# # NOTICE: Permission is hereby granted, free of charge,","files (the \"Software\"), # to deal in the Software without restriction, including without","np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name, item,","# Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic","session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session):","Software without restriction, including without limitation the # rights to use, copy, modify,","substantial portions of the Software. # ============================================================================== \"\"\" Feedable Dataset - Abstract class","self.proto_fields, 'Feature %s is not in proto fields.' % feature proto_field = self.proto_fields[feature]","proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs):","item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) #","the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or #",":param cluster_config: Optional. If set, the cluster configuration will be used for distributed","right dtype for feature in item: assert feature in self.proto_fields, 'Feature %s is","isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting and flattening #","__init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the","distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config","Fixed Len - Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field,","encountered. :return: A tornado.concurrent.Future that will be set with the results when they","# # NOTICE: Permission is hereby granted, free of charge, to any person","and casts/pads the item accordingly \"\"\" # Checking all features in items, padding","been started \"\"\" return self._is_started @property def is_initialized(self): \"\"\" Determines if the iterator","# ============================================================================== \"\"\" Feedable Dataset - Abstract class responsible for feeding data inside","class responsible for feeding data inside a model \"\"\" from abc import ABCMeta,","- Just converting # Fixed Len - Padding and converting if proto_field.dtype is","persons to whom the Software is # furnished to do so, subject to","publish, distribute, sublicense, and/or # sell copies of the Software, and to permit","\"\"\" return self._is_initialized @property def is_closing(self): \"\"\" Determines if the dataset is closing","self._is_initialized @property def is_closing(self): \"\"\" Determines if the dataset is closing \"\"\" return","conditions: # # The above copyright notice and this permission notice shall be","session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes the dataset","= dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will","\"\"\" Computes the outputs of a name using item as inout :param queue_name:","# Copyright 2019 - <NAME> # # NOTICE: Permission is hereby granted, free","obtaining # a copy of this software and associated documentation files (the \"Software\"),","to permit persons to whom the Software is # furnished to do so,","Len - Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField):","@property def can_support_iterator(self): \"\"\" Determines if the dataset can support an iterator or","the Software without restriction, including without limitation the # rights to use, copy,","# to deal in the Software without restriction, including without limitation the #","item :param item: A dictionary with the fields required for that queue :param","for that queue :param retry_on_failure: Boolean that indicates to retry querying from the","# ============================================================================== # Copyright 2019 - <NAME> # # NOTICE: Permission is hereby","that will be set with the results when they become available \"\"\" raise","any person obtaining # a copy of this software and associated documentation files","set, the cluster configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder","= np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name,",":param retry_on_failure: Boolean that indicates to retry querying from the model if an","of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional. If set,","dataset :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise","\"\"\" Determines if the dataset can support an iterator or if it is","Permission is hereby granted, free of charge, to any person obtaining # a","for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder","so, subject to the following conditions: # # The above copyright notice and","get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes sure the item","model \"\"\" from abc import ABCMeta, abstractmethod import logging import numpy as np","the iterator is initialized \"\"\" return self._is_initialized @property def is_closing(self): \"\"\" Determines if","class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic feedable dataset \"\"\" def __init__(self,","- Abstract class responsible for feeding data inside a model \"\"\" from abc",":return: A tornado.concurrent.Future that will be set with the results when they become","dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be","name using item as inout :param queue_name: The name of the queue where","NotImplementedError() @property def is_started(self): \"\"\" Determines if the dataset has been started \"\"\"","the proto-fields and generation methods :param cluster_config: Optional. If set, the cluster configuration","continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] =","required protofields and casts/pads the item accordingly \"\"\" # Checking all features in","to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\"","the right dtype for feature in item: assert feature in self.proto_fields, 'Feature %s","proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] =","to the following conditions: # # The above copyright notice and this permission","NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args,","dataset (and its iterator) :param session: The TensorFlow session to use. :type session:","Builds a SessionRunHook for the MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops the",":param item: A dictionary with the fields required for that queue :param retry_on_failure:","(RPC) dataset \"\"\" raise NotImplementedError() @property def is_started(self): \"\"\" Determines if the dataset","portions of the Software. # ============================================================================== \"\"\" Feedable Dataset - Abstract class responsible","cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default","restriction, including without limitation the # rights to use, copy, modify, merge, publish,","data inside a model \"\"\" from abc import ABCMeta, abstractmethod import logging import","this software and associated documentation files (the \"Software\"), # to deal in the","def initialize(self, session): \"\"\" Initializes the dataset (and its iterator) :param session: The","\"\"\" Determines if the dataset has been started \"\"\" return self._is_started @property def","initialize(self, session): \"\"\" Initializes the dataset (and its iterator) :param session: The TensorFlow","The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def","item: A dictionary with the fields required for that queue :param retry_on_failure: Boolean","(and its iterator) :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session","and generation methods :param cluster_config: Optional. If set, the cluster configuration will be","tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item","\"\"\" return self._is_closing @abstractmethod def build(self): \"\"\" Builds the dataset \"\"\" raise NotImplementedError()","\"Software\"), # to deal in the Software without restriction, including without limitation the","Software is # furnished to do so, subject to the following conditions: #","cluster_config: Optional. If set, the cluster configuration will be used for distributed training.","self.close() @property def can_support_iterator(self): \"\"\" Determines if the dataset can support an iterator","self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) #","a generic feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder:","cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features","dataset \"\"\" raise NotImplementedError() @property def is_started(self): \"\"\" Determines if the dataset has","is closing \"\"\" return self._is_closing @abstractmethod def build(self): \"\"\" Builds the dataset \"\"\"","dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config:","initialized \"\"\" return self._is_initialized @property def is_closing(self): \"\"\" Determines if the dataset is","VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta):","an iterator or if it is a remote (RPC) dataset \"\"\" raise NotImplementedError()","\"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {}","support an iterator or if it is a remote (RPC) dataset \"\"\" raise","its iterator) :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\"","to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting","object is a generic feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor","the dataset (and its iterator) :param session: The TensorFlow session to use. :type","Var Len - Converting and flattening # Scalar - Just converting # Fixed","self.default_features = {} # Will be used as default if features are missing","iterator or if it is a remote (RPC) dataset \"\"\" raise NotImplementedError() @property","not in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to","converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten()","elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature],","Boolean that indicates to retry querying from the model if an error is","\"\"\" This object is a generic feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None):","in items, padding them and converting them to the right dtype for feature","Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines if the dataset can support","is_initialized(self): \"\"\" Determines if the iterator is initialized \"\"\" return self._is_initialized @property def","A dictionary with the fields required for that queue :param retry_on_failure: Boolean that","dtype for feature in item: assert feature in self.proto_fields, 'Feature %s is not","permission notice shall be included in all # copies or substantial portions of","Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic feedable","software and associated documentation files (the \"Software\"), # to deal in the Software","# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell","of the Software. # ============================================================================== \"\"\" Feedable Dataset - Abstract class responsible for","diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object","the item respects the required protofields and casts/pads the item accordingly \"\"\" #","person obtaining # a copy of this software and associated documentation files (the","proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return","notice and this permission notice shall be included in all # copies or","TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self,","has been started \"\"\" return self._is_started @property def is_initialized(self): \"\"\" Determines if the","padding them and converting them to the right dtype for feature in item:","queue :param retry_on_failure: Boolean that indicates to retry querying from the model if","isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod","raise NotImplementedError() @property def is_started(self): \"\"\" Determines if the dataset has been started","to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of","= None self._is_started = False self._is_initialized = False self._is_closing = False def __del__(self):","containing the proto-fields and generation methods :param cluster_config: Optional. If set, the cluster","abc import ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import","self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default if","*args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self,","TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self, *args,","False self._is_closing = False def __del__(self): \"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self):","If set, the cluster configuration will be used for distributed training. :type dataset_builder:","@property def is_closing(self): \"\"\" Determines if the dataset is closing \"\"\" return self._is_closing","and to permit persons to whom the Software is # furnished to do","This object is a generic feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\"","the following conditions: # # The above copyright notice and this permission notice","SessionRunHook for the MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops the dataset \"\"\"","they become available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook","retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of a name using item as inout","# furnished to do so, subject to the following conditions: # # The","import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from","the Software, and to permit persons to whom the Software is # furnished","to put the item :param item: A dictionary with the fields required for","self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} #","import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__)","False self._is_initialized = False self._is_closing = False def __del__(self): \"\"\" Destructor \"\"\" self.close()","name of the queue where to put the item :param item: A dictionary","responsible for feeding data inside a model \"\"\" from abc import ABCMeta, abstractmethod","used as default if features are missing self.session = None self.iterator = None","to do so, subject to the following conditions: # # The above copyright","def is_closing(self): \"\"\" Determines if the dataset is closing \"\"\" return self._is_closing @abstractmethod","item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self,","all # copies or substantial portions of the Software. # ============================================================================== \"\"\" Feedable","\"\"\" # Checking all features in items, padding them and converting them to","and flattening # Scalar - Just converting # Fixed Len - Padding and","if the iterator is initialized \"\"\" return self._is_initialized @property def is_closing(self): \"\"\" Determines","An instance of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional.","hereby granted, free of charge, to any person obtaining # a copy of","tornado.concurrent.Future that will be set with the results when they become available \"\"\"","__del__(self): \"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines if the dataset","inside a model \"\"\" from abc import ABCMeta, abstractmethod import logging import numpy","flattening # Scalar - Just converting # Fixed Len - Padding and converting","whom the Software is # furnished to do so, subject to the following","and converting them to the right dtype for feature in item: assert feature","available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook for the","make_session_run_hook(): \"\"\" Builds a SessionRunHook for the MonitoredTrainingSession object \"\"\" def close(self): \"\"\"","isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype)","granted, free of charge, to any person obtaining # a copy of this","\"\"\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation",":param queue_name: The name of the queue where to put the item :param","closing \"\"\" return self._is_closing @abstractmethod def build(self): \"\"\" Builds the dataset \"\"\" raise","the item :param item: A dictionary with the fields required for that queue","documentation files (the \"Software\"), # to deal in the Software without restriction, including","results when they become available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds","dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields","None self.iterator = None self._is_started = False self._is_initialized = False self._is_closing = False","proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to lists if","fields required for that queue :param retry_on_failure: Boolean that indicates to retry querying","Optional. If set, the cluster configuration will be used for distributed training. :type","used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder =","The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self,","{} # Will be used as default if features are missing self.session =","dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes sure the","dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields","is # furnished to do so, subject to the following conditions: # #","and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature],","Converting and flattening # Scalar - Just converting # Fixed Len - Padding","# sell copies of the Software, and to permit persons to whom the","def is_started(self): \"\"\" Determines if the dataset has been started \"\"\" return self._is_started","abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField","- Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature]","outputs of a name using item as inout :param queue_name: The name of","cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and","return self._is_closing @abstractmethod def build(self): \"\"\" Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod","@property def is_initialized(self): \"\"\" Determines if the iterator is initialized \"\"\" return self._is_initialized","\"\"\" from abc import ABCMeta, abstractmethod import logging import numpy as np from","iterator is initialized \"\"\" return self._is_initialized @property def is_closing(self): \"\"\" Determines if the","distribute, sublicense, and/or # sell copies of the Software, and to permit persons","\"\"\" Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\"","Makes sure the item respects the required protofields and casts/pads the item accordingly","when they become available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a","list(item[feature]) # Var Len - Converting and flattening # Scalar - Just converting","set with the results when they become available \"\"\" raise NotImplementedError() @staticmethod def","is encountered. :return: A tornado.concurrent.Future that will be set with the results when","= False def __del__(self): \"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines","def get_feedable_item(self, *args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs)","import ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder,","Dataset - Abstract class responsible for feeding data inside a model \"\"\" from","as default if features are missing self.session = None self.iterator = None self._is_started","the required protofields and casts/pads the item accordingly \"\"\" # Checking all features","item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field,","using item as inout :param queue_name: The name of the queue where to","querying from the model if an error is encountered. :return: A tornado.concurrent.Future that","import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import","a name using item as inout :param queue_name: The name of the queue","def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing","tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes the dataset (and","# Var Len - Converting and flattening # Scalar - Just converting #","sublicense, and/or # sell copies of the Software, and to permit persons to","FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic feedable dataset \"\"\" def __init__(self, dataset_builder,","the dataset has been started \"\"\" return self._is_started @property def is_initialized(self): \"\"\" Determines","\"\"\" raise NotImplementedError() @property def is_started(self): \"\"\" Determines if the dataset has been","item as inout :param queue_name: The name of the queue where to put","Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts the","\"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines if the dataset can support an","\"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes the dataset (and its","ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField,","\"\"\" Determines if the iterator is initialized \"\"\" return self._is_initialized @property def is_closing(self):","with the fields required for that queue :param retry_on_failure: Boolean that indicates to","@property def is_started(self): \"\"\" Determines if the dataset has been started \"\"\" return","Software, and to permit persons to whom the Software is # furnished to","import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is","to the right dtype for feature in item: assert feature in self.proto_fields, 'Feature","\"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines if the dataset can","is initialized \"\"\" return self._is_initialized @property def is_closing(self): \"\"\" Determines if the dataset","build(self): \"\"\" Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self, session): \"\"\"","# copies or substantial portions of the Software. # ============================================================================== \"\"\" Feedable Dataset","copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software,","# NOTICE: Permission is hereby granted, free of charge, to any person obtaining","methods :param cluster_config: Optional. If set, the cluster configuration will be used for","in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to lists","diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields =","is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape:","without restriction, including without limitation the # rights to use, copy, modify, merge,","item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of","Starts the dataset :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session","session): \"\"\" Initializes the dataset (and its iterator) :param session: The TensorFlow session","use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes","Determines if the dataset can support an iterator or if it is a","can_support_iterator(self): \"\"\" Determines if the dataset can support an iterator or if it","the outputs of a name using item as inout :param queue_name: The name","# Fixed Len - Padding and converting if proto_field.dtype is None: continue elif","Determines if the dataset is closing \"\"\" return self._is_closing @abstractmethod def build(self): \"\"\"","from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This","the dataset can support an iterator or if it is a remote (RPC)","furnished to do so, subject to the following conditions: # # The above","and this permission notice shall be included in all # copies or substantial","set): item[feature] = list(item[feature]) # Var Len - Converting and flattening # Scalar","training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config =","= cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as","them to the right dtype for feature in item: assert feature in self.proto_fields,","get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of a name using","the dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts the dataset","FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def","@abstractmethod def start(self, session): \"\"\" Starts the dataset :param session: The TensorFlow session","self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes sure the item respects the required","error is encountered. :return: A tornado.concurrent.Future that will be set with the results","item: assert feature in self.proto_fields, 'Feature %s is not in proto fields.' %","`BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional. If set, the","Abstract class responsible for feeding data inside a model \"\"\" from abc import","elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature],","def can_support_iterator(self): \"\"\" Determines if the dataset can support an iterator or if","is not in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets","the dataset :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\"","BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class","feature in item: assert feature in self.proto_fields, 'Feature %s is not in proto","items, padding them and converting them to the right dtype for feature in","the cluster configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type","# Checking all features in items, padding them and converting them to the","not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape),","for feature in item: assert feature in self.proto_fields, 'Feature %s is not in",":param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation methods :param","self._is_started @property def is_initialized(self): \"\"\" Determines if the iterator is initialized \"\"\" return","modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and","from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER","\"\"\" Determines if the dataset is closing \"\"\" return self._is_closing @abstractmethod def build(self):","\"\"\" Feedable Dataset - Abstract class responsible for feeding data inside a model","Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes","that queue :param retry_on_failure: Boolean that indicates to retry querying from the model","NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts the dataset :param session: The TensorFlow","raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook for the MonitoredTrainingSession object","copies of the Software, and to permit persons to whom the Software is","will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\"","in item: assert feature in self.proto_fields, 'Feature %s is not in proto fields.'","will be set with the results when they become available \"\"\" raise NotImplementedError()","for feeding data inside a model \"\"\" from abc import ABCMeta, abstractmethod import","\"\"\" Initializes the dataset (and its iterator) :param session: The TensorFlow session to","return self._is_started @property def is_initialized(self): \"\"\" Determines if the iterator is initialized \"\"\"","notice shall be included in all # copies or substantial portions of the","get_feedable_item(self, *args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def","@staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook for the MonitoredTrainingSession object \"\"\" def","in all # copies or substantial portions of the Software. # ============================================================================== \"\"\"","if an error is encountered. :return: A tornado.concurrent.Future that will be set with","a model \"\"\" from abc import ABCMeta, abstractmethod import logging import numpy as","proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype)","a remote (RPC) dataset \"\"\" raise NotImplementedError() @property def is_started(self): \"\"\" Determines if","self._is_initialized = False self._is_closing = False def __del__(self): \"\"\" Destructor \"\"\" self.close() @property","shall be included in all # copies or substantial portions of the Software.","accordingly \"\"\" # Checking all features in items, padding them and converting them","item respects the required protofields and casts/pads the item accordingly \"\"\" # Checking","retry querying from the model if an error is encountered. :return: A tornado.concurrent.Future","= {} # Will be used as default if features are missing self.session","them and converting them to the right dtype for feature in item: assert","The above copyright notice and this permission notice shall be included in all","self._is_closing @abstractmethod def build(self): \"\"\" Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod def","session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod","generic feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An","charge, to any person obtaining # a copy of this software and associated","return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes sure the item respects the","self.session = None self.iterator = None self._is_started = False self._is_initialized = False self._is_closing","pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a","return self._is_initialized @property def is_closing(self): \"\"\" Determines if the dataset is closing \"\"\"","the Software. # ============================================================================== \"\"\" Feedable Dataset - Abstract class responsible for feeding","proto-fields and generation methods :param cluster_config: Optional. If set, the cluster configuration will","feature proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature]","%s is not in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting","an error is encountered. :return: A tornado.concurrent.Future that will be set with the","a copy of this software and associated documentation files (the \"Software\"), # to","raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\" return","'Feature %s is not in proto fields.' % feature proto_field = self.proto_fields[feature] #","numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list","item accordingly \"\"\" # Checking all features in items, padding them and converting","\"\"\" Starts the dataset :param session: The TensorFlow session to use. :type session:","is_started(self): \"\"\" Determines if the dataset has been started \"\"\" return self._is_started @property","feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance","default if features are missing self.session = None self.iterator = None self._is_started =","can support an iterator or if it is a remote (RPC) dataset \"\"\"","Just converting # Fixed Len - Padding and converting if proto_field.dtype is None:","= np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning","logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic feedable dataset \"\"\" def","sell copies of the Software, and to permit persons to whom the Software","Copyright 2019 - <NAME> # # NOTICE: Permission is hereby granted, free of","# # The above copyright notice and this permission notice shall be included","BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default if features are","copies or substantial portions of the Software. # ============================================================================== \"\"\" Feedable Dataset -","if it is a remote (RPC) dataset \"\"\" raise NotImplementedError() @property def is_started(self):","None self._is_started = False self._is_initialized = False self._is_closing = False def __del__(self): \"\"\"","= logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic feedable dataset \"\"\"","use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the","NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes the dataset (and its iterator) :param","**kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item):","\"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook for the MonitoredTrainingSession","be used as default if features are missing self.session = None self.iterator =","cluster configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config:","prepare_item(self, item): \"\"\" Makes sure the item respects the required protofields and casts/pads","VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif","of charge, to any person obtaining # a copy of this software and","def start(self, session): \"\"\" Starts the dataset :param session: The TensorFlow session to","dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance of","is_closing(self): \"\"\" Determines if the dataset is closing \"\"\" return self._is_closing @abstractmethod def",":type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\" Calls the","dataset is closing \"\"\" return self._is_closing @abstractmethod def build(self): \"\"\" Builds the dataset","self._is_started = False self._is_initialized = False self._is_closing = False def __del__(self): \"\"\" Destructor","if the dataset is closing \"\"\" return self._is_closing @abstractmethod def build(self): \"\"\" Builds","including without limitation the # rights to use, copy, modify, merge, publish, distribute,","from abc import ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder","- Converting and flattening # Scalar - Just converting # Fixed Len -","elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item","the model if an error is encountered. :return: A tornado.concurrent.Future that will be","casts/pads the item accordingly \"\"\" # Checking all features in items, padding them","if features are missing self.session = None self.iterator = None self._is_started = False","to retry querying from the model if an error is encountered. :return: A","if the dataset has been started \"\"\" return self._is_started @property def is_initialized(self): \"\"\"","Calls the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes","be set with the results when they become available \"\"\" raise NotImplementedError() @staticmethod","if the dataset can support an iterator or if it is a remote","\"\"\" return self._is_started @property def is_initialized(self): \"\"\" Determines if the iterator is initialized","to whom the Software is # furnished to do so, subject to the","False def __del__(self): \"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines if","deal in the Software without restriction, including without limitation the # rights to","A tornado.concurrent.Future that will be set with the results when they become available","be included in all # copies or substantial portions of the Software. #","np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants","or substantial portions of the Software. # ============================================================================== \"\"\" Feedable Dataset - Abstract","= False self._is_initialized = False self._is_closing = False def __del__(self): \"\"\" Destructor \"\"\"","of this software and associated documentation files (the \"Software\"), # to deal in",":type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes the","of the queue where to put the item :param item: A dictionary with","Scalar - Just converting # Fixed Len - Padding and converting if proto_field.dtype","def is_initialized(self): \"\"\" Determines if the iterator is initialized \"\"\" return self._is_initialized @property","the queue where to put the item :param item: A dictionary with the","def build(self): \"\"\" Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self, session):","proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature] =","converting them to the right dtype for feature in item: assert feature in","the MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops the dataset \"\"\" self._is_closing =","retry_on_failure: Boolean that indicates to retry querying from the model if an error","do so, subject to the following conditions: # # The above copyright notice","\"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes sure the item respects",":type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config","session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def","\"\"\" raise NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts the dataset :param session:","dataset has been started \"\"\" return self._is_started @property def is_initialized(self): \"\"\" Determines if","# Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var","a SessionRunHook for the MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops the dataset","for the MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops the dataset \"\"\" self._is_closing","Determines if the dataset has been started \"\"\" return self._is_started @property def is_initialized(self):","**kwargs) def prepare_item(self, item): \"\"\" Makes sure the item respects the required protofields","= None self.iterator = None self._is_started = False self._is_initialized = False self._is_closing =","configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig","np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature]","from the model if an error is encountered. :return: A tornado.concurrent.Future that will","permit persons to whom the Software is # furnished to do so, subject","copy of this software and associated documentation files (the \"Software\"), # to deal","# Will be used as default if features are missing self.session = None","without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense,","model if an error is encountered. :return: A tornado.concurrent.Future that will be set","queue where to put the item :param item: A dictionary with the fields","item): \"\"\" Makes sure the item respects the required protofields and casts/pads the","required for that queue :param retry_on_failure: Boolean that indicates to retry querying from","The name of the queue where to put the item :param item: A","= list(item[feature]) # Var Len - Converting and flattening # Scalar - Just","or if it is a remote (RPC) dataset \"\"\" raise NotImplementedError() @property def","protofields and casts/pads the item accordingly \"\"\" # Checking all features in items,","of a name using item as inout :param queue_name: The name of the","are missing self.session = None self.iterator = None self._is_started = False self._is_initialized =","is a generic feedable dataset \"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param","diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features =","Software. # ============================================================================== \"\"\" Feedable Dataset - Abstract class responsible for feeding data","dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts the dataset :param","queue_name: The name of the queue where to put the item :param item:","included in all # copies or substantial portions of the Software. # ==============================================================================","= self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature])","# The above copyright notice and this permission notice shall be included in","@abstractmethod def build(self): \"\"\" Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self,","the dataset is closing \"\"\" return self._is_closing @abstractmethod def build(self): \"\"\" Builds the","above copyright notice and this permission notice shall be included in all #","proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True,","become available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook for","self.iterator = None self._is_started = False self._is_initialized = False self._is_closing = False def","NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\" Builds a SessionRunHook for the MonitoredTrainingSession object \"\"\"","raise NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts the dataset :param session: The","return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs","indicates to retry querying from the model if an error is encountered. :return:","Initializes the dataset (and its iterator) :param session: The TensorFlow session to use.","Computes the outputs of a name using item as inout :param queue_name: The","all features in items, padding them and converting them to the right dtype","associated documentation files (the \"Software\"), # to deal in the Software without restriction,","and associated documentation files (the \"Software\"), # to deal in the Software without","in the Software without restriction, including without limitation the # rights to use,","dictionary with the fields required for that queue :param retry_on_failure: Boolean that indicates","with the results when they become available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook():","converting # Fixed Len - Padding and converting if proto_field.dtype is None: continue","np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item","Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] =","session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs):","- <NAME> # # NOTICE: Permission is hereby granted, free of charge, to","def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of a name","self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used","instance of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional. If","this permission notice shall be included in all # copies or substantial portions","Determines if the iterator is initialized \"\"\" return self._is_initialized @property def is_closing(self): \"\"\"","dataset can support an iterator or if it is a remote (RPC) dataset","the dataset_builder get_feedable_item \"\"\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \"\"\" Makes sure","remote (RPC) dataset \"\"\" raise NotImplementedError() @property def is_started(self): \"\"\" Determines if the","Checking all features in items, padding them and converting them to the right","# a copy of this software and associated documentation files (the \"Software\"), #","to any person obtaining # a copy of this software and associated documentation","is hereby granted, free of charge, to any person obtaining # a copy","# Scalar - Just converting # Fixed Len - Padding and converting if","that indicates to retry querying from the model if an error is encountered.","def prepare_item(self, item): \"\"\" Makes sure the item respects the required protofields and","<NAME> # # NOTICE: Permission is hereby granted, free of charge, to any","inout :param queue_name: The name of the queue where to put the item","============================================================================== # Copyright 2019 - <NAME> # # NOTICE: Permission is hereby granted,","to deal in the Software without restriction, including without limitation the # rights","following conditions: # # The above copyright notice and this permission notice shall","logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model","of the Software, and to permit persons to whom the Software is #","item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of a name using item as","the fields required for that queue :param retry_on_failure: Boolean that indicates to retry","\"\"\" def __init__(self, dataset_builder, cluster_config=None): \"\"\" Constructor :param dataset_builder: An instance of `BaseBuilder`","= False self._is_closing = False def __del__(self): \"\"\" Destructor \"\"\" self.close() @property def","features in items, padding them and converting them to the right dtype for","None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature]","the item accordingly \"\"\" # Checking all features in items, padding them and","in self.proto_fields, 'Feature %s is not in proto fields.' % feature proto_field =","item[feature] = list(item[feature]) # Var Len - Converting and flattening # Scalar -",":type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields)","free of charge, to any person obtaining # a copy of this software","============================================================================== \"\"\" Feedable Dataset - Abstract class responsible for feeding data inside a","respects the required protofields and casts/pads the item accordingly \"\"\" # Checking all","<filename>diplomacy_research/models/datasets/feedable_dataset.py # ============================================================================== # Copyright 2019 - <NAME> # # NOTICE: Permission is","Will be used as default if features are missing self.session = None self.iterator","(the \"Software\"), # to deal in the Software without restriction, including without limitation","and/or # sell copies of the Software, and to permit persons to whom","put the item :param item: A dictionary with the fields required for that","started \"\"\" return self._is_started @property def is_initialized(self): \"\"\" Determines if the iterator is","be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \"\"\" self.dataset_builder","MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops the dataset \"\"\" self._is_closing = True","Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len","# Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\"","rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies","Feedable Dataset - Abstract class responsible for feeding data inside a model \"\"\"","LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\" This object is a generic feedable dataset","is a remote (RPC) dataset \"\"\" raise NotImplementedError() @property def is_started(self): \"\"\" Determines","session): \"\"\" Starts the dataset :param session: The TensorFlow session to use. :type","to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\"","assert feature in self.proto_fields, 'Feature %s is not in proto fields.' % feature","def __del__(self): \"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\" Determines if the","raise NotImplementedError() @abstractmethod def initialize(self, session): \"\"\" Initializes the dataset (and its iterator)","self._is_closing = False def __del__(self): \"\"\" Destructor \"\"\" self.close() @property def can_support_iterator(self): \"\"\"","generation methods :param cluster_config: Optional. If set, the cluster configuration will be used","copyright notice and this permission notice shall be included in all # copies","\"\"\" Builds the dataset \"\"\" raise NotImplementedError() @abstractmethod def start(self, session): \"\"\" Starts","item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the",":param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError()","\"\"\" Makes sure the item respects the required protofields and casts/pads the item","feature in self.proto_fields, 'Feature %s is not in proto fields.' % feature proto_field","NOTICE: Permission is hereby granted, free of charge, to any person obtaining #","proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not","**kwargs): \"\"\" Computes the outputs of a name using item as inout :param","where to put the item :param item: A dictionary with the fields required","missing self.session = None self.iterator = None self._is_started = False self._is_initialized = False","queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of a name using item","iterator) :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \"\"\" raise","start(self, session): \"\"\" Starts the dataset :param session: The TensorFlow session to use.","feeding data inside a model \"\"\" from abc import ABCMeta, abstractmethod import logging","if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting and flattening","@abstractmethod def initialize(self, session): \"\"\" Initializes the dataset (and its iterator) :param session:","lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting and","def make_session_run_hook(): \"\"\" Builds a SessionRunHook for the MonitoredTrainingSession object \"\"\" def close(self):","sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len -","\"\"\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\" Calls the dataset_builder get_feedable_item \"\"\"","if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif","= BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default if features","Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation methods","limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or","features are missing self.session = None self.iterator = None self._is_started = False self._is_initialized","as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list #","use. :type session: tensorflow.python.client.session.Session \"\"\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \"\"\" Calls","@abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \"\"\" Computes the outputs of a","the results when they become available \"\"\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \"\"\"","% feature proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set):","\"\"\" Builds a SessionRunHook for the MonitoredTrainingSession object \"\"\" def close(self): \"\"\" Stops","merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to","the Software is # furnished to do so, subject to the following conditions:","subject to the following conditions: # # The above copyright notice and this","= np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField):"],"string":"[\n \"as inout :param queue_name: The name of the queue where to put the\",\n \"FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\"\",\n \"it is a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\"\",\n \"fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature],\",\n \"session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder\",\n \"diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER =\",\n \"sure the item respects the required protofields and casts/pads the item accordingly \\\"\\\"\\\"\",\n \"Len - Converting and flattening # Scalar - Just converting # Fixed Len\",\n \"2019 - <NAME> # # NOTICE: Permission is hereby granted, free of charge,\",\n \"files (the \\\"Software\\\"), # to deal in the Software without restriction, including without\",\n \"np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name, item,\",\n \"# Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic\",\n \"session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session):\",\n \"Software without restriction, including without limitation the # rights to use, copy, modify,\",\n \"substantial portions of the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class\",\n \"self.proto_fields, 'Feature %s is not in proto fields.' % feature proto_field = self.proto_fields[feature]\",\n \"proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs):\",\n \"item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) #\",\n \"the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or #\",\n \":param cluster_config: Optional. If set, the cluster configuration will be used for distributed\",\n \"right dtype for feature in item: assert feature in self.proto_fields, 'Feature %s is\",\n \"isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting and flattening #\",\n \"__init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the\",\n \"distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config\",\n \"Fixed Len - Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field,\",\n \"encountered. :return: A tornado.concurrent.Future that will be set with the results when they\",\n \"# # NOTICE: Permission is hereby granted, free of charge, to any person\",\n \"and casts/pads the item accordingly \\\"\\\"\\\" # Checking all features in items, padding\",\n \"been started \\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if the iterator\",\n \"# ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class responsible for feeding data inside\",\n \"class responsible for feeding data inside a model \\\"\\\"\\\" from abc import ABCMeta,\",\n \"- Just converting # Fixed Len - Padding and converting if proto_field.dtype is\",\n \"persons to whom the Software is # furnished to do so, subject to\",\n \"publish, distribute, sublicense, and/or # sell copies of the Software, and to permit\",\n \"\\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if the dataset is closing\",\n \"self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if the dataset is closing \\\"\\\"\\\" return\",\n \"conditions: # # The above copyright notice and this permission notice shall be\",\n \"session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the dataset\",\n \"= dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will\",\n \"\\\"\\\"\\\" Computes the outputs of a name using item as inout :param queue_name:\",\n \"# Copyright 2019 - <NAME> # # NOTICE: Permission is hereby granted, free\",\n \"obtaining # a copy of this software and associated documentation files (the \\\"Software\\\"),\",\n \"to permit persons to whom the Software is # furnished to do so,\",\n \"Len - Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField):\",\n \"@property def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can support an iterator or\",\n \"the Software without restriction, including without limitation the # rights to use, copy,\",\n \"# to deal in the Software without restriction, including without limitation the #\",\n \"item :param item: A dictionary with the fields required for that queue :param\",\n \"for that queue :param retry_on_failure: Boolean that indicates to retry querying from the\",\n \"# ============================================================================== # Copyright 2019 - <NAME> # # NOTICE: Permission is hereby\",\n \"that will be set with the results when they become available \\\"\\\"\\\" raise\",\n \"any person obtaining # a copy of this software and associated documentation files\",\n \"set, the cluster configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder\",\n \"= np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name,\",\n \":param retry_on_failure: Boolean that indicates to retry querying from the model if an\",\n \"of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional. If set,\",\n \"dataset :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise\",\n \"\\\"\\\"\\\" Determines if the dataset can support an iterator or if it is\",\n \"Permission is hereby granted, free of charge, to any person obtaining # a\",\n \"for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder\",\n \"so, subject to the following conditions: # # The above copyright notice and\",\n \"get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure the item\",\n \"model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod import logging import numpy as np\",\n \"the iterator is initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if\",\n \"class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic feedable dataset \\\"\\\"\\\" def __init__(self,\",\n \"- Abstract class responsible for feeding data inside a model \\\"\\\"\\\" from abc\",\n \":return: A tornado.concurrent.Future that will be set with the results when they become\",\n \"dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be\",\n \"name using item as inout :param queue_name: The name of the queue where\",\n \"NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if the dataset has been started \\\"\\\"\\\"\",\n \"the proto-fields and generation methods :param cluster_config: Optional. If set, the cluster configuration\",\n \"continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] =\",\n \"required protofields and casts/pads the item accordingly \\\"\\\"\\\" # Checking all features in\",\n \"to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\"\",\n \"the right dtype for feature in item: assert feature in self.proto_fields, 'Feature %s\",\n \"proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] =\",\n \"to the following conditions: # # The above copyright notice and this permission\",\n \"NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args,\",\n \"dataset (and its iterator) :param session: The TensorFlow session to use. :type session:\",\n \"Builds a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops the\",\n \":param item: A dictionary with the fields required for that queue :param retry_on_failure:\",\n \"(RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if the dataset\",\n \"portions of the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class responsible\",\n \"cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default\",\n \"restriction, including without limitation the # rights to use, copy, modify, merge, publish,\",\n \"data inside a model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod import logging import\",\n \"this software and associated documentation files (the \\\"Software\\\"), # to deal in the\",\n \"def initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and its iterator) :param session: The\",\n \"\\\"\\\"\\\" Determines if the dataset has been started \\\"\\\"\\\" return self._is_started @property def\",\n \"initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and its iterator) :param session: The TensorFlow\",\n \"The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def\",\n \"item: A dictionary with the fields required for that queue :param retry_on_failure: Boolean\",\n \"(and its iterator) :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session\",\n \"and generation methods :param cluster_config: Optional. If set, the cluster configuration will be\",\n \"tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item\",\n \"\\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError()\",\n \"\\\"Software\\\"), # to deal in the Software without restriction, including without limitation the\",\n \"Software is # furnished to do so, subject to the following conditions: #\",\n \"cluster_config: Optional. If set, the cluster configuration will be used for distributed training.