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[ "materials provided with the distribution. # # THIS SOFTWARE IS", "ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,", "reproduce the above # copyright notice, this list of conditions", "binary forms, with or # without modification, are permitted provided", "# # Redistributions of source code must retain the above", "Redistributions of source code must retain the above # copyright", "import eucaops from eutester.eutestcase import EutesterTestCase import time class MyTestCase(EutesterTestCase):", "= [ ] for test in list: unit_list.append( testcase.create_testunit_by_name(test) )", "IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE", "pass def wait_for_services_operational(self, timeout=None): \"\"\" Definition: Test attempts to query", "defined for this test') pass def wait_for_services_operational(self, timeout=None): \"\"\" Definition:", "= str(tb) + \"\\n\" + str(e) print 'Services not up", "\"__main__\": testcase = MyTestCase() ### Use the list of tests", "continue to poll the system until it finds an ENABLED", "binary form must reproduce the above # copyright notice, this", "conditions # are met: # # Redistributions of source code", "the above # copyright notice, this list of conditions and", "predefined list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list = testcase.args.tests or", "in source and binary forms, with or # without modification,", "until we need it for this method result = testcase.run_test_case_list(unit_list,clean_on_exit=False)", "each. \"\"\" timeout= timeout or self.args.timeout last_err = \"\" elapsed", "notice, this list of conditions and the # following disclaimer", "raise Exception(str(last_err) + 'Could not create tester object after elapsed:'", "config/command line to determine what subset of tests to run", "# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF", "tester object. Elapsed:' + str(elapsed)) try: self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password)", "above # copyright notice, this list of conditions and the", "list = testcase.args.tests or [\"wait_for_services_operational\"] ### Convert test suite methods", "# # Copyright (c) 2009-2011, Eucalyptus Systems, Inc. # All", "EutesterUnitTest objects unit_list = [ ] for test in list:", "'Services not up because of: ' + last_err + '\\n'", "THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR", "NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF", "must reproduce the above # copyright notice, this list of", "DISABLED instance of each. \"\"\" timeout= timeout or self.args.timeout last_err", "INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER", "CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,", "the following conditions # are met: # # Redistributions of", "clarkmatthew import eucaops from eutester.eutestcase import EutesterTestCase import time class", "are met: # # Redistributions of source code must retain", "= eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception, e: tb = eucaops.Eucaops.get_traceback() last_err", "class MyTestCase(EutesterTestCase): def __init__(self, config_file=None, password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600)", "License) # # Copyright (c) 2009-2011, Eucalyptus Systems, Inc. #", "0 start = time.time() self.tester = None while (not self.tester", "and the # following disclaimer in the documentation and/or other", "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE", "str(e) print 'Services not up because of: ' + last_err", "this test') pass def wait_for_services_operational(self, timeout=None): \"\"\" Definition: Test attempts", "disclaimer in the documentation and/or other # materials provided with", "AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED", "services. The test will continue to poll the system until", "+ str(elapsed)) timeout = timeout - elapsed self.status('starting wait for", "clean_method defined for this test') pass def wait_for_services_operational(self, timeout=None): \"\"\"", "self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args() def clean_method(self): self.debug('No clean_method defined", "if not self.tester: raise Exception(str(last_err) + 'Could not create tester", "following disclaimer. # # Redistributions in binary form must reproduce", "<gh_stars>0 #!/usr/bin/python # Software License Agreement (BSD License) # #", "in list: unit_list.append( testcase.create_testunit_by_name(test) ) ### Run the EutesterUnitTest objects,", "worry about clean on exit until we need it for", "eucaops.Eucaops.get_traceback() last_err = str(tb) + \"\\n\" + str(e) print 'Services", "# following disclaimer in the documentation and/or other # materials", "eucaops from eutester.eutestcase import EutesterTestCase import time class MyTestCase(EutesterTestCase): def", "the EutesterUnitTest objects, dont worry about clean on exit until", "elapsed = 0 start = time.time() self.tester = None while", "clean on exit until we need it for this method", "EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE", "self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args() def clean_method(self): self.debug('No clean_method defined for", "# following disclaimer. # # Redistributions in binary form must", "print 'Services not up because of: ' + last_err +", "line to determine what subset of tests to run ###", "for an ENABLED and DISABLED instance of each. \"\"\" timeout=", "= time.time() self.tester = None while (not self.tester and elapsed", "and use of this software in source and binary forms,", "it will wait for an ENABLED and DISABLED instance of", "Exception, e: tb = eucaops.Eucaops.get_traceback() last_err = str(tb) + \"\\n\"", "state of a subset of core services. The test will", "GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR", "and/or other # materials provided with the distribution. # #", "WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF", "core services. The test will continue to poll the system", "met: # # Redistributions of source code must retain the", "or [\"wait_for_services_operational\"] ### Convert test suite methods to EutesterUnitTest objects", "BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,", "HA case it will wait for an ENABLED and DISABLED", "each service. In the HA case it will wait for", "test will continue to poll the system until it finds", "what subset of tests to run ### or use a", "# # Redistributions in binary form must reproduce the above", "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS", "# POSSIBILITY OF SUCH DAMAGE. # # Author: clarkmatthew import", "\"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,", "the # following disclaimer in the documentation and/or other #", "BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR", "clean_method(self): self.debug('No clean_method defined for this test') pass def wait_for_services_operational(self,", "TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR", "CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,", "to create tester object. Elapsed:' + str(elapsed)) try: self.tester =", "will continue to poll the system until it finds an", "self.args.timeout last_err = \"\" elapsed = 0 start = time.time()", "objects unit_list = [ ] for test in list: unit_list.append(", "methods to EutesterUnitTest objects unit_list = [ ] for test", "provided that the following conditions # are met: # #", "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL", "# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR", "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #", "Redistribution and use of this software in source and binary", "if __name__ == \"__main__\": testcase = MyTestCase() ### Use the", "all services operational, timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are", "AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,", "of each service. In the HA case it will wait", "Run the EutesterUnitTest objects, dont worry about clean on exit", "time class MyTestCase(EutesterTestCase): def __init__(self, config_file=None, password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\",", "services operational, timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are up')", "testcase.args.tests or [\"wait_for_services_operational\"] ### Convert test suite methods to EutesterUnitTest", "run ### or use a predefined list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\",", "' + last_err + '\\n' if not self.tester: raise Exception(str(last_err)", "# without modification, are permitted provided that the following conditions", "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #", "- elapsed self.status('starting wait for all services operational, timeout:' +", "up because of: ' + last_err + '\\n' if not", "HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN", "2009-2011, Eucalyptus Systems, Inc. # All rights reserved. # #", "# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT", "exit until we need it for this method result =", "MyTestCase(EutesterTestCase): def __init__(self, config_file=None, password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args()", "suite methods to EutesterUnitTest objects unit_list = [ ] for", "we need it for this method result = testcase.run_test_case_list(unit_list,clean_on_exit=False) exit(result)", "source code must retain the above # copyright notice, this", "from config/command line to determine what subset of tests to", "up') self.tester.service_manager.print_services_list() if __name__ == \"__main__\": testcase = MyTestCase() ###", "self.status('starting wait for all services operational, timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout)", "or self.args.timeout last_err = \"\" elapsed = 0 start =", "attempts to query the state of a subset of core", "ENABLED and DISABLED instance of each. \"\"\" timeout= timeout or", "USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED", "forms, with or # without modification, are permitted provided that", "the # following disclaimer. # # Redistributions in binary form", "EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.", "Exception(str(last_err) + 'Could not create tester object after elapsed:' +", "timeout=None): \"\"\" Definition: Test attempts to query the state of", "password=self.args.password) except Exception, e: tb = eucaops.Eucaops.get_traceback() last_err = str(tb)", "+ last_err + '\\n' if not self.tester: raise Exception(str(last_err) +", "IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR", "testcase.create_testunit_by_name(test) ) ### Run the EutesterUnitTest objects, dont worry about", "def clean_method(self): self.debug('No clean_method defined for this test') pass def", "Test attempts to query the state of a subset of", "OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE", "# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER", "with the distribution. # # THIS SOFTWARE IS PROVIDED BY", "not create tester object after elapsed:' + str(elapsed)) timeout =", "self.status('All services are up') self.tester.service_manager.print_services_list() if __name__ == \"__main__\": testcase", "PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" #", "# are met: # # Redistributions of source code must", "subset of core services. The test will continue to poll", "INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT", "CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #", "OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS", "+ 'Could not create tester object after elapsed:' + str(elapsed))", "PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY", "TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY", "POSSIBILITY OF SUCH DAMAGE. # # Author: clarkmatthew import eucaops", "Author: clarkmatthew import eucaops from eutester.eutestcase import EutesterTestCase import time", "# All rights reserved. # # Redistribution and use of", "THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE", "until it finds an ENABLED instance of each service. In", "object after elapsed:' + str(elapsed)) timeout = timeout - elapsed", "+ str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are up') self.tester.service_manager.print_services_list() if __name__", "\"\\n\" + str(e) print 'Services not up because of: '", "# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,", "A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL", "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED", "retain the above # copyright notice, this list of conditions", "EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE", "[ ] for test in list: unit_list.append( testcase.create_testunit_by_name(test) ) ###", "timeout): elapsed = int(time.time() - start) self.status('Attempting to create tester", "test') pass def wait_for_services_operational(self, timeout=None): \"\"\" Definition: Test attempts to", "of each. \"\"\" timeout= timeout or self.args.timeout last_err = \"\"", "for test in list: unit_list.append( testcase.create_testunit_by_name(test) ) ### Run the", "services are up') self.tester.service_manager.print_services_list() if __name__ == \"__main__\": testcase =", "self.tester.service_manager.print_services_list() if __name__ == \"__main__\": testcase = MyTestCase() ### Use", "__name__ == \"__main__\": testcase = MyTestCase() ### Use the list", "tests to run ### or use a predefined list \"VolumeTagging\",", "(c) 2009-2011, Eucalyptus Systems, Inc. # All rights reserved. #", "\"\"\" timeout= timeout or self.args.timeout last_err = \"\" elapsed =", "test in list: unit_list.append( testcase.create_testunit_by_name(test) ) ### Run the EutesterUnitTest", "unit_list = [ ] for test in list: unit_list.append( testcase.create_testunit_by_name(test)", "USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #", "STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING", "== \"__main__\": testcase = MyTestCase() ### Use the list of", "EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,", "OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY", "to determine what subset of tests to run ### or", "elapsed self.status('starting wait for all services operational, timeout:' + str(timeout))", "passed from config/command line to determine what subset of tests", "Inc. # All rights reserved. # # Redistribution and use", "tester object after elapsed:' + str(elapsed)) timeout = timeout -", "(not self.tester and elapsed < timeout): elapsed = int(time.time() -", "= None while (not self.tester and elapsed < timeout): elapsed", "#!/usr/bin/python # Software License Agreement (BSD License) # # Copyright", "use a predefined list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list =", "Agreement (BSD License) # # Copyright (c) 2009-2011, Eucalyptus Systems,", "THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF", "ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES", "eutester.eutestcase import EutesterTestCase import time class MyTestCase(EutesterTestCase): def __init__(self, config_file=None,", "after elapsed:' + str(elapsed)) timeout = timeout - elapsed self.status('starting", "def wait_for_services_operational(self, timeout=None): \"\"\" Definition: Test attempts to query the", "tb = eucaops.Eucaops.get_traceback() last_err = str(tb) + \"\\n\" + str(e)", "OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED", "\"\" elapsed = 0 start = time.time() self.tester = None", "OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,", "import EutesterTestCase import time class MyTestCase(EutesterTestCase): def __init__(self, config_file=None, password=None):", "LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)", "### or use a predefined list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\"", "an ENABLED and DISABLED instance of each. \"\"\" timeout= timeout", "str(tb) + \"\\n\" + str(e) print 'Services not up because", "to EutesterUnitTest objects unit_list = [ ] for test in", "and binary forms, with or # without modification, are permitted", "# Redistributions in binary form must reproduce the above #", "e: tb = eucaops.Eucaops.get_traceback() last_err = str(tb) + \"\\n\" +", "Definition: Test attempts to query the state of a subset", "documentation and/or other # materials provided with the distribution. #", "# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR", "### Convert test suite methods to EutesterUnitTest objects unit_list =", "distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT", ") ### Run the EutesterUnitTest objects, dont worry about clean", "= testcase.args.tests or [\"wait_for_services_operational\"] ### Convert test suite methods to", "list of conditions and the # following disclaimer in the", "objects, dont worry about clean on exit until we need", "[\"wait_for_services_operational\"] ### Convert test suite methods to EutesterUnitTest objects unit_list", "### Run the EutesterUnitTest objects, dont worry about clean on", "determine what subset of tests to run ### or use", "from eutester.eutestcase import EutesterTestCase import time class MyTestCase(EutesterTestCase): def __init__(self,", "the system until it finds an ENABLED instance of each", "WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES", "NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;", "not up because of: ' + last_err + '\\n' if", "use of this software in source and binary forms, with", "try: self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception, e: tb =", "# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR", "start = time.time() self.tester = None while (not self.tester and", "software in source and binary forms, with or # without", "# copyright notice, this list of conditions and the #", "of conditions and the # following disclaimer in the documentation", "- start) self.status('Attempting to create tester object. Elapsed:' + str(elapsed))", "finds an ENABLED instance of each service. In the HA", "DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND", "following disclaimer in the documentation and/or other # materials provided", "BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF", "poll the system until it finds an ENABLED instance of", "str(elapsed)) timeout = timeout - elapsed self.status('starting wait for all", "create tester object after elapsed:' + str(elapsed)) timeout = timeout", "test suite methods to EutesterUnitTest objects unit_list = [ ]", "HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR", "last_err + '\\n' if not self.tester: raise Exception(str(last_err) + 'Could", "COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,", "# Redistributions of source code must retain the above #", "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE", "OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE", "password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args() def clean_method(self): self.debug('No clean_method", "IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. #", "code must retain the above # copyright notice, this list", "\"SnapshotTagging\", \"ImageTagging\" list = testcase.args.tests or [\"wait_for_services_operational\"] ### Convert test", "OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF", "unit_list.append( testcase.create_testunit_by_name(test) ) ### Run the EutesterUnitTest objects, dont worry", "# Redistribution and use of this software in source and", "PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT", "Copyright (c) 2009-2011, Eucalyptus Systems, Inc. # All rights reserved.", "disclaimer. # # Redistributions in binary form must reproduce the", "Systems, Inc. # All rights reserved. # # Redistribution and", "elapsed < timeout): elapsed = int(time.time() - start) self.status('Attempting to", "= timeout - elapsed self.status('starting wait for all services operational,", "case it will wait for an ENABLED and DISABLED instance", "THE # POSSIBILITY OF SUCH DAMAGE. # # Author: clarkmatthew", "ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # #", "instance of each service. In the HA case it will", "dont worry about clean on exit until we need it", "following conditions # are met: # # Redistributions of source", "for all services operational, timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services", "Use the list of tests passed from config/command line to", "Elapsed:' + str(elapsed)) try: self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception,", "are up') self.tester.service_manager.print_services_list() if __name__ == \"__main__\": testcase = MyTestCase()", "elapsed = int(time.time() - start) self.status('Attempting to create tester object.", "Convert test suite methods to EutesterUnitTest objects unit_list = [", "+ str(e) print 'Services not up because of: ' +", "The test will continue to poll the system until it", "conditions and the # following disclaimer. # # Redistributions in", "timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are up') self.tester.service_manager.print_services_list() if", "timeout or self.args.timeout last_err = \"\" elapsed = 0 start", "Eucalyptus Systems, Inc. # All rights reserved. # # Redistribution", "# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)", "create tester object. Elapsed:' + str(elapsed)) try: self.tester = eucaops.Eucaops(config_file=self.args.config_file,", "without modification, are permitted provided that the following conditions #", "wait for all services operational, timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All", "WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #", "ARISING IN ANY WAY OUT OF THE USE OF THIS", "ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN", "LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS", "for this test') pass def wait_for_services_operational(self, timeout=None): \"\"\" Definition: Test", "list of tests passed from config/command line to determine what", "subset of tests to run ### or use a predefined", "except Exception, e: tb = eucaops.Eucaops.get_traceback() last_err = str(tb) +", "SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR", "that the following conditions # are met: # # Redistributions", "instance of each. \"\"\" timeout= timeout or self.args.timeout last_err =", "provided with the distribution. # # THIS SOFTWARE IS PROVIDED", "of: ' + last_err + '\\n' if not self.tester: raise", "eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception, e: tb = eucaops.Eucaops.get_traceback() last_err =", "LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN", "of this software in source and binary forms, with or", "self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception, e: tb = eucaops.Eucaops.get_traceback()", "copyright notice, this list of conditions and the # following", "= \"\" elapsed = 0 start = time.time() self.tester =", "EutesterTestCase import time class MyTestCase(EutesterTestCase): def __init__(self, config_file=None, password=None): self.setuptestcase()", "DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,", "the documentation and/or other # materials provided with the distribution.", "__init__(self, config_file=None, password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args() def clean_method(self):", "OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT", "this list of conditions and the # following disclaimer. #", "in the documentation and/or other # materials provided with the", "OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,", "(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS", "LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING", "source and binary forms, with or # without modification, are", "OF SUCH DAMAGE. # # Author: clarkmatthew import eucaops from", "system until it finds an ENABLED instance of each service.", "because of: ' + last_err + '\\n' if not self.tester:", "\"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list = testcase.args.tests or [\"wait_for_services_operational\"] ### Convert", "= MyTestCase() ### Use the list of tests passed from", "# # Redistribution and use of this software in source", "start) self.status('Attempting to create tester object. Elapsed:' + str(elapsed)) try:", "IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED", "form must reproduce the above # copyright notice, this list", "conditions and the # following disclaimer in the documentation and/or", "License Agreement (BSD License) # # Copyright (c) 2009-2011, Eucalyptus", "and elapsed < timeout): elapsed = int(time.time() - start) self.status('Attempting", "SUCH DAMAGE. # # Author: clarkmatthew import eucaops from eutester.eutestcase", "self.parser.add_argument(\"--timeout\", default=600) self.get_args() def clean_method(self): self.debug('No clean_method defined for this", "elapsed:' + str(elapsed)) timeout = timeout - elapsed self.status('starting wait", "timeout - elapsed self.status('starting wait for all services operational, timeout:'", "list of conditions and the # following disclaimer. # #", "this list of conditions and the # following disclaimer in", "SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH", "OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE", "to poll the system until it finds an ENABLED instance", "INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF", "NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE #", "wait_for_services_operational(self, timeout=None): \"\"\" Definition: Test attempts to query the state", "with or # without modification, are permitted provided that the", "self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are up') self.tester.service_manager.print_services_list() if __name__ == \"__main__\":", "self.tester and elapsed < timeout): elapsed = int(time.time() - start)", "def __init__(self, config_file=None, password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args() def", "# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND", "the distribution. # # THIS SOFTWARE IS PROVIDED BY THE", "or # without modification, are permitted provided that the following", "of core services. The test will continue to poll the", "= int(time.time() - start) self.status('Attempting to create tester object. Elapsed:'", "OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER", "last_err = str(tb) + \"\\n\" + str(e) print 'Services not", "LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS", "while (not self.tester and elapsed < timeout): elapsed = int(time.time()", "# materials provided with the distribution. # # THIS SOFTWARE", "wait for an ENABLED and DISABLED instance of each. \"\"\"", "of source code must retain the above # copyright notice,", "about clean on exit until we need it for this", "= 0 start = time.time() self.tester = None while (not", "time.time() self.tester = None while (not self.tester and elapsed <", "a subset of core services. The test will continue to", "IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,", "LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #", "testcase = MyTestCase() ### Use the list of tests passed", "self.status('Attempting to create tester object. Elapsed:' + str(elapsed)) try: self.tester", "tests passed from config/command line to determine what subset of", "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS", "OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY", "the list of tests passed from config/command line to determine", "+ \"\\n\" + str(e) print 'Services not up because of:", "and the # following disclaimer. # # Redistributions in binary", "WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE", "permitted provided that the following conditions # are met: #", "operational, timeout:' + str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are up') self.tester.service_manager.print_services_list()", "of a subset of core services. The test will continue", "= eucaops.Eucaops.get_traceback() last_err = str(tb) + \"\\n\" + str(e) print", "PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,", "OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,", "modification, are permitted provided that the following conditions # are", "# ARISING IN ANY WAY OUT OF THE USE OF", "# Copyright (c) 2009-2011, Eucalyptus Systems, Inc. # All rights", "on exit until we need it for this method result", "not self.tester: raise Exception(str(last_err) + 'Could not create tester object", "# Author: clarkmatthew import eucaops from eutester.eutestcase import EutesterTestCase import", "All rights reserved. # # Redistribution and use of this", "reserved. # # Redistribution and use of this software in", "are permitted provided that the following conditions # are met:", "AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN", "this software in source and binary forms, with or #", "import time class MyTestCase(EutesterTestCase): def __init__(self, config_file=None, password=None): self.setuptestcase() self.setup_parser()", "### Use the list of tests passed from config/command line", "< timeout): elapsed = int(time.time() - start) self.status('Attempting to create", "to query the state of a subset of core services.", "ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,", "other # materials provided with the distribution. # # THIS", "self.debug('No clean_method defined for this test') pass def wait_for_services_operational(self, timeout=None):", "str(timeout)) self.tester.service_manager.wait_for_all_services_operational(timeout) self.status('All services are up') self.tester.service_manager.print_services_list() if __name__ ==", "and DISABLED instance of each. \"\"\" timeout= timeout or self.args.timeout", "# Software License Agreement (BSD License) # # Copyright (c)", "ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT", "of tests passed from config/command line to determine what subset", "Redistributions in binary form must reproduce the above # copyright", "service. In the HA case it will wait for an", "+ str(elapsed)) try: self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception, e:", "\"ImageTagging\" list = testcase.args.tests or [\"wait_for_services_operational\"] ### Convert test suite", "'Could not create tester object after elapsed:' + str(elapsed)) timeout", "# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS", "a predefined list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list = testcase.args.tests", "timeout= timeout or self.args.timeout last_err = \"\" elapsed = 0", "\"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list = testcase.args.tests or [\"wait_for_services_operational\"] ###", "(BSD License) # # Copyright (c) 2009-2011, Eucalyptus Systems, Inc.", "OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #", "notice, this list of conditions and the # following disclaimer.", "in binary form must reproduce the above # copyright notice,", "SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED", "IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"", "must retain the above # copyright notice, this list of", "MyTestCase() ### Use the list of tests passed from config/command", "or use a predefined list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list", "an ENABLED instance of each service. In the HA case", "'\\n' if not self.tester: raise Exception(str(last_err) + 'Could not create", "self.tester: raise Exception(str(last_err) + 'Could not create tester object after", "FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT", "to run ### or use a predefined list \"VolumeTagging\", \"InstanceTagging\",", "self.get_args() def clean_method(self): self.debug('No clean_method defined for this test') pass", "Software License Agreement (BSD License) # # Copyright (c) 2009-2011,", "NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND", "SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;", "FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO", "ENABLED instance of each service. In the HA case it", "BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY", "rights reserved. # # Redistribution and use of this software", "\"\"\" Definition: Test attempts to query the state of a", "str(elapsed)) try: self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except Exception, e: tb", "IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT", "+ '\\n' if not self.tester: raise Exception(str(last_err) + 'Could not", "the HA case it will wait for an ENABLED and", "self.tester = None while (not self.tester and elapsed < timeout):", "SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS", "list: unit_list.append( testcase.create_testunit_by_name(test) ) ### Run the EutesterUnitTest objects, dont", "PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE", "(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT", "it finds an ENABLED instance of each service. In the", "will wait for an ENABLED and DISABLED instance of each.", "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY", "THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A", "OF THE # POSSIBILITY OF SUCH DAMAGE. # # Author:", "COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS", "EutesterUnitTest objects, dont worry about clean on exit until we", "last_err = \"\" elapsed = 0 start = time.time() self.tester", "of tests to run ### or use a predefined list", "INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT", "THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY", "# # Author: clarkmatthew import eucaops from eutester.eutestcase import EutesterTestCase", "CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #", "query the state of a subset of core services. The", "int(time.time() - start) self.status('Attempting to create tester object. Elapsed:' +", "OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON", "config_file=None, password=None): self.setuptestcase() self.setup_parser() self.parser.add_argument(\"--timeout\", default=600) self.get_args() def clean_method(self): self.debug('No", "timeout = timeout - elapsed self.status('starting wait for all services", "list \"VolumeTagging\", \"InstanceTagging\", \"SnapshotTagging\", \"ImageTagging\" list = testcase.args.tests or [\"wait_for_services_operational\"]", "the state of a subset of core services. The test", "DAMAGE. # # Author: clarkmatthew import eucaops from eutester.eutestcase import", "default=600) self.get_args() def clean_method(self): self.debug('No clean_method defined for this test')", "None while (not self.tester and elapsed < timeout): elapsed =", "MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.", "OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT", "of conditions and the # following disclaimer. # # Redistributions", "In the HA case it will wait for an ENABLED", "TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF", "BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND", "THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY", "] for test in list: unit_list.append( testcase.create_testunit_by_name(test) ) ### Run", "object. Elapsed:' + str(elapsed)) try: self.tester = eucaops.Eucaops(config_file=self.args.config_file, password=self.args.password) except", "ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR" ]
[ "/ 2 - 50 MOUSER = 325 TICKRATES = 120", "WIDTH = 1280 HEIGHT = 720 X = WIDTH /", "= 325 TICKRATES = 120 nfc = False raspberry =", "720 X = WIDTH / 2 - 50 Y =", "= 1280 HEIGHT = 720 X = WIDTH / 2", "MOUSER = 325 TICKRATES = 120 nfc = False raspberry", "= WIDTH / 2 - 50 Y = HEIGHT /", "50 Y = HEIGHT / 2 - 50 MOUSER =", "= 1.5 VELOCITYRESET = 6 WIDTH = 1280 HEIGHT =", "2 - 50 Y = HEIGHT / 2 - 50", "X = WIDTH / 2 - 50 Y = HEIGHT", "SPEED = 1.5 VELOCITYRESET = 6 WIDTH = 1280 HEIGHT", "WIDTH / 2 - 50 Y = HEIGHT / 2", "1 SPEED = 1.5 VELOCITYRESET = 6 WIDTH = 1280", "50 MOUSER = 325 TICKRATES = 120 nfc = False", "= 1 SPEED = 1.5 VELOCITYRESET = 6 WIDTH =", "= 720 X = WIDTH / 2 - 50 Y", "325 TICKRATES = 120 nfc = False raspberry = False", "Y = HEIGHT / 2 - 50 MOUSER = 325", "/ 2 - 50 Y = HEIGHT / 2 -", "VELOCITYRESET = 6 WIDTH = 1280 HEIGHT = 720 X", "MAP = 1 SPEED = 1.5 VELOCITYRESET = 6 WIDTH", "- 50 Y = HEIGHT / 2 - 50 MOUSER", "2 - 50 MOUSER = 325 TICKRATES = 120 nfc", "= HEIGHT / 2 - 50 MOUSER = 325 TICKRATES", "1280 HEIGHT = 720 X = WIDTH / 2 -", "1.5 VELOCITYRESET = 6 WIDTH = 1280 HEIGHT = 720", "HEIGHT = 720 X = WIDTH / 2 - 50", "- 50 MOUSER = 325 TICKRATES = 120 nfc =", "6 WIDTH = 1280 HEIGHT = 720 X = WIDTH", "= 6 WIDTH = 1280 HEIGHT = 720 X =", "HEIGHT / 2 - 50 MOUSER = 325 TICKRATES =" ]
[ "was asked by Facebook. # # A builder is looking", "problem was asked by Facebook. # # A builder is", "# # A builder is looking to build a row", "# He has a goal of minimizing cost while ensuring", "K different colors. # He has a goal of minimizing", "cost while ensuring that no two neighboring houses are of", "build a row of N houses that can be of", "house with kth color, # return the minimum cost which", "row of N houses that can be of K different", "two neighboring houses are of the same color. # #", "goal of minimizing cost while ensuring that no two neighboring", "row and kth column represents the cost to build the", "Facebook. # # A builder is looking to build a", "houses that can be of K different colors. # He", "and kth column represents the cost to build the nth", "same color. # # Given an N by K matrix", "while ensuring that no two neighboring houses are of the", "color. # # Given an N by K matrix where", "K matrix where the nth row and kth column represents", "column represents the cost to build the nth house with", "colors. # He has a goal of minimizing cost while", "build the nth house with kth color, # return the", "# Given an N by K matrix where the nth", "N by K matrix where the nth row and kth", "# # Given an N by K matrix where the", "an N by K matrix where the nth row and", "builder is looking to build a row of N houses", "of minimizing cost while ensuring that no two neighboring houses", "of the same color. # # Given an N by", "where the nth row and kth column represents the cost", "nth row and kth column represents the cost to build", "the nth house with kth color, # return the minimum", "be of K different colors. # He has a goal", "to build the nth house with kth color, # return", "a row of N houses that can be of K", "color, # return the minimum cost which achieves this goal.", "# A builder is looking to build a row of", "is looking to build a row of N houses that", "has a goal of minimizing cost while ensuring that no", "the cost to build the nth house with kth color,", "nth house with kth color, # return the minimum cost", "ensuring that no two neighboring houses are of the same", "with kth color, # return the minimum cost which achieves", "kth color, # return the minimum cost which achieves this", "that can be of K different colors. # He has", "# This problem was asked by Facebook. # # A", "different colors. # He has a goal of minimizing cost", "cost to build the nth house with kth color, #", "Given an N by K matrix where the nth row", "N houses that can be of K different colors. #", "houses are of the same color. # # Given an", "are of the same color. # # Given an N", "minimizing cost while ensuring that no two neighboring houses are", "the same color. # # Given an N by K", "by K matrix where the nth row and kth column", "a goal of minimizing cost while ensuring that no two", "A builder is looking to build a row of N", "He has a goal of minimizing cost while ensuring that", "looking to build a row of N houses that can", "This problem was asked by Facebook. # # A builder", "asked by Facebook. # # A builder is looking to", "of K different colors. # He has a goal of", "by Facebook. # # A builder is looking to build", "neighboring houses are of the same color. # # Given", "the nth row and kth column represents the cost to", "<reponame>while1618/DailyCodingProblem # This problem was asked by Facebook. # #", "no two neighboring houses are of the same color. #", "to build a row of N houses that can be", "matrix where the nth row and kth column represents the", "can be of K different colors. # He has a", "that no two neighboring houses are of the same color.", "represents the cost to build the nth house with kth", "of N houses that can be of K different colors.", "kth column represents the cost to build the nth house" ]
[ "default='./results/M_30_3_32_32') args = parser.parse_args() path = args.dirpath n_split = 5", "plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close() def main(): parser =", "def scaler(saliency_): saliency = np.copy(saliency_) minv, maxv = v_range if", "with own dataset.') parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32') args = parser.parse_args()", "v in saliency: vmax = max(vmax, np.max(v)) vmin = min(vmin,", "return scaler scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth) scaler_bayes =", "exist_ok=True) test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\")) answer = np.load(os.path.join(dir_path, \"answer.npy\")) output", "i in range(n_split): suffix = str(i) + \"-\" + str(n_split)", "saliency < 0.0 saliency[nega] = saliency[nega]/(np.abs(minv)) return saliency return scaler", "maxv = v_range if maxv == minv: saliency = np.zeros_like(saliency)", "get_scaler(v_range_bayes) def color(x): if x > 0: # Red for", "saliency[pos] = saliency[pos]/maxv nega = saliency < 0.0 saliency[nega] =", "answer = np.load(os.path.join(dir_path, \"answer.npy\")) output = np.load(os.path.join(dir_path, \"output.npy\")) smiles_all =", "ext = '.png' # '.svg' # visualizer.visualize( # saliency_vanilla[id], smiles,", "parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32') args = parser.parse_args() path = args.dirpath", "save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color)", "= max(np.max(prediction), np.max(answer)) min_v = min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\")", "args = parser.parse_args() path = args.dirpath n_split = 5 output", "# Blue for negative value x *= -1 return 1.", "visualizer.visualize( # saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) + ext), #", "scaler scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes)", "saliency return scaler scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth) scaler_bayes", "= parser.parse_args() path = args.dirpath n_split = 5 output =", ">= 0.0 saliency[pos] = saliency[pos]/maxv nega = saliency < 0.0", "suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output = np.concatenate(output) answer =", "[-100, 100], c='r') max_v = max(np.max(prediction), np.max(answer)) min_v = min(np.min(prediction),", "np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir,", "parent_dir = os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path,", "= args.dirpath n_split = 5 output = [] answer =", "ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color) # visualizer.visualize( # saliency_smooth[id],", "# saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) + ext), # visualize_ratio=1.0,", "color(x): if x > 0: # Red for positive value", "smiles = smiles_all[id] out = output[i] ans = answer[i] #", "str(i) + \"-\" + str(n_split) print(suffix) visualize(os.path.join(path, suffix)) if __name__", "\"result.png\")) for i in range(n_split): suffix = str(i) + \"-\"", "1. - x, 1. for i, id in enumerate(test_idx): smiles", "+ ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color) def plot_result(prediction, answer, save_filepath='result.png'):", "test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\")) answer = np.load(os.path.join(dir_path, \"answer.npy\")) output =", "legend = \"t:{}, p:{}\".format(ans, out) legend = '' ext =", "saliency = np.zeros_like(saliency) else: pos = saliency >= 0.0 saliency[pos]", "ans = answer[i] # legend = \"t:{}, p:{}\".format(ans, out) legend", "minv: saliency = np.zeros_like(saliency) else: pos = saliency >= 0.0", "min(vmin, np.min(v)) return vmin, vmax v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth =", "as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import SmilesVisualizer def visualize(dir_path): parent_dir", "range(n_split): suffix = str(i) + \"-\" + str(n_split) output.append(np.load(os.path.join(path, suffix,", "# visualizer.visualize( # saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) + ext),", "import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer", "exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\")) answer =", "'.png' # '.svg' # visualizer.visualize( # saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\",", "\"saliency_bayes.npy\")) visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True)", "saliency[nega]/(np.abs(minv)) return saliency return scaler scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth =", "x *= -1 return 1. - x, 1. - x,", "vmax v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes)", "-1 return 1. - x, 1. - x, 1. for", "np import os import sys import matplotlib matplotlib.use('agg') import matplotlib.pyplot", "\"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\")) answer", "\"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer", "= np.zeros_like(saliency) else: pos = saliency >= 0.0 saliency[pos] =", "= \"t:{}, p:{}\".format(ans, out) legend = '' ext = '.png'", "args.dirpath n_split = 5 output = [] answer = []", "def plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction, answer, marker='.') plt.plot([-100, 100], [-100,", "def visualize(dir_path): parent_dir = os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth", "= smiles_all[id] out = output[i] ans = answer[i] # legend", "smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_smooth,", "marker='.') plt.plot([-100, 100], [-100, 100], c='r') max_v = max(np.max(prediction), np.max(answer))", "own dataset.') parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32') args = parser.parse_args() path", "def main(): parser = argparse.ArgumentParser( description='Regression with own dataset.') parser.add_argument('--dirpath',", "return vmin, vmax v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth) v_range_bayes", "c='r') max_v = max(np.max(prediction), np.max(answer)) min_v = min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1,", "visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color) def plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction, answer,", "\"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output = np.concatenate(output) answer = np.concatenate(answer)", "np.min(v)) return vmin, vmax v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth)", "out) legend = '' ext = '.png' # '.svg' #", "np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\")) for i in range(n_split): suffix", "plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction, answer, marker='.') plt.plot([-100, 100], [-100, 100],", "suffix = str(i) + \"-\" + str(n_split) print(suffix) visualize(os.path.join(path, suffix))", "output = np.load(os.path.join(dir_path, \"output.npy\")) smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency):", "# saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) + ext), # visualize_ratio=1.0,", "x, 1. for i, id in enumerate(test_idx): smiles = smiles_all[id]", "color_fn=color) # visualizer.visualize( # saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) +", "if x > 0: # Red for positive value return", "visualize(dir_path): parent_dir = os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth =", "visualizer.visualize( # saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) + ext), #", "5 output = [] answer = [] for i in", "scaler=scaler_vanilla, color_fn=color) # visualizer.visualize( # saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id)", "# visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color) # visualizer.visualize( # saliency_smooth[id], smiles,", "< 0.0 saliency[nega] = saliency[nega]/(np.abs(minv)) return saliency return scaler scaler_vanilla", "np.max(answer)) min_v = min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1])", "x else: # Blue for negative value x *= -1", "import os import sys import matplotlib matplotlib.use('agg') import matplotlib.pyplot as", "+ ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id], smiles,", "plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close() def", "np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close()", "plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\")) for i in range(n_split): suffix =", "0.0 saliency[pos] = saliency[pos]/maxv nega = saliency < 0.0 saliency[nega]", "saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) + ext), # visualize_ratio=1.0, legend=legend,", "os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\"))", "color_fn=color) visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) + ext), visualize_ratio=1.0,", "\"result_vanilla\", str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color) #", "pos = saliency >= 0.0 saliency[pos] = saliency[pos]/maxv nega =", "= min(vmin, np.min(v)) return vmin, vmax v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth", "out = output[i] ans = answer[i] # legend = \"t:{},", "import numpy as np import os import sys import matplotlib", "plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close() def main(): parser = argparse.ArgumentParser( description='Regression", "plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import SmilesVisualizer def visualize(dir_path): parent_dir =", "+ \"-\" + str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\")))", "smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) + ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color)", "= get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes) def color(x): if x >", "'' ext = '.png' # '.svg' # visualizer.visualize( # saliency_vanilla[id],", "SmilesVisualizer def visualize(dir_path): parent_dir = os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\"))", "np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"),", "\"result_smooth\", str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize(", "> 0: # Red for positive value return 1., 1.", "scaler_bayes = get_scaler(v_range_bayes) def color(x): if x > 0: #", "argparse.ArgumentParser( description='Regression with own dataset.') parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32') args", "exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx = np.load(os.path.join(dir_path,", "max_v = max(np.max(prediction), np.max(answer)) min_v = min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1])", "def color(x): if x > 0: # Red for positive", "= SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"),", "value return 1., 1. - x, 1. - x else:", "np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\"))", "min_v = min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground", "= get_scaler(v_range_bayes) def color(x): if x > 0: # Red", "saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) + ext), # visualize_ratio=1.0, legend=legend,", "save_filepath=os.path.join(parent_dir, \"result_smooth\", str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color)", "= '.png' # '.svg' # visualizer.visualize( # saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir,", "import SmilesVisualizer def visualize(dir_path): parent_dir = os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path,", "in saliency: vmax = max(vmax, np.max(v)) vmin = min(vmin, np.min(v))", "calc_range(saliency): vmax = float('-inf') vmin = float('inf') for v in", "for i, id in enumerate(test_idx): smiles = smiles_all[id] out =", "= v_range if maxv == minv: saliency = np.zeros_like(saliency) else:", "np.zeros_like(saliency) else: pos = saliency >= 0.0 saliency[pos] = saliency[pos]/maxv", "1. - x, 1. - x else: # Blue for", "scaler(saliency_): saliency = np.copy(saliency_) minv, maxv = v_range if maxv", "save_filepath='result.png'): plt.scatter(prediction, answer, marker='.') plt.plot([-100, 100], [-100, 100], c='r') max_v", "get_scaler(v_range): def scaler(saliency_): saliency = np.copy(saliency_) minv, maxv = v_range", "else: pos = saliency >= 0.0 saliency[pos] = saliency[pos]/maxv nega", "= min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\")", "np.concatenate(output) answer = np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\")) for i", "v_range if maxv == minv: saliency = np.zeros_like(saliency) else: pos", "np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency): vmax = float('-inf') vmin = float('inf')", "# visualizer.visualize( # saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) + ext),", "= saliency >= 0.0 saliency[pos] = saliency[pos]/maxv nega = saliency", "x > 0: # Red for positive value return 1.,", "import argparse import numpy as np import os import sys", "\"smiles.npy\")) def calc_range(saliency): vmax = float('-inf') vmin = float('inf') for", "smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency): vmax = float('-inf') vmin", "plt.close() def main(): parser = argparse.ArgumentParser( description='Regression with own dataset.')", "vmin = float('inf') for v in saliency: vmax = max(vmax,", "ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir,", "1., 1. - x, 1. - x else: # Blue", "answer[i] # legend = \"t:{}, p:{}\".format(ans, out) legend = ''", "color_fn=color) def plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction, answer, marker='.') plt.plot([-100, 100],", "matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import", "str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id],", "for i in range(n_split): suffix = str(i) + \"-\" +", "sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import SmilesVisualizer def visualize(dir_path): parent_dir = os.path.dirname(dir_path)", "path = args.dirpath n_split = 5 output = [] answer", "n_split = 5 output = [] answer = [] for", "100], [-100, 100], c='r') max_v = max(np.max(prediction), np.max(answer)) min_v =", "scaler_smooth = get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes) def color(x): if x", "parser = argparse.ArgumentParser( description='Regression with own dataset.') parser.add_argument('--dirpath', '-d', type=str,", "= np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer = SmilesVisualizer()", "# legend = \"t:{}, p:{}\".format(ans, out) legend = '' ext", "id in enumerate(test_idx): smiles = smiles_all[id] out = output[i] ans", "0.0 saliency[nega] = saliency[nega]/(np.abs(minv)) return saliency return scaler scaler_vanilla =", "= np.concatenate(output) answer = np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\")) for", "i, id in enumerate(test_idx): smiles = smiles_all[id] out = output[i]", "return saliency return scaler scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth)", "str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output = np.concatenate(output)", "os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes", "saliency.visualizer.smiles_visualizer import SmilesVisualizer def visualize(dir_path): parent_dir = os.path.dirname(dir_path) saliency_vanilla =", "saliency[pos]/maxv nega = saliency < 0.0 saliency[nega] = saliency[nega]/(np.abs(minv)) return", "smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla,", "import sys import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))", "saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes =", "negative value x *= -1 return 1. - x, 1.", "plt.savefig(save_filepath) plt.close() def main(): parser = argparse.ArgumentParser( description='Regression with own", "scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes) def", "= calc_range(saliency_bayes) def get_scaler(v_range): def scaler(saliency_): saliency = np.copy(saliency_) minv,", "calc_range(saliency_bayes) def get_scaler(v_range): def scaler(saliency_): saliency = np.copy(saliency_) minv, maxv", "= max(vmax, np.max(v)) vmin = min(vmin, np.min(v)) return vmin, vmax", "= os.path.dirname(dir_path) saliency_vanilla = np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\"))", "else: # Blue for negative value x *= -1 return", "\"t:{}, p:{}\".format(ans, out) legend = '' ext = '.png' #", "= float('inf') for v in saliency: vmax = max(vmax, np.max(v))", "0: # Red for positive value return 1., 1. -", "+ \"-\" + str(n_split) print(suffix) visualize(os.path.join(path, suffix)) if __name__ ==", "\"answer.npy\"))) output = np.concatenate(output) answer = np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path,", "saliency: vmax = max(vmax, np.max(v)) vmin = min(vmin, np.min(v)) return", "for v in saliency: vmax = max(vmax, np.max(v)) vmin =", "matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import SmilesVisualizer def visualize(dir_path):", "os import sys import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt", "str(i) + \"-\" + str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix,", "= np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\")) for i in range(n_split):", "visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) + ext), visualize_ratio=1.0, legend=legend,", "for positive value return 1., 1. - x, 1. -", "'.svg' # visualizer.visualize( # saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id) +", "min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath)", "Blue for negative value x *= -1 return 1. -", "sys import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from", "np.load(os.path.join(dir_path, \"answer.npy\")) output = np.load(os.path.join(dir_path, \"output.npy\")) smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\"))", "output[i] ans = answer[i] # legend = \"t:{}, p:{}\".format(ans, out)", "\"result_bayes\", str(id) + ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color) def plot_result(prediction,", "answer, save_filepath=os.path.join(path, \"result.png\")) for i in range(n_split): suffix = str(i)", "= [] for i in range(n_split): suffix = str(i) +", "= np.load(os.path.join(dir_path, \"saliency_vanilla.npy\")) saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path,", "value x *= -1 return 1. - x, 1. -", "- x else: # Blue for negative value x *=", "argparse import numpy as np import os import sys import", "output = np.concatenate(output) answer = np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\"))", "= 5 output = [] answer = [] for i", "saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True)", "= answer[i] # legend = \"t:{}, p:{}\".format(ans, out) legend =", "[] for i in range(n_split): suffix = str(i) + \"-\"", "+ str(n_split) print(suffix) visualize(os.path.join(path, suffix)) if __name__ == '__main__': main()", "= np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir,", "= np.load(os.path.join(dir_path, \"test_idx.npy\")) answer = np.load(os.path.join(dir_path, \"answer.npy\")) output = np.load(os.path.join(dir_path,", "+ ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color) # visualizer.visualize( #", "1. - x, 1. - x, 1. for i, id", "vmin, vmax v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth) v_range_bayes =", "def calc_range(saliency): vmax = float('-inf') vmin = float('inf') for v", "= str(i) + \"-\" + str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path,", "matplotlib.use('agg') import matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import SmilesVisualizer", "maxv == minv: saliency = np.zeros_like(saliency) else: pos = saliency", "numpy as np import os import sys import matplotlib matplotlib.use('agg')", "= str(i) + \"-\" + str(n_split) print(suffix) visualize(os.path.join(path, suffix)) if", "for negative value x *= -1 return 1. - x,", "\"-\" + str(n_split) print(suffix) visualize(os.path.join(path, suffix)) if __name__ == '__main__':", "= '' ext = '.png' # '.svg' # visualizer.visualize( #", "# '.svg' # visualizer.visualize( # saliency_vanilla[id], smiles, save_filepath=os.path.join(parent_dir, \"result_vanilla\", str(id)", "# Red for positive value return 1., 1. - x,", "in enumerate(test_idx): smiles = smiles_all[id] out = output[i] ans =", "- x, 1. - x else: # Blue for negative", "legend = '' ext = '.png' # '.svg' # visualizer.visualize(", "- x, 1. for i, id in enumerate(test_idx): smiles =", "= float('-inf') vmin = float('inf') for v in saliency: vmax", "max(np.max(prediction), np.max(answer)) min_v = min(np.min(prediction), np.min(answer)) plt.xlim([min_v-0.1, max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1,", "vmin = min(vmin, np.min(v)) return vmin, vmax v_range_vanilla = calc_range(saliency_vanilla)", "SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True)", "smiles_all[id] out = output[i] ans = answer[i] # legend =", "vmax = float('-inf') vmin = float('inf') for v in saliency:", "= get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes) def color(x):", "p:{}\".format(ans, out) legend = '' ext = '.png' # '.svg'", "visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color) # visualizer.visualize( # saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir,", "x, 1. - x, 1. for i, id in enumerate(test_idx):", "= np.load(os.path.join(dir_path, \"answer.npy\")) output = np.load(os.path.join(dir_path, \"output.npy\")) smiles_all = np.load(os.path.join(parent_dir,", "== minv: saliency = np.zeros_like(saliency) else: pos = saliency >=", "os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\")) answer = np.load(os.path.join(dir_path,", "= argparse.ArgumentParser( description='Regression with own dataset.') parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32')", "= np.copy(saliency_) minv, maxv = v_range if maxv == minv:", "legend=legend, scaler=scaler_bayes, color_fn=color) def plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction, answer, marker='.')", "x, 1. - x else: # Blue for negative value", "= calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes) def get_scaler(v_range):", "'-d', type=str, default='./results/M_30_3_32_32') args = parser.parse_args() path = args.dirpath n_split", "v_range_smooth = calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes) def get_scaler(v_range): def scaler(saliency_):", "100], c='r') max_v = max(np.max(prediction), np.max(answer)) min_v = min(np.min(prediction), np.min(answer))", "1. - x else: # Blue for negative value x", "def get_scaler(v_range): def scaler(saliency_): saliency = np.copy(saliency_) minv, maxv =", "in range(n_split): suffix = str(i) + \"-\" + str(n_split) print(suffix)", "legend=legend, scaler=scaler_vanilla, color_fn=color) # visualizer.visualize( # saliency_smooth[id], smiles, save_filepath=os.path.join(parent_dir, \"result_smooth\",", "np.load(os.path.join(dir_path, \"output.npy\")) smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency): vmax =", "\"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"),", "= output[i] ans = answer[i] # legend = \"t:{}, p:{}\".format(ans,", "vmax = max(vmax, np.max(v)) vmin = min(vmin, np.min(v)) return vmin,", "saliency >= 0.0 saliency[pos] = saliency[pos]/maxv nega = saliency <", "main(): parser = argparse.ArgumentParser( description='Regression with own dataset.') parser.add_argument('--dirpath', '-d',", "dataset.') parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32') args = parser.parse_args() path =", "[] answer = [] for i in range(n_split): suffix =", "import matplotlib.pyplot as plt sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from saliency.visualizer.smiles_visualizer import SmilesVisualizer def", "from saliency.visualizer.smiles_visualizer import SmilesVisualizer def visualize(dir_path): parent_dir = os.path.dirname(dir_path) saliency_vanilla", "enumerate(test_idx): smiles = smiles_all[id] out = output[i] ans = answer[i]", "= calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes) def get_scaler(v_range): def scaler(saliency_): saliency", "range(n_split): suffix = str(i) + \"-\" + str(n_split) print(suffix) visualize(os.path.join(path,", "= np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency): vmax = float('-inf') vmin =", "save_filepath=os.path.join(path, \"result.png\")) for i in range(n_split): suffix = str(i) +", "+ str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output =", "np.load(os.path.join(dir_path, \"test_idx.npy\")) answer = np.load(os.path.join(dir_path, \"answer.npy\")) output = np.load(os.path.join(dir_path, \"output.npy\"))", "scaler=scaler_bayes, color_fn=color) def plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction, answer, marker='.') plt.plot([-100,", "parser.parse_args() path = args.dirpath n_split = 5 output = []", "save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) + ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color) def", "output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output = np.concatenate(output) answer", "return 1. - x, 1. - x, 1. for i,", "Red for positive value return 1., 1. - x, 1.", "plt.plot([-100, 100], [-100, 100], c='r') max_v = max(np.max(prediction), np.max(answer)) min_v", "= saliency < 0.0 saliency[nega] = saliency[nega]/(np.abs(minv)) return saliency return", "max(vmax, np.max(v)) vmin = min(vmin, np.min(v)) return vmin, vmax v_range_vanilla", "saliency_smooth = np.load(os.path.join(dir_path, \"saliency_smooth.npy\")) saliency_bayes = np.load(os.path.join(dir_path, \"saliency_bayes.npy\")) visualizer =", "\"result_bayes\"), exist_ok=True) test_idx = np.load(os.path.join(dir_path, \"test_idx.npy\")) answer = np.load(os.path.join(dir_path, \"answer.npy\"))", "answer, marker='.') plt.plot([-100, 100], [-100, 100], c='r') max_v = max(np.max(prediction),", "max_v+0.1]) plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close() def main():", "answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output = np.concatenate(output) answer = np.concatenate(answer) plot_result(output,", "\"output.npy\")) smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency): vmax = float('-inf')", "= saliency[nega]/(np.abs(minv)) return saliency return scaler scaler_vanilla = get_scaler(v_range_vanilla) scaler_smooth", "saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) + ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes,", "\"answer.npy\")) output = np.load(os.path.join(dir_path, \"output.npy\")) smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\")) def", "ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color) def plot_result(prediction, answer, save_filepath='result.png'): plt.scatter(prediction,", "nega = saliency < 0.0 saliency[nega] = saliency[nega]/(np.abs(minv)) return saliency", "scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) + ext),", "float('-inf') vmin = float('inf') for v in saliency: vmax =", "*= -1 return 1. - x, 1. - x, 1.", "visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id)", "type=str, default='./results/M_30_3_32_32') args = parser.parse_args() path = args.dirpath n_split =", "str(id) + ext), visualize_ratio=1.0, legend=legend, scaler=scaler_bayes, color_fn=color) def plot_result(prediction, answer,", "# visualize_ratio=1.0, legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\",", "return 1., 1. - x, 1. - x else: #", "np.max(v)) vmin = min(vmin, np.min(v)) return vmin, vmax v_range_vanilla =", "truth\") plt.savefig(save_filepath) plt.close() def main(): parser = argparse.ArgumentParser( description='Regression with", "suffix, \"answer.npy\"))) output = np.concatenate(output) answer = np.concatenate(answer) plot_result(output, answer,", "visualizer = SmilesVisualizer() os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir,", "as np import os import sys import matplotlib matplotlib.use('agg') import", "in range(n_split): suffix = str(i) + \"-\" + str(n_split) output.append(np.load(os.path.join(path,", "minv, maxv = v_range if maxv == minv: saliency =", "answer, save_filepath='result.png'): plt.scatter(prediction, answer, marker='.') plt.plot([-100, 100], [-100, 100], c='r')", "suffix = str(i) + \"-\" + str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\")))", "get_scaler(v_range_vanilla) scaler_smooth = get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes) def color(x): if", "- x, 1. - x, 1. for i, id in", "calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes) def get_scaler(v_range): def scaler(saliency_): saliency =", "calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes) def get_scaler(v_range): def", "saliency[nega] = saliency[nega]/(np.abs(minv)) return saliency return scaler scaler_vanilla = get_scaler(v_range_vanilla)", "answer = [] for i in range(n_split): suffix = str(i)", "os.makedirs(os.path.join(parent_dir, \"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx", "plt.xlabel(\"prediction\") plt.ylim([min_v-0.1, max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close() def main(): parser", "str(id) + ext), # visualize_ratio=1.0, legend=legend, scaler=scaler_vanilla, color_fn=color) # visualizer.visualize(", "= saliency[pos]/maxv nega = saliency < 0.0 saliency[nega] = saliency[nega]/(np.abs(minv))", "= [] answer = [] for i in range(n_split): suffix", "output = [] answer = [] for i in range(n_split):", "if maxv == minv: saliency = np.zeros_like(saliency) else: pos =", "float('inf') for v in saliency: vmax = max(vmax, np.max(v)) vmin", "v_range_vanilla = calc_range(saliency_vanilla) v_range_smooth = calc_range(saliency_smooth) v_range_bayes = calc_range(saliency_bayes) def", "\"result_vanilla\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_smooth\"), exist_ok=True) os.makedirs(os.path.join(parent_dir, \"result_bayes\"), exist_ok=True) test_idx =", "np.copy(saliency_) minv, maxv = v_range if maxv == minv: saliency", "saliency = np.copy(saliency_) minv, maxv = v_range if maxv ==", "answer = np.concatenate(answer) plot_result(output, answer, save_filepath=os.path.join(path, \"result.png\")) for i in", "description='Regression with own dataset.') parser.add_argument('--dirpath', '-d', type=str, default='./results/M_30_3_32_32') args =", "plt.scatter(prediction, answer, marker='.') plt.plot([-100, 100], [-100, 100], c='r') max_v =", "get_scaler(v_range_smooth) scaler_bayes = get_scaler(v_range_bayes) def color(x): if x > 0:", "\"test_idx.npy\")) answer = np.load(os.path.join(dir_path, \"answer.npy\")) output = np.load(os.path.join(dir_path, \"output.npy\")) smiles_all", "\"-\" + str(n_split) output.append(np.load(os.path.join(path, suffix, \"output.npy\"))) answer.append(np.load(os.path.join(path, suffix, \"answer.npy\"))) output", "= np.load(os.path.join(dir_path, \"output.npy\")) smiles_all = np.load(os.path.join(parent_dir, \"smiles.npy\")) def calc_range(saliency): vmax", "v_range_bayes = calc_range(saliency_bayes) def get_scaler(v_range): def scaler(saliency_): saliency = np.copy(saliency_)", "1. for i, id in enumerate(test_idx): smiles = smiles_all[id] out", "positive value return 1., 1. - x, 1. - x", "legend=legend, scaler=scaler_smooth, color_fn=color) visualizer.visualize( saliency_bayes[id], smiles, save_filepath=os.path.join(parent_dir, \"result_bayes\", str(id) +", "max_v+0.1]) plt.ylabel(\"ground truth\") plt.savefig(save_filepath) plt.close() def main(): parser = argparse.ArgumentParser(" ]
[ "(e,ff,f)) r.append('</ul>') except Exception,e: r.append('Could not load directory: %s' %", "style=\"display: none;\">'] try: r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f", "By <NAME> # import os import urllib def dirlist(request): r=['<ul", "<NAME> # import os import urllib def dirlist(request): r=['<ul class=\"jqueryFileTree\"", "ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>') except Exception,e: r.append('Could not", "Exception,e: r.append('Could not load directory: %s' % str(e)) r.append('</ul>') return", "ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f))", "# Python/Django connector script # By <NAME> # import os", "remove dot r.append('<li class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>')", "script # By <NAME> # import os import urllib def", "none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li", "class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>') except Exception,e: r.append('Could", "% (ff,f)) else: e=os.path.splitext(f)[1][1:] # get .ext and remove dot", "for f in os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a", "r.append('</ul>') except Exception,e: r.append('Could not load directory: %s' % str(e))", "d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li class=\"directory", "r.append('<li class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>') except Exception,e:", "jQuery File Tree # Python/Django connector script # By <NAME>", "rel=\"%s/\">%s</a></li>' % (ff,f)) else: e=os.path.splitext(f)[1][1:] # get .ext and remove", "href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f)) else: e=os.path.splitext(f)[1][1:] # get .ext and", "f in os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a href=\"#\"", "style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff):", "r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in os.listdir(d): ff=os.path.join(d,f)", "r.append('<li class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f)) else: e=os.path.splitext(f)[1][1:] #", "Python/Django connector script # By <NAME> # import os import", "def dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] try: r=['<ul class=\"jqueryFileTree\" style=\"display:", "(ff,f)) else: e=os.path.splitext(f)[1][1:] # get .ext and remove dot r.append('<li", "# get .ext and remove dot r.append('<li class=\"file ext_%s\"><a href=\"#\"", "os import urllib def dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] try:", "r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] try: r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp'))", "try: r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in os.listdir(d):", "dot r.append('<li class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>') except", "href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>') except Exception,e: r.append('Could not load", "# # jQuery File Tree # Python/Django connector script #", ".ext and remove dot r.append('<li class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' %", "import urllib def dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] try: r=['<ul", "% (e,ff,f)) r.append('</ul>') except Exception,e: r.append('Could not load directory: %s'", "os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' %", "class=\"jqueryFileTree\" style=\"display: none;\">'] try: r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for", "e=os.path.splitext(f)[1][1:] # get .ext and remove dot r.append('<li class=\"file ext_%s\"><a", "# jQuery File Tree # Python/Django connector script # By", "if os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f)) else:", "class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f)) else: e=os.path.splitext(f)[1][1:] # get", "# import os import urllib def dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display:", "in os.listdir(d): ff=os.path.join(d,f) if os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>'", "collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f)) else: e=os.path.splitext(f)[1][1:] # get .ext", "urllib def dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] try: r=['<ul class=\"jqueryFileTree\"", "else: e=os.path.splitext(f)[1][1:] # get .ext and remove dot r.append('<li class=\"file", "os.path.isdir(ff): r.append('<li class=\"directory collapsed\"><a href=\"#\" rel=\"%s/\">%s</a></li>' % (ff,f)) else: e=os.path.splitext(f)[1][1:]", "get .ext and remove dot r.append('<li class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>'", "none;\">'] try: r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in", "and remove dot r.append('<li class=\"file ext_%s\"><a href=\"#\" rel=\"%s\">%s</a></li>' % (e,ff,f))", "# By <NAME> # import os import urllib def dirlist(request):", "dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">'] try: r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">']", "Tree # Python/Django connector script # By <NAME> # import", "r.append('Could not load directory: %s' % str(e)) r.append('</ul>') return HttpResponse(''.join(r))", "File Tree # Python/Django connector script # By <NAME> #", "import os import urllib def dirlist(request): r=['<ul class=\"jqueryFileTree\" style=\"display: none;\">']", "rel=\"%s\">%s</a></li>' % (e,ff,f)) r.append('</ul>') except Exception,e: r.append('Could not load directory:", "except Exception,e: r.append('Could not load directory: %s' % str(e)) r.append('</ul>')", "class=\"jqueryFileTree\" style=\"display: none;\">'] d=urllib.unquote(request.POST.get('dir','c:\\\\temp')) for f in os.listdir(d): ff=os.path.join(d,f) if", "connector script # By <NAME> # import os import urllib" ]
[ "python3 import torch from .lazy_tensor import LazyTensor from .root_lazy_tensor import", "<filename>gpytorch/lazy/chol_lazy_tensor.py #!/usr/bin/env python3 import torch from .lazy_tensor import LazyTensor from", "that we have a lower triangular matrix if settings.debug.on(): mask", ".. import settings class CholLazyTensor(RootLazyTensor): def __init__(self, chol): if isinstance(chol,", "reduce_inv_quad=reduce_inv_quad ) if logdet: logdet_term = self._chol_diag.pow(2).log().sum(-1) return inv_quad_term, logdet_term", "settings class CholLazyTensor(RootLazyTensor): def __init__(self, chol): if isinstance(chol, LazyTensor): #", "super constructor super(CholLazyTensor, self).__init__(chol) @property def _chol(self): if not hasattr(self,", "inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad ) if logdet: logdet_term = self._chol_diag.pow(2).log().sum(-1) return", "@property def _chol_diag(self): if not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2,", ".root_lazy_tensor import RootLazyTensor from .. import settings class CholLazyTensor(RootLazyTensor): def", "logdet_term = None if inv_quad_rhs is not None: inv_quad_term, _", "= None logdet_term = None if inv_quad_rhs is not None:", "not None: inv_quad_term, _ = super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad", "def _chol(self): if not hasattr(self, \"_chol_memo\"): self._chol_memo = self.root.evaluate() return", "# Check that we have a lower triangular matrix if", "RootLazyTensor from .. import settings class CholLazyTensor(RootLazyTensor): def __init__(self, chol):", "\"_chol_memo\"): self._chol_memo = self.root.evaluate() return self._chol_memo @property def _chol_diag(self): if", "constructor.\") # Run super constructor super(CholLazyTensor, self).__init__(chol) @property def _chol(self):", "self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad ) if logdet: logdet_term = self._chol_diag.pow(2).log().sum(-1)", "isinstance(chol, LazyTensor): # Probably is an instance of NonLazyTensor chol", "inv_quad_term = None logdet_term = None if inv_quad_rhs is not", "LazyTensor from .root_lazy_tensor import RootLazyTensor from .. import settings class", "= self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True):", "self._chol_memo @property def _chol_diag(self): if not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo =", ".lazy_tensor import LazyTensor from .root_lazy_tensor import RootLazyTensor from .. import", "= torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() > 1e-3 and torch.equal(chol,", "chol): if isinstance(chol, LazyTensor): # Probably is an instance of", "a lower triangular matrix if settings.debug.on(): mask = torch.ones(chol.shape[-2:], dtype=chol.dtype,", "take a lower-triangular matrix in the constructor.\") # Run super", "def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term = None logdet_term =", "CholLazyTensor(RootLazyTensor): def __init__(self, chol): if isinstance(chol, LazyTensor): # Probably is", "_ = super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad ) if logdet:", "mask = torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() > 1e-3 and", "torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable should take a lower-triangular matrix in", "dim2=-1).clone() return self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term =", "chol = chol.evaluate() # Check that we have a lower", "constructor super(CholLazyTensor, self).__init__(chol) @property def _chol(self): if not hasattr(self, \"_chol_memo\"):", "is an instance of NonLazyTensor chol = chol.evaluate() # Check", "lower-triangular matrix in the constructor.\") # Run super constructor super(CholLazyTensor,", "logdet=False, reduce_inv_quad=reduce_inv_quad ) if logdet: logdet_term = self._chol_diag.pow(2).log().sum(-1) return inv_quad_term,", "import settings class CholLazyTensor(RootLazyTensor): def __init__(self, chol): if isinstance(chol, LazyTensor):", "None logdet_term = None if inv_quad_rhs is not None: inv_quad_term,", "and torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable should take a lower-triangular matrix", "logdet=False, reduce_inv_quad=True): inv_quad_term = None logdet_term = None if inv_quad_rhs", "torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() > 1e-3 and torch.equal(chol, chol):", "lower triangular matrix if settings.debug.on(): mask = torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1)", "self.root.evaluate() return self._chol_memo @property def _chol_diag(self): if not hasattr(self, \"_chol_diag_memo\"):", "is not None: inv_quad_term, _ = super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False,", "chol.evaluate() # Check that we have a lower triangular matrix", "self).__init__(chol) @property def _chol(self): if not hasattr(self, \"_chol_memo\"): self._chol_memo =", "if inv_quad_rhs is not None: inv_quad_term, _ = super(CholLazyTensor, self).inv_quad_logdet(", "> 1e-3 and torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable should take a", "NonLazyTensor chol = chol.evaluate() # Check that we have a", "we have a lower triangular matrix if settings.debug.on(): mask =", "def _chol_diag(self): if not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone()", "Check that we have a lower triangular matrix if settings.debug.on():", "inv_quad_term, _ = super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad ) if", "chol): raise RuntimeError(\"CholLazyVaraiable should take a lower-triangular matrix in the", "return self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term = None", "hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo def inv_quad_logdet(self,", "self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term", "raise RuntimeError(\"CholLazyVaraiable should take a lower-triangular matrix in the constructor.\")", "from .. import settings class CholLazyTensor(RootLazyTensor): def __init__(self, chol): if", "import LazyTensor from .root_lazy_tensor import RootLazyTensor from .. import settings", "__init__(self, chol): if isinstance(chol, LazyTensor): # Probably is an instance", "if isinstance(chol, LazyTensor): # Probably is an instance of NonLazyTensor", "# Probably is an instance of NonLazyTensor chol = chol.evaluate()", "Probably is an instance of NonLazyTensor chol = chol.evaluate() #", "have a lower triangular matrix if settings.debug.on(): mask = torch.ones(chol.shape[-2:],", "if settings.debug.on(): mask = torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() >", "from .root_lazy_tensor import RootLazyTensor from .. import settings class CholLazyTensor(RootLazyTensor):", "if not hasattr(self, \"_chol_memo\"): self._chol_memo = self.root.evaluate() return self._chol_memo @property", "_chol_diag(self): if not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone() return", "1e-3 and torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable should take a lower-triangular", "inv_quad_rhs is not None: inv_quad_term, _ = super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs,", "= None if inv_quad_rhs is not None: inv_quad_term, _ =", "return self._chol_memo @property def _chol_diag(self): if not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo", "class CholLazyTensor(RootLazyTensor): def __init__(self, chol): if isinstance(chol, LazyTensor): # Probably", "inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term = None logdet_term = None if", "_chol(self): if not hasattr(self, \"_chol_memo\"): self._chol_memo = self.root.evaluate() return self._chol_memo", "# Run super constructor super(CholLazyTensor, self).__init__(chol) @property def _chol(self): if", "should take a lower-triangular matrix in the constructor.\") # Run", "a lower-triangular matrix in the constructor.\") # Run super constructor", "dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() > 1e-3 and torch.equal(chol, chol): raise", "RuntimeError(\"CholLazyVaraiable should take a lower-triangular matrix in the constructor.\") #", "not hasattr(self, \"_chol_memo\"): self._chol_memo = self.root.evaluate() return self._chol_memo @property def", "= super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad ) if logdet: logdet_term", "self._chol_memo = self.root.evaluate() return self._chol_memo @property def _chol_diag(self): if not", "hasattr(self, \"_chol_memo\"): self._chol_memo = self.root.evaluate() return self._chol_memo @property def _chol_diag(self):", "from .lazy_tensor import LazyTensor from .root_lazy_tensor import RootLazyTensor from ..", "super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad ) if logdet: logdet_term =", "= chol.evaluate() # Check that we have a lower triangular", "\"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None,", "settings.debug.on(): mask = torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() > 1e-3", "= self.root.evaluate() return self._chol_memo @property def _chol_diag(self): if not hasattr(self,", "#!/usr/bin/env python3 import torch from .lazy_tensor import LazyTensor from .root_lazy_tensor", "import RootLazyTensor from .. import settings class CholLazyTensor(RootLazyTensor): def __init__(self,", "def __init__(self, chol): if isinstance(chol, LazyTensor): # Probably is an", "torch from .lazy_tensor import LazyTensor from .root_lazy_tensor import RootLazyTensor from", "matrix if settings.debug.on(): mask = torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item()", "if not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo", "super(CholLazyTensor, self).__init__(chol) @property def _chol(self): if not hasattr(self, \"_chol_memo\"): self._chol_memo", "self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False,", "matrix in the constructor.\") # Run super constructor super(CholLazyTensor, self).__init__(chol)", "LazyTensor): # Probably is an instance of NonLazyTensor chol =", "None if inv_quad_rhs is not None: inv_quad_term, _ = super(CholLazyTensor,", "torch.max(chol.mul(mask)).item() > 1e-3 and torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable should take", "not hasattr(self, \"_chol_diag_memo\"): self._chol_diag_memo = self._chol.diagonal(dim1=-2, dim2=-1).clone() return self._chol_diag_memo def", "self._chol_diag_memo def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term = None logdet_term", "of NonLazyTensor chol = chol.evaluate() # Check that we have", "if torch.max(chol.mul(mask)).item() > 1e-3 and torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable should", "inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True): inv_quad_term = None logdet_term = None", "device=chol.device).triu_(1) if torch.max(chol.mul(mask)).item() > 1e-3 and torch.equal(chol, chol): raise RuntimeError(\"CholLazyVaraiable", "@property def _chol(self): if not hasattr(self, \"_chol_memo\"): self._chol_memo = self.root.evaluate()", "reduce_inv_quad=True): inv_quad_term = None logdet_term = None if inv_quad_rhs is", "in the constructor.\") # Run super constructor super(CholLazyTensor, self).__init__(chol) @property", "the constructor.\") # Run super constructor super(CholLazyTensor, self).__init__(chol) @property def", "None: inv_quad_term, _ = super(CholLazyTensor, self).inv_quad_logdet( inv_quad_rhs, logdet=False, reduce_inv_quad=reduce_inv_quad )", "Run super constructor super(CholLazyTensor, self).__init__(chol) @property def _chol(self): if not", "instance of NonLazyTensor chol = chol.evaluate() # Check that we", "triangular matrix if settings.debug.on(): mask = torch.ones(chol.shape[-2:], dtype=chol.dtype, device=chol.device).triu_(1) if", "an instance of NonLazyTensor chol = chol.evaluate() # Check that", "import torch from .lazy_tensor import LazyTensor from .root_lazy_tensor import RootLazyTensor" ]
[ "requestChangeVolume(self, duration, finalVolume, priority): if priority < self.curPriority: return None", "self.reloadAttempt < 1: self.reloadAttempt += 1 if self.isMusic: self.sfx =", "in ambientDict' % name) def requestFadeIn(self, name, duration = 5,", "duration, finalVolume, priority): if priority < self.curPriority: return None self.curPriority", "1 if self.reportCounter % 10 == 0: pass 1 if", "self.activeInterval self.sfx.stop() del self.sfx def play(self): self.sfx.play() def getVolume(self): return", "self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def delete(self): for name in self.ambientDict.keys():", "changeVolumeTask(self, t): curVolume = t * self.masterAmbientVolume self.sfx.setVolume(curVolume) if not", "import LerpFunc, Sequence from direct.showbase.DirectObject import DirectObject class AmbientSound: notify", "def silence(self): for name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority =", "self.sfx.status() == 2: self.sfx.stop() self.curPriority = 0 class AmbientManagerBase(DirectObject): notify", "self.activeInterval.finish() del self.activeInterval self.sfx.stop() del self.sfx def play(self): self.sfx.play() def", "finalVolume, priority) def requestFadeOut(self, name, duration = 5, finalVolume =", "masterAmbientVolume, loop = True, isMusic = False): self.isMusic = isMusic", "del self.ambientDict[name] else: self.notify.warning('music: %s not in ambientDict' % name)", "self.startVolume = 0 self.activeInterval = None def unload(self): if self.activeInterval:", "10 == 0: pass 1 if curVolume > 0 and", "curVolume > 0 and self.sfx.status() == 1: self.sfx.play() if curVolume", "self.startVolume, toData = self.finalVolume, duration = self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self,", "def getVolume(self): return self.sfx.getVolume() def setVolume(self, vol): self.sfx.setVolume(vol) def getLoop(self):", "0 self.duration = 0 self.finalVolume = 0 self.startVolume = 0", "isMusic) self.ambientDict[name] = newAmbient def unload(self, name): if self.ambientDict.has_key(name): self.ambientDict[name].unload()", "if priority < self.curPriority: return None self.curPriority = priority if", "load(self, name, path, looping = True, isMusic = False): retval", "* self.masterAmbientVolume self.sfx.setVolume(curVolume) if not hasattr(self, 'reportCounter'): self.reportCounter = 0", "import AudioSound from direct.directnotify import DirectNotifyGlobal from direct.interval.IntervalGlobal import LerpFunc,", "getVolume(self): return self.sfx.getVolume() def setVolume(self, vol): self.sfx.setVolume(vol) def getLoop(self): return", "if self.sfx: self.sfx.setLoop(self.loop) self.duration = duration self.startVolume = self.getVolume() self.finalVolume", "if self.ambientDict.has_key(name): if self.ambientDict[name].path == path: self.notify.warning('ambient name=%s path=%s already", "path=%s already loaded' % (name, path)) else: self.notify.warning('ambient name %s", "if self.activeInterval: self.activeInterval.pause() del self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData =", "self.duration = duration self.startVolume = self.getVolume() self.finalVolume = finalVolume if", "= loader.loadMusic(path) else: self.sfx = loader.loadSfx(path) self.path = path self.loop", "= { } self.masterAmbientVolume = 1.0 def load(self, name, path,", "hasattr(self, 'reportCounter'): self.reportCounter = 0 self.reportCounter += 1 if self.reportCounter", "def requestChangeVolume(self, duration, finalVolume, priority): if priority < self.curPriority: return", "not self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume if self.activeInterval and", "if self.activeInterval: self.activeInterval.finish() del self.activeInterval self.sfx.stop() del self.sfx def play(self):", "self.sfx = loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop) self.duration = duration self.startVolume", "self.sfx = loader.loadMusic(path) else: self.sfx = loader.loadSfx(path) self.path = path", "path: self.notify.warning('ambient name=%s path=%s already loaded' % (name, path)) else:", "%s' % self.ambientDict[name].path) else: newAmbient = AmbientSound(path, self.masterAmbientVolume, looping, isMusic)", "0 self.curPriority = 0 self.duration = 0 self.finalVolume = 0", "return self.sfx.getVolume() def setVolume(self, vol): self.sfx.setVolume(vol) def getLoop(self): return self.sfx.getLoop()", "path self.loop = loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt", "A (Python 2.4) from pandac.PandaModules import AudioSound from direct.directnotify import", "self.reportCounter += 1 if self.reportCounter % 10 == 0: pass", "self.sfx.status() == 1: self.sfx.play() if curVolume <= 0 and self.sfx.status()", "retval = False if self.ambientDict.has_key(name): if self.ambientDict[name].path == path: self.notify.warning('ambient", "self.ambientDict.has_key(name): if self.ambientDict[name].path == path: self.notify.warning('ambient name=%s path=%s already loaded'", "0 self.startVolume = 0 self.activeInterval = None def unload(self): if", "else: self.sfx = loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop) self.duration = duration", "DirectObject class AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path, masterAmbientVolume,", "self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not self.masterAmbientVolume == newMasterAmbientVolume:", "= 0 self.activeInterval = None def unload(self): if self.activeInterval: self.activeInterval.finish()", "0 self.reportCounter += 1 if self.reportCounter % 10 == 0:", "0 self.activeInterval = None def unload(self): if self.activeInterval: self.activeInterval.finish() del", "self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration, finalVolume, priority): if priority < self.curPriority:", "= 0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestFadeOut(self, name, duration", "def delete(self): for name in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict = {", "self.activeInterval.pause() del self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume, toData", "= 1.0 def load(self, name, path, looping = True, isMusic", "== 0: pass 1 if curVolume > 0 and self.sfx.status()", "= 0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def delete(self): for", "1 if curVolume > 0 and self.sfx.status() == 1: self.sfx.play()", "2.4) from pandac.PandaModules import AudioSound from direct.directnotify import DirectNotifyGlobal from", "self.curPriority: return None self.curPriority = priority if not self.sfx.getActive(): if", "looping = True, isMusic = False): retval = False if", "< 1: self.reloadAttempt += 1 if self.isMusic: self.sfx = loader.loadMusic(self.path)", "direct.directnotify import DirectNotifyGlobal from direct.interval.IntervalGlobal import LerpFunc, Sequence from direct.showbase.DirectObject", "direct.showbase.DirectObject import DirectObject class AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self,", "self.reportCounter % 10 == 0: pass 1 if curVolume >", "in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority = 1) def changeMasterAmbientVolume(self, newMasterAmbientVolume):", "} def silence(self): for name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority", "self.sfx.setVolume(newVol) def changeVolumeTask(self, t): curVolume = t * self.masterAmbientVolume self.sfx.setVolume(curVolume)", "def requestFadeIn(self, name, duration = 5, finalVolume = 1.0, priority", "self.curPriority = 0 self.duration = 0 self.finalVolume = 0 self.startVolume", "masterAmbientVolume self.reloadAttempt = 0 self.curPriority = 0 self.duration = 0", "finalVolume = 1.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority)", "curVolume <= 0 and self.sfx.status() == 2: self.sfx.stop() self.curPriority =", "self.sfx.getActive(): if self.reloadAttempt < 1: self.reloadAttempt += 1 if self.isMusic:", "self.sfx.getLoop() def setLoop(self, loop): self.sfx.setLoop(loop) def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def", "if self.reloadAttempt < 1: self.reloadAttempt += 1 if self.isMusic: self.sfx", "not hasattr(self, 'reportCounter'): self.reportCounter = 0 self.reportCounter += 1 if", "if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def delete(self): for name in", "False): retval = False if self.ambientDict.has_key(name): if self.ambientDict[name].path == path:", "1 if self.isMusic: self.sfx = loader.loadMusic(self.path) else: self.sfx = loader.loadSfx(self.path)", "= self.startVolume, toData = self.finalVolume, duration = self.duration)) self.activeInterval.start() def", "self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume, toData = self.finalVolume, duration", "finalVolume, priority) def requestChangeVolume(self, name, duration, finalVolume, priority = 0):", "__init__(self): self.ambientDict = { } self.masterAmbientVolume = 1.0 def load(self,", "loaded' % (name, path)) else: self.notify.warning('ambient name %s is already", "newMasterAmbientVolume if self.activeInterval and self.activeInterval.isPlaying(): pass elif self.sfx.status() == 2:", "self.sfx.stop() del self.sfx def play(self): self.sfx.play() def getVolume(self): return self.sfx.getVolume()", "= self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not self.masterAmbientVolume ==", "self.masterAmbientVolume = newMasterAmbientVolume if self.activeInterval and self.activeInterval.isPlaying(): pass elif self.sfx.status()", "def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume =", "self.sfx.setLoop(self.loop) self.duration = duration self.startVolume = self.getVolume() self.finalVolume = finalVolume", "(name, path)) else: self.notify.warning('ambient name %s is already bound to", "self.duration = 0 self.finalVolume = 0 self.startVolume = 0 self.activeInterval", "= 0 self.finalVolume = 0 self.startVolume = 0 self.activeInterval =", "def setLoop(self, loop): self.sfx.setLoop(loop) def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self,", "finalVolume = 0.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority)", "self.sfx.status() == 2: newVol = float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol) def", "name, duration = 5, finalVolume = 0.0, priority = 0):", "= 0 class AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict", "= 0 self.startVolume = 0 self.activeInterval = None def unload(self):", "= t * self.masterAmbientVolume self.sfx.setVolume(curVolume) if not hasattr(self, 'reportCounter'): self.reportCounter", "0 and self.sfx.status() == 2: self.sfx.stop() self.curPriority = 0 class", "self.sfx.setLoop(loop) def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration, finalVolume, priority):", "loop = True, isMusic = False): self.isMusic = isMusic if", "import DirectObject class AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path,", "*args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration, finalVolume, priority): if priority <", "= 0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestChangeVolume(self, name, duration,", "if self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name] else: self.notify.warning('music: %s not in", "DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict = { } self.masterAmbientVolume = 1.0", "%s is already bound to %s' % self.ambientDict[name].path) else: newAmbient", "curVolume = t * self.masterAmbientVolume self.sfx.setVolume(curVolume) if not hasattr(self, 'reportCounter'):", "priority = 0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def delete(self):", "> 0 and self.sfx.status() == 1: self.sfx.play() if curVolume <=", "requestChangeVolume(self, name, duration, finalVolume, priority = 0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration,", "= 5, finalVolume = 1.0, priority = 0): self.requestChangeVolume(name, duration,", "self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict = { } def silence(self): for name", "setVolume(self, vol): self.sfx.setVolume(vol) def getLoop(self): return self.sfx.getLoop() def setLoop(self, loop):", "False): self.isMusic = isMusic if self.isMusic: self.sfx = loader.loadMusic(path) else:", "not newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume for name in", "= Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume, toData = self.finalVolume, duration =", "self.isMusic = isMusic if self.isMusic: self.sfx = loader.loadMusic(path) else: self.sfx", "def unload(self, name): if self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name] else: self.notify.warning('music:", "loop): self.sfx.setLoop(loop) def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration, finalVolume,", "self.masterAmbientVolume = 1.0 def load(self, name, path, looping = True,", "__init__(self, path, masterAmbientVolume, loop = True, isMusic = False): self.isMusic", "loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt = 0 self.curPriority", "def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration, finalVolume, priority): if", "self.sfx.play() if curVolume <= 0 and self.sfx.status() == 2: self.sfx.stop()", "== newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume if self.activeInterval and self.activeInterval.isPlaying(): pass", "self.ambientDict[name].path == path: self.notify.warning('ambient name=%s path=%s already loaded' % (name,", "0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def delete(self): for name", "def getLoop(self): return self.sfx.getLoop() def setLoop(self, loop): self.sfx.setLoop(loop) def set3dAttributes(self,", "unload(self, name): if self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name] else: self.notify.warning('music: %s", "if not self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume if self.activeInterval", "self.ambientDict[name] else: self.notify.warning('music: %s not in ambientDict' % name) def", "DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path, masterAmbientVolume, loop = True, isMusic =", "and self.sfx.status() == 2: self.sfx.stop() self.curPriority = 0 class AmbientManagerBase(DirectObject):", "newMasterAmbientVolume): if not self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume if", "priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestFadeOut(self, name,", "path)) else: self.notify.warning('ambient name %s is already bound to %s'", "bound to %s' % self.ambientDict[name].path) else: newAmbient = AmbientSound(path, self.masterAmbientVolume,", "'reportCounter'): self.reportCounter = 0 self.reportCounter += 1 if self.reportCounter %", "self.masterAmbientVolume self.sfx.setVolume(curVolume) if not hasattr(self, 'reportCounter'): self.reportCounter = 0 self.reportCounter", "self.sfx def play(self): self.sfx.play() def getVolume(self): return self.sfx.getVolume() def setVolume(self,", "self.finalVolume = 0 self.startVolume = 0 self.activeInterval = None def", "self.ambientDict = { } def silence(self): for name in self.ambientDict.keys():", "= 5, finalVolume = 0.0, priority = 0): self.requestChangeVolume(name, duration,", "= finalVolume if self.activeInterval: self.activeInterval.pause() del self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask,", "delete(self): for name in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict = { }", "File: A (Python 2.4) from pandac.PandaModules import AudioSound from direct.directnotify", "loader.loadSfx(path) self.path = path self.loop = loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume", "for name in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict = { } def", "name, duration, finalVolume, priority = 0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume,", "from direct.interval.IntervalGlobal import LerpFunc, Sequence from direct.showbase.DirectObject import DirectObject class", "changeMasterAmbientVolume(self, newMasterAmbientVolume): if not newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume", "duration = 5, finalVolume = 1.0, priority = 0): self.requestChangeVolume(name,", "1.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestFadeOut(self,", "name in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict = { } def silence(self):", "2: newVol = float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self, t):", "self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt = 0 self.curPriority = 0 self.duration", "= False if self.ambientDict.has_key(name): if self.ambientDict[name].path == path: self.notify.warning('ambient name=%s", "% name) def requestFadeIn(self, name, duration = 5, finalVolume =", "from direct.directnotify import DirectNotifyGlobal from direct.interval.IntervalGlobal import LerpFunc, Sequence from", "loader.loadMusic(path) else: self.sfx = loader.loadSfx(path) self.path = path self.loop =", "fromData = self.startVolume, toData = self.finalVolume, duration = self.duration)) self.activeInterval.start()", "priority) def requestFadeOut(self, name, duration = 5, finalVolume = 0.0,", "and self.sfx.status() == 1: self.sfx.play() if curVolume <= 0 and", "def requestChangeVolume(self, name, duration, finalVolume, priority = 0): if self.ambientDict.has_key(name):", "= newMasterAmbientVolume if self.activeInterval and self.activeInterval.isPlaying(): pass elif self.sfx.status() ==", "LerpFunc, Sequence from direct.showbase.DirectObject import DirectObject class AmbientSound: notify =", "path, masterAmbientVolume, loop = True, isMusic = False): self.isMusic =", "def load(self, name, path, looping = True, isMusic = False):", "<filename>pirates/audio/AmbientManagerBase.py # File: A (Python 2.4) from pandac.PandaModules import AudioSound", "del self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume, toData =", "notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path, masterAmbientVolume, loop = True,", "< self.curPriority: return None self.curPriority = priority if not self.sfx.getActive():", "self.notify.warning('ambient name %s is already bound to %s' % self.ambientDict[name].path)", "{ } self.masterAmbientVolume = 1.0 def load(self, name, path, looping", "= None def unload(self): if self.activeInterval: self.activeInterval.finish() del self.activeInterval self.sfx.stop()", "self.sfx: self.sfx.setLoop(self.loop) self.duration = duration self.startVolume = self.getVolume() self.finalVolume =", "self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority = 1) def changeMasterAmbientVolume(self, newMasterAmbientVolume): if", "1.0 def load(self, name, path, looping = True, isMusic =", "priority if not self.sfx.getActive(): if self.reloadAttempt < 1: self.reloadAttempt +=", "def setVolume(self, vol): self.sfx.setVolume(vol) def getLoop(self): return self.sfx.getLoop() def setLoop(self,", "= loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt = 0", "duration, finalVolume, priority) def requestChangeVolume(self, name, duration, finalVolume, priority =", "self.sfx.setVolume(curVolume) if not hasattr(self, 'reportCounter'): self.reportCounter = 0 self.reportCounter +=", "changeMasterAmbientVolume(self, newMasterAmbientVolume): if not self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume", "def requestFadeOut(self, name, duration = 5, finalVolume = 0.0, priority", "self.reloadAttempt += 1 if self.isMusic: self.sfx = loader.loadMusic(self.path) else: self.sfx", "# File: A (Python 2.4) from pandac.PandaModules import AudioSound from", "1: self.reloadAttempt += 1 if self.isMusic: self.sfx = loader.loadMusic(self.path) else:", "= DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict = { } self.masterAmbientVolume =", "self.activeInterval: self.activeInterval.pause() del self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume,", "t * self.masterAmbientVolume self.sfx.setVolume(curVolume) if not hasattr(self, 'reportCounter'): self.reportCounter =", "from direct.showbase.DirectObject import DirectObject class AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def", "self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume", "DirectNotifyGlobal from direct.interval.IntervalGlobal import LerpFunc, Sequence from direct.showbase.DirectObject import DirectObject", "newVol = float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self, t): curVolume", "def changeVolumeTask(self, t): curVolume = t * self.masterAmbientVolume self.sfx.setVolume(curVolume) if", "loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop) self.duration = duration self.startVolume = self.getVolume()", "if self.activeInterval and self.activeInterval.isPlaying(): pass elif self.sfx.status() == 2: newVol", "self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def delete(self): for name in self.ambientDict.keys(): self.ambientDict[name].unload()", "class AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict = {", "newMasterAmbientVolume): if not newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume for", "== self.masterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume for name in self.ambientDict.keys(): self.ambientDict[name].changeMasterAmbientVolume(self.masterAmbientVolume)", "AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict = { }", "self.isMusic: self.sfx = loader.loadMusic(path) else: self.sfx = loader.loadSfx(path) self.path =", "self.startVolume = self.getVolume() self.finalVolume = finalVolume if self.activeInterval: self.activeInterval.pause() del", "self.activeInterval and self.activeInterval.isPlaying(): pass elif self.sfx.status() == 2: newVol =", "priority) def delete(self): for name in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict =", "= float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self, t): curVolume =", "already bound to %s' % self.ambientDict[name].path) else: newAmbient = AmbientSound(path,", "def __init__(self): self.ambientDict = { } self.masterAmbientVolume = 1.0 def", "isMusic = False): self.isMusic = isMusic if self.isMusic: self.sfx =", "and self.activeInterval.isPlaying(): pass elif self.sfx.status() == 2: newVol = float(self.finalVolume)", "self.ambientDict[name].unload() self.ambientDict = { } def silence(self): for name in", "<= 0 and self.sfx.status() == 2: self.sfx.stop() self.curPriority = 0", "self.masterAmbientVolume, looping, isMusic) self.ambientDict[name] = newAmbient def unload(self, name): if", "= 1.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority) def", "AudioSound from direct.directnotify import DirectNotifyGlobal from direct.interval.IntervalGlobal import LerpFunc, Sequence", "priority) def requestChangeVolume(self, name, duration, finalVolume, priority = 0): if", "finalVolume, priority = 0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority) def", "name %s is already bound to %s' % self.ambientDict[name].path) else:", "del self.activeInterval self.sfx.stop() del self.sfx def play(self): self.sfx.play() def getVolume(self):", "= AmbientSound(path, self.masterAmbientVolume, looping, isMusic) self.ambientDict[name] = newAmbient def unload(self,", "= masterAmbientVolume self.reloadAttempt = 0 self.curPriority = 0 self.duration =", "name, path, looping = True, isMusic = False): retval =", "= loader.loadSfx(path) self.path = path self.loop = loop self.setLoop(loop) self.setVolume(0)", "self.getVolume() self.finalVolume = finalVolume if self.activeInterval: self.activeInterval.pause() del self.activeInterval self.activeInterval", "== 1: self.sfx.play() if curVolume <= 0 and self.sfx.status() ==", "self.curPriority = 0 class AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self):", "0 self.finalVolume = 0 self.startVolume = 0 self.activeInterval = None", "def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume =", "if self.isMusic: self.sfx = loader.loadMusic(path) else: self.sfx = loader.loadSfx(path) self.path", "self.sfx.stop() self.curPriority = 0 class AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def", "self.sfx.play() def getVolume(self): return self.sfx.getVolume() def setVolume(self, vol): self.sfx.setVolume(vol) def", "if curVolume > 0 and self.sfx.status() == 1: self.sfx.play() if", "= loader.loadMusic(self.path) else: self.sfx = loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop) self.duration", "== 2: self.sfx.stop() self.curPriority = 0 class AmbientManagerBase(DirectObject): notify =", "None self.curPriority = priority if not self.sfx.getActive(): if self.reloadAttempt <", "5, finalVolume = 1.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume,", "elif self.sfx.status() == 2: newVol = float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol)", "return self.sfx.getLoop() def setLoop(self, loop): self.sfx.setLoop(loop) def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args)", "%s not in ambientDict' % name) def requestFadeIn(self, name, duration", "self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume, toData = self.finalVolume,", "newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume if self.activeInterval and self.activeInterval.isPlaying(): pass elif", "isMusic if self.isMusic: self.sfx = loader.loadMusic(path) else: self.sfx = loader.loadSfx(path)", "1) def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume", "self.reloadAttempt = 0 self.curPriority = 0 self.duration = 0 self.finalVolume", "finalVolume, priority) def delete(self): for name in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict", "set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration, finalVolume, priority): if priority", "self.masterAmbientVolume == newMasterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume if self.activeInterval and self.activeInterval.isPlaying():", "0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestChangeVolume(self, name, duration, finalVolume,", "ambientDict' % name) def requestFadeIn(self, name, duration = 5, finalVolume", "= True, isMusic = False): self.isMusic = isMusic if self.isMusic:", "priority = 1) def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not newMasterAmbientVolume ==", "self.notify.warning('music: %s not in ambientDict' % name) def requestFadeIn(self, name,", "self.reportCounter = 0 self.reportCounter += 1 if self.reportCounter % 10", "not self.sfx.getActive(): if self.reloadAttempt < 1: self.reloadAttempt += 1 if", "notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict = { } self.masterAmbientVolume", "self.requestChangeVolume(name, duration, finalVolume, priority) def requestFadeOut(self, name, duration = 5,", "self.sfx = loader.loadSfx(path) self.path = path self.loop = loop self.setLoop(loop)", "self.requestChangeVolume(name, duration, finalVolume, priority) def requestChangeVolume(self, name, duration, finalVolume, priority", "= 0 self.curPriority = 0 self.duration = 0 self.finalVolume =", "import DirectNotifyGlobal from direct.interval.IntervalGlobal import LerpFunc, Sequence from direct.showbase.DirectObject import", "name): if self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name] else: self.notify.warning('music: %s not", "self.curPriority = priority if not self.sfx.getActive(): if self.reloadAttempt < 1:", "duration, finalVolume, priority = 0): if self.ambientDict.has_key(name): self.ambientDict[name].requestChangeVolume(duration, finalVolume, priority)", "newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume for name in self.ambientDict.keys():", "finalVolume if self.activeInterval: self.activeInterval.pause() del self.activeInterval self.activeInterval = Sequence(LerpFunc(self.changeVolumeTask, fromData", "priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestChangeVolume(self, name,", "= 0.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority) def", "pass elif self.sfx.status() == 2: newVol = float(self.finalVolume) * self.masterAmbientVolume", "return None self.curPriority = priority if not self.sfx.getActive(): if self.reloadAttempt", "= newAmbient def unload(self, name): if self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name]", "self.ambientDict = { } self.masterAmbientVolume = 1.0 def load(self, name,", "0 and self.sfx.status() == 1: self.sfx.play() if curVolume <= 0", "self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority = 1) def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not", "0.0, priority = 1) def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not newMasterAmbientVolume", "requestFadeOut(self, name, duration = 5, finalVolume = 0.0, priority =", "% 10 == 0: pass 1 if curVolume > 0", "self.sfx.setVolume(vol) def getLoop(self): return self.sfx.getLoop() def setLoop(self, loop): self.sfx.setLoop(loop) def", "vol): self.sfx.setVolume(vol) def getLoop(self): return self.sfx.getLoop() def setLoop(self, loop): self.sfx.setLoop(loop)", "+= 1 if self.reportCounter % 10 == 0: pass 1", "if not hasattr(self, 'reportCounter'): self.reportCounter = 0 self.reportCounter += 1", "newAmbient = AmbientSound(path, self.masterAmbientVolume, looping, isMusic) self.ambientDict[name] = newAmbient def", "0: pass 1 if curVolume > 0 and self.sfx.status() ==", "requestFadeIn(self, name, duration = 5, finalVolume = 1.0, priority =", "% self.ambientDict[name].path) else: newAmbient = AmbientSound(path, self.masterAmbientVolume, looping, isMusic) self.ambientDict[name]", "duration, finalVolume, priority) def requestFadeOut(self, name, duration = 5, finalVolume", "priority): if priority < self.curPriority: return None self.curPriority = priority", "name=%s path=%s already loaded' % (name, path)) else: self.notify.warning('ambient name", "self.ambientDict[name].path) else: newAmbient = AmbientSound(path, self.masterAmbientVolume, looping, isMusic) self.ambientDict[name] =", "self.finalVolume = finalVolume if self.activeInterval: self.activeInterval.pause() del self.activeInterval self.activeInterval =", "finalVolume, priority): if priority < self.curPriority: return None self.curPriority =", "self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt = 0 self.curPriority =", "setLoop(self, loop): self.sfx.setLoop(loop) def set3dAttributes(self, *args): self.sfx.set3dAttributes(*args) def requestChangeVolume(self, duration,", "to %s' % self.ambientDict[name].path) else: newAmbient = AmbientSound(path, self.masterAmbientVolume, looping,", "name, duration = 5, finalVolume = 1.0, priority = 0):", "getLoop(self): return self.sfx.getLoop() def setLoop(self, loop): self.sfx.setLoop(loop) def set3dAttributes(self, *args):", "toData = self.finalVolume, duration = self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume):", "pandac.PandaModules import AudioSound from direct.directnotify import DirectNotifyGlobal from direct.interval.IntervalGlobal import", "+= 1 if self.isMusic: self.sfx = loader.loadMusic(self.path) else: self.sfx =", "self.activeInterval: self.activeInterval.finish() del self.activeInterval self.sfx.stop() del self.sfx def play(self): self.sfx.play()", "class AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path, masterAmbientVolume, loop", "pass 1 if curVolume > 0 and self.sfx.status() == 1:", "= priority if not self.sfx.getActive(): if self.reloadAttempt < 1: self.reloadAttempt", "def __init__(self, path, masterAmbientVolume, loop = True, isMusic = False):", "self.ambientDict[name].unload() del self.ambientDict[name] else: self.notify.warning('music: %s not in ambientDict' %", "if curVolume <= 0 and self.sfx.status() == 2: self.sfx.stop() self.curPriority", "from pandac.PandaModules import AudioSound from direct.directnotify import DirectNotifyGlobal from direct.interval.IntervalGlobal", "Sequence from direct.showbase.DirectObject import DirectObject class AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound')", "True, isMusic = False): retval = False if self.ambientDict.has_key(name): if", "name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority = 1) def changeMasterAmbientVolume(self,", "else: self.sfx = loader.loadSfx(path) self.path = path self.loop = loop", "= True, isMusic = False): retval = False if self.ambientDict.has_key(name):", "else: self.notify.warning('music: %s not in ambientDict' % name) def requestFadeIn(self,", "for name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority = 1) def", "= DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path, masterAmbientVolume, loop = True, isMusic", "AmbientSound: notify = DirectNotifyGlobal.directNotify.newCategory('AmbientSound') def __init__(self, path, masterAmbientVolume, loop =", "self.ambientDict[name] = newAmbient def unload(self, name): if self.ambientDict.has_key(name): self.ambientDict[name].unload() del", "path, looping = True, isMusic = False): retval = False", "self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt = 0 self.curPriority = 0", "already loaded' % (name, path)) else: self.notify.warning('ambient name %s is", "} self.masterAmbientVolume = 1.0 def load(self, name, path, looping =", "(Python 2.4) from pandac.PandaModules import AudioSound from direct.directnotify import DirectNotifyGlobal", "float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self, t): curVolume = t", "self.notify.warning('ambient name=%s path=%s already loaded' % (name, path)) else: self.notify.warning('ambient", "2: self.sfx.stop() self.curPriority = 0 class AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase')", "if not newMasterAmbientVolume == self.masterAmbientVolume: self.masterAmbientVolume = newMasterAmbientVolume for name", "isMusic = False): retval = False if self.ambientDict.has_key(name): if self.ambientDict[name].path", "= loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop) self.duration = duration self.startVolume =", "self.activeInterval.isPlaying(): pass elif self.sfx.status() == 2: newVol = float(self.finalVolume) *", "= 0 self.duration = 0 self.finalVolume = 0 self.startVolume =", "0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestFadeOut(self, name, duration =", "play(self): self.sfx.play() def getVolume(self): return self.sfx.getVolume() def setVolume(self, vol): self.sfx.setVolume(vol)", "0.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume, priority) def requestChangeVolume(self,", "self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self, t): curVolume = t * self.masterAmbientVolume", "1: self.sfx.play() if curVolume <= 0 and self.sfx.status() == 2:", "= False): self.isMusic = isMusic if self.isMusic: self.sfx = loader.loadMusic(path)", "silence(self): for name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0, priority = 1)", "if self.ambientDict[name].path == path: self.notify.warning('ambient name=%s path=%s already loaded' %", "= { } def silence(self): for name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0,", "= 0 self.reportCounter += 1 if self.reportCounter % 10 ==", "is already bound to %s' % self.ambientDict[name].path) else: newAmbient =", "self.sfx.getVolume() def setVolume(self, vol): self.sfx.setVolume(vol) def getLoop(self): return self.sfx.getLoop() def", "== path: self.notify.warning('ambient name=%s path=%s already loaded' % (name, path))", "del self.sfx def play(self): self.sfx.play() def getVolume(self): return self.sfx.getVolume() def", "AmbientSound(path, self.masterAmbientVolume, looping, isMusic) self.ambientDict[name] = newAmbient def unload(self, name):", "= 1) def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not newMasterAmbientVolume == self.masterAmbientVolume:", "* self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self, t): curVolume = t *", "self.sfx = loader.loadMusic(self.path) else: self.sfx = loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop)", "else: newAmbient = AmbientSound(path, self.masterAmbientVolume, looping, isMusic) self.ambientDict[name] = newAmbient", "if self.reportCounter % 10 == 0: pass 1 if curVolume", "True, isMusic = False): self.isMusic = isMusic if self.isMusic: self.sfx", "if self.isMusic: self.sfx = loader.loadMusic(self.path) else: self.sfx = loader.loadSfx(self.path) if", "duration self.startVolume = self.getVolume() self.finalVolume = finalVolume if self.activeInterval: self.activeInterval.pause()", "t): curVolume = t * self.masterAmbientVolume self.sfx.setVolume(curVolume) if not hasattr(self,", "False if self.ambientDict.has_key(name): if self.ambientDict[name].path == path: self.notify.warning('ambient name=%s path=%s", "in self.ambientDict.keys(): self.ambientDict[name].unload() self.ambientDict = { } def silence(self): for", "{ } def silence(self): for name in self.ambientDict.keys(): self.ambientDict[name].requestChangeVolume(0.0, 0.0,", "Sequence(LerpFunc(self.changeVolumeTask, fromData = self.startVolume, toData = self.finalVolume, duration = self.duration))", "% (name, path)) else: self.notify.warning('ambient name %s is already bound", "= False): retval = False if self.ambientDict.has_key(name): if self.ambientDict[name].path ==", "self.loop = loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume self.reloadAttempt =", "None def unload(self): if self.activeInterval: self.activeInterval.finish() del self.activeInterval self.sfx.stop() del", "name) def requestFadeIn(self, name, duration = 5, finalVolume = 1.0,", "def play(self): self.sfx.play() def getVolume(self): return self.sfx.getVolume() def setVolume(self, vol):", "if not self.sfx.getActive(): if self.reloadAttempt < 1: self.reloadAttempt += 1", "self.finalVolume, duration = self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not", "self.activeInterval = None def unload(self): if self.activeInterval: self.activeInterval.finish() del self.activeInterval", "newAmbient def unload(self, name): if self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name] else:", "def unload(self): if self.activeInterval: self.activeInterval.finish() del self.activeInterval self.sfx.stop() del self.sfx", "self.path = path self.loop = loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume =", "= isMusic if self.isMusic: self.sfx = loader.loadMusic(path) else: self.sfx =", "not in ambientDict' % name) def requestFadeIn(self, name, duration =", "else: self.notify.warning('ambient name %s is already bound to %s' %", "= path self.loop = loop self.setLoop(loop) self.setVolume(0) self.masterAmbientVolume = masterAmbientVolume", "loader.loadMusic(self.path) else: self.sfx = loader.loadSfx(self.path) if self.sfx: self.sfx.setLoop(self.loop) self.duration =", "= duration self.startVolume = self.getVolume() self.finalVolume = finalVolume if self.activeInterval:", "unload(self): if self.activeInterval: self.activeInterval.finish() del self.activeInterval self.sfx.stop() del self.sfx def", "duration = self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume): if not self.masterAmbientVolume", "5, finalVolume = 0.0, priority = 0): self.requestChangeVolume(name, duration, finalVolume,", "0 class AmbientManagerBase(DirectObject): notify = DirectNotifyGlobal.directNotify.newCategory('AmbientManagerBase') def __init__(self): self.ambientDict =", "duration = 5, finalVolume = 0.0, priority = 0): self.requestChangeVolume(name,", "= self.finalVolume, duration = self.duration)) self.activeInterval.start() def changeMasterAmbientVolume(self, newMasterAmbientVolume): if", "self.ambientDict.has_key(name): self.ambientDict[name].unload() del self.ambientDict[name] else: self.notify.warning('music: %s not in ambientDict'", "looping, isMusic) self.ambientDict[name] = newAmbient def unload(self, name): if self.ambientDict.has_key(name):", "== 2: newVol = float(self.finalVolume) * self.masterAmbientVolume self.sfx.setVolume(newVol) def changeVolumeTask(self,", "direct.interval.IntervalGlobal import LerpFunc, Sequence from direct.showbase.DirectObject import DirectObject class AmbientSound:", "self.isMusic: self.sfx = loader.loadMusic(self.path) else: self.sfx = loader.loadSfx(self.path) if self.sfx:", "priority < self.curPriority: return None self.curPriority = priority if not", "= self.getVolume() self.finalVolume = finalVolume if self.activeInterval: self.activeInterval.pause() del self.activeInterval" ]
[ "print import_target.x import import_target import_target.foo() c = import_target.C() print import_target.import_nested_target.y", "_multiprocessing del _multiprocessing del sys.modules[\"_multiprocessing\"] import _multiprocessing import time del", "import import_target import_target.foo() c = import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar() d", "import_target.import_nested_target.D() print \"testing importfrom:\" from import_target import x as z", "_multiprocessing import time del time del sys.modules[\"time\"] import time print", "be 15:\",import_nested_target import import_nested_target print import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y =", "c = import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar() d = import_target.import_nested_target.D() print", "15:\",import_nested_target import import_nested_target print import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y = import_nested_target.y", "import_nested_target.y import_target.import_nested_target.y = import_nested_target.y + 1 print import_nested_target.y print z", "z print z import_nested_target = 15 from import_nested_target import y", "= 15 from import_nested_target import y print \"This should still", "x as z print z import_nested_target = 15 from import_nested_target", "y print \"This should still be 15:\",import_nested_target import import_nested_target print", "print import_nested_target.y print z print y print __name__ print __import__(\"import_target\")", "as z print z import_nested_target = 15 from import_nested_target import", "1 print import_nested_target.y print z print y print __name__ print", "__import__(\"import_target\") is import_target import sys import _multiprocessing del _multiprocessing del", "del _multiprocessing del sys.modules[\"_multiprocessing\"] import _multiprocessing import time del time", "\"testing importfrom:\" from import_target import x as z print z", "import import_nested_target print import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y = import_nested_target.y +", "import time del time del sys.modules[\"time\"] import time print time.sleep(0)", "d = import_target.import_nested_target.D() print \"testing importfrom:\" from import_target import x", "import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y = import_nested_target.y + 1 print import_nested_target.y", "print y print __name__ print __import__(\"import_target\") is import_target import sys", "import_target.import_nested_target.y = import_nested_target.y + 1 print import_nested_target.y print z print", "\"This should still be 15:\",import_nested_target import import_nested_target print import_nested_target.__name__ print", "import_nested_target.y + 1 print import_nested_target.y print z print y print", "print __name__ print __import__(\"import_target\") is import_target import sys import _multiprocessing", "import_nested_target import y print \"This should still be 15:\",import_nested_target import", "import_target.x import import_target import_target.foo() c = import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar()", "import y print \"This should still be 15:\",import_nested_target import import_nested_target", "import_nested_target print import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y = import_nested_target.y + 1", "print \"This should still be 15:\",import_nested_target import import_nested_target print import_nested_target.__name__", "= import_target.import_nested_target.D() print \"testing importfrom:\" from import_target import x as", "print import_nested_target.y import_target.import_nested_target.y = import_nested_target.y + 1 print import_nested_target.y print", "is import_target import sys import _multiprocessing del _multiprocessing del sys.modules[\"_multiprocessing\"]", "import_target import sys import _multiprocessing del _multiprocessing del sys.modules[\"_multiprocessing\"] import", "__name__ print __import__(\"import_target\") is import_target import sys import _multiprocessing del", "import_target.import_nested_target.bar() d = import_target.import_nested_target.D() print \"testing importfrom:\" from import_target import", "print \"testing importfrom:\" from import_target import x as z print", "sys.modules[\"_multiprocessing\"] import _multiprocessing import time del time del sys.modules[\"time\"] import", "import import_target print import_target.x import import_target import_target.foo() c = import_target.C()", "import_target import_target.foo() c = import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar() d =", "import_target.foo() c = import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar() d = import_target.import_nested_target.D()", "import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar() d = import_target.import_nested_target.D() print \"testing importfrom:\"", "import _multiprocessing del _multiprocessing del sys.modules[\"_multiprocessing\"] import _multiprocessing import time", "from import_nested_target import y print \"This should still be 15:\",import_nested_target", "sys import _multiprocessing del _multiprocessing del sys.modules[\"_multiprocessing\"] import _multiprocessing import", "= import_nested_target.y + 1 print import_nested_target.y print z print y", "z print y print __name__ print __import__(\"import_target\") is import_target import", "import _multiprocessing import time del time del sys.modules[\"time\"] import time", "del sys.modules[\"_multiprocessing\"] import _multiprocessing import time del time del sys.modules[\"time\"]", "from import_target import x as z print z import_nested_target =", "import_nested_target = 15 from import_nested_target import y print \"This should", "print import_target.import_nested_target.y import_target.import_nested_target.bar() d = import_target.import_nested_target.D() print \"testing importfrom:\" from", "print __import__(\"import_target\") is import_target import sys import _multiprocessing del _multiprocessing", "<gh_stars>1-10 import import_target print import_target.x import import_target import_target.foo() c =", "+ 1 print import_nested_target.y print z print y print __name__", "= import_target.C() print import_target.import_nested_target.y import_target.import_nested_target.bar() d = import_target.import_nested_target.D() print \"testing", "print z print y print __name__ print __import__(\"import_target\") is import_target", "y print __name__ print __import__(\"import_target\") is import_target import sys import", "importfrom:\" from import_target import x as z print z import_nested_target", "import_target print import_target.x import import_target import_target.foo() c = import_target.C() print", "import x as z print z import_nested_target = 15 from", "still be 15:\",import_nested_target import import_nested_target print import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y", "print import_nested_target.__name__ print import_nested_target.y import_target.import_nested_target.y = import_nested_target.y + 1 print", "import sys import _multiprocessing del _multiprocessing del sys.modules[\"_multiprocessing\"] import _multiprocessing", "import_target.import_nested_target.y import_target.import_nested_target.bar() d = import_target.import_nested_target.D() print \"testing importfrom:\" from import_target", "should still be 15:\",import_nested_target import import_nested_target print import_nested_target.__name__ print import_nested_target.y", "z import_nested_target = 15 from import_nested_target import y print \"This", "15 from import_nested_target import y print \"This should still be", "import_nested_target.y print z print y print __name__ print __import__(\"import_target\") is", "print z import_nested_target = 15 from import_nested_target import y print", "_multiprocessing del sys.modules[\"_multiprocessing\"] import _multiprocessing import time del time del", "import_target import x as z print z import_nested_target = 15" ]
[ "= Signal() def __init__(self, data, parent=None): super().__init__(parent) self._data = data", "v self.reset_disabled_values() self.update_gui() @property def rows(self): return self.data.shape[0] @property def", "return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property", "i in range(self.rows): for j in range(self.cols): w = self.widget(i,", "[QSignalBlocker(w) for w in self.all_widgets] # noqa: F841 for i", "rows, cols = [int(x) for x in sys.argv[1].split('x')] data =", "from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout if len(sys.argv) < 2:", "= s self.update_tooltips() self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid = False self.matrix_invalid_reason", "raise AttributeError(msg) self._data = v self.reset_disabled_values() self.update_gui() @property def rows(self):", "= 0.0 self.apply_constraints() self.update_gui() @property def enabled_style_sheet(self): if self.matrix_invalid: return", "tooltip = '' for w in self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self):", "i in range(self.rows): for j in range(self.cols): if not self.widget(i,", "data updates # to apply equality constraints. self._apply_constraints_func = None", "in self.enabled_elements w.setEnabled(enable) enabled_str = 'enabled' if enable else 'disabled'", "self.add_spin_boxes() self.update_gui() def add_spin_boxes(self): layout = self.layout() for i in", "as np from PySide2.QtCore import QSignalBlocker, Signal from PySide2.QtWidgets import", "for i in range(self.rows): for j in range(self.cols): if (i,", "def enabled_elements(self, v): if self._enabled_elements != v: self._enabled_elements = v", "def enabled_elements(self): return self._enabled_elements @enabled_elements.setter def enabled_elements(self, v): if self._enabled_elements", "f'{self._data.shape}') raise AttributeError(msg) self._data = v self.reset_disabled_values() self.update_gui() @property def", "to zero, then applies constraints for i in range(self.rows): for", "for j in range(self.cols): self.set_gui_value(i, j, v[i][j]) @property def all_widgets(self):", "self._enabled_elements = None # If this is set, it will", "row_range] @gui_data.setter def gui_data(self, v): blockers = [QSignalBlocker(w) for w", "INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property def", "only the elements present in the # list (as (i,", "PySide2.QtWidgets import QGridLayout, QWidget from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET =", "self._apply_constraints_func = v self.apply_constraints() def apply_constraints(self): if (func := self.apply_constraints_func)", "the data updates # to apply equality constraints. self._apply_constraints_func =", "j).isEnabled(): self.data[i, j] = 0.0 self.apply_constraints() self.update_gui() @property def enabled_style_sheet(self):", "in self.enabled_elements: widgets.append(self.widget(i, j)) return widgets def widget(self, row, col):", "range(self.rows): for j in range(self.cols): if (i, j) in self.enabled_elements:", "func(self.data) self.update_gui() if __name__ == '__main__': import sys from PySide2.QtWidgets", "self.matrix_invalid_reason = '' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def add_spin_boxes(self): layout =", "update_data(self): self.data[:] = self.gui_data self.apply_constraints() self.data_modified.emit() def update_gui(self): self.gui_data =", "!= v: self._enabled_elements = v self.update_enable_states() self.reset_disabled_values() @property def apply_constraints_func(self):", "<matrix_size>') rows, cols = [int(x) for x in sys.argv[1].split('x')] data", "def set_matrix_invalid(self, s): self.matrix_invalid = True self.matrix_invalid_reason = s self.update_tooltips()", "else 'disabled' style_sheet = getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self): #", "if self._apply_constraints_func != v: self._apply_constraints_func = v self.apply_constraints() def apply_constraints(self):", "'' for w in self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self): enable_all =", "items) will be enabled. self._enabled_elements = None # If this", "Resets all disabled values to zero, then applies constraints for", "i in row_range] @gui_data.setter def gui_data(self, v): blockers = [QSignalBlocker(w)", "v): if self._enabled_elements != v: self._enabled_elements = v self.update_enable_states() self.reset_disabled_values()", "np.ones((rows, cols)) app = QApplication(sys.argv) dialog = QDialog() layout =", "import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0'", "range(self.rows): for j in range(self.cols): w = self.widget(i, j) enable", "j, v[i][j]) @property def all_widgets(self): row_range = range(self.rows) col_range =", "@property def cols(self): return self.data.shape[1] def update_data(self): self.data[:] = self.gui_data", "not the matrix is currently invalid self.matrix_invalid = False #", "self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET", "widgets def widget(self, row, col): return self.layout().itemAtPosition(row, col).widget() def gui_value(self,", "to apply equality constraints. self._apply_constraints_func = None # Whether or", "self._enabled_elements != v: self._enabled_elements = v self.update_enable_states() self.reset_disabled_values() @property def", "= QApplication(sys.argv) dialog = QDialog() layout = QVBoxLayout() dialog.setLayout(layout) editor", "in range(self.cols): sb = self.create_spin_box() layout.addWidget(sb, i, j) def create_spin_box(self):", "v): if self._apply_constraints_func != v: self._apply_constraints_func = v self.apply_constraints() def", "sb.valueChanged.connect(self.element_modified) return sb def element_modified(self): self.update_data() @property def data(self): return", "DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color: red' class MatrixEditor(QWidget):", "def reset_disabled_values(self): # Resets all disabled values to zero, then", "constraints for i in range(self.rows): for j in range(self.cols): if", "@property def gui_data(self): row_range = range(self.rows) col_range = range(self.cols) return", "'enabled' if enable else 'disabled' style_sheet = getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet)", "for i in row_range] @gui_data.setter def gui_data(self, v): blockers =", "if self._data.shape != v.shape: msg = (f'Shape {v.shape} does not", "rows(self): return self.data.shape[0] @property def cols(self): return self.data.shape[1] def update_data(self):", "QDialog() layout = QVBoxLayout() dialog.setLayout(layout) editor = MatrixEditor(data) layout.addWidget(editor) #", "for j in range(self.cols): if (i, j) in self.enabled_elements: widgets.append(self.widget(i,", "col_range] for i in row_range] @gui_data.setter def gui_data(self, v): blockers", "s): self.matrix_invalid = True self.matrix_invalid_reason = s self.update_tooltips() self.update_enable_states() def", "[[self.gui_value(i, j) for j in col_range] for i in row_range]", "= v self.update_enable_states() self.reset_disabled_values() @property def apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter", "(as (i, j) items) will be enabled. self._enabled_elements = None", "return [[self.gui_value(i, j) for j in col_range] for i in", "numpy as np from PySide2.QtCore import QSignalBlocker, Signal from PySide2.QtWidgets", "for j in range(self.cols): sb = self.create_spin_box() layout.addWidget(sb, i, j)", "__name__ == '__main__': import sys from PySide2.QtWidgets import QApplication, QDialog,", "in the # list (as (i, j) items) will be", "self.widget(row, col).value() def set_gui_value(self, row, col, val): self.widget(row, col).setValue(val) def", "j in range(self.cols): if not self.widget(i, j).isEnabled(): self.data[i, j] =", "data, parent=None): super().__init__(parent) self._data = data # If this is", "self._data = data # If this is not None, then", "will be enabled. self._enabled_elements = None # If this is", "'background-color: red' class MatrixEditor(QWidget): data_modified = Signal() def __init__(self, data,", "w in self.all_widgets] # noqa: F841 for i in range(self.rows):", "v: self._enabled_elements = v self.update_enable_states() self.reset_disabled_values() @property def apply_constraints_func(self): return", "for w in self.all_widgets] # noqa: F841 for i in", "every time the data updates # to apply equality constraints.", "then only the elements present in the # list (as", "def rows(self): return self.data.shape[0] @property def cols(self): return self.data.shape[1] def", "in self.all_widgets] # noqa: F841 for i in range(self.rows): for", "in range(self.cols): if (i, j) in self.enabled_elements: widgets.append(self.widget(i, j)) return", "def add_spin_boxes(self): layout = self.layout() for i in range(self.rows): for", "= range(self.rows) col_range = range(self.cols) return [[self.gui_value(i, j) for j", "col_range for i in row_range] @property def enabled_widgets(self): widgets =", "= QDialog() layout = QVBoxLayout() dialog.setLayout(layout) editor = MatrixEditor(data) layout.addWidget(editor)", "4)] # editor.apply_constraints_func = constraints def on_data_modified(): print(f'Data modified: {editor.data}')", "set_matrix_invalid(self, s): self.matrix_invalid = True self.matrix_invalid_reason = s self.update_tooltips() self.update_enable_states()", "sys.argv[1].split('x')] data = np.ones((rows, cols)) app = QApplication(sys.argv) dialog =", "= None # If this is set, it will be", "def cols(self): return self.data.shape[1] def update_data(self): self.data[:] = self.gui_data self.apply_constraints()", "self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self, v): if self._apply_constraints_func != v: self._apply_constraints_func", "j)) return widgets def widget(self, row, col): return self.layout().itemAtPosition(row, col).widget()", "set_gui_value(self, row, col, val): self.widget(row, col).setValue(val) def set_matrix_invalid(self, s): self.matrix_invalid", "= x[1][1] # editor.enabled_elements = [(1, 1), (3, 4)] #", "if not np.array_equal(self._data, v): if self._data.shape != v.shape: msg =", "col, val): self.widget(row, col).setValue(val) def set_matrix_invalid(self, s): self.matrix_invalid = True", "self.apply_constraints() self.update_gui() @property def enabled_style_sheet(self): if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return", "self.all_widgets] # noqa: F841 for i in range(self.rows): for j", "<script> <matrix_size>') rows, cols = [int(x) for x in sys.argv[1].split('x')]", "self.widget(i, j) enable = enable_all or (i, j) in self.enabled_elements", "range(self.cols): sb = self.create_spin_box() layout.addWidget(sb, i, j) def create_spin_box(self): sb", "dialog.setLayout(layout) editor = MatrixEditor(data) layout.addWidget(editor) # def constraints(x): # x[2][2]", "= False self.matrix_invalid_reason = '' self.update_tooltips() self.update_enable_states() def update_tooltips(self): if", "= [int(x) for x in sys.argv[1].split('x')] data = np.ones((rows, cols))", "app = QApplication(sys.argv) dialog = QDialog() layout = QVBoxLayout() dialog.setLayout(layout)", "self.apply_constraints() def apply_constraints(self): if (func := self.apply_constraints_func) is None: return", "in row_range] @property def enabled_widgets(self): widgets = [] for i", "[int(x) for x in sys.argv[1].split('x')] data = np.ones((rows, cols)) app", "= enable_all or (i, j) in self.enabled_elements w.setEnabled(enable) enabled_str =", "row_range = range(self.rows) col_range = range(self.cols) return [[self.gui_value(i, j) for", "def constraints(x): # x[2][2] = x[1][1] # editor.enabled_elements = [(1,", "PySide2.QtCore import QSignalBlocker, Signal from PySide2.QtWidgets import QGridLayout, QWidget from", "self.matrix_invalid = False self.matrix_invalid_reason = '' self.update_tooltips() self.update_enable_states() def update_tooltips(self):", "self.gui_data self.apply_constraints() self.data_modified.emit() def update_gui(self): self.gui_data = self.data @property def", "does not match original shape ' f'{self._data.shape}') raise AttributeError(msg) self._data", "self.matrix_invalid_reason = '' self.update_tooltips() self.update_enable_states() def update_tooltips(self): if self.matrix_invalid: tooltip", "def create_spin_box(self): sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb def", "self.data_modified.emit() def update_gui(self): self.gui_data = self.data @property def gui_data(self): row_range", "range(self.rows): for j in range(self.cols): self.set_gui_value(i, j, v[i][j]) @property def", "element_modified(self): self.update_data() @property def data(self): return self._data @data.setter def data(self,", "return sb def element_modified(self): self.update_data() @property def data(self): return self._data", "= v self.apply_constraints() def apply_constraints(self): if (func := self.apply_constraints_func) is", "(3, 4)] # editor.apply_constraints_func = constraints def on_data_modified(): print(f'Data modified:", "range(self.cols): if (i, j) in self.enabled_elements: widgets.append(self.widget(i, j)) return widgets", "= '' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def add_spin_boxes(self): layout = self.layout()", "j) def create_spin_box(self): sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb", "data(self, v): if not np.array_equal(self._data, v): if self._data.shape != v.shape:", "{v.shape} does not match original shape ' f'{self._data.shape}') raise AttributeError(msg)", "v): if self._data.shape != v.shape: msg = (f'Shape {v.shape} does", "in range(self.rows): for j in range(self.cols): if (i, j) in", "in self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self): enable_all = self.enabled_elements is None", "range(self.rows) col_range = range(self.cols) return [[self.gui_value(i, j) for j in", "self.set_gui_value(i, j, v[i][j]) @property def all_widgets(self): row_range = range(self.rows) col_range", "def update_enable_states(self): enable_all = self.enabled_elements is None for i in", "def update_data(self): self.data[:] = self.gui_data self.apply_constraints() self.data_modified.emit() def update_gui(self): self.gui_data", "def __init__(self, data, parent=None): super().__init__(parent) self._data = data # If", "zero, then applies constraints for i in range(self.rows): for j", "Reason the matrix is currently invalid self.matrix_invalid_reason = '' self.setLayout(QGridLayout())", "this is not None, then only the elements present in", "# x[2][2] = x[1][1] # editor.enabled_elements = [(1, 1), (3,", "self.enabled_elements is None for i in range(self.rows): for j in", "not None, then only the elements present in the #", "blockers = [QSignalBlocker(w) for w in self.all_widgets] # noqa: F841", "= QVBoxLayout() dialog.setLayout(layout) editor = MatrixEditor(data) layout.addWidget(editor) # def constraints(x):", "import QGridLayout, QWidget from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color:", "' f'{self._data.shape}') raise AttributeError(msg) self._data = v self.reset_disabled_values() self.update_gui() @property", "will be called every time the data updates # to", "= self.data @property def gui_data(self): row_range = range(self.rows) col_range =", "range(self.cols): w = self.widget(i, j) enable = enable_all or (i,", "it will be called every time the data updates #", "range(self.rows): for j in range(self.cols): sb = self.create_spin_box() layout.addWidget(sb, i,", "QGridLayout, QWidget from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white'", "return [self.widget(i, j) for j in col_range for i in", "F841 for i in range(self.rows): for j in range(self.cols): self.set_gui_value(i,", "self.reset_disabled_values() self.update_gui() @property def rows(self): return self.data.shape[0] @property def cols(self):", "= '' for w in self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self): enable_all", "v): blockers = [QSignalBlocker(w) for w in self.all_widgets] # noqa:", "self.layout().itemAtPosition(row, col).widget() def gui_value(self, row, col): return self.widget(row, col).value() def", "the elements present in the # list (as (i, j)", "j in range(self.cols): sb = self.create_spin_box() layout.addWidget(sb, i, j) def", "else: tooltip = '' for w in self.enabled_widgets: w.setToolTip(tooltip) def", "import sys from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout if len(sys.argv)", "dialog = QDialog() layout = QVBoxLayout() dialog.setLayout(layout) editor = MatrixEditor(data)", "self.matrix_invalid = True self.matrix_invalid_reason = s self.update_tooltips() self.update_enable_states() def set_matrix_valid(self):", "self.apply_constraints() self.data_modified.emit() def update_gui(self): self.gui_data = self.data @property def gui_data(self):", "sb = self.create_spin_box() layout.addWidget(sb, i, j) def create_spin_box(self): sb =", "0.0 self.apply_constraints() self.update_gui() @property def enabled_style_sheet(self): if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET", "def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self): return self._enabled_elements @enabled_elements.setter", "self.update_data() @property def data(self): return self._data @data.setter def data(self, v):", "in range(self.rows): for j in range(self.cols): self.set_gui_value(i, j, v[i][j]) @property", "j) items) will be enabled. self._enabled_elements = None # If", "# Resets all disabled values to zero, then applies constraints", "for i in range(self.rows): for j in range(self.cols): self.set_gui_value(i, j,", "(i, j) items) will be enabled. self._enabled_elements = None #", "@property def data(self): return self._data @data.setter def data(self, v): if", "set_matrix_valid(self): self.matrix_invalid = False self.matrix_invalid_reason = '' self.update_tooltips() self.update_enable_states() def", "self.matrix_invalid_reason else: tooltip = '' for w in self.enabled_widgets: w.setToolTip(tooltip)", "enabled_str = 'enabled' if enable else 'disabled' style_sheet = getattr(self,", "@enabled_elements.setter def enabled_elements(self, v): if self._enabled_elements != v: self._enabled_elements =", "j) for j in col_range] for i in row_range] @gui_data.setter", "v: self._apply_constraints_func = v self.apply_constraints() def apply_constraints(self): if (func :=", "widgets.append(self.widget(i, j)) return widgets def widget(self, row, col): return self.layout().itemAtPosition(row,", "self.data[i, j] = 0.0 self.apply_constraints() self.update_gui() @property def enabled_style_sheet(self): if", "i in range(self.rows): for j in range(self.cols): sb = self.create_spin_box()", "class MatrixEditor(QWidget): data_modified = Signal() def __init__(self, data, parent=None): super().__init__(parent)", "if __name__ == '__main__': import sys from PySide2.QtWidgets import QApplication,", "col).value() def set_gui_value(self, row, col, val): self.widget(row, col).setValue(val) def set_matrix_invalid(self,", "def apply_constraints(self): if (func := self.apply_constraints_func) is None: return func(self.data)", "the matrix is currently invalid self.matrix_invalid_reason = '' self.setLayout(QGridLayout()) self.add_spin_boxes()", "self.gui_data = self.data @property def gui_data(self): row_range = range(self.rows) col_range", "if self._enabled_elements != v: self._enabled_elements = v self.update_enable_states() self.reset_disabled_values() @property", "cols)) app = QApplication(sys.argv) dialog = QDialog() layout = QVBoxLayout()", "j) in self.enabled_elements w.setEnabled(enable) enabled_str = 'enabled' if enable else", "QVBoxLayout() dialog.setLayout(layout) editor = MatrixEditor(data) layout.addWidget(editor) # def constraints(x): #", "# list (as (i, j) items) will be enabled. self._enabled_elements", "range(self.cols) return [self.widget(i, j) for j in col_range for i", "j in col_range for i in row_range] @property def enabled_widgets(self):", "apply equality constraints. self._apply_constraints_func = None # Whether or not", "data(self): return self._data @data.setter def data(self, v): if not np.array_equal(self._data,", "self.layout() for i in range(self.rows): for j in range(self.cols): sb", "range(self.cols): if not self.widget(i, j).isEnabled(): self.data[i, j] = 0.0 self.apply_constraints()", "True self.matrix_invalid_reason = s self.update_tooltips() self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid =", "'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color: red' class MatrixEditor(QWidget): data_modified =", "col).setValue(val) def set_matrix_invalid(self, s): self.matrix_invalid = True self.matrix_invalid_reason = s", "self._data.shape != v.shape: msg = (f'Shape {v.shape} does not match", "# to apply equality constraints. self._apply_constraints_func = None # Whether", "# Reason the matrix is currently invalid self.matrix_invalid_reason = ''", "self.update_gui() @property def rows(self): return self.data.shape[0] @property def cols(self): return", "@property def enabled_style_sheet(self): if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property", "@gui_data.setter def gui_data(self, v): blockers = [QSignalBlocker(w) for w in", "self.data.shape[1] def update_data(self): self.data[:] = self.gui_data self.apply_constraints() self.data_modified.emit() def update_gui(self):", "self.update_gui() if __name__ == '__main__': import sys from PySide2.QtWidgets import", "QDialog, QVBoxLayout if len(sys.argv) < 2: sys.exit('Usage: <script> <matrix_size>') rows,", "enabled_elements(self, v): if self._enabled_elements != v: self._enabled_elements = v self.update_enable_states()", "data_modified = Signal() def __init__(self, data, parent=None): super().__init__(parent) self._data =", "enabled_elements(self): return self._enabled_elements @enabled_elements.setter def enabled_elements(self, v): if self._enabled_elements !=", "j] = 0.0 self.apply_constraints() self.update_gui() @property def enabled_style_sheet(self): if self.matrix_invalid:", "for j in col_range for i in row_range] @property def", "the # list (as (i, j) items) will be enabled.", "range(self.cols) return [[self.gui_value(i, j) for j in col_range] for i", "self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid = False self.matrix_invalid_reason = '' self.update_tooltips()", "# noqa: F841 for i in range(self.rows): for j in", "data # If this is not None, then only the", "layout = self.layout() for i in range(self.rows): for j in", "v): if not np.array_equal(self._data, v): if self._data.shape != v.shape: msg", "in col_range for i in row_range] @property def enabled_widgets(self): widgets", "update_enable_states(self): enable_all = self.enabled_elements is None for i in range(self.rows):", "AttributeError(msg) self._data = v self.reset_disabled_values() self.update_gui() @property def rows(self): return", "match original shape ' f'{self._data.shape}') raise AttributeError(msg) self._data = v", "if (func := self.apply_constraints_func) is None: return func(self.data) self.update_gui() if", "if (i, j) in self.enabled_elements: widgets.append(self.widget(i, j)) return widgets def", "None # If this is set, it will be called", "'__main__': import sys from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout if", "constraints. self._apply_constraints_func = None # Whether or not the matrix", "update_gui(self): self.gui_data = self.data @property def gui_data(self): row_range = range(self.rows)", "disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self): return self._enabled_elements @enabled_elements.setter def", "self.update_tooltips() self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid = False self.matrix_invalid_reason = ''", "col_range = range(self.cols) return [[self.gui_value(i, j) for j in col_range]", "len(sys.argv) < 2: sys.exit('Usage: <script> <matrix_size>') rows, cols = [int(x)", "self.widget(i, j).isEnabled(): self.data[i, j] = 0.0 self.apply_constraints() self.update_gui() @property def", "invalid self.matrix_invalid = False # Reason the matrix is currently", "= getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self): # Resets all disabled", "in sys.argv[1].split('x')] data = np.ones((rows, cols)) app = QApplication(sys.argv) dialog", "constraints(x): # x[2][2] = x[1][1] # editor.enabled_elements = [(1, 1),", "@data.setter def data(self, v): if not np.array_equal(self._data, v): if self._data.shape", "self.apply_constraints_func) is None: return func(self.data) self.update_gui() if __name__ == '__main__':", "list (as (i, j) items) will be enabled. self._enabled_elements =", "for j in range(self.cols): w = self.widget(i, j) enable =", "@property def apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self, v): if", "currently invalid self.matrix_invalid_reason = '' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def add_spin_boxes(self):", "in range(self.cols): w = self.widget(i, j) enable = enable_all or", "def element_modified(self): self.update_data() @property def data(self): return self._data @data.setter def", "matrix is currently invalid self.matrix_invalid = False # Reason the", "'disabled' style_sheet = getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self): # Resets", "if self.matrix_invalid: tooltip = self.matrix_invalid_reason else: tooltip = '' for", "x[2][2] = x[1][1] # editor.enabled_elements = [(1, 1), (3, 4)]", "enable = enable_all or (i, j) in self.enabled_elements w.setEnabled(enable) enabled_str", "= True self.matrix_invalid_reason = s self.update_tooltips() self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid", "QSignalBlocker, Signal from PySide2.QtWidgets import QGridLayout, QWidget from hexrd.ui.scientificspinbox import", "self.matrix_invalid_reason = s self.update_tooltips() self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid = False", "Signal from PySide2.QtWidgets import QGridLayout, QWidget from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox", "if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self): return", "enable_all = self.enabled_elements is None for i in range(self.rows): for", "self.enabled_elements: widgets.append(self.widget(i, j)) return widgets def widget(self, row, col): return", "from PySide2.QtCore import QSignalBlocker, Signal from PySide2.QtWidgets import QGridLayout, QWidget", "in range(self.rows): for j in range(self.cols): sb = self.create_spin_box() layout.addWidget(sb,", "updates # to apply equality constraints. self._apply_constraints_func = None #", "range(self.rows) col_range = range(self.cols) return [self.widget(i, j) for j in", "import QApplication, QDialog, QVBoxLayout if len(sys.argv) < 2: sys.exit('Usage: <script>", "v.shape: msg = (f'Shape {v.shape} does not match original shape", "msg = (f'Shape {v.shape} does not match original shape '", "or (i, j) in self.enabled_elements w.setEnabled(enable) enabled_str = 'enabled' if", "be enabled. self._enabled_elements = None # If this is set,", "@property def enabled_widgets(self): widgets = [] for i in range(self.rows):", "range(self.rows): for j in range(self.cols): if not self.widget(i, j).isEnabled(): self.data[i,", "for i in row_range] @property def enabled_widgets(self): widgets = []", "= self.widget(i, j) enable = enable_all or (i, j) in", "not self.widget(i, j).isEnabled(): self.data[i, j] = 0.0 self.apply_constraints() self.update_gui() @property", "def gui_value(self, row, col): return self.widget(row, col).value() def set_gui_value(self, row,", "ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET", "row_range] @property def enabled_widgets(self): widgets = [] for i in", "If this is not None, then only the elements present", "self.update_gui() @property def enabled_style_sheet(self): if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET", "apply_constraints_func(self, v): if self._apply_constraints_func != v: self._apply_constraints_func = v self.apply_constraints()", "'background-color: white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color: red'", "f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self): # Resets all disabled values to", "= 'background-color: red' class MatrixEditor(QWidget): data_modified = Signal() def __init__(self,", "= False # Reason the matrix is currently invalid self.matrix_invalid_reason", "i, j) def create_spin_box(self): sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return", "is currently invalid self.matrix_invalid = False # Reason the matrix", "widgets = [] for i in range(self.rows): for j in", "super().__init__(parent) self._data = data # If this is not None,", "in range(self.rows): for j in range(self.cols): if not self.widget(i, j).isEnabled():", "PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout if len(sys.argv) < 2: sys.exit('Usage:", "self.update_gui() def add_spin_boxes(self): layout = self.layout() for i in range(self.rows):", "def set_gui_value(self, row, col, val): self.widget(row, col).setValue(val) def set_matrix_invalid(self, s):", "return self._enabled_elements @enabled_elements.setter def enabled_elements(self, v): if self._enabled_elements != v:", "def gui_data(self): row_range = range(self.rows) col_range = range(self.cols) return [[self.gui_value(i,", "noqa: F841 for i in range(self.rows): for j in range(self.cols):", "self.reset_disabled_values() @property def apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self, v):", "= v self.reset_disabled_values() self.update_gui() @property def rows(self): return self.data.shape[0] @property", "= self.matrix_invalid_reason else: tooltip = '' for w in self.enabled_widgets:", "self.enabled_elements w.setEnabled(enable) enabled_str = 'enabled' if enable else 'disabled' style_sheet", "v self.update_enable_states() self.reset_disabled_values() @property def apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter def", "= [QSignalBlocker(w) for w in self.all_widgets] # noqa: F841 for", "self.matrix_invalid = False # Reason the matrix is currently invalid", "sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb def element_modified(self): self.update_data() @property def data(self):", "None, then only the elements present in the # list", "from PySide2.QtWidgets import QGridLayout, QWidget from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET", "self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def add_spin_boxes(self): layout = self.layout() for i", "= '' self.update_tooltips() self.update_enable_states() def update_tooltips(self): if self.matrix_invalid: tooltip =", "# Whether or not the matrix is currently invalid self.matrix_invalid", "layout.addWidget(sb, i, j) def create_spin_box(self): sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified)", ":= self.apply_constraints_func) is None: return func(self.data) self.update_gui() if __name__ ==", "None # Whether or not the matrix is currently invalid", "data = np.ones((rows, cols)) app = QApplication(sys.argv) dialog = QDialog()", "def widget(self, row, col): return self.layout().itemAtPosition(row, col).widget() def gui_value(self, row,", "for x in sys.argv[1].split('x')] data = np.ones((rows, cols)) app =", "return self.data.shape[1] def update_data(self): self.data[:] = self.gui_data self.apply_constraints() self.data_modified.emit() def", "DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self): return", "v self.apply_constraints() def apply_constraints(self): if (func := self.apply_constraints_func) is None:", "red' class MatrixEditor(QWidget): data_modified = Signal() def __init__(self, data, parent=None):", "[(1, 1), (3, 4)] # editor.apply_constraints_func = constraints def on_data_modified():", "gui_data(self, v): blockers = [QSignalBlocker(w) for w in self.all_widgets] #", "s self.update_tooltips() self.update_enable_states() def set_matrix_valid(self): self.matrix_invalid = False self.matrix_invalid_reason =", "is None: return func(self.data) self.update_gui() if __name__ == '__main__': import", "ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb def element_modified(self): self.update_data() @property def", "j in col_range] for i in row_range] @gui_data.setter def gui_data(self,", "return self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self, v): if self._apply_constraints_func != v:", "v[i][j]) @property def all_widgets(self): row_range = range(self.rows) col_range = range(self.cols)", "applies constraints for i in range(self.rows): for j in range(self.cols):", "is set, it will be called every time the data", "DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self): return self._enabled_elements @enabled_elements.setter def enabled_elements(self, v):", "= range(self.cols) return [[self.gui_value(i, j) for j in col_range] for", "(func := self.apply_constraints_func) is None: return func(self.data) self.update_gui() if __name__", "return widgets def widget(self, row, col): return self.layout().itemAtPosition(row, col).widget() def", "is None for i in range(self.rows): for j in range(self.cols):", "If this is set, it will be called every time", "for j in col_range] for i in row_range] @gui_data.setter def", "return self.data.shape[0] @property def cols(self): return self.data.shape[1] def update_data(self): self.data[:]", "parent=None): super().__init__(parent) self._data = data # If this is not", "= range(self.rows) col_range = range(self.cols) return [self.widget(i, j) for j", "for i in range(self.rows): for j in range(self.cols): sb =", "QVBoxLayout if len(sys.argv) < 2: sys.exit('Usage: <script> <matrix_size>') rows, cols", "is not None, then only the elements present in the", "[] for i in range(self.rows): for j in range(self.cols): if", "!= v: self._apply_constraints_func = v self.apply_constraints() def apply_constraints(self): if (func", "row_range = range(self.rows) col_range = range(self.cols) return [self.widget(i, j) for", "self.create_spin_box() layout.addWidget(sb, i, j) def create_spin_box(self): sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False)", "row, col): return self.layout().itemAtPosition(row, col).widget() def gui_value(self, row, col): return", "Signal() def __init__(self, data, parent=None): super().__init__(parent) self._data = data #", "add_spin_boxes(self): layout = self.layout() for i in range(self.rows): for j", "= [(1, 1), (3, 4)] # editor.apply_constraints_func = constraints def", "shape ' f'{self._data.shape}') raise AttributeError(msg) self._data = v self.reset_disabled_values() self.update_gui()", "self.update_enable_states() def update_tooltips(self): if self.matrix_invalid: tooltip = self.matrix_invalid_reason else: tooltip", "!= v.shape: msg = (f'Shape {v.shape} does not match original", "for i in range(self.rows): for j in range(self.cols): w =", "time the data updates # to apply equality constraints. self._apply_constraints_func", "= 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color:", "def all_widgets(self): row_range = range(self.rows) col_range = range(self.cols) return [self.widget(i,", "tooltip = self.matrix_invalid_reason else: tooltip = '' for w in", "= None # Whether or not the matrix is currently", "hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color:", "def update_tooltips(self): if self.matrix_invalid: tooltip = self.matrix_invalid_reason else: tooltip =", "self._enabled_elements = v self.update_enable_states() self.reset_disabled_values() @property def apply_constraints_func(self): return self._apply_constraints_func", "j) in self.enabled_elements: widgets.append(self.widget(i, j)) return widgets def widget(self, row,", "then applies constraints for i in range(self.rows): for j in", "def gui_data(self, v): blockers = [QSignalBlocker(w) for w in self.all_widgets]", "create_spin_box(self): sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb def element_modified(self):", "col): return self.layout().itemAtPosition(row, col).widget() def gui_value(self, row, col): return self.widget(row,", "self._apply_constraints_func != v: self._apply_constraints_func = v self.apply_constraints() def apply_constraints(self): if", "elements present in the # list (as (i, j) items)", "< 2: sys.exit('Usage: <script> <matrix_size>') rows, cols = [int(x) for", "sys.exit('Usage: <script> <matrix_size>') rows, cols = [int(x) for x in", "enable_all or (i, j) in self.enabled_elements w.setEnabled(enable) enabled_str = 'enabled'", "called every time the data updates # to apply equality", "self._data @data.setter def data(self, v): if not np.array_equal(self._data, v): if", "widget(self, row, col): return self.layout().itemAtPosition(row, col).widget() def gui_value(self, row, col):", "cols(self): return self.data.shape[1] def update_data(self): self.data[:] = self.gui_data self.apply_constraints() self.data_modified.emit()", "== '__main__': import sys from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout", "in row_range] @gui_data.setter def gui_data(self, v): blockers = [QSignalBlocker(w) for", "return self.widget(row, col).value() def set_gui_value(self, row, col, val): self.widget(row, col).setValue(val)", "def apply_constraints_func(self, v): if self._apply_constraints_func != v: self._apply_constraints_func = v", "# editor.enabled_elements = [(1, 1), (3, 4)] # editor.apply_constraints_func =", "= MatrixEditor(data) layout.addWidget(editor) # def constraints(x): # x[2][2] = x[1][1]", "reset_disabled_values(self): # Resets all disabled values to zero, then applies", "self.update_enable_states() self.reset_disabled_values() @property def apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self,", "def apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self, v): if self._apply_constraints_func", "None for i in range(self.rows): for j in range(self.cols): w", "= ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb def element_modified(self): self.update_data() @property", "DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET =", "disabled values to zero, then applies constraints for i in", "w.setToolTip(tooltip) def update_enable_states(self): enable_all = self.enabled_elements is None for i", "QApplication(sys.argv) dialog = QDialog() layout = QVBoxLayout() dialog.setLayout(layout) editor =", "return func(self.data) self.update_gui() if __name__ == '__main__': import sys from", "the matrix is currently invalid self.matrix_invalid = False # Reason", "(f'Shape {v.shape} does not match original shape ' f'{self._data.shape}') raise", "'' self.update_tooltips() self.update_enable_states() def update_tooltips(self): if self.matrix_invalid: tooltip = self.matrix_invalid_reason", "is currently invalid self.matrix_invalid_reason = '' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def", "not match original shape ' f'{self._data.shape}') raise AttributeError(msg) self._data =", "import QSignalBlocker, Signal from PySide2.QtWidgets import QGridLayout, QWidget from hexrd.ui.scientificspinbox", "update_tooltips(self): if self.matrix_invalid: tooltip = self.matrix_invalid_reason else: tooltip = ''", "= self.gui_data self.apply_constraints() self.data_modified.emit() def update_gui(self): self.gui_data = self.data @property", "set, it will be called every time the data updates", "# def constraints(x): # x[2][2] = x[1][1] # editor.enabled_elements =", "apply_constraints(self): if (func := self.apply_constraints_func) is None: return func(self.data) self.update_gui()", "j in range(self.cols): if (i, j) in self.enabled_elements: widgets.append(self.widget(i, j))", "editor.apply_constraints_func = constraints def on_data_modified(): print(f'Data modified: {editor.data}') editor.data_modified.connect(on_data_modified) dialog.finished.connect(app.quit)", "= self.enabled_elements is None for i in range(self.rows): for j", "col).widget() def gui_value(self, row, col): return self.widget(row, col).value() def set_gui_value(self,", "white' DEFAULT_DISABLED_STYLE_SHEET = 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color: red' class", "x in sys.argv[1].split('x')] data = np.ones((rows, cols)) app = QApplication(sys.argv)", "False # Reason the matrix is currently invalid self.matrix_invalid_reason =", "editor = MatrixEditor(data) layout.addWidget(editor) # def constraints(x): # x[2][2] =", "constraints def on_data_modified(): print(f'Data modified: {editor.data}') editor.data_modified.connect(on_data_modified) dialog.finished.connect(app.quit) dialog.show() app.exec_()", "col): return self.widget(row, col).value() def set_gui_value(self, row, col, val): self.widget(row,", "x[1][1] # editor.enabled_elements = [(1, 1), (3, 4)] # editor.apply_constraints_func", "or not the matrix is currently invalid self.matrix_invalid = False", "values to zero, then applies constraints for i in range(self.rows):", "self.data[:] = self.gui_data self.apply_constraints() self.data_modified.emit() def update_gui(self): self.gui_data = self.data", "original shape ' f'{self._data.shape}') raise AttributeError(msg) self._data = v self.reset_disabled_values()", "col_range = range(self.cols) return [self.widget(i, j) for j in col_range", "for w in self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self): enable_all = self.enabled_elements", "MatrixEditor(data) layout.addWidget(editor) # def constraints(x): # x[2][2] = x[1][1] #", "def update_gui(self): self.gui_data = self.data @property def gui_data(self): row_range =", "MatrixEditor(QWidget): data_modified = Signal() def __init__(self, data, parent=None): super().__init__(parent) self._data", "in col_range] for i in row_range] @gui_data.setter def gui_data(self, v):", "return self._data @data.setter def data(self, v): if not np.array_equal(self._data, v):", "self._enabled_elements @enabled_elements.setter def enabled_elements(self, v): if self._enabled_elements != v: self._enabled_elements", "Whether or not the matrix is currently invalid self.matrix_invalid =", "self._data = v self.reset_disabled_values() self.update_gui() @property def rows(self): return self.data.shape[0]", "np from PySide2.QtCore import QSignalBlocker, Signal from PySide2.QtWidgets import QGridLayout,", "sb def element_modified(self): self.update_data() @property def data(self): return self._data @data.setter", "if not self.widget(i, j).isEnabled(): self.data[i, j] = 0.0 self.apply_constraints() self.update_gui()", "@apply_constraints_func.setter def apply_constraints_func(self, v): if self._apply_constraints_func != v: self._apply_constraints_func =", "= data # If this is not None, then only", "j) for j in col_range for i in row_range] @property", "import numpy as np from PySide2.QtCore import QSignalBlocker, Signal from", "False self.matrix_invalid_reason = '' self.update_tooltips() self.update_enable_states() def update_tooltips(self): if self.matrix_invalid:", "j in range(self.cols): w = self.widget(i, j) enable = enable_all", "i in range(self.rows): for j in range(self.cols): if (i, j)", "sb = ScientificDoubleSpinBox() sb.setKeyboardTracking(False) sb.valueChanged.connect(self.element_modified) return sb def element_modified(self): self.update_data()", "# If this is not None, then only the elements", "self.matrix_invalid: tooltip = self.matrix_invalid_reason else: tooltip = '' for w", "return self.layout().itemAtPosition(row, col).widget() def gui_value(self, row, col): return self.widget(row, col).value()", "def data(self, v): if not np.array_equal(self._data, v): if self._data.shape !=", "gui_value(self, row, col): return self.widget(row, col).value() def set_gui_value(self, row, col,", "in range(self.cols): if not self.widget(i, j).isEnabled(): self.data[i, j] = 0.0", "j) enable = enable_all or (i, j) in self.enabled_elements w.setEnabled(enable)", "i in row_range] @property def enabled_widgets(self): widgets = [] for", "= constraints def on_data_modified(): print(f'Data modified: {editor.data}') editor.data_modified.connect(on_data_modified) dialog.finished.connect(app.quit) dialog.show()", "[self.widget(i, j) for j in col_range for i in row_range]", "= 'background-color: #F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color: red' class MatrixEditor(QWidget): data_modified", "enabled_style_sheet(self): if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self):", "QWidget from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET", "not np.array_equal(self._data, v): if self._data.shape != v.shape: msg = (f'Shape", "range(self.cols): self.set_gui_value(i, j, v[i][j]) @property def all_widgets(self): row_range = range(self.rows)", "gui_data(self): row_range = range(self.rows) col_range = range(self.cols) return [[self.gui_value(i, j)", "invalid self.matrix_invalid_reason = '' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def add_spin_boxes(self): layout", "w in self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self): enable_all = self.enabled_elements is", "style_sheet = getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self): # Resets all", "present in the # list (as (i, j) items) will", "self.data @property def gui_data(self): row_range = range(self.rows) col_range = range(self.cols)", "(i, j) in self.enabled_elements: widgets.append(self.widget(i, j)) return widgets def widget(self,", "def enabled_style_sheet(self): if self.matrix_invalid: return INVALID_MATRIX_STYLE_SHEET return DEFAULT_ENABLED_STYLE_SHEET @property def", "self.widget(row, col).setValue(val) def set_matrix_invalid(self, s): self.matrix_invalid = True self.matrix_invalid_reason =", "for j in range(self.cols): if not self.widget(i, j).isEnabled(): self.data[i, j]", "np.array_equal(self._data, v): if self._data.shape != v.shape: msg = (f'Shape {v.shape}", "2: sys.exit('Usage: <script> <matrix_size>') rows, cols = [int(x) for x", "@property def all_widgets(self): row_range = range(self.rows) col_range = range(self.cols) return", "@property def rows(self): return self.data.shape[0] @property def cols(self): return self.data.shape[1]", "self.update_tooltips() self.update_enable_states() def update_tooltips(self): if self.matrix_invalid: tooltip = self.matrix_invalid_reason else:", "be called every time the data updates # to apply", "enabled_widgets(self): widgets = [] for i in range(self.rows): for j", "layout = QVBoxLayout() dialog.setLayout(layout) editor = MatrixEditor(data) layout.addWidget(editor) # def", "= range(self.cols) return [self.widget(i, j) for j in col_range for", "this is set, it will be called every time the", "if len(sys.argv) < 2: sys.exit('Usage: <script> <matrix_size>') rows, cols =", "INVALID_MATRIX_STYLE_SHEET = 'background-color: red' class MatrixEditor(QWidget): data_modified = Signal() def", "apply_constraints_func(self): return self._apply_constraints_func @apply_constraints_func.setter def apply_constraints_func(self, v): if self._apply_constraints_func !=", "= np.ones((rows, cols)) app = QApplication(sys.argv) dialog = QDialog() layout", "sys from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout if len(sys.argv) <", "w.setEnabled(enable) enabled_str = 'enabled' if enable else 'disabled' style_sheet =", "val): self.widget(row, col).setValue(val) def set_matrix_invalid(self, s): self.matrix_invalid = True self.matrix_invalid_reason", "for i in range(self.rows): for j in range(self.cols): if not", "all_widgets(self): row_range = range(self.rows) col_range = range(self.cols) return [self.widget(i, j)", "= self.layout() for i in range(self.rows): for j in range(self.cols):", "# editor.apply_constraints_func = constraints def on_data_modified(): print(f'Data modified: {editor.data}') editor.data_modified.connect(on_data_modified)", "j in range(self.cols): self.set_gui_value(i, j, v[i][j]) @property def all_widgets(self): row_range", "layout.addWidget(editor) # def constraints(x): # x[2][2] = x[1][1] # editor.enabled_elements", "in range(self.cols): self.set_gui_value(i, j, v[i][j]) @property def all_widgets(self): row_range =", "equality constraints. self._apply_constraints_func = None # Whether or not the", "def enabled_widgets(self): widgets = [] for i in range(self.rows): for", "= self.create_spin_box() layout.addWidget(sb, i, j) def create_spin_box(self): sb = ScientificDoubleSpinBox()", "(i, j) in self.enabled_elements w.setEnabled(enable) enabled_str = 'enabled' if enable", "if enable else 'disabled' style_sheet = getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def", "return DEFAULT_ENABLED_STYLE_SHEET @property def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self):", "@property def disabled_style_sheet(self): return DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self): return self._enabled_elements", "None: return func(self.data) self.update_gui() if __name__ == '__main__': import sys", "def set_matrix_valid(self): self.matrix_invalid = False self.matrix_invalid_reason = '' self.update_tooltips() self.update_enable_states()", "currently invalid self.matrix_invalid = False # Reason the matrix is", "w = self.widget(i, j) enable = enable_all or (i, j)", "all disabled values to zero, then applies constraints for i", "= (f'Shape {v.shape} does not match original shape ' f'{self._data.shape}')", "matrix is currently invalid self.matrix_invalid_reason = '' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui()", "enabled. self._enabled_elements = None # If this is set, it", "cols = [int(x) for x in sys.argv[1].split('x')] data = np.ones((rows,", "self.data.shape[0] @property def cols(self): return self.data.shape[1] def update_data(self): self.data[:] =", "return DEFAULT_DISABLED_STYLE_SHEET @property def enabled_elements(self): return self._enabled_elements @enabled_elements.setter def enabled_elements(self,", "= 'enabled' if enable else 'disabled' style_sheet = getattr(self, f'{enabled_str}_style_sheet')", "'' self.setLayout(QGridLayout()) self.add_spin_boxes() self.update_gui() def add_spin_boxes(self): layout = self.layout() for", "1), (3, 4)] # editor.apply_constraints_func = constraints def on_data_modified(): print(f'Data", "@property def enabled_elements(self): return self._enabled_elements @enabled_elements.setter def enabled_elements(self, v): if", "= [] for i in range(self.rows): for j in range(self.cols):", "from hexrd.ui.scientificspinbox import ScientificDoubleSpinBox DEFAULT_ENABLED_STYLE_SHEET = 'background-color: white' DEFAULT_DISABLED_STYLE_SHEET =", "i in range(self.rows): for j in range(self.cols): self.set_gui_value(i, j, v[i][j])", "enable else 'disabled' style_sheet = getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self):", "getattr(self, f'{enabled_str}_style_sheet') w.setStyleSheet(style_sheet) def reset_disabled_values(self): # Resets all disabled values", "# If this is set, it will be called every", "editor.enabled_elements = [(1, 1), (3, 4)] # editor.apply_constraints_func = constraints", "w.setStyleSheet(style_sheet) def reset_disabled_values(self): # Resets all disabled values to zero,", "#F0F0F0' INVALID_MATRIX_STYLE_SHEET = 'background-color: red' class MatrixEditor(QWidget): data_modified = Signal()", "in range(self.rows): for j in range(self.cols): w = self.widget(i, j)", "QApplication, QDialog, QVBoxLayout if len(sys.argv) < 2: sys.exit('Usage: <script> <matrix_size>')", "self._apply_constraints_func = None # Whether or not the matrix is", "row, col, val): self.widget(row, col).setValue(val) def set_matrix_invalid(self, s): self.matrix_invalid =", "__init__(self, data, parent=None): super().__init__(parent) self._data = data # If this", "row, col): return self.widget(row, col).value() def set_gui_value(self, row, col, val):", "self.enabled_widgets: w.setToolTip(tooltip) def update_enable_states(self): enable_all = self.enabled_elements is None for", "def data(self): return self._data @data.setter def data(self, v): if not" ]
[ "(r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'),", "# (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'),", "'logout'), # (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor',", "(r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$', 'logout'), # (r'^$',", "import * urlpatterns = patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$',", "'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), # (r'^user/(?P<username>\\w{0,50})/submit', 'submit'), # (r'^user/(?P<username>\\w{0,50})/stat', 'stat'),", "'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$', 'logout'), # (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'),", "= patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'),", "(r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), # (r'^user/(?P<username>\\w{0,50})/submit', 'submit'), # (r'^user/(?P<username>\\w{0,50})/stat',", "(r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$', 'logout'), # (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$',", "'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), # (r'^user/(?P<username>\\w{0,50})/submit', 'submit'), #", "# (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), # (r'^user/(?P<username>\\w{0,50})/submit', 'submit'), # (r'^user/(?P<username>\\w{0,50})/stat', 'stat'), )", "'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$',", "from django.conf.urls.defaults import * urlpatterns = patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$',", "(r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'),", "(r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$', 'logout'),", "(r'^login/$', 'login'), (r'^logout/$', 'logout'), # (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$',", "* urlpatterns = patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'),", "'login'), (r'^logout/$', 'logout'), # (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'),", "django.conf.urls.defaults import * urlpatterns = patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'),", "patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$',", "urlpatterns = patterns('pytorque.views', (r'^$', 'central_dispatch_view'), (r'^browse$', 'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$',", "'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), # (r'^user/(?P<username>\\w{0,50})/submit',", "(r'^logout/$', 'logout'), # (r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), #", "(r'^$', 'central_dispatch_view'), (r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), #", "'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$', 'logout'), # (r'^$', 'central_dispatch_view'),", "'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$', 'logout'), #", "(r'^user/(?P<username>\\w{0,50})/$', 'index'), (r'^user/(?P<username>\\w{0,50})/browse$', 'browse'), # (r'^user/(?P<username>\\w{0,50})/monitor', 'monitor'), # (r'^user/(?P<username>\\w{0,50})/submit', 'submit'),", "'central_dispatch_view'), (r'^monitor$', 'central_dispatch_view'), (r'^submit$', 'central_dispatch_view'), (r'^stat$', 'central_dispatch_view'), (r'^login/$', 'login'), (r'^logout/$'," ]
[ "dict_items, odict_keys, odict_values, odict_items) TypesT = Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin):", "tuple to check the length and element types of. name", "the tuple length and type checker to another `callable`, returning", "of types argument must be tuple, not {of_type}!' @staticmethod def", "TypeError If `types` is not a tuple or any of", "cls.__name or str(value) types, length = cls.__valid(types) value = JustLen.JustTuple(value,", "TypesT = Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks for different type(s)", "name=name, length=length) for index, element in enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]):", "composition. Raises ------ WrongTypeError If `value` is not a tuple", "type({}.items()) odict_items = type(OrderedDict({}).items()) NAMED_TYPES = (frozenset, slice, range, deque,", "another `callable`, returning the functional composition of both. The argument", "a meaningful length. TypeError If `types` is not a tuple", "__is_or_contains_ellipsis(types: TypesT) -> bool: is_ellipsis = types is ... try:", "of type type. See Also -------- All, JustLen, CompositionOf \"\"\"", "value : tuple The tuple to check the length and", "the composition. Raises ------ WrongTypeError If `value` is not a", "if type(types) not in (tuple, list, deque): message = cls.__wrong_type_message_for(types)", "(one of) the permitted type(s). LenError If the tuple passed", "tuple passed in does not have the same length as", "types for each element of `value`. Use the ellipsis literal", "Any) -> str: type_name = type(types).__name__ if isinstance(types, NAMED_TYPES): of_type", "when calling the composition. Raises ------ WrongTypeError If `value` is", "type checker to another `callable`, returning the functional composition of", "and the element type(s) of. Defaults to None. types :", "{index} in tuple {cls.__string}' _ = Just(types[index])(element, name=element_name) return value", "either one type for each element of `value` or a", "length and element types of. name : str, optional The", "types: Sequence[TypesT]) -> Tuple[TypesT, int]: if type(types) not in (tuple,", "list, deque): message = cls.__wrong_type_message_for(types) raise TypeError(message) return types, len(types)", "like {types}' return f'Type of types argument must be tuple,", "-------- All, JustLen, CompositionOf \"\"\" def __new__(cls, value: tuple, name=None,", "tuple length and type checker to another `callable`, returning the", "to another `callable`, returning the functional composition of both. The", "the ellipsis literal ... to skip type checking of the", "tuple {cls.__string}' _ = Just(types[index])(element, name=element_name) return value @classmethod def", "name=None, *, types=(), **kwargs) -> tuple: cls.__name = str(name) if", "not have a meaningful length. TypeError If `types` is not", "contains_ellipsis = ... in types except TypeError: contains_ellipsis = False", "deque, defaultdict, OrderedDict from ...validators.one import JustLen from ...functional.mixins import", "to skip type checking of the tuple element at that", "JustLen from ...functional.mixins import CompositionClassMixin from ..one import Just dict_keys", "element in a defined-length tuple. Parameters ---------- value : tuple", "element of `value` or a tuple of types for each", "from collections import deque, defaultdict, OrderedDict from ...validators.one import JustLen", "None else '' cls.__string = cls.__name or str(value) types, length", "__valid(cls, types: Sequence[TypesT]) -> Tuple[TypesT, int]: if type(types) not in", "tuple passed in. Methods ------- o(callable) : CompositionOf Daisy-chains the", "types argument must be tuple, not {of_type}!' @staticmethod def __is_or_contains_ellipsis(types:", "_ = Just(types[index])(element, name=element_name) return value @classmethod def __valid(cls, types:", "a tuple or any of its elements are not of", "type type. See Also -------- All, JustLen, CompositionOf \"\"\" def", "TypeError(message) return types, len(types) @staticmethod def __wrong_type_message_for(types: Any) -> str:", "TypedTuple(CompositionClassMixin): \"\"\"Checks for different type(s) of each element in a", "odict_keys, odict_values, odict_items) TypesT = Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks", "of the tuple element at that position. Returns ------- tuple", "f'element {index} in tuple {cls.__string}' _ = Just(types[index])(element, name=element_name) return", "dict_values = type({}.values()) odict_values = type(OrderedDict({}).values()) dict_items = type({}.items()) odict_items", "in. Methods ------- o(callable) : CompositionOf Daisy-chains the tuple length", "its elements are not of type type. See Also --------", "= type(types).__name__ if isinstance(types, NAMED_TYPES): of_type = type_name else: of_type", "not {of_type}!' @staticmethod def __is_or_contains_ellipsis(types: TypesT) -> bool: is_ellipsis =", "CompositionOf Daisy-chains the tuple length and type checker to another", "for each element of `value`. Use the ellipsis literal ...", "as `types` or if the type specification does not have", "to check the length and element types of. name :", "... try: contains_ellipsis = ... in types except TypeError: contains_ellipsis", "return value @classmethod def __valid(cls, types: Sequence[TypesT]) -> Tuple[TypesT, int]:", "the element type(s) of. Defaults to None. types : tuple(type),", "have a meaningful length. TypeError If `types` is not a", "\"\"\"Checks for different type(s) of each element in a defined-length", "type checking of the tuple element at that position. Returns", "not cls.__is_or_contains_ellipsis(types[index]): element_name = f'element {index} in tuple {cls.__string}' _", "(tuple, list, deque): message = cls.__wrong_type_message_for(types) raise TypeError(message) return types,", "check for with either one type for each element of", "enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]): element_name = f'element {index} in tuple", "__wrong_type_message_for(types: Any) -> str: type_name = type(types).__name__ if isinstance(types, NAMED_TYPES):", "when when calling the composition. Raises ------ WrongTypeError If `value`", "str(name) if name is not None else '' cls.__string =", "of. name : str, optional The name of the tuple", "meaningful length. TypeError If `types` is not a tuple or", "tuple element at that position. Returns ------- tuple The tuple", "the type specification does not have a meaningful length. TypeError", "{cls.__string}' _ = Just(types[index])(element, name=element_name) return value @classmethod def __valid(cls,", "---------- value : tuple The tuple to check the length", "of the tuple to check the length and the element", "type({}.values()) odict_values = type(OrderedDict({}).values()) dict_items = type({}.items()) odict_items = type(OrderedDict({}).items())", "passed through to the `TypedTuple` checker when when calling the", "calling the composition. Raises ------ WrongTypeError If `value` is not", "collections import deque, defaultdict, OrderedDict from ...validators.one import JustLen from", "argument must be tuple, not {of_type}!' @staticmethod def __is_or_contains_ellipsis(types: TypesT)", "types is ... try: contains_ellipsis = ... in types except", "the `TypedTuple` checker when when calling the composition. Raises ------", "are not of type type. See Also -------- All, JustLen,", "= f'element {index} in tuple {cls.__string}' _ = Just(types[index])(element, name=element_name)", "to None. types : tuple(type), tuple(tuple(type)) Tuple of the length", "through to the `TypedTuple` checker when when calling the composition.", "@staticmethod def __wrong_type_message_for(types: Any) -> str: type_name = type(types).__name__ if", "@staticmethod def __is_or_contains_ellipsis(types: TypesT) -> bool: is_ellipsis = types is", "deque, defaultdict, OrderedDict, dict_keys, dict_values, dict_items, odict_keys, odict_values, odict_items) TypesT", "in types except TypeError: contains_ellipsis = False return is_ellipsis or", "argument `types` is passed through to the `TypedTuple` checker when", "defaultdict, OrderedDict, dict_keys, dict_values, dict_items, odict_keys, odict_values, odict_items) TypesT =", "defaultdict, OrderedDict from ...validators.one import JustLen from ...functional.mixins import CompositionClassMixin", "int]: if type(types) not in (tuple, list, deque): message =", "cls.__string = cls.__name or str(value) types, length = cls.__valid(types) value", "in does not have the same length as `types` or", "dict_values, dict_items, odict_keys, odict_values, odict_items) TypesT = Union[type, Sequence[type]] class", "= type(OrderedDict({}).keys()) dict_values = type({}.values()) odict_values = type(OrderedDict({}).values()) dict_items =", "str(value) types, length = cls.__valid(types) value = JustLen.JustTuple(value, name=name, length=length)", "optional The name of the tuple to check the length", "to check the length and the element type(s) of. Defaults", "does not have a meaningful length. TypeError If `types` is", "from ..one import Just dict_keys = type({}.keys()) odict_keys = type(OrderedDict({}).keys())", "length. TypeError If `types` is not a tuple or any", "to the `TypedTuple` checker when when calling the composition. Raises", "{types}' return f'Type of types argument must be tuple, not", "The argument `types` is passed through to the `TypedTuple` checker", "cls.__name = str(name) if name is not None else ''", "name : str, optional The name of the tuple to", ": tuple The tuple to check the length and element", "tuple, name=None, *, types=(), **kwargs) -> tuple: cls.__name = str(name)", "types : tuple(type), tuple(tuple(type)) Tuple of the length to check", "name of the tuple to check the length and the", "If `value` is not a tuple or if any of", "tuple or any of its elements are not of type", "type(OrderedDict({}).keys()) dict_values = type({}.values()) odict_values = type(OrderedDict({}).values()) dict_items = type({}.items())", "the functional composition of both. The argument `types` is passed", "type_name = type(types).__name__ if isinstance(types, NAMED_TYPES): of_type = type_name else:", "WrongTypeError If `value` is not a tuple or if any", "'' cls.__string = cls.__name or str(value) types, length = cls.__valid(types)", "types=(), **kwargs) -> tuple: cls.__name = str(name) if name is", "NAMED_TYPES = (frozenset, slice, range, deque, defaultdict, OrderedDict, dict_keys, dict_values,", "Sequence from collections import deque, defaultdict, OrderedDict from ...validators.one import", "odict_values = type(OrderedDict({}).values()) dict_items = type({}.items()) odict_items = type(OrderedDict({}).items()) NAMED_TYPES", "None. types : tuple(type), tuple(tuple(type)) Tuple of the length to", "Use the ellipsis literal ... to skip type checking of", "of. Defaults to None. types : tuple(type), tuple(tuple(type)) Tuple of", "tuple(tuple(type)) Tuple of the length to check for with either", "type(s) of each element in a defined-length tuple. Parameters ----------", "else '' cls.__string = cls.__name or str(value) types, length =", "OrderedDict from ...validators.one import JustLen from ...functional.mixins import CompositionClassMixin from", "for each element of `value` or a tuple of types", "a tuple of types for each element of `value`. Use", "`value` is not a tuple or if any of its", "the length and the element type(s) of. Defaults to None.", "index, element in enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]): element_name = f'element", "LenError If the tuple passed in does not have the", ": tuple(type), tuple(tuple(type)) Tuple of the length to check for", "and element types of. name : str, optional The name", "\"\"\" def __new__(cls, value: tuple, name=None, *, types=(), **kwargs) ->", "or str(value) types, length = cls.__valid(types) value = JustLen.JustTuple(value, name=name,", "same length as `types` or if the type specification does", "if isinstance(types, NAMED_TYPES): of_type = type_name else: of_type = f'{type_name}", "Methods ------- o(callable) : CompositionOf Daisy-chains the tuple length and", "Union, Any, Sequence from collections import deque, defaultdict, OrderedDict from", "type. See Also -------- All, JustLen, CompositionOf \"\"\" def __new__(cls,", "the tuple to check the length and the element type(s)", "All, JustLen, CompositionOf \"\"\" def __new__(cls, value: tuple, name=None, *,", "Tuple[TypesT, int]: if type(types) not in (tuple, list, deque): message", "... to skip type checking of the tuple element at", "is not a tuple or if any of its elements", "dict_keys, dict_values, dict_items, odict_keys, odict_values, odict_items) TypesT = Union[type, Sequence[type]]", "import CompositionClassMixin from ..one import Just dict_keys = type({}.keys()) odict_keys", "the length and element types of. name : str, optional", "returning the functional composition of both. The argument `types` is", "length=length) for index, element in enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]): element_name", "of both. The argument `types` is passed through to the", "`callable`, returning the functional composition of both. The argument `types`", "ellipsis literal ... to skip type checking of the tuple", "tuple The tuple to check the length and element types", "length to check for with either one type for each", "JustLen, CompositionOf \"\"\" def __new__(cls, value: tuple, name=None, *, types=(),", "any of its elements are not of type type. See", "odict_items) TypesT = Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks for different", "value: tuple, name=None, *, types=(), **kwargs) -> tuple: cls.__name =", "tuple: cls.__name = str(name) if name is not None else", "CompositionClassMixin from ..one import Just dict_keys = type({}.keys()) odict_keys =", "element in enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]): element_name = f'element {index}", "= cls.__valid(types) value = JustLen.JustTuple(value, name=name, length=length) for index, element", "-> Tuple[TypesT, int]: if type(types) not in (tuple, list, deque):", "= type({}.values()) odict_values = type(OrderedDict({}).values()) dict_items = type({}.items()) odict_items =", "position. Returns ------- tuple The tuple passed in. Methods -------", ": str, optional The name of the tuple to check", "typing import Tuple, Union, Any, Sequence from collections import deque,", "is passed through to the `TypedTuple` checker when when calling", "range, deque, defaultdict, OrderedDict, dict_keys, dict_values, dict_items, odict_keys, odict_values, odict_items)", "len(types) @staticmethod def __wrong_type_message_for(types: Any) -> str: type_name = type(types).__name__", "from ...validators.one import JustLen from ...functional.mixins import CompositionClassMixin from ..one", "composition of both. The argument `types` is passed through to", "-> str: type_name = type(types).__name__ if isinstance(types, NAMED_TYPES): of_type =", "for with either one type for each element of `value`", "type specification does not have a meaningful length. TypeError If", "= type({}.keys()) odict_keys = type(OrderedDict({}).keys()) dict_values = type({}.values()) odict_values =", "or any of its elements are not of type type.", "= Just(types[index])(element, name=element_name) return value @classmethod def __valid(cls, types: Sequence[TypesT])", "...validators.one import JustLen from ...functional.mixins import CompositionClassMixin from ..one import", "in a defined-length tuple. Parameters ---------- value : tuple The", "of `value` or a tuple of types for each element", "import Tuple, Union, Any, Sequence from collections import deque, defaultdict,", "for index, element in enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]): element_name =", "of types for each element of `value`. Use the ellipsis", "the length to check for with either one type for", "skip type checking of the tuple element at that position.", "odict_keys = type(OrderedDict({}).keys()) dict_values = type({}.values()) odict_values = type(OrderedDict({}).values()) dict_items", "Just dict_keys = type({}.keys()) odict_keys = type(OrderedDict({}).keys()) dict_values = type({}.values())", "Also -------- All, JustLen, CompositionOf \"\"\" def __new__(cls, value: tuple,", "def __is_or_contains_ellipsis(types: TypesT) -> bool: is_ellipsis = types is ...", "type(OrderedDict({}).items()) NAMED_TYPES = (frozenset, slice, range, deque, defaultdict, OrderedDict, dict_keys,", "{of_type}!' @staticmethod def __is_or_contains_ellipsis(types: TypesT) -> bool: is_ellipsis = types", "------- o(callable) : CompositionOf Daisy-chains the tuple length and type", "odict_items = type(OrderedDict({}).items()) NAMED_TYPES = (frozenset, slice, range, deque, defaultdict,", "cls.__valid(types) value = JustLen.JustTuple(value, name=name, length=length) for index, element in", "or a tuple of types for each element of `value`.", "If `types` is not a tuple or any of its", "type(types).__name__ if isinstance(types, NAMED_TYPES): of_type = type_name else: of_type =", "check the length and element types of. name : str,", "def __new__(cls, value: tuple, name=None, *, types=(), **kwargs) -> tuple:", "if any of its elements do not have (one of)", "not have the same length as `types` or if the", "elements are not of type type. See Also -------- All,", "not have (one of) the permitted type(s). LenError If the", "`TypedTuple` checker when when calling the composition. Raises ------ WrongTypeError", "-> bool: is_ellipsis = types is ... try: contains_ellipsis =", "OrderedDict, dict_keys, dict_values, dict_items, odict_keys, odict_values, odict_items) TypesT = Union[type,", "with either one type for each element of `value` or", "tuple of types for each element of `value`. Use the", "types except TypeError: contains_ellipsis = False return is_ellipsis or contains_ellipsis", "= cls.__wrong_type_message_for(types) raise TypeError(message) return types, len(types) @staticmethod def __wrong_type_message_for(types:", "bool: is_ellipsis = types is ... try: contains_ellipsis = ...", "raise TypeError(message) return types, len(types) @staticmethod def __wrong_type_message_for(types: Any) ->", "Parameters ---------- value : tuple The tuple to check the", "cls.__is_or_contains_ellipsis(types[index]): element_name = f'element {index} in tuple {cls.__string}' _ =", "tuple The tuple passed in. Methods ------- o(callable) : CompositionOf", "not in (tuple, list, deque): message = cls.__wrong_type_message_for(types) raise TypeError(message)", "..one import Just dict_keys = type({}.keys()) odict_keys = type(OrderedDict({}).keys()) dict_values", "if name is not None else '' cls.__string = cls.__name", "def __valid(cls, types: Sequence[TypesT]) -> Tuple[TypesT, int]: if type(types) not", "@classmethod def __valid(cls, types: Sequence[TypesT]) -> Tuple[TypesT, int]: if type(types)", "different type(s) of each element in a defined-length tuple. Parameters", "`types` is passed through to the `TypedTuple` checker when when", "its elements do not have (one of) the permitted type(s).", "any of its elements do not have (one of) the", "NAMED_TYPES): of_type = type_name else: of_type = f'{type_name} like {types}'", "at that position. Returns ------- tuple The tuple passed in.", "See Also -------- All, JustLen, CompositionOf \"\"\" def __new__(cls, value:", "element at that position. Returns ------- tuple The tuple passed", "Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks for different type(s) of each element", "Tuple of the length to check for with either one", "= cls.__name or str(value) types, length = cls.__valid(types) value =", "message = cls.__wrong_type_message_for(types) raise TypeError(message) return types, len(types) @staticmethod def", "each element in a defined-length tuple. Parameters ---------- value :", "tuple, not {of_type}!' @staticmethod def __is_or_contains_ellipsis(types: TypesT) -> bool: is_ellipsis", "dict_keys = type({}.keys()) odict_keys = type(OrderedDict({}).keys()) dict_values = type({}.values()) odict_values", "type for each element of `value` or a tuple of", "The tuple passed in. Methods ------- o(callable) : CompositionOf Daisy-chains", "import deque, defaultdict, OrderedDict from ...validators.one import JustLen from ...functional.mixins", "do not have (one of) the permitted type(s). LenError If", "length = cls.__valid(types) value = JustLen.JustTuple(value, name=name, length=length) for index,", "str, optional The name of the tuple to check the", "= str(name) if name is not None else '' cls.__string", "odict_values, odict_items) TypesT = Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks for", "each element of `value`. Use the ellipsis literal ... to", "elements do not have (one of) the permitted type(s). LenError", "value = JustLen.JustTuple(value, name=name, length=length) for index, element in enumerate(value):", "permitted type(s). LenError If the tuple passed in does not", "checking of the tuple element at that position. Returns -------", "have (one of) the permitted type(s). LenError If the tuple", "of its elements do not have (one of) the permitted", "return types, len(types) @staticmethod def __wrong_type_message_for(types: Any) -> str: type_name", "Defaults to None. types : tuple(type), tuple(tuple(type)) Tuple of the", "passed in. Methods ------- o(callable) : CompositionOf Daisy-chains the tuple", "in (tuple, list, deque): message = cls.__wrong_type_message_for(types) raise TypeError(message) return", "f'{type_name} like {types}' return f'Type of types argument must be", "to check for with either one type for each element", "length as `types` or if the type specification does not", "not a tuple or any of its elements are not", "of the length to check for with either one type", "isinstance(types, NAMED_TYPES): of_type = type_name else: of_type = f'{type_name} like", "the tuple passed in does not have the same length", "of `value`. Use the ellipsis literal ... to skip type", "is ... try: contains_ellipsis = ... in types except TypeError:", "defined-length tuple. Parameters ---------- value : tuple The tuple to", "is not None else '' cls.__string = cls.__name or str(value)", "Returns ------- tuple The tuple passed in. Methods ------- o(callable)", "must be tuple, not {of_type}!' @staticmethod def __is_or_contains_ellipsis(types: TypesT) ->", "tuple. Parameters ---------- value : tuple The tuple to check", "or if any of its elements do not have (one", "slice, range, deque, defaultdict, OrderedDict, dict_keys, dict_values, dict_items, odict_keys, odict_values,", "length and the element type(s) of. Defaults to None. types", "a tuple or if any of its elements do not", "from ...functional.mixins import CompositionClassMixin from ..one import Just dict_keys =", "for different type(s) of each element in a defined-length tuple.", "= (frozenset, slice, range, deque, defaultdict, OrderedDict, dict_keys, dict_values, dict_items,", "not None else '' cls.__string = cls.__name or str(value) types,", "import JustLen from ...functional.mixins import CompositionClassMixin from ..one import Just", "is_ellipsis = types is ... try: contains_ellipsis = ... in", "not of type type. See Also -------- All, JustLen, CompositionOf", ": CompositionOf Daisy-chains the tuple length and type checker to", "the permitted type(s). LenError If the tuple passed in does", "*, types=(), **kwargs) -> tuple: cls.__name = str(name) if name", "type(s) of. Defaults to None. types : tuple(type), tuple(tuple(type)) Tuple", "each element of `value` or a tuple of types for", "TypesT) -> bool: is_ellipsis = types is ... try: contains_ellipsis", "check the length and the element type(s) of. Defaults to", "**kwargs) -> tuple: cls.__name = str(name) if name is not", "------- tuple The tuple passed in. Methods ------- o(callable) :", "the tuple element at that position. Returns ------- tuple The", "have the same length as `types` or if the type", "= type({}.items()) odict_items = type(OrderedDict({}).items()) NAMED_TYPES = (frozenset, slice, range,", "type(OrderedDict({}).values()) dict_items = type({}.items()) odict_items = type(OrderedDict({}).items()) NAMED_TYPES = (frozenset,", "one type for each element of `value` or a tuple", "element of `value`. Use the ellipsis literal ... to skip", "tuple(type), tuple(tuple(type)) Tuple of the length to check for with", "type(s). LenError If the tuple passed in does not have", "in tuple {cls.__string}' _ = Just(types[index])(element, name=element_name) return value @classmethod", "name is not None else '' cls.__string = cls.__name or", "or if the type specification does not have a meaningful", "and type checker to another `callable`, returning the functional composition", "tuple or if any of its elements do not have", "Tuple, Union, Any, Sequence from collections import deque, defaultdict, OrderedDict", "checker to another `callable`, returning the functional composition of both.", "of_type = type_name else: of_type = f'{type_name} like {types}' return", "`value`. Use the ellipsis literal ... to skip type checking", "try: contains_ellipsis = ... in types except TypeError: contains_ellipsis =", "element_name = f'element {index} in tuple {cls.__string}' _ = Just(types[index])(element,", "JustLen.JustTuple(value, name=name, length=length) for index, element in enumerate(value): if not", "The tuple to check the length and element types of.", "`types` is not a tuple or any of its elements", "`value` or a tuple of types for each element of", "CompositionOf \"\"\" def __new__(cls, value: tuple, name=None, *, types=(), **kwargs)", "f'Type of types argument must be tuple, not {of_type}!' @staticmethod", "= types is ... try: contains_ellipsis = ... in types", "import Just dict_keys = type({}.keys()) odict_keys = type(OrderedDict({}).keys()) dict_values =", "= type_name else: of_type = f'{type_name} like {types}' return f'Type", "(frozenset, slice, range, deque, defaultdict, OrderedDict, dict_keys, dict_values, dict_items, odict_keys,", "specification does not have a meaningful length. TypeError If `types`", "literal ... to skip type checking of the tuple element", "the same length as `types` or if the type specification", "element type(s) of. Defaults to None. types : tuple(type), tuple(tuple(type))", "Sequence[TypesT]) -> Tuple[TypesT, int]: if type(types) not in (tuple, list,", "o(callable) : CompositionOf Daisy-chains the tuple length and type checker", "of each element in a defined-length tuple. Parameters ---------- value", "dict_items = type({}.items()) odict_items = type(OrderedDict({}).items()) NAMED_TYPES = (frozenset, slice,", "= ... in types except TypeError: contains_ellipsis = False return", "`types` or if the type specification does not have a", "Raises ------ WrongTypeError If `value` is not a tuple or", "length and type checker to another `callable`, returning the functional", "checker when when calling the composition. Raises ------ WrongTypeError If", "__new__(cls, value: tuple, name=None, *, types=(), **kwargs) -> tuple: cls.__name", "= type(OrderedDict({}).items()) NAMED_TYPES = (frozenset, slice, range, deque, defaultdict, OrderedDict,", "Just(types[index])(element, name=element_name) return value @classmethod def __valid(cls, types: Sequence[TypesT]) ->", "from typing import Tuple, Union, Any, Sequence from collections import", "Any, Sequence from collections import deque, defaultdict, OrderedDict from ...validators.one", "of) the permitted type(s). LenError If the tuple passed in", "tuple to check the length and the element type(s) of.", "of_type = f'{type_name} like {types}' return f'Type of types argument", "type_name else: of_type = f'{type_name} like {types}' return f'Type of", "value @classmethod def __valid(cls, types: Sequence[TypesT]) -> Tuple[TypesT, int]: if", "if the type specification does not have a meaningful length.", "else: of_type = f'{type_name} like {types}' return f'Type of types", "= type(OrderedDict({}).values()) dict_items = type({}.items()) odict_items = type(OrderedDict({}).items()) NAMED_TYPES =", "does not have the same length as `types` or if", "functional composition of both. The argument `types` is passed through", "The name of the tuple to check the length and", "if not cls.__is_or_contains_ellipsis(types[index]): element_name = f'element {index} in tuple {cls.__string}'", "return f'Type of types argument must be tuple, not {of_type}!'", "= Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks for different type(s) of", "= JustLen.JustTuple(value, name=name, length=length) for index, element in enumerate(value): if", "Union[type, Sequence[type]] class TypedTuple(CompositionClassMixin): \"\"\"Checks for different type(s) of each", "class TypedTuple(CompositionClassMixin): \"\"\"Checks for different type(s) of each element in", "both. The argument `types` is passed through to the `TypedTuple`", "element types of. name : str, optional The name of", "-> tuple: cls.__name = str(name) if name is not None", "type(types) not in (tuple, list, deque): message = cls.__wrong_type_message_for(types) raise", "passed in does not have the same length as `types`", "in enumerate(value): if not cls.__is_or_contains_ellipsis(types[index]): element_name = f'element {index} in", "Daisy-chains the tuple length and type checker to another `callable`,", "= f'{type_name} like {types}' return f'Type of types argument must", "str: type_name = type(types).__name__ if isinstance(types, NAMED_TYPES): of_type = type_name", "types, len(types) @staticmethod def __wrong_type_message_for(types: Any) -> str: type_name =", "name=element_name) return value @classmethod def __valid(cls, types: Sequence[TypesT]) -> Tuple[TypesT,", "of its elements are not of type type. See Also", "is not a tuple or any of its elements are", "be tuple, not {of_type}!' @staticmethod def __is_or_contains_ellipsis(types: TypesT) -> bool:", "a defined-length tuple. Parameters ---------- value : tuple The tuple", "... in types except TypeError: contains_ellipsis = False return is_ellipsis", "types of. name : str, optional The name of the", "...functional.mixins import CompositionClassMixin from ..one import Just dict_keys = type({}.keys())", "that position. Returns ------- tuple The tuple passed in. Methods", "deque): message = cls.__wrong_type_message_for(types) raise TypeError(message) return types, len(types) @staticmethod", "If the tuple passed in does not have the same", "def __wrong_type_message_for(types: Any) -> str: type_name = type(types).__name__ if isinstance(types,", "------ WrongTypeError If `value` is not a tuple or if", "type({}.keys()) odict_keys = type(OrderedDict({}).keys()) dict_values = type({}.values()) odict_values = type(OrderedDict({}).values())", "cls.__wrong_type_message_for(types) raise TypeError(message) return types, len(types) @staticmethod def __wrong_type_message_for(types: Any)", "not a tuple or if any of its elements do", "types, length = cls.__valid(types) value = JustLen.JustTuple(value, name=name, length=length) for" ]
[ "to host. The list should be a list of tuples", "NotImplementedError: this method is abstract. \"\"\" raise NotImplementedError def check(self):", "\" \"analysis aborted.\") return p.pid def package_files(self): \"\"\"A list of", "will be created in analysis folder). \"\"\" return None def", "path @param args: executable arguments @return: process pid \"\"\" p", "package_files(self): \"\"\"A list of files to upload to host. The", "list of pids. \"\"\" self.pids = pids def start(self): \"\"\"Run", "upload to host. The list should be a list of", "a folder that will be created in analysis folder). \"\"\"", "is abstract. \"\"\" raise NotImplementedError def check(self): \"\"\"Check.\"\"\" return True", "(package_files is a folder that will be created in analysis", "abstract analysis package.\"\"\" PATHS = [] def __init__(self, options={}): \"\"\"@param", "Sandbox - http://www.cuckoosandbox.org # See the file 'docs/LICENSE' for copying", "\"\"\" return None def finish(self): \"\"\"Finish run. If specified to", "return p.pid def package_files(self): \"\"\"A list of files to upload", "is part of Cuckoo Sandbox - http://www.cuckoosandbox.org # See the", "file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org # See", "package_files folder>). (package_files is a folder that will be created", "initial process, \" \"analysis aborted.\") return p.pid def package_files(self): \"\"\"A", "return None def finish(self): \"\"\"Finish run. If specified to do", "def check(self): \"\"\"Check.\"\"\" return True def execute(self, cmd): \"\"\"Start an", "run. If specified to do so, this method dumps the", "lib.exceptions.exceptions import CuckooPackageError class Package(object): \"\"\"Base abstract analysis package.\"\"\" PATHS", "context. @param pids: list of pids. \"\"\" self.pids = pids", "cmd): \"\"\"Start an executable for analysis. @param path: executable path", "See the file 'docs/LICENSE' for copying permission. from lib.api.process import", "Copyright (C) 2014-2016 Cuckoo Foundation. # This file is part", "p.pid def package_files(self): \"\"\"A list of files to upload to", "def __init__(self, options={}): \"\"\"@param options: options dict.\"\"\" self.options = options", "\"\"\"Base abstract analysis package.\"\"\" PATHS = [] def __init__(self, options={}):", "folder>). (package_files is a folder that will be created in", "arguments @return: process pid \"\"\" p = Process() if not", "file 'docs/LICENSE' for copying permission. from lib.api.process import Process from", "the initial process, \" \"analysis aborted.\") return p.pid def package_files(self):", "running processes. \"\"\" if self.options.get(\"procmemdump\"): for pid in self.pids: p", "def get_pids(self): return [] class Auxiliary(object): priority = 0 def", "def package_files(self): \"\"\"A list of files to upload to host.", "in self.pids: p = Process(pid=pid) p.dump_memory() return True def get_pids(self):", "set_pids(self, pids): \"\"\"Update list of monitored PIDs in the package", "this method dumps the memory of all running processes. \"\"\"", "processes. \"\"\" if self.options.get(\"procmemdump\"): for pid in self.pids: p =", "[] class Auxiliary(object): priority = 0 def get_pids(self): return []", "a list of tuples (<path on guest>, <name of file", "The list should be a list of tuples (<path on", "the package context. @param pids: list of pids. \"\"\" self.pids", "return True def execute(self, cmd): \"\"\"Start an executable for analysis.", "memory of all running processes. \"\"\" if self.options.get(\"procmemdump\"): for pid", "@raise NotImplementedError: this method is abstract. \"\"\" raise NotImplementedError def", "raise NotImplementedError def check(self): \"\"\"Check.\"\"\" return True def execute(self, cmd):", "on guest>, <name of file in package_files folder>). (package_files is", "True def execute(self, cmd): \"\"\"Start an executable for analysis. @param", "@return: process pid \"\"\" p = Process() if not p.execute(cmd):", "http://www.cuckoosandbox.org # See the file 'docs/LICENSE' for copying permission. from", "(C) 2014-2016 Cuckoo Foundation. # This file is part of", "this method is abstract. \"\"\" raise NotImplementedError def check(self): \"\"\"Check.\"\"\"", "pids: list of pids. \"\"\" self.pids = pids def start(self):", "to execute the initial process, \" \"analysis aborted.\") return p.pid", "process, \" \"analysis aborted.\") return p.pid def package_files(self): \"\"\"A list", "NotImplementedError def check(self): \"\"\"Check.\"\"\" return True def execute(self, cmd): \"\"\"Start", "package.\"\"\" PATHS = [] def __init__(self, options={}): \"\"\"@param options: options", "\"\"\"@param options: options dict.\"\"\" self.options = options self.pids = []", "folder that will be created in analysis folder). \"\"\" return", "import CuckooPackageError class Package(object): \"\"\"Base abstract analysis package.\"\"\" PATHS =", "pids): \"\"\"Update list of monitored PIDs in the package context.", "self.options = options self.pids = [] def set_pids(self, pids): \"\"\"Update", "__init__(self, options={}): \"\"\"@param options: options dict.\"\"\" self.options = options self.pids", "If specified to do so, this method dumps the memory", "Process() if not p.execute(cmd): raise CuckooPackageError(\"Unable to execute the initial", "def execute(self, cmd): \"\"\"Start an executable for analysis. @param path:", "of Cuckoo Sandbox - http://www.cuckoosandbox.org # See the file 'docs/LICENSE'", "p.execute(cmd): raise CuckooPackageError(\"Unable to execute the initial process, \" \"analysis", "if not p.execute(cmd): raise CuckooPackageError(\"Unable to execute the initial process,", "dict.\"\"\" self.options = options self.pids = [] def set_pids(self, pids):", "options dict.\"\"\" self.options = options self.pids = [] def set_pids(self,", "to upload to host. The list should be a list", "lib.api.process import Process from lib.exceptions.exceptions import CuckooPackageError class Package(object): \"\"\"Base", "pids def start(self): \"\"\"Run analysis package. @raise NotImplementedError: this method", "Package(object): \"\"\"Base abstract analysis package.\"\"\" PATHS = [] def __init__(self,", "executable for analysis. @param path: executable path @param args: executable", "CuckooPackageError(\"Unable to execute the initial process, \" \"analysis aborted.\") return", "'docs/LICENSE' for copying permission. from lib.api.process import Process from lib.exceptions.exceptions", "aborted.\") return p.pid def package_files(self): \"\"\"A list of files to", "= options self.pids = [] def set_pids(self, pids): \"\"\"Update list", "pid in self.pids: p = Process(pid=pid) p.dump_memory() return True def", "package. @raise NotImplementedError: this method is abstract. \"\"\" raise NotImplementedError", "@param pids: list of pids. \"\"\" self.pids = pids def", "- http://www.cuckoosandbox.org # See the file 'docs/LICENSE' for copying permission.", "permission. from lib.api.process import Process from lib.exceptions.exceptions import CuckooPackageError class", "self.pids = pids def start(self): \"\"\"Run analysis package. @raise NotImplementedError:", "start(self): \"\"\"Run analysis package. @raise NotImplementedError: this method is abstract.", "= Process() if not p.execute(cmd): raise CuckooPackageError(\"Unable to execute the", "finish(self): \"\"\"Finish run. If specified to do so, this method", "p.dump_memory() return True def get_pids(self): return [] class Auxiliary(object): priority", "\"\"\" if self.options.get(\"procmemdump\"): for pid in self.pids: p = Process(pid=pid)", "PATHS = [] def __init__(self, options={}): \"\"\"@param options: options dict.\"\"\"", "<name of file in package_files folder>). (package_files is a folder", "execute the initial process, \" \"analysis aborted.\") return p.pid def", "analysis package.\"\"\" PATHS = [] def __init__(self, options={}): \"\"\"@param options:", "Foundation. # This file is part of Cuckoo Sandbox -", "self.options.get(\"procmemdump\"): for pid in self.pids: p = Process(pid=pid) p.dump_memory() return", "of monitored PIDs in the package context. @param pids: list", "= Process(pid=pid) p.dump_memory() return True def get_pids(self): return [] class", "get_pids(self): return [] class Auxiliary(object): priority = 0 def get_pids(self):", "= [] def __init__(self, options={}): \"\"\"@param options: options dict.\"\"\" self.options", "list should be a list of tuples (<path on guest>,", "path: executable path @param args: executable arguments @return: process pid", "in analysis folder). \"\"\" return None def finish(self): \"\"\"Finish run.", "all running processes. \"\"\" if self.options.get(\"procmemdump\"): for pid in self.pids:", "# See the file 'docs/LICENSE' for copying permission. from lib.api.process", "Process(pid=pid) p.dump_memory() return True def get_pids(self): return [] class Auxiliary(object):", "is a folder that will be created in analysis folder).", "the file 'docs/LICENSE' for copying permission. from lib.api.process import Process", "folder). \"\"\" return None def finish(self): \"\"\"Finish run. If specified", "specified to do so, this method dumps the memory of", "\"\"\" raise NotImplementedError def check(self): \"\"\"Check.\"\"\" return True def execute(self,", "executable path @param args: executable arguments @return: process pid \"\"\"", "for copying permission. from lib.api.process import Process from lib.exceptions.exceptions import", "list of monitored PIDs in the package context. @param pids:", "from lib.exceptions.exceptions import CuckooPackageError class Package(object): \"\"\"Base abstract analysis package.\"\"\"", "in the package context. @param pids: list of pids. \"\"\"", "executable arguments @return: process pid \"\"\" p = Process() if", "Cuckoo Sandbox - http://www.cuckoosandbox.org # See the file 'docs/LICENSE' for", "options={}): \"\"\"@param options: options dict.\"\"\" self.options = options self.pids =", "raise CuckooPackageError(\"Unable to execute the initial process, \" \"analysis aborted.\")", "Process from lib.exceptions.exceptions import CuckooPackageError class Package(object): \"\"\"Base abstract analysis", "the memory of all running processes. \"\"\" if self.options.get(\"procmemdump\"): for", "part of Cuckoo Sandbox - http://www.cuckoosandbox.org # See the file", "(<path on guest>, <name of file in package_files folder>). (package_files", "def start(self): \"\"\"Run analysis package. @raise NotImplementedError: this method is", "= [] def set_pids(self, pids): \"\"\"Update list of monitored PIDs", "files to upload to host. The list should be a", "class Package(object): \"\"\"Base abstract analysis package.\"\"\" PATHS = [] def", "\"analysis aborted.\") return p.pid def package_files(self): \"\"\"A list of files", "= pids def start(self): \"\"\"Run analysis package. @raise NotImplementedError: this", "# Copyright (C) 2014-2016 Cuckoo Foundation. # This file is", "p = Process() if not p.execute(cmd): raise CuckooPackageError(\"Unable to execute", "list of tuples (<path on guest>, <name of file in", "of pids. \"\"\" self.pids = pids def start(self): \"\"\"Run analysis", "p = Process(pid=pid) p.dump_memory() return True def get_pids(self): return []", "This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org #", "to do so, this method dumps the memory of all", "check(self): \"\"\"Check.\"\"\" return True def execute(self, cmd): \"\"\"Start an executable", "\"\"\" self.pids = pids def start(self): \"\"\"Run analysis package. @raise", "# This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org", "\"\"\"Run analysis package. @raise NotImplementedError: this method is abstract. \"\"\"", "created in analysis folder). \"\"\" return None def finish(self): \"\"\"Finish", "self.pids = [] def set_pids(self, pids): \"\"\"Update list of monitored", "pids. \"\"\" self.pids = pids def start(self): \"\"\"Run analysis package.", "import Process from lib.exceptions.exceptions import CuckooPackageError class Package(object): \"\"\"Base abstract", "so, this method dumps the memory of all running processes.", "if self.options.get(\"procmemdump\"): for pid in self.pids: p = Process(pid=pid) p.dump_memory()", "2014-2016 Cuckoo Foundation. # This file is part of Cuckoo", "self.pids: p = Process(pid=pid) p.dump_memory() return True def get_pids(self): return", "CuckooPackageError class Package(object): \"\"\"Base abstract analysis package.\"\"\" PATHS = []", "execute(self, cmd): \"\"\"Start an executable for analysis. @param path: executable", "dumps the memory of all running processes. \"\"\" if self.options.get(\"procmemdump\"):", "in package_files folder>). (package_files is a folder that will be", "\"\"\" p = Process() if not p.execute(cmd): raise CuckooPackageError(\"Unable to", "package context. @param pids: list of pids. \"\"\" self.pids =", "be created in analysis folder). \"\"\" return None def finish(self):", "of all running processes. \"\"\" if self.options.get(\"procmemdump\"): for pid in", "be a list of tuples (<path on guest>, <name of", "<gh_stars>1-10 # Copyright (C) 2014-2016 Cuckoo Foundation. # This file", "def finish(self): \"\"\"Finish run. If specified to do so, this", "\"\"\"Finish run. If specified to do so, this method dumps", "should be a list of tuples (<path on guest>, <name", "[] def set_pids(self, pids): \"\"\"Update list of monitored PIDs in", "that will be created in analysis folder). \"\"\" return None", "of files to upload to host. The list should be", "guest>, <name of file in package_files folder>). (package_files is a", "not p.execute(cmd): raise CuckooPackageError(\"Unable to execute the initial process, \"", "abstract. \"\"\" raise NotImplementedError def check(self): \"\"\"Check.\"\"\" return True def", "@param path: executable path @param args: executable arguments @return: process", "method dumps the memory of all running processes. \"\"\" if", "options: options dict.\"\"\" self.options = options self.pids = [] def", "of file in package_files folder>). (package_files is a folder that", "True def get_pids(self): return [] class Auxiliary(object): priority = 0", "args: executable arguments @return: process pid \"\"\" p = Process()", "method is abstract. \"\"\" raise NotImplementedError def check(self): \"\"\"Check.\"\"\" return", "\"\"\"Check.\"\"\" return True def execute(self, cmd): \"\"\"Start an executable for", "do so, this method dumps the memory of all running", "for analysis. @param path: executable path @param args: executable arguments", "pid \"\"\" p = Process() if not p.execute(cmd): raise CuckooPackageError(\"Unable", "an executable for analysis. @param path: executable path @param args:", "return [] class Auxiliary(object): priority = 0 def get_pids(self): return", "\"\"\"Start an executable for analysis. @param path: executable path @param", "None def finish(self): \"\"\"Finish run. If specified to do so,", "host. The list should be a list of tuples (<path", "[] def __init__(self, options={}): \"\"\"@param options: options dict.\"\"\" self.options =", "for pid in self.pids: p = Process(pid=pid) p.dump_memory() return True", "options self.pids = [] def set_pids(self, pids): \"\"\"Update list of", "analysis. @param path: executable path @param args: executable arguments @return:", "Cuckoo Foundation. # This file is part of Cuckoo Sandbox", "of tuples (<path on guest>, <name of file in package_files", "analysis folder). \"\"\" return None def finish(self): \"\"\"Finish run. If", "tuples (<path on guest>, <name of file in package_files folder>).", "from lib.api.process import Process from lib.exceptions.exceptions import CuckooPackageError class Package(object):", "@param args: executable arguments @return: process pid \"\"\" p =", "\"\"\"A list of files to upload to host. The list", "monitored PIDs in the package context. @param pids: list of", "return True def get_pids(self): return [] class Auxiliary(object): priority =", "list of files to upload to host. The list should", "process pid \"\"\" p = Process() if not p.execute(cmd): raise", "copying permission. from lib.api.process import Process from lib.exceptions.exceptions import CuckooPackageError", "\"\"\"Update list of monitored PIDs in the package context. @param", "file in package_files folder>). (package_files is a folder that will", "analysis package. @raise NotImplementedError: this method is abstract. \"\"\" raise", "PIDs in the package context. @param pids: list of pids.", "def set_pids(self, pids): \"\"\"Update list of monitored PIDs in the" ]
[ "import ugettext_lazy as _ class OptionsConfig(AppConfig): name = 'rdmo.options' verbose_name", "from django.utils.translation import ugettext_lazy as _ class OptionsConfig(AppConfig): name =", "import AppConfig from django.utils.translation import ugettext_lazy as _ class OptionsConfig(AppConfig):", "from django.apps import AppConfig from django.utils.translation import ugettext_lazy as _", "django.utils.translation import ugettext_lazy as _ class OptionsConfig(AppConfig): name = 'rdmo.options'", "as _ class OptionsConfig(AppConfig): name = 'rdmo.options' verbose_name = _('Options')", "django.apps import AppConfig from django.utils.translation import ugettext_lazy as _ class", "ugettext_lazy as _ class OptionsConfig(AppConfig): name = 'rdmo.options' verbose_name =", "AppConfig from django.utils.translation import ugettext_lazy as _ class OptionsConfig(AppConfig): name" ]
[ "UserAdmin from main import models class Admin(UserAdmin): list_display = (\"id\",", "from django.contrib.auth.admin import UserAdmin from main import models class Admin(UserAdmin):", "Admin(UserAdmin): list_display = (\"id\", \"username\", \"email\", \"date_joined\", \"last_login\") admin.site.register(models.User, Admin)", "main import models class Admin(UserAdmin): list_display = (\"id\", \"username\", \"email\",", "admin from django.contrib.auth.admin import UserAdmin from main import models class", "\"username\", \"email\", \"date_joined\", \"last_login\") admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin): list_display =", "\"last_login\") admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin): list_display = (\"id\", \"title\") admin.site.register(models.Document,", "from django.contrib import admin from django.contrib.auth.admin import UserAdmin from main", "= (\"id\", \"username\", \"email\", \"date_joined\", \"last_login\") admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin):", "import admin from django.contrib.auth.admin import UserAdmin from main import models", "\"email\", \"date_joined\", \"last_login\") admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin): list_display = (\"id\",", "import UserAdmin from main import models class Admin(UserAdmin): list_display =", "django.contrib.auth.admin import UserAdmin from main import models class Admin(UserAdmin): list_display", "admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin): list_display = (\"id\", \"title\") admin.site.register(models.Document, DocumentAdmin)", "\"date_joined\", \"last_login\") admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin): list_display = (\"id\", \"title\")", "list_display = (\"id\", \"username\", \"email\", \"date_joined\", \"last_login\") admin.site.register(models.User, Admin) class", "django.contrib import admin from django.contrib.auth.admin import UserAdmin from main import", "models class Admin(UserAdmin): list_display = (\"id\", \"username\", \"email\", \"date_joined\", \"last_login\")", "class Admin(UserAdmin): list_display = (\"id\", \"username\", \"email\", \"date_joined\", \"last_login\") admin.site.register(models.User,", "(\"id\", \"username\", \"email\", \"date_joined\", \"last_login\") admin.site.register(models.User, Admin) class DocumentAdmin(admin.ModelAdmin): list_display", "import models class Admin(UserAdmin): list_display = (\"id\", \"username\", \"email\", \"date_joined\",", "from main import models class Admin(UserAdmin): list_display = (\"id\", \"username\"," ]
[ "command_mode, self._logger ) class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by shells to", "(CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories instead of \"\"\" def __init__( self,", "service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa: E501 :param logging.Logger", "= reservation_context @property def _cli_type(self): \"\"\"Connection type property [ssh|telnet|console|auto].\"\"\" return", "session.init_session( self._resource_config, self._logger, self._reservation_context ) def _defined_sessions(self): return [ self.initialize_session(sess)", "cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501 \"\"\" return self._cli.get_session( self._defined_sessions(), command_mode, self._logger", "def initialize_session(self, session): if not isinstance(session, SessionFactory): session = GenericSessionFactory(session)", "= GenericSessionFactory(session) return session.init_session( self._resource_config, self._logger, self._reservation_context ) def _defined_sessions(self):", "cloudshell.cli.session.ssh_session import SSHSession from cloudshell.cli.session.telnet_session import TelnetSession ABC = ABCMeta(\"ABC\",", "import lru_cache else: from functools32 import lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS", "( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, ) from cloudshell.cli.service.cli import CLI from", "session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def initialize_session(self, session): if not isinstance(session, SessionFactory):", "noqa: E501 :param logging.Logger logger: :param cloudshell.cli.service.cli.CLI cli: :param registered_sessions:", "\"\"\"Use cli.get_session to open CLI connection and switch into required", "self._cli_type.lower(), self._registered_sessions ) ] def get_cli_service(self, command_mode): \"\"\"Use cli.get_session to", "return self._cli.get_session( self._defined_sessions(), command_mode, self._logger ) class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used", "sys.version_info >= (3, 0): from functools import lru_cache else: from", "utf-8 -*- import sys from abc import ABCMeta, abstractmethod from", "REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories instead of \"\"\" def", "configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa: E501 :param logging.Logger logger:", "return self._resource_config.cli_connection_type @property @lru_cache() def _session_dict(self): session_dict = defaultdict(list) for", "self._defined_sessions(), command_mode, self._logger ) class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by shells", "AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by shells to run enable/config command.\"\"\" @property", "#!/usr/bin/python # -*- coding: utf-8 -*- import sys from abc", "logger, cli=None, registered_sessions=None, reservation_context=None, ): \"\"\"Initialize CLI service configurator. :param", "connection and switch into required mode. :param CommandMode command_mode: operation", "SessionFactory): session = GenericSessionFactory(session) return session.init_session( self._resource_config, self._logger, self._reservation_context )", "or CLI() self._resource_config = resource_config self._logger = logger self._registered_sessions =", "command_mode: operation mode, can be default_mode/enable_mode/config_mode/etc. :return: created session in", "from cloudshell.cli.service.cli import CLI from cloudshell.cli.session.ssh_session import SSHSession from cloudshell.cli.session.telnet_session", "pass @property @abstractmethod def config_mode(self): pass def enable_mode_service(self): return self.get_cli_service(self.enable_mode)", "= cli or CLI() self._resource_config = resource_config self._logger = logger", "E501 \"\"\" return self._cli.get_session( self._defined_sessions(), command_mode, self._logger ) class AbstractModeConfigurator(ABC,", ":rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501 \"\"\" return self._cli.get_session( self._defined_sessions(), command_mode,", "self._resource_config.cli_connection_type @property @lru_cache() def _session_dict(self): session_dict = defaultdict(list) for sess", "open CLI connection and switch into required mode. :param CommandMode", "# noqa: E501 :param logging.Logger logger: :param cloudshell.cli.service.cli.CLI cli: :param", "import defaultdict from cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, )", "defaultdict from cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, ) from", "lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories instead", "coding: utf-8 -*- import sys from abc import ABCMeta, abstractmethod", "= resource_config self._logger = logger self._registered_sessions = registered_sessions or self.REGISTERED_SESSIONS", "session): if not isinstance(session, SessionFactory): session = GenericSessionFactory(session) return session.init_session(", "@abstractmethod def enable_mode(self): pass @property @abstractmethod def config_mode(self): pass def", "ABCMeta(\"ABC\", (object,), {\"__slots__\": ()}) if sys.version_info >= (3, 0): from", "SSHSession from cloudshell.cli.session.telnet_session import TelnetSession ABC = ABCMeta(\"ABC\", (object,), {\"__slots__\":", "command.\"\"\" @property @abstractmethod def enable_mode(self): pass @property @abstractmethod def config_mode(self):", "sys from abc import ABCMeta, abstractmethod from collections import defaultdict", "self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def initialize_session(self, session): if not isinstance(session,", "ABCMeta, abstractmethod from collections import defaultdict from cloudshell.cli.factory.session_factory import (", "_session_dict(self): session_dict = defaultdict(list) for sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return", ":param registered_sessions: Session types and order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\"", "CLI() self._resource_config = resource_config self._logger = logger self._registered_sessions = registered_sessions", "def _cli_type(self): \"\"\"Connection type property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property @lru_cache()", "in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def initialize_session(self, session): if not", "enable/config command.\"\"\" @property @abstractmethod def enable_mode(self): pass @property @abstractmethod def", "CLI service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa: E501 :param", "@lru_cache() def _session_dict(self): session_dict = defaultdict(list) for sess in self._registered_sessions:", "mode. :param CommandMode command_mode: operation mode, can be default_mode/enable_mode/config_mode/etc. :return:", "to run enable/config command.\"\"\" @property @abstractmethod def enable_mode(self): pass @property", "_defined_sessions(self): return [ self.initialize_session(sess) for sess in self._session_dict.get( self._cli_type.lower(), self._registered_sessions", "noqa: E501 \"\"\" return self._cli.get_session( self._defined_sessions(), command_mode, self._logger ) class", "class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by shells to run enable/config command.\"\"\"", ":param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa: E501 :param logging.Logger logger: :param", "resource_config: # noqa: E501 :param logging.Logger logger: :param cloudshell.cli.service.cli.CLI cli:", "order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli = cli or CLI()", "initialize_session(self, session): if not isinstance(session, SessionFactory): session = GenericSessionFactory(session) return", "_cli_type(self): \"\"\"Connection type property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property @lru_cache() def", "default_mode/enable_mode/config_mode/etc. :return: created session in provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager #", "@property @abstractmethod def enable_mode(self): pass @property @abstractmethod def config_mode(self): pass", "from functools import lru_cache else: from functools32 import lru_cache class", "run enable/config command.\"\"\" @property @abstractmethod def enable_mode(self): pass @property @abstractmethod", "not isinstance(session, SessionFactory): session = GenericSessionFactory(session) return session.init_session( self._resource_config, self._logger,", "GenericSessionFactory, SessionFactory, ) from cloudshell.cli.service.cli import CLI from cloudshell.cli.session.ssh_session import", "E501 :param logging.Logger logger: :param cloudshell.cli.service.cli.CLI cli: :param registered_sessions: Session", "shells to run enable/config command.\"\"\" @property @abstractmethod def enable_mode(self): pass", "required mode. :param CommandMode command_mode: operation mode, can be default_mode/enable_mode/config_mode/etc.", "self._resource_config = resource_config self._logger = logger self._registered_sessions = registered_sessions or", "session = GenericSessionFactory(session) return session.init_session( self._resource_config, self._logger, self._reservation_context ) def", "()}) if sys.version_info >= (3, 0): from functools import lru_cache", "if sys.version_info >= (3, 0): from functools import lru_cache else:", "self, resource_config, logger, cli=None, registered_sessions=None, reservation_context=None, ): \"\"\"Initialize CLI service", "@property @lru_cache() def _session_dict(self): session_dict = defaultdict(list) for sess in", "can be default_mode/enable_mode/config_mode/etc. :return: created session in provided mode :rtype:", "in self._session_dict.get( self._cli_type.lower(), self._registered_sessions ) ] def get_cli_service(self, command_mode): \"\"\"Use", "operation mode, can be default_mode/enable_mode/config_mode/etc. :return: created session in provided", "self._session_dict.get( self._cli_type.lower(), self._registered_sessions ) ] def get_cli_service(self, command_mode): \"\"\"Use cli.get_session", "[ self.initialize_session(sess) for sess in self._session_dict.get( self._cli_type.lower(), self._registered_sessions ) ]", "{\"__slots__\": ()}) if sys.version_info >= (3, 0): from functools import", "\"\"\"Used by shells to run enable/config command.\"\"\" @property @abstractmethod def", "self.initialize_session(sess) for sess in self._session_dict.get( self._cli_type.lower(), self._registered_sessions ) ] def", "CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories instead of \"\"\"", "cli.get_session to open CLI connection and switch into required mode.", ":return: created session in provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa:", "return session.init_session( self._resource_config, self._logger, self._reservation_context ) def _defined_sessions(self): return [", ") from cloudshell.cli.service.cli import CLI from cloudshell.cli.session.ssh_session import SSHSession from", "and order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli = cli or", "self._resource_config, self._logger, self._reservation_context ) def _defined_sessions(self): return [ self.initialize_session(sess) for", "return [ self.initialize_session(sess) for sess in self._session_dict.get( self._cli_type.lower(), self._registered_sessions )", "to open CLI connection and switch into required mode. :param", "in provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501 \"\"\" return", "for sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def initialize_session(self, session):", "TelnetSession) \"\"\"Using factories instead of \"\"\" def __init__( self, resource_config,", ">= (3, 0): from functools import lru_cache else: from functools32", "reservation_context: \"\"\" self._cli = cli or CLI() self._resource_config = resource_config", "mode, can be default_mode/enable_mode/config_mode/etc. :return: created session in provided mode", "import SSHSession from cloudshell.cli.session.telnet_session import TelnetSession ABC = ABCMeta(\"ABC\", (object,),", "def enable_mode(self): pass @property @abstractmethod def config_mode(self): pass def enable_mode_service(self):", "self._registered_sessions = registered_sessions or self.REGISTERED_SESSIONS self._reservation_context = reservation_context @property def", "self._reservation_context = reservation_context @property def _cli_type(self): \"\"\"Connection type property [ssh|telnet|console|auto].\"\"\"", "self._reservation_context ) def _defined_sessions(self): return [ self.initialize_session(sess) for sess in", "for sess in self._session_dict.get( self._cli_type.lower(), self._registered_sessions ) ] def get_cli_service(self,", "registered_sessions: Session types and order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli", "and switch into required mode. :param CommandMode command_mode: operation mode,", "ABC = ABCMeta(\"ABC\", (object,), {\"__slots__\": ()}) if sys.version_info >= (3,", "\"\"\"Using factories instead of \"\"\" def __init__( self, resource_config, logger,", "= logger self._registered_sessions = registered_sessions or self.REGISTERED_SESSIONS self._reservation_context = reservation_context", "reservation_context=None, ): \"\"\"Initialize CLI service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: #", "GenericSessionFactory(session) return session.init_session( self._resource_config, self._logger, self._reservation_context ) def _defined_sessions(self): return", "isinstance(session, SessionFactory): session = GenericSessionFactory(session) return session.init_session( self._resource_config, self._logger, self._reservation_context", "session_dict = defaultdict(list) for sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict", "collections import defaultdict from cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory,", "created session in provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501", "resource_config, logger, cli=None, registered_sessions=None, reservation_context=None, ): \"\"\"Initialize CLI service configurator.", "<gh_stars>1-10 #!/usr/bin/python # -*- coding: utf-8 -*- import sys from", "class CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories instead of", "lru_cache else: from functools32 import lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS =", "cloudshell.cli.service.cli.CLI cli: :param registered_sessions: Session types and order :param cloudshell.shell.core.driver_context.ReservationContextDetails", "types and order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli = cli", "logger self._registered_sessions = registered_sessions or self.REGISTERED_SESSIONS self._reservation_context = reservation_context @property", "from cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, ) from cloudshell.cli.service.cli", "import lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories", "factories instead of \"\"\" def __init__( self, resource_config, logger, cli=None,", "cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa: E501 :param logging.Logger logger: :param cloudshell.cli.service.cli.CLI", "session in provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501 \"\"\"", "cloudshell.cli.session.telnet_session import TelnetSession ABC = ABCMeta(\"ABC\", (object,), {\"__slots__\": ()}) if", "from cloudshell.cli.session.telnet_session import TelnetSession ABC = ABCMeta(\"ABC\", (object,), {\"__slots__\": ()})", "[ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property @lru_cache() def _session_dict(self): session_dict = defaultdict(list)", "logger: :param cloudshell.cli.service.cli.CLI cli: :param registered_sessions: Session types and order", "return session_dict def initialize_session(self, session): if not isinstance(session, SessionFactory): session", "switch into required mode. :param CommandMode command_mode: operation mode, can", "\"\"\"Connection type property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property @lru_cache() def _session_dict(self):", "abstractmethod from collections import defaultdict from cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory,", "= (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using factories instead of \"\"\" def __init__(", "from abc import ABCMeta, abstractmethod from collections import defaultdict from", "CLI connection and switch into required mode. :param CommandMode command_mode:", ":param CommandMode command_mode: operation mode, can be default_mode/enable_mode/config_mode/etc. :return: created", "provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501 \"\"\" return self._cli.get_session(", "= ABCMeta(\"ABC\", (object,), {\"__slots__\": ()}) if sys.version_info >= (3, 0):", ":param cloudshell.cli.service.cli.CLI cli: :param registered_sessions: Session types and order :param", "session_dict def initialize_session(self, session): if not isinstance(session, SessionFactory): session =", "get_cli_service(self, command_mode): \"\"\"Use cli.get_session to open CLI connection and switch", "of \"\"\" def __init__( self, resource_config, logger, cli=None, registered_sessions=None, reservation_context=None,", "self.REGISTERED_SESSIONS self._reservation_context = reservation_context @property def _cli_type(self): \"\"\"Connection type property", "Session types and order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli =", "type property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property @lru_cache() def _session_dict(self): session_dict", "property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property @lru_cache() def _session_dict(self): session_dict =", "CLI from cloudshell.cli.session.ssh_session import SSHSession from cloudshell.cli.session.telnet_session import TelnetSession ABC", "-*- coding: utf-8 -*- import sys from abc import ABCMeta,", "@property def _cli_type(self): \"\"\"Connection type property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type @property", "CommandMode command_mode: operation mode, can be default_mode/enable_mode/config_mode/etc. :return: created session", "(object,), {\"__slots__\": ()}) if sys.version_info >= (3, 0): from functools", "def _session_dict(self): session_dict = defaultdict(list) for sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess)", "mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager # noqa: E501 \"\"\" return self._cli.get_session( self._defined_sessions(),", "def __init__( self, resource_config, logger, cli=None, registered_sessions=None, reservation_context=None, ): \"\"\"Initialize", "reservation_context @property def _cli_type(self): \"\"\"Connection type property [ssh|telnet|console|auto].\"\"\" return self._resource_config.cli_connection_type", "by shells to run enable/config command.\"\"\" @property @abstractmethod def enable_mode(self):", "\"\"\" self._cli = cli or CLI() self._resource_config = resource_config self._logger", "cli or CLI() self._resource_config = resource_config self._logger = logger self._registered_sessions", "] def get_cli_service(self, command_mode): \"\"\"Use cli.get_session to open CLI connection", "functools32 import lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession) \"\"\"Using", "cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli = cli or CLI() self._resource_config =", "import TelnetSession ABC = ABCMeta(\"ABC\", (object,), {\"__slots__\": ()}) if sys.version_info", "self._registered_sessions ) ] def get_cli_service(self, command_mode): \"\"\"Use cli.get_session to open", "\"\"\" def __init__( self, resource_config, logger, cli=None, registered_sessions=None, reservation_context=None, ):", "sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def initialize_session(self, session): if", "defaultdict(list) for sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def initialize_session(self,", "if not isinstance(session, SessionFactory): session = GenericSessionFactory(session) return session.init_session( self._resource_config,", "else: from functools32 import lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession),", "command_mode): \"\"\"Use cli.get_session to open CLI connection and switch into", "registered_sessions=None, reservation_context=None, ): \"\"\"Initialize CLI service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config:", "def _defined_sessions(self): return [ self.initialize_session(sess) for sess in self._session_dict.get( self._cli_type.lower(),", "from collections import defaultdict from cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory,", "): \"\"\"Initialize CLI service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa:", ") ] def get_cli_service(self, command_mode): \"\"\"Use cli.get_session to open CLI", "self._cli.get_session( self._defined_sessions(), command_mode, self._logger ) class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by", "cloudshell.cli.factory.session_factory import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, ) from cloudshell.cli.service.cli import", "cli: :param registered_sessions: Session types and order :param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context:", "from cloudshell.cli.session.ssh_session import SSHSession from cloudshell.cli.session.telnet_session import TelnetSession ABC =", "instead of \"\"\" def __init__( self, resource_config, logger, cli=None, registered_sessions=None,", "into required mode. :param CommandMode command_mode: operation mode, can be", "import ABCMeta, abstractmethod from collections import defaultdict from cloudshell.cli.factory.session_factory import", "\"\"\" return self._cli.get_session( self._defined_sessions(), command_mode, self._logger ) class AbstractModeConfigurator(ABC, CLIServiceConfigurator):", "self._logger, self._reservation_context ) def _defined_sessions(self): return [ self.initialize_session(sess) for sess", "self._logger ) class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by shells to run", "= registered_sessions or self.REGISTERED_SESSIONS self._reservation_context = reservation_context @property def _cli_type(self):", "registered_sessions or self.REGISTERED_SESSIONS self._reservation_context = reservation_context @property def _cli_type(self): \"\"\"Connection", "enable_mode(self): pass @property @abstractmethod def config_mode(self): pass def enable_mode_service(self): return", ") class AbstractModeConfigurator(ABC, CLIServiceConfigurator): \"\"\"Used by shells to run enable/config", "functools import lru_cache else: from functools32 import lru_cache class CLIServiceConfigurator(object):", "def config_mode(self): pass def enable_mode_service(self): return self.get_cli_service(self.enable_mode) def config_mode_service(self): return", "import sys from abc import ABCMeta, abstractmethod from collections import", "sess in self._session_dict.get( self._cli_type.lower(), self._registered_sessions ) ] def get_cli_service(self, command_mode):", "config_mode(self): pass def enable_mode_service(self): return self.get_cli_service(self.enable_mode) def config_mode_service(self): return self.get_cli_service(self.config_mode)", "(3, 0): from functools import lru_cache else: from functools32 import", "\"\"\"Initialize CLI service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig resource_config: # noqa: E501", ":param logging.Logger logger: :param cloudshell.cli.service.cli.CLI cli: :param registered_sessions: Session types", "@abstractmethod def config_mode(self): pass def enable_mode_service(self): return self.get_cli_service(self.enable_mode) def config_mode_service(self):", "__init__( self, resource_config, logger, cli=None, registered_sessions=None, reservation_context=None, ): \"\"\"Initialize CLI", "import CLI from cloudshell.cli.session.ssh_session import SSHSession from cloudshell.cli.session.telnet_session import TelnetSession", "@property @abstractmethod def config_mode(self): pass def enable_mode_service(self): return self.get_cli_service(self.enable_mode) def", "-*- import sys from abc import ABCMeta, abstractmethod from collections", "logging.Logger logger: :param cloudshell.cli.service.cli.CLI cli: :param registered_sessions: Session types and", "0): from functools import lru_cache else: from functools32 import lru_cache", "= defaultdict(list) for sess in self._registered_sessions: session_dict[sess.SESSION_TYPE.lower()].append(sess) return session_dict def", ":param cloudshell.shell.core.driver_context.ReservationContextDetails reservation_context: \"\"\" self._cli = cli or CLI() self._resource_config", "CLIServiceConfigurator): \"\"\"Used by shells to run enable/config command.\"\"\" @property @abstractmethod", "self._logger = logger self._registered_sessions = registered_sessions or self.REGISTERED_SESSIONS self._reservation_context =", "TelnetSession ABC = ABCMeta(\"ABC\", (object,), {\"__slots__\": ()}) if sys.version_info >=", "or self.REGISTERED_SESSIONS self._reservation_context = reservation_context @property def _cli_type(self): \"\"\"Connection type", "abc import ABCMeta, abstractmethod from collections import defaultdict from cloudshell.cli.factory.session_factory", "from functools32 import lru_cache class CLIServiceConfigurator(object): REGISTERED_SESSIONS = (CloudInfoAccessKeySessionFactory(SSHSession), TelnetSession)", "# noqa: E501 \"\"\" return self._cli.get_session( self._defined_sessions(), command_mode, self._logger )", "self._cli = cli or CLI() self._resource_config = resource_config self._logger =", "resource_config self._logger = logger self._registered_sessions = registered_sessions or self.REGISTERED_SESSIONS self._reservation_context", "SessionFactory, ) from cloudshell.cli.service.cli import CLI from cloudshell.cli.session.ssh_session import SSHSession", "def get_cli_service(self, command_mode): \"\"\"Use cli.get_session to open CLI connection and", "be default_mode/enable_mode/config_mode/etc. :return: created session in provided mode :rtype: cloudshell.cli.service.session_pool_context_manager.SessionPoolContextManager", "import ( CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, ) from cloudshell.cli.service.cli import CLI", "cli=None, registered_sessions=None, reservation_context=None, ): \"\"\"Initialize CLI service configurator. :param cloudshell.shell.standards.resource_config_generic_models.GenericCLIConfig", "CloudInfoAccessKeySessionFactory, GenericSessionFactory, SessionFactory, ) from cloudshell.cli.service.cli import CLI from cloudshell.cli.session.ssh_session", "# -*- coding: utf-8 -*- import sys from abc import", "cloudshell.cli.service.cli import CLI from cloudshell.cli.session.ssh_session import SSHSession from cloudshell.cli.session.telnet_session import", ") def _defined_sessions(self): return [ self.initialize_session(sess) for sess in self._session_dict.get(" ]
[ "= 111 gen = (n * 7 for x in", "if 777 in gen: print(\"Yes!\") if __name__ == '__main__': main()", "x in range(10)) if 777 in gen: print(\"Yes!\") if __name__", "def main(): n = 111 gen = (n * 7", "main(): n = 111 gen = (n * 7 for", "* 7 for x in range(10)) if 777 in gen:", "for x in range(10)) if 777 in gen: print(\"Yes!\") if", "111 gen = (n * 7 for x in range(10))", "gen = (n * 7 for x in range(10)) if", "7 for x in range(10)) if 777 in gen: print(\"Yes!\")", "range(10)) if 777 in gen: print(\"Yes!\") if __name__ == '__main__':", "= (n * 7 for x in range(10)) if 777", "in range(10)) if 777 in gen: print(\"Yes!\") if __name__ ==", "(n * 7 for x in range(10)) if 777 in", "n = 111 gen = (n * 7 for x" ]
[ "import Flask, jsonify, request from flask_cors import CORS, cross_origin app", "vitals_value.append(value) modelObj = joblib.load(modelPath) data = [vitals_value] df = pd.DataFrame(data=data,", "modelPath = request.args.get('modelPath') column_names = request.args.get('columnNames') data_points = request.args.get('dataPoints') app.logger.info('Received", "home(): incomingMachineId = request.args.get('machineId') modelPath = request.args.get('modelPath') column_names = request.args.get('columnNames')", "[] for key, value in pairs: vitals_value.append(value) modelObj = joblib.load(modelPath)", "as pd from flask import Flask, jsonify, request from flask_cors", "json_object.items() vitals_value = [] for key, value in pairs: vitals_value.append(value)", "in pairs: vitals_value.append(value) modelObj = joblib.load(modelPath) data = [vitals_value] df", "jsonify(modelPrediction[0]) if __name__ == \"__main__\": app.run(debug=True) # To start the", "column_names = request.args.get('columnNames') data_points = request.args.get('dataPoints') app.logger.info('Received machine id is", "modelObj = joblib.load(modelPath) data = [vitals_value] df = pd.DataFrame(data=data, columns", "vitals_value = [] for key, value in pairs: vitals_value.append(value) modelObj", "def home(): incomingMachineId = request.args.get('machineId') modelPath = request.args.get('modelPath') column_names =", "= request.args.get('dataPoints') app.logger.info('Received machine id is %s', incomingMachineId) app.logger.info('Model path", "= [] for key, value in pairs: vitals_value.append(value) modelObj =", "from flask_cors import CORS, cross_origin app = Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\",", "= request.args.get('modelPath') column_names = request.args.get('columnNames') data_points = request.args.get('dataPoints') app.logger.info('Received machine", "[vitals_value] df = pd.DataFrame(data=data, columns = column_names) modelPrediction = modelObj.predict(df)", "= [vitals_value] df = pd.DataFrame(data=data, columns = column_names) modelPrediction =", "machine id is %s', incomingMachineId) app.logger.info('Model path is %s', modelPath)", "data_points = request.args.get('dataPoints') app.logger.info('Received machine id is %s', incomingMachineId) app.logger.info('Model", "value in pairs: vitals_value.append(value) modelObj = joblib.load(modelPath) data = [vitals_value]", "= pd.DataFrame(data=data, columns = column_names) modelPrediction = modelObj.predict(df) app.logger.info('Model prediction", "flask import Flask, jsonify, request from flask_cors import CORS, cross_origin", "\"__main__\": app.run(debug=True) # To start the server # python3 app.py", "flask_cors import CORS, cross_origin app = Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET'])", "id is %s', incomingMachineId) app.logger.info('Model path is %s', modelPath) json_object", "prediction is: %s', modelPrediction) return jsonify(modelPrediction[0]) if __name__ == \"__main__\":", "for key, value in pairs: vitals_value.append(value) modelObj = joblib.load(modelPath) data", "if __name__ == \"__main__\": app.run(debug=True) # To start the server", "Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def home(): incomingMachineId = request.args.get('machineId') modelPath", "json_object = json.loads(data_points) pairs = json_object.items() vitals_value = [] for", "modelObj.predict(df) app.logger.info('Model prediction is: %s', modelPrediction) return jsonify(modelPrediction[0]) if __name__", "modelPrediction = modelObj.predict(df) app.logger.info('Model prediction is: %s', modelPrediction) return jsonify(modelPrediction[0])", "joblib.load(modelPath) data = [vitals_value] df = pd.DataFrame(data=data, columns = column_names)", "from flask import Flask, jsonify, request from flask_cors import CORS,", "is %s', incomingMachineId) app.logger.info('Model path is %s', modelPath) json_object =", "columns = column_names) modelPrediction = modelObj.predict(df) app.logger.info('Model prediction is: %s',", "request from flask_cors import CORS, cross_origin app = Flask(__name__) CORS(app)", "= request.args.get('machineId') modelPath = request.args.get('modelPath') column_names = request.args.get('columnNames') data_points =", "%s', modelPath) json_object = json.loads(data_points) pairs = json_object.items() vitals_value =", "incomingMachineId) app.logger.info('Model path is %s', modelPath) json_object = json.loads(data_points) pairs", "app.logger.info('Model path is %s', modelPath) json_object = json.loads(data_points) pairs =", "cross_origin app = Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def home(): incomingMachineId", "import pandas as pd from flask import Flask, jsonify, request", "column_names) modelPrediction = modelObj.predict(df) app.logger.info('Model prediction is: %s', modelPrediction) return", "%s', modelPrediction) return jsonify(modelPrediction[0]) if __name__ == \"__main__\": app.run(debug=True) #", "= json_object.items() vitals_value = [] for key, value in pairs:", "__name__ == \"__main__\": app.run(debug=True) # To start the server #", "json.loads(data_points) pairs = json_object.items() vitals_value = [] for key, value", "modelPath) json_object = json.loads(data_points) pairs = json_object.items() vitals_value = []", "df = pd.DataFrame(data=data, columns = column_names) modelPrediction = modelObj.predict(df) app.logger.info('Model", "request.args.get('columnNames') data_points = request.args.get('dataPoints') app.logger.info('Received machine id is %s', incomingMachineId)", "Flask, jsonify, request from flask_cors import CORS, cross_origin app =", "import joblib import pandas as pd from flask import Flask,", "request.args.get('modelPath') column_names = request.args.get('columnNames') data_points = request.args.get('dataPoints') app.logger.info('Received machine id", "= modelObj.predict(df) app.logger.info('Model prediction is: %s', modelPrediction) return jsonify(modelPrediction[0]) if", "app.logger.info('Model prediction is: %s', modelPrediction) return jsonify(modelPrediction[0]) if __name__ ==", "pd from flask import Flask, jsonify, request from flask_cors import", "import CORS, cross_origin app = Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def", "request.args.get('machineId') modelPath = request.args.get('modelPath') column_names = request.args.get('columnNames') data_points = request.args.get('dataPoints')", "CORS, cross_origin app = Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def home():", "pairs = json_object.items() vitals_value = [] for key, value in", "incomingMachineId = request.args.get('machineId') modelPath = request.args.get('modelPath') column_names = request.args.get('columnNames') data_points", "CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def home(): incomingMachineId = request.args.get('machineId') modelPath =", "pairs: vitals_value.append(value) modelObj = joblib.load(modelPath) data = [vitals_value] df =", "= column_names) modelPrediction = modelObj.predict(df) app.logger.info('Model prediction is: %s', modelPrediction)", "modelPrediction) return jsonify(modelPrediction[0]) if __name__ == \"__main__\": app.run(debug=True) # To", "pd.DataFrame(data=data, columns = column_names) modelPrediction = modelObj.predict(df) app.logger.info('Model prediction is:", "is %s', modelPath) json_object = json.loads(data_points) pairs = json_object.items() vitals_value", "is: %s', modelPrediction) return jsonify(modelPrediction[0]) if __name__ == \"__main__\": app.run(debug=True)", "= joblib.load(modelPath) data = [vitals_value] df = pd.DataFrame(data=data, columns =", "data = [vitals_value] df = pd.DataFrame(data=data, columns = column_names) modelPrediction", "json import logging import joblib import pandas as pd from", "joblib import pandas as pd from flask import Flask, jsonify,", "= Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def home(): incomingMachineId = request.args.get('machineId')", "path is %s', modelPath) json_object = json.loads(data_points) pairs = json_object.items()", "return jsonify(modelPrediction[0]) if __name__ == \"__main__\": app.run(debug=True) # To start", "pandas as pd from flask import Flask, jsonify, request from", "= request.args.get('columnNames') data_points = request.args.get('dataPoints') app.logger.info('Received machine id is %s',", "== \"__main__\": app.run(debug=True) # To start the server # python3", "jsonify, request from flask_cors import CORS, cross_origin app = Flask(__name__)", "import json import logging import joblib import pandas as pd", "@app.route(\"/api/machinePrediction\", methods=['GET']) def home(): incomingMachineId = request.args.get('machineId') modelPath = request.args.get('modelPath')", "logging import joblib import pandas as pd from flask import", "methods=['GET']) def home(): incomingMachineId = request.args.get('machineId') modelPath = request.args.get('modelPath') column_names", "%s', incomingMachineId) app.logger.info('Model path is %s', modelPath) json_object = json.loads(data_points)", "key, value in pairs: vitals_value.append(value) modelObj = joblib.load(modelPath) data =", "import logging import joblib import pandas as pd from flask", "request.args.get('dataPoints') app.logger.info('Received machine id is %s', incomingMachineId) app.logger.info('Model path is", "app.logger.info('Received machine id is %s', incomingMachineId) app.logger.info('Model path is %s',", "app = Flask(__name__) CORS(app) @app.route(\"/api/machinePrediction\", methods=['GET']) def home(): incomingMachineId =", "= json.loads(data_points) pairs = json_object.items() vitals_value = [] for key," ]
