fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

790d63f065ecd4e3b6431e8fcb5ae11dec6027e2

2,530

py

Python

livelossplot/keras_plot.py

kkanska/livelossplot

20715b3c16656e22a1371e0b9bb45ae4a7718a71

[ "MIT" ]

1

2019-03-07T12:49:58.000Z

livelossplot/keras_plot.py

kkanska/livelossplot

20715b3c16656e22a1371e0b9bb45ae4a7718a71

[ "MIT" ]

null

livelossplot/keras_plot.py

kkanska/livelossplot

20715b3c16656e22a1371e0b9bb45ae4a7718a71

[ "MIT" ]

null

from __future__ import division from keras.callbacks import Callback from .generic_plot import PlotLosses metric2printable = { "acc": "Accuracy", "mean_squared_error": "Mean squared error", "mean_absolute_error": "Mean absolute error", "mean_absolute_percentage_error": "Mean absolute percentage error", # etc "categorical_crossentropy": "Log-loss", "sparse_categorical_crossentropy": "Log-loss", "binary_crossentropy": "Log-loss", "kullback_leibler_divergence": "Log-loss" } def loss2name(loss): if hasattr(loss, '__call__'): # if passed as a function return loss.__name__ else: # if passed as a string return loss class PlotLossesKeras(Callback): def __init__(self, **kwargs): super(PlotLossesKeras, self).__init__() self.liveplot = PlotLosses(**kwargs) def on_train_begin(self, logs={}): self.liveplot.set_metrics([ metric for metric in self.params['metrics'] if not metric.startswith('val_') ]) # slightly convolved due to model.complie(loss=...) stuff # vide https://github.com/keras-team/keras/blob/master/keras/engine/training.py if isinstance(self.model.loss, list): losses = self.model.loss elif isinstance(self.model.loss, dict): losses = list(self.model.loss.values()) else: # by far the most common scenario losses = [self.model.loss] metric2printable_updated = metric2printable.copy() loss_name = loss2name(losses[0]) metric2printable_updated['loss'] =\ "{} (cost function)".format(metric2printable_updated.get(loss_name, loss_name)) if len(losses) > 1: for output_name, loss in zip(self.model.output_names, losses): loss_name = loss2name(loss) metric2printable_updated['{}_loss'.format(output_name)] =\ "{} ({})".format(metric2printable_updated.get(loss_name, loss_name), output_name) else: for output_name in self.model.output_names: metric2printable_updated['{}_loss'.format(output_name)] =\ "{} ({})".format(metric2printable_updated.get(loss_name, loss_name), output_name) self.liveplot.metric2title = metric2printable_updated self.liveplot.set_max_epoch(self.params['epochs']) def on_epoch_end(self, epoch, logs={}): self.liveplot.update(logs.copy()) self.liveplot.draw()

36.142857

101

0.641107

from __future__ import division from keras.callbacks import Callback from .generic_plot import PlotLosses metric2printable = { "acc": "Accuracy", "mean_squared_error": "Mean squared error", "mean_absolute_error": "Mean absolute error", "mean_absolute_percentage_error": "Mean absolute percentage error", "categorical_crossentropy": "Log-loss", "sparse_categorical_crossentropy": "Log-loss", "binary_crossentropy": "Log-loss", "kullback_leibler_divergence": "Log-loss" } def loss2name(loss): if hasattr(loss, '__call__'): return loss.__name__ else: return loss class PlotLossesKeras(Callback): def __init__(self, **kwargs): super(PlotLossesKeras, self).__init__() self.liveplot = PlotLosses(**kwargs) def on_train_begin(self, logs={}): self.liveplot.set_metrics([ metric for metric in self.params['metrics'] if not metric.startswith('val_') ]) if isinstance(self.model.loss, list): losses = self.model.loss elif isinstance(self.model.loss, dict): losses = list(self.model.loss.values()) else: losses = [self.model.loss] metric2printable_updated = metric2printable.copy() loss_name = loss2name(losses[0]) metric2printable_updated['loss'] =\ "{} (cost function)".format(metric2printable_updated.get(loss_name, loss_name)) if len(losses) > 1: for output_name, loss in zip(self.model.output_names, losses): loss_name = loss2name(loss) metric2printable_updated['{}_loss'.format(output_name)] =\ "{} ({})".format(metric2printable_updated.get(loss_name, loss_name), output_name) else: for output_name in self.model.output_names: metric2printable_updated['{}_loss'.format(output_name)] =\ "{} ({})".format(metric2printable_updated.get(loss_name, loss_name), output_name) self.liveplot.metric2title = metric2printable_updated self.liveplot.set_max_epoch(self.params['epochs']) def on_epoch_end(self, epoch, logs={}): self.liveplot.update(logs.copy()) self.liveplot.draw()

true

790d64784359740d132bbba12433a5a99da3ca03

77,727

py

Python

bert4keras/models.py

CurisZhou/bert4keras

216f408b0501a1e6e6903c7a6271213d88f7725c

[ "Apache-2.0" ]

null

bert4keras/models.py

CurisZhou/bert4keras

216f408b0501a1e6e6903c7a6271213d88f7725c

[ "Apache-2.0" ]

null

bert4keras/models.py

CurisZhou/bert4keras

216f408b0501a1e6e6903c7a6271213d88f7725c

[ "Apache-2.0" ]

null

#! -*- coding: utf-8 -*- # 主要模型 import numpy as np from bert4keras.layers import * from bert4keras.snippets import insert_arguments from bert4keras.snippets import delete_arguments from bert4keras.snippets import is_string from keras.models import Model import json class Transformer(object): """模型基类 """ def __init__( self, vocab_size, # 词表大小 hidden_size, # 编码维度 num_hidden_layers, # Transformer总层数 num_attention_heads, # Attention的头数 intermediate_size, # FeedForward的隐层维度 hidden_act, # FeedForward隐层的激活函数 dropout_rate=None, # Dropout比例 embedding_size=None, # 是否指定embedding_size attention_head_size=None, # Attention中V的head_size attention_key_size=None, # Attention中Q,K的head_size sequence_length=None, # 是否固定序列长度 keep_tokens=None, # 要保留的词ID列表 compound_tokens=None, # 扩展Embedding residual_attention_scores=False, # Attention矩阵加残差 layers=None, # 外部传入的Keras层 prefix=None, # 层名前缀 name=None, # 模型名称 **kwargs ): if keep_tokens is not None: vocab_size = len(keep_tokens) if compound_tokens is not None: vocab_size += len(compound_tokens) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.attention_head_size = attention_head_size or hidden_size // num_attention_heads self.attention_key_size = attention_key_size or self.attention_head_size self.intermediate_size = intermediate_size self.dropout_rate = dropout_rate or 0 self.hidden_act = hidden_act self.embedding_size = embedding_size or hidden_size self.sequence_length = sequence_length self.keep_tokens = keep_tokens self.compound_tokens = compound_tokens self.attention_bias = None self.position_bias = None self.attention_scores = None self.residual_attention_scores = residual_attention_scores self.layers = {} if layers is None else layers self.prefix = prefix or '' self.name = name self.built = False def build( self, attention_caches=None, layer_norm_cond=None, layer_norm_cond_hidden_size=None, layer_norm_cond_hidden_act=None, additional_input_layers=None, **kwargs ): """模型构建函数 attention_caches：为Attention的K,V的缓存序列字典，格式为 {Attention层名: [K缓存, V缓存]}； layer_norm_*系列参数：实现Conditional Layer Normalization时使用，用来实现以“固定长度向量”为条件的条件Bert。 """ if self.built: return None # Input inputs = self.get_inputs() self.set_inputs(inputs, additional_input_layers) # Other self.attention_caches = attention_caches or {} self.layer_norm_conds = [ layer_norm_cond, layer_norm_cond_hidden_size, layer_norm_cond_hidden_act or 'linear', ] # Call outputs = self.call(inputs) self.set_outputs(outputs) # Model self.model = Model(self.inputs, self.outputs, name=self.name) self.built = True def call(self, inputs): """定义模型的执行流程 """ # Embedding outputs = self.apply_embeddings(inputs) # Main for i in range(self.num_hidden_layers): outputs = self.apply_main_layers(outputs, i) # Final outputs = self.apply_final_layers(outputs) return outputs def prefixed(self, name): """给名字加前缀 """ if name is not None: return self.prefix + name def apply(self, inputs=None, layer=None, arguments=None, **kwargs): """通过apply调用层会自动重用同名层 inputs: 上一层的输出； layer: 要调用的层类名； arguments: 传递给layer.call的参数； kwargs: 传递给层初始化的参数。 """ if layer is Dropout and self.dropout_rate == 0: return inputs if layer is MultiHeadAttention and self.residual_attention_scores: kwargs['return_attention_scores'] = True arguments = arguments or {} name = self.prefixed(kwargs.get('name')) kwargs['name'] = name if name not in self.layers: layer = layer(**kwargs) name = layer.name self.layers[name] = layer if inputs is None: return self.layers[name] else: if isinstance(self.layers[name], MultiHeadAttention): if name in self.attention_caches: # 如果检测到Cache的传入，那么自动在Key,Value处拼接起来 k_cache, v_cache = self.attention_caches[name] k_name, v_name = name + '-Cached-Key', name + '-Cached-Value' k = Concatenate1D(name=k_name)([k_cache, inputs[1]]) v = Concatenate1D(name=v_name)([v_cache, inputs[2]]) inputs = inputs[:1] + [k, v] + inputs[3:] if self.residual_attention_scores: # 如果使用残差Attention矩阵，则给每个Attention矩阵加上前上一层的Attention # 矩阵，这对应RealFormer设计（https://arxiv.org/abs/2012.11747）。目前 # 该实现还相对粗糙，可能欠缺通用性。 if self.attention_scores is not None: if arguments.get('a_bias'): a_bias = Add(name=name + '-Attention-Bias' )([inputs[3], self.attention_scores]) else: a_bias = self.attention_scores inputs = inputs[:3] + [a_bias] + inputs[4:] arguments['a_bias'] = True o, a = self.layers[name](inputs, **arguments) self.attention_scores = a return o return self.layers[name](inputs, **arguments) def get_inputs(self): raise NotImplementedError def apply_embeddings(self, inputs): raise NotImplementedError def apply_main_layers(self, inputs, index): raise NotImplementedError def apply_final_layers(self, inputs): raise NotImplementedError def compute_attention_bias(self, inputs=None): """定义每一层的Attention Bias """ return self.attention_bias def compute_position_bias(self, inputs=None): """定义每一层的Position Bias（一般相对位置编码用） """ return self.position_bias def set_inputs(self, inputs, additional_input_layers=None): """设置input和inputs属性 """ if inputs is None: inputs = [] elif not isinstance(inputs, list): inputs = [inputs] inputs = inputs[:] if additional_input_layers is not None: if not isinstance(additional_input_layers, list): additional_input_layers = [additional_input_layers] inputs.extend(additional_input_layers) self.inputs = inputs if len(inputs) > 1: self.input = inputs else: self.input = inputs[0] def set_outputs(self, outputs): """设置output和oututs属性 """ if not isinstance(outputs, list): outputs = [outputs] outputs = outputs[:] self.outputs = outputs if len(outputs) > 1: self.output = outputs else: self.output = outputs[0] @property def initializer(self): """默认使用截断正态分布初始化 """ return keras.initializers.TruncatedNormal(stddev=0.02) def simplify(self, inputs): """将list中的None过滤掉 """ inputs = [i for i in inputs if i is not None] if len(inputs) == 1: inputs = inputs[0] return inputs def load_embeddings(self, embeddings): """处理Embedding层权重 """ if self.keep_tokens is not None: embeddings = embeddings[self.keep_tokens] if self.compound_tokens is not None: ext_embeddings = [] for item in self.compound_tokens: if isinstance(item, list): item = (item, [1] * len(item)) ext_embeddings.append( np.average(embeddings[item[0]], 0, item[1]) ) embeddings = np.concatenate([embeddings, ext_embeddings], 0) return embeddings def load_variable(self, checkpoint, name): """加载单个变量的函数 """ if isinstance(checkpoint, dict): return checkpoint[name] else: return tf.train.load_variable(checkpoint, name) def create_variable(self, name, value, dtype=None): """创建一个变量 """ dtype = dtype or K.floatx() return K.variable( self.initializer(value.shape, dtype), dtype, name=name ), value def variable_mapping(self): """构建keras层与checkpoint的变量名之间的映射表 """ return {} def load_weights_from_checkpoint(self, checkpoint, mapping=None): """根据mapping从checkpoint加载权重 """ mapping = mapping or self.variable_mapping() mapping = {self.prefixed(k): v for k, v in mapping.items()} mapping = {k: v for k, v in mapping.items() if k in self.layers} weight_value_pairs = [] for layer, variables in mapping.items(): layer = self.layers[layer] weights = layer.trainable_weights values = [self.load_variable(checkpoint, v) for v in variables] if isinstance(layer, MultiHeadAttention): """如果key_size不等于head_size，则可以通过正交矩阵将相应的权重投影到合适的shape。 """ count = 2 if layer.use_bias: count += 2 heads = self.num_attention_heads head_size = self.attention_head_size key_size = self.attention_key_size W = np.linalg.qr(np.random.randn(key_size, head_size))[0].T if layer.attention_scale: W = W * key_size**0.25 / head_size**0.25 for i in range(count): w, v = weights[i], values[i] w_shape, v_shape = K.int_shape(w), v.shape if w_shape[-1] != v_shape[-1]: pre_shape = w_shape[:-1] v = v.reshape(pre_shape + (heads, head_size)) v = np.dot(v, W) v = v.reshape(pre_shape + (heads * key_size,)) values[i] = v weight_value_pairs.extend(zip(weights, values)) K.batch_set_value(weight_value_pairs) def save_weights_as_checkpoint(self, filename, mapping=None, dtype=None): """根据mapping将权重保存为checkpoint格式 """ mapping = mapping or self.variable_mapping() mapping = {self.prefixed(k): v for k, v in mapping.items()} mapping = {k: v for k, v in mapping.items() if k in self.layers} with tf.Graph().as_default(): all_variables, all_values = [], [] for layer, variables in mapping.items(): layer = self.layers[layer] values = K.batch_get_value(layer.trainable_weights) for name, value in zip(variables, values): variable, value = self.create_variable(name, value, dtype) all_variables.append(variable) all_values.append(value) with tf.Session() as sess: K.batch_set_value(zip(all_variables, all_values)) saver = tf.train.Saver() saver.save(sess, filename) class LM_Mask(object): """定义下三角Attention Mask（语言模型用） """ def compute_attention_bias(self, inputs=None): """通过idxs序列的比较来得到对应的mask """ if self.attention_bias is None: def lm_mask(s): seq_len = K.shape(s)[1] idxs = K.arange(0, seq_len) mask = idxs[None, :] <= idxs[:, None] mask = K.cast(mask, K.floatx()) return -(1 - mask[None, None]) * 1e12 self.attention_bias = self.apply( inputs=self.inputs[0], layer=Lambda, function=lm_mask, name='Attention-LM-Mask' ) return self.attention_bias class UniLM_Mask(object): """定义UniLM的Attention Mask（Seq2Seq模型用）其中source和target的分区，由segment_ids来表示。 UniLM: https://arxiv.org/abs/1905.03197 """ def compute_attention_bias(self, inputs=None): """通过idxs序列的比较来得到对应的mask """ if self.attention_bias is None: def unilm_mask(s): idxs = K.cumsum(s, axis=1) mask = idxs[:, None, :] <= idxs[:, :, None] mask = K.cast(mask, K.floatx()) return -(1 - mask[:, None]) * 1e12 self.attention_bias = self.apply( inputs=self.inputs[1], layer=Lambda, function=unilm_mask, name='Attention-UniLM-Mask' ) return self.attention_bias class BERT(Transformer): """构建BERT模型 """ def __init__( self, max_position, # 序列最大长度 segment_vocab_size=2, # segment总数目 with_pool=False, # 是否包含Pool部分 with_nsp=False, # 是否包含NSP部分 with_mlm=False, # 是否包含MLM部分 hierarchical_position=None, # 是否层次分解位置编码 custom_position_ids=False, # 是否自行传入位置id shared_segment_embeddings=False, # 若True，则segment跟token共用embedding **kwargs # 其余参数 ): super(BERT, self).__init__(**kwargs) self.max_position = max_position self.segment_vocab_size = segment_vocab_size self.with_pool = with_pool self.with_nsp = with_nsp self.with_mlm = with_mlm self.hierarchical_position = hierarchical_position self.custom_position_ids = custom_position_ids self.shared_segment_embeddings = shared_segment_embeddings if self.with_nsp and not self.with_pool: self.with_pool = True def get_inputs(self): """BERT的输入是token_ids和segment_ids （但允许自行传入位置id，以实现一些特殊需求） """ x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Token' ) inputs = [x_in] if self.segment_vocab_size > 0: s_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Segment' ) inputs.append(s_in) if self.custom_position_ids: p_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Position' ) inputs.append(p_in) return inputs def apply_embeddings(self, inputs): """BERT的embedding是token、position、segment三者embedding之和 """ inputs = inputs[:] x = inputs.pop(0) if self.segment_vocab_size > 0: s = inputs.pop(0) if self.custom_position_ids: p = inputs.pop(0) else: p = None z = self.layer_norm_conds[0] x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) if self.segment_vocab_size > 0: if self.shared_segment_embeddings: name = 'Embedding-Token' else: name = 'Embedding-Segment' s = self.apply( inputs=s, layer=Embedding, input_dim=self.segment_vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, name=name ) x = self.apply( inputs=[x, s], layer=Add, name='Embedding-Token-Segment' ) x = self.apply( inputs=self.simplify([x, p]), layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, custom_position_ids=self.custom_position_ids, name='Embedding-Position' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Embedding-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): """BERT的主体是基于Self-Attention的模块顺序：Att --> Add --> LN --> FFN --> Add --> LN """ x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) # Self Attention xi, x, arguments = x, [x, x, x], {'a_bias': None} if attention_mask is not None: arguments['a_bias'] = True x.append(attention_mask) x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) # Feed Forward xi = x x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) return x def apply_final_layers(self, inputs): """根据剩余参数决定输出 """ x = inputs z = self.layer_norm_conds[0] outputs = [x] if self.with_pool: # Pooler部分（提取CLS向量） x = outputs[0] x = self.apply( inputs=x, layer=Lambda, function=lambda x: x[:, 0], name='Pooler' ) pool_activation = 'tanh' if self.with_pool is True else self.with_pool x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, activation=pool_activation, kernel_initializer=self.initializer, name='Pooler-Dense' ) if self.with_nsp: # Next Sentence Prediction部分 x = self.apply( inputs=x, layer=Dense, units=2, activation='softmax', kernel_initializer=self.initializer, name='NSP-Proba' ) outputs.append(x) if self.with_mlm: # Masked Language Model部分 x = outputs[0] x = self.apply( inputs=x, layer=Dense, units=self.embedding_size, activation=self.hidden_act, kernel_initializer=self.initializer, name='MLM-Dense' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='MLM-Norm' ) x = self.apply( inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token' ) x = self.apply(inputs=x, layer=BiasAdd, name='MLM-Bias') mlm_activation = 'softmax' if self.with_mlm is True else self.with_mlm x = self.apply( inputs=x, layer=Activation, activation=mlm_activation, name='MLM-Activation' ) outputs.append(x) if len(outputs) == 1: outputs = outputs[0] elif len(outputs) == 2: outputs = outputs[1] else: outputs = outputs[1:] return outputs def load_variable(self, checkpoint, name): """加载单个变量的函数 """ variable = super(BERT, self).load_variable(checkpoint, name) if name in [ 'bert/embeddings/word_embeddings', 'cls/predictions/output_bias', ]: return self.load_embeddings(variable) elif name == 'cls/seq_relationship/output_weights': return variable.T else: return variable def create_variable(self, name, value, dtype=None): """在tensorflow中创建一个变量 """ if name == 'cls/seq_relationship/output_weights': value = value.T return super(BERT, self).create_variable(name, value, dtype) def variable_mapping(self): """映射到官方BERT权重格式 """ mapping = { 'Embedding-Token': ['bert/embeddings/word_embeddings'], 'Embedding-Segment': ['bert/embeddings/token_type_embeddings'], 'Embedding-Position': ['bert/embeddings/position_embeddings'], 'Embedding-Norm': [ 'bert/embeddings/LayerNorm/beta', 'bert/embeddings/LayerNorm/gamma', ], 'Embedding-Mapping': [ 'bert/encoder/embedding_hidden_mapping_in/kernel', 'bert/encoder/embedding_hidden_mapping_in/bias', ], 'Pooler-Dense': [ 'bert/pooler/dense/kernel', 'bert/pooler/dense/bias', ], 'NSP-Proba': [ 'cls/seq_relationship/output_weights', 'cls/seq_relationship/output_bias', ], 'MLM-Dense': [ 'cls/predictions/transform/dense/kernel', 'cls/predictions/transform/dense/bias', ], 'MLM-Norm': [ 'cls/predictions/transform/LayerNorm/beta', 'cls/predictions/transform/LayerNorm/gamma', ], 'MLM-Bias': ['cls/predictions/output_bias'], } for i in range(self.num_hidden_layers): prefix = 'bert/encoder/layer_%d/' % i mapping.update({ 'Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'attention/self/query/kernel', prefix + 'attention/self/query/bias', prefix + 'attention/self/key/kernel', prefix + 'attention/self/key/bias', prefix + 'attention/self/value/kernel', prefix + 'attention/self/value/bias', prefix + 'attention/output/dense/kernel', prefix + 'attention/output/dense/bias', ], 'Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'attention/output/LayerNorm/beta', prefix + 'attention/output/LayerNorm/gamma', ], 'Transformer-%d-FeedForward' % i: [ prefix + 'intermediate/dense/kernel', prefix + 'intermediate/dense/bias', prefix + 'output/dense/kernel', prefix + 'output/dense/bias', ], 'Transformer-%d-FeedForward-Norm' % i: [ prefix + 'output/LayerNorm/beta', prefix + 'output/LayerNorm/gamma', ], }) return mapping class ALBERT(BERT): """构建ALBERT模型 """ def apply_main_layers(self, inputs, index): """ALBERT的主体是基于Self-Attention的模块顺序：Att --> Add --> LN --> FFN --> Add --> LN """ x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-MultiHeadSelfAttention' feed_forward_name = 'Transformer-FeedForward' attention_mask = self.compute_attention_bias(index) # Self Attention xi, x, arguments = x, [x, x, x], {'a_bias': None} if attention_mask is not None: arguments['a_bias'] = True x.append(attention_mask) x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) # Feed Forward xi = x x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) return x def variable_mapping(self): """映射到官方ALBERT权重格式 """ mapping = super(ALBERT, self).variable_mapping() prefix = 'bert/encoder/transformer/group_0/inner_group_0/' mapping.update({ 'Transformer-MultiHeadSelfAttention': [ prefix + 'attention_1/self/query/kernel', prefix + 'attention_1/self/query/bias', prefix + 'attention_1/self/key/kernel', prefix + 'attention_1/self/key/bias', prefix + 'attention_1/self/value/kernel', prefix + 'attention_1/self/value/bias', prefix + 'attention_1/output/dense/kernel', prefix + 'attention_1/output/dense/bias', ], 'Transformer-MultiHeadSelfAttention-Norm': [ prefix + 'LayerNorm/beta', prefix + 'LayerNorm/gamma', ], 'Transformer-FeedForward': [ prefix + 'ffn_1/intermediate/dense/kernel', prefix + 'ffn_1/intermediate/dense/bias', prefix + 'ffn_1/intermediate/output/dense/kernel', prefix + 'ffn_1/intermediate/output/dense/bias', ], 'Transformer-FeedForward-Norm': [ prefix + 'LayerNorm_1/beta', prefix + 'LayerNorm_1/gamma', ], }) return mapping class ALBERT_Unshared(BERT): """解开ALBERT共享约束，当成BERT用 """ def variable_mapping(self): """映射到官方ALBERT权重格式 """ mapping = super(ALBERT_Unshared, self).variable_mapping() prefix = 'bert/encoder/transformer/group_0/inner_group_0/' for i in range(self.num_hidden_layers): mapping.update({ 'Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'attention_1/self/query/kernel', prefix + 'attention_1/self/query/bias', prefix + 'attention_1/self/key/kernel', prefix + 'attention_1/self/key/bias', prefix + 'attention_1/self/value/kernel', prefix + 'attention_1/self/value/bias', prefix + 'attention_1/output/dense/kernel', prefix + 'attention_1/output/dense/bias', ], 'Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'LayerNorm/beta', prefix + 'LayerNorm/gamma', ], 'Transformer-%d-FeedForward' % i: [ prefix + 'ffn_1/intermediate/dense/kernel', prefix + 'ffn_1/intermediate/dense/bias', prefix + 'ffn_1/intermediate/output/dense/kernel', prefix + 'ffn_1/intermediate/output/dense/bias', ], 'Transformer-%d-FeedForward-Norm' % i: [ prefix + 'LayerNorm_1/beta', prefix + 'LayerNorm_1/gamma', ], }) return mapping class NEZHA(BERT): """华为推出的NAZHA模型链接：https://arxiv.org/abs/1909.00204 """ def apply_embeddings(self, inputs): """NEZHA的embedding是token、segment两者embedding之和 """ inputs = inputs[:] x = inputs.pop(0) if self.segment_vocab_size > 0: s = inputs.pop(0) z = self.layer_norm_conds[0] x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) if self.segment_vocab_size > 0: if self.shared_segment_embeddings: name = 'Embedding-Token' else: name = 'Embedding-Segment' s = self.apply( inputs=s, layer=Embedding, input_dim=2, output_dim=self.embedding_size, embeddings_initializer=self.initializer, name=name ) x = self.apply( inputs=[x, s], layer=Add, name='Embedding-Token-Segment' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Embedding-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): """NEZHA的主体是基于Self-Attention的模块顺序：Att --> Add --> LN --> FFN --> Add --> LN """ x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) position_bias = self.compute_position_bias(x) # Self Attention xi, x = x, [x, x, x, position_bias] arguments = {'a_bias': None, 'p_bias': 'typical_relative'} if attention_mask is not None: arguments['a_bias'] = True x.insert(3, attention_mask) x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) # Feed Forward xi = x x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) return x def compute_position_bias(self, inputs=None): """经典相对位置编码 """ if self.position_bias is None: x = inputs self.position_bias = self.apply( inputs=[x, x], layer=RelativePositionEmbedding, input_dim=2 * 64 + 1, output_dim=self.attention_head_size, embeddings_initializer='Sinusoidal', name='Embedding-Relative-Position', trainable=False ) return self.position_bias class ELECTRA(BERT): """Google推出的ELECTRA模型链接：https://arxiv.org/abs/2003.10555 """ @insert_arguments(with_discriminator=False) @delete_arguments('with_pool', 'with_mlm') def __init__( self, max_position, # 序列最大长度 **kwargs # 其余参数 ): super(ELECTRA, self).__init__(max_position, **kwargs) def apply_final_layers(self, inputs): x = inputs if self.with_discriminator: if self.with_discriminator is True: final_activation = 'sigmoid' else: final_activation = self.with_discriminator x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, activation=self.hidden_act, kernel_initializer=self.initializer, name='Discriminator-Dense' ) x = self.apply( inputs=x, layer=Dense, units=1, activation=final_activation, kernel_initializer=self.initializer, name='Discriminator-Prediction' ) return x def load_variable(self, checkpoint, name): """加载单个变量的函数 """ variable = super(ELECTRA, self).load_variable(checkpoint, name) if name == 'electra/embeddings/word_embeddings': return self.load_embeddings(variable) else: return variable def variable_mapping(self): mapping = super(ELECTRA, self).variable_mapping() mapping['Embedding-Mapping'] = [ 'electra/embeddings_project/kernel', 'electra/embeddings_project/bias', ] mapping = { k: [i.replace('bert/', 'electra/') for i in v] for k, v in mapping.items() } mapping['Discriminator-Dense'] = [ 'discriminator_predictions/dense/kernel', 'discriminator_predictions/dense/bias', ] mapping['Discriminator-Prediction'] = [ 'discriminator_predictions/dense_1/kernel', 'discriminator_predictions/dense_1/bias', ] return mapping class GPT(LM_Mask, BERT): """构建GPT模型链接：https://github.com/openai/finetune-transformer-lm """ @insert_arguments(final_activation='softmax') @delete_arguments('with_pool', 'with_mlm') def __init__(self, **kwargs): super(GPT, self).__init__(**kwargs) def apply_embeddings(self, inputs): """GPT的embedding是token、position、segment三者embedding之和跟BERT的主要区别是三者相加之后没有加LayerNormalization层。 """ inputs = inputs[:] x = inputs.pop(0) if self.segment_vocab_size > 0: s = inputs.pop(0) if self.custom_position_ids: p = inputs.pop(0) else: p = None x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) if self.segment_vocab_size > 0: if self.shared_segment_embeddings: name = 'Embedding-Token' else: name = 'Embedding-Segment' s = self.apply( inputs=s, layer=Embedding, input_dim=self.segment_vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, name=name ) x = self.apply( inputs=[x, s], layer=Add, name='Embedding-Token-Segment' ) x = self.apply( inputs=self.simplify([x, p]), layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, custom_position_ids=self.custom_position_ids, name='Embedding-Position' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_final_layers(self, inputs): """剩余部分 """ x = inputs # Language Model部分 x = self.apply( inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Activation, activation=self.final_activation, name='LM-Activation' ) return x def load_variable(self, checkpoint, name): """加载单个变量的函数 """ variable = super(GPT, self).load_variable(checkpoint, name) if name == 'gpt/embeddings/word_embeddings': return self.load_embeddings(variable) else: return variable def variable_mapping(self): """映射到TF版GPT权重格式 """ mapping = super(GPT, self).variable_mapping() mapping = { k: [ i.replace('bert/', 'gpt/').replace('encoder', 'transformer') for i in v ] for k, v in mapping.items() } return mapping class GPT2(GPT): """构建GPT2模型链接: https://github.com/openai/gpt-2 """ def get_inputs(self): """GPT2的输入是token_ids """ x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Token' ) return x_in def apply_embeddings(self, inputs): """GPT2的embedding是token、position两者embedding之和 """ x = inputs x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, name='Embedding-Position' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): """GPT2的主体是基于Self-Attention的模块顺序：LN --> Att --> Add --> LN --> FFN --> Add """ x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) # Self Attention xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) x = self.apply( inputs=[x, x, x, attention_mask], layer=MultiHeadAttention, arguments={'a_bias': True}, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) # Feed Forward xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) return x def apply_final_layers(self, inputs): """剩余部分 """ x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Output-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Output-Dropout' ) x = super(GPT2, self).apply_final_layers(x) return x def variable_mapping(self): """映射到TF版GPT2权重格式 """ mapping = super(GPT2, self).variable_mapping() mapping = { k: [i.replace('output/LayerNorm', 'input/LayerNorm') for i in v] for k, v in mapping.items() } mapping['Output-Norm'] = [ 'gpt/output/LayerNorm/beta', 'gpt/output/LayerNorm/gamma', ] return mapping class GPT2_ML(GPT): """构建GPT2_ML模型链接: https://github.com/imcaspar/gpt2-ml 注意：GPT2_ML虽然号称GPT2，但是它的结构其实更接近GPT，它自称GPT2的原因大概是因为它开源的版本参数量达到了GPT2的15亿参数。 """ def get_inputs(self): """GPT2_ML的输入是token_ids """ x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Token' ) return x_in def apply_embeddings(self, inputs): """GPT2_ML的embedding是token、position两者embedding之和 """ x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, name='Embedding-Position' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Embedding-Norm' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): """GPT2_ML的主体是基于Self-Attention的模块顺序：Att --> LN --> FFN --> Add --> LN """ x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) # Self Attention xi, x, arguments = x, [x, x, x, attention_mask], {'a_bias': True} x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) # Feed Forward xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm-0' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm-1' % feed_forward_name ) return x def load_variable(self, checkpoint, name): """加载单个变量的函数 """ variable = super(GPT2_ML, self).load_variable(checkpoint, name) if name == 'newslm/embeddings/word_embed': return self.load_embeddings(variable) else: return variable def variable_mapping(self): """映射到官方GPT2_ML权重格式 """ mapping = { 'Embedding-Token': ['newslm/embeddings/word_embed'], 'Embedding-Position': ['newslm/embeddings/pos_embed'], 'Embedding-Norm': [ 'newslm/embeddings/LayerNorm_embed_norm/beta', 'newslm/embeddings/LayerNorm_embed_norm/gamma', ], } for i in range(self.num_hidden_layers): prefix = 'newslm/layer%02d/' % i mapping.update({ 'Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'query_layer/kernel', prefix + 'query_layer/bias', prefix + 'key_layer/kernel', prefix + 'key_layer/bias', prefix + 'value_layer/kernel', prefix + 'value_layer/bias', prefix + 'context_projection_layer/kernel', prefix + 'context_projection_layer/bias', ], 'Transformer-%d-FeedForward-Norm-0' % i: [ prefix + 'LayerNorm_mlp_ln0/beta', prefix + 'LayerNorm_mlp_ln0/gamma', ], 'Transformer-%d-FeedForward' % i: [ prefix + 'intermediate/kernel', prefix + 'intermediate/bias', prefix + 'output/kernel', prefix + 'output/bias', ], 'Transformer-%d-FeedForward-Norm-1' % i: [ prefix + 'LayerNorm_mlp_ln1/beta', prefix + 'LayerNorm_mlp_ln1/gamma', ], }) return mapping class T5_Base(Transformer): """Google的T5模型（基类）注意T5有两个版本，一开始放出来的版本称为t5.1.0，而后来放出了一个升级版本称为t5.1.1，两者结构略有不同，包括后来放出来的多国语言版T5也采用了t5.1.1的结构。 t5.1.0: https://github.com/google-research/text-to-text-transfer-transformer t5.1.1: https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511 multilingual-t5: https://github.com/google-research/multilingual-t5 """ @insert_arguments(version='t5.1.0') def __init__(self, **kwargs): super(T5_Base, self).__init__(**kwargs) def load_variable(self, checkpoint, name): """加载单个变量的函数 """ variable = super(T5_Base, self).load_variable(checkpoint, name) if name == 'shared/embedding': return self.load_embeddings(variable) elif name == 'decoder/logits/kernel': return self.load_embeddings(variable.T).T elif 'relative_attention_bias' in name: return variable.T else: return variable def create_variable(self, name, value, dtype=None): """在tensorflow中创建一个变量 """ if 'relative_attention_bias' in name: value = value.T return super(T5_Base, self).create_variable(name, value, dtype) def variable_mapping(self): """映射到官方T5权重格式 """ mapping = { 'Embedding-Token': ['shared/embedding'], 'Encoder-Embedding-Relative-Position': [ 'encoder/block_000/layer_000/SelfAttention/relative_attention_bias' ], 'Encoder-Output-Norm': ['encoder/final_layer_norm/scale'], 'Decoder-Embedding-Relative-Position': [ 'decoder/block_000/layer_000/SelfAttention/relative_attention_bias', ], 'Decoder-Output-Norm': ['decoder/final_layer_norm/scale'], } for i in range(self.num_hidden_layers): # Encoder主体 prefix = 'encoder/block_%03d/' % i mapping.update({ 'Encoder-Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'layer_000/SelfAttention/q', prefix + 'layer_000/SelfAttention/k', prefix + 'layer_000/SelfAttention/v', prefix + 'layer_000/SelfAttention/o', ], 'Encoder-Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'layer_000/layer_norm/scale', ], 'Encoder-Transformer-%d-FeedForward' % i: [ prefix + 'layer_001/DenseReluDense/wi/kernel', prefix + 'layer_001/DenseReluDense/wo/kernel', ], 'Encoder-Transformer-%d-FeedForward-Norm' % i: [ prefix + 'layer_001/layer_norm/scale', ], }) # Decoder主体 prefix = 'decoder/block_%03d/' % i mapping.update({ 'Decoder-Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'layer_000/SelfAttention/q', prefix + 'layer_000/SelfAttention/k', prefix + 'layer_000/SelfAttention/v', prefix + 'layer_000/SelfAttention/o', ], 'Decoder-Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'layer_000/layer_norm/scale', ], 'Decoder-Transformer-%d-MultiHeadCrossAttention' % i: [ prefix + 'layer_001/EncDecAttention/q', prefix + 'layer_001/EncDecAttention/k', prefix + 'layer_001/EncDecAttention/v', prefix + 'layer_001/EncDecAttention/o', ], 'Decoder-Transformer-%d-MultiHeadCrossAttention-Norm' % i: [ prefix + 'layer_001/layer_norm/scale', ], 'Decoder-Transformer-%d-FeedForward' % i: [ prefix + 'layer_002/DenseReluDense/wi/kernel', prefix + 'layer_002/DenseReluDense/wo/kernel', ], 'Decoder-Transformer-%d-FeedForward-Norm' % i: [ prefix + 'layer_002/layer_norm/scale', ], }) if self.version == 't5.1.1': mapping['Encoder-Output-Norm'] = ['encoder/rms_norm/scale'] mapping['Decoder-Output-Norm'] = ['decoder/rms_norm/scale'] mapping['Decoder-Output-LM'] = ['decoder/logits/kernel'] mapping = { k: [i.replace('layer_norm', 'rms_norm') for i in v] for k, v in mapping.items() } for i in range(self.num_hidden_layers): for layer in [ 'Encoder-Transformer-%d-FeedForward' % i, 'Decoder-Transformer-%d-FeedForward' % i ]: mapping[layer] = [ mapping[layer][0][:-7] + '_0' + mapping[layer][0][-7:], mapping[layer][0][:-7] + '_1' + mapping[layer][0][-7:], mapping[layer][1] ] return mapping class T5_Encoder(T5_Base): """Google的T5模型（Encoder） """ def get_inputs(self): """T5的Encoder的输入只有token_ids """ x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Encoder-Input-Token' ) return x_in def apply_embeddings(self, inputs): """T5的embedding只有token embedding，并把relative position embedding准备好，待attention使用。 """ x = inputs x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Encoder-Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Encoder-Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): """T5的Encoder的主体是基于Self-Attention的模块顺序：LN --> Att --> Add --> LN --> FFN --> Add """ x = inputs z = self.layer_norm_conds[0] attention_name = 'Encoder-Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Encoder-Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) position_bias = self.compute_position_bias(x) # Self Attention xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) x = self.apply( inputs=[x, x, x, position_bias], layer=MultiHeadAttention, arguments={'p_bias': 't5_relative'}, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, use_bias=False, attention_scale=False, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) # Feed Forward xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, use_bias=False, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) return x def apply_final_layers(self, inputs): """剩余部分 """ x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Encoder-Output-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Encoder-Output-Dropout' ) return x def compute_position_bias(self, inputs=None): """T5相对位置编码 """ if self.position_bias is None: x = inputs p = self.apply( inputs=[x, x], layer=RelativePositionEmbeddingT5, input_dim=32, output_dim=self.num_attention_heads, bidirectional=True, embeddings_initializer=self.initializer, name='Encoder-Embedding-Relative-Position' ) self.position_bias = p return self.position_bias class T5_Decoder(LM_Mask, T5_Base): """Google的T5模型（Decoder） """ def __init__(self, with_lm=True, **kwargs): super(T5_Decoder, self).__init__(**kwargs) self.with_lm = with_lm def get_inputs(self): """T5的Decoder的输入为context序列和token_ids """ c_in = self.apply( layer=Input, shape=(self.sequence_length, self.hidden_size), name='Input-Context' ) x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Decoder-Input-Token' ) return [c_in, x_in] def apply_embeddings(self, inputs): """T5的embedding只有token embedding，并把relative position embedding准备好，待attention使用。 """ c, x = inputs x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Decoder-Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Decoder-Embedding-Mapping' ) return [c, x] def apply_main_layers(self, inputs, index): """T5的Dencoder主体是基于Self-Attention、Cross-Attention的模块顺序：LN --> Att1 --> Add --> LN --> Att2 --> Add --> LN --> FFN --> Add """ c, x = inputs z = self.layer_norm_conds[0] self_attention_name = 'Decoder-Transformer-%d-MultiHeadSelfAttention' % index cross_attention_name = 'Decoder-Transformer-%d-MultiHeadCrossAttention' % index feed_forward_name = 'Decoder-Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) position_bias = self.compute_position_bias([x, c]) # Self Attention xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % self_attention_name ) x = self.apply( inputs=[x, x, x, attention_mask, position_bias[0]], layer=MultiHeadAttention, arguments={ 'a_bias': True, 'p_bias': 't5_relative' }, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, use_bias=False, attention_scale=False, kernel_initializer=self.initializer, name=self_attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % self_attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % self_attention_name ) # Cross Attention xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % cross_attention_name ) x = self.apply( inputs=[x, c, c, position_bias[1]], layer=MultiHeadAttention, arguments={ 'a_bias': None, 'p_bias': 't5_relative' }, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, use_bias=False, attention_scale=False, kernel_initializer=self.initializer, name=cross_attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % cross_attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % cross_attention_name ) # Feed Forward xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, use_bias=False, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) return [c, x] def apply_final_layers(self, inputs): """剩余部分 """ c, x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Decoder-Output-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Decoder-Output-Dropout' ) x = self.apply( inputs=x, layer=Lambda, function=lambda x: x / np.sqrt(self.hidden_size), mask=lambda i, m: m, name='Decoder-Output-Scale' ) if self.with_lm: # 预测token概率部分 if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.embedding_size, kernel_initializer=self.initializer, name='Decoder-Output-Mapping' ) lm_activation = 'softmax' if self.with_lm is True else self.with_lm if self.version == 't5.1.0': x = self.apply( inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Activation, activation=lm_activation, name='Dencoder-Output-LM-Activation' ) else: x = self.apply( inputs=x, layer=Dense, units=self.vocab_size, activation=lm_activation, use_bias=False, kernel_initializer=self.initializer, name='Decoder-Output-LM' ) return x def compute_attention_bias(self, inputs=None): """修改LM Mask的序列长度（从 self.inputs[0] 改为 self.inputs[1] ） """ old_inputs = self.inputs[:] self.inputs = [old_inputs[1]] mask = super(T5_Decoder, self).compute_attention_bias(inputs) self.inputs = old_inputs return mask def compute_position_bias(self, inputs=None): """T5相对位置编码 """ if self.position_bias is None: x, c = inputs p1 = self.apply( inputs=[x, x], layer=RelativePositionEmbeddingT5, input_dim=32, output_dim=self.num_attention_heads, bidirectional=False, embeddings_initializer=self.initializer, name='Decoder-Embedding-Relative-Position' ) p2 = self.apply( inputs=[x, c], layer=RelativePositionEmbeddingT5, input_dim=32, output_dim=self.num_attention_heads, bidirectional=False, embeddings_initializer=self.initializer, name='Decoder-Embedding-Relative-Position' ) self.position_bias = (p1, p2) return self.position_bias class T5(T5_Base): """Google的T5模型（Encoder-Decoder） """ def __init__(self, **kwargs): super(T5, self).__init__(**kwargs) kwargs['layers'] = self.layers e_name, d_name = 'Encoder', 'Decoder' if 'name' in kwargs: e_name = '%s_%s' % (kwargs['name'], e_name) d_name = '%s_%s' % (kwargs['name'], d_name) del kwargs['name'] # 防止重复传参 self._encoder = T5_Encoder(name=e_name, **kwargs) self._decoder = T5_Decoder(name=d_name, **kwargs) def build(self, **kwargs): """同时构建Encoder和Decoder """ self._encoder.build(**kwargs) self._decoder.build(**kwargs) self.encoder = self._encoder.model self.decoder = self._decoder.model self.inputs = self.encoder.inputs + self.decoder.inputs[1:] self.outputs = self.decoder( self.encoder.outputs + self.decoder.inputs[1:] ) self.model = Model(self.inputs, self.outputs) def extend_with_language_model(BaseModel): """添加下三角的Attention Mask（语言模型用） """ class LanguageModel(LM_Mask, BaseModel): """带下三角Attention Mask的派生模型 """ def __init__(self, *args, **kwargs): super(LanguageModel, self).__init__(*args, **kwargs) self.with_mlm = self.with_mlm or True return LanguageModel def extend_with_unified_language_model(BaseModel): """添加UniLM的Attention Mask（Seq2Seq模型用） """ class UnifiedLanguageModel(UniLM_Mask, BaseModel): """带UniLM的Attention Mask的派生模型 UniLM: https://arxiv.org/abs/1905.03197 """ def __init__(self, *args, **kwargs): super(UnifiedLanguageModel, self).__init__(*args, **kwargs) self.with_mlm = self.with_mlm or True return UnifiedLanguageModel def build_transformer_model( config_path=None, checkpoint_path=None, model='bert', application='encoder', return_keras_model=True, **kwargs ): """根据配置文件构建模型，可选加载checkpoint权重 """ configs = {} if config_path is not None: configs.update(json.load(open(config_path))) configs.update(kwargs) if 'max_position' not in configs: configs['max_position'] = configs.get('max_position_embeddings', 512) if 'dropout_rate' not in configs: configs['dropout_rate'] = configs.get('hidden_dropout_prob') if 'segment_vocab_size' not in configs: configs['segment_vocab_size'] = configs.get('type_vocab_size', 2) models = { 'bert': BERT, 'albert': ALBERT, 'albert_unshared': ALBERT_Unshared, 'roberta': BERT, 'nezha': NEZHA, 'electra': ELECTRA, 'gpt': GPT, 'gpt2': GPT2, 'gpt2_ml': GPT2_ML, 't5': T5, 't5_encoder': T5_Encoder, 't5_decoder': T5_Decoder, 't5.1.0': T5, 't5.1.0_encoder': T5_Encoder, 't5.1.0_decoder': T5_Decoder, 't5.1.1': T5, 't5.1.1_encoder': T5_Encoder, 't5.1.1_decoder': T5_Decoder, } if is_string(model): model = model.lower() MODEL = models[model] else: MODEL = model application = application.lower() if application in ['lm', 'unilm'] and model in ['electra', 't5']: raise ValueError( '"%s" model can not be used as "%s" application.\n' % (model, application) ) if application == 'lm': MODEL = extend_with_language_model(MODEL) elif application == 'unilm': MODEL = extend_with_unified_language_model(MODEL) if model.startswith('t5.1.1'): configs['version'] = 't5.1.1' transformer = MODEL(**configs) # 此处以Transformer类中的build()函数创建模型. transformer.build(**configs) if checkpoint_path is not None: transformer.load_weights_from_checkpoint(checkpoint_path) if return_keras_model: return transformer.model else: return transformer