\",\n \"self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can support an iterator\",\n \"self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) #\",\n \"a generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder:\",\n \"cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features\",\n \"dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if the dataset has\",\n \"is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\"\",\n \"dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config:\",\n \"initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if the dataset is\",\n \"VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta):\",\n \"an iterator or if it is a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError()\",\n \"\\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {}\",\n \"support an iterator or if it is a remote (RPC) dataset \\\"\\\"\\\" raise\",\n \"its iterator) :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\"\",\n \"to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting\",\n \"object is a generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor\",\n \"the dataset (and its iterator) :param session: The TensorFlow session to use. :type\",\n \"Var Len - Converting and flattening # Scalar - Just converting # Fixed\",\n \"self.default_features = {} # Will be used as default if features are missing\",\n \"iterator or if it is a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property\",\n \"not in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to\",\n \"converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten()\",\n \"elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature],\",\n \"Boolean that indicates to retry querying from the model if an error is\",\n \"\\\"\\\"\\\" This object is a generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None):\",\n \"in items, padding them and converting them to the right dtype for feature\",\n \"Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can support\",\n \"is_initialized(self): \\\"\\\"\\\" Determines if the iterator is initialized \\\"\\\"\\\" return self._is_initialized @property def\",\n \"A dictionary with the fields required for that queue :param retry_on_failure: Boolean that\",\n \"dtype for feature in item: assert feature in self.proto_fields, 'Feature %s is not\",\n \"permission notice shall be included in all # copies or substantial portions of\",\n \"Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic feedable\",\n \"software and associated documentation files (the \\\"Software\\\"), # to deal in the Software\",\n \"# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell\",\n \"of the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class responsible for\",\n \"diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object\",\n \"the item respects the required protofields and casts/pads the item accordingly \\\"\\\"\\\" #\",\n \"person obtaining # a copy of this software and associated documentation files (the\",\n \"proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return\",\n \"notice and this permission notice shall be included in all # copies or\",\n \"TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self,\",\n \"has been started \\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if the\",\n \"padding them and converting them to the right dtype for feature in item:\",\n \"queue :param retry_on_failure: Boolean that indicates to retry querying from the model if\",\n \"isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod\",\n \"raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if the dataset has been started\",\n \"to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of\",\n \"= None self._is_started = False self._is_initialized = False self._is_closing = False def __del__(self):\",\n \"containing the proto-fields and generation methods :param cluster_config: Optional. If set, the cluster\",\n \"abc import ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import\",\n \"self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default if\",\n \"*args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self,\",\n \"TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args,\",\n \"False self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self):\",\n \"If set, the cluster configuration will be used for distributed training. :type dataset_builder:\",\n \"@property def is_closing(self): \\\"\\\"\\\" Determines if the dataset is closing \\\"\\\"\\\" return self._is_closing\",\n \"and to permit persons to whom the Software is # furnished to do\",\n \"This object is a generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\"\",\n \"the following conditions: # # The above copyright notice and this permission notice\",\n \"SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops the dataset \\\"\\\"\\\"\",\n \"they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook\",\n \"retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of a name using item as inout\",\n \"# furnished to do so, subject to the following conditions: # # The\",\n \"import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from\",\n \"the Software, and to permit persons to whom the Software is # furnished\",\n \"to put the item :param item: A dictionary with the fields required for\",\n \"self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} #\",\n \"import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__)\",\n \"False self._is_initialized = False self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close()\",\n \"name of the queue where to put the item :param item: A dictionary\",\n \"responsible for feeding data inside a model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod\",\n \"used as default if features are missing self.session = None self.iterator = None\",\n \"to do so, subject to the following conditions: # # The above copyright\",\n \"def is_closing(self): \\\"\\\"\\\" Determines if the dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod\",\n \"item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self,\",\n \"all # copies or substantial portions of the Software. # ============================================================================== \\\"\\\"\\\" Feedable\",\n \"\\\"\\\"\\\" # Checking all features in items, padding them and converting them to\",\n \"and flattening # Scalar - Just converting # Fixed Len - Padding and\",\n \"if the iterator is initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines\",\n \"An instance of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional.\",\n \"hereby granted, free of charge, to any person obtaining # a copy of\",\n \"tornado.concurrent.Future that will be set with the results when they become available \\\"\\\"\\\"\",\n \"__del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset\",\n \"inside a model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod import logging import numpy\",\n \"flattening # Scalar - Just converting # Fixed Len - Padding and converting\",\n \"whom the Software is # furnished to do so, subject to the following\",\n \"and converting them to the right dtype for feature in item: assert feature\",\n \"available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the\",\n \"make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\"\",\n \"isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype)\",\n \"granted, free of charge, to any person obtaining # a copy of this\",\n \"\\\"\\\"\\\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation\",\n \":param queue_name: The name of the queue where to put the item :param\",\n \"closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise\",\n \"the item :param item: A dictionary with the fields required for that queue\",\n \"documentation files (the \\\"Software\\\"), # to deal in the Software without restriction, including\",\n \"results when they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds\",\n \"dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields\",\n \"None self.iterator = None self._is_started = False self._is_initialized = False self._is_closing = False\",\n \"proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to lists if\",\n \"fields required for that queue :param retry_on_failure: Boolean that indicates to retry querying\",\n \"Optional. If set, the cluster configuration will be used for distributed training. :type\",\n \"used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder =\",\n \"The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self,\",\n \"{} # Will be used as default if features are missing self.session =\",\n \"dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure the\",\n \"dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields\",\n \"is # furnished to do so, subject to the following conditions: # #\",\n \"and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature],\",\n \"Converting and flattening # Scalar - Just converting # Fixed Len - Padding\",\n \"# sell copies of the Software, and to permit persons to whom the\",\n \"def is_started(self): \\\"\\\"\\\" Determines if the dataset has been started \\\"\\\"\\\" return self._is_started\",\n \"abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField\",\n \"- Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature]\",\n \"outputs of a name using item as inout :param queue_name: The name of\",\n \"cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and\",\n \"return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod\",\n \"@property def is_initialized(self): \\\"\\\"\\\" Determines if the iterator is initialized \\\"\\\"\\\" return self._is_initialized\",\n \"\\\"\\\"\\\" from abc import ABCMeta, abstractmethod import logging import numpy as np from\",\n \"iterator is initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if the\",\n \"distribute, sublicense, and/or # sell copies of the Software, and to permit persons\",\n \"\\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\"\",\n \"Makes sure the item respects the required protofields and casts/pads the item accordingly\",\n \"when they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a\",\n \"list(item[feature]) # Var Len - Converting and flattening # Scalar - Just converting\",\n \"set with the results when they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def\",\n \"is encountered. :return: A tornado.concurrent.Future that will be set with the results when\",\n \"= False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines\",\n \"def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs)\",\n \"import ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder,\",\n \"Dataset - Abstract class responsible for feeding data inside a model \\\"\\\"\\\" from\",\n \"as default if features are missing self.session = None self.iterator = None self._is_started\",\n \"the required protofields and casts/pads the item accordingly \\\"\\\"\\\" # Checking all features\",\n \"item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field,\",\n \"using item as inout :param queue_name: The name of the queue where to\",\n \"querying from the model if an error is encountered. :return: A tornado.concurrent.Future that\",\n \"import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import\",\n \"a name using item as inout :param queue_name: The name of the queue\",\n \"def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance of `BaseBuilder` containing\",\n \"tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and\",\n \"# Var Len - Converting and flattening # Scalar - Just converting #\",\n \"sublicense, and/or # sell copies of the Software, and to permit persons to\",\n \"FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder,\",\n \"the dataset has been started \\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines\",\n \"\\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if the dataset has been\",\n \"item as inout :param queue_name: The name of the queue where to put\",\n \"Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts the\",\n \"\\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can support an\",\n \"\\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and its\",\n \"ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField,\",\n \"\\\"\\\"\\\" Determines if the iterator is initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self):\",\n \"with the fields required for that queue :param retry_on_failure: Boolean that indicates to\",\n \"@property def is_started(self): \\\"\\\"\\\" Determines if the dataset has been started \\\"\\\"\\\" return\",\n \"Software, and to permit persons to whom the Software is # furnished to\",\n \"import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is\",\n \"to the right dtype for feature in item: assert feature in self.proto_fields, 'Feature\",\n \"\\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can\",\n \"is initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if the dataset\",\n \"build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\"\",\n \"# copies or substantial portions of the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset\",\n \"copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software,\",\n \"# NOTICE: Permission is hereby granted, free of charge, to any person obtaining\",\n \"methods :param cluster_config: Optional. If set, the cluster configuration will be used for\",\n \"in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets to lists\",\n \"diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields =\",\n \"is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape:\",\n \"without restriction, including without limitation the # rights to use, copy, modify, merge,\",\n \"item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of\",\n \"Starts the dataset :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session\",\n \"session): \\\"\\\"\\\" Initializes the dataset (and its iterator) :param session: The TensorFlow session\",\n \"use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes\",\n \"Determines if the dataset can support an iterator or if it is a\",\n \"can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can support an iterator or if it\",\n \"the outputs of a name using item as inout :param queue_name: The name\",\n \"# Fixed Len - Padding and converting if proto_field.dtype is None: continue elif\",\n \"Determines if the dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\"\",\n \"from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This\",\n \"the dataset can support an iterator or if it is a remote (RPC)\",\n \"furnished to do so, subject to the following conditions: # # The above\",\n \"and this permission notice shall be included in all # copies or substantial\",\n \"set): item[feature] = list(item[feature]) # Var Len - Converting and flattening # Scalar\",\n \"training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config =\",\n \"= cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as\",\n \"them to the right dtype for feature in item: assert feature in self.proto_fields,\",\n \"get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of a name using\",\n \"the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts the dataset\",\n \"FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def\",\n \"@abstractmethod def start(self, session): \\\"\\\"\\\" Starts the dataset :param session: The TensorFlow session\",\n \"self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure the item respects the required\",\n \"error is encountered. :return: A tornado.concurrent.Future that will be set with the results\",\n \"item: assert feature in self.proto_fields, 'Feature %s is not in proto fields.' %\",\n \"`BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional. If set, the\",\n \"Abstract class responsible for feeding data inside a model \\\"\\\"\\\" from abc import\",\n \"elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature],\",\n \"def can_support_iterator(self): \\\"\\\"\\\" Determines if the dataset can support an iterator or if\",\n \"is not in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting sets\",\n \"the dataset :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\"\",\n \"BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class\",\n \"feature in item: assert feature in self.proto_fields, 'Feature %s is not in proto\",\n \"items, padding them and converting them to the right dtype for feature in\",\n \"the cluster configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type\",\n \"# Checking all features in items, padding them and converting them to the\",\n \"not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape),\",\n \"for feature in item: assert feature in self.proto_fields, 'Feature %s is not in\",\n \":param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation methods :param\",\n \"self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if the iterator is initialized \\\"\\\"\\\" return\",\n \"modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and\",\n \"from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER\",\n \"\\\"\\\"\\\" Determines if the dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self):\",\n \"\\\"\\\"\\\" Feedable Dataset - Abstract class responsible for feeding data inside a model\",\n \"Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes\",\n \"that queue :param retry_on_failure: Boolean that indicates to retry querying from the model\",\n \"NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts the dataset :param session: The TensorFlow\",\n \"raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession object\",\n \"copies of the Software, and to permit persons to whom the Software is\",\n \"will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\"\",\n \"in item: assert feature in self.proto_fields, 'Feature %s is not in proto fields.'\",\n \"will be set with the results when they become available \\\"\\\"\\\" raise NotImplementedError()\",\n \"for feeding data inside a model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod import\",\n \"\\\"\\\"\\\" Initializes the dataset (and its iterator) :param session: The TensorFlow session to\",\n \"return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if the iterator is initialized \\\"\\\"\\\"\",\n \"notice shall be included in all # copies or substantial portions of the\",\n \"get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def\",\n \"@staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def\",\n \"in all # copies or substantial portions of the Software. # ============================================================================== \\\"\\\"\\\"\",\n \"if an error is encountered. :return: A tornado.concurrent.Future that will be set with\",\n \"a model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod import logging import numpy as\",\n \"proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype)\",\n \"a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if\",\n \"self._is_initialized = False self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property\",\n \"shall be included in all # copies or substantial portions of the Software.\",\n \"accordingly \\\"\\\"\\\" # Checking all features in items, padding them and converting them\",\n \"item respects the required protofields and casts/pads the item accordingly \\\"\\\"\\\" # Checking\",\n \"retry querying from the model if an error is encountered. :return: A tornado.concurrent.Future\",\n \"= {} # Will be used as default if features are missing self.session\",\n \"them and converting them to the right dtype for feature in item: assert\",\n \"The above copyright notice and this permission notice shall be included in all\",\n \"self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def\",\n \"session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod\",\n \"generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An\",\n \"charge, to any person obtaining # a copy of this software and associated\",\n \"return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure the item respects the\",\n \"self.session = None self.iterator = None self._is_started = False self._is_initialized = False self._is_closing\",\n \"pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a\",\n \"return self._is_initialized @property def is_closing(self): \\\"\\\"\\\" Determines if the dataset is closing \\\"\\\"\\\"\",\n \"the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class responsible for feeding\",\n \"proto-fields and generation methods :param cluster_config: Optional. If set, the cluster configuration will\",\n \"feature proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature]\",\n \"%s is not in proto fields.' % feature proto_field = self.proto_fields[feature] # Converting\",\n \"an error is encountered. :return: A tornado.concurrent.Future that will be set with the\",\n \"a copy of this software and associated documentation files (the \\\"Software\\\"), # to\",\n \"raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return\",\n \"'Feature %s is not in proto fields.' % feature proto_field = self.proto_fields[feature] #\",\n \"numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list\",\n \"item accordingly \\\"\\\"\\\" # Checking all features in items, padding them and converting\",\n \"\\\"\\\"\\\" Starts the dataset :param session: The TensorFlow session to use. :type session:\",\n \"is_started(self): \\\"\\\"\\\" Determines if the dataset has been started \\\"\\\"\\\" return self._is_started @property\",\n \"feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance\",\n \"default if features are missing self.session = None self.iterator = None self._is_started =\",\n \"can support an iterator or if it is a remote (RPC) dataset \\\"\\\"\\\"\",\n \"Just converting # Fixed Len - Padding and converting if proto_field.dtype is None:\",\n \"= np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning\",\n \"logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic feedable dataset \\\"\\\"\\\" def\",\n \"sell copies of the Software, and to permit persons to whom the Software\",\n \"Copyright 2019 - <NAME> # # NOTICE: Permission is hereby granted, free of\",\n \"# # The above copyright notice and this permission notice shall be included\",\n \"BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default if features are\",\n \"copies or substantial portions of the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset -\",\n \"if it is a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self):\",\n \"None self._is_started = False self._is_initialized = False self._is_closing = False def __del__(self): \\\"\\\"\\\"\",\n \"= logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic feedable dataset \\\"\\\"\\\"\",\n \"use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the\",\n \"NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and its iterator) :param\",\n \"**kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item):\",\n \"\\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession\",\n \"be used as default if features are missing self.session = None self.iterator =\",\n \"cluster configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config:\",\n \"prepare_item(self, item): \\\"\\\"\\\" Makes sure the item respects the required protofields and casts/pads\",\n \"VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif\",\n \"of charge, to any person obtaining # a copy of this software and\",\n \"def start(self, session): \\\"\\\"\\\" Starts the dataset :param session: The TensorFlow session to\",\n \"dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance of\",\n \"is_closing(self): \\\"\\\"\\\" Determines if the dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def\",\n \":type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the\",\n \"dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset\",\n \"self._is_started = False self._is_initialized = False self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor\",\n \"if the dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds\",\n \"including without limitation the # rights to use, copy, modify, merge, publish, distribute,\",\n \"from abc import ABCMeta, abstractmethod import logging import numpy as np from diplomacy_research.models.datasets.base_builder\",\n \"- Converting and flattening # Scalar - Just converting # Fixed Len -\",\n \"elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item return item\",\n \"the model if an error is encountered. :return: A tornado.concurrent.Future that will be\",\n \"casts/pads the item accordingly \\\"\\\"\\\" # Checking all features in items, padding them\",\n \"if features are missing self.session = None self.iterator = None self._is_started = False\",\n \"to retry querying from the model if an error is encountered. :return: A\",\n \"if the dataset has been started \\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\"\",\n \"Calls the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes\",\n \"be set with the results when they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod\",\n \"if the dataset can support an iterator or if it is a remote\",\n \"\\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if the iterator is initialized\",\n \"to whom the Software is # furnished to do so, subject to the\",\n \"False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if\",\n \"deal in the Software without restriction, including without limitation the # rights to\",\n \"A tornado.concurrent.Future that will be set with the results when they become available\",\n \"be included in all # copies or substantial portions of the Software. #\",\n \"np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list # Constants\",\n \"or substantial portions of the Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract\",\n \"= False self._is_initialized = False self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\"\",\n \"of this software and associated documentation files (the \\\"Software\\\"), # to deal in\",\n \":type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the\",\n \"of the queue where to put the item :param item: A dictionary with\",\n \"Scalar - Just converting # Fixed Len - Padding and converting if proto_field.dtype\",\n \"def is_initialized(self): \\\"\\\"\\\" Determines if the iterator is initialized \\\"\\\"\\\" return self._is_initialized @property\",\n \"the queue where to put the item :param item: A dictionary with the\",\n \"def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session):\",\n \"proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature] =\",\n \"converting them to the right dtype for feature in item: assert feature in\",\n \"the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops the dataset \\\"\\\"\\\" self._is_closing =\",\n \"retry_on_failure: Boolean that indicates to retry querying from the model if an error\",\n \"do so, subject to the following conditions: # # The above copyright notice\",\n \"\\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure the item respects\",\n \":type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config\",\n \"session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def\",\n \"\\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts the dataset :param session:\",\n \"dataset has been started \\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if\",\n \"# Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var\",\n \"a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops the dataset\",\n \"for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops the dataset \\\"\\\"\\\" self._is_closing\",\n \"Determines if the dataset has been started \\\"\\\"\\\" return self._is_started @property def is_initialized(self):\",\n \"**kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure the item respects the required protofields\",\n \"= None self.iterator = None self._is_started = False self._is_initialized = False self._is_closing =\",\n \"configuration will be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig\",\n \"np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature]\",\n \"from the model if an error is encountered. :return: A tornado.concurrent.Future that will\",\n \"permit persons to whom the Software is # furnished to do so, subject\",\n \"copy of this software and associated documentation files (the \\\"Software\\\"), # to deal\",\n \"# Will be used as default if features are missing self.session = None\",\n \"without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense,\",\n \"model if an error is encountered. :return: A tornado.concurrent.Future that will be set\",\n \"queue where to put the item :param item: A dictionary with the fields\",\n \"item): \\\"\\\"\\\" Makes sure the item respects the required protofields and casts/pads the\",\n \"required for that queue :param retry_on_failure: Boolean that indicates to retry querying from\",\n \"The name of the queue where to put the item :param item: A\",\n \"= list(item[feature]) # Var Len - Converting and flattening # Scalar - Just\",\n \"or if it is a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def\",\n \"protofields and casts/pads the item accordingly \\\"\\\"\\\" # Checking all features in items,\",\n \"of a name using item as inout :param queue_name: The name of the\",\n \"are missing self.session = None self.iterator = None self._is_started = False self._is_initialized =\",\n \"is a generic feedable dataset \\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param\",\n \"diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features =\",\n \"Software. # ============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class responsible for feeding data\",\n \"dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts the dataset :param\",\n \"queue_name: The name of the queue where to put the item :param item:\",\n \"included in all # copies or substantial portions of the Software. # ==============================================================================\",\n \"= self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature])\",\n \"# The above copyright notice and this permission notice shall be included in\",\n \"@abstractmethod def build(self): \\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self,\",\n \"the dataset is closing \\\"\\\"\\\" return self._is_closing @abstractmethod def build(self): \\\"\\\"\\\" Builds the\",\n \"above copyright notice and this permission notice shall be included in all #\",\n \"proto_field.shape), proto_field.dtype) # Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True,\",\n \"become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for\",\n \"self.iterator = None self._is_started = False self._is_initialized = False self._is_closing = False def\",\n \"NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\"\",\n \"raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts the dataset :param session: The\",\n \"return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs\",\n \"indicates to retry querying from the model if an error is encountered. :return:\",\n \"Initializes the dataset (and its iterator) :param session: The TensorFlow session to use.\",\n \"Computes the outputs of a name using item as inout :param queue_name: The\",\n \"all features in items, padding them and converting them to the right dtype\",\n \"associated documentation files (the \\\"Software\\\"), # to deal in the Software without restriction,\",\n \"and associated documentation files (the \\\"Software\\\"), # to deal in the Software without\",\n \"in the Software without restriction, including without limitation the # rights to use,\",\n \"dictionary with the fields required for that queue :param retry_on_failure: Boolean that indicates\",\n \"with the results when they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook():\",\n \"converting # Fixed Len - Padding and converting if proto_field.dtype is None: continue\",\n \"np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField): item[feature] = np.array(pad_list(item[feature], proto_field.shape), proto_field.dtype) # Returning item\",\n \"Padding and converting if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] =\",\n \"session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs):\",\n \"- <NAME> # # NOTICE: Permission is hereby granted, free of charge, to\",\n \"def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of a name\",\n \"self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used\",\n \"instance of `BaseBuilder` containing the proto-fields and generation methods :param cluster_config: Optional. If\",\n \"this permission notice shall be included in all # copies or substantial portions\",\n \"Determines if the iterator is initialized \\\"\\\"\\\" return self._is_initialized @property def is_closing(self): \\\"\\\"\\\"\",\n \"dataset can support an iterator or if it is a remote (RPC) dataset\",\n \"the dataset_builder get_feedable_item \\\"\\\"\\\" return self.dataset_builder.get_feedable_item(*args, **kwargs) def prepare_item(self, item): \\\"\\\"\\\" Makes sure\",\n \"remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines if the\",\n \"Checking all features in items, padding them and converting them to the right\",\n \"# a copy of this software and associated documentation files (the \\\"Software\\\"), #\",\n \"to any person obtaining # a copy of this software and associated documentation\",\n \"is hereby granted, free of charge, to any person obtaining # a copy\",\n \"# Scalar - Just converting # Fixed Len - Padding and converting if\",\n \"that indicates to retry querying from the model if an error is encountered.\",\n \"def prepare_item(self, item): \\\"\\\"\\\" Makes sure the item respects the required protofields and\",\n \"<NAME> # # NOTICE: Permission is hereby granted, free of charge, to any\",\n \"inout :param queue_name: The name of the queue where to put the item\",\n \"============================================================================== # Copyright 2019 - <NAME> # # NOTICE: Permission is hereby granted,\",\n \"to deal in the Software without restriction, including without limitation the # rights\",\n \"following conditions: # # The above copyright notice and this permission notice shall\",\n \"logging import numpy as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model\",\n \"of the Software, and to permit persons to whom the Software is #\",\n \"item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of a name using item as\",\n \"the fields required for that queue :param retry_on_failure: Boolean that indicates to retry\",\n \"\\\"\\\"\\\" def __init__(self, dataset_builder, cluster_config=None): \\\"\\\"\\\" Constructor :param dataset_builder: An instance of `BaseBuilder`\",\n \"= False self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def\",\n \"features in items, padding them and converting them to the right dtype for\",\n \"None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature]\",\n \"the item accordingly \\\"\\\"\\\" # Checking all features in items, padding them and\",\n \"in self.proto_fields, 'Feature %s is not in proto fields.' % feature proto_field =\",\n \"item[feature] = list(item[feature]) # Var Len - Converting and flattening # Scalar -\",\n \":type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder = dataset_builder self.cluster_config = cluster_config self.proto_fields = BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields)\",\n \"free of charge, to any person obtaining # a copy of this software\",\n \"============================================================================== \\\"\\\"\\\" Feedable Dataset - Abstract class responsible for feeding data inside a\",\n \"respects the required protofields and casts/pads the item accordingly \\\"\\\"\\\" # Checking all\",\n \"<filename>diplomacy_research/models/datasets/feedable_dataset.py # ============================================================================== # Copyright 2019 - <NAME> # # NOTICE: Permission is\",\n \"Will be used as default if features are missing self.session = None self.iterator\",\n \"(the \\\"Software\\\"), # to deal in the Software without restriction, including without limitation\",\n \"and/or # sell copies of the Software, and to permit persons to whom\",\n \"put the item :param item: A dictionary with the fields required for that\",\n \"started \\\"\\\"\\\" return self._is_started @property def is_initialized(self): \\\"\\\"\\\" Determines if the iterator is\",\n \"be used for distributed training. :type dataset_builder: diplomacy_research.models.datasets.base_builder.BaseBuilder :type cluster_config: diplomacy_research.utils.cluster.ClusterConfig \\\"\\\"\\\" self.dataset_builder\",\n \"MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops the dataset \\\"\\\"\\\" self._is_closing = True\",\n \"Converting sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len\",\n \"# Returning item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\"\",\n \"rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies\",\n \"Feedable Dataset - Abstract class responsible for feeding data inside a model \\\"\\\"\\\"\",\n \"LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \\\"\\\"\\\" This object is a generic feedable dataset\",\n \"is a remote (RPC) dataset \\\"\\\"\\\" raise NotImplementedError() @property def is_started(self): \\\"\\\"\\\" Determines\",\n \"session): \\\"\\\"\\\" Starts the dataset :param session: The TensorFlow session to use. :type\",\n \"to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\"\",\n \"assert feature in self.proto_fields, 'Feature %s is not in proto fields.' % feature\",\n \"def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\" Determines if the\",\n \"raise NotImplementedError() @abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and its iterator)\",\n \"self._is_closing = False def __del__(self): \\\"\\\"\\\" Destructor \\\"\\\"\\\" self.close() @property def can_support_iterator(self): \\\"\\\"\\\"\",\n \"generation methods :param cluster_config: Optional. If set, the cluster configuration will be used\",\n \"copyright notice and this permission notice shall be included in all # copies\",\n \"\\\"\\\"\\\" Builds the dataset \\\"\\\"\\\" raise NotImplementedError() @abstractmethod def start(self, session): \\\"\\\"\\\" Starts\",\n \"item return item @abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the\",\n \":param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError()\",\n \"\\\"\\\"\\\" Makes sure the item respects the required protofields and casts/pads the item\",\n \"feature in self.proto_fields, 'Feature %s is not in proto fields.' % feature proto_field\",\n \"NOTICE: Permission is hereby granted, free of charge, to any person obtaining #\",\n \"proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif not\",\n \"**kwargs): \\\"\\\"\\\" Computes the outputs of a name using item as inout :param\",\n \"where to put the item :param item: A dictionary with the fields required\",\n \"missing self.session = None self.iterator = None self._is_started = False self._is_initialized = False\",\n \"queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of a name using item\",\n \"iterator) :param session: The TensorFlow session to use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise\",\n \"start(self, session): \\\"\\\"\\\" Starts the dataset :param session: The TensorFlow session to use.\",\n \"feeding data inside a model \\\"\\\"\\\" from abc import ABCMeta, abstractmethod import logging\",\n \"if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting and flattening\",\n \"@abstractmethod def initialize(self, session): \\\"\\\"\\\" Initializes the dataset (and its iterator) :param session:\",\n \"lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len - Converting and\",\n \"def make_session_run_hook(): \\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self):\",\n \"sets to lists if isinstance(item[feature], set): item[feature] = list(item[feature]) # Var Len -\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls the dataset_builder get_feedable_item \\\"\\\"\\\"\",\n \"if proto_field.dtype is None: continue elif isinstance(proto_field, VarProtoField): item[feature] = np.array(item[feature], proto_field.dtype).flatten() elif\",\n \"= BaseBuilder.parse_sparse_fields(dataset_builder.proto_fields) self.default_features = {} # Will be used as default if features\",\n \"Constructor :param dataset_builder: An instance of `BaseBuilder` containing the proto-fields and generation methods\",\n \"limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\",\n \"features are missing self.session = None self.iterator = None self._is_started = False self._is_initialized\",\n \"as np from diplomacy_research.models.datasets.base_builder import BaseBuilder, VarProtoField, FixedProtoField from diplomacy_research.utils.model import pad_list #\",\n \"use. :type session: tensorflow.python.client.session.Session \\\"\\\"\\\" raise NotImplementedError() def get_feedable_item(self, *args, **kwargs): \\\"\\\"\\\" Calls\",\n \"@abstractmethod def get_results(self, queue_name, item, retry_on_failure=True, **kwargs): \\\"\\\"\\\" Computes the outputs of a\",\n \"the results when they become available \\\"\\\"\\\" raise NotImplementedError() @staticmethod def make_session_run_hook(): \\\"\\\"\\\"\",\n \"% feature proto_field = self.proto_fields[feature] # Converting sets to lists if isinstance(item[feature], set):\",\n \"\\\"\\\"\\\" Builds a SessionRunHook for the MonitoredTrainingSession object \\\"\\\"\\\" def close(self): \\\"\\\"\\\" Stops\",\n \"merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to\",\n \"the Software is # furnished to do so, subject to the following conditions:\",\n \"subject to the following conditions: # # The above copyright notice and this\",\n \"= np.array(item[feature], proto_field.dtype).flatten() elif not proto_field.shape: item[feature] = np.array(item[feature], proto_field.dtype) elif isinstance(proto_field, FixedProtoField):\"\n]","isConversational":false}}},{"rowIdx":12864189,"cells":{"ngram":{"kind":"list like","value":["def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] =","params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour']","try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]),","Exception as ex: print(str(ex)) return shapes def text(obj): texts = list() if texts_args","len(data) >= 2: return [data[0], data[1]] return None def colour(obj): data = params.split(',')","from PIL import ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb']","7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj):","[int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg =","'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] =","[(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >=","7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6:","except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list()","obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]]","= lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if len(line_arg) >=","[data[0], data[1]] return None def colour(obj): data = params.split(',') if (params := obj.get('colour'))",">= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >=","params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] =","bg = None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >=","int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)])","else None return None def album(obj): data = params.split(',') if (params := obj.get('album'))","= rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g')","int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])),","2: return [data[0], data[1]] return None def colour(obj): data = params.split(',') if (params","PIL import ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] =","None: return op if 0 <= (op := int(a)) <= 255 else None","ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args :=","if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b')","(int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None if not obj.get('store')","# ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg =","= color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles')","2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args =",">= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)])","shapes def text(obj): texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';')","a := o if (o := obj.get('a')) else None: return op if 0","shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]),",":= obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for","obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if","if data and len(data) >= 2: return [data[0], data[1]] return None def colour(obj):","text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]),","height = int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj): rgb","int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append(","data = params.split(',') if (params := obj.get('album')) else None if data and len(data)","opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour'] = colour(obj)",">= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >=","else None if data and len(data) >= 2: return [data[0], data[1]] return None","= list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf'","= point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else","int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]),","= opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour'] =","colour(obj) params['album'] = album(obj) return params def opacity(obj): if a := o if","= int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300')","def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if bg: if","if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf, 30)]) return","album(obj) return params def opacity(obj): if a := o if (o := obj.get('a'))","len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None","for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append(","params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj)","7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]),","not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0],","params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] =","obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args","(int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]),","int(data[6]))] return None def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',')","if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]),","False params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj): if a","int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj): rgb = (obj.get('rgb')","list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf","len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf, 30)]) return texts","int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return lines","data and len(data) >= 2: return [data[0], data[1]] return None def colour(obj): data","in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]),","= ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity']","op if 0 <= (op := int(a)) <= 255 else None return None","int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]),","bg_args else: return None def point(obj): return None if not obj.get('point') else float(obj.get('point'))","def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or","= (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1]","ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg) >=","= int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj): rgb =","(int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]),","shape): shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i","obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height","elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if","import ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj)","lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in","'0'] return bg_args else: return None def point(obj): return None if not obj.get('point')","i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])),","ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf,","album(obj): data = params.split(',') if (params := obj.get('album')) else None if data and","= store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o","line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])),","if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return","int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append(","int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex))","len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0,","params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original'))","return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0]","try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])),","int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return","'400') height = int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj):","None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args","rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else",":= obj.get('a')) else None: return op if 0 <= (op := int(a)) <=","return params def opacity(obj): if a := o if (o := obj.get('a')) else",":= obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',')","size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] =","(params := obj.get('colour')) else None if data and len(data) >= 7: return [int(data[0]),","if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]),","shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10:","int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])),","else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args","(0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0,","= i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]),","rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r')","for i in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])),","data = params.split(',') if (params := obj.get('colour')) else None if data and len(data)","lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg =","int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])),","'300') return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] =","255 else None return None def album(obj): data = params.split(',') if (params :=","float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height')","if a := o if (o := obj.get('a')) else None: return op if","or obj.get('h') or '300') return width, height def color(obj): rgb = (obj.get('rgb') or","data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))]","ex: print(str(ex)) return shapes def text(obj): texts = list() if texts_args := obj.get('texts'):","print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape):",">= 2: return [data[0], data[1]] return None def colour(obj): data = params.split(',') if","0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0,","if (o := obj.get('a')) else None: return op if 0 <= (op :=","= list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args:","0, 0)]) except Exception as ex: print(str(ex)) return shapes def text(obj): texts =","def colour(obj): data = params.split(',') if (params := obj.get('colour')) else None if data","int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)])","= None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2:","except Exception as ex: print(str(ex)) return shapes def text(obj): texts = list() if","'200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if","point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else False","int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300') return","ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',')","i in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]),",">= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))])",">= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4:","params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses')","params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if","bg_args = [bg[0], '0'] return bg_args else: return None def point(obj): return None","return [data[0], data[1]] return None def colour(obj): data = params.split(',') if (params :=","len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as","obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]),","4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except","= '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if","o if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj)","else None: return op if 0 <= (op := int(a)) <= 255 else",":= obj.get('album')) else None if data and len(data) >= 2: return [data[0], data[1]]","obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i","analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj)","line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store']","if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if","def line(obj): lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for","params['original'] = o