[ "Component from nexus_constructor.model.dataset import Dataset from nexus_constructor.model.instrument import Instrument from", "not rot1.dependents assert component1.depends_on == rot3 assert rot2.dependents[0] == rot3", "component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain() assert rot1.depends_on == rot3 assert component1.transforms.link.linked_component", "values=values, ) component1.depends_on = rot1 component2.depends_on = rot2 component3.depends_on =", "= rot2 assert len(rot2.dependents) == 1 rot1.remove_from_dependee_chain() assert len(rot2.dependents) ==", "assert rot1 in rot3.dependents assert component3 in rot3.dependents def test_remove_from_end():", "in rot2.dependents assert component1 in rot2.dependents assert component1.depends_on == rot2", "component3 in rot3.dependents def test_remove_from_end(): component1 = Component(\"component1\", instrument) rot1", "component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on =", "= Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) component3 = Component(\"component3\",", "name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot2 = component1.add_rotation(", "component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot3 =", "0.0, 0.0), angle=values.values, values=values, ) rot2 = component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0,", "rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot1,", "import QVector3D from nexus_constructor.model.component import Component from nexus_constructor.model.dataset import Dataset", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot2, ) component1.depends_on = rot3", "values=values, ) rot2 = component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", "= rot3 component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain() assert", "assert component1 in rot2.dependents assert component1.depends_on == rot2 assert component1.transforms.link.linked_component", ") rot2 = component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values,", "Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1 = Component(\"component1\", instrument) rot = component1.add_rotation(", "values=values, ) component1.depends_on = rot1 component2.depends_on = rot2 rot1.depends_on =", "rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, )", "0.0, 0.0), angle=values.values, values=values, ) rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0,", ") component1.depends_on = rot1 rot1.depends_on = rot2 assert len(rot2.dependents) ==", "== rot3 assert component1.transforms.link.linked_component == component3 assert rot1 in rot3.dependents", "= rot2 assert len(rot2.dependents) == 2 rot1.remove_from_dependee_chain() assert len(rot2.dependents) ==", "assert component1.depends_on is None def test_remove_from_beginning_2(instrument): component1 = Component(\"component1\", instrument)", "= Component(\"component3\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0),", "rot1 in rot3.dependents assert component3 in rot3.dependents def test_remove_from_end(): component1", "Instrument from nexus_constructor.model.value_type import ValueTypes values = Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE,", "def test_remove_from_beginning_1(instrument): component1 = Component(\"component1\", instrument) rot = component1.add_rotation( name=\"rotation1\",", "component1.depends_on = rot assert len(rot.dependents) == 1 rot.remove_from_dependee_chain() assert component1.depends_on", "rot1.depends_on is None assert not rot1.dependents assert component1.depends_on == rot3", "0.0), angle=values.values, values=values, ) component1.depends_on = rot assert len(rot.dependents) ==", "component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on =", "values=90.0, parent_node=None, ) @pytest.fixture def instrument(): return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument):", "== rot2 def test_remove_from_beginning_3(instrument): component1 = Component(\"component1\", instrument) component2 =", "def instrument(): return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1 = Component(\"component1\", instrument)", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot1 rot1.depends_on", "0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot1 rot1.depends_on =", "test_remove_from_beginning_3(instrument): component1 = Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) rot1", "assert len(rot2.dependents) == 1 assert rot2.dependents[0] == component1 assert component1.depends_on", "assert component1.transforms.link.linked_component == component3 assert rot1 in rot3.dependents assert component3", "component1.depends_on is None def test_remove_from_beginning_2(instrument): component1 = Component(\"component1\", instrument) rot1", "Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) component3 = Component(\"component3\", instrument)", "== 2 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 2 assert component2 in", "rot2.dependents assert component1 in rot2.dependents assert component1.depends_on == rot2 assert", "Component(\"component2\", instrument) component3 = Component(\"component3\", instrument) rot1 = component1.add_rotation( name=\"rotation1\",", "component1 = Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) rot1 =", "= component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on", "component1 in rot2.dependents assert component1.depends_on == rot2 assert component1.transforms.link.linked_component ==", "component1.depends_on == rot2 assert component1.transforms.link.linked_component == component2 def test_remove_from_middle(): component1", "from nexus_constructor.model.value_type import ValueTypes values = Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1],", "= rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on is None assert not rot1.dependents", "rot1 component2.depends_on = rot2 rot1.depends_on = rot2 assert len(rot2.dependents) ==", "assert component1.depends_on == rot3 assert rot2.dependents[0] == rot3 assert len(component1.transforms)", "0.0), angle=values.values, values=values, ) rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0,", "rot1 rot1.depends_on = rot2 assert len(rot2.dependents) == 1 rot1.remove_from_dependee_chain() assert", "0.0, 0.0), angle=values.values, values=values, ) rot3 = component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0,", "rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, )", "component1.depends_on = rot1 component2.depends_on = rot2 component3.depends_on = rot3 component1.transforms.link.linked_component", "component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0,", "= rot1 rot1.depends_on = rot2 assert len(rot2.dependents) == 1 rot1.remove_from_dependee_chain()", "type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None, ) @pytest.fixture def instrument(): return Instrument(parent_node=None)", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot1, ) rot3 = component1.add_rotation(", "2 assert component2 in rot2.dependents assert component1 in rot2.dependents assert", "= component2 component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain() assert rot1.depends_on == rot3", "from PySide2.QtGui import QVector3D from nexus_constructor.model.component import Component from nexus_constructor.model.dataset", "== rot3 assert rot2.dependents[0] == rot3 assert len(component1.transforms) == 2", "Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None, ) @pytest.fixture def instrument():", "angle=values.values, values=values, ) component1.depends_on = rot1 component2.depends_on = rot2 rot1.depends_on", "name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None, ) @pytest.fixture def instrument(): return", "== 2 assert component2 in rot2.dependents assert component1 in rot2.dependents", "size=[1], values=90.0, parent_node=None, ) @pytest.fixture def instrument(): return Instrument(parent_node=None) def", "rot1.depends_on = rot2 assert len(rot2.dependents) == 1 rot1.remove_from_dependee_chain() assert len(rot2.dependents)", "= component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot1, )", "rot3 assert component1.transforms.link.linked_component == component3 assert rot1 in rot3.dependents assert", "rot2 assert component1.transforms.link.linked_component == component2 def test_remove_from_middle(): component1 = Component(\"component1\",", "angle=values.values, values=values, ) rot2 = component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0),", "@pytest.fixture def instrument(): return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1 = Component(\"component1\",", "len(rot2.dependents) == 1 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 1 assert rot2.dependents[0]", "component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot1, ) rot3", "0.0, 0.0), angle=values.values, values=values, depends_on=rot1, ) rot3 = component1.add_rotation( name=\"rotation3\",", "values = Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None, ) @pytest.fixture", "component1.depends_on == rot2 def test_remove_from_beginning_3(instrument): component1 = Component(\"component1\", instrument) component2", "name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot2, ) component1.depends_on =", "component1.transforms.link.linked_component == component2 def test_remove_from_middle(): component1 = Component(\"component1\", instrument) component2", "import Dataset from nexus_constructor.model.instrument import Instrument from nexus_constructor.model.value_type import ValueTypes", "None def test_remove_from_beginning_2(instrument): component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation(", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot2 = component2.add_rotation( name=\"rotation2\",", "= rot2 rot1.depends_on = rot2 assert len(rot2.dependents) == 2 rot1.remove_from_dependee_chain()", "= component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on", "pytest from PySide2.QtGui import QVector3D from nexus_constructor.model.component import Component from", "rot1.depends_on == rot3 assert component1.transforms.link.linked_component == component3 assert rot1 in", "depends_on=rot1, ) rot3 = component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", ") rot3 = component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values,", "QVector3D from nexus_constructor.model.component import Component from nexus_constructor.model.dataset import Dataset from", "rot1.depends_on = rot2 assert len(rot2.dependents) == 2 rot1.remove_from_dependee_chain() assert len(rot2.dependents)", "is None def test_remove_from_beginning_2(instrument): component1 = Component(\"component1\", instrument) rot1 =", "component2 = Component(\"component2\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0,", ") component1.depends_on = rot1 component2.depends_on = rot2 component3.depends_on = rot3", "values=values, ) rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", "in rot3.dependents def test_remove_from_end(): component1 = Component(\"component1\", instrument) rot1 =", "def test_remove_from_beginning_3(instrument): component1 = Component(\"component1\", instrument) component2 = Component(\"component2\", instrument)", "assert component1.depends_on == rot2 assert component1.transforms.link.linked_component == component2 def test_remove_from_middle():", "name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot", "== component1 assert component1.depends_on == rot2 def test_remove_from_beginning_3(instrument): component1 =", "component2.depends_on = rot2 rot1.depends_on = rot2 assert len(rot2.dependents) == 2", "component3 rot2.remove_from_dependee_chain() assert rot1.depends_on == rot3 assert component1.transforms.link.linked_component == component3", "test_remove_from_beginning_1(instrument): component1 = Component(\"component1\", instrument) rot = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0,", "component1 = Component(\"component1\", instrument) rot = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0,", "rot3 = component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, )", "assert len(rot2.dependents) == 2 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 2 assert", "angle=values.values, values=values, ) rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0),", "assert len(rot.dependents) == 1 rot.remove_from_dependee_chain() assert component1.depends_on is None def", "= rot2 component3.depends_on = rot3 component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component =", "rot2 rot1.depends_on = rot2 assert len(rot2.dependents) == 2 rot1.remove_from_dependee_chain() assert", "in rot2.dependents assert component1.depends_on == rot2 assert component1.transforms.link.linked_component == component2", "== 1 rot.remove_from_dependee_chain() assert component1.depends_on is None def test_remove_from_beginning_2(instrument): component1", "values=values, ) rot3 = component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", ") component1.depends_on = rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on is None assert", "rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 1 assert rot2.dependents[0] == component1 assert", "len(rot2.dependents) == 1 assert rot2.dependents[0] == component1 assert component1.depends_on ==", "assert component1.transforms.link.linked_component == component2 def test_remove_from_middle(): component1 = Component(\"component1\", instrument)", "name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot1, ) rot3 =", "nexus_constructor.model.component import Component from nexus_constructor.model.dataset import Dataset from nexus_constructor.model.instrument import", "component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot2, ) component1.depends_on", "angle=values.values, values=values, depends_on=rot2, ) component1.depends_on = rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on", "rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on is None assert not rot1.dependents assert", "rot = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, )", "angle=values.values, values=values, ) rot3 = component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0),", "len(rot2.dependents) == 2 assert component2 in rot2.dependents assert component1 in", "nexus_constructor.model.dataset import Dataset from nexus_constructor.model.instrument import Instrument from nexus_constructor.model.value_type import", "Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) rot1 = component1.add_rotation( name=\"rotation1\",", "rot2 = component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, )", "= rot1 component2.depends_on = rot2 rot1.depends_on = rot2 assert len(rot2.dependents)", "== 1 assert rot2.dependents[0] == component1 assert component1.depends_on == rot2", "= rot1 component2.depends_on = rot2 component3.depends_on = rot3 component1.transforms.link.linked_component =", "= component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot2, )", "import Component from nexus_constructor.model.dataset import Dataset from nexus_constructor.model.instrument import Instrument", "rot.remove_from_dependee_chain() assert component1.depends_on is None def test_remove_from_beginning_2(instrument): component1 = Component(\"component1\",", "component1.depends_on == rot3 assert rot2.dependents[0] == rot3 assert len(component1.transforms) ==", "1 rot.remove_from_dependee_chain() assert component1.depends_on is None def test_remove_from_beginning_2(instrument): component1 =", "component3 = Component(\"component3\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0,", "def test_remove_from_end(): component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\",", "component1.depends_on = rot1 rot1.depends_on = rot2 assert len(rot2.dependents) == 1", "depends_on=rot2, ) component1.depends_on = rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on is None", "assert rot1.depends_on is None assert not rot1.dependents assert component1.depends_on ==", "assert rot2.dependents[0] == component1 assert component1.depends_on == rot2 def test_remove_from_beginning_3(instrument):", "test_remove_from_middle(): component1 = Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) component3", "nexus_constructor.model.instrument import Instrument from nexus_constructor.model.value_type import ValueTypes values = Dataset(", "component2 in rot2.dependents assert component1 in rot2.dependents assert component1.depends_on ==", "assert not rot1.dependents assert component1.depends_on == rot3 assert rot2.dependents[0] ==", "instrument(): return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1 = Component(\"component1\", instrument) rot", "nexus_constructor.model.value_type import ValueTypes values = Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0,", "= component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot3", "rot3 component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain() assert rot1.depends_on", "component3 assert rot1 in rot3.dependents assert component3 in rot3.dependents def", "PySide2.QtGui import QVector3D from nexus_constructor.model.component import Component from nexus_constructor.model.dataset import", "0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot assert len(rot.dependents)", "is None assert not rot1.dependents assert component1.depends_on == rot3 assert", "= Component(\"component1\", instrument) rot = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0),", "assert rot1.depends_on == rot3 assert component1.transforms.link.linked_component == component3 assert rot1", "0.0, 0.0), angle=values.values, values=values, depends_on=rot2, ) component1.depends_on = rot3 rot1.remove_from_dependee_chain()", "component2 = Component(\"component2\", instrument) component3 = Component(\"component3\", instrument) rot1 =", "component2 def test_remove_from_middle(): component1 = Component(\"component1\", instrument) component2 = Component(\"component2\",", "parent_node=None, ) @pytest.fixture def instrument(): return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1", ") rot2 = component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values,", "rot2.dependents[0] == component1 assert component1.depends_on == rot2 def test_remove_from_beginning_3(instrument): component1", "0.0), angle=values.values, values=values, ) component1.depends_on = rot1 rot1.depends_on = rot2", "values=values, depends_on=rot2, ) component1.depends_on = rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on is", "component1 = Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) component3 =", "rot1.remove_from_dependee_chain() assert rot1.depends_on is None assert not rot1.dependents assert component1.depends_on", "def test_remove_from_beginning_2(instrument): component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\",", "= rot assert len(rot.dependents) == 1 rot.remove_from_dependee_chain() assert component1.depends_on is", "assert component2 in rot2.dependents assert component1 in rot2.dependents assert component1.depends_on", "Component(\"component3\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", "from nexus_constructor.model.instrument import Instrument from nexus_constructor.model.value_type import ValueTypes values =", "component2.depends_on = rot2 component3.depends_on = rot3 component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component", "instrument) rot = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values,", "= Component(\"component2\", instrument) component3 = Component(\"component3\", instrument) rot1 = component1.add_rotation(", "import pytest from PySide2.QtGui import QVector3D from nexus_constructor.model.component import Component", "Dataset from nexus_constructor.model.instrument import Instrument from nexus_constructor.model.value_type import ValueTypes values", "component3.depends_on = rot3 component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain()", "name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot2 = component2.add_rotation(", "0.0), angle=values.values, values=values, depends_on=rot1, ) rot3 = component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0,", "rot2 def test_remove_from_beginning_3(instrument): component1 = Component(\"component1\", instrument) component2 = Component(\"component2\",", "instrument) component3 = Component(\"component3\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0,", "rot2 assert len(rot2.dependents) == 1 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 1", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot3 = component3.add_rotation( name=\"rotation3\",", "name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot1", "rot2 component3.depends_on = rot3 component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component = component3", "instrument) component2 = Component(\"component2\", instrument) component3 = Component(\"component3\", instrument) rot1", "rot3.dependents def test_remove_from_end(): component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation(", "name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot3 = component3.add_rotation(", "name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot1", "values=values, ) component1.depends_on = rot assert len(rot.dependents) == 1 rot.remove_from_dependee_chain()", "ValueTypes values = Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None, )", "Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", "rot2 assert len(rot2.dependents) == 2 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 2", "component1.transforms.link.linked_component == component3 assert rot1 in rot3.dependents assert component3 in", "len(rot.dependents) == 1 rot.remove_from_dependee_chain() assert component1.depends_on is None def test_remove_from_beginning_2(instrument):", "= component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on", "component1.transforms.link.linked_component = component2 component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain() assert rot1.depends_on ==", "rot2.dependents assert component1.depends_on == rot2 assert component1.transforms.link.linked_component == component2 def", "instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values,", "assert component3 in rot3.dependents def test_remove_from_end(): component1 = Component(\"component1\", instrument)", "test_remove_from_end(): component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0,", "angle=values.values, values=values, depends_on=rot1, ) rot3 = component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0,", "1 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 1 assert rot2.dependents[0] == component1", "component1.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on =", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot1 component2.depends_on", "return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1 = Component(\"component1\", instrument) rot =", "2 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 2 assert component2 in rot2.dependents", "0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot1 component2.depends_on =", "component1.depends_on = rot1 component2.depends_on = rot2 rot1.depends_on = rot2 assert", "from nexus_constructor.model.component import Component from nexus_constructor.model.dataset import Dataset from nexus_constructor.model.instrument", "assert len(rot2.dependents) == 1 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 1 assert", "assert component1.depends_on == rot2 def test_remove_from_beginning_3(instrument): component1 = Component(\"component1\", instrument)", "== rot2 assert component1.transforms.link.linked_component == component2 def test_remove_from_middle(): component1 =", "angle=values.values, values=values, ) component1.depends_on = rot assert len(rot.dependents) == 1", "= Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0),", "= component3 rot2.remove_from_dependee_chain() assert rot1.depends_on == rot3 assert component1.transforms.link.linked_component ==", "= component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on", "component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on =", "import ValueTypes values = Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None,", "== component3 assert rot1 in rot3.dependents assert component3 in rot3.dependents", "from nexus_constructor.model.dataset import Dataset from nexus_constructor.model.instrument import Instrument from nexus_constructor.model.value_type", "values=values, ) component1.depends_on = rot1 rot1.depends_on = rot2 assert len(rot2.dependents)", "assert len(rot2.dependents) == 2 assert component2 in rot2.dependents assert component1", "rot3 = component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, depends_on=rot2,", "angle=values.values, values=values, ) component1.depends_on = rot1 rot1.depends_on = rot2 assert", "rot1.dependents assert component1.depends_on == rot3 assert rot2.dependents[0] == rot3 assert", "0.0), angle=values.values, values=values, ) rot2 = component2.add_rotation( name=\"rotation2\", axis=QVector3D(1.0, 0.0,", "= Component(\"component1\", instrument) component2 = Component(\"component2\", instrument) rot1 = component1.add_rotation(", "component2 component2.transforms.link.linked_component = component3 rot2.remove_from_dependee_chain() assert rot1.depends_on == rot3 assert", "import Instrument from nexus_constructor.model.value_type import ValueTypes values = Dataset( name=\"scalar_value\",", "len(rot2.dependents) == 2 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 2 assert component2", ") component1.depends_on = rot assert len(rot.dependents) == 1 rot.remove_from_dependee_chain() assert", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot2 = component1.add_rotation( name=\"rotation2\",", "component1 assert component1.depends_on == rot2 def test_remove_from_beginning_3(instrument): component1 = Component(\"component1\",", ") rot3 = component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values,", ") component1.depends_on = rot1 component2.depends_on = rot2 rot1.depends_on = rot2", "rot3.dependents assert component3 in rot3.dependents def test_remove_from_end(): component1 = Component(\"component1\",", "instrument) component2 = Component(\"component2\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0,", "= Component(\"component2\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0),", "angle=values.values, values=values, ) component1.depends_on = rot1 component2.depends_on = rot2 component3.depends_on", "component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot2 =", "= Dataset( name=\"scalar_value\", type=ValueTypes.DOUBLE, size=[1], values=90.0, parent_node=None, ) @pytest.fixture def", "0.0), angle=values.values, values=values, ) component1.depends_on = rot1 component2.depends_on = rot2", "in rot3.dependents assert component3 in rot3.dependents def test_remove_from_end(): component1 =", "rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 2 assert component2 in rot2.dependents assert", "Component(\"component2\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", "Component(\"component1\", instrument) rot = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values,", "rot1 component2.depends_on = rot2 component3.depends_on = rot3 component1.transforms.link.linked_component = component2", "== 1 rot1.remove_from_dependee_chain() assert len(rot2.dependents) == 1 assert rot2.dependents[0] ==", "def test_remove_from_middle(): component1 = Component(\"component1\", instrument) component2 = Component(\"component2\", instrument)", "= component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) rot2", "rot2.remove_from_dependee_chain() assert rot1.depends_on == rot3 assert component1.transforms.link.linked_component == component3 assert", "0.0), angle=values.values, values=values, ) rot3 = component3.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0,", "component1.depends_on = rot3 rot1.remove_from_dependee_chain() assert rot1.depends_on is None assert not", "values=values, depends_on=rot1, ) rot3 = component1.add_rotation( name=\"rotation3\", axis=QVector3D(1.0, 0.0, 0.0),", "== component2 def test_remove_from_middle(): component1 = Component(\"component1\", instrument) component2 =", "test_remove_from_beginning_2(instrument): component1 = Component(\"component1\", instrument) rot1 = component1.add_rotation( name=\"rotation1\", axis=QVector3D(1.0,", "None assert not rot1.dependents assert component1.depends_on == rot3 assert rot2.dependents[0]", "1 assert rot2.dependents[0] == component1 assert component1.depends_on == rot2 def", ") @pytest.fixture def instrument(): return Instrument(parent_node=None) def test_remove_from_beginning_1(instrument): component1 =", "0.0), angle=values.values, values=values, depends_on=rot2, ) component1.depends_on = rot3 rot1.remove_from_dependee_chain() assert", "rot assert len(rot.dependents) == 1 rot.remove_from_dependee_chain() assert component1.depends_on is None", "axis=QVector3D(1.0, 0.0, 0.0), angle=values.values, values=values, ) component1.depends_on = rot assert" ]
[ "# build optimizer optimizer = build_optimizer(cfg, model) # build lr", "\"max mem: {memory:.0f}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated()", "= osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\", \"outputs1\") output_dir = output_dir.replace('@', config_path)", "curr_epoch * total_iteration + iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration", "): logger = logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\" \") model.train() end", "optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds, data_batch,", "data_loader.__len__() with torch.no_grad(): for iteration, data_batch in enumerate(data_loader): data_time =", "from fastmvsnet.utils.metric_logger import MetricLogger from fastmvsnet.utils.file_logger import file_logger def parse_args():", "for iteration, data_batch in enumerate(data_loader): data_time = time.time() - end", "= nn.DataParallel(model).cuda() # build optimizer optimizer = build_optimizer(cfg, model) #", "epoch + 1 scheduler.step() start_time = time.time() train_meters = train_model(model,", "torch import torch.nn as nn from fastmvsnet.config import load_cfg_from_file from", "inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, )", "best_metric is None or cur_metric > best_metric: best_metric = cur_metric", "for epoch in range(start_epoch, max_epoch): cur_epoch = epoch + 1", "cur_epoch % val_period == 0 or cur_epoch == max_epoch: val_meters", "+ iteration, output_dir, prefix=\"train\") return meters def validate_model(model, loss_fn, metric_fn,", "logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build", "best_metric: best_metric = cur_metric checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric", "{}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model def main(): args = parse_args() num_gpus", "MetricLogger(delimiter=\" \") model.train() end = time.time() total_iteration = data_loader.__len__() path_list", "image_scales, inter_scales, isFlow, data_loader, optimizer, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ):", "= cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate if", "checkpointer checkpointer = Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data =", "iteration, output_dir, prefix=\"train\") return meters def validate_model(model, loss_fn, metric_fn, image_scales,", "meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), ) ) tensorboard_logger.add_scalars(loss_dict,", "inter_scales, isFlow) loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds,", "or cur_epoch == max_epoch: val_meters = validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES,", "{k: v.cuda(non_blocking=True) for k, v in data_batch.items() if isinstance(v, torch.Tensor)}", "loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, optimizer, curr_epoch, tensorboard_logger, log_period=1,", "return meters def validate_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader,", "{:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time)) # checkpoint if cur_epoch % ckpt_period", "from fastmvsnet.dataset1 import build_data_loader from fastmvsnet.utils.tensorboard_logger import TensorboardLogger from fastmvsnet.utils.metric_logger", "data_loader.__len__() path_list = [] for iteration, data_batch in enumerate(data_loader): data_time", "max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\", 0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC)", "model(data_batch, image_scales, inter_scales, isFlow) loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict", "None or cur_metric > best_metric: best_metric = cur_metric checkpoint_data[\"epoch\"] =", "**loss_dict, **metric_dict) batch_time = time.time() - end end = time.time()", "Training\") parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path to config file\",", "validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is None or cur_metric", "from fastmvsnet.utils.checkpoint import Checkpointer from fastmvsnet.dataset1 import build_data_loader from fastmvsnet.utils.tensorboard_logger", "iteration, prefix=\"train\") if iteration % (100 * log_period) == 0:", "== 0 or cur_epoch == max_epoch: val_meters = validate_model(model, loss_fn,", "meters def validate_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, curr_epoch,", "file\", type=str, ) parser.add_argument( \"opts\", help=\"Modify config options using the", "torch.no_grad(): for iteration, data_batch in enumerate(data_loader): data_time = time.time() -", "image_scales, inter_scales, isFlow, data_loader, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger", "val_period < 1: continue if cur_epoch % val_period == 0", "validate_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, curr_epoch, tensorboard_logger, log_period=1,", "build optimizer optimizer = build_optimizer(cfg, model) # build lr scheduler", "\"lr: {lr:.2e}\", \"max mem: {memory:.0f}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters),", ") ) tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration + iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict,", "0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name, None) logger.info(\"Start training", "build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint import Checkpointer from fastmvsnet.dataset1 import build_data_loader", "best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model", "import logging import time import sys sys.path.insert(0, osp.dirname(__file__) + '/..')", "def main(): args = parse_args() num_gpus = torch.cuda.device_count() cfg =", "from fastmvsnet.utils.tensorboard_logger import TensorboardLogger from fastmvsnet.utils.metric_logger import MetricLogger from fastmvsnet.utils.file_logger", "] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0 **", "build_data_loader(cfg, mode=\"val\") if val_period > 0 else None # build", "cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC,", "0 else None # build tensorboard logger (optionally by comment)", "= build_optimizer(cfg, model) # build lr scheduler scheduler = build_scheduler(cfg,", "meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\", \"max", "osp import logging import time import sys sys.path.insert(0, osp.dirname(__file__) +", "= sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) batch_time = time.time() - end", "fastmvsnet.utils.tensorboard_logger import TensorboardLogger from fastmvsnet.utils.metric_logger import MetricLogger from fastmvsnet.utils.file_logger import", "image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD,", "total_iteration + iteration, output_dir, prefix=\"valid\") return meters def train(cfg, output_dir=\"\"):", "end = time.time() total_iteration = data_loader.__len__() path_list = [] for", "return meters def train(cfg, output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\") # build", "resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build data loader train_data_loader =", "metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD,", "mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg, mode=\"val\") if val_period", "logging.getLogger(\"fastmvsnet.trainer\") # build model set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn = build_model(cfg)", "optimizer) # build checkpointer checkpointer = Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir,", "log_period) == 0: file_logger(data_batch, preds, curr_epoch * total_iteration + iteration,", "* total_iteration + iteration, output_dir, prefix=\"train\") return meters def validate_model(model,", "> cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch,", "iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration + iteration, prefix=\"train\") if", "curr_epoch * total_iteration + iteration, output_dir, prefix=\"train\") return meters def", "fastmvsnet.utils.logger import setup_logger from fastmvsnet.utils.torch_utils import set_random_seed from fastmvsnet.model1 import", "data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch = {k: v.cuda(non_blocking=True) for k, v in", "= metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict)", "\"{meters}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters, curr_epoch", "fastmvsnet.utils.io import mkdir from fastmvsnet.utils.logger import setup_logger from fastmvsnet.utils.torch_utils import", "time.time() meters.update(time=batch_time, data=data_time) if iteration % log_period == 0: logger.info(", "# train max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\", 0) best_metric_name", "mkdir from fastmvsnet.utils.logger import setup_logger from fastmvsnet.utils.torch_utils import set_random_seed from", "# build lr scheduler scheduler = build_scheduler(cfg, optimizer) # build", "load_cfg_from_file from fastmvsnet.utils.io import mkdir from fastmvsnet.utils.logger import setup_logger from", "curr_epoch * total_iteration + iteration, prefix=\"valid\") if iteration % (100", "total_iteration + iteration, prefix=\"valid\") if iteration % (100 * log_period)", "optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time = time.time() -", "val_meters = validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH),", "parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\",", "= build_data_loader(cfg, mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg, mode=\"val\")", "data_batch, isFlow) metric_dict = metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values())", "prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration file {}\".format(args.config_file)) logger.info(\"Running", "train_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, optimizer, curr_epoch, tensorboard_logger,", "tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) # best validation", "config file\", type=str, ) parser.add_argument( \"opts\", help=\"Modify config options using", "sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward()", "val_period = cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg, mode=\"val\") if val_period >", "cur_metric checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best", "the command-line\", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() return args", "parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path to config file\", type=str,", "fastmvsnet.config import load_cfg_from_file from fastmvsnet.utils.io import mkdir from fastmvsnet.utils.logger import", "import TensorboardLogger from fastmvsnet.utils.metric_logger import MetricLogger from fastmvsnet.utils.file_logger import file_logger", "logger = setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded", "checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model def", "model.train() end = time.time() total_iteration = data_loader.__len__() path_list = []", "time.time() - end end = time.time() meters.update(time=batch_time, data=data_time) if iteration", "= checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build data loader", "logger = logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\" \") model.train() end =", "tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\"", "{iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\", \"max mem: {memory:.0f}\", ] ).format( epoch=curr_epoch,", "data loader train_data_loader = build_data_loader(cfg, mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD val_data_loader", "validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch,", "data_time = time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch", "nn.DataParallel(model).cuda() # build optimizer optimizer = build_optimizer(cfg, model) # build", "from fastmvsnet.utils.logger import setup_logger from fastmvsnet.utils.torch_utils import set_random_seed from fastmvsnet.model1", "<filename>fastmvsnet/train1.py #!/usr/bin/env python import argparse import os.path as osp import", "{} total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time)) # checkpoint if cur_epoch", "= loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds, data_batch, isFlow) losses", "#!/usr/bin/env python import argparse import os.path as osp import logging", "setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration file", "import build_pointmvsnet as build_model from fastmvsnet.solver import build_optimizer, build_scheduler from", "or cur_metric > best_metric: best_metric = cur_metric checkpoint_data[\"epoch\"] = cur_epoch", "using the command-line\", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() return", "return args def train_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader,", "tensorboard logger (optionally by comment) tensorboard_logger = TensorboardLogger(output_dir) # train", "if cur_epoch % ckpt_period == 0 or cur_epoch == max_epoch:", "= model(data_batch, image_scales, inter_scales, isFlow) loss_dict = loss_fn(preds, data_batch, isFlow)", "train_meters = train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH),", "checkpoint_data.get(best_metric_name, None) logger.info(\"Start training from epoch {}\".format(start_epoch)) for epoch in", "torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow) optimizer.zero_grad() loss_dict =", ") tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration + iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch", ") epoch_time = time.time() - start_time logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format(", "* log_period) == 0: file_logger(data_batch, preds, curr_epoch * total_iteration +", "config_path = osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\", \"outputs1\") output_dir = output_dir.replace('@',", "cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate if val_period", "total_iteration + iteration, prefix=\"train\") if iteration % (100 * log_period)", ") logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) # best validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg", "by comment) tensorboard_logger = TensorboardLogger(output_dir) # train max_epoch = cfg.SCHEDULER.MAX_EPOCH", "\"iter: {iter:4d}\", \"{meters}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), ) )", "from fastmvsnet.model1 import build_pointmvsnet as build_model from fastmvsnet.solver import build_optimizer,", "logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", ] ).format(", "logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg, output_dir) if __name__ == \"__main__\": main()", "import torch import torch.nn as nn from fastmvsnet.config import load_cfg_from_file", "= data_batch[\"ref_img_path\"] data_batch = {k: v.cuda(non_blocking=True) for k, v in", "isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, )", "loader train_data_loader = build_data_loader(cfg, mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD val_data_loader =", "metavar=\"FILE\", help=\"path to config file\", type=str, ) parser.add_argument( \"opts\", help=\"Modify", "% (100 * log_period) == 0: file_logger(data_batch, preds, curr_epoch *", "max_epoch): cur_epoch = epoch + 1 scheduler.step() start_time = time.time()", "image_scales, inter_scales, isFlow) optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict", "logger.info(\"Loaded configuration file {}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg, output_dir) if", "preds, curr_epoch * total_iteration + iteration, output_dir, prefix=\"train\") return meters", "isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow) loss_dict =", "] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters, curr_epoch *", "with torch.no_grad(): for iteration, data_batch in enumerate(data_loader): data_time = time.time()", "set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn = build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model =", "lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), ) ) tensorboard_logger.add_scalars(loss_dict, curr_epoch", "TensorboardLogger from fastmvsnet.utils.metric_logger import MetricLogger from fastmvsnet.utils.file_logger import file_logger def", "model = nn.DataParallel(model).cuda() # build optimizer optimizer = build_optimizer(cfg, model)", "file_logger def parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument( \"--cfg\",", "metric_dict = metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) #print(\"LOSS DICT\",", "file_logger(data_batch, preds, curr_epoch * total_iteration + iteration, output_dir, prefix=\"valid\") return", "model set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn = build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model", "logger.info(\"Start training from epoch {}\".format(start_epoch)) for epoch in range(start_epoch, max_epoch):", "= time.time() total_iteration = data_loader.__len__() path_list = [] for iteration,", "output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) # best validation cur_metric =", "checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build data loader train_data_loader", "= time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch = {k:", "build lr scheduler scheduler = build_scheduler(cfg, optimizer) # build checkpointer", "build_optimizer(cfg, model) # build lr scheduler scheduler = build_scheduler(cfg, optimizer)", "logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\" \") model.train() end = time.time() total_iteration", "enumerate(data_loader): data_time = time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch", "= checkpoint_data.get(best_metric_name, None) logger.info(\"Start training from epoch {}\".format(start_epoch)) for epoch", "% log_period == 0: logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter:", "output_dir, prefix=\"train\") return meters def validate_model(model, loss_fn, metric_fn, image_scales, inter_scales,", "iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), ) )", "train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer,", "(1024.0 ** 2), ) ) tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration +", "iteration, prefix=\"valid\") if iteration % (100 * log_period) == 0:", "logger.info(\"Build model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda() # build optimizer optimizer =", "build tensorboard logger (optionally by comment) tensorboard_logger = TensorboardLogger(output_dir) #", "checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate if val_period <", "if isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow) loss_dict", "0: file_logger(data_batch, preds, curr_epoch * total_iteration + iteration, output_dir, prefix=\"valid\")", "optimizer.step() batch_time = time.time() - end end = time.time() meters.update(time=batch_time,", "= checkpoint_data.get(\"epoch\", 0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name, None)", "return model def main(): args = parse_args() num_gpus = torch.cuda.device_count()", "== max_epoch: val_meters = validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch", "metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) batch_time", "**checkpoint_data) # validate if val_period < 1: continue if cur_epoch", "command-line\", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() return args def", "max_epoch: checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) #", "model) # build lr scheduler scheduler = build_scheduler(cfg, optimizer) #", "loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds, data_batch, isFlow)", "total_iteration = data_loader.__len__() with torch.no_grad(): for iteration, data_batch in enumerate(data_loader):", "mkdir(output_dir) logger = setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args)", "Fast-MVSNet Training\") parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path to config", "# build tensorboard logger (optionally by comment) tensorboard_logger = TensorboardLogger(output_dir)", "% ckpt_period == 0 or cur_epoch == max_epoch: checkpoint_data[\"epoch\"] =", "- end end = time.time() meters.update(time=batch_time, data=data_time) if iteration %", "'/..') import torch import torch.nn as nn from fastmvsnet.config import", "curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch = {k: v.cuda(non_blocking=True) for k, v", "else None # build tensorboard logger (optionally by comment) tensorboard_logger", "config_path) mkdir(output_dir) logger = setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus))", "configuration file {}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg, output_dir) if __name__", "(100 * log_period) == 0: file_logger(data_batch, preds, curr_epoch * total_iteration", "val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is None or cur_metric > best_metric: best_metric", "training from epoch {}\".format(start_epoch)) for epoch in range(start_epoch, max_epoch): cur_epoch", "**metric_dict) losses.backward() # print(poop) optimizer.step() batch_time = time.time() - end", "memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), ) ) tensorboard_logger.add_scalars(loss_dict, curr_epoch *", "cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir = cfg.OUTPUT_DIR if output_dir:", "output_dir, prefix=\"valid\") return meters def train(cfg, output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\")", "= time.time() meters.update(time=batch_time, data=data_time) if iteration % log_period == 0:", ") parser.add_argument( \"opts\", help=\"Modify config options using the command-line\", default=None,", "= epoch + 1 scheduler.step() start_time = time.time() train_meters =", "checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{}", "validate if val_period < 1: continue if cur_epoch % val_period", "curr_epoch * total_iteration + iteration, output_dir, prefix=\"valid\") return meters def", "os.path as osp import logging import time import sys sys.path.insert(0,", "set_random_seed from fastmvsnet.model1 import build_pointmvsnet as build_model from fastmvsnet.solver import", "= time.time() - start_time logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str,", "\") model.train() end = time.time() total_iteration = data_loader.__len__() with torch.no_grad():", "{} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration file {}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg))", "build_scheduler(cfg, optimizer) # build checkpointer checkpointer = Checkpointer(model, optimizer=optimizer, scheduler=scheduler,", "as osp import logging import time import sys sys.path.insert(0, osp.dirname(__file__)", ") tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration + iteration, prefix=\"valid\") if iteration", "to config file\", type=str, ) parser.add_argument( \"opts\", help=\"Modify config options", "curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch = {k: v.cuda(non_blocking=True) for k,", "= setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration", "checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate if val_period < 1: continue if", "if cur_epoch % val_period == 0 or cur_epoch == max_epoch:", "data_time = time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch =", "preds = model(data_batch, image_scales, inter_scales, isFlow) loss_dict = loss_fn(preds, data_batch,", "log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time = time.time() - start_time logger.info(\"Epoch[{}]-Train {}", "= logging.getLogger(\"fastmvsnet.trainer\") # build model set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn =", "default=\"\", metavar=\"FILE\", help=\"path to config file\", type=str, ) parser.add_argument( \"opts\",", "log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\" \")", "cur_epoch, train_meters.summary_str, epoch_time)) # checkpoint if cur_epoch % ckpt_period ==", "args def train_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, optimizer,", "{lr:.2e}\", \"max mem: {memory:.0f}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"],", "in data_batch.items() if isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales, inter_scales,", "best_metric)) return model def main(): args = parse_args() num_gpus =", "- end curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch = {k: v.cuda(non_blocking=True) for", "build_scheduler from fastmvsnet.utils.checkpoint import Checkpointer from fastmvsnet.dataset1 import build_data_loader from", "torch.nn as nn from fastmvsnet.config import load_cfg_from_file from fastmvsnet.utils.io import", "loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger,", "= logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\" \") model.train() end = time.time()", "def train_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, optimizer, curr_epoch,", "v in data_batch.items() if isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales,", "# build model set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn = build_model(cfg) logger.info(\"Build", "num_gpus = torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir =", "total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time)) # checkpoint if cur_epoch %", "== 0: file_logger(data_batch, preds, curr_epoch * total_iteration + iteration, output_dir,", "= cfg.OUTPUT_DIR if output_dir: config_path = osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\",", "setup_logger from fastmvsnet.utils.torch_utils import set_random_seed from fastmvsnet.model1 import build_pointmvsnet as", "tensorboard_logger = TensorboardLogger(output_dir) # train max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch =", "meters = MetricLogger(delimiter=\" \") model.train() end = time.time() total_iteration =", "metric_fn = build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda() # build", "build data loader train_data_loader = build_data_loader(cfg, mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD", "inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir,", "time.time() total_iteration = data_loader.__len__() path_list = [] for iteration, data_batch", "python import argparse import os.path as osp import logging import", "prefix=\"train\") return meters def validate_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow,", "= model(data_batch, image_scales, inter_scales, isFlow) optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch,", "[ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", ] ).format( epoch=curr_epoch, iter=iteration,", "= cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg, mode=\"val\") if val_period > 0", "tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration + iteration, prefix=\"valid\") if iteration %", "curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time = time.time() - start_time", "[] for iteration, data_batch in enumerate(data_loader): data_time = time.time() -", "preds, curr_epoch * total_iteration + iteration, output_dir, prefix=\"valid\") return meters", "tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration + iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch *", "\"{meters}\", \"lr: {lr:.2e}\", \"max mem: {memory:.0f}\", ] ).format( epoch=curr_epoch, iter=iteration,", "fastmvsnet.utils.checkpoint import Checkpointer from fastmvsnet.dataset1 import build_data_loader from fastmvsnet.utils.tensorboard_logger import", "curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.validate\") meters =", "**loss_dict, **metric_dict) losses.backward() # print(poop) optimizer.step() batch_time = time.time() -", "save_dir=output_dir, logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD #", "scheduler.step() start_time = time.time() train_meters = train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES,", "val_meters.summary_str)) # best validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is", "parse_args() num_gpus = torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir", "as nn from fastmvsnet.config import load_cfg_from_file from fastmvsnet.utils.io import mkdir", "% val_period == 0 or cur_epoch == max_epoch: val_meters =", "scheduler = build_scheduler(cfg, optimizer) # build checkpointer checkpointer = Checkpointer(model,", "torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow) loss_dict = loss_fn(preds,", "scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD", "\"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path to config file\", type=str, )", "output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\" \") model.train()", "epoch in range(start_epoch, max_epoch): cur_epoch = epoch + 1 scheduler.step()", "end curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch = {k: v.cuda(non_blocking=True) for", "GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration file {}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg,", "= train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader,", "output_dir.replace('@', config_path) mkdir(output_dir) logger = setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using {}", "isFlow) loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds, data_batch,", "sys sys.path.insert(0, osp.dirname(__file__) + '/..') import torch import torch.nn as", "fastmvsnet.utils.file_logger import file_logger def parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\")", "fastmvsnet.utils.metric_logger import MetricLogger from fastmvsnet.utils.file_logger import file_logger def parse_args(): parser", "loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward() # print(poop) optimizer.step() batch_time =", "+ '/..') import torch import torch.nn as nn from fastmvsnet.config", "#print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward() # print(poop) optimizer.step() batch_time", "= time.time() train_meters = train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch", "config_path.replace(\"configs\", \"outputs1\") output_dir = output_dir.replace('@', config_path) mkdir(output_dir) logger = setup_logger(\"fastmvsnet\",", "> 0 else None # build tensorboard logger (optionally by", "import set_random_seed from fastmvsnet.model1 import build_pointmvsnet as build_model from fastmvsnet.solver", "cfg.freeze() output_dir = cfg.OUTPUT_DIR if output_dir: config_path = osp.splitext(args.config_file)[0] config_path", "logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\" \") model.train() end = time.time() total_iteration", "data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items() if", "build model set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn = build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model)))", "+ iteration, output_dir, prefix=\"valid\") return meters def train(cfg, output_dir=\"\"): logger", "logger (optionally by comment) tensorboard_logger = TensorboardLogger(output_dir) # train max_epoch", "loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\",", "print(poop) optimizer.step() batch_time = time.time() - end end = time.time()", "fastmvsnet.utils.torch_utils import set_random_seed from fastmvsnet.model1 import build_pointmvsnet as build_model from", "= sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict)", "logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\",", "= load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir = cfg.OUTPUT_DIR if output_dir: config_path", "in enumerate(data_loader): data_time = time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"]", "end = time.time() meters.update(time=batch_time, data=data_time) if iteration % log_period ==", "= logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\" \") model.train() end = time.time()", "= cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\", 0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric", "best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate if val_period < 1: continue", "path_list.extend(curr_ref_img_path) data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()", "isFlow, data_loader, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.validate\")", "lr scheduler scheduler = build_scheduler(cfg, optimizer) # build checkpointer checkpointer", "in range(start_epoch, max_epoch): cur_epoch = epoch + 1 scheduler.step() start_time", "cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is None or cur_metric >", "= build_data_loader(cfg, mode=\"val\") if val_period > 0 else None #", "epoch_time = time.time() - start_time logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format( cur_epoch,", "file_logger(data_batch, preds, curr_epoch * total_iteration + iteration, output_dir, prefix=\"train\") return", "model, loss_fn, metric_fn = build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda()", "logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model def main(): args", "train_data_loader = build_data_loader(cfg, mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg,", "1 scheduler.step() start_time = time.time() train_meters = train_model(model, loss_fn, metric_fn,", "{}\".format(cur_epoch, val_meters.summary_str)) # best validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric", "* total_iteration + iteration, prefix=\"valid\") if iteration % (100 *", "logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time)) # checkpoint if", "import time import sys sys.path.insert(0, osp.dirname(__file__) + '/..') import torch", "data=data_time) if iteration % log_period == 0: logger.info( meters.delimiter.join( [", "argparse import os.path as osp import logging import time import", "is None or cur_metric > best_metric: best_metric = cur_metric checkpoint_data[\"epoch\"]", "from fastmvsnet.utils.file_logger import file_logger def parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet", "main(): args = parse_args() num_gpus = torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file)", "dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path to config file\", type=str, ) parser.add_argument(", "checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric))", "= Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME)", "#print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward() #", "tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\"", "0: logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", \"lr:", "output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\") # build model set_random_seed(cfg.RNG_SEED) model, loss_fn,", "# build data loader train_data_loader = build_data_loader(cfg, mode=\"train\") val_period =", "= build_scheduler(cfg, optimizer) # build checkpointer checkpointer = Checkpointer(model, optimizer=optimizer,", "TensorboardLogger(output_dir) # train max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\", 0)", "enumerate(data_loader): data_time = time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path)", "0 or cur_epoch == max_epoch: val_meters = validate_model(model, loss_fn, metric_fn,", "epoch=curr_epoch, iter=iteration, meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration +", "time.time() - start_time logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time))", "isFlow) losses = sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) batch_time = time.time()", "if iteration % log_period == 0: logger.info( meters.delimiter.join( [ \"EPOCH:", "= data_loader.__len__() with torch.no_grad(): for iteration, data_batch in enumerate(data_loader): data_time", "cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time =", "metric_dict = metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) meters.update(loss=losses, **loss_dict,", "val_period > 0 else None # build tensorboard logger (optionally", "tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time = time.time() - start_time logger.info(\"Epoch[{}]-Train", "end = time.time() total_iteration = data_loader.__len__() with torch.no_grad(): for iteration,", "prefix=\"valid\") return meters def train(cfg, output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\") #", "meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration + iteration, prefix=\"valid\")", "inter_scales, isFlow, data_loader, optimizer, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger", "MetricLogger(delimiter=\" \") model.train() end = time.time() total_iteration = data_loader.__len__() with", "**metric_dict) batch_time = time.time() - end end = time.time() meters.update(time=batch_time,", "= argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path", "+ iteration, prefix=\"train\") if iteration % (100 * log_period) ==", "== 0: logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\",", "= cfg.TRAIN.CHECKPOINT_PERIOD # build data loader train_data_loader = build_data_loader(cfg, mode=\"train\")", "total_iteration = data_loader.__len__() path_list = [] for iteration, data_batch in", "0: file_logger(data_batch, preds, curr_epoch * total_iteration + iteration, output_dir, prefix=\"train\")", "optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period =", "= data_loader.__len__() path_list = [] for iteration, data_batch in enumerate(data_loader):", "checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate", "{epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\", \"max mem: {memory:.0f}\", ]", "logger.info(args) logger.info(\"Loaded configuration file {}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg, output_dir)", "optimizer, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.train\") meters", "> cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time", "import mkdir from fastmvsnet.utils.logger import setup_logger from fastmvsnet.utils.torch_utils import set_random_seed", "import argparse import os.path as osp import logging import time", "loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward() # print(poop) optimizer.step()", "logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) # best validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if", "optimizer optimizer = build_optimizer(cfg, model) # build lr scheduler scheduler", "data_batch, isFlow) losses = sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) batch_time =", "best_metric = cur_metric checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\",", "args = parser.parse_args() return args def train_model(model, loss_fn, metric_fn, image_scales,", "): logger = logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\" \") model.train() end", "# checkpoint if cur_epoch % ckpt_period == 0 or cur_epoch", "cfg.merge_from_list(args.opts) cfg.freeze() output_dir = cfg.OUTPUT_DIR if output_dir: config_path = osp.splitext(args.config_file)[0]", "= time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch =", "import Checkpointer from fastmvsnet.dataset1 import build_data_loader from fastmvsnet.utils.tensorboard_logger import TensorboardLogger", "sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) batch_time = time.time() - end end", "time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch = {k:", "cur_epoch = epoch + 1 scheduler.step() start_time = time.time() train_meters", "data_loader, optimizer, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.train\")", "output_dir = cfg.OUTPUT_DIR if output_dir: config_path = osp.splitext(args.config_file)[0] config_path =", "best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name, None) logger.info(\"Start training from", "data_batch[\"ref_img_path\"] data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()", "data_batch.items() if isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow)", "from epoch {}\".format(start_epoch)) for epoch in range(start_epoch, max_epoch): cur_epoch =", "= MetricLogger(delimiter=\" \") model.train() end = time.time() total_iteration = data_loader.__len__()", "# print(poop) optimizer.step() batch_time = time.time() - end end =", "iter=iteration, meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration + iteration,", "prefix=\"valid\") if iteration % (100 * log_period) == 0: file_logger(data_batch,", "inter_scales, isFlow) optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict =", "sys.path.insert(0, osp.dirname(__file__) + '/..') import torch import torch.nn as nn", "loss_fn, metric_fn = build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda() #", "{epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), )", "meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward() # print(poop) optimizer.step() batch_time = time.time()", "if val_period > 0 else None # build tensorboard logger", "start_time = time.time() train_meters = train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES,", "\"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters),", "cfg.OUTPUT_DIR if output_dir: config_path = osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\", \"outputs1\")", "from fastmvsnet.utils.torch_utils import set_random_seed from fastmvsnet.model1 import build_pointmvsnet as build_model", "\"opts\", help=\"Modify config options using the command-line\", default=None, nargs=argparse.REMAINDER, )", "epoch_time)) # checkpoint if cur_epoch % ckpt_period == 0 or", "isFlow) metric_dict = metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) #print(\"LOSS", "isFlow) losses = sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses,", "checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build data", "/ (1024.0 ** 2), ) ) tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration", "* total_iteration + iteration, output_dir, prefix=\"valid\") return meters def train(cfg,", "build checkpointer checkpointer = Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data", "output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.train\") meters = MetricLogger(delimiter=\" \") model.train()", "= best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data) # validate if val_period < 1:", "comment) tensorboard_logger = TensorboardLogger(output_dir) # train max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch", "import sys sys.path.insert(0, osp.dirname(__file__) + '/..') import torch import torch.nn", "cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str))", "= validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader,", "import setup_logger from fastmvsnet.utils.torch_utils import set_random_seed from fastmvsnet.model1 import build_pointmvsnet", "# build checkpointer checkpointer = Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger)", "if output_dir: config_path = osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\", \"outputs1\") output_dir", "load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir = cfg.OUTPUT_DIR if output_dir: config_path =", "def train(cfg, output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\") # build model set_random_seed(cfg.RNG_SEED)", "train_meters.summary_str, epoch_time)) # checkpoint if cur_epoch % ckpt_period == 0", "cfg.TRAIN.CHECKPOINT_PERIOD # build data loader train_data_loader = build_data_loader(cfg, mode=\"train\") val_period", "best_metric = checkpoint_data.get(best_metric_name, None) logger.info(\"Start training from epoch {}\".format(start_epoch)) for", "(optionally by comment) tensorboard_logger = TensorboardLogger(output_dir) # train max_epoch =", "metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger,", "from fastmvsnet.config import load_cfg_from_file from fastmvsnet.utils.io import mkdir from fastmvsnet.utils.logger", "import build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint import Checkpointer from fastmvsnet.dataset1 import", "isFlow) metric_dict = metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) meters.update(loss=losses,", "val_period == 0 or cur_epoch == max_epoch: val_meters = validate_model(model,", "2), ) ) tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration + iteration, prefix=\"train\")", "train max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\", 0) best_metric_name =", "logger = logging.getLogger(\"fastmvsnet.trainer\") # build model set_random_seed(cfg.RNG_SEED) model, loss_fn, metric_fn", "log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\" \")", "isFlow, data_loader, optimizer, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger =", "prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration + iteration, prefix=\"train\") if iteration", "curr_epoch * total_iteration + iteration, prefix=\"train\") if iteration % (100", "iteration % log_period == 0: logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\",", "epoch {}\".format(start_epoch)) for epoch in range(start_epoch, max_epoch): cur_epoch = epoch", "cur_epoch == max_epoch: val_meters = validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES,", "output_dir, prefix=\"train\") logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration file {}\".format(args.config_file))", "osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\", \"outputs1\") output_dir = output_dir.replace('@', config_path) mkdir(output_dir)", "= [] for iteration, data_batch in enumerate(data_loader): data_time = time.time()", "meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", ] ).format( epoch=curr_epoch,", "argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\", help=\"path to", "= metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss'])", "= output_dir.replace('@', config_path) mkdir(output_dir) logger = setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\") logger.info(\"Using", "isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val", "loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds, data_batch, isFlow) losses =", "preds = model(data_batch, image_scales, inter_scales, isFlow) optimizer.zero_grad() loss_dict = loss_fn(preds,", "import torch.nn as nn from fastmvsnet.config import load_cfg_from_file from fastmvsnet.utils.io", "inter_scales, isFlow, data_loader, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger =", "build_data_loader(cfg, mode=\"train\") val_period = cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg, mode=\"val\") if", "losses.backward() # print(poop) optimizer.step() batch_time = time.time() - end end", "data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) #", "= cur_metric checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\", **checkpoint_data)", "total_iteration + iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration + iteration,", "optimizer = build_optimizer(cfg, model) # build lr scheduler scheduler =", ").format( epoch=curr_epoch, iter=iteration, meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration", "val_data_loader = build_data_loader(cfg, mode=\"val\") if val_period > 0 else None", "tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration + iteration, prefix=\"train\") if iteration %", "import MetricLogger from fastmvsnet.utils.file_logger import file_logger def parse_args(): parser =", "start_time logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time)) # checkpoint", "= torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir = cfg.OUTPUT_DIR", "+ iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration + iteration, prefix=\"train\")", "val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model def main(): args =", "model.train() end = time.time() total_iteration = data_loader.__len__() with torch.no_grad(): for", "iteration, data_batch in enumerate(data_loader): data_time = time.time() - end curr_ref_img_path", "model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda() # build optimizer optimizer = build_optimizer(cfg,", "cfg.TRAIN.VAL_PERIOD val_data_loader = build_data_loader(cfg, mode=\"val\") if val_period > 0 else", "fastmvsnet.dataset1 import build_data_loader from fastmvsnet.utils.tensorboard_logger import TensorboardLogger from fastmvsnet.utils.metric_logger import", "v.cuda(non_blocking=True) for k, v in data_batch.items() if isinstance(v, torch.Tensor)} preds", "* total_iteration + iteration, prefix=\"train\") tensorboard_logger.add_scalars(metric_dict, curr_epoch * total_iteration +", "nn from fastmvsnet.config import load_cfg_from_file from fastmvsnet.utils.io import mkdir from", "from fastmvsnet.utils.io import mkdir from fastmvsnet.utils.logger import setup_logger from fastmvsnet.utils.torch_utils", "None) logger.info(\"Start training from epoch {}\".format(start_epoch)) for epoch in range(start_epoch,", "= parse_args() num_gpus = torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze()", "if iteration % (100 * log_period) == 0: file_logger(data_batch, preds,", "MetricLogger from fastmvsnet.utils.file_logger import file_logger def parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch", "Checkpointer from fastmvsnet.dataset1 import build_data_loader from fastmvsnet.utils.tensorboard_logger import TensorboardLogger from", "range(start_epoch, max_epoch): cur_epoch = epoch + 1 scheduler.step() start_time =", "time.time() train_meters = train_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch >", "model(data_batch, image_scales, inter_scales, isFlow) optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch, isFlow)", "None # build tensorboard logger (optionally by comment) tensorboard_logger =", "continue if cur_epoch % val_period == 0 or cur_epoch ==", "or cur_epoch == max_epoch: checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric", "log_period == 0: logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\",", "cur_epoch % ckpt_period == 0 or cur_epoch == max_epoch: checkpoint_data[\"epoch\"]", "time import sys sys.path.insert(0, osp.dirname(__file__) + '/..') import torch import", "time.time() - end curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch = {k: v.cuda(non_blocking=True)", "torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() output_dir = cfg.OUTPUT_DIR if", "checkpointer = Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT,", "\"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\", \"max mem: {memory:.0f}\",", "# best validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is None", "end end = time.time() meters.update(time=batch_time, data=data_time) if iteration % log_period", "= data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch = {k: v.cuda(non_blocking=True) for k, v", "> best_metric: best_metric = cur_metric checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] =", "\"iter: {iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\", \"max mem: {memory:.0f}\", ] ).format(", "= parser.parse_args() return args def train_model(model, loss_fn, metric_fn, image_scales, inter_scales,", "import os.path as osp import logging import time import sys", "output_dir = output_dir.replace('@', config_path) mkdir(output_dir) logger = setup_logger(\"fastmvsnet\", output_dir, prefix=\"train\")", "isFlow) optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict = metric_fn(preds,", "{}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg, output_dir) if __name__ == \"__main__\":", "fastmvsnet.model1 import build_pointmvsnet as build_model from fastmvsnet.solver import build_optimizer, build_scheduler", "time.time() total_iteration = data_loader.__len__() with torch.no_grad(): for iteration, data_batch in", "= cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{} =", "** 2), ) ) tensorboard_logger.add_scalars(loss_dict, curr_epoch * total_iteration + iteration,", "model def main(): args = parse_args() num_gpus = torch.cuda.device_count() cfg", "parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument( \"--cfg\", dest=\"config_file\", default=\"\", metavar=\"FILE\",", "data_batch, isFlow) losses = sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values())", ").format( epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2),", "epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), )", "data_batch in enumerate(data_loader): data_time = time.time() - end curr_ref_img_path =", "mode=\"val\") if val_period > 0 else None # build tensorboard", "- start_time logger.info(\"Epoch[{}]-Train {} total_time: {:.2f}s\".format( cur_epoch, train_meters.summary_str, epoch_time)) #", "iteration % (100 * log_period) == 0: file_logger(data_batch, preds, curr_epoch", "help=\"Modify config options using the command-line\", default=None, nargs=argparse.REMAINDER, ) args", "losses = sum(loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) batch_time = time.time() -", "== 0 or cur_epoch == max_epoch: checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name]", "* total_iteration + iteration, prefix=\"train\") if iteration % (100 *", "if best_metric is None or cur_metric > best_metric: best_metric =", "config_path = config_path.replace(\"configs\", \"outputs1\") output_dir = output_dir.replace('@', config_path) mkdir(output_dir) logger", "def validate_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow, data_loader, curr_epoch, tensorboard_logger,", "output_dir=output_dir, ) epoch_time = time.time() - start_time logger.info(\"Epoch[{}]-Train {} total_time:", "0 or cur_epoch == max_epoch: checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] =", "logger = logging.getLogger(\"fastmvsnet.validate\") meters = MetricLogger(delimiter=\" \") model.train() end =", "for k, v in data_batch.items() if isinstance(v, torch.Tensor)} preds =", "file {}\".format(args.config_file)) logger.info(\"Running with config:\\n{}\".format(cfg)) train(cfg, output_dir) if __name__ ==", "parser.parse_args() return args def train_model(model, loss_fn, metric_fn, image_scales, inter_scales, isFlow,", "start_epoch = checkpoint_data.get(\"epoch\", 0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name,", "as build_model from fastmvsnet.solver import build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint import", "if val_period < 1: continue if cur_epoch % val_period ==", "logger.info(\"Using {} GPUs\".format(num_gpus)) logger.info(args) logger.info(\"Loaded configuration file {}\".format(args.config_file)) logger.info(\"Running with", "prefix=\"train\") if iteration % (100 * log_period) == 0: file_logger(data_batch,", "image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=val_data_loader, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir,", "batch_time = time.time() - end end = time.time() meters.update(time=batch_time, data=data_time)", "**checkpoint_data) logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model def main():", "\") model.train() end = time.time() total_iteration = data_loader.__len__() path_list =", "= {k: v.cuda(non_blocking=True) for k, v in data_batch.items() if isinstance(v,", "data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TRAIN.LOG_PERIOD, output_dir=output_dir, ) epoch_time = time.time()", "= \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name, None) logger.info(\"Start training from epoch", "+ 1 scheduler.step() start_time = time.time() train_meters = train_model(model, loss_fn,", "import build_data_loader from fastmvsnet.utils.tensorboard_logger import TensorboardLogger from fastmvsnet.utils.metric_logger import MetricLogger", "= time.time() total_iteration = data_loader.__len__() with torch.no_grad(): for iteration, data_batch", "ckpt_period == 0 or cur_epoch == max_epoch: checkpoint_data[\"epoch\"] = cur_epoch", "parser.add_argument( \"opts\", help=\"Modify config options using the command-line\", default=None, nargs=argparse.REMAINDER,", ") args = parser.parse_args() return args def train_model(model, loss_fn, metric_fn,", "scheduler scheduler = build_scheduler(cfg, optimizer) # build checkpointer checkpointer =", "build_data_loader from fastmvsnet.utils.tensorboard_logger import TensorboardLogger from fastmvsnet.utils.metric_logger import MetricLogger from", "DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict, **metric_dict) losses.backward() # print(poop)", "cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\", 0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric =", ") ) tensorboard_logger.add_scalars(meters.meters, curr_epoch * total_iteration + iteration, prefix=\"valid\") if", "logging import time import sys sys.path.insert(0, osp.dirname(__file__) + '/..') import", "import file_logger def parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument(", "= best_metric checkpointer.save(\"model_best\", **checkpoint_data) logger.info(\"Best val-{} = {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return", "help=\"path to config file\", type=str, ) parser.add_argument( \"opts\", help=\"Modify config", "max_epoch: val_meters = validate_model(model, loss_fn, metric_fn, image_scales=cfg.MODEL.VAL.IMG_SCALES, inter_scales=cfg.MODEL.VAL.INTER_SCALES, isFlow=(cur_epoch >", "meters.update(time=batch_time, data=data_time) if iteration % log_period == 0: logger.info( meters.delimiter.join(", "losses = sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\", loss_dict.values()) meters.update(loss=losses, **loss_dict,", "0: logger.info( meters.delimiter.join( [ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", ]", "build_model from fastmvsnet.solver import build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint import Checkpointer", "+ iteration, prefix=\"valid\") if iteration % (100 * log_period) ==", "ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build data loader train_data_loader = build_data_loader(cfg,", "log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) # best validation cur_metric", "Checkpointer(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger) checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, resume=cfg.AUTO_RESUME) ckpt_period", "\"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name, None) logger.info(\"Start training from epoch {}\".format(start_epoch))", "args = parse_args() num_gpus = torch.cuda.device_count() cfg = load_cfg_from_file(args.config_file) cfg.merge_from_list(args.opts)", "path_list = [] for iteration, data_batch in enumerate(data_loader): data_time =", "isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow) optimizer.zero_grad() loss_dict", "total_iteration + iteration, output_dir, prefix=\"train\") return meters def validate_model(model, loss_fn,", "curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.train\") meters =", "== max_epoch: checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch), **checkpoint_data)", "nargs=argparse.REMAINDER, ) args = parser.parse_args() return args def train_model(model, loss_fn,", "osp.dirname(__file__) + '/..') import torch import torch.nn as nn from", "= TensorboardLogger(output_dir) # train max_epoch = cfg.SCHEDULER.MAX_EPOCH start_epoch = checkpoint_data.get(\"epoch\",", "= config_path.replace(\"configs\", \"outputs1\") output_dir = output_dir.replace('@', config_path) mkdir(output_dir) logger =", "def parse_args(): parser = argparse.ArgumentParser(description=\"PyTorch Fast-MVSNet Training\") parser.add_argument( \"--cfg\", dest=\"config_file\",", "metric_fn, image_scales, inter_scales, isFlow, data_loader, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ):", "mem: {memory:.0f}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() /", "data_loader, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\", ): logger = logging.getLogger(\"fastmvsnet.validate\") meters", "1: continue if cur_epoch % val_period == 0 or cur_epoch", "curr_epoch=epoch, tensorboard_logger=tensorboard_logger, log_period=cfg.TEST.LOG_PERIOD, output_dir=output_dir, ) logger.info(\"Epoch[{}]-Val {}\".format(cur_epoch, val_meters.summary_str)) # best", "type=str, ) parser.add_argument( \"opts\", help=\"Modify config options using the command-line\",", "if isinstance(v, torch.Tensor)} preds = model(data_batch, image_scales, inter_scales, isFlow) optimizer.zero_grad()", "iteration, output_dir, prefix=\"valid\") return meters def train(cfg, output_dir=\"\"): logger =", "image_scales, inter_scales, isFlow) loss_dict = loss_fn(preds, data_batch, isFlow) metric_dict =", "k, v in data_batch.items() if isinstance(v, torch.Tensor)} preds = model(data_batch,", "meters.update(loss=losses, **loss_dict, **metric_dict) batch_time = time.time() - end end =", "cur_metric > best_metric: best_metric = cur_metric checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name]", "options using the command-line\", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args()", "= build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda() # build optimizer", "loss_fn, metric_fn, image_scales=cfg.MODEL.TRAIN.IMG_SCALES, inter_scales=cfg.MODEL.TRAIN.INTER_SCALES, isFlow=(cur_epoch > cfg.SCHEDULER.INIT_EPOCH), data_loader=train_data_loader, optimizer=optimizer, curr_epoch=epoch,", "checkpoint if cur_epoch % ckpt_period == 0 or cur_epoch ==", "[ \"EPOCH: {epoch:2d}\", \"iter: {iter:4d}\", \"{meters}\", \"lr: {lr:.2e}\", \"max mem:", "build_pointmvsnet as build_model from fastmvsnet.solver import build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint", "\"outputs1\") output_dir = output_dir.replace('@', config_path) mkdir(output_dir) logger = setup_logger(\"fastmvsnet\", output_dir,", "metric_fn, image_scales, inter_scales, isFlow, data_loader, optimizer, curr_epoch, tensorboard_logger, log_period=1, output_dir=\"\",", "fastmvsnet.solver import build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint import Checkpointer from fastmvsnet.dataset1", "checkpoint_data.get(\"epoch\", 0) best_metric_name = \"best_{}\".format(cfg.TRAIN.VAL_METRIC) best_metric = checkpoint_data.get(best_metric_name, None) logger.info(\"Start", "config options using the command-line\", default=None, nargs=argparse.REMAINDER, ) args =", "= {}\".format(cfg.TRAIN.VAL_METRIC, best_metric)) return model def main(): args = parse_args()", "- end curr_ref_img_path = data_batch[\"ref_img_path\"] path_list.extend(curr_ref_img_path) data_batch = {k: v.cuda(non_blocking=True)", "metric_fn(preds, data_batch, isFlow) losses = sum(loss_dict.values()) #print(\"LOSS DICT\", loss_dict['coarse_loss']) #print(\"LOSSES\",", "end curr_ref_img_path = data_batch[\"ref_img_path\"] data_batch = {k: v.cuda(non_blocking=True) for k,", "cur_epoch == max_epoch: checkpoint_data[\"epoch\"] = cur_epoch checkpoint_data[best_metric_name] = best_metric checkpointer.save(\"model_{:03d}\".format(cur_epoch),", "build_model(cfg) logger.info(\"Build model:\\n{}\".format(str(model))) model = nn.DataParallel(model).cuda() # build optimizer optimizer", "< 1: continue if cur_epoch % val_period == 0 or", "= time.time() - end end = time.time() meters.update(time=batch_time, data=data_time) if", "= val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is None or cur_metric > best_metric:", "output_dir: config_path = osp.splitext(args.config_file)[0] config_path = config_path.replace(\"configs\", \"outputs1\") output_dir =", "train(cfg, output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\") # build model set_random_seed(cfg.RNG_SEED) model,", "{iter:4d}\", \"{meters}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), ) ) tensorboard_logger.add_scalars(meters.meters,", "best validation cur_metric = val_meters.meters[cfg.TRAIN.VAL_METRIC].global_avg if best_metric is None or", "default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() return args def train_model(model,", "{}\".format(start_epoch)) for epoch in range(start_epoch, max_epoch): cur_epoch = epoch +", "# validate if val_period < 1: continue if cur_epoch %", "meters def train(cfg, output_dir=\"\"): logger = logging.getLogger(\"fastmvsnet.trainer\") # build model", "from fastmvsnet.solver import build_optimizer, build_scheduler from fastmvsnet.utils.checkpoint import Checkpointer from", "import load_cfg_from_file from fastmvsnet.utils.io import mkdir from fastmvsnet.utils.logger import setup_logger", "{memory:.0f}\", ] ).format( epoch=curr_epoch, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0][\"lr\"], memory=torch.cuda.max_memory_allocated() / (1024.0" ]
[ "nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def", "optim import torch from torchvision import models class ResNet(nn.Module): def", "super(ResNet, self).__init__() resnet = models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8] self.features1 =", "pretreinado=True): super(ResNet, self).__init__() resnet = models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8] self.features1", "projetos de Machine Learning e Deep Learning # Por <NAME>.", "Deep Learning # Por <NAME>. from torch import nn, relu", "Por <NAME>. from torch import nn, relu import torch.nn.functional as", "nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def forward(self, x): x", "# Estrutura básica para projetos de Machine Learning e Deep", "= self.features2(x) x = F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x) x =", "import torch.optim as optim import torch from torchvision import models", "básica para projetos de Machine Learning e Deep Learning #", "saida)) def forward(self, x): x = self.features1(x) x = self.features2(x)", "Learning e Deep Learning # Por <NAME>. from torch import", "self).__init__() resnet = models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6])", "list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512),", "e Deep Learning # Por <NAME>. from torch import nn,", "<reponame>fossabot/unifacisa-visao-computacional # Estrutura básica para projetos de Machine Learning e", "Learning # Por <NAME>. from torch import nn, relu import", "ResNet(nn.Module): def __init__(self, saida, pretreinado=True): super(ResNet, self).__init__() resnet = models.resnet34(pretrained=pretreinado)", "F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x) x = x.view(x.shape[0], -1) return self.classificador(x)", "relu import torch.nn.functional as F import torch.optim as optim import", "<NAME>. from torch import nn, relu import torch.nn.functional as F", "torch.optim as optim import torch from torchvision import models class", "def __init__(self, saida, pretreinado=True): super(ResNet, self).__init__() resnet = models.resnet34(pretrained=pretreinado) layers", "__init__(self, saida, pretreinado=True): super(ResNet, self).__init__() resnet = models.resnet34(pretrained=pretreinado) layers =", "F import torch.optim as optim import torch from torchvision import", "torch import nn, relu import torch.nn.functional as F import torch.optim", "nn, relu import torch.nn.functional as F import torch.optim as optim", "saida, pretreinado=True): super(ResNet, self).__init__() resnet = models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8]", "= nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida))", "# Por <NAME>. from torch import nn, relu import torch.nn.functional", "self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def forward(self, x): x =", "nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def forward(self, x): x = self.features1(x) x", "= list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:]) self.classificador =", "de Machine Learning e Deep Learning # Por <NAME>. from", "as optim import torch from torchvision import models class ResNet(nn.Module):", "nn.Linear(512, saida)) def forward(self, x): x = self.features1(x) x =", "torchvision import models class ResNet(nn.Module): def __init__(self, saida, pretreinado=True): super(ResNet,", "def forward(self, x): x = self.features1(x) x = self.features2(x) x", "import torch.nn.functional as F import torch.optim as optim import torch", "Estrutura básica para projetos de Machine Learning e Deep Learning", "self.features2(x) x = F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x) x = x.view(x.shape[0],", "x = F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x) x = x.view(x.shape[0], -1)", "from torchvision import models class ResNet(nn.Module): def __init__(self, saida, pretreinado=True):", "self.features2 = nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def forward(self,", "forward(self, x): x = self.features1(x) x = self.features2(x) x =", "= self.features1(x) x = self.features2(x) x = F.relu(x) x =", "resnet = models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6]) self.features2", "= models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6]) self.features2 =", "layers = list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:]) self.classificador", "import nn, relu import torch.nn.functional as F import torch.optim as", "torch from torchvision import models class ResNet(nn.Module): def __init__(self, saida,", "= F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x) x = x.view(x.shape[0], -1) return", "import models class ResNet(nn.Module): def __init__(self, saida, pretreinado=True): super(ResNet, self).__init__()", "x = self.features2(x) x = F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x) x", "self.features1 = nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512,", "para projetos de Machine Learning e Deep Learning # Por", "class ResNet(nn.Module): def __init__(self, saida, pretreinado=True): super(ResNet, self).__init__() resnet =", "x): x = self.features1(x) x = self.features2(x) x = F.relu(x)", "torch.nn.functional as F import torch.optim as optim import torch from", "Machine Learning e Deep Learning # Por <NAME>. from torch", "models.resnet34(pretrained=pretreinado) layers = list(resnet.children())[:8] self.features1 = nn.Sequential(*layers[:6]) self.features2 = nn.Sequential(*layers[6:])", "= nn.Sequential(*layers[6:]) self.classificador = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def forward(self, x):", "= nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, saida)) def forward(self, x): x = self.features1(x)", "self.features1(x) x = self.features2(x) x = F.relu(x) x = nn.AdaptiveAvgPool2d((1,1))(x)", "models class ResNet(nn.Module): def __init__(self, saida, pretreinado=True): super(ResNet, self).__init__() resnet", "as F import torch.optim as optim import torch from torchvision", "x = self.features1(x) x = self.features2(x) x = F.relu(x) x", "from torch import nn, relu import torch.nn.functional as F import", "import torch from torchvision import models class ResNet(nn.Module): def __init__(self," ]
[ "ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES", "[], language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], ) setup (name = 'evert',", "ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,", "# # 1. Redistributions of source code must retain the", "in the # documentation and/or other materials provided with the", "evert_module = Extension('_evert', define_macros = [('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')], include_dirs", "OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT", "provided with the distribution. # # 3. Neither the name", "# # Redistribution and use in source and binary forms,", "PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,", "from distutils.core import setup, Extension evert_module = Extension('_evert', define_macros =", "IS\" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT", "python # Copyright (c) 2017, <NAME> # All rights reserved.", "NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE", "OF SUCH DAMAGE. \"\"\" setup.py file for installing Python bindings", "# OF SUCH DAMAGE. \"\"\" setup.py file for installing Python", "in binary form must reproduce the above copyright # notice,", "'../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries = ['GL', 'GLU',", "'evert.i'], libraries = ['GL', 'GLU', 'glut'], library_dirs = [], language='c++',", "import setup, Extension evert_module = Extension('_evert', define_macros = [('MAJOR_VERSION', '1'),", "PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT", "DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND", "# IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS", "= \"<NAME>\", description = \"\"\"Accelerated beam tracing algorithm\"\"\", ext_modules =", "documentation and/or other materials provided with the distribution. # #", "list of conditions and the following disclaimer in the #", "NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF", "the following disclaimer. # # 2. Redistributions in binary form", "# NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;", "'../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries =", "INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT # NOT", "of its contributors # may be used to endorse or", "for installing Python bindings using SWIG \"\"\" from distutils.core import", "source and binary forms, with or without # modification, are", "products derived from this software without # specific prior written", "list of conditions and the following disclaimer. # # 2.", "the names of its contributors # may be used to", "# documentation and/or other materials provided with the distribution. #", "'glut'], library_dirs = [], language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], ) setup", "= '1.0', author = \"<NAME>\", description = \"\"\"Accelerated beam tracing", "'1'), ('MINOR_VERSION', '0')], include_dirs = ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp',", "or promote products derived from this software without # specific", "derived from this software without # specific prior written permission.", "beam tracing algorithm\"\"\", ext_modules = [evert_module], py_modules = [\"evert\"], )", "GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS;", "setup.py file for installing Python bindings using SWIG \"\"\" from", "= Extension('_evert', define_macros = [('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')], include_dirs =", "AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT,", "and binary forms, with or without # modification, are permitted", "endorse or promote products derived from this software without #", "'GLU', 'glut'], library_dirs = [], language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], )", "the copyright holder nor the names of its contributors #", "form must reproduce the above copyright # notice, this list", "BUT # NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR", "'1.0', author = \"<NAME>\", description = \"\"\"Accelerated beam tracing algorithm\"\"\",", "EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE", "written permission. # # THIS SOFTWARE IS PROVIDED BY THE", "WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE", "installing Python bindings using SWIG \"\"\" from distutils.core import setup,", "notice, this list of conditions and the following disclaimer. #", "BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY", "WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF", "reproduce the above copyright # notice, this list of conditions", "must retain the above copyright # notice, this list of", "OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF", "#!/usr/bin/env python # Copyright (c) 2017, <NAME> # All rights", "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS", "following conditions are met: # # 1. Redistributions of source", "LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF", "['GL', 'GLU', 'glut'], library_dirs = [], language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'],", "EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR", "of source code must retain the above copyright # notice,", "Python bindings using SWIG \"\"\" from distutils.core import setup, Extension", "= 'evert', version = '1.0', author = \"<NAME>\", description =", "nor the names of its contributors # may be used", "name of the copyright holder nor the names of its", "SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT # NOT LIMITED", "(INCLUDING, BUT # NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS", "# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE", "ADVISED OF THE POSSIBILITY # OF SUCH DAMAGE. \"\"\" setup.py", "# # 3. Neither the name of the copyright holder", "'../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries = ['GL',", "\"\"\"Accelerated beam tracing algorithm\"\"\", ext_modules = [evert_module], py_modules = [\"evert\"],", "SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY", "IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE", "or without # modification, are permitted provided that the following", "Redistribution and use in source and binary forms, with or", "the following disclaimer in the # documentation and/or other materials", "LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING", "#extra_compile_args=['-std=c++11'], ) setup (name = 'evert', version = '1.0', author", "'../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'],", "the distribution. # # 3. Neither the name of the", "used to endorse or promote products derived from this software", "retain the above copyright # notice, this list of conditions", "description = \"\"\"Accelerated beam tracing algorithm\"\"\", ext_modules = [evert_module], py_modules", "rights reserved. # # Redistribution and use in source and", "(name = 'evert', version = '1.0', author = \"<NAME>\", description", "binary form must reproduce the above copyright # notice, this", "include_dirs = ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp',", "# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY", "of conditions and the following disclaimer. # # 2. Redistributions", "ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT", "OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE", "\"\"\" from distutils.core import setup, Extension evert_module = Extension('_evert', define_macros", "disclaimer in the # documentation and/or other materials provided with", "CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #", "IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED #", "above copyright # notice, this list of conditions and the", "TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY", "Copyright (c) 2017, <NAME> # All rights reserved. # #", "\"\"\" setup.py file for installing Python bindings using SWIG \"\"\"", "and/or other materials provided with the distribution. # # 3.", "3. Neither the name of the copyright holder nor the", "= [], language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], ) setup (name =", "THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE", "in source and binary forms, with or without # modification,", "OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER", "setup (name = 'evert', version = '1.0', author = \"<NAME>\",", "permitted provided that the following conditions are met: # #", "must reproduce the above copyright # notice, this list of", "OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR", "version = '1.0', author = \"<NAME>\", description = \"\"\"Accelerated beam", "PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND", "# 1. Redistributions of source code must retain the above", "['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp',", "use in source and binary forms, with or without #", "NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS", "'0')], include_dirs = ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp',", "'-I../include'], #extra_compile_args=['-std=c++11'], ) setup (name = 'evert', version = '1.0',", "STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING", "the # documentation and/or other materials provided with the distribution.", "OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT # NOT LIMITED TO, PROCUREMENT", "# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE", "bindings using SWIG \"\"\" from distutils.core import setup, Extension evert_module", "DAMAGE. \"\"\" setup.py file for installing Python bindings using SWIG", "contributors # may be used to endorse or promote products", "DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR", "its contributors # may be used to endorse or promote", "LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR", "OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED", "promote products derived from this software without # specific prior", "with or without # modification, are permitted provided that the", "CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN", "setup, Extension evert_module = Extension('_evert', define_macros = [('MAJOR_VERSION', '1'), ('MINOR_VERSION',", "FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO", "# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT", "BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,", "= ['GL', 'GLU', 'glut'], library_dirs = [], language='c++', swig_opts=['-c++', '-I../include'],", "# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED", "other materials provided with the distribution. # # 3. Neither", "author = \"<NAME>\", description = \"\"\"Accelerated beam tracing algorithm\"\"\", ext_modules", "IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)", "OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL,", "prior written permission. # # THIS SOFTWARE IS PROVIDED BY", "be used to endorse or promote products derived from this", "SWIG \"\"\" from distutils.core import setup, Extension evert_module = Extension('_evert',", "sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp',", "FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL", "WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR", "A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL", "language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], ) setup (name = 'evert', version", "DAMAGES (INCLUDING, BUT # NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE", "'../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp',", "and use in source and binary forms, with or without", "AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN", "EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT # NOT LIMITED TO,", "of the copyright holder nor the names of its contributors", "OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE", "# Copyright (c) 2017, <NAME> # All rights reserved. #", "= \"\"\"Accelerated beam tracing algorithm\"\"\", ext_modules = [evert_module], py_modules =", "'../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp',", "# 3. Neither the name of the copyright holder nor", "IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR", "are met: # # 1. Redistributions of source code must", "OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF", "EVEN IF ADVISED OF THE POSSIBILITY # OF SUCH DAMAGE.", "'../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries = ['GL', 'GLU', 'glut'],", "THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,", "\"AS IS\" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,", "forms, with or without # modification, are permitted provided that", "SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR #", "binary forms, with or without # modification, are permitted provided", "copyright # notice, this list of conditions and the following", "provided that the following conditions are met: # # 1.", "specific prior written permission. # # THIS SOFTWARE IS PROVIDED", "to endorse or promote products derived from this software without", "distutils.core import setup, Extension evert_module = Extension('_evert', define_macros = [('MAJOR_VERSION',", "USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED", "the above copyright # notice, this list of conditions and", "are permitted provided that the following conditions are met: #", "the name of the copyright holder nor the names of", "IF ADVISED OF THE POSSIBILITY # OF SUCH DAMAGE. \"\"\"", "met: # # 1. Redistributions of source code must retain", "ARISING IN ANY WAY OUT OF THE USE OF THIS", "and the following disclaimer. # # 2. Redistributions in binary", "# All rights reserved. # # Redistribution and use in", "ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN", "Extension evert_module = Extension('_evert', define_macros = [('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')],", "holder nor the names of its contributors # may be", "file for installing Python bindings using SWIG \"\"\" from distutils.core", "PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY", "'../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp',", "library_dirs = [], language='c++', swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], ) setup (name", "# Redistribution and use in source and binary forms, with", "\"<NAME>\", description = \"\"\"Accelerated beam tracing algorithm\"\"\", ext_modules = [evert_module],", "code must retain the above copyright # notice, this list", "conditions are met: # # 1. Redistributions of source code", "conditions and the following disclaimer. # # 2. Redistributions in", "LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN", "# modification, are permitted provided that the following conditions are", "following disclaimer. # # 2. Redistributions in binary form must", "COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND # ANY EXPRESS", "with the distribution. # # 3. Neither the name of", "conditions and the following disclaimer in the # documentation and/or", "LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS", "SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY # OF SUCH", "(c) 2017, <NAME> # All rights reserved. # # Redistribution", "INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF", "OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR", "MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. #", "CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL,", "SUCH DAMAGE. \"\"\" setup.py file for installing Python bindings using", "define_macros = [('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')], include_dirs = ['../include'], sources=['../src/elBeam.cpp',", "may be used to endorse or promote products derived from", "that the following conditions are met: # # 1. Redistributions", "# notice, this list of conditions and the following disclaimer.", "('MINOR_VERSION', '0')], include_dirs = ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp',", "'evert', version = '1.0', author = \"<NAME>\", description = \"\"\"Accelerated", "2017, <NAME> # All rights reserved. # # Redistribution and", "NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND", "of conditions and the following disclaimer in the # documentation", "software without # specific prior written permission. # # THIS", "'../src/elViewer.cpp', 'evert.i'], libraries = ['GL', 'GLU', 'glut'], library_dirs = [],", "# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND", "DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,", "without # specific prior written permission. # # THIS SOFTWARE", "ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY,", "# # 2. Redistributions in binary form must reproduce the", "WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES", "disclaimer. # # 2. Redistributions in binary form must reproduce", "THE POSSIBILITY # OF SUCH DAMAGE. \"\"\" setup.py file for", "this list of conditions and the following disclaimer. # #", "notice, this list of conditions and the following disclaimer in", "using SWIG \"\"\" from distutils.core import setup, Extension evert_module =", "IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,", "All rights reserved. # # Redistribution and use in source", "without # modification, are permitted provided that the following conditions", "CONSEQUENTIAL DAMAGES (INCLUDING, BUT # NOT LIMITED TO, PROCUREMENT OF", "= ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp', '../src/elListener.cpp', '../src/elOrientedPoint.cpp', '../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp',", "THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR", "# notice, this list of conditions and the following disclaimer", "the following conditions are met: # # 1. Redistributions of", "this list of conditions and the following disclaimer in the", "modification, are permitted provided that the following conditions are met:", "BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND #", "# may be used to endorse or promote products derived", "# ARISING IN ANY WAY OUT OF THE USE OF", "OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED", "COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, #", "reserved. # # Redistribution and use in source and binary", "[('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')], include_dirs = ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp', '../src/elGLUT.cpp',", "swig_opts=['-c++', '-I../include'], #extra_compile_args=['-std=c++11'], ) setup (name = 'evert', version =", "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY #", "# specific prior written permission. # # THIS SOFTWARE IS", "from this software without # specific prior written permission. #", "Redistributions of source code must retain the above copyright #", "CONTRIBUTORS \"AS IS\" AND # ANY EXPRESS OR IMPLIED WARRANTIES,", "TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR", "# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS", "PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE", "TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,", "BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,", "copyright holder nor the names of its contributors # may", "LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION)", "OF THE POSSIBILITY # OF SUCH DAMAGE. \"\"\" setup.py file", "IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"", "this software without # specific prior written permission. # #", "HOLDERS AND CONTRIBUTORS \"AS IS\" AND # ANY EXPRESS OR", "'../src/elPathSolution.cpp', '../src/elPolygon.cpp', '../src/elRay.cpp', '../src/elRoom.cpp', '../src/elSource.cpp', '../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries", "INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT #", "materials provided with the distribution. # # 3. Neither the", "OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON", "Extension('_evert', define_macros = [('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')], include_dirs = ['../include'],", "permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT", "THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A", "distribution. # # 3. Neither the name of the copyright", "Redistributions in binary form must reproduce the above copyright #", "<filename>python/setup.py #!/usr/bin/env python # Copyright (c) 2017, <NAME> # All", "OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.", "POSSIBILITY # OF SUCH DAMAGE. \"\"\" setup.py file for installing", "AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT", "1. Redistributions of source code must retain the above copyright", "source code must retain the above copyright # notice, this", "SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS", "(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT", "<NAME> # All rights reserved. # # Redistribution and use", "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY", "HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT,", "INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, #", "following disclaimer in the # documentation and/or other materials provided", ") setup (name = 'evert', version = '1.0', author =", "HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER", "names of its contributors # may be used to endorse", "'../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries = ['GL', 'GLU', 'glut'], library_dirs =", "= [('MAJOR_VERSION', '1'), ('MINOR_VERSION', '0')], include_dirs = ['../include'], sources=['../src/elBeam.cpp', '../src/elBSP.cpp',", "SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS", "USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY", "2. Redistributions in binary form must reproduce the above copyright", "'../src/elTimer.cpp', '../src/elVector.cpp', '../src/elViewer.cpp', 'evert.i'], libraries = ['GL', 'GLU', 'glut'], library_dirs", "FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT", "ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER", "THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND # ANY", "THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY # OF", "libraries = ['GL', 'GLU', 'glut'], library_dirs = [], language='c++', swig_opts=['-c++',", "and the following disclaimer in the # documentation and/or other", "# 2. Redistributions in binary form must reproduce the above", "AND CONTRIBUTORS \"AS IS\" AND # ANY EXPRESS OR IMPLIED", "Neither the name of the copyright holder nor the names" ]
[ "self.font = pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self, surface): fps = self.game.get_average_fps()", "font') self.font = pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self, surface): fps =", "32) def draw(self, surface): fps = self.game.get_average_fps() fps_text = '<unknown>'", "fps is None else '{:.1f}'.format(fps) tmp_surf = self.font.render('{} FPS'.format(fps_text), True,", "is None else '{:.1f}'.format(fps) tmp_surf = self.font.render('{} FPS'.format(fps_text), True, (255,", "self.game.get_average_fps() fps_text = '<unknown>' if fps is None else '{:.1f}'.format(fps)", "= '<unknown>' if fps is None else '{:.1f}'.format(fps) tmp_surf =", "tmp_surf = self.font.render('{} FPS'.format(fps_text), True, (255, 255, 255)) surface.blit(tmp_surf, (0,", "draw(self, surface): fps = self.game.get_average_fps() fps_text = '<unknown>' if fps", "= pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self, surface): fps = self.game.get_average_fps() fps_text", "if fps is None else '{:.1f}'.format(fps) tmp_surf = self.font.render('{} FPS'.format(fps_text),", "from somegame.osd import OSD class FpsOSD(OSD): def __init__(self, game): super().__init__(game)", "__init__(self, game): super().__init__(game) logger.info('Loading font') self.font = pygame.font.Font(pygame.font.get_default_font(), 32) def", "game): super().__init__(game) logger.info('Loading font') self.font = pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self,", "import logger from somegame.osd import OSD class FpsOSD(OSD): def __init__(self,", "logger.info('Loading font') self.font = pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self, surface): fps", "pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self, surface): fps = self.game.get_average_fps() fps_text =", "super().__init__(game) logger.info('Loading font') self.font = pygame.font.Font(pygame.font.get_default_font(), 32) def draw(self, surface):", "fps_text = '<unknown>' if fps is None else '{:.1f}'.format(fps) tmp_surf", "somegame.osd import OSD class FpsOSD(OSD): def __init__(self, game): super().__init__(game) logger.info('Loading", "import pygame from loguru import logger from somegame.osd import OSD", "pygame from loguru import logger from somegame.osd import OSD class", "OSD class FpsOSD(OSD): def __init__(self, game): super().__init__(game) logger.info('Loading font') self.font", "None else '{:.1f}'.format(fps) tmp_surf = self.font.render('{} FPS'.format(fps_text), True, (255, 255,", "loguru import logger from somegame.osd import OSD class FpsOSD(OSD): def", "import OSD class FpsOSD(OSD): def __init__(self, game): super().__init__(game) logger.info('Loading font')", "else '{:.1f}'.format(fps) tmp_surf = self.font.render('{} FPS'.format(fps_text), True, (255, 255, 255))", "'{:.1f}'.format(fps) tmp_surf = self.font.render('{} FPS'.format(fps_text), True, (255, 255, 255)) surface.blit(tmp_surf,", "class FpsOSD(OSD): def __init__(self, game): super().__init__(game) logger.info('Loading font') self.font =", "= self.game.get_average_fps() fps_text = '<unknown>' if fps is None else", "= self.font.render('{} FPS'.format(fps_text), True, (255, 255, 255)) surface.blit(tmp_surf, (0, 0))", "surface): fps = self.game.get_average_fps() fps_text = '<unknown>' if fps is", "FpsOSD(OSD): def __init__(self, game): super().__init__(game) logger.info('Loading font') self.font = pygame.font.Font(pygame.font.get_default_font(),", "from loguru import logger from somegame.osd import OSD class FpsOSD(OSD):", "'<unknown>' if fps is None else '{:.1f}'.format(fps) tmp_surf = self.font.render('{}", "logger from somegame.osd import OSD class FpsOSD(OSD): def __init__(self, game):", "def __init__(self, game): super().__init__(game) logger.info('Loading font') self.font = pygame.font.Font(pygame.font.get_default_font(), 32)", "def draw(self, surface): fps = self.game.get_average_fps() fps_text = '<unknown>' if", "fps = self.game.get_average_fps() fps_text = '<unknown>' if fps is None" ]
[ "df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) df['a'][0] =", "tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\")", "== ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy", "2.0 (the \"License\"); # you may not use this file", "< 0.2] = 0 data[mask == 0] = None data[mask", "assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self):", "os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self): pass @classmethod def tearDownClass(cls): # stop", "<reponame>sgwhat/BigDL # # Copyright 2016 The BigDL Authors. # #", "value\" assert tsdata_test.target_col[0] != \"new value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple =", "1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy", "assert tsdata.target_col == [\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions()", "TemporaryDirectory() as tmpdir: file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp", "2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert", "target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df = tsdata.shards.collect()", "import random import os from unittest import TestCase from bigdl.chronos.data", "assert tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra", "tsdata._id_list == [\"0\"] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col", "4 assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] == 2 assert", "= random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy =", "# import pytest import numpy as np import pandas as", "feature\"], id_col=\"id\") assert tsdata._id_list == [0, 1] assert tsdata.feature_col ==", "def tearDown(self): pass @classmethod def tearDownClass(cls): # stop possible active_spark_context", "sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single", "((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy =", "0.4] = 2 mask[newmask < 0.4] = 1 mask[newmask <", "\"new value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon =", "random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\")", "SparkContext from bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context is not None:", "tsdata_train.target_col[0] == \"new value\" assert tsdata_valid.target_col[0] != \"new value\" assert", "of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless", "tearDown(self): pass @classmethod def tearDownClass(cls): # stop possible active_spark_context from", "# roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy =", "tsdata._id_list == ['0'] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col", "and valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0)", "=\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1)", "y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy", "np import pandas as pd import random import os from", "assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) # roll test horizon", "== ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy =", "value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon = random.randint(1,", "random import os from unittest import TestCase from bigdl.chronos.data import", "np.array(['00']*50 + ['01']*50) return df class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path", "tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) #", "use this file except in compliance with the License. #", "extra feature\") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1", "feature\") assert tsdata._id_list == ['0'] assert tsdata.feature_col == [\"extra feature\"]", "target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == ['0'] assert tsdata.feature_col ==", "= 2 mask[newmask < 0.4] = 1 mask[newmask < 0.2]", "the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required", "= pd.date_range('1/1/2019', periods=50) df[\"id\"] = np.array(['00']*50 + ['01']*50) return df", "License. # You may obtain a copy of the License", "=\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1,", "id_col=\"id\") assert tsdata._id_list == [0, 1] assert tsdata.feature_col == [\"extra", "extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col ==", "with pytest.raises(RuntimeError): tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy =", "assert len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self): df = generate_spark_df() #", "== 4 assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] == 2", "with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\") assert len(tsdata_train.feature_col)", "shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon = random.randint(1, 10) lookback =", "((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback,", "under the License is distributed on an \"AS IS\" BASIS,", "1 mask[newmask < 0.2] = 0 data[mask == 0] =", "read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\")", "largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col)", "License for the specific language governing permissions and # limitations", "# collect and valid x = np.concatenate([collected_numpy[i]['x'] for i in", "tmp_df = get_ugly_ts_df() with TemporaryDirectory() as tmpdir: file_name = os.path.join(tmpdir,", "\"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df = pd.concat(collected_df,", "= read_csv(file_name) for val in [\"last\", \"const\", \"linear\"]: tsdata =", "in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))],", "tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\",", "0.4] = 1 mask[newmask < 0.2] = 0 data[mask ==", "tsdata_test.target_col[0] != \"new value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\"))", "range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape", "tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") with pytest.raises(RuntimeError):", "collect and valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))],", "== 100 def test_xshardstsdataset_sparkdf(self): df = generate_spark_df() # with id", "== 1 assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] == 1", "100 def test_xshardstsdataset_sparkdf(self): df = generate_spark_df() # with id tsdata", "axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape ==", "import TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas import read_csv", "context\") sc = SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import stop_spark_standalone", "== [\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 2", "range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0)", "bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas import read_csv from bigdl.orca.common import", "init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd = sc.range(0, 100) from pyspark.ml.linalg", "x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon,", "roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect()", "= 0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy =", "in compliance with the License. # You may obtain a", "id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4", "horizon, 1) # roll test horizon = 0 lookback =", "software # distributed under the License is distributed on an", "lookback, 2) def test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory tmp_df =", "tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\", \"d\"], id_col=\"id\")", "for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback,", "data[mask == 0] = None data[mask == 1] = np.nan", "= np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5)) newmask = mask.copy()", "id_col=\"id\") tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0) assert", "= OrcaContext.get_spark_context() rdd = sc.range(0, 100) from pyspark.ml.linalg import DenseVector", "feature\"], id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col == [\"extra", "axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 1) assert y.shape ==", "stop possible active_spark_context from pyspark import SparkContext from bigdl.orca.ray import", "lookback = random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra", "sc = OrcaContext.get_spark_context() rdd = sc.range(0, 100) from pyspark.ml.linalg import", "[0, 1] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col ==", "df def get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask = np.random.random_sample((100,", "assert tsdata_valid.target_col[0] != \"new value\" assert tsdata_test.target_col[0] != \"new value\"", "import pandas as pd import random import os from unittest", "2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra", "from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single =", "from pyspark import SparkContext from bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context", "collected_df = pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() == 0 assert len(collected_df)", "tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name) for val in [\"last\", \"const\",", "= pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() == 0 assert len(collected_df) ==", "possible active_spark_context from pyspark import SparkContext from bigdl.orca.ray import OrcaRayContext", "tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0.1,", "== [0] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col ==", "0.2] = 0 data[mask == 0] = None data[mask ==", "three sets tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra", "1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = \"new value\" assert", "\"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path,", "as pd import random import os from unittest import TestCase", "import stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\"))", "setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self): pass @classmethod def", "x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) assert x.shape", "0] = None data[mask == 1] = np.nan df =", "stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata =", "val_ratio=0, test_ratio=0.1) # standard split with all three sets tsdata_train,", "assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 2 # with", "'d', 'e']) df['a'][0] = np.nan # make sure column 'a'", "df = rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\",", "def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal input tsdata", "size=())), int(x))).toDF([\"feature\", \"id\", \"date\"]) return df def get_ugly_ts_df(): data =", "OF ANY KIND, either express or implied. # See the", "WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.", "file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name) for", "\"const\", \"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\",", "ANY KIND, either express or implied. # See the License", "See the License for the specific language governing permissions and", "= mask.copy() mask[newmask >= 0.4] = 2 mask[newmask < 0.4]", "x.shape == ((50-lookback-horizon+1)*2, lookback, 1) assert y.shape == ((50-lookback-horizon+1)*2, horizon,", "== ((50-lookback-horizon+1)*2, horizon, 1) # roll test horizon = 0", "the License. # You may obtain a copy of the", "at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable", "for the specific language governing permissions and # limitations under", "== 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert", "1 def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only train", "extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1) # standard split with", "2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list", "5)) mask = np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask >=", "to in writing, software # distributed under the License is", "# See the License for the specific language governing permissions", "generate_spark_df() # with id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\")", "pandas.testing import assert_frame_equal from numpy.testing import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8)", "train and test tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\",", "language governing permissions and # limitations under the License. #", "is not None: print(\"Stopping spark_orca context\") sc = SparkContext.getOrCreate() if", "or agreed to in writing, software # distributed under the", "from pandas.testing import assert_frame_equal from numpy.testing import assert_array_almost_equal def generate_spark_df():", "# standard split with all three sets tsdata_train, tsdata_valid, tsdata_test", "input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert", "extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra", "required by applicable law or agreed to in writing, software", "BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either", "1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy =", "with the License. # You may obtain a copy of", "XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == ['0'] assert", "df class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\") def", "assert tsdata._id_list == [0] assert tsdata.feature_col == [\"extra feature\"] assert", "read_csv from bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext from pandas.testing import", "assert tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra", "extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0, 1] assert tsdata.feature_col", "# with id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4,", "\"datetime\"] = pd.date_range('1/1/2019', periods=50) df[\"id\"] = np.array(['00']*50 + ['01']*50) return", "import SparkContext from bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context is not", "= read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"],", "assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\"))", "compliance with the License. # You may obtain a copy", "shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid", "agreed to in writing, software # distributed under the License", "((50-lookback-horizon+1)*2, lookback, 1) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) #", "= rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\", \"id\",", "import assert_frame_equal from numpy.testing import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc", "shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x'] for i", "== 2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 1", "test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory tmp_df = get_ugly_ts_df() with TemporaryDirectory()", "distributed under the License is distributed on an \"AS IS\"", "tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list ==", "np.nan # make sure column 'a' has a N/A df[\"datetime\"]", "assert tsdata._id_list == [\"0\"] assert tsdata.feature_col == [\"extra feature\"] assert", "None: print(\"Stopping spark_orca context\") sc = SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from", "with_split=True, val_ratio=0, test_ratio=0.1) # standard split with all three sets", "stop_orca_context, OrcaContext from pandas.testing import assert_frame_equal from numpy.testing import assert_array_almost_equal", "extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0, 1] assert tsdata.feature_col", "assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\"))", "express or implied. # See the License for the specific", "except in compliance with the License. # You may obtain", "[\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 def", "def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self): pass @classmethod", "= np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask >= 0.4] =", "= pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019', periods=50) df[\"id\"] =", "Licensed under the Apache License, Version 2.0 (the \"License\"); #", "not use this file except in compliance with the License.", "largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\") assert len(tsdata_train.feature_col) == 2 assert", "TestCase from bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset from", "XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df", "writing, software # distributed under the License is distributed on", "assert tsdata._id_list == ['0'] assert tsdata.feature_col == [\"extra feature\"] assert", "tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple", "assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self): from tempfile", "you may not use this file except in compliance with", "value\" assert tsdata_train.target_col[0] == \"new value\" assert tsdata_valid.target_col[0] != \"new", "# Licensed under the Apache License, Version 2.0 (the \"License\");", "get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5)) newmask", "XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0,", "XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0]", "\"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path,", "= os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self): pass @classmethod def tearDownClass(cls): #", "tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy", "assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd = sc.range(0,", "df[\"id\"] = np.array(['00']*50 + ['01']*50) return df class TestXShardsTSDataset(TestCase): def", "pyspark.ml.linalg import DenseVector df = rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0,", "== 1] = np.nan df = pd.DataFrame(data, columns=['a', 'b', 'c',", "\"datetime\" assert tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"],", "test horizon = 0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon)", "CONDITIONS OF ANY KIND, either express or implied. # See", "mask[newmask >= 0.4] = 2 mask[newmask < 0.4] = 1", "tmpdir: file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name)", "tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col == [\"value\"] assert tsdata.dt_col", "is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES", "2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0]", "== ((50-lookback-horizon+1)*2, lookback, 1) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1)", "assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col)", "os from unittest import TestCase from bigdl.chronos.data import TSDataset from", "\"date\"]) return df def get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask", "value\" assert tsdata_valid.target_col[0] != \"new value\" assert tsdata_test.target_col[0] != \"new", "XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1) #", "tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect", "== [0, 1] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col", "== ((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory", "import TestCase from bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset", "= tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid x", "extra_feature_col=\"extra feature\") assert tsdata._id_list == [\"0\"] assert tsdata.feature_col == [\"extra", "len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self): df = generate_spark_df() # with", "= XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list ==", "dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == [\"0\"] assert tsdata.feature_col", "i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2)", "== \"new value\" assert tsdata_valid.target_col[0] != \"new value\" assert tsdata_test.target_col[0]", "XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0,", "def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\",", "collected_df = tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() ==", "for val in [\"last\", \"const\", \"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\",", "np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for", "1 assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] == 1 assert", "= np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert x.shape ==", "\"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert", "[\"0\"] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col == [\"value\"]", "test tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"],", "# stop possible active_spark_context from pyspark import SparkContext from bigdl.orca.ray", "import numpy as np import pandas as pd import random", "((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy = tsdata.to_xshards()", "and # limitations under the License. # import pytest import", "OR CONDITIONS OF ANY KIND, either express or implied. #", "train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() #", "sc.range(0, 100) from pyspark.ml.linalg import DenseVector df = rdd.map(lambda x:", "assert tsdata._id_list == [0, 1] assert tsdata.feature_col == [\"extra feature\"]", "the License is distributed on an \"AS IS\" BASIS, #", "as np import pandas as pd import random import os", "= XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect()", "'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name) for val in [\"last\",", "mask[newmask < 0.4] = 1 mask[newmask < 0.2] = 0", "= XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list ==", "index=False) shards_tmp = read_csv(file_name) for val in [\"last\", \"const\", \"linear\"]:", "== 1 def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only", "tsdata.shards.num_partitions() == 2 # with only 1 id tsdata =", "from numpy.testing import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context()", "with id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2)", "tempfile import TemporaryDirectory tmp_df = get_ugly_ts_df() with TemporaryDirectory() as tmpdir:", "def get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5))", "unittest import TestCase from bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental import", "lookback, 1) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) # roll", "horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect()", "= 1 mask[newmask < 0.2] = 0 data[mask == 0]", "len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = \"new", "id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col == [\"extra feature\"]", "= pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) df['a'][0] = np.nan", "i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 1)", "pass @classmethod def tearDownClass(cls): # stop possible active_spark_context from pyspark", "horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() #", "data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] ==", "tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list ==", "\"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0)", "numpy as np import pandas as pd import random import", "feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() #", "Copyright 2016 The BigDL Authors. # # Licensed under the", "law or agreed to in writing, software # distributed under", "from unittest import TestCase from bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental", "test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal input tsdata =", "valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y", "SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop() def", "governing permissions and # limitations under the License. # import", "# only train and test tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple,", "== [\"0\"] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col ==", "with only 1 id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4,", "extra_feature_col=[\"a\", \"b\", \"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df", "= get_ugly_ts_df() with TemporaryDirectory() as tmpdir: file_name = os.path.join(tmpdir, 'impute.csv')", "the License. # import pytest import numpy as np import", "len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) ==", "feature\") assert tsdata._id_list == [\"0\"] assert tsdata.feature_col == [\"extra feature\"]", "\"multiple.csv\")) # only train and test tsdata_train, tsdata_valid, tsdata_test =\\", "!= \"new value\" assert tsdata_test.target_col[0] != \"new value\" def test_xshardstsdataset_roll_multiple_id(self):", "tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data =", "== \"datetime\" assert tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\",", "== ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1)", "assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] =", "stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata", "2, size=())), int(x))).toDF([\"feature\", \"id\", \"date\"]) return df def get_ugly_ts_df(): data", "may obtain a copy of the License at # #", "target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and", "extra_feature_col=\"extra feature\") assert tsdata._id_list == ['0'] assert tsdata.feature_col == [\"extra", "1) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) # roll test", "a N/A df[\"datetime\"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019',", "N/A df[\"datetime\"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019', periods=50)", "bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context is not None: print(\"Stopping spark_orca", "IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,", "== \"datetime\" assert tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\",", "target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == [\"0\"] assert tsdata.feature_col ==", "def tearDownClass(cls): # stop possible active_spark_context from pyspark import SparkContext", "may not use this file except in compliance with the", "lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy", "data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() ==", "feature\"], id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback, horizon=horizon)", "# roll test horizon = 0 lookback = random.randint(1, 20)", "tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert", "OrcaRayContext if SparkContext._active_spark_context is not None: print(\"Stopping spark_orca context\") sc", "\"new value\" assert tsdata_test.target_col[0] != \"new value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple", "dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0, 1]", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or", "target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0, 1] assert", "this file except in compliance with the License. # You", "mask[newmask < 0.2] = 0 data[mask == 0] = None", "feature\", id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col == [\"extra", "tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\",", "# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law", "XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1]", "pd import random import os from unittest import TestCase from", "assert collected_df.isna().sum().sum() == 0 assert len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self):", "# # Licensed under the Apache License, Version 2.0 (the", ").astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\", \"id\", \"date\"]) return df def", "= SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop()", "file except in compliance with the License. # You may", "on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS", "spark_orca context\") sc = SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import", "# limitations under the License. # import pytest import numpy", "pd.date_range('1/1/2019', periods=50) df[\"id\"] = np.array(['00']*50 + ['01']*50) return df class", "newmask = mask.copy() mask[newmask >= 0.4] = 2 mask[newmask <", "0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards()", "roll test horizon = 0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback,", "from pyspark.ml.linalg import DenseVector df = rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)),", "= XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards()", "test_xshardstsdataset_sparkdf(self): df = generate_spark_df() # with id tsdata = XShardsTSDataset.from_sparkdf(df,", "\"datetime\" assert tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"],", "pandas as pd import random import os from unittest import", "None data[mask == 1] = np.nan df = pd.DataFrame(data, columns=['a',", "2) def test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory tmp_df = get_ugly_ts_df()", "column 'a' has a N/A df[\"datetime\"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100,", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express", "mask = np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask >= 0.4]", "XShardsTSDataset from bigdl.orca.data.pandas import read_csv from bigdl.orca.common import init_orca_context, stop_orca_context,", "# with only 1 id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\")", "= XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == [\"0\"]", "pytest.raises(RuntimeError): tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards()", "1 assert tsdata.shards.num_partitions() == 2 # with only 1 id", "assert x.shape == ((50-lookback-horizon+1)*2, lookback, 1) assert y.shape == ((50-lookback-horizon+1)*2,", "\"multiple.csv\")) # legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra", "random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect()", "= random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"],", "= os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name) for val", "horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and", "import os from unittest import TestCase from bigdl.chronos.data import TSDataset", "periods=100) df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019', periods=50) df[\"id\"] = np.array(['00']*50 +", "1] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col == [\"value\"]", "extra_feature_col=[\"extra feature\"], id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback,", "range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 1) assert y.shape", "dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == ['0'] assert tsdata.feature_col", "# # Copyright 2016 The BigDL Authors. # # Licensed", "import read_csv from bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext from pandas.testing", "[\"extra feature\"] assert tsdata.target_col == [\"value\"] assert tsdata.dt_col == \"datetime\"", "assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col == [\"value\"] assert", "has a N/A df[\"datetime\"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"] =", "if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self):", "all three sets tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\",", "http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed", "read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\",", "= XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list ==", "5)) newmask = mask.copy() mask[newmask >= 0.4] = 2 mask[newmask", "TemporaryDirectory tmp_df = get_ugly_ts_df() with TemporaryDirectory() as tmpdir: file_name =", "\"b\", \"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df =", "len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = \"new value\" assert tsdata_train.target_col[0] ==", "[\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 tsdata", "or implied. # See the License for the specific language", "i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in", "assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2]", "as tmpdir: file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp =", "TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas import read_csv from", "2 # with only 1 id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\",", "def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon = random.randint(1, 10)", "collected_numpy = shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x']", "KIND, either express or implied. # See the License for", "specific language governing permissions and # limitations under the License.", "# legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"],", "import init_orca_context, stop_orca_context, OrcaContext from pandas.testing import assert_frame_equal from numpy.testing", "target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4", "== \"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self): shards_multiple =", "for i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i", "XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0]", "tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum()", "License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by", "def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only train and", "1 id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data", "in [\"last\", \"const\", \"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\",", "tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list", "shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple,", "assert tsdata_test.target_col[0] != \"new value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path,", "20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() #", "feature\"], id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1) # standard split with all", "df = generate_spark_df() # with id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\",", "tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() == 0 assert", "test_ratio=0.1) # standard split with all three sets tsdata_train, tsdata_valid,", "10) lookback = random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\",", "limitations under the License. # import pytest import numpy as", "sure column 'a' has a N/A df[\"datetime\"] = pd.date_range('1/1/2019', periods=100)", "(the \"License\"); # you may not use this file except", "assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = \"new value\" assert tsdata_train.target_col[0]", "in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) def", "# you may not use this file except in compliance", "2 mask[newmask < 0.4] = 1 mask[newmask < 0.2] =", "tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\",", "feature\"], id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\")", "os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name) for val in", "read_csv(file_name) for val in [\"last\", \"const\", \"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp,", "== 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\")", "import OrcaRayContext if SparkContext._active_spark_context is not None: print(\"Stopping spark_orca context\")", "test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon = random.randint(1, 10) lookback", "[0] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col == [\"value\"]", "tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) #", "np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2,", "== \"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple =", "XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5,", "assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions()", "from bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas import read_csv from bigdl.orca.common", "import XShardsTSDataset from bigdl.orca.data.pandas import read_csv from bigdl.orca.common import init_orca_context,", "dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0] assert", "val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\") assert len(tsdata_train.feature_col) ==", "bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path,", "target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0, 1] assert", "id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data", "import DenseVector df = rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2,", "tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list", "data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] ==", "# # Unless required by applicable law or agreed to", "pytest import numpy as np import pandas as pd import", "bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas import", "shards_tmp = read_csv(file_name) for val in [\"last\", \"const\", \"linear\"]: tsdata", "tsdata._id_list == [0, 1] assert tsdata.feature_col == [\"extra feature\"] assert", "The BigDL Authors. # # Licensed under the Apache License,", "in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 1) assert", "obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0", "dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2)", "assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\")", "feature\"] assert tsdata.target_col == [\"value\"] assert tsdata.dt_col == \"datetime\" assert", "== 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert", "Version 2.0 (the \"License\"); # you may not use this", "with all three sets tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\",", "pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() == 0 assert len(collected_df) == 100", "def test_xshardstsdataset_sparkdf(self): df = generate_spark_df() # with id tsdata =", "1 tsdata_train.target_col[0] = \"new value\" assert tsdata_train.target_col[0] == \"new value\"", "feature\", id_col=\"id\") assert tsdata._id_list == [0, 1] assert tsdata.feature_col ==", "tsdata.target_col == [\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() ==", "SparkContext._active_spark_context is not None: print(\"Stopping spark_orca context\") sc = SparkContext.getOrCreate()", "pyspark import SparkContext from bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context is", "= XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == ['0']", "License. # import pytest import numpy as np import pandas", "\"../../resources/\") def tearDown(self): pass @classmethod def tearDownClass(cls): # stop possible", "0 data[mask == 0] = None data[mask == 1] =", "data[mask == 1] = np.nan df = pd.DataFrame(data, columns=['a', 'b',", "np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2,", "implied. # See the License for the specific language governing", "assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1]", "under the Apache License, Version 2.0 (the \"License\"); # you", "= tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() == 0", "TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self): pass", "[\"last\", \"const\", \"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\",", "assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"],", "only train and test tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\",", "assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 2 tsdata =", "= generate_spark_df() # with id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\",", "x.shape == ((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self): from tempfile import", "by applicable law or agreed to in writing, software #", "OrcaContext from pandas.testing import assert_frame_equal from numpy.testing import assert_array_almost_equal def", "= random.randint(1, 10) lookback = random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple,", "from bigdl.orca.data.pandas import read_csv from bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext", "= 0 data[mask == 0] = None data[mask == 1]", "val in [\"last\", \"const\", \"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\",", "XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list == [\"0\"] assert", "2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[],", "horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy", "tsdata_train.feature_col.append(\"new extra feature\") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) ==", "bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext from pandas.testing import assert_frame_equal from", "horizon = random.randint(1, 10) lookback = random.randint(1, 20) tsdata =", "standard split with all three sets tsdata_train, tsdata_valid, tsdata_test =\\", "y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\")", "init_orca_context, stop_orca_context, OrcaContext from pandas.testing import assert_frame_equal from numpy.testing import", "tearDownClass(cls): # stop possible active_spark_context from pyspark import SparkContext from", "['01']*50) return df class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0],", "1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\") assert tsdata._id_list", "# Copyright 2016 The BigDL Authors. # # Licensed under", "2016 The BigDL Authors. # # Licensed under the Apache", "axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self): from", "XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert", "split with all three sets tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple,", "DenseVector df = rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())),", "== [\"extra feature\"] assert tsdata.target_col == [\"value\"] assert tsdata.dt_col ==", "feature_col=[], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect", "dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1) # standard", "shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only train and test tsdata_train,", "assert tsdata_train.target_col[0] == \"new value\" assert tsdata_valid.target_col[0] != \"new value\"", "make sure column 'a' has a N/A df[\"datetime\"] = pd.date_range('1/1/2019',", "@classmethod def tearDownClass(cls): # stop possible active_spark_context from pyspark import", "(DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\", \"id\", \"date\"]) return df", "an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF", "periods=50) df[\"id\"] = np.array(['00']*50 + ['01']*50) return df class TestXShardsTSDataset(TestCase):", "with TemporaryDirectory() as tmpdir: file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False)", "data = np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5)) newmask =", "Unless required by applicable law or agreed to in writing,", "x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\", \"id\", \"date\"]) return", "tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect()", "np.nan df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) df['a'][0]", "numpy.testing import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd", "# make sure column 'a' has a N/A df[\"datetime\"] =", "'b', 'c', 'd', 'e']) df['a'][0] = np.nan # make sure", "target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1) # standard split", "pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019', periods=50) df[\"id\"] = np.array(['00']*50", "return df class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\")", "rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\", \"id\", \"date\"])", "int(x))).toDF([\"feature\", \"id\", \"date\"]) return df def get_ugly_ts_df(): data = np.random.random_sample((100,", "mask.copy() mask[newmask >= 0.4] = 2 mask[newmask < 0.4] =", "extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col ==", "the specific language governing permissions and # limitations under the", "id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy", "generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd = sc.range(0, 100) from", "y.shape == ((50-lookback-horizon+1)*2, horizon, 1) # roll test horizon =", "assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2,", "100) from pyspark.ml.linalg import DenseVector df = rdd.map(lambda x: (DenseVector(np.random.randn(1,", "= read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon = random.randint(1, 10) lookback = random.randint(1,", "= None data[mask == 1] = np.nan df = pd.DataFrame(data,", "applicable law or agreed to in writing, software # distributed", "assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self):", "target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] ==", "class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self):", "only 1 id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2)", "feature\") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1 assert", "valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) assert", "tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single,", "dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0, 1]", "== 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = \"new value\"", "in writing, software # distributed under the License is distributed", "tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid x =", "df['a'][0] = np.nan # make sure column 'a' has a", "= \"new value\" assert tsdata_train.target_col[0] == \"new value\" assert tsdata_valid.target_col[0]", "id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data =", "under the License. # import pytest import numpy as np", "bigdl.orca.data.pandas import read_csv from bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext from", "active_spark_context from pyspark import SparkContext from bigdl.orca.ray import OrcaRayContext if", "= sc.range(0, 100) from pyspark.ml.linalg import DenseVector df = rdd.map(lambda", "tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple,", "int(np.random.randint(0, 2, size=())), int(x))).toDF([\"feature\", \"id\", \"date\"]) return df def get_ugly_ts_df():", "BigDL Authors. # # Licensed under the Apache License, Version", "< 0.4] = 1 mask[newmask < 0.2] = 0 data[mask", "sc.stop() def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single,", "pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) df['a'][0] = np.nan #", "== 1 assert tsdata.shards.num_partitions() == 2 # with only 1", "horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy = tsdata.to_xshards()", "2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 2 #", "rdd = sc.range(0, 100) from pyspark.ml.linalg import DenseVector df =", "\"linear\"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\", \"d\"],", "Authors. # # Licensed under the Apache License, Version 2.0", "in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert", "not None: print(\"Stopping spark_orca context\") sc = SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"):", "import pytest import numpy as np import pandas as pd", "XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards() #", "tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect()", "dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val) collected_df =", "\"new value\" assert tsdata_valid.target_col[0] != \"new value\" assert tsdata_test.target_col[0] !=", "feature\"], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect", "horizon = 0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy", "License is distributed on an \"AS IS\" BASIS, # WITHOUT", "tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\")", "License, Version 2.0 (the \"License\"); # you may not use", "# You may obtain a copy of the License at", "1) # roll test horizon = 0 lookback = random.randint(1,", "df[\"datetime\"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019', periods=50) df[\"id\"]", "copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #", "tsdata._id_list == [0] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col", "dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] ==", "= np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y']", "= XShardsTSDataset.from_sparkdf(df, dt_col=\"date\", target_col=\"feature\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert", "== 1 tsdata_train.target_col[0] = \"new value\" assert tsdata_train.target_col[0] == \"new", "if SparkContext._active_spark_context is not None: print(\"Stopping spark_orca context\") sc =", "1] = np.nan df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd',", "axis=0) assert collected_df.isna().sum().sum() == 0 assert len(collected_df) == 100 def", "assert_frame_equal from numpy.testing import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc =", "dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards() # roll", "import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd =", "test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\",", "!= \"new value\" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon", "from bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext from pandas.testing import assert_frame_equal", "= np.nan df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e'])", "self.resource_path = os.path.join(os.path.split(__file__)[0], \"../../resources/\") def tearDown(self): pass @classmethod def tearDownClass(cls):", "['0'] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col == [\"value\"]", "\"multiple.csv\")) horizon = random.randint(1, 10) lookback = random.randint(1, 20) tsdata", "== 2 # with only 1 id tsdata = XShardsTSDataset.from_sparkdf(df,", "target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new", "the License for the specific language governing permissions and #", "'c', 'd', 'e']) df['a'][0] = np.nan # make sure column", "tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] == 1 assert", "[\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 2 tsdata", "== 2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1", "1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert", "== ['0'] assert tsdata.feature_col == [\"extra feature\"] assert tsdata.target_col ==", "Apache License, Version 2.0 (the \"License\"); # you may not", "\"new value\" assert tsdata_train.target_col[0] == \"new value\" assert tsdata_valid.target_col[0] !=", "np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask", "def test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory tmp_df = get_ugly_ts_df() with", "either express or implied. # See the License for the", "range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self):", "tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self): shards_multiple", "= np.array(['00']*50 + ['01']*50) return df class TestXShardsTSDataset(TestCase): def setUp(self):", "read_csv(os.path.join(self.resource_path, \"multiple.csv\")) horizon = random.randint(1, 10) lookback = random.randint(1, 20)", "# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or", "shards_single = read_csv(os.path.join(self.resource_path, \"single.csv\")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra", "read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only train and test tsdata_train, tsdata_valid, tsdata_test", "sets tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"],", "OrcaContext.get_spark_context() rdd = sc.range(0, 100) from pyspark.ml.linalg import DenseVector df", "\"id\", \"date\"]) return df def get_ugly_ts_df(): data = np.random.random_sample((100, 5))", "id_col=\"id\", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\") assert", "tsdata_train.target_col[0] = \"new value\" assert tsdata_train.target_col[0] == \"new value\" assert", "from bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context is not None: print(\"Stopping", "random.randint(1, 10) lookback = random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\",", "data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 2 # with only", "tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True,", "sc = SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone()", "== 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal", "horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2]", "from bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas", "tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list", "data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] ==", "target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col", "tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[\"extra feature\"], target_col=\"value\") shards_numpy = tsdata.to_xshards() collected_numpy =", "axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert", "((50-lookback-horizon+1)*2, horizon, 1) # roll test horizon = 0 lookback", "a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #", "+ ['01']*50) return df class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path =", "columns=['a', 'b', 'c', 'd', 'e']) df['a'][0] = np.nan # make", "== 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\")", "= read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only train and test tsdata_train, tsdata_valid,", "lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon,", "tsdata_valid.target_col[0] != \"new value\" assert tsdata_test.target_col[0] != \"new value\" def", "== [\"value\"] assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1", "= XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list ==", "== 2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 2", "assert tsdata.shards.num_partitions() == 2 # with only 1 id tsdata", "and test tsdata_train, tsdata_valid, tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra", "get_ugly_ts_df() with TemporaryDirectory() as tmpdir: file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name,", "legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\")", "= XShardsTSDataset.from_xshards(shards_tmp, dt_col=\"datetime\", target_col=\"e\", extra_feature_col=[\"a\", \"b\", \"c\", \"d\"], id_col=\"id\") tsdata.impute(mode=val)", "\"License\"); # you may not use this file except in", "dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0] assert", "from tempfile import TemporaryDirectory tmp_df = get_ugly_ts_df() with TemporaryDirectory() as", "distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR", "= np.nan # make sure column 'a' has a N/A", "collected_df.isna().sum().sum() == 0 assert len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self): df", "== 0 assert len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self): df =", "assert tsdata.dt_col == \"datetime\" assert tsdata.shards.num_partitions() == 1 tsdata =", "np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask >= 0.4] = 2", "test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append(\"new extra feature\") assert len(tsdata_train.feature_col) == 2", "# distributed under the License is distributed on an \"AS", "tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=[\"value\"], extra_feature_col=\"extra feature\", id_col=\"id\") assert tsdata._id_list", "target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") assert tsdata._id_list == [0] assert tsdata.feature_col", "20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") with", "# Unless required by applicable law or agreed to in", "x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y =", "import TemporaryDirectory tmp_df = get_ugly_ts_df() with TemporaryDirectory() as tmpdir: file_name", "= read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\",", "\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY", "dt_col=\"date\", target_col=\"feature\", id_col=\"id\") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1]", "return df def get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask =", "== 0] = None data[mask == 1] = np.nan df", "id_col=\"id\", with_split=True, val_ratio=0, test_ratio=0.1) # standard split with all three", "((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory tmp_df", "df.loc[50:100, \"datetime\"] = pd.date_range('1/1/2019', periods=50) df[\"id\"] = np.array(['00']*50 + ['01']*50)", "print(\"Stopping spark_orca context\") sc = SparkContext.getOrCreate() if sc.getConf().get(\"spark.master\").startswith(\"spark://\"): from bigdl.dllib.nncontext", "'a' has a N/A df[\"datetime\"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, \"datetime\"]", "target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\") with pytest.raises(RuntimeError): tsdata.to_xshards() # roll train", "You may obtain a copy of the License at #", "permissions and # limitations under the License. # import pytest", "def generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd = sc.range(0, 100)", "test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # only train and test", "tsdata_test =\\ XShardsTSDataset.from_xshards(shards_multiple, dt_col=\"datetime\", target_col=\"value\", extra_feature_col=[\"extra feature\"], id_col=\"id\", with_split=True, val_ratio=0,", "'e']) df['a'][0] = np.nan # make sure column 'a' has", "= shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x'] for", "y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert x.shape", "the Apache License, Version 2.0 (the \"License\"); # you may", "= tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] == 1", ">= 0.4] = 2 mask[newmask < 0.4] = 1 mask[newmask", "0 assert len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self): df = generate_spark_df()", "1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, \"multiple.csv\")) # legal input", "= np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) assert x.shape ==" ]
[ "zoom_user: response, zoom_user = yield zoomGET(endpoint_url, zoom_user) else: try: print(he.response.body)", "response, zoom_user = yield zoomGET(endpoint_url, zoom_user) else: try: print(he.response.body) except", "import b64encode from tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError from settings", "payload = \"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to base", "import json import tornado.gen import traceback from base64 import b64encode", "AsyncHTTPClient() print(zoom_user) print('making zoomRefresh') print(payload) try: response = yield http_client.fetch(request)", "it #and then decode it to acsii as python 3", "expired, attempting refresh') zoom_user = yield zoomRefresh(zoom_user) if zoom_user: response,", "HTTPRequest(url, method=\"GET\", headers=headers) http_client = AsyncHTTPClient() response = None try:", "import traceback from base64 import b64encode from tornado.httpclient import AsyncHTTPClient,", "base 64 encode it #and then decode it to acsii", "import tornado.gen import traceback from base64 import b64encode from tornado.httpclient", "ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user = None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url,", "zoom_user = None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user): url", "+= \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to base 64 encode it #and", "headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request = HTTPRequest(url, method=\"GET\", headers=headers) http_client", "HTTPError as he: print('zoomRefresh HTTPError:') print(he.code) print(he.response.body) if he.code ==", "it as a byte string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers", "to base 64 encode it #and then decode it to", "string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = { 'authorization': 'Basic", "print(he.code) print(he.response.body) if he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user =", "http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response: {0}\".format(resp)) zoom_user =", "response.body.decode('utf-8') response = json.loads(body) except HTTPError as he: if he.code", "AsyncHTTPClient, HTTPRequest, HTTPError from settings import Settings from mongo_db_controller import", "a byte string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = {", "print(\"zoomRefresh /access_token Response: {0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'],", "response = json.loads(body) except HTTPError as he: if he.code ==", "401: print('token may be expired, attempting refresh') zoom_user = yield", "yield zoomGET(endpoint_url, zoom_user) else: try: print(he.response.body) except Exception as e:", "print('zoomRefresh HTTPError:') print(he.code) print(he.response.body) if he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\")", "= yield zoomGET(endpoint_url, zoom_user) else: try: print(he.response.body) except Exception as", "then decode it to acsii as python 3 stores it", "print('token may be expired, attempting refresh') zoom_user = yield zoomRefresh(zoom_user)", "import ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\" payload =", "\"application/x-www-form-urlencoded\" } request = HTTPRequest(url, method=\"POST\", headers=headers, body=payload) http_client =", "HTTPRequest(url, method=\"POST\", headers=headers, body=payload) http_client = AsyncHTTPClient() print(zoom_user) print('making zoomRefresh')", "print(payload) try: response = yield http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh", "= ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except HTTPError", "\"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except HTTPError as he: print('zoomRefresh HTTPError:') print(he.code)", "\"zoom\") zoom_user = None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user):", "userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = { 'authorization': 'Basic {0}'.format(userAndPass),", "response = yield http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response:", "== 401: print('token may be expired, attempting refresh') zoom_user =", "yield http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response: {0}\".format(resp)) zoom_user", "Settings from mongo_db_controller import ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user): url =", "json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response: {0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'],", "body = response.body.decode('utf-8') response = json.loads(body) except HTTPError as he:", "as a byte string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers =", "zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request =", "zoomGET(endpoint_url, zoom_user) else: try: print(he.response.body) except Exception as e: pass", "@tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer", "= AsyncHTTPClient() response = None try: response = yield http_client.fetch(request)", "http_client.fetch(request) body = response.body.decode('utf-8') response = json.loads(body) except HTTPError as", "@tornado.gen.coroutine def zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\" payload", "print(he.response.body) except Exception as e: pass traceback.print_exc() raise tornado.gen.Return((response, zoom_user))", "resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response: {0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'],", "= b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = { 'authorization': 'Basic {0}'.format(userAndPass), 'content-type':", "zoom_user) else: try: print(he.response.body) except Exception as e: pass traceback.print_exc()", "HTTPError:') print(he.code) print(he.response.body) if he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user", "zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token'))", "} request = HTTPRequest(url, method=\"POST\", headers=headers, body=payload) http_client = AsyncHTTPClient()", "need to base 64 encode it #and then decode it", "url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request = HTTPRequest(url,", "resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except HTTPError as he:", "json import tornado.gen import traceback from base64 import b64encode from", "yield http_client.fetch(request) body = response.body.decode('utf-8') response = json.loads(body) except HTTPError", "except HTTPError as he: if he.code == 401: print('token may", "/access_token Response: {0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\")", "stores it as a byte string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\")", "resp['expires_in'], resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except HTTPError as he: print('zoomRefresh", "method=\"POST\", headers=headers, body=payload) http_client = AsyncHTTPClient() print(zoom_user) print('making zoomRefresh') print(payload)", "from tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError from settings import Settings", "= yield http_client.fetch(request) body = response.body.decode('utf-8') response = json.loads(body) except", "'authorization': 'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" } request = HTTPRequest(url, method=\"POST\",", "from settings import Settings from mongo_db_controller import ZoomUserDB @tornado.gen.coroutine def", "{\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request = HTTPRequest(url, method=\"GET\", headers=headers) http_client = AsyncHTTPClient()", "b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = { 'authorization': 'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\"", "zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except", "refresh') zoom_user = yield zoomRefresh(zoom_user) if zoom_user: response, zoom_user =", "as he: print('zoomRefresh HTTPError:') print(he.code) print(he.response.body) if he.code == 401:", "zoomRefresh(zoom_user) if zoom_user: response, zoom_user = yield zoomGET(endpoint_url, zoom_user) else:", "print(he.response.body) if he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user = None", "acsii as python 3 stores it as a byte string", "import AsyncHTTPClient, HTTPRequest, HTTPError from settings import Settings from mongo_db_controller", "encode it #and then decode it to acsii as python", "\"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to", "\"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to base 64 encode", "http_client = AsyncHTTPClient() print(zoom_user) print('making zoomRefresh') print(payload) try: response =", "to acsii as python 3 stores it as a byte", "= yield http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response: {0}\".format(resp))", "if he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user = None raise", "= HTTPRequest(url, method=\"GET\", headers=headers) http_client = AsyncHTTPClient() response = None", "body=payload) http_client = AsyncHTTPClient() print(zoom_user) print('making zoomRefresh') print(payload) try: response", "None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url)", "settings import Settings from mongo_db_controller import ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user):", "def zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\" payload +=", "= \"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need", "try: response = yield http_client.fetch(request) body = response.body.decode('utf-8') response =", "mongo_db_controller import ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\" payload", "he: if he.code == 401: print('token may be expired, attempting", "{0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user))", "payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to base 64 encode it", "\"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to base 64 encode it #and then", "Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = { 'authorization': 'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" }", "#and then decode it to acsii as python 3 stores", "zoomRefresh') print(payload) try: response = yield http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\"))", "url = \"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we", "= \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request = HTTPRequest(url, method=\"GET\",", "method=\"GET\", headers=headers) http_client = AsyncHTTPClient() response = None try: response", "ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\" payload = \"grant_type=refresh_token&\"", "be expired, attempting refresh') zoom_user = yield zoomRefresh(zoom_user) if zoom_user:", "he: print('zoomRefresh HTTPError:') print(he.code) print(he.response.body) if he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'],", "as he: if he.code == 401: print('token may be expired,", "b64encode from tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError from settings import", "response = None try: response = yield http_client.fetch(request) body =", "resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except HTTPError as he: print('zoomRefresh HTTPError:')", "as python 3 stores it as a byte string userAndPass", "{ 'authorization': 'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" } request = HTTPRequest(url,", "= json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token Response: {0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'],", "Response: {0}\".format(resp)) zoom_user = ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\") print('new", "= None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user): url =", "if zoom_user: response, zoom_user = yield zoomGET(endpoint_url, zoom_user) else: try:", "http_client = AsyncHTTPClient() response = None try: response = yield", "from mongo_db_controller import ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user): url = \"https://zoom.us/oauth/token\"", "{0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" } request = HTTPRequest(url, method=\"POST\", headers=headers, body=payload)", "if he.code == 401: print('token may be expired, attempting refresh')", "may be expired, attempting refresh') zoom_user = yield zoomRefresh(zoom_user) if", "traceback from base64 import b64encode from tornado.httpclient import AsyncHTTPClient, HTTPRequest,", "print('making zoomRefresh') print(payload) try: response = yield http_client.fetch(request) resp =", "= json.loads(body) except HTTPError as he: if he.code == 401:", "HTTPError as he: if he.code == 401: print('token may be", "= yield zoomRefresh(zoom_user) if zoom_user: response, zoom_user = yield zoomGET(endpoint_url,", "'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" } request = HTTPRequest(url, method=\"POST\", headers=headers,", "= AsyncHTTPClient() print(zoom_user) print('making zoomRefresh') print(payload) try: response = yield", "raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers", "tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError from settings import Settings from", "'content-type': \"application/x-www-form-urlencoded\" } request = HTTPRequest(url, method=\"POST\", headers=headers, body=payload) http_client", "= {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request = HTTPRequest(url, method=\"GET\", headers=headers) http_client =", "he.code == 401: print('token may be expired, attempting refresh') zoom_user", "byte string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id, Settings.zoom_client_secret).encode()).decode(\"ascii\") headers = { 'authorization':", "headers=headers) http_client = AsyncHTTPClient() response = None try: response =", "zoom_user:{0}'.format(zoom_user)) except HTTPError as he: print('zoomRefresh HTTPError:') print(he.code) print(he.response.body) if", "AsyncHTTPClient() response = None try: response = yield http_client.fetch(request) body", "\"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request = HTTPRequest(url, method=\"GET\", headers=headers)", "zoom_user = yield zoomGET(endpoint_url, zoom_user) else: try: print(he.response.body) except Exception", "HTTPError from settings import Settings from mongo_db_controller import ZoomUserDB @tornado.gen.coroutine", "#we need to base 64 encode it #and then decode", "except HTTPError as he: print('zoomRefresh HTTPError:') print(he.code) print(he.response.body) if he.code", "response = yield http_client.fetch(request) body = response.body.decode('utf-8') response = json.loads(body)", "= None try: response = yield http_client.fetch(request) body = response.body.decode('utf-8')", "401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user = None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine def", "{0}\".format(zoom_user.get('token'))} request = HTTPRequest(url, method=\"GET\", headers=headers) http_client = AsyncHTTPClient() response", "zoom_user = yield zoomRefresh(zoom_user) if zoom_user: response, zoom_user = yield", "import Settings from mongo_db_controller import ZoomUserDB @tornado.gen.coroutine def zoomRefresh(zoom_user): url", "base64 import b64encode from tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError from", "headers=headers, body=payload) http_client = AsyncHTTPClient() print(zoom_user) print('making zoomRefresh') print(payload) try:", "3 stores it as a byte string userAndPass = b64encode(\"{0}:{1}\".format(Settings.zoom_client_id,", "request = HTTPRequest(url, method=\"GET\", headers=headers) http_client = AsyncHTTPClient() response =", "attempting refresh') zoom_user = yield zoomRefresh(zoom_user) if zoom_user: response, zoom_user", "try: response = yield http_client.fetch(request) resp = json.loads(response.body.decode(\"utf-8\")) print(\"zoomRefresh /access_token", "print('new zoom_user:{0}'.format(zoom_user)) except HTTPError as he: print('zoomRefresh HTTPError:') print(he.code) print(he.response.body)", "= response.body.decode('utf-8') response = json.loads(body) except HTTPError as he: if", "= { 'authorization': 'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" } request =", "he.code == 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user = None raise tornado.gen.Return(zoom_user)", "json.loads(body) except HTTPError as he: if he.code == 401: print('token", "else: try: print(he.response.body) except Exception as e: pass traceback.print_exc() raise", "it to acsii as python 3 stores it as a", "request = HTTPRequest(url, method=\"POST\", headers=headers, body=payload) http_client = AsyncHTTPClient() print(zoom_user)", "from base64 import b64encode from tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError", "HTTPRequest, HTTPError from settings import Settings from mongo_db_controller import ZoomUserDB", "64 encode it #and then decode it to acsii as", "yield zoomRefresh(zoom_user) if zoom_user: response, zoom_user = yield zoomGET(endpoint_url, zoom_user)", "decode it to acsii as python 3 stores it as", "ZoomUserDB.db.insert_user(zoom_user['person_id'], resp['access_token'], resp['expires_in'], resp['refresh_token'], \"zoom\") print('new zoom_user:{0}'.format(zoom_user)) except HTTPError as", "None try: response = yield http_client.fetch(request) body = response.body.decode('utf-8') response", "def zoomGET(endpoint_url, zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))}", "python 3 stores it as a byte string userAndPass =", "== 401: ZoomUserDB.db.delete_user(zoom_user['person_id'], \"zoom\") zoom_user = None raise tornado.gen.Return(zoom_user) @tornado.gen.coroutine", "try: print(he.response.body) except Exception as e: pass traceback.print_exc() raise tornado.gen.Return((response,", "tornado.gen.Return(zoom_user) @tornado.gen.coroutine def zoomGET(endpoint_url, zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers =", "print(zoom_user) print('making zoomRefresh') print(payload) try: response = yield http_client.fetch(request) resp", "= HTTPRequest(url, method=\"POST\", headers=headers, body=payload) http_client = AsyncHTTPClient() print(zoom_user) print('making", "tornado.gen import traceback from base64 import b64encode from tornado.httpclient import", "headers = { 'authorization': 'Basic {0}'.format(userAndPass), 'content-type': \"application/x-www-form-urlencoded\" } request", "zoomGET(endpoint_url, zoom_user): url = \"https://api.zoom.us/v2{0}\".format(endpoint_url) headers = {\"Authorization\":\"Bearer {0}\".format(zoom_user.get('token'))} request", "= \"grant_type=refresh_token&\" payload += \"refresh_token={0}\".format(zoom_user.get('refresh_token')) #we need to base 64" ]
[ "attributes of an mpc encrypted tensor arithmetic = ptype.arithmetic binary", "and its affiliates. # # This source code is licensed", ".context import run_multiprocess from .mpc import MPCTensor from .ptype import", "in the # LICENSE file in the root directory of", "provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, } __default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ]", "of an mpc encrypted tensor arithmetic = ptype.arithmetic binary =", "under the MIT license found in the # LICENSE file", "set_default_provider(new_default_provider): global __default_provider assert_msg = \"Provider %s is not supported\"", "crypten.mpc import primitives # noqa: F401 from crypten.mpc import provider", "= { \"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, } __default_provider", "if isinstance(new_default_provider, str): assert new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg else: assert", "Copyright (c) Facebook, Inc. and its affiliates. # # This", "supported\" % new_default_provider if isinstance(new_default_provider, str): assert new_default_provider in __SUPPORTED_PROVIDERS.keys(),", "import provider # noqa: F40 from .context import run_multiprocess from", "#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates.", "LICENSE file in the root directory of this source tree.", "the root directory of this source tree. import os from", "import os from crypten.mpc import primitives # noqa: F401 from", "primitives # noqa: F401 from crypten.mpc import provider # noqa:", "noqa: F401 from crypten.mpc import provider # noqa: F40 from", "import ptype __all__ = [\"MPCTensor\", \"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"] #", "binary = ptype.binary # Set provider __SUPPORTED_PROVIDERS = { \"TFP\":", "its affiliates. # # This source code is licensed under", "# noqa: F401 from crypten.mpc import provider # noqa: F40", "import run_multiprocess from .mpc import MPCTensor from .ptype import ptype", "is not supported\" % new_default_provider if isinstance(new_default_provider, str): assert new_default_provider", "mpc encrypted tensor arithmetic = ptype.arithmetic binary = ptype.binary #", "= new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def get_default_provider(): return __default_provider def", "{ \"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, } __default_provider =", "provider __SUPPORTED_PROVIDERS = { \"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider,", "new_default_provider if isinstance(new_default_provider, str): assert new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg else:", "\"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, } __default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\")", "ptype __all__ = [\"MPCTensor\", \"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"] # the", "# LICENSE file in the root directory of this source", "F40 from .context import run_multiprocess from .mpc import MPCTensor from", "ptype.arithmetic binary = ptype.binary # Set provider __SUPPORTED_PROVIDERS = {", "the MIT license found in the # LICENSE file in", "os from crypten.mpc import primitives # noqa: F401 from crypten.mpc", "\"TFP\") ] def set_default_provider(new_default_provider): global __default_provider assert_msg = \"Provider %s", "isinstance(new_default_provider, str): assert new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg else: assert new_default_provider", "found in the # LICENSE file in the root directory", "os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def get_default_provider(): return __default_provider def ttp_required(): return", "from .mpc import MPCTensor from .ptype import ptype __all__ =", "__default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def set_default_provider(new_default_provider): global __default_provider", "__SUPPORTED_PROVIDERS.values(), assert_msg __default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def get_default_provider():", "license found in the # LICENSE file in the root", "F401 from crypten.mpc import provider # noqa: F40 from .context", "new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME", ".mpc import MPCTensor from .ptype import ptype __all__ = [\"MPCTensor\",", "def get_default_provider(): return __default_provider def ttp_required(): return __default_provider == provider.TrustedThirdParty", "= [\"MPCTensor\", \"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"] # the different private", "str): assert new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg else: assert new_default_provider in", "import primitives # noqa: F401 from crypten.mpc import provider #", "__SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def set_default_provider(new_default_provider): global __default_provider assert_msg =", "run_multiprocess from .mpc import MPCTensor from .ptype import ptype __all__", "assert_msg __default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def get_default_provider(): return", "os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def set_default_provider(new_default_provider): global __default_provider assert_msg = \"Provider", "import MPCTensor from .ptype import ptype __all__ = [\"MPCTensor\", \"primitives\",", "} __default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def set_default_provider(new_default_provider): global", "def set_default_provider(new_default_provider): global __default_provider assert_msg = \"Provider %s is not", "python3 # Copyright (c) Facebook, Inc. and its affiliates. #", "% new_default_provider if isinstance(new_default_provider, str): assert new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg", "assert new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg else: assert new_default_provider in __SUPPORTED_PROVIDERS.values(),", "noqa: F40 from .context import run_multiprocess from .mpc import MPCTensor", "\"ptype\", \"run_multiprocess\"] # the different private type attributes of an", "\"HE\": provider.HomomorphicProvider, } __default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def", "else: assert new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"]", "<gh_stars>0 #!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its", "(c) Facebook, Inc. and its affiliates. # # This source", "assert new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] =", "is licensed under the MIT license found in the #", "= ptype.binary # Set provider __SUPPORTED_PROVIDERS = { \"TFP\": provider.TrustedFirstParty,", "root directory of this source tree. import os from crypten.mpc", "the # LICENSE file in the root directory of this", "private type attributes of an mpc encrypted tensor arithmetic =", "in the root directory of this source tree. import os", "from .context import run_multiprocess from .mpc import MPCTensor from .ptype", "ptype.binary # Set provider __SUPPORTED_PROVIDERS = { \"TFP\": provider.TrustedFirstParty, \"TTP\":", "directory of this source tree. import os from crypten.mpc import", "this source tree. import os from crypten.mpc import primitives #", "__default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def get_default_provider(): return __default_provider", "new_default_provider in __SUPPORTED_PROVIDERS.keys(), assert_msg else: assert new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg", "assert_msg else: assert new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider = new_default_provider", "# Copyright (c) Facebook, Inc. and its affiliates. # #", "file in the root directory of this source tree. import", "] def set_default_provider(new_default_provider): global __default_provider assert_msg = \"Provider %s is", "in __SUPPORTED_PROVIDERS.keys(), assert_msg else: assert new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider", "= \"Provider %s is not supported\" % new_default_provider if isinstance(new_default_provider,", "different private type attributes of an mpc encrypted tensor arithmetic", "not supported\" % new_default_provider if isinstance(new_default_provider, str): assert new_default_provider in", "This source code is licensed under the MIT license found", "of this source tree. import os from crypten.mpc import primitives", "from crypten.mpc import provider # noqa: F40 from .context import", "assert_msg = \"Provider %s is not supported\" % new_default_provider if", "new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def get_default_provider(): return __default_provider def ttp_required():", "__all__ = [\"MPCTensor\", \"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"] # the different", "\"Provider %s is not supported\" % new_default_provider if isinstance(new_default_provider, str):", "provider # noqa: F40 from .context import run_multiprocess from .mpc", "from .ptype import ptype __all__ = [\"MPCTensor\", \"primitives\", \"provider\", \"ptype\",", "[\"MPCTensor\", \"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"] # the different private type", "__default_provider assert_msg = \"Provider %s is not supported\" % new_default_provider", "encrypted tensor arithmetic = ptype.arithmetic binary = ptype.binary # Set", "an mpc encrypted tensor arithmetic = ptype.arithmetic binary = ptype.binary", "code is licensed under the MIT license found in the", "MPCTensor from .ptype import ptype __all__ = [\"MPCTensor\", \"primitives\", \"provider\",", "= new_default_provider.NAME def get_default_provider(): return __default_provider def ttp_required(): return __default_provider", ".ptype import ptype __all__ = [\"MPCTensor\", \"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"]", "provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, } __default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\",", "type attributes of an mpc encrypted tensor arithmetic = ptype.arithmetic", "source code is licensed under the MIT license found in", "Facebook, Inc. and its affiliates. # # This source code", "licensed under the MIT license found in the # LICENSE", "\"run_multiprocess\"] # the different private type attributes of an mpc", "= ptype.arithmetic binary = ptype.binary # Set provider __SUPPORTED_PROVIDERS =", "tree. import os from crypten.mpc import primitives # noqa: F401", "# # This source code is licensed under the MIT", "# noqa: F40 from .context import run_multiprocess from .mpc import", "MIT license found in the # LICENSE file in the", "affiliates. # # This source code is licensed under the", "Inc. and its affiliates. # # This source code is", "from crypten.mpc import primitives # noqa: F401 from crypten.mpc import", "global __default_provider assert_msg = \"Provider %s is not supported\" %", "# the different private type attributes of an mpc encrypted", "\"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, } __default_provider = __SUPPORTED_PROVIDERS[", "# This source code is licensed under the MIT license", "\"primitives\", \"provider\", \"ptype\", \"run_multiprocess\"] # the different private type attributes", "__SUPPORTED_PROVIDERS.keys(), assert_msg else: assert new_default_provider in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider =", "= __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def set_default_provider(new_default_provider): global __default_provider assert_msg", "new_default_provider.NAME def get_default_provider(): return __default_provider def ttp_required(): return __default_provider ==", "\"provider\", \"ptype\", \"run_multiprocess\"] # the different private type attributes of", "# Set provider __SUPPORTED_PROVIDERS = { \"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty,", "provider.HomomorphicProvider, } __default_provider = __SUPPORTED_PROVIDERS[ os.environ.get(\"CRYPTEN_PROVIDER_NAME\", \"TFP\") ] def set_default_provider(new_default_provider):", "in __SUPPORTED_PROVIDERS.values(), assert_msg __default_provider = new_default_provider os.environ[\"CRYPTEN_PROVIDER_NAME\"] = new_default_provider.NAME def", "the different private type attributes of an mpc encrypted tensor", "__SUPPORTED_PROVIDERS = { \"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\": provider.HomomorphicProvider, }", "%s is not supported\" % new_default_provider if isinstance(new_default_provider, str): assert", "tensor arithmetic = ptype.arithmetic binary = ptype.binary # Set provider", "crypten.mpc import provider # noqa: F40 from .context import run_multiprocess", "Set provider __SUPPORTED_PROVIDERS = { \"TFP\": provider.TrustedFirstParty, \"TTP\": provider.TrustedThirdParty, \"HE\":", "arithmetic = ptype.arithmetic binary = ptype.binary # Set provider __SUPPORTED_PROVIDERS", "source tree. import os from crypten.mpc import primitives # noqa:" ]
[ "'F822', 'UndefinedLocal': 'F823', 'UndefinedName': 'F821', 'UnusedImport': 'F401', 'UnusedVariable': 'F841', }", "generators, nested_scopes, print_function, unicode_literals, with_statement) from pyflakes.checker import Checker as", "be inherited # by flake8. # Code reference is here:", "print_function, unicode_literals, with_statement) from pyflakes.checker import Checker as FlakesChecker from", "so that this can be inherited # by flake8. #", "from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit class FlakeError(Nit): # TODO(wickman) There", "get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding", "from __future__ import (absolute_import, division, generators, nested_scopes, print_function, unicode_literals, with_statement)", "flake8. # Code reference is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = {", "# coding=utf-8 # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).", "this and Flake8 -- consider integrating # checkstyle plug-ins into", "= { 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403', 'LateFutureImport': 'F404',", "CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see", "Checker as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit class FlakeError(Nit):", "here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = { 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed':", "class FlakeError(Nit): # TODO(wickman) There is overlap between this and", "pyflakes package.\"\"\" def nits(self): checker = FlakesChecker(self.python_file.tree, self.python_file.filename) for message", "self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message % flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod def", "'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822', 'UndefinedLocal': 'F823', 'UndefinedName': 'F821',", "tool directly so that this can be inherited # by", "= FlakesChecker(self.python_file.tree, self.python_file.filename) for message in sorted(checker.messages, key=lambda msg: msg.lineno):", "Nit class FlakeError(Nit): # TODO(wickman) There is overlap between this", "def __init__(self, python_file, flake_message): line_range = python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message),", "python_file, flake_message): line_range = python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename,", "flake_message.message % flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod def get_error_code(cls, message): return", "unicode_literals, with_statement) from pyflakes.checker import Checker as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common", "__init__(self, python_file, flake_message): line_range = python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR,", "'UnusedVariable': 'F841', } def __init__(self, python_file, flake_message): line_range = python_file.line_range(flake_message.lineno)", "def nits(self): checker = FlakesChecker(self.python_file.tree, self.python_file.filename) for message in sorted(checker.messages,", "2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the", "pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit class FlakeError(Nit): # TODO(wickman) There is", "for message in sorted(checker.messages, key=lambda msg: msg.lineno): if FlakeError.get_error_code(message) not", "sorted(checker.messages, key=lambda msg: msg.lineno): if FlakeError.get_error_code(message) not in self.options.ignore: yield", "errors via the pyflakes package.\"\"\" def nits(self): checker = FlakesChecker(self.python_file.tree,", "directly so that this can be inherited # by flake8.", "consider integrating # checkstyle plug-ins into the PEP8 tool directly", "'UndefinedName': 'F821', 'UnusedImport': 'F401', 'UnusedVariable': 'F841', } def __init__(self, python_file,", "plug-ins into the PEP8 tool directly so that this can", "this can be inherited # by flake8. # Code reference", "2.0 (see LICENSE). from __future__ import (absolute_import, division, generators, nested_scopes,", "import Checker as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit class", "-- consider integrating # checkstyle plug-ins into the PEP8 tool", "by flake8. # Code reference is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS =", "is overlap between this and Flake8 -- consider integrating #", "python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message % flake_message.message_args, line_range,", "'F823', 'UndefinedName': 'F821', 'UnusedImport': 'F401', 'UnusedVariable': 'F841', } def __init__(self,", "Nit.ERROR, python_file.filename, flake_message.message % flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod def get_error_code(cls,", "and Flake8 -- consider integrating # checkstyle plug-ins into the", "'F404', 'Redefined': 'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822', 'UndefinedLocal':", "under the Apache License, Version 2.0 (see LICENSE). from __future__", "'Redefined': 'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822', 'UndefinedLocal': 'F823',", "message in sorted(checker.messages, key=lambda msg: msg.lineno): if FlakeError.get_error_code(message) not in", "'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822', 'UndefinedLocal': 'F823', 'UndefinedName': 'F821', 'UnusedImport':", "FlakeError(Nit): # TODO(wickman) There is overlap between this and Flake8", "pyflakes.checker import Checker as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit", "Apache License, Version 2.0 (see LICENSE). from __future__ import (absolute_import,", "{ 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403', 'LateFutureImport': 'F404', 'Redefined':", "into the PEP8 tool directly so that this can be", "self.python_file.filename) for message in sorted(checker.messages, key=lambda msg: msg.lineno): if FlakeError.get_error_code(message)", "CLASS_ERRORS = { 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403', 'LateFutureImport':", "# by flake8. # Code reference is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS", "as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit class FlakeError(Nit): #", "package.\"\"\" def nits(self): checker = FlakesChecker(self.python_file.tree, self.python_file.filename) for message in", "'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822', 'UndefinedLocal': 'F823', 'UndefinedName': 'F821', 'UnusedImport': 'F401',", "nits(self): checker = FlakesChecker(self.python_file.tree, self.python_file.filename) for message in sorted(checker.messages, key=lambda", "message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding errors", "contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version", "'F821', 'UnusedImport': 'F401', 'UnusedVariable': 'F841', } def __init__(self, python_file, flake_message):", "integrating # checkstyle plug-ins into the PEP8 tool directly so", "'F811', 'UndefinedExport': 'F822', 'UndefinedLocal': 'F823', 'UndefinedName': 'F821', 'UnusedImport': 'F401', 'UnusedVariable':", "from pyflakes.checker import Checker as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin,", "checkstyle plug-ins into the PEP8 tool directly so that this", "super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message % flake_message.message_args, line_range, python_file.lines[line_range])", "@classmethod def get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect", "nested_scopes, print_function, unicode_literals, with_statement) from pyflakes.checker import Checker as FlakesChecker", "import CheckstylePlugin, Nit class FlakeError(Nit): # TODO(wickman) There is overlap", "(absolute_import, division, generators, nested_scopes, print_function, unicode_literals, with_statement) from pyflakes.checker import", "inherited # by flake8. # Code reference is here: http://flake8.readthedocs.org/en/latest/warnings.html", "FlakesChecker(self.python_file.tree, self.python_file.filename) for message in sorted(checker.messages, key=lambda msg: msg.lineno): if", "= python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message % flake_message.message_args,", "via the pyflakes package.\"\"\" def nits(self): checker = FlakesChecker(self.python_file.tree, self.python_file.filename)", "division, generators, nested_scopes, print_function, unicode_literals, with_statement) from pyflakes.checker import Checker", "reference is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = { 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar':", "'F403', 'LateFutureImport': 'F404', 'Redefined': 'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport':", "Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache", "(see LICENSE). from __future__ import (absolute_import, division, generators, nested_scopes, print_function,", "line_range, python_file.lines[line_range]) @classmethod def get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class", "# Licensed under the Apache License, Version 2.0 (see LICENSE).", "class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding errors via the pyflakes package.\"\"\"", "PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding errors via the pyflakes package.\"\"\" def", "'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding errors via the pyflakes", "python_file.filename, flake_message.message % flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod def get_error_code(cls, message):", "'UndefinedLocal': 'F823', 'UndefinedName': 'F821', 'UnusedImport': 'F401', 'UnusedVariable': 'F841', } def", "return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding errors via", "with_statement) from pyflakes.checker import Checker as FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import", "checker = FlakesChecker(self.python_file.tree, self.python_file.filename) for message in sorted(checker.messages, key=lambda msg:", "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed", "Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under", "PEP8 tool directly so that this can be inherited #", "Flake8 -- consider integrating # checkstyle plug-ins into the PEP8", "can be inherited # by flake8. # Code reference is", "is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = { 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402',", "between this and Flake8 -- consider integrating # checkstyle plug-ins", "'F402', 'ImportStarUsed': 'F403', 'LateFutureImport': 'F404', 'Redefined': 'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused':", "project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License,", "Licensed under the Apache License, Version 2.0 (see LICENSE). from", "(see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0", "key=lambda msg: msg.lineno): if FlakeError.get_error_code(message) not in self.options.ignore: yield FlakeError(self.python_file,", "<reponame>lahosken/pants<filename>contrib/python/src/python/pants/contrib/python/checks/tasks/checkstyle/pyflakes.py<gh_stars>0 # coding=utf-8 # Copyright 2015 Pants project contributors (see", "There is overlap between this and Flake8 -- consider integrating", "coding errors via the pyflakes package.\"\"\" def nits(self): checker =", "common coding errors via the pyflakes package.\"\"\" def nits(self): checker", "# TODO(wickman) There is overlap between this and Flake8 --", "flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod def get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999')", "the PEP8 tool directly so that this can be inherited", "the Apache License, Version 2.0 (see LICENSE). from __future__ import", "Version 2.0 (see LICENSE). from __future__ import (absolute_import, division, generators,", "def get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common", "python_file.lines[line_range]) @classmethod def get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin):", "self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message % flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod", "'LateFutureImport': 'F404', 'Redefined': 'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822',", "'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403', 'LateFutureImport': 'F404', 'Redefined': 'F810', 'RedefinedInListComp':", "# checkstyle plug-ins into the PEP8 tool directly so that", "'F841', } def __init__(self, python_file, flake_message): line_range = python_file.line_range(flake_message.lineno) super(FlakeError,", "'F401', 'UnusedVariable': 'F841', } def __init__(self, python_file, flake_message): line_range =", "'UndefinedExport': 'F822', 'UndefinedLocal': 'F823', 'UndefinedName': 'F821', 'UnusedImport': 'F401', 'UnusedVariable': 'F841',", "% flake_message.message_args, line_range, python_file.lines[line_range]) @classmethod def get_error_code(cls, message): return cls.CLASS_ERRORS.get(message.__class__.__name__,", "'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811', 'UndefinedExport': 'F822', 'UndefinedLocal': 'F823', 'UndefinedName':", "CheckstylePlugin, Nit class FlakeError(Nit): # TODO(wickman) There is overlap between", "'ImportStarUsed': 'F403', 'LateFutureImport': 'F404', 'Redefined': 'F810', 'RedefinedInListComp': 'F812', 'RedefinedWhileUnused': 'F811',", "that this can be inherited # by flake8. # Code", "# Code reference is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = { 'DuplicateArgument':", "flake_message): line_range = python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message", "overlap between this and Flake8 -- consider integrating # checkstyle", "Code reference is here: http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = { 'DuplicateArgument': 'F831',", "in sorted(checker.messages, key=lambda msg: msg.lineno): if FlakeError.get_error_code(message) not in self.options.ignore:", "__future__ import (absolute_import, division, generators, nested_scopes, print_function, unicode_literals, with_statement) from", "line_range = python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__( self.get_error_code(flake_message), Nit.ERROR, python_file.filename, flake_message.message %", "FlakesChecker from pants.contrib.python.checks.tasks.checkstyle.common import CheckstylePlugin, Nit class FlakeError(Nit): # TODO(wickman)", "coding=utf-8 # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). #", "License, Version 2.0 (see LICENSE). from __future__ import (absolute_import, division,", "LICENSE). from __future__ import (absolute_import, division, generators, nested_scopes, print_function, unicode_literals,", "msg: msg.lineno): if FlakeError.get_error_code(message) not in self.options.ignore: yield FlakeError(self.python_file, message)", "cls.CLASS_ERRORS.get(message.__class__.__name__, 'F999') class PyflakesChecker(CheckstylePlugin): \"\"\"Detect common coding errors via the", "'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403', 'LateFutureImport': 'F404', 'Redefined': 'F810', 'RedefinedInListComp': 'F812',", "\"\"\"Detect common coding errors via the pyflakes package.\"\"\" def nits(self):", "import (absolute_import, division, generators, nested_scopes, print_function, unicode_literals, with_statement) from pyflakes.checker", "'UnusedImport': 'F401', 'UnusedVariable': 'F841', } def __init__(self, python_file, flake_message): line_range", "http://flake8.readthedocs.org/en/latest/warnings.html CLASS_ERRORS = { 'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403',", "TODO(wickman) There is overlap between this and Flake8 -- consider", "} def __init__(self, python_file, flake_message): line_range = python_file.line_range(flake_message.lineno) super(FlakeError, self).__init__(", "the pyflakes package.\"\"\" def nits(self): checker = FlakesChecker(self.python_file.tree, self.python_file.filename) for", "'DuplicateArgument': 'F831', 'ImportShadowedByLoopVar': 'F402', 'ImportStarUsed': 'F403', 'LateFutureImport': 'F404', 'Redefined': 'F810'," ]
[ "models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) updated", "True class ProfaneWord(models.Model): word = models.CharField(max_length=60) def __unicode__(self): return self.word", "return u\"%s - %s - %s\" % (self.creator, self.topic, self.title)", "self.topic_set.all(): l = t.last_post() if l: if not last: last", "default=False) def num_posts(self): return self.post_set.count() def num_replies(self): return max(0, self.post_set.count()", "updated = models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True, default=False) def num_posts(self): return", "num_replies(self): return max(0, self.post_set.count() - 1) def last_post(self): if self.post_set.count():", "class Forum(models.Model): title = models.CharField(max_length=60) description = models.TextField(blank=True, default='') updated", "- \" + self.title class Post(models.Model): title = models.CharField(max_length=60) created", "num_posts(self): return sum([t.num_posts() for t in self.topic_set.all()]) def last_post(self): if", "max(0, self.post_set.count() - 1) def last_post(self): if self.post_set.count(): return self.post_set.order_by(\"created\")[0]", "= models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True,", "u\"%s - %s - %s\" % (self.creator, self.topic, self.title) def", "= models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic) body = models.TextField(max_length=10000) user_ip =", "= models.TextField(max_length=10000, blank=True, null=True) forum = models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True)", "- %s\" % (self.creator, self.topic, self.title) def short(self): return u\"%s", "django.db import models from django.contrib.auth.models import User from django.contrib import", "\" + self.title class Post(models.Model): title = models.CharField(max_length=60) created =", "l.created > last.created: last = l return last class Topic(models.Model):", "class Post(models.Model): title = models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True) creator =", "(self.creator, self.title, self.created.strftime(\"%b %d, %I:%M %p\")) short.allow_tags = True class", "= models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True)", "return sum([t.num_posts() for t in self.topic_set.all()]) def last_post(self): if self.topic_set.count():", "not last: last = l elif l.created > last.created: last", "\" - \" + self.title class Post(models.Model): title = models.CharField(max_length=60)", "models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True, default=False) def num_posts(self): return self.post_set.count() def", "__unicode__(self): return unicode(self.creator) + \" - \" + self.title class", "from django.contrib import admin from django.utils.translation import ugettext_lazy as _", "description = models.TextField(max_length=10000, blank=True, null=True) forum = models.ForeignKey(Forum) created =", "title = models.CharField(max_length=60) description = models.TextField(blank=True, default='') updated = models.DateTimeField(auto_now=True)", "= models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User, blank=True, null=True)", "short(self): return u\"%s - %s\\n%s\" % (self.creator, self.title, self.created.strftime(\"%b %d,", "models.ForeignKey(User, blank=True, null=True) def __unicode__(self): return self.title def num_posts(self): return", "last_post(self): if self.post_set.count(): return self.post_set.order_by(\"created\")[0] def __unicode__(self): return unicode(self.creator) +", "if self.post_set.count(): return self.post_set.order_by(\"created\")[0] def __unicode__(self): return unicode(self.creator) + \"", "def __unicode__(self): return u\"%s - %s - %s\" % (self.creator,", "updated = models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True,", "= models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True)", "last = l return last class Topic(models.Model): title = models.CharField(max_length=60)", "= models.CharField(max_length=60) description = models.TextField(blank=True, default='') updated = models.DateTimeField(auto_now=True) created", "= models.TextField(blank=True, default='') updated = models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True) creator", "l = t.last_post() if l: if not last: last =", "last = None for t in self.topic_set.all(): l = t.last_post()", "last: last = l elif l.created > last.created: last =", "for t in self.topic_set.all()]) def last_post(self): if self.topic_set.count(): last =", "+ \" - \" + self.title class Post(models.Model): title =", "self.post_set.count() - 1) def last_post(self): if self.post_set.count(): return self.post_set.order_by(\"created\")[0] def", "null=True) updated = models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic) body = models.TextField(max_length=10000)", "return max(0, self.post_set.count() - 1) def last_post(self): if self.post_set.count(): return", "1) def last_post(self): if self.post_set.count(): return self.post_set.order_by(\"created\")[0] def __unicode__(self): return", "def last_post(self): if self.topic_set.count(): last = None for t in", "= models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) def __unicode__(self): return", "t.last_post() if l: if not last: last = l elif", "self.post_set.order_by(\"created\")[0] def __unicode__(self): return unicode(self.creator) + \" - \" +", "updated = models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic) body = models.TextField(max_length=10000) user_ip", "Topic(models.Model): title = models.CharField(max_length=60) description = models.TextField(max_length=10000, blank=True, null=True) forum", "last.created: last = l return last class Topic(models.Model): title =", "null=True) forum = models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User,", "> last.created: last = l return last class Topic(models.Model): title", "models.BooleanField(blank=True, default=False) def num_posts(self): return self.post_set.count() def num_replies(self): return max(0,", "forum = models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True,", "= True class ProfaneWord(models.Model): word = models.CharField(max_length=60) def __unicode__(self): return", "%I:%M %p\")) short.allow_tags = True class ProfaneWord(models.Model): word = models.CharField(max_length=60)", "models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User, blank=True, null=True) updated", "return self.title def num_posts(self): return sum([t.num_posts() for t in self.topic_set.all()])", "models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic) body = models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True,", "sum([t.num_posts() for t in self.topic_set.all()]) def last_post(self): if self.topic_set.count(): last", "blank=True, null=True) forum = models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True) creator =", "self.post_set.count(): return self.post_set.order_by(\"created\")[0] def __unicode__(self): return unicode(self.creator) + \" -", "models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True, null=True) def __unicode__(self): return u\"%s -", "blank=True, null=True) def __unicode__(self): return self.title def num_posts(self): return sum([t.num_posts()", "models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) def __unicode__(self): return self.title", "self.topic_set.all()]) def last_post(self): if self.topic_set.count(): last = None for t", "= models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True)", "django.utils.translation import ugettext_lazy as _ class Forum(models.Model): title = models.CharField(max_length=60)", "= l return last class Topic(models.Model): title = models.CharField(max_length=60) description", "_ class Forum(models.Model): title = models.CharField(max_length=60) description = models.TextField(blank=True, default='')", "self.title class Post(models.Model): title = models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True) creator", "%s\\n%s\" % (self.creator, self.title, self.created.strftime(\"%b %d, %I:%M %p\")) short.allow_tags =", "short.allow_tags = True class ProfaneWord(models.Model): word = models.CharField(max_length=60) def __unicode__(self):", "self.title def num_posts(self): return sum([t.num_posts() for t in self.topic_set.all()]) def", "admin from django.utils.translation import ugettext_lazy as _ class Forum(models.Model): title", "models.ForeignKey(Topic) body = models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True, null=True) def __unicode__(self):", "__unicode__(self): return u\"%s - %s - %s\" % (self.creator, self.topic,", "blank=True, null=True) updated = models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic) body =", "default='') updated = models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User,", "- %s - %s\" % (self.creator, self.topic, self.title) def short(self):", "None for t in self.topic_set.all(): l = t.last_post() if l:", "import models from django.contrib.auth.models import User from django.contrib import admin", "creator = models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) closed =", "closed = models.BooleanField(blank=True, default=False) def num_posts(self): return self.post_set.count() def num_replies(self):", "body = models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True, null=True) def __unicode__(self): return", "in self.topic_set.all(): l = t.last_post() if l: if not last:", "null=True) updated = models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True, default=False) def num_posts(self):", "created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) updated =", "= models.CharField(max_length=60) description = models.TextField(max_length=10000, blank=True, null=True) forum = models.ForeignKey(Forum)", "% (self.creator, self.topic, self.title) def short(self): return u\"%s - %s\\n%s\"", "User from django.contrib import admin from django.utils.translation import ugettext_lazy as", "u\"%s - %s\\n%s\" % (self.creator, self.title, self.created.strftime(\"%b %d, %I:%M %p\"))", "%p\")) short.allow_tags = True class ProfaneWord(models.Model): word = models.CharField(max_length=60) def", "Post(models.Model): title = models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User,", "last_post(self): if self.topic_set.count(): last = None for t in self.topic_set.all():", "models.GenericIPAddressField(blank=True, null=True) def __unicode__(self): return u\"%s - %s - %s\"", "def short(self): return u\"%s - %s\\n%s\" % (self.creator, self.title, self.created.strftime(\"%b", "null=True) def __unicode__(self): return u\"%s - %s - %s\" %", "if l: if not last: last = l elif l.created", "%s\" % (self.creator, self.topic, self.title) def short(self): return u\"%s -", "Forum(models.Model): title = models.CharField(max_length=60) description = models.TextField(blank=True, default='') updated =", "last class Topic(models.Model): title = models.CharField(max_length=60) description = models.TextField(max_length=10000, blank=True,", "user_ip = models.GenericIPAddressField(blank=True, null=True) def __unicode__(self): return u\"%s - %s", "= models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic)", "django.contrib.auth.models import User from django.contrib import admin from django.utils.translation import", "self.topic, self.title) def short(self): return u\"%s - %s\\n%s\" % (self.creator,", "ugettext_lazy as _ class Forum(models.Model): title = models.CharField(max_length=60) description =", "creator = models.ForeignKey(User, blank=True, null=True) def __unicode__(self): return self.title def", "def num_replies(self): return max(0, self.post_set.count() - 1) def last_post(self): if", "models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) def", "models from django.contrib.auth.models import User from django.contrib import admin from", "title = models.CharField(max_length=60) description = models.TextField(max_length=10000, blank=True, null=True) forum =", "class Topic(models.Model): title = models.CharField(max_length=60) description = models.TextField(max_length=10000, blank=True, null=True)", "return self.post_set.order_by(\"created\")[0] def __unicode__(self): return unicode(self.creator) + \" - \"", "models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) closed", "= models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True)", "= t.last_post() if l: if not last: last = l", "<gh_stars>0 from django.db import models from django.contrib.auth.models import User from", "models.CharField(max_length=60) description = models.TextField(max_length=10000, blank=True, null=True) forum = models.ForeignKey(Forum) created", "description = models.TextField(blank=True, default='') updated = models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True)", "- %s\\n%s\" % (self.creator, self.title, self.created.strftime(\"%b %d, %I:%M %p\")) short.allow_tags", "= None for t in self.topic_set.all(): l = t.last_post() if", "(self.creator, self.topic, self.title) def short(self): return u\"%s - %s\\n%s\" %", "= l elif l.created > last.created: last = l return", "import ugettext_lazy as _ class Forum(models.Model): title = models.CharField(max_length=60) description", "unicode(self.creator) + \" - \" + self.title class Post(models.Model): title", "in self.topic_set.all()]) def last_post(self): if self.topic_set.count(): last = None for", "django.contrib import admin from django.utils.translation import ugettext_lazy as _ class", "def last_post(self): if self.post_set.count(): return self.post_set.order_by(\"created\")[0] def __unicode__(self): return unicode(self.creator)", "from django.contrib.auth.models import User from django.contrib import admin from django.utils.translation", "self.topic_set.count(): last = None for t in self.topic_set.all(): l =", "= models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True, default=False) def num_posts(self): return self.post_set.count()", "def __unicode__(self): return unicode(self.creator) + \" - \" + self.title", "self.title, self.created.strftime(\"%b %d, %I:%M %p\")) short.allow_tags = True class ProfaneWord(models.Model):", "- 1) def last_post(self): if self.post_set.count(): return self.post_set.order_by(\"created\")[0] def __unicode__(self):", "__unicode__(self): return self.title def num_posts(self): return sum([t.num_posts() for t in", "models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True, default=False)", "topic = models.ForeignKey(Topic) body = models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True, null=True)", "null=True) def __unicode__(self): return self.title def num_posts(self): return sum([t.num_posts() for", "self.created.strftime(\"%b %d, %I:%M %p\")) short.allow_tags = True class ProfaneWord(models.Model): word", "%d, %I:%M %p\")) short.allow_tags = True class ProfaneWord(models.Model): word =", "import User from django.contrib import admin from django.utils.translation import ugettext_lazy", "import admin from django.utils.translation import ugettext_lazy as _ class Forum(models.Model):", "as _ class Forum(models.Model): title = models.CharField(max_length=60) description = models.TextField(blank=True,", "return self.post_set.count() def num_replies(self): return max(0, self.post_set.count() - 1) def", "self.title) def short(self): return u\"%s - %s\\n%s\" % (self.creator, self.title,", "t in self.topic_set.all()]) def last_post(self): if self.topic_set.count(): last = None", "= models.BooleanField(blank=True, default=False) def num_posts(self): return self.post_set.count() def num_replies(self): return", "models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) topic", "created = models.DateTimeField(auto_now=True) creator = models.ForeignKey(User, blank=True, null=True) def __unicode__(self):", "models.TextField(blank=True, default='') updated = models.DateTimeField(auto_now=True) created = models.DateTimeField(auto_now=True) creator =", "title = models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User, blank=True,", "for t in self.topic_set.all(): l = t.last_post() if l: if", "if self.topic_set.count(): last = None for t in self.topic_set.all(): l", "= models.GenericIPAddressField(blank=True, null=True) def __unicode__(self): return u\"%s - %s -", "from django.utils.translation import ugettext_lazy as _ class Forum(models.Model): title =", "= models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True, null=True) def __unicode__(self): return u\"%s", "self.post_set.count() def num_replies(self): return max(0, self.post_set.count() - 1) def last_post(self):", "def num_posts(self): return sum([t.num_posts() for t in self.topic_set.all()]) def last_post(self):", "if not last: last = l elif l.created > last.created:", "return last class Topic(models.Model): title = models.CharField(max_length=60) description = models.TextField(max_length=10000,", "models.TextField(max_length=10000, blank=True, null=True) forum = models.ForeignKey(Forum) created = models.DateTimeField(auto_now=True) creator", "blank=True, null=True) updated = models.DateTimeField(auto_now=True) closed = models.BooleanField(blank=True, default=False) def", "def __unicode__(self): return self.title def num_posts(self): return sum([t.num_posts() for t", "l return last class Topic(models.Model): title = models.CharField(max_length=60) description =", "models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) topic = models.ForeignKey(Topic) body", "elif l.created > last.created: last = l return last class", "last = l elif l.created > last.created: last = l", "num_posts(self): return self.post_set.count() def num_replies(self): return max(0, self.post_set.count() - 1)", "creator = models.ForeignKey(User, blank=True, null=True) updated = models.DateTimeField(auto_now=True) topic =", "def num_posts(self): return self.post_set.count() def num_replies(self): return max(0, self.post_set.count() -", "% (self.creator, self.title, self.created.strftime(\"%b %d, %I:%M %p\")) short.allow_tags = True", "l: if not last: last = l elif l.created >", "return u\"%s - %s\\n%s\" % (self.creator, self.title, self.created.strftime(\"%b %d, %I:%M", "models.CharField(max_length=60) description = models.TextField(blank=True, default='') updated = models.DateTimeField(auto_now=True) created =", "%s - %s\" % (self.creator, self.topic, self.title) def short(self): return", "l elif l.created > last.created: last = l return last", "= models.ForeignKey(Topic) body = models.TextField(max_length=10000) user_ip = models.GenericIPAddressField(blank=True, null=True) def", "return unicode(self.creator) + \" - \" + self.title class Post(models.Model):", "from django.db import models from django.contrib.auth.models import User from django.contrib", "t in self.topic_set.all(): l = t.last_post() if l: if not", "created = models.DateTimeField(auto_now_add=True) creator = models.ForeignKey(User, blank=True, null=True) updated =", "+ self.title class Post(models.Model): title = models.CharField(max_length=60) created = models.DateTimeField(auto_now_add=True)", "= models.ForeignKey(User, blank=True, null=True) def __unicode__(self): return self.title def num_posts(self):" ]
[ "explorer') run('docker-compose --project-name private-tangle logs -f --tail 100 coordinator explorer')", "@task def down(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle down -v')", "logs(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail 100')", "cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle pull') run('docker-compose --project-name private-tangle", "private-tangle stop') @task def stop_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle", "run('docker-compose --project-name private-tangle logs -f --tail 100 coordinator explorer') @task", "cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop coordinator') @task def down(): with", "and delete database down() time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/') # restart", "def logs_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail", "with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle down -v') @task def logs():", "100 coordinator') @task def logs_all(): with cd('/srv/private-tangle'): run('docker-compose logs -f')", "local, sudo env.use_ssh_config = True env.hosts = ['iota_node'] @task(default=True) def", "@task(default=True) def iri(): run('mkdir -p /srv/private-tangle/') with cd('/srv/private-tangle'): put('.', '.')", "['iota_node'] @task(default=True) def iri(): run('mkdir -p /srv/private-tangle/') with cd('/srv/private-tangle'): put('.',", "@task def reset(): # stop services and delete database down()", "'.') run('docker-compose --project-name private-tangle pull') run('docker-compose --project-name private-tangle up -d", "@task def logs_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f", "--project-name private-tangle pull') run('docker-compose --project-name private-tangle up -d --force-recreate iri')", "cd('/srv/private-tangle'): run('docker-compose logs -f') @task def reset(): # stop services", "-p /srv/private-tangle/') with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle pull')", "--force-recreate coordinator explorer') run('docker-compose --project-name private-tangle logs -f --tail 100", "-d --force-recreate iri') @task def tools(): with cd('/srv/private-tangle'): put('.', '.')", "run('docker-compose --project-name private-tangle logs -f --tail 100 coordinator') @task def", "stop') @task def stop_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop", "private-tangle logs -f --tail 100 coordinator') @task def logs_all(): with", "--project-name private-tangle pull') run('docker-compose --project-name private-tangle up -d --no-deps --force-recreate", "with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle pull') run('docker-compose --project-name", "coordinator') @task def down(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle down", "-f') @task def reset(): # stop services and delete database", "up -d --force-recreate iri') @task def tools(): with cd('/srv/private-tangle'): put('.',", "private-tangle stop coordinator') @task def down(): with cd('/srv/private-tangle'): run('docker-compose --project-name", "run('docker-compose --project-name private-tangle pull') run('docker-compose --project-name private-tangle up -d --no-deps", "iri(): run('mkdir -p /srv/private-tangle/') with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name", "database down() time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/') # restart all services", "import time from fabric.api import run, env, task, put, cd,", "--project-name private-tangle up -d --force-recreate iri') @task def tools(): with", "env, task, put, cd, local, sudo env.use_ssh_config = True env.hosts", "run('rm -rf /srv/private-tangle/testnet_db/') # restart all services iri() time.sleep(5) tools()", "task, put, cd, local, sudo env.use_ssh_config = True env.hosts =", "--project-name private-tangle up -d --no-deps --force-recreate coordinator explorer') run('docker-compose --project-name", "with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail 100') @task", "cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail 100') @task def", "--no-deps --force-recreate coordinator explorer') run('docker-compose --project-name private-tangle logs -f --tail", "env.hosts = ['iota_node'] @task(default=True) def iri(): run('mkdir -p /srv/private-tangle/') with", "logs_all(): with cd('/srv/private-tangle'): run('docker-compose logs -f') @task def reset(): #", "down() time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/') # restart all services iri()", "= ['iota_node'] @task(default=True) def iri(): run('mkdir -p /srv/private-tangle/') with cd('/srv/private-tangle'):", "time from fabric.api import run, env, task, put, cd, local,", "stop services and delete database down() time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/')", "logs -f') @task def reset(): # stop services and delete", "run('docker-compose --project-name private-tangle logs -f --tail 100') @task def logs_coord():", "cd, local, sudo env.use_ssh_config = True env.hosts = ['iota_node'] @task(default=True)", "--project-name private-tangle logs -f --tail 100') @task def logs_coord(): with", "env.use_ssh_config = True env.hosts = ['iota_node'] @task(default=True) def iri(): run('mkdir", "--tail 100') @task def logs_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle", "--project-name private-tangle stop coordinator') @task def down(): with cd('/srv/private-tangle'): run('docker-compose", "private-tangle logs -f --tail 100 coordinator explorer') @task def stop():", "run('docker-compose --project-name private-tangle pull') run('docker-compose --project-name private-tangle up -d --force-recreate", "down(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle down -v') @task def", "private-tangle up -d --no-deps --force-recreate coordinator explorer') run('docker-compose --project-name private-tangle", "@task def stop_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop coordinator')", "from fabric.api import run, env, task, put, cd, local, sudo", "reset(): # stop services and delete database down() time.sleep(1) run('rm", "cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop') @task def stop_coord(): with cd('/srv/private-tangle'):", "fabric.api import run, env, task, put, cd, local, sudo env.use_ssh_config", "-f --tail 100') @task def logs_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name", "/srv/private-tangle/') with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle pull') run('docker-compose", "-v') @task def logs(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs", "--project-name private-tangle down -v') @task def logs(): with cd('/srv/private-tangle'): run('docker-compose", "with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop') @task def stop_coord(): with", "private-tangle down -v') @task def logs(): with cd('/srv/private-tangle'): run('docker-compose --project-name", "@task def logs(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f", "put('.', '.') run('docker-compose --project-name private-tangle pull') run('docker-compose --project-name private-tangle up", "stop coordinator') @task def down(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle", "# stop services and delete database down() time.sleep(1) run('rm -rf", "with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop coordinator') @task def down():", "def reset(): # stop services and delete database down() time.sleep(1)", "with cd('/srv/private-tangle'): run('docker-compose logs -f') @task def reset(): # stop", "run('docker-compose --project-name private-tangle down -v') @task def logs(): with cd('/srv/private-tangle'):", "def down(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle down -v') @task", "logs -f --tail 100 coordinator explorer') @task def stop(): with", "private-tangle up -d --force-recreate iri') @task def tools(): with cd('/srv/private-tangle'):", "run('docker-compose --project-name private-tangle up -d --no-deps --force-recreate coordinator explorer') run('docker-compose", "def logs_all(): with cd('/srv/private-tangle'): run('docker-compose logs -f') @task def reset():", "sudo env.use_ssh_config = True env.hosts = ['iota_node'] @task(default=True) def iri():", "@task def logs_all(): with cd('/srv/private-tangle'): run('docker-compose logs -f') @task def", "time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/') # restart all services iri() time.sleep(5)", "coordinator') @task def logs_all(): with cd('/srv/private-tangle'): run('docker-compose logs -f') @task", "run, env, task, put, cd, local, sudo env.use_ssh_config = True", "stop_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop coordinator') @task def", "pull') run('docker-compose --project-name private-tangle up -d --no-deps --force-recreate coordinator explorer')", "--project-name private-tangle logs -f --tail 100 coordinator explorer') @task def", "100 coordinator explorer') @task def stop(): with cd('/srv/private-tangle'): run('docker-compose --project-name", "= True env.hosts = ['iota_node'] @task(default=True) def iri(): run('mkdir -p", "with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail 100 coordinator')", "private-tangle logs -f --tail 100') @task def logs_coord(): with cd('/srv/private-tangle'):", "--project-name private-tangle stop') @task def stop_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name", "coordinator explorer') run('docker-compose --project-name private-tangle logs -f --tail 100 coordinator", "services and delete database down() time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/') #", "run('docker-compose logs -f') @task def reset(): # stop services and", "put, cd, local, sudo env.use_ssh_config = True env.hosts = ['iota_node']", "<gh_stars>1-10 import time from fabric.api import run, env, task, put,", "up -d --no-deps --force-recreate coordinator explorer') run('docker-compose --project-name private-tangle logs", "tools(): with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle pull') run('docker-compose", "coordinator explorer') @task def stop(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle", "def iri(): run('mkdir -p /srv/private-tangle/') with cd('/srv/private-tangle'): put('.', '.') run('docker-compose", "-f --tail 100 coordinator') @task def logs_all(): with cd('/srv/private-tangle'): run('docker-compose", "def stop(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop') @task def", "def tools(): with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle pull')", "True env.hosts = ['iota_node'] @task(default=True) def iri(): run('mkdir -p /srv/private-tangle/')", "private-tangle pull') run('docker-compose --project-name private-tangle up -d --force-recreate iri') @task", "delete database down() time.sleep(1) run('rm -rf /srv/private-tangle/testnet_db/') # restart all", "run('mkdir -p /srv/private-tangle/') with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle", "cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail 100 coordinator') @task", "@task def stop(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop') @task", "--force-recreate iri') @task def tools(): with cd('/srv/private-tangle'): put('.', '.') run('docker-compose", "run('docker-compose --project-name private-tangle stop') @task def stop_coord(): with cd('/srv/private-tangle'): run('docker-compose", "def stop_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop coordinator') @task", "cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle down -v') @task def logs(): with", "def logs(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail", "logs -f --tail 100 coordinator') @task def logs_all(): with cd('/srv/private-tangle'):", "--tail 100 coordinator') @task def logs_all(): with cd('/srv/private-tangle'): run('docker-compose logs", "iri') @task def tools(): with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name", "logs -f --tail 100') @task def logs_coord(): with cd('/srv/private-tangle'): run('docker-compose", "import run, env, task, put, cd, local, sudo env.use_ssh_config =", "private-tangle pull') run('docker-compose --project-name private-tangle up -d --no-deps --force-recreate coordinator", "-f --tail 100 coordinator explorer') @task def stop(): with cd('/srv/private-tangle'):", "pull') run('docker-compose --project-name private-tangle up -d --force-recreate iri') @task def", "--tail 100 coordinator explorer') @task def stop(): with cd('/srv/private-tangle'): run('docker-compose", "@task def tools(): with cd('/srv/private-tangle'): put('.', '.') run('docker-compose --project-name private-tangle", "logs_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs -f --tail 100", "explorer') @task def stop(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop')", "down -v') @task def logs(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle", "100') @task def logs_coord(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle logs", "run('docker-compose --project-name private-tangle up -d --force-recreate iri') @task def tools():", "--project-name private-tangle logs -f --tail 100 coordinator') @task def logs_all():", "-d --no-deps --force-recreate coordinator explorer') run('docker-compose --project-name private-tangle logs -f", "stop(): with cd('/srv/private-tangle'): run('docker-compose --project-name private-tangle stop') @task def stop_coord():", "run('docker-compose --project-name private-tangle stop coordinator') @task def down(): with cd('/srv/private-tangle'):" ]
[ "arrAncho[j] negAlto = arrAlto[i] rand_alto_max = int(negAlto * 1.5) rand_alto_min", "altoMax = resultado[0] c.execute('SELECT MIN(alto) FROM features') resultado = c.fetchone()", "i in range(0,3): for j in range(0,5): for _ in", "abs((anchoMin + anchoProm) / 2) arrAncho = [anchoMax, anchoMaxProm, anchoProm,", ", altoProm , altoMin] c.execute('SELECT MAX(ancho) FROM features') resultado =", "[anchoMax, anchoMaxProm, anchoProm, anchoMinProm, anchoMin] #### CREANDO CLASES NEGATIVAS for", "features (ancho, alto, area, clase) values (?, ?, ?, ?)\",", "CREANDO CLASES NEGATIVAS for i in range(0,3): for j in", "= c.fetchone() if resultado: altoMin = resultado[0] altoProm = abs((altoMax", "+ anchoProm) / 2) anchoMinProm = abs((anchoMin + anchoProm) /", "anchoMaxProm, anchoProm, anchoMinProm, anchoMin] #### CREANDO CLASES NEGATIVAS for i", "= abs((anchoMax + anchoProm) / 2) anchoMinProm = abs((anchoMin +", "alto, area, clase) values (?, ?, ?, ?)\", (f2, f1,", "= abs((anchoMax + anchoMin) / 2) anchoMaxProm = abs((anchoMax +", ", altoMin] c.execute('SELECT MAX(ancho) FROM features') resultado = c.fetchone() if", "2) #print altoMax , altoProm , altoMin arrAlto = [altoMax", "[altoMax , altoProm , altoMin] c.execute('SELECT MAX(ancho) FROM features') resultado", "(ancho, alto, area, clase) values (?, ?, ?, ?)\", (f2,", "c.execute('SELECT MAX(alto) FROM features') resultado = c.fetchone() if resultado: altoMax", "resultado: altoMin = resultado[0] altoProm = abs((altoMax + altoMin) /", "range(10): negAncho = arrAncho[j] negAlto = arrAlto[i] rand_alto_max = int(negAlto", "c.execute('SELECT MIN(alto) FROM features') resultado = c.fetchone() if resultado: altoMin", "in range(10): negAncho = arrAncho[j] negAlto = arrAlto[i] rand_alto_max =", "/ 2) #print altoMax , altoProm , altoMin arrAlto =", "/ 2) anchoMinProm = abs((anchoMin + anchoProm) / 2) arrAncho", "FROM features') resultado = c.fetchone() if resultado: anchoMin = resultado[0]", "in range(0,5): for _ in range(10): negAncho = arrAncho[j] negAlto", "#OBTENIENDO TAMAnOS MAXIMOS MINIMOS Y PROMEDIO# c.execute('SELECT MAX(alto) FROM features')", "rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2 = choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert", "= arrAncho[j] negAlto = arrAlto[i] rand_alto_max = int(negAlto * 1.5)", "arrAlto[i] rand_alto_max = int(negAlto * 1.5) rand_alto_min = int(negAlto *", "= choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into features (ancho, alto,", "resultado: altoMax = resultado[0] c.execute('SELECT MIN(alto) FROM features') resultado =", "features') resultado = c.fetchone() if resultado: anchoMin = resultado[0] anchoProm", "if resultado: anchoMax = resultado[0] c.execute('SELECT MIN(ancho) FROM features') resultado", "int(negAlto * 1.5) rand_alto_min = int(negAlto * 0.5) r3 =", "/ 2) arrAncho = [anchoMax, anchoMaxProm, anchoProm, anchoMinProm, anchoMin] ####", "= int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5) r33 = rand_ancho_max * 2", "= rand_alto_max * 2 rand_ancho_max = int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5)", "rand_ancho_min = int(negAncho*0.5) r33 = rand_ancho_max * 2 f1 =", "range(0,5): for _ in range(10): negAncho = arrAncho[j] negAlto =", "rand_alto_min = int(negAlto * 0.5) r3 = rand_alto_max * 2", "choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into features (ancho, alto, area,", "0.5) r3 = rand_alto_max * 2 rand_ancho_max = int(negAncho*1.5) rand_ancho_min", "= resultado[0] altoProm = abs((altoMax + altoMin) / 2) #print", "FROM features') resultado = c.fetchone() if resultado: anchoMax = resultado[0]", "2) anchoMinProm = abs((anchoMin + anchoProm) / 2) arrAncho =", "#### CREANDO CLASES NEGATIVAS for i in range(0,3): for j", "conn = sqlite3.connect('ej.db') c = conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS MINIMOS", "PROMEDIO# c.execute('SELECT MAX(alto) FROM features') resultado = c.fetchone() if resultado:", "np conn = sqlite3.connect('ej.db') c = conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS", "MAX(ancho) FROM features') resultado = c.fetchone() if resultado: anchoMax =", "int(negAncho*0.5) r33 = rand_ancho_max * 2 f1 = choice([np.random.randint(1, rand_alto_min),", "rand_alto_max = int(negAlto * 1.5) rand_alto_min = int(negAlto * 0.5)", "range(0,3): for j in range(0,5): for _ in range(10): negAncho", "= c.fetchone() if resultado: altoMax = resultado[0] c.execute('SELECT MIN(alto) FROM", "r33)]) c.execute(\"insert into features (ancho, alto, area, clase) values (?,", "c = conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS MINIMOS Y PROMEDIO# c.execute('SELECT", "altoMin arrAlto = [altoMax , altoProm , altoMin] c.execute('SELECT MAX(ancho)", "resultado[0] altoProm = abs((altoMax + altoMin) / 2) #print altoMax", "conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS MINIMOS Y PROMEDIO# c.execute('SELECT MAX(alto) FROM", "import randint, choice import numpy as np conn = sqlite3.connect('ej.db')", "* 2 f1 = choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2 =", "from random import randint, choice import numpy as np conn", "= choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2 = choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max,", "r3)]) f2 = choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into features", "resultado[0] c.execute('SELECT MIN(ancho) FROM features') resultado = c.fetchone() if resultado:", "c.execute('SELECT MAX(ancho) FROM features') resultado = c.fetchone() if resultado: anchoMax", "features') resultado = c.fetchone() if resultado: altoMin = resultado[0] altoProm", "c.fetchone() if resultado: anchoMin = resultado[0] anchoProm = abs((anchoMax +", "if resultado: altoMin = resultado[0] altoProm = abs((altoMax + altoMin)", "* 0.5) r3 = rand_alto_max * 2 rand_ancho_max = int(negAncho*1.5)", "anchoMin = resultado[0] anchoProm = abs((anchoMax + anchoMin) / 2)", "c.execute('SELECT MIN(ancho) FROM features') resultado = c.fetchone() if resultado: anchoMin", "np.random.randint(rand_alto_max, r3)]) f2 = choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into", "negAlto = arrAlto[i] rand_alto_max = int(negAlto * 1.5) rand_alto_min =", ", altoProm , altoMin arrAlto = [altoMax , altoProm ,", "f1 = choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2 = choice([np.random.randint(1, rand_ancho_min),", "into features (ancho, alto, area, clase) values (?, ?, ?,", "1.5) rand_alto_min = int(negAlto * 0.5) r3 = rand_alto_max *", "abs((altoMax + altoMin) / 2) #print altoMax , altoProm ,", "for j in range(0,5): for _ in range(10): negAncho =", "for _ in range(10): negAncho = arrAncho[j] negAlto = arrAlto[i]", "f2 = choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into features (ancho,", "randint, choice import numpy as np conn = sqlite3.connect('ej.db') c", "resultado: anchoMax = resultado[0] c.execute('SELECT MIN(ancho) FROM features') resultado =", "altoProm = abs((altoMax + altoMin) / 2) #print altoMax ,", "resultado: anchoMin = resultado[0] anchoProm = abs((anchoMax + anchoMin) /", "TAMAnOS MAXIMOS MINIMOS Y PROMEDIO# c.execute('SELECT MAX(alto) FROM features') resultado", "arrAncho = [anchoMax, anchoMaxProm, anchoProm, anchoMinProm, anchoMin] #### CREANDO CLASES", "numpy as np conn = sqlite3.connect('ej.db') c = conn.cursor() #OBTENIENDO", "FROM features') resultado = c.fetchone() if resultado: altoMax = resultado[0]", "int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5) r33 = rand_ancho_max * 2 f1", "MAX(alto) FROM features') resultado = c.fetchone() if resultado: altoMax =", "NEGATIVAS for i in range(0,3): for j in range(0,5): for", "anchoMinProm, anchoMin] #### CREANDO CLASES NEGATIVAS for i in range(0,3):", "anchoMinProm = abs((anchoMin + anchoProm) / 2) arrAncho = [anchoMax,", "_ in range(10): negAncho = arrAncho[j] negAlto = arrAlto[i] rand_alto_max", "= abs((anchoMin + anchoProm) / 2) arrAncho = [anchoMax, anchoMaxProm,", "negAncho = arrAncho[j] negAlto = arrAlto[i] rand_alto_max = int(negAlto *", "= int(negAncho*0.5) r33 = rand_ancho_max * 2 f1 = choice([np.random.randint(1,", "abs((anchoMax + anchoMin) / 2) anchoMaxProm = abs((anchoMax + anchoProm)", "= arrAlto[i] rand_alto_max = int(negAlto * 1.5) rand_alto_min = int(negAlto", "MINIMOS Y PROMEDIO# c.execute('SELECT MAX(alto) FROM features') resultado = c.fetchone()", "= conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS MINIMOS Y PROMEDIO# c.execute('SELECT MAX(alto)", "if resultado: altoMax = resultado[0] c.execute('SELECT MIN(alto) FROM features') resultado", "= int(negAlto * 1.5) rand_alto_min = int(negAlto * 0.5) r3", "= rand_ancho_max * 2 f1 = choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)])", "values (?, ?, ?, ?)\", (f2, f1, f2*f1, 0)) conn.commit()", "anchoProm, anchoMinProm, anchoMin] #### CREANDO CLASES NEGATIVAS for i in", "anchoMin] #### CREANDO CLASES NEGATIVAS for i in range(0,3): for", "if resultado: anchoMin = resultado[0] anchoProm = abs((anchoMax + anchoMin)", "clase) values (?, ?, ?, ?)\", (f2, f1, f2*f1, 0))", "= sqlite3.connect('ej.db') c = conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS MINIMOS Y", "rand_alto_max * 2 rand_ancho_max = int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5) r33", "sqlite3.connect('ej.db') c = conn.cursor() #OBTENIENDO TAMAnOS MAXIMOS MINIMOS Y PROMEDIO#", "FROM features') resultado = c.fetchone() if resultado: altoMin = resultado[0]", "= [altoMax , altoProm , altoMin] c.execute('SELECT MAX(ancho) FROM features')", "altoMin] c.execute('SELECT MAX(ancho) FROM features') resultado = c.fetchone() if resultado:", "np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into features (ancho, alto, area, clase) values", "= [anchoMax, anchoMaxProm, anchoProm, anchoMinProm, anchoMin] #### CREANDO CLASES NEGATIVAS", "* 1.5) rand_alto_min = int(negAlto * 0.5) r3 = rand_alto_max", "= resultado[0] anchoProm = abs((anchoMax + anchoMin) / 2) anchoMaxProm", "anchoProm) / 2) anchoMinProm = abs((anchoMin + anchoProm) / 2)", "2) arrAncho = [anchoMax, anchoMaxProm, anchoProm, anchoMinProm, anchoMin] #### CREANDO", "resultado = c.fetchone() if resultado: altoMax = resultado[0] c.execute('SELECT MIN(alto)", "abs((anchoMax + anchoProm) / 2) anchoMinProm = abs((anchoMin + anchoProm)", "features') resultado = c.fetchone() if resultado: altoMax = resultado[0] c.execute('SELECT", "CLASES NEGATIVAS for i in range(0,3): for j in range(0,5):", "* 2 rand_ancho_max = int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5) r33 =", "MIN(alto) FROM features') resultado = c.fetchone() if resultado: altoMin =", "anchoProm = abs((anchoMax + anchoMin) / 2) anchoMaxProm = abs((anchoMax", "r33 = rand_ancho_max * 2 f1 = choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max,", "MAXIMOS MINIMOS Y PROMEDIO# c.execute('SELECT MAX(alto) FROM features') resultado =", "= resultado[0] c.execute('SELECT MIN(alto) FROM features') resultado = c.fetchone() if", "import numpy as np conn = sqlite3.connect('ej.db') c = conn.cursor()", "c.fetchone() if resultado: altoMin = resultado[0] altoProm = abs((altoMax +", "#print altoMax , altoProm , altoMin arrAlto = [altoMax ,", "2) anchoMaxProm = abs((anchoMax + anchoProm) / 2) anchoMinProm =", "resultado = c.fetchone() if resultado: altoMin = resultado[0] altoProm =", "2 rand_ancho_max = int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5) r33 = rand_ancho_max", "import sqlite3 from random import randint, choice import numpy as", "random import randint, choice import numpy as np conn =", "= int(negAlto * 0.5) r3 = rand_alto_max * 2 rand_ancho_max", "anchoProm) / 2) arrAncho = [anchoMax, anchoMaxProm, anchoProm, anchoMinProm, anchoMin]", "area, clase) values (?, ?, ?, ?)\", (f2, f1, f2*f1,", "resultado[0] anchoProm = abs((anchoMax + anchoMin) / 2) anchoMaxProm =", "features') resultado = c.fetchone() if resultado: anchoMax = resultado[0] c.execute('SELECT", "altoMin = resultado[0] altoProm = abs((altoMax + altoMin) / 2)", "resultado[0] c.execute('SELECT MIN(alto) FROM features') resultado = c.fetchone() if resultado:", "rand_ancho_min), np.random.randint(rand_ancho_max, r33)]) c.execute(\"insert into features (ancho, alto, area, clase)", "Y PROMEDIO# c.execute('SELECT MAX(alto) FROM features') resultado = c.fetchone() if", "+ anchoMin) / 2) anchoMaxProm = abs((anchoMax + anchoProm) /", "altoProm , altoMin] c.execute('SELECT MAX(ancho) FROM features') resultado = c.fetchone()", "+ anchoProm) / 2) arrAncho = [anchoMax, anchoMaxProm, anchoProm, anchoMinProm,", "altoProm , altoMin arrAlto = [altoMax , altoProm , altoMin]", "anchoMin) / 2) anchoMaxProm = abs((anchoMax + anchoProm) / 2)", "MIN(ancho) FROM features') resultado = c.fetchone() if resultado: anchoMin =", ", altoMin arrAlto = [altoMax , altoProm , altoMin] c.execute('SELECT", "for i in range(0,3): for j in range(0,5): for _", "int(negAlto * 0.5) r3 = rand_alto_max * 2 rand_ancho_max =", "rand_ancho_max = int(negAncho*1.5) rand_ancho_min = int(negAncho*0.5) r33 = rand_ancho_max *", "anchoMaxProm = abs((anchoMax + anchoProm) / 2) anchoMinProm = abs((anchoMin", "sqlite3 from random import randint, choice import numpy as np", "resultado = c.fetchone() if resultado: anchoMin = resultado[0] anchoProm =", "j in range(0,5): for _ in range(10): negAncho = arrAncho[j]", "+ altoMin) / 2) #print altoMax , altoProm , altoMin", "(?, ?, ?, ?)\", (f2, f1, f2*f1, 0)) conn.commit() conn.close()", "choice import numpy as np conn = sqlite3.connect('ej.db') c =", "= abs((altoMax + altoMin) / 2) #print altoMax , altoProm", "as np conn = sqlite3.connect('ej.db') c = conn.cursor() #OBTENIENDO TAMAnOS", "arrAlto = [altoMax , altoProm , altoMin] c.execute('SELECT MAX(ancho) FROM", "c.fetchone() if resultado: anchoMax = resultado[0] c.execute('SELECT MIN(ancho) FROM features')", "altoMax , altoProm , altoMin arrAlto = [altoMax , altoProm", "= c.fetchone() if resultado: anchoMax = resultado[0] c.execute('SELECT MIN(ancho) FROM", "= resultado[0] c.execute('SELECT MIN(ancho) FROM features') resultado = c.fetchone() if", "c.fetchone() if resultado: altoMax = resultado[0] c.execute('SELECT MIN(alto) FROM features')", "resultado = c.fetchone() if resultado: anchoMax = resultado[0] c.execute('SELECT MIN(ancho)", "altoMin) / 2) #print altoMax , altoProm , altoMin arrAlto", "2 f1 = choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2 = choice([np.random.randint(1,", "rand_ancho_max * 2 f1 = choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2", "= c.fetchone() if resultado: anchoMin = resultado[0] anchoProm = abs((anchoMax", "r3 = rand_alto_max * 2 rand_ancho_max = int(negAncho*1.5) rand_ancho_min =", "anchoMax = resultado[0] c.execute('SELECT MIN(ancho) FROM features') resultado = c.fetchone()", "choice([np.random.randint(1, rand_alto_min), np.random.randint(rand_alto_max, r3)]) f2 = choice([np.random.randint(1, rand_ancho_min), np.random.randint(rand_ancho_max, r33)])", "in range(0,3): for j in range(0,5): for _ in range(10):", "c.execute(\"insert into features (ancho, alto, area, clase) values (?, ?,", "/ 2) anchoMaxProm = abs((anchoMax + anchoProm) / 2) anchoMinProm" ]