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import numpy as np from bert4keras.layers import * from bert4keras.snippets import insert_arguments from bert4keras.snippets import delete_arguments from bert4keras.snippets import is_string from keras.models import Model import json class Transformer(object): def __init__( self, vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, dropout_rate=None, embedding_size=None, attention_head_size=None, attention_key_size=None, sequence_length=None, keep_tokens=None, compound_tokens=None, residual_attention_scores=False, layers=None, prefix=None, name=None, **kwargs ): if keep_tokens is not None: vocab_size = len(keep_tokens) if compound_tokens is not None: vocab_size += len(compound_tokens) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.attention_head_size = attention_head_size or hidden_size // num_attention_heads self.attention_key_size = attention_key_size or self.attention_head_size self.intermediate_size = intermediate_size self.dropout_rate = dropout_rate or 0 self.hidden_act = hidden_act self.embedding_size = embedding_size or hidden_size self.sequence_length = sequence_length self.keep_tokens = keep_tokens self.compound_tokens = compound_tokens self.attention_bias = None self.position_bias = None self.attention_scores = None self.residual_attention_scores = residual_attention_scores self.layers = {} if layers is None else layers self.prefix = prefix or '' self.name = name self.built = False def build( self, attention_caches=None, layer_norm_cond=None, layer_norm_cond_hidden_size=None, layer_norm_cond_hidden_act=None, additional_input_layers=None, **kwargs ): if self.built: return None inputs = self.get_inputs() self.set_inputs(inputs, additional_input_layers) self.attention_caches = attention_caches or {} self.layer_norm_conds = [ layer_norm_cond, layer_norm_cond_hidden_size, layer_norm_cond_hidden_act or 'linear', ] outputs = self.call(inputs) self.set_outputs(outputs) self.model = Model(self.inputs, self.outputs, name=self.name) self.built = True def call(self, inputs): outputs = self.apply_embeddings(inputs) for i in range(self.num_hidden_layers): outputs = self.apply_main_layers(outputs, i) outputs = self.apply_final_layers(outputs) return outputs def prefixed(self, name): if name is not None: return self.prefix + name def apply(self, inputs=None, layer=None, arguments=None, **kwargs): if layer is Dropout and self.dropout_rate == 0: return inputs if layer is MultiHeadAttention and self.residual_attention_scores: kwargs['return_attention_scores'] = True arguments = arguments or {} name = self.prefixed(kwargs.get('name')) kwargs['name'] = name if name not in self.layers: layer = layer(**kwargs) name = layer.name self.layers[name] = layer if inputs is None: return self.layers[name] else: if isinstance(self.layers[name], MultiHeadAttention): if name in self.attention_caches: k_cache, v_cache = self.attention_caches[name] k_name, v_name = name + '-Cached-Key', name + '-Cached-Value' k = Concatenate1D(name=k_name)([k_cache, inputs[1]]) v = Concatenate1D(name=v_name)([v_cache, inputs[2]]) inputs = inputs[:1] + [k, v] + inputs[3:] if self.residual_attention_scores: if self.attention_scores is not None: if arguments.get('a_bias'): a_bias = Add(name=name + '-Attention-Bias' )([inputs[3], self.attention_scores]) else: a_bias = self.attention_scores inputs = inputs[:3] + [a_bias] + inputs[4:] arguments['a_bias'] = True o, a = self.layers[name](inputs, **arguments) self.attention_scores = a return o return self.layers[name](inputs, **arguments) def get_inputs(self): raise NotImplementedError def apply_embeddings(self, inputs): raise NotImplementedError def apply_main_layers(self, inputs, index): raise NotImplementedError def apply_final_layers(self, inputs): raise NotImplementedError def compute_attention_bias(self, inputs=None): return self.attention_bias def compute_position_bias(self, inputs=None): return self.position_bias def set_inputs(self, inputs, additional_input_layers=None): if inputs is None: inputs = [] elif not isinstance(inputs, list): inputs = [inputs] inputs = inputs[:] if additional_input_layers is not None: if not isinstance(additional_input_layers, list): additional_input_layers = [additional_input_layers] inputs.extend(additional_input_layers) self.inputs = inputs if len(inputs) > 1: self.input = inputs else: self.input = inputs[0] def set_outputs(self, outputs): if not isinstance(outputs, list): outputs = [outputs] outputs = outputs[:] self.outputs = outputs if len(outputs) > 1: self.output = outputs else: self.output = outputs[0] @property def initializer(self): return keras.initializers.TruncatedNormal(stddev=0.02) def simplify(self, inputs): inputs = [i for i in inputs if i is not None] if len(inputs) == 1: inputs = inputs[0] return inputs def load_embeddings(self, embeddings): if self.keep_tokens is not None: embeddings = embeddings[self.keep_tokens] if self.compound_tokens is not None: ext_embeddings = [] for item in self.compound_tokens: if isinstance(item, list): item = (item, [1] * len(item)) ext_embeddings.append( np.average(embeddings[item[0]], 0, item[1]) ) embeddings = np.concatenate([embeddings, ext_embeddings], 0) return embeddings def load_variable(self, checkpoint, name): if isinstance(checkpoint, dict): return checkpoint[name] else: return tf.train.load_variable(checkpoint, name) def create_variable(self, name, value, dtype=None): dtype = dtype or K.floatx() return K.variable( self.initializer(value.shape, dtype), dtype, name=name ), value def variable_mapping(self): return {} def load_weights_from_checkpoint(self, checkpoint, mapping=None): mapping = mapping or self.variable_mapping() mapping = {self.prefixed(k): v for k, v in mapping.items()} mapping = {k: v for k, v in mapping.items() if k in self.layers} weight_value_pairs = [] for layer, variables in mapping.items(): layer = self.layers[layer] weights = layer.trainable_weights values = [self.load_variable(checkpoint, v) for v in variables] if isinstance(layer, MultiHeadAttention): count = 2 if layer.use_bias: count += 2 heads = self.num_attention_heads head_size = self.attention_head_size key_size = self.attention_key_size W = np.linalg.qr(np.random.randn(key_size, head_size))[0].T if layer.attention_scale: W = W * key_size**0.25 / head_size**0.25 for i in range(count): w, v = weights[i], values[i] w_shape, v_shape = K.int_shape(w), v.shape if w_shape[-1] != v_shape[-1]: pre_shape = w_shape[:-1] v = v.reshape(pre_shape + (heads, head_size)) v = np.dot(v, W) v = v.reshape(pre_shape + (heads * key_size,)) values[i] = v weight_value_pairs.extend(zip(weights, values)) K.batch_set_value(weight_value_pairs) def save_weights_as_checkpoint(self, filename, mapping=None, dtype=None): mapping = mapping or self.variable_mapping() mapping = {self.prefixed(k): v for k, v in mapping.items()} mapping = {k: v for k, v in mapping.items() if k in self.layers} with tf.Graph().as_default(): all_variables, all_values = [], [] for layer, variables in mapping.items(): layer = self.layers[layer] values = K.batch_get_value(layer.trainable_weights) for name, value in zip(variables, values): variable, value = self.create_variable(name, value, dtype) all_variables.append(variable) all_values.append(value) with tf.Session() as sess: K.batch_set_value(zip(all_variables, all_values)) saver = tf.train.Saver() saver.save(sess, filename) class LM_Mask(object): def compute_attention_bias(self, inputs=None): if self.attention_bias is None: def lm_mask(s): seq_len = K.shape(s)[1] idxs = K.arange(0, seq_len) mask = idxs[None, :] <= idxs[:, None] mask = K.cast(mask, K.floatx()) return -(1 - mask[None, None]) * 1e12 self.attention_bias = self.apply( inputs=self.inputs[0], layer=Lambda, function=lm_mask, name='Attention-LM-Mask' ) return self.attention_bias class UniLM_Mask(object): def compute_attention_bias(self, inputs=None): if self.attention_bias is None: def unilm_mask(s): idxs = K.cumsum(s, axis=1) mask = idxs[:, None, :] <= idxs[:, :, None] mask = K.cast(mask, K.floatx()) return -(1 - mask[:, None]) * 1e12 self.attention_bias = self.apply( inputs=self.inputs[1], layer=Lambda, function=unilm_mask, name='Attention-UniLM-Mask' ) return self.attention_bias class BERT(Transformer): def __init__( self, max_position, segment_vocab_size=2, with_pool=False, with_nsp=False, with_mlm=False, hierarchical_position=None, custom_position_ids=False, shared_segment_embeddings=False, **kwargs ): super(BERT, self).__init__(**kwargs) self.max_position = max_position self.segment_vocab_size = segment_vocab_size self.with_pool = with_pool self.with_nsp = with_nsp self.with_mlm = with_mlm self.hierarchical_position = hierarchical_position self.custom_position_ids = custom_position_ids self.shared_segment_embeddings = shared_segment_embeddings if self.with_nsp and not self.with_pool: self.with_pool = True def get_inputs(self): x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Token' ) inputs = [x_in] if self.segment_vocab_size > 0: s_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Segment' ) inputs.append(s_in) if self.custom_position_ids: p_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Position' ) inputs.append(p_in) return inputs def apply_embeddings(self, inputs): inputs = inputs[:] x = inputs.pop(0) if self.segment_vocab_size > 0: s = inputs.pop(0) if self.custom_position_ids: p = inputs.pop(0) else: p = None z = self.layer_norm_conds[0] x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) if self.segment_vocab_size > 0: if self.shared_segment_embeddings: name = 'Embedding-Token' else: name = 'Embedding-Segment' s = self.apply( inputs=s, layer=Embedding, input_dim=self.segment_vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, name=name ) x = self.apply( inputs=[x, s], layer=Add, name='Embedding-Token-Segment' ) x = self.apply( inputs=self.simplify([x, p]), layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, custom_position_ids=self.custom_position_ids, name='Embedding-Position' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Embedding-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) xi, x, arguments = x, [x, x, x], {'a_bias': None} if attention_mask is not None: arguments['a_bias'] = True x.append(attention_mask) x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) xi = x x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) return x def apply_final_layers(self, inputs): x = inputs z = self.layer_norm_conds[0] outputs = [x] if self.with_pool: x = outputs[0] x = self.apply( inputs=x, layer=Lambda, function=lambda x: x[:, 0], name='Pooler' ) pool_activation = 'tanh' if self.with_pool is True else self.with_pool x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, activation=pool_activation, kernel_initializer=self.initializer, name='Pooler-Dense' ) if self.with_nsp: x = self.apply( inputs=x, layer=Dense, units=2, activation='softmax', kernel_initializer=self.initializer, name='NSP-Proba' ) outputs.append(x) if self.with_mlm: x = outputs[0] x = self.apply( inputs=x, layer=Dense, units=self.embedding_size, activation=self.hidden_act, kernel_initializer=self.initializer, name='MLM-Dense' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='MLM-Norm' ) x = self.apply( inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token' ) x = self.apply(inputs=x, layer=BiasAdd, name='MLM-Bias') mlm_activation = 'softmax' if self.with_mlm is True else self.with_mlm x = self.apply( inputs=x, layer=Activation, activation=mlm_activation, name='MLM-Activation' ) outputs.append(x) if len(outputs) == 1: outputs = outputs[0] elif len(outputs) == 2: outputs = outputs[1] else: outputs = outputs[1:] return outputs def load_variable(self, checkpoint, name): variable = super(BERT, self).load_variable(checkpoint, name) if name in [ 'bert/embeddings/word_embeddings', 'cls/predictions/output_bias', ]: return self.load_embeddings(variable) elif name == 'cls/seq_relationship/output_weights': return variable.T else: return variable def create_variable(self, name, value, dtype=None): if name == 'cls/seq_relationship/output_weights': value = value.T return super(BERT, self).create_variable(name, value, dtype) def variable_mapping(self): mapping = { 'Embedding-Token': ['bert/embeddings/word_embeddings'], 'Embedding-Segment': ['bert/embeddings/token_type_embeddings'], 'Embedding-Position': ['bert/embeddings/position_embeddings'], 'Embedding-Norm': [ 'bert/embeddings/LayerNorm/beta', 'bert/embeddings/LayerNorm/gamma', ], 'Embedding-Mapping': [ 'bert/encoder/embedding_hidden_mapping_in/kernel', 'bert/encoder/embedding_hidden_mapping_in/bias', ], 'Pooler-Dense': [ 'bert/pooler/dense/kernel', 'bert/pooler/dense/bias', ], 'NSP-Proba': [ 'cls/seq_relationship/output_weights', 'cls/seq_relationship/output_bias', ], 'MLM-Dense': [ 'cls/predictions/transform/dense/kernel', 'cls/predictions/transform/dense/bias', ], 'MLM-Norm': [ 'cls/predictions/transform/LayerNorm/beta', 'cls/predictions/transform/LayerNorm/gamma', ], 'MLM-Bias': ['cls/predictions/output_bias'], } for i in range(self.num_hidden_layers): prefix = 'bert/encoder/layer_%d/' % i mapping.update({ 'Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'attention/self/query/kernel', prefix + 'attention/self/query/bias', prefix + 'attention/self/key/kernel', prefix + 'attention/self/key/bias', prefix + 'attention/self/value/kernel', prefix + 'attention/self/value/bias', prefix + 'attention/output/dense/kernel', prefix + 'attention/output/dense/bias', ], 'Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'attention/output/LayerNorm/beta', prefix + 'attention/output/LayerNorm/gamma', ], 'Transformer-%d-FeedForward' % i: [ prefix + 'intermediate/dense/kernel', prefix + 'intermediate/dense/bias', prefix + 'output/dense/kernel', prefix + 'output/dense/bias', ], 'Transformer-%d-FeedForward-Norm' % i: [ prefix + 'output/LayerNorm/beta', prefix + 'output/LayerNorm/gamma', ], }) return mapping class ALBERT(BERT): def apply_main_layers(self, inputs, index): x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-MultiHeadSelfAttention' feed_forward_name = 'Transformer-FeedForward' attention_mask = self.compute_attention_bias(index) xi, x, arguments = x, [x, x, x], {'a_bias': None} if attention_mask is not None: arguments['a_bias'] = True x.append(attention_mask) x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) xi = x x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) return x def variable_mapping(self): mapping = super(ALBERT, self).variable_mapping() prefix = 'bert/encoder/transformer/group_0/inner_group_0/' mapping.update({ 'Transformer-MultiHeadSelfAttention': [ prefix + 'attention_1/self/query/kernel', prefix + 'attention_1/self/query/bias', prefix + 'attention_1/self/key/kernel', prefix + 'attention_1/self/key/bias', prefix + 'attention_1/self/value/kernel', prefix + 'attention_1/self/value/bias', prefix + 'attention_1/output/dense/kernel', prefix + 'attention_1/output/dense/bias', ], 'Transformer-MultiHeadSelfAttention-Norm': [ prefix + 'LayerNorm/beta', prefix + 'LayerNorm/gamma', ], 'Transformer-FeedForward': [ prefix + 'ffn_1/intermediate/dense/kernel', prefix + 'ffn_1/intermediate/dense/bias', prefix + 'ffn_1/intermediate/output/dense/kernel', prefix + 'ffn_1/intermediate/output/dense/bias', ], 'Transformer-FeedForward-Norm': [ prefix + 'LayerNorm_1/beta', prefix + 'LayerNorm_1/gamma', ], }) return mapping class ALBERT_Unshared(BERT): def variable_mapping(self): mapping = super(ALBERT_Unshared, self).variable_mapping() prefix = 'bert/encoder/transformer/group_0/inner_group_0/' for i in range(self.num_hidden_layers): mapping.update({ 'Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'attention_1/self/query/kernel', prefix + 'attention_1/self/query/bias', prefix + 'attention_1/self/key/kernel', prefix + 'attention_1/self/key/bias', prefix + 'attention_1/self/value/kernel', prefix + 'attention_1/self/value/bias', prefix + 'attention_1/output/dense/kernel', prefix + 'attention_1/output/dense/bias', ], 'Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'LayerNorm/beta', prefix + 'LayerNorm/gamma', ], 'Transformer-%d-FeedForward' % i: [ prefix + 'ffn_1/intermediate/dense/kernel', prefix + 'ffn_1/intermediate/dense/bias', prefix + 'ffn_1/intermediate/output/dense/kernel', prefix + 'ffn_1/intermediate/output/dense/bias', ], 'Transformer-%d-FeedForward-Norm' % i: [ prefix + 'LayerNorm_1/beta', prefix + 'LayerNorm_1/gamma', ], }) return mapping class NEZHA(BERT): def apply_embeddings(self, inputs): inputs = inputs[:] x = inputs.pop(0) if self.segment_vocab_size > 0: s = inputs.pop(0) z = self.layer_norm_conds[0] x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) if self.segment_vocab_size > 0: if self.shared_segment_embeddings: name = 'Embedding-Token' else: name = 'Embedding-Segment' s = self.apply( inputs=s, layer=Embedding, input_dim=2, output_dim=self.embedding_size, embeddings_initializer=self.initializer, name=name ) x = self.apply( inputs=[x, s], layer=Add, name='Embedding-Token-Segment' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Embedding-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) position_bias = self.compute_position_bias(x) xi, x = x, [x, x, x, position_bias] arguments = {'a_bias': None, 'p_bias': 'typical_relative'} if attention_mask is not None: arguments['a_bias'] = True x.insert(3, attention_mask) x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) xi = x x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) return x def compute_position_bias(self, inputs=None): if self.position_bias is None: x = inputs self.position_bias = self.apply( inputs=[x, x], layer=RelativePositionEmbedding, input_dim=2 * 64 + 1, output_dim=self.attention_head_size, embeddings_initializer='Sinusoidal', name='Embedding-Relative-Position', trainable=False ) return self.position_bias class ELECTRA(BERT): @insert_arguments(with_discriminator=False) @delete_arguments('with_pool', 'with_mlm') def __init__( self, max_position, **kwargs ): super(ELECTRA, self).__init__(max_position, **kwargs) def apply_final_layers(self, inputs): x = inputs if self.with_discriminator: if self.with_discriminator is True: final_activation = 'sigmoid' else: final_activation = self.with_discriminator x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, activation=self.hidden_act, kernel_initializer=self.initializer, name='Discriminator-Dense' ) x = self.apply( inputs=x, layer=Dense, units=1, activation=final_activation, kernel_initializer=self.initializer, name='Discriminator-Prediction' ) return x def load_variable(self, checkpoint, name): variable = super(ELECTRA, self).load_variable(checkpoint, name) if name == 'electra/embeddings/word_embeddings': return self.load_embeddings(variable) else: return variable def variable_mapping(self): mapping = super(ELECTRA, self).variable_mapping() mapping['Embedding-Mapping'] = [ 'electra/embeddings_project/kernel', 'electra/embeddings_project/bias', ] mapping = { k: [i.replace('bert/', 'electra/') for i in v] for k, v in mapping.items() } mapping['Discriminator-Dense'] = [ 'discriminator_predictions/dense/kernel', 'discriminator_predictions/dense/bias', ] mapping['Discriminator-Prediction'] = [ 'discriminator_predictions/dense_1/kernel', 'discriminator_predictions/dense_1/bias', ] return mapping class GPT(LM_Mask, BERT): @insert_arguments(final_activation='softmax') @delete_arguments('with_pool', 'with_mlm') def __init__(self, **kwargs): super(GPT, self).__init__(**kwargs) def apply_embeddings(self, inputs): inputs = inputs[:] x = inputs.pop(0) if self.segment_vocab_size > 0: s = inputs.pop(0) if self.custom_position_ids: p = inputs.pop(0) else: p = None x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) if self.segment_vocab_size > 0: if self.shared_segment_embeddings: name = 'Embedding-Token' else: name = 'Embedding-Segment' s = self.apply( inputs=s, layer=Embedding, input_dim=self.segment_vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, name=name ) x = self.apply( inputs=[x, s], layer=Add, name='Embedding-Token-Segment' ) x = self.apply( inputs=self.simplify([x, p]), layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, custom_position_ids=self.custom_position_ids, name='Embedding-Position' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_final_layers(self, inputs): x = inputs x = self.apply( inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Activation, activation=self.final_activation, name='LM-Activation' ) return x def load_variable(self, checkpoint, name): variable = super(GPT, self).load_variable(checkpoint, name) if name == 'gpt/embeddings/word_embeddings': return self.load_embeddings(variable) else: return variable def variable_mapping(self): mapping = super(GPT, self).variable_mapping() mapping = { k: [ i.replace('bert/', 'gpt/').replace('encoder', 'transformer') for i in v ] for k, v in mapping.items() } return mapping class GPT2(GPT): def get_inputs(self): x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Token' ) return x_in def apply_embeddings(self, inputs): x = inputs x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, name='Embedding-Position' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) x = self.apply( inputs=[x, x, x, attention_mask], layer=MultiHeadAttention, arguments={'a_bias': True}, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) return x def apply_final_layers(self, inputs): x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Output-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Output-Dropout' ) x = super(GPT2, self).apply_final_layers(x) return x def variable_mapping(self): mapping = super(GPT2, self).variable_mapping() mapping = { k: [i.replace('output/LayerNorm', 'input/LayerNorm') for i in v] for k, v in mapping.items() } mapping['Output-Norm'] = [ 'gpt/output/LayerNorm/beta', 'gpt/output/LayerNorm/gamma', ] return mapping class GPT2_ML(GPT): def get_inputs(self): x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Input-Token' ) return x_in def apply_embeddings(self, inputs): x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=PositionEmbedding, input_dim=self.max_position, output_dim=self.embedding_size, merge_mode='add', hierarchical=self.hierarchical_position, embeddings_initializer=self.initializer, name='Embedding-Position' ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Embedding-Norm' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): x = inputs z = self.layer_norm_conds[0] attention_name = 'Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) xi, x, arguments = x, [x, x, x, attention_mask], {'a_bias': True} x = self.apply( inputs=x, layer=MultiHeadAttention, arguments=arguments, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm-0' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, epsilon=1e-5, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm-1' % feed_forward_name ) return x def load_variable(self, checkpoint, name): variable = super(GPT2_ML, self).load_variable(checkpoint, name) if name == 'newslm/embeddings/word_embed': return self.load_embeddings(variable) else: return variable def variable_mapping(self): mapping = { 'Embedding-Token': ['newslm/embeddings/word_embed'], 'Embedding-Position': ['newslm/embeddings/pos_embed'], 'Embedding-Norm': [ 'newslm/embeddings/LayerNorm_embed_norm/beta', 'newslm/embeddings/LayerNorm_embed_norm/gamma', ], } for i in range(self.num_hidden_layers): prefix = 'newslm/layer%02d/' % i mapping.update({ 'Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'query_layer/kernel', prefix + 'query_layer/bias', prefix + 'key_layer/kernel', prefix + 'key_layer/bias', prefix + 'value_layer/kernel', prefix + 'value_layer/bias', prefix + 'context_projection_layer/kernel', prefix + 'context_projection_layer/bias', ], 'Transformer-%d-FeedForward-Norm-0' % i: [ prefix + 'LayerNorm_mlp_ln0/beta', prefix + 'LayerNorm_mlp_ln0/gamma', ], 'Transformer-%d-FeedForward' % i: [ prefix + 'intermediate/kernel', prefix + 'intermediate/bias', prefix + 'output/kernel', prefix + 'output/bias', ], 'Transformer-%d-FeedForward-Norm-1' % i: [ prefix + 'LayerNorm_mlp_ln1/beta', prefix + 'LayerNorm_mlp_ln1/gamma', ], }) return mapping class T5_Base(Transformer): @insert_arguments(version='t5.1.0') def __init__(self, **kwargs): super(T5_Base, self).__init__(**kwargs) def load_variable(self, checkpoint, name): variable = super(T5_Base, self).load_variable(checkpoint, name) if name == 'shared/embedding': return self.load_embeddings(variable) elif name == 'decoder/logits/kernel': return self.load_embeddings(variable.T).T elif 'relative_attention_bias' in name: return variable.T else: return variable def create_variable(self, name, value, dtype=None): if 'relative_attention_bias' in name: value = value.T return super(T5_Base, self).create_variable(name, value, dtype) def variable_mapping(self): mapping = { 'Embedding-Token': ['shared/embedding'], 'Encoder-Embedding-Relative-Position': [ 'encoder/block_000/layer_000/SelfAttention/relative_attention_bias' ], 'Encoder-Output-Norm': ['encoder/final_layer_norm/scale'], 'Decoder-Embedding-Relative-Position': [ 'decoder/block_000/layer_000/SelfAttention/relative_attention_bias', ], 'Decoder-Output-Norm': ['decoder/final_layer_norm/scale'], } for i in range(self.num_hidden_layers): prefix = 'encoder/block_%03d/' % i mapping.update({ 'Encoder-Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'layer_000/SelfAttention/q', prefix + 'layer_000/SelfAttention/k', prefix + 'layer_000/SelfAttention/v', prefix + 'layer_000/SelfAttention/o', ], 'Encoder-Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'layer_000/layer_norm/scale', ], 'Encoder-Transformer-%d-FeedForward' % i: [ prefix + 'layer_001/DenseReluDense/wi/kernel', prefix + 'layer_001/DenseReluDense/wo/kernel', ], 'Encoder-Transformer-%d-FeedForward-Norm' % i: [ prefix + 'layer_001/layer_norm/scale', ], }) prefix = 'decoder/block_%03d/' % i mapping.update({ 'Decoder-Transformer-%d-MultiHeadSelfAttention' % i: [ prefix + 'layer_000/SelfAttention/q', prefix + 'layer_000/SelfAttention/k', prefix + 'layer_000/SelfAttention/v', prefix + 'layer_000/SelfAttention/o', ], 'Decoder-Transformer-%d-MultiHeadSelfAttention-Norm' % i: [ prefix + 'layer_000/layer_norm/scale', ], 'Decoder-Transformer-%d-MultiHeadCrossAttention' % i: [ prefix + 'layer_001/EncDecAttention/q', prefix + 'layer_001/EncDecAttention/k', prefix + 'layer_001/EncDecAttention/v', prefix + 'layer_001/EncDecAttention/o', ], 'Decoder-Transformer-%d-MultiHeadCrossAttention-Norm' % i: [ prefix + 'layer_001/layer_norm/scale', ], 'Decoder-Transformer-%d-FeedForward' % i: [ prefix + 'layer_002/DenseReluDense/wi/kernel', prefix + 'layer_002/DenseReluDense/wo/kernel', ], 'Decoder-Transformer-%d-FeedForward-Norm' % i: [ prefix + 'layer_002/layer_norm/scale', ], }) if self.version == 't5.1.1': mapping['Encoder-Output-Norm'] = ['encoder/rms_norm/scale'] mapping['Decoder-Output-Norm'] = ['decoder/rms_norm/scale'] mapping['Decoder-Output-LM'] = ['decoder/logits/kernel'] mapping = { k: [i.replace('layer_norm', 'rms_norm') for i in v] for k, v in mapping.items() } for i in range(self.num_hidden_layers): for layer in [ 'Encoder-Transformer-%d-FeedForward' % i, 'Decoder-Transformer-%d-FeedForward' % i ]: mapping[layer] = [ mapping[layer][0][:-7] + '_0' + mapping[layer][0][-7:], mapping[layer][0][:-7] + '_1' + mapping[layer][0][-7:], mapping[layer][1] ] return mapping class T5_Encoder(T5_Base): def get_inputs(self): x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Encoder-Input-Token' ) return x_in def apply_embeddings(self, inputs): x = inputs x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Encoder-Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Encoder-Embedding-Mapping' ) return x def apply_main_layers(self, inputs, index): x = inputs z = self.layer_norm_conds[0] attention_name = 'Encoder-Transformer-%d-MultiHeadSelfAttention' % index feed_forward_name = 'Encoder-Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) position_bias = self.compute_position_bias(x) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % attention_name ) x = self.apply( inputs=[x, x, x, position_bias], layer=MultiHeadAttention, arguments={'p_bias': 't5_relative'}, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, use_bias=False, attention_scale=False, kernel_initializer=self.initializer, name=attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % attention_name ) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, use_bias=False, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) return x def apply_final_layers(self, inputs): x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Encoder-Output-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Encoder-Output-Dropout' ) return x def compute_position_bias(self, inputs=None): if self.position_bias is None: x = inputs p = self.apply( inputs=[x, x], layer=RelativePositionEmbeddingT5, input_dim=32, output_dim=self.num_attention_heads, bidirectional=True, embeddings_initializer=self.initializer, name='Encoder-Embedding-Relative-Position' ) self.position_bias = p return self.position_bias class T5_Decoder(LM_Mask, T5_Base): def __init__(self, with_lm=True, **kwargs): super(T5_Decoder, self).__init__(**kwargs) self.with_lm = with_lm def get_inputs(self): c_in = self.apply( layer=Input, shape=(self.sequence_length, self.hidden_size), name='Input-Context' ) x_in = self.apply( layer=Input, shape=(self.sequence_length,), name='Decoder-Input-Token' ) return [c_in, x_in] def apply_embeddings(self, inputs): c, x = inputs x = self.apply( inputs=x, layer=Embedding, input_dim=self.vocab_size, output_dim=self.embedding_size, embeddings_initializer=self.initializer, mask_zero=True, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Decoder-Embedding-Dropout' ) if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.hidden_size, kernel_initializer=self.initializer, name='Decoder-Embedding-Mapping' ) return [c, x] def apply_main_layers(self, inputs, index): c, x = inputs z = self.layer_norm_conds[0] self_attention_name = 'Decoder-Transformer-%d-MultiHeadSelfAttention' % index cross_attention_name = 'Decoder-Transformer-%d-MultiHeadCrossAttention' % index feed_forward_name = 'Decoder-Transformer-%d-FeedForward' % index attention_mask = self.compute_attention_bias(index) position_bias = self.compute_position_bias([x, c]) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % self_attention_name ) x = self.apply( inputs=[x, x, x, attention_mask, position_bias[0]], layer=MultiHeadAttention, arguments={ 'a_bias': True, 'p_bias': 't5_relative' }, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, use_bias=False, attention_scale=False, kernel_initializer=self.initializer, name=self_attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % self_attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % self_attention_name ) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % cross_attention_name ) x = self.apply( inputs=[x, c, c, position_bias[1]], layer=MultiHeadAttention, arguments={ 'a_bias': None, 'p_bias': 't5_relative' }, heads=self.num_attention_heads, head_size=self.attention_head_size, out_dim=self.hidden_size, key_size=self.attention_key_size, use_bias=False, attention_scale=False, kernel_initializer=self.initializer, name=cross_attention_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % cross_attention_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % cross_attention_name ) xi = x x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='%s-Norm' % feed_forward_name ) x = self.apply( inputs=x, layer=FeedForward, units=self.intermediate_size, activation=self.hidden_act, use_bias=False, kernel_initializer=self.initializer, name=feed_forward_name ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='%s-Dropout' % feed_forward_name ) x = self.apply( inputs=[xi, x], layer=Add, name='%s-Add' % feed_forward_name ) return [c, x] def apply_final_layers(self, inputs): c, x = inputs z = self.layer_norm_conds[0] x = self.apply( inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-6, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Decoder-Output-Norm' ) x = self.apply( inputs=x, layer=Dropout, rate=self.dropout_rate, name='Decoder-Output-Dropout' ) x = self.apply( inputs=x, layer=Lambda, function=lambda x: x / np.sqrt(self.hidden_size), mask=lambda i, m: m, name='Decoder-Output-Scale' ) if self.with_lm: if self.embedding_size != self.hidden_size: x = self.apply( inputs=x, layer=Dense, units=self.embedding_size, kernel_initializer=self.initializer, name='Decoder-Output-Mapping' ) lm_activation = 'softmax' if self.with_lm is True else self.with_lm if self.version == 't5.1.0': x = self.apply( inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token' ) x = self.apply( inputs=x, layer=Activation, activation=lm_activation, name='Dencoder-Output-LM-Activation' ) else: x = self.apply( inputs=x, layer=Dense, units=self.vocab_size, activation=lm_activation, use_bias=False, kernel_initializer=self.initializer, name='Decoder-Output-LM' ) return x def compute_attention_bias(self, inputs=None): old_inputs = self.inputs[:] self.inputs = [old_inputs[1]] mask = super(T5_Decoder, self).compute_attention_bias(inputs) self.inputs = old_inputs return mask def compute_position_bias(self, inputs=None): if self.position_bias is None: x, c = inputs p1 = self.apply( inputs=[x, x], layer=RelativePositionEmbeddingT5, input_dim=32, output_dim=self.num_attention_heads, bidirectional=False, embeddings_initializer=self.initializer, name='Decoder-Embedding-Relative-Position' ) p2 = self.apply( inputs=[x, c], layer=RelativePositionEmbeddingT5, input_dim=32, output_dim=self.num_attention_heads, bidirectional=False, embeddings_initializer=self.initializer, name='Decoder-Embedding-Relative-Position' ) self.position_bias = (p1, p2) return self.position_bias class T5(T5_Base): def __init__(self, **kwargs): super(T5, self).__init__(**kwargs) kwargs['layers'] = self.layers e_name, d_name = 'Encoder', 'Decoder' if 'name' in kwargs: e_name = '%s_%s' % (kwargs['name'], e_name) d_name = '%s_%s' % (kwargs['name'], d_name) del kwargs['name'] self._encoder = T5_Encoder(name=e_name, **kwargs) self._decoder = T5_Decoder(name=d_name, **kwargs) def build(self, **kwargs): self._encoder.build(**kwargs) self._decoder.build(**kwargs) self.encoder = self._encoder.model self.decoder = self._decoder.model self.inputs = self.encoder.inputs + self.decoder.inputs[1:] self.outputs = self.decoder( self.encoder.outputs + self.decoder.inputs[1:] ) self.model = Model(self.inputs, self.outputs) def extend_with_language_model(BaseModel): class LanguageModel(LM_Mask, BaseModel): def __init__(self, *args, **kwargs): super(LanguageModel, self).__init__(*args, **kwargs) self.with_mlm = self.with_mlm or True return LanguageModel def extend_with_unified_language_model(BaseModel): class UnifiedLanguageModel(UniLM_Mask, BaseModel): def __init__(self, *args, **kwargs): super(UnifiedLanguageModel, self).__init__(*args, **kwargs) self.with_mlm = self.with_mlm or True return UnifiedLanguageModel def build_transformer_model( config_path=None, checkpoint_path=None, model='bert', application='encoder', return_keras_model=True, **kwargs ): configs = {} if config_path is not None: configs.update(json.load(open(config_path))) configs.update(kwargs) if 'max_position' not in configs: configs['max_position'] = configs.get('max_position_embeddings', 512) if 'dropout_rate' not in configs: configs['dropout_rate'] = configs.get('hidden_dropout_prob') if 'segment_vocab_size' not in configs: configs['segment_vocab_size'] = configs.get('type_vocab_size', 2) models = { 'bert': BERT, 'albert': ALBERT, 'albert_unshared': ALBERT_Unshared, 'roberta': BERT, 'nezha': NEZHA, 'electra': ELECTRA, 'gpt': GPT, 'gpt2': GPT2, 'gpt2_ml': GPT2_ML, 't5': T5, 't5_encoder': T5_Encoder, 't5_decoder': T5_Decoder, 't5.1.0': T5, 't5.1.0_encoder': T5_Encoder, 't5.1.0_decoder': T5_Decoder, 't5.1.1': T5, 't5.1.1_encoder': T5_Encoder, 't5.1.1_decoder': T5_Decoder, } if is_string(model): model = model.lower() MODEL = models[model] else: MODEL = model application = application.lower() if application in ['lm', 'unilm'] and model in ['electra', 't5']: raise ValueError( '"%s" model can not be used as "%s" application.\n' % (model, application) ) if application == 'lm': MODEL = extend_with_language_model(MODEL) elif application == 'unilm': MODEL = extend_with_unified_language_model(MODEL) if model.startswith('t5.1.1'): configs['version'] = 't5.1.1' transformer = MODEL(**configs) transformer.build(**configs) if checkpoint_path is not None: transformer.load_weights_from_checkpoint(checkpoint_path) if return_keras_model: return transformer.model else: return transformer

true

790d665955845251d27d1b13cc3d9cea5240ebcc

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py

Python

manage.py

Tianny/incepiton_mysql

8ef86d19f26e22a39b4fac99ea0b4286c3226b6f

[ "MIT" ]

74

2018-01-04T09:36:32.000Z

2018-09-06T07:13:57.000Z

manage.py

Tianny/incepiton-mysql

8ef86d19f26e22a39b4fac99ea0b4286c3226b6f

[ "MIT" ]

1

2018-02-24T09:00:15.000Z

2018-04-20T02:08:52.000Z

manage.py

Tianny/incepiton_mysql

8ef86d19f26e22a39b4fac99ea0b4286c3226b6f

[ "MIT" ]

23

2018-01-13T05:26:22.000Z

2018-07-05T13:34:07.000Z

import os from werkzeug.security import generate_password_hash from flask_script import Manager, Shell, Command, Option from flask_migrate import Migrate, MigrateCommand from app import db from app import create_app from app.models import User app = create_app(os.getenv('FLASK_CONFIG') or 'default') manager = Manager(app) migrate = Migrate(app, db) class CreateUser(Command): option_list = ( Option('--name', '-n', dest='name'), Option('--password', '-p', dest='password'), Option('--email', '-e', dest='email') ) def run(self, name, password, email): user = User() user.name = name user.hash_pass = generate_password_hash(password) user.email = email db.session.add(user) db.session.commit() def make_shell_context(): return dict(app=app, db=db, User=User) manager.add_command('shell', Shell(make_context=make_shell_context)) manager.add_command('db', MigrateCommand) manager.add_command('create_user', CreateUser()) if __name__ == '__main__': manager.run()

24.767442

68

0.684507

import os from werkzeug.security import generate_password_hash from flask_script import Manager, Shell, Command, Option from flask_migrate import Migrate, MigrateCommand from app import db from app import create_app from app.models import User app = create_app(os.getenv('FLASK_CONFIG') or 'default') manager = Manager(app) migrate = Migrate(app, db) class CreateUser(Command): option_list = ( Option('--name', '-n', dest='name'), Option('--password', '-p', dest='password'), Option('--email', '-e', dest='email') ) def run(self, name, password, email): user = User() user.name = name user.hash_pass = generate_password_hash(password) user.email = email db.session.add(user) db.session.commit() def make_shell_context(): return dict(app=app, db=db, User=User) manager.add_command('shell', Shell(make_context=make_shell_context)) manager.add_command('db', MigrateCommand) manager.add_command('create_user', CreateUser()) if __name__ == '__main__': manager.run()

true

790d674cff7ae664cf013b310e5bf3172325ca2f

562

py

Python

src/SRM-684/istr.py

mikefeneley/topcoder

175a7a05367c0458a900a3fea16af68ae5ee53ec

[ "MIT" ]

null

src/SRM-684/istr.py

mikefeneley/topcoder

175a7a05367c0458a900a3fea16af68ae5ee53ec

[ "MIT" ]

null

src/SRM-684/istr.py

mikefeneley/topcoder

175a7a05367c0458a900a3fea16af68ae5ee53ec

[ "MIT" ]

null

import operator class Istr: def count(self, s, k): letters = {} for letter in s: if letter not in letters: letters[letter] = 1 else: letters[letter] += 1 for i in range(0, k): index = max(letters.iteritems(), key=operator.itemgetter(1))[0] letters[index] -= 1 score = 0 for element in letters: val = letters[element] * letters[element] score += val return score

21.615385

77

0.455516

import operator class Istr: def count(self, s, k): letters = {} for letter in s: if letter not in letters: letters[letter] = 1 else: letters[letter] += 1 for i in range(0, k): index = max(letters.iteritems(), key=operator.itemgetter(1))[0] letters[index] -= 1 score = 0 for element in letters: val = letters[element] * letters[element] score += val return score

true

790d675788b5a41ef28bcd938287045fb6fc3696

219

py

Python

src/day_16/src/models/__init__.py

chenyuanqi/python-training

3bbc2a45304de57cbc23b46da1ba7d38c8fad6e0

[ "Apache-2.0" ]

null

src/day_16/src/models/__init__.py

chenyuanqi/python-training

3bbc2a45304de57cbc23b46da1ba7d38c8fad6e0

[ "Apache-2.0" ]

null

src/day_16/src/models/__init__.py

chenyuanqi/python-training

3bbc2a45304de57cbc23b46da1ba7d38c8fad6e0

[ "Apache-2.0" ]

null

#!/usr/bin/env python # _*_ coding:utf-8 _*_ # Created by vikey on 2018/2/17 from __future__ import print_function from __future__ import unicode_literals def main(): pass if __name__ == "__main__": main()

14.6

39

0.707763

from __future__ import print_function from __future__ import unicode_literals def main(): pass if __name__ == "__main__": main()

true

790d682d59733918b6d1a5934d0417de1016946a

2,061

py

Python

simple-telnet-deception.py

raresteak/simple-telnet-deception

f7d1271ccaf01d5d6b206e88a52402f0240323b3

[ "BSD-2-Clause" ]

null

simple-telnet-deception.py

raresteak/simple-telnet-deception

f7d1271ccaf01d5d6b206e88a52402f0240323b3

[ "BSD-2-Clause" ]

2

2021-10-05T15:59:10.000Z

2021-10-05T16:31:38.000Z

simple-telnet-deception.py

raresteak/simple-telnet-deception

f7d1271ccaf01d5d6b206e88a52402f0240323b3

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 # purpose: Mimics a simple telnet daemon login prompts and records output # starts a tcp listener on port and address with variables defined below # author: Raresteak # date: 6 October 2021 # version: 3 import datetime import socket HOST = '127.0.0.1' PORT = 2323 FILE = "stn-results.json" fh = open(FILE, "a") with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.bind((HOST, PORT)) s.listen() while True: conn, addr = s.accept() with conn: timeNow = datetime.datetime.now() conn.send(b'Warning: Telnet is not a secure protocol, and it is recommended to use Stelnet.\n\nLogin authentication\n\n\nUsername: ') username = "" while True: data = conn.recv(1024) if not data: break else: try: username = data.decode("utf-8").rstrip() except UnicodeDecodeError: username = "cancelledInput" conn.send(b'Password: ') password = "" while True: data = conn.recv(1024) if not data: break else: try: password = data.decode("utf-8").rstrip() except UnicodeDecodeError: password = "cancelledInput" conn.sendall(b'\b \b') break break output = str("{ \"time\": \"" + timeNow.strftime('%Y-%m-%dT%H:%M:%S') + "\", \"src.ip\": \"" + addr[0] + "\", \"username\": \"" + username + "\", \"password\": \"" + password + "\" }") print(output) fh.write(output + "\n")

37.472727

145

0.447841

import datetime import socket HOST = '127.0.0.1' PORT = 2323 FILE = "stn-results.json" fh = open(FILE, "a") with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.bind((HOST, PORT)) s.listen() while True: conn, addr = s.accept() with conn: timeNow = datetime.datetime.now() conn.send(b'Warning: Telnet is not a secure protocol, and it is recommended to use Stelnet.\n\nLogin authentication\n\n\nUsername: ') username = "" while True: data = conn.recv(1024) if not data: break else: try: username = data.decode("utf-8").rstrip() except UnicodeDecodeError: username = "cancelledInput" conn.send(b'Password: ') password = "" while True: data = conn.recv(1024) if not data: break else: try: password = data.decode("utf-8").rstrip() except UnicodeDecodeError: password = "cancelledInput" conn.sendall(b'\b \b') break break output = str("{ \"time\": \"" + timeNow.strftime('%Y-%m-%dT%H:%M:%S') + "\", \"src.ip\": \"" + addr[0] + "\", \"username\": \"" + username + "\", \"password\": \"" + password + "\" }") print(output) fh.write(output + "\n")

true

790d685e82700fc2d434189758494db076f50329

6,133

py

Python

predict/ensemble.py

DataArk/CHIP2021-Task1-Top1

e352198d96d31c60541e4a271f20cc23b3ab6b92

[ "Apache-2.0" ]

15

2021-12-18T06:08:55.000Z

2022-03-30T00:41:45.000Z

predict/ensemble.py

confstantine/nlp-task

cb152e885bc6f6f1243a12ad90b1c715eb548736

[ "Apache-2.0" ]

1

2021-12-20T05:57:37.000Z

2021-12-20T13:43:07.000Z

predict/ensemble.py

DataArk/CHIP2021-Task1-Top1

e352198d96d31c60541e4a271f20cc23b3ab6b92

[ "Apache-2.0" ]

1

2021-12-27T04:49:35.000Z

import codecs import json from tqdm import tqdm import copy submit_result2 = [] with codecs.open('dialog_chinese-macbert.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result2.append(dialogue_) submit_result4 = [] with codecs.open('macbert2-f-f.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result4.append(dialogue_) submit_result3 = [] with codecs.open('macbert2-f.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result3.append(dialogue_) submit_result5 = [] with codecs.open('mcbert.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result5.append(dialogue_) submit_result6 = [] with codecs.open('medbert.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result6.append(dialogue_) submit_result = [] with codecs.open('macbert2-f.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) for content_idx_, contents_ in enumerate(dialogue_['dialog_info']): terms_ = contents_['ner'] if len(terms_) != 0: idx_ = 0 for _ner_idx, term_ in enumerate(terms_): if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阳性' and dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阴性' and dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '不标注': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阳性': if submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '其他': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result4[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阴性': if submit_result4[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '不标注': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result4[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阴性': if submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '不标注': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] # elif submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '其他': # dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result6[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阳性': if submit_result6[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '其他': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result6[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] submit_result.append(dialogue_) with open('./result.txt', 'w', encoding='utf-8') as output_data: for json_content in submit_result: output_data.write(json.dumps(json_content, ensure_ascii=False) + '\n')

51.537815

268

0.612098

import codecs import json from tqdm import tqdm import copy submit_result2 = [] with codecs.open('dialog_chinese-macbert.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result2.append(dialogue_) submit_result4 = [] with codecs.open('macbert2-f-f.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result4.append(dialogue_) submit_result3 = [] with codecs.open('macbert2-f.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result3.append(dialogue_) submit_result5 = [] with codecs.open('mcbert.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result5.append(dialogue_) submit_result6 = [] with codecs.open('medbert.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) submit_result6.append(dialogue_) submit_result = [] with codecs.open('macbert2-f.txt', mode='r', encoding='utf8') as f: reader = f.readlines(f) data_list = [] for dialogue_idx_, dialogue_ in enumerate(tqdm(reader)): dialogue_ = json.loads(dialogue_) for content_idx_, contents_ in enumerate(dialogue_['dialog_info']): terms_ = contents_['ner'] if len(terms_) != 0: idx_ = 0 for _ner_idx, term_ in enumerate(terms_): if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阳性' and dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阴性' and dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result3[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '不标注': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阳性': if submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '其他': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result2[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result4[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阴性': if submit_result4[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '不标注': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result4[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阴性': if submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '不标注': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result5[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] elif dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] != submit_result6[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr']: if dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '阳性': if submit_result6[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] == '其他': dialogue_['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] = submit_result6[dialogue_idx_]['dialog_info'][content_idx_]['ner'][_ner_idx]['attr'] submit_result.append(dialogue_) with open('./result.txt', 'w', encoding='utf-8') as output_data: for json_content in submit_result: output_data.write(json.dumps(json_content, ensure_ascii=False) + '\n')

true

790d687e1a550df847e9c24f9d009229bbdf2d1b

108

py

Python

tests/test_apps/helloworld/hello.py

BlueMoon55/flask_test

eb32cf47b336dcf633cf4d85ed03478c003a74d7

[ "BSD-3-Clause" ]

null

tests/test_apps/helloworld/hello.py

BlueMoon55/flask_test

eb32cf47b336dcf633cf4d85ed03478c003a74d7

[ "BSD-3-Clause" ]

null

tests/test_apps/helloworld/hello.py

BlueMoon55/flask_test

eb32cf47b336dcf633cf4d85ed03478c003a74d7

[ "BSD-3-Clause" ]

null

from flask import Flask app = Flask(__name__) @app.route("/") def hello(): return "Hello World!"

15.428571

26

0.62963

from flask import Flask app = Flask(__name__) @app.route("/") def hello(): return "Hello World!"

true

790d6881b84d575211ea1bf0221cc97754b74dc7

2,532

py

Python

examples/pybullet/examples/vrEvent.py

frk2/bullet3

225d823e4dc3f952c6c39920c3f87390383e0602

[ "Zlib" ]

27

2018-05-21T14:28:10.000Z

2021-12-31T03:12:35.000Z

examples/pybullet/examples/vrEvent.py

frk2/bullet3

225d823e4dc3f952c6c39920c3f87390383e0602

[ "Zlib" ]

2

2018-02-02T21:26:09.000Z

2018-02-06T19:05:24.000Z

examples/pybullet/examples/vrEvent.py

frk2/bullet3

225d823e4dc3f952c6c39920c3f87390383e0602

[ "Zlib" ]

13

2019-11-08T12:48:44.000Z

2022-01-04T04:13:33.000Z

# See pybullet quickstart guide here: # https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit# # Create a Tiltbrush-like app, drawing lines using any controller # Line width can be changed import pybullet as p CONTROLLER_ID = 0 POSITION=1 ORIENTATION=2 NUM_MOVE_EVENTS=5 BUTTONS=6 ANALOG_AXIS=8 #assume that the VR physics server is already started before c = p.connect(p.SHARED_MEMORY) print(c) if (c<0): p.connect(p.GUI) p.setInternalSimFlags(0)#don't load default robot assets etc p.resetSimulation() p.loadURDF("plane.urdf") prevPosition=[[0,0,0]]*p.VR_MAX_CONTROLLERS colors=[0.,0.5,0.5]*p.VR_MAX_CONTROLLERS widths = [3]*p.VR_MAX_CONTROLLERS #use a few default colors colors[0] = [0,0,0] colors[1] = [0.5,0,0] colors[2] = [0,0.5,0] colors[3] = [0,0,0.5] colors[4] = [0.5,0.5,0.] colors[5] = [.5,.5,.5] controllerId = -1 pt=[0,0,0] print("waiting for VR controller trigger") while (controllerId<0): events = p.getVREvents() for e in (events): if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN): controllerId = e[CONTROLLER_ID] if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN): controllerId = e[CONTROLLER_ID] print("Using controllerId="+str(controllerId)) while True: events = p.getVREvents(allAnalogAxes=1) for e in (events): if (e[CONTROLLER_ID]==controllerId ): for a in range(10): print("analog axis"+str(a)+"="+str(e[8][a])) if (e[BUTTONS][33]&p.VR_BUTTON_WAS_TRIGGERED): prevPosition[e[CONTROLLER_ID]] = e[POSITION] if (e[BUTTONS][32]&p.VR_BUTTON_WAS_TRIGGERED): widths[e[CONTROLLER_ID]]=widths[e[0]]+1 if (widths[e[CONTROLLER_ID]]>20): widths[e[CONTROLLER_ID]] = 1 if (e[BUTTONS][1]&p.VR_BUTTON_WAS_TRIGGERED): p.resetSimulation() #p.setGravity(0,0,-10) p.removeAllUserDebugItems() p.loadURDF("plane.urdf") if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN): pt = prevPosition[e[CONTROLLER_ID]] #print(prevPosition[e[0]]) print("e[POSITION]") print(e[POSITION]) print("pt") print(pt) diff = [pt[0]-e[POSITION][0],pt[1]-e[POSITION][1],pt[2]-e[POSITION][2]] lenSqr = diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2] ptDistThreshold = 0.01 if (lenSqr>(ptDistThreshold*ptDistThreshold)): p.addUserDebugLine(e[POSITION],prevPosition[e[CONTROLLER_ID]],colors[e[CONTROLLER_ID]],widths[e[CONTROLLER_ID]]) #p.loadURDF("cube_small.urdf",e[1]) colors[e[CONTROLLER_ID]] = [1-colors[e[CONTROLLER_ID]][0],1-colors[e[CONTROLLER_ID]][1],1-colors[e[CONTROLLER_ID]][2]] prevPosition[e[CONTROLLER_ID]] = e[POSITION]

30.142857

122

0.699052

import pybullet as p CONTROLLER_ID = 0 POSITION=1 ORIENTATION=2 NUM_MOVE_EVENTS=5 BUTTONS=6 ANALOG_AXIS=8 c = p.connect(p.SHARED_MEMORY) print(c) if (c<0): p.connect(p.GUI) p.setInternalSimFlags(0) p.resetSimulation() p.loadURDF("plane.urdf") prevPosition=[[0,0,0]]*p.VR_MAX_CONTROLLERS colors=[0.,0.5,0.5]*p.VR_MAX_CONTROLLERS widths = [3]*p.VR_MAX_CONTROLLERS #use a few default colors colors[0] = [0,0,0] colors[1] = [0.5,0,0] colors[2] = [0,0.5,0] colors[3] = [0,0,0.5] colors[4] = [0.5,0.5,0.] colors[5] = [.5,.5,.5] controllerId = -1 pt=[0,0,0] print("waiting for VR controller trigger") while (controllerId<0): events = p.getVREvents() for e in (events): if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN): controllerId = e[CONTROLLER_ID] if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN): controllerId = e[CONTROLLER_ID] print("Using controllerId="+str(controllerId)) while True: events = p.getVREvents(allAnalogAxes=1) for e in (events): if (e[CONTROLLER_ID]==controllerId ): for a in range(10): print("analog axis"+str(a)+"="+str(e[8][a])) if (e[BUTTONS][33]&p.VR_BUTTON_WAS_TRIGGERED): prevPosition[e[CONTROLLER_ID]] = e[POSITION] if (e[BUTTONS][32]&p.VR_BUTTON_WAS_TRIGGERED): widths[e[CONTROLLER_ID]]=widths[e[0]]+1 if (widths[e[CONTROLLER_ID]]>20): widths[e[CONTROLLER_ID]] = 1 if (e[BUTTONS][1]&p.VR_BUTTON_WAS_TRIGGERED): p.resetSimulation() #p.setGravity(0,0,-10) p.removeAllUserDebugItems() p.loadURDF("plane.urdf") if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN): pt = prevPosition[e[CONTROLLER_ID]] #print(prevPosition[e[0]]) print("e[POSITION]") print(e[POSITION]) print("pt") print(pt) diff = [pt[0]-e[POSITION][0],pt[1]-e[POSITION][1],pt[2]-e[POSITION][2]] lenSqr = diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2] ptDistThreshold = 0.01 if (lenSqr>(ptDistThreshold*ptDistThreshold)): p.addUserDebugLine(e[POSITION],prevPosition[e[CONTROLLER_ID]],colors[e[CONTROLLER_ID]],widths[e[CONTROLLER_ID]]) #p.loadURDF("cube_small.urdf",e[1]) colors[e[CONTROLLER_ID]] = [1-colors[e[CONTROLLER_ID]][0],1-colors[e[CONTROLLER_ID]][1],1-colors[e[CONTROLLER_ID]][2]] prevPosition[e[CONTROLLER_ID]] = e[POSITION]

true

790d6934b720f8d7e4a229513ef60df1a9ed7fd8

6,669

py

Python

dino/main.py

bartlomiej-kedziora/games

00ff00566bd7c0cd444161f3edf6cd7e1f4abb62

[ "MIT" ]

null

dino/main.py

bartlomiej-kedziora/games

00ff00566bd7c0cd444161f3edf6cd7e1f4abb62

[ "MIT" ]

null

dino/main.py

bartlomiej-kedziora/games

00ff00566bd7c0cd444161f3edf6cd7e1f4abb62

[ "MIT" ]

null

import pgzero import pgzrun import random from pgzero.actor import Actor __all__ = ["pgzrun", "pgzero"] from pgzero.clock import clock from pgzero.keyboard import keyboard from pgzero.loaders import sounds clouds = [Actor('cloud1', (200, 200)), Actor('cloud2', (400, 300)), Actor('cloud3', (600, 200)), Actor('cloud1', (800, 300))] obstacles = [Actor('cactus', (random.randint(900, 1000), 495)), Actor('cactus', (random.randint(1200, 1500), 495)), Actor('cactus', (random.randint(1500, 2000), 495))] player = Actor('p3_stand', (100, 484)) # 0 - game not started # 1 - game just stared # 2 - finished game = 0 # frame that is currently running frame = 0 # player movement speed and direction jump = 0 # 0 - jump is available # 1 - jump is forbidden jump_blocked = 0 cloud_speed = 2 game_time = 0 # cactus movement speed game_speed = 8 # 0 - game running # 1 - game blocked jump_unblocked = 0 def draw(): global game screen.clear() screen.fill('#cff4f7') for i in range((screen.width // 70) + 1): screen.blit('grass', (i * 70, screen.height - 70)) for cloud in clouds: cloud.draw() for obstacle in obstacles: obstacle.draw() screen.draw.text( align_text_time(game_time), midright=(screen.width - 50, 50), fontname="roboto_mono_bold", color="orange", fontsize=45 ) player.draw() if game == 0: screen.draw.text( "Wcisnij spacje", center=(screen.width / 2, screen.height / 2), color="orange", fontsize=60 ) if game == 2: screen.draw.text( "Koniec gry", center=(screen.width / 2, screen.height / 2), color="red", fontsize=60 ) screen.draw.text( "Wcisnij spacje aby zagrac jeszcze raz", center=(screen.width / 2, screen.height - 200), color="red", fontsize=30 ) def update(): global game global jump global jump_blocked global jump_unblocked if keyboard.SPACE and jump_unblocked == 0: if game == 0 or game == 2: jump_blocked = 1 clock.schedule_unique(unblock_jump, 0.3) reset() game = 1 if jump_blocked == 0: jump = -18 jump_blocked = 1 sounds.jingles_jump.play() animation() jump_fall() move_cloud() move_obstacle() check_collision() # change difficulty level, increase game and clouds speed def change_difficulty_level(): global game_speed global cloud_speed if game_speed < 16: game_speed += 1 cloud_speed += 1 # reset global variables def reset(): global frame global game global jump global jump_blocked global cloud_speed global game_speed global game_time if game == 2: frame = 0 game = 0 jump = 0 jump_blocked = 1 cloud_speed = 2 game_speed = 8 game_time = 0 player.pos = (100, 484) clouds[0].pos = (200, 200) clouds[1].pos = (400, 300) clouds[2].pos = (600, 200) clouds[3].pos = (800, 300) obstacles[0].pos = (random.randint(900, 1000), 495) obstacles[1].pos = (random.randint(1200, 1500), 495) obstacles[2].pos = (random.randint(1500, 2000), 495) clock.unschedule(change_difficulty_level) # change difficulty level every 20s clock.schedule_interval(change_difficulty_level, 20) def unblock_game(): global jump_unblocked jump_unblocked = 0 # check collision with cactus def check_collision(): global game global jump_unblocked if game == 1: for i in obstacles: if player.collidepoint(i.x, i.y): game = 2 sounds.jingles_end.play() jump_unblocked = 1 # unblock game in 2 sec clock.schedule_unique(unblock_game, 2.0) def move_obstacle(): global game_speed global game if game == 1: for i in range(len(obstacles)): # decrease x for all obstacles about speed value obstacles[i].x -= game_speed # if obstacles is out of screen get random position if obstacles[i].x + 35 < 0: obstacles[i].x = random.randint(900, 1500) # if obstacles have the same position as other or is too close, move it about 400 for j in range(0, len(obstacles)): if j != i and abs(obstacles[i].x - obstacles[j].x < 300): obstacles[i].x += 400 # triggered every 0.1s increasing game time about 1s def measure_time(): global game_time global game if game == 0: game_time = 0 elif game == 1: game_time +=1 def align_text_time(time): text = "0" * (5 - len(str(time))) text += str(time) return text def move_cloud(): global cloud_speed global game if game == 1: # move clouds x pos about cloud speed for cloud in clouds: cloud.x -= cloud_speed # if cloud out of screen move it to right side if cloud.x + 64 < 0: cloud.x = screen.width + 32 def unblock_jump(): global jump_blocked jump_blocked = 0 def jump_fall(): global jump global frame if jump != 0: # block animation frame = 0 player.y += jump # if player on the ground unblock if player.y >= 484: unblock_jump() jump = 0 # if player jumped start falling if player.y <= 250: jump *= (-1) # player animation def animation(): global frame if game == 1: if frame == 0: player.image = 'p3_walk01' if frame == 1: player.image = 'p3_walk02' if frame == 2: player.image = 'p3_walk03' if frame == 3: player.image = 'p3_walk04' if frame == 4: player.image = 'p3_walk05' if frame == 5: player.image = 'p3_walk06' if frame == 6: player.image = 'p3_walk07' if frame == 7: player.image = 'p3_walk08' if frame == 8: player.image = 'p3_walk09' if frame == 9: player.image = 'p3_walk10' if frame == 10: player.image = 'p3_walk11' frame += 1 # result is 0 or less than 11 frame %= 11 clock.schedule_interval(measure_time, 0.1) clock.schedule_interval(change_difficulty_level, 20) pgzrun.go()