if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album']",">= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args","= i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf,","(int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0,","return None def album(obj): data = params.split(',') if (params := obj.get('album')) else None","data[1]] return None def colour(obj): data = params.split(',') if (params := obj.get('colour')) else","= [bg[0], '0'] return bg_args else: return None def point(obj): return None if","= rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj):",":= int(a)) <= 255 else None return None def album(obj): data = params.split(',')","None def point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj): width","4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex))","(int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0,","dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj,","obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300') return width, height","if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf =","text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in","int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'): line_args =","'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj)","None def album(obj): data = params.split(',') if (params := obj.get('album')) else None if","if (params := obj.get('album')) else None if data and len(data) >= 2: return","return None def colour(obj): data = params.split(',') if (params := obj.get('colour')) else None","10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif","width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if","return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg","def text(obj): texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') #","list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try:","def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r')","return bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else:","obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if",":= o if (o := obj.get('a')) else None: return op if 0 <=","len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >=","None def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if bg:","params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj,","bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0],","ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf,","= size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles']","elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception",":= obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return params def","ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point']","'/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg)","obj.get('a')) else None: return op if 0 <= (op := int(a)) <= 255","obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if","obj.get('album')) else None if data and len(data) >= 2: return [data[0], data[1]] return","def point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj): width =","shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in","params def opacity(obj): if a := o if (o := obj.get('a')) else None:","= rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b')","def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';')","if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg","obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] =","texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]),","else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]),","None if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]),","as ex: print(str(ex)) return shapes def text(obj): texts = list() if texts_args :=","and len(data) >= 2: return [data[0], data[1]] return None def colour(obj): data =","if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args =","(0, 0, 0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape):","texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]),","= dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] =","= [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0], '0']",">= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)])","return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'):","opacity(obj): if a := o if (o := obj.get('a')) else None: return op","(int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]),","(params := obj.get('album')) else None if data and len(data) >= 2: return [data[0],","or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1]","text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])),","if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g')","else False params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj): if","int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except","(0, 0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes","[bg[0], '0'] return bg_args else: return None def point(obj): return None if not","= text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] =","None if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w')","rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not","(op := int(a)) <= 255 else None return None def album(obj): data =","rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines",">= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def","return shapes def text(obj): texts = list() if texts_args := obj.get('texts'): text_args =","text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2],","i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))])","(o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return params","int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4:",">= 1: bg_args = [bg[0], '0'] return bg_args else: return None def point(obj):","texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf =","or '300') return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0]",":= obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg = i.split(',')","return None def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if","bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else: return","0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes =","color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts']","int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return lines def","0 <= (op := int(a)) <= 255 else None return None def album(obj):","len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else: return None def","for i in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]),","int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0,","obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args :=","int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]),","int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])),","if 0 <= (op := int(a)) <= 255 else None return None def","Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if","else None if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])),","params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original']","height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not","<= 255 else None return None def album(obj): data = params.split(',') if (params","1: bg_args = [bg[0], '0'] return bg_args else: return None def point(obj): return","int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])),","bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args","texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf'","store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg)","colour(obj): data = params.split(',') if (params := obj.get('colour')) else None if data and","len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0,","rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else","shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg)","in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]),","text(obj): texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf","None if data and len(data) >= 2: return [data[0], data[1]] return None def","len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1:","def album(obj): data = params.split(',') if (params := obj.get('album')) else None if data","or '400') height = int(obj.get('height') or obj.get('h') or '300') return width, height def","line(obj): lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i","int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]),","return op if 0 <= (op := int(a)) <= 255 else None return","if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >=","ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for","(0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes def text(obj): texts","int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as ex:","shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg)","if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines =","int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'): line_args","obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2])","rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def","= list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args:","= colour(obj) params['album'] = album(obj) return params def opacity(obj): if a := o","int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None if not","not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400')","obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj):","return None if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or","= '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg) >= 7:","len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args)","return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args","o if (o := obj.get('a')) else None: return op if 0 <= (op","i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]),","print(str(ex)) return shapes def text(obj): texts = list() if texts_args := obj.get('texts'): text_args","def opacity(obj): if a := o if (o := obj.get('a')) else None: return","= texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args:","None return None def album(obj): data = params.split(',') if (params := obj.get('album')) else","return None def point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj):","i in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2],","int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]),","obj.get('h') or '300') return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',')","params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj): if a :=","else: return None def point(obj): return None if not obj.get('point') else float(obj.get('point')) def","[bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return",":= obj.get('colour')) else None if data and len(data) >= 7: return [int(data[0]), (int(data[1]),","= line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj)","0, 0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes","in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]),","else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return","elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0,","size(obj): width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h')","0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0),","int(a)) <= 255 else None return None def album(obj): data = params.split(',') if","and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return","elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0,","= album(obj) return params def opacity(obj): if a := o if (o :=","'/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]),","0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes def","<= (op := int(a)) <= 255 else None return None def album(obj): data","params.split(',') if (params := obj.get('album')) else None if data and len(data) >= 2:","rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not","params['album'] = album(obj) return params def opacity(obj): if a := o if (o","int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])),","= o if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] =","not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2]","int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';')","not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return","= params.split(',') if (params := obj.get('colour')) else None if data and len(data) >=","else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height =","bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg)","or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300') return width,",">= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex:","else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2]","int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]),","int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None if","list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try:","= i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))])","lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args =","int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]),","as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args","int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0,","point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width')","int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7:","(int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None","shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]),","ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] =","not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list()","i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif","text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o","texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg","= params.split(',') if (params := obj.get('album')) else None if data and len(data) >=","= shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >=","params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses']","if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg","None def colour(obj): data = params.split(',') if (params := obj.get('colour')) else None if","7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif","color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else","if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif","(obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] =","(int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]),","(o := obj.get('a')) else None: return op if 0 <= (op := int(a))","shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception","int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0),","0)]) except Exception as ex: print(str(ex)) return shapes def text(obj): texts = list()","[(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as","int(data[5]), int(data[6]))] return None def store(obj): bg = None if not obj.get('store') else","shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg =","int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])),","if (params := obj.get('colour')) else None if data and len(data) >= 7: return","if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else: return None","int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj,","if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or","width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or","params.split(',') if (params := obj.get('colour')) else None if data and len(data) >= 7:","len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg)","30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf, 30)])","6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3:","= ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj)","line_args = lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if len(line_arg)","shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg)","obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if","store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o :=","lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]),","line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]),","lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7:","text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2],","0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes def text(obj):","ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines']","[(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >=","lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return","len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]),","return bg_args else: return None def point(obj): return None if not obj.get('point') else","obj.get('colour')) else None if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]),","if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg)"],"string":"[\n \"def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] =\",\n \"params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour']\",\n \"try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]),\",\n \"Exception as ex: print(str(ex)) return shapes def text(obj): texts = list() if texts_args\",\n \"len(data) >= 2: return [data[0], data[1]] return None def colour(obj): data = params.split(',')\",\n \"from PIL import ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb']\",\n \"7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj):\",\n \"[int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg =\",\n \"'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] =\",\n \"[(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >=\",\n \"7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6:\",\n \"except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list()\",\n \"obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]]\",\n \"= lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if len(line_arg) >=\",\n \"[data[0], data[1]] return None def colour(obj): data = params.split(',') if (params := obj.get('colour'))\",\n \">= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >=\",\n \"params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] =\",\n \"bg = None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >=\",\n \"int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)])\",\n \"else None return None def album(obj): data = params.split(',') if (params := obj.get('album'))\",\n \"= rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g')\",\n \"int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])),\",\n \"2: return [data[0], data[1]] return None def colour(obj): data = params.split(',') if (params\",\n \"PIL import ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] =\",\n \"None: return op if 0 <= (op := int(a)) <= 255 else None\",\n \"ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args :=\",\n \"if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b')\",\n \"(int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None if not obj.get('store')\",\n \"# ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg =\",\n \"= color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles')\",\n \"2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args =\",\n \">= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)])\",\n \"shapes def text(obj): texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';')\",\n \"a := o if (o := obj.get('a')) else None: return op if 0\",\n \"shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]),\",\n \":= obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for\",\n \"obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if\",\n \"if data and len(data) >= 2: return [data[0], data[1]] return None def colour(obj):\",\n \"text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]),\",\n \"height = int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj): rgb\",\n \"int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append(\",\n \"data = params.split(',') if (params := obj.get('album')) else None if data and len(data)\",\n \"opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour'] = colour(obj)\",\n \">= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >=\",\n \"else None if data and len(data) >= 2: return [data[0], data[1]] return None\",\n \"= list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf'\",\n \"= point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else\",\n \"int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]),\",\n \"= opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour'] =\",\n \"colour(obj) params['album'] = album(obj) return params def opacity(obj): if a := o if\",\n \"= int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300')\",\n \"def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if bg: if\",\n \"if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf, 30)]) return\",\n \"album(obj) return params def opacity(obj): if a := o if (o := obj.get('a'))\",\n \"len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None\",\n \"for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append(\",\n \"params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj)\",\n \"7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]),\",\n \"not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0],\",\n \"params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] =\",\n \"obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args\",\n \"(int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]),\",\n \"int(data[6]))] return None def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',')\",\n \"if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]),\",\n \"False params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj): if a\",\n \"int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj): rgb = (obj.get('rgb')\",\n \"list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf\",\n \"len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf, 30)]) return texts\",\n \"int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return lines\",\n \"data and len(data) >= 2: return [data[0], data[1]] return None def colour(obj): data\",\n \"in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]),\",\n \"= ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity']\",\n \"op if 0 <= (op := int(a)) <= 255 else None return None\",\n \"int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]),\",\n \"bg_args else: return None def point(obj): return None if not obj.get('point') else float(obj.get('point'))\",\n \"def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or\",\n \"= (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1]\",\n \"ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg) >=\",\n \"= int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj): rgb =\",\n \"(int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]),\",\n \"shape): shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i\",\n \"obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height\",\n \"elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if\",\n \"import ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj)\",\n \"lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in\",\n \"'0'] return bg_args else: return None def point(obj): return None if not obj.get('point')\",\n \"i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])),\",\n \"ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf,\",\n \"album(obj): data = params.split(',') if (params := obj.get('album')) else None if data and\",\n \"= store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o\",\n \"line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])),\",\n \"if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return\",\n \"int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append(\",\n \"int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex))\",\n \"len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0,\",\n \"params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original'))\",\n \"return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0]\",\n \"try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])),\",\n \"int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return\",\n \"'400') height = int(obj.get('height') or obj.get('h') or '300') return width, height def color(obj):\",\n \"None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args\",\n \"rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else\",\n \":= obj.get('a')) else None: return op if 0 <= (op := int(a)) <=\",\n \"return params def opacity(obj): if a := o if (o := obj.get('a')) else\",\n \":= obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',')\",\n \"size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] =\",\n \"(params := obj.get('colour')) else None if data and len(data) >= 7: return [int(data[0]),\",\n \"if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]),\",\n \"shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10:\",\n \"int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])),\",\n \"else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args\",\n \"(0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0,\",\n \"= i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]),\",\n \"rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r')\",\n \"for i in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])),\",\n \"data = params.split(',') if (params := obj.get('colour')) else None if data and len(data)\",\n \"lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg =\",\n \"int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])),\",\n \"'300') return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] =\",\n \"255 else None return None def album(obj): data = params.split(',') if (params :=\",\n \"float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height')\",\n \"if a := o if (o := obj.get('a')) else None: return op if\",\n \"or obj.get('h') or '300') return width, height def color(obj): rgb = (obj.get('rgb') or\",\n \"data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))]\",\n \"ex: print(str(ex)) return shapes def text(obj): texts = list() if texts_args := obj.get('texts'):\",\n \"print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape):\",\n \">= 2: return [data[0], data[1]] return None def colour(obj): data = params.split(',') if\",\n \"0, 0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0,\",\n \"if (o := obj.get('a')) else None: return op if 0 <= (op :=\",\n \"= list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args:\",\n \"0, 0)]) except Exception as ex: print(str(ex)) return shapes def text(obj): texts =\",\n \"def colour(obj): data = params.split(',') if (params := obj.get('colour')) else None if data\",\n \"int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)])\",\n \"= None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2:\",\n \"except Exception as ex: print(str(ex)) return shapes def text(obj): texts = list() if\",\n \"'200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if\",\n \"point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else False\",\n \"int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300') return\",\n \"ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',')\",\n \"i in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]),\",\n \">= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))])\",\n \">= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4:\",\n \"params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses')\",\n \"params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if\",\n \"bg_args = [bg[0], '0'] return bg_args else: return None def point(obj): return None\",\n \"return [data[0], data[1]] return None def colour(obj): data = params.split(',') if (params :=\",\n \"len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as\",\n \"obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]),\",\n \"4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except\",\n \"= '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if\",\n \"o if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj)\",\n \"else None: return op if 0 <= (op := int(a)) <= 255 else\",\n \":= obj.get('album')) else None if data and len(data) >= 2: return [data[0], data[1]]\",\n \"obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i\",\n \"analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj)\",\n \"line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store']\",\n \"if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if\",\n \"def line(obj): lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for\",\n \"params['original'] = o if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album']\",\n \">= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args\",\n \"= i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf,\",\n \"(int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0,\",\n \"return None def album(obj): data = params.split(',') if (params := obj.get('album')) else None\",\n \"data[1]] return None def colour(obj): data = params.split(',') if (params := obj.get('colour')) else\",\n \"= [bg[0], '0'] return bg_args else: return None def point(obj): return None if\",\n \"= rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj):\",\n \":= int(a)) <= 255 else None return None def album(obj): data = params.split(',')\",\n \"None def point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj): width\",\n \"4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex))\",\n \"(int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0,\",\n \"dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj,\",\n \"obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300') return width, height\",\n \"if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf =\",\n \"text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in\",\n \"int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'): line_args =\",\n \"'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj)\",\n \"None def album(obj): data = params.split(',') if (params := obj.get('album')) else None if\",\n \"if (params := obj.get('album')) else None if data and len(data) >= 2: return\",\n \"return None def colour(obj): data = params.split(',') if (params := obj.get('colour')) else None\",\n \"10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif\",\n \"width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if\",\n \"return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg\",\n \"def text(obj): texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') #\",\n \"list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try:\",\n \"def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r')\",\n \"return bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else:\",\n \"obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if\",\n \":= o if (o := obj.get('a')) else None: return op if 0 <=\",\n \"len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >=\",\n \"None def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if bg:\",\n \"params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj,\",\n \"bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0],\",\n \"ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf,\",\n \"= size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles']\",\n \"elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception\",\n \":= obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return params def\",\n \"ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point']\",\n \"'/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg)\",\n \"obj.get('a')) else None: return op if 0 <= (op := int(a)) <= 255\",\n \"obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if\",\n \"obj.get('album')) else None if data and len(data) >= 2: return [data[0], data[1]] return\",\n \"def point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj): width =\",\n \"shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in\",\n \"params def opacity(obj): if a := o if (o := obj.get('a')) else None:\",\n \"= rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b')\",\n \"def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';')\",\n \"if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg\",\n \"obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] =\",\n \"texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]),\",\n \"else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]),\",\n \"None if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]),\",\n \"as ex: print(str(ex)) return shapes def text(obj): texts = list() if texts_args :=\",\n \"and len(data) >= 2: return [data[0], data[1]] return None def colour(obj): data =\",\n \"if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args =\",\n \"(0, 0, 0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape):\",\n \"texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]),\",\n \"= dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses'] =\",\n \"= [bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0], '0']\",\n \">= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)])\",\n \"return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'):\",\n \"opacity(obj): if a := o if (o := obj.get('a')) else None: return op\",\n \"(int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]),\",\n \"(params := obj.get('album')) else None if data and len(data) >= 2: return [data[0],\",\n \"or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1]\",\n \"text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])),\",\n \"if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g')\",\n \"else False params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj): if\",\n \"int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except\",\n \"(0, 0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes\",\n \"[bg[0], '0'] return bg_args else: return None def point(obj): return None if not\",\n \"= text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] =\",\n \"None if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w')\",\n \"rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not\",\n \"(op := int(a)) <= 255 else None return None def album(obj): data =\",\n \"rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines\",\n \">= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def\",\n \"return shapes def text(obj): texts = list() if texts_args := obj.get('texts'): text_args =\",\n \"text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2],\",\n \"i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))])\",\n \"(o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return params\",\n \"int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg) >= 4:\",\n \">= 1: bg_args = [bg[0], '0'] return bg_args else: return None def point(obj):\",\n \"texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf =\",\n \"or '300') return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0]\",\n \":= obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try: line_arg = i.split(',')\",\n \"return None def store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if\",\n \"bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else: return\",\n \"0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes =\",\n \"color(obj) params['lines'] = line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts']\",\n \"int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return lines def\",\n \"0 <= (op := int(a)) <= 255 else None return None def album(obj):\",\n \"len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else: return None def\",\n \"for i in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]),\",\n \"int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0,\",\n \"obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args :=\",\n \"int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]),\",\n \"int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])),\",\n \"if 0 <= (op := int(a)) <= 255 else None return None def\",\n \"Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if\",\n \"else None if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])),\",\n \"params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original']\",\n \"height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not\",\n \"<= 255 else None return None def album(obj): data = params.split(',') if (params\",\n \"1: bg_args = [bg[0], '0'] return bg_args else: return None def point(obj): return\",\n \"int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])),\",\n \"bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args\",\n \"texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf'\",\n \"store(obj): bg = None if not obj.get('store') else obj.get('store').split(',') if bg: if len(bg)\",\n \"colour(obj): data = params.split(',') if (params := obj.get('colour')) else None if data and\",\n \"len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0,\",\n \"rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else\",\n \"shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg)\",\n \"in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]),\",\n \"text(obj): texts = list() if texts_args := obj.get('texts'): text_args = texts_args.split(';') # ttf\",\n \"None if data and len(data) >= 2: return [data[0], data[1]] return None def\",\n \"len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >= 1:\",\n \"def album(obj): data = params.split(',') if (params := obj.get('album')) else None if data\",\n \"or '400') height = int(obj.get('height') or obj.get('h') or '300') return width, height def\",\n \"line(obj): lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i\",\n \"int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]),\",\n \"return op if 0 <= (op := int(a)) <= 255 else None return\",\n \"if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg) >=\",\n \"ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for\",\n \"(0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes def text(obj): texts\",\n \"int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as ex:\",\n \"shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif len(shape_arg)\",\n \"if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines =\",\n \"int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'): line_args\",\n \"obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2])\",\n \"rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def\",\n \"= list() if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args:\",\n \"= colour(obj) params['album'] = album(obj) return params def opacity(obj): if a := o\",\n \"int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None if not\",\n \"not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400')\",\n \"obj.get('original')) else False params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj):\",\n \"return None if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or\",\n \"= '/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg) >= 7:\",\n \"len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args)\",\n \"return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args\",\n \"o if (o := obj.get('a')) else None: return op if 0 <= (op\",\n \"i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]),\",\n \"print(str(ex)) return shapes def text(obj): texts = list() if texts_args := obj.get('texts'): text_args\",\n \"def opacity(obj): if a := o if (o := obj.get('a')) else None: return\",\n \"= texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args:\",\n \"None return None def album(obj): data = params.split(',') if (params := obj.get('album')) else\",\n \"return None def point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj):\",\n \"i in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2],\",\n \"int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]),\",\n \"obj.get('h') or '300') return width, height def color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',')\",\n \"params['colour'] = colour(obj) params['album'] = album(obj) return params def opacity(obj): if a :=\",\n \"else: return None def point(obj): return None if not obj.get('point') else float(obj.get('point')) def\",\n \"[bg[0], bg[1]] return bg_args if len(bg) >= 1: bg_args = [bg[0], '0'] return\",\n \":= obj.get('colour')) else None if data and len(data) >= 7: return [int(data[0]), (int(data[1]),\",\n \"= line(obj) params['ellipses'] = ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj)\",\n \"0, 0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes\",\n \"in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]),\",\n \"else obj.get('store').split(',') if bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return\",\n \"elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0,\",\n \"size(obj): width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h')\",\n \"0)]) elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0),\",\n \"int(a)) <= 255 else None return None def album(obj): data = params.split(',') if\",\n \"and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return\",\n \"elif len(shape_arg) >= 4: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0,\",\n \"= album(obj) return params def opacity(obj): if a := o if (o :=\",\n \"'/project/Image/font.ttf' for i in text_args: text_arg = i.split(',') if len(text_arg) >= 7: texts.append([(int(text_arg[0]),\",\n \"0, 0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes def\",\n \"<= (op := int(a)) <= 255 else None return None def album(obj): data\",\n \"params.split(',') if (params := obj.get('album')) else None if data and len(data) >= 2:\",\n \"rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not\",\n \"params['album'] = album(obj) return params def opacity(obj): if a := o if (o\",\n \"int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])),\",\n \"= o if (o := obj.get('original')) else False params['colour'] = colour(obj) params['album'] =\",\n \"not obj.get('r') else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2]\",\n \"int(rgb[2]) def line(obj): lines = list() if lines_args := obj.get('lines'): line_args = lines_args.split(';')\",\n \"not obj.get('g') else obj.get('g') rgb[2] = rgb[2] if not obj.get('b') else obj.get('b') return\",\n \"= params.split(',') if (params := obj.get('colour')) else None if data and len(data) >=\",\n \"else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or '400') height =\",\n \"bg: if len(bg) >= 2: bg_args = [bg[0], bg[1]] return bg_args if len(bg)\",\n \"or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or '300') return width,\",\n \">= 4: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex:\",\n \"else obj.get('r') rgb[1] = rgb[1] if not obj.get('g') else obj.get('g') rgb[2] = rgb[2]\",\n \"int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg) >= 6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]),\",\n \"int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None if\",\n \"list() if lines_args := obj.get('lines'): line_args = lines_args.split(';') for i in line_args: try:\",\n \"= i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))])\",\n \"lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args := obj.get(shape): shape_args =\",\n \"int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]),\",\n \"as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj, shape): shapes = list() if shapes_args\",\n \"int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0,\",\n \"point(obj): return None if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width')\",\n \"int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7:\",\n \"(int(data[1]), int(data[2]), int(data[3])), (int(data[4]), int(data[5]), int(data[6]))] return None def store(obj): bg = None\",\n \"shape_arg = i.split(',') if len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]),\",\n \"ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] =\",\n \"not obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list()\",\n \"i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif\",\n \"text(obj) params['store'] = store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o\",\n \"texts_args.split(';') # ttf = '/home/ahri/code/AhriImage/Image/font.ttf' ttf = '/project/Image/font.ttf' for i in text_args: text_arg\",\n \"= params.split(',') if (params := obj.get('album')) else None if data and len(data) >=\",\n \"= shapes_args.split(';') for i in shape_args: try: shape_arg = i.split(',') if len(shape_arg) >=\",\n \"params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] = line(obj) params['ellipses']\",\n \"if shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg\",\n \"None def colour(obj): data = params.split(',') if (params := obj.get('colour')) else None if\",\n \"7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (0, 0, 0)]) elif\",\n \"color(obj): rgb = (obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else\",\n \"if len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif\",\n \"(obj.get('rgb') or '200,200,200').split(',') rgb[0] = rgb[0] if not obj.get('r') else obj.get('r') rgb[1] =\",\n \"(int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >= 7: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]),\",\n \"(o := obj.get('a')) else None: return op if 0 <= (op := int(a))\",\n \"shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception\",\n \"int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0),\",\n \"0)]) except Exception as ex: print(str(ex)) return shapes def text(obj): texts = list()\",\n \"[(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (0, 0, 0), (0, 0, 0)]) except Exception as\",\n \"int(data[5]), int(data[6]))] return None def store(obj): bg = None if not obj.get('store') else\",\n \"shapes_args := obj.get(shape): shape_args = shapes_args.split(';') for i in shape_args: try: shape_arg =\",\n \"int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])),\",\n \"if (params := obj.get('colour')) else None if data and len(data) >= 7: return\",\n \"if len(bg) >= 1: bg_args = [bg[0], '0'] return bg_args else: return None\",\n \"int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return lines def ellipse_and_rectangle(obj,\",\n \"if not obj.get('point') else float(obj.get('point')) def size(obj): width = int(obj.get('width') or obj.get('w') or\",\n \"width = int(obj.get('width') or obj.get('w') or '400') height = int(obj.get('height') or obj.get('h') or\",\n \"params.split(',') if (params := obj.get('colour')) else None if data and len(data) >= 7:\",\n \"len(text_arg) >= 7: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, int(text_arg[6]))]) elif len(text_arg)\",\n \"30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (0, 0, 0), ImageFont.truetype(ttf, 30)])\",\n \"6: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3:\",\n \"= ellipse_and_rectangle(obj, 'ellipses') params['rectangles'] = ellipse_and_rectangle(obj, 'rectangles') params['texts'] = text(obj) params['store'] = store(obj)\",\n \"line_args = lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if len(line_arg)\",\n \"shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg)\",\n \"obj.get('b') else obj.get('b') return int(rgb[0]), int(rgb[1]), int(rgb[2]) def line(obj): lines = list() if\",\n \"store(obj) params['point'] = point(obj) params['opacity'] = opacity(obj) params['original'] = o if (o :=\",\n \"lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg) >= 4: lines.append([(int(line_arg[0]), int(line_arg[1]),\",\n \"line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]),\",\n \"lines_args.split(';') for i in line_args: try: line_arg = i.split(',') if len(line_arg) >= 7:\",\n \"text_arg[2], (int(text_arg[3]), int(text_arg[3]), int(text_arg[5])), ImageFont.truetype(ttf, 30)]) if len(text_args) >= 3: texts.append([(int(text_arg[0]), int(text_arg[1])), text_arg[2],\",\n \"0), (0, 0, 0)]) except Exception as ex: print(str(ex)) return shapes def text(obj):\",\n \"ImageFont def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines']\",\n \"[(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]), int(shape_arg[9]))]) elif len(shape_arg) >=\",\n \"lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (0, 0, 0)]) except Exception as ex: print(str(ex)) return\",\n \"len(shape_arg) >= 10: shapes.append( [(int(shape_arg[0]), int(shape_arg[1]), int(shape_arg[2]), int(shape_arg[3])), (int(shape_arg[4]), int(shape_arg[5]), int(shape_arg[6])), (int(shape_arg[7]), int(shape_arg[8]),\",\n \"return bg_args else: return None def point(obj): return None if not obj.get('point') else\",\n \"obj.get('colour')) else None if data and len(data) >= 7: return [int(data[0]), (int(data[1]), int(data[2]),\",\n \"if len(line_arg) >= 7: lines.append([(int(line_arg[0]), int(line_arg[1]), int(line_arg[2]), int(line_arg[3])), (int(line_arg[4]), int(line_arg[5]), int(line_arg[6]))]) elif len(line_arg)\"\n]","isConversational":false}}},{"rowIdx":12864190,"cells":{"ngram":{"kind":"list like","value":["shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in shape.table.iter_cells(): print(cell.text)","for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table:","p = Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame:","Presentation p = Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if","import Presentation p = Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes:","Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if","from pptx import Presentation p = Presentation('a.pptx') for slide in p.slides: for shape","= Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text)","in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell","p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in","slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for","for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in shape.table.iter_cells():","<filename>pythonoffice/ppt_get.py<gh_stars>1-10 from pptx import Presentation p = Presentation('a.pptx') for slide in p.slides: for","pptx import Presentation p = Presentation('a.pptx') for slide in p.slides: for shape in"],"string":"[\n \"shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in shape.table.iter_cells(): print(cell.text)\",\n \"for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table:\",\n \"p = Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame:\",\n \"Presentation p = Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if\",\n \"import Presentation p = Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes:\",\n \"Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if\",\n \"from pptx import Presentation p = Presentation('a.pptx') for slide in p.slides: for shape\",\n \"= Presentation('a.pptx') for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text)\",\n \"in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell\",\n \"p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in\",\n \"slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for\",\n \"for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in shape.table.iter_cells():\",\n \"<filename>pythonoffice/ppt_get.py<gh_stars>1-10 from pptx import Presentation p = Presentation('a.pptx') for slide in p.slides: for\",\n \"pptx import Presentation p = Presentation('a.pptx') for slide in p.slides: for shape in\"\n]","isConversational":false}}},{"rowIdx":12864191,"cells":{"ngram":{"kind":"list like","value":["the ones # that are not so sparse and that I FEEL makes","picking the ones # that are not so sparse and that I FEEL","bors and yahoo data''' import numpy as np import pandas as pd from","sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True)","include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if","are not so sparse and that I FEEL makes # makes sense to","merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name)","yahoo_name: str, result_filename: str): ''' Get filenames for csv files from Oslo Bors","str): ''' Get filenames for csv files from Oslo Bors and Yahoo Finance","Finance and merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats","df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__': merge_bors_and_yahoo_dfs(cng.BORS_CSV_NAME,","# Some of the features from Yahoo Finance # are very sparse, so","''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from","oslo bors and yahoo data''' import numpy as np import pandas as pd","I FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined =","pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename:","to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME)","df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__","import pandas as pd from pprint import pprint import scrapeconfig as cng def","yahoo data''' import numpy as np import pandas as pd from pprint import","from Yahoo Finance # are very sparse, so here I am picking the","pd from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name:","'''Combines oslo bors and yahoo data''' import numpy as np import pandas as","Finance # are very sparse, so here I am picking the ones #","for csv files from Oslo Bors and Yahoo Finance and merges them to","Some of the features from Yahoo Finance # are very sparse, so here","that I FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined","of the features from Yahoo Finance # are very sparse, so here I","merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for csv files from","def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for csv files","dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features","files from Oslo Bors and Yahoo Finance and merges them to one large","Oslo Bors and Yahoo Finance and merges them to one large dataset. '''","= pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance","not so sparse and that I FEEL makes # makes sense to include.","# makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON)","here I am picking the ones # that are not so sparse and","the features from Yahoo Finance # are very sparse, so here I am","cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for csv","import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get","that are not so sparse and that I FEEL makes # makes sense","sparse and that I FEEL makes # makes sense to include. df_stats =","filenames for csv files from Oslo Bors and Yahoo Finance and merges them","am picking the ones # that are not so sparse and that I","scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames","df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance # are","makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON,","str, result_filename: str): ''' Get filenames for csv files from Oslo Bors and","makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats,","import numpy as np import pandas as pd from pprint import pprint import","data''' import numpy as np import pandas as pd from pprint import pprint","sparse, so here I am picking the ones # that are not so","pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): '''","as np import pandas as pd from pprint import pprint import scrapeconfig as","and yahoo data''' import numpy as np import pandas as pd from pprint","so here I am picking the ones # that are not so sparse","csv files from Oslo Bors and Yahoo Finance and merges them to one","pandas as pd from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name:","them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) #","numpy as np import pandas as pd from pprint import pprint import scrapeconfig","and Yahoo Finance and merges them to one large dataset. ''' df_bors =","from Oslo Bors and Yahoo Finance and merges them to one large dataset.","so sparse and that I FEEL makes # makes sense to include. df_stats","np import pandas as pd from pprint import pprint import scrapeconfig as cng","and that I FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES]","pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance #","to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some","result_filename: str): ''' Get filenames for csv files from Oslo Bors and Yahoo","# that are not so sparse and that I FEEL makes # makes","''' Get filenames for csv files from Oslo Bors and Yahoo Finance and","large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the","Bors and Yahoo Finance and merges them to one large dataset. ''' df_bors","from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str,","df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__':","and merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats =","ones # that are not so sparse and that I FEEL makes #","are very sparse, so here I am picking the ones # that are","as pd from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str,","Get filenames for csv files from Oslo Bors and Yahoo Finance and merges","= pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__': merge_bors_and_yahoo_dfs(cng.BORS_CSV_NAME, cng.YAHOO_CSV_NAME,","= pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance # are very","one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of","pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__': merge_bors_and_yahoo_dfs(cng.BORS_CSV_NAME, cng.YAHOO_CSV_NAME, cng.FINALDATASET_FILENAME)","df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo","FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors,","features from Yahoo Finance # are very sparse, so here I am picking","import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str):","Yahoo Finance # are very sparse, so here I am picking the ones","# are very sparse, so here I am picking the ones # that","as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for","= df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ ==","pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance # are very sparse,","very sparse, so here I am picking the ones # that are not","Yahoo Finance and merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name)","I am picking the ones # that are not so sparse and that","str, yahoo_name: str, result_filename: str): ''' Get filenames for csv files from Oslo"],"string":"[\n \"the ones # that are not so sparse and that I FEEL makes\",\n \"picking the ones # that are not so sparse and that I FEEL\",\n \"bors and yahoo data''' import numpy as np import pandas as pd from\",\n \"sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True)\",\n \"include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if\",\n \"are not so sparse and that I FEEL makes # makes sense to\",\n \"merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name)\",\n \"yahoo_name: str, result_filename: str): ''' Get filenames for csv files from Oslo Bors\",\n \"str): ''' Get filenames for csv files from Oslo Bors and Yahoo Finance\",\n \"Finance and merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats\",\n \"df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__': merge_bors_and_yahoo_dfs(cng.BORS_CSV_NAME,\",\n \"# Some of the features from Yahoo Finance # are very sparse, so\",\n \"''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from\",\n \"oslo bors and yahoo data''' import numpy as np import pandas as pd\",\n \"I FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined =\",\n \"pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename:\",\n \"to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME)\",\n \"df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__\",\n \"import pandas as pd from pprint import pprint import scrapeconfig as cng def\",\n \"yahoo data''' import numpy as np import pandas as pd from pprint import\",\n \"from Yahoo Finance # are very sparse, so here I am picking the\",\n \"pd from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name:\",\n \"'''Combines oslo bors and yahoo data''' import numpy as np import pandas as\",\n \"Finance # are very sparse, so here I am picking the ones #\",\n \"for csv files from Oslo Bors and Yahoo Finance and merges them to\",\n \"Some of the features from Yahoo Finance # are very sparse, so here\",\n \"that I FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined\",\n \"of the features from Yahoo Finance # are very sparse, so here I\",\n \"merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for csv files from\",\n \"def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for csv files\",\n \"dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features\",\n \"files from Oslo Bors and Yahoo Finance and merges them to one large\",\n \"Oslo Bors and Yahoo Finance and merges them to one large dataset. '''\",\n \"= pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance\",\n \"not so sparse and that I FEEL makes # makes sense to include.