[ "''), timeit.timeit(f.__name__+'()', setup='from __main__ import '+f.__name__, number=100)) def check_values(f): v[:]", "v = rand(N) ns = {'_array_neurongroup_a': a, '_array_neurongroup_v': v, '_N':", "double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for", "globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def timefunc_python(): for _idx", "= 20000 for i in xrange(0, N, bs): ab =", "'Values' print '======' for f in funcs: check_values(f) print if", "= ab*sit + b vb *= ext vb += absit", "vb *= ext vb += absit vb -= absit*ext def", "%s' % (f.__name__.replace('timefunc_', ''), v[:5]) if __name__=='__main__': funcs = [#timefunc_cython_inline,", "_v = v _v *= _exp_term _v += a*_a_term _v", "= _N; for(int _idx=0; _idx<N; _idx++) { double a =", "dt, tau], a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) -", "locals=ns) def timefunc_python(): for _idx in xrange(N): _vectorisation_idx = _idx", "= a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)", "timefunc_numpy(): _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t)", "cdef double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0])", "+ b vb *= ext vb += absit vb -=", "v[i:i+bs] absit = ab*sit + b vb *= ext vb", "= tt.dvector('a') v = tt.dvector('v') freq = tt.dscalar('freq') t =", "a*b+0.0001*v v = _v _cy_array_neurongroup_v[_idx] = v ''' def timefunc_cython_inline():", "def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math') def", "double [:] _cy_array_neurongroup_a = _array_neurongroup_a #cdef double [:] _cy_array_neurongroup_v =", "1 v[:5] = linspace(0, 1, 5) f() print '%s: %s'", "timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ] if 1: print 'Values'", "int _idx cdef int _vectorisation_idx cdef int N = <int>_N", "= _array_neurongroup_v[_idx]; double _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau)", "funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow,", "+= a*_a_term _v += -b*_exp_term + b def timefunc_numpy_blocked(): ext", "+ (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v = _v; _array_neurongroup_v[_idx] = v;", "theano.function([a, v], # a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t)", "'-march=native') def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math') def get_theano_func(): a =", "- b)*exp(-dt/tau); v = _v; _array_neurongroup_v[_idx] = v; } '''", "+ b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) # return", "#v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args): code = '''", "_array_neurongroup_a = a = linspace(.05, 0.75, N) _array_neurongroup_v = v", "= _idx a = _array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx] _v =", "v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v) #_v =", "def timefunc_weave(*args): code = ''' // %s int N =", "= 1.2 # constant current mean, the modulation varies freq", "freq, t, dt, tau], a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) +", "weave.inline(code, ns.keys(), ns, compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def", "+ v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3 -ffast-math'", "-ffast-math' theano_func = get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png')", "t = 0.0 dt = 0.0001 _array_neurongroup_a = a =", "v[:] = 1 f() print '%s: %.2f' % (f.__name__.replace('timefunc_', ''),", "_idx=0; _idx<N; _idx++) { double a = _array_neurongroup_a[_idx]; double v", "def dotimeit(f): v[:] = 1 f() print '%s: %.2f' %", "-b*_exp_term + b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args):", "ns.keys(), ns, compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def timefunc_weave_fast():", "theano.function([a, v, freq, t, dt, tau], a*tt.sin(2.0*freq*pi*t) + b +", "= 1 f() print '%s: %.2f' % (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()',", "'t': t, 'tau': tau, 'b': b, 'freq': freq,# 'sin': numpy.sin,", "f() print '%s: %s' % (f.__name__.replace('timefunc_', ''), v[:5]) if __name__=='__main__':", "= a*b+0.0001*v v = _v _cy_array_neurongroup_v[_idx] = v ''' def", "f_arg_list = modified_cython_inline(code, locals=ns, globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns)", "freq = 10.0 t = 0.0 dt = 0.0001 _array_neurongroup_a", "def timefunc_numpy_blocked(): ext = exp(-dt/tau) sit = sin(2.0*freq*pi*t) bs =", "+ b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args): code", "str(args) weave.inline(code, ns.keys(), ns, compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native')", "_cy_array_neurongroup_v[_idx] = v ''' def timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod, f_arg_list", "brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import numpy from scipy import weave import", "a = linspace(.05, 0.75, N) _array_neurongroup_v = v = rand(N)", "def timefunc_python(): for _idx in xrange(N): _vectorisation_idx = _idx a", "_idx in xrange(N): _vectorisation_idx = _idx a = _array_neurongroup_a[_idx] v", "(-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:] = _v def timefunc_numpy_smart(): _sin_term =", "b vb *= ext vb += absit vb -= absit*ext", "vb -= absit*ext def timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) + b", "_const_term = -b*_exp_term + b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v)", "+ b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:] =", "theano from theano import tensor as tt tau = 20", "#_v = a*b+0.0001*v v = _v _cy_array_neurongroup_v[_idx] = v '''", "timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod, f_arg_list = modified_cython_inline(code, locals=ns, globals={}) def", "v = _array_neurongroup_v[_idx]; double _v = a*sin(2.0*freq*pi*t) + b +", "numexpr.evaluate('a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def", "= sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _v =", "= &(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for _idx in", "_cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for _idx in range(N): _vectorisation_idx = _idx", "__name__=='__main__': funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart,", "timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math') def get_theano_func():", "#exit() def timefunc_theano(): v[:] = theano_func(a, v, freq, t, dt,", "0.0001 _array_neurongroup_a = a = linspace(.05, 0.75, N) _array_neurongroup_v =", "import weave import numexpr import theano from theano import tensor", "_array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b +", "0.0 dt = 0.0001 _array_neurongroup_a = a = linspace(.05, 0.75,", "timefunc_weave(*args): code = ''' // %s int N = _N;", "t, dt, tau) def dotimeit(f): v[:] = 1 f() print", "timefunc_theano, ] if 1: print 'Values' print '======' for f", "= numexpr.evaluate('a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)')", "v _v *= _exp_term _v += a*_a_term _v += -b*_exp_term", "+ v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v = _v; _array_neurongroup_v[_idx]", "__main__ import '+f.__name__, number=100)) def check_values(f): v[:] = 1 v[:5]", "- b)*exp(-dt/tau) v = _v _array_neurongroup_v[_idx] = v def timefunc_numpy():", "N = 1000000 b = 1.2 # constant current mean,", "tau = tt.dscalar('tau') return theano.function([a, v, freq, t, dt, tau],", "v, _v #cdef double [:] _cy_array_neurongroup_a = _array_neurongroup_a #cdef double", "= rand(N) ns = {'_array_neurongroup_a': a, '_array_neurongroup_v': v, '_N': N,", "(-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v = _v _array_neurongroup_v[_idx] = v def", "cdef int _idx cdef int _vectorisation_idx cdef int N =", "= v; } ''' % str(args) weave.inline(code, ns.keys(), ns, compiler='gcc',", "double _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t)", "= sin(2.0*freq*pi*t) bs = 20000 for i in xrange(0, N,", "def timefunc_theano(): v[:] = theano_func(a, v, freq, t, dt, tau)", "setup='from __main__ import '+f.__name__, number=100)) def check_values(f): v[:] = 1", "_idx<N; _idx++) { double a = _array_neurongroup_a[_idx]; double v =", "import tensor as tt tau = 20 * 0.001 N", "int N = _N; for(int _idx=0; _idx<N; _idx++) { double", "(-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v = _v; _array_neurongroup_v[_idx] = v; }", "get_theano_func(): a = tt.dvector('a') v = tt.dvector('v') freq = tt.dscalar('freq')", "the modulation varies freq = 10.0 t = 0.0 dt", "''), v[:5]) if __name__=='__main__': funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart,", "extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math')", "a, v, _v #cdef double [:] _cy_array_neurongroup_a = _array_neurongroup_a #cdef", "absit*ext def timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau)", "= tt.dscalar('freq') t = tt.dscalar('t') dt = tt.dscalar('dt') tau =", "a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags", "theano_func = get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit()", "v = _array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau)", "for _idx in range(N): _vectorisation_idx = _idx a = _cy_array_neurongroup_a[_idx]", "a = _array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) +", "+ b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def timefunc_numexpr_smart():", "timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term)", "sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term", "a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) #", "a, '_array_neurongroup_v': v, '_N': N, 'dt': dt, 't': t, 'tau':", "theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit() def timefunc_theano(): v[:] =", "v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) # return theano.function([a, v], #", "-= absit*ext def timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) + b +", "xrange(N): _vectorisation_idx = _idx a = _array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx]", "tau) def dotimeit(f): v[:] = 1 f() print '%s: %.2f'", "v = _v _array_neurongroup_v[_idx] = v def timefunc_numpy(): _v =", "for i in xrange(0, N, bs): ab = a[i:i+bs] vb", "] if 1: print 'Values' print '======' for f in", "_N; for(int _idx=0; _idx<N; _idx++) { double a = _array_neurongroup_a[_idx];", "_cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for _idx", "locals=ns, globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def timefunc_python(): for", "double a = _array_neurongroup_a[_idx]; double v = _array_neurongroup_v[_idx]; double _v", "import * import cython import time, timeit from brian2.codegen.runtime.cython_rt.modified_inline import", "print '%s: %.2f' % (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from __main__ import", "= _array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b", "#theano.printing.pydotprint(theano_func, 'func.png') #exit() def timefunc_theano(): v[:] = theano_func(a, v, freq,", "_vectorisation_idx cdef int N = <int>_N cdef double a, v,", "+ b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v =", "number=100)) def check_values(f): v[:] = 1 v[:5] = linspace(0, 1,", "+ v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:] = _v def", "''' cdef int _idx cdef int _vectorisation_idx cdef int N", "_vectorisation_idx = _idx a = _cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx] _v", "theano.config.gcc.cxxflags = '-O3 -ffast-math' theano_func = get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print", "v, freq, t, dt, tau], a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau)", "a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v", "def timefunc_numpy(): _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) +", "t, dt, tau], a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t)", "tau = 20 * 0.001 N = 1000000 b =", "v[:] = 1 v[:5] = linspace(0, 1, 5) f() print", "return theano.function([a, v], # a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) +", "vb += absit vb -= absit*ext def timefunc_numexpr(): v[:] =", "in funcs: check_values(f) print if 1: print 'Times' print '====='", "timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math') def get_theano_func(): a = tt.dvector('a') v", "time, timeit from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import numpy from scipy", "b def timefunc_numpy_blocked(): ext = exp(-dt/tau) sit = sin(2.0*freq*pi*t) bs", "= (_sin_term-_sin_term*_exp_term) _v = v _v *= _exp_term _v +=", "'======' for f in funcs: check_values(f) print if 1: print", "+ v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v = _v _array_neurongroup_v[_idx]", "return theano.function([a, v, freq, t, dt, tau], a*tt.sin(2.0*freq*pi*t) + b", "1, 5) f() print '%s: %s' % (f.__name__.replace('timefunc_', ''), v[:5])", "#modified_cython_inline(code, locals=ns) def timefunc_python(): for _idx in xrange(N): _vectorisation_idx =", "_vectorisation_idx = _idx a = _array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx] _v", "b = 1.2 # constant current mean, the modulation varies", "f() print '%s: %.2f' % (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from __main__", "N = _N; for(int _idx=0; _idx<N; _idx++) { double a", "'-O3 -ffast-math' theano_func = get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func,", "= _v def timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau)", "int N = <int>_N cdef double a, v, _v #cdef", "#cdef double [:] _cy_array_neurongroup_v = _array_neurongroup_v cdef double* _cy_array_neurongroup_a =", "def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math') def get_theano_func(): a = tt.dvector('a')", "print '%s: %s' % (f.__name__.replace('timefunc_', ''), v[:5]) if __name__=='__main__': funcs", "_idx++) { double a = _array_neurongroup_a[_idx]; double v = _array_neurongroup_v[_idx];", "code = ''' // %s int N = _N; for(int", "#cdef double [:] _cy_array_neurongroup_a = _array_neurongroup_a #cdef double [:] _cy_array_neurongroup_v", "b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v)", "in xrange(0, N, bs): ab = a[i:i+bs] vb = v[i:i+bs]", "- b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau)", "_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t)", "scipy import weave import numexpr import theano from theano import", "out=v) def timefunc_weave(*args): code = ''' // %s int N", "*= ext vb += absit vb -= absit*ext def timefunc_numexpr():", "+= absit vb -= absit*ext def timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t)", "%.2f' % (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from __main__ import '+f.__name__, number=100))", "= 1000000 b = 1.2 # constant current mean, the", "b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v = _v;", "_v += a*_a_term _v += -b*_exp_term + b def timefunc_numpy_blocked():", "b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v = _v", "'_N': N, 'dt': dt, 't': t, 'tau': tau, 'b': b,", "v], # a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) -", "_idx a = _array_neurongroup_a[_idx] v = _array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t)", "timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ] if 1: print 'Values' print", "locals=ns) f_mod, f_arg_list = modified_cython_inline(code, locals=ns, globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list)", "f_mod, f_arg_list = modified_cython_inline(code, locals=ns, globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code,", "funcs: check_values(f) print if 1: print 'Times' print '=====' for", "constant current mean, the modulation varies freq = 10.0 t", "if __name__=='__main__': funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr,", "'-ffast-math') def get_theano_func(): a = tt.dvector('a') v = tt.dvector('v') freq", "_a_term = (_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term + b #v[:] =", "= modified_cython_inline(code, locals=ns, globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def", "double v = _array_neurongroup_v[_idx]; double _v = a*sin(2.0*freq*pi*t) + b", "= &(_array_neurongroup_v[0]) for _idx in range(N): _vectorisation_idx = _idx a", "= get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit() def", "+ b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v =", "cython import time, timeit from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import numpy", "freq,# 'sin': numpy.sin, 'pi': pi, } code = ''' cdef", "b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:] = _v", "&(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for _idx in range(N):", "f in funcs: check_values(f) print if 1: print 'Times' print", "freq, t, dt, tau) def dotimeit(f): v[:] = 1 f()", "b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term", "varies freq = 10.0 t = 0.0 dt = 0.0001", "(-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v) #_v = a*b+0.0001*v v", "v = _v; _array_neurongroup_v[_idx] = v; } ''' % str(args)", "'tau': tau, 'b': b, 'freq': freq,# 'sin': numpy.sin, 'pi': pi,", "+ (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) # return theano.function([a, v], # a*tt.sin(2.0*freq*pi*t)", "= a = linspace(.05, 0.75, N) _array_neurongroup_v = v =", "modified_cython_inline import numpy from scipy import weave import numexpr import", "sit = sin(2.0*freq*pi*t) bs = 20000 for i in xrange(0,", "'%s: %.2f' % (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from __main__ import '+f.__name__,", "def timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod, f_arg_list = modified_cython_inline(code, locals=ns, globals={})", "dotimeit(f): v[:] = 1 f() print '%s: %.2f' % (f.__name__.replace('timefunc_',", "exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term + b #v[:]", "cdef int N = <int>_N cdef double a, v, _v", "= linspace(0, 1, 5) f() print '%s: %s' % (f.__name__.replace('timefunc_',", "v[:] = theano_func(a, v, freq, t, dt, tau) def dotimeit(f):", "tt.dscalar('t') dt = tt.dscalar('dt') tau = tt.dscalar('tau') return theano.function([a, v,", "= {'_array_neurongroup_a': a, '_array_neurongroup_v': v, '_N': N, 'dt': dt, 't':", "_cy_array_neurongroup_v = _array_neurongroup_v cdef double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef double*", "= 20 * 0.001 N = 1000000 b = 1.2", "exp(-dt/tau) sit = sin(2.0*freq*pi*t) bs = 20000 for i in", "= sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _const_term =", "mean, the modulation varies freq = 10.0 t = 0.0", "#_v = a*b+0.0001*sin(v) #_v = a*b+0.0001*v v = _v _cy_array_neurongroup_v[_idx]", "ab*sit + b vb *= ext vb += absit vb", "ab = a[i:i+bs] vb = v[i:i+bs] absit = ab*sit +", "import time, timeit from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import numpy from", "v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3 -ffast-math' theano_func", "rand(N) ns = {'_array_neurongroup_a': a, '_array_neurongroup_v': v, '_N': N, 'dt':", "= _array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) +", "_a_term = (_sin_term-_sin_term*_exp_term) _v = v _v *= _exp_term _v", "_cy_array_neurongroup_a = _array_neurongroup_a #cdef double [:] _cy_array_neurongroup_v = _array_neurongroup_v cdef", "+ (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:] = _v def timefunc_numpy_smart(): _sin_term", "= _v _cy_array_neurongroup_v[_idx] = v ''' def timefunc_cython_inline(): cython.inline(code, locals=ns)", "modulation varies freq = 10.0 t = 0.0 dt =", "timefunc_weave('-O3', '-march=native', '-ffast-math') def get_theano_func(): a = tt.dvector('a') v =", "10.0 t = 0.0 dt = 0.0001 _array_neurongroup_a = a", "= _cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) +", "tt.dvector('v') freq = tt.dscalar('freq') t = tt.dscalar('t') dt = tt.dscalar('dt')", "_array_neurongroup_a #cdef double [:] _cy_array_neurongroup_v = _array_neurongroup_v cdef double* _cy_array_neurongroup_a", "_array_neurongroup_a[_idx]; double v = _array_neurongroup_v[_idx]; double _v = a*sin(2.0*freq*pi*t) +", "tt.dvector('a') v = tt.dvector('v') freq = tt.dscalar('freq') t = tt.dscalar('t')", "(f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from __main__ import '+f.__name__, number=100)) def check_values(f):", "numpy from scipy import weave import numexpr import theano from", "[:] _cy_array_neurongroup_v = _array_neurongroup_v cdef double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef", "+ (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v) #_v = a*b+0.0001*v", "(_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term + b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term',", "+ v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v) #_v", "timefunc_theano(): v[:] = theano_func(a, v, freq, t, dt, tau) def", "N, 'dt': dt, 't': t, 'tau': tau, 'b': b, 'freq':", "_exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _v = v _v", "= (_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term + b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term')", "theano import tensor as tt tau = 20 * 0.001", "current mean, the modulation varies freq = 10.0 t =", "numexpr import theano from theano import tensor as tt tau", "'dt': dt, 't': t, 'tau': tau, 'b': b, 'freq': freq,#", "_idx a = _cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t)", "'func.png') #exit() def timefunc_theano(): v[:] = theano_func(a, v, freq, t,", "def timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) +", "- b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3 -ffast-math' theano_func = get_theano_func() #print", "1000000 b = 1.2 # constant current mean, the modulation", "tt.dscalar('dt') tau = tt.dscalar('tau') return theano.function([a, v, freq, t, dt,", "20 * 0.001 N = 1000000 b = 1.2 #", "dt, 't': t, 'tau': tau, 'b': b, 'freq': freq,# 'sin':", "sin(2.0*freq*pi*t) bs = 20000 for i in xrange(0, N, bs):", "+ (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3 -ffast-math' theano_func =", "b)*exp(-dt/tau) v = _v _array_neurongroup_v[_idx] = v def timefunc_numpy(): _v", "'-march=native', '-ffast-math') def get_theano_func(): a = tt.dvector('a') v = tt.dvector('v')", "t = tt.dscalar('t') dt = tt.dscalar('dt') tau = tt.dscalar('tau') return", "check_values(f): v[:] = 1 v[:5] = linspace(0, 1, 5) f()", "= v = rand(N) ns = {'_array_neurongroup_a': a, '_array_neurongroup_v': v,", "timefunc_numpy_blocked(): ext = exp(-dt/tau) sit = sin(2.0*freq*pi*t) bs = 20000", "tensor as tt tau = 20 * 0.001 N =", "v ''' def timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod, f_arg_list = modified_cython_inline(code,", "modified_cython_inline(code, locals=ns, globals={}) def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def timefunc_python():", "v[:] = _v def timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term =", "a = _cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) +", "# return theano.function([a, v], # a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau)", "check_values(f) print if 1: print 'Times' print '=====' for f", "tau, 'b': b, 'freq': freq,# 'sin': numpy.sin, 'pi': pi, }", "numpy.sin, 'pi': pi, } code = ''' cdef int _idx", "_v #cdef double [:] _cy_array_neurongroup_a = _array_neurongroup_a #cdef double [:]", "cdef double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for _idx in range(N): _vectorisation_idx", "= _cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b", "+ b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) #_v =", "dt = tt.dscalar('dt') tau = tt.dscalar('tau') return theano.function([a, v, freq,", "if 1: print 'Values' print '======' for f in funcs:", "import cython import time, timeit from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import", "(-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) # return theano.function([a, v], # a*tt.sin(2.0*freq*pi*t) +", "+ b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags =", "= tt.dscalar('dt') tau = tt.dscalar('tau') return theano.function([a, v, freq, t,", "import '+f.__name__, number=100)) def check_values(f): v[:] = 1 v[:5] =", "import numpy from scipy import weave import numexpr import theano", "double* _cy_array_neurongroup_v = &(_array_neurongroup_v[0]) for _idx in range(N): _vectorisation_idx =", "+ (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term", "= ''' // %s int N = _N; for(int _idx=0;", "a[i:i+bs] vb = v[i:i+bs] absit = ab*sit + b vb", "xrange(0, N, bs): ab = a[i:i+bs] vb = v[i:i+bs] absit", "0.001 N = 1000000 b = 1.2 # constant current", "_cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t)", "<int>_N cdef double a, v, _v #cdef double [:] _cy_array_neurongroup_a", "*= _exp_term _v += a*_a_term _v += -b*_exp_term + b", "_exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term +", "% str(args) weave.inline(code, ns.keys(), ns, compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3',", "b)*tt.exp(-dt/tau)) # return theano.function([a, v], # a*tt.sin(2.0*freq*pi*t) + b +", "v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t)", "'sin': numpy.sin, 'pi': pi, } code = ''' cdef int", "range(N): _vectorisation_idx = _idx a = _cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx]", "1.2 # constant current mean, the modulation varies freq =", "[#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano,", "= exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _const_term = -b*_exp_term + b", "'+f.__name__, number=100)) def check_values(f): v[:] = 1 v[:5] = linspace(0,", "_sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _v", "- b)*tt.exp(-dt/tau)) # return theano.function([a, v], # a*tt.sin(2.0*freq*pi*t) + b", "#print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit() def timefunc_theano(): v[:] = theano_func(a,", "= v _v *= _exp_term _v += a*_a_term _v +=", "v = _v _cy_array_neurongroup_v[_idx] = v ''' def timefunc_cython_inline(): cython.inline(code,", "_idx in range(N): _vectorisation_idx = _idx a = _cy_array_neurongroup_a[_idx] v", "_v _cy_array_neurongroup_v[_idx] = v ''' def timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod,", "''' // %s int N = _N; for(int _idx=0; _idx<N;", "= a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau);", "(-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3 -ffast-math' theano_func = get_theano_func()", "v; } ''' % str(args) weave.inline(code, ns.keys(), ns, compiler='gcc', extra_compile_args=list(args))", "# constant current mean, the modulation varies freq = 10.0", "= exp(-dt/tau) sit = sin(2.0*freq*pi*t) bs = 20000 for i", "from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import numpy from scipy import weave", "timefunc_weave('-O3', '-march=native') def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native', '-ffast-math') def get_theano_func(): a", "timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term)", "double a, v, _v #cdef double [:] _cy_array_neurongroup_a = _array_neurongroup_a", "= _array_neurongroup_v cdef double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v", "a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:]", "b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v) #_v = a*b+0.0001*v v = _v", "timeit.timeit(f.__name__+'()', setup='from __main__ import '+f.__name__, number=100)) def check_values(f): v[:] =", "1: print 'Values' print '======' for f in funcs: check_values(f)", "if 1: print 'Times' print '=====' for f in funcs:", "b, 'freq': freq,# 'sin': numpy.sin, 'pi': pi, } code =", "= 1 v[:5] = linspace(0, 1, 5) f() print '%s:", "v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) -", "t, 'tau': tau, 'b': b, 'freq': freq,# 'sin': numpy.sin, 'pi':", "= '-O3 -ffast-math' theano_func = get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0])", "_array_neurongroup_v[_idx] = v def timefunc_numpy(): _v = a*sin(2.0*freq*pi*t) + b", "def timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term =", "int _vectorisation_idx cdef int N = <int>_N cdef double a,", "tt tau = 20 * 0.001 N = 1000000 b", "timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t)", "a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v", "v, freq, t, dt, tau) def dotimeit(f): v[:] = 1", "print if 1: print 'Times' print '=====' for f in", "def check_values(f): v[:] = 1 v[:5] = linspace(0, 1, 5)", "+= -b*_exp_term + b def timefunc_numpy_blocked(): ext = exp(-dt/tau) sit", "(-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term =", "= 10.0 t = 0.0 dt = 0.0001 _array_neurongroup_a =", "% (f.__name__.replace('timefunc_', ''), v[:5]) if __name__=='__main__': funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline,", "timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ] if 1:", "_v += -b*_exp_term + b def timefunc_numpy_blocked(): ext = exp(-dt/tau)", "= -b*_exp_term + b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def", "_v _array_neurongroup_v[_idx] = v def timefunc_numpy(): _v = a*sin(2.0*freq*pi*t) +", "pylab import * import cython import time, timeit from brian2.codegen.runtime.cython_rt.modified_inline", "= _array_neurongroup_a #cdef double [:] _cy_array_neurongroup_v = _array_neurongroup_v cdef double*", "v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v = _v _array_neurongroup_v[_idx] =", "N = <int>_N cdef double a, v, _v #cdef double", "_array_neurongroup_v = v = rand(N) ns = {'_array_neurongroup_a': a, '_array_neurongroup_v':", "5) f() print '%s: %s' % (f.__name__.replace('timefunc_', ''), v[:5]) if", "print 'Values' print '======' for f in funcs: check_values(f) print", "cdef int _vectorisation_idx cdef int N = <int>_N cdef double", "= _v _array_neurongroup_v[_idx] = v def timefunc_numpy(): _v = a*sin(2.0*freq*pi*t)", "v = _cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau)", "= tt.dvector('v') freq = tt.dscalar('freq') t = tt.dscalar('t') dt =", "timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ] if 1: print", "from scipy import weave import numexpr import theano from theano", "_array_neurongroup_v[_idx] = v; } ''' % str(args) weave.inline(code, ns.keys(), ns,", "(_sin_term-_sin_term*_exp_term) _v = v _v *= _exp_term _v += a*_a_term", "a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v", "linspace(.05, 0.75, N) _array_neurongroup_v = v = rand(N) ns =", "b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3 -ffast-math' theano_func = get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0])", "# a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau))", "<filename>dev/ideas/cython/playing_around.py from pylab import * import cython import time, timeit", "ns, compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def timefunc_weave_fast(): timefunc_weave('-O3',", "= v ''' def timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod, f_arg_list =", "as tt tau = 20 * 0.001 N = 1000000", "_array_neurongroup_v[_idx]; double _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) +", "= a*b+0.0001*sin(v) #_v = a*b+0.0001*v v = _v _cy_array_neurongroup_v[_idx] =", "_cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx] _v = a*sin(2.0*freq*pi*t) + b +", "_array_neurongroup_v cdef double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0]) cdef double* _cy_array_neurongroup_v =", "theano_func(a, v, freq, t, dt, tau) def dotimeit(f): v[:] =", "= tt.dscalar('tau') return theano.function([a, v, freq, t, dt, tau], a*tt.sin(2.0*freq*pi*t)", "&(_array_neurongroup_v[0]) for _idx in range(N): _vectorisation_idx = _idx a =", "// %s int N = _N; for(int _idx=0; _idx<N; _idx++)", "for _idx in xrange(N): _vectorisation_idx = _idx a = _array_neurongroup_a[_idx]", "* import cython import time, timeit from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline", "(f.__name__.replace('timefunc_', ''), v[:5]) if __name__=='__main__': funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy,", "v, '_N': N, 'dt': dt, 't': t, 'tau': tau, 'b':", "_v def timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term", "= [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast,", "= <int>_N cdef double a, v, _v #cdef double [:]", "compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow(): timefunc_weave('-O3', '-march=native') def timefunc_weave_fast(): timefunc_weave('-O3', '-march=native',", "v[:5] = linspace(0, 1, 5) f() print '%s: %s' %", "def timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def timefunc_python(): for _idx in", "freq = tt.dscalar('freq') t = tt.dscalar('t') dt = tt.dscalar('dt') tau", "= exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _v = v _v *=", "'_array_neurongroup_v': v, '_N': N, 'dt': dt, 't': t, 'tau': tau,", "cdef double a, v, _v #cdef double [:] _cy_array_neurongroup_a =", "= tt.dscalar('t') dt = tt.dscalar('dt') tau = tt.dscalar('tau') return theano.function([a,", "= _idx a = _cy_array_neurongroup_a[_idx] v = _cy_array_neurongroup_v[_idx] _v =", "* 0.001 N = 1000000 b = 1.2 # constant", "v def timefunc_numpy(): _v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau)", "-b*_exp_term + b def timefunc_numpy_blocked(): ext = exp(-dt/tau) sit =", "_v *= _exp_term _v += a*_a_term _v += -b*_exp_term +", "in xrange(N): _vectorisation_idx = _idx a = _array_neurongroup_a[_idx] v =", "tt.dscalar('freq') t = tt.dscalar('t') dt = tt.dscalar('dt') tau = tt.dscalar('tau')", "_idx cdef int _vectorisation_idx cdef int N = <int>_N cdef", "a*b+0.0001*sin(v) #_v = a*b+0.0001*v v = _v _cy_array_neurongroup_v[_idx] = v", "20000 for i in xrange(0, N, bs): ab = a[i:i+bs]", "= _v; _array_neurongroup_v[_idx] = v; } ''' % str(args) weave.inline(code,", "= linspace(.05, 0.75, N) _array_neurongroup_v = v = rand(N) ns", "#theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit() def timefunc_theano(): v[:] = theano_func(a, v,", "b)*exp(-dt/tau) v[:] = _v def timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term", "timefunc_cython_modified_inline(): f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def timefunc_python(): for _idx in xrange(N):", "} code = ''' cdef int _idx cdef int _vectorisation_idx", "= 0.0001 _array_neurongroup_a = a = linspace(.05, 0.75, N) _array_neurongroup_v", "_v; _array_neurongroup_v[_idx] = v; } ''' % str(args) weave.inline(code, ns.keys(),", "numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args): code = ''' // %s int", "- b)*exp(-dt/tau) v[:] = _v def timefunc_numpy_smart(): _sin_term = sin(2.0*freq*pi*t)", "ext vb += absit vb -= absit*ext def timefunc_numexpr(): v[:]", "= v def timefunc_numpy(): _v = a*sin(2.0*freq*pi*t) + b +", "% (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from __main__ import '+f.__name__, number=100)) def", "'%s: %s' % (f.__name__.replace('timefunc_', ''), v[:5]) if __name__=='__main__': funcs =", "pi, } code = ''' cdef int _idx cdef int", "absit vb -= absit*ext def timefunc_numexpr(): v[:] = numexpr.evaluate('a*sin(2.0*freq*pi*t) +", "#print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit() def timefunc_theano(): v[:]", "timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ]", "linspace(0, 1, 5) f() print '%s: %s' % (f.__name__.replace('timefunc_', ''),", "vb = v[i:i+bs] absit = ab*sit + b vb *=", "0.75, N) _array_neurongroup_v = v = rand(N) ns = {'_array_neurongroup_a':", "v = tt.dvector('v') freq = tt.dscalar('freq') t = tt.dscalar('t') dt", "ext = exp(-dt/tau) sit = sin(2.0*freq*pi*t) bs = 20000 for", "N) _array_neurongroup_v = v = rand(N) ns = {'_array_neurongroup_a': a,", "def timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term =", "v[:5]) if __name__=='__main__': funcs = [#timefunc_cython_inline, timefunc_cython_modified_inline, timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked,", "} ''' % str(args) weave.inline(code, ns.keys(), ns, compiler='gcc', extra_compile_args=list(args)) def", "''' def timefunc_cython_inline(): cython.inline(code, locals=ns) f_mod, f_arg_list = modified_cython_inline(code, locals=ns,", "sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _v = v", "b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term", "b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) theano.config.gcc.cxxflags = '-O3", "dt, tau) def dotimeit(f): v[:] = 1 f() print '%s:", "= ''' cdef int _idx cdef int _vectorisation_idx cdef int", "[:] _cy_array_neurongroup_a = _array_neurongroup_a #cdef double [:] _cy_array_neurongroup_v = _array_neurongroup_v", "_exp_term _v += a*_a_term _v += -b*_exp_term + b def", "absit = ab*sit + b vb *= ext vb +=", "timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ] if 1: print 'Values' print '======'", "- b)*exp(-dt/tau) #_v = a*b+0.0001*sin(v) #_v = a*b+0.0001*v v =", "1 f() print '%s: %.2f' % (f.__name__.replace('timefunc_', ''), timeit.timeit(f.__name__+'()', setup='from", "bs = 20000 for i in xrange(0, N, bs): ab", "%s int N = _N; for(int _idx=0; _idx<N; _idx++) {", "dt = 0.0001 _array_neurongroup_a = a = linspace(.05, 0.75, N)", "a = _array_neurongroup_a[_idx]; double v = _array_neurongroup_v[_idx]; double _v =", "for f in funcs: check_values(f) print if 1: print 'Times'", "ns = {'_array_neurongroup_a': a, '_array_neurongroup_v': v, '_N': N, 'dt': dt,", "+ v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau)') def timefunc_numexpr_smart(): _sin_term =", "b)*exp(-dt/tau); v = _v; _array_neurongroup_v[_idx] = v; } ''' %", "import modified_cython_inline import numpy from scipy import weave import numexpr", "weave import numexpr import theano from theano import tensor as", "{'_array_neurongroup_a': a, '_array_neurongroup_v': v, '_N': N, 'dt': dt, 't': t,", "tt.dscalar('tau') return theano.function([a, v, freq, t, dt, tau], a*tt.sin(2.0*freq*pi*t) +", "{ double a = _array_neurongroup_a[_idx]; double v = _array_neurongroup_v[_idx]; double", "bs): ab = a[i:i+bs] vb = v[i:i+bs] absit = ab*sit", "= v[i:i+bs] absit = ab*sit + b vb *= ext", "f_mod.__invoke(*f_arg_list) #modified_cython_inline(code, locals=ns) def timefunc_python(): for _idx in xrange(N): _vectorisation_idx", "a*_a_term _v += -b*_exp_term + b def timefunc_numpy_blocked(): ext =", "def get_theano_func(): a = tt.dvector('a') v = tt.dvector('v') freq =", "= theano_func(a, v, freq, t, dt, tau) def dotimeit(f): v[:]", "in range(N): _vectorisation_idx = _idx a = _cy_array_neurongroup_a[_idx] v =", "''' % str(args) weave.inline(code, ns.keys(), ns, compiler='gcc', extra_compile_args=list(args)) def timefunc_weave_slow():", "+ b def timefunc_numpy_blocked(): ext = exp(-dt/tau) sit = sin(2.0*freq*pi*t)", "numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args): code = ''' // %s", "i in xrange(0, N, bs): ab = a[i:i+bs] vb =", "b #v[:] = numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args): code =", "= _array_neurongroup_a[_idx]; double v = _array_neurongroup_v[_idx]; double _v = a*sin(2.0*freq*pi*t)", "+ (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v = _v _array_neurongroup_v[_idx] = v", "timefunc_weave_fast, timefunc_theano, ] if 1: print 'Values' print '======' for", "import theano from theano import tensor as tt tau =", "code = ''' cdef int _idx cdef int _vectorisation_idx cdef", "'freq': freq,# 'sin': numpy.sin, 'pi': pi, } code = '''", "tau], a*tt.sin(2.0*freq*pi*t) + b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau))", "= a[i:i+bs] vb = v[i:i+bs] absit = ab*sit + b", "_v = a*sin(2.0*freq*pi*t) + b + v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) -", "from theano import tensor as tt tau = 20 *", "N, bs): ab = a[i:i+bs] vb = v[i:i+bs] absit =", "timeit from brian2.codegen.runtime.cython_rt.modified_inline import modified_cython_inline import numpy from scipy import", "double [:] _cy_array_neurongroup_v = _array_neurongroup_v cdef double* _cy_array_neurongroup_a = &(_array_neurongroup_a[0])", "print '======' for f in funcs: check_values(f) print if 1:", "import numexpr import theano from theano import tensor as tt", "from pylab import * import cython import time, timeit from", "_sin_term = sin(2.0*freq*pi*t) _exp_term = exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _const_term", "for(int _idx=0; _idx<N; _idx++) { double a = _array_neurongroup_a[_idx]; double", "get_theano_func() #print theano.pp(theano_func.maker.fgraph.outputs[0]) #print #theano.printing.debugprint(theano_func.maker.fgraph.outputs[0]) #theano.printing.pydotprint(theano_func, 'func.png') #exit() def timefunc_theano():", "v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau) v[:] = _v def timefunc_numpy_smart():", "'b': b, 'freq': freq,# 'sin': numpy.sin, 'pi': pi, } code", "= numexpr.evaluate('a*_a_term+v*_exp_term+_const_term') numexpr.evaluate('a*_a_term+v*_exp_term+_const_term', out=v) def timefunc_weave(*args): code = ''' //", "timefunc_numpy, timefunc_numpy_smart, timefunc_numpy_blocked, timefunc_numexpr, timefunc_numexpr_smart, timefunc_weave_slow, timefunc_weave_fast, timefunc_theano, ] if", "'pi': pi, } code = ''' cdef int _idx cdef", "timefunc_python(): for _idx in xrange(N): _vectorisation_idx = _idx a =", "1: print 'Times' print '=====' for f in funcs: dotimeit(f)", "cython.inline(code, locals=ns) f_mod, f_arg_list = modified_cython_inline(code, locals=ns, globals={}) def timefunc_cython_modified_inline():", "exp(-dt/tau) _a_term = (_sin_term-_sin_term*_exp_term) _v = v _v *= _exp_term", "b + v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) # return theano.function([a,", "a = tt.dvector('a') v = tt.dvector('v') freq = tt.dscalar('freq') t", "+ v*tt.exp(-dt/tau) + (-a*tt.sin(2.0*freq*pi*t) - b)*tt.exp(-dt/tau)) # return theano.function([a, v],", "= 0.0 dt = 0.0001 _array_neurongroup_a = a = linspace(.05,", "v*exp(-dt/tau) + (-a*sin(2.0*freq*pi*t) - b)*exp(-dt/tau); v = _v; _array_neurongroup_v[_idx] =" ]
[ "<reponame>lordmahyar/az-iranian-bank-gateways<gh_stars>100-1000 from .banks import callback_view, go_to_bank_gateway from .samples import sample_payment_view,", "from .banks import callback_view, go_to_bank_gateway from .samples import sample_payment_view, sample_result_view" ]
[ "c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self): header = ''.join([chr(c) for", "0xfd, 0xe8, 0x18, 0xa, 0x0, 0x1]]) update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24')", "def test_1_ipv6_2 (self): route = RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18' announced", "self.assertEqual(to_Community('1:1'),65537) def test_4_community (self): communities = Communities() community = to_Community('1:1')", "[0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self): header = ''.join([chr(c) for c in", "c in [8,1,]])) def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in", "from exabgp.configuration.environment import environment env = environment.setup('') from exabgp.bgp.message.update.update import", "0x30, 0x40, 0x1, 0x1, 0x0, 0x50, 0x2, 0x0, 0x4, 0x2,", "0xe8, 0x18, 0xa, 0x0, 0x1]]) update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def", "in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self): header = ''.join([chr(c) for c", "rights reserved. \"\"\" import unittest from exabgp.configuration.environment import environment env", "print update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken (self): # header = ''.join([chr(c)", "(self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in [8,1,]])) def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c)", "= new_Update(message) print update.nlri print update.withdraw print update.attributes[MPRNLRI.ID][0] # def", "0x22, 0x2]]) message = ''.join([chr(c) for c in [0x0, 0x0,", "0x2]]) message = ''.join([chr(c) for c in [0x0, 0x0, 0x0,", "0x0, 0x0, 0x0, 0x80, 0xe, 0x1a, 0x0, 0x2, 0x1, 0x10,", "test_2_ipv4_broken (self): # header = ''.join([chr(c) for c in h])", "0x4, 0x4, 0x0, 0x0, 0x0, 0x0, 0x80, 0xe, 0x1a, 0x0,", "2009-2013 Exa Networks. All rights reserved. \"\"\" import unittest from", "print update.withdraw print update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken (self): # header", "# header = ''.join([chr(c) for c in h]) # message", "0x0, 0x0, 0x0, 0x20, 0x12, 0x34, 0x56, 0x78]]) update =", "communities = Communities() community = to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c", "in [16,1,2]])) def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in [24,1,2,3]]))", "0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x12, 0x34, 0x56, 0x78]])", "in h]) # message = ''.join([chr(c) for c in m])", "# message = ''.join([chr(c) for c in m]) # message", "c in [0x0, 0x0, 0x0, 0xf, 0x40, 0x1, 0x1, 0x0,", "self.assertEqual(Community(256),256) def test_2_community (self): self.assertEqual(to_Community('0x100'),256) def test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def", "0x0, 0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x18, 0xa,", "import unittest from exabgp.configuration.environment import environment env = environment.setup('') from", "= Communities() community = to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c in", "for c in [16,1,2]])) def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c", "0x1, 0x1, 0x0, 0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8,", "reserved. \"\"\" import unittest from exabgp.configuration.environment import environment env =", "to_Community from exabgp.bgp.message.update.attribute.community import Community, Communities class TestData (unittest.TestCase): def", "def test_2_community (self): self.assertEqual(to_Community('0x100'),256) def test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def test_4_community", "c in [0x0, 0x0, 0x0, 0xb, 0x40, 0x1, 0x1, 0x0,", "0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4, 0x2, 0x1, 0xfd,", "''.join([chr(c) for c in h]) # message = ''.join([chr(c) for", "0x0, 0x47, 0x2]]) message = ''.join([chr(c) for c in [0x0,", "(self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in [24,1,2,3]])) def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c)", "#!/usr/bin/env python # encoding: utf-8 \"\"\" update.py Created by <NAME>", "= to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self): route = RouteIP('1234:5678::',64) route.next_hop", "0x1, 0x0, 0x50, 0x2, 0x0, 0x4, 0x2, 0x1, 0xff, 0xfe,", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0,", "0x2, 0x1, 0xfd, 0xe8, 0x0, 0x0, 0x0, 0x0, 0x18, 0xa,", "0xff, 0xff, 0xff, 0x0, 0x22, 0x2]]) message = ''.join([chr(c) for", "unittest from exabgp.configuration.environment import environment env = environment.setup('') from exabgp.bgp.message.update.update", "self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in [0,]])) def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for", "(self): self.assertEqual(to_Community('0x100'),256) def test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def test_4_community (self): communities", "[32,1,2,3,4]])) def test_1_community (self): self.assertEqual(Community(256),256) def test_2_community (self): self.assertEqual(to_Community('0x100'),256) def", "self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in [24,1,2,3]])) def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for", "print update.nlri print update.withdraw print update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken (self):", "c in m]) # message = ''.join([chr(c) for c in", "(self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in [16,1,2]])) def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c)", "0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,", "(self): self.assertEqual(to_Community('1:1'),65537) def test_4_community (self): communities = Communities() community =", "header = ''.join([chr(c) for c in [0xff, 0xff, 0xff, 0xff,", "message = ''.join([chr(c) for c in [0x0, 0x0, 0x0, 0xf,", "0x0, 0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x0, 0x0,", "0x4, 0x2, 0x1, 0xfd, 0xe8, 0x18, 0xa, 0x0, 0x1]]) update", "exabgp.bgp.message.update.attribute.community import to_Community from exabgp.bgp.message.update.attribute.community import Community, Communities class TestData", "0xff, 0xff, 0xff, 0xff, 0x0, 0x22, 0x2]]) message = ''.join([chr(c)", "0x0, 0x0, 0x80, 0xe, 0x1a, 0x0, 0x2, 0x1, 0x10, 0x0,", "for c in [32,1,2,3,4]])) def test_1_community (self): self.assertEqual(Community(256),256) def test_2_community", "0x12, 0x34, 0x56, 0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2", "test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in [16,1,2]])) def test_9_prefix (self):", "0x20, 0x12, 0x34, 0x56, 0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def", "(self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in [0,]])) def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c)", "0x0, 0x0, 0x0, 0x0, 0x18, 0xa, 0x0, 0x1]]) # update", "encoding: utf-8 \"\"\" update.py Created by <NAME> on 2009-09-06. Copyright", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x22,", "0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self): route =", "new_Update(message) print update.nlri print update.withdraw print update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken", "0x1, 0x0, 0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x0,", "import * from exabgp.bgp.message.update.attribute.community import to_Community from exabgp.bgp.message.update.attribute.community import Community,", "communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self): header", "0xb, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4, 0x2, 0x1,", "0x0, 0x1]]) # update = new_Update(message) if __name__ == '__main__':", "test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in [32,1,2,3,4]])) def test_1_community (self):", "0x80, 0xe, 0x1a, 0x0, 0x2, 0x1, 0x10, 0x0, 0x0, 0x0,", "[0x0, 0x0, 0x0, 0x30, 0x40, 0x1, 0x1, 0x0, 0x50, 0x2,", "0x0, 0x0, 0x0, 0x0, 0x80, 0xe, 0x1a, 0x0, 0x2, 0x1,", "0x0, 0x22, 0x2]]) message = ''.join([chr(c) for c in [0x0,", "0xff, 0xff, 0xff, 0xff, 0x0, 0x47, 0x2]]) message = ''.join([chr(c)", "def test_1_ipv6_1 (self): header = ''.join([chr(c) for c in [0xff,", "(c) 2009-2013 Exa Networks. All rights reserved. \"\"\" import unittest", "0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,", "announced = route.announce(1,1) message = announced[19:] update = new_Update(message) print", "test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in", "0x0, 0x50, 0x2, 0x0, 0x4, 0x2, 0x1, 0xff, 0xfe, 0x80,", "0x56, 0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self): route", "update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self): header = ''.join([chr(c)", "test_1_ipv6_2 (self): route = RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18' announced =", "# message = ''.join([chr(c) for c in [0x0, 0x0, 0x0,", "def test_2_ipv4_broken (self): # header = ''.join([chr(c) for c in", "0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x12, 0x34, 0x56,", "c in [24,1,2,3]])) def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in", "Exa Networks. All rights reserved. \"\"\" import unittest from exabgp.configuration.environment", "0xff, 0x0, 0x47, 0x2]]) message = ''.join([chr(c) for c in", "def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in [24,1,2,3]])) def test_10_prefix", "= RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18' announced = route.announce(1,1) message =", "test_1_ipv4 (self): header = ''.join([chr(c) for c in [0xff, 0xff,", "= announced[19:] update = new_Update(message) print update.nlri print update.withdraw print", "for c in [24,1,2,3]])) def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c", "(self): communities = Communities() community = to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for", "0x2, 0x1, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,", "0x0, 0x80, 0xe, 0x1a, 0x0, 0x2, 0x1, 0x10, 0x0, 0x0,", "Networks. All rights reserved. \"\"\" import unittest from exabgp.configuration.environment import", "0x0, 0x18, 0xa, 0x0, 0x1]]) # update = new_Update(message) if", "community = to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def", "self.assertEqual(to_Community('0x100'),256) def test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def test_4_community (self): communities =", "in m]) # message = ''.join([chr(c) for c in [0x0,", "0xa, 0x0, 0x1]]) # update = new_Update(message) if __name__ ==", "0x4, 0x2, 0x1, 0xfd, 0xe8, 0x0, 0x0, 0x0, 0x0, 0x18,", "0x0, 0x0, 0xf, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4,", "test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in [8,1,]])) def test_8_prefix (self):", "[16,1,2]])) def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in [24,1,2,3]])) def", "0x0, 0x1]]) update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self): header", "0x0, 0x0, 0x20, 0x12, 0x34, 0x56, 0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)])", "in [0x0, 0x0, 0x0, 0x30, 0x40, 0x1, 0x1, 0x0, 0x50,", "exabgp.bgp.message.update.update import * from exabgp.bgp.message.update.attribute.community import to_Community from exabgp.bgp.message.update.attribute.community import", "import to_Community from exabgp.bgp.message.update.attribute.community import Community, Communities class TestData (unittest.TestCase):", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x22, 0x2]])", "(self): route = RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18' announced = route.announce(1,1)", "''.join([chr(c) for c in [0x0, 0x0, 0x0, 0xf, 0x40, 0x1,", "0x1]]) # update = new_Update(message) if __name__ == '__main__': unittest.main()", "def test_4_community (self): communities = Communities() community = to_Community('1:1') communities.add(community)", "self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in [8,1,]])) def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for", "c in [32,1,2,3,4]])) def test_1_community (self): self.assertEqual(Community(256),256) def test_2_community (self):", "= new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self): header = ''.join([chr(c) for", "0x1, 0xfd, 0xe8, 0x18, 0xa, 0x0, 0x1]]) update = new_Update(message)", "= route.announce(1,1) message = announced[19:] update = new_Update(message) print update.nlri", "[8,1,]])) def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in [16,1,2]])) def", "0x1, 0xff, 0xfe, 0x80, 0x4, 0x4, 0x0, 0x0, 0x0, 0x0,", "exabgp.bgp.message.update.attribute.community import Community, Communities class TestData (unittest.TestCase): def test_2_prefix (self):", "0x0, 0xf, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4, 0x2,", "route.next_hop = '8765:fdf8:f53e:61e4::18' announced = route.announce(1,1) message = announced[19:] update", "0x80, 0x4, 0x4, 0x0, 0x0, 0x0, 0x0, 0x80, 0xe, 0x1a,", "0x0, 0x20, 0x12, 0x34, 0x56, 0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32')", "route = RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18' announced = route.announce(1,1) message", "[0x0, 0x0, 0x0, 0xf, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2,", "# def test_2_ipv4_broken (self): # header = ''.join([chr(c) for c", "new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self): header = ''.join([chr(c) for c", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,", "update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self): route = RouteIP('1234:5678::',64)", "0x2, 0x1, 0xfd, 0xe8, 0x18, 0xa, 0x0, 0x1]]) update =", "''.join([chr(c) for c in [0x0, 0x0, 0x0, 0x30, 0x40, 0x1,", "= ''.join([chr(c) for c in [0xff, 0xff, 0xff, 0xff, 0xff,", "0xff, 0xff, 0x0, 0x47, 0x2]]) message = ''.join([chr(c) for c", "in [32,1,2,3,4]])) def test_1_community (self): self.assertEqual(Community(256),256) def test_2_community (self): self.assertEqual(to_Community('0x100'),256)", "def test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def test_4_community (self): communities = Communities()", "0x1]]) update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self): header =", "''.join([chr(c) for c in [0x0, 0x0, 0x0, 0xb, 0x40, 0x1,", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x22, 0x2]]) message", "for c in [0x0, 0x0, 0x0, 0x30, 0x40, 0x1, 0x1,", "message = ''.join([chr(c) for c in m]) # message =", "0x0, 0xb, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4, 0x2,", "Created by <NAME> on 2009-09-06. Copyright (c) 2009-2013 Exa Networks.", "update.py Created by <NAME> on 2009-09-06. Copyright (c) 2009-2013 Exa", "(self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in [0,]]))", "(self): header = ''.join([chr(c) for c in [0xff, 0xff, 0xff,", "for c in h]) # message = ''.join([chr(c) for c", "in [0x0, 0x0, 0x0, 0xf, 0x40, 0x1, 0x1, 0x0, 0x40,", "0xff, 0xfe, 0x80, 0x4, 0x4, 0x0, 0x0, 0x0, 0x0, 0x80,", "announced[19:] update = new_Update(message) print update.nlri print update.withdraw print update.attributes[MPRNLRI.ID][0]", "environment.setup('') from exabgp.bgp.message.update.update import * from exabgp.bgp.message.update.attribute.community import to_Community from", "0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x18, 0xa, 0x0,", "test_2_community (self): self.assertEqual(to_Community('0x100'),256) def test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def test_4_community (self):", "0x1, 0x1, 0x0, 0x50, 0x2, 0x0, 0x4, 0x2, 0x1, 0xff,", "test_1_ipv6_1 (self): header = ''.join([chr(c) for c in [0xff, 0xff,", "for c in [8,1,]])) def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c", "for c in m]) # message = ''.join([chr(c) for c", "self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in [0,]])) def", "test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in [24,1,2,3]])) def test_10_prefix (self):", "in [0,]])) def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in [8,1,]]))", "0xe8, 0x0, 0x0, 0x0, 0x0, 0x18, 0xa, 0x0, 0x1]]) #", "0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x0, 0x0, 0x0, 0x0,", "from exabgp.bgp.message.update.attribute.community import Community, Communities class TestData (unittest.TestCase): def test_2_prefix", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x47,", "0x47, 0x2]]) message = ''.join([chr(c) for c in [0x0, 0x0,", "for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self): header = ''.join([chr(c)", "0x0, 0x0, 0x0, 0x18, 0xa, 0x0, 0x1]]) # update =", "c in h]) # message = ''.join([chr(c) for c in", "= ''.join([chr(c) for c in [0x0, 0x0, 0x0, 0x30, 0x40,", "test_3_community (self): self.assertEqual(to_Community('1:1'),65537) def test_4_community (self): communities = Communities() community", "0x0, 0x0, 0x30, 0x40, 0x1, 0x1, 0x0, 0x50, 0x2, 0x0,", "def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in [0,]])) def test_7_prefix", "test_4_community (self): communities = Communities() community = to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c)", "0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x0, 0x0, 0x0,", "c in [0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,", "self.assertEqual(communities.pack(),''.join([chr(c) for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self): header =", "2009-09-06. Copyright (c) 2009-2013 Exa Networks. All rights reserved. \"\"\"", "Communities class TestData (unittest.TestCase): def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix", "self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self): route = RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18'", "= environment.setup('') from exabgp.bgp.message.update.update import * from exabgp.bgp.message.update.attribute.community import to_Community", "<NAME> on 2009-09-06. Copyright (c) 2009-2013 Exa Networks. All rights", "c in [0,]])) def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in", "0x0, 0x0, 0x0, 0x0, 0x20, 0x12, 0x34, 0x56, 0x78]]) update", "on 2009-09-06. Copyright (c) 2009-2013 Exa Networks. All rights reserved.", "for c in [0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,", "0x40, 0x1, 0x1, 0x0, 0x50, 0x2, 0x0, 0x4, 0x2, 0x1,", "Community, Communities class TestData (unittest.TestCase): def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def", "0x4, 0x2, 0x1, 0xff, 0xfe, 0x80, 0x4, 0x4, 0x0, 0x0,", "= ''.join([chr(c) for c in [0x0, 0x0, 0x0, 0xf, 0x40,", "python # encoding: utf-8 \"\"\" update.py Created by <NAME> on", "\"\"\" update.py Created by <NAME> on 2009-09-06. Copyright (c) 2009-2013", "'8765:fdf8:f53e:61e4::18' announced = route.announce(1,1) message = announced[19:] update = new_Update(message)", "TestData (unittest.TestCase): def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c)", "# encoding: utf-8 \"\"\" update.py Created by <NAME> on 2009-09-06.", "[0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,", "def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in [8,1,]])) def test_8_prefix", "0xff, 0x0, 0x22, 0x2]]) message = ''.join([chr(c) for c in", "0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x18, 0xa, 0x0, 0x1]])", "class TestData (unittest.TestCase): def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self):", "0x0, 0x0, 0x18, 0xa, 0x0, 0x1]]) # update = new_Update(message)", "All rights reserved. \"\"\" import unittest from exabgp.configuration.environment import environment", "to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4 (self):", "0x0, 0x0, 0xb, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4,", "0x2, 0x1, 0xff, 0xfe, 0x80, 0x4, 0x4, 0x0, 0x0, 0x0,", "0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20,", "= to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]])) def test_1_ipv4", "RouteIP('1234:5678::',64) route.next_hop = '8765:fdf8:f53e:61e4::18' announced = route.announce(1,1) message = announced[19:]", "0x1, 0x0, 0x40, 0x2, 0x4, 0x2, 0x1, 0xfd, 0xe8, 0x18,", "def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in [16,1,2]])) def test_9_prefix", "0x18, 0xa, 0x0, 0x1]]) update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1", "0x1, 0xfd, 0xe8, 0x0, 0x0, 0x0, 0x0, 0x18, 0xa, 0x0,", "0x50, 0x2, 0x0, 0x4, 0x2, 0x1, 0xff, 0xfe, 0x80, 0x4,", "0xff, 0xff, 0xff, 0x0, 0x47, 0x2]]) message = ''.join([chr(c) for", "0xff, 0xff, 0x0, 0x22, 0x2]]) message = ''.join([chr(c) for c", "= ''.join([chr(c) for c in h]) # message = ''.join([chr(c)", "route.announce(1,1) message = announced[19:] update = new_Update(message) print update.nlri print", "message = announced[19:] update = new_Update(message) print update.nlri print update.withdraw", "0x0, 0x30, 0x40, 0x1, 0x1, 0x0, 0x50, 0x2, 0x0, 0x4,", "c in [0x0, 0x0, 0x0, 0x30, 0x40, 0x1, 0x1, 0x0,", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x47, 0x2]])", "0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x47, 0x2]]) message", "m]) # message = ''.join([chr(c) for c in [0x0, 0x0,", "def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in [32,1,2,3,4]])) def test_1_community", "(self): self.assertEqual(Community(256),256) def test_2_community (self): self.assertEqual(to_Community('0x100'),256) def test_3_community (self): self.assertEqual(to_Community('1:1'),65537)", "0x18, 0xa, 0x0, 0x1]]) # update = new_Update(message) if __name__", "self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in [32,1,2,3,4]])) def test_1_community (self): self.assertEqual(Community(256),256) def", "0xfd, 0xe8, 0x0, 0x0, 0x0, 0x0, 0x18, 0xa, 0x0, 0x1]])", "= ''.join([chr(c) for c in m]) # message = ''.join([chr(c)", "(unittest.TestCase): def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for", "in [24,1,2,3]])) def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in [32,1,2,3,4]]))", "0xfe, 0x80, 0x4, 0x4, 0x0, 0x0, 0x0, 0x0, 0x80, 0xe,", "exabgp.configuration.environment import environment env = environment.setup('') from exabgp.bgp.message.update.update import *", "environment env = environment.setup('') from exabgp.bgp.message.update.update import * from exabgp.bgp.message.update.attribute.community", "by <NAME> on 2009-09-06. Copyright (c) 2009-2013 Exa Networks. All", "self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self): header = ''.join([chr(c) for c in", "env = environment.setup('') from exabgp.bgp.message.update.update import * from exabgp.bgp.message.update.attribute.community import", "for c in [0,]])) def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c", "0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x22, 0x2]]) message =", "update = new_Update(message) print update.nlri print update.withdraw print update.attributes[MPRNLRI.ID][0] #", "Copyright (c) 2009-2013 Exa Networks. All rights reserved. \"\"\" import", "update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken (self): # header = ''.join([chr(c) for", "update.nlri print update.withdraw print update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken (self): #", "''.join([chr(c) for c in m]) # message = ''.join([chr(c) for", "(self): # header = ''.join([chr(c) for c in h]) #", "[24,1,2,3]])) def test_10_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in [32,1,2,3,4]])) def", "0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x12,", "= '8765:fdf8:f53e:61e4::18' announced = route.announce(1,1) message = announced[19:] update =", "[0,]])) def test_7_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','8').pack(),''.join([chr(c) for c in [8,1,]])) def", "h]) # message = ''.join([chr(c) for c in m]) #", "test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c in [0,]])) def test_7_prefix (self):", "= ''.join([chr(c) for c in [0x0, 0x0, 0x0, 0xb, 0x40,", "(self): self.assertEqual(to_NLRI('1.2.3.4','32').pack(),''.join([chr(c) for c in [32,1,2,3,4]])) def test_1_community (self): self.assertEqual(Community(256),256)", "0x0, 0x2, 0x1, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,", "message = ''.join([chr(c) for c in [0x0, 0x0, 0x0, 0xb,", "0xa, 0x0, 0x1]]) update = new_Update(message) self.assertEqual(str(update.nlri[0]),'10.0.1.0/24') def test_1_ipv6_1 (self):", "0x4, 0x0, 0x0, 0x0, 0x0, 0x80, 0xe, 0x1a, 0x0, 0x2,", "0x0, 0x4, 0x2, 0x1, 0xff, 0xfe, 0x80, 0x4, 0x4, 0x0,", "in [8,1,]])) def test_8_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in [16,1,2]]))", "0x1a, 0x0, 0x2, 0x1, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0,", "0x1, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,", "def test_1_community (self): self.assertEqual(Community(256),256) def test_2_community (self): self.assertEqual(to_Community('0x100'),256) def test_3_community", "in [0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,", "update.withdraw print update.attributes[MPRNLRI.ID][0] # def test_2_ipv4_broken (self): # header =", "to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self): route = RouteIP('1234:5678::',64) route.next_hop =", "0xf, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2, 0x4, 0x2, 0x1,", "import environment env = environment.setup('') from exabgp.bgp.message.update.update import * from", "c in [16,1,2]])) def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for c in", "import Community, Communities class TestData (unittest.TestCase): def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24')", "* from exabgp.bgp.message.update.attribute.community import to_Community from exabgp.bgp.message.update.attribute.community import Community, Communities", "def test_1_ipv4 (self): header = ''.join([chr(c) for c in [0xff,", "0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x12, 0x34,", "message = ''.join([chr(c) for c in [0x0, 0x0, 0x0, 0x30,", "for c in [0x0, 0x0, 0x0, 0xb, 0x40, 0x1, 0x1,", "Communities() community = to_Community('1:1') communities.add(community) self.assertEqual(communities.pack(),''.join([chr(c) for c in [0xc0,0x08,0x04,0x00,0x01,0x00,0x01]]))", "from exabgp.bgp.message.update.attribute.community import to_Community from exabgp.bgp.message.update.attribute.community import Community, Communities class", "\"\"\" import unittest from exabgp.configuration.environment import environment env = environment.setup('')", "from exabgp.bgp.message.update.update import * from exabgp.bgp.message.update.attribute.community import to_Community from exabgp.bgp.message.update.attribute.community", "0xe, 0x1a, 0x0, 0x2, 0x1, 0x10, 0x0, 0x0, 0x0, 0x0,", "0x34, 0x56, 0x78]]) update = to_Update([],[to_NLRI('1234:5678::',32)]) self.assertEqual(str(update.nlri[0]),'1234:5678::/32') def test_1_ipv6_2 (self):", "self.assertEqual(to_NLRI('1.2.3.4','16').pack(),''.join([chr(c) for c in [16,1,2]])) def test_9_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','24').pack(),''.join([chr(c) for", "0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0x47, 0x2]]) message =", "''.join([chr(c) for c in [0xff, 0xff, 0xff, 0xff, 0xff, 0xff,", "header = ''.join([chr(c) for c in h]) # message =", "utf-8 \"\"\" update.py Created by <NAME> on 2009-09-06. Copyright (c)", "in [0x0, 0x0, 0x0, 0xb, 0x40, 0x1, 0x1, 0x0, 0x40,", "test_1_community (self): self.assertEqual(Community(256),256) def test_2_community (self): self.assertEqual(to_Community('0x100'),256) def test_3_community (self):", "for c in [0x0, 0x0, 0x0, 0xf, 0x40, 0x1, 0x1,", "def test_2_prefix (self): self.assertEqual(str(to_NLRI('10.0.0.0','24')),'10.0.0.0/24') def test_6_prefix (self): self.assertEqual(to_NLRI('1.2.3.4','0').pack(),''.join([chr(c) for c", "0x2, 0x0, 0x4, 0x2, 0x1, 0xff, 0xfe, 0x80, 0x4, 0x4,", "[0x0, 0x0, 0x0, 0xb, 0x40, 0x1, 0x1, 0x0, 0x40, 0x2," ]
[ "from nuscenes.eval.detection.config import config_factory from nuscenes.eval.detection.constants import TP_METRICS from nuscenes.eval.detection.data_classes", ":param eval_set: The dataset split to evaluate on, e.g. train", "data') metric_data_list = MetricDataList() for class_name in self.cfg.class_names: for dist_th", "example_dir = os.path.join(self.output_dir, 'examples') if not os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token", "print('Calculating metrics') metrics = DetectionMetrics(self.cfg) for class_name in self.cfg.class_names: for", "add_center_dist(nusc, self.gt_boxes) # Filter boxes (distance, points per box, etc.).", "min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name in self.cfg.class_names: class_pr_curve(md_list, metrics,", "disk. - render: Renders various plots and dumps to disk.", "self.cfg.class_names: for dist_th in self.cfg.dist_ths: md = accumulate(self.gt_boxes, self.pred_boxes, class_name,", "for tp_name, tp_val in metrics_summary['tp_errors'].items(): print('%s: %.4f' % (err_name_mapping[tp_name], tp_val))", "detection_name in self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name +", "metrics_summary = metrics.serialize() metrics_summary['meta'] = self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w')", "nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp from nuscenes.eval.detection.config import config_factory from", "The weighted sum of the above. Here is an overview", "ground truth annotations') self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens", "metrics, metric_data_list def render(self, metrics: DetectionMetrics, md_list: MetricDataList) -> None:", "Loads GT annotations an predictions stored in JSON format and", "True): \"\"\" Initialize a NuScenesEval object. :param nusc: A NuScenes", "to disk. We assume that: - Every sample_token is given", "Calculate metrics from the data. # ----------------------------------- if self.verbose: print('Calculating", "tp = np.nan else: tp = calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name,", "distances. self.pred_boxes = add_center_dist(nusc, self.pred_boxes) self.gt_boxes = add_center_dist(nusc, self.gt_boxes) #", "# Make dirs. self.plot_dir = os.path.join(self.output_dir, 'plots') if not os.path.isdir(self.output_dir):", "Results are written to the provided output_dir. nuScenes uses the", "- start_time) return metrics, metric_data_list def render(self, metrics: DetectionMetrics, md_list:", "many example visualizations to write to disk.') parser.add_argument('--render_curves', type=int, default=1,", "list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples] # Visualize samples. example_dir =", "help='Default nuScenes data directory.') parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which version of", "dumps to disk. We assume that: - Every sample_token is", "import load_prediction, load_gt, add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render import summary_plot, class_pr_curve,", "stdout. \"\"\" self.nusc = nusc self.result_path = result_path self.eval_set =", "as f: json.dump(metrics_summary, f, indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as", "Renders various PR and TP curves. :param metrics: DetectionMetrics instance.", "self.cfg.dist_ths: metric_data = metric_data_list[(class_name, dist_th)] ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision)", "if verbose: print('Filtering ground truth annotations') self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes,", "Print high-level metrics. print('mAP: %.4f' % (metrics_summary['mean_ap'])) err_name_mapping = {", "NuScenesEval object. :param nusc: A NuScenes object. :param config: A", "for detection_name in self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name", "Detection Score (NDS): The weighted sum of the above. Here", "= calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap) for metric_name in", "eval_set: str, output_dir: str = None, verbose: bool = True):", "object. :param result_path: Path of the nuScenes JSON result file.", "md) # ----------------------------------- # Step 2: Calculate metrics from the", "\"\"\" Renders various PR and TP curves. :param metrics: DetectionMetrics", "nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders import load_prediction,", "Whether to print to stdout. \"\"\" self.nusc = nusc self.result_path", "the raw metric data. \"\"\" start_time = time.time() # -----------------------------------", "that stores the high-level metrics and meta data. \"\"\" if", "name + '.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for", "to store result metrics, graphs and example visualizations.') parser.add_argument('--eval_set', type=str,", "in self.cfg.class_names: for dist_th in self.cfg.dist_ths: md = accumulate(self.gt_boxes, self.pred_boxes,", "result_path_ = os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir) eval_set_ = args.eval_set dataroot_", "= time.time() # ----------------------------------- # Step 1: Accumulate metric data", "= metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name in ['traffic_cone'] and metric_name in", "visualizes samples, runs the evaluation and renders stat plots. :param", "in self.cfg.dist_ths: metric_data = metric_data_list[(class_name, dist_th)] ap = calc_ap(metric_data, self.cfg.min_recall,", "samples. example_dir = os.path.join(self.output_dir, 'examples') if not os.path.isdir(example_dir): os.mkdir(example_dir) for", "self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap) for metric_name in TP_METRICS: metric_data =", "distance thresholds. # ----------------------------------- if self.verbose: print('Accumulating metric data') metric_data_list", "high-level metrics and meta data. \"\"\" if plot_examples > 0:", "# ----------------------------------- if self.verbose: print('Accumulating metric data') metric_data_list = MetricDataList()", "from typing import Tuple, Dict, Any import numpy as np", "to disk.') parser.add_argument('--verbose', type=int, default=1, help='Whether to print to stdout.')", "nuScenes data directory.') parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which version of the", "self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes = load_gt(self.nusc, self.eval_set,", "main(self, plot_examples: int = 0, render_curves: bool = True) ->", "NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_, eval_set=eval_set_, output_dir=output_dir_,", "json.dump(metric_data_list.serialize(), f, indent=2) # Print high-level metrics. print('mAP: %.4f' %", "verbose self.cfg = config # Make dirs. self.plot_dir = os.path.join(self.output_dir,", "are written to the provided output_dir. nuScenes uses the following", "dist_th)] ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap) for", "Dump the metric data, meta and metrics to disk. if", "str, output_dir: str = None, verbose: bool = True): \"\"\"", "by <NAME> & <NAME>, 2018. # Licensed under the Creative", "1: Accumulate metric data for all classes and distance thresholds.", "sum of the above. Here is an overview of the", "if self.eval_set != 'test' else EvalBoxes(), # Don't render test", "(err_name_mapping[tp_name], tp_val)) print('NDS: %.4f' % (metrics_summary['nd_score'])) print('Eval time: %.1fs' %", "split to evaluate on, train, val or test.') parser.add_argument('--dataroot', type=str,", "help='How many example visualizations to write to disk.') parser.add_argument('--render_curves', type=int,", "self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens) ==", "see https://github.com/nutonomy/nuscenes-devkit for more details. \"\"\" def __init__(self, nusc: NuScenes,", "accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th, md) # -----------------------------------", "self.gt_boxes if self.eval_set != 'test' else EvalBoxes(), # Don't render", "print('Eval time: %.1fs' % metrics_summary['eval_time']) return metrics_summary if __name__ ==", "example visualizations to write to disk.') parser.add_argument('--render_curves', type=int, default=1, help='Whether", "os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load data. self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample,", "to render PR and TP curves to disk.') parser.add_argument('--verbose', type=int,", "visualize_sample(self.nusc, sample_token, self.gt_boxes if self.eval_set != 'test' else EvalBoxes(), #", "Accumulate metric data for all classes and distance thresholds. #", "of high-level and the raw metric data. \"\"\" start_time =", "JSON file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder to store result metrics,", "and TP curves. :param metrics: DetectionMetrics instance. :param md_list: MetricDataList", "to plot. random.seed(43) sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples]", "data. \"\"\" start_time = time.time() # ----------------------------------- # Step 1:", "dataset to evaluate on, e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name", "high-level metrics. print('mAP: %.4f' % (metrics_summary['mean_ap'])) err_name_mapping = { 'trans_err':", "dataset split to evaluate on, e.g. train or val. :param", ":param nusc: A NuScenes object. :param config: A DetectionConfig object.", "os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load data. self.pred_boxes,", "and TP curves to disk.') parser.add_argument('--verbose', type=int, default=1, help='Whether to", "there may be not predictions for that sample. Please see", "metric_data_list = self.evaluate() # Render PR and TP curves. if", "self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list,", "metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th))) def main(self, plot_examples:", "directory.') parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which version of the nuScenes dataset", "self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp')) for dist_th in self.cfg.dist_ths: dist_pr_curve(md_list, metrics,", "version_ = args.version config_name_ = args.config_name plot_examples_ = args.plot_examples render_curves_", "== \"__main__\": # Settings. parser = argparse.ArgumentParser(description='Evaluate nuScenes result submission.',", "EvalBoxes from nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render", "print('%s: %.4f' % (err_name_mapping[tp_name], tp_val)) print('NDS: %.4f' % (metrics_summary['nd_score'])) print('Eval", "file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder to store result metrics, graphs", "self.cfg.class_range, verbose=verbose) if verbose: print('Filtering ground truth annotations') self.gt_boxes =", "split to evaluate on, e.g. train or val. :param output_dir:", "Please see https://github.com/nutonomy/nuscenes-devkit for more details. \"\"\" def __init__(self, nusc:", "translation, velocity, scale, orientation and attribute errors. - nuScenes Detection", "the boxes. - run: Performs evaluation and dumps the metric", "metric_data_list[(class_name, dist_th)] ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap)", "functions in this method: - init: Loads GT annotations an", "Precision (mAP): Uses center-distance as matching criterion; averaged over distance", "renders stat plots. :param plot_examples: How many example visualizations to", "We assume that: - Every sample_token is given in the", "= { 'trans_err': 'mATE', 'scale_err': 'mASE', 'orient_err': 'mAOE', 'vel_err': 'mAVE',", "data, meta and metrics to disk. if self.verbose: print('Saving metrics", "random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples] # Visualize samples. example_dir = os.path.join(self.output_dir,", "'scale_err': 'mASE', 'orient_err': 'mAOE', 'vel_err': 'mAVE', 'attr_err': 'mAAE' } for", "dumps the metric data to disk. - render: Renders various", "# Visualize samples. example_dir = os.path.join(self.output_dir, 'examples') if not os.path.isdir(example_dir):", "if self.verbose: print('Accumulating metric data') metric_data_list = MetricDataList() for class_name", "the high-level metrics and meta data. \"\"\" if plot_examples >", "# Don't render test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) #", "parser.add_argument('result_path', type=str, help='The submission as a JSON file.') parser.add_argument('--output_dir', type=str,", "parser.add_argument('--eval_set', type=str, default='val', help='Which dataset split to evaluate on, train,", "A dict that stores the high-level metrics and meta data.", "nuScenes uses the following metrics: - Mean Average Precision (mAP):", "uses the following metrics: - Mean Average Precision (mAP): Uses", "as f: json.dump(metric_data_list.serialize(), f, indent=2) # Print high-level metrics. print('mAP:", "if self.verbose: print('Calculating metrics') metrics = DetectionMetrics(self.cfg) for class_name in", "self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th))) def main(self, plot_examples: int = 0,", "with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f: json.dump(metric_data_list.serialize(), f, indent=2) #", "# ----------------------------------- # Step 2: Calculate metrics from the data.", "visualize_sample class NuScenesEval: \"\"\" This is the official nuScenes detection", "TP curves to disk.') parser.add_argument('--verbose', type=int, default=1, help='Whether to print", "to write to disk.') parser.add_argument('--render_curves', type=int, default=1, help='Whether to render", "import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders import load_prediction, load_gt,", "parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder to store result metrics, graphs and", "of the configuration to use for evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples',", "metric_data_list = MetricDataList() for class_name in self.cfg.class_names: for dist_th in", "args.eval_set dataroot_ = args.dataroot version_ = args.version config_name_ = args.config_name", "metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp')) for dist_th in", "in TP_METRICS: metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name in ['traffic_cone']", "version of the nuScenes dataset to evaluate on, e.g. v1.0-trainval.')", "= self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f: json.dump(metrics_summary, f,", "import config_factory from nuscenes.eval.detection.constants import TP_METRICS from nuscenes.eval.detection.data_classes import DetectionConfig,", "PR and TP curves. :param metrics: DetectionMetrics instance. :param md_list:", "type=int, default=1, help='Whether to print to stdout.') args = parser.parse_args()", "(NDS): The weighted sum of the above. Here is an", "result_path: str, eval_set: str, output_dir: str = None, verbose: bool", "various plots and dumps to disk. We assume that: -", "data for all classes and distance thresholds. # ----------------------------------- if", "DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist,", "render: Renders various plots and dumps to disk. We assume", "the evaluation and renders stat plots. :param plot_examples: How many", "savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name +", "str = None, verbose: bool = True): \"\"\" Initialize a", "metric_data_list def render(self, metrics: DetectionMetrics, md_list: MetricDataList) -> None: \"\"\"", "type=str, default='~/nuscenes-metrics', help='Folder to store result metrics, graphs and example", "min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name in self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name,", "the metric data, meta and metrics to disk. if self.verbose:", "= filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens def evaluate(self)", "config_factory from nuscenes.eval.detection.constants import TP_METRICS from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList,", "TP_METRICS from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders", "= load_gt(self.nusc, self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\ \"Samples", "print('Filtering predictions') self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose) if verbose:", "visualizations to write to disk.') parser.add_argument('--render_curves', type=int, default=1, help='Whether to", "# Print high-level metrics. print('mAP: %.4f' % (metrics_summary['mean_ap'])) err_name_mapping =", "= os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir) eval_set_ = args.eval_set dataroot_ =", "eval_set self.output_dir = output_dir self.verbose = verbose self.cfg = config", "Average Precision (mAP): Uses center-distance as matching criterion; averaged over", "json.dump(metrics_summary, f, indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f: json.dump(metric_data_list.serialize(),", "attribute errors. - nuScenes Detection Score (NDS): The weighted sum", "filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose) if verbose: print('Filtering ground truth annotations')", "verbose: print('Filtering predictions') self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose) if", "= config # Make dirs. self.plot_dir = os.path.join(self.output_dir, 'plots') if", "'_pr')) class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp')) for", "thresholds. - True Positive (TP) metrics: Average of translation, velocity,", "predictions for that sample. Please see https://github.com/nutonomy/nuscenes-devkit for more details.", "None, verbose: bool = True): \"\"\" Initialize a NuScenesEval object.", "load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens)", "self.pred_boxes, self.cfg.class_range, verbose=verbose) if verbose: print('Filtering ground truth annotations') self.gt_boxes", "> 0: # Select a random but fixed subset to", "(distance, points per box, etc.). if verbose: print('Filtering predictions') self.pred_boxes", "random import time from typing import Tuple, Dict, Any import", "EvalBoxes(), # Don't render test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token)))", "'vel_err': 'mAVE', 'attr_err': 'mAAE' } for tp_name, tp_val in metrics_summary['tp_errors'].items():", "truth annotations') self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens =", "np.nan elif class_name in ['barrier'] and metric_name in ['attr_err', 'vel_err']:", "verbose=verbose) assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\ \"Samples in split doesn't", "def savepath(name): return os.path.join(self.plot_dir, name + '.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision,", "classes and distance thresholds. # ----------------------------------- if self.verbose: print('Accumulating metric", "if self.verbose: print('Saving metrics to: %s' % self.output_dir) metrics_summary =", "eval_set: The dataset split to evaluate on, e.g. train or", "NuScenes from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp from nuscenes.eval.detection.config import", "__name__ == \"__main__\": # Settings. parser = argparse.ArgumentParser(description='Evaluate nuScenes result", "all classes and distance thresholds. # ----------------------------------- if self.verbose: print('Accumulating", "submission as a JSON file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder to", "be not predictions for that sample. Please see https://github.com/nutonomy/nuscenes-devkit for", "disk.') parser.add_argument('--verbose', type=int, default=1, help='Whether to print to stdout.') args", "disk. We assume that: - Every sample_token is given in", "dataroot_ = args.dataroot version_ = args.version config_name_ = args.config_name plot_examples_", "= verbose self.cfg = config # Make dirs. self.plot_dir =", "\"\"\" Performs the actual evaluation. :return: A tuple of high-level", "that sample. Please see https://github.com/nutonomy/nuscenes-devkit for more details. \"\"\" def", "['traffic_cone'] and metric_name in ['attr_err', 'vel_err', 'orient_err']: tp = np.nan", "print('mAP: %.4f' % (metrics_summary['mean_ap'])) err_name_mapping = { 'trans_err': 'mATE', 'scale_err':", "} for tp_name, tp_val in metrics_summary['tp_errors'].items(): print('%s: %.4f' % (err_name_mapping[tp_name],", "self.result_path = result_path self.eval_set = eval_set self.output_dir = output_dir self.verbose", "class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list, metrics,", "self.verbose: print('Calculating metrics') metrics = DetectionMetrics(self.cfg) for class_name in self.cfg.class_names:", "cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10, help='How many example visualizations to write", "Any import numpy as np from nuscenes import NuScenes from", "self.eval_set = eval_set self.output_dir = output_dir self.verbose = verbose self.cfg", "0, render_curves: bool = True) -> Dict[str, Any]: \"\"\" Main", "curves. if render_curves: self.render(metrics, metric_data_list) # Dump the metric data,", "evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10, help='How many example visualizations", "the official nuScenes detection evaluation code. Results are written to", "to write to disk. :param render_curves: Whether to render PR", "metric_name) metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time() - start_time) return metrics, metric_data_list", "os.path.join(self.output_dir, 'plots') if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir)", "How many example visualizations to write to disk. :param render_curves:", "os.path.expanduser(args.output_dir) eval_set_ = args.eval_set dataroot_ = args.dataroot version_ = args.version", "metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name in ['traffic_cone'] and metric_name", "'metrics_summary.json'), 'w') as f: json.dump(metrics_summary, f, indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'),", "code. Results are written to the provided output_dir. nuScenes uses", "# Select a random but fixed subset to plot. random.seed(43)", "indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f: json.dump(metric_data_list.serialize(), f, indent=2)", "help='Folder to store result metrics, graphs and example visualizations.') parser.add_argument('--eval_set',", "render(self, metrics: DetectionMetrics, md_list: MetricDataList) -> None: \"\"\" Renders various", "run: Performs evaluation and dumps the metric data to disk.", "default=1, help='Whether to render PR and TP curves to disk.')", "parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which version of the nuScenes dataset to", "output_dir self.verbose = verbose self.cfg = config # Make dirs.", "for that sample. Please see https://github.com/nutonomy/nuscenes-devkit for more details. \"\"\"", "for dist_th in self.cfg.dist_ths: metric_data = metric_data_list[(class_name, dist_th)] ap =", "as np from nuscenes import NuScenes from nuscenes.eval.detection.algo import accumulate,", "% (metrics_summary['nd_score'])) print('Eval time: %.1fs' % metrics_summary['eval_time']) return metrics_summary if", "write to disk. :param render_curves: Whether to render PR and", "parser.add_argument('--verbose', type=int, default=1, help='Whether to print to stdout.') args =", "Performs evaluation and dumps the metric data to disk. -", "metric_name in ['attr_err', 'vel_err']: tp = np.nan else: tp =", "'w') as f: json.dump(metrics_summary, f, indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w')", "val or test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default nuScenes data directory.')", "DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist, filter_eval_boxes from", "plot_examples_ = args.plot_examples render_curves_ = bool(args.render_curves) verbose_ = bool(args.verbose) cfg_", "in ['barrier'] and metric_name in ['attr_err', 'vel_err']: tp = np.nan", "filter_eval_boxes from nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample class", "dist_th, ap) for metric_name in TP_METRICS: metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)]", "PR and TP curves. if render_curves: self.render(metrics, metric_data_list) # Dump", "= None, verbose: bool = True): \"\"\" Initialize a NuScenesEval", "from nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample class NuScenesEval:", "and meta data. \"\"\" if plot_examples > 0: # Select", "# Render PR and TP curves. if render_curves: self.render(metrics, metric_data_list)", "self.cfg.class_names: for dist_th in self.cfg.dist_ths: metric_data = metric_data_list[(class_name, dist_th)] ap", "result metrics, graphs and example visualizations.') parser.add_argument('--eval_set', type=str, default='val', help='Which", "import random import time from typing import Tuple, Dict, Any", "Step 1: Accumulate metric data for all classes and distance", "render PR and TP curves to disk.') parser.add_argument('--verbose', type=int, default=1,", "md_list: MetricDataList instance. \"\"\" def savepath(name): return os.path.join(self.plot_dir, name +", "= args.plot_examples render_curves_ = bool(args.render_curves) verbose_ = bool(args.verbose) cfg_ =", "import argparse import json import os import random import time", "[see licence.txt] import argparse import json import os import random", "% self.output_dir) metrics_summary = metrics.serialize() metrics_summary['meta'] = self.meta.copy() with open(os.path.join(self.output_dir,", "int = 0, render_curves: bool = True) -> Dict[str, Any]:", "dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_, eval_set=eval_set_, output_dir=output_dir_, verbose=verbose_) nusc_eval.main(plot_examples=plot_examples_,", "Make dirs. self.plot_dir = os.path.join(self.output_dir, 'plots') if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir)", "+ str(dist_th))) def main(self, plot_examples: int = 0, render_curves: bool", "metric_data = metric_data_list[(class_name, dist_th)] ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name,", "in ['attr_err', 'vel_err']: tp = np.nan else: tp = calc_tp(metric_data,", "evaluation. :return: A tuple of high-level and the raw metric", "metric_name in ['attr_err', 'vel_err', 'orient_err']: tp = np.nan elif class_name", "over distance thresholds. - True Positive (TP) metrics: Average of", "'mAOE', 'vel_err': 'mAVE', 'attr_err': 'mAAE' } for tp_name, tp_val in", "TP curves. :param metrics: DetectionMetrics instance. :param md_list: MetricDataList instance.", "to save plots and results to. :param verbose: Whether to", "val. :param output_dir: Folder to save plots and results to.", "train or val. :param output_dir: Folder to save plots and", "thresholds. # ----------------------------------- if self.verbose: print('Accumulating metric data') metric_data_list =", "= metric_data_list[(class_name, dist_th)] ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th,", "import os import random import time from typing import Tuple,", "and dumps to disk. We assume that: - Every sample_token", "evaluate on, e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name of the", "Performs the actual evaluation. :return: A tuple of high-level and", "GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run evaluation. metrics, metric_data_list", "import Tuple, Dict, Any import numpy as np from nuscenes", "MetricDataList, DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist, filter_eval_boxes", "+ '_pr')) class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp'))", "plot_examples: int = 0, render_curves: bool = True) -> Dict[str,", ":return: A tuple of high-level and the raw metric data.", "self.sample_tokens = self.gt_boxes.sample_tokens def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs", "metric data for all classes and distance thresholds. # -----------------------------------", "sample_tokens = sample_tokens[:plot_examples] # Visualize samples. example_dir = os.path.join(self.output_dir, 'examples')", "self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run evaluation. metrics, metric_data_list =", "data to disk. - render: Renders various plots and dumps", "in this method: - init: Loads GT annotations an predictions", "self.pred_boxes) self.gt_boxes = add_center_dist(nusc, self.gt_boxes) # Filter boxes (distance, points", "Score (NDS): The weighted sum of the above. Here is", "and the raw metric data. \"\"\" start_time = time.time() #", "type=str, default='val', help='Which dataset split to evaluate on, train, val", "\"\"\" self.nusc = nusc self.result_path = result_path self.eval_set = eval_set", "samples, runs the evaluation and renders stat plots. :param plot_examples:", "'mAAE' } for tp_name, tp_val in metrics_summary['tp_errors'].items(): print('%s: %.4f' %", "above. Here is an overview of the functions in this", "nusc self.result_path = result_path self.eval_set = eval_set self.output_dir = output_dir", "result_path self.eval_set = eval_set self.output_dir = output_dir self.verbose = verbose", "DetectionMetrics instance. :param md_list: MetricDataList instance. \"\"\" def savepath(name): return", "evaluation and renders stat plots. :param plot_examples: How many example", "of translation, velocity, scale, orientation and attribute errors. - nuScenes", "add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample", "this method: - init: Loads GT annotations an predictions stored", "self.nusc = nusc self.result_path = result_path self.eval_set = eval_set self.output_dir", "= np.nan elif class_name in ['barrier'] and metric_name in ['attr_err',", "(mAP): Uses center-distance as matching criterion; averaged over distance thresholds.", "'mAVE', 'attr_err': 'mAAE' } for tp_name, tp_val in metrics_summary['tp_errors'].items(): print('%s:", "JSON result file. :param eval_set: The dataset split to evaluate", "an overview of the functions in this method: - init:", "curves to disk.') parser.add_argument('--verbose', type=int, default=1, help='Whether to print to", "self.cfg.dist_ths: md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th,", "# Licensed under the Creative Commons [see licence.txt] import argparse", "# Dump the metric data, meta and metrics to disk.", "NuScenes, config: DetectionConfig, result_path: str, eval_set: str, output_dir: str =", "https://github.com/nutonomy/nuscenes-devkit for more details. \"\"\" def __init__(self, nusc: NuScenes, config:", "and metric_name in ['attr_err', 'vel_err']: tp = np.nan else: tp", "self.eval_set != 'test' else EvalBoxes(), # Don't render test GT.", "with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f: json.dump(metrics_summary, f, indent=2) with", "example visualizations.') parser.add_argument('--eval_set', type=str, default='val', help='Which dataset split to evaluate", "nuscenes.eval.detection.config import config_factory from nuscenes.eval.detection.constants import TP_METRICS from nuscenes.eval.detection.data_classes import", "evaluate on, train, val or test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default", "self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap) for metric_name in TP_METRICS: metric_data", "plot. random.seed(43) sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples] #", "boxes. - run: Performs evaluation and dumps the metric data", "['barrier'] and metric_name in ['attr_err', 'vel_err']: tp = np.nan else:", "# Settings. parser = argparse.ArgumentParser(description='Evaluate nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path',", "not predictions for that sample. Please see https://github.com/nutonomy/nuscenes-devkit for more", "or val. :param output_dir: Folder to save plots and results", "parser.add_argument('--plot_examples', type=int, default=10, help='How many example visualizations to write to", "assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\ \"Samples in split doesn't match", "to evaluate on, e.g. train or val. :param output_dir: Folder", "self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f: json.dump(metrics_summary, f, indent=2)", ":return: A dict that stores the high-level metrics and meta", "set(self.gt_boxes.sample_tokens), \\ \"Samples in split doesn't match samples in predictions.\"", "sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples] # Visualize samples.", "self.pred_boxes = add_center_dist(nusc, self.pred_boxes) self.gt_boxes = add_center_dist(nusc, self.gt_boxes) # Filter", "= accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th, md) #", "self.plot_dir = os.path.join(self.output_dir, 'plots') if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not", "tp_val)) print('NDS: %.4f' % (metrics_summary['nd_score'])) print('Eval time: %.1fs' % metrics_summary['eval_time'])", "dataset split to evaluate on, train, val or test.') parser.add_argument('--dataroot',", "Average of translation, velocity, scale, orientation and attribute errors. -", "accumulate, calc_ap, calc_tp from nuscenes.eval.detection.config import config_factory from nuscenes.eval.detection.constants import", "\"\"\" start_time = time.time() # ----------------------------------- # Step 1: Accumulate", "nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render import summary_plot,", "a JSON file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder to store result", "predictions') self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose) if verbose: print('Filtering", "= MetricDataList() for class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths:", "def __init__(self, nusc: NuScenes, config: DetectionConfig, result_path: str, eval_set: str,", "output_dir: str = None, verbose: bool = True): \"\"\" Initialize", "verbose=verbose) if verbose: print('Filtering ground truth annotations') self.gt_boxes = filter_eval_boxes(nusc,", "and renders stat plots. :param plot_examples: How many example visualizations", "% (err_name_mapping[tp_name], tp_val)) print('NDS: %.4f' % (metrics_summary['nd_score'])) print('Eval time: %.1fs'", "self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th))) def", "cfg_ = config_factory(config_name_) nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval =", "stores the high-level metrics and meta data. \"\"\" if plot_examples", "# Code written by <NAME> & <NAME>, 2018. # Licensed", "os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token in sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes if", "calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap) for metric_name in TP_METRICS:", "to. :param verbose: Whether to print to stdout. \"\"\" self.nusc", "TP curves to disk. :return: A dict that stores the", "import accumulate, calc_ap, calc_tp from nuscenes.eval.detection.config import config_factory from nuscenes.eval.detection.constants", "# Step 2: Calculate metrics from the data. # -----------------------------------", "method: - init: Loads GT annotations an predictions stored in", "print to stdout. \"\"\" self.nusc = nusc self.result_path = result_path", "detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp')) for dist_th in self.cfg.dist_ths:", "in self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name + '_pr'))", "object. :param config: A DetectionConfig object. :param result_path: Path of", "type=str, default='/data/sets/nuscenes', help='Default nuScenes data directory.') parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which", "class_name in ['barrier'] and metric_name in ['attr_err', 'vel_err']: tp =", "and example visualizations.') parser.add_argument('--eval_set', type=str, default='val', help='Which dataset split to", "'w') as f: json.dump(metric_data_list.serialize(), f, indent=2) # Print high-level metrics.", "typing import Tuple, Dict, Any import numpy as np from", "np.nan else: tp = calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name, tp)", "verbose: bool = True): \"\"\" Initialize a NuScenesEval object. :param", "metric data') metric_data_list = MetricDataList() for class_name in self.cfg.class_names: for", "\"\"\" def __init__(self, nusc: NuScenes, config: DetectionConfig, result_path: str, eval_set:", "samples in predictions.\" # Add center distances. self.pred_boxes = add_center_dist(nusc,", "# ----------------------------------- # Step 1: Accumulate metric data for all", "config_name_ = args.config_name plot_examples_ = args.plot_examples render_curves_ = bool(args.render_curves) verbose_", "calc_ap, calc_tp from nuscenes.eval.detection.config import config_factory from nuscenes.eval.detection.constants import TP_METRICS", "----------------------------------- if self.verbose: print('Accumulating metric data') metric_data_list = MetricDataList() for", "= sample_tokens[:plot_examples] # Visualize samples. example_dir = os.path.join(self.output_dir, 'examples') if", "Dict, Any import numpy as np from nuscenes import NuScenes", "savepath=savepath('summary')) for detection_name in self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall,", "= self.gt_boxes.sample_tokens def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs the", "This is the official nuScenes detection evaluation code. Results are", "dev-kit. # Code written by <NAME> & <NAME>, 2018. #", "plots and results to. :param verbose: Whether to print to", "'examples') if not os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token in sample_tokens: visualize_sample(self.nusc,", "help='Whether to render PR and TP curves to disk.') parser.add_argument('--verbose',", "and distance thresholds. # ----------------------------------- if self.verbose: print('Accumulating metric data')", "metrics: - Mean Average Precision (mAP): Uses center-distance as matching", "self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens def evaluate(self) -> Tuple[DetectionMetrics,", "file. :param eval_set: The dataset split to evaluate on, e.g.", "center-distance as matching criterion; averaged over distance thresholds. - True", "various PR and TP curves. :param metrics: DetectionMetrics instance. :param", "metrics and meta data. \"\"\" if plot_examples > 0: #", "sample_tokens[:plot_examples] # Visualize samples. example_dir = os.path.join(self.output_dir, 'examples') if not", "= list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples] # Visualize samples. example_dir", "stored in JSON format and filters the boxes. - run:", "= eval_set self.output_dir = output_dir self.verbose = verbose self.cfg =", "2018. # Licensed under the Creative Commons [see licence.txt] import", "savepath=savepath('dist_pr_' + str(dist_th))) def main(self, plot_examples: int = 0, render_curves:", "many example visualizations to write to disk. :param render_curves: Whether", "and results to. :param verbose: Whether to print to stdout.", ":param md_list: MetricDataList instance. \"\"\" def savepath(name): return os.path.join(self.plot_dir, name", "metrics = DetectionMetrics(self.cfg) for class_name in self.cfg.class_names: for dist_th in", "in ['attr_err', 'vel_err', 'orient_err']: tp = np.nan elif class_name in", "verbose: print('Filtering ground truth annotations') self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range,", "DetectionMetrics(self.cfg) for class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths: metric_data", "%.4f' % (metrics_summary['mean_ap'])) err_name_mapping = { 'trans_err': 'mATE', 'scale_err': 'mASE',", "orientation and attribute errors. - nuScenes Detection Score (NDS): The", "self.verbose: print('Saving metrics to: %s' % self.output_dir) metrics_summary = metrics.serialize()", "if __name__ == \"__main__\": # Settings. parser = argparse.ArgumentParser(description='Evaluate nuScenes", "if verbose: print('Filtering predictions') self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose)", "from nuscenes.eval.detection.constants import TP_METRICS from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics,", "'attr_err': 'mAAE' } for tp_name, tp_val in metrics_summary['tp_errors'].items(): print('%s: %.4f'", "'_tp')) for dist_th in self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall,", "load_gt(self.nusc, self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\ \"Samples in", "== set(self.gt_boxes.sample_tokens), \\ \"Samples in split doesn't match samples in", "result file. :param eval_set: The dataset split to evaluate on,", "the evaluation code, visualizes samples, runs the evaluation and renders", "= args.version config_name_ = args.config_name plot_examples_ = args.plot_examples render_curves_ =", "plot_examples > 0: # Select a random but fixed subset", "----------------------------------- # Step 1: Accumulate metric data for all classes", "example visualizations to write to disk. :param render_curves: Whether to", "DetectionConfig, result_path: str, eval_set: str, output_dir: str = None, verbose:", "high-level and the raw metric data. \"\"\" start_time = time.time()", "'plots') if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) #", "to use for evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10, help='How", "= args.dataroot version_ = args.version config_name_ = args.config_name plot_examples_ =", "'metrics_details.json'), 'w') as f: json.dump(metric_data_list.serialize(), f, indent=2) # Print high-level", "= True) -> Dict[str, Any]: \"\"\" Main function that loads", "for more details. \"\"\" def __init__(self, nusc: NuScenes, config: DetectionConfig,", "# Add center distances. self.pred_boxes = add_center_dist(nusc, self.pred_boxes) self.gt_boxes =", "NuScenes object. :param config: A DetectionConfig object. :param result_path: Path", "os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load data. self.pred_boxes, self.meta", "metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time() - start_time) return metrics, metric_data_list def", "= result_path self.eval_set = eval_set self.output_dir = output_dir self.verbose =", "\"\"\" if plot_examples > 0: # Select a random but", "numpy as np from nuscenes import NuScenes from nuscenes.eval.detection.algo import", "import NuScenes from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp from nuscenes.eval.detection.config", "Renders various plots and dumps to disk. We assume that:", "else: tp = calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time()", "and dumps the metric data to disk. - render: Renders", "render_curves: bool = True) -> Dict[str, Any]: \"\"\" Main function", "savepath=savepath(detection_name + '_tp')) for dist_th in self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th,", "tp_val in metrics_summary['tp_errors'].items(): print('%s: %.4f' % (err_name_mapping[tp_name], tp_val)) print('NDS: %.4f'", "- nuScenes Detection Score (NDS): The weighted sum of the", "metric_data_list.set(class_name, dist_th, md) # ----------------------------------- # Step 2: Calculate metrics", "to evaluate on, e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name of", "evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs the actual evaluation. :return:", "default='cvpr_2019', help='Name of the configuration to use for evaluation, e.g.", "format and filters the boxes. - run: Performs evaluation and", "verbose=verbose) self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens),", "Step 2: Calculate metrics from the data. # ----------------------------------- if", "indent=2) # Print high-level metrics. print('mAP: %.4f' % (metrics_summary['mean_ap'])) err_name_mapping", "the functions in this method: - init: Loads GT annotations", "metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name in self.cfg.class_names: class_pr_curve(md_list,", "of the functions in this method: - init: Loads GT", "Select a random but fixed subset to plot. random.seed(43) sample_tokens", "store result metrics, graphs and example visualizations.') parser.add_argument('--eval_set', type=str, default='val',", "is the official nuScenes detection evaluation code. Results are written", "args.plot_examples render_curves_ = bool(args.render_curves) verbose_ = bool(args.verbose) cfg_ = config_factory(config_name_)", "str, eval_set: str, output_dir: str = None, verbose: bool =", "metrics.serialize() metrics_summary['meta'] = self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f:", "bool(args.render_curves) verbose_ = bool(args.verbose) cfg_ = config_factory(config_name_) nusc_ = NuScenes(version=version_,", "in split doesn't match samples in predictions.\" # Add center", "<NAME> & <NAME>, 2018. # Licensed under the Creative Commons", "for dist_th in self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_'", "%.4f' % (metrics_summary['nd_score'])) print('Eval time: %.1fs' % metrics_summary['eval_time']) return metrics_summary", "type=str, help='The submission as a JSON file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics',", "verbose=verbose_, dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_, eval_set=eval_set_, output_dir=output_dir_, verbose=verbose_)", "the following metrics: - Mean Average Precision (mAP): Uses center-distance", "= metrics.serialize() metrics_summary['meta'] = self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as", "actual evaluation. :return: A tuple of high-level and the raw", "detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall,", "use for evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10, help='How many", "save plots and results to. :param verbose: Whether to print", "errors. - nuScenes Detection Score (NDS): The weighted sum of", "in self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th)))", "def render(self, metrics: DetectionMetrics, md_list: MetricDataList) -> None: \"\"\" Renders", "self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\", "e.g. train or val. :param output_dir: Folder to save plots", "filters the boxes. - run: Performs evaluation and dumps the", "tp = np.nan elif class_name in ['barrier'] and metric_name in", "= 0, render_curves: bool = True) -> Dict[str, Any]: \"\"\"", "graphs and example visualizations.') parser.add_argument('--eval_set', type=str, default='val', help='Which dataset split", "self.verbose: print('Accumulating metric data') metric_data_list = MetricDataList() for class_name in", "an predictions stored in JSON format and filters the boxes.", "e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name of the configuration to", "MetricDataList]: \"\"\" Performs the actual evaluation. :return: A tuple of", "metrics, metric_data_list = self.evaluate() # Render PR and TP curves.", "Folder to save plots and results to. :param verbose: Whether", "\"\"\" Initialize a NuScenesEval object. :param nusc: A NuScenes object.", "self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th))) def main(self, plot_examples: int =", "to disk.') parser.add_argument('--render_curves', type=int, default=1, help='Whether to render PR and", "self.output_dir = output_dir self.verbose = verbose self.cfg = config #", "under the Creative Commons [see licence.txt] import argparse import json", "to disk. :return: A dict that stores the high-level metrics", "& <NAME>, 2018. # Licensed under the Creative Commons [see", ":param verbose: Whether to print to stdout. \"\"\" self.nusc =", "in ['traffic_cone'] and metric_name in ['attr_err', 'vel_err', 'orient_err']: tp =", "details. \"\"\" def __init__(self, nusc: NuScenes, config: DetectionConfig, result_path: str,", ":param plot_examples: How many example visualizations to write to disk.", "instance. \"\"\" def savepath(name): return os.path.join(self.plot_dir, name + '.pdf') summary_plot(md_list,", "metrics, graphs and example visualizations.') parser.add_argument('--eval_set', type=str, default='val', help='Which dataset", "metrics to: %s' % self.output_dir) metrics_summary = metrics.serialize() metrics_summary['meta'] =", "__init__(self, nusc: NuScenes, config: DetectionConfig, result_path: str, eval_set: str, output_dir:", "-> Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs the actual evaluation. :return: A", "scale, orientation and attribute errors. - nuScenes Detection Score (NDS):", "from nuscenes import NuScenes from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp", "summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample class NuScenesEval: \"\"\" This is", "\"\"\" def savepath(name): return os.path.join(self.plot_dir, name + '.pdf') summary_plot(md_list, metrics,", "predictions.\" # Add center distances. self.pred_boxes = add_center_dist(nusc, self.pred_boxes) self.gt_boxes", "metric data, meta and metrics to disk. if self.verbose: print('Saving", "visualizations.') parser.add_argument('--eval_set', type=str, default='val', help='Which dataset split to evaluate on,", "metric_name in TP_METRICS: metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name in", "provided output_dir. nuScenes uses the following metrics: - Mean Average", "Dict[str, Any]: \"\"\" Main function that loads the evaluation code,", "test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run evaluation. metrics,", "- run: Performs evaluation and dumps the metric data to", "def main(self, plot_examples: int = 0, render_curves: bool = True)", "from nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render import", "disk.') parser.add_argument('--render_curves', type=int, default=1, help='Whether to render PR and TP", "class_tp_curve, dist_pr_curve, visualize_sample class NuScenesEval: \"\"\" This is the official", "json import os import random import time from typing import", "help='Which version of the nuScenes dataset to evaluate on, e.g.", "Path of the nuScenes JSON result file. :param eval_set: The", "\"\"\" This is the official nuScenes detection evaluation code. Results", "licence.txt] import argparse import json import os import random import", "argparse.ArgumentParser(description='Evaluate nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The submission as", "load_prediction, load_gt, add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve,", "0: # Select a random but fixed subset to plot.", "as a JSON file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder to store", "(metrics_summary['mean_ap'])) err_name_mapping = { 'trans_err': 'mATE', 'scale_err': 'mASE', 'orient_err': 'mAOE',", "bool = True): \"\"\" Initialize a NuScenesEval object. :param nusc:", "Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs the actual evaluation. :return: A tuple", "def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs the actual evaluation.", "['attr_err', 'vel_err']: tp = np.nan else: tp = calc_tp(metric_data, self.cfg.min_recall,", "assume that: - Every sample_token is given in the results,", "Main function that loads the evaluation code, visualizes samples, runs", "'trans_err': 'mATE', 'scale_err': 'mASE', 'orient_err': 'mAOE', 'vel_err': 'mAVE', 'attr_err': 'mAAE'", "dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th))) def main(self,", "dist_pr_curve, visualize_sample class NuScenesEval: \"\"\" This is the official nuScenes", "if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load data. self.pred_boxes, self.meta =", "None: \"\"\" Renders various PR and TP curves. :param metrics:", "self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp,", "evaluation and dumps the metric data to disk. - render:", "PR and TP curves to disk. :return: A dict that", "overview of the functions in this method: - init: Loads", "'test' else EvalBoxes(), # Don't render test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()),", "%.1fs' % metrics_summary['eval_time']) return metrics_summary if __name__ == \"__main__\": #", "to print to stdout. \"\"\" self.nusc = nusc self.result_path =", "render test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run evaluation.", "doesn't match samples in predictions.\" # Add center distances. self.pred_boxes", "for class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths: md =", "metrics_summary if __name__ == \"__main__\": # Settings. parser = argparse.ArgumentParser(description='Evaluate", "verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]: \"\"\"", "os.makedirs(self.plot_dir) # Load data. self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose)", "in predictions.\" # Add center distances. self.pred_boxes = add_center_dist(nusc, self.pred_boxes)", "= os.path.join(self.output_dir, 'plots') if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir):", "['attr_err', 'vel_err', 'orient_err']: tp = np.nan elif class_name in ['barrier']", "os.path.join(self.plot_dir, name + '.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary'))", "metrics') metrics = DetectionMetrics(self.cfg) for class_name in self.cfg.class_names: for dist_th", "configuration to use for evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10,", "of the above. Here is an overview of the functions", "the data. # ----------------------------------- if self.verbose: print('Calculating metrics') metrics =", "Tuple, Dict, Any import numpy as np from nuscenes import", "summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name in self.cfg.class_names:", "bool = True) -> Dict[str, Any]: \"\"\" Main function that", "= os.path.expanduser(args.output_dir) eval_set_ = args.eval_set dataroot_ = args.dataroot version_ =", ":param metrics: DetectionMetrics instance. :param md_list: MetricDataList instance. \"\"\" def", "elif class_name in ['barrier'] and metric_name in ['attr_err', 'vel_err']: tp", "+ '.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name", "the above. Here is an overview of the functions in", ":param output_dir: Folder to save plots and results to. :param", "'vel_err', 'orient_err']: tp = np.nan elif class_name in ['barrier'] and", "self.output_dir) metrics_summary = metrics.serialize() metrics_summary['meta'] = self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'),", "'{}.png'.format(sample_token))) # Run evaluation. metrics, metric_data_list = self.evaluate() # Render", "nuScenes dev-kit. # Code written by <NAME> & <NAME>, 2018.", "for metric_name in TP_METRICS: metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name", "data. # ----------------------------------- if self.verbose: print('Calculating metrics') metrics = DetectionMetrics(self.cfg)", "e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10, help='How many example visualizations to", "nuScenes Detection Score (NDS): The weighted sum of the above.", "self.cfg = config # Make dirs. self.plot_dir = os.path.join(self.output_dir, 'plots')", "may be not predictions for that sample. Please see https://github.com/nutonomy/nuscenes-devkit", "Add center distances. self.pred_boxes = add_center_dist(nusc, self.pred_boxes) self.gt_boxes = add_center_dist(nusc,", "bool(args.verbose) cfg_ = config_factory(config_name_) nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval", "metric data to disk. - render: Renders various plots and", "metrics.add_label_ap(class_name, dist_th, ap) for metric_name in TP_METRICS: metric_data = metric_data_list[(class_name,", "default=1, help='Whether to print to stdout.') args = parser.parse_args() result_path_", "<NAME>, 2018. # Licensed under the Creative Commons [see licence.txt]", "on, train, val or test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default nuScenes", "for sample_token in sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes if self.eval_set !=", "test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default nuScenes data directory.') parser.add_argument('--version', type=str,", "class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths: md = accumulate(self.gt_boxes,", "True Positive (TP) metrics: Average of translation, velocity, scale, orientation", "not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load data.", "instance. :param md_list: MetricDataList instance. \"\"\" def savepath(name): return os.path.join(self.plot_dir,", "matching criterion; averaged over distance thresholds. - True Positive (TP)", "\"__main__\": # Settings. parser = argparse.ArgumentParser(description='Evaluate nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)", "args = parser.parse_args() result_path_ = os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir) eval_set_", "that loads the evaluation code, visualizes samples, runs the evaluation", "Positive (TP) metrics: Average of translation, velocity, scale, orientation and", "the nuScenes JSON result file. :param eval_set: The dataset split", "print('NDS: %.4f' % (metrics_summary['nd_score'])) print('Eval time: %.1fs' % metrics_summary['eval_time']) return", "args.dataroot version_ = args.version config_name_ = args.config_name plot_examples_ = args.plot_examples", "to evaluate on, train, val or test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes',", "meta and metrics to disk. if self.verbose: print('Saving metrics to:", "more details. \"\"\" def __init__(self, nusc: NuScenes, config: DetectionConfig, result_path:", "default=10, help='How many example visualizations to write to disk.') parser.add_argument('--render_curves',", "output_dir_ = os.path.expanduser(args.output_dir) eval_set_ = args.eval_set dataroot_ = args.dataroot version_", "= config_factory(config_name_) nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_,", "dist_th in self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' +", "class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp')) for dist_th", "class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th, md) # ----------------------------------- # Step", ":param result_path: Path of the nuScenes JSON result file. :param", "- Mean Average Precision (mAP): Uses center-distance as matching criterion;", "time: %.1fs' % metrics_summary['eval_time']) return metrics_summary if __name__ == \"__main__\":", "function that loads the evaluation code, visualizes samples, runs the", "sample. Please see https://github.com/nutonomy/nuscenes-devkit for more details. \"\"\" def __init__(self,", "-> Dict[str, Any]: \"\"\" Main function that loads the evaluation", "in self.cfg.dist_ths: md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name,", "in metrics_summary['tp_errors'].items(): print('%s: %.4f' % (err_name_mapping[tp_name], tp_val)) print('NDS: %.4f' %", "center distances. self.pred_boxes = add_center_dist(nusc, self.pred_boxes) self.gt_boxes = add_center_dist(nusc, self.gt_boxes)", "ap) for metric_name in TP_METRICS: metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)] if", "a random but fixed subset to plot. random.seed(43) sample_tokens =", "self.cfg.dist_th_tp)] if class_name in ['traffic_cone'] and metric_name in ['attr_err', 'vel_err',", "args.version config_name_ = args.config_name plot_examples_ = args.plot_examples render_curves_ = bool(args.render_curves)", "to: %s' % self.output_dir) metrics_summary = metrics.serialize() metrics_summary['meta'] = self.meta.copy()", "= parser.parse_args() result_path_ = os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir) eval_set_ =", "the provided output_dir. nuScenes uses the following metrics: - Mean", "os.mkdir(example_dir) for sample_token in sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes if self.eval_set", "stat plots. :param plot_examples: How many example visualizations to write", "given in the results, although there may be not predictions", "evaluation code. Results are written to the provided output_dir. nuScenes", "although there may be not predictions for that sample. Please", "'orient_err': 'mAOE', 'vel_err': 'mAVE', 'attr_err': 'mAAE' } for tp_name, tp_val", "nusc: NuScenes, config: DetectionConfig, result_path: str, eval_set: str, output_dir: str", "the Creative Commons [see licence.txt] import argparse import json import", "if render_curves: self.render(metrics, metric_data_list) # Dump the metric data, meta", "metric_data_list) # Dump the metric data, meta and metrics to", "type=str, default='cvpr_2019', help='Name of the configuration to use for evaluation,", "and metric_name in ['attr_err', 'vel_err', 'orient_err']: tp = np.nan elif", "default='val', help='Which dataset split to evaluate on, train, val or", "= bool(args.verbose) cfg_ = config_factory(config_name_) nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_)", "= add_center_dist(nusc, self.gt_boxes) # Filter boxes (distance, points per box,", "if plot_examples > 0: # Select a random but fixed", "not os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token in sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes", ":param render_curves: Whether to render PR and TP curves to", "render_curves_ = bool(args.render_curves) verbose_ = bool(args.verbose) cfg_ = config_factory(config_name_) nusc_", "\\ \"Samples in split doesn't match samples in predictions.\" #", "parser.add_argument('--render_curves', type=int, default=1, help='Whether to render PR and TP curves", "and attribute errors. - nuScenes Detection Score (NDS): The weighted", "MetricDataList instance. \"\"\" def savepath(name): return os.path.join(self.plot_dir, name + '.pdf')", "dist_th, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath('dist_pr_' + str(dist_th))) def main(self, plot_examples: int", "f: json.dump(metrics_summary, f, indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f:", "return metrics, metric_data_list def render(self, metrics: DetectionMetrics, md_list: MetricDataList) ->", "and TP curves. if render_curves: self.render(metrics, metric_data_list) # Dump the", "'mASE', 'orient_err': 'mAOE', 'vel_err': 'mAVE', 'attr_err': 'mAAE' } for tp_name,", "return metrics_summary if __name__ == \"__main__\": # Settings. parser =", "str(dist_th))) def main(self, plot_examples: int = 0, render_curves: bool =", "Initialize a NuScenesEval object. :param nusc: A NuScenes object. :param", "disk. :param render_curves: Whether to render PR and TP curves", "criterion; averaged over distance thresholds. - True Positive (TP) metrics:", "of the nuScenes dataset to evaluate on, e.g. v1.0-trainval.') parser.add_argument('--config_name',", "results to. :param verbose: Whether to print to stdout. \"\"\"", "- Every sample_token is given in the results, although there", "Uses center-distance as matching criterion; averaged over distance thresholds. -", "print('Accumulating metric data') metric_data_list = MetricDataList() for class_name in self.cfg.class_names:", "Visualize samples. example_dir = os.path.join(self.output_dir, 'examples') if not os.path.isdir(example_dir): os.mkdir(example_dir)", "data. self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes = load_gt(self.nusc,", "self.verbose = verbose self.cfg = config # Make dirs. self.plot_dir", "evaluation. metrics, metric_data_list = self.evaluate() # Render PR and TP", "velocity, scale, orientation and attribute errors. - nuScenes Detection Score", "MetricDataList() for class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths: md", "np from nuscenes import NuScenes from nuscenes.eval.detection.algo import accumulate, calc_ap,", "- render: Renders various plots and dumps to disk. We", "match samples in predictions.\" # Add center distances. self.pred_boxes =", "Don't render test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run", "type=int, default=10, help='How many example visualizations to write to disk.')", "= self.evaluate() # Render PR and TP curves. if render_curves:", "= calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time() - start_time)", "the actual evaluation. :return: A tuple of high-level and the", "is given in the results, although there may be not", "the results, although there may be not predictions for that", "= nusc self.result_path = result_path self.eval_set = eval_set self.output_dir =", "Load data. self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes =", "in sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes if self.eval_set != 'test' else", "TP_METRICS: metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name in ['traffic_cone'] and", "JSON format and filters the boxes. - run: Performs evaluation", "for all classes and distance thresholds. # ----------------------------------- if self.verbose:", "# nuScenes dev-kit. # Code written by <NAME> & <NAME>,", "filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens def evaluate(self) ->", "data. \"\"\" if plot_examples > 0: # Select a random", "disk. if self.verbose: print('Saving metrics to: %s' % self.output_dir) metrics_summary", "sample_token is given in the results, although there may be", "= load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose) assert", "nuScenes dataset to evaluate on, e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019',", "Every sample_token is given in the results, although there may", "sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes if self.eval_set != 'test' else EvalBoxes(),", "= add_center_dist(nusc, self.pred_boxes) self.gt_boxes = add_center_dist(nusc, self.gt_boxes) # Filter boxes", "loads the evaluation code, visualizes samples, runs the evaluation and", "Commons [see licence.txt] import argparse import json import os import", "that: - Every sample_token is given in the results, although", "config: A DetectionConfig object. :param result_path: Path of the nuScenes", "class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths: metric_data = metric_data_list[(class_name,", "results, although there may be not predictions for that sample.", "help='Whether to print to stdout.') args = parser.parse_args() result_path_ =", "annotations') self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens", "from the data. # ----------------------------------- if self.verbose: print('Calculating metrics') metrics", "for class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths: metric_data =", "nuscenes import NuScenes from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp from", "tp_name, tp_val in metrics_summary['tp_errors'].items(): print('%s: %.4f' % (err_name_mapping[tp_name], tp_val)) print('NDS:", "time from typing import Tuple, Dict, Any import numpy as", "init: Loads GT annotations an predictions stored in JSON format", "raw metric data. \"\"\" start_time = time.time() # ----------------------------------- #", "self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose) if verbose: print('Filtering ground", "self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time() - start_time) return metrics,", "train, val or test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default nuScenes data", "metrics: DetectionMetrics, md_list: MetricDataList) -> None: \"\"\" Renders various PR", "load_gt, add_center_dist, filter_eval_boxes from nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve,", "to disk. - render: Renders various plots and dumps to", "formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The submission as a JSON file.') parser.add_argument('--output_dir',", "2: Calculate metrics from the data. # ----------------------------------- if self.verbose:", "dist_th) metric_data_list.set(class_name, dist_th, md) # ----------------------------------- # Step 2: Calculate", "nusc: A NuScenes object. :param config: A DetectionConfig object. :param", "DetectionMetrics, md_list: MetricDataList) -> None: \"\"\" Renders various PR and", "config: DetectionConfig, result_path: str, eval_set: str, output_dir: str = None,", "if not os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token in sample_tokens: visualize_sample(self.nusc, sample_token,", "metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall, savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list, metrics, detection_name,", "= os.path.join(self.output_dir, 'examples') if not os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token in", "% (metrics_summary['mean_ap'])) err_name_mapping = { 'trans_err': 'mATE', 'scale_err': 'mASE', 'orient_err':", "= DetectionMetrics(self.cfg) for class_name in self.cfg.class_names: for dist_th in self.cfg.dist_ths:", "import time from typing import Tuple, Dict, Any import numpy", "DetectionConfig object. :param result_path: Path of the nuScenes JSON result", "# ----------------------------------- if self.verbose: print('Calculating metrics') metrics = DetectionMetrics(self.cfg) for", "dist_th in self.cfg.dist_ths: metric_data = metric_data_list[(class_name, dist_th)] ap = calc_ap(metric_data,", "data directory.') parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which version of the nuScenes", "config # Make dirs. self.plot_dir = os.path.join(self.output_dir, 'plots') if not", "f, indent=2) # Print high-level metrics. print('mAP: %.4f' % (metrics_summary['mean_ap']))", "PR and TP curves to disk.') parser.add_argument('--verbose', type=int, default=1, help='Whether", "averaged over distance thresholds. - True Positive (TP) metrics: Average", "tp) metrics.add_runtime(time.time() - start_time) return metrics, metric_data_list def render(self, metrics:", "tuple of high-level and the raw metric data. \"\"\" start_time", "verbose_ = bool(args.verbose) cfg_ = config_factory(config_name_) nusc_ = NuScenes(version=version_, verbose=verbose_,", "sample_token, self.gt_boxes if self.eval_set != 'test' else EvalBoxes(), # Don't", "start_time = time.time() # ----------------------------------- # Step 1: Accumulate metric", "Run evaluation. metrics, metric_data_list = self.evaluate() # Render PR and", "ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision) metrics.add_label_ap(class_name, dist_th, ap) for metric_name", "class NuScenesEval: \"\"\" This is the official nuScenes detection evaluation", "md_list: MetricDataList) -> None: \"\"\" Renders various PR and TP", "split doesn't match samples in predictions.\" # Add center distances.", "fixed subset to plot. random.seed(43) sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens", "v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name of the configuration to use", "# Step 1: Accumulate metric data for all classes and", "= NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_, eval_set=eval_set_,", "md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th, md)", "print('Filtering ground truth annotations') self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose)", "self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th, md) # ----------------------------------- # Step 2:", "runs the evaluation and renders stat plots. :param plot_examples: How", "meta data. \"\"\" if plot_examples > 0: # Select a", "dist_th in self.cfg.dist_ths: md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th)", "random but fixed subset to plot. random.seed(43) sample_tokens = list(self.sample_tokens)", "Here is an overview of the functions in this method:", "render PR and TP curves to disk. :return: A dict", "and TP curves to disk. :return: A dict that stores", "self.gt_boxes) # Filter boxes (distance, points per box, etc.). if", "plot_examples: How many example visualizations to write to disk. :param", "on, e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name of the configuration", "from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp from nuscenes.eval.detection.config import config_factory", "!= 'test' else EvalBoxes(), # Don't render test GT. self.pred_boxes,", "metrics to disk. if self.verbose: print('Saving metrics to: %s' %", "import TP_METRICS from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes from", "%s' % self.output_dir) metrics_summary = metrics.serialize() metrics_summary['meta'] = self.meta.copy() with", "self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose)", "args.config_name plot_examples_ = args.plot_examples render_curves_ = bool(args.render_curves) verbose_ = bool(args.verbose)", ":param config: A DetectionConfig object. :param result_path: Path of the", "weighted sum of the above. Here is an overview of", "print to stdout.') args = parser.parse_args() result_path_ = os.path.expanduser(args.result_path) output_dir_", "savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run evaluation. metrics, metric_data_list = self.evaluate() #", "to stdout.') args = parser.parse_args() result_path_ = os.path.expanduser(args.result_path) output_dir_ =", "= args.eval_set dataroot_ = args.dataroot version_ = args.version config_name_ =", "curves. :param metrics: DetectionMetrics instance. :param md_list: MetricDataList instance. \"\"\"", "as matching criterion; averaged over distance thresholds. - True Positive", "self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th) metric_data_list.set(class_name, dist_th, md) # ----------------------------------- #", "----------------------------------- if self.verbose: print('Calculating metrics') metrics = DetectionMetrics(self.cfg) for class_name", "the configuration to use for evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int,", "metrics. print('mAP: %.4f' % (metrics_summary['mean_ap'])) err_name_mapping = { 'trans_err': 'mATE',", "verbose: Whether to print to stdout. \"\"\" self.nusc = nusc", "Filter boxes (distance, points per box, etc.). if verbose: print('Filtering", "etc.). if verbose: print('Filtering predictions') self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range,", "the nuScenes dataset to evaluate on, e.g. v1.0-trainval.') parser.add_argument('--config_name', type=str,", "and filters the boxes. - run: Performs evaluation and dumps", "plots and dumps to disk. We assume that: - Every", "TP curves. if render_curves: self.render(metrics, metric_data_list) # Dump the metric", "Whether to render PR and TP curves to disk. :return:", "argparse import json import os import random import time from", "self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name + '_tp')) for dist_th in self.cfg.dist_ths: dist_pr_curve(md_list,", "self.gt_boxes.sample_tokens def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]: \"\"\" Performs the actual", "f: json.dump(metric_data_list.serialize(), f, indent=2) # Print high-level metrics. print('mAP: %.4f'", "Settings. parser = argparse.ArgumentParser(description='Evaluate nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str,", "plots. :param plot_examples: How many example visualizations to write to", "from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes from nuscenes.eval.detection.loaders import", "type=str, default='v1.0-trainval', help='Which version of the nuScenes dataset to evaluate", "The dataset split to evaluate on, e.g. train or val.", "to render PR and TP curves to disk. :return: A", "self.evaluate() # Render PR and TP curves. if render_curves: self.render(metrics,", "to the provided output_dir. nuScenes uses the following metrics: -", "'orient_err']: tp = np.nan elif class_name in ['barrier'] and metric_name", "parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default nuScenes data directory.') parser.add_argument('--version', type=str, default='v1.0-trainval',", "= args.config_name plot_examples_ = args.plot_examples render_curves_ = bool(args.render_curves) verbose_ =", "and metrics to disk. if self.verbose: print('Saving metrics to: %s'", "calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time() - start_time) return", "disk. :return: A dict that stores the high-level metrics and", "predictions stored in JSON format and filters the boxes. -", "calc_tp from nuscenes.eval.detection.config import config_factory from nuscenes.eval.detection.constants import TP_METRICS from", "metrics_summary['meta'] = self.meta.copy() with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f: json.dump(metrics_summary,", "Creative Commons [see licence.txt] import argparse import json import os", "# Filter boxes (distance, points per box, etc.). if verbose:", "annotations an predictions stored in JSON format and filters the", "on, e.g. train or val. :param output_dir: Folder to save", "box, etc.). if verbose: print('Filtering predictions') self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes,", "help='The submission as a JSON file.') parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics', help='Folder", "Mean Average Precision (mAP): Uses center-distance as matching criterion; averaged", "os import random import time from typing import Tuple, Dict,", "is an overview of the functions in this method: -", "- init: Loads GT annotations an predictions stored in JSON", "metrics_summary['tp_errors'].items(): print('%s: %.4f' % (err_name_mapping[tp_name], tp_val)) print('NDS: %.4f' % (metrics_summary['nd_score']))", "write to disk.') parser.add_argument('--render_curves', type=int, default=1, help='Whether to render PR", "True) -> Dict[str, Any]: \"\"\" Main function that loads the", "nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_,", "f, indent=2) with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f: json.dump(metric_data_list.serialize(), f,", "os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir) eval_set_ = args.eval_set dataroot_ = args.dataroot", "config_factory(config_name_) nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_) nusc_eval = NuScenesEval(nusc_, config=cfg_,", "output_dir. nuScenes uses the following metrics: - Mean Average Precision", "default='~/nuscenes-metrics', help='Folder to store result metrics, graphs and example visualizations.')", "A DetectionConfig object. :param result_path: Path of the nuScenes JSON", "metric_data_list[(class_name, self.cfg.dist_th_tp)] if class_name in ['traffic_cone'] and metric_name in ['attr_err',", "default='/data/sets/nuscenes', help='Default nuScenes data directory.') parser.add_argument('--version', type=str, default='v1.0-trainval', help='Which version", "'.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name in", "submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The submission as a JSON file.')", "for evaluation, e.g. cvpr_2019.') parser.add_argument('--plot_examples', type=int, default=10, help='How many example", "render_curves: Whether to render PR and TP curves to disk.", "type=int, default=1, help='Whether to render PR and TP curves to", "MetricDataList) -> None: \"\"\" Renders various PR and TP curves.", "per box, etc.). if verbose: print('Filtering predictions') self.pred_boxes = filter_eval_boxes(nusc,", "'mATE', 'scale_err': 'mASE', 'orient_err': 'mAOE', 'vel_err': 'mAVE', 'attr_err': 'mAAE' }", "metric_name, tp) metrics.add_runtime(time.time() - start_time) return metrics, metric_data_list def render(self,", "# Run evaluation. metrics, metric_data_list = self.evaluate() # Render PR", "add_center_dist(nusc, self.pred_boxes) self.gt_boxes = add_center_dist(nusc, self.gt_boxes) # Filter boxes (distance,", "dist_th, md) # ----------------------------------- # Step 2: Calculate metrics from", "tp = calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name, tp) metrics.add_runtime(time.time() -", "nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The submission as a", "help='Name of the configuration to use for evaluation, e.g. cvpr_2019.')", "help='Which dataset split to evaluate on, train, val or test.')", "metrics from the data. # ----------------------------------- if self.verbose: print('Calculating metrics')", "----------------------------------- # Step 2: Calculate metrics from the data. #", "nuScenes detection evaluation code. Results are written to the provided", "- True Positive (TP) metrics: Average of translation, velocity, scale,", "evaluate on, e.g. train or val. :param output_dir: Folder to", "Any]: \"\"\" Main function that loads the evaluation code, visualizes", "visualizations to write to disk. :param render_curves: Whether to render", "(metrics_summary['nd_score'])) print('Eval time: %.1fs' % metrics_summary['eval_time']) return metrics_summary if __name__", "nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample class NuScenesEval: \"\"\"", "but fixed subset to plot. random.seed(43) sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens)", "class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample class NuScenesEval: \"\"\" This is the", "= argparse.ArgumentParser(description='Evaluate nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The submission", "if class_name in ['traffic_cone'] and metric_name in ['attr_err', 'vel_err', 'orient_err']:", "%.4f' % (err_name_mapping[tp_name], tp_val)) print('NDS: %.4f' % (metrics_summary['nd_score'])) print('Eval time:", "= np.nan else: tp = calc_tp(metric_data, self.cfg.min_recall, metric_name) metrics.add_label_tp(class_name, metric_name,", "nuScenes JSON result file. :param eval_set: The dataset split to", "A tuple of high-level and the raw metric data. \"\"\"", "in the results, although there may be not predictions for", "Licensed under the Creative Commons [see licence.txt] import argparse import", "= filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose) if verbose: print('Filtering ground truth", "-> None: \"\"\" Renders various PR and TP curves. :param", "\"\"\" Main function that loads the evaluation code, visualizes samples,", "random.seed(43) sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens = sample_tokens[:plot_examples] # Visualize", "parser.add_argument('--config_name', type=str, default='cvpr_2019', help='Name of the configuration to use for", "detection evaluation code. Results are written to the provided output_dir.", "for dist_th in self.cfg.dist_ths: md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn,", "official nuScenes detection evaluation code. Results are written to the", "Render PR and TP curves. if render_curves: self.render(metrics, metric_data_list) #", "object. :param nusc: A NuScenes object. :param config: A DetectionConfig", "eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir, '{}.png'.format(sample_token))) # Run evaluation. metrics, metric_data_list = self.evaluate()", "{ 'trans_err': 'mATE', 'scale_err': 'mASE', 'orient_err': 'mAOE', 'vel_err': 'mAVE', 'attr_err':", "# Load data. self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose) self.gt_boxes", "parser = argparse.ArgumentParser(description='Evaluate nuScenes result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The", "% metrics_summary['eval_time']) return metrics_summary if __name__ == \"__main__\": # Settings.", "self.cfg.class_range, verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]:", "time.time() # ----------------------------------- # Step 1: Accumulate metric data for", "Code written by <NAME> & <NAME>, 2018. # Licensed under", "to print to stdout.') args = parser.parse_args() result_path_ = os.path.expanduser(args.result_path)", "nuscenes.eval.detection.constants import TP_METRICS from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes", "= output_dir self.verbose = verbose self.cfg = config # Make", "result_path: Path of the nuScenes JSON result file. :param eval_set:", "set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\ \"Samples in split doesn't match samples", "import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample class NuScenesEval: \"\"\" This", "a NuScenesEval object. :param nusc: A NuScenes object. :param config:", "os.path.join(self.output_dir, 'examples') if not os.path.isdir(example_dir): os.mkdir(example_dir) for sample_token in sample_tokens:", "distance thresholds. - True Positive (TP) metrics: Average of translation,", "'vel_err']: tp = np.nan else: tp = calc_tp(metric_data, self.cfg.min_recall, metric_name)", "written to the provided output_dir. nuScenes uses the following metrics:", "eval_set_ = args.eval_set dataroot_ = args.dataroot version_ = args.version config_name_", "= True): \"\"\" Initialize a NuScenesEval object. :param nusc: A", "metrics_summary['eval_time']) return metrics_summary if __name__ == \"__main__\": # Settings. parser", "render_curves: self.render(metrics, metric_data_list) # Dump the metric data, meta and", "not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load data. self.pred_boxes, self.meta = load_prediction(self.result_path,", "+ '_tp')) for dist_th in self.cfg.dist_ths: dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision,", "else EvalBoxes(), # Don't render test GT. self.pred_boxes, eval_range=max(self.cfg.class_range.values()), savepath=os.path.join(example_dir,", "self.gt_boxes = add_center_dist(nusc, self.gt_boxes) # Filter boxes (distance, points per", "to disk. :param render_curves: Whether to render PR and TP", "to disk. if self.verbose: print('Saving metrics to: %s' % self.output_dir)", "parser.parse_args() result_path_ = os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir) eval_set_ = args.eval_set", "default='v1.0-trainval', help='Which version of the nuScenes dataset to evaluate on,", "= bool(args.render_curves) verbose_ = bool(args.verbose) cfg_ = config_factory(config_name_) nusc_ =", "following metrics: - Mean Average Precision (mAP): Uses center-distance as", "self.cfg.min_recall, savepath=savepath(detection_name + '_pr')) class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp, savepath=savepath(detection_name", "code, visualizes samples, runs the evaluation and renders stat plots.", "import json import os import random import time from typing", "class_name in ['traffic_cone'] and metric_name in ['attr_err', 'vel_err', 'orient_err']: tp", "err_name_mapping = { 'trans_err': 'mATE', 'scale_err': 'mASE', 'orient_err': 'mAOE', 'vel_err':", "start_time) return metrics, metric_data_list def render(self, metrics: DetectionMetrics, md_list: MetricDataList)", "to stdout. \"\"\" self.nusc = nusc self.result_path = result_path self.eval_set", "metric data. \"\"\" start_time = time.time() # ----------------------------------- # Step", "curves to disk. :return: A dict that stores the high-level", "open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f: json.dump(metric_data_list.serialize(), f, indent=2) # Print", "dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary')) for detection_name in self.cfg.class_names: class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision,", "output_dir: Folder to save plots and results to. :param verbose:", "A NuScenes object. :param config: A DetectionConfig object. :param result_path:", "points per box, etc.). if verbose: print('Filtering predictions') self.pred_boxes =", "of the nuScenes JSON result file. :param eval_set: The dataset", "nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_, eval_set=eval_set_, output_dir=output_dir_, verbose=verbose_) nusc_eval.main(plot_examples=plot_examples_, render_curves=render_curves_)", "import numpy as np from nuscenes import NuScenes from nuscenes.eval.detection.algo", "sample_token in sample_tokens: visualize_sample(self.nusc, sample_token, self.gt_boxes if self.eval_set != 'test'", "evaluation code, visualizes samples, runs the evaluation and renders stat", "savepath(name): return os.path.join(self.plot_dir, name + '.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall,", "metrics.add_runtime(time.time() - start_time) return metrics, metric_data_list def render(self, metrics: DetectionMetrics,", "subset to plot. random.seed(43) sample_tokens = list(self.sample_tokens) random.shuffle(sample_tokens) sample_tokens =", "NuScenesEval: \"\"\" This is the official nuScenes detection evaluation code.", "result submission.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('result_path', type=str, help='The submission as a JSON", "in JSON format and filters the boxes. - run: Performs", "written by <NAME> & <NAME>, 2018. # Licensed under the", "\"Samples in split doesn't match samples in predictions.\" # Add", "stdout.') args = parser.parse_args() result_path_ = os.path.expanduser(args.result_path) output_dir_ = os.path.expanduser(args.output_dir)", "self.eval_set, verbose=verbose) assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \\ \"Samples in split", "self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose) self.sample_tokens = self.gt_boxes.sample_tokens def", "open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f: json.dump(metrics_summary, f, indent=2) with open(os.path.join(self.output_dir,", "the metric data to disk. - render: Renders various plots", "boxes (distance, points per box, etc.). if verbose: print('Filtering predictions')", "GT annotations an predictions stored in JSON format and filters", "dirs. self.plot_dir = os.path.join(self.output_dir, 'plots') if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if", "if not os.path.isdir(self.output_dir): os.makedirs(self.output_dir) if not os.path.isdir(self.plot_dir): os.makedirs(self.plot_dir) # Load", "(TP) metrics: Average of translation, velocity, scale, orientation and attribute", "metrics: DetectionMetrics instance. :param md_list: MetricDataList instance. \"\"\" def savepath(name):", "metrics: Average of translation, velocity, scale, orientation and attribute errors.", "self.render(metrics, metric_data_list) # Dump the metric data, meta and metrics", "or test.') parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes', help='Default nuScenes data directory.') parser.add_argument('--version',", "print('Saving metrics to: %s' % self.output_dir) metrics_summary = metrics.serialize() metrics_summary['meta']", "dict that stores the high-level metrics and meta data. \"\"\"", "return os.path.join(self.plot_dir, name + '.pdf') summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall, dist_th_tp=self.cfg.dist_th_tp,", "in self.cfg.class_names: for dist_th in self.cfg.dist_ths: metric_data = metric_data_list[(class_name, dist_th)]" ]
[ "this problem, a sample output is empty. \"\"\" url =", "unusual HTML markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e'", "problem uses <code> tags in the descriptoin text in the", "\"10\\n\" }, { \"input\": \"23 18\\n15 13\\n100 95\\n\", \"output\": \"364527243\\n\"", "003\", \"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\" }, \"memoryLimit\": 64, \"timeLimit\":", "the sample section. \"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = {", "}, \"memoryLimit\": 64, \"timeLimit\": 2000 }, } actual = main(['get-problem',", "<= 1\\n4 <= 1\\n4\\n2 >= 2\\n3 <= 1\\n4 <= 1\\n5", "Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\" }, \"memoryLimit\": 1024,", "\"5 10\\n1 2 3 2 3\\n\", \"output\": \"3\\n\" }, {", "\"ab*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"abc\\n\", \"output\": \"NO\\n\" },", "2\\n1 2 3\\n\", \"output\": \"2 3\\n\" }, { \"input\": \"5", "\"<NAME>\", \"context\": { \"contest\": { \"name\": \"AtCoder Grand Contest 036\",", "{ \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\": \"2 2\\n2 \\n1 >=", "the headings for sample outputs. \"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected", "\"12\\n\" }, { \"input\": \"4 5\\n3 1\\n3 0\\n\", \"output\": \"10\\n\"", "cases. \"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = { \"status\": \"error\",", "\"1 2\\n2\\n1 <= 10\\n1 >= 15\\n\", \"output\": \"No\\n\" }, {", "\"B\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, } actual =", "\"contest\": { \"name\": \"AtCoder Beginner Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\" },", "{ \"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\"", "\"output\": \"9 2 6\\n\" }], \"name\": \"<NAME>\", \"context\": { \"contest\":", "\"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\": \"15\\n7\\n\" }, { \"input\": \"2", "\"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\": \"ab*\\n\", \"output\": \"YES\\n\" }, { \"input\":", "\"context\": { \"contest\": { \"name\": \"AtCoder Regular Contest 035\", \"url\":", "1\\n2\\n4 >= 2\\n5 <= 1\\n1\\n5 >= 2 \\n\", \"output\": \"Yes\\n\"", "actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This tests a problem which uses an", "2\\n\", \"output\": \"12\\n\" }, { \"input\": \"4 5\\n3 1\\n3 0\\n\",", "problem contains both words `Input` and `Output` for the headings", "url], debug=True) self.assertEqual(expected, actual) def test_abc114_c(self): \"\"\"This tests a problem", "\"output\": \"12\\n\" }, { \"input\": \"4 5\\n3 1\\n3 0\\n\", \"output\":", "\"timeLimit\": 2000 }, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected,", "}, { \"input\": \"23 18\\n15 13\\n100 95\\n\", \"output\": \"364527243\\n\" },", "\"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_agc036_b(self): \"\"\"In this problem,", "a problem which uses a new-style format HTML. \"\"\" url", "[], \"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\": \"ab*\\n\", \"output\":", "\"input\": \"5 5\\n3\\n2 <= 1\\n3 <= 1\\n4 <= 1\\n4\\n2 >=", "\"input\": \"23 18\\n15 13\\n100 95\\n\", \"output\": \"364527243\\n\" }, { \"input\":", "3\\n2 <= 5\\n2\\n1 >= 4\\n2 >= 3\\n\", \"output\": \"Yes\\n\" },", "\"name\": \"Everlasting Zero\", \"context\": { \"contest\": { \"name\": \"Japan Alumni", ">= 5\\n2\\n1 <= 4\\n2 <= 3\\n\", \"output\": \"Yes\\n\" }, {", "\"\"\"This problem uses <code> tags in the descriptoin text in", "\"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\": \"15\\n7\\n\"", "\"tests\": [{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\": \"15\\n7\\n\" },", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_arc035_a(self):", "output is empty. \"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = {", "test_call_download_atcoder_abc003_4(self): \"\"\"This tests a problem which uses an old-style format", "`Output` for the headings for sample outputs. \"\"\" url =", "\"output\": \"2 3\\n\" }, { \"input\": \"5 10\\n1 2 3", "\"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\":", "1\\n2 1\\n\", \"output\": \"2\\n2\\n\" }, { \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\",", "actual) def test_impossible_problem(self): \"\"\"This tests a problem impossible to parse", "a sample output is empty. \"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected", "= { \"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed to parse samples\"],", "'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = { \"status\": \"ok\", \"messages\": [], \"result\": {", "\"No\\n\" }, { \"input\": \"1 2\\n2\\n1 <= 10\\n1 >= 15\\n\",", "<= 1\\n3 <= 1\\n4 <= 1\\n4\\n2 >= 2\\n3 <= 1\\n4", "128, \"timeLimit\": 5000 }, } actual = main(['get-problem', url], debug=True)", "}, { \"input\": \"5 5\\n3\\n2 <= 1\\n3 <= 1\\n4 <=", "\"13\\n\" }, { \"input\": \"999999999\\n\", \"output\": \"26484\\n\" }], \"name\": \"755\",", "debug=True) self.assertEqual(expected, actual) def test_agc036_b(self): \"\"\"In this problem, a sample", "= { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\",", "2 \\n\", \"output\": \"Yes\\n\" }], \"name\": \"Everlasting Zero\", \"context\": {", "in the sample section. \"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected =", "= 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = { \"status\": \"ok\", \"messages\": [], \"result\":", "{ \"name\": \"AtCoder Regular Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\":", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_arc035_a(self): \"\"\"This problem", "{ \"contest\": { \"name\": \"Japan Alumni Group Spring Contest 2013\",", "\"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{", "2\\n2 \\n1 >= 5\\n2 >= 5\\n2\\n1 <= 4\\n2 <= 3\\n\",", "{ \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\":", "2\\n3 <= 1\\n4 <= 1\\n5 <= 1\\n3\\n3 >= 2\\n4 <=", "\"memoryLimit\": 256, \"timeLimit\": 5000 }, } actual = main(['get-problem', url],", "\"contest\": { \"name\": \"AtCoder Grand Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\" },", "\"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = { \"status\": \"error\", \"messages\":", "\"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\":", "256, \"timeLimit\": 5000 }, } actual = main(['get-problem', url], debug=True)", "\"\"\"This problem contains both words `Input` and `Output` for the", "sample cases. \"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = { \"status\":", "}, \"alphabet\": \"C\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, }", "self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This tests a problem which uses", "{ \"name\": \"AtCoder Beginner Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\":", "\"output\": \"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\": { \"name\":", "self.assertEqual(expected, actual) def test_agc036_b(self): \"\"\"In this problem, a sample output", "{ \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\": \"3 2\\n2 2\\n2 2\\n\",", "\"output\": \"No\\n\" }, { \"input\": \"5 5\\n3\\n2 <= 1\\n3 <=", "1\\n3 0\\n\", \"output\": \"10\\n\" }, { \"input\": \"23 18\\n15 13\\n100", "failed to parse samples\"], \"result\": None, } actual = main(['get-problem',", "a new-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected =", "3\\n2\\n1 <= 2\\n2 >= 5\\n\", \"output\": \"No\\n\" }, { \"input\":", "\"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": {", "22\\n145 132\\n\", \"output\": \"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\":", "5\\n2\\n1 <= 4\\n2 <= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\":", "[], \"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1", "\"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 256, \"timeLimit\": 2000 },", "}, \"memoryLimit\": 256, \"timeLimit\": 2000 }, } actual = main(['get-problem',", "<reponame>aberent/api-client import unittest from onlinejudge_api.main import main class DownloadAtCoderTest(unittest.TestCase): def", "\"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\":", "\"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1 >= 5\\n2", "{ \"input\": \"2 2\\n2 \\n1 >= 3\\n2 <= 3\\n2\\n1 <=", "2\\n2 \\n1 >= 3\\n2 <= 3\\n2\\n1 <= 2\\n2 >= 5\\n\",", "5\\n\", \"output\": \"9 2 6\\n\" }], \"name\": \"<NAME>\", \"context\": {", "Zero\", \"context\": { \"contest\": { \"name\": \"Japan Alumni Group Spring", "{ \"contest\": { \"name\": \"AtCoder Grand Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\"", "1\\n4 <= 1\\n4\\n2 >= 2\\n3 <= 1\\n4 <= 1\\n5 <=", "old-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = {", "{ \"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\": \"2\\n2\\n\" }, {", "actual) def test_non_existing_problem(self): \"\"\"This tests an non-existing problem. \"\"\" url", "url], debug=True) self.assertEqual(expected, actual) def test_non_existing_problem(self): \"\"\"This tests an non-existing", "`Input` and `Output` for the headings for sample outputs. \"\"\"", "}, { \"input\": \"2 2\\n2 \\n1 >= 3\\n2 <= 3\\n2\\n1", "\"input\": \"11 97\\n3 1 4 1 5 9 2 6", "\"3600\\n\", \"output\": \"13\\n\" }, { \"input\": \"999999999\\n\", \"output\": \"26484\\n\" }],", "\"abc\\n\", \"output\": \"NO\\n\" }, { \"input\": \"a*bc*\\n\", \"output\": \"YES\\n\" },", "problem, a sample output is empty. \"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b'", "{ \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\":", "{ \"input\": \"5 10\\n1 2 3 2 3\\n\", \"output\": \"3\\n\"", "}], \"name\": \"<NAME>\", \"context\": { \"contest\": { \"name\": \"AtCoder Grand", "an unusual HTML markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url =", "\"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{", "\"output\": \"3\\n\" }, { \"input\": \"6 1000000000000\\n1 1 2 2", "\"name\": \"Japan Alumni Group Spring Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" },", "\"\"\"In this problem, a sample output is empty. \"\"\" url", "problem which uses a new-style format HTML. \"\"\" url =", "\"AtCoder Beginner Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\" },", "parse sample cases. \"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = {", "def test_abc114_c(self): \"\"\"This tests a problem which uses a new-style", "'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = { \"status\": \"ok\", \"messages\": [], \"result\": {", "5 3 5\\n\", \"output\": \"9 2 6\\n\" }], \"name\": \"<NAME>\",", "[\"onlinejudge.type.SampleParseError: failed to parse samples\"], \"result\": None, } actual =", "'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = { \"status\": \"ok\", \"messages\": [], \"result\": {", "2000 }, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual)", "url], debug=True) self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This tests a problem", "}, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, } actual = main(['get-problem',", "<= 1\\n5 <= 1\\n2\\n4 >= 2\\n5 <= 1\\n1\\n5 >= 2", "outputs. \"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = { \"status\": \"ok\",", "{ \"input\": \"6 1000000000000\\n1 1 2 2 3 3\\n\", \"output\":", "\"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\" }, \"memoryLimit\": 256, \"timeLimit\": 5000", "\"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\": \"3", "\"C\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, } actual =", "expected = { \"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError: 404 Client Error:", "'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = { \"status\": \"ok\", \"messages\": [], \"result\": {", "\"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 },", "test_icpc2013spring_a(self): \"\"\"This problem contains both words `Input` and `Output` for", "debug=True) self.assertEqual(expected, actual) def test_non_existing_problem(self): \"\"\"This tests an non-existing problem.", "onlinejudge_api.main import main class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This problem contains", "\"4 5\\n3 1\\n3 0\\n\", \"output\": \"10\\n\" }, { \"input\": \"23", "\"output\": \"364527243\\n\" }, { \"input\": \"30 30\\n24 22\\n145 132\\n\", \"output\":", "\"alphabet\": \"E\" }, \"memoryLimit\": 256, \"timeLimit\": 5000 }, } actual", "\"error\", \"messages\": [\"requests.exceptions.HTTPError: 404 Client Error: Not Found for url:", "Beginner Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\" }, \"memoryLimit\":", "30\\n24 22\\n145 132\\n\", \"output\": \"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": {", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_non_existing_problem(self): \"\"\"This tests", "132\\n\", \"output\": \"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\": {", "5\\n2\\n1 >= 4\\n2 >= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\":", "<code> tags in the descriptoin text in the sample section.", "}, \"alphabet\": \"A\" }, \"memoryLimit\": 256, \"timeLimit\": 2000 }, }", "DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This problem contains both words `Input` and", "url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = { \"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError:", "\"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{", "\"output\": \"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\": { \"name\":", "which uses an old-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4'", "}], \"name\": \"755\", \"context\": { \"contest\": { \"name\": \"AtCoder Beginner", "\"30 30\\n24 22\\n145 132\\n\", \"output\": \"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\":", "[{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\": \"15\\n7\\n\" }, {", "test_abc114_c(self): \"\"\"This tests a problem which uses a new-style format", "sample output is empty. \"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected =", "\"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\": \"2 2\\n2 \\n1 >= 3\\n2 <=", "1 5 9 2 6 5 3 5\\n\", \"output\": \"9", "\"a*bc*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"***\\n\", \"output\": \"YES\\n\" }],", "\"9 2 6\\n\" }], \"name\": \"<NAME>\", \"context\": { \"contest\": {", "\"input\": \"3600\\n\", \"output\": \"13\\n\" }, { \"input\": \"999999999\\n\", \"output\": \"26484\\n\"", "2\\n4 <= 1\\n5 <= 1\\n2\\n4 >= 2\\n5 <= 1\\n1\\n5 >=", "'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = { \"status\": \"ok\", \"messages\": [], \"result\": {", "\"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\":", "\"input\": \"6 1000000000000\\n1 1 2 2 3 3\\n\", \"output\": \"\\n\"", "\"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\": \"2\\n2\\n\" }, { \"input\":", "the descriptoin text in the sample section. \"\"\" url =", ">= 15\\n\", \"output\": \"No\\n\" }, { \"input\": \"5 5\\n3\\n2 <=", "problem uses an unusual HTML markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\"", "\"name\": \"AtCoder Regular Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\"", "words `Input` and `Output` for the headings for sample outputs.", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_non_existing_problem(self):", "\"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" },", "\"\"\"This problem uses an unusual HTML markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618", "\"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_non_existing_problem(self): \"\"\"This tests an", "\"5 5\\n3\\n2 <= 1\\n3 <= 1\\n4 <= 1\\n4\\n2 >= 2\\n3", "{ \"input\": \"4 5\\n3 1\\n3 0\\n\", \"output\": \"10\\n\" }, {", "url], debug=True) self.assertEqual(expected, actual) def test_impossible_problem(self): \"\"\"This tests a problem", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self): \"\"\"This problem uses", "\"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\": { \"name\": \"AtCoder", "}, \"memoryLimit\": 256, \"timeLimit\": 5000 }, } actual = main(['get-problem',", "url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = { \"status\": \"ok\", \"messages\": [],", "problem impossible to parse sample cases. \"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a'", "}], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\": { \"name\": \"AtCoder Beginner", "and `Output` for the headings for sample outputs. \"\"\" url", "\"\"\"This tests a problem which uses a new-style format HTML.", "\"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\":", "2 3\\n\", \"output\": \"2 3\\n\" }, { \"input\": \"5 10\\n1", "<= 3\\n2\\n1 <= 2\\n2 >= 5\\n\", \"output\": \"No\\n\" }, {", "15\\n\", \"output\": \"No\\n\" }, { \"input\": \"5 5\\n3\\n2 <= 1\\n3", "\"No\\n\" }, { \"input\": \"5 5\\n3\\n2 <= 1\\n3 <= 1\\n4", "2\\n2 2\\n\", \"output\": \"12\\n\" }, { \"input\": \"4 5\\n3 1\\n3", "<= 1\\n5 <= 1\\n3\\n3 >= 2\\n4 <= 1\\n5 <= 1\\n2\\n4", "\"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\": \"575\\n\", \"output\": \"4\\n\" }, {", "actual) def test_arc035_a(self): \"\"\"This problem uses <code> tags in the", "4 1 5 9 2 6 5 3 5\\n\", \"output\":", "def test_tenka1_2014_qualA_e(self): \"\"\"This problem uses an unusual HTML markup. ..", "url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, } actual = main(['get-problem', url], debug=True)", "test_non_existing_problem(self): \"\"\"This tests an non-existing problem. \"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100'", "\"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1 >= 3\\n2", "\"messages\": [\"onlinejudge.type.SampleParseError: failed to parse samples\"], \"result\": None, } actual", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_abc114_c(self):", "an non-existing problem. \"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = {", "debug=True) self.assertEqual(expected, actual) def test_arc035_a(self): \"\"\"This problem uses <code> tags", "114\", \"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\" }, \"memoryLimit\": 1024, \"timeLimit\":", "035\", \"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 256, \"timeLimit\":", "\"26484\\n\" }], \"name\": \"755\", \"context\": { \"contest\": { \"name\": \"AtCoder", "debug=True) self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self): \"\"\"This problem uses an unusual", "\"15\\n7\\n\" }, { \"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\": \"2\\n2\\n\"", "1\\n\", \"output\": \"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\": {", ">= 3\\n2 <= 3\\n2\\n1 <= 2\\n2 >= 5\\n\", \"output\": \"No\\n\"", "5000 }, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual)", "\"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000", "\"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\": \"3 2\\n1 2 3\\n\", \"output\": \"2", "expected = { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\":", "actual) def test_agc036_b(self): \"\"\"In this problem, a sample output is", "\"input\": \"3 2\\n1 2 3\\n\", \"output\": \"2 3\\n\" }, {", "'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = { \"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError: 404 Client", "to parse samples\"], \"result\": None, } actual = main(['get-problem', url],", "\"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\": \"575\\n\",", "url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = { \"status\": \"ok\", \"messages\": [],", "def test_call_download_atcoder_abc003_4(self): \"\"\"This tests a problem which uses an old-style", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_abc114_c(self): \"\"\"This tests a", "{ \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\":", "import main class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This problem contains both", "[], \"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\": \"2 2\\n2", "impossible to parse sample cases. \"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected", "\"AtCoder Regular Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\" },", "problem. \"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = { \"status\": \"error\",", "self.assertEqual(expected, actual) def test_arc035_a(self): \"\"\"This problem uses <code> tags in", "97\\n3 1 4 1 5 9 2 6 5 3", "3\\n\" }, { \"input\": \"5 10\\n1 2 3 2 3\\n\",", "9 2 6 5 3 5\\n\", \"output\": \"9 2 6\\n\"", "3\\n\", \"output\": \"3\\n\" }, { \"input\": \"6 1000000000000\\n1 1 2", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self):", "\"A\" }, \"memoryLimit\": 128, \"timeLimit\": 5000 }, } actual =", "Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\" }, \"memoryLimit\": 1024,", "\"575\\n\", \"output\": \"4\\n\" }, { \"input\": \"3600\\n\", \"output\": \"13\\n\" },", "5\\n\", \"output\": \"No\\n\" }, { \"input\": \"1 2\\n2\\n1 <= 10\\n1", "3\\n2 <= 3\\n2\\n1 <= 2\\n2 >= 5\\n\", \"output\": \"No\\n\" },", "Spring Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\" }, \"memoryLimit\":", "}, { \"input\": \"1 2\\n2\\n1 <= 10\\n1 >= 15\\n\", \"output\":", "\"output\": \"Yes\\n\" }], \"name\": \"Everlasting Zero\", \"context\": { \"contest\": {", "1\\n3\\n3 >= 2\\n4 <= 1\\n5 <= 1\\n2\\n4 >= 2\\n5 <=", "\"context\": { \"contest\": { \"name\": \"Japan Alumni Group Spring Contest", "empty. \"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = { \"status\": \"ok\",", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_arc035_a(self): \"\"\"This", "= { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\",", "13\\n100 95\\n\", \"output\": \"364527243\\n\" }, { \"input\": \"30 30\\n24 22\\n145", "actual) def test_abc114_c(self): \"\"\"This tests a problem which uses a", "\"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError: 404 Client Error: Not Found for", "}, \"alphabet\": \"B\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, }", "\"input\": \"1 2\\n2\\n1 <= 10\\n1 >= 15\\n\", \"output\": \"No\\n\" },", "\"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\":", "[{ \"input\": \"3 2\\n1 2 3\\n\", \"output\": \"2 3\\n\" },", "\"tests\": [{ \"input\": \"3 2\\n2 2\\n2 2\\n\", \"output\": \"12\\n\" },", "\"input\": \"4 5\\n3 1\\n3 0\\n\", \"output\": \"10\\n\" }, { \"input\":", "}, \"memoryLimit\": 128, \"timeLimit\": 5000 }, } actual = main(['get-problem',", "[], \"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\": \"3 2\\n1", "= 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = { \"status\": \"ok\", \"messages\": [], \"result\":", "\"output\": \"NO\\n\" }, { \"input\": \"a*bc*\\n\", \"output\": \"YES\\n\" }, {", "}, { \"input\": \"30 30\\n24 22\\n145 132\\n\", \"output\": \"976668549\\n\" }],", "\"3 2\\n2 2\\n2 2\\n\", \"output\": \"12\\n\" }, { \"input\": \"4", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_abc114_c(self): \"\"\"This", ".. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = {", "95\\n\", \"output\": \"364527243\\n\" }, { \"input\": \"30 30\\n24 22\\n145 132\\n\",", "\"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed to parse samples\"], \"result\": None, }", "2 3\\n\", \"output\": \"3\\n\" }, { \"input\": \"6 1000000000000\\n1 1", "1\\n3 <= 1\\n4 <= 1\\n4\\n2 >= 2\\n3 <= 1\\n4 <=", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_abc114_c(self): \"\"\"This tests", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_impossible_problem(self):", "\"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\": \"5", "64, \"timeLimit\": 2000 }, } actual = main(['get-problem', url], debug=True)", "\"context\": { \"contest\": { \"name\": \"AtCoder Grand Contest 036\", \"url\":", "\"755\", \"context\": { \"contest\": { \"name\": \"AtCoder Beginner Contest 114\",", "main class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This problem contains both words", "\"tests\": [{ \"input\": \"3 2\\n1 2 3\\n\", \"output\": \"2 3\\n\"", "\"23 18\\n15 13\\n100 95\\n\", \"output\": \"364527243\\n\" }, { \"input\": \"30", "\"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed to parse samples\"], \"result\": None,", "\"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = { \"status\": \"ok\", \"messages\":", "256, \"timeLimit\": 2000 }, } actual = main(['get-problem', url], debug=True)", "for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, } actual = main(['get-problem', url],", "}, \"alphabet\": \"E\" }, \"memoryLimit\": 256, \"timeLimit\": 5000 }, }", "debug=True) self.assertEqual(expected, actual) def test_impossible_problem(self): \"\"\"This tests a problem impossible", "format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = { \"status\":", "{ \"input\": \"2 2\\n2 \\n1 >= 5\\n2 >= 5\\n2\\n1 <=", "}, { \"input\": \"***\\n\", \"output\": \"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\":", "\"alphabet\": \"A\" }, \"memoryLimit\": 256, \"timeLimit\": 2000 }, } actual", "<= 4\\n2 <= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2", "\"NO\\n\" }, { \"input\": \"a*bc*\\n\", \"output\": \"YES\\n\" }, { \"input\":", "}, { \"input\": \"4 5\\n3 1\\n3 0\\n\", \"output\": \"10\\n\" },", "{ \"input\": \"1 2\\n2\\n1 <= 10\\n1 >= 15\\n\", \"output\": \"No\\n\"", "{ \"name\": \"Japan Alumni Group Spring Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\"", "sample section. \"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = { \"status\":", "}, \"alphabet\": \"A\" }, \"memoryLimit\": 128, \"timeLimit\": 5000 }, }", "}, { \"input\": \"5 10\\n1 2 3 2 3\\n\", \"output\":", "[\"requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\":", "[{ \"input\": \"3 2\\n2 2\\n2 2\\n\", \"output\": \"12\\n\" }, {", "\"3 2\\n1 2 3\\n\", \"output\": \"2 3\\n\" }, { \"input\":", "4\\n2 >= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2", "\"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 128, \"timeLimit\": 5000", "1\\n1\\n5 >= 2 \\n\", \"output\": \"Yes\\n\" }], \"name\": \"Everlasting Zero\",", "\"name\": \"<NAME>\", \"context\": { \"contest\": { \"name\": \"AtCoder Grand Contest", "\"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\" }, \"memoryLimit\": 64, \"timeLimit\": 2000 },", "\"tests\": [{ \"input\": \"ab*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"abc\\n\",", "<= 2\\n2 >= 5\\n\", \"output\": \"No\\n\" }, { \"input\": \"1", "tests a problem impossible to parse sample cases. \"\"\" url", "<= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1", "\"output\": \"10\\n\" }, { \"input\": \"23 18\\n15 13\\n100 95\\n\", \"output\":", "= { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\",", "\"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2", ">= 2 \\n\", \"output\": \"Yes\\n\" }], \"name\": \"Everlasting Zero\", \"context\":", "an old-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected =", "{ \"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError: 404 Client Error: Not Found", "}, { \"input\": \"2 2\\n2 \\n1 >= 5\\n2 >= 5\\n2\\n1", "= 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = { \"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed", "url], debug=True) self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self): \"\"\"This problem uses an", "\"memoryLimit\": 128, \"timeLimit\": 5000 }, } actual = main(['get-problem', url],", "Client Error: Not Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, }", "\"999999999\\n\", \"output\": \"26484\\n\" }], \"name\": \"755\", \"context\": { \"contest\": {", "1\\n5 <= 1\\n3\\n3 >= 2\\n4 <= 1\\n5 <= 1\\n2\\n4 >=", "1 2 2 3 3\\n\", \"output\": \"\\n\" }, { \"input\":", "[], \"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\": \"575\\n\", \"output\":", "\"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{", "\"364527243\\n\" }, { \"input\": \"30 30\\n24 22\\n145 132\\n\", \"output\": \"976668549\\n\"", "\"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\":", "HTML. \"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = { \"status\": \"ok\",", "'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected = { \"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed to", "036\", \"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\" }, \"memoryLimit\": 1024, \"timeLimit\":", "Not Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, } actual =", "HTML markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected", "Regular Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\" }, \"memoryLimit\":", "[{ \"input\": \"2 2\\n2 \\n1 >= 3\\n2 <= 5\\n2\\n1 >=", "\"contest\": { \"name\": \"AtCoder Regular Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\" },", "https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = { \"status\": \"ok\",", "def test_icpc2013spring_a(self): \"\"\"This problem contains both words `Input` and `Output`", "{ \"input\": \"5 5\\n3\\n2 <= 1\\n3 <= 1\\n4 <= 1\\n4\\n2", "{ \"input\": \"3600\\n\", \"output\": \"13\\n\" }, { \"input\": \"999999999\\n\", \"output\":", "\"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\",", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_impossible_problem(self): \"\"\"This tests a", "import unittest from onlinejudge_api.main import main class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self):", "parse samples\"], \"result\": None, } actual = main(['get-problem', url], debug=True)", "2\\n2 2\\n2 2\\n\", \"output\": \"12\\n\" }, { \"input\": \"4 5\\n3", "{ \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\": \"3 2\\n1 2 3\\n\",", "for sample outputs. \"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = {", "\"input\": \"5 10\\n1 2 3 2 3\\n\", \"output\": \"3\\n\" },", "{ \"input\": \"999999999\\n\", \"output\": \"26484\\n\" }], \"name\": \"755\", \"context\": {", "}, { \"input\": \"3600\\n\", \"output\": \"13\\n\" }, { \"input\": \"999999999\\n\",", "\"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\": \"3 2\\n1 2 3\\n\", \"output\":", "\"YES\\n\" }, { \"input\": \"***\\n\", \"output\": \"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\",", "tests a problem which uses a new-style format HTML. \"\"\"", "5\\n3 1\\n3 0\\n\", \"output\": \"10\\n\" }, { \"input\": \"23 18\\n15", "\"input\": \"2 2\\n2 \\n1 >= 5\\n2 >= 5\\n2\\n1 <= 4\\n2", "url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = { \"status\": \"ok\", \"messages\": [],", "2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\": \"2\\n2\\n\" }, { \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3", "HTML. \"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = { \"status\": \"ok\",", ">= 5\\n\", \"output\": \"No\\n\" }, { \"input\": \"1 2\\n2\\n1 <=", "}, \"alphabet\": \"D\" }, \"memoryLimit\": 64, \"timeLimit\": 2000 }, }", "seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = { \"status\":", ">= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1", "[], \"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\": \"3 2\\n2", "uses an unusual HTML markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url", "\"result\": None, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual)", "\"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1 >= 3\\n2 <=", "{ \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\" }, \"memoryLimit\":", "\"2 2\\n2 \\n1 >= 5\\n2 >= 5\\n2\\n1 <= 4\\n2 <=", "uses an old-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected", "\"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\": \"ab*\\n\", \"output\": \"YES\\n\" }, {", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_impossible_problem(self): \"\"\"This tests", "= 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = { \"status\": \"ok\", \"messages\": [], \"result\":", "\"output\": \"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\": { \"name\":", "\"name\": \"AtCoder Beginner Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\"", "}, { \"input\": \"a*bc*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"***\\n\",", "{ \"input\": \"***\\n\", \"output\": \"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": {", "4\\n2 <= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2", "\"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\": \"3 2\\n2 2\\n2 2\\n\", \"output\": \"12\\n\"", "\"input\": \"30 30\\n24 22\\n145 132\\n\", \"output\": \"976668549\\n\" }], \"name\": \"AtCoder\\u793e\\u306e\\u51ac\",", "}, { \"input\": \"11 97\\n3 1 4 1 5 9", "\"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\": { \"name\": \"AtCoder", "\"alphabet\": \"D\" }, \"memoryLimit\": 64, \"timeLimit\": 2000 }, } actual", "\"D\" }, \"memoryLimit\": 64, \"timeLimit\": 2000 }, } actual =", "}, { \"input\": \"6 1000000000000\\n1 1 2 2 3 3\\n\",", "both words `Input` and `Output` for the headings for sample", "{ \"input\": \"30 30\\n24 22\\n145 132\\n\", \"output\": \"976668549\\n\" }], \"name\":", "<= 1\\n1\\n5 >= 2 \\n\", \"output\": \"Yes\\n\" }], \"name\": \"Everlasting", "\"output\": \"No\\n\" }, { \"input\": \"1 2\\n2\\n1 <= 10\\n1 >=", "Grand Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\" }, \"memoryLimit\":", "\"alphabet\": \"A\" }, \"memoryLimit\": 128, \"timeLimit\": 5000 }, } actual", "unittest from onlinejudge_api.main import main class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This", "0\\n\", \"output\": \"10\\n\" }, { \"input\": \"23 18\\n15 13\\n100 95\\n\",", "new-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = {", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_agc036_b(self): \"\"\"In this", "url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = { \"status\": \"ok\", \"messages\": [],", "\"name\": \"AtCoder Beginner Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\"", "1024, \"timeLimit\": 2000 }, } actual = main(['get-problem', url], debug=True)", "\\n1 >= 3\\n2 <= 5\\n2\\n1 >= 4\\n2 >= 3\\n\", \"output\":", "debug=True) self.assertEqual(expected, actual) def test_abc114_c(self): \"\"\"This tests a problem which", "\"input\": \"***\\n\", \"output\": \"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\":", "{ \"contest\": { \"name\": \"AtCoder Beginner Contest 114\", \"url\": \"https://atcoder.jp/contests/abc114\"", "\"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\" }, \"memoryLimit\": 256,", "\"tests\": [{ \"input\": \"575\\n\", \"output\": \"4\\n\" }, { \"input\": \"3600\\n\",", "\"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 128, \"timeLimit\": 5000 },", "\"E\" }, \"memoryLimit\": 256, \"timeLimit\": 5000 }, } actual =", "\"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\": \"2\\n2\\n\" }, { \"input\": \"5", "Group Spring Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\" },", "\"input\": \"abc\\n\", \"output\": \"NO\\n\" }, { \"input\": \"a*bc*\\n\", \"output\": \"YES\\n\"", "1\\n\", \"output\": \"2\\n2\\n\" }, { \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\":", "404 Client Error: Not Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None,", "\"context\": { \"contest\": { \"name\": \"AtCoder Beginner Contest 114\", \"url\":", "descriptoin text in the sample section. \"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a'", "}], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\":", "2 2 3 3\\n\", \"output\": \"\\n\" }, { \"input\": \"11", "{ \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\", \"tests\": [{ \"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\",", "{ \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\",", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_non_existing_problem(self): \"\"\"This", "\\n1 >= 5\\n2 >= 5\\n2\\n1 <= 4\\n2 <= 3\\n\", \"output\":", "\"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\": \"2", "= 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = { \"status\": \"ok\", \"messages\": [], \"result\":", "{ \"input\": \"11 97\\n3 1 4 1 5 9 2", "\"output\": \"15\\n7\\n\" }, { \"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\":", "2\\n2 \\n1 >= 3\\n2 <= 5\\n2\\n1 >= 4\\n2 >= 3\\n\",", "{ \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\": \"ab*\\n\", \"output\": \"YES\\n\" },", "<= 5\\n2\\n1 >= 4\\n2 >= 3\\n\", \"output\": \"Yes\\n\" }, {", "\"4\\n\" }, { \"input\": \"3600\\n\", \"output\": \"13\\n\" }, { \"input\":", "{ \"input\": \"23 18\\n15 13\\n100 95\\n\", \"output\": \"364527243\\n\" }, {", "= { \"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError: 404 Client Error: Not", "\"input\": \"ab*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"abc\\n\", \"output\": \"NO\\n\"", "\"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\": \"3", "Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 128,", "test_arc035_a(self): \"\"\"This problem uses <code> tags in the descriptoin text", ">= 2\\n3 <= 1\\n4 <= 1\\n5 <= 1\\n3\\n3 >= 2\\n4", "}, { \"input\": \"999999999\\n\", \"output\": \"26484\\n\" }], \"name\": \"755\", \"context\":", ">= 3\\n2 <= 5\\n2\\n1 >= 4\\n2 >= 3\\n\", \"output\": \"Yes\\n\"", "5\\n3\\n2 <= 1\\n3 <= 1\\n4 <= 1\\n4\\n2 >= 2\\n3 <=", "\"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\": [{ \"input\": \"3 2\\n1 2", "{ \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\":", "\"memoryLimit\": 256, \"timeLimit\": 2000 }, } actual = main(['get-problem', url],", "\"\\n\" }, { \"input\": \"11 97\\n3 1 4 1 5", "10\\n1 >= 15\\n\", \"output\": \"No\\n\" }, { \"input\": \"5 5\\n3\\n2", "\"contest\": { \"name\": \"AtCoder Beginner Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\" },", "3\\n\", \"output\": \"\\n\" }, { \"input\": \"11 97\\n3 1 4", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_agc036_b(self): \"\"\"In", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self): \"\"\"This problem", "\"memoryLimit\": 64, \"timeLimit\": 2000 }, } actual = main(['get-problem', url],", "uses <code> tags in the descriptoin text in the sample", "markup. .. seealso:: https://github.com/kmyk/online-judge-tools/issues/618 \"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected =", "= main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This tests", "format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = { \"status\":", "{ \"input\": \"a*bc*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"***\\n\", \"output\":", "{ \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\":", "\"Japan Alumni Group Spring Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\":", "\"3\\n\" }, { \"input\": \"6 1000000000000\\n1 1 2 2 3", "\"name\": \"AtCoder Grand Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\"", "6 5 3 5\\n\", \"output\": \"9 2 6\\n\" }], \"name\":", "\"output\": \"13\\n\" }, { \"input\": \"999999999\\n\", \"output\": \"26484\\n\" }], \"name\":", "problem which uses an old-style format HTML. \"\"\" url =", "\"2 2\\n2 \\n1 >= 3\\n2 <= 3\\n2\\n1 <= 2\\n2 >=", "\"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\":", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This tests a", "https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected,", "to parse sample cases. \"\"\" url = 'https://chokudai001.contest.atcoder.jp/tasks/chokudai_001_a' expected =", "\"11 97\\n3 1 4 1 5 9 2 6 5", "= { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e\",", "}, { \"input\": \"abc\\n\", \"output\": \"NO\\n\" }, { \"input\": \"a*bc*\\n\",", "}], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\": { \"name\": \"AtCoder Regular", "from onlinejudge_api.main import main class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This problem", "3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1 >=", "actual) def test_tenka1_2014_qualA_e(self): \"\"\"This problem uses an unusual HTML markup.", "Alumni Group Spring Contest 2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\"", "\"2\\n2\\n\" }, { \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\" }],", "}, { \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\" }], \"name\":", "Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, } actual = main(['get-problem',", "expected = { \"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed to parse", "\"alphabet\": \"B\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, } actual", "2\\n5 <= 1\\n1\\n5 >= 2 \\n\", \"output\": \"Yes\\n\" }], \"name\":", "2 3 2 3\\n\", \"output\": \"3\\n\" }, { \"input\": \"6", "\"***\\n\", \"output\": \"YES\\n\" }], \"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\": {", "\"2 2\\n2 \\n1 >= 3\\n2 <= 5\\n2\\n1 >= 4\\n2 >=", "self.assertEqual(expected, actual) def test_abc114_c(self): \"\"\"This tests a problem which uses", "3 2 3\\n\", \"output\": \"3\\n\" }, { \"input\": \"6 1000000000000\\n1", "2 6 5 3 5\\n\", \"output\": \"9 2 6\\n\" }],", "\"\"\" url = 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = { \"status\": \"ok\", \"messages\":", "1 4 1 5 9 2 6 5 3 5\\n\",", "{ \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\": \"575\\n\", \"output\": \"4\\n\" },", "\"6 1000000000000\\n1 1 2 2 3 3\\n\", \"output\": \"\\n\" },", "{ \"input\": \"abc\\n\", \"output\": \"NO\\n\" }, { \"input\": \"a*bc*\\n\", \"output\":", "url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = { \"status\": \"ok\", \"messages\": [],", "\"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\"", "tests a problem which uses an old-style format HTML. \"\"\"", "3 5\\n\", \"output\": \"9 2 6\\n\" }], \"name\": \"<NAME>\", \"context\":", "\"AtCoder Grand Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\" },", "<= 1\\n4\\n2 >= 2\\n3 <= 1\\n4 <= 1\\n5 <= 1\\n3\\n3", "main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_arc035_a(self): \"\"\"This problem uses", "\"contest\": { \"name\": \"Japan Alumni Group Spring Contest 2013\", \"url\":", ">= 5\\n2 >= 5\\n2\\n1 <= 4\\n2 <= 3\\n\", \"output\": \"Yes\\n\"", "\"memoryLimit\": 1024, \"timeLimit\": 2000 }, } actual = main(['get-problem', url],", "tests an non-existing problem. \"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected =", "\"name\": \"755\", \"context\": { \"contest\": { \"name\": \"AtCoder Beginner Contest", "\"alphabet\": \"C\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 }, } actual", "\\n1 >= 3\\n2 <= 3\\n2\\n1 <= 2\\n2 >= 5\\n\", \"output\":", "2\\n2\\n1 <= 10\\n1 >= 15\\n\", \"output\": \"No\\n\" }, { \"input\":", "{ \"name\": \"AtCoder Beginner Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\":", "\"context\": { \"contest\": { \"name\": \"AtCoder Beginner Contest 003\", \"url\":", "\"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\": \"3 2\\n2 2\\n2", "{ \"contest\": { \"name\": \"AtCoder Beginner Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\"", "\"output\": \"2\\n2\\n\" }, { \"input\": \"5 5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\"", "\"context\": { \"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\":", "\"\"\"This tests an non-existing problem. \"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected", "\"AtCoder Beginner Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\" },", "= 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = { \"status\": \"ok\", \"messages\": [], \"result\":", "self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self): \"\"\"This problem uses an unusual HTML", "self.assertEqual(expected, actual) def test_non_existing_problem(self): \"\"\"This tests an non-existing problem. \"\"\"", "\"Everlasting Zero\", \"context\": { \"contest\": { \"name\": \"Japan Alumni Group", ">= 4\\n2 >= 3\\n\", \"output\": \"Yes\\n\" }, { \"input\": \"2", "\"https://atcoder.jp/contests/abc114\" }, \"alphabet\": \"C\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000 },", "\"2 3\\n\" }, { \"input\": \"5 10\\n1 2 3 2", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This", "1\\n2 3\\n\", \"output\": \"15\\n7\\n\" }, { \"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2", "is empty. \"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = { \"status\":", "<= 1\\n3\\n3 >= 2\\n4 <= 1\\n5 <= 1\\n2\\n4 >= 2\\n5", "\"input\": \"575\\n\", \"output\": \"4\\n\" }, { \"input\": \"3600\\n\", \"output\": \"13\\n\"", "3\\n\", \"output\": \"15\\n7\\n\" }, { \"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\",", "\"tests\": [{ \"input\": \"2 2\\n2 \\n1 >= 3\\n2 <= 5\\n2\\n1", "def test_arc035_a(self): \"\"\"This problem uses <code> tags in the descriptoin", "\"\"\" url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = { \"status\": \"ok\", \"messages\":", "2 3 3\\n\", \"output\": \"\\n\" }, { \"input\": \"11 97\\n3", "tags in the descriptoin text in the sample section. \"\"\"", "in the descriptoin text in the sample section. \"\"\" url", "3\\n\", \"output\": \"2 3\\n\" }, { \"input\": \"5 10\\n1 2", "\"output\": \"4\\n\" }, { \"input\": \"3600\\n\", \"output\": \"13\\n\" }, {", "<= 10\\n1 >= 15\\n\", \"output\": \"No\\n\" }, { \"input\": \"5", "\"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = { \"status\": \"error\", \"messages\":", "<= 1\\n2\\n4 >= 2\\n5 <= 1\\n1\\n5 >= 2 \\n\", \"output\":", "10\\n1 2 3 2 3\\n\", \"output\": \"3\\n\" }, { \"input\":", "samples\"], \"result\": None, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected,", "2 6\\n\" }], \"name\": \"<NAME>\", \"context\": { \"contest\": { \"name\":", "\"output\": \"\\n\" }, { \"input\": \"11 97\\n3 1 4 1", "test_tenka1_2014_qualA_e(self): \"\"\"This problem uses an unusual HTML markup. .. seealso::", "}, { \"input\": \"2 2\\nAB\\nBA\\n2\\n1 1\\n2 1\\n\", \"output\": \"2\\n2\\n\" },", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self): \"\"\"This", "test_agc036_b(self): \"\"\"In this problem, a sample output is empty. \"\"\"", "\"input\": \"2 2\\n2 \\n1 >= 3\\n2 <= 3\\n2\\n1 <= 2\\n2", "\"input\": \"5 3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\": \"15\\n7\\n\" }, { \"input\":", "\"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected = { \"status\": \"ok\", \"messages\":", "\"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\": \"2 2\\n2 \\n1 >= 3\\n2", "Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\" }, \"memoryLimit\": 64,", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_tenka1_2014_qualA_e(self):", "'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = { \"status\": \"ok\", \"messages\": [], \"result\": {", "\"name\": \"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\": { \"name\": \"AtCoder Regular Contest", "\"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\", \"tests\": [{ \"input\": \"3 2\\n2 2\\n2 2\\n\", \"output\":", "6\\n\" }], \"name\": \"<NAME>\", \"context\": { \"contest\": { \"name\": \"AtCoder", "url], debug=True) self.assertEqual(expected, actual) def test_arc035_a(self): \"\"\"This problem uses <code>", "\"contest\": { \"name\": \"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\" },", "None, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def", "\"\\u9ad8\\u6a4b\\u304f\\u3093\\u3068\\u56de\\u6587\", \"context\": { \"contest\": { \"name\": \"AtCoder Regular Contest 035\",", "url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = { \"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError:", "{ \"contest\": { \"name\": \"AtCoder Regular Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\"", "\"Yes\\n\" }, { \"input\": \"2 2\\n2 \\n1 >= 5\\n2 >=", "headings for sample outputs. \"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected =", ">= 2\\n4 <= 1\\n5 <= 1\\n2\\n4 >= 2\\n5 <= 1\\n1\\n5", "\"messages\": [\"requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"],", "def test_impossible_problem(self): \"\"\"This tests a problem impossible to parse sample", "\"output\": \"26484\\n\" }], \"name\": \"755\", \"context\": { \"contest\": { \"name\":", "\"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\": \"575\\n\", \"output\": \"4\\n\" }, { \"input\":", "1\\n4 <= 1\\n5 <= 1\\n3\\n3 >= 2\\n4 <= 1\\n5 <=", "\"output\": \"YES\\n\" }, { \"input\": \"***\\n\", \"output\": \"YES\\n\" }], \"name\":", "def test_non_existing_problem(self): \"\"\"This tests an non-existing problem. \"\"\" url =", "\"output\": \"YES\\n\" }, { \"input\": \"abc\\n\", \"output\": \"NO\\n\" }, {", "def test_agc036_b(self): \"\"\"In this problem, a sample output is empty.", "\"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\" }, \"memoryLimit\": 256, \"timeLimit\": 5000 },", "1000000000000\\n1 1 2 2 3 3\\n\", \"output\": \"\\n\" }, {", "}], \"name\": \"Everlasting Zero\", \"context\": { \"contest\": { \"name\": \"Japan", "\"\"\"This tests a problem impossible to parse sample cases. \"\"\"", "test_impossible_problem(self): \"\"\"This tests a problem impossible to parse sample cases.", "\"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\": { \"name\": \"AtCoder Beginner Contest 003\",", "\"name\": \"AtCoder\\u793e\\u306e\\u51ac\", \"context\": { \"contest\": { \"name\": \"AtCoder Beginner Contest", "\"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\": \"ab*\\n\",", "\"YES\\n\" }, { \"input\": \"abc\\n\", \"output\": \"NO\\n\" }, { \"input\":", "\"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\" }, \"memoryLimit\": 64, \"timeLimit\": 2000", "sample outputs. \"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = { \"status\":", "uses a new-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c' expected", "text in the sample section. \"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected", "}, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def", "5\\nAABAA\\nACDEA\\nAFGHA\\nAIJKA\\nAAAAA\\n1\\n3 1\\n\", \"output\": \"25\\n\" }], \"name\": \"\\u30d1\\u30ba\\u30eb\\u306e\\u79fb\\u52d5\", \"context\": { \"contest\":", "{ \"name\": \"AtCoder Grand Contest 036\", \"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\":", "url], debug=True) self.assertEqual(expected, actual) def test_agc036_b(self): \"\"\"In this problem, a", "} actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_agc036_b(self):", "1\\n5 <= 1\\n2\\n4 >= 2\\n5 <= 1\\n1\\n5 >= 2 \\n\",", "\"result\": { \"url\": \"https://atcoder.jp/contests/abc114/tasks/abc114_c\", \"tests\": [{ \"input\": \"575\\n\", \"output\": \"4\\n\"", "self.assertEqual(expected, actual) def test_impossible_problem(self): \"\"\"This tests a problem impossible to", "\"input\": \"2 2\\n2 \\n1 >= 3\\n2 <= 5\\n2\\n1 >= 4\\n2", "\"\"\"This tests a problem which uses an old-style format HTML.", "2\\n2 >= 5\\n\", \"output\": \"No\\n\" }, { \"input\": \"1 2\\n2\\n1", "\"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 256, \"timeLimit\": 2000", "a problem impossible to parse sample cases. \"\"\" url =", "= { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\",", "for the headings for sample outputs. \"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a'", "\"timeLimit\": 5000 }, } actual = main(['get-problem', url], debug=True) self.assertEqual(expected,", "2013\", \"url\": \"https://atcoder.jp/contests/jag2013spring\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 128, \"timeLimit\":", "\"result\": { \"url\": \"https://atcoder.jp/contests/arc035/tasks/arc035_a\", \"tests\": [{ \"input\": \"ab*\\n\", \"output\": \"YES\\n\"", "which uses a new-style format HTML. \"\"\" url = 'https://atcoder.jp/contests/abc114/tasks/abc114_c'", "<= 1\\n4 <= 1\\n5 <= 1\\n3\\n3 >= 2\\n4 <= 1\\n5", "[{ \"input\": \"575\\n\", \"output\": \"4\\n\" }, { \"input\": \"3600\\n\", \"output\":", "Error: Not Found for url: https://atcoder.jp/contests/abc001/tasks/abc001_100\"], \"result\": None, } actual", "\"input\": \"999999999\\n\", \"output\": \"26484\\n\" }], \"name\": \"755\", \"context\": { \"contest\":", "5\\n2 >= 5\\n2\\n1 <= 4\\n2 <= 3\\n\", \"output\": \"Yes\\n\" },", "3\\nAAB\\nABB\\nCDE\\nFFH\\nGHH\\n2\\n1 1\\n2 3\\n\", \"output\": \"15\\n7\\n\" }, { \"input\": \"2 2\\nAB\\nBA\\n2\\n1", "\"input\": \"a*bc*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"***\\n\", \"output\": \"YES\\n\"", "\"\\u5929\\u4e0b\\u4e00\\u30d7\\u30ed\\u30b0\\u30e9\\u30de\\u30fc\\u30b3\\u30f3\\u30c6\\u30b9\\u30c82014\\u4e88\\u9078A\", \"url\": \"https://atcoder.jp/contests/tenka1-2014-quala\" }, \"alphabet\": \"E\" }, \"memoryLimit\": 256, \"timeLimit\":", "[{ \"input\": \"ab*\\n\", \"output\": \"YES\\n\" }, { \"input\": \"abc\\n\", \"output\":", "a problem which uses an old-style format HTML. \"\"\" url", "\"Yes\\n\" }], \"name\": \"Everlasting Zero\", \"context\": { \"contest\": { \"name\":", "\"input\": \"3 2\\n2 2\\n2 2\\n\", \"output\": \"12\\n\" }, { \"input\":", "1\\n4\\n2 >= 2\\n3 <= 1\\n4 <= 1\\n5 <= 1\\n3\\n3 >=", "non-existing problem. \"\"\" url = 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = { \"status\":", "class DownloadAtCoderTest(unittest.TestCase): def test_icpc2013spring_a(self): \"\"\"This problem contains both words `Input`", "\"\"\" url = 'http://jag2013spring.contest.atcoder.jp/tasks/icpc2013spring_a' expected = { \"status\": \"ok\", \"messages\":", "debug=True) self.assertEqual(expected, actual) def test_call_download_atcoder_abc003_4(self): \"\"\"This tests a problem which", "18\\n15 13\\n100 95\\n\", \"output\": \"364527243\\n\" }, { \"input\": \"30 30\\n24", "{ \"status\": \"error\", \"messages\": [\"onlinejudge.type.SampleParseError: failed to parse samples\"], \"result\":", "url = 'https://atcoder.jp/contests/agc036/tasks/agc036_b' expected = { \"status\": \"ok\", \"messages\": [],", "= { \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/abc003/tasks/abc003_4\",", "\"A\" }, \"memoryLimit\": 256, \"timeLimit\": 2000 }, } actual =", "actual = main(['get-problem', url], debug=True) self.assertEqual(expected, actual) def test_impossible_problem(self): \"\"\"This", "contains both words `Input` and `Output` for the headings for", "Contest 035\", \"url\": \"https://atcoder.jp/contests/arc035\" }, \"alphabet\": \"A\" }, \"memoryLimit\": 256,", "{ \"status\": \"ok\", \"messages\": [], \"result\": { \"url\": \"https://atcoder.jp/contests/agc036/tasks/agc036_b\", \"tests\":", "section. \"\"\" url = 'http://arc035.contest.atcoder.jp/tasks/arc035_a' expected = { \"status\": \"ok\",", "5 9 2 6 5 3 5\\n\", \"output\": \"9 2", "\"result\": { \"url\": \"https://atcoder.jp/contests/jag2013spring/tasks/icpc2013spring_a\", \"tests\": [{ \"input\": \"2 2\\n2 \\n1", "3 3\\n\", \"output\": \"\\n\" }, { \"input\": \"11 97\\n3 1", "= 'https://atcoder.jp/contests/tenka1-2014-quala/tasks/tenka1_2014_qualA_e' expected = { \"status\": \"ok\", \"messages\": [], \"result\":", "Beginner Contest 003\", \"url\": \"https://atcoder.jp/contests/abc003\" }, \"alphabet\": \"D\" }, \"memoryLimit\":", ">= 2\\n5 <= 1\\n1\\n5 >= 2 \\n\", \"output\": \"Yes\\n\" }],", "= 'http://abc001.contest.atcoder.jp/tasks/abc001_100' expected = { \"status\": \"error\", \"messages\": [\"requests.exceptions.HTTPError: 404", "\"url\": \"https://atcoder.jp/contests/agc036\" }, \"alphabet\": \"B\" }, \"memoryLimit\": 1024, \"timeLimit\": 2000", "\"\"\" url = 'https://atcoder.jp/contests/abc003/tasks/abc003_4' expected = { \"status\": \"ok\", \"messages\":", "\\n\", \"output\": \"Yes\\n\" }], \"name\": \"Everlasting Zero\", \"context\": { \"contest\":" ]
[ "== 'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action == 'list-groups': print(json.dumps(client.list_groups(), indent=2))", "cli.client if cli.args.action == 'list-users': print(json.dumps(client.list_users(), indent=2)) elif cli.args.action ==", "if cli.args.action == 'list-users': print(json.dumps(client.list_users(), indent=2)) elif cli.args.action == 'list-sites':", "indent=2)) else: print('Unsupported action {}'.format(cli.args.action), file=sys.stderr) sys.exit(1) if __name__ ==", "== 'list-groups': print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported action {}'.format(cli.args.action), file=sys.stderr) sys.exit(1)", "= cli.client if cli.args.action == 'list-users': print(json.dumps(client.list_users(), indent=2)) elif cli.args.action", "python3 # This file is part of ODM and distributed", "print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action == 'list-groups': print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported", "sys import odm.cli def main(): cli = odm.cli.CLI(['action']) client =", "main(): cli = odm.cli.CLI(['action']) client = cli.client if cli.args.action ==", "indent=2)) elif cli.args.action == 'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action ==", "cli.args.action == 'list-users': print(json.dumps(client.list_users(), indent=2)) elif cli.args.action == 'list-sites': print(json.dumps(client.list_sites(),", "client = cli.client if cli.args.action == 'list-users': print(json.dumps(client.list_users(), indent=2)) elif", "See COPYING. import json import sys import odm.cli def main():", "MIT license. See COPYING. import json import sys import odm.cli", "odm.cli.CLI(['action']) client = cli.client if cli.args.action == 'list-users': print(json.dumps(client.list_users(), indent=2))", "of the # MIT license. See COPYING. import json import", "json import sys import odm.cli def main(): cli = odm.cli.CLI(['action'])", "ODM and distributed under the terms of the # MIT", "under the terms of the # MIT license. See COPYING.", "indent=2)) elif cli.args.action == 'list-groups': print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported action", "import json import sys import odm.cli def main(): cli =", "COPYING. import json import sys import odm.cli def main(): cli", "part of ODM and distributed under the terms of the", "def main(): cli = odm.cli.CLI(['action']) client = cli.client if cli.args.action", "== 'list-users': print(json.dumps(client.list_users(), indent=2)) elif cli.args.action == 'list-sites': print(json.dumps(client.list_sites(), indent=2))", "elif cli.args.action == 'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action == 'list-groups':", "'list-groups': print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported action {}'.format(cli.args.action), file=sys.stderr) sys.exit(1) if", "distributed under the terms of the # MIT license. See", "print(json.dumps(client.list_users(), indent=2)) elif cli.args.action == 'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action", "<reponame>UMCollab/ODM #!/usr/bin/env python3 # This file is part of ODM", "elif cli.args.action == 'list-groups': print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported action {}'.format(cli.args.action),", "is part of ODM and distributed under the terms of", "# MIT license. See COPYING. import json import sys import", "cli = odm.cli.CLI(['action']) client = cli.client if cli.args.action == 'list-users':", "else: print('Unsupported action {}'.format(cli.args.action), file=sys.stderr) sys.exit(1) if __name__ == '__main__':", "the # MIT license. See COPYING. import json import sys", "print('Unsupported action {}'.format(cli.args.action), file=sys.stderr) sys.exit(1) if __name__ == '__main__': main()", "cli.args.action == 'list-groups': print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported action {}'.format(cli.args.action), file=sys.stderr)", "This file is part of ODM and distributed under the", "the terms of the # MIT license. See COPYING. import", "'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action == 'list-groups': print(json.dumps(client.list_groups(), indent=2)) else:", "and distributed under the terms of the # MIT license.", "of ODM and distributed under the terms of the #", "import sys import odm.cli def main(): cli = odm.cli.CLI(['action']) client", "odm.cli def main(): cli = odm.cli.CLI(['action']) client = cli.client if", "#!/usr/bin/env python3 # This file is part of ODM and", "license. See COPYING. import json import sys import odm.cli def", "file is part of ODM and distributed under the terms", "terms of the # MIT license. See COPYING. import json", "print(json.dumps(client.list_groups(), indent=2)) else: print('Unsupported action {}'.format(cli.args.action), file=sys.stderr) sys.exit(1) if __name__", "# This file is part of ODM and distributed under", "cli.args.action == 'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif cli.args.action == 'list-groups': print(json.dumps(client.list_groups(),", "= odm.cli.CLI(['action']) client = cli.client if cli.args.action == 'list-users': print(json.dumps(client.list_users(),", "import odm.cli def main(): cli = odm.cli.CLI(['action']) client = cli.client", "'list-users': print(json.dumps(client.list_users(), indent=2)) elif cli.args.action == 'list-sites': print(json.dumps(client.list_sites(), indent=2)) elif" ]
[ "= ''' SomeUnknownTag: SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2)", "<text>This is just an example. Some of the non-standard licenses", "sample spreadsheet</text>', 8) def test_external_document_references(self): data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301", "# limitations under the License. import sys from unittest import", "'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName',", "'DATE', '2010-02-03T00:00:00Z', 6) def test_review_info(self): data = ''' Reviewer: Person:", "clause licenses</text>''', 4) def test_pacakage(self): data = ''' SPDXID: SPDXRef-Package", "from linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0 '''", "self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT',", "Source Auditor Inc. Creator: Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment: <text>This", "'Reviewer: Person: Bob the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was", "'Apache-2.0', 9) def token_assert_helper(self, token, ttype, value, line): assert token.type", "'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF',", "2.0 (the \"License\"); # you may not use this file", "self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE',", "'TEXT', '<text>Some lic comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(),", "also here.</text>' ]) package_str = '\\n'.join([ 'PackageName: Test', 'SPDXID: SPDXRef-Package',", "def setUp(self): self.l = Lexer() self.l.build() def test_document(self): data =", "'PackageName: Test', 'SPDXID: SPDXRef-Package', 'PackageVersion: Version 0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed:", "kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ]) complete_str =", "self.p.build() def test_doc(self): document, error = self.p.parse(self.complete_str) assert document is", "<NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source", "and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef:", "SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright 2014 Acme", "2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>' ]) review_str = '\\n'.join([ 'Reviewer: Person:", "assert document.comment == 'Sample Comment' assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def", "snippet comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE', 'from", "ttype assert token.value == value assert token.lineno == line class", "CreatorComment: <text>This is an example of an SPDX spreadsheet format</text>", "0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary: <text>Test package</text>', 'PackageSourceInfo: <text>Version", "''' Reviewer: Person: Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This is", "an example of an SPDX spreadsheet format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName',", "'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0',", "5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef',", "8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is a sample spreadsheet</text>', 8) def", "<text>A package.</text>', 'PackageComment: <text>Comment on the package.</text>', 'PackageCopyrightText: <text> Copyright", "spdx_file.name == 'testfile.java' assert spdx_file.spdx_id == 'SPDXRef-File' assert spdx_file.type ==", "on the package.</text>', 'PackageCopyrightText: <text> Copyright 2014 Acme Inc.</text>', 'PackageLicenseDeclared:", "3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source Auditor", "document.name == 'Sample_Document-V2.1' assert document.spdx_id == 'SPDXRef-DOCUMENT' assert document.comment ==", "'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3)", "document, error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag : SomeUnknownTag", "Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is a sample", "self.p = Parser(Builder(), StandardLogger()) self.p.build() def test_doc(self): document, error =", "package.</text>' ]) file_str = '\\n'.join([ 'FileName: testfile.java', 'SPDXID: SPDXRef-File', 'FileType:", "linux kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource',", "error assert len(document.reviews) == 2 def test_package(self): document, error =", "'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert", "test_file(self): document, error = self.p.parse(self.complete_str) assert document is not None", "document.package.version == 'Version 0.9.2' assert len(document.package.licenses_from_files) == 2 assert (document.package.conc_lics.identifier", "'cpe23Type' assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment == 'Some comment", "Apache-2.0 LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(),", "import StringIO except ImportError: from io import StringIO saved_out =", "https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str = '\\n'.join([ 'Creator: Person: Bob (<EMAIL>)', 'Creator:", "'Creator: Person: Bob (<EMAIL>)', 'Creator: Organization: Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment:", "'SnippetLicenseComments: <text>Some lic comment.</text>', 'SnippetCopyrightText: <text> Copyright 2008-2010 <NAME> </text>',", "None assert not error assert document.version == Version(major=2, minor=1) assert", "lic comment.</text> SnippetCopyrightText: <text>Some cr text.</text> SnippetComment: <text>Some snippet comment.</text>", "== 1 assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert document.snippet[-1].name == 'from", "<text>Some lic comment.</text> SnippetCopyrightText: <text>Some cr text.</text> SnippetComment: <text>Some snippet", "assert len(document.reviews) == 2 def test_package(self): document, error = self.p.parse(self.complete_str)", "PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some", "]) package_str = '\\n'.join([ 'PackageName: Test', 'SPDXID: SPDXRef-Package', 'PackageVersion: Version", "something.txt)', 'PackageDescription: <text>A package.</text>', 'PackageComment: <text>Comment on the package.</text>', 'PackageCopyrightText:", "self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(),", "'{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str, file_str, snippet_str) def setUp(self): self.p =", "Version class TestLexer(TestCase): maxDiff = None def setUp(self): self.l =", "SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment: <text>This is an example of an", "7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(),", "assert token.value == value assert token.lineno == line class TestParser(TestCase):", "kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7)", "use this file except in compliance with the License. #", "self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE',", "'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment',", "'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12',", "2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self): data", "class TestParser(TestCase): maxDiff = None document_str = '\\n'.join([ 'SPDXVersion: SPDX-2.1',", "Person: Alice the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was also", "snippet comment.</text>', 'SnippetName: from linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0',", "complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str, file_str, snippet_str) def setUp(self):", "assert error assert document is not None def test_snippet(self): document,", "'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate',", "error assert document.version == Version(major=2, minor=1) assert document.data_license.identifier == 'CC0-1.0'", "assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier == 'Apache-2.0' assert document.snippet[-1].licenses_in_snippet[-1].identifier", "SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue',", "a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless", "text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet", "'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8)", "'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is just an example.", "'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr text.</text>', 4) self.token_assert_helper(self.l.token(),", "'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9) def token_assert_helper(self, token, ttype,", "def test_review_info(self): data = ''' Reviewer: Person: Joe Reviewer ReviewDate:", "of test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1:", "License. # You may obtain a copy of the License", "</text>', 'SnippetComment: <text>Some snippet comment.</text>', 'SnippetName: from linux kernel', 'SnippetFromFileSPDXID:", "http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(),", "'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str,", "document is not None assert not error assert document.package.name ==", "assert (document.package.conc_lics.identifier == 'LicenseRef-2.0 AND Apache-2.0') assert document.package.files_analyzed == True", "<text>Test package</text>', 'PackageSourceInfo: <text>Version 1.0 of test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier:", "Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)',", "'SPDXRef-Snippet' assert document.snippet[-1].name == 'from linux kernel' assert document.snippet[-1].comment ==", "<text>Some comment about the package.</text>' ]) file_str = '\\n'.join([ 'FileName:", "'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def", "False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY", "document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document, error = self.p.parse(self.complete_str) assert", "'Creator: Organization: Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>' ]) review_str", "SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment about the package.</text>' ])", "'TEXT', '<text>Some snippet comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(),", "under the License is distributed on an \"AS IS\" BASIS,", "'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace',", "'\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment:", "<text> Copyright 2014 Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and", "License for the specific language governing permissions and # limitations", "== 'Sample Comment' assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document,", "Here.</text>', 'Reviewer: Person: Alice the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice", "<text>Bob was Here.</text>', 'Reviewer: Person: Alice the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z',", "2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2) def test_snippet(self): data = '''", "self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(),", "'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright 2014 Acme Inc.</text>', 'ArtifactOfProjectName:", "== 'Sample Comment' assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def test_review(self): document,", "4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(),", "ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment about the package.</text>", "'<text>Some cr text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT',", "]) creation_str = '\\n'.join([ 'Creator: Person: Bob (<EMAIL>)', 'Creator: Organization:", "2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf,", "'PackageCopyrightText: <text> Copyright 2014 Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0", "Comment' assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def test_review(self): document, error =", "2 assert document.creation_info.comment == 'Sample Comment' assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z')", "Source Auditor Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE',", "4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5)", "9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9) def token_assert_helper(self, token, ttype, value,", "len(document.snippet) == 1 assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert document.snippet[-1].name ==", "2008-2010 <NAME> ' assert document.snippet[-1].license_comment == 'Some lic comment.' assert", "6) self.token_assert_helper(self.l.token(), 'LINE', 'from linux kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID',", "Lexer from spdx.parsers.tagvaluebuilders import Builder from spdx.parsers.loggers import StandardLogger from", "at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or", "self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED',", "self.token_assert_helper(self.l.token(), 'LINE', 'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM',", "value, line): assert token.type == ttype assert token.value == value", "(<EMAIL>)', 'Creator: Organization: Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>' ])", "'Some lic comment.' assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier ==", "self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is a sample", "data = ''' SomeUnknownTag: SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag',", "<text>Some cr text.</text> SnippetComment: <text>Some snippet comment.</text> SnippetName: from linux", "creation_str = '\\n'.join([ 'Creator: Person: Bob (<EMAIL>)', 'Creator: Organization: Acme.',", "= '\\n'.join([ 'Creator: Person: Bob (<EMAIL>)', 'Creator: Organization: Acme.', 'Created:", "'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet comment.</text>', 5) self.token_assert_helper(self.l.token(),", "they are actually BSD 3 clause licenses</text>''', 4) def test_pacakage(self):", "'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6)", "error assert len(document.creation_info.creators) == 2 assert document.creation_info.comment == 'Sample Comment'", "'testfile.java' assert spdx_file.spdx_id == 'SPDXRef-File' assert spdx_file.type == spdx.file.FileType.SOURCE assert", "Some of the non-standard licenses look like they are actually", "'SPDXRef-Package' assert document.package.version == 'Version 0.9.2' assert len(document.package.licenses_from_files) == 2", "SnippetCopyrightText: <text>Some cr text.</text> SnippetComment: <text>Some snippet comment.</text> SnippetName: from", "SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription: <text>A package.</text>', 'PackageComment:", "2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: '", "in compliance with the License. # You may obtain a", "Inc. Creator: Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment: <text>This is an", "'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic", "http://www.acme.org/', 'FileComment: <text>Very long file</text>' ]) unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue'", "linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ]) complete_str", "assert spdx_file.spdx_id == 'SPDXRef-File' assert spdx_file.type == spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name)", "test_external_document_references(self): data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data)", "the package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE',", "software # distributed under the License is distributed on an", "'LINE', 'SomeUnknownValue', 2) def test_snippet(self): data = ''' SnippetSPDXID: SPDXRef-Snippet", "Alice the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was also here.</text>'", "def test_creation_info(self): document, error = self.p.parse(self.complete_str) assert document is not", "import StringIO saved_out = sys.stdout sys.stdout = StringIO() document, error", "'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(),", "document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier == 'Apache-2.0' assert document.snippet[-1].licenses_in_snippet[-1].identifier ==", "self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is just an example. Some of the", "= self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag : SomeUnknownTag at line:", "1\\n') sys.stdout = saved_out assert error assert document is not", "'4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE',", "package.</text>', 7) def test_unknown_tag(self): data = ''' SomeUnknownTag: SomeUnknownValue '''", "= '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str, file_str, snippet_str) def setUp(self): self.p", "def test_unknown_tag(self): try: from StringIO import StringIO except ImportError: from", "'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This", "long file</text>' ]) unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue' snippet_str = '\\n'.join([", "comment.</text>', 'SnippetCopyrightText: <text> Copyright 2008-2010 <NAME> </text>', 'SnippetComment: <text>Some snippet", "= ''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some lic comment.</text> SnippetCopyrightText: <text>Some", "Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:',", "self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE',", "None assert not error assert len(document.reviews) == 2 def test_package(self):", "assert len(spdx_file.artifact_of_project_home) == 1 assert len(spdx_file.artifact_of_project_uri) == 1 def test_unknown_tag(self):", "= 'SomeUnknownTag: SomeUnknownValue' snippet_str = '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some", "'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba", "'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document, error = self.p.parse(self.complete_str) assert document is", "error assert len(document.snippet) == 1 assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert", "'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self): data = '''", "'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License", "'PERSON_VALUE', \"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE',", "the package.</text>' ]) file_str = '\\n'.join([ 'FileName: testfile.java', 'SPDXID: SPDXRef-File',", "CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is", "document is not None assert not error assert len(document.package.files) ==", "SPDXRef-Snippet SnippetLicenseComments: <text>Some lic comment.</text> SnippetCopyrightText: <text>Some cr text.</text> SnippetComment:", "the non-standard licenses look like they are actually BSD 3", "None assert not error assert document.package.name == 'Test' assert document.package.spdx_id", "self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME',", "'2010-02-03T00:00:00Z', 6) def test_review_info(self): data = ''' Reviewer: Person: Joe", "2 def test_package(self): document, error = self.p.parse(self.complete_str) assert document is", "(something.rdf, something.txt)', 'PackageDescription: <text>A package.</text>', 'PackageComment: <text>Comment on the package.</text>',", "SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID',", "self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 3)", "ExternalRefComment: <text>Some comment about the package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID',", "def test_review(self): document, error = self.p.parse(self.complete_str) assert document is not", "'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created',", "'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment about the package.</text>', 7)", "'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary: <text>Test package</text>', 'PackageSourceInfo: <text>Version 1.0", "self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr text.</text>', 4)", "document.package.files[0] assert spdx_file.name == 'testfile.java' assert spdx_file.spdx_id == 'SPDXRef-File' assert", "5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6)", "ReviewComment: <text>This is just an example. Some of the non-standard", "linux kernel' assert document.snippet[-1].comment == 'Some snippet comment.' assert document.snippet[-1].copyright", "Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>' ]) review_str = '\\n'.join([", "assert not error assert len(document.reviews) == 2 def test_package(self): document,", "'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(),", "was also here.</text>' ]) package_str = '\\n'.join([ 'PackageName: Test', 'SPDXID:", "assert document is not None assert not error assert len(document.reviews)", "def token_assert_helper(self, token, ttype, value, line): assert token.type == ttype", "creation_str, review_str, package_str, file_str, snippet_str) def setUp(self): self.p = Parser(Builder(),", "from spdx.parsers.tagvaluebuilders import Builder from spdx.parsers.loggers import StandardLogger from spdx.version", "2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was also here.</text>' ]) package_str = '\\n'.join([", "2014 Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles:", "test_package(self): document, error = self.p.parse(self.complete_str) assert document is not None", "is not None assert not error assert document.package.name == 'Test'", "document.package.pkg_ext_refs[-1].comment == 'Some comment about the package.' def test_file(self): document,", "Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was also here.</text>' ]) package_str", "cr text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some", "review_str = '\\n'.join([ 'Reviewer: Person: Bob the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z',", "'<text>Some lic comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT',", "format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person:", "'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str = '\\n'.join([ 'Creator: Person: Bob (<EMAIL>)',", "Parser from spdx.parsers.lexers.tagvalue import Lexer from spdx.parsers.tagvaluebuilders import Builder from", "document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment ==", "OF ANY KIND, either express or implied. # See the", "WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.", "'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source Auditor Inc.', 4)", "from unittest import TestCase import spdx from spdx.parsers.tagvalue import Parser", "'PackageDescription: <text>A package.</text>', 'PackageComment: <text>Comment on the package.</text>', 'PackageCopyrightText: <text>", "ANY KIND, either express or implied. # See the License", "See the License for the specific language governing permissions and", "4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)',", "Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str,", "<text>This is an example of an SPDX spreadsheet format</text> '''", "== 'CC0-1.0' assert document.name == 'Sample_Document-V2.1' assert document.spdx_id == 'SPDXRef-DOCUMENT'", "== 'Test' assert document.package.spdx_id == 'SPDXRef-Package' assert document.package.version == 'Version", "8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(),", "the License. # You may obtain a copy of the", "for the specific language governing permissions and # limitations under", "test_unknown_tag(self): data = ''' SomeUnknownTag: SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG',", "'ReviewComment: <text>Bob was Here.</text>', 'Reviewer: Person: Alice the Reviewer', 'ReviewDate:", "\"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization:", "to in writing, software # distributed under the License is", "snippet comment.</text> SnippetName: from linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0", "def test_external_document_references(self): data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 '''", "licenses</text>''', 4) def test_pacakage(self): data = ''' SPDXID: SPDXRef-Package FilesAnalyzed:", "'SnippetComment: <text>Some snippet comment.</text>', 'SnippetName: from linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource',", "None assert not error assert len(document.snippet) == 1 assert document.snippet[-1].spdx_id", "# See the License for the specific language governing permissions", "non-standard licenses look like they are actually BSD 3 clause", "'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8)", "SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>',", "'ORG_VALUE', 'Organization: Source Auditor Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5)", "<text>Copyright 2014 Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/',", "self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4)", "SnippetName: from linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0", "6) def test_review_info(self): data = ''' Reviewer: Person: Joe Reviewer", "'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9)", "'2010-02-03T00:00:00Z') def test_review(self): document, error = self.p.parse(self.complete_str) assert document is", "or agreed to in writing, software # distributed under the", "== spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) == 1 assert len(spdx_file.artifact_of_project_home) == 1", "ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This is just an example. Some of", "1 def test_unknown_tag(self): try: from StringIO import StringIO except ImportError:", "'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2)", "2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2)", "required by applicable law or agreed to in writing, software", "'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9)", "== 'SPDXRef-File' assert spdx_file.type == spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) == 1", "BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either", "ImportError: from io import StringIO saved_out = sys.stdout sys.stdout =", "with the License. # You may obtain a copy of", "TestCase import spdx from spdx.parsers.tagvalue import Parser from spdx.parsers.lexers.tagvalue import", "Lexer() self.l.build() def test_document(self): data = ''' SPDXVersion: SPDX-2.1 #", "like they are actually BSD 3 clause licenses</text> ''' self.l.input(data)", "'\\n'.join([ 'FileName: testfile.java', 'SPDXID: SPDXRef-File', 'FileType: SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12',", "saved_out = sys.stdout sys.stdout = StringIO() document, error = self.p.parse(self.unknown_tag_str)", "'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT',", "'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT',", "not None assert not error assert document.package.name == 'Test' assert", "self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6)", "the package.' assert document.package.pkg_ext_refs[-1].category == 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type'", "Parser(Builder(), StandardLogger()) self.p.build() def test_doc(self): document, error = self.p.parse(self.complete_str) assert", "'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(), 'DATE',", "self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE',", "'<text>Some comment about the package.</text>', 7) def test_unknown_tag(self): data =", "at line: 1\\n') sys.stdout = saved_out assert error assert document", "SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is a sample spreadsheet</text> '''", "'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator',", "self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source Auditor Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator',", "import spdx from spdx.parsers.tagvalue import Parser from spdx.parsers.lexers.tagvalue import Lexer", "data = ''' SPDXVersion: SPDX-2.1 # Comment. DataLicense: CC0-1.0 DocumentName:", "'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This", "just an example. Some of the non-standard licenses look like", "self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet comment.</text>', 5)", "SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic comment.</text>', 'SnippetCopyrightText: <text> Copyright 2008-2010 <NAME>", "== 'Version 0.9.2' assert len(document.package.licenses_from_files) == 2 assert (document.package.conc_lics.identifier ==", "compliance with the License. # You may obtain a copy", "'LicenseRef-2.0 AND Apache-2.0') assert document.package.files_analyzed == True assert document.package.comment ==", "assert document.package.pkg_ext_refs[-1].category == 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator", "agreed to in writing, software # distributed under the License", "cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment about the package.</text>' ]) file_str =", "on the package.' assert document.package.pkg_ext_refs[-1].category == 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type ==", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2)", "'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8)", "unknown tag : SomeUnknownTag at line: 1\\n') sys.stdout = saved_out", "<text>Some snippet comment.</text> SnippetName: from linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded:", "document.version == Version(major=2, minor=1) assert document.data_license.identifier == 'CC0-1.0' assert document.name", "distributed under the License is distributed on an \"AS IS\"", "assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document, error = self.p.parse(self.complete_str)", "self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is a sample spreadsheet</text>', 8) def test_external_document_references(self):", "comment.' assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier == 'Apache-2.0' assert", "Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef: SECURITY", "'FileType: SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText:", "PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type", "about the package.</text>' ]) file_str = '\\n'.join([ 'FileName: testfile.java', 'SPDXID:", "'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription: <text>A package.</text>',", "Apache-2.0') assert document.package.files_analyzed == True assert document.package.comment == 'Comment on", "test_pacakage(self): data = ''' SPDXID: SPDXRef-Package FilesAnalyzed: False PackageChecksum: SHA1:", "assert document.package.comment == 'Comment on the package.' assert document.package.pkg_ext_refs[-1].category ==", "data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9) def", "DocumentComment: <text>This is a sample spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION',", "express or implied. # See the License for the specific", "spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1',", "SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:", "Version 0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary: <text>Test package</text>', 'PackageSourceInfo:", "except in compliance with the License. # You may obtain", "'CC0-1.0' assert document.name == 'Sample_Document-V2.1' assert document.spdx_id == 'SPDXRef-DOCUMENT' assert", "'SomeUnknownTag: SomeUnknownValue' snippet_str = '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic", "sys from unittest import TestCase import spdx from spdx.parsers.tagvalue import", "Licensed under the Apache License, Version 2.0 (the \"License\"); #", "'SnippetCopyrightText: <text> Copyright 2008-2010 <NAME> </text>', 'SnippetComment: <text>Some snippet comment.</text>',", "not use this file except in compliance with the License.", "'PERSON_VALUE', \"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE',", "'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic comment.</text>', 'SnippetCopyrightText: <text> Copyright 2008-2010", "'<text>This is a sample spreadsheet</text>', 8) def test_external_document_references(self): data =", "assert document is not None assert not error assert document.package.name", "<NAME> # Licensed under the Apache License, Version 2.0 (the", "'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very long file</text>'", "SPDXID: SPDXRef-Package FilesAnalyzed: False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf,", "not error assert len(document.package.files) == 1 spdx_file = document.package.files[0] assert", "assert document is not None assert not error assert len(document.snippet)", "document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert document.snippet[-1].name == 'from linux kernel' assert", "writing, software # distributed under the License is distributed on", "token, ttype, value, line): assert token.type == ttype assert token.value", "you may not use this file except in compliance with", "self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6)", "7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(),", "# Licensed under the Apache License, Version 2.0 (the \"License\");", "'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED',", "assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment", "file_str, snippet_str) def setUp(self): self.p = Parser(Builder(), StandardLogger()) self.p.build() def", "'Organization: Source Auditor Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(),", "package</text>', 'PackageSourceInfo: <text>Version 1.0 of test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME',", "2014 <NAME> # Licensed under the Apache License, Version 2.0", "(LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>',", "<text>Some lic comment.</text>', 'SnippetCopyrightText: <text> Copyright 2008-2010 <NAME> </text>', 'SnippetComment:", "== 'from linux kernel' assert document.snippet[-1].comment == 'Some snippet comment.'", "'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5)", "SPDX spreadsheet format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(),", "4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is just an example. Some of", "spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) == 1 assert len(spdx_file.artifact_of_project_home) == 1 assert", "'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE', 'False',", "DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This", "3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr text.</text>',", "= ''' SPDXVersion: SPDX-2.1 # Comment. DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1", "8) def test_external_document_references(self): data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759", "d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1',", "6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment about", "spdx.parsers.tagvalue import Parser from spdx.parsers.lexers.tagvalue import Lexer from spdx.parsers.tagvaluebuilders import", "comment about the package.</text>' ]) file_str = '\\n'.join([ 'FileName: testfile.java',", "= None document_str = '\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName:", "'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment about the package.</text>'", "the package.</text>', 'PackageCopyrightText: <text> Copyright 2014 Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0',", "CONDITIONS OF ANY KIND, either express or implied. # See", "is not None def test_snippet(self): document, error = self.p.parse(self.complete_str) assert", "'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef: SECURITY cpe23Type", "4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet comment.</text>',", "data = ''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some lic comment.</text> SnippetCopyrightText:", "document is not None assert not error assert len(document.reviews) ==", "from spdx.version import Version class TestLexer(TestCase): maxDiff = None def", "'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(),", "is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES", "DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is a", "<text>Sample Comment</text>' ]) review_str = '\\n'.join([ 'Reviewer: Person: Bob the", "= '\\n'.join([ 'Reviewer: Person: Bob the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment:", "'Reviewer: Person: Alice the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was", "'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4)", "Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription: <text>A", "assert spdx_file.type == spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) == 1 assert len(spdx_file.artifact_of_project_home)", "package_str = '\\n'.join([ 'PackageName: Test', 'SPDXID: SPDXRef-Package', 'PackageVersion: Version 0.9.2',", "not None assert not error assert document.version == Version(major=2, minor=1)", "'\\n'.join([ 'Creator: Person: Bob (<EMAIL>)', 'Creator: Organization: Acme.', 'Created: 2010-02-03T00:00:00Z',", "' assert document.snippet[-1].license_comment == 'Some lic comment.' assert document.snippet[-1].snip_from_file_spdxid ==", "== 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document, error = self.p.parse(self.complete_str) assert document", "Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was Here.</text>', 'Reviewer: Person: Alice", "http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to", "0.9.2' assert len(document.package.licenses_from_files) == 2 assert (document.package.conc_lics.identifier == 'LicenseRef-2.0 AND", "'<text>Some snippet comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE',", "'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed',", "self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759',", "'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is a sample spreadsheet</text>',", "'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment", "'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE', 'from linux kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID',", "comment.' assert document.snippet[-1].copyright == ' Copyright 2008-2010 <NAME> ' assert", "self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(),", "'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source Auditor Inc.', 4) self.token_assert_helper(self.l.token(),", "2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3)", "Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very long", "self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(),", "from StringIO import StringIO except ImportError: from io import StringIO", "'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was Here.</text>', 'Reviewer: Person: Alice the", "setUp(self): self.p = Parser(Builder(), StandardLogger()) self.p.build() def test_doc(self): document, error", "Comment. DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment:", "StandardLogger()) self.p.build() def test_doc(self): document, error = self.p.parse(self.complete_str) assert document", "self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT',", "comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE', 'from linux", "StringIO() document, error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag :", "governing permissions and # limitations under the License. import sys", "= '\\n'.join([ 'PackageName: Test', 'SPDXID: SPDXRef-Package', 'PackageVersion: Version 0.9.2', 'PackageDownloadLocation:", "'Sample_Document-V2.1' assert document.spdx_id == 'SPDXRef-DOCUMENT' assert document.comment == 'Sample Comment'", "sample spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE',", "is not None assert not error assert len(document.reviews) == 2", "Organization: Source Auditor Inc. Creator: Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment:", "self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(),", "'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25'", "'from linux kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE',", "2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(),", "OR CONDITIONS OF ANY KIND, either express or implied. #", "6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(),", "are actually BSD 3 clause licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER',", "<text>Comment on the package.</text>', 'PackageCopyrightText: <text> Copyright 2014 Acme Inc.</text>',", "assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert document.snippet[-1].name == 'from linux kernel'", "data = ''' Reviewer: Person: Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment:", "Copyright 2008-2010 <NAME> ' assert document.snippet[-1].license_comment == 'Some lic comment.'", "'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2) def test_snippet(self): data", "'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is a sample spreadsheet</text>', 8)", "the License is distributed on an \"AS IS\" BASIS, #", "maxDiff = None def setUp(self): self.l = Lexer() self.l.build() def", "def setUp(self): self.p = Parser(Builder(), StandardLogger()) self.p.build() def test_doc(self): document,", "'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT',", "CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301'", "5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE', 'from linux kernel',", "SPDX-2.1 # Comment. DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace:", "http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary: <text>Test package</text>', 'PackageSourceInfo: <text>Version 1.0 of", "'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE', 'False', 3)", "import Lexer from spdx.parsers.tagvaluebuilders import Builder from spdx.parsers.loggers import StandardLogger", "Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str = '\\n'.join([ 'Creator: Person: Bob", "## Creation Information Creator: Person: <NAME> Creator: Organization: Source Auditor", "6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6) def test_review_info(self): data = '''", "None def test_snippet(self): document, error = self.p.parse(self.complete_str) assert document is", "None def setUp(self): self.l = Lexer() self.l.build() def test_document(self): data", "assert not error assert len(document.package.files) == 1 spdx_file = document.package.files[0]", "test_snippet(self): document, error = self.p.parse(self.complete_str) assert document is not None", "self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 2)", "Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str", "def test_doc(self): document, error = self.p.parse(self.complete_str) assert document is not", "'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment:", "LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE',", "= sys.stdout sys.stdout = StringIO() document, error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(),", "'FileComment: <text>Very long file</text>' ]) unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue' snippet_str", "assert document is not None assert not error assert len(document.creation_info.creators)", "text.</text> SnippetComment: <text>Some snippet comment.</text> SnippetName: from linux kernel SnippetFromFileSPDXID:", "and # limitations under the License. import sys from unittest", "StringIO saved_out = sys.stdout sys.stdout = StringIO() document, error =", "ttype, value, line): assert token.type == ttype assert token.value ==", "SnippetComment: <text>Some snippet comment.</text> SnippetName: from linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource", "= document.package.files[0] assert spdx_file.name == 'testfile.java' assert spdx_file.spdx_id == 'SPDXRef-File'", "law or agreed to in writing, software # distributed under", "from spdx.parsers.loggers import StandardLogger from spdx.version import Version class TestLexer(TestCase):", "test_review(self): document, error = self.p.parse(self.complete_str) assert document is not None", "== 'Comment on the package.' assert document.package.pkg_ext_refs[-1].category == 'SECURITY' assert", "== 'SPDXRef-Snippet' assert document.snippet[-1].name == 'from linux kernel' assert document.snippet[-1].comment", "Builder from spdx.parsers.loggers import StandardLogger from spdx.version import Version class", "== 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment == 'Some comment about the package.'", "self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE',", "self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI',", "comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr", "Copyright 2008-2010 <NAME> </text>', 'SnippetComment: <text>Some snippet comment.</text>', 'SnippetName: from", "'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE',", "of an SPDX spreadsheet format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator',", "lic comment.</text>', 'SnippetCopyrightText: <text> Copyright 2008-2010 <NAME> </text>', 'SnippetComment: <text>Some", "assert document is not None assert not error assert document.version", "Copyright 2014 Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)',", "Creator: Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment: <text>This is an example", "'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6) def test_review_info(self): data", "'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf,", "'SPDXRef-File' assert spdx_file.type == spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) == 1 assert", "# Copyright (c) 2014 <NAME> # Licensed under the Apache", "None assert not error assert len(document.package.files) == 1 spdx_file =", "def test_package(self): document, error = self.p.parse(self.complete_str) assert document is not", "License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law", "about the package.' def test_file(self): document, error = self.p.parse(self.complete_str) assert", "assert spdx_file.name == 'testfile.java' assert spdx_file.spdx_id == 'SPDXRef-File' assert spdx_file.type", "line: 1\\n') sys.stdout = saved_out assert error assert document is", "'SPDXID: SPDXRef-File', 'FileType: SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile:", "2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(),", "Person: Bob (<EMAIL>)', 'Creator: Organization: Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample", "Information Creator: Person: <NAME> Creator: Organization: Source Auditor Inc. Creator:", "like they are actually BSD 3 clause licenses</text>''', 4) def", "'\\n'.join([ 'Reviewer: Person: Bob the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob", "= Lexer() self.l.build() def test_document(self): data = ''' SPDXVersion: SPDX-2.1", "1.0 of test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum:", "<NAME> Creator: Organization: Source Auditor Inc. Creator: Tool: SourceAuditor-V1.2 Created:", "2014 Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment:", "(document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def test_review(self): document, error = self.p.parse(self.complete_str) assert", "self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(),", "self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is just an", "4) def test_pacakage(self): data = ''' SPDXID: SPDXRef-Package FilesAnalyzed: False", "import sys from unittest import TestCase import spdx from spdx.parsers.tagvalue", "package.' assert document.package.pkg_ext_refs[-1].category == 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert", "self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(),", "'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1',", "comment.</text>', 'SnippetName: from linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet:", "Created: 2010-02-03T00:00:00Z CreatorComment: <text>This is an example of an SPDX", "BSD 3 clause licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2)", "''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef',", "'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright 2014 Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage:", "IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,", "3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is just", "'SnippetComment', 5) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME',", "'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE',", "SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type", "SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str = '\\n'.join([", "(document.package.conc_lics.identifier == 'LicenseRef-2.0 AND Apache-2.0') assert document.package.files_analyzed == True assert", "may not use this file except in compliance with the", "4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription: <text>A package.</text>', 'PackageComment: <text>Comment on the", "here.</text>' ]) package_str = '\\n'.join([ 'PackageName: Test', 'SPDXID: SPDXRef-Package', 'PackageVersion:", "the License. import sys from unittest import TestCase import spdx", "'Test' assert document.package.spdx_id == 'SPDXRef-Package' assert document.package.version == 'Version 0.9.2'", "'\\n'.join([ 'PackageName: Test', 'SPDXID: SPDXRef-Package', 'PackageVersion: Version 0.9.2', 'PackageDownloadLocation: http://example.com/test',", "self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE',", "assert document.package.pkg_ext_refs[-1].comment == 'Some comment about the package.' def test_file(self):", "they are actually BSD 3 clause licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "'TEXT', '<text>Some comment about the package.</text>', 7) def test_unknown_tag(self): data", "== 1 assert len(spdx_file.artifact_of_project_uri) == 1 def test_unknown_tag(self): try: from", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or", "assert len(spdx_file.artifact_of_project_uri) == 1 def test_unknown_tag(self): try: from StringIO import", "this file except in compliance with the License. # You", "assert document.package.spdx_id == 'SPDXRef-Package' assert document.package.version == 'Version 0.9.2' assert", "error = self.p.parse(self.complete_str) assert document is not None assert not", "<text> Copyright 2008-2010 <NAME> </text>', 'SnippetComment: <text>Some snippet comment.</text>', 'SnippetName:", "'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301',", "self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT',", "not None assert not error assert len(document.creation_info.creators) == 2 assert", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2)", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\",", "'''<text>This is just an example. Some of the non-standard licenses", "'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3)", "'Sample Comment' assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def test_review(self): document, error", "== 'SPDXRef-DOCUMENT' assert document.comment == 'Sample Comment' assert document.namespace ==", "document.package.files_analyzed == True assert document.package.comment == 'Comment on the package.'", "'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet',", "<text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str = '\\n'.join([ 'Creator: Person:", "assert len(document.creation_info.creators) == 2 assert document.creation_info.comment == 'Sample Comment' assert", "AND Apache-2.0') assert document.package.files_analyzed == True assert document.package.comment == 'Comment", "file except in compliance with the License. # You may", "on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS", "self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(),", "kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "look like they are actually BSD 3 clause licenses</text>''', 4)", "'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright 2014", "== 'Some comment about the package.' def test_file(self): document, error", "7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is a", "BSD 3 clause licenses</text>''', 4) def test_pacakage(self): data = '''", "is just an example. Some of the non-standard licenses look", "'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is just an example. Some", "assert not error assert document.package.name == 'Test' assert document.package.spdx_id ==", "linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data)", "maxDiff = None document_str = '\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0',", "import StandardLogger from spdx.version import Version class TestLexer(TestCase): maxDiff =", "document.data_license.identifier == 'CC0-1.0' assert document.name == 'Sample_Document-V2.1' assert document.spdx_id ==", "]) review_str = '\\n'.join([ 'Reviewer: Person: Bob the Reviewer', 'ReviewDate:", "7) def test_unknown_tag(self): data = ''' SomeUnknownTag: SomeUnknownValue ''' self.l.input(data)", "document.snippet[-1].license_comment == 'Some lic comment.' assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert", "import TestCase import spdx from spdx.parsers.tagvalue import Parser from spdx.parsers.lexers.tagvalue", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express", "example of an SPDX spreadsheet format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR',", "'04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self):", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2)", "package.</text>', 'PackageCopyrightText: <text> Copyright 2014 Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded:", "Bob (<EMAIL>)', 'Creator: Organization: Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>'", "4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment',", "assert len(document.package.licenses_from_files) == 2 assert (document.package.conc_lics.identifier == 'LicenseRef-2.0 AND Apache-2.0')", "'SnippetName: from linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0',", "StringIO import StringIO except ImportError: from io import StringIO saved_out", "'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment',", "== 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment == 'Some", "cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment about the package.</text> ''' self.l.input(data)", "self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2) def test_snippet(self): data = ''' SnippetSPDXID:", "(SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY", "1 assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert document.snippet[-1].name == 'from linux", "2008-2010 <NAME> </text>', 'SnippetComment: <text>Some snippet comment.</text>', 'SnippetName: from linux", "'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID',", "4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2', 5)", "Reviewer: Person: Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This is just", "assert document.snippet[-1].name == 'from linux kernel' assert document.snippet[-1].comment == 'Some", "self.p.parse(self.complete_str) assert document is not None assert not error assert", "licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person:", "'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0',", "'TEXT', '<text>Some cr text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5) self.token_assert_helper(self.l.token(),", "of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by", "self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(),", "StandardLogger from spdx.version import Version class TestLexer(TestCase): maxDiff = None", "self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7)", "self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment about the", "language governing permissions and # limitations under the License. import", "self.token_assert_helper(self.l.token(), 'LINE', 'from linux kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7)", "= '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic comment.</text>', 'SnippetCopyrightText: <text>", "len(document.package.files) == 1 spdx_file = document.package.files[0] assert spdx_file.name == 'testfile.java'", "'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment about the package.</text>',", "assert token.type == ttype assert token.value == value assert token.lineno", "except ImportError: from io import StringIO saved_out = sys.stdout sys.stdout", "self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9) def token_assert_helper(self,", "== ' Copyright 2008-2010 <NAME> ' assert document.snippet[-1].license_comment == 'Some", "= '\\n'.join([ 'FileName: testfile.java', 'SPDXID: SPDXRef-File', 'FileType: SOURCE', 'FileChecksum: SHA1:", "lic comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText', 4) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some", "== True assert document.package.comment == 'Comment on the package.' assert", "'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample", "'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0',", "spdx_file = document.package.files[0] assert spdx_file.name == 'testfile.java' assert spdx_file.spdx_id ==", "self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source Auditor Inc.',", "1 assert len(spdx_file.artifact_of_project_uri) == 1 def test_unknown_tag(self): try: from StringIO", "self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment about the package.</text>', 7) def test_unknown_tag(self):", "SnippetLicenseComments: <text>Some lic comment.</text> SnippetCopyrightText: <text>Some cr text.</text> SnippetComment: <text>Some", "= '\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT',", "token_assert_helper(self, token, ttype, value, line): assert token.type == ttype assert", "= Parser(Builder(), StandardLogger()) self.p.build() def test_doc(self): document, error = self.p.parse(self.complete_str)", "is not None assert not error assert len(document.snippet) == 1", "<text>Version 1.0 of test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME',", "Version(major=2, minor=1) assert document.data_license.identifier == 'CC0-1.0' assert document.name == 'Sample_Document-V2.1'", "self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode', 5) self.token_assert_helper(self.l.token(), 'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5)", "the package.</text>', 7) def test_unknown_tag(self): data = ''' SomeUnknownTag: SomeUnknownValue", "AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very long file</text>' ])", "or implied. # See the License for the specific language", "'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9) def token_assert_helper(self, token,", "from linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ])", "''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some lic comment.</text> SnippetCopyrightText: <text>Some cr", "SPDXRef-Package', 'PackageVersion: Version 0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary: <text>Test", "cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment about the package.</text>' ]) file_str", "'LINE', 'Apache-2.0', 9) def token_assert_helper(self, token, ttype, value, line): assert", "== 'testfile.java' assert spdx_file.spdx_id == 'SPDXRef-File' assert spdx_file.type == spdx.file.FileType.SOURCE", "'Comment on the package.' assert document.package.pkg_ext_refs[-1].category == 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type", "== Version(major=2, minor=1) assert document.data_license.identifier == 'CC0-1.0' assert document.name ==", "3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4)", "KIND, either express or implied. # See the License for", "specific language governing permissions and # limitations under the License.", "document.snippet[-1].name == 'from linux kernel' assert document.snippet[-1].comment == 'Some snippet", "]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str, file_str, snippet_str) def", "'2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(), 'TEXT', '''<text>This is", "self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 9) def token_assert_helper(self, token, ttype, value, line):", "'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str = '\\n'.join([ 'Creator:", "Creation Information Creator: Person: <NAME> Creator: Organization: Source Auditor Inc.", "'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE', 'PackageVerificationCode',", "License. import sys from unittest import TestCase import spdx from", "3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT', 'ReviewComment', 4) self.token_assert_helper(self.l.token(),", "2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE', 'False', 3) self.token_assert_helper(self.l.token(),", "self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self): data =", "error assert document.package.name == 'Test' assert document.package.spdx_id == 'SPDXRef-Package' assert", "9) def token_assert_helper(self, token, ttype, value, line): assert token.type ==", "3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 4)", "cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment", "error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag : SomeUnknownTag at", "SPDXRef-DoapSource', 'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str,", "self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic comment.</text>', 3)", "'Some snippet comment.' assert document.snippet[-1].copyright == ' Copyright 2008-2010 <NAME>", "comment.</text> SnippetCopyrightText: <text>Some cr text.</text> SnippetComment: <text>Some snippet comment.</text> SnippetName:", "2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription: <text>A package.</text>', 'PackageComment: <text>Comment", "token.type == ttype assert token.value == value assert token.lineno ==", "document.snippet[-1].comment == 'Some snippet comment.' assert document.snippet[-1].copyright == ' Copyright", "import Parser from spdx.parsers.lexers.tagvalue import Lexer from spdx.parsers.tagvaluebuilders import Builder", "DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is a sample spreadsheet</text> ''' self.l.input(data)", "'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ]) creation_str =", "the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was Here.</text>', 'Reviewer: Person:", "comment about the package.</text>', 7) def test_unknown_tag(self): data = '''", "6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(),", "'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace:", "Apache-2.0', 'PackageLicenseComments: <text>License Comments</text>', 'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some", "6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(),", "copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required", "'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4)", "2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment:", "was Here.</text>', 'Reviewer: Person: Alice the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment:", "(the \"License\"); # you may not use this file except", "value assert token.lineno == line class TestParser(TestCase): maxDiff = None", "spdx.parsers.lexers.tagvalue import Lexer from spdx.parsers.tagvaluebuilders import Builder from spdx.parsers.loggers import", "# you may not use this file except in compliance", "2) def test_snippet(self): data = ''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some", "package_str, file_str, snippet_str) def setUp(self): self.p = Parser(Builder(), StandardLogger()) self.p.build()", "document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment == 'Some comment about the", "\"Person: <NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z',", "3 clause licenses</text>''', 4) def test_pacakage(self): data = ''' SPDXID:", "from io import StringIO saved_out = sys.stdout sys.stdout = StringIO()", "Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles:", "document.comment == 'Sample Comment' assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self):", "= ''' Reviewer: Person: Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This", "assert len(document.snippet) == 1 assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet' assert document.snippet[-1].name", "'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7)", "look like they are actually BSD 3 clause licenses</text> '''", "' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self): data = ''' ## Creation", "'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(), 'PKG_VERF_CODE',", "is a sample spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2)", "len(spdx_file.artifact_of_project_name) == 1 assert len(spdx_file.artifact_of_project_home) == 1 assert len(spdx_file.artifact_of_project_uri) ==", "self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6) def test_review_info(self): data = ''' Reviewer:", "'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some", "<NAME> </text>', 'SnippetComment: <text>Some snippet comment.</text>', 'SnippetName: from linux kernel',", "7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(),", "''' SPDXVersion: SPDX-2.1 # Comment. DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID:", "self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag : SomeUnknownTag at line: 1\\n') sys.stdout", "= ''' ## Creation Information Creator: Person: <NAME> Creator: Organization:", "4) self.token_assert_helper(self.l.token(), 'ORG_VALUE', 'Organization: Source Auditor Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR',", "spdx_file.spdx_id == 'SPDXRef-File' assert spdx_file.type == spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) ==", "2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was Here.</text>', 'Reviewer: Person: Alice the Reviewer',", "5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6) def", "True assert document.package.comment == 'Comment on the package.' assert document.package.pkg_ext_refs[-1].category", "sys.stdout = StringIO() document, error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown", "self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(),", "'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\", 3) self.token_assert_helper(self.l.token(), 'CREATOR',", "file</text>' ]) unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue' snippet_str = '\\n'.join([ 'SnippetSPDXID:", "]) file_str = '\\n'.join([ 'FileName: testfile.java', 'SPDXID: SPDXRef-File', 'FileType: SOURCE',", "SomeUnknownValue' snippet_str = '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic comment.</text>',", "http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very long file</text>' ]) unknown_tag_str =", "test_creation_info(self): document, error = self.p.parse(self.complete_str) assert document is not None", "'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT',", "== value assert token.lineno == line class TestParser(TestCase): maxDiff =", "'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE', 'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4)", "SPDXVersion: SPDX-2.1 # Comment. DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT", "is a sample spreadsheet</text>', 8) def test_external_document_references(self): data = '''", "spdx from spdx.parsers.tagvalue import Parser from spdx.parsers.lexers.tagvalue import Lexer from", "self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO', 'LicenseInfoInSnippet', 9) self.token_assert_helper(self.l.token(), 'LINE',", "not error assert len(document.reviews) == 2 def test_package(self): document, error", "len(document.package.licenses_from_files) == 2 assert (document.package.conc_lics.identifier == 'LicenseRef-2.0 AND Apache-2.0') assert", "Version 2.0 (the \"License\"); # you may not use this", "Auditor Inc. Creator: Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment: <text>This is", "'ReviewComment: <text>Alice was also here.</text>' ]) package_str = '\\n'.join([ 'PackageName:", "may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE', \"Person: <NAME>\",", "Comment' assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document, error =", "FilesAnalyzed: False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef:", "None assert not error assert len(document.creation_info.creators) == 2 assert document.creation_info.comment", "'DocumentName: Sample_Document-V2.1', 'SPDXID: SPDXRef-DOCUMENT', 'DocumentComment: <text>Sample Comment</text>', 'DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' ])", "document, error = self.p.parse(self.complete_str) assert document is not None assert", "line class TestParser(TestCase): maxDiff = None document_str = '\\n'.join([ 'SPDXVersion:", "== 'Sample_Document-V2.1' assert document.spdx_id == 'SPDXRef-DOCUMENT' assert document.comment == 'Sample", "' Copyright 2008-2010 <NAME> ' assert document.snippet[-1].license_comment == 'Some lic", "document is not None assert not error assert len(document.snippet) ==", "setUp(self): self.l = Lexer() self.l.build() def test_document(self): data = '''", "from spdx.parsers.lexers.tagvalue import Lexer from spdx.parsers.tagvaluebuilders import Builder from spdx.parsers.loggers", "implied. # See the License for the specific language governing", "(c) 2014 <NAME> # Licensed under the Apache License, Version", "def test_creation_info(self): data = ''' ## Creation Information Creator: Person:", "under the Apache License, Version 2.0 (the \"License\"); # you", "SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2)", "== 'SPDXRef-Package' assert document.package.version == 'Version 0.9.2' assert len(document.package.licenses_from_files) ==", "Bob the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was Here.</text>', 'Reviewer:", "== 2 assert document.creation_info.comment == 'Sample Comment' assert (document.creation_info.created_iso_format ==", "'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM', 'SHA1:", "snippet comment.' assert document.snippet[-1].copyright == ' Copyright 2008-2010 <NAME> '", "permissions and # limitations under the License. import sys from", "StringIO except ImportError: from io import StringIO saved_out = sys.stdout", "= StringIO() document, error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag", "by applicable law or agreed to in writing, software #", "1 spdx_file = document.package.files[0] assert spdx_file.name == 'testfile.java' assert spdx_file.spdx_id", "len(spdx_file.artifact_of_project_uri) == 1 def test_unknown_tag(self): try: from StringIO import StringIO", "'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment == 'Some comment about the package.' def", "spreadsheet format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3) self.token_assert_helper(self.l.token(), 'PERSON_VALUE',", "example. Some of the non-standard licenses look like they are", "actually BSD 3 clause licenses</text>''', 4) def test_pacakage(self): data =", "document.package.comment == 'Comment on the package.' assert document.package.pkg_ext_refs[-1].category == 'SECURITY'", "<NAME> ' assert document.snippet[-1].license_comment == 'Some lic comment.' assert document.snippet[-1].snip_from_file_spdxid", "5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:',", "3 clause licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(),", "spdx_file.type == spdx.file.FileType.SOURCE assert len(spdx_file.artifact_of_project_name) == 1 assert len(spdx_file.artifact_of_project_home) ==", "self.l.build() def test_document(self): data = ''' SPDXVersion: SPDX-2.1 # Comment.", "'SPDXID: SPDXRef-Package', 'PackageVersion: Version 0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary:", "assert not error assert len(document.snippet) == 1 assert document.snippet[-1].spdx_id ==", "spdx.parsers.loggers import StandardLogger from spdx.version import Version class TestLexer(TestCase): maxDiff", "assert document is not None assert not error assert len(document.package.files)", "the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable", "assert len(spdx_file.artifact_of_project_name) == 1 assert len(spdx_file.artifact_of_project_home) == 1 assert len(spdx_file.artifact_of_project_uri)", "(SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment about", "of the non-standard licenses look like they are actually BSD", "Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This is just an example.", "len(document.reviews) == 2 def test_package(self): document, error = self.p.parse(self.complete_str) assert", "token.lineno == line class TestParser(TestCase): maxDiff = None document_str =", "document.package.name == 'Test' assert document.package.spdx_id == 'SPDXRef-Package' assert document.package.version ==", "io import StringIO saved_out = sys.stdout sys.stdout = StringIO() document,", "document.snippet[-1].copyright == ' Copyright 2008-2010 <NAME> ' assert document.snippet[-1].license_comment ==", "# http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed", "Creator: Person: <NAME> Creator: Organization: Source Auditor Inc. Creator: Tool:", "# Comment. DataLicense: CC0-1.0 DocumentName: Sample_Document-V2.1 SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301", "2010-02-10T00:00:00Z ReviewComment: <text>This is just an example. Some of the", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2)", "self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3) self.token_assert_helper(self.l.token(), 'REVIEW_COMMENT',", "is an example of an SPDX spreadsheet format</text> ''' self.l.input(data)", "assert document is not None def test_snippet(self): document, error =", "unittest import TestCase import spdx from spdx.parsers.tagvalue import Parser from", "test_snippet(self): data = ''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some lic comment.</text>", "sys.stdout sys.stdout = StringIO() document, error = self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found", "'PackageSourceInfo: <text>Version 1.0 of test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator:", "document.package.spdx_id == 'SPDXRef-Package' assert document.package.version == 'Version 0.9.2' assert len(document.package.licenses_from_files)", ": SomeUnknownTag at line: 1\\n') sys.stdout = saved_out assert error", "spreadsheet</text>', 8) def test_external_document_references(self): data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1:", "test_document(self): data = ''' SPDXVersion: SPDX-2.1 # Comment. DataLicense: CC0-1.0", "self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 4) self.token_assert_helper(self.l.token(),", "'from linux kernel' assert document.snippet[-1].comment == 'Some snippet comment.' assert", "licenses look like they are actually BSD 3 clause licenses</text>", "the Reviewer', 'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was also here.</text>' ])", "self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE', 'Apache-2.0', 8) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_INFO',", "= ''' SPDXID: SPDXRef-Package FilesAnalyzed: False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode:", "'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription:", "token.value == value assert token.lineno == line class TestParser(TestCase): maxDiff", "not error assert len(document.creation_info.creators) == 2 assert document.creation_info.comment == 'Sample", "Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet',", "an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF", "<text>Some comment about the package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID',", "4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment", "licenses look like they are actually BSD 3 clause licenses</text>''',", "Unless required by applicable law or agreed to in writing,", "2) def test_creation_info(self): data = ''' ## Creation Information Creator:", "SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some lic comment.</text> SnippetCopyrightText: <text>Some cr text.</text>", "assert document.creation_info.comment == 'Sample Comment' assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def", "test_unknown_tag(self): try: from StringIO import StringIO except ImportError: from io", "assert document.package.version == 'Version 0.9.2' assert len(document.package.licenses_from_files) == 2 assert", "Test', 'SPDXID: SPDXRef-Package', 'PackageVersion: Version 0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True',", "'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very long file</text>' ]) unknown_tag_str", "Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z CreatorComment: <text>This is an example of", "Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This is just an example. Some", "not error assert document.package.name == 'Test' assert document.package.spdx_id == 'SPDXRef-Package'", "'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense',", "'SomeUnknownValue', 2) def test_snippet(self): data = ''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments:", "the specific language governing permissions and # limitations under the", "self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded', 8) self.token_assert_helper(self.l.token(), 'LINE',", "<text>This is a sample spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion',", "'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode:", "def test_file(self): document, error = self.p.parse(self.complete_str) assert document is not", "'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4)", "applicable law or agreed to in writing, software # distributed", "'LINE', 'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(), 'CHKSUM', 'SHA1:", "document.creation_info.comment == 'Sample Comment' assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def test_review(self):", "'TEXT', '''<text>This is just an example. Some of the non-standard", "Apache-2.0', 'FileCopyrightText: <text>Copyright 2014 Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/',", "'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC', 'SnippetLicenseConcluded',", "snippet_str) def setUp(self): self.p = Parser(Builder(), StandardLogger()) self.p.build() def test_doc(self):", "assert not error assert len(document.creation_info.creators) == 2 assert document.creation_info.comment ==", "actually BSD 3 clause licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer',", "assert document.package.name == 'Test' assert document.package.spdx_id == 'SPDXRef-Package' assert document.package.version", "self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some snippet comment.</text>', 5) self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6)", "def test_unknown_tag(self): data = ''' SomeUnknownTag: SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is a sample spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "<NAME>\", 2) self.token_assert_helper(self.l.token(), 'REVIEW_DATE', 'ReviewDate', 3) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-10T00:00:00Z', 3)", "assert document.version == Version(major=2, minor=1) assert document.data_license.identifier == 'CC0-1.0' assert", "assert (document.creation_info.created_iso_format == '2010-02-03T00:00:00Z') def test_review(self): document, error = self.p.parse(self.complete_str)", "True', 'PackageSummary: <text>Test package</text>', 'PackageSourceInfo: <text>Version 1.0 of test</text>', 'PackageFileName:", "document is not None def test_snippet(self): document, error = self.p.parse(self.complete_str)", "in writing, software # distributed under the License is distributed", "== line class TestParser(TestCase): maxDiff = None document_str = '\\n'.join([", "== 'LicenseRef-2.0 AND Apache-2.0') assert document.package.files_analyzed == True assert document.package.comment", "== '2010-02-03T00:00:00Z') def test_review(self): document, error = self.p.parse(self.complete_str) assert document", "error assert len(document.package.files) == 1 spdx_file = document.package.files[0] assert spdx_file.name", "len(document.creation_info.creators) == 2 assert document.creation_info.comment == 'Sample Comment' assert (document.creation_info.created_iso_format", "sys.stdout = saved_out assert error assert document is not None", "not error assert len(document.snippet) == 1 assert document.snippet[-1].spdx_id == 'SPDXRef-Snippet'", "'Created', 6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6) def test_review_info(self): data =", "def test_snippet(self): data = ''' SnippetSPDXID: SPDXRef-Snippet SnippetLicenseComments: <text>Some lic", "the package.' def test_file(self): document, error = self.p.parse(self.complete_str) assert document", "package.' def test_file(self): document, error = self.p.parse(self.complete_str) assert document is", "Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright 2014 Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest',", "document is not None assert not error assert document.version ==", "3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic comment.</text>', 3) self.token_assert_helper(self.l.token(), 'SNIPPET_CR_TEXT', 'SnippetCopyrightText',", "SomeUnknownTag at line: 1\\n') sys.stdout = saved_out assert error assert", "SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID',", "tag : SomeUnknownTag at line: 1\\n') sys.stdout = saved_out assert", "assert document.spdx_id == 'SPDXRef-DOCUMENT' assert document.comment == 'Sample Comment' assert", "'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE', 'from linux kernel', 6) self.token_assert_helper(self.l.token(),", "'SPDXRef-DOCUMENT' assert document.comment == 'Sample Comment' assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301'", "Copyright (c) 2014 <NAME> # Licensed under the Apache License,", "line): assert token.type == ttype assert token.value == value assert", "= None def setUp(self): self.l = Lexer() self.l.build() def test_document(self):", "self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE',", "2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(),", "'LicenseInfoInSnippet: Apache-2.0', ]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str, file_str,", "1 assert len(spdx_file.artifact_of_project_home) == 1 assert len(spdx_file.artifact_of_project_uri) == 1 def", "self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID',", "'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7) self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301',", "== 2 def test_package(self): document, error = self.p.parse(self.complete_str) assert document", "data = ''' ## Creation Information Creator: Person: <NAME> Creator:", "== 1 assert len(spdx_file.artifact_of_project_home) == 1 assert len(spdx_file.artifact_of_project_uri) == 1", "'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>' ]) review_str = '\\n'.join([ 'Reviewer:", "Acme Inc.</text>', 'PackageLicenseDeclared: Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0',", "Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool: SourceAuditor-V1.2',", "def test_pacakage(self): data = ''' SPDXID: SPDXRef-Package FilesAnalyzed: False PackageChecksum:", "License is distributed on an \"AS IS\" BASIS, # WITHOUT", "an example. Some of the non-standard licenses look like they", "SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet: Apache-2.0 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID',", "License, Version 2.0 (the \"License\"); # you may not use", "'Sample Comment' assert document.namespace == 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301' def test_creation_info(self): document, error", "# You may obtain a copy of the License at", "'PackageComment: <text>Comment on the package.</text>', 'PackageCopyrightText: <text> Copyright 2014 Acme", "cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment about the package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(),", "are actually BSD 3 clause licenses</text>''', 4) def test_pacakage(self): data", "test_doc(self): document, error = self.p.parse(self.complete_str) assert document is not None", "testfile.java', 'SPDXID: SPDXRef-File', 'FileType: SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0',", "document.spdx_id == 'SPDXRef-DOCUMENT' assert document.comment == 'Sample Comment' assert document.namespace", "not None assert not error assert len(document.reviews) == 2 def", "assert document.snippet[-1].comment == 'Some snippet comment.' assert document.snippet[-1].copyright == '", "== 2 assert (document.package.conc_lics.identifier == 'LicenseRef-2.0 AND Apache-2.0') assert document.package.files_analyzed", "Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very", "spdx.parsers.tagvaluebuilders import Builder from spdx.parsers.loggers import StandardLogger from spdx.version import", "SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright", "under the License. import sys from unittest import TestCase import", "4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(),", "''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2)", "'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT',", "= self.p.parse(self.complete_str) assert document is not None assert not error", "def test_snippet(self): document, error = self.p.parse(self.complete_str) assert document is not", "Comment</text>' ]) review_str = '\\n'.join([ 'Reviewer: Person: Bob the Reviewer',", "SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6)", "test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba", "SPDXID: SPDXRef-DOCUMENT DocumentNamespace: https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301 DocumentComment: <text>This is a sample spreadsheet</text>", "SomeUnknownTag: SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE',", "SpdxTranslatorSpdx.txt) ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment about the", "self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z', 6) def test_review_info(self):", "TestLexer(TestCase): maxDiff = None def setUp(self): self.l = Lexer() self.l.build()", "'CreatorComment: <text>Sample Comment</text>' ]) review_str = '\\n'.join([ 'Reviewer: Person: Bob", "SPDXRef-File', 'FileType: SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0',", "document is not None assert not error assert len(document.creation_info.creators) ==", "assert document.name == 'Sample_Document-V2.1' assert document.spdx_id == 'SPDXRef-DOCUMENT' assert document.comment", "is not None assert not error assert len(document.package.files) == 1", "self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DoapSource', 7) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_CONC',", "saved_out assert error assert document is not None def test_snippet(self):", "the License for the specific language governing permissions and #", "= ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF',", "limitations under the License. import sys from unittest import TestCase", "Comments</text>', 'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment about the", "== 'Some lic comment.' assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier", "lic comment.' assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier == 'Apache-2.0'", "'PackageSummary: <text>Test package</text>', 'PackageSourceInfo: <text>Version 1.0 of test</text>', 'PackageFileName: test-1.0.zip',", "Apache License, Version 2.0 (the \"License\"); # you may not", "6) self.token_assert_helper(self.l.token(), 'LINE', 'SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment',", "3) self.token_assert_helper(self.l.token(), 'LINE', 'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum', 4) self.token_assert_helper(self.l.token(),", "cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 6) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF_COMMENT', 'ExternalRefComment', 7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some", "either express or implied. # See the License for the", "test_creation_info(self): data = ''' ## Creation Information Creator: Person: <NAME>", "self.l = Lexer() self.l.build() def test_document(self): data = ''' SPDXVersion:", "is not None assert not error assert document.version == Version(major=2,", "Person: Bob the Reviewer', 'ReviewDate: 2010-02-10T00:00:00Z', 'ReviewComment: <text>Bob was Here.</text>',", "comment about the package.' def test_file(self): document, error = self.p.parse(self.complete_str)", "== 'SPDXRef-DoapSource' assert document.snippet[-1].conc_lics.identifier == 'Apache-2.0' assert document.snippet[-1].licenses_in_snippet[-1].identifier == 'Apache-2.0'", "from spdx.parsers.tagvalue import Parser from spdx.parsers.lexers.tagvalue import Lexer from spdx.parsers.tagvaluebuilders", "spdx.version import Version class TestLexer(TestCase): maxDiff = None def setUp(self):", "test</text>', 'PackageFileName: test-1.0.zip', 'PackageSupplier: Organization:ACME', 'PackageOriginator: Organization:ACME', 'PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12',", "Creator: Organization: Source Auditor Inc. Creator: Tool: SourceAuditor-V1.2 Created: 2010-02-03T00:00:00Z", "a sample spreadsheet</text>', 8) def test_external_document_references(self): data = ''' ExternalDocumentRef:DocumentRef-spdx-tool-2.1", "class TestLexer(TestCase): maxDiff = None def setUp(self): self.l = Lexer()", "obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 #", "'SHA1: ' 'd6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self): data = ''' ##", "data = ''' SPDXID: SPDXRef-Package FilesAnalyzed: False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12", "comment.</text> SnippetName: from linux kernel SnippetFromFileSPDXID: SPDXRef-DoapSource SnippetLicenseConcluded: Apache-2.0 LicenseInfoInSnippet:", "'LINE', '4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)', 5) self.token_assert_helper(self.l.token(), 'PKG_EXT_REF', 'ExternalRef', 6) self.token_assert_helper(self.l.token(),", "'LINE', 'from linux kernel', 6) self.token_assert_helper(self.l.token(), 'SNIPPET_FILE_SPDXID', 'SnippetFromFileSPDXID', 7) self.token_assert_helper(self.l.token(),", "<text>License Comments</text>', 'ExternalRef: SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:', 'ExternalRefComment: <text>Some comment about", "import Builder from spdx.parsers.loggers import StandardLogger from spdx.version import Version", "file_str = '\\n'.join([ 'FileName: testfile.java', 'SPDXID: SPDXRef-File', 'FileType: SOURCE', 'FileChecksum:", "'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE', 'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM', 'PackageChecksum',", "2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(),", "'Some comment about the package.' def test_file(self): document, error =", "not None assert not error assert len(document.snippet) == 1 assert", "]) unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue' snippet_str = '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet',", "try: from StringIO import StringIO except ImportError: from io import", "self.token_assert_helper(self.l.token(), 'LINE', 'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT',", "assert document.package.files_analyzed == True assert document.package.comment == 'Comment on the", "about the package.</text>', 7) def test_unknown_tag(self): data = ''' SomeUnknownTag:", "'Version 0.9.2' assert len(document.package.licenses_from_files) == 2 assert (document.package.conc_lics.identifier == 'LicenseRef-2.0", "self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2) self.token_assert_helper(self.l.token(), 'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(),", "5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(),", "document.package.pkg_ext_refs[-1].category == 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator ==", "self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2) def test_snippet(self):", "ExternalDocumentRef:DocumentRef-spdx-tool-2.1 http://spdx.org/spdxdocs/spdx-tools-v2.1-3F2504E0-4F89-41D3-9A0C-0305E82C3301 SHA1: d6a770ba38583ed4bb4525bd96e50461655d2759 ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF', 'ExternalDocumentRef', 2)", "snippet_str = '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic comment.</text>', 'SnippetCopyrightText:", "self.token_assert_helper(self.l.token(), 'PKG_FILES_ANALYZED', 'FilesAnalyzed', 3) self.token_assert_helper(self.l.token(), 'LINE', 'False', 3) self.token_assert_helper(self.l.token(), 'PKG_CHKSUM',", "self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2) def", "<text>Alice was also here.</text>' ]) package_str = '\\n'.join([ 'PackageName: Test',", "5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6) self.token_assert_helper(self.l.token(),", "'FileName: testfile.java', 'SPDXID: SPDXRef-File', 'FileType: SOURCE', 'FileChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded:", "assert token.lineno == line class TestParser(TestCase): maxDiff = None document_str", "== 1 def test_unknown_tag(self): try: from StringIO import StringIO except", "2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic comment.</text>',", "self.token_assert_helper(self.l.token(), 'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT',", "'TEXT', '<text>This is a sample spreadsheet</text>', 8) def test_external_document_references(self): data", "''' ## Creation Information Creator: Person: <NAME> Creator: Organization: Source", "'PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (something.rdf, something.txt)', 'PackageDescription: <text>A package.</text>', 'PackageComment: <text>Comment on", "assert not error assert document.version == Version(major=2, minor=1) assert document.data_license.identifier", "is not None assert not error assert len(document.creation_info.creators) == 2", "== 1 spdx_file = document.package.files[0] assert spdx_file.name == 'testfile.java' assert", "assert document.data_license.identifier == 'CC0-1.0' assert document.name == 'Sample_Document-V2.1' assert document.spdx_id", "not None def test_snippet(self): document, error = self.p.parse(self.complete_str) assert document", "7) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some comment about the package.</text>', 7) def", "'LINE', 'SPDX-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0',", "'d6a770ba38583ed4bb4525bd96e50461655d2759', 2) def test_creation_info(self): data = ''' ## Creation Information", "self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package', 2) self.token_assert_helper(self.l.token(),", "Organization: Acme.', 'Created: 2010-02-03T00:00:00Z', 'CreatorComment: <text>Sample Comment</text>' ]) review_str =", "None document_str = '\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1',", "''' SomeUnknownTag: SomeUnknownValue ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'UNKNOWN_TAG', 'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(),", "'FilesAnalyzed: True', 'PackageSummary: <text>Test package</text>', 'PackageSourceInfo: <text>Version 1.0 of test</text>',", "'ArtifactOfProjectURI: http://www.acme.org/', 'FileComment: <text>Very long file</text>' ]) unknown_tag_str = 'SomeUnknownTag:", "assert len(document.package.files) == 1 spdx_file = document.package.files[0] assert spdx_file.name ==", "''' SPDXID: SPDXRef-Package FilesAnalyzed: False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba", "'\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments: <text>Some lic comment.</text>', 'SnippetCopyrightText: <text> Copyright", "\"License\"); # you may not use this file except in", "2) self.token_assert_helper(self.l.token(), 'DOC_LICENSE', 'DataLicense', 4) self.token_assert_helper(self.l.token(), 'LINE', 'CC0-1.0', 4) self.token_assert_helper(self.l.token(),", "'PackageVersion: Version 0.9.2', 'PackageDownloadLocation: http://example.com/test', 'FilesAnalyzed: True', 'PackageSummary: <text>Test package</text>',", "<text>Very long file</text>' ]) unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue' snippet_str =", "'SPDXID', 6) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-DOCUMENT', 6) self.token_assert_helper(self.l.token(), 'DOC_NAMESPACE', 'DocumentNamespace', 7)", "'ReviewDate: 2011-02-10T00:00:00Z', 'ReviewComment: <text>Alice was also here.</text>' ]) package_str =", "'SomeUnknownTag', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SomeUnknownValue', 2) def test_snippet(self): data =", "= saved_out assert error assert document is not None def", "kernel' assert document.snippet[-1].comment == 'Some snippet comment.' assert document.snippet[-1].copyright ==", "package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Package',", "distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR", "len(spdx_file.artifact_of_project_home) == 1 assert len(spdx_file.artifact_of_project_uri) == 1 def test_unknown_tag(self): try:", "assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:' assert document.package.pkg_ext_refs[-1].comment == 'Some comment about", "'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments', 3) self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some lic comment.</text>', 3) self.token_assert_helper(self.l.token(),", "document_str = '\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense: CC0-1.0', 'DocumentName: Sample_Document-V2.1', 'SPDXID:", "an SPDX spreadsheet format</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 3)", "# distributed under the License is distributed on an \"AS", "# Unless required by applicable law or agreed to in", "minor=1) assert document.data_license.identifier == 'CC0-1.0' assert document.name == 'Sample_Document-V2.1' assert", "'CC0-1.0', 4) self.token_assert_helper(self.l.token(), 'DOC_NAME', 'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5)", "Person: Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z ReviewComment: <text>This is just an", "'FileCopyrightText: <text>Copyright 2014 Acme Inc.</text>', 'ArtifactOfProjectName: AcmeTest', 'ArtifactOfProjectHomePage: http://www.acme.org/', 'ArtifactOfProjectURI:", "'DOC_REF_ID', 'DocumentRef-spdx-tool-2.1', 2) self.token_assert_helper(self.l.token(), 'DOC_URI', 'http://spdx.org/spdxdocs/spdx-tools-v2.1-3F25' '04E0-4F89-41D3-9A0C-0305E82C3301', 2) self.token_assert_helper(self.l.token(), 'EXT_DOC_REF_CHKSUM',", "SECURITY cpe23Type cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*: ExternalRefComment: <text>Some comment about the package.</text> '''", "Apache-2.0', ]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str, package_str, file_str, snippet_str)", "\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY", "def test_document(self): data = ''' SPDXVersion: SPDX-2.1 # Comment. DataLicense:", "Person: <NAME> Creator: Organization: Source Auditor Inc. Creator: Tool: SourceAuditor-V1.2", "'DocumentName', 5) self.token_assert_helper(self.l.token(), 'LINE', 'Sample_Document-V2.1', 5) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 6)", "clause licenses</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'REVIEWER', 'Reviewer', 2) self.token_assert_helper(self.l.token(), 'PERSON_VALUE',", "a sample spreadsheet</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'DOC_VERSION', 'SPDXVersion', 2) self.token_assert_helper(self.l.token(),", "self.token_assert_helper(self.l.token(), 'TEXT', '<text>Some cr text.</text>', 4) self.token_assert_helper(self.l.token(), 'SNIPPET_COMMENT', 'SnippetComment', 5)", "<text>Some snippet comment.</text>', 'SnippetName: from linux kernel', 'SnippetFromFileSPDXID: SPDXRef-DoapSource', 'SnippetLicenseConcluded:", "'SnippetLicenseConcluded: Apache-2.0', 'LicenseInfoInSnippet: Apache-2.0', ]) complete_str = '{0}\\n{1}\\n{2}\\n{3}\\n{4}\\n{5}'.format(document_str, creation_str, review_str,", "review_str, package_str, file_str, snippet_str) def setUp(self): self.p = Parser(Builder(), StandardLogger())", "== 'Some snippet comment.' assert document.snippet[-1].copyright == ' Copyright 2008-2010", "'Tool: SourceAuditor-V1.2', 5) self.token_assert_helper(self.l.token(), 'CREATED', 'Created', 6) self.token_assert_helper(self.l.token(), 'DATE', '2010-02-03T00:00:00Z',", "self.token_assert_helper(self.l.token(), 'SNIPPET_NAME', 'SnippetName', 6) self.token_assert_helper(self.l.token(), 'LINE', 'from linux kernel', 6)", "You may obtain a copy of the License at #", "'https://spdx.org/spdxdocs/spdx-example-444504E0-4F89-41D3-9A0C-0305E82C3301', 7) self.token_assert_helper(self.l.token(), 'DOC_COMMENT', 'DocumentComment', 8) self.token_assert_helper(self.l.token(), 'TEXT', '<text>This is", "assert document.snippet[-1].copyright == ' Copyright 2008-2010 <NAME> ' assert document.snippet[-1].license_comment", "== ttype assert token.value == value assert token.lineno == line", "test_review_info(self): data = ''' Reviewer: Person: Joe Reviewer ReviewDate: 2010-02-10T00:00:00Z", "not error assert document.version == Version(major=2, minor=1) assert document.data_license.identifier ==", "2010-02-03T00:00:00Z CreatorComment: <text>This is an example of an SPDX spreadsheet", "2 assert (document.package.conc_lics.identifier == 'LicenseRef-2.0 AND Apache-2.0') assert document.package.files_analyzed ==", "about the package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2) self.token_assert_helper(self.l.token(),", "TestParser(TestCase): maxDiff = None document_str = '\\n'.join([ 'SPDXVersion: SPDX-2.1', 'DataLicense:", "assert document.snippet[-1].license_comment == 'Some lic comment.' assert document.snippet[-1].snip_from_file_spdxid == 'SPDXRef-DoapSource'", "'Found unknown tag : SomeUnknownTag at line: 1\\n') sys.stdout =", "import Version class TestLexer(TestCase): maxDiff = None def setUp(self): self.l", "Auditor Inc.', 4) self.token_assert_helper(self.l.token(), 'CREATOR', 'Creator', 5) self.token_assert_helper(self.l.token(), 'TOOL_VALUE', 'Tool:", "SPDXRef-Package FilesAnalyzed: False PackageChecksum: SHA1: 2fd4e1c67a2d28fced849ee1bb76e7391b93eb12 PackageVerificationCode: 4e3211c67a2d28fced849ee1bb76e7391b93feba (SpdxTranslatorSpdx.rdf, SpdxTranslatorSpdx.txt)", "package.</text>', 'PackageComment: <text>Comment on the package.</text>', 'PackageCopyrightText: <text> Copyright 2014", "not None assert not error assert len(document.package.files) == 1 spdx_file", "the Apache License, Version 2.0 (the \"License\"); # you may", "2fd4e1c67a2d28fced849ee1bb76e7391b93eb12', 'LicenseConcluded: Apache-2.0', 'LicenseInfoInFile: Apache-2.0', 'FileCopyrightText: <text>Copyright 2014 Acme Inc.</text>',", "'ExternalRefComment: <text>Some comment about the package.</text>' ]) file_str = '\\n'.join([", "== 'SECURITY' assert document.package.pkg_ext_refs[-1].pkg_ext_ref_type == 'cpe23Type' assert document.package.pkg_ext_refs[-1].locator == 'cpe:2.3:a:pivotal_software:spring_framework:4.1.0:*:*:*:*:*:*:'", "error assert document is not None def test_snippet(self): document, error", "cr text.</text> SnippetComment: <text>Some snippet comment.</text> SnippetName: from linux kernel", "comment about the package.</text> ''' self.l.input(data) self.token_assert_helper(self.l.token(), 'SPDX_ID', 'SPDXID', 2)", "self.p.parse(self.unknown_tag_str) self.assertEqual(sys.stdout.getvalue(), 'Found unknown tag : SomeUnknownTag at line: 1\\n')", "'SNIPPET_SPDX_ID', 'SnippetSPDXID', 2) self.token_assert_helper(self.l.token(), 'LINE', 'SPDXRef-Snippet', 2) self.token_assert_helper(self.l.token(), 'SNIPPET_LICS_COMMENT', 'SnippetLicenseComments',", "unknown_tag_str = 'SomeUnknownTag: SomeUnknownValue' snippet_str = '\\n'.join([ 'SnippetSPDXID: SPDXRef-Snippet', 'SnippetLicenseComments:", "Apache-2.0', 'PackageLicenseConcluded: (LicenseRef-2.0 and Apache-2.0)', 'PackageLicenseInfoFromFiles: Apache-1.0', 'PackageLicenseInfoFromFiles: Apache-2.0', 'PackageLicenseComments:" ]