24.791822

97

0.562453

import pgzero import pgzrun import random from pgzero.actor import Actor __all__ = ["pgzrun", "pgzero"] from pgzero.clock import clock from pgzero.keyboard import keyboard from pgzero.loaders import sounds clouds = [Actor('cloud1', (200, 200)), Actor('cloud2', (400, 300)), Actor('cloud3', (600, 200)), Actor('cloud1', (800, 300))] obstacles = [Actor('cactus', (random.randint(900, 1000), 495)), Actor('cactus', (random.randint(1200, 1500), 495)), Actor('cactus', (random.randint(1500, 2000), 495))] player = Actor('p3_stand', (100, 484)) game = 0 frame = 0 jump = 0 jump_blocked = 0 cloud_speed = 2 game_time = 0 game_speed = 8 jump_unblocked = 0 def draw(): global game screen.clear() screen.fill('#cff4f7') for i in range((screen.width // 70) + 1): screen.blit('grass', (i * 70, screen.height - 70)) for cloud in clouds: cloud.draw() for obstacle in obstacles: obstacle.draw() screen.draw.text( align_text_time(game_time), midright=(screen.width - 50, 50), fontname="roboto_mono_bold", color="orange", fontsize=45 ) player.draw() if game == 0: screen.draw.text( "Wcisnij spacje", center=(screen.width / 2, screen.height / 2), color="orange", fontsize=60 ) if game == 2: screen.draw.text( "Koniec gry", center=(screen.width / 2, screen.height / 2), color="red", fontsize=60 ) screen.draw.text( "Wcisnij spacje aby zagrac jeszcze raz", center=(screen.width / 2, screen.height - 200), color="red", fontsize=30 ) def update(): global game global jump global jump_blocked global jump_unblocked if keyboard.SPACE and jump_unblocked == 0: if game == 0 or game == 2: jump_blocked = 1 clock.schedule_unique(unblock_jump, 0.3) reset() game = 1 if jump_blocked == 0: jump = -18 jump_blocked = 1 sounds.jingles_jump.play() animation() jump_fall() move_cloud() move_obstacle() check_collision() def change_difficulty_level(): global game_speed global cloud_speed if game_speed < 16: game_speed += 1 cloud_speed += 1 def reset(): global frame global game global jump global jump_blocked global cloud_speed global game_speed global game_time if game == 2: frame = 0 game = 0 jump = 0 jump_blocked = 1 cloud_speed = 2 game_speed = 8 game_time = 0 player.pos = (100, 484) clouds[0].pos = (200, 200) clouds[1].pos = (400, 300) clouds[2].pos = (600, 200) clouds[3].pos = (800, 300) obstacles[0].pos = (random.randint(900, 1000), 495) obstacles[1].pos = (random.randint(1200, 1500), 495) obstacles[2].pos = (random.randint(1500, 2000), 495) clock.unschedule(change_difficulty_level) clock.schedule_interval(change_difficulty_level, 20) def unblock_game(): global jump_unblocked jump_unblocked = 0 def check_collision(): global game global jump_unblocked if game == 1: for i in obstacles: if player.collidepoint(i.x, i.y): game = 2 sounds.jingles_end.play() jump_unblocked = 1 clock.schedule_unique(unblock_game, 2.0) def move_obstacle(): global game_speed global game if game == 1: for i in range(len(obstacles)): obstacles[i].x -= game_speed if obstacles[i].x + 35 < 0: obstacles[i].x = random.randint(900, 1500) for j in range(0, len(obstacles)): if j != i and abs(obstacles[i].x - obstacles[j].x < 300): obstacles[i].x += 400 def measure_time(): global game_time global game if game == 0: game_time = 0 elif game == 1: game_time +=1 def align_text_time(time): text = "0" * (5 - len(str(time))) text += str(time) return text def move_cloud(): global cloud_speed global game if game == 1: for cloud in clouds: cloud.x -= cloud_speed if cloud.x + 64 < 0: cloud.x = screen.width + 32 def unblock_jump(): global jump_blocked jump_blocked = 0 def jump_fall(): global jump global frame if jump != 0: frame = 0 player.y += jump if player.y >= 484: unblock_jump() jump = 0 if player.y <= 250: jump *= (-1) def animation(): global frame if game == 1: if frame == 0: player.image = 'p3_walk01' if frame == 1: player.image = 'p3_walk02' if frame == 2: player.image = 'p3_walk03' if frame == 3: player.image = 'p3_walk04' if frame == 4: player.image = 'p3_walk05' if frame == 5: player.image = 'p3_walk06' if frame == 6: player.image = 'p3_walk07' if frame == 7: player.image = 'p3_walk08' if frame == 8: player.image = 'p3_walk09' if frame == 9: player.image = 'p3_walk10' if frame == 10: player.image = 'p3_walk11' frame += 1 frame %= 11 clock.schedule_interval(measure_time, 0.1) clock.schedule_interval(change_difficulty_level, 20) pgzrun.go()

true

790d69a0863f817c9600d9648b29c56aefa86acd

2,180

py

Python

venv/lib/python3.8/site-packages/vsts/release/v4_1/models/release_task_attachment.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/vsts/release/v4_1/models/release_task_attachment.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/vsts/release/v4_1/models/release_task_attachment.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

2

2021-05-23T16:46:31.000Z

2021-05-26T23:51:09.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from msrest.serialization import Model class ReleaseTaskAttachment(Model): """ReleaseTaskAttachment. :param _links: :type _links: :class:`ReferenceLinks <release.v4_1.models.ReferenceLinks>` :param created_on: :type created_on: datetime :param modified_by: :type modified_by: :class:`IdentityRef <release.v4_1.models.IdentityRef>` :param modified_on: :type modified_on: datetime :param name: :type name: str :param record_id: :type record_id: str :param timeline_id: :type timeline_id: str :param type: :type type: str """ _attribute_map = { '_links': {'key': '_links', 'type': 'ReferenceLinks'}, 'created_on': {'key': 'createdOn', 'type': 'iso-8601'}, 'modified_by': {'key': 'modifiedBy', 'type': 'IdentityRef'}, 'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'}, 'name': {'key': 'name', 'type': 'str'}, 'record_id': {'key': 'recordId', 'type': 'str'}, 'timeline_id': {'key': 'timelineId', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'} } def __init__(self, _links=None, created_on=None, modified_by=None, modified_on=None, name=None, record_id=None, timeline_id=None, type=None): super(ReleaseTaskAttachment, self).__init__() self._links = _links self.created_on = created_on self.modified_by = modified_by self.modified_on = modified_on self.name = name self.record_id = record_id self.timeline_id = timeline_id self.type = type

40.37037

146

0.544495

from msrest.serialization import Model class ReleaseTaskAttachment(Model): _attribute_map = { '_links': {'key': '_links', 'type': 'ReferenceLinks'}, 'created_on': {'key': 'createdOn', 'type': 'iso-8601'}, 'modified_by': {'key': 'modifiedBy', 'type': 'IdentityRef'}, 'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'}, 'name': {'key': 'name', 'type': 'str'}, 'record_id': {'key': 'recordId', 'type': 'str'}, 'timeline_id': {'key': 'timelineId', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'} } def __init__(self, _links=None, created_on=None, modified_by=None, modified_on=None, name=None, record_id=None, timeline_id=None, type=None): super(ReleaseTaskAttachment, self).__init__() self._links = _links self.created_on = created_on self.modified_by = modified_by self.modified_on = modified_on self.name = name self.record_id = record_id self.timeline_id = timeline_id self.type = type

true

790d69c2fe14e452fd81adf6722dffb4d6179f1f

41

py

Python

recover_unseeded.py

maziara/deluge-feed-innoreader

874ae84d5f75569a6749e44f8c525e484aa801b7

[ "MIT" ]

8

2016-07-31T01:58:00.000Z

2020-09-30T01:18:34.000Z

recover_unseeded.py

maziara/deluge-feed-innoreader

874ae84d5f75569a6749e44f8c525e484aa801b7

[ "MIT" ]

null

recover_unseeded.py

maziara/deluge-feed-innoreader

874ae84d5f75569a6749e44f8c525e484aa801b7

[ "MIT" ]

null

import main main.recover_unseeded_items()

20.5

29

0.878049

import main main.recover_unseeded_items()

true

790d6a1d89941bbe74cb2ee771b7995accfaed15

6,752

py

Python

src/ggrc_workflows/migrations/versions/20150707143127_44047daa31a9_add_non_adjusted_next_cycle_start_date.py

zidarsk8/ggrc-core

2509c989eddf434249d3bef50c21e08dbf56c1a4

[ "ECL-2.0", "Apache-2.0" ]

1

2018-01-03T02:49:23.000Z

src/ggrc_workflows/migrations/versions/20150707143127_44047daa31a9_add_non_adjusted_next_cycle_start_date.py

zidarsk8/ggrc-core

2509c989eddf434249d3bef50c21e08dbf56c1a4

[ "ECL-2.0", "Apache-2.0" ]

6

2015-04-25T13:15:15.000Z

2019-03-21T22:38:01.000Z

src/ggrc_workflows/migrations/versions/20150707143127_44047daa31a9_add_non_adjusted_next_cycle_start_date.py

zidarsk8/ggrc-core

2509c989eddf434249d3bef50c21e08dbf56c1a4

[ "ECL-2.0", "Apache-2.0" ]

null

# Copyright (C) 2017 Google Inc. # Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file> """Add non-adjusted next cycle start date Revision ID: 44047daa31a9 Revises: 1431e7094e26 Create Date: 2015-07-07 14:31:27.780564 """ # revision identifiers, used by Alembic. revision = '44047daa31a9' down_revision = '4840f4760f4b' from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql from datetime import date from ggrc.app import app from ggrc import settings, db import ggrc_workflows.models as models from ggrc_workflows import adjust_next_cycle_start_date from ggrc_workflows.services.workflow_cycle_calculator import \ get_cycle_calculator def upgrade(): op.add_column('workflows', sa.Column('non_adjusted_next_cycle_start_date', sa.Date(), nullable=True)) # If somebody deleted all the tasks we must clear the next cycle start # date workflows = db.session.query(models.Workflow) \ .filter( models.Workflow.next_cycle_start_date != None, models.Workflow.recurrences == True, models.Workflow.status == 'Active', models.Workflow.next_cycle_start_date < date.today() ).all() for workflow in workflows: tasks_start_days = [task.relative_start_day for tg in workflow.task_groups for task in tg.task_group_tasks] tasks_end_days = [task.relative_end_day for tg in workflow.task_groups for task in tg.task_group_tasks] if ((not all(tasks_start_days) and not all(tasks_end_days)) or (not tasks_start_days and not tasks_end_days)): app.logger.warning( "Removing NCSD from expired WF {} because no tasks are " "set up. Current NCSD: {}".format( workflow.id, workflow.next_cycle_start_date )) workflow.next_cycle_start_date = None db.session.add(workflow) workflows = db.session.query(models.Workflow) \ .filter( models.Workflow.next_cycle_start_date != None, models.Workflow.non_adjusted_next_cycle_start_date == None, models.Workflow.recurrences == True, models.Workflow.status == 'Active', models.Workflow.next_cycle_start_date >= date.today() ).all() for workflow in workflows: tasks_start_days = [task.relative_start_day for tg in workflow.task_groups for task in tg.task_group_tasks] tasks_end_days = [task.relative_end_day for tg in workflow.task_groups for task in tg.task_group_tasks] # We must skip tasks that don't have start days and end days defined if ((not all(tasks_start_days) and not all(tasks_end_days)) or (not tasks_start_days and not tasks_end_days)): append_msg = "" if workflow.next_cycle_start_date: workflow.next_cycle_start_date = None append_msg += (" Removing existing next cycle start date " "because none are configured.") db.session.add(workflow) app.logger.warning( "Skipping active WF {0} because no tasks " "are set up.{1}".format( workflow.id, append_msg )) continue pre_compute_ncsd = workflow.next_cycle_start_date last_cycle_start_date = None if workflow.cycles: last_cycle_start_date = max([c.start_date for c in workflow.cycles]) if last_cycle_start_date: base_date = last_cycle_start_date else: base_date = base_date.today() base_date = max(base_date, workflow.next_cycle_start_date) calculator = get_cycle_calculator(workflow, base_date=base_date) if workflow.frequency in {"weekly", "monthly"}: nancsd_day = min( v['relative_start'] for v in calculator.reified_tasks.values()) nancsd_month = None else: nancsd_month, nancsd_day = min( v['relative_start'] for v in calculator.reified_tasks.values()) nancsd_date = calculator.relative_day_to_date( relative_day=nancsd_day, relative_month=nancsd_month, base_date=base_date) if last_cycle_start_date: while calculator.adjust_date(nancsd_date) <= last_cycle_start_date: base_date = base_date + calculator.time_delta nancsd_date = calculator.relative_day_to_date( relative_day=nancsd_day, relative_month=nancsd_month, base_date=base_date ) else: base_date = base_date - calculator.time_delta while calculator.adjust_date(nancsd_date) <= pre_compute_ncsd: base_date = base_date + calculator.time_delta nancsd_date = calculator.relative_day_to_date( relative_day=nancsd_day, relative_month=nancsd_month, base_date=base_date ) workflow.non_adjusted_next_cycle_start_date = nancsd_date workflow.next_cycle_start_date = calculator.adjust_date(nancsd_date) post_compute_ncsd = workflow.next_cycle_start_date start_dates = ["{}/{}".format( task.relative_start_month, task.relative_start_day) for tg in workflow.task_groups for task in tg.task_group_tasks] end_dates = ["{}/{}".format( task.relative_end_month, task.relative_end_day) for tg in workflow.task_groups for task in tg.task_group_tasks] if pre_compute_ncsd != post_compute_ncsd: app.logger.warning( "Adjusted NCSD for workflow {}. " "Freq: {}, PRE: {}, Last cycle: {}, POST: {}, NON: {}," "tasks start: {}, tasks end: {},".format( workflow.id, workflow.frequency[:2], pre_compute_ncsd, last_cycle_start_date, post_compute_ncsd, workflow.non_adjusted_next_cycle_start_date, start_dates, end_dates)) db.session.add(workflow) # Save db.session.commit() def downgrade(): op.drop_column('workflows', 'non_adjusted_next_cycle_start_date')

38.146893

80

0.598045

revision = '44047daa31a9' down_revision = '4840f4760f4b' from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql from datetime import date from ggrc.app import app from ggrc import settings, db import ggrc_workflows.models as models from ggrc_workflows import adjust_next_cycle_start_date from ggrc_workflows.services.workflow_cycle_calculator import \ get_cycle_calculator def upgrade(): op.add_column('workflows', sa.Column('non_adjusted_next_cycle_start_date', sa.Date(), nullable=True)) workflows = db.session.query(models.Workflow) \ .filter( models.Workflow.next_cycle_start_date != None, models.Workflow.recurrences == True, models.Workflow.status == 'Active', models.Workflow.next_cycle_start_date < date.today() ).all() for workflow in workflows: tasks_start_days = [task.relative_start_day for tg in workflow.task_groups for task in tg.task_group_tasks] tasks_end_days = [task.relative_end_day for tg in workflow.task_groups for task in tg.task_group_tasks] if ((not all(tasks_start_days) and not all(tasks_end_days)) or (not tasks_start_days and not tasks_end_days)): app.logger.warning( "Removing NCSD from expired WF {} because no tasks are " "set up. Current NCSD: {}".format( workflow.id, workflow.next_cycle_start_date )) workflow.next_cycle_start_date = None db.session.add(workflow) workflows = db.session.query(models.Workflow) \ .filter( models.Workflow.next_cycle_start_date != None, models.Workflow.non_adjusted_next_cycle_start_date == None, models.Workflow.recurrences == True, models.Workflow.status == 'Active', models.Workflow.next_cycle_start_date >= date.today() ).all() for workflow in workflows: tasks_start_days = [task.relative_start_day for tg in workflow.task_groups for task in tg.task_group_tasks] tasks_end_days = [task.relative_end_day for tg in workflow.task_groups for task in tg.task_group_tasks] if ((not all(tasks_start_days) and not all(tasks_end_days)) or (not tasks_start_days and not tasks_end_days)): append_msg = "" if workflow.next_cycle_start_date: workflow.next_cycle_start_date = None append_msg += (" Removing existing next cycle start date " "because none are configured.") db.session.add(workflow) app.logger.warning( "Skipping active WF {0} because no tasks " "are set up.{1}".format( workflow.id, append_msg )) continue pre_compute_ncsd = workflow.next_cycle_start_date last_cycle_start_date = None if workflow.cycles: last_cycle_start_date = max([c.start_date for c in workflow.cycles]) if last_cycle_start_date: base_date = last_cycle_start_date else: base_date = base_date.today() base_date = max(base_date, workflow.next_cycle_start_date) calculator = get_cycle_calculator(workflow, base_date=base_date) if workflow.frequency in {"weekly", "monthly"}: nancsd_day = min( v['relative_start'] for v in calculator.reified_tasks.values()) nancsd_month = None else: nancsd_month, nancsd_day = min( v['relative_start'] for v in calculator.reified_tasks.values()) nancsd_date = calculator.relative_day_to_date( relative_day=nancsd_day, relative_month=nancsd_month, base_date=base_date) if last_cycle_start_date: while calculator.adjust_date(nancsd_date) <= last_cycle_start_date: base_date = base_date + calculator.time_delta nancsd_date = calculator.relative_day_to_date( relative_day=nancsd_day, relative_month=nancsd_month, base_date=base_date ) else: base_date = base_date - calculator.time_delta while calculator.adjust_date(nancsd_date) <= pre_compute_ncsd: base_date = base_date + calculator.time_delta nancsd_date = calculator.relative_day_to_date( relative_day=nancsd_day, relative_month=nancsd_month, base_date=base_date ) workflow.non_adjusted_next_cycle_start_date = nancsd_date workflow.next_cycle_start_date = calculator.adjust_date(nancsd_date) post_compute_ncsd = workflow.next_cycle_start_date start_dates = ["{}/{}".format( task.relative_start_month, task.relative_start_day) for tg in workflow.task_groups for task in tg.task_group_tasks] end_dates = ["{}/{}".format( task.relative_end_month, task.relative_end_day) for tg in workflow.task_groups for task in tg.task_group_tasks] if pre_compute_ncsd != post_compute_ncsd: app.logger.warning( "Adjusted NCSD for workflow {}. " "Freq: {}, PRE: {}, Last cycle: {}, POST: {}, NON: {}," "tasks start: {}, tasks end: {},".format( workflow.id, workflow.frequency[:2], pre_compute_ncsd, last_cycle_start_date, post_compute_ncsd, workflow.non_adjusted_next_cycle_start_date, start_dates, end_dates)) db.session.add(workflow) # Save db.session.commit() def downgrade(): op.drop_column('workflows', 'non_adjusted_next_cycle_start_date')

true

790d6a227b95a008a2221d0e4dbd56cad8afaad3

152,075

py

Python

kapua-client/python-client/swagger_client/api/devices_api.py

liang-faan/SmartIOT-Diec

8336a4b558295295f10a82cf350d8b7ff3fb9f5c

[ "MIT" ]

null

kapua-client/python-client/swagger_client/api/devices_api.py

liang-faan/SmartIOT-Diec

8336a4b558295295f10a82cf350d8b7ff3fb9f5c

[ "MIT" ]

null

kapua-client/python-client/swagger_client/api/devices_api.py

liang-faan/SmartIOT-Diec

8336a4b558295295f10a82cf350d8b7ff3fb9f5c

[ "MIT" ]

null

# coding: utf-8 """ Eclipse Kapua REST API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from swagger_client.api_client import ApiClient class DevicesApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def device_asset_filtered_get(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of assets # noqa: E501 Returns the list of all the Assets installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_filtered_get(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :param DeviceAssets body: The filter of the request :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_filtered_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_asset_filtered_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_asset_filtered_get_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of assets # noqa: E501 Returns the list of all the Assets installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_filtered_get_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :param DeviceAssets body: The filter of the request :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_filtered_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_filtered_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_filtered_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_asset_get(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of assets # noqa: E501 Returns the list of all the Assets installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_get(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_asset_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_asset_get_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of assets # noqa: E501 Returns the list of all the Assets installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_get_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_asset_read(self, scope_id, device_id, **kwargs): # noqa: E501 """Reads asset channel values # noqa: E501 Returns the value read from the asset channel # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_read(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :param DeviceAssets body: The filter of the read request :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_read_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_asset_read_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_asset_read_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Reads asset channel values # noqa: E501 Returns the value read from the asset channel # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_read_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :param DeviceAssets body: The filter of the read request :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_read" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_read`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_read`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets/_read', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_asset_write(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of assets # noqa: E501 Returns the list of all the Assets installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_write(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :param DeviceAssets body: The values to write to the asset channels :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_write_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_asset_write_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_asset_write_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of assets # noqa: E501 Returns the list of all the Assets installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_asset_write_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :param DeviceAssets body: The values to write to the asset channels :return: DeviceAssets If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_write" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_write`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_write`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets/_write', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_bundle_get(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of bundles # noqa: E501 Returns the list of all the Bundles installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_bundle_get(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceBundles If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_bundle_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_bundle_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_bundle_get_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of bundles # noqa: E501 Returns the list of all the Bundles installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_bundle_get_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceBundles If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_bundle_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_bundle_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_bundle_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/bundles', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceBundles', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_bundle_start(self, scope_id, device_id, bundle_id, **kwargs): # noqa: E501 """Start a bundle # noqa: E501 Starts the specified bundle # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_bundle_start(scope_id, device_id, bundle_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param str bundle_id: the ID of the bundle to start (required) :param int timeout: The timeout of the operation in milliseconds :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_bundle_start_with_http_info(scope_id, device_id, bundle_id, **kwargs) # noqa: E501 else: (data) = self.device_bundle_start_with_http_info(scope_id, device_id, bundle_id, **kwargs) # noqa: E501 return data def device_bundle_start_with_http_info(self, scope_id, device_id, bundle_id, **kwargs): # noqa: E501 """Start a bundle # noqa: E501 Starts the specified bundle # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_bundle_start_with_http_info(scope_id, device_id, bundle_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param str bundle_id: the ID of the bundle to start (required) :param int timeout: The timeout of the operation in milliseconds :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'bundle_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_bundle_start" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_bundle_start`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_bundle_start`") # noqa: E501 # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params or params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `device_bundle_start`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/bundles/{bundleId}/_start', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_bundle_stop(self, scope_id, device_id, bundle_id, **kwargs): # noqa: E501 """Stop a bundle # noqa: E501 Stops the specified bundle # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_bundle_stop(scope_id, device_id, bundle_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param str bundle_id: the ID of the bundle to stop (required) :param int timeout: The timeout of the operation in milliseconds :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_bundle_stop_with_http_info(scope_id, device_id, bundle_id, **kwargs) # noqa: E501 else: (data) = self.device_bundle_stop_with_http_info(scope_id, device_id, bundle_id, **kwargs) # noqa: E501 return data def device_bundle_stop_with_http_info(self, scope_id, device_id, bundle_id, **kwargs): # noqa: E501 """Stop a bundle # noqa: E501 Stops the specified bundle # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_bundle_stop_with_http_info(scope_id, device_id, bundle_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device. (required) :param str device_id: The id of the device (required) :param str bundle_id: the ID of the bundle to stop (required) :param int timeout: The timeout of the operation in milliseconds :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'bundle_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_bundle_stop" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_bundle_stop`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_bundle_stop`") # noqa: E501 # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params or params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `device_bundle_stop`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/bundles/{bundleId}/_stop', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_command_execute(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Executes a command # noqa: E501 Executes a remote command on a device and return the command output. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_command_execute(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param DeviceCommandInput body: The input command (required) :param int timeout: The timeout of the command execution :return: DeviceCommandOutput If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_command_execute_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_command_execute_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_command_execute_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Executes a command # noqa: E501 Executes a remote command on a device and return the command output. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_command_execute_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param DeviceCommandInput body: The input command (required) :param int timeout: The timeout of the command execution :return: DeviceCommandOutput If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_command_execute" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_command_execute`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_command_execute`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_command_execute`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/commands/_execute', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceCommandOutput', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_component_get(self, scope_id, device_id, component_id, **kwargs): # noqa: E501 """Gets the configuration of a component on a device # noqa: E501 Returns the configuration of a device or the configuration of the OSGi component identified with specified PID (service's persistent identity). In the OSGi framework, the service's persistent identity is defined as the name attribute of the Component Descriptor XML file; at runtime, the same value is also available in the component.name and in the service.pid attributes of the Component Configuration. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_component_get(scope_id, device_id, component_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param str component_id: An optional id of the component to get the configuration for (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_component_get_with_http_info(scope_id, device_id, component_id, **kwargs) # noqa: E501 else: (data) = self.device_configuration_component_get_with_http_info(scope_id, device_id, component_id, **kwargs) # noqa: E501 return data def device_configuration_component_get_with_http_info(self, scope_id, device_id, component_id, **kwargs): # noqa: E501 """Gets the configuration of a component on a device # noqa: E501 Returns the configuration of a device or the configuration of the OSGi component identified with specified PID (service's persistent identity). In the OSGi framework, the service's persistent identity is defined as the name attribute of the Component Descriptor XML file; at runtime, the same value is also available in the component.name and in the service.pid attributes of the Component Configuration. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_component_get_with_http_info(scope_id, device_id, component_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param str component_id: An optional id of the component to get the configuration for (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'component_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_component_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_component_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_component_get`") # noqa: E501 # verify the required parameter 'component_id' is set if ('component_id' not in params or params['component_id'] is None): raise ValueError("Missing the required parameter `component_id` when calling `device_configuration_component_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'component_id' in params: path_params['componentId'] = params['component_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations/{componentId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_component_update(self, scope_id, device_id, component_id, body, **kwargs): # noqa: E501 """Updates the configuration of a component on a device # noqa: E501 Updates a device component configuration # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_component_update(scope_id, device_id, component_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param str component_id: The component id to update (required) :param DeviceComponentConfiguration body: The component configuration to send to the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_component_update_with_http_info(scope_id, device_id, component_id, body, **kwargs) # noqa: E501 else: (data) = self.device_configuration_component_update_with_http_info(scope_id, device_id, component_id, body, **kwargs) # noqa: E501 return data def device_configuration_component_update_with_http_info(self, scope_id, device_id, component_id, body, **kwargs): # noqa: E501 """Updates the configuration of a component on a device # noqa: E501 Updates a device component configuration # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_component_update_with_http_info(scope_id, device_id, component_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param str component_id: The component id to update (required) :param DeviceComponentConfiguration body: The component configuration to send to the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'component_id', 'body', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_component_update" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_component_update`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_component_update`") # noqa: E501 # verify the required parameter 'component_id' is set if ('component_id' not in params or params['component_id'] is None): raise ValueError("Missing the required parameter `component_id` when calling `device_configuration_component_update`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_configuration_component_update`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'component_id' in params: path_params['componentId'] = params['component_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations/{componentId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_get(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets the device configurations # noqa: E501 Returns the current configuration of a device # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_get(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_configuration_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_configuration_get_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets the device configurations # noqa: E501 Returns the current configuration of a device # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_get_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_update(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Updates a device configuration # noqa: E501 Updates a device configuration # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_update(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param DeviceConfiguration body: The configuration to send to the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_update_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_configuration_update_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_configuration_update_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Updates a device configuration # noqa: E501 Updates a device configuration # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_configuration_update_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device. (required) :param str device_id: The id of the device (required) :param DeviceConfiguration body: The configuration to send to the device (required) :param int timeout: The timeout of the operation in milliseconds :return: DeviceConfiguration If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_update" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_update`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_update`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_configuration_update`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_count(self, scope_id, body, **kwargs): # noqa: E501 """Counts the Devices # noqa: E501 Counts the Devices with the given DeviceQuery parameter returning the number of matching Devices # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_count(scope_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to count results (required) :param DeviceQuery body: The DeviceQuery to use to filter count results (required) :return: CountResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_count_with_http_info(scope_id, body, **kwargs) # noqa: E501 else: (data) = self.device_count_with_http_info(scope_id, body, **kwargs) # noqa: E501 return data def device_count_with_http_info(self, scope_id, body, **kwargs): # noqa: E501 """Counts the Devices # noqa: E501 Counts the Devices with the given DeviceQuery parameter returning the number of matching Devices # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_count_with_http_info(scope_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to count results (required) :param DeviceQuery body: The DeviceQuery to use to filter count results (required) :return: CountResult If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_count" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_count`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_count`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/_count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='CountResult', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_create(self, scope_id, body, **kwargs): # noqa: E501 """Create an Device # noqa: E501 Creates a new Device based on the information provided in DeviceCreator parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_create(scope_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to create the Device. (required) :param DeviceCreator body: Provides the information for the new Device to be created (required) :return: Device If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_create_with_http_info(scope_id, body, **kwargs) # noqa: E501 else: (data) = self.device_create_with_http_info(scope_id, body, **kwargs) # noqa: E501 return data def device_create_with_http_info(self, scope_id, body, **kwargs): # noqa: E501 """Create an Device # noqa: E501 Creates a new Device based on the information provided in DeviceCreator parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_create_with_http_info(scope_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to create the Device. (required) :param DeviceCreator body: Provides the information for the new Device to be created (required) :return: Device If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_create" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_create`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_create`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Device', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_delete(self, scope_id, device_id, **kwargs): # noqa: E501 """Delete a Device # noqa: E501 Deletes the Device specified by the \"deviceId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_delete(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device to delete. (required) :param str device_id: The id of the Device to be deleted (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_delete_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_delete_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_delete_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Delete a Device # noqa: E501 Deletes the Device specified by the \"deviceId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_delete_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device to delete. (required) :param str device_id: The id of the Device to be deleted (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_delete`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_count(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Counts the DeviceEvents # noqa: E501 Counts the DeviceEvents with the given DeviceEventQuery parameter returning the number of matching DeviceEvents # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_count(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to count results. (required) :param str device_id: The id of the Device in which to count results (required) :param DeviceEventQuery body: The DeviceEventQuery to use to filter count results (required) :return: CountResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_count_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_event_count_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_event_count_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Counts the DeviceEvents # noqa: E501 Counts the DeviceEvents with the given DeviceEventQuery parameter returning the number of matching DeviceEvents # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_count_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to count results. (required) :param str device_id: The id of the Device in which to count results (required) :param DeviceEventQuery body: The DeviceEventQuery to use to filter count results (required) :return: CountResult If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_count" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_count`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_count`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_event_count`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/_count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='CountResult', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_delete(self, scope_id, device_id, device_event_id, **kwargs): # noqa: E501 """Delete a DeviceEvent # noqa: E501 Deletes the DeviceEvent specified by the \"deviceEventId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_delete(scope_id, device_id, device_event_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: (required) :param str device_id: The id of the Device in which to delete the event. (required) :param str device_event_id: The id of the DeviceEvent to be deleted (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_delete_with_http_info(scope_id, device_id, device_event_id, **kwargs) # noqa: E501 else: (data) = self.device_event_delete_with_http_info(scope_id, device_id, device_event_id, **kwargs) # noqa: E501 return data def device_event_delete_with_http_info(self, scope_id, device_id, device_event_id, **kwargs): # noqa: E501 """Delete a DeviceEvent # noqa: E501 Deletes the DeviceEvent specified by the \"deviceEventId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_delete_with_http_info(scope_id, device_id, device_event_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: (required) :param str device_id: The id of the Device in which to delete the event. (required) :param str device_event_id: The id of the DeviceEvent to be deleted (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'device_event_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_delete`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_delete`") # noqa: E501 # verify the required parameter 'device_event_id' is set if ('device_event_id' not in params or params['device_event_id'] is None): raise ValueError("Missing the required parameter `device_event_id` when calling `device_event_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'device_event_id' in params: path_params['deviceEventId'] = params['device_event_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/{deviceEventId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_find(self, scope_id, device_id, device_event_id, **kwargs): # noqa: E501 """Get an DeviceEvent # noqa: E501 Returns the DeviceEvent specified by the \"deviceEventId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_find(scope_id, device_id, device_event_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the requested DeviceEvent. (required) :param str device_id: The id of the requested Device (required) :param str device_event_id: The id of the requested DeviceEvent (required) :return: DeviceEvent If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_find_with_http_info(scope_id, device_id, device_event_id, **kwargs) # noqa: E501 else: (data) = self.device_event_find_with_http_info(scope_id, device_id, device_event_id, **kwargs) # noqa: E501 return data def device_event_find_with_http_info(self, scope_id, device_id, device_event_id, **kwargs): # noqa: E501 """Get an DeviceEvent # noqa: E501 Returns the DeviceEvent specified by the \"deviceEventId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_find_with_http_info(scope_id, device_id, device_event_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the requested DeviceEvent. (required) :param str device_id: The id of the requested Device (required) :param str device_event_id: The id of the requested DeviceEvent (required) :return: DeviceEvent If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'device_event_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_find" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_find`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_find`") # noqa: E501 # verify the required parameter 'device_event_id' is set if ('device_event_id' not in params or params['device_event_id'] is None): raise ValueError("Missing the required parameter `device_event_id` when calling `device_event_find`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'device_event_id' in params: path_params['deviceEventId'] = params['device_event_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/{deviceEventId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceEvent', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_query(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Queries the DeviceEvents # noqa: E501 Queries the DeviceEvents with the given DeviceEvents parameter returning all matching DeviceEvents # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_query(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param str device_id: The id of the Device in which to search results (required) :param DeviceEventQuery body: The DeviceEventQuery to use to filter results. (required) :return: DeviceEventListResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_query_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_event_query_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_event_query_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Queries the DeviceEvents # noqa: E501 Queries the DeviceEvents with the given DeviceEvents parameter returning all matching DeviceEvents # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_query_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param str device_id: The id of the Device in which to search results (required) :param DeviceEventQuery body: The DeviceEventQuery to use to filter results. (required) :return: DeviceEventListResult If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_query" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_query`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_query`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_event_query`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/_query', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceEventListResult', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_simple_query(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets the DeviceEvent list in the scope # noqa: E501 Returns the list of all the deviceEvents associated to the current selected scope. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_simple_query(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param str device_id: The client id to filter results. (required) :param str resource: The resource of the DeviceEvent in which to search results :param int offset: The result set offset. :param int limit: The result set limit. :return: DeviceEventListResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_simple_query_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_event_simple_query_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_event_simple_query_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets the DeviceEvent list in the scope # noqa: E501 Returns the list of all the deviceEvents associated to the current selected scope. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_event_simple_query_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param str device_id: The client id to filter results. (required) :param str resource: The resource of the DeviceEvent in which to search results :param int offset: The result set offset. :param int limit: The result set limit. :return: DeviceEventListResult If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'resource', 'offset', 'limit'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_simple_query" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_simple_query`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_simple_query`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'resource' in params: query_params.append(('resource', params['resource'])) # noqa: E501 if 'offset' in params: query_params.append(('offset', params['offset'])) # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceEventListResult', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_find(self, scope_id, device_id, **kwargs): # noqa: E501 """Get a Device # noqa: E501 Returns the Device specified by the \"deviceId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_find(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the requested Device (required) :param str device_id: The id of the requested Device (required) :return: Device If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_find_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_find_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_find_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Get a Device # noqa: E501 Returns the Device specified by the \"deviceId\" path parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_find_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the requested Device (required) :param str device_id: The id of the requested Device (required) :return: Device If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_find" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_find`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_find`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Device', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_package_download(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Installs a package # noqa: E501 Installs a package into the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_package_download(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device (required) :param str device_id: The id of the device (required) :param DevicePackageDownloadRequest body: Mandatory object with all the informations needed to download and install a package (required) :param int timeout: The timeout of the operation :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_package_download_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_package_download_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_package_download_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Installs a package # noqa: E501 Installs a package into the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_package_download_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device (required) :param str device_id: The id of the device (required) :param DevicePackageDownloadRequest body: Mandatory object with all the informations needed to download and install a package (required) :param int timeout: The timeout of the operation :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_package_download" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_package_download`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_package_download`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_package_download`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/packages/_download', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_package_get(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of packages # noqa: E501 Returns the list of all the packages installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_package_get(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation :return: DevicePackages If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_package_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_package_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_package_get_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of packages # noqa: E501 Returns the list of all the packages installed on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_package_get_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation :return: DevicePackages If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_package_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_package_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_package_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/packages', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DevicePackages', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_package_uninstall(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Uninstalls a package # noqa: E501 Uninstalls a package into the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_package_uninstall(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device (required) :param str device_id: The id of the device (required) :param DevicePackageUninstallRequest body: Mandatory object with all the informations needed to uninstall a package (required) :param int timeout: The timeout of the operation :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_package_uninstall_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_package_uninstall_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_package_uninstall_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Uninstalls a package # noqa: E501 Uninstalls a package into the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_package_uninstall_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the Device (required) :param str device_id: The id of the device (required) :param DevicePackageUninstallRequest body: Mandatory object with all the informations needed to uninstall a package (required) :param int timeout: The timeout of the operation :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_package_uninstall" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_package_uninstall`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_package_uninstall`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_package_uninstall`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/packages/_uninstall', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_query(self, scope_id, body, **kwargs): # noqa: E501 """Queries the Devices # noqa: E501 Queries the Devices with the given Devices parameter returning all matching Devices # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_query(scope_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param DeviceQuery body: The DeviceQuery to use to filter results. (required) :return: DeviceListResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_query_with_http_info(scope_id, body, **kwargs) # noqa: E501 else: (data) = self.device_query_with_http_info(scope_id, body, **kwargs) # noqa: E501 return data def device_query_with_http_info(self, scope_id, body, **kwargs): # noqa: E501 """Queries the Devices # noqa: E501 Queries the Devices with the given Devices parameter returning all matching Devices # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_query_with_http_info(scope_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param DeviceQuery body: The DeviceQuery to use to filter results. (required) :return: DeviceListResult If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_query" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_query`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_query`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/_query', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceListResult', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_request_send(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Sends a request # noqa: E501 Sends a request message to a device # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_request_send(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param JsonGenericRequestMessage body: The input request (required) :param int timeout: The timeout of the request execution :return: JsonGenericResponseMessage If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_request_send_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_request_send_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_request_send_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Sends a request # noqa: E501 Sends a request message to a device # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_request_send_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param JsonGenericRequestMessage body: The input request (required) :param int timeout: The timeout of the request execution :return: JsonGenericResponseMessage If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_request_send" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_request_send`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_request_send`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_request_send`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/requests', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='JsonGenericResponseMessage', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_simple_query(self, scope_id, **kwargs): # noqa: E501 """Gets the Device list in the scope # noqa: E501 Returns the list of all the devices associated to the current selected scope. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_simple_query(scope_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param str tag_id: The tag id to filter results. :param str client_id: The client id to filter results. :param str status: The connection status to filter results. :param list[str] fetch_attributes: Additional attributes to be returned. Allowed values: connection, lastEvent :param int offset: The result set offset. :param int limit: The result set limit. :return: DeviceListResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_simple_query_with_http_info(scope_id, **kwargs) # noqa: E501 else: (data) = self.device_simple_query_with_http_info(scope_id, **kwargs) # noqa: E501 return data def device_simple_query_with_http_info(self, scope_id, **kwargs): # noqa: E501 """Gets the Device list in the scope # noqa: E501 Returns the list of all the devices associated to the current selected scope. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_simple_query_with_http_info(scope_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId in which to search results. (required) :param str tag_id: The tag id to filter results. :param str client_id: The client id to filter results. :param str status: The connection status to filter results. :param list[str] fetch_attributes: Additional attributes to be returned. Allowed values: connection, lastEvent :param int offset: The result set offset. :param int limit: The result set limit. :return: DeviceListResult If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'tag_id', 'client_id', 'status', 'fetch_attributes', 'offset', 'limit'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_simple_query" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_simple_query`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 query_params = [] if 'tag_id' in params: query_params.append(('tagId', params['tag_id'])) # noqa: E501 if 'client_id' in params: query_params.append(('clientId', params['client_id'])) # noqa: E501 if 'status' in params: query_params.append(('status', params['status'])) # noqa: E501 if 'fetch_attributes' in params: query_params.append(('fetchAttributes', params['fetch_attributes'])) # noqa: E501 collection_formats['fetchAttributes'] = 'multi' # noqa: E501 if 'offset' in params: query_params.append(('offset', params['offset'])) # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceListResult', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_snapshot_get(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of snapshots # noqa: E501 Returns the list of all the Snapshots available on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_snapshot_get(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation :return: DeviceSnapshots If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_snapshot_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 else: (data) = self.device_snapshot_get_with_http_info(scope_id, device_id, **kwargs) # noqa: E501 return data def device_snapshot_get_with_http_info(self, scope_id, device_id, **kwargs): # noqa: E501 """Gets a list of snapshots # noqa: E501 Returns the list of all the Snapshots available on the device. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_snapshot_get_with_http_info(scope_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param int timeout: The timeout of the operation :return: DeviceSnapshots If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_snapshot_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_snapshot_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_snapshot_get`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/snapshots', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceSnapshots', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_snapshot_rollback(self, scope_id, device_id, snapshot_id, **kwargs): # noqa: E501 """Gets a list of snapshots # noqa: E501 Updates the configuration of a device rolling back a given snapshot ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_snapshot_rollback(scope_id, device_id, snapshot_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param str snapshot_id: the ID of the snapshot to rollback to (required) :param int timeout: The timeout of the operation :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_snapshot_rollback_with_http_info(scope_id, device_id, snapshot_id, **kwargs) # noqa: E501 else: (data) = self.device_snapshot_rollback_with_http_info(scope_id, device_id, snapshot_id, **kwargs) # noqa: E501 return data def device_snapshot_rollback_with_http_info(self, scope_id, device_id, snapshot_id, **kwargs): # noqa: E501 """Gets a list of snapshots # noqa: E501 Updates the configuration of a device rolling back a given snapshot ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_snapshot_rollback_with_http_info(scope_id, device_id, snapshot_id, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the device (required) :param str device_id: The id of the device (required) :param str snapshot_id: the ID of the snapshot to rollback to (required) :param int timeout: The timeout of the operation :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'snapshot_id', 'timeout'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_snapshot_rollback" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_snapshot_rollback`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_snapshot_rollback`") # noqa: E501 # verify the required parameter 'snapshot_id' is set if ('snapshot_id' not in params or params['snapshot_id'] is None): raise ValueError("Missing the required parameter `snapshot_id` when calling `device_snapshot_rollback`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 if 'snapshot_id' in params: path_params['snapshotId'] = params['snapshot_id'] # noqa: E501 query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/snapshots/{snapshotId}/_rollback', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_update(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Update a Device # noqa: E501 Updates a new Device based on the information provided in the Device parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_update(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the requested Device. (required) :param str device_id: The id of the requested Device (required) :param Device body: The modified Device whose attributed need to be updated (required) :return: Device If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_update_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 else: (data) = self.device_update_with_http_info(scope_id, device_id, body, **kwargs) # noqa: E501 return data def device_update_with_http_info(self, scope_id, device_id, body, **kwargs): # noqa: E501 """Update a Device # noqa: E501 Updates a new Device based on the information provided in the Device parameter. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.device_update_with_http_info(scope_id, device_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param str scope_id: The ScopeId of the requested Device. (required) :param str device_id: The id of the requested Device (required) :param Device body: The modified Device whose attributed need to be updated (required) :return: Device If the method is called asynchronously, returns the request thread. """ all_params = ['scope_id', 'device_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_update" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scope_id' is set if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_update`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_update`") # noqa: E501 # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_update`") # noqa: E501 collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] # noqa: E501 if 'device_id' in params: path_params['deviceId'] = params['device_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/xml', 'application/json']) # noqa: E501 # Authentication setting auth_settings = ['kapuaAccessToken'] # noqa: E501 return self.api_client.call_api( '/{scopeId}/devices/{deviceId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Device', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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from __future__ import absolute_import import re import six from swagger_client.api_client import ApiClient class DevicesApi(object): def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def device_asset_filtered_get(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_filtered_get_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_asset_filtered_get_with_http_info(scope_id, device_id, **kwargs) return data def device_asset_filtered_get_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_filtered_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_filtered_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_filtered_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_asset_get(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_get_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_asset_get_with_http_info(scope_id, device_id, **kwargs) return data def device_asset_get_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_asset_read(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_read_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_asset_read_with_http_info(scope_id, device_id, **kwargs) return data def device_asset_read_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_read" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_read`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_read`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets/_read', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_asset_write(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_asset_write_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_asset_write_with_http_info(scope_id, device_id, **kwargs) return data def device_asset_write_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_asset_write" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_asset_write`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_asset_write`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/assets/_write', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceAssets', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_bundle_get(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_bundle_get_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_bundle_get_with_http_info(scope_id, device_id, **kwargs) return data def device_bundle_get_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_bundle_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_bundle_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_bundle_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/bundles', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceBundles', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_bundle_start(self, scope_id, device_id, bundle_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_bundle_start_with_http_info(scope_id, device_id, bundle_id, **kwargs) else: (data) = self.device_bundle_start_with_http_info(scope_id, device_id, bundle_id, **kwargs) return data def device_bundle_start_with_http_info(self, scope_id, device_id, bundle_id, **kwargs): all_params = ['scope_id', 'device_id', 'bundle_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_bundle_start" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_bundle_start`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_bundle_start`") if ('bundle_id' not in params or params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `device_bundle_start`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/bundles/{bundleId}/_start', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_bundle_stop(self, scope_id, device_id, bundle_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_bundle_stop_with_http_info(scope_id, device_id, bundle_id, **kwargs) else: (data) = self.device_bundle_stop_with_http_info(scope_id, device_id, bundle_id, **kwargs) return data def device_bundle_stop_with_http_info(self, scope_id, device_id, bundle_id, **kwargs): all_params = ['scope_id', 'device_id', 'bundle_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_bundle_stop" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_bundle_stop`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_bundle_stop`") if ('bundle_id' not in params or params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `device_bundle_stop`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/bundles/{bundleId}/_stop', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_command_execute(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_command_execute_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_command_execute_with_http_info(scope_id, device_id, body, **kwargs) return data def device_command_execute_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_command_execute" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_command_execute`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_command_execute`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_command_execute`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/commands/_execute', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceCommandOutput', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_component_get(self, scope_id, device_id, component_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_component_get_with_http_info(scope_id, device_id, component_id, **kwargs) else: (data) = self.device_configuration_component_get_with_http_info(scope_id, device_id, component_id, **kwargs) return data def device_configuration_component_get_with_http_info(self, scope_id, device_id, component_id, **kwargs): all_params = ['scope_id', 'device_id', 'component_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_component_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_component_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_component_get`") if ('component_id' not in params or params['component_id'] is None): raise ValueError("Missing the required parameter `component_id` when calling `device_configuration_component_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'component_id' in params: path_params['componentId'] = params['component_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations/{componentId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_component_update(self, scope_id, device_id, component_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_component_update_with_http_info(scope_id, device_id, component_id, body, **kwargs) else: (data) = self.device_configuration_component_update_with_http_info(scope_id, device_id, component_id, body, **kwargs) return data def device_configuration_component_update_with_http_info(self, scope_id, device_id, component_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'component_id', 'body', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_component_update" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_component_update`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_component_update`") if ('component_id' not in params or params['component_id'] is None): raise ValueError("Missing the required parameter `component_id` when calling `device_configuration_component_update`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_configuration_component_update`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'component_id' in params: path_params['componentId'] = params['component_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations/{componentId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_get(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_get_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_configuration_get_with_http_info(scope_id, device_id, **kwargs) return data def device_configuration_get_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_configuration_update(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_configuration_update_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_configuration_update_with_http_info(scope_id, device_id, body, **kwargs) return data def device_configuration_update_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_configuration_update" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_configuration_update`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_configuration_update`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_configuration_update`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/configurations', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceConfiguration', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_count(self, scope_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_count_with_http_info(scope_id, body, **kwargs) else: (data) = self.device_count_with_http_info(scope_id, body, **kwargs) return data def device_count_with_http_info(self, scope_id, body, **kwargs): all_params = ['scope_id', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_count" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_count`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_count`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/_count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='CountResult', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_create(self, scope_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_create_with_http_info(scope_id, body, **kwargs) else: (data) = self.device_create_with_http_info(scope_id, body, **kwargs) return data def device_create_with_http_info(self, scope_id, body, **kwargs): all_params = ['scope_id', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_create" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_create`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_create`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Device', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_delete(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_delete_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_delete_with_http_info(scope_id, device_id, **kwargs) return data def device_delete_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_delete" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_delete`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_delete`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_count(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_count_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_event_count_with_http_info(scope_id, device_id, body, **kwargs) return data def device_event_count_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_count" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_count`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_count`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_event_count`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/_count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='CountResult', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_delete(self, scope_id, device_id, device_event_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_delete_with_http_info(scope_id, device_id, device_event_id, **kwargs) else: (data) = self.device_event_delete_with_http_info(scope_id, device_id, device_event_id, **kwargs) return data def device_event_delete_with_http_info(self, scope_id, device_id, device_event_id, **kwargs): all_params = ['scope_id', 'device_id', 'device_event_id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_delete" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_delete`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_delete`") if ('device_event_id' not in params or params['device_event_id'] is None): raise ValueError("Missing the required parameter `device_event_id` when calling `device_event_delete`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'device_event_id' in params: path_params['deviceEventId'] = params['device_event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/{deviceEventId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_find(self, scope_id, device_id, device_event_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_find_with_http_info(scope_id, device_id, device_event_id, **kwargs) else: (data) = self.device_event_find_with_http_info(scope_id, device_id, device_event_id, **kwargs) return data def device_event_find_with_http_info(self, scope_id, device_id, device_event_id, **kwargs): all_params = ['scope_id', 'device_id', 'device_event_id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_find" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_find`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_find`") if ('device_event_id' not in params or params['device_event_id'] is None): raise ValueError("Missing the required parameter `device_event_id` when calling `device_event_find`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'device_event_id' in params: path_params['deviceEventId'] = params['device_event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/{deviceEventId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceEvent', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_query(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_query_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_event_query_with_http_info(scope_id, device_id, body, **kwargs) return data def device_event_query_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_query" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_query`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_query`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_event_query`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events/_query', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceEventListResult', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_event_simple_query(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_event_simple_query_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_event_simple_query_with_http_info(scope_id, device_id, **kwargs) return data def device_event_simple_query_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'resource', 'offset', 'limit'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_event_simple_query" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_event_simple_query`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_event_simple_query`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'resource' in params: query_params.append(('resource', params['resource'])) if 'offset' in params: query_params.append(('offset', params['offset'])) if 'limit' in params: query_params.append(('limit', params['limit'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/events', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceEventListResult', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_find(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_find_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_find_with_http_info(scope_id, device_id, **kwargs) return data def device_find_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_find" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_find`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_find`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Device', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_package_download(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_package_download_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_package_download_with_http_info(scope_id, device_id, body, **kwargs) return data def device_package_download_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_package_download" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_package_download`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_package_download`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_package_download`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/packages/_download', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_package_get(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_package_get_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_package_get_with_http_info(scope_id, device_id, **kwargs) return data def device_package_get_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_package_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_package_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_package_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/packages', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DevicePackages', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_package_uninstall(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_package_uninstall_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_package_uninstall_with_http_info(scope_id, device_id, body, **kwargs) return data def device_package_uninstall_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_package_uninstall" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_package_uninstall`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_package_uninstall`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_package_uninstall`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/packages/_uninstall', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_query(self, scope_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_query_with_http_info(scope_id, body, **kwargs) else: (data) = self.device_query_with_http_info(scope_id, body, **kwargs) return data def device_query_with_http_info(self, scope_id, body, **kwargs): all_params = ['scope_id', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_query" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_query`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_query`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/_query', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceListResult', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_request_send(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_request_send_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_request_send_with_http_info(scope_id, device_id, body, **kwargs) return data def device_request_send_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_request_send" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_request_send`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_request_send`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_request_send`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/requests', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='JsonGenericResponseMessage', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_simple_query(self, scope_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_simple_query_with_http_info(scope_id, **kwargs) else: (data) = self.device_simple_query_with_http_info(scope_id, **kwargs) return data def device_simple_query_with_http_info(self, scope_id, **kwargs): all_params = ['scope_id', 'tag_id', 'client_id', 'status', 'fetch_attributes', 'offset', 'limit'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_simple_query" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_simple_query`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] query_params = [] if 'tag_id' in params: query_params.append(('tagId', params['tag_id'])) if 'client_id' in params: query_params.append(('clientId', params['client_id'])) if 'status' in params: query_params.append(('status', params['status'])) if 'fetch_attributes' in params: query_params.append(('fetchAttributes', params['fetch_attributes'])) collection_formats['fetchAttributes'] = 'multi' if 'offset' in params: query_params.append(('offset', params['offset'])) if 'limit' in params: query_params.append(('limit', params['limit'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceListResult', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_snapshot_get(self, scope_id, device_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_snapshot_get_with_http_info(scope_id, device_id, **kwargs) else: (data) = self.device_snapshot_get_with_http_info(scope_id, device_id, **kwargs) return data def device_snapshot_get_with_http_info(self, scope_id, device_id, **kwargs): all_params = ['scope_id', 'device_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_snapshot_get" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_snapshot_get`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_snapshot_get`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'application/xml']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/snapshots', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='DeviceSnapshots', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_snapshot_rollback(self, scope_id, device_id, snapshot_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_snapshot_rollback_with_http_info(scope_id, device_id, snapshot_id, **kwargs) else: (data) = self.device_snapshot_rollback_with_http_info(scope_id, device_id, snapshot_id, **kwargs) return data def device_snapshot_rollback_with_http_info(self, scope_id, device_id, snapshot_id, **kwargs): all_params = ['scope_id', 'device_id', 'snapshot_id', 'timeout'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_snapshot_rollback" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_snapshot_rollback`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_snapshot_rollback`") if ('snapshot_id' not in params or params['snapshot_id'] is None): raise ValueError("Missing the required parameter `snapshot_id` when calling `device_snapshot_rollback`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] if 'snapshot_id' in params: path_params['snapshotId'] = params['snapshot_id'] query_params = [] if 'timeout' in params: query_params.append(('timeout', params['timeout'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}/snapshots/{snapshotId}/_rollback', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def device_update(self, scope_id, device_id, body, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.device_update_with_http_info(scope_id, device_id, body, **kwargs) else: (data) = self.device_update_with_http_info(scope_id, device_id, body, **kwargs) return data def device_update_with_http_info(self, scope_id, device_id, body, **kwargs): all_params = ['scope_id', 'device_id', 'body'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method device_update" % key ) params[key] = val del params['kwargs'] if ('scope_id' not in params or params['scope_id'] is None): raise ValueError("Missing the required parameter `scope_id` when calling `device_update`") if ('device_id' not in params or params['device_id'] is None): raise ValueError("Missing the required parameter `device_id` when calling `device_update`") if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `device_update`") collection_formats = {} path_params = {} if 'scope_id' in params: path_params['scopeId'] = params['scope_id'] if 'device_id' in params: path_params['deviceId'] = params['device_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/xml', 'application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/xml', 'application/json']) auth_settings = ['kapuaAccessToken'] return self.api_client.call_api( '/{scopeId}/devices/{deviceId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Device', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

true

790d6a3efac390378876a5be53e7f10b1530fb5c

28,191

py

Python

tests/migrations/test_writer.py

robgolding/django

1d0bab0bfd77edcf1228d45bf654457a8ff1890d

[ "PSF-2.0", "BSD-3-Clause" ]