\",\n \"# makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON)\",\n \"here I am picking the ones # that are not so sparse and\",\n \"the features from Yahoo Finance # are very sparse, so here I am\",\n \"cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for csv\",\n \"import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get\",\n \"that are not so sparse and that I FEEL makes # makes sense\",\n \"sparse and that I FEEL makes # makes sense to include. df_stats =\",\n \"filenames for csv files from Oslo Bors and Yahoo Finance and merges them\",\n \"am picking the ones # that are not so sparse and that I\",\n \"scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames\",\n \"df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance # are\",\n \"makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON,\",\n \"str, result_filename: str): ''' Get filenames for csv files from Oslo Bors and\",\n \"makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats,\",\n \"import numpy as np import pandas as pd from pprint import pprint import\",\n \"data''' import numpy as np import pandas as pd from pprint import pprint\",\n \"sparse, so here I am picking the ones # that are not so\",\n \"pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): '''\",\n \"as np import pandas as pd from pprint import pprint import scrapeconfig as\",\n \"and yahoo data''' import numpy as np import pandas as pd from pprint\",\n \"so here I am picking the ones # that are not so sparse\",\n \"csv files from Oslo Bors and Yahoo Finance and merges them to one\",\n \"pandas as pd from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name:\",\n \"them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) #\",\n \"numpy as np import pandas as pd from pprint import pprint import scrapeconfig\",\n \"and Yahoo Finance and merges them to one large dataset. ''' df_bors =\",\n \"from Oslo Bors and Yahoo Finance and merges them to one large dataset.\",\n \"so sparse and that I FEEL makes # makes sense to include. df_stats\",\n \"np import pandas as pd from pprint import pprint import scrapeconfig as cng\",\n \"and that I FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES]\",\n \"pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance #\",\n \"to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some\",\n \"result_filename: str): ''' Get filenames for csv files from Oslo Bors and Yahoo\",\n \"# that are not so sparse and that I FEEL makes # makes\",\n \"''' Get filenames for csv files from Oslo Bors and Yahoo Finance and\",\n \"large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the\",\n \"Bors and Yahoo Finance and merges them to one large dataset. ''' df_bors\",\n \"from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str,\",\n \"df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__':\",\n \"and merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats =\",\n \"ones # that are not so sparse and that I FEEL makes #\",\n \"are very sparse, so here I am picking the ones # that are\",\n \"as pd from pprint import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str,\",\n \"Get filenames for csv files from Oslo Bors and Yahoo Finance and merges\",\n \"= pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__': merge_bors_and_yahoo_dfs(cng.BORS_CSV_NAME, cng.YAHOO_CSV_NAME,\",\n \"= pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance # are very\",\n \"one large dataset. ''' df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of\",\n \"pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ == '__main__': merge_bors_and_yahoo_dfs(cng.BORS_CSV_NAME, cng.YAHOO_CSV_NAME, cng.FINALDATASET_FILENAME)\",\n \"df_bors = pd.read_csv(bors_name) df_stats = pd.read_csv(yahoo_name) # Some of the features from Yahoo\",\n \"FEEL makes # makes sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors,\",\n \"features from Yahoo Finance # are very sparse, so here I am picking\",\n \"import pprint import scrapeconfig as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str):\",\n \"Yahoo Finance # are very sparse, so here I am picking the ones\",\n \"# are very sparse, so here I am picking the ones # that\",\n \"as cng def merge_bors_and_yahoo_dfs(bors_name: str, yahoo_name: str, result_filename: str): ''' Get filenames for\",\n \"= df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS_ON) df_combined.set_index(cng.MERGE_DFS_ON, inplace=True) df_combined.to_csv(cng.FINALDATASET_FILENAME) if __name__ ==\",\n \"pd.read_csv(yahoo_name) # Some of the features from Yahoo Finance # are very sparse,\",\n \"very sparse, so here I am picking the ones # that are not\",\n \"Yahoo Finance and merges them to one large dataset. ''' df_bors = pd.read_csv(bors_name)\",\n \"I am picking the ones # that are not so sparse and that\",\n \"str, yahoo_name: str, result_filename: str): ''' Get filenames for csv files from Oslo\"\n]","isConversational":false}}},{"rowIdx":12864192,"cells":{"ngram":{"kind":"list like","value":["# print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if","matplotlib import fileDispose import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表","import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench","word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench","re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else:","= 0 for i in range(len(text)): if(i % n == 0): lable =","if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n","def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = [] lable =","def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i","\"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = [] lable = 0 for","= get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for j in","0 for i in range(len(text)): if(i % n == 0): lable = i","elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else: # qita +=","if(i % n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return","0 # shuangzi = 0 # qita = 0 # for k in","np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = [] lable","sentence in bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j >=","return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench:","int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex =","1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return","== 2): # shuangzi += 1 # else: # qita += 1 #","import matplotlib import fileDispose import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围",">= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n =","1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else: #","# for i, (k, v) in enumerate(word_listindex.items()): # if i in range(0, 10):","j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j","continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2)","print(bench[:10]) # danzi = 0 # shuangzi = 0 # qita = 0","qita = 0 # for k in word_listindex: # if (len(word_listindex[k]) == 1):","# for k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi +=","co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i in","== 1): # danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi","bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in","get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for j in range(1,window+1):","# print(bench[:10]) # danzi = 0 # shuangzi = 0 # qita =","in range(len(text)): if(i % n == 0): lable = i continue else: text[lable].extend(text[i])","bench = get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for j","print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if i","共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = [] lable = 0 for i in","range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] +=","= 0 # qita = 0 # for k in word_listindex: # if","in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n]","re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i in range(len(sentence)):","danzi = 0 # shuangzi = 0 # qita = 0 # for","1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence =","print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if i in range(0,","qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k,","i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]])","bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi = 0","shuangzi = 0 # qita = 0 # for k in word_listindex: #","= fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench =","continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) #","return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json')","in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <=","in word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1 # elif","bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n","# if (len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k]) ==","1 # else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita)","0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]])","fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) #","int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1","\"\"\" bench = [] lable = 0 for i in range(len(text)): if(i %","== 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex):","n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def","as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = []","+= 1 # else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) #","= get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi = 0 #","1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in","+= 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v)","= co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10])","in bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0):","n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n]","= int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] +=","bench = [] lable = 0 for i in range(len(text)): if(i % n","for i in range(len(text)): if(i % n == 0): lable = i continue","1): # danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi +=","0 # for k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi","i, (k, v) in enumerate(word_listindex.items()): # if i in range(0, 10): # print(k,","fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2)","shuangzi += 1 # else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi)","# danzi = 0 # shuangzi = 0 # qita = 0 #","# shuangzi = 0 # qita = 0 # for k in word_listindex:","= 0 # for k in word_listindex: # if (len(word_listindex[k]) == 1): #","= 0 # shuangzi = 0 # qita = 0 # for k","danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1 #","range(len(text)): if(i % n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable])","text：文本列表 n：几句话合并 \"\"\" bench = [] lable = 0 for i in range(len(text)):","lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int)","range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)):","= int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex","n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json')","# bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi =","for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1","else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex)","n：几句话合并 \"\"\" bench = [] lable = 0 for i in range(len(text)): if(i","co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) #","if (len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k]) == 2):","print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): #","for i, (k, v) in enumerate(word_listindex.items()): # if i in range(0, 10): #","=np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for","[] lable = 0 for i in range(len(text)): if(i % n == 0):","k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1 #","+= 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence","lable = 0 for i in range(len(text)): if(i % n == 0): lable","0 # qita = 0 # for k in word_listindex: # if (len(word_listindex[k])","i in range(len(text)): if(i % n == 0): lable = i continue else:","= [] lable = 0 for i in range(len(text)): if(i % n ==","# danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1","# fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi","get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi = 0 # qita","# qita = 0 # for k in word_listindex: # if (len(word_listindex[k]) ==","import fileDispose import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并","# print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if i in","(k, v) in enumerate(word_listindex.items()): # if i in range(0, 10): # print(k, v)","i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench =","for k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1","fileDispose import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\"","text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence","re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json')","if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix","(len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k]) == 2): #","print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi","2): # shuangzi += 1 # else: # qita += 1 # print('单个字词个数：',danzi)","for sentence in bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j","(len(word_listindex[k]) == 2): # shuangzi += 1 # else: # qita += 1","# shuangzi += 1 # else: # qita += 1 # print('单个字词个数：',danzi) #","total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) #","<= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list","0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix","= fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1])","+= 1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else:","= i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench","for i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n =","# else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) #","% n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench","bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for","<filename>co-occurrence_matrix.py import matplotlib import fileDispose import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int)","# print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()):","len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list =","# print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 #","else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for","re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence)","get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = [] lable = 0 for i","+= 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue","get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench = [] lable = 0","fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi =","np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0","# elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else: # qita","numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \"\"\" bench =","# qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i,","co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) #","else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for","word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k])"],"string":"[\n \"# print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if\",\n \"matplotlib import fileDispose import numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表\",\n \"import numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench\",\n \"word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench\",\n \"re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else:\",\n \"= 0 for i in range(len(text)): if(i % n == 0): lable =\",\n \"if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n\",\n \"def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable =\",\n \"def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i\",\n \"\\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable = 0 for\",\n \"= get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for j in\",\n \"0 for i in range(len(text)): if(i % n == 0): lable = i\",\n \"elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else: # qita +=\",\n \"if(i % n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return\",\n \"0 # shuangzi = 0 # qita = 0 # for k in\",\n \"np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable\",\n \"sentence in bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j >=\",\n \"return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench:\",\n \"int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex =\",\n \"1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return\",\n \"== 2): # shuangzi += 1 # else: # qita += 1 #\",\n \"import matplotlib import fileDispose import numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围\",\n \">= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n =\",\n \"1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else: #\",\n \"# for i, (k, v) in enumerate(word_listindex.items()): # if i in range(0, 10):\",\n \"j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j\",\n \"continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2)\",\n \"print(bench[:10]) # danzi = 0 # shuangzi = 0 # qita = 0\",\n \"qita = 0 # for k in word_listindex: # if (len(word_listindex[k]) == 1):\",\n \"# for k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi +=\",\n \"co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i in\",\n \"== 1): # danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi\",\n \"bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in\",\n \"get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for j in range(1,window+1):\",\n \"# print(bench[:10]) # danzi = 0 # shuangzi = 0 # qita =\",\n \"in range(len(text)): if(i % n == 0): lable = i continue else: text[lable].extend(text[i])\",\n \"bench = get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for j\",\n \"print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if i\",\n \"共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable = 0 for i in\",\n \"range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] +=\",\n \"= 0 # qita = 0 # for k in word_listindex: # if\",\n \"in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n]\",\n \"re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i in range(len(sentence)):\",\n \"danzi = 0 # shuangzi = 0 # qita = 0 # for\",\n \"1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence =\",\n \"print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if i in range(0,\",\n \"qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k,\",\n \"i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]])\",\n \"bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi = 0\",\n \"shuangzi = 0 # qita = 0 # for k in word_listindex: #\",\n \"= fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench =\",\n \"continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) #\",\n \"return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json')\",\n \"in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <=\",\n \"in word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1 # elif\",\n \"bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n\",\n \"# if (len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k]) ==\",\n \"1 # else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita)\",\n \"0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]])\",\n \"fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) #\",\n \"int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1\",\n \"\\\"\\\"\\\" bench = [] lable = 0 for i in range(len(text)): if(i %\",\n \"== 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex):\",\n \"n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def\",\n \"as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = []\",\n \"+= 1 # else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) #\",\n \"= get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi = 0 #\",\n \"1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in\",\n \"+= 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v)\",\n \"= co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10])\",\n \"in bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0):\",\n \"n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n]\",\n \"= int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] +=\",\n \"bench = [] lable = 0 for i in range(len(text)): if(i % n\",\n \"for i in range(len(text)): if(i % n == 0): lable = i continue\",\n \"1): # danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi +=\",\n \"0 # for k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi\",\n \"i, (k, v) in enumerate(word_listindex.items()): # if i in range(0, 10): # print(k,\",\n \"fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2)\",\n \"shuangzi += 1 # else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi)\",\n \"# danzi = 0 # shuangzi = 0 # qita = 0 #\",\n \"# shuangzi = 0 # qita = 0 # for k in word_listindex:\",\n \"= 0 # for k in word_listindex: # if (len(word_listindex[k]) == 1): #\",\n \"= 0 # shuangzi = 0 # qita = 0 # for k\",\n \"danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1 #\",\n \"range(len(text)): if(i % n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable])\",\n \"text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable = 0 for i in range(len(text)):\",\n \"lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int)\",\n \"range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1 if(i+j <= len(sentence)):\",\n \"= int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex\",\n \"n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json')\",\n \"# bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi =\",\n \"for j in range(1,window+1): if(i-j >= 0): n = int(word_listindex[sentence[i-j]]) re_matrix[i,n] += 1\",\n \"else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex)\",\n \"n：几句话合并 \\\"\\\"\\\" bench = [] lable = 0 for i in range(len(text)): if(i\",\n \"co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) #\",\n \"if (len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k]) == 2):\",\n \"print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): #\",\n \"for i, (k, v) in enumerate(word_listindex.items()): # if i in range(0, 10): #\",\n \"=np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for i in range(len(sentence)): for\",\n \"[] lable = 0 for i in range(len(text)): if(i % n == 0):\",\n \"k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1 #\",\n \"+= 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence\",\n \"lable = 0 for i in range(len(text)): if(i % n == 0): lable\",\n \"0 # qita = 0 # for k in word_listindex: # if (len(word_listindex[k])\",\n \"i in range(len(text)): if(i % n == 0): lable = i continue else:\",\n \"= [] lable = 0 for i in range(len(text)): if(i % n ==\",\n \"# danzi += 1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1\",\n \"# fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi\",\n \"get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi = 0 # qita\",\n \"# qita = 0 # for k in word_listindex: # if (len(word_listindex[k]) ==\",\n \"import fileDispose import numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并\",\n \"# print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if i in\",\n \"(k, v) in enumerate(word_listindex.items()): # if i in range(0, 10): # print(k, v)\",\n \"i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench =\",\n \"for k in word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1\",\n \"fileDispose import numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\"\",\n \"text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence\",\n \"re_matrix[i,n] += 1 else: continue return re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json')\",\n \"if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix\",\n \"(len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k]) == 2): #\",\n \"print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 # shuangzi\",\n \"2): # shuangzi += 1 # else: # qita += 1 # print('单个字词个数：',danzi)\",\n \"for sentence in bench: for i in range(len(sentence)): for j in range(1,window+1): if(i-j\",\n \"(len(word_listindex[k]) == 2): # shuangzi += 1 # else: # qita += 1\",\n \"# shuangzi += 1 # else: # qita += 1 # print('单个字词个数：',danzi) #\",\n \"total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) #\",\n \"<= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list\",\n \"0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix\",\n \"= fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1])\",\n \"+= 1 # elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else:\",\n \"= i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench\",\n \"for i in range(len(sentence)): for j in range(1,window+1): if(i-j >= 0): n =\",\n \"# else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) #\",\n \"% n == 0): lable = i continue else: text[lable].extend(text[i]) bench.append(text[lable]) return bench\",\n \"bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for sentence in bench: for\",\n \"<filename>co-occurrence_matrix.py import matplotlib import fileDispose import numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int)\",\n \"# print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()):\",\n \"len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue return re_matrix total_list =\",\n \"# print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0 #\",\n \"else: # qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for\",\n \"re_matrix total_list = fileDispose.getFile('total_list.json') word_listindex = fileDispose.getFile('allcut_word_listindex.json') co_occurrence = co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence)\",\n \"get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable = 0 for i\",\n \"+= 1 if(i+j <= len(sentence)): n = int(word_listindex[sentence[i+j-1]]) re_matrix[i,n] += 1 else: continue\",\n \"get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench = [] lable = 0\",\n \"fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi =\",\n \"np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) # danzi = 0\",\n \"# elif (len(word_listindex[k]) == 2): # shuangzi += 1 # else: # qita\",\n \"numpy as np def get_bench(text,n): \\\"\\\"\\\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表 n：几句话合并 \\\"\\\"\\\" bench =\",\n \"# qita += 1 # print('单个字词个数：',danzi) # print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i,\",\n \"co_occurrence_matrix_for_word(2,total_list,word_listindex) # fileDispose.writeToFile(co_occurrence.tolist(),'./Data/train/co_occurrence.json') np.savetxt('./Data/train/co_occurrence.txt',co_occurrence) # print(co_occurrence[:,1]) # bench = get_bench(total_list,2) # print(bench[:10]) #\",\n \"else: text[lable].extend(text[i]) bench.append(text[lable]) return bench def co_occurrence_matrix_for_word(window,text,word_listindex): re_matrix =np.zeros((len(word_listindex),len(word_listindex)),dtype=int) bench = get_bench(text,2) for\",\n \"word_listindex: # if (len(word_listindex[k]) == 1): # danzi += 1 # elif (len(word_listindex[k])\"\n]","isConversational":false}}},{"rowIdx":12864193,"cells":{"ngram":{"kind":"list like","value":["True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5","alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\",","len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\"","data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs","type=dir_path, help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\",","costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)),","fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5),","CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\"","9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':'","3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1","len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs","1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]),","args.function popsize = 10 data_LTO = {} data_CSA = {} with open(lto_path) as","1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1)","np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) +","= 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] =","plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen *","plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend()","np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1,","rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path):","datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family']","LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path',","timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time))","1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp =","import seaborn as sns import matplotlib.pyplot as plt import argparse from datetime import","= datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path,","rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon']","import argparse from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"] =","numpy as np import seaborn as sns import matplotlib.pyplot as plt import argparse","parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to","1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1","plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma","= 32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not","plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10)","argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to a file\"% path) parser = argparse.ArgumentParser(description='Script","Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in","\"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function,","1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1)","json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval = generations *","generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\",","len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs","costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"])","default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path","= json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x',","linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp)","32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s","data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"])","rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize']","= generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step","import json import numpy as np import seaborn as sns import matplotlib.pyplot as","costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"],","= 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] =","LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\"))","Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\",","popsize = 10 data_LTO = {} data_CSA = {} with open(lto_path) as json_file:","= 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] =","plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\",","type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time =","which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5),","exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function,","LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"])","costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now() time","rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width']","if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to","= 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] =","'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30","CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now() time =","plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations,","+ 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma","= 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] =","for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average","label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma","linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std","os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' %","LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]),","len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) +","= True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] =","('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25,","6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32","plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1,","matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize']","bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50)","with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA =","is not a valid path to a file\"% path) parser = argparse.ArgumentParser(description='Script to","parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the","data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval = generations * popsize","costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs","np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std","return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to a file\"%","data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average","color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"])","(type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num","fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for","data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path,","seaborn as sns import matplotlib.pyplot as plt import argparse from datetime import datetime","[str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function)","32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32","import datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False","dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path","file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path =","args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize","plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations,","as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval = generations","= ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0,","10 data_LTO = {} data_CSA = {} with open(lto_path) as json_file: data_LTO =","32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a","1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6","plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"])","LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma","def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid","json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations =","= \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp)","not a valid path to a file\"% path) parser = argparse.ArgumentParser(description='Script to plot","':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both'","default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function","costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs","plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\")","color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"])","as np import seaborn as sns import matplotlib.pyplot as plt import argparse from","= 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else: raise","stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)])","CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average","1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)),","data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4,","as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations","plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type =","popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1,","costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"],","plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average","default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function","= args.lto_path csa_path = args.csa_path function = args.function popsize = 10 data_LTO =","costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type =","6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6","= str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf()","data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average","plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\"))","plt import argparse from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"]","'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3","= os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf'","+ 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) +","= {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file:","32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32","= argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO","* popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50)","gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma","import numpy as np import seaborn as sns import matplotlib.pyplot as plt import","help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\")","rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth']","CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)),","for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)),","label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp) plot_file","color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp) plot_file =","from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex']","= 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] =","step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]),","args.csa_path function = args.function popsize = 10 data_LTO = {} data_CSA = {}","LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)),","LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std","path to a file\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.')","= 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] =","plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen","file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str,","raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to a file\"% path) parser =","type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path csa_path =","test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path,","argparse from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"] = \"40\"","rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which']","plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10,","rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0)","1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path","output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file =","from matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif'","alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\",","data_LTO = {} data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file)","= 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] =","= ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] =","costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs","rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width']","fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] +","= 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] =","rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size']","parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being","rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize']","sns import matplotlib.pyplot as plt import argparse from datetime import datetime sns.set() from","<reponame>shercklo/LTO-CMA import os import json import numpy as np import seaborn as sns","to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data file.\",","'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k'","file\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path","LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)),","np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std","function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\",","= (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle']","parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize = 10","= 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] =","LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4,","6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if","with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval","type=dir_path, help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the","help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA","np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) +","rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is","Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) +","rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha']","ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)]","else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to a file\"% path) parser","alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma","= \"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type,","being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function","import os import json import numpy as np import seaborn as sns import","+ [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs","10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)),","rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k'","rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True","help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path","False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] =","rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size']","data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path","lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize = 10 data_LTO","+ 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs","Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for","plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"],","path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to a file\"% path)","data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4,","\"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file","plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std","costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"],","= 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return","CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path","data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average","= len(data_LTO[\"Average costs LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25,","+ 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"],","open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval =","= 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] =","data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"])","os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\"readable_dir:%s is not a valid path to a","the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data file.\",","json import numpy as np import seaborn as sns import matplotlib.pyplot as plt","len(data_LTO[\"Average costs LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2)","import matplotlib.pyplot as plt import argparse from datetime import datetime sns.set() from matplotlib","valid path to a file\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test","'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6","stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma","* 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"])","args.lto_path csa_path = args.csa_path function = args.function popsize = 10 data_LTO = {}","plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations,","to the LTO data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data","CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma","label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs","{} data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path)","1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"],","LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"])","LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\",","sns.set() from matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family'] =","plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function =","LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma","csa_path = args.csa_path function = args.function popsize = 10 data_LTO = {} data_CSA","matplotlib.pyplot as plt import argparse from datetime import datetime sns.set() from matplotlib import","data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std","generations = len(data_LTO[\"Average costs LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0,","= {} data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with","plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective","+ 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path =","= 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] =","as plt import argparse from datetime import datetime sns.set() from matplotlib import rcParams","Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\",","= 10 data_LTO = {} data_CSA = {} with open(lto_path) as json_file: data_LTO","rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width']","len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma","data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs","data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as","plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time","1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\")","CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"])","costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)),","path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to","color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now()","data file.\", default=os.path.join(\"..\",\"examples\",\"10BBOB\",\"GallaghersGaussian21hi_LTO.json\")) parser.add_argument('--csa_path', type=dir_path, help=\"Path to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function',","rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size']","linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp","parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path = args.lto_path csa_path","a valid path to a file\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO","to a file\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path',","= 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] =","str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x',","= parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize =","CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True)","fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen *","fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen","np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1,","LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"])","np import seaborn as sns import matplotlib.pyplot as plt import argparse from datetime","= 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path):","plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)]","= 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] =","os import json import numpy as np import seaborn as sns import matplotlib.pyplot","costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"])","rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor']","6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6","LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average","plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] +","plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor')","datetime import datetime sns.set() from matplotlib import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] =","plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std","which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1,","gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs","in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"],","CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]),","[str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma","rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else:","the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args =","{} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA","* 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) +","json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"])","% (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2)","type = \"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' %","= 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color']","in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"],","FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10)","costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs","np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1,","CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1,","color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"],","datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file),","plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function)","os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type,","alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type","function = args.function popsize = 10 data_LTO = {} data_CSA = {} with","data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now()","plt.legend() type = \"ObjectiveValue\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf'","LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1,","= False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor']","rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k'","rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize']","0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True'","costs LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num","step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), np.subtract(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]),","= json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\"Average costs","CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args()","import rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] =","(16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] =","CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4,","+ [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1,","[str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average","a file\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path,","label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"StepSize\" output_path = os.path.join(\"..