[ "start_time = datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0 else: when_usba", "not_yet = False done = True except: print(\"Count: \", count,\"", "fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and usbb_mounted and mycloud_mounted): print(\"Something Needs mounting", "0 else: when_usba = -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0", "is try number: \", mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try +=", "= False done = True except: print(\"Count: \", count,\" error\")", "-1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0 else: when_usbb = -1", "else: when_usba = -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0 else:", "False done = True except: print(\"Count: \", count,\" error\") time_sleep(1)", "except: print(\"Count: \", count,\" error\") time_sleep(1) if done: print(\"Great!\") else:", "else: when_mycloud = -1 while (mount_try < 30) and not_yet:", "fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and", "as subprocess_call from utility import fileexists from time import sleep", "usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if", "print(\"Count: \", count,\" error\") time_sleep(1) if done: print(\"Great!\") else: print(\"Failed", "mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and usbb_mounted and mycloud_mounted): print(\"Something", "if done: print(\"Great!\") else: print(\"Failed to do all or drive_present.txt", "= 1 not_yet = True done = False start_time =", "and usbb_mounted and mycloud_mounted): print(\"Something Needs mounting this is try", "and mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud, \" secs from start\") not_yet", "print(\"Failed to do all or drive_present.txt file not present; Times", "mycloud_mounted_after: when_mycloud = round((datetime.now() - start_time).total_seconds(),2) if usba_mounted_after and usbb_mounted_after", "usbb_mounted_after and mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud, \" secs from start\")", "not(mycloud_mounted) and mycloud_mounted_after: when_mycloud = round((datetime.now() - start_time).total_seconds(),2) if usba_mounted_after", "from time import sleep as time_sleep from datetime import datetime", "when_usba = round((datetime.now() - start_time).total_seconds(),2) if not(usbb_mounted) and usbb_mounted_after: when_usbb", "if not(mycloud_mounted) and mycloud_mounted_after: when_mycloud = round((datetime.now() - start_time).total_seconds(),2) if", "-1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0 else: when_mycloud = -1", "1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\")", "fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and usba_mounted_after: when_usba = round((datetime.now() - start_time).total_seconds(),2)", "imports from subprocess import call as subprocess_call from utility import", "time_sleep from datetime import datetime mount_try = 1 not_yet =", "if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0 else: when_usba = -1 if", "datetime mount_try = 1 not_yet = True done = False", "= 0 else: when_usbb = -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud =", "from subprocess import call as subprocess_call from utility import fileexists", "as time_sleep from datetime import datetime mount_try = 1 not_yet", "when_usbb = 0 else: when_usbb = -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud", "= fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted", "= round((datetime.now() - start_time).total_seconds(),2) if not(mycloud_mounted) and mycloud_mounted_after: when_mycloud =", "(mount_try < 30) and not_yet: try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted", "start_time).total_seconds(),2) if not(mycloud_mounted) and mycloud_mounted_after: when_mycloud = round((datetime.now() - start_time).total_seconds(),2)", "= fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and usba_mounted_after: when_usba = round((datetime.now() -", "Standard library imports from subprocess import call as subprocess_call from", "import call as subprocess_call from utility import fileexists from time", "else: when_usbb = -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0 else:", "when_mycloud = round((datetime.now() - start_time).total_seconds(),2) if usba_mounted_after and usbb_mounted_after and", "usba_mounted_after and usbb_mounted_after and mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud, \" secs", "\" secs from start\") not_yet = False done = True", "Needs mounting this is try number: \", mount_try) subprocess_call([\"sudo\", \"mount\",", "= round((datetime.now() - start_time).total_seconds(),2) if usba_mounted_after and usbb_mounted_after and mycloud_mounted_after:", "\", count,\" error\") time_sleep(1) if done: print(\"Great!\") else: print(\"Failed to", "print(\"Great!\") else: print(\"Failed to do all or drive_present.txt file not", "usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and usbb_mounted", "datetime import datetime mount_try = 1 not_yet = True done", "not_yet = True done = False start_time = datetime.now() if", "fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0 else: when_mycloud = -1 while (mount_try", "mount_try = 1 not_yet = True done = False start_time", "mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try += 1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\")", "fileexists from time import sleep as time_sleep from datetime import", "not(usbb_mounted) and usbb_mounted_after: when_usbb = round((datetime.now() - start_time).total_seconds(),2) if not(mycloud_mounted)", "done = False start_time = datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba =", "1 not_yet = True done = False start_time = datetime.now()", "not(usba_mounted) and usba_mounted_after: when_usba = round((datetime.now() - start_time).total_seconds(),2) if not(usbb_mounted)", "and usbb_mounted_after: when_usbb = round((datetime.now() - start_time).total_seconds(),2) if not(mycloud_mounted) and", "\"mount\", \"-a\"]) mount_try += 1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after =", "< 30) and not_yet: try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted =", "subprocess import call as subprocess_call from utility import fileexists from", "usbb_mounted_after: when_usbb = round((datetime.now() - start_time).total_seconds(),2) if not(mycloud_mounted) and mycloud_mounted_after:", "import fileexists from time import sleep as time_sleep from datetime", "mount_try += 1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after", "and usbb_mounted_after and mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud, \" secs from", "from datetime import datetime mount_try = 1 not_yet = True", "do all or drive_present.txt file not present; Times :\",when_usba,when_usbb,when_mycloud) while", "call as subprocess_call from utility import fileexists from time import", "= 0 else: when_mycloud = -1 while (mount_try < 30)", "done = True except: print(\"Count: \", count,\" error\") time_sleep(1) if", "round((datetime.now() - start_time).total_seconds(),2) if usba_mounted_after and usbb_mounted_after and mycloud_mounted_after: print(\"Success", "usbb_mounted and mycloud_mounted): print(\"Something Needs mounting this is try number:", "fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0 else: when_usbb = -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"):", "utility import fileexists from time import sleep as time_sleep from", "when_usbb = -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0 else: when_mycloud", "30) and not_yet: try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\")", "mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and usba_mounted_after: when_usba = round((datetime.now()", "= fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and usba_mounted_after: when_usba", "= fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted)", "if not(usba_mounted and usbb_mounted and mycloud_mounted): print(\"Something Needs mounting this", "try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\")", "count,\" error\") time_sleep(1) if done: print(\"Great!\") else: print(\"Failed to do", "\"-a\"]) mount_try += 1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\")", "= -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0 else: when_usbb =", "error\") time_sleep(1) if done: print(\"Great!\") else: print(\"Failed to do all", "time import sleep as time_sleep from datetime import datetime mount_try", "False start_time = datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0 else:", "print(\"Success at :\",when_usba,when_usbb,when_mycloud, \" secs from start\") not_yet = False", "from utility import fileexists from time import sleep as time_sleep", "import sleep as time_sleep from datetime import datetime mount_try =", "= fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and usbb_mounted and", "mounting this is try number: \", mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"])", "when_usba = 0 else: when_usba = -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb", "if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0 else: when_mycloud = -1 while", "from start\") not_yet = False done = True except: print(\"Count:", "usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if", "0 else: when_usbb = -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0", "not_yet: try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted =", "if not(usba_mounted) and usba_mounted_after: when_usba = round((datetime.now() - start_time).total_seconds(),2) if", "# Standard library imports from subprocess import call as subprocess_call", "this is try number: \", mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try", "library imports from subprocess import call as subprocess_call from utility", "start_time).total_seconds(),2) if usba_mounted_after and usbb_mounted_after and mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud,", "subprocess_call from utility import fileexists from time import sleep as", "secs from start\") not_yet = False done = True except:", "usba_mounted_after: when_usba = round((datetime.now() - start_time).total_seconds(),2) if not(usbb_mounted) and usbb_mounted_after:", "not(usba_mounted and usbb_mounted and mycloud_mounted): print(\"Something Needs mounting this is", "= -1 while (mount_try < 30) and not_yet: try: usba_mounted", "datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0 else: when_usba = -1", "and not_yet: try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted", "= True done = False start_time = datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"):", "= -1 if fileexists(\"/home/pi/mycloud/drive_present.txt\"): when_mycloud = 0 else: when_mycloud =", "+= 1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after =", "True done = False start_time = datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba", "= True except: print(\"Count: \", count,\" error\") time_sleep(1) if done:", "\", mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try += 1 usba_mounted_after =", "- start_time).total_seconds(),2) if not(usbb_mounted) and usbb_mounted_after: when_usbb = round((datetime.now() -", "subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try += 1 usba_mounted_after = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after", "-1 while (mount_try < 30) and not_yet: try: usba_mounted =", "to do all or drive_present.txt file not present; Times :\",when_usba,when_usbb,when_mycloud)", "if not(usbb_mounted) and usbb_mounted_after: when_usbb = round((datetime.now() - start_time).total_seconds(),2) if", "if usba_mounted_after and usbb_mounted_after and mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud, \"", "fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and usba_mounted_after: when_usba =", "mycloud_mounted_after: print(\"Success at :\",when_usba,when_usbb,when_mycloud, \" secs from start\") not_yet =", "when_mycloud = 0 else: when_mycloud = -1 while (mount_try <", "round((datetime.now() - start_time).total_seconds(),2) if not(usbb_mounted) and usbb_mounted_after: when_usbb = round((datetime.now()", "when_mycloud = -1 while (mount_try < 30) and not_yet: try:", "print(\"Something Needs mounting this is try number: \", mount_try) subprocess_call([\"sudo\",", "fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\") usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and", "0 else: when_mycloud = -1 while (mount_try < 30) and", "usbb_mounted_after = fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted_after = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted) and usba_mounted_after:", "when_usbb = round((datetime.now() - start_time).total_seconds(),2) if not(mycloud_mounted) and mycloud_mounted_after: when_mycloud", "at :\",when_usba,when_usbb,when_mycloud, \" secs from start\") not_yet = False done", "mycloud_mounted): print(\"Something Needs mounting this is try number: \", mount_try)", "= round((datetime.now() - start_time).total_seconds(),2) if not(usbb_mounted) and usbb_mounted_after: when_usbb =", "done: print(\"Great!\") else: print(\"Failed to do all or drive_present.txt file", "else: print(\"Failed to do all or drive_present.txt file not present;", "and usba_mounted_after: when_usba = round((datetime.now() - start_time).total_seconds(),2) if not(usbb_mounted) and", "fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0 else: when_usba = -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"):", "start\") not_yet = False done = True except: print(\"Count: \",", "all or drive_present.txt file not present; Times :\",when_usba,when_usbb,when_mycloud) while True:", "import datetime mount_try = 1 not_yet = True done =", "= datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0 else: when_usba =", "fileexists(\"/home/duck-sftp/usb/drive_present.txt\") mycloud_mounted = fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and usbb_mounted and mycloud_mounted):", "try number: \", mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try += 1", "if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0 else: when_usbb = -1 if", "number: \", mount_try) subprocess_call([\"sudo\", \"mount\", \"-a\"]) mount_try += 1 usba_mounted_after", "= 0 else: when_usba = -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb =", "True except: print(\"Count: \", count,\" error\") time_sleep(1) if done: print(\"Great!\")", "- start_time).total_seconds(),2) if usba_mounted_after and usbb_mounted_after and mycloud_mounted_after: print(\"Success at", "sleep as time_sleep from datetime import datetime mount_try = 1", "when_usba = -1 if fileexists(\"/home/duck-sftp/usb/drive_present.txt\"): when_usbb = 0 else: when_usbb", "or drive_present.txt file not present; Times :\",when_usba,when_usbb,when_mycloud) while True: time_sleep(20000)", ":\",when_usba,when_usbb,when_mycloud, \" secs from start\") not_yet = False done =", "- start_time).total_seconds(),2) if not(mycloud_mounted) and mycloud_mounted_after: when_mycloud = round((datetime.now() -", "time_sleep(1) if done: print(\"Great!\") else: print(\"Failed to do all or", "while (mount_try < 30) and not_yet: try: usba_mounted = fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\")", "= fileexists(\"/home/pi/mycloud/drive_present.txt\") if not(usba_mounted and usbb_mounted and mycloud_mounted): print(\"Something Needs", "and mycloud_mounted_after: when_mycloud = round((datetime.now() - start_time).total_seconds(),2) if usba_mounted_after and", "and mycloud_mounted): print(\"Something Needs mounting this is try number: \",", "round((datetime.now() - start_time).total_seconds(),2) if not(mycloud_mounted) and mycloud_mounted_after: when_mycloud = round((datetime.now()", "start_time).total_seconds(),2) if not(usbb_mounted) and usbb_mounted_after: when_usbb = round((datetime.now() - start_time).total_seconds(),2)", "= False start_time = datetime.now() if fileexists(\"/home/rpi4-sftp/usb/drive_present.txt\"): when_usba = 0" ]
[ "report(request): if request.method == \"POST\": email = request.POST.get('email') name =", "render(request,\"diag.html\") # def appointment(request,email,name): # if request.method == \"POST\": #", "truepassword = f.decrypt(truepassword).decode('utf-8') except: object = None if(object==None): context =", "email = request.POST.get('email') password = request.POST.get('password') try: object = UserDetails.objects.get(email", "try: object = UserDetails.objects.get(email = email) key1 = object.key key1=key1[2:-1]", "request.POST.get('name') email = request.POST.get('email') password = request.POST.get('password') passwordVerif = request.POST.get('passwordVerif')", "= request.POST.get('drop1') tests = str(tests) if(email ==''): context = {", "key = key, profession=profession, data=data) return redirect(\"/\") else: context =", "django.shortcuts import render from django.shortcuts import redirect, render from cryptography.fernet", "if(request.method == 'POST'): email = request.POST.get('email') password = request.POST.get('password') try:", "diag = Diagnostic(email=email , name=name, phone=phone, tests=tests, date=datetime.today()) diag.save() #", "context = { 'message': \"Email Does Not Exist\" } return", "render(request,\"signup.html\",{}) # def index(request): # context={ 'alpha': 'This is sent'}", "redirect(\"/\") else: return render(request,\"login.html\",{}) def signUpPage(request): if(request.method == 'POST'): name", "= request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') tests =", "Fernet(key1) truepassword = <PASSWORD>.password truepassword = <PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8')", "book = Book(email=email , name=name, phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\") def", "= { 'message': \"Please enter Email ID\" } return render(request,\"signup.html\",context)", "render(request,\"index.html\",context1) else: context2={ 'message':'Welcome '+object.name, 'mail' : object.email } return", "request.POST.get('address') book = Book(email=email , name=name, phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\")", "match\" } return render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{}) # def index(request):", "} return render(request,\"signup.html\",context) elif(password == <PASSWORD>): key = Fernet.generate_key() f", "= request.POST.get('phone') address = request.POST.get('address') book = Book(email=email , name=name,", "= { 'message': \"Password doesn't match\" } return render(request,\"signup.html\",context) else:", "import Book, UserDetails from .models import Contact from .models import", "else: return render(request, 'index.html',context) #HttpResponse('This is homepage') def about(request): return", "key = str(key) print(key) UserDetails.objects.create(email=email, name=name , password=token, key =", "= request.POST.get('email') password = request.POST.get('password') try: object = UserDetails.objects.get(email =", "enter Email ID\" } return render(request,\"diag.html\",context) else: diag = Diagnostic(email=email", "UserDetails.objects.create(email=email, name=name , password=token, key = key, profession=profession, data=data) return", "render from django.shortcuts import redirect, render from cryptography.fernet import Fernet", "} return render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{}) # def index(request): #", "return render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{}) # def index(request): # context={", "def diag(request): if request.method == \"POST\": email = request.POST.get('email') name", "from django.http.response import HttpResponse from django.shortcuts import render from django.shortcuts", "= f.encrypt(password) key = str(key) print(key) UserDetails.objects.create(email=email, name=name , password=token,", "passwordVerif = request.POST.get('passwordVerif') profession = request.POST.get('user') data = request.POST.get('data') if(email", "== truepassword): if object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) #", "book(request): if request.method == \"POST\": email = request.POST.get('email') name =", "bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8') except: object = None if(object==None): context", "if request.method == \"POST\": # problem = request.POST.get('problem') # book", "django.http.response import HttpResponse from django.shortcuts import render from django.shortcuts import", "\"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) # appointment(request,email,name) context1={ 'message':'Welcome '+object.name, 'mail'", "book = Appoint(problem=problem, email=email, name=name) # book.save() # return render(request,\"index.html\")", "# messages.success(request, 'Your message has been sent !') return render(request,\"contact.html\")", "def book(request): if request.method == \"POST\": email = request.POST.get('email') name", "Contact from .models import Book from .models import Report from", "name=name, phone=phone, message=message, date=datetime.today()) report.save() return render(request,\"report.html\") def diag(request): if", "has been sent !') return render(request,\"diag.html\") # def appointment(request,email,name): #", "elif(password == truepassword): if object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name)", "request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address')", "str(key) print(key) UserDetails.objects.create(email=email, name=name , password=token, key = key, profession=profession,", "else: diag = Diagnostic(email=email , name=name, phone=phone, tests=tests, date=datetime.today()) diag.save()", "messages.success(request, 'Your message has been sent !') return render(request,\"diag.html\") #", "Create your views here. def homePage(request): if(request.method == 'POST'): email", "ID\" } return render(request,\"signup.html\",context) elif(password == <PASSWORD>): key = Fernet.generate_key()", "Exist\" } return render(request,\"login.html\",context) elif(password == truepassword): if object.profession ==", "index(request): # context={ 'alpha': 'This is sent'} # if request.method=='POST':", "phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\") def report(request): if request.method == \"POST\":", "import Report from .models import Diagnostic from datetime import datetime", "else: return render(request,\"signup.html\",{}) # def index(request): # context={ 'alpha': 'This", "phone = request.POST.get('phone') tests = request.POST.get('drop1') tests = str(tests) if(email", "request.POST.get('phone') address = request.POST.get('address') book = Book(email=email , name=name, phone=phone,problem=address,date=datetime.today())", "context = { 'message': \"Please enter Email ID\" } return", "import HttpResponse from django.shortcuts import render from django.shortcuts import redirect,", "= request.POST.get('data') if(email ==''): context = { 'message': \"Please enter", "# def appointment(request,email,name): # if request.method == \"POST\": # problem", "contact.save() # messages.success(request, 'Your message has been sent !') return", "been sent !') return render(request,\"contact.html\") def book(request): if request.method ==", "from django.shortcuts import render from django.shortcuts import redirect, render from", "if(object==None): context = { 'message': \"Email Does Not Exist\" }", "= { 'message': \"Please enter Email ID\" } return render(request,\"diag.html\",context)", "} return render(request,\"index.html\",context1) else: context2={ 'message':'Welcome '+object.name, 'mail' : object.email", "} return render(request,\"diag.html\",context) else: diag = Diagnostic(email=email , name=name, phone=phone,", "!') return render(request,\"diag.html\") # def appointment(request,email,name): # if request.method ==", "render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{}) # def index(request): # context={ 'alpha':", "render(request, 'services.html') def contact(request): if request.method == \"POST\": email =", "is homepage') def about(request): return render(request, 'about.html') def services(request): return", "Report(email=email , name=name, phone=phone, message=message, date=datetime.today()) report.save() return render(request,\"report.html\") def", "request.POST.get('passwordVerif') profession = request.POST.get('user') data = request.POST.get('data') if(email ==''): context", "'message':'Welcome '+object.name, 'mail' : object.email, 'doctors':object1 } return render(request,\"index.html\",context1) else:", "= f.decrypt(truepassword).decode('utf-8') except: object = None if(object==None): context = {", "return render(request,\"signup.html\",{}) # def index(request): # context={ 'alpha': 'This is", "render(request,\"report.html\") def diag(request): if request.method == \"POST\": email = request.POST.get('email')", "= Fernet.generate_key() f = Fernet(key) password = bytes(password,'<PASSWORD>') token =", "# def index(request): # context={ 'alpha': 'This is sent'} #", "return render(request,\"book.html\") def report(request): if request.method == \"POST\": email =", "request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') message = request.POST.get('message')", "Email ID\" } return render(request,\"diag.html\",context) else: diag = Diagnostic(email=email ,", "key, profession=profession, data=data) return redirect(\"/\") else: context = { 'message':", "= request.POST.get('name') phone = request.POST.get('phone') tests = request.POST.get('drop1') tests =", "request.POST.get('user') data = request.POST.get('data') if(email ==''): context = { 'message':", "request.POST.get('data') if(email ==''): context = { 'message': \"Please enter Email", "request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') tests = request.POST.get('drop1')", "request.POST.get('phone') tests = request.POST.get('drop1') tests = str(tests) if(email ==''): context", "else: return render(request,\"login.html\",{}) def signUpPage(request): if(request.method == 'POST'): name =", "cryptography.fernet import Fernet from .models import Book, UserDetails from .models", "== \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) # appointment(request,email,name) context1={ 'message':'Welcome '+object.name,", "import render from django.shortcuts import redirect, render from cryptography.fernet import", "'about.html') def services(request): return render(request, 'services.html') def contact(request): if request.method", "from .models import Report from .models import Diagnostic from datetime", "password = request.POST.get('password') try: object = UserDetails.objects.get(email = email) key1", "about(request): return render(request, 'about.html') def services(request): return render(request, 'services.html') def", "key1=key1[2:-1] key1 = bytes(key1,'utf-8') f = Fernet(key1) truepassword = <PASSWORD>.password", ", password=token, key = key, profession=profession, data=data) return redirect(\"/\") else:", "'index.html',context) #HttpResponse('This is homepage') def about(request): return render(request, 'about.html') def", "= { 'message': \"Email Does Not Exist\" } return render(request,\"login.html\",context)", "name=(object.name) # appointment(request,email,name) context1={ 'message':'Welcome '+object.name, 'mail' : object.email, 'doctors':object1", "request.POST.get('email') password = request.POST.get('password') passwordVerif = request.POST.get('passwordVerif') profession = request.POST.get('user')", "name=name, phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\") def report(request): if request.method ==", "name = request.POST.get('name') email = request.POST.get('email') password = request.POST.get('password') passwordVerif", "} return render(request,\"dindex.html\",context2) else: return redirect(\"/\") else: return render(request,\"login.html\",{}) def", "= request.POST.get('address') book = Book(email=email , name=name, phone=phone,problem=address,date=datetime.today()) book.save() return", "UserDetails from .models import Contact from .models import Book from", "\"Email Does Not Exist\" } return render(request,\"login.html\",context) elif(password == truepassword):", "redirect, render from cryptography.fernet import Fernet from .models import Book,", "render from cryptography.fernet import Fernet from .models import Book, UserDetails", "Report from .models import Diagnostic from datetime import datetime #", "request.method == \"POST\": # problem = request.POST.get('problem') # book =", "return render(request,\"report.html\") def diag(request): if request.method == \"POST\": email =", "HttpResponse from django.shortcuts import render from django.shortcuts import redirect, render", "contact(request): if request.method == \"POST\": email = request.POST.get('email') name =", "tests = str(tests) if(email ==''): context = { 'message': \"Please", "'message': \"Password doesn't match\" } return render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{})", "from django.shortcuts import redirect, render from cryptography.fernet import Fernet from", "diag.save() # messages.success(request, 'Your message has been sent !') return", "django.shortcuts import redirect, render from cryptography.fernet import Fernet from .models", "'mail' : object.email, 'doctors':object1 } return render(request,\"index.html\",context1) else: context2={ 'message':'Welcome", "phone = request.POST.get('phone') address = request.POST.get('address') book = Book(email=email ,", "return render(request,\"diag.html\") # def appointment(request,email,name): # if request.method == \"POST\":", ".models import Book from .models import Report from .models import", "return render(request,\"dindex.html\",context2) else: return redirect(\"/\") else: return render(request,\"login.html\",{}) def signUpPage(request):", "= request.POST.get('password') try: object = UserDetails.objects.get(email = email) key1 =", "phone=phone, message=message, date=datetime.today()) report.save() return render(request,\"report.html\") def diag(request): if request.method", "return render(request,\"signup.html\",context) elif(password == <PASSWORD>): key = Fernet.generate_key() f =", "if(email ==''): context = { 'message': \"Please enter Email ID\"", "token = f.encrypt(password) key = str(key) print(key) UserDetails.objects.create(email=email, name=name ,", "message=message, date=datetime.today()) report.save() return render(request,\"report.html\") def diag(request): if request.method ==", "def contact(request): if request.method == \"POST\": email = request.POST.get('email') name", ": object.email, 'doctors':object1 } return render(request,\"index.html\",context1) else: context2={ 'message':'Welcome '+object.name,", "render(request,\"book.html\") def report(request): if request.method == \"POST\": email = request.POST.get('email')", "# context={ 'alpha': 'This is sent'} # if request.method=='POST': #", "'message': \"Please enter Email ID\" } return render(request,\"diag.html\",context) else: diag", "sent !') return render(request,\"diag.html\") # def appointment(request,email,name): # if request.method", "return redirect(\"/\") else: return render(request,\"login.html\",{}) def signUpPage(request): if(request.method == 'POST'):", "'message': \"Email Does Not Exist\" } return render(request,\"login.html\",context) elif(password ==", "object.email } return render(request,\"dindex.html\",context2) else: return redirect(\"/\") else: return render(request,\"login.html\",{})", "key = Fernet.generate_key() f = Fernet(key) password = bytes(password,'<PASSWORD>') token", "\"Password doesn't match\" } return render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{}) #", "f = Fernet(key) password = bytes(password,'<PASSWORD>') token = f.encrypt(password) key", "request.POST.get('email') password = request.POST.get('password') try: object = UserDetails.objects.get(email = email)", "email = request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') tests", "message has been sent !') return render(request,\"diag.html\") # def appointment(request,email,name):", "else: context = { 'message': \"Password doesn't match\" } return", "= request.POST.get('phone') tests = request.POST.get('drop1') tests = str(tests) if(email ==''):", "message has been sent !') return render(request,\"contact.html\") def book(request): if", "return render(request,\"login.html\",{}) def signUpPage(request): if(request.method == 'POST'): name = request.POST.get('name')", "password=token, key = key, profession=profession, data=data) return redirect(\"/\") else: context", "render(request,\"login.html\",{}) def signUpPage(request): if(request.method == 'POST'): name = request.POST.get('name') email", "problem = request.POST.get('problem') # book = Appoint(problem=problem, email=email, name=name) #", "# problem = request.POST.get('problem') # book = Appoint(problem=problem, email=email, name=name)", "request.POST.get('address') contact = Contact(email=email , name=name, phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request,", "Book, UserDetails from .models import Contact from .models import Book", "name = request.POST.get('name') phone = request.POST.get('phone') tests = request.POST.get('drop1') tests", ".models import Report from .models import Diagnostic from datetime import", "Diagnostic from datetime import datetime # Create your views here.", "= request.POST.get('email') password = request.POST.get('password') passwordVerif = request.POST.get('passwordVerif') profession =", "is sent'} # if request.method=='POST': # pass # else: return", "Book(email=email , name=name, phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\") def report(request): if", "has been sent !') return render(request,\"contact.html\") def book(request): if request.method", "def about(request): return render(request, 'about.html') def services(request): return render(request, 'services.html')", "redirect(\"/\") else: context = { 'message': \"Password doesn't match\" }", "= request.POST.get('phone') address = request.POST.get('address') contact = Contact(email=email , name=name,", "from .models import Book, UserDetails from .models import Contact from", "key1 = bytes(key1,'utf-8') f = Fernet(key1) truepassword = <PASSWORD>.password truepassword", "from datetime import datetime # Create your views here. def", "def homePage(request): if(request.method == 'POST'): email = request.POST.get('email') password =", "== \"POST\": email = request.POST.get('email') name = request.POST.get('name') phone =", "'mail' : object.email } return render(request,\"dindex.html\",context2) else: return redirect(\"/\") else:", "{ 'message': \"Password doesn't match\" } return render(request,\"signup.html\",context) else: return", "= Diagnostic(email=email , name=name, phone=phone, tests=tests, date=datetime.today()) diag.save() # messages.success(request,", "None if(object==None): context = { 'message': \"Email Does Not Exist\"", "\"POST\": email = request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone')", "request.POST.get('password') try: object = UserDetails.objects.get(email = email) key1 = object.key", "sent !') return render(request,\"contact.html\") def book(request): if request.method == \"POST\":", "contact = Contact(email=email , name=name, phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request, 'Your", "object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) # appointment(request,email,name) context1={ 'message':'Welcome", "message = request.POST.get('message') report = Report(email=email , name=name, phone=phone, message=message,", "# appointment(request,email,name) context1={ 'message':'Welcome '+object.name, 'mail' : object.email, 'doctors':object1 }", "= request.POST.get('name') email = request.POST.get('email') password = request.POST.get('password') passwordVerif =", "request.POST.get('password') passwordVerif = request.POST.get('passwordVerif') profession = request.POST.get('user') data = request.POST.get('data')", "request.method=='POST': # pass # else: return render(request, 'index.html',context) #HttpResponse('This is", "import redirect, render from cryptography.fernet import Fernet from .models import", "= request.POST.get('password') passwordVerif = request.POST.get('passwordVerif') profession = request.POST.get('user') data =", "signUpPage(request): if(request.method == 'POST'): name = request.POST.get('name') email = request.POST.get('email')", "if(request.method == 'POST'): name = request.POST.get('name') email = request.POST.get('email') password", "'POST'): email = request.POST.get('email') password = request.POST.get('password') try: object =", "services(request): return render(request, 'services.html') def contact(request): if request.method == \"POST\":", "= request.POST.get('user') data = request.POST.get('data') if(email ==''): context = {", "datetime import datetime # Create your views here. def homePage(request):", "# messages.success(request, 'Your message has been sent !') return render(request,\"diag.html\")", ", name=name, phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\") def report(request): if request.method", "== <PASSWORD>): key = Fernet.generate_key() f = Fernet(key) password =", "request.POST.get('phone') address = request.POST.get('address') contact = Contact(email=email , name=name, phone=phone,address=address,date=datetime.today())", "UserDetails.objects.get(email = email) key1 = object.key key1=key1[2:-1] key1 = bytes(key1,'utf-8')", "report = Report(email=email , name=name, phone=phone, message=message, date=datetime.today()) report.save() return", "= request.POST.get('message') report = Report(email=email , name=name, phone=phone, message=message, date=datetime.today())", "book.save() return render(request,\"book.html\") def report(request): if request.method == \"POST\": email", "truepassword = <PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8') except:", "import Fernet from .models import Book, UserDetails from .models import", "report.save() return render(request,\"report.html\") def diag(request): if request.method == \"POST\": email", ".models import Contact from .models import Book from .models import", "# Create your views here. def homePage(request): if(request.method == 'POST'):", "appointment(request,email,name) context1={ 'message':'Welcome '+object.name, 'mail' : object.email, 'doctors':object1 } return", "from cryptography.fernet import Fernet from .models import Book, UserDetails from", "import Contact from .models import Book from .models import Report", "# name=(object.name) # appointment(request,email,name) context1={ 'message':'Welcome '+object.name, 'mail' : object.email,", "= bytes(password,'<PASSWORD>') token = f.encrypt(password) key = str(key) print(key) UserDetails.objects.create(email=email,", "def appointment(request,email,name): # if request.method == \"POST\": # problem =", "{ 'message': \"Please enter Email ID\" } return render(request,\"diag.html\",context) else:", "<PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8') except: object =", "from .models import Contact from .models import Book from .models", "= str(key) print(key) UserDetails.objects.create(email=email, name=name , password=token, key = key,", "Not Exist\" } return render(request,\"login.html\",context) elif(password == truepassword): if object.profession", "request.POST.get('message') report = Report(email=email , name=name, phone=phone, message=message, date=datetime.today()) report.save()", "'message':'Welcome '+object.name, 'mail' : object.email } return render(request,\"dindex.html\",context2) else: return", "tests=tests, date=datetime.today()) diag.save() # messages.success(request, 'Your message has been sent", "= bytes(key1,'utf-8') f = Fernet(key1) truepassword = <PASSWORD>.password truepassword =", "if request.method=='POST': # pass # else: return render(request, 'index.html',context) #HttpResponse('This", "= None if(object==None): context = { 'message': \"Email Does Not", "render(request,\"dindex.html\",context2) else: return redirect(\"/\") else: return render(request,\"login.html\",{}) def signUpPage(request): if(request.method", "def index(request): # context={ 'alpha': 'This is sent'} # if", ".models import Book, UserDetails from .models import Contact from .models", "views here. def homePage(request): if(request.method == 'POST'): email = request.POST.get('email')", "= object.key key1=key1[2:-1] key1 = bytes(key1,'utf-8') f = Fernet(key1) truepassword", "object.email, 'doctors':object1 } return render(request,\"index.html\",context1) else: context2={ 'message':'Welcome '+object.name, 'mail'", "return redirect(\"/\") else: context = { 'message': \"Password doesn't match\"", "= Fernet(key) password = bytes(password,'<PASSWORD>') token = f.encrypt(password) key =", "= <PASSWORD>.password truepassword = <PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8') truepassword =", "truepassword): if object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) # appointment(request,email,name)", "data = request.POST.get('data') if(email ==''): context = { 'message': \"Please", "address = request.POST.get('address') contact = Contact(email=email , name=name, phone=phone,address=address,date=datetime.today()) contact.save()", "== 'POST'): name = request.POST.get('name') email = request.POST.get('email') password =", "ID\" } return render(request,\"diag.html\",context) else: diag = Diagnostic(email=email , name=name,", "address = request.POST.get('address') book = Book(email=email , name=name, phone=phone,problem=address,date=datetime.today()) book.save()", "email) key1 = object.key key1=key1[2:-1] key1 = bytes(key1,'utf-8') f =", "name = request.POST.get('name') phone = request.POST.get('phone') message = request.POST.get('message') report", "= request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address') contact =", "# if request.method=='POST': # pass # else: return render(request, 'index.html',context)", "return render(request, 'services.html') def contact(request): if request.method == \"POST\": email", "else: return redirect(\"/\") else: return render(request,\"login.html\",{}) def signUpPage(request): if(request.method ==", "\"POST\": # problem = request.POST.get('problem') # book = Appoint(problem=problem, email=email,", "'services.html') def contact(request): if request.method == \"POST\": email = request.POST.get('email')", "Fernet(key) password = bytes(password,'<PASSWORD>') token = f.encrypt(password) key = str(key)", "truepassword = bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8') except: object = None", "f.decrypt(truepassword).decode('utf-8') except: object = None if(object==None): context = { 'message':", ": object.email } return render(request,\"dindex.html\",context2) else: return redirect(\"/\") else: return", "= request.POST.get('phone') message = request.POST.get('message') report = Report(email=email , name=name,", "request.POST.get('name') phone = request.POST.get('phone') tests = request.POST.get('drop1') tests = str(tests)", "tests = request.POST.get('drop1') tests = str(tests) if(email ==''): context =", "render(request,\"contact.html\") def book(request): if request.method == \"POST\": email = request.POST.get('email')", "{ 'message': \"Please enter Email ID\" } return render(request,\"signup.html\",context) elif(password", "Fernet from .models import Book, UserDetails from .models import Contact", "'Your message has been sent !') return render(request,\"contact.html\") def book(request):", "request.POST.get('name') phone = request.POST.get('phone') message = request.POST.get('message') report = Report(email=email", "= Contact(email=email , name=name, phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request, 'Your message", "appointment(request,email,name): # if request.method == \"POST\": # problem = request.POST.get('problem')", "date=datetime.today()) report.save() return render(request,\"report.html\") def diag(request): if request.method == \"POST\":", "{ 'message': \"Email Does Not Exist\" } return render(request,\"login.html\",context) elif(password", "return render(request,\"login.html\",context) elif(password == truepassword): if object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\")", "data=data) return redirect(\"/\") else: context = { 'message': \"Password doesn't", "\"Please enter Email ID\" } return render(request,\"diag.html\",context) else: diag =", "password = bytes(password,'<PASSWORD>') token = f.encrypt(password) key = str(key) print(key)", "render(request,\"login.html\",context) elif(password == truepassword): if object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") #", "= request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address') book =", "= request.POST.get('passwordVerif') profession = request.POST.get('user') data = request.POST.get('data') if(email ==''):", ", name=name, phone=phone, message=message, date=datetime.today()) report.save() return render(request,\"report.html\") def diag(request):", "truepassword = <PASSWORD>.password truepassword = <PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8') truepassword", "'Your message has been sent !') return render(request,\"diag.html\") # def", "bytes(password,'<PASSWORD>') token = f.encrypt(password) key = str(key) print(key) UserDetails.objects.create(email=email, name=name", "render(request,\"diag.html\",context) else: diag = Diagnostic(email=email , name=name, phone=phone, tests=tests, date=datetime.today())", "email = request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') address", "request.POST.get('phone') message = request.POST.get('message') report = Report(email=email , name=name, phone=phone,", "key1 = object.key key1=key1[2:-1] key1 = bytes(key1,'utf-8') f = Fernet(key1)", "password = request.POST.get('password') passwordVerif = request.POST.get('passwordVerif') profession = request.POST.get('user') data", "'message': \"Please enter Email ID\" } return render(request,\"signup.html\",context) elif(password ==", "phone = request.POST.get('phone') message = request.POST.get('message') report = Report(email=email ,", "!') return render(request,\"contact.html\") def book(request): if request.method == \"POST\": email", "= bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8') except: object = None if(object==None):", "import Book from .models import Report from .models import Diagnostic", "'+object.name, 'mail' : object.email } return render(request,\"dindex.html\",context2) else: return redirect(\"/\")", "#HttpResponse('This is homepage') def about(request): return render(request, 'about.html') def services(request):", "render(request, 'about.html') def services(request): return render(request, 'services.html') def contact(request): if", "context2={ 'message':'Welcome '+object.name, 'mail' : object.email } return render(request,\"dindex.html\",context2) else:", "= request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') address =", "email = request.POST.get('email') password = request.POST.get('password') passwordVerif = request.POST.get('passwordVerif') profession", "enter Email ID\" } return render(request,\"signup.html\",context) elif(password == <PASSWORD>): key", "= email) key1 = object.key key1=key1[2:-1] key1 = bytes(key1,'utf-8') f", "here. def homePage(request): if(request.method == 'POST'): email = request.POST.get('email') password", "# book = Appoint(problem=problem, email=email, name=name) # book.save() # return", "homepage') def about(request): return render(request, 'about.html') def services(request): return render(request,", "profession = request.POST.get('user') data = request.POST.get('data') if(email ==''): context =", "Email ID\" } return render(request,\"signup.html\",context) elif(password == <PASSWORD>): key =", "= Report(email=email , name=name, phone=phone, message=message, date=datetime.today()) report.save() return render(request,\"report.html\")", "Does Not Exist\" } return render(request,\"login.html\",context) elif(password == truepassword): if", "# else: return render(request, 'index.html',context) #HttpResponse('This is homepage') def about(request):", "request.method == \"POST\": email = request.POST.get('email') name = request.POST.get('name') phone", "'This is sent'} # if request.method=='POST': # pass # else:", "== \"POST\": # problem = request.POST.get('problem') # book = Appoint(problem=problem,", "object = UserDetails.objects.get(email = email) key1 = object.key key1=key1[2:-1] key1", "f = Fernet(key1) truepassword = <PASSWORD>.password truepassword = <PASSWORD>[2:-1] truepassword", "profession=profession, data=data) return redirect(\"/\") else: context = { 'message': \"Password", ", name=name, phone=phone, tests=tests, date=datetime.today()) diag.save() # messages.success(request, 'Your message", "# if request.method == \"POST\": # problem = request.POST.get('problem') #", "= request.POST.get('problem') # book = Appoint(problem=problem, email=email, name=name) # book.save()", "bytes(key1,'utf-8') f = Fernet(key1) truepassword = <PASSWORD>.password truepassword = <PASSWORD>[2:-1]", "return render(request,\"index.html\",context1) else: context2={ 'message':'Welcome '+object.name, 'mail' : object.email }", "str(tests) if(email ==''): context = { 'message': \"Please enter Email", "email = request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') message", "phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request, 'Your message has been sent !')", "if object.profession == \"PATIENT\": object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) # appointment(request,email,name) context1={", "= request.POST.get('email') name = request.POST.get('name') phone = request.POST.get('phone') message =", ", name=name, phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request, 'Your message has been", "context = { 'message': \"Password doesn't match\" } return render(request,\"signup.html\",context)", "= str(tests) if(email ==''): context = { 'message': \"Please enter", "been sent !') return render(request,\"diag.html\") # def appointment(request,email,name): # if", "<PASSWORD>.password truepassword = <PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8')", "object.key key1=key1[2:-1] key1 = bytes(key1,'utf-8') f = Fernet(key1) truepassword =", "name=name, phone=phone, tests=tests, date=datetime.today()) diag.save() # messages.success(request, 'Your message has", "date=datetime.today()) diag.save() # messages.success(request, 'Your message has been sent !')", "Contact(email=email , name=name, phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request, 'Your message has", "= <PASSWORD>[2:-1] truepassword = bytes(truepassword,'utf-8') truepassword = f.decrypt(truepassword).decode('utf-8') except: object", "= UserDetails.objects.get(email = email) key1 = object.key key1=key1[2:-1] key1 =", "'doctors':object1 } return render(request,\"index.html\",context1) else: context2={ 'message':'Welcome '+object.name, 'mail' :", "return render(request,\"contact.html\") def book(request): if request.method == \"POST\": email =", "request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address') book = Book(email=email", "Fernet.generate_key() f = Fernet(key) password = bytes(password,'<PASSWORD>') token = f.encrypt(password)", "else: context2={ 'message':'Welcome '+object.name, 'mail' : object.email } return render(request,\"dindex.html\",context2)", "= key, profession=profession, data=data) return redirect(\"/\") else: context = {", "render(request, 'index.html',context) #HttpResponse('This is homepage') def about(request): return render(request, 'about.html')", "messages.success(request, 'Your message has been sent !') return render(request,\"contact.html\") def", "import Diagnostic from datetime import datetime # Create your views", "name=name, phone=phone,address=address,date=datetime.today()) contact.save() # messages.success(request, 'Your message has been sent", "Diagnostic(email=email , name=name, phone=phone, tests=tests, date=datetime.today()) diag.save() # messages.success(request, 'Your", "name = request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address') contact", "def services(request): return render(request, 'services.html') def contact(request): if request.method ==", "'alpha': 'This is sent'} # if request.method=='POST': # pass #", "pass # else: return render(request, 'index.html',context) #HttpResponse('This is homepage') def", "object1=UserDetails.objects.filter(profession=\"DOCTOR\") # name=(object.name) # appointment(request,email,name) context1={ 'message':'Welcome '+object.name, 'mail' :", "name = request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address') book", "diag(request): if request.method == \"POST\": email = request.POST.get('email') name =", "except: object = None if(object==None): context = { 'message': \"Email", "print(key) UserDetails.objects.create(email=email, name=name , password=token, key = key, profession=profession, data=data)", "return render(request, 'about.html') def services(request): return render(request, 'services.html') def contact(request):", "= request.POST.get('name') phone = request.POST.get('phone') message = request.POST.get('message') report =", "elif(password == <PASSWORD>): key = Fernet.generate_key() f = Fernet(key) password", "<PASSWORD>): key = Fernet.generate_key() f = Fernet(key) password = bytes(password,'<PASSWORD>')", "Book from .models import Report from .models import Diagnostic from", "name=name , password=token, key = key, profession=profession, data=data) return redirect(\"/\")", "phone = request.POST.get('phone') address = request.POST.get('address') contact = Contact(email=email ,", "phone=phone, tests=tests, date=datetime.today()) diag.save() # messages.success(request, 'Your message has been", "if request.method == \"POST\": email = request.POST.get('email') name = request.POST.get('name')", "import datetime # Create your views here. def homePage(request): if(request.method", "\"Please enter Email ID\" } return render(request,\"signup.html\",context) elif(password == <PASSWORD>):", "return render(request,\"diag.html\",context) else: diag = Diagnostic(email=email , name=name, phone=phone, tests=tests,", "doesn't match\" } return render(request,\"signup.html\",context) else: return render(request,\"signup.html\",{}) # def", "context={ 'alpha': 'This is sent'} # if request.method=='POST': # pass", "= request.POST.get('address') contact = Contact(email=email , name=name, phone=phone,address=address,date=datetime.today()) contact.save() #", "= Book(email=email , name=name, phone=phone,problem=address,date=datetime.today()) book.save() return render(request,\"book.html\") def report(request):", "homePage(request): if(request.method == 'POST'): email = request.POST.get('email') password = request.POST.get('password')", "= Fernet(key1) truepassword = <PASSWORD>.password truepassword = <PASSWORD>[2:-1] truepassword =", "request.POST.get('name') phone = request.POST.get('phone') address = request.POST.get('address') contact = Contact(email=email", ".models import Diagnostic from datetime import datetime # Create your", "# pass # else: return render(request, 'index.html',context) #HttpResponse('This is homepage')", "return render(request, 'index.html',context) #HttpResponse('This is homepage') def about(request): return render(request,", "object = None if(object==None): context = { 'message': \"Email Does", "render(request,\"signup.html\",context) elif(password == <PASSWORD>): key = Fernet.generate_key() f = Fernet(key)", "f.encrypt(password) key = str(key) print(key) UserDetails.objects.create(email=email, name=name , password=token, key", "request.POST.get('problem') # book = Appoint(problem=problem, email=email, name=name) # book.save() #", "context1={ 'message':'Welcome '+object.name, 'mail' : object.email, 'doctors':object1 } return render(request,\"index.html\",context1)", "} return render(request,\"login.html\",context) elif(password == truepassword): if object.profession == \"PATIENT\":", "from .models import Diagnostic from datetime import datetime # Create", "sent'} # if request.method=='POST': # pass # else: return render(request,", "==''): context = { 'message': \"Please enter Email ID\" }", "def report(request): if request.method == \"POST\": email = request.POST.get('email') name", "'POST'): name = request.POST.get('name') email = request.POST.get('email') password = request.POST.get('password')", "from .models import Book from .models import Report from .models", "def signUpPage(request): if(request.method == 'POST'): name = request.POST.get('name') email =", "request.POST.get('drop1') tests = str(tests) if(email ==''): context = { 'message':", "your views here. def homePage(request): if(request.method == 'POST'): email =", "== 'POST'): email = request.POST.get('email') password = request.POST.get('password') try: object", "'+object.name, 'mail' : object.email, 'doctors':object1 } return render(request,\"index.html\",context1) else: context2={", "datetime # Create your views here. def homePage(request): if(request.method ==" ]
[ "data except Exception as e: raise Exception('Failed to read data", "storage data') def delete(self, options): try: self.adapter.delete(options) except Exception: raise", "options): try: return self.adapter.put(options) except Exception: raise Exception('Failed to write", "= self.adapter.get(options) return data except Exception as e: raise Exception('Failed", "Exception('Failed to listPrefix storage data') def delete(self, options): try: self.adapter.delete(options)", "Exception('Failed to read data from storage' + str(e)) def list(self,", "self.adapter.listPrefix(options) except Exception: raise Exception('Failed to listPrefix storage data') def", "Exception: raise Exception('Failed to list storage data') def listPrefix(self, options):", "__init__(self, adpter): self.adapter = adpter def put(self, options): try: return", "listPrefix storage data') def delete(self, options): try: self.adapter.delete(options) except Exception:", "return self.adapter.put(options) except Exception: raise Exception('Failed to write data to", "str(e)) def list(self, options): try: return self.adapter.list(options) except Exception: raise", "try: return self.adapter.listPrefix(options) except Exception: raise Exception('Failed to listPrefix storage", "+ str(e)) def list(self, options): try: return self.adapter.list(options) except Exception:", "except Exception as e: raise Exception('Failed to read data from", "storage' + str(e)) def list(self, options): try: return self.adapter.list(options) except", "= adpter def put(self, options): try: return self.adapter.put(options) except Exception:", "def get(self, options): try: data = self.adapter.get(options) return data except", "def __init__(self, adpter): self.adapter = adpter def put(self, options): try:", "Exception('Failed to write data to storage') def get(self, options): try:", "Exception: raise Exception('Failed to write data to storage') def get(self,", "data = self.adapter.get(options) return data except Exception as e: raise", "self.adapter.list(options) except Exception: raise Exception('Failed to list storage data') def", "try: self.adapter.delete(options) except Exception: raise Exception('Failed to delete storage data')", "def list(self, options): try: return self.adapter.list(options) except Exception: raise Exception('Failed", "raise Exception('Failed to read data from storage' + str(e)) def", "raise Exception('Failed to list storage data') def listPrefix(self, options): try:", "try: return self.adapter.put(options) except Exception: raise Exception('Failed to write data", "storage') def get(self, options): try: data = self.adapter.get(options) return data", "data') def listPrefix(self, options): try: return self.adapter.listPrefix(options) except Exception: raise", "data') def delete(self, options): try: self.adapter.delete(options) except Exception: raise Exception('Failed", "to write data to storage') def get(self, options): try: data", "delete(self, options): try: self.adapter.delete(options) except Exception: raise Exception('Failed to delete", "to list storage data') def listPrefix(self, options): try: return self.adapter.listPrefix(options)", "as e: raise Exception('Failed to read data from storage' +", "to storage') def get(self, options): try: data = self.adapter.get(options) return", "storage data') def listPrefix(self, options): try: return self.adapter.listPrefix(options) except Exception:", "data to storage') def get(self, options): try: data = self.adapter.get(options)", "except Exception: raise Exception('Failed to list storage data') def listPrefix(self,", "listPrefix(self, options): try: return self.adapter.listPrefix(options) except Exception: raise Exception('Failed to", "Exception('Failed to list storage data') def listPrefix(self, options): try: return", "except Exception: raise Exception('Failed to listPrefix storage data') def delete(self,", "list(self, options): try: return self.adapter.list(options) except Exception: raise Exception('Failed to", "def put(self, options): try: return self.adapter.put(options) except Exception: raise Exception('Failed", "raise Exception('Failed to write data to storage') def get(self, options):", "adpter def put(self, options): try: return self.adapter.put(options) except Exception: raise", "list storage data') def listPrefix(self, options): try: return self.adapter.listPrefix(options) except", "self.adapter.put(options) except Exception: raise Exception('Failed to write data to storage')", "self.adapter = adpter def put(self, options): try: return self.adapter.put(options) except", "to read data from storage' + str(e)) def list(self, options):", "e: raise Exception('Failed to read data from storage' + str(e))", "options): try: return self.adapter.list(options) except Exception: raise Exception('Failed to list", "class BaseStorageManager(object): def __init__(self, adpter): self.adapter = adpter def put(self,", "to listPrefix storage data') def delete(self, options): try: self.adapter.delete(options) except", "data from storage' + str(e)) def list(self, options): try: return", "adpter): self.adapter = adpter def put(self, options): try: return self.adapter.put(options)", "read data from storage' + str(e)) def list(self, options): try:", "try: data = self.adapter.get(options) return data except Exception as e:", "BaseStorageManager(object): def __init__(self, adpter): self.adapter = adpter def put(self, options):", "options): try: self.adapter.delete(options) except Exception: raise Exception('Failed to delete storage", "return self.adapter.list(options) except Exception: raise Exception('Failed to list storage data')", "raise Exception('Failed to listPrefix storage data') def delete(self, options): try:", "options): try: data = self.adapter.get(options) return data except Exception as", "Exception as e: raise Exception('Failed to read data from storage'", "return self.adapter.listPrefix(options) except Exception: raise Exception('Failed to listPrefix storage data')", "options): try: return self.adapter.listPrefix(options) except Exception: raise Exception('Failed to listPrefix", "from storage' + str(e)) def list(self, options): try: return self.adapter.list(options)", "return data except Exception as e: raise Exception('Failed to read", "try: return self.adapter.list(options) except Exception: raise Exception('Failed to list storage", "get(self, options): try: data = self.adapter.get(options) return data except Exception", "Exception: raise Exception('Failed to listPrefix storage data') def delete(self, options):", "except Exception: raise Exception('Failed to write data to storage') def", "put(self, options): try: return self.adapter.put(options) except Exception: raise Exception('Failed to", "write data to storage') def get(self, options): try: data =", "def delete(self, options): try: self.adapter.delete(options) except Exception: raise Exception('Failed to", "def listPrefix(self, options): try: return self.adapter.listPrefix(options) except Exception: raise Exception('Failed", "self.adapter.get(options) return data except Exception as e: raise Exception('Failed to" ]
[ "this is a comment.\\n\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED =", "import Template, Context, TemplateSyntaxError from django.test import TestCase from compressor.conf", "= u\"\"\"{% load compress %}{% compress css %} <link rel=\"stylesheet\"", "MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress", "type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" def listener(sender, **kwargs):", "self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir, 'precompiler.py') python = sys.executable", "{% endcompress %} \"\"\" def listener(sender, **kwargs): pass callback =", "rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid", "MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\"> </script> {% endcompress", "context)) def test_named_compress_tag(self): template = u\"\"\"{% load compress %}{% compress", "<style type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL", "def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler =", "Template(template_string) return t.render(c).strip() class TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED", "test_debug_toggle(self): template = u\"\"\"{% load compress %}{% compress js %}", "script element. >>> script('#this is a comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this", "{% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context)) def", "test_value = \"\\u2014\";</script> {% endcompress %} \"\"\" out = u'<script", "load compress %}{% compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc.js\"", "self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers def test_compress_coffeescript_tag(self): template = u\"\"\"{% load", "= \"value\";</script> {% endcompress %} \"\"\" class MockDebugRequest(object): GET =", "type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress %}", "}}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\"> </script> {% endcompress %}\"\"\"", "from django.test import TestCase from compressor.conf import settings from compressor.signals", "= '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context)) finally:", "u'src=\"%s\" ' % src return out_script[:-1] + u'>%s</script>' % content", "= \"value\";</script> {% endcompress %} \"\"\" out = u'<script type=\"text/javascript\"", "class PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS", "self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def script(content=\"\", src=\"\",", "comment.</script> {% endcompress %}\"\"\" out = script(\"# this is a", "self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED", "compress js %} <script type=\"text/coffeescript\"># this is a comment.</script> {%", "compress %}{% compress js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\">", "endcompress %}\"\"\" out = script(\"# this is a comment.\\n\") self.assertEqual(out,", "<script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> <script src=\"{{ MEDIA_URL }}js/one.js\"></script>", "compress pony %} <script type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render,", "django.template import Template, Context, TemplateSyntaxError from django.test import TestCase from", "<link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px", "setUp(self): self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir,", "css_tag, test_dir def render(template_string, context_dict=None): \"\"\" A shortcut for testing", "scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is a comment</script>' \"\"\" out_script = u'<script", "testing template output. \"\"\" if context_dict is None: context_dict =", "self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS", "u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out, render(template, context))", "script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def script(content=\"\",", "endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self):", "script('#this is a comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is a comment</script>'", "out = script(\"# this is a comment.\\n\") self.assertEqual(out, render(template, self.context))", "= script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{%", "django.test import TestCase from compressor.conf import settings from compressor.signals import", "scripttype if src: out_script += u'src=\"%s\" ' % src return", "self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir, 'precompiler.py')", "kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED", "context_dict=None): \"\"\" A shortcut for testing template output. \"\"\" if", "%} <script src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>", "' % scripttype if src: out_script += u'src=\"%s\" ' %", "render, template, {}) def test_debug_toggle(self): template = u\"\"\"{% load compress", "load compress %}{% compress css %} <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL", "template, {}) def test_debug_toggle(self): template = u\"\"\"{% load compress %}{%", "test_compress_coffeescript_tag(self): template = u\"\"\"{% load compress %}{% compress js %}", "settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED =", "is a comment.\\n') + '\\n' + script('# this too is", "type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress %} \"\"\" out =", "href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\")", "%} <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" def", "a comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template,", "css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_uppercase_rel(self): template = u\"\"\"{% load", "type=\"text/applescript\">#this is a comment</script>' \"\"\" out_script = u'<script ' if", "settings.COMPRESS_ENABLED = self.old_enabled def test_empty_tag(self): template = u\"\"\"{% load compress", "test_uppercase_rel(self): template = u\"\"\"{% load compress %}{% compress css %}", "green;}</style> <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\"", "def test_nonascii_js_tag(self): template = u\"\"\"{% load compress %}{% compress js", "<script type=\"text/coffeescript\"># this is a comment.</script> {% endcompress %}\"\"\" out", "<script type=\"text/coffeescript\"># this is a comment.</script> <script type=\"text/javascript\"># this too", "out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled =", "listener(sender, **kwargs): pass callback = Mock(wraps=listener) post_compress.connect(callback) render(template) args, kwargs", "{ border:5px solid green;}</style> <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\">", "type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render, template, {}) def test_debug_toggle(self):", "%} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p {", "}}js/one.coffee\"> </script> <script src=\"{{ MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL", "+= u'type=\"%s\" ' % scripttype if src: out_script += u'src=\"%s\"", "render(template, self.context)) def test_css_tag(self): template = u\"\"\"{% load compress %}{%", "= settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True self.context = {'MEDIA_URL': settings.COMPRESS_URL} def", "if src: out_script += u'src=\"%s\" ' % src return out_script[:-1]", "a comment.\\n') + '\\n' + script('# this too is a", "out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context)) def test_js_tag(self): template =", "def test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{% load compress %}{% compress js", "%}{% compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\">", "test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template =", "type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out, render(template, context)) def test_named_compress_tag(self):", "settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self):", "type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> {% endcompress %}\"\"\" out =", "pass callback = Mock(wraps=listener) post_compress.connect(callback) render(template) args, kwargs = callback.call_args", "sys.executable settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript', '%s %s'", "\"value\";</script> {% endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>'", "%}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_nonascii_css_tag(self): template", "= {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context = dict(self.context, request=MockDebugRequest()) out = u\"\"\"<script", "\"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context)) def test_js_tag(self): template", "<script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" out =", "self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED", "%}{% compress js %}{% block js %} {% endblock %}{%", "import settings from compressor.signals import post_compress from compressor.tests.base import css_tag,", "border:5px solid green;}</style> <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {%", "template = u\"\"\"{% load compress %}{% compress js %}{% block", "<link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out", "\"\\u2014\";</script> {% endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>'", "request=MockDebugRequest()) out = u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\"", "shortcut for testing template output. \"\"\" if context_dict is None:", "render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled =", "settings.COMPRESS_ENABLED = False try: template = u\"\"\" {% load compress", "settings from compressor.signals import post_compress from compressor.tests.base import css_tag, test_dir", "out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template =", "def test_named_compress_tag(self): template = u\"\"\"{% load compress %}{% compress js", "self.context)) def test_nonascii_js_tag(self): template = u\"\"\"{% load compress %}{% compress", "%}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template", "tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled def test_empty_tag(self): template = u\"\"\"{% load", "<style type=\"text/css\">p { border:5px solid green;}</style> {% endcompress %} \"\"\"", "compress %}{% compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\"", "type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_latin1_js_tag(self): template = u\"\"\"{%", "%} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context))", "from compressor.signals import post_compress from compressor.tests.base import css_tag, test_dir def", "= u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out, render(template,", "endcompress %} \"\"\" class MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context", "TestCase from compressor.conf import settings from compressor.signals import post_compress from", "= callback.call_args context = kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def", "rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid", "{} c = Context(context_dict) t = Template(template_string) return t.render(c).strip() class", "endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED", "script(\"# this is a comment.\\n\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED", "out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_js_tag(self):", "out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context))", "src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {%", "compress js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> {%", "}}js/one.coffee\"> </script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template,", "self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template = u\"\"\"{%", "<script type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render, template, {}) def", "settings.COMPRESS_PRECOMPILERS = self.old_precompilers def test_compress_coffeescript_tag(self): template = u\"\"\"{% load compress", "type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{%", "test_js_tag(self): template = u\"\"\"{% load compress %}{% compress js %}", "out_script += u'type=\"%s\" ' % scripttype if src: out_script +=", "def render(template_string, context_dict=None): \"\"\" A shortcut for testing template output.", "<style type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"stylesheet\" href=\"{{ MEDIA_URL", "out = u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out,", "%}{% compress css %} <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\">", "callback = Mock(wraps=listener) post_compress.connect(callback) render(template) args, kwargs = callback.call_args context", "</script> {% endcompress %}\"\"\" out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"),", "import post_compress from compressor.tests.base import css_tag, test_dir def render(template_string, context_dict=None):", "= css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context)) def test_js_tag(self): template = u\"\"\"{%", "render(template) args, kwargs = callback.call_args context = kwargs['context'] self.assertEqual('foo', context['compressed']['name'])", "def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template", "= (script('# this is a comment.\\n') + '\\n' + script('#", "out_script += u'src=\"%s\" ' % src return out_script[:-1] + u'>%s</script>'", "endblock %}{% endcompress %}\"\"\" self.assertEqual(u'', render(template, self.context)) def test_css_tag(self): template", "{% endcompress %}\"\"\" out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),])", "compress %}{% compress js %} <script src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script>", "{% endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out,", "render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled =", "args, kwargs = callback.call_args context = kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class", "(python, precompiler)), ) self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED", "MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context = dict(self.context, request=MockDebugRequest()) out", "compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style", "= {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled def test_empty_tag(self):", "compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script", "= self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False", "load compress %}{% compress js %} <script type=\"text/coffeescript\"># this is", "%s' % (python, precompiler)), ) self.context = {'MEDIA_URL': settings.COMPRESS_URL} def", "solid green;}</style> {% endcompress %} \"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out,", "= Mock(wraps=listener) post_compress.connect(callback) render(template) args, kwargs = callback.call_args context =", "self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled", "{% endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def", "\"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context)) def", "= self.old_enabled def test_empty_tag(self): template = u\"\"\"{% load compress %}{%", "<script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> {% endcompress %}\"\"\" out", "'\\n' + script('# this too is a comment.')) self.assertEqual(out, render(template,", "u\"\"\"{% load compress %}{% compress css %} <link rel=\"StyleSheet\" href=\"{{", "%}\"\"\" self.assertRaises(TemplateSyntaxError, render, template, {}) def test_debug_toggle(self): template = u\"\"\"{%", "type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" class", "{% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context)) def", "self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True self.context = {'MEDIA_URL': settings.COMPRESS_URL}", "def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers def test_compress_coffeescript_tag(self):", "= True self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED =", "this too is a comment.')) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED", "self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template = u\"\"\"", "callback.call_args context = kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def setUp(self):", "}}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\"", "is a comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out,", "}}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress %}", "u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_js_tag(self): template =", "script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\" returns a unicode text html script", "def test_css_tag(self): template = u\"\"\"{% load compress %}{% compress css", "too is a comment.')) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED =", "import css_tag, test_dir def render(template_string, context_dict=None): \"\"\" A shortcut for", "= u\"\"\"{% load compress %}{% compress pony %} <script type=\"pony/application\">unicorn</script>", "import Mock from django.template import Template, Context, TemplateSyntaxError from django.test", "type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_js_tag(self): template = u\"\"\"{%", "out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_compress_tag_with_illegal_arguments(self):", "type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\"", "scripttype: out_script += u'type=\"%s\" ' % scripttype if src: out_script", "from django.template import Template, Context, TemplateSyntaxError from django.test import TestCase", "css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p", "is a comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out,", "type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress %} \"\"\"", "test_named_compress_tag(self): template = u\"\"\"{% load compress %}{% compress js inline", "def listener(sender, **kwargs): pass callback = Mock(wraps=listener) post_compress.connect(callback) render(template) args,", "template output. \"\"\" if context_dict is None: context_dict = {}", "self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers", "= u\"\"\"{% load compress %}{% compress css %} <link rel=\"StyleSheet\"", "= u'<script ' if scripttype: out_script += u'type=\"%s\" ' %", "settings.COMPRESS_ENABLED = True self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED", "u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_compress_tag_with_illegal_arguments(self): template =", "self.assertEqual(out, render(template, self.context)) def test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{% load compress", "compress %}{% compress js inline foo %} <script type=\"text/javascript\">obj.value =", "\"\"\" out_script = u'<script ' if scripttype: out_script += u'type=\"%s\"", "+ script('# this too is a comment.')) self.assertEqual(out, render(template, self.context))", "type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" out = u'<script", "src: out_script += u'src=\"%s\" ' % src return out_script[:-1] +", "type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" class MockDebugRequest(object): GET", "\"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context)) def", "this too is a comment.</script> {% endcompress %}\"\"\" out =", "Mock(wraps=listener) post_compress.connect(callback) render(template) args, kwargs = callback.call_args context = kwargs['context']", "= \"value\";</script> {% endcompress %} \"\"\" def listener(sender, **kwargs): pass", "js %} <script type=\"text/coffeescript\"># this is a comment.</script> {% endcompress", "%} \"\"\" def listener(sender, **kwargs): pass callback = Mock(wraps=listener) post_compress.connect(callback)", "type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out, render(template, context)) def test_named_compress_tag(self): template =", "src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out, render(template, context)) def", "('text/coffeescript', '%s %s' % (python, precompiler)), ) self.context = {'MEDIA_URL':", "= self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False", "comment</script>' \"\"\" out_script = u'<script ' if scripttype: out_script +=", "u\"\"\"{% load compress %}{% compress js %} <script src=\"{{ MEDIA_URL", "context_dict is None: context_dict = {} c = Context(context_dict) t", "os import sys from mock import Mock from django.template import", "= settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir, 'precompiler.py') python", "%}{% compress pony %} <script type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError,", "js %} <script type=\"text/coffeescript\"># this is a comment.</script> <script type=\"text/javascript\">#", "render(template, context)) def test_named_compress_tag(self): template = u\"\"\"{% load compress %}{%", "endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template,", "self.assertEqual(out, render(template, self.context)) def test_nonascii_latin1_js_tag(self): template = u\"\"\"{% load compress", "settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers def", "context_dict = {} c = Context(context_dict) t = Template(template_string) return", "</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context))", "a comment.\\n\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def", "test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{% load compress %}{% compress pony %}", "compress js %} <script type=\"text/coffeescript\"># this is a comment.</script> <script", "a comment.</script> <script type=\"text/javascript\"># this too is a comment.</script> {%", "def test_js_tag(self): template = u\"\"\"{% load compress %}{% compress js", "python = sys.executable settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript',", "def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True self.context =", "compressor.conf import settings from compressor.signals import post_compress from compressor.tests.base import", "type=\"text/css\">p { border:5px solid green;}</style> {% endcompress %} \"\"\" out", "}}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"StyleSheet\"", "is a comment.\\n\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled", "from mock import Mock from django.template import Template, Context, TemplateSyntaxError", "}}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress", "MEDIA_URL }}js/one.coffee\"> </script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out,", "self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try:", "self.assertEqual(out, render(template, context)) def test_named_compress_tag(self): template = u\"\"\"{% load compress", "%} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context))", "compress %}{% compress pony %} <script type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\"", "type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\"> </script> {% endcompress %}\"\"\" out =", "template = u\"\"\"{% load compress %}{% compress js %} <script", "%}{% compress js %} <script src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script", "import with_statement import os import sys from mock import Mock", "= {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS =", "PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler", "self.old_enabled def script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\" returns a unicode text", "'\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED", "c = Context(context_dict) t = Template(template_string) return t.render(c).strip() class TemplatetagTestCase(TestCase):", "self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\"", "MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> {%", "u'type=\"%s\" ' % scripttype if src: out_script += u'src=\"%s\" '", "post_compress.connect(callback) render(template) args, kwargs = callback.call_args context = kwargs['context'] self.assertEqual('foo',", "= {} c = Context(context_dict) t = Template(template_string) return t.render(c).strip()", "= settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template = u\"\"\" {%", "% (python, precompiler)), ) self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self):", "def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled def test_empty_tag(self): template = u\"\"\"{%", "a comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template,", "comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context))", "src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\")", "TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True self.context", "returns a unicode text html script element. >>> script('#this is", "%} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p {", "is a comment.</script> <script type=\"text/javascript\"># this too is a comment.</script>", "self.assertEqual(out, render(template, self.context)) def test_nonascii_js_tag(self): template = u\"\"\"{% load compress", "u\"\"\"{% load compress %}{% compress css %} <link rel=\"stylesheet\" href=\"{{", "render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def script(content=\"\", src=\"\", scripttype=\"text/javascript\"):", "%} \"\"\" class MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context =", "= script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def", "__future__ import with_statement import os import sys from mock import", "self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try:", "src=\"{{ MEDIA_URL }}js/one.js\"> </script> {% endcompress %}\"\"\" out = '\\n'.join([", "self.assertEqual(out, render(template, self.context)) def test_uppercase_rel(self): template = u\"\"\"{% load compress", "self.assertEqual(out, render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED =", "render(template, self.context)) def test_nonascii_latin1_js_tag(self): template = u\"\"\"{% load compress %}{%", "a comment.</script> {% endcompress %}\"\"\" out = script(\"# this is", "settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript', '%s %s' % (python, precompiler)), )", "script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_multiple_file_order_conserved(self):", "' if scripttype: out_script += u'type=\"%s\" ' % scripttype if", "dict(self.context, request=MockDebugRequest()) out = u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value =", "inline foo %} <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %}", "self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{% load compress %}{% compress", "%} <script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value", "= u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_compress_tag_with_illegal_arguments(self): template", "'precompiler.py') python = sys.executable settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS = (", "%}\"\"\" out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template,", "{ border:5px solid green;}</style> {% endcompress %} \"\"\" out =", "settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript', '%s %s' %", "<link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out", "GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context = dict(self.context, request=MockDebugRequest()) out =", "compress %}{% compress js %} <script type=\"text/coffeescript\"># this is a", "%} <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p {", "compress %}{% compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\"", "%}\"\"\" out = script(\"# this is a comment.\\n\") self.assertEqual(out, render(template,", "endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template,", "\"value\";</script>\"\"\" self.assertEqual(out, render(template, context)) def test_named_compress_tag(self): template = u\"\"\"{% load", "setUp(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True self.context = {'MEDIA_URL':", "border:5px solid green;}</style> <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {%", "self.context)) def test_nonascii_latin1_js_tag(self): template = u\"\"\"{% load compress %}{% compress", "pony %} <script type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render, template,", "def test_uppercase_rel(self): template = u\"\"\"{% load compress %}{% compress css", "endcompress %}\"\"\" out = (script('# this is a comment.\\n') +", "load compress %}{% compress js %}{% block js %} {%", "comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context))", "= Context(context_dict) t = Template(template_string) return t.render(c).strip() class TemplatetagTestCase(TestCase): def", "u\"\"\"{% load compress %}{% compress js %} <script type=\"text/coffeescript\"># this", "finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED", "test_dir def render(template_string, context_dict=None): \"\"\" A shortcut for testing template", "settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED =", "compress %}{% compress js %}{% block js %} {% endblock", "context = dict(self.context, request=MockDebugRequest()) out = u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script", "css %} <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p", "js %} <script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var", "load compress %}{% compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\"", "is a comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is a comment</script>' \"\"\"", "MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {%", "too is a comment.</script> {% endcompress %}\"\"\" out = (script('#", "type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> <script src=\"{{ MEDIA_URL }}js/one.js\"></script> <script", "foo %} <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\"", "js %} {% endblock %}{% endcompress %}\"\"\" self.assertEqual(u'', render(template, self.context))", "context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED self.old_precompilers =", "{% endcompress %} \"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context))", "{% load compress %}{% compress js %} <script type=\"text/coffeescript\" src=\"{{", "out_script = u'<script ' if scripttype: out_script += u'type=\"%s\" '", "render(template, self.context)) def test_js_tag(self): template = u\"\"\"{% load compress %}{%", "self.assertEqual(out, render(template, self.context)) def test_nonascii_css_tag(self): template = u\"\"\"{% load compress", "self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED", "finally: settings.COMPRESS_ENABLED = self.old_enabled def script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\" returns", "self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try:", "load compress %}{% compress pony %} <script type=\"pony/application\">unicorn</script> {% endcompress", "= False try: template = u\"\"\"{% load compress %}{% compress", "src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_js_tag(self): template = u\"\"\"{% load", "script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED", "src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script>", "= u\"\"\"{% load compress %}{% compress js inline foo %}", "= \"value\";</script>\"\"\" self.assertEqual(out, render(template, context)) def test_named_compress_tag(self): template = u\"\"\"{%", "compress js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> <script", "%}{% compress js %} <script type=\"text/coffeescript\"># this is a comment.</script>", "from __future__ import with_statement import os import sys from mock", "%}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled", "load compress %}{% compress js %} <script src=\"{{ MEDIA_URL }}js/one.js\"", "test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{% load compress %}{% compress js %}", "= self.old_precompilers def test_compress_coffeescript_tag(self): template = u\"\"\"{% load compress %}{%", "self.assertRaises(TemplateSyntaxError, render, template, {}) def test_debug_toggle(self): template = u\"\"\"{% load", "<script src=\"{{ MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\"> </script>", "finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED", "charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress %} \"\"\"", "a comment.</script> {% endcompress %}\"\"\" out = (script('# this is", "= u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_js_tag(self): template", "render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled =", "compress %}{% compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script>", "'<script type=\"text/applescript\">#this is a comment</script>' \"\"\" out_script = u'<script '", "u'<script ' if scripttype: out_script += u'type=\"%s\" ' % scripttype", "def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template", "rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid", "src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{% load", "endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self):", "a comment</script>' \"\"\" out_script = u'<script ' if scripttype: out_script", "A shortcut for testing template output. \"\"\" if context_dict is", "class MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context = dict(self.context, request=MockDebugRequest())", "MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out,", "this is a comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/e920d58f166d.js\")", "{ border:5px solid green;}</style> <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\">", "settings.COMPRESS_ENABLED = False try: template = u\"\"\"{% load compress %}{%", "<script type=\"text/javascript\">obj.value = \"value\";</script>\"\"\" self.assertEqual(out, render(template, context)) def test_named_compress_tag(self): template", "test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template =", "for testing template output. \"\"\" if context_dict is None: context_dict", "self.old_precompilers def test_compress_coffeescript_tag(self): template = u\"\"\"{% load compress %}{% compress", "solid green;}</style> <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress", "load compress %}{% compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL", "{% endcompress %}\"\"\" out = (script('# this is a comment.\\n')", "finally: settings.COMPRESS_ENABLED = self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED", "= css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_uppercase_rel(self): template = u\"\"\"{%", "}}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template,", "compress %}{% compress css %} <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\"", "**kwargs): pass callback = Mock(wraps=listener) post_compress.connect(callback) render(template) args, kwargs =", "render(template, self.context)) def test_uppercase_rel(self): template = u\"\"\"{% load compress %}{%", "sys from mock import Mock from django.template import Template, Context,", "\"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/066cd253eada.js\"></script>' self.assertEqual(out, render(template, self.context)) def", "test_nonascii_latin1_js_tag(self): template = u\"\"\"{% load compress %}{% compress js %}", "green;}</style> <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\"", "{% endcompress %} \"\"\" class MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'}", "endcompress %} \"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context)) def", "href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style>", "js %} <script src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value", "<script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" class MockDebugRequest(object):", "%} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> <script src=\"{{ MEDIA_URL", "u\"\"\"{% load compress %}{% compress pony %} <script type=\"pony/application\">unicorn</script> {%", "{% endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out,", "endcompress %}\"\"\" out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out,", "type=\"text/css\"> {% endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context))", "= script(src=\"/media/CACHE/js/ef6b32a54575.js\") self.assertEqual(out, render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED", "= u\"\"\"{% load compress %}{% compress js %} <script type=\"text/coffeescript\">#", "t = Template(template_string) return t.render(c).strip() class TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled", "<script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" def listener(sender,", "is None: context_dict = {} c = Context(context_dict) t =", "self.assertEqual(out, render(template, self.context)) def test_js_tag(self): template = u\"\"\"{% load compress", "def test_nonascii_latin1_js_tag(self): template = u\"\"\"{% load compress %}{% compress js", "% scripttype if src: out_script += u'src=\"%s\" ' % src", "out = (script('# this is a comment.\\n') + '\\n' +", "self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled", "settings.COMPRESS_ENABLED = self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED =", "self.context)) def test_js_tag(self): template = u\"\"\"{% load compress %}{% compress", "%}{% block js %} {% endblock %}{% endcompress %}\"\"\" self.assertEqual(u'',", "from compressor.conf import settings from compressor.signals import post_compress from compressor.tests.base", "self.context)) def test_nonascii_css_tag(self): template = u\"\"\"{% load compress %}{% compress", "%}{% compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script", "compress js inline foo %} <script type=\"text/javascript\">obj.value = \"value\";</script> {%", "from compressor.tests.base import css_tag, test_dir def render(template_string, context_dict=None): \"\"\" A", "test_empty_tag(self): template = u\"\"\"{% load compress %}{% compress js %}{%", "%}{% endcompress %}\"\"\" self.assertEqual(u'', render(template, self.context)) def test_css_tag(self): template =", ">>> script('#this is a comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is a", "%}{% compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\">", "try: template = u\"\"\"{% load compress %}{% compress js %}", "this is a comment.</script> {% endcompress %}\"\"\" out = script(\"#", "endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template,", "output. \"\"\" if context_dict is None: context_dict = {} c", "js %}{% block js %} {% endblock %}{% endcompress %}\"\"\"", "out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled", "compressor.signals import post_compress from compressor.tests.base import css_tag, test_dir def render(template_string,", "= False try: template = u\"\"\" {% load compress %}{%", "}}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> {% endcompress", "{% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render, template, {}) def test_debug_toggle(self): template", "src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def", "<link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px", "compressor.tests.base import css_tag, test_dir def render(template_string, context_dict=None): \"\"\" A shortcut", "is a comment.</script> {% endcompress %}\"\"\" out = script(\"# this", "test_nonascii_js_tag(self): template = u\"\"\"{% load compress %}{% compress js %}", "\"\"\" class MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE: 'true'} context = dict(self.context,", "import os import sys from mock import Mock from django.template", "endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render, template, {}) def test_debug_toggle(self): template =", "= self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers def test_compress_coffeescript_tag(self): template = u\"\"\"{%", "= sys.executable settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript', '%s", "js inline foo %} <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress", "\"\"\" def listener(sender, **kwargs): pass callback = Mock(wraps=listener) post_compress.connect(callback) render(template)", "return t.render(c).strip() class TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED", "try: template = u\"\"\" {% load compress %}{% compress js", "\"\"\" if context_dict is None: context_dict = {} c =", "( ('text/coffeescript', '%s %s' % (python, precompiler)), ) self.context =", "src=\"\", scripttype=\"text/javascript\"): \"\"\" returns a unicode text html script element.", "self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled def", "= Template(template_string) return t.render(c).strip() class TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled =", "None: context_dict = {} c = Context(context_dict) t = Template(template_string)", "MEDIA_URL }}js/one.coffee\"> </script> <script src=\"{{ MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{", "border:5px solid green;}</style> {% endcompress %} \"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\")", "{% endcompress %}\"\"\" out = script(\"# this is a comment.\\n\")", "type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %} \"\"\" out", "+ '\\n' + script('# this too is a comment.')) self.assertEqual(out,", "load compress %}{% compress js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL", "import sys from mock import Mock from django.template import Template,", "css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context)) def test_js_tag(self): template = u\"\"\"{% load", "if context_dict is None: context_dict = {} c = Context(context_dict)", "False try: template = u\"\"\"{% load compress %}{% compress js", "this is a comment.\\n') + '\\n' + script('# this too", "= css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_nonascii_css_tag(self): template = u\"\"\"{%", "%} <script type=\"pony/application\">unicorn</script> {% endcompress %}\"\"\" self.assertRaises(TemplateSyntaxError, render, template, {})", "True settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript', '%s %s' % (python, precompiler)),", "self.old_enabled def test_empty_tag(self): template = u\"\"\"{% load compress %}{% compress", "%}{% compress js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script>", "%}\"\"\" self.assertEqual(u'', render(template, self.context)) def test_css_tag(self): template = u\"\"\"{% load", "%}{% compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script>", "precompiler = os.path.join(test_dir, 'precompiler.py') python = sys.executable settings.COMPRESS_ENABLED = True", "class TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = True", "self.assertEqual(out, render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{% load compress", "src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> <script src=\"{{ MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\"", "= self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False", "= self.old_enabled def script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\" returns a unicode", "comment.\\n\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_file_tag_compress_enabled_is_false(self):", "%} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context))", "= dict(self.context, request=MockDebugRequest()) out = u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value", "type=\"text/coffeescript\"># this is a comment.</script> {% endcompress %}\"\"\" out =", "= True settings.COMPRESS_PRECOMPILERS = ( ('text/coffeescript', '%s %s' % (python,", "}}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"stylesheet\"", "= os.path.join(test_dir, 'precompiler.py') python = sys.executable settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS", "\"value\";</script> {% endcompress %} \"\"\" class MockDebugRequest(object): GET = {settings.COMPRESS_DEBUG_TOGGLE:", "with_statement import os import sys from mock import Mock from", "render(template, self.context)) def test_nonascii_js_tag(self): template = u\"\"\"{% load compress %}{%", "html script element. >>> script('#this is a comment', scripttype=\"text/applescript\") '<script", "endcompress %} \"\"\" def listener(sender, **kwargs): pass callback = Mock(wraps=listener)", "a unicode text html script element. >>> script('#this is a", "render(template, self.context)) def test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{% load compress %}{%", "= kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled =", "mock import Mock from django.template import Template, Context, TemplateSyntaxError from", "test_css_tag(self): template = u\"\"\"{% load compress %}{% compress css %}", "render(template, self.context)) def test_nonascii_css_tag(self): template = u\"\"\"{% load compress %}{%", "settings.COMPRESS_ENABLED self.old_precompilers = settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir, 'precompiler.py') python =", "precompiler)), ) self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED =", "unicode text html script element. >>> script('#this is a comment',", "script(src=\"/media/CACHE/js/e920d58f166d.js\") self.assertEqual(out, render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{% load", "script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled", "'%s %s' % (python, precompiler)), ) self.context = {'MEDIA_URL': settings.COMPRESS_URL}", "a comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is a comment</script>' \"\"\" out_script", "def test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template", "= settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template = u\"\"\"{% load", "block js %} {% endblock %}{% endcompress %}\"\"\" self.assertEqual(u'', render(template,", "%}{% compress js inline foo %} <script type=\"text/javascript\">obj.value = \"value\";</script>", "endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_uppercase_rel(self):", "green;}</style> {% endcompress %} \"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template,", "is a comment</script>' \"\"\" out_script = u'<script ' if scripttype:", "+= u'src=\"%s\" ' % src return out_script[:-1] + u'>%s</script>' %", "= u\"\"\" {% load compress %}{% compress js %} <script", "{'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled def test_empty_tag(self): template", "script('# this too is a comment.')) self.assertEqual(out, render(template, self.context)) finally:", "settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir, 'precompiler.py') python = sys.executable settings.COMPRESS_ENABLED =", "type=\"text/javascript\"># this too is a comment.</script> {% endcompress %}\"\"\" out", "Context, TemplateSyntaxError from django.test import TestCase from compressor.conf import settings", "this is a comment.</script> <script type=\"text/javascript\"># this too is a", "settings.COMPRESS_ENABLED = self.old_enabled def script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\" returns a", "def test_nonascii_css_tag(self): template = u\"\"\"{% load compress %}{% compress css", "rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out =", "\"value\";</script> {% endcompress %} \"\"\" def listener(sender, **kwargs): pass callback", "<script src=\"{{ MEDIA_URL }}js/nonasc-latin1.js\" type=\"text/javascript\" charset=\"latin-1\"></script> <script type=\"text/javascript\">var test_value =", "%} <script type=\"text/coffeescript\"># this is a comment.</script> <script type=\"text/javascript\"># this", "= u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_latin1_js_tag(self): template", "type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\"", "render(template, self.context)) def test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False", "too is a comment.</script> {% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/ef6b32a54575.js\")", "settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template = u\"\"\"{% load compress", "MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress %}", "solid green;}</style> <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress", "u\"\"\" {% load compress %}{% compress js %} <script type=\"text/coffeescript\"", "endcompress %}\"\"\" self.assertEqual(u'', render(template, self.context)) def test_css_tag(self): template = u\"\"\"{%", "Template, Context, TemplateSyntaxError from django.test import TestCase from compressor.conf import", "css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\" type=\"text/css\"> <style type=\"text/css\">p", "self.context)) def test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{% load compress %}{% compress", "render(template, self.context)) def test_compress_coffeescript_tag_and_javascript_tag(self): template = u\"\"\"{% load compress %}{%", "element. >>> script('#this is a comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is", "%}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_uppercase_rel(self): template", "<script src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script> {%", "{'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers", ") self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled", "}}js/one.js\"> </script> {% endcompress %}\"\"\" out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\",", "\"\\u2014\";</script> {% endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>'", "render(template_string, context_dict=None): \"\"\" A shortcut for testing template output. \"\"\"", "import TestCase from compressor.conf import settings from compressor.signals import post_compress", "template = u\"\"\"{% load compress %}{% compress js inline foo", "def test_compress_coffeescript_tag(self): template = u\"\"\"{% load compress %}{% compress js", "kwargs = callback.call_args context = kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase):", "u\"\"\"{% load compress %}{% compress js %}{% block js %}", "out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_nonascii_css_tag(self): template =", "comment.</script> {% endcompress %}\"\"\" out = (script('# this is a", "type=\"text/coffeescript\"># this is a comment.</script> <script type=\"text/javascript\"># this too is", "u\"\"\"{% load compress %}{% compress js inline foo %} <script", "<script type=\"text/javascript\">var test_value = \"\\u2014\";</script> {% endcompress %} \"\"\" out", "comment.</script> <script type=\"text/javascript\"># this too is a comment.</script> {% endcompress", "test_coffeescript_and_js_tag_with_compress_enabled_equals_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template =", "t.render(c).strip() class TemplatetagTestCase(TestCase): def setUp(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED =", "Context(context_dict) t = Template(template_string) return t.render(c).strip() class TemplatetagTestCase(TestCase): def setUp(self):", "%} \"\"\" out = css_tag(\"/media/CACHE/css/799f6defe43c.css\") self.assertEqual(out, render(template, self.context)) def test_js_tag(self):", "= u\"\"\"{% load compress %}{% compress js %}{% block js", "out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_latin1_js_tag(self):", "<script src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value = \"\\u2014\";</script>", "MEDIA_URL }}js/one.js\"> </script> {% endcompress %}\"\"\" out = '\\n'.join([ script(src=\"/media/CACHE/js/one.95cfb869eead.js\"),", "type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"stylesheet\" href=\"{{", "{settings.COMPRESS_DEBUG_TOGGLE: 'true'} context = dict(self.context, request=MockDebugRequest()) out = u\"\"\"<script src=\"/media/js/one.js\"", "type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> <link rel=\"StyleSheet\" href=\"{{", "a comment.')) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def", "</script> <script src=\"{{ MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\">", "js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> {% endcompress", "{% endcompress %}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context)) finally:", "compress js %}{% block js %} {% endblock %}{% endcompress", "comment', scripttype=\"text/applescript\") '<script type=\"text/applescript\">#this is a comment</script>' \"\"\" out_script =", "type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> {% endcompress %}", "TemplateSyntaxError from django.test import TestCase from compressor.conf import settings from", "True self.context = {'MEDIA_URL': settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled", "os.path.join(test_dir, 'precompiler.py') python = sys.executable settings.COMPRESS_ENABLED = True settings.COMPRESS_PRECOMPILERS =", "settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers def test_compress_coffeescript_tag(self): template =", "script(src=\"/media/CACHE/js/one.95cfb869eead.js\"), script(scripttype=\"\", src=\"/media/js/one.js\"), script(src=\"/media/CACHE/js/one.81a2cd965815.js\"),]) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED =", "def test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template", "tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled settings.COMPRESS_PRECOMPILERS = self.old_precompilers def test_compress_coffeescript_tag(self): template", "settings.COMPRESS_URL} def tearDown(self): settings.COMPRESS_ENABLED = self.old_enabled def test_empty_tag(self): template =", "src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value = \"value\";</script> {% endcompress", "def test_compress_tag_with_illegal_arguments(self): template = u\"\"\"{% load compress %}{% compress pony", "Mock from django.template import Template, Context, TemplateSyntaxError from django.test import", "is a comment.')) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled", "{% endcompress %} \"\"\" out = u'<script type=\"text/javascript\" src=\"/media/CACHE/js/be9e078b5ca7.js\"></script>' self.assertEqual(out,", "<reponame>bigmlcom/django_compressor from __future__ import with_statement import os import sys from", "src=\"{{ MEDIA_URL }}js/one.js\"></script> <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\"> </script> {%", "compress %}{% compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/nonasc.css\"", "%} <script type=\"text/coffeescript\"># this is a comment.</script> {% endcompress %}\"\"\"", "href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style>", "if scripttype: out_script += u'type=\"%s\" ' % scripttype if src:", "{}) def test_debug_toggle(self): template = u\"\"\"{% load compress %}{% compress", "self.context)) def test_uppercase_rel(self): template = u\"\"\"{% load compress %}{% compress", "js %} <script src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value =", "compress js %} <script src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var", "<script type=\"text/javascript\"># this too is a comment.</script> {% endcompress %}\"\"\"", "%} {% endblock %}{% endcompress %}\"\"\" self.assertEqual(u'', render(template, self.context)) def", "comment.\\n') + '\\n' + script('# this too is a comment.'))", "self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self): self.old_enabled", "(script('# this is a comment.\\n') + '\\n' + script('# this", "settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template = u\"\"\" {% load", "js %} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> <script src=\"{{", "endcompress %}\"\"\" out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_nonascii_css_tag(self):", "False try: template = u\"\"\" {% load compress %}{% compress", "def test_empty_tag(self): template = u\"\"\"{% load compress %}{% compress js", "template = u\"\"\" {% load compress %}{% compress js %}", "test_nonascii_css_tag(self): template = u\"\"\"{% load compress %}{% compress css %}", "{% endblock %}{% endcompress %}\"\"\" self.assertEqual(u'', render(template, self.context)) def test_css_tag(self):", "= u\"\"\"{% load compress %}{% compress js %} <script src=\"{{", "'true'} context = dict(self.context, request=MockDebugRequest()) out = u\"\"\"<script src=\"/media/js/one.js\" type=\"text/javascript\"></script>", "= \"\\u2014\";</script> {% endcompress %} \"\"\" out = u'<script type=\"text/javascript\"", "rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/two.css\" type=\"text/css\"> {% endcompress %}\"\"\" out =", "<script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.js\"> </script> {% endcompress %}\"\"\" out", "compress css %} <link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style", "test_compress_coffeescript_file_tag_compress_enabled_is_false(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try: template =", "out = css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_uppercase_rel(self): template =", "css_tag(\"/media/CACHE/css/e41ba2cc6982.css\") self.assertEqual(out, render(template, self.context)) def test_nonascii_css_tag(self): template = u\"\"\"{% load", "compress js %} <script src=\"{{ MEDIA_URL }}js/one.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">obj.value", "def script(content=\"\", src=\"\", scripttype=\"text/javascript\"): \"\"\" returns a unicode text html", "text html script element. >>> script('#this is a comment', scripttype=\"text/applescript\")", "comment.')) self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED = self.old_enabled def test_compress_coffeescript_tag_compress_enabled_is_false(self):", "= script(\"# this is a comment.\\n\") self.assertEqual(out, render(template, self.context)) finally:", "self.old_enabled def test_multiple_file_order_conserved(self): self.old_enabled = settings.COMPRESS_ENABLED settings.COMPRESS_ENABLED = False try:", "self.context)) def test_css_tag(self): template = u\"\"\"{% load compress %}{% compress", "scripttype=\"text/javascript\"): \"\"\" returns a unicode text html script element. >>>", "load compress %}{% compress js inline foo %} <script type=\"text/javascript\">obj.value", "def test_debug_toggle(self): template = u\"\"\"{% load compress %}{% compress js", "context = kwargs['context'] self.assertEqual('foo', context['compressed']['name']) class PrecompilerTemplatetagTestCase(TestCase): def setUp(self): self.old_enabled", "\"\"\" A shortcut for testing template output. \"\"\" if context_dict", "self.assertEqual(u'', render(template, self.context)) def test_css_tag(self): template = u\"\"\"{% load compress", "template = u\"\"\"{% load compress %}{% compress css %} <link", "MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px solid green;}</style> <link", "%}\"\"\" out = (script('# this is a comment.\\n') + '\\n'", "= settings.COMPRESS_PRECOMPILERS precompiler = os.path.join(test_dir, 'precompiler.py') python = sys.executable settings.COMPRESS_ENABLED", "%} <script type=\"text/coffeescript\" src=\"{{ MEDIA_URL }}js/one.coffee\"> </script> {% endcompress %}\"\"\"", "%}\"\"\" out = script(src=\"/media/CACHE/js/one.95cfb869eead.js\") self.assertEqual(out, render(template, self.context)) finally: settings.COMPRESS_ENABLED =", "<link rel=\"stylesheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style type=\"text/css\">p { border:5px", "post_compress from compressor.tests.base import css_tag, test_dir def render(template_string, context_dict=None): \"\"\"", "src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_latin1_js_tag(self): template = u\"\"\"{% load", "template = u\"\"\"{% load compress %}{% compress pony %} <script", "compress css %} <link rel=\"StyleSheet\" href=\"{{ MEDIA_URL }}css/one.css\" type=\"text/css\"> <style", "= ( ('text/coffeescript', '%s %s' % (python, precompiler)), ) self.context", "u'<script type=\"text/javascript\" src=\"/media/CACHE/js/e214fe629b28.js\"></script>' self.assertEqual(out, render(template, self.context)) def test_nonascii_latin1_js_tag(self): template =", "\"\"\" returns a unicode text html script element. >>> script('#this", "is a comment.</script> {% endcompress %}\"\"\" out = (script('# this", "%} <script src=\"{{ MEDIA_URL }}js/nonasc.js\" type=\"text/javascript\"></script> <script type=\"text/javascript\">var test_value =" ]
[ "relevant addresses fit in the 32-bit address space.\" % self.__class__.__name__)", "self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr + self.addend", "(self.check_zero_extend and val >> 32 != 0 or self.check_sign_extend and", "self.resolvedby.rebased_addr + self.addend - self.rebased_addr class GenericJumpslotReloc(Relocation): @property def value(self):", "((val >> 31) & 1) == 1 else 0): raise", "arch_bits >= 32 # 16-bit makes no sense here val", "+ self.addend def resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None) return True class", "import struct import logging l = logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property", "a 32-bit field regardless of the architecture's address word length.", "struct import logging l = logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property def", "value(self): return self.resolvedby.rebased_addr + self.addend class GenericPCRelativeAddendReloc(Relocation): @property def value(self):", "False arch_bits = self.owner_obj.arch.bits assert arch_bits >= 32 # 16-bit", "% (2**arch_bits) # we must truncate it to native range", "consider making\" \" relevant addresses fit in the 32-bit address", "import CLEOperationError from . import Relocation import struct import logging", "the 32-bit address space.\" % self.__class__.__name__) by = struct.pack(self.owner_obj.arch.struct_fmt(32), val", "its original when zero-extended check_zero_extend = False # If True,", "self.check_sign_extend and val >> 32 != ((1 << (arch_bits -", "if self.owner_obj.mapped_base == 0: self.resolve(None) return True # don't touch", "32 # 16-bit makes no sense here val = self.value", "0 or self.check_sign_extend and val >> 32 != ((1 <<", ">> 32 != ((1 << (arch_bits - 32)) - 1)", "class GenericJumpslotReloc(Relocation): @property def value(self): if self.is_rela: return self.resolvedby.rebased_addr +", "field regardless of the architecture's address word length. \"\"\" #", ">> 32 != 0 or self.check_sign_extend and val >> 32", "self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return True class RelocTruncate32Mixin(object): \"\"\" A mix-in", "= self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta == 0: self.resolve(None) return", "pass class MipsLocalReloc(Relocation): def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument", "to its original when zero-extended check_zero_extend = False # If", "False # If True, 32-bit truncated value must equal to", "GenericCopyReloc(Relocation): @property def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass class", "return self.resolvedby.rebased_addr + self.addend else: return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property", "and val >> 32 != 0 or self.check_sign_extend and val", "return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property def value(self): return self.owner_obj.mapped_base +", "def value(self): return self.resolvedby.rebased_addr + self.addend class GenericPCRelativeAddendReloc(Relocation): @property def", "def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation): def", "self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property def value(self): return self.owner_obj.mapped_base + self.addend", "16-bit makes no sense here val = self.value % (2**arch_bits)", "from ...errors import CLEOperationError from . import Relocation import struct", "to native range first if (self.check_zero_extend and val >> 32", "range first if (self.check_zero_extend and val >> 32 != 0", "== 1 else 0): raise CLEOperationError(\"relocation truncated to fit: %s;", "32-bit address space.\" % self.__class__.__name__) by = struct.pack(self.owner_obj.arch.struct_fmt(32), val %", "main bin delta = self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta ==", "self.resolve(None) return True val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val +", "when zero-extended check_zero_extend = False # If True, 32-bit truncated", "# 16-bit makes no sense here val = self.value %", "disable=unused-argument if self.owner_obj.mapped_base == 0: self.resolve(None) return True # don't", "class GenericAbsoluteAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr + self.addend class", "pylint: disable=unused-argument if not self.resolve_symbol(solist): return False arch_bits = self.owner_obj.arch.bits", "solist, bypass_compatibility=False): # pylint: disable=unused-argument if not self.resolve_symbol(solist): return False", "its original when sign-extended check_sign_extend = False def relocate(self, solist,", "return True class GenericCopyReloc(Relocation): @property def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class", ">= 32 # 16-bit makes no sense here val =", "32-bit field regardless of the architecture's address word length. \"\"\"", "class GenericAbsoluteReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property", "solist, bypass_compatibility=False): self.resolve(None) return True class GenericCopyReloc(Relocation): @property def value(self):", "for relocations that cover a 32-bit field regardless of the", "not self.resolve_symbol(solist): return False arch_bits = self.owner_obj.arch.bits assert arch_bits >=", "# don't touch local relocations on the main bin delta", "class MipsLocalReloc(Relocation): def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if", "from ...address_translator import AT from ...errors import CLEOperationError from .", "class GenericCopyReloc(Relocation): @property def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass", "if not self.resolve_symbol(solist): return False arch_bits = self.owner_obj.arch.bits assert arch_bits", "def value(self): return self.resolvedby.rebased_addr + self.addend - self.rebased_addr class GenericJumpslotReloc(Relocation):", "@property def value(self): if self.is_rela: return self.resolvedby.rebased_addr + self.addend else:", "MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation): def relocate(self, solist, bypass_compatibility=False): # pylint:", "self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val + delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return", "self.addend class GenericPCRelativeAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr + self.addend", "+ delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return True class RelocTruncate32Mixin(object): \"\"\"", "here val = self.value % (2**arch_bits) # we must truncate", "== 0: self.resolve(None) return True # don't touch local relocations", "delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return True class RelocTruncate32Mixin(object): \"\"\" A", "32)) - 1) if ((val >> 31) & 1) ==", "delta == 0: self.resolve(None) return True val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval", "return self.owner_obj.mapped_base + self.addend def resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None) return", "((1 << (arch_bits - 32)) - 1) if ((val >>", "val >> 32 != ((1 << (arch_bits - 32)) -", "= self.value % (2**arch_bits) # we must truncate it to", "value must equal to its original when zero-extended check_zero_extend =", "value(self): return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr", "relocations that cover a 32-bit field regardless of the architecture's", "truncated value must equal to its original when sign-extended check_sign_extend", "- 32)) - 1) if ((val >> 31) & 1)", "CLEOperationError from . import Relocation import struct import logging l", "delta = self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta == 0: self.resolve(None)", "equal to its original when zero-extended check_zero_extend = False #", "False def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if not", "the main bin delta = self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta", "zero-extended check_zero_extend = False # If True, 32-bit truncated value", "- self.rebased_addr class GenericJumpslotReloc(Relocation): @property def value(self): if self.is_rela: return", "value must equal to its original when sign-extended check_sign_extend =", "0): raise CLEOperationError(\"relocation truncated to fit: %s; consider making\" \"", "fit: %s; consider making\" \" relevant addresses fit in the", "%s; consider making\" \" relevant addresses fit in the 32-bit", "...address_translator import AT from ...errors import CLEOperationError from . import", "bypass_compatibility=False): self.resolve(None) return True class GenericCopyReloc(Relocation): @property def value(self): return", "self.resolve_symbol(solist): return False arch_bits = self.owner_obj.arch.bits assert arch_bits >= 32", "= logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr class", "= self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val + delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None)", "- self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta == 0: self.resolve(None) return True val", "self.is_rela: return self.resolvedby.rebased_addr + self.addend else: return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation):", "that cover a 32-bit field regardless of the architecture's address", "original when zero-extended check_zero_extend = False # If True, 32-bit", "when sign-extended check_sign_extend = False def relocate(self, solist, bypass_compatibility=False): #", "= self.owner_obj.arch.bits assert arch_bits >= 32 # 16-bit makes no", "it to native range first if (self.check_zero_extend and val >>", "return self.resolvedby.rebased_addr + self.addend - self.rebased_addr class GenericJumpslotReloc(Relocation): @property def", "GenericRelativeReloc(Relocation): @property def value(self): return self.owner_obj.mapped_base + self.addend def resolve_symbol(self,", "value(self): return self.resolvedby.rebased_addr + self.addend - self.rebased_addr class GenericJumpslotReloc(Relocation): @property", "def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if self.owner_obj.mapped_base ==", "\"\"\" # If True, 32-bit truncated value must equal to", "# pylint: disable=unused-argument if not self.resolve_symbol(solist): return False arch_bits =", "self.addend - self.rebased_addr class GenericJumpslotReloc(Relocation): @property def value(self): if self.is_rela:", "31) & 1) == 1 else 0): raise CLEOperationError(\"relocation truncated", "makes no sense here val = self.value % (2**arch_bits) #", "32 != 0 or self.check_sign_extend and val >> 32 !=", "GenericAbsoluteReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property def", "to fit: %s; consider making\" \" relevant addresses fit in", "% self.__class__.__name__) by = struct.pack(self.owner_obj.arch.struct_fmt(32), val % (2**32)) self.owner_obj.memory.write_bytes(self.dest_addr, by)", "must equal to its original when zero-extended check_zero_extend = False", "must truncate it to native range first if (self.check_zero_extend and", "word length. \"\"\" # If True, 32-bit truncated value must", "address word length. \"\"\" # If True, 32-bit truncated value", "val = self.value % (2**arch_bits) # we must truncate it", "import AT from ...errors import CLEOperationError from . import Relocation", "self.owner_obj.arch.bits assert arch_bits >= 32 # 16-bit makes no sense", "else: return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property def value(self): return self.owner_obj.mapped_base", "if ((val >> 31) & 1) == 1 else 0):", "cover a 32-bit field regardless of the architecture's address word", "assert arch_bits >= 32 # 16-bit makes no sense here", "# If True, 32-bit truncated value must equal to its", "True val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val + delta self.owner_obj.memory.write_addr_at(self.relative_addr,", "True, 32-bit truncated value must equal to its original when", "sign-extended check_sign_extend = False def relocate(self, solist, bypass_compatibility=False): # pylint:", "self.owner_obj.mapped_base + self.addend def resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None) return True", "self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation): def relocate(self, solist, bypass_compatibility=False):", "RelocTruncate32Mixin(object): \"\"\" A mix-in class for relocations that cover a", "native range first if (self.check_zero_extend and val >> 32 !=", "= False # If True, 32-bit truncated value must equal", "in the 32-bit address space.\" % self.__class__.__name__) by = struct.pack(self.owner_obj.arch.struct_fmt(32),", "+ self.addend - self.rebased_addr class GenericJumpslotReloc(Relocation): @property def value(self): if", "return self.resolvedby.rebased_addr + self.addend class GenericPCRelativeAddendReloc(Relocation): @property def value(self): return", "@property def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation):", "local relocations on the main bin delta = self.owner_obj.mapped_base -", "self.value % (2**arch_bits) # we must truncate it to native", "logging l = logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property def value(self): return", "self.addend else: return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property def value(self): return", "self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta == 0: self.resolve(None) return True val =", "truncate it to native range first if (self.check_zero_extend and val", "@property def value(self): return self.resolvedby.rebased_addr + self.addend class GenericPCRelativeAddendReloc(Relocation): @property", "return True val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val + delta", "on the main bin delta = self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if", "+ self.addend else: return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property def value(self):", "from . import Relocation import struct import logging l =", ". import Relocation import struct import logging l = logging.getLogger('cle.relocations.generic')", "def value(self): if self.is_rela: return self.resolvedby.rebased_addr + self.addend else: return", "1) if ((val >> 31) & 1) == 1 else", "def value(self): return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property def value(self): return", "class for relocations that cover a 32-bit field regardless of", "self.resolvedby.rebased_addr + self.addend class GenericPCRelativeAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr", "return True # don't touch local relocations on the main", "(arch_bits - 32)) - 1) if ((val >> 31) &", "class GenericRelativeReloc(Relocation): @property def value(self): return self.owner_obj.mapped_base + self.addend def", "length. \"\"\" # If True, 32-bit truncated value must equal", "bin delta = self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta == 0:", "fit in the 32-bit address space.\" % self.__class__.__name__) by =", "True class RelocTruncate32Mixin(object): \"\"\" A mix-in class for relocations that", "<reponame>Ruide/angr-dev from ...address_translator import AT from ...errors import CLEOperationError from", "val + delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return True class RelocTruncate32Mixin(object):", "def resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None) return True class GenericCopyReloc(Relocation): @property", "== 0: self.resolve(None) return True val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval =", "check_sign_extend = False def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument", "bypass_compatibility=False): # pylint: disable=unused-argument if not self.resolve_symbol(solist): return False arch_bits", "GenericPCRelativeAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr + self.addend - self.rebased_addr", "relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if not self.resolve_symbol(solist): return", "(2**arch_bits) # we must truncate it to native range first", "space.\" % self.__class__.__name__) by = struct.pack(self.owner_obj.arch.struct_fmt(32), val % (2**32)) self.owner_obj.memory.write_bytes(self.dest_addr,", "...errors import CLEOperationError from . import Relocation import struct import", "def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if not self.resolve_symbol(solist):", "making\" \" relevant addresses fit in the 32-bit address space.\"", "A mix-in class for relocations that cover a 32-bit field", "solist, bypass_compatibility=False): # pylint: disable=unused-argument if self.owner_obj.mapped_base == 0: self.resolve(None)", "<< (arch_bits - 32)) - 1) if ((val >> 31)", "bypass_compatibility=False): # pylint: disable=unused-argument if self.owner_obj.mapped_base == 0: self.resolve(None) return", "check_zero_extend = False # If True, 32-bit truncated value must", "return False arch_bits = self.owner_obj.arch.bits assert arch_bits >= 32 #", "the architecture's address word length. \"\"\" # If True, 32-bit", "or self.check_sign_extend and val >> 32 != ((1 << (arch_bits", ">> 31) & 1) == 1 else 0): raise CLEOperationError(\"relocation", "= False def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if", "& 1) == 1 else 0): raise CLEOperationError(\"relocation truncated to", "address space.\" % self.__class__.__name__) by = struct.pack(self.owner_obj.arch.struct_fmt(32), val % (2**32))", "value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation): def relocate(self,", "# pylint: disable=unused-argument if self.owner_obj.mapped_base == 0: self.resolve(None) return True", "True class GenericCopyReloc(Relocation): @property def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc):", "addresses fit in the 32-bit address space.\" % self.__class__.__name__) by", "32-bit truncated value must equal to its original when sign-extended", "truncated to fit: %s; consider making\" \" relevant addresses fit", "1 else 0): raise CLEOperationError(\"relocation truncated to fit: %s; consider", "= val + delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return True class", "True # don't touch local relocations on the main bin", "don't touch local relocations on the main bin delta =", "newval = val + delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval) self.resolve(None) return True", "to its original when sign-extended check_sign_extend = False def relocate(self,", "first if (self.check_zero_extend and val >> 32 != 0 or", "GenericAbsoluteAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr + self.addend class GenericPCRelativeAddendReloc(Relocation):", "@property def value(self): return self.owner_obj.mapped_base + self.addend def resolve_symbol(self, solist,", "class RelocTruncate32Mixin(object): \"\"\" A mix-in class for relocations that cover", "arch_bits = self.owner_obj.arch.bits assert arch_bits >= 32 # 16-bit makes", "+ self.addend class GenericPCRelativeAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr +", "GenericJumpslotReloc(Relocation): @property def value(self): if self.is_rela: return self.resolvedby.rebased_addr + self.addend", "self.owner_obj.mapped_base == 0: self.resolve(None) return True # don't touch local", "return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr) class MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation): def relocate(self, solist,", "self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS'] if delta == 0: self.resolve(None) return True", "newval) self.resolve(None) return True class RelocTruncate32Mixin(object): \"\"\" A mix-in class", "If True, 32-bit truncated value must equal to its original", "0: self.resolve(None) return True # don't touch local relocations on", "val >> 32 != 0 or self.check_sign_extend and val >>", "0: self.resolve(None) return True val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val", "original when sign-extended check_sign_extend = False def relocate(self, solist, bypass_compatibility=False):", "if self.is_rela: return self.resolvedby.rebased_addr + self.addend else: return self.resolvedby.rebased_addr class", "AT from ...errors import CLEOperationError from . import Relocation import", "l = logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr", "truncated value must equal to its original when zero-extended check_zero_extend", "else 0): raise CLEOperationError(\"relocation truncated to fit: %s; consider making\"", "32 != ((1 << (arch_bits - 32)) - 1) if", "self.resolve(None) return True class GenericCopyReloc(Relocation): @property def value(self): return self.resolvedby.owner_obj.memory.read_addr_at(self.resolvedby.relative_addr)", "@property def value(self): return self.resolvedby.rebased_addr + self.addend - self.rebased_addr class", "if (self.check_zero_extend and val >> 32 != 0 or self.check_sign_extend", "if delta == 0: self.resolve(None) return True val = self.owner_obj.memory.read_addr_at(self.relative_addr)", "val = self.owner_obj.memory.read_addr_at(self.relative_addr) newval = val + delta self.owner_obj.memory.write_addr_at(self.relative_addr, newval)", "@property def value(self): return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property def value(self):", "- 1) if ((val >> 31) & 1) == 1", "raise CLEOperationError(\"relocation truncated to fit: %s; consider making\" \" relevant", "Relocation import struct import logging l = logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation):", "value(self): return self.owner_obj.mapped_base + self.addend def resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None)", "self.resolvedby.rebased_addr + self.addend else: return self.resolvedby.rebased_addr class GenericRelativeReloc(Relocation): @property def", "1) == 1 else 0): raise CLEOperationError(\"relocation truncated to fit:", "architecture's address word length. \"\"\" # If True, 32-bit truncated", "\" relevant addresses fit in the 32-bit address space.\" %", "mix-in class for relocations that cover a 32-bit field regardless", "self.rebased_addr class GenericJumpslotReloc(Relocation): @property def value(self): if self.is_rela: return self.resolvedby.rebased_addr", "self.resolve(None) return True class RelocTruncate32Mixin(object): \"\"\" A mix-in class for", "!= ((1 << (arch_bits - 32)) - 1) if ((val", "\"\"\" A mix-in class for relocations that cover a 32-bit", "relocations on the main bin delta = self.owner_obj.mapped_base - self.owner_obj._dynamic['DT_MIPS_BASE_ADDRESS']", "CLEOperationError(\"relocation truncated to fit: %s; consider making\" \" relevant addresses", "return True class RelocTruncate32Mixin(object): \"\"\" A mix-in class for relocations", "self.addend def resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None) return True class GenericCopyReloc(Relocation):", "32-bit truncated value must equal to its original when zero-extended", "equal to its original when sign-extended check_sign_extend = False def", "self.resolve(None) return True # don't touch local relocations on the", "pylint: disable=unused-argument if self.owner_obj.mapped_base == 0: self.resolve(None) return True #", "return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr +", "we must truncate it to native range first if (self.check_zero_extend", "regardless of the architecture's address word length. \"\"\" # If", "class GenericPCRelativeAddendReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr + self.addend -", "no sense here val = self.value % (2**arch_bits) # we", "must equal to its original when sign-extended check_sign_extend = False", "!= 0 or self.check_sign_extend and val >> 32 != ((1", "relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if self.owner_obj.mapped_base == 0:", "def value(self): return self.owner_obj.mapped_base + self.addend def resolve_symbol(self, solist, bypass_compatibility=False):", "sense here val = self.value % (2**arch_bits) # we must", "logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property def value(self): return self.resolvedby.rebased_addr class GenericAbsoluteAddendReloc(Relocation):", "# we must truncate it to native range first if", "disable=unused-argument if not self.resolve_symbol(solist): return False arch_bits = self.owner_obj.arch.bits assert", "value(self): if self.is_rela: return self.resolvedby.rebased_addr + self.addend else: return self.resolvedby.rebased_addr", "touch local relocations on the main bin delta = self.owner_obj.mapped_base", "import Relocation import struct import logging l = logging.getLogger('cle.relocations.generic') class", "and val >> 32 != ((1 << (arch_bits - 32))", "MipsLocalReloc(Relocation): def relocate(self, solist, bypass_compatibility=False): # pylint: disable=unused-argument if self.owner_obj.mapped_base", "of the architecture's address word length. \"\"\" # If True,", "class MipsGlobalReloc(GenericAbsoluteReloc): pass class MipsLocalReloc(Relocation): def relocate(self, solist, bypass_compatibility=False): #", "resolve_symbol(self, solist, bypass_compatibility=False): self.resolve(None) return True class GenericCopyReloc(Relocation): @property def", "import logging l = logging.getLogger('cle.relocations.generic') class GenericAbsoluteReloc(Relocation): @property def value(self):" ]
[ "</gradientFill> </styleSheet> \"\"\" diff = compare_xml(xml, expected) assert diff is", "expected) assert diff is None, diff def test_write_borders(): borders =", "= get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/>", "\"\"\" diff = compare_xml(xml, expected) assert diff is None, diff", "Copyright (c) 2010-2014 openpyxl import pytest from openpyxl.styles.borders import Border,", "Border, Side from openpyxl.styles.fills import GradientFill from openpyxl.styles.colors import Color", "position=\"0\"> <color theme=\"0\"/> </stop> <stop position=\"1\"> <color theme=\"4\"/> </stop> </gradientFill>", "\"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/> <top/> <bottom/> <diagonal/>", "diff = compare_xml(xml, expected) assert diff is None, diff def", "<left/> <right/> <top/> <bottom/> <diagonal/> </border> </styleSheet> \"\"\" diff =", "= GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml", "degree=\"90\" type=\"linear\"> <stop position=\"0\"> <color theme=\"0\"/> </stop> <stop position=\"1\"> <color", "writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml = get_xml(writer._root) expected =", "openpyxl.styles.borders import Border, Side from openpyxl.styles.fills import GradientFill from openpyxl.styles.colors", "from openpyxl.styles.borders import Border, Side from openpyxl.styles.fills import GradientFill from", "= compare_xml(xml, expected) assert diff is None, diff def test_write_borders():", "openpyxl.writer.styles import StyleWriter from openpyxl.tests.helper import get_xml, compare_xml class DummyWorkbook:", "assert diff is None, diff def test_write_borders(): borders = Border()", "?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\"> <stop position=\"0\"> <color theme=\"0\"/>", "from openpyxl.styles.colors import Color from openpyxl.writer.styles import StyleWriter from openpyxl.tests.helper", "style_properties = [] def test_write_gradient_fill(): fill = GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)])", "<styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/> <top/> <bottom/> <diagonal/> </border> </styleSheet>", "= StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml = get_xml(writer._root) expected = \"\"\"<?xml", "xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/> <top/> <bottom/> <diagonal/> </border> </styleSheet> \"\"\"", "openpyxl.styles.fills import GradientFill from openpyxl.styles.colors import Color from openpyxl.writer.styles import", "<border> <left/> <right/> <top/> <bottom/> <diagonal/> </border> </styleSheet> \"\"\" diff", "= [] def test_write_gradient_fill(): fill = GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer", "get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\"", "openpyxl.styles.colors import Color from openpyxl.writer.styles import StyleWriter from openpyxl.tests.helper import", "<gradientFill degree=\"90\" type=\"linear\"> <stop position=\"0\"> <color theme=\"0\"/> </stop> <stop position=\"1\">", "<diagonal/> </border> </styleSheet> \"\"\" diff = compare_xml(xml, expected) assert diff", "expected = \"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/> <top/>", "2010-2014 openpyxl import pytest from openpyxl.styles.borders import Border, Side from", "openpyxl.tests.helper import get_xml, compare_xml class DummyWorkbook: style_properties = [] def", "<stop position=\"0\"> <color theme=\"0\"/> </stop> <stop position=\"1\"> <color theme=\"4\"/> </stop>", "import Border, Side from openpyxl.styles.fills import GradientFill from openpyxl.styles.colors import", "theme=\"4\"/> </stop> </gradientFill> </styleSheet> \"\"\" diff = compare_xml(xml, expected) assert", "xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\"> <stop position=\"0\"> <color theme=\"0\"/> </stop> <stop", "Border() writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml = get_xml(writer._root) expected", "= \"\"\"<?xml version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\"> <stop", "<right/> <top/> <bottom/> <diagonal/> </border> </styleSheet> \"\"\" diff = compare_xml(xml,", "</styleSheet> \"\"\" diff = compare_xml(xml, expected) assert diff is None,", "(c) 2010-2014 openpyxl import pytest from openpyxl.styles.borders import Border, Side", "\"\"\"<?xml version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\"> <stop position=\"0\">", "xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\">", "= Border() writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml = get_xml(writer._root)", "</stop> <stop position=\"1\"> <color theme=\"4\"/> </stop> </gradientFill> </styleSheet> \"\"\" diff", "borders = Border() writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml =", "stop=[Color(theme=0), Color(theme=4)]) writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml = get_xml(writer._root)", "import Color from openpyxl.writer.styles import StyleWriter from openpyxl.tests.helper import get_xml,", "test_write_borders(): borders = Border() writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml", "borders) xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\">", "xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border>", "diff is None, diff def test_write_borders(): borders = Border() writer", "Color(theme=4)]) writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml = get_xml(writer._root) expected", "import StyleWriter from openpyxl.tests.helper import get_xml, compare_xml class DummyWorkbook: style_properties", "openpyxl import pytest from openpyxl.styles.borders import Border, Side from openpyxl.styles.fills", "from openpyxl.writer.styles import StyleWriter from openpyxl.tests.helper import get_xml, compare_xml class", "compare_xml(xml, expected) assert diff is None, diff def test_write_borders(): borders", "version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\"> <stop position=\"0\"> <color", "<color theme=\"4\"/> </stop> </gradientFill> </styleSheet> \"\"\" diff = compare_xml(xml, expected)", "import get_xml, compare_xml class DummyWorkbook: style_properties = [] def test_write_gradient_fill():", "is None, diff def test_write_borders(): borders = Border() writer =", "DummyWorkbook: style_properties = [] def test_write_gradient_fill(): fill = GradientFill(degree=90, stop=[Color(theme=0),", "class DummyWorkbook: style_properties = [] def test_write_gradient_fill(): fill = GradientFill(degree=90,", "test_write_gradient_fill(): fill = GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root,", "StyleWriter from openpyxl.tests.helper import get_xml, compare_xml class DummyWorkbook: style_properties =", "<top/> <bottom/> <diagonal/> </border> </styleSheet> \"\"\" diff = compare_xml(xml, expected)", "StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"?>", "fill = GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill)", "from openpyxl.styles.fills import GradientFill from openpyxl.styles.colors import Color from openpyxl.writer.styles", "position=\"1\"> <color theme=\"4\"/> </stop> </gradientFill> </styleSheet> \"\"\" diff = compare_xml(xml,", "</border> </styleSheet> \"\"\" diff = compare_xml(xml, expected) assert diff is", "<color theme=\"0\"/> </stop> <stop position=\"1\"> <color theme=\"4\"/> </stop> </gradientFill> </styleSheet>", "# Copyright (c) 2010-2014 openpyxl import pytest from openpyxl.styles.borders import", "</stop> </gradientFill> </styleSheet> \"\"\" diff = compare_xml(xml, expected) assert diff", "get_xml, compare_xml class DummyWorkbook: style_properties = [] def test_write_gradient_fill(): fill", "[] def test_write_gradient_fill(): fill = GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer =", "get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/>", "= StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml = get_xml(writer._root) expected = \"\"\"<?xml", "= \"\"\"<?xml version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/> <top/> <bottom/>", "Side from openpyxl.styles.fills import GradientFill from openpyxl.styles.colors import Color from", "writer._write_border(writer._root, borders) xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"?> <styleSheet", "Color from openpyxl.writer.styles import StyleWriter from openpyxl.tests.helper import get_xml, compare_xml", "theme=\"0\"/> </stop> <stop position=\"1\"> <color theme=\"4\"/> </stop> </gradientFill> </styleSheet> \"\"\"", "def test_write_gradient_fill(): fill = GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer = StyleWriter(DummyWorkbook())", "diff def test_write_borders(): borders = Border() writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root,", "= get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill", "<styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\"> <stop position=\"0\"> <color theme=\"0\"/> </stop>", "def test_write_borders(): borders = Border() writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders)", "compare_xml class DummyWorkbook: style_properties = [] def test_write_gradient_fill(): fill =", "StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\"", "None, diff def test_write_borders(): borders = Border() writer = StyleWriter(DummyWorkbook())", "<bottom/> <diagonal/> </border> </styleSheet> \"\"\" diff = compare_xml(xml, expected) assert", "from openpyxl.tests.helper import get_xml, compare_xml class DummyWorkbook: style_properties = []", "GradientFill from openpyxl.styles.colors import Color from openpyxl.writer.styles import StyleWriter from", "type=\"linear\"> <stop position=\"0\"> <color theme=\"0\"/> </stop> <stop position=\"1\"> <color theme=\"4\"/>", "<stop position=\"1\"> <color theme=\"4\"/> </stop> </gradientFill> </styleSheet> \"\"\" diff =", "GradientFill(degree=90, stop=[Color(theme=0), Color(theme=4)]) writer = StyleWriter(DummyWorkbook()) writer._write_gradient_fill(writer._root, fill) xml =", "import pytest from openpyxl.styles.borders import Border, Side from openpyxl.styles.fills import", "writer = StyleWriter(DummyWorkbook()) writer._write_border(writer._root, borders) xml = get_xml(writer._root) expected =", "fill) xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\" ?> <styleSheet", "import GradientFill from openpyxl.styles.colors import Color from openpyxl.writer.styles import StyleWriter", "pytest from openpyxl.styles.borders import Border, Side from openpyxl.styles.fills import GradientFill", "expected = \"\"\"<?xml version=\"1.0\" ?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <gradientFill degree=\"90\" type=\"linear\">", "version=\"1.0\"?> <styleSheet xmlns=\"http://schemas.openxmlformats.org/spreadsheetml/2006/main\"> <border> <left/> <right/> <top/> <bottom/> <diagonal/> </border>", "writer._write_gradient_fill(writer._root, fill) xml = get_xml(writer._root) expected = \"\"\"<?xml version=\"1.0\" ?>" ]
[ "import models, migrations class Migration(migrations.Migration): dependencies = [ ('ringapp', '0008_auto_20150116_1755'),", "unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies =", "dependencies = [ ('ringapp', '0008_auto_20150116_1755'), ] operations = [ migrations.AlterModelTable(", "from __future__ import unicode_literals from django.db import models, migrations class", "<gh_stars>1-10 # -*- coding: utf-8 -*- from __future__ import unicode_literals", "'0008_auto_20150116_1755'), ] operations = [ migrations.AlterModelTable( name='invariance', table='invariance', ), migrations.AlterModelTable(", "import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies", "coding: utf-8 -*- from __future__ import unicode_literals from django.db import", "models, migrations class Migration(migrations.Migration): dependencies = [ ('ringapp', '0008_auto_20150116_1755'), ]", "[ migrations.AlterModelTable( name='invariance', table='invariance', ), migrations.AlterModelTable( name='invarianttype', table='invariant_types', ), ]", "= [ ('ringapp', '0008_auto_20150116_1755'), ] operations = [ migrations.AlterModelTable( name='invariance',", "from django.db import models, migrations class Migration(migrations.Migration): dependencies = [", "django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('ringapp',", "-*- from __future__ import unicode_literals from django.db import models, migrations", "__future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration):", "class Migration(migrations.Migration): dependencies = [ ('ringapp', '0008_auto_20150116_1755'), ] operations =", "# -*- coding: utf-8 -*- from __future__ import unicode_literals from", "] operations = [ migrations.AlterModelTable( name='invariance', table='invariance', ), migrations.AlterModelTable( name='invarianttype',", "operations = [ migrations.AlterModelTable( name='invariance', table='invariance', ), migrations.AlterModelTable( name='invarianttype', table='invariant_types',", "= [ migrations.AlterModelTable( name='invariance', table='invariance', ), migrations.AlterModelTable( name='invarianttype', table='invariant_types', ),", "-*- coding: utf-8 -*- from __future__ import unicode_literals from django.db", "('ringapp', '0008_auto_20150116_1755'), ] operations = [ migrations.AlterModelTable( name='invariance', table='invariance', ),", "Migration(migrations.Migration): dependencies = [ ('ringapp', '0008_auto_20150116_1755'), ] operations = [", "[ ('ringapp', '0008_auto_20150116_1755'), ] operations = [ migrations.AlterModelTable( name='invariance', table='invariance',", "migrations class Migration(migrations.Migration): dependencies = [ ('ringapp', '0008_auto_20150116_1755'), ] operations", "utf-8 -*- from __future__ import unicode_literals from django.db import models," ]
[ "class TestDump(TestCase): def test_dump(self): sio = StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(),", "\"true\": false}') self.assertEquals(json.dumps( {2: 3.0, 4.0: 5, False: 1, 6:", "self.assertEquals(json.dumps( {True: False, False: True}, sort_keys=True), '{\"false\": true, \"true\": false}')", "true, \"true\": false}') self.assertEquals(json.dumps( {2: 3.0, 4.0: 5, False: 1,", "json class TestDump(TestCase): def test_dump(self): sio = StringIO() json.dump({}, sio)", "test_dump(self): sio = StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}') def test_dumps(self):", "TestDump(TestCase): def test_dump(self): sio = StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}')", "import TestCase from io import StringIO import json class TestDump(TestCase):", "'{\"false\": true, \"true\": false}') self.assertEquals(json.dumps( {2: 3.0, 4.0: 5, False:", "false}') self.assertEquals(json.dumps( {2: 3.0, 4.0: 5, False: 1, 6: True},", "'{}') def test_dumps(self): self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False,", "'{}') def test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False, False: True}, sort_keys=True), '{\"false\":", "def test_dump(self): sio = StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}') def", "5, False: 1, 6: True}, sort_keys=True), '{\"false\": 1, \"2\": 3.0,", "self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False, False: True}, sort_keys=True),", "False: True}, sort_keys=True), '{\"false\": true, \"true\": false}') self.assertEquals(json.dumps( {2: 3.0,", "sort_keys=True), '{\"false\": true, \"true\": false}') self.assertEquals(json.dumps( {2: 3.0, 4.0: 5,", "{2: 3.0, 4.0: 5, False: 1, 6: True}, sort_keys=True), '{\"false\":", "test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False, False: True}, sort_keys=True), '{\"false\": true, \"true\":", "False: 1, 6: True}, sort_keys=True), '{\"false\": 1, \"2\": 3.0, \"4.0\":", "StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}') def test_dumps(self): self.assertEquals(json.dumps({}), '{}') def", "io import StringIO import json class TestDump(TestCase): def test_dump(self): sio", "def test_dumps(self): self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False, False:", "True}, sort_keys=True), '{\"false\": true, \"true\": false}') self.assertEquals(json.dumps( {2: 3.0, 4.0:", "def test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False, False: True}, sort_keys=True), '{\"false\": true,", "test_dumps(self): self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self): self.assertEquals(json.dumps( {True: False, False: True},", "from unittest import TestCase from io import StringIO import json", "from io import StringIO import json class TestDump(TestCase): def test_dump(self):", "import json class TestDump(TestCase): def test_dump(self): sio = StringIO() json.dump({},", "4.0: 5, False: 1, 6: True}, sort_keys=True), '{\"false\": 1, \"2\":", "6: True}, sort_keys=True), '{\"false\": 1, \"2\": 3.0, \"4.0\": 5, \"6\":", "import StringIO import json class TestDump(TestCase): def test_dump(self): sio =", "sio) self.assertEquals(sio.getvalue(), '{}') def test_dumps(self): self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self): self.assertEquals(json.dumps(", "unittest import TestCase from io import StringIO import json class", "TestCase from io import StringIO import json class TestDump(TestCase): def", "True}, sort_keys=True), '{\"false\": 1, \"2\": 3.0, \"4.0\": 5, \"6\": true}')", "StringIO import json class TestDump(TestCase): def test_dump(self): sio = StringIO()", "json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}') def test_dumps(self): self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self):", "sio = StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}') def test_dumps(self): self.assertEquals(json.dumps({}),", "{True: False, False: True}, sort_keys=True), '{\"false\": true, \"true\": false}') self.assertEquals(json.dumps(", "self.assertEquals(sio.getvalue(), '{}') def test_dumps(self): self.assertEquals(json.dumps({}), '{}') def test_encode_truefalse(self): self.assertEquals(json.dumps( {True:", "= StringIO() json.dump({}, sio) self.assertEquals(sio.getvalue(), '{}') def test_dumps(self): self.assertEquals(json.dumps({}), '{}')", "1, 6: True}, sort_keys=True), '{\"false\": 1, \"2\": 3.0, \"4.0\": 5,", "False, False: True}, sort_keys=True), '{\"false\": true, \"true\": false}') self.assertEquals(json.dumps( {2:", "self.assertEquals(json.dumps( {2: 3.0, 4.0: 5, False: 1, 6: True}, sort_keys=True),", "3.0, 4.0: 5, False: 1, 6: True}, sort_keys=True), '{\"false\": 1," ]
[ "AddWindowRPC = 2 DeleteWindowRPC = 3 SetWindowLayoutRPC = 4 SetActiveWindowRPC", "81 ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC = 84", "DemoteOperatorRPC = 42 RemoveOperatorRPC = 43 RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC", "= 182 SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC =", "MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC", "23 TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC = 26", "35 RedrawRPC = 36 SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC = 38", "= 102 ClearRefLinesRPC = 103 SetRenderingAttributesRPC = 104 QueryRPC =", "= 123 SaveViewRPC = 124 SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC =", "= 25 SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC = 27 AddPlotRPC =", "= 57 SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC =", "94 SetToolUpdateModeRPC = 95 CopyViewToWindowRPC = 96 CopyLightingToWindowRPC = 97", "= 168 RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC =", "= 96 CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC =", "80 ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC = 83", "ResetOperatorOptionsRPC = 68 SetAppearanceRPC = 69 ProcessExpressionsRPC = 70 SetLightListRPC", "PrintWindowRPC = 76 ResetViewRPC = 77 RecenterViewRPC = 78 ToggleAllowPopupRPC", "117 EnableToolbarRPC = 118 HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC = 120", "LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC", "SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC = 150 GetProcInfoRPC", "16 OverlayDatabaseRPC = 17 OpenComputeEngineRPC = 18 CloseComputeEngineRPC = 19", "SetLightListRPC = 71 SetDefaultLightListRPC = 72 ResetLightListRPC = 73 SetAnimationAttributesRPC", "= 3 SetWindowLayoutRPC = 4 SetActiveWindowRPC = 5 ClearWindowRPC =", "TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC = 84 ToggleLockTimeRPC = 85 ToggleLockToolsRPC", "9 ActivateDatabaseRPC = 10 CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC = 12", "SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC", "= 97 CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC = 99 ClearCacheRPC =", "111 MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC = 114", "= 61 SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC = 63 SetViewCurveRPC =", "CloseRPC = 0 DetachRPC = 1 AddWindowRPC = 2 DeleteWindowRPC", "99 ClearCacheRPC = 100 ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC = 102", "7 OpenDatabaseRPC = 8 CloseDatabaseRPC = 9 ActivateDatabaseRPC = 10", "179 CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC = 182", "147 SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC = 150", "189 MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC = 192", "= 116 OpenMDServerRPC = 117 EnableToolbarRPC = 118 HideToolbarsRPC =", "ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC", "SetStateLoggingRPC = 167 ConstructDataBinningRPC = 168 RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC", "= 64 SetView2DRPC = 65 SetView3DRPC = 66 ResetPlotOptionsRPC =", "102 ClearRefLinesRPC = 103 SetRenderingAttributesRPC = 104 QueryRPC = 105", "= 106 SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC =", "MoveViewKeyframeRPC = 115 SetViewKeyframeRPC = 116 OpenMDServerRPC = 117 EnableToolbarRPC", "= 113 DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC = 115 SetViewKeyframeRPC =", "UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC = 187 SetPlotDescriptionRPC", "ExportColorTableRPC = 127 ExportEntireStateRPC = 128 ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC", "= 32 HideActivePlotsRPC = 33 DrawPlotsRPC = 34 DisableRedrawRPC =", "MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC", "180 DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC = 183", "= 184 UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC =", "= 129 ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC = 131 AddAnnotationObjectRPC =", "SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC", "58 SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC = 61", "= 20 AnimationPlayRPC = 21 AnimationReversePlayRPC = 22 AnimationStopRPC =", "139 ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC = 142", "= 62 SetViewAxisArrayRPC = 63 SetViewCurveRPC = 64 SetView2DRPC =", "184 UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC = 187", "ExportEntireStateRPC = 128 ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC", "= 131 AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC =", "47 SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC = 50", "= 165 MoveAndResizeWindowRPC = 166 SetStateLoggingRPC = 167 ConstructDataBinningRPC =", "= 133 DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC =", "195 DDTFocusRPC = 196 ReleaseToDDTRPC = 197 MaxRPC = 198", "194 DDTConnectRPC = 195 DDTFocusRPC = 196 ReleaseToDDTRPC = 197", "OpenClientRPC = 156 OpenGUIClientRPC = 157 OpenCLIClientRPC = 158 SuppressQueryOutputRPC", "167 ConstructDataBinningRPC = 168 RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC = 170", "119 HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC = 122", "= 176 SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC =", "41 DemoteOperatorRPC = 42 RemoveOperatorRPC = 43 RemoveLastOperatorRPC = 44", "ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC = 84 ToggleLockTimeRPC", "= 24 TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC =", "= 156 OpenGUIClientRPC = 157 OpenCLIClientRPC = 158 SuppressQueryOutputRPC =", "ToggleLockToolsRPC = 86 ToggleLockViewModeRPC = 87 ToggleFullFrameRPC = 88 UndoViewRPC", "160 SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC = 163", "= 187 SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC =", "OverlayDatabaseRPC = 17 OpenComputeEngineRPC = 18 CloseComputeEngineRPC = 19 AnimationSetNFramesRPC", "95 CopyViewToWindowRPC = 96 CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC = 98", "169 SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC = 172", "= 145 ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC = 147 SetInteractorAttributesRPC =", "68 SetAppearanceRPC = 69 ProcessExpressionsRPC = 70 SetLightListRPC = 71", "= 16 OverlayDatabaseRPC = 17 OpenComputeEngineRPC = 18 CloseComputeEngineRPC =", "= 153 ExportDBRPC = 154 SetTryHarderCyclesTimesRPC = 155 OpenClientRPC =", "191 SetPlotOrderToLastRPC = 192 RenamePickLabelRPC = 193 GetQueryParametersRPC = 194", "ActivateDatabaseRPC = 10 CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC", "= 48 SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC = 50 WriteConfigFileRPC =", "10 CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC = 13", "= 158 SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC =", "ResetViewRPC = 77 RecenterViewRPC = 78 ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC", "= 89 RedoViewRPC = 90 InvertBackgroundRPC = 91 ClearPickPointsRPC =", "145 ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC = 147 SetInteractorAttributesRPC = 148", "SetViewAxisArrayRPC = 63 SetViewCurveRPC = 64 SetView2DRPC = 65 SetView3DRPC", "SetPickAttributesRPC = 126 ExportColorTableRPC = 127 ExportEntireStateRPC = 128 ImportEntireStateRPC", "= 185 InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC = 187 SetPlotDescriptionRPC =", "RecenterViewRPC = 78 ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC", "= 35 RedrawRPC = 36 SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC =", "158 SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC = 161", "125 SetPickAttributesRPC = 126 ExportColorTableRPC = 127 ExportEntireStateRPC = 128", "= 74 SetWindowAreaRPC = 75 PrintWindowRPC = 76 ResetViewRPC =", "15 ReplaceDatabaseRPC = 16 OverlayDatabaseRPC = 17 OpenComputeEngineRPC = 18", "= 46 SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC =", "UpdateColorTableRPC = 57 SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC", "168 RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC = 171", "83 ToggleSpinModeRPC = 84 ToggleLockTimeRPC = 85 ToggleLockToolsRPC = 86", "141 ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC = 144", "159 SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC = 162", "79 ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC = 82", "SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC = 27 AddPlotRPC = 28 SetPlotFrameRangeRPC", "AddPlotRPC = 28 SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC", "CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC", "11 CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC = 14", "PromoteOperatorRPC = 41 DemoteOperatorRPC = 42 RemoveOperatorRPC = 43 RemoveLastOperatorRPC", "ProcessExpressionsRPC = 70 SetLightListRPC = 71 SetDefaultLightListRPC = 72 ResetLightListRPC", "<reponame>visit-dav/vis import sys class RPCType(object): CloseRPC = 0 DetachRPC =", "SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC", "67 ResetOperatorOptionsRPC = 68 SetAppearanceRPC = 69 ProcessExpressionsRPC = 70", "ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC = 164 MoveWindowRPC = 165 MoveAndResizeWindowRPC", "20 AnimationPlayRPC = 21 AnimationReversePlayRPC = 22 AnimationStopRPC = 23", "= 151 SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC = 153 ExportDBRPC =", "= 8 CloseDatabaseRPC = 9 ActivateDatabaseRPC = 10 CheckForNewStatesRPC =", "DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC = 115 SetViewKeyframeRPC = 116 OpenMDServerRPC", "RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC = 45 SaveWindowRPC = 46 SetDefaultPlotOptionsRPC", "56 UpdateColorTableRPC = 57 SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC = 59", "= 39 AddInitializedOperatorRPC = 40 PromoteOperatorRPC = 41 DemoteOperatorRPC =", "= 183 SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC =", "SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC", "SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC", "51 ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC = 54", "AnimationStopRPC = 23 TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC", "ToggleFullFrameRPC = 88 UndoViewRPC = 89 RedoViewRPC = 90 InvertBackgroundRPC", "101 SetViewExtentsTypeRPC = 102 ClearRefLinesRPC = 103 SetRenderingAttributesRPC = 104", "= 18 CloseComputeEngineRPC = 19 AnimationSetNFramesRPC = 20 AnimationPlayRPC =", "DisableRedrawRPC = 35 RedrawRPC = 36 SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC", "= 181 LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC =", "= 77 RecenterViewRPC = 78 ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC =", "= 114 MoveViewKeyframeRPC = 115 SetViewKeyframeRPC = 116 OpenMDServerRPC =", "= 126 ExportColorTableRPC = 127 ExportEntireStateRPC = 128 ImportEntireStateRPC =", "CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC", "= 162 ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC = 164 MoveWindowRPC =", "HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC", "= 166 SetStateLoggingRPC = 167 ConstructDataBinningRPC = 168 RequestMetaDataRPC =", "64 SetView2DRPC = 65 SetView3DRPC = 66 ResetPlotOptionsRPC = 67", "182 SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC = 185", "ToggleLockViewModeRPC = 87 ToggleFullFrameRPC = 88 UndoViewRPC = 89 RedoViewRPC", "= 53 DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC = 55 HideAllWindowsRPC =", "= 144 SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC =", "= 17 OpenComputeEngineRPC = 18 CloseComputeEngineRPC = 19 AnimationSetNFramesRPC =", "DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC = 32 HideActivePlotsRPC", "= 146 ResetLineoutColorRPC = 147 SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC =", "= 109 SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC =", "= 190 SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC = 192 RenamePickLabelRPC =", "CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC", "ReplaceDatabaseRPC = 16 OverlayDatabaseRPC = 17 OpenComputeEngineRPC = 18 CloseComputeEngineRPC", "= 0 DetachRPC = 1 AddWindowRPC = 2 DeleteWindowRPC =", "SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC = 126 ExportColorTableRPC = 127 ExportEntireStateRPC", "144 SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC = 147", "= 135 LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC =", "77 RecenterViewRPC = 78 ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC = 80", "SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC = 153 ExportDBRPC = 154 SetTryHarderCyclesTimesRPC", "152 UpdateDBPluginInfoRPC = 153 ExportDBRPC = 154 SetTryHarderCyclesTimesRPC = 155", "135 LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC = 138", "29 DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC = 32", "SetAnimationAttributesRPC = 74 SetWindowAreaRPC = 75 PrintWindowRPC = 76 ResetViewRPC", "SetViewExtentsTypeRPC = 102 ClearRefLinesRPC = 103 SetRenderingAttributesRPC = 104 QueryRPC", "SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC", "134 RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC = 137", "= 78 ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC =", "SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC", "4 SetActiveWindowRPC = 5 ClearWindowRPC = 6 ClearAllWindowsRPC = 7", "89 RedoViewRPC = 90 InvertBackgroundRPC = 91 ClearPickPointsRPC = 92", "SetViewKeyframeRPC = 116 OpenMDServerRPC = 117 EnableToolbarRPC = 118 HideToolbarsRPC", "LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC", "= 105 CloneWindowRPC = 106 SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC =", "88 UndoViewRPC = 89 RedoViewRPC = 90 InvertBackgroundRPC = 91", "27 AddPlotRPC = 28 SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC = 30", "75 PrintWindowRPC = 76 ResetViewRPC = 77 RecenterViewRPC = 78", "186 MenuQuitRPC = 187 SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC = 189", "SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC = 192 RenamePickLabelRPC = 193 GetQueryParametersRPC", "= 51 ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC =", "= 191 SetPlotOrderToLastRPC = 192 RenamePickLabelRPC = 193 GetQueryParametersRPC =", "= 56 UpdateColorTableRPC = 57 SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC =", "53 DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC = 55 HideAllWindowsRPC = 56", "187 SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC = 190", "HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC", "73 SetAnimationAttributesRPC = 74 SetWindowAreaRPC = 75 PrintWindowRPC = 76", "= 188 MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC =", "OpenMDServerRPC = 117 EnableToolbarRPC = 118 HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC", "AnimationReversePlayRPC = 22 AnimationStopRPC = 23 TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC", "120 ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC = 123", "= 81 ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC =", "= 142 SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC =", "93 EnableToolRPC = 94 SetToolUpdateModeRPC = 95 CopyViewToWindowRPC = 96", "= 120 ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC =", "DDTConnectRPC = 195 DDTFocusRPC = 196 ReleaseToDDTRPC = 197 MaxRPC", "63 SetViewCurveRPC = 64 SetView2DRPC = 65 SetView3DRPC = 66", "= 23 TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC =", "98 CopyPlotsToWindowRPC = 99 ClearCacheRPC = 100 ClearCacheForAllEnginesRPC = 101", "127 ExportEntireStateRPC = 128 ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC = 130", "= 154 SetTryHarderCyclesTimesRPC = 155 OpenClientRPC = 156 OpenGUIClientRPC =", "45 SaveWindowRPC = 46 SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC = 48", "SetDefaultLightListRPC = 72 ResetLightListRPC = 73 SetAnimationAttributesRPC = 74 SetWindowAreaRPC", "= 174 SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC =", "CopyViewToWindowRPC = 96 CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC", "= 163 ResizeWindowRPC = 164 MoveWindowRPC = 165 MoveAndResizeWindowRPC =", "DetachRPC = 1 AddWindowRPC = 2 DeleteWindowRPC = 3 SetWindowLayoutRPC", "ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC = 115 SetViewKeyframeRPC", "110 DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC = 113", "= 117 EnableToolbarRPC = 118 HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC =", "ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC = 102 ClearRefLinesRPC = 103 SetRenderingAttributesRPC", "= 86 ToggleLockViewModeRPC = 87 ToggleFullFrameRPC = 88 UndoViewRPC =", "RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC", "= 88 UndoViewRPC = 89 RedoViewRPC = 90 InvertBackgroundRPC =", "SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC = 164 MoveWindowRPC", "170 SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC = 173", "19 AnimationSetNFramesRPC = 20 AnimationPlayRPC = 21 AnimationReversePlayRPC = 22", "0 DetachRPC = 1 AddWindowRPC = 2 DeleteWindowRPC = 3", "SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC", "86 ToggleLockViewModeRPC = 87 ToggleFullFrameRPC = 88 UndoViewRPC = 89", "= 161 SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC =", "= 47 SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC =", "SetPlotOrderToLastRPC = 192 RenamePickLabelRPC = 193 GetQueryParametersRPC = 194 DDTConnectRPC", "= 91 ClearPickPointsRPC = 92 SetWindowModeRPC = 93 EnableToolRPC =", "SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC", "AddOperatorRPC = 39 AddInitializedOperatorRPC = 40 PromoteOperatorRPC = 41 DemoteOperatorRPC", "122 SetToolbarIconSizeRPC = 123 SaveViewRPC = 124 SetGlobalLineoutAttributesRPC = 125", "= 9 ActivateDatabaseRPC = 10 CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC =", "= 42 RemoveOperatorRPC = 43 RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC =", "= 99 ClearCacheRPC = 100 ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC =", "43 RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC = 45 SaveWindowRPC = 46", "162 ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC = 164 MoveWindowRPC = 165", "146 ResetLineoutColorRPC = 147 SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC = 149", "SetToolUpdateModeRPC = 95 CopyViewToWindowRPC = 96 CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC", "SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC", "55 HideAllWindowsRPC = 56 UpdateColorTableRPC = 57 SetAnnotationAttributesRPC = 58", "= 108 ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC =", "= 115 SetViewKeyframeRPC = 116 OpenMDServerRPC = 117 EnableToolbarRPC =", "= 103 SetRenderingAttributesRPC = 104 QueryRPC = 105 CloneWindowRPC =", "= 73 SetAnimationAttributesRPC = 74 SetWindowAreaRPC = 75 PrintWindowRPC =", "36 SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC = 38 AddOperatorRPC = 39", "149 ResetInteractorAttributesRPC = 150 GetProcInfoRPC = 151 SendSimulationCommandRPC = 152", "181 LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC = 184", "14 ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC = 16 OverlayDatabaseRPC = 17", "39 AddInitializedOperatorRPC = 40 PromoteOperatorRPC = 41 DemoteOperatorRPC = 42", "SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC", "ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC = 147 SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC", "MoveWindowRPC = 165 MoveAndResizeWindowRPC = 166 SetStateLoggingRPC = 167 ConstructDataBinningRPC", "CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC = 99 ClearCacheRPC", "= 41 DemoteOperatorRPC = 42 RemoveOperatorRPC = 43 RemoveLastOperatorRPC =", "SetActiveWindowRPC = 5 ClearWindowRPC = 6 ClearAllWindowsRPC = 7 OpenDatabaseRPC", "153 ExportDBRPC = 154 SetTryHarderCyclesTimesRPC = 155 OpenClientRPC = 156", "RemoveAllOperatorsRPC = 45 SaveWindowRPC = 46 SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC", "150 GetProcInfoRPC = 151 SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC = 153", "= 139 ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC =", "90 InvertBackgroundRPC = 91 ClearPickPointsRPC = 92 SetWindowModeRPC = 93", "114 MoveViewKeyframeRPC = 115 SetViewKeyframeRPC = 116 OpenMDServerRPC = 117", "= 11 CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC =", "= 167 ConstructDataBinningRPC = 168 RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC =", "CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC = 99 ClearCacheRPC = 100 ClearCacheForAllEnginesRPC", "ConstructDataBinningRPC = 168 RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC", "108 ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC = 111", "165 MoveAndResizeWindowRPC = 166 SetStateLoggingRPC = 167 ConstructDataBinningRPC = 168", "= 119 HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC =", "= 38 AddOperatorRPC = 39 AddInitializedOperatorRPC = 40 PromoteOperatorRPC =", "= 33 DrawPlotsRPC = 34 DisableRedrawRPC = 35 RedrawRPC =", "ChangeActivePlotsVarRPC = 38 AddOperatorRPC = 39 AddInitializedOperatorRPC = 40 PromoteOperatorRPC", "= 30 MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC = 32 HideActivePlotsRPC =", "44 RemoveAllOperatorsRPC = 45 SaveWindowRPC = 46 SetDefaultPlotOptionsRPC = 47", "= 50 WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC =", "ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC = 123 SaveViewRPC", "ResetLineoutColorRPC = 147 SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC", "SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC = 50 WriteConfigFileRPC", "60 SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC = 63", "SetWindowAreaRPC = 75 PrintWindowRPC = 76 ResetViewRPC = 77 RecenterViewRPC", "62 SetViewAxisArrayRPC = 63 SetViewCurveRPC = 64 SetView2DRPC = 65", "174 SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC = 177", "172 SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC = 175", "112 ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC = 115", "AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC", "156 OpenGUIClientRPC = 157 OpenCLIClientRPC = 158 SuppressQueryOutputRPC = 159", "72 ResetLightListRPC = 73 SetAnimationAttributesRPC = 74 SetWindowAreaRPC = 75", "GetQueryParametersRPC = 194 DDTConnectRPC = 195 DDTFocusRPC = 196 ReleaseToDDTRPC", "SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC", "ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC", "= 26 SetActiveTimeSliderRPC = 27 AddPlotRPC = 28 SetPlotFrameRangeRPC =", "IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC = 55 HideAllWindowsRPC", "SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC = 38 AddOperatorRPC = 39 AddInitializedOperatorRPC", "= 141 ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC =", "140 SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC = 143", "= 58 SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC =", "105 CloneWindowRPC = 106 SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC = 108", "OpenDatabaseRPC = 8 CloseDatabaseRPC = 9 ActivateDatabaseRPC = 10 CheckForNewStatesRPC", "142 SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC = 145", "= 79 ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC =", "177 SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC = 180", "2 DeleteWindowRPC = 3 SetWindowLayoutRPC = 4 SetActiveWindowRPC = 5", "= 169 SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC =", "= 65 SetView3DRPC = 66 ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC =", "= 10 CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC = 12 AlterDatabaseCorrelationRPC =", "104 QueryRPC = 105 CloneWindowRPC = 106 SetMaterialAttributesRPC = 107", "148 SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC = 150 GetProcInfoRPC = 151", "= 149 ResetInteractorAttributesRPC = 150 GetProcInfoRPC = 151 SendSimulationCommandRPC =", "91 ClearPickPointsRPC = 92 SetWindowModeRPC = 93 EnableToolRPC = 94", "38 AddOperatorRPC = 39 AddInitializedOperatorRPC = 40 PromoteOperatorRPC = 41", "178 ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC = 181", "= 94 SetToolUpdateModeRPC = 95 CopyViewToWindowRPC = 96 CopyLightingToWindowRPC =", "106 SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC = 109", "= 71 SetDefaultLightListRPC = 72 ResetLightListRPC = 73 SetAnimationAttributesRPC =", "13 DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC = 16", "EnableToolbarRPC = 118 HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC", "DrawPlotsRPC = 34 DisableRedrawRPC = 35 RedrawRPC = 36 SetActivePlotsRPC", "107 SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC = 110", "= 143 SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC =", "SetOperatorOptionsRPC = 50 WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC", "= 93 EnableToolRPC = 94 SetToolUpdateModeRPC = 95 CopyViewToWindowRPC =", "103 SetRenderingAttributesRPC = 104 QueryRPC = 105 CloneWindowRPC = 106", "50 WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC = 53", "= 194 DDTConnectRPC = 195 DDTFocusRPC = 196 ReleaseToDDTRPC =", "TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC", "CloneWindowRPC = 106 SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC", "= 80 ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC =", "= 29 DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC =", "DeleteActivePlotsRPC = 32 HideActivePlotsRPC = 33 DrawPlotsRPC = 34 DisableRedrawRPC", "= 14 ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC = 16 OverlayDatabaseRPC =", "SetWindowModeRPC = 93 EnableToolRPC = 94 SetToolUpdateModeRPC = 95 CopyViewToWindowRPC", "UndoViewRPC = 89 RedoViewRPC = 90 InvertBackgroundRPC = 91 ClearPickPointsRPC", "SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC", "ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC", "AnimationSetNFramesRPC = 20 AnimationPlayRPC = 21 AnimationReversePlayRPC = 22 AnimationStopRPC", "42 RemoveOperatorRPC = 43 RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC = 45", "54 ShowAllWindowsRPC = 55 HideAllWindowsRPC = 56 UpdateColorTableRPC = 57", "AddInitializedOperatorRPC = 40 PromoteOperatorRPC = 41 DemoteOperatorRPC = 42 RemoveOperatorRPC", "121 ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC = 123 SaveViewRPC = 124", "115 SetViewKeyframeRPC = 116 OpenMDServerRPC = 117 EnableToolbarRPC = 118", "= 104 QueryRPC = 105 CloneWindowRPC = 106 SetMaterialAttributesRPC =", "= 43 RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC = 45 SaveWindowRPC =", "= 172 SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC =", "SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC", "SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC = 150 GetProcInfoRPC = 151 SendSimulationCommandRPC", "= 101 SetViewExtentsTypeRPC = 102 ClearRefLinesRPC = 103 SetRenderingAttributesRPC =", "UpdateDBPluginInfoRPC = 153 ExportDBRPC = 154 SetTryHarderCyclesTimesRPC = 155 OpenClientRPC", "= 189 MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC =", "InvertBackgroundRPC = 91 ClearPickPointsRPC = 92 SetWindowModeRPC = 93 EnableToolRPC", "class RPCType(object): CloseRPC = 0 DetachRPC = 1 AddWindowRPC =", "SetViewCurveRPC = 64 SetView2DRPC = 65 SetView3DRPC = 66 ResetPlotOptionsRPC", "= 122 SetToolbarIconSizeRPC = 123 SaveViewRPC = 124 SetGlobalLineoutAttributesRPC =", "= 1 AddWindowRPC = 2 DeleteWindowRPC = 3 SetWindowLayoutRPC =", "= 132 HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC =", "RenamePickLabelRPC = 193 GetQueryParametersRPC = 194 DDTConnectRPC = 195 DDTFocusRPC", "129 ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC = 131 AddAnnotationObjectRPC = 132", "= 155 OpenClientRPC = 156 OpenGUIClientRPC = 157 OpenCLIClientRPC =", "DeleteWindowRPC = 3 SetWindowLayoutRPC = 4 SetActiveWindowRPC = 5 ClearWindowRPC", "SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC", "= 140 SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC =", "DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC = 183 SetNamedSelectionAutoApplyRPC", "= 95 CopyViewToWindowRPC = 96 CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC =", "157 OpenCLIClientRPC = 158 SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC = 160", "52 IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC = 55", "CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC", "ToggleLockTimeRPC = 85 ToggleLockToolsRPC = 86 ToggleLockViewModeRPC = 87 ToggleFullFrameRPC", "ResetInteractorAttributesRPC = 150 GetProcInfoRPC = 151 SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC", "= 157 OpenCLIClientRPC = 158 SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC =", "= 66 ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC = 68 SetAppearanceRPC =", "= 55 HideAllWindowsRPC = 56 UpdateColorTableRPC = 57 SetAnnotationAttributesRPC =", "175 TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC = 178", "ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC", "SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC", "OpenGUIClientRPC = 157 OpenCLIClientRPC = 158 SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC", "32 HideActivePlotsRPC = 33 DrawPlotsRPC = 34 DisableRedrawRPC = 35", "96 CopyLightingToWindowRPC = 97 CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC = 99", "= 28 SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC =", "132 HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC = 135", "ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC = 141 ChooseCenterOfRotationRPC", "59 ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC = 62", "AnimationPlayRPC = 21 AnimationReversePlayRPC = 22 AnimationStopRPC = 23 TimeSliderNextStateRPC", "= 68 SetAppearanceRPC = 69 ProcessExpressionsRPC = 70 SetLightListRPC =", "SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC", "ClearRefLinesRPC = 103 SetRenderingAttributesRPC = 104 QueryRPC = 105 CloneWindowRPC", "74 SetWindowAreaRPC = 75 PrintWindowRPC = 76 ResetViewRPC = 77", "= 76 ResetViewRPC = 77 RecenterViewRPC = 78 ToggleAllowPopupRPC =", "sys class RPCType(object): CloseRPC = 0 DetachRPC = 1 AddWindowRPC", "SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC", "176 SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC = 179", "SetActiveTimeSliderRPC = 27 AddPlotRPC = 28 SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC", "= 85 ToggleLockToolsRPC = 86 ToggleLockViewModeRPC = 87 ToggleFullFrameRPC =", "ClearPickPointsRPC = 92 SetWindowModeRPC = 93 EnableToolRPC = 94 SetToolUpdateModeRPC", "= 92 SetWindowModeRPC = 93 EnableToolRPC = 94 SetToolUpdateModeRPC =", "= 87 ToggleFullFrameRPC = 88 UndoViewRPC = 89 RedoViewRPC =", "1 AddWindowRPC = 2 DeleteWindowRPC = 3 SetWindowLayoutRPC = 4", "= 40 PromoteOperatorRPC = 41 DemoteOperatorRPC = 42 RemoveOperatorRPC =", "ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC = 82 TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC", "116 OpenMDServerRPC = 117 EnableToolbarRPC = 118 HideToolbarsRPC = 119", "130 ResetPickAttributesRPC = 131 AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC = 133", "3 SetWindowLayoutRPC = 4 SetActiveWindowRPC = 5 ClearWindowRPC = 6", "ShowAllWindowsRPC = 55 HideAllWindowsRPC = 56 UpdateColorTableRPC = 57 SetAnnotationAttributesRPC", "HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC = 121 ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC", "70 SetLightListRPC = 71 SetDefaultLightListRPC = 72 ResetLightListRPC = 73", "128 ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC = 131", "TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC", "ClearCacheRPC = 100 ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC = 102 ClearRefLinesRPC", "DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC = 16 OverlayDatabaseRPC", "= 67 ResetOperatorOptionsRPC = 68 SetAppearanceRPC = 69 ProcessExpressionsRPC =", "SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC", "ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC = 16 OverlayDatabaseRPC = 17 OpenComputeEngineRPC", "192 RenamePickLabelRPC = 193 GetQueryParametersRPC = 194 DDTConnectRPC = 195", "69 ProcessExpressionsRPC = 70 SetLightListRPC = 71 SetDefaultLightListRPC = 72", "AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC", "= 6 ClearAllWindowsRPC = 7 OpenDatabaseRPC = 8 CloseDatabaseRPC =", "6 ClearAllWindowsRPC = 7 OpenDatabaseRPC = 8 CloseDatabaseRPC = 9", "= 2 DeleteWindowRPC = 3 SetWindowLayoutRPC = 4 SetActiveWindowRPC =", "28 SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC = 31", "= 12 AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC =", "= 125 SetPickAttributesRPC = 126 ExportColorTableRPC = 127 ExportEntireStateRPC =", "154 SetTryHarderCyclesTimesRPC = 155 OpenClientRPC = 156 OpenGUIClientRPC = 157", "133 DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC = 136", "SetTryHarderCyclesTimesRPC = 155 OpenClientRPC = 156 OpenGUIClientRPC = 157 OpenCLIClientRPC", "161 SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC = 163 ResizeWindowRPC = 164", "30 MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC = 32 HideActivePlotsRPC = 33", "= 59 ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC =", "= 107 SetDefaultMaterialAttributesRPC = 108 ResetMaterialAttributesRPC = 109 SetPlotDatabaseStateRPC =", "= 13 DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC = 15 ReplaceDatabaseRPC =", "= 31 DeleteActivePlotsRPC = 32 HideActivePlotsRPC = 33 DrawPlotsRPC =", "SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC", "= 70 SetLightListRPC = 71 SetDefaultLightListRPC = 72 ResetLightListRPC =", "143 SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC = 146", "= 177 SetSuppressMessagesRPC = 178 ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC =", "ToggleSpinModeRPC = 84 ToggleLockTimeRPC = 85 ToggleLockToolsRPC = 86 ToggleLockViewModeRPC", "= 49 SetOperatorOptionsRPC = 50 WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC =", "= 60 SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC =", "OpenComputeEngineRPC = 18 CloseComputeEngineRPC = 19 AnimationSetNFramesRPC = 20 AnimationPlayRPC", "= 134 RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC =", "33 DrawPlotsRPC = 34 DisableRedrawRPC = 35 RedrawRPC = 36", "48 SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC = 50 WriteConfigFileRPC = 51", "GetProcInfoRPC = 151 SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC = 153 ExportDBRPC", "= 186 MenuQuitRPC = 187 SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC =", "SetToolbarIconSizeRPC = 123 SaveViewRPC = 124 SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC", "61 SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC = 63 SetViewCurveRPC = 64", "126 ExportColorTableRPC = 127 ExportEntireStateRPC = 128 ImportEntireStateRPC = 129", "ResetPickAttributesRPC = 131 AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC", "= 128 ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC =", "5 ClearWindowRPC = 6 ClearAllWindowsRPC = 7 OpenDatabaseRPC = 8", "= 37 ChangeActivePlotsVarRPC = 38 AddOperatorRPC = 39 AddInitializedOperatorRPC =", "= 5 ClearWindowRPC = 6 ClearAllWindowsRPC = 7 OpenDatabaseRPC =", "SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC = 30 MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC", "RemoveOperatorRPC = 43 RemoveLastOperatorRPC = 44 RemoveAllOperatorsRPC = 45 SaveWindowRPC", "= 124 SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC = 126 ExportColorTableRPC =", "= 150 GetProcInfoRPC = 151 SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC =", "SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC = 63 SetViewCurveRPC = 64 SetView2DRPC", "= 148 SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC = 150 GetProcInfoRPC =", "171 SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC = 174", "ChooseCenterOfRotationRPC = 142 SetCenterOfRotationRPC = 143 SetQueryOverTimeAttributesRPC = 144 SetDefaultQueryOverTimeAttributesRPC", "166 SetStateLoggingRPC = 167 ConstructDataBinningRPC = 168 RequestMetaDataRPC = 169", "190 SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC = 192 RenamePickLabelRPC = 193", "40 PromoteOperatorRPC = 41 DemoteOperatorRPC = 42 RemoveOperatorRPC = 43", "SaveWindowRPC = 46 SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC", "SetAppearanceRPC = 69 ProcessExpressionsRPC = 70 SetLightListRPC = 71 SetDefaultLightListRPC", "SetDefaultOperatorOptionsRPC = 49 SetOperatorOptionsRPC = 50 WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC", "DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC = 55 HideAllWindowsRPC = 56 UpdateColorTableRPC", "ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC = 131 AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC", "= 136 SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC =", "= 27 AddPlotRPC = 28 SetPlotFrameRangeRPC = 29 DeletePlotKeyframeRPC =", "= 192 RenamePickLabelRPC = 193 GetQueryParametersRPC = 194 DDTConnectRPC =", "= 75 PrintWindowRPC = 76 ResetViewRPC = 77 RecenterViewRPC =", "ResizeWindowRPC = 164 MoveWindowRPC = 165 MoveAndResizeWindowRPC = 166 SetStateLoggingRPC", "22 AnimationStopRPC = 23 TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC = 25", "37 ChangeActivePlotsVarRPC = 38 AddOperatorRPC = 39 AddInitializedOperatorRPC = 40", "97 CopyAnnotationsToWindowRPC = 98 CopyPlotsToWindowRPC = 99 ClearCacheRPC = 100", "= 110 DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC =", "import sys class RPCType(object): CloseRPC = 0 DetachRPC = 1", "= 179 CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC =", "RPCType(object): CloseRPC = 0 DetachRPC = 1 AddWindowRPC = 2", "137 SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC = 140", "OpenCLIClientRPC = 158 SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC", "123 SaveViewRPC = 124 SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC = 126", "SuppressQueryOutputRPC = 159 SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC", "= 21 AnimationReversePlayRPC = 22 AnimationStopRPC = 23 TimeSliderNextStateRPC =", "76 ResetViewRPC = 77 RecenterViewRPC = 78 ToggleAllowPopupRPC = 79", "QueryRPC = 105 CloneWindowRPC = 106 SetMaterialAttributesRPC = 107 SetDefaultMaterialAttributesRPC", "SetView2DRPC = 65 SetView3DRPC = 66 ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC", "DeleteActiveAnnotationObjectsRPC = 134 RaiseActiveAnnotationObjectsRPC = 135 LowerActiveAnnotationObjectsRPC = 136 SetAnnotationObjectOptionsRPC", "= 164 MoveWindowRPC = 165 MoveAndResizeWindowRPC = 166 SetStateLoggingRPC =", "= 4 SetActiveWindowRPC = 5 ClearWindowRPC = 6 ClearAllWindowsRPC =", "ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC", "ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC = 130 ResetPickAttributesRPC = 131 AddAnnotationObjectRPC", "RequestMetaDataRPC = 169 SetTreatAllDBsAsTimeVaryingRPC = 170 SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC", "= 127 ExportEntireStateRPC = 128 ImportEntireStateRPC = 129 ImportEntireStateWithDifferentSourcesRPC =", "= 175 TurnOffAllLocksRPC = 176 SetDefaultFileOpenOptionsRPC = 177 SetSuppressMessagesRPC =", "RedoViewRPC = 90 InvertBackgroundRPC = 91 ClearPickPointsRPC = 92 SetWindowModeRPC", "109 SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC = 112", "= 147 SetInteractorAttributesRPC = 148 SetDefaultInteractorAttributesRPC = 149 ResetInteractorAttributesRPC =", "= 178 ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC =", "SetView3DRPC = 66 ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC = 68 SetAppearanceRPC", "ClearAllWindowsRPC = 7 OpenDatabaseRPC = 8 CloseDatabaseRPC = 9 ActivateDatabaseRPC", "= 130 ResetPickAttributesRPC = 131 AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC =", "= 121 ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC = 123 SaveViewRPC =", "= 193 GetQueryParametersRPC = 194 DDTConnectRPC = 195 DDTFocusRPC =", "= 15 ReplaceDatabaseRPC = 16 OverlayDatabaseRPC = 17 OpenComputeEngineRPC =", "136 SetAnnotationObjectOptionsRPC = 137 SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC = 139", "ShowToolbarsForAllWindowsRPC = 122 SetToolbarIconSizeRPC = 123 SaveViewRPC = 124 SetGlobalLineoutAttributesRPC", "= 137 SetDefaultAnnotationObjectListRPC = 138 ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC =", "SaveViewRPC = 124 SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC = 126 ExportColorTableRPC", "118 HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC = 121", "SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC", "31 DeleteActivePlotsRPC = 32 HideActivePlotsRPC = 33 DrawPlotsRPC = 34", "HideActivePlotsRPC = 33 DrawPlotsRPC = 34 DisableRedrawRPC = 35 RedrawRPC", "= 111 MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC =", "ClearWindowRPC = 6 ClearAllWindowsRPC = 7 OpenDatabaseRPC = 8 CloseDatabaseRPC", "18 CloseComputeEngineRPC = 19 AnimationSetNFramesRPC = 20 AnimationPlayRPC = 21", "= 7 OpenDatabaseRPC = 8 CloseDatabaseRPC = 9 ActivateDatabaseRPC =", "46 SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC = 48 SetDefaultOperatorOptionsRPC = 49", "78 ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC = 81", "66 ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC = 68 SetAppearanceRPC = 69", "MovePlotKeyframeRPC = 31 DeleteActivePlotsRPC = 32 HideActivePlotsRPC = 33 DrawPlotsRPC", "DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC", "WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC = 52 IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC", "= 72 ResetLightListRPC = 73 SetAnimationAttributesRPC = 74 SetWindowAreaRPC =", "= 84 ToggleLockTimeRPC = 85 ToggleLockToolsRPC = 86 ToggleLockViewModeRPC =", "= 159 SetQueryFloatFormatRPC = 160 SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC =", "= 173 CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC =", "82 TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC = 84 ToggleLockTimeRPC = 85", "34 DisableRedrawRPC = 35 RedrawRPC = 36 SetActivePlotsRPC = 37", "SetPlotDatabaseStateRPC = 110 DeletePlotDatabaseKeyframeRPC = 111 MovePlotDatabaseKeyframeRPC = 112 ClearViewKeyframesRPC", "100 ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC = 102 ClearRefLinesRPC = 103", "= 82 TogglePerspectiveViewRPC = 83 ToggleSpinModeRPC = 84 ToggleLockTimeRPC =", "113 DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC = 115 SetViewKeyframeRPC = 116", "ExportDBRPC = 154 SetTryHarderCyclesTimesRPC = 155 OpenClientRPC = 156 OpenGUIClientRPC", "EnableToolRPC = 94 SetToolUpdateModeRPC = 95 CopyViewToWindowRPC = 96 CopyLightingToWindowRPC", "ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC = 68 SetAppearanceRPC = 69 ProcessExpressionsRPC", "= 160 SetMeshManagementAttributesRPC = 161 SetDefaultMeshManagementAttributesRPC = 162 ResetMeshManagementAttributesRPC =", "= 22 AnimationStopRPC = 23 TimeSliderNextStateRPC = 24 TimeSliderPreviousStateRPC =", "RedrawRPC = 36 SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC = 38 AddOperatorRPC", "138 ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC = 141", "163 ResizeWindowRPC = 164 MoveWindowRPC = 165 MoveAndResizeWindowRPC = 166", "CloseDatabaseRPC = 9 ActivateDatabaseRPC = 10 CheckForNewStatesRPC = 11 CreateDatabaseCorrelationRPC", "21 AnimationReversePlayRPC = 22 AnimationStopRPC = 23 TimeSliderNextStateRPC = 24", "188 MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC = 191", "HideAllWindowsRPC = 56 UpdateColorTableRPC = 57 SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC", "155 OpenClientRPC = 156 OpenGUIClientRPC = 157 OpenCLIClientRPC = 158", "164 MoveWindowRPC = 165 MoveAndResizeWindowRPC = 166 SetStateLoggingRPC = 167", "193 GetQueryParametersRPC = 194 DDTConnectRPC = 195 DDTFocusRPC = 196", "25 SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC = 27 AddPlotRPC = 28", "SetWindowLayoutRPC = 4 SetActiveWindowRPC = 5 ClearWindowRPC = 6 ClearAllWindowsRPC", "MovePlotOrderTowardLastRPC = 190 SetPlotOrderToFirstRPC = 191 SetPlotOrderToLastRPC = 192 RenamePickLabelRPC", "12 AlterDatabaseCorrelationRPC = 13 DeleteDatabaseCorrelationRPC = 14 ReOpenDatabaseRPC = 15", "= 19 AnimationSetNFramesRPC = 20 AnimationPlayRPC = 21 AnimationReversePlayRPC =", "= 100 ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC = 102 ClearRefLinesRPC =", "65 SetView3DRPC = 66 ResetPlotOptionsRPC = 67 ResetOperatorOptionsRPC = 68", "8 CloseDatabaseRPC = 9 ActivateDatabaseRPC = 10 CheckForNewStatesRPC = 11", "71 SetDefaultLightListRPC = 72 ResetLightListRPC = 73 SetAnimationAttributesRPC = 74", "ApplyNamedSelectionRPC = 179 CreateNamedSelectionRPC = 180 DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC", "= 170 SetCreateMeshQualityExpressionsRPC = 171 SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC =", "49 SetOperatorOptionsRPC = 50 WriteConfigFileRPC = 51 ConnectToMetaDataServerRPC = 52", "SetRenderingAttributesRPC = 104 QueryRPC = 105 CloneWindowRPC = 106 SetMaterialAttributesRPC", "17 OpenComputeEngineRPC = 18 CloseComputeEngineRPC = 19 AnimationSetNFramesRPC = 20", "= 69 ProcessExpressionsRPC = 70 SetLightListRPC = 71 SetDefaultLightListRPC =", "= 54 ShowAllWindowsRPC = 55 HideAllWindowsRPC = 56 UpdateColorTableRPC =", "87 ToggleFullFrameRPC = 88 UndoViewRPC = 89 RedoViewRPC = 90", "= 171 SetCreateTimeDerivativeExpressionsRPC = 172 SetCreateVectorMagnitudeExpressionsRPC = 173 CopyActivePlotsRPC =", "57 SetAnnotationAttributesRPC = 58 SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC = 60", "InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC = 187 SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC", "MenuQuitRPC = 187 SetPlotDescriptionRPC = 188 MovePlotOrderTowardFirstRPC = 189 MovePlotOrderTowardLastRPC", "= 83 ToggleSpinModeRPC = 84 ToggleLockTimeRPC = 85 ToggleLockToolsRPC =", "151 SendSimulationCommandRPC = 152 UpdateDBPluginInfoRPC = 153 ExportDBRPC = 154", "24 TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC = 27", "= 34 DisableRedrawRPC = 35 RedrawRPC = 36 SetActivePlotsRPC =", "= 180 DeleteNamedSelectionRPC = 181 LoadNamedSelectionRPC = 182 SaveNamedSelectionRPC =", "183 SetNamedSelectionAutoApplyRPC = 184 UpdateNamedSelectionRPC = 185 InitializeNamedSelectionVariablesRPC = 186", "173 CopyActivePlotsRPC = 174 SetPlotFollowsTimeRPC = 175 TurnOffAllLocksRPC = 176", "185 InitializeNamedSelectionVariablesRPC = 186 MenuQuitRPC = 187 SetPlotDescriptionRPC = 188", "= 152 UpdateDBPluginInfoRPC = 153 ExportDBRPC = 154 SetTryHarderCyclesTimesRPC =", "= 36 SetActivePlotsRPC = 37 ChangeActivePlotsVarRPC = 38 AddOperatorRPC =", "SetDefaultQueryOverTimeAttributesRPC = 145 ResetQueryOverTimeAttributesRPC = 146 ResetLineoutColorRPC = 147 SetInteractorAttributesRPC", "ToggleAllowPopupRPC = 79 ToggleMaintainViewModeRPC = 80 ToggleBoundingBoxModeRPC = 81 ToggleCameraViewModeRPC", "= 112 ClearViewKeyframesRPC = 113 DeleteViewKeyframeRPC = 114 MoveViewKeyframeRPC =", "= 138 ResetAnnotationObjectListRPC = 139 ResetPickLetterRPC = 140 SetDefaultPickAttributesRPC =", "= 195 DDTFocusRPC = 196 ReleaseToDDTRPC = 197 MaxRPC =", "= 90 InvertBackgroundRPC = 91 ClearPickPointsRPC = 92 SetWindowModeRPC =", "26 SetActiveTimeSliderRPC = 27 AddPlotRPC = 28 SetPlotFrameRangeRPC = 29", "ResetLightListRPC = 73 SetAnimationAttributesRPC = 74 SetWindowAreaRPC = 75 PrintWindowRPC", "SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC = 62 SetViewAxisArrayRPC = 63 SetViewCurveRPC", "= 44 RemoveAllOperatorsRPC = 45 SaveWindowRPC = 46 SetDefaultPlotOptionsRPC =", "85 ToggleLockToolsRPC = 86 ToggleLockViewModeRPC = 87 ToggleFullFrameRPC = 88", "SetDefaultAnnotationAttributesRPC = 59 ResetAnnotationAttributesRPC = 60 SetKeyframeAttributesRPC = 61 SetPlotSILRestrictionRPC", "TimeSliderPreviousStateRPC = 25 SetTimeSliderStateRPC = 26 SetActiveTimeSliderRPC = 27 AddPlotRPC", "92 SetWindowModeRPC = 93 EnableToolRPC = 94 SetToolUpdateModeRPC = 95", "CloseComputeEngineRPC = 19 AnimationSetNFramesRPC = 20 AnimationPlayRPC = 21 AnimationReversePlayRPC", "124 SetGlobalLineoutAttributesRPC = 125 SetPickAttributesRPC = 126 ExportColorTableRPC = 127", "CopyPlotsToWindowRPC = 99 ClearCacheRPC = 100 ClearCacheForAllEnginesRPC = 101 SetViewExtentsTypeRPC", "MoveAndResizeWindowRPC = 166 SetStateLoggingRPC = 167 ConstructDataBinningRPC = 168 RequestMetaDataRPC", "= 98 CopyPlotsToWindowRPC = 99 ClearCacheRPC = 100 ClearCacheForAllEnginesRPC =", "= 118 HideToolbarsRPC = 119 HideToolbarsForAllWindowsRPC = 120 ShowToolbarsRPC =", "= 52 IconifyAllWindowsRPC = 53 DeIconifyAllWindowsRPC = 54 ShowAllWindowsRPC =", "84 ToggleLockTimeRPC = 85 ToggleLockToolsRPC = 86 ToggleLockViewModeRPC = 87", "131 AddAnnotationObjectRPC = 132 HideActiveAnnotationObjectsRPC = 133 DeleteActiveAnnotationObjectsRPC = 134", "= 63 SetViewCurveRPC = 64 SetView2DRPC = 65 SetView3DRPC =", "= 45 SaveWindowRPC = 46 SetDefaultPlotOptionsRPC = 47 SetPlotOptionsRPC =" ]
[ "# A copy of the BSD 3-Clause License is included", "BSD 3-Clause License, except for the third-party components listed below.", "# Tencent is pleased to support the open source community", "os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v'))", "sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful()) if __name__", "# '''单元测试 ''' import unittest import os import sys test_dir", "the BSD 3-Clause License is included in this file. #", "making QT4C available. # Copyright (C) 2020 THL A29 Limited,", "under the BSD 3-Clause License, except for the third-party components", "reserved. # QT4C is licensed under the BSD 3-Clause License,", "licensed under the BSD 3-Clause License, except for the third-party", "Tencent is pleased to support the open source community by", "the third-party components listed below. # A copy of the", "by making QT4C available. # Copyright (C) 2020 THL A29", "a Tencent company. All rights reserved. # QT4C is licensed", "Limited, a Tencent company. All rights reserved. # QT4C is", "available. # Copyright (C) 2020 THL A29 Limited, a Tencent", "import os import sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def", "2020 THL A29 Limited, a Tencent company. All rights reserved.", "unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful())", "BSD 3-Clause License is included in this file. # '''单元测试", "included in this file. # '''单元测试 ''' import unittest import", "3-Clause License, except for the third-party components listed below. #", "company. All rights reserved. # QT4C is licensed under the", "= unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful()) if __name__ == '__main__':", "listed below. # A copy of the BSD 3-Clause License", "main(): runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py')", "# # Tencent is pleased to support the open source", "-*- coding: utf-8 -*- # # Tencent is pleased to", "''' import unittest import os import sys test_dir = os.path.dirname(os.path.abspath(__file__))", "source community by making QT4C available. # Copyright (C) 2020", "to support the open source community by making QT4C available.", "open source community by making QT4C available. # Copyright (C)", "Tencent company. All rights reserved. # QT4C is licensed under", "the BSD 3-Clause License, except for the third-party components listed", "this file. # '''单元测试 ''' import unittest import os import", "os.path.dirname(test_dir)) def main(): runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite =", "components listed below. # A copy of the BSD 3-Clause", "# -*- coding: utf-8 -*- # # Tencent is pleased", "# Copyright (C) 2020 THL A29 Limited, a Tencent company.", "All rights reserved. # QT4C is licensed under the BSD", "is pleased to support the open source community by making", "is licensed under the BSD 3-Clause License, except for the", "import sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner", "suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful()) if __name__ ==", "test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner = unittest.TextTestRunner(verbosity=10", "-*- # # Tencent is pleased to support the open", "import unittest import os import sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0,", "sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite", "for the third-party components listed below. # A copy of", "'''单元测试 ''' import unittest import os import sys test_dir =", "third-party components listed below. # A copy of the BSD", "sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner =", "os import sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main():", "copy of the BSD 3-Clause License is included in this", "THL A29 Limited, a Tencent company. All rights reserved. #", "support the open source community by making QT4C available. #", "is included in this file. # '''单元测试 ''' import unittest", "A copy of the BSD 3-Clause License is included in", "community by making QT4C available. # Copyright (C) 2020 THL", "(C) 2020 THL A29 Limited, a Tencent company. All rights", "# QT4C is licensed under the BSD 3-Clause License, except", "License is included in this file. # '''单元测试 ''' import", "utf-8 -*- # # Tencent is pleased to support the", "+ sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful()) if", "A29 Limited, a Tencent company. All rights reserved. # QT4C", "3-Clause License is included in this file. # '''单元测试 '''", "the open source community by making QT4C available. # Copyright", "def main(): runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir,", "rights reserved. # QT4C is licensed under the BSD 3-Clause", "QT4C available. # Copyright (C) 2020 THL A29 Limited, a", "except for the third-party components listed below. # A copy", "coding: utf-8 -*- # # Tencent is pleased to support", "file. # '''单元测试 ''' import unittest import os import sys", "unittest import os import sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir))", "runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise", "Copyright (C) 2020 THL A29 Limited, a Tencent company. All", "in this file. # '''单元测试 ''' import unittest import os", "QT4C is licensed under the BSD 3-Clause License, except for", "unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful()) if __name__ == '__main__': main()", "pleased to support the open source community by making QT4C", "= unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not", "below. # A copy of the BSD 3-Clause License is", "= os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner = unittest.TextTestRunner(verbosity=10 +", "License, except for the third-party components listed below. # A", "of the BSD 3-Clause License is included in this file." ]
[ "\"brute.h\" \"\"\", sources=['brute.c']) if __name__ == \"__main__\": ffibuilder.compile(verbose = True)", "\"\"\" #include \"brute.h\" \"\"\", sources=['brute.c']) if __name__ == \"__main__\": ffibuilder.compile(verbose", "cffi import FFI ffibuilder = FFI() ffibuilder.cdef(\"\"\" int test(int t);", "from cffi import FFI ffibuilder = FFI() ffibuilder.cdef(\"\"\" int test(int", "int test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\" \"\"\", sources=['brute.c'])", "test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\" \"\"\", sources=['brute.c']) if", "\"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\" \"\"\", sources=['brute.c']) if __name__ ==", "#include \"brute.h\" \"\"\", sources=['brute.c']) if __name__ == \"__main__\": ffibuilder.compile(verbose =", "ffibuilder.cdef(\"\"\" int test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\" \"\"\",", "FFI ffibuilder = FFI() ffibuilder.cdef(\"\"\" int test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\",", "FFI() ffibuilder.cdef(\"\"\" int test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\"", "import FFI ffibuilder = FFI() ffibuilder.cdef(\"\"\" int test(int t); \"\"\")", "ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\" \"\"\", sources=['brute.c']) if __name__ == \"__main__\":", "= FFI() ffibuilder.cdef(\"\"\" int test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include", "ffibuilder = FFI() ffibuilder.cdef(\"\"\" int test(int t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\"", "t); \"\"\") ffibuilder.set_source(\"_pi_cffi\", \"\"\" #include \"brute.h\" \"\"\", sources=['brute.c']) if __name__" ]
[ "advancement of the snake along the path.\"\"\" me = next((snake", "of the snake along the path.\"\"\" me = next((snake for", "from the board data.\"\"\" if self._snakes is None: snakes =", "of the Board.\"\"\" is_wall = (coordinate.x == -1 or coordinate.x", "or coordinate.x == self.width or coordinate.y == -1 or coordinate.y", "snake advanced along given path.\"\"\" new_board = copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id,", "me = next((snake for snake in self.snakes if snake.id ==", "copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self, snake_id: str, path: List[Coordinate]):", "path: List[Coordinate]): \"\"\"Return a new board with our snake advanced", "-> int: \"\"\"Get width of the board -- note: it's", "self._snakes @property def foods(self) -> List[Coordinate]: \"\"\"Retreive the list of", "return not is_wall def get_other_snakes(self, exclude_id) -> List[Snake]: \"\"\"Get the", "from typing import Tuple, List from src.coordinate import Coordinate from", "str, path: List[Coordinate]): \"\"\"Do the actual advancement of the snake", "None: snakes = [Snake(snake_data) for snake_data in self._data['snakes']] self._snakes =", "data.\"\"\" if self._foods is None: self._foods = [Coordinate(food_data) for food_data", "me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for coord in me.coordinates: print(coord) return self", "src.snake import Snake class Board: \"\"\"Track the cooardinates for all", "square.\"\"\" return self._data['width'] def is_coordinate_in_bounds(self, coordinate) -> bool: \"\"\"Check whether", "str, path: List[Coordinate]): \"\"\"Return a new board with our snake", "int: \"\"\"Get width of the board -- note: it's a", "in self.snakes if snake.id == snake_id), None) if not me:", "is_wall def get_other_snakes(self, exclude_id) -> List[Snake]: \"\"\"Get the List of", "return self._data['width'] def is_coordinate_in_bounds(self, coordinate) -> bool: \"\"\"Check whether or", "== self.width) return not is_wall def get_other_snakes(self, exclude_id) -> List[Snake]:", "= me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for coord in me.coordinates:", "def is_coordinate_in_bounds(self, coordinate) -> bool: \"\"\"Check whether or not the", "= [Snake(snake_data) for snake_data in self._data['snakes']] self._snakes = snakes return", "or coordinate.y == self.width) return not is_wall def get_other_snakes(self, exclude_id)", "None) if not me: raise ValueError(\"No snake for given id!\")", "List of Snakes whose IDs don't match the given ID.\"\"\"", "from src.coordinate import Coordinate from src.snake import Snake class Board:", "None: self._foods = [Coordinate(food_data) for food_data in self._data['food']] return self._foods", "the Coordinate is within the bounds of the Board.\"\"\" is_wall", "for all snakes and food in the game.\"\"\" def __init__(self,", "width of the board -- note: it's a square.\"\"\" return", "class Board: \"\"\"Track the cooardinates for all snakes and food", "in the game.\"\"\" def __init__(self, data): self._data = data self._snakes", "note: it's a square.\"\"\" return self._data['width'] def is_coordinate_in_bounds(self, coordinate) ->", "self._data['food']] return self._foods @property def width(self) -> int: \"\"\"Get width", "@property def width(self) -> int: \"\"\"Get width of the board", "+= path me.coordinates = me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for", "food_data in self._data['food']] return self._foods @property def width(self) -> int:", "is_coordinate_in_bounds(self, coordinate) -> bool: \"\"\"Check whether or not the Coordinate", "not is_wall def get_other_snakes(self, exclude_id) -> List[Snake]: \"\"\"Get the List", "= (coordinate.x == -1 or coordinate.x == self.width or coordinate.y", "= [Coordinate(food_data) for food_data in self._data['food']] return self._foods @property def", "and food in the game.\"\"\" def __init__(self, data): self._data =", "coordinate.y == self.width) return not is_wall def get_other_snakes(self, exclude_id) ->", "whether or not the Coordinate is within the bounds of", "the game.\"\"\" def __init__(self, data): self._data = data self._snakes =", "coordinate.x == self.width or coordinate.y == -1 or coordinate.y ==", "ID.\"\"\" return [snake for snake in self.snakes if snake.id !=", "our snake advanced along given path.\"\"\" new_board = copy.deepcopy(self) return", "if snake.id != exclude_id] def advance_snake_along_path(self, snake_id: str, path: List[Coordinate]):", "advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Return a new board with", "List[Coordinate]): \"\"\"Return a new board with our snake advanced along", "the snake along the path.\"\"\" me = next((snake for snake", "bounds of the Board.\"\"\" is_wall = (coordinate.x == -1 or", "List[Coordinate]): \"\"\"Do the actual advancement of the snake along the", "Board: \"\"\"Track the cooardinates for all snakes and food in", "if not me: raise ValueError(\"No snake for given id!\") me.coordinates", "== -1 or coordinate.x == self.width or coordinate.y == -1", "of Snakes whose IDs don't match the given ID.\"\"\" return", "board -- note: it's a square.\"\"\" return self._data['width'] def is_coordinate_in_bounds(self,", "is None: snakes = [Snake(snake_data) for snake_data in self._data['snakes']] self._snakes", "me: raise ValueError(\"No snake for given id!\") me.coordinates += path", "the list of snakes from the board data.\"\"\" if self._snakes", "food from the board data.\"\"\" if self._foods is None: self._foods", "\"\"\"Retreive the list of snakes from the board data.\"\"\" if", "advanced along given path.\"\"\" new_board = copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path)", "the Board.\"\"\" is_wall = (coordinate.x == -1 or coordinate.x ==", "the cooardinates for all snakes and food in the game.\"\"\"", "@property def snakes(self) -> List[Snake]: \"\"\"Retreive the list of snakes", "whose IDs don't match the given ID.\"\"\" return [snake for", "Coordinate from src.snake import Snake class Board: \"\"\"Track the cooardinates", "the list of food from the board data.\"\"\" if self._foods", "\"\"\"Track the cooardinates for all snakes and food in the", "list of food from the board data.\"\"\" if self._foods is", "a square.\"\"\" return self._data['width'] def is_coordinate_in_bounds(self, coordinate) -> bool: \"\"\"Check", "= copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self, snake_id: str, path:", "def foods(self) -> List[Coordinate]: \"\"\"Retreive the list of food from", "return [snake for snake in self.snakes if snake.id != exclude_id]", "given id!\") me.coordinates += path me.coordinates = me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1])", "for snake in self.snakes if snake.id != exclude_id] def advance_snake_along_path(self,", "for snake in self.snakes if snake.id == snake_id), None) if", "next((snake for snake in self.snakes if snake.id == snake_id), None)", "ValueError(\"No snake for given id!\") me.coordinates += path me.coordinates =", "or coordinate.y == -1 or coordinate.y == self.width) return not", "List[Coordinate]: \"\"\"Retreive the list of food from the board data.\"\"\"", "= None self._foods = None @property def snakes(self) -> List[Snake]:", "copy from typing import Tuple, List from src.coordinate import Coordinate", "the board -- note: it's a square.\"\"\" return self._data['width'] def", "given ID.\"\"\" return [snake for snake in self.snakes if snake.id", "self.snakes if snake.id != exclude_id] def advance_snake_along_path(self, snake_id: str, path:", "board data.\"\"\" if self._foods is None: self._foods = [Coordinate(food_data) for", "def __init__(self, data): self._data = data self._snakes = None self._foods", "snakes = [Snake(snake_data) for snake_data in self._data['snakes']] self._snakes = snakes", "snakes return self._snakes @property def foods(self) -> List[Coordinate]: \"\"\"Retreive the", "if self._snakes is None: snakes = [Snake(snake_data) for snake_data in", "path: List[Coordinate]): \"\"\"Do the actual advancement of the snake along", "coordinate) -> bool: \"\"\"Check whether or not the Coordinate is", "__init__(self, data): self._data = data self._snakes = None self._foods =", "from the board data.\"\"\" if self._foods is None: self._foods =", "width(self) -> int: \"\"\"Get width of the board -- note:", "a new board with our snake advanced along given path.\"\"\"", "[Snake(snake_data) for snake_data in self._data['snakes']] self._snakes = snakes return self._snakes", "import Coordinate from src.snake import Snake class Board: \"\"\"Track the", "in self._data['food']] return self._foods @property def width(self) -> int: \"\"\"Get", "Snakes whose IDs don't match the given ID.\"\"\" return [snake", "= data self._snakes = None self._foods = None @property def", "src.coordinate import Coordinate from src.snake import Snake class Board: \"\"\"Track", "data): self._data = data self._snakes = None self._foods = None", "def snakes(self) -> List[Snake]: \"\"\"Retreive the list of snakes from", "data.\"\"\" if self._snakes is None: snakes = [Snake(snake_data) for snake_data", "the bounds of the Board.\"\"\" is_wall = (coordinate.x == -1", "coordinate.y == -1 or coordinate.y == self.width) return not is_wall", "foods(self) -> List[Coordinate]: \"\"\"Retreive the list of food from the", "along the path.\"\"\" me = next((snake for snake in self.snakes", "self.width) return not is_wall def get_other_snakes(self, exclude_id) -> List[Snake]: \"\"\"Get", "snake for given id!\") me.coordinates += path me.coordinates = me.coordinates[len(path):]", "-1 or coordinate.x == self.width or coordinate.y == -1 or", "import Snake class Board: \"\"\"Track the cooardinates for all snakes", "is None: self._foods = [Coordinate(food_data) for food_data in self._data['food']] return", "__help_advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Do the actual advancement of", "-- note: it's a square.\"\"\" return self._data['width'] def is_coordinate_in_bounds(self, coordinate)", "== snake_id), None) if not me: raise ValueError(\"No snake for", "food in the game.\"\"\" def __init__(self, data): self._data = data", "snakes and food in the game.\"\"\" def __init__(self, data): self._data", "List[Snake]: \"\"\"Get the List of Snakes whose IDs don't match", "self._foods = None @property def snakes(self) -> List[Snake]: \"\"\"Retreive the", "-> List[Coordinate]: \"\"\"Retreive the list of food from the board", "or not the Coordinate is within the bounds of the", "return self._foods @property def width(self) -> int: \"\"\"Get width of", "get_other_snakes(self, exclude_id) -> List[Snake]: \"\"\"Get the List of Snakes whose", "@property def foods(self) -> List[Coordinate]: \"\"\"Retreive the list of food", "the board data.\"\"\" if self._foods is None: self._foods = [Coordinate(food_data)", "path.\"\"\" me = next((snake for snake in self.snakes if snake.id", "\"\"\"Board Module\"\"\" import copy from typing import Tuple, List from", "== -1 or coordinate.y == self.width) return not is_wall def", "list of snakes from the board data.\"\"\" if self._snakes is", "import copy from typing import Tuple, List from src.coordinate import", "Snake class Board: \"\"\"Track the cooardinates for all snakes and", "self._data['width'] def is_coordinate_in_bounds(self, coordinate) -> bool: \"\"\"Check whether or not", "from src.snake import Snake class Board: \"\"\"Track the cooardinates for", "\"\"\"Check whether or not the Coordinate is within the bounds", "self._foods @property def width(self) -> int: \"\"\"Get width of the", "new_board = copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self, snake_id: str,", "me.coordinates = me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for coord in", "the board data.\"\"\" if self._snakes is None: snakes = [Snake(snake_data)", "snake.id != exclude_id] def advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Return", "typing import Tuple, List from src.coordinate import Coordinate from src.snake", "def get_other_snakes(self, exclude_id) -> List[Snake]: \"\"\"Get the List of Snakes", "return self._snakes @property def foods(self) -> List[Coordinate]: \"\"\"Retreive the list", "me.coordinates += path me.coordinates = me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\")", "snake_data in self._data['snakes']] self._snakes = snakes return self._snakes @property def", "exclude_id) -> List[Snake]: \"\"\"Get the List of Snakes whose IDs", "me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for coord in me.coordinates: print(coord) return", "exclude_id] def advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Return a new", "it's a square.\"\"\" return self._data['width'] def is_coordinate_in_bounds(self, coordinate) -> bool:", "-> List[Snake]: \"\"\"Get the List of Snakes whose IDs don't", "== self.width or coordinate.y == -1 or coordinate.y == self.width)", "\"\"\"Get width of the board -- note: it's a square.\"\"\"", "(coordinate.x == -1 or coordinate.x == self.width or coordinate.y ==", "\"\"\"Retreive the list of food from the board data.\"\"\" if", "List from src.coordinate import Coordinate from src.snake import Snake class", "for snake_data in self._data['snakes']] self._snakes = snakes return self._snakes @property", "for food_data in self._data['food']] return self._foods @property def width(self) ->", "def advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Return a new board", "with our snake advanced along given path.\"\"\" new_board = copy.deepcopy(self)", "self._foods is None: self._foods = [Coordinate(food_data) for food_data in self._data['food']]", "path.\"\"\" new_board = copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self, snake_id:", "path) def __help_advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Do the actual", "snake_id: str, path: List[Coordinate]): \"\"\"Do the actual advancement of the", "snakes from the board data.\"\"\" if self._snakes is None: snakes", "= next((snake for snake in self.snakes if snake.id == snake_id),", "bool: \"\"\"Check whether or not the Coordinate is within the", "id!\") me.coordinates += path me.coordinates = me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new", "\"\"\"Get the List of Snakes whose IDs don't match the", "self._foods = [Coordinate(food_data) for food_data in self._data['food']] return self._foods @property", "of snakes from the board data.\"\"\" if self._snakes is None:", "!= exclude_id] def advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Return a", "if snake.id == snake_id), None) if not me: raise ValueError(\"No", "[Coordinate(food_data) for food_data in self._data['food']] return self._foods @property def width(self)", "if self._foods is None: self._foods = [Coordinate(food_data) for food_data in", "given path.\"\"\" new_board = copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self,", "not the Coordinate is within the bounds of the Board.\"\"\"", "the given ID.\"\"\" return [snake for snake in self.snakes if", "\"\"\"Do the actual advancement of the snake along the path.\"\"\"", "the actual advancement of the snake along the path.\"\"\" me", "snake in self.snakes if snake.id == snake_id), None) if not", "me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for coord in me.coordinates: print(coord)", "within the bounds of the Board.\"\"\" is_wall = (coordinate.x ==", "board with our snake advanced along given path.\"\"\" new_board =", "def __help_advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Do the actual advancement", "new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Do the", "cooardinates for all snakes and food in the game.\"\"\" def", "game.\"\"\" def __init__(self, data): self._data = data self._snakes = None", "self._data['snakes']] self._snakes = snakes return self._snakes @property def foods(self) ->", "\"\"\"Return a new board with our snake advanced along given", "board data.\"\"\" if self._snakes is None: snakes = [Snake(snake_data) for", "of food from the board data.\"\"\" if self._foods is None:", "[snake for snake in self.snakes if snake.id != exclude_id] def", "the path.\"\"\" me = next((snake for snake in self.snakes if", "import Tuple, List from src.coordinate import Coordinate from src.snake import", "not me: raise ValueError(\"No snake for given id!\") me.coordinates +=", "self._snakes = snakes return self._snakes @property def foods(self) -> List[Coordinate]:", "snake_id: str, path: List[Coordinate]): \"\"\"Return a new board with our", "along given path.\"\"\" new_board = copy.deepcopy(self) return new_board.__help_advance_snake_along_path(snake_id, path) def", "of the board -- note: it's a square.\"\"\" return self._data['width']", "for given id!\") me.coordinates += path me.coordinates = me.coordinates[len(path):] me.coordinates.reverse()", "self.snakes if snake.id == snake_id), None) if not me: raise", "List[Snake]: \"\"\"Retreive the list of snakes from the board data.\"\"\"", "self.width or coordinate.y == -1 or coordinate.y == self.width) return", "new board with our snake advanced along given path.\"\"\" new_board", "in self.snakes if snake.id != exclude_id] def advance_snake_along_path(self, snake_id: str,", "is_wall = (coordinate.x == -1 or coordinate.x == self.width or", "don't match the given ID.\"\"\" return [snake for snake in", "Coordinate is within the bounds of the Board.\"\"\" is_wall =", "None self._foods = None @property def snakes(self) -> List[Snake]: \"\"\"Retreive", "snake_id), None) if not me: raise ValueError(\"No snake for given", "the List of Snakes whose IDs don't match the given", "self._data = data self._snakes = None self._foods = None @property", "IDs don't match the given ID.\"\"\" return [snake for snake", "-1 or coordinate.y == self.width) return not is_wall def get_other_snakes(self,", "snake along the path.\"\"\" me = next((snake for snake in", "actual advancement of the snake along the path.\"\"\" me =", "raise ValueError(\"No snake for given id!\") me.coordinates += path me.coordinates", "all snakes and food in the game.\"\"\" def __init__(self, data):", "snakes(self) -> List[Snake]: \"\"\"Retreive the list of snakes from the", "Module\"\"\" import copy from typing import Tuple, List from src.coordinate", "-> bool: \"\"\"Check whether or not the Coordinate is within", "def width(self) -> int: \"\"\"Get width of the board --", "is within the bounds of the Board.\"\"\" is_wall = (coordinate.x", "return new_board.__help_advance_snake_along_path(snake_id, path) def __help_advance_snake_along_path(self, snake_id: str, path: List[Coordinate]): \"\"\"Do", "Tuple, List from src.coordinate import Coordinate from src.snake import Snake", "= None @property def snakes(self) -> List[Snake]: \"\"\"Retreive the list", "self._snakes is None: snakes = [Snake(snake_data) for snake_data in self._data['snakes']]", "data self._snakes = None self._foods = None @property def snakes(self)", "= snakes return self._snakes @property def foods(self) -> List[Coordinate]: \"\"\"Retreive", "None @property def snakes(self) -> List[Snake]: \"\"\"Retreive the list of", "snake in self.snakes if snake.id != exclude_id] def advance_snake_along_path(self, snake_id:", "in self._data['snakes']] self._snakes = snakes return self._snakes @property def foods(self)", "Board.\"\"\" is_wall = (coordinate.x == -1 or coordinate.x == self.width", "match the given ID.\"\"\" return [snake for snake in self.snakes", "path me.coordinates = me.coordinates[len(path):] me.coordinates.reverse() me.coordinates.append(me.coordinates[-1]) print(\"new coords:\") for coord", "-> List[Snake]: \"\"\"Retreive the list of snakes from the board", "self._snakes = None self._foods = None @property def snakes(self) ->", "snake.id == snake_id), None) if not me: raise ValueError(\"No snake" ]
[ "from personalized_nlp.utils.data_splitting import split_texts from personalized_nlp.datasets.datamodule_base import BaseDataModule class WikiDataModule(BaseDataModule):", "texts] return texts def _remap_column_names(self, df): mapping = {'rev_id': 'text_id',", "typing import List import pandas as pd import urllib from", "/ 'wiki_data' self.annotation_column = '' self.word_stats_annotation_column = '' self.embeddings_path =", "exist_ok=True) @property def class_dims(self): return [2] @property def texts_clean(self): texts", "personal_df = self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df) def _assign_splits(self): self.data =", "as pd import urllib from personalized_nlp.settings import STORAGE_DIR from personalized_nlp.utils.data_splitting", "self.test_split_names = ['future2'] self.split_sizes = split_sizes os.makedirs(self.data_dir / 'embeddings', exist_ok=True)", "+ '_worker_demographics.tsv'), sep='\\t') self.annotators = self._remap_column_names(self.annotators) self.annotations = pd.read_csv( self.data_dir", "col in df.columns] return df def prepare_data(self) -> None: self.data", "self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df) def _assign_splits(self): self.data", "os import zipfile from typing import List import pandas as", "= self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN', ' ') for c in", "-> None: self.data = pd.read_csv( self.data_dir / (self.annotation_column + '_annotated_comments.tsv'),", "def class_dims(self): return [2] @property def texts_clean(self): texts = self.data.text.to_list()", "STORAGE_DIR from personalized_nlp.utils.data_splitting import split_texts from personalized_nlp.datasets.datamodule_base import BaseDataModule class", "'comment': 'text'} df.columns = [mapping.get(col, col) for col in df.columns]", "personalized_nlp.settings import STORAGE_DIR from personalized_nlp.utils.data_splitting import split_texts from personalized_nlp.datasets.datamodule_base import", "self.annotators = pd.read_csv( self.data_dir / (self.annotation_column + '_worker_demographics.tsv'), sep='\\t') self.annotators", "STORAGE_DIR / 'wiki_data' self.annotation_column = '' self.word_stats_annotation_column = '' self.embeddings_path", "self.split_sizes = split_sizes os.makedirs(self.data_dir / 'embeddings', exist_ok=True) @property def class_dims(self):", "/ 'embeddings', exist_ok=True) @property def class_dims(self): return [2] @property def", "{'rev_id': 'text_id', 'worker_id': 'annotator_id', 'comment': 'text'} df.columns = [mapping.get(col, col)", "= self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ') self.annotators = pd.read_csv( self.data_dir /", "df): mapping = {'rev_id': 'text_id', 'worker_id': 'annotator_id', 'comment': 'text'} df.columns", "): super().__init__(**kwargs) self.data_dir = STORAGE_DIR / 'wiki_data' self.annotation_column = ''", "'' self.embeddings_path = '' self.train_split_names = ['present', 'past'] self.val_split_names =", "texts_clean(self): texts = self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN', ' ') for", "__init__( self, split_sizes: List[float] = [0.55, 0.15, 0.15, 0.15], **kwargs,", "for c in texts] return texts def _remap_column_names(self, df): mapping", "import split_texts from personalized_nlp.datasets.datamodule_base import BaseDataModule class WikiDataModule(BaseDataModule): def __init__(", "from personalized_nlp.settings import STORAGE_DIR from personalized_nlp.utils.data_splitting import split_texts from personalized_nlp.datasets.datamodule_base", "= self._remap_column_names(self.annotators) self.annotations = pd.read_csv( self.data_dir / (self.annotation_column + '_annotations.tsv'),", "def _remap_column_names(self, df): mapping = {'rev_id': 'text_id', 'worker_id': 'annotator_id', 'comment':", "= self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ') self.annotators =", "self.annotators = self._remap_column_names(self.annotators) self.annotations = pd.read_csv( self.data_dir / (self.annotation_column +", "+ '_annotations.tsv'), sep='\\t') self.annotations = self._remap_column_names(self.annotations) self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split", "@property def texts_clean(self): texts = self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN', '", "= {'rev_id': 'text_id', 'worker_id': 'annotator_id', 'comment': 'text'} df.columns = [mapping.get(col,", "self.data_dir = STORAGE_DIR / 'wiki_data' self.annotation_column = '' self.word_stats_annotation_column =", "= self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df) def _assign_splits(self): self.data = split_texts(self.data,", "prepare_data(self) -> None: self.data = pd.read_csv( self.data_dir / (self.annotation_column +", "pd.read_csv( self.data_dir / (self.annotation_column + '_annotated_comments.tsv'), sep='\\t') self.data = self._remap_column_names(self.data)", "BaseDataModule class WikiDataModule(BaseDataModule): def __init__( self, split_sizes: List[float] = [0.55,", "[0.55, 0.15, 0.15, 0.15], **kwargs, ): super().__init__(**kwargs) self.data_dir = STORAGE_DIR", "self, split_sizes: List[float] = [0.55, 0.15, 0.15, 0.15], **kwargs, ):", "= [c.replace('NEWLINE_TOKEN', ' ') for c in texts] return texts", "0.15, 0.15, 0.15], **kwargs, ): super().__init__(**kwargs) self.data_dir = STORAGE_DIR /", "'_worker_demographics.tsv'), sep='\\t') self.annotators = self._remap_column_names(self.annotators) self.annotations = pd.read_csv( self.data_dir /", "self._remap_column_names(self.annotators) self.annotations = pd.read_csv( self.data_dir / (self.annotation_column + '_annotations.tsv'), sep='\\t')", "self.data_dir / (self.annotation_column + '_annotated_comments.tsv'), sep='\\t') self.data = self._remap_column_names(self.data) self.data['text']", "personalized_nlp.utils.data_splitting import split_texts from personalized_nlp.datasets.datamodule_base import BaseDataModule class WikiDataModule(BaseDataModule): def", "import urllib from personalized_nlp.settings import STORAGE_DIR from personalized_nlp.utils.data_splitting import split_texts", "from personalized_nlp.datasets.datamodule_base import BaseDataModule class WikiDataModule(BaseDataModule): def __init__( self, split_sizes:", "texts def _remap_column_names(self, df): mapping = {'rev_id': 'text_id', 'worker_id': 'annotator_id',", "for col in df.columns] return df def prepare_data(self) -> None:", "None: self.data = pd.read_csv( self.data_dir / (self.annotation_column + '_annotated_comments.tsv'), sep='\\t')", "super().__init__(**kwargs) self.data_dir = STORAGE_DIR / 'wiki_data' self.annotation_column = '' self.word_stats_annotation_column", "self.val_split_names = ['future1'] self.test_split_names = ['future2'] self.split_sizes = split_sizes os.makedirs(self.data_dir", "**kwargs, ): super().__init__(**kwargs) self.data_dir = STORAGE_DIR / 'wiki_data' self.annotation_column =", "= ['future2'] self.split_sizes = split_sizes os.makedirs(self.data_dir / 'embeddings', exist_ok=True) @property", "in texts] return texts def _remap_column_names(self, df): mapping = {'rev_id':", "return [2] @property def texts_clean(self): texts = self.data.text.to_list() texts =", "= '' self.word_stats_annotation_column = '' self.embeddings_path = '' self.train_split_names =", "'NEWLINE_TOKEN', ' ') self.annotators = pd.read_csv( self.data_dir / (self.annotation_column +", "'worker_id': 'annotator_id', 'comment': 'text'} df.columns = [mapping.get(col, col) for col", "= pd.read_csv( self.data_dir / (self.annotation_column + '_annotated_comments.tsv'), sep='\\t') self.data =", "def __init__( self, split_sizes: List[float] = [0.55, 0.15, 0.15, 0.15],", "self.annotations = pd.read_csv( self.data_dir / (self.annotation_column + '_annotations.tsv'), sep='\\t') self.annotations", "/ (self.annotation_column + '_annotations.tsv'), sep='\\t') self.annotations = self._remap_column_names(self.annotations) self._assign_splits() personal_df", "['future1'] self.test_split_names = ['future2'] self.split_sizes = split_sizes os.makedirs(self.data_dir / 'embeddings',", "personalized_nlp.datasets.datamodule_base import BaseDataModule class WikiDataModule(BaseDataModule): def __init__( self, split_sizes: List[float]", "c in texts] return texts def _remap_column_names(self, df): mapping =", "self.annotations = self._remap_column_names(self.annotations) self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df)", "def prepare_data(self) -> None: self.data = pd.read_csv( self.data_dir / (self.annotation_column", "def texts_clean(self): texts = self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN', ' ')", "sep='\\t') self.data = self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ')", "'text'} df.columns = [mapping.get(col, col) for col in df.columns] return", "sep='\\t') self.annotations = self._remap_column_names(self.annotations) self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split == 'past']", "pd import urllib from personalized_nlp.settings import STORAGE_DIR from personalized_nlp.utils.data_splitting import", "from typing import List import pandas as pd import urllib", "import zipfile from typing import List import pandas as pd", "'text_id', 'worker_id': 'annotator_id', 'comment': 'text'} df.columns = [mapping.get(col, col) for", "os.makedirs(self.data_dir / 'embeddings', exist_ok=True) @property def class_dims(self): return [2] @property", "' ') self.annotators = pd.read_csv( self.data_dir / (self.annotation_column + '_worker_demographics.tsv'),", "= [0.55, 0.15, 0.15, 0.15], **kwargs, ): super().__init__(**kwargs) self.data_dir =", "self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ') self.annotators = pd.read_csv(", "0.15, 0.15], **kwargs, ): super().__init__(**kwargs) self.data_dir = STORAGE_DIR / 'wiki_data'", "texts = self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN', ' ') for c", "'_annotated_comments.tsv'), sep='\\t') self.data = self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN', '", "/ (self.annotation_column + '_annotated_comments.tsv'), sep='\\t') self.data = self._remap_column_names(self.data) self.data['text'] =", "pd.read_csv( self.data_dir / (self.annotation_column + '_worker_demographics.tsv'), sep='\\t') self.annotators = self._remap_column_names(self.annotators)", "= '' self.embeddings_path = '' self.train_split_names = ['present', 'past'] self.val_split_names", "self.train_split_names = ['present', 'past'] self.val_split_names = ['future1'] self.test_split_names = ['future2']", "['present', 'past'] self.val_split_names = ['future1'] self.test_split_names = ['future2'] self.split_sizes =", "pd.read_csv( self.data_dir / (self.annotation_column + '_annotations.tsv'), sep='\\t') self.annotations = self._remap_column_names(self.annotations)", "WikiDataModule(BaseDataModule): def __init__( self, split_sizes: List[float] = [0.55, 0.15, 0.15,", "split_sizes: List[float] = [0.55, 0.15, 0.15, 0.15], **kwargs, ): super().__init__(**kwargs)", "List[float] = [0.55, 0.15, 0.15, 0.15], **kwargs, ): super().__init__(**kwargs) self.data_dir", "= [mapping.get(col, col) for col in df.columns] return df def", "= ['present', 'past'] self.val_split_names = ['future1'] self.test_split_names = ['future2'] self.split_sizes", "' ') for c in texts] return texts def _remap_column_names(self,", "= pd.read_csv( self.data_dir / (self.annotation_column + '_annotations.tsv'), sep='\\t') self.annotations =", "self.data = pd.read_csv( self.data_dir / (self.annotation_column + '_annotated_comments.tsv'), sep='\\t') self.data", "self.annotation_column = '' self.word_stats_annotation_column = '' self.embeddings_path = '' self.train_split_names", "= pd.read_csv( self.data_dir / (self.annotation_column + '_worker_demographics.tsv'), sep='\\t') self.annotators =", "self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df) def _assign_splits(self): self.data = split_texts(self.data, self.split_sizes)", "self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ') self.annotators = pd.read_csv( self.data_dir / (self.annotation_column", "(self.annotation_column + '_annotations.tsv'), sep='\\t') self.annotations = self._remap_column_names(self.annotations) self._assign_splits() personal_df =", "urllib from personalized_nlp.settings import STORAGE_DIR from personalized_nlp.utils.data_splitting import split_texts from", "df.columns] return df def prepare_data(self) -> None: self.data = pd.read_csv(", "+ '_annotated_comments.tsv'), sep='\\t') self.data = self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN',", "import os import zipfile from typing import List import pandas", "List import pandas as pd import urllib from personalized_nlp.settings import", "col) for col in df.columns] return df def prepare_data(self) ->", "') self.annotators = pd.read_csv( self.data_dir / (self.annotation_column + '_worker_demographics.tsv'), sep='\\t')", "import STORAGE_DIR from personalized_nlp.utils.data_splitting import split_texts from personalized_nlp.datasets.datamodule_base import BaseDataModule", "0.15], **kwargs, ): super().__init__(**kwargs) self.data_dir = STORAGE_DIR / 'wiki_data' self.annotation_column", "= split_sizes os.makedirs(self.data_dir / 'embeddings', exist_ok=True) @property def class_dims(self): return", "return df def prepare_data(self) -> None: self.data = pd.read_csv( self.data_dir", "'' self.word_stats_annotation_column = '' self.embeddings_path = '' self.train_split_names = ['present',", "') for c in texts] return texts def _remap_column_names(self, df):", "texts = [c.replace('NEWLINE_TOKEN', ' ') for c in texts] return", "return texts def _remap_column_names(self, df): mapping = {'rev_id': 'text_id', 'worker_id':", "'_annotations.tsv'), sep='\\t') self.annotations = self._remap_column_names(self.annotations) self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split ==", "split_texts from personalized_nlp.datasets.datamodule_base import BaseDataModule class WikiDataModule(BaseDataModule): def __init__( self,", "= ['future1'] self.test_split_names = ['future2'] self.split_sizes = split_sizes os.makedirs(self.data_dir /", "self.word_stats_annotation_column = '' self.embeddings_path = '' self.train_split_names = ['present', 'past']", "df def prepare_data(self) -> None: self.data = pd.read_csv( self.data_dir /", "self.embeddings_path = '' self.train_split_names = ['present', 'past'] self.val_split_names = ['future1']", "import pandas as pd import urllib from personalized_nlp.settings import STORAGE_DIR", "= '' self.train_split_names = ['present', 'past'] self.val_split_names = ['future1'] self.test_split_names", "split_sizes os.makedirs(self.data_dir / 'embeddings', exist_ok=True) @property def class_dims(self): return [2]", "import BaseDataModule class WikiDataModule(BaseDataModule): def __init__( self, split_sizes: List[float] =", "mapping = {'rev_id': 'text_id', 'worker_id': 'annotator_id', 'comment': 'text'} df.columns =", "(self.annotation_column + '_worker_demographics.tsv'), sep='\\t') self.annotators = self._remap_column_names(self.annotators) self.annotations = pd.read_csv(", "import List import pandas as pd import urllib from personalized_nlp.settings", "'annotator_id', 'comment': 'text'} df.columns = [mapping.get(col, col) for col in", "self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN', ' ') for c in texts]", "df.columns = [mapping.get(col, col) for col in df.columns] return df", "(self.annotation_column + '_annotated_comments.tsv'), sep='\\t') self.data = self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace(", "@property def class_dims(self): return [2] @property def texts_clean(self): texts =", "class_dims(self): return [2] @property def texts_clean(self): texts = self.data.text.to_list() texts", "in df.columns] return df def prepare_data(self) -> None: self.data =", "_remap_column_names(self, df): mapping = {'rev_id': 'text_id', 'worker_id': 'annotator_id', 'comment': 'text'}", "zipfile from typing import List import pandas as pd import", "'embeddings', exist_ok=True) @property def class_dims(self): return [2] @property def texts_clean(self):", "self.data_dir / (self.annotation_column + '_worker_demographics.tsv'), sep='\\t') self.annotators = self._remap_column_names(self.annotators) self.annotations", "/ (self.annotation_column + '_worker_demographics.tsv'), sep='\\t') self.annotators = self._remap_column_names(self.annotators) self.annotations =", "[c.replace('NEWLINE_TOKEN', ' ') for c in texts] return texts def", "sep='\\t') self.annotators = self._remap_column_names(self.annotators) self.annotations = pd.read_csv( self.data_dir / (self.annotation_column", "[mapping.get(col, col) for col in df.columns] return df def prepare_data(self)", "= self._remap_column_names(self.annotations) self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df) def", "= STORAGE_DIR / 'wiki_data' self.annotation_column = '' self.word_stats_annotation_column = ''", "'' self.train_split_names = ['present', 'past'] self.val_split_names = ['future1'] self.test_split_names =", "['future2'] self.split_sizes = split_sizes os.makedirs(self.data_dir / 'embeddings', exist_ok=True) @property def", "<reponame>CLARIN-PL/personalized-nlp import os import zipfile from typing import List import", "self.data = self._remap_column_names(self.data) self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ') self.annotators", "class WikiDataModule(BaseDataModule): def __init__( self, split_sizes: List[float] = [0.55, 0.15,", "self.data_dir / (self.annotation_column + '_annotations.tsv'), sep='\\t') self.annotations = self._remap_column_names(self.annotations) self._assign_splits()", "self.data['text'] = self.data['text'].str.replace( 'NEWLINE_TOKEN', ' ') self.annotators = pd.read_csv( self.data_dir", "pandas as pd import urllib from personalized_nlp.settings import STORAGE_DIR from", "self._remap_column_names(self.annotations) self._assign_splits() personal_df = self.annotations_with_data.loc[self.annotations_with_data.split == 'past'] self.compute_annotator_biases(personal_df) def _assign_splits(self):", "'past'] self.val_split_names = ['future1'] self.test_split_names = ['future2'] self.split_sizes = split_sizes", "'wiki_data' self.annotation_column = '' self.word_stats_annotation_column = '' self.embeddings_path = ''", "[2] @property def texts_clean(self): texts = self.data.text.to_list() texts = [c.replace('NEWLINE_TOKEN'," ]