5

2019-10-17T21:29:53.000Z

2021-06-23T16:27:02.000Z

tests/migrations/test_writer.py

robgolding/django

1d0bab0bfd77edcf1228d45bf654457a8ff1890d

[ "PSF-2.0", "BSD-3-Clause" ]

10

2016-05-19T21:54:42.000Z

2019-08-09T15:59:50.000Z

tests/migrations/test_writer.py

robgolding/django

1d0bab0bfd77edcf1228d45bf654457a8ff1890d

[ "PSF-2.0", "BSD-3-Clause" ]

11

2019-09-14T20:57:30.000Z

2022-01-19T17:59:26.000Z

import datetime import decimal import enum import functools import math import os import re import uuid from unittest import mock import custom_migration_operations.more_operations import custom_migration_operations.operations from django import get_version from django.conf import SettingsReference, settings from django.core.validators import EmailValidator, RegexValidator from django.db import migrations, models from django.db.migrations.serializer import BaseSerializer from django.db.migrations.writer import MigrationWriter, OperationWriter from django.test import SimpleTestCase from django.utils.deconstruct import deconstructible from django.utils.functional import SimpleLazyObject from django.utils.timezone import get_default_timezone, get_fixed_timezone, utc from django.utils.translation import gettext_lazy as _ from .models import FoodManager, FoodQuerySet class Money(decimal.Decimal): def deconstruct(self): return ( '%s.%s' % (self.__class__.__module__, self.__class__.__name__), [str(self)], {} ) class TestModel1: def upload_to(self): return '/somewhere/dynamic/' thing = models.FileField(upload_to=upload_to) class OperationWriterTests(SimpleTestCase): def test_empty_signature(self): operation = custom_migration_operations.operations.TestOperation() buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.TestOperation(\n' '),' ) def test_args_signature(self): operation = custom_migration_operations.operations.ArgsOperation(1, 2) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ArgsOperation(\n' ' arg1=1,\n' ' arg2=2,\n' '),' ) def test_kwargs_signature(self): operation = custom_migration_operations.operations.KwargsOperation(kwarg1=1) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.KwargsOperation(\n' ' kwarg1=1,\n' '),' ) def test_args_kwargs_signature(self): operation = custom_migration_operations.operations.ArgsKwargsOperation(1, 2, kwarg2=4) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ArgsKwargsOperation(\n' ' arg1=1,\n' ' arg2=2,\n' ' kwarg2=4,\n' '),' ) def test_nested_args_signature(self): operation = custom_migration_operations.operations.ArgsOperation( custom_migration_operations.operations.ArgsOperation(1, 2), custom_migration_operations.operations.KwargsOperation(kwarg1=3, kwarg2=4) ) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ArgsOperation(\n' ' arg1=custom_migration_operations.operations.ArgsOperation(\n' ' arg1=1,\n' ' arg2=2,\n' ' ),\n' ' arg2=custom_migration_operations.operations.KwargsOperation(\n' ' kwarg1=3,\n' ' kwarg2=4,\n' ' ),\n' '),' ) def test_multiline_args_signature(self): operation = custom_migration_operations.operations.ArgsOperation("test\n arg1", "test\narg2") buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, "custom_migration_operations.operations.ArgsOperation(\n" " arg1='test\\n arg1',\n" " arg2='test\\narg2',\n" ")," ) def test_expand_args_signature(self): operation = custom_migration_operations.operations.ExpandArgsOperation([1, 2]) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ExpandArgsOperation(\n' ' arg=[\n' ' 1,\n' ' 2,\n' ' ],\n' '),' ) def test_nested_operation_expand_args_signature(self): operation = custom_migration_operations.operations.ExpandArgsOperation( arg=[ custom_migration_operations.operations.KwargsOperation( kwarg1=1, kwarg2=2, ), ] ) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ExpandArgsOperation(\n' ' arg=[\n' ' custom_migration_operations.operations.KwargsOperation(\n' ' kwarg1=1,\n' ' kwarg2=2,\n' ' ),\n' ' ],\n' '),' ) class WriterTests(SimpleTestCase): """ Tests the migration writer (makes migration files from Migration instances) """ def safe_exec(self, string, value=None): d = {} try: exec(string, globals(), d) except Exception as e: if value: self.fail("Could not exec %r (from value %r): %s" % (string.strip(), value, e)) else: self.fail("Could not exec %r: %s" % (string.strip(), e)) return d def serialize_round_trip(self, value): string, imports = MigrationWriter.serialize(value) return self.safe_exec("%s\ntest_value_result = %s" % ("\n".join(imports), string), value)['test_value_result'] def assertSerializedEqual(self, value): self.assertEqual(self.serialize_round_trip(value), value) def assertSerializedResultEqual(self, value, target): self.assertEqual(MigrationWriter.serialize(value), target) def assertSerializedFieldEqual(self, value): new_value = self.serialize_round_trip(value) self.assertEqual(value.__class__, new_value.__class__) self.assertEqual(value.max_length, new_value.max_length) self.assertEqual(value.null, new_value.null) self.assertEqual(value.unique, new_value.unique) def test_serialize_numbers(self): self.assertSerializedEqual(1) self.assertSerializedEqual(1.2) self.assertTrue(math.isinf(self.serialize_round_trip(float("inf")))) self.assertTrue(math.isinf(self.serialize_round_trip(float("-inf")))) self.assertTrue(math.isnan(self.serialize_round_trip(float("nan")))) self.assertSerializedEqual(decimal.Decimal('1.3')) self.assertSerializedResultEqual( decimal.Decimal('1.3'), ("Decimal('1.3')", {'from decimal import Decimal'}) ) self.assertSerializedEqual(Money('1.3')) self.assertSerializedResultEqual( Money('1.3'), ("migrations.test_writer.Money('1.3')", {'import migrations.test_writer'}) ) def test_serialize_constants(self): self.assertSerializedEqual(None) self.assertSerializedEqual(True) self.assertSerializedEqual(False) def test_serialize_strings(self): self.assertSerializedEqual(b"foobar") string, imports = MigrationWriter.serialize(b"foobar") self.assertEqual(string, "b'foobar'") self.assertSerializedEqual("föobár") string, imports = MigrationWriter.serialize("foobar") self.assertEqual(string, "'foobar'") def test_serialize_multiline_strings(self): self.assertSerializedEqual(b"foo\nbar") string, imports = MigrationWriter.serialize(b"foo\nbar") self.assertEqual(string, "b'foo\\nbar'") self.assertSerializedEqual("föo\nbár") string, imports = MigrationWriter.serialize("foo\nbar") self.assertEqual(string, "'foo\\nbar'") def test_serialize_collections(self): self.assertSerializedEqual({1: 2}) self.assertSerializedEqual(["a", 2, True, None]) self.assertSerializedEqual({2, 3, "eighty"}) self.assertSerializedEqual({"lalalala": ["yeah", "no", "maybe"]}) self.assertSerializedEqual(_('Hello')) def test_serialize_builtin_types(self): self.assertSerializedEqual([list, tuple, dict, set, frozenset]) self.assertSerializedResultEqual( [list, tuple, dict, set, frozenset], ("[list, tuple, dict, set, frozenset]", set()) ) def test_serialize_lazy_objects(self): pattern = re.compile(r'^foo$') lazy_pattern = SimpleLazyObject(lambda: pattern) self.assertEqual(self.serialize_round_trip(lazy_pattern), pattern) def test_serialize_enums(self): class TextEnum(enum.Enum): A = 'a-value' B = 'value-b' class BinaryEnum(enum.Enum): A = b'a-value' B = b'value-b' class IntEnum(enum.IntEnum): A = 1 B = 2 self.assertSerializedResultEqual( TextEnum.A, ("migrations.test_writer.TextEnum('a-value')", {'import migrations.test_writer'}) ) self.assertSerializedResultEqual( BinaryEnum.A, ("migrations.test_writer.BinaryEnum(b'a-value')", {'import migrations.test_writer'}) ) self.assertSerializedResultEqual( IntEnum.B, ("migrations.test_writer.IntEnum(2)", {'import migrations.test_writer'}) ) field = models.CharField(default=TextEnum.B, choices=[(m.value, m) for m in TextEnum]) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.CharField(choices=[" "('a-value', migrations.test_writer.TextEnum('a-value')), " "('value-b', migrations.test_writer.TextEnum('value-b'))], " "default=migrations.test_writer.TextEnum('value-b'))" ) field = models.CharField(default=BinaryEnum.B, choices=[(m.value, m) for m in BinaryEnum]) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.CharField(choices=[" "(b'a-value', migrations.test_writer.BinaryEnum(b'a-value')), " "(b'value-b', migrations.test_writer.BinaryEnum(b'value-b'))], " "default=migrations.test_writer.BinaryEnum(b'value-b'))" ) field = models.IntegerField(default=IntEnum.A, choices=[(m.value, m) for m in IntEnum]) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.IntegerField(choices=[" "(1, migrations.test_writer.IntEnum(1)), " "(2, migrations.test_writer.IntEnum(2))], " "default=migrations.test_writer.IntEnum(1))" ) def test_serialize_uuid(self): self.assertSerializedEqual(uuid.uuid1()) self.assertSerializedEqual(uuid.uuid4()) uuid_a = uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8') uuid_b = uuid.UUID('c7853ec1-2ea3-4359-b02d-b54e8f1bcee2') self.assertSerializedResultEqual( uuid_a, ("uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8')", {'import uuid'}) ) self.assertSerializedResultEqual( uuid_b, ("uuid.UUID('c7853ec1-2ea3-4359-b02d-b54e8f1bcee2')", {'import uuid'}) ) field = models.UUIDField(choices=((uuid_a, 'UUID A'), (uuid_b, 'UUID B')), default=uuid_a) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.UUIDField(choices=[" "(uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8'), 'UUID A'), " "(uuid.UUID('c7853ec1-2ea3-4359-b02d-b54e8f1bcee2'), 'UUID B')], " "default=uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8'))" ) def test_serialize_functions(self): with self.assertRaisesMessage(ValueError, 'Cannot serialize function: lambda'): self.assertSerializedEqual(lambda x: 42) self.assertSerializedEqual(models.SET_NULL) string, imports = MigrationWriter.serialize(models.SET(42)) self.assertEqual(string, 'models.SET(42)') self.serialize_round_trip(models.SET(42)) def test_serialize_datetime(self): self.assertSerializedEqual(datetime.datetime.utcnow()) self.assertSerializedEqual(datetime.datetime.utcnow) self.assertSerializedEqual(datetime.datetime.today()) self.assertSerializedEqual(datetime.datetime.today) self.assertSerializedEqual(datetime.date.today()) self.assertSerializedEqual(datetime.date.today) self.assertSerializedEqual(datetime.datetime.now().time()) self.assertSerializedEqual(datetime.datetime(2014, 1, 1, 1, 1, tzinfo=get_default_timezone())) self.assertSerializedEqual(datetime.datetime(2013, 12, 31, 22, 1, tzinfo=get_fixed_timezone(180))) self.assertSerializedResultEqual( datetime.datetime(2014, 1, 1, 1, 1), ("datetime.datetime(2014, 1, 1, 1, 1)", {'import datetime'}) ) self.assertSerializedResultEqual( datetime.datetime(2012, 1, 1, 1, 1, tzinfo=utc), ( "datetime.datetime(2012, 1, 1, 1, 1, tzinfo=utc)", {'import datetime', 'from django.utils.timezone import utc'}, ) ) def test_serialize_fields(self): self.assertSerializedFieldEqual(models.CharField(max_length=255)) self.assertSerializedResultEqual( models.CharField(max_length=255), ("models.CharField(max_length=255)", {"from django.db import models"}) ) self.assertSerializedFieldEqual(models.TextField(null=True, blank=True)) self.assertSerializedResultEqual( models.TextField(null=True, blank=True), ("models.TextField(blank=True, null=True)", {'from django.db import models'}) ) def test_serialize_settings(self): self.assertSerializedEqual(SettingsReference(settings.AUTH_USER_MODEL, "AUTH_USER_MODEL")) self.assertSerializedResultEqual( SettingsReference("someapp.model", "AUTH_USER_MODEL"), ("settings.AUTH_USER_MODEL", {"from django.conf import settings"}) ) def test_serialize_iterators(self): self.assertSerializedResultEqual( ((x, x * x) for x in range(3)), ("((0, 0), (1, 1), (2, 4))", set()) ) def test_serialize_compiled_regex(self): """ Make sure compiled regex can be serialized. """ regex = re.compile(r'^\w+$') self.assertSerializedEqual(regex) def test_serialize_class_based_validators(self): """ Ticket #22943: Test serialization of class-based validators, including compiled regexes. """ validator = RegexValidator(message="hello") string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator(message='hello')") self.serialize_round_trip(validator) # Test with a compiled regex. validator = RegexValidator(regex=re.compile(r'^\w+$')) string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator(regex=re.compile('^\\\\w+$'))") self.serialize_round_trip(validator) # Test a string regex with flag validator = RegexValidator(r'^[0-9]+$', flags=re.S) string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator('^[0-9]+$', flags=re.RegexFlag(16))") self.serialize_round_trip(validator) # Test message and code validator = RegexValidator('^[-a-zA-Z0-9_]+$', 'Invalid', 'invalid') string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator('^[-a-zA-Z0-9_]+$', 'Invalid', 'invalid')") self.serialize_round_trip(validator) # Test with a subclass. validator = EmailValidator(message="hello") string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.EmailValidator(message='hello')") self.serialize_round_trip(validator) validator = deconstructible(path="migrations.test_writer.EmailValidator")(EmailValidator)(message="hello") string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "migrations.test_writer.EmailValidator(message='hello')") validator = deconstructible(path="custom.EmailValidator")(EmailValidator)(message="hello") with self.assertRaisesMessage(ImportError, "No module named 'custom'"): MigrationWriter.serialize(validator) validator = deconstructible(path="django.core.validators.EmailValidator2")(EmailValidator)(message="hello") with self.assertRaisesMessage(ValueError, "Could not find object EmailValidator2 in django.core.validators."): MigrationWriter.serialize(validator) def test_serialize_empty_nonempty_tuple(self): """ Ticket #22679: makemigrations generates invalid code for (an empty tuple) default_permissions = () """ empty_tuple = () one_item_tuple = ('a',) many_items_tuple = ('a', 'b', 'c') self.assertSerializedEqual(empty_tuple) self.assertSerializedEqual(one_item_tuple) self.assertSerializedEqual(many_items_tuple) def test_serialize_range(self): string, imports = MigrationWriter.serialize(range(1, 5)) self.assertEqual(string, 'range(1, 5)') self.assertEqual(imports, set()) def test_serialize_builtins(self): string, imports = MigrationWriter.serialize(range) self.assertEqual(string, 'range') self.assertEqual(imports, set()) def test_serialize_unbound_method_reference(self): """An unbound method used within a class body can be serialized.""" self.serialize_round_trip(TestModel1.thing) def test_serialize_local_function_reference(self): """A reference in a local scope can't be serialized.""" class TestModel2: def upload_to(self): return "somewhere dynamic" thing = models.FileField(upload_to=upload_to) with self.assertRaisesMessage(ValueError, 'Could not find function upload_to in migrations.test_writer'): self.serialize_round_trip(TestModel2.thing) def test_serialize_managers(self): self.assertSerializedEqual(models.Manager()) self.assertSerializedResultEqual( FoodQuerySet.as_manager(), ('migrations.models.FoodQuerySet.as_manager()', {'import migrations.models'}) ) self.assertSerializedEqual(FoodManager('a', 'b')) self.assertSerializedEqual(FoodManager('x', 'y', c=3, d=4)) def test_serialize_frozensets(self): self.assertSerializedEqual(frozenset()) self.assertSerializedEqual(frozenset("let it go")) def test_serialize_set(self): self.assertSerializedEqual(set()) self.assertSerializedResultEqual(set(), ('set()', set())) self.assertSerializedEqual({'a'}) self.assertSerializedResultEqual({'a'}, ("{'a'}", set())) def test_serialize_timedelta(self): self.assertSerializedEqual(datetime.timedelta()) self.assertSerializedEqual(datetime.timedelta(minutes=42)) def test_serialize_functools_partial(self): value = functools.partial(datetime.timedelta, 1, seconds=2) result = self.serialize_round_trip(value) self.assertEqual(result.func, value.func) self.assertEqual(result.args, value.args) self.assertEqual(result.keywords, value.keywords) def test_serialize_functools_partialmethod(self): value = functools.partialmethod(datetime.timedelta, 1, seconds=2) result = self.serialize_round_trip(value) self.assertIsInstance(result, functools.partialmethod) self.assertEqual(result.func, value.func) self.assertEqual(result.args, value.args) self.assertEqual(result.keywords, value.keywords) def test_serialize_type_none(self): self.assertSerializedEqual(type(None)) def test_simple_migration(self): """ Tests serializing a simple migration. """ fields = { 'charfield': models.DateTimeField(default=datetime.datetime.utcnow), 'datetimefield': models.DateTimeField(default=datetime.datetime.utcnow), } options = { 'verbose_name': 'My model', 'verbose_name_plural': 'My models', } migration = type("Migration", (migrations.Migration,), { "operations": [ migrations.CreateModel("MyModel", tuple(fields.items()), options, (models.Model,)), migrations.CreateModel("MyModel2", tuple(fields.items()), bases=(models.Model,)), migrations.CreateModel( name="MyModel3", fields=tuple(fields.items()), options=options, bases=(models.Model,) ), migrations.DeleteModel("MyModel"), migrations.AddField("OtherModel", "datetimefield", fields["datetimefield"]), ], "dependencies": [("testapp", "some_other_one")], }) writer = MigrationWriter(migration) output = writer.as_string() # We don't test the output formatting - that's too fragile. # Just make sure it runs for now, and that things look alright. result = self.safe_exec(output) self.assertIn("Migration", result) def test_migration_path(self): test_apps = [ 'migrations.migrations_test_apps.normal', 'migrations.migrations_test_apps.with_package_model', 'migrations.migrations_test_apps.without_init_file', ] base_dir = os.path.dirname(os.path.dirname(__file__)) for app in test_apps: with self.modify_settings(INSTALLED_APPS={'append': app}): migration = migrations.Migration('0001_initial', app.split('.')[-1]) expected_path = os.path.join(base_dir, *(app.split('.') + ['migrations', '0001_initial.py'])) writer = MigrationWriter(migration) self.assertEqual(writer.path, expected_path) def test_custom_operation(self): migration = type("Migration", (migrations.Migration,), { "operations": [ custom_migration_operations.operations.TestOperation(), custom_migration_operations.operations.CreateModel(), migrations.CreateModel("MyModel", (), {}, (models.Model,)), custom_migration_operations.more_operations.TestOperation() ], "dependencies": [] }) writer = MigrationWriter(migration) output = writer.as_string() result = self.safe_exec(output) self.assertIn("custom_migration_operations", result) self.assertNotEqual( result['custom_migration_operations'].operations.TestOperation, result['custom_migration_operations'].more_operations.TestOperation ) def test_sorted_imports(self): """ #24155 - Tests ordering of imports. """ migration = type("Migration", (migrations.Migration,), { "operations": [ migrations.AddField("mymodel", "myfield", models.DateTimeField( default=datetime.datetime(2012, 1, 1, 1, 1, tzinfo=utc), )), ] }) writer = MigrationWriter(migration) output = writer.as_string() self.assertIn( "import datetime\n" "from django.db import migrations, models\n" "from django.utils.timezone import utc\n", output ) def test_migration_file_header_comments(self): """ Test comments at top of file. """ migration = type("Migration", (migrations.Migration,), { "operations": [] }) dt = datetime.datetime(2015, 7, 31, 4, 40, 0, 0, tzinfo=utc) with mock.patch('django.db.migrations.writer.now', lambda: dt): for include_header in (True, False): with self.subTest(include_header=include_header): writer = MigrationWriter(migration, include_header) output = writer.as_string() self.assertEqual( include_header, output.startswith( "# Generated by Django %s on 2015-07-31 04:40\n\n" % get_version() ) ) if not include_header: # Make sure the output starts with something that's not # a comment or indentation or blank line self.assertRegex(output.splitlines(keepends=True)[0], r"^[^#\s]+") def test_models_import_omitted(self): """ django.db.models shouldn't be imported if unused. """ migration = type("Migration", (migrations.Migration,), { "operations": [ migrations.AlterModelOptions( name='model', options={'verbose_name': 'model', 'verbose_name_plural': 'models'}, ), ] }) writer = MigrationWriter(migration) output = writer.as_string() self.assertIn("from django.db import migrations\n", output) def test_deconstruct_class_arguments(self): # Yes, it doesn't make sense to use a class as a default for a # CharField. It does make sense for custom fields though, for example # an enumfield that takes the enum class as an argument. class DeconstructibleInstances: def deconstruct(self): return ('DeconstructibleInstances', [], {}) string = MigrationWriter.serialize(models.CharField(default=DeconstructibleInstances))[0] self.assertEqual(string, "models.CharField(default=migrations.test_writer.DeconstructibleInstances)") def test_register_serializer(self): class ComplexSerializer(BaseSerializer): def serialize(self): return 'complex(%r)' % self.value, {} MigrationWriter.register_serializer(complex, ComplexSerializer) self.assertSerializedEqual(complex(1, 2)) MigrationWriter.unregister_serializer(complex) with self.assertRaisesMessage(ValueError, 'Cannot serialize: (1+2j)'): self.assertSerializedEqual(complex(1, 2)) def test_register_non_serializer(self): with self.assertRaisesMessage(ValueError, "'TestModel1' must inherit from 'BaseSerializer'."): MigrationWriter.register_serializer(complex, TestModel1)

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import datetime import decimal import enum import functools import math import os import re import uuid from unittest import mock import custom_migration_operations.more_operations import custom_migration_operations.operations from django import get_version from django.conf import SettingsReference, settings from django.core.validators import EmailValidator, RegexValidator from django.db import migrations, models from django.db.migrations.serializer import BaseSerializer from django.db.migrations.writer import MigrationWriter, OperationWriter from django.test import SimpleTestCase from django.utils.deconstruct import deconstructible from django.utils.functional import SimpleLazyObject from django.utils.timezone import get_default_timezone, get_fixed_timezone, utc from django.utils.translation import gettext_lazy as _ from .models import FoodManager, FoodQuerySet class Money(decimal.Decimal): def deconstruct(self): return ( '%s.%s' % (self.__class__.__module__, self.__class__.__name__), [str(self)], {} ) class TestModel1: def upload_to(self): return '/somewhere/dynamic/' thing = models.FileField(upload_to=upload_to) class OperationWriterTests(SimpleTestCase): def test_empty_signature(self): operation = custom_migration_operations.operations.TestOperation() buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.TestOperation(\n' '),' ) def test_args_signature(self): operation = custom_migration_operations.operations.ArgsOperation(1, 2) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ArgsOperation(\n' ' arg1=1,\n' ' arg2=2,\n' '),' ) def test_kwargs_signature(self): operation = custom_migration_operations.operations.KwargsOperation(kwarg1=1) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.KwargsOperation(\n' ' kwarg1=1,\n' '),' ) def test_args_kwargs_signature(self): operation = custom_migration_operations.operations.ArgsKwargsOperation(1, 2, kwarg2=4) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ArgsKwargsOperation(\n' ' arg1=1,\n' ' arg2=2,\n' ' kwarg2=4,\n' '),' ) def test_nested_args_signature(self): operation = custom_migration_operations.operations.ArgsOperation( custom_migration_operations.operations.ArgsOperation(1, 2), custom_migration_operations.operations.KwargsOperation(kwarg1=3, kwarg2=4) ) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ArgsOperation(\n' ' arg1=custom_migration_operations.operations.ArgsOperation(\n' ' arg1=1,\n' ' arg2=2,\n' ' ),\n' ' arg2=custom_migration_operations.operations.KwargsOperation(\n' ' kwarg1=3,\n' ' kwarg2=4,\n' ' ),\n' '),' ) def test_multiline_args_signature(self): operation = custom_migration_operations.operations.ArgsOperation("test\n arg1", "test\narg2") buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, "custom_migration_operations.operations.ArgsOperation(\n" " arg1='test\\n arg1',\n" " arg2='test\\narg2',\n" ")," ) def test_expand_args_signature(self): operation = custom_migration_operations.operations.ExpandArgsOperation([1, 2]) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ExpandArgsOperation(\n' ' arg=[\n' ' 1,\n' ' 2,\n' ' ],\n' '),' ) def test_nested_operation_expand_args_signature(self): operation = custom_migration_operations.operations.ExpandArgsOperation( arg=[ custom_migration_operations.operations.KwargsOperation( kwarg1=1, kwarg2=2, ), ] ) buff, imports = OperationWriter(operation, indentation=0).serialize() self.assertEqual(imports, {'import custom_migration_operations.operations'}) self.assertEqual( buff, 'custom_migration_operations.operations.ExpandArgsOperation(\n' ' arg=[\n' ' custom_migration_operations.operations.KwargsOperation(\n' ' kwarg1=1,\n' ' kwarg2=2,\n' ' ),\n' ' ],\n' '),' ) class WriterTests(SimpleTestCase): def safe_exec(self, string, value=None): d = {} try: exec(string, globals(), d) except Exception as e: if value: self.fail("Could not exec %r (from value %r): %s" % (string.strip(), value, e)) else: self.fail("Could not exec %r: %s" % (string.strip(), e)) return d def serialize_round_trip(self, value): string, imports = MigrationWriter.serialize(value) return self.safe_exec("%s\ntest_value_result = %s" % ("\n".join(imports), string), value)['test_value_result'] def assertSerializedEqual(self, value): self.assertEqual(self.serialize_round_trip(value), value) def assertSerializedResultEqual(self, value, target): self.assertEqual(MigrationWriter.serialize(value), target) def assertSerializedFieldEqual(self, value): new_value = self.serialize_round_trip(value) self.assertEqual(value.__class__, new_value.__class__) self.assertEqual(value.max_length, new_value.max_length) self.assertEqual(value.null, new_value.null) self.assertEqual(value.unique, new_value.unique) def test_serialize_numbers(self): self.assertSerializedEqual(1) self.assertSerializedEqual(1.2) self.assertTrue(math.isinf(self.serialize_round_trip(float("inf")))) self.assertTrue(math.isinf(self.serialize_round_trip(float("-inf")))) self.assertTrue(math.isnan(self.serialize_round_trip(float("nan")))) self.assertSerializedEqual(decimal.Decimal('1.3')) self.assertSerializedResultEqual( decimal.Decimal('1.3'), ("Decimal('1.3')", {'from decimal import Decimal'}) ) self.assertSerializedEqual(Money('1.3')) self.assertSerializedResultEqual( Money('1.3'), ("migrations.test_writer.Money('1.3')", {'import migrations.test_writer'}) ) def test_serialize_constants(self): self.assertSerializedEqual(None) self.assertSerializedEqual(True) self.assertSerializedEqual(False) def test_serialize_strings(self): self.assertSerializedEqual(b"foobar") string, imports = MigrationWriter.serialize(b"foobar") self.assertEqual(string, "b'foobar'") self.assertSerializedEqual("föobár") string, imports = MigrationWriter.serialize("foobar") self.assertEqual(string, "'foobar'") def test_serialize_multiline_strings(self): self.assertSerializedEqual(b"foo\nbar") string, imports = MigrationWriter.serialize(b"foo\nbar") self.assertEqual(string, "b'foo\\nbar'") self.assertSerializedEqual("föo\nbár") string, imports = MigrationWriter.serialize("foo\nbar") self.assertEqual(string, "'foo\\nbar'") def test_serialize_collections(self): self.assertSerializedEqual({1: 2}) self.assertSerializedEqual(["a", 2, True, None]) self.assertSerializedEqual({2, 3, "eighty"}) self.assertSerializedEqual({"lalalala": ["yeah", "no", "maybe"]}) self.assertSerializedEqual(_('Hello')) def test_serialize_builtin_types(self): self.assertSerializedEqual([list, tuple, dict, set, frozenset]) self.assertSerializedResultEqual( [list, tuple, dict, set, frozenset], ("[list, tuple, dict, set, frozenset]", set()) ) def test_serialize_lazy_objects(self): pattern = re.compile(r'^foo$') lazy_pattern = SimpleLazyObject(lambda: pattern) self.assertEqual(self.serialize_round_trip(lazy_pattern), pattern) def test_serialize_enums(self): class TextEnum(enum.Enum): A = 'a-value' B = 'value-b' class BinaryEnum(enum.Enum): A = b'a-value' B = b'value-b' class IntEnum(enum.IntEnum): A = 1 B = 2 self.assertSerializedResultEqual( TextEnum.A, ("migrations.test_writer.TextEnum('a-value')", {'import migrations.test_writer'}) ) self.assertSerializedResultEqual( BinaryEnum.A, ("migrations.test_writer.BinaryEnum(b'a-value')", {'import migrations.test_writer'}) ) self.assertSerializedResultEqual( IntEnum.B, ("migrations.test_writer.IntEnum(2)", {'import migrations.test_writer'}) ) field = models.CharField(default=TextEnum.B, choices=[(m.value, m) for m in TextEnum]) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.CharField(choices=[" "('a-value', migrations.test_writer.TextEnum('a-value')), " "('value-b', migrations.test_writer.TextEnum('value-b'))], " "default=migrations.test_writer.TextEnum('value-b'))" ) field = models.CharField(default=BinaryEnum.B, choices=[(m.value, m) for m in BinaryEnum]) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.CharField(choices=[" "(b'a-value', migrations.test_writer.BinaryEnum(b'a-value')), " "(b'value-b', migrations.test_writer.BinaryEnum(b'value-b'))], " "default=migrations.test_writer.BinaryEnum(b'value-b'))" ) field = models.IntegerField(default=IntEnum.A, choices=[(m.value, m) for m in IntEnum]) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.IntegerField(choices=[" "(1, migrations.test_writer.IntEnum(1)), " "(2, migrations.test_writer.IntEnum(2))], " "default=migrations.test_writer.IntEnum(1))" ) def test_serialize_uuid(self): self.assertSerializedEqual(uuid.uuid1()) self.assertSerializedEqual(uuid.uuid4()) uuid_a = uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8') uuid_b = uuid.UUID('c7853ec1-2ea3-4359-b02d-b54e8f1bcee2') self.assertSerializedResultEqual( uuid_a, ("uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8')", {'import uuid'}) ) self.assertSerializedResultEqual( uuid_b, ("uuid.UUID('c7853ec1-2ea3-4359-b02d-b54e8f1bcee2')", {'import uuid'}) ) field = models.UUIDField(choices=((uuid_a, 'UUID A'), (uuid_b, 'UUID B')), default=uuid_a) string = MigrationWriter.serialize(field)[0] self.assertEqual( string, "models.UUIDField(choices=[" "(uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8'), 'UUID A'), " "(uuid.UUID('c7853ec1-2ea3-4359-b02d-b54e8f1bcee2'), 'UUID B')], " "default=uuid.UUID('5c859437-d061-4847-b3f7-e6b78852f8c8'))" ) def test_serialize_functions(self): with self.assertRaisesMessage(ValueError, 'Cannot serialize function: lambda'): self.assertSerializedEqual(lambda x: 42) self.assertSerializedEqual(models.SET_NULL) string, imports = MigrationWriter.serialize(models.SET(42)) self.assertEqual(string, 'models.SET(42)') self.serialize_round_trip(models.SET(42)) def test_serialize_datetime(self): self.assertSerializedEqual(datetime.datetime.utcnow()) self.assertSerializedEqual(datetime.datetime.utcnow) self.assertSerializedEqual(datetime.datetime.today()) self.assertSerializedEqual(datetime.datetime.today) self.assertSerializedEqual(datetime.date.today()) self.assertSerializedEqual(datetime.date.today) self.assertSerializedEqual(datetime.datetime.now().time()) self.assertSerializedEqual(datetime.datetime(2014, 1, 1, 1, 1, tzinfo=get_default_timezone())) self.assertSerializedEqual(datetime.datetime(2013, 12, 31, 22, 1, tzinfo=get_fixed_timezone(180))) self.assertSerializedResultEqual( datetime.datetime(2014, 1, 1, 1, 1), ("datetime.datetime(2014, 1, 1, 1, 1)", {'import datetime'}) ) self.assertSerializedResultEqual( datetime.datetime(2012, 1, 1, 1, 1, tzinfo=utc), ( "datetime.datetime(2012, 1, 1, 1, 1, tzinfo=utc)", {'import datetime', 'from django.utils.timezone import utc'}, ) ) def test_serialize_fields(self): self.assertSerializedFieldEqual(models.CharField(max_length=255)) self.assertSerializedResultEqual( models.CharField(max_length=255), ("models.CharField(max_length=255)", {"from django.db import models"}) ) self.assertSerializedFieldEqual(models.TextField(null=True, blank=True)) self.assertSerializedResultEqual( models.TextField(null=True, blank=True), ("models.TextField(blank=True, null=True)", {'from django.db import models'}) ) def test_serialize_settings(self): self.assertSerializedEqual(SettingsReference(settings.AUTH_USER_MODEL, "AUTH_USER_MODEL")) self.assertSerializedResultEqual( SettingsReference("someapp.model", "AUTH_USER_MODEL"), ("settings.AUTH_USER_MODEL", {"from django.conf import settings"}) ) def test_serialize_iterators(self): self.assertSerializedResultEqual( ((x, x * x) for x in range(3)), ("((0, 0), (1, 1), (2, 4))", set()) ) def test_serialize_compiled_regex(self): regex = re.compile(r'^\w+$') self.assertSerializedEqual(regex) def test_serialize_class_based_validators(self): validator = RegexValidator(message="hello") string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator(message='hello')") self.serialize_round_trip(validator) validator = RegexValidator(regex=re.compile(r'^\w+$')) string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator(regex=re.compile('^\\\\w+$'))") self.serialize_round_trip(validator) validator = RegexValidator(r'^[0-9]+$', flags=re.S) string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator('^[0-9]+$', flags=re.RegexFlag(16))") self.serialize_round_trip(validator) validator = RegexValidator('^[-a-zA-Z0-9_]+$', 'Invalid', 'invalid') string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.RegexValidator('^[-a-zA-Z0-9_]+$', 'Invalid', 'invalid')") self.serialize_round_trip(validator) validator = EmailValidator(message="hello") string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "django.core.validators.EmailValidator(message='hello')") self.serialize_round_trip(validator) validator = deconstructible(path="migrations.test_writer.EmailValidator")(EmailValidator)(message="hello") string = MigrationWriter.serialize(validator)[0] self.assertEqual(string, "migrations.test_writer.EmailValidator(message='hello')") validator = deconstructible(path="custom.EmailValidator")(EmailValidator)(message="hello") with self.assertRaisesMessage(ImportError, "No module named 'custom'"): MigrationWriter.serialize(validator) validator = deconstructible(path="django.core.validators.EmailValidator2")(EmailValidator)(message="hello") with self.assertRaisesMessage(ValueError, "Could not find object EmailValidator2 in django.core.validators."): MigrationWriter.serialize(validator) def test_serialize_empty_nonempty_tuple(self): empty_tuple = () one_item_tuple = ('a',) many_items_tuple = ('a', 'b', 'c') self.assertSerializedEqual(empty_tuple) self.assertSerializedEqual(one_item_tuple) self.assertSerializedEqual(many_items_tuple) def test_serialize_range(self): string, imports = MigrationWriter.serialize(range(1, 5)) self.assertEqual(string, 'range(1, 5)') self.assertEqual(imports, set()) def test_serialize_builtins(self): string, imports = MigrationWriter.serialize(range) self.assertEqual(string, 'range') self.assertEqual(imports, set()) def test_serialize_unbound_method_reference(self): self.serialize_round_trip(TestModel1.thing) def test_serialize_local_function_reference(self): class TestModel2: def upload_to(self): return "somewhere dynamic" thing = models.FileField(upload_to=upload_to) with self.assertRaisesMessage(ValueError, 'Could not find function upload_to in migrations.test_writer'): self.serialize_round_trip(TestModel2.thing) def test_serialize_managers(self): self.assertSerializedEqual(models.Manager()) self.assertSerializedResultEqual( FoodQuerySet.as_manager(), ('migrations.models.FoodQuerySet.as_manager()', {'import migrations.models'}) ) self.assertSerializedEqual(FoodManager('a', 'b')) self.assertSerializedEqual(FoodManager('x', 'y', c=3, d=4)) def test_serialize_frozensets(self): self.assertSerializedEqual(frozenset()) self.assertSerializedEqual(frozenset("let it go")) def test_serialize_set(self): self.assertSerializedEqual(set()) self.assertSerializedResultEqual(set(), ('set()', set())) self.assertSerializedEqual({'a'}) self.assertSerializedResultEqual({'a'}, ("{'a'}", set())) def test_serialize_timedelta(self): self.assertSerializedEqual(datetime.timedelta()) self.assertSerializedEqual(datetime.timedelta(minutes=42)) def test_serialize_functools_partial(self): value = functools.partial(datetime.timedelta, 1, seconds=2) result = self.serialize_round_trip(value) self.assertEqual(result.func, value.func) self.assertEqual(result.args, value.args) self.assertEqual(result.keywords, value.keywords) def test_serialize_functools_partialmethod(self): value = functools.partialmethod(datetime.timedelta, 1, seconds=2) result = self.serialize_round_trip(value) self.assertIsInstance(result, functools.partialmethod) self.assertEqual(result.func, value.func) self.assertEqual(result.args, value.args) self.assertEqual(result.keywords, value.keywords) def test_serialize_type_none(self): self.assertSerializedEqual(type(None)) def test_simple_migration(self): fields = { 'charfield': models.DateTimeField(default=datetime.datetime.utcnow), 'datetimefield': models.DateTimeField(default=datetime.datetime.utcnow), } options = { 'verbose_name': 'My model', 'verbose_name_plural': 'My models', } migration = type("Migration", (migrations.Migration,), { "operations": [ migrations.CreateModel("MyModel", tuple(fields.items()), options, (models.Model,)), migrations.CreateModel("MyModel2", tuple(fields.items()), bases=(models.Model,)), migrations.CreateModel( name="MyModel3", fields=tuple(fields.items()), options=options, bases=(models.Model,) ), migrations.DeleteModel("MyModel"), migrations.AddField("OtherModel", "datetimefield", fields["datetimefield"]), ], "dependencies": [("testapp", "some_other_one")], }) writer = MigrationWriter(migration) output = writer.as_string() result = self.safe_exec(output) self.assertIn("Migration", result) def test_migration_path(self): test_apps = [ 'migrations.migrations_test_apps.normal', 'migrations.migrations_test_apps.with_package_model', 'migrations.migrations_test_apps.without_init_file', ] base_dir = os.path.dirname(os.path.dirname(__file__)) for app in test_apps: with self.modify_settings(INSTALLED_APPS={'append': app}): migration = migrations.Migration('0001_initial', app.split('.')[-1]) expected_path = os.path.join(base_dir, *(app.split('.') + ['migrations', '0001_initial.py'])) writer = MigrationWriter(migration) self.assertEqual(writer.path, expected_path) def test_custom_operation(self): migration = type("Migration", (migrations.Migration,), { "operations": [ custom_migration_operations.operations.TestOperation(), custom_migration_operations.operations.CreateModel(), migrations.CreateModel("MyModel", (), {}, (models.Model,)), custom_migration_operations.more_operations.TestOperation() ], "dependencies": [] }) writer = MigrationWriter(migration) output = writer.as_string() result = self.safe_exec(output) self.assertIn("custom_migration_operations", result) self.assertNotEqual( result['custom_migration_operations'].operations.TestOperation, result['custom_migration_operations'].more_operations.TestOperation ) def test_sorted_imports(self): migration = type("Migration", (migrations.Migration,), { "operations": [ migrations.AddField("mymodel", "myfield", models.DateTimeField( default=datetime.datetime(2012, 1, 1, 1, 1, tzinfo=utc), )), ] }) writer = MigrationWriter(migration) output = writer.as_string() self.assertIn( "import datetime\n" "from django.db import migrations, models\n" "from django.utils.timezone import utc\n", output ) def test_migration_file_header_comments(self): migration = type("Migration", (migrations.Migration,), { "operations": [] }) dt = datetime.datetime(2015, 7, 31, 4, 40, 0, 0, tzinfo=utc) with mock.patch('django.db.migrations.writer.now', lambda: dt): for include_header in (True, False): with self.subTest(include_header=include_header): writer = MigrationWriter(migration, include_header) output = writer.as_string() self.assertEqual( include_header, output.startswith( "# Generated by Django %s on 2015-07-31 04:40\n\n" % get_version() ) ) if not include_header: # a comment or indentation or blank line self.assertRegex(output.splitlines(keepends=True)[0], r"^[^#\s]+") def test_models_import_omitted(self): migration = type("Migration", (migrations.Migration,), { "operations": [ migrations.AlterModelOptions( name='model', options={'verbose_name': 'model', 'verbose_name_plural': 'models'}, ), ] }) writer = MigrationWriter(migration) output = writer.as_string() self.assertIn("from django.db import migrations\n", output) def test_deconstruct_class_arguments(self): # Yes, it doesn't make sense to use a class as a default for a class DeconstructibleInstances: def deconstruct(self): return ('DeconstructibleInstances', [], {}) string = MigrationWriter.serialize(models.CharField(default=DeconstructibleInstances))[0] self.assertEqual(string, "models.CharField(default=migrations.test_writer.DeconstructibleInstances)") def test_register_serializer(self): class ComplexSerializer(BaseSerializer): def serialize(self): return 'complex(%r)' % self.value, {} MigrationWriter.register_serializer(complex, ComplexSerializer) self.assertSerializedEqual(complex(1, 2)) MigrationWriter.unregister_serializer(complex) with self.assertRaisesMessage(ValueError, 'Cannot serialize: (1+2j)'): self.assertSerializedEqual(complex(1, 2)) def test_register_non_serializer(self): with self.assertRaisesMessage(ValueError, "'TestModel1' must inherit from 'BaseSerializer'."): MigrationWriter.register_serializer(complex, TestModel1)

true

790d6b4ad1b4eae37dac46c169794c84eb65e406

6,486

py

Python

tests/openapi/test_validation.py

dreuse/kinto

533037ad421b63419f9883653a428683c67d43b8

[ "Apache-2.0" ]

null

tests/openapi/test_validation.py

dreuse/kinto

533037ad421b63419f9883653a428683c67d43b8

[ "Apache-2.0" ]

null

tests/openapi/test_validation.py

dreuse/kinto

533037ad421b63419f9883653a428683c67d43b8

[ "Apache-2.0" ]

null

from bravado_core.request import IncomingRequest, unmarshal_request from bravado_core.swagger20_validator import ValidationError from .support import OpenAPITest class OpenAPIRequestsValidationTest(OpenAPITest): def setUp(self): super().setUp() self.request = IncomingRequest() self.request.path = {} self.request.headers = {} self.request.query = {} self.request._json = {} self.request.json = lambda: self.request._json def test_validate_bucket_path(self): self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].get_bucket ) def test_validate_groups_path(self): self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Groups"].get_groups ) def test_validate_group_path(self): paths = [{}, {"bucket_id": "b1"}, {"id": "g1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Groups"].get_group, ) def test_validate_collections_path(self): self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Collections"].get_collections, ) def test_validate_collection_path(self): paths = [{}, {"bucket_id": "b1"}, {"id": "c1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Collections"].get_collection, ) def test_validate_records_path(self): paths = [{}, {"bucket_id": "b1"}, {"collection_id": "c1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Records"].get_records, ) def test_validate_record_path(self): paths = [{}, {"bucket_id": "b1", "collection_id": "c1"}, {"id": "r1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Records"].get_record, ) def test_validate_data(self): bodies = [{"data": "aaa"}] for body in bodies: self.request._json = body self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].create_bucket, ) def test_validate_permissions(self): bodies = [ {"permissions": "aaa"}, {"permissions": {"read": "aaa"}}, {"permissions": {"read": [111]}}, ] for body in bodies: self.request._json = body self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].create_bucket, ) def test_validate_queries(self): queries = [{"_since": "aaa"}, {"_before": "aaa"}, {"_limit": "aaa"}, {"_token": {}}] for query in queries: self.request.query = query self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].get_buckets, ) def test_validate_headers(self): headers = [{"If-None-Match": "123"}, {"If-Match": "123"}] for head in headers: self.request.headers = head self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].get_buckets, ) def test_validate_batch_requests_method(self): self.request._json = {"requests": [{"method": "AAA", "path": "/buckets/b1"}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_requests_path(self): self.request._json = {"requests": [{"method": "GET", "path": 123}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_requests_body(self): self.request._json = {"requests": [{"method": "GET", "path": "/buckets/b1", "body": []}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_requests_header(self): self.request._json = { "requests": [{"method": "GET", "path": "/buckets/b1", "body": {}, "headers": []}] } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults(self): self.request._json = { "defaults": [], "requests": [{"method": "GET", "path": "/buckets/b1"}], } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults_method(self): self.request._json = {"defaults": {"method": "AAA"}, "requests": [{"path": "/buckets/b1"}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults_body(self): self.request._json = { "defaults": {"body": []}, "requests": [{"method": "PUT", "path": "/buckets/b1"}], } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults_headers(self): self.request._json = { "defaults": {"headers": []}, "requests": [{"method": "GET", "path": "/buckets/b1"}], } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch )

34.870968

99

0.557046

from bravado_core.request import IncomingRequest, unmarshal_request from bravado_core.swagger20_validator import ValidationError from .support import OpenAPITest class OpenAPIRequestsValidationTest(OpenAPITest): def setUp(self): super().setUp() self.request = IncomingRequest() self.request.path = {} self.request.headers = {} self.request.query = {} self.request._json = {} self.request.json = lambda: self.request._json def test_validate_bucket_path(self): self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].get_bucket ) def test_validate_groups_path(self): self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Groups"].get_groups ) def test_validate_group_path(self): paths = [{}, {"bucket_id": "b1"}, {"id": "g1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Groups"].get_group, ) def test_validate_collections_path(self): self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Collections"].get_collections, ) def test_validate_collection_path(self): paths = [{}, {"bucket_id": "b1"}, {"id": "c1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Collections"].get_collection, ) def test_validate_records_path(self): paths = [{}, {"bucket_id": "b1"}, {"collection_id": "c1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Records"].get_records, ) def test_validate_record_path(self): paths = [{}, {"bucket_id": "b1", "collection_id": "c1"}, {"id": "r1"}] for path in paths: self.request.path = path self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Records"].get_record, ) def test_validate_data(self): bodies = [{"data": "aaa"}] for body in bodies: self.request._json = body self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].create_bucket, ) def test_validate_permissions(self): bodies = [ {"permissions": "aaa"}, {"permissions": {"read": "aaa"}}, {"permissions": {"read": [111]}}, ] for body in bodies: self.request._json = body self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].create_bucket, ) def test_validate_queries(self): queries = [{"_since": "aaa"}, {"_before": "aaa"}, {"_limit": "aaa"}, {"_token": {}}] for query in queries: self.request.query = query self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].get_buckets, ) def test_validate_headers(self): headers = [{"If-None-Match": "123"}, {"If-Match": "123"}] for head in headers: self.request.headers = head self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Buckets"].get_buckets, ) def test_validate_batch_requests_method(self): self.request._json = {"requests": [{"method": "AAA", "path": "/buckets/b1"}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_requests_path(self): self.request._json = {"requests": [{"method": "GET", "path": 123}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_requests_body(self): self.request._json = {"requests": [{"method": "GET", "path": "/buckets/b1", "body": []}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_requests_header(self): self.request._json = { "requests": [{"method": "GET", "path": "/buckets/b1", "body": {}, "headers": []}] } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults(self): self.request._json = { "defaults": [], "requests": [{"method": "GET", "path": "/buckets/b1"}], } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults_method(self): self.request._json = {"defaults": {"method": "AAA"}, "requests": [{"path": "/buckets/b1"}]} self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults_body(self): self.request._json = { "defaults": {"body": []}, "requests": [{"method": "PUT", "path": "/buckets/b1"}], } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch ) def test_validate_batch_defaults_headers(self): self.request._json = { "defaults": {"headers": []}, "requests": [{"method": "GET", "path": "/buckets/b1"}], } self.assertRaises( ValidationError, unmarshal_request, self.request, self.resources["Batch"].batch )

true

790d6bc82351cc457b84198eafb931c2ef75e9b8

17,632

py

Python

tests/bugs/core_5275_test.py

reevespaul/firebird-qa

98f16f425aa9ab8ee63b86172f959d63a2d76f21

[ "MIT" ]

null

tests/bugs/core_5275_test.py

reevespaul/firebird-qa

98f16f425aa9ab8ee63b86172f959d63a2d76f21

[ "MIT" ]

null

tests/bugs/core_5275_test.py

reevespaul/firebird-qa

98f16f425aa9ab8ee63b86172f959d63a2d76f21

[ "MIT" ]

null

#coding:utf-8 # # id: bugs.core_5275 # title: CORE-5275: Expression index may become inconsistent if CREATE INDEX was interrupted after b-tree creation but before commiting # decription: # This test (and CORE- ticket) has been created after wrong initial implementation of test for CORE-1746. # Scenario: # 1. ISQL_1 is launched as child async. process, inserts 1000 rows and then falls in pause (delay) ~10 seconds; # 2. ISQL_2 is launched as child async. process in Tx = WAIT, tries to create index on the table which is handled # by ISQL_1 and immediatelly falls in pause because of waiting for table lock. # 3. ISQL_3 is launched in SYNC mode and does 'DELETE FROM MON$ATTACHMENTS' thus forcing other attachments to be # closed with raising 00803/connection shutdown. # 4. Repeat step 1. On WI-T4.0.0.258 this step lead to: # "invalid SEND request (167), file: JrdStatement.cpp line: 325", 100% reproducilbe. # # Checked on WI-V2.5.6.27017 (SC), WI-V3.0.1.32539 (SS/SC/CS), WI-T4.0.0.262 (SS) - works fine. # # Beside above mentioned steps, we also: # 1) compare content of old/new firebird.log (difference): it should NOT contain line "consistency check"; # 2) run database online validation: it should NOT report any error in the database. # # :::::::::::::::::::::::::::::::::::::::: NB :::::::::::::::::::::::::::::::::::: # 18.08.2020. FB 4.x has incompatible behaviour with all previous versions since build 4.0.0.2131 (06-aug-2020): # statement 'alter sequence <seq_name> restart with 0' changes rdb$generators.rdb$initial_value to -1 thus next call # gen_id(<seq_name>,1) will return 0 (ZERO!) rather than 1. # See also CORE-6084 and its fix: https://github.com/FirebirdSQL/firebird/commit/23dc0c6297825b2e9006f4d5a2c488702091033d # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # This is considered as *expected* and is noted in doc/README.incompatibilities.3to4.txt # # Because of this, it was decided to replace 'alter sequence restart...' with subtraction of two gen values: # c = gen_id(<g>, -gen_id(<g>, 0)) -- see procedure sp_restart_sequences. # # Checked on: # 4.0.0.2164 SS: 15.932s. # 4.0.0.2119 SS: 16.141s. # 4.0.0.2164 CS: 17.549s. # 3.0.7.33356 SS: 17.446s. # 3.0.7.33356 CS: 18.321s. # 2.5.9.27150 SC: 13.768s. # # tracker_id: CORE-5275 # min_versions: ['2.5.6'] # versions: 2.5.6 # qmid: None import pytest from firebird.qa import db_factory, isql_act, Action # version: 2.5.6 # resources: None substitutions_1 = [('0: CREATE INDEX LOG: RDB_EXPR_BLOB.*', '0: CREATE INDEX LOG: RDB_EXPR_BLOB'), ('BULK_INSERT_START.*', 'BULK_INSERT_START'), ('.*KILLED BY DATABASE ADMINISTRATOR.*', ''), ('BULK_INSERT_FINISH.*', 'BULK_INSERT_FINISH'), ('CREATE_INDX_START.*', 'CREATE_INDX_START'), ('AFTER LINE.*', 'AFTER LINE'), ('RECORDS AFFECTED:.*', 'RECORDS AFFECTED:'), ('[0-9][0-9]:[0-9][0-9]:[0-9][0-9].[0-9][0-9]', ''), ('RELATION [0-9]{3,4}', 'RELATION')] init_script_1 = """""" db_1 = db_factory(sql_dialect=3, init=init_script_1) # test_script_1 #--- # import os # import time # import difflib # import subprocess # # os.environ["ISC_USER"] = user_name # os.environ["ISC_PASSWORD"] = user_password # db_file=db_conn.database_name # engine =str(db_conn.engine_version) # # db_conn.close() # # #-------------------------------------------- # # def flush_and_close(file_handle): # # https://docs.python.org/2/library/os.html#os.fsync # # If you're starting with a Python file object f, # # first do f.flush(), and # # then do os.fsync(f.fileno()), to ensure that all internal buffers associated with f are written to disk. # global os # # file_handle.flush() # if file_handle.mode not in ('r', 'rb') and file_handle.name != os.devnull: # # otherwise: "OSError: [Errno 9] Bad file descriptor"! # os.fsync(file_handle.fileno()) # file_handle.close() # # #-------------------------------------------- # # def cleanup( f_names_list ): # global os # for i in range(len( f_names_list )): # if type(f_names_list[i]) == file: # del_name = f_names_list[i].name # elif type(f_names_list[i]) == str: # del_name = f_names_list[i] # else: # print('Unrecognized type of element:', f_names_list[i], ' - can not be treated as file.') # del_name = None # # if del_name and os.path.isfile( del_name ): # os.remove( del_name ) # # #-------------------------------------------- # # def svc_get_fb_log( engine, f_fb_log ): # # import subprocess # # if engine.startswith('2.5'): # get_firebird_log_key='action_get_ib_log' # else: # get_firebird_log_key='action_get_fb_log' # # # C:\\MIX # irebird\\oldfb251in # bsvcmgr localhost:service_mgr -user sysdba -password masterkey action_get_ib_log # subprocess.call([ context['fbsvcmgr_path'], # "localhost:service_mgr", # get_firebird_log_key # ], # stdout=f_fb_log, stderr=subprocess.STDOUT # ) # # return # # sql_ddl=''' # create or alter procedure sp_ins(n int) as begin end; # # recreate table test(x int unique using index test_x, s varchar(10) default 'qwerty' ); # # set term ^; # execute block as # begin # execute statement 'drop sequence g'; # when any do begin end # end # ^ # set term ;^ # commit; # create sequence g; # commit; # # set term ^; # create or alter procedure sp_ins(n int) as # begin # while (n>0) do # begin # insert into test( x ) values( gen_id(g,1) ); # n = n - 1; # end # end # ^ # set term ;^ # commit; # ''' # runProgram('isql',[dsn],sql_ddl) # # f_fblog_before=open( os.path.join(context['temp_directory'],'tmp_5275_fblog_before.txt'), 'w') # svc_get_fb_log( engine, f_fblog_before ) # flush_and_close( f_fblog_before ) # # ######################################################### # # rows_to_add=1000 # # sql_bulk_insert=''' set bail on; # set list on; # # -- DISABLED 19.08.2020: alter sequence g restart with 0; # # delete from test; # commit; # set transaction lock timeout 10; -- THIS LOCK TIMEOUT SERVES ONLY FOR DELAY, see below auton Tx start. # # select current_timestamp as bulk_insert_start from rdb$database; # set term ^; # execute block as # declare i int; # begin # i = gen_id(g, -gen_id(g, 0)); -- restart sequence, since 19.08.2020 # execute procedure sp_ins( %(rows_to_add)s ); # begin # -- ######################################################### # -- ####################### D E L A Y ##################### # -- ######################################################### # in autonomous transaction do # insert into test( x ) values( %(rows_to_add)s ); -- this will cause delay because of duplicate in index # when any do # begin # i = gen_id(g,1); # end # end # end # ^ # set term ;^ # commit; # select current_timestamp as bulk_insert_finish from rdb$database; # ''' # # sql_create_indx=''' set bail on; # set list on; # set blob all; # select # iif( gen_id(g,0) > 0 and gen_id(g,0) < 1 + %(rows_to_add)s, # 'OK, IS RUNNING', # iif( gen_id(g,0) <=0, # 'WRONG: not yet started, current gen_id='||gen_id(g,0), # 'WRONG: already finished, rows_to_add='||%(rows_to_add)s ||', current gen_id='||gen_id(g,0) # ) # ) as inserts_state, # current_timestamp as create_indx_start # from rdb$database; # set autoddl off; # commit; # # set echo on; # set transaction %(tx_decl)s; # # create index test_%(idx_name)s on test computed by( %(idx_expr)s ); # set echo off; # commit; # # select # iif( gen_id(g,0) >= 1 + %(rows_to_add)s, # 'OK, FINISHED', # 'SOMETHING WRONG: current gen_id=' || gen_id(g,0)||', rows_to_add='||%(rows_to_add)s # ) as inserts_state # from rdb$database; # # set count on; # select # rdb$index_name # ,coalesce(rdb$unique_flag,0) as rdb$unique_flag # ,coalesce(rdb$index_inactive,0) as rdb$index_inactive # ,rdb$expression_source as rdb_expr_blob # from rdb$indices ri # where ri.rdb$index_name = upper( 'test_%(idx_name)s' ) # ; # set count off; # set echo on; # set plan on; # select 1 from test where %(idx_expr)s > '' rows 0; # set plan off; # set echo off; # commit; # drop index test_%(idx_name)s; # commit; # ''' # # sql_kill_att=''' set count on; # set list on; # commit; # delete from mon$attachments where mon$attachment_id<>current_connection; # ''' # # f_kill_att_sql = open( os.path.join(context['temp_directory'],'tmp_5275_kill_att.sql' ), 'w') # f_kill_att_sql.write( sql_kill_att ) # flush_and_close( f_kill_att_sql ) # # tx_param=['WAIT','WAIT'] # # for i in range(len(tx_param)): # # f_bulk_insert_sql = open( os.path.join(context['temp_directory'],'tmp_5275_ins.sql'), 'w') # f_bulk_insert_sql.write(sql_bulk_insert % locals() ) # flush_and_close( f_bulk_insert_sql ) # # tx_decl=tx_param[i] # idx_name=tx_decl.replace(' ','_') # idx_expr="'"+idx_name+"'|| s" # # f_create_indx_sql = open( os.path.join(context['temp_directory'],'tmp_5275_idx_%s.sql' % str(i) ), 'w') # f_create_indx_sql.write( sql_create_indx % locals() ) # flush_and_close( f_create_indx_sql ) # # f_bulk_insert_log = open( os.path.join(context['temp_directory'],'tmp_5275_ins_%s.log' % str(i) ), 'w') # f_create_indx_log = open( os.path.join(context['temp_directory'],'tmp_5275_idx_%s.log' % str(i) ), 'w') # # p_bulk_insert=subprocess.Popen( [context['isql_path'], dsn, "-q", "-i", f_bulk_insert_sql.name ], # stdout = f_bulk_insert_log, # stderr = subprocess.STDOUT # ) # # # 3.0 Classic: seems that it requires at least 2 seconds for ISQL be loaded into memory. # time.sleep(2) # # p_create_indx=subprocess.Popen( [context['isql_path'], dsn, "-q", "-i", f_create_indx_sql.name ], # stdout = f_create_indx_log, # stderr = subprocess.STDOUT # ) # time.sleep(2) # # f_kill_att_log = open( os.path.join(context['temp_directory'],'tmp_5275_kill_att.log' ), 'w') # # subprocess.call( [context['isql_path'], dsn, "-q", "-i", f_kill_att_sql.name ], # stdout = f_kill_att_log, # stderr = subprocess.STDOUT # ) # flush_and_close( f_kill_att_log ) # # # 11.05.2017, FB 4.0 only! # # Following messages can appear after 'connection shutdown' # # (letter from dimitr, 08-may-2017 20:41): # # isc_att_shut_killed: Killed by database administrator # # isc_att_shut_idle: Idle timeout expired # # isc_att_shut_db_down: Database is shutdown # # isc_att_shut_engine: Engine is shutdown # # # do NOT remove this delay, otherwise ISQL logs in 2.5.x will contain NO text with error message # # STATEMENT FAILED, SQLSTATE = 08003 / CONNECTION SHUTDOWN: # time.sleep(1) # # p_create_indx.terminate() # p_bulk_insert.terminate() # # flush_and_close( f_bulk_insert_log ) # flush_and_close( f_create_indx_log ) # # with open( f_bulk_insert_log.name,'r') as f: # for line in f: # if line.split(): # print( str(i)+': BULK INSERTS LOG: '+line.strip().upper() ) # # with open( f_create_indx_log.name,'r') as f: # for line in f: # if line.split(): # print( str(i)+': CREATE INDEX LOG: '+line.strip().upper() ) # # with open( f_kill_att_log.name,'r') as f: # for line in f: # if line.split(): # print( str(i)+': KILL ATTACH LOG: '+line.upper() ) # # # cleanup (nitermediate): # ######### # time.sleep(1) # cleanup( (f_bulk_insert_sql, f_create_indx_sql, f_bulk_insert_log, f_create_indx_log, f_kill_att_log) ) # # # ------------------------------------------------------------------------------------------ # # f_fblog_after=open( os.path.join(context['temp_directory'],'tmp_5275_fblog_after.txt'), 'w') # svc_get_fb_log( engine, f_fblog_after ) # flush_and_close( f_fblog_after ) # # # Now we can compare two versions of firebird.log and check their difference. # ################################# # # oldfb=open(f_fblog_before.name, 'r') # newfb=open(f_fblog_after.name, 'r') # # difftext = ''.join(difflib.unified_diff( # oldfb.readlines(), # newfb.readlines() # )) # oldfb.close() # newfb.close() # # f_diff_txt=open( os.path.join(context['temp_directory'],'tmp_5275_diff.txt'), 'w') # f_diff_txt.write(difftext) # flush_and_close( f_diff_txt ) # # # This should be empty: # ####################### # with open( f_diff_txt.name,'r') as f: # for line in f: # # internal Firebird consistency check (invalid SEND request (167), file: JrdStatement.cpp line: 325) # if 'consistency check' in line: # print('NEW IN FIREBIRD.LOG: '+line.upper()) # # # #-------------------------------------------------------------------------------------------- # # f_validate_log=open( os.path.join(context['temp_directory'],'tmp_5275_validate.log'), "w") # f_validate_err=open( os.path.join(context['temp_directory'],'tmp_5275_validate.err'), "w") # # subprocess.call([context['fbsvcmgr_path'],"localhost:service_mgr", # "action_validate", # "dbname", "$(DATABASE_LOCATION)bugs.core_5275.fdb" # ], # stdout=f_validate_log, # stderr=f_validate_err) # flush_and_close( f_validate_log ) # flush_and_close( f_validate_err ) # # with open( f_validate_log.name,'r') as f: # for line in f: # if line.split(): # print( 'VALIDATION STDOUT: '+line.upper() ) # # with open( f_validate_err.name,'r') as f: # for line in f: # if line.split(): # print( 'VALIDATION STDERR: '+line.upper() ) # # # cleanup # ######### # time.sleep(1) # cleanup( (f_validate_log, f_validate_err, f_kill_att_sql, f_fblog_before, f_fblog_after, f_diff_txt) ) # #--- #act_1 = python_act('db_1', test_script_1, substitutions=substitutions_1) expected_stdout_1 = """ 0: BULK INSERTS LOG: BULK_INSERT_START 0: BULK INSERTS LOG: STATEMENT FAILED, SQLSTATE = 08003 0: BULK INSERTS LOG: CONNECTION SHUTDOWN 0: BULK INSERTS LOG: AFTER LINE 0: CREATE INDEX LOG: INSERTS_STATE OK, IS RUNNING 0: CREATE INDEX LOG: CREATE_INDX_START 0: CREATE INDEX LOG: SET TRANSACTION WAIT; 0: CREATE INDEX LOG: CREATE INDEX TEST_WAIT ON TEST COMPUTED BY( 'WAIT'|| S ); 0: CREATE INDEX LOG: SET ECHO OFF; 0: CREATE INDEX LOG: STATEMENT FAILED, SQLSTATE = 08003 0: CREATE INDEX LOG: CONNECTION SHUTDOWN 0: CREATE INDEX LOG: AFTER LINE 0: KILL ATTACH LOG: RECORDS AFFECTED: 1: BULK INSERTS LOG: BULK_INSERT_START 1: BULK INSERTS LOG: STATEMENT FAILED, SQLSTATE = 08003 1: BULK INSERTS LOG: CONNECTION SHUTDOWN 1: BULK INSERTS LOG: AFTER LINE 1: CREATE INDEX LOG: INSERTS_STATE OK, IS RUNNING 1: CREATE INDEX LOG: CREATE_INDX_START 1: CREATE INDEX LOG: SET TRANSACTION WAIT; 1: CREATE INDEX LOG: CREATE INDEX TEST_WAIT ON TEST COMPUTED BY( 'WAIT'|| S ); 1: CREATE INDEX LOG: SET ECHO OFF; 1: CREATE INDEX LOG: STATEMENT FAILED, SQLSTATE = 08003 1: CREATE INDEX LOG: CONNECTION SHUTDOWN 1: CREATE INDEX LOG: AFTER LINE 1: KILL ATTACH LOG: RECORDS AFFECTED: VALIDATION STDOUT: 20:05:26.86 VALIDATION STARTED VALIDATION STDOUT: 20:05:26.86 RELATION 128 (TEST) VALIDATION STDOUT: 20:05:26.86 PROCESS POINTER PAGE 0 OF 1 VALIDATION STDOUT: 20:05:26.86 INDEX 1 (TEST_X) VALIDATION STDOUT: 20:05:26.86 RELATION 128 (TEST) IS OK VALIDATION STDOUT: 20:05:26.86 VALIDATION FINISHED """ @pytest.mark.version('>=2.5.6') @pytest.mark.xfail def test_1(db_1): pytest.fail("Test not IMPLEMENTED")

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import pytest from firebird.qa import db_factory, isql_act, Action substitutions_1 = [('0: CREATE INDEX LOG: RDB_EXPR_BLOB.*', '0: CREATE INDEX LOG: RDB_EXPR_BLOB'), ('BULK_INSERT_START.*', 'BULK_INSERT_START'), ('.*KILLED BY DATABASE ADMINISTRATOR.*', ''), ('BULK_INSERT_FINISH.*', 'BULK_INSERT_FINISH'), ('CREATE_INDX_START.*', 'CREATE_INDX_START'), ('AFTER LINE.*', 'AFTER LINE'), ('RECORDS AFFECTED:.*', 'RECORDS AFFECTED:'), ('[0-9][0-9]:[0-9][0-9]:[0-9][0-9].[0-9][0-9]', ''), ('RELATION [0-9]{3,4}', 'RELATION')] init_script_1 = """""" db_1 = db_factory(sql_dialect=3, init=init_script_1) global os # # file_handle.flush() # if file_handle.mode not in ('r', 'rb') and file_handle.name != os.devnull: # # otherwise: "OSError: [Errno 9] Bad file descriptor"! # os.fsync(file_handle.fileno()) # file_handle.close() # # #-------------------------------------------- # # def cleanup( f_names_list ): # global os # for i in range(len( f_names_list )): # if type(f_names_list[i]) == file: # del_name = f_names_list[i].name # elif type(f_names_list[i]) == str: # del_name = f_names_list[i] # else: # print('Unrecognized type of element:', f_names_list[i], ' - can not be treated as file.') # del_name = None # # if del_name and os.path.isfile( del_name ): # os.remove( del_name ) # # #-------------------------------------------- # # def svc_get_fb_log( engine, f_fb_log ): # # import subprocess # # if engine.startswith('2.5'): # get_firebird_log_key='action_get_ib_log' # else: # get_firebird_log_key='action_get_fb_log' # # # C:\\MIX # irebird\\oldfb251in # bsvcmgr localhost:service_mgr -user sysdba -password masterkey action_get_ib_log # subprocess.call([ context['fbsvcmgr_path'], # "localhost:service_mgr", # get_firebird_log_key # ], # stdout=f_fb_log, stderr=subprocess.STDOUT # ) # # return # # sql_ddl=''' # create or alter procedure sp_ins(n int) as begin end; # # recreate table test(x int unique using index test_x, s varchar(10) default 'qwerty' ); # # set term ^; # execute block as # begin # execute statement 'drop sequence g'; # when any do begin end # end # ^ # set term ;^ # commit; # create sequence g; # commit; # # set term ^; # create or alter procedure sp_ins(n int) as # begin # while (n>0) do # begin # insert into test( x ) values( gen_id(g,1) ); # n = n - 1; # end # end # ^ # set term ;^ # commit; # ''' # runProgram('isql',[dsn],sql_ddl) # # f_fblog_before=open( os.path.join(context['temp_directory'],'tmp_5275_fblog_before.txt'), 'w') # svc_get_fb_log( engine, f_fblog_before ) # flush_and_close( f_fblog_before ) # # ######################################################### # # rows_to_add=1000 # # sql_bulk_insert=''' set bail on; # set list on; # # -- DISABLED 19.08.2020: alter sequence g restart with 0; # # delete from test; # commit; # set transaction lock timeout 10; -- THIS LOCK TIMEOUT SERVES ONLY FOR DELAY, see below auton Tx start. # # select current_timestamp as bulk_insert_start from rdb$database; # set term ^; # execute block as # declare i int; # begin # i = gen_id(g, -gen_id(g, 0)); -- restart sequence, since 19.08.2020 # execute procedure sp_ins( %(rows_to_add)s ); # begin # -- ######################################################### # -- ####################### D E L A Y ##################### # -- ######################################################### # in autonomous transaction do # insert into test( x ) values( %(rows_to_add)s ); -- this will cause delay because of duplicate in index # when any do # begin # i = gen_id(g,1); # end # end # end # ^ # set term ;^ # commit; # select current_timestamp as bulk_insert_finish from rdb$database; # ''' # # sql_create_indx=''' set bail on; # set list on; # set blob all; # select # iif( gen_id(g,0) > 0 and gen_id(g,0) < 1 + %(rows_to_add)s, # 'OK, IS RUNNING', # iif( gen_id(g,0) <=0, # 'WRONG: not yet started, current gen_id='||gen_id(g,0), # 'WRONG: already finished, rows_to_add='||%(rows_to_add)s ||', current gen_id='||gen_id(g,0) # ) # ) as inserts_state, # current_timestamp as create_indx_start # from rdb$database; # set autoddl off; # commit; # # set echo on; # set transaction %(tx_decl)s; # # create index test_%(idx_name)s on test computed by( %(idx_expr)s ); # set echo off; # commit; # # select # iif( gen_id(g,0) >= 1 + %(rows_to_add)s, # 'OK, FINISHED', # 'SOMETHING WRONG: current gen_id=' || gen_id(g,0)||', rows_to_add='||%(rows_to_add)s # ) as inserts_state # from rdb$database; # # set count on; # select # rdb$index_name # ,coalesce(rdb$unique_flag,0) as rdb$unique_flag # ,coalesce(rdb$index_inactive,0) as rdb$index_inactive # ,rdb$expression_source as rdb_expr_blob # from rdb$indices ri # where ri.rdb$index_name = upper( 'test_%(idx_name)s' ) # ; # set count off; # set echo on; # set plan on; # select 1 from test where %(idx_expr)s > '' rows 0; # set plan off; # set echo off; # commit; # drop index test_%(idx_name)s; # commit; # ''' # # sql_kill_att=''' set count on; # set list on; # commit; # delete from mon$attachments where mon$attachment_id<>current_connection; # ''' # # f_kill_att_sql = open( os.path.join(context['temp_directory'],'tmp_5275_kill_att.sql' ), 'w') # f_kill_att_sql.write( sql_kill_att ) # flush_and_close( f_kill_att_sql ) # # tx_param=['WAIT','WAIT'] # # for i in range(len(tx_param)): # # f_bulk_insert_sql = open( os.path.join(context['temp_directory'],'tmp_5275_ins.sql'), 'w') # f_bulk_insert_sql.write(sql_bulk_insert % locals() ) # flush_and_close( f_bulk_insert_sql ) # # tx_decl=tx_param[i] # idx_name=tx_decl.replace(' ','_') # idx_expr="'"+idx_name+"'|| s" # # f_create_indx_sql = open( os.path.join(context['temp_directory'],'tmp_5275_idx_%s.sql' % str(i) ), 'w') # f_create_indx_sql.write( sql_create_indx % locals() ) # flush_and_close( f_create_indx_sql ) # # f_bulk_insert_log = open( os.path.join(context['temp_directory'],'tmp_5275_ins_%s.log' % str(i) ), 'w') # f_create_indx_log = open( os.path.join(context['temp_directory'],'tmp_5275_idx_%s.log' % str(i) ), 'w') # # p_bulk_insert=subprocess.Popen( [context['isql_path'], dsn, "-q", "-i", f_bulk_insert_sql.name ], # stdout = f_bulk_insert_log, # stderr = subprocess.STDOUT # ) # # # 3.0 Classic: seems that it requires at least 2 seconds for ISQL be loaded into memory. # time.sleep(2) # # p_create_indx=subprocess.Popen( [context['isql_path'], dsn, "-q", "-i", f_create_indx_sql.name ], # stdout = f_create_indx_log, # stderr = subprocess.STDOUT # ) # time.sleep(2) # # f_kill_att_log = open( os.path.join(context['temp_directory'],'tmp_5275_kill_att.log' ), 'w') # # subprocess.call( [context['isql_path'], dsn, "-q", "-i", f_kill_att_sql.name ], # stdout = f_kill_att_log, # stderr = subprocess.STDOUT # ) # flush_and_close( f_kill_att_log ) # # # 11.05.2017, FB 4.0 only! # # Following messages can appear after 'connection shutdown' # # (letter from dimitr, 08-may-2017 20:41): # # isc_att_shut_killed: Killed by database administrator # # isc_att_shut_idle: Idle timeout expired # # isc_att_shut_db_down: Database is shutdown # # isc_att_shut_engine: Engine is shutdown # # # do NOT remove this delay, otherwise ISQL logs in 2.5.x will contain NO text with error message # # STATEMENT FAILED, SQLSTATE = 08003 / CONNECTION SHUTDOWN: # time.sleep(1) # # p_create_indx.terminate() # p_bulk_insert.terminate() # # flush_and_close( f_bulk_insert_log ) # flush_and_close( f_create_indx_log ) # # with open( f_bulk_insert_log.name,'r') as f: # for line in f: # if line.split(): # print( str(i)+': BULK INSERTS LOG: '+line.strip().upper() ) # # with open( f_create_indx_log.name,'r') as f: # for line in f: # if line.split(): # print( str(i)+': CREATE INDEX LOG: '+line.strip().upper() ) # # with open( f_kill_att_log.name,'r') as f: # for line in f: # if line.split(): # print( str(i)+': KILL ATTACH LOG: '+line.upper() ) # # # cleanup (nitermediate): # ######### # time.sleep(1) # cleanup( (f_bulk_insert_sql, f_create_indx_sql, f_bulk_insert_log, f_create_indx_log, f_kill_att_log) ) # # # ------------------------------------------------------------------------------------------ # # f_fblog_after=open( os.path.join(context['temp_directory'],'tmp_5275_fblog_after.txt'), 'w') # svc_get_fb_log( engine, f_fblog_after ) # flush_and_close( f_fblog_after ) # # # Now we can compare two versions of firebird.log and check their difference. # ################################# # # oldfb=open(f_fblog_before.name, 'r') # newfb=open(f_fblog_after.name, 'r') # # difftext = ''.join(difflib.unified_diff( # oldfb.readlines(), # newfb.readlines() # )) # oldfb.close() # newfb.close() # # f_diff_txt=open( os.path.join(context['temp_directory'],'tmp_5275_diff.txt'), 'w') # f_diff_txt.write(difftext) # flush_and_close( f_diff_txt ) # # # This should be empty: # ####################### # with open( f_diff_txt.name,'r') as f: # for line in f: # # internal Firebird consistency check (invalid SEND request (167), file: JrdStatement.cpp line: 325) # if 'consistency check' in line: # print('NEW IN FIREBIRD.LOG: '+line.upper()) # # # #-------------------------------------------------------------------------------------------- # # f_validate_log=open( os.path.join(context['temp_directory'],'tmp_5275_validate.log'), "w") # f_validate_err=open( os.path.join(context['temp_directory'],'tmp_5275_validate.err'), "w") # # subprocess.call([context['fbsvcmgr_path'],"localhost:service_mgr", # "action_validate", # "dbname", "$(DATABASE_LOCATION)bugs.core_5275.fdb" # ], # stdout=f_validate_log, # stderr=f_validate_err) # flush_and_close( f_validate_log ) # flush_and_close( f_validate_err ) # # with open( f_validate_log.name,'r') as f: # for line in f: # if line.split(): # print( 'VALIDATION STDOUT: '+line.upper() ) # # with open( f_validate_err.name,'r') as f: # for line in f: # if line.split(): # print( 'VALIDATION STDERR: '+line.upper() ) # # # cleanup # ######### # time.sleep(1) # cleanup( (f_validate_log, f_validate_err, f_kill_att_sql, f_fblog_before, f_fblog_after, f_diff_txt) ) # #--- #act_1 = python_act('db_1', test_script_1, substitutions=substitutions_1) expected_stdout_1 = """ 0: BULK INSERTS LOG: BULK_INSERT_START 0: BULK INSERTS LOG: STATEMENT FAILED, SQLSTATE = 08003 0: BULK INSERTS LOG: CONNECTION SHUTDOWN 0: BULK INSERTS LOG: AFTER LINE 0: CREATE INDEX LOG: INSERTS_STATE OK, IS RUNNING 0: CREATE INDEX LOG: CREATE_INDX_START 0: CREATE INDEX LOG: SET TRANSACTION WAIT; 0: CREATE INDEX LOG: CREATE INDEX TEST_WAIT ON TEST COMPUTED BY( 'WAIT'|| S ); 0: CREATE INDEX LOG: SET ECHO OFF; 0: CREATE INDEX LOG: STATEMENT FAILED, SQLSTATE = 08003 0: CREATE INDEX LOG: CONNECTION SHUTDOWN 0: CREATE INDEX LOG: AFTER LINE 0: KILL ATTACH LOG: RECORDS AFFECTED: 1: BULK INSERTS LOG: BULK_INSERT_START 1: BULK INSERTS LOG: STATEMENT FAILED, SQLSTATE = 08003 1: BULK INSERTS LOG: CONNECTION SHUTDOWN 1: BULK INSERTS LOG: AFTER LINE 1: CREATE INDEX LOG: INSERTS_STATE OK, IS RUNNING 1: CREATE INDEX LOG: CREATE_INDX_START 1: CREATE INDEX LOG: SET TRANSACTION WAIT; 1: CREATE INDEX LOG: CREATE INDEX TEST_WAIT ON TEST COMPUTED BY( 'WAIT'|| S ); 1: CREATE INDEX LOG: SET ECHO OFF; 1: CREATE INDEX LOG: STATEMENT FAILED, SQLSTATE = 08003 1: CREATE INDEX LOG: CONNECTION SHUTDOWN 1: CREATE INDEX LOG: AFTER LINE 1: KILL ATTACH LOG: RECORDS AFFECTED: VALIDATION STDOUT: 20:05:26.86 VALIDATION STARTED VALIDATION STDOUT: 20:05:26.86 RELATION 128 (TEST) VALIDATION STDOUT: 20:05:26.86 PROCESS POINTER PAGE 0 OF 1 VALIDATION STDOUT: 20:05:26.86 INDEX 1 (TEST_X) VALIDATION STDOUT: 20:05:26.86 RELATION 128 (TEST) IS OK VALIDATION STDOUT: 20:05:26.86 VALIDATION FINISHED """ @pytest.mark.version('>=2.5.6') @pytest.mark.xfail def test_1(db_1): pytest.fail("Test not IMPLEMENTED")

true

790d6c64277cf767cc545c71a99683f5f5160fa9

962

py

Python

saas/backend/debug/urls.py

nannan00/bk-iam-saas

217600fa6e5fd466fff9c33c20c4dbd7c69f77d9

[ "MIT" ]

7

2021-08-13T03:48:16.000Z

2021-12-20T15:31:38.000Z

saas/backend/debug/urls.py

nannan00/bk-iam-saas

217600fa6e5fd466fff9c33c20c4dbd7c69f77d9

[ "MIT" ]

456

2021-08-16T02:13:57.000Z

2022-03-30T10:02:49.000Z

saas/backend/debug/urls.py

nannan00/bk-iam-saas

217600fa6e5fd466fff9c33c20c4dbd7c69f77d9

[ "MIT" ]

17

2021-08-10T04:08:46.000Z

2022-03-14T14:24:36.000Z

# -*- coding: utf-8 -*- """ TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-权限中心(BlueKing-IAM) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from django.urls import path from . import views urlpatterns = [ path("", views.DebugViewSet.as_view({"get": "list"}), name="debug.list_debug"), path("<str:id>/", views.DebugViewSet.as_view({"get": "retrieve"}), name="debug.detail"), ]

50.631579

115

0.754678

from django.urls import path from . import views urlpatterns = [ path("", views.DebugViewSet.as_view({"get": "list"}), name="debug.list_debug"), path("<str:id>/", views.DebugViewSet.as_view({"get": "retrieve"}), name="debug.detail"), ]

true

790d6cb3a155ec439cad0318ff3fa9dce5e228ef

267

py

Python

pylint_django_translations/plugin.py

troyjfarrell/pylint_django_translations

b6c5349379024cdc5445499229bc31330591049a

[ "BSD-3-Clause" ]

null

pylint_django_translations/plugin.py

troyjfarrell/pylint_django_translations

b6c5349379024cdc5445499229bc31330591049a

[ "BSD-3-Clause" ]

null

pylint_django_translations/plugin.py

troyjfarrell/pylint_django_translations

b6c5349379024cdc5445499229bc31330591049a

[ "BSD-3-Clause" ]

null

"Plugin registration" from pylint.lint import PyLinter from .checkers import register_checkers from .suppression import suppress_warnings def register(linter: PyLinter) -> None: "Register the plugin" register_checkers(linter) suppress_warnings(linter)

22.25

42

0.790262

from pylint.lint import PyLinter from .checkers import register_checkers from .suppression import suppress_warnings def register(linter: PyLinter) -> None: register_checkers(linter) suppress_warnings(linter)

true

790d6e47e1a19163d57d299b3382141bc440e8c5

11,598

py

Python

Cinder/Ocata/extend/fc_zone_helper.py

doubletao318/New

1be04d22592af4150a58129e4169d2ea1df25379

[ "Apache-2.0" ]

14

2019-05-25T01:55:50.000Z

2021-02-23T06:54:06.000Z

Cinder/Ocata/extend/fc_zone_helper.py

doubletao318/New

1be04d22592af4150a58129e4169d2ea1df25379

[ "Apache-2.0" ]

4

2019-12-31T08:46:30.000Z

2021-10-30T09:27:58.000Z

Cinder/Ocata/extend/fc_zone_helper.py

doubletao318/New

1be04d22592af4150a58129e4169d2ea1df25379

[ "Apache-2.0" ]

17

2019-07-31T03:13:07.000Z

2022-02-21T08:09:15.000Z

# Copyright (c) 2015 Huawei Technologies Co., Ltd. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from oslo_config import cfg from oslo_log import log as logging from oslo_utils import excutils from oslo_utils import importutils from cinder import exception from cinder.i18n import _ from cinder.i18n import _LE from cinder.i18n import _LI from cinder.volume import configuration as config from cinder.zonemanager import utils as fczm_utils LOG = logging.getLogger(__name__) controller_list = ['A', 'B', 'C', 'D'] zone_manager_opts = [ cfg.StrOpt('zone_driver', default='cinder.zonemanager.drivers.brocade.brcd_fc_zone_driver' '.BrcdFCZoneDriver', help='FC Zone Driver responsible for zone management') ] class FCZoneHelper(object): """FC zone helper for Huawei driver.""" def __init__(self, zm, client): self.zm = zm self.client = client def _check_fc_port_and_init(self, wwns, hostid, fabric_map, nsinfos): """Check FC port on array and wwn on host is connected to switch. If no FC port on array is connected to switch or no ini on host is connected to switch, raise a error. """ if not fabric_map: msg = _('No FC port on array is connected to switch.') LOG.error(msg) raise exception.CinderException(msg) no_wwn_connected_to_switch = True for wwn in wwns: formatted_initiator = fczm_utils.get_formatted_wwn(wwn) for fabric in fabric_map: nsinfo = nsinfos[fabric] if formatted_initiator in nsinfo: no_wwn_connected_to_switch = False self.client.ensure_fc_initiator_added(wwn, hostid) break if no_wwn_connected_to_switch: msg = _('No wwn on host is connected to switch.') LOG.error(msg) raise exception.CinderException(msg) def build_ini_tgt_map(self, wwns, host_id, port_list, is_add): fabric_map = self.zm.get_san_context(port_list) nsinfos = {} cfgmap_from_fabrics = {} for fabric in fabric_map: nsinfos[fabric] = self._get_nameserver_info(fabric) cfgmap_from_fabric = self._get_active_zone_set(fabric) cfgmap_from_fabrics[fabric] = cfgmap_from_fabric self._check_fc_port_and_init(wwns, host_id, fabric_map, nsinfos) return self._build_ini_tgt_map(wwns, is_add, nsinfos, cfgmap_from_fabrics) def _build_ini_tgt_map(self, wwns, need_add_con, nsinfos, cfgmap_from_fabrics): tgt_port_wwns = [] init_targ_map_total = {} fabric_maps = {} for contr in controller_list: port_list_from_contr = self.client.get_fc_ports_from_contr(contr) if port_list_from_contr: fabric_map = self.zm.get_san_context(port_list_from_contr) fabric_maps[contr] = fabric_map for wwn in wwns: init_targ_map = {} tmp_port_list = [] tgt_port_for_map = [] tmp_flag = False need_new_zone = False for contr in fabric_maps: (fc_port_for_zone, tmp_flag) = \ self._get_one_fc_port_for_zone(wwn, contr, nsinfos, cfgmap_from_fabrics, fabric_maps) if tmp_flag: need_new_zone = True if fc_port_for_zone: tgt_port_wwns.append(fc_port_for_zone) if not tmp_flag: tgt_port_for_map.append(fc_port_for_zone) if tmp_flag: tmp_port_list.append(fc_port_for_zone) init_targ_map[wwn] = tmp_port_list LOG.debug("tmp_port_list: %s" % tmp_port_list) init_targ_map_total[wwn] = tgt_port_for_map if need_new_zone and need_add_con: LOG.debug("Got init_targ_map to create zone: %s" % init_targ_map) self.zm.add_connection(init_targ_map) tgt_port_wwns = list(set(tgt_port_wwns)) return (tgt_port_wwns, init_targ_map_total) def _get_fabric_vendor(self): zone_config = config.Configuration(zone_manager_opts, 'fc-zone-manager') fabric_driver = zone_config.zone_driver LOG.debug('Using fabric driver: %s' % fabric_driver) driver_vendor = None try: driver_vendor = fabric_driver.split('.')[3] except Exception: msg = _('Get fabric driver vendor error.') LOG.exception(msg) raise exception.VolumeBackendAPIException(data=msg) return driver_vendor def _get_nameserver_info(self, fabric): driver_vendor = self._get_fabric_vendor() if driver_vendor == 'brocade': nsinfo = self._get_brcd_nsinfo(fabric) elif driver_vendor == 'cisco': nsinfo = self._get_cisco_nsinfo(fabric) else: msg = ('Unsupported fabric, vendor name: %s.' % driver_vendor) LOG.error(msg) raise exception.VolumeBackendAPIException(data=msg) return nsinfo def _get_cisco_config(self, fabric): fabric_ip = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_address') fabric_user = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_user') fabric_pwd = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_password') fabric_port = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_port') zoning_vsan = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_zoning_vsan') return (fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) def _get_brcd_nsinfo(self, fabric): conn = self.zm.driver._get_cli_client(fabric) try: nsinfo = conn.get_nameserver_info() LOG.debug("name server info from fabric: %s", nsinfo) conn.cleanup() except exception.BrocadeZoningCliException: if not conn.is_supported_firmware(): msg = _("Unsupported firmware on switch %s. Make sure " "switch is running firmware v6.4 or higher." ) % conn.switch_ip LOG.error(msg) raise exception.FCZoneDriverException(msg) with excutils.save_and_reraise_exception(): LOG.exception(_LE("Error getting name server info.")) except Exception: msg = _("Failed to get name server info.") LOG.exception(msg) raise exception.FCZoneDriverException(msg) return nsinfo def _get_cisco_nsinfo(self, fabric): (fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) = ( self._get_cisco_config(fabric)) try: conn = importutils.import_object( self.zm.driver.configuration.cisco_sb_connector, ipaddress=fabric_ip, username=fabric_user, password=fabric_pwd, port=fabric_port, vsan=zoning_vsan) nsinfo = conn.get_nameserver_info() LOG.debug("name server info from fabric: %s", nsinfo) conn.cleanup() except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.exception(_LE("Error getting show fcns database " "info.")) except Exception: msg = ("Failed to get show fcns database info.") LOG.exception(msg) raise exception.FCZoneDriverException(msg) return nsinfo def _get_one_fc_port_for_zone(self, initiator, contr, nsinfos, cfgmap_from_fabrics, fabric_maps): """Get on FC port per one controller. task flow: 1. Get all the FC port from the array. 2. Filter out ports belonged to the specific controller and the status is connected. 3. Filter out ports connected to the fabric configured in cinder.conf. 4. Get active zones set from switch. 5. Find a port according to three cases. """ LOG.info(_LI("Get in function _get_one_fc_port_for_zone. " "Initiator: %s"), initiator) formatted_initiator = fczm_utils.get_formatted_wwn(initiator) fabric_map = fabric_maps[contr] if not fabric_map: return (None, False) port_zone_number_map = {} for fabric in fabric_map: LOG.info(_LI("Dealing with fabric: %s"), fabric) nsinfo = nsinfos[fabric] if formatted_initiator not in nsinfo: continue final_port_list_per_fabric = fabric_map[fabric] cfgmap_from_fabric = cfgmap_from_fabrics[fabric] zones_members = cfgmap_from_fabric['zones'].values() for port in final_port_list_per_fabric: port_zone_number_map[port] = 0 formatted_port = fczm_utils.get_formatted_wwn(port) for zones_member in zones_members: if formatted_port in zones_member: # For the second case use. if formatted_initiator in zones_member: # First case: found a port in the same # zone with the given initiator. return (port, False) # For the third case use. port_zone_number_map[port] += 1 if port_zone_number_map == {}: return (None, False) temp_list = [] temp_list = sorted(port_zone_number_map.items(), key=lambda d: d[1]) # Third case: find a port referenced in fewest zone. return (temp_list[0][0], True) def _get_active_zone_set(self, fabric): driver_vendor = self._get_fabric_vendor() if driver_vendor == 'brocade': conn = self.zm.driver._get_cli_client(fabric) cfgmap_from_fabric = self.zm.driver._get_active_zone_set(conn) conn.cleanup() elif driver_vendor == 'cisco': (fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) = ( self._get_cisco_config(fabric)) cfgmap_from_fabric = self.zm.driver.get_active_zone_set( fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) else: msg = ('Unsupported fabric, vendor name: %s.' % driver_vendor) LOG.error(msg) raise exception.VolumeBackendAPIException(data=msg) return cfgmap_from_fabric

41.274021

79

0.604846

from oslo_config import cfg from oslo_log import log as logging from oslo_utils import excutils from oslo_utils import importutils from cinder import exception from cinder.i18n import _ from cinder.i18n import _LE from cinder.i18n import _LI from cinder.volume import configuration as config from cinder.zonemanager import utils as fczm_utils LOG = logging.getLogger(__name__) controller_list = ['A', 'B', 'C', 'D'] zone_manager_opts = [ cfg.StrOpt('zone_driver', default='cinder.zonemanager.drivers.brocade.brcd_fc_zone_driver' '.BrcdFCZoneDriver', help='FC Zone Driver responsible for zone management') ] class FCZoneHelper(object): def __init__(self, zm, client): self.zm = zm self.client = client def _check_fc_port_and_init(self, wwns, hostid, fabric_map, nsinfos): if not fabric_map: msg = _('No FC port on array is connected to switch.') LOG.error(msg) raise exception.CinderException(msg) no_wwn_connected_to_switch = True for wwn in wwns: formatted_initiator = fczm_utils.get_formatted_wwn(wwn) for fabric in fabric_map: nsinfo = nsinfos[fabric] if formatted_initiator in nsinfo: no_wwn_connected_to_switch = False self.client.ensure_fc_initiator_added(wwn, hostid) break if no_wwn_connected_to_switch: msg = _('No wwn on host is connected to switch.') LOG.error(msg) raise exception.CinderException(msg) def build_ini_tgt_map(self, wwns, host_id, port_list, is_add): fabric_map = self.zm.get_san_context(port_list) nsinfos = {} cfgmap_from_fabrics = {} for fabric in fabric_map: nsinfos[fabric] = self._get_nameserver_info(fabric) cfgmap_from_fabric = self._get_active_zone_set(fabric) cfgmap_from_fabrics[fabric] = cfgmap_from_fabric self._check_fc_port_and_init(wwns, host_id, fabric_map, nsinfos) return self._build_ini_tgt_map(wwns, is_add, nsinfos, cfgmap_from_fabrics) def _build_ini_tgt_map(self, wwns, need_add_con, nsinfos, cfgmap_from_fabrics): tgt_port_wwns = [] init_targ_map_total = {} fabric_maps = {} for contr in controller_list: port_list_from_contr = self.client.get_fc_ports_from_contr(contr) if port_list_from_contr: fabric_map = self.zm.get_san_context(port_list_from_contr) fabric_maps[contr] = fabric_map for wwn in wwns: init_targ_map = {} tmp_port_list = [] tgt_port_for_map = [] tmp_flag = False need_new_zone = False for contr in fabric_maps: (fc_port_for_zone, tmp_flag) = \ self._get_one_fc_port_for_zone(wwn, contr, nsinfos, cfgmap_from_fabrics, fabric_maps) if tmp_flag: need_new_zone = True if fc_port_for_zone: tgt_port_wwns.append(fc_port_for_zone) if not tmp_flag: tgt_port_for_map.append(fc_port_for_zone) if tmp_flag: tmp_port_list.append(fc_port_for_zone) init_targ_map[wwn] = tmp_port_list LOG.debug("tmp_port_list: %s" % tmp_port_list) init_targ_map_total[wwn] = tgt_port_for_map if need_new_zone and need_add_con: LOG.debug("Got init_targ_map to create zone: %s" % init_targ_map) self.zm.add_connection(init_targ_map) tgt_port_wwns = list(set(tgt_port_wwns)) return (tgt_port_wwns, init_targ_map_total) def _get_fabric_vendor(self): zone_config = config.Configuration(zone_manager_opts, 'fc-zone-manager') fabric_driver = zone_config.zone_driver LOG.debug('Using fabric driver: %s' % fabric_driver) driver_vendor = None try: driver_vendor = fabric_driver.split('.')[3] except Exception: msg = _('Get fabric driver vendor error.') LOG.exception(msg) raise exception.VolumeBackendAPIException(data=msg) return driver_vendor def _get_nameserver_info(self, fabric): driver_vendor = self._get_fabric_vendor() if driver_vendor == 'brocade': nsinfo = self._get_brcd_nsinfo(fabric) elif driver_vendor == 'cisco': nsinfo = self._get_cisco_nsinfo(fabric) else: msg = ('Unsupported fabric, vendor name: %s.' % driver_vendor) LOG.error(msg) raise exception.VolumeBackendAPIException(data=msg) return nsinfo def _get_cisco_config(self, fabric): fabric_ip = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_address') fabric_user = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_user') fabric_pwd = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_password') fabric_port = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_fc_fabric_port') zoning_vsan = self.zm.driver.fabric_configs[fabric].safe_get( 'cisco_zoning_vsan') return (fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) def _get_brcd_nsinfo(self, fabric): conn = self.zm.driver._get_cli_client(fabric) try: nsinfo = conn.get_nameserver_info() LOG.debug("name server info from fabric: %s", nsinfo) conn.cleanup() except exception.BrocadeZoningCliException: if not conn.is_supported_firmware(): msg = _("Unsupported firmware on switch %s. Make sure " "switch is running firmware v6.4 or higher." ) % conn.switch_ip LOG.error(msg) raise exception.FCZoneDriverException(msg) with excutils.save_and_reraise_exception(): LOG.exception(_LE("Error getting name server info.")) except Exception: msg = _("Failed to get name server info.") LOG.exception(msg) raise exception.FCZoneDriverException(msg) return nsinfo def _get_cisco_nsinfo(self, fabric): (fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) = ( self._get_cisco_config(fabric)) try: conn = importutils.import_object( self.zm.driver.configuration.cisco_sb_connector, ipaddress=fabric_ip, username=fabric_user, password=fabric_pwd, port=fabric_port, vsan=zoning_vsan) nsinfo = conn.get_nameserver_info() LOG.debug("name server info from fabric: %s", nsinfo) conn.cleanup() except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.exception(_LE("Error getting show fcns database " "info.")) except Exception: msg = ("Failed to get show fcns database info.") LOG.exception(msg) raise exception.FCZoneDriverException(msg) return nsinfo def _get_one_fc_port_for_zone(self, initiator, contr, nsinfos, cfgmap_from_fabrics, fabric_maps): LOG.info(_LI("Get in function _get_one_fc_port_for_zone. " "Initiator: %s"), initiator) formatted_initiator = fczm_utils.get_formatted_wwn(initiator) fabric_map = fabric_maps[contr] if not fabric_map: return (None, False) port_zone_number_map = {} for fabric in fabric_map: LOG.info(_LI("Dealing with fabric: %s"), fabric) nsinfo = nsinfos[fabric] if formatted_initiator not in nsinfo: continue final_port_list_per_fabric = fabric_map[fabric] cfgmap_from_fabric = cfgmap_from_fabrics[fabric] zones_members = cfgmap_from_fabric['zones'].values() for port in final_port_list_per_fabric: port_zone_number_map[port] = 0 formatted_port = fczm_utils.get_formatted_wwn(port) for zones_member in zones_members: if formatted_port in zones_member: if formatted_initiator in zones_member: return (port, False) port_zone_number_map[port] += 1 if port_zone_number_map == {}: return (None, False) temp_list = [] temp_list = sorted(port_zone_number_map.items(), key=lambda d: d[1]) return (temp_list[0][0], True) def _get_active_zone_set(self, fabric): driver_vendor = self._get_fabric_vendor() if driver_vendor == 'brocade': conn = self.zm.driver._get_cli_client(fabric) cfgmap_from_fabric = self.zm.driver._get_active_zone_set(conn) conn.cleanup() elif driver_vendor == 'cisco': (fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) = ( self._get_cisco_config(fabric)) cfgmap_from_fabric = self.zm.driver.get_active_zone_set( fabric_ip, fabric_user, fabric_pwd, fabric_port, zoning_vsan) else: msg = ('Unsupported fabric, vendor name: %s.' % driver_vendor) LOG.error(msg) raise exception.VolumeBackendAPIException(data=msg) return cfgmap_from_fabric

true

790d6e48c3ce711d691ffe339851840bd6867634

5,282

py

Python

modules/transformer.py

riokt/video-paragraph

2da3298819e73809af495457db2cf1dfffad712f

[ "MIT" ]

null

modules/transformer.py

riokt/video-paragraph

2da3298819e73809af495457db2cf1dfffad712f

[ "MIT" ]

null

modules/transformer.py

riokt/video-paragraph

2da3298819e73809af495457db2cf1dfffad712f

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import framework.configbase import math import time import numpy as np from modules.transformer_encoder import Encoder from modules.transformer_decoder import Decoder decay1 = [(i+1)*20**(-1) for i in range(20)] decay2 = [1-(i+1)*50**(-1) for i in range(50)] class TransformerConfig(framework.configbase.ModuleConfig): def __init__(self): super(TransformerConfig, self).__init__() self.vocab = 0 self.max_words_in_sent = 150 self.ft_dim = 4096 self.d_model = 512 self.enc_n_layers = 3 self.dec_n_layers = 3 self.heads = 8 self.dropout = 0.1 self.keyframes = False self.rl = False self.document_freq = None class Transformer(nn.Module): def __init__(self, config): super(Transformer, self).__init__() self.config = config self.encoder = Encoder(self.config.ft_dim, self.config.d_model, self.config.enc_n_layers, self.config.heads, self.config.dropout, self.config.keyframes, act=True) self.decoder = Decoder(self.config.vocab, self.config.d_model, self.config.dec_n_layers, self.config.heads, self.config.dropout, act=True) self.dropout = nn.Dropout(self.config.dropout) self.logit = nn.Linear(self.config.d_model, self.config.vocab) self.logit.weight = self.decoder.embed.embed.weight self.remove_gate = nn.Linear(self.config.d_model, 1) self.add_gate = nn.Linear(self.config.d_model, 1) self.q_linear = nn.Linear(self.config.d_model, self.config.d_model, bias=False) self.next_attn = nn.Linear(2*self.config.d_model, 1) self.init_weights() def init_weights(self,): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, src, trg, src_mask, trg_mask): e_outputs, org_key, select = self.encoder(src, src_mask) add_state = torch.tensor(decay2[:e_outputs.size(1)]+[0]*max(0,e_outputs.size(1)-50)).cuda().unsqueeze(0).unsqueeze(-1) memory_bank = e_outputs * add_state d_output, attn_weights = [], [] for i in range(1, trg.size(1)+1): word, attn, _ = self.decoder(trg[:,i-1].unsqueeze(1), memory_bank, src_mask, trg_mask[:,i-1,i-1].unsqueeze(1), step=i-1) d_output.append(word[:,-1]) attn_weights.append(attn[:,:,-1].mean(dim=1)) memory_bank, add_state = self.update_memory(memory_bank, add_state, e_outputs, attn_weights[-20:], d_output[-20:]) output = self.logit(torch.cat([_.unsqueeze(1) for _ in d_output], 1)) return output, org_key, select def update_memory(self, memory_bank, add_state, e_outputs, attn, query_s): remove_prob = torch.sigmoid(self.remove_gate(query_s[-1])).unsqueeze(-1) add_prob = torch.sigmoid(self.add_gate(query_s[-1])).unsqueeze(-1) temp = torch.softmax(torch.tensor(decay1[20-len(attn):]).cuda(), dim=-1) attn = sum([attn[i]*temp[i] for i in range(len(attn))]).unsqueeze(-1) # remove for diversity query_s = sum([query_s[i]*temp[i] for i in range(len(query_s))]) sim = torch.sigmoid(torch.matmul(memory_bank, self.q_linear(query_s).unsqueeze(-1))) memory_bank = memory_bank * (1 - remove_prob * attn * sim) # add for coherence last_ctx = (e_outputs * attn).sum(dim=1, keepdim=True) next_attn = torch.sigmoid(self.next_attn(torch.cat([e_outputs,last_ctx.expand_as(e_outputs)], dim=-1))) memory_bank = memory_bank + e_outputs * (1-add_state) * (add_prob*next_attn) add_state = add_state + (1-add_state) * (add_prob*next_attn) return memory_bank, add_state def sample(self, src, src_mask, decoding='greedy'): init_tok = 2 eos_tok = 3 if self.config.keyframes: e_outputs, src_mask = self.encoder.get_keyframes(src, src_mask) else: e_outputs, _, _ = self.encoder(src, src_mask) add_state = torch.tensor(decay2[:e_outputs.size(1)]+[0]*max(0,e_outputs.size(1)-50)).cuda().unsqueeze(0).unsqueeze(-1) memory_bank = e_outputs * add_state outputs = torch.ones(src.size(0), 1).fill_(init_tok).long().cuda() seqLogprobs = torch.zeros(src.size(0), 60).cuda() attn_weights, d_output = [], [] for i in range(1, 60): trg_mask = self.nopeak_mask(i) word, attn, _ = self.decoder(outputs[:,-1].unsqueeze(1), memory_bank, src_mask, trg_mask[:,-1,-1].unsqueeze(1), step=i-1) attn_weights.append(attn[:,:,-1].mean(dim=1)) d_output.append(word[:,-1]) out = self.logit(word) logprobs = F.log_softmax(out[:,-1], dim=-1) if decoding == 'greedy': _, next_word = torch.max(logprobs, dim=1) next_word = next_word.unsqueeze(-1) else: probs = torch.exp(logprobs.data).cpu() next_word = torch.multinomial(probs, 1).cuda() seqLogprobs[:,i] = logprobs.gather(1, next_word).view(-1) outputs = torch.cat([outputs, next_word], dim=1) memory_bank, add_state = self.update_memory(memory_bank, add_state, e_outputs, attn_weights[-20:], d_output[-20:]) attn_weights = torch.cat([_.unsqueeze(1) for _ in attn_weights], dim=1) return outputs, seqLogprobs, attn_weights def nopeak_mask(self, size): np_mask = np.triu(np.ones((1, size, size)), k=1).astype('uint8') np_mask = Variable(torch.from_numpy(np_mask) == 0).cuda() return np_mask