\",\"plots\") os.makedirs(output_path, exist_ok=True) timestamp =","stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs","data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args = parser.parse_args() lto_path","+ 1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs","rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def","fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10,","1)), np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]),","len(data_CSA[\"Average costs CSA\"]) + 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type","step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1,","costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"],","open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file)","FEval\", fontsize=50) plt.ylabel(\"Objective Value\", fontsize=50) plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen","[str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\"Average costs LTO\"])","color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1,","rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth']","as sns import matplotlib.pyplot as plt import argparse from datetime import datetime sns.set()","= args.csa_path function = args.function popsize = 10 data_LTO = {} data_CSA =","ncol=2) plt.xlabel(\"Num FEval\", fontsize=50) plt.ylabel(\"Step Size\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen","\"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] =","len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma","LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma LTO\"],","linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]),","rcParams rcParams[\"font.size\"] = \"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0,","color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]),","data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average","plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average","CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std","len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average","CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"],","rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size']","30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1","time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50)","'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32","Sigma LTO\"]), np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) +","+ 1)), np.subtract(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"],","rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width']","step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks()","LTO\"], linewidth=4, label=\"LTO\", color=sns.xkcd_rgb[\"magenta\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\"Average costs CSA\"]) + 1)), np.subtract(data_CSA[\"Average costs CSA\"],","np.add(data_CSA[\"Sigma CSA\"], data_CSA[\"Std Sigma CSA\"]), color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma","color=sns.xkcd_rgb[\"green\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\"Sigma CSA\"]) + 1)), data_CSA[\"Sigma CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend()","json.load(json_file) generations = len(data_LTO[\"Average costs LTO\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor')","np.subtract(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), np.add(data_LTO[\"Average costs LTO\"], data_LTO[\"Std costs LTO\"]), alpha=0.1,","'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] =","= \"40\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon']","argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\"Path to the LTO data","np.add(data_LTO[\"Sigma LTO\"], data_LTO[\"Std Sigma LTO\"]), color=sns.xkcd_rgb[\"magenta\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) + 1)), data_LTO[\"Sigma","CSA\"]), np.add(data_CSA[\"Average costs CSA\"], data_CSA[\"Std costs CSA\"]), alpha=0.1, color=sns.xkcd_rgb[\"green\"]) plt.plot(list(np.arange(1, len(data_CSA[\"Average costs CSA\"])","10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\"Sigma LTO\"]) +","+ 1)), data_CSA[\"Average costs CSA\"], linewidth=4, label=\"CSA\", color=sns.xkcd_rgb[\"green\"]) plt.legend() type = \"ObjectiveValue\" timestamp","plt.xscale(\"log\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in","to the CSA data file.\", default=os.path.join(\"..\",\"data\",\"PPSN_LTO_Data\",\"CSA_Data\",\"CSA_Plots_10D\",\"GallaghersGaussian21hi.json\")) parser.add_argument('--function', type=str, help=\"Function being plotted\", default=\"GallaghersGaussian21hi\") args","time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight')","= 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] =","= args.function popsize = 10 data_LTO = {} data_CSA = {} with open(lto_path)","num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\"Num FEval\", fontsize=50)"],"string":"[\n \"True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5\",\n \"alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\",\",\n \"len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\"\",\n \"data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs\",\n \"type=dir_path, help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\",\",\n \"costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)),\",\n \"fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5),\",\n \"CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\"\",\n \"9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':'\",\n \"3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1\",\n \"len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs\",\n \"1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]),\",\n \"args.function popsize = 10 data_LTO = {} data_CSA = {} with open(lto_path) as\",\n \"1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1)\",\n \"np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) +\",\n \"= 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] =\",\n \"plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen *\",\n \"plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend()\",\n \"np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1,\",\n \"rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path):\",\n \"datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family']\",\n \"LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path',\",\n \"timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time))\",\n \"1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp =\",\n \"import seaborn as sns import matplotlib.pyplot as plt import argparse from datetime import\",\n \"= datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path,\",\n \"rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon']\",\n \"import argparse from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] =\",\n \"numpy as np import seaborn as sns import matplotlib.pyplot as plt import argparse\",\n \"parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to\",\n \"1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1\",\n \"plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma\",\n \"= 32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not\",\n \"plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10)\",\n \"argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to a file\\\"% path) parser = argparse.ArgumentParser(description='Script\",\n \"Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in\",\n \"\\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function,\",\n \"1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1)\",\n \"json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations *\",\n \"generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\",\",\n \"len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs\",\n \"costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"])\",\n \"default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path\",\n \"= json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x',\",\n \"linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp)\",\n \"32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s\",\n \"data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"])\",\n \"rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize']\",\n \"= generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step\",\n \"import json import numpy as np import seaborn as sns import matplotlib.pyplot as\",\n \"costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"],\",\n \"= 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] =\",\n \"LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\"))\",\n \"Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\",\",\n \"popsize = 10 data_LTO = {} data_CSA = {} with open(lto_path) as json_file:\",\n \"= 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] =\",\n \"plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\",\",\n \"type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time =\",\n \"which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5),\",\n \"exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function,\",\n \"LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"])\",\n \"costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time\",\n \"rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width']\",\n \"if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to\",\n \"= 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] =\",\n \"'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30\",\n \"CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time =\",\n \"plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations,\",\n \"+ 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma\",\n \"= 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] =\",\n \"for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average\",\n \"label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma\",\n \"linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std\",\n \"os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' %\",\n \"LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]),\",\n \"len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) +\",\n \"= True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] =\",\n \"('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25,\",\n \"6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32\",\n \"plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1,\",\n \"matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize']\",\n \"bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50)\",\n \"with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA =\",\n \"is not a valid path to a file\\\"% path) parser = argparse.ArgumentParser(description='Script to\",\n \"parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the\",\n \"data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations * popsize\",\n \"costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs\",\n \"np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std\",\n \"return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to a file\\\"%\",\n \"data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average\",\n \"color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"])\",\n \"(type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num\",\n \"fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for\",\n \"data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path,\",\n \"seaborn as sns import matplotlib.pyplot as plt import argparse from datetime import datetime\",\n \"[str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function)\",\n \"32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32\",\n \"import datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False\",\n \"dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path\",\n \"file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path =\",\n \"args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize\",\n \"plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations,\",\n \"as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations\",\n \"= ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0,\",\n \"10 data_LTO = {} data_CSA = {} with open(lto_path) as json_file: data_LTO =\",\n \"32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a\",\n \"1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6\",\n \"plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"])\",\n \"LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma\",\n \"def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid\",\n \"json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations =\",\n \"= \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp)\",\n \"not a valid path to a file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot\",\n \"':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both'\",\n \"default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function\",\n \"costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs\",\n \"plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\")\",\n \"color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"])\",\n \"as np import seaborn as sns import matplotlib.pyplot as plt import argparse from\",\n \"= 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else: raise\",\n \"stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)])\",\n \"CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average\",\n \"1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)),\",\n \"data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4,\",\n \"as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations\",\n \"plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type =\",\n \"popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1,\",\n \"costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"],\",\n \"plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average\",\n \"default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function\",\n \"= args.lto_path csa_path = args.csa_path function = args.function popsize = 10 data_LTO =\",\n \"costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type =\",\n \"6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6\",\n \"= str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf()\",\n \"data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average\",\n \"plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\"))\",\n \"plt import argparse from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"]\",\n \"'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3\",\n \"= os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf'\",\n \"+ 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) +\",\n \"= {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file:\",\n \"32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32\",\n \"= argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO\",\n \"* popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50)\",\n \"gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma\",\n \"import numpy as np import seaborn as sns import matplotlib.pyplot as plt import\",\n \"help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\")\",\n \"rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth']\",\n \"CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)),\",\n \"for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)),\",\n \"label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp) plot_file\",\n \"color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp) plot_file =\",\n \"from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex']\",\n \"= 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] =\",\n \"step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]),\",\n \"args.csa_path function = args.function popsize = 10 data_LTO = {} data_CSA = {}\",\n \"LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)),\",\n \"LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std\",\n \"path to a file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.')\",\n \"= 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] =\",\n \"plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen\",\n \"file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str,\",\n \"raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to a file\\\"% path) parser =\",\n \"type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path csa_path =\",\n \"test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path,\",\n \"argparse from datetime import datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\"\",\n \"rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which']\",\n \"plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10,\",\n \"rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0)\",\n \"1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path\",\n \"output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file =\",\n \"from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif'\",\n \"alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\",\",\n \"data_LTO = {} data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file)\",\n \"= 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] =\",\n \"= ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] =\",\n \"costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs\",\n \"rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width']\",\n \"fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] +\",\n \"= 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] =\",\n \"rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size']\",\n \"parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being\",\n \"rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize']\",\n \"sns import matplotlib.pyplot as plt import argparse from datetime import datetime sns.set() from\",\n \"<reponame>shercklo/LTO-CMA import os import json import numpy as np import seaborn as sns\",\n \"to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data file.\\\",\",\n \"'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k'\",\n \"file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path\",\n \"LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)),\",\n \"np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std\",\n \"function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\",\",\n \"= (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle']\",\n \"parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize = 10\",\n \"= 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] =\",\n \"LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4,\",\n \"6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if\",\n \"with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval\",\n \"type=dir_path, help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the\",\n \"help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA\",\n \"np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) +\",\n \"rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is\",\n \"Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) +\",\n \"rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha']\",\n \"ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)]\",\n \"else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to a file\\\"% path) parser\",\n \"alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma\",\n \"= \\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type,\",\n \"being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function\",\n \"import os import json import numpy as np import seaborn as sns import\",\n \"+ [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs\",\n \"10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)),\",\n \"rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k'\",\n \"rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True\",\n \"help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path\",\n \"False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] =\",\n \"rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size']\",\n \"data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path\",\n \"lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize = 10 data_LTO\",\n \"+ 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs\",\n \"Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for\",\n \"plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"],\",\n \"path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to a file\\\"% path)\",\n \"data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4,\",\n \"\\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file\",\n \"plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std\",\n \"costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"],\",\n \"= 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return\",\n \"CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path\",\n \"data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average\",\n \"= len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25,\",\n \"+ 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"],\",\n \"open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval =\",\n \"= 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] =\",\n \"data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"])\",\n \"os.path.isfile(path): return path else: raise argparse.ArgumentTypeError(\\\"readable_dir:%s is not a valid path to a\",\n \"the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data file.\\\",\",\n \"json import numpy as np import seaborn as sns import matplotlib.pyplot as plt\",\n \"len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2)\",\n \"import matplotlib.pyplot as plt import argparse from datetime import datetime sns.set() from matplotlib\",\n \"valid path to a file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test\",\n \"'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6\",\n \"stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma\",\n \"* 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"])\",\n \"args.lto_path csa_path = args.csa_path function = args.function popsize = 10 data_LTO = {}\",\n \"plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations,\",\n \"to the LTO data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data\",\n \"CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma\",\n \"label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs\",\n \"{} data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path)\",\n \"1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"],\",\n \"LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"])\",\n \"LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\",\",\n \"sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] =\",\n \"plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function =\",\n \"LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma\",\n \"csa_path = args.csa_path function = args.function popsize = 10 data_LTO = {} data_CSA\",\n \"matplotlib.pyplot as plt import argparse from datetime import datetime sns.set() from matplotlib import\",\n \"data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std\",\n \"generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0,\",\n \"= {} data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with\",\n \"plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective\",\n \"+ 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path =\",\n \"= 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] =\",\n \"as plt import argparse from datetime import datetime sns.set() from matplotlib import rcParams\",\n \"Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\",\",\n \"= 10 data_LTO = {} data_CSA = {} with open(lto_path) as json_file: data_LTO\",\n \"rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width']\",\n \"len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma\",\n \"data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs\",\n \"data_CSA = {} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as\",\n \"plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time\",\n \"1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\")\",\n \"CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"])\",\n \"costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)),\",\n \"path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to\",\n \"color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp = datetime.now()\",\n \"data file.\\\", default=os.path.join(\\\"..\\\",\\\"examples\\\",\\\"10BBOB\\\",\\\"GallaghersGaussian21hi_LTO.json\\\")) parser.add_argument('--csa_path', type=dir_path, help=\\\"Path to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function',\",\n \"rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size']\",\n \"linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp\",\n \"parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path = args.lto_path csa_path\",\n \"a valid path to a file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO\",\n \"to a file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path',\",\n \"= 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] =\",\n \"str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x',\",\n \"= parser.parse_args() lto_path = args.lto_path csa_path = args.csa_path function = args.function popsize =\",\n \"CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True)\",\n \"fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen *\",\n \"fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen\",\n \"np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1,\",\n \"LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"])\",\n \"np import seaborn as sns import matplotlib.pyplot as plt import argparse from datetime\",\n \"= 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path):\",\n \"plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)]\",\n \"= 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] =\",\n \"os import json import numpy as np import seaborn as sns import matplotlib.pyplot\",\n \"costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"])\",\n \"rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor']\",\n \"6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6\",\n \"LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average\",\n \"plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] +\",\n \"plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor')\",\n \"datetime import datetime sns.set() from matplotlib import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] =\",\n \"plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std\",\n \"which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1,\",\n \"gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs\",\n \"in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"],\",\n \"CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]),\",\n \"[str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma\",\n \"rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def dir_path(path): if os.path.isfile(path): return path else:\",\n \"the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args =\",\n \"{} with open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA\",\n \"* 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) +\",\n \"json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"])\",\n \"% (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2)\",\n \"type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' %\",\n \"= 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color']\",\n \"in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"],\",\n \"FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10)\",\n \"costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1, len(data_LTO[\\\"Average costs\",\n \"np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1,\",\n \"CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1,\",\n \"color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"],\",\n \"datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file),\",\n \"plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function)\",\n \"os.makedirs(output_path, exist_ok=True) timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type,\",\n \"alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type\",\n \"function = args.function popsize = 10 data_LTO = {} data_CSA = {} with\",\n \"data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now()\",\n \"plt.legend() type = \\\"ObjectiveValue\\\" timestamp = datetime.now() time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf'\",\n \"LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.plot(list(np.arange(1,\",\n \"= False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor']\",\n \"rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k'\",\n \"rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize']\",\n \"0.5 rcParams['axes.linewidth'] = 3 rcParams['axes.edgecolor'] = 'k' rcParams['axes.grid.which'] = 'both' rcParams['legend.frameon'] = 'True'\",\n \"costs LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num\",\n \"step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]),\",\n \"= json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file) generations = len(data_LTO[\\\"Average costs\",\n \"CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args()\",\n \"import rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] =\",\n \"(16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] =\",\n \"CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4,\",\n \"+ [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1,\",\n \"[str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average\",\n \"a file\\\"% path) parser = argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path,\",\n \"label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"StepSize\\\" output_path = os.path.join(\\\"..\\\",\\\"plots\\\") os.makedirs(output_path, exist_ok=True) timestamp =\",\n \"stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs\",\n \"data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args = parser.parse_args() lto_path\",\n \"+ 1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs\",\n \"rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width'] = 1 rcParams['xtick.labelsize'] = 32 rcParams['ytick.labelsize'] = 32 def\",\n \"fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10,\",\n \"1)), np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]),\",\n \"len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type\",\n \"step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1,\",\n \"costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"],\",\n \"open(lto_path) as json_file: data_LTO = json.load(json_file) with open(csa_path) as json_file: data_CSA = json.load(json_file)\",\n \"FEval\\\", fontsize=50) plt.ylabel(\\\"Objective Value\\\", fontsize=50) plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen\",\n \"[str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Average costs LTO\\\"])\",\n \"color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1,\",\n \"rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth']\",\n \"as sns import matplotlib.pyplot as plt import argparse from datetime import datetime sns.set()\",\n \"= args.csa_path function = args.function popsize = 10 data_LTO = {} data_CSA =\",\n \"ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50) plt.ylabel(\\\"Step Size\\\", fontsize=50) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen\",\n \"\\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] =\",\n \"len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma\",\n \"LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma LTO\\\"],\",\n \"linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]),\",\n \"rcParams rcParams[\\\"font.size\\\"] = \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0,\",\n \"color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]),\",\n \"data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average\",\n \"plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average\",\n \"CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std\",\n \"len(data_LTO[\\\"Average costs LTO\\\"]) + 1)), data_LTO[\\\"Average costs LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average\",\n \"CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"],\",\n \"rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size']\",\n \"30 rcParams['ytick.major.size'] = 32 rcParams['ytick.major.width'] = 6 rcParams['ytick.minor.size'] = 6 rcParams['ytick.minor.width'] = 1\",\n \"time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight') plt.clf() plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50)\",\n \"'both' rcParams['legend.frameon'] = 'True' rcParams['legend.framealpha'] = 1 rcParams['legend.fontsize'] = 30 rcParams['ytick.major.size'] = 32\",\n \"Sigma LTO\\\"]), np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) +\",\n \"+ 1)), np.subtract(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"],\",\n \"rcParams['ytick.minor.width'] = 1 rcParams['xtick.major.size'] = 32 rcParams['xtick.major.width'] = 6 rcParams['xtick.minor.size'] = 6 rcParams['xtick.minor.width']\",\n \"step=generations//5), [str(10)] + [str(gen * 10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks()\",\n \"LTO\\\"], linewidth=4, label=\\\"LTO\\\", color=sns.xkcd_rgb[\\\"magenta\\\"]) plt.fill_between(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"]) + 1)), np.subtract(data_CSA[\\\"Average costs CSA\\\"],\",\n \"np.add(data_CSA[\\\"Sigma CSA\\\"], data_CSA[\\\"Std Sigma CSA\\\"]), color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma\",\n \"color=sns.xkcd_rgb[\\\"green\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_CSA[\\\"Sigma CSA\\\"]) + 1)), data_CSA[\\\"Sigma CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend()\",\n \"json.load(json_file) generations = len(data_LTO[\\\"Average costs LTO\\\"]) num_feval = generations * popsize plt.tick_params(axis='x', which='minor')\",\n \"np.subtract(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), np.add(data_LTO[\\\"Average costs LTO\\\"], data_LTO[\\\"Std costs LTO\\\"]), alpha=0.1,\",\n \"'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon'] = True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] =\",\n \"= \\\"40\\\" rcParams['text.usetex'] = False rcParams['font.family'] = 'serif' rcParams['figure.figsize'] = (16.0, 9.0) rcParams['figure.frameon']\",\n \"argparse.ArgumentParser(description='Script to plot LTO test data.') parser.add_argument('--lto_path', type=dir_path, help=\\\"Path to the LTO data\",\n \"np.add(data_LTO[\\\"Sigma LTO\\\"], data_LTO[\\\"Std Sigma LTO\\\"]), color=sns.xkcd_rgb[\\\"magenta\\\"], alpha=0.1) plt.plot(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) + 1)), data_LTO[\\\"Sigma\",\n \"CSA\\\"]), np.add(data_CSA[\\\"Average costs CSA\\\"], data_CSA[\\\"Std costs CSA\\\"]), alpha=0.1, color=sns.xkcd_rgb[\\\"green\\\"]) plt.plot(list(np.arange(1, len(data_CSA[\\\"Average costs CSA\\\"])\",\n \"10) for gen in np.arange(start=10, stop=generations, step=generations//5)]) plt.xticks() plt.title(function) plt.fill_between(list(np.arange(1, len(data_LTO[\\\"Sigma LTO\\\"]) +\",\n \"+ 1)), data_CSA[\\\"Average costs CSA\\\"], linewidth=4, label=\\\"CSA\\\", color=sns.xkcd_rgb[\\\"green\\\"]) plt.legend() type = \\\"ObjectiveValue\\\" timestamp\",\n \"plt.xscale(\\\"log\\\") plt.title(function) plt.xticks(np.arange(start=1, stop=generations, step=generations//5), [str(10)] + [str(gen * 10) for gen in\",\n \"to the CSA data file.\\\", default=os.path.join(\\\"..\\\",\\\"data\\\",\\\"PPSN_LTO_Data\\\",\\\"CSA_Data\\\",\\\"CSA_Plots_10D\\\",\\\"GallaghersGaussian21hi.json\\\")) parser.add_argument('--function', type=str, help=\\\"Function being plotted\\\", default=\\\"GallaghersGaussian21hi\\\") args\",\n \"time = str(timestamp) plot_file = ('Plot_%s_%s_%s.pdf' % (type, function, time)) plt.savefig(os.path.join(output_path, plot_file), bbox_inches='tight')\",\n \"= 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5 rcParams['axes.linewidth'] =\",\n \"= args.function popsize = 10 data_LTO = {} data_CSA = {} with open(lto_path)\",\n \"num_feval = generations * popsize plt.tick_params(axis='x', which='minor') plt.legend(loc=0, fontsize=25, ncol=2) plt.xlabel(\\\"Num FEval\\\", fontsize=50)\"\n]","isConversational":false}}},{"rowIdx":12864194,"cells":{"ngram":{"kind":"list like","value":["= [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free","class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking',","2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies =","operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']], max_length=20),","] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']],","django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations","# Generated by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models","models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField(","<reponame>invinst/CPDBv2_backend<gh_stars>10-100 # Generated by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations,","= [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']], max_length=20), ),","dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text',","'0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No","from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ]","on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [","Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type',","[ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']], max_length=20), ), ]","[ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'],","import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations =","migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [","('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction',","Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies","08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'),","Generated by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class","by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration):","2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics',"],"string":"[\n \"= [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free\",\n \"class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking',\",\n \"2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies =\",\n \"operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']], max_length=20),\",\n \"] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']],\",\n \"django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations\",\n \"# Generated by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models\",\n \"models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField(\",\n \"<reponame>invinst/CPDBv2_backend<gh_stars>10-100 # Generated by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations,\",\n \"= [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']], max_length=20), ),\",\n \"dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text',\",\n \"'0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No\",\n \"from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ]\",\n \"on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [\",\n \"Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type',\",\n \"[ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction', 'No Interaction']], max_length=20), ), ]\",\n \"[ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'],\",\n \"import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations =\",\n \"migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [\",\n \"('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free Text'], ['no_interaction',\",\n \"Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies\",\n \"08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'),\",\n \"Generated by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class\",\n \"by Django 2.1.3 on 2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration):\",\n \"2018-11-23 08:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('analytics',\"\n]","isConversational":false}}},{"rowIdx":12864195,"cells":{"ngram":{"kind":"list like","value":["<filename>LaureatsBackEnd-master/laureats/migrations/0008_etudiant.py # Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations,","[ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE,","parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ],","migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations","to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'],","django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'),","from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats',","models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations =","('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], },","3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration):","= [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement',","models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], }, bases=('laureats.laureat',),","class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant',","] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False,","Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import","[ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='',","migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)),","('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)),","operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')),","models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription',","Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class","primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={","name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude',","('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True,","max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], }, bases=('laureats.laureat',), ),","on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies","# Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models","import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ]","= [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True,","import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [","2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies =","django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel(","('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], }, bases=('laureats.laureat',), ), ]","Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[","fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='',","on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()),","by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion","dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr',","'0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True,","20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [","serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering':"],"string":"[\n \"<filename>LaureatsBackEnd-master/laureats/migrations/0008_etudiant.py # Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations,\",\n \"[ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE,\",\n \"parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ],\",\n \"migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations\",\n \"to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'],\",\n \"django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'),\",\n \"from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats',\",\n \"models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations =\",\n \"('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], },\",\n \"3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration):\",\n \"= [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement',\",\n \"models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], }, bases=('laureats.laureat',),\",\n \"class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant',\",\n \"] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False,\",\n \"Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import\",\n \"[ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='',\",\n \"migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)),\",\n \"('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)),\",\n \"operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')),\",\n \"models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription',\",\n \"Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class\",\n \"primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={\",\n \"name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude',\",\n \"('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True,\",\n \"max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], }, bases=('laureats.laureat',), ),\",\n \"on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies\",\n \"# Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models\",\n \"import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ]\",\n \"= [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True,\",\n \"import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [\",\n \"2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies =\",\n \"django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel(\",\n \"('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering': ['new_date_inscription'], }, bases=('laureats.laureat',), ), ]\",\n \"Migration(migrations.Migration): dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[\",\n \"fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='',\",\n \"on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()),\",\n \"by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations, models import django.db.models.deletion\",\n \"dependencies = [ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr',\",\n \"'0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True,\",\n \"20:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [\",\n \"serialize=False, to='laureats.Laureat')), ('etablissement', models.CharField(default='', max_length=100)), ('sujet_etude', models.CharField(default='', max_length=255)), ('new_date_inscription', models.DateField()), ], options={ 'ordering':\"\n]","isConversational":false}}},{"rowIdx":12864196,"cells":{"ngram":{"kind":"list like","value":["import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def","torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) ->","torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends =","# It's not strictly necessary to multiply the span indices by the mask","import torch from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import","elif num_width_embeddings is not None or span_width_embedding_dim is not None: raise ConfigurationError( \"To","span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask =","def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None,","* span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) #","= span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor,","None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor,","def get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim()","overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn","None or span_width_embedding_dim is not None: raise ConfigurationError( \"To use a span width","import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int,","or span_width_embedding_dim is not None: raise ConfigurationError( \"To use a span width embedding","dim=-1)] if span_indices_mask is not None: # It's not strictly necessary to multiply","-> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None:","def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is","get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return","False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths =","num_width_embeddings is not None or span_width_embedding_dim is not None: raise ConfigurationError( \"To use","span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask","None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings","span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None:","by the mask here, # but it's possible that the span representation was","input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool =","# but it's possible that the span representation was padded with something other","span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: # Embed","torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size,","num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings =","@overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask:","None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for","not strictly necessary to multiply the span indices by the mask here, #","allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks","span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends","# than 0 (such as -1, which would be an invalid index), so","so we do so anyway to # be safe. span_starts = span_starts *","span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to","set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num,","span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is","-> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if","- span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths)","mask here, # but it's possible that the span representation was padded with","self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor,","embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings","self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not None:","span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None:","span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num,","widths and concatenate to the rest of the representations. if self._bucket_widths: span_widths =","ConfigurationError( \"To use a span width embedding representation, you must\" \"specify both num_width_buckets","index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not strictly","to the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends -","class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int","None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding","<filename>spanmb/models/span_extractor.py import torch from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor","util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings:","overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util","[index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's","from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from","torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: # Embed the span","if span_indices_mask is not None: # It's not strictly necessary to multiply the","num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings,","for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not","necessary to multiply the span indices by the mask here, # but it's","super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is","Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__(","sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape","representation was padded with something other # than 0 (such as -1, which","# [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask","It's not strictly necessary to multiply the span indices by the mask here,","# [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is","self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor","span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings *","MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int =","span widths and concatenate to the rest of the representations. if self._bucket_widths: span_widths","if num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding(","def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor","so anyway to # be safe. span_starts = span_starts * span_indices_mask span_ends =","= util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts","a span width embedding representation, you must\" \"specify both num_width_buckets and span_width_embedding_dim.\" )","span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not strictly necessary to","shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if","util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings","span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings =","torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings * span_indices_mask.unsqueeze(-1) return","* span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if","forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor =","= [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: #","in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not strictly necessary","\"specify both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding = None def get_input_dim(self) ->","and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings","would be an invalid index), so we do so anyway to # be","we do so anyway to # be safe. span_starts = span_starts * span_indices_mask","util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float()","to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings =","= torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings * span_indices_mask.unsqueeze(-1)",") else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self)","other # than 0 (such as -1, which would be an invalid index),","torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding","= span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] #","an invalid index), so we do so anyway to # be safe. span_starts","use a span width embedding representation, you must\" \"specify both num_width_buckets and span_width_embedding_dim.