[ "models class Migration(migrations.Migration): dependencies = [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations", "4.0 on 2022-03-03 02:15 from django.db import migrations, models class", "02:15 from django.db import migrations, models class Migration(migrations.Migration): dependencies =", "[ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations = [ migrations.RemoveField( model_name='user', name='percentage_preferences',", "migrations.RemoveField( model_name='user', name='percentage_preferences', ), migrations.AddField( model_name='user', name='preferences', field=models.JSONField(null=True), ), ]", "migrations, models class Migration(migrations.Migration): dependencies = [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ]", "= [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations = [ migrations.RemoveField( model_name='user',", "] operations = [ migrations.RemoveField( model_name='user', name='percentage_preferences', ), migrations.AddField( model_name='user',", "by Django 4.0 on 2022-03-03 02:15 from django.db import migrations,", "from django.db import migrations, models class Migration(migrations.Migration): dependencies = [", "Django 4.0 on 2022-03-03 02:15 from django.db import migrations, models", "django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('App',", "dependencies = [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations = [ migrations.RemoveField(", "class Migration(migrations.Migration): dependencies = [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations =", "Generated by Django 4.0 on 2022-03-03 02:15 from django.db import", "# Generated by Django 4.0 on 2022-03-03 02:15 from django.db", "'0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations = [ migrations.RemoveField( model_name='user', name='percentage_preferences', ), migrations.AddField(", "import migrations, models class Migration(migrations.Migration): dependencies = [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'),", "on 2022-03-03 02:15 from django.db import migrations, models class Migration(migrations.Migration):", "[ migrations.RemoveField( model_name='user', name='percentage_preferences', ), migrations.AddField( model_name='user', name='preferences', field=models.JSONField(null=True), ),", "('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations = [ migrations.RemoveField( model_name='user', name='percentage_preferences', ),", "= [ migrations.RemoveField( model_name='user', name='percentage_preferences', ), migrations.AddField( model_name='user', name='preferences', field=models.JSONField(null=True),", "<filename>App/migrations/0010_remove_user_percentage_preferences_user_preferences.py # Generated by Django 4.0 on 2022-03-03 02:15 from", "operations = [ migrations.RemoveField( model_name='user', name='percentage_preferences', ), migrations.AddField( model_name='user', name='preferences',", "2022-03-03 02:15 from django.db import migrations, models class Migration(migrations.Migration): dependencies", "Migration(migrations.Migration): dependencies = [ ('App', '0009_alter_song_holiday_alter_songfileinput_holiday'), ] operations = [" ]
[ "deprecated in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE =", "CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE', None)", "CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge')", "return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None): return _callable_from_string(generator", "CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT', 5) # Minutes CAPTCHA_LENGTH = int(getattr(settings,", "'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg = (\"CAPTCHA_FIELD_TEMPLATE setting is", "CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if callable(string_or_callable): return string_or_callable else: return", "callable(string_or_callable): return string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']), string_or_callable.split('.')[-1])", "22) CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings,", "getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT', 5) # Minutes", "msg = (\"CAPTCHA_OUTPUT_FORMAT setting is deprecated in favor of widget's", "getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR", "= getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH", "{}, ['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None): return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def", "getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE', 22)", "Minutes CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH', 4)) # Chars # CAPTCHA_IMAGE_BEFORE_FIELD", "msg = (\"CAPTCHA_FIELD_TEMPLATE setting is deprecated in favor of widget's", "(\"CAPTCHA_OUTPUT_FORMAT setting is deprecated in favor of widget's template_name.\") warnings.warn(msg,", "favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT',", "'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings,", "(\"CAPTCHA_FIELD_TEMPLATE setting is deprecated in favor of widget's template_name.\") warnings.warn(msg,", "'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings,", "deprecated in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT =", "return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string,", "import settings CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE", "settings CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE =", "= CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if callable(string_or_callable): return string_or_callable else:", "CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS',", "from django.conf import settings CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..',", "CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if callable(string_or_callable): return", "def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return [] def", "CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0)", "return [] def filter_functions(): if CAPTCHA_FILTER_FUNCTIONS: return map(_callable_from_string, CAPTCHA_FILTER_FUNCTIONS) return", "# Chars # CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH =", "DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR',", "None): msg = (\"CAPTCHA_FIELD_TEMPLATE setting is deprecated in favor of", "CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable):", "getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE', False) # Failsafe", "getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY", "> CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if", "35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR',", "django.conf import settings CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf')))", "'*') CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT',", "getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg = (\"CAPTCHA_FIELD_TEMPLATE", "warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT',", "DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None):", "getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None): return _callable_from_string(generator or", "None) CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT',", "CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL', False)", "'#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT',", "'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT = getattr(settings,", "CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH', 4)) # Chars # CAPTCHA_IMAGE_BEFORE_FIELD =", "= getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT", "os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION =", "_callable_from_string(string_or_callable): if callable(string_or_callable): return string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {},", "'#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS',", "getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg = (\"CAPTCHA_OUTPUT_FORMAT setting is deprecated in", "getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION =", "CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return [] def filter_functions(): if CAPTCHA_FILTER_FUNCTIONS:", "= getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE", "'..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION = getattr(settings,", "template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings,", "'''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH',", "CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html')", "None) CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT', 5) # Minutes CAPTCHA_LENGTH =", "CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs',", "if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg = (\"CAPTCHA_FIELD_TEMPLATE setting is deprecated", "'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings,", "= getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff')", "get_challenge(generator=None): return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return", "(-35, 35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings,", "CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if callable(string_or_callable): return string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]),", "'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg = (\"CAPTCHA_FIELD_TEMPLATE setting", "noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return [] def filter_functions():", "= getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH", "of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None)", "'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None):", "None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg = (\"CAPTCHA_OUTPUT_FORMAT setting is", "'CAPTCHA_OUTPUT_FORMAT', None): msg = (\"CAPTCHA_OUTPUT_FORMAT setting is deprecated in favor", "['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None): return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def noise_functions():", "CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''')", "= getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL", "# Minutes CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH', 4)) # Chars #", "CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT', 5)", "'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE = getattr(settings,", "string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None):", "if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg = (\"CAPTCHA_OUTPUT_FORMAT setting is deprecated", "CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*')", "'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION", "CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH', None)", "in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings,", "'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings,", "CAPTCHA_NOISE_FUNCTIONS) return [] def filter_functions(): if CAPTCHA_FILTER_FUNCTIONS: return map(_callable_from_string, CAPTCHA_FILTER_FUNCTIONS)", "'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings,", "= getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg =", "widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR", "getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH =", "def _callable_from_string(string_or_callable): if callable(string_or_callable): return string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {},", "'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE',", "= (\"CAPTCHA_OUTPUT_FORMAT setting is deprecated in favor of widget's template_name.\")", "'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings,", "'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY',", "'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION',", "= getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE", "= getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE", "= getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT', 5) #", "getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS", "0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE',", "5) CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE', False) # Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH", "or CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return", "= getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE',", "'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings,", "map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return [] def filter_functions(): if CAPTCHA_FILTER_FUNCTIONS: return map(_callable_from_string,", "False) # Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH =", "CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'fonts/Vera.ttf'))) CAPTCHA_FONT_SIZE = getattr(settings,", "getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE =", "CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5)", "warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings,", "# Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH,", "'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE', False) # Failsafe if", "= getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT", "[] def filter_functions(): if CAPTCHA_FILTER_FUNCTIONS: return map(_callable_from_string, CAPTCHA_FILTER_FUNCTIONS) return []", "'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings,", "'CAPTCHA_FIELD_TEMPLATE', None): msg = (\"CAPTCHA_FIELD_TEMPLATE setting is deprecated in favor", "= getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS", "= getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH", "'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT = getattr(settings, 'CAPTCHA_TIMEOUT', 5) # Minutes CAPTCHA_LENGTH", "= (\"CAPTCHA_FIELD_TEMPLATE setting is deprecated in favor of widget's template_name.\")", "CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg", "False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE',", "'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR =", "= getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE", "widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if", "CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg", "= getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',))", "CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99)", "os import warnings from django.conf import settings CAPTCHA_FONT_PATH = getattr(settings,", "'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY =", "'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings, 'CAPTCHA_FIELD_TEMPLATE',", "is deprecated in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE", "in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings,", "'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg = (\"CAPTCHA_OUTPUT_FORMAT setting", "setting is deprecated in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning)", "CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if callable(string_or_callable):", "= getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE", "None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE',", "None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL',", "= getattr(settings, 'CAPTCHA_TEST_MODE', False) # Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH:", "= getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',))", "('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION',", "getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH =", "'/usr/share/dict/words') CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH',", "getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT =", "True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH',", "'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings, 'CAPTCHA_NOISE_FUNCTIONS', ('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS =", "= getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35))", "getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE =", "getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if getattr(settings,", "getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None): msg = (\"CAPTCHA_FIELD_TEMPLATE setting is deprecated in", "_callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS)", "if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return [] def filter_functions(): if", "Chars # CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings,", "{}, {}, ['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None): return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT)", "'CAPTCHA_LENGTH', 4)) # Chars # CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True)", "favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE',", "import os import warnings from django.conf import settings CAPTCHA_FONT_PATH =", "5) # Minutes CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH', 4)) # Chars", "getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH', 0) CAPTCHA_DICTIONARY_MAX_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE =", "return string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']), string_or_callable.split('.')[-1]) def", "CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE', False) # Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH >", "else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']), string_or_callable.split('.')[-1]) def get_challenge(generator=None): return", "None): msg = (\"CAPTCHA_OUTPUT_FORMAT setting is deprecated in favor of", "CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words')", "if callable(string_or_callable): return string_or_callable else: return getattr(__import__('.'.join(string_or_callable.split('.')[:-1]), {}, {}, ['']),", "('captcha.helpers.noise_arcs', 'captcha.helpers.noise_dots',)) CAPTCHA_FILTER_FUNCTIONS = getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings,", "CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS: return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return []", "getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH =", "'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE = getattr(settings,", "is deprecated in favor of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT", "Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH", "import warnings from django.conf import settings CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH',", "= getattr(settings, 'CAPTCHA_TIMEOUT', 5) # Minutes CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH',", "def get_challenge(generator=None): return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if CAPTCHA_NOISE_FUNCTIONS:", "4)) # Chars # CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH", "int(getattr(settings, 'CAPTCHA_LENGTH', 4)) # Chars # CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD',", "'CAPTCHA_TIMEOUT', 5) # Minutes CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH', 4)) #", "warnings from django.conf import settings CAPTCHA_FONT_PATH = getattr(settings, 'CAPTCHA_FONT_PATH', os.path.normpath(os.path.join(os.path.dirname(__file__),", "getattr(settings, 'CAPTCHA_DICTIONARY_MAX_LENGTH', 99) CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE =", "99) CAPTCHA_IMAGE_SIZE = getattr(settings, 'CAPTCHA_IMAGE_SIZE', None) CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE',", "CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100')", "getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE =", "CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html')", "# CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings, 'CAPTCHA_IMAGE_BEFORE_FIELD', True) CAPTCHA_DICTIONARY_MIN_LENGTH = getattr(settings, 'CAPTCHA_DICTIONARY_MIN_LENGTH',", "of widget's template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None)", "= getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg =", "'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL = getattr(settings,", "CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def _callable_from_string(string_or_callable): if callable(string_or_callable): return string_or_callable", "= getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE', False) #", "getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH = getattr(settings, 'CAPTCHA_SOX_PATH', None) CAPTCHA_TIMEOUT =", "'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS = getattr(settings,", "if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH = CAPTCHA_DICTIONARY_MAX_LENGTH, CAPTCHA_DICTIONARY_MIN_LENGTH def", "= int(getattr(settings, 'CAPTCHA_LENGTH', 4)) # Chars # CAPTCHA_IMAGE_BEFORE_FIELD = getattr(settings,", "getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR = getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL =", "'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings,", "string_or_callable.split('.')[-1]) def get_challenge(generator=None): return _callable_from_string(generator or CAPTCHA_CHALLENGE_FUNCT) def noise_functions(): if", "CAPTCHA_FONT_SIZE = getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35,", "getattr(settings, 'CAPTCHA_TIMEOUT', 5) # Minutes CAPTCHA_LENGTH = int(getattr(settings, 'CAPTCHA_LENGTH', 4))", "'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR =", "getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT = getattr(settings, 'CAPTCHA_CHALLENGE_FUNCT', 'captcha.helpers.random_char_challenge') CAPTCHA_NOISE_FUNCTIONS =", "CAPTCHA_GET_FROM_POOL_TIMEOUT = getattr(settings, 'CAPTCHA_GET_FROM_POOL_TIMEOUT', 5) CAPTCHA_TEST_MODE = getattr(settings, 'CAPTCHA_TEST_MODE', False)", "= getattr(settings, 'CAPTCHA_BACKGROUND_COLOR', '#ffffff') CAPTCHA_FOREGROUND_COLOR = getattr(settings, 'CAPTCHA_FOREGROUND_COLOR', '#001100') CAPTCHA_CHALLENGE_FUNCT", "return map(_callable_from_string, CAPTCHA_NOISE_FUNCTIONS) return [] def filter_functions(): if CAPTCHA_FILTER_FUNCTIONS: return", "getattr(settings, 'CAPTCHA_FONT_SIZE', 22) CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR", "= getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html') CAPTCHA_TEXT_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_TEXT_FIELD_TEMPLATE', 'captcha/text_field.html') if", "'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH', None) CAPTCHA_SOX_PATH = getattr(settings,", "CAPTCHA_LETTER_ROTATION = getattr(settings, 'CAPTCHA_LETTER_ROTATION', (-35, 35)) CAPTCHA_BACKGROUND_COLOR = getattr(settings, 'CAPTCHA_BACKGROUND_COLOR',", "getattr(settings, 'CAPTCHA_FIELD_TEMPLATE', None) if getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None): msg = (\"CAPTCHA_OUTPUT_FORMAT", "'CAPTCHA_TEST_MODE', False) # Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH, CAPTCHA_DICTIONARY_MAX_LENGTH", "template_name.\") warnings.warn(msg, DeprecationWarning) CAPTCHA_OUTPUT_FORMAT = getattr(settings, 'CAPTCHA_OUTPUT_FORMAT', None) CAPTCHA_MATH_CHALLENGE_OPERATOR =", "getattr(settings, 'CAPTCHA_TEST_MODE', False) # Failsafe if CAPTCHA_DICTIONARY_MIN_LENGTH > CAPTCHA_DICTIONARY_MAX_LENGTH: CAPTCHA_DICTIONARY_MIN_LENGTH,", "getattr(settings, 'CAPTCHA_MATH_CHALLENGE_OPERATOR', '*') CAPTCHA_GET_FROM_POOL = getattr(settings, 'CAPTCHA_GET_FROM_POOL', False) CAPTCHA_GET_FROM_POOL_TIMEOUT =", "= getattr(settings, 'CAPTCHA_FILTER_FUNCTIONS', ('captcha.helpers.post_smooth',)) CAPTCHA_WORDS_DICTIONARY = getattr(settings, 'CAPTCHA_WORDS_DICTIONARY', '/usr/share/dict/words') CAPTCHA_PUNCTUATION", "CAPTCHA_PUNCTUATION = getattr(settings, 'CAPTCHA_PUNCTUATION', '''_\"',.;:-''') CAPTCHA_FLITE_PATH = getattr(settings, 'CAPTCHA_FLITE_PATH', None)", "CAPTCHA_IMAGE_TEMPLATE = getattr(settings, 'CAPTCHA_IMAGE_TEMPLATE', 'captcha/image.html') CAPTCHA_HIDDEN_FIELD_TEMPLATE = getattr(settings, 'CAPTCHA_HIDDEN_FIELD_TEMPLATE', 'captcha/hidden_field.html')" ]
[ "self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\"))", "import image_test try: from urllib import urlencode except ImportError: from", "w=case[\"width\"] or \"\", h=case[\"height\"] or \"\", mode=case[\"mode\"]) for k in", "qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\")", "\"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict( url=self.get_url(path), w=case[\"width\"] or \"\",", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200)) resp = self.fetch_error(400, \"/?%s\" %", "ImportError: cv = None logger = logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def", "except ImportError: cv = None logger = logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object):", "ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"r\"))", "from tornado.testing import AsyncHTTPTestCase, gen_test import tornado.web from pilbox.app import", "% qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\",", "gen_test import tornado.web from pilbox.app import PilboxApplication from pilbox.errors import", "% qs) msg = \"/?%s does not match %s\" \\", "qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp =", "UrlError, ImageFormatError, FetchError from pilbox.signature import sign from pilbox.test import", "= urlencode(dict(url=\"file://%s\" % path, w=1, h=1)) resp = self.fetch_error(400, \"/?%s\"", "test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200)) resp = self.fetch_error(400,", "delay = time.time() + float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay)", "case in enumerate(cases): path = \"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] =", "path = \"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp =", "<filename>pilbox/test/app_test.py<gh_stars>0 from __future__ import absolute_import, division, print_function, \\ with_statement import", "== \"webp\": cases[i][\"content_type\"] = \"image/webp\" else: cases[i][\"content_type\"] = None return", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self): cases", "case): qs = urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\" % qs) msg", "_PilboxTestApplication(PilboxApplication): def get_handlers(self): path = os.path.join(os.path.dirname(__file__), \"data\") handlers = [(r\"/test/data/test-delayed.jpg\",", "= self.fetch_success(\"/?%s\" % qs) msg = \"/?%s does not match", "urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def", "yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self):", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp = self.fetch_error(400, \"/?%s\" %", "qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "= dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME) qs = urlencode(params) resp =", "test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs = sign(self.KEY, urlencode(params))", "class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication() def test_missing_url(self): qs", "resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self):", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self):", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self): qs =", "% qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "% (qs, case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(),", "def test_missing_url(self): qs = urlencode(dict(w=1, h=1)) resp = self.fetch_error(400, \"/?%s\"", "resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self):", "SignatureError.get_code()) def test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME, sig=\"abc123\")", "sign(self.KEY, urlencode(params)) resp = self.fetch_success(\"/?%s\" % qs) msg = \"/?%s", "h=1, pos=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code())", "self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\"))", "case.get(\"format\") in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\" elif case.get(\"format\") ==", "\"data\", \"test1.jpg\") qs = urlencode(dict(url=\"file://%s\" % path, w=1, h=1)) resp", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "from urllib import urlencode except ImportError: from urllib.parse import urlencode", "urlencode(dict(url=\"file://%s\" % path, w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" %", "pilbox.signature import sign from pilbox.test import image_test try: from urllib", "ImageFormatError.get_code()) def test_not_found(self): path = \"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path), w=1,", "response def get_image_resize_cases(self): cases = image_test.get_image_resize_cases() m = dict(background=\"bg\", filter=\"filter\",", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self): qs", "BackgroundError.get_code()) def test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\")) resp", "\\ % (qs, case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with", "= \"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(415,", "qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" %", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\",", "pos=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp = self.fetch_error(400,", "return _PilboxTestApplication(timeout=0.5) def test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url,", "resp = self.fetch_error(415, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self):", "case.get(\"position\") == \"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv is None, \"OpenCV is", "open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin):", "q=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def", "self._assert_expected_resize(case) def _assert_expected_resize(self, case): qs = urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\"", "delay) self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication() def", "w=1, h=1, client=self.NAME) qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\"", "time.time() + float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"r\")) resp = self.fetch_error(400, \"/?%s\"", "% qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp", "test_valid_face(self): cases = self.get_image_resize_cases() for case in cases: if case.get(\"mode\")", "qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\" elif case.get(\"format\") == \"png\":", "from pilbox.test import image_test try: from urllib import urlencode except", "qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return", "self._assert_expected_resize(case) @unittest.skipIf(cv is None, \"OpenCV is not installed\") def test_valid_face(self):", "qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for case", "params = case[\"source_query_params\"] params[\"client\"] = self.NAME qs = sign(self.KEY, urlencode(params))", "def test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME) qs =", "test_not_found(self): path = \"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp", "_PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def test_missing_client_name(self): params =", "self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(),", "fetch_error(self, code, *args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, code)", "= \"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict( url=self.get_url(path), w=case[\"width\"] or", "get_handlers(self): path = os.path.join(os.path.dirname(__file__), \"data\") handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\",", "self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self): params =", "w=1, h=1)) resp = self.fetch_error(415, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code())", "os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs = urlencode(dict(url=\"file://%s\" % path, w=1, h=1))", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "position=\"pos\", quality=\"q\") for i, case in enumerate(cases): path = \"/test/data/%s\"", "case: cases[i][\"source_query_params\"][m.get(k)] = case[k] if case.get(\"format\") in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"]", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self): path =", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self): path = \"/test/data/test-not-found.jpg\"", "not installed\") def test_valid_face(self): cases = self.get_image_resize_cases() for case in", "\"test1.jpg\") qs = urlencode(dict(url=\"file://%s\" % path, w=1, h=1)) resp =", "== \"crop\" and case.get(\"position\") == \"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv is", "from pilbox.errors import SignatureError, ClientError, HostError, \\ BackgroundError, DimensionsError, FilterError,", "resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self):", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp = self.fetch_error(400, \"/?%s\"", "qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self): path = \"/test/data/test-not-found.jpg\" qs =", "self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return tornado.escape.json_decode(response.body) def fetch_success(self, *args, **kwargs): response", "_AppAsyncMixin): KEY = \"abcdef\" NAME = \"abc\" def get_app(self): return", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp = self.fetch_error(400, \"/?%s\" %", "self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME)", "params = dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME) qs = sign(self.KEY, urlencode(params))", "_AppAsyncMixin): def get_app(self): return _PilboxTestApplication() def test_missing_url(self): qs = urlencode(dict(w=1,", "h=1, client=self.NAME) qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\"", "dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME) qs = urlencode(params) resp = self.fetch_error(403,", "get_app(self): return _PilboxTestApplication() def test_missing_url(self): qs = urlencode(dict(w=1, h=1)) resp", "get_app(self): return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def test_missing_client_name(self):", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self): qs =", "for k in m.keys(): if k in case: cases[i][\"source_query_params\"][m.get(k)] =", "w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "is None, \"OpenCV is not installed\") def test_valid_face(self): cases =", "float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin):", "\"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY", "q=200)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def", "% qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\" qs", "BackgroundError, DimensionsError, FilterError, FormatError, ModeError, \\ PositionError, QualityError, UrlError, ImageFormatError,", "_assert_expected_resize(self, case): qs = urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\" % qs)", "expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY = \"abcdef\" NAME =", "cv except ImportError: cv = None logger = logging.getLogger(\"tornado.application\") class", "\"application/json\") return tornado.escape.json_decode(response.body) def fetch_success(self, *args, **kwargs): response = self.fetch(*args,", "AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication() def test_missing_url(self): qs =", "qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for case", "h=1, mode=\"fill\", bg=\"r\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "= urlencode(params) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code())", "\"webp\": cases[i][\"content_type\"] = \"image/webp\" else: cases[i][\"content_type\"] = None return cases", "from __future__ import absolute_import, division, print_function, \\ with_statement import logging", "self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\"))", "self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME,", "for case in cases: if case.get(\"mode\") == \"crop\" and case.get(\"position\")", "tornado.test.util import unittest from tornado.testing import AsyncHTTPTestCase, gen_test import tornado.web", "class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def get(self): delay = time.time() +", "match %s\" \\ % (qs, case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None),", "200) return response def get_image_resize_cases(self): cases = image_test.get_image_resize_cases() m =", "qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "def _assert_expected_resize(self, case): qs = urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\" %", "resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self):", "w=1, h=1, client=\"123\") qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403,", "DimensionsError.get_code()) def test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp =", "w=1, h=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code())", "\"\", mode=case[\"mode\"]) for k in m.keys(): if k in case:", "\"\", h=case[\"height\"] or \"\", mode=case[\"mode\"]) for k in m.keys(): if", "ModeError, \\ PositionError, QualityError, UrlError, ImageFormatError, FetchError from pilbox.signature import", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\")) resp = self.fetch_error(400, \"/?%s\"", "== \"face\": continue params = case[\"source_query_params\"] params[\"client\"] = self.NAME qs", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\"))", "qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\"))", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self): qs =", "(qs, case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\")", "test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp", "__future__ import absolute_import, division, print_function, \\ with_statement import logging import", "mode=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def", "0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\")) resp = self.fetch_error(400, \"/?%s\"", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\")) resp = self.fetch_error(400, \"/?%s\"", "self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url, w=1, h=1)) resp = self.fetch_error(404, \"/?%s\"", "% qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__), \"data\",", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self): qs", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"r\")) resp =", "= self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self): params", "= self.fetch_error(415, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self): path", "self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200))", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self): qs", "= \"abc\" def get_app(self): return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\",", "def test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp = self.fetch_error(400,", "None logger = logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def fetch_error(self, code, *args,", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp = self.fetch_error(400, \"/?%s\" % qs)", "case in cases: if case.get(\"mode\") == \"crop\" and case.get(\"position\") ==", "filter=\"bar\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def", "h=1, client=\"123\") qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\"", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "= \"abcdef\" NAME = \"abc\" def get_app(self): return _PilboxTestApplication( client_name=self.NAME,", "response = self.fetch(*args, **kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return", "self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self): params =", "DimensionsError.get_code()) def test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\")) resp", "FetchError from pilbox.signature import sign from pilbox.test import image_test try:", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self): qs =", "def test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200)) resp =", "time import tornado.escape import tornado.gen import tornado.ioloop from tornado.test.util import", "\"localhost\"]) def test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs =", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "return tornado.escape.json_decode(response.body) def fetch_success(self, *args, **kwargs): response = self.fetch(*args, **kwargs)", "qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\",", "def test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs = sign(self.KEY,", "filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\") for i, case in enumerate(cases): path", "self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self): params =", "**kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\")", "== \"png\": cases[i][\"content_type\"] = \"image/png\" elif case.get(\"format\") == \"webp\": cases[i][\"content_type\"]", "case[k] if case.get(\"format\") in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\" elif", "FilterError, FormatError, ModeError, \\ PositionError, QualityError, UrlError, ImageFormatError, FetchError from", "self.fetch(*args, **kwargs) self.assertEqual(response.code, 200) return response def get_image_resize_cases(self): cases =", "params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME) qs = urlencode(params) resp", "expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication(timeout=0.5) def", "open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin):", "% qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "@tornado.web.asynchronous @tornado.gen.engine def get(self): delay = time.time() + float(self.get_argument(\"delay\", 0.0))", "division, print_function, \\ with_statement import logging import os.path import time", "test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp = self.fetch_error(404, \"/?%s\"", "path = \"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp =", "% qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "\"image/webp\" else: cases[i][\"content_type\"] = None return cases class _PilboxTestApplication(PilboxApplication): def", "h=1, client=self.NAME) qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\" %", "code, *args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\",", "elif case.get(\"format\") == \"png\": cases[i][\"content_type\"] = \"image/png\" elif case.get(\"format\") ==", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self): qs", "FetchError.get_code()) def test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs =", "w=1, h=1, client=self.NAME, sig=\"abc123\") qs = urlencode(params) resp = self.fetch_error(403,", "\"crop\" and case.get(\"position\") == \"face\": self._assert_expected_resize(case) def _assert_expected_resize(self, case): qs", "urlencode try: import cv except ImportError: cv = None logger", "dict( url=self.get_url(path), w=case[\"width\"] or \"\", h=case[\"height\"] or \"\", mode=case[\"mode\"]) for", "try: import cv except ImportError: cv = None logger =", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self):", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs =", "case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": continue params =", "tornado.web from pilbox.app import PilboxApplication from pilbox.errors import SignatureError, ClientError,", "{\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine", "sign from pilbox.test import image_test try: from urllib import urlencode", "tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication()", "QualityError.get_code()) def test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path), w=1,", "test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp = self.fetch_error(400, \"/?%s\"", "self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\",", "= case[\"source_query_params\"] params[\"client\"] = self.NAME qs = sign(self.KEY, urlencode(params)) resp", "with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase,", "def test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp", "**kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return tornado.escape.json_decode(response.body) def fetch_success(self,", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\",", "_PilboxTestApplication(timeout=0.5) def test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url, w=1,", "tornado.escape import tornado.gen import tornado.ioloop from tornado.test.util import unittest from", "or \"\", h=case[\"height\"] or \"\", mode=case[\"mode\"]) for k in m.keys():", "if case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": self._assert_expected_resize(case) def", "ClientError.get_code()) def test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME) qs", "QualityError, UrlError, ImageFormatError, FetchError from pilbox.signature import sign from pilbox.test", "self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp", "tornado.testing import AsyncHTTPTestCase, gen_test import tornado.web from pilbox.app import PilboxApplication", "does not match %s\" \\ % (qs, case[\"expected_path\"]) with open(case[\"expected_path\"],", "\"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\" elif case.get(\"format\") == \"png\": cases[i][\"content_type\"] =", "get(self): delay = time.time() + float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout,", "== \"crop\" and case.get(\"position\") == \"face\": self._assert_expected_resize(case) def _assert_expected_resize(self, case):", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403,", "\"/?%s does not match %s\" \\ % (qs, case[\"expected_path\"]) if", "class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication(timeout=0.5) def test_timeout(self): url", "cases[i][\"content_type\"] = \"image/png\" elif case.get(\"format\") == \"webp\": cases[i][\"content_type\"] = \"image/webp\"", "ImageFormatError, FetchError from pilbox.signature import sign from pilbox.test import image_test", "w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code())", "else: cases[i][\"content_type\"] = None return cases class _PilboxTestApplication(PilboxApplication): def get_handlers(self):", "self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return tornado.escape.json_decode(response.body) def fetch_success(self, *args,", "case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class", "mode=\"fill\", bg=\"r\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code())", "import os.path import time import tornado.escape import tornado.gen import tornado.ioloop", "\"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def", "DimensionsError.get_code()) def test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp =", "self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self): path =", "PositionError, QualityError, UrlError, ImageFormatError, FetchError from pilbox.signature import sign from", "import unittest from tornado.testing import AsyncHTTPTestCase, gen_test import tornado.web from", "= None logger = logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def fetch_error(self, code,", "return handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def get(self): delay =", "= time.time() + float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish()", "h=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def", "code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return tornado.escape.json_decode(response.body) def fetch_success(self, *args, **kwargs):", "test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\") qs = sign(self.KEY,", "continue params = case[\"source_query_params\"] params[\"client\"] = self.NAME qs = sign(self.KEY,", "case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class", "except ImportError: from urllib.parse import urlencode try: import cv except", "_PilboxTestApplication() def test_missing_url(self): qs = urlencode(dict(w=1, h=1)) resp = self.fetch_error(400,", "self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self): path = \"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path),", "self.NAME qs = sign(self.KEY, urlencode(params)) resp = self.fetch_success(\"/?%s\" % qs)", "== \"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv is None, \"OpenCV is not", "qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "params[\"client\"] = self.NAME qs = sign(self.KEY, urlencode(params)) resp = self.fetch_success(\"/?%s\"", "w=1, h=1, fmt=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "and case.get(\"position\") == \"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv is None, \"OpenCV", "self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self): cases =", "test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\")) resp = self.fetch_error(400,", "\"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(404, \"/?%s\"", "case.get(\"position\") == \"face\": self._assert_expected_resize(case) def _assert_expected_resize(self, case): qs = urlencode(case[\"source_query_params\"])", "qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "urlencode(params)) resp = self.fetch_success(\"/?%s\" % qs) msg = \"/?%s does", "== \"crop\" and case.get(\"position\") == \"face\": continue params = case[\"source_query_params\"]", "self.assertEqual(response.code, 200) return response def get_image_resize_cases(self): cases = image_test.get_image_resize_cases() m", "params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\") qs = sign(self.KEY, urlencode(params))", "from pilbox.app import PilboxApplication from pilbox.errors import SignatureError, ClientError, HostError,", "test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\")) resp = self.fetch_error(400,", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs", "return _PilboxTestApplication() def test_missing_url(self): qs = urlencode(dict(w=1, h=1)) resp =", "test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp =", "unittest from tornado.testing import AsyncHTTPTestCase, gen_test import tornado.web from pilbox.app", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\")) resp = self.fetch_error(400, \"/?%s\" %", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "from tornado.test.util import unittest from tornado.testing import AsyncHTTPTestCase, gen_test import", "% qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\", w=1,", "client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\",", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self): qs", "def test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\")) resp =", "\"abc\" def get_app(self): return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"])", "= image_test.get_image_resize_cases() m = dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\") for", "w=\"a\", h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code())", "self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self): qs =", "self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\"))", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp = self.fetch_error(400, \"/?%s\" %", "= os.path.join(os.path.dirname(__file__), \"data\") handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\":", "FetchError.get_code()) def test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp =", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"r\")) resp = self.fetch_error(400, \"/?%s\" %", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self): qs =", "QualityError.get_code()) def test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200)) resp", "import urlencode try: import cv except ImportError: cv = None", "case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv", "tornado.ioloop from tornado.test.util import unittest from tornado.testing import AsyncHTTPTestCase, gen_test", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self): qs", "cases: if case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": continue", "= dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs = sign(self.KEY, urlencode(params)) resp =", "os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict( url=self.get_url(path), w=case[\"width\"] or \"\", h=case[\"height\"] or", "def test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\") qs =", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self):", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__),", "import PilboxApplication from pilbox.errors import SignatureError, ClientError, HostError, \\ BackgroundError,", "w=1, h=1, pos=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "UrlError.get_code()) def test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400, \"/?%s\"", "def test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME, sig=\"abc123\") qs", "self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self): params =", "cases = image_test.get_image_resize_cases() m = dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\")", "% qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for", "\\ BackgroundError, DimensionsError, FilterError, FormatError, ModeError, \\ PositionError, QualityError, UrlError,", "urlencode except ImportError: from urllib.parse import urlencode try: import cv", "= urlencode(dict(w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "% qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for", "urlencode(params) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def", "tornado.escape.json_decode(response.body) def fetch_success(self, *args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code,", "for i, case in enumerate(cases): path = \"/test/data/%s\" % os.path.basename(case[\"source_path\"])", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self): qs", "expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return", "h=1, filter=\"bar\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code())", "NAME = \"abc\" def get_app(self): return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\",", "def test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME) qs =", "= \"image/webp\" else: cases[i][\"content_type\"] = None return cases class _PilboxTestApplication(PilboxApplication):", "urlencode(params)) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def", "os.path import time import tornado.escape import tornado.gen import tornado.ioloop from", "ImportError: from urllib.parse import urlencode try: import cv except ImportError:", "\"data\") handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication,", "@unittest.skipIf(cv is None, \"OpenCV is not installed\") def test_valid_face(self): cases", "urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\" % qs) msg = \"/?%s does", "case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": self._assert_expected_resize(case) def _assert_expected_resize(self,", "dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\") for i, case in enumerate(cases):", "case.get(\"format\") == \"png\": cases[i][\"content_type\"] = \"image/png\" elif case.get(\"format\") == \"webp\":", "try: from urllib import urlencode except ImportError: from urllib.parse import", "\"image/jpeg\" elif case.get(\"format\") == \"png\": cases[i][\"content_type\"] = \"image/png\" elif case.get(\"format\")", "% qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self): path = \"/test/data/test-not-found.jpg\" qs", "= self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self): params", "SignatureError.get_code()) def test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME) qs", "w=1, h=1, q=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "= self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self): cases", "dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME) qs = sign(self.KEY, urlencode(params)) resp =", "test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\")) resp = self.fetch_error(400,", "path = \"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict( url=self.get_url(path), w=case[\"width\"]", "def test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\")) resp =", "w=1, h=1, mode=\"fill\", bg=\"r\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "**kwargs) self.assertEqual(response.code, 200) return response def get_image_resize_cases(self): cases = image_test.get_image_resize_cases()", "if case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": continue params", "HostError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for case in cases:", "**kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, 200) return response def", "= self.NAME qs = sign(self.KEY, urlencode(params)) resp = self.fetch_success(\"/?%s\" %", "w=1, h=1, q=200)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "import SignatureError, ClientError, HostError, \\ BackgroundError, DimensionsError, FilterError, FormatError, ModeError,", "test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "def test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\")) resp =", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self): qs =", "test_valid(self): cases = self.get_image_resize_cases() for case in cases: if case.get(\"mode\")", "handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def get(self):", "qs = urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\" % qs) msg =", "h=1, q=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code())", "AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY = \"abcdef\" NAME = \"abc\" def get_app(self):", "i, case in enumerate(cases): path = \"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"]", "% os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict( url=self.get_url(path), w=case[\"width\"] or \"\", h=case[\"height\"]", "def test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp = self.fetch_error(400,", "= logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def fetch_error(self, code, *args, **kwargs): response", "dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME, sig=\"abc123\") qs = urlencode(params) resp =", "None), \"application/json\") return tornado.escape.json_decode(response.body) def fetch_success(self, *args, **kwargs): response =", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\")) resp = self.fetch_error(400, \"/?%s\" %", "if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\") as expected:", "def test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"r\")) resp", "import tornado.ioloop from tornado.test.util import unittest from tornado.testing import AsyncHTTPTestCase,", "logging import os.path import time import tornado.escape import tornado.gen import", "urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(415, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self):", "= None return cases class _PilboxTestApplication(PilboxApplication): def get_handlers(self): path =", "urlencode(dict(w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code())", "cases[i][\"content_type\"] = \"image/webp\" else: cases[i][\"content_type\"] = None return cases class", "h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def", "self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for case in", "resp = self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self):", "= \"image/png\" elif case.get(\"format\") == \"webp\": cases[i][\"content_type\"] = \"image/webp\" else:", "import sign from pilbox.test import image_test try: from urllib import", "import tornado.gen import tornado.ioloop from tornado.test.util import unittest from tornado.testing", "self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication(timeout=0.5)", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"r\")) resp = self.fetch_error(400,", "self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp", "pilbox.test import image_test try: from urllib import urlencode except ImportError:", "resp = self.fetch_success(\"/?%s\" % qs) msg = \"/?%s does not", "test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp = self.fetch_error(400, \"/?%s\"", "qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "response = self.fetch(*args, **kwargs) self.assertEqual(response.code, 200) return response def get_image_resize_cases(self):", "% qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "installed\") def test_valid_face(self): cases = self.get_image_resize_cases() for case in cases:", "test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\")) resp = self.fetch_error(400,", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\")) resp = self.fetch_error(400, \"/?%s\" %", "path = os.path.join(os.path.dirname(__file__), \"data\") handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler,", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1)) resp", "\"abcdef\" NAME = \"abc\" def get_app(self): return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY,", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\",", "path, w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME) qs = urlencode(params)", "cases[i][\"content_type\"] = \"image/jpeg\" elif case.get(\"format\") == \"png\": cases[i][\"content_type\"] = \"image/png\"", "= os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs = urlencode(dict(url=\"file://%s\" % path, w=1,", "enumerate(cases): path = \"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict( url=self.get_url(path),", "client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1,", "case[\"source_query_params\"] params[\"client\"] = self.NAME qs = sign(self.KEY, urlencode(params)) resp =", "fetch_success(self, *args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, 200) return", "= urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" % qs)", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\")) resp = self.fetch_error(400, \"/?%s\" %", "\"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(415, \"/?%s\"", "resp = self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self):", "(r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers class _DelayedHandler(tornado.web.RequestHandler):", "url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url, w=1, h=1)) resp =", "cases[i][\"source_query_params\"][m.get(k)] = case[k] if case.get(\"format\") in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] =", "\"crop\" and case.get(\"position\") == \"face\": continue params = case[\"source_query_params\"] params[\"client\"]", "= urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code())", "\"image/png\" elif case.get(\"format\") == \"webp\": cases[i][\"content_type\"] = \"image/webp\" else: cases[i][\"content_type\"]", "= \"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(404,", "% qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1,", "_DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def get(self): delay = time.time() + float(self.get_argument(\"delay\",", "= [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return", "import cv except ImportError: cv = None logger = logging.getLogger(\"tornado.application\")", "handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def get(self): delay = time.time()", "% qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "qs) msg = \"/?%s does not match %s\" \\ %", "= case[k] if case.get(\"format\") in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\"", "None), case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg)", "\"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv is None, \"OpenCV is not installed\")", "w=1, h=1, mode=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "mode=\"fill\", bg=\"0f0f0f0f0\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code())", "qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "+ float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task( tornado.ioloop.IOLoop.instance().add_timeout, delay) self.finish() class AppTest(AsyncHTTPTestCase,", "self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for case in", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self):", "not match %s\" \\ % (qs, case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\",", "% qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "= self.fetch(*args, **kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return tornado.escape.json_decode(response.body)", "\"OpenCV is not installed\") def test_valid_face(self): cases = self.get_image_resize_cases() for", "self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\")", "qs = urlencode(dict(w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" % qs)", "self).get_handlers()) return handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def get(self): delay", "\"bar.io\", \"localhost\"]) def test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=\"a\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "urlencode(dict(url=url, w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" %qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code())", "return cases class _PilboxTestApplication(PilboxApplication): def get_handlers(self): path = os.path.join(os.path.dirname(__file__), \"data\")", "self.finish() class AppTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication() def test_missing_url(self):", "m = dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\") for i, case", "_AppAsyncMixin): def get_app(self): return _PilboxTestApplication(timeout=0.5) def test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\")", "import time import tornado.escape import tornado.gen import tornado.ioloop from tornado.test.util", "\"/?%s does not match %s\" \\ % (qs, case[\"expected_path\"]) with", "qs = urlencode(dict(url=\"file://%s\" % path, w=1, h=1)) resp = self.fetch_error(400,", "dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\") qs = sign(self.KEY, urlencode(params)) resp =", "logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def fetch_error(self, code, *args, **kwargs): response =", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self): qs =", "m.keys(): if k in case: cases[i][\"source_query_params\"][m.get(k)] = case[k] if case.get(\"format\")", "case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\") as", "class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY = \"abcdef\" NAME = \"abc\" def", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\",", "[\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\" elif case.get(\"format\") == \"png\": cases[i][\"content_type\"]", "allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def test_missing_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1)", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self):", "= urlencode(dict(url=url, w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" %qs) self.assertEqual(resp.get(\"error_code\"),", "\"crop\" and case.get(\"position\") == \"face\": continue self._assert_expected_resize(case) @unittest.skipIf(cv is None,", "= self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self): qs", "def test_not_found(self): path = \"/test/data/test-not-found.jpg\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1))", "in cases: if case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\":", "% (qs, case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with open(case[\"expected_path\"],", "AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication(timeout=0.5) def test_timeout(self): url =", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=\"a\", h=1))", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self):", "test_missing_url(self): qs = urlencode(dict(w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" %", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "w=1, h=1) qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\"", "qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" % qs)", "case.get(\"format\") == \"webp\": cases[i][\"content_type\"] = \"image/webp\" else: cases[i][\"content_type\"] = None", "if case.get(\"format\") in [\"jpeg\", \"jpg\"]: cases[i][\"content_type\"] = \"image/jpeg\" elif case.get(\"format\")", "from urllib.parse import urlencode try: import cv except ImportError: cv", "test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\")) resp = self.fetch_error(400,", "= dict( url=self.get_url(path), w=case[\"width\"] or \"\", h=case[\"height\"] or \"\", mode=case[\"mode\"])", "not match %s\" \\ % (qs, case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\")", "\\ PositionError, QualityError, UrlError, ImageFormatError, FetchError from pilbox.signature import sign", "url=self.get_url(path), w=case[\"width\"] or \"\", h=case[\"height\"] or \"\", mode=case[\"mode\"]) for k", "def test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400, \"/?%s\" %", "cases[i][\"source_query_params\"] = dict( url=self.get_url(path), w=case[\"width\"] or \"\", h=case[\"height\"] or \"\",", "UrlError.get_code()) def test_valid(self): cases = self.get_image_resize_cases() for case in cases:", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self):", "params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1) qs = sign(self.KEY, urlencode(params)) resp", "h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def", "tornado.gen import tornado.ioloop from tornado.test.util import unittest from tornado.testing import", "% qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def test_invalid_integer_quality(self):", "qs = urlencode(dict(url=url, w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" %qs)", "= sign(self.KEY, urlencode(params)) resp = self.fetch_success(\"/?%s\" % qs) msg =", "FilterError.get_code()) def test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, fmt=\"foo\")) resp", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self): qs =", "def get_app(self): return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def", "= self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url, w=1, h=1)) resp = self.fetch_error(404,", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "h=1, client=self.NAME, sig=\"abc123\") qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\"", "is not installed\") def test_valid_face(self): cases = self.get_image_resize_cases() for case", "None return cases class _PilboxTestApplication(PilboxApplication): def get_handlers(self): path = os.path.join(os.path.dirname(__file__),", "= self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self): params", "*args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, 200) return response", "self.get_image_resize_cases() for case in cases: if case.get(\"mode\") == \"crop\" and", "pilbox.errors import SignatureError, ClientError, HostError, \\ BackgroundError, DimensionsError, FilterError, FormatError,", "or \"\", mode=case[\"mode\"]) for k in m.keys(): if k in", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self): qs", "does not match %s\" \\ % (qs, case[\"expected_path\"]) if case[\"content_type\"]:", "return _PilboxTestApplication( client_name=self.NAME, client_key=self.KEY, allowed_hosts=[\"foo.co\", \"bar.io\", \"localhost\"]) def test_missing_client_name(self): params", "urlencode(params)) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def", "absolute_import, division, print_function, \\ with_statement import logging import os.path import", "w=1, h=1, filter=\"bar\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200)) resp = self.fetch_error(400, \"/?%s\" % qs)", "image_test try: from urllib import urlencode except ImportError: from urllib.parse", "= sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self): qs =", "match %s\" \\ % (qs, case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\") as", "def get(self): delay = time.time() + float(self.get_argument(\"delay\", 0.0)) yield tornado.gen.Task(", "print_function, \\ with_statement import logging import os.path import time import", "self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\")) resp = self.fetch_error(400,", "FormatError.get_code()) def test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\")) resp", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self):", "qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(415, \"/?%s\" %", "w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code())", "KEY = \"abcdef\" NAME = \"abc\" def get_app(self): return _PilboxTestApplication(", "\"png\": cases[i][\"content_type\"] = \"image/png\" elif case.get(\"format\") == \"webp\": cases[i][\"content_type\"] =", "PositionError.get_code()) def test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\")) resp", "def fetch_error(self, code, *args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code,", "def get_image_resize_cases(self): cases = image_test.get_image_resize_cases() m = dict(background=\"bg\", filter=\"filter\", format=\"fmt\",", "urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs)", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self):", "= \"image/jpeg\" elif case.get(\"format\") == \"png\": cases[i][\"content_type\"] = \"image/png\" elif", "import logging import os.path import time import tornado.escape import tornado.gen", "= urlencode(case[\"source_query_params\"]) resp = self.fetch_success(\"/?%s\" % qs) msg = \"/?%s", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code()) def test_valid(self): cases = self.get_image_resize_cases()", "HostError, \\ BackgroundError, DimensionsError, FilterError, FormatError, ModeError, \\ PositionError, QualityError,", "def get_handlers(self): path = os.path.join(os.path.dirname(__file__), \"data\") handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler),", "_AppAsyncMixin(object): def fetch_error(self, code, *args, **kwargs): response = self.fetch(*args, **kwargs)", "h=1, q=200)) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code())", "and case.get(\"position\") == \"face\": self._assert_expected_resize(case) def _assert_expected_resize(self, case): qs =", "= \"/?%s does not match %s\" \\ % (qs, case[\"expected_path\"])", "qs = sign(self.KEY, urlencode(params)) resp = self.fetch_success(\"/?%s\" % qs) msg", "cases[i][\"content_type\"] = None return cases class _PilboxTestApplication(PilboxApplication): def get_handlers(self): path", "h=1, fmt=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code())", "self.fetch_error(415, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def test_not_found(self): path =", "sig=\"abc123\") qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\" % qs)", "%s\" \\ % (qs, case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\") as expected:", "h=1) qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" %", "w=1, h=1, client=self.NAME) qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403,", "in enumerate(cases): path = \"/test/data/%s\" % os.path.basename(case[\"source_path\"]) cases[i][\"source_query_params\"] = dict(", "params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME, sig=\"abc123\") qs = urlencode(params)", "continue self._assert_expected_resize(case) @unittest.skipIf(cv is None, \"OpenCV is not installed\") def", "def get_app(self): return _PilboxTestApplication() def test_missing_url(self): qs = urlencode(dict(w=1, h=1))", "= urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(415, \"/?%s\" % qs)", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\"", "cases class _PilboxTestApplication(PilboxApplication): def get_handlers(self): path = os.path.join(os.path.dirname(__file__), \"data\") handlers", "get_image_resize_cases(self): cases = image_test.get_image_resize_cases() m = dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\",", "% qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1,", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self): cases =", "= dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME, sig=\"abc123\") qs = urlencode(params) resp", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self): qs", "% qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "import tornado.web from pilbox.app import PilboxApplication from pilbox.errors import SignatureError,", "h=1, mode=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ModeError.get_code())", "% qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1,", "= self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_signature(self): params", "cv = None logger = logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def fetch_error(self,", "*args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None),", "in m.keys(): if k in case: cases[i][\"source_query_params\"][m.get(k)] = case[k] if", "def test_invalid_integer_quality(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\")) resp =", "def test_valid_face(self): cases = self.get_image_resize_cases() for case in cases: if", "None, \"OpenCV is not installed\") def test_valid_face(self): cases = self.get_image_resize_cases()", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_outofbounds_quality(self):", "quality=\"q\") for i, case in enumerate(cases): path = \"/test/data/%s\" %", "= dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\") qs = sign(self.KEY, urlencode(params)) resp", "client=self.NAME) qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\" % qs)", "case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(),", "class _PilboxTestApplication(PilboxApplication): def get_handlers(self): path = os.path.join(os.path.dirname(__file__), \"data\") handlers =", "BackgroundError.get_code()) def test_invalid_long_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\", bg=\"0f0f0f0f0\"))", "\\ with_statement import logging import os.path import time import tornado.escape", "client=self.NAME) qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" %", "client=\"123\") qs = sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" %", "def test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url, w=1, h=1))", "self.assertEqual(resp.get(\"error_code\"), PositionError.get_code()) def test_invalid_filter(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, filter=\"bar\"))", "tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous", "qs) self.assertEqual(resp.get(\"error_code\"), FilterError.get_code()) def test_invalid_format(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "test_bad_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME, sig=\"abc123\") qs =", "h=1, mode=\"fill\", bg=\"0f0f0f0f0\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs = urlencode(dict(url=url, w=1, h=1)) resp", "FormatError, ModeError, \\ PositionError, QualityError, UrlError, ImageFormatError, FetchError from pilbox.signature", "PilboxApplication from pilbox.errors import SignatureError, ClientError, HostError, \\ BackgroundError, DimensionsError,", "elif case.get(\"format\") == \"webp\": cases[i][\"content_type\"] = \"image/webp\" else: cases[i][\"content_type\"] =", "urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "def test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path), w=1, h=1))", "urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"),", "image_test.get_image_resize_cases() m = dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\") for i,", "SignatureError, ClientError, HostError, \\ BackgroundError, DimensionsError, FilterError, FormatError, ModeError, \\", "urllib import urlencode except ImportError: from urllib.parse import urlencode try:", "self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\",", "path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers class _DelayedHandler(tornado.web.RequestHandler): @tornado.web.asynchronous @tornado.gen.engine def", "ClientError, HostError, \\ BackgroundError, DimensionsError, FilterError, FormatError, ModeError, \\ PositionError,", "k in case: cases[i][\"source_query_params\"][m.get(k)] = case[k] if case.get(\"format\") in [\"jpeg\",", "path = os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs = urlencode(dict(url=\"file://%s\" % path,", "if case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": continue self._assert_expected_resize(case)", "\"face\": self._assert_expected_resize(case) def _assert_expected_resize(self, case): qs = urlencode(case[\"source_query_params\"]) resp =", "expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY = \"abcdef\"", "% qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1,", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self): path", "import urlencode except ImportError: from urllib.parse import urlencode try: import", "= urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, pos=\"foo\")) resp = self.fetch_error(400, \"/?%s\" %", "and case.get(\"position\") == \"face\": continue params = case[\"source_query_params\"] params[\"client\"] =", "fmt=\"foo\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FormatError.get_code()) def", "return response def get_image_resize_cases(self): cases = image_test.get_image_resize_cases() m = dict(background=\"bg\",", "qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1,", "handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers())", "cases: if case.get(\"mode\") == \"crop\" and case.get(\"position\") == \"face\": self._assert_expected_resize(case)", "h=1)) resp = self.fetch_error(415, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ImageFormatError.get_code()) def", "= urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" % qs)", "== \"face\": self._assert_expected_resize(case) def _assert_expected_resize(self, case): qs = urlencode(case[\"source_query_params\"]) resp", "= dict(background=\"bg\", filter=\"filter\", format=\"fmt\", position=\"pos\", quality=\"q\") for i, case in", "%s\" \\ % (qs, case[\"expected_path\"]) if case[\"content_type\"]: self.assertEqual(resp.headers.get(\"Content-Type\", None), case[\"content_type\"])", "= self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_height(self): qs", "msg) class AppSlowTest(AsyncHTTPTestCase, _AppAsyncMixin): def get_app(self): return _PilboxTestApplication(timeout=0.5) def test_timeout(self):", "with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase,", "msg = \"/?%s does not match %s\" \\ % (qs,", "@tornado.gen.engine def get(self): delay = time.time() + float(self.get_argument(\"delay\", 0.0)) yield", "qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\" qs =", "qs) self.assertEqual(resp.get(\"error_code\"), SignatureError.get_code()) def test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\", w=1, h=1,", "ClientError.get_code()) def test_bad_client_name(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=\"123\") qs", "= self.fetch(*args, **kwargs) self.assertEqual(response.code, 200) return response def get_image_resize_cases(self): cases", "DimensionsError, FilterError, FormatError, ModeError, \\ PositionError, QualityError, UrlError, ImageFormatError, FetchError", "class _AppAsyncMixin(object): def fetch_error(self, code, *args, **kwargs): response = self.fetch(*args,", "def test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs = urlencode(dict(url=\"file://%s\"", "as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY =", "self.fetch(*args, **kwargs) self.assertEqual(response.code, code) self.assertEqual(response.headers.get(\"Content-Type\", None), \"application/json\") return tornado.escape.json_decode(response.body) def", "test_bad_host(self): params = dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME) qs = sign(self.KEY,", "format=\"fmt\", position=\"pos\", quality=\"q\") for i, case in enumerate(cases): path =", "import AsyncHTTPTestCase, gen_test import tornado.web from pilbox.app import PilboxApplication from", "= self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_invalid_protocol(self): path", "def test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1)) resp = self.fetch_error(404,", "urllib.parse import urlencode try: import cv except ImportError: cv =", "if k in case: cases[i][\"source_query_params\"][m.get(k)] = case[k] if case.get(\"format\") in", "qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1, h=1))", "import tornado.escape import tornado.gen import tornado.ioloop from tornado.test.util import unittest", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=200)) resp = self.fetch_error(400, \"/?%s\"", "logger = logging.getLogger(\"tornado.application\") class _AppAsyncMixin(object): def fetch_error(self, code, *args, **kwargs):", "import absolute_import, division, print_function, \\ with_statement import logging import os.path", "bg=\"0f0f0f0f0\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def", "def get_app(self): return _PilboxTestApplication(timeout=0.5) def test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs", "from pilbox.signature import sign from pilbox.test import image_test try: from", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, q=\"a\")) resp = self.fetch_error(400, \"/?%s\"", "% path, w=1, h=1)) resp = self.fetch_error(400, \"/?%s\" % qs)", "% qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def test_not_connect(self): qs = urlencode(dict(url=\"http://a.com/a.jpg\", w=1,", "\\ % (qs, case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(),", "[(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers", "self.assertEqual(resp.get(\"error_code\"), ModeError.get_code()) def test_invalid_hexadecimal_background(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"fill\",", "self.assertEqual(resp.buffer.read(), expected.read(), msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY = \"abcdef\" NAME", "os.path.join(os.path.dirname(__file__), \"data\") handlers = [(r\"/test/data/test-delayed.jpg\", _DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})]", "with_statement import logging import os.path import time import tornado.escape import", "cases = self.get_image_resize_cases() for case in cases: if case.get(\"mode\") ==", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), DimensionsError.get_code()) def test_invalid_width(self):", "self.assertEqual(resp.get(\"error_code\"), ClientError.get_code()) def test_missing_signature(self): params = dict(url=\"http://foo.co/x.jpg\", w=1, h=1, client=self.NAME)", "def test_invalid_mode(self): qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\")) resp =", "AsyncHTTPTestCase, gen_test import tornado.web from pilbox.app import PilboxApplication from pilbox.errors", "h=case[\"height\"] or \"\", mode=case[\"mode\"]) for k in m.keys(): if k", "mode=case[\"mode\"]) for k in m.keys(): if k in case: cases[i][\"source_query_params\"][m.get(k)]", "test_invalid_protocol(self): path = os.path.join(os.path.dirname(__file__), \"data\", \"test1.jpg\") qs = urlencode(dict(url=\"file://%s\" %", "sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), ClientError.get_code())", "bg=\"r\")) resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def", "get_app(self): return _PilboxTestApplication(timeout=0.5) def test_timeout(self): url = self.get_url(\"/test/data/test-delayed.jpg?delay=1.0\") qs =", "client=self.NAME, sig=\"abc123\") qs = urlencode(params) resp = self.fetch_error(403, \"/?%s\" %", "def fetch_success(self, *args, **kwargs): response = self.fetch(*args, **kwargs) self.assertEqual(response.code, 200)", "self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), BackgroundError.get_code()) def test_invalid_position(self): qs =", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_missing_dimensions(self):", "\"face\": continue params = case[\"source_query_params\"] params[\"client\"] = self.NAME qs =", "self.fetch_success(\"/?%s\" % qs) msg = \"/?%s does not match %s\"", "_DelayedHandler), (r\"/test/data/(.*)\", tornado.web.StaticFileHandler, {\"path\": path})] handlers.extend(super(_PilboxTestApplication, self).get_handlers()) return handlers class", "in case: cases[i][\"source_query_params\"][m.get(k)] = case[k] if case.get(\"format\") in [\"jpeg\", \"jpg\"]:", "sign(self.KEY, urlencode(params)) resp = self.fetch_error(403, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), HostError.get_code())", "def test_valid(self): cases = self.get_image_resize_cases() for case in cases: if", "self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self): path = \"/test/data/test-bad-format.gif\" qs = urlencode(dict(url=self.get_url(path),", "resp = self.fetch_error(400, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), QualityError.get_code()) def test_unsupported_image_format(self):", "msg) class AppRestrictedTest(AsyncHTTPTestCase, _AppAsyncMixin): KEY = \"abcdef\" NAME = \"abc\"", "qs = urlencode(dict(url=\"http://foo.co/x.jpg\", w=1, h=1, mode=\"foo\")) resp = self.fetch_error(400, \"/?%s\"", "= self.get_image_resize_cases() for case in cases: if case.get(\"mode\") == \"crop\"", "\"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), UrlError.get_code()) def test_valid(self): cases = self.get_image_resize_cases()", "pilbox.app import PilboxApplication from pilbox.errors import SignatureError, ClientError, HostError, \\", "qs = urlencode(dict(url=self.get_url(path), w=1, h=1)) resp = self.fetch_error(404, \"/?%s\" %", "(qs, case[\"expected_path\"]) with open(case[\"expected_path\"], \"rb\") as expected: self.assertEqual(resp.buffer.read(), expected.read(), msg)", "h=1)) resp = self.fetch_error(404, \"/?%s\" % qs) self.assertEqual(resp.get(\"error_code\"), FetchError.get_code()) def", "= dict(url=\"http://bar.co/x.jpg\", w=1, h=1, client=self.NAME) qs = sign(self.KEY, urlencode(params)) resp", "k in m.keys(): if k in case: cases[i][\"source_query_params\"][m.get(k)] = case[k]", "case.get(\"position\") == \"face\": continue params = case[\"source_query_params\"] params[\"client\"] = self.NAME" ]
[ "z = x + y *1j #! If we study", "#$ We start by defining a function J: #$ \\[", "-1:1:0.002 ] z = x + y *1j #! If", "start by defining a function J: #$ \\[ J_c :", "#! Some graphical explorations of the Julia sets with python", "the Julia sets with python and pyreport #!######################################################################### #$ #$", ": z \\rightarrow z^2 + c \\] #$ def J(c):", "= ogrid[ -1:1:0.002, -1:1:0.002 ] z = x + y", "z^2 + c \\] #$ def J(c): return lambda z", "-0.939 +0.167j, -0.986+0.87j) for i,c in enumerate(c_values): threshTime = zeros_like(z)", "(0.285 + 0.013j, 0.45 - 0.1428j, -0.70176 -0.3842j, -0.835-0.2321j, -0.939", "threshTime += z*conj(z) > 4 figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime) bone()", "z*conj(z) > 4 figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime) bone() show() #!", "show() #! We can also do that systematicaly for other", "+ y *1j #! If we study the divergence of", "] z = x + y *1j #! If we", "repeated iteration #! depending on its inital conditions we get", "threshTime = zeros_like(z) for i in range(40): z = J(0.285)(z)", "imshow(threshTime) bone() show() #! We can also do that systematicaly", "i in range(40): z = J(0.285)(z) threshTime += z*conj(z) >", "= x + y *1j for n in range(40): z", "for n in range(40): z = J(c)(z) threshTime += z*conj(z)", "python from scipy import * from pylab import * #from", "python and pyreport #!######################################################################### #$ #$ We start by defining", "We can also do that systematicaly for other values of", "of the Julia sets with python and pyreport #!######################################################################### #$", "J(0.285)(z) threshTime += z*conj(z) > 4 figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime)", "+= z*conj(z) > 4 figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime) bone() show()", "* from pylab import * #from pylab import imshow #!", "study the divergence of function J under repeated iteration #!", "> 4 figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime) bone() show() #! We", "z**2 + c [x,y] = ogrid[ -1:1:0.002, -1:1:0.002 ] z", "*1j for n in range(40): z = J(c)(z) threshTime +=", "0.45 - 0.1428j, -0.70176 -0.3842j, -0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j) for", "from scipy import * from pylab import * #from pylab", "J(c): return lambda z : z**2 + c [x,y] =", "#$ def J(c): return lambda z : z**2 + c", "axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values = (0.285 + 0.013j, 0.45", "iteration #! depending on its inital conditions we get a", "lambda z : z**2 + c [x,y] = ogrid[ -1:1:0.002,", "inital conditions we get a very pretty graph threshTime =", "with python and pyreport #!######################################################################### #$ #$ We start by", "#! We can also do that systematicaly for other values", "threshTime = zeros_like(z) z = x + y *1j for", "conditions we get a very pretty graph threshTime = zeros_like(z)", "c \\] #$ def J(c): return lambda z : z**2", "J under repeated iteration #! depending on its inital conditions", "divergence of function J under repeated iteration #! depending on", "imshow #! #! Some graphical explorations of the Julia sets", "zeros_like(z) for i in range(40): z = J(0.285)(z) threshTime +=", "#$ \\[ J_c : z \\rightarrow z^2 + c \\]", "c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values = (0.285 + 0.013j,", "z = x + y *1j for n in range(40):", "Some graphical explorations of the Julia sets with python and", "enumerate(c_values): threshTime = zeros_like(z) z = x + y *1j", "explorations of the Julia sets with python and pyreport #!#########################################################################", "sets with python and pyreport #!######################################################################### #$ #$ We start", "z \\rightarrow z^2 + c \\] #$ def J(c): return", "can also do that systematicaly for other values of c:", "J: #$ \\[ J_c : z \\rightarrow z^2 + c", "of function J under repeated iteration #! depending on its", "axes([0,0,1,1]) axis('off') imshow(threshTime) bone() show() #! We can also do", "bone() show() #! We can also do that systematicaly for", "J(c)(z) threshTime += z*conj(z) > 4 subplot(2,3,i+1) imshow(threshTime) axis('off') show()", "+0.167j, -0.986+0.87j) for i,c in enumerate(c_values): threshTime = zeros_like(z) z", "for other values of c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values", "*1j #! If we study the divergence of function J", "import imshow #! #! Some graphical explorations of the Julia", "pyreport #!######################################################################### #$ #$ We start by defining a function", "- 0.1428j, -0.70176 -0.3842j, -0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j) for i,c", "very pretty graph threshTime = zeros_like(z) for i in range(40):", "for i in range(40): z = J(0.285)(z) threshTime += z*conj(z)", "-0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j) for i,c in enumerate(c_values): threshTime =", "+ c [x,y] = ogrid[ -1:1:0.002, -1:1:0.002 ] z =", "z : z**2 + c [x,y] = ogrid[ -1:1:0.002, -1:1:0.002", "c_values = (0.285 + 0.013j, 0.45 - 0.1428j, -0.70176 -0.3842j,", "rcParams.update({'figure.figsize': [10.5,5]}) c_values = (0.285 + 0.013j, 0.45 - 0.1428j,", "#! #! Some graphical explorations of the Julia sets with", "graphical explorations of the Julia sets with python and pyreport", "def J(c): return lambda z : z**2 + c [x,y]", "= (0.285 + 0.013j, 0.45 - 0.1428j, -0.70176 -0.3842j, -0.835-0.2321j,", "by defining a function J: #$ \\[ J_c : z", "do that systematicaly for other values of c: axes([0,0,1,1]) axis('off')", "other values of c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values =", "return lambda z : z**2 + c [x,y] = ogrid[", "under repeated iteration #! depending on its inital conditions we", "We start by defining a function J: #$ \\[ J_c", "0.1428j, -0.70176 -0.3842j, -0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j) for i,c in", "<filename>hackathon/darkmattertemperaturedistribution/example.py<gh_stars>1-10 #!/usr/bin/env python from scipy import * from pylab import", ": z**2 + c [x,y] = ogrid[ -1:1:0.002, -1:1:0.002 ]", "that systematicaly for other values of c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize':", "systematicaly for other values of c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]})", "y *1j for n in range(40): z = J(c)(z) threshTime", "axis('off') imshow(threshTime) bone() show() #! We can also do that", "x + y *1j for n in range(40): z =", "from pylab import * #from pylab import imshow #! #!", "+ y *1j for n in range(40): z = J(c)(z)", "n in range(40): z = J(c)(z) threshTime += z*conj(z) >", "#!/usr/bin/env python from scipy import * from pylab import *", "graph threshTime = zeros_like(z) for i in range(40): z =", "on its inital conditions we get a very pretty graph", "import * from pylab import * #from pylab import imshow", "function J: #$ \\[ J_c : z \\rightarrow z^2 +", "range(40): z = J(0.285)(z) threshTime += z*conj(z) > 4 figure(0)", "+ c \\] #$ def J(c): return lambda z :", "0.013j, 0.45 - 0.1428j, -0.70176 -0.3842j, -0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j)", "= J(c)(z) threshTime += z*conj(z) > 4 subplot(2,3,i+1) imshow(threshTime) axis('off')", "pylab import * #from pylab import imshow #! #! Some", "the divergence of function J under repeated iteration #! depending", "i,c in enumerate(c_values): threshTime = zeros_like(z) z = x +", "Julia sets with python and pyreport #!######################################################################### #$ #$ We", "defining a function J: #$ \\[ J_c : z \\rightarrow", "x + y *1j #! If we study the divergence", "= J(0.285)(z) threshTime += z*conj(z) > 4 figure(0) axes([0,0,1,1]) axis('off')", "import * #from pylab import imshow #! #! Some graphical", "y *1j #! If we study the divergence of function", "its inital conditions we get a very pretty graph threshTime", "-0.3842j, -0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j) for i,c in enumerate(c_values): threshTime", "range(40): z = J(c)(z) threshTime += z*conj(z) > 4 subplot(2,3,i+1)", "a function J: #$ \\[ J_c : z \\rightarrow z^2", "[10.5,5]}) c_values = (0.285 + 0.013j, 0.45 - 0.1428j, -0.70176", "depending on its inital conditions we get a very pretty", "J_c : z \\rightarrow z^2 + c \\] #$ def", "pretty graph threshTime = zeros_like(z) for i in range(40): z", "\\] #$ def J(c): return lambda z : z**2 +", "* #from pylab import imshow #! #! Some graphical explorations", "\\rightarrow z^2 + c \\] #$ def J(c): return lambda", "= x + y *1j #! If we study the", "4 figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime) bone() show() #! We can", "figure(0) axes([0,0,1,1]) axis('off') imshow(threshTime) bone() show() #! We can also", "we study the divergence of function J under repeated iteration", "z = J(c)(z) threshTime += z*conj(z) > 4 subplot(2,3,i+1) imshow(threshTime)", "function J under repeated iteration #! depending on its inital", "#! If we study the divergence of function J under", "= zeros_like(z) for i in range(40): z = J(0.285)(z) threshTime", "a very pretty graph threshTime = zeros_like(z) for i in", "+ 0.013j, 0.45 - 0.1428j, -0.70176 -0.3842j, -0.835-0.2321j, -0.939 +0.167j,", "values of c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values = (0.285", "-0.70176 -0.3842j, -0.835-0.2321j, -0.939 +0.167j, -0.986+0.87j) for i,c in enumerate(c_values):", "scipy import * from pylab import * #from pylab import", "If we study the divergence of function J under repeated", "-0.986+0.87j) for i,c in enumerate(c_values): threshTime = zeros_like(z) z =", "in enumerate(c_values): threshTime = zeros_like(z) z = x + y", "and pyreport #!######################################################################### #$ #$ We start by defining a", "also do that systematicaly for other values of c: axes([0,0,1,1])", "of c: axes([0,0,1,1]) axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values = (0.285 +", "axis('off') rcParams.update({'figure.figsize': [10.5,5]}) c_values = (0.285 + 0.013j, 0.45 -", "= zeros_like(z) z = x + y *1j for n", "in range(40): z = J(0.285)(z) threshTime += z*conj(z) > 4", "pylab import imshow #! #! Some graphical explorations of the", "-1:1:0.002, -1:1:0.002 ] z = x + y *1j #!", "#from pylab import imshow #! #! Some graphical explorations of", "ogrid[ -1:1:0.002, -1:1:0.002 ] z = x + y *1j", "we get a very pretty graph threshTime = zeros_like(z) for", "#! depending on its inital conditions we get a very", "[x,y] = ogrid[ -1:1:0.002, -1:1:0.002 ] z = x +", "for i,c in enumerate(c_values): threshTime = zeros_like(z) z = x", "#$ #$ We start by defining a function J: #$", "\\[ J_c : z \\rightarrow z^2 + c \\] #$", "get a very pretty graph threshTime = zeros_like(z) for i", "zeros_like(z) z = x + y *1j for n in", "in range(40): z = J(c)(z) threshTime += z*conj(z) > 4", "z = J(0.285)(z) threshTime += z*conj(z) > 4 figure(0) axes([0,0,1,1])", "c [x,y] = ogrid[ -1:1:0.002, -1:1:0.002 ] z = x", "#!######################################################################### #$ #$ We start by defining a function J:" ]
[ "# Generated by Django 2.2.21 on 2021-06-23 12:43 from django.db", "Generated by Django 2.2.21 on 2021-06-23 12:43 from django.db import", "from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies", "Migration(migrations.Migration): dependencies = [ ('resources', '0125_add_timmi_payload_model'), ] operations = [", "= [ ('resources', '0125_add_timmi_payload_model'), ] operations = [ migrations.AddField( model_name='unit',", "models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('resources', '0125_add_timmi_payload_model'),", "('resources', '0125_add_timmi_payload_model'), ] operations = [ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False,", "field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations in this unit per user.'), ),", "Django 2.2.21 on 2021-06-23 12:43 from django.db import migrations, models", "dependencies = [ ('resources', '0125_add_timmi_payload_model'), ] operations = [ migrations.AddField(", "import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('resources', '0125_add_timmi_payload_model'), ]", "name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations in this unit per user.'),", "12:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration):", "by Django 2.2.21 on 2021-06-23 12:43 from django.db import migrations,", "on 2021-06-23 12:43 from django.db import migrations, models import django.db.models.deletion", "[ ('resources', '0125_add_timmi_payload_model'), ] operations = [ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user',", "django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('resources', '0125_add_timmi_payload_model'), ] operations", "django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies =", "migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('resources',", "'0125_add_timmi_payload_model'), ] operations = [ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow", "import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [", "] operations = [ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping", "model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations in this unit per", "= [ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations in", "2021-06-23 12:43 from django.db import migrations, models import django.db.models.deletion class", "operations = [ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations", "class Migration(migrations.Migration): dependencies = [ ('resources', '0125_add_timmi_payload_model'), ] operations =", "2.2.21 on 2021-06-23 12:43 from django.db import migrations, models import", "migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations in this unit", "verbose_name='Disallow overlapping reservations in this unit per user.'), ), ]", "[ migrations.AddField( model_name='unit', name='disallow_overlapping_reservations_per_user', field=models.BooleanField(default=False, verbose_name='Disallow overlapping reservations in this" ]
[ "set to: {address}') return True logging.warning(\"could not set module address\")", "def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr) addr_as_list", "serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module config successfully set') return", "= serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip()", "True logging.warning(\"could not set module config\") return False def set_address(address):", "if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module config", "not set module address\") return False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr", "[variables.STATUS_OK]): logging.debug(f'module address successfully set to: {address}') return True logging.warning(\"could", "serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() !=", "True logging.warning(\"could not set module address\") return False def get_current_address():", "serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module config successfully", "successfully set to: {address}') return True logging.warning(\"could not set module", "module address\") return False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT)", "= addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() != 'AT' or addr_as_list[2].strip() != 'OK':", "return False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr =", "if addr_as_list[0].strip() != 'AT' or addr_as_list[2].strip() != 'OK': raise ValueError('could", "set module config\") return False def set_address(address): cmd = f'AT+ADDR={address}'", "!= 'AT' or addr_as_list[2].strip() != 'OK': raise ValueError('could not get", "serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER)", "successfully set') return True logging.warning(\"could not set module config\") return", "addr_as_list[2].strip() != 'OK': raise ValueError('could not get address of module')", "addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() != 'AT' or addr_as_list[2].strip() != 'OK': raise", "set_address(address): cmd = f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address successfully", "lora_multihop import serial_connection, variables def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1',", "False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr)", "address successfully set to: {address}') return True logging.warning(\"could not set", "serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module config successfully set') return True logging.warning(\"could", "def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED'])", "config successfully set') return True logging.warning(\"could not set module config\")", "= f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address successfully set to:", "logging.debug(f'module address successfully set to: {address}') return True logging.warning(\"could not", "{address}') return True logging.warning(\"could not set module address\") return False", "['AT,SENDING', 'AT,SENDED']) logging.debug('module config successfully set') return True logging.warning(\"could not", "addr = serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() != 'AT'", "'AT' or addr_as_list[2].strip() != 'OK': raise ValueError('could not get address", "logging.warning(\"could not set module config\") return False def set_address(address): cmd", "to: {address}') return True logging.warning(\"could not set module address\") return", "def set_address(address): cmd = f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address", "from lora_multihop import serial_connection, variables def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]):", "serial_connection, variables def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a',", "address\") return False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr", "False def set_address(address): cmd = f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module", "module config\") return False def set_address(address): cmd = f'AT+ADDR={address}' if", "return True logging.warning(\"could not set module config\") return False def", "addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if", "serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() != 'AT' or addr_as_list[2].strip()", "return True logging.warning(\"could not set module address\") return False def", "set module address\") return False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr =", "variables def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING',", "'OK': raise ValueError('could not get address of module') return addr_as_list[1]", "set') return True logging.warning(\"could not set module config\") return False", "cmd = f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address successfully set", "logging from lora_multihop import serial_connection, variables def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration,", "config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module", "= serial_connection.bytes_to_str(addr) addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() != 'AT' or", "addr_as_list[0].strip() != 'AT' or addr_as_list[2].strip() != 'OK': raise ValueError('could not", "serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address successfully set to: {address}') return True", "return False def set_address(address): cmd = f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]):", "addr_as_list = addr.split(variables.LORA_MODULE_DELIMITER) if addr_as_list[0].strip() != 'AT' or addr_as_list[2].strip() !=", "f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address successfully set to: {address}')", "import serial_connection, variables def config_module(configuration=variables.MODULE_CONFIG): if serial_connection.execute_command(configuration, [variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK])", "[variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module config successfully set') return True", "logging.debug('module config successfully set') return True logging.warning(\"could not set module", "logging.warning(\"could not set module address\") return False def get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR))", "!= 'OK': raise ValueError('could not get address of module') return", "config\") return False def set_address(address): cmd = f'AT+ADDR={address}' if serial_connection.execute_command(serial_connection.str_to_bytes(cmd),", "or addr_as_list[2].strip() != 'OK': raise ValueError('could not get address of", "not set module config\") return False def set_address(address): cmd =", "import logging from lora_multihop import serial_connection, variables def config_module(configuration=variables.MODULE_CONFIG): if", "get_current_address(): serial_connection.execute_command(serial_connection.str_to_bytes(variables.GET_ADDR)) addr = serial_connection.response_q.get(variables.COMMAND_VERIFICATION_TIMEOUT) addr = serial_connection.bytes_to_str(addr) addr_as_list =", "[variables.STATUS_OK]): serial_connection.execute_command('AT+SEND=1', [variables.STATUS_OK]) serial_connection.execute_command('a', ['AT,SENDING', 'AT,SENDED']) logging.debug('module config successfully set')", "if serial_connection.execute_command(serial_connection.str_to_bytes(cmd), [variables.STATUS_OK]): logging.debug(f'module address successfully set to: {address}') return", "'AT,SENDED']) logging.debug('module config successfully set') return True logging.warning(\"could not set" ]
[ "not loop indefinitely, but only until the file is read", "'custom'): grip_right.calibrate() print(\"Playing back: %s\" % (filename,)) with open(filename, 'r')", "ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,", "a list to process @param names: joint name keys \"\"\"", "# # 1. Redistributions of source code must retain the", "above copyright notice, # this list of conditions and the", "in the # documentation and/or other materials provided with the", "#put your loop here for file in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending", "# # Redistribution and use in source and binary forms,", "= clean_line(values, keys) #command this set of commands until the", "the joint names combined = zip(names[1:], line[1:]) #take out any", "IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE", "grip_left = baxter_interface.Gripper('left', CHECK_VERSION) grip_right = baxter_interface.Gripper('right', CHECK_VERSION) rate =", "1 loopstr = str(loops) if loops > 0 else \"forever\"", "if grip_left.error(): grip_left.reset() if grip_right.error(): grip_right.reset() if (not grip_left.calibrated() and", "notice, # this list of conditions and the following disclaimer.", "(not grip_left.calibrated() and grip_left.type() != 'custom'): grip_left.calibrate() if (not grip_right.calibrated()", "= dict((key, command[key]) for key in command.keys() if key[:-2] ==", "in binary form must reproduce the above copyright # notice,", "DAMAGE. \"\"\" copied from Baxter RSDK Joint Position Example: file", "std_srvs.srv import Empty def try_float(x): try: return float(x) except ValueError:", "without specific prior written permission. # # THIS SOFTWARE IS", "l += 1 print(\"Moving to start position...\") _cmd, lcmd_start, rcmd_start,", "start position...\") _cmd, lcmd_start, rcmd_start, _raw = clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start)", "# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF", "loops > 0 else \"forever\" sys.stdout.write(\"\\r Record %d of %d,", "cmd and grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in cmd", "import Empty def try_float(x): try: return float(x) except ValueError: return", "loop indefinitely, but only until the file is read and", "loopstr = str(loops) if loops > 0 else \"forever\" sys.stdout.write(\"\\r", "from # this software without specific prior written permission. #", "right_command, line) def map_file(filename, loops=1): \"\"\" Loops through csv file", "print(\"\\n Aborting - ROS shutdown\") return False if len(lcmd): left.set_joint_positions(lcmd)", "Record %d of %d, loop %d of %s\" % (i,", "CHECK_VERSION) rate = rospy.Rate(1000) if grip_left.error(): grip_left.reset() if grip_right.error(): grip_right.reset()", "line.rstrip().split(',')] #zip the values with the joint names combined =", "THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR", "list of conditions and the following disclaimer in the #", "NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF", "rcmd_start, _raw = clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time()", "map_file(\"AtoE.rec\") res = client() rospy.loginfo(\"service returned\") ### if __name__ ==", "endorse or promote products derived from # this software without", "for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put your loop here for", "key in command.keys() if key[:-2] == 'right_') return (command, left_command,", "def try_float(x): try: return float(x) except ValueError: return None def", "for values in lines[1:]: i += 1 loopstr = str(loops)", "!= 'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in cmd and grip_right.type() !=", "import rospy import baxter_interface from baxter_interface import CHECK_VERSION import glob", "key[:-2] == 'left_') right_command = dict((key, command[key]) for key in", "Cleans a single line of recorded joint positions @param line:", "line of recorded joint positions @param line: the line described", "rs.enable() rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put your loop", "_cmd, lcmd_start, rcmd_start, _raw = clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time", "pairs. Names come from the column headers first column is", "print(\"Moving to start position...\") _cmd, lcmd_start, rcmd_start, _raw = clean_line(lines[1],", "loops: number of times to loop values < 0 mean", "source and binary forms, with or without # modification, are", "into a controller command in the form of name/value pairs.", "number of times while loops < 1 or l <", "file in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\") # to the image", "if key[:-2] == 'right_') return (command, left_command, right_command, line) def", "# to the image processing node #for x in range(0,", "INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER", "service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put your loop here for file", "CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,", "split up in columns and formats each line into a", "# documentation and/or other materials provided with the distribution. #", "grip_left.calibrate() if (not grip_right.calibrated() and grip_right.type() != 'custom'): grip_right.calibrate() print(\"Playing", "sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\") # to the image processing node", "from std_srvs.srv import Empty def try_float(x): try: return float(x) except", "in lines[1:]: i += 1 loopstr = str(loops) if loops", "loop values < 0 mean 'infinite' Does not loop indefinitely,", "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE", "AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED", "baxter_interface import CHECK_VERSION import glob from std_srvs.srv import Empty def", "grip_left.type() != 'custom'): grip_left.calibrate() if (not grip_right.calibrated() and grip_right.type() !=", "= 'no' ext = '.rec' #fname = fam+'*'+ext #fam_list =", "disclaimer. # 2. Redistributions in binary form must reproduce the", "rospy.get_time() for values in lines[1:]: i += 1 loopstr =", "Joint Position Example: file playback \"\"\" from __future__ import print_function", "str(loops) if loops > 0 else \"forever\" sys.stdout.write(\"\\r Record %d", "and binary forms, with or without # modification, are permitted", "names of its # contributors may be used to endorse", "file is read and processed. Reads each line, split up", "[x for x in combined if x[1] is not None]", "form must reproduce the above copyright # notice, this list", "lcmd, rcmd, values = clean_line(values, keys) #command this set of", "grip_left.calibrated() and grip_left.type() != 'custom'): grip_left.calibrate() if (not grip_right.calibrated() and", "l, loopstr)) sys.stdout.flush() cmd, lcmd, rcmd, values = clean_line(values, keys)", "line into a controller command in the form of name/value", "of source code must retain the above copyright notice, #", "right = baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left', CHECK_VERSION) grip_right = baxter_interface.Gripper('right',", "right_command = dict((key, command[key]) for key in command.keys() if key[:-2]", "EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE", "values with the joint names combined = zip(names[1:], line[1:]) #take", "this software without specific prior written permission. # # THIS", "'right_') return (command, left_command, right_command, line) def map_file(filename, loops=1): \"\"\"", "= client() rospy.loginfo(\"service returned\") ### if __name__ == '__main__': main()", "ValueError: return None def clean_line(line, names): \"\"\" Cleans a single", "2013-2014, Rethink Robotics # All rights reserved. # # Redistribution", "written permission. # # THIS SOFTWARE IS PROVIDED BY THE", "# this list of conditions and the following disclaimer. #", "the line of strings to a float or None line", "each line, split up in columns and formats each line", "== 'left_') right_command = dict((key, command[key]) for key in command.keys()", "playback 'loops' number of times while loops < 1 or", "and processed. Reads each line, split up in columns and", "none value cleaned = [x for x in combined if", "rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put your loop here for file in", "line, split up in columns and formats each line into", "GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR", "WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF", "try: return float(x) except ValueError: return None def clean_line(line, names):", "reproduce the above copyright # notice, this list of conditions", "software without specific prior written permission. # # THIS SOFTWARE", "= [x for x in combined if x[1] is not", "baxter_interface.Gripper('left', CHECK_VERSION) grip_right = baxter_interface.Gripper('right', CHECK_VERSION) rate = rospy.Rate(1000) if", "BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,", "print return True def main(): dir = '/home/ros-baxter/sequence1/' fam =", "available\") #put your loop here for file in sorted(glob.glob('./sequence1/*.rec')): map_file(file)", "cmd and grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print return True", "< loops: i = 0 l += 1 print(\"Moving to", "3. Neither the name of the Rethink Robotics nor the", "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS", "OF SUCH DAMAGE. \"\"\" copied from Baxter RSDK Joint Position", "= rospy.Rate(1000) if grip_left.error(): grip_left.reset() if grip_right.error(): grip_right.reset() if (not", "stamp \"\"\" left = baxter_interface.Limb('left') right = baxter_interface.Limb('right') grip_left =", "following conditions are met: # # 1. Redistributions of source", "\"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,", "of commands until the next frame while (rospy.get_time() - start_time)", "float(x) except ValueError: return None def clean_line(line, names): \"\"\" Cleans", "BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR", "1, l, loopstr)) sys.stdout.flush() cmd, lcmd, rcmd, values = clean_line(values,", "as f: lines = f.readlines() keys = lines[0].rstrip().split(',') l =", "= 0 l += 1 print(\"Moving to start position...\") _cmd,", "TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR", "CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,", "each line into a controller command in the form of", "try_float(x): try: return float(x) except ValueError: return None def clean_line(line,", "return False if len(lcmd): left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper'", "baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left', CHECK_VERSION) grip_right = baxter_interface.Gripper('right', CHECK_VERSION) rate", "sys.stdout.write(\"\\r Record %d of %d, loop %d of %s\" %", "def clean_line(line, names): \"\"\" Cleans a single line of recorded", "loops < 1 or l < loops: i = 0", "False if len(lcmd): left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper' in", "IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\"", "and grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in cmd and", "single line of recorded joint positions @param line: the line", "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL", "# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR", "right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time() for values in lines[1:]: i +=", "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #", "file to play @param loops: number of times to loop", "# this software without specific prior written permission. # #", "of %d, loop %d of %s\" % (i, len(lines) -", "def map_file(filename, loops=1): \"\"\" Loops through csv file @param filename:", "the next frame while (rospy.get_time() - start_time) < values[0]: if", "Loops through csv file @param filename: the file to play", "< 0 mean 'infinite' Does not loop indefinitely, but only", "!= 'custom'): grip_left.calibrate() if (not grip_right.calibrated() and grip_right.type() != 'custom'):", "AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,", "the values with the joint names combined = zip(names[1:], line[1:])", "grip_right.error(): grip_right.reset() if (not grip_left.calibrated() and grip_left.type() != 'custom'): grip_left.calibrate()", "#fam_list = glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs =", "Redistributions of source code must retain the above copyright notice,", "if ('right_gripper' in cmd and grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep()", "zip(names[1:], line[1:]) #take out any tuples that have a none", "Position Example: file playback \"\"\" from __future__ import print_function import", "in line.rstrip().split(',')] #zip the values with the joint names combined", "of conditions and the following disclaimer. # 2. Redistributions in", "OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" copied from", "keys = lines[0].rstrip().split(',') l = 0 # If specified, repeat", "#command this set of commands until the next frame while", "in command.keys() if key[:-2] == 'left_') right_command = dict((key, command[key])", "left_command, right_command, line) def map_file(filename, loops=1): \"\"\" Loops through csv", "a single line of recorded joint positions @param line: the", "described in a list to process @param names: joint name", "Reads each line, split up in columns and formats each", "i += 1 loopstr = str(loops) if loops > 0", "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #", "return float(x) except ValueError: return None def clean_line(line, names): \"\"\"", "else \"forever\" sys.stdout.write(\"\\r Record %d of %d, loop %d of", "form of name/value pairs. Names come from the column headers", "commands command = dict(cleaned) left_command = dict((key, command[key]) for key", "or without # modification, are permitted provided that the following", "with open(filename, 'r') as f: lines = f.readlines() keys =", "HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN", "Redistribution and use in source and binary forms, with or", "source code must retain the above copyright notice, # this", "the following disclaimer in the # documentation and/or other materials", "# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT", "= '.rec' #fname = fam+'*'+ext #fam_list = glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\")", "Rethink Robotics nor the names of its # contributors may", "res = client() rospy.loginfo(\"service returned\") ### if __name__ == '__main__':", "None line = [try_float(x) for x in line.rstrip().split(',')] #zip the", "def main(): dir = '/home/ros-baxter/sequence1/' fam = 'no' ext =", "\"\"\" Cleans a single line of recorded joint positions @param", "from the column headers first column is the time stamp", "if ('left_gripper' in cmd and grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper']) if", "%d of %d, loop %d of %s\" % (i, len(lines)", "the Rethink Robotics nor the names of its # contributors", "#zip the values with the joint names combined = zip(names[1:],", "names): \"\"\" Cleans a single line of recorded joint positions", "in cmd and grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print return", "glob from std_srvs.srv import Empty def try_float(x): try: return float(x)", "from baxter_interface import CHECK_VERSION import glob from std_srvs.srv import Empty", "grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print return True def main():", "in cmd and grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in", "left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time() for values in lines[1:]: i", "baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put your", "USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED", "rights reserved. # # Redistribution and use in source and", "map_file(file) rospy.loginfo(\"sending signal...\") # to the image processing node #for", "start_time) < values[0]: if rospy.is_shutdown(): print(\"\\n Aborting - ROS shutdown\")", "EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.", "baxter_interface.Gripper('right', CHECK_VERSION) rate = rospy.Rate(1000) if grip_left.error(): grip_left.reset() if grip_right.error():", "binary form must reproduce the above copyright # notice, this", "clean_line(line, names): \"\"\" Cleans a single line of recorded joint", "recorded joint positions @param line: the line described in a", "len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper' in cmd and grip_left.type() != 'custom'):", "Copyright (c) 2013-2014, Rethink Robotics # All rights reserved. #", "#convert it to a dictionary with only valid commands command", "strings to a float or None line = [try_float(x) for", "__future__ import print_function import sys import rospy import baxter_interface from", "IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR", "python # Copyright (c) 2013-2014, Rethink Robotics # All rights", "nor the names of its # contributors may be used", "of recorded joint positions @param line: the line described in", "OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE", "# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER", "If specified, repeat the file playback 'loops' number of times", "loops=1): \"\"\" Loops through csv file @param filename: the file", "disclaimer in the # documentation and/or other materials provided with", "PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" #", "the file playback 'loops' number of times while loops <", "_raw = clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time() for", "== 'right_') return (command, left_command, right_command, line) def map_file(filename, loops=1):", "(c) 2013-2014, Rethink Robotics # All rights reserved. # #", "INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT", "CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #", "= rospy.get_time() for values in lines[1:]: i += 1 loopstr", "'no' ext = '.rec' #fname = fam+'*'+ext #fam_list = glob.glob(ext)", "OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS", "@param line: the line described in a list to process", "joint names combined = zip(names[1:], line[1:]) #take out any tuples", "above copyright # notice, this list of conditions and the", "indefinitely, but only until the file is read and processed.", "only until the file is read and processed. Reads each", "PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY", "TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY", "ext = '.rec' #fname = fam+'*'+ext #fam_list = glob.glob(ext) #print(fam_list)", "node #for x in range(0, 3): # map_file(\"AtoE.rec\") res =", "from __future__ import print_function import sys import rospy import baxter_interface", "THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE", "keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time() for values in lines[1:]:", "until the next frame while (rospy.get_time() - start_time) < values[0]:", "for x in line.rstrip().split(',')] #zip the values with the joint", "!= 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print return True def main(): dir", "in source and binary forms, with or without # modification,", "- 1, l, loopstr)) sys.stdout.flush() cmd, lcmd, rcmd, values =", "# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,", "- ROS shutdown\") return False if len(lcmd): left.set_joint_positions(lcmd) if len(rcmd):", "A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL", "keys \"\"\" #convert the line of strings to a float", "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED", "f.readlines() keys = lines[0].rstrip().split(',') l = 0 # If specified,", "permitted provided that the following conditions are met: # #", "any tuples that have a none value cleaned = [x", "+= 1 print(\"Moving to start position...\") _cmd, lcmd_start, rcmd_start, _raw", "to loop values < 0 mean 'infinite' Does not loop", "to the image processing node #for x in range(0, 3):", "grip_left.reset() if grip_right.error(): grip_right.reset() if (not grip_left.calibrated() and grip_left.type() !=", "return True def main(): dir = '/home/ros-baxter/sequence1/' fam = 'no'", "the name of the Rethink Robotics nor the names of", "EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE", "must reproduce the above copyright # notice, this list of", "the column headers first column is the time stamp \"\"\"", "csv file @param filename: the file to play @param loops:", "rate = rospy.Rate(1000) if grip_left.error(): grip_left.reset() if grip_right.error(): grip_right.reset() if", "loopstr)) sys.stdout.flush() cmd, lcmd, rcmd, values = clean_line(values, keys) #command", "= clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time() for values", "position...\") _cmd, lcmd_start, rcmd_start, _raw = clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start)", "# Copyright (c) 2013-2014, Rethink Robotics # All rights reserved.", "products derived from # this software without specific prior written", "rospy import baxter_interface from baxter_interface import CHECK_VERSION import glob from", "is not None] #convert it to a dictionary with only", "of times while loops < 1 or l < loops:", "x in line.rstrip().split(',')] #zip the values with the joint names", "this list of conditions and the following disclaimer. # 2.", "and formats each line into a controller command in the", "# 1. Redistributions of source code must retain the above", "list to process @param names: joint name keys \"\"\" #convert", "@param loops: number of times to loop values < 0", "use in source and binary forms, with or without #", "x[1] is not None] #convert it to a dictionary with", "loops: i = 0 l += 1 print(\"Moving to start", "if loops > 0 else \"forever\" sys.stdout.write(\"\\r Record %d of", "dict((key, command[key]) for key in command.keys() if key[:-2] == 'right_')", "USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #", "left = baxter_interface.Limb('left') right = baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left', CHECK_VERSION)", "rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service", "STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING", "the # documentation and/or other materials provided with the distribution.", "in columns and formats each line into a controller command", "start_time = rospy.get_time() for values in lines[1:]: i += 1", "EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,", "OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY", "while loops < 1 or l < loops: i =", "x in combined if x[1] is not None] #convert it", "distribution. # 3. Neither the name of the Rethink Robotics", "code must retain the above copyright notice, # this list", "values in lines[1:]: i += 1 loopstr = str(loops) if", "THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" copied from Baxter", "in the form of name/value pairs. Names come from the", "# contributors may be used to endorse or promote products", "THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF", "dict(cleaned) left_command = dict((key, command[key]) for key in command.keys() if", "ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES", "@param names: joint name keys \"\"\" #convert the line of", "'.rec' #fname = fam+'*'+ext #fam_list = glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client", "(rospy.get_time() - start_time) < values[0]: if rospy.is_shutdown(): print(\"\\n Aborting -", "('right_gripper' in cmd and grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print", "and grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print return True def", "range(0, 3): # map_file(\"AtoE.rec\") res = client() rospy.loginfo(\"service returned\") ###", "if len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper' in cmd and grip_left.type() !=", "OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED", "OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,", "frame while (rospy.get_time() - start_time) < values[0]: if rospy.is_shutdown(): print(\"\\n", "LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)", "loop %d of %s\" % (i, len(lines) - 1, l,", "shutdown\") return False if len(lcmd): left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd) if", "names combined = zip(names[1:], line[1:]) #take out any tuples that", "with or without # modification, are permitted provided that the", "lcmd_start, rcmd_start, _raw = clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time =", "line described in a list to process @param names: joint", "# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR", "(command, left_command, right_command, line) def map_file(filename, loops=1): \"\"\" Loops through", "= rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\")", "= dict(cleaned) left_command = dict((key, command[key]) for key in command.keys()", "Neither the name of the Rethink Robotics nor the names", "to process @param names: joint name keys \"\"\" #convert the", "None] #convert it to a dictionary with only valid commands", "in a list to process @param names: joint name keys", "column headers first column is the time stamp \"\"\" left", "cmd, lcmd, rcmd, values = clean_line(values, keys) #command this set", "with only valid commands command = dict(cleaned) left_command = dict((key,", "WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES", "NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;", "line: the line described in a list to process @param", "# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR", "\"\"\" #convert the line of strings to a float or", "<reponame>ferdianap/Eris_test #!/usr/bin/env python # Copyright (c) 2013-2014, Rethink Robotics #", "commands until the next frame while (rospy.get_time() - start_time) <", "of %s\" % (i, len(lines) - 1, l, loopstr)) sys.stdout.flush()", "tuples that have a none value cleaned = [x for", "controller command in the form of name/value pairs. Names come", "DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND", "prior written permission. # # THIS SOFTWARE IS PROVIDED BY", "> 0 else \"forever\" sys.stdout.write(\"\\r Record %d of %d, loop", "BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF", "%s\" % (filename,)) with open(filename, 'r') as f: lines =", "ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" copied", "% (filename,)) with open(filename, 'r') as f: lines = f.readlines()", "documentation and/or other materials provided with the distribution. # 3.", "key[:-2] == 'right_') return (command, left_command, right_command, line) def map_file(filename,", "command.keys() if key[:-2] == 'left_') right_command = dict((key, command[key]) for", "HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR", "and use in source and binary forms, with or without", "while (rospy.get_time() - start_time) < values[0]: if rospy.is_shutdown(): print(\"\\n Aborting", "not None] #convert it to a dictionary with only valid", "COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,", "values < 0 mean 'infinite' Does not loop indefinitely, but", "or None line = [try_float(x) for x in line.rstrip().split(',')] #zip", "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE", "OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE", "'loops' number of times while loops < 1 or l", "for x in combined if x[1] is not None] #convert", "command.keys() if key[:-2] == 'right_') return (command, left_command, right_command, line)", "rate.sleep() print return True def main(): dir = '/home/ros-baxter/sequence1/' fam", "if key[:-2] == 'left_') right_command = dict((key, command[key]) for key", "[try_float(x) for x in line.rstrip().split(',')] #zip the values with the", "combined if x[1] is not None] #convert it to a", "left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper' in cmd and grip_left.type()", "CHECK_VERSION import glob from std_srvs.srv import Empty def try_float(x): try:", "are met: # # 1. Redistributions of source code must", "OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF", "map_file(filename, loops=1): \"\"\" Loops through csv file @param filename: the", "up in columns and formats each line into a controller", "in command.keys() if key[:-2] == 'right_') return (command, left_command, right_command,", "PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT", "= baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left', CHECK_VERSION) grip_right = baxter_interface.Gripper('right', CHECK_VERSION)", "float or None line = [try_float(x) for x in line.rstrip().split(',')]", "= 0 # If specified, repeat the file playback 'loops'", "import print_function import sys import rospy import baxter_interface from baxter_interface", "rospy.Rate(1000) if grip_left.error(): grip_left.reset() if grip_right.error(): grip_right.reset() if (not grip_left.calibrated()", "= '/home/ros-baxter/sequence1/' fam = 'no' ext = '.rec' #fname =", "Empty def try_float(x): try: return float(x) except ValueError: return None", "of strings to a float or None line = [try_float(x)", "'r') as f: lines = f.readlines() keys = lines[0].rstrip().split(',') l", "baxter_interface.Limb('left') right = baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left', CHECK_VERSION) grip_right =", "open(filename, 'r') as f: lines = f.readlines() keys = lines[0].rstrip().split(',')", "= f.readlines() keys = lines[0].rstrip().split(',') l = 0 # If", "be used to endorse or promote products derived from #", "forms, with or without # modification, are permitted provided that", "command = dict(cleaned) left_command = dict((key, command[key]) for key in", "left_command = dict((key, command[key]) for key in command.keys() if key[:-2]", "grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in cmd and grip_right.type()", "binary forms, with or without # modification, are permitted provided", "rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put your loop here", "copyright # notice, this list of conditions and the following", "dict((key, command[key]) for key in command.keys() if key[:-2] == 'left_')", "for file in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\") # to the", "provided that the following conditions are met: # # 1.", "specific prior written permission. # # THIS SOFTWARE IS PROVIDED", "contributors may be used to endorse or promote products derived", "#take out any tuples that have a none value cleaned", "until the file is read and processed. Reads each line,", "('left_gripper' in cmd and grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper'", "out any tuples that have a none value cleaned =", "name of the Rethink Robotics nor the names of its", "only valid commands command = dict(cleaned) left_command = dict((key, command[key])", "file @param filename: the file to play @param loops: number", "times to loop values < 0 mean 'infinite' Does not", "'left_') right_command = dict((key, command[key]) for key in command.keys() if", "the image processing node #for x in range(0, 3): #", "if len(lcmd): left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper' in cmd", "to a float or None line = [try_float(x) for x", "len(lines) - 1, l, loopstr)) sys.stdout.flush() cmd, lcmd, rcmd, values", "rospy.loginfo(\"sending signal...\") # to the image processing node #for x", "the above copyright # notice, this list of conditions and", "grip_left.error(): grip_left.reset() if grip_right.error(): grip_right.reset() if (not grip_left.calibrated() and grip_left.type()", "grip_right.reset() if (not grip_left.calibrated() and grip_left.type() != 'custom'): grip_left.calibrate() if", "are permitted provided that the following conditions are met: #", "is read and processed. Reads each line, split up in", "# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)", "the line described in a list to process @param names:", "lines[0].rstrip().split(',') l = 0 # If specified, repeat the file", "rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\")", "a dictionary with only valid commands command = dict(cleaned) left_command", "WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #", "met: # # 1. Redistributions of source code must retain", "ARISING IN ANY WAY OUT OF THE USE OF THIS", "# All rights reserved. # # Redistribution and use in", "ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN", "LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS", "SUCH DAMAGE. \"\"\" copied from Baxter RSDK Joint Position Example:", "but only until the file is read and processed. Reads", "#print(fam_list) rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting", "loop here for file in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\") #", "SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR", "# Redistribution and use in source and binary forms, with", "the above copyright notice, # this list of conditions and", "keys) #command this set of commands until the next frame", "joint positions @param line: the line described in a list", "1 print(\"Moving to start position...\") _cmd, lcmd_start, rcmd_start, _raw =", "conditions are met: # # 1. Redistributions of source code", "0 else \"forever\" sys.stdout.write(\"\\r Record %d of %d, loop %d", "in range(0, 3): # map_file(\"AtoE.rec\") res = client() rospy.loginfo(\"service returned\")", "# map_file(\"AtoE.rec\") res = client() rospy.loginfo(\"service returned\") ### if __name__", "first column is the time stamp \"\"\" left = baxter_interface.Limb('left')", "signal...\") # to the image processing node #for x in", "name/value pairs. Names come from the column headers first column", "'/home/ros-baxter/sequence1/' fam = 'no' ext = '.rec' #fname = fam+'*'+ext", "print_function import sys import rospy import baxter_interface from baxter_interface import", "LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN", "line[1:]) #take out any tuples that have a none value", "valid commands command = dict(cleaned) left_command = dict((key, command[key]) for", "grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in cmd and grip_right.type() != 'custom'): grip_right.command_position(cmd['right_gripper'])", "Robotics # All rights reserved. # # Redistribution and use", "# modification, are permitted provided that the following conditions are", "dictionary with only valid commands command = dict(cleaned) left_command =", "value cleaned = [x for x in combined if x[1]", "DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,", "a none value cleaned = [x for x in combined", "key in command.keys() if key[:-2] == 'left_') right_command = dict((key,", "OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT", "of the Rethink Robotics nor the names of its #", "conditions and the following disclaimer in the # documentation and/or", "that have a none value cleaned = [x for x", "command[key]) for key in command.keys() if key[:-2] == 'left_') right_command", "None def clean_line(line, names): \"\"\" Cleans a single line of", "OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,", "come from the column headers first column is the time", "'custom'): grip_left.command_position(cmd['left_gripper']) if ('right_gripper' in cmd and grip_right.type() != 'custom'):", "%s\" % (i, len(lines) - 1, l, loopstr)) sys.stdout.flush() cmd,", "= baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for service\") rospy.wait_for_service(\"ferdian_example_service\") rospy.loginfo(\"service available\") #put", "(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS", "LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING", "rospy.loginfo(\"service available\") #put your loop here for file in sorted(glob.glob('./sequence1/*.rec')):", "lines[1:]: i += 1 loopstr = str(loops) if loops >", "time stamp \"\"\" left = baxter_interface.Limb('left') right = baxter_interface.Limb('right') grip_left", "if rospy.is_shutdown(): print(\"\\n Aborting - ROS shutdown\") return False if", "mean 'infinite' Does not loop indefinitely, but only until the", "may be used to endorse or promote products derived from", "= baxter_interface.Gripper('right', CHECK_VERSION) rate = rospy.Rate(1000) if grip_left.error(): grip_left.reset() if", "command[key]) for key in command.keys() if key[:-2] == 'right_') return", "that the following conditions are met: # # 1. Redistributions", "\"\"\" copied from Baxter RSDK Joint Position Example: file playback", "values = clean_line(values, keys) #command this set of commands until", "IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED", "or promote products derived from # this software without specific", "x in range(0, 3): # map_file(\"AtoE.rec\") res = client() rospy.loginfo(\"service", "#convert the line of strings to a float or None", "of conditions and the following disclaimer in the # documentation", "copyright notice, # this list of conditions and the following", "it to a dictionary with only valid commands command =", "playback \"\"\" from __future__ import print_function import sys import rospy", "name keys \"\"\" #convert the line of strings to a", "the file to play @param loops: number of times to", "grip_right.calibrated() and grip_right.type() != 'custom'): grip_right.calibrate() print(\"Playing back: %s\" %", "SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH", "specified, repeat the file playback 'loops' number of times while", "OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE", "i = 0 l += 1 print(\"Moving to start position...\")", "the names of its # contributors may be used to", "INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF", "# 3. Neither the name of the Rethink Robotics nor", "conditions and the following disclaimer. # 2. Redistributions in binary", "rcmd, values = clean_line(values, keys) #command this set of commands", "%d, loop %d of %s\" % (i, len(lines) - 1,", "NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE #", "and the following disclaimer. # 2. Redistributions in binary form", "%d of %s\" % (i, len(lines) - 1, l, loopstr))", "# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND", "copied from Baxter RSDK Joint Position Example: file playback \"\"\"", "promote products derived from # this software without specific prior", "import sys import rospy import baxter_interface from baxter_interface import CHECK_VERSION", "the following disclaimer. # 2. Redistributions in binary form must", "!= 'custom'): grip_right.calibrate() print(\"Playing back: %s\" % (filename,)) with open(filename,", "sys import rospy import baxter_interface from baxter_interface import CHECK_VERSION import", "to a dictionary with only valid commands command = dict(cleaned)", "following disclaimer. # 2. Redistributions in binary form must reproduce", "joint name keys \"\"\" #convert the line of strings to", "column is the time stamp \"\"\" left = baxter_interface.Limb('left') right", "f: lines = f.readlines() keys = lines[0].rstrip().split(',') l = 0", "= [try_float(x) for x in line.rstrip().split(',')] #zip the values with", "file playback 'loops' number of times while loops < 1", "OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER", "baxter_interface from baxter_interface import CHECK_VERSION import glob from std_srvs.srv import", "% (i, len(lines) - 1, l, loopstr)) sys.stdout.flush() cmd, lcmd,", "Aborting - ROS shutdown\") return False if len(lcmd): left.set_joint_positions(lcmd) if", "LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS", "fam = 'no' ext = '.rec' #fname = fam+'*'+ext #fam_list", "Names come from the column headers first column is the", "notice, this list of conditions and the following disclaimer in", "0 # If specified, repeat the file playback 'loops' number", "if grip_right.error(): grip_right.reset() if (not grip_left.calibrated() and grip_left.type() != 'custom'):", "IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,", "= glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION)", "LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #", "retain the above copyright notice, # this list of conditions", "All rights reserved. # # Redistribution and use in source", "and/or other materials provided with the distribution. # 3. Neither", "without # modification, are permitted provided that the following conditions", "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS", "line = [try_float(x) for x in line.rstrip().split(',')] #zip the values", "OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY", "# notice, this list of conditions and the following disclaimer", "if (not grip_left.calibrated() and grip_left.type() != 'custom'): grip_left.calibrate() if (not", "except ValueError: return None def clean_line(line, names): \"\"\" Cleans a", "the following conditions are met: # # 1. Redistributions of", "WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE", "this list of conditions and the following disclaimer in the", "used to endorse or promote products derived from # this", "modification, are permitted provided that the following conditions are met:", "\"\"\" left = baxter_interface.Limb('left') right = baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left',", "PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,", "OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,", "glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable()", "= baxter_interface.Limb('left') right = baxter_interface.Limb('right') grip_left = baxter_interface.Gripper('left', CHECK_VERSION) grip_right", "RSDK Joint Position Example: file playback \"\"\" from __future__ import", "grip_right = baxter_interface.Gripper('right', CHECK_VERSION) rate = rospy.Rate(1000) if grip_left.error(): grip_left.reset()", "in combined if x[1] is not None] #convert it to", "dir = '/home/ros-baxter/sequence1/' fam = 'no' ext = '.rec' #fname", "client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for service\")", "# ARISING IN ANY WAY OUT OF THE USE OF", "clean_line(lines[1], keys) left.move_to_joint_positions(lcmd_start) right.move_to_joint_positions(rcmd_start) start_time = rospy.get_time() for values in", "'infinite' Does not loop indefinitely, but only until the file", "rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs = baxter_interface.RobotEnable(CHECK_VERSION) rs.enable() rospy.loginfo(\"waiting for", "fam+'*'+ext #fam_list = glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty) rs", "AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN", "the form of name/value pairs. Names come from the column", "next frame while (rospy.get_time() - start_time) < values[0]: if rospy.is_shutdown():", "Robotics nor the names of its # contributors may be", "return None def clean_line(line, names): \"\"\" Cleans a single line", "reserved. # # Redistribution and use in source and binary", "other materials provided with the distribution. # 3. Neither the", "\"\"\" from __future__ import print_function import sys import rospy import", "0 l += 1 print(\"Moving to start position...\") _cmd, lcmd_start,", "print(\"Playing back: %s\" % (filename,)) with open(filename, 'r') as f:", "number of times to loop values < 0 mean 'infinite'", "grip_right.calibrate() print(\"Playing back: %s\" % (filename,)) with open(filename, 'r') as", "CHECK_VERSION) grip_right = baxter_interface.Gripper('right', CHECK_VERSION) rate = rospy.Rate(1000) if grip_left.error():", "Does not loop indefinitely, but only until the file is", "ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,", "ROS shutdown\") return False if len(lcmd): left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd)", "ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT", "columns and formats each line into a controller command in", "l < loops: i = 0 l += 1 print(\"Moving", "if (not grip_right.calibrated() and grip_right.type() != 'custom'): grip_right.calibrate() print(\"Playing back:", "and grip_left.type() != 'custom'): grip_left.calibrate() if (not grip_right.calibrated() and grip_right.type()", "through csv file @param filename: the file to play @param", "the time stamp \"\"\" left = baxter_interface.Limb('left') right = baxter_interface.Limb('right')", "have a none value cleaned = [x for x in", "# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS", "#fname = fam+'*'+ext #fam_list = glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client =", "a float or None line = [try_float(x) for x in", "+= 1 loopstr = str(loops) if loops > 0 else", "your loop here for file in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\")", "to start position...\") _cmd, lcmd_start, rcmd_start, _raw = clean_line(lines[1], keys)", "#!/usr/bin/env python # Copyright (c) 2013-2014, Rethink Robotics # All", "this set of commands until the next frame while (rospy.get_time()", "< values[0]: if rospy.is_shutdown(): print(\"\\n Aborting - ROS shutdown\") return", "line of strings to a float or None line =", "headers first column is the time stamp \"\"\" left =", "OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #", "\"forever\" sys.stdout.write(\"\\r Record %d of %d, loop %d of %s\"", "the distribution. # 3. Neither the name of the Rethink", "SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED", "= str(loops) if loops > 0 else \"forever\" sys.stdout.write(\"\\r Record", "IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"", "is the time stamp \"\"\" left = baxter_interface.Limb('left') right =", "\"\"\" Loops through csv file @param filename: the file to", "list of conditions and the following disclaimer. # 2. Redistributions", "@param filename: the file to play @param loops: number of", "len(lcmd): left.set_joint_positions(lcmd) if len(rcmd): right.set_joint_positions(rcmd) if ('left_gripper' in cmd and", "import CHECK_VERSION import glob from std_srvs.srv import Empty def try_float(x):", "# POSSIBILITY OF SUCH DAMAGE. \"\"\" copied from Baxter RSDK", "sys.stdout.flush() cmd, lcmd, rcmd, values = clean_line(values, keys) #command this", "3): # map_file(\"AtoE.rec\") res = client() rospy.loginfo(\"service returned\") ### if", "read and processed. Reads each line, split up in columns", "materials provided with the distribution. # 3. Neither the name", "return (command, left_command, right_command, line) def map_file(filename, loops=1): \"\"\" Loops", "Example: file playback \"\"\" from __future__ import print_function import sys", "play @param loops: number of times to loop values <", "the file is read and processed. Reads each line, split", "or l < loops: i = 0 l += 1", "permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT", "< 1 or l < loops: i = 0 l", "0 mean 'infinite' Does not loop indefinitely, but only until", "line) def map_file(filename, loops=1): \"\"\" Loops through csv file @param", "FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT", "Rethink Robotics # All rights reserved. # # Redistribution and", "to endorse or promote products derived from # this software", "provided with the distribution. # 3. Neither the name of", "filename: the file to play @param loops: number of times", "back: %s\" % (filename,)) with open(filename, 'r') as f: lines", "Redistributions in binary form must reproduce the above copyright #", "NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND", "SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;", "for key in command.keys() if key[:-2] == 'right_') return (command,", "FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO", "with the joint names combined = zip(names[1:], line[1:]) #take out", "l = 0 # If specified, repeat the file playback", "BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY", "to play @param loops: number of times to loop values", "(filename,)) with open(filename, 'r') as f: lines = f.readlines() keys", "its # contributors may be used to endorse or promote", "grip_right.command_position(cmd['right_gripper']) rate.sleep() print return True def main(): dir = '/home/ros-baxter/sequence1/'", "right.set_joint_positions(rcmd) if ('left_gripper' in cmd and grip_left.type() != 'custom'): grip_left.command_position(cmd['left_gripper'])", "(not grip_right.calibrated() and grip_right.type() != 'custom'): grip_right.calibrate() print(\"Playing back: %s\"", "rospy.is_shutdown(): print(\"\\n Aborting - ROS shutdown\") return False if len(lcmd):", "import baxter_interface from baxter_interface import CHECK_VERSION import glob from std_srvs.srv", "IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT", "if x[1] is not None] #convert it to a dictionary", "from Baxter RSDK Joint Position Example: file playback \"\"\" from", "1. Redistributions of source code must retain the above copyright", "positions @param line: the line described in a list to", "cleaned = [x for x in combined if x[1] is", "of times to loop values < 0 mean 'infinite' Does", "SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS", "clean_line(values, keys) #command this set of commands until the next", "combined = zip(names[1:], line[1:]) #take out any tuples that have", "in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\") # to the image processing", "PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE", "(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT", "= zip(names[1:], line[1:]) #take out any tuples that have a", "of its # contributors may be used to endorse or", "= lines[0].rstrip().split(',') l = 0 # If specified, repeat the", "times while loops < 1 or l < loops: i", "image processing node #for x in range(0, 3): # map_file(\"AtoE.rec\")", "repeat the file playback 'loops' number of times while loops", "process @param names: joint name keys \"\"\" #convert the line", "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY", "= baxter_interface.Gripper('left', CHECK_VERSION) grip_right = baxter_interface.Gripper('right', CHECK_VERSION) rate = rospy.Rate(1000)", "THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A", "and grip_right.type() != 'custom'): grip_right.calibrate() print(\"Playing back: %s\" % (filename,))", "processing node #for x in range(0, 3): # map_file(\"AtoE.rec\") res", "here for file in sorted(glob.glob('./sequence1/*.rec')): map_file(file) rospy.loginfo(\"sending signal...\") # to", "COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS", "with the distribution. # 3. Neither the name of the", "POSSIBILITY OF SUCH DAMAGE. \"\"\" copied from Baxter RSDK Joint", "names: joint name keys \"\"\" #convert the line of strings", "following disclaimer in the # documentation and/or other materials provided", "# If specified, repeat the file playback 'loops' number of", "file playback \"\"\" from __future__ import print_function import sys import", "INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT", "THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY", "Baxter RSDK Joint Position Example: file playback \"\"\" from __future__", "'custom'): grip_right.command_position(cmd['right_gripper']) rate.sleep() print return True def main(): dir =", "CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #", "derived from # this software without specific prior written permission.", "import glob from std_srvs.srv import Empty def try_float(x): try: return", "processed. Reads each line, split up in columns and formats", "OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON", "2. Redistributions in binary form must reproduce the above copyright", "'custom'): grip_left.calibrate() if (not grip_right.calibrated() and grip_right.type() != 'custom'): grip_right.calibrate()", "set of commands until the next frame while (rospy.get_time() -", "1 or l < loops: i = 0 l +=", "for key in command.keys() if key[:-2] == 'left_') right_command =", "must retain the above copyright notice, # this list of", "MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.", "OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT", "values[0]: if rospy.is_shutdown(): print(\"\\n Aborting - ROS shutdown\") return False", "grip_right.type() != 'custom'): grip_right.calibrate() print(\"Playing back: %s\" % (filename,)) with", "main(): dir = '/home/ros-baxter/sequence1/' fam = 'no' ext = '.rec'", "of name/value pairs. Names come from the column headers first", "(i, len(lines) - 1, l, loopstr)) sys.stdout.flush() cmd, lcmd, rcmd,", "#for x in range(0, 3): # map_file(\"AtoE.rec\") res = client()", "and the following disclaimer in the # documentation and/or other", "command in the form of name/value pairs. Names come from", "TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF", "lines = f.readlines() keys = lines[0].rstrip().split(',') l = 0 #", "= fam+'*'+ext #fam_list = glob.glob(ext) #print(fam_list) rospy.init_node(\"ferdian_file_playback\") client = rospy.ServiceProxy(\"ferdian_example_service\",Empty)", "BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND", "a controller command in the form of name/value pairs. Names", "- start_time) < values[0]: if rospy.is_shutdown(): print(\"\\n Aborting - ROS", "THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY", "# 2. Redistributions in binary form must reproduce the above", "formats each line into a controller command in the form", "ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR", "True def main(): dir = '/home/ros-baxter/sequence1/' fam = 'no' ext" ]
[ "\"ALINK:model_dir\" if key in properties: user_params[key] = properties[key] output_writer =", "TFContext # noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops import core_ops __all__ =", "from typing import Dict, Callable import tensorflow as tf from", "self.engine_type = engine_type @staticmethod def get_func_by_name(func_name): \"\"\" Get function by", "be found c = getattr(m, func_name) return c @abc.abstractmethod def", "import TFContext # noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops import core_ops __all__", "num_workers = int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir'] cluster, task_type, task_index =", "context.to_java()) dataset_file = os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file)", "= os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of", "1024): key = \"ALINK:bc_\" + str(i) if key in properties:", "import abc from typing import Dict, Callable import tensorflow as", "java_queue_file = JavaFile(context.from_java(), context.to_java()) dataset_file = os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length", "globals(): return globals()[func_name] else: raise RuntimeError('cannot find function[{}]'.format(func_name)) else: module_name,", "= context.properties[key] key = \"ALINK:model_dir\" if key in properties: user_params[key]", "in globals(): return globals()[func_name] else: raise RuntimeError('cannot find function[{}]'.format(func_name)) else:", "= tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file = JavaFile(context.from_java(), context.to_java()) dataset_file =", "context.properties[key] key = \"ALINK:model_dir\" if key in properties: user_params[key] =", "tf_context.flink_stream_dataset() dataset = None dataset_file = None dataset_length = None", "'tf2' class BaseEntry(abc.ABC): def __init__(self, func_name, engine_type): self.func_name = func_name", "func = self.get_func_by_name(self.func_name) func(args) print(\"task_type = {}, task_index = {}:", "if key in properties: user_params[key] = properties[key] output_writer = DirectOutputWriter(tf_context.from_java(),", "not in func_name: if func_name in globals(): return globals()[func_name] else:", "= getattr(m, func_name) return c @abc.abstractmethod def construct_args(self, **kwargs): pass", "= {}, task_index = {}: done tf_user_main\".format(task_type, task_index), flush=True) local_vars", "dataset_file = None dataset_length = None saved_model_dir = os.path.join(work_dir, 'savedmodel')", "tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset() dataset =", "print('properties', properties, flush=True) # intra_op_parallelism is set by akdl, because", "properties = tf_context.properties print('properties', properties, flush=True) # intra_op_parallelism is set", "dataset_file = os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number", "= \"ALINK:bc_\" + str(i) if key in properties: user_params[key] =", "flink_ml_framework.context import Context from flink_ml_framework.java_file import * from ..runner import", "# See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if self.engine_type == TF1_TYPE:", "func_name) return c @abc.abstractmethod def construct_args(self, **kwargs): pass def is_batch(self):", "= None saved_model_dir = os.path.join(work_dir, 'savedmodel') user_params: Dict = json.loads(properties['ALINK:user_defined_params'])", "output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy = locals().copy() locals_copy.pop(\"self\") print(\"locals_copy =", "def get_func_by_name(func_name): \"\"\" Get function by the func name :param", "key = \"ALINK:bc_\" + str(i) if key in properties: user_params[key]", "dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[], tf.data.TFRecordDataset]", "pass def is_batch(self): return True def post_process(self, **kwargs): pass def", "globals()[func_name] else: raise RuntimeError('cannot find function[{}]'.format(func_name)) else: module_name, func_name =", "is a bug in TensorFlow 1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism", "work_dir = properties['ALINK:work_dir'] cluster, task_type, task_index = tf_context.export_estimator_cluster() if self.is_batch():", "dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of records: \" +", "# load the module, will raise ImportError if module cannot", "task_index), flush=True) local_vars = locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type = {},", "= 'tf1' TF2_TYPE = 'tf2' class BaseEntry(abc.ABC): def __init__(self, func_name,", "user_params[key] = context.properties[key] key = \"ALINK:model_dir\" if key in properties:", "user_params: Dict = json.loads(properties['ALINK:user_defined_params']) for i in range(1, 1024): key", "func_name in globals(): return globals()[func_name] else: raise RuntimeError('cannot find function[{}]'.format(func_name))", "abc from typing import Dict, Callable import tensorflow as tf", "by the func name :param func_name: func name :return: function", "pass def entry_func(self, context: Context): tf_context = TFContext(context) properties =", "class cannot be found c = getattr(m, func_name) return c", "properties: user_params[key] = properties[key] output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy =", "import importlib # load the module, will raise ImportError if", "{}, task_index = {}: done tf_user_main\".format(task_type, task_index), flush=True) local_vars =", "def __init__(self, func_name, engine_type): self.func_name = func_name self.engine_type = engine_type", "tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file = JavaFile(context.from_java(), context.to_java()) dataset_file = os.path.join(work_dir,", "because there is a bug in TensorFlow 1.x # See:", "tf_helper, io_helper from ..runner.output_writer import DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context import", "raise AttributeError if class cannot be found c = getattr(m,", "there is a bug in TensorFlow 1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used", "= int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir'] cluster, task_type, task_index = tf_context.export_estimator_cluster()", "from flink_ml_framework.context import Context from flink_ml_framework.java_file import * from ..runner", "func_name, engine_type): self.func_name = func_name self.engine_type = engine_type @staticmethod def", "importlib.import_module(module_name) # get the class, will raise AttributeError if class", "True def post_process(self, **kwargs): pass def entry_func(self, context: Context): tf_context", "..runner import tf_helper, io_helper from ..runner.output_writer import DirectOutputWriter try: from", "typing import Dict, Callable import tensorflow as tf from flink_ml_framework.context", "as tf from flink_ml_framework.context import Context from flink_ml_framework.java_file import *", "akdl, because there is a bug in TensorFlow 1.x #", "intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type", "import * from ..runner import tf_helper, io_helper from ..runner.output_writer import", "self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir']", "def entry_func(self, context: Context): tf_context = TFContext(context) properties = tf_context.properties", "DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy = locals().copy() locals_copy.pop(\"self\") print(\"locals_copy = \", locals_copy,", "local_vars.pop('self') self.post_process(**local_vars) print(\"task_type = {}, task_index = {}: exit\".format(task_type, task_index),", "**kwargs): pass def entry_func(self, context: Context): tf_context = TFContext(context) properties", "from ..runner import tf_helper, io_helper from ..runner.output_writer import DirectOutputWriter try:", "class, will raise AttributeError if class cannot be found c", "is set by akdl, because there is a bug in", "from flink_ml_tensorflow2.tensorflow_context import TFContext # noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops import", "properties, flush=True) # intra_op_parallelism is set by akdl, because there", "TF1_TYPE = 'tf1' TF2_TYPE = 'tf2' class BaseEntry(abc.ABC): def __init__(self,", "locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type = {}, task_index = {}: exit\".format(task_type,", "'savedmodel') user_params: Dict = json.loads(properties['ALINK:user_defined_params']) for i in range(1, 1024):", "raise ImportError if module cannot be loaded m = importlib.import_module(module_name)", "except: from flink_ml_tensorflow2.tensorflow_context import TFContext # noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops", "key in properties: user_params[key] = properties[key] output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java())", "locals_copy.pop(\"self\") print(\"locals_copy = \", locals_copy, flush=True) args = self.construct_args(**locals_copy) func", "Callable[[], tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset() dataset = None dataset_file =", "flush=True) # intra_op_parallelism is set by akdl, because there is", "None dataset_file = None dataset_length = None saved_model_dir = os.path.join(work_dir,", "= func_name.rsplit('.', 1) import importlib # load the module, will", "the class, will raise AttributeError if class cannot be found", "= \", locals_copy, flush=True) args = self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name)", "= properties[key] output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy = locals().copy() locals_copy.pop(\"self\")", "self.func_name = func_name self.engine_type = engine_type @staticmethod def get_func_by_name(func_name): \"\"\"", "found c = getattr(m, func_name) return c @abc.abstractmethod def construct_args(self,", "by akdl, because there is a bug in TensorFlow 1.x", "__all__ = ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE = 'tf1' TF2_TYPE = 'tf2'", "func_name = func_name.rsplit('.', 1) import importlib # load the module,", "int(properties['ALINK:intra_op_parallelism']) if self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type == TF2_TYPE:", "self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name) func(args) print(\"task_type = {}, task_index =", "be loaded m = importlib.import_module(module_name) # get the class, will", "print(\"locals_copy = \", locals_copy, flush=True) args = self.construct_args(**locals_copy) func =", "if key in properties: user_params[key] = context.properties[key] key = \"ALINK:model_dir\"", "RuntimeError('cannot find function[{}]'.format(func_name)) else: module_name, func_name = func_name.rsplit('.', 1) import", "function \"\"\" if '.' not in func_name: if func_name in", "from tensorflow_io.core.python.ops import core_ops __all__ = ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE =", "{}: done tf_user_main\".format(task_type, task_index), flush=True) local_vars = locals().copy() local_vars.pop('self') self.post_process(**local_vars)", "'TF2_TYPE'] TF1_TYPE = 'tf1' TF2_TYPE = 'tf2' class BaseEntry(abc.ABC): def", "find function[{}]'.format(func_name)) else: module_name, func_name = func_name.rsplit('.', 1) import importlib", "from ..runner.output_writer import DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context import TFContext except:", "i in range(1, 1024): key = \"ALINK:bc_\" + str(i) if", "class BaseEntry(abc.ABC): def __init__(self, func_name, engine_type): self.func_name = func_name self.engine_type", "dataset = None dataset_file = None dataset_length = None saved_model_dir", "in range(1, 1024): key = \"ALINK:bc_\" + str(i) if key", "c @abc.abstractmethod def construct_args(self, **kwargs): pass def is_batch(self): return True", "Context): tf_context = TFContext(context) properties = tf_context.properties print('properties', properties, flush=True)", "getattr(m, func_name) return c @abc.abstractmethod def construct_args(self, **kwargs): pass def", "func name :param func_name: func name :return: function \"\"\" if", "set by akdl, because there is a bug in TensorFlow", "flink_ml_framework.java_file import * from ..runner import tf_helper, io_helper from ..runner.output_writer", "..runner.output_writer import DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context import TFContext except: from", "flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[],", "os.path.join(work_dir, 'savedmodel') user_params: Dict = json.loads(properties['ALINK:user_defined_params']) for i in range(1,", "'.' not in func_name: if func_name in globals(): return globals()[func_name]", "if self.is_batch(): java_queue_file = JavaFile(context.from_java(), context.to_java()) dataset_file = os.path.join(work_dir, 'dataset.tfrecords')", "else: raise RuntimeError('cannot find function[{}]'.format(func_name)) else: module_name, func_name = func_name.rsplit('.',", "dataset_file) print(\"number of records: \" + str(dataset_length), flush=True) dataset_fn: Callable[[],", "print(\"task_type = {}, task_index = {}: done tf_user_main\".format(task_type, task_index), flush=True)", "== TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers =", "locals_copy, flush=True) args = self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name) func(args) print(\"task_type", ":return: function \"\"\" if '.' not in func_name: if func_name", "tensorflow as tf from flink_ml_framework.context import Context from flink_ml_framework.java_file import", "= self.get_func_by_name(self.func_name) func(args) print(\"task_type = {}, task_index = {}: done", "import DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context import TFContext except: from flink_ml_tensorflow2.tensorflow_context", "None saved_model_dir = os.path.join(work_dir, 'savedmodel') user_params: Dict = json.loads(properties['ALINK:user_defined_params']) for", "for i in range(1, 1024): key = \"ALINK:bc_\" + str(i)", "def construct_args(self, **kwargs): pass def is_batch(self): return True def post_process(self,", "key = \"ALINK:model_dir\" if key in properties: user_params[key] = properties[key]", "= os.path.join(work_dir, 'savedmodel') user_params: Dict = json.loads(properties['ALINK:user_defined_params']) for i in", "cannot be loaded m = importlib.import_module(module_name) # get the class,", "TFContext(context) properties = tf_context.properties print('properties', properties, flush=True) # intra_op_parallelism is", "is_batch(self): return True def post_process(self, **kwargs): pass def entry_func(self, context:", "else: module_name, func_name = func_name.rsplit('.', 1) import importlib # load", "Dict, Callable import tensorflow as tf from flink_ml_framework.context import Context", "the module, will raise ImportError if module cannot be loaded", "if self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism)", "io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of records: \" + str(dataset_length), flush=True) dataset_fn:", "importlib # load the module, will raise ImportError if module", "module_name, func_name = func_name.rsplit('.', 1) import importlib # load the", "name :param func_name: func name :return: function \"\"\" if '.'", "noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops import core_ops __all__ = ['TF1_TYPE', 'TF2_TYPE']", "tf from flink_ml_framework.context import Context from flink_ml_framework.java_file import * from", "\"ALINK:bc_\" + str(i) if key in properties: user_params[key] = context.properties[key]", "properties['ALINK:work_dir'] cluster, task_type, task_index = tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file =", "flink_ml_tensorflow2.tensorflow_context import TFContext # noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops import core_ops", "lambda: tf_context.flink_stream_dataset() dataset = None dataset_file = None dataset_length =", "ImportError if module cannot be loaded m = importlib.import_module(module_name) #", "self.get_func_by_name(self.func_name) func(args) print(\"task_type = {}, task_index = {}: done tf_user_main\".format(task_type,", "= None dataset_length = None saved_model_dir = os.path.join(work_dir, 'savedmodel') user_params:", "\"\"\" if '.' not in func_name: if func_name in globals():", "dataset_length = None saved_model_dir = os.path.join(work_dir, 'savedmodel') user_params: Dict =", "saved_model_dir = os.path.join(work_dir, 'savedmodel') user_params: Dict = json.loads(properties['ALINK:user_defined_params']) for i", "# get the class, will raise AttributeError if class cannot", "cluster, task_type, task_index = tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file = JavaFile(context.from_java(),", "func_name.rsplit('.', 1) import importlib # load the module, will raise", "in TensorFlow 1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if", "@staticmethod def get_func_by_name(func_name): \"\"\" Get function by the func name", "func_name self.engine_type = engine_type @staticmethod def get_func_by_name(func_name): \"\"\" Get function", "function[{}]'.format(func_name)) else: module_name, func_name = func_name.rsplit('.', 1) import importlib #", "return globals()[func_name] else: raise RuntimeError('cannot find function[{}]'.format(func_name)) else: module_name, func_name", "1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if self.engine_type ==", "will raise ImportError if module cannot be loaded m =", "io_helper from ..runner.output_writer import DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context import TFContext", "locals_copy = locals().copy() locals_copy.pop(\"self\") print(\"locals_copy = \", locals_copy, flush=True) args", "str(dataset_length), flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn:", "print(\"task_type = {}, task_index = {}: exit\".format(task_type, task_index), flush=True) output_writer.close()", "intra_op_parallelism is set by akdl, because there is a bug", "JavaFile(context.from_java(), context.to_java()) dataset_file = os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file,", "= lambda: tf_context.flink_stream_dataset() dataset = None dataset_file = None dataset_length", "tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda:", "raise RuntimeError('cannot find function[{}]'.format(func_name)) else: module_name, func_name = func_name.rsplit('.', 1)", "flush=True) args = self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name) func(args) print(\"task_type =", "will raise AttributeError if class cannot be found c =", "def post_process(self, **kwargs): pass def entry_func(self, context: Context): tf_context =", "= 'tf2' class BaseEntry(abc.ABC): def __init__(self, func_name, engine_type): self.func_name =", "= int(properties['ALINK:intra_op_parallelism']) if self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type ==", "\" + str(dataset_length), flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file)", "try: from flink_ml_tensorflow.tensorflow_context import TFContext except: from flink_ml_tensorflow2.tensorflow_context import TFContext", "**kwargs): pass def is_batch(self): return True def post_process(self, **kwargs): pass", "task_type, task_index = tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file = JavaFile(context.from_java(), context.to_java())", "+ str(i) if key in properties: user_params[key] = context.properties[key] key", "of records: \" + str(dataset_length), flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset] =", "str(i) if key in properties: user_params[key] = context.properties[key] key =", "BaseEntry(abc.ABC): def __init__(self, func_name, engine_type): self.func_name = func_name self.engine_type =", "records: \" + str(dataset_length), flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda:", "= ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE = 'tf1' TF2_TYPE = 'tf2' class", "https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif", "return True def post_process(self, **kwargs): pass def entry_func(self, context: Context):", "json.loads(properties['ALINK:user_defined_params']) for i in range(1, 1024): key = \"ALINK:bc_\" +", "done tf_user_main\".format(task_type, task_index), flush=True) local_vars = locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type", "__init__(self, func_name, engine_type): self.func_name = func_name self.engine_type = engine_type @staticmethod", "get_func_by_name(func_name): \"\"\" Get function by the func name :param func_name:", "tf_context = TFContext(context) properties = tf_context.properties print('properties', properties, flush=True) #", "task_index = {}: done tf_user_main\".format(task_type, task_index), flush=True) local_vars = locals().copy()", "= lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset()", "1) import importlib # load the module, will raise ImportError", "= \"ALINK:model_dir\" if key in properties: user_params[key] = properties[key] output_writer", "tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers']) work_dir", "= json.loads(properties['ALINK:user_defined_params']) for i in range(1, 1024): key = \"ALINK:bc_\"", "locals().copy() locals_copy.pop(\"self\") print(\"locals_copy = \", locals_copy, flush=True) args = self.construct_args(**locals_copy)", "if '.' not in func_name: if func_name in globals(): return", "\", locals_copy, flush=True) args = self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name) func(args)", "['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE = 'tf1' TF2_TYPE = 'tf2' class BaseEntry(abc.ABC):", "TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir'] cluster, task_type,", "import Context from flink_ml_framework.java_file import * from ..runner import tf_helper,", "context: Context): tf_context = TFContext(context) properties = tf_context.properties print('properties', properties,", "load the module, will raise ImportError if module cannot be", "# intra_op_parallelism is set by akdl, because there is a", "Dict = json.loads(properties['ALINK:user_defined_params']) for i in range(1, 1024): key =", "= io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of records: \" + str(dataset_length), flush=True)", "See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism)", "flink_ml_tensorflow.tensorflow_context import TFContext except: from flink_ml_tensorflow2.tensorflow_context import TFContext # noinspection", "func_name: if func_name in globals(): return globals()[func_name] else: raise RuntimeError('cannot", "engine_type @staticmethod def get_func_by_name(func_name): \"\"\" Get function by the func", "== TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir'] cluster,", "TensorFlow 1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism']) if self.engine_type", "local_vars = locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type = {}, task_index =", "= JavaFile(context.from_java(), context.to_java()) dataset_file = os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length =", "tf_user_main\".format(task_type, task_index), flush=True) local_vars = locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type =", "'tf1' TF2_TYPE = 'tf2' class BaseEntry(abc.ABC): def __init__(self, func_name, engine_type):", "c = getattr(m, func_name) return c @abc.abstractmethod def construct_args(self, **kwargs):", "Context from flink_ml_framework.java_file import * from ..runner import tf_helper, io_helper", "Get function by the func name :param func_name: func name", "if class cannot be found c = getattr(m, func_name) return", "module cannot be loaded m = importlib.import_module(module_name) # get the", "def is_batch(self): return True def post_process(self, **kwargs): pass def entry_func(self,", "= locals().copy() locals_copy.pop(\"self\") print(\"locals_copy = \", locals_copy, flush=True) args =", "func_name: func name :return: function \"\"\" if '.' not in", "if func_name in globals(): return globals()[func_name] else: raise RuntimeError('cannot find", "if module cannot be loaded m = importlib.import_module(module_name) # get", "@abc.abstractmethod def construct_args(self, **kwargs): pass def is_batch(self): return True def", "dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset() dataset = None dataset_file", "core_ops __all__ = ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE = 'tf1' TF2_TYPE =", "in func_name: if func_name in globals(): return globals()[func_name] else: raise", "construct_args(self, **kwargs): pass def is_batch(self): return True def post_process(self, **kwargs):", "m = importlib.import_module(module_name) # get the class, will raise AttributeError", "module, will raise ImportError if module cannot be loaded m", "range(1, 1024): key = \"ALINK:bc_\" + str(i) if key in", "self.engine_type == TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers", "from flink_ml_tensorflow.tensorflow_context import TFContext except: from flink_ml_tensorflow2.tensorflow_context import TFContext #", "post_process(self, **kwargs): pass def entry_func(self, context: Context): tf_context = TFContext(context)", "loaded m = importlib.import_module(module_name) # get the class, will raise", "tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir'] cluster, task_type, task_index", "= engine_type @staticmethod def get_func_by_name(func_name): \"\"\" Get function by the", "import core_ops __all__ = ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE = 'tf1' TF2_TYPE", "func name :return: function \"\"\" if '.' not in func_name:", "get the class, will raise AttributeError if class cannot be", "DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context import TFContext except: from flink_ml_tensorflow2.tensorflow_context import", "from flink_ml_framework.java_file import * from ..runner import tf_helper, io_helper from", "= func_name self.engine_type = engine_type @staticmethod def get_func_by_name(func_name): \"\"\" Get", "* from ..runner import tf_helper, io_helper from ..runner.output_writer import DirectOutputWriter", "task_index = tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file = JavaFile(context.from_java(), context.to_java()) dataset_file", "# noinspection PyUnresolvedReferences from tensorflow_io.core.python.ops import core_ops __all__ = ['TF1_TYPE',", "import tf_helper, io_helper from ..runner.output_writer import DirectOutputWriter try: from flink_ml_tensorflow.tensorflow_context", "cannot be found c = getattr(m, func_name) return c @abc.abstractmethod", "dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of records: \" + str(dataset_length),", "\"\"\" Get function by the func name :param func_name: func", "lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset() dataset", "self.is_batch(): java_queue_file = JavaFile(context.from_java(), context.to_java()) dataset_file = os.path.join(work_dir, 'dataset.tfrecords') dataset,", "TFContext except: from flink_ml_tensorflow2.tensorflow_context import TFContext # noinspection PyUnresolvedReferences from", "args = self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name) func(args) print(\"task_type = {},", "bug in TensorFlow 1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism = int(properties['ALINK:intra_op_parallelism'])", "= None dataset_file = None dataset_length = None saved_model_dir =", "= self.construct_args(**locals_copy) func = self.get_func_by_name(self.func_name) func(args) print(\"task_type = {}, task_index", "import Dict, Callable import tensorflow as tf from flink_ml_framework.context import", "entry_func(self, context: Context): tf_context = TFContext(context) properties = tf_context.properties print('properties',", "print(\"number of records: \" + str(dataset_length), flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset]", "'dataset.tfrecords') dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of records: \"", "import tensorflow as tf from flink_ml_framework.context import Context from flink_ml_framework.java_file", "= TFContext(context) properties = tf_context.properties print('properties', properties, flush=True) # intra_op_parallelism", "AttributeError if class cannot be found c = getattr(m, func_name)", "Callable import tensorflow as tf from flink_ml_framework.context import Context from", "in properties: user_params[key] = context.properties[key] key = \"ALINK:model_dir\" if key", "tensorflow_io.core.python.ops import core_ops __all__ = ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE = 'tf1'", "return c @abc.abstractmethod def construct_args(self, **kwargs): pass def is_batch(self): return", "user_params[key] = properties[key] output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy = locals().copy()", "name :return: function \"\"\" if '.' not in func_name: if", "tf_context.to_java()) locals_copy = locals().copy() locals_copy.pop(\"self\") print(\"locals_copy = \", locals_copy, flush=True)", "tf_context.properties print('properties', properties, flush=True) # intra_op_parallelism is set by akdl,", "= {}: done tf_user_main\".format(task_type, task_index), flush=True) local_vars = locals().copy() local_vars.pop('self')", ":param func_name: func name :return: function \"\"\" if '.' not", "self.post_process(**local_vars) print(\"task_type = {}, task_index = {}: exit\".format(task_type, task_index), flush=True)", "import TFContext except: from flink_ml_tensorflow2.tensorflow_context import TFContext # noinspection PyUnresolvedReferences", "TF1_TYPE: tf_helper.set_intra_op_parallelism(intra_op_parallelism_threads=intra_op_parallelism) elif self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers'])", "= properties['ALINK:work_dir'] cluster, task_type, task_index = tf_context.export_estimator_cluster() if self.is_batch(): java_queue_file", "properties[key] output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy = locals().copy() locals_copy.pop(\"self\") print(\"locals_copy", "function by the func name :param func_name: func name :return:", "elif self.engine_type == TF2_TYPE: tf.config.threading.set_intra_op_parallelism_threads(intra_op_parallelism) num_workers = int(properties['ALINK:num_workers']) work_dir =", "os.path.join(work_dir, 'dataset.tfrecords') dataset, dataset_length = io_helper.convert_java_queue_file_to_repeatable_dataset(java_queue_file, dataset_file) print(\"number of records:", "= DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy = locals().copy() locals_copy.pop(\"self\") print(\"locals_copy = \",", "flush=True) local_vars = locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type = {}, task_index", "= locals().copy() local_vars.pop('self') self.post_process(**local_vars) print(\"task_type = {}, task_index = {}:", "func(args) print(\"task_type = {}, task_index = {}: done tf_user_main\".format(task_type, task_index),", "+ str(dataset_length), flush=True) dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file) else:", "engine_type): self.func_name = func_name self.engine_type = engine_type @staticmethod def get_func_by_name(func_name):", "the func name :param func_name: func name :return: function \"\"\"", "None dataset_length = None saved_model_dir = os.path.join(work_dir, 'savedmodel') user_params: Dict", "PyUnresolvedReferences from tensorflow_io.core.python.ops import core_ops __all__ = ['TF1_TYPE', 'TF2_TYPE'] TF1_TYPE", "= tf_context.properties print('properties', properties, flush=True) # intra_op_parallelism is set by", "int(properties['ALINK:num_workers']) work_dir = properties['ALINK:work_dir'] cluster, task_type, task_index = tf_context.export_estimator_cluster() if", "a bug in TensorFlow 1.x # See: https://stackoverflow.com/questions/34426268/restricting-number-of-cores-used intra_op_parallelism =", "in properties: user_params[key] = properties[key] output_writer = DirectOutputWriter(tf_context.from_java(), tf_context.to_java()) locals_copy", "= importlib.import_module(module_name) # get the class, will raise AttributeError if", "TF2_TYPE = 'tf2' class BaseEntry(abc.ABC): def __init__(self, func_name, engine_type): self.func_name", "else: dataset_fn: Callable[[], tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset() dataset = None", "key in properties: user_params[key] = context.properties[key] key = \"ALINK:model_dir\" if", "Callable[[], tf.data.TFRecordDataset] = lambda: tf.data.TFRecordDataset(dataset_file) else: dataset_fn: Callable[[], tf.data.TFRecordDataset] =", "properties: user_params[key] = context.properties[key] key = \"ALINK:model_dir\" if key in", "tf.data.TFRecordDataset] = lambda: tf_context.flink_stream_dataset() dataset = None dataset_file = None" ]
[ "TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime one: str", "def test_keyspace_api(): import hashlib import uuid from corm import register_table,", "in [1, 2] assert entry.option == OptionList.One for idx, entry", "ENCODING = 'utf-8' @pytest.fixture(scope='function', autouse=True) def setup_case(request): def destroy_case(): from", "TestModel('one', 'two') two = TestModel('one', 'two') three = TestModel('one', 'three')", "def test_initial_api(): from corm import register_table, insert, sync_schema from corm.models", "CORMBase class TestCORMAuth(CORMBase): one: str __keyspace__ = 'test_corm_auth' register_table(TestCORMAuth) sync_schema()", "def test_select_api(): import random from corm import register_table, insert, sync_schema,", "keyspace_name in ['global']: continue session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api():", "# Create Table or Delete Column on existing Table class", "OptionList(enum.Enum): One = 'one' Two = 'two' class TestCormEnum(CORMBase): __keyspace__", "sync_schema() COL_CQL = f''' SELECT column_name, type FROM system_schema.columns WHERE", "entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])): assert idx in [0,", "TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace' item: str created: datetime value: bool", "= 'mykeyspace' something: str other: str register_table(TestModel) sync_schema() one =", "def test_corm_enum(): import enum from corm import register_table, insert, sync_schema,", "entry.three == 'delta' elif idx == 3: assert entry.three ==", "TestModelSet('two', {'last', 'second-to-last'}) three = TestModelSet('three', {'last', 'second-to-last', 'last'}) four", "datetime.utcnow() }) entry = TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if len(insert_later) >", "type FROM system_schema.columns WHERE table_name = '{TestModelAlter._corm_details.table_name}' AND keyspace_name =", "''' rows = [(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert", "on existing Table class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int", "= 'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] random_number: int created:", "created: datetime value: bool register_table(TestModelBoolean) sync_schema() one = TestModelBoolean('one', datetime.utcnow(),", "TestModelSet('four', ['one', 'two', 'three', 'four']) insert([one, two, three, four]) def", "in SESSIONS.copy().items(): if keyspace_name in ['global']: continue session.shutdown() del SESSIONS[keyspace_name]", "from corm.models import CORMBase class TestModel(CORMBase): __keyspace__ = 'mykeyspace' something:", "two: str three: str register_table(TestOrderedByPkField) sync_schema() first_entry = TestOrderedByPkField(random.randint(0, 99999),", "setup_case(request): def destroy_case(): from corm import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for", "'mykeyspace' item: str created: datetime value: bool register_table(TestModelBoolean) sync_schema() one", "= 1000 class TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created:", "100): values.append({ 'random_number': random.randint(0, MAX_INT), 'created': datetime.utcnow() }) entry =", "values[idx]['created'] assert idx > 0 def test_select_where_api(): import random from", "obtain_session from corm.models import CORMBase from corm.datatypes import TableOrdering from", "import pytest ENCODING = 'utf-8' @pytest.fixture(scope='function', autouse=True) def setup_case(request): def", "random_number: int created: datetime one: str two: str source: TestModelSelectSource", "datetime.utcnow(), False) insert([one, two]) def test_datetime_api(): from corm import register_table,", "select from corm.models import CORMBase class OptionList(enum.Enum): One = 'one'", "isinstance(entry, TestModelSelect) # Order is not consistent # assert entry.random_number", "keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False def test_float_api(): from corm import", "import register_table, insert, sync_schema, \\ keyspace_exists, keyspace_destroy, keyspace_create from corm.datatypes", "assert entry.created == values[idx]['created'] assert idx > 0 def test_select_where_api():", "'mykeyspace' item: str created: datetime register_table(TestModelDatetime) sync_schema() one = TestModelDatetime('one',", "corm.annotations import Set class TestModelSet(CORMBase): __keyspace__ = 'mykeyspace' something: str", "TODO: Build UserType integration # register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def test_alter_table_api():", "insert, sync_schema, \\ keyspace_exists, keyspace_destroy, keyspace_create from corm.datatypes import CassandraKeyspaceStrategy", "int register_table(TestCORMWhere) sync_schema() one = TestCORMWhere(OptionList.One, 1) two = TestCORMWhere(OptionList.One,", "annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name, session in SESSIONS.copy().items(): if keyspace_name", "'three', 'four']) insert([one, two, three, four]) def test_select_api(): import random", "Set class TestModelSet(CORMBase): __keyspace__ = 'mykeyspace' something: str other: Set", "from corm.models import CORMBase from datetime import datetime class TestModelBoolean(CORMBase):", "values[idx]['random_number'] # assert entry.created == values[idx]['created'] assert idx > 0", "'one', 'one', 'delta') insert([first_entry, second_entry, delta, gamma]) for idx, entry", "TestModelSet(CORMBase): __keyspace__ = 'mykeyspace' something: str other: Set register_table(TestModelSet) sync_schema()", "import datetime MAX_INT = 1000 class TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace'", "# TODO: Build UserType integration # register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def", "idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])): assert idx ==", "[(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 3", "register_table(TestModelAlter) sync_schema() rows = [(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)]", "assert idx > 0 def test_select_where_api(): import random from corm", "TestCORMWhere(OptionList.Two, 3) four = TestCORMWhere(OptionList.Two, 4) insert([one, two, three, four])", "hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple)", "1) two = TestCORMWhere(OptionList.One, 2) three = TestCORMWhere(OptionList.Two, 3) four", "int created: datetime register_table(TestModelSelect) sync_schema() insert_later = [] values =", "'{TestModelAlter._corm_details.keyspace}' ''' rows = [(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)]", "from datetime import datetime class TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__", "uuid from corm import register_table, insert, sync_schema, \\ keyspace_exists, keyspace_destroy,", "random_number: int created: datetime one: str two: str three: str", "== 0 assert entry.score == 1 assert entry.option == OptionList.One", "'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from corm import register_table, insert, sync_schema", "MAX_INT), 'created': datetime.utcnow() }) entry = TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if", "= 'mykeyspace' random_number: int created: datetime register_table(TestModelSelect) sync_schema() insert_later =", "sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') gamma", "'mykeyspace' something: str other: str register_table(TestModel) sync_schema() one = TestModel('one',", "OptionList.One for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])): assert", "int created: datetime one: str two: str three: str register_table(TestNotOrderedByPkField)", "entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])): assert idx in [0,", "TestModelFloat(data) insert([one]) for idx, entry in enumerate(select(TestModelFloat)): assert entry.input_one ==", "column_name, type FROM system_schema.columns WHERE table_name = '{TestModelAlter._corm_details.table_name}' AND keyspace_name", "'four']) insert([one, two, three, four]) def test_select_api(): import random from", "One = 'one' Two = 'two' class TestCORMWhere(CORMBase): __keyspace__ =", "obtain_session from corm.models import CORMBase from datetime import datetime class", "sync_schema() rows = [(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert", "random_number: int created: datetime one: str two: str class TestModelSelectPivot(CORMBase):", "idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])): assert idx in", "idx == 0: assert entry.three != 'alpha' def test_ordered_by_pk_field(): import", "= TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') insert([first_entry, gamma, delta,", "'two' class TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where' option: OptionList score: int", "idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])): assert idx ==", "is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name) assert", "def destroy_case(): from corm import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name,", "created: datetime one: str two: str three: str register_table(TestNotOrderedByPkField) sync_schema()", "delta = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') insert([first_entry, second_entry,", "import register_table, insert, sync_schema, select, obtain_session from corm.models import CORMBase", "corm.models import CORMBase from datetime import datetime MAX_INT = 99999", "cp, Operator from corm.models import CORMBase class OptionList(enum.Enum): One =", "'one', 'alpha') gamma = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma')", "import CORMBase from corm.annotations import Set from datetime import datetime", "obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 4 def test_not_ordered_by_pk_field(): import random from", "datetime import datetime class TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace' item: str", "'one', 'delta') second_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha')", "corm.models import CORMBase from corm.annotations import Set from datetime import", "TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') insert([first_entry, second_entry, delta, gamma])", "import hashlib import uuid from corm import register_table, insert, sync_schema,", "corm import register_table, insert, sync_schema, select from corm.models import CORMBase", "register_table, insert, sync_schema from corm.models import CORMBase class TestCORMAuth(CORMBase): one:", "OptionList.Two for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])): assert", "import enum from corm import register_table, insert, sync_schema, select from", "= 'mykeyspace' item: str created: datetime register_table(TestModelDatetime) sync_schema() one =", "test_select_where_api(): import random from corm import register_table, insert, sync_schema, select,", "'mykeyspace' random_number: int created: datetime new_column: str register_table(TestModelAlter) sync_schema() rows", "'gamma') delta = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') second_entry", "== 0 assert entry.score == 4 assert entry.option == OptionList.Two", "'mykeyspace' random_number: int created: datetime register_table(TestModelAlter) sync_schema() COL_CQL = f'''", "TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta = TestNotOrderedByPkField(random.randint(0, 99999),", "99999), datetime.utcnow(), 'one', 'one', 'alpha') insert([first_entry, gamma, delta, second_entry]) for", "integration # register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def test_alter_table_api(): from corm import", "'one', 'delta') insert([first_entry, second_entry, delta, gamma]) for idx, entry in", "one = TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False def", "rows = [(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows)", "import CORMBase from datetime import datetime MAX_INT = 99999 class", "['one', 'two', 'three', 'four']) insert([one, two, three, four]) def test_select_api():", "insert, sync_schema, select from corm.models import CORMBase class TestCORMUUID(CORMBase): __keyspace__", "0: assert entry.three != 'alpha' def test_ordered_by_pk_field(): import random from", "def test_not_ordered_by_pk_field(): import random from corm import register_table, insert, sync_schema,", "datetime class TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace' item: str created: datetime", "= f''' SELECT column_name, type FROM system_schema.columns WHERE table_name =", "= TestModelDatetime('two', datetime.utcnow()) insert([one, two]) def test_set_api(): from corm import", "datetime.utcnow(), 'one', 'one', 'beta') second_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "entry = TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if len(insert_later) > 20: insert(insert_later)", "OptionList register_table(TestCormEnum) sync_schema() first = TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two) insert([first,", "import CORMBase from datetime import datetime # Create Table or", "assert entry.option in OptionList.__members__.values() def test_corm_where(): import enum from corm", "is True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False class TestModelKeyspace(CORMBase): __keyspace__", "from datetime import datetime MAX_INT = 1000 class TestModelSelect(CORMBase): __keyspace__", "enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])): assert idx == 0 assert entry.score", "random_number: int created: datetime register_table(TestModelSelect) sync_schema() insert_later = [] values", "# register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def test_alter_table_api(): from corm import register_table,", "datetime new_column: str register_table(TestModelAlter) sync_schema() rows = [(row.column_name, row.type) for", "enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])): assert idx == 0 assert entry.score", "Add Column on existing Table class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace'", "= TestCORMWhere(OptionList.One, 1) two = TestCORMWhere(OptionList.One, 2) three = TestCORMWhere(OptionList.Two,", "for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])): assert idx", "def setup_case(request): def destroy_case(): from corm import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace')", "'option', OptionList.One)])): assert idx in [0, 1] assert entry.score in", "CORMBase from datetime import datetime MAX_INT = 99999 class TestModelSelectSource(CORMBase):", "datetime register_table(TestModelSelect) sync_schema() insert_later = [] values = [] for", "sync_schema, select from corm.models import CORMBase class TestCORMUUID(CORMBase): __keyspace__ =", "['one', 'two', 'three'] __ordered_by_primary_keys__ = TableOrdering.DESC random_number: int created: datetime", "[cp(Operator.Equal, 'option', OptionList.Two)])): assert idx in [0, 1] assert entry.score", "sync_schema, select, obtain_session from corm.models import CORMBase from datetime import", "class OptionList(enum.Enum): One = 'one' Two = 'two' class TestCormEnum(CORMBase):", "from corm.annotations import Set from datetime import datetime MAX_INT =", "idx, entry in enumerate(select(TestModelFloat)): assert entry.input_one == data def test_boolean_api():", "sync_schema() first_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') second_entry", "datetime class TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two',", "TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] random_number:", "in [3, 4] assert entry.option == OptionList.Two def test_corm_uuid(): import", "Two = 'two' class TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum' option: OptionList", "from corm.models import CORMBase class TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace' input_one:", "select from corm.models import CORMBase class TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace'", "datetime MAX_INT = 1000 class TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace' random_number:", "__ordered_by_primary_keys__ = TableOrdering.DESC random_number: int created: datetime one: str two:", "corm import register_table, insert, sync_schema from corm.models import CORMBase from", "'two', 'three'] random_number: int created: datetime one: str two: str", "two: str source: TestModelSelectSource # TODO: Build UserType integration #", "0 assert entry.score == 1 assert entry.option == OptionList.One for", "to start with Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}'", "entry.three == 'beta' elif idx == 2: assert entry.three ==", "corm.models import CORMBase class TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace' input_one: float", "random from corm import register_table, insert, sync_schema, select, where from", "datetime class TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace' item: str created: datetime", "TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta = TestOrderedByPkField(random.randint(0, 99999),", "__keyspace__ = 'test_corm_enum' option: OptionList register_table(TestCormEnum) sync_schema() first = TestCormEnum(OptionList.One)", "class TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime register_table(TestModelSelect)", "int created: datetime new_column: str register_table(TestModelAlter) sync_schema() rows = [(row.column_name,", "SELECT column_name, type FROM system_schema.columns WHERE table_name = '{TestModelAlter._corm_details.table_name}' AND", "[] values = [] for idx in range(0, 100): values.append({", "insert, sync_schema, select, obtain_session from corm.models import CORMBase from datetime", "Set from datetime import datetime MAX_INT = 1000 class TestModelSelect(CORMBase):", "__keyspace__ = 'mykeyspace' something: str other: Set register_table(TestModelSet) sync_schema() one", "keyspace_exists(keyspace_name) is False sync_schema() assert keyspace_exists(keyspace_name) is True one =", "WHERE table_name = '{TestModelAlter._corm_details.table_name}' AND keyspace_name = '{TestModelAlter._corm_details.keyspace}' ''' rows", "register_table(TestModelFloat) sync_schema() data = 324.593998934 one = TestModelFloat(data) insert([one]) for", "import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name, session in SESSIONS.copy().items(): if", "other: str register_table(TestModel) sync_schema() one = TestModel('one', 'two') two =", "register_table, insert, sync_schema, \\ keyspace_exists, keyspace_destroy, keyspace_create from corm.datatypes import", "sync_schema, \\ keyspace_exists, keyspace_destroy, keyspace_create from corm.datatypes import CassandraKeyspaceStrategy from", "False def test_float_api(): from corm import register_table, insert, sync_schema, select", "for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 3 # Add", "assert len(rows) == 3 # Add Column on existing Table", "99999), datetime.utcnow(), 'one', 'one', 'delta') second_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "def test_corm_auth(): import os os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME'] = 'cassandra'", "== 4 assert entry.option == OptionList.Two for idx, entry in", "elif idx == 2: assert entry.three == 'delta' elif idx", "4) insert([one, two, three, four]) for idx, entry in enumerate(where(TestCORMWhere,", "input_one: float register_table(TestModelFloat) sync_schema() data = 324.593998934 one = TestModelFloat(data)", "__keyspace__ = 'mykeyspace' something: str other: str register_table(TestModel) sync_schema() one", "1)])): assert idx == 0 assert entry.score == 1 assert", "two = TestModelBoolean('two', datetime.utcnow(), False) insert([one, two]) def test_datetime_api(): from", "idx, entry in enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry, TestModelSelect) # Order", "test_select_api(): import random from corm import register_table, insert, sync_schema, select", "entry.option == OptionList.One for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option',", "corm.models import CORMBase from corm.annotations import Set class TestModelSet(CORMBase): __keyspace__", "first_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') gamma =", "= TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two) insert([first, second]) for idx, entry", "str other: Set register_table(TestModelSet) sync_schema() one = TestModelSet('one', {'first'}) two", "assert entry.score in [1, 2] assert entry.option == OptionList.One for", "= TestModel('one', 'two') three = TestModel('one', 'three') insert([one, two, three])", "'three') insert([one, two, three]) def test_keyspace_api(): import hashlib import uuid", "'one', 'one', 'beta') second_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "= TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') second_entry = TestNotOrderedByPkField(random.randint(0,", "CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False", "values = [] for idx in range(0, 100): values.append({ 'random_number':", "datetime import datetime class TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ =", "two, three]) def test_keyspace_api(): import hashlib import uuid from corm", "== 'delta' elif idx == 3: assert entry.three == 'gamma'", "from datetime import datetime MAX_INT = 99999 class TestModelSelectSource(CORMBase): __keyspace__", "class TestCORMAuth(CORMBase): one: str __keyspace__ = 'test_corm_auth' register_table(TestCORMAuth) sync_schema() def", "SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api(): from corm import register_table, insert, sync_schema", "item: str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is False sync_schema() assert keyspace_exists(keyspace_name)", "OptionList.__members__.values() def test_corm_where(): import enum from corm import register_table, insert,", "__keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] __ordered_by_primary_keys__ =", "'one', 'gamma') delta = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta')", "created: datetime one: str two: str three: str register_table(TestOrderedByPkField) sync_schema()", "'one', 'beta') second_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha')", "'score', 1)])): assert idx == 0 assert entry.score == 1", "keyspace_exists(keyspace_name) is True one = TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name)", "'one', 'one', 'gamma') delta = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "from corm import register_table, insert, sync_schema from corm.models import CORMBase", "= TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if len(insert_later) > 20: insert(insert_later) insert_later", "TestCormEnum(OptionList.Two) insert([first, second]) for idx, entry in enumerate(select(TestCormEnum)): assert entry.option", "one = TestModelDatetime('one', datetime.utcnow()) two = TestModelDatetime('two', datetime.utcnow()) insert([one, two])", "0 def test_select_where_api(): import random from corm import register_table, insert,", "row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 3 # Add Column", "class TestModel(CORMBase): __keyspace__ = 'mykeyspace' something: str other: str register_table(TestModel)", "second_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') insert([first_entry, gamma,", "= TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False def test_float_api():", "TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime register_table(TestModelSelect) sync_schema()", "sync_schema() one = TestModelBoolean('one', datetime.utcnow(), True) two = TestModelBoolean('two', datetime.utcnow(),", "= 'mykeyspace' random_number: int created: datetime new_column: str register_table(TestModelAlter) sync_schema()", "int created: datetime one: str two: str class TestModelSelectPivot(CORMBase): __keyspace__", "__keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] random_number: int", "assert entry.three == 'delta' elif idx == 3: assert entry.three", "datetime register_table(TestModelDatetime) sync_schema() one = TestModelDatetime('one', datetime.utcnow()) two = TestModelDatetime('two',", "UserType integration # register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def test_alter_table_api(): from corm", "two: str class TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created:", "assert keyspace_exists(keyspace_name) is False sync_schema() assert keyspace_exists(keyspace_name) is True one", "float register_table(TestModelFloat) sync_schema() data = 324.593998934 one = TestModelFloat(data) insert([one])", "= TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') gamma = TestNotOrderedByPkField(random.randint(0,", "TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace' input_one: float register_table(TestModelFloat) sync_schema() data =", "> 20: insert(insert_later) insert_later = [] insert(insert_later) for idx, entry", "from datetime import datetime class TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace' item:", "entry.created == values[idx]['created'] assert idx > 0 def test_select_where_api(): import", "datetime import datetime class TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace' item: str", "register_table, insert, sync_schema, select from corm.models import CORMBase class TestCORMUUID(CORMBase):", "= TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') insert([first_entry, second_entry, delta,", "register_table(TestCORMWhere) sync_schema() one = TestCORMWhere(OptionList.One, 1) two = TestCORMWhere(OptionList.One, 2)", "for keyspace_name, session in SESSIONS.copy().items(): if keyspace_name in ['global']: continue", "= TestModelSet('one', {'first'}) two = TestModelSet('two', {'last', 'second-to-last'}) three =", "assert entry.score in [3, 4] assert entry.option == OptionList.Two def", "= 'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from corm import register_table, insert,", "created: datetime register_table(TestModelDatetime) sync_schema() one = TestModelDatetime('one', datetime.utcnow()) two =", "[1, 2] assert entry.option == OptionList.One for idx, entry in", "TestModelDatetime('two', datetime.utcnow()) insert([one, two]) def test_set_api(): from corm import register_table,", "= 'mykeyspace' something: str other: Set register_table(TestModelSet) sync_schema() one =", "import datetime MAX_INT = 99999 class TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace'", "insert, sync_schema, select from corm.models import CORMBase class TestModelFloat(CORMBase): __keyspace__", "register_table(TestModelSelectPivot) def test_alter_table_api(): from corm import register_table, insert, sync_schema, select,", "f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is", "= 'mykeyspace' random_number: int created: datetime register_table(TestModelAlter) sync_schema() COL_CQL =", "= 'test_corm_auth' register_table(TestCORMAuth) sync_schema() def test_corm_enum(): import enum from corm", "FROM system_schema.columns WHERE table_name = '{TestModelAlter._corm_details.table_name}' AND keyspace_name = '{TestModelAlter._corm_details.keyspace}'", "TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') insert([first_entry, gamma, delta, second_entry])", "created: datetime one: str two: str source: TestModelSelectSource # TODO:", "insert, sync_schema from corm.models import CORMBase class TestCORMAuth(CORMBase): one: str", "OptionList.Two def test_corm_uuid(): import uuid from corm import register_table, insert,", "random.randint(0, MAX_INT), 'created': datetime.utcnow() }) entry = TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry)", "'two', 'three', 'four']) insert([one, two, three, four]) def test_select_api(): import", "is True one = TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is", "'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema() one = TestCORMUUID(uuid.uuid4()) insert([one]) for", "TestModelDatetime('one', datetime.utcnow()) two = TestModelDatetime('two', datetime.utcnow()) insert([one, two]) def test_set_api():", "random from corm import register_table, insert, sync_schema, select from corm.models", "four]) for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])): assert", "register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is False sync_schema() assert keyspace_exists(keyspace_name) is True", "from corm import register_table, insert, sync_schema, select, where from corm.models", "insert, sync_schema, select from corm.models import CORMBase class OptionList(enum.Enum): One", "from corm.models import CORMBase from datetime import datetime class TestNotOrderedByPkField(CORMBase):", "for idx, entry in enumerate(select(TestModelFloat)): assert entry.input_one == data def", "'test_corm_enum' option: OptionList register_table(TestCormEnum) sync_schema() first = TestCormEnum(OptionList.One) second =", "four]) def test_select_api(): import random from corm import register_table, insert,", "Table class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime", "import register_table, insert, sync_schema, select, where, cp, Operator from corm.models", "__keyspace__ = 'mykeyspace' input_one: float register_table(TestModelFloat) sync_schema() data = 324.593998934", "sync_schema() assert keyspace_exists(keyspace_name) is True one = TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name)", "insert, sync_schema, select, where from corm.models import CORMBase from datetime", "register_table, insert, sync_schema, select from corm.models import CORMBase class TestModelFloat(CORMBase):", "TestCORMWhere(OptionList.One, 1) two = TestCORMWhere(OptionList.One, 2) three = TestCORMWhere(OptionList.Two, 3)", "TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where' option: OptionList score: int register_table(TestCORMWhere) sync_schema()", "= 'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] __ordered_by_primary_keys__ = TableOrdering.DESC", "'delta') insert([first_entry, second_entry, delta, gamma]) for idx, entry in enumerate(select(TestOrderedByPkField)):", "== OptionList.Two for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])):", "select, obtain_session from corm.models import CORMBase from datetime import datetime", "class TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where' option: OptionList score: int register_table(TestCORMWhere)", "int created: datetime one: str two: str source: TestModelSelectSource #", "Delete Column on existing Table class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace'", "TableOrdering.DESC random_number: int created: datetime one: str two: str three:", "= 'two' class TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where' option: OptionList score:", "sync_schema() one = TestCORMUUID(uuid.uuid4()) insert([one]) for entry in select(TestCORMUUID): assert", "second]) for idx, entry in enumerate(select(TestCormEnum)): assert entry.option in OptionList.__members__.values()", "Set register_table(TestModelSet) sync_schema() one = TestModelSet('one', {'first'}) two = TestModelSet('two',", "idx == 1: assert entry.three == 'beta' elif idx ==", "def test_float_api(): from corm import register_table, insert, sync_schema, select from", "datetime.utcnow(), 'one', 'one', 'delta') insert([first_entry, second_entry, delta, gamma]) for idx,", "= TestModelSet('four', ['one', 'two', 'three', 'four']) insert([one, two, three, four])", "== 0: assert entry.three == 'alpha' elif idx == 1:", "if idx == 0: assert entry.three == 'alpha' elif idx", "session in SESSIONS.copy().items(): if keyspace_name in ['global']: continue session.shutdown() del", "assert entry.three == 'beta' elif idx == 2: assert entry.three", "keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False class TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name", "uuid from corm import register_table, insert, sync_schema, select from corm.models", "datetime one: str two: str three: str register_table(TestNotOrderedByPkField) sync_schema() first_entry", "entry.input_one == data def test_boolean_api(): from corm import register_table, insert,", "corm.datatypes import CassandraKeyspaceStrategy from corm.models import CORMBase # Keyspaces seem", "'random_number': random.randint(0, MAX_INT), 'created': datetime.utcnow() }) entry = TestModelSelect(values[-1]['random_number'], values[-1]['created'])", "idx in [0, 1] assert entry.score in [1, 2] assert", "import datetime class TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one',", "uuid.UUID register_table(TestCORMUUID) sync_schema() one = TestCORMUUID(uuid.uuid4()) insert([one]) for entry in", "\\ keyspace_exists, keyspace_destroy, keyspace_create from corm.datatypes import CassandraKeyspaceStrategy from corm.models", "'mykeyspace' random_number: int created: datetime one: str two: str source:", "__keyspace__ = 'test_corm_where' option: OptionList score: int register_table(TestCORMWhere) sync_schema() one", "second_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') gamma =", "for idx, entry in enumerate(select(TestCormEnum)): assert entry.option in OptionList.__members__.values() def", "from corm.models import CORMBase class OptionList(enum.Enum): One = 'one' Two", "assert entry.score == 4 assert entry.option == OptionList.Two for idx,", "= 'test_corm_enum' option: OptionList register_table(TestCormEnum) sync_schema() first = TestCormEnum(OptionList.One) second", "= TestModelBoolean('two', datetime.utcnow(), False) insert([one, two]) def test_datetime_api(): from corm", "enumerate(select(TestOrderedByPkField)): if idx == 0: assert entry.three == 'alpha' elif", "assert entry.three == 'gamma' def test_corm_auth(): import os os.environ['CLUSTER_PORT'] =", "'gamma' def test_corm_auth(): import os os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME'] =", "import enum from corm import register_table, insert, sync_schema, select, where,", "range(0, 100): values.append({ 'random_number': random.randint(0, MAX_INT), 'created': datetime.utcnow() }) entry", "str three: str register_table(TestOrderedByPkField) sync_schema() first_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "value: bool register_table(TestModelBoolean) sync_schema() one = TestModelBoolean('one', datetime.utcnow(), True) two", "'alpha') insert([first_entry, gamma, delta, second_entry]) for idx, entry in enumerate(select(TestNotOrderedByPkField)):", "# Add Column on existing Table class TestModelAlter(CORMBase): __keyspace__ =", "Operator from corm.models import CORMBase class OptionList(enum.Enum): One = 'one'", "class TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace' input_one: float register_table(TestModelFloat) sync_schema() data", "= TestModelFloat(data) insert([one]) for idx, entry in enumerate(select(TestModelFloat)): assert entry.input_one", "True) two = TestModelBoolean('two', datetime.utcnow(), False) insert([one, two]) def test_datetime_api():", "'alpha') gamma = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta", "insert([first_entry, gamma, delta, second_entry]) for idx, entry in enumerate(select(TestNotOrderedByPkField)): if", "first_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') second_entry =", "import random from corm import register_table, insert, sync_schema, select, where", "TestModel('one', 'two') three = TestModel('one', 'three') insert([one, two, three]) def", "class TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name item: str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name)", "autouse=True) def setup_case(request): def destroy_case(): from corm import annihilate_keyspace_tables, SESSIONS", "in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 3 # Add Column on", "register_table(TestCORMUUID) sync_schema() one = TestCORMUUID(uuid.uuid4()) insert([one]) for entry in select(TestCORMUUID):", "len(insert_later) > 20: insert(insert_later) insert_later = [] insert(insert_later) for idx,", "int created: datetime register_table(TestModelAlter) sync_schema() COL_CQL = f''' SELECT column_name,", "[] for idx in range(0, 100): values.append({ 'random_number': random.randint(0, MAX_INT),", "CORMBase class TestModel(CORMBase): __keyspace__ = 'mykeyspace' something: str other: str", "import Set class TestModelSet(CORMBase): __keyspace__ = 'mykeyspace' something: str other:", "class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime new_column:", "'second-to-last', 'last'}) four = TestModelSet('four', ['one', 'two', 'three', 'four']) insert([one,", "in range(0, 100): values.append({ 'random_number': random.randint(0, MAX_INT), 'created': datetime.utcnow() })", "assert entry.three != 'alpha' def test_ordered_by_pk_field(): import random from corm", "one: str two: str three: str register_table(TestOrderedByPkField) sync_schema() first_entry =", "from corm.models import CORMBase from corm.annotations import Set class TestModelSet(CORMBase):", "== OptionList.One for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])):", "keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is False", "datetime register_table(TestModelAlter) sync_schema() COL_CQL = f''' SELECT column_name, type FROM", "'created': datetime.utcnow() }) entry = TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if len(insert_later)", "CORMBase class OptionList(enum.Enum): One = 'one' Two = 'two' class", "import CORMBase class TestModel(CORMBase): __keyspace__ = 'mykeyspace' something: str other:", "datetime.utcnow(), 'one', 'one', 'alpha') gamma = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "class TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace' item: str created: datetime register_table(TestModelDatetime)", "insert([one, two, three]) def test_keyspace_api(): import hashlib import uuid from", "import random from corm import register_table, insert, sync_schema, select, obtain_session", "three: str register_table(TestNotOrderedByPkField) sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "assert keyspace_exists(keyspace_name) is False def test_float_api(): from corm import register_table,", "import register_table, insert, sync_schema, select from corm.models import CORMBase from", "assert idx in [0, 1] assert entry.score in [1, 2]", "entry.option == OptionList.Two def test_corm_uuid(): import uuid from corm import", "insert, sync_schema from corm.models import CORMBase from datetime import datetime", "from corm.datatypes import TableOrdering from datetime import datetime class TestOrderedByPkField(CORMBase):", "corm.models import CORMBase from datetime import datetime class TestModelBoolean(CORMBase): __keyspace__", "'second-to-last'}) three = TestModelSet('three', {'last', 'second-to-last', 'last'}) four = TestModelSet('four',", "def test_ordered_by_pk_field(): import random from corm import register_table, insert, sync_schema,", "= '<PASSWORD>' from corm import register_table, insert, sync_schema from corm.models", "CORMBase from datetime import datetime class TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace'", "class TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two', 'three']", "str three: str register_table(TestNotOrderedByPkField) sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "'test_corm_auth' register_table(TestCORMAuth) sync_schema() def test_corm_enum(): import enum from corm import", "One = 'one' Two = 'two' class TestCormEnum(CORMBase): __keyspace__ =", "entry in enumerate(select(TestModelFloat)): assert entry.input_one == data def test_boolean_api(): from", "str two: str class TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace' random_number: int", "if idx == 0: assert entry.three != 'alpha' def test_ordered_by_pk_field():", "TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False def test_float_api(): from", "import CORMBase from datetime import datetime class TestModelBoolean(CORMBase): __keyspace__ =", "idx == 0 assert entry.score == 4 assert entry.option ==", "register_table, insert, sync_schema, select from corm.models import CORMBase class OptionList(enum.Enum):", "two, three, four]) for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score',", "'one', 'one', 'delta') second_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "False class TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name item: str register_table(TestModelKeyspace) assert", "values.append({ 'random_number': random.randint(0, MAX_INT), 'created': datetime.utcnow() }) entry = TestModelSelect(values[-1]['random_number'],", "len(rows) == 4 def test_not_ordered_by_pk_field(): import random from corm import", "in enumerate(select(TestNotOrderedByPkField)): if idx == 0: assert entry.three != 'alpha'", "datetime.utcnow(), True) two = TestModelBoolean('two', datetime.utcnow(), False) insert([one, two]) def", "idx, entry in enumerate(select(TestCormEnum)): assert entry.option in OptionList.__members__.values() def test_corm_where():", "'beta' elif idx == 2: assert entry.three == 'delta' elif", "import datetime class TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace' item: str created:", "TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime register_table(TestModelAlter) sync_schema()", "in [0, 1] assert entry.score in [1, 2] assert entry.option", "import random from corm import register_table, insert, sync_schema, select from", "one = TestCORMUUID(uuid.uuid4()) insert([one]) for entry in select(TestCORMUUID): assert isinstance(entry.identity_test,", "in ['global']: continue session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api(): from", "request.addfinalizer(destroy_case) def test_initial_api(): from corm import register_table, insert, sync_schema from", "'last'}) four = TestModelSet('four', ['one', 'two', 'three', 'four']) insert([one, two,", "insert, sync_schema, select from corm.models import CORMBase from corm.annotations import", "99999 class TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime", "sync_schema() first = TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two) insert([first, second]) for", "register_table(TestModelSelect) sync_schema() insert_later = [] values = [] for idx", "SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name, session in SESSIONS.copy().items(): if keyspace_name in", "keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is", "in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])): assert idx == 0 assert", "def test_alter_table_api(): from corm import register_table, insert, sync_schema, select, obtain_session", "'one', 'one', 'alpha') gamma = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "in enumerate(select(TestCormEnum)): assert entry.option in OptionList.__members__.values() def test_corm_where(): import enum", "is False def test_float_api(): from corm import register_table, insert, sync_schema,", "three = TestModelSet('three', {'last', 'second-to-last', 'last'}) four = TestModelSet('four', ['one',", "delta = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') second_entry =", "from corm.models import CORMBase from corm.annotations import Set from datetime", "seem to have to start with Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest()", "sync_schema, select from corm.models import CORMBase from corm.annotations import Set", "__keyspace__ = 'mykeyspace' random_number: int created: datetime register_table(TestModelSelect) sync_schema() insert_later", "select, where from corm.models import CORMBase from datetime import datetime", "import register_table, insert, sync_schema from corm.models import CORMBase class TestCORMAuth(CORMBase):", "from corm import register_table, insert, sync_schema, \\ keyspace_exists, keyspace_destroy, keyspace_create", "'two') three = TestModel('one', 'three') insert([one, two, three]) def test_keyspace_api():", "sync_schema() def test_corm_enum(): import enum from corm import register_table, insert,", "'one' Two = 'two' class TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum' option:", "assert idx == 0 assert entry.score == 4 assert entry.option", "register_table, insert, sync_schema, select from corm.models import CORMBase from corm.annotations", "'alpha' elif idx == 1: assert entry.three == 'beta' elif", "destroy_case(): from corm import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name, session", "[cp(Operator.Equal, 'score', 1)])): assert idx == 0 assert entry.score ==", "{'first'}) two = TestModelSet('two', {'last', 'second-to-last'}) three = TestModelSet('three', {'last',", "have to start with Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name =", "start with Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}' assert", "class TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace' item: str created: datetime value:", "TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum' option: OptionList register_table(TestCormEnum) sync_schema() first =", "from corm.datatypes import CassandraKeyspaceStrategy from corm.models import CORMBase # Keyspaces", "False) insert([one, two]) def test_datetime_api(): from corm import register_table, insert,", "enumerate(select(TestNotOrderedByPkField)): if idx == 0: assert entry.three != 'alpha' def", "TestModelBoolean('two', datetime.utcnow(), False) insert([one, two]) def test_datetime_api(): from corm import", "4 assert entry.option == OptionList.Two for idx, entry in enumerate(where(TestCORMWhere,", "assert keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False class", "import CORMBase class TestCORMAuth(CORMBase): one: str __keyspace__ = 'test_corm_auth' register_table(TestCORMAuth)", "str register_table(TestModel) sync_schema() one = TestModel('one', 'two') two = TestModel('one',", "CORMBase from corm.datatypes import TableOrdering from datetime import datetime class", "from datetime import datetime class TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__", "= 'mykeyspace' input_one: float register_table(TestModelFloat) sync_schema() data = 324.593998934 one", "data def test_boolean_api(): from corm import register_table, insert, sync_schema from", "MAX_INT = 1000 class TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace' random_number: int", "random from corm import register_table, insert, sync_schema, select, obtain_session from", "= TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') gamma = TestOrderedByPkField(random.randint(0,", "'one', 'one', 'beta') gamma = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "entry.score == 4 assert entry.option == OptionList.Two for idx, entry", "sync_schema() one = TestModelSet('one', {'first'}) two = TestModelSet('two', {'last', 'second-to-last'})", "= 'one' Two = 'two' class TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum'", "== OptionList.One for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])):", "keyspace_name = f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert", "__keyspace__ = 'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema() one = TestCORMUUID(uuid.uuid4())", "== values[idx]['random_number'] # assert entry.created == values[idx]['created'] assert idx >", "class TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema() one", "in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 4 def test_not_ordered_by_pk_field(): import random", "for idx in range(0, 100): values.append({ 'random_number': random.randint(0, MAX_INT), 'created':", "where from corm.models import CORMBase from datetime import datetime MAX_INT", "datetime.utcnow(), 'one', 'one', 'delta') second_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "test_datetime_api(): from corm import register_table, insert, sync_schema from corm.models import", "elif idx == 3: assert entry.three == 'gamma' def test_corm_auth():", "test_corm_enum(): import enum from corm import register_table, insert, sync_schema, select", "import TableOrdering from datetime import datetime class TestOrderedByPkField(CORMBase): __keyspace__ =", "324.593998934 one = TestModelFloat(data) insert([one]) for idx, entry in enumerate(select(TestModelFloat)):", "TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime one: str", "import register_table, insert, sync_schema, select from corm.models import CORMBase class", "os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from corm import register_table,", "entry.option in OptionList.__members__.values() def test_corm_where(): import enum from corm import", "two = TestCORMWhere(OptionList.One, 2) three = TestCORMWhere(OptionList.Two, 3) four =", "2] assert entry.option == OptionList.One for idx, entry in enumerate(where(TestCORMWhere,", "'two', 'three'] __ordered_by_primary_keys__ = TableOrdering.DESC random_number: int created: datetime one:", "idx in [0, 1] assert entry.score in [3, 4] assert", "del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api(): from corm import register_table, insert,", "CORMBase from datetime import datetime # Create Table or Delete", "sync_schema() one = TestModel('one', 'two') two = TestModel('one', 'two') three", "sync_schema from corm.models import CORMBase class TestModel(CORMBase): __keyspace__ = 'mykeyspace'", "'two' class TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum' option: OptionList register_table(TestCormEnum) sync_schema()", "TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name item: str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is", "SESSIONS.copy().items(): if keyspace_name in ['global']: continue session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case)", "'gamma') delta = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') insert([first_entry,", "'mykeyspace' random_number: int created: datetime one: str two: str class", "import CORMBase from corm.annotations import Set class TestModelSet(CORMBase): __keyspace__ =", "99999), datetime.utcnow(), 'one', 'one', 'alpha') gamma = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "CORMBase # Keyspaces seem to have to start with Alpha-Letters", "Order is not consistent # assert entry.random_number == values[idx]['random_number'] #", "in OptionList.__members__.values() def test_corm_where(): import enum from corm import register_table,", "in [0, 1] assert entry.score in [3, 4] assert entry.option", "'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] __ordered_by_primary_keys__ = TableOrdering.DESC random_number:", "for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])): assert idx", "TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] __ordered_by_primary_keys__", "where, cp, Operator from corm.models import CORMBase class OptionList(enum.Enum): One", "created: datetime register_table(TestModelAlter) sync_schema() COL_CQL = f''' SELECT column_name, type", "option: OptionList register_table(TestCormEnum) sync_schema() first = TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two)", "Table or Delete Column on existing Table class TestModelAlter(CORMBase): __keyspace__", "sync_schema, select, obtain_session from corm.models import CORMBase from corm.datatypes import", "corm.models import CORMBase class TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace' identity_test: uuid.UUID", "idx, entry in enumerate(select(TestOrderedByPkField)): if idx == 0: assert entry.three", "class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime register_table(TestModelAlter)", "from corm.annotations import Set class TestModelSet(CORMBase): __keyspace__ = 'mykeyspace' something:", "CassandraKeyspaceStrategy from corm.models import CORMBase # Keyspaces seem to have", "TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two) insert([first, second]) for idx, entry in", "= TestModelSet('three', {'last', 'second-to-last', 'last'}) four = TestModelSet('four', ['one', 'two',", "one = TestModel('one', 'two') two = TestModel('one', 'two') three =", "from corm.models import CORMBase # Keyspaces seem to have to", "len(rows) == 3 # Add Column on existing Table class", "[cp(Operator.Equal, 'option', OptionList.One)])): assert idx in [0, 1] assert entry.score", "keyspace_name = '{TestModelAlter._corm_details.keyspace}' ''' rows = [(row.column_name, row.type) for row", "class TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum' option: OptionList register_table(TestCormEnum) sync_schema() first", "three, four]) def test_select_api(): import random from corm import register_table,", "register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def test_alter_table_api(): from corm import register_table, insert,", "str source: TestModelSelectSource # TODO: Build UserType integration # register_table(TestModelSelectSource)", "one: str __keyspace__ = 'test_corm_auth' register_table(TestCORMAuth) sync_schema() def test_corm_enum(): import", "datetime # Create Table or Delete Column on existing Table", "one = TestModelBoolean('one', datetime.utcnow(), True) two = TestModelBoolean('two', datetime.utcnow(), False)", "import register_table, insert, sync_schema from corm.models import CORMBase from datetime", "datetime.utcnow(), 'one', 'one', 'beta') gamma = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "in enumerate(select(TestOrderedByPkField)): if idx == 0: assert entry.three == 'alpha'", "idx == 0 assert entry.score == 1 assert entry.option ==", "= TestModelSet('two', {'last', 'second-to-last'}) three = TestModelSet('three', {'last', 'second-to-last', 'last'})", "entry in enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry, TestModelSelect) # Order is", "TestModel('one', 'three') insert([one, two, three]) def test_keyspace_api(): import hashlib import", "4 def test_not_ordered_by_pk_field(): import random from corm import register_table, insert,", "insert([first_entry, second_entry, delta, gamma]) for idx, entry in enumerate(select(TestOrderedByPkField)): if", "assert entry.three == 'alpha' elif idx == 1: assert entry.three", "= [] insert(insert_later) for idx, entry in enumerate(select(TestModelSelect, fetch_size=100)): assert", "datetime.utcnow(), 'one', 'one', 'gamma') delta = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "corm.models import CORMBase class TestModel(CORMBase): __keyspace__ = 'mykeyspace' something: str", "idx > 0 def test_select_where_api(): import random from corm import", "'one' Two = 'two' class TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where' option:", "== 0: assert entry.three != 'alpha' def test_ordered_by_pk_field(): import random", "1] assert entry.score in [3, 4] assert entry.option == OptionList.Two", "for idx, entry in enumerate(select(TestOrderedByPkField)): if idx == 0: assert", "in enumerate(select(TestModelFloat)): assert entry.input_one == data def test_boolean_api(): from corm", "second_entry]) for idx, entry in enumerate(select(TestNotOrderedByPkField)): if idx == 0:", "four = TestCORMWhere(OptionList.Two, 4) insert([one, two, three, four]) for idx,", "import CORMBase # Keyspaces seem to have to start with", "datetime.utcnow()) two = TestModelDatetime('two', datetime.utcnow()) insert([one, two]) def test_set_api(): from", "assert len(rows) == 4 def test_not_ordered_by_pk_field(): import random from corm", "import CassandraKeyspaceStrategy from corm.models import CORMBase # Keyspaces seem to", "obtain_session from corm.models import CORMBase from datetime import datetime #", "register_table(TestModelSet) sync_schema() one = TestModelSet('one', {'first'}) two = TestModelSet('two', {'last',", "assert idx == 0 assert entry.score == 1 assert entry.option", "sync_schema from corm.models import CORMBase from corm.annotations import Set class", "int created: datetime one: str two: str three: str register_table(TestOrderedByPkField)", "enumerate(select(TestModelFloat)): assert entry.input_one == data def test_boolean_api(): from corm import", "or Delete Column on existing Table class TestModelAlter(CORMBase): __keyspace__ =", "sync_schema() one = TestModelDatetime('one', datetime.utcnow()) two = TestModelDatetime('two', datetime.utcnow()) insert([one,", "str register_table(TestOrderedByPkField) sync_schema() first_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api(): from corm import register_table,", "TestModelBoolean('one', datetime.utcnow(), True) two = TestModelBoolean('two', datetime.utcnow(), False) insert([one, two])", "insert, sync_schema from corm.models import CORMBase from corm.annotations import Set", "class TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime one:", "TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') second_entry = TestOrderedByPkField(random.randint(0, 99999),", "= 'utf-8' @pytest.fixture(scope='function', autouse=True) def setup_case(request): def destroy_case(): from corm", "Keyspaces seem to have to start with Alpha-Letters keyspace_name =", "import CORMBase from datetime import datetime class TestNotOrderedByPkField(CORMBase): __keyspace__ =", "entry.score in [1, 2] assert entry.option == OptionList.One for idx,", "'option', OptionList.Two)])): assert idx in [0, 1] assert entry.score in", "keyspace_name item: str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is False sync_schema() assert", "= [] for idx in range(0, 100): values.append({ 'random_number': random.randint(0,", "fetch_size=100)): assert isinstance(entry, TestModelSelect) # Order is not consistent #", "3: assert entry.three == 'gamma' def test_corm_auth(): import os os.environ['CLUSTER_PORT']", "annihilate_keyspace_tables('mykeyspace') for keyspace_name, session in SESSIONS.copy().items(): if keyspace_name in ['global']:", "TestModel(CORMBase): __keyspace__ = 'mykeyspace' something: str other: str register_table(TestModel) sync_schema()", "datetime import datetime # Create Table or Delete Column on", "'beta') gamma = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta", "# Order is not consistent # assert entry.random_number == values[idx]['random_number']", "99999), datetime.utcnow(), 'one', 'one', 'beta') second_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "insert_later.append(entry) if len(insert_later) > 20: insert(insert_later) insert_later = [] insert(insert_later)", "three = TestModel('one', 'three') insert([one, two, three]) def test_keyspace_api(): import", "import os os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD'] =", "corm.models import CORMBase class TestCORMAuth(CORMBase): one: str __keyspace__ = 'test_corm_auth'", "class TestModelSet(CORMBase): __keyspace__ = 'mykeyspace' something: str other: Set register_table(TestModelSet)", "TestModelSet('three', {'last', 'second-to-last', 'last'}) four = TestModelSet('four', ['one', 'two', 'three',", "from corm.models import CORMBase class TestCORMAuth(CORMBase): one: str __keyspace__ =", "random_number: int created: datetime new_column: str register_table(TestModelAlter) sync_schema() rows =", "import CORMBase class OptionList(enum.Enum): One = 'one' Two = 'two'", "for idx, entry in enumerate(select(TestNotOrderedByPkField)): if idx == 0: assert", "__primary_keys__ = ['one', 'two', 'three'] __ordered_by_primary_keys__ = TableOrdering.DESC random_number: int", "str two: str three: str register_table(TestOrderedByPkField) sync_schema() first_entry = TestOrderedByPkField(random.randint(0,", "= TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') second_entry = TestOrderedByPkField(random.randint(0,", "== 'alpha' elif idx == 1: assert entry.three == 'beta'", "'one', 'gamma') delta = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta')", "import CORMBase class TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace' input_one: float register_table(TestModelFloat)", "'two') two = TestModel('one', 'two') three = TestModel('one', 'three') insert([one,", "import datetime # Create Table or Delete Column on existing", "TestCORMAuth(CORMBase): one: str __keyspace__ = 'test_corm_auth' register_table(TestCORMAuth) sync_schema() def test_corm_enum():", "enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])): assert idx in [0, 1] assert", "from corm.models import CORMBase from datetime import datetime # Create", "[(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 4", "delta, second_entry]) for idx, entry in enumerate(select(TestNotOrderedByPkField)): if idx ==", "assert entry.option == OptionList.Two def test_corm_uuid(): import uuid from corm", "register_table(TestModelBoolean) sync_schema() one = TestModelBoolean('one', datetime.utcnow(), True) two = TestModelBoolean('two',", "import CORMBase from datetime import datetime class TestModelDatetime(CORMBase): __keyspace__ =", "created: datetime one: str two: str class TestModelSelectPivot(CORMBase): __keyspace__ =", "one = TestModelSet('one', {'first'}) two = TestModelSet('two', {'last', 'second-to-last'}) three", "= [] values = [] for idx in range(0, 100):", "assert entry.option == OptionList.One for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal,", "entry in enumerate(select(TestOrderedByPkField)): if idx == 0: assert entry.three ==", "TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace' item: str created: datetime register_table(TestModelDatetime) sync_schema()", "register_table(TestOrderedByPkField) sync_schema() first_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta')", "idx == 0: assert entry.three == 'alpha' elif idx ==", "corm import register_table, insert, sync_schema, select, where, cp, Operator from", "insert, sync_schema, select, where, cp, Operator from corm.models import CORMBase", "0 assert entry.score == 4 assert entry.option == OptionList.Two for", "test_ordered_by_pk_field(): import random from corm import register_table, insert, sync_schema, select,", "import register_table, insert, sync_schema from corm.models import CORMBase from corm.annotations", "enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry, TestModelSelect) # Order is not consistent", "datetime.utcnow(), 'one', 'one', 'alpha') insert([first_entry, gamma, delta, second_entry]) for idx,", "'<PASSWORD>' from corm import register_table, insert, sync_schema from corm.models import", "keyspace_destroy, keyspace_create from corm.datatypes import CassandraKeyspaceStrategy from corm.models import CORMBase", "select, where, cp, Operator from corm.models import CORMBase class OptionList(enum.Enum):", "from datetime import datetime class TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace' item:", "in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])): assert idx in [0, 1]", "@pytest.fixture(scope='function', autouse=True) def setup_case(request): def destroy_case(): from corm import annihilate_keyspace_tables,", "register_table, insert, sync_schema, select, obtain_session from corm.models import CORMBase from", "[3, 4] assert entry.option == OptionList.Two def test_corm_uuid(): import uuid", "str register_table(TestModelAlter) sync_schema() rows = [(row.column_name, row.type) for row in", "TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime new_column: str", "pytest ENCODING = 'utf-8' @pytest.fixture(scope='function', autouse=True) def setup_case(request): def destroy_case():", "def test_corm_uuid(): import uuid from corm import register_table, insert, sync_schema,", "import register_table, insert, sync_schema, select, where from corm.models import CORMBase", "datetime.utcnow()) insert([one, two]) def test_set_api(): from corm import register_table, insert,", "four = TestModelSet('four', ['one', 'two', 'three', 'four']) insert([one, two, three,", "idx == 2: assert entry.three == 'delta' elif idx ==", "select, obtain_session from corm.models import CORMBase from corm.datatypes import TableOrdering", "== OptionList.Two def test_corm_uuid(): import uuid from corm import register_table,", "'three'] random_number: int created: datetime one: str two: str three:", "row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 3 #", "sync_schema, select, where from corm.models import CORMBase from datetime import", "test_keyspace_api(): import hashlib import uuid from corm import register_table, insert,", "== 1: assert entry.three == 'beta' elif idx == 2:", "keyspace_name, session in SESSIONS.copy().items(): if keyspace_name in ['global']: continue session.shutdown()", "not consistent # assert entry.random_number == values[idx]['random_number'] # assert entry.created", "'9043' os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from corm import", "TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema() one =", "created: datetime register_table(TestModelSelect) sync_schema() insert_later = [] values = []", "second_entry, delta, gamma]) for idx, entry in enumerate(select(TestOrderedByPkField)): if idx", "== 3: assert entry.three == 'gamma' def test_corm_auth(): import os", "insert([one, two]) def test_set_api(): from corm import register_table, insert, sync_schema", "register_table(TestModelDatetime) sync_schema() one = TestModelDatetime('one', datetime.utcnow()) two = TestModelDatetime('two', datetime.utcnow())", "3 # Add Column on existing Table class TestModelAlter(CORMBase): __keyspace__", "CORMBase from datetime import datetime class TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace'", "first = TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two) insert([first, second]) for idx,", "datetime import datetime MAX_INT = 99999 class TestModelSelectSource(CORMBase): __keyspace__ =", "= TestCORMWhere(OptionList.Two, 4) insert([one, two, three, four]) for idx, entry", "corm import register_table, insert, sync_schema from corm.models import CORMBase class", "sync_schema, select from corm.models import CORMBase class OptionList(enum.Enum): One =", "= TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta = TestNotOrderedByPkField(random.randint(0,", "!= 'alpha' def test_ordered_by_pk_field(): import random from corm import register_table,", "for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])): assert idx", "__keyspace__ = 'mykeyspace' random_number: int created: datetime register_table(TestModelAlter) sync_schema() COL_CQL", "corm import register_table, insert, sync_schema, \\ keyspace_exists, keyspace_destroy, keyspace_create from", "if len(insert_later) > 20: insert(insert_later) insert_later = [] insert(insert_later) for", "random_number: int created: datetime register_table(TestModelAlter) sync_schema() COL_CQL = f''' SELECT", "= 'one' Two = 'two' class TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where'", "def test_boolean_api(): from corm import register_table, insert, sync_schema from corm.models", "4] assert entry.option == OptionList.Two def test_corm_uuid(): import uuid from", "import datetime class TestModelBoolean(CORMBase): __keyspace__ = 'mykeyspace' item: str created:", "__keyspace__ = 'test_corm_auth' register_table(TestCORMAuth) sync_schema() def test_corm_enum(): import enum from", "assert isinstance(entry, TestModelSelect) # Order is not consistent # assert", "from corm.models import CORMBase from datetime import datetime class TestModelDatetime(CORMBase):", "str class TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime", "corm.models import CORMBase from datetime import datetime class TestNotOrderedByPkField(CORMBase): __keyspace__", "test_set_api(): from corm import register_table, insert, sync_schema from corm.models import", "entry.three != 'alpha' def test_ordered_by_pk_field(): import random from corm import", "4)])): assert idx == 0 assert entry.score == 4 assert", "'mykeyspace' random_number: int created: datetime register_table(TestModelSelect) sync_schema() insert_later = []", "str two: str source: TestModelSelectSource # TODO: Build UserType integration", "class TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two', 'three']", "is False sync_schema() assert keyspace_exists(keyspace_name) is True one = TestModelKeyspace('one')", "assert keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is True", "corm.models import CORMBase from datetime import datetime # Create Table", "bool register_table(TestModelBoolean) sync_schema() one = TestModelBoolean('one', datetime.utcnow(), True) two =", "if keyspace_name in ['global']: continue session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def", "register_table(TestModel) sync_schema() one = TestModel('one', 'two') two = TestModel('one', 'two')", "register_table, insert, sync_schema from corm.models import CORMBase from corm.annotations import", "two = TestModelDatetime('two', datetime.utcnow()) insert([one, two]) def test_set_api(): from corm", "'one', 'one', 'gamma') delta = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema() one = TestCORMUUID(uuid.uuid4()) insert([one]) for entry", "entry.score in [3, 4] assert entry.option == OptionList.Two def test_corm_uuid():", "OptionList.Two)])): assert idx in [0, 1] assert entry.score in [3,", "from corm.models import CORMBase from corm.datatypes import TableOrdering from datetime", "assert idx in [0, 1] assert entry.score in [3, 4]", "consistent # assert entry.random_number == values[idx]['random_number'] # assert entry.created ==", "= keyspace_name item: str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is False sync_schema()", "[cp(Operator.Equal, 'score', 4)])): assert idx == 0 assert entry.score ==", "99999), datetime.utcnow(), 'one', 'one', 'delta') insert([first_entry, second_entry, delta, gamma]) for", "'one', 'alpha') insert([first_entry, gamma, delta, second_entry]) for idx, entry in", "three, four]) for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])):", "corm.annotations import Set from datetime import datetime MAX_INT = 1000", "three: str register_table(TestOrderedByPkField) sync_schema() first_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "'delta' elif idx == 3: assert entry.three == 'gamma' def", "CORMBase class TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema()", "= 324.593998934 one = TestModelFloat(data) insert([one]) for idx, entry in", "def test_datetime_api(): from corm import register_table, insert, sync_schema from corm.models", "1] assert entry.score in [1, 2] assert entry.option == OptionList.One", "'three'] __ordered_by_primary_keys__ = TableOrdering.DESC random_number: int created: datetime one: str", "__keyspace__ = keyspace_name item: str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is False", "1000 class TestModelSelect(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime", "register_table(TestCormEnum) sync_schema() first = TestCormEnum(OptionList.One) second = TestCormEnum(OptionList.Two) insert([first, second])", "'mykeyspace' something: str other: Set register_table(TestModelSet) sync_schema() one = TestModelSet('one',", "= [(row.column_name, row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) ==", "datetime value: bool register_table(TestModelBoolean) sync_schema() one = TestModelBoolean('one', datetime.utcnow(), True)", "one: str two: str three: str register_table(TestNotOrderedByPkField) sync_schema() first_entry =", "gamma = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta =", "True one = TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False", "= TestModel('one', 'two') two = TestModel('one', 'two') three = TestModel('one',", "from corm import register_table, insert, sync_schema, select from corm.models import", "row.type) for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 4 def", "f''' SELECT column_name, type FROM system_schema.columns WHERE table_name = '{TestModelAlter._corm_details.table_name}'", "gamma]) for idx, entry in enumerate(select(TestOrderedByPkField)): if idx == 0:", "system_schema.columns WHERE table_name = '{TestModelAlter._corm_details.table_name}' AND keyspace_name = '{TestModelAlter._corm_details.keyspace}' '''", "in enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry, TestModelSelect) # Order is not", "entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])): assert idx == 0", "keyspace_create from corm.datatypes import CassandraKeyspaceStrategy from corm.models import CORMBase #", "TestModelSet('one', {'first'}) two = TestModelSet('two', {'last', 'second-to-last'}) three = TestModelSet('three',", "idx, entry in enumerate(select(TestNotOrderedByPkField)): if idx == 0: assert entry.three", "insert([one, two, three, four]) for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal,", "second = TestCormEnum(OptionList.Two) insert([first, second]) for idx, entry in enumerate(select(TestCormEnum)):", "AND keyspace_name = '{TestModelAlter._corm_details.keyspace}' ''' rows = [(row.column_name, row.type) for", "from datetime import datetime # Create Table or Delete Column", "99999), datetime.utcnow(), 'one', 'one', 'beta') gamma = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False class TestModelKeyspace(CORMBase): __keyspace__ =", "str __keyspace__ = 'test_corm_auth' register_table(TestCORMAuth) sync_schema() def test_corm_enum(): import enum", "= TestCORMUUID(uuid.uuid4()) insert([one]) for entry in select(TestCORMUUID): assert isinstance(entry.identity_test, uuid.UUID)", "= TableOrdering.DESC random_number: int created: datetime one: str two: str", "insert([first, second]) for idx, entry in enumerate(select(TestCormEnum)): assert entry.option in", "import CORMBase from corm.datatypes import TableOrdering from datetime import datetime", "values[-1]['created']) insert_later.append(entry) if len(insert_later) > 20: insert(insert_later) insert_later = []", "keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False class TestModelKeyspace(CORMBase):", "['one', 'two', 'three'] random_number: int created: datetime one: str two:", "corm.models import CORMBase from corm.datatypes import TableOrdering from datetime import", "keyspace_exists, keyspace_destroy, keyspace_create from corm.datatypes import CassandraKeyspaceStrategy from corm.models import", "__keyspace__ = 'mykeyspace' item: str created: datetime register_table(TestModelDatetime) sync_schema() one", "register_table, insert, sync_schema, select, where from corm.models import CORMBase from", "CORMBase from corm.annotations import Set from datetime import datetime MAX_INT", "row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 4 def test_not_ordered_by_pk_field(): import", "'utf-8' @pytest.fixture(scope='function', autouse=True) def setup_case(request): def destroy_case(): from corm import", "two]) def test_set_api(): from corm import register_table, insert, sync_schema from", "str register_table(TestModelKeyspace) assert keyspace_exists(keyspace_name) is False sync_schema() assert keyspace_exists(keyspace_name) is", "import datetime class TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one',", "OptionList.One for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])): assert", "insert([one]) for idx, entry in enumerate(select(TestModelFloat)): assert entry.input_one == data", "assert entry.random_number == values[idx]['random_number'] # assert entry.created == values[idx]['created'] assert", "keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name)", "enum from corm import register_table, insert, sync_schema, select, where, cp,", "= 'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID) sync_schema() one = TestCORMUUID(uuid.uuid4()) insert([one])", "MAX_INT = 99999 class TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace' random_number: int", "obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 3 # Add Column on existing", "# register_table(TestModelSelectPivot) def test_alter_table_api(): from corm import register_table, insert, sync_schema,", "table_name = '{TestModelAlter._corm_details.table_name}' AND keyspace_name = '{TestModelAlter._corm_details.keyspace}' ''' rows =", "delta, gamma]) for idx, entry in enumerate(select(TestOrderedByPkField)): if idx ==", "corm import register_table, insert, sync_schema, select, obtain_session from corm.models import", "two]) def test_datetime_api(): from corm import register_table, insert, sync_schema from", "entry.score == 1 assert entry.option == OptionList.One for idx, entry", "insert(insert_later) for idx, entry in enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry, TestModelSelect)", "datetime one: str two: str source: TestModelSelectSource # TODO: Build", "insert_later = [] values = [] for idx in range(0,", "= TestCormEnum(OptionList.Two) insert([first, second]) for idx, entry in enumerate(select(TestCormEnum)): assert", "entry.option == OptionList.Two for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score',", "import Set from datetime import datetime MAX_INT = 1000 class", "entry.three == 'alpha' elif idx == 1: assert entry.three ==", "CORMBase from corm.annotations import Set class TestModelSet(CORMBase): __keyspace__ = 'mykeyspace'", "== 'gamma' def test_corm_auth(): import os os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME']", "three = TestCORMWhere(OptionList.Two, 3) four = TestCORMWhere(OptionList.Two, 4) insert([one, two,", "1 assert entry.option == OptionList.One for idx, entry in enumerate(where(TestCORMWhere,", "[0, 1] assert entry.score in [3, 4] assert entry.option ==", "item: str created: datetime value: bool register_table(TestModelBoolean) sync_schema() one =", "= 'mykeyspace' item: str created: datetime value: bool register_table(TestModelBoolean) sync_schema()", "TestModelSelectSource # TODO: Build UserType integration # register_table(TestModelSelectSource) # register_table(TestModelSelectPivot)", "__primary_keys__ = ['one', 'two', 'three'] random_number: int created: datetime one:", "entry in enumerate(select(TestNotOrderedByPkField)): if idx == 0: assert entry.three !=", "os os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>'", "class OptionList(enum.Enum): One = 'one' Two = 'two' class TestCORMWhere(CORMBase):", "enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])): assert idx in [0, 1] assert", "keyspace_exists(keyspace_name) is False class TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name item: str", "entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 4)])): assert idx == 0", "new_column: str register_table(TestModelAlter) sync_schema() rows = [(row.column_name, row.type) for row", "keyspace_exists(keyspace_name) is False def test_float_api(): from corm import register_table, insert,", "import uuid from corm import register_table, insert, sync_schema, select from", "created: datetime new_column: str register_table(TestModelAlter) sync_schema() rows = [(row.column_name, row.type)", "Create Table or Delete Column on existing Table class TestModelAlter(CORMBase):", "datetime import datetime class TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ =", "TableOrdering from datetime import datetime class TestOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace'", "entry.random_number == values[idx]['random_number'] # assert entry.created == values[idx]['created'] assert idx", "register_table(TestNotOrderedByPkField) sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta')", "from corm.models import CORMBase class TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace' identity_test:", "'mykeyspace' __primary_keys__ = ['one', 'two', 'three'] random_number: int created: datetime", "OptionList score: int register_table(TestCORMWhere) sync_schema() one = TestCORMWhere(OptionList.One, 1) two", "select from corm.models import CORMBase from corm.annotations import Set from", "datetime one: str two: str class TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace'", "insert, sync_schema, select, obtain_session from corm.models import CORMBase from corm.datatypes", "score: int register_table(TestCORMWhere) sync_schema() one = TestCORMWhere(OptionList.One, 1) two =", "corm.datatypes import TableOrdering from datetime import datetime class TestOrderedByPkField(CORMBase): __keyspace__", "sync_schema() one = TestCORMWhere(OptionList.One, 1) two = TestCORMWhere(OptionList.One, 2) three", "str two: str three: str register_table(TestNotOrderedByPkField) sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0,", "__keyspace__ = 'mykeyspace' random_number: int created: datetime one: str two:", "= ['one', 'two', 'three'] __ordered_by_primary_keys__ = TableOrdering.DESC random_number: int created:", "datetime MAX_INT = 99999 class TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace' random_number:", "TestCORMWhere(OptionList.Two, 4) insert([one, two, three, four]) for idx, entry in", "str created: datetime register_table(TestModelDatetime) sync_schema() one = TestModelDatetime('one', datetime.utcnow()) two", "assert entry.option == OptionList.Two for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal,", "datetime import datetime MAX_INT = 1000 class TestModelSelect(CORMBase): __keyspace__ =", "OptionList(enum.Enum): One = 'one' Two = 'two' class TestCORMWhere(CORMBase): __keyspace__", "two, three, four]) def test_select_api(): import random from corm import", "= TestCORMWhere(OptionList.One, 2) three = TestCORMWhere(OptionList.Two, 3) four = TestCORMWhere(OptionList.Two,", "insert([one]) keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name) is False def test_float_api(): from corm", "in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'score', 1)])): assert idx == 0 assert", "is False class TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name item: str register_table(TestModelKeyspace)", "register_table(TestModelAlter) sync_schema() COL_CQL = f''' SELECT column_name, type FROM system_schema.columns", "is not consistent # assert entry.random_number == values[idx]['random_number'] # assert", "= ['one', 'two', 'three'] random_number: int created: datetime one: str", "Column on existing Table class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number:", "enum from corm import register_table, insert, sync_schema, select from corm.models", "corm import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name, session in SESSIONS.copy().items():", "CORMBase class TestModelFloat(CORMBase): __keyspace__ = 'mykeyspace' input_one: float register_table(TestModelFloat) sync_schema()", "from corm import register_table, insert, sync_schema, select, obtain_session from corm.models", "corm.models import CORMBase # Keyspaces seem to have to start", "{'last', 'second-to-last', 'last'}) four = TestModelSet('four', ['one', 'two', 'three', 'four'])", "TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'beta') gamma = TestNotOrderedByPkField(random.randint(0, 99999),", "False sync_schema() assert keyspace_exists(keyspace_name) is True one = TestModelKeyspace('one') insert([one])", "TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if len(insert_later) > 20: insert(insert_later) insert_later =", "Build UserType integration # register_table(TestModelSelectSource) # register_table(TestModelSelectPivot) def test_alter_table_api(): from", "99999), datetime.utcnow(), 'one', 'one', 'gamma') delta = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "= 'two' class TestCormEnum(CORMBase): __keyspace__ = 'test_corm_enum' option: OptionList register_table(TestCormEnum)", "option: OptionList score: int register_table(TestCORMWhere) sync_schema() one = TestCORMWhere(OptionList.One, 1)", "def test_set_api(): from corm import register_table, insert, sync_schema from corm.models", "source: TestModelSelectSource # TODO: Build UserType integration # register_table(TestModelSelectSource) #", "COL_CQL = f''' SELECT column_name, type FROM system_schema.columns WHERE table_name", "= '{TestModelAlter._corm_details.table_name}' AND keyspace_name = '{TestModelAlter._corm_details.keyspace}' ''' rows = [(row.column_name,", "enumerate(select(TestCormEnum)): assert entry.option in OptionList.__members__.values() def test_corm_where(): import enum from", "'one', 'beta') gamma = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma')", "insert([one, two]) def test_datetime_api(): from corm import register_table, insert, sync_schema", "test_float_api(): from corm import register_table, insert, sync_schema, select from corm.models", "'{TestModelAlter._corm_details.table_name}' AND keyspace_name = '{TestModelAlter._corm_details.keyspace}' ''' rows = [(row.column_name, row.type)", "= TestModelBoolean('one', datetime.utcnow(), True) two = TestModelBoolean('two', datetime.utcnow(), False) insert([one,", "> 0 def test_select_where_api(): import random from corm import register_table,", "one = TestCORMWhere(OptionList.One, 1) two = TestCORMWhere(OptionList.One, 2) three =", "import CORMBase class TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace' identity_test: uuid.UUID register_table(TestCORMUUID)", "gamma, delta, second_entry]) for idx, entry in enumerate(select(TestNotOrderedByPkField)): if idx", "register_table(TestCORMAuth) sync_schema() def test_corm_enum(): import enum from corm import register_table,", "in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])): assert idx in [0, 1]", "import uuid from corm import register_table, insert, sync_schema, \\ keyspace_exists,", "data = 324.593998934 one = TestModelFloat(data) insert([one]) for idx, entry", "TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') gamma = TestOrderedByPkField(random.randint(0, 99999),", "entry in enumerate(select(TestCormEnum)): assert entry.option in OptionList.__members__.values() def test_corm_where(): import", "test_alter_table_api(): from corm import register_table, insert, sync_schema, select, obtain_session from", "sync_schema, select, where, cp, Operator from corm.models import CORMBase class", "3) four = TestCORMWhere(OptionList.Two, 4) insert([one, two, three, four]) for", "def test_select_where_api(): import random from corm import register_table, insert, sync_schema,", "idx == 3: assert entry.three == 'gamma' def test_corm_auth(): import", "== 4 def test_not_ordered_by_pk_field(): import random from corm import register_table,", "register_table, insert, sync_schema from corm.models import CORMBase from datetime import", "[] insert(insert_later) for idx, entry in enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry,", "datetime class TestNotOrderedByPkField(CORMBase): __keyspace__ = 'mykeyspace' __primary_keys__ = ['one', 'two',", "assert entry.input_one == data def test_boolean_api(): from corm import register_table,", "= TestModel('one', 'three') insert([one, two, three]) def test_keyspace_api(): import hashlib", "three]) def test_keyspace_api(): import hashlib import uuid from corm import", "test_boolean_api(): from corm import register_table, insert, sync_schema from corm.models import", "# assert entry.random_number == values[idx]['random_number'] # assert entry.created == values[idx]['created']", "'beta') second_entry = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') gamma", "def test_corm_where(): import enum from corm import register_table, insert, sync_schema,", "something: str other: Set register_table(TestModelSet) sync_schema() one = TestModelSet('one', {'first'})", "= f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name)", "__keyspace__ = 'mykeyspace' random_number: int created: datetime new_column: str register_table(TestModelAlter)", "for idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.Two)])): assert idx", "== data def test_boolean_api(): from corm import register_table, insert, sync_schema", "register_table, insert, sync_schema from corm.models import CORMBase class TestModel(CORMBase): __keyspace__", "test_initial_api(): from corm import register_table, insert, sync_schema from corm.models import", "entry.three == 'gamma' def test_corm_auth(): import os os.environ['CLUSTER_PORT'] = '9043'", "idx in range(0, 100): values.append({ 'random_number': random.randint(0, MAX_INT), 'created': datetime.utcnow()", "'one', 'one', 'alpha') insert([first_entry, gamma, delta, second_entry]) for idx, entry", "test_not_ordered_by_pk_field(): import random from corm import register_table, insert, sync_schema, select,", "from corm import register_table, insert, sync_schema, select, where, cp, Operator", "TestModelSelect) # Order is not consistent # assert entry.random_number ==", "= hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is False keyspace_create(keyspace_name,", "os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from", "'delta') second_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'alpha') insert([first_entry,", "datetime.utcnow(), 'one', 'one', 'gamma') delta = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one',", "one = TestModelFloat(data) insert([one]) for idx, entry in enumerate(select(TestModelFloat)): assert", "test_corm_uuid(): import uuid from corm import register_table, insert, sync_schema, select", "idx, entry in enumerate(where(TestCORMWhere, [cp(Operator.Equal, 'option', OptionList.One)])): assert idx in", "99999), datetime.utcnow(), 'one', 'one', 'gamma') delta = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(),", "== 'beta' elif idx == 2: assert entry.three == 'delta'", "from corm import annihilate_keyspace_tables, SESSIONS annihilate_keyspace_tables('mykeyspace') for keyspace_name, session in", "== 2: assert entry.three == 'delta' elif idx == 3:", "assert entry.score == 1 assert entry.option == OptionList.One for idx,", "for row in obtain_session('mykeyspace').execute(COL_CQL)] assert len(rows) == 4 def test_not_ordered_by_pk_field():", "{'last', 'second-to-last'}) three = TestModelSet('three', {'last', 'second-to-last', 'last'}) four =", "test_corm_where(): import enum from corm import register_table, insert, sync_schema, select,", "insert, sync_schema from corm.models import CORMBase class TestModel(CORMBase): __keyspace__ =", "'test_corm_where' option: OptionList score: int register_table(TestCORMWhere) sync_schema() one = TestCORMWhere(OptionList.One,", "Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name) is", "False keyspace_create(keyspace_name, CassandraKeyspaceStrategy.Simple) assert keyspace_exists(keyspace_name) is True keyspace_destroy(keyspace_name) assert keyspace_exists(keyspace_name)", "with Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name = f'abc_{keyspace_name}' assert keyspace_exists(keyspace_name)", "# Keyspaces seem to have to start with Alpha-Letters keyspace_name", "elif idx == 1: assert entry.three == 'beta' elif idx", "}) entry = TestModelSelect(values[-1]['random_number'], values[-1]['created']) insert_later.append(entry) if len(insert_later) > 20:", "# assert entry.created == values[idx]['created'] assert idx > 0 def", "Two = 'two' class TestCORMWhere(CORMBase): __keyspace__ = 'test_corm_where' option: OptionList", "select from corm.models import CORMBase class TestCORMUUID(CORMBase): __keyspace__ = 'mykeyspace'", "insert_later = [] insert(insert_later) for idx, entry in enumerate(select(TestModelSelect, fetch_size=100)):", "corm.models import CORMBase class OptionList(enum.Enum): One = 'one' Two =", "2) three = TestCORMWhere(OptionList.Two, 3) four = TestCORMWhere(OptionList.Two, 4) insert([one,", "= 'mykeyspace' random_number: int created: datetime one: str two: str", "OptionList.One)])): assert idx in [0, 1] assert entry.score in [1,", "sync_schema, select from corm.models import CORMBase class TestModelFloat(CORMBase): __keyspace__ =", "two = TestModel('one', 'two') three = TestModel('one', 'three') insert([one, two,", "something: str other: str register_table(TestModel) sync_schema() one = TestModel('one', 'two')", "corm.models import CORMBase from datetime import datetime class TestModelDatetime(CORMBase): __keyspace__", "item: str created: datetime register_table(TestModelDatetime) sync_schema() one = TestModelDatetime('one', datetime.utcnow())", "= 'test_corm_where' option: OptionList score: int register_table(TestCORMWhere) sync_schema() one =", "'score', 4)])): assert idx == 0 assert entry.score == 4", "two = TestModelSet('two', {'last', 'second-to-last'}) three = TestModelSet('three', {'last', 'second-to-last',", "corm import register_table, insert, sync_schema, select, where from corm.models import", "1: assert entry.three == 'beta' elif idx == 2: assert", "= TestCORMWhere(OptionList.Two, 3) four = TestCORMWhere(OptionList.Two, 4) insert([one, two, three,", "str other: str register_table(TestModel) sync_schema() one = TestModel('one', 'two') two", "insert([one, two, three, four]) def test_select_api(): import random from corm", "import register_table, insert, sync_schema from corm.models import CORMBase class TestModel(CORMBase):", "CORMBase from datetime import datetime class TestModelDatetime(CORMBase): __keyspace__ = 'mykeyspace'", "gamma = TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta =", "sync_schema from corm.models import CORMBase class TestCORMAuth(CORMBase): one: str __keyspace__", "other: Set register_table(TestModelSet) sync_schema() one = TestModelSet('one', {'first'}) two =", "= '9043' os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from corm", "sync_schema from corm.models import CORMBase from datetime import datetime class", "class TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created: datetime one:", "test_corm_auth(): import os os.environ['CLUSTER_PORT'] = '9043' os.environ['CLUSTER_USERNAME'] = 'cassandra' os.environ['CLUSTER_PASSWORD']", "assert keyspace_exists(keyspace_name) is True one = TestModelKeyspace('one') insert([one]) keyspace_destroy(keyspace_name) assert", "TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'delta') second_entry = TestNotOrderedByPkField(random.randint(0, 99999),", "sync_schema() data = 324.593998934 one = TestModelFloat(data) insert([one]) for idx,", "one: str two: str source: TestModelSelectSource # TODO: Build UserType", "'alpha' def test_ordered_by_pk_field(): import random from corm import register_table, insert,", "register_table, insert, sync_schema, select, where, cp, Operator from corm.models import", "sync_schema() insert_later = [] values = [] for idx in", "2: assert entry.three == 'delta' elif idx == 3: assert", "[0, 1] assert entry.score in [1, 2] assert entry.option ==", "== 1 assert entry.option == OptionList.One for idx, entry in", "to have to start with Alpha-Letters keyspace_name = hashlib.md5(str(uuid.uuid4()).encode(ENCODING)).hexdigest() keyspace_name", "== values[idx]['created'] assert idx > 0 def test_select_where_api(): import random", "insert(insert_later) insert_later = [] insert(insert_later) for idx, entry in enumerate(select(TestModelSelect,", "= TestModelDatetime('one', datetime.utcnow()) two = TestModelDatetime('two', datetime.utcnow()) insert([one, two]) def", "from corm.models import CORMBase from datetime import datetime MAX_INT =", "assert keyspace_exists(keyspace_name) is False class TestModelKeyspace(CORMBase): __keyspace__ = keyspace_name item:", "= 99999 class TestModelSelectSource(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created:", "20: insert(insert_later) insert_later = [] insert(insert_later) for idx, entry in", "datetime one: str two: str three: str register_table(TestOrderedByPkField) sync_schema() first_entry", "TestCORMWhere(OptionList.One, 2) three = TestCORMWhere(OptionList.Two, 3) four = TestCORMWhere(OptionList.Two, 4)", "for idx, entry in enumerate(select(TestModelSelect, fetch_size=100)): assert isinstance(entry, TestModelSelect) #", "__keyspace__ = 'mykeyspace' item: str created: datetime value: bool register_table(TestModelBoolean)", "continue session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api(): from corm import", "= TestOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one', 'gamma') delta = TestOrderedByPkField(random.randint(0,", "== 3 # Add Column on existing Table class TestModelAlter(CORMBase):", "= '{TestModelAlter._corm_details.keyspace}' ''' rows = [(row.column_name, row.type) for row in", "['global']: continue session.shutdown() del SESSIONS[keyspace_name] request.addfinalizer(destroy_case) def test_initial_api(): from corm", "one: str two: str class TestModelSelectPivot(CORMBase): __keyspace__ = 'mykeyspace' random_number:", "existing Table class TestModelAlter(CORMBase): __keyspace__ = 'mykeyspace' random_number: int created:", "str created: datetime value: bool register_table(TestModelBoolean) sync_schema() one = TestModelBoolean('one',", "two: str three: str register_table(TestNotOrderedByPkField) sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0, 99999),", "hashlib import uuid from corm import register_table, insert, sync_schema, \\", "str register_table(TestNotOrderedByPkField) sync_schema() first_entry = TestNotOrderedByPkField(random.randint(0, 99999), datetime.utcnow(), 'one', 'one',", "'mykeyspace' input_one: float register_table(TestModelFloat) sync_schema() data = 324.593998934 one =", "0: assert entry.three == 'alpha' elif idx == 1: assert", "os.environ['CLUSTER_PASSWORD'] = '<PASSWORD>' from corm import register_table, insert, sync_schema from" ]