44.762712

166

0.683453

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import framework.configbase import math import time import numpy as np from modules.transformer_encoder import Encoder from modules.transformer_decoder import Decoder decay1 = [(i+1)*20**(-1) for i in range(20)] decay2 = [1-(i+1)*50**(-1) for i in range(50)] class TransformerConfig(framework.configbase.ModuleConfig): def __init__(self): super(TransformerConfig, self).__init__() self.vocab = 0 self.max_words_in_sent = 150 self.ft_dim = 4096 self.d_model = 512 self.enc_n_layers = 3 self.dec_n_layers = 3 self.heads = 8 self.dropout = 0.1 self.keyframes = False self.rl = False self.document_freq = None class Transformer(nn.Module): def __init__(self, config): super(Transformer, self).__init__() self.config = config self.encoder = Encoder(self.config.ft_dim, self.config.d_model, self.config.enc_n_layers, self.config.heads, self.config.dropout, self.config.keyframes, act=True) self.decoder = Decoder(self.config.vocab, self.config.d_model, self.config.dec_n_layers, self.config.heads, self.config.dropout, act=True) self.dropout = nn.Dropout(self.config.dropout) self.logit = nn.Linear(self.config.d_model, self.config.vocab) self.logit.weight = self.decoder.embed.embed.weight self.remove_gate = nn.Linear(self.config.d_model, 1) self.add_gate = nn.Linear(self.config.d_model, 1) self.q_linear = nn.Linear(self.config.d_model, self.config.d_model, bias=False) self.next_attn = nn.Linear(2*self.config.d_model, 1) self.init_weights() def init_weights(self,): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, src, trg, src_mask, trg_mask): e_outputs, org_key, select = self.encoder(src, src_mask) add_state = torch.tensor(decay2[:e_outputs.size(1)]+[0]*max(0,e_outputs.size(1)-50)).cuda().unsqueeze(0).unsqueeze(-1) memory_bank = e_outputs * add_state d_output, attn_weights = [], [] for i in range(1, trg.size(1)+1): word, attn, _ = self.decoder(trg[:,i-1].unsqueeze(1), memory_bank, src_mask, trg_mask[:,i-1,i-1].unsqueeze(1), step=i-1) d_output.append(word[:,-1]) attn_weights.append(attn[:,:,-1].mean(dim=1)) memory_bank, add_state = self.update_memory(memory_bank, add_state, e_outputs, attn_weights[-20:], d_output[-20:]) output = self.logit(torch.cat([_.unsqueeze(1) for _ in d_output], 1)) return output, org_key, select def update_memory(self, memory_bank, add_state, e_outputs, attn, query_s): remove_prob = torch.sigmoid(self.remove_gate(query_s[-1])).unsqueeze(-1) add_prob = torch.sigmoid(self.add_gate(query_s[-1])).unsqueeze(-1) temp = torch.softmax(torch.tensor(decay1[20-len(attn):]).cuda(), dim=-1) attn = sum([attn[i]*temp[i] for i in range(len(attn))]).unsqueeze(-1) query_s = sum([query_s[i]*temp[i] for i in range(len(query_s))]) sim = torch.sigmoid(torch.matmul(memory_bank, self.q_linear(query_s).unsqueeze(-1))) memory_bank = memory_bank * (1 - remove_prob * attn * sim) last_ctx = (e_outputs * attn).sum(dim=1, keepdim=True) next_attn = torch.sigmoid(self.next_attn(torch.cat([e_outputs,last_ctx.expand_as(e_outputs)], dim=-1))) memory_bank = memory_bank + e_outputs * (1-add_state) * (add_prob*next_attn) add_state = add_state + (1-add_state) * (add_prob*next_attn) return memory_bank, add_state def sample(self, src, src_mask, decoding='greedy'): init_tok = 2 eos_tok = 3 if self.config.keyframes: e_outputs, src_mask = self.encoder.get_keyframes(src, src_mask) else: e_outputs, _, _ = self.encoder(src, src_mask) add_state = torch.tensor(decay2[:e_outputs.size(1)]+[0]*max(0,e_outputs.size(1)-50)).cuda().unsqueeze(0).unsqueeze(-1) memory_bank = e_outputs * add_state outputs = torch.ones(src.size(0), 1).fill_(init_tok).long().cuda() seqLogprobs = torch.zeros(src.size(0), 60).cuda() attn_weights, d_output = [], [] for i in range(1, 60): trg_mask = self.nopeak_mask(i) word, attn, _ = self.decoder(outputs[:,-1].unsqueeze(1), memory_bank, src_mask, trg_mask[:,-1,-1].unsqueeze(1), step=i-1) attn_weights.append(attn[:,:,-1].mean(dim=1)) d_output.append(word[:,-1]) out = self.logit(word) logprobs = F.log_softmax(out[:,-1], dim=-1) if decoding == 'greedy': _, next_word = torch.max(logprobs, dim=1) next_word = next_word.unsqueeze(-1) else: probs = torch.exp(logprobs.data).cpu() next_word = torch.multinomial(probs, 1).cuda() seqLogprobs[:,i] = logprobs.gather(1, next_word).view(-1) outputs = torch.cat([outputs, next_word], dim=1) memory_bank, add_state = self.update_memory(memory_bank, add_state, e_outputs, attn_weights[-20:], d_output[-20:]) attn_weights = torch.cat([_.unsqueeze(1) for _ in attn_weights], dim=1) return outputs, seqLogprobs, attn_weights def nopeak_mask(self, size): np_mask = np.triu(np.ones((1, size, size)), k=1).astype('uint8') np_mask = Variable(torch.from_numpy(np_mask) == 0).cuda() return np_mask

true

790d6ea3e263f841adc2a3adb570394e159cd2d3

157

py

Python

alarmexception.py

Megha-Bose/Brick-Breaker-Game

b543ec8277193dcca0ec15afab4a4775744b9587

[ "MIT" ]

1

2021-04-08T04:15:36.000Z

alarmexception.py

Megha-Bose/Brick-Breaker-Game

b543ec8277193dcca0ec15afab4a4775744b9587

[ "MIT" ]

null

alarmexception.py

Megha-Bose/Brick-Breaker-Game

b543ec8277193dcca0ec15afab4a4775744b9587

[ "MIT" ]

null

''' Taking characters from terminal without pressing enter for movements ''' from __future__ import print_function class AlarmException(Exception): pass

31.4

76

0.802548

from __future__ import print_function class AlarmException(Exception): pass

true

790d6edd3e6ec87c0fb40dea98f50155369e3bae

56,304

py

Python

datadog_checks_base/datadog_checks/base/checks/openmetrics/mixins.py

vbarbaresi/integrations-core

ab26ab1cd6c28a97c1ad1177093a93659658c7aa

[ "BSD-3-Clause" ]

null

datadog_checks_base/datadog_checks/base/checks/openmetrics/mixins.py

vbarbaresi/integrations-core

ab26ab1cd6c28a97c1ad1177093a93659658c7aa

[ "BSD-3-Clause" ]

2

2021-04-26T13:37:48.000Z

2021-04-26T13:37:49.000Z

datadog_checks_base/datadog_checks/base/checks/openmetrics/mixins.py

vbarbaresi/integrations-core

ab26ab1cd6c28a97c1ad1177093a93659658c7aa

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2018-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) from __future__ import division import copy from fnmatch import translate from math import isinf, isnan from os.path import isfile from re import compile import requests from prometheus_client.samples import Sample from six import PY3, iteritems, string_types from ...config import is_affirmative from ...errors import CheckException from ...utils.common import to_native_string from ...utils.http import RequestsWrapper from .. import AgentCheck from ..libs.prometheus import text_fd_to_metric_families if PY3: long = int class OpenMetricsScraperMixin(object): # pylint: disable=E1101 # This class is not supposed to be used by itself, it provides scraping behavior but # need to be within a check in the end REQUESTS_CHUNK_SIZE = 1024 * 10 # use 10kb as chunk size when using the Stream feature in requests.get # indexes in the sample tuple of core.Metric SAMPLE_NAME = 0 SAMPLE_LABELS = 1 SAMPLE_VALUE = 2 MICROS_IN_S = 1000000 MINUS_INF = float("-inf") TELEMETRY_GAUGE_MESSAGE_SIZE = "payload.size" TELEMETRY_COUNTER_METRICS_BLACKLIST_COUNT = "metrics.blacklist.count" TELEMETRY_COUNTER_METRICS_INPUT_COUNT = "metrics.input.count" TELEMETRY_COUNTER_METRICS_IGNORE_COUNT = "metrics.ignored.count" TELEMETRY_COUNTER_METRICS_PROCESS_COUNT = "metrics.processed.count" METRIC_TYPES = ['counter', 'gauge', 'summary', 'histogram'] KUBERNETES_TOKEN_PATH = '/var/run/secrets/kubernetes.io/serviceaccount/token' def __init__(self, *args, **kwargs): # Initialize AgentCheck's base class super(OpenMetricsScraperMixin, self).__init__(*args, **kwargs) def create_scraper_configuration(self, instance=None): """ Creates a scraper configuration. If instance does not specify a value for a configuration option, the value will default to the `init_config`. Otherwise, the `default_instance` value will be used. A default mixin configuration will be returned if there is no instance. """ if 'openmetrics_endpoint' in instance: raise CheckException('The setting `openmetrics_endpoint` is only available for Agent version 7 or later') # We can choose to create a default mixin configuration for an empty instance if instance is None: instance = {} # Supports new configuration options config = copy.deepcopy(instance) # Set the endpoint endpoint = instance.get('prometheus_url') if instance and endpoint is None: raise CheckException("You have to define a prometheus_url for each prometheus instance") config['prometheus_url'] = endpoint # `NAMESPACE` is the prefix metrics will have. Need to be hardcoded in the # child check class. namespace = instance.get('namespace') # Check if we have a namespace if instance and namespace is None: if self.default_namespace is None: raise CheckException("You have to define a namespace for each prometheus check") namespace = self.default_namespace config['namespace'] = namespace # Retrieve potential default instance settings for the namespace default_instance = self.default_instances.get(namespace, {}) # `metrics_mapper` is a dictionary where the keys are the metrics to capture # and the values are the corresponding metrics names to have in datadog. # Note: it is empty in the parent class but will need to be # overloaded/hardcoded in the final check not to be counted as custom metric. # Metrics are preprocessed if no mapping metrics_mapper = {} # We merge list and dictionaries from optional defaults & instance settings metrics = default_instance.get('metrics', []) + instance.get('metrics', []) for metric in metrics: if isinstance(metric, string_types): metrics_mapper[metric] = metric else: metrics_mapper.update(metric) config['metrics_mapper'] = metrics_mapper # `_wildcards_re` is a Pattern object used to match metric wildcards config['_wildcards_re'] = None wildcards = set() for metric in config['metrics_mapper']: if "*" in metric: wildcards.add(translate(metric)) if wildcards: config['_wildcards_re'] = compile('|'.join(wildcards)) # `prometheus_metrics_prefix` allows to specify a prefix that all # prometheus metrics should have. This can be used when the prometheus # endpoint we are scrapping allows to add a custom prefix to it's # metrics. config['prometheus_metrics_prefix'] = instance.get( 'prometheus_metrics_prefix', default_instance.get('prometheus_metrics_prefix', '') ) # `label_joins` holds the configuration for extracting 1:1 labels from # a target metric to all metric matching the label, example: # self.label_joins = { # 'kube_pod_info': { # 'labels_to_match': ['pod'], # 'labels_to_get': ['node', 'host_ip'] # } # } config['label_joins'] = default_instance.get('label_joins', {}) config['label_joins'].update(instance.get('label_joins', {})) # `_label_mapping` holds the additionals label info to add for a specific # label value, example: # self._label_mapping = { # 'pod': { # 'dd-agent-9s1l1': { # "node": "yolo", # "host_ip": "yey" # } # } # } config['_label_mapping'] = {} # `_active_label_mapping` holds a dictionary of label values found during the run # to cleanup the label_mapping of unused values, example: # self._active_label_mapping = { # 'pod': { # 'dd-agent-9s1l1': True # } # } config['_active_label_mapping'] = {} # `_watched_labels` holds the sets of labels to watch for enrichment config['_watched_labels'] = {} config['_dry_run'] = True # Some metrics are ignored because they are duplicates or introduce a # very high cardinality. Metrics included in this list will be silently # skipped without a 'Unable to handle metric' debug line in the logs config['ignore_metrics'] = instance.get('ignore_metrics', default_instance.get('ignore_metrics', [])) config['_ignored_metrics'] = set() # `_ignored_re` is a Pattern object used to match ignored metric patterns config['_ignored_re'] = None ignored_patterns = set() # Separate ignored metric names and ignored patterns in different sets for faster lookup later for metric in config['ignore_metrics']: if '*' in metric: ignored_patterns.add(translate(metric)) else: config['_ignored_metrics'].add(metric) if ignored_patterns: config['_ignored_re'] = compile('|'.join(ignored_patterns)) # Ignore metrics based on label keys or specific label values config['ignore_metrics_by_labels'] = instance.get( 'ignore_metrics_by_labels', default_instance.get('ignore_metrics_by_labels', {}) ) # If you want to send the buckets as tagged values when dealing with histograms, # set send_histograms_buckets to True, set to False otherwise. config['send_histograms_buckets'] = is_affirmative( instance.get('send_histograms_buckets', default_instance.get('send_histograms_buckets', True)) ) # If you want the bucket to be non cumulative and to come with upper/lower bound tags # set non_cumulative_buckets to True, enabled when distribution metrics are enabled. config['non_cumulative_buckets'] = is_affirmative( instance.get('non_cumulative_buckets', default_instance.get('non_cumulative_buckets', False)) ) # Send histograms as datadog distribution metrics config['send_distribution_buckets'] = is_affirmative( instance.get('send_distribution_buckets', default_instance.get('send_distribution_buckets', False)) ) # Non cumulative buckets are mandatory for distribution metrics if config['send_distribution_buckets'] is True: config['non_cumulative_buckets'] = True # If you want to send `counter` metrics as monotonic counts, set this value to True. # Set to False if you want to instead send those metrics as `gauge`. config['send_monotonic_counter'] = is_affirmative( instance.get('send_monotonic_counter', default_instance.get('send_monotonic_counter', True)) ) # If you want `counter` metrics to be submitted as both gauges and monotonic counts. Set this value to True. config['send_monotonic_with_gauge'] = is_affirmative( instance.get('send_monotonic_with_gauge', default_instance.get('send_monotonic_with_gauge', False)) ) config['send_distribution_counts_as_monotonic'] = is_affirmative( instance.get( 'send_distribution_counts_as_monotonic', default_instance.get('send_distribution_counts_as_monotonic', False), ) ) config['send_distribution_sums_as_monotonic'] = is_affirmative( instance.get( 'send_distribution_sums_as_monotonic', default_instance.get('send_distribution_sums_as_monotonic', False), ) ) # If the `labels_mapper` dictionary is provided, the metrics labels names # in the `labels_mapper` will use the corresponding value as tag name # when sending the gauges. config['labels_mapper'] = default_instance.get('labels_mapper', {}) config['labels_mapper'].update(instance.get('labels_mapper', {})) # Rename bucket "le" label to "upper_bound" config['labels_mapper']['le'] = 'upper_bound' # `exclude_labels` is an array of labels names to exclude. Those labels # will just not be added as tags when submitting the metric. config['exclude_labels'] = default_instance.get('exclude_labels', []) + instance.get('exclude_labels', []) # `type_overrides` is a dictionary where the keys are prometheus metric names # and the values are a metric type (name as string) to use instead of the one # listed in the payload. It can be used to force a type on untyped metrics. # Note: it is empty in the parent class but will need to be # overloaded/hardcoded in the final check not to be counted as custom metric. config['type_overrides'] = default_instance.get('type_overrides', {}) config['type_overrides'].update(instance.get('type_overrides', {})) # `_type_override_patterns` is a dictionary where we store Pattern objects # that match metric names as keys, and their corresponding metric type overrrides as values. config['_type_override_patterns'] = {} with_wildcards = set() for metric, type in iteritems(config['type_overrides']): if '*' in metric: config['_type_override_patterns'][compile(translate(metric))] = type with_wildcards.add(metric) # cleanup metric names with wildcards from the 'type_overrides' dict for metric in with_wildcards: del config['type_overrides'][metric] # Some metrics are retrieved from differents hosts and often # a label can hold this information, this transfers it to the hostname config['label_to_hostname'] = instance.get('label_to_hostname', default_instance.get('label_to_hostname', None)) # In combination to label_as_hostname, allows to add a common suffix to the hostnames # submitted. This can be used for instance to discriminate hosts between clusters. config['label_to_hostname_suffix'] = instance.get( 'label_to_hostname_suffix', default_instance.get('label_to_hostname_suffix', None) ) # Add a 'health' service check for the prometheus endpoint config['health_service_check'] = is_affirmative( instance.get('health_service_check', default_instance.get('health_service_check', True)) ) # Can either be only the path to the certificate and thus you should specify the private key # or it can be the path to a file containing both the certificate & the private key config['ssl_cert'] = instance.get('ssl_cert', default_instance.get('ssl_cert', None)) # Needed if the certificate does not include the private key # # /!\ The private key to your local certificate must be unencrypted. # Currently, Requests does not support using encrypted keys. config['ssl_private_key'] = instance.get('ssl_private_key', default_instance.get('ssl_private_key', None)) # The path to the trusted CA used for generating custom certificates config['ssl_ca_cert'] = instance.get('ssl_ca_cert', default_instance.get('ssl_ca_cert', None)) # Whether or not to validate SSL certificates config['ssl_verify'] = is_affirmative(instance.get('ssl_verify', default_instance.get('ssl_verify', True))) # Extra http headers to be sent when polling endpoint config['extra_headers'] = default_instance.get('extra_headers', {}) config['extra_headers'].update(instance.get('extra_headers', {})) # Timeout used during the network request config['prometheus_timeout'] = instance.get( 'prometheus_timeout', default_instance.get('prometheus_timeout', 10) ) # Authentication used when polling endpoint config['username'] = instance.get('username', default_instance.get('username', None)) config['password'] = instance.get('password', default_instance.get('password', None)) # Custom tags that will be sent with each metric config['custom_tags'] = instance.get('tags', []) # Additional tags to be sent with each metric config['_metric_tags'] = [] # List of strings to filter the input text payload on. If any line contains # one of these strings, it will be filtered out before being parsed. # INTERNAL FEATURE, might be removed in future versions config['_text_filter_blacklist'] = [] # Whether or not to use the service account bearer token for authentication # if 'bearer_token_path' is not set, we use /var/run/secrets/kubernetes.io/serviceaccount/token # as a default path to get the token. config['bearer_token_auth'] = is_affirmative( instance.get('bearer_token_auth', default_instance.get('bearer_token_auth', False)) ) # Can be used to get a service account bearer token from files # other than /var/run/secrets/kubernetes.io/serviceaccount/token # 'bearer_token_auth' should be enabled. config['bearer_token_path'] = instance.get('bearer_token_path', default_instance.get('bearer_token_path', None)) # The service account bearer token to be used for authentication config['_bearer_token'] = self._get_bearer_token(config['bearer_token_auth'], config['bearer_token_path']) config['telemetry'] = is_affirmative(instance.get('telemetry', default_instance.get('telemetry', False))) # The metric name services use to indicate build information config['metadata_metric_name'] = instance.get( 'metadata_metric_name', default_instance.get('metadata_metric_name') ) # Map of metadata key names to label names config['metadata_label_map'] = instance.get( 'metadata_label_map', default_instance.get('metadata_label_map', {}) ) config['_default_metric_transformers'] = {} if config['metadata_metric_name'] and config['metadata_label_map']: config['_default_metric_transformers'][config['metadata_metric_name']] = self.transform_metadata # Whether or not to enable flushing of the first value of monotonic counts config['_successfully_executed'] = False return config def get_http_handler(self, scraper_config): """ Get http handler for a specific scraper config. The http handler is cached using `prometheus_url` as key. """ prometheus_url = scraper_config['prometheus_url'] if prometheus_url in self._http_handlers: return self._http_handlers[prometheus_url] # TODO: Deprecate this behavior in Agent 8 if scraper_config['ssl_ca_cert'] is False: scraper_config['ssl_verify'] = False # TODO: Deprecate this behavior in Agent 8 if scraper_config['ssl_verify'] is False: scraper_config.setdefault('tls_ignore_warning', True) http_handler = self._http_handlers[prometheus_url] = RequestsWrapper( scraper_config, self.init_config, self.HTTP_CONFIG_REMAPPER, self.log ) headers = http_handler.options['headers'] bearer_token = scraper_config['_bearer_token'] if bearer_token is not None: headers['Authorization'] = 'Bearer {}'.format(bearer_token) # TODO: Determine if we really need this headers.setdefault('accept-encoding', 'gzip') # Explicitly set the content type we accept headers.setdefault('accept', 'text/plain') return http_handler def reset_http_config(self): """ You may need to use this when configuration is determined dynamically during every check run, such as when polling an external resource like the Kubelet. """ self._http_handlers.clear() def parse_metric_family(self, response, scraper_config): """ Parse the MetricFamily from a valid `requests.Response` object to provide a MetricFamily object. The text format uses iter_lines() generator. """ if response.encoding is None: response.encoding = 'utf-8' input_gen = response.iter_lines(chunk_size=self.REQUESTS_CHUNK_SIZE, decode_unicode=True) if scraper_config['_text_filter_blacklist']: input_gen = self._text_filter_input(input_gen, scraper_config) for metric in text_fd_to_metric_families(input_gen): self._send_telemetry_counter( self.TELEMETRY_COUNTER_METRICS_INPUT_COUNT, len(metric.samples), scraper_config ) type_override = scraper_config['type_overrides'].get(metric.name) if type_override: metric.type = type_override elif scraper_config['_type_override_patterns']: for pattern, new_type in iteritems(scraper_config['_type_override_patterns']): if pattern.search(metric.name): metric.type = new_type break if metric.type not in self.METRIC_TYPES: continue metric.name = self._remove_metric_prefix(metric.name, scraper_config) yield metric def _text_filter_input(self, input_gen, scraper_config): """ Filters out the text input line by line to avoid parsing and processing metrics we know we don't want to process. This only works on `text/plain` payloads, and is an INTERNAL FEATURE implemented for the kubelet check :param input_get: line generator :output: generator of filtered lines """ for line in input_gen: for item in scraper_config['_text_filter_blacklist']: if item in line: self._send_telemetry_counter(self.TELEMETRY_COUNTER_METRICS_BLACKLIST_COUNT, 1, scraper_config) break else: # No blacklist matches, passing the line through yield line def _remove_metric_prefix(self, metric, scraper_config): prometheus_metrics_prefix = scraper_config['prometheus_metrics_prefix'] return metric[len(prometheus_metrics_prefix) :] if metric.startswith(prometheus_metrics_prefix) else metric def scrape_metrics(self, scraper_config): """ Poll the data from Prometheus and return the metrics as a generator. """ response = self.poll(scraper_config) if scraper_config['telemetry']: if 'content-length' in response.headers: content_len = int(response.headers['content-length']) else: content_len = len(response.content) self._send_telemetry_gauge(self.TELEMETRY_GAUGE_MESSAGE_SIZE, content_len, scraper_config) try: # no dry run if no label joins if not scraper_config['label_joins']: scraper_config['_dry_run'] = False elif not scraper_config['_watched_labels']: watched = scraper_config['_watched_labels'] watched['sets'] = {} watched['keys'] = {} watched['singles'] = set() for key, val in iteritems(scraper_config['label_joins']): labels = [] if 'labels_to_match' in val: labels = val['labels_to_match'] elif 'label_to_match' in val: self.log.warning("`label_to_match` is being deprecated, please use `labels_to_match`") if isinstance(val['label_to_match'], list): labels = val['label_to_match'] else: labels = [val['label_to_match']] if labels: s = frozenset(labels) watched['sets'][key] = s watched['keys'][key] = ','.join(s) if len(labels) == 1: watched['singles'].add(labels[0]) for metric in self.parse_metric_family(response, scraper_config): yield metric # Set dry run off scraper_config['_dry_run'] = False # Garbage collect unused mapping and reset active labels for metric, mapping in list(iteritems(scraper_config['_label_mapping'])): for key in list(mapping): if ( metric in scraper_config['_active_label_mapping'] and key not in scraper_config['_active_label_mapping'][metric] ): del scraper_config['_label_mapping'][metric][key] scraper_config['_active_label_mapping'] = {} finally: response.close() def process(self, scraper_config, metric_transformers=None): """ Polls the data from Prometheus and submits them as Datadog metrics. `endpoint` is the metrics endpoint to use to poll metrics from Prometheus Note that if the instance has a `tags` attribute, it will be pushed automatically as additional custom tags and added to the metrics """ transformers = scraper_config['_default_metric_transformers'].copy() if metric_transformers: transformers.update(metric_transformers) for metric in self.scrape_metrics(scraper_config): self.process_metric(metric, scraper_config, metric_transformers=transformers) scraper_config['_successfully_executed'] = True def transform_metadata(self, metric, scraper_config): labels = metric.samples[0][self.SAMPLE_LABELS] for metadata_name, label_name in iteritems(scraper_config['metadata_label_map']): if label_name in labels: self.set_metadata(metadata_name, labels[label_name]) def _metric_name_with_namespace(self, metric_name, scraper_config): namespace = scraper_config['namespace'] if not namespace: return metric_name return '{}.{}'.format(namespace, metric_name) def _telemetry_metric_name_with_namespace(self, metric_name, scraper_config): namespace = scraper_config['namespace'] if not namespace: return '{}.{}'.format('telemetry', metric_name) return '{}.{}.{}'.format(namespace, 'telemetry', metric_name) def _send_telemetry_gauge(self, metric_name, val, scraper_config): if scraper_config['telemetry']: metric_name_with_namespace = self._telemetry_metric_name_with_namespace(metric_name, scraper_config) # Determine the tags to send custom_tags = scraper_config['custom_tags'] tags = list(custom_tags) tags.extend(scraper_config['_metric_tags']) self.gauge(metric_name_with_namespace, val, tags=tags) def _send_telemetry_counter(self, metric_name, val, scraper_config, extra_tags=None): if scraper_config['telemetry']: metric_name_with_namespace = self._telemetry_metric_name_with_namespace(metric_name, scraper_config) # Determine the tags to send custom_tags = scraper_config['custom_tags'] tags = list(custom_tags) tags.extend(scraper_config['_metric_tags']) if extra_tags: tags.extend(extra_tags) self.count(metric_name_with_namespace, val, tags=tags) def _store_labels(self, metric, scraper_config): # If targeted metric, store labels if metric.name not in scraper_config['label_joins']: return watched = scraper_config['_watched_labels'] matching_labels = watched['sets'][metric.name] mapping_key = watched['keys'][metric.name] labels_to_get = scraper_config['label_joins'][metric.name]['labels_to_get'] get_all = '*' in labels_to_get match_all = mapping_key == '*' for sample in metric.samples: # metadata-only metrics that are used for label joins are always equal to 1 # this is required for metrics where all combinations of a state are sent # but only the active one is set to 1 (others are set to 0) # example: kube_pod_status_phase in kube-state-metrics if sample[self.SAMPLE_VALUE] != 1: continue sample_labels = sample[self.SAMPLE_LABELS] sample_labels_keys = sample_labels.keys() if match_all or matching_labels.issubset(sample_labels_keys): label_dict = dict() if get_all: for label_name, label_value in iteritems(sample_labels): if label_name in matching_labels: continue label_dict[label_name] = label_value else: for label_name in labels_to_get: if label_name in sample_labels: label_dict[label_name] = sample_labels[label_name] if match_all: mapping_value = '*' else: mapping_value = ','.join([sample_labels[l] for l in matching_labels]) scraper_config['_label_mapping'].setdefault(mapping_key, {}).setdefault(mapping_value, {}).update( label_dict ) def _join_labels(self, metric, scraper_config): # Filter metric to see if we can enrich with joined labels if not scraper_config['label_joins']: return label_mapping = scraper_config['_label_mapping'] active_label_mapping = scraper_config['_active_label_mapping'] watched = scraper_config['_watched_labels'] sets = watched['sets'] keys = watched['keys'] singles = watched['singles'] for sample in metric.samples: sample_labels = sample[self.SAMPLE_LABELS] sample_labels_keys = sample_labels.keys() # Match with wildcard label # Label names are [a-zA-Z0-9_]*, so no risk of collision if '*' in singles: active_label_mapping.setdefault('*', {})['*'] = True if '*' in label_mapping and '*' in label_mapping['*']: sample_labels.update(label_mapping['*']['*']) # Match with single labels matching_single_labels = singles.intersection(sample_labels_keys) for label in matching_single_labels: mapping_key = label mapping_value = sample_labels[label] active_label_mapping.setdefault(mapping_key, {})[mapping_value] = True if mapping_key in label_mapping and mapping_value in label_mapping[mapping_key]: sample_labels.update(label_mapping[mapping_key][mapping_value]) # Match with tuples of labels for key, mapping_key in iteritems(keys): if mapping_key in matching_single_labels: continue matching_labels = sets[key] if matching_labels.issubset(sample_labels_keys): matching_values = [sample_labels[l] for l in matching_labels] mapping_value = ','.join(matching_values) active_label_mapping.setdefault(mapping_key, {})[mapping_value] = True if mapping_key in label_mapping and mapping_value in label_mapping[mapping_key]: sample_labels.update(label_mapping[mapping_key][mapping_value]) def _ignore_metrics_by_label(self, scraper_config, metric_name, sample): ignore_metrics_by_label = scraper_config['ignore_metrics_by_labels'] sample_labels = sample[self.SAMPLE_LABELS] for label_key, label_values in ignore_metrics_by_label.items(): if not label_values: self.log.debug( "Skipping filter label `%s` with an empty values list, did you mean to use '*' wildcard?", label_key ) elif '*' in label_values: # Wildcard '*' means all metrics with label_key will be ignored self.log.debug("Detected wildcard for label `%s`", label_key) if label_key in sample_labels.keys(): self.log.debug("Skipping metric `%s` due to label key matching: %s", metric_name, label_key) return True else: for val in label_values: if label_key in sample_labels and sample_labels[label_key] == val: self.log.debug( "Skipping metric `%s` due to label `%s` value matching: %s", metric_name, label_key, val ) return True return False def process_metric(self, metric, scraper_config, metric_transformers=None): """ Handle a Prometheus metric according to the following flow: - search `scraper_config['metrics_mapper']` for a prometheus.metric to datadog.metric mapping - call check method with the same name as the metric - log info if none of the above worked `metric_transformers` is a dict of `<metric name>:<function to run when the metric name is encountered>` """ # If targeted metric, store labels self._store_labels(metric, scraper_config) if scraper_config['ignore_metrics']: if metric.name in scraper_config['_ignored_metrics']: self._send_telemetry_counter( self.TELEMETRY_COUNTER_METRICS_IGNORE_COUNT, len(metric.samples), scraper_config ) return # Ignore the metric if scraper_config['_ignored_re'] and scraper_config['_ignored_re'].search(metric.name): # Metric must be ignored scraper_config['_ignored_metrics'].add(metric.name) self._send_telemetry_counter( self.TELEMETRY_COUNTER_METRICS_IGNORE_COUNT, len(metric.samples), scraper_config ) return # Ignore the metric self._send_telemetry_counter(self.TELEMETRY_COUNTER_METRICS_PROCESS_COUNT, len(metric.samples), scraper_config) if self._filter_metric(metric, scraper_config): return # Ignore the metric # Filter metric to see if we can enrich with joined labels self._join_labels(metric, scraper_config) if scraper_config['_dry_run']: return try: self.submit_openmetric(scraper_config['metrics_mapper'][metric.name], metric, scraper_config) except KeyError: if metric_transformers is not None and metric.name in metric_transformers: try: # Get the transformer function for this specific metric transformer = metric_transformers[metric.name] transformer(metric, scraper_config) except Exception as err: self.log.warning('Error handling metric: %s - error: %s', metric.name, err) return # check for wilcards in transformers for transformer_name, transformer in iteritems(metric_transformers): if transformer_name.endswith('*') and metric.name.startswith(transformer_name[:-1]): transformer(metric, scraper_config, transformer_name) # try matching wildcards if scraper_config['_wildcards_re'] and scraper_config['_wildcards_re'].search(metric.name): self.submit_openmetric(metric.name, metric, scraper_config) return self.log.debug( 'Skipping metric `%s` as it is not defined in the metrics mapper, ' 'has no transformer function, nor does it match any wildcards.', metric.name, ) def poll(self, scraper_config, headers=None): """ Returns a valid `requests.Response`, otherwise raise requests.HTTPError if the status code of the response isn't valid - see `response.raise_for_status()` The caller needs to close the requests.Response. Custom headers can be added to the default headers. """ endpoint = scraper_config.get('prometheus_url') # Should we send a service check for when we make a request health_service_check = scraper_config['health_service_check'] service_check_name = self._metric_name_with_namespace('prometheus.health', scraper_config) service_check_tags = ['endpoint:{}'.format(endpoint)] service_check_tags.extend(scraper_config['custom_tags']) try: response = self.send_request(endpoint, scraper_config, headers) except requests.exceptions.SSLError: self.log.error("Invalid SSL settings for requesting %s endpoint", endpoint) raise except IOError: if health_service_check: self.service_check(service_check_name, AgentCheck.CRITICAL, tags=service_check_tags) raise try: response.raise_for_status() if health_service_check: self.service_check(service_check_name, AgentCheck.OK, tags=service_check_tags) return response except requests.HTTPError: response.close() if health_service_check: self.service_check(service_check_name, AgentCheck.CRITICAL, tags=service_check_tags) raise def send_request(self, endpoint, scraper_config, headers=None): kwargs = {} if headers: kwargs['headers'] = headers http_handler = self.get_http_handler(scraper_config) return http_handler.get(endpoint, stream=True, **kwargs) def get_hostname_for_sample(self, sample, scraper_config): """ Expose the label_to_hostname mapping logic to custom handler methods """ return self._get_hostname(None, sample, scraper_config) def submit_openmetric(self, metric_name, metric, scraper_config, hostname=None): """ For each sample in the metric, report it as a gauge with all labels as tags except if a labels `dict` is passed, in which case keys are label names we'll extract and corresponding values are tag names we'll use (eg: {'node': 'node'}). Histograms generate a set of values instead of a unique metric. `send_histograms_buckets` is used to specify if you want to send the buckets as tagged values when dealing with histograms. `custom_tags` is an array of `tag:value` that will be added to the metric when sending the gauge to Datadog. """ if metric.type in ["gauge", "counter", "rate"]: metric_name_with_namespace = self._metric_name_with_namespace(metric_name, scraper_config) for sample in metric.samples: if self._ignore_metrics_by_label(scraper_config, metric_name, sample): continue val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue custom_hostname = self._get_hostname(hostname, sample, scraper_config) # Determine the tags to send tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) if metric.type == "counter" and scraper_config['send_monotonic_counter']: self.monotonic_count( metric_name_with_namespace, val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif metric.type == "rate": self.rate(metric_name_with_namespace, val, tags=tags, hostname=custom_hostname) else: self.gauge(metric_name_with_namespace, val, tags=tags, hostname=custom_hostname) # Metric is a "counter" but legacy behavior has "send_as_monotonic" defaulted to False # Submit metric as monotonic_count with appended name if metric.type == "counter" and scraper_config['send_monotonic_with_gauge']: self.monotonic_count( metric_name_with_namespace + '.total', val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif metric.type == "histogram": self._submit_gauges_from_histogram(metric_name, metric, scraper_config) elif metric.type == "summary": self._submit_gauges_from_summary(metric_name, metric, scraper_config) else: self.log.error("Metric type %s unsupported for metric %s.", metric.type, metric_name) def _get_hostname(self, hostname, sample, scraper_config): """ If hostname is None, look at label_to_hostname setting """ if ( hostname is None and scraper_config['label_to_hostname'] is not None and sample[self.SAMPLE_LABELS].get(scraper_config['label_to_hostname']) ): hostname = sample[self.SAMPLE_LABELS][scraper_config['label_to_hostname']] suffix = scraper_config['label_to_hostname_suffix'] if suffix is not None: hostname += suffix return hostname def _submit_gauges_from_summary(self, metric_name, metric, scraper_config, hostname=None): """ Extracts metrics from a prometheus summary metric and sends them as gauges """ for sample in metric.samples: val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if self._ignore_metrics_by_label(scraper_config, metric_name, sample): continue custom_hostname = self._get_hostname(hostname, sample, scraper_config) if sample[self.SAMPLE_NAME].endswith("_sum"): tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) self._submit_distribution_count( scraper_config['send_distribution_sums_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.sum".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif sample[self.SAMPLE_NAME].endswith("_count"): tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) self._submit_distribution_count( scraper_config['send_distribution_counts_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.count".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) else: try: quantile = sample[self.SAMPLE_LABELS]["quantile"] except KeyError: # TODO: In the Prometheus spec the 'quantile' label is optional, but it's not clear yet # what we should do in this case. Let's skip for now and submit the rest of metrics. message = ( '"quantile" label not present in metric %r. ' 'Quantile-less summary metrics are not currently supported. Skipping...' ) self.log.debug(message, metric_name) continue sample[self.SAMPLE_LABELS]["quantile"] = str(float(quantile)) tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) self.gauge( "{}.quantile".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, ) def _submit_gauges_from_histogram(self, metric_name, metric, scraper_config, hostname=None): """ Extracts metrics from a prometheus histogram and sends them as gauges """ if scraper_config['non_cumulative_buckets']: self._decumulate_histogram_buckets(metric) for sample in metric.samples: val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if self._ignore_metrics_by_label(scraper_config, metric_name, sample): continue custom_hostname = self._get_hostname(hostname, sample, scraper_config) if sample[self.SAMPLE_NAME].endswith("_sum") and not scraper_config['send_distribution_buckets']: tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname) self._submit_distribution_count( scraper_config['send_distribution_sums_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.sum".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif sample[self.SAMPLE_NAME].endswith("_count") and not scraper_config['send_distribution_buckets']: tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname) if scraper_config['send_histograms_buckets']: tags.append("upper_bound:none") self._submit_distribution_count( scraper_config['send_distribution_counts_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.count".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif scraper_config['send_histograms_buckets'] and sample[self.SAMPLE_NAME].endswith("_bucket"): if scraper_config['send_distribution_buckets']: self._submit_sample_histogram_buckets(metric_name, sample, scraper_config, hostname) elif "Inf" not in sample[self.SAMPLE_LABELS]["le"] or scraper_config['non_cumulative_buckets']: sample[self.SAMPLE_LABELS]["le"] = str(float(sample[self.SAMPLE_LABELS]["le"])) tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname) self._submit_distribution_count( scraper_config['send_distribution_counts_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.count".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) def _compute_bucket_hash(self, tags): # we need the unique context for all the buckets # hence we remove the "le" tag return hash(frozenset(sorted((k, v) for k, v in iteritems(tags) if k != 'le'))) def _decumulate_histogram_buckets(self, metric): """ Decumulate buckets in a given histogram metric and adds the lower_bound label (le being upper_bound) """ bucket_values_by_context_upper_bound = {} for sample in metric.samples: if sample[self.SAMPLE_NAME].endswith("_bucket"): context_key = self._compute_bucket_hash(sample[self.SAMPLE_LABELS]) if context_key not in bucket_values_by_context_upper_bound: bucket_values_by_context_upper_bound[context_key] = {} bucket_values_by_context_upper_bound[context_key][float(sample[self.SAMPLE_LABELS]["le"])] = sample[ self.SAMPLE_VALUE ] sorted_buckets_by_context = {} for context in bucket_values_by_context_upper_bound: sorted_buckets_by_context[context] = sorted(bucket_values_by_context_upper_bound[context]) # Tuples (lower_bound, upper_bound, value) bucket_tuples_by_context_upper_bound = {} for context in sorted_buckets_by_context: for i, upper_b in enumerate(sorted_buckets_by_context[context]): if i == 0: if context not in bucket_tuples_by_context_upper_bound: bucket_tuples_by_context_upper_bound[context] = {} if upper_b > 0: # positive buckets start at zero bucket_tuples_by_context_upper_bound[context][upper_b] = ( 0, upper_b, bucket_values_by_context_upper_bound[context][upper_b], ) else: # negative buckets start at -inf bucket_tuples_by_context_upper_bound[context][upper_b] = ( self.MINUS_INF, upper_b, bucket_values_by_context_upper_bound[context][upper_b], ) continue tmp = ( bucket_values_by_context_upper_bound[context][upper_b] - bucket_values_by_context_upper_bound[context][sorted_buckets_by_context[context][i - 1]] ) bucket_tuples_by_context_upper_bound[context][upper_b] = ( sorted_buckets_by_context[context][i - 1], upper_b, tmp, ) # modify original metric to inject lower_bound & modified value for i, sample in enumerate(metric.samples): if not sample[self.SAMPLE_NAME].endswith("_bucket"): continue context_key = self._compute_bucket_hash(sample[self.SAMPLE_LABELS]) matching_bucket_tuple = bucket_tuples_by_context_upper_bound[context_key][ float(sample[self.SAMPLE_LABELS]["le"]) ] # Replacing the sample tuple sample[self.SAMPLE_LABELS]["lower_bound"] = str(matching_bucket_tuple[0]) metric.samples[i] = Sample(sample[self.SAMPLE_NAME], sample[self.SAMPLE_LABELS], matching_bucket_tuple[2]) def _submit_sample_histogram_buckets(self, metric_name, sample, scraper_config, hostname=None): if "lower_bound" not in sample[self.SAMPLE_LABELS] or "le" not in sample[self.SAMPLE_LABELS]: self.log.warning( "Metric: %s was not containing required bucket boundaries labels: %s", metric_name, sample[self.SAMPLE_LABELS], ) return sample[self.SAMPLE_LABELS]["le"] = str(float(sample[self.SAMPLE_LABELS]["le"])) sample[self.SAMPLE_LABELS]["lower_bound"] = str(float(sample[self.SAMPLE_LABELS]["lower_bound"])) if sample[self.SAMPLE_LABELS]["le"] == sample[self.SAMPLE_LABELS]["lower_bound"]: # this can happen for -inf/-inf bucket that we don't want to send (always 0) self.log.warning( "Metric: %s has bucket boundaries equal, skipping: %s", metric_name, sample[self.SAMPLE_LABELS] ) return tags = self._metric_tags(metric_name, sample[self.SAMPLE_VALUE], sample, scraper_config, hostname) self.submit_histogram_bucket( self._metric_name_with_namespace(metric_name, scraper_config), sample[self.SAMPLE_VALUE], float(sample[self.SAMPLE_LABELS]["lower_bound"]), float(sample[self.SAMPLE_LABELS]["le"]), True, hostname, tags, ) def _submit_distribution_count( self, monotonic, send_monotonic_with_gauge, metric_name, value, tags=None, hostname=None, flush_first_value=False, ): if monotonic: self.monotonic_count(metric_name, value, tags=tags, hostname=hostname, flush_first_value=flush_first_value) else: self.gauge(metric_name, value, tags=tags, hostname=hostname) if send_monotonic_with_gauge: self.monotonic_count( metric_name + ".total", value, tags=tags, hostname=hostname, flush_first_value=flush_first_value ) def _metric_tags(self, metric_name, val, sample, scraper_config, hostname=None): custom_tags = scraper_config['custom_tags'] _tags = list(custom_tags) _tags.extend(scraper_config['_metric_tags']) for label_name, label_value in iteritems(sample[self.SAMPLE_LABELS]): if label_name not in scraper_config['exclude_labels']: tag_name = scraper_config['labels_mapper'].get(label_name, label_name) _tags.append('{}:{}'.format(to_native_string(tag_name), to_native_string(label_value))) return self._finalize_tags_to_submit( _tags, metric_name, val, sample, custom_tags=custom_tags, hostname=hostname ) def _is_value_valid(self, val): return not (isnan(val) or isinf(val)) def _get_bearer_token(self, bearer_token_auth, bearer_token_path): if bearer_token_auth is False: return None path = None if bearer_token_path is not None: if isfile(bearer_token_path): path = bearer_token_path else: self.log.error("File not found: %s", bearer_token_path) elif isfile(self.KUBERNETES_TOKEN_PATH): path = self.KUBERNETES_TOKEN_PATH if path is None: self.log.error("Cannot get bearer token from bearer_token_path or auto discovery") raise IOError("Cannot get bearer token from bearer_token_path or auto discovery") try: with open(path, 'r') as f: return f.read().rstrip() except Exception as err: self.log.error("Cannot get bearer token from path: %s - error: %s", path, err) raise def _histogram_convert_values(self, metric_name, converter): def _convert(metric, scraper_config=None): for index, sample in enumerate(metric.samples): val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if sample[self.SAMPLE_NAME].endswith("_sum"): lst = list(sample) lst[self.SAMPLE_VALUE] = converter(val) metric.samples[index] = tuple(lst) elif sample[self.SAMPLE_NAME].endswith("_bucket") and "Inf" not in sample[self.SAMPLE_LABELS]["le"]: sample[self.SAMPLE_LABELS]["le"] = str(converter(float(sample[self.SAMPLE_LABELS]["le"]))) self.submit_openmetric(metric_name, metric, scraper_config) return _convert def _histogram_from_microseconds_to_seconds(self, metric_name): return self._histogram_convert_values(metric_name, lambda v: v / self.MICROS_IN_S) def _histogram_from_seconds_to_microseconds(self, metric_name): return self._histogram_convert_values(metric_name, lambda v: v * self.MICROS_IN_S) def _summary_convert_values(self, metric_name, converter): def _convert(metric, scraper_config=None): for index, sample in enumerate(metric.samples): val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if sample[self.SAMPLE_NAME].endswith("_count"): continue else: lst = list(sample) lst[self.SAMPLE_VALUE] = converter(val) metric.samples[index] = tuple(lst) self.submit_openmetric(metric_name, metric, scraper_config) return _convert def _summary_from_microseconds_to_seconds(self, metric_name): return self._summary_convert_values(metric_name, lambda v: v / self.MICROS_IN_S) def _summary_from_seconds_to_microseconds(self, metric_name): return self._summary_convert_values(metric_name, lambda v: v * self.MICROS_IN_S)