\"","span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size]","than 0 (such as -1, which would be an invalid index), so we","else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings],","padded with something other # than 0 (such as -1, which would be","num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool = False, )","bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding =","span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings","allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim:","None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings","span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends -","num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is","self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None,","not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor:","not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None","\"To use a span width embedding representation, you must\" \"specify both num_width_buckets and","strictly necessary to multiply the span indices by the mask here, # but","here, # but it's possible that the span representation was padded with something","# set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size,","the span widths and concatenate to the rest of the representations. if self._bucket_widths:","self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends","be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask #","but it's possible that the span representation was padded with something other #","concatenate to the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends","ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim:","sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, )","span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not","of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings )","must\" \"specify both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding = None def get_input_dim(self)","torch from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor","= num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is","from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class","None, span_width_embedding_dim: int = None, bucket_widths: bool = False, ) -> None: super().__init__()","is not None or span_width_embedding_dim is not None: raise ConfigurationError( \"To use a","= False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths","the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else:","import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None,","span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not","= None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans)","span_width_embedding_dim: int = None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim","span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask","= input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None","= Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is","embedding representation, you must\" \"specify both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding =","num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int: return","and span_width_embedding_dim.\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim","you must\" \"specify both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding = None def","else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) ->","None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1,","int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides","import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import","invalid index), so we do so anyway to # be safe. span_starts =","multiply the span indices by the mask here, # but it's possible that","return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim","not None or span_width_embedding_dim is not None: raise ConfigurationError( \"To use a span","span indices by the mask here, # but it's possible that the span","something other # than 0 (such as -1, which would be an invalid","int = None, span_width_embedding_dim: int = None, bucket_widths: bool = False, ) ->","+ self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask:","= None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1)","* span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings,","representation, you must\" \"specify both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding = None","None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans) span_starts,","to multiply the span indices by the mask here, # but it's possible","int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None: return","span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings =","indices by the mask here, # but it's possible that the span representation","representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths","it's possible that the span representation was padded with something other # than","self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None,","None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif","None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or","(batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask","2)[0] # span_mask if self._span_width_embedding is not None: # Embed the span widths","from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from","bool = False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings","emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: #","span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices)","is not None: # Embed the span widths and concatenate to the rest","span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings * span_indices_mask.unsqueeze(-1) return span_max_embeddings","if self._span_width_embedding is not None: # Embed the span widths and concatenate to","self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim +","not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim )","the span representation was padded with something other # than 0 (such as","__init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths:","with something other # than 0 (such as -1, which would be an","self._span_width_embedding is not None: # Embed the span widths and concatenate to the","unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size]","possible that the span representation was padded with something other # than 0","self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool","span width embedding representation, you must\" \"specify both num_width_buckets and span_width_embedding_dim.\" ) else:","which would be an invalid index), so we do so anyway to #","be an invalid index), so we do so anyway to # be safe.","span_mask if self._span_width_embedding is not None: # Embed the span widths and concatenate","- span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is","-> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in","is not None: raise ConfigurationError( \"To use a span width embedding representation, you","from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self,","= torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: # Embed the","# Embed the span widths and concatenate to the rest of the representations.","span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings","Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding","span_width_embedding_dim is not None: raise ConfigurationError( \"To use a span width embedding representation,","both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int:","is not None: # It's not strictly necessary to multiply the span indices","span representation was padded with something other # than 0 (such as -1,","-1, which would be an invalid index), so we do so anyway to","anyway to # be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends","# be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask","int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool = False,","span_indices_mask is not None: # It's not strictly necessary to multiply the span","0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings,","= bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding","None: # It's not strictly necessary to multiply the span indices by the","from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from","import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import","self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim","the span indices by the mask here, # but it's possible that the","# span_mask if self._span_width_embedding is not None: # Embed the span widths and","return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices:","if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def","span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0]","self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not","= None, span_width_embedding_dim: int = None, bucket_widths: bool = False, ) -> None:",") -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths","not None: # Embed the span widths and concatenate to the rest of","not None: raise ConfigurationError( \"To use a span width embedding representation, you must\"","and concatenate to the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values(","Embed the span widths and concatenate to the rest of the representations. if","allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int =","if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths =","is not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim","= self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return",") -> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index","import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import","embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is not None: raise","from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int","None: raise ConfigurationError( \"To use a span width embedding representation, you must\" \"specify","(such as -1, which would be an invalid index), so we do so","= span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width,","emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings","# shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)]","the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts,","self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings","raise ConfigurationError( \"To use a span width embedding representation, you must\" \"specify both","span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set","span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None:","self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim","self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int:","[batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings","is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not","= None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim = input_dim","not None: # It's not strictly necessary to multiply the span indices by","max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0","rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings","num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not","= None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if","span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None:","span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding","Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is not","span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask","self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward(","safe. span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size,","num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is not None:","= util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings *","int = None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim =","bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings =","span_width_embedding_dim.\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def","was padded with something other # than 0 (such as -1, which would","that the span representation was padded with something other # than 0 (such","allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor):",") elif num_width_embeddings is not None or span_width_embedding_dim is not None: raise ConfigurationError(","return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor =","0 (such as -1, which would be an invalid index), so we do","-> int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim","width embedding representation, you must\" \"specify both num_width_buckets and span_width_embedding_dim.\" ) else: self._span_width_embedding","None: # Embed the span widths and concatenate to the rest of the","to # be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends *","= span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if","as -1, which would be an invalid index), so we do so anyway","index), so we do so anyway to # be safe. span_starts = span_starts","span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() #",") else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings,","span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1)","the mask here, # but it's possible that the span representation was padded","is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self,","input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and","get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not","do so anyway to # be safe. span_starts = span_starts * span_indices_mask span_ends","torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None: #","[batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not","SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError"],"string":"[\n \"import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def\",\n \"torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) ->\",\n \"torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends =\",\n \"# It's not strictly necessary to multiply the span indices by the mask\",\n \"import torch from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import\",\n \"elif num_width_embeddings is not None or span_width_embedding_dim is not None: raise ConfigurationError( \\\"To\",\n \"span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask =\",\n \"def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None,\",\n \"* span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) #\",\n \"= span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor,\",\n \"None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor,\",\n \"def get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim()\",\n \"overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn\",\n \"None or span_width_embedding_dim is not None: raise ConfigurationError( \\\"To use a span width\",\n \"import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int,\",\n \"or span_width_embedding_dim is not None: raise ConfigurationError( \\\"To use a span width embedding\",\n \"dim=-1)] if span_indices_mask is not None: # It's not strictly necessary to multiply\",\n \"-> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None:\",\n \"def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is\",\n \"get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return\",\n \"False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths =\",\n \"num_width_embeddings is not None or span_width_embedding_dim is not None: raise ConfigurationError( \\\"To use\",\n \"span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask\",\n \"None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings\",\n \"span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None:\",\n \"by the mask here, # but it's possible that the span representation was\",\n \"input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool =\",\n \"# but it's possible that the span representation was padded with something other\",\n \"span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: # Embed\",\n \"torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size,\",\n \"num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings =\",\n \"@overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask:\",\n \"None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for\",\n \"not strictly necessary to multiply the span indices by the mask here, #\",\n \"allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks\",\n \"span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends\",\n \"# than 0 (such as -1, which would be an invalid index), so\",\n \"so we do so anyway to # be safe. span_starts = span_starts *\",\n \"span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to\",\n \"set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num,\",\n \"span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is\",\n \"-> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if\",\n \"- span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths)\",\n \"mask here, # but it's possible that the span representation was padded with\",\n \"self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor,\",\n \"embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings\",\n \"self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not None:\",\n \"span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None:\",\n \"span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num,\",\n \"widths and concatenate to the rest of the representations. if self._bucket_widths: span_widths =\",\n \"ConfigurationError( \\\"To use a span width embedding representation, you must\\\" \\\"specify both num_width_buckets\",\n \"index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not strictly\",\n \"to the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends -\",\n \"class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int\",\n \"None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding\",\n \"<filename>spanmb/models/span_extractor.py import torch from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor\",\n \"util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings:\",\n \"overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util\",\n \"[index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's\",\n \"from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from\",\n \"torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: # Embed the span\",\n \"if span_indices_mask is not None: # It's not strictly necessary to multiply the\",\n \"num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings,\",\n \"for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not\",\n \"necessary to multiply the span indices by the mask here, # but it's\",\n \"super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is\",\n \"Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__(\",\n \"sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape\",\n \"representation was padded with something other # than 0 (such as -1, which\",\n \"# [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask\",\n \"It's not strictly necessary to multiply the span indices by the mask here,\",\n \"# [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is\",\n \"self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor\",\n \"span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings *\",\n \"MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int =\",\n \"span widths and concatenate to the rest of the representations. if self._bucket_widths: span_widths\",\n \"if num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding(\",\n \"def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor\",\n \"so anyway to # be safe. span_starts = span_starts * span_indices_mask span_ends =\",\n \"= util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts\",\n \"a span width embedding representation, you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" )\",\n \"span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not strictly necessary to\",\n \"shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if\",\n \"util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings\",\n \"span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings =\",\n \"torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings * span_indices_mask.unsqueeze(-1) return\",\n \"* span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if\",\n \"forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor =\",\n \"= [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None: #\",\n \"in span_indices.split(1, dim=-1)] if span_indices_mask is not None: # It's not strictly necessary\",\n \"\\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def get_input_dim(self) ->\",\n \"and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings\",\n \"would be an invalid index), so we do so anyway to # be\",\n \"we do so anyway to # be safe. span_starts = span_starts * span_indices_mask\",\n \"util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float()\",\n \"to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings =\",\n \"= torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings * span_indices_mask.unsqueeze(-1)\",\n \") else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self)\",\n \"other # than 0 (such as -1, which would be an invalid index),\",\n \"torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding\",\n \"= span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] #\",\n \"an invalid index), so we do so anyway to # be safe. span_starts\",\n \"use a span width embedding representation, you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\"\",\n \"span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size]\",\n \"than 0 (such as -1, which would be an invalid index), so we\",\n \"else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings],\",\n \"padded with something other # than 0 (such as -1, which would be\",\n \"num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool = False, )\",\n \"bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding =\",\n \"span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings\",\n \"allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim:\",\n \"None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings\",\n \"span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends -\",\n \"num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is\",\n \"self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None,\",\n \"not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor:\",\n \"not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None\",\n \"\\\"To use a span width embedding representation, you must\\\" \\\"specify both num_width_buckets and\",\n \"strictly necessary to multiply the span indices by the mask here, # but\",\n \"here, # but it's possible that the span representation was padded with something\",\n \"# set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size,\",\n \"the span widths and concatenate to the rest of the representations. if self._bucket_widths:\",\n \"self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths = span_ends\",\n \"be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask #\",\n \"but it's possible that the span representation was padded with something other #\",\n \"concatenate to the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends\",\n \"ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim:\",\n \"sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, )\",\n \"span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not\",\n \"of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings )\",\n \"must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def get_input_dim(self)\",\n \"torch from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor\",\n \"= num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is\",\n \"from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class\",\n \"None, span_width_embedding_dim: int = None, bucket_widths: bool = False, ) -> None: super().__init__()\",\n \"is not None or span_width_embedding_dim is not None: raise ConfigurationError( \\\"To use a\",\n \"= False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths\",\n \"the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else:\",\n \"import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int = None,\",\n \"span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not\",\n \"= None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans)\",\n \"span_width_embedding_dim: int = None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim\",\n \"span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask\",\n \"= input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None\",\n \"= Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is\",\n \"embedding representation, you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding =\",\n \"num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int: return\",\n \"and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim\",\n \"you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def\",\n \"else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) ->\",\n \"None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1,\",\n \"int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides\",\n \"import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import\",\n \"invalid index), so we do so anyway to # be safe. span_starts =\",\n \"multiply the span indices by the mask here, # but it's possible that\",\n \"return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim\",\n \"not None or span_width_embedding_dim is not None: raise ConfigurationError( \\\"To use a span\",\n \"span indices by the mask here, # but it's possible that the span\",\n \"something other # than 0 (such as -1, which would be an invalid\",\n \"int = None, span_width_embedding_dim: int = None, bucket_widths: bool = False, ) ->\",\n \"+ self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask:\",\n \"= None, ) -> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1)\",\n \"* span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings,\",\n \"representation, you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None\",\n \"None, span_indices_mask: torch.BoolTensor = None, ) -> None: # shape (batch_size, num_spans) span_starts,\",\n \"to multiply the span indices by the mask here, # but it's possible\",\n \"int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None: return\",\n \"span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings =\",\n \"indices by the mask here, # but it's possible that the span representation\",\n \"representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths\",\n \"it's possible that the span representation was padded with something other # than\",\n \"self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None,\",\n \"None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif\",\n \"None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or\",\n \"(batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask\",\n \"2)[0] # span_mask if self._span_width_embedding is not None: # Embed the span widths\",\n \"from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from\",\n \"bool = False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings\",\n \"emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: #\",\n \"span_ends * span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices)\",\n \"is not None: # Embed the span widths and concatenate to the rest\",\n \"span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings * span_indices_mask.unsqueeze(-1) return span_max_embeddings\",\n \"if self._span_width_embedding is not None: # Embed the span widths and concatenate to\",\n \"self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not None: return self._input_dim +\",\n \"not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim )\",\n \"the span representation was padded with something other # than 0 (such as\",\n \"__init__( self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths:\",\n \"with something other # than 0 (such as -1, which would be an\",\n \"self._span_width_embedding is not None: # Embed the span widths and concatenate to the\",\n \"unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size]\",\n \"possible that the span representation was padded with something other # than 0\",\n \"self, input_dim: int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool\",\n \"span width embedding representation, you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else:\",\n \"which would be an invalid index), so we do so anyway to #\",\n \"be an invalid index), so we do so anyway to # be safe.\",\n \"span_mask if self._span_width_embedding is not None: # Embed the span widths and concatenate\",\n \"- span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is\",\n \"-> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in\",\n \"is not None: raise ConfigurationError( \\\"To use a span width embedding representation, you\",\n \"from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self,\",\n \"= torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not None: # Embed the\",\n \"# Embed the span widths and concatenate to the rest of the representations.\",\n \"span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings\",\n \"Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding\",\n \"span_width_embedding_dim is not None: raise ConfigurationError( \\\"To use a span width embedding representation,\",\n \"both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int:\",\n \"is not None: # It's not strictly necessary to multiply the span indices\",\n \"span representation was padded with something other # than 0 (such as -1,\",\n \"-1, which would be an invalid index), so we do so anyway to\",\n \"anyway to # be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends\",\n \"# be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask\",\n \"int, num_width_embeddings: int = None, span_width_embedding_dim: int = None, bucket_widths: bool = False,\",\n \"span_indices_mask is not None: # It's not strictly necessary to multiply the span\",\n \"0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings,\",\n \"= bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not None: self._span_width_embedding\",\n \"None: # It's not strictly necessary to multiply the span indices by the\",\n \"from torch.nn.parameter import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from\",\n \"import Parameter from overrides import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import\",\n \"self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim\",\n \"the span indices by the mask here, # but it's possible that the\",\n \"# span_mask if self._span_width_embedding is not None: # Embed the span widths and\",\n \"return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices:\",\n \"if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def\",\n \"span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0]\",\n \"self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not\",\n \"= None, span_width_embedding_dim: int = None, bucket_widths: bool = False, ) -> None:\",\n \") -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths\",\n \"not None: # Embed the span widths and concatenate to the rest of\",\n \"not None: raise ConfigurationError( \\\"To use a span width embedding representation, you must\\\"\",\n \"and concatenate to the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values(\",\n \"Embed the span widths and concatenate to the rest of the representations. if\",\n \"allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int =\",\n \"if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings ) else: span_widths =\",\n \"is not None and span_width_embedding_dim is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim\",\n \"= self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return\",\n \") -> None: # shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index\",\n \"import overrides from allennlp.modules.span_extractors.span_extractor import SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import\",\n \"embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is not None: raise\",\n \"from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def __init__( self, input_dim: int, num_width_embeddings: int\",\n \"None: raise ConfigurationError( \\\"To use a span width embedding representation, you must\\\" \\\"specify\",\n \"(such as -1, which would be an invalid index), so we do so\",\n \"= span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size, span_num, max_span_width,\",\n \"emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings\",\n \"# shape (batch_size, num_spans) span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)]\",\n \"the rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts,\",\n \"self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask is not None: return span_max_embeddings\",\n \"raise ConfigurationError( \\\"To use a span width embedding representation, you must\\\" \\\"specify both\",\n \"span_indices_mask # [batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set\",\n \"span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None:\",\n \"self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim\",\n \"self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int:\",\n \"[batch_size, span_num, max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings\",\n \"is not None: self._span_width_embedding = Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not\",\n \"= None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim = input_dim\",\n \"not None: # It's not strictly necessary to multiply the span indices by\",\n \"max_span_width, emb_size] span_embeddings, span_mask = util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0\",\n \"rest of the representations. if self._bucket_widths: span_widths = util.bucket_values( span_ends - span_starts, num_total_buckets=self._num_width_embeddings\",\n \"num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and span_width_embedding_dim is not\",\n \"= None def get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if\",\n \"span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)] if span_indices_mask is not None:\",\n \"span_mask.unsqueeze(-1).float() # [batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding\",\n \"Embedding( num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is not\",\n \"span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if span_indices_mask\",\n \"self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward(\",\n \"safe. span_starts = span_starts * span_indices_mask span_ends = span_ends * span_indices_mask # [batch_size,\",\n \"num_embeddings=num_width_embeddings, embedding_dim=span_width_embedding_dim ) elif num_width_embeddings is not None or span_width_embedding_dim is not None:\",\n \"= util.batched_span_select(sequence_tensor, span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings *\",\n \"int = None, bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim =\",\n \"bucket_widths: bool = False, ) -> None: super().__init__() self._input_dim = input_dim self._num_width_embeddings =\",\n \"span_width_embedding_dim.\\\" ) else: self._span_width_embedding = None def get_input_dim(self) -> int: return self._input_dim def\",\n \"was padded with something other # than 0 (such as -1, which would\",\n \"that the span representation was padded with something other # than 0 (such\",\n \"allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor):\",\n \") elif num_width_embeddings is not None or span_width_embedding_dim is not None: raise ConfigurationError(\",\n \"return self._input_dim @overrides def forward( self, sequence_tensor: torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor =\",\n \"0 (such as -1, which would be an invalid index), so we do\",\n \"-> int: if self._span_width_embedding is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim\",\n \"width embedding representation, you must\\\" \\\"specify both num_width_buckets and span_width_embedding_dim.\\\" ) else: self._span_width_embedding\",\n \"None: # Embed the span widths and concatenate to the rest of the\",\n \"to # be safe. span_starts = span_starts * span_indices_mask span_ends = span_ends *\",\n \"= span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1) if\",\n \"as -1, which would be an invalid index), so we do so anyway\",\n \"index), so we do so anyway to # be safe. span_starts = span_starts\",\n \"span_indices) # set unmask embeddings to 0 span_embeddings = span_embeddings * span_mask.unsqueeze(-1).float() #\",\n \") else: span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings,\",\n \"span_widths = span_ends - span_starts span_width_embeddings = self._span_width_embedding(span_widths) span_max_embeddings = torch.cat([span_max_embeddings, span_width_embeddings], -1)\",\n \"the mask here, # but it's possible that the span representation was padded\",\n \"is not None: return self._input_dim + self._span_width_embedding.get_output_dim() return self._input_dim @overrides def forward( self,\",\n \"input_dim self._num_width_embeddings = num_width_embeddings self._bucket_widths = bucket_widths if num_width_embeddings is not None and\",\n \"get_input_dim(self) -> int: return self._input_dim def get_output_dim(self) -> int: if self._span_width_embedding is not\",\n \"do so anyway to # be safe. span_starts = span_starts * span_indices_mask span_ends\",\n \"torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) -> None: #\",\n \"[batch_size, span_num, emb_size] span_max_embeddings = torch.max(span_embeddings, 2)[0] # span_mask if self._span_width_embedding is not\",\n \"SpanExtractor from allennlp.modules.token_embedders.embedding import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError\"\n]","isConversational":false}}},{"rowIdx":12864197,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":12864198,"cells":{"ngram":{"kind":"list like","value":["power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode","not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if","def preset_modes(self): \"\"\"Return the list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self,","ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER","'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID","= { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO:","# _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending","return None @property def preset_modes(self): \"\"\"Return the list of available preset modes.\"\"\" return","None @property def swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode):","def hvac_modes(self): \"\"\"Return the list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self,","PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2',","PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list of supported features.\"\"\" return self._support_flags @property","'max': 21 } } # self._model = model # _LOGGER.info(f'Setting model to {model}')","\"\"\"Set new power level.\"\"\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise","= 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco'","swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return","@property def supported_features(self): \"\"\"Return the list of supported features.\"\"\" return self._support_flags @property def","def swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new","of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\"","temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the","'1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101',","'5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class","set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def","== STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \"\"\"Return the supported","minimum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None","ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE =","'104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE:","return None @property def swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self,","self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \"\"\"Set new","else: return None @property def swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def","ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON:","dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode from api status.","list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode from","swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level =","self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property","return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list of available HVAC modes.\"\"\" return","+ ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID =","min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min']","!= STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \"\"\"Set new target","@property def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return","the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return","if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def","21 } } # self._model = model # _LOGGER.info(f'Setting model to {model}') @property","ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF:","WiFi Heater device. \"\"\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE,","ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE","PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list of supported features.\"\"\"","\"\"\"Return the temperature we try to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE])","'3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE","self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \"\"\"Return the","return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature we try to reach.\"\"\" if","self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self):","temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and","STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE:","= 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1',","return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat or Off.\"\"\"","= self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return","Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise","} SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a","must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE],","new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current","'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2',","import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import","Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE,","GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5',","'106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = {","= 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO:","self._model = model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the","'12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270","unit of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature we try","target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None:","features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return the polling state.\"\"\" return True @property","= 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE","= 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON =","ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return","\"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the heater you have\"\"\"","dps_mode) @property def preset_mode(self): \"\"\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode","the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC mode, ie","STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF,","of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature we try to","of the climate device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return the unit of","modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode]","'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO","= self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in","= self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE","PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE,","None @property def preset_modes(self): \"\"\"Return the list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys())","\"\"\"Return the polling state.\"\"\" return True @property def name(self): \"\"\"Return the name of","SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature'","STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE:","new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}')","= int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set","device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit","ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER =","{ STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max':","preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the power","def target_temperature_step(self): \"\"\"Return the supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def","api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode =","step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\"","dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO:","'3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE |","not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be","to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode","True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL","ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR","dps_mode) else: return None @property def swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys())","HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C',","the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif","target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\"min\"]}","self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode","= 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE:","'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 } } # self._model","dps_mode) else: return None @property def preset_modes(self): \"\"\"Return the list of available preset","(str): The device's name. device (GoldairTuyaDevice): The device API instance.\"\"\" self._device = device","is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode","self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature))","\"\"\"Representation of a Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater. Args:","HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE:","self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the","hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self):","= self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load","self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs):","@property def swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set","the heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The device API","ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = {","homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE,","maximum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None","'1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101',","'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation","return None def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not","ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO =","37 }, STATE_ECO: { 'min': 5, 'max': 21 } } # self._model =","import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE","<= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\"min\"]} and","def hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE])","elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the current temperature.\"\"\"","name (str): The device's name. device (GoldairTuyaDevice): The device API instance.\"\"\" self._device =","return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property","the supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the","import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import","power level.\"\"\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power","temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def","**kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE)","\"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature","self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT:","available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode","'106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE:","\"\"\"Return the list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set","}, STATE_ECO: { 'min': 5, 'max': 21 } } # self._model = model","= self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature","dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode","'min': 5, 'max': 21 } } # self._model = model # _LOGGER.info(f'Setting model","heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater. Args: name (str): The device's name.","not in keys: _LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting","({target_temperature}) must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT:","set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <=","{json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else:","\"\"\"Return the supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return","STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property","\"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC mode,","the list of supported features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return the polling","| SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\" def","'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 } }","'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1',","ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not","def name(self): \"\"\"Return the name of the climate device.\"\"\" return self._device.name @property def","correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID","PROPERTY_TO_DPS_ID depending on the model of the heater you have\"\"\" if self._device.model ==","STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode]","False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF'","\"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else:","= swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level","def should_poll(self): \"\"\"Return the polling state.\"\"\" return True @property def name(self): \"\"\"Return the","target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You","The device's name. device (GoldairTuyaDevice): The device API instance.\"\"\" self._device = device self._support_flags","if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level]",") from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT,","STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys())","to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the model","you have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def","\"\"\"Return current HVAC mode, ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode","from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from","} POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4':","ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT:","else: return None @property def target_temperature_step(self): \"\"\"Return the supported step of target temperature.\"\"\"","WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater. Args: name (str): The device's","temperature ({target_temperature}) must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode ==","target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot","in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']:","swing_mode): \"\"\"Set new power level.\"\"\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys():","ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE =","\"\"\"Return the name of the climate device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return","limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between","def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return","preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def","and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO:","GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \"\"\"Return the list of available","\"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\"","is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature):","_LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO","{ 'min': 5, 'max': 21 } } # self._model = model # _LOGGER.info(f'Setting","preset_mode(self): \"\"\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys","ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE,","set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if","@property def preset_mode(self): \"\"\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode =","preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was:","mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode","'2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS =","POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level) def update(self):","@property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the","mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the power level.\"\"\"","temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat or","{ STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop',","device's name. device (GoldairTuyaDevice): The device API instance.\"\"\" self._device = device self._support_flags =","self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat or Off.\"\"\" dps_mode","'4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE |","dps_mode) @property def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode","( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import (","} } # self._model = model # _LOGGER.info(f'Setting model to {model}') @property def","== STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE])","STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1':","self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\"","temperature we try to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode","self._device.name @property def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit @property def","depending on the model of the heater you have\"\"\" if self._device.model == \"GECO270\":","climate device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return","Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is","self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze","device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min':","STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2':","a Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater. Args: name (str):","target_temperature(self): \"\"\"Return the temperature we try to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return","if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def","current HVAC mode, ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is","power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level =","return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list of supported features.\"\"\" return self._support_flags","PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6',","return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \"\"\"Return the supported step of","True @property def name(self): \"\"\"Return the name of the climate device.\"\"\" return self._device.name","list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode)","== ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return","instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = {","if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode","the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature","temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <=","try to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO:","5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 } } #","\"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else:","of the heater you have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return","@property def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self):","Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater. Args: name (str): The","def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE))","( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice","f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode ==","current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id)","@property def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode ==","ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE:","SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair","self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return","= 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER =","ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON:","in keys: _LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting to","supported features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return the polling state.\"\"\" return True","HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE","new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not","{model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the model of","ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' }","'6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } #","'1': '1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' }","dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return","\"\"\"Return the list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set","STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2',","return None @property def target_temperature_step(self): \"\"\"Return the supported step of target temperature.\"\"\" return","\"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return","Goldair WiFi Heater device. \"\"\" import logging import json from homeassistant.const import (","else: return None @property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and","{ HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO',","API instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS =","ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER","load correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode},","should_poll(self): \"\"\"Return the polling state.\"\"\" return True @property def name(self): \"\"\"Return the name","'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto':","dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current preset mode, ie","not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \"\"\"Return the","set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def","'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto'","@property def hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat or Off.\"\"\" dps_mode =","if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return",") if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature)","set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level = swing_mode if new_level not in","{ ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12',","if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def","Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if","'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_'","return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if","None @property def target_temperature_step(self): \"\"\"Return the supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP","'2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE","else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list of supported features.\"\"\" return","_LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not","'3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104',","= 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4',","ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON","available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode","supported_features(self): \"\"\"Return the list of supported features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return","= 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE =","!= STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \"\"\"Return the maximum","'105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE:","raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' )","state.\"\"\" return True @property def name(self): \"\"\"Return the name of the climate device.\"\"\"","model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the","else: return None @property def preset_modes(self): \"\"\"Return the list of available preset modes.\"\"\"","ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE:","STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\"","'1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS","ValueError('You cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not","GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK:","return None @property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode","| SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\" def __init__(self, device):","self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \"\"\"Return the","None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode","of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level","None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list","'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of","device (GoldairTuyaDevice): The device API instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP","polling state.\"\"\" return True @property def name(self): \"\"\"Return the name of the climate","@property def target_temperature(self): \"\"\"Return the temperature we try to reach.\"\"\" if self.preset_mode ==","if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property","if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must","in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level) def","def target_temperature(self): \"\"\"Return the temperature we try to reach.\"\"\" if self.preset_mode == STATE_COMFORT:","{ 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5',","mode, ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None:","between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif","_LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on","return self._device.name @property def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\" return self._device.temperature_unit @property","'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode'","\"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return","dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self):","levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level = swing_mode","if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else:","SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37","'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user'","ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3',","device. \"\"\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE","def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE:","'5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106'","target_temperature) @property def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self):","heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The device API instance.\"\"\"","ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL","the list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new","HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__)","was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None:","dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else:","'4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105',","== \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list","self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 },","= { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT:","ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270","@property def preset_modes(self): \"\"\"Return the list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def","elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \"\"\"Return","target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode","} HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT:","preset_modes(self): \"\"\"Return the list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode):","ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE )","ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE","GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \"\"\"List of power levels.\"\"\" return","= 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort'","{ ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12',","max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max']","'4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE","'2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE","heater you have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property","of supported features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return the polling state.\"\"\" return","None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature =","Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else:","= SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max':","preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits","have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self):","dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list of available HVAC","} # self._model = model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self):","@property def should_poll(self): \"\"\"Return the polling state.\"\"\" return True @property def name(self): \"\"\"Return","Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could","POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4',","self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct","return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \"\"\"Return the list of","ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = {","STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return","return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE],","HVAC mode, ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not","kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self,","the list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new","TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE,","not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature","== STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits =","logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate","= { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT:","# GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE:","new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL],","= list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode","list of supported features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return the polling state.\"\"\"","{ 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 }","we try to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode ==","measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature we try to reach.\"\"\"","\"\"\"Initialize the heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The device","the name of the climate device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return the","self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min':","target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the current","= logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO =","from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE,","'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3': '3',","SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\"","is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \"\"\"Return","self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \"\"\"Return the supported step of target","STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \"\"\"Return the supported step","GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list of available","= model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct","Args: name (str): The device's name. device (GoldairTuyaDevice): The device API instance.\"\"\" self._device","class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the","from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode","logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto'","STATE_ECO: { 'min': 5, 'max': 21 } } # self._model = model #","def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current","= 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE =","} PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL =","hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if","self._support_flags @property def should_poll(self): \"\"\"Return the polling state.\"\"\" return True @property def name(self):","= 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None","@property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE:","self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode !=","<= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between '","def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property","preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return","return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level = swing_mode if","None @property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode !=","or Off.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode)","= 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL =","'101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False,","'5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice):","and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \"\"\"Set","ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if","is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return","mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}')","@property def hvac_modes(self): \"\"\"Return the list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def","mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current preset mode,","return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode","status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C'","import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from","ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE =","preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode =","' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode","limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}'","== STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self):","custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT","{dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return","modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode]","kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode =","reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE])","SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE =","def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the heater","\"\"\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys =","= 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze'","model of the heater you have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else:","\"\"\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE )","@property def target_temperature_step(self): \"\"\"Return the supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property","supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum","= PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode =","STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21","_LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode","self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def","swing_modes(self): \"\"\"List of power levels.\"\"\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power","json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice","None def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None:","list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode)","SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK","homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate","return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def","the heater you have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID","of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if","\"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is","self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature})","@property def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE:","def preset_mode(self): \"\"\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE])","SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize","self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode ==","not load correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was:","to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return","get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the heater you","ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE:","and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \"\"\"Return","ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None","level.\"\"\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level:","ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT =","def supported_features(self): \"\"\"Return the list of supported features.\"\"\" return self._support_flags @property def should_poll(self):","be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature)","of a Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater. Args: name","HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current preset mode, ie Comfort, Eco,","'3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105',","def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property","self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def","'2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104',","def min_temp(self): \"\"\"Return the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return","= { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5,","on the model of the heater you have\"\"\" if self._device.model == \"GECO270\": return","'4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106'","STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \"\"\"Return the maximum temperature.\"\"\"","== STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None","dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self):","@property def name(self): \"\"\"Return the name of the climate device.\"\"\" return self._device.name @property","current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC","1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: {","mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError(","return self._support_flags @property def should_poll(self): \"\"\"Return the polling state.\"\"\" return True @property def","HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current preset","ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL:","GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault'","'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE","\"\"\" Goldair WiFi Heater device. \"\"\" import logging import json from homeassistant.const import","not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level)","self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5,","ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' }","'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on'","None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \"\"\"Return the list","import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import","level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode ==","keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset","the temperature we try to reach.\"\"\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif","raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if",") from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK =","model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get the correct PROPERTY_TO_DPS_ID","5, 'max': 21 } } # self._model = model # _LOGGER.info(f'Setting model to","if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def","'101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 =","preset_mode): \"\"\"Set new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self):","= { 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5':","the climate device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return the unit of measurement.\"\"\"","ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True }","the unit of measurement.\"\"\" return self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature we","if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self,","dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not","name. device (GoldairTuyaDevice): The device API instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS","= device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: {","'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK:","keys: _LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting to Comfort')","'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT","ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1',","The device API instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1","== ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \"\"\"List of","return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \"\"\"List of power levels.\"\"\"","def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level = swing_mode if new_level not","\"GECO270\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list of","} # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4',","the minimum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return","PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE])","import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT =","'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3',","preset mode, ie Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if","= { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop':","= HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \"\"\"Return current preset mode, ie Comfort,","device API instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS","Heater device. \"\"\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS,","device): \"\"\"Initialize the heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The","temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property","def __init__(self, device): \"\"\"Initialize the heater. Args: name (str): The device's name. device","'104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True","was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode)","the correct PROPERTY_TO_DPS_ID depending on the model of the heater you have\"\"\" if","return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list of","{limits[\"max\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE],","homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const","__init__(self, device): \"\"\"Initialize the heater. Args: name (str): The device's name. device (GoldairTuyaDevice):","(GoldairTuyaDevice): The device API instance.\"\"\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP =","\"\"\"Return the list of supported features.\"\"\" return self._support_flags @property def should_poll(self): \"\"\"Return the","hvac_modes(self): \"\"\"Return the list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode):","HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' }","if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.')","GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\" def __init__(self, device): \"\"\"Initialize the heater.","self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self):","ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL:","ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' +","HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode =","self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if","set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise","from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from","PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = {","ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE:","dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \"\"\"List","new power level.\"\"\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid","of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset mode.\"\"\"","name(self): \"\"\"Return the name of the climate device.\"\"\" return self._device.name @property def temperature_unit(self):","f'Target temperature ({target_temperature}) must be between ' f'{limits[\"min\"]} and {limits[\"max\"]}' ) if preset_mode","list of available HVAC modes.\"\"\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC","self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE)","else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the list of available HVAC modes.\"\"\"","# self._model = model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \"\"\"Get","the polling state.\"\"\" return True @property def name(self): \"\"\"Return the name of the","STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3':","'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE:","if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode)","raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level) def update(self): self._device.refresh()","preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature","temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE))","the model of the heater you have\"\"\" if self._device.model == \"GECO270\": return PROPERTY_TO_DPS_ID_GECO270","if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode from api","'12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = {","'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property","correct PROPERTY_TO_DPS_ID depending on the model of the heater you have\"\"\" if self._device.model","name of the climate device.\"\"\" return self._device.name @property def temperature_unit(self): \"\"\"Return the unit","new preset mode.\"\"\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \"\"\"Return the","Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys:","'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level'","dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return","from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock'","ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \"\"\"List of power","\"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL])","Comfort, Eco, Anti-freeze.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in","target_temperature_step(self): \"\"\"Return the supported step of target temperature.\"\"\" return self._TEMPERATURE_STEP @property def min_temp(self):","'105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE","STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property","list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \"\"\"Set new power level.\"\"\" new_level = swing_mode if new_level","elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self):","int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the","self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \"\"\"Return the","cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min']","def swing_mode(self): \"\"\"Return the power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER:","self._device.temperature_unit @property def target_temperature(self): \"\"\"Return the temperature we try to reach.\"\"\" if self.preset_mode","else: return None def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if kwargs.get(ATTR_PRESET_MODE) is","return True @property def name(self): \"\"\"Return the name of the climate device.\"\"\" return","return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \"\"\"Set new target temperatures.\"\"\" if","return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \"\"\"Set new HVAC mode.\"\"\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE],","self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL,","SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi heater.\"\"\" def __init__(self,","limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target","= SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \"\"\"Representation of a Goldair WiFi","current temperature.\"\"\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \"\"\"Return current HVAC mode, ie Heat","def max_temp(self): \"\"\"Return the maximum temperature.\"\"\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return","not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \"\"\"Return the","return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \"\"\"Return the list of supported","list of available preset modes.\"\"\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \"\"\"Set new preset"],"string":"[\n \"power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode\",\n \"not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if\",\n \"def preset_modes(self): \\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self,\",\n \"ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER\",\n \"'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID\",\n \"= { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO:\",\n \"# _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending\",\n \"return None @property def preset_modes(self): \\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\" return\",\n \"None @property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode):\",\n \"def hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self,\",\n \"PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2',\",\n \"PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return self._support_flags @property\",\n \"'max': 21 } } # self._model = model # _LOGGER.info(f'Setting model to {model}')\",\n \"\\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise\",\n \"= 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco'\",\n \"swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return\",\n \"@property def supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return self._support_flags @property def\",\n \"def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new\",\n \"of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\"\",\n \"temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the\",\n \"'1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101',\",\n \"'5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class\",\n \"set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def\",\n \"== STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported\",\n \"minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None\",\n \"ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE =\",\n \"'104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE:\",\n \"return None @property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self,\",\n \"self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new\",\n \"else: return None @property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def\",\n \"ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON:\",\n \"dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode from api status.\",\n \"list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode from\",\n \"swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level =\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property\",\n \"return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\" return\",\n \"+ ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID =\",\n \"min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min']\",\n \"!= STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target\",\n \"@property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return\",\n \"the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return\",\n \"if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def\",\n \"21 } } # self._model = model # _LOGGER.info(f'Setting model to {model}') @property\",\n \"ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF:\",\n \"WiFi Heater device. \\\"\\\"\\\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE,\",\n \"ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE\",\n \"PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\"\",\n \"\\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE])\",\n \"'3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE\",\n \"self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \\\"\\\"\\\"Return the\",\n \"return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\" if\",\n \"self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self):\",\n \"temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and\",\n \"STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE:\",\n \"= 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1',\",\n \"return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat or Off.\\\"\\\"\\\"\",\n \"= self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return\",\n \"Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise\",\n \"} SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a\",\n \"must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE],\",\n \"new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current\",\n \"'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2',\",\n \"import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import\",\n \"Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE,\",\n \"GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5',\",\n \"'106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = {\",\n \"= 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO:\",\n \"self._model = model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the\",\n \"'12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270\",\n \"unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature we try\",\n \"target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None:\",\n \"features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property\",\n \"= 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE\",\n \"= 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON =\",\n \"ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return\",\n \"\\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the heater you have\\\"\\\"\\\"\",\n \"dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode\",\n \"the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie\",\n \"STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF,\",\n \"of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature we try to\",\n \"of the climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit of\",\n \"modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode]\",\n \"'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO\",\n \"= self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in\",\n \"= self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE\",\n \"PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE,\",\n \"None @property def preset_modes(self): \\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys())\",\n \"\\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return the name of\",\n \"SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature'\",\n \"STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE:\",\n \"new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}')\",\n \"= int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set\",\n \"device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit\",\n \"ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER =\",\n \"{ STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max':\",\n \"preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power\",\n \"def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def\",\n \"api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode =\",\n \"step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\"\",\n \"dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO:\",\n \"'3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE |\",\n \"not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be\",\n \"to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode\",\n \"True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL\",\n \"ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR\",\n \"dps_mode) else: return None @property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys())\",\n \"HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C',\",\n \"the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif\",\n \"target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]}\",\n \"self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode\",\n \"= 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE:\",\n \"'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 } } # self._model\",\n \"dps_mode) else: return None @property def preset_modes(self): \\\"\\\"\\\"Return the list of available preset\",\n \"(str): The device's name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device = device\",\n \"is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode\",\n \"self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature))\",\n \"\\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args:\",\n \"HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE:\",\n \"self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the\",\n \"hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self):\",\n \"= self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load\",\n \"self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs):\",\n \"@property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set\",\n \"the heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The device API\",\n \"ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = {\",\n \"homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE,\",\n \"maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None\",\n \"'1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101',\",\n \"'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation\",\n \"return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not\",\n \"ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO =\",\n \"37 }, STATE_ECO: { 'min': 5, 'max': 21 } } # self._model =\",\n \"import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE\",\n \"<= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and\",\n \"def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE])\",\n \"elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\"\",\n \"name (str): The device's name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device =\",\n \"return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property\",\n \"the supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the\",\n \"import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import\",\n \"power level.\\\"\\\"\\\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power\",\n \"temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def\",\n \"**kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE)\",\n \"\\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature\",\n \"self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT:\",\n \"available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode\",\n \"'106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE:\",\n \"\\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set\",\n \"}, STATE_ECO: { 'min': 5, 'max': 21 } } # self._model = model\",\n \"= self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature\",\n \"dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode\",\n \"'min': 5, 'max': 21 } } # self._model = model # _LOGGER.info(f'Setting model\",\n \"heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name (str): The device's name.\",\n \"not in keys: _LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting\",\n \"({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT:\",\n \"set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <=\",\n \"{json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else:\",\n \"\\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return\",\n \"STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property\",\n \"\\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode,\",\n \"the list of supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling\",\n \"| SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def\",\n \"'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 } }\",\n \"'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1',\",\n \"ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not\",\n \"def name(self): \\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\" return self._device.name @property def\",\n \"correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID\",\n \"PROPERTY_TO_DPS_ID depending on the model of the heater you have\\\"\\\"\\\" if self._device.model ==\",\n \"STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode]\",\n \"False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF'\",\n \"\\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else:\",\n \"= swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level\",\n \"def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return the\",\n \"target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You\",\n \"The device's name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device = device self._support_flags\",\n \"if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level]\",\n \") from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT,\",\n \"STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys())\",\n \"to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the model\",\n \"you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def\",\n \"\\\"\\\"\\\"Return current HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode\",\n \"from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from\",\n \"} POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4':\",\n \"ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT:\",\n \"else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\"\",\n \"WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name (str): The device's\",\n \"temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode ==\",\n \"target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot\",\n \"in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']:\",\n \"swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys():\",\n \"ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE =\",\n \"\\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return\",\n \"limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between\",\n \"def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return\",\n \"preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def\",\n \"and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO:\",\n \"GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \\\"\\\"\\\"Return the list of available\",\n \"\\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\"\",\n \"is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature):\",\n \"_LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO\",\n \"{ 'min': 5, 'max': 21 } } # self._model = model # _LOGGER.info(f'Setting\",\n \"preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys\",\n \"ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE,\",\n \"set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if\",\n \"@property def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode =\",\n \"preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was:\",\n \"mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode\",\n \"'2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS =\",\n \"POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level) def update(self):\",\n \"@property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the\",\n \"mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\"\",\n \"temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat or\",\n \"{ STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop',\",\n \"device's name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device = device self._support_flags =\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode\",\n \"'4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE |\",\n \"dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode\",\n \"( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import (\",\n \"} } # self._model = model # _LOGGER.info(f'Setting model to {model}') @property def\",\n \"== STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE])\",\n \"STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1':\",\n \"self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\"\",\n \"temperature we try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode\",\n \"self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def\",\n \"depending on the model of the heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\":\",\n \"climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return\",\n \"Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is\",\n \"self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze\",\n \"device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min':\",\n \"STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2':\",\n \"a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name (str):\",\n \"target_temperature(self): \\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return\",\n \"if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def\",\n \"current HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is\",\n \"power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level =\",\n \"return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return self._support_flags\",\n \"PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6',\",\n \"return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of\",\n \"True @property def name(self): \\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\" return self._device.name\",\n \"list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode)\",\n \"== ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return\",\n \"instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = {\",\n \"if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode\",\n \"the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature\",\n \"temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <=\",\n \"try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO:\",\n \"5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 } } #\",\n \"\\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else:\",\n \"of the heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return\",\n \"@property def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self):\",\n \"Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name (str): The\",\n \"def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE))\",\n \"( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice\",\n \"f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode ==\",\n \"current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id)\",\n \"@property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode ==\",\n \"ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE:\",\n \"SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair\",\n \"self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return\",\n \"= 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER =\",\n \"ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON:\",\n \"in keys: _LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting to\",\n \"supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True\",\n \"HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE\",\n \"new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not\",\n \"{model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the model of\",\n \"ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' }\",\n \"'6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } #\",\n \"'1': '1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' }\",\n \"dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return\",\n \"\\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set\",\n \"STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2',\",\n \"return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\" return\",\n \"\\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return\",\n \"Goldair WiFi Heater device. \\\"\\\"\\\" import logging import json from homeassistant.const import (\",\n \"else: return None @property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and\",\n \"{ HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO',\",\n \"API instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS =\",\n \"ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER\",\n \"load correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode},\",\n \"should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return the name\",\n \"'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto':\",\n \"dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie\",\n \"not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \\\"\\\"\\\"Return the\",\n \"set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def\",\n \"'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto'\",\n \"@property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode =\",\n \"if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return\",\n \") if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature)\",\n \"set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode if new_level not in\",\n \"{ ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12',\",\n \"if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def\",\n \"Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if\",\n \"'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_'\",\n \"return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if\",\n \"None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP\",\n \"'2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE\",\n \"else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return\",\n \"_LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not\",\n \"'3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104',\",\n \"= 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4',\",\n \"ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON\",\n \"available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode\",\n \"supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return\",\n \"= 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE =\",\n \"!= STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \\\"\\\"\\\"Return the maximum\",\n \"'105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE:\",\n \"raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' )\",\n \"state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\"\",\n \"model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the\",\n \"else: return None @property def preset_modes(self): \\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\"\",\n \"ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE:\",\n \"STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\"\",\n \"'1', '2': '2', '3': '3', '4': '4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS\",\n \"ValueError('You cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not\",\n \"GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK:\",\n \"return None @property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode\",\n \"| SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device):\",\n \"self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the\",\n \"None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode\",\n \"of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level\",\n \"None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list\",\n \"'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of\",\n \"device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP\",\n \"polling state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return the name of the climate\",\n \"@property def target_temperature(self): \\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\" if self.preset_mode ==\",\n \"if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property\",\n \"if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must\",\n \"in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level) def\",\n \"def target_temperature(self): \\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT:\",\n \"{ 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5',\",\n \"mode, ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None:\",\n \"between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif\",\n \"_LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on\",\n \"return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property\",\n \"'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode'\",\n \"\\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return\",\n \"dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self):\",\n \"levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode\",\n \"if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else:\",\n \"SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37\",\n \"'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user'\",\n \"ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3',\",\n \"device. \\\"\\\"\\\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE\",\n \"def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE:\",\n \"'5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106'\",\n \"target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self):\",\n \"heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\"\",\n \"ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL\",\n \"the list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new\",\n \"HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__)\",\n \"was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None:\",\n \"dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else:\",\n \"'4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105',\",\n \"== \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list\",\n \"self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 },\",\n \"= { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT:\",\n \"ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270\",\n \"@property def preset_modes(self): \\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def\",\n \"elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return\",\n \"target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode\",\n \"} HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT:\",\n \"preset_modes(self): \\\"\\\"\\\"Return the list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode):\",\n \"ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE )\",\n \"ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE\",\n \"GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return\",\n \"= 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort'\",\n \"{ ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12',\",\n \"max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max']\",\n \"'4', '5': '5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE\",\n \"'2', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE\",\n \"heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property\",\n \"of supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return\",\n \"None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature =\",\n \"Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else:\",\n \"= SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max':\",\n \"preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits\",\n \"have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self):\",\n \"dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC\",\n \"} # self._model = model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self):\",\n \"@property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return\",\n \"Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could\",\n \"POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4',\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct\",\n \"return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \\\"\\\"\\\"Return the list of\",\n \"ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = {\",\n \"STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return\",\n \"return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE],\",\n \"HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not\",\n \"kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self,\",\n \"the list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new\",\n \"TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE,\",\n \"not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature\",\n \"== STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits =\",\n \"logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate\",\n \"= { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT:\",\n \"# GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE:\",\n \"new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL],\",\n \"= list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode\",\n \"list of supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\"\",\n \"{ 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21 }\",\n \"we try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode ==\",\n \"measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\"\",\n \"\\\"\\\"\\\"Initialize the heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The device\",\n \"the name of the climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the\",\n \"self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min':\",\n \"target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current\",\n \"= logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO =\",\n \"from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE,\",\n \"'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3': '3',\",\n \"SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\"\",\n \"is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \\\"\\\"\\\"Return\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target\",\n \"STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step\",\n \"GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list of available\",\n \"= model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct\",\n \"Args: name (str): The device's name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device\",\n \"class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the\",\n \"from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode\",\n \"logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto'\",\n \"STATE_ECO: { 'min': 5, 'max': 21 } } # self._model = model #\",\n \"def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current\",\n \"= 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE =\",\n \"} PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL =\",\n \"hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if\",\n \"self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property def name(self):\",\n \"= 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None\",\n \"@property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE:\",\n \"self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode !=\",\n \"<= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between '\",\n \"def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property\",\n \"preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return\",\n \"return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode if\",\n \"None @property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode !=\",\n \"or Off.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_HVAC_MODE]) if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode)\",\n \"= 'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL =\",\n \"'101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False,\",\n \"'5', 'Auto': 'auto' } SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice):\",\n \"and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set\",\n \"ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if\",\n \"is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return\",\n \"mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}')\",\n \"@property def hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def\",\n \"mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset mode,\",\n \"return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode\",\n \"status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was: {dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C'\",\n \"import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from\",\n \"ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE =\",\n \"preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode =\",\n \"' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode\",\n \"limits['max']: raise ValueError( f'Target temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}'\",\n \"== STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self):\",\n \"custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT\",\n \"{dps_mode}, PROPERTY_TO_DPS_ID was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return\",\n \"modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode]\",\n \"kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode =\",\n \"reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE])\",\n \"SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE =\",\n \"def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the heater\",\n \"\\\"\\\"\\\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys =\",\n \"= 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze'\",\n \"model of the heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else:\",\n \"\\\"\\\"\\\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE )\",\n \"@property def target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property\",\n \"supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum\",\n \"= PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode =\",\n \"STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5, 'max': 21\",\n \"_LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode\",\n \"self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def\",\n \"swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\" return list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power\",\n \"json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice\",\n \"None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None:\",\n \"list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode)\",\n \"SUPPORT_SWING_MODE ) from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK\",\n \"homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from custom_components.goldair_climate\",\n \"return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def\",\n \"the heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID\",\n \"of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if\",\n \"\\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is\",\n \"self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target temperature ({target_temperature})\",\n \"@property def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE:\",\n \"def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE])\",\n \"SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize\",\n \"self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode ==\",\n \"not load correct preset mode from api status. Defaulting to Comfort') _LOGGER.debug(f'dps_mode was:\",\n \"to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return\",\n \"get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID depending on the model of the heater you\",\n \"ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE:\",\n \"and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \\\"\\\"\\\"Return\",\n \"ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None\",\n \"level.\\\"\\\"\\\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level:\",\n \"ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT =\",\n \"def supported_features(self): \\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self):\",\n \"be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature)\",\n \"of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name\",\n \"HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort, Eco,\",\n \"'3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105',\",\n \"def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def\",\n \"'2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104',\",\n \"def min_temp(self): \\\"\\\"\\\"Return the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return\",\n \"= { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: { 'min': 5,\",\n \"on the model of the heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return\",\n \"'4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106'\",\n \"STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\"\",\n \"== STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None\",\n \"dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self):\",\n \"@property def name(self): \\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\" return self._device.name @property\",\n \"current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC\",\n \"1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5, 'max': 37 }, STATE_ECO: {\",\n \"mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError(\",\n \"return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the polling state.\\\"\\\"\\\" return True @property def\",\n \"HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset\",\n \"ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL:\",\n \"GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT = 'fault'\",\n \"'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT = 'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE\",\n \"\\\"\\\"\\\" Goldair WiFi Heater device. \\\"\\\"\\\" import logging import json from homeassistant.const import\",\n \"not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level)\",\n \"self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: { 'min': 5,\",\n \"ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' }\",\n \"'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on'\",\n \"None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property def preset_modes(self): \\\"\\\"\\\"Return the list\",\n \"import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import\",\n \"level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode ==\",\n \"keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset\",\n \"the temperature we try to reach.\\\"\\\"\\\" if self.preset_mode == STATE_COMFORT: return self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif\",\n \"raise ValueError('You cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if\",\n \") from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK =\",\n \"model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get the correct PROPERTY_TO_DPS_ID\",\n \"5, 'max': 21 } } # self._model = model # _LOGGER.info(f'Setting model to\",\n \"if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def\",\n \"'101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 =\",\n \"preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self):\",\n \"= { 'Stop': 'stop', '1': '1', '2': '2', '3': '3', '4': '4', '5':\",\n \"the climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit of measurement.\\\"\\\"\\\"\",\n \"ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True }\",\n \"the unit of measurement.\\\"\\\"\\\" return self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature we\",\n \"if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self,\",\n \"dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys: _LOGGER.debug(f'Could not\",\n \"name. device (GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS\",\n \"= device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS = { STATE_COMFORT: {\",\n \"'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3', ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK:\",\n \"keys: _LOGGER.debug(f'Could not load correct preset mode from api status. Defaulting to Comfort')\",\n \"'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' + ATTR_TARGET_TEMPERATURE STATE_COMFORT\",\n \"ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1',\",\n \"The device API instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1\",\n \"== ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \\\"\\\"\\\"List of\",\n \"return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \\\"\\\"\\\"List of power levels.\\\"\\\"\\\"\",\n \"def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode if new_level not\",\n \"\\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list of\",\n \"} # GOLDAIR GECO270 PROPERTY_TO_DPS_ID_GECO270 = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4',\",\n \"the minimum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return\",\n \"PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE])\",\n \"import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock' ATTR_FAULT =\",\n \"'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '2', ATTR_TEMPERATURE: '3',\",\n \"preset mode, ie Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if\",\n \"= { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop':\",\n \"= HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE], dps_mode) @property def preset_mode(self): \\\"\\\"\\\"Return current preset mode, ie Comfort,\",\n \"device API instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP = 1 self._TEMPERATURE_LIMITS\",\n \"Heater device. \\\"\\\"\\\" import logging import json from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS,\",\n \"device): \\\"\\\"\\\"Initialize the heater. Args: name (str): The device's name. device (GoldairTuyaDevice): The\",\n \"temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return self._TEMPERATURE_LIMITS[self.preset_mode]['min'] else: return None @property\",\n \"def __init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name (str): The device's name. device\",\n \"'104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True\",\n \"was: {json.dumps(self.get_property_to_dps_id)}') dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode)\",\n \"the correct PROPERTY_TO_DPS_ID depending on the model of the heater you have\\\"\\\"\\\" if\",\n \"return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list of\",\n \"{limits[\\\"max\\\"]}' ) if preset_mode == STATE_COMFORT: self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE],\",\n \"homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, STATE_UNAVAILABLE ) from homeassistant.components.climate import ClimateDevice from homeassistant.components.climate.const\",\n \"__init__(self, device): \\\"\\\"\\\"Initialize the heater. Args: name (str): The device's name. device (GoldairTuyaDevice):\",\n \"(GoldairTuyaDevice): The device API instance.\\\"\\\"\\\" self._device = device self._support_flags = SUPPORT_FLAGS self._TEMPERATURE_STEP =\",\n \"\\\"\\\"\\\"Return the list of supported features.\\\"\\\"\\\" return self._support_flags @property def should_poll(self): \\\"\\\"\\\"Return the\",\n \"hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode):\",\n \"HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' }\",\n \"if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature in Anti-freeze mode.')\",\n \"GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self, device): \\\"\\\"\\\"Initialize the heater.\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE]) elif self.preset_mode == STATE_ECO: return self._device.get_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE]) else: return None @property def target_temperature_step(self):\",\n \"ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE: '3', ATTR_TEMPERATURE: '4', ATTR_PRESET_MODE: '5', ATTR_CHILD_LOCK: '2', ATTR_FAULT: '12', ATTR_POWER_LEVEL:\",\n \"ATTR_POWER_LEVEL = 'power_level' ATTR_DISPLAY_ON = 'display_on' ATTR_POWER_MODE = 'power_mode' ATTR_ECO_TARGET_TEMPERATURE = 'eco_' +\",\n \"HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode =\",\n \"self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if\",\n \"set_target_temperature(self, target_temperature): target_temperature = int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise\",\n \"from homeassistant.components.climate.const import ( ATTR_HVAC_MODE, ATTR_PRESET_MODE, HVAC_MODE_OFF, HVAC_MODE_HEAT, SUPPORT_TARGET_TEMPERATURE, SUPPORT_PRESET_MODE, SUPPORT_SWING_MODE ) from\",\n \"PRESET_MODE_TO_DPS_MODE = { STATE_COMFORT: 'C', STATE_ECO: 'ECO', STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = {\",\n \"ATTR_PRESET_MODE: '4', ATTR_CHILD_LOCK: '6', ATTR_FAULT: '12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE:\",\n \"dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \\\"\\\"\\\"List\",\n \"new power level.\\\"\\\"\\\" new_level = swing_mode if new_level not in POWER_LEVEL_TO_DPS_LEVEL.keys(): raise ValueError(f'Invalid\",\n \"of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset mode.\\\"\\\"\\\"\",\n \"name(self): \\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self):\",\n \"f'Target temperature ({target_temperature}) must be between ' f'{limits[\\\"min\\\"]} and {limits[\\\"max\\\"]}' ) if preset_mode\",\n \"list of available HVAC modes.\\\"\\\"\\\" return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC\",\n \"self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE)\",\n \"else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the list of available HVAC modes.\\\"\\\"\\\"\",\n \"# self._model = model # _LOGGER.info(f'Setting model to {model}') @property def get_property_to_dps_id(self): \\\"\\\"\\\"Get\",\n \"the polling state.\\\"\\\"\\\" return True @property def name(self): \\\"\\\"\\\"Return the name of the\",\n \"STATE_ANTI_FREEZE: 'AF' } POWER_LEVEL_TO_DPS_LEVEL = { 'Stop': 'stop', '1': '1', '2': '2', '3':\",\n \"'Comfort' STATE_ECO = 'Eco' STATE_ANTI_FREEZE = 'Anti-freeze' PROPERTY_TO_DPS_ID = { ATTR_HVAC_MODE: '1', ATTR_TARGET_TEMPERATURE:\",\n \"if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode)\",\n \"raise ValueError(f'Invalid power level: {new_level}') dps_level = POWER_LEVEL_TO_DPS_LEVEL[new_level] self._device.set_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL], dps_level) def update(self): self._device.refresh()\",\n \"preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the temperature\",\n \"temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is not None: self.set_preset_mode(kwargs.get(ATTR_PRESET_MODE)) if kwargs.get(ATTR_TEMPERATURE) is not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE))\",\n \"the model of the heater you have\\\"\\\"\\\" if self._device.model == \\\"GECO270\\\": return PROPERTY_TO_DPS_ID_GECO270\",\n \"if dps_mode not in keys: _LOGGER.debug(f'Could not load correct preset mode from api\",\n \"'12', ATTR_POWER_LEVEL: '101', ATTR_DISPLAY_ON: '104', ATTR_POWER_MODE: '105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = {\",\n \"'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return None @property\",\n \"correct PROPERTY_TO_DPS_ID depending on the model of the heater you have\\\"\\\"\\\" if self._device.model\",\n \"name of the climate device.\\\"\\\"\\\" return self._device.name @property def temperature_unit(self): \\\"\\\"\\\"Return the unit\",\n \"new preset mode.\\\"\\\"\\\" dps_mode = PRESET_MODE_TO_DPS_MODE[preset_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_PRESET_MODE], dps_mode) @property def swing_mode(self): \\\"\\\"\\\"Return the\",\n \"Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in keys:\",\n \"'child_lock' ATTR_FAULT = 'fault' ATTR_POWER_MODE_AUTO = 'auto' ATTR_POWER_MODE_USER = 'user' ATTR_POWER_LEVEL = 'power_level'\",\n \"dps_mode = 'C' if dps_mode is not None: return GoldairTuyaDevice.get_key_for_value(PRESET_MODE_TO_DPS_MODE, dps_mode) else: return\",\n \"from custom_components.goldair_climate import GoldairTuyaDevice _LOGGER = logging.getLogger(__name__) ATTR_TARGET_TEMPERATURE = 'target_temperature' ATTR_CHILD_LOCK = 'child_lock'\",\n \"ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self): \\\"\\\"\\\"List of power\",\n \"\\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL])\",\n \"Comfort, Eco, Anti-freeze.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_PRESET_MODE]) keys = list(self.get_property_to_dps_id) if dps_mode not in\",\n \"target_temperature_step(self): \\\"\\\"\\\"Return the supported step of target temperature.\\\"\\\"\\\" return self._TEMPERATURE_STEP @property def min_temp(self):\",\n \"'105', ATTR_ECO_TARGET_TEMPERATURE: '106' } HVAC_MODE_TO_DPS_MODE = { HVAC_MODE_OFF: False, HVAC_MODE_HEAT: True } PRESET_MODE_TO_DPS_MODE\",\n \"STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property\",\n \"list(POWER_LEVEL_TO_DPS_LEVEL.keys()) def set_swing_mode(self, swing_mode): \\\"\\\"\\\"Set new power level.\\\"\\\"\\\" new_level = swing_mode if new_level\",\n \"elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL, dps_mode) else: return None @property def swing_modes(self):\",\n \"int(round(target_temperature)) preset_mode = self.preset_mode if preset_mode == STATE_ANTI_FREEZE: raise ValueError('You cannot set the\",\n \"self._device.set_property(self.get_property_to_dps_id[ATTR_TARGET_TEMPERATURE], target_temperature) elif preset_mode == STATE_ECO: self._device.set_property(self.get_property_to_dps_id[ATTR_ECO_TARGET_TEMPERATURE], target_temperature) @property def current_temperature(self): \\\"\\\"\\\"Return the\",\n \"cannot set the temperature in Anti-freeze mode.') limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min']\",\n \"def swing_mode(self): \\\"\\\"\\\"Return the power level.\\\"\\\"\\\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER:\",\n \"self._device.temperature_unit @property def target_temperature(self): \\\"\\\"\\\"Return the temperature we try to reach.\\\"\\\"\\\" if self.preset_mode\",\n \"else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if kwargs.get(ATTR_PRESET_MODE) is\",\n \"return True @property def name(self): \\\"\\\"\\\"Return the name of the climate device.\\\"\\\"\\\" return\",\n \"return self._TEMPERATURE_LIMITS[self.preset_mode]['max'] else: return None def set_temperature(self, **kwargs): \\\"\\\"\\\"Set new target temperatures.\\\"\\\"\\\" if\",\n \"return list(HVAC_MODE_TO_DPS_MODE.keys()) def set_hvac_mode(self, hvac_mode): \\\"\\\"\\\"Set new HVAC mode.\\\"\\\"\\\" dps_mode = HVAC_MODE_TO_DPS_MODE[hvac_mode] self._device.set_property(self.get_property_to_dps_id[ATTR_HVAC_MODE],\",\n \"self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode == ATTR_POWER_MODE_AUTO: return GoldairTuyaDevice.get_key_for_value(POWER_LEVEL_TO_DPS_LEVEL,\",\n \"SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi heater.\\\"\\\"\\\" def __init__(self,\",\n \"limits = self._TEMPERATURE_LIMITS[preset_mode] if not limits['min'] <= target_temperature <= limits['max']: raise ValueError( f'Target\",\n \"= SUPPORT_TARGET_TEMPERATURE | SUPPORT_PRESET_MODE | SUPPORT_SWING_MODE class GoldairHeater(ClimateDevice): \\\"\\\"\\\"Representation of a Goldair WiFi\",\n \"current temperature.\\\"\\\"\\\" return self._device.get_property(self.get_property_to_dps_id[ATTR_TEMPERATURE]) @property def hvac_mode(self): \\\"\\\"\\\"Return current HVAC mode, ie Heat\",\n \"def max_temp(self): \\\"\\\"\\\"Return the maximum temperature.\\\"\\\"\\\" if self.preset_mode and self.preset_mode != STATE_ANTI_FREEZE: return\",\n \"not None: return GoldairTuyaDevice.get_key_for_value(HVAC_MODE_TO_DPS_MODE, dps_mode) else: return STATE_UNAVAILABLE @property def hvac_modes(self): \\\"\\\"\\\"Return the\",\n \"return PROPERTY_TO_DPS_ID_GECO270 else: return PROPERTY_TO_DPS_ID @property def supported_features(self): \\\"\\\"\\\"Return the list of supported\",\n \"list of available preset modes.\\\"\\\"\\\" return list(PRESET_MODE_TO_DPS_MODE.keys()) def set_preset_mode(self, preset_mode): \\\"\\\"\\\"Set new preset\"\n]","isConversational":false}}},{"rowIdx":12864199,"cells":{"ngram":{"kind":"list like","value":["from 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ngram listlengths 0 67.8k |
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[
"TMS Global Mercator pyramid\" self.tileSize = tileSize self.initialResolution = 2 * math.pi *",
"GlobalGeodetic (based on EPSG:4326) for OpenLayers Base Map and Google Earth compatible tiles",
"= math.log( math.tan((90 + lat) * math.pi / 360.0 )) / (math.pi /",
"= 156543.03392804062 What is the difference be... |
[
"= ['name', 'product_image', 'price', 'discount_price', 'slug', 'label'] inlines = [ProductImageModel] list_per_page = 3",
"from .models import Product, CartProduct, Order, Address, Payment, Coupon, Refund, Setting, ProductImages, Profile,",
"readonly_fields = ['stripe_charge_id', 'paypal_order_key', 'paypal_us... |
[
"('amount', models.DecimalField(decimal_places=2, max_digits=9)), ('amount_received', models.DecimalField(decimal_places=2, default=Decimal('0'), max_digits=9)), ('bitcoin_amount', models.PositiveIntegerField()), ('bitcoin_amount_received', models.PositiveIntegerField(default=0)), ('bitcoin_uri', models.TextField(b... |
[
"parknn = min(5, n_samples, self.X_.shape[0]) Xnew, y = SMOTE(sampling_strategy = parss, k_neighbors =",
"return self.mname_ # ============================================================================= # # TEST # # from sklearn.datasets import make_classification",
"import matplotlib.pyplot as plt # plt.scat... |
[
"True: j = random.randint(0 , 119) if i != j: break while True:",
"119) while True: j = random.randint(0 , 119) if i != j: break",
"m != l: break return i , j , z , k ,",
"random.randint(0 , 119) if z != i and z != j: break k",
"= random.randint(0 , 119) if z != i and z != j: break",
"i and z != j: break ... |
[
"-1 shutil.copy(eachfile, targetDir + os.path.basename(eachfile)) print eachfile + \" copy succeeded!\" cmd =",
"\"--------------------------------------------------\" def feature_generator_query(target_dir): ''' 根据图片文件列表，获取线上系统 查询视频帧 .bow .hash .txt 信息 :param target_dir: endswith",
"= [] img_names = get_all_fi... |
[
"walk of a tree (left, right, parent)\"\"\" def post_order_walk(node, result: list, left: Callable,",
"q.put(node) tree.append(node) if current_parent is not None: if (is_full(current_parent)): current_parent = q.get(block=False) add_child(current_parent,",
"= [] post_order_walk(tree[0], post_order_walk_result,... |
[
"out_shp: int corresponding to the output image shape on the considered axis. \"\"\"",
"{} reverse = True if not broadcastable: reverse = False a_idx = [None]",
"s in node_a.shape: idx = node_a.shape.index(s) all_ops.append(idx) else: assert s in node_b.shape, f\"Output",
"0 if s in a_map: start = a_map[s] si... |
[
"Lower directory 참조 def function_2(): print(\"function_2 of second module imported\") def call_function_in_first_module(): print(\"called",
"import first_module # Upper directory 에서 Lower directory 참조 def function_2(): print(\"function_2 of",
"경로 sys.path.append(UPPER_PATH) # 상위 경로를 추가 from my_tools import firs... |
[
"x.pop('kind') yield 'user-id', x.pop('user-id') yield 'group-id', x.pop('group-id') yield 'environ', Map(x.pop('environ')) yield 'memory-limit', x.pop('memory-limit')",
"BaseLoader except ImportError: from yaml import SafeDumper as BaseDumper from yaml import SafeLoader",
"def tag_map_repr(self, value): return... |
[
"tuple = (0.05, 0.05, 0.08, 1.0) canvas_dark_mode_clear_color: tuple = (0.05, 0.05, 0.08, 1.0)",
"str = \":/dark/stylesheet.qss\" light_stylesheet: str = \":/light/stylesheet.qss\" canvas_light_mode_clear_color: tuple = (0.05, 0.05, 0.08,",
"\":/dark/stylesheet.qss\" light_stylesheet: str = \":/light/stylesheet... |
[
"'correct_switched', 'incorrect_switched', 'is_switchable', 'is_associative', 'translated']) return df def generate_json(df): json_rows = [] for",
"else wsc_json[i+1]['substitution'] # noqa E226 correct_sentence = correct_sentence.replace('recieved', 'received') incorrect_sentence = incorrect_sentence.replace('re... |
[
"i in range(mat['R'].shape[0])] print(len(actualHs),actualHs[0].shape) for i in range(mat['R'].shape[0]): for k in range(mat['R'].shape[1]-1): actualHs[i]",
"np.dot(Hs[i][k] , actualHs[i]) print(actualHs[0].shape) registeredModel = [] #registeredmodel[0][x][0] cointains the array of points",
"matlab.engine impo... |
[
"headers_useragents=[] headers_referers=[] request_counter=0 flag=0 safe=0 def inc_counter(): global request_counter request_counter+=1 def set_flag(val): global",
"1.0') headers_useragents.append(':robot/1.0 (linux) ( admin e-mail: undefined http://www.neofonie.de/loesungen/search/robot.html )') headers_useragen... |
[
"with open(path) as csvfile: df = pd.read_csv(csvfile) url = df['url'] products = extract_products_json(url)",
"'./stores.csv' import pandas as pd from shopify import extract_products_json result = '' with",
"extract_products_json result = '' with open(path) as csvfile: df = pd.read_csv(csvfile) url =",
"resu... |
[
"result.append(\"\\\\u%.4X\" % ord(c)) else: result.append(c) return \"\".join(result) def to_json(obj): t = type(obj) if",
"query.get() def to_json(self): return to_json({'name': self.name, 'count': self.count}) class Version(db.Model): current_suite = db.StringProperty()",
"'\\f': '\\\\f', '\\n': '\\\\n', '\\... |
[
"setuptools import setup, find_packages with open('requirements.txt') as f: required = f.read().splitlines() setup( name='taming-transformers',",
"from setuptools import setup, find_packages with open('requirements.txt') as f: required = f.read().splitlines() setup(",
"setup, find_packages with open('requiremen... |
[
"the grouping matrix. It is important to # extract the values in the",
"return self._compute_r_stat(grouping_tri) def _compute_r_stat(self, grouping_tri): # within r_W = np.mean(self._ranked_dists[grouping_tri]) # between r_B",
"matrix where True means that the two objects are in the same #",
"means that the ... |
[] |
[
"supports PySide2 and 6 but needs to adapt to whats already imported NOT",
"<reponame>ewerybody/siding<gh_stars>0 \"\"\" singlesiding supports PySide2 and 6 but needs to adapt to whats",
"in sys.modules: from PySide2 import QtCore, QtWidgets, QtNetwork else: try: from PySide6 import",
"PySide2 import QtCore, ... |
[
"main(direc): direc_list = get_direc(direc) for file in direc_list: file_code = file.split('/')[-1].split('.')[0] table_csv =",
"all files under the directory\"\"\" path = os.path.join(direc, direc+'png', '*png') direc_list = glob.glob(path)",
"directory\"\"\" path = os.path.join(direc, direc+'png', '*png') dir... |
[] |
[
"aname = 'Anonymous' # get article title atitle = soup.find(class_=\"_21349 india none _4ca8e\")",
"= link # get page text page = requests.get(url) # parse with BFS",
"get the text only text = paragraph.get_text() paragraphtext.append(text) # combine all paragraphs into",
"india none _4ca8e\") thetitle = atit... |
[
"prices[i-1]) dp_i_k_1[i][j] = max(dp_i_k_1[i-1][j], dp_i_k_0[i-1][j-1] - prices[i-1]) return dp_i_k_0[nLen][k] # 边界 solution =",
"len(prices) = 2 assert(solution.maxProfit(1, [1,4]) == 3) assert(solution.maxProfit(2, [1,4]) == 3) assert(solution.maxProfit(2, [4,1])",
"0 dp_i_1 = -sys.maxsize for i in range(nLe... |
[
"import LogicPy.flipflops as flipflops import LogicPy.combination_logic as combination_logic import LogicPy.display_terminals as display_terminals import",
"LogicPy.main_functions as main_functions import LogicPy.conversion as conversion import LogicPy.gates as gates import LogicPy.flipflops",
"LogicPy.conversi... |
[
"count in range(size): message += str(random.randint(0,1)) return message n = 32 * 10000",
"'' for count in range(size): message += str(random.randint(0,1)) return message n = 32",
"random binary string def generateMessage (size): message = '' for count in range(size):",
"string def generateMessage (size): me... |
[
"in fobj.readlines()]) git_repo = Repo(\"dvc\") hashes = [git_repo.commit(t).hexsha + os.linesep for t in",
"convert_to_sha(tags_filename=\"tags.txt\", hashes_filename=\"hashes.txt\"): tags = [] with open(tags_filename, \"r\") as fobj: tags.extend([l.strip() for l",
"os from git import Repo def convert_to_sha(t... |
[] |
[
"CCA = 1 SECOND = 2 def start(update, context): username = update.message.from_user.username first_name",
"your CCAs.\" % ( first_name), reply_markup=reply_markup) return CCA def end(update, context): query =",
"import InlineKeyboardButton, InlineKeyboardMarkup from telegram.ext import ConversationHandler impor... |
[
"<gh_stars>0 from kivy.uix.widget import Widget from kivy.uix.floatlayout import FloatLayout class RootWidget(FloatLayout): def __init__(self,",
"kivy.uix.widget import Widget from kivy.uix.floatlayout import FloatLayout class RootWidget(FloatLayout): def __init__(self, **kwargs): super().__init__(**kwargs)",
"... |
[
"__init__(self, *dirs): self.dirname = BASE_DIR / self.default_dir / '/'.join(*dirs) mkdir_if_not_exists(self.dirname) def write_file(self, name,",
"language) -> bool: basename = remove_win_special_char(name) file = self.dirname / ''.join([basename, get_extension(language)]) if",
"'w', encoding='utf-8') as f: f... |
[
"9.0; en-US)', 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET",
"= { 'house_renting.pipelines.HouseRentingPipeline': 100, 'house_renting.pipelines.DuplicatesPipeline': 200, 'scrapy.pipelines.images.ImagesPipeline': 300, 'house_renting.pipelines.ESPipeline': 400, } IMAGES_STORE =... |
[
"(1-q_j) * (1-q_k) def circuit_011(self, q_i, q_j, q_k): \"\"\"Implements a circuit that checks",
"the nonsensical string 000.\"\"\" return (1-q_i) * (1-q_j) * (1-q_k) def circuit_011(self, q_i,",
"r in range(maximum)]) for k in ['y', 'z']]) return prefactor * \\ (",
"j, 'x+'), 'x-': self.sum_string(i, j, 'x-... |
[
"django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations =",
"models.CharField(choices=[('unrated', 'Unrated'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6',",
"('animated_film', ... |
[
"the axis and way to manipulate the axis appearance. \"\"\" @mark_backend_unsupported def enable_zooming(self,",
"self.colorbar.finalize() del self.colorbar def add_line( self, id: str, orientation: str, position: float, bounds:",
"\"\"\"Cursor on a plot.\"\"\" #: x_value = Float() #: y_value = Float() #:",
"... |
[] |
[
"False def __init__(self, *args, **kwargs): super(Node, self).__init__(*args, **kwargs) self.sockets = {} self.processes =",
"stop(self): try: self._process.terminate() self._process.wait() except psutil.NoSuchProcess: pass def restart(self): print(\"restarting %s\" % self)",
"self._services.append((name, c.get... |
[
"exitAction.setShortcut('Ctrl+Q') exitAction.setStatusTip('Exit application') exitAction.triggered.connect(QtGui.QApplication.quit) self.statusBar().showMessage('Ready') # menubar menubar = self.menuBar() menubar.setNativeMenuBar(False) fileMenu =",
"self.toolbar2 = QtGui.QToolBar('name') self.toolbar2.setToolBut... |
[
"nextNode=None): self.val = value self.next = nextNode class MyLinkedList(object): def __init__(self): \"\"\" Initialize",
"\"\"\" Append a node of value val to the last element of the",
"linked list. If index equals to the length of linked list, the node",
"curr = curr.next def addAtHead(self, val): \"\"\" A... |
[
"\"LMDG\": { \"title\": \"LMD-G\", \"color\": \"tab:red\", }, \"ROCKE3D\": { \"title\": \"ROCKE-3D\", \"color\": \"tab:green\",",
"\"color\": \"tab:red\", }, \"ROCKE3D\": { \"title\": \"ROCKE-3D\", \"color\": \"tab:green\", }, \"UM\": { \"title\":",
"}, \"LMDG\": { \"title\": \"LMD-G\", \"color\": \"tab:red\", ... |
[
"[], [] for _ in range(trials + 1): x, y = random(), random()",
"+ \" trials processing time: \" + str(time.process_time() - start)) return 4 *",
"time import logging import matplotlib.pyplot as plt logger = logging.getLogger() logging.basicConfig(level=logging.INFO) def estimate_pi(trials):",
"/ trials def g... |
[
"[] phenotype_structured = self.phenotype_structured.all() for phenotype in self.phenotype_structured.all(): data.append('<b>'+phenotype.id+'</b>: '+phenotype.label) return data def",
") name = models.CharField(verbose_name='Performance Metric Name', max_length=100, null=False) # ex: \"Odds Ratio\" name_short",
... |
[
"train_dataloader(self): return DataLoader( dataset=self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, ) def val_dataloader(self): return DataLoader( dataset=self.valid_dataset,",
"dataset=self.valid_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_w... |
[
"init_token is properly handled. field = data.TextField(fix_length=4, init_token=\"<bos>\") minibatch = [[\"a\", \"sentence\", \"of\",",
"== expected_output assert field.decode(np.asarray(word_idxs)) == expected_output # Single caption word_idxs = [0, 3,",
"# Test init_token is properly handled. field = data.Te... |
[
"'UNDEFINED' SITTINGOUT_STR = 'Sitting Out' BASE_DIR = '/home/nonroot/Projects/' #Change this path HH_PATH =",
"SLEEP_MILLISECONDS = 750 GET_SESSION_URL = 'https://sealswithclubs.eu/sealsloginhandler.php?login=X&JSON=1&Version=v0.2.18' USER_AGENT_STR = 'python-requests/1.1.0 CPython/2.7.3 Windows/7' SERVICE_URL =... |
[
"StringIO # h( h(1) + h(2) ) # 0df4085b3a65bd26ca6ab608c0f70c41213f77e56bc5b33bd9899db5d39a7cd8 # h( h(3) +",
"% 2 != 0: hashes.append(hashes[-1]) i = 0 j = 0 while i",
"+ 1 < len(hashes): hashes[j] = sha256(str(hashes[i] + hashes[i + 1]).encode('utf-8')).hexdigest() i +=",
"bitcoin core implementation ''' im... |
[
"P def f(x): P = 4e9 return ones(shape(x)) * P x = arange(0,3e6,100)",
"label='Moon') plt.plot(x,mars(x), label='Mars') plt.plot(x,earth(x), label='Earth') plt.plot(x,jupiter(x), label='Jupiter') plt.axis([0,3e6,0,y_max]) plt.legend() #~ plt.plot([1,2,3,4]) #~ plt.ylabel('some",
"5510 * 9.807 * x return P def j... |
[
"count else: for i, square in enumerate(squares): # Find the biggest square that",
"in square_nums: if is_divided_by(n - k, count - 1): return True return False",
"= min(dp[i], dp[i - square] + 1) return dp[-1] # Greedy DFS 1",
"1, squares[i:]) # Greedy DFS 2 class Solution: def numSquares(self, n): def is_di... |
[
"selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support.expected_conditions import ( url_contains, url_matches ) url = 'https://selenium.dunossauro.live/aula_10_c.html'",
"browser = Firefox() browser.get(url) wdw = WebDriverWait(browser, 10) links = browser.find_elements_by_css_selecto... |
[
"else: raise NotImplementedError(f\"Datatype {datatype} not implemented\") def buildSnak(propertyId: str, value): datatype = getPropertyType(propertyId)",
"\"math\", \"commonsMedia\", ]: if type(value) == dict: return {\"value\": value, \"type\": \"string\"} elif",
"not convert type {type(value)} to datatype {d... |
[
"0. self.counterparty_bond_repo_rate = 0. self.variation_margin_interest_rate = 0.1 self.stock_repo_rate = 0.07 * np.ones(dimension, dtype=float)",
"np.zeros(num_of_assets, dtype=float) bank_bond_yield = 0 counterparty_bond_yield = 0 counterparty_bond_repo_rate = 0 variation_margin_interest_rate =",
"expect_bas... |
[
"iter(self._containers) def __repr__(self): self._get_containers() return f\"<{self.__class__.__name__}: {repr(self._containers)}>\" def _get_containers(self): containers = [] for",
"\"\"\" kwargs = {\"node\": self._node, \"vmid\": self._vmid} return self._api.suspend_container(**kwargs) def resume(self) -> str:"... |
[
"a, b, c, d) MAX = max(MAX, a, b, c, d) return (MIN,",
"* b else: assert False def MinAndMax(i, j): MIN = float('inf') MAX =",
"op == '-': return a - b elif op == '*': return a",
"MAX = float('-inf') for k in range(i, j): a = evalt(M[i][k], M[k +",
"op = [dataset[i] for i in range(1, len(dataset), 2)] m, M,... |
[
"str 'DEN', 'BUF', etc. Returns: HTML string \"\"\" url = f\"http://www.espn.com/nfl/team/roster/_/name/{team_code}\" return self.get(url,",
"Tag): player[\"source_player_name\"] = child.string player[\"source_player_id\"] = child.attrs.get(\"playerid\") results.append(player) return results @staticmethod def wee... |
[
"s3_bucket=None): \"\"\" Upload a dataframe to redshift via s3. Parameters ---------- name: str",
"= True except ImportError: HAS_PYMSSQL = False DBPY_PROFILE_ID = \".db.py_\" S3_PROFILE_ID = \".db.py_s3_\"",
"and rendered using handlebars. union: bool Whether or not \"UNION ALL\" handlebars templates.",
"chu... |
[
"<reponame>LucasLaibly/Intrusion class Development(object): DEBUG: True class Testing(object): DEBUG: False class Production(object): DEBUG: False"
] |
[] |
[
"hidden_dim=1024, num_filters=128, kernel_size=5, name=\"attention block\", rate=0.1, residual=True, last=False, **kwargs): super(AttentionBlock, self).__init__(name=name, **kwargs) self.attention_layer",
"self.activation_layer = Activation('relu') self.dense1 = Dense(hidden_dim/2) self.dense2 = Dense(hidden_dim)... |
[
"= result[\"Tag\"] print(Tags) allTag = \"\" for row in result.index: allTag = allTag",
"as np import nltk nltk.download('punkt') import os import nltk.corpus from nltk.probability import FreqDist",
"allTag = \"\" for row in result.index: allTag = allTag + \" \"",
"word_tokenize # read result result = pd.read... |
[
"== '__main__': # path = 'D:/Desktop/artigos/rdml.pdf' # #id = 20367574 # print(get_data_from_pdf_path(path, '<EMAIL>'))",
"Bio import Entrez import os import sys sys.path.append(os.path.dirname(os.path.dirname(__file__))) import nltk from data_structures.document import",
"= '<KEY>' try: title = get_title_from... |
[] |
[
"api_base_url = 'https://api.thetvdb.com' resource_base_url_per_ep = 'https://thetvdb.com/banners/' headers = {} def get_jwt(): data =",
"resource_base_url = 'https://thetvdb.com' api_base_url = 'https://api.thetvdb.com' resource_base_url_per_ep = 'https://thetvdb.com/banners/' headers = {} def",
"def get_image... |
[
"Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python",
"find_packages setup( name='blueliv-python-sdk', version='2.3.0', description='Blueliv API SDK for Python', url='https://github.com/Blueliv/api-python-sdk', author='Blueliv', author_email='<EMAIL>', license='MIT',",
":: Py... |
[
"# Equation 2 def eq2(x): return (pow(x, 2) - 4) def eq2_d(x): return",
"Sample Equations # # Description: Sample functions (and their derivatives) # to be",
"Equations # # Description: Sample functions (and their derivatives) # to be used",
"5*x + 1 def eq1_d(x): return 5*(x**4) - 5 # Equation 2 def",
"(an... |
[
"y.ndim - 1 y_in = y.astype(xp.float64) lam1 = xp.broadcast_to(lam, (y_in.shape[0], 1)).astype(xp.float64) if lam2",
"xp.max(xp.abs(cdf - norm_cdf(ys, xp.mean(yp, axis=1), xp.var(yp, axis=1))), axis=1) ks_mask = ks < best_ks",
"ret[zero_mask] = xp.exp(y_in[zero_mask]) - lam2[zero_mask] ret[~zero_mask] = (lam1[~... |
[
"1 def DZ_main(): filename = sys.argv[1] df = pd.read_csv(filename) if DZstats == 1:",
"define lower limit for probability density plots (PDPs) and kernel density estimates (KDEs)",
"intersample_results = intersample(df, xmin, xmax, xint) print(intersample_results) if PlotDistributions == 1: fig, axs",
"sys.a... |
[
"Activation : 0.4228725100457559 V Eta Concentration : 0.0007181421727400468 V Eta Ohmic : 0.006390481776561363",
"0.2 E : 6.068413961701556 V Eta Activation : 0.23146009851376736 V Eta Concentration :",
": 0.41280968341523216 W ########### I : 0.4 E : 6.068410886366294 V Eta Activation",
"4.0 E : 6.144553093... |
[
":param MLLD_functions: this class has several functions that are usually used by myself.",
"\"\"\" var_average = np.average(variable) var_std = np.std(variable) new_variable = [] for i in",
"for i in range(variable.size): new_variable_i = (variable[i] - var_average)/var_std new_variable.append(new_variable_i) ... |
[
"json import torch from editsql.data_util import atis_batch from editsql.data_util.atis_data import ATISDataset from editsql.data_util.interaction",
"= comparisons else: eval_output[\"diff\"] = comparisons write_json_log_results(eval_output, CURRENT_DIR / \"evaluation/results\") # ------------ Batch",
"the firs... |
[
"shutil import sys import tempfile from observations.r.rep_vict import rep_vict def test_rep_vict(): \"\"\"Test module",
"downloading rep_vict.csv and testing shape of extracted data has 8 rows and 8",
"import rep_vict def test_rep_vict(): \"\"\"Test module rep_vict.py by downloading rep_vict.csv and testing sh... |
[
"validation accuracy as a performance measure def test_synthetic_data(): \"\"\" Generate the synthetic data",
"= ut.compute_cross_validation_error(X, y, x_control, NUM_FOLDS, loss_function, apply_fairness_constraints, apply_accuracy_constraint, sep_constraint, ['s1'], [{} for i",
"lf # loss funcs that can be op... |
[
"BlogPosts().get() try: message = self._BLOG_MESSAGE.format( name=random_blog['title'], url=random_blog['link'] ) PostTweet().post(message) except: print(\"Error posting to",
"message, random_blog['title'], random_blog['link'] ) except: print(\"Error posting to LinkedIn\") pass #self.blog() def oss(self):",
"Ch... |
[
"env = os.environ.copy() env.update(new_env) abs_bname = os.path.join(apppath, binary_name) abs_ihex_name = abs_bname + \".ihex\"",
"longskips, n_emptys, ccas, nodemaps): n_empty = NemptyTuple(*n_empty) cca = CcaTuple(*cca) simdir = \"sink%03d_snd%02d_p%02d_c%02d_e%.2f_ns%02d_nt%02d_na%02d_ds%02d_dt%02d_da%02d_sy... |
[
"+= val * n self.count += n self.avg = self.sum / self.count if",
"model, criterion, optimizer, epoch, output_dir, tb_log_dir, writer_dict): batch_time = AverageMeter() data_time = AverageMeter()",
"'.join(['| {:.3f}'.format(value) for value in values]) + ' |' ) class AverageMeter(object): \"\"\"Computes",
"n... |
[
"b'Thanks'), (b'SUPPORTER', 'Idol Supporter'), (b'LOVER', 'Idol Lover'), (b'AMBASSADOR', 'Idol Ambassador'), (b'PRODUCER', 'Idol Producer'),",
"class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('web', '0001_initial'), ] operations = [ migrations.Cr... |
[
"set version = \"' + str(self.context.version) + '\" %}'}, ) __all__ = [\"Sync\"]",
"= \"{}\"'.format(self.context.source(\"date\")), }, ) def fix_recipe(self) -> None: if self.has(\"recipe\"): self.replace_substrs( self.context.path_source(\"recipe\"), {\"{%",
"/ self.context.project / \"__init__.py\", { \"__s... |
[
"data. asol : string Name of the variable on the grid. \"\"\" X,",
"else: values = (-8 * numpy.pi**2 * numpy.cos(2 * numpy.pi * X) *",
"* numpy.pi * Y)) else: values = (-8 * numpy.pi**2 * numpy.cos(2 *",
"the right-hand side of the Poisson system. Arguments --------- grid : flowx.Grid object",
"grid.y) if(u... |
[] |
[
"for t in reversed(range(0, len(dOut))): dAct = dH[t] * self.activation.d(outputs[t]) ''' the following",
"dCache is not None: dCell[-1] += dCache['dCell'] dH[-1] += dCache['dHidden'] for t in",
"cache[l]['cn'] if 'cn' in cache[l] else None, 'h0': cache[l]['hn'] if 'hn' in cache[l]",
"optimality ''' tmp_ = np... |
[
"root node of tree # @return an integer def isSameTree(self, A, B): if",
"__name__ == \"__main__\": root1, root1.left, root1.right = TreeNode(1), TreeNode(2), TreeNode(3) root2, root2.left, root2.right",
"root1.right = TreeNode(1), TreeNode(2), TreeNode(3) root2, root2.left, root2.right = TreeNode(1), TreeNode(... |
[] |
[
"10:00:02 GMT\\r\\nAccept-Ranges: bytes\\r\\nContent-Length: 5092104\\r\\nX-Content-Type-Options: nosniff\\r\\nConnection: close\\r\\nContent-Type: application/pdf\\r\\nStrict-Transport-Security: max-age=31536000; includeSubDomains; preload;\\r\\nX-Frame-Options: SAMEORIGIN\\r\\nX-XSS-Protection: 1; mode=block\\r\\... |
[
"None: raise TypeError(self.messages.ILLEGAL_ARGUMENT_NONE_TYPE.value) if not isinstance(data, pandas.DataFrame): raise TypeError(self.messages.ILLEGAL_ARGUMENT_TYPE.value) if not all(column in",
":param data_segments: :param selected_columns: :param axis: :return: \"\"\" try: data = None for",
"mode is None or... |
[
"struct unpacked_wo_header = unpacked[len(asdict(packet_header)) : :] # Get telemetry for each active car",
"allocate to position on car (fl, fr, rl, rr). Args: telemetry_values(list): List of",
"m_header: PacketHeader m_car_telemetry_data: List[CarTelemetryData] m_mfd_panel_index: ctypes.c_uint8 m_mfd_panel_in... |
[
"Takes 9-class (categorical) hamming weight labels and reduces it to 2 semi-fixed classes.",
"weight labels and reduces it to 2 classes: semi-fixed and random. \"\"\" hamming_weight",
"traces, encode(la_bit, 2) if __name__ == '__main__': trace_set = TraceSetHW(Database.ascad, Pollution(PollutionType.gauss, 0), ... |
[
"ForbiddenError: raise HTTPException(403, f\"Application not setup for the repository {action.repo}\") except NotFoundError: raise",
"react_to_pr(action: ActionIn): gh = login_as_installation(action) issue = get_pr(gh, action).issue() issue._post( issue._api + '/reactions',",
"get_pr(gh, action).create_comment(... |
[
"WordNetLemmatizer() ''' print(lemmatizer.lemmatize(\"cacti\")) print(lemmatizer.lemmatize(\"geese\")) print(lemmatizer.lemmatize(\"rocks\")) print(lemmatizer.lemmatize(\"python\")) ''' #default pos=\"n\"(noun) #\"a\" = adjective, \"v\"",
"#lemmas give back actual words, usually better then stemmers print(lemmati... |
[
"location = models.ForeignKey(Location,on_delete=models.CASCADE) # one image belongs to a single location category =",
"image belongs to a single category name= models.CharField(max_length=200) description = models.TextField(max_length=300) image =",
"slug = models.SlugField(unique=True) def __str__(self): retu... |
[
"as inout :param queue_name: The name of the queue where to put the",
"FixedProtoField from diplomacy_research.utils.model import pad_list # Constants LOGGER = logging.getLogger(__name__) class FeedableDataset(metaclass=ABCMeta): \"\"\"",
"it is a remote (RPC) dataset \"\"\" raise NotImplementedError() @propert... |
[
"def analysis(obj): params = dict() params['size'] = size(obj) params['rgb'] = color(obj) params['lines'] =",
"params['opacity'] = opacity(obj) params['original'] = o if (o := obj.get('original')) else False params['colour']",
"try: line_arg = i.split(',') if len(line_arg) >= 7: lines.append([(int(line_arg[0]),... |
[
"shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table: for cell in shape.table.iter_cells(): print(cell.text)",
"for slide in p.slides: for shape in slide.shapes: if shape.has_text_frame: print(shape.text_frame.text) if shape.has_table:",
"p = Presentation('a.pptx') fo... |
[
"the ones # that are not so sparse and that I FEEL makes",
"picking the ones # that are not so sparse and that I FEEL",
"bors and yahoo data''' import numpy as np import pandas as pd from",
"sense to include. df_stats = df_stats[cng.SELECTED_FEATURES] df_combined = pd.merge(df_bors, df_stats, on=cng.MERGE_DFS... |
[
"# print('双字词个数：',shuangzi) # print('其他字词个数：',qita) # for i, (k, v) in enumerate(word_listindex.items()): # if",
"matplotlib import fileDispose import numpy as np def get_bench(text,n): \"\"\" get_bench(list,int) 共现矩阵窗口循环的范围，对联选择上下联两句做循环范围 text：文本列表",
"import numpy as np def get_bench(text,n): \"\"\" get_bench(... |
[
"True rcParams['figure.edgecolor'] = 'k' rcParams['grid.color'] = 'k' rcParams['grid.linestyle'] = ':' rcParams['grid.linewidth'] = 0.5",
"alpha=0.1, color=sns.xkcd_rgb[\"magenta\"]) plt.plot(list(np.arange(1, len(data_LTO[\"Average costs LTO\"]) + 1)), data_LTO[\"Average costs LTO\"], linewidth=4, label=\"LTO\",... |
[
"= [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterField( model_name='searchtracking', name='query_type', field=models.CharField(choices=[['free_text', 'Free",
"class Migration(migrations.Migration): dependencies = [ ('analytics', '0003_searchtracking'), ] operations = [ migrations.AlterF... |
[
"<filename>LaureatsBackEnd-master/laureats/migrations/0008_etudiant.py # Generated by Django 3.0.2 on 2020-01-10 20:43 from django.db import migrations,",
"[ ('laureats', '0007_laureat_filiere'), ] operations = [ migrations.CreateModel( name='Etudiant', fields=[ ('laureat_ptr', models.OneToOneField(auto_created=T... |
[
"import Embedding from allennlp.nn import util from allennlp.common.checks import ConfigurationError class MaxSpanExtractor(SpanExtractor): def",
"torch.FloatTensor, span_indices: torch.LongTensor, sequence_mask: torch.BoolTensor = None, span_indices_mask: torch.BoolTensor = None, ) ->",
"torch.BoolTensor = Non... |
[] |
[
"power level.\"\"\" dps_mode = self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_MODE]) if dps_mode == ATTR_POWER_MODE_USER: return self._device.get_property(self.get_property_to_dps_id[ATTR_POWER_LEVEL]) elif dps_mode",
"not None: self.set_target_temperature(kwargs.get(ATTR_TEMPERATURE)) def set_t... |
[
"from .importer import * from .settings import * __all__ = [ 'DraftstarsCSVImporter', 'DraftstarsNFLSettings'",
"<filename>pydfs_lineup_optimizer/sites/draftstarsnfl/__init__.py from .importer import * from .settings import * __all__ = [ 'DraftstarsCSVImporter',",
".importer import * from .settings import * __a... |