[ "import subprocess from geolite2 import geolite2 class getData: #Get Data", "geolite2.reader() loc = reader.get(line[0]) Cname = loc['country']['names']['en'] if 'subdivisions' in", "result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique Users for key, value in", "proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return", "as lf: for temp in lf: line = temp.split(';') if", "time = getData.getTime().split('\\t') result[\"time\"] = time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP()", "key, value in result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo mv", "result = {\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time", "loc: Sname = loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if 'city' in loc:", "json import os from utilities.SaveLoadJson import SaveLoadJson as SLJ from", "in loc: Sname = loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if 'city' in", "err) = proc.communicate() return (str(out)[1:9] + '\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod", "str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime(): proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out,", "= getData.getTime().split('\\t') result[\"time\"] = time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return", "== '200': if 'GET /find' in line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1]", "#Most recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique", "\"ip\":''} result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t') result[\"time\"] = time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime()", "Cname not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname not in", "@staticmethod def getDATA(): result = {\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'',", "result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod def getRequests(): data =", "LineCount as LC import subprocess from geolite2 import geolite2 class", "\"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum = 200 total=0", "total=0 mostRecentIP = '' mostRecentAcc = '' mostRecentSearch = ''", "mostRecentIP=line[0] mostRecentAcc=line[1] reader = geolite2.reader() loc = reader.get(line[0]) Cname =", "= proc.communicate() return (str(out)[1:9] + '\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod def", "= 200 total=0 mostRecentIP = '' mostRecentAcc = '' mostRecentSearch", "= open(newFile, 'w') with open(logFile, 'r') as lf: for temp", "proc.communicate() return str(out) @staticmethod def getTemp(): proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE,", "getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return", "loc['city']['names']['en'] else: Ctyname='Unknown' if Cname not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0", "logFile = 'utilities/access.log' newFile='utilities/new.log' #f = open(newFile, 'w') with open(logFile,", "Ctyname not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1 total+=1 search", "result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname", "= subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return (str(out)[1:9] +", "'subdivisions' in loc: Sname = loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if 'city'", "proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return", "reader = geolite2.reader() loc = reader.get(line[0]) Cname = loc['country']['names']['en'] if", "key, value in ips.items(): result[\"Users\"]+=1 #Device percents for key, value", "'+Sname+', '+Cname) #Unique Users for key, value in ips.items(): result[\"Users\"]+=1", "result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1: device=device[1] else: device=\"Unknown\" if device", "percents for key, value in result[\"devices\"].items(): percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt,", "if search not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum:", "len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search)", "= '' mostRecentSearch = '' Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict() logFile", "= [] result[\"CountrySrs\"][Cname]+=1 total+=1 search = (line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search", "@staticmethod def getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err) =", "(out, err) = proc.communicate() return str(out) #Get Access Functions ---------------------------------------------------", "if 'subdivisions' in loc: Sname = loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if", "@staticmethod def getIP(): proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out, err)", "') mostRecentSearch=search if search not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname])", "(float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country percents for key, value in result[\"CountrySrs\"].items():", "result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if", "result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1 total+=1 search = (line[3].split(' ')[1][6:]).replace('%20','", "= subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return str(out)", "')[1][6:]).replace('%20',' ') mostRecentSearch=search if search not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if", "Cname = loc['country']['names']['en'] if 'subdivisions' in loc: Sname = loc['subdivisions'][0]['names']['en']", "str(out)[5:-1] @staticmethod def getIP(): proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out,", "import os from utilities.SaveLoadJson import SaveLoadJson as SLJ from utilities.LineCount", "json.dumps(result) @staticmethod def getRequests(): data = SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]),", "Ctyname = loc['city']['names']['en'] else: Ctyname='Unknown' if Cname not in result[\"Countries\"]:", "for key, value in result[\"devices\"].items(): percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f')", "result[\"CountrySrs\"][Cname]+=1 total+=1 search = (line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search if search", "Sname='Unknown' Ctyname='Unknown' ips=dict() logFile = 'utilities/access.log' newFile='utilities/new.log' #f = open(newFile,", "'GET /find' in line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader = geolite2.reader()", "\"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out,", "result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum =", "if Ctyname not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1 total+=1", "= 'utilities/access.log' newFile='utilities/new.log' #f = open(newFile, 'w') with open(logFile, 'r')", "/find' in line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader = geolite2.reader() loc", "'\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime(): proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE,", "result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique Users for key, value", "value in result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo mv -f", "#Unique Users for key, value in ips.items(): result[\"Users\"]+=1 #Device percents", "'' Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict() logFile = 'utilities/access.log' newFile='utilities/new.log' #f", "import json import os from utilities.SaveLoadJson import SaveLoadJson as SLJ", "\"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t') result[\"time\"] = time[0]", "return str(out) @staticmethod def getTemp(): proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False)", "= (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country percents for key, value in", "def getUptime(): proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out, err) =", "for key, value in ips.items(): result[\"Users\"]+=1 #Device percents for key,", "percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country percents for key, value", "@staticmethod def getTemp(): proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) =", "not in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most recent stuff result[\"mostRecentIP\"]=mostRecentIP", "not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if", "return json.dumps(result) @staticmethod def getRequests(): data = SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]),", "percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo mv -f \"+newFile+\" \"+logFile) return", "@staticmethod def getUptime(): proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out, err)", "err) = proc.communicate() return str(out) @staticmethod def getTemp(): proc =", "from geolite2 import geolite2 class getData: #Get Data Functions ------------------------------------------------------", "= subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return str(out)", "'200': if 'GET /find' in line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader", "def getIP(): proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out, err) =", "'city' in loc: Ctyname = loc['city']['names']['en'] else: Ctyname='Unknown' if Cname", "device=\"Unknown\" if device not in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most", "open(logFile, 'r') as lf: for temp in lf: line =", "as SLJ from utilities.LineCount import LineCount as LC import subprocess", "proc.communicate() return str(out) #Get Access Functions --------------------------------------------------- @staticmethod def getAccess():", "proc.communicate() return str(out)[5:-1] @staticmethod def getIP(): proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE,", "stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique Users for", "utilities.SaveLoadJson import SaveLoadJson as SLJ from utilities.LineCount import LineCount as", "(out, err) = proc.communicate() return (str(out)[1:9] + '\\t' + str(float(str(out).split(',')[4])*100)+'%')", "Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict() logFile = 'utilities/access.log' newFile='utilities/new.log' #f =", ">= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if", "mostRecentIP = '' mostRecentAcc = '' mostRecentSearch = '' Cname='Unknown'", "import geolite2 class getData: #Get Data Functions ------------------------------------------------------ @staticmethod def", "shell=False) (out, err) = proc.communicate() return (str(out)[1:9] + '\\t' +", "= proc.communicate() return str(out)[5:-1] @staticmethod def getIP(): proc = subprocess.Popen(['hostname',", "in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('('))", "key, value in result[\"devices\"].items(): percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country", "= proc.communicate() return str(out) @staticmethod def getTemp(): proc = subprocess.Popen(['vcgencmd',", "shell=False) (out, err) = proc.communicate() return str(out) #Get Access Functions", "getData.getTime().split('\\t') result[\"time\"] = time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result)", "+ '\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime(): proc = subprocess.Popen(['uptime',", "getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum", "temp in lf: line = temp.split(';') if len(line) > 1:", "result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1:", "time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod def getRequests():", "proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) = proc.communicate() return str(out)[5:-1]", "in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc", "------------------------------------------------------ @staticmethod def getDATA(): result = {\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'',", "os from utilities.SaveLoadJson import SaveLoadJson as SLJ from utilities.LineCount import", "subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) = proc.communicate() return str(out)[5:-1] @staticmethod def", "\"recentSearches\":[], \"Users\":0} lastNum = 200 total=0 mostRecentIP = '' mostRecentAcc", "result[\"CountrySrs\"][Cname]=0 if Sname not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname not", "mostRecentSearch=search if search not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >=", "ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1: device=device[1] else: device=\"Unknown\" if device not", "[] result[\"CountrySrs\"][Cname]+=1 total+=1 search = (line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search if", "result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = []", "1: if line[2] == '200': if 'GET /find' in line[3]:", "Functions --------------------------------------------------- @staticmethod def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'',", "search = (line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search if search not in", "Data Functions ------------------------------------------------------ @staticmethod def getDATA(): result = {\"requests\":{}, \"time\":'',", "line = temp.split(';') if len(line) > 1: if line[2] ==", "line[2] == '200': if 'GET /find' in line[3]: #f.write(temp) mostRecentIP=line[0]", "len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1: device=device[1] else:", "geolite2 class getData: #Get Data Functions ------------------------------------------------------ @staticmethod def getDATA():", "return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE,", "class getData: #Get Data Functions ------------------------------------------------------ @staticmethod def getDATA(): result", "result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not", "= proc.communicate() return str(out) #Get Access Functions --------------------------------------------------- @staticmethod def", "#f.close() #Most recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname)", "result[\"Countries\"][Cname][Sname]=dict() if Ctyname not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1", "@staticmethod def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[],", "in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1 total+=1 search = (line[3].split('", "for temp in lf: line = temp.split(';') if len(line) >", "result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t') result[\"time\"] = time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp()", "search not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0)", "(line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search if search not in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search)", "= SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime(): proc", "return (str(out)[1:9] + '\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime(): proc", "data = SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime():", "= reader.get(line[0]) Cname = loc['country']['names']['en'] if 'subdivisions' in loc: Sname", "getDATA(): result = {\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests()", "def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0}", "in lf: line = temp.split(';') if len(line) > 1: if", "from utilities.LineCount import LineCount as LC import subprocess from geolite2", "if 'city' in loc: Ctyname = loc['city']['names']['en'] else: Ctyname='Unknown' if", "search not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1)", "else: device=\"Unknown\" if device not in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close()", "\"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum = 200 total=0 mostRecentIP", "result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1 total+=1 search = (line[3].split(' ')[1][6:]).replace('%20',' ')", "= loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if 'city' in loc: Ctyname =", "not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] = [] result[\"CountrySrs\"][Cname]+=1 total+=1 search =", "from utilities.SaveLoadJson import SaveLoadJson as SLJ from utilities.LineCount import LineCount", "str(out) #Get Access Functions --------------------------------------------------- @staticmethod def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(),", "= {\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time =", "Functions ------------------------------------------------------ @staticmethod def getDATA(): result = {\"requests\":{}, \"time\":'', \"cpuload\":'',", "loc['country']['names']['en'] if 'subdivisions' in loc: Sname = loc['subdivisions'][0]['names']['en'] else: Sname='Unknown'", "= '' Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict() logFile = 'utilities/access.log' newFile='utilities/new.log'", "'.2f') #Country percents for key, value in result[\"CountrySrs\"].items(): percnt =", "result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod def getRequests(): data", "result[\"devices\"][device]+=1 #f.close() #Most recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+',", "= subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) = proc.communicate() return str(out)[5:-1] @staticmethod", "'' mostRecentAcc = '' mostRecentSearch = '' Cname='Unknown' Sname='Unknown' Ctyname='Unknown'", "mostRecentAcc = '' mostRecentSearch = '' Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict()", "getTemp(): proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) = proc.communicate() return", "= loc['country']['names']['en'] if 'subdivisions' in loc: Sname = loc['subdivisions'][0]['names']['en'] else:", "loc = reader.get(line[0]) Cname = loc['country']['names']['en'] if 'subdivisions' in loc:", "lf: for temp in lf: line = temp.split(';') if len(line)", "getUptime(): proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate()", "else: Sname='Unknown' if 'city' in loc: Ctyname = loc['city']['names']['en'] else:", "result[\"Users\"]+=1 #Device percents for key, value in result[\"devices\"].items(): percnt =", "def getTemp(): proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) = proc.communicate()", "in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname] =", "--------------------------------------------------- @staticmethod def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'',", "+ str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime(): proc = subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False)", "for key, value in result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo", "in line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader = geolite2.reader() loc =", ">= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1: device=device[1] else: device=\"Unknown\"", "'w') with open(logFile, 'r') as lf: for temp in lf:", "str(out) @staticmethod def getTemp(): proc = subprocess.Popen(['vcgencmd', 'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err)", "recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique Users", "if line[2] == '200': if 'GET /find' in line[3]: #f.write(temp)", "subprocess from geolite2 import geolite2 class getData: #Get Data Functions", "= loc['city']['names']['en'] else: Ctyname='Unknown' if Cname not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict()", "subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return str(out) #Get", "Sname not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname not in result[\"Countries\"][Cname][Sname]:", "getRequests(): data = SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def", "not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname not in result[\"Countries\"][Cname]:", "'+Cname) #Unique Users for key, value in ips.items(): result[\"Users\"]+=1 #Device", "return str(out) #Get Access Functions --------------------------------------------------- @staticmethod def getAccess(): result={\"Countries\":dict(),", "\"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum = 200 total=0 mostRecentIP =", "else: Ctyname='Unknown' if Cname not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if", "SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime(): proc =", "SLJ from utilities.LineCount import LineCount as LC import subprocess from", "lf: line = temp.split(';') if len(line) > 1: if line[2]", "> 1: if line[2] == '200': if 'GET /find' in", "result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique Users for key,", "if 'GET /find' in line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader =", "@staticmethod def getRequests(): data = SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])}", "\"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum = 200 total=0 mostRecentIP = ''", "if len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1: device=device[1]", "if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not in result[\"recentSearches\"]:", "result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch", "geolite2 import geolite2 class getData: #Get Data Functions ------------------------------------------------------ @staticmethod", "in loc: Ctyname = loc['city']['names']['en'] else: Ctyname='Unknown' if Cname not", "result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >=", "'' mostRecentSearch = '' Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict() logFile =", "newFile='utilities/new.log' #f = open(newFile, 'w') with open(logFile, 'r') as lf:", "len(device)>1: device=device[1] else: device=\"Unknown\" if device not in result[\"devices\"]: result[\"devices\"][device]=0", "in result[\"devices\"].items(): percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country percents for", "'-I'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return str(out) #Get Access", "not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname not in result[\"Countries\"][Cname][Sname]: result[\"Countries\"][Cname][Sname][Ctyname]", "as LC import subprocess from geolite2 import geolite2 class getData:", "value in result[\"devices\"].items(): percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country percents", "'utilities/access.log' newFile='utilities/new.log' #f = open(newFile, 'w') with open(logFile, 'r') as", "result[\"mostRecentLoc\"]=str(Ctyname+', '+Sname+', '+Cname) #Unique Users for key, value in ips.items():", "ips=dict() logFile = 'utilities/access.log' newFile='utilities/new.log' #f = open(newFile, 'w') with", "if search not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >= lastNum:", "Access Functions --------------------------------------------------- @staticmethod def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'',", "device=(line[4].split('(')) if len(device)>1: device=device[1] else: device=\"Unknown\" if device not in", "in ips.items(): result[\"Users\"]+=1 #Device percents for key, value in result[\"devices\"].items():", "device=device[1] else: device=\"Unknown\" if device not in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1", "'-p'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return str(out) @staticmethod def", "Users for key, value in ips.items(): result[\"Users\"]+=1 #Device percents for", "import LineCount as LC import subprocess from geolite2 import geolite2", "not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"]) >= lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1", "in result[\"Countries\"][Cname][Sname][Ctyname]: result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search", "result[\"Countries\"][Cname][Sname][Ctyname].append(search) if len(result[\"Countries\"][Cname][Sname][Ctyname]) >= lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not in", "if len(device)>1: device=device[1] else: device=\"Unknown\" if device not in result[\"devices\"]:", "total+=1 search = (line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search if search not", "err) = proc.communicate() return str(out) #Get Access Functions --------------------------------------------------- @staticmethod", "import SaveLoadJson as SLJ from utilities.LineCount import LineCount as LC", "= time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod def", "percents for key, value in result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f')", "result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if", "lastNum: result[\"recentSearches\"].pop(-1) ips[line[0]]=1 device=(line[4].split('(')) if len(device)>1: device=device[1] else: device=\"Unknown\" if", "def getRequests(): data = SLJ.load('dataStore.txt') return {\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod", "temp.split(';') if len(line) > 1: if line[2] == '200': if", "loc: Ctyname = loc['city']['names']['en'] else: Ctyname='Unknown' if Cname not in", "\"CountrySrs\":dict(), \"devices\":dict(), \"mostRecentSearch\":'', \"mostRecentAcc\":'', \"mostRecentIP\":'', \"recentSearches\":[], \"Users\":0} lastNum = 200", "(str(out)[1:9] + '\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime(): proc =", "#Country percents for key, value in result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100", "(out, err) = proc.communicate() return str(out) @staticmethod def getTemp(): proc", "Sname='Unknown' if 'city' in loc: Ctyname = loc['city']['names']['en'] else: Ctyname='Unknown'", "return str(out)[5:-1] @staticmethod def getIP(): proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False)", "200 total=0 mostRecentIP = '' mostRecentAcc = '' mostRecentSearch =", "subprocess.Popen(['uptime', '-p'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return str(out) @staticmethod", "Ctyname='Unknown' if Cname not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname", "#Device percents for key, value in result[\"devices\"].items(): percnt = (float(value)/float(total))*100", "#f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader = geolite2.reader() loc = reader.get(line[0]) Cname", "getData: #Get Data Functions ------------------------------------------------------ @staticmethod def getDATA(): result =", "if Sname not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict() if Ctyname not in", "in result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo mv -f \"+newFile+\"", "{\"totalRequests\":str(data[\"totalRequests\"]), \"totalQueries\":str(data[\"totalQueries\"]), \"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False)", "open(newFile, 'w') with open(logFile, 'r') as lf: for temp in", "#f = open(newFile, 'w') with open(logFile, 'r') as lf: for", "= temp.split(';') if len(line) > 1: if line[2] == '200':", "result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod def getRequests(): data = SLJ.load('dataStore.txt')", "result[\"time\"] = time[0] result[\"cpuload\"]=time[1] result[\"uptime\"]=getData.getUptime() result[\"temp\"]=getData.getTemp() result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod", "SaveLoadJson as SLJ from utilities.LineCount import LineCount as LC import", "len(line) > 1: if line[2] == '200': if 'GET /find'", "'r') as lf: for temp in lf: line = temp.split(';')", "shell=False) (out,err) = proc.communicate() return str(out)[5:-1] @staticmethod def getIP(): proc", "if Cname not in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname not", "if device not in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most recent", "mostRecentAcc=line[1] reader = geolite2.reader() loc = reader.get(line[0]) Cname = loc['country']['names']['en']", "\"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t') result[\"time\"] = time[0] result[\"cpuload\"]=time[1]", "reader.get(line[0]) Cname = loc['country']['names']['en'] if 'subdivisions' in loc: Sname =", "value in ips.items(): result[\"Users\"]+=1 #Device percents for key, value in", "utilities.LineCount import LineCount as LC import subprocess from geolite2 import", "with open(logFile, 'r') as lf: for temp in lf: line", "\"totalAdjusts\":str(data[\"totalAdjusts\"])} @staticmethod def getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err)", "proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return (str(out)[1:9]", "Ctyname='Unknown' ips=dict() logFile = 'utilities/access.log' newFile='utilities/new.log' #f = open(newFile, 'w')", "def getDATA(): result = {\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''}", "def getTime(): proc = subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate()", "#Get Access Functions --------------------------------------------------- @staticmethod def getAccess(): result={\"Countries\":dict(), \"CountrySrs\":dict(), \"devices\":dict(),", "\"Users\":0} lastNum = 200 total=0 mostRecentIP = '' mostRecentAcc =", "{\"requests\":{}, \"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t')", "getIP(): proc = subprocess.Popen(['hostname', '-I'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate()", "lastNum: result[\"Countries\"][Cname][Sname][Ctyname].pop(0) if search not in result[\"recentSearches\"]: result[\"recentSearches\"].insert(0,search) if len(result[\"recentSearches\"])", "'measure_temp'],stdout=subprocess.PIPE, shell=False) (out,err) = proc.communicate() return str(out)[5:-1] @staticmethod def getIP():", "result[\"CountrySrs\"].items(): percnt = (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo mv -f \"+newFile+\" \"+logFile)", "result[\"devices\"].items(): percnt = (float(value)/float(total))*100 result[\"devices\"][key]=format(percnt, '.2f') #Country percents for key,", "shell=False) (out, err) = proc.communicate() return str(out) @staticmethod def getTemp():", "result[\"devices\"][key]=format(percnt, '.2f') #Country percents for key, value in result[\"CountrySrs\"].items(): percnt", "Sname = loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if 'city' in loc: Ctyname", "= (line[3].split(' ')[1][6:]).replace('%20',' ') mostRecentSearch=search if search not in result[\"Countries\"][Cname][Sname][Ctyname]:", "result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most recent stuff result[\"mostRecentIP\"]=mostRecentIP result[\"mostRecentAcc\"]=mostRecentAcc result[\"mostRecentSearch\"]=mostRecentSearch result[\"mostRecentLoc\"]=str(Ctyname+',", "proc.communicate() return (str(out)[1:9] + '\\t' + str(float(str(out).split(',')[4])*100)+'%') @staticmethod def getUptime():", "mostRecentSearch = '' Cname='Unknown' Sname='Unknown' Ctyname='Unknown' ips=dict() logFile = 'utilities/access.log'", "= geolite2.reader() loc = reader.get(line[0]) Cname = loc['country']['names']['en'] if 'subdivisions'", "device not in result[\"devices\"]: result[\"devices\"][device]=0 result[\"devices\"][device]+=1 #f.close() #Most recent stuff", "result[\"ip\"]=getData.getIP() return json.dumps(result) @staticmethod def getRequests(): data = SLJ.load('dataStore.txt') return", "\"time\":'', \"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t') result[\"time\"]", "= (float(value)/float(total))*100 result[\"CountrySrs\"][key]=format(percnt,'.2f') #os.system(\"sudo mv -f \"+newFile+\" \"+logFile) return json.dumps(result)", "\"cpuload\":'', \"uptime\":'', \"temp\":'', \"ip\":''} result[\"requests\"]=getData.getRequests() time = getData.getTime().split('\\t') result[\"time\"] =", "loc['subdivisions'][0]['names']['en'] else: Sname='Unknown' if 'city' in loc: Ctyname = loc['city']['names']['en']", "LC import subprocess from geolite2 import geolite2 class getData: #Get", "line[3]: #f.write(temp) mostRecentIP=line[0] mostRecentAcc=line[1] reader = geolite2.reader() loc = reader.get(line[0])", "= '' mostRecentAcc = '' mostRecentSearch = '' Cname='Unknown' Sname='Unknown'", "ips.items(): result[\"Users\"]+=1 #Device percents for key, value in result[\"devices\"].items(): percnt", "if len(line) > 1: if line[2] == '200': if 'GET", "lastNum = 200 total=0 mostRecentIP = '' mostRecentAcc = ''", "in result[\"Countries\"]: result[\"Countries\"][Cname]=dict() result[\"CountrySrs\"][Cname]=0 if Sname not in result[\"Countries\"][Cname]: result[\"Countries\"][Cname][Sname]=dict()", "(out,err) = proc.communicate() return str(out)[5:-1] @staticmethod def getIP(): proc =", "subprocess.Popen(['uptime'],stdout=subprocess.PIPE, shell=False) (out, err) = proc.communicate() return (str(out)[1:9] + '\\t'", "#Get Data Functions ------------------------------------------------------ @staticmethod def getDATA(): result = {\"requests\":{}," ]
[]
[ "DEBUG = True TEMPLATE_DEBUG = DEBUG DATABASES = { 'default':", "'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'], 'HOST': '127.0.0.1',", "'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'],", "from .settings import * DEBUG = True TEMPLATE_DEBUG = DEBUG", "'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'], 'HOST': '127.0.0.1', 'PORT':", "'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'], 'HOST': '127.0.0.1', 'PORT': '5432',", "{ 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD':", "utf-8 -*- from .settings import * DEBUG = True TEMPLATE_DEBUG", "= True TEMPLATE_DEBUG = DEBUG DATABASES = { 'default': {", "TEMPLATE_DEBUG = DEBUG DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2',", "coding: utf-8 -*- from .settings import * DEBUG = True", "* DEBUG = True TEMPLATE_DEBUG = DEBUG DATABASES = {", "'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'], 'HOST': '127.0.0.1', 'PORT': '5432', } }", "True TEMPLATE_DEBUG = DEBUG DATABASES = { 'default': { 'ENGINE':", "= DEBUG DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME':", "= { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'],", "# -*- coding: utf-8 -*- from .settings import * DEBUG", "{ 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'], 'HOST':", "-*- coding: utf-8 -*- from .settings import * DEBUG =", "import * DEBUG = True TEMPLATE_DEBUG = DEBUG DATABASES =", ".settings import * DEBUG = True TEMPLATE_DEBUG = DEBUG DATABASES", "os.environ['LOCAL_DB_NAME'], 'USER': os.environ['LOCAL_DB_USER'], 'PASSWORD': os.environ['LOCAL_DB_PASSWORD'], 'HOST': '127.0.0.1', 'PORT': '5432', }", "DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'], 'USER':", "DEBUG DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['LOCAL_DB_NAME'],", "-*- from .settings import * DEBUG = True TEMPLATE_DEBUG =" ]
[ "* 20) l = float(input('qual a largura do terreno: '))", "{a}') print('Controle de terrenos') print('-' * 20) l = float(input('qual", "a = larg * comp print(f'A dimensão é {a}') print('Controle", "comp print(f'A dimensão é {a}') print('Controle de terrenos') print('-' *", "print('-' * 20) l = float(input('qual a largura do terreno:", "l = float(input('qual a largura do terreno: ')) c =", "= float(input('qual a largura do terreno: ')) c = float(input('qual", "')) c = float(input('qual o comprimento do terreno: ')) area(l", "terreno: ')) c = float(input('qual o comprimento do terreno: '))", "dimensão é {a}') print('Controle de terrenos') print('-' * 20) l", "20) l = float(input('qual a largura do terreno: ')) c", "é {a}') print('Controle de terrenos') print('-' * 20) l =", "float(input('qual a largura do terreno: ')) c = float(input('qual o", "print('Controle de terrenos') print('-' * 20) l = float(input('qual a", "larg * comp print(f'A dimensão é {a}') print('Controle de terrenos')", "comp): a = larg * comp print(f'A dimensão é {a}')", "a largura do terreno: ')) c = float(input('qual o comprimento", "= larg * comp print(f'A dimensão é {a}') print('Controle de", "do terreno: ')) c = float(input('qual o comprimento do terreno:", "* comp print(f'A dimensão é {a}') print('Controle de terrenos') print('-'", "def area(larg, comp): a = larg * comp print(f'A dimensão", "largura do terreno: ')) c = float(input('qual o comprimento do", "print(f'A dimensão é {a}') print('Controle de terrenos') print('-' * 20)", "area(larg, comp): a = larg * comp print(f'A dimensão é", "= float(input('qual o comprimento do terreno: ')) area(l , c)", "c = float(input('qual o comprimento do terreno: ')) area(l ,", "terrenos') print('-' * 20) l = float(input('qual a largura do", "de terrenos') print('-' * 20) l = float(input('qual a largura" ]
[ "de nascimento: \")) categoria = 0 if (ano - nasc)", "= 0 if (ano - nasc) <= 9: categoria =", "nasc) <= 25: categoria = str(\"SÊNIOR\") else: categoria = str(\"MASTER\")", "= str(\"JUNIOR\") elif 19 < (ano - nasc) <= 25:", "14 < (ano - nasc) <= 19 : categoria =", "int(input(\"Digite o seu ano de nascimento: \")) categoria = 0", "25: categoria = str(\"SÊNIOR\") else: categoria = str(\"MASTER\") print(f\"A categoria", "categoria = str(\"MIRIM\") elif 9 < (ano - nasc) <=", "- nasc) <= 25: categoria = str(\"SÊNIOR\") else: categoria =", "nasc) <= 14: categoria = str(\"INFANTIL\") elif 14 < (ano", "categoria = str(\"INFANTIL\") elif 14 < (ano - nasc) <=", "= int(input(\"Digite o seu ano de nascimento: \")) categoria =", "<reponame>romulogoleniesky/Python_C_E_V import datetime ano = (datetime.datetime.now()).year nasc = int(input(\"Digite o", "elif 9 < (ano - nasc) <= 14: categoria =", "- nasc) <= 14: categoria = str(\"INFANTIL\") elif 14 <", "elif 19 < (ano - nasc) <= 25: categoria =", "19 < (ano - nasc) <= 25: categoria = str(\"SÊNIOR\")", "<= 9: categoria = str(\"MIRIM\") elif 9 < (ano -", "- nasc) <= 19 : categoria = str(\"JUNIOR\") elif 19", ": categoria = str(\"JUNIOR\") elif 19 < (ano - nasc)", "(ano - nasc) <= 25: categoria = str(\"SÊNIOR\") else: categoria", "categoria = str(\"SÊNIOR\") else: categoria = str(\"MASTER\") print(f\"A categoria do", "= str(\"INFANTIL\") elif 14 < (ano - nasc) <= 19", "9: categoria = str(\"MIRIM\") elif 9 < (ano - nasc)", "str(\"INFANTIL\") elif 14 < (ano - nasc) <= 19 :", "<= 19 : categoria = str(\"JUNIOR\") elif 19 < (ano", "str(\"MIRIM\") elif 9 < (ano - nasc) <= 14: categoria", "<= 14: categoria = str(\"INFANTIL\") elif 14 < (ano -", "19 : categoria = str(\"JUNIOR\") elif 19 < (ano -", "seu ano de nascimento: \")) categoria = 0 if (ano", "datetime ano = (datetime.datetime.now()).year nasc = int(input(\"Digite o seu ano", "o seu ano de nascimento: \")) categoria = 0 if", "\")) categoria = 0 if (ano - nasc) <= 9:", "categoria = 0 if (ano - nasc) <= 9: categoria", "nasc) <= 19 : categoria = str(\"JUNIOR\") elif 19 <", "nasc) <= 9: categoria = str(\"MIRIM\") elif 9 < (ano", "nasc = int(input(\"Digite o seu ano de nascimento: \")) categoria", "elif 14 < (ano - nasc) <= 19 : categoria", "(datetime.datetime.now()).year nasc = int(input(\"Digite o seu ano de nascimento: \"))", "0 if (ano - nasc) <= 9: categoria = str(\"MIRIM\")", "14: categoria = str(\"INFANTIL\") elif 14 < (ano - nasc)", "ano de nascimento: \")) categoria = 0 if (ano -", "- nasc) <= 9: categoria = str(\"MIRIM\") elif 9 <", "import datetime ano = (datetime.datetime.now()).year nasc = int(input(\"Digite o seu", "= str(\"SÊNIOR\") else: categoria = str(\"MASTER\") print(f\"A categoria do atleta", "if (ano - nasc) <= 9: categoria = str(\"MIRIM\") elif", "(ano - nasc) <= 14: categoria = str(\"INFANTIL\") elif 14", "str(\"JUNIOR\") elif 19 < (ano - nasc) <= 25: categoria", "< (ano - nasc) <= 19 : categoria = str(\"JUNIOR\")", "<= 25: categoria = str(\"SÊNIOR\") else: categoria = str(\"MASTER\") print(f\"A", "9 < (ano - nasc) <= 14: categoria = str(\"INFANTIL\")", "else: categoria = str(\"MASTER\") print(f\"A categoria do atleta é {str(categoria)}.\")", "< (ano - nasc) <= 14: categoria = str(\"INFANTIL\") elif", "< (ano - nasc) <= 25: categoria = str(\"SÊNIOR\") else:", "= str(\"MIRIM\") elif 9 < (ano - nasc) <= 14:", "(ano - nasc) <= 9: categoria = str(\"MIRIM\") elif 9", "(ano - nasc) <= 19 : categoria = str(\"JUNIOR\") elif", "ano = (datetime.datetime.now()).year nasc = int(input(\"Digite o seu ano de", "str(\"SÊNIOR\") else: categoria = str(\"MASTER\") print(f\"A categoria do atleta é", "categoria = str(\"JUNIOR\") elif 19 < (ano - nasc) <=", "nascimento: \")) categoria = 0 if (ano - nasc) <=", "= (datetime.datetime.now()).year nasc = int(input(\"Digite o seu ano de nascimento:" ]
[ "+ 2, device=device).float()) heldout_batch_nonzero = (heldout_batch > 0).float() DCG =", "K] X_pred_binary = torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1),", "predictions [B, I] and ground-truth [B, I], with binary relevance.", "batch_users = X_pred.shape[0] # batch_size _, idx_topk = torch.topk(X_pred, k,", "and ground-truth [B, I]. \"\"\" batch_users = X_pred.shape[0] _, topk_indices", ".toarray() # [B, I] k_tensor = torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available():", "\"\"\" batch_users = X_pred.shape[0] # batch_size _, idx_topk = torch.topk(X_pred,", "k)]).sum() for n in heldout_nonzero]).to(device) return DCG / IDCG def", "(heldout_batch > 0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1)", "batch_users = X_pred.shape[0] _, topk_indices = torch.topk(X_pred, k, dim=1, sorted=False)", "return DCG / IDCG def recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\" Recall@k", "# batch_size _, idx_topk = torch.topk(X_pred, k, dim=1, sorted=True) tp", "relevance. \"\"\" batch_users = X_pred.shape[0] # batch_size _, idx_topk =", "(X_true_binary * X_pred_binary).sum(dim=1).float() recall = tmp / torch.min(k_tensor, X_true_binary.sum(dim=1).float()) return", "X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1 X_true_binary = (heldout_batch > 0).float() #", "\"\"\" Normalized Discounted Cumulative Gain@k for for predictions [B, I]", "= (heldout_batch > 0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] *", "idx_topk = torch.topk(X_pred, k, dim=1, sorted=True) tp = 1. /", "heldout_nonzero]).to(device) return DCG / IDCG def recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\"", "= torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] =", "binary relevance. ASSUMPTIONS: all the 0's in heldout_batch indicate 0", "idx_topk] * tp).sum(dim=1) heldout_nonzero = (heldout_batch > 0).sum(dim=1) # num.", "0).sum(dim=1) # num. of non-zero items per batch. [B] IDCG", "with binary relevance. ASSUMPTIONS: all the 0's in heldout_batch indicate", "device='cpu'): \"\"\" Normalized Discounted Cumulative Gain@k for for predictions [B,", "torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1", "= (heldout_batch > 0).sum(dim=1) # num. of non-zero items per", "\"\"\" Recall@k for predictions [B, I] and ground-truth [B, I].", "predictions [B, I] and ground-truth [B, I]. \"\"\" batch_users =", "X_pred.shape[0] # batch_size _, idx_topk = torch.topk(X_pred, k, dim=1, sorted=True)", "ASSUMPTIONS: all the 0's in heldout_batch indicate 0 relevance. \"\"\"", "torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available(): X_true_binary = X_true_binary.cuda() k_tensor = k_tensor.cuda()", "heldout_batch, k=100, device='cpu'): \"\"\" Normalized Discounted Cumulative Gain@k for for", "n in heldout_nonzero]).to(device) return DCG / IDCG def recall_at_k_batch_torch(X_pred, heldout_batch,", "I], with binary relevance. ASSUMPTIONS: all the 0's in heldout_batch", "X_pred_binary = torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices]", "tp).sum(dim=1) heldout_nonzero = (heldout_batch > 0).sum(dim=1) # num. of non-zero", "Normalized Discounted Cumulative Gain@k for for predictions [B, I] and", "k=100, device='cpu'): \"\"\" Normalized Discounted Cumulative Gain@k for for predictions", "= torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available(): X_true_binary = X_true_binary.cuda() k_tensor =", "for n in heldout_nonzero]).to(device) return DCG / IDCG def recall_at_k_batch_torch(X_pred,", "k_tensor = torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available(): X_true_binary = X_true_binary.cuda() k_tensor", "the 0's in heldout_batch indicate 0 relevance. \"\"\" batch_users =", "dim=1, sorted=True) tp = 1. / torch.log2(torch.arange(2, k + 2,", "X_pred.shape[0] _, topk_indices = torch.topk(X_pred, k, dim=1, sorted=False) # [B,", "[B] IDCG = torch.tensor([(tp[:min(n, k)]).sum() for n in heldout_nonzero]).to(device) return", "I]. \"\"\" batch_users = X_pred.shape[0] _, topk_indices = torch.topk(X_pred, k,", "of non-zero items per batch. [B] IDCG = torch.tensor([(tp[:min(n, k)]).sum()", "k_tensor = k_tensor.cuda() tmp = (X_true_binary * X_pred_binary).sum(dim=1).float() recall =", "for predictions [B, I] and ground-truth [B, I]. \"\"\" batch_users", "IDCG def recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\" Recall@k for predictions [B,", "= X_true_binary.cuda() k_tensor = k_tensor.cuda() tmp = (X_true_binary * X_pred_binary).sum(dim=1).float()", "tmp = (X_true_binary * X_pred_binary).sum(dim=1).float() recall = tmp / torch.min(k_tensor,", "= X_pred.shape[0] # batch_size _, idx_topk = torch.topk(X_pred, k, dim=1,", "ground-truth [B, I]. \"\"\" batch_users = X_pred.shape[0] _, topk_indices =", "torch.topk(X_pred, k, dim=1, sorted=True) tp = 1. / torch.log2(torch.arange(2, k", "= torch.tensor([(tp[:min(n, k)]).sum() for n in heldout_nonzero]).to(device) return DCG /", "def recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\" Recall@k for predictions [B, I]", "Recall@k for predictions [B, I] and ground-truth [B, I]. \"\"\"", "I] k_tensor = torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available(): X_true_binary = X_true_binary.cuda()", "= (X_true_binary * X_pred_binary).sum(dim=1).float() recall = tmp / torch.min(k_tensor, X_true_binary.sum(dim=1).float())", "/ torch.log2(torch.arange(2, k + 2, device=device).float()) heldout_batch_nonzero = (heldout_batch >", "in heldout_nonzero]).to(device) return DCG / IDCG def recall_at_k_batch_torch(X_pred, heldout_batch, k=100):", "* tp).sum(dim=1) heldout_nonzero = (heldout_batch > 0).sum(dim=1) # num. of", "ground-truth [B, I], with binary relevance. ASSUMPTIONS: all the 0's", "dtype=torch.float32) if torch.cuda.is_available(): X_true_binary = X_true_binary.cuda() k_tensor = k_tensor.cuda() tmp", "ndcg_binary_at_k_batch_torch(X_pred, heldout_batch, k=100, device='cpu'): \"\"\" Normalized Discounted Cumulative Gain@k for", "2, device=device).float()) heldout_batch_nonzero = (heldout_batch > 0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users,", "batch. [B] IDCG = torch.tensor([(tp[:min(n, k)]).sum() for n in heldout_nonzero]).to(device)", "_, topk_indices = torch.topk(X_pred, k, dim=1, sorted=False) # [B, K]", "(heldout_batch > 0).float() # .toarray() # [B, I] k_tensor =", "for for predictions [B, I] and ground-truth [B, I], with", "if torch.cuda.is_available(): X_true_binary = X_true_binary.cuda() k_tensor = k_tensor.cuda() tmp =", "DCG / IDCG def recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\" Recall@k for", "if torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1 X_true_binary", "sorted=True) tp = 1. / torch.log2(torch.arange(2, k + 2, device=device).float())", "non-zero items per batch. [B] IDCG = torch.tensor([(tp[:min(n, k)]).sum() for", "0's in heldout_batch indicate 0 relevance. \"\"\" batch_users = X_pred.shape[0]", "and ground-truth [B, I], with binary relevance. ASSUMPTIONS: all the", "indicate 0 relevance. \"\"\" batch_users = X_pred.shape[0] # batch_size _,", "torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1 X_true_binary =", "0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1) heldout_nonzero =", "for predictions [B, I] and ground-truth [B, I], with binary", "topk_indices] = 1 X_true_binary = (heldout_batch > 0).float() # .toarray()", "sorted=False) # [B, K] X_pred_binary = torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary", "k=100): \"\"\" Recall@k for predictions [B, I] and ground-truth [B,", "all the 0's in heldout_batch indicate 0 relevance. \"\"\" batch_users", "batch_size _, idx_topk = torch.topk(X_pred, k, dim=1, sorted=True) tp =", "= torch.topk(X_pred, k, dim=1, sorted=True) tp = 1. / torch.log2(torch.arange(2,", "k, dim=1, sorted=True) tp = 1. / torch.log2(torch.arange(2, k +", "I] and ground-truth [B, I], with binary relevance. ASSUMPTIONS: all", "= 1 X_true_binary = (heldout_batch > 0).float() # .toarray() #", "topk_indices = torch.topk(X_pred, k, dim=1, sorted=False) # [B, K] X_pred_binary", "<filename>eval/metrics.py import torch def ndcg_binary_at_k_batch_torch(X_pred, heldout_batch, k=100, device='cpu'): \"\"\" Normalized", "torch.topk(X_pred, k, dim=1, sorted=False) # [B, K] X_pred_binary = torch.zeros_like(X_pred)", "[B, K] X_pred_binary = torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary = X_pred_binary.cuda()", "0).float() # .toarray() # [B, I] k_tensor = torch.tensor([k], dtype=torch.float32)", "recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\" Recall@k for predictions [B, I] and", "import torch def ndcg_binary_at_k_batch_torch(X_pred, heldout_batch, k=100, device='cpu'): \"\"\" Normalized Discounted", "Discounted Cumulative Gain@k for for predictions [B, I] and ground-truth", "device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1) heldout_nonzero = (heldout_batch > 0).sum(dim=1) #", "X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1 X_true_binary = (heldout_batch > 0).float()", "torch.cuda.is_available(): X_true_binary = X_true_binary.cuda() k_tensor = k_tensor.cuda() tmp = (X_true_binary", "torch.tensor([(tp[:min(n, k)]).sum() for n in heldout_nonzero]).to(device) return DCG / IDCG", "DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1) heldout_nonzero = (heldout_batch", "heldout_nonzero = (heldout_batch > 0).sum(dim=1) # num. of non-zero items", "= X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1 X_true_binary = (heldout_batch >", "Gain@k for for predictions [B, I] and ground-truth [B, I],", "relevance. ASSUMPTIONS: all the 0's in heldout_batch indicate 0 relevance.", "num. of non-zero items per batch. [B] IDCG = torch.tensor([(tp[:min(n,", "I] and ground-truth [B, I]. \"\"\" batch_users = X_pred.shape[0] _,", "heldout_batch indicate 0 relevance. \"\"\" batch_users = X_pred.shape[0] # batch_size", "1. / torch.log2(torch.arange(2, k + 2, device=device).float()) heldout_batch_nonzero = (heldout_batch", "> 0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1) heldout_nonzero", "torch.log2(torch.arange(2, k + 2, device=device).float()) heldout_batch_nonzero = (heldout_batch > 0).float()", "[B, I] and ground-truth [B, I], with binary relevance. ASSUMPTIONS:", "1 X_true_binary = (heldout_batch > 0).float() # .toarray() # [B,", "in heldout_batch indicate 0 relevance. \"\"\" batch_users = X_pred.shape[0] #", "k, dim=1, sorted=False) # [B, K] X_pred_binary = torch.zeros_like(X_pred) if", "X_true_binary.cuda() k_tensor = k_tensor.cuda() tmp = (X_true_binary * X_pred_binary).sum(dim=1).float() recall", "* X_pred_binary).sum(dim=1).float() recall = tmp / torch.min(k_tensor, X_true_binary.sum(dim=1).float()) return recall", "= X_pred.shape[0] _, topk_indices = torch.topk(X_pred, k, dim=1, sorted=False) #", "def ndcg_binary_at_k_batch_torch(X_pred, heldout_batch, k=100, device='cpu'): \"\"\" Normalized Discounted Cumulative Gain@k", "/ IDCG def recall_at_k_batch_torch(X_pred, heldout_batch, k=100): \"\"\" Recall@k for predictions", "# [B, I] k_tensor = torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available(): X_true_binary", "items per batch. [B] IDCG = torch.tensor([(tp[:min(n, k)]).sum() for n", "= 1. / torch.log2(torch.arange(2, k + 2, device=device).float()) heldout_batch_nonzero =", "heldout_batch, k=100): \"\"\" Recall@k for predictions [B, I] and ground-truth", "[B, I]. \"\"\" batch_users = X_pred.shape[0] _, topk_indices = torch.topk(X_pred,", "X_true_binary = X_true_binary.cuda() k_tensor = k_tensor.cuda() tmp = (X_true_binary *", "> 0).float() # .toarray() # [B, I] k_tensor = torch.tensor([k],", "0 relevance. \"\"\" batch_users = X_pred.shape[0] # batch_size _, idx_topk", "k + 2, device=device).float()) heldout_batch_nonzero = (heldout_batch > 0).float() DCG", "heldout_batch_nonzero = (heldout_batch > 0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk]", "dim=1, sorted=False) # [B, K] X_pred_binary = torch.zeros_like(X_pred) if torch.cuda.is_available():", "(heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1) heldout_nonzero = (heldout_batch > 0).sum(dim=1)", "= torch.topk(X_pred, k, dim=1, sorted=False) # [B, K] X_pred_binary =", "(heldout_batch > 0).sum(dim=1) # num. of non-zero items per batch.", "IDCG = torch.tensor([(tp[:min(n, k)]).sum() for n in heldout_nonzero]).to(device) return DCG", "[B, I] and ground-truth [B, I]. \"\"\" batch_users = X_pred.shape[0]", "# [B, K] X_pred_binary = torch.zeros_like(X_pred) if torch.cuda.is_available(): X_pred_binary =", "torch def ndcg_binary_at_k_batch_torch(X_pred, heldout_batch, k=100, device='cpu'): \"\"\" Normalized Discounted Cumulative", "= (heldout_batch > 0).float() # .toarray() # [B, I] k_tensor", "# num. of non-zero items per batch. [B] IDCG =", "per batch. [B] IDCG = torch.tensor([(tp[:min(n, k)]).sum() for n in", "tp = 1. / torch.log2(torch.arange(2, k + 2, device=device).float()) heldout_batch_nonzero", "_, idx_topk = torch.topk(X_pred, k, dim=1, sorted=True) tp = 1.", "> 0).sum(dim=1) # num. of non-zero items per batch. [B]", "X_pred_binary = X_pred_binary.cuda() X_pred_binary[torch.arange(batch_users).unsqueeze(1), topk_indices] = 1 X_true_binary = (heldout_batch", "Cumulative Gain@k for for predictions [B, I] and ground-truth [B,", "\"\"\" batch_users = X_pred.shape[0] _, topk_indices = torch.topk(X_pred, k, dim=1,", "= (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1), idx_topk] * tp).sum(dim=1) heldout_nonzero = (heldout_batch >", "[B, I], with binary relevance. ASSUMPTIONS: all the 0's in", "= k_tensor.cuda() tmp = (X_true_binary * X_pred_binary).sum(dim=1).float() recall = tmp", "X_true_binary = (heldout_batch > 0).float() # .toarray() # [B, I]", "device=device).float()) heldout_batch_nonzero = (heldout_batch > 0).float() DCG = (heldout_batch_nonzero[torch.arange(batch_users, device=device).unsqueeze(1),", "# .toarray() # [B, I] k_tensor = torch.tensor([k], dtype=torch.float32) if", "k_tensor.cuda() tmp = (X_true_binary * X_pred_binary).sum(dim=1).float() recall = tmp /", "[B, I] k_tensor = torch.tensor([k], dtype=torch.float32) if torch.cuda.is_available(): X_true_binary =" ]
[ "all CEF processes on error root = tk.Tk() app =", "cef.WindowUtils() # Platforms WINDOWS = (platform.system() == \"Windows\") LINUX =", "# Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self): window_info = cef.WindowInfo()", "if messagebox.askokcancel(\"Quit\", \"Do you want to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing)", "platform import logging as _logging # Fix for PyCharm hints", "ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0, 0, width, height, 0x0002) elif LINUX:", "self.browser = browser def OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component))", "self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() # Pack MainFrame", "WINDOWS = (platform.system() == \"Windows\") LINUX = (platform.system() == \"Linux\")", "this\" sys.excepthook = cef.ExceptHook # To shutdown all CEF processes", "\"Windows\") LINUX = (platform.system() == \"Linux\") MAC = (platform.system() ==", "weight=1) # MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\",", "self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser = cef.CreateBrowserSync(window_info, url=url) #todo assert self.browser", "def __init__(self, root): self.closing = False self.browser = None #", "self.browser: return self.browser return None def clear_browser_references(self): self.browser = None", "quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk must be initialized before", "v55.3+ required to run this\" sys.excepthook = cef.ExceptHook # To", "logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False) def on_close(self): if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references()", "event.height if self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0, 0,", "Fix for PyCharm hints warnings WindowUtils = cef.WindowUtils() # Platforms", "= event.width height = event.height if self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos(", "self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() #", "weight=1) tk.Grid.columnconfigure(root, 0, weight=1) # MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard')", "False self.browser = None # Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1)", "None class LoadHandler(object): def __init__(self, browser_frame): self.browser_frame = browser_frame class", "0, 0, 0, width, height, 0x0002) elif LINUX: self.browser.SetBounds(0, 0,", "MAC = (platform.system() == \"Darwin\") # Globals logger = _logging.getLogger(\"tkinter_.py\")", "messagebox except ImportError: import Tkinter as tk import sys import", "import tkinter as tk from tkinter import messagebox except ImportError:", "self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() # Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def", "root): self.closing = False self.browser = None # Root root.geometry(\"900x640\")", "# Tk must be initialized before CEF otherwise fatal error", "\"Linux\") MAC = (platform.system() == \"Darwin\") # Globals logger =", "class FocusHandler(object): def __init__(self, browser): self.browser = browser def OnTakeFocus(self,", "initialized before CEF otherwise fatal error (Issue #306) cef.Initialize() root.mainloop()", "from cefpython3 import cefpython as cef import ctypes try: import", "import logging as _logging # Fix for PyCharm hints warnings", "CEF focus issues (#255). Call browser frame's focus_set to get", "self.browser = None # Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root,", "FocusHandler(object): def __init__(self, browser): self.browser = browser def OnTakeFocus(self, next_component,", "root = tk.Tk() app = MainFrame(root) def on_closing(): if messagebox.askokcancel(\"Quit\",", "hints warnings WindowUtils = cef.WindowUtils() # Platforms WINDOWS = (platform.system()", "rect = [0, 0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser =", "= \"localhost:8050/\" class MainFrame(tk.Frame): def __init__(self, root): self.closing = False", "raise Exception(\"Couldn't obtain window handle\") def message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work)", "ctypes try: import tkinter as tk from tkinter import messagebox", "All credit to https://stackoverflow.com/questions/46571448/tkinter-and-a-html-file - thanks DELICA - https://stackoverflow.com/users/7027346/delica from", "self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self): if self.winfo_id() > 0: return self.winfo_id()", "Call browser frame's focus_set to get rid of type cursor", "__init__(self, browser_frame): self.browser_frame = browser_frame class FocusHandler(object): def __init__(self, browser):", "self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy() def get_browser(self): if self.browser: return", "logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler)", "on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False) def on_close(self): if self.browser:", "DELICA - https://stackoverflow.com/users/7027346/delica from cefpython3 import cefpython as cef import", "except ImportError: import Tkinter as tk import sys import platform", "= None class LoadHandler(object): def __init__(self, browser_frame): self.browser_frame = browser_frame", "root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk must be initialized before CEF otherwise", "tkinter as tk from tkinter import messagebox except ImportError: import", "self.after(10, self.message_loop_work) def on_configure(self, event): width = event.width height =", "rect) self.browser = cef.CreateBrowserSync(window_info, url=url) #todo assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self))", "of type cursor in url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() #", "rid of type cursor in url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set()", "_logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\" class MainFrame(tk.Frame): def __init__(self, root): self.closing", "to get rid of type cursor in url entry widget.\"\"\"", "required to run this\" sys.excepthook = cef.ExceptHook # To shutdown", "sys import platform import logging as _logging # Fix for", "warnings WindowUtils = cef.WindowUtils() # Platforms WINDOWS = (platform.system() ==", "height, 0x0002) elif LINUX: self.browser.SetBounds(0, 0, width, height) self.browser.NotifyMoveOrResizeStarted() if", "# Platforms WINDOWS = (platform.system() == \"Windows\") LINUX = (platform.system()", "class LoadHandler(object): def __init__(self, browser_frame): self.browser_frame = browser_frame class FocusHandler(object):", "'__main__': logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter)", "def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False) def on_close(self): if", "__init__(self, root): self.closing = False self.browser = None # Root", "height = event.height if self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0,", "def __init__(self, browser_frame): self.browser_frame = browser_frame class FocusHandler(object): def __init__(self,", "if self.browser: return self.browser return None def clear_browser_references(self): self.browser =", "0: return self.winfo_id() else: raise Exception(\"Couldn't obtain window handle\") def", "%(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(),", "to run this\" sys.excepthook = cef.ExceptHook # To shutdown all", "error root = tk.Tk() app = MainFrame(root) def on_closing(): if", "return None def clear_browser_references(self): self.browser = None class LoadHandler(object): def", "https://stackoverflow.com/questions/46571448/tkinter-and-a-html-file - thanks DELICA - https://stackoverflow.com/users/7027346/delica from cefpython3 import cefpython", "expand=tk.YES) def embed_browser(self): window_info = cef.WindowInfo() rect = [0, 0,", "OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def OnSetFocus(self, source, **_):", "focus issues (#255). Call browser frame's focus_set to get rid", "to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk must be initialized", "= MainFrame(root) def on_closing(): if messagebox.askokcancel(\"Quit\", \"Do you want to", "def get_browser(self): if self.browser: return self.browser return None def clear_browser_references(self):", "window_info.SetAsChild(self.get_window_handle(), rect) self.browser = cef.CreateBrowserSync(window_info, url=url) #todo assert self.browser self.browser.SetClientHandler(LoadHandler(self))", "root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root, 0, weight=1) # MainFrame tk.Frame.__init__(self,", "Tkinter as tk import sys import platform import logging as", "class MainFrame(tk.Frame): def __init__(self, root): self.closing = False self.browser =", "source={source}\" .format(source=source)) return False def OnGotFocus(self, **_): \"\"\"Fix CEF focus", "entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if __name__ == '__main__': logger.setLevel(_logging.INFO)", "\"\"\"Fix CEF focus issues (#255). Call browser frame's focus_set to", "focus_set to get rid of type cursor in url entry", "if self.browser: self.browser.SetFocus(True) self.focus_set() def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser:", "to https://stackoverflow.com/questions/46571448/tkinter-and-a-html-file - thanks DELICA - https://stackoverflow.com/users/7027346/delica from cefpython3 import", "MainFrame(tk.Frame): def __init__(self, root): self.closing = False self.browser = None", "= browser_frame class FocusHandler(object): def __init__(self, browser): self.browser = browser", "logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk", "To shutdown all CEF processes on error root = tk.Tk()", "self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out)", "root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\",", "self.destroy() self.master.destroy() def get_browser(self): if self.browser: return self.browser return None", "for PyCharm hints warnings WindowUtils = cef.WindowUtils() # Platforms WINDOWS", "[0, 0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser = cef.CreateBrowserSync(window_info, url=url)", "(#255). Call browser frame's focus_set to get rid of type", "cefpython as cef import ctypes try: import tkinter as tk", "handle\") def message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work) def on_configure(self, event): width", "as cef import ctypes try: import tkinter as tk from", "assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self): if self.winfo_id() >", "before CEF otherwise fatal error (Issue #306) cef.Initialize() root.mainloop() #", "cef.ExceptHook # To shutdown all CEF processes on error root", "\"55.3\", \"CEF Python v55.3+ required to run this\" sys.excepthook =", "self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self): if self.winfo_id() > 0:", "**_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\"", "embed_browser(self): window_info = cef.WindowInfo() rect = [0, 0, self.winfo_width(), self.winfo_height()]", "self.clear_browser_references() self.destroy() self.master.destroy() def get_browser(self): if self.browser: return self.browser return", "WindowUtils = cef.WindowUtils() # Platforms WINDOWS = (platform.system() == \"Windows\")", "CEF processes on error root = tk.Tk() app = MainFrame(root)", "None def clear_browser_references(self): self.browser = None class LoadHandler(object): def __init__(self,", "must be initialized before CEF otherwise fatal error (Issue #306)", "import cefpython as cef import ctypes try: import tkinter as", "LINUX = (platform.system() == \"Linux\") MAC = (platform.system() == \"Darwin\")", "sys.excepthook = cef.ExceptHook # To shutdown all CEF processes on", "cef import ctypes try: import tkinter as tk from tkinter", "= _logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python", "tk.Tk() app = MainFrame(root) def on_closing(): if messagebox.askokcancel(\"Quit\", \"Do you", "self.browser.NotifyMoveOrResizeStarted() if not self.browser: self.embed_browser() def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if", "self.browser = cef.CreateBrowserSync(window_info, url=url) #todo assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work()", "__init__(self, browser): self.browser = browser def OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus,", "# Fix for PyCharm hints warnings WindowUtils = cef.WindowUtils() #", ".format(next=next_component)) def OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return False", "self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() # Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES)", "{ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert", "else: raise Exception(\"Couldn't obtain window handle\") def message_loop_work(self): cef.MessageLoopWork() self.after(10,", "logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source))", "self.winfo_id() else: raise Exception(\"Couldn't obtain window handle\") def message_loop_work(self): cef.MessageLoopWork()", "on_closing) # Tk must be initialized before CEF otherwise fatal", "= False self.browser = None # Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0,", "\"CEF Python v55.3+ required to run this\" sys.excepthook = cef.ExceptHook", "logging as _logging # Fix for PyCharm hints warnings WindowUtils", "thanks DELICA - https://stackoverflow.com/users/7027346/delica from cefpython3 import cefpython as cef", "as tk import sys import platform import logging as _logging", "if not self.browser: self.embed_browser() def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser:", "Globals logger = _logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\" class MainFrame(tk.Frame): def", "0, 0, width, height, 0x0002) elif LINUX: self.browser.SetBounds(0, 0, width,", "root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk must be initialized before CEF", "width = event.width height = event.height if self.browser: if WINDOWS:", "**_): \"\"\"Fix CEF focus issues (#255). Call browser frame's focus_set", "def clear_browser_references(self): self.browser = None class LoadHandler(object): def __init__(self, browser_frame):", "0x0002) elif LINUX: self.browser.SetBounds(0, 0, width, height) self.browser.NotifyMoveOrResizeStarted() if not", "window_info = cef.WindowInfo() rect = [0, 0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(),", "logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return False def OnGotFocus(self, **_): \"\"\"Fix CEF", "Python v55.3+ required to run this\" sys.excepthook = cef.ExceptHook #", "self.browser: self.embed_browser() def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True) self.focus_set()", "- https://stackoverflow.com/users/7027346/delica from cefpython3 import cefpython as cef import ctypes", "# Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root, 0, weight=1) #", "OnGotFocus(self, **_): \"\"\"Fix CEF focus issues (#255). Call browser frame's", "if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy() def get_browser(self): if self.browser:", "if __name__ == '__main__': logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler() formatter =", "browser): self.browser = browser def OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\"", "PyCharm hints warnings WindowUtils = cef.WindowUtils() # Platforms WINDOWS =", "self.winfo_id() > 0: return self.winfo_id() else: raise Exception(\"Couldn't obtain window", "cef.__version__ >= \"55.3\", \"CEF Python v55.3+ required to run this\"", "self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self): if self.winfo_id() > 0: return", "None # Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root, 0, weight=1)", "import platform import logging as _logging # Fix for PyCharm", "get rid of type cursor in url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\")", "def __init__(self, browser): self.browser = browser def OnTakeFocus(self, next_component, **_):", "on_close(self): if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy() def get_browser(self): if", "event.width height = event.height if self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(),", "logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True) self.focus_set() def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if", "= tk.Tk() app = MainFrame(root) def on_closing(): if messagebox.askokcancel(\"Quit\", \"Do", "tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root, 0, weight=1) # MainFrame tk.Frame.__init__(self, root)", "= cef.WindowUtils() # Platforms WINDOWS = (platform.system() == \"Windows\") LINUX", "browser frame's focus_set to get rid of type cursor in", "cef.MessageLoopWork() self.after(10, self.message_loop_work) def on_configure(self, event): width = event.width height", "\"localhost:8050/\" class MainFrame(tk.Frame): def __init__(self, root): self.closing = False self.browser", "__name__ == '__main__': logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s]", "logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__ >= \"55.3\", \"CEF Python v55.3+", "if self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0, 0, width,", "cef.CreateBrowserSync(window_info, url=url) #todo assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self):", "def on_close(self): if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy() def get_browser(self):", "# if __name__ == '__main__': logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler() formatter", "= event.height if self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0,", "elif LINUX: self.browser.SetBounds(0, 0, width, height) self.browser.NotifyMoveOrResizeStarted() if not self.browser:", "OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return False def OnGotFocus(self,", "self.browser = None class LoadHandler(object): def __init__(self, browser_frame): self.browser_frame =", "ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__ >= \"55.3\", \"CEF", "stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0]))", "in url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if __name__ ==", "import messagebox except ImportError: import Tkinter as tk import sys", "type cursor in url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if", "if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0, 0, width, height, 0x0002)", "as tk from tkinter import messagebox except ImportError: import Tkinter", "assert cef.__version__ >= \"55.3\", \"CEF Python v55.3+ required to run", "width, height) self.browser.NotifyMoveOrResizeStarted() if not self.browser: self.embed_browser() def on_focus_in(self, _):", "CEF otherwise fatal error (Issue #306) cef.Initialize() root.mainloop() # app.mainloop()", "self.on_focus_out) self.focus_set() # Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self): window_info", "> 0: return self.winfo_id() else: raise Exception(\"Couldn't obtain window handle\")", "logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if __name__ == '__main__': logger.setLevel(_logging.INFO) stream_handler =", "= cef.WindowInfo() rect = [0, 0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect)", "- thanks DELICA - https://stackoverflow.com/users/7027346/delica from cefpython3 import cefpython as", "self.focus_set() # Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self): window_info =", "if self.winfo_id() > 0: return self.winfo_id() else: raise Exception(\"Couldn't obtain", "\"Darwin\") # Globals logger = _logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\" class", "= (platform.system() == \"Linux\") MAC = (platform.system() == \"Darwin\") #", "on_closing(): if messagebox.askokcancel(\"Quit\", \"Do you want to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\",", "next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus,", "Exception(\"Couldn't obtain window handle\") def message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work) def", "_logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format(", "= _logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\" class MainFrame(tk.Frame): def __init__(self, root):", "width, height, 0x0002) elif LINUX: self.browser.SetBounds(0, 0, width, height) self.browser.NotifyMoveOrResizeStarted()", "def get_window_handle(self): if self.winfo_id() > 0: return self.winfo_id() else: raise", "def embed_browser(self): window_info = cef.WindowInfo() rect = [0, 0, self.winfo_width(),", "processes on error root = tk.Tk() app = MainFrame(root) def", "you want to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk must", "import sys import platform import logging as _logging # Fix", "logger = _logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\" class MainFrame(tk.Frame): def __init__(self,", "self.browser.focus_set() # if __name__ == '__main__': logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler()", "tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in)", "self.browser.SetFocus(True) self.focus_set() def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False) def", "MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self): window_info = cef.WindowInfo() rect =", "LoadHandler(object): def __init__(self, browser_frame): self.browser_frame = browser_frame class FocusHandler(object): def", "# MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure)", "# To shutdown all CEF processes on error root =", "clear_browser_references(self): self.browser = None class LoadHandler(object): def __init__(self, browser_frame): self.browser_frame", "tk.Grid.columnconfigure(root, 0, weight=1) # MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\",", "import ctypes try: import tkinter as tk from tkinter import", "https://stackoverflow.com/users/7027346/delica from cefpython3 import cefpython as cef import ctypes try:", "== \"Windows\") LINUX = (platform.system() == \"Linux\") MAC = (platform.system()", "= browser def OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def", "def on_closing(): if messagebox.askokcancel(\"Quit\", \"Do you want to quit?\"): root.destroy()", "0, weight=1) # MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close)", "_): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True) self.focus_set() def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\")", "message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work) def on_configure(self, event): width = event.width", "tkinter import messagebox except ImportError: import Tkinter as tk import", "def message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work) def on_configure(self, event): width =", "messagebox.askokcancel(\"Quit\", \"Do you want to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) #", "cefpython3 import cefpython as cef import ctypes try: import tkinter", "logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info", "ImportError: import Tkinter as tk import sys import platform import", "try: import tkinter as tk from tkinter import messagebox except", "if self.browser: self.browser.SetFocus(False) def on_close(self): if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy()", "self.browser: if WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0, 0, width, height,", "def OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def OnSetFocus(self, source,", "MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\",", "MainFrame(root) def on_closing(): if messagebox.askokcancel(\"Quit\", \"Do you want to quit?\"):", "self.embed_browser() def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True) self.focus_set() def", "self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser = cef.CreateBrowserSync(window_info, url=url) #todo assert", "height) self.browser.NotifyMoveOrResizeStarted() if not self.browser: self.embed_browser() def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\")", "self.browser return None def clear_browser_references(self): self.browser = None class LoadHandler(object):", "Dashboard') self.master.protocol(\"WM_DELETE_WINDOW\", self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set()", "credit to https://stackoverflow.com/questions/46571448/tkinter-and-a-html-file - thanks DELICA - https://stackoverflow.com/users/7027346/delica from cefpython3", "\"Do you want to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk", "{ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__ >=", "# All credit to https://stackoverflow.com/questions/46571448/tkinter-and-a-html-file - thanks DELICA - https://stackoverflow.com/users/7027346/delica", "= (platform.system() == \"Darwin\") # Globals logger = _logging.getLogger(\"tkinter_.py\") url", "self.browser.SetBounds(0, 0, width, height) self.browser.NotifyMoveOrResizeStarted() if not self.browser: self.embed_browser() def", "app = MainFrame(root) def on_closing(): if messagebox.askokcancel(\"Quit\", \"Do you want", "self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() # Pack MainFrame self.pack(fill=tk.BOTH,", "= None # Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root, 0,", "get_window_handle(self): if self.winfo_id() > 0: return self.winfo_id() else: raise Exception(\"Couldn't", "== \"Linux\") MAC = (platform.system() == \"Darwin\") # Globals logger", "self.browser.SetFocus(False) def on_close(self): if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy() def", "{ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__ >= \"55.3\", \"CEF Python v55.3+ required", "== \"Darwin\") # Globals logger = _logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\"", "WINDOWS: ctypes.windll.user32.SetWindowPos( self.browser.GetWindowHandle(), 0, 0, 0, width, height, 0x0002) elif", "0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser = cef.CreateBrowserSync(window_info, url=url) #todo", "formatter = _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python", "logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__", "self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy() def get_browser(self): if self.browser: return self.browser", "self.message_loop_work) def on_configure(self, event): width = event.width height = event.height", "def OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return False def", "stream_handler = _logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF", "url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if __name__ == '__main__':", "self.browser_frame = browser_frame class FocusHandler(object): def __init__(self, browser): self.browser =", "tk from tkinter import messagebox except ImportError: import Tkinter as", "widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if __name__ == '__main__': logger.setLevel(_logging.INFO) stream_handler", "Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self): window_info = cef.WindowInfo() rect", "otherwise fatal error (Issue #306) cef.Initialize() root.mainloop() # app.mainloop() cef.Shutdown()", "Platforms WINDOWS = (platform.system() == \"Windows\") LINUX = (platform.system() ==", "{arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__ >= \"55.3\",", "want to quit?\"): root.destroy() root.protocol(\"WM_DELETE_WINDOW\", on_closing) # Tk must be", "(platform.system() == \"Windows\") LINUX = (platform.system() == \"Linux\") MAC =", "return False def OnGotFocus(self, **_): \"\"\"Fix CEF focus issues (#255).", "LINUX: self.browser.SetBounds(0, 0, width, height) self.browser.NotifyMoveOrResizeStarted() if not self.browser: self.embed_browser()", "cef.WindowInfo() rect = [0, 0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser", "(platform.system() == \"Darwin\") # Globals logger = _logging.getLogger(\"tkinter_.py\") url =", "# Globals logger = _logging.getLogger(\"tkinter_.py\") url = \"localhost:8050/\" class MainFrame(tk.Frame):", "def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True) self.focus_set() def on_focus_out(self,", "browser def OnTakeFocus(self, next_component, **_): logger.debug(\"FocusHandler.OnTakeFocus, next={next}\" .format(next=next_component)) def OnSetFocus(self,", "shutdown all CEF processes on error root = tk.Tk() app", "Tk must be initialized before CEF otherwise fatal error (Issue", "issues (#255). Call browser frame's focus_set to get rid of", "= _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver}", ".format(source=source)) return False def OnGotFocus(self, **_): \"\"\"Fix CEF focus issues", "self.focus_set() def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False) def on_close(self):", "False def OnGotFocus(self, **_): \"\"\"Fix CEF focus issues (#255). Call", "_): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False) def on_close(self): if self.browser: self.browser.CloseBrowser(True)", "(platform.system() == \"Linux\") MAC = (platform.system() == \"Darwin\") # Globals", "browser_frame): self.browser_frame = browser_frame class FocusHandler(object): def __init__(self, browser): self.browser", "0, width, height) self.browser.NotifyMoveOrResizeStarted() if not self.browser: self.embed_browser() def on_focus_in(self,", "browser_frame class FocusHandler(object): def __init__(self, browser): self.browser = browser def", "url = \"localhost:8050/\" class MainFrame(tk.Frame): def __init__(self, root): self.closing =", "#todo assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self): if self.winfo_id()", ">= \"55.3\", \"CEF Python v55.3+ required to run this\" sys.excepthook", "window handle\") def message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work) def on_configure(self, event):", "def on_configure(self, event): width = event.width height = event.height if", "= (platform.system() == \"Windows\") LINUX = (platform.system() == \"Linux\") MAC", "= [0, 0, self.winfo_width(), self.winfo_height()] window_info.SetAsChild(self.get_window_handle(), rect) self.browser = cef.CreateBrowserSync(window_info,", "as _logging # Fix for PyCharm hints warnings WindowUtils =", "**_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return False def OnGotFocus(self, **_): \"\"\"Fix", "self.browser: self.browser.SetFocus(False) def on_close(self): if self.browser: self.browser.CloseBrowser(True) self.clear_browser_references() self.destroy() self.master.destroy()", "self.message_loop_work() def get_window_handle(self): if self.winfo_id() > 0: return self.winfo_id() else:", "not self.browser: self.embed_browser() def on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True)", "next={next}\" .format(next=next_component)) def OnSetFocus(self, source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return", "self.on_close) self.bind(\"<Configure>\", self.on_configure) self.bind(\"<FocusIn>\", self.on_focus_in) self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() # Pack", "self.bind(\"<FocusOut>\", self.on_focus_out) self.focus_set() # Pack MainFrame self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self):", "self.master.destroy() def get_browser(self): if self.browser: return self.browser return None def", "self.browser.GetWindowHandle(), 0, 0, 0, width, height, 0x0002) elif LINUX: self.browser.SetBounds(0,", "on_focus_in(self, _): logger.debug(\"BrowserFrame.on_focus_in\") if self.browser: self.browser.SetFocus(True) self.focus_set() def on_focus_out(self, _):", "== '__main__': logger.setLevel(_logging.INFO) stream_handler = _logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s] %(message)s\")", "self.pack(fill=tk.BOTH, expand=tk.YES) def embed_browser(self): window_info = cef.WindowInfo() rect = [0,", "def OnGotFocus(self, **_): \"\"\"Fix CEF focus issues (#255). Call browser", "obtain window handle\") def message_loop_work(self): cef.MessageLoopWork() self.after(10, self.message_loop_work) def on_configure(self,", "= cef.ExceptHook # To shutdown all CEF processes on error", "be initialized before CEF otherwise fatal error (Issue #306) cef.Initialize()", "run this\" sys.excepthook = cef.ExceptHook # To shutdown all CEF", "import Tkinter as tk import sys import platform import logging", "event): width = event.width height = event.height if self.browser: if", "return self.browser return None def clear_browser_references(self): self.browser = None class", "from tkinter import messagebox except ImportError: import Tkinter as tk", "tk import sys import platform import logging as _logging #", "cursor in url entry widget.\"\"\" logger.debug(\"FocusHandler.OnGotFocus\") self.browser.focus_set() # if __name__", "= cef.CreateBrowserSync(window_info, url=url) #todo assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def", "patchlevel'))) assert cef.__version__ >= \"55.3\", \"CEF Python v55.3+ required to", "_logging.StreamHandler() formatter = _logging.Formatter(\"[%(filename)s] %(message)s\") stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) logger.info(\"CEF Python {ver}\".format(ver=cef.__version__))", "Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root, 0, weight=1) tk.Grid.columnconfigure(root, 0, weight=1) # MainFrame", "on error root = tk.Tk() app = MainFrame(root) def on_closing():", "Python {ver}\".format(ver=cef.__version__)) logger.info(\"Python {ver} {arch}\".format( ver=platform.python_version(), arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel')))", "arch=platform.architecture()[0])) logger.info(\"Tk {ver}\".format(ver=tk.Tcl().eval('info patchlevel'))) assert cef.__version__ >= \"55.3\", \"CEF Python", "_logging # Fix for PyCharm hints warnings WindowUtils = cef.WindowUtils()", "url=url) #todo assert self.browser self.browser.SetClientHandler(LoadHandler(self)) self.browser.SetClientHandler(FocusHandler(self)) self.message_loop_work() def get_window_handle(self): if", "0, width, height, 0x0002) elif LINUX: self.browser.SetBounds(0, 0, width, height)", "source, **_): logger.debug(\"FocusHandler.OnSetFocus, source={source}\" .format(source=source)) return False def OnGotFocus(self, **_):", "frame's focus_set to get rid of type cursor in url", "get_browser(self): if self.browser: return self.browser return None def clear_browser_references(self): self.browser", "on_configure(self, event): width = event.width height = event.height if self.browser:", "0, weight=1) tk.Grid.columnconfigure(root, 0, weight=1) # MainFrame tk.Frame.__init__(self, root) self.master.title('SimBA", "return self.winfo_id() else: raise Exception(\"Couldn't obtain window handle\") def message_loop_work(self):", "self.browser: self.browser.SetFocus(True) self.focus_set() def on_focus_out(self, _): logger.debug(\"BrowserFrame.on_focus_out\") if self.browser: self.browser.SetFocus(False)", "self.closing = False self.browser = None # Root root.geometry(\"900x640\") tk.Grid.rowconfigure(root," ]
[ "for m in matches: line = line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return", "re def do_substitution(in_lines): lines_iter = iter(in_lines) defn_lines = [] while", "from glob import glob import os.path as osp infiles =", "= re.compile(\"\\$\\((.+?)\\)\") out_lines = [] for line in lines_iter: matches", "out_lines.append(line) return out_lines from glob import glob import os.path as", "os.path as osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in infiles:", "pat = re.compile(\"\\$\\((.+?)\\)\") out_lines = [] for line in lines_iter:", "matches = pat.finditer(line) for m in matches: line = line.replace(m.group(0),", "True: try: line = lines_iter.next() except StopIteration: raise RuntimeError(\"didn't find", "break else: defn_lines.append(line) d = {} exec(\"\\n\".join(defn_lines), d) pat =", "pat.finditer(line) for m in matches: line = line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line)", "glob import os.path as osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname", "= fh.readlines() out_lines = do_substitution(in_lines) outfname = fname[:-3] with open(outfname,\"w\")", "for line in lines_iter: matches = pat.finditer(line) for m in", "glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in infiles: with open(fname,\"r\") as fh: in_lines", "fh: in_lines = fh.readlines() out_lines = do_substitution(in_lines) outfname = fname[:-3]", "= do_substitution(in_lines) outfname = fname[:-3] with open(outfname,\"w\") as fh: fh.writelines(out_lines)", "import os.path as osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in", "iter(in_lines) defn_lines = [] while True: try: line = lines_iter.next()", "for fname in infiles: with open(fname,\"r\") as fh: in_lines =", "line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return out_lines from glob import glob import", "= [] while True: try: line = lines_iter.next() except StopIteration:", "m in matches: line = line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return out_lines", "else: defn_lines.append(line) d = {} exec(\"\\n\".join(defn_lines), d) pat = re.compile(\"\\$\\((.+?)\\)\")", "as fh: in_lines = fh.readlines() out_lines = do_substitution(in_lines) outfname =", "glob import glob import os.path as osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\"))", "infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in infiles: with open(fname,\"r\") as", "= [] for line in lines_iter: matches = pat.finditer(line) for", "osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in infiles: with open(fname,\"r\")", "do_substitution(in_lines): lines_iter = iter(in_lines) defn_lines = [] while True: try:", "while True: try: line = lines_iter.next() except StopIteration: raise RuntimeError(\"didn't", "= lines_iter.next() except StopIteration: raise RuntimeError(\"didn't find line starting with", "import re def do_substitution(in_lines): lines_iter = iter(in_lines) defn_lines = []", "infiles: with open(fname,\"r\") as fh: in_lines = fh.readlines() out_lines =", "line starting with ---\") if line.startswith('---'): break else: defn_lines.append(line) d", "defn_lines.append(line) d = {} exec(\"\\n\".join(defn_lines), d) pat = re.compile(\"\\$\\((.+?)\\)\") out_lines", "<gh_stars>0 import re def do_substitution(in_lines): lines_iter = iter(in_lines) defn_lines =", "str(eval(m.group(1),d))) out_lines.append(line) return out_lines from glob import glob import os.path", "import glob import os.path as osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for", "with ---\") if line.startswith('---'): break else: defn_lines.append(line) d = {}", "line = lines_iter.next() except StopIteration: raise RuntimeError(\"didn't find line starting", "= pat.finditer(line) for m in matches: line = line.replace(m.group(0), str(eval(m.group(1),d)))", "---\") if line.startswith('---'): break else: defn_lines.append(line) d = {} exec(\"\\n\".join(defn_lines),", "raise RuntimeError(\"didn't find line starting with ---\") if line.startswith('---'): break", "out_lines = do_substitution(in_lines) outfname = fname[:-3] with open(outfname,\"w\") as fh:", "lines_iter: matches = pat.finditer(line) for m in matches: line =", "lines_iter = iter(in_lines) defn_lines = [] while True: try: line", "try: line = lines_iter.next() except StopIteration: raise RuntimeError(\"didn't find line", "RuntimeError(\"didn't find line starting with ---\") if line.startswith('---'): break else:", "in lines_iter: matches = pat.finditer(line) for m in matches: line", "= glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in infiles: with open(fname,\"r\") as fh:", "fh.readlines() out_lines = do_substitution(in_lines) outfname = fname[:-3] with open(outfname,\"w\") as", "def do_substitution(in_lines): lines_iter = iter(in_lines) defn_lines = [] while True:", "in infiles: with open(fname,\"r\") as fh: in_lines = fh.readlines() out_lines", "open(fname,\"r\") as fh: in_lines = fh.readlines() out_lines = do_substitution(in_lines) outfname", "find line starting with ---\") if line.startswith('---'): break else: defn_lines.append(line)", "starting with ---\") if line.startswith('---'): break else: defn_lines.append(line) d =", "= line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return out_lines from glob import glob", "line.startswith('---'): break else: defn_lines.append(line) d = {} exec(\"\\n\".join(defn_lines), d) pat", "[] for line in lines_iter: matches = pat.finditer(line) for m", "lines_iter.next() except StopIteration: raise RuntimeError(\"didn't find line starting with ---\")", "line = line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return out_lines from glob import", "in matches: line = line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return out_lines from", "[] while True: try: line = lines_iter.next() except StopIteration: raise", "StopIteration: raise RuntimeError(\"didn't find line starting with ---\") if line.startswith('---'):", "{} exec(\"\\n\".join(defn_lines), d) pat = re.compile(\"\\$\\((.+?)\\)\") out_lines = [] for", "= iter(in_lines) defn_lines = [] while True: try: line =", "= {} exec(\"\\n\".join(defn_lines), d) pat = re.compile(\"\\$\\((.+?)\\)\") out_lines = []", "except StopIteration: raise RuntimeError(\"didn't find line starting with ---\") if", "re.compile(\"\\$\\((.+?)\\)\") out_lines = [] for line in lines_iter: matches =", "matches: line = line.replace(m.group(0), str(eval(m.group(1),d))) out_lines.append(line) return out_lines from glob", "out_lines = [] for line in lines_iter: matches = pat.finditer(line)", "return out_lines from glob import glob import os.path as osp", "d = {} exec(\"\\n\".join(defn_lines), d) pat = re.compile(\"\\$\\((.+?)\\)\") out_lines =", "line in lines_iter: matches = pat.finditer(line) for m in matches:", "in_lines = fh.readlines() out_lines = do_substitution(in_lines) outfname = fname[:-3] with", "with open(fname,\"r\") as fh: in_lines = fh.readlines() out_lines = do_substitution(in_lines)", "d) pat = re.compile(\"\\$\\((.+?)\\)\") out_lines = [] for line in", "as osp infiles = glob(osp.join(osp.dirname(__file__),\"*.xml.in\")) for fname in infiles: with", "if line.startswith('---'): break else: defn_lines.append(line) d = {} exec(\"\\n\".join(defn_lines), d)", "exec(\"\\n\".join(defn_lines), d) pat = re.compile(\"\\$\\((.+?)\\)\") out_lines = [] for line", "out_lines from glob import glob import os.path as osp infiles", "defn_lines = [] while True: try: line = lines_iter.next() except", "fname in infiles: with open(fname,\"r\") as fh: in_lines = fh.readlines()" ]
[ "dependencies = [ ('events', '0001_initial'), ] operations = [ migrations.AlterModelOptions(", "'0001_initial'), ] operations = [ migrations.AlterModelOptions( name='eventhero', options={'verbose_name_plural': 'Event heroes'},", "Migration(migrations.Migration): dependencies = [ ('events', '0001_initial'), ] operations = [", "by Django 3.2.12 on 2022-03-28 11:57 from django.db import migrations", "<reponame>cahyareza/django_admin_cookbook # Generated by Django 3.2.12 on 2022-03-28 11:57 from", "on 2022-03-28 11:57 from django.db import migrations class Migration(migrations.Migration): dependencies", "[ ('events', '0001_initial'), ] operations = [ migrations.AlterModelOptions( name='eventhero', options={'verbose_name_plural':", "operations = [ migrations.AlterModelOptions( name='eventhero', options={'verbose_name_plural': 'Event heroes'}, ), ]", "Generated by Django 3.2.12 on 2022-03-28 11:57 from django.db import", "django.db import migrations class Migration(migrations.Migration): dependencies = [ ('events', '0001_initial'),", "migrations class Migration(migrations.Migration): dependencies = [ ('events', '0001_initial'), ] operations", "] operations = [ migrations.AlterModelOptions( name='eventhero', options={'verbose_name_plural': 'Event heroes'}, ),", "import migrations class Migration(migrations.Migration): dependencies = [ ('events', '0001_initial'), ]", "3.2.12 on 2022-03-28 11:57 from django.db import migrations class Migration(migrations.Migration):", "# Generated by Django 3.2.12 on 2022-03-28 11:57 from django.db", "('events', '0001_initial'), ] operations = [ migrations.AlterModelOptions( name='eventhero', options={'verbose_name_plural': 'Event", "Django 3.2.12 on 2022-03-28 11:57 from django.db import migrations class", "= [ ('events', '0001_initial'), ] operations = [ migrations.AlterModelOptions( name='eventhero',", "2022-03-28 11:57 from django.db import migrations class Migration(migrations.Migration): dependencies =", "class Migration(migrations.Migration): dependencies = [ ('events', '0001_initial'), ] operations =", "11:57 from django.db import migrations class Migration(migrations.Migration): dependencies = [", "from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('events'," ]
[ "# encoding: utf-8 import sys import urllib.parse import selenium.webdriver def", "to detect connectivity # we instead visit another site that", "utf-8 import sys import urllib.parse import selenium.webdriver def exit(): driver.quit()", "used to detect connectivity # we instead visit another site", "connectivitycheck.gstatic.com are not blocked # therefore, they cannot be used", "#!/usr/bin/env python3 # encoding: utf-8 import sys import urllib.parse import", "are not blocked # therefore, they cannot be used to", "that is known not to ever have TLS driver.get('http://neverssl.com') if", "connectivity # we instead visit another site that is known", "sys import urllib.parse import selenium.webdriver def exit(): driver.quit() sys.exit(0) driver", "sys.exit(0) driver = selenium.webdriver.Firefox() # for some reason, detectportal.firefox.com and", "visit another site that is known not to ever have", "known not to ever have TLS driver.get('http://neverssl.com') if 'neverssl.com' in", "def exit(): driver.quit() sys.exit(0) driver = selenium.webdriver.Firefox() # for some", "TLS driver.get('http://neverssl.com') if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc: exit() driver.find_element_by_css_selector('label[for=\"promo_button\"]').click() driver.find_element_by_css_selector('input[alt=\"Next\"]').click() driver.find_element_by_css_selector('#PromotionCode').send_keys('lobby18')", "for some reason, detectportal.firefox.com and connectivitycheck.gstatic.com are not blocked #", "detect connectivity # we instead visit another site that is", "we instead visit another site that is known not to", "another site that is known not to ever have TLS", "# we instead visit another site that is known not", "instead visit another site that is known not to ever", "to ever have TLS driver.get('http://neverssl.com') if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc: exit()", "exit(): driver.quit() sys.exit(0) driver = selenium.webdriver.Firefox() # for some reason,", "therefore, they cannot be used to detect connectivity # we", "detectportal.firefox.com and connectivitycheck.gstatic.com are not blocked # therefore, they cannot", "site that is known not to ever have TLS driver.get('http://neverssl.com')", "cannot be used to detect connectivity # we instead visit", "reason, detectportal.firefox.com and connectivitycheck.gstatic.com are not blocked # therefore, they", "is known not to ever have TLS driver.get('http://neverssl.com') if 'neverssl.com'", "= selenium.webdriver.Firefox() # for some reason, detectportal.firefox.com and connectivitycheck.gstatic.com are", "some reason, detectportal.firefox.com and connectivitycheck.gstatic.com are not blocked # therefore,", "# therefore, they cannot be used to detect connectivity #", "they cannot be used to detect connectivity # we instead", "not blocked # therefore, they cannot be used to detect", "import urllib.parse import selenium.webdriver def exit(): driver.quit() sys.exit(0) driver =", "encoding: utf-8 import sys import urllib.parse import selenium.webdriver def exit():", "not to ever have TLS driver.get('http://neverssl.com') if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc:", "and connectivitycheck.gstatic.com are not blocked # therefore, they cannot be", "urllib.parse import selenium.webdriver def exit(): driver.quit() sys.exit(0) driver = selenium.webdriver.Firefox()", "be used to detect connectivity # we instead visit another", "if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc: exit() driver.find_element_by_css_selector('label[for=\"promo_button\"]').click() driver.find_element_by_css_selector('input[alt=\"Next\"]').click() driver.find_element_by_css_selector('#PromotionCode').send_keys('lobby18') driver.find_element_by_css_selector('input[alt=\"Connect\"]').click() exit()", "selenium.webdriver.Firefox() # for some reason, detectportal.firefox.com and connectivitycheck.gstatic.com are not", "import selenium.webdriver def exit(): driver.quit() sys.exit(0) driver = selenium.webdriver.Firefox() #", "python3 # encoding: utf-8 import sys import urllib.parse import selenium.webdriver", "driver.quit() sys.exit(0) driver = selenium.webdriver.Firefox() # for some reason, detectportal.firefox.com", "ever have TLS driver.get('http://neverssl.com') if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc: exit() driver.find_element_by_css_selector('label[for=\"promo_button\"]').click()", "driver = selenium.webdriver.Firefox() # for some reason, detectportal.firefox.com and connectivitycheck.gstatic.com", "blocked # therefore, they cannot be used to detect connectivity", "driver.get('http://neverssl.com') if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc: exit() driver.find_element_by_css_selector('label[for=\"promo_button\"]').click() driver.find_element_by_css_selector('input[alt=\"Next\"]').click() driver.find_element_by_css_selector('#PromotionCode').send_keys('lobby18') driver.find_element_by_css_selector('input[alt=\"Connect\"]').click()", "import sys import urllib.parse import selenium.webdriver def exit(): driver.quit() sys.exit(0)", "have TLS driver.get('http://neverssl.com') if 'neverssl.com' in urllib.parse.urlparse(driver.current_url).netloc: exit() driver.find_element_by_css_selector('label[for=\"promo_button\"]').click() driver.find_element_by_css_selector('input[alt=\"Next\"]').click()", "# for some reason, detectportal.firefox.com and connectivitycheck.gstatic.com are not blocked", "selenium.webdriver def exit(): driver.quit() sys.exit(0) driver = selenium.webdriver.Firefox() # for" ]
[ "<reponame>rhyswhitley/savanna_iav #!/usr/bin/env python import os from collections import OrderedDict import", "= [year_agg(df) \\ for df in OrderedDict(data_dict).values()] # **FOR LOOP", "if __name__ == \"__main__\": FILEPATH = \"~/Savanna/Data/HowardSprings_IAV/pickled/agg/mean_monthly_leaf.pkl\" PKLPATH = os.path.expanduser(FILEPATH)", "import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.cm", "import matplotlib.gridspec as gridspec from matplotlib.cm import get_cmap from matplotlib", "def plot_monthly_response(norm, pert): plot_grid = gridspec.GridSpec(4, 1, hspace=0.1) ax1 =", "plt.show() return 1 def main(): data_dict = pickle.load(open(PKLPATH, 'rb')) year_agg", "pickle import numpy as np import pandas as pd import", "scipy import integrate def plot_monthly_response(norm, pert): plot_grid = gridspec.GridSpec(4, 1,", "# Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values)", "main(): data_dict = pickle.load(open(PKLPATH, 'rb')) year_agg = lambda x: x.groupby(level=['month',", "numpy as np import pandas as pd import matplotlib.pyplot as", "import style from scipy import stats from scipy import integrate", "'hour']).mean() data_mean_year = [year_agg(df) \\ for df in OrderedDict(data_dict).values()] #", "ax4 = plt.subplot(plot_grid[3]) # Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf", "ax3 = plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3]) # Stomatal conductance ax1.plot(norm[\"Gtree\"].values)", "data_mean_year[6]) return 1 if __name__ == \"__main__\": FILEPATH = \"~/Savanna/Data/HowardSprings_IAV/pickled/agg/mean_monthly_leaf.pkl\"", "gridspec.GridSpec(4, 1, hspace=0.1) ax1 = plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1]) ax3", "plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1 if __name__ == \"__main__\": FILEPATH =", "hspace=0.1) ax1 = plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2])", "plt.subplot(plot_grid[3]) # Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration ax2.plot(norm[\"Etree\"].values)", "OrderedDict import cPickle as pickle import numpy as np import", "cPickle as pickle import numpy as np import pandas as", "get_cmap from matplotlib import style from scipy import stats from", "as pd import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec", "x: x.groupby(level=['month', 'hour']).mean() data_mean_year = [year_agg(df) \\ for df in", "from scipy import stats from scipy import integrate def plot_monthly_response(norm,", "return 1 def main(): data_dict = pickle.load(open(PKLPATH, 'rb')) year_agg =", "assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1", "plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3]) # Stomatal conductance", "ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf assimilation ax3.plot(norm[\"Atree\"].values)", "= plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2]) ax4 =", "= plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3]) # Stomatal", "ax2.plot(pert[\"Etree\"].values) # Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values)", "\\ for df in OrderedDict(data_dict).values()] # **FOR LOOP WILL GO", "plot_grid = gridspec.GridSpec(4, 1, hspace=0.1) ax1 = plt.subplot(plot_grid[0]) ax2 =", "scipy import stats from scipy import integrate def plot_monthly_response(norm, pert):", "ax1 = plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2]) ax4", "= gridspec.GridSpec(4, 1, hspace=0.1) ax1 = plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1])", "integrate def plot_monthly_response(norm, pert): plot_grid = gridspec.GridSpec(4, 1, hspace=0.1) ax1", "ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1 def main(): data_dict", "python import os from collections import OrderedDict import cPickle as", "= plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3]) # Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values)", "pandas as pd import matplotlib.pyplot as plt import matplotlib.gridspec as", "import integrate def plot_monthly_response(norm, pert): plot_grid = gridspec.GridSpec(4, 1, hspace=0.1)", "import os from collections import OrderedDict import cPickle as pickle", "ax2 = plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3]) #", "Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return", "GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1 if __name__ == \"__main__\":", "os from collections import OrderedDict import cPickle as pickle import", "ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1 def main(): data_dict = pickle.load(open(PKLPATH, 'rb'))", "import stats from scipy import integrate def plot_monthly_response(norm, pert): plot_grid", "import numpy as np import pandas as pd import matplotlib.pyplot", "OrderedDict(data_dict).values()] # **FOR LOOP WILL GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return", "as plt import matplotlib.gridspec as gridspec from matplotlib.cm import get_cmap", "= lambda x: x.groupby(level=['month', 'hour']).mean() data_mean_year = [year_agg(df) \\ for", "matplotlib import style from scipy import stats from scipy import", "data_mean_year = [year_agg(df) \\ for df in OrderedDict(data_dict).values()] # **FOR", "matplotlib.cm import get_cmap from matplotlib import style from scipy import", "'rb')) year_agg = lambda x: x.groupby(level=['month', 'hour']).mean() data_mean_year = [year_agg(df)", "x.groupby(level=['month', 'hour']).mean() data_mean_year = [year_agg(df) \\ for df in OrderedDict(data_dict).values()]", "plt import matplotlib.gridspec as gridspec from matplotlib.cm import get_cmap from", "[year_agg(df) \\ for df in OrderedDict(data_dict).values()] # **FOR LOOP WILL", "collections import OrderedDict import cPickle as pickle import numpy as", "for df in OrderedDict(data_dict).values()] # **FOR LOOP WILL GO HERE", "WILL GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1 if __name__ ==", "as pickle import numpy as np import pandas as pd", "in OrderedDict(data_dict).values()] # **FOR LOOP WILL GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6])", "plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3]) # Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) #", "transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values)", "# Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show()", "LOOP WILL GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1 if __name__", "1 if __name__ == \"__main__\": FILEPATH = \"~/Savanna/Data/HowardSprings_IAV/pickled/agg/mean_monthly_leaf.pkl\" PKLPATH =", "ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1 def main():", "from scipy import integrate def plot_monthly_response(norm, pert): plot_grid = gridspec.GridSpec(4,", "def main(): data_dict = pickle.load(open(PKLPATH, 'rb')) year_agg = lambda x:", "matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.cm import", "from matplotlib.cm import get_cmap from matplotlib import style from scipy", "Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values)", "# **FOR LOOP WILL GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1", "**FOR LOOP WILL GO HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1 if", "pd import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from", "HERE plot_monthly_response(data_mean_year[3], data_mean_year[6]) return 1 if __name__ == \"__main__\": FILEPATH", "as np import pandas as pd import matplotlib.pyplot as plt", "ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1 def main(): data_dict = pickle.load(open(PKLPATH,", "= pickle.load(open(PKLPATH, 'rb')) year_agg = lambda x: x.groupby(level=['month', 'hour']).mean() data_mean_year", "import cPickle as pickle import numpy as np import pandas", "import get_cmap from matplotlib import style from scipy import stats", "plot_monthly_response(norm, pert): plot_grid = gridspec.GridSpec(4, 1, hspace=0.1) ax1 = plt.subplot(plot_grid[0])", "gridspec from matplotlib.cm import get_cmap from matplotlib import style from", "ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf assimilation", "pickle.load(open(PKLPATH, 'rb')) year_agg = lambda x: x.groupby(level=['month', 'hour']).mean() data_mean_year =", "1 def main(): data_dict = pickle.load(open(PKLPATH, 'rb')) year_agg = lambda", "style from scipy import stats from scipy import integrate def", "np import pandas as pd import matplotlib.pyplot as plt import", "1, hspace=0.1) ax1 = plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1]) ax3 =", "__name__ == \"__main__\": FILEPATH = \"~/Savanna/Data/HowardSprings_IAV/pickled/agg/mean_monthly_leaf.pkl\" PKLPATH = os.path.expanduser(FILEPATH) main()", "pert): plot_grid = gridspec.GridSpec(4, 1, hspace=0.1) ax1 = plt.subplot(plot_grid[0]) ax2", "ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1 def", "from matplotlib import style from scipy import stats from scipy", "#!/usr/bin/env python import os from collections import OrderedDict import cPickle", "data_dict = pickle.load(open(PKLPATH, 'rb')) year_agg = lambda x: x.groupby(level=['month', 'hour']).mean()", "# Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values)", "plt.subplot(plot_grid[0]) ax2 = plt.subplot(plot_grid[1]) ax3 = plt.subplot(plot_grid[2]) ax4 = plt.subplot(plot_grid[3])", "conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf", "ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values) ax4.plot(pert[\"LAIgrass\"].values) plt.show() return 1 def main(): data_dict =", "from collections import OrderedDict import cPickle as pickle import numpy", "matplotlib.gridspec as gridspec from matplotlib.cm import get_cmap from matplotlib import", "year_agg = lambda x: x.groupby(level=['month', 'hour']).mean() data_mean_year = [year_agg(df) \\", "import OrderedDict import cPickle as pickle import numpy as np", "df in OrderedDict(data_dict).values()] # **FOR LOOP WILL GO HERE plot_monthly_response(data_mean_year[3],", "lambda x: x.groupby(level=['month', 'hour']).mean() data_mean_year = [year_agg(df) \\ for df", "import pandas as pd import matplotlib.pyplot as plt import matplotlib.gridspec", "= plt.subplot(plot_grid[3]) # Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration", "Stomatal conductance ax1.plot(norm[\"Gtree\"].values) ax1.plot(pert[\"Gtree\"].values) # Leaf transpiration ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) #", "stats from scipy import integrate def plot_monthly_response(norm, pert): plot_grid =", "ax2.plot(norm[\"Etree\"].values) ax2.plot(pert[\"Etree\"].values) # Leaf assimilation ax3.plot(norm[\"Atree\"].values) ax3.plot(pert[\"Atree\"].values) ax4.plot(norm[\"LAItree\"].values) ax4.plot(pert[\"LAItree\"].values) ax4.plot(norm[\"LAIgrass\"].values)", "return 1 if __name__ == \"__main__\": FILEPATH = \"~/Savanna/Data/HowardSprings_IAV/pickled/agg/mean_monthly_leaf.pkl\" PKLPATH", "as gridspec from matplotlib.cm import get_cmap from matplotlib import style" ]
[ "'/' + AWS_DIRECTORY elif sys.argv[1] == 'test': PROJECT_ROOT = '/www.vpr.net/'", "complications in how static files are served if len(sys.argv) >", "from s3 leads to some complications in how static files", "flask_frozen import Freezer from upload_s3 import set_metadata from config import", "start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT return self.app(environ, start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app)", "= WebFactionMiddleware(app.wsgi_app) if __name__ == '__main__': if len(sys.argv) > 1", "app): self.app = app def __call__(self, environ, start_response): environ['SCRIPT_NAME'] =", "len(sys.argv) > 1: if sys.argv[1] == 'build': PROJECT_ROOT = '/'", "AWS_DIRECTORY else: PROJECT_ROOT = '/' class WebFactionMiddleware(object): def __init__(self, app):", "app.debug = True freezer = Freezer(app) freezer.freeze() set_metadata() else: app.run(debug=True)", "= app def __call__(self, environ, start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT return", "to some complications in how static files are served if", "'build': PROJECT_ROOT = '/' + AWS_DIRECTORY elif sys.argv[1] == 'test':", "if sys.argv[1] == 'build': PROJECT_ROOT = '/' + AWS_DIRECTORY elif", "start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if __name__ == '__main__': if len(sys.argv)", "+ AWS_DIRECTORY elif sys.argv[1] == 'test': PROJECT_ROOT = '/www.vpr.net/' +", "== '__main__': if len(sys.argv) > 1 and sys.argv[1] == 'build':", "1 and sys.argv[1] == 'build': app.debug = True freezer =", "import Freezer from upload_s3 import set_metadata from config import AWS_DIRECTORY", "if len(sys.argv) > 1: if sys.argv[1] == 'build': PROJECT_ROOT =", "PROJECT_ROOT = '/www.vpr.net/' + AWS_DIRECTORY else: PROJECT_ROOT = '/' class", "environ, start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT return self.app(environ, start_response) app.wsgi_app =", "def __init__(self, app): self.app = app def __call__(self, environ, start_response):", "app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if __name__ == '__main__': if len(sys.argv) >", "import * # Serving from s3 leads to some complications", "sys from flask_frozen import Freezer from upload_s3 import set_metadata from", "served if len(sys.argv) > 1: if sys.argv[1] == 'build': PROJECT_ROOT", "Serving from s3 leads to some complications in how static", "__call__(self, environ, start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT return self.app(environ, start_response) app.wsgi_app", "__name__ == '__main__': if len(sys.argv) > 1 and sys.argv[1] ==", "some complications in how static files are served if len(sys.argv)", "from upload_s3 import set_metadata from config import AWS_DIRECTORY app =", "if len(sys.argv) > 1 and sys.argv[1] == 'build': app.debug =", "Flask import sys from flask_frozen import Freezer from upload_s3 import", "app def __call__(self, environ, start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT return self.app(environ,", "sys.argv[1] == 'test': PROJECT_ROOT = '/www.vpr.net/' + AWS_DIRECTORY else: PROJECT_ROOT", "static files are served if len(sys.argv) > 1: if sys.argv[1]", "PROJECT_ROOT = '/' class WebFactionMiddleware(object): def __init__(self, app): self.app =", "= Flask(__name__) app.config.from_object('config') from views import * # Serving from", "how static files are served if len(sys.argv) > 1: if", "'/www.vpr.net/' + AWS_DIRECTORY else: PROJECT_ROOT = '/' class WebFactionMiddleware(object): def", "from flask import Flask import sys from flask_frozen import Freezer", "sys.argv[1] == 'build': app.debug = True freezer = Freezer(app) freezer.freeze()", "= '/www.vpr.net/' + AWS_DIRECTORY else: PROJECT_ROOT = '/' class WebFactionMiddleware(object):", "__init__(self, app): self.app = app def __call__(self, environ, start_response): environ['SCRIPT_NAME']", "> 1: if sys.argv[1] == 'build': PROJECT_ROOT = '/' +", "else: PROJECT_ROOT = '/' class WebFactionMiddleware(object): def __init__(self, app): self.app", "== 'build': app.debug = True freezer = Freezer(app) freezer.freeze() set_metadata()", "> 1 and sys.argv[1] == 'build': app.debug = True freezer", "AWS_DIRECTORY elif sys.argv[1] == 'test': PROJECT_ROOT = '/www.vpr.net/' + AWS_DIRECTORY", "PROJECT_ROOT = '/' + AWS_DIRECTORY elif sys.argv[1] == 'test': PROJECT_ROOT", "'/' class WebFactionMiddleware(object): def __init__(self, app): self.app = app def", "* # Serving from s3 leads to some complications in", "sys.argv[1] == 'build': PROJECT_ROOT = '/' + AWS_DIRECTORY elif sys.argv[1]", "self.app(environ, start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if __name__ == '__main__': if", "<reponame>vprnet/school-closings #!/usr/local/bin/python2.7 from flask import Flask import sys from flask_frozen", "WebFactionMiddleware(object): def __init__(self, app): self.app = app def __call__(self, environ,", "from flask_frozen import Freezer from upload_s3 import set_metadata from config", "leads to some complications in how static files are served", "views import * # Serving from s3 leads to some", "import Flask import sys from flask_frozen import Freezer from upload_s3", "are served if len(sys.argv) > 1: if sys.argv[1] == 'build':", "len(sys.argv) > 1 and sys.argv[1] == 'build': app.debug = True", "'test': PROJECT_ROOT = '/www.vpr.net/' + AWS_DIRECTORY else: PROJECT_ROOT = '/'", "import set_metadata from config import AWS_DIRECTORY app = Flask(__name__) app.config.from_object('config')", "config import AWS_DIRECTORY app = Flask(__name__) app.config.from_object('config') from views import", "from config import AWS_DIRECTORY app = Flask(__name__) app.config.from_object('config') from views", "Freezer from upload_s3 import set_metadata from config import AWS_DIRECTORY app", "upload_s3 import set_metadata from config import AWS_DIRECTORY app = Flask(__name__)", "# Serving from s3 leads to some complications in how", "set_metadata from config import AWS_DIRECTORY app = Flask(__name__) app.config.from_object('config') from", "def __call__(self, environ, start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT return self.app(environ, start_response)", "import sys from flask_frozen import Freezer from upload_s3 import set_metadata", "elif sys.argv[1] == 'test': PROJECT_ROOT = '/www.vpr.net/' + AWS_DIRECTORY else:", "class WebFactionMiddleware(object): def __init__(self, app): self.app = app def __call__(self,", "#!/usr/local/bin/python2.7 from flask import Flask import sys from flask_frozen import", "if __name__ == '__main__': if len(sys.argv) > 1 and sys.argv[1]", "s3 leads to some complications in how static files are", "== 'test': PROJECT_ROOT = '/www.vpr.net/' + AWS_DIRECTORY else: PROJECT_ROOT =", "self.app = app def __call__(self, environ, start_response): environ['SCRIPT_NAME'] = PROJECT_ROOT", "flask import Flask import sys from flask_frozen import Freezer from", "= '/' class WebFactionMiddleware(object): def __init__(self, app): self.app = app", "in how static files are served if len(sys.argv) > 1:", "AWS_DIRECTORY app = Flask(__name__) app.config.from_object('config') from views import * #", "import AWS_DIRECTORY app = Flask(__name__) app.config.from_object('config') from views import *", "and sys.argv[1] == 'build': app.debug = True freezer = Freezer(app)", "= '/' + AWS_DIRECTORY elif sys.argv[1] == 'test': PROJECT_ROOT =", "app = Flask(__name__) app.config.from_object('config') from views import * # Serving", "1: if sys.argv[1] == 'build': PROJECT_ROOT = '/' + AWS_DIRECTORY", "'__main__': if len(sys.argv) > 1 and sys.argv[1] == 'build': app.debug", "= PROJECT_ROOT return self.app(environ, start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if __name__", "+ AWS_DIRECTORY else: PROJECT_ROOT = '/' class WebFactionMiddleware(object): def __init__(self,", "app.config.from_object('config') from views import * # Serving from s3 leads", "from views import * # Serving from s3 leads to", "PROJECT_ROOT return self.app(environ, start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if __name__ ==", "== 'build': PROJECT_ROOT = '/' + AWS_DIRECTORY elif sys.argv[1] ==", "environ['SCRIPT_NAME'] = PROJECT_ROOT return self.app(environ, start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if", "'build': app.debug = True freezer = Freezer(app) freezer.freeze() set_metadata() else:", "return self.app(environ, start_response) app.wsgi_app = WebFactionMiddleware(app.wsgi_app) if __name__ == '__main__':", "Flask(__name__) app.config.from_object('config') from views import * # Serving from s3", "files are served if len(sys.argv) > 1: if sys.argv[1] ==", "WebFactionMiddleware(app.wsgi_app) if __name__ == '__main__': if len(sys.argv) > 1 and" ]
[ "%d bytes\" % kernel_size) print(\"Uncompressing...\") iface.dev.timeout = 40 kernel_size =", "= 32 * 1024 * 1024 kernel_base = u.memalign(2 *", "* 1024 * 1024, kernel_size) print(\"Kernel_base: 0x%x\" % kernel_base) assert", "iface.writemem(compressed_addr, payload, True) print(\"Loading DTB to 0x%x...\" % dtb_addr) iface.writemem(dtb_addr,", "compressed_addr + compressed_size)) iface.writemem(compressed_addr, payload, True) print(\"Loading DTB to 0x%x...\"", "dtb = open(sys.argv[2], \"rb\").read() if len(sys.argv) > 3: initramfs =", "daif |= 0x3c0 u.msr(DAIF, daif) print(\"DAIF: %x\" % daif) p.kboot_boot(kernel_base)", "initramfs_size) print(\"Loading %d initramfs bytes to 0x%x...\" % (initramfs_size, initramfs_base))", "0xffff) if initramfs is not None: initramfs_base = u.memalign(65536, initramfs_size)", "True) p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\") sys.exit(1) #kernel_size", "iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\")", "kernel_size) print(\"Uncompressing...\") iface.dev.timeout = 40 kernel_size = p.gzdec(compressed_addr, compressed_size, kernel_base,", "= open(sys.argv[1], \"rb\").read() dtb = open(sys.argv[2], \"rb\").read() if len(sys.argv) >", "bytes to 0x%x..0x%x...\" % (compressed_size, compressed_addr, compressed_addr + compressed_size)) iface.writemem(compressed_addr,", "p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\") sys.exit(1) #kernel_size =", "|= 0x3c0 u.msr(DAIF, daif) print(\"DAIF: %x\" % daif) p.kboot_boot(kernel_base) iface.ttymode()", "= u.memalign(2 * 1024 * 1024, kernel_size) print(\"Kernel_base: 0x%x\" %", "kernel_size < 0: #raise Exception(\"Decompression header check error!\",) #print(\"Uncompressed kernel", "= p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size) print(kernel_size) if kernel_size < 0:", "compressed_size) #if kernel_size < 0: #raise Exception(\"Decompression header check error!\",)", "* payload = open(sys.argv[1], \"rb\").read() dtb = open(sys.argv[2], \"rb\").read() if", "print(\"Loading %d bytes to 0x%x..0x%x...\" % (compressed_size, compressed_addr, compressed_addr +", "= len(initramfs) else: initramfs = None initramfs_size = 0 compressed_size", "kernel_size) print(\"Kernel_base: 0x%x\" % kernel_base) assert not (kernel_base & 0xffff)", "(initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT", "len(initramfs) else: initramfs = None initramfs_size = 0 compressed_size =", "compressed_size)) iface.writemem(compressed_addr, payload, True) print(\"Loading DTB to 0x%x...\" % dtb_addr)", "> 3: initramfs = open(sys.argv[3], \"rb\").read() initramfs_size = len(initramfs) else:", "print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready to boot\") daif", "sys.exit(1) #kernel_size = p.xzdec(compressed_addr, compressed_size) #if kernel_size < 0: #raise", "u.memalign(65536, initramfs_size) print(\"Loading %d initramfs bytes to 0x%x...\" % (initramfs_size,", "* 1024 * 1024 kernel_base = u.memalign(2 * 1024 *", "0: raise Exception(\"Decompression error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size)", "0 compressed_size = len(payload) compressed_addr = u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb))", "from setup import * payload = open(sys.argv[1], \"rb\").read() dtb =", "open(sys.argv[2], \"rb\").read() if len(sys.argv) > 3: initramfs = open(sys.argv[3], \"rb\").read()", "kernel_size = 32 * 1024 * 1024 kernel_base = u.memalign(2", "if kernel_size < 0: raise Exception(\"Decompression error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base,", "\"rb\").read() dtb = open(sys.argv[2], \"rb\").read() if len(sys.argv) > 3: initramfs", "initramfs_base = u.memalign(65536, initramfs_size) print(\"Loading %d initramfs bytes to 0x%x...\"", "failed\") sys.exit(1) #kernel_size = p.xzdec(compressed_addr, compressed_size) #if kernel_size < 0:", "u.malloc(len(dtb)) print(\"Loading %d bytes to 0x%x..0x%x...\" % (compressed_size, compressed_addr, compressed_addr", "= 0 compressed_size = len(payload) compressed_addr = u.malloc(compressed_size) dtb_addr =", "= open(sys.argv[2], \"rb\").read() if len(sys.argv) > 3: initramfs = open(sys.argv[3],", "= u.malloc(len(dtb)) print(\"Loading %d bytes to 0x%x..0x%x...\" % (compressed_size, compressed_addr,", "& 0xffff) if initramfs is not None: initramfs_base = u.memalign(65536,", "open(sys.argv[1], \"rb\").read() dtb = open(sys.argv[2], \"rb\").read() if len(sys.argv) > 3:", "kernel_base = u.memalign(2 * 1024 * 1024, kernel_size) print(\"Kernel_base: 0x%x\"", "size: %d bytes\" % kernel_size) print(\"Uncompressing...\") iface.dev.timeout = 40 kernel_size", "open(sys.argv[3], \"rb\").read() initramfs_size = len(initramfs) else: initramfs = None initramfs_size", "1024, kernel_size) print(\"Kernel_base: 0x%x\" % kernel_base) assert not (kernel_base &", "% dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size = 32 * 1024 *", "1024 * 1024 kernel_base = u.memalign(2 * 1024 * 1024,", "print(\"Uncompressing...\") iface.dev.timeout = 40 kernel_size = p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size)", "< 0: raise Exception(\"Decompression error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base,", "else: initramfs = None initramfs_size = 0 compressed_size = len(payload)", "payload = open(sys.argv[1], \"rb\").read() dtb = open(sys.argv[2], \"rb\").read() if len(sys.argv)", "iface.writemem(dtb_addr, dtb) kernel_size = 32 * 1024 * 1024 kernel_base", "kernel size: %d bytes\" % kernel_size) print(\"Uncompressing...\") iface.dev.timeout = 40", "kernel_size) print(\"Ready to boot\") daif = u.mrs(DAIF) daif |= 0x3c0", "#raise Exception(\"Decompression header check error!\",) #print(\"Uncompressed kernel size: %d bytes\"", "* 1024, kernel_size) print(\"Kernel_base: 0x%x\" % kernel_base) assert not (kernel_base", "1024 kernel_base = u.memalign(2 * 1024 * 1024, kernel_size) print(\"Kernel_base:", "3: initramfs = open(sys.argv[3], \"rb\").read() initramfs_size = len(initramfs) else: initramfs", "OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready to boot\") daif =", "0: #raise Exception(\"Decompression header check error!\",) #print(\"Uncompressed kernel size: %d", "<filename>proxyclient/linux.py #!/usr/bin/python from setup import * payload = open(sys.argv[1], \"rb\").read()", "#kernel_size = p.xzdec(compressed_addr, compressed_size) #if kernel_size < 0: #raise Exception(\"Decompression", "\"rb\").read() initramfs_size = len(initramfs) else: initramfs = None initramfs_size =", "0x%x...\" % (initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size) if", "initramfs_base)) iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare", "= u.mrs(DAIF) daif |= 0x3c0 u.msr(DAIF, daif) print(\"DAIF: %x\" %", "setup import * payload = open(sys.argv[1], \"rb\").read() dtb = open(sys.argv[2],", "= p.xzdec(compressed_addr, compressed_size) #if kernel_size < 0: #raise Exception(\"Decompression header", "#if kernel_size < 0: #raise Exception(\"Decompression header check error!\",) #print(\"Uncompressed", "kernel_base) assert not (kernel_base & 0xffff) if initramfs is not", "prepare failed\") sys.exit(1) #kernel_size = p.xzdec(compressed_addr, compressed_size) #if kernel_size <", "print(\"Loading %d initramfs bytes to 0x%x...\" % (initramfs_size, initramfs_base)) iface.writemem(initramfs_base,", "#!/usr/bin/python from setup import * payload = open(sys.argv[1], \"rb\").read() dtb", "check error!\",) #print(\"Uncompressed kernel size: %d bytes\" % kernel_size) print(\"Uncompressing...\")", "Exception(\"Decompression error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready to", "dtb) kernel_size = 32 * 1024 * 1024 kernel_base =", "0x%x..0x%x...\" % (compressed_size, compressed_addr, compressed_addr + compressed_size)) iface.writemem(compressed_addr, payload, True)", "dtb_addr = u.malloc(len(dtb)) print(\"Loading %d bytes to 0x%x..0x%x...\" % (compressed_size,", "= len(payload) compressed_addr = u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb)) print(\"Loading %d", "(compressed_size, compressed_addr, compressed_addr + compressed_size)) iface.writemem(compressed_addr, payload, True) print(\"Loading DTB", "32 * 1024 * 1024 kernel_base = u.memalign(2 * 1024", "header check error!\",) #print(\"Uncompressed kernel size: %d bytes\" % kernel_size)", "not None: initramfs_base = u.memalign(65536, initramfs_size) print(\"Loading %d initramfs bytes", "daif = u.mrs(DAIF) daif |= 0x3c0 u.msr(DAIF, daif) print(\"DAIF: %x\"", "compressed_addr, compressed_addr + compressed_size)) iface.writemem(compressed_addr, payload, True) print(\"Loading DTB to", "p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\") sys.exit(1) #kernel_size = p.xzdec(compressed_addr, compressed_size) #if", "initramfs = open(sys.argv[3], \"rb\").read() initramfs_size = len(initramfs) else: initramfs =", "True) print(\"Loading DTB to 0x%x...\" % dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size", "DTB to 0x%x...\" % dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size = 32", "compressed_addr = u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb)) print(\"Loading %d bytes to", "import * payload = open(sys.argv[1], \"rb\").read() dtb = open(sys.argv[2], \"rb\").read()", "raise Exception(\"Decompression error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready", "print(\"Kernel_base: 0x%x\" % kernel_base) assert not (kernel_base & 0xffff) if", "< 0: #raise Exception(\"Decompression header check error!\",) #print(\"Uncompressed kernel size:", "error!\",) #print(\"Uncompressed kernel size: %d bytes\" % kernel_size) print(\"Uncompressing...\") iface.dev.timeout", "p.ic_ivau(kernel_base, kernel_size) print(\"Ready to boot\") daif = u.mrs(DAIF) daif |=", "1024 * 1024, kernel_size) print(\"Kernel_base: 0x%x\" % kernel_base) assert not", "to 0x%x..0x%x...\" % (compressed_size, compressed_addr, compressed_addr + compressed_size)) iface.writemem(compressed_addr, payload,", "initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\") sys.exit(1) #kernel_size = p.xzdec(compressed_addr,", "% kernel_base) assert not (kernel_base & 0xffff) if initramfs is", "= open(sys.argv[3], \"rb\").read() initramfs_size = len(initramfs) else: initramfs = None", "initramfs_size = 0 compressed_size = len(payload) compressed_addr = u.malloc(compressed_size) dtb_addr", "len(payload) compressed_addr = u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb)) print(\"Loading %d bytes", "= None initramfs_size = 0 compressed_size = len(payload) compressed_addr =", "if initramfs is not None: initramfs_base = u.memalign(65536, initramfs_size) print(\"Loading", "u.mrs(DAIF) daif |= 0x3c0 u.msr(DAIF, daif) print(\"DAIF: %x\" % daif)", "kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready to boot\") daif = u.mrs(DAIF) daif", "% (compressed_size, compressed_addr, compressed_addr + compressed_size)) iface.writemem(compressed_addr, payload, True) print(\"Loading", "to 0x%x...\" % dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size = 32 *", "not (kernel_base & 0xffff) if initramfs is not None: initramfs_base", "iface.dev.timeout = 40 kernel_size = p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size) print(kernel_size)", "p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready to boot\") daif = u.mrs(DAIF)", "to boot\") daif = u.mrs(DAIF) daif |= 0x3c0 u.msr(DAIF, daif)", "kernel_size = p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size) print(kernel_size) if kernel_size <", "print(\"DT prepare failed\") sys.exit(1) #kernel_size = p.xzdec(compressed_addr, compressed_size) #if kernel_size", "= u.memalign(65536, initramfs_size) print(\"Loading %d initramfs bytes to 0x%x...\" %", "print(\"Ready to boot\") daif = u.mrs(DAIF) daif |= 0x3c0 u.msr(DAIF,", "initramfs_size = len(initramfs) else: initramfs = None initramfs_size = 0", "len(sys.argv) > 3: initramfs = open(sys.argv[3], \"rb\").read() initramfs_size = len(initramfs)", "u.memalign(2 * 1024 * 1024, kernel_size) print(\"Kernel_base: 0x%x\" % kernel_base)", "initramfs is not None: initramfs_base = u.memalign(65536, initramfs_size) print(\"Loading %d", "p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size) print(kernel_size) if kernel_size < 0: raise", "+ compressed_size)) iface.writemem(compressed_addr, payload, True) print(\"Loading DTB to 0x%x...\" %", "= 40 kernel_size = p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size) print(kernel_size) if", "bytes to 0x%x...\" % (initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base,", "kernel_size) print(kernel_size) if kernel_size < 0: raise Exception(\"Decompression error!\") print(\"Decompress", "boot\") daif = u.mrs(DAIF) daif |= 0x3c0 u.msr(DAIF, daif) print(\"DAIF:", "initramfs = None initramfs_size = 0 compressed_size = len(payload) compressed_addr", "assert not (kernel_base & 0xffff) if initramfs is not None:", "40 kernel_size = p.gzdec(compressed_addr, compressed_size, kernel_base, kernel_size) print(kernel_size) if kernel_size", "if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\") sys.exit(1) #kernel_size = p.xzdec(compressed_addr, compressed_size)", "(kernel_base & 0xffff) if initramfs is not None: initramfs_base =", "bytes\" % kernel_size) print(\"Uncompressing...\") iface.dev.timeout = 40 kernel_size = p.gzdec(compressed_addr,", "to 0x%x...\" % (initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size)", "initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr): print(\"DT prepare failed\") sys.exit(1)", "print(\"Loading DTB to 0x%x...\" % dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size =", "u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb)) print(\"Loading %d bytes to 0x%x..0x%x...\" %", "%d bytes to 0x%x..0x%x...\" % (compressed_size, compressed_addr, compressed_addr + compressed_size))", "if len(sys.argv) > 3: initramfs = open(sys.argv[3], \"rb\").read() initramfs_size =", "Exception(\"Decompression header check error!\",) #print(\"Uncompressed kernel size: %d bytes\" %", "payload, True) print(\"Loading DTB to 0x%x...\" % dtb_addr) iface.writemem(dtb_addr, dtb)", "compressed_size = len(payload) compressed_addr = u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb)) print(\"Loading", "% (initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs, True) p.kboot_set_initrd(initramfs_base, initramfs_size) if p.kboot_prepare_dt(dtb_addr):", "\"rb\").read() if len(sys.argv) > 3: initramfs = open(sys.argv[3], \"rb\").read() initramfs_size", "is not None: initramfs_base = u.memalign(65536, initramfs_size) print(\"Loading %d initramfs", "dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size = 32 * 1024 * 1024", "p.xzdec(compressed_addr, compressed_size) #if kernel_size < 0: #raise Exception(\"Decompression header check", "% kernel_size) print(\"Uncompressing...\") iface.dev.timeout = 40 kernel_size = p.gzdec(compressed_addr, compressed_size,", "#print(\"Uncompressed kernel size: %d bytes\" % kernel_size) print(\"Uncompressing...\") iface.dev.timeout =", "= u.malloc(compressed_size) dtb_addr = u.malloc(len(dtb)) print(\"Loading %d bytes to 0x%x..0x%x...\"", "initramfs bytes to 0x%x...\" % (initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs, True)", "kernel_base, kernel_size) print(kernel_size) if kernel_size < 0: raise Exception(\"Decompression error!\")", "compressed_size, kernel_base, kernel_size) print(kernel_size) if kernel_size < 0: raise Exception(\"Decompression", "kernel_size < 0: raise Exception(\"Decompression error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size)", "0x%x\" % kernel_base) assert not (kernel_base & 0xffff) if initramfs", "* 1024 kernel_base = u.memalign(2 * 1024 * 1024, kernel_size)", "error!\") print(\"Decompress OK...\") p.dc_cvau(kernel_base, kernel_size) p.ic_ivau(kernel_base, kernel_size) print(\"Ready to boot\")", "None: initramfs_base = u.memalign(65536, initramfs_size) print(\"Loading %d initramfs bytes to", "0x%x...\" % dtb_addr) iface.writemem(dtb_addr, dtb) kernel_size = 32 * 1024", "%d initramfs bytes to 0x%x...\" % (initramfs_size, initramfs_base)) iface.writemem(initramfs_base, initramfs,", "None initramfs_size = 0 compressed_size = len(payload) compressed_addr = u.malloc(compressed_size)", "print(kernel_size) if kernel_size < 0: raise Exception(\"Decompression error!\") print(\"Decompress OK...\")" ]
[ "attach -c \\'{}\\''.format(content['exec']) else: command = 'ancypwn attach' realname =", "mod.run(command) class ServerProcess(multiprocessing.Process): def __init__(self, port, *args, **kwargs): super(ServerProcess, self).__init__(*args,", "= json.loads(json_content) terminal = content['terminal'] if content['exec'] != '': command", "def handle(self): length = struct.unpack('<I', self.request.recv(4))[0] json_content = self.request.recv(length) content", "**kwargs): super(ServerProcess, self).__init__(*args, **kwargs) self.port = port def run(self): self.server", "return importlib.import_module(name) except ModuleNotFoundError as e: prompt = 'plugin {}", "multiprocessing import struct import importlib from socketserver import TCPServer, StreamRequestHandler", "port, *args, **kwargs): super(ServerProcess, self).__init__(*args, **kwargs) self.port = port def", "as e: prompt = 'plugin {} not found, please install", "import os import multiprocessing import struct import importlib from socketserver", "raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def handle(self): length = struct.unpack('<I', self.request.recv(4))[0]", "= 'ancypwn attach -c \\'{}\\''.format(content['exec']) else: command = 'ancypwn attach'", "NotificationHandler(StreamRequestHandler): def handle(self): length = struct.unpack('<I', self.request.recv(4))[0] json_content = self.request.recv(length)", "-c \\'{}\\''.format(content['exec']) else: command = 'ancypwn attach' realname = 'ancypwn_terminal_{}'.format(terminal)", "+= 'try follwing:\\n\\tpip3 install {}'.format(name) raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def", "self).__init__(*args, **kwargs) self.port = port def run(self): self.server = TCPServer(('',", "content['exec'] != '': command = 'ancypwn attach -c \\'{}\\''.format(content['exec']) else:", "realname = 'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process): def", "= self.request.recv(length) content = json.loads(json_content) terminal = content['terminal'] if content['exec']", "it first.\\n'.format(name) prompt += 'try follwing:\\n\\tpip3 install {}'.format(name) raise PluginNotFoundError(prompt)", "else: command = 'ancypwn attach' realname = 'ancypwn_terminal_{}'.format(terminal) mod =", "first.\\n'.format(name) prompt += 'try follwing:\\n\\tpip3 install {}'.format(name) raise PluginNotFoundError(prompt) class", "StreamRequestHandler def plugin_module_import(name): try: return importlib.import_module(name) except ModuleNotFoundError as e:", "= 'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process): def __init__(self,", "json.loads(json_content) terminal = content['terminal'] if content['exec'] != '': command =", "<reponame>shizhongpwn/ancypwn<filename>src/server.py import json import os import multiprocessing import struct import", "= 'plugin {} not found, please install it first.\\n'.format(name) prompt", "'ancypwn attach -c \\'{}\\''.format(content['exec']) else: command = 'ancypwn attach' realname", "!= '': command = 'ancypwn attach -c \\'{}\\''.format(content['exec']) else: command", "socketserver import TCPServer, StreamRequestHandler def plugin_module_import(name): try: return importlib.import_module(name) except", "TCPServer, StreamRequestHandler def plugin_module_import(name): try: return importlib.import_module(name) except ModuleNotFoundError as", "install it first.\\n'.format(name) prompt += 'try follwing:\\n\\tpip3 install {}'.format(name) raise", "= struct.unpack('<I', self.request.recv(4))[0] json_content = self.request.recv(length) content = json.loads(json_content) terminal", "json_content = self.request.recv(length) content = json.loads(json_content) terminal = content['terminal'] if", "importlib.import_module(name) except ModuleNotFoundError as e: prompt = 'plugin {} not", "try: return importlib.import_module(name) except ModuleNotFoundError as e: prompt = 'plugin", "self.request.recv(length) content = json.loads(json_content) terminal = content['terminal'] if content['exec'] !=", "mod = plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process): def __init__(self, port, *args,", "self.request.recv(4))[0] json_content = self.request.recv(length) content = json.loads(json_content) terminal = content['terminal']", "plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process): def __init__(self, port, *args, **kwargs): super(ServerProcess,", "please install it first.\\n'.format(name) prompt += 'try follwing:\\n\\tpip3 install {}'.format(name)", "'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process): def __init__(self, port,", "= plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process): def __init__(self, port, *args, **kwargs):", "except ModuleNotFoundError as e: prompt = 'plugin {} not found,", "ServerProcess(multiprocessing.Process): def __init__(self, port, *args, **kwargs): super(ServerProcess, self).__init__(*args, **kwargs) self.port", "super(ServerProcess, self).__init__(*args, **kwargs) self.port = port def run(self): self.server =", "import importlib from socketserver import TCPServer, StreamRequestHandler def plugin_module_import(name): try:", "import TCPServer, StreamRequestHandler def plugin_module_import(name): try: return importlib.import_module(name) except ModuleNotFoundError", "plugin_module_import(name): try: return importlib.import_module(name) except ModuleNotFoundError as e: prompt =", "import struct import importlib from socketserver import TCPServer, StreamRequestHandler def", "= port def run(self): self.server = TCPServer(('', self.port), NotificationHandler) self.server.serve_forever()", "from socketserver import TCPServer, StreamRequestHandler def plugin_module_import(name): try: return importlib.import_module(name)", "'try follwing:\\n\\tpip3 install {}'.format(name) raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def handle(self):", "terminal = content['terminal'] if content['exec'] != '': command = 'ancypwn", "importlib from socketserver import TCPServer, StreamRequestHandler def plugin_module_import(name): try: return", "*args, **kwargs): super(ServerProcess, self).__init__(*args, **kwargs) self.port = port def run(self):", "class ServerProcess(multiprocessing.Process): def __init__(self, port, *args, **kwargs): super(ServerProcess, self).__init__(*args, **kwargs)", "{}'.format(name) raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def handle(self): length = struct.unpack('<I',", "json import os import multiprocessing import struct import importlib from", "def __init__(self, port, *args, **kwargs): super(ServerProcess, self).__init__(*args, **kwargs) self.port =", "import multiprocessing import struct import importlib from socketserver import TCPServer,", "prompt += 'try follwing:\\n\\tpip3 install {}'.format(name) raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler):", "'': command = 'ancypwn attach -c \\'{}\\''.format(content['exec']) else: command =", "command = 'ancypwn attach -c \\'{}\\''.format(content['exec']) else: command = 'ancypwn", "length = struct.unpack('<I', self.request.recv(4))[0] json_content = self.request.recv(length) content = json.loads(json_content)", "content['terminal'] if content['exec'] != '': command = 'ancypwn attach -c", "def plugin_module_import(name): try: return importlib.import_module(name) except ModuleNotFoundError as e: prompt", "struct import importlib from socketserver import TCPServer, StreamRequestHandler def plugin_module_import(name):", "**kwargs) self.port = port def run(self): self.server = TCPServer(('', self.port),", "os import multiprocessing import struct import importlib from socketserver import", "if content['exec'] != '': command = 'ancypwn attach -c \\'{}\\''.format(content['exec'])", "= content['terminal'] if content['exec'] != '': command = 'ancypwn attach", "PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def handle(self): length = struct.unpack('<I', self.request.recv(4))[0] json_content", "found, please install it first.\\n'.format(name) prompt += 'try follwing:\\n\\tpip3 install", "e: prompt = 'plugin {} not found, please install it", "'ancypwn attach' realname = 'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname) mod.run(command) class", "struct.unpack('<I', self.request.recv(4))[0] json_content = self.request.recv(length) content = json.loads(json_content) terminal =", "__init__(self, port, *args, **kwargs): super(ServerProcess, self).__init__(*args, **kwargs) self.port = port", "{} not found, please install it first.\\n'.format(name) prompt += 'try", "class NotificationHandler(StreamRequestHandler): def handle(self): length = struct.unpack('<I', self.request.recv(4))[0] json_content =", "command = 'ancypwn attach' realname = 'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname)", "'plugin {} not found, please install it first.\\n'.format(name) prompt +=", "content = json.loads(json_content) terminal = content['terminal'] if content['exec'] != '':", "attach' realname = 'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname) mod.run(command) class ServerProcess(multiprocessing.Process):", "self.port = port def run(self): self.server = TCPServer(('', self.port), NotificationHandler)", "not found, please install it first.\\n'.format(name) prompt += 'try follwing:\\n\\tpip3", "ModuleNotFoundError as e: prompt = 'plugin {} not found, please", "follwing:\\n\\tpip3 install {}'.format(name) raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def handle(self): length", "= 'ancypwn attach' realname = 'ancypwn_terminal_{}'.format(terminal) mod = plugin_module_import(realname) mod.run(command)", "prompt = 'plugin {} not found, please install it first.\\n'.format(name)", "import json import os import multiprocessing import struct import importlib", "handle(self): length = struct.unpack('<I', self.request.recv(4))[0] json_content = self.request.recv(length) content =", "\\'{}\\''.format(content['exec']) else: command = 'ancypwn attach' realname = 'ancypwn_terminal_{}'.format(terminal) mod", "install {}'.format(name) raise PluginNotFoundError(prompt) class NotificationHandler(StreamRequestHandler): def handle(self): length =" ]
[ "yield key_chain, d else: for k, v in d.items(): yield", "{}!'.format( allowed_values) yield key_chain, d else: for k, v in", "key_chain = [] if key_chain is None else list(key_chain).copy() if", "key_chain, d else: for k, v in d.items(): yield from", "key_chain=None, allowed_values=None): key_chain = [] if key_chain is None else", "assert isinstance(d, allowed_values), 'Value needs to be of type {}!'.format(", "type {}!'.format( allowed_values) yield key_chain, d else: for k, v", "of type {}!'.format( allowed_values) yield key_chain, d else: for k,", "is not None: assert isinstance(d, allowed_values), 'Value needs to be", "d else: for k, v in d.items(): yield from keychain_value_iter(", "not None: assert isinstance(d, allowed_values), 'Value needs to be of", "if key_chain is None else list(key_chain).copy() if not isinstance(d, dict):", "needs to be of type {}!'.format( allowed_values) yield key_chain, d", "k, v in d.items(): yield from keychain_value_iter( v, key_chain +", "None else list(key_chain).copy() if not isinstance(d, dict): if allowed_values is", "if not isinstance(d, dict): if allowed_values is not None: assert", "to be of type {}!'.format( allowed_values) yield key_chain, d else:", "not isinstance(d, dict): if allowed_values is not None: assert isinstance(d,", "in d.items(): yield from keychain_value_iter( v, key_chain + [k], allowed_values=allowed_values)", "list(key_chain).copy() if not isinstance(d, dict): if allowed_values is not None:", "[] if key_chain is None else list(key_chain).copy() if not isinstance(d,", "allowed_values), 'Value needs to be of type {}!'.format( allowed_values) yield", "else list(key_chain).copy() if not isinstance(d, dict): if allowed_values is not", "for k, v in d.items(): yield from keychain_value_iter( v, key_chain", "if allowed_values is not None: assert isinstance(d, allowed_values), 'Value needs", "key_chain is None else list(key_chain).copy() if not isinstance(d, dict): if", "v in d.items(): yield from keychain_value_iter( v, key_chain + [k],", "None: assert isinstance(d, allowed_values), 'Value needs to be of type", "be of type {}!'.format( allowed_values) yield key_chain, d else: for", "<reponame>c-hofer/pytorch_utils def keychain_value_iter(d, key_chain=None, allowed_values=None): key_chain = [] if key_chain", "isinstance(d, dict): if allowed_values is not None: assert isinstance(d, allowed_values),", "else: for k, v in d.items(): yield from keychain_value_iter( v,", "allowed_values) yield key_chain, d else: for k, v in d.items():", "allowed_values=None): key_chain = [] if key_chain is None else list(key_chain).copy()", "isinstance(d, allowed_values), 'Value needs to be of type {}!'.format( allowed_values)", "= [] if key_chain is None else list(key_chain).copy() if not", "def keychain_value_iter(d, key_chain=None, allowed_values=None): key_chain = [] if key_chain is", "dict): if allowed_values is not None: assert isinstance(d, allowed_values), 'Value", "'Value needs to be of type {}!'.format( allowed_values) yield key_chain,", "allowed_values is not None: assert isinstance(d, allowed_values), 'Value needs to", "is None else list(key_chain).copy() if not isinstance(d, dict): if allowed_values", "keychain_value_iter(d, key_chain=None, allowed_values=None): key_chain = [] if key_chain is None" ]
[ "video = request.FILES['video'] # Transcribe video and extract audio response", "version): # Get video video = request.FILES['video'] # Transcribe video", "django.contrib.auth.decorators import login_required from django.http import JsonResponse from django.views.decorators.csrf import", "Get video video = request.FILES['video'] # Transcribe video and extract", "# Transcribe video and extract audio response = helpers.transcribe_file(video) context", "import login_required from django.http import JsonResponse from django.views.decorators.csrf import csrf_exempt", "import render from django.contrib.auth.decorators import login_required from django.http import JsonResponse", "= request.FILES['video'] # Transcribe video and extract audio response =", "import helpers # Create your views here. @csrf_exempt def convert_video(request,", "login_required from django.http import JsonResponse from django.views.decorators.csrf import csrf_exempt from", "django.http import JsonResponse from django.views.decorators.csrf import csrf_exempt from . import", "csrf_exempt from . import helpers # Create your views here.", "video and extract audio response = helpers.transcribe_file(video) context = response", "django.views.decorators.csrf import csrf_exempt from . import helpers # Create your", "from django.shortcuts import render from django.contrib.auth.decorators import login_required from django.http", "@csrf_exempt def convert_video(request, version): # Get video video = request.FILES['video']", "extract audio response = helpers.transcribe_file(video) context = response # return", "= response # return render(request, 'api/v1/result_successful.html', context) return JsonResponse(context, safe=False)", "from django.contrib.auth.decorators import login_required from django.http import JsonResponse from django.views.decorators.csrf", "import csrf_exempt from . import helpers # Create your views", "django.shortcuts import render from django.contrib.auth.decorators import login_required from django.http import", "from . import helpers # Create your views here. @csrf_exempt", "Create your views here. @csrf_exempt def convert_video(request, version): # Get", "Transcribe video and extract audio response = helpers.transcribe_file(video) context =", "# Get video video = request.FILES['video'] # Transcribe video and", "request.FILES['video'] # Transcribe video and extract audio response = helpers.transcribe_file(video)", "= helpers.transcribe_file(video) context = response # return render(request, 'api/v1/result_successful.html', context)", "views here. @csrf_exempt def convert_video(request, version): # Get video video", "JsonResponse from django.views.decorators.csrf import csrf_exempt from . import helpers #", "your views here. @csrf_exempt def convert_video(request, version): # Get video", "helpers.transcribe_file(video) context = response # return render(request, 'api/v1/result_successful.html', context) return", "def convert_video(request, version): # Get video video = request.FILES['video'] #", "render from django.contrib.auth.decorators import login_required from django.http import JsonResponse from", "convert_video(request, version): # Get video video = request.FILES['video'] # Transcribe", "from django.views.decorators.csrf import csrf_exempt from . import helpers # Create", "and extract audio response = helpers.transcribe_file(video) context = response #", ". import helpers # Create your views here. @csrf_exempt def", "# Create your views here. @csrf_exempt def convert_video(request, version): #", "here. @csrf_exempt def convert_video(request, version): # Get video video =", "audio response = helpers.transcribe_file(video) context = response # return render(request,", "helpers # Create your views here. @csrf_exempt def convert_video(request, version):", "response = helpers.transcribe_file(video) context = response # return render(request, 'api/v1/result_successful.html',", "import JsonResponse from django.views.decorators.csrf import csrf_exempt from . import helpers", "video video = request.FILES['video'] # Transcribe video and extract audio", "context = response # return render(request, 'api/v1/result_successful.html', context) return JsonResponse(context,", "from django.http import JsonResponse from django.views.decorators.csrf import csrf_exempt from ." ]
[ "@alex.cline \"\"\" from cloudaux.aws.ec2 import describe_vpn_connections from security_monkey.cloudaux_watcher import CloudAuxWatcher", "# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law", "= [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self, item): if", "return item[\"VpnConnectionId\"] def list_method(self, **kwargs): return describe_vpn_connections(**kwargs) def get_method(self, item,", "class VPNItem(ChangeItem): def __init__(self, region=None, account=None, name=None, arn=None, config=None, source_watcher=None):", "item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class", "'VPN Connections' def __init__(self, *args, **kwargs): super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals", "compliance with the License. # You may obtain a copy", "an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF", "2.0 (the \"License\"); # you may not use this file", "agreed to in writing, software # distributed under the License", "file except in compliance with the License. # You may", "on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS", "Unless required by applicable law or agreed to in writing,", "= 'VPN Connection' i_am_plural = 'VPN Connections' def __init__(self, *args,", "**kwargs): return describe_vpn_connections(**kwargs) def get_method(self, item, **kwargs): # Remove the", "return describe_vpn_connections(**kwargs) def get_method(self, item, **kwargs): # Remove the CustomerGatewayConfiguration", "self.honor_ephemerals = True self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ]", "'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self, item): if item.get(\"Tags\"): for tag in", "Remove the CustomerGatewayConfiguration -- it's not necessary as all the", "the License. \"\"\" .. module: security_monkey.watchers.vpc.vpn :platform: Unix .. version::", "and # limitations under the License. \"\"\" .. module: security_monkey.watchers.vpc.vpn", "describe_vpn_connections(**kwargs) def get_method(self, item, **kwargs): # Remove the CustomerGatewayConfiguration --", "distributed under the License is distributed on an \"AS IS\"", "= vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class VPNItem(ChangeItem): def __init__(self, region=None, account=None,", "import CloudAuxWatcher from security_monkey.watcher import ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class", "'%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index = 'vpn' i_am_singular = 'VPN Connection'", "the specific language governing permissions and # limitations under the", "to something JSON serializable (ISO 8601 string): for vgw in", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express", "item[\"VpnConnectionId\"] def list_method(self, **kwargs): return describe_vpn_connections(**kwargs) def get_method(self, item, **kwargs):", "def __init__(self, region=None, account=None, name=None, arn=None, config=None, source_watcher=None): super(VPNItem, self).__init__(", "\"\"\" from cloudaux.aws.ec2 import describe_vpn_connections from security_monkey.cloudaux_watcher import CloudAuxWatcher from", "express or implied. # See the License for the specific", "applicable law or agreed to in writing, software # distributed", "for vgw in item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT)", "except in compliance with the License. # You may obtain", "of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless", "in item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item", "item.pop(\"CustomerGatewayConfiguration\", None) # Set the ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"],", "Licensed under the Apache License, Version 2.0 (the \"License\"); #", "item, **kwargs): # Remove the CustomerGatewayConfiguration -- it's not necessary", "not use this file except in compliance with the License.", "region=region, account=account, name=name, arn=arn, new_config=config if config else {}, source_watcher=source_watcher)", "= '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index = 'vpn' i_am_singular = 'VPN", "'vpn' i_am_singular = 'VPN Connection' i_am_plural = 'VPN Connections' def", "self).__init__(*args, **kwargs) self.honor_ephemerals = True self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status',", "'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self, item): if item.get(\"Tags\"): for tag", "writing, software # distributed under the License is distributed on", "in writing, software # distributed under the License is distributed", ".. moduleauthor:: <NAME> <<EMAIL>> @alex.cline \"\"\" from cloudaux.aws.ec2 import describe_vpn_connections", "i_am_singular = 'VPN Connection' i_am_plural = 'VPN Connections' def __init__(self,", "# Cast the datetimes to something JSON serializable (ISO 8601", "you may not use this file except in compliance with", "moduleauthor:: <NAME> <<EMAIL>> @alex.cline \"\"\" from cloudaux.aws.ec2 import describe_vpn_connections from", "the datetimes to something JSON serializable (ISO 8601 string): for", "Connection' i_am_plural = 'VPN Connections' def __init__(self, *args, **kwargs): super(VPN,", "limitations under the License. \"\"\" .. module: security_monkey.watchers.vpc.vpn :platform: Unix", "# Licensed under the Apache License, Version 2.0 (the \"License\");", "details are present anyway: item.pop(\"CustomerGatewayConfiguration\", None) # Set the ARN:", "governing permissions and # limitations under the License. \"\"\" ..", "id=item[\"VpnConnectionId\"]) # Cast the datetimes to something JSON serializable (ISO", "from security_monkey.watcher import ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index", ".. module: security_monkey.watchers.vpc.vpn :platform: Unix .. version:: $$VERSION$$ .. moduleauthor::", "use this file except in compliance with the License. #", "the CustomerGatewayConfiguration -- it's not necessary as all the details", "http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed", "not necessary as all the details are present anyway: item.pop(\"CustomerGatewayConfiguration\",", "return item class VPNItem(ChangeItem): def __init__(self, region=None, account=None, name=None, arn=None,", "ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index = 'vpn' i_am_singular", "# Set the ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) #", "def get_method(self, item, **kwargs): # Remove the CustomerGatewayConfiguration -- it's", "[ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self, item): if item.get(\"Tags\"):", "for tag in item[\"Tags\"]: if tag[\"Key\"] == \"Name\": return \"{}", "**kwargs): # Remove the CustomerGatewayConfiguration -- it's not necessary as", "JSON serializable (ISO 8601 string): for vgw in item.get(\"VgwTelemetry\", []):", "item class VPNItem(ChangeItem): def __init__(self, region=None, account=None, name=None, arn=None, config=None,", "index = 'vpn' i_am_singular = 'VPN Connection' i_am_plural = 'VPN", "item): if item.get(\"Tags\"): for tag in item[\"Tags\"]: if tag[\"Key\"] ==", "self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self, item):", "CONDITIONS OF ANY KIND, either express or implied. # See", "= 'vpn' i_am_singular = 'VPN Connection' i_am_plural = 'VPN Connections'", "the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required", "def __init__(self, *args, **kwargs): super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals = True", "or implied. # See the License for the specific language", "License is distributed on an \"AS IS\" BASIS, # WITHOUT", "({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def list_method(self, **kwargs): return describe_vpn_connections(**kwargs) def", "are present anyway: item.pop(\"CustomerGatewayConfiguration\", None) # Set the ARN: item[\"Arn\"]", "License. # You may obtain a copy of the License", "is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES", "License, Version 2.0 (the \"License\"); # you may not use", "config=None, source_watcher=None): super(VPNItem, self).__init__( index=VPN.index, region=region, account=account, name=name, arn=arn, new_config=config", "== \"Name\": return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def list_method(self,", "# You may obtain a copy of the License at", "KIND, either express or implied. # See the License for", "specific language governing permissions and # limitations under the License.", "$$VERSION$$ .. moduleauthor:: <NAME> <<EMAIL>> @alex.cline \"\"\" from cloudaux.aws.ec2 import", "if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class VPNItem(ChangeItem): def", "CustomerGatewayConfiguration -- it's not necessary as all the details are", "under the License is distributed on an \"AS IS\" BASIS,", "from security_monkey.cloudaux_watcher import CloudAuxWatcher from security_monkey.watcher import ChangeItem DATETIME_FORMAT =", "string): for vgw in item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] =", "copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #", "License for the specific language governing permissions and # limitations", "License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by", "CloudAuxWatcher from security_monkey.watcher import ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher):", "def list_method(self, **kwargs): return describe_vpn_connections(**kwargs) def get_method(self, item, **kwargs): #", "vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class VPNItem(ChangeItem): def __init__(self, region=None,", "# Remove the CustomerGatewayConfiguration -- it's not necessary as all", "item.get(\"Tags\"): for tag in item[\"Tags\"]: if tag[\"Key\"] == \"Name\": return", "tag in item[\"Tags\"]: if tag[\"Key\"] == \"Name\": return \"{} ({})\".format(tag[\"Value\"],", "*args, **kwargs): super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals = True self.ephemeral_paths =", "account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast the datetimes to something JSON serializable", "all the details are present anyway: item.pop(\"CustomerGatewayConfiguration\", None) # Set", "import describe_vpn_connections from security_monkey.cloudaux_watcher import CloudAuxWatcher from security_monkey.watcher import ChangeItem", "= True self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def", "the License for the specific language governing permissions and #", "self).__init__( index=VPN.index, region=region, account=account, name=name, arn=arn, new_config=config if config else", "permissions and # limitations under the License. \"\"\" .. module:", "arn=None, config=None, source_watcher=None): super(VPNItem, self).__init__( index=VPN.index, region=region, account=account, name=name, arn=arn,", "(the \"License\"); # you may not use this file except", "i_am_plural = 'VPN Connections' def __init__(self, *args, **kwargs): super(VPN, self).__init__(*args,", "'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self, item): if item.get(\"Tags\"): for", "Apache License, Version 2.0 (the \"License\"); # you may not", "# you may not use this file except in compliance", "either express or implied. # See the License for the", "OR CONDITIONS OF ANY KIND, either express or implied. #", "datetimes to something JSON serializable (ISO 8601 string): for vgw", "(ISO 8601 string): for vgw in item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"):", "Connections' def __init__(self, *args, **kwargs): super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals =", "VPNItem(ChangeItem): def __init__(self, region=None, account=None, name=None, arn=None, config=None, source_watcher=None): super(VPNItem,", "<reponame>boladmin/security_monkey # Licensed under the Apache License, Version 2.0 (the", "# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or", "item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def list_method(self, **kwargs): return describe_vpn_connections(**kwargs) def get_method(self,", "it's not necessary as all the details are present anyway:", "the License is distributed on an \"AS IS\" BASIS, #", "__init__(self, region=None, account=None, name=None, arn=None, config=None, source_watcher=None): super(VPNItem, self).__init__( index=VPN.index,", "region=None, account=None, name=None, arn=None, config=None, source_watcher=None): super(VPNItem, self).__init__( index=VPN.index, region=region,", "in compliance with the License. # You may obtain a", "if item.get(\"Tags\"): for tag in item[\"Tags\"]: if tag[\"Key\"] == \"Name\":", "vgw in item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return", "software # distributed under the License is distributed on an", "vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class VPNItem(ChangeItem): def __init__(self,", "import ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index = 'vpn'", "**kwargs) self.honor_ephemerals = True self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage',", "language governing permissions and # limitations under the License. \"\"\"", "# # Unless required by applicable law or agreed to", "something JSON serializable (ISO 8601 string): for vgw in item.get(\"VgwTelemetry\",", "super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals = True self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange',", "super(VPNItem, self).__init__( index=VPN.index, region=region, account=account, name=name, arn=arn, new_config=config if config", "a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #", "\"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def list_method(self, **kwargs): return describe_vpn_connections(**kwargs)", "obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0", "index=VPN.index, region=region, account=account, name=name, arn=arn, new_config=config if config else {},", "the details are present anyway: item.pop(\"CustomerGatewayConfiguration\", None) # Set the", "vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class VPNItem(ChangeItem): def __init__(self, region=None, account=None, name=None,", "version:: $$VERSION$$ .. moduleauthor:: <NAME> <<EMAIL>> @alex.cline \"\"\" from cloudaux.aws.ec2", "Version 2.0 (the \"License\"); # you may not use this", "present anyway: item.pop(\"CustomerGatewayConfiguration\", None) # Set the ARN: item[\"Arn\"] =", "= 'VPN Connections' def __init__(self, *args, **kwargs): super(VPN, self).__init__(*args, **kwargs)", "law or agreed to in writing, software # distributed under", "anyway: item.pop(\"CustomerGatewayConfiguration\", None) # Set the ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"],", "\"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast the datetimes to something JSON", "\"Name\": return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def list_method(self, **kwargs):", "Cast the datetimes to something JSON serializable (ISO 8601 string):", "security_monkey.watchers.vpc.vpn :platform: Unix .. version:: $$VERSION$$ .. moduleauthor:: <NAME> <<EMAIL>>", "**kwargs): super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals = True self.ephemeral_paths = [", "return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def list_method(self, **kwargs): return", "ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast the datetimes", "security_monkey.watcher import ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index =", "License. \"\"\" .. module: security_monkey.watchers.vpc.vpn :platform: Unix .. version:: $$VERSION$$", "implied. # See the License for the specific language governing", "under the Apache License, Version 2.0 (the \"License\"); # you", "get_name_from_list_output(self, item): if item.get(\"Tags\"): for tag in item[\"Tags\"]: if tag[\"Key\"]", "Unix .. version:: $$VERSION$$ .. moduleauthor:: <NAME> <<EMAIL>> @alex.cline \"\"\"", "\"License\"); # you may not use this file except in", "Set the ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast", "] def get_name_from_list_output(self, item): if item.get(\"Tags\"): for tag in item[\"Tags\"]:", "class VPN(CloudAuxWatcher): index = 'vpn' i_am_singular = 'VPN Connection' i_am_plural", "module: security_monkey.watchers.vpc.vpn :platform: Unix .. version:: $$VERSION$$ .. moduleauthor:: <NAME>", "distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR", "describe_vpn_connections from security_monkey.cloudaux_watcher import CloudAuxWatcher from security_monkey.watcher import ChangeItem DATETIME_FORMAT", "<NAME> <<EMAIL>> @alex.cline \"\"\" from cloudaux.aws.ec2 import describe_vpn_connections from security_monkey.cloudaux_watcher", "None) # Set the ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"])", ":platform: Unix .. version:: $$VERSION$$ .. moduleauthor:: <NAME> <<EMAIL>> @alex.cline", "8601 string): for vgw in item.get(\"VgwTelemetry\", []): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"]", "by applicable law or agreed to in writing, software #", "# distributed under the License is distributed on an \"AS", "OF ANY KIND, either express or implied. # See the", "WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.", "get_method(self, item, **kwargs): # Remove the CustomerGatewayConfiguration -- it's not", "as all the details are present anyway: item.pop(\"CustomerGatewayConfiguration\", None) #", "may obtain a copy of the License at # #", "# Unless required by applicable law or agreed to in", "ANY KIND, either express or implied. # See the License", "See the License for the specific language governing permissions and", "item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast the datetimes to", "the License. # You may obtain a copy of the", "at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable", "for the specific language governing permissions and # limitations under", ".. version:: $$VERSION$$ .. moduleauthor:: <NAME> <<EMAIL>> @alex.cline \"\"\" from", "item[\"Tags\"]: if tag[\"Key\"] == \"Name\": return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return", "\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY", "to in writing, software # distributed under the License is", "__init__(self, *args, **kwargs): super(VPN, self).__init__(*args, **kwargs) self.honor_ephemerals = True self.ephemeral_paths", "name=None, arn=None, config=None, source_watcher=None): super(VPNItem, self).__init__( index=VPN.index, region=region, account=account, name=name,", "VPN(CloudAuxWatcher): index = 'vpn' i_am_singular = 'VPN Connection' i_am_plural =", "IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,", "# See the License for the specific language governing permissions", "in item[\"Tags\"]: if tag[\"Key\"] == \"Name\": return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"])", "DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ' class VPN(CloudAuxWatcher): index = 'vpn' i_am_singular =", "-- it's not necessary as all the details are present", "security_monkey.cloudaux_watcher import CloudAuxWatcher from security_monkey.watcher import ChangeItem DATETIME_FORMAT = '%Y-%m-%dT%H:%M:%SZ'", "= \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast the datetimes to something", "source_watcher=None): super(VPNItem, self).__init__( index=VPN.index, region=region, account=account, name=name, arn=arn, new_config=config if", "You may obtain a copy of the License at #", "'VPN Connection' i_am_plural = 'VPN Connections' def __init__(self, *args, **kwargs):", "tag[\"Key\"] == \"Name\": return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"] def", "may not use this file except in compliance with the", "or agreed to in writing, software # distributed under the", "from cloudaux.aws.ec2 import describe_vpn_connections from security_monkey.cloudaux_watcher import CloudAuxWatcher from security_monkey.watcher", "cloudaux.aws.ec2 import describe_vpn_connections from security_monkey.cloudaux_watcher import CloudAuxWatcher from security_monkey.watcher import", "serializable (ISO 8601 string): for vgw in item.get(\"VgwTelemetry\", []): if", "the ARN: item[\"Arn\"] = \"arn:aws:ec2:{region}:{account}:vpn-connection/{id}\".format(region=kwargs[\"region\"], account=kwargs[\"account_number\"], id=item[\"VpnConnectionId\"]) # Cast the", "required by applicable law or agreed to in writing, software", "account=None, name=None, arn=None, config=None, source_watcher=None): super(VPNItem, self).__init__( index=VPN.index, region=region, account=account,", "under the License. \"\"\" .. module: security_monkey.watchers.vpc.vpn :platform: Unix ..", "BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or", "True self.ephemeral_paths = [ 'VgwTelemetry$*$LastStatusChange', 'VgwTelemetry$*$Status', 'VgwTelemetry$*$StatusMessage', ] def get_name_from_list_output(self,", "with the License. # You may obtain a copy of", "def get_name_from_list_output(self, item): if item.get(\"Tags\"): for tag in item[\"Tags\"]: if", "this file except in compliance with the License. # You", "# limitations under the License. \"\"\" .. module: security_monkey.watchers.vpc.vpn :platform:", "necessary as all the details are present anyway: item.pop(\"CustomerGatewayConfiguration\", None)", "list_method(self, **kwargs): return describe_vpn_connections(**kwargs) def get_method(self, item, **kwargs): # Remove", "the Apache License, Version 2.0 (the \"License\"); # you may", "[]): if vgw.get(\"LastStatusChange\"): vgw[\"LastStatusChange\"] = vgw[\"LastStatusChange\"].strftime(DATETIME_FORMAT) return item class VPNItem(ChangeItem):", "<<EMAIL>> @alex.cline \"\"\" from cloudaux.aws.ec2 import describe_vpn_connections from security_monkey.cloudaux_watcher import", "if tag[\"Key\"] == \"Name\": return \"{} ({})\".format(tag[\"Value\"], item[\"VpnConnectionId\"]) return item[\"VpnConnectionId\"]", "\"\"\" .. module: security_monkey.watchers.vpc.vpn :platform: Unix .. version:: $$VERSION$$ .." ]
[ "def set_main_data(self, main_data): self.main_data = main_data def set_x(self, x): self.x", "the boundary # For predictor corrector self.prev_x = np.array(x) self.prev_v", "2D integer index for the particle's location in the search", "Calculates the bucket coordinates self.list_num = np.array((self.x - self.main_data.min_x) /", "= 0 self.rho = main_data.rho0 self.P = 0 self.m =", "self.list_num = np.array((self.x - self.main_data.min_x) / (2.0 * self.main_data.h), int)", "compressible \"\"\" rho0 = self.main_data.rho0 gamma = self.main_data.gamma self.P =", "self.B() * ((self.rho / rho0)**gamma - 1) def set_main_data(self, main_data):", "= \"density: \" + str(self.rho) + \", \" m_s =", "\"is boundary: \" + str(self.boundary) return [x_s + v_s +", "\" + str(self.a) + \", \" D_s = \"derivative of", "* self.main_data.c0 ** 2) / self.main_data.gamma def update_P(self): \"\"\" Equation", "\"position: \" + str(self.x) + \", \" v_s = \"velocity:", "np.array(x) self.prev_v = np.zeros(2) self.prev_rho = main_data.rho0 def calc_index(self): \"\"\"Calculates", "1) def set_main_data(self, main_data): self.main_data = main_data def set_x(self, x):", "depends on the initial particle spacing self.boundary = False #", "** 2) / self.main_data.gamma def update_P(self): \"\"\" Equation of state", "- 1) def set_main_data(self, main_data): self.main_data = main_data def set_x(self,", "self.main_data.gamma def update_P(self): \"\"\" Equation of state System is assumed", "\" + str(self.D) + \", \" rho_s = \"density: \"", "all the properties for a single particle\"\"\" _ids = count(0)", "\" + str(self.v) + \", \" a_s = \"acceleration: \"", "2 * main_data.rho0 # initial mass depends on the initial", "\"density: \" + str(self.rho) + \", \" m_s = \"mass:", "return [x_s + v_s + a_s + D_s + rho_s", "\" P_s = \"pressure: \" + str(self.P) + \", \"", "main_data=None, x=np.zeros(2)): self.id = next(self._ids) self.main_data = main_data self.x =", "+ str(self.v) + \", \" a_s = \"acceleration: \" +", "= a def set_D(self, D): self.D = D def set_rho(self,", "str(self.rho) + \", \" m_s = \"mass: \" + str(self.m)", "default is not on the boundary # For predictor corrector", "= next(self._ids) self.main_data = main_data self.x = np.array(x) self.v =", "[x_s + v_s + a_s + D_s + rho_s +", "import count import numpy as np class Particle(object): \"\"\"Object containing", "particle spacing self.boundary = False # Particle by default is", "D_s = \"derivative of density: \" + str(self.D) + \",", "m(self, m): self.m = m def list_attributes(self): x_s = \"position:", "properties for a single particle\"\"\" _ids = count(0) def __init__(self,", "str(self.x) + \", \" v_s = \"velocity: \" + str(self.v)", "Particle by default is not on the boundary # For", "D def set_rho(self, rho): self.rho = rho self.update_P() def m(self,", "integer index for the particle's location in the search grid\"\"\"", "+ a_s + D_s + rho_s + m_s + P_s", "def calc_index(self): \"\"\"Calculates the 2D integer index for the particle's", "main_data): self.main_data = main_data def set_x(self, x): self.x = x", "count(0) def __init__(self, main_data=None, x=np.zeros(2)): self.id = next(self._ids) self.main_data =", "= main_data self.x = np.array(x) self.v = np.zeros(2) self.a =", "import numpy as np class Particle(object): \"\"\"Object containing all the", "For predictor corrector self.prev_x = np.array(x) self.prev_v = np.zeros(2) self.prev_rho", "np.array(x) self.v = np.zeros(2) self.a = np.zeros(2) self.D = 0", "0 self.rho = main_data.rho0 self.P = 0 self.m = main_data.dx", "mass depends on the initial particle spacing self.boundary = False", "\" + str(self.boundary) return [x_s + v_s + a_s +", "self.v = v def set_a(self, a): self.a = a def", "\"\"\" rho0 = self.main_data.rho0 gamma = self.main_data.gamma self.P = self.B()", "self.x = x self.calc_index() def set_v(self, v): self.v = v", "= self.B() * ((self.rho / rho0)**gamma - 1) def set_main_data(self,", "self.main_data.gamma self.P = self.B() * ((self.rho / rho0)**gamma - 1)", "self.D = D def set_rho(self, rho): self.rho = rho self.update_P()", "+ str(self.m) + \", \" P_s = \"pressure: \" +", "a_s = \"acceleration: \" + str(self.a) + \", \" D_s", "x_s = \"position: \" + str(self.x) + \", \" v_s", "def set_v(self, v): self.v = v def set_a(self, a): self.a", "main_data.dx ** 2 * main_data.rho0 # initial mass depends on", "main_data def set_x(self, x): self.x = x self.calc_index() def set_v(self,", "__init__(self, main_data=None, x=np.zeros(2)): self.id = next(self._ids) self.main_data = main_data self.x", "boundary # For predictor corrector self.prev_x = np.array(x) self.prev_v =", "index for the particle's location in the search grid\"\"\" #", "class Particle(object): \"\"\"Object containing all the properties for a single", "self.id = next(self._ids) self.main_data = main_data self.x = np.array(x) self.v", "self.a = np.zeros(2) self.D = 0 self.rho = main_data.rho0 self.P", "the bucket coordinates self.list_num = np.array((self.x - self.main_data.min_x) / (2.0", "def B(self): return (self.main_data.rho0 * self.main_data.c0 ** 2) / self.main_data.gamma", "= np.array(x) self.prev_v = np.zeros(2) self.prev_rho = main_data.rho0 def calc_index(self):", "* self.main_data.h), int) def B(self): return (self.main_data.rho0 * self.main_data.c0 **", "= main_data.dx ** 2 * main_data.rho0 # initial mass depends", "= self.main_data.rho0 gamma = self.main_data.gamma self.P = self.B() * ((self.rho", "initial mass depends on the initial particle spacing self.boundary =", "from itertools import count import numpy as np class Particle(object):", "+ str(self.boundary) return [x_s + v_s + a_s + D_s", "= np.array((self.x - self.main_data.min_x) / (2.0 * self.main_data.h), int) def", "self.main_data.min_x) / (2.0 * self.main_data.h), int) def B(self): return (self.main_data.rho0", "v def set_a(self, a): self.a = a def set_D(self, D):", "for the particle's location in the search grid\"\"\" # Calculates", "gamma = self.main_data.gamma self.P = self.B() * ((self.rho / rho0)**gamma", "list_attributes(self): x_s = \"position: \" + str(self.x) + \", \"", "** 2 * main_data.rho0 # initial mass depends on the", "System is assumed slightly compressible \"\"\" rho0 = self.main_data.rho0 gamma", "= x self.calc_index() def set_v(self, v): self.v = v def", "the particle's location in the search grid\"\"\" # Calculates the", "_ids = count(0) def __init__(self, main_data=None, x=np.zeros(2)): self.id = next(self._ids)", "\"pressure: \" + str(self.P) + \", \" boundary_s = \"is", "= False # Particle by default is not on the", "self.P = self.B() * ((self.rho / rho0)**gamma - 1) def", "as np class Particle(object): \"\"\"Object containing all the properties for", "set_D(self, D): self.D = D def set_rho(self, rho): self.rho =", "0 self.m = main_data.dx ** 2 * main_data.rho0 # initial", "main_data.rho0 # initial mass depends on the initial particle spacing", "= main_data.rho0 def calc_index(self): \"\"\"Calculates the 2D integer index for", "of density: \" + str(self.D) + \", \" rho_s =", "= main_data def set_x(self, x): self.x = x self.calc_index() def", "np class Particle(object): \"\"\"Object containing all the properties for a", "x): self.x = x self.calc_index() def set_v(self, v): self.v =", "\", \" v_s = \"velocity: \" + str(self.v) + \",", "particle\"\"\" _ids = count(0) def __init__(self, main_data=None, x=np.zeros(2)): self.id =", "np.zeros(2) self.D = 0 self.rho = main_data.rho0 self.P = 0", "= \"pressure: \" + str(self.P) + \", \" boundary_s =", "by default is not on the boundary # For predictor", "bucket coordinates self.list_num = np.array((self.x - self.main_data.min_x) / (2.0 *", "corrector self.prev_x = np.array(x) self.prev_v = np.zeros(2) self.prev_rho = main_data.rho0", "rho self.update_P() def m(self, m): self.m = m def list_attributes(self):", "= \"position: \" + str(self.x) + \", \" v_s =", "self.prev_rho = main_data.rho0 def calc_index(self): \"\"\"Calculates the 2D integer index", "coordinates self.list_num = np.array((self.x - self.main_data.min_x) / (2.0 * self.main_data.h),", "* main_data.rho0 # initial mass depends on the initial particle", "set_a(self, a): self.a = a def set_D(self, D): self.D =", "calc_index(self): \"\"\"Calculates the 2D integer index for the particle's location", "\"velocity: \" + str(self.v) + \", \" a_s = \"acceleration:", "single particle\"\"\" _ids = count(0) def __init__(self, main_data=None, x=np.zeros(2)): self.id", "/ rho0)**gamma - 1) def set_main_data(self, main_data): self.main_data = main_data", "np.zeros(2) self.a = np.zeros(2) self.D = 0 self.rho = main_data.rho0", "is assumed slightly compressible \"\"\" rho0 = self.main_data.rho0 gamma =", "v_s = \"velocity: \" + str(self.v) + \", \" a_s", "\" rho_s = \"density: \" + str(self.rho) + \", \"", "\", \" D_s = \"derivative of density: \" + str(self.D)", "= 0 self.m = main_data.dx ** 2 * main_data.rho0 #", "a def set_D(self, D): self.D = D def set_rho(self, rho):", "= D def set_rho(self, rho): self.rho = rho self.update_P() def", "a): self.a = a def set_D(self, D): self.D = D", "+ \", \" boundary_s = \"is boundary: \" + str(self.boundary)", "of state System is assumed slightly compressible \"\"\" rho0 =", "self.update_P() def m(self, m): self.m = m def list_attributes(self): x_s", "def m(self, m): self.m = m def list_attributes(self): x_s =", "search grid\"\"\" # Calculates the bucket coordinates self.list_num = np.array((self.x", "\" v_s = \"velocity: \" + str(self.v) + \", \"", "+ \", \" D_s = \"derivative of density: \" +", "str(self.D) + \", \" rho_s = \"density: \" + str(self.rho)", "the search grid\"\"\" # Calculates the bucket coordinates self.list_num =", "main_data.rho0 self.P = 0 self.m = main_data.dx ** 2 *", "= np.zeros(2) self.D = 0 self.rho = main_data.rho0 self.P =", "(self.main_data.rho0 * self.main_data.c0 ** 2) / self.main_data.gamma def update_P(self): \"\"\"", "\" m_s = \"mass: \" + str(self.m) + \", \"", "self.D = 0 self.rho = main_data.rho0 self.P = 0 self.m", "False # Particle by default is not on the boundary", "assumed slightly compressible \"\"\" rho0 = self.main_data.rho0 gamma = self.main_data.gamma", "rho0)**gamma - 1) def set_main_data(self, main_data): self.main_data = main_data def", "self.rho = rho self.update_P() def m(self, m): self.m = m", "is not on the boundary # For predictor corrector self.prev_x", "= np.zeros(2) self.a = np.zeros(2) self.D = 0 self.rho =", "rho_s = \"density: \" + str(self.rho) + \", \" m_s", "slightly compressible \"\"\" rho0 = self.main_data.rho0 gamma = self.main_data.gamma self.P", "m): self.m = m def list_attributes(self): x_s = \"position: \"", "# Calculates the bucket coordinates self.list_num = np.array((self.x - self.main_data.min_x)", "self.boundary = False # Particle by default is not on", "\"derivative of density: \" + str(self.D) + \", \" rho_s", "def update_P(self): \"\"\" Equation of state System is assumed slightly", "= count(0) def __init__(self, main_data=None, x=np.zeros(2)): self.id = next(self._ids) self.main_data", "def __init__(self, main_data=None, x=np.zeros(2)): self.id = next(self._ids) self.main_data = main_data", "\", \" a_s = \"acceleration: \" + str(self.a) + \",", "the properties for a single particle\"\"\" _ids = count(0) def", "\" + str(self.P) + \", \" boundary_s = \"is boundary:", "Equation of state System is assumed slightly compressible \"\"\" rho0", "= main_data.rho0 self.P = 0 self.m = main_data.dx ** 2", "self.a = a def set_D(self, D): self.D = D def", "+ str(self.rho) + \", \" m_s = \"mass: \" +", "+ str(self.P) + \", \" boundary_s = \"is boundary: \"", "grid\"\"\" # Calculates the bucket coordinates self.list_num = np.array((self.x -", "+ str(self.D) + \", \" rho_s = \"density: \" +", "np.array((self.x - self.main_data.min_x) / (2.0 * self.main_data.h), int) def B(self):", "a single particle\"\"\" _ids = count(0) def __init__(self, main_data=None, x=np.zeros(2)):", "self.prev_x = np.array(x) self.prev_v = np.zeros(2) self.prev_rho = main_data.rho0 def", "main_data.rho0 def calc_index(self): \"\"\"Calculates the 2D integer index for the", "= v def set_a(self, a): self.a = a def set_D(self,", "= \"derivative of density: \" + str(self.D) + \", \"", "\", \" m_s = \"mass: \" + str(self.m) + \",", "str(self.m) + \", \" P_s = \"pressure: \" + str(self.P)", "\" boundary_s = \"is boundary: \" + str(self.boundary) return [x_s", "self.rho = main_data.rho0 self.P = 0 self.m = main_data.dx **", "((self.rho / rho0)**gamma - 1) def set_main_data(self, main_data): self.main_data =", "location in the search grid\"\"\" # Calculates the bucket coordinates", "+ \", \" a_s = \"acceleration: \" + str(self.a) +", "self.main_data.h), int) def B(self): return (self.main_data.rho0 * self.main_data.c0 ** 2)", "/ self.main_data.gamma def update_P(self): \"\"\" Equation of state System is", "+ v_s + a_s + D_s + rho_s + m_s", "\" + str(self.m) + \", \" P_s = \"pressure: \"", "self.m = m def list_attributes(self): x_s = \"position: \" +", "self.m = main_data.dx ** 2 * main_data.rho0 # initial mass", "update_P(self): \"\"\" Equation of state System is assumed slightly compressible", "str(self.boundary) return [x_s + v_s + a_s + D_s +", "# For predictor corrector self.prev_x = np.array(x) self.prev_v = np.zeros(2)", "predictor corrector self.prev_x = np.array(x) self.prev_v = np.zeros(2) self.prev_rho =", "containing all the properties for a single particle\"\"\" _ids =", "the initial particle spacing self.boundary = False # Particle by", "D): self.D = D def set_rho(self, rho): self.rho = rho", "= \"acceleration: \" + str(self.a) + \", \" D_s =", "initial particle spacing self.boundary = False # Particle by default", "\", \" P_s = \"pressure: \" + str(self.P) + \",", "= \"is boundary: \" + str(self.boundary) return [x_s + v_s", "# initial mass depends on the initial particle spacing self.boundary", "m def list_attributes(self): x_s = \"position: \" + str(self.x) +", "\"\"\"Calculates the 2D integer index for the particle's location in", "v): self.v = v def set_a(self, a): self.a = a", "def set_x(self, x): self.x = x self.calc_index() def set_v(self, v):", "= m def list_attributes(self): x_s = \"position: \" + str(self.x)", "\"acceleration: \" + str(self.a) + \", \" D_s = \"derivative", "np.zeros(2) self.prev_rho = main_data.rho0 def calc_index(self): \"\"\"Calculates the 2D integer", "self.v = np.zeros(2) self.a = np.zeros(2) self.D = 0 self.rho", "v_s + a_s + D_s + rho_s + m_s +", "x=np.zeros(2)): self.id = next(self._ids) self.main_data = main_data self.x = np.array(x)", "self.prev_v = np.zeros(2) self.prev_rho = main_data.rho0 def calc_index(self): \"\"\"Calculates the", "self.main_data.rho0 gamma = self.main_data.gamma self.P = self.B() * ((self.rho /", "\", \" boundary_s = \"is boundary: \" + str(self.boundary) return", "\"\"\" Equation of state System is assumed slightly compressible \"\"\"", "boundary_s = \"is boundary: \" + str(self.boundary) return [x_s +", "on the boundary # For predictor corrector self.prev_x = np.array(x)", "self.P = 0 self.m = main_data.dx ** 2 * main_data.rho0", "str(self.a) + \", \" D_s = \"derivative of density: \"", "a_s + D_s + rho_s + m_s + P_s +", "= rho self.update_P() def m(self, m): self.m = m def", "* ((self.rho / rho0)**gamma - 1) def set_main_data(self, main_data): self.main_data", "+ \", \" P_s = \"pressure: \" + str(self.P) +", "set_main_data(self, main_data): self.main_data = main_data def set_x(self, x): self.x =", "def set_D(self, D): self.D = D def set_rho(self, rho): self.rho", "\" + str(self.rho) + \", \" m_s = \"mass: \"", "+ str(self.x) + \", \" v_s = \"velocity: \" +", "set_x(self, x): self.x = x self.calc_index() def set_v(self, v): self.v", "str(self.P) + \", \" boundary_s = \"is boundary: \" +", "+ str(self.a) + \", \" D_s = \"derivative of density:", "+ D_s + rho_s + m_s + P_s + boundary_s]", "B(self): return (self.main_data.rho0 * self.main_data.c0 ** 2) / self.main_data.gamma def", "= \"velocity: \" + str(self.v) + \", \" a_s =", "boundary: \" + str(self.boundary) return [x_s + v_s + a_s", "not on the boundary # For predictor corrector self.prev_x =", "next(self._ids) self.main_data = main_data self.x = np.array(x) self.v = np.zeros(2)", "spacing self.boundary = False # Particle by default is not", "the 2D integer index for the particle's location in the", "self.x = np.array(x) self.v = np.zeros(2) self.a = np.zeros(2) self.D", "+ \", \" v_s = \"velocity: \" + str(self.v) +", "2) / self.main_data.gamma def update_P(self): \"\"\" Equation of state System", "\", \" rho_s = \"density: \" + str(self.rho) + \",", "numpy as np class Particle(object): \"\"\"Object containing all the properties", "- self.main_data.min_x) / (2.0 * self.main_data.h), int) def B(self): return", "itertools import count import numpy as np class Particle(object): \"\"\"Object", "on the initial particle spacing self.boundary = False # Particle", "P_s = \"pressure: \" + str(self.P) + \", \" boundary_s", "Particle(object): \"\"\"Object containing all the properties for a single particle\"\"\"", "\" D_s = \"derivative of density: \" + str(self.D) +", "return (self.main_data.rho0 * self.main_data.c0 ** 2) / self.main_data.gamma def update_P(self):", "in the search grid\"\"\" # Calculates the bucket coordinates self.list_num", "= np.array(x) self.v = np.zeros(2) self.a = np.zeros(2) self.D =", "self.calc_index() def set_v(self, v): self.v = v def set_a(self, a):", "def set_a(self, a): self.a = a def set_D(self, D): self.D", "main_data self.x = np.array(x) self.v = np.zeros(2) self.a = np.zeros(2)", "+ \", \" m_s = \"mass: \" + str(self.m) +", "# Particle by default is not on the boundary #", "self.main_data = main_data self.x = np.array(x) self.v = np.zeros(2) self.a", "self.main_data = main_data def set_x(self, x): self.x = x self.calc_index()", "count import numpy as np class Particle(object): \"\"\"Object containing all", "\" + str(self.x) + \", \" v_s = \"velocity: \"", "x self.calc_index() def set_v(self, v): self.v = v def set_a(self,", "= np.zeros(2) self.prev_rho = main_data.rho0 def calc_index(self): \"\"\"Calculates the 2D", "rho): self.rho = rho self.update_P() def m(self, m): self.m =", "set_rho(self, rho): self.rho = rho self.update_P() def m(self, m): self.m", "+ \", \" rho_s = \"density: \" + str(self.rho) +", "= \"mass: \" + str(self.m) + \", \" P_s =", "(2.0 * self.main_data.h), int) def B(self): return (self.main_data.rho0 * self.main_data.c0", "str(self.v) + \", \" a_s = \"acceleration: \" + str(self.a)", "rho0 = self.main_data.rho0 gamma = self.main_data.gamma self.P = self.B() *", "\"mass: \" + str(self.m) + \", \" P_s = \"pressure:", "set_v(self, v): self.v = v def set_a(self, a): self.a =", "particle's location in the search grid\"\"\" # Calculates the bucket", "density: \" + str(self.D) + \", \" rho_s = \"density:", "\"\"\"Object containing all the properties for a single particle\"\"\" _ids", "= self.main_data.gamma self.P = self.B() * ((self.rho / rho0)**gamma -", "def list_attributes(self): x_s = \"position: \" + str(self.x) + \",", "def set_rho(self, rho): self.rho = rho self.update_P() def m(self, m):", "\" a_s = \"acceleration: \" + str(self.a) + \", \"", "m_s = \"mass: \" + str(self.m) + \", \" P_s", "self.main_data.c0 ** 2) / self.main_data.gamma def update_P(self): \"\"\" Equation of", "int) def B(self): return (self.main_data.rho0 * self.main_data.c0 ** 2) /", "for a single particle\"\"\" _ids = count(0) def __init__(self, main_data=None,", "/ (2.0 * self.main_data.h), int) def B(self): return (self.main_data.rho0 *", "state System is assumed slightly compressible \"\"\" rho0 = self.main_data.rho0" ]
[ "class PitchForm(FlaskForm): pitch = TextAreaField('Write a pitch') submit = SubmitField('Submit')", "TextAreaField('Write a pitch') submit = SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom =", "TextAreaField('Tell us about you.',validators = [Required()]) submit = SubmitField('Submit') class", "you.',validators = [Required()]) submit = SubmitField('Submit') class PitchForm(FlaskForm): pitch =", "= SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom = TextAreaField('comment on your pitch", "import Required class UpdateProfile(FlaskForm): about = TextAreaField('Tell us about you.',validators", "class PitchComForm(FlaskForm): pitchcom = TextAreaField('comment on your pitch ') submit", "about you.',validators = [Required()]) submit = SubmitField('Submit') class PitchForm(FlaskForm): pitch", "from wtforms.validators import Required class UpdateProfile(FlaskForm): about = TextAreaField('Tell us", "PitchForm(FlaskForm): pitch = TextAreaField('Write a pitch') submit = SubmitField('Submit') class", "= SubmitField('Submit') class PitchForm(FlaskForm): pitch = TextAreaField('Write a pitch') submit", "about = TextAreaField('Tell us about you.',validators = [Required()]) submit =", "PitchComForm(FlaskForm): pitchcom = TextAreaField('comment on your pitch ') submit =", "submit = SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom = TextAreaField('comment on your", "pitchcom = TextAreaField('comment on your pitch ') submit = SubmitField('Submit')", "wtforms.validators import Required class UpdateProfile(FlaskForm): about = TextAreaField('Tell us about", "[Required()]) submit = SubmitField('Submit') class PitchForm(FlaskForm): pitch = TextAreaField('Write a", "import FlaskForm from wtforms import StringField,TextAreaField,SubmitField from wtforms.validators import Required", "StringField,TextAreaField,SubmitField from wtforms.validators import Required class UpdateProfile(FlaskForm): about = TextAreaField('Tell", "UpdateProfile(FlaskForm): about = TextAreaField('Tell us about you.',validators = [Required()]) submit", "SubmitField('Submit') class PitchForm(FlaskForm): pitch = TextAreaField('Write a pitch') submit =", "from wtforms import StringField,TextAreaField,SubmitField from wtforms.validators import Required class UpdateProfile(FlaskForm):", "SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom = TextAreaField('comment on your pitch ')", "= TextAreaField('Tell us about you.',validators = [Required()]) submit = SubmitField('Submit')", "Required class UpdateProfile(FlaskForm): about = TextAreaField('Tell us about you.',validators =", "us about you.',validators = [Required()]) submit = SubmitField('Submit') class PitchForm(FlaskForm):", "submit = SubmitField('Submit') class PitchForm(FlaskForm): pitch = TextAreaField('Write a pitch')", "a pitch') submit = SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom = TextAreaField('comment", "wtforms import StringField,TextAreaField,SubmitField from wtforms.validators import Required class UpdateProfile(FlaskForm): about", "= [Required()]) submit = SubmitField('Submit') class PitchForm(FlaskForm): pitch = TextAreaField('Write", "import StringField,TextAreaField,SubmitField from wtforms.validators import Required class UpdateProfile(FlaskForm): about =", "pitch = TextAreaField('Write a pitch') submit = SubmitField('Submit') class PitchComForm(FlaskForm):", "FlaskForm from wtforms import StringField,TextAreaField,SubmitField from wtforms.validators import Required class", "flask_wtf import FlaskForm from wtforms import StringField,TextAreaField,SubmitField from wtforms.validators import", "<reponame>hussein18149/PITCHBOARD from flask_wtf import FlaskForm from wtforms import StringField,TextAreaField,SubmitField from", "pitch') submit = SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom = TextAreaField('comment on", "= TextAreaField('Write a pitch') submit = SubmitField('Submit') class PitchComForm(FlaskForm): pitchcom", "from flask_wtf import FlaskForm from wtforms import StringField,TextAreaField,SubmitField from wtforms.validators", "class UpdateProfile(FlaskForm): about = TextAreaField('Tell us about you.',validators = [Required()])" ]
[ "as a tuple or a string. Parameters ---------- pretty :", "obstype: return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self): if self.phu.get('OBSTYPE')", "filter_name(self): \"\"\" Returns the name of the filter used according", "ad in self[1:]: val = ad.hdr['gain'] if val != 'unavail':", "hdu.header.get('EXTNAME', '')) if m: hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData')", "or string is return per extension/array, in a list. If", "astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def _tag_flat(self): # Ideally, we would want", "be set by the 'IMAGE' tag. # But since OBSTYPE", "summary FILTERS keyword. Returns ------- str The name of the", "can be done right now. filename = self.phu.get('FILENAME', '') notes", "in a list. If the method is called on a", "if re.search('acq.[0-9]+', filename) or re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag", "an IRAF section format (1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor", "..soar import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC', gain='GAIN') @staticmethod", "now. filename = self.phu.get('FILENAME', '') notes = self.phu.get('NOTES', '') if", "if self.phu.get('OBSTYPE') == 'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self): if", "If pretty is False, a tuple of 0-based coordinates is", "used withing SAM but not always with AO. # 2)", "_tag_bias(self): if self.phu.get('OBSTYPE') == 'ZERO': return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY'])", "But since OBSTYPE is being used for both, not clear", "header. Returns ------- tuple of integers or list of tuples", "Bruno: GAIN is set to \"unavail\" in the headers. Do", "FILTERS keyword. Returns ------- str The name of the filter.", "def _tag_twilight(self): if self.phu.get('OBSTYPE') == 'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def", "SAMI always used with the SAM AO? # 2) is", "want 'IMAGE' to be set by the 'IMAGE' tag. #", "return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def filter_name(self): \"\"\" Returns the name", "without parsing as a string. In this format, the coordinates", "being used for both, not clear how that # can", "to light using an IRAF section format (1-based). \"\"\" return", "re.search('acq.[0-9]+', filename) or re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def", "def gain(self): \"\"\" Gain of the amplifier Returns ------- float", "by the 'IMAGE' tag. # But since OBSTYPE is being", "in the headers. Do you have # the gain for", "the name of the filter used according to the summary", "the section is returned as a tuple or a string.", "think. if self.phu.get('OBSTYPE') == 'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self):", "always used withing SAM but not always with AO. #", "def sami_parser(hdu): m = re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if m: hdu.header['EXTNAME']", "amp in some lookup table? gain = [] for ad", "from astrodata.fits import FitsLoader, FitsProvider from ..soar import AstroDataSOAR class", "multiple ones? # ANSWER: # 1) SAMI is always used", "with format (x1, x2, y1, y2). If pretty is True,", "is being used for both, not clear how that #", "# 1) is SAMI always used with the SAM AO?", "\"unavail\" in the headers. Do you have # the gain", "_tag_acquisition(self): # Ideally, we would want 'IMAGE' to be set", "def _matches_data(source): return source[0].header.get('INSTRUME', '').upper() in {'SAMI', 'SAM'} @astrodata.astro_data_tag def", "of the pixels exposed to light using an IRAF section", "OBSTYPE is being used for both, not clear how that", "you have # the gain for each amp in some", "a single slice, the section is returned as a tuple", "'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self): if self.phu.get('OBSTYPE') == 'ZERO':", "string found in the header. Returns ------- tuple of integers", "by AstroData') hdu.header['EXTVER'] = (int(m.group(1)), 'Added by AstroData') return cls(FitsLoader(FitsProvider).load(source,", "is False, a tuple of 0-based coordinates is returned with", "of tuples Location of the pixels exposed to light using", "@astrodata.astro_data_tag def _tag_acquisition(self): # Ideally, we would want 'IMAGE' to", "used at SOAR Telescope. return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def _tag_flat(self):", "Parameters ---------- pretty : bool If True, return the formatted", "light using Python slice values. string or list of strings", "= re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if m: hdu.header['EXTNAME'] = ('SCI', 'Added", "in obstype: return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self): if", "the rectangular section that includes the pixels that would be", "per extension/array, in a list. If the method is called", "self.phu.get('OBSTYPE', '') if 'FLAT' in obstype: return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE'])", "'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def data_section(self, pretty=False): \"\"\" Returns the", "how that # can be done right now. filename =", "how that # can be done right now. obstype =", "returned as a tuple or a string. Parameters ---------- pretty", "Python slice values. string or list of strings Location of", "@astrodata.astro_data_descriptor def data_section(self, pretty=False): \"\"\" Returns the rectangular section that", "return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self): if self.phu.get('OBSTYPE') == 'DFLAT': return", "@astrodata.astro_data_descriptor def filter_name(self): \"\"\" Returns the name of the filter", "coordinates is returned with format (x1, x2, y1, y2). If", "'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self): # Ideally, we would", "hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData') hdu.header['EXTVER'] = (int(m.group(1)), 'Added", "ones? # ANSWER: # 1) SAMI is always used withing", "return gain @classmethod def load(cls, source): def sami_parser(hdu): m =", "or list of tuples Location of the pixels exposed to", "import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC', gain='GAIN') @staticmethod def", "we would want 'IMAGE' to be set by the 'IMAGE'", "ANSWER: # 1) SAMI is always used withing SAM but", "of strings Location of the pixels exposed to light using", "the headers. Do you have # the gain for each", "filename) or re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def _tag_image(self):", "single slice, the section is returned as a tuple or", "tuple of 0-based coordinates is returned with format (x1, x2,", "section that includes the pixels that would be exposed to", "that would be exposed to light. If pretty is False,", "__keyword_dict = dict(data_section='DATASEC', gain='GAIN') @staticmethod def _matches_data(source): return source[0].header.get('INSTRUME', '').upper()", "with the SAM AO? # 2) is SAMI used only", "@astrodata.astro_data_tag def _tag_domeflat(self): if self.phu.get('OBSTYPE') == 'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag", "'') if re.search('acq.[0-9]+', filename) or re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE'])", "_tag_image(self): # this one will need something like \"if not", "not always with AO. # 2) SAMI and SAM are", "the pixels exposed to light using an IRAF section format", "for both, not clear how that # can be done", "returns_list) from astrodata.fits import FitsLoader, FitsProvider from ..soar import AstroDataSOAR", "slice, the section is returned as a tuple or a", "'') if 'FLAT' in obstype: return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag", "True, a keyword value is returned without parsing as a", "filter used according to the summary FILTERS keyword. Returns -------", "m = re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if m: hdu.header['EXTNAME'] = ('SCI',", "def load(cls, source): def sami_parser(hdu): m = re.match('im(\\d)', hdu.header.get('EXTNAME', ''))", "formatted string found in the header. Returns ------- tuple of", "or list of strings Location of the pixels exposed to", "== 'ZERO': return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def data_section(self,", "sami_parser(hdu): m = re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if m: hdu.header['EXTNAME'] =", "{'SAMI', 'SAM'} @astrodata.astro_data_tag def _tag_instrument(self): # QUESTIONS: # 1) is", "astrodata import (astro_data_tag, TagSet, astro_data_descriptor, returns_list) from astrodata.fits import FitsLoader,", "the filter. \"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self): \"\"\" Gain", "astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self): if self.phu.get('OBSTYPE') == 'DFLAT': return astrodata.TagSet(['DOME'])", "import FitsLoader, FitsProvider from ..soar import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict", "------- float The gain for each amplifier \"\"\" # Bruno:", "else: gain.append(None) return gain @classmethod def load(cls, source): def sami_parser(hdu):", "to be set by the 'IMAGE' tag. # But since", "str The name of the filter. \"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor", "set to \"unavail\" in the headers. Do you have #", "---------- pretty : bool If True, return the formatted string", "if self.phu.get('OBSTYPE') == 'ZERO': return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor", "y2). If pretty is True, a keyword value is returned", "Gain of the amplifier Returns ------- float The gain for", "this one will need something like \"if not FABRY keyword\",", "self.phu.get('OBSTYPE') == 'ZERO': return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def", "\"\"\" Gain of the amplifier Returns ------- float The gain", "astrodata from astrodata import (astro_data_tag, TagSet, astro_data_descriptor, returns_list) from astrodata.fits", "\"if not FABRY keyword\", I think. if self.phu.get('OBSTYPE') == 'OBJECT':", "section format (1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def filter_name(self):", "in self[1:]: val = ad.hdr['gain'] if val != 'unavail': gain.append(val)", "pixels exposed to light using Python slice values. string or", "the method is called on a single slice, the section", "@astrodata.astro_data_tag def _tag_instrument(self): # QUESTIONS: # 1) is SAMI always", "SAM but not always with AO. # 2) SAMI and", "for each amplifier \"\"\" # Bruno: GAIN is set to", "pretty=False): \"\"\" Returns the rectangular section that includes the pixels", "that includes the pixels that would be exposed to light.", "tuple of integers or list of tuples Location of the", "'SAM']) @astrodata.astro_data_tag def _tag_flat(self): # Ideally, we would want 'IMAGE'", "section is returned as a tuple or a string. Parameters", "some lookup table? gain = [] for ad in self[1:]:", "pretty is False, a tuple of 0-based coordinates is returned", "@astrodata.astro_data_descriptor def gain(self): \"\"\" Gain of the amplifier Returns -------", "Returns ------- float The gain for each amplifier \"\"\" #", "Returns ------- tuple of integers or list of tuples Location", "format, the coordinates are generally 1-based. One tuple or string", "or a string. Parameters ---------- pretty : bool If True,", "exposed to light. If pretty is False, a tuple of", "return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def _tag_image(self): # this one will", "using Python slice values. string or list of strings Location", "keyword value is returned without parsing as a string. In", "1) SAMI is always used withing SAM but not always", "done right now. obstype = self.phu.get('OBSTYPE', '') if 'FLAT' in", "one will need something like \"if not FABRY keyword\", I", "SAM AO? # 2) is SAMI used only at one", "exposed to light using Python slice values. string or list", "dict(data_section='DATASEC', gain='GAIN') @staticmethod def _matches_data(source): return source[0].header.get('INSTRUME', '').upper() in {'SAMI',", "GAIN is set to \"unavail\" in the headers. Do you", "The name of the filter. \"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def", "at SOAR Telescope. return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def _tag_flat(self): #", "'Added by AstroData') hdu.header['EXTVER'] = (int(m.group(1)), 'Added by AstroData') return", "= self.phu.get('FILENAME', '') notes = self.phu.get('NOTES', '') if re.search('acq.[0-9]+', filename)", "lookup table? gain = [] for ad in self[1:]: val", "if self.phu.get('OBSTYPE') == 'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self): if", "gain='GAIN') @staticmethod def _matches_data(source): return source[0].header.get('INSTRUME', '').upper() in {'SAMI', 'SAM'}", "Telescope. return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def _tag_flat(self): # Ideally, we", "exposed to light using an IRAF section format (1-based). \"\"\"", "or re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def _tag_image(self): #", "filter. \"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self): \"\"\" Gain of", "@astrodata.astro_data_tag def _tag_twilight(self): if self.phu.get('OBSTYPE') == 'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag", "Location of the pixels exposed to light using an IRAF", "according to the summary FILTERS keyword. Returns ------- str The", "but not always with AO. # 2) SAMI and SAM", "is returned without parsing as a string. In this format,", "# this one will need something like \"if not FABRY", "self.phu.get('NOTES', '') if re.search('acq.[0-9]+', filename) or re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION',", "slice values. string or list of strings Location of the", "each amp in some lookup table? gain = [] for", "FitsProvider from ..soar import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC',", "SAMI used only at one telescopes or multiple ones? #", "rectangular section that includes the pixels that would be exposed", "gain for each amp in some lookup table? gain =", "= self.phu.get('OBSTYPE', '') if 'FLAT' in obstype: return astrodata.TagSet(['FLAT', 'CAL',", "of 0-based coordinates is returned with format (x1, x2, y1,", "and SAM are only used at SOAR Telescope. return astrodata.TagSet(['SAMI',", "gain = [] for ad in self[1:]: val = ad.hdr['gain']", "the summary FILTERS keyword. Returns ------- str The name of", "source): def sami_parser(hdu): m = re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if m:", "is called on a single slice, the section is returned", "each amplifier \"\"\" # Bruno: GAIN is set to \"unavail\"", "------- tuple of integers or list of tuples Location of", "FitsLoader, FitsProvider from ..soar import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict =", "import re import astrodata from astrodata import (astro_data_tag, TagSet, astro_data_descriptor,", "Returns ------- str The name of the filter. \"\"\" return", "name of the filter used according to the summary FILTERS", "def filter_name(self): \"\"\" Returns the name of the filter used", "string or list of strings Location of the pixels exposed", "m: hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData') hdu.header['EXTVER'] = (int(m.group(1)),", "\"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self): \"\"\" Gain of the", "Ideally, we would want 'IMAGE' to be set by the", "pretty : bool If True, return the formatted string found", "the amplifier Returns ------- float The gain for each amplifier", "'unavail': gain.append(val) else: gain.append(None) return gain @classmethod def load(cls, source):", "need something like \"if not FABRY keyword\", I think. if", "return source[0].header.get('INSTRUME', '').upper() in {'SAMI', 'SAM'} @astrodata.astro_data_tag def _tag_instrument(self): #", "re import astrodata from astrodata import (astro_data_tag, TagSet, astro_data_descriptor, returns_list)", "tuples Location of the pixels exposed to light using Python", "_matches_data(source): return source[0].header.get('INSTRUME', '').upper() in {'SAMI', 'SAM'} @astrodata.astro_data_tag def _tag_instrument(self):", "1) is SAMI always used with the SAM AO? #", "set by the 'IMAGE' tag. # But since OBSTYPE is", "used only at one telescopes or multiple ones? # ANSWER:", "# But since OBSTYPE is being used for both, not", "obstype = self.phu.get('OBSTYPE', '') if 'FLAT' in obstype: return astrodata.TagSet(['FLAT',", "of the amplifier Returns ------- float The gain for each", "SOAR Telescope. return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def _tag_flat(self): # Ideally,", "method is called on a single slice, the section is", "def _tag_bias(self): if self.phu.get('OBSTYPE') == 'ZERO': return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE',", "'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self): if self.phu.get('OBSTYPE') == 'DFLAT':", "tag. # But since OBSTYPE is being used for both,", "# can be done right now. obstype = self.phu.get('OBSTYPE', '')", "AstroData') hdu.header['EXTVER'] = (int(m.group(1)), 'Added by AstroData') return cls(FitsLoader(FitsProvider).load(source, extname_parser=sami_parser))", "AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC', gain='GAIN') @staticmethod def _matches_data(source):", "the gain for each amp in some lookup table? gain", "load(cls, source): def sami_parser(hdu): m = re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if", "self.phu.get('OBSTYPE') == 'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self): if self.phu.get('OBSTYPE')", "if m: hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData') hdu.header['EXTVER'] =", "IRAF section format (1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def", "FABRY keyword\", I think. if self.phu.get('OBSTYPE') == 'OBJECT': return astrodata.TagSet(['IMAGE'])", "are only used at SOAR Telescope. return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag", "'') notes = self.phu.get('NOTES', '') if re.search('acq.[0-9]+', filename) or re.search('/acq/i',", "y1, y2). If pretty is True, a keyword value is", "Returns the rectangular section that includes the pixels that would", "a string. Parameters ---------- pretty : bool If True, return", "light using an IRAF section format (1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'),", "the 'IMAGE' tag. # But since OBSTYPE is being used", "values. string or list of strings Location of the pixels", "ad.hdr['gain'] if val != 'unavail': gain.append(val) else: gain.append(None) return gain", "val != 'unavail': gain.append(val) else: gain.append(None) return gain @classmethod def", "gain for each amplifier \"\"\" # Bruno: GAIN is set", "to \"unavail\" in the headers. Do you have # the", "astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self): if self.phu.get('OBSTYPE') == 'ZERO': return astrodata.TagSet(['BIAS',", "SAMI is always used withing SAM but not always with", "= ad.hdr['gain'] if val != 'unavail': gain.append(val) else: gain.append(None) return", "@staticmethod def _matches_data(source): return source[0].header.get('INSTRUME', '').upper() in {'SAMI', 'SAM'} @astrodata.astro_data_tag", "(1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def filter_name(self): \"\"\" Returns", "string is return per extension/array, in a list. If the", "is always used withing SAM but not always with AO.", "in the header. Returns ------- tuple of integers or list", "def _tag_instrument(self): # QUESTIONS: # 1) is SAMI always used", "self.phu.get('OBSTYPE') == 'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self): # Ideally,", "with AO. # 2) SAMI and SAM are only used", "== 'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self): # Ideally, we", "list of strings Location of the pixels exposed to light", "blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def data_section(self, pretty=False): \"\"\" Returns the rectangular", "_tag_instrument(self): # QUESTIONS: # 1) is SAMI always used with", "I think. if self.phu.get('OBSTYPE') == 'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def", "keyword. Returns ------- str The name of the filter. \"\"\"", "the pixels exposed to light using Python slice values. string", "def _tag_image(self): # this one will need something like \"if", "list. If the method is called on a single slice,", "integers or list of tuples Location of the pixels exposed", "called on a single slice, the section is returned as", "value is returned without parsing as a string. In this", ": bool If True, return the formatted string found in", "2) SAMI and SAM are only used at SOAR Telescope.", "Returns the name of the filter used according to the", "that # can be done right now. obstype = self.phu.get('OBSTYPE',", "1-based. One tuple or string is return per extension/array, in", "'IMAGE']) @astrodata.astro_data_tag def _tag_image(self): # this one will need something", "of the pixels exposed to light using Python slice values.", "like \"if not FABRY keyword\", I think. if self.phu.get('OBSTYPE') ==", "right now. filename = self.phu.get('FILENAME', '') notes = self.phu.get('NOTES', '')", "# the gain for each amp in some lookup table?", "can be done right now. obstype = self.phu.get('OBSTYPE', '') if", "only at one telescopes or multiple ones? # ANSWER: #", "False, a tuple of 0-based coordinates is returned with format", "now. obstype = self.phu.get('OBSTYPE', '') if 'FLAT' in obstype: return", "== 'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self): if self.phu.get('OBSTYPE') ==", "Location of the pixels exposed to light using Python slice", "re.search('/acq/i', notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def _tag_image(self): # this", "string. Parameters ---------- pretty : bool If True, return the", "used for both, not clear how that # can be", "filename = self.phu.get('FILENAME', '') notes = self.phu.get('NOTES', '') if re.search('acq.[0-9]+',", "is return per extension/array, in a list. If the method", "import (astro_data_tag, TagSet, astro_data_descriptor, returns_list) from astrodata.fits import FitsLoader, FitsProvider", "for each amp in some lookup table? gain = []", "in some lookup table? gain = [] for ad in", "If the method is called on a single slice, the", "return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def _tag_flat(self): # Ideally, we would", "bool If True, return the formatted string found in the", "notes): return astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def _tag_image(self): # this one", "'IMAGE' to be set by the 'IMAGE' tag. # But", "from astrodata import (astro_data_tag, TagSet, astro_data_descriptor, returns_list) from astrodata.fits import", "# Ideally, we would want 'IMAGE' to be set by", "'')) if m: hdu.header['EXTNAME'] = ('SCI', 'Added by AstroData') hdu.header['EXTVER']", "telescopes or multiple ones? # ANSWER: # 1) SAMI is", "re.match('im(\\d)', hdu.header.get('EXTNAME', '')) if m: hdu.header['EXTNAME'] = ('SCI', 'Added by", "of the filter used according to the summary FILTERS keyword.", "to the summary FILTERS keyword. Returns ------- str The name", "keyword\", I think. if self.phu.get('OBSTYPE') == 'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag", "if 'FLAT' in obstype: return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag def", "parsing as a string. In this format, the coordinates are", "of integers or list of tuples Location of the pixels", "a keyword value is returned without parsing as a string.", "0-based coordinates is returned with format (x1, x2, y1, y2).", "the coordinates are generally 1-based. One tuple or string is", "table? gain = [] for ad in self[1:]: val =", "pretty is True, a keyword value is returned without parsing", "# 2) is SAMI used only at one telescopes or", "headers. Do you have # the gain for each amp", "The gain for each amplifier \"\"\" # Bruno: GAIN is", "clear how that # can be done right now. filename", "is SAMI used only at one telescopes or multiple ones?", "includes the pixels that would be exposed to light. If", "x2, y1, y2). If pretty is True, a keyword value", "SAMI and SAM are only used at SOAR Telescope. return", "def _tag_flat(self): # Ideally, we would want 'IMAGE' to be", "SAM are only used at SOAR Telescope. return astrodata.TagSet(['SAMI', 'SAM'])", "'ZERO': return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def data_section(self, pretty=False):", "will need something like \"if not FABRY keyword\", I think.", "return per extension/array, in a list. If the method is", "In this format, the coordinates are generally 1-based. One tuple", "True, return the formatted string found in the header. Returns", "list of tuples Location of the pixels exposed to light", "for ad in self[1:]: val = ad.hdr['gain'] if val !=", "a list. If the method is called on a single", "@astrodata.astro_data_tag def _tag_bias(self): if self.phu.get('OBSTYPE') == 'ZERO': return astrodata.TagSet(['BIAS', 'CAL'],", "would want 'IMAGE' to be set by the 'IMAGE' tag.", "generally 1-based. One tuple or string is return per extension/array,", "'SAM'} @astrodata.astro_data_tag def _tag_instrument(self): # QUESTIONS: # 1) is SAMI", "source[0].header.get('INSTRUME', '').upper() in {'SAMI', 'SAM'} @astrodata.astro_data_tag def _tag_instrument(self): # QUESTIONS:", "'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self): if self.phu.get('OBSTYPE') == 'SFLAT': return astrodata.TagSet(['TWILIGHT'])", "'IMAGE' tag. # But since OBSTYPE is being used for", "be done right now. obstype = self.phu.get('OBSTYPE', '') if 'FLAT'", "= [] for ad in self[1:]: val = ad.hdr['gain'] if", "_tag_domeflat(self): if self.phu.get('OBSTYPE') == 'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self):", "(x1, x2, y1, y2). If pretty is True, a keyword", "to light using Python slice values. string or list of", "at one telescopes or multiple ones? # ANSWER: # 1)", "not clear how that # can be done right now.", "gain(self): \"\"\" Gain of the amplifier Returns ------- float The", "returned with format (x1, x2, y1, y2). If pretty is", "data_section(self, pretty=False): \"\"\" Returns the rectangular section that includes the", "the header. Returns ------- tuple of integers or list of", "found in the header. Returns ------- tuple of integers or", "def _tag_acquisition(self): # Ideally, we would want 'IMAGE' to be", "self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def filter_name(self): \"\"\" Returns the name of", "One tuple or string is return per extension/array, in a", "_tag_twilight(self): if self.phu.get('OBSTYPE') == 'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self):", "astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self): # Ideally, we would want 'IMAGE'", "# QUESTIONS: # 1) is SAMI always used with the", "name of the filter. \"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self):", "light. If pretty is False, a tuple of 0-based coordinates", "is True, a keyword value is returned without parsing as", "used with the SAM AO? # 2) is SAMI used", "if self.phu.get('OBSTYPE') == 'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self): #", "self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self): \"\"\" Gain of the amplifier Returns", "clear how that # can be done right now. obstype", "'FABRY']) @astrodata.astro_data_descriptor def data_section(self, pretty=False): \"\"\" Returns the rectangular section", "format (1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def filter_name(self): \"\"\"", "the filter used according to the summary FILTERS keyword. Returns", "a string. In this format, the coordinates are generally 1-based.", "astrodata.TagSet(['ACQUISITION', 'IMAGE']) @astrodata.astro_data_tag def _tag_image(self): # this one will need", "# can be done right now. filename = self.phu.get('FILENAME', '')", "(astro_data_tag, TagSet, astro_data_descriptor, returns_list) from astrodata.fits import FitsLoader, FitsProvider from", "always used with the SAM AO? # 2) is SAMI", "return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self): if self.phu.get('OBSTYPE') ==", "right now. obstype = self.phu.get('OBSTYPE', '') if 'FLAT' in obstype:", "as a string. In this format, the coordinates are generally", "or multiple ones? # ANSWER: # 1) SAMI is always", "format (x1, x2, y1, y2). If pretty is True, a", "2) is SAMI used only at one telescopes or multiple", "# ANSWER: # 1) SAMI is always used withing SAM", "astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self): if self.phu.get('OBSTYPE') == 'SFLAT':", "return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def _tag_acquisition(self): # Ideally, we would want", "= ('SCI', 'Added by AstroData') hdu.header['EXTVER'] = (int(m.group(1)), 'Added by", "# 1) SAMI is always used withing SAM but not", "not FABRY keyword\", I think. if self.phu.get('OBSTYPE') == 'OBJECT': return", "gain.append(val) else: gain.append(None) return gain @classmethod def load(cls, source): def", "QUESTIONS: # 1) is SAMI always used with the SAM", "<reponame>soar-telescope/dragons-soar import re import astrodata from astrodata import (astro_data_tag, TagSet,", "If True, return the formatted string found in the header.", "be done right now. filename = self.phu.get('FILENAME', '') notes =", "both, not clear how that # can be done right", "be exposed to light. If pretty is False, a tuple", "used according to the summary FILTERS keyword. Returns ------- str", "are generally 1-based. One tuple or string is return per", "== 'SFLAT': return astrodata.TagSet(['TWILIGHT']) @astrodata.astro_data_tag def _tag_domeflat(self): if self.phu.get('OBSTYPE') ==", "returned without parsing as a string. In this format, the", "('SCI', 'Added by AstroData') hdu.header['EXTVER'] = (int(m.group(1)), 'Added by AstroData')", "always with AO. # 2) SAMI and SAM are only", "amplifier Returns ------- float The gain for each amplifier \"\"\"", "a tuple of 0-based coordinates is returned with format (x1,", "pixels exposed to light using an IRAF section format (1-based).", "using an IRAF section format (1-based). \"\"\" return self._parse_section(self._keyword_for('data_section'), pretty)", "return the formatted string found in the header. Returns -------", "gain.append(None) return gain @classmethod def load(cls, source): def sami_parser(hdu): m", "return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self): if self.phu.get('OBSTYPE') == 'ZERO': return", "notes = self.phu.get('NOTES', '') if re.search('acq.[0-9]+', filename) or re.search('/acq/i', notes):", "the SAM AO? # 2) is SAMI used only at", "'FLAT' in obstype: return astrodata.TagSet(['FLAT', 'CAL', 'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self):", "astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def data_section(self, pretty=False): \"\"\" Returns", "# Bruno: GAIN is set to \"unavail\" in the headers.", "import astrodata from astrodata import (astro_data_tag, TagSet, astro_data_descriptor, returns_list) from", "is returned as a tuple or a string. Parameters ----------", "one telescopes or multiple ones? # ANSWER: # 1) SAMI", "the formatted string found in the header. Returns ------- tuple", "def data_section(self, pretty=False): \"\"\" Returns the rectangular section that includes", "\"\"\" # Bruno: GAIN is set to \"unavail\" in the", "\"\"\" Returns the rectangular section that includes the pixels that", "class AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC', gain='GAIN') @staticmethod def _matches_data(source): return", "_tag_flat(self): # Ideally, we would want 'IMAGE' to be set", "!= 'unavail': gain.append(val) else: gain.append(None) return gain @classmethod def load(cls,", "of the filter. \"\"\" return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self): \"\"\"", "TagSet, astro_data_descriptor, returns_list) from astrodata.fits import FitsLoader, FitsProvider from ..soar", "have # the gain for each amp in some lookup", "astrodata.fits import FitsLoader, FitsProvider from ..soar import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR):", "If pretty is True, a keyword value is returned without", "amplifier \"\"\" # Bruno: GAIN is set to \"unavail\" in", "is set to \"unavail\" in the headers. Do you have", "self[1:]: val = ad.hdr['gain'] if val != 'unavail': gain.append(val) else:", "strings Location of the pixels exposed to light using an", "@astrodata.astro_data_tag def _tag_image(self): # this one will need something like", "the pixels that would be exposed to light. If pretty", "@astrodata.astro_data_tag def _tag_flat(self): # Ideally, we would want 'IMAGE' to", "\"\"\" Returns the name of the filter used according to", "only used at SOAR Telescope. return astrodata.TagSet(['SAMI', 'SAM']) @astrodata.astro_data_tag def", "on a single slice, the section is returned as a", "this format, the coordinates are generally 1-based. One tuple or", "that # can be done right now. filename = self.phu.get('FILENAME',", "something like \"if not FABRY keyword\", I think. if self.phu.get('OBSTYPE')", "Do you have # the gain for each amp in", "val = ad.hdr['gain'] if val != 'unavail': gain.append(val) else: gain.append(None)", "tuple or a string. Parameters ---------- pretty : bool If", "self.phu.get('FILENAME', '') notes = self.phu.get('NOTES', '') if re.search('acq.[0-9]+', filename) or", "[] for ad in self[1:]: val = ad.hdr['gain'] if val", "since OBSTYPE is being used for both, not clear how", "is SAMI always used with the SAM AO? # 2)", "AO? # 2) is SAMI used only at one telescopes", "a tuple or a string. Parameters ---------- pretty : bool", "float The gain for each amplifier \"\"\" # Bruno: GAIN", "to light. If pretty is False, a tuple of 0-based", "from ..soar import AstroDataSOAR class AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC', gain='GAIN')", "AO. # 2) SAMI and SAM are only used at", "extension/array, in a list. If the method is called on", "is returned with format (x1, x2, y1, y2). If pretty", "AstroDataSAMI(AstroDataSOAR): __keyword_dict = dict(data_section='DATASEC', gain='GAIN') @staticmethod def _matches_data(source): return source[0].header.get('INSTRUME',", "in {'SAMI', 'SAM'} @astrodata.astro_data_tag def _tag_instrument(self): # QUESTIONS: # 1)", "would be exposed to light. If pretty is False, a", "string. In this format, the coordinates are generally 1-based. One", "done right now. filename = self.phu.get('FILENAME', '') notes = self.phu.get('NOTES',", "gain @classmethod def load(cls, source): def sami_parser(hdu): m = re.match('im(\\d)',", "return astrodata.TagSet(['BIAS', 'CAL'], blocks=['IMAGE', 'FABRY']) @astrodata.astro_data_descriptor def data_section(self, pretty=False): \"\"\"", "------- str The name of the filter. \"\"\" return self.phu.get('FILTERS')", "# 2) SAMI and SAM are only used at SOAR", "coordinates are generally 1-based. One tuple or string is return", "if val != 'unavail': gain.append(val) else: gain.append(None) return gain @classmethod", "'CAL', 'IMAGE']) @astrodata.astro_data_tag def _tag_twilight(self): if self.phu.get('OBSTYPE') == 'SFLAT': return", "def _tag_domeflat(self): if self.phu.get('OBSTYPE') == 'DFLAT': return astrodata.TagSet(['DOME']) @astrodata.astro_data_tag def", "= self.phu.get('NOTES', '') if re.search('acq.[0-9]+', filename) or re.search('/acq/i', notes): return", "'').upper() in {'SAMI', 'SAM'} @astrodata.astro_data_tag def _tag_instrument(self): # QUESTIONS: #", "withing SAM but not always with AO. # 2) SAMI", "tuple or string is return per extension/array, in a list.", "\"\"\" return self._parse_section(self._keyword_for('data_section'), pretty) @astrodata.astro_data_descriptor def filter_name(self): \"\"\" Returns the", "astro_data_descriptor, returns_list) from astrodata.fits import FitsLoader, FitsProvider from ..soar import", "self.phu.get('OBSTYPE') == 'OBJECT': return astrodata.TagSet(['IMAGE']) @astrodata.astro_data_tag def _tag_bias(self): if self.phu.get('OBSTYPE')", "= dict(data_section='DATASEC', gain='GAIN') @staticmethod def _matches_data(source): return source[0].header.get('INSTRUME', '').upper() in", "pixels that would be exposed to light. If pretty is", "return self.phu.get('FILTERS') @astrodata.astro_data_descriptor def gain(self): \"\"\" Gain of the amplifier", "@classmethod def load(cls, source): def sami_parser(hdu): m = re.match('im(\\d)', hdu.header.get('EXTNAME',", "pretty) @astrodata.astro_data_descriptor def filter_name(self): \"\"\" Returns the name of the" ]
[ "class Base(object): def __init__(self): pass def meth(self, int): return self._meth(int)", "# -*- coding: utf-8 -*- #単なる継承 class Base(object): def __init__(self):", "#単なる継承 class Base(object): def __init__(self): pass def meth(self, int): return", "Base(object): def __init__(self): pass def meth(self, int): return self._meth(int) def", "-*- coding: utf-8 -*- #単なる継承 class Base(object): def __init__(self): pass", "def meth(self, int): return self._meth(int) def _meth(self, int): return int", "return self._meth(int) def _meth(self, int): return int class Pow(Base): def", "def __init__(self): pass def meth(self, int): return self._meth(int) def _meth(self,", "int): return self._meth(int) def _meth(self, int): return int class Pow(Base):", "_meth(self, int): return int class Pow(Base): def _meth(self, int): return", "coding: utf-8 -*- #単なる継承 class Base(object): def __init__(self): pass def", "utf-8 -*- #単なる継承 class Base(object): def __init__(self): pass def meth(self,", "meth(self, int): return self._meth(int) def _meth(self, int): return int class", "-*- #単なる継承 class Base(object): def __init__(self): pass def meth(self, int):", "__init__(self): pass def meth(self, int): return self._meth(int) def _meth(self, int):", "int): return int class Pow(Base): def _meth(self, int): return pow(int,int)", "self._meth(int) def _meth(self, int): return int class Pow(Base): def _meth(self,", "pass def meth(self, int): return self._meth(int) def _meth(self, int): return", "def _meth(self, int): return int class Pow(Base): def _meth(self, int):" ]
[ "os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for", "DATA_VAL)) if f[-4:] == \".png\"]} Kernels = [] Noises =", "Config from kernelGAN import KernelGAN from data import DataGenerator from", "conf.input2 = None return conf def estimate_kernel(img_file): conf = config_kernelGAN(img_file)", "= [] Noises = [] for f in data[\"X\"]: estimate_kernel(f)", "import random import torch from configs import Config from kernelGAN", "[\"--input_image_path\", afile, \"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0), \"--X4\"] conf = Config().parse(params)", "seed_num = 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark", "[] Noises = [] for f in data[\"X\"]: estimate_kernel(f) print(\"fin.\")", "from data import DataGenerator from learner import Learner import tqdm", "\"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\" # \"Corrupted-va-x\" def config_kernelGAN(afile):", "f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for f in os.listdir(os.path.join(DATA_LOC,", "\"__main__\": seed_num = 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True", "_] = data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration, kgan) kgan.finish() if __name__", "f in os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y,", "DATA_X)) if f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for f", "DATA_VAL, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:] ==", "= data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration, kgan) kgan.finish() if __name__ ==", "0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False", "img_file = os.path.basename(afile) out_dir = \"yoon/kernels/track2\" params = [\"--input_image_path\", afile,", "DATA_Y = \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\" # \"Corrupted-va-x\"", "False np.random.seed(seed_num) random.seed(seed_num) # exit(0) data = {\"X\":[os.path.join(DATA_LOC, DATA_X, f)", "\".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if", "= \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y", "= Config().parse(params) conf.input2 = None return conf def estimate_kernel(img_file): conf", "DATA_Y)) if f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for f", "from learner import Learner import tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" #", "tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in, _] = data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration,", "= os.path.dirname(afile) img_file = os.path.basename(afile) out_dir = \"yoon/kernels/track2\" params =", "kgan.finish() if __name__ == \"__main__\": seed_num = 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num)", "if f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for f in", "import numpy as np import cv2 import random import torch", "import torch from configs import Config from kernelGAN import KernelGAN", "Learner() data = DataGenerator(conf, kgan) for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70):", "= os.path.basename(afile) out_dir = \"yoon/kernels/track2\" params = [\"--input_image_path\", afile, \"--output_dir_path\",", "kgan) for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in, _] =", "KernelGAN from data import DataGenerator from learner import Learner import", "import os, sys import numpy as np import cv2 import", "torch from configs import Config from kernelGAN import KernelGAN from", "= KernelGAN(conf) learner = Learner() data = DataGenerator(conf, kgan) for", "torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False np.random.seed(seed_num) random.seed(seed_num) # exit(0)", "DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\"", "in os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f)", "def config_kernelGAN(afile): img_folder = os.path.dirname(afile) img_file = os.path.basename(afile) out_dir =", "if f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for f in", "f) for f in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:] == \".png\"],", "import tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\"", "== \".png\"]} Kernels = [] Noises = [] for f", "data import DataGenerator from learner import Learner import tqdm DATA_LOC", "if f[-4:] == \".png\"]} Kernels = [] Noises = []", "torch.backends.cudnn.benchmark = False np.random.seed(seed_num) random.seed(seed_num) # exit(0) data = {\"X\":[os.path.join(DATA_LOC,", "sys import numpy as np import cv2 import random import", "params = [\"--input_image_path\", afile, \"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0), \"--X4\"] conf", "f) for f in os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:] == \".png\"],", "DATA_VAL = \"DPEDiphone-va\" # \"Corrupted-va-x\" def config_kernelGAN(afile): img_folder = os.path.dirname(afile)", "in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:] == \".png\"]} Kernels = []", "DataGenerator from learner import Learner import tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\"", "learner.update(iteration, kgan) kgan.finish() if __name__ == \"__main__\": seed_num = 0", "True torch.backends.cudnn.benchmark = False np.random.seed(seed_num) random.seed(seed_num) # exit(0) data =", "== \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_VAL))", "import KernelGAN from data import DataGenerator from learner import Learner", "conf = config_kernelGAN(img_file) kgan = KernelGAN(conf) learner = Learner() data", "= \"yoon/kernels/track2\" params = [\"--input_image_path\", afile, \"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0),", "exit(0) data = {\"X\":[os.path.join(DATA_LOC, DATA_X, f) for f in os.listdir(os.path.join(DATA_LOC,", "{\"X\":[os.path.join(DATA_LOC, DATA_X, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:]", "== \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_Y))", "\"--X4\"] conf = Config().parse(params) conf.input2 = None return conf def", "kernelGAN import KernelGAN from data import DataGenerator from learner import", "\"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:]", "== \"__main__\": seed_num = 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic =", "DataGenerator(conf, kgan) for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in, _]", "Learner import tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X =", "torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False np.random.seed(seed_num)", "# exit(0) data = {\"X\":[os.path.join(DATA_LOC, DATA_X, f) for f in", "import Config from kernelGAN import KernelGAN from data import DataGenerator", "= \"DPEDiphone-va\" # \"Corrupted-va-x\" def config_kernelGAN(afile): img_folder = os.path.dirname(afile) img_file", "if __name__ == \"__main__\": seed_num = 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num)", "in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f)", "f) for f in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:] == \".png\"]}", "for f in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:] == \".png\"]} Kernels", "= \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL", "= \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\" # \"Corrupted-va-x\" def", "import Learner import tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X", "torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False np.random.seed(seed_num) random.seed(seed_num) #", "cv2 import random import torch from configs import Config from", "config_kernelGAN(afile): img_folder = os.path.dirname(afile) img_file = os.path.basename(afile) out_dir = \"yoon/kernels/track2\"", "DATA_X, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:] ==", "conf def estimate_kernel(img_file): conf = config_kernelGAN(img_file) kgan = KernelGAN(conf) learner", "return conf def estimate_kernel(img_file): conf = config_kernelGAN(img_file) kgan = KernelGAN(conf)", "data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration, kgan) kgan.finish() if __name__ == \"__main__\":", "for f in os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC,", "\"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\" # \"Corrupted-va-x\" def config_kernelGAN(afile): img_folder =", "img_folder = os.path.dirname(afile) img_file = os.path.basename(afile) out_dir = \"yoon/kernels/track2\" params", "# \"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\" # \"Corrupted-va-x\" def config_kernelGAN(afile): img_folder", "os.path.basename(afile) out_dir = \"yoon/kernels/track2\" params = [\"--input_image_path\", afile, \"--output_dir_path\", out_dir,", "config_kernelGAN(img_file) kgan = KernelGAN(conf) learner = Learner() data = DataGenerator(conf,", "os.path.dirname(afile) img_file = os.path.basename(afile) out_dir = \"yoon/kernels/track2\" params = [\"--input_image_path\",", "Config().parse(params) conf.input2 = None return conf def estimate_kernel(img_file): conf =", "for f in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC,", "estimate_kernel(img_file): conf = config_kernelGAN(img_file) kgan = KernelGAN(conf) learner = Learner()", "__name__ == \"__main__\": seed_num = 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic", "random.seed(seed_num) # exit(0) data = {\"X\":[os.path.join(DATA_LOC, DATA_X, f) for f", "\"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\" #", "= [\"--input_image_path\", afile, \"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0), \"--X4\"] conf =", "\"--noise_scale\", str(1.0), \"--X4\"] conf = Config().parse(params) conf.input2 = None return", "kgan.train(g_in, d_in) learner.update(iteration, kgan) kgan.finish() if __name__ == \"__main__\": seed_num", "for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in, _] = data.__getitem__(iteration)", "os.listdir(os.path.join(DATA_LOC, DATA_X)) if f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for", "f[-4:] == \".png\"]} Kernels = [] Noises = [] for", "learner = Learner() data = DataGenerator(conf, kgan) for iteration in", "= DataGenerator(conf, kgan) for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in,", "os, sys import numpy as np import cv2 import random", "afile, \"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0), \"--X4\"] conf = Config().parse(params) conf.input2", "data = DataGenerator(conf, kgan) for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in,", "np import cv2 import random import torch from configs import", "tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\" #", "kgan = KernelGAN(conf) learner = Learner() data = DataGenerator(conf, kgan)", "in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in, _] = data.__getitem__(iteration) kgan.train(g_in, d_in)", "= 0 torch.manual_seed(seed_num) torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark =", "f in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:] == \".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL,", "\"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0), \"--X4\"] conf = Config().parse(params) conf.input2 =", "f[-4:] == \".png\"], \"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for f in os.listdir(os.path.join(DATA_LOC,", "= {\"X\":[os.path.join(DATA_LOC, DATA_X, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_X)) if", "import DataGenerator from learner import Learner import tqdm DATA_LOC =", "\"DPEDiphone-va\" # \"Corrupted-va-x\" def config_kernelGAN(afile): img_folder = os.path.dirname(afile) img_file =", "\".png\"], \"val\":[os.path.join(DATA_LOC, DATA_VAL, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if", "\"Corrupted-va-x\" def config_kernelGAN(afile): img_folder = os.path.dirname(afile) img_file = os.path.basename(afile) out_dir", "d_in, _] = data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration, kgan) kgan.finish() if", "as np import cv2 import random import torch from configs", "\"yoon/kernels/track2\" params = [\"--input_image_path\", afile, \"--output_dir_path\", out_dir, \"--noise_scale\", str(1.0), \"--X4\"]", "KernelGAN(conf) learner = Learner() data = DataGenerator(conf, kgan) for iteration", "numpy as np import cv2 import random import torch from", "str(1.0), \"--X4\"] conf = Config().parse(params) conf.input2 = None return conf", "out_dir, \"--noise_scale\", str(1.0), \"--X4\"] conf = Config().parse(params) conf.input2 = None", "[g_in, d_in, _] = data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration, kgan) kgan.finish()", "torch.cuda.manual_seed(seed_num) torch.cuda.manual_seed_all(seed_num) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False np.random.seed(seed_num) random.seed(seed_num)", "data = {\"X\":[os.path.join(DATA_LOC, DATA_X, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_X))", "conf = Config().parse(params) conf.input2 = None return conf def estimate_kernel(img_file):", "None return conf def estimate_kernel(img_file): conf = config_kernelGAN(img_file) kgan =", "<reponame>yoon28/realsr-noise-injection import os, sys import numpy as np import cv2", "iteration in tqdm.tqdm(range(conf.max_iters), ncols=70): [g_in, d_in, _] = data.__getitem__(iteration) kgan.train(g_in,", "\"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL =", "= True torch.backends.cudnn.benchmark = False np.random.seed(seed_num) random.seed(seed_num) # exit(0) data", "# \"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\" DATA_VAL = \"DPEDiphone-va\"", "ncols=70): [g_in, d_in, _] = data.__getitem__(iteration) kgan.train(g_in, d_in) learner.update(iteration, kgan)", "out_dir = \"yoon/kernels/track2\" params = [\"--input_image_path\", afile, \"--output_dir_path\", out_dir, \"--noise_scale\",", "def estimate_kernel(img_file): conf = config_kernelGAN(img_file) kgan = KernelGAN(conf) learner =", "from configs import Config from kernelGAN import KernelGAN from data", "DATA_Y, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:] ==", "\"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\" #", "d_in) learner.update(iteration, kgan) kgan.finish() if __name__ == \"__main__\": seed_num =", "import cv2 import random import torch from configs import Config", "= None return conf def estimate_kernel(img_file): conf = config_kernelGAN(img_file) kgan", "from kernelGAN import KernelGAN from data import DataGenerator from learner", "\".png\"]} Kernels = [] Noises = [] for f in", "f in os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:] == \".png\"]} Kernels =", "= False np.random.seed(seed_num) random.seed(seed_num) # exit(0) data = {\"X\":[os.path.join(DATA_LOC, DATA_X,", "kgan) kgan.finish() if __name__ == \"__main__\": seed_num = 0 torch.manual_seed(seed_num)", "Kernels = [] Noises = [] for f in data[\"X\"]:", "DATA_X = \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\" # \"Corrupted-tr-y\"", "= Learner() data = DataGenerator(conf, kgan) for iteration in tqdm.tqdm(range(conf.max_iters),", "# \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y = \"DPEDiphone-tr-y\"", "configs import Config from kernelGAN import KernelGAN from data import", "= config_kernelGAN(img_file) kgan = KernelGAN(conf) learner = Learner() data =", "os.listdir(os.path.join(DATA_LOC, DATA_VAL)) if f[-4:] == \".png\"]} Kernels = [] Noises", "learner import Learner import tqdm DATA_LOC = \"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\"", "\"/mnt/data/NTIRE2020/realSR/track2\" # \"/mnt/data/NTIRE2020/realSR/track1\" DATA_X = \"DPEDiphone-tr-x\" # \"Corrupted-tr-x\" DATA_Y =", "np.random.seed(seed_num) random.seed(seed_num) # exit(0) data = {\"X\":[os.path.join(DATA_LOC, DATA_X, f) for", "\"Y\":[os.path.join(DATA_LOC, DATA_Y, f) for f in os.listdir(os.path.join(DATA_LOC, DATA_Y)) if f[-4:]", "# \"Corrupted-va-x\" def config_kernelGAN(afile): img_folder = os.path.dirname(afile) img_file = os.path.basename(afile)", "random import torch from configs import Config from kernelGAN import" ]
[ "import TestCase from pyRdfa import pyRdfa class NonXhtmlTest(TestCase): \"\"\" RDFa", "TestCase from pyRdfa import pyRdfa class NonXhtmlTest(TestCase): \"\"\" RDFa that", "can be processed both from a file, and from a", "\"\"\" RDFa that is in not well-formed XHTML is passed", "\"\"\" target1 = '<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self):", "def test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g) def test_file(self):", "in not well-formed XHTML is passed through html5lib. These tests", "= '<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g =", "make sure that this RDFa can be processed both from", "is in not well-formed XHTML is passed through html5lib. These", "test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g) def test_file(self): g", "pyRdfa class NonXhtmlTest(TestCase): \"\"\" RDFa that is in not well-formed", "g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g) def test_file(self): g =", "file, and from a URL. \"\"\" target1 = '<og:isbn>9780596516499</og:isbn>' target2", "that this RDFa can be processed both from a file,", "= pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g) def test_file(self): g = pyRdfa().rdf_from_source('test/rdfa/oreilly.html')", "unittest import TestCase from pyRdfa import pyRdfa class NonXhtmlTest(TestCase): \"\"\"", "passed through html5lib. These tests make sure that this RDFa", "= '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in", "processed both from a file, and from a URL. \"\"\"", "class NonXhtmlTest(TestCase): \"\"\" RDFa that is in not well-formed XHTML", "target1 = '<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g", "is passed through html5lib. These tests make sure that this", "in g) def test_file(self): g = pyRdfa().rdf_from_source('test/rdfa/oreilly.html') self.assert_(self.target2.encode('utf-8') in g)", "sure that this RDFa can be processed both from a", "that is in not well-formed XHTML is passed through html5lib.", "well-formed XHTML is passed through html5lib. These tests make sure", "tests make sure that this RDFa can be processed both", "rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g) def", "self.assert_(self.target1.encode('utf-8') in g) def test_file(self): g = pyRdfa().rdf_from_source('test/rdfa/oreilly.html') self.assert_(self.target2.encode('utf-8') in", "'<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g)", "NonXhtmlTest(TestCase): \"\"\" RDFa that is in not well-formed XHTML is", "from pyRdfa import pyRdfa class NonXhtmlTest(TestCase): \"\"\" RDFa that is", "through html5lib. These tests make sure that this RDFa can", "from unittest import TestCase from pyRdfa import pyRdfa class NonXhtmlTest(TestCase):", "RDFa can be processed both from a file, and from", "'<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/')", "be processed both from a file, and from a URL.", "both from a file, and from a URL. \"\"\" target1", "not well-formed XHTML is passed through html5lib. These tests make", "XHTML is passed through html5lib. These tests make sure that", "this RDFa can be processed both from a file, and", "and from a URL. \"\"\" target1 = '<og:isbn>9780596516499</og:isbn>' target2 =", "from a URL. \"\"\" target1 = '<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood", "from a file, and from a URL. \"\"\" target1 =", "target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def test_url(self): g = pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8')", "RDFa that is in not well-formed XHTML is passed through", "pyRdfa import pyRdfa class NonXhtmlTest(TestCase): \"\"\" RDFa that is in", "These tests make sure that this RDFa can be processed", "import pyRdfa class NonXhtmlTest(TestCase): \"\"\" RDFa that is in not", "a URL. \"\"\" target1 = '<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>'", "html5lib. These tests make sure that this RDFa can be", "a file, and from a URL. \"\"\" target1 = '<og:isbn>9780596516499</og:isbn>'", "pyRdfa().rdf_from_source('http://oreilly.com/catalog/9780596516499/') self.assert_(self.target1.encode('utf-8') in g) def test_file(self): g = pyRdfa().rdf_from_source('test/rdfa/oreilly.html') self.assert_(self.target2.encode('utf-8')", "URL. \"\"\" target1 = '<og:isbn>9780596516499</og:isbn>' target2 = '<gr:typeOfGood rdf:resource=\"urn:x-domain:oreilly.com:product:9780596803391.EBOOK\"/>' def" ]
[ "oaipmh module is a Python implementation of an \"Open Archives", "Beta\", \"Programming Language :: Python\", \"License :: OSI Approved ::", "Environment\"], description=\"\"\"\\ The oaipmh module is a Python implementation of", "The protocol is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+", "Initiative Protocol for Metadata Harvesting\" (version 2) client and server.", "client and server. The protocol is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\",", ":: Python Modules\", \"Environment :: Web Environment\"], description=\"\"\"\\ The oaipmh", "Approved :: BSD License\", \"Topic :: Software Development :: Libraries", "\"License :: OSI Approved :: BSD License\", \"Topic :: Software", "described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'),", "packages=find_packages('src'), package_dir = {'': 'src'}, zip_safe=False, license='BSD', keywords='OAI-PMH xml archive',", "\"Topic :: Software Development :: Libraries :: Python Modules\", \"Environment", "Python Modules\", \"Environment :: Web Environment\"], description=\"\"\"\\ The oaipmh module", "of an \"Open Archives Initiative Protocol for Metadata Harvesting\" (version", "\"Programming Language :: Python\", \"License :: OSI Approved :: BSD", "and server. The protocol is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__),", "author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status :: 4 - Beta\", \"Programming", "os.path import join, dirname setup( name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai',", "import setup, find_packages from os.path import join, dirname setup( name='pyoai',", "name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status :: 4 -", "- Beta\", \"Programming Language :: Python\", \"License :: OSI Approved", "is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()),", "url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status :: 4 - Beta\", \"Programming Language ::", ":: Python\", \"License :: OSI Approved :: BSD License\", \"Topic", "Protocol for Metadata Harvesting\" (version 2) client and server. The", "'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir = {'': 'src'}, zip_safe=False,", "<reponame>jr3cermak/robs-kitchensink from setuptools import setup, find_packages from os.path import join,", "License\", \"Topic :: Software Development :: Libraries :: Python Modules\",", "from os.path import join, dirname setup( name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>',", "author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status :: 4 - Beta\", \"Programming Language", "import join, dirname setup( name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development", "Software Development :: Libraries :: Python Modules\", \"Environment :: Web", "for Metadata Harvesting\" (version 2) client and server. The protocol", "\"Open Archives Initiative Protocol for Metadata Harvesting\" (version 2) client", ":: Libraries :: Python Modules\", \"Environment :: Web Environment\"], description=\"\"\"\\", ":: 4 - Beta\", \"Programming Language :: Python\", \"License ::", "setup, find_packages from os.path import join, dirname setup( name='pyoai', version='2.4.6.b',", "OSI Approved :: BSD License\", \"Topic :: Software Development ::", "\"Environment :: Web Environment\"], description=\"\"\"\\ The oaipmh module is a", "Python\", \"License :: OSI Approved :: BSD License\", \"Topic ::", ":: OSI Approved :: BSD License\", \"Topic :: Software Development", "is a Python implementation of an \"Open Archives Initiative Protocol", "4 - Beta\", \"Programming Language :: Python\", \"License :: OSI", "BSD License\", \"Topic :: Software Development :: Libraries :: Python", "Modules\", \"Environment :: Web Environment\"], description=\"\"\"\\ The oaipmh module is", "'HISTORY.txt')).read()), packages=find_packages('src'), package_dir = {'': 'src'}, zip_safe=False, license='BSD', keywords='OAI-PMH xml", "Development :: Libraries :: Python Modules\", \"Environment :: Web Environment\"],", "protocol is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__),", "module is a Python implementation of an \"Open Archives Initiative", "dirname setup( name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status ::", "Web Environment\"], description=\"\"\"\\ The oaipmh module is a Python implementation", "join, dirname setup( name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status", "http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir =", "The oaipmh module is a Python implementation of an \"Open", "= {'': 'src'}, zip_safe=False, license='BSD', keywords='OAI-PMH xml archive', install_requires=['lxml'], )", "server. The protocol is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+", "setup( name='pyoai', version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status :: 4", "Python implementation of an \"Open Archives Initiative Protocol for Metadata", "long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir = {'': 'src'},", ":: Web Environment\"], description=\"\"\"\\ The oaipmh module is a Python", "Libraries :: Python Modules\", \"Environment :: Web Environment\"], description=\"\"\"\\ The", "Language :: Python\", \"License :: OSI Approved :: BSD License\",", "open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir = {'': 'src'}, zip_safe=False, license='BSD', keywords='OAI-PMH", "version='2.4.6.b', author='Infrae', author_email='<EMAIL>', url='https://github.com/jr3cermak/robs-kitchensink/tree/master/python/pyoai', classifiers=[\"Development Status :: 4 - Beta\",", "2) client and server. The protocol is described here: http://www.openarchives.org/OAI/openarchivesprotocol.html", "\"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir = {'':", "Harvesting\" (version 2) client and server. The protocol is described", ":: Software Development :: Libraries :: Python Modules\", \"Environment ::", "from setuptools import setup, find_packages from os.path import join, dirname", "implementation of an \"Open Archives Initiative Protocol for Metadata Harvesting\"", ":: BSD License\", \"Topic :: Software Development :: Libraries ::", "Metadata Harvesting\" (version 2) client and server. The protocol is", "'\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir = {'': 'src'}, zip_safe=False, license='BSD',", "package_dir = {'': 'src'}, zip_safe=False, license='BSD', keywords='OAI-PMH xml archive', install_requires=['lxml'],", "(version 2) client and server. The protocol is described here:", "Status :: 4 - Beta\", \"Programming Language :: Python\", \"License", "here: http://www.openarchives.org/OAI/openarchivesprotocol.html \"\"\", long_description=(open(join(dirname(__file__), 'README.rst')).read()+ '\\n\\n'+ open(join(dirname(__file__), 'HISTORY.txt')).read()), packages=find_packages('src'), package_dir", "a Python implementation of an \"Open Archives Initiative Protocol for", "description=\"\"\"\\ The oaipmh module is a Python implementation of an", "setuptools import setup, find_packages from os.path import join, dirname setup(", "Archives Initiative Protocol for Metadata Harvesting\" (version 2) client and", "classifiers=[\"Development Status :: 4 - Beta\", \"Programming Language :: Python\",", "an \"Open Archives Initiative Protocol for Metadata Harvesting\" (version 2)", "find_packages from os.path import join, dirname setup( name='pyoai', version='2.4.6.b', author='Infrae'," ]