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from __future__ import division import copy from fnmatch import translate from math import isinf, isnan from os.path import isfile from re import compile import requests from prometheus_client.samples import Sample from six import PY3, iteritems, string_types from ...config import is_affirmative from ...errors import CheckException from ...utils.common import to_native_string from ...utils.http import RequestsWrapper from .. import AgentCheck from ..libs.prometheus import text_fd_to_metric_families if PY3: long = int class OpenMetricsScraperMixin(object): REQUESTS_CHUNK_SIZE = 1024 * 10 SAMPLE_NAME = 0 SAMPLE_LABELS = 1 SAMPLE_VALUE = 2 MICROS_IN_S = 1000000 MINUS_INF = float("-inf") TELEMETRY_GAUGE_MESSAGE_SIZE = "payload.size" TELEMETRY_COUNTER_METRICS_BLACKLIST_COUNT = "metrics.blacklist.count" TELEMETRY_COUNTER_METRICS_INPUT_COUNT = "metrics.input.count" TELEMETRY_COUNTER_METRICS_IGNORE_COUNT = "metrics.ignored.count" TELEMETRY_COUNTER_METRICS_PROCESS_COUNT = "metrics.processed.count" METRIC_TYPES = ['counter', 'gauge', 'summary', 'histogram'] KUBERNETES_TOKEN_PATH = '/var/run/secrets/kubernetes.io/serviceaccount/token' def __init__(self, *args, **kwargs): super(OpenMetricsScraperMixin, self).__init__(*args, **kwargs) def create_scraper_configuration(self, instance=None): if 'openmetrics_endpoint' in instance: raise CheckException('The setting `openmetrics_endpoint` is only available for Agent version 7 or later') # We can choose to create a default mixin configuration for an empty instance if instance is None: instance = {} # Supports new configuration options config = copy.deepcopy(instance) # Set the endpoint endpoint = instance.get('prometheus_url') if instance and endpoint is None: raise CheckException("You have to define a prometheus_url for each prometheus instance") config['prometheus_url'] = endpoint # `NAMESPACE` is the prefix metrics will have. Need to be hardcoded in the # child check class. namespace = instance.get('namespace') # Check if we have a namespace if instance and namespace is None: if self.default_namespace is None: raise CheckException("You have to define a namespace for each prometheus check") namespace = self.default_namespace config['namespace'] = namespace # Retrieve potential default instance settings for the namespace default_instance = self.default_instances.get(namespace, {}) # `metrics_mapper` is a dictionary where the keys are the metrics to capture # and the values are the corresponding metrics names to have in datadog. # Note: it is empty in the parent class but will need to be # overloaded/hardcoded in the final check not to be counted as custom metric. # Metrics are preprocessed if no mapping metrics_mapper = {} # We merge list and dictionaries from optional defaults & instance settings metrics = default_instance.get('metrics', []) + instance.get('metrics', []) for metric in metrics: if isinstance(metric, string_types): metrics_mapper[metric] = metric else: metrics_mapper.update(metric) config['metrics_mapper'] = metrics_mapper # `_wildcards_re` is a Pattern object used to match metric wildcards config['_wildcards_re'] = None wildcards = set() for metric in config['metrics_mapper']: if "*" in metric: wildcards.add(translate(metric)) if wildcards: config['_wildcards_re'] = compile('|'.join(wildcards)) # `prometheus_metrics_prefix` allows to specify a prefix that all # prometheus metrics should have. This can be used when the prometheus # endpoint we are scrapping allows to add a custom prefix to it's config['prometheus_metrics_prefix'] = instance.get( 'prometheus_metrics_prefix', default_instance.get('prometheus_metrics_prefix', '') ) config['label_joins'] = default_instance.get('label_joins', {}) config['label_joins'].update(instance.get('label_joins', {})) config['_label_mapping'] = {} config['_active_label_mapping'] = {} config['_watched_labels'] = {} config['_dry_run'] = True config['ignore_metrics'] = instance.get('ignore_metrics', default_instance.get('ignore_metrics', [])) config['_ignored_metrics'] = set() config['_ignored_re'] = None ignored_patterns = set() for metric in config['ignore_metrics']: if '*' in metric: ignored_patterns.add(translate(metric)) else: config['_ignored_metrics'].add(metric) if ignored_patterns: config['_ignored_re'] = compile('|'.join(ignored_patterns)) config['ignore_metrics_by_labels'] = instance.get( 'ignore_metrics_by_labels', default_instance.get('ignore_metrics_by_labels', {}) ) config['send_histograms_buckets'] = is_affirmative( instance.get('send_histograms_buckets', default_instance.get('send_histograms_buckets', True)) ) config['non_cumulative_buckets'] = is_affirmative( instance.get('non_cumulative_buckets', default_instance.get('non_cumulative_buckets', False)) ) config['send_distribution_buckets'] = is_affirmative( instance.get('send_distribution_buckets', default_instance.get('send_distribution_buckets', False)) ) if config['send_distribution_buckets'] is True: config['non_cumulative_buckets'] = True config['send_monotonic_counter'] = is_affirmative( instance.get('send_monotonic_counter', default_instance.get('send_monotonic_counter', True)) ) config['send_monotonic_with_gauge'] = is_affirmative( instance.get('send_monotonic_with_gauge', default_instance.get('send_monotonic_with_gauge', False)) ) config['send_distribution_counts_as_monotonic'] = is_affirmative( instance.get( 'send_distribution_counts_as_monotonic', default_instance.get('send_distribution_counts_as_monotonic', False), ) ) config['send_distribution_sums_as_monotonic'] = is_affirmative( instance.get( 'send_distribution_sums_as_monotonic', default_instance.get('send_distribution_sums_as_monotonic', False), ) ) config['labels_mapper'] = default_instance.get('labels_mapper', {}) config['labels_mapper'].update(instance.get('labels_mapper', {})) config['labels_mapper']['le'] = 'upper_bound' config['exclude_labels'] = default_instance.get('exclude_labels', []) + instance.get('exclude_labels', []) config['type_overrides'] = default_instance.get('type_overrides', {}) config['type_overrides'].update(instance.get('type_overrides', {})) config['_type_override_patterns'] = {} with_wildcards = set() for metric, type in iteritems(config['type_overrides']): if '*' in metric: config['_type_override_patterns'][compile(translate(metric))] = type with_wildcards.add(metric) for metric in with_wildcards: del config['type_overrides'][metric] config['label_to_hostname'] = instance.get('label_to_hostname', default_instance.get('label_to_hostname', None)) config['label_to_hostname_suffix'] = instance.get( 'label_to_hostname_suffix', default_instance.get('label_to_hostname_suffix', None) ) config['health_service_check'] = is_affirmative( instance.get('health_service_check', default_instance.get('health_service_check', True)) ) config['ssl_cert'] = instance.get('ssl_cert', default_instance.get('ssl_cert', None)) config['ssl_private_key'] = instance.get('ssl_private_key', default_instance.get('ssl_private_key', None)) config['ssl_ca_cert'] = instance.get('ssl_ca_cert', default_instance.get('ssl_ca_cert', None)) config['ssl_verify'] = is_affirmative(instance.get('ssl_verify', default_instance.get('ssl_verify', True))) config['extra_headers'] = default_instance.get('extra_headers', {}) config['extra_headers'].update(instance.get('extra_headers', {})) config['prometheus_timeout'] = instance.get( 'prometheus_timeout', default_instance.get('prometheus_timeout', 10) ) config['username'] = instance.get('username', default_instance.get('username', None)) config['password'] = instance.get('password', default_instance.get('password', None)) config['custom_tags'] = instance.get('tags', []) config['_metric_tags'] = [] config['_text_filter_blacklist'] = [] config['bearer_token_auth'] = is_affirmative( instance.get('bearer_token_auth', default_instance.get('bearer_token_auth', False)) ) config['bearer_token_path'] = instance.get('bearer_token_path', default_instance.get('bearer_token_path', None)) config['_bearer_token'] = self._get_bearer_token(config['bearer_token_auth'], config['bearer_token_path']) config['telemetry'] = is_affirmative(instance.get('telemetry', default_instance.get('telemetry', False))) config['metadata_metric_name'] = instance.get( 'metadata_metric_name', default_instance.get('metadata_metric_name') ) config['metadata_label_map'] = instance.get( 'metadata_label_map', default_instance.get('metadata_label_map', {}) ) config['_default_metric_transformers'] = {} if config['metadata_metric_name'] and config['metadata_label_map']: config['_default_metric_transformers'][config['metadata_metric_name']] = self.transform_metadata config['_successfully_executed'] = False return config def get_http_handler(self, scraper_config): prometheus_url = scraper_config['prometheus_url'] if prometheus_url in self._http_handlers: return self._http_handlers[prometheus_url] if scraper_config['ssl_ca_cert'] is False: scraper_config['ssl_verify'] = False if scraper_config['ssl_verify'] is False: scraper_config.setdefault('tls_ignore_warning', True) http_handler = self._http_handlers[prometheus_url] = RequestsWrapper( scraper_config, self.init_config, self.HTTP_CONFIG_REMAPPER, self.log ) headers = http_handler.options['headers'] bearer_token = scraper_config['_bearer_token'] if bearer_token is not None: headers['Authorization'] = 'Bearer {}'.format(bearer_token) headers.setdefault('accept-encoding', 'gzip') headers.setdefault('accept', 'text/plain') return http_handler def reset_http_config(self): self._http_handlers.clear() def parse_metric_family(self, response, scraper_config): if response.encoding is None: response.encoding = 'utf-8' input_gen = response.iter_lines(chunk_size=self.REQUESTS_CHUNK_SIZE, decode_unicode=True) if scraper_config['_text_filter_blacklist']: input_gen = self._text_filter_input(input_gen, scraper_config) for metric in text_fd_to_metric_families(input_gen): self._send_telemetry_counter( self.TELEMETRY_COUNTER_METRICS_INPUT_COUNT, len(metric.samples), scraper_config ) type_override = scraper_config['type_overrides'].get(metric.name) if type_override: metric.type = type_override elif scraper_config['_type_override_patterns']: for pattern, new_type in iteritems(scraper_config['_type_override_patterns']): if pattern.search(metric.name): metric.type = new_type break if metric.type not in self.METRIC_TYPES: continue metric.name = self._remove_metric_prefix(metric.name, scraper_config) yield metric def _text_filter_input(self, input_gen, scraper_config): for line in input_gen: for item in scraper_config['_text_filter_blacklist']: if item in line: self._send_telemetry_counter(self.TELEMETRY_COUNTER_METRICS_BLACKLIST_COUNT, 1, scraper_config) break else: yield line def _remove_metric_prefix(self, metric, scraper_config): prometheus_metrics_prefix = scraper_config['prometheus_metrics_prefix'] return metric[len(prometheus_metrics_prefix) :] if metric.startswith(prometheus_metrics_prefix) else metric def scrape_metrics(self, scraper_config): response = self.poll(scraper_config) if scraper_config['telemetry']: if 'content-length' in response.headers: content_len = int(response.headers['content-length']) else: content_len = len(response.content) self._send_telemetry_gauge(self.TELEMETRY_GAUGE_MESSAGE_SIZE, content_len, scraper_config) try: if not scraper_config['label_joins']: scraper_config['_dry_run'] = False elif not scraper_config['_watched_labels']: watched = scraper_config['_watched_labels'] watched['sets'] = {} watched['keys'] = {} watched['singles'] = set() for key, val in iteritems(scraper_config['label_joins']): labels = [] if 'labels_to_match' in val: labels = val['labels_to_match'] elif 'label_to_match' in val: self.log.warning("`label_to_match` is being deprecated, please use `labels_to_match`") if isinstance(val['label_to_match'], list): labels = val['label_to_match'] else: labels = [val['label_to_match']] if labels: s = frozenset(labels) watched['sets'][key] = s watched['keys'][key] = ','.join(s) if len(labels) == 1: watched['singles'].add(labels[0]) for metric in self.parse_metric_family(response, scraper_config): yield metric scraper_config['_dry_run'] = False for metric, mapping in list(iteritems(scraper_config['_label_mapping'])): for key in list(mapping): if ( metric in scraper_config['_active_label_mapping'] and key not in scraper_config['_active_label_mapping'][metric] ): del scraper_config['_label_mapping'][metric][key] scraper_config['_active_label_mapping'] = {} finally: response.close() def process(self, scraper_config, metric_transformers=None): transformers = scraper_config['_default_metric_transformers'].copy() if metric_transformers: transformers.update(metric_transformers) for metric in self.scrape_metrics(scraper_config): self.process_metric(metric, scraper_config, metric_transformers=transformers) scraper_config['_successfully_executed'] = True def transform_metadata(self, metric, scraper_config): labels = metric.samples[0][self.SAMPLE_LABELS] for metadata_name, label_name in iteritems(scraper_config['metadata_label_map']): if label_name in labels: self.set_metadata(metadata_name, labels[label_name]) def _metric_name_with_namespace(self, metric_name, scraper_config): namespace = scraper_config['namespace'] if not namespace: return metric_name return '{}.{}'.format(namespace, metric_name) def _telemetry_metric_name_with_namespace(self, metric_name, scraper_config): namespace = scraper_config['namespace'] if not namespace: return '{}.{}'.format('telemetry', metric_name) return '{}.{}.{}'.format(namespace, 'telemetry', metric_name) def _send_telemetry_gauge(self, metric_name, val, scraper_config): if scraper_config['telemetry']: metric_name_with_namespace = self._telemetry_metric_name_with_namespace(metric_name, scraper_config) custom_tags = scraper_config['custom_tags'] tags = list(custom_tags) tags.extend(scraper_config['_metric_tags']) self.gauge(metric_name_with_namespace, val, tags=tags) def _send_telemetry_counter(self, metric_name, val, scraper_config, extra_tags=None): if scraper_config['telemetry']: metric_name_with_namespace = self._telemetry_metric_name_with_namespace(metric_name, scraper_config) custom_tags = scraper_config['custom_tags'] tags = list(custom_tags) tags.extend(scraper_config['_metric_tags']) if extra_tags: tags.extend(extra_tags) self.count(metric_name_with_namespace, val, tags=tags) def _store_labels(self, metric, scraper_config): if metric.name not in scraper_config['label_joins']: return watched = scraper_config['_watched_labels'] matching_labels = watched['sets'][metric.name] mapping_key = watched['keys'][metric.name] labels_to_get = scraper_config['label_joins'][metric.name]['labels_to_get'] get_all = '*' in labels_to_get match_all = mapping_key == '*' for sample in metric.samples: if sample[self.SAMPLE_VALUE] != 1: continue sample_labels = sample[self.SAMPLE_LABELS] sample_labels_keys = sample_labels.keys() if match_all or matching_labels.issubset(sample_labels_keys): label_dict = dict() if get_all: for label_name, label_value in iteritems(sample_labels): if label_name in matching_labels: continue label_dict[label_name] = label_value else: for label_name in labels_to_get: if label_name in sample_labels: label_dict[label_name] = sample_labels[label_name] if match_all: mapping_value = '*' else: mapping_value = ','.join([sample_labels[l] for l in matching_labels]) scraper_config['_label_mapping'].setdefault(mapping_key, {}).setdefault(mapping_value, {}).update( label_dict ) def _join_labels(self, metric, scraper_config): if not scraper_config['label_joins']: return label_mapping = scraper_config['_label_mapping'] active_label_mapping = scraper_config['_active_label_mapping'] watched = scraper_config['_watched_labels'] sets = watched['sets'] keys = watched['keys'] singles = watched['singles'] for sample in metric.samples: sample_labels = sample[self.SAMPLE_LABELS] sample_labels_keys = sample_labels.keys() if '*' in singles: active_label_mapping.setdefault('*', {})['*'] = True if '*' in label_mapping and '*' in label_mapping['*']: sample_labels.update(label_mapping['*']['*']) matching_single_labels = singles.intersection(sample_labels_keys) for label in matching_single_labels: mapping_key = label mapping_value = sample_labels[label] active_label_mapping.setdefault(mapping_key, {})[mapping_value] = True if mapping_key in label_mapping and mapping_value in label_mapping[mapping_key]: sample_labels.update(label_mapping[mapping_key][mapping_value]) for key, mapping_key in iteritems(keys): if mapping_key in matching_single_labels: continue matching_labels = sets[key] if matching_labels.issubset(sample_labels_keys): matching_values = [sample_labels[l] for l in matching_labels] mapping_value = ','.join(matching_values) active_label_mapping.setdefault(mapping_key, {})[mapping_value] = True if mapping_key in label_mapping and mapping_value in label_mapping[mapping_key]: sample_labels.update(label_mapping[mapping_key][mapping_value]) def _ignore_metrics_by_label(self, scraper_config, metric_name, sample): ignore_metrics_by_label = scraper_config['ignore_metrics_by_labels'] sample_labels = sample[self.SAMPLE_LABELS] for label_key, label_values in ignore_metrics_by_label.items(): if not label_values: self.log.debug( "Skipping filter label `%s` with an empty values list, did you mean to use '*' wildcard?", label_key ) elif '*' in label_values: self.log.debug("Detected wildcard for label `%s`", label_key) if label_key in sample_labels.keys(): self.log.debug("Skipping metric `%s` due to label key matching: %s", metric_name, label_key) return True else: for val in label_values: if label_key in sample_labels and sample_labels[label_key] == val: self.log.debug( "Skipping metric `%s` due to label `%s` value matching: %s", metric_name, label_key, val ) return True return False def process_metric(self, metric, scraper_config, metric_transformers=None): self._store_labels(metric, scraper_config) if scraper_config['ignore_metrics']: if metric.name in scraper_config['_ignored_metrics']: self._send_telemetry_counter( self.TELEMETRY_COUNTER_METRICS_IGNORE_COUNT, len(metric.samples), scraper_config ) return if scraper_config['_ignored_re'] and scraper_config['_ignored_re'].search(metric.name): scraper_config['_ignored_metrics'].add(metric.name) self._send_telemetry_counter( self.TELEMETRY_COUNTER_METRICS_IGNORE_COUNT, len(metric.samples), scraper_config ) return self._send_telemetry_counter(self.TELEMETRY_COUNTER_METRICS_PROCESS_COUNT, len(metric.samples), scraper_config) if self._filter_metric(metric, scraper_config): return self._join_labels(metric, scraper_config) if scraper_config['_dry_run']: return try: self.submit_openmetric(scraper_config['metrics_mapper'][metric.name], metric, scraper_config) except KeyError: if metric_transformers is not None and metric.name in metric_transformers: try: transformer = metric_transformers[metric.name] transformer(metric, scraper_config) except Exception as err: self.log.warning('Error handling metric: %s - error: %s', metric.name, err) return for transformer_name, transformer in iteritems(metric_transformers): if transformer_name.endswith('*') and metric.name.startswith(transformer_name[:-1]): transformer(metric, scraper_config, transformer_name) if scraper_config['_wildcards_re'] and scraper_config['_wildcards_re'].search(metric.name): self.submit_openmetric(metric.name, metric, scraper_config) return self.log.debug( 'Skipping metric `%s` as it is not defined in the metrics mapper, ' 'has no transformer function, nor does it match any wildcards.', metric.name, ) def poll(self, scraper_config, headers=None): endpoint = scraper_config.get('prometheus_url') health_service_check = scraper_config['health_service_check'] service_check_name = self._metric_name_with_namespace('prometheus.health', scraper_config) service_check_tags = ['endpoint:{}'.format(endpoint)] service_check_tags.extend(scraper_config['custom_tags']) try: response = self.send_request(endpoint, scraper_config, headers) except requests.exceptions.SSLError: self.log.error("Invalid SSL settings for requesting %s endpoint", endpoint) raise except IOError: if health_service_check: self.service_check(service_check_name, AgentCheck.CRITICAL, tags=service_check_tags) raise try: response.raise_for_status() if health_service_check: self.service_check(service_check_name, AgentCheck.OK, tags=service_check_tags) return response except requests.HTTPError: response.close() if health_service_check: self.service_check(service_check_name, AgentCheck.CRITICAL, tags=service_check_tags) raise def send_request(self, endpoint, scraper_config, headers=None): kwargs = {} if headers: kwargs['headers'] = headers http_handler = self.get_http_handler(scraper_config) return http_handler.get(endpoint, stream=True, **kwargs) def get_hostname_for_sample(self, sample, scraper_config): return self._get_hostname(None, sample, scraper_config) def submit_openmetric(self, metric_name, metric, scraper_config, hostname=None): if metric.type in ["gauge", "counter", "rate"]: metric_name_with_namespace = self._metric_name_with_namespace(metric_name, scraper_config) for sample in metric.samples: if self._ignore_metrics_by_label(scraper_config, metric_name, sample): continue val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue custom_hostname = self._get_hostname(hostname, sample, scraper_config) tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) if metric.type == "counter" and scraper_config['send_monotonic_counter']: self.monotonic_count( metric_name_with_namespace, val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif metric.type == "rate": self.rate(metric_name_with_namespace, val, tags=tags, hostname=custom_hostname) else: self.gauge(metric_name_with_namespace, val, tags=tags, hostname=custom_hostname) if metric.type == "counter" and scraper_config['send_monotonic_with_gauge']: self.monotonic_count( metric_name_with_namespace + '.total', val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif metric.type == "histogram": self._submit_gauges_from_histogram(metric_name, metric, scraper_config) elif metric.type == "summary": self._submit_gauges_from_summary(metric_name, metric, scraper_config) else: self.log.error("Metric type %s unsupported for metric %s.", metric.type, metric_name) def _get_hostname(self, hostname, sample, scraper_config): if ( hostname is None and scraper_config['label_to_hostname'] is not None and sample[self.SAMPLE_LABELS].get(scraper_config['label_to_hostname']) ): hostname = sample[self.SAMPLE_LABELS][scraper_config['label_to_hostname']] suffix = scraper_config['label_to_hostname_suffix'] if suffix is not None: hostname += suffix return hostname def _submit_gauges_from_summary(self, metric_name, metric, scraper_config, hostname=None): for sample in metric.samples: val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if self._ignore_metrics_by_label(scraper_config, metric_name, sample): continue custom_hostname = self._get_hostname(hostname, sample, scraper_config) if sample[self.SAMPLE_NAME].endswith("_sum"): tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) self._submit_distribution_count( scraper_config['send_distribution_sums_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.sum".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif sample[self.SAMPLE_NAME].endswith("_count"): tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) self._submit_distribution_count( scraper_config['send_distribution_counts_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.count".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) else: try: quantile = sample[self.SAMPLE_LABELS]["quantile"] except KeyError: # what we should do in this case. Let's skip for now and submit the rest of metrics. message = ( '"quantile" label not present in metric %r. ' 'Quantile-less summary metrics are not currently supported. Skipping...' ) self.log.debug(message, metric_name) continue sample[self.SAMPLE_LABELS]["quantile"] = str(float(quantile)) tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname=custom_hostname) self.gauge( "{}.quantile".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, ) def _submit_gauges_from_histogram(self, metric_name, metric, scraper_config, hostname=None): if scraper_config['non_cumulative_buckets']: self._decumulate_histogram_buckets(metric) for sample in metric.samples: val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if self._ignore_metrics_by_label(scraper_config, metric_name, sample): continue custom_hostname = self._get_hostname(hostname, sample, scraper_config) if sample[self.SAMPLE_NAME].endswith("_sum") and not scraper_config['send_distribution_buckets']: tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname) self._submit_distribution_count( scraper_config['send_distribution_sums_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.sum".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif sample[self.SAMPLE_NAME].endswith("_count") and not scraper_config['send_distribution_buckets']: tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname) if scraper_config['send_histograms_buckets']: tags.append("upper_bound:none") self._submit_distribution_count( scraper_config['send_distribution_counts_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.count".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) elif scraper_config['send_histograms_buckets'] and sample[self.SAMPLE_NAME].endswith("_bucket"): if scraper_config['send_distribution_buckets']: self._submit_sample_histogram_buckets(metric_name, sample, scraper_config, hostname) elif "Inf" not in sample[self.SAMPLE_LABELS]["le"] or scraper_config['non_cumulative_buckets']: sample[self.SAMPLE_LABELS]["le"] = str(float(sample[self.SAMPLE_LABELS]["le"])) tags = self._metric_tags(metric_name, val, sample, scraper_config, hostname) self._submit_distribution_count( scraper_config['send_distribution_counts_as_monotonic'], scraper_config['send_monotonic_with_gauge'], "{}.count".format(self._metric_name_with_namespace(metric_name, scraper_config)), val, tags=tags, hostname=custom_hostname, flush_first_value=scraper_config['_successfully_executed'], ) def _compute_bucket_hash(self, tags): return hash(frozenset(sorted((k, v) for k, v in iteritems(tags) if k != 'le'))) def _decumulate_histogram_buckets(self, metric): bucket_values_by_context_upper_bound = {} for sample in metric.samples: if sample[self.SAMPLE_NAME].endswith("_bucket"): context_key = self._compute_bucket_hash(sample[self.SAMPLE_LABELS]) if context_key not in bucket_values_by_context_upper_bound: bucket_values_by_context_upper_bound[context_key] = {} bucket_values_by_context_upper_bound[context_key][float(sample[self.SAMPLE_LABELS]["le"])] = sample[ self.SAMPLE_VALUE ] sorted_buckets_by_context = {} for context in bucket_values_by_context_upper_bound: sorted_buckets_by_context[context] = sorted(bucket_values_by_context_upper_bound[context]) bucket_tuples_by_context_upper_bound = {} for context in sorted_buckets_by_context: for i, upper_b in enumerate(sorted_buckets_by_context[context]): if i == 0: if context not in bucket_tuples_by_context_upper_bound: bucket_tuples_by_context_upper_bound[context] = {} if upper_b > 0: bucket_tuples_by_context_upper_bound[context][upper_b] = ( 0, upper_b, bucket_values_by_context_upper_bound[context][upper_b], ) else: bucket_tuples_by_context_upper_bound[context][upper_b] = ( self.MINUS_INF, upper_b, bucket_values_by_context_upper_bound[context][upper_b], ) continue tmp = ( bucket_values_by_context_upper_bound[context][upper_b] - bucket_values_by_context_upper_bound[context][sorted_buckets_by_context[context][i - 1]] ) bucket_tuples_by_context_upper_bound[context][upper_b] = ( sorted_buckets_by_context[context][i - 1], upper_b, tmp, ) for i, sample in enumerate(metric.samples): if not sample[self.SAMPLE_NAME].endswith("_bucket"): continue context_key = self._compute_bucket_hash(sample[self.SAMPLE_LABELS]) matching_bucket_tuple = bucket_tuples_by_context_upper_bound[context_key][ float(sample[self.SAMPLE_LABELS]["le"]) ] sample[self.SAMPLE_LABELS]["lower_bound"] = str(matching_bucket_tuple[0]) metric.samples[i] = Sample(sample[self.SAMPLE_NAME], sample[self.SAMPLE_LABELS], matching_bucket_tuple[2]) def _submit_sample_histogram_buckets(self, metric_name, sample, scraper_config, hostname=None): if "lower_bound" not in sample[self.SAMPLE_LABELS] or "le" not in sample[self.SAMPLE_LABELS]: self.log.warning( "Metric: %s was not containing required bucket boundaries labels: %s", metric_name, sample[self.SAMPLE_LABELS], ) return sample[self.SAMPLE_LABELS]["le"] = str(float(sample[self.SAMPLE_LABELS]["le"])) sample[self.SAMPLE_LABELS]["lower_bound"] = str(float(sample[self.SAMPLE_LABELS]["lower_bound"])) if sample[self.SAMPLE_LABELS]["le"] == sample[self.SAMPLE_LABELS]["lower_bound"]: self.log.warning( "Metric: %s has bucket boundaries equal, skipping: %s", metric_name, sample[self.SAMPLE_LABELS] ) return tags = self._metric_tags(metric_name, sample[self.SAMPLE_VALUE], sample, scraper_config, hostname) self.submit_histogram_bucket( self._metric_name_with_namespace(metric_name, scraper_config), sample[self.SAMPLE_VALUE], float(sample[self.SAMPLE_LABELS]["lower_bound"]), float(sample[self.SAMPLE_LABELS]["le"]), True, hostname, tags, ) def _submit_distribution_count( self, monotonic, send_monotonic_with_gauge, metric_name, value, tags=None, hostname=None, flush_first_value=False, ): if monotonic: self.monotonic_count(metric_name, value, tags=tags, hostname=hostname, flush_first_value=flush_first_value) else: self.gauge(metric_name, value, tags=tags, hostname=hostname) if send_monotonic_with_gauge: self.monotonic_count( metric_name + ".total", value, tags=tags, hostname=hostname, flush_first_value=flush_first_value ) def _metric_tags(self, metric_name, val, sample, scraper_config, hostname=None): custom_tags = scraper_config['custom_tags'] _tags = list(custom_tags) _tags.extend(scraper_config['_metric_tags']) for label_name, label_value in iteritems(sample[self.SAMPLE_LABELS]): if label_name not in scraper_config['exclude_labels']: tag_name = scraper_config['labels_mapper'].get(label_name, label_name) _tags.append('{}:{}'.format(to_native_string(tag_name), to_native_string(label_value))) return self._finalize_tags_to_submit( _tags, metric_name, val, sample, custom_tags=custom_tags, hostname=hostname ) def _is_value_valid(self, val): return not (isnan(val) or isinf(val)) def _get_bearer_token(self, bearer_token_auth, bearer_token_path): if bearer_token_auth is False: return None path = None if bearer_token_path is not None: if isfile(bearer_token_path): path = bearer_token_path else: self.log.error("File not found: %s", bearer_token_path) elif isfile(self.KUBERNETES_TOKEN_PATH): path = self.KUBERNETES_TOKEN_PATH if path is None: self.log.error("Cannot get bearer token from bearer_token_path or auto discovery") raise IOError("Cannot get bearer token from bearer_token_path or auto discovery") try: with open(path, 'r') as f: return f.read().rstrip() except Exception as err: self.log.error("Cannot get bearer token from path: %s - error: %s", path, err) raise def _histogram_convert_values(self, metric_name, converter): def _convert(metric, scraper_config=None): for index, sample in enumerate(metric.samples): val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if sample[self.SAMPLE_NAME].endswith("_sum"): lst = list(sample) lst[self.SAMPLE_VALUE] = converter(val) metric.samples[index] = tuple(lst) elif sample[self.SAMPLE_NAME].endswith("_bucket") and "Inf" not in sample[self.SAMPLE_LABELS]["le"]: sample[self.SAMPLE_LABELS]["le"] = str(converter(float(sample[self.SAMPLE_LABELS]["le"]))) self.submit_openmetric(metric_name, metric, scraper_config) return _convert def _histogram_from_microseconds_to_seconds(self, metric_name): return self._histogram_convert_values(metric_name, lambda v: v / self.MICROS_IN_S) def _histogram_from_seconds_to_microseconds(self, metric_name): return self._histogram_convert_values(metric_name, lambda v: v * self.MICROS_IN_S) def _summary_convert_values(self, metric_name, converter): def _convert(metric, scraper_config=None): for index, sample in enumerate(metric.samples): val = sample[self.SAMPLE_VALUE] if not self._is_value_valid(val): self.log.debug("Metric value is not supported for metric %s", sample[self.SAMPLE_NAME]) continue if sample[self.SAMPLE_NAME].endswith("_count"): continue else: lst = list(sample) lst[self.SAMPLE_VALUE] = converter(val) metric.samples[index] = tuple(lst) self.submit_openmetric(metric_name, metric, scraper_config) return _convert def _summary_from_microseconds_to_seconds(self, metric_name): return self._summary_convert_values(metric_name, lambda v: v / self.MICROS_IN_S) def _summary_from_seconds_to_microseconds(self, metric_name): return self._summary_convert_values(metric_name, lambda v: v * self.MICROS_IN_S)

true

790d6ee6eaa7f2a7b19763772641feb2cf553339

10,276

py

Python

test/units/formats/office/test_xlxtr.py

bronxc/refinery

9448facf48a0008f27861dd1a5ee8f5218e6bb86

[ "BSD-3-Clause" ]

1

2022-02-13T20:57:15.000Z

test/units/formats/office/test_xlxtr.py

bronxc/refinery

9448facf48a0008f27861dd1a5ee8f5218e6bb86

[ "BSD-3-Clause" ]

null

test/units/formats/office/test_xlxtr.py

bronxc/refinery

9448facf48a0008f27861dd1a5ee8f5218e6bb86

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import base64 import lzma from ... import TestUnitBase from refinery.units.formats.office.xlxtr import _ref2rc, _rc2ref class TestCellIndexConverter(TestUnitBase): def test_concistency(self): for row in range(1, 12): for col in range(1, 12): ref = _rc2ref(row, col) r, c = _ref2rc(ref) self.assertEqual((r, c), (row, col), F'({row},{col}) -> {ref} -> ({r}, {c}) != ({row},{col})') class TestExcelExtractor(TestUnitBase): def test_regular_xlsx(self): data = self.TEST_XLSX unit = self.load() self.assertEqual(unit(data), B'Binary\nRefinery.\nBinary Refinery.') xl1 = self.load('A1', 'R33', squeeze=True)(data) xl2 = self.load('2#E10')(data) xl3 = self.load('Refinery#E10')(data) self.assertEqual(xl2, xl3) self.assertEqual(xl1, b'BinaryRefinery.') self.assertEqual(xl2, b'Binary Refinery.') TEST_XLSX = lzma.decompress(base64.b85decode( '{Wp48S^xk9=GL@E0stWa8~^|S5YJf5;3PvDAzc6{61-q2m(dT*lz$@h&uisO-M2S>G=qQEROhS?T`LVCl<0*Kr;j=qGZrTMa1_{74oZ0B;H_q6z{0fO2`#4p' 'Z(%@Rrb2l^+DIK4qbHHF_tmNDpz&Y$NlI-C6c(59S<hkLEM^A)s!{gk@qKO#f!<CU&7G31h2%4o%gM*%hC-@#t>rmqA<7aPOjP!YEkx*jkYln_Gs2{7ZcSSp' 'k%^+f{8_0fK#=AnGd4nKnS~b32=88*Gzk18vHibqY6IP;P8rsEd*hi%t(hYl<vzGV#mly+rRuPU?H$RjiOhkC&_Y^=3@n*lF-L-p{&*dA>A$-1cYhlULYXE~' '9lRf#_`OFa&uH^H|E#>F1+<slwderZG)kz>f=O+S%CnbmT=-*EXvyp=?C!#p@e|yqJFol$s>T6*DyGIxp^}#q4f#_*{FEDNWty4CtIr9?l}dTd2ZvRe4c(lw' 'DABO4`<xHUA!rFO$CY0pMP$7Ch|~lYzBzW26csva+1m`if>ts<6(kc$R^2wfYI_u<Q|ve2LG39foqnwf%7wRQd2S-u4FHQJN@YT;52pT!6{VrFCidv$Fyf;}' 'rH559u)j4P7JILO$#(5+ZYcGMZALFyO?bVadG%NCWt)~F^p=Pm29lCFbYt)Fedzu<1zSy|M+}&@hOGrpf$f_=Y#DSA@|#f687|=g$UxDWWJKOTp)mW6TzZ=^' 'p2l)f#+eE2G<HArbYwZE!pb>bRES(cfK<g8_b)!Kft2?rXK}=vK3~G(CX^_QX)BQi&gU31F}4c4VcB7TrBk^r&0ca1okiuv1q4^388j~{y%RNKdMWD;q7$3l' '#C;mMydS27!Koh*Bsd(dJ8m~*nz#&cRltJuz`RD02l;!L145|lg~%t7)#pZ6bT%^@aB5v|Mx2gU?|0@qMh{gR9r!(5QDnF8uc&l@Th{F@viY>d61j#TIyb8X' '61@K*a|ghIpbVLNf7H)(W5>emQ41R#dw<#Af~ZpQO|)JqOd_Vj*kk+pzMMj@w+^G{FQH|dL4#ia(qX?XVK!~^yYHeq(&}Ngxfz31xqCY)rD*@_3Pyn>pc~Wn' 'MYDkF4kdF2tAi&B|JQ~s4)B9`NTUl4qos<(L1M+~{2d!BjkqBUb0%v1*kgIrF+ptfh}s0W$bSkIfJEba^sYW_lhRuUo-$5(Fftuy6p{|&N2JPAGBvqFg`%Q)' '1cB<NMLt8qVvugS&hO*6_B9Kg?C_=TOZyGd>o8}DAXwo}7%+6|%=!Q&@h){<N`TgzUUJ67cJdcdXo;y#hyb@#8t&HY8P=kV)6}2jZhORE^Qab?zfQf7B_xQV' 'RK!+xABFg{33KMQ{4`>l&=iyiPUfI)c<LSMZ$G<RZa2rC=p3JGN`2;6a?#<4(EV$(=VK)cnGq^2NNZgPm;XW_n&r%)Tv0l1<R+xEEgpr*wA|*#_J_;WjMhx*' '2_V1cq6SWKO|ImPFM#_s4uUlRF5$o<bxhE8EI!Cp;wWYl$Rwb5FtH|uR2(*WCRKe{RcePa){nOIYL{IHzSvbnG=TE4j4@A1=U$eDy?6P-nQ|;;P(T(jnSv=m' 'A&Rh1<Lz=W1J+!8u%iw8-_zZAtJcr2%@WV=+r{F4QyRi-NYdmBUk!FaGe5&&sf5vL_S1fe>CT`VFqQJ@BYH?72AFt;%Y}5m9zy2-<(iY_-&tjDSa4w0OtaO1' '8tKtv_^&+^2ur(e<A~BD=}W({XC6cTLgOQNXL9dl25Uj~y?U_xM??>jmwHU+ICMbW#mHy;%;FmR7XxDT&|UA)JmOx6IY-%2Nzf6u%Ak^&L#DrA=cJ-qL+2V4' 'QaEix%b9zxe1xNE5#G23ON{#;_>8Kk9uORLt@ysrPLTL;n@tE%n;XrSU|Lbfw)ow=_ou8?#%|lEmF1WDbL}FKuGMr+{x400xau(;+mVCbvi;c!7;xGT@yFdV' 'O%KZ3Zd7>8k{6`<kvAq=;*cc=8so}&t<|n@0JZ0ilyz;t_j^nrUr_nSS-~|bLvwY%)Eezn(t5`=4(yJ3=C)R^NZ7aBvqw##zY<>uu=C59T>6kOvA{kgk@|v`' 's>pkG(&hxNnj-cSvL;G~#$Ew`FZiF$IM+7ut?;osAW_o%bvrhoYq6nZm9@=HAw>h4Pp#i=u)I}zReJI81}J1NlhYYmCJI!K?zcp6@Y#8Z3MQwQRUxzknnlp5' 'Rl_cFj`Wt<CU*@+s1`HvyHy~l=e_`sA<(R)nIRh{g7LFc>#eyLlRNK~<0x(GE1^FLwOTD6)j;!)u7?|Ed8uB8efa1bHZN)eQzTas@ce)BAOmvmldGs|(&vx<' '5<<8Fy}}2W=u;!65A`@sm;bxZvSJ7?a@dwF?Hm9qA<e_Li%pFt+<IhChQmdjO{g%kg(jDtI-dwJFT9Gy@;{Nj;_p=$7QGZ6J(<db_mP^Z0@hL`fMm~^emi-<' '#U}<C;1S7UX&q{)L&*;Bb4F4&hy!RF0|TGtm9!CB-zUI~7+XmC5f#gR?25`_79+(~-tv8S?S4f!r4*c$F!XRrO<4{vh^|w`l%t?0J>547bF1x6nFKL1FZME8' 'x>xF18ESM1s;wm*-x&m$NDpw?@x=<tlcE)STJnr9{NuK;#i6_2MYCPl%4Zq^9*$^R372ua6jwv>oH^mR0ioqk%%)Awns;#lrjXkIhYB_Vt*Pr*oTgse6Uazr' 'd)yUnaZ|Z`9?Q6aTHa2@m4`pd_?E;;Re)&<*otbim^DZ!V{~?+t%H;U2&V8O9CkMdW*tOzBErCD-E}{=Nl%~-`;W#E5$bMF8A-TOVDt09^K)tTG2cvWxLh%9' 'cuC?O7rL(QbGlAASV!M6dTB)pfy|#N5k4(Mdd*7+Mb<Fc^fR3BfFeEzF^|<<jpBXBM&T8{-77eX)1)UjzwbB1E&LZ4khDM^66En##rJ{5FB;62)1u0P(WW!?' 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96.943396

130

0.672635

import base64 import lzma from ... import TestUnitBase from refinery.units.formats.office.xlxtr import _ref2rc, _rc2ref class TestCellIndexConverter(TestUnitBase): def test_concistency(self): for row in range(1, 12): for col in range(1, 12): ref = _rc2ref(row, col) r, c = _ref2rc(ref) self.assertEqual((r, c), (row, col), F'({row},{col}) -> {ref} -> ({r}, {c}) != ({row},{col})') class TestExcelExtractor(TestUnitBase): def test_regular_xlsx(self): data = self.TEST_XLSX unit = self.load() self.assertEqual(unit(data), B'Binary\nRefinery.\nBinary Refinery.') xl1 = self.load('A1', 'R33', squeeze=True)(data) xl2 = self.load('2#E10')(data) xl3 = self.load('Refinery#E10')(data) self.assertEqual(xl2, xl3) self.assertEqual(xl1, b'BinaryRefinery.') self.assertEqual(xl2, b'Binary Refinery.') TEST_XLSX = lzma.decompress(base64.b85decode( '{Wp48S^xk9=GL@E0stWa8~^|S5YJf5;3PvDAzc6{61-q2m(dT*lz$@h&uisO-M2S>G=qQEROhS?T`LVCl<0*Kr;j=qGZrTMa1_{74oZ0B;H_q6z{0fO2`#4p' 'Z(%@Rrb2l^+DIK4qbHHF_tmNDpz&Y$NlI-C6c(59S<hkLEM^A)s!{gk@qKO#f!<CU&7G31h2%4o%gM*%hC-@#t>rmqA<7aPOjP!YEkx*jkYln_Gs2{7ZcSSp' 'k%^+f{8_0fK#=AnGd4nKnS~b32=88*Gzk18vHibqY6IP;P8rsEd*hi%t(hYl<vzGV#mly+rRuPU?H$RjiOhkC&_Y^=3@n*lF-L-p{&*dA>A$-1cYhlULYXE~' '9lRf#_`OFa&uH^H|E#>F1+<slwderZG)kz>f=O+S%CnbmT=-*EXvyp=?C!#p@e|yqJFol$s>T6*DyGIxp^}#q4f#_*{FEDNWty4CtIr9?l}dTd2ZvRe4c(lw' 'DABO4`<xHUA!rFO$CY0pMP$7Ch|~lYzBzW26csva+1m`if>ts<6(kc$R^2wfYI_u<Q|ve2LG39foqnwf%7wRQd2S-u4FHQJN@YT;52pT!6{VrFCidv$Fyf;}' 'rH559u)j4P7JILO$#(5+ZYcGMZALFyO?bVadG%NCWt)~F^p=Pm29lCFbYt)Fedzu<1zSy|M+}&@hOGrpf$f_=Y#DSA@|#f687|=g$UxDWWJKOTp)mW6TzZ=^' 'p2l)f#+eE2G<HArbYwZE!pb>bRES(cfK<g8_b)!Kft2?rXK}=vK3~G(CX^_QX)BQi&gU31F}4c4VcB7TrBk^r&0ca1okiuv1q4^388j~{y%RNKdMWD;q7$3l' '#C;mMydS27!Koh*Bsd(dJ8m~*nz#&cRltJuz`RD02l;!L145|lg~%t7)#pZ6bT%^@aB5v|Mx2gU?|0@qMh{gR9r!(5QDnF8uc&l@Th{F@viY>d61j#TIyb8X' '61@K*a|ghIpbVLNf7H)(W5>emQ41R#dw<#Af~ZpQO|)JqOd_Vj*kk+pzMMj@w+^G{FQH|dL4#ia(qX?XVK!~^yYHeq(&}Ngxfz31xqCY)rD*@_3Pyn>pc~Wn' 'MYDkF4kdF2tAi&B|JQ~s4)B9`NTUl4qos<(L1M+~{2d!BjkqBUb0%v1*kgIrF+ptfh}s0W$bSkIfJEba^sYW_lhRuUo-$5(Fftuy6p{|&N2JPAGBvqFg`%Q)' '1cB<NMLt8qVvugS&hO*6_B9Kg?C_=TOZyGd>o8}DAXwo}7%+6|%=!Q&@h){<N`TgzUUJ67cJdcdXo;y#hyb@#8t&HY8P=kV)6}2jZhORE^Qab?zfQf7B_xQV' 'RK!+xABFg{33KMQ{4`>l&=iyiPUfI)c<LSMZ$G<RZa2rC=p3JGN`2;6a?#<4(EV$(=VK)cnGq^2NNZgPm;XW_n&r%)Tv0l1<R+xEEgpr*wA|*#_J_;WjMhx*' '2_V1cq6SWKO|ImPFM#_s4uUlRF5$o<bxhE8EI!Cp;wWYl$Rwb5FtH|uR2(*WCRKe{RcePa){nOIYL{IHzSvbnG=TE4j4@A1=U$eDy?6P-nQ|;;P(T(jnSv=m' 'A&Rh1<Lz=W1J+!8u%iw8-_zZAtJcr2%@WV=+r{F4QyRi-NYdmBUk!FaGe5&&sf5vL_S1fe>CT`VFqQJ@BYH?72AFt;%Y}5m9zy2-<(iY_-&tjDSa4w0OtaO1' '8tKtv_^&+^2ur(e<A~BD=}W({XC6cTLgOQNXL9dl25Uj~y?U_xM??>jmwHU+ICMbW#mHy;%;FmR7XxDT&|UA)JmOx6IY-%2Nzf6u%Ak^&L#DrA=cJ-qL+2V4' 'QaEix%b9zxe1xNE5#G23ON{#;_>8Kk9uORLt@ysrPLTL;n@tE%n;XrSU|Lbfw)ow=_ou8?#%|lEmF1WDbL}FKuGMr+{x400xau(;+mVCbvi;c!7;xGT@yFdV' 'O%KZ3Zd7>8k{6`<kvAq=;*cc=8so}&t<|n@0JZ0ilyz;t_j^nrUr_nSS-~|bLvwY%)Eezn(t5`=4(yJ3=C)R^NZ7aBvqw##zY<>uu=C59T>6kOvA{kgk@|v`' 's>pkG(&hxNnj-cSvL;G~#$Ew`FZiF$IM+7ut?;osAW_o%bvrhoYq6nZm9@=HAw>h4Pp#i=u)I}zReJI81}J1NlhYYmCJI!K?zcp6@Y#8Z3MQwQRUxzknnlp5' 'Rl_cFj`Wt<CU*@+s1`HvyHy~l=e_`sA<(R)nIRh{g7LFc>#eyLlRNK~<0x(GE1^FLwOTD6)j;!)u7?|Ed8uB8efa1bHZN)eQzTas@ce)BAOmvmldGs|(&vx<' '5<<8Fy}}2W=u;!65A`@sm;bxZvSJ7?a@dwF?Hm9qA<e_Li%pFt+<IhChQmdjO{g%kg(jDtI-dwJFT9Gy@;{Nj;_p=$7QGZ6J(<db_mP^Z0@hL`fMm~^emi-<' '#U}<C;1S7UX&q{)L&*;Bb4F4&hy!RF0|TGtm9!CB-zUI~7+XmC5f#gR?25`_79+(~-tv8S?S4f!r4*c$F!XRrO<4{vh^|w`l%t?0J>547bF1x6nFKL1FZME8' 'x>xF18ESM1s;wm*-x&m$NDpw?@x=<tlcE)STJnr9{NuK;#i6_2MYCPl%4Zq^9*$^R372ua6jwv>oH^mR0ioqk%%)Awns;#lrjXkIhYB_Vt*Pr*oTgse6Uazr' 'd)yUnaZ|Z`9?Q6aTHa2@m4`pd_?E;;Re)&<*otbim^DZ!V{~?+t%H;U2&V8O9CkMdW*tOzBErCD-E}{=Nl%~-`;W#E5$bMF8A-TOVDt09^K)tTG2cvWxLh%9' 'cuC?O7rL(QbGlAASV!M6dTB)pfy|#N5k4(Mdd*7+Mb<Fc^fR3BfFeEzF^|<<jpBXBM&T8{-77eX)1)UjzwbB1E&LZ4khDM^66En##rJ{5FB;62)1u0P(WW!?' 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true

790d70b112d173624d86dc5ffce0b1d185b8a479

14,962

py

Python

examples/eg1/eg1.py

SagarRoy1996/TabularDataExtraction

59b05dde00272e7f04f56b89bd2139e3a4e252e5

[ "Apache-2.0" ]

null

examples/eg1/eg1.py

SagarRoy1996/TabularDataExtraction

59b05dde00272e7f04f56b89bd2139e3a4e252e5

[ "Apache-2.0" ]

null

examples/eg1/eg1.py

SagarRoy1996/TabularDataExtraction

59b05dde00272e7f04f56b89bd2139e3a4e252e5

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import os import re from math import radians, degrees import numpy as np import pandas as pd import cv2 from pdftabextract import imgproc from pdftabextract.geom import pt from pdftabextract.common import read_xml, parse_pages, save_page_grids from pdftabextract.textboxes import rotate_textboxes, sorted_by_attr from pdftabextract.clustering import (find_clusters_1d_break_dist, calc_cluster_centers_1d, zip_clusters_and_values) from pdftabextract.splitpages import split_page_texts, create_split_pages_dict_structure from pdftabextract.extract import make_grid_from_positions, fit_texts_into_grid, datatable_to_dataframe #%% Some constants #DATAPATH = 'data/' #DATAPATH = 'ip/' #OUTPUTPATH = 'generated_output/' #OUTPUTPATH = 'op/' #INPUT_XML = 'output.xml' #INPUT_XML = 'output.xml' DATAPATH = 'data/' OUTPUTPATH = 'generated_output/' INPUT_XML = 'schoollist_1.pdf.xml' MIN_ROW_HEIGHT = 260 # minimum height of a row in pixels, measured in the scanned pages MIN_COL_WIDTH = 194 # very important. the minimum width of a column in pixels, measured in the scanned pages #%% Some helper functions def save_image_w_lines(iproc_obj, imgfilebasename, orig_img_as_background, file_suffix_prefix=''): file_suffix = 'lines-orig' if orig_img_as_background else 'lines' img_lines = iproc_obj.draw_lines(orig_img_as_background=orig_img_as_background) img_lines_file = os.path.join(OUTPUTPATH, '%s-%s.png' % (imgfilebasename, file_suffix_prefix + file_suffix)) print("> saving image with detected lines to '%s'" % img_lines_file) cv2.imwrite(img_lines_file, img_lines) #%% Read the XML # Load the XML that was generated with pdftohtml xmltree, xmlroot = read_xml(os.path.join(DATAPATH, INPUT_XML)) # parse it and generate a dict of pages pages = parse_pages(xmlroot, require_image=True) #%% Split the scanned double pages so that we can later process the lists page-by-page split_texts_and_images = [] # list of tuples with (double page, split text boxes, split images) for p_num, p in pages.items(): # get the image file of the scanned page imgfilebasename = p['image'][:p['image'].rindex('.')] imgfile = os.path.join(DATAPATH, p['image']) print("page %d: detecting lines in image file '%s'..." % (p_num, imgfile)) # create an image processing object with the scanned page iproc_obj = imgproc.ImageProc(imgfile) # calculate the scaling of the image file in relation to the text boxes coordinate system dimensions page_scaling_x = iproc_obj.img_w / p['width'] page_scaling_y = iproc_obj.img_h / p['height'] image_scaling = (page_scaling_x, # scaling in X-direction page_scaling_y) # scaling in Y-direction # detect the lines in the double pages lines_hough = iproc_obj.detect_lines(canny_low_thresh=50, canny_high_thresh=150, canny_kernel_size=3, hough_rho_res=1, hough_theta_res=np.pi/500, hough_votes_thresh=350) print("> found %d lines" % len(lines_hough)) save_image_w_lines(iproc_obj, imgfilebasename, True, 'bothpages-') # find the vertical line that separates both sides sep_line_img_x = iproc_obj.find_pages_separator_line(dist_thresh=MIN_COL_WIDTH/2) sep_line_page_x = sep_line_img_x / page_scaling_x print("> found pages separator line at %f (image space position) / %f (page space position)" % (sep_line_img_x, sep_line_page_x)) # split the scanned double page at the separator line split_images = iproc_obj.split_image(sep_line_img_x) # split the textboxes at the separator line split_texts = split_page_texts(p, sep_line_page_x) split_texts_and_images.append((p, split_texts, split_images)) # generate a new XML and "pages" dict structure from the split pages split_pages_xmlfile = os.path.join(OUTPUTPATH, INPUT_XML[:INPUT_XML.rindex('.')] + '.split.xml') print("> saving split pages XML to '%s'" % split_pages_xmlfile) split_tree, split_root, split_pages = create_split_pages_dict_structure(split_texts_and_images, save_to_output_path=split_pages_xmlfile) # we don't need the original double pages any more, we'll work with 'split_pages' del pages #%% Detect clusters of horizontal lines using the image processing module and rotate back or deskew pages hori_lines_clusters = {} pages_image_scaling = {} # scaling of the scanned page image in relation to the OCR page dimensions for each page for p_num, p in split_pages.items(): # get the image file of the scanned page imgfilebasename = p['image'][:p['image'].rindex('.')] imgfile = os.path.join(OUTPUTPATH, p['image']) print("page %d: detecting lines in image file '%s'..." % (p_num, imgfile)) # create an image processing object with the scanned page iproc_obj = imgproc.ImageProc(imgfile) # calculate the scaling of the image file in relation to the text boxes coordinate system dimensions page_scaling_x = iproc_obj.img_w / p['width'] page_scaling_y = iproc_obj.img_h / p['height'] pages_image_scaling[p_num] = (page_scaling_x, # scaling in X-direction page_scaling_y) # scaling in Y-direction # detect the lines lines_hough = iproc_obj.detect_lines(canny_low_thresh=50, canny_high_thresh=150, canny_kernel_size=3, hough_rho_res=1, hough_theta_res=np.pi/500, hough_votes_thresh=round(0.2 * iproc_obj.img_w)) print("> found %d lines" % len(lines_hough)) save_image_w_lines(iproc_obj, imgfilebasename, True) save_image_w_lines(iproc_obj, imgfilebasename, False) # find rotation or skew # the parameters are: # 1. the minimum threshold in radians for a rotation to be counted as such # 2. the maximum threshold for the difference between horizontal and vertical line rotation (to detect skew) # 3. an optional threshold to filter out "stray" lines whose angle is too far apart from the median angle of # all other lines that go in the same direction (no effect here) rot_or_skew_type, rot_or_skew_radians = iproc_obj.find_rotation_or_skew(radians(0.5), # uses "lines_hough" radians(1), omit_on_rot_thresh=radians(0.5)) # rotate back text boxes # since often no vertical lines can be detected and hence it cannot be determined if the page is rotated or skewed, # we assume that it's always rotated if rot_or_skew_type is not None: print("> rotating back by %f°" % -degrees(rot_or_skew_radians)) rotate_textboxes(p, -rot_or_skew_radians, pt(0, 0)) # rotate back detected lines lines_hough = iproc_obj.apply_found_rotation_or_skew(rot_or_skew_type, -rot_or_skew_radians) save_image_w_lines(iproc_obj, imgfilebasename + '-repaired', True) save_image_w_lines(iproc_obj, imgfilebasename + '-repaired', False) # cluster the detected *horizontal* lines using find_clusters_1d_break_dist as simple clustering function # (break on distance MIN_ROW_HEIGHT/2) # additionally, remove all cluster sections that are considered empty # a cluster is considered empty when the number of text boxes in it is below 10% of the median number of text boxes # per cluster section hori_clusters = iproc_obj.find_clusters(imgproc.DIRECTION_HORIZONTAL, find_clusters_1d_break_dist, remove_empty_cluster_sections_use_texts=p['texts'], # use this page's textboxes remove_empty_cluster_sections_n_texts_ratio=0.1, # 10% rule remove_empty_cluster_sections_scaling=page_scaling_y, # the positions are in "scanned image space" -> we scale them to "text box space" dist_thresh=MIN_ROW_HEIGHT/2) print("> found %d clusters" % len(hori_clusters)) if len(hori_clusters) > 0: # draw the clusters img_w_clusters = iproc_obj.draw_line_clusters(imgproc.DIRECTION_HORIZONTAL, hori_clusters) save_img_file = os.path.join(OUTPUTPATH, '%s-hori-clusters.png' % imgfilebasename) print("> saving image with detected horizontal clusters to '%s'" % save_img_file) cv2.imwrite(save_img_file, img_w_clusters) hori_lines_clusters[p_num] = hori_clusters else: print("> no horizontal line clusters found") # save split and repaired XML (i.e. XML with deskewed textbox positions) output_files_basename = INPUT_XML[:INPUT_XML.rindex('.')] repaired_xmlfile = os.path.join(OUTPUTPATH, output_files_basename + '.split.repaired.xml') print("saving split and repaired XML file to '%s'..." % repaired_xmlfile) split_tree.write(repaired_xmlfile) #%% Determine the rows and columns of the tables pttrn_schoolnum = re.compile(r'^\d{6}$') # a valid school number indicates a table row page_grids = {} print("detecting rows and columns...") for p_num, p in split_pages.items(): scaling_x, scaling_y = pages_image_scaling[p_num] # try to find out the table rows in this page using the horizontal lines that were detected before hori_lines = list(np.array(calc_cluster_centers_1d(hori_lines_clusters[p_num])) / scaling_y) hori_lines.append(p['height']) # last line: page bottom prev_line_y = 0 row_texts = [] row_positions = [] in_table = False # is True when the current segment is a real table row (not a table header or surrounding text) for line_y in hori_lines: # get all texts in this row segment_texts = [t for t in p['texts'] if prev_line_y < t['bottom'] <= line_y] if not segment_texts: continue # skip empty rows # try to find the start and the end of the table for t in segment_texts: t_val = t['value'].strip() if pttrn_schoolnum.search(t_val): # if this matches, we found the start of the table if not in_table: in_table = True row_positions.append(prev_line_y) break else: if in_table: # we found the end of the table in_table = False if in_table: # this is a table row, so add the texts and row positions to the respective lists row_texts.append(segment_texts) row_positions.append(line_y) prev_line_y = line_y # try to find out the table columns in this page using the distribution of x-coordinates of the left position of # each text box in all rows text_xs = [] for texts in row_texts: text_xs.extend([t['left'] for t in texts]) text_xs = np.array(text_xs) # make clusters of x positions text_xs_clusters = find_clusters_1d_break_dist(text_xs, dist_thresh=MIN_COL_WIDTH/2/scaling_x) text_xs_clusters_w_values = zip_clusters_and_values(text_xs_clusters, text_xs) col_positions = calc_cluster_centers_1d(text_xs_clusters_w_values) # remove falsely identified columns (i.e. merge columns with only a few text boxes) filtered_col_positions = [] n_rows = len(row_positions) n_cols = len(col_positions) if n_cols > 1 and n_rows > 1: top_y = row_positions[0] bottom_y = row_positions[-1] # append the rightmost text's right border as the last column border rightmost_pos = sorted_by_attr(p['texts'], 'right')[-1]['right'] col_positions.append(rightmost_pos) # merge columns with few text boxes texts_in_table = [t for t in p['texts'] if top_y < t['top'] + t['height']/2 <= bottom_y] prev_col_x = col_positions[0] for col_x in col_positions[1:]: col_texts = [t for t in texts_in_table if prev_col_x < t['left'] + t['width']/2 <= col_x] if len(col_texts) >= n_rows: # there should be at least one text box per row filtered_col_positions.append(prev_col_x) last_col_x = col_x prev_col_x = col_x # manually add border for the last column because it has very few or no text boxes filtered_col_positions.append(filtered_col_positions[-1] + (rightmost_pos - filtered_col_positions[-1]) / 2) filtered_col_positions.append(rightmost_pos) # create the grid if filtered_col_positions: grid = make_grid_from_positions(filtered_col_positions, row_positions) n_rows = len(grid) n_cols = len(grid[0]) print("> page %d: grid with %d rows, %d columns" % (p_num, n_rows, n_cols)) page_grids[p_num] = grid else: # this happens for the first page as there's no table on that print("> page %d: no table found" % p_num) # save the page grids # After you created the page grids, you should then check that they're correct using pdf2xml-viewer's # loadGridFile() function page_grids_file = os.path.join(OUTPUTPATH, output_files_basename + '.pagegrids.json') print("saving page grids JSON file to '%s'" % page_grids_file) save_page_grids(page_grids, page_grids_file) #%% Create data frames (requires pandas library) # For sake of simplicity, we will just fit the text boxes into the grid, merge the texts in their cells (splitting text # boxes to separate lines if necessary) and output the result. Normally, you would do some more parsing here, e.g. # extracting the address components from the second column. full_df = pd.DataFrame() print("fitting text boxes into page grids and generating final output...") for p_num, p in split_pages.items(): if p_num not in page_grids: continue # happens when no table was detected print("> page %d" % p_num) datatable, unmatched_texts = fit_texts_into_grid(p['texts'], page_grids[p_num], return_unmatched_texts=True) df = datatable_to_dataframe(datatable, split_texts_in_lines=True) df['from_page'] = p_num full_df = full_df.append(df, ignore_index=True) print("extracted %d rows from %d pages" % (len(full_df), len(split_pages))) csv_output_file = os.path.join(OUTPUTPATH, output_files_basename + '.csv') print("saving extracted data to '%s'" % csv_output_file) full_df.to_csv(csv_output_file, index=False) excel_output_file = os.path.join(OUTPUTPATH, output_files_basename + '.xlsx') print("saving extracted data to '%s'" % excel_output_file) full_df.to_excel(excel_output_file, index=False)