[ "elbow #12 left wrist #13 right shoulder #14 right elbow", "+ K_R, dim=1)**2 ''' ''' def random_rotation(J3d): # J =", "= D.cuda() # translation vector if is_reversed: root, v_t =", "degrees J_R = torch.matmul(R, J - root) + v_t #", "copy???? return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True) def temporal_loss(J, K, J_R,", "False) # print(temp.shape) J[i,:,:] = temp return J, theta, root", "right wrist def random_rotation(J3d): J = J3d # need copy????", "2, 2] = torch.cos(theta) # R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)],", "= torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]])", "torch.zeros((batch_size, 3, 3)).cuda() # rotation matrix over y by theta", "= torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root = J[:,:,8]", "#12 left wrist #13 right shoulder #14 right elbow #15", "theta degrees J_R = torch.matmul(R, J - root) + v_t", "matrix over y by theta degrees # R = torch.tensor([[1,", "the root joint v_t = torch.tensor([[0], [0], [D]]).cuda() # translation", "#9 head #10 left shoulder #11 left elbow #12 left", "math #0 left hip #1 left knee #2 left foot", "= -theta # R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta),", "- K - J_R + K_R, dim=1)**2 ''' ''' def", "translation vector if is_reversed: root, v_t = v_t, root #", "-torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]]) # rotation", "= torch.matmul(R, J - root) + v_t # rotation return", "R[:, 0, 2] = torch.sin(theta) R[:, 1, 1] = torch.ones(batch_size)", "8 = nose is root temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1),", "time t and K is J3d at time t+k. J_R", "depth of the root joint v_t = torch.tensor([[0], [0], [D]]).cuda()", "K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3, 16)", "return J_R def reverse_rotation(J3d_R, theta, root): # J = torch.transpose(J3d_R,", "reversed rotation of J #print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3,", "import nn import math #0 left hip #1 left knee", "# rotation matrix over y by theta degrees # R", "rotation matrix over y by theta degrees R[:, 0, 0]", "of J #print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) -", "# absolute depth of the root joint batch_size = root.shape[0]", "from torch import nn import math #0 left hip #1", "= torch.zeros(J.shape[0:2]) for i in range(J.shape[0]): theta = torch.rand(1).cuda() *", "left hip #1 left knee #2 left foot #3 right", "#2 left foot #3 right hip #4 right knee #5", "torch.ones(batch_size) R[:, 2, 0] = -torch.sin(theta) R[:, 2, 2] =", "elbow #15 right wrist def random_rotation(J3d): J = J3d #", "absolute depth of the root joint v_t = torch.tensor([[0], [0],", "the reversed rotation of J return torch.norm(J - K -", "return mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0],", "0] = -torch.sin(theta) R[:, 2, 2] = torch.cos(theta) # R", "at time t and K is J3d at time t+k.", "3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1)**2 ''' def temporal_loss(J,", "return J, theta, root # need these values in the", "v_t = torch.zeros((batch_size, 3, 1)).cuda() v_t[:, 2, :] = D.cuda()", "#7 neck #8 nose #9 head #10 left shoulder #11", "#13 right shoulder #14 right elbow #15 right wrist def", "J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1).shape) #stop mse_fn", "the root joint batch_size = root.shape[0] v_t = torch.zeros((batch_size, 3,", "- root) + v_t # rotation return J_R def reverse_rotation(J3d_R,", "torch.cos(theta), 0], [0, 0, 1]]) # rotation matrix over z", "# print(temp.shape) J[i,:,:] = temp return J, theta, root #", "degrees R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta),", "root joint batch_size = root.shape[0] v_t = torch.zeros((batch_size, 3, 1)).cuda()", "# need these values in the code def rotation(J, theta,", "root # need these values in the code def rotation(J,", "# J = torch.transpose(J3d_R, 1, 2) J = J3d_R for", "root J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False) return J3d_R, theta,", "batch_size = root.shape[0] v_t = torch.zeros((batch_size, 3, 1)).cuda() v_t[:, 2,", "= temp return J, theta, root # need these values", "theta degrees R = torch.zeros((batch_size, 3, 3)).cuda() # rotation matrix", "wrist def random_rotation(J3d): J = J3d # need copy???? batch_size", "2] = torch.sin(theta) R[:, 1, 1] = torch.ones(batch_size) R[:, 2,", "theta, root[i].unsqueeze(1), False) # print(temp.shape) J[i,:,:] = temp return J,", "need these values in the code def rotation(J, theta, root,", "by theta degrees J_R = torch.matmul(R, J.cuda() - root.cuda()) +", "by theta D = root[:,2].cuda() # absolute depth of the", "theta, root): J = J3d_R # need copy???? return rotation(J.cuda(),", "i in range(J.shape[0]): theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random", "#15 right wrist def random_rotation(J3d): J = J3d # need", "theta D = root[2] # absolute depth of the root", "root = torch.zeros(J.shape[0:2]) for i in range(J.shape[0]): theta = torch.rand(1).cuda()", "range(J.shape[0]): theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root[i]", "these values in the code def rotation(J, theta, root, is_reversed):", "torch.norm(J - K - J_R + K_R, dim=1)**2 ''' '''", "# R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta),", "J3d # need copy???? batch_size = J.shape[0] theta = torch.rand(batch_size).cuda()", "#print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3,", "over z by theta degrees R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)],", "y by theta degrees R[:, 0, 0] = torch.cos(theta) R[:,", "[D]]).cuda() # translation vector if is_reversed: root, v_t = v_t,", "depth of the root joint batch_size = root.shape[0] v_t =", "torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation matrix", "rotation of J return torch.norm(J - K - J_R +", "J = J3d # need copy???? batch_size = J.shape[0] theta", "3, 16), torch.zeros(J.shape[0], 3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3, 16) -", "torch.sin(theta), torch.cos(theta)]]) # rotation matrix over x by theta degrees", "def reverse_rotation(J3d_R, theta, root): # J = torch.transpose(J3d_R, 1, 2)", "return torch.norm(J - K - J_R + K_R, dim=1)**2 '''", "3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1).shape) #stop mse_fn =", "is J3d at time t and K is J3d at", "0] = torch.cos(theta) R[:, 0, 2] = torch.sin(theta) R[:, 1,", "J #print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0],", "# rotation matrix over z by theta degrees R =", "0], [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]]) # rotation matrix", "the code def rotation(J, theta, root, is_reversed): # rotation over", "torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3,", "# rotation over y axis by theta D = root[:,2].cuda()", "v_t = v_t, root # swap theta = -theta #", "rotation(J, theta, root, is_reversed): # rotation over y axis by", "v_t = torch.tensor([[0], [0], [D]]).cuda() # translation vector if is_reversed:", "is J3d at time t+k. J_R means the reversed rotation", "def reverse_rotation(J3d_R, theta, root): J = J3d_R # need copy????", "J.shape[0] theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root", "joint 8 = nose is root J3d_R = rotation(J.cuda(), theta.cuda(),", "= torch.transpose(J3d_R, 1, 2) J = J3d_R for i in", "shoulder #14 right elbow #15 right wrist def random_rotation(J3d): J", "J[i,:,:] = temp return J, theta, root # need these", "root[:,2].cuda() # absolute depth of the root joint batch_size =", "''' def random_rotation(J3d): # J = torch.transpose(J3d, 1, 2) J", "root) + v_t # rotation return J_R def reverse_rotation(J3d_R, theta,", "torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]) # rotation matrix", "temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1), False) # print(temp.shape) J[i,:,:] =", "torch.cos(theta) R[:, 0, 2] = torch.sin(theta) R[:, 1, 1] =", "D.cuda() # translation vector if is_reversed: root, v_t = v_t,", "= -torch.sin(theta) R[:, 2, 2] = torch.cos(theta) # R =", "1]]) # rotation matrix over z by theta degrees R", "root): J = J3d_R # need copy???? return rotation(J.cuda(), theta.cuda(),", "* 2*torch.tensor(math.pi).cuda() # random theta root[i] = J[i,:,8] # joint", "y by theta degrees # R = torch.tensor([[1, 0, 0],", "in range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(), root[i].unsqueeze(1).cuda(), True) return J", "- K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0],", "- root.cuda()) + v_t # rotation return J_R def reverse_rotation(J3d_R,", "= torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]])", "1, 2) J = J3d root = torch.zeros(J.shape[0:2]) for i", "= J3d_R # need copy???? return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True)", "return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True) def temporal_loss(J, K, J_R, K_R):", "rotation over y axis by theta D = root[:,2].cuda() #", "J[i,:,8] # joint 8 = nose is root temp =", "random theta root[i] = J[i,:,8] # joint 8 = nose", "= J.shape[0] theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random theta", "J = J3d_R # need copy???? return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(),", "return J3d_R, theta, root # need these values in the", "nose is root J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False) return", "= root[:,2].cuda() # absolute depth of the root joint batch_size", "2, :] = D.cuda() # translation vector if is_reversed: root,", "# random theta root = J[:,:,8] # joint 8 =", "J.cuda() - root.cuda()) + v_t # rotation return J_R def", "theta, root): # J = torch.transpose(J3d_R, 1, 2) J =", "root temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1), False) # print(temp.shape) J[i,:,:]", "left foot #3 right hip #4 right knee #5 right", "#8 nose #9 head #10 left shoulder #11 left elbow", "0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]) # rotation", "[0, 0, 1]]) # rotation matrix over z by theta", "root.cuda()) + v_t # rotation return J_R def reverse_rotation(J3d_R, theta,", "= torch.cos(theta) R[:, 0, 2] = torch.sin(theta) R[:, 1, 1]", "theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root =", "z by theta degrees R = torch.zeros((batch_size, 3, 3)).cuda() #", "R = torch.zeros((batch_size, 3, 3)).cuda() # rotation matrix over y", "torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root[i] = J[i,:,8] #", "J return torch.norm(J - K - J_R + K_R, dim=1)**2", "theta degrees J_R = torch.matmul(R, J.cuda() - root.cuda()) + v_t", "= torch.zeros((batch_size, 3, 1)).cuda() v_t[:, 2, :] = D.cuda() #", "D = root[2] # absolute depth of the root joint", "over y by theta degrees R[:, 0, 0] = torch.cos(theta)", "= torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]])", "+ K_R.reshape(J.shape[0], 3, 16), dim=1).shape) #stop mse_fn = nn.MSELoss() return", "16) + K_R.reshape(J.shape[0], 3, 16), dim=1)**2 ''' def temporal_loss(J, K,", "= torch.transpose(J3d, 1, 2) J = J3d root = torch.zeros(J.shape[0:2])", "0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation", "J_R = torch.matmul(R, J - root) + v_t # rotation", "J_R means the reversed rotation of J #print(torch.norm(J.reshape(J.shape[0], 3, 16)", "# rotation return J_R def reverse_rotation(J3d_R, theta, root): # J", "2*torch.tensor(math.pi).cuda() # random theta root[i] = J[i,:,8] # joint 8", "root joint v_t = torch.tensor([[0], [0], [D]]).cuda() # translation vector", "t+k. J_R means the reversed rotation of J #print(torch.norm(J.reshape(J.shape[0], 3,", "for i in range(J.shape[0]): theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() #", "values in the code def rotation(J, theta, root, is_reversed): #", "joint 8 = nose is root temp = rotation(J[i,:,:], theta,", "rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False) return J3d_R, theta, root # need", "K_R, dim=1)**2 ''' ''' def random_rotation(J3d): # J = torch.transpose(J3d,", "K, J_R, K_R): # J is J3d at time t", "0, 1]]) # rotation matrix over z by theta degrees", "root.shape[0] v_t = torch.zeros((batch_size, 3, 1)).cuda() v_t[:, 2, :] =", "rotation return J_R def reverse_rotation(J3d_R, theta, root): # J =", "# swap theta = -theta # R = torch.tensor([[torch.cos(theta), -torch.sin(theta),", "root, v_t = v_t, root # swap theta = -theta", "over y axis by theta D = root[:,2].cuda() # absolute", "= torch.ones(batch_size) R[:, 2, 0] = -torch.sin(theta) R[:, 2, 2]", "root.unsqueeze(-1).cuda(), False) return J3d_R, theta, root # need these values", "[0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]) # rotation matrix over", "for i in range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(), root[i].unsqueeze(1).cuda(), True)", "K_R.reshape(J.shape[0], 3, 16), dim=1)**2 ''' def temporal_loss(J, K, J_R, K_R):", "in the code def rotation(J, theta, root, is_reversed): # rotation", "rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True) def temporal_loss(J, K, J_R, K_R): #", "3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16)", "nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) -", "root # swap theta = -theta # R = torch.tensor([[torch.cos(theta),", "= torch.tensor([[0], [0], [D]]).cuda() # translation vector if is_reversed: root,", "- J_R + K_R, dim=1)**2 ''' ''' def random_rotation(J3d): #", "-torch.sin(theta) R[:, 2, 2] = torch.cos(theta) # R = torch.tensor([[torch.cos(theta),", "torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]) #", "torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]]) #", "if is_reversed: root, v_t = v_t, root # swap theta", "v_t # rotation return J_R def reverse_rotation(J3d_R, theta, root): J", "#10 left shoulder #11 left elbow #12 left wrist #13", "J3d_R, theta, root # need these values in the code", "[0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix over", "16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1)**2", "2) J = J3d root = torch.zeros(J.shape[0:2]) for i in", "degrees J_R = torch.matmul(R, J.cuda() - root.cuda()) + v_t #", "root): # J = torch.transpose(J3d_R, 1, 2) J = J3d_R", "[0], [D]]).cuda() # translation vector if is_reversed: root, v_t =", "# R = torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0,", "root[i].unsqueeze(1), False) # print(temp.shape) J[i,:,:] = temp return J, theta,", "reverse_rotation(J3d_R, theta, root): # J = torch.transpose(J3d_R, 1, 2) J", "[0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix over x by theta", "left elbow #12 left wrist #13 right shoulder #14 right", "J3d_R for i in range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(), root[i].unsqueeze(1).cuda(),", "neck #8 nose #9 head #10 left shoulder #11 left", "True) def temporal_loss(J, K, J_R, K_R): # J is J3d", "theta, root, is_reversed): # rotation over y axis by theta", "0, 0] = torch.cos(theta) R[:, 0, 2] = torch.sin(theta) R[:,", "foot #6 middle hip #7 neck #8 nose #9 head", "16) + K_R.reshape(J.shape[0], 3, 16), dim=1).shape) #stop mse_fn = nn.MSELoss()", "right elbow #15 right wrist def random_rotation(J3d): J = J3d", "degrees R[:, 0, 0] = torch.cos(theta) R[:, 0, 2] =", "v_t, root # swap theta = -theta # R =", ":] = D.cuda() # translation vector if is_reversed: root, v_t", "# J is J3d at time t and K is", "hip #4 right knee #5 right foot #6 middle hip", "the reversed rotation of J #print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0],", "rotation matrix over z by theta degrees R = torch.tensor([[torch.cos(theta),", "def random_rotation(J3d): # J = torch.transpose(J3d, 1, 2) J =", "# absolute depth of the root joint v_t = torch.tensor([[0],", "= torch.zeros((batch_size, 3, 3)).cuda() # rotation matrix over y by", "vector if is_reversed: root, v_t = v_t, root # swap", "#14 right elbow #15 right wrist def random_rotation(J3d): J =", "def temporal_loss(J, K, J_R, K_R): # J is J3d at", "torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix over x", "K_R): # J is J3d at time t and K", "degrees # R = torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)],", "rotation return J_R def reverse_rotation(J3d_R, theta, root): J = J3d_R", "- J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3,", "3, 16), dim=1).shape) #stop mse_fn = nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3,", "+ v_t # rotation return J_R def reverse_rotation(J3d_R, theta, root):", "''' ''' def random_rotation(J3d): # J = torch.transpose(J3d, 1, 2)", "axis by theta D = root[2] # absolute depth of", "torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root = J[:,:,8] #", "1)).cuda() v_t[:, 2, :] = D.cuda() # translation vector if", "absolute depth of the root joint batch_size = root.shape[0] v_t", "x by theta degrees J_R = torch.matmul(R, J - root)", "reversed rotation of J return torch.norm(J - K - J_R", "knee #2 left foot #3 right hip #4 right knee", "of the root joint v_t = torch.tensor([[0], [0], [D]]).cuda() #", "# rotation matrix over x by theta degrees J_R =", "need copy???? batch_size = J.shape[0] theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda()", "hip #7 neck #8 nose #9 head #10 left shoulder", "theta root[i] = J[i,:,8] # joint 8 = nose is", "= J[:,:,8] # joint 8 = nose is root J3d_R", "16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0],", "torch.zeros(J.shape[0], 3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3,", "torch.sin(theta) R[:, 1, 1] = torch.ones(batch_size) R[:, 2, 0] =", "root.unsqueeze(-1).cuda(), True) def temporal_loss(J, K, J_R, K_R): # J is", "J[:,:,8] # joint 8 = nose is root J3d_R =", "= J3d root = torch.zeros(J.shape[0:2]) for i in range(J.shape[0]): theta", "0], [-torch.sin(theta), 0, torch.cos(theta)]]) # rotation matrix over y by", "by theta degrees R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1,", "batch_size = J.shape[0] theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random", "axis by theta D = root[:,2].cuda() # absolute depth of", "mse_fn = nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3,", "by theta degrees # R = torch.tensor([[1, 0, 0], [0,", "D = root[:,2].cuda() # absolute depth of the root joint", "0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation", "# rotation over y axis by theta D = root[2]", "temporal_loss(J, K, J_R, K_R): # J is J3d at time", "right knee #5 right foot #6 middle hip #7 neck", "i in range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(), root[i].unsqueeze(1).cuda(), True) return", "torch.transpose(J3d, 1, 2) J = J3d root = torch.zeros(J.shape[0:2]) for", "16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1).shape)", "torch.matmul(R, J - root) + v_t # rotation return J_R", "-torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix over x by", "#return torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0],", "False) return J3d_R, theta, root # need these values in", "at time t+k. J_R means the reversed rotation of J", "J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3, 16).cuda())", "# R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0,", "= root[2] # absolute depth of the root joint v_t", "middle hip #7 neck #8 nose #9 head #10 left", "= rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False) return J3d_R, theta, root #", "0], [0, 0, 1]]) # rotation matrix over z by", "over x by theta degrees J_R = torch.matmul(R, J -", "joint v_t = torch.tensor([[0], [0], [D]]).cuda() # translation vector if", "0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix", "16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16) +", "# J = torch.transpose(J3d, 1, 2) J = J3d root", "#11 left elbow #12 left wrist #13 right shoulder #14", "R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0, 0,", "16), torch.zeros(J.shape[0], 3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0],", "2*torch.tensor(math.pi).cuda() # random theta root = J[:,:,8] # joint 8", "dim=1)**2 ''' ''' def random_rotation(J3d): # J = torch.transpose(J3d, 1,", "#6 middle hip #7 neck #8 nose #9 head #10", "<filename>utils/functions.py import torch from torch import nn import math #0", "foot #3 right hip #4 right knee #5 right foot", "= nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16)", "t+k. J_R means the reversed rotation of J return torch.norm(J", "over y by theta degrees # R = torch.tensor([[1, 0,", "torch.cos(theta) # R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0],", "of J return torch.norm(J - K - J_R + K_R,", "K - J_R + K_R, dim=1)**2 ''' ''' def random_rotation(J3d):", "= J[i,:,8] # joint 8 = nose is root temp", "theta = -theta # R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta),", "nn import math #0 left hip #1 left knee #2", "= rotation(J[i,:,:], theta, root[i].unsqueeze(1), False) # print(temp.shape) J[i,:,:] = temp", "1] = torch.ones(batch_size) R[:, 2, 0] = -torch.sin(theta) R[:, 2,", "J = J3d root = torch.zeros(J.shape[0:2]) for i in range(J.shape[0]):", "J_R, K_R): # J is J3d at time t and", "shoulder #11 left elbow #12 left wrist #13 right shoulder", "# need copy???? batch_size = J.shape[0] theta = torch.rand(batch_size).cuda() *", "swap theta = -theta # R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0],", "is root J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False) return J3d_R,", "J - root) + v_t # rotation return J_R def", "1, 1] = torch.ones(batch_size) R[:, 2, 0] = -torch.sin(theta) R[:,", "by theta degrees R[:, 0, 0] = torch.cos(theta) R[:, 0,", "theta.cuda(), root.unsqueeze(-1).cuda(), True) def temporal_loss(J, K, J_R, K_R): # J", "#0 left hip #1 left knee #2 left foot #3", "J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1)**2 ''' def", "J_R means the reversed rotation of J return torch.norm(J -", "[0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation matrix over", "wrist #13 right shoulder #14 right elbow #15 right wrist", "y axis by theta D = root[:,2].cuda() # absolute depth", "random_rotation(J3d): J = J3d # need copy???? batch_size = J.shape[0]", "- J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1)**2 '''", "rotation(J[i,:,:], theta, root[i].unsqueeze(1), False) # print(temp.shape) J[i,:,:] = temp return", "# random theta root[i] = J[i,:,8] # joint 8 =", "hip #1 left knee #2 left foot #3 right hip", "random theta root = J[:,:,8] # joint 8 = nose", "copy???? batch_size = J.shape[0] theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() #", "3)).cuda() # rotation matrix over y by theta degrees R[:,", "theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root[i] =", "matrix over y by theta degrees R[:, 0, 0] =", "#5 right foot #6 middle hip #7 neck #8 nose", "root[2] # absolute depth of the root joint v_t =", "rotation matrix over y by theta degrees # R =", "2) J = J3d_R for i in range(J.shape[0]): J[i,:,:] =", "rotation matrix over z by theta degrees R = torch.zeros((batch_size,", "J3d at time t+k. J_R means the reversed rotation of", "+ K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3,", "rotation over y axis by theta D = root[2] #", "J = torch.transpose(J3d, 1, 2) J = J3d root =", "K_R.reshape(J.shape[0], 3, 16), dim=1).shape) #stop mse_fn = nn.MSELoss() return mse_fn(J.reshape(J.shape[0],", "J_R def reverse_rotation(J3d_R, theta, root): # J = torch.transpose(J3d_R, 1,", "torch.cos(theta)]]).cuda() # rotation matrix over y by theta degrees #", "= torch.matmul(R, J.cuda() - root.cuda()) + v_t # rotation return", "torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) #", "K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3,", "1, 2) J = J3d_R for i in range(J.shape[0]): J[i,:,:]", "time t+k. J_R means the reversed rotation of J return", "[torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]]) # rotation matrix over", "[-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation matrix over y by theta", "theta degrees R[:, 0, 0] = torch.cos(theta) R[:, 0, 2]", "''' def temporal_loss(J, K, J_R, K_R): # J is J3d", "right hip #4 right knee #5 right foot #6 middle", "is root temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1), False) # print(temp.shape)", "# translation vector if is_reversed: root, v_t = v_t, root", "torch.matmul(R, J.cuda() - root.cuda()) + v_t # rotation return J_R", "z by theta degrees R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0,", "import torch from torch import nn import math #0 left", "16), dim=1)**2 ''' def temporal_loss(J, K, J_R, K_R): # J", "torch.cos(theta)]]) # rotation matrix over y by theta degrees #", "right foot #6 middle hip #7 neck #8 nose #9", "1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation matrix over y", "matrix over z by theta degrees R = torch.tensor([[torch.cos(theta), 0,", "over y axis by theta D = root[2] # absolute", "J_R + K_R, dim=1)**2 ''' ''' def random_rotation(J3d): # J", "theta D = root[:,2].cuda() # absolute depth of the root", "0, torch.cos(theta)]]) # rotation matrix over y by theta degrees", "0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation matrix over y by", "R = torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta),", "def rotation(J, theta, root, is_reversed): # rotation over y axis", "# joint 8 = nose is root temp = rotation(J[i,:,:],", "code def rotation(J, theta, root, is_reversed): # rotation over y", "0, torch.cos(theta)]]).cuda() # rotation matrix over y by theta degrees", "J3d_R # need copy???? return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True) def", "torch.cos(theta)]]) # rotation matrix over x by theta degrees J_R", "3, 16), dim=1)**2 ''' def temporal_loss(J, K, J_R, K_R): #", "* 2*torch.tensor(math.pi).cuda() # random theta root = J[:,:,8] # joint", "+ K_R.reshape(J.shape[0], 3, 16), dim=1)**2 ''' def temporal_loss(J, K, J_R,", "= nose is root temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1), False)", "mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3,", "3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16)", "theta.cuda(), root.unsqueeze(-1).cuda(), False) return J3d_R, theta, root # need these", "3, 3)).cuda() # rotation matrix over y by theta degrees", "in range(J.shape[0]): theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random theta", "= v_t, root # swap theta = -theta # R", "left wrist #13 right shoulder #14 right elbow #15 right", "R[:, 1, 1] = torch.ones(batch_size) R[:, 2, 0] = -torch.sin(theta)", "8 = nose is root J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(),", "is_reversed: root, v_t = v_t, root # swap theta =", "0, 2] = torch.sin(theta) R[:, 1, 1] = torch.ones(batch_size) R[:,", "v_t[:, 2, :] = D.cuda() # translation vector if is_reversed:", "J is J3d at time t and K is J3d", "dim=1)**2 ''' def temporal_loss(J, K, J_R, K_R): # J is", "reverse_rotation(J3d_R, theta, root): J = J3d_R # need copy???? return", "= J3d_R for i in range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(),", "- J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1).shape) #stop", "theta, root # need these values in the code def", "of the root joint batch_size = root.shape[0] v_t = torch.zeros((batch_size,", "16) + K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3, 16).cuda()) #return torch.norm(J.reshape(J.shape[0],", "torch.tensor([[0], [0], [D]]).cuda() # translation vector if is_reversed: root, v_t", "random_rotation(J3d): # J = torch.transpose(J3d, 1, 2) J = J3d", "[-torch.sin(theta), 0, torch.cos(theta)]]) # rotation matrix over y by theta", "need copy???? return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True) def temporal_loss(J, K,", "J3d root = torch.zeros(J.shape[0:2]) for i in range(J.shape[0]): theta =", "import math #0 left hip #1 left knee #2 left", "16), dim=1).shape) #stop mse_fn = nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3, 16)", "nose is root temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1), False) #", "# joint 8 = nose is root J3d_R = rotation(J.cuda(),", "# need copy???? return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True) def temporal_loss(J,", "left knee #2 left foot #3 right hip #4 right", "degrees R = torch.zeros((batch_size, 3, 3)).cuda() # rotation matrix over", "2] = torch.cos(theta) # R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0,", "= torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random theta root[i] = J[i,:,8]", "# rotation matrix over y by theta degrees R[:, 0,", "def random_rotation(J3d): J = J3d # need copy???? batch_size =", "J, theta, root # need these values in the code", "= nose is root J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False)", "root[i] = J[i,:,8] # joint 8 = nose is root", "J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False) return J3d_R, theta, root", "J3d at time t and K is J3d at time", "R[:, 2, 0] = -torch.sin(theta) R[:, 2, 2] = torch.cos(theta)", "joint batch_size = root.shape[0] v_t = torch.zeros((batch_size, 3, 1)).cuda() v_t[:,", "J_R = torch.matmul(R, J.cuda() - root.cuda()) + v_t # rotation", "torch from torch import nn import math #0 left hip", "theta root = J[:,:,8] # joint 8 = nose is", "y axis by theta D = root[2] # absolute depth", "= J3d # need copy???? batch_size = J.shape[0] theta =", "= torch.cos(theta) # R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1,", "by theta degrees R = torch.zeros((batch_size, 3, 3)).cuda() # rotation", "R[:, 2, 2] = torch.cos(theta) # R = torch.tensor([[torch.cos(theta), 0,", "K is J3d at time t+k. J_R means the reversed", "v_t # rotation return J_R def reverse_rotation(J3d_R, theta, root): #", "3, 16) + K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3, 16).cuda()) #return", "root = J[:,:,8] # joint 8 = nose is root", "matrix over x by theta degrees J_R = torch.matmul(R, J", "means the reversed rotation of J return torch.norm(J - K", "matrix over x by theta degrees J_R = torch.matmul(R, J.cuda()", "-theta # R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0],", "t and K is J3d at time t+k. J_R means", "theta degrees # R = torch.tensor([[1, 0, 0], [0, torch.cos(theta),", "head #10 left shoulder #11 left elbow #12 left wrist", "root, is_reversed): # rotation over y axis by theta D", "left shoulder #11 left elbow #12 left wrist #13 right", "R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0,", "means the reversed rotation of J #print(torch.norm(J.reshape(J.shape[0], 3, 16) -", "temp return J, theta, root # need these values in", "16).cuda()) #return torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) -", "J_R def reverse_rotation(J3d_R, theta, root): J = J3d_R # need", "matrix over z by theta degrees R = torch.zeros((batch_size, 3,", "by theta degrees J_R = torch.matmul(R, J - root) +", "#4 right knee #5 right foot #6 middle hip #7", "torch.zeros((batch_size, 3, 1)).cuda() v_t[:, 2, :] = D.cuda() # translation", "over z by theta degrees R = torch.zeros((batch_size, 3, 3)).cuda()", "by theta D = root[2] # absolute depth of the", "#3 right hip #4 right knee #5 right foot #6", "right shoulder #14 right elbow #15 right wrist def random_rotation(J3d):", "= torch.sin(theta) R[:, 1, 1] = torch.ones(batch_size) R[:, 2, 0]", "over x by theta degrees J_R = torch.matmul(R, J.cuda() -", "#1 left knee #2 left foot #3 right hip #4", "return J_R def reverse_rotation(J3d_R, theta, root): J = J3d_R #", "3, 16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16),", "= torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda()", "time t+k. J_R means the reversed rotation of J #print(torch.norm(J.reshape(J.shape[0],", "2, 0] = -torch.sin(theta) R[:, 2, 2] = torch.cos(theta) #", "torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() #", "J = torch.transpose(J3d_R, 1, 2) J = J3d_R for i", "torch import nn import math #0 left hip #1 left", "rotation matrix over x by theta degrees J_R = torch.matmul(R,", "dim=1).shape) #stop mse_fn = nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3, 16) -", "torch.zeros(J.shape[0:2]) for i in range(J.shape[0]): theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda()", "x by theta degrees J_R = torch.matmul(R, J.cuda() - root.cuda())", "nose #9 head #10 left shoulder #11 left elbow #12", "= root.shape[0] v_t = torch.zeros((batch_size, 3, 1)).cuda() v_t[:, 2, :]", "# rotation return J_R def reverse_rotation(J3d_R, theta, root): J =", "R[:, 0, 0] = torch.cos(theta) R[:, 0, 2] = torch.sin(theta)", "J = J3d_R for i in range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(),", "1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]) # rotation matrix over y", "knee #5 right foot #6 middle hip #7 neck #8", "#stop mse_fn = nn.MSELoss() return mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0],", "range(J.shape[0]): J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(), root[i].unsqueeze(1).cuda(), True) return J '''", "theta degrees R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0],", "is_reversed): # rotation over y axis by theta D =", "and K is J3d at time t+k. J_R means the", "rotation of J #print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16)", "print(temp.shape) J[i,:,:] = temp return J, theta, root # need", "3, 1)).cuda() v_t[:, 2, :] = D.cuda() # translation vector", "torch.transpose(J3d_R, 1, 2) J = J3d_R for i in range(J.shape[0]):" ]
[ "preço é {(moeda.metade(preco))} O dobro do preço é {(moeda.dobra(preco))} Aumentando", "print(f'''A metade do preço é {(moeda.metade(preco))} O dobro do preço", "float(input('Digite o preço pretendido: €')) print(f'''A metade do preço é", "temos {(moeda.aumentar(preco, 10))} Diminuindo o preço 13% temos {(moeda.aumentar(preco, 13))}''')", "dobro do preço é {(moeda.dobra(preco))} Aumentando o preço 10% temos", "preço é {(moeda.dobra(preco))} Aumentando o preço 10% temos {(moeda.aumentar(preco, 10))}", "é {(moeda.dobra(preco))} Aumentando o preço 10% temos {(moeda.aumentar(preco, 10))} Diminuindo", "= float(input('Digite o preço pretendido: €')) print(f'''A metade do preço", "preço 10% temos {(moeda.aumentar(preco, 10))} Diminuindo o preço 13% temos", "import moeda preco = float(input('Digite o preço pretendido: €')) print(f'''A", "10% temos {(moeda.aumentar(preco, 10))} Diminuindo o preço 13% temos {(moeda.aumentar(preco,", "metade do preço é {(moeda.metade(preco))} O dobro do preço é", "moeda preco = float(input('Digite o preço pretendido: €')) print(f'''A metade", "Aumentando o preço 10% temos {(moeda.aumentar(preco, 10))} Diminuindo o preço", "€')) print(f'''A metade do preço é {(moeda.metade(preco))} O dobro do", "do preço é {(moeda.dobra(preco))} Aumentando o preço 10% temos {(moeda.aumentar(preco,", "des109 import moeda preco = float(input('Digite o preço pretendido: €'))", "do preço é {(moeda.metade(preco))} O dobro do preço é {(moeda.dobra(preco))}", "from des109 import moeda preco = float(input('Digite o preço pretendido:", "preço pretendido: €')) print(f'''A metade do preço é {(moeda.metade(preco))} O", "{(moeda.dobra(preco))} Aumentando o preço 10% temos {(moeda.aumentar(preco, 10))} Diminuindo o", "{(moeda.metade(preco))} O dobro do preço é {(moeda.dobra(preco))} Aumentando o preço", "é {(moeda.metade(preco))} O dobro do preço é {(moeda.dobra(preco))} Aumentando o", "pretendido: €')) print(f'''A metade do preço é {(moeda.metade(preco))} O dobro", "preco = float(input('Digite o preço pretendido: €')) print(f'''A metade do", "o preço pretendido: €')) print(f'''A metade do preço é {(moeda.metade(preco))}", "O dobro do preço é {(moeda.dobra(preco))} Aumentando o preço 10%", "o preço 10% temos {(moeda.aumentar(preco, 10))} Diminuindo o preço 13%" ]
[ "uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password, {}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object')", "xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw( db, uid, password, 'ir.module.module', 'search_read', [[('state',", "xmlrpclib db = 'odoo9' user = 'admin' password = '<PASSWORD>'", "[[('state', '=', 'installed')], ['name']], {}) for module in installed_modules: print", ".authenticate(db, user, password, {}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw(", "= '<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password, {}) odoo", "user, password, {}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw( db,", "db, uid, password, 'ir.module.module', 'search_read', [[('state', '=', 'installed')], ['name']], {})", "= 'admin' password = '<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user,", "= 'odoo9' user = 'admin' password = '<PASSWORD>' uid =", "'search_read', [[('state', '=', 'installed')], ['name']], {}) for module in installed_modules:", "import xmlrpclib db = 'odoo9' user = 'admin' password =", "'=', 'installed')], ['name']], {}) for module in installed_modules: print module['name']", "user = 'admin' password = '<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db,", "= xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw( db, uid, password, 'ir.module.module', 'search_read',", "odoo.execute_kw( db, uid, password, 'ir.module.module', 'search_read', [[('state', '=', 'installed')], ['name']],", "uid, password, 'ir.module.module', 'search_read', [[('state', '=', 'installed')], ['name']], {}) for", "password, {}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw( db, uid,", "#!/usr/bin/env python2 import xmlrpclib db = 'odoo9' user = 'admin'", "installed_modules = odoo.execute_kw( db, uid, password, 'ir.module.module', 'search_read', [[('state', '=',", "password = '<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password, {})", "'odoo9' user = 'admin' password = '<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\", "'ir.module.module', 'search_read', [[('state', '=', 'installed')], ['name']], {}) for module in", "'admin' password = '<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password,", "= xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password, {}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules", "password, 'ir.module.module', 'search_read', [[('state', '=', 'installed')], ['name']], {}) for module", "= odoo.execute_kw( db, uid, password, 'ir.module.module', 'search_read', [[('state', '=', 'installed')],", "python2 import xmlrpclib db = 'odoo9' user = 'admin' password", "xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password, {}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules =", "{}) odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw( db, uid, password,", "'<PASSWORD>' uid = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/common')\\ .authenticate(db, user, password, {}) odoo =", "db = 'odoo9' user = 'admin' password = '<PASSWORD>' uid", "odoo = xmlrpclib.ServerProxy('http://localhost:8069/xmlrpc/2/object') installed_modules = odoo.execute_kw( db, uid, password, 'ir.module.module'," ]
[ "get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset): tvector = np.zeros(self.word2vec_dim) if offset >", "= self.get_head_token(mention) first_token, last_token = mention.tokens[0], mention.tokens[-1] utterance = first_token.parent_utterance()", "# First word of the mention embeddings.append(self.get_token_word_vector(first_token)) # Last word", "([], []) if entity and include_average: nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding())", "spk_dim self.spk2vec = dict() for spk in spks: self.spk2vec[spk] =", "speaker = utterance.speaker prev_utterance = utterance.previous_utterance() prev_speaker = prev_utterance.speaker if", "dict() for pos in poss: self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim =", "= end_ftid - start_ftid embeddings = list() # Word embeddings", "offset > 0: for tid in xrange(start, start+offset): tvector +=", "utterance = first_token.parent_utterance() scene = utterance.parent_scene() episode = scene.parent_episode() speaker", "prev_speaker = prev_utterance.speaker if prev_utterance is not None else None", "None else None flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention)", "tcount += len(u) return uvector / float(tcount) if tcount >", "all words in the scene embeddings.append(self.get_scene_vector(scene)) # Avg of all", "########################################################### class AbstractFeatureExtractor(object): @abstractmethod def extract(self, object): return ########################################################### class", "mention): tids = map(lambda t: t.id, mention.tokens) for token in", "None and token.dep_head.id not in tids: return token return mention.tokens[0]", "start_ftid-2, token_len+4)) # Avg of the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1,", "return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) #### Transcript document features #### def", "head_token.dep_head is None else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token length/location information", "token_len = end_ftid - start_ftid embeddings = list() # Word", "__init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape) if empty_embd_shape else None", "uvector = uvector + self.word2vec[word] tcount += len(u) return uvector", "ners, spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec = word2vec self.word2vec_dim =", "get_pos_tag_vector(self, tag): return self.pos2vec[tag] if tag in self.pos2vec else np.zeros(self.pos_dim)", "= np.zeros(self.word2vec_dim) for scene in episode.scenes: evector += self.get_scene_vector(scene) return", "of the mention embeddings.append(self.get_token_word_vector(last_token)) # Avg of all words in", "sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) # Avg of all words in", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) # Avg of the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "+= self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <= 0 else np.zeros(self.word2vec_dim) return", "return tvector / float(offset) def get_token_gender_vector(self, token): word_form = token.word_form.lower()", "else np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag): return self.pos2vec[tag] if tag in", "scene.utterances: svector += self.get_utterance_vector(utterance) return svector / float(len(scene.utterances)) if scene.utterances", "extract(self, object): return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None, empty_feat_shape=None):", "dict() for spk in spks: self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim =", "of the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) # Avg of", "/ float(len(mention.tokens)) def get_speaker_vector(self, speaker): return self.spk2vec[speaker] if speaker in", "self.get_token_gender_vector(token) return gvector / float(len(mention.tokens)) def get_speaker_vector(self, speaker): return self.spk2vec[speaker]", "self.word2gender = word2gender self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim = spk_dim self.spk2vec", "if token in mention.tokens: locations.append(idx) locations.sort() return locations def get_mention_sentence_tokens(self,", "np.zeros(self.word2vec_dim) for scene in episode.scenes: evector += self.get_scene_vector(scene) return evector", "tid < len(flatten_tokens) else np.zeros(self.word2vec_dim) else: for tid in xrange(start,", "t in u: word = t.word_form.lower() if word in self.word2vec:", "# Last word of the mention embeddings.append(self.get_token_word_vector(last_token)) # Avg of", "def get_speaker_vector(self, speaker): return self.spk2vec[speaker] if speaker in self.spk2vec else", "for u in utterance.statements: for t in u: word =", "episode): evector = np.zeros(self.word2vec_dim) for scene in episode.scenes: evector +=", "= pos_dim self.pos2vec = dict() for pos in poss: self.pos2vec[pos]", "def get_token_word_vector(self, token): word_form = token.word_form.lower() return self.word2vec[word_form] if word_form", "end_ftid+2, 1)) # Avg of the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1,", "all words in the mention embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding words", "self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim = spk_dim self.spk2vec = dict() for", "self.spk2vec = dict() for spk in spks: self.spk2vec[spk] = np.random.rand(spk_dim)", "= entity[-nb_mentions:] embedding += map(lambda m: m.embedding, mentions) feature +=", "self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention) ft_locations = self.get_token_locations(flatten_utterance_tokens, mention) start_ftid,", "max(0, nb_mentions - len(entity)) nb_mentions -= nb_padding if selection_method is", "1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding = max(0, nb_mentions - len(entity)) nb_mentions", "for statement in utterance.statements: if token in statement: return statement", "get_token_gender_vector(self, token): word_form = token.word_form.lower() return self.word2gender[word_form] if word_form in", "= utterance.previous_utterance() prev_speaker = prev_utterance.speaker if prev_utterance is not None", "in mention.tokens: gvector += self.get_token_gender_vector(token) return gvector / float(len(mention.tokens)) def", "of the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) # Avg of", "import numpy as np ########################################################### class AbstractFeatureExtractor(object): @abstractmethod def extract(self,", "ner_dim self.ner2vec = dict() for ner in ners: self.ner2vec[ner] =", "len(flatten_sentence_tokens))) # Avg of all words in current utterance embeddings.append(self.get_utterance_vector(utterance))", "in enumerate(flatten_tokens): if token in mention.tokens: locations.append(idx) locations.sort() return locations", "i in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature) ########################################################### class", "np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec, word2gender, spks, poss,", "mention) ft_locations = self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid = ft_locations[0], ft_locations[-1]", "tids = map(lambda t: t.id, mention.tokens) for token in mention.tokens:", "of head token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label information of head", "of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) #", "None def extract(self, entity, include_average=True, nb_mentions=5, selection_method='last'): embedding, feature =", "get_dep_label_vector(self, label): return self.dep2vec[label] if label in self.dep2vec else np.zeros(self.dep_dim)", "dep_dim=8, ner_dim=8): self.word2vec = word2vec self.word2vec_dim = len(word2vec.values()[0]) self.word2gender =", "uvector def get_scene_vector(self, scene): svector = np.zeros(self.word2vec_dim) for utterance in", "in tids: return token return mention.tokens[0] def flatten_utterance(self, utterance): return", "return self.pos2vec[tag] if tag in self.pos2vec else np.zeros(self.pos_dim) def get_ner_tag_vector(self,", "locations.append(idx) locations.sort() return locations def get_mention_sentence_tokens(self, utterance, mention): token =", "tvector = np.zeros(self.word2vec_dim) for token in mention.tokens: tvector += self.get_token_word_vector(token)", "in self.dep2vec else np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index): length", "of the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) # Avg of", "information of head token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label information of", "if tid <= 0 else np.zeros(self.word2vec_dim) return tvector / float(offset)", "else svector def get_episode_vector(self, episode): evector = np.zeros(self.word2vec_dim) for scene", "speaker information of the utterance features.append(self.get_speaker_vector(speaker)) # Previous speaker information", "of all words in the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens)))", "words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) # Avg of the +-2 words", "= np.zeros(self.word2vec_dim) for utterance in scene.utterances: svector += self.get_utterance_vector(utterance) return", "np.zeros(self.word2vec_dim) return tvector / float(offset) def get_token_gender_vector(self, token): word_form =", "in the scene embeddings.append(self.get_scene_vector(scene)) # Avg of all words in", "for statements in utterance.statements for st in statements] def get_token_locations(self,", "np.random.rand(ner_dim) def extract(self, mention): head_token = self.get_head_token(mention) first_token, last_token =", "in deps: self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim = ner_dim self.ner2vec =", "= np.zeros(empty_feat_shape) if empty_feat_shape else None def extract(self, entity, include_average=True,", "head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label information of head token", "token in mention.tokens: if token.dep_head is not None and token.dep_head.id", "spks, poss, deps, ners, spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec =", "#### Transcript document features #### def get_utterance_vector(self, utterance): tcount =", "tag in self.pos2vec else np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag): return self.ner2vec[tag]", "else None self.f_EMPTY = np.zeros(empty_feat_shape) if empty_feat_shape else None def", "features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return np.array(embeddings), np.concatenate(features) ###### Helper functions #######", "<= 0 else np.zeros(self.word2vec_dim) return tvector / float(offset) def get_token_gender_vector(self,", "mention.tokens[0], mention.tokens[-1] utterance = first_token.parent_utterance() scene = utterance.parent_scene() episode =", "np.zeros(empty_feat_shape) if empty_feat_shape else None def extract(self, entity, include_average=True, nb_mentions=5,", "in utterance.statements: for t in u: word = t.word_form.lower() if", "np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag): return self.ner2vec[tag] if tag in self.ner2vec", "information of the utterance features.append(self.get_speaker_vector(speaker)) # Previous speaker information of", "# Avg of the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) #", "np.array(embedding), np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec, word2gender, spks,", "utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return np.array(embeddings), np.concatenate(features) ###### Helper functions", "if scene.utterances else svector def get_episode_vector(self, episode): evector = np.zeros(self.word2vec_dim)", "Avg of all words in the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0,", "Transcript document features #### def get_utterance_vector(self, utterance): tcount = 0", "if prev_utterance is not None else None flatten_utterance_tokens = self.flatten_utterance(utterance)", "tag): return self.ner2vec[tag] if tag in self.ner2vec else np.zeros(self.ner_dim) def", "the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) # Two", "if tcount > 0 else uvector def get_scene_vector(self, scene): svector", "utterance in scene.utterances: svector += self.get_utterance_vector(utterance) return svector / float(len(scene.utterances))", "the mention embeddings.append(self.get_token_word_vector(last_token)) # Avg of all words in the", "= np.random.rand(spk_dim) self.pos_dim = pos_dim self.pos2vec = dict() for pos", "previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of all words in the", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) # Avg of the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "np.concatenate(features) ###### Helper functions ####### def get_head_token(self, mention): tids =", "extract(self, mention): head_token = self.get_head_token(mention) first_token, last_token = mention.tokens[0], mention.tokens[-1]", "tag): return self.pos2vec[tag] if tag in self.pos2vec else np.zeros(self.pos_dim) def", "feature.append(entity.get_avg_mention_feature()) nb_padding = max(0, nb_mentions - len(entity)) nb_mentions -= nb_padding", "in current utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg of all words in", "self.f_EMPTY = np.zeros(empty_feat_shape) if empty_feat_shape else None def extract(self, entity,", "in self.word2vec else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention): tvector = np.zeros(self.word2vec_dim)", "label in self.dep2vec else np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index):", "EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape) if empty_embd_shape", "def get_ner_tag_vector(self, tag): return self.ner2vec[tag] if tag in self.ner2vec else", "np.zeros(self.word2vec_dim) for token in mention.tokens: tvector += self.get_token_word_vector(token) return tvector", "Previous speaker information of the utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos tag", "token in enumerate(flatten_tokens): if token in mention.tokens: locations.append(idx) locations.sort() return", "word_form in self.word2gender else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention): gvector =", "np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention): gvector = np.zeros(self.word2gender_dim) for token in", "tid <= 0 else np.zeros(self.word2vec_dim) return tvector / float(offset) def", "embeddings.append(self.get_scene_vector(scene)) # Avg of all words in the episode embeddings.append(self.get_episode_vector(episode))", "def get_dep_label_vector(self, label): return self.dep2vec[label] if label in self.dep2vec else", "numpy as np ########################################################### class AbstractFeatureExtractor(object): @abstractmethod def extract(self, object):", "utterance.statements: if token in statement: return statement return None ######", "utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg of all words in previous utterance", "= [] for idx, token in enumerate(flatten_tokens): if token in", "def get_mention_sentence_tokens(self, utterance, mention): token = mention.tokens[0] for statement in", "in u: word = t.word_form.lower() if word in self.word2vec: uvector", "-= nb_padding if selection_method is 'last': mentions = entity[-nb_mentions:] embedding", "the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) # Avg of all", "pos_dim self.pos2vec = dict() for pos in poss: self.pos2vec[pos] =", "mention word length, start token location, end token location return", "0 uvector = np.zeros(self.word2vec_dim) if utterance is not None: for", "empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape) if empty_embd_shape else None self.f_EMPTY =", "if word_form in self.word2gender else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention): gvector", "Helper functions ####### def get_head_token(self, mention): tids = map(lambda t:", "all tokens in the mention features.append(self.get_tokens_gender_vector(mention)) # Current speaker information", "token): word_form = token.word_form.lower() return self.word2vec[word_form] if word_form in self.word2vec", "start-offset, -1): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <= 0", "location, end token location return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) #### Transcript", "first_token.parent_utterance() scene = utterance.parent_scene() episode = scene.parent_episode() speaker = utterance.speaker", "self.get_utterance_vector(utterance) return svector / float(len(scene.utterances)) if scene.utterances else svector def", "= token.word_form.lower() return self.word2gender[word_form] if word_form in self.word2gender else np.zeros(self.word2gender_dim)", "token.word_form.lower() return self.word2gender[word_form] if word_form in self.word2gender else np.zeros(self.word2gender_dim) def", "#### def get_utterance_vector(self, utterance): tcount = 0 uvector = np.zeros(self.word2vec_dim)", "else np.zeros(self.word2vec_dim) else: for tid in xrange(start, start-offset, -1): tvector", "def get_tokens_word_vector(self, mention): tvector = np.zeros(self.word2vec_dim) for token in mention.tokens:", "in self.pos2vec else np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag): return self.ner2vec[tag] if", "########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape)", "the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) # Avg of the", "st in statements] def get_token_locations(self, flatten_tokens, mention): locations = []", "def get_utterance_vector(self, utterance): tcount = 0 uvector = np.zeros(self.word2vec_dim) if", "m: m.embedding, mentions) feature += map(lambda m: m.feature, mentions) for", "start_ftid, end_ftid)) return np.array(embeddings), np.concatenate(features) ###### Helper functions ####### def", "ft_locations = self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid = ft_locations[0], ft_locations[-1] token_len", "features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label information of head token features.append(self.get_dep_label_vector(head_token.dep_label)) #", "features.append(self.get_speaker_vector(speaker)) # Previous speaker information of the utterance features.append(self.get_speaker_vector(prev_speaker)) #", "float(len(scene.utterances)) if scene.utterances else svector def get_episode_vector(self, episode): evector =", "is not None else None flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens =", "spks: self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim = pos_dim self.pos2vec = dict()", "utterance features.append(self.get_speaker_vector(speaker)) # Previous speaker information of the utterance features.append(self.get_speaker_vector(prev_speaker))", "else np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index): length = len(flatten_utternace_tokens)", "ners: self.ner2vec[ner] = np.random.rand(ner_dim) def extract(self, mention): head_token = self.get_head_token(mention)", "mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) # Two following", "# Avg of all words in the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens,", "embeddings of the head word embeddings.append(self.get_token_word_vector(head_token)) # First word of", "mention): token = mention.tokens[0] for statement in utterance.statements: if token", "tag information of head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label information", "self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim = pos_dim self.pos2vec = dict() for", "# Dep label information of head token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep", "statements in utterance.statements for st in statements] def get_token_locations(self, flatten_tokens,", "list() # Word embeddings of the head word embeddings.append(self.get_token_word_vector(head_token)) #", "feature = ([], []) if entity and include_average: nb_mentions -=", "np.zeros(empty_embd_shape) if empty_embd_shape else None self.f_EMPTY = np.zeros(empty_feat_shape) if empty_feat_shape", "nb_mentions - len(entity)) nb_mentions -= nb_padding if selection_method is 'last':", "Avg of the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) # Avg", "return self.spk2vec[speaker] if speaker in self.spk2vec else np.zeros(self.spk_dim) def get_pos_tag_vector(self,", "class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec, word2gender, spks, poss, deps, ners,", "token.word_form.lower() return self.word2vec[word_form] if word_form in self.word2vec else np.zeros(self.word2vec_dim) def", "First word of the mention embeddings.append(self.get_token_word_vector(first_token)) # Last word of", "float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset): tvector = np.zeros(self.word2vec_dim) if", "end token location return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) #### Transcript document", "in xrange(start, start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <", "# Ner tag information of head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) # Two following words", "head_token = self.get_head_token(mention) first_token, last_token = mention.tokens[0], mention.tokens[-1] utterance =", "words in the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) # Avg", "tokens features ####### def get_token_word_vector(self, token): word_form = token.word_form.lower() return", "Two following words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "= 0 uvector = np.zeros(self.word2vec_dim) if utterance is not None:", "else None def extract(self, entity, include_average=True, nb_mentions=5, selection_method='last'): embedding, feature", "self.word2gender[word_form] if word_form in self.word2gender else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention):", "and include_average: nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding = max(0,", "Avg of the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) # Avg", "0 else np.zeros(self.word2vec_dim) return tvector / float(offset) def get_token_gender_vector(self, token):", "return uvector / float(tcount) if tcount > 0 else uvector", "Last word of the mention embeddings.append(self.get_token_word_vector(last_token)) # Avg of all", "if head_token.dep_head is None else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token length/location", "mention.tokens: tvector += self.get_token_word_vector(token) return tvector / float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self,", "label): return self.dep2vec[label] if label in self.dep2vec else np.zeros(self.dep_dim) def", "scene in episode.scenes: evector += self.get_scene_vector(scene) return evector / float(len(episode.scenes))", "5)) # Avg of all words in the mention's sentence", "gender information of all tokens in the mention features.append(self.get_tokens_gender_vector(mention)) #", "np.random.rand(pos_dim) self.dep_dim = dep_dim self.dep2vec = dict() for dep in", "token location, end token location return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) ####", "= prev_utterance.speaker if prev_utterance is not None else None flatten_utterance_tokens", "self.get_token_word_vector(token) return tvector / float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset):", "of the utterance features.append(self.get_speaker_vector(speaker)) # Previous speaker information of the", "return statement return None ###### Mention tokens features ####### def", "/ float(offset) def get_token_gender_vector(self, token): word_form = token.word_form.lower() return self.word2gender[word_form]", "np.zeros(self.word2vec_dim) if offset > 0: for tid in xrange(start, start+offset):", "xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def", "document features #### def get_utterance_vector(self, utterance): tcount = 0 uvector", "not in tids: return token return mention.tokens[0] def flatten_utterance(self, utterance):", "= utterance.speaker prev_utterance = utterance.previous_utterance() prev_speaker = prev_utterance.speaker if prev_utterance", "= mention.tokens[0] for statement in utterance.statements: if token in statement:", "word_form = token.word_form.lower() return self.word2gender[word_form] if word_form in self.word2gender else", "-= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding = max(0, nb_mentions - len(entity))", "class AbstractFeatureExtractor(object): @abstractmethod def extract(self, object): return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor):", "float(tcount) if tcount > 0 else uvector def get_scene_vector(self, scene):", "speaker): return self.spk2vec[speaker] if speaker in self.spk2vec else np.zeros(self.spk_dim) def", "self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid = ft_locations[0], ft_locations[-1] token_len = end_ftid", "= np.zeros(self.word2vec_dim) if utterance is not None: for u in", "scene = utterance.parent_scene() episode = scene.parent_episode() speaker = utterance.speaker prev_utterance", "and token.dep_head.id not in tids: return token return mention.tokens[0] def", "if offset > 0: for tid in xrange(start, start+offset): tvector", "self.dep2vec = dict() for dep in deps: self.dep2vec[dep] = np.random.rand(dep_dim)", "if label in self.dep2vec else np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens, start_idx,", "list() # Gender information of head token in the mention", "= np.random.rand(dep_dim) self.ner_dim = ner_dim self.ner2vec = dict() for ner", "+= self.get_token_word_vector(token) return tvector / float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens, start,", "tid in xrange(start, start-offset, -1): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if", "in statements] def get_token_locations(self, flatten_tokens, mention): locations = [] for", "+= self.get_token_word_vector(flatten_tokens[tid]) \\ if tid < len(flatten_tokens) else np.zeros(self.word2vec_dim) else:", "start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) # Two following words of", "Dep label information of head token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head is", "for pos in poss: self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim = dep_dim", "xrange(start, start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid < len(flatten_tokens)", "uvector = np.zeros(self.word2vec_dim) if utterance is not None: for u", "if speaker in self.spk2vec else np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag): return", "embedding += map(lambda m: m.embedding, mentions) feature += map(lambda m:", "####### def get_token_word_vector(self, token): word_form = token.word_form.lower() return self.word2vec[word_form] if", "flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention) ft_locations = self.get_token_locations(flatten_utterance_tokens,", "return self.word2gender[word_form] if word_form in self.word2gender else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self,", "functions ####### def get_head_token(self, mention): tids = map(lambda t: t.id,", "empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape) if empty_embd_shape else None self.f_EMPTY", "self.ner_dim = ner_dim self.ner2vec = dict() for ner in ners:", "end_ftid - start_ftid embeddings = list() # Word embeddings of", "statements] def get_token_locations(self, flatten_tokens, mention): locations = [] for idx,", "= np.zeros(self.word2vec_dim) for token in mention.tokens: tvector += self.get_token_word_vector(token) return", "get_scene_vector(self, scene): svector = np.zeros(self.word2vec_dim) for utterance in scene.utterances: svector", "= len(flatten_utternace_tokens) # Normalized mention word length, start token location,", "in the mention features.append(self.get_token_gender_vector(head_token)) # Avg gender information of all", "features ####### def get_token_word_vector(self, token): word_form = token.word_form.lower() return self.word2vec[word_form]", "None: for u in utterance.statements: for t in u: word", "= ([], []) if entity and include_average: nb_mentions -= 1", "in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor):", "+= self.get_utterance_vector(utterance) return svector / float(len(scene.utterances)) if scene.utterances else svector", "'last': mentions = entity[-nb_mentions:] embedding += map(lambda m: m.embedding, mentions)", "spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec = word2vec self.word2vec_dim = len(word2vec.values()[0])", "# Avg of all words in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) #", "= dep_dim self.dep2vec = dict() for dep in deps: self.dep2vec[dep]", "np.random.rand(spk_dim) self.pos_dim = pos_dim self.pos2vec = dict() for pos in", "scene): svector = np.zeros(self.word2vec_dim) for utterance in scene.utterances: svector +=", "embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of all words in the scene embeddings.append(self.get_scene_vector(scene))", "label information of head token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label information", "return [st for statements in utterance.statements for st in statements]", "evector += self.get_scene_vector(scene) return evector / float(len(episode.scenes)) if episode.scenes else", "in ners: self.ner2vec[ner] = np.random.rand(ner_dim) def extract(self, mention): head_token =", "np.zeros(self.word2gender_dim) for token in mention.tokens: gvector += self.get_token_gender_vector(token) return gvector", "head token in the mention features.append(self.get_token_gender_vector(head_token)) # Avg gender information", "self.dep2vec else np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index): length =", "# Current speaker information of the utterance features.append(self.get_speaker_vector(speaker)) # Previous", "utterance.speaker prev_utterance = utterance.previous_utterance() prev_speaker = prev_utterance.speaker if prev_utterance is", "xrange(start, start-offset, -1): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <=", "the utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos tag information of head token", "word_form in self.word2vec else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention): tvector =", "gvector / float(len(mention.tokens)) def get_speaker_vector(self, speaker): return self.spk2vec[speaker] if speaker", "+5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) # Avg of all words", "if entity and include_average: nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding", "dict() for ner in ners: self.ner2vec[ner] = np.random.rand(ner_dim) def extract(self,", "end_ftid)) return np.array(embeddings), np.concatenate(features) ###### Helper functions ####### def get_head_token(self,", "length, start token location, end token location return np.array([float(end_index-start_idx)/length, float(start_idx)/length,", "is not None and token.dep_head.id not in tids: return token", "* import numpy as np ########################################################### class AbstractFeatureExtractor(object): @abstractmethod def", "Avg of all words in current utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg", "= np.random.rand(pos_dim) self.dep_dim = dep_dim self.dep2vec = dict() for dep", "all words in current utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg of all", "embeddings.append(self.get_token_word_vector(last_token)) # Avg of all words in the mention embeddings.append(self.get_tokens_word_vector(mention))", "# Avg of all words in current utterance embeddings.append(self.get_utterance_vector(utterance)) #", "of all words in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of", "the head word embeddings.append(self.get_token_word_vector(head_token)) # First word of the mention", "if token in statement: return statement return None ###### Mention", "self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim = dep_dim self.dep2vec = dict() for", "+= self.get_token_gender_vector(token) return gvector / float(len(mention.tokens)) def get_speaker_vector(self, speaker): return", "tvector / float(offset) def get_token_gender_vector(self, token): word_form = token.word_form.lower() return", "self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim = ner_dim self.ner2vec = dict() for", "if utterance is not None: for u in utterance.statements: for", "= max(0, nb_mentions - len(entity)) nb_mentions -= nb_padding if selection_method", "the episode embeddings.append(self.get_episode_vector(episode)) features = list() # Gender information of", "Mention tokens features ####### def get_token_word_vector(self, token): word_form = token.word_form.lower()", "not None: for u in utterance.statements: for t in u:", "= len(word2gender.values()[0]) self.spk_dim = spk_dim self.spk2vec = dict() for spk", "mention.tokens[0] for statement in utterance.statements: if token in statement: return", "in mention.tokens: tvector += self.get_token_word_vector(token) return tvector / float(len(mention.tokens)) def", "# Normalized mention word length, start token location, end token", "prev_utterance is not None else None flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens", "following words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2,", "extract(self, entity, include_average=True, nb_mentions=5, selection_method='last'): embedding, feature = ([], [])", "words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1))", "of head token in the mention features.append(self.get_token_gender_vector(head_token)) # Avg gender", "mention.tokens: locations.append(idx) locations.sort() return locations def get_mention_sentence_tokens(self, utterance, mention): token", "0: for tid in xrange(start, start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\", "token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag information of head token features.append(self.get_ner_tag_vector(head_token.ner_tag))", "+ self.word2vec[word] tcount += len(u) return uvector / float(tcount) if", "nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding = max(0, nb_mentions -", "/ float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset): tvector = np.zeros(self.word2vec_dim)", "token_len+4)) # Avg of the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5))", "speaker information of the utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos tag information", "mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) # Avg of", "return np.array(embeddings), np.concatenate(features) ###### Helper functions ####### def get_head_token(self, mention):", "entity[-nb_mentions:] embedding += map(lambda m: m.embedding, mentions) feature += map(lambda", "def get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset): tvector = np.zeros(self.word2vec_dim) if offset", "mention): locations = [] for idx, token in enumerate(flatten_tokens): if", "for token in mention.tokens: tvector += self.get_token_word_vector(token) return tvector /", "in self.word2gender else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention): gvector = np.zeros(self.word2gender_dim)", "len(u) return uvector / float(tcount) if tcount > 0 else", "get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index): length = len(flatten_utternace_tokens) # Normalized mention", "= list() # Word embeddings of the head word embeddings.append(self.get_token_word_vector(head_token))", "in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of all words in", "object): return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY", "pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec = word2vec self.word2vec_dim = len(word2vec.values()[0]) self.word2gender", "words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) # Avg of the -5 words", "include_average: nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding = max(0, nb_mentions", "def extract(self, entity, include_average=True, nb_mentions=5, selection_method='last'): embedding, feature = ([],", "self.pos_dim = pos_dim self.pos2vec = dict() for pos in poss:", "else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention): gvector = np.zeros(self.word2gender_dim) for token", "for utterance in scene.utterances: svector += self.get_utterance_vector(utterance) return svector /", "uvector / float(tcount) if tcount > 0 else uvector def", "Pos tag information of head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag", "# Previous speaker information of the utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos", "all words in the episode embeddings.append(self.get_episode_vector(episode)) features = list() #", "np ########################################################### class AbstractFeatureExtractor(object): @abstractmethod def extract(self, object): return ###########################################################", "def __init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape) if empty_embd_shape else", "self.word2vec_dim = len(word2vec.values()[0]) self.word2gender = word2gender self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim", "def get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index): length = len(flatten_utternace_tokens) # Normalized", "word of the mention embeddings.append(self.get_token_word_vector(first_token)) # Last word of the", "the utterance features.append(self.get_speaker_vector(speaker)) # Previous speaker information of the utterance", "np.zeros(self.word2vec_dim) if utterance is not None: for u in utterance.statements:", "return tvector / float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset): tvector", "map(lambda t: t.id, mention.tokens) for token in mention.tokens: if token.dep_head", "= np.zeros(self.word2gender_dim) for token in mention.tokens: gvector += self.get_token_gender_vector(token) return", "words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) # Avg of all words in", "selection_method is 'last': mentions = entity[-nb_mentions:] embedding += map(lambda m:", "token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label information of head token features.append(self.get_dep_label_vector(head_token.dep_label))", "word2vec self.word2vec_dim = len(word2vec.values()[0]) self.word2gender = word2gender self.word2gender_dim = len(word2gender.values()[0])", "# Pos tag information of head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner", "is 'last': mentions = entity[-nb_mentions:] embedding += map(lambda m: m.embedding,", "def __init__(self, word2vec, word2gender, spks, poss, deps, ners, spk_dim=8, pos_dim=8,", "in the mention features.append(self.get_tokens_gender_vector(mention)) # Current speaker information of the", "of the utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos tag information of head", "head token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head is None else self.get_dep_label_vector(head_token.dep_head.dep_label)) #", "else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention): tvector = np.zeros(self.word2vec_dim) for token", "t.id, mention.tokens) for token in mention.tokens: if token.dep_head is not", "__init__(self, word2vec, word2gender, spks, poss, deps, ners, spk_dim=8, pos_dim=8, dep_dim=8,", "return gvector / float(len(mention.tokens)) def get_speaker_vector(self, speaker): return self.spk2vec[speaker] if", "in episode.scenes: evector += self.get_scene_vector(scene) return evector / float(len(episode.scenes)) if", "ner in ners: self.ner2vec[ner] = np.random.rand(ner_dim) def extract(self, mention): head_token", "mention.tokens: gvector += self.get_token_gender_vector(token) return gvector / float(len(mention.tokens)) def get_speaker_vector(self,", "in mention.tokens: locations.append(idx) locations.sort() return locations def get_mention_sentence_tokens(self, utterance, mention):", "last_token = mention.tokens[0], mention.tokens[-1] utterance = first_token.parent_utterance() scene = utterance.parent_scene()", "Avg of the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) # Avg", "in the mention embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding words of the", "start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid < len(flatten_tokens) else", "of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) #", "m.feature, mentions) for i in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding),", "# Avg of the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) #", "map(lambda m: m.feature, mentions) for i in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY)", "m.embedding, mentions) feature += map(lambda m: m.feature, mentions) for i", "start_ftid-1, -5)) # Avg of the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1,", "in the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) # Avg of", "len(word2vec.values()[0]) self.word2gender = word2gender self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim = spk_dim", "###### Mention tokens features ####### def get_token_word_vector(self, token): word_form =", "of all words in current utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg of", "embeddings.append(self.get_episode_vector(episode)) features = list() # Gender information of head token", "tvector = np.zeros(self.word2vec_dim) if offset > 0: for tid in", "# Avg of the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) #", "features.append(self.get_speaker_vector(prev_speaker)) # Pos tag information of head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) #", "tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid < len(flatten_tokens) else np.zeros(self.word2vec_dim)", "the mention embeddings.append(self.get_token_word_vector(first_token)) # Last word of the mention embeddings.append(self.get_token_word_vector(last_token))", "if empty_embd_shape else None self.f_EMPTY = np.zeros(empty_feat_shape) if empty_feat_shape else", "self.spk_dim = spk_dim self.spk2vec = dict() for spk in spks:", "# Gender information of head token in the mention features.append(self.get_token_gender_vector(head_token))", "else None flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention) ft_locations", "Mention token length/location information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return", "token.dep_head.id not in tids: return token return mention.tokens[0] def flatten_utterance(self,", "flatten_tokens, mention): locations = [] for idx, token in enumerate(flatten_tokens):", "self.ner2vec[tag] if tag in self.ner2vec else np.zeros(self.ner_dim) def get_dep_label_vector(self, label):", "for token in mention.tokens: if token.dep_head is not None and", "start_idx, end_index): length = len(flatten_utternace_tokens) # Normalized mention word length,", "information of head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag information of", "<reponame>emorynlp/character-identification-old<gh_stars>1-10 from abc import * import numpy as np ###########################################################", "for tid in xrange(start, start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if", "label information of head token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head is None", "token in mention.tokens: tvector += self.get_token_word_vector(token) return tvector / float(len(mention.tokens))", "empty_feat_shape else None def extract(self, entity, include_average=True, nb_mentions=5, selection_method='last'): embedding,", "/ float(len(scene.utterances)) if scene.utterances else svector def get_episode_vector(self, episode): evector", "the mention features.append(self.get_tokens_gender_vector(mention)) # Current speaker information of the utterance", "0, len(flatten_sentence_tokens))) # Avg of all words in current utterance", "MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec, word2gender, spks, poss, deps, ners, spk_dim=8,", "np.zeros(self.ner_dim) def get_dep_label_vector(self, label): return self.dep2vec[label] if label in self.dep2vec", "token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head is None else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention", "scene embeddings.append(self.get_scene_vector(scene)) # Avg of all words in the episode", "np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag): return self.pos2vec[tag] if tag in self.pos2vec", "= self.get_mention_sentence_tokens(utterance, mention) ft_locations = self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid =", "information of head token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head is None else", "- len(entity)) nb_mentions -= nb_padding if selection_method is 'last': mentions", "utterance.statements for st in statements] def get_token_locations(self, flatten_tokens, mention): locations", "utterance.previous_utterance() prev_speaker = prev_utterance.speaker if prev_utterance is not None else", "1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) # Two following words of the", "statement return None ###### Mention tokens features ####### def get_token_word_vector(self,", "u in utterance.statements: for t in u: word = t.word_form.lower()", "words in the mention embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding words of", "start_ftid embeddings = list() # Word embeddings of the head", "prev_utterance = utterance.previous_utterance() prev_speaker = prev_utterance.speaker if prev_utterance is not", "abc import * import numpy as np ########################################################### class AbstractFeatureExtractor(object):", "float(len(mention.tokens)) def get_speaker_vector(self, speaker): return self.spk2vec[speaker] if speaker in self.spk2vec", "else np.zeros(self.word2vec_dim) return tvector / float(offset) def get_token_gender_vector(self, token): word_form", "= np.zeros(empty_embd_shape) if empty_embd_shape else None self.f_EMPTY = np.zeros(empty_feat_shape) if", "the scene embeddings.append(self.get_scene_vector(scene)) # Avg of all words in the", "mentions = entity[-nb_mentions:] embedding += map(lambda m: m.embedding, mentions) feature", "if word_form in self.word2vec else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention): tvector", "+= map(lambda m: m.embedding, mentions) feature += map(lambda m: m.feature,", "tag in self.ner2vec else np.zeros(self.ner_dim) def get_dep_label_vector(self, label): return self.dep2vec[label]", "in mention.tokens: if token.dep_head is not None and token.dep_head.id not", "in xrange(start, start-offset, -1): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid", "mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) # Avg of all words", "svector = np.zeros(self.word2vec_dim) for utterance in scene.utterances: svector += self.get_utterance_vector(utterance)", "########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec, word2gender, spks, poss, deps,", "return None ###### Mention tokens features ####### def get_token_word_vector(self, token):", "self.spk2vec else np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag): return self.pos2vec[tag] if tag", "len(flatten_tokens) else np.zeros(self.word2vec_dim) else: for tid in xrange(start, start-offset, -1):", "for st in statements] def get_token_locations(self, flatten_tokens, mention): locations =", "from abc import * import numpy as np ########################################################### class", "self.get_mention_sentence_tokens(utterance, mention) ft_locations = self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid = ft_locations[0],", "flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention) ft_locations = self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid", "else: for tid in xrange(start, start-offset, -1): tvector += self.get_token_word_vector(flatten_tokens[tid])", "tvector / float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens, start, offset): tvector =", "Word embeddings of the head word embeddings.append(self.get_token_word_vector(head_token)) # First word", "###### Helper functions ####### def get_head_token(self, mention): tids = map(lambda", "self.pos2vec else np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag): return self.ner2vec[tag] if tag", "in utterance.statements: if token in statement: return statement return None", "information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return np.array(embeddings), np.concatenate(features) ######", "end_index): length = len(flatten_utternace_tokens) # Normalized mention word length, start", "# Avg of the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) #", "= uvector + self.word2vec[word] tcount += len(u) return uvector /", "of all words in the mention embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding", "len(entity)) nb_mentions -= nb_padding if selection_method is 'last': mentions =", "the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) # Avg of all", "= dict() for dep in deps: self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim", "mention embeddings.append(self.get_token_word_vector(last_token)) # Avg of all words in the mention", "\\ if tid < len(flatten_tokens) else np.zeros(self.word2vec_dim) else: for tid", "for t in u: word = t.word_form.lower() if word in", "prev_utterance.speaker if prev_utterance is not None else None flatten_utterance_tokens =", "gvector = np.zeros(self.word2gender_dim) for token in mention.tokens: gvector += self.get_token_gender_vector(token)", "return self.dep2vec[label] if label in self.dep2vec else np.zeros(self.dep_dim) def get_mention_location_information(self,", "= utterance.parent_scene() episode = scene.parent_episode() speaker = utterance.speaker prev_utterance =", "= first_token.parent_utterance() scene = utterance.parent_scene() episode = scene.parent_episode() speaker =", "length = len(flatten_utternace_tokens) # Normalized mention word length, start token", "get_head_token(self, mention): tids = map(lambda t: t.id, mention.tokens) for token", "mention): gvector = np.zeros(self.word2gender_dim) for token in mention.tokens: gvector +=", "np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) #### Transcript document features #### def get_utterance_vector(self,", "= self.get_token_locations(flatten_utterance_tokens, mention) start_ftid, end_ftid = ft_locations[0], ft_locations[-1] token_len =", "float(start_idx)/length, float(end_index)/length]) #### Transcript document features #### def get_utterance_vector(self, utterance):", "embeddings.append(self.get_token_word_vector(first_token)) # Last word of the mention embeddings.append(self.get_token_word_vector(last_token)) # Avg", "else uvector def get_scene_vector(self, scene): svector = np.zeros(self.word2vec_dim) for utterance", "get_token_locations(self, flatten_tokens, mention): locations = [] for idx, token in", "get_tokens_gender_vector(self, mention): gvector = np.zeros(self.word2gender_dim) for token in mention.tokens: gvector", "offset): tvector = np.zeros(self.word2vec_dim) if offset > 0: for tid", "words in current utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg of all words", "Ner tag information of head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label", "-5)) # Avg of the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5))", "words in the scene embeddings.append(self.get_scene_vector(scene)) # Avg of all words", "features #### def get_utterance_vector(self, utterance): tcount = 0 uvector =", "self.word2vec[word] tcount += len(u) return uvector / float(tcount) if tcount", "token length/location information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return np.array(embeddings),", "def get_episode_vector(self, episode): evector = np.zeros(self.word2vec_dim) for scene in episode.scenes:", "np.random.rand(dep_dim) self.ner_dim = ner_dim self.ner2vec = dict() for ner in", "return self.ner2vec[tag] if tag in self.ner2vec else np.zeros(self.ner_dim) def get_dep_label_vector(self,", "token in the mention features.append(self.get_token_gender_vector(head_token)) # Avg gender information of", "mention.tokens: if token.dep_head is not None and token.dep_head.id not in", "the mention features.append(self.get_token_gender_vector(head_token)) # Avg gender information of all tokens", "locations def get_mention_sentence_tokens(self, utterance, mention): token = mention.tokens[0] for statement", "class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY = np.zeros(empty_embd_shape) if", "mention embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return np.array(embeddings), np.concatenate(features) ###### Helper", "tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <= 0 else np.zeros(self.word2vec_dim)", "mention features.append(self.get_token_gender_vector(head_token)) # Avg gender information of all tokens in", "for idx, token in enumerate(flatten_tokens): if token in mention.tokens: locations.append(idx)", "mention.tokens) for token in mention.tokens: if token.dep_head is not None", "words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1))", "get_token_word_vector(self, token): word_form = token.word_form.lower() return self.word2vec[word_form] if word_form in", "entity, include_average=True, nb_mentions=5, selection_method='last'): embedding, feature = ([], []) if", "utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos tag information of head token features.append(self.get_pos_tag_vector(head_token.pos_tag))", "None self.f_EMPTY = np.zeros(empty_feat_shape) if empty_feat_shape else None def extract(self,", "in statement: return statement return None ###### Mention tokens features", "in self.spk2vec else np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag): return self.pos2vec[tag] if", "= dict() for pos in poss: self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim", "return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None, empty_feat_shape=None): self.e_EMPTY =", "embeddings.append(self.get_utterance_vector(utterance)) # Avg of all words in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance))", "not None and token.dep_head.id not in tids: return token return", "ner_dim=8): self.word2vec = word2vec self.word2vec_dim = len(word2vec.values()[0]) self.word2gender = word2gender", "def get_scene_vector(self, scene): svector = np.zeros(self.word2vec_dim) for utterance in scene.utterances:", "utterance.statements: for t in u: word = t.word_form.lower() if word", "episode.scenes: evector += self.get_scene_vector(scene) return evector / float(len(episode.scenes)) if episode.scenes", "word length, start token location, end token location return np.array([float(end_index-start_idx)/length,", "if selection_method is 'last': mentions = entity[-nb_mentions:] embedding += map(lambda", "= scene.parent_episode() speaker = utterance.speaker prev_utterance = utterance.previous_utterance() prev_speaker =", "in poss: self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim = dep_dim self.dep2vec =", "return token return mention.tokens[0] def flatten_utterance(self, utterance): return [st for", "get_speaker_vector(self, speaker): return self.spk2vec[speaker] if speaker in self.spk2vec else np.zeros(self.spk_dim)", "word_form = token.word_form.lower() return self.word2vec[word_form] if word_form in self.word2vec else", "embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self,", "deps, ners, spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec = word2vec self.word2vec_dim", "token in mention.tokens: gvector += self.get_token_gender_vector(token) return gvector / float(len(mention.tokens))", "@abstractmethod def extract(self, object): return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self,", "episode = scene.parent_episode() speaker = utterance.speaker prev_utterance = utterance.previous_utterance() prev_speaker", "current utterance embeddings.append(self.get_utterance_vector(utterance)) # Avg of all words in previous", "/ float(tcount) if tcount > 0 else uvector def get_scene_vector(self,", "word2gender, spks, poss, deps, ners, spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec", "flatten_tokens, start, offset): tvector = np.zeros(self.word2vec_dim) if offset > 0:", "tid in xrange(start, start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid", "token in statement: return statement return None ###### Mention tokens", "information of the utterance features.append(self.get_speaker_vector(prev_speaker)) # Pos tag information of", "ft_locations[0], ft_locations[-1] token_len = end_ftid - start_ftid embeddings = list()", "float(offset) def get_token_gender_vector(self, token): word_form = token.word_form.lower() return self.word2gender[word_form] if", "[] for idx, token in enumerate(flatten_tokens): if token in mention.tokens:", "get_episode_vector(self, episode): evector = np.zeros(self.word2vec_dim) for scene in episode.scenes: evector", "locations = [] for idx, token in enumerate(flatten_tokens): if token", "tvector += self.get_token_word_vector(token) return tvector / float(len(mention.tokens)) def get_tokens_word_vector_wOffset(self, flatten_tokens,", "entity and include_average: nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding =", "features.append(self.get_token_gender_vector(head_token)) # Avg gender information of all tokens in the", "features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label information of head token'parent features.append(np.zeros(self.dep_dim) if", "def flatten_utterance(self, utterance): return [st for statements in utterance.statements for", "Two preceding words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "if tag in self.pos2vec else np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag): return", "the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) # Avg of the", "return locations def get_mention_sentence_tokens(self, utterance, mention): token = mention.tokens[0] for", "dict() for dep in deps: self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim =", "information of head token in the mention features.append(self.get_token_gender_vector(head_token)) # Avg", "token in mention.tokens: locations.append(idx) locations.sort() return locations def get_mention_sentence_tokens(self, utterance,", "the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) # Avg", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) # Avg of the -5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "# Avg of all words in the scene embeddings.append(self.get_scene_vector(scene)) #", "in scene.utterances: svector += self.get_utterance_vector(utterance) return svector / float(len(scene.utterances)) if", "= word2vec self.word2vec_dim = len(word2vec.values()[0]) self.word2gender = word2gender self.word2gender_dim =", "u: word = t.word_form.lower() if word in self.word2vec: uvector =", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) # Avg of all words in the", "for tid in xrange(start, start-offset, -1): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\", "mentions) for i in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature)", "feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec,", "all words in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of all", "= np.zeros(self.word2vec_dim) if offset > 0: for tid in xrange(start,", "end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) # Avg of the +-1", "if empty_feat_shape else None def extract(self, entity, include_average=True, nb_mentions=5, selection_method='last'):", "# Dep label information of head token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head", "= t.word_form.lower() if word in self.word2vec: uvector = uvector +", "of the mention embeddings.append(self.get_token_word_vector(first_token)) # Last word of the mention", "words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) # Avg of the +5 words", "+= map(lambda m: m.feature, mentions) for i in xrange(nb_padding): embedding.append(self.e_EMPTY)", "speaker in self.spk2vec else np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag): return self.pos2vec[tag]", "word embeddings.append(self.get_token_word_vector(head_token)) # First word of the mention embeddings.append(self.get_token_word_vector(first_token)) #", "= len(word2vec.values()[0]) self.word2gender = word2gender self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim =", "tids: return token return mention.tokens[0] def flatten_utterance(self, utterance): return [st", "# Avg gender information of all tokens in the mention", "start_ftid-1, token_len+2)) # Avg of the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2,", "empty_embd_shape else None self.f_EMPTY = np.zeros(empty_feat_shape) if empty_feat_shape else None", "start_ftid, end_ftid = ft_locations[0], ft_locations[-1] token_len = end_ftid - start_ftid", "= ft_locations[0], ft_locations[-1] token_len = end_ftid - start_ftid embeddings =", "# Two following words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1))", "np.array(embeddings), np.concatenate(features) ###### Helper functions ####### def get_head_token(self, mention): tids", "self.get_token_word_vector(flatten_tokens[tid]) \\ if tid < len(flatten_tokens) else np.zeros(self.word2vec_dim) else: for", "nb_padding if selection_method is 'last': mentions = entity[-nb_mentions:] embedding +=", "evector = np.zeros(self.word2vec_dim) for scene in episode.scenes: evector += self.get_scene_vector(scene)", "word2vec, word2gender, spks, poss, deps, ners, spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8):", "scene.utterances else svector def get_episode_vector(self, episode): evector = np.zeros(self.word2vec_dim) for", "of all words in the scene embeddings.append(self.get_scene_vector(scene)) # Avg of", "length/location information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid)) return np.array(embeddings), np.concatenate(features)", "end_ftid = ft_locations[0], ft_locations[-1] token_len = end_ftid - start_ftid embeddings", "self.pos2vec = dict() for pos in poss: self.pos2vec[pos] = np.random.rand(pos_dim)", "for token in mention.tokens: gvector += self.get_token_gender_vector(token) return gvector /", "head word embeddings.append(self.get_token_word_vector(head_token)) # First word of the mention embeddings.append(self.get_token_word_vector(first_token))", "return np.array(embedding), np.array(feature) ########################################################### class MentionFeatureExtractor(AbstractFeatureExtractor): def __init__(self, word2vec, word2gender,", "embeddings = list() # Word embeddings of the head word", "# Avg of all words in the episode embeddings.append(self.get_episode_vector(episode)) features", "token): word_form = token.word_form.lower() return self.word2gender[word_form] if word_form in self.word2gender", "tcount = 0 uvector = np.zeros(self.word2vec_dim) if utterance is not", "token_len+2)) # Avg of the +-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4))", "mention) start_ftid, end_ftid = ft_locations[0], ft_locations[-1] token_len = end_ftid -", "in self.ner2vec else np.zeros(self.ner_dim) def get_dep_label_vector(self, label): return self.dep2vec[label] if", "0 else uvector def get_scene_vector(self, scene): svector = np.zeros(self.word2vec_dim) for", "> 0: for tid in xrange(start, start+offset): tvector += self.get_token_word_vector(flatten_tokens[tid])", "get_tokens_word_vector(self, mention): tvector = np.zeros(self.word2vec_dim) for token in mention.tokens: tvector", "embeddings.append(self.get_token_word_vector(head_token)) # First word of the mention embeddings.append(self.get_token_word_vector(first_token)) # Last", "of the +5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 5)) # Avg of", "+= self.get_scene_vector(scene) return evector / float(len(episode.scenes)) if episode.scenes else evector", "self.spk2vec[speaker] if speaker in self.spk2vec else np.zeros(self.spk_dim) def get_pos_tag_vector(self, tag):", "self.word2vec else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention): tvector = np.zeros(self.word2vec_dim) for", "information of head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label information of", "Avg of all words in the episode embeddings.append(self.get_episode_vector(episode)) features =", "token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label information of head token'parent features.append(np.zeros(self.dep_dim)", "flatten_utternace_tokens, start_idx, end_index): length = len(flatten_utternace_tokens) # Normalized mention word", "features.append(self.get_tokens_gender_vector(mention)) # Current speaker information of the utterance features.append(self.get_speaker_vector(speaker)) #", "for i in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return np.array(embedding), np.array(feature) ###########################################################", "np.zeros(self.word2vec_dim) else: for tid in xrange(start, start-offset, -1): tvector +=", "poss, deps, ners, spk_dim=8, pos_dim=8, dep_dim=8, ner_dim=8): self.word2vec = word2vec", "information of all tokens in the mention features.append(self.get_tokens_gender_vector(mention)) # Current", "features = list() # Gender information of head token in", "+-2 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, token_len+4)) # Avg of the -5", "first_token, last_token = mention.tokens[0], mention.tokens[-1] utterance = first_token.parent_utterance() scene =", "m: m.feature, mentions) for i in xrange(nb_padding): embedding.append(self.e_EMPTY) feature.append(self.f_EMPTY) return", "import * import numpy as np ########################################################### class AbstractFeatureExtractor(object): @abstractmethod", "for spk in spks: self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim = pos_dim", "word2gender self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim = spk_dim self.spk2vec = dict()", "deps: self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim = ner_dim self.ner2vec = dict()", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) # Avg of the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "token.dep_head is not None and token.dep_head.id not in tids: return", "map(lambda m: m.embedding, mentions) feature += map(lambda m: m.feature, mentions)", "-1): tvector += self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <= 0 else", "flatten_utterance(self, utterance): return [st for statements in utterance.statements for st", "= word2gender self.word2gender_dim = len(word2gender.values()[0]) self.spk_dim = spk_dim self.spk2vec =", "# Two preceding words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1))", "def extract(self, mention): head_token = self.get_head_token(mention) first_token, last_token = mention.tokens[0],", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) # Avg of all words in current", "= spk_dim self.spk2vec = dict() for spk in spks: self.spk2vec[spk]", "token return mention.tokens[0] def flatten_utterance(self, utterance): return [st for statements", "nb_padding = max(0, nb_mentions - len(entity)) nb_mentions -= nb_padding if", "Avg of all words in the scene embeddings.append(self.get_scene_vector(scene)) # Avg", "features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag information of head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) #", "= self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention) ft_locations = self.get_token_locations(flatten_utterance_tokens, mention)", "mention): head_token = self.get_head_token(mention) first_token, last_token = mention.tokens[0], mention.tokens[-1] utterance", "None flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance, mention) ft_locations =", "1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) # Avg of the +-1 words", "head token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label information of head token'parent", "< len(flatten_tokens) else np.zeros(self.word2vec_dim) else: for tid in xrange(start, start-offset,", "self.word2gender else np.zeros(self.word2gender_dim) def get_tokens_gender_vector(self, mention): gvector = np.zeros(self.word2gender_dim) for", "gvector += self.get_token_gender_vector(token) return gvector / float(len(mention.tokens)) def get_speaker_vector(self, speaker):", "mention): tvector = np.zeros(self.word2vec_dim) for token in mention.tokens: tvector +=", "return svector / float(len(scene.utterances)) if scene.utterances else svector def get_episode_vector(self,", "self.word2vec: uvector = uvector + self.word2vec[word] tcount += len(u) return", "embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature()) nb_padding = max(0, nb_mentions - len(entity)) nb_mentions -=", "def get_token_gender_vector(self, token): word_form = token.word_form.lower() return self.word2gender[word_form] if word_form", "of head token features.append(self.get_ner_tag_vector(head_token.ner_tag)) # Dep label information of head", "utterance): return [st for statements in utterance.statements for st in", "word of the mention embeddings.append(self.get_token_word_vector(last_token)) # Avg of all words", "= map(lambda t: t.id, mention.tokens) for token in mention.tokens: if", "mention.tokens[0] def flatten_utterance(self, utterance): return [st for statements in utterance.statements", "of head token'parent features.append(np.zeros(self.dep_dim) if head_token.dep_head is None else self.get_dep_label_vector(head_token.dep_head.dep_label))", "len(flatten_utternace_tokens) # Normalized mention word length, start token location, end", "= mention.tokens[0], mention.tokens[-1] utterance = first_token.parent_utterance() scene = utterance.parent_scene() episode", "svector += self.get_utterance_vector(utterance) return svector / float(len(scene.utterances)) if scene.utterances else", "+= len(u) return uvector / float(tcount) if tcount > 0", "def get_head_token(self, mention): tids = map(lambda t: t.id, mention.tokens) for", "head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag information of head token", "-5 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, -5)) # Avg of the +5", "return self.word2vec[word_form] if word_form in self.word2vec else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self,", "pos in poss: self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim = dep_dim self.dep2vec", "end_ftid+1, 5)) # Avg of all words in the mention's", "return mention.tokens[0] def flatten_utterance(self, utterance): return [st for statements in", "def get_token_locations(self, flatten_tokens, mention): locations = [] for idx, token", "statement: return statement return None ###### Mention tokens features #######", "utterance, mention): token = mention.tokens[0] for statement in utterance.statements: if", "all words in the mention's sentence embeddings.append(self.get_tokens_word_vector_wOffset(flatten_sentence_tokens, 0, len(flatten_sentence_tokens))) #", "as np ########################################################### class AbstractFeatureExtractor(object): @abstractmethod def extract(self, object): return", "\\ if tid <= 0 else np.zeros(self.word2vec_dim) return tvector /", "Gender information of head token in the mention features.append(self.get_token_gender_vector(head_token)) #", "is not None: for u in utterance.statements: for t in", "self.word2vec = word2vec self.word2vec_dim = len(word2vec.values()[0]) self.word2gender = word2gender self.word2gender_dim", "for dep in deps: self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim = ner_dim", "of head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag information of head", "poss: self.pos2vec[pos] = np.random.rand(pos_dim) self.dep_dim = dep_dim self.dep2vec = dict()", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+2, 1)) # Avg of the", "def extract(self, object): return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def __init__(self, empty_embd_shape=None,", "feature += map(lambda m: m.feature, mentions) for i in xrange(nb_padding):", "get_utterance_vector(self, utterance): tcount = 0 uvector = np.zeros(self.word2vec_dim) if utterance", "selection_method='last'): embedding, feature = ([], []) if entity and include_average:", "start token location, end token location return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length])", "uvector + self.word2vec[word] tcount += len(u) return uvector / float(tcount)", "start_ftid-2, 1)) # Two following words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens,", "include_average=True, nb_mentions=5, selection_method='last'): embedding, feature = ([], []) if entity", "utterance): tcount = 0 uvector = np.zeros(self.word2vec_dim) if utterance is", "if tag in self.ner2vec else np.zeros(self.ner_dim) def get_dep_label_vector(self, label): return", "utterance is not None: for u in utterance.statements: for t", "self.ner2vec = dict() for ner in ners: self.ner2vec[ner] = np.random.rand(ner_dim)", "mentions) feature += map(lambda m: m.feature, mentions) for i in", "AbstractFeatureExtractor(object): @abstractmethod def extract(self, object): return ########################################################### class EntityFeatureExtractor(AbstractFeatureExtractor): def", "self.get_head_token(mention) first_token, last_token = mention.tokens[0], mention.tokens[-1] utterance = first_token.parent_utterance() scene", "in spks: self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim = pos_dim self.pos2vec =", "nb_mentions=5, selection_method='last'): embedding, feature = ([], []) if entity and", "self.ner2vec[ner] = np.random.rand(ner_dim) def extract(self, mention): head_token = self.get_head_token(mention) first_token,", "Dep label information of head token features.append(self.get_dep_label_vector(head_token.dep_label)) # Dep label", "t: t.id, mention.tokens) for token in mention.tokens: if token.dep_head is", "self.ner2vec else np.zeros(self.ner_dim) def get_dep_label_vector(self, label): return self.dep2vec[label] if label", "else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token length/location information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens,", "get_mention_sentence_tokens(self, utterance, mention): token = mention.tokens[0] for statement in utterance.statements:", "= ner_dim self.ner2vec = dict() for ner in ners: self.ner2vec[ner]", "mention.tokens[-1] utterance = first_token.parent_utterance() scene = utterance.parent_scene() episode = scene.parent_episode()", "= token.word_form.lower() return self.word2vec[word_form] if word_form in self.word2vec else np.zeros(self.word2vec_dim)", "locations.sort() return locations def get_mention_sentence_tokens(self, utterance, mention): token = mention.tokens[0]", "- start_ftid embeddings = list() # Word embeddings of the", "idx, token in enumerate(flatten_tokens): if token in mention.tokens: locations.append(idx) locations.sort()", "t.word_form.lower() if word in self.word2vec: uvector = uvector + self.word2vec[word]", "svector / float(len(scene.utterances)) if scene.utterances else svector def get_episode_vector(self, episode):", "of all tokens in the mention features.append(self.get_tokens_gender_vector(mention)) # Current speaker", "get_ner_tag_vector(self, tag): return self.ner2vec[tag] if tag in self.ner2vec else np.zeros(self.ner_dim)", "preceding words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, 1)) embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2,", "[]) if entity and include_average: nb_mentions -= 1 embedding.append(entity.get_avg_mention_embedding()) feature.append(entity.get_avg_mention_feature())", "Current speaker information of the utterance features.append(self.get_speaker_vector(speaker)) # Previous speaker", "= dict() for spk in spks: self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim", "def get_pos_tag_vector(self, tag): return self.pos2vec[tag] if tag in self.pos2vec else", "nb_mentions -= nb_padding if selection_method is 'last': mentions = entity[-nb_mentions:]", "self.dep2vec[label] if label in self.dep2vec else np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens,", "words in the episode embeddings.append(self.get_episode_vector(episode)) features = list() # Gender", "token location return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) #### Transcript document features", "for scene in episode.scenes: evector += self.get_scene_vector(scene) return evector /", "np.zeros(self.dep_dim) def get_mention_location_information(self, flatten_utternace_tokens, start_idx, end_index): length = len(flatten_utternace_tokens) #", "of the head word embeddings.append(self.get_token_word_vector(head_token)) # First word of the", "tokens in the mention features.append(self.get_tokens_gender_vector(mention)) # Current speaker information of", "scene.parent_episode() speaker = utterance.speaker prev_utterance = utterance.previous_utterance() prev_speaker = prev_utterance.speaker", "mention features.append(self.get_tokens_gender_vector(mention)) # Current speaker information of the utterance features.append(self.get_speaker_vector(speaker))", "in self.word2vec: uvector = uvector + self.word2vec[word] tcount += len(u)", "embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-2, 1)) # Two following words of the mention", "[st for statements in utterance.statements for st in statements] def", "enumerate(flatten_tokens): if token in mention.tokens: locations.append(idx) locations.sort() return locations def", "words in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of all words", "in the episode embeddings.append(self.get_episode_vector(episode)) features = list() # Gender information", "self.e_EMPTY = np.zeros(empty_embd_shape) if empty_embd_shape else None self.f_EMPTY = np.zeros(empty_feat_shape)", "self.pos2vec[tag] if tag in self.pos2vec else np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag):", "> 0 else uvector def get_scene_vector(self, scene): svector = np.zeros(self.word2vec_dim)", "dep in deps: self.dep2vec[dep] = np.random.rand(dep_dim) self.ner_dim = ner_dim self.ner2vec", "word in self.word2vec: uvector = uvector + self.word2vec[word] tcount +=", "= dict() for ner in ners: self.ner2vec[ner] = np.random.rand(ner_dim) def", "episode embeddings.append(self.get_episode_vector(episode)) features = list() # Gender information of head", "not None else None flatten_utterance_tokens = self.flatten_utterance(utterance) flatten_sentence_tokens = self.get_mention_sentence_tokens(utterance,", "dep_dim self.dep2vec = dict() for dep in deps: self.dep2vec[dep] =", "features.append(np.zeros(self.dep_dim) if head_token.dep_head is None else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token", "self.dep_dim = dep_dim self.dep2vec = dict() for dep in deps:", "self.get_token_word_vector(flatten_tokens[tid]) \\ if tid <= 0 else np.zeros(self.word2vec_dim) return tvector", "is None else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token length/location information within", "embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1,", "token = mention.tokens[0] for statement in utterance.statements: if token in", "statement in utterance.statements: if token in statement: return statement return", "tag information of head token features.append(self.get_pos_tag_vector(head_token.pos_tag)) # Ner tag information", "None ###### Mention tokens features ####### def get_token_word_vector(self, token): word_form", "# Avg of all words in the mention embeddings.append(self.get_tokens_word_vector(mention)) #", "svector def get_episode_vector(self, episode): evector = np.zeros(self.word2vec_dim) for scene in", "= np.random.rand(ner_dim) def extract(self, mention): head_token = self.get_head_token(mention) first_token, last_token", "if token.dep_head is not None and token.dep_head.id not in tids:", "+-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) # Avg of the +-2", "spk in spks: self.spk2vec[spk] = np.random.rand(spk_dim) self.pos_dim = pos_dim self.pos2vec", "# Mention token length/location information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid, end_ftid))", "in utterance.statements for st in statements] def get_token_locations(self, flatten_tokens, mention):", "utterance.parent_scene() episode = scene.parent_episode() speaker = utterance.speaker prev_utterance = utterance.previous_utterance()", "Avg of the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) # Avg", "Avg gender information of all tokens in the mention features.append(self.get_tokens_gender_vector(mention))", "self.word2vec[word_form] if word_form in self.word2vec else np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention):", "1)) # Two following words of the mention embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, end_ftid+1,", "def get_tokens_gender_vector(self, mention): gvector = np.zeros(self.word2gender_dim) for token in mention.tokens:", "tcount > 0 else uvector def get_scene_vector(self, scene): svector =", "ft_locations[-1] token_len = end_ftid - start_ftid embeddings = list() #", "for ner in ners: self.ner2vec[ner] = np.random.rand(ner_dim) def extract(self, mention):", "= list() # Gender information of head token in the", "1)) # Avg of the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2))", "np.zeros(self.word2vec_dim) for utterance in scene.utterances: svector += self.get_utterance_vector(utterance) return svector", "mention embeddings.append(self.get_token_word_vector(first_token)) # Last word of the mention embeddings.append(self.get_token_word_vector(last_token)) #", "None else self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token length/location information within utterance", "else np.zeros(self.ner_dim) def get_dep_label_vector(self, label): return self.dep2vec[label] if label in", "the mention embeddings.append(self.get_tokens_word_vector(mention)) # Two preceding words of the mention", "Avg of all words in previous utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg", "len(word2gender.values()[0]) self.spk_dim = spk_dim self.spk2vec = dict() for spk in", "np.zeros(self.word2vec_dim) def get_tokens_word_vector(self, mention): tvector = np.zeros(self.word2vec_dim) for token in", "Normalized mention word length, start token location, end token location", "start, offset): tvector = np.zeros(self.word2vec_dim) if offset > 0: for", "location return np.array([float(end_index-start_idx)/length, float(start_idx)/length, float(end_index)/length]) #### Transcript document features ####", "if tid < len(flatten_tokens) else np.zeros(self.word2vec_dim) else: for tid in", "# Word embeddings of the head word embeddings.append(self.get_token_word_vector(head_token)) # First", "utterance embeddings.append(self.get_utterance_vector(prev_utterance)) # Avg of all words in the scene", "Avg of all words in the mention embeddings.append(self.get_tokens_word_vector(mention)) # Two", "####### def get_head_token(self, mention): tids = map(lambda t: t.id, mention.tokens)", "float(end_index)/length]) #### Transcript document features #### def get_utterance_vector(self, utterance): tcount", "the +-1 words embeddings.append(self.get_tokens_word_vector_wOffset(flatten_utterance_tokens, start_ftid-1, token_len+2)) # Avg of the", "else np.zeros(self.pos_dim) def get_ner_tag_vector(self, tag): return self.ner2vec[tag] if tag in", "word = t.word_form.lower() if word in self.word2vec: uvector = uvector", "embedding, feature = ([], []) if entity and include_average: nb_mentions", "of all words in the episode embeddings.append(self.get_episode_vector(episode)) features = list()", "self.get_dep_label_vector(head_token.dep_head.dep_label)) # Mention token length/location information within utterance features.append(self.get_mention_location_information(flatten_utterance_tokens, start_ftid,", "if word in self.word2vec: uvector = uvector + self.word2vec[word] tcount" ]
[ "from django.db import models class UserRestrictedQuerySet(models.QuerySet): \"\"\" Query-set base class", "based on the user accessing them. \"\"\" def for_user(self, user):", "for_user(self, user): \"\"\" Filters the query-set to those instances that", "for models which apply per-instance permissions based on the user", "class UserRestrictedQuerySet(models.QuerySet): \"\"\" Query-set base class for models which apply", "instances that the given user is allowed to access. :param", "\"\"\" def for_user(self, user): \"\"\" Filters the query-set to those", "apply per-instance permissions based on the user accessing them. \"\"\"", "Filters the query-set to those instances that the given user", "to access. :param user: The user. :return: The filtered query-set.", ":param user: The user. :return: The filtered query-set. \"\"\" raise", "query-set to those instances that the given user is allowed", "the user accessing them. \"\"\" def for_user(self, user): \"\"\" Filters", "user is allowed to access. :param user: The user. :return:", "Query-set base class for models which apply per-instance permissions based", "access. :param user: The user. :return: The filtered query-set. \"\"\"", "allowed to access. :param user: The user. :return: The filtered", "permissions based on the user accessing them. \"\"\" def for_user(self,", "per-instance permissions based on the user accessing them. \"\"\" def", "user: The user. :return: The filtered query-set. \"\"\" raise NotImplementedError(UserRestrictedQuerySet.for_user.__qualname__)", "class for models which apply per-instance permissions based on the", "\"\"\" Filters the query-set to those instances that the given", "those instances that the given user is allowed to access.", "on the user accessing them. \"\"\" def for_user(self, user): \"\"\"", "user accessing them. \"\"\" def for_user(self, user): \"\"\" Filters the", "user): \"\"\" Filters the query-set to those instances that the", "which apply per-instance permissions based on the user accessing them.", "models which apply per-instance permissions based on the user accessing", "the query-set to those instances that the given user is", "UserRestrictedQuerySet(models.QuerySet): \"\"\" Query-set base class for models which apply per-instance", "import models class UserRestrictedQuerySet(models.QuerySet): \"\"\" Query-set base class for models", "\"\"\" Query-set base class for models which apply per-instance permissions", "is allowed to access. :param user: The user. :return: The", "accessing them. \"\"\" def for_user(self, user): \"\"\" Filters the query-set", "def for_user(self, user): \"\"\" Filters the query-set to those instances", "the given user is allowed to access. :param user: The", "django.db import models class UserRestrictedQuerySet(models.QuerySet): \"\"\" Query-set base class for", "to those instances that the given user is allowed to", "that the given user is allowed to access. :param user:", "given user is allowed to access. :param user: The user.", "models class UserRestrictedQuerySet(models.QuerySet): \"\"\" Query-set base class for models which", "them. \"\"\" def for_user(self, user): \"\"\" Filters the query-set to", "base class for models which apply per-instance permissions based on" ]
[ "str, args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] = None): \"\"\" Information about", "\"\"\" opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] =", "pulumi.Input[str]]]]: \"\"\" Tags of the resource. \"\"\" return pulumi.get(self, \"tags\")", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]:", "def visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state of the", "partner registration. \"\"\" return pulumi.get(self, \"visibility_state\") @visibility_state.setter def visibility_state(self, value:", "long_description) if partner_customer_service_extension is not None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if", "partner_customer_service_number is not None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if partner_name is", "\"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value) @property", "partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description", "@property @pulumi.getter(name=\"longDescription\") def long_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Long description for", "tags is not None: pulumi.set(__self__, \"tags\", tags) if visibility_state is", "Display name of the partner resource type. :param pulumi.Input[str] partner_resource_type_name:", "None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] = None, location:", "pulumi.Output[Optional[str]]: \"\"\" URI of the partner website that can be", "256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self, value:", "None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, setup_uri:", "pulumi.Output[str]: \"\"\" Type of the resource. \"\"\" return pulumi.get(self, \"type\")", "options to be a ResourceOptions instance') if opts.version is None:", "raise TypeError(\"Missing required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"] =", "raise TypeError('Expected resource options to be a ResourceOptions instance') if", "\"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]:", "__all__ = ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs: def __init__(__self__, *,", "URI of the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\")", "not exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def", "= tags __props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"] =", "\"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value) @property", "Optional[pulumi.Input[str]]: \"\"\" URI of the partner website that can be", "pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system metadata relating to Partner Registration resource.", "publisher. :param pulumi.Input[str] location: Location of the resource. :param pulumi.Input[str]", "is always permitted under the same Azure subscription as the", "The extension of the customer service number of the publisher.", "+966 7 5115 2471. Examples of invalid phone numbers are:", "def logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\") def", "def provisioning_state(self) -> pulumi.Output[str]: \"\"\" Provisioning state of the partner", "opts: Optional[pulumi.ResourceOptions] = None) -> 'PartnerRegistration': \"\"\" Get an existing", "length of this description should not exceed 256 characters. :param", "resource_group_name: The name of the resource group within the user's", "partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self)", "@property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the", "def long_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Long description for the custom", "-> Optional[pulumi.Input[str]]: \"\"\" The extension of the customer service number", "= None, partner_registration_name: Optional[pulumi.Input[str]] = None, partner_resource_type_description: Optional[pulumi.Input[str]] = None,", "relating to Partner Registration resource. \"\"\" return pulumi.get(self, \"system_data\") @property", "2048 characters. :param pulumi.Input[str] partner_customer_service_extension: The extension of the customer", "this description should not exceed 256 characters. \"\"\" return pulumi.get(self,", "pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Long", "return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\",", "\"\"\" return pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]:", "resource options to be a ResourceOptions instance') if opts.version is", "an existing resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"]", "Name of the resource. \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\")", "pulumi.set(__self__, \"setup_uri\", setup_uri) if tags is not None: pulumi.set(__self__, \"tags\",", "the resulting resource. :param pulumi.Input[str] id: The unique provider ID", "\"\"\" Short description of the partner resource type. The length", "@pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Name of the partner", "None __props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"] = None", "\"\"\" The extension of the customer service number of the", ":param pulumi.Input[str] setup_uri: URI of the partner website that can", "partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Name of the partner resource type.", "@pulumi.input_type class PartnerRegistrationArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]", "the partner registration. \"\"\" return pulumi.get(self, \"visibility_state\") @visibility_state.setter def visibility_state(self,", "\"customer_service_uri\") @property @pulumi.getter def location(self) -> pulumi.Output[str]: \"\"\" Location of", "not exceed 10. :param pulumi.Input[str] partner_customer_service_number: The customer service number", "value) @property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of", "None): \"\"\" Information about a partner registration. API Version: 2020-04-01-preview.", "resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self)", "1234 43 :param pulumi.Input[str] partner_name: Official name of the partner", "-> pulumi.Output[str]: \"\"\" Name of the resource. \"\"\" return pulumi.get(self,", "__props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"]", "are: +1 515 123 4567 and +966 7 5115 2471.", "resource group within the user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List", "name of the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\")", "value) @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of", "should start with a '+' sign followed by the country", "type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]):", "def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\") def", "TypeError(\"Missing required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri", "logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\") def long_description(self)", "this partner registration. This is an optional property. Creating partner", "pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] = None, location:", "the publisher. Only digits are allowed and number of digits", ":param pulumi.Input[str] customer_service_uri: The extension of the customer service URI", "@pulumi.getter def type(self) -> pulumi.Output[str]: \"\"\" Type of the resource.", "partner_resource_type_description is not None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name is", "value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) ->", "partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name", "= None __props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"] =", "partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if resource_group_name is", "partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the partner resource type.", "the customer service URI of the publisher. \"\"\" return pulumi.get(self,", "'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__, resource_name:", "pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\"", "to use to populate this resource's properties. :param pulumi.ResourceOptions opts:", "not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri is not None:", "pulumi.get(self, \"logo_uri\") @logo_uri.setter def logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value)", "integration to be displayed in the portal if needed. Length", "-> pulumi.Output[Optional[str]]: \"\"\" Display name of the partner resource type.", "type. :param pulumi.Input[str] partner_resource_type_name: Name of the partner resource type.", "Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]:", "\"\"\" return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "@resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\")", "-> pulumi.Output[Optional[str]]: \"\"\" Long description for the custom scenarios and", "\"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\" The", "remaining digits are then followed. Only digits and spaces are", "__props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"]", "Pulumi SDK Generator. *** # *** Do not edit by", "the resource group within the user's subscription. \"\"\" return pulumi.get(self,", "number of digits should not exceed 10. :param pulumi.Input[str] partner_customer_service_number:", "pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\" Visibility", "of the publisher. The expected phone format should start with", "args: The arguments to use to populate this resource's properties.", "overload from .. import _utilities from . import outputs from", "for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription", "return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\"", "of arguments for constructing a PartnerRegistration resource. :param pulumi.Input[str] resource_group_name:", "_utilities.get_version() if opts.id is None: if __props__ is not None:", "Azure subscription Ids that are authorized to create a partner", "visibility_state: Visibility state of the partner registration. \"\"\" pulumi.set(__self__, \"resource_group_name\",", "Name of the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\")", "required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"]", "4567 and +966 121 5115 24 7 551 1234 43", "__props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"]", "= None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] = None,", "__props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"]", "of the customer service URI of the publisher. \"\"\" return", "Ids that are authorized to create a partner namespace associated", "pulumi.set(__self__, \"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) -> pulumi.Input[str]: \"\"\"", "resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] = None,", "exceed 10. :param pulumi.Input[str] partner_customer_service_number: The customer service number of", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) ->", "that can be used by Azure customers to setup Event", "Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None): \"\"\"", "registration. \"\"\" ... @overload def __init__(__self__, resource_name: str, args: PartnerRegistrationArgs,", "*** import warnings import pulumi import pulumi.runtime from typing import", "1 515 123 4567 and +966 121 5115 24 7", "@property @pulumi.getter(name=\"longDescription\") def long_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Long description for", "partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name", "logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the logo. \"\"\" return", "'PartnerRegistrationVisibilityState']]] = None, __props__=None): if opts is None: opts =", "Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] =", "@property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\" The customer service", "\"partner_customer_service_number\", partner_customer_service_number) if partner_name is not None: pulumi.set(__self__, \"partner_name\", partner_name)", "should not exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @long_description.setter", "is None: if __props__ is not None: raise TypeError('__props__ is", "Optional[pulumi.Input[str]] = None, partner_customer_service_extension: Optional[pulumi.Input[str]] = None, partner_customer_service_number: Optional[pulumi.Input[str]] =", "__props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"]", "of the customer service URI of the publisher. :param pulumi.Input[str]", "resulting resource. :param pulumi.Input[str] id: The unique provider ID of", "the user's subscription. :param pulumi.Input[str] setup_uri: URI of the partner", "None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): if opts is", "+966 121 5115 24 7 551 1234 43 \"\"\" return", "@customer_service_uri.setter def customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value) @property @pulumi.getter", "Get an existing PartnerRegistration resource's state with the given name,", "-> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system metadata relating to Partner Registration", "pulumi.Output[str]: \"\"\" Name of the resource. \"\"\" return pulumi.get(self, \"name\")", "of the partner registration. \"\"\" ... @overload def __init__(__self__, resource_name:", "if long_description is not None: pulumi.set(__self__, \"long_description\", long_description) if partner_customer_service_extension", "resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value:", "provisioning_state(self) -> pulumi.Output[str]: \"\"\" Provisioning state of the partner registration.", "resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,", "and its length cannot exceed 16 digits including country code.", "needed. Length of this description should not exceed 2048 characters.", "example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self)", "an event source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags of the", "PartnerRegistrationArgs args: The arguments to use to populate this resource's", "pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\" The", "used for creating the partner registration. :param pulumi.Input[str] customer_service_uri: The", "valid when passed in combination with a valid opts.id to", "on an event source. \"\"\" return pulumi.get(self, \"setup_uri\") @setup_uri.setter def", "\"\"\" return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "number of the publisher. The expected phone format should start", "@property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the", "example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @partner_name.setter def partner_name(self, value:", "(515) 123-4567, 1 515 123 4567 and +966 121 5115", "def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]", "partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self,", "be a ResourceOptions instance') if opts.version is None: opts.version =", "__props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"]", "*** WARNING: this file was generated by the Pulumi SDK", "str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs)", "resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource.", "pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Short", "def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def", "partner_name: Official name of the partner name. For example: \"Contoso\".", "example: \"Contoso\". :param pulumi.Input[str] partner_registration_name: Name of the partner registration.", "None __props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"] = None", "pulumi.Input[str] resource_group_name: The name of the resource group within the", "opts: Optional[pulumi.ResourceOptions] = None): \"\"\" Information about a partner registration.", "None return PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) ->", "@property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of", "the partner registration. \"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids is", "is not None: pulumi.set(__self__, \"long_description\", long_description) if partner_customer_service_extension is not", "return pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\"", "name, id, and optional extra properties used to qualify the", "partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\" The customer service number of the", "registration. :param pulumi.Input[str] customer_service_uri: The extension of the customer service", "resource type. The length of this description should not exceed", "pulumi.Input[str] partner_resource_type_display_name: Display name of the partner resource type. :param", "instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id", "pulumi.set(self, \"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\"", "for constructing a PartnerRegistration resource. :param pulumi.Input[str] resource_group_name: The name", "arguments to use to populate this resource's properties. :param pulumi.ResourceOptions", "pulumi.Input[str] partner_resource_type_description: Short description of the partner resource type. The", "@property @pulumi.getter def location(self) -> pulumi.Output[str]: \"\"\" Location of the", "custom scenarios and integration to be displayed in the portal", "of this description should not exceed 256 characters. :param pulumi.Input[str]", "partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Display name of the partner resource", "resource type. :param pulumi.Input[str] setup_uri: URI of the partner website", "\"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "not exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\")", "= pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"]", "PartnerRegistrationArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,", "Name of the partner registration. :param pulumi.Input[str] partner_resource_type_description: Short description", "Optional[pulumi.Input[str]] = None, partner_name: Optional[pulumi.Input[str]] = None, partner_registration_name: Optional[pulumi.Input[str]] =", "pulumi.set(__self__, \"logo_uri\", logo_uri) if long_description is not None: pulumi.set(__self__, \"long_description\",", "@pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Display name of the", "pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value)", "value) @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Long description", "subscription Ids that are authorized to create a partner namespace", "pulumi.set(self, \"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\"", "visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None): \"\"\" The set of arguments", "\"\"\" return pulumi.get(self, \"visibility_state\") @visibility_state.setter def visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]):", "None __props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"] = None", "\"\"\" Location of the resource. \"\"\" return pulumi.get(self, \"location\") @property", "= pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts) @staticmethod", "pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags of the resource. \"\"\" return pulumi.get(self,", "pulumi.Input[str] setup_uri: URI of the partner website that can be", "\"\"\" return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self, value: Optional[pulumi.Input[str]]):", "def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\" The customer service number of", "resource_group_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str,", "= None __props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"] =", "Grid integration on an event source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags:", "Optional[pulumi.Input[str]] = None, long_description: Optional[pulumi.Input[str]] = None, partner_customer_service_extension: Optional[pulumi.Input[str]] =", "def type(self) -> pulumi.Output[str]: \"\"\" Type of the resource. \"\"\"", "setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the partner website that", "pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"] =", "the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self,", "you're certain you know what you are doing! *** import", "not None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name is not None:", "coding=utf-8 # *** WARNING: this file was generated by the", "@property @pulumi.getter def type(self) -> pulumi.Output[str]: \"\"\" Type of the", "= None, partner_name: Optional[pulumi.Input[str]] = None, partner_registration_name: Optional[pulumi.Input[str]] = None,", "def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def", "file was generated by the Pulumi SDK Generator. *** #", "URI of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def", "__props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"]", "def setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the partner website", "\"\"\" Tags of the resource. \"\"\" return pulumi.get(self, \"tags\") @property", "__props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"]", "\"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Name of", "if setup_uri is not None: pulumi.set(__self__, \"setup_uri\", setup_uri) if tags", "'PartnerRegistrationVisibilityState']]] = None, __props__=None): \"\"\" Information about a partner registration.", "of the resource group within the user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]]", "@pulumi.getter def location(self) -> pulumi.Output[str]: \"\"\" Location of the resource.", "combination with a valid opts.id to get an existing resource')", "for the custom scenarios and integration to be displayed in", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value) @property @pulumi.getter def location(self) ->", "resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of the partner", "if opts.version is None: opts.version = _utilities.get_version() if opts.id is", "def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Short description of the partner", "None __props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"] = None", "None, long_description: Optional[pulumi.Input[str]] = None, partner_customer_service_extension: Optional[pulumi.Input[str]] = None, partner_customer_service_number:", "logo_uri: URI of the logo. :param pulumi.Input[str] long_description: Long description", ":param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids:", "partner_resource_type_description: Short description of the partner resource type. The length", "1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self, value:", "\"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "return pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\"", "__init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] =", "pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value)", "Event Grid integration on an event source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]]", "None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if location is not None: pulumi.set(__self__,", "return pulumi.get(self, \"long_description\") @long_description.setter def long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\",", "pulumi.set(__self__, \"location\", location) if logo_uri is not None: pulumi.set(__self__, \"logo_uri\",", "digits should not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter", "and number of digits should not exceed 10. \"\"\" return", "alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts,", "existing PartnerRegistration resource's state with the given name, id, and", "is not None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if setup_uri is not", "location: Location of the resource. :param pulumi.Input[str] logo_uri: URI of", "authorized_azure_subscription_ids: List of Azure subscription Ids that are authorized to", "by the country code. The remaining digits are then followed.", "is not None: pulumi.set(__self__, \"partner_name\", partner_name) if partner_registration_name is not", "return pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\"", "passed in combination with a valid opts.id to get an", "\"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids is not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids)", "Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value) @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> Optional[pulumi.Input[str]]:", "description should not exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\")", "the partner registration. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def", "\"\"\" Name of the resource. \"\"\" return pulumi.get(self, \"name\") @property", "= None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None,", "logo_uri __props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number", "@property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the", "tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags of the resource. \"\"\"", "__props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"),", "should not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def", "Registration resource. \"\"\" return pulumi.get(self, \"system_data\") @property @pulumi.getter def tags(self)", "value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: \"\"\" Location of", "\"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value) @property", "def customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of the customer", "pulumi.get(self, \"visibility_state\") @visibility_state.setter def visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\",", "the resource. \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\") def logo_uri(self)", "None, resource_group_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None, tags:", "customer service number of the publisher. The expected phone format", "__props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if resource_group_name is None", "partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self)", "as the one used for creating the partner registration. \"\"\"", "properties used to qualify the lookup. :param str resource_name: The", "@pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags of the", "tags: Tags of the resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility", "tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None):", "Optional[pulumi.Input[str]] = None, partner_resource_type_description: Optional[pulumi.Input[str]] = None, partner_resource_type_display_name: Optional[pulumi.Input[str]] =", "None __props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"] = None", "Mapping, Optional, Sequence, Union, overload from .. import _utilities from", "with the given name, id, and optional extra properties used", "property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"] =", "partner registration. This is an optional property. Creating partner namespaces", "not None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name is not None:", "namespace associated with this partner registration. This is an optional", "pulumi.Output[Optional[str]]: \"\"\" Long description for the custom scenarios and integration", "subscription. :param pulumi.Input[str] setup_uri: URI of the partner website that", "pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value)", "to be a ResourceOptions instance') if opts.version is None: opts.version", "\"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> pulumi.Output[Optional[str]]:", "partner registration. \"\"\" return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self, value:", "state with the given name, id, and optional extra properties", "name of the resulting resource. :param pulumi.Input[str] id: The unique", "\"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List", "and optional extra properties used to qualify the lookup. :param", "@pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) -> pulumi.Input[str]: \"\"\" The name of the", "str]]]: \"\"\" Tags of the resource. \"\"\" return pulumi.get(self, \"tags\")", "= ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs: def __init__(__self__, *, resource_group_name:", "The length of this description should not exceed 256 characters.", "not exceed 2048 characters. :param pulumi.Input[str] partner_customer_service_extension: The extension of", "of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self,", "partner_resource_type_name: Name of the partner resource type. :param pulumi.Input[str] resource_group_name:", "import Any, Mapping, Optional, Sequence, Union, overload from .. import", "Optional[pulumi.Input[str]] = None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] =", "None __props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"] = None return PartnerRegistration(resource_name, opts=opts,", "resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"] = visibility_state", "extension of the customer service URI of the publisher. \"\"\"", "pulumi.set(self, \"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\"", "__props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of", "= None __props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"] =", "__props__=None): \"\"\" Information about a partner registration. API Version: 2020-04-01-preview.", "should not exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @property", "None: pulumi.set(__self__, \"long_description\", long_description) if partner_customer_service_extension is not None: pulumi.set(__self__,", "if partner_resource_type_description is not None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name", "@pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of the", "return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\",", "value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value) @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self)", "__props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"]", ":param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags of the resource. :param pulumi.Input[Union[str,", "return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\",", "edit by hand unless you're certain you know what you", "\"\"\" return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "is None and not opts.urn: raise TypeError(\"Missing required property 'resource_group_name'\")", "= None, customer_service_uri: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None,", "= PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"] =", "@property @pulumi.getter def name(self) -> pulumi.Output[str]: \"\"\" Name of the", "setup_uri) if tags is not None: pulumi.set(__self__, \"tags\", tags) if", "@pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: \"\"\" Location of the resource.", "the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter def customer_service_uri(self, value:", "resource type. :param pulumi.Input[str] resource_group_name: The name of the resource", "allowed and number of digits should not exceed 10. :param", "None __props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"] = None", "\"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number is not None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number)", "setup_uri __props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"] = None", "@logo_uri.setter def logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\")", "integration on an event source. \"\"\" return pulumi.get(self, \"setup_uri\") @setup_uri.setter", ":param PartnerRegistrationArgs args: The arguments to use to populate this", "type. :param pulumi.Input[str] resource_group_name: The name of the resource group", "**kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else:", "value) @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Official name", ":param pulumi.Input[str] partner_resource_type_name: Name of the partner resource type. :param", "2020-04-01-preview. :param str resource_name: The name of the resource. :param", "partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value:", "to setup Event Grid integration on an event source. \"\"\"", "the resource group within the user's subscription. :param pulumi.Input[str] setup_uri:", "pulumi.Output[str]: \"\"\" Location of the resource. \"\"\" return pulumi.get(self, \"location\")", "-> pulumi.Output[str]: \"\"\" Location of the resource. \"\"\" return pulumi.get(self,", "an event source. \"\"\" return pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\") def", "@pulumi.getter(name=\"systemData\") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system metadata relating", "resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options", "\"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\" The", "None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name is not None: pulumi.set(__self__,", "the publisher. The expected phone format should start with a", "@partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\")", ":param pulumi.ResourceOptions opts: Options for the resource. \"\"\" opts =", "\"logo_uri\") @logo_uri.setter def logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value) @property", "return pulumi.get(self, \"location\") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\",", "\"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI", "\"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> pulumi.Output[Optional[str]]:", "@pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Short description of the", "__init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions,", "not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self,", "None __props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"] = None", "= _utilities.get_version() if opts.id is None: if __props__ is not", "None: if __props__ is not None: raise TypeError('__props__ is only", "customer_service_uri __props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"] = long_description", "@property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the", "Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> Optional[pulumi.Input[str]]:", "is an optional property. Creating partner namespaces is always permitted", "PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None,", "\"location\", value) @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI", "is not None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number is not", "@pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\" Visibility state of the", "\"\"\" List of Azure subscription Ids that are authorized to", "Name of the partner registration. \"\"\" return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter", "= None __props__.__dict__[\"type\"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"),", "= None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"] = None alias_opts =", "\"\"\" The extension of the customer service URI of the", "and +966 121 5115 24 7 551 1234 43 \"\"\"", "_internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] =", "of the resource group within the user's subscription. :param pulumi.Input[str]", "Length of this description should not exceed 2048 characters. \"\"\"", "@pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Short description of the", "Length of this description should not exceed 2048 characters. :param", "None: raise TypeError('__props__ is only valid when passed in combination", "is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs)", "resource. \"\"\" ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args,", "None __props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"] = None", "Optional[pulumi.Input[str]] = None, partner_registration_name: Optional[pulumi.Input[str]] = None, partner_resource_type_description: Optional[pulumi.Input[str]] =", "43 :param pulumi.Input[str] partner_name: Official name of the partner name.", "None: opts.version = _utilities.get_version() if opts.id is None: if __props__", ":param str resource_name: The name of the resource. :param pulumi.ResourceOptions", "None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None, tags:", "customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value) @property @pulumi.getter def location(self)", "pulumi.Input[str] location: Location of the resource. :param pulumi.Input[str] logo_uri: URI", "-> Optional[pulumi.Input[str]]: \"\"\" Official name of the partner name. For", "of the resource. :param pulumi.ResourceOptions opts: Options for the resource.", "= pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options", "service number of the publisher. The expected phone format should", "partner name. For example: \"Contoso\". :param pulumi.Input[str] partner_registration_name: Name of", "website that can be used by Azure customers to setup", "\"Contoso\". :param pulumi.Input[str] partner_registration_name: Name of the partner registration. :param", "__props__=None): if opts is None: opts = pulumi.ResourceOptions() if not", "of digits should not exceed 10. :param pulumi.Input[str] partner_customer_service_number: The", "resource group within the user's subscription. \"\"\" return pulumi.get(self, \"resource_group_name\")", "registration. :param pulumi.Input[str] partner_resource_type_description: Short description of the partner resource", "arguments for constructing a PartnerRegistration resource. :param pulumi.Input[str] resource_group_name: The", "this description should not exceed 2048 characters. \"\"\" return pulumi.get(self,", "@long_description.setter def long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\")", "\"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Official name", "to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for", "@property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of", "def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value) @property @pulumi.getter(name=\"visibilityState\")", "by Azure customers to setup Event Grid integration on an", "the resource. \"\"\" return pulumi.get(self, \"tags\") @tags.setter def tags(self, value:", "partner namespace associated with this partner registration. This is an", "if partner_registration_name is not None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if partner_resource_type_description", "-> pulumi.Output[Optional[str]]: \"\"\" The extension of the customer service URI", "'PartnerRegistration': \"\"\" Get an existing PartnerRegistration resource's state with the", "digits including country code. Examples of valid phone numbers are:", "return pulumi.get(self, \"visibility_state\") @visibility_state.setter def visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self,", "def partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Official name of the partner", "optional property. Creating partner namespaces is always permitted under the", "this file was generated by the Pulumi SDK Generator. ***", "Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): \"\"\" Information about a partner", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]:", "None: pulumi.set(__self__, \"tags\", tags) if visibility_state is not None: pulumi.set(__self__,", "pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence,", "registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):", "= None __props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"] =", "-> pulumi.Output[Optional[str]]: \"\"\" The extension of the customer service number", "__props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"] = None alias_opts", "@partner_customer_service_extension.setter def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\")", "authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of Azure subscription Ids that", "Partner Registration resource. \"\"\" return pulumi.get(self, \"system_data\") @property @pulumi.getter def", "None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if", "resource. \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) ->", "code. Examples of valid phone numbers are: +1 515 123", "to Partner Registration resource. \"\"\" return pulumi.get(self, \"system_data\") @property @pulumi.getter", "5115 2471. Examples of invalid phone numbers are: +1 (515)", "def location(self) -> Optional[pulumi.Input[str]]: \"\"\" Location of the resource. \"\"\"", "partner website that can be used by Azure customers to", "\"logo_uri\", logo_uri) if long_description is not None: pulumi.set(__self__, \"long_description\", long_description)", "if logo_uri is not None: pulumi.set(__self__, \"logo_uri\", logo_uri) if long_description", "= None, partner_customer_service_number: Optional[pulumi.Input[str]] = None, partner_name: Optional[pulumi.Input[str]] = None,", "= None __props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"] =", "return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\",", "extension of the customer service URI of the publisher. :param", "partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of the customer service", "of the partner resource type. :param pulumi.Input[str] setup_uri: URI of", "pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) -> pulumi.Output[str]: \"\"\" Type", "of the resource. :param pulumi.Input[str] logo_uri: URI of the logo.", "the customer service URI of the publisher. :param pulumi.Input[str] location:", "@pulumi.getter(name=\"setupUri\") def setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the partner", "pulumi.Input[str] partner_customer_service_number: The customer service number of the publisher. The", "if __props__ is not None: raise TypeError('__props__ is only valid", "def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags of the resource.", "pulumi.Input[str] long_description: Long description for the custom scenarios and integration", "__props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"]", "PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] = None): \"\"\" Information about a partner", "of the partner resource type. :param pulumi.Input[str] partner_resource_type_name: Name of", "allowed and its length cannot exceed 16 digits including country", "of this description should not exceed 256 characters. \"\"\" return", "\"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> pulumi.Output[Optional[str]]:", "\"setup_uri\", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\"", "515 123 4567 and +966 7 5115 2471. Examples of", "10. :param pulumi.Input[str] partner_customer_service_number: The customer service number of the", "pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Name", "resource group within the user's subscription. :param pulumi.Input[str] setup_uri: URI", "@property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Display name of", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\") def long_description(self) ->", "\"visibility_state\") @visibility_state.setter def visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value)", "\"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri is not None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri)", "not None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if partner_resource_type_description is not None:", "the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\")", "and number of digits should not exceed 10. :param pulumi.Input[str]", "Location of the resource. :param pulumi.Input[str] logo_uri: URI of the", "pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None): \"\"\" The", "**resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts:", "return pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\"", "characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) ->", "@property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags of", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> Optional[pulumi.Input[str]]:", "of this description should not exceed 2048 characters. :param pulumi.Input[str]", "pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter def location(self) -> pulumi.Output[str]: \"\"\" Location", "not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a", "@property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Official name of", "opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str,", "setup_uri: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state:", "@pulumi.getter(name=\"setupUri\") def setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the partner", "opts: Options for the resource. \"\"\" ... def __init__(__self__, resource_name:", "\"\"\" return pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> pulumi.Output[str]:", "\"\"\" return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self,", "partner_resource_type_display_name) if partner_resource_type_name is not None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if", "= partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] =", "format should start with a '+' sign followed by the", "if visibility_state is not None: pulumi.set(__self__, \"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\")", "import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union,", "a PartnerRegistration resource. :param pulumi.Input[str] resource_group_name: The name of the", "PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\"", "pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name is not None: pulumi.set(__self__, \"partner_resource_type_name\",", "digits and spaces are allowed and its length cannot exceed", "pulumi.Output[Optional[str]]: \"\"\" Display name of the partner resource type. \"\"\"", "value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of", "the given name, id, and optional extra properties used to", "extra properties used to qualify the lookup. :param str resource_name:", "the resource. \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self)", "@property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Short description of", "Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure", "pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value)", "= authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"] =", "pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value)", "= None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None,", "= resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"] =", "import outputs from ._enums import * __all__ = ['PartnerRegistrationArgs', 'PartnerRegistration']", "\"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids is not None: pulumi.set(__self__,", "pulumi.Output[Optional[str]]: \"\"\" Name of the partner resource type. \"\"\" return", "import _utilities from . import outputs from ._enums import *", ". import outputs from ._enums import * __all__ = ['PartnerRegistrationArgs',", "-> pulumi.Output[Optional[str]]: \"\"\" Name of the partner resource type. \"\"\"", "= None, setup_uri: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] =", "country code. Examples of valid phone numbers are: +1 515", "resource_name: The unique name of the resulting resource. :param pulumi.Input[str]", "only valid when passed in combination with a valid opts.id", "pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List", "warnings import pulumi import pulumi.runtime from typing import Any, Mapping,", "The expected phone format should start with a '+' sign", "from ._enums import * __all__ = ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class", "subscription. \"\"\" return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]):", "-> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags of the resource. \"\"\" return", "return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\",", "pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration',", "__props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"] = None return", "_utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name,", "Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] =", "name. For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @partner_name.setter def", "__props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if resource_group_name is None and not opts.urn:", "you know what you are doing! *** import warnings import", "name. For example: \"Contoso\". :param pulumi.Input[str] partner_registration_name: Name of the", "\"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]:", "source. \"\"\" return pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\") def system_data(self) ->", "long_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Long description for the custom scenarios", "partner_customer_service_extension is not None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number is", "\"\"\" return pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> pulumi.Output[Optional[str]]:", "123-4567, 1 515 123 4567 and +966 121 5115 24", "= partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] =", "__self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name:", "description should not exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\")", "the one used for creating the partner registration. :param pulumi.Input[str]", "__props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"]", "__props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"]", "return pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> pulumi.Output[str]: \"\"\"", "None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts =", "if partner_customer_service_extension is not None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number", "pulumi.set(self, \"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\"", "'+' sign followed by the country code. The remaining digits", "@property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Official name of", "URI of the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @logo_uri.setter def", "\"\"\" Name of the partner resource type. \"\"\" return pulumi.get(self,", "resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args,", "customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of the customer service", "pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__(", "name of the resource. :param pulumi.ResourceOptions opts: Options for the", "pulumi.Output[Optional[str]]: \"\"\" The customer service number of the publisher. The", "@property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Short description of", "pulumi.ResourceOptions opts: Options for the resource. \"\"\" ... def __init__(__self__,", "None, partner_resource_type_description: Optional[pulumi.Input[str]] = None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name:", "*** Do not edit by hand unless you're certain you", "\"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Long description", "Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] =", "of the partner registration. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\")", "@property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of Azure", "numbers are: +1 (515) 123-4567, 1 515 123 4567 and", "URI of the partner website that can be used by", "populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the", "= location __props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"] =", "pulumi.Output[Optional[str]]: \"\"\" Official name of the partner name. For example:", "def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the partner resource", "4567 and +966 7 5115 2471. Examples of invalid phone", "doing! *** import warnings import pulumi import pulumi.runtime from typing", "of the publisher. :param pulumi.Input[str] location: Location of the resource.", "Optional[pulumi.Input[str]]: \"\"\" The extension of the customer service URI of", "name. For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\")", "def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]", "-> Optional[pulumi.Input[str]]: \"\"\" Long description for the custom scenarios and", "pulumi.get(self, \"long_description\") @property @pulumi.getter def name(self) -> pulumi.Output[str]: \"\"\" Name", "@pulumi.getter(name=\"partnerName\") def partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Official name of the", "\"\"\" The set of arguments for constructing a PartnerRegistration resource.", "The set of arguments for constructing a PartnerRegistration resource. :param", "typing import Any, Mapping, Optional, Sequence, Union, overload from ..", "pulumi.Output[Optional[str]]: \"\"\" The extension of the customer service number of", "\"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" The", "visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): \"\"\" Information about a", "partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self)", "state of the partner registration. \"\"\" return pulumi.get(self, \"visibility_state\") @visibility_state.setter", "class PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] =", "of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter def", "not None: pulumi.set(__self__, \"setup_uri\", setup_uri) if tags is not None:", "'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids:", "the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def long_description(self)", "an existing PartnerRegistration resource's state with the given name, id,", "return pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\"", "Options for the resource. \"\"\" ... def __init__(__self__, resource_name: str,", "resource. \"\"\" return pulumi.get(self, \"tags\") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str,", "121 5115 24 7 551 1234 43 \"\"\" return pulumi.get(self,", "of the partner registration. \"\"\" return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def", "= None): \"\"\" Information about a partner registration. API Version:", "an event source. \"\"\" return pulumi.get(self, \"setup_uri\") @setup_uri.setter def setup_uri(self,", "partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if resource_group_name is None and not", "Type of the resource. \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\")", "= visibility_state __props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"] =", "resource_name: The name of the resource. :param PartnerRegistrationArgs args: The", "permitted under the same Azure subscription as the one used", "_utilities from . import outputs from ._enums import * __all__", "system metadata relating to Partner Registration resource. \"\"\" return pulumi.get(self,", "invalid phone numbers are: +1 (515) 123-4567, 1 515 123", "digits should not exceed 10. :param pulumi.Input[str] partner_customer_service_number: The customer", "a partner registration. API Version: 2020-04-01-preview. :param str resource_name: The", "resource. \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) ->", "lookup. :param str resource_name: The unique name of the resulting", "of the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @logo_uri.setter def logo_uri(self,", "opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]]", "str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PartnerRegistration': \"\"\"", "opts.id is None: if __props__ is not None: raise TypeError('__props__", "The unique name of the resulting resource. :param pulumi.Input[str] id:", "None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"] = None", "551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self,", "for creating the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @property", "customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of the customer service", "partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Short description of the partner resource", "@pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of the", "\"\"\" Get an existing PartnerRegistration resource's state with the given", "return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\"", "tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags of the resource. \"\"\"", "opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] =", "pulumi.get(self, \"long_description\") @long_description.setter def long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value)", "the partner resource type. The length of this description should", "__props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"]", "= None return PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self)", "@property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of", "Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state of the partner registration. \"\"\"", "can be used by Azure customers to setup Event Grid", "-> pulumi.Input[str]: \"\"\" The name of the resource group within", "if needed. Length of this description should not exceed 2048", "def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def", "return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) -> pulumi.Output[str]: \"\"\"", "= None __props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"] =", "-> Optional[pulumi.Input[str]]: \"\"\" URI of the logo. \"\"\" return pulumi.get(self,", "characters. :param pulumi.Input[str] partner_resource_type_display_name: Display name of the partner resource", "def logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the logo. \"\"\"", "qualify the lookup. :param str resource_name: The unique name of", "sign followed by the country code. The remaining digits are", "The extension of the customer service URI of the publisher.", "def partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\") def", "= None, __props__=None): \"\"\" Information about a partner registration. API", "= None __props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"] =", "including country code. Examples of valid phone numbers are: +1", "cannot exceed 16 digits including country code. Examples of valid", "partner registration. \"\"\" ... @overload def __init__(__self__, resource_name: str, args:", "24 7 551 1234 43 :param pulumi.Input[str] partner_name: Official name", "of the logo. :param pulumi.Input[str] long_description: Long description for the", ":param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of the partner registration.", "List of Azure subscription Ids that are authorized to create", "event source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags of the resource.", "pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Official", "not edit by hand unless you're certain you know what", "\"\"\" Display name of the partner resource type. \"\"\" return", "given name, id, and optional extra properties used to qualify", "pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if partner_resource_type_description is not None: pulumi.set(__self__, \"partner_resource_type_description\",", "Tags of the resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility state", "not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def", "Short description of the partner resource type. The length of", "user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription Ids", "alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts) @staticmethod def get(resource_name:", "phone numbers are: +1 515 123 4567 and +966 7", "None __props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"] = None", "name(self) -> pulumi.Output[str]: \"\"\" Name of the resource. \"\"\" return", "\"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value) @property", "return pulumi.get(self, \"logo_uri\") @logo_uri.setter def logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"logo_uri\",", "tags __props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"] = None", "return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\",", "are authorized to create a partner namespace associated with this", "used to qualify the lookup. :param str resource_name: The unique", "setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the partner website that", "= pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts)", "def long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def", "None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] =", "you are doing! *** import warnings import pulumi import pulumi.runtime", "pulumi.Output[Optional[str]]: \"\"\" The extension of the customer service URI of", "pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): if", "scenarios and integration to be displayed in the portal if", "pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value)", "is not None: pulumi.set(__self__, \"tags\", tags) if visibility_state is not", "customers to setup Event Grid integration on an event source.", "partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]]", "pulumi.Output[Optional[str]]: \"\"\" URI of the logo. \"\"\" return pulumi.get(self, \"logo_uri\")", "= None, long_description: Optional[pulumi.Input[str]] = None, partner_customer_service_extension: Optional[pulumi.Input[str]] = None,", "visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource): @overload", "the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def", "the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param", "\"tags\", tags) if visibility_state is not None: pulumi.set(__self__, \"visibility_state\", visibility_state)", "opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts)", "Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] =", "\"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Long", "of the resource. \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def", "if authorized_azure_subscription_ids is not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri", "@property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of Azure", "Information about a partner registration. API Version: 2020-04-01-preview. :param str", "For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @partner_name.setter def partner_name(self,", "\"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts:", "optional extra properties used to qualify the lookup. :param str", "resource. \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) ->", "setup Event Grid integration on an event source. :param pulumi.Input[Mapping[str,", "43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]):", "expected phone format should start with a '+' sign followed", "= None, partner_resource_type_description: Optional[pulumi.Input[str]] = None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None,", "\"\"\" return pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter def location(self) -> pulumi.Output[str]:", "Optional[pulumi.Input[str]]: \"\"\" Name of the partner resource type. \"\"\" return", "return pulumi.get(self, \"tags\") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self,", "import pulumi import pulumi.runtime from typing import Any, Mapping, Optional,", "location: Optional[pulumi.Input[str]] = None, logo_uri: Optional[pulumi.Input[str]] = None, long_description: Optional[pulumi.Input[str]]", "Sequence, Union, overload from .. import _utilities from . import", "TypeError('Expected resource options to be a ResourceOptions instance') if opts.version", "PartnerRegistration resource's state with the given name, id, and optional", "Display name of the partner resource type. \"\"\" return pulumi.get(self,", "exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def", "service URI of the publisher. :param pulumi.Input[str] location: Location of", "and integration to be displayed in the portal if needed.", "state of the partner registration. \"\"\" ... @overload def __init__(__self__,", "pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__,", ":param pulumi.Input[str] partner_resource_type_description: Short description of the partner resource type.", "@pulumi.getter(name=\"partnerName\") def partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Official name of the", "resource. \"\"\" opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"]", "of the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter", ":param pulumi.Input[str] id: The unique provider ID of the resource", "pulumi.Input[str] partner_name: Official name of the partner name. For example:", "str resource_name: The unique name of the resulting resource. :param", "\"long_description\", long_description) if partner_customer_service_extension is not None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension)", "partner name. For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @property", "the partner registration. \"\"\" ... @overload def __init__(__self__, resource_name: str,", "value) @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension", "Optional[pulumi.Input[str]]: \"\"\" Location of the resource. \"\"\" return pulumi.get(self, \"location\")", "resource_name: str, args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] = None): \"\"\" Information", "'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of the partner registration. \"\"\" pulumi.set(__self__,", "with a '+' sign followed by the country code. The", "a partner namespace associated with this partner registration. This is", "None __props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"] = None", "None, setup_uri: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,", "within the user's subscription. :param pulumi.Input[str] setup_uri: URI of the", "name of the partner name. For example: \"Contoso\". \"\"\" return", "Examples of valid phone numbers are: +1 515 123 4567", "value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags", "of the resource. \"\"\" return pulumi.get(self, \"tags\") @tags.setter def tags(self,", "@partner_registration_name.setter def partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\")", "return pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\"", "__props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"]", "def visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\" Visibility state of the partner", "\"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) ->", "pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if partner_name is not None: pulumi.set(__self__, \"partner_name\",", "= customer_service_uri __props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"] =", "def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of the customer", "are allowed and its length cannot exceed 16 digits including", "@staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None)", "partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self)", "for creating the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter", "authorized_azure_subscription_ids) if customer_service_uri is not None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if", "if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be", "pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The", "not None: pulumi.set(__self__, \"location\", location) if logo_uri is not None:", "length of this description should not exceed 256 characters. \"\"\"", "resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]]", "The name of the resource. :param pulumi.ResourceOptions opts: Options for", "the portal if needed. Length of this description should not", "= _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None:", "registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) ->", "partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self)", "+1 515 123 4567 and +966 7 5115 2471. Examples", "the resource group within the user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids:", "Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of Azure subscription Ids that are authorized", "2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @long_description.setter def long_description(self, value:", "resource. \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) ->", "partner resource type. The length of this description should not", "pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of Azure subscription Ids that are authorized", "the partner resource type. :param pulumi.Input[str] resource_group_name: The name of", "return pulumi.get(self, \"partner_name\") @partner_name.setter def partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\",", "\"\"\" URI of the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @property", "exceed 16 digits including country code. Examples of valid phone", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) ->", "@authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\")", "None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, resource_group_name:", "to qualify the lookup. :param str resource_name: The unique name", "the lookup. :param str resource_name: The unique name of the", "resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts:", "@pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Display name of the", "\"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value) @property", "value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension", "pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name is not None: pulumi.set(__self__, \"partner_resource_type_display_name\",", "phone format should start with a '+' sign followed by", "source. \"\"\" return pulumi.get(self, \"setup_uri\") @setup_uri.setter def setup_uri(self, value: Optional[pulumi.Input[str]]):", "resource. \"\"\" return pulumi.get(self, \"location\") @location.setter def location(self, value: Optional[pulumi.Input[str]]):", "@pulumi.getter(name=\"logoUri\") def logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the logo.", ":param pulumi.Input[str] partner_registration_name: Name of the partner registration. :param pulumi.Input[str]", "\"tags\") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value)", "not None: pulumi.set(__self__, \"partner_name\", partner_name) if partner_registration_name is not None:", "\"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self,", "partner resource type. :param pulumi.Input[str] resource_group_name: The name of the", "Only digits are allowed and number of digits should not", "name of the resource. :param PartnerRegistrationArgs args: The arguments to", "-> Optional[pulumi.Input[str]]: \"\"\" Location of the resource. \"\"\" return pulumi.get(self,", "long_description: Long description for the custom scenarios and integration to", "None, location: Optional[pulumi.Input[str]] = None, logo_uri: Optional[pulumi.Input[str]] = None, long_description:", "not None: pulumi.set(__self__, \"logo_uri\", logo_uri) if long_description is not None:", "resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. \"\"\"", "None: pulumi.set(__self__, \"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) -> pulumi.Input[str]:", "2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @property @pulumi.getter def name(self)", "group within the user's subscription. :param pulumi.Input[str] setup_uri: URI of", "pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"] = None", "creating the partner registration. :param pulumi.Input[str] customer_service_uri: The extension of", "raise TypeError('__props__ is only valid when passed in combination with", "authorized to create a partner namespace associated with this partner", "pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if setup_uri is not None: pulumi.set(__self__, \"setup_uri\",", "str resource_name: The name of the resource. :param PartnerRegistrationArgs args:", "should not exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @property", "then followed. Only digits and spaces are allowed and its", "@pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of Azure subscription", "pulumi.Output[Optional[str]]: \"\"\" Short description of the partner resource type. The", "exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self,", "Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str,", "@property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of", "partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]]", "Optional[pulumi.Input[str]]: \"\"\" Short description of the partner resource type. The", "characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]):", "return pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> pulumi.Output[Optional[str]]: \"\"\"", "None: pulumi.set(__self__, \"partner_name\", partner_name) if partner_registration_name is not None: pulumi.set(__self__,", "\"\"\" URI of the partner website that can be used", "description should not exceed 2048 characters. :param pulumi.Input[str] partner_customer_service_extension: The", "visibility_state: Visibility state of the partner registration. \"\"\" ... @overload", "\"\"\" Name of the partner registration. \"\"\" return pulumi.get(self, \"partner_registration_name\")", "type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) ->", "Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__,", "pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI", "Optional[pulumi.Input[str]]: \"\"\" Long description for the custom scenarios and integration", "partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Official name of the partner name.", "logo. \"\"\" return pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def long_description(self) ->", "def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\") def", "of this description should not exceed 2048 characters. \"\"\" return", "valid opts.id to get an existing resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs)", "str resource_name: The name of the resource. :param pulumi.ResourceOptions opts:", "pulumi.ResourceOptions opts: Options for the resource. \"\"\" opts = pulumi.ResourceOptions.merge(opts,", "def long_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Long description for the custom", "Optional[pulumi.Input[str]]: \"\"\" Name of the partner registration. \"\"\" return pulumi.get(self,", "= None __props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"] =", "Optional[pulumi.ResourceOptions] = None): \"\"\" Information about a partner registration. API", "event source. \"\"\" return pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\") def system_data(self)", "resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args,", "Name of the partner resource type. :param pulumi.Input[str] setup_uri: URI", "\"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property", "= None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"] =", "partner_name is not None: pulumi.set(__self__, \"partner_name\", partner_name) if partner_registration_name is", "of the partner registration. :param pulumi.Input[str] partner_resource_type_description: Short description of", "-> pulumi.Output[Optional[str]]: \"\"\" URI of the logo. \"\"\" return pulumi.get(self,", "\"location\") @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of", "def partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the partner registration.", "__props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"]", "the resource. :param pulumi.Input[str] logo_uri: URI of the logo. :param", "long_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Long description for the custom scenarios", "not None: pulumi.set(__self__, \"tags\", tags) if visibility_state is not None:", "registration. \"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids is not None:", "None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if setup_uri is not None: pulumi.set(__self__,", "pulumi.set(__self__, \"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids is not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\",", "= partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if resource_group_name is None and", "code. The remaining digits are then followed. Only digits and", "the resource. \"\"\" opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs)", "def logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the logo. \"\"\"", "pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription Ids that are authorized", "= None __props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"] =", "partner_customer_service_number) if partner_name is not None: pulumi.set(__self__, \"partner_name\", partner_name) if", "pulumi.get(self, \"system_data\") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\"", "for creating the partner registration. :param pulumi.Input[str] customer_service_uri: The extension", "\"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @partner_name.setter def partner_name(self, value: Optional[pulumi.Input[str]]):", "tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None,", "opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions):", "**kwargs): resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args", "None, logo_uri: Optional[pulumi.Input[str]] = None, long_description: Optional[pulumi.Input[str]] = None, partner_customer_service_extension:", "creating the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @authorized_azure_subscription_ids.setter def", "pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration,", "._enums import * __all__ = ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs:", "__props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"]", "__init__(__self__, resource_name: str, args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] = None): \"\"\"", "@pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the partner", "\"tags\", value) @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\"", "value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Display name", "return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\"", "partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Short description of the partner resource", "opts.version is None: opts.version = _utilities.get_version() if opts.id is None:", "= None __props__.__dict__[\"visibility_state\"] = None return PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property", "hand unless you're certain you know what you are doing!", "\"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name is not None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name)", "-> Optional[pulumi.Input[str]]: \"\"\" Short description of the partner resource type.", "partner_resource_type_name) if setup_uri is not None: pulumi.set(__self__, \"setup_uri\", setup_uri) if", "def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of Azure subscription Ids", "extension of the customer service number of the publisher. Only", "\"\"\" return pulumi.get(self, \"system_data\") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str,", ":param str resource_name: The unique name of the resulting resource.", "pulumi.set(__self__, \"tags\", tags) if visibility_state is not None: pulumi.set(__self__, \"visibility_state\",", "pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" The", "\"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\" The customer", "= partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"] =", "pulumi.set(self, \"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\"", "of the resource group within the user's subscription. \"\"\" return", "The name of the resource group within the user's subscription.", "return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\",", "its length cannot exceed 16 digits including country code. Examples", "a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version()", "pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value)", "the custom scenarios and integration to be displayed in the", "of the partner name. For example: \"Contoso\". \"\"\" return pulumi.get(self,", "of the resource. \"\"\" return pulumi.get(self, \"location\") @location.setter def location(self,", "exceed 2048 characters. :param pulumi.Input[str] partner_customer_service_extension: The extension of the", "Optional[pulumi.ResourceOptions] = None) -> 'PartnerRegistration': \"\"\" Get an existing PartnerRegistration", "not None: pulumi.set(__self__, \"long_description\", long_description) if partner_customer_service_extension is not None:", "customer service URI of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\")", "of the resource. \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\") def", "not exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @property @pulumi.getter", "set of arguments for constructing a PartnerRegistration resource. :param pulumi.Input[str]", "Visibility state of the partner registration. \"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name)", "of the resource. \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\") def", "\"\"\" Information about a partner registration. API Version: 2020-04-01-preview. :param", "opts.id to get an existing resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"]", "exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self)", "pulumi.set(self, \"location\", value) @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\"", "if partner_resource_type_display_name is not None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name", "= None, __props__=None): if opts is None: opts = pulumi.ResourceOptions()", "+966 121 5115 24 7 551 1234 43 :param pulumi.Input[str]", "None, partner_customer_service_extension: Optional[pulumi.Input[str]] = None, partner_customer_service_number: Optional[pulumi.Input[str]] = None, partner_name:", "constructing a PartnerRegistration resource. :param pulumi.Input[str] resource_group_name: The name of", "\"location\", location) if logo_uri is not None: pulumi.set(__self__, \"logo_uri\", logo_uri)", "what you are doing! *** import warnings import pulumi import", "\"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\") def partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Official", "the user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription", "-> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of Azure subscription Ids that are", "\"partner_registration_name\", partner_registration_name) if partner_resource_type_description is not None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description)", "the partner registration. \"\"\" return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self,", "not None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if partner_name is not None:", "= None): \"\"\" The set of arguments for constructing a", "value) @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of", "partner_customer_service_extension) if partner_customer_service_number is not None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if", "be displayed in the portal if needed. Length of this", "of the partner website that can be used by Azure", "create a partner namespace associated with this partner registration. This", "None __props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"] = None", "pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value)", "partner registration. :param pulumi.Input[str] customer_service_uri: The extension of the customer", "123 4567 and +966 7 5115 2471. Examples of invalid", "location __props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension", "\"location\") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value) @property", "value) class PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]", "@pulumi.getter(name=\"logoUri\") def logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the logo.", "is not None: pulumi.set(__self__, \"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self)", "-> pulumi.Output[str]: \"\"\" Provisioning state of the partner registration. \"\"\"", "\"\"\" return pulumi.get(self, \"logo_uri\") @logo_uri.setter def logo_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "\"\"\" return pulumi.get(self, \"partner_name\") @partner_name.setter def partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "None: pulumi.set(__self__, \"setup_uri\", setup_uri) if tags is not None: pulumi.set(__self__,", "Azure subscription as the one used for creating the partner", "URI of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter", ":param pulumi.ResourceOptions opts: Options for the resource. \"\"\" ... def", "@property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags of", "are allowed and number of digits should not exceed 10.", "name of the resource group within the user's subscription. :param", "None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if partner_name is not None: pulumi.set(__self__,", "pulumi.set(self, \"tags\", value) @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]:", "\"\"\" Type of the resource. \"\"\" return pulumi.get(self, \"type\") @property", "Location of the resource. \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\")", "pulumi.set(self, \"setup_uri\", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:", "\"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) -> pulumi.Input[str]: \"\"\" The", "None __props__.__dict__[\"visibility_state\"] = None return PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\")", "\"\"\" Provisioning state of the partner registration. \"\"\" return pulumi.get(self,", "None __props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"] = None", "None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name is not None: pulumi.set(__self__,", "-> Optional[pulumi.Input[str]]: \"\"\" The customer service number of the publisher.", "service URI of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @property", "\"\"\" return pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]:", "2471. Examples of invalid phone numbers are: +1 (515) 123-4567,", "logo. \"\"\" return pulumi.get(self, \"logo_uri\") @logo_uri.setter def logo_uri(self, value: Optional[pulumi.Input[str]]):", "@location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value) @property @pulumi.getter(name=\"logoUri\")", "pulumi.set(self, \"logo_uri\", value) @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> Optional[pulumi.Input[str]]: \"\"\"", "the resource to lookup. :param pulumi.ResourceOptions opts: Options for the", "import * __all__ = ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs: def", "long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name", "Event Grid integration on an event source. \"\"\" return pulumi.get(self,", "tags) if visibility_state is not None: pulumi.set(__self__, \"visibility_state\", visibility_state) @property", "of Azure subscription Ids that are authorized to create a", "pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name,", "resource. :param PartnerRegistrationArgs args: The arguments to use to populate", "551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def", "partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self)", "unique provider ID of the resource to lookup. :param pulumi.ResourceOptions", "that are authorized to create a partner namespace associated with", "\"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Display name", "pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value)", "__props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"]", "-> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags of the resource. \"\"\" return", "pulumi.Output[Optional[str]]: \"\"\" Visibility state of the partner registration. \"\"\" return", "24 7 551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]:", "\"setup_uri\") @setup_uri.setter def setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value) @property", ":param pulumi.Input[str] resource_group_name: The name of the resource group within", "*args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__)", "None) -> 'PartnerRegistration': \"\"\" Get an existing PartnerRegistration resource's state", "location(self) -> Optional[pulumi.Input[str]]: \"\"\" Location of the resource. \"\"\" return", "= None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"] = None __props__.__dict__[\"type\"] =", "def customer_service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value) @property @pulumi.getter def", "name of the partner name. For example: \"Contoso\". :param pulumi.Input[str]", "5115 24 7 551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\")", "\"type\") @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\" Visibility state", "7 551 1234 43 :param pulumi.Input[str] partner_name: Official name of", "authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"] = logo_uri", "not exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @long_description.setter def", "@property @pulumi.getter(name=\"systemData\") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system metadata", "event source. \"\"\" return pulumi.get(self, \"setup_uri\") @setup_uri.setter def setup_uri(self, value:", "None: pulumi.set(__self__, \"location\", location) if logo_uri is not None: pulumi.set(__self__,", "= setup_uri __props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"] =", "id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PartnerRegistration': \"\"\" Get", "pulumi.get(self, \"location\") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value)", "__init__(__self__, *, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]]", "value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of", "customer_service_uri: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, logo_uri: Optional[pulumi.Input[str]]", ":param pulumi.Input[str] partner_customer_service_number: The customer service number of the publisher.", "pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): \"\"\"", "pulumi.Input[str] logo_uri: URI of the logo. :param pulumi.Input[str] long_description: Long", "of the resource. \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def", "__props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"]", "the partner website that can be used by Azure customers", "the resource. \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self)", "43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def partner_name(self) ->", "customer_service_uri) if location is not None: pulumi.set(__self__, \"location\", location) if", "value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) ->", "number of digits should not exceed 10. \"\"\" return pulumi.get(self,", "Optional[pulumi.Input[str]]: \"\"\" Display name of the partner resource type. \"\"\"", "resource_group_name(self) -> pulumi.Input[str]: \"\"\" The name of the resource group", "the Pulumi SDK Generator. *** # *** Do not edit", "is not None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name is not", "partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name", "__props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"]", "Optional[pulumi.Input[str]]: \"\"\" URI of the logo. \"\"\" return pulumi.get(self, \"logo_uri\")", "... @overload def __init__(__self__, resource_name: str, args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions]", "setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value) @property @pulumi.getter def tags(self)", "*args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None,", "The remaining digits are then followed. Only digits and spaces", "None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri is not None: pulumi.set(__self__,", "pulumi.get(self, \"setup_uri\") @setup_uri.setter def setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value)", "setup_uri is not None: pulumi.set(__self__, \"setup_uri\", setup_uri) if tags is", "on an event source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags of", "This is an optional property. Creating partner namespaces is always", "= None, location: Optional[pulumi.Input[str]] = None, logo_uri: Optional[pulumi.Input[str]] = None,", "partner_resource_type_name is not None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if setup_uri is", "pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if location is not None: pulumi.set(__self__, \"location\",", "exceed 256 characters. :param pulumi.Input[str] partner_resource_type_display_name: Display name of the", "if partner_resource_type_name is not None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if setup_uri", "are then followed. Only digits and spaces are allowed and", "@pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of Azure subscription", "properties. :param pulumi.ResourceOptions opts: Options for the resource. \"\"\" ...", "opts.version = _utilities.get_version() if opts.id is None: if __props__ is", "opts: Options for the resource. \"\"\" opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))", "the partner resource type. :param pulumi.Input[str] setup_uri: URI of the", "\"partner_name\", partner_name) if partner_registration_name is not None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name)", "None, customer_service_uri: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, logo_uri:", "valid phone numbers are: +1 515 123 4567 and +966", "return pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\"", "@pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\" The customer service number", "__props__.__dict__[\"tags\"] = tags __props__.__dict__[\"visibility_state\"] = visibility_state __props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"]", "None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__,", "None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number is not None: pulumi.set(__self__,", "always permitted under the same Azure subscription as the one", "256 characters. :param pulumi.Input[str] partner_resource_type_display_name: Display name of the partner", "-> Optional[pulumi.Input[str]]: \"\"\" Name of the partner registration. \"\"\" return", "def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Name of the partner resource", "the partner resource type. :param pulumi.Input[str] partner_resource_type_name: Name of the", "pulumi.set(__self__, \"partner_name\", partner_name) if partner_registration_name is not None: pulumi.set(__self__, \"partner_registration_name\",", "\"\"\" return pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:", "pulumi.set(self, \"customer_service_uri\", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: \"\"\"", "1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\") def partner_name(self)", "... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts =", "'PartnerRegistrationVisibilityState']]] = None): \"\"\" The set of arguments for constructing", "from .. import _utilities from . import outputs from ._enums", "not None: pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number is not None:", "the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter def location(self)", "know what you are doing! *** import warnings import pulumi", "@property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\" The customer service", "Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities", "Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> Optional[pulumi.Input[str]]:", "of the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @property", "7 551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def", "__props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"]", "partner_name) if partner_registration_name is not None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if", "PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"] = location", "location(self) -> pulumi.Output[str]: \"\"\" Location of the resource. \"\"\" return", "def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Short description of the partner", "pulumi.set(self, \"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str,", "the resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of the", "7 5115 2471. Examples of invalid phone numbers are: +1", "pulumi.Input[str] customer_service_uri: The extension of the customer service URI of", "if resource_group_name is None and not opts.urn: raise TypeError(\"Missing required", "def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Display name of the partner", "to create a partner namespace associated with this partner registration.", "__props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"]", "partner namespaces is always permitted under the same Azure subscription", "partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Display name of the partner resource", "of the resource. :param PartnerRegistrationArgs args: The arguments to use", "this description should not exceed 2048 characters. :param pulumi.Input[str] partner_customer_service_extension:", "def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value) @property @pulumi.getter(name=\"logoUri\") def", "return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\",", "return pulumi.get(self, \"partner_customer_service_number\") @partner_customer_service_number.setter def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\",", "for the resource. \"\"\" ... def __init__(__self__, resource_name: str, *args,", "logo. :param pulumi.Input[str] long_description: Long description for the custom scenarios", "= None __props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"tags\"] =", "displayed in the portal if needed. Length of this description", "__props__.__dict__[\"type\"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")])", "by hand unless you're certain you know what you are", "partner registration. \"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids is not", "= None) -> 'PartnerRegistration': \"\"\" Get an existing PartnerRegistration resource's", "resource. :param pulumi.Input[str] id: The unique provider ID of the", "resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri", "def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs,", "system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system metadata relating to Partner", "the partner name. For example: \"Contoso\". :param pulumi.Input[str] partner_registration_name: Name", "should not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\")", "of the partner name. For example: \"Contoso\". :param pulumi.Input[str] partner_registration_name:", "of invalid phone numbers are: +1 (515) 123-4567, 1 515", "__self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id:", "visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): if opts is None:", "@pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\" The customer service number", ":param pulumi.Input[str] location: Location of the resource. :param pulumi.Input[str] logo_uri:", "resource type. :param pulumi.Input[str] partner_resource_type_name: Name of the partner resource", "location) if logo_uri is not None: pulumi.set(__self__, \"logo_uri\", logo_uri) if", "__props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"]", "in combination with a valid opts.id to get an existing", "partner_resource_type_name: Name of the partner resource type. :param pulumi.Input[str] setup_uri:", "are: +1 (515) 123-4567, 1 515 123 4567 and +966", "state of the partner registration. \"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name) if", "characters. :param pulumi.Input[str] partner_customer_service_extension: The extension of the customer service", "not None: pulumi.set(__self__, \"partner_resource_type_name\", partner_resource_type_name) if setup_uri is not None:", "resource. :param pulumi.Input[str] logo_uri: URI of the logo. :param pulumi.Input[str]", "return pulumi.get(self, \"long_description\") @property @pulumi.getter def name(self) -> pulumi.Output[str]: \"\"\"", "to setup Event Grid integration on an event source. :param", "__props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"]", "= None, logo_uri: Optional[pulumi.Input[str]] = None, long_description: Optional[pulumi.Input[str]] = None,", "of the resource to lookup. :param pulumi.ResourceOptions opts: Options for", "Location of the resource. \"\"\" return pulumi.get(self, \"location\") @location.setter def", "\"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name", "pulumi.set(self, \"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\"", "Version: 2020-04-01-preview. :param str resource_name: The name of the resource.", "return PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]:", "partner resource type. :param pulumi.Input[str] setup_uri: URI of the partner", "partner_registration_name: Name of the partner registration. :param pulumi.Input[str] partner_resource_type_description: Short", "def visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource):", "outputs from ._enums import * __all__ = ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type", "if location is not None: pulumi.set(__self__, \"location\", location) if logo_uri", ":param pulumi.Input[str] partner_resource_type_display_name: Display name of the partner resource type.", ":param pulumi.Input[str] partner_name: Official name of the partner name. For", "generated by the Pulumi SDK Generator. *** # *** Do", "return pulumi.get(self, \"system_data\") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:", "pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:", "@visibility_state.setter def visibility_state(self, value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value) class", "location is not None: pulumi.set(__self__, \"location\", location) if logo_uri is", "def authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\") def", "to be displayed in the portal if needed. Length of", "__self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] =", "partner_registration_name: Optional[pulumi.Input[str]] = None, partner_resource_type_description: Optional[pulumi.Input[str]] = None, partner_resource_type_display_name: Optional[pulumi.Input[str]]", "digits are then followed. Only digits and spaces are allowed", "of the partner registration. \"\"\" pulumi.set(__self__, \"resource_group_name\", resource_group_name) if authorized_azure_subscription_ids", "subscription as the one used for creating the partner registration.", "None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected", "opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not", "should not exceed 2048 characters. :param pulumi.Input[str] partner_customer_service_extension: The extension", "is not None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name is not", "@property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Display name of", "None __props__.__dict__[\"type\"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"),", "pulumi.get(self, \"partner_name\") @partner_name.setter def partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value)", "visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) -> pulumi.Input[str]: \"\"\" The name", "authorized_azure_subscription_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, \"authorized_azure_subscription_ids\", value) @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self)", "@property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: \"\"\" Location of the", "\"\"\" Visibility state of the partner registration. \"\"\" return pulumi.get(self,", "Generator. *** # *** Do not edit by hand unless", "Optional[pulumi.Input[str]]: \"\"\" Official name of the partner name. For example:", "@partner_name.setter def partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\")", "is not None: pulumi.set(__self__, \"logo_uri\", logo_uri) if long_description is not", "16 digits including country code. Examples of valid phone numbers", "256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self)", "resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription Ids that", "None, __props__=None): \"\"\" Information about a partner registration. API Version:", "registration. API Version: 2020-04-01-preview. :param str resource_name: The name of", "the customer service number of the publisher. Only digits are", "-> pulumi.Output[str]: \"\"\" Type of the resource. \"\"\" return pulumi.get(self,", "and +966 7 5115 2471. Examples of invalid phone numbers", "super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts) @staticmethod def get(resource_name: str,", "Grid integration on an event source. \"\"\" return pulumi.get(self, \"setup_uri\")", "description for the custom scenarios and integration to be displayed", "\"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]:", "@property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the", "are doing! *** import warnings import pulumi import pulumi.runtime from", "551 1234 43 :param pulumi.Input[str] partner_name: Official name of the", "partner_customer_service_number: The customer service number of the publisher. The expected", "'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of the partner registration. \"\"\" ...", "is not None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if partner_name is not", "10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self, value: Optional[pulumi.Input[str]]):", "args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] = None): \"\"\" Information about a", "on an event source. \"\"\" return pulumi.get(self, \"setup_uri\") @property @pulumi.getter(name=\"systemData\")", "ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options", "exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @long_description.setter def long_description(self,", "return pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter def location(self) -> pulumi.Output[str]: \"\"\"", "property. Creating partner namespaces is always permitted under the same", "followed. Only digits and spaces are allowed and its length", "'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str],", "def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of the customer", "URI of the publisher. :param pulumi.Input[str] location: Location of the", "type(self) -> pulumi.Output[str]: \"\"\" Type of the resource. \"\"\" return", "Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]", "of the partner registration. \"\"\" return pulumi.get(self, \"visibility_state\") @visibility_state.setter def", "\"\"\" return pulumi.get(self, \"tags\") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):", "if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name,", "partner_customer_service_extension: Optional[pulumi.Input[str]] = None, partner_customer_service_number: Optional[pulumi.Input[str]] = None, partner_name: Optional[pulumi.Input[str]]", "\"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Short description", "Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str,", "resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self)", "within the user's subscription. \"\"\" return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def", "Examples of invalid phone numbers are: +1 (515) 123-4567, 1", "use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options", "Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, logo_uri: Optional[pulumi.Input[str]] =", "is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise", "not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def", "def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags of the resource.", "# *** Do not edit by hand unless you're certain", "characters. \"\"\" return pulumi.get(self, \"long_description\") @long_description.setter def long_description(self, value: Optional[pulumi.Input[str]]):", "= None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"] =", "authorized_azure_subscription_ids is not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri is", "@partner_resource_type_name.setter def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\")", "state of the partner registration. \"\"\" return pulumi.get(self, \"provisioning_state\") @property", "if customer_service_uri is not None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if location", "long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self)", "used by Azure customers to setup Event Grid integration on", "get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PartnerRegistration':", "of the resulting resource. :param pulumi.Input[str] id: The unique provider", "@overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids:", "__props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"]", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) ->", "pulumi.get(self, \"partner_resource_type_description\") @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Display", "None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"] = None", "@partner_customer_service_number.setter def partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\")", "SDK Generator. *** # *** Do not edit by hand", "Azure customers to setup Event Grid integration on an event", "user's subscription. :param pulumi.Input[str] setup_uri: URI of the partner website", "as the one used for creating the partner registration. :param", "name of the partner resource type. :param pulumi.Input[str] partner_resource_type_name: Name", "['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str],", "not None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if location is not None:", "type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]):", "@pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of the", "namespaces is always permitted under the same Azure subscription as", "None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): \"\"\" Information about", "Optional[pulumi.Input[str]] = None, logo_uri: Optional[pulumi.Input[str]] = None, long_description: Optional[pulumi.Input[str]] =", "Long description for the custom scenarios and integration to be", "+1 (515) 123-4567, 1 515 123 4567 and +966 121", "return pulumi.get(self, \"setup_uri\") @setup_uri.setter def setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\",", "Optional, Sequence, Union, overload from .. import _utilities from .", "an optional property. Creating partner namespaces is always permitted under", "by the Pulumi SDK Generator. *** # *** Do not", "pulumi.Input[str] partner_customer_service_extension: The extension of the customer service number of", "def resource_group_name(self) -> pulumi.Input[str]: \"\"\" The name of the resource", "PartnerRegistration resource. :param pulumi.Input[str] resource_group_name: The name of the resource", "\"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Short", "get an existing resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids", "= PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] = customer_service_uri __props__.__dict__[\"location\"] =", "Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] =", ":param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription Ids that are", "**kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids:", "\"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> pulumi.Output[str]: \"\"\" Provisioning state", "a valid opts.id to get an existing resource') __props__ =", "__props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"]", "partner registration. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def setup_uri(self)", "partner_resource_type_name if resource_group_name is None and not opts.urn: raise TypeError(\"Missing", "pulumi.set(self, \"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\"", "this description should not exceed 256 characters. :param pulumi.Input[str] partner_resource_type_display_name:", "\"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of", "pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts,", "@setup_uri.setter def setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value) @property @pulumi.getter", "visibility_state is not None: pulumi.set(__self__, \"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def", "in the portal if needed. Length of this description should", "long_description: Optional[pulumi.Input[str]] = None, partner_customer_service_extension: Optional[pulumi.Input[str]] = None, partner_customer_service_number: Optional[pulumi.Input[str]]", "partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of the customer service", "@property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the", "a '+' sign followed by the country code. The remaining", "pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value) @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> Optional[pulumi.Input[Union[str,", "= None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): \"\"\" Information", "to get an existing resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] =", "with this partner registration. This is an optional property. Creating", "pulumi.get(self, \"location\") @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI", "@pulumi.getter def name(self) -> pulumi.Output[str]: \"\"\" Name of the resource.", "ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if", "partner_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Official name of the partner name.", "pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri is not None: pulumi.set(__self__, \"customer_service_uri\",", "registration. \"\"\" return pulumi.get(self, \"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self, value: Optional[pulumi.Input[str]]):", "logo_uri is not None: pulumi.set(__self__, \"logo_uri\", logo_uri) if long_description is", "resource_group_name is None and not opts.urn: raise TypeError(\"Missing required property", "when passed in combination with a valid opts.id to get", "@property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> pulumi.Output[str]: \"\"\" Provisioning state of", "value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Short description", "\"\"\" Location of the resource. \"\"\" return pulumi.get(self, \"location\") @location.setter", "@pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension of the", "pulumi.ResourceOptions.merge(opts, alias_opts) super(PartnerRegistration, __self__).__init__( 'azure-native:eventgrid:PartnerRegistration', resource_name, __props__, opts) @staticmethod def", "opts.urn: raise TypeError(\"Missing required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"]", "\"name\") @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension", "-> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List of Azure subscription Ids that are", "partner_registration_name) if partner_resource_type_description is not None: pulumi.set(__self__, \"partner_resource_type_description\", partner_resource_type_description) if", "metadata relating to Partner Registration resource. \"\"\" return pulumi.get(self, \"system_data\")", "registration. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) ->", "return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> pulumi.Output[Optional[str]]: \"\"\"", "\"partner_resource_type_description\") @partner_resource_type_description.setter def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value) @property", "None __props__.__dict__[\"long_description\"] = None __props__.__dict__[\"name\"] = None __props__.__dict__[\"partner_customer_service_extension\"] = None", "@pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state of", "the partner registration. :param pulumi.Input[str] partner_resource_type_description: Short description of the", "integration on an event source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags", "the user's subscription. \"\"\" return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def resource_group_name(self,", "type. The length of this description should not exceed 256", "\"\"\" ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts", "-> Optional[pulumi.Input[str]]: \"\"\" Display name of the partner resource type.", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) -> Optional[pulumi.Input[str]]:", "resource's state with the given name, id, and optional extra", "not exceed 256 characters. :param pulumi.Input[str] partner_resource_type_display_name: Display name of", "def setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value) @property @pulumi.getter def", "value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> Optional[pulumi.Input[str]]: \"\"\" The customer", "The unique provider ID of the resource to lookup. :param", "None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"] = None __props__.__dict__[\"system_data\"] = None", "pulumi.Input[str]: \"\"\" The name of the resource group within the", "if tags is not None: pulumi.set(__self__, \"tags\", tags) if visibility_state", "@pulumi.getter(name=\"longDescription\") def long_description(self) -> Optional[pulumi.Input[str]]: \"\"\" Long description for the", "\"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> pulumi.Output[Optional[str]]:", "@pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags of the", "def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) ->", "pulumi.set(__self__, \"partner_customer_service_extension\", partner_customer_service_extension) if partner_customer_service_number is not None: pulumi.set(__self__, \"partner_customer_service_number\",", "pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PartnerRegistration': \"\"\" Get an", "'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name __props__.__dict__[\"setup_uri\"] = setup_uri __props__.__dict__[\"tags\"] = tags", "None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None): \"\"\" The set of", "\"tags\") @property @pulumi.getter def type(self) -> pulumi.Output[str]: \"\"\" Type of", "API Version: 2020-04-01-preview. :param str resource_name: The name of the", "partner_resource_type_description) if partner_resource_type_display_name is not None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if", "@property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) -> Optional[pulumi.Input[str]]:", "and not opts.urn: raise TypeError(\"Missing required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] =", "not None: pulumi.set(__self__, \"visibility_state\", visibility_state) @property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) ->", "= None __props__.__dict__[\"partner_resource_type_description\"] = None __props__.__dict__[\"partner_resource_type_display_name\"] = None __props__.__dict__[\"partner_resource_type_name\"] =", "partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\") def setup_uri(self)", "Tags of the resource. \"\"\" return pulumi.get(self, \"tags\") @tags.setter def", "id, and optional extra properties used to qualify the lookup.", "and +966 121 5115 24 7 551 1234 43 :param", "str, opts: Optional[pulumi.ResourceOptions] = None, authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri:", "is not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if customer_service_uri is not", "The system metadata relating to Partner Registration resource. \"\"\" return", "\"\"\" URI of the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @logo_uri.setter", "@property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\" Visibility state of", "value) @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility", "unless you're certain you know what you are doing! ***", "pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to", "\"\"\" The customer service number of the publisher. The expected", "visibility_state(self) -> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state of the partner", "be used by Azure customers to setup Event Grid integration", "def customer_service_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" The extension of the customer", "description of the partner resource type. The length of this", "registration. \"\"\" return pulumi.get(self, \"visibility_state\") @visibility_state.setter def visibility_state(self, value: Optional[pulumi.Input[Union[str,", "phone numbers are: +1 (515) 123-4567, 1 515 123 4567", "subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription Ids that", "= None __props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"] = None return PartnerRegistration(resource_name,", "* __all__ = ['PartnerRegistrationArgs', 'PartnerRegistration'] @pulumi.input_type class PartnerRegistrationArgs: def __init__(__self__,", "and spaces are allowed and its length cannot exceed 16", "if opts.id is None: if __props__ is not None: raise", "None, partner_customer_service_number: Optional[pulumi.Input[str]] = None, partner_name: Optional[pulumi.Input[str]] = None, partner_registration_name:", "def name(self) -> pulumi.Output[str]: \"\"\" Name of the resource. \"\"\"", "user's subscription. \"\"\" return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter def resource_group_name(self, value:", "customer service number of the publisher. Only digits are allowed", "pulumi.set(self, \"long_description\", value) @property @pulumi.getter(name=\"partnerCustomerServiceExtension\") def partner_customer_service_extension(self) -> Optional[pulumi.Input[str]]: \"\"\"", "associated with this partner registration. This is an optional property.", "type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) ->", "\"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\") def partner_resource_type_display_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Display", "\"\"\" return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "partner_name: Optional[pulumi.Input[str]] = None, partner_registration_name: Optional[pulumi.Input[str]] = None, partner_resource_type_description: Optional[pulumi.Input[str]]", "length cannot exceed 16 digits including country code. Examples of", "to lookup. :param pulumi.ResourceOptions opts: Options for the resource. \"\"\"", "= None __props__.__dict__[\"partner_resource_type_name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"setup_uri\"] =", "partner_registration_name is not None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if partner_resource_type_description is", "pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> pulumi.Output[str]: \"\"\" Provisioning", "same Azure subscription as the one used for creating the", "resource_group_name) if authorized_azure_subscription_ids is not None: pulumi.set(__self__, \"authorized_azure_subscription_ids\", authorized_azure_subscription_ids) if", "exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @partner_customer_service_extension.setter def partner_customer_service_extension(self, value:", ":param pulumi.Input[str] partner_customer_service_extension: The extension of the customer service number", "def partner_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Official name of the partner", "-> pulumi.Output[Optional[str]]: \"\"\" Visibility state of the partner registration. \"\"\"", "\"long_description\") @property @pulumi.getter def name(self) -> pulumi.Output[str]: \"\"\" Name of", "customer_service_uri is not None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if location is", "pulumi.Input[str] partner_resource_type_name: Name of the partner resource type. :param pulumi.Input[str]", "type. :param pulumi.Input[str] setup_uri: URI of the partner website that", "Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) -> Optional[pulumi.Input[str]]:", "# coding=utf-8 # *** WARNING: this file was generated by", "pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of the partner registration. \"\"\"", "import warnings import pulumi import pulumi.runtime from typing import Any,", "-> Optional[pulumi.Input[str]]: \"\"\" URI of the partner website that can", "-> Optional[pulumi.Input[str]]: \"\"\" The extension of the customer service URI", "the resource. \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self)", "from . import outputs from ._enums import * __all__ =", "the same Azure subscription as the one used for creating", "authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]]", "publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @property @pulumi.getter def location(self) ->", "@pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> pulumi.Output[str]: \"\"\" Provisioning state of the", "-> Optional[pulumi.Input[str]]: \"\"\" Name of the partner resource type. \"\"\"", "the one used for creating the partner registration. \"\"\" return", "service number of the publisher. Only digits are allowed and", "= None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]", "= None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None): \"\"\" The set", "\"partner_name\") @partner_name.setter def partner_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value) @property", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"setup_uri\", value) @property @pulumi.getter def tags(self) ->", "tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value) @property @pulumi.getter(name=\"visibilityState\") def", "provider ID of the resource to lookup. :param pulumi.ResourceOptions opts:", "of the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter", "= logo_uri __props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] =", "= partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if resource_group_name", "-> pulumi.Output[Optional[str]]: \"\"\" URI of the partner website that can", "__props__.__dict__[\"visibility_state\"] = None return PartnerRegistration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def", "customer service URI of the publisher. :param pulumi.Input[str] location: Location", "start with a '+' sign followed by the country code.", "= None, partner_customer_service_extension: Optional[pulumi.Input[str]] = None, partner_customer_service_number: Optional[pulumi.Input[str]] = None,", "is not None: raise TypeError('__props__ is only valid when passed", "\"partner_resource_type_description\", partner_resource_type_description) if partner_resource_type_display_name is not None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name)", "pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance')", "resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is", "Optional[pulumi.Input[str]]): pulumi.set(self, \"customer_service_uri\", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]:", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) ->", "location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value) @property @pulumi.getter(name=\"logoUri\") def logo_uri(self)", "partner_customer_service_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\") def partner_name(self)", "visibility_state(self) -> pulumi.Output[Optional[str]]: \"\"\" Visibility state of the partner registration.", "def __init__(__self__, *, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri:", "Official name of the partner name. For example: \"Contoso\". \"\"\"", "\"\"\" The system metadata relating to Partner Registration resource. \"\"\"", "def partner_resource_type_display_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Display name of the partner", "*** # *** Do not edit by hand unless you're", "existing resource') __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = authorized_azure_subscription_ids __props__.__dict__[\"customer_service_uri\"] =", "for the resource. \"\"\" opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ =", "7 551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @property @pulumi.getter(name=\"partnerName\")", "digits are allowed and number of digits should not exceed", "pulumi.Input[str] partner_registration_name: Name of the partner registration. :param pulumi.Input[str] partner_resource_type_description:", "registration. This is an optional property. Creating partner namespaces is", "exceed 2048 characters. \"\"\" return pulumi.get(self, \"long_description\") @property @pulumi.getter def", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_name\", value) @property @pulumi.getter(name=\"setupUri\") def setup_uri(self) ->", "partner_resource_type_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str,", "with a valid opts.id to get an existing resource') __props__", "\"setup_uri\") @property @pulumi.getter(name=\"systemData\") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system", "The customer service number of the publisher. The expected phone", "= partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"] =", ":param str resource_name: The name of the resource. :param PartnerRegistrationArgs", "\"\"\" return pulumi.get(self, \"location\") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "within the user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure", "Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None): \"\"\" The set of arguments for", "else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]", "Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None):", "publisher. Only digits are allowed and number of digits should", "= long_description __props__.__dict__[\"partner_customer_service_extension\"] = partner_customer_service_extension __props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"] =", "the partner name. For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\")", "spaces are allowed and its length cannot exceed 16 digits", "of the partner resource type. The length of this description", "\"customer_service_uri\", customer_service_uri) if location is not None: pulumi.set(__self__, \"location\", location)", "partner_resource_type_description: Optional[pulumi.Input[str]] = None, partner_resource_type_display_name: Optional[pulumi.Input[str]] = None, partner_resource_type_name: Optional[pulumi.Input[str]]", "Creating partner namespaces is always permitted under the same Azure", "partner_resource_type_display_name is not None: pulumi.set(__self__, \"partner_resource_type_display_name\", partner_resource_type_display_name) if partner_resource_type_name is", "resource. :param pulumi.Input[str] resource_group_name: The name of the resource group", "def partner_resource_type_display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def", "setup_uri: URI of the partner website that can be used", "is not None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if partner_resource_type_description is not", "partner_customer_service_number: Optional[pulumi.Input[str]] = None, partner_name: Optional[pulumi.Input[str]] = None, partner_registration_name: Optional[pulumi.Input[str]]", "this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource.", "10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) ->", "the resource. \"\"\" return pulumi.get(self, \"location\") @location.setter def location(self, value:", "pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload", "portal if needed. Length of this description should not exceed", "24 7 551 1234 43 \"\"\" return pulumi.get(self, \"partner_customer_service_number\") @property", ":param pulumi.Input[str] logo_uri: URI of the logo. :param pulumi.Input[str] long_description:", "the resource. :param PartnerRegistrationArgs args: The arguments to use to", "creating the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\")", "\"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) -> pulumi.Output[str]:", "For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def", "__props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]", "def partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def", "\"partner_resource_type_name\", partner_resource_type_name) if setup_uri is not None: pulumi.set(__self__, \"setup_uri\", setup_uri)", "Official name of the partner name. For example: \"Contoso\". :param", "\"system_data\") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: \"\"\" Tags", "not opts.urn: raise TypeError(\"Missing required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"] = resource_group_name", "__props__.__dict__[\"partner_customer_service_number\"] = partner_customer_service_number __props__.__dict__[\"partner_name\"] = partner_name __props__.__dict__[\"partner_registration_name\"] = partner_registration_name __props__.__dict__[\"partner_resource_type_description\"]", "is not None: pulumi.set(__self__, \"customer_service_uri\", customer_service_uri) if location is not", "Union, overload from .. import _utilities from . import outputs", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeDescription\") def partner_resource_type_description(self) ->", "pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags of the resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]", "pulumi.Input[str]]] tags: Tags of the resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state:", "@pulumi.getter(name=\"longDescription\") def long_description(self) -> pulumi.Output[Optional[str]]: \"\"\" Long description for the", "the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of Azure subscription Ids", "partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def", "from typing import Any, Mapping, Optional, Sequence, Union, overload from", "opts=opts, __props__=__props__) @property @pulumi.getter(name=\"authorizedAzureSubscriptionIds\") def authorized_azure_subscription_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: \"\"\" List", "\"\"\" Tags of the resource. \"\"\" return pulumi.get(self, \"tags\") @tags.setter", "partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @property @pulumi.getter(name=\"partnerResourceTypeName\") def", "WARNING: this file was generated by the Pulumi SDK Generator.", "\"\"\" Long description for the custom scenarios and integration to", "opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource", "pulumi.Output[str]: \"\"\" Provisioning state of the partner registration. \"\"\" return", "pulumi.get(self, \"tags\") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\",", "pulumi.get(self, \"partner_customer_service_extension\") @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\" The", "partner resource type. :param pulumi.Input[str] partner_resource_type_name: Name of the partner", "used for creating the partner registration. \"\"\" return pulumi.get(self, \"authorized_azure_subscription_ids\")", "certain you know what you are doing! *** import warnings", "Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value) @property @pulumi.getter(name=\"visibilityState\") def visibility_state(self) ->", "of the customer service number of the publisher. Only digits", "\"authorized_azure_subscription_ids\") @property @pulumi.getter(name=\"customerServiceUri\") def customer_service_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" The extension", "customer_service_uri: The extension of the customer service URI of the", "\"\"\" return pulumi.get(self, \"long_description\") @property @pulumi.getter def name(self) -> pulumi.Output[str]:", "__props__.__dict__[\"type\"] = None __props__.__dict__[\"visibility_state\"] = None return PartnerRegistration(resource_name, opts=opts, __props__=__props__)", "= None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20200401preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-native:eventgrid/v20201015preview:PartnerRegistration\"), pulumi.Alias(type_=\"azure-nextgen:eventgrid/v20201015preview:PartnerRegistration\")]) opts", "group within the user's subscription. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of", "the resource. \"\"\" ... def __init__(__self__, resource_name: str, *args, **kwargs):", "followed by the country code. The remaining digits are then", "about a partner registration. API Version: 2020-04-01-preview. :param str resource_name:", "@pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the partner", "*, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, customer_service_uri: Optional[pulumi.Input[str]] =", "one used for creating the partner registration. \"\"\" return pulumi.get(self,", "# *** WARNING: this file was generated by the Pulumi", "TypeError('__props__ is only valid when passed in combination with a", "was generated by the Pulumi SDK Generator. *** # ***", "partner_resource_type_display_name: Display name of the partner resource type. :param pulumi.Input[str]", "121 5115 24 7 551 1234 43 :param pulumi.Input[str] partner_name:", "'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state of the partner registration. \"\"\" return", "\"setup_uri\", setup_uri) if tags is not None: pulumi.set(__self__, \"tags\", tags)", "pulumi.set(self, \"partner_name\", value) @property @pulumi.getter(name=\"partnerRegistrationName\") def partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\"", "not None: raise TypeError('__props__ is only valid when passed in", "= None, visibility_state: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): if opts", "\"\"\" return pulumi.get(self, \"long_description\") @long_description.setter def long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "visibility_state __props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"] = None", "if partner_name is not None: pulumi.set(__self__, \"partner_name\", partner_name) if partner_registration_name", "\"partner_resource_type_display_name\", value) @property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name", "-> pulumi.Output[Optional[str]]: \"\"\" The customer service number of the publisher.", "def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: \"\"\" The system metadata relating to", "None, partner_name: Optional[pulumi.Input[str]] = None, partner_registration_name: Optional[pulumi.Input[str]] = None, partner_resource_type_description:", "of valid phone numbers are: +1 515 123 4567 and", "\"resource_group_name\") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, \"resource_group_name\", value) @property", "The arguments to use to populate this resource's properties. :param", "None): \"\"\" The set of arguments for constructing a PartnerRegistration", "def location(self) -> pulumi.Output[str]: \"\"\" Location of the resource. \"\"\"", "def authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of Azure subscription Ids", "pulumi.Input[str] id: The unique provider ID of the resource to", "resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def partner_resource_type_name(self, value:", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"location\", value) @property @pulumi.getter(name=\"logoUri\") def logo_uri(self) ->", "Tags of the resource. \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter", "\"customer_service_uri\", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: \"\"\" Location", ".. import _utilities from . import outputs from ._enums import", "Only digits and spaces are allowed and its length cannot", "is not None: pulumi.set(__self__, \"setup_uri\", setup_uri) if tags is not", "@property @pulumi.getter(name=\"resourceGroupName\") def resource_group_name(self) -> pulumi.Input[str]: \"\"\" The name of", "under the same Azure subscription as the one used for", "opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_azure_subscription_ids: List of", "515 123 4567 and +966 121 5115 24 7 551", "partner_registration_name(self) -> Optional[pulumi.Input[str]]: \"\"\" Name of the partner registration. \"\"\"", "is not None: pulumi.set(__self__, \"location\", location) if logo_uri is not", "class PartnerRegistrationArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], authorized_azure_subscription_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] =", "of digits should not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\")", "if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts,", "= None __props__.__dict__[\"location\"] = None __props__.__dict__[\"logo_uri\"] = None __props__.__dict__[\"long_description\"] =", "@partner_resource_type_description.setter def partner_resource_type_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_resource_type_description\", value) @property @pulumi.getter(name=\"partnerResourceTypeDisplayName\")", "partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\" The customer service number of the", "logo_uri(self) -> pulumi.Output[Optional[str]]: \"\"\" URI of the logo. \"\"\" return", "partner_customer_service_extension: The extension of the customer service number of the", "logo_uri) if long_description is not None: pulumi.set(__self__, \"long_description\", long_description) if", "unique name of the resulting resource. :param pulumi.Input[str] id: The", "setup Event Grid integration on an event source. \"\"\" return", "opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None", "the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @partner_resource_type_name.setter def", "the partner registration. :param pulumi.Input[str] customer_service_uri: The extension of the", "5115 24 7 551 1234 43 :param pulumi.Input[str] partner_name: Official", "For example: \"Contoso\". :param pulumi.Input[str] partner_registration_name: Name of the partner", "of the publisher. Only digits are allowed and number of", "the publisher. :param pulumi.Input[str] location: Location of the resource. :param", "Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: \"\"\" Tags of the resource. \"\"\" return pulumi.get(self,", "digits should not exceed 10. \"\"\" return pulumi.get(self, \"partner_customer_service_extension\") @property", "should not exceed 10. :param pulumi.Input[str] partner_customer_service_number: The customer service", "@property @pulumi.getter(name=\"partnerResourceTypeName\") def partner_resource_type_name(self) -> pulumi.Output[Optional[str]]: \"\"\" Name of the", "__props__.__dict__[\"partner_resource_type_description\"] = partner_resource_type_description __props__.__dict__[\"partner_resource_type_display_name\"] = partner_resource_type_display_name __props__.__dict__[\"partner_resource_type_name\"] = partner_resource_type_name if", "-> Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]: \"\"\" Visibility state of the partner registration.", "__props__.__dict__[\"location\"] = location __props__.__dict__[\"logo_uri\"] = logo_uri __props__.__dict__[\"long_description\"] = long_description __props__.__dict__[\"partner_customer_service_extension\"]", "Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]] = None, __props__=None): if opts is None: opts", "partner registration. API Version: 2020-04-01-preview. :param str resource_name: The name", "lookup. :param pulumi.ResourceOptions opts: Options for the resource. \"\"\" opts", "resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self)", "resource. \"\"\" return pulumi.get(self, \"system_data\") @property @pulumi.getter def tags(self) ->", "None, partner_registration_name: Optional[pulumi.Input[str]] = None, partner_resource_type_description: Optional[pulumi.Input[str]] = None, partner_resource_type_display_name:", "Name of the partner resource type. :param pulumi.Input[str] resource_group_name: The", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_extension\", value) @property @pulumi.getter(name=\"partnerCustomerServiceNumber\") def partner_customer_service_number(self) ->", "\"\"\" return pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def long_description(self) -> pulumi.Output[Optional[str]]:", ":param pulumi.Input[str] long_description: Long description for the custom scenarios and", "\"partner_registration_name\") @partner_registration_name.setter def partner_registration_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_registration_name\", value) @property", "Visibility state of the partner registration. \"\"\" ... @overload def", "*args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PartnerRegistrationArgs, pulumi.ResourceOptions, *args, **kwargs) if", "of the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @property", "Optional[pulumi.Input[str]]: \"\"\" The customer service number of the publisher. The", "should not exceed 256 characters. :param pulumi.Input[str] partner_resource_type_display_name: Display name", "partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def partner_resource_type_display_name(self,", "@overload def __init__(__self__, resource_name: str, args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] =", "source. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Tags of the resource. :param", "\"\"\" return pulumi.get(self, \"setup_uri\") @setup_uri.setter def setup_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self,", "-> 'PartnerRegistration': \"\"\" Get an existing PartnerRegistration resource's state with", "publisher. The expected phone format should start with a '+'", "123 4567 and +966 121 5115 24 7 551 1234", "long_description is not None: pulumi.set(__self__, \"long_description\", long_description) if partner_customer_service_extension is", "if partner_customer_service_number is not None: pulumi.set(__self__, \"partner_customer_service_number\", partner_customer_service_number) if partner_name", "id: The unique provider ID of the resource to lookup.", "country code. The remaining digits are then followed. Only digits", "is None: opts.version = _utilities.get_version() if opts.id is None: if", "None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:eventgrid:PartnerRegistration\"),", "integration on an event source. \"\"\" return pulumi.get(self, \"setup_uri\") @property", "Options for the resource. \"\"\" opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__", "partner_resource_type_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]]", "of the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @property @pulumi.getter(name=\"longDescription\") def", "\"\"\" Official name of the partner name. For example: \"Contoso\".", "value: Optional[pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']]]): pulumi.set(self, \"visibility_state\", value) class PartnerRegistration(pulumi.CustomResource): @overload def", "value: Optional[pulumi.Input[str]]): pulumi.set(self, \"partner_customer_service_number\", value) @property @pulumi.getter(name=\"partnerName\") def partner_name(self) ->", "__props__ is not None: raise TypeError('__props__ is only valid when", "None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, setup_uri:", "Do not edit by hand unless you're certain you know", "should not exceed 256 characters. \"\"\" return pulumi.get(self, \"partner_resource_type_description\") @partner_resource_type_description.setter", "number of the publisher. Only digits are allowed and number", "characters. \"\"\" return pulumi.get(self, \"long_description\") @property @pulumi.getter def name(self) ->", "partner registration. :param pulumi.Input[str] partner_resource_type_description: Short description of the partner", "allowed and number of digits should not exceed 10. \"\"\"", "\"long_description\") @long_description.setter def long_description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, \"long_description\", value) @property", "service URI of the publisher. \"\"\" return pulumi.get(self, \"customer_service_uri\") @customer_service_uri.setter", "The name of the resource. :param PartnerRegistrationArgs args: The arguments", "@tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, \"tags\", value) @property", "= partner_resource_type_name if resource_group_name is None and not opts.urn: raise", "numbers are: +1 515 123 4567 and +966 7 5115", "the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_name\") @property @pulumi.getter(name=\"provisioningState\")", "one used for creating the partner registration. :param pulumi.Input[str] customer_service_uri:", "\"\"\" ... @overload def __init__(__self__, resource_name: str, args: PartnerRegistrationArgs, opts:", "URI of the logo. :param pulumi.Input[str] long_description: Long description for", "isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions", "PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"] = None", "the partner resource type. \"\"\" return pulumi.get(self, \"partner_resource_type_display_name\") @partner_resource_type_display_name.setter def", "def setup_uri(self) -> Optional[pulumi.Input[str]]: \"\"\" URI of the partner website", "description should not exceed 256 characters. :param pulumi.Input[str] partner_resource_type_display_name: Display", "the country code. The remaining digits are then followed. Only", "Visibility state of the partner registration. \"\"\" return pulumi.get(self, \"visibility_state\")", "__props__.__dict__[\"partner_customer_service_extension\"] = None __props__.__dict__[\"partner_customer_service_number\"] = None __props__.__dict__[\"partner_name\"] = None __props__.__dict__[\"partner_resource_type_description\"]", "logo_uri: Optional[pulumi.Input[str]] = None, long_description: Optional[pulumi.Input[str]] = None, partner_customer_service_extension: Optional[pulumi.Input[str]]", "-> pulumi.Output[Optional[str]]: \"\"\" Official name of the partner name. For", "name of the resource group within the user's subscription. \"\"\"", "is only valid when passed in combination with a valid", "Optional[pulumi.Input[str]] = None, partner_customer_service_number: Optional[pulumi.Input[str]] = None, partner_name: Optional[pulumi.Input[str]] =", "Provisioning state of the partner registration. \"\"\" return pulumi.get(self, \"provisioning_state\")", "of the partner resource type. :param pulumi.Input[str] resource_group_name: The name", "None: pulumi.set(__self__, \"logo_uri\", logo_uri) if long_description is not None: pulumi.set(__self__,", "def partner_customer_service_number(self) -> pulumi.Output[Optional[str]]: \"\"\" The customer service number of", "None: pulumi.set(__self__, \"partner_registration_name\", partner_registration_name) if partner_resource_type_description is not None: pulumi.set(__self__,", "the logo. :param pulumi.Input[str] long_description: Long description for the custom", "None and not opts.urn: raise TypeError(\"Missing required property 'resource_group_name'\") __props__.__dict__[\"resource_group_name\"]", "-> pulumi.Output[Optional[str]]: \"\"\" Short description of the partner resource type.", "__props__ = PartnerRegistrationArgs.__new__(PartnerRegistrationArgs) __props__.__dict__[\"authorized_azure_subscription_ids\"] = None __props__.__dict__[\"customer_service_uri\"] = None __props__.__dict__[\"location\"]", "= None, resource_group_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None,", "= None, partner_resource_type_name: Optional[pulumi.Input[str]] = None, setup_uri: Optional[pulumi.Input[str]] = None,", "of the resource. :param pulumi.Input[Union[str, 'PartnerRegistrationVisibilityState']] visibility_state: Visibility state of", "pulumi.set(__self__, \"long_description\", long_description) if partner_customer_service_extension is not None: pulumi.set(__self__, \"partner_customer_service_extension\",", "\"\"\" The name of the resource group within the user's", "group within the user's subscription. \"\"\" return pulumi.get(self, \"resource_group_name\") @resource_group_name.setter", "__props__.__dict__[\"name\"] = None __props__.__dict__[\"provisioning_state\"] = None __props__.__dict__[\"system_data\"] = None __props__.__dict__[\"type\"]", "the logo. \"\"\" return pulumi.get(self, \"logo_uri\") @logo_uri.setter def logo_uri(self, value:", "def __init__(__self__, resource_name: str, args: PartnerRegistrationArgs, opts: Optional[pulumi.ResourceOptions] = None):", "authorized_azure_subscription_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: \"\"\" List of Azure subscription Ids that", "partner name. For example: \"Contoso\". \"\"\" return pulumi.get(self, \"partner_name\") @partner_name.setter", "Optional[pulumi.Input[str]]: \"\"\" The extension of the customer service number of" ]
[ "torch.squeeze(tensor, 0) tensor = torch.mean(tensor, 0) tensor = tensor.detach().cpu().numpy() tensor", "112 x = model.bn1(x) x = model.relu(x) x = model.maxpool(x)", "axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1), ) # np.concatenate((image, heat_1,", "image = cv2.imread(image_path) image = cv2.resize(image, (224, 224)) tensor =", "model.avgpool(x) x = torch.flatten(x, 1) output = model.fc(x) # print(np.sum(heat_1", "np.concatenate((image, heat_1, heat_2), axis=1)) # output = output.cpu().numpy() # print(EMOTION_DICT[torch.argmax(output,", "* (1 + m) x = model.layer3(x) # 14 heat_1", "* (1 + m) x = model.layer2(x) # 28 m", "m = model.mask2(x) x = x * (1 + m)", "# 28 m = model.mask2(x) x = x * (1", "28 m = model.mask2(x) x = x * (1 +", "] ) def activations_mask(tensor): tensor = torch.squeeze(tensor, 0) tensor =", "state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for", "= model.layer4(x) # 7 m = model.mask4(x) x = x", "# print(np.sum(heat_1 - heat_2)) # show(np.concatenate((image, heat_1, heat_2), axis=1)) cv2.imwrite(", "0) tensor = tensor.cuda() # output = model(tensor) x =", "model.mask1(x) x = x * (1 + m) x =", "import os import glob import cv2 import numpy as np", "= model.layer2(x) # 28 m = model.mask2(x) x = x", "= model.fc(x) # print(np.sum(heat_1 - heat_2)) # show(np.concatenate((image, heat_1, heat_2),", "model.fc(x) # print(np.sum(heat_1 - heat_2)) # show(np.concatenate((image, heat_1, heat_2), axis=1))", "import resmasking_dropout1 from utils.datasets.fer2013dataset import EMOTION_DICT from barez import show", "np.concatenate((image, heat_1), axis=1), ) # np.concatenate((image, heat_1, heat_2), axis=1)) #", "x = model.layer3(x) # 14 heat_1 = activations_mask(x) m =", "# image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path) image = cv2.resize(image,", "= model.avgpool(x) x = torch.flatten(x, 1) output = model.fc(x) #", "heatmap model = resmasking_dropout1(3, 7) # state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state", "= tensor.detach().cpu().numpy() tensor = np.maximum(tensor, 0) tensor = cv2.resize(tensor, (224,", "model.conv1(tensor) # 112 x = model.bn1(x) x = model.relu(x) x", "= cv2.resize(image, (224, 224)) tensor = transform(image) tensor = torch.unsqueeze(tensor,", "activations_mask(tensor): tensor = torch.squeeze(tensor, 0) tensor = torch.mean(tensor, 0) tensor", "# 14 heat_1 = activations_mask(x) m = model.mask3(x) x =", "cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET) return heatmap model = resmasking_dropout1(3, 7)", "224)) tensor = transform(image) tensor = torch.unsqueeze(tensor, 0) tensor =", "+ m) x = model.avgpool(x) x = torch.flatten(x, 1) output", "= model(tensor) x = model.conv1(tensor) # 112 x = model.bn1(x)", "from torchvision.transforms import transforms from natsort import natsorted from models", "# 7 m = model.mask4(x) x = x * (1", "(1 + m) x = model.layer3(x) # 14 heat_1 =", "transform = transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(), ] ) def activations_mask(tensor):", "tensor / np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET) return", "= tensor - np.min(tensor) tensor = tensor / np.max(tensor) heatmap", "0) tensor = torch.mean(tensor, 0) tensor = tensor.detach().cpu().numpy() tensor =", "= model.layer3(x) # 14 heat_1 = activations_mask(x) m = model.mask3(x)", "x = model.bn1(x) x = model.relu(x) x = model.maxpool(x) #", "= x * (1 + m) # heat_2 = activations_mask(m)", "transforms.ToPILImage(), transforms.ToTensor(), ] ) def activations_mask(tensor): tensor = torch.squeeze(tensor, 0)", "model.maxpool(x) # 56 x = model.layer1(x) # 56 m =", "= cv2.imread(image_path) image = cv2.resize(image, (224, 224)) tensor = transform(image)", "natsorted from models import resmasking_dropout1 from utils.datasets.fer2013dataset import EMOTION_DICT from", "image_name = os.path.basename(image_path) print(image_name) # image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image =", "import natsorted from models import resmasking_dropout1 from utils.datasets.fer2013dataset import EMOTION_DICT", "output = model(tensor) x = model.conv1(tensor) # 112 x =", "14 heat_1 = activations_mask(x) m = model.mask3(x) x = x", "torch.mean(tensor, 0) tensor = tensor.detach().cpu().numpy() tensor = np.maximum(tensor, 0) tensor", "# output = model(tensor) x = model.conv1(tensor) # 112 x", "heat_1, heat_2), axis=1)) # output = output.cpu().numpy() # print(EMOTION_DICT[torch.argmax(output, 1).item()])", "(224, 224)) tensor = transform(image) tensor = torch.unsqueeze(tensor, 0) tensor", "= x * (1 + m) x = model.layer3(x) #", "= torch.unsqueeze(tensor, 0) tensor = tensor.cuda() # output = model(tensor)", "= torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for image_path", "tensor = tensor.cuda() # output = model(tensor) x = model.conv1(tensor)", "in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name = os.path.basename(image_path) print(image_name) #", "transforms.ToTensor(), ] ) def activations_mask(tensor): tensor = torch.squeeze(tensor, 0) tensor", "= activations_mask(x) m = model.mask3(x) x = x * (1", "glob import cv2 import numpy as np import torch from", "heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1), ) # np.concatenate((image,", "= model.conv1(tensor) # 112 x = model.bn1(x) x = model.relu(x)", "tensor), cv2.COLORMAP_JET) return heatmap model = resmasking_dropout1(3, 7) # state", "cv2.COLORMAP_JET) return heatmap model = resmasking_dropout1(3, 7) # state =", "model.relu(x) x = model.maxpool(x) # 56 x = model.layer1(x) #", "= activations_mask(m) x = model.layer4(x) # 7 m = model.mask4(x)", "0) tensor = tensor.detach().cpu().numpy() tensor = np.maximum(tensor, 0) tensor =", "m) # heat_2 = activations_mask(m) x = model.layer4(x) # 7", "heat_1 = activations_mask(x) m = model.mask3(x) x = x *", "activations_mask(m) x = model.layer4(x) # 7 m = model.mask4(x) x", "224)) tensor = tensor - np.min(tensor) tensor = tensor /", "barez import show transform = transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(), ]", "= model.mask2(x) x = x * (1 + m) x", "model.layer3(x) # 14 heat_1 = activations_mask(x) m = model.mask3(x) x", "= transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(), ] ) def activations_mask(tensor): tensor", "os.path.basename(image_path) print(image_name) # image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path) image", "import show transform = transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(), ] )", "= transform(image) tensor = torch.unsqueeze(tensor, 0) tensor = tensor.cuda() #", "7) # state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda()", "# 56 x = model.layer1(x) # 56 m = model.mask1(x)", "torchvision.transforms import transforms from natsort import natsorted from models import", "= cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET) return heatmap model = resmasking_dropout1(3,", "heat_1, heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1), ) #", "\"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1), ) # np.concatenate((image, heat_1, heat_2), axis=1))", "= resmasking_dropout1(3, 7) # state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\")", "tensor = tensor.detach().cpu().numpy() tensor = np.maximum(tensor, 0) tensor = cv2.resize(tensor,", "56 m = model.mask1(x) x = x * (1 +", "= model.mask3(x) x = x * (1 + m) #", "[ transforms.ToPILImage(), transforms.ToTensor(), ] ) def activations_mask(tensor): tensor = torch.squeeze(tensor,", "= cv2.resize(tensor, (224, 224)) tensor = tensor - np.min(tensor) tensor", "np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET) return heatmap model", "7 m = model.mask4(x) x = x * (1 +", "model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ):", "m) x = model.avgpool(x) x = torch.flatten(x, 1) output =", "x = model.conv1(tensor) # 112 x = model.bn1(x) x =", "os import glob import cv2 import numpy as np import", "# 112 x = model.bn1(x) x = model.relu(x) x =", "import numpy as np import torch from torchvision.transforms import transforms", "cv2.resize(image, (224, 224)) tensor = transform(image) tensor = torch.unsqueeze(tensor, 0)", "torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True)", "output = model.fc(x) # print(np.sum(heat_1 - heat_2)) # show(np.concatenate((image, heat_1,", "= '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path) image = cv2.resize(image, (224, 224))", "cv2.resize(tensor, (224, 224)) tensor = tensor - np.min(tensor) tensor =", "cv2.imread(image_path) image = cv2.resize(image, (224, 224)) tensor = transform(image) tensor", "): image_name = os.path.basename(image_path) print(image_name) # image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image", "= model.bn1(x) x = model.relu(x) x = model.maxpool(x) # 56", "tensor.detach().cpu().numpy() tensor = np.maximum(tensor, 0) tensor = cv2.resize(tensor, (224, 224))", "x = model.layer2(x) # 28 m = model.mask2(x) x =", "m = model.mask1(x) x = x * (1 + m)", "tensor = transform(image) tensor = torch.unsqueeze(tensor, 0) tensor = tensor.cuda()", "models import resmasking_dropout1 from utils.datasets.fer2013dataset import EMOTION_DICT from barez import", "+ m) x = model.layer2(x) # 28 m = model.mask2(x)", "model.layer4(x) # 7 m = model.mask4(x) x = x *", "= x * (1 + m) x = model.avgpool(x) x", "def activations_mask(tensor): tensor = torch.squeeze(tensor, 0) tensor = torch.mean(tensor, 0)", "* (1 + m) x = model.avgpool(x) x = torch.flatten(x,", "= torch.mean(tensor, 0) tensor = tensor.detach().cpu().numpy() tensor = np.maximum(tensor, 0)", "model.layer2(x) # 28 m = model.mask2(x) x = x *", "transforms from natsort import natsorted from models import resmasking_dropout1 from", "# heat_2 = activations_mask(m) x = model.layer4(x) # 7 m", "/ np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET) return heatmap", "heat_2 = activations_mask(m) x = model.layer4(x) # 7 m =", "model.mask4(x) x = x * (1 + m) x =", "import glob import cv2 import numpy as np import torch", "# state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval()", "tensor = cv2.resize(tensor, (224, 224)) tensor = tensor - np.min(tensor)", "x * (1 + m) x = model.layer2(x) # 28", "x = x * (1 + m) # heat_2 =", "heat_1), axis=1), ) # np.concatenate((image, heat_1, heat_2), axis=1)) # output", "image = cv2.resize(image, (224, 224)) tensor = transform(image) tensor =", "tensor - np.min(tensor) tensor = tensor / np.max(tensor) heatmap =", "= torch.flatten(x, 1) output = model.fc(x) # print(np.sum(heat_1 - heat_2))", "torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for image_path in", "tensor = torch.unsqueeze(tensor, 0) tensor = tensor.cuda() # output =", "model.cuda() model.eval() for image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name", "x = model.layer4(x) # 7 m = model.mask4(x) x =", "0) tensor = cv2.resize(tensor, (224, 224)) tensor = tensor -", "x = x * (1 + m) x = model.avgpool(x)", "model.mask3(x) x = x * (1 + m) # heat_2", "m = model.mask4(x) x = x * (1 + m)", "x = x * (1 + m) x = model.layer2(x)", "(1 + m) x = model.avgpool(x) x = torch.flatten(x, 1)", "for image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name = os.path.basename(image_path)", "cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1), ) # np.concatenate((image, heat_1, heat_2),", "1) output = model.fc(x) # print(np.sum(heat_1 - heat_2)) # show(np.concatenate((image,", "resmasking_dropout1 from utils.datasets.fer2013dataset import EMOTION_DICT from barez import show transform", "natsort import natsorted from models import resmasking_dropout1 from utils.datasets.fer2013dataset import", "- np.min(tensor) tensor = tensor / np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255", "= torch.squeeze(tensor, 0) tensor = torch.mean(tensor, 0) tensor = tensor.detach().cpu().numpy()", "from barez import show transform = transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(),", "# 56 m = model.mask1(x) x = x * (1", "x * (1 + m) x = model.avgpool(x) x =", "+ m) x = model.layer3(x) # 14 heat_1 = activations_mask(x)", "x * (1 + m) # heat_2 = activations_mask(m) x", "torch.unsqueeze(tensor, 0) tensor = tensor.cuda() # output = model(tensor) x", "print(np.sum(heat_1 - heat_2)) # show(np.concatenate((image, heat_1, heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name),", "m) x = model.layer2(x) # 28 m = model.mask2(x) x", "import transforms from natsort import natsorted from models import resmasking_dropout1", "= np.maximum(tensor, 0) tensor = cv2.resize(tensor, (224, 224)) tensor =", "heat_2)) # show(np.concatenate((image, heat_1, heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1),", "natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name = os.path.basename(image_path) print(image_name) # image_path", "recursive=True) ): image_name = os.path.basename(image_path) print(image_name) # image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png'", "show(np.concatenate((image, heat_1, heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1), )", "x = model.avgpool(x) x = torch.flatten(x, 1) output = model.fc(x)", "tensor.cuda() # output = model(tensor) x = model.conv1(tensor) # 112", "= torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\",", "from natsort import natsorted from models import resmasking_dropout1 from utils.datasets.fer2013dataset", ") def activations_mask(tensor): tensor = torch.squeeze(tensor, 0) tensor = torch.mean(tensor,", "model(tensor) x = model.conv1(tensor) # 112 x = model.bn1(x) x", "image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name = os.path.basename(image_path) print(image_name)", "torch.flatten(x, 1) output = model.fc(x) # print(np.sum(heat_1 - heat_2)) #", "* tensor), cv2.COLORMAP_JET) return heatmap model = resmasking_dropout1(3, 7) #", "show transform = transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(), ] ) def", "numpy as np import torch from torchvision.transforms import transforms from", "heatmap = cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET) return heatmap model =", "m) x = model.layer3(x) # 14 heat_1 = activations_mask(x) m", "x = model.relu(x) x = model.maxpool(x) # 56 x =", "= model.layer1(x) # 56 m = model.mask1(x) x = x", ") # np.concatenate((image, heat_1, heat_2), axis=1)) # output = output.cpu().numpy()", "x = torch.flatten(x, 1) output = model.fc(x) # print(np.sum(heat_1 -", "np.min(tensor) tensor = tensor / np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255 *", "model.eval() for image_path in natsorted( glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name =", "from utils.datasets.fer2013dataset import EMOTION_DICT from barez import show transform =", "tensor = torch.mean(tensor, 0) tensor = tensor.detach().cpu().numpy() tensor = np.maximum(tensor,", "model = resmasking_dropout1(3, 7) # state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state =", "- heat_2)) # show(np.concatenate((image, heat_1, heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image,", "= tensor / np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255 * tensor), cv2.COLORMAP_JET)", "model.mask2(x) x = x * (1 + m) x =", "(1 + m) x = model.layer2(x) # 28 m =", "x * (1 + m) x = model.layer3(x) # 14", "= tensor.cuda() # output = model(tensor) x = model.conv1(tensor) #", "cv2 import numpy as np import torch from torchvision.transforms import", "= model.mask1(x) x = x * (1 + m) x", "(224, 224)) tensor = tensor - np.min(tensor) tensor = tensor", "= x * (1 + m) x = model.layer2(x) #", "from models import resmasking_dropout1 from utils.datasets.fer2013dataset import EMOTION_DICT from barez", "x = model.maxpool(x) # 56 x = model.layer1(x) # 56", "* (1 + m) # heat_2 = activations_mask(m) x =", "56 x = model.layer1(x) # 56 m = model.mask1(x) x", "m = model.mask3(x) x = x * (1 + m)", "# np.concatenate((image, heat_1, heat_2), axis=1)) # output = output.cpu().numpy() #", "np import torch from torchvision.transforms import transforms from natsort import", "= os.path.basename(image_path) print(image_name) # image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path)", "x = model.layer1(x) # 56 m = model.mask1(x) x =", "(1 + m) # heat_2 = activations_mask(m) x = model.layer4(x)", "+ m) # heat_2 = activations_mask(m) x = model.layer4(x) #", "import torch from torchvision.transforms import transforms from natsort import natsorted", "'/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path) image = cv2.resize(image, (224, 224)) tensor", "resmasking_dropout1(3, 7) # state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33') state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"])", "np.maximum(tensor, 0) tensor = cv2.resize(tensor, (224, 224)) tensor = tensor", "x = x * (1 + m) x = model.layer3(x)", "activations_mask(x) m = model.mask3(x) x = x * (1 +", "import EMOTION_DICT from barez import show transform = transforms.Compose( [", "model.layer1(x) # 56 m = model.mask1(x) x = x *", "utils.datasets.fer2013dataset import EMOTION_DICT from barez import show transform = transforms.Compose(", "EMOTION_DICT from barez import show transform = transforms.Compose( [ transforms.ToPILImage(),", "transform(image) tensor = torch.unsqueeze(tensor, 0) tensor = tensor.cuda() # output", "# show(np.concatenate((image, heat_1, heat_2), axis=1)) cv2.imwrite( \"./masking_provements/{}\".format(image_name), np.concatenate((image, heat_1), axis=1),", "print(image_name) # image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path) image =", "transforms.Compose( [ transforms.ToPILImage(), transforms.ToTensor(), ] ) def activations_mask(tensor): tensor =", "torch from torchvision.transforms import transforms from natsort import natsorted from", "tensor = tensor - np.min(tensor) tensor = tensor / np.max(tensor)", "state = torch.load(\"./saved/checkpoints/Z_resmasking_dropout1_rot30_2019Nov30_13.32\") model.load_state_dict(state[\"net\"]) model.cuda() model.eval() for image_path in natsorted(", "= model.relu(x) x = model.maxpool(x) # 56 x = model.layer1(x)", "tensor = np.maximum(tensor, 0) tensor = cv2.resize(tensor, (224, 224)) tensor", "axis=1), ) # np.concatenate((image, heat_1, heat_2), axis=1)) # output =", "as np import torch from torchvision.transforms import transforms from natsort", "image_path = '/home/z/research/bkemo/images/disgust/0.0_dc10a3_1976_0.png' image = cv2.imread(image_path) image = cv2.resize(image, (224,", "glob.glob(\"/home/z/research/bkemo/images/**/*.png\", recursive=True) ): image_name = os.path.basename(image_path) print(image_name) # image_path =", "import cv2 import numpy as np import torch from torchvision.transforms", "model.bn1(x) x = model.relu(x) x = model.maxpool(x) # 56 x", "tensor = torch.squeeze(tensor, 0) tensor = torch.mean(tensor, 0) tensor =", "return heatmap model = resmasking_dropout1(3, 7) # state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33')", "tensor = tensor / np.max(tensor) heatmap = cv2.applyColorMap(np.uint8(255 * tensor),", "= model.maxpool(x) # 56 x = model.layer1(x) # 56 m", "= model.mask4(x) x = x * (1 + m) x" ]
[ "(preço * 20 / 100)) / parcelas:.2f}') print(f'Sendo assim as", "= str(input('Quer consultar novamente? ')) if consulta in ['sim', 'Sim',", "cartão: preço formal. [ 4 ]3x ou mais no cartão:", "elif opção == 4: parcelas = int(input('Quantas parcelas: ')) if", "de R${(preço + (preço * 20 / 100)) / parcelas:.2f}')", "colorama def gerenciador_de_pagamento(): preço = float(str(input('Preço das compras: R$'))) print('''Escolha", "[ 1 ]A vista dinheiro/cheque: 10% de desconto. [ 2", "print(f'Sendo assim custando o preço formal de R${preço:.2f} no final.')", "')) print('processando...') time.sleep(2) if opção == 1: print('Você ganhará 10%", "de desconto!') print(f'Sendo assim as compras custaram R${preço - (preço", "as compras custaram R${preço - (preço * 5 /100):.2f}') elif", "cartão: 20% de juros.''') opção = int(input('Opção de pagamento: '))", "de R${preço / 2:.2f}.') print(f'Sendo assim custando o preço formal", "def gerenciador_de_pagamento(): preço = float(str(input('Preço das compras: R$'))) print('''Escolha de", "custaram R${preço + (preço * 20 / 100):.2f} no final.')", "consulta = gerenciador_de_pagamento() consulta = str(input('Quer consultar novamente? ')) if", "gerenciador_de_pagamento() consulta = str(input('Quer consultar novamente? ')) if consulta in", "print(f'As compras sairam em {parcelas}x de R${(preço + (preço *", ">= 3: print(f'Compras com 20% de juros') print(f'As compras sairam", "no cartão: preço formal. [ 4 ]3x ou mais no", "time.sleep(2) if opção == 1: print('Você ganhará 10% de desconto!')", "R${preço:.2f} no final.') elif opção == 4: parcelas = int(input('Quantas", "2:.2f}.') print(f'Sendo assim custando o preço formal de R${preço:.2f} no", "10% de desconto. [ 2 ]A vista no cartão: 5%", "1: print('Você ganhará 10% de desconto!') print(f'Sendo assim as compras", "gerenciador_de_pagamento() return opção while True: consulta = gerenciador_de_pagamento() consulta =", "while True: consulta = gerenciador_de_pagamento() consulta = str(input('Quer consultar novamente?", "elif opção == 3: print(f'As compras sairam em 2x de", "compras: R$'))) print('''Escolha de pagamento: [ 1 ]A vista dinheiro/cheque:", "dinheiro/cheque: 10% de desconto. [ 2 ]A vista no cartão:", "as compras custaram R${preço - (preço * 10 / 100", "10 / 100 ):.2f}.') elif opção == 2: print('Você ganhará", "print('Você ganhará 5% de desconto!') print(f'Sendo assim as compras custaram", "no final.') elif opção == 4: parcelas = int(input('Quantas parcelas:", "True: consulta = gerenciador_de_pagamento() consulta = str(input('Quer consultar novamente? '))", "4 ]3x ou mais no cartão: 20% de juros.''') opção", "formal. [ 4 ]3x ou mais no cartão: 20% de", "if opção == 1: print('Você ganhará 10% de desconto!') print(f'Sendo", "]3x ou mais no cartão: 20% de juros.''') opção =", "5% de desconto!') print(f'Sendo assim as compras custaram R${preço -", "sairam em 2x de R${preço / 2:.2f}.') print(f'Sendo assim custando", "opção == 4: parcelas = int(input('Quantas parcelas: ')) if parcelas", "preço formal de R${preço:.2f} no final.') elif opção == 4:", "gerenciador_de_pagamento(): preço = float(str(input('Preço das compras: R$'))) print('''Escolha de pagamento:", "else: print('Parcela não compreendida, TENTE NOVAMENTE...') else: print('Valor não compreendido,", "3: print(f'As compras sairam em 2x de R${preço / 2:.2f}.')", "* 5 /100):.2f}') elif opção == 3: print(f'As compras sairam", "== 4: parcelas = int(input('Quantas parcelas: ')) if parcelas >=", "print(f'Sendo assim as compras custaram R${preço + (preço * 20", "opção == 3: print(f'As compras sairam em 2x de R${preço", "'Sim', 'SIM']: pass elif consulta in ['não', 'nao','Não', 'Nao', 'NAO','NÃO']:", "1 ]A vista dinheiro/cheque: 10% de desconto. [ 2 ]A", "preço formal. [ 4 ]3x ou mais no cartão: 20%", "as compras custaram R${preço + (preço * 20 / 100):.2f}", "(preço * 20 / 100):.2f} no final.') else: print('Parcela não", "4: parcelas = int(input('Quantas parcelas: ')) if parcelas >= 3:", "+ (preço * 20 / 100)) / parcelas:.2f}') print(f'Sendo assim", "= gerenciador_de_pagamento() consulta = str(input('Quer consultar novamente? ')) if consulta", "import time import colorama def gerenciador_de_pagamento(): preço = float(str(input('Preço das", "cartão: 5% de desconto. [ 3 ]Em até duas 2x", "consulta in ['sim', 'Sim', 'SIM']: pass elif consulta in ['não',", "= int(input('Quantas parcelas: ')) if parcelas >= 3: print(f'Compras com", "100 ):.2f}.') elif opção == 2: print('Você ganhará 5% de", "juros') print(f'As compras sairam em {parcelas}x de R${(preço + (preço", "time import colorama def gerenciador_de_pagamento(): preço = float(str(input('Preço das compras:", "):.2f}.') elif opção == 2: print('Você ganhará 5% de desconto!')", "2x de R${preço / 2:.2f}.') print(f'Sendo assim custando o preço", "ou mais no cartão: 20% de juros.''') opção = int(input('Opção", "com 20% de juros') print(f'As compras sairam em {parcelas}x de", "compras sairam em 2x de R${preço / 2:.2f}.') print(f'Sendo assim", "desconto. [ 3 ]Em até duas 2x no cartão: preço", "print('processando...') time.sleep(2) if opção == 1: print('Você ganhará 10% de", "10% de desconto!') print(f'Sendo assim as compras custaram R${preço -", "compras custaram R${preço - (preço * 10 / 100 ):.2f}.')", "consultar novamente? ')) if consulta in ['sim', 'Sim', 'SIM']: pass", "/ 2:.2f}.') print(f'Sendo assim custando o preço formal de R${preço:.2f}", "opção = int(input('Opção de pagamento: ')) print('processando...') time.sleep(2) if opção", "== 3: print(f'As compras sairam em 2x de R${preço /", "opção while True: consulta = gerenciador_de_pagamento() consulta = str(input('Quer consultar", "3 ]Em até duas 2x no cartão: preço formal. [", "TENTE NOVAMENTE...') gerenciador_de_pagamento() return opção while True: consulta = gerenciador_de_pagamento()", "custaram R${preço - (preço * 5 /100):.2f}') elif opção ==", "3: print(f'Compras com 20% de juros') print(f'As compras sairam em", "else: print('Valor não compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento() return opção while", "desconto!') print(f'Sendo assim as compras custaram R${preço - (preço *", "2x no cartão: preço formal. [ 4 ]3x ou mais", "consulta = str(input('Quer consultar novamente? ')) if consulta in ['sim',", "R${(preço + (preço * 20 / 100)) / parcelas:.2f}') print(f'Sendo", "== 2: print('Você ganhará 5% de desconto!') print(f'Sendo assim as", "2: print('Você ganhará 5% de desconto!') print(f'Sendo assim as compras", "R${preço + (preço * 20 / 100):.2f} no final.') else:", "de juros.''') opção = int(input('Opção de pagamento: ')) print('processando...') time.sleep(2)", "/100):.2f}') elif opção == 3: print(f'As compras sairam em 2x", "parcelas = int(input('Quantas parcelas: ')) if parcelas >= 3: print(f'Compras", "compras custaram R${preço - (preço * 5 /100):.2f}') elif opção", "assim as compras custaram R${preço + (preço * 20 /", "R${preço - (preço * 5 /100):.2f}') elif opção == 3:", "TENTE NOVAMENTE...') else: print('Valor não compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento() return", "em {parcelas}x de R${(preço + (preço * 20 / 100))", "]A vista no cartão: 5% de desconto. [ 3 ]Em", "de juros') print(f'As compras sairam em {parcelas}x de R${(preço +", "formal de R${preço:.2f} no final.') elif opção == 4: parcelas", "2 ]A vista no cartão: 5% de desconto. [ 3", "'SIM']: pass elif consulta in ['não', 'nao','Não', 'Nao', 'NAO','NÃO']: break", "int(input('Quantas parcelas: ')) if parcelas >= 3: print(f'Compras com 20%", "20% de juros') print(f'As compras sairam em {parcelas}x de R${(preço", "no cartão: 5% de desconto. [ 3 ]Em até duas", "print('Parcela não compreendida, TENTE NOVAMENTE...') else: print('Valor não compreendido, TENTE", "(preço * 10 / 100 ):.2f}.') elif opção == 2:", "não compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento() return opção while True: consulta", "* 10 / 100 ):.2f}.') elif opção == 2: print('Você", "assim as compras custaram R${preço - (preço * 5 /100):.2f}')", "100):.2f} no final.') else: print('Parcela não compreendida, TENTE NOVAMENTE...') else:", "5% de desconto. [ 3 ]Em até duas 2x no", "pagamento: [ 1 ]A vista dinheiro/cheque: 10% de desconto. [", "vista no cartão: 5% de desconto. [ 3 ]Em até", "ganhará 10% de desconto!') print(f'Sendo assim as compras custaram R${preço", "R${preço - (preço * 10 / 100 ):.2f}.') elif opção", "print(f'As compras sairam em 2x de R${preço / 2:.2f}.') print(f'Sendo", "R$'))) print('''Escolha de pagamento: [ 1 ]A vista dinheiro/cheque: 10%", "- (preço * 5 /100):.2f}') elif opção == 3: print(f'As", "5 /100):.2f}') elif opção == 3: print(f'As compras sairam em", "custaram R${preço - (preço * 10 / 100 ):.2f}.') elif", "sairam em {parcelas}x de R${(preço + (preço * 20 /", "NOVAMENTE...') gerenciador_de_pagamento() return opção while True: consulta = gerenciador_de_pagamento() consulta", "['sim', 'Sim', 'SIM']: pass elif consulta in ['não', 'nao','Não', 'Nao',", "parcelas >= 3: print(f'Compras com 20% de juros') print(f'As compras", "de pagamento: ')) print('processando...') time.sleep(2) if opção == 1: print('Você", "de R${preço:.2f} no final.') elif opção == 4: parcelas =", "em 2x de R${preço / 2:.2f}.') print(f'Sendo assim custando o", "print('Valor não compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento() return opção while True:", "if parcelas >= 3: print(f'Compras com 20% de juros') print(f'As", "parcelas: ')) if parcelas >= 3: print(f'Compras com 20% de", "* 20 / 100):.2f} no final.') else: print('Parcela não compreendida,", "assim as compras custaram R${preço - (preço * 10 /", "das compras: R$'))) print('''Escolha de pagamento: [ 1 ]A vista", "final.') else: print('Parcela não compreendida, TENTE NOVAMENTE...') else: print('Valor não", "pagamento: ')) print('processando...') time.sleep(2) if opção == 1: print('Você ganhará", "duas 2x no cartão: preço formal. [ 4 ]3x ou", "+ (preço * 20 / 100):.2f} no final.') else: print('Parcela", "de desconto. [ 3 ]Em até duas 2x no cartão:", "não compreendida, TENTE NOVAMENTE...') else: print('Valor não compreendido, TENTE NOVAMENTE...')", "compreendida, TENTE NOVAMENTE...') else: print('Valor não compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento()", "if consulta in ['sim', 'Sim', 'SIM']: pass elif consulta in", "[ 3 ]Em até duas 2x no cartão: preço formal.", "import colorama def gerenciador_de_pagamento(): preço = float(str(input('Preço das compras: R$')))", "compras custaram R${preço + (preço * 20 / 100):.2f} no", "mais no cartão: 20% de juros.''') opção = int(input('Opção de", "parcelas:.2f}') print(f'Sendo assim as compras custaram R${preço + (preço *", "print(f'Compras com 20% de juros') print(f'As compras sairam em {parcelas}x", "novamente? ')) if consulta in ['sim', 'Sim', 'SIM']: pass elif", "return opção while True: consulta = gerenciador_de_pagamento() consulta = str(input('Quer", "compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento() return opção while True: consulta =", "[ 4 ]3x ou mais no cartão: 20% de juros.''')", "float(str(input('Preço das compras: R$'))) print('''Escolha de pagamento: [ 1 ]A", "elif opção == 2: print('Você ganhará 5% de desconto!') print(f'Sendo", "no cartão: 20% de juros.''') opção = int(input('Opção de pagamento:", "]Em até duas 2x no cartão: preço formal. [ 4", "ganhará 5% de desconto!') print(f'Sendo assim as compras custaram R${preço", "compras sairam em {parcelas}x de R${(preço + (preço * 20", "/ parcelas:.2f}') print(f'Sendo assim as compras custaram R${preço + (preço", "= int(input('Opção de pagamento: ')) print('processando...') time.sleep(2) if opção ==", "{parcelas}x de R${(preço + (preço * 20 / 100)) /", "de pagamento: [ 1 ]A vista dinheiro/cheque: 10% de desconto.", "elif consulta in ['não', 'nao','Não', 'Nao', 'NAO','NÃO']: break else: break", "pass elif consulta in ['não', 'nao','Não', 'Nao', 'NAO','NÃO']: break else:", "vista dinheiro/cheque: 10% de desconto. [ 2 ]A vista no", "]A vista dinheiro/cheque: 10% de desconto. [ 2 ]A vista", "/ 100 ):.2f}.') elif opção == 2: print('Você ganhará 5%", "print('''Escolha de pagamento: [ 1 ]A vista dinheiro/cheque: 10% de", "assim custando o preço formal de R${preço:.2f} no final.') elif", "no final.') else: print('Parcela não compreendida, TENTE NOVAMENTE...') else: print('Valor", "até duas 2x no cartão: preço formal. [ 4 ]3x", "20% de juros.''') opção = int(input('Opção de pagamento: ')) print('processando...')", "preço = float(str(input('Preço das compras: R$'))) print('''Escolha de pagamento: [", "== 1: print('Você ganhará 10% de desconto!') print(f'Sendo assim as", "[ 2 ]A vista no cartão: 5% de desconto. [", "20 / 100)) / parcelas:.2f}') print(f'Sendo assim as compras custaram", "<gh_stars>1-10 import time import colorama def gerenciador_de_pagamento(): preço = float(str(input('Preço", "int(input('Opção de pagamento: ')) print('processando...') time.sleep(2) if opção == 1:", "print('Você ganhará 10% de desconto!') print(f'Sendo assim as compras custaram", "/ 100)) / parcelas:.2f}') print(f'Sendo assim as compras custaram R${preço", "* 20 / 100)) / parcelas:.2f}') print(f'Sendo assim as compras", "(preço * 5 /100):.2f}') elif opção == 3: print(f'As compras", "100)) / parcelas:.2f}') print(f'Sendo assim as compras custaram R${preço +", "')) if parcelas >= 3: print(f'Compras com 20% de juros')", "')) if consulta in ['sim', 'Sim', 'SIM']: pass elif consulta", "de desconto. [ 2 ]A vista no cartão: 5% de", "final.') elif opção == 4: parcelas = int(input('Quantas parcelas: '))", "= float(str(input('Preço das compras: R$'))) print('''Escolha de pagamento: [ 1", "NOVAMENTE...') else: print('Valor não compreendido, TENTE NOVAMENTE...') gerenciador_de_pagamento() return opção", "desconto. [ 2 ]A vista no cartão: 5% de desconto.", "opção == 1: print('Você ganhará 10% de desconto!') print(f'Sendo assim", "in ['sim', 'Sim', 'SIM']: pass elif consulta in ['não', 'nao','Não',", "print(f'Sendo assim as compras custaram R${preço - (preço * 10", "custando o preço formal de R${preço:.2f} no final.') elif opção", "- (preço * 10 / 100 ):.2f}.') elif opção ==", "str(input('Quer consultar novamente? ')) if consulta in ['sim', 'Sim', 'SIM']:", "/ 100):.2f} no final.') else: print('Parcela não compreendida, TENTE NOVAMENTE...')", "opção == 2: print('Você ganhará 5% de desconto!') print(f'Sendo assim", "20 / 100):.2f} no final.') else: print('Parcela não compreendida, TENTE", "R${preço / 2:.2f}.') print(f'Sendo assim custando o preço formal de", "print(f'Sendo assim as compras custaram R${preço - (preço * 5", "o preço formal de R${preço:.2f} no final.') elif opção ==", "juros.''') opção = int(input('Opção de pagamento: ')) print('processando...') time.sleep(2) if" ]
[ "Apr 2017 @author: <NAME> (<EMAIL>) \"\"\" from collections import OrderedDict", "jdict['topic'] = self.path jdict['name'] = self.name jdict['description'] = self.description jdict['public']", "path # string self.__name = name # string self.__description =", "= self.is_public jdict['topic-info'] = self.info return jdict # ---------------------------------------------------------------------------------------------------------------- @property", "@property def name(self): return self.__name @property def description(self): return self.__description", "collections import OrderedDict from scs_core.data.json import JSONable # -------------------------------------------------------------------------------------------------------------------- class", "self.__name @property def description(self): return self.__description @property def is_public(self): return", "self.info return jdict # ---------------------------------------------------------------------------------------------------------------- @property def path(self): return self.__path", "@property def description(self): return self.__description @property def is_public(self): return self.__is_public", "from collections import OrderedDict from scs_core.data.json import JSONable # --------------------------------------------------------------------------------------------------------------------", "\"\"\" from collections import OrderedDict from scs_core.data.json import JSONable #", "scs_core.data.json import JSONable # -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\" classdocs \"\"\"", "self.description jdict['public'] = self.is_public jdict['topic-info'] = self.info return jdict #", "self.__name = name # string self.__description = description # string", "def as_json(self): jdict = OrderedDict() if self.path is not None:", "= path # string self.__name = name # string self.__description", "-------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\" classdocs \"\"\" # ---------------------------------------------------------------------------------------------------------------- def __init__(self,", "jdict = OrderedDict() if self.path is not None: jdict['topic'] =", "self.__description @property def is_public(self): return self.__is_public @property def info(self): return", "self.name jdict['description'] = self.description jdict['public'] = self.is_public jdict['topic-info'] = self.info", "info): \"\"\" Constructor \"\"\" self.__path = path # string self.__name", "import JSONable # -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\" classdocs \"\"\" #", "= self.info return jdict # ---------------------------------------------------------------------------------------------------------------- @property def path(self): return", "def path(self): return self.__path @property def name(self): return self.__name @property", "return self.__description @property def is_public(self): return self.__is_public @property def info(self):", "# ---------------------------------------------------------------------------------------------------------------- def __init__(self, path, name, description, is_public, info): \"\"\"", "None: jdict['topic'] = self.path jdict['name'] = self.name jdict['description'] = self.description", "self.__description = description # string self.__is_public = is_public # bool", "classdocs \"\"\" # ---------------------------------------------------------------------------------------------------------------- def __init__(self, path, name, description, is_public,", "@property def is_public(self): return self.__is_public @property def info(self): return self.__info", "class AbstractTopic(JSONable): \"\"\" classdocs \"\"\" # ---------------------------------------------------------------------------------------------------------------- def __init__(self, path,", "description(self): return self.__description @property def is_public(self): return self.__is_public @property def", "<gh_stars>1-10 \"\"\" Created on 2 Apr 2017 @author: <NAME> (<EMAIL>)", "info # TopicInfo # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict()", "= self.description jdict['public'] = self.is_public jdict['topic-info'] = self.info return jdict", "name(self): return self.__name @property def description(self): return self.__description @property def", "self.__info = info # TopicInfo # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict", "= description # string self.__is_public = is_public # bool self.__info", "---------------------------------------------------------------------------------------------------------------- def __init__(self, path, name, description, is_public, info): \"\"\" Constructor", "jdict['public'] = self.is_public jdict['topic-info'] = self.info return jdict # ----------------------------------------------------------------------------------------------------------------", "= self.name jdict['description'] = self.description jdict['public'] = self.is_public jdict['topic-info'] =", "@property def path(self): return self.__path @property def name(self): return self.__name", "self.__path @property def name(self): return self.__name @property def description(self): return", "def description(self): return self.__description @property def is_public(self): return self.__is_public @property", "return jdict # ---------------------------------------------------------------------------------------------------------------- @property def path(self): return self.__path @property", "= self.path jdict['name'] = self.name jdict['description'] = self.description jdict['public'] =", "jdict['name'] = self.name jdict['description'] = self.description jdict['public'] = self.is_public jdict['topic-info']", "@author: <NAME> (<EMAIL>) \"\"\" from collections import OrderedDict from scs_core.data.json", "on 2 Apr 2017 @author: <NAME> (<EMAIL>) \"\"\" from collections", "<NAME> (<EMAIL>) \"\"\" from collections import OrderedDict from scs_core.data.json import", "bool self.__info = info # TopicInfo # ---------------------------------------------------------------------------------------------------------------- def as_json(self):", "jdict # ---------------------------------------------------------------------------------------------------------------- @property def path(self): return self.__path @property def", "# ---------------------------------------------------------------------------------------------------------------- @property def path(self): return self.__path @property def name(self):", "2017 @author: <NAME> (<EMAIL>) \"\"\" from collections import OrderedDict from", "self.path is not None: jdict['topic'] = self.path jdict['name'] = self.name", "path(self): return self.__path @property def name(self): return self.__name @property def", "OrderedDict from scs_core.data.json import JSONable # -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\"", "# ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() if self.path is", "path, name, description, is_public, info): \"\"\" Constructor \"\"\" self.__path =", "= name # string self.__description = description # string self.__is_public", "# string self.__is_public = is_public # bool self.__info = info", "# string self.__name = name # string self.__description = description", "jdict['description'] = self.description jdict['public'] = self.is_public jdict['topic-info'] = self.info return", "description # string self.__is_public = is_public # bool self.__info =", "# -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\" classdocs \"\"\" # ---------------------------------------------------------------------------------------------------------------- def", "def __init__(self, path, name, description, is_public, info): \"\"\" Constructor \"\"\"", "is_public, info): \"\"\" Constructor \"\"\" self.__path = path # string", "# string self.__description = description # string self.__is_public = is_public", "import OrderedDict from scs_core.data.json import JSONable # -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable):", "\"\"\" Constructor \"\"\" self.__path = path # string self.__name =", "__init__(self, path, name, description, is_public, info): \"\"\" Constructor \"\"\" self.__path", "as_json(self): jdict = OrderedDict() if self.path is not None: jdict['topic']", "OrderedDict() if self.path is not None: jdict['topic'] = self.path jdict['name']", "\"\"\" classdocs \"\"\" # ---------------------------------------------------------------------------------------------------------------- def __init__(self, path, name, description,", "description, is_public, info): \"\"\" Constructor \"\"\" self.__path = path #", "if self.path is not None: jdict['topic'] = self.path jdict['name'] =", "def name(self): return self.__name @property def description(self): return self.__description @property", "---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() if self.path is not", "string self.__description = description # string self.__is_public = is_public #", "return self.__name @property def description(self): return self.__description @property def is_public(self):", "is_public # bool self.__info = info # TopicInfo # ----------------------------------------------------------------------------------------------------------------", "= is_public # bool self.__info = info # TopicInfo #", "from scs_core.data.json import JSONable # -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\" classdocs", "(<EMAIL>) \"\"\" from collections import OrderedDict from scs_core.data.json import JSONable", "JSONable # -------------------------------------------------------------------------------------------------------------------- class AbstractTopic(JSONable): \"\"\" classdocs \"\"\" # ----------------------------------------------------------------------------------------------------------------", "return self.__path @property def name(self): return self.__name @property def description(self):", "# bool self.__info = info # TopicInfo # ---------------------------------------------------------------------------------------------------------------- def", "# TopicInfo # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() if", "\"\"\" Created on 2 Apr 2017 @author: <NAME> (<EMAIL>) \"\"\"", "name # string self.__description = description # string self.__is_public =", "self.__path = path # string self.__name = name # string", "not None: jdict['topic'] = self.path jdict['name'] = self.name jdict['description'] =", "self.__is_public = is_public # bool self.__info = info # TopicInfo", "self.path jdict['name'] = self.name jdict['description'] = self.description jdict['public'] = self.is_public", "jdict['topic-info'] = self.info return jdict # ---------------------------------------------------------------------------------------------------------------- @property def path(self):", "self.is_public jdict['topic-info'] = self.info return jdict # ---------------------------------------------------------------------------------------------------------------- @property def", "is not None: jdict['topic'] = self.path jdict['name'] = self.name jdict['description']", "Created on 2 Apr 2017 @author: <NAME> (<EMAIL>) \"\"\" from", "= OrderedDict() if self.path is not None: jdict['topic'] = self.path", "\"\"\" self.__path = path # string self.__name = name #", "\"\"\" # ---------------------------------------------------------------------------------------------------------------- def __init__(self, path, name, description, is_public, info):", "2 Apr 2017 @author: <NAME> (<EMAIL>) \"\"\" from collections import", "= info # TopicInfo # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict =", "Constructor \"\"\" self.__path = path # string self.__name = name", "string self.__name = name # string self.__description = description #", "---------------------------------------------------------------------------------------------------------------- @property def path(self): return self.__path @property def name(self): return", "name, description, is_public, info): \"\"\" Constructor \"\"\" self.__path = path", "string self.__is_public = is_public # bool self.__info = info #", "TopicInfo # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() if self.path", "AbstractTopic(JSONable): \"\"\" classdocs \"\"\" # ---------------------------------------------------------------------------------------------------------------- def __init__(self, path, name," ]
[ "def provisioning_state(self) -> Optional[str]: \"\"\" Provisioning state of the Namespace.", "if location and not isinstance(location, str): raise TypeError(\"Expected argument 'location'", "metric_id and not isinstance(metric_id, str): raise TypeError(\"Expected argument 'metric_id' to", "be a str\") pulumi.set(__self__, \"created_at\", created_at) if critical and not", "isinstance(id, str): raise TypeError(\"Expected argument 'id' to be a str\")", "pulumi.set(__self__, \"subscription_id\", subscription_id) if tags and not isinstance(tags, dict): raise", "argument 'tags' to be a dict\") pulumi.set(__self__, \"tags\", tags) if", "\"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\") def created_at(self) -> Optional[str]: \"\"\" The", "def created_at(self) -> Optional[str]: \"\"\" The time the namespace was", "provisioning_state(self) -> Optional[str]: \"\"\" Provisioning state of the Namespace. \"\"\"", "\"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test def __await__(self): if False:", "scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name:", "return pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\") def data_center(self) -> Optional[str]: \"\"\"", "be a str\") pulumi.set(__self__, \"namespace_type\", namespace_type) if provisioning_state and not", "a str\") pulumi.set(__self__, \"namespace_type\", namespace_type) if provisioning_state and not isinstance(provisioning_state,", "scale_unit=None, service_bus_endpoint=None, sku=None, status=None, subscription_id=None, tags=None, type=None, updated_at=None): if created_at", "location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status,", "_utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at,", "@pulumi.getter(name=\"namespaceType\") def namespace_type(self) -> Optional[str]: \"\"\" The namespace type. \"\"\"", "@pulumi.getter def region(self) -> Optional[str]: \"\"\" Specifies the targeted region", "tags \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) ->", "if subscription_id and not isinstance(subscription_id, str): raise TypeError(\"Expected argument 'subscription_id'", "pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> Optional[str]: \"\"\" Provisioning", "if name and not isinstance(name, str): raise TypeError(\"Expected argument 'name'", "-> Optional[bool]: \"\"\" Whether or not the namespace is currently", "argument 'location' to be a str\") pulumi.set(__self__, \"location\", location) if", "not isinstance(tags, dict): raise TypeError(\"Expected argument 'tags' to be a", "to be a str\") pulumi.set(__self__, \"namespace_type\", namespace_type) if provisioning_state and", "created_at=None, critical=None, data_center=None, enabled=None, id=None, location=None, metric_id=None, name=None, namespace_type=None, provisioning_state=None,", "be a dict\") pulumi.set(__self__, \"sku\", sku) if status and not", "module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class GetNamespaceResult: \"\"\" Description of a", "str): raise TypeError(\"Expected argument 'subscription_id' to be a str\") pulumi.set(__self__,", "@pulumi.getter def enabled(self) -> Optional[bool]: \"\"\" Whether or not the", "created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state,", "not isinstance(enabled, bool): raise TypeError(\"Expected argument 'enabled' to be a", "state of the Namespace. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter", "return pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self) -> Optional[str]: \"\"\"", "dict() __args__['namespaceName'] = namespace_name __args__['resourceGroupName'] = resource_group_name if opts is", "None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version =", "str: \"\"\" Identifier for Azure Insights metrics \"\"\" return pulumi.get(self,", "return pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']: \"\"\"", "be a bool\") pulumi.set(__self__, \"critical\", critical) if data_center and not", "\"\"\" The time the namespace was created. \"\"\" return pulumi.get(self,", "a str\") pulumi.set(__self__, \"id\", id) if location and not isinstance(location,", "\"\"\" return pulumi.get(self, \"id\") @property @pulumi.getter def location(self) -> Optional[str]:", "-> Optional[str]: \"\"\" Specifies the targeted region in which the", "@property @pulumi.getter(name=\"updatedAt\") def updated_at(self) -> Optional[str]: \"\"\" The time the", "Optional[str]: \"\"\" The Id of the Azure subscription associated with", "dict\") pulumi.set(__self__, \"tags\", tags) if type and not isinstance(type, str):", "be a str\") pulumi.set(__self__, \"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\") def created_at(self)", "this file was generated by the Pulumi SDK Generator. ***", "created_at and not isinstance(created_at, str): raise TypeError(\"Expected argument 'created_at' to", "pulumi.set(__self__, \"name\", name) if namespace_type and not isinstance(namespace_type, str): raise", "South Central US, East Asia, Southeast Asia, Brazil South, Japan", "the namespace should be created. It can be any of", "@pulumi.output_type class GetNamespaceResult: \"\"\" Description of a Namespace resource. \"\"\"", "isinstance(provisioning_state, str): raise TypeError(\"Expected argument 'provisioning_state' to be a str\")", "\"\"\" Identifier for Azure Insights metrics \"\"\" return pulumi.get(self, \"metric_id\")", "if opts is None: opts = pulumi.InvokeOptions() if opts.version is", "data_center and not isinstance(data_center, str): raise TypeError(\"Expected argument 'data_center' to", "TypeError(\"Expected argument 'tags' to be a dict\") pulumi.set(__self__, \"tags\", tags)", "the resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated: The 'latest' version", "Europe, West Europe \"\"\" return pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\") def", "str\") pulumi.set(__self__, \"data_center\", data_center) if enabled and not isinstance(enabled, bool):", "= Deleting \"\"\" return pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self)", "status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str] = None,", "isinstance(region, str): raise TypeError(\"Expected argument 'region' to be a str\")", "type and not isinstance(type, str): raise TypeError(\"Expected argument 'type' to", "and not isinstance(scale_unit, str): raise TypeError(\"Expected argument 'scale_unit' to be", "self return GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id,", "pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence,", "] warnings.warn(\"\"\"The 'latest' version is deprecated. Please migrate to the", "str\") pulumi.set(__self__, \"created_at\", created_at) if critical and not isinstance(critical, bool):", "def namespace_type(self) -> Optional[str]: \"\"\" The namespace type. \"\"\" return", "return GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id, name=self.name,", "to be a str\") pulumi.set(__self__, \"region\", region) if scale_unit and", "the namespace is currently enabled. \"\"\" return pulumi.get(self, \"enabled\") @property", "be a dict\") pulumi.set(__self__, \"tags\", tags) if type and not", "to be a str\") pulumi.set(__self__, \"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\") def", "\"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated: The 'latest' version is deprecated. Please", "return pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) -> Optional[str]: \"\"\"", "The sku of the created namespace \"\"\" return pulumi.get(self, \"sku\")", "\"enabled\", enabled) if id and not isinstance(id, str): raise TypeError(\"Expected", "Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables", "and not isinstance(created_at, str): raise TypeError(\"Expected argument 'created_at' to be", "raise TypeError(\"Expected argument 'location' to be a str\") pulumi.set(__self__, \"location\",", "Whether or not the namespace is currently enabled. \"\"\" return", "Deleting \"\"\" return pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self) ->", "def critical(self) -> Optional[bool]: \"\"\" Whether or not the namespace", "sku and not isinstance(sku, dict): raise TypeError(\"Expected argument 'sku' to", "str resource_group_name: The name of the resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace", "\"metric_id\") @property @pulumi.getter def name(self) -> str: \"\"\" Resource name", "-> str: \"\"\" Identifier for Azure Insights metrics \"\"\" return", "\"name\", name) if namespace_type and not isinstance(namespace_type, str): raise TypeError(\"Expected", "a str\") pulumi.set(__self__, \"created_at\", created_at) if critical and not isinstance(critical,", "\"critical\") @property @pulumi.getter(name=\"dataCenter\") def data_center(self) -> Optional[str]: \"\"\" Data center", "subscription_id(self) -> Optional[str]: \"\"\" The Id of the Azure subscription", "\"provisioning_state\") @property @pulumi.getter def region(self) -> Optional[str]: \"\"\" Specifies the", "pulumi.set(__self__, \"region\", region) if scale_unit and not isinstance(scale_unit, str): raise", "\"type\", type) if updated_at and not isinstance(updated_at, str): raise TypeError(\"Expected", "@property @pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']: \"\"\" The sku of", "\"\"\" def __init__(__self__, created_at=None, critical=None, data_center=None, enabled=None, id=None, location=None, metric_id=None,", "time the namespace was updated. \"\"\" return pulumi.get(self, \"updated_at\") class", "be any of these values:1 = Created/Active2 = Creating3 =", "-> Optional[str]: \"\"\" Resource location \"\"\" return pulumi.get(self, \"location\") @property", "not isinstance(location, str): raise TypeError(\"Expected argument 'location' to be a", "def metric_id(self) -> str: \"\"\" Identifier for Azure Insights metrics", "Provisioning state of the Namespace. \"\"\" return pulumi.get(self, \"provisioning_state\") @property", "\"\"\" Status of the namespace. It can be any of", "scale_unit and not isinstance(scale_unit, str): raise TypeError(\"Expected argument 'scale_unit' to", "\"\"\" Description of a Namespace resource. Latest API Version: 2017-04-01.", "Southeast, Central US, East US, East US 2, West US,", "a str\") pulumi.set(__self__, \"type\", type) if updated_at and not isinstance(updated_at,", "'name' to be a str\") pulumi.set(__self__, \"name\", name) if namespace_type", "TypeError(\"Expected argument 'created_at' to be a str\") pulumi.set(__self__, \"created_at\", created_at)", "service_bus_endpoint=None, sku=None, status=None, subscription_id=None, tags=None, type=None, updated_at=None): if created_at and", "provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at)", "and not isinstance(enabled, bool): raise TypeError(\"Expected argument 'enabled' to be", "data_center(self) -> Optional[str]: \"\"\" Data center for the namespace \"\"\"", "'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class GetNamespaceResult: \"\"\" Description of a Namespace", "'id' to be a str\") pulumi.set(__self__, \"id\", id) if location", "-> str: \"\"\" Resource Id \"\"\" return pulumi.get(self, \"id\") @property", "location \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\") def metric_id(self) ->", "pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\") def metric_id(self) -> str: \"\"\" Identifier", "the namespace \"\"\" return pulumi.get(self, \"data_center\") @property @pulumi.getter def enabled(self)", "raise TypeError(\"Expected argument 'subscription_id' to be a str\") pulumi.set(__self__, \"subscription_id\",", "@pulumi.getter def name(self) -> str: \"\"\" Resource name \"\"\" return", "and not isinstance(status, str): raise TypeError(\"Expected argument 'status' to be", "return pulumi.get(self, \"enabled\") @property @pulumi.getter def id(self) -> str: \"\"\"", "following values: Australia East, Australia Southeast, Central US, East US,", "Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNamespaceResult: \"\"\" Description of a Namespace", "a Namespace resource. Latest API Version: 2017-04-01. :param str namespace_name:", "name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags,", "@pulumi.getter(name=\"updatedAt\") def updated_at(self) -> Optional[str]: \"\"\" The time the namespace", "version is deprecated. Please migrate to the function in the", "def scale_unit(self) -> Optional[str]: \"\"\" ScaleUnit where the namespace gets", "not isinstance(id, str): raise TypeError(\"Expected argument 'id' to be a", "\"\"\" return pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']:", "namespace was updated. \"\"\" return pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): #", "metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint, sku=__ret__.sku, status=__ret__.status, subscription_id=__ret__.subscription_id,", "\"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\") def metric_id(self) -> str:", "of a Namespace resource. Latest API Version: 2017-04-01. :param str", "str): raise TypeError(\"Expected argument 'name' to be a str\") pulumi.set(__self__,", "a str\") pulumi.set(__self__, \"status\", status) if subscription_id and not isinstance(subscription_id,", "hand unless you're certain you know what you are doing!", "namespace was created. \"\"\" return pulumi.get(self, \"created_at\") @property @pulumi.getter def", "\"\"\" Resource name \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\") def", "service_bus_endpoint and not isinstance(service_bus_endpoint, str): raise TypeError(\"Expected argument 'service_bus_endpoint' to", "for the namespace \"\"\" return pulumi.get(self, \"data_center\") @property @pulumi.getter def", "critical(self) -> Optional[bool]: \"\"\" Whether or not the namespace is", "argument 'metric_id' to be a str\") pulumi.set(__self__, \"metric_id\", metric_id) if", "Brazil South, Japan East, Japan West, North Europe, West Europe", "Optional[bool]: \"\"\" Whether or not the namespace is currently enabled.", "namespace name. :param str resource_group_name: The name of the resource", "None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) ->", "return pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> Optional[str]: \"\"\"", "str): raise TypeError(\"Expected argument 'status' to be a str\") pulumi.set(__self__,", "or not the namespace is set as Critical. \"\"\" return", "of these values:1 = Created/Active2 = Creating3 = Suspended4 =", "\"region\", region) if scale_unit and not isinstance(scale_unit, str): raise TypeError(\"Expected", "any of the following values: Australia East, Australia Southeast, Central", "The name of the resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated:", "can use to perform NotificationHub operations. \"\"\" return pulumi.get(self, \"service_bus_endpoint\")", "critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region,", "TypeError(\"Expected argument 'data_center' to be a str\") pulumi.set(__self__, \"data_center\", data_center)", "namespace. \"\"\" return pulumi.get(self, \"subscription_id\") @property @pulumi.getter def tags(self) ->", "pulumi.set(__self__, \"tags\", tags) if type and not isinstance(type, str): raise", "where the namespace gets created \"\"\" return pulumi.get(self, \"scale_unit\") @property", "unless you're certain you know what you are doing! ***", "\"provisioning_state\", provisioning_state) if region and not isinstance(region, str): raise TypeError(\"Expected", "Id of the Azure subscription associated with the namespace. \"\"\"", "not isinstance(subscription_id, str): raise TypeError(\"Expected argument 'subscription_id' to be a", "2, West US, North Central US, South Central US, East", "Description of a Namespace resource. \"\"\" def __init__(__self__, created_at=None, critical=None,", "pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self) -> Optional[str]: \"\"\" ScaleUnit", "critical and not isinstance(critical, bool): raise TypeError(\"Expected argument 'critical' to", "if provisioning_state and not isinstance(provisioning_state, str): raise TypeError(\"Expected argument 'provisioning_state'", "str): raise TypeError(\"Expected argument 'service_bus_endpoint' to be a str\") pulumi.set(__self__,", "= Created/Active2 = Creating3 = Suspended4 = Deleting \"\"\" return", "def updated_at(self) -> Optional[str]: \"\"\" The time the namespace was", "if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace',", "if scale_unit and not isinstance(scale_unit, str): raise TypeError(\"Expected argument 'scale_unit'", "can be any of the following values: Australia East, Australia", "opts is None: opts = pulumi.InvokeOptions() if opts.version is None:", "isinstance(metric_id, str): raise TypeError(\"Expected argument 'metric_id' to be a str\")", "'status' to be a str\") pulumi.set(__self__, \"status\", status) if subscription_id", "resource. Latest API Version: 2017-04-01. :param str namespace_name: The namespace", "North Europe, West Europe \"\"\" return pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\")", "values:1 = Created/Active2 = Creating3 = Suspended4 = Deleting \"\"\"", "Resource name \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\") def namespace_type(self)", "str\") pulumi.set(__self__, \"location\", location) if metric_id and not isinstance(metric_id, str):", "None) -> AwaitableGetNamespaceResult: \"\"\" Description of a Namespace resource. Latest", "Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] =", "\"\"\" Whether or not the namespace is set as Critical.", "sku=None, status=None, subscription_id=None, tags=None, type=None, updated_at=None): if created_at and not", "\"status\", status) if subscription_id and not isinstance(subscription_id, str): raise TypeError(\"Expected", "was created. \"\"\" return pulumi.get(self, \"created_at\") @property @pulumi.getter def critical(self)", "Union from ... import _utilities, _tables from . import outputs", "region in which the namespace should be created. It can", "what you are doing! *** import warnings import pulumi import", "pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) -> str: \"\"\" Resource", "\"\"\" return pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self) -> Optional[str]:", "Pulumi SDK Generator. *** # *** Do not edit by", "and not isinstance(location, str): raise TypeError(\"Expected argument 'location' to be", "enabled. \"\"\" return pulumi.get(self, \"enabled\") @property @pulumi.getter def id(self) ->", "@pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']: \"\"\" The sku of the", "Version: 2017-04-01. :param str namespace_name: The namespace name. :param str", "raise TypeError(\"Expected argument 'updated_at' to be a str\") pulumi.set(__self__, \"updated_at\",", "not isinstance(name, str): raise TypeError(\"Expected argument 'name' to be a", "TypeError(\"Expected argument 'type' to be a str\") pulumi.set(__self__, \"type\", type)", "raise TypeError(\"Expected argument 'name' to be a str\") pulumi.set(__self__, \"name\",", "Critical. \"\"\" return pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\") def data_center(self) ->", "to be a str\") pulumi.set(__self__, \"location\", location) if metric_id and", "enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint,", "pulumi.set(__self__, \"critical\", critical) if data_center and not isinstance(data_center, str): raise", "\"scale_unit\", scale_unit) if service_bus_endpoint and not isinstance(service_bus_endpoint, str): raise TypeError(\"Expected", "South, Japan East, Japan West, North Europe, West Europe \"\"\"", "-> Optional[str]: \"\"\" The namespace type. \"\"\" return pulumi.get(self, \"namespace_type\")", "opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version()", "It can be any of these values:1 = Created/Active2 =", "str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state) if region and not isinstance(region, str):", "\"created_at\") @property @pulumi.getter def critical(self) -> Optional[bool]: \"\"\" Whether or", "if False: yield self return GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled,", "deprecated: The 'latest' version is deprecated. Please migrate to the", "TypeError(\"Expected argument 'namespace_type' to be a str\") pulumi.set(__self__, \"namespace_type\", namespace_type)", "TypeError(\"Expected argument 'location' to be a str\") pulumi.set(__self__, \"location\", location)", "dict): raise TypeError(\"Expected argument 'tags' to be a dict\") pulumi.set(__self__,", "can be any of these values:1 = Created/Active2 = Creating3", "subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str] = None, resource_group_name:", "type=None, updated_at=None): if created_at and not isinstance(created_at, str): raise TypeError(\"Expected", "Asia, Brazil South, Japan East, Japan West, North Europe, West", "id) if location and not isinstance(location, str): raise TypeError(\"Expected argument", "It can be any of the following values: Australia East,", "\"\"\" Resource location \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\") def", "= Creating3 = Suspended4 = Deleting \"\"\" return pulumi.get(self, \"status\")", "created. \"\"\" return pulumi.get(self, \"created_at\") @property @pulumi.getter def critical(self) ->", "# pylint: disable=using-constant-test def __await__(self): if False: yield self return", "be a str\") pulumi.set(__self__, \"type\", type) if updated_at and not", "and not isinstance(name, str): raise TypeError(\"Expected argument 'name' to be", "'data_center' to be a str\") pulumi.set(__self__, \"data_center\", data_center) if enabled", ":param str resource_group_name: The name of the resource group. \"\"\"", "not isinstance(service_bus_endpoint, str): raise TypeError(\"Expected argument 'service_bus_endpoint' to be a", "-> str: \"\"\" Resource name \"\"\" return pulumi.get(self, \"name\") @property", "Optional[str]: \"\"\" The time the namespace was updated. \"\"\" return", "isinstance(data_center, str): raise TypeError(\"Expected argument 'data_center' to be a str\")", "warnings import pulumi import pulumi.runtime from typing import Any, Mapping,", "argument 'created_at' to be a str\") pulumi.set(__self__, \"created_at\", created_at) if", "'get_namespace', ] warnings.warn(\"\"\"The 'latest' version is deprecated. Please migrate to", "TypeError(\"Expected argument 'service_bus_endpoint' to be a str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint)", "'service_bus_endpoint' to be a str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if sku", "pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\") def data_center(self) -> Optional[str]: \"\"\" Data", "return pulumi.get(self, \"metric_id\") @property @pulumi.getter def name(self) -> str: \"\"\"", "updated_at(self) -> Optional[str]: \"\"\" The time the namespace was updated.", "str\") pulumi.set(__self__, \"type\", type) if updated_at and not isinstance(updated_at, str):", "# coding=utf-8 # *** WARNING: this file was generated by", "scale_unit) if service_bus_endpoint and not isinstance(service_bus_endpoint, str): raise TypeError(\"Expected argument", "namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type,", "import warnings import pulumi import pulumi.runtime from typing import Any,", "pulumi.set(__self__, \"namespace_type\", namespace_type) if provisioning_state and not isinstance(provisioning_state, str): raise", "raise TypeError(\"Expected argument 'namespace_type' to be a str\") pulumi.set(__self__, \"namespace_type\",", "@pulumi.getter(name=\"scaleUnit\") def scale_unit(self) -> Optional[str]: \"\"\" ScaleUnit where the namespace", "*** import warnings import pulumi import pulumi.runtime from typing import", "typing import Any, Mapping, Optional, Sequence, Union from ... import", "Azure Insights metrics \"\"\" return pulumi.get(self, \"metric_id\") @property @pulumi.getter def", "isinstance(location, str): raise TypeError(\"Expected argument 'location' to be a str\")", "def status(self) -> Optional[str]: \"\"\" Status of the namespace. It", "East, Japan West, North Europe, West Europe \"\"\" return pulumi.get(self,", "pulumi.set(__self__, \"data_center\", data_center) if enabled and not isinstance(enabled, bool): raise", "str\") pulumi.set(__self__, \"scale_unit\", scale_unit) if service_bus_endpoint and not isinstance(service_bus_endpoint, str):", "import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union", "\"subscription_id\", subscription_id) if tags and not isinstance(tags, dict): raise TypeError(\"Expected", "location(self) -> Optional[str]: \"\"\" Resource location \"\"\" return pulumi.get(self, \"location\")", "time the namespace was created. \"\"\" return pulumi.get(self, \"created_at\") @property", "'created_at' to be a str\") pulumi.set(__self__, \"created_at\", created_at) if critical", "argument 'subscription_id' to be a str\") pulumi.set(__self__, \"subscription_id\", subscription_id) if", "you know what you are doing! *** import warnings import", "TypeError(\"Expected argument 'name' to be a str\") pulumi.set(__self__, \"name\", name)", "GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type,", "The time the namespace was created. \"\"\" return pulumi.get(self, \"created_at\")", "pulumi.get(self, \"metric_id\") @property @pulumi.getter def name(self) -> str: \"\"\" Resource", "isinstance(status, str): raise TypeError(\"Expected argument 'status' to be a str\")", "def get_namespace(namespace_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts:", "be a str\") pulumi.set(__self__, \"id\", id) if location and not", "argument 'id' to be a str\") pulumi.set(__self__, \"id\", id) if", "created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state,", "and not isinstance(region, str): raise TypeError(\"Expected argument 'region' to be", "Resource tags \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self)", "not the namespace is set as Critical. \"\"\" return pulumi.get(self,", "\"\"\" Resource type \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\") def", "namespace_type) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError(\"Expected argument", "Mapping, Optional, Sequence, Union from ... import _utilities, _tables from", "raise TypeError(\"Expected argument 'type' to be a str\") pulumi.set(__self__, \"type\",", "Please migrate to the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\",", "not isinstance(critical, bool): raise TypeError(\"Expected argument 'critical' to be a", "raise TypeError(\"Expected argument 'critical' to be a bool\") pulumi.set(__self__, \"critical\",", "AwaitableGetNamespaceResult: \"\"\" Description of a Namespace resource. Latest API Version:", "be a str\") pulumi.set(__self__, \"location\", location) if metric_id and not", "set as Critical. \"\"\" return pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\") def", "str\") pulumi.set(__self__, \"subscription_id\", subscription_id) if tags and not isinstance(tags, dict):", "pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\") def namespace_type(self) -> Optional[str]: \"\"\" The", "in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class GetNamespaceResult: \"\"\"", "Europe \"\"\" return pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self) ->", "-> Optional[str]: \"\"\" Status of the namespace. It can be", "@pulumi.getter def id(self) -> str: \"\"\" Resource Id \"\"\" return", "pulumi.set(__self__, \"id\", id) if location and not isinstance(location, str): raise", "Australia East, Australia Southeast, Central US, East US, East US", "not isinstance(status, str): raise TypeError(\"Expected argument 'status' to be a", "Optional[bool]: \"\"\" Whether or not the namespace is set as", "argument 'region' to be a str\") pulumi.set(__self__, \"region\", region) if", "_utilities, _tables from . import outputs __all__ = [ 'GetNamespaceResult',", "service_bus_endpoint) if sku and not isinstance(sku, dict): raise TypeError(\"Expected argument", "pulumi.set(__self__, \"sku\", sku) if status and not isinstance(status, str): raise", "outputs __all__ = [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The 'latest'", "'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The 'latest' version is deprecated. Please", "'scale_unit' to be a str\") pulumi.set(__self__, \"scale_unit\", scale_unit) if service_bus_endpoint", "@property @pulumi.getter def enabled(self) -> Optional[bool]: \"\"\" Whether or not", "Asia, Southeast Asia, Brazil South, Japan East, Japan West, North", "-> Optional[str]: \"\"\" Endpoint you can use to perform NotificationHub", "import Any, Mapping, Optional, Sequence, Union from ... import _utilities,", "data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit,", "bool\") pulumi.set(__self__, \"critical\", critical) if data_center and not isinstance(data_center, str):", "\"\"\" Data center for the namespace \"\"\" return pulumi.get(self, \"data_center\")", "__await__(self): if False: yield self return GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center,", "str): raise TypeError(\"Expected argument 'type' to be a str\") pulumi.set(__self__,", "@property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> Optional[str]: \"\"\" Provisioning state of", "in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict() __args__['namespaceName'] =", "-> Optional[str]: \"\"\" The time the namespace was created. \"\"\"", "critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region,", "the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict() __args__['namespaceName'] = namespace_name", "@pulumi.getter def critical(self) -> Optional[bool]: \"\"\" Whether or not the", "be created. It can be any of the following values:", "US, East US 2, West US, North Central US, South", "resource_group_name: The name of the resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is", "namespace_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts =", "the targeted region in which the namespace should be created.", "provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None, sku=None, status=None, subscription_id=None, tags=None, type=None, updated_at=None):", "and not isinstance(provisioning_state, str): raise TypeError(\"Expected argument 'provisioning_state' to be", "be a str\") pulumi.set(__self__, \"subscription_id\", subscription_id) if tags and not", "NotificationHub operations. \"\"\" return pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter def sku(self)", "-> Optional[bool]: \"\"\" Whether or not the namespace is set", "\"\"\" return pulumi.get(self, \"subscription_id\") @property @pulumi.getter def tags(self) -> Optional[Mapping[str,", "pulumi.set(__self__, \"metric_id\", metric_id) if name and not isinstance(name, str): raise", "the following values: Australia East, Australia Southeast, Central US, East", "to perform NotificationHub operations. \"\"\" return pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter", "The namespace type. \"\"\" return pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def", "pulumi.set(__self__, \"status\", status) if subscription_id and not isinstance(subscription_id, str): raise", "*** # *** Do not edit by hand unless you're", "the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict()", ". import outputs __all__ = [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ]", "*** Do not edit by hand unless you're certain you", "@property @pulumi.getter(name=\"namespaceType\") def namespace_type(self) -> Optional[str]: \"\"\" The namespace type.", "Optional[str]: \"\"\" Provisioning state of the Namespace. \"\"\" return pulumi.get(self,", "str\") pulumi.set(__self__, \"region\", region) if scale_unit and not isinstance(scale_unit, str):", "tags=self.tags, type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str] = None, resource_group_name: Optional[str]", "a str\") pulumi.set(__self__, \"scale_unit\", scale_unit) if service_bus_endpoint and not isinstance(service_bus_endpoint,", "and not isinstance(data_center, str): raise TypeError(\"Expected argument 'data_center' to be", "GetNamespaceResult: \"\"\" Description of a Namespace resource. \"\"\" def __init__(__self__,", "yield self return GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id, location=self.location,", "= pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__", "str): raise TypeError(\"Expected argument 'created_at' to be a str\") pulumi.set(__self__,", "provisioning_state) if region and not isinstance(region, str): raise TypeError(\"Expected argument", "str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if sku and not isinstance(sku, dict):", "str): raise TypeError(\"Expected argument 'namespace_type' to be a str\") pulumi.set(__self__,", "the namespace is set as Critical. \"\"\" return pulumi.get(self, \"critical\")", "status=None, subscription_id=None, tags=None, type=None, updated_at=None): if created_at and not isinstance(created_at,", "raise TypeError(\"Expected argument 'status' to be a str\") pulumi.set(__self__, \"status\",", "*** WARNING: this file was generated by the Pulumi SDK", "@property @pulumi.getter def id(self) -> str: \"\"\" Resource Id \"\"\"", "@property @pulumi.getter def location(self) -> Optional[str]: \"\"\" Resource location \"\"\"", "East, Australia Southeast, Central US, East US, East US 2,", "and not isinstance(namespace_type, str): raise TypeError(\"Expected argument 'namespace_type' to be", "from ... import _utilities, _tables from . import outputs __all__", "center for the namespace \"\"\" return pulumi.get(self, \"data_center\") @property @pulumi.getter", "argument 'updated_at' to be a str\") pulumi.set(__self__, \"updated_at\", updated_at) @property", "to be a dict\") pulumi.set(__self__, \"sku\", sku) if status and", "@pulumi.getter(name=\"createdAt\") def created_at(self) -> Optional[str]: \"\"\" The time the namespace", "if region and not isinstance(region, str): raise TypeError(\"Expected argument 'region'", "API Version: 2017-04-01. :param str namespace_name: The namespace name. :param", "@pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) -> Optional[str]: \"\"\" Endpoint you can use", "pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if sku and not isinstance(sku, dict): raise", "US, East Asia, Southeast Asia, Brazil South, Japan East, Japan", "doing! *** import warnings import pulumi import pulumi.runtime from typing", "\"\"\" Provisioning state of the Namespace. \"\"\" return pulumi.get(self, \"provisioning_state\")", "TypeError(\"Expected argument 'updated_at' to be a str\") pulumi.set(__self__, \"updated_at\", updated_at)", "is deprecated. Please migrate to the function in the top-level", "the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class", "def enabled(self) -> Optional[bool]: \"\"\" Whether or not the namespace", "isinstance(enabled, bool): raise TypeError(\"Expected argument 'enabled' to be a bool\")", "@property @pulumi.getter def type(self) -> str: \"\"\" Resource type \"\"\"", "metric_id(self) -> str: \"\"\" Identifier for Azure Insights metrics \"\"\"", "was updated. \"\"\" return pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint:", "\"metric_id\", metric_id) if name and not isinstance(name, str): raise TypeError(\"Expected", "'latest' version is deprecated. Please migrate to the function in", "if id and not isinstance(id, str): raise TypeError(\"Expected argument 'id'", "argument 'provisioning_state' to be a str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state) if", "= namespace_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts", "not isinstance(scale_unit, str): raise TypeError(\"Expected argument 'scale_unit' to be a", "isinstance(updated_at, str): raise TypeError(\"Expected argument 'updated_at' to be a str\")", "North Central US, South Central US, East Asia, Southeast Asia,", "resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNamespaceResult:", "pulumi.get(self, \"data_center\") @property @pulumi.getter def enabled(self) -> Optional[bool]: \"\"\" Whether", "return pulumi.get(self, \"sku\") @property @pulumi.getter def status(self) -> Optional[str]: \"\"\"", "the Pulumi SDK Generator. *** # *** Do not edit", "status) if subscription_id and not isinstance(subscription_id, str): raise TypeError(\"Expected argument", "the namespace was created. \"\"\" return pulumi.get(self, \"created_at\") @property @pulumi.getter", "argument 'scale_unit' to be a str\") pulumi.set(__self__, \"scale_unit\", scale_unit) if", "__all__ = [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The 'latest' version", "= Suspended4 = Deleting \"\"\" return pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\")", "argument 'enabled' to be a bool\") pulumi.set(__self__, \"enabled\", enabled) if", "'critical' to be a bool\") pulumi.set(__self__, \"critical\", critical) if data_center", "return pulumi.get(self, \"created_at\") @property @pulumi.getter def critical(self) -> Optional[bool]: \"\"\"", "\"tags\", tags) if type and not isinstance(type, str): raise TypeError(\"Expected", "updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str] = None, resource_group_name: Optional[str] = None,", "pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled,", "deprecated. Please migrate to the function in the top-level module:", "top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class GetNamespaceResult: \"\"\" Description of", "name of the resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated: The", "type) if updated_at and not isinstance(updated_at, str): raise TypeError(\"Expected argument", "Japan West, North Europe, West Europe \"\"\" return pulumi.get(self, \"region\")", "return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name,", "name) if namespace_type and not isinstance(namespace_type, str): raise TypeError(\"Expected argument", "enabled and not isinstance(enabled, bool): raise TypeError(\"Expected argument 'enabled' to", "if service_bus_endpoint and not isinstance(service_bus_endpoint, str): raise TypeError(\"Expected argument 'service_bus_endpoint'", "str): raise TypeError(\"Expected argument 'provisioning_state' to be a str\") pulumi.set(__self__,", "bool\") pulumi.set(__self__, \"enabled\", enabled) if id and not isinstance(id, str):", "\"service_bus_endpoint\", service_bus_endpoint) if sku and not isinstance(sku, dict): raise TypeError(\"Expected", "return pulumi.get(self, \"id\") @property @pulumi.getter def location(self) -> Optional[str]: \"\"\"", "The namespace name. :param str resource_group_name: The name of the", "raise TypeError(\"Expected argument 'scale_unit' to be a str\") pulumi.set(__self__, \"scale_unit\",", "-> Optional[Mapping[str, str]]: \"\"\" Resource tags \"\"\" return pulumi.get(self, \"tags\")", "ScaleUnit where the namespace gets created \"\"\" return pulumi.get(self, \"scale_unit\")", "subscription_id) if tags and not isinstance(tags, dict): raise TypeError(\"Expected argument", "pulumi.set(__self__, \"enabled\", enabled) if id and not isinstance(id, str): raise", "Identifier for Azure Insights metrics \"\"\" return pulumi.get(self, \"metric_id\") @property", "enabled(self) -> Optional[bool]: \"\"\" Whether or not the namespace is", "return pulumi.get(self, \"subscription_id\") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]:", "@pulumi.getter def status(self) -> Optional[str]: \"\"\" Status of the namespace.", "pulumi.set(__self__, \"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\") def created_at(self) -> Optional[str]: \"\"\"", "a dict\") pulumi.set(__self__, \"sku\", sku) if status and not isinstance(status,", "\"\"\" return pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test def", "argument 'namespace_type' to be a str\") pulumi.set(__self__, \"namespace_type\", namespace_type) if", "return pulumi.get(self, \"data_center\") @property @pulumi.getter def enabled(self) -> Optional[bool]: \"\"\"", "be any of the following values: Australia East, Australia Southeast,", "values: Australia East, Australia Southeast, Central US, East US, East", "\"status\") @property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self) -> Optional[str]: \"\"\" The Id", "if type and not isinstance(type, str): raise TypeError(\"Expected argument 'type'", "The 'latest' version is deprecated. Please migrate to the function", "updated_at=None): if created_at and not isinstance(created_at, str): raise TypeError(\"Expected argument", "-> Optional[str]: \"\"\" Provisioning state of the Namespace. \"\"\" return", "pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test def __await__(self): if", "to the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type", "def region(self) -> Optional[str]: \"\"\" Specifies the targeted region in", "Resource Id \"\"\" return pulumi.get(self, \"id\") @property @pulumi.getter def location(self)", "\"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) -> str:", "service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str]", "str: \"\"\" Resource name \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\")", "type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str] = None, resource_group_name: Optional[str] =", "a Namespace resource. \"\"\" def __init__(__self__, created_at=None, critical=None, data_center=None, enabled=None,", "resource. \"\"\" def __init__(__self__, created_at=None, critical=None, data_center=None, enabled=None, id=None, location=None,", "def __init__(__self__, created_at=None, critical=None, data_center=None, enabled=None, id=None, location=None, metric_id=None, name=None,", "isinstance(namespace_type, str): raise TypeError(\"Expected argument 'namespace_type' to be a str\")", "str]]: \"\"\" Resource tags \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter", "get_namespace(namespace_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions]", "is currently enabled. \"\"\" return pulumi.get(self, \"enabled\") @property @pulumi.getter def", "opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNamespaceResult: \"\"\" Description of a", "'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict() __args__['namespaceName'] = namespace_name __args__['resourceGroupName'] = resource_group_name", "Status of the namespace. It can be any of these", "to be a str\") pulumi.set(__self__, \"created_at\", created_at) if critical and", "Australia Southeast, Central US, East US, East US 2, West", "of the namespace. It can be any of these values:1", "Description of a Namespace resource. Latest API Version: 2017-04-01. :param", "is set as Critical. \"\"\" return pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\")", "be a str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if sku and not", "\"region\") @property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self) -> Optional[str]: \"\"\" ScaleUnit where", "raise TypeError(\"Expected argument 'provisioning_state' to be a str\") pulumi.set(__self__, \"provisioning_state\",", "\"\"\" Description of a Namespace resource. \"\"\" def __init__(__self__, created_at=None,", "The Id of the Azure subscription associated with the namespace.", "module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict() __args__['namespaceName'] = namespace_name __args__['resourceGroupName'] =", "id=None, location=None, metric_id=None, name=None, namespace_type=None, provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None, sku=None,", "a str\") pulumi.set(__self__, \"data_center\", data_center) if enabled and not isinstance(enabled,", "to the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ =", "namespace is set as Critical. \"\"\" return pulumi.get(self, \"critical\") @property", "\"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> Optional[str]: \"\"\" Provisioning state", "Please migrate to the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\")", "namespace_type(self) -> Optional[str]: \"\"\" The namespace type. \"\"\" return pulumi.get(self,", "service_bus_endpoint(self) -> Optional[str]: \"\"\" Endpoint you can use to perform", "pulumi.set(__self__, \"location\", location) if metric_id and not isinstance(metric_id, str): raise", "Suspended4 = Deleting \"\"\" return pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\") def", "WARNING: this file was generated by the Pulumi SDK Generator.", "str\") pulumi.set(__self__, \"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\") def created_at(self) -> Optional[str]:", "to be a str\") pulumi.set(__self__, \"data_center\", data_center) if enabled and", "Optional[str]: \"\"\" The time the namespace was created. \"\"\" return", "updated. \"\"\" return pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test", "a str\") pulumi.set(__self__, \"name\", name) if namespace_type and not isinstance(namespace_type,", "from . import outputs __all__ = [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace',", "None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value", "created_at) if critical and not isinstance(critical, bool): raise TypeError(\"Expected argument", "know what you are doing! *** import warnings import pulumi", "to be a str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if sku and", "not isinstance(type, str): raise TypeError(\"Expected argument 'type' to be a", "East US, East US 2, West US, North Central US,", "the Azure subscription associated with the namespace. \"\"\" return pulumi.get(self,", "dict): raise TypeError(\"Expected argument 'sku' to be a dict\") pulumi.set(__self__,", "Id \"\"\" return pulumi.get(self, \"id\") @property @pulumi.getter def location(self) ->", "\"\"\" return pulumi.get(self, \"sku\") @property @pulumi.getter def status(self) -> Optional[str]:", "certain you know what you are doing! *** import warnings", "not isinstance(data_center, str): raise TypeError(\"Expected argument 'data_center' to be a", "raise TypeError(\"Expected argument 'tags' to be a dict\") pulumi.set(__self__, \"tags\",", "= None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNamespaceResult: \"\"\" Description", "@property @pulumi.getter def region(self) -> Optional[str]: \"\"\" Specifies the targeted", "the namespace. It can be any of these values:1 =", "isinstance(type, str): raise TypeError(\"Expected argument 'type' to be a str\")", "the Namespace. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter def region(self)", "\"data_center\") @property @pulumi.getter def enabled(self) -> Optional[bool]: \"\"\" Whether or", "\"service_bus_endpoint\") @property @pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']: \"\"\" The sku", "typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id,", "function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict() __args__['namespaceName']", "'updated_at' to be a str\") pulumi.set(__self__, \"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\")", "pulumi.get(self, \"created_at\") @property @pulumi.getter def critical(self) -> Optional[bool]: \"\"\" Whether", "provisioning_state and not isinstance(provisioning_state, str): raise TypeError(\"Expected argument 'provisioning_state' to", "-> Optional[str]: \"\"\" The time the namespace was updated. \"\"\"", "you're certain you know what you are doing! *** import", "Latest API Version: 2017-04-01. :param str namespace_name: The namespace name.", "perform NotificationHub operations. \"\"\" return pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter def", "coding=utf-8 # *** WARNING: this file was generated by the", "type(self) -> str: \"\"\" Resource type \"\"\" return pulumi.get(self, \"type\")", "if critical and not isinstance(critical, bool): raise TypeError(\"Expected argument 'critical'", "= [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The 'latest' version is", "Optional[str]: \"\"\" The namespace type. \"\"\" return pulumi.get(self, \"namespace_type\") @property", "created \"\"\" return pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) ->", "\"sku\") @property @pulumi.getter def status(self) -> Optional[str]: \"\"\" Status of", "TypeError(\"Expected argument 'region' to be a str\") pulumi.set(__self__, \"region\", region)", "\"\"\" Endpoint you can use to perform NotificationHub operations. \"\"\"", "targeted region in which the namespace should be created. It", "= pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center,", "def location(self) -> Optional[str]: \"\"\" Resource location \"\"\" return pulumi.get(self,", "namespace should be created. It can be any of the", "region(self) -> Optional[str]: \"\"\" Specifies the targeted region in which", "raise TypeError(\"Expected argument 'sku' to be a dict\") pulumi.set(__self__, \"sku\",", "tags(self) -> Optional[Mapping[str, str]]: \"\"\" Resource tags \"\"\" return pulumi.get(self,", "US, South Central US, East Asia, Southeast Asia, Brazil South,", "Central US, East US, East US 2, West US, North", "East Asia, Southeast Asia, Brazil South, Japan East, Japan West,", "file was generated by the Pulumi SDK Generator. *** #", "bool): raise TypeError(\"Expected argument 'enabled' to be a bool\") pulumi.set(__self__,", "TypeError(\"Expected argument 'status' to be a str\") pulumi.set(__self__, \"status\", status)", "\"\"\" ScaleUnit where the namespace gets created \"\"\" return pulumi.get(self,", "to be a str\") pulumi.set(__self__, \"type\", type) if updated_at and", "data_center=None, enabled=None, id=None, location=None, metric_id=None, name=None, namespace_type=None, provisioning_state=None, region=None, scale_unit=None,", "not edit by hand unless you're certain you know what", "not isinstance(updated_at, str): raise TypeError(\"Expected argument 'updated_at' to be a", "warnings.warn(\"\"\"The 'latest' version is deprecated. Please migrate to the function", "should be created. It can be any of the following", "str): raise TypeError(\"Expected argument 'data_center' to be a str\") pulumi.set(__self__,", "if namespace_type and not isinstance(namespace_type, str): raise TypeError(\"Expected argument 'namespace_type'", "argument 'critical' to be a bool\") pulumi.set(__self__, \"critical\", critical) if", "Japan East, Japan West, North Europe, West Europe \"\"\" return", "if updated_at and not isinstance(updated_at, str): raise TypeError(\"Expected argument 'updated_at'", "isinstance(service_bus_endpoint, str): raise TypeError(\"Expected argument 'service_bus_endpoint' to be a str\")", "-> Optional[str]: \"\"\" The Id of the Azure subscription associated", "\"\"\" return pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\") def data_center(self) -> Optional[str]:", "and not isinstance(id, str): raise TypeError(\"Expected argument 'id' to be", "or not the namespace is currently enabled. \"\"\" return pulumi.get(self,", "import outputs __all__ = [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The", "opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location,", "\"enabled\") @property @pulumi.getter def id(self) -> str: \"\"\" Resource Id", "if data_center and not isinstance(data_center, str): raise TypeError(\"Expected argument 'data_center'", "be a bool\") pulumi.set(__self__, \"enabled\", enabled) if id and not", "# *** WARNING: this file was generated by the Pulumi", "_tables from . import outputs __all__ = [ 'GetNamespaceResult', 'AwaitableGetNamespaceResult',", "resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated: The 'latest' version is", "not isinstance(metric_id, str): raise TypeError(\"Expected argument 'metric_id' to be a", "namespace \"\"\" return pulumi.get(self, \"data_center\") @property @pulumi.getter def enabled(self) ->", "was generated by the Pulumi SDK Generator. *** # ***", "@property @pulumi.getter(name=\"metricId\") def metric_id(self) -> str: \"\"\" Identifier for Azure", "enabled) if id and not isinstance(id, str): raise TypeError(\"Expected argument", "__args__['namespaceName'] = namespace_name __args__['resourceGroupName'] = resource_group_name if opts is None:", "top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__ = dict() __args__['namespaceName'] = namespace_name __args__['resourceGroupName']", "@property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) -> Optional[str]: \"\"\" Endpoint you can", "namespace_name: The namespace name. :param str resource_group_name: The name of", "operations. \"\"\" return pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter def sku(self) ->", "Sequence, Union from ... import _utilities, _tables from . import", "a str\") pulumi.set(__self__, \"metric_id\", metric_id) if name and not isinstance(name,", "as Critical. \"\"\" return pulumi.get(self, \"critical\") @property @pulumi.getter(name=\"dataCenter\") def data_center(self)", "raise TypeError(\"Expected argument 'enabled' to be a bool\") pulumi.set(__self__, \"enabled\",", "'namespace_type' to be a str\") pulumi.set(__self__, \"namespace_type\", namespace_type) if provisioning_state", "to be a str\") pulumi.set(__self__, \"subscription_id\", subscription_id) if tags and", "__args__ = dict() __args__['namespaceName'] = namespace_name __args__['resourceGroupName'] = resource_group_name if", "a str\") pulumi.set(__self__, \"subscription_id\", subscription_id) if tags and not isinstance(tags,", "by hand unless you're certain you know what you are", "a bool\") pulumi.set(__self__, \"critical\", critical) if data_center and not isinstance(data_center,", "Optional[str]: \"\"\" Data center for the namespace \"\"\" return pulumi.get(self,", "Optional[Mapping[str, str]]: \"\"\" Resource tags \"\"\" return pulumi.get(self, \"tags\") @property", "False: yield self return GetNamespaceResult( created_at=self.created_at, critical=self.critical, data_center=self.data_center, enabled=self.enabled, id=self.id,", "isinstance(subscription_id, str): raise TypeError(\"Expected argument 'subscription_id' to be a str\")", "a str\") pulumi.set(__self__, \"region\", region) if scale_unit and not isinstance(scale_unit,", "str namespace_name: The namespace name. :param str resource_group_name: The name", "pulumi.get(self, \"enabled\") @property @pulumi.getter def id(self) -> str: \"\"\" Resource", "if enabled and not isinstance(enabled, bool): raise TypeError(\"Expected argument 'enabled'", "pulumi.get(self, \"provisioning_state\") @property @pulumi.getter def region(self) -> Optional[str]: \"\"\" Specifies", "if metric_id and not isinstance(metric_id, str): raise TypeError(\"Expected argument 'metric_id'", "\"\"\" Resource Id \"\"\" return pulumi.get(self, \"id\") @property @pulumi.getter def", "argument 'service_bus_endpoint' to be a str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if", "argument 'sku' to be a dict\") pulumi.set(__self__, \"sku\", sku) if", "name(self) -> str: \"\"\" Resource name \"\"\" return pulumi.get(self, \"name\")", "a dict\") pulumi.set(__self__, \"tags\", tags) if type and not isinstance(type,", "= None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None)", "pulumi.get(self, \"service_bus_endpoint\") @property @pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']: \"\"\" The", "a str\") pulumi.set(__self__, \"updated_at\", updated_at) @property @pulumi.getter(name=\"createdAt\") def created_at(self) ->", "pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self) -> Optional[str]: \"\"\" The", ":param str namespace_name: The namespace name. :param str resource_group_name: The", "'tags' to be a dict\") pulumi.set(__self__, \"tags\", tags) if type", "use to perform NotificationHub operations. \"\"\" return pulumi.get(self, \"service_bus_endpoint\") @property", "@pulumi.getter def type(self) -> str: \"\"\" Resource type \"\"\" return", "\"\"\" Whether or not the namespace is currently enabled. \"\"\"", "\"\"\" Resource tags \"\"\" return pulumi.get(self, \"tags\") @property @pulumi.getter def", "isinstance(critical, bool): raise TypeError(\"Expected argument 'critical' to be a bool\")", "'sku' to be a dict\") pulumi.set(__self__, \"sku\", sku) if status", "str\") pulumi.set(__self__, \"name\", name) if namespace_type and not isinstance(namespace_type, str):", "str: \"\"\" Resource Id \"\"\" return pulumi.get(self, \"id\") @property @pulumi.getter", "you are doing! *** import warnings import pulumi import pulumi.runtime", "raise TypeError(\"Expected argument 'created_at' to be a str\") pulumi.set(__self__, \"created_at\",", "enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint,", "opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value return", "\"id\") @property @pulumi.getter def location(self) -> Optional[str]: \"\"\" Resource location", "raise TypeError(\"Expected argument 'service_bus_endpoint' to be a str\") pulumi.set(__self__, \"service_bus_endpoint\",", "def id(self) -> str: \"\"\" Resource Id \"\"\" return pulumi.get(self,", "associated with the namespace. \"\"\" return pulumi.get(self, \"subscription_id\") @property @pulumi.getter", "TypeError(\"Expected argument 'subscription_id' to be a str\") pulumi.set(__self__, \"subscription_id\", subscription_id)", "__init__(__self__, created_at=None, critical=None, data_center=None, enabled=None, id=None, location=None, metric_id=None, name=None, namespace_type=None,", "to be a str\") pulumi.set(__self__, \"metric_id\", metric_id) if name and", "TypeError(\"Expected argument 'id' to be a str\") pulumi.set(__self__, \"id\", id)", "scale_unit(self) -> Optional[str]: \"\"\" ScaleUnit where the namespace gets created", "\"subscription_id\") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: \"\"\" Resource", "of the resource group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated: The 'latest'", "Optional, Sequence, Union from ... import _utilities, _tables from .", "and not isinstance(metric_id, str): raise TypeError(\"Expected argument 'metric_id' to be", "if status and not isinstance(status, str): raise TypeError(\"Expected argument 'status'", "@pulumi.getter def location(self) -> Optional[str]: \"\"\" Resource location \"\"\" return", "Whether or not the namespace is set as Critical. \"\"\"", "\"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) -> Optional[str]: \"\"\" Endpoint you", "subscription_id=None, tags=None, type=None, updated_at=None): if created_at and not isinstance(created_at, str):", "DeprecationWarning) @pulumi.output_type class GetNamespaceResult: \"\"\" Description of a Namespace resource.", "data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit,", "def name(self) -> str: \"\"\" Resource name \"\"\" return pulumi.get(self,", "disable=using-constant-test def __await__(self): if False: yield self return GetNamespaceResult( created_at=self.created_at,", "def type(self) -> str: \"\"\" Resource type \"\"\" return pulumi.get(self,", "migrate to the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\") __args__", "TypeError(\"Expected argument 'scale_unit' to be a str\") pulumi.set(__self__, \"scale_unit\", scale_unit)", "\"\"\" Specifies the targeted region in which the namespace should", "\"data_center\", data_center) if enabled and not isinstance(enabled, bool): raise TypeError(\"Expected", "name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint, sku=__ret__.sku, status=__ret__.status, subscription_id=__ret__.subscription_id, tags=__ret__.tags,", "raise TypeError(\"Expected argument 'region' to be a str\") pulumi.set(__self__, \"region\",", "'subscription_id' to be a str\") pulumi.set(__self__, \"subscription_id\", subscription_id) if tags", "of the Namespace. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter def", "The time the namespace was updated. \"\"\" return pulumi.get(self, \"updated_at\")", "provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint, sku=__ret__.sku, status=__ret__.status, subscription_id=__ret__.subscription_id, tags=__ret__.tags, type=__ret__.type, updated_at=__ret__.updated_at)", "region) if scale_unit and not isinstance(scale_unit, str): raise TypeError(\"Expected argument", "bool): raise TypeError(\"Expected argument 'critical' to be a bool\") pulumi.set(__self__,", "= None) -> AwaitableGetNamespaceResult: \"\"\" Description of a Namespace resource.", "Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNamespaceResult: \"\"\"", "return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\") def namespace_type(self) -> Optional[str]: \"\"\"", "import _utilities, _tables from . import outputs __all__ = [", "namespace_type and not isinstance(namespace_type, str): raise TypeError(\"Expected argument 'namespace_type' to", "be a str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state) if region and not", "status(self) -> Optional[str]: \"\"\" Status of the namespace. It can", "location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint, sku=__ret__.sku, status=__ret__.status,", "namespace_type=None, provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None, sku=None, status=None, subscription_id=None, tags=None, type=None,", "2017-04-01. :param str namespace_name: The namespace name. :param str resource_group_name:", "name=None, namespace_type=None, provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None, sku=None, status=None, subscription_id=None, tags=None,", "namespace is currently enabled. \"\"\" return pulumi.get(self, \"enabled\") @property @pulumi.getter", "def data_center(self) -> Optional[str]: \"\"\" Data center for the namespace", "id(self) -> str: \"\"\" Resource Id \"\"\" return pulumi.get(self, \"id\")", "Optional[str]: \"\"\" Endpoint you can use to perform NotificationHub operations.", "is deprecated: The 'latest' version is deprecated. Please migrate to", "str): raise TypeError(\"Expected argument 'id' to be a str\") pulumi.set(__self__,", "Optional[str]: \"\"\" Resource location \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\")", "\"type\") @property @pulumi.getter(name=\"updatedAt\") def updated_at(self) -> Optional[str]: \"\"\" The time", "West US, North Central US, South Central US, East Asia,", "tags) if type and not isinstance(type, str): raise TypeError(\"Expected argument", "location=None, metric_id=None, name=None, namespace_type=None, provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None, sku=None, status=None,", "to be a dict\") pulumi.set(__self__, \"tags\", tags) if type and", "Insights metrics \"\"\" return pulumi.get(self, \"metric_id\") @property @pulumi.getter def name(self)", "pulumi.set(__self__, \"scale_unit\", scale_unit) if service_bus_endpoint and not isinstance(service_bus_endpoint, str): raise", "id=self.id, location=self.location, metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku,", "def service_bus_endpoint(self) -> Optional[str]: \"\"\" Endpoint you can use to", "\"\"\" return pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self) -> Optional[str]:", "@pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: \"\"\" Resource tags \"\"\"", "import pulumi import pulumi.runtime from typing import Any, Mapping, Optional,", "raise TypeError(\"Expected argument 'metric_id' to be a str\") pulumi.set(__self__, \"metric_id\",", "the namespace. \"\"\" return pulumi.get(self, \"subscription_id\") @property @pulumi.getter def tags(self)", "\"created_at\", created_at) if critical and not isinstance(critical, bool): raise TypeError(\"Expected", "isinstance(created_at, str): raise TypeError(\"Expected argument 'created_at' to be a str\")", "\"namespace_type\", namespace_type) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError(\"Expected", "generated by the Pulumi SDK Generator. *** # *** Do", "not isinstance(sku, dict): raise TypeError(\"Expected argument 'sku' to be a", "pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from", "name. :param str resource_group_name: The name of the resource group.", "you can use to perform NotificationHub operations. \"\"\" return pulumi.get(self,", "Namespace resource. \"\"\" def __init__(__self__, created_at=None, critical=None, data_center=None, enabled=None, id=None,", "str): raise TypeError(\"Expected argument 'scale_unit' to be a str\") pulumi.set(__self__,", "@property @pulumi.getter def critical(self) -> Optional[bool]: \"\"\" Whether or not", "US, North Central US, South Central US, East Asia, Southeast", "sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at) def get_namespace(namespace_name: Optional[str] =", "subscription associated with the namespace. \"\"\" return pulumi.get(self, \"subscription_id\") @property", "\"\"\" return pulumi.get(self, \"enabled\") @property @pulumi.getter def id(self) -> str:", "and not isinstance(tags, dict): raise TypeError(\"Expected argument 'tags' to be", "\"\"\" The sku of the created namespace \"\"\" return pulumi.get(self,", "type \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\") def updated_at(self) ->", "'region' to be a str\") pulumi.set(__self__, \"region\", region) if scale_unit", "Namespace resource. Latest API Version: 2017-04-01. :param str namespace_name: The", "\"critical\", critical) if data_center and not isinstance(data_center, str): raise TypeError(\"Expected", "enabled=None, id=None, location=None, metric_id=None, name=None, namespace_type=None, provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None,", "region=None, scale_unit=None, service_bus_endpoint=None, sku=None, status=None, subscription_id=None, tags=None, type=None, updated_at=None): if", "and not isinstance(sku, dict): raise TypeError(\"Expected argument 'sku' to be", "to be a str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state) if region and", "of the following values: Australia East, Australia Southeast, Central US,", "\"\"\" return pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> Optional[str]:", "and not isinstance(updated_at, str): raise TypeError(\"Expected argument 'updated_at' to be", "location) if metric_id and not isinstance(metric_id, str): raise TypeError(\"Expected argument", "@property @pulumi.getter(name=\"dataCenter\") def data_center(self) -> Optional[str]: \"\"\" Data center for", "\"id\", id) if location and not isinstance(location, str): raise TypeError(\"Expected", "-> str: \"\"\" Resource type \"\"\" return pulumi.get(self, \"type\") @property", "created_at(self) -> Optional[str]: \"\"\" The time the namespace was created.", "def tags(self) -> Optional[Mapping[str, str]]: \"\"\" Resource tags \"\"\" return", "Resource type \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\") def updated_at(self)", "namespace type. \"\"\" return pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self)", "__ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical,", "\"tags\") @property @pulumi.getter def type(self) -> str: \"\"\" Resource type", "Generator. *** # *** Do not edit by hand unless", "[ 'GetNamespaceResult', 'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The 'latest' version is deprecated.", "Endpoint you can use to perform NotificationHub operations. \"\"\" return", "\"location\", location) if metric_id and not isinstance(metric_id, str): raise TypeError(\"Expected", "Optional[str]: \"\"\" ScaleUnit where the namespace gets created \"\"\" return", "'metric_id' to be a str\") pulumi.set(__self__, \"metric_id\", metric_id) if name", "be a str\") pulumi.set(__self__, \"region\", region) if scale_unit and not", "= _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult(", "type. \"\"\" return pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\") def provisioning_state(self) ->", "pylint: disable=using-constant-test def __await__(self): if False: yield self return GetNamespaceResult(", "def __await__(self): if False: yield self return GetNamespaceResult( created_at=self.created_at, critical=self.critical,", "= dict() __args__['namespaceName'] = namespace_name __args__['resourceGroupName'] = resource_group_name if opts", "Namespace. \"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter def region(self) ->", "\"\"\" return pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) -> Optional[str]:", "pulumi.get(self, \"id\") @property @pulumi.getter def location(self) -> Optional[str]: \"\"\" Resource", "these values:1 = Created/Active2 = Creating3 = Suspended4 = Deleting", "return pulumi.get(self, \"status\") @property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self) -> Optional[str]: \"\"\"", "__args__, opts=opts, typ=GetNamespaceResult).value return AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id,", "@pulumi.getter(name=\"metricId\") def metric_id(self) -> str: \"\"\" Identifier for Azure Insights", "return pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test def __await__(self):", "region and not isinstance(region, str): raise TypeError(\"Expected argument 'region' to", "\"location\") @property @pulumi.getter(name=\"metricId\") def metric_id(self) -> str: \"\"\" Identifier for", "a str\") pulumi.set(__self__, \"location\", location) if metric_id and not isinstance(metric_id,", "None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNamespaceResult: \"\"\" Description of", "West, North Europe, West Europe \"\"\" return pulumi.get(self, \"region\") @property", "argument 'name' to be a str\") pulumi.set(__self__, \"name\", name) if", "are doing! *** import warnings import pulumi import pulumi.runtime from", "metric_id=None, name=None, namespace_type=None, provisioning_state=None, region=None, scale_unit=None, service_bus_endpoint=None, sku=None, status=None, subscription_id=None,", "argument 'data_center' to be a str\") pulumi.set(__self__, \"data_center\", data_center) if", "to be a str\") pulumi.set(__self__, \"scale_unit\", scale_unit) if service_bus_endpoint and", "Azure subscription associated with the namespace. \"\"\" return pulumi.get(self, \"subscription_id\")", "created. It can be any of the following values: Australia", "region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id, tags=self.tags, type=self.type, updated_at=self.updated_at) def", "West Europe \"\"\" return pulumi.get(self, \"region\") @property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self)", "resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version", "pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ =", "pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self) -> Optional[str]: \"\"\" Endpoint", "class AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test def __await__(self): if False: yield", "isinstance(name, str): raise TypeError(\"Expected argument 'name' to be a str\")", "= resource_group_name if opts is None: opts = pulumi.InvokeOptions() if", "subscription_id and not isinstance(subscription_id, str): raise TypeError(\"Expected argument 'subscription_id' to", "'location' to be a str\") pulumi.set(__self__, \"location\", location) if metric_id", "updated_at and not isinstance(updated_at, str): raise TypeError(\"Expected argument 'updated_at' to", "Specifies the targeted region in which the namespace should be", "return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter def region(self) -> Optional[str]: \"\"\"", "tags=None, type=None, updated_at=None): if created_at and not isinstance(created_at, str): raise", "Central US, East Asia, Southeast Asia, Brazil South, Japan East,", "the created namespace \"\"\" return pulumi.get(self, \"sku\") @property @pulumi.getter def", "TypeError(\"Expected argument 'enabled' to be a bool\") pulumi.set(__self__, \"enabled\", enabled)", "'enabled' to be a bool\") pulumi.set(__self__, \"enabled\", enabled) if id", "opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__,", "to be a str\") pulumi.set(__self__, \"id\", id) if location and", "@property @pulumi.getter def name(self) -> str: \"\"\" Resource name \"\"\"", "metrics \"\"\" return pulumi.get(self, \"metric_id\") @property @pulumi.getter def name(self) ->", "-> Optional['outputs.SkuResponse']: \"\"\" The sku of the created namespace \"\"\"", "namespace \"\"\" return pulumi.get(self, \"sku\") @property @pulumi.getter def status(self) ->", "edit by hand unless you're certain you know what you", "str\") pulumi.set(__self__, \"namespace_type\", namespace_type) if provisioning_state and not isinstance(provisioning_state, str):", "\"\"\" The namespace type. \"\"\" return pulumi.get(self, \"namespace_type\") @property @pulumi.getter(name=\"provisioningState\")", "def sku(self) -> Optional['outputs.SkuResponse']: \"\"\" The sku of the created", "@pulumi.getter(name=\"dataCenter\") def data_center(self) -> Optional[str]: \"\"\" Data center for the", "metric_id=self.metric_id, name=self.name, namespace_type=self.namespace_type, provisioning_state=self.provisioning_state, region=self.region, scale_unit=self.scale_unit, service_bus_endpoint=self.service_bus_endpoint, sku=self.sku, status=self.status, subscription_id=self.subscription_id,", "\"\"\" return pulumi.get(self, \"data_center\") @property @pulumi.getter def enabled(self) -> Optional[bool]:", "Optional[str]: \"\"\" Specifies the targeted region in which the namespace", "to be a bool\") pulumi.set(__self__, \"critical\", critical) if data_center and", "migrate to the function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning)", "any of these values:1 = Created/Active2 = Creating3 = Suspended4", "East US 2, West US, North Central US, South Central", "pulumi.get(self, \"sku\") @property @pulumi.getter def status(self) -> Optional[str]: \"\"\" Status", "tags and not isinstance(tags, dict): raise TypeError(\"Expected argument 'tags' to", "status and not isinstance(status, str): raise TypeError(\"Expected argument 'status' to", "Do not edit by hand unless you're certain you know", "str\") pulumi.set(__self__, \"id\", id) if location and not isinstance(location, str):", "@property @pulumi.getter(name=\"scaleUnit\") def scale_unit(self) -> Optional[str]: \"\"\" ScaleUnit where the", "namespace gets created \"\"\" return pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def", "gets created \"\"\" return pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\") def service_bus_endpoint(self)", "data_center) if enabled and not isinstance(enabled, bool): raise TypeError(\"Expected argument", "argument 'type' to be a str\") pulumi.set(__self__, \"type\", type) if", "which the namespace should be created. It can be any", "@pulumi.getter(name=\"subscriptionId\") def subscription_id(self) -> Optional[str]: \"\"\" The Id of the", "str\") pulumi.set(__self__, \"status\", status) if subscription_id and not isinstance(subscription_id, str):", "not isinstance(region, str): raise TypeError(\"Expected argument 'region' to be a", "not the namespace is currently enabled. \"\"\" return pulumi.get(self, \"enabled\")", "str): raise TypeError(\"Expected argument 'location' to be a str\") pulumi.set(__self__,", "isinstance(tags, dict): raise TypeError(\"Expected argument 'tags' to be a dict\")", "__args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions()", "dict\") pulumi.set(__self__, \"sku\", sku) if status and not isinstance(status, str):", "Created/Active2 = Creating3 = Suspended4 = Deleting \"\"\" return pulumi.get(self,", "pulumi.set(__self__, \"provisioning_state\", provisioning_state) if region and not isinstance(region, str): raise", "Creating3 = Suspended4 = Deleting \"\"\" return pulumi.get(self, \"status\") @property", "id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint, sku=__ret__.sku,", "# *** Do not edit by hand unless you're certain", "'type' to be a str\") pulumi.set(__self__, \"type\", type) if updated_at", "if tags and not isinstance(tags, dict): raise TypeError(\"Expected argument 'tags'", "is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:notificationhubs/latest:getNamespace', __args__, opts=opts,", "not isinstance(created_at, str): raise TypeError(\"Expected argument 'created_at' to be a", "if created_at and not isinstance(created_at, str): raise TypeError(\"Expected argument 'created_at'", "return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\") def updated_at(self) -> Optional[str]: \"\"\"", "and not isinstance(type, str): raise TypeError(\"Expected argument 'type' to be", "return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\") def metric_id(self) -> str: \"\"\"", "class GetNamespaceResult: \"\"\" Description of a Namespace resource. \"\"\" def", "Central US, South Central US, East Asia, Southeast Asia, Brazil", "pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\") def updated_at(self) -> Optional[str]: \"\"\" The", "\"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\") def namespace_type(self) -> Optional[str]:", "Optional[str]: \"\"\" Status of the namespace. It can be any", "argument 'status' to be a str\") pulumi.set(__self__, \"status\", status) if", "TypeError(\"Expected argument 'metric_id' to be a str\") pulumi.set(__self__, \"metric_id\", metric_id)", "-> AwaitableGetNamespaceResult: \"\"\" Description of a Namespace resource. Latest API", "metric_id) if name and not isinstance(name, str): raise TypeError(\"Expected argument", "str): raise TypeError(\"Expected argument 'region' to be a str\") pulumi.set(__self__,", "critical=None, data_center=None, enabled=None, id=None, location=None, metric_id=None, name=None, namespace_type=None, provisioning_state=None, region=None,", "TypeError(\"Expected argument 'critical' to be a bool\") pulumi.set(__self__, \"critical\", critical)", "be a str\") pulumi.set(__self__, \"status\", status) if subscription_id and not", "\"name\") @property @pulumi.getter(name=\"namespaceType\") def namespace_type(self) -> Optional[str]: \"\"\" The namespace", "\"\"\" return pulumi.get(self, \"created_at\") @property @pulumi.getter def critical(self) -> Optional[bool]:", "isinstance(scale_unit, str): raise TypeError(\"Expected argument 'scale_unit' to be a str\")", "\"sku\", sku) if status and not isinstance(status, str): raise TypeError(\"Expected", "namespace_type=__ret__.namespace_type, provisioning_state=__ret__.provisioning_state, region=__ret__.region, scale_unit=__ret__.scale_unit, service_bus_endpoint=__ret__.service_bus_endpoint, sku=__ret__.sku, status=__ret__.status, subscription_id=__ret__.subscription_id, tags=__ret__.tags, type=__ret__.type,", "for Azure Insights metrics \"\"\" return pulumi.get(self, \"metric_id\") @property @pulumi.getter", "SDK Generator. *** # *** Do not edit by hand", "pulumi.log.warn(\"\"\"get_namespace is deprecated: The 'latest' version is deprecated. Please migrate", "AwaitableGetNamespaceResult(GetNamespaceResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self", "is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version", "pulumi.set(__self__, \"type\", type) if updated_at and not isinstance(updated_at, str): raise", "raise TypeError(\"Expected argument 'data_center' to be a str\") pulumi.set(__self__, \"data_center\",", "sku(self) -> Optional['outputs.SkuResponse']: \"\"\" The sku of the created namespace", "be a str\") pulumi.set(__self__, \"data_center\", data_center) if enabled and not", "a str\") pulumi.set(__self__, \"service_bus_endpoint\", service_bus_endpoint) if sku and not isinstance(sku,", "\"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\") def updated_at(self) -> Optional[str]:", "str): raise TypeError(\"Expected argument 'metric_id' to be a str\") pulumi.set(__self__,", "from typing import Any, Mapping, Optional, Sequence, Union from ...", "\"\"\" The Id of the Azure subscription associated with the", "-> Optional[str]: \"\"\" ScaleUnit where the namespace gets created \"\"\"", "group. \"\"\" pulumi.log.warn(\"\"\"get_namespace is deprecated: The 'latest' version is deprecated.", "AwaitableGetNamespaceResult( created_at=__ret__.created_at, critical=__ret__.critical, data_center=__ret__.data_center, enabled=__ret__.enabled, id=__ret__.id, location=__ret__.location, metric_id=__ret__.metric_id, name=__ret__.name, namespace_type=__ret__.namespace_type,", "str\") pulumi.set(__self__, \"metric_id\", metric_id) if name and not isinstance(name, str):", "the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class GetNamespaceResult: \"\"\" Description", "Optional['outputs.SkuResponse']: \"\"\" The sku of the created namespace \"\"\" return", "a str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state) if region and not isinstance(region,", "\"\"\" return pulumi.get(self, \"metric_id\") @property @pulumi.getter def name(self) -> str:", "function in the top-level module: 'azure-native:notificationhubs:getNamespace'.\"\"\", DeprecationWarning) @pulumi.output_type class GetNamespaceResult:", "in which the namespace should be created. It can be", "@property @pulumi.getter def status(self) -> Optional[str]: \"\"\" Status of the", "not isinstance(provisioning_state, str): raise TypeError(\"Expected argument 'provisioning_state' to be a", "sku) if status and not isinstance(status, str): raise TypeError(\"Expected argument", "def subscription_id(self) -> Optional[str]: \"\"\" The Id of the Azure", "sku of the created namespace \"\"\" return pulumi.get(self, \"sku\") @property", "namespace. It can be any of these values:1 = Created/Active2", "pulumi.get(self, \"subscription_id\") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: \"\"\"", "TypeError(\"Expected argument 'sku' to be a dict\") pulumi.set(__self__, \"sku\", sku)", "raise TypeError(\"Expected argument 'id' to be a str\") pulumi.set(__self__, \"id\",", "if sku and not isinstance(sku, dict): raise TypeError(\"Expected argument 'sku'", "str: \"\"\" Resource type \"\"\" return pulumi.get(self, \"type\") @property @pulumi.getter(name=\"updatedAt\")", "by the Pulumi SDK Generator. *** # *** Do not", "not isinstance(namespace_type, str): raise TypeError(\"Expected argument 'namespace_type' to be a", "'provisioning_state' to be a str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state) if region", "created namespace \"\"\" return pulumi.get(self, \"sku\") @property @pulumi.getter def status(self)", "the namespace was updated. \"\"\" return pulumi.get(self, \"updated_at\") class AwaitableGetNamespaceResult(GetNamespaceResult):", "id and not isinstance(id, str): raise TypeError(\"Expected argument 'id' to", "isinstance(sku, dict): raise TypeError(\"Expected argument 'sku' to be a dict\")", "to be a bool\") pulumi.set(__self__, \"enabled\", enabled) if id and", "pulumi.set(__self__, \"created_at\", created_at) if critical and not isinstance(critical, bool): raise", "@property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: \"\"\" Resource tags", "-> Optional[str]: \"\"\" Data center for the namespace \"\"\" return", "of a Namespace resource. \"\"\" def __init__(__self__, created_at=None, critical=None, data_center=None,", "updated_at) @property @pulumi.getter(name=\"createdAt\") def created_at(self) -> Optional[str]: \"\"\" The time", "to be a str\") pulumi.set(__self__, \"status\", status) if subscription_id and", "the namespace gets created \"\"\" return pulumi.get(self, \"scale_unit\") @property @pulumi.getter(name=\"serviceBusEndpoint\")", "name and not isinstance(name, str): raise TypeError(\"Expected argument 'name' to", "US 2, West US, North Central US, South Central US,", "be a str\") pulumi.set(__self__, \"metric_id\", metric_id) if name and not", "Data center for the namespace \"\"\" return pulumi.get(self, \"data_center\") @property", "currently enabled. \"\"\" return pulumi.get(self, \"enabled\") @property @pulumi.getter def id(self)", "a bool\") pulumi.set(__self__, \"enabled\", enabled) if id and not isinstance(id,", "Resource location \"\"\" return pulumi.get(self, \"location\") @property @pulumi.getter(name=\"metricId\") def metric_id(self)", "@pulumi.getter(name=\"provisioningState\") def provisioning_state(self) -> Optional[str]: \"\"\" Provisioning state of the", "and not isinstance(service_bus_endpoint, str): raise TypeError(\"Expected argument 'service_bus_endpoint' to be", "and not isinstance(critical, bool): raise TypeError(\"Expected argument 'critical' to be", "... import _utilities, _tables from . import outputs __all__ =", "of the created namespace \"\"\" return pulumi.get(self, \"sku\") @property @pulumi.getter", "with the namespace. \"\"\" return pulumi.get(self, \"subscription_id\") @property @pulumi.getter def", "@property @pulumi.getter(name=\"createdAt\") def created_at(self) -> Optional[str]: \"\"\" The time the", "be a str\") pulumi.set(__self__, \"scale_unit\", scale_unit) if service_bus_endpoint and not", "str): raise TypeError(\"Expected argument 'updated_at' to be a str\") pulumi.set(__self__,", "of the Azure subscription associated with the namespace. \"\"\" return", "return pulumi.get(self, \"tags\") @property @pulumi.getter def type(self) -> str: \"\"\"", "location and not isinstance(location, str): raise TypeError(\"Expected argument 'location' to", "and not isinstance(subscription_id, str): raise TypeError(\"Expected argument 'subscription_id' to be", "US, East US, East US 2, West US, North Central", "Southeast Asia, Brazil South, Japan East, Japan West, North Europe,", "\"\"\" return pulumi.get(self, \"provisioning_state\") @property @pulumi.getter def region(self) -> Optional[str]:", "to be a str\") pulumi.set(__self__, \"name\", name) if namespace_type and", "@property @pulumi.getter(name=\"subscriptionId\") def subscription_id(self) -> Optional[str]: \"\"\" The Id of", "'AwaitableGetNamespaceResult', 'get_namespace', ] warnings.warn(\"\"\"The 'latest' version is deprecated. Please migrate", "name \"\"\" return pulumi.get(self, \"name\") @property @pulumi.getter(name=\"namespaceType\") def namespace_type(self) ->", "\"\"\" The time the namespace was updated. \"\"\" return pulumi.get(self,", "TypeError(\"Expected argument 'provisioning_state' to be a str\") pulumi.set(__self__, \"provisioning_state\", provisioning_state)", "be a str\") pulumi.set(__self__, \"name\", name) if namespace_type and not", "critical) if data_center and not isinstance(data_center, str): raise TypeError(\"Expected argument" ]
[ "= json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(), TerminalFormatter())) def print_json_str(json_str): print(highlight(json_str,", "import highlight from pygments.lexers import JsonLexer from pygments.formatters import TerminalFormatter", "TerminalFormatter def print_json_obj(json_object): json_str = json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(),", "highlight from pygments.lexers import JsonLexer from pygments.formatters import TerminalFormatter def", "from pygments.formatters import TerminalFormatter def print_json_obj(json_object): json_str = json.dumps(json_object, indent=4,", "from pygments.lexers import JsonLexer from pygments.formatters import TerminalFormatter def print_json_obj(json_object):", "pygments.lexers import JsonLexer from pygments.formatters import TerminalFormatter def print_json_obj(json_object): json_str", "import json from pygments import highlight from pygments.lexers import JsonLexer", "print_json_obj(json_object): json_str = json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(), TerminalFormatter())) def", "import TerminalFormatter def print_json_obj(json_object): json_str = json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str,", "json_str = json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(), TerminalFormatter())) def print_json_str(json_str):", "from pygments import highlight from pygments.lexers import JsonLexer from pygments.formatters", "json from pygments import highlight from pygments.lexers import JsonLexer from", "def print_json_obj(json_object): json_str = json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(), TerminalFormatter()))", "import JsonLexer from pygments.formatters import TerminalFormatter def print_json_obj(json_object): json_str =", "pygments.formatters import TerminalFormatter def print_json_obj(json_object): json_str = json.dumps(json_object, indent=4, sort_keys=True)", "JsonLexer from pygments.formatters import TerminalFormatter def print_json_obj(json_object): json_str = json.dumps(json_object,", "indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(), TerminalFormatter())) def print_json_str(json_str): print(highlight(json_str, JsonLexer(), TerminalFormatter()))", "json.dumps(json_object, indent=4, sort_keys=True) print(highlight(json_str, JsonLexer(), TerminalFormatter())) def print_json_str(json_str): print(highlight(json_str, JsonLexer(),", "pygments import highlight from pygments.lexers import JsonLexer from pygments.formatters import" ]
[ "* msg_n + ['REL_SPEED'] * msg_n, RADAR_MSGS_C * 2 +", "errors for ii in self.updated_messages: # ii should be the", "None ret = car.RadarData.new_message() errors = [] if not self.rcp.can_valid:", "car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel = float('nan')", "# REL_SPEED set to 0, factor/offset to this # TODO", "= len(RADAR_MSGS_C) # list of [(signal name, message name or", "in cpt: # c_* message self.pts[trackId].dRel = cpt['LONG_DIST'] # from", "1074, 255), # ('LONG_DIST', 1075, 255), # The factor and", "honda only checks the last message, # toyota checks all", "the message ID as a number cpt = self.rcp.vl[ii] trackId", "self.pts[trackId].dRel = cpt['LONG_DIST'] # from front of car # our", "in self.updated_messages: # ii should be the message ID as", "self.pts[trackId].trackId = trackId self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured", "CANParser from cereal import car from selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C", "RADAR_MSGS_C[0]) // 2 if address in RADAR_MSGS_D: return (address -", "want a list, not a dictionary. Filter out LONG_DIST==0 because", "used for? # honda only checks the last message, #", "# LONG_DIST, LAT_DIST RADAR_MSGS_D, # REL_SPEED [0] * msg_n +", "if not self.rcp.can_valid: errors.append(\"canError\") ret.errors = errors for ii in", "self.pts = {} self.delay = 0 # Delay of radar", "ii should be the message ID as a number cpt", "len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C) #", "self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel", "= float('nan') self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured = True if 'LONG_DIST'", "['REL_SPEED'] * msg_n, RADAR_MSGS_C * 2 + # LONG_DIST, LAT_DIST", "1072, 255), # ('LONG_DIST', 1073, 255), # ('LONG_DIST', 1074, 255),", "LAT_DIST RADAR_MSGS_D, # REL_SPEED [0] * msg_n + # LONG_DIST", "actually used for? # honda only checks the last message,", "= [] if not self.rcp.can_valid: errors.append(\"canError\") ret.errors = errors for", "a number cpt = self.rcp.vl[ii] trackId = _address_to_track(ii) if trackId", "# honda only checks the last message, # toyota checks", "# TODO what are the checks actually used for? #", "can_strings): vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg not in self.updated_messages:", "applied by the dbc parsing library, so the # default", "def __init__(self, CP): self.pts = {} self.delay = 0 #", "do we want? checks = list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n +", "raise ValueError(\"radar received unexpected address %d\" % address) class RadarInterface(RadarInterfaceBase):", "len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n =", "msg_n + # LONG_DIST [-1000] * msg_n + # LAT_DIST", "msg_n = len(RADAR_MSGS_C) # list of [(signal name, message name", "LONG_DIST [-1000] * msg_n + # LAT_DIST [-146.278] * msg_n))", "(address - RADAR_MSGS_C[0]) // 2 if address in RADAR_MSGS_D: return", "to the right in car's frame. # TODO what does", "msg_n)) # REL_SPEED set to 0, factor/offset to this #", "out LONG_DIST==0 because that means it's not valid. ret.points =", "* msg_n, RADAR_MSGS_C * 2 + # LONG_DIST, LAT_DIST RADAR_MSGS_D,", "# d_* message self.pts[trackId].vRel = cpt['REL_SPEED'] # We want a", "#!/usr/bin/env python3 import os from opendbc.can.parser import CANParser from cereal", "[-1000] * msg_n + # LAT_DIST [-146.278] * msg_n)) #", "number cpt = self.rcp.vl[ii] trackId = _address_to_track(ii) if trackId not", "_create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C) # list of", "('LONG_DIST', 1073, 255), # ('LONG_DIST', 1074, 255), # ('LONG_DIST', 1075,", "the right in car's frame. # TODO what does yRel", "# REL_SPEED [0] * msg_n + # LONG_DIST [-1000] *", "self.updated_messages = set() self.trigger_msg = LAST_MSG def update(self, can_strings): vls", "only checks the last message, # toyota checks all the", "signals, checks, 1) def _address_to_track(address): if address in RADAR_MSGS_C: return", "# 20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1) def _address_to_track(address):", "y axis, left is positive else: # d_* message self.pts[trackId].vRel", "unexpected address %d\" % address) class RadarInterface(RadarInterfaceBase): def __init__(self, CP):", "to this # TODO what are the checks actually used", "factor/offset are applied. signals = list(zip(['LONG_DIST'] * msg_n + ['LAT_DIST']", "self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg not in self.updated_messages: return None ret", "= True if 'LONG_DIST' in cpt: # c_* message self.pts[trackId].dRel", "self.pts[trackId].vRel = cpt['REL_SPEED'] # We want a list, not a", "if address in RADAR_MSGS_D: return (address - RADAR_MSGS_D[0]) // 2", "the factor/offset are applied. signals = list(zip(['LONG_DIST'] * msg_n +", "the # default values should be after the factor/offset are", "CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1) def _address_to_track(address): if address in RADAR_MSGS_C:", "values), (...)] # [('RADAR_STATE', 1024, 0), # ('LONG_DIST', 1072, 255),", "RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2, 2)) # c_ messages 706,...,724", "- RADAR_MSGS_C[0]) // 2 if address in RADAR_MSGS_D: return (address", "name, message name or number, initial values), (...)] # [('RADAR_STATE',", "self.delay = 0 # Delay of radar #TUNE self.rcp =", "self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId self.pts[trackId].aRel = float('nan')", "the checks actually used for? # honda only checks the", "= 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C) # list of [(signal name,", "message self.pts[trackId].dRel = cpt['LONG_DIST'] # from front of car #", "[20]*msg_n)) # 20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1) def", "checks the last message, # toyota checks all the messages.", "def update(self, can_strings): vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg not", "what does yRel want? self.pts[trackId].yRel = cpt['LAT_DIST'] # in car", "signals = list(zip(['LONG_DIST'] * msg_n + ['LAT_DIST'] * msg_n +", "list(zip(['LONG_DIST'] * msg_n + ['LAT_DIST'] * msg_n + ['REL_SPEED'] *", "ID as a number cpt = self.rcp.vl[ii] trackId = _address_to_track(ii)", "* msg_n + ['LAT_DIST'] * msg_n + ['REL_SPEED'] * msg_n,", "+ ['LAT_DIST'] * msg_n + ['REL_SPEED'] * msg_n, RADAR_MSGS_C *", "# ii should be the message ID as a number", "= car.RadarData.new_message() errors = [] if not self.rcp.can_valid: errors.append(\"canError\") ret.errors", "from front of car # our lat_dist is positive to", "# ('LONG_DIST', 1074, 255), # ('LONG_DIST', 1075, 255), # The", "lat_dist is positive to the right in car's frame. #", "all the messages. Which do we want? checks = list(zip(RADAR_MSGS_C", "in self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId self.pts[trackId].aRel =", "toyota checks all the messages. Which do we want? checks", "0, factor/offset to this # TODO what are the checks", "= cpt['LONG_DIST'] # from front of car # our lat_dist", "= max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def", "ret.points = [x for x in self.pts.values() if x.dRel !=", "for? # honda only checks the last message, # toyota", "255), # ('LONG_DIST', 1074, 255), # ('LONG_DIST', 1075, 255), #", "[0] * msg_n + # LONG_DIST [-1000] * msg_n +", "messages LAST_MSG = max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C) +", "2 if address in RADAR_MSGS_D: return (address - RADAR_MSGS_D[0]) //", "# 20Hz (0.05s) [20]*msg_n)) # 20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals,", "trackId self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured = True", "# c_* message self.pts[trackId].dRel = cpt['LONG_DIST'] # from front of", "ret.errors = errors for ii in self.updated_messages: # ii should", "= float('nan') self.pts[trackId].measured = True if 'LONG_DIST' in cpt: #", "axis, left is positive else: # d_* message self.pts[trackId].vRel =", "that means it's not valid. ret.points = [x for x", "# LAT_DIST [-146.278] * msg_n)) # REL_SPEED set to 0,", "if self.trigger_msg not in self.updated_messages: return None ret = car.RadarData.new_message()", "[] if not self.rcp.can_valid: errors.append(\"canError\") ret.errors = errors for ii", "for ii in self.updated_messages: # ii should be the message", "list, not a dictionary. Filter out LONG_DIST==0 because that means", "# our lat_dist is positive to the right in car's", "dictionary. Filter out LONG_DIST==0 because that means it's not valid.", "os from opendbc.can.parser import CANParser from cereal import car from", "are applied. signals = list(zip(['LONG_DIST'] * msg_n + ['LAT_DIST'] *", "# Delay of radar #TUNE self.rcp = _create_radar_can_parser() self.updated_messages =", "errors = [] if not self.rcp.can_valid: errors.append(\"canError\") ret.errors = errors", "number, initial values), (...)] # [('RADAR_STATE', 1024, 0), # ('LONG_DIST',", "RADAR_MSGS_C * 2 + # LONG_DIST, LAT_DIST RADAR_MSGS_D, # REL_SPEED", "REL_SPEED set to 0, factor/offset to this # TODO what", "is positive else: # d_* message self.pts[trackId].vRel = cpt['REL_SPEED'] #", "# The factor and offset are applied by the dbc", "list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n + # 20Hz (0.05s) [20]*msg_n)) #", "be after the factor/offset are applied. signals = list(zip(['LONG_DIST'] *", "cereal import car from selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2,", "as a number cpt = self.rcp.vl[ii] trackId = _address_to_track(ii) if", "car frame's y axis, left is positive else: # d_*", "list(range(0x2c2, 0x2d4+2, 2)) # c_ messages 706,...,724 RADAR_MSGS_D = list(range(0x2a2,", "want? checks = list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n + # 20Hz", "# [('RADAR_STATE', 1024, 0), # ('LONG_DIST', 1072, 255), # ('LONG_DIST',", "2)) # c_ messages 706,...,724 RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2, 2))", "+ RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f", "message name or number, initial values), (...)] # [('RADAR_STATE', 1024,", "of radar #TUNE self.rcp = _create_radar_can_parser() self.updated_messages = set() self.trigger_msg", "in RADAR_MSGS_C: return (address - RADAR_MSGS_C[0]) // 2 if address", "in car frame's y axis, left is positive else: #", "# ('LONG_DIST', 1073, 255), # ('LONG_DIST', 1074, 255), # ('LONG_DIST',", "self.trigger_msg not in self.updated_messages: return None ret = car.RadarData.new_message() errors", "a list, not a dictionary. Filter out LONG_DIST==0 because that", "after the factor/offset are applied. signals = list(zip(['LONG_DIST'] * msg_n", "= 0 # Delay of radar #TUNE self.rcp = _create_radar_can_parser()", "# TODO what does yRel want? self.pts[trackId].yRel = cpt['LAT_DIST'] #", "= LAST_MSG def update(self, can_strings): vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if", "values should be after the factor/offset are applied. signals =", "car from selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2, 2))", "+ ['REL_SPEED'] * msg_n, RADAR_MSGS_C * 2 + # LONG_DIST,", "the dbc parsing library, so the # default values should", "# list of [(signal name, message name or number, initial", "= list(range(0x2a2, 0x2b4+2, 2)) # d_ messages LAST_MSG = max(RADAR_MSGS_C", "checks, 1) def _address_to_track(address): if address in RADAR_MSGS_C: return (address", "library, so the # default values should be after the", "if 'LONG_DIST' in cpt: # c_* message self.pts[trackId].dRel = cpt['LONG_DIST']", "= _address_to_track(ii) if trackId not in self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message()", "return (address - RADAR_MSGS_C[0]) // 2 if address in RADAR_MSGS_D:", "self.trigger_msg = LAST_MSG def update(self, can_strings): vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls)", "+ len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C)", "are applied by the dbc parsing library, so the #", "706,...,724 RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2, 2)) # d_ messages LAST_MSG", "= list(range(0x2c2, 0x2d4+2, 2)) # c_ messages 706,...,724 RADAR_MSGS_D =", "{} self.delay = 0 # Delay of radar #TUNE self.rcp", "python3 import os from opendbc.can.parser import CANParser from cereal import", "0), # ('LONG_DIST', 1072, 255), # ('LONG_DIST', 1073, 255), #", "self.updated_messages: return None ret = car.RadarData.new_message() errors = [] if", "frame's y axis, left is positive else: # d_* message", "message ID as a number cpt = self.rcp.vl[ii] trackId =", "* msg_n + # LAT_DIST [-146.278] * msg_n)) # REL_SPEED", "self.updated_messages: # ii should be the message ID as a", "RADAR_MSGS_D[0]) // 2 raise ValueError(\"radar received unexpected address %d\" %", "be the message ID as a number cpt = self.rcp.vl[ii]", "LONG_DIST==0 because that means it's not valid. ret.points = [x", "RADAR_MSGS_D: return (address - RADAR_MSGS_D[0]) // 2 raise ValueError(\"radar received", "from selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2, 2)) #", "= self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg not in self.updated_messages: return None", "address %d\" % address) class RadarInterface(RadarInterfaceBase): def __init__(self, CP): self.pts", "TODO what does yRel want? self.pts[trackId].yRel = cpt['LAT_DIST'] # in", "return (address - RADAR_MSGS_D[0]) // 2 raise ValueError(\"radar received unexpected", "list of [(signal name, message name or number, initial values),", "= cpt['LAT_DIST'] # in car frame's y axis, left is", "255), # ('LONG_DIST', 1073, 255), # ('LONG_DIST', 1074, 255), #", "c_* message self.pts[trackId].dRel = cpt['LONG_DIST'] # from front of car", "address in RADAR_MSGS_D: return (address - RADAR_MSGS_D[0]) // 2 raise", "of car # our lat_dist is positive to the right", "should be after the factor/offset are applied. signals = list(zip(['LONG_DIST']", "address) class RadarInterface(RadarInterfaceBase): def __init__(self, CP): self.pts = {} self.delay", "= _create_radar_can_parser() self.updated_messages = set() self.trigger_msg = LAST_MSG def update(self,", "if address in RADAR_MSGS_C: return (address - RADAR_MSGS_C[0]) // 2", "RADAR_MSGS_C: return (address - RADAR_MSGS_C[0]) // 2 if address in", "in self.updated_messages: return None ret = car.RadarData.new_message() errors = []", "20Hz (0.05s) [20]*msg_n)) # 20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals, checks,", "We want a list, not a dictionary. Filter out LONG_DIST==0", "checks = list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n + # 20Hz (0.05s)", "vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg not in self.updated_messages: return", "1) def _address_to_track(address): if address in RADAR_MSGS_C: return (address -", "'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C) # list of [(signal name, message", "if trackId not in self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId =", "LAT_DIST [-146.278] * msg_n)) # REL_SPEED set to 0, factor/offset", "LAST_MSG def update(self, can_strings): vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg", "* 2 + # LONG_DIST, LAT_DIST RADAR_MSGS_D, # REL_SPEED [0]", "- RADAR_MSGS_D[0]) // 2 raise ValueError(\"radar received unexpected address %d\"", "self.pts[trackId].measured = True if 'LONG_DIST' in cpt: # c_* message", "class RadarInterface(RadarInterfaceBase): def __init__(self, CP): self.pts = {} self.delay =", "list(range(0x2a2, 0x2b4+2, 2)) # d_ messages LAST_MSG = max(RADAR_MSGS_C +", "want? self.pts[trackId].yRel = cpt['LAT_DIST'] # in car frame's y axis,", "255), # The factor and offset are applied by the", "% address) class RadarInterface(RadarInterfaceBase): def __init__(self, CP): self.pts = {}", "the last message, # toyota checks all the messages. Which", "# default values should be after the factor/offset are applied.", "car.RadarData.new_message() errors = [] if not self.rcp.can_valid: errors.append(\"canError\") ret.errors =", "RADAR_MSGS_D, [20]*msg_n + # 20Hz (0.05s) [20]*msg_n)) # 20Hz (0.05s)", "True if 'LONG_DIST' in cpt: # c_* message self.pts[trackId].dRel =", "[x for x in self.pts.values() if x.dRel != 0] self.updated_messages.clear()", "what are the checks actually used for? # honda only", "RadarInterface(RadarInterfaceBase): def __init__(self, CP): self.pts = {} self.delay = 0", "not in self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId self.pts[trackId].aRel", "update(self, can_strings): vls = self.rcp.update_strings(can_strings) self.updated_messages.update(vls) if self.trigger_msg not in", "Delay of radar #TUNE self.rcp = _create_radar_can_parser() self.updated_messages = set()", "for x in self.pts.values() if x.dRel != 0] self.updated_messages.clear() return", "# in car frame's y axis, left is positive else:", "positive to the right in car's frame. # TODO what", "to 0, factor/offset to this # TODO what are the", "errors.append(\"canError\") ret.errors = errors for ii in self.updated_messages: # ii", "trackId = _address_to_track(ii) if trackId not in self.pts: self.pts[trackId] =", "the messages. Which do we want? checks = list(zip(RADAR_MSGS_C +", "ii in self.updated_messages: # ii should be the message ID", "checks actually used for? # honda only checks the last", "CP): self.pts = {} self.delay = 0 # Delay of", "not self.rcp.can_valid: errors.append(\"canError\") ret.errors = errors for ii in self.updated_messages:", "# LONG_DIST [-1000] * msg_n + # LAT_DIST [-146.278] *", "applied. signals = list(zip(['LONG_DIST'] * msg_n + ['LAT_DIST'] * msg_n", "right in car's frame. # TODO what does yRel want?", "message, # toyota checks all the messages. Which do we", "* msg_n + # LONG_DIST [-1000] * msg_n + #", "1073, 255), # ('LONG_DIST', 1074, 255), # ('LONG_DIST', 1075, 255),", "2 + # LONG_DIST, LAT_DIST RADAR_MSGS_D, # REL_SPEED [0] *", "#TUNE self.rcp = _create_radar_can_parser() self.updated_messages = set() self.trigger_msg = LAST_MSG", "return None ret = car.RadarData.new_message() errors = [] if not", "cpt: # c_* message self.pts[trackId].dRel = cpt['LONG_DIST'] # from front", "msg_n + ['REL_SPEED'] * msg_n, RADAR_MSGS_C * 2 + #", "are the checks actually used for? # honda only checks", "parsing library, so the # default values should be after", "_address_to_track(ii) if trackId not in self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId", "x in self.pts.values() if x.dRel != 0] self.updated_messages.clear() return ret", "// 2 raise ValueError(\"radar received unexpected address %d\" % address)", "means it's not valid. ret.points = [x for x in", "msg_n, RADAR_MSGS_C * 2 + # LONG_DIST, LAT_DIST RADAR_MSGS_D, #", "len(RADAR_MSGS_C) # list of [(signal name, message name or number,", "RADAR_MSGS_D, # REL_SPEED [0] * msg_n + # LONG_DIST [-1000]", "valid. ret.points = [x for x in self.pts.values() if x.dRel", "by the dbc parsing library, so the # default values", "float('nan') self.pts[trackId].measured = True if 'LONG_DIST' in cpt: # c_*", "ValueError(\"radar received unexpected address %d\" % address) class RadarInterface(RadarInterfaceBase): def", "2)) # d_ messages LAST_MSG = max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS", "not valid. ret.points = [x for x in self.pts.values() if", "= car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel =", "('LONG_DIST', 1075, 255), # The factor and offset are applied", "# ('LONG_DIST', 1075, 255), # The factor and offset are", "set to 0, factor/offset to this # TODO what are", "last message, # toyota checks all the messages. Which do", "in car's frame. # TODO what does yRel want? self.pts[trackId].yRel", "max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def _create_radar_can_parser():", "0x2b4+2, 2)) # d_ messages LAST_MSG = max(RADAR_MSGS_C + RADAR_MSGS_D)", "import car from selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2,", "because that means it's not valid. ret.points = [x for", "d_ messages LAST_MSG = max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C)", "= {} self.delay = 0 # Delay of radar #TUNE", "set() self.trigger_msg = LAST_MSG def update(self, can_strings): vls = self.rcp.update_strings(can_strings)", "[('RADAR_STATE', 1024, 0), # ('LONG_DIST', 1072, 255), # ('LONG_DIST', 1073,", "20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1) def _address_to_track(address): if", "= errors for ii in self.updated_messages: # ii should be", "REL_SPEED [0] * msg_n + # LONG_DIST [-1000] * msg_n", "self.rcp = _create_radar_can_parser() self.updated_messages = set() self.trigger_msg = LAST_MSG def", "and offset are applied by the dbc parsing library, so", "1024, 0), # ('LONG_DIST', 1072, 255), # ('LONG_DIST', 1073, 255),", "so the # default values should be after the factor/offset", "import RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2, 2)) # c_ messages", "RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f =", "is positive to the right in car's frame. # TODO", "('LONG_DIST', 1074, 255), # ('LONG_DIST', 1075, 255), # The factor", "this # TODO what are the checks actually used for?", "address in RADAR_MSGS_C: return (address - RADAR_MSGS_C[0]) // 2 if", "= trackId self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured =", "= list(zip(['LONG_DIST'] * msg_n + ['LAT_DIST'] * msg_n + ['REL_SPEED']", "* msg_n)) # REL_SPEED set to 0, factor/offset to this", "or number, initial values), (...)] # [('RADAR_STATE', 1024, 0), #", "checks all the messages. Which do we want? checks =", "def _create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C) # list", "[20]*msg_n + # 20Hz (0.05s) [20]*msg_n)) # 20Hz (0.05s) return", "def _address_to_track(address): if address in RADAR_MSGS_C: return (address - RADAR_MSGS_C[0])", "should be the message ID as a number cpt =", "= len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n", "float('nan') self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured = True if 'LONG_DIST' in", "self.updated_messages.update(vls) if self.trigger_msg not in self.updated_messages: return None ret =", "__init__(self, CP): self.pts = {} self.delay = 0 # Delay", "initial values), (...)] # [('RADAR_STATE', 1024, 0), # ('LONG_DIST', 1072,", "= [x for x in self.pts.values() if x.dRel != 0]", "self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured = True if 'LONG_DIST' in cpt:", "our lat_dist is positive to the right in car's frame.", "= list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n + # 20Hz (0.05s) [20]*msg_n))", "(0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1) def _address_to_track(address): if address", "+ # 20Hz (0.05s) [20]*msg_n)) # 20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0],", "of [(signal name, message name or number, initial values), (...)]", "255), # ('LONG_DIST', 1075, 255), # The factor and offset", "not in self.updated_messages: return None ret = car.RadarData.new_message() errors =", "trackId not in self.pts: self.pts[trackId] = car.RadarData.RadarPoint.new_message() self.pts[trackId].trackId = trackId", "messages. Which do we want? checks = list(zip(RADAR_MSGS_C + RADAR_MSGS_D,", "ret = car.RadarData.new_message() errors = [] if not self.rcp.can_valid: errors.append(\"canError\")", "factor and offset are applied by the dbc parsing library,", "return CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1) def _address_to_track(address): if address in", "+ # LAT_DIST [-146.278] * msg_n)) # REL_SPEED set to", "cpt['REL_SPEED'] # We want a list, not a dictionary. Filter", "_create_radar_can_parser() self.updated_messages = set() self.trigger_msg = LAST_MSG def update(self, can_strings):", "%d\" % address) class RadarInterface(RadarInterfaceBase): def __init__(self, CP): self.pts =", "# We want a list, not a dictionary. Filter out", "(0.05s) [20]*msg_n)) # 20Hz (0.05s) return CANParser(os.path.splitext(dbc_f)[0], signals, checks, 1)", "frame. # TODO what does yRel want? self.pts[trackId].yRel = cpt['LAT_DIST']", "[(signal name, message name or number, initial values), (...)] #", "radar #TUNE self.rcp = _create_radar_can_parser() self.updated_messages = set() self.trigger_msg =", "Which do we want? checks = list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n", "self.pts[trackId].aRel = float('nan') self.pts[trackId].yvRel = float('nan') self.pts[trackId].measured = True if", "positive else: # d_* message self.pts[trackId].vRel = cpt['REL_SPEED'] # We", "factor/offset to this # TODO what are the checks actually", "not a dictionary. Filter out LONG_DIST==0 because that means it's", "import os from opendbc.can.parser import CANParser from cereal import car", "left is positive else: # d_* message self.pts[trackId].vRel = cpt['REL_SPEED']", "yRel want? self.pts[trackId].yRel = cpt['LAT_DIST'] # in car frame's y", "# d_ messages LAST_MSG = max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS =", "+ RADAR_MSGS_D, [20]*msg_n + # 20Hz (0.05s) [20]*msg_n)) # 20Hz", "LONG_DIST, LAT_DIST RADAR_MSGS_D, # REL_SPEED [0] * msg_n + #", "does yRel want? self.pts[trackId].yRel = cpt['LAT_DIST'] # in car frame's", "default values should be after the factor/offset are applied. signals", "self.rcp.vl[ii] trackId = _address_to_track(ii) if trackId not in self.pts: self.pts[trackId]", "offset are applied by the dbc parsing library, so the", "car's frame. # TODO what does yRel want? self.pts[trackId].yRel =", "# ('LONG_DIST', 1072, 255), # ('LONG_DIST', 1073, 255), # ('LONG_DIST',", "cpt['LONG_DIST'] # from front of car # our lat_dist is", "dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc' msg_n = len(RADAR_MSGS_C) # list of [(signal", "The factor and offset are applied by the dbc parsing", "# c_ messages 706,...,724 RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2, 2)) #", "a dictionary. Filter out LONG_DIST==0 because that means it's not", "# toyota checks all the messages. Which do we want?", "= set() self.trigger_msg = LAST_MSG def update(self, can_strings): vls =", "LAST_MSG = max(RADAR_MSGS_C + RADAR_MSGS_D) NUMBER_MSGS = len(RADAR_MSGS_C) + len(RADAR_MSGS_D)", "// 2 if address in RADAR_MSGS_D: return (address - RADAR_MSGS_D[0])", "NUMBER_MSGS = len(RADAR_MSGS_C) + len(RADAR_MSGS_D) def _create_radar_can_parser(): dbc_f = 'chrysler_pacifica_2017_hybrid_private_fusion.dbc'", "msg_n + # LAT_DIST [-146.278] * msg_n)) # REL_SPEED set", "from cereal import car from selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C =", "from opendbc.can.parser import CANParser from cereal import car from selfdrive.car.interfaces", "+ # LONG_DIST [-1000] * msg_n + # LAT_DIST [-146.278]", "in RADAR_MSGS_D: return (address - RADAR_MSGS_D[0]) // 2 raise ValueError(\"radar", "self.rcp.can_valid: errors.append(\"canError\") ret.errors = errors for ii in self.updated_messages: #", "(...)] # [('RADAR_STATE', 1024, 0), # ('LONG_DIST', 1072, 255), #", "= self.rcp.vl[ii] trackId = _address_to_track(ii) if trackId not in self.pts:", "'LONG_DIST' in cpt: # c_* message self.pts[trackId].dRel = cpt['LONG_DIST'] #", "RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2, 2)) # c_ messages 706,...,724 RADAR_MSGS_D", "front of car # our lat_dist is positive to the", "messages 706,...,724 RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2, 2)) # d_ messages", "received unexpected address %d\" % address) class RadarInterface(RadarInterfaceBase): def __init__(self,", "1075, 255), # The factor and offset are applied by", "cpt['LAT_DIST'] # in car frame's y axis, left is positive", "name or number, initial values), (...)] # [('RADAR_STATE', 1024, 0),", "self.pts[trackId].yRel = cpt['LAT_DIST'] # in car frame's y axis, left", "2 raise ValueError(\"radar received unexpected address %d\" % address) class", "_address_to_track(address): if address in RADAR_MSGS_C: return (address - RADAR_MSGS_C[0]) //", "msg_n + ['LAT_DIST'] * msg_n + ['REL_SPEED'] * msg_n, RADAR_MSGS_C", "0x2d4+2, 2)) # c_ messages 706,...,724 RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2,", "import CANParser from cereal import car from selfdrive.car.interfaces import RadarInterfaceBase", "it's not valid. ret.points = [x for x in self.pts.values()", "dbc parsing library, so the # default values should be", "Filter out LONG_DIST==0 because that means it's not valid. ret.points", "= cpt['REL_SPEED'] # We want a list, not a dictionary.", "opendbc.can.parser import CANParser from cereal import car from selfdrive.car.interfaces import", "# from front of car # our lat_dist is positive", "else: # d_* message self.pts[trackId].vRel = cpt['REL_SPEED'] # We want", "cpt = self.rcp.vl[ii] trackId = _address_to_track(ii) if trackId not in", "RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2, 2)) # d_ messages LAST_MSG =", "['LAT_DIST'] * msg_n + ['REL_SPEED'] * msg_n, RADAR_MSGS_C * 2", "+ # LONG_DIST, LAT_DIST RADAR_MSGS_D, # REL_SPEED [0] * msg_n", "(address - RADAR_MSGS_D[0]) // 2 raise ValueError(\"radar received unexpected address", "message self.pts[trackId].vRel = cpt['REL_SPEED'] # We want a list, not", "[-146.278] * msg_n)) # REL_SPEED set to 0, factor/offset to", "we want? checks = list(zip(RADAR_MSGS_C + RADAR_MSGS_D, [20]*msg_n + #", "('LONG_DIST', 1072, 255), # ('LONG_DIST', 1073, 255), # ('LONG_DIST', 1074,", "d_* message self.pts[trackId].vRel = cpt['REL_SPEED'] # We want a list,", "c_ messages 706,...,724 RADAR_MSGS_D = list(range(0x2a2, 0x2b4+2, 2)) # d_", "TODO what are the checks actually used for? # honda", "car # our lat_dist is positive to the right in", "0 # Delay of radar #TUNE self.rcp = _create_radar_can_parser() self.updated_messages", "selfdrive.car.interfaces import RadarInterfaceBase RADAR_MSGS_C = list(range(0x2c2, 0x2d4+2, 2)) # c_" ]
[ "ccmake command line tool \"\"\" import subprocess name = 'ccmake'", "returns successful \"\"\" res = subprocess.call('ccmake .', cwd=build_dir, shell=True) return", "cmake project :param build_dir: directory where ccmake should run :returns:", "= ['linux', 'osx'] optional = True not_found = \"required for", "not_found = \"required for 'fips config' functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir)", "if ccmake is in the path \"\"\" try: out =", "tool \"\"\" import subprocess name = 'ccmake' platforms = ['linux',", "ccmake is in the path \"\"\" try: out = subprocess.check_output(['ccmake',", "build_dir: directory where ccmake should run :returns: True if ccmake", "run :returns: True if ccmake returns successful \"\"\" res =", "out = subprocess.check_output(['ccmake', '--version']) return True except (OSError, subprocess.CalledProcessError): return", ":param build_dir: directory where ccmake should run :returns: True if", "= True not_found = \"required for 'fips config' functionality\" #-------------------------------------------------------------------------------", "'fips config' functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir) : \"\"\"test if ccmake", "'osx'] optional = True not_found = \"required for 'fips config'", "project :param build_dir: directory where ccmake should run :returns: True", "'--version']) return True except (OSError, subprocess.CalledProcessError): return False #------------------------------------------------------------------------------- def", "def run(build_dir) : \"\"\"run ccmake to configure cmake project :param", "line tool \"\"\" import subprocess name = 'ccmake' platforms =", "platforms = ['linux', 'osx'] optional = True not_found = \"required", "for 'fips config' functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir) : \"\"\"test if", "try: out = subprocess.check_output(['ccmake', '--version']) return True except (OSError, subprocess.CalledProcessError):", "(OSError, subprocess.CalledProcessError): return False #------------------------------------------------------------------------------- def run(build_dir) : \"\"\"run ccmake", "path \"\"\" try: out = subprocess.check_output(['ccmake', '--version']) return True except", "where ccmake should run :returns: True if ccmake returns successful", "= \"required for 'fips config' functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir) :", "path :returns: True if ccmake is in the path \"\"\"", "is in the path \"\"\" try: out = subprocess.check_output(['ccmake', '--version'])", "for ccmake command line tool \"\"\" import subprocess name =", "successful \"\"\" res = subprocess.call('ccmake .', cwd=build_dir, shell=True) return res", "functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir) : \"\"\"test if ccmake is in", "should run :returns: True if ccmake returns successful \"\"\" res", "in the path :returns: True if ccmake is in the", "in the path \"\"\" try: out = subprocess.check_output(['ccmake', '--version']) return", "return False #------------------------------------------------------------------------------- def run(build_dir) : \"\"\"run ccmake to configure", "command line tool \"\"\" import subprocess name = 'ccmake' platforms", "the path \"\"\" try: out = subprocess.check_output(['ccmake', '--version']) return True", "config' functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir) : \"\"\"test if ccmake is", "name = 'ccmake' platforms = ['linux', 'osx'] optional = True", "= 'ccmake' platforms = ['linux', 'osx'] optional = True not_found", "<gh_stars>100-1000 \"\"\" wrapper for ccmake command line tool \"\"\" import", "ccmake is in the path :returns: True if ccmake is", "True not_found = \"required for 'fips config' functionality\" #------------------------------------------------------------------------------- def", ": \"\"\"run ccmake to configure cmake project :param build_dir: directory", "\"\"\"run ccmake to configure cmake project :param build_dir: directory where", "res = subprocess.call('ccmake .', cwd=build_dir, shell=True) return res == 0", ":returns: True if ccmake returns successful \"\"\" res = subprocess.call('ccmake", ":returns: True if ccmake is in the path \"\"\" try:", "subprocess name = 'ccmake' platforms = ['linux', 'osx'] optional =", "the path :returns: True if ccmake is in the path", "is in the path :returns: True if ccmake is in", "#------------------------------------------------------------------------------- def run(build_dir) : \"\"\"run ccmake to configure cmake project", "run(build_dir) : \"\"\"run ccmake to configure cmake project :param build_dir:", ": \"\"\"test if ccmake is in the path :returns: True", "ccmake should run :returns: True if ccmake returns successful \"\"\"", "\"\"\" import subprocess name = 'ccmake' platforms = ['linux', 'osx']", "check_exists(fips_dir) : \"\"\"test if ccmake is in the path :returns:", "['linux', 'osx'] optional = True not_found = \"required for 'fips", "False #------------------------------------------------------------------------------- def run(build_dir) : \"\"\"run ccmake to configure cmake", "optional = True not_found = \"required for 'fips config' functionality\"", "True if ccmake returns successful \"\"\" res = subprocess.call('ccmake .',", "= subprocess.check_output(['ccmake', '--version']) return True except (OSError, subprocess.CalledProcessError): return False", "configure cmake project :param build_dir: directory where ccmake should run", "True except (OSError, subprocess.CalledProcessError): return False #------------------------------------------------------------------------------- def run(build_dir) :", "\"required for 'fips config' functionality\" #------------------------------------------------------------------------------- def check_exists(fips_dir) : \"\"\"test", "if ccmake is in the path :returns: True if ccmake", "directory where ccmake should run :returns: True if ccmake returns", "import subprocess name = 'ccmake' platforms = ['linux', 'osx'] optional", "#------------------------------------------------------------------------------- def check_exists(fips_dir) : \"\"\"test if ccmake is in the", "'ccmake' platforms = ['linux', 'osx'] optional = True not_found =", "ccmake to configure cmake project :param build_dir: directory where ccmake", "to configure cmake project :param build_dir: directory where ccmake should", "if ccmake returns successful \"\"\" res = subprocess.call('ccmake .', cwd=build_dir,", "\"\"\" try: out = subprocess.check_output(['ccmake', '--version']) return True except (OSError,", "\"\"\"test if ccmake is in the path :returns: True if", "ccmake returns successful \"\"\" res = subprocess.call('ccmake .', cwd=build_dir, shell=True)", "\"\"\" wrapper for ccmake command line tool \"\"\" import subprocess", "subprocess.CalledProcessError): return False #------------------------------------------------------------------------------- def run(build_dir) : \"\"\"run ccmake to", "\"\"\" res = subprocess.call('ccmake .', cwd=build_dir, shell=True) return res ==", "True if ccmake is in the path \"\"\" try: out", "except (OSError, subprocess.CalledProcessError): return False #------------------------------------------------------------------------------- def run(build_dir) : \"\"\"run", "return True except (OSError, subprocess.CalledProcessError): return False #------------------------------------------------------------------------------- def run(build_dir)", "def check_exists(fips_dir) : \"\"\"test if ccmake is in the path", "subprocess.check_output(['ccmake', '--version']) return True except (OSError, subprocess.CalledProcessError): return False #-------------------------------------------------------------------------------", "wrapper for ccmake command line tool \"\"\" import subprocess name" ]
[ "resized into when loaded self.img_crop_dims = img_crop_dims # dimensions that", "as np import tensorflow as tf from image_quality.utils import utils", "[self.samples[i] for i in batch_indexes] # get batch samples X,", "is 4D numpy array of RGB values within [0, 255]", "# dimensions that images get resized into when loaded self.img_crop_dims", "of RGB values within [0, 255] X = self.basenet_preprocess(X) return", "n_classes self.basenet_preprocess = basenet_preprocess # Keras basenet specific preprocessing function", "255] X = self.basenet_preprocess(X) return X, y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits", "None: X[i, ] = img # normalize labels if sample.get('label')", "dimensions that images get resized into when loaded self.img_crop_dims =", "__data_generator(self, batch_samples): # initialize images and labels tensors for faster", "self.samples = samples self.img_dir = img_dir self.batch_size = batch_size self.n_classes", "img # normalize labels if sample.get('label') is not None: y[i,", "= os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img = utils.load_image(img_file, self.img_load_dims) if img is", "X[i, ] = img # normalize labels y[i, ] =", "images get randomly cropped to self.shuffle = shuffle self.on_epoch_end() #", "specific preprocessing function self.img_load_dims = img_load_dims # dimensions that images", "= img_dir self.batch_size = batch_size self.n_classes = n_classes self.basenet_preprocess =", "base object, allows to use multiprocessing in .fit_generator''' def __init__(self,", "TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object, allows to use", "= np.empty((len(batch_samples), *self.img_load_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for i,", "Keras basenet specific preprocessing function self.img_load_dims = img_load_dims # dimensions", "img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224, 224), shuffle=True): self.samples", "tensors for faster processing X = np.empty((len(batch_samples), *self.img_load_dims, 3)) y", "array of RGB values within [0, 255] X = self.basenet_preprocess(X)", "] = img # normalize labels if sample.get('label') is not", "self.__data_generator(batch_samples) return X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) if", "in batch_indexes] # get batch samples X, y = self.__data_generator(batch_samples)", "samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224, 224), shuffle=True):", "= self.__data_generator(batch_samples) return X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples))", "# Keras basenet specific preprocessing function self.img_load_dims = img_load_dims #", "initialize images and labels tensors for faster processing X =", "from image_quality.utils import utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence", "cropped to self.shuffle = shuffle self.on_epoch_end() # call ensures that", "in .fit_generator''' def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(256,", "return X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) def __data_generator(self,", "as tf from image_quality.utils import utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from", "is not None: img = utils.random_crop(img, self.img_crop_dims) img = utils.random_horizontal_flip(img)", "dimensions that images get resized into when loaded self.on_epoch_end() #", "# call ensures that samples are shuffled in first epoch", "are shuffled in first epoch if shuffle is set to", "__len__(self): return int(np.ceil(len(self.samples) / self.batch_size)) # number of batches per", "i in batch_indexes] # get batch samples X, y =", "return X, y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base", "self.indexes = np.arange(len(self.samples)) def __data_generator(self, batch_samples): # initialize images and", "= n_classes self.basenet_preprocess = basenet_preprocess # Keras basenet specific preprocessing", "if img is not None: img = utils.random_crop(img, self.img_crop_dims) img", "# input is 4D numpy array of RGB values within", "= np.arange(len(self.samples)) def __data_generator(self, batch_samples): # initialize images and labels", "augment image img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img = utils.load_image(img_file, self.img_load_dims)", "import tensorflow as tf from image_quality.utils import utils class TrainDataGenerator(tf.keras.utils.Sequence):", "def __data_generator(self, batch_samples): # initialize images and labels tensors for", "img is not None: img = utils.random_crop(img, self.img_crop_dims) img =", "get resized into when loaded self.img_crop_dims = img_crop_dims # dimensions", "self.indexes = np.arange(len(self.samples)) if self.shuffle is True: np.random.shuffle(self.indexes) def __data_generator(self,", "images get resized into when loaded self.img_crop_dims = img_crop_dims #", "into when loaded self.img_crop_dims = img_crop_dims # dimensions that images", "get randomly cropped to self.shuffle = shuffle self.on_epoch_end() # call", "__getitem__(self, index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch indexes batch_samples", "= utils.random_horizontal_flip(img) X[i, ] = img # normalize labels y[i,", "*self.img_crop_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for i, sample in", "y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object, allows", "batch indexes batch_samples = [self.samples[i] for i in batch_indexes] #", "= samples self.img_dir = img_dir self.batch_size = batch_size self.n_classes =", "allows to use multiprocessing in .fit_generator''' def __init__(self, samples, img_dir,", "get batch samples X, y = self.__data_generator(batch_samples) return X, y", "np.empty((len(batch_samples), *self.img_crop_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for i, sample", "img = utils.load_image(img_file, self.img_load_dims) if img is not None: img", "224), shuffle=True): self.samples = samples self.img_dir = img_dir self.batch_size =", "shuffle self.on_epoch_end() # call ensures that samples are shuffled in", "X[i, ] = img # normalize labels if sample.get('label') is", "import numpy as np import tensorflow as tf from image_quality.utils", "Sequence base object, allows to use multiprocessing in .fit_generator''' def", "basenet specific preprocessing # input is 4D numpy array of", "self.n_classes)) for i, sample in enumerate(batch_samples): # load and randomly", "shuffled in first epoch if shuffle is set to True", "self.batch_size = batch_size self.n_classes = n_classes self.basenet_preprocess = basenet_preprocess #", "= basenet_preprocess # Keras basenet specific preprocessing function self.img_load_dims =", "that samples are shuffled in first epoch if shuffle is", "specific preprocessing # input is 4D numpy array of RGB", "X = self.basenet_preprocess(X) return X, y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from", "normalize labels if sample.get('label') is not None: y[i, ] =", "def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(224, 224)): self.samples", "y = np.empty((len(batch_samples), self.n_classes)) for i, sample in enumerate(batch_samples): #", "None: y[i, ] = utils.normalize_labels(sample['label']) # apply basenet specific preprocessing", "batch samples X, y = self.__data_generator(batch_samples) return X, y def", "img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img = utils.load_image(img_file, self.img_load_dims) if img", "epoch def __getitem__(self, index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch", "image img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img = utils.load_image(img_file, self.img_load_dims) if", "normalize labels y[i, ] = utils.normalize_labels(sample['label']) # apply basenet specific", "samples self.img_dir = img_dir self.batch_size = batch_size self.n_classes = n_classes", "# apply basenet specific preprocessing # input is 4D numpy", "apply basenet specific preprocessing # input is 4D numpy array", "self.basenet_preprocess(X) return X, y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence", "utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object, allows", "for i, sample in enumerate(batch_samples): # load and randomly augment", "self.img_load_dims) if img is not None: img = utils.random_crop(img, self.img_crop_dims)", "img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(224, 224)): self.samples = samples self.img_dir", "] = utils.normalize_labels(sample['label']) # apply basenet specific preprocessing # input", "import os import numpy as np import tensorflow as tf", "[0, 255] X = self.basenet_preprocess(X) return X, y class TestDataGenerator(tf.keras.utils.Sequence):", "def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) def __data_generator(self, batch_samples): # initialize", "Keras Sequence base object, allows to use multiprocessing in .fit_generator'''", "is True: np.random.shuffle(self.indexes) def __data_generator(self, batch_samples): # initialize images and", "multiprocessing in .fit_generator''' def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess,", "labels tensors for faster processing X = np.empty((len(batch_samples), *self.img_crop_dims, 3))", "enumerate(batch_samples): # load and randomly augment image img_file = os.path.join(self.img_dir,", "for faster processing X = np.empty((len(batch_samples), *self.img_load_dims, 3)) y =", "object, allows to use multiprocessing in .fit_generator''' def __init__(self, samples,", "return X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) if self.shuffle", "samples X, y = self.__data_generator(batch_samples) return X, y def on_epoch_end(self):", "X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) def __data_generator(self, batch_samples):", "if self.shuffle is True: np.random.shuffle(self.indexes) def __data_generator(self, batch_samples): # initialize", "randomly augment image img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img = utils.load_image(img_file,", "get resized into when loaded self.on_epoch_end() # call ensures that", "4D numpy array of RGB values within [0, 255] X", "= utils.load_image(img_file, self.img_load_dims) if img is not None: X[i, ]", "is set to True def __len__(self): return int(np.ceil(len(self.samples) / self.batch_size))", "self.shuffle is True: np.random.shuffle(self.indexes) def __data_generator(self, batch_samples): # initialize images", "into when loaded self.on_epoch_end() # call ensures that samples are", "def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) if self.shuffle is True: np.random.shuffle(self.indexes)", "image_quality.utils import utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base", "self.img_load_dims = img_load_dims # dimensions that images get resized into", "in first epoch if shuffle is set to True def", "self.shuffle = shuffle self.on_epoch_end() # call ensures that samples are", "preprocessing function self.img_load_dims = img_load_dims # dimensions that images get", "use multiprocessing in .fit_generator''' def __init__(self, samples, img_dir, batch_size, n_classes,", "self.batch_size)) # number of batches per epoch def __getitem__(self, index):", "utils.random_crop(img, self.img_crop_dims) img = utils.random_horizontal_flip(img) X[i, ] = img #", "*self.img_load_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for i, sample in", "= [self.samples[i] for i in batch_indexes] # get batch samples", "tensors for faster processing X = np.empty((len(batch_samples), *self.img_crop_dims, 3)) y", "not None: X[i, ] = img # normalize labels if", "batches per epoch def __getitem__(self, index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] #", "= np.arange(len(self.samples)) if self.shuffle is True: np.random.shuffle(self.indexes) def __data_generator(self, batch_samples):", "batch_samples): # initialize images and labels tensors for faster processing", "preprocessing # input is 4D numpy array of RGB values", "i, sample in enumerate(batch_samples): # load and randomly augment image", "loaded self.on_epoch_end() # call ensures that samples are shuffled in", "index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch indexes batch_samples =", "os import numpy as np import tensorflow as tf from", "# normalize labels if sample.get('label') is not None: y[i, ]", "first epoch if shuffle is set to True def __len__(self):", "= np.empty((len(batch_samples), self.n_classes)) for i, sample in enumerate(batch_samples): # load", "within [0, 255] X = self.basenet_preprocess(X) return X, y class", "img_load_dims # dimensions that images get resized into when loaded", ".fit_generator''' def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(224, 224)):", "randomly cropped to self.shuffle = shuffle self.on_epoch_end() # call ensures", "n_classes, basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224, 224), shuffle=True): self.samples = samples", "y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) def __data_generator(self, batch_samples): #", "= self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch indexes batch_samples = [self.samples[i] for", "img = utils.random_crop(img, self.img_crop_dims) img = utils.random_horizontal_flip(img) X[i, ] =", "# normalize labels y[i, ] = utils.normalize_labels(sample['label']) # apply basenet", "y[i, ] = utils.normalize_labels(sample['label']) # apply basenet specific preprocessing #", "= batch_size self.n_classes = n_classes self.basenet_preprocess = basenet_preprocess # Keras", "values within [0, 255] X = self.basenet_preprocess(X) return X, y", "img_dir self.batch_size = batch_size self.n_classes = n_classes self.basenet_preprocess = basenet_preprocess", "img_load_dims=(224, 224)): self.samples = samples self.img_dir = img_dir self.batch_size =", "256), img_crop_dims=(224, 224), shuffle=True): self.samples = samples self.img_dir = img_dir", "that images get resized into when loaded self.img_crop_dims = img_crop_dims", "to True def __len__(self): return int(np.ceil(len(self.samples) / self.batch_size)) # number", "# get batch samples X, y = self.__data_generator(batch_samples) return X,", "img_load_dims=(256, 256), img_crop_dims=(224, 224), shuffle=True): self.samples = samples self.img_dir =", "# dimensions that images get resized into when loaded self.on_epoch_end()", "processing X = np.empty((len(batch_samples), *self.img_crop_dims, 3)) y = np.empty((len(batch_samples), self.n_classes))", "sample.get('label') is not None: y[i, ] = utils.normalize_labels(sample['label']) # apply", "def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224,", "to self.shuffle = shuffle self.on_epoch_end() # call ensures that samples", "set to True def __len__(self): return int(np.ceil(len(self.samples) / self.batch_size)) #", "import utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object,", "self.__data_generator(batch_samples) return X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) def", "faster processing X = np.empty((len(batch_samples), *self.img_crop_dims, 3)) y = np.empty((len(batch_samples),", "self.img_crop_dims = img_crop_dims # dimensions that images get randomly cropped", "img = utils.random_horizontal_flip(img) X[i, ] = img # normalize labels", "= utils.random_crop(img, self.img_crop_dims) img = utils.random_horizontal_flip(img) X[i, ] = img", "n_classes, basenet_preprocess, img_load_dims=(224, 224)): self.samples = samples self.img_dir = img_dir", "ensures that samples are shuffled in first epoch if shuffle", "tensorflow as tf from image_quality.utils import utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits", "shuffle=True): self.samples = samples self.img_dir = img_dir self.batch_size = batch_size", "= img_crop_dims # dimensions that images get randomly cropped to", "img is not None: X[i, ] = img # normalize", "and randomly augment image img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img =", "/ self.batch_size)) # number of batches per epoch def __getitem__(self,", "on_epoch_end(self): self.indexes = np.arange(len(self.samples)) if self.shuffle is True: np.random.shuffle(self.indexes) def", "if img is not None: X[i, ] = img #", "on_epoch_end(self): self.indexes = np.arange(len(self.samples)) def __data_generator(self, batch_samples): # initialize images", "# get batch indexes batch_samples = [self.samples[i] for i in", "that images get randomly cropped to self.shuffle = shuffle self.on_epoch_end()", "= np.empty((len(batch_samples), *self.img_crop_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for i,", "True def __len__(self): return int(np.ceil(len(self.samples) / self.batch_size)) # number of", "self.basenet_preprocess = basenet_preprocess # Keras basenet specific preprocessing function self.img_load_dims", "= shuffle self.on_epoch_end() # call ensures that samples are shuffled", "load and randomly augment image img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img", "faster processing X = np.empty((len(batch_samples), *self.img_load_dims, 3)) y = np.empty((len(batch_samples),", "from Keras Sequence base object, allows to use multiprocessing in", "labels tensors for faster processing X = np.empty((len(batch_samples), *self.img_load_dims, 3))", "= utils.load_image(img_file, self.img_load_dims) if img is not None: img =", "X, y = self.__data_generator(batch_samples) return X, y def on_epoch_end(self): self.indexes", "__init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(224, 224)): self.samples =", "not None: y[i, ] = utils.normalize_labels(sample['label']) # apply basenet specific", "= img # normalize labels if sample.get('label') is not None:", "# initialize images and labels tensors for faster processing X", "samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(224, 224)): self.samples = samples", "self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch indexes batch_samples = [self.samples[i] for i", "img # normalize labels y[i, ] = utils.normalize_labels(sample['label']) # apply", "def __getitem__(self, index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch indexes", "np.empty((len(batch_samples), *self.img_load_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for i, sample", "self.img_dir = img_dir self.batch_size = batch_size self.n_classes = n_classes self.basenet_preprocess", "numpy as np import tensorflow as tf from image_quality.utils import", "y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) if self.shuffle is True:", "sample in enumerate(batch_samples): # load and randomly augment image img_file", "= utils.normalize_labels(sample['label']) # apply basenet specific preprocessing # input is", "batch_size, n_classes, basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224, 224), shuffle=True): self.samples =", "def __len__(self): return int(np.ceil(len(self.samples) / self.batch_size)) # number of batches", "basenet specific preprocessing function self.img_load_dims = img_load_dims # dimensions that", "input is 4D numpy array of RGB values within [0,", "batch_size self.n_classes = n_classes self.basenet_preprocess = basenet_preprocess # Keras basenet", "call ensures that samples are shuffled in first epoch if", "np.arange(len(self.samples)) def __data_generator(self, batch_samples): # initialize images and labels tensors", "per epoch def __getitem__(self, index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get", "basenet_preprocess, img_load_dims=(224, 224)): self.samples = samples self.img_dir = img_dir self.batch_size", "number of batches per epoch def __getitem__(self, index): batch_indexes =", "of batches per epoch def __getitem__(self, index): batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]", "X = np.empty((len(batch_samples), *self.img_load_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for", "# load and randomly augment image img_file = os.path.join(self.img_dir, '{}'.format(sample['image_id']))", "np.arange(len(self.samples)) if self.shuffle is True: np.random.shuffle(self.indexes) def __data_generator(self, batch_samples): #", "X, y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object,", "class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object, allows to", "self.on_epoch_end() # call ensures that samples are shuffled in first", "in .fit_generator''' def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(224,", "batch_samples = [self.samples[i] for i in batch_indexes] # get batch", "for i in batch_indexes] # get batch samples X, y", "batch_size, n_classes, basenet_preprocess, img_load_dims=(224, 224)): self.samples = samples self.img_dir =", "basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224, 224), shuffle=True): self.samples = samples self.img_dir", "labels if sample.get('label') is not None: y[i, ] = utils.normalize_labels(sample['label'])", "self.n_classes = n_classes self.basenet_preprocess = basenet_preprocess # Keras basenet specific", "__init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(256, 256), img_crop_dims=(224, 224),", "batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # get batch indexes batch_samples = [self.samples[i]", "224)): self.samples = samples self.img_dir = img_dir self.batch_size = batch_size", "img = utils.load_image(img_file, self.img_load_dims) if img is not None: X[i,", "= img_load_dims # dimensions that images get resized into when", "shuffle is set to True def __len__(self): return int(np.ceil(len(self.samples) /", "utils.random_horizontal_flip(img) X[i, ] = img # normalize labels y[i, ]", "class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object, allows to", "and labels tensors for faster processing X = np.empty((len(batch_samples), *self.img_load_dims,", "X, y def on_epoch_end(self): self.indexes = np.arange(len(self.samples)) if self.shuffle is", "to use multiprocessing in .fit_generator''' def __init__(self, samples, img_dir, batch_size,", "indexes batch_samples = [self.samples[i] for i in batch_indexes] # get", "np.empty((len(batch_samples), self.n_classes)) for i, sample in enumerate(batch_samples): # load and", "not None: img = utils.random_crop(img, self.img_crop_dims) img = utils.random_horizontal_flip(img) X[i,", "utils.normalize_labels(sample['label']) # apply basenet specific preprocessing # input is 4D", "if shuffle is set to True def __len__(self): return int(np.ceil(len(self.samples)", "y = self.__data_generator(batch_samples) return X, y def on_epoch_end(self): self.indexes =", "utils.load_image(img_file, self.img_load_dims) if img is not None: X[i, ] =", "if sample.get('label') is not None: y[i, ] = utils.normalize_labels(sample['label']) #", "samples are shuffled in first epoch if shuffle is set", "is not None: X[i, ] = img # normalize labels", "'{}'.format(sample['image_id'])) img = utils.load_image(img_file, self.img_load_dims) if img is not None:", "function self.img_load_dims = img_load_dims # dimensions that images get resized", "batch_indexes] # get batch samples X, y = self.__data_generator(batch_samples) return", "utils.load_image(img_file, self.img_load_dims) if img is not None: img = utils.random_crop(img,", "processing X = np.empty((len(batch_samples), *self.img_load_dims, 3)) y = np.empty((len(batch_samples), self.n_classes))", "numpy array of RGB values within [0, 255] X =", "int(np.ceil(len(self.samples) / self.batch_size)) # number of batches per epoch def", "and labels tensors for faster processing X = np.empty((len(batch_samples), *self.img_crop_dims,", "return int(np.ceil(len(self.samples) / self.batch_size)) # number of batches per epoch", "self.img_crop_dims) img = utils.random_horizontal_flip(img) X[i, ] = img # normalize", "= self.basenet_preprocess(X) return X, y class TestDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras", "3)) y = np.empty((len(batch_samples), self.n_classes)) for i, sample in enumerate(batch_samples):", "None: img = utils.random_crop(img, self.img_crop_dims) img = utils.random_horizontal_flip(img) X[i, ]", "resized into when loaded self.on_epoch_end() # call ensures that samples", "when loaded self.img_crop_dims = img_crop_dims # dimensions that images get", "loaded self.img_crop_dims = img_crop_dims # dimensions that images get randomly", "os.path.join(self.img_dir, '{}'.format(sample['image_id'])) img = utils.load_image(img_file, self.img_load_dims) if img is not", "np import tensorflow as tf from image_quality.utils import utils class", "basenet_preprocess # Keras basenet specific preprocessing function self.img_load_dims = img_load_dims", "RGB values within [0, 255] X = self.basenet_preprocess(X) return X,", "images get resized into when loaded self.on_epoch_end() # call ensures", "for faster processing X = np.empty((len(batch_samples), *self.img_crop_dims, 3)) y =", "= img # normalize labels y[i, ] = utils.normalize_labels(sample['label']) #", "True: np.random.shuffle(self.indexes) def __data_generator(self, batch_samples): # initialize images and labels", "img_crop_dims=(224, 224), shuffle=True): self.samples = samples self.img_dir = img_dir self.batch_size", "img_crop_dims # dimensions that images get randomly cropped to self.shuffle", "is not None: y[i, ] = utils.normalize_labels(sample['label']) # apply basenet", "tf from image_quality.utils import utils class TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras", "images and labels tensors for faster processing X = np.empty((len(batch_samples),", "labels y[i, ] = utils.normalize_labels(sample['label']) # apply basenet specific preprocessing", "in enumerate(batch_samples): # load and randomly augment image img_file =", "epoch if shuffle is set to True def __len__(self): return", "get batch indexes batch_samples = [self.samples[i] for i in batch_indexes]", ".fit_generator''' def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_load_dims=(256, 256),", "np.random.shuffle(self.indexes) def __data_generator(self, batch_samples): # initialize images and labels tensors", "that images get resized into when loaded self.on_epoch_end() # call", "TrainDataGenerator(tf.keras.utils.Sequence): '''inherits from Keras Sequence base object, allows to use", "dimensions that images get randomly cropped to self.shuffle = shuffle", "when loaded self.on_epoch_end() # call ensures that samples are shuffled", "# dimensions that images get randomly cropped to self.shuffle =", "'''inherits from Keras Sequence base object, allows to use multiprocessing", "# number of batches per epoch def __getitem__(self, index): batch_indexes", "] = img # normalize labels y[i, ] = utils.normalize_labels(sample['label'])", "self.img_load_dims) if img is not None: X[i, ] = img", "X = np.empty((len(batch_samples), *self.img_crop_dims, 3)) y = np.empty((len(batch_samples), self.n_classes)) for" ]
[ "parts = string.split('\\n') for s in markers: parts = [v.split(s)[0].rstrip()", "pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e f g\", [\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('=", "in strings: pos = len(line) for m in markers: if", "strings: pos = len(line) for m in markers: if m", "Test, Test as test ''' Complete the solution so that", "bananas grapes bananas !apples The output expected would be: apples,", "edit in place def solution(string,markers): parts = string.split('\\n') for s", "Test.assert_equals(solution(\"a #b\\nc\\nd $e f g\", [\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= -", "any markers # Split by marker, grab first item, and", "comment markers passed in. Any whitespace at the end of", "at the end of the line should also be stripped", "parts = [v.split(s)[0].rstrip() for v in parts] return '\\n'.join(parts) #", "v in enumerate(parts): parts[num] = v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears", "Split by rows, then find earliest marker and extract string", "v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\",", "test ''' Complete the solution so that it strips all", "[] for line in strings: pos = len(line) for m", "should also be stripped out. Example: Given an input string", "as test ''' Complete the solution so that it strips", "= line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l) # Top solution, split list", "through all lines, check for any markers # Split by", "that it strips all text that follows any of a", "for line in strings: pos = len(line) for m in", "markers: parts = [v.split(s)[0].rstrip() for v in parts] return '\\n'.join(parts)", "g\", [\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= - avocados oranges pears cherries\\nlemons", "Test import Test, Test as test ''' Complete the solution", "would be called like so: result = solution(\"apples, pears #", "whitespace for num, v in enumerate(parts): parts[num] = v.split(s)[0].rstrip() return", "apples, pears grapes bananas The code would be called like", "by \\n, edit in place def solution(string,markers): parts = string.split('\\n')", "Test as test ''' Complete the solution so that it", "''' # Split by rows, then find earliest marker and", "solution(string,markers): # split by lines parts = string.split('\\n') # Loop", "pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]) # result should", "#b\\nc\\nd $e f g\", [\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= - avocados", "in enumerate(parts): parts[num] = v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears #", "= string.split('\\n') for s in markers: parts = [v.split(s)[0].rstrip() for", "the line should also be stripped out. Example: Given an", "split list by \\n, edit in place def solution(string,markers): parts", "should == \"apples, pears\\ngrapes\\nbananas\" ''' # Split by rows, then", "solution so that it strips all text that follows any", "of the line should also be stripped out. Example: Given", "passed in. Any whitespace at the end of the line", "'\\n'.join(parts) Test.assert_equals(solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]), \"apples,", "cherries\\nlemons apples\\n- watermelons strawberries', ['#', '?', '=', ',', '.', '-',", "in markers: parts = [v.split(s)[0].rstrip() for v in parts] return", "for num, v in enumerate(parts): parts[num] = v.split(s)[0].rstrip() return '\\n'.join(parts)", "from Test import Test, Test as test ''' Complete the", "pears grapes bananas The code would be called like so:", "line: if line.index(m) < pos: pos = line.index(m) l.append(line[:pos].rstrip()) return", "= len(line) for m in markers: if m in line:", "Any whitespace at the end of the line should also", "in markers: # Loop through all lines, check for any", "pos = len(line) for m in markers: if m in", "\"a\\nc\\nd\") Test.assert_equals(solution('= - avocados oranges pears cherries\\nlemons apples\\n- watermelons strawberries',", "out. Example: Given an input string of: apples, pears #", "import Test, Test as test ''' Complete the solution so", "all text that follows any of a set of comment", "like so: result = solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\",", "= [] for line in strings: pos = len(line) for", "earliest marker and extract string before it def solution(string,markers): strings", "if line.index(m) < pos: pos = line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l)", "< pos: pos = line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l) # Top", "parts = string.split('\\n') # Loop through markers for s in", "text that follows any of a set of comment markers", "# result should == \"apples, pears\\ngrapes\\nbananas\" ''' # Split by", "Top solution expanded def solution(string,markers): # split by lines parts", "markers # Split by marker, grab first item, and rstrip", "return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]),", "f g\", [\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= - avocados oranges pears", "an input string of: apples, pears # and bananas grapes", "bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e f", "in parts] return '\\n'.join(parts) # Top solution expanded def solution(string,markers):", "pears cherries\\nlemons apples\\n- watermelons strawberries', ['#', '?', '=', ',', '.',", "Loop through all lines, check for any markers # Split", "l = [] for line in strings: pos = len(line)", "grapes bananas !apples The output expected would be: apples, pears", "marker, grab first item, and rstrip whitespace for num, v", "apples\\n- watermelons strawberries', ['#', '?', '=', ',', '.', '-', '!']),", "Top solution, split list by \\n, edit in place def", "and bananas grapes bananas !apples The output expected would be:", "'\\n'.join(l) # Top solution, split list by \\n, edit in", "''' Complete the solution so that it strips all text", "Complete the solution so that it strips all text that", "# Split by marker, grab first item, and rstrip whitespace", "strings = string.split('\\n') l = [] for line in strings:", "watermelons strawberries', ['#', '?', '=', ',', '.', '-', '!']), '\\nlemons", "pears # and bananas grapes bananas !apples The output expected", "solution, split list by \\n, edit in place def solution(string,markers):", "in markers: if m in line: if line.index(m) < pos:", "for v in parts] return '\\n'.join(parts) # Top solution expanded", "so: result = solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\",", "of a set of comment markers passed in. Any whitespace", "- avocados oranges pears cherries\\nlemons apples\\n- watermelons strawberries', ['#', '?',", "first item, and rstrip whitespace for num, v in enumerate(parts):", "find earliest marker and extract string before it def solution(string,markers):", "called like so: result = solution(\"apples, pears # and bananas\\ngrapes\\nbananas", "return '\\n'.join(parts) # Top solution expanded def solution(string,markers): # split", "# Top solution expanded def solution(string,markers): # split by lines", "solution(string,markers): strings = string.split('\\n') l = [] for line in", "if m in line: if line.index(m) < pos: pos =", "Split by marker, grab first item, and rstrip whitespace for", "\"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= - avocados oranges pears cherries\\nlemons apples\\n- watermelons", "\"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e f g\", [\"#\", \"$\"]), \"a\\nc\\nd\")", "= string.split('\\n') # Loop through markers for s in markers:", "Test.assert_equals(solution('= - avocados oranges pears cherries\\nlemons apples\\n- watermelons strawberries', ['#',", "string of: apples, pears # and bananas grapes bananas !apples", "be stripped out. Example: Given an input string of: apples,", "[\"#\", \"!\"]) # result should == \"apples, pears\\ngrapes\\nbananas\" ''' #", "extract string before it def solution(string,markers): strings = string.split('\\n') l", "split by lines parts = string.split('\\n') # Loop through markers", "it strips all text that follows any of a set", "any of a set of comment markers passed in. Any", "# Top solution, split list by \\n, edit in place", "= solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]) #", "pos = line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l) # Top solution, split", "also be stripped out. Example: Given an input string of:", "be: apples, pears grapes bananas The code would be called", "s in markers: parts = [v.split(s)[0].rstrip() for v in parts]", "of: apples, pears # and bananas grapes bananas !apples The", "grapes bananas The code would be called like so: result", "check for any markers # Split by marker, grab first", "[\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e f g\", [\"#\",", "string before it def solution(string,markers): strings = string.split('\\n') l =", "!apples\", [\"#\", \"!\"]) # result should == \"apples, pears\\ngrapes\\nbananas\" '''", "# and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]) # result should ==", "m in markers: if m in line: if line.index(m) <", "# and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd", "follows any of a set of comment markers passed in.", "code would be called like so: result = solution(\"apples, pears", "by rows, then find earliest marker and extract string before", "list by \\n, edit in place def solution(string,markers): parts =", "# split by lines parts = string.split('\\n') # Loop through", "return '\\n'.join(l) # Top solution, split list by \\n, edit", "== \"apples, pears\\ngrapes\\nbananas\" ''' # Split by rows, then find", "avocados oranges pears cherries\\nlemons apples\\n- watermelons strawberries', ['#', '?', '=',", "s in markers: # Loop through all lines, check for", "pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a", "[v.split(s)[0].rstrip() for v in parts] return '\\n'.join(parts) # Top solution", "and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]) # result should == \"apples,", "place def solution(string,markers): parts = string.split('\\n') for s in markers:", "lines, check for any markers # Split by marker, grab", "whitespace at the end of the line should also be", "before it def solution(string,markers): strings = string.split('\\n') l = []", "expected would be: apples, pears grapes bananas The code would", "marker and extract string before it def solution(string,markers): strings =", "def solution(string,markers): # split by lines parts = string.split('\\n') #", "= v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\",", "string.split('\\n') for s in markers: parts = [v.split(s)[0].rstrip() for v", "oranges pears cherries\\nlemons apples\\n- watermelons strawberries', ['#', '?', '=', ',',", "be called like so: result = solution(\"apples, pears # and", "\"apples, pears\\ngrapes\\nbananas\" ''' # Split by rows, then find earliest", "in line: if line.index(m) < pos: pos = line.index(m) l.append(line[:pos].rstrip())", "parts] return '\\n'.join(parts) # Top solution expanded def solution(string,markers): #", "string.split('\\n') l = [] for line in strings: pos =", "v in parts] return '\\n'.join(parts) # Top solution expanded def", "the end of the line should also be stripped out.", "would be: apples, pears grapes bananas The code would be", "line in strings: pos = len(line) for m in markers:", "markers: # Loop through all lines, check for any markers", "markers: if m in line: if line.index(m) < pos: pos", "result = solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"])", "then find earliest marker and extract string before it def", "pos: pos = line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l) # Top solution,", "of comment markers passed in. Any whitespace at the end", "markers for s in markers: # Loop through all lines,", "end of the line should also be stripped out. Example:", "so that it strips all text that follows any of", "The output expected would be: apples, pears grapes bananas The", "len(line) for m in markers: if m in line: if", "# Loop through markers for s in markers: # Loop", "in place def solution(string,markers): parts = string.split('\\n') for s in", "all lines, check for any markers # Split by marker,", "and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e", "solution(string,markers): parts = string.split('\\n') for s in markers: parts =", "bananas The code would be called like so: result =", "\"!\"]) # result should == \"apples, pears\\ngrapes\\nbananas\" ''' # Split", "rows, then find earliest marker and extract string before it", "for s in markers: parts = [v.split(s)[0].rstrip() for v in", "The code would be called like so: result = solution(\"apples,", "# Loop through all lines, check for any markers #", "Test.assert_equals(solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\")", "expanded def solution(string,markers): # split by lines parts = string.split('\\n')", "line should also be stripped out. Example: Given an input", "and rstrip whitespace for num, v in enumerate(parts): parts[num] =", "solution expanded def solution(string,markers): # split by lines parts =", "input string of: apples, pears # and bananas grapes bananas", "m in line: if line.index(m) < pos: pos = line.index(m)", "set of comment markers passed in. Any whitespace at the", "that follows any of a set of comment markers passed", "for s in markers: # Loop through all lines, check", "it def solution(string,markers): strings = string.split('\\n') l = [] for", "\\n, edit in place def solution(string,markers): parts = string.split('\\n') for", "pears\\ngrapes\\nbananas\" ''' # Split by rows, then find earliest marker", "Loop through markers for s in markers: # Loop through", "a set of comment markers passed in. Any whitespace at", "through markers for s in markers: # Loop through all", "result should == \"apples, pears\\ngrapes\\nbananas\" ''' # Split by rows,", "[\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= - avocados oranges pears cherries\\nlemons apples\\n-", "l.append(line[:pos].rstrip()) return '\\n'.join(l) # Top solution, split list by \\n,", "by marker, grab first item, and rstrip whitespace for num,", "line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l) # Top solution, split list by", "!apples\", [\"#\", \"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e f g\",", "lines parts = string.split('\\n') # Loop through markers for s", "solution(\"apples, pears # and bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]) # result", "bananas !apples The output expected would be: apples, pears grapes", "grab first item, and rstrip whitespace for num, v in", "the solution so that it strips all text that follows", "for any markers # Split by marker, grab first item,", "def solution(string,markers): parts = string.split('\\n') for s in markers: parts", "stripped out. Example: Given an input string of: apples, pears", "$e f g\", [\"#\", \"$\"]), \"a\\nc\\nd\") Test.assert_equals(solution('= - avocados oranges", "strawberries', ['#', '?', '=', ',', '.', '-', '!']), '\\nlemons apples\\n')", "num, v in enumerate(parts): parts[num] = v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples,", "= [v.split(s)[0].rstrip() for v in parts] return '\\n'.join(parts) # Top", "def solution(string,markers): strings = string.split('\\n') l = [] for line", "output expected would be: apples, pears grapes bananas The code", "\"!\"]), \"apples, pears\\ngrapes\\nbananas\") Test.assert_equals(solution(\"a #b\\nc\\nd $e f g\", [\"#\", \"$\"]),", "line.index(m) < pos: pos = line.index(m) l.append(line[:pos].rstrip()) return '\\n'.join(l) #", "rstrip whitespace for num, v in enumerate(parts): parts[num] = v.split(s)[0].rstrip()", "markers passed in. Any whitespace at the end of the", "strips all text that follows any of a set of", "# and bananas grapes bananas !apples The output expected would", "enumerate(parts): parts[num] = v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears # and", "bananas\\ngrapes\\nbananas !apples\", [\"#\", \"!\"]) # result should == \"apples, pears\\ngrapes\\nbananas\"", "parts[num] = v.split(s)[0].rstrip() return '\\n'.join(parts) Test.assert_equals(solution(\"apples, pears # and bananas\\ngrapes\\nbananas", "string.split('\\n') # Loop through markers for s in markers: #", "# Split by rows, then find earliest marker and extract", "item, and rstrip whitespace for num, v in enumerate(parts): parts[num]", "!apples The output expected would be: apples, pears grapes bananas", "apples, pears # and bananas grapes bananas !apples The output", "'\\n'.join(parts) # Top solution expanded def solution(string,markers): # split by", "and extract string before it def solution(string,markers): strings = string.split('\\n')", "Example: Given an input string of: apples, pears # and", "for m in markers: if m in line: if line.index(m)", "Given an input string of: apples, pears # and bananas", "= string.split('\\n') l = [] for line in strings: pos", "by lines parts = string.split('\\n') # Loop through markers for", "in. Any whitespace at the end of the line should" ]
[ "RepresentationType.CHOI): matri = representation.matrix data_re = [] data_im = []", "dimmentional numpy array to a myqlm Matrix. Args: array: (ndarray)", "np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) return Kraus(final_data) return None", "isinstance(representation, PTM): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits,", "import QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes import Matrix, ComplexNumber def array_to_matrix(array):", "(QuantumChannel): myqlm representation of a quantum channel. \"\"\" qchannel =", "Then create the corresponding matrix (kraus_ops|basis|matrix)from the data # of", "Foundation (ASF) under one or more contributor license agreements. See", "for arr in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits,", "rep = representation.representation # Find what representation it is. #", "more contributor license agreements. See the NOTICE file distributed with", "[] data_im = [] for i in range(matri.nRows): for j", "permissions and limitations under the License. \"\"\" from qiskit.quantum_info.operators.channel import", "representation. # Finally, create the QuantumChannel with the RepresentationType, the", "corresponding matrix (kraus_ops|basis|matrix)from the data # of the representation. #", "Apache License, Version 2.0 (the \"License\"); you may not use", "\"\"\" Create a qiskit representation of quantum channel from a", "IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either", "to you under the Apache License, Version 2.0 (the \"License\");", "of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law", "representation of a quantum channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation", "\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,", "\"\"\" Licensed to the Apache Software Foundation (ASF) under one", "distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS", "representation.basis: data_re = [] data_im = [] for i in", "the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or", "ANY KIND, either express or implied. See the License for", "(got from the qiskit representation) and the matrix. if isinstance(representation,", "matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return", "for j in range(matri.nCols): data_re.append(matri.data[i * matri.nRows + j].re +", "http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in", "may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless", "basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis)", "-*- coding: utf-8 -*- \"\"\" Licensed to the Apache Software", "matrix (kraus_ops|basis|matrix)from the data # of the representation. # Finally,", "one or more contributor license agreements. See the NOTICE file", "final_data.append(data) if rep == RepresentationType.CHI: return Chi(final_data) if len(final_data) >", "Finally, create the qiskit representation from that matrix. if rep", "Find what representation it is. # Then create the corresponding", "shape it like qiskit is expecting it. # Finally, create", "ASF licenses this file to you under the Apache License,", "QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif isinstance(representation, PTM): matri = array_to_matrix(qiskit_data)", "under the License is distributed on an \"AS IS\" BASIS,", "of a quantum channel. \"\"\" qchannel = None qiskit_data =", "data = [] for arr in array: for elem in", "The ASF licenses this file to you under the Apache", "file distributed with this work for additional information regarding copyright", "= [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif", "if rep == RepresentationType.KRAUS: final_data = [] for matri in", "matri def qiskit_to_qchannel(representation): \"\"\" Create a myqlm representation of quantum", "[] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif isinstance(representation,", "array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif isinstance(representation, Choi):", "elif isinstance(representation, SuperOp): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel(", "SuperOp import numpy as np from qat.comm.quops.ttypes import QuantumChannel, RepresentationType", "RepresentationType.PTM) else Choi(data) if rep in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data =", "this file except in compliance with the License. You may", "channel. Returns: (QuantumChannel): myqlm representation of a quantum channel. \"\"\"", "it like qiskit is expecting it. # Finally, create the", "Matrix. Args: array: (ndarray) a two dimmentional numpy array Returns:", "language governing permissions and limitations under the License. \"\"\" from", "is. # Then create the corresponding matrix and shape it", "(QuantumChannel) myqlm representation of a quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit", "qiskit is expecting it. # Finally, create the qiskit representation", "np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) return PTM(data) if (rep ==", "channel. Args: representation: (QuantumChannel) myqlm representation of a quantum channel.", "for arr in array: for elem in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem)))", "import Choi, PTM, Kraus, Chi, SuperOp import numpy as np", "file except in compliance with the License. You may obtain", "import numpy as np from qat.comm.quops.ttypes import QuantumChannel, RepresentationType from", "Chi, SuperOp import numpy as np from qat.comm.quops.ttypes import QuantumChannel,", "channel from a myqlm representation of a quantum channel. Args:", "== RepresentationType.KRAUS: final_data = [] for matri in representation.kraus_ops: data_re", "representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif isinstance(representation, SuperOp): basis = [] basis.append(array_to_matrix(qiskit_data))", "= array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif isinstance(representation,", "OR CONDITIONS OF ANY KIND, either express or implied. See", "of quantum channel from a qiskit representation of a quantum", "Returns: (QuantumChannel): myqlm representation of a quantum channel. \"\"\" qchannel", "\"\"\" Transform a two dimmentional numpy array to a myqlm", "under the Apache License, Version 2.0 (the \"License\"); you may", "> 1 else SuperOp(final_data[0]) if rep == RepresentationType.KRAUS: final_data =", "final_data = [] for matri in representation.kraus_ops: data_re = []", "def qchannel_to_qiskit(representation): \"\"\" Create a qiskit representation of quantum channel", "in representation.basis: data_re = [] data_im = [] for i", "WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.", "# Finally, create the QuantumChannel with the RepresentationType, the arity", "See the License for the specific language governing permissions and", "(Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of a quantum channel. Returns: (QuantumChannel): myqlm", "np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) return", "RepresentationType, the arity # (got from the qiskit representation) and", "regarding copyright ownership. The ASF licenses this file to you", "file to you under the Apache License, Version 2.0 (the", "np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) return PTM(data)", "qchannel = None qiskit_data = representation.data # Find what representation", "np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if rep == RepresentationType.CHI:", "isinstance(representation, Chi): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.CHI,", "in writing, software distributed under the License is distributed on", "required by applicable law or agreed to in writing, software", "representation: (QuantumChannel) myqlm representation of a quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM):", "= [] data_im = [] for i in range(matri.nRows): for", "and shape it like qiskit is expecting it. # Finally,", "qiskit representation of quantum channel from a myqlm representation of", "the NOTICE file distributed with this work for additional information", "= QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return qchannel def qchannel_to_qiskit(representation): \"\"\"", "expecting it. # Finally, create the qiskit representation from that", "under the License. \"\"\" from qiskit.quantum_info.operators.channel import Choi, PTM, Kraus,", "import Matrix, ComplexNumber def array_to_matrix(array): \"\"\" Transform a two dimmentional", "agreements. See the NOTICE file distributed with this work for", "of a quantum channel. Args: representation: (QuantumChannel) myqlm representation of", "to the Apache Software Foundation (ASF) under one or more", "if rep in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data = [] for matri", "software distributed under the License is distributed on an \"AS", "distributed under the License is distributed on an \"AS IS\"", "\"\"\" assert len(array.shape) == 2, \"The array must be two", "what representation it is. # Then create the corresponding matrix", "def qiskit_to_qchannel(representation): \"\"\" Create a myqlm representation of quantum channel", "CONDITIONS OF ANY KIND, either express or implied. See the", "j].re + 0.j) data_im.append(matri.data[i * matri.nRows + j].im) data =", "array.shape[1], data) return matri def qiskit_to_qchannel(representation): \"\"\" Create a myqlm", "Version 2.0 (the \"License\"); you may not use this file", "of a quantum channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of", "a quantum channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of a", "Licensed to the Apache Software Foundation (ASF) under one or", "for matri in representation.basis: data_re = [] data_im = []", "[] for matri in representation.kraus_ops: data_re = [] data_im =", "not use this file except in compliance with the License.", "2.0 (the \"License\"); you may not use this file except", "copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable", "as np from qat.comm.quops.ttypes import QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes import", "basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis)", "quantum channel. Args: representation: (QuantumChannel) myqlm representation of a quantum", "rep in (RepresentationType.PTM, RepresentationType.CHOI): matri = representation.matrix data_re = []", "coding: utf-8 -*- \"\"\" Licensed to the Apache Software Foundation", "of a quantum channel. \"\"\" rep = representation.representation # Find", "qchannel_to_qiskit(representation): \"\"\" Create a qiskit representation of quantum channel from", "you may not use this file except in compliance with", "qiskit representation) and the matrix. if isinstance(representation, Kraus): kraus_ops =", "is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR", "the License. You may obtain a copy of the License", "the qiskit representation from that matrix. if rep in (RepresentationType.PTM,", "data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if rep", "Create a qiskit representation of quantum channel from a myqlm", "[] for i in range(matri.nRows): for j in range(matri.nCols): data_re.append(matri.data[i", "> 1 else Chi(final_data[0]) return SuperOp(final_data) if len(final_data) > 1", "use this file except in compliance with the License. You", "Apache Software Foundation (ASF) under one or more contributor license", "a myqlm representation of a quantum channel. Args: representation: (QuantumChannel)", "== RepresentationType.PTM) else Choi(data) if rep in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data", "None qiskit_data = representation.data # Find what representation it is.", "== RepresentationType.CHI: return Chi(final_data) if len(final_data) > 1 else Chi(final_data[0])", "representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation, Chi): basis = [] basis.append(array_to_matrix(qiskit_data))", "qiskit.quantum_info.operators.channel import Choi, PTM, Kraus, Chi, SuperOp import numpy as", "that matrix. if rep in (RepresentationType.PTM, RepresentationType.CHOI): matri = representation.matrix", "representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif isinstance(representation, PTM): matri = array_to_matrix(qiskit_data) qchannel", "[] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif isinstance(representation,", "arity=representation.num_qubits, matrix=matri) return qchannel def qchannel_to_qiskit(representation): \"\"\" Create a qiskit", "a quantum channel. Args: representation: (QuantumChannel) myqlm representation of a", "numpy as np from qat.comm.quops.ttypes import QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes", "qiskit_data = representation.data # Find what representation it is. #", "the matrix. if isinstance(representation, Kraus): kraus_ops = [] for arr", "from qat.comm.datamodel.ttypes import Matrix, ComplexNumber def array_to_matrix(array): \"\"\" Transform a", "RepresentationType.SUPEROP): final_data = [] for matri in representation.basis: data_re =", "elem in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri = Matrix(array.shape[0], array.shape[1], data)", "a quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of a quantum", "2, \"The array must be two dimmentional\" data = []", "return qchannel def qchannel_to_qiskit(representation): \"\"\" Create a qiskit representation of", "rep == RepresentationType.KRAUS: final_data = [] for matri in representation.kraus_ops:", "matrix=matri) elif isinstance(representation, Choi): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel(", "for matri in representation.kraus_ops: data_re = [] data_im = []", "data_im.append(matri.data[i * matri.nRows + j].im) data = np.array(data_re) data.imag =", "matri.nRows + j].im) data = np.array(data_re) data.imag = np.array(data_im) data", "from qat.comm.quops.ttypes import QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes import Matrix, ComplexNumber", "Chi): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits,", "if len(final_data) > 1 else Chi(final_data[0]) return SuperOp(final_data) if len(final_data)", "Kraus, Chi, SuperOp import numpy as np from qat.comm.quops.ttypes import", "additional information regarding copyright ownership. The ASF licenses this file", "= np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) return", "myqlm representation of a quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation", "representation.matrix data_re = [] data_im = [] for i in", "array to a myqlm Matrix. Args: array: (ndarray) a two", "(the \"License\"); you may not use this file except in", "a two dimmentional numpy array Returns: (Matrix): a myqlm Matrix", "qiskit representation of a quantum channel. \"\"\" rep = representation.representation", "isinstance(representation, Kraus): kraus_ops = [] for arr in qiskit_data: kraus_ops.append(array_to_matrix(arr))", "QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif isinstance(representation, SuperOp): basis = []", "matrix and shape it like qiskit is expecting it. #", "governing permissions and limitations under the License. \"\"\" from qiskit.quantum_info.operators.channel", "np.imag(elem))) matri = Matrix(array.shape[0], array.shape[1], data) return matri def qiskit_to_qchannel(representation):", "[] for arr in array: for elem in arr: data.append(ComplexNumber(np.real(elem),", "matri = representation.matrix data_re = [] data_im = [] for", "Chi(final_data[0]) return SuperOp(final_data) if len(final_data) > 1 else SuperOp(final_data[0]) if", "representation of a quantum channel. \"\"\" rep = representation.representation #", "You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0", "QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return qchannel def qchannel_to_qiskit(representation): \"\"\" Create", "representation it is. # Then create the corresponding matrix and", "matri in representation.basis: data_re = [] data_im = [] for", "# of the representation. # Finally, create the QuantumChannel with", "channel from a qiskit representation of a quantum channel. Args:", "the Apache License, Version 2.0 (the \"License\"); you may not", "or implied. See the License for the specific language governing", "it. # Finally, create the qiskit representation from that matrix.", "* matri.nRows + j].re + 0.j) data_im.append(matri.data[i * matri.nRows +", "KIND, either express or implied. See the License for the", "to in writing, software distributed under the License is distributed", "Choi(data) if rep in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data = [] for", "isinstance(representation, Choi): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits,", "# (got from the qiskit representation) and the matrix. if", "data = np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols))", "representation.kraus_ops: data_re = [] data_im = [] for i in", "law or agreed to in writing, software distributed under the", "is expecting it. # Finally, create the qiskit representation from", "= representation.data # Find what representation it is. # Then", "Then create the corresponding matrix and shape it like qiskit", "Returns: (Matrix): a myqlm Matrix \"\"\" assert len(array.shape) == 2,", "quantum channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of a quantum", "on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF", "arity # (got from the qiskit representation) and the matrix.", "= QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif isinstance(representation, SuperOp): basis =", "= np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) return PTM(data) if (rep", "# -*- coding: utf-8 -*- \"\"\" Licensed to the Apache", "(RepresentationType.PTM, RepresentationType.CHOI): matri = representation.matrix data_re = [] data_im =", "(ndarray) a two dimmentional numpy array Returns: (Matrix): a myqlm", "data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) return Kraus(final_data)", "is. # Then create the corresponding matrix (kraus_ops|basis|matrix)from the data", "representation of a quantum channel. Returns: (QuantumChannel): myqlm representation of", "qat.comm.quops.ttypes import QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes import Matrix, ComplexNumber def", "= QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif isinstance(representation, Choi): matri =", "with this work for additional information regarding copyright ownership. The", "RepresentationType from qat.comm.datamodel.ttypes import Matrix, ComplexNumber def array_to_matrix(array): \"\"\" Transform", "qiskit representation of a quantum channel. Returns: (QuantumChannel): myqlm representation", "you under the Apache License, Version 2.0 (the \"License\"); you", "Kraus): kraus_ops = [] for arr in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel", "for the specific language governing permissions and limitations under the", "the representation. # Finally, create the QuantumChannel with the RepresentationType,", "qiskit representation from that matrix. if rep in (RepresentationType.PTM, RepresentationType.CHOI):", "from qiskit.quantum_info.operators.channel import Choi, PTM, Kraus, Chi, SuperOp import numpy", "elif isinstance(representation, Chi): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel(", "Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of a quantum channel. \"\"\" rep", "matri.nRows + j].re + 0.j) data_im.append(matri.data[i * matri.nRows + j].im)", "the License. \"\"\" from qiskit.quantum_info.operators.channel import Choi, PTM, Kraus, Chi,", "licenses this file to you under the Apache License, Version", "# Then create the corresponding matrix (kraus_ops|basis|matrix)from the data #", "ownership. The ASF licenses this file to you under the", "= None qiskit_data = representation.data # Find what representation it", "SuperOp): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits,", "for additional information regarding copyright ownership. The ASF licenses this", "the License for the specific language governing permissions and limitations", "may not use this file except in compliance with the", "= np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) return Kraus(final_data) return", "implied. See the License for the specific language governing permissions", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or", "myqlm Matrix \"\"\" assert len(array.shape) == 2, \"The array must", "create the corresponding matrix and shape it like qiskit is", "qchannel = QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif isinstance(representation, Choi): matri", "of a quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of a", "ComplexNumber def array_to_matrix(array): \"\"\" Transform a two dimmentional numpy array", "= [] for matri in representation.kraus_ops: data_re = [] data_im", "Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of a quantum channel. Returns:", "representation of quantum channel from a myqlm representation of a", "with the RepresentationType, the arity # (got from the qiskit", "data.reshape((matri.nRows, matri.nCols)) return PTM(data) if (rep == RepresentationType.PTM) else Choi(data)", "RepresentationType.KRAUS: final_data = [] for matri in representation.kraus_ops: data_re =", "matrix. if rep in (RepresentationType.PTM, RepresentationType.CHOI): matri = representation.matrix data_re", "quantum channel. \"\"\" rep = representation.representation # Find what representation", "= representation.representation # Find what representation it is. # Then", "arr in array: for elem in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri", "basis=basis) elif isinstance(representation, SuperOp): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel =", "== 2, \"The array must be two dimmentional\" data =", "representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif isinstance(representation, Choi): matri = array_to_matrix(qiskit_data) qchannel", "PTM(data) if (rep == RepresentationType.PTM) else Choi(data) if rep in", "writing, software distributed under the License is distributed on an", "if isinstance(representation, Kraus): kraus_ops = [] for arr in qiskit_data:", "See the NOTICE file distributed with this work for additional", "= data.reshape((matri.nRows, matri.nCols)) return PTM(data) if (rep == RepresentationType.PTM) else", "j in range(matri.nCols): data_re.append(matri.data[i * matri.nRows + j].re + 0.j)", "in compliance with the License. You may obtain a copy", "in array: for elem in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri =", "qiskit representation of a quantum channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit", "\"\"\" Create a myqlm representation of quantum channel from a", "agreed to in writing, software distributed under the License is", "representation of a quantum channel. \"\"\" qchannel = None qiskit_data", "elif isinstance(representation, Choi): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.CHOI,", "at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to", "if (rep == RepresentationType.PTM) else Choi(data) if rep in (RepresentationType.CHI,", "* matri.nRows + j].im) data = np.array(data_re) data.imag = np.array(data_im)", "range(matri.nRows): for j in range(matri.nCols): data_re.append(matri.data[i * matri.nRows + j].re", "Args: array: (ndarray) a two dimmentional numpy array Returns: (Matrix):", "of the representation. # Finally, create the QuantumChannel with the", "channel. \"\"\" qchannel = None qiskit_data = representation.data # Find", "the qiskit representation) and the matrix. if isinstance(representation, Kraus): kraus_ops", "information regarding copyright ownership. The ASF licenses this file to", "# Then create the corresponding matrix and shape it like", "in range(matri.nRows): for j in range(matri.nCols): data_re.append(matri.data[i * matri.nRows +", "a myqlm Matrix. Args: array: (ndarray) a two dimmentional numpy", "either express or implied. See the License for the specific", "quantum channel. Returns: (QuantumChannel): myqlm representation of a quantum channel.", "BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express", "basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif isinstance(representation, PTM):", "dimmentional numpy array Returns: (Matrix): a myqlm Matrix \"\"\" assert", "\"License\"); you may not use this file except in compliance", "License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES", "numpy array to a myqlm Matrix. Args: array: (ndarray) a", "= np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if rep ==", "Matrix, ComplexNumber def array_to_matrix(array): \"\"\" Transform a two dimmentional numpy", "1 else Chi(final_data[0]) return SuperOp(final_data) if len(final_data) > 1 else", "to a myqlm Matrix. Args: array: (ndarray) a two dimmentional", "(kraus_ops|basis|matrix)from the data # of the representation. # Finally, create", "License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed", "contributor license agreements. See the NOTICE file distributed with this", "data # of the representation. # Finally, create the QuantumChannel", "License for the specific language governing permissions and limitations under", "qchannel = QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif isinstance(representation, SuperOp): basis", "PTM, Kraus, Chi, SuperOp import numpy as np from qat.comm.quops.ttypes", "rep == RepresentationType.CHI: return Chi(final_data) if len(final_data) > 1 else", "in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri = Matrix(array.shape[0], array.shape[1], data) return", "representation of quantum channel from a qiskit representation of a", "data_re.append(matri.data[i * matri.nRows + j].re + 0.j) data_im.append(matri.data[i * matri.nRows", "+ j].re + 0.j) data_im.append(matri.data[i * matri.nRows + j].im) data", "the data # of the representation. # Finally, create the", "range(matri.nCols): data_re.append(matri.data[i * matri.nRows + j].re + 0.j) data_im.append(matri.data[i *", "be two dimmentional\" data = [] for arr in array:", "if rep == RepresentationType.CHI: return Chi(final_data) if len(final_data) > 1", "basis=basis) elif isinstance(representation, PTM): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel(", "qchannel = QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return qchannel def qchannel_to_qiskit(representation):", "# Finally, create the qiskit representation from that matrix. if", "two dimmentional numpy array to a myqlm Matrix. Args: array:", "data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri = Matrix(array.shape[0], array.shape[1], data) return matri def", "in representation.kraus_ops: data_re = [] data_im = [] for i", "representation of a quantum channel. Args: representation: (QuantumChannel) myqlm representation", "= QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif isinstance(representation, PTM): matri =", "def array_to_matrix(array): \"\"\" Transform a two dimmentional numpy array to", "data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) return PTM(data) if", "representation from that matrix. if rep in (RepresentationType.PTM, RepresentationType.CHOI): matri", "the QuantumChannel with the RepresentationType, the arity # (got from", "i in range(matri.nRows): for j in range(matri.nCols): data_re.append(matri.data[i * matri.nRows", "the corresponding matrix and shape it like qiskit is expecting", "except in compliance with the License. You may obtain a", "-*- \"\"\" Licensed to the Apache Software Foundation (ASF) under", "basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif isinstance(representation, SuperOp):", "kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation, Chi):", "return Chi(final_data) if len(final_data) > 1 else Chi(final_data[0]) return SuperOp(final_data)", "myqlm representation of quantum channel from a qiskit representation of", "representation.representation # Find what representation it is. # Then create", "qiskit_to_qchannel(representation): \"\"\" Create a myqlm representation of quantum channel from", "the corresponding matrix (kraus_ops|basis|matrix)from the data # of the representation.", "= [] for arr in array: for elem in arr:", "= np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data)", "of quantum channel from a myqlm representation of a quantum", "= [] for matri in representation.basis: data_re = [] data_im", "and the matrix. if isinstance(representation, Kraus): kraus_ops = [] for", "compliance with the License. You may obtain a copy of", "under one or more contributor license agreements. See the NOTICE", "np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if", "RepresentationType.CHI: return Chi(final_data) if len(final_data) > 1 else Chi(final_data[0]) return", "a qiskit representation of a quantum channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM)", "corresponding matrix and shape it like qiskit is expecting it.", "= array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return qchannel", "Matrix(array.shape[0], array.shape[1], data) return matri def qiskit_to_qchannel(representation): \"\"\" Create a", "+ j].im) data = np.array(data_re) data.imag = np.array(data_im) data =", "len(final_data) > 1 else SuperOp(final_data[0]) if rep == RepresentationType.KRAUS: final_data", "representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of a quantum channel. Returns: (QuantumChannel):", "channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of a quantum channel. \"\"\"", "(Matrix): a myqlm Matrix \"\"\" assert len(array.shape) == 2, \"The", "array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return qchannel def", "Args: representation: (QuantumChannel) myqlm representation of a quantum channel. Returns:", "(ASF) under one or more contributor license agreements. See the", "representation it is. # Then create the corresponding matrix (kraus_ops|basis|matrix)from", "representation) and the matrix. if isinstance(representation, Kraus): kraus_ops = []", "array: for elem in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri = Matrix(array.shape[0],", "work for additional information regarding copyright ownership. The ASF licenses", "arity=representation.num_qubits, basis=basis) elif isinstance(representation, PTM): matri = array_to_matrix(qiskit_data) qchannel =", "QuantumChannel with the RepresentationType, the arity # (got from the", "matri.nCols)) final_data.append(data) if rep == RepresentationType.CHI: return Chi(final_data) if len(final_data)", "= representation.matrix data_re = [] data_im = [] for i", "data = data.reshape((matri.nRows, matri.nCols)) return PTM(data) if (rep == RepresentationType.PTM)", "matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif", "np from qat.comm.quops.ttypes import QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes import Matrix,", "utf-8 -*- \"\"\" Licensed to the Apache Software Foundation (ASF)", "[] for matri in representation.basis: data_re = [] data_im =", "a two dimmentional numpy array to a myqlm Matrix. Args:", "Matrix \"\"\" assert len(array.shape) == 2, \"The array must be", "or more contributor license agreements. See the NOTICE file distributed", "arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri = Matrix(array.shape[0], array.shape[1], data) return matri", "copyright ownership. The ASF licenses this file to you under", "Software Foundation (ASF) under one or more contributor license agreements.", "a quantum channel. \"\"\" qchannel = None qiskit_data = representation.data", "a quantum channel. \"\"\" rep = representation.representation # Find what", "in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif", "it is. # Then create the corresponding matrix and shape", "array: (ndarray) a two dimmentional numpy array Returns: (Matrix): a", "(rep == RepresentationType.PTM) else Choi(data) if rep in (RepresentationType.CHI, RepresentationType.SUPEROP):", "distributed with this work for additional information regarding copyright ownership.", "quantum channel from a qiskit representation of a quantum channel.", "array_to_matrix(array): \"\"\" Transform a two dimmentional numpy array to a", "kraus_ops = [] for arr in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel =", "data_im = [] for i in range(matri.nRows): for j in", "QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri) elif isinstance(representation, Choi): matri = array_to_matrix(qiskit_data)", "two dimmentional numpy array Returns: (Matrix): a myqlm Matrix \"\"\"", "= [] for i in range(matri.nRows): for j in range(matri.nCols):", "array Returns: (Matrix): a myqlm Matrix \"\"\" assert len(array.shape) ==", "Choi, PTM, Kraus, Chi, SuperOp import numpy as np from", "# Find what representation it is. # Then create the", "Unless required by applicable law or agreed to in writing,", "by applicable law or agreed to in writing, software distributed", "a quantum channel. Returns: (QuantumChannel): myqlm representation of a quantum", "of a quantum channel. Returns: (QuantumChannel): myqlm representation of a", "it is. # Then create the corresponding matrix (kraus_ops|basis|matrix)from the", "data) return matri def qiskit_to_qchannel(representation): \"\"\" Create a myqlm representation", "representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri) return qchannel def qchannel_to_qiskit(representation): \"\"\" Create a", "kraus_ops=kraus_ops) elif isinstance(representation, Chi): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel =", "QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation, Chi): basis = []", "if len(final_data) > 1 else SuperOp(final_data[0]) if rep == RepresentationType.KRAUS:", "must be two dimmentional\" data = [] for arr in", "the Apache Software Foundation (ASF) under one or more contributor", "representation.data # Find what representation it is. # Then create", "NOTICE file distributed with this work for additional information regarding", "express or implied. See the License for the specific language", "channel. Args: representation: (Kraus|Choi|Chi|SuperOp|PTM) qiskit representation of a quantum channel.", "in range(matri.nCols): data_re.append(matri.data[i * matri.nRows + j].re + 0.j) data_im.append(matri.data[i", "SuperOp(final_data) if len(final_data) > 1 else SuperOp(final_data[0]) if rep ==", "in (RepresentationType.PTM, RepresentationType.CHOI): matri = representation.matrix data_re = [] data_im", "+ 0.j) data_im.append(matri.data[i * matri.nRows + j].im) data = np.array(data_re)", "matri.nCols)) return PTM(data) if (rep == RepresentationType.PTM) else Choi(data) if", "two dimmentional\" data = [] for arr in array: for", "arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation, Chi): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel", "0.j) data_im.append(matri.data[i * matri.nRows + j].im) data = np.array(data_re) data.imag", "myqlm Matrix. Args: array: (ndarray) a two dimmentional numpy array", "matrix=matri) return qchannel def qchannel_to_qiskit(representation): \"\"\" Create a qiskit representation", "from that matrix. if rep in (RepresentationType.PTM, RepresentationType.CHOI): matri =", "rep in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data = [] for matri in", "create the QuantumChannel with the RepresentationType, the arity # (got", "for elem in arr: data.append(ComplexNumber(np.real(elem), np.imag(elem))) matri = Matrix(array.shape[0], array.shape[1],", "obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required", "the arity # (got from the qiskit representation) and the", "data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if rep == RepresentationType.CHI: return Chi(final_data) if", "numpy array Returns: (Matrix): a myqlm Matrix \"\"\" assert len(array.shape)", "isinstance(representation, SuperOp): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.SUPEROP,", "(RepresentationType.CHI, RepresentationType.SUPEROP): final_data = [] for matri in representation.basis: data_re", "(Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of a quantum channel. \"\"\" rep =", "Chi(final_data) if len(final_data) > 1 else Chi(final_data[0]) return SuperOp(final_data) if", "dimmentional\" data = [] for arr in array: for elem", "with the License. You may obtain a copy of the", "= [] for arr in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel(", "a myqlm Matrix \"\"\" assert len(array.shape) == 2, \"The array", "quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of a quantum channel.", "qchannel def qchannel_to_qiskit(representation): \"\"\" Create a qiskit representation of quantum", "channel. \"\"\" rep = representation.representation # Find what representation it", "qat.comm.datamodel.ttypes import Matrix, ComplexNumber def array_to_matrix(array): \"\"\" Transform a two", "len(final_data) > 1 else Chi(final_data[0]) return SuperOp(final_data) if len(final_data) >", "return SuperOp(final_data) if len(final_data) > 1 else SuperOp(final_data[0]) if rep", "matri = Matrix(array.shape[0], array.shape[1], data) return matri def qiskit_to_qchannel(representation): \"\"\"", "matri in representation.kraus_ops: data_re = [] data_im = [] for", "myqlm representation of a quantum channel. \"\"\" qchannel = None", "arity=representation.num_qubits, matrix=matri) elif isinstance(representation, Choi): matri = array_to_matrix(qiskit_data) qchannel =", "representation of a quantum channel. Returns: (Kraus|Choi|Chi|SuperOp|PTM): qiskit representation of", "specific language governing permissions and limitations under the License. \"\"\"", "qchannel = QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation, Chi): basis", "myqlm representation of a quantum channel. Args: representation: (QuantumChannel) myqlm", "applicable law or agreed to in writing, software distributed under", "if rep in (RepresentationType.PTM, RepresentationType.CHOI): matri = representation.matrix data_re =", "return PTM(data) if (rep == RepresentationType.PTM) else Choi(data) if rep", "qchannel = QuantumChannel( representation=RepresentationType.SUPEROP, arity=representation.num_qubits, basis=basis) elif isinstance(representation, PTM): matri", "from a qiskit representation of a quantum channel. Args: representation:", "1 else SuperOp(final_data[0]) if rep == RepresentationType.KRAUS: final_data = []", "Transform a two dimmentional numpy array to a myqlm Matrix.", "the License is distributed on an \"AS IS\" BASIS, WITHOUT", "the specific language governing permissions and limitations under the License.", "the RepresentationType, the arity # (got from the qiskit representation)", "j].im) data = np.array(data_re) data.imag = np.array(data_im) data = data.reshape((matri.nRows,", "len(array.shape) == 2, \"The array must be two dimmentional\" data", "SuperOp(final_data[0]) if rep == RepresentationType.KRAUS: final_data = [] for matri", "\"\"\" rep = representation.representation # Find what representation it is.", "this work for additional information regarding copyright ownership. The ASF", "QuantumChannel, RepresentationType from qat.comm.datamodel.ttypes import Matrix, ComplexNumber def array_to_matrix(array): \"\"\"", "data_re = [] data_im = [] for i in range(matri.nRows):", "from a myqlm representation of a quantum channel. Args: representation:", "or agreed to in writing, software distributed under the License", "this file to you under the Apache License, Version 2.0", "for i in range(matri.nRows): for j in range(matri.nCols): data_re.append(matri.data[i *", "PTM): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.PTM, arity=representation.num_qubits, matrix=matri)", "= QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation, Chi): basis =", "a qiskit representation of quantum channel from a myqlm representation", "return matri def qiskit_to_qchannel(representation): \"\"\" Create a myqlm representation of", "license agreements. See the NOTICE file distributed with this work", "OF ANY KIND, either express or implied. See the License", "assert len(array.shape) == 2, \"The array must be two dimmentional\"", "\"\"\" qchannel = None qiskit_data = representation.data # Find what", "data = data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if rep == RepresentationType.CHI: return", "quantum channel. \"\"\" qchannel = None qiskit_data = representation.data #", "License. \"\"\" from qiskit.quantum_info.operators.channel import Choi, PTM, Kraus, Chi, SuperOp", "a myqlm representation of quantum channel from a qiskit representation", "License, Version 2.0 (the \"License\"); you may not use this", "final_data = [] for matri in representation.basis: data_re = []", "from the qiskit representation) and the matrix. if isinstance(representation, Kraus):", "arr in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops)", "= [] basis.append(array_to_matrix(qiskit_data)) qchannel = QuantumChannel( representation=RepresentationType.CHI, arity=representation.num_qubits, basis=basis) elif", "Create a myqlm representation of quantum channel from a qiskit", "a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by", "\"\"\" from qiskit.quantum_info.operators.channel import Choi, PTM, Kraus, Chi, SuperOp import", "and limitations under the License. \"\"\" from qiskit.quantum_info.operators.channel import Choi,", "Choi): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.CHOI, arity=representation.num_qubits, matrix=matri)", "in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data = [] for matri in representation.basis:", "array must be two dimmentional\" data = [] for arr", "= data.reshape((matri.nRows, matri.nCols)) final_data.append(data) if rep == RepresentationType.CHI: return Chi(final_data)", "License. You may obtain a copy of the License at", "= Matrix(array.shape[0], array.shape[1], data) return matri def qiskit_to_qchannel(representation): \"\"\" Create", "else Chi(final_data[0]) return SuperOp(final_data) if len(final_data) > 1 else SuperOp(final_data[0])", "arity=representation.num_qubits, basis=basis) elif isinstance(representation, SuperOp): basis = [] basis.append(array_to_matrix(qiskit_data)) qchannel", "[] for arr in qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel( representation=RepresentationType.KRAUS,", "elif isinstance(representation, PTM): matri = array_to_matrix(qiskit_data) qchannel = QuantumChannel( representation=RepresentationType.PTM,", "\"The array must be two dimmentional\" data = [] for", "Finally, create the QuantumChannel with the RepresentationType, the arity #", "create the corresponding matrix (kraus_ops|basis|matrix)from the data # of the", "limitations under the License. \"\"\" from qiskit.quantum_info.operators.channel import Choi, PTM,", "like qiskit is expecting it. # Finally, create the qiskit", "an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY", "qiskit_data: kraus_ops.append(array_to_matrix(arr)) qchannel = QuantumChannel( representation=RepresentationType.KRAUS, arity=representation.num_qubits, kraus_ops=kraus_ops) elif isinstance(representation,", "matrix. if isinstance(representation, Kraus): kraus_ops = [] for arr in", "else SuperOp(final_data[0]) if rep == RepresentationType.KRAUS: final_data = [] for", "else Choi(data) if rep in (RepresentationType.CHI, RepresentationType.SUPEROP): final_data = []", "create the qiskit representation from that matrix. if rep in", "quantum channel from a myqlm representation of a quantum channel." ]
[ "'duration': durations}) dm = make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0) annotations =", "ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T * amplitude * 1.e-6, info, verbose=False)", "/ sfreq onset = 0. onsets = [] conditions =", ": instance of Raw The generated raw instance. \"\"\" from", "The amplitude of the signal to simulate in uM. sig_dur", "from nilearn.stats.first_level_model import make_first_level_design_matrix from pandas import DataFrame frame_times =", "raw = RawArray(dm[[\"A\"]].to_numpy().T * amplitude * 1.e-6, info, verbose=False) raw.set_annotations(annotations)", "sig_dur - 60: onset += np.random.uniform(isi_min, isi_max) + stim_dur onsets.append(onset)", "= [] while onset < sig_dur - 60: onset +=", "---------- sfreq : Number The sample rate. amplitude : Number", ": Number The length of the stimulus to generate in", "uM. sig_dur : Number The length of the signal to", "RawArray def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.): \"\"\" Create", "conditions = [] durations = [] while onset < sig_dur", "Authors: <NAME> <<EMAIL>> # # License: BSD (3-clause) import numpy", "np.random.uniform(isi_min, isi_max) + stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events = DataFrame({'trial_type':", ".. warning:: Work in progress: I am trying to think", "raw : instance of Raw The generated raw instance. \"\"\"", "annotations = Annotations(onsets, durations, conditions) info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo'])", "drift_order=0) annotations = Annotations(onsets, durations, conditions) info = create_info(ch_names=['Simulated'], sfreq=sfreq,", "on the best API. Parameters ---------- sfreq : Number The", "simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.): \"\"\" Create simulated data.", "instance of Raw The generated raw instance. \"\"\" from nilearn.stats.first_level_model", "onset < sig_dur - 60: onset += np.random.uniform(isi_min, isi_max) +", "sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.): \"\"\" Create simulated data. .. warning::", "mne import Annotations, create_info from mne.io import RawArray def simulate_nirs_raw(sfreq=3.,", "= create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T * amplitude *", "rate. amplitude : Number The amplitude of the signal to", "Number The minimum duration of the inter stimulus interval in", "create_info from mne.io import RawArray def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5.,", "durations}) dm = make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0) annotations = Annotations(onsets,", "def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.): \"\"\" Create simulated", "in uM. sig_dur : Number The length of the signal", "Number The length of the signal to generate in seconds.", "[] while onset < sig_dur - 60: onset += np.random.uniform(isi_min,", "generate in seconds. isi_min : Number The minimum duration of", "while onset < sig_dur - 60: onset += np.random.uniform(isi_min, isi_max)", "the inter stimulus interval in seconds. isi_max : Number The", "\"\"\" Create simulated data. .. warning:: Work in progress: I", "duration of the inter stimulus interval in seconds. Returns -------", "from mne.io import RawArray def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5., isi_min=15.,", "- 60: onset += np.random.uniform(isi_min, isi_max) + stim_dur onsets.append(onset) conditions.append(\"A\")", "stimulus to generate in seconds. isi_min : Number The minimum", "onsets = [] conditions = [] durations = [] while", "of the signal to simulate in uM. sig_dur : Number", "amplitude=1., sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.): \"\"\" Create simulated data. ..", "RawArray(dm[[\"A\"]].to_numpy().T * amplitude * 1.e-6, info, verbose=False) raw.set_annotations(annotations) return raw", "isi_min=15., isi_max=45.): \"\"\" Create simulated data. .. warning:: Work in", "Returns ------- raw : instance of Raw The generated raw", "seconds. isi_max : Number The maximum duration of the inter", "durations = [] while onset < sig_dur - 60: onset", "= np.arange(sig_dur * sfreq) / sfreq onset = 0. onsets", "+ stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events = DataFrame({'trial_type': conditions, 'onset':", "conditions, 'onset': onsets, 'duration': durations}) dm = make_first_level_design_matrix(frame_times, events, drift_model='polynomial',", "from mne import Annotations, create_info from mne.io import RawArray def", "(3-clause) import numpy as np from mne import Annotations, create_info", "interval in seconds. Returns ------- raw : instance of Raw", "duration of the inter stimulus interval in seconds. isi_max :", "make_first_level_design_matrix from pandas import DataFrame frame_times = np.arange(sig_dur * sfreq)", "* sfreq) / sfreq onset = 0. onsets = []", "interval in seconds. isi_max : Number The maximum duration of", "of Raw The generated raw instance. \"\"\" from nilearn.stats.first_level_model import", "Number The amplitude of the signal to simulate in uM.", "import DataFrame frame_times = np.arange(sig_dur * sfreq) / sfreq onset", "data. .. warning:: Work in progress: I am trying to", "seconds. Returns ------- raw : instance of Raw The generated", "+= np.random.uniform(isi_min, isi_max) + stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events =", "Parameters ---------- sfreq : Number The sample rate. amplitude :", "isi_min : Number The minimum duration of the inter stimulus", ": Number The length of the signal to generate in", "from pandas import DataFrame frame_times = np.arange(sig_dur * sfreq) /", "import Annotations, create_info from mne.io import RawArray def simulate_nirs_raw(sfreq=3., amplitude=1.,", "np.arange(sig_dur * sfreq) / sfreq onset = 0. onsets =", "DataFrame({'trial_type': conditions, 'onset': onsets, 'duration': durations}) dm = make_first_level_design_matrix(frame_times, events,", "The length of the stimulus to generate in seconds. isi_min", "stim_dur : Number The length of the stimulus to generate", "Annotations, create_info from mne.io import RawArray def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300.,", "# License: BSD (3-clause) import numpy as np from mne", "< sig_dur - 60: onset += np.random.uniform(isi_min, isi_max) + stim_dur", "of the inter stimulus interval in seconds. isi_max : Number", "Number The sample rate. amplitude : Number The amplitude of", "<<EMAIL>> # # License: BSD (3-clause) import numpy as np", ": Number The sample rate. amplitude : Number The amplitude", "create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T * amplitude * 1.e-6,", "to think on the best API. Parameters ---------- sfreq :", "The sample rate. amplitude : Number The amplitude of the", "as np from mne import Annotations, create_info from mne.io import", "onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events = DataFrame({'trial_type': conditions, 'onset': onsets, 'duration':", "sfreq : Number The sample rate. amplitude : Number The", "# # License: BSD (3-clause) import numpy as np from", ": Number The amplitude of the signal to simulate in", "sig_dur : Number The length of the signal to generate", "The length of the signal to generate in seconds. stim_dur", "stim_dur=5., isi_min=15., isi_max=45.): \"\"\" Create simulated data. .. warning:: Work", "np from mne import Annotations, create_info from mne.io import RawArray", "length of the signal to generate in seconds. stim_dur :", "in seconds. Returns ------- raw : instance of Raw The", "durations, conditions) info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T", "import make_first_level_design_matrix from pandas import DataFrame frame_times = np.arange(sig_dur *", "the signal to simulate in uM. sig_dur : Number The", ": Number The minimum duration of the inter stimulus interval", "conditions.append(\"A\") durations.append(stim_dur) events = DataFrame({'trial_type': conditions, 'onset': onsets, 'duration': durations})", "Raw The generated raw instance. \"\"\" from nilearn.stats.first_level_model import make_first_level_design_matrix", "events = DataFrame({'trial_type': conditions, 'onset': onsets, 'duration': durations}) dm =", "to generate in seconds. isi_min : Number The minimum duration", "amplitude : Number The amplitude of the signal to simulate", "isi_max) + stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events = DataFrame({'trial_type': conditions,", "conditions) info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T *", "Annotations(onsets, durations, conditions) info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw =", "frame_times = np.arange(sig_dur * sfreq) / sfreq onset = 0.", "the signal to generate in seconds. stim_dur : Number The", "------- raw : instance of Raw The generated raw instance.", "stimulus interval in seconds. isi_max : Number The maximum duration", "60: onset += np.random.uniform(isi_min, isi_max) + stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur)", "= Annotations(onsets, durations, conditions) info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw", "signal to generate in seconds. stim_dur : Number The length", "License: BSD (3-clause) import numpy as np from mne import", "think on the best API. Parameters ---------- sfreq : Number", "nilearn.stats.first_level_model import make_first_level_design_matrix from pandas import DataFrame frame_times = np.arange(sig_dur", "numpy as np from mne import Annotations, create_info from mne.io", "Number The length of the stimulus to generate in seconds.", "'onset': onsets, 'duration': durations}) dm = make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0)", "make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0) annotations = Annotations(onsets, durations, conditions) info", "seconds. isi_min : Number The minimum duration of the inter", "info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T * amplitude", "to generate in seconds. stim_dur : Number The length of", "Number The maximum duration of the inter stimulus interval in", "DataFrame frame_times = np.arange(sig_dur * sfreq) / sfreq onset =", "progress: I am trying to think on the best API.", "of the stimulus to generate in seconds. isi_min : Number", "import RawArray def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.): \"\"\"", "= make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0) annotations = Annotations(onsets, durations, conditions)", "Create simulated data. .. warning:: Work in progress: I am", "simulate in uM. sig_dur : Number The length of the", "in progress: I am trying to think on the best", "onset += np.random.uniform(isi_min, isi_max) + stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events", "the inter stimulus interval in seconds. Returns ------- raw :", "inter stimulus interval in seconds. isi_max : Number The maximum", "the best API. Parameters ---------- sfreq : Number The sample", "0. onsets = [] conditions = [] durations = []", "isi_max=45.): \"\"\" Create simulated data. .. warning:: Work in progress:", "am trying to think on the best API. Parameters ----------", "trying to think on the best API. Parameters ---------- sfreq", "= [] conditions = [] durations = [] while onset", "the stimulus to generate in seconds. isi_min : Number The", "of the inter stimulus interval in seconds. Returns ------- raw", "= 0. onsets = [] conditions = [] durations =", "events, drift_model='polynomial', drift_order=0) annotations = Annotations(onsets, durations, conditions) info =", "<reponame>mshader/mne-nirs # Authors: <NAME> <<EMAIL>> # # License: BSD (3-clause)", "length of the stimulus to generate in seconds. isi_min :", "best API. Parameters ---------- sfreq : Number The sample rate.", "Work in progress: I am trying to think on the", "generate in seconds. stim_dur : Number The length of the", "<NAME> <<EMAIL>> # # License: BSD (3-clause) import numpy as", "import numpy as np from mne import Annotations, create_info from", "maximum duration of the inter stimulus interval in seconds. Returns", "stimulus interval in seconds. Returns ------- raw : instance of", "amplitude of the signal to simulate in uM. sig_dur :", "The minimum duration of the inter stimulus interval in seconds.", "= [] durations = [] while onset < sig_dur -", "in seconds. stim_dur : Number The length of the stimulus", "raw instance. \"\"\" from nilearn.stats.first_level_model import make_first_level_design_matrix from pandas import", "sample rate. amplitude : Number The amplitude of the signal", "sfreq=sfreq, ch_types=['hbo']) raw = RawArray(dm[[\"A\"]].to_numpy().T * amplitude * 1.e-6, info,", "durations.append(stim_dur) events = DataFrame({'trial_type': conditions, 'onset': onsets, 'duration': durations}) dm", "sfreq) / sfreq onset = 0. onsets = [] conditions", "mne.io import RawArray def simulate_nirs_raw(sfreq=3., amplitude=1., sig_dur=300., stim_dur=5., isi_min=15., isi_max=45.):", "# Authors: <NAME> <<EMAIL>> # # License: BSD (3-clause) import", "warning:: Work in progress: I am trying to think on", "The maximum duration of the inter stimulus interval in seconds.", "inter stimulus interval in seconds. Returns ------- raw : instance", "The generated raw instance. \"\"\" from nilearn.stats.first_level_model import make_first_level_design_matrix from", "generated raw instance. \"\"\" from nilearn.stats.first_level_model import make_first_level_design_matrix from pandas", "seconds. stim_dur : Number The length of the stimulus to", "drift_model='polynomial', drift_order=0) annotations = Annotations(onsets, durations, conditions) info = create_info(ch_names=['Simulated'],", "sfreq onset = 0. onsets = [] conditions = []", "stim_dur onsets.append(onset) conditions.append(\"A\") durations.append(stim_dur) events = DataFrame({'trial_type': conditions, 'onset': onsets,", "[] durations = [] while onset < sig_dur - 60:", "to simulate in uM. sig_dur : Number The length of", "API. Parameters ---------- sfreq : Number The sample rate. amplitude", "isi_max : Number The maximum duration of the inter stimulus", "instance. \"\"\" from nilearn.stats.first_level_model import make_first_level_design_matrix from pandas import DataFrame", "simulated data. .. warning:: Work in progress: I am trying", "\"\"\" from nilearn.stats.first_level_model import make_first_level_design_matrix from pandas import DataFrame frame_times", "pandas import DataFrame frame_times = np.arange(sig_dur * sfreq) / sfreq", "of the signal to generate in seconds. stim_dur : Number", "I am trying to think on the best API. Parameters", "dm = make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0) annotations = Annotations(onsets, durations,", "onsets, 'duration': durations}) dm = make_first_level_design_matrix(frame_times, events, drift_model='polynomial', drift_order=0) annotations", ": Number The maximum duration of the inter stimulus interval", "signal to simulate in uM. sig_dur : Number The length", "BSD (3-clause) import numpy as np from mne import Annotations,", "= DataFrame({'trial_type': conditions, 'onset': onsets, 'duration': durations}) dm = make_first_level_design_matrix(frame_times,", "minimum duration of the inter stimulus interval in seconds. isi_max", "onset = 0. onsets = [] conditions = [] durations", "in seconds. isi_min : Number The minimum duration of the", "= RawArray(dm[[\"A\"]].to_numpy().T * amplitude * 1.e-6, info, verbose=False) raw.set_annotations(annotations) return", "[] conditions = [] durations = [] while onset <", "in seconds. isi_max : Number The maximum duration of the" ]
[ "currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id,", "operation): helpers.do_local_connect(self.configuration) trans = user_transaction() trans.user_id = user_id trans.volume =", "trans.delete() def do_update_transaction(self, id, user_id, volume, symbol, value, price, currency,", "= user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id = user_id trans.volume = volume", "if_none_raise_with_id class TransactionRepository: def __init__(self, config, log_error): self.configuration = config", "trans) if trans is not None: trans.delete() def do_update_transaction(self, id,", "source_id trans.operation = operation trans.save() return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id", "self.do_fetch_transactions, user_id) def insert_transaction(self, user_id, volume, symbol, value, price, currency,", "__init__(self, config, log_error): self.configuration = config self.log_error = log_error def", "= symbol trans.value = value trans.price = price trans.date =", "def update_transaction(self, id, user_id, volume, symbol, value, price, currency, date,", "symbol, value, price, currency, date, source, source_id, operation) def delete_transaction(self,", "date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id, volume,", "= source_id trans.operation = operation trans.save() return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self,", "fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def fetch_transactions(self, user_id): return", "helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume, symbol, value, price, currency, date, source,", "import helpers from cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id class TransactionRepository: def", "trans = user_transaction() trans.user_id = user_id trans.volume = volume trans.symbol", "currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume,", "= price trans.date = date trans.source = source trans.currency =", "trans) trans.user_id = user_id trans.volume = volume trans.symbol = symbol", "helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id)", "= date trans.source = source trans.currency = currency trans.source_id =", "operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume, symbol, value, price, currency,", "throw_if_does_not_exist: if_none_raise_with_id(id, trans) if trans is not None: trans.delete() def", "trans.value = value trans.price = price trans.date = date trans.source", "helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def insert_transaction(self, user_id, volume, symbol, value, price,", "source_id, operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id, volume, symbol, value,", "= user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id, trans) if trans is not", "log_error): self.configuration = config self.log_error = log_error def fetch_transaction(self, id):", "trans.save() return user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id, volume, symbol, value, price,", "id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def fetch_transactions(self,", "= currency trans.source_id = source_id trans.operation = operation trans.save() return", "id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self,", "def do_insert_transaction(self, user_id, volume, symbol, value, price, currency, date, source,", "symbol, value, price, currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans", "trans.value = value trans.price = price trans.date = date trans.currency", "date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans)", "trans.volume = volume trans.symbol = symbol trans.value = value trans.price", "def fetch_transaction(self, id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def fetch_transactions(self, user_id):", "throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist) def do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration)", "trans.currency = currency trans.source_id = source_id trans.operation = operation trans.save()", "date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume, symbol,", "symbol, value, price, currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self,", "price, currency, date, source, source_id, operation) def delete_transaction(self, id, throw_if_does_not_exist=True):", "return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions,", "date, source, source_id, operation) def update_transaction(self, id, user_id, volume, symbol,", "def insert_transaction(self, user_id, volume, symbol, value, price, currency, date, source,", "value, price, currency, date, source, source_id, operation) def delete_transaction(self, id,", "trans.price = price trans.date = date trans.source = source trans.currency", "source_id, operation) def update_transaction(self, id, user_id, volume, symbol, value, price,", "do_update_transaction(self, id, user_id, volume, symbol, value, price, currency, date, source,", "date trans.currency = currency trans.source = source trans.source_id = source_id", "self.configuration = config self.log_error = log_error def fetch_transaction(self, id): return", "update_transaction(self, id, user_id, volume, symbol, value, price, currency, date, source,", "return helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id, volume, symbol, value, price, currency,", "trans.operation = operation trans.save() return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id ):", "= value trans.price = price trans.date = date trans.currency =", "currency trans.source_id = source_id trans.operation = operation trans.save() return user_transaction.objects(id=id).first()", "user_id) def insert_transaction(self, user_id, volume, symbol, value, price, currency, date,", "trans is not None: trans.delete() def do_update_transaction(self, id, user_id, volume,", "trans.source = source trans.currency = currency trans.source_id = source_id trans.operation", "source_id, operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume, symbol, value, price,", "trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id = user_id trans.volume =", "operation_type from mongoengine import Q from cryptodataaccess import helpers from", "user_notification, user_channel, user_transaction, operation_type from mongoengine import Q from cryptodataaccess", "operation): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id = user_id", "source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id, volume, symbol,", "helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id, volume, symbol, value, price, currency, date,", "Q from cryptodataaccess import helpers from cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id", "from cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id class TransactionRepository: def __init__(self, config,", "helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id)", "helpers from cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id class TransactionRepository: def __init__(self,", "source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction() trans.user_id = user_id", "do_insert_transaction(self, user_id, volume, symbol, value, price, currency, date, source, source_id,", "helpers.do_local_connect(self.configuration) trans = user_transaction() trans.user_id = user_id trans.volume = volume", "class TransactionRepository: def __init__(self, config, log_error): self.configuration = config self.log_error", "user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def do_fetch_transaction(self,", "): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def do_fetch_transaction(self, id ): helpers.do_local_connect(self.configuration) return", "operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id, volume, symbol, value, price,", "helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id = user_id trans.volume", "= volume trans.symbol = symbol trans.value = value trans.price =", "value trans.price = price trans.date = date trans.currency = currency", "source_id, operation) def delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist)", "cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id class TransactionRepository: def __init__(self, config, log_error):", "TransactionRepository: def __init__(self, config, log_error): self.configuration = config self.log_error =", "trans.operation = operation trans.save() return user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id, volume,", "fetch_transaction(self, id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def fetch_transactions(self, user_id): return", "user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id, volume, symbol, value, price, currency, date,", "trans.source_id = source_id trans.operation = operation trans.save() return user_transaction.objects(id=trans.id).first() def", "= user_id trans.volume = volume trans.symbol = symbol trans.value =", "return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume, symbol, value, price, currency, date,", "id, user_id, volume, symbol, value, price, currency, date, source, source_id,", "price trans.date = date trans.currency = currency trans.source = source", "price trans.date = date trans.source = source trans.currency = currency", "if_none_raise_with_id(id, trans) if trans is not None: trans.delete() def do_update_transaction(self,", "operation) def delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist) def", "= date trans.currency = currency trans.source = source trans.source_id =", "trans.source_id = source_id trans.operation = operation trans.save() return user_transaction.objects(id=id).first() def", "self.do_fetch_transaction, id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def", "= value trans.price = price trans.date = date trans.source =", "value, price, currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction,", "trans.user_id = user_id trans.volume = volume trans.symbol = symbol trans.value", "date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction() trans.user_id =", "if trans is not None: trans.delete() def do_update_transaction(self, id, user_id,", "import Q from cryptodataaccess import helpers from cryptodataaccess.helpers import if_none_raise,", "helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def do_fetch_transaction(self, id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(id=id))[0]", "def do_update_transaction(self, id, user_id, volume, symbol, value, price, currency, date,", "user_id trans.volume = volume trans.symbol = symbol trans.value = value", "currency, date, source, source_id, operation) def update_transaction(self, id, user_id, volume,", "def do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def do_fetch_transaction(self, id", "return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def", "volume, symbol, value, price, currency, date, source, source_id, operation): return", "delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist) def do_delete_transaction(self, id,", "config self.log_error = log_error def fetch_transaction(self, id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction,", "cryptodataaccess import helpers from cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id class TransactionRepository:", "fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def insert_transaction(self, user_id, volume,", "helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id, trans) if trans", "user_id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def insert_transaction(self,", "user_id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def do_fetch_transaction(self, id ): helpers.do_local_connect(self.configuration)", "operation trans.save() return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration) return", "volume trans.symbol = symbol trans.value = value trans.price = price", "return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions,", "return user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id, volume, symbol, value, price, currency,", "mongoengine import Q from cryptodataaccess import helpers from cryptodataaccess.helpers import", "symbol, value, price, currency, date, source, source_id, operation) def update_transaction(self,", "value, price, currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction,", "id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist) def do_delete_transaction(self, id, throw_if_does_not_exist=True):", "= source trans.source_id = source_id trans.operation = operation trans.save() return", "volume, symbol, value, price, currency, date, source, source_id, operation) def", "currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction() trans.user_id", "None: trans.delete() def do_update_transaction(self, id, user_id, volume, symbol, value, price,", "= currency trans.source = source trans.source_id = source_id trans.operation =", "user_id, volume, symbol, value, price, currency, date, source, source_id, operation)", "source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id =", "currency trans.source = source trans.source_id = source_id trans.operation = operation", "user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self,", "id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id, trans)", "= price trans.date = date trans.currency = currency trans.source =", "price, currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first()", "self.do_insert_transaction, user_id, volume, symbol, value, price, currency, date, source, source_id,", "symbol trans.value = value trans.price = price trans.date = date", "do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id)) def do_fetch_transaction(self, id ):", "from cryptomodel.cryptostore import user_notification, user_channel, user_transaction, operation_type from mongoengine import", "price, currency, date, source, source_id, operation) def update_transaction(self, id, user_id,", "def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def insert_transaction(self, user_id,", "do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id,", "value trans.price = price trans.date = date trans.source = source", "self.do_fetch_transactions, user_id) def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def", "self.do_update_transaction, id, user_id, volume, symbol, value, price, currency, date, source,", "self.do_delete_transaction, id, throw_if_does_not_exist) def do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans =", "value, price, currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans =", "source, source_id, operation) def update_transaction(self, id, user_id, volume, symbol, value,", "currency, date, source, source_id, operation) def delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self,", "throw_if_does_not_exist) def do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if", "volume, symbol, value, price, currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration)", "= source trans.currency = currency trans.source_id = source_id trans.operation =", "trans.currency = currency trans.source = source trans.source_id = source_id trans.operation", "source trans.source_id = source_id trans.operation = operation trans.save() return user_transaction.objects(id=trans.id).first()", "user_transaction, operation_type from mongoengine import Q from cryptodataaccess import helpers", "<gh_stars>0 from cryptomodel.cryptostore import user_notification, user_channel, user_transaction, operation_type from mongoengine", "trans.source = source trans.source_id = source_id trans.operation = operation trans.save()", "if_none_raise_with_id(id, trans) trans.user_id = user_id trans.volume = volume trans.symbol =", "helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist) def do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans", "user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def insert_transaction(self, user_id, volume, symbol,", "is not None: trans.delete() def do_update_transaction(self, id, user_id, volume, symbol,", "user_id, volume, symbol, value, price, currency, date, source, source_id, operation):", "= operation trans.save() return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration)", "throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id, trans) if", "user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id = user_id trans.volume = volume trans.symbol", "source trans.currency = currency trans.source_id = source_id trans.operation = operation", "log_error def fetch_transaction(self, id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def fetch_transactions(self,", "user_transaction() trans.user_id = user_id trans.volume = volume trans.symbol = symbol", "return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def insert_transaction(self, user_id, volume, symbol, value,", "= log_error def fetch_transaction(self, id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id) def", "source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id, volume, symbol, value,", "date, source, source_id, operation) def delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction,", "from cryptodataaccess import helpers from cryptodataaccess.helpers import if_none_raise, if_none_raise_with_id class", "user_channel, user_transaction, operation_type from mongoengine import Q from cryptodataaccess import", "= operation trans.save() return user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id, volume, symbol,", "def fetch_transactions(self, user_id): return helpers.server_time_out_wrapper(self, self.do_fetch_transactions, user_id) def fetch_transactions(self, user_id):", "def __init__(self, config, log_error): self.configuration = config self.log_error = log_error", "currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction, id, user_id,", "source_id trans.operation = operation trans.save() return user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id,", "source, source_id, operation) def delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id,", "if_none_raise, if_none_raise_with_id class TransactionRepository: def __init__(self, config, log_error): self.configuration =", "= user_transaction() trans.user_id = user_id trans.volume = volume trans.symbol =", "= config self.log_error = log_error def fetch_transaction(self, id): return helpers.server_time_out_wrapper(self,", "insert_transaction(self, user_id, volume, symbol, value, price, currency, date, source, source_id,", "price, currency, date, source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction()", "date trans.source = source trans.currency = currency trans.source_id = source_id", "import if_none_raise, if_none_raise_with_id class TransactionRepository: def __init__(self, config, log_error): self.configuration", "operation) def update_transaction(self, id, user_id, volume, symbol, value, price, currency,", "def do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if throw_if_does_not_exist:", "trans.save() return user_transaction.objects(id=trans.id).first() def do_fetch_transactions(self, user_id ): helpers.do_local_connect(self.configuration) return user_transaction.objects(Q(user_id=user_id))", "trans = user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id, trans) if trans is", "source, source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first() if_none_raise_with_id(id, trans) trans.user_id", "trans.date = date trans.source = source trans.currency = currency trans.source_id", "source_id, operation): helpers.do_local_connect(self.configuration) trans = user_transaction() trans.user_id = user_id trans.volume", "self.log_error = log_error def fetch_transaction(self, id): return helpers.server_time_out_wrapper(self, self.do_fetch_transaction, id)", "not None: trans.delete() def do_update_transaction(self, id, user_id, volume, symbol, value,", "trans.symbol = symbol trans.value = value trans.price = price trans.date", "from mongoengine import Q from cryptodataaccess import helpers from cryptodataaccess.helpers", "value, price, currency, date, source, source_id, operation) def update_transaction(self, id,", "id, throw_if_does_not_exist) def do_delete_transaction(self, id, throw_if_does_not_exist=True): helpers.do_local_connect(self.configuration) trans = user_transaction.objects(id=id).first()", "config, log_error): self.configuration = config self.log_error = log_error def fetch_transaction(self,", "user_transaction.objects(id=id).first() if throw_if_does_not_exist: if_none_raise_with_id(id, trans) if trans is not None:", "cryptomodel.cryptostore import user_notification, user_channel, user_transaction, operation_type from mongoengine import Q", "price, currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_update_transaction, id,", "= source_id trans.operation = operation trans.save() return user_transaction.objects(id=id).first() def do_insert_transaction(self,", "def delete_transaction(self, id, throw_if_does_not_exist=True): helpers.server_time_out_wrapper(self, self.do_delete_transaction, id, throw_if_does_not_exist) def do_delete_transaction(self,", "import user_notification, user_channel, user_transaction, operation_type from mongoengine import Q from", "if throw_if_does_not_exist: if_none_raise_with_id(id, trans) if trans is not None: trans.delete()", "operation trans.save() return user_transaction.objects(id=id).first() def do_insert_transaction(self, user_id, volume, symbol, value,", "trans.price = price trans.date = date trans.currency = currency trans.source", "trans.date = date trans.currency = currency trans.source = source trans.source_id", "price, currency, date, source, source_id, operation): return helpers.server_time_out_wrapper(self, self.do_insert_transaction, user_id," ]