46.610592

180

0.679054

import os import re from math import radians, degrees import numpy as np import pandas as pd import cv2 from pdftabextract import imgproc from pdftabextract.geom import pt from pdftabextract.common import read_xml, parse_pages, save_page_grids from pdftabextract.textboxes import rotate_textboxes, sorted_by_attr from pdftabextract.clustering import (find_clusters_1d_break_dist, calc_cluster_centers_1d, zip_clusters_and_values) from pdftabextract.splitpages import split_page_texts, create_split_pages_dict_structure from pdftabextract.extract import make_grid_from_positions, fit_texts_into_grid, datatable_to_dataframe DATAPATH = 'data/' OUTPUTPATH = 'generated_output/' INPUT_XML = 'schoollist_1.pdf.xml' MIN_ROW_HEIGHT = 260 MIN_COL_WIDTH = 194 def save_image_w_lines(iproc_obj, imgfilebasename, orig_img_as_background, file_suffix_prefix=''): file_suffix = 'lines-orig' if orig_img_as_background else 'lines' img_lines = iproc_obj.draw_lines(orig_img_as_background=orig_img_as_background) img_lines_file = os.path.join(OUTPUTPATH, '%s-%s.png' % (imgfilebasename, file_suffix_prefix + file_suffix)) print("> saving image with detected lines to '%s'" % img_lines_file) cv2.imwrite(img_lines_file, img_lines) xmltree, xmlroot = read_xml(os.path.join(DATAPATH, INPUT_XML)) pages = parse_pages(xmlroot, require_image=True) split_texts_and_images = [] for p_num, p in pages.items(): imgfilebasename = p['image'][:p['image'].rindex('.')] imgfile = os.path.join(DATAPATH, p['image']) print("page %d: detecting lines in image file '%s'..." % (p_num, imgfile)) iproc_obj = imgproc.ImageProc(imgfile) page_scaling_x = iproc_obj.img_w / p['width'] page_scaling_y = iproc_obj.img_h / p['height'] image_scaling = (page_scaling_x, page_scaling_y) lines_hough = iproc_obj.detect_lines(canny_low_thresh=50, canny_high_thresh=150, canny_kernel_size=3, hough_rho_res=1, hough_theta_res=np.pi/500, hough_votes_thresh=350) print("> found %d lines" % len(lines_hough)) save_image_w_lines(iproc_obj, imgfilebasename, True, 'bothpages-') sep_line_img_x = iproc_obj.find_pages_separator_line(dist_thresh=MIN_COL_WIDTH/2) sep_line_page_x = sep_line_img_x / page_scaling_x print("> found pages separator line at %f (image space position) / %f (page space position)" % (sep_line_img_x, sep_line_page_x)) split_images = iproc_obj.split_image(sep_line_img_x) split_texts = split_page_texts(p, sep_line_page_x) split_texts_and_images.append((p, split_texts, split_images)) split_pages_xmlfile = os.path.join(OUTPUTPATH, INPUT_XML[:INPUT_XML.rindex('.')] + '.split.xml') print("> saving split pages XML to '%s'" % split_pages_xmlfile) split_tree, split_root, split_pages = create_split_pages_dict_structure(split_texts_and_images, save_to_output_path=split_pages_xmlfile) del pages hori_lines_clusters = {} pages_image_scaling = {} for p_num, p in split_pages.items(): imgfilebasename = p['image'][:p['image'].rindex('.')] imgfile = os.path.join(OUTPUTPATH, p['image']) print("page %d: detecting lines in image file '%s'..." % (p_num, imgfile)) iproc_obj = imgproc.ImageProc(imgfile) page_scaling_x = iproc_obj.img_w / p['width'] page_scaling_y = iproc_obj.img_h / p['height'] pages_image_scaling[p_num] = (page_scaling_x, page_scaling_y) lines_hough = iproc_obj.detect_lines(canny_low_thresh=50, canny_high_thresh=150, canny_kernel_size=3, hough_rho_res=1, hough_theta_res=np.pi/500, hough_votes_thresh=round(0.2 * iproc_obj.img_w)) print("> found %d lines" % len(lines_hough)) save_image_w_lines(iproc_obj, imgfilebasename, True) save_image_w_lines(iproc_obj, imgfilebasename, False) rot_or_skew_type, rot_or_skew_radians = iproc_obj.find_rotation_or_skew(radians(0.5), radians(1), omit_on_rot_thresh=radians(0.5)) if rot_or_skew_type is not None: print("> rotating back by %f°" % -degrees(rot_or_skew_radians)) rotate_textboxes(p, -rot_or_skew_radians, pt(0, 0)) # rotate back detected lines lines_hough = iproc_obj.apply_found_rotation_or_skew(rot_or_skew_type, -rot_or_skew_radians) save_image_w_lines(iproc_obj, imgfilebasename + '-repaired', True) save_image_w_lines(iproc_obj, imgfilebasename + '-repaired', False) # cluster the detected *horizontal* lines using find_clusters_1d_break_dist as simple clustering function # (break on distance MIN_ROW_HEIGHT/2) # additionally, remove all cluster sections that are considered empty # a cluster is considered empty when the number of text boxes in it is below 10% of the median number of text boxes # per cluster section hori_clusters = iproc_obj.find_clusters(imgproc.DIRECTION_HORIZONTAL, find_clusters_1d_break_dist, remove_empty_cluster_sections_use_texts=p['texts'], # use this page's textboxes remove_empty_cluster_sections_n_texts_ratio=0.1, remove_empty_cluster_sections_scaling=page_scaling_y, dist_thresh=MIN_ROW_HEIGHT/2) print("> found %d clusters" % len(hori_clusters)) if len(hori_clusters) > 0: img_w_clusters = iproc_obj.draw_line_clusters(imgproc.DIRECTION_HORIZONTAL, hori_clusters) save_img_file = os.path.join(OUTPUTPATH, '%s-hori-clusters.png' % imgfilebasename) print("> saving image with detected horizontal clusters to '%s'" % save_img_file) cv2.imwrite(save_img_file, img_w_clusters) hori_lines_clusters[p_num] = hori_clusters else: print("> no horizontal line clusters found") output_files_basename = INPUT_XML[:INPUT_XML.rindex('.')] repaired_xmlfile = os.path.join(OUTPUTPATH, output_files_basename + '.split.repaired.xml') print("saving split and repaired XML file to '%s'..." % repaired_xmlfile) split_tree.write(repaired_xmlfile) pttrn_schoolnum = re.compile(r'^\d{6}$') page_grids = {} print("detecting rows and columns...") for p_num, p in split_pages.items(): scaling_x, scaling_y = pages_image_scaling[p_num] hori_lines = list(np.array(calc_cluster_centers_1d(hori_lines_clusters[p_num])) / scaling_y) hori_lines.append(p['height']) prev_line_y = 0 row_texts = [] row_positions = [] in_table = False for line_y in hori_lines: segment_texts = [t for t in p['texts'] if prev_line_y < t['bottom'] <= line_y] if not segment_texts: continue for t in segment_texts: t_val = t['value'].strip() if pttrn_schoolnum.search(t_val): if not in_table: in_table = True row_positions.append(prev_line_y) break else: if in_table: in_table = False if in_table: row_texts.append(segment_texts) row_positions.append(line_y) prev_line_y = line_y text_xs = [] for texts in row_texts: text_xs.extend([t['left'] for t in texts]) text_xs = np.array(text_xs) text_xs_clusters = find_clusters_1d_break_dist(text_xs, dist_thresh=MIN_COL_WIDTH/2/scaling_x) text_xs_clusters_w_values = zip_clusters_and_values(text_xs_clusters, text_xs) col_positions = calc_cluster_centers_1d(text_xs_clusters_w_values) filtered_col_positions = [] n_rows = len(row_positions) n_cols = len(col_positions) if n_cols > 1 and n_rows > 1: top_y = row_positions[0] bottom_y = row_positions[-1] rightmost_pos = sorted_by_attr(p['texts'], 'right')[-1]['right'] col_positions.append(rightmost_pos) # merge columns with few text boxes texts_in_table = [t for t in p['texts'] if top_y < t['top'] + t['height']/2 <= bottom_y] prev_col_x = col_positions[0] for col_x in col_positions[1:]: col_texts = [t for t in texts_in_table if prev_col_x < t['left'] + t['width']/2 <= col_x] if len(col_texts) >= n_rows: # there should be at least one text box per row filtered_col_positions.append(prev_col_x) last_col_x = col_x prev_col_x = col_x # manually add border for the last column because it has very few or no text boxes filtered_col_positions.append(filtered_col_positions[-1] + (rightmost_pos - filtered_col_positions[-1]) / 2) filtered_col_positions.append(rightmost_pos) # create the grid if filtered_col_positions: grid = make_grid_from_positions(filtered_col_positions, row_positions) n_rows = len(grid) n_cols = len(grid[0]) print("> page %d: grid with %d rows, %d columns" % (p_num, n_rows, n_cols)) page_grids[p_num] = grid else: # this happens for the first page as there's no table on that print("> page %d: no table found" % p_num) page_grids_file = os.path.join(OUTPUTPATH, output_files_basename + '.pagegrids.json') print("saving page grids JSON file to '%s'" % page_grids_file) save_page_grids(page_grids, page_grids_file) full_df = pd.DataFrame() print("fitting text boxes into page grids and generating final output...") for p_num, p in split_pages.items(): if p_num not in page_grids: continue print("> page %d" % p_num) datatable, unmatched_texts = fit_texts_into_grid(p['texts'], page_grids[p_num], return_unmatched_texts=True) df = datatable_to_dataframe(datatable, split_texts_in_lines=True) df['from_page'] = p_num full_df = full_df.append(df, ignore_index=True) print("extracted %d rows from %d pages" % (len(full_df), len(split_pages))) csv_output_file = os.path.join(OUTPUTPATH, output_files_basename + '.csv') print("saving extracted data to '%s'" % csv_output_file) full_df.to_csv(csv_output_file, index=False) excel_output_file = os.path.join(OUTPUTPATH, output_files_basename + '.xlsx') print("saving extracted data to '%s'" % excel_output_file) full_df.to_excel(excel_output_file, index=False)

true

790d70c7303ba08660f1fc2fc19df3a6b93b2447

4,492

py

Python

manga_py/base_classes/base.py

theincognito-inc/manga-dl

899905bafb6c6891815b58cce41eaff32a682570

[ "MIT" ]

1

2020-11-19T00:40:49.000Z

manga_py/base_classes/base.py

eduhoribe/manga-py

fe7eb2e08532b3c75b4f7ac8cc4132f0e7a65eb4

[ "MIT" ]

null

manga_py/base_classes/base.py

eduhoribe/manga-py

fe7eb2e08532b3c75b4f7ac8cc4132f0e7a65eb4

[ "MIT" ]

null

from logging import warning from os import path from typing import Optional, List from lxml.html import HtmlElement from manga_py.http import Http from .params import ProviderParams class Base(ProviderParams): _storage = None _params = None _image_params = None _http_kwargs = None __http = None __arguments = None chapter_id = 0 quiet = False original_url = None def __init__(self): self._storage = { 'cookies': {}, 'main_content': None, 'chapters': [], 'current_chapter': 0, 'current_file': 0, 'proxies': {}, 'domain_uri': None, } self._params = { 'destination': 'Manga', 'cf-protect': False, } self._image_params = { 'crop': (0, 0, 0, 0), # 'crop': (left, upper, right, lower) 'auto_crop': False, # 'auto_crop': True, } self._http_kwargs = {} def _archive_type(self) -> str: arc_type = 'zip' if self._params['cbz']: arc_type = 'cbz' return arc_type def get_url(self): return self._params['url'] def _build_http_params(self, params): if params is None: params = {} params.setdefault('allow_webp', not self._params.get('disallow_webp', None)) params.setdefault('referer', self._storage.get('referer', self.domain)) params.setdefault('user_agent', self._get_user_agent()) params.setdefault('proxies', self._storage.get('proxies', None)) params.setdefault('cookies', self._storage.get('cookies', None)) params.setdefault('kwargs', self._http_kwargs) return params def http(self, new=False, params=None) -> Http: http_params = self._build_http_params(params) if new: http = Http(**http_params) return http elif not self.__http: self.__http = Http(**http_params) return self.__http def http_get(self, url: str, headers: dict = None, cookies: dict = None): return self.http().get(url=url, headers=headers, cookies=cookies) def http_post(self, url: str, headers: dict = None, cookies: dict = None, data=()): return self.http().post(url=url, headers=headers, cookies=cookies, data=data) def _get_user_agent(self): ua_storage = self._storage.get('user_agent', None) ua_params = self._params.get('user_agent', None) if self._params.get('cf_scrape', False): return ua_storage return ua_params @classmethod def __normalize_chapters(cls, n, element): if isinstance(element, HtmlElement): return n(element.get('href')) if isinstance(element, str): return n(element) return element def _prepare_chapters(self, chapters): n = self.http().normalize_uri items = [] if chapters and len(chapters): for i in chapters: url = self.__normalize_chapters(n, i) items.append(url) else: warning('Chapters list empty. Check %s' % self.get_url()) return items def get_current_file(self): return self._storage['files'][self._storage['current_file']] def book_meta(self) -> dict: return {} def _image_name(self, idx, filename): if idx is None: idx = self._storage['current_file'] fn, extension = path.splitext(filename) _path = '{:0>3}_{}'.format(idx, fn) if self._params['rename_pages']: _path = '{:0>3}'.format(idx) return _path + extension def chapter_for_json(self) -> str: return self.chapter def put_info_json(self, meta): # manga_name, url, directory pass def _fill_arguments(self, arguments: List[str]): know_args = [ 'login', 'password', 'language', 'translator', ] if self.__arguments is None: self.__arguments = {} for arg in arguments: key, value = arg.split('=', 1) # type: str, str if key in know_args: self.__arguments[key] = value def arg(self, key: str) -> Optional[str]: if self.__arguments is None: return None return self.__arguments.get(key) def allow_auto_change_url(self): return True

29.748344

87

0.573241

from logging import warning from os import path from typing import Optional, List from lxml.html import HtmlElement from manga_py.http import Http from .params import ProviderParams class Base(ProviderParams): _storage = None _params = None _image_params = None _http_kwargs = None __http = None __arguments = None chapter_id = 0 quiet = False original_url = None def __init__(self): self._storage = { 'cookies': {}, 'main_content': None, 'chapters': [], 'current_chapter': 0, 'current_file': 0, 'proxies': {}, 'domain_uri': None, } self._params = { 'destination': 'Manga', 'cf-protect': False, } self._image_params = { 'crop': (0, 0, 0, 0), 'auto_crop': False, } self._http_kwargs = {} def _archive_type(self) -> str: arc_type = 'zip' if self._params['cbz']: arc_type = 'cbz' return arc_type def get_url(self): return self._params['url'] def _build_http_params(self, params): if params is None: params = {} params.setdefault('allow_webp', not self._params.get('disallow_webp', None)) params.setdefault('referer', self._storage.get('referer', self.domain)) params.setdefault('user_agent', self._get_user_agent()) params.setdefault('proxies', self._storage.get('proxies', None)) params.setdefault('cookies', self._storage.get('cookies', None)) params.setdefault('kwargs', self._http_kwargs) return params def http(self, new=False, params=None) -> Http: http_params = self._build_http_params(params) if new: http = Http(**http_params) return http elif not self.__http: self.__http = Http(**http_params) return self.__http def http_get(self, url: str, headers: dict = None, cookies: dict = None): return self.http().get(url=url, headers=headers, cookies=cookies) def http_post(self, url: str, headers: dict = None, cookies: dict = None, data=()): return self.http().post(url=url, headers=headers, cookies=cookies, data=data) def _get_user_agent(self): ua_storage = self._storage.get('user_agent', None) ua_params = self._params.get('user_agent', None) if self._params.get('cf_scrape', False): return ua_storage return ua_params @classmethod def __normalize_chapters(cls, n, element): if isinstance(element, HtmlElement): return n(element.get('href')) if isinstance(element, str): return n(element) return element def _prepare_chapters(self, chapters): n = self.http().normalize_uri items = [] if chapters and len(chapters): for i in chapters: url = self.__normalize_chapters(n, i) items.append(url) else: warning('Chapters list empty. Check %s' % self.get_url()) return items def get_current_file(self): return self._storage['files'][self._storage['current_file']] def book_meta(self) -> dict: return {} def _image_name(self, idx, filename): if idx is None: idx = self._storage['current_file'] fn, extension = path.splitext(filename) _path = '{:0>3}_{}'.format(idx, fn) if self._params['rename_pages']: _path = '{:0>3}'.format(idx) return _path + extension def chapter_for_json(self) -> str: return self.chapter def put_info_json(self, meta): pass def _fill_arguments(self, arguments: List[str]): know_args = [ 'login', 'password', 'language', 'translator', ] if self.__arguments is None: self.__arguments = {} for arg in arguments: key, value = arg.split('=', 1) if key in know_args: self.__arguments[key] = value def arg(self, key: str) -> Optional[str]: if self.__arguments is None: return None return self.__arguments.get(key) def allow_auto_change_url(self): return True

true

790d70e3cbbdaaa46d1decb3dcc65fb133d8e02c

18,752

py

Python

cmdb/views_ajax.py

bopopescu/dbsupport

9b0f767cebc338fe22f5f3435a8d261101ea35dd

[ "Apache-2.0" ]

2

2019-04-20T06:10:49.000Z

2020-06-11T08:11:46.000Z

cmdb/views_ajax.py

bopopescu/dbsupport

9b0f767cebc338fe22f5f3435a8d261101ea35dd

[ "Apache-2.0" ]

null

cmdb/views_ajax.py

bopopescu/dbsupport

9b0f767cebc338fe22f5f3435a8d261101ea35dd

[ "Apache-2.0" ]

1

2020-07-22T02:57:46.000Z

# -*- coding: UTF-8 -*- import datetime import json from django.contrib.auth.hashers import check_password, make_password from django.core import serializers from django.db import connection from django.http import HttpResponse from django.shortcuts import render from django.views.decorators.csrf import csrf_exempt from cmdb.models import host, hostUser, dbGroup, dbInstance from utils.jsonExt import DateEncoder from utils.logUtil import getLogger # from cmdb.models import dbCluster logger = getLogger() @csrf_exempt def addChangeHostInfo(request): ''' 新增主机修改主机 ''' v_hostId = request.POST.get('host_id') v_businessName = request.POST.get('business_name') v_serviceEnv = request.POST.get('service_env') v_hostName = request.POST.get('host_name') v_intranetIpAddr = request.POST.get('intranet_ipaddr') v_publicIpAddr = request.POST.get('public_ipaddr') v_sshPort = request.POST.get('ssh_port') v_hostType = request.POST.get('host_type') v_hostRole = request.POST.get('host_role') v_hostDesc = request.POST.get('host_desc') print(v_hostId, v_businessName, v_serviceEnv, v_hostName, v_intranetIpAddr, v_publicIpAddr, v_sshPort, v_hostType, v_hostRole, v_hostDesc) if v_hostId == '' or v_hostId is None: # 新增 try: hostObj = host(businessName=v_businessName, serviceEnv=v_serviceEnv, hostName=v_hostName, intranetIpAddr=v_intranetIpAddr, publicIpAddr=v_publicIpAddr, sshPort=v_sshPort, hostType=v_hostType, hostRole=v_hostRole, hostDesc=v_hostDesc) hostObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: result = {'status':2, 'msg':'保存失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: # 修改 try: hostObj = host.objects.filter(id=v_hostId) hostObj.update(businessName=v_businessName, serviceEnv=v_serviceEnv, hostName=v_hostName, intranetIpAddr=v_intranetIpAddr, publicIpAddr=v_publicIpAddr, sshPort=v_sshPort, hostType=v_hostType, hostRole=v_hostRole, hostDesc=v_hostDesc) # masterConfigObj.save() result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: result = {'status':2, 'msg':'修改失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getHostDetailInfo(request): hostId = request.POST['hostId'] try: hostObj = host.objects.get(id=hostId) hostJson = hostObj.toJSON() result = {'status':1, 'msg':'请求成功', 'obj':hostJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def delHost(request): hostId = request.POST['hostId'] if hostId == "" or hostId is None: result = {'status':3, 'msg':'未选中任何记录!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: delResult = host.objects.filter(id=hostId).delete() print(delResult) result = {'status':1, 'msg':'删除成功!', 'data':delResult} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'删除失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def addChangeHostUserInfo(request): ''' 新增主机用户修改主机用户 ''' v_hostUserId = request.POST.get('host_user_id') v_hostId = request.POST.get('host_id') v_hostUser = request.POST.get('host_user') v_hostPasswd = request.POST.get('host_passwd') v_userDesc = request.POST.get('user_desc') print(v_hostUserId, v_hostId, v_hostUser, v_hostPasswd, v_userDesc) if v_hostUserId == '' or v_hostUserId is None: # 新增 try: hostObj = host.objects.get(id=v_hostId) hostUserObj = hostUser(hostUser=v_hostUser, hostPasswd=v_hostPasswd, userDesc=v_userDesc, host=hostObj) hostUserObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'保存失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: # 修改 try: hostUserObj = hostUser.objects.filter(id=v_hostUserId) hostUserObj.update(hostUser=v_hostUser, hostPasswd=v_hostPasswd, userDesc=v_userDesc) # masterConfigObj.save() result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'修改失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getHostUserDetailInfo(request): hostUserId = request.POST['hostUserId'].strip() try: hostUserInfo = hostUser.objects.filter(id=hostUserId) hostUserJson = serializers.serialize("json", hostUserInfo, use_natural_foreign_keys=True) result = {'status':1, 'msg':'请求成功', 'hostUserJson':hostUserJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def delHostUser(request): hostUserId = request.POST['hostUserId'] if hostUserId == "" or hostUserId is None: result = {'status':3, 'msg':'未选中任何记录!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: delResult = hostUser.objects.filter(id=hostUserId).delete() print(delResult) logger.error(delResult) result = {'status':1, 'msg':'删除成功!', 'data':delResult} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) logger.error(e) result = {'status':2, 'msg':'删除失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def addChangeDbGroupInfo(request): ''' 新增数据库组修改数据库组 ''' v_groupId = request.POST.get('group_id') v_businessName = request.POST.get('business_name') v_groupName = request.POST.get('group_name') v_groupStatus = request.POST.get('group_status') v_groupDesc = request.POST.get('group_desc') v_groupEnv = request.POST.get('group_env') print(v_groupId, v_businessName, v_groupName, v_groupEnv, v_groupStatus, v_groupDesc) logger.info("保存或修改数据库组信息，接收前端参数：", v_groupId, v_businessName, v_groupName, v_groupEnv, v_groupStatus, v_groupDesc) if v_groupId == '' or v_groupId is None: # 新增 try: dbGroupObj = dbGroup(businessName=v_businessName, groupName=v_groupName, groupEnv=v_groupEnv, groupStatus=v_groupStatus, groupDesc=v_groupDesc) dbGroupObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'保存失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: # 修改 try: dbGroupObj = dbGroup.objects.filter(id=v_groupId) dbGroupObj.update(businessName=v_businessName, groupName=v_groupName, groupEnv=v_groupEnv, groupStatus=v_groupStatus, groupDesc=v_groupDesc) # masterConfigObj.save() result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'修改失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') # @csrf_exempt # def getDbClusterDetailInfo(request): # clusterId = request.POST['clusterId'] # # try: # dbClusterObj = dbCluster.objects.get(id=clusterId) # dbClusterJson = dbClusterObj.toJSON() # # result = {'status':1, 'msg':'请求成功', 'obj':dbClusterJson} # print(result) # return HttpResponse(json.dumps(result), content_type='application/json') # except Exception as e: # print(e) # result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getDbGroupDetailInfo(request): groupId = request.POST['groupId'] try: dbGroupObj = dbGroup.objects.get(id=groupId) dbGroupJson = dbGroupObj.toJSON() result = {'status':1, 'msg':'请求成功', 'obj':dbGroupJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def addChangeDbInstanceInfo(request): ''' 新增数据库实例修改数据库实例 ''' v_instanceId = request.POST.get('instance_id') v_groupId = request.POST.get('group_id') v_host_id = request.POST.get('host_id') v_instanceName = request.POST.get('instance_env') v_instanceType = request.POST.get('instance_type') v_portNum = request.POST.get('port_num') v_instanceRole = request.POST.get('instance_role') v_instanceStatus = request.POST.get('instance_status') v_instanceDesc = request.POST.get('instance_desc') print(v_instanceId, v_groupId, v_host_id, v_instanceName, v_instanceType, v_portNum, v_instanceRole, v_instanceStatus, v_instanceDesc) logger.info("保存或修改数据库实例信息，接收前端参数：", v_instanceId, v_groupId, v_host_id, v_instanceName, v_instanceType, v_portNum, v_instanceRole, v_instanceStatus, v_instanceDesc) if v_instanceId == '' or v_instanceId is None: # 新增 try: dbGroupObj = dbGroup.objects.get(id=v_groupId) hostObj = host.objects.get(id=v_host_id) print(hostObj) dbInstanceObj = dbInstance(groupName=dbGroupObj, host=hostObj, instanceName=v_instanceName, instanceType=v_instanceType, portNum=v_portNum, instanceRole=v_instanceRole, instanceStatus=v_instanceStatus, instanceDesc=v_instanceDesc) dbInstanceObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) logger.error(str(e)) result = {'status':2, 'msg':'保存失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: # 修改 try: dbGroupObj = dbGroup.objects.get(id=v_groupId) hostObj = host.objects.get(id=v_host_id) dbInstanceObj = dbInstance.objects.filter(id=v_instanceId) dbInstanceObj.update(groupName=dbGroupObj, host=hostObj, instanceName=v_instanceName, instanceType=v_instanceType, portNum=v_portNum, instanceRole=v_instanceRole, instanceStatus=v_instanceStatus, instanceDesc=v_instanceDesc) # masterConfigObj.save() result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'修改失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getDbInstanceDetailInfo(request): instanceId = request.POST['instanceId'].strip() try: dbInstanceInfo = dbInstance.objects.filter(id=instanceId) dbInstanceJson = serializers.serialize("json", dbInstanceInfo, use_natural_foreign_keys=True) result = {'status':1, 'msg':'请求成功', 'dbInstanceJson':dbInstanceJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') # conn = connection.cursor() # try: # conn.execute('SELECT cdi.*, ch.host_name, ch.intranet_ip_addr, cdg.group_name FROM cmdb_db_instance cdi inner join cmdb_host ch on cdi.host = ch.id inner join cmdb_db_group cdg on cdi.db_group = cdg.id WHERE cdi.id = %s', [instanceId]) # dbInstanceInfo = conn.fetchall() # print(dbInstanceInfo) # dbInstanceJson = serializers.serialize("json", dbInstanceInfo) # result = {'status':1, 'msg':'请求成功', 'dbInstanceInfo':dbInstanceInfo} # print(result) # return HttpResponse(json.dumps(result, cls=DateEncoder), content_type='application/json') # except Exception as e: # print(e) # result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') # finally: # conn.close() # try: # dbInstanceInfo = dbInstance.objects.raw('SELECT * FROM cmdb_db_instance WHERE id = %d', [instanceId]) # dbInstanceJson = serializers.serialize("json", dbInstanceInfo) # # print(dbInstanceJson[0].fields.host) # print(type(dbInstanceJson[0].fields.host)) # # hostInfo = host.objects.raw('SELECT * FROM cmdb_host WHERE id = %d', [int(dbInstanceJson[0].fields.host)]) # hostJson = serializers.serialize("json", hostInfo) # print(hostJson) # # result = {'status':1, 'msg':'请求成功', 'dbInstanceJson':dbInstanceJson} # print(result) # return HttpResponse(json.dumps(result), content_type='application/json') # except Exception as e: # print(e) # result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def delDbInstance(request): instanceId = request.POST['instanceId'] if instanceId == "" or instanceId is None: result = {'status':3, 'msg':'未选中任何记录!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: delResult = dbInstance.objects.filter(id=instanceId).delete() print(delResult) logger.error(delResult) result = {'status':1, 'msg':'删除成功!', 'data':delResult} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) logger.error(e) result = {'status':2, 'msg':'删除失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') # @csrf_exempt # def addChangeDbClusterInfo(request): # ''' # 新增集群信息 # 修改集群信息 # ''' # v_clusterId = request.POST.get('cluster_id') # v_clusterName = request.POST.get('cluster_name') # v_clusterStatus = request.POST.get('cluster_status') # v_clusterDesc = request.POST.get('cluster_desc') # # print("begin add Cluster: ", v_clusterId, v_clusterName, v_clusterStatus, v_clusterDesc) # # if v_clusterId == '' or v_clusterId is None: # # 新增 # try: # dbClusterObj = dbCluster(clusterName=v_clusterName, clusterStatus=v_clusterStatus, clusterDesc=v_clusterDesc) # dbClusterObj.save() # result = {'status':1, 'msg':'保存成功!', 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') # except Exception as e: # logger.error(str(e)) # result = {'status':2, 'msg':'保存失败!', 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') # else: # # 修改 # try: # dbClusterObj = dbCluster.objects.filter(id=v_clusterId) # dbClusterObj.update(clusterName=v_clusterName, clusterStatus=v_clusterStatus, clusterDesc=v_clusterDesc) # # masterConfigObj.save() # result = {'status':1, 'msg':'修改成功!', 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') # except Exception as e: # logger.error(str(e)) # result = {'status':2, 'msg':'修改失败!', 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') # # @csrf_exempt # def delDbCluster(request): # v_clusterId = request.POST['cluster_id'] # # if v_clusterId == "" or v_clusterId is None: # result = {'status':3, 'msg':'未选中任何记录!', 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json') # else: # try: # delResult = dbCluster.objects.filter(id=v_clusterId).delete() # print(delResult) # logger.info(delResult) # result = {'status':1, 'msg':'删除成功!', 'data':delResult} # return HttpResponse(json.dumps(result), content_type='application/json') # except Exception as e: # print(e) # logger.error(e) # result = {'status':2, 'msg':'删除失败!', 'data':''} # return HttpResponse(json.dumps(result), content_type='application/json')

44.330969

245

0.624307

import datetime import json from django.contrib.auth.hashers import check_password, make_password from django.core import serializers from django.db import connection from django.http import HttpResponse from django.shortcuts import render from django.views.decorators.csrf import csrf_exempt from cmdb.models import host, hostUser, dbGroup, dbInstance from utils.jsonExt import DateEncoder from utils.logUtil import getLogger logger = getLogger() @csrf_exempt def addChangeHostInfo(request): v_hostId = request.POST.get('host_id') v_businessName = request.POST.get('business_name') v_serviceEnv = request.POST.get('service_env') v_hostName = request.POST.get('host_name') v_intranetIpAddr = request.POST.get('intranet_ipaddr') v_publicIpAddr = request.POST.get('public_ipaddr') v_sshPort = request.POST.get('ssh_port') v_hostType = request.POST.get('host_type') v_hostRole = request.POST.get('host_role') v_hostDesc = request.POST.get('host_desc') print(v_hostId, v_businessName, v_serviceEnv, v_hostName, v_intranetIpAddr, v_publicIpAddr, v_sshPort, v_hostType, v_hostRole, v_hostDesc) if v_hostId == '' or v_hostId is None: try: hostObj = host(businessName=v_businessName, serviceEnv=v_serviceEnv, hostName=v_hostName, intranetIpAddr=v_intranetIpAddr, publicIpAddr=v_publicIpAddr, sshPort=v_sshPort, hostType=v_hostType, hostRole=v_hostRole, hostDesc=v_hostDesc) hostObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: result = {'status':2, 'msg':'保存失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: hostObj = host.objects.filter(id=v_hostId) hostObj.update(businessName=v_businessName, serviceEnv=v_serviceEnv, hostName=v_hostName, intranetIpAddr=v_intranetIpAddr, publicIpAddr=v_publicIpAddr, sshPort=v_sshPort, hostType=v_hostType, hostRole=v_hostRole, hostDesc=v_hostDesc) result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: result = {'status':2, 'msg':'修改失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getHostDetailInfo(request): hostId = request.POST['hostId'] try: hostObj = host.objects.get(id=hostId) hostJson = hostObj.toJSON() result = {'status':1, 'msg':'请求成功', 'obj':hostJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def delHost(request): hostId = request.POST['hostId'] if hostId == "" or hostId is None: result = {'status':3, 'msg':'未选中任何记录!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: delResult = host.objects.filter(id=hostId).delete() print(delResult) result = {'status':1, 'msg':'删除成功!', 'data':delResult} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'删除失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def addChangeHostUserInfo(request): v_hostUserId = request.POST.get('host_user_id') v_hostId = request.POST.get('host_id') v_hostUser = request.POST.get('host_user') v_hostPasswd = request.POST.get('host_passwd') v_userDesc = request.POST.get('user_desc') print(v_hostUserId, v_hostId, v_hostUser, v_hostPasswd, v_userDesc) if v_hostUserId == '' or v_hostUserId is None: try: hostObj = host.objects.get(id=v_hostId) hostUserObj = hostUser(hostUser=v_hostUser, hostPasswd=v_hostPasswd, userDesc=v_userDesc, host=hostObj) hostUserObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'保存失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: hostUserObj = hostUser.objects.filter(id=v_hostUserId) hostUserObj.update(hostUser=v_hostUser, hostPasswd=v_hostPasswd, userDesc=v_userDesc) result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'修改失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getHostUserDetailInfo(request): hostUserId = request.POST['hostUserId'].strip() try: hostUserInfo = hostUser.objects.filter(id=hostUserId) hostUserJson = serializers.serialize("json", hostUserInfo, use_natural_foreign_keys=True) result = {'status':1, 'msg':'请求成功', 'hostUserJson':hostUserJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def delHostUser(request): hostUserId = request.POST['hostUserId'] if hostUserId == "" or hostUserId is None: result = {'status':3, 'msg':'未选中任何记录!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: delResult = hostUser.objects.filter(id=hostUserId).delete() print(delResult) logger.error(delResult) result = {'status':1, 'msg':'删除成功!', 'data':delResult} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) logger.error(e) result = {'status':2, 'msg':'删除失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def addChangeDbGroupInfo(request): v_groupId = request.POST.get('group_id') v_businessName = request.POST.get('business_name') v_groupName = request.POST.get('group_name') v_groupStatus = request.POST.get('group_status') v_groupDesc = request.POST.get('group_desc') v_groupEnv = request.POST.get('group_env') print(v_groupId, v_businessName, v_groupName, v_groupEnv, v_groupStatus, v_groupDesc) logger.info("保存或修改数据库组信息，接收前端参数：", v_groupId, v_businessName, v_groupName, v_groupEnv, v_groupStatus, v_groupDesc) if v_groupId == '' or v_groupId is None: try: dbGroupObj = dbGroup(businessName=v_businessName, groupName=v_groupName, groupEnv=v_groupEnv, groupStatus=v_groupStatus, groupDesc=v_groupDesc) dbGroupObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'保存失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: dbGroupObj = dbGroup.objects.filter(id=v_groupId) dbGroupObj.update(businessName=v_businessName, groupName=v_groupName, groupEnv=v_groupEnv, groupStatus=v_groupStatus, groupDesc=v_groupDesc) result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'修改失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getDbGroupDetailInfo(request): groupId = request.POST['groupId'] try: dbGroupObj = dbGroup.objects.get(id=groupId) dbGroupJson = dbGroupObj.toJSON() result = {'status':1, 'msg':'请求成功', 'obj':dbGroupJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def addChangeDbInstanceInfo(request): v_instanceId = request.POST.get('instance_id') v_groupId = request.POST.get('group_id') v_host_id = request.POST.get('host_id') v_instanceName = request.POST.get('instance_env') v_instanceType = request.POST.get('instance_type') v_portNum = request.POST.get('port_num') v_instanceRole = request.POST.get('instance_role') v_instanceStatus = request.POST.get('instance_status') v_instanceDesc = request.POST.get('instance_desc') print(v_instanceId, v_groupId, v_host_id, v_instanceName, v_instanceType, v_portNum, v_instanceRole, v_instanceStatus, v_instanceDesc) logger.info("保存或修改数据库实例信息，接收前端参数：", v_instanceId, v_groupId, v_host_id, v_instanceName, v_instanceType, v_portNum, v_instanceRole, v_instanceStatus, v_instanceDesc) if v_instanceId == '' or v_instanceId is None: try: dbGroupObj = dbGroup.objects.get(id=v_groupId) hostObj = host.objects.get(id=v_host_id) print(hostObj) dbInstanceObj = dbInstance(groupName=dbGroupObj, host=hostObj, instanceName=v_instanceName, instanceType=v_instanceType, portNum=v_portNum, instanceRole=v_instanceRole, instanceStatus=v_instanceStatus, instanceDesc=v_instanceDesc) dbInstanceObj.save() result = {'status':1, 'msg':'保存成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) logger.error(str(e)) result = {'status':2, 'msg':'保存失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: dbGroupObj = dbGroup.objects.get(id=v_groupId) hostObj = host.objects.get(id=v_host_id) dbInstanceObj = dbInstance.objects.filter(id=v_instanceId) dbInstanceObj.update(groupName=dbGroupObj, host=hostObj, instanceName=v_instanceName, instanceType=v_instanceType, portNum=v_portNum, instanceRole=v_instanceRole, instanceStatus=v_instanceStatus, instanceDesc=v_instanceDesc) result = {'status':1, 'msg':'修改成功!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: logger.error(str(e)) result = {'status':2, 'msg':'修改失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def getDbInstanceDetailInfo(request): instanceId = request.POST['instanceId'].strip() try: dbInstanceInfo = dbInstance.objects.filter(id=instanceId) dbInstanceJson = serializers.serialize("json", dbInstanceInfo, use_natural_foreign_keys=True) result = {'status':1, 'msg':'请求成功', 'dbInstanceJson':dbInstanceJson} print(result) return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) result = {'status':2, 'msg':'请求失败!'+str(e), 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') @csrf_exempt def delDbInstance(request): instanceId = request.POST['instanceId'] if instanceId == "" or instanceId is None: result = {'status':3, 'msg':'未选中任何记录!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') else: try: delResult = dbInstance.objects.filter(id=instanceId).delete() print(delResult) logger.error(delResult) result = {'status':1, 'msg':'删除成功!', 'data':delResult} return HttpResponse(json.dumps(result), content_type='application/json') except Exception as e: print(e) logger.error(e) result = {'status':2, 'msg':'删除失败!', 'data':''} return HttpResponse(json.dumps(result), content_type='application/json') # 新增集群信息 # 修改集群信息 # '''

true

790d7258611c71f85ea414a0d78f516bb3b1cbb3

219

py

Python

blink_001.py

luisC62/RPi_Pico_Examples

d2fb34e6ec0835d9265b3bd750add9e2da3eabf7

[ "MIT" ]

null

blink_001.py

luisC62/RPi_Pico_Examples

d2fb34e6ec0835d9265b3bd750add9e2da3eabf7

[ "MIT" ]

null

blink_001.py

luisC62/RPi_Pico_Examples

d2fb34e6ec0835d9265b3bd750add9e2da3eabf7

[ "MIT" ]

null

from machine import Pin import utime led = Pin(28, Pin.OUT) onboard_led = Pin(25, Pin.OUT) led.low() onboard_led.high() while True: led.toggle() onboard_led.toggle() print("Toggle") utime.sleep(0.5)

18.25

30

0.666667

from machine import Pin import utime led = Pin(28, Pin.OUT) onboard_led = Pin(25, Pin.OUT) led.low() onboard_led.high() while True: led.toggle() onboard_led.toggle() print("Toggle") utime.sleep(0.5)

true

790d728eeac14afc437d0301467e95f1c6a85fee

978

py

Python

SRC/December-Batch/02_class/01_list.py

archeranimesh/fantastic-waffle

74274be44a469dac765379624c489cd5952e9b7c

[ "MIT" ]

null

SRC/December-Batch/02_class/01_list.py

archeranimesh/fantastic-waffle

74274be44a469dac765379624c489cd5952e9b7c

[ "MIT" ]

null

SRC/December-Batch/02_class/01_list.py

archeranimesh/fantastic-waffle

74274be44a469dac765379624c489cd5952e9b7c

[ "MIT" ]

null

a = [] # append element at the end. a.append(2) a.append(3) print(a) # insert at a specific location. a.insert(0, 5) a.insert(10, 5) print(a) # when specified a position not in list, it inserts at the end. a.insert(100, 6) print(a) # Deleting elements from a list. a.remove(5) # removes the first occurence of value passed print(a, len(a)) del a[0] print(a, len(a)) # access the last element print(a[-1]) # Printing a list print(len(a)) for item in range(len(a)): # the len is not inclusive print("(", item, ", ", a[item], ")") print("-" * 30) for item in range(0, len(a), 1): # the len is not inclusive print("(", item, ", ", a[item], ")") print("-" * 30) # Reverse printing a list for item in range(len(a) - 1, -1, -1): # the len is not inclusive print("(", item, ", ", a[item], ")") print("-" * 30) # Jump a certain number of times. for item in range(0, len(a), 2): # the len is not inclusive print("(", item, ", ", a[item], ")") print("-" * 30)

22.227273

66

0.604294

a = [] a.append(2) a.append(3) print(a) a.insert(0, 5) a.insert(10, 5) print(a) a.insert(100, 6) print(a) a.remove(5) print(a, len(a)) del a[0] print(a, len(a)) print(a[-1]) print(len(a)) for item in range(len(a)): print("(", item, ", ", a[item], ")") print("-" * 30) for item in range(0, len(a), 1): print("(", item, ", ", a[item], ")") print("-" * 30) for item in range(len(a) - 1, -1, -1): print("(", item, ", ", a[item], ")") print("-" * 30) for item in range(0, len(a), 2): print("(", item, ", ", a[item], ")") print("-" * 30)

true

790d742ca9e0602fc2e720daaa6e3b8267c06812

2,395

py

Python

data/p4VQE/R1/benchmark/startQiskit_Class82.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p4VQE/R1/benchmark/startQiskit_Class82.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p4VQE/R1/benchmark/startQiskit_Class82.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=3 # total number=9 import numpy as np from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ import networkx as nx from qiskit.visualization import plot_histogram from typing import * from pprint import pprint from math import log2 from collections import Counter from qiskit.test.mock import FakeVigo, FakeYorktown kernel = 'circuit/bernstein' def make_circuit(n:int) -> QuantumCircuit: # circuit begin input_qubit = QuantumRegister(n,"qc") prog = QuantumCircuit(input_qubit) prog.h(input_qubit[0]) # number=1 prog.h(input_qubit[1]) # number=2 prog.h(input_qubit[2]) # number=3 prog.h(input_qubit[3]) # number=4 for edge in E: k = edge[0] l = edge[1] prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1]) prog.p(gamma, k) prog.p(gamma, l) prog.rx(2 * beta, range(len(V))) prog.swap(input_qubit[1],input_qubit[0]) # number=5 prog.swap(input_qubit[1],input_qubit[0]) # number=6 prog.y(input_qubit[3]) # number=7 prog.y(input_qubit[3]) # number=8 # circuit end return prog if __name__ == '__main__': n = 4 V = np.arange(0, n, 1) E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)] G = nx.Graph() G.add_nodes_from(V) G.add_weighted_edges_from(E) step_size = 0.1 a_gamma = np.arange(0, np.pi, step_size) a_beta = np.arange(0, np.pi, step_size) a_gamma, a_beta = np.meshgrid(a_gamma, a_beta) F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * ( 1 + np.cos(4 * a_gamma) ** 2) result = np.where(F1 == np.amax(F1)) a = list(zip(result[0], result[1]))[0] gamma = a[0] * step_size beta = a[1] * step_size prog = make_circuit(4) sample_shot =5200 writefile = open("../data/startQiskit_Class82.csv", "w") # prog.draw('mpl', filename=(kernel + '.png')) backend = BasicAer.get_backend('statevector_simulator') circuit1 = transpile(prog, FakeYorktown()) prog = circuit1 info = execute(prog,backend=backend, shots=sample_shot).result().get_counts() print(info, file=writefile) print("results end", file=writefile) print(circuit1.depth(), file=writefile) print(circuit1, file=writefile) writefile.close()

27.215909

118

0.634238

import numpy as np from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ import networkx as nx from qiskit.visualization import plot_histogram from typing import * from pprint import pprint from math import log2 from collections import Counter from qiskit.test.mock import FakeVigo, FakeYorktown kernel = 'circuit/bernstein' def make_circuit(n:int) -> QuantumCircuit: input_qubit = QuantumRegister(n,"qc") prog = QuantumCircuit(input_qubit) prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[3]) for edge in E: k = edge[0] l = edge[1] prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1]) prog.p(gamma, k) prog.p(gamma, l) prog.rx(2 * beta, range(len(V))) prog.swap(input_qubit[1],input_qubit[0]) prog.swap(input_qubit[1],input_qubit[0]) prog.y(input_qubit[3]) prog.y(input_qubit[3]) return prog if __name__ == '__main__': n = 4 V = np.arange(0, n, 1) E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)] G = nx.Graph() G.add_nodes_from(V) G.add_weighted_edges_from(E) step_size = 0.1 a_gamma = np.arange(0, np.pi, step_size) a_beta = np.arange(0, np.pi, step_size) a_gamma, a_beta = np.meshgrid(a_gamma, a_beta) F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * ( 1 + np.cos(4 * a_gamma) ** 2) result = np.where(F1 == np.amax(F1)) a = list(zip(result[0], result[1]))[0] gamma = a[0] * step_size beta = a[1] * step_size prog = make_circuit(4) sample_shot =5200 writefile = open("../data/startQiskit_Class82.csv", "w") backend = BasicAer.get_backend('statevector_simulator') circuit1 = transpile(prog, FakeYorktown()) prog = circuit1 info = execute(prog,backend=backend, shots=sample_shot).result().get_counts() print(info, file=writefile) print("results end", file=writefile) print(circuit1.depth(), file=writefile) print(circuit1, file=writefile) writefile.close()

true

790d74b84c68d02413bfbc62e01e8661e782f03d

3,650

py

Python

support_files/scraping/entries/proj_2062/proj_2062/middlewares.py

miccaldas/new_rss

9580887ac44b5c3e4c4ed5045478f2c7fef36afe

[ "MIT" ]

null

support_files/scraping/entries/proj_2062/proj_2062/middlewares.py

miccaldas/new_rss

9580887ac44b5c3e4c4ed5045478f2c7fef36afe

[ "MIT" ]

null

support_files/scraping/entries/proj_2062/proj_2062/middlewares.py

miccaldas/new_rss

9580887ac44b5c3e4c4ed5045478f2c7fef36afe

[ "MIT" ]

null

# Define here the models for your spider middleware # # See documentation in: # https://docs.scrapy.org/en/latest/topics/spider-middleware.html from scrapy import signals # useful for handling different item types with a single interface from itemadapter import is_item, ItemAdapter class Proj2062SpiderMiddleware: # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the spider middleware does not modify the # passed objects. @classmethod def from_crawler(cls, crawler): # This method is used by Scrapy to create your spiders. s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_spider_input(self, response, spider): # Called for each response that goes through the spider # middleware and into the spider. # Should return None or raise an exception. return None def process_spider_output(self, response, result, spider): # Called with the results returned from the Spider, after # it has processed the response. # Must return an iterable of Request, or item objects. for i in result: yield i def process_spider_exception(self, response, exception, spider): # Called when a spider or process_spider_input() method # (from other spider middleware) raises an exception. # Should return either None or an iterable of Request or item objects. pass def process_start_requests(self, start_requests, spider): # Called with the start requests of the spider, and works # similarly to the process_spider_output() method, except # that it doesn’t have a response associated. # Must return only requests (not items). for r in start_requests: yield r def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name) class Proj2062DownloaderMiddleware: # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the downloader middleware does not modify the # passed objects. @classmethod def from_crawler(cls, crawler): # This method is used by Scrapy to create your spiders. s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_request(self, request, spider): # Called for each request that goes through the downloader # middleware. # Must either: # - return None: continue processing this request # - or return a Response object # - or return a Request object # - or raise IgnoreRequest: process_exception() methods of # installed downloader middleware will be called return None def process_response(self, request, response, spider): # Called with the response returned from the downloader. # Must either; # - return a Response object # - return a Request object # - or raise IgnoreRequest return response def process_exception(self, request, exception, spider): # Called when a download handler or a process_request() # (from other downloader middleware) raises an exception. # Must either: # - return None: continue processing this exception # - return a Response object: stops process_exception() chain # - return a Request object: stops process_exception() chain pass def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name)

35.096154

78

0.674521

from scrapy import signals from itemadapter import is_item, ItemAdapter class Proj2062SpiderMiddleware: @classmethod def from_crawler(cls, crawler): s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_spider_input(self, response, spider): return None def process_spider_output(self, response, result, spider): for i in result: yield i def process_spider_exception(self, response, exception, spider): pass def process_start_requests(self, start_requests, spider): for r in start_requests: yield r def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name) class Proj2062DownloaderMiddleware: @classmethod def from_crawler(cls, crawler): s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_request(self, request, spider): return None def process_response(self, request, response, spider): return response def process_exception(self, request, exception, spider): pass def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name)

true

790d750c0073c6e8d5d7bf30cbf1030e3f3b4896

132

py

Python

Beginner/1173.py

pedrodanieljardim/DesafiosURI-feitos-em-JAVA

4e727e1b08e01f527d0b7b884c268643f1472ded

[ "MIT" ]

1

2022-03-19T18:06:25.000Z

Beginner/1173.py

pedrodanieljardim/beecrowd

4e727e1b08e01f527d0b7b884c268643f1472ded

[ "MIT" ]

null

Beginner/1173.py

pedrodanieljardim/beecrowd

4e727e1b08e01f527d0b7b884c268643f1472ded

[ "MIT" ]

null

n = [] v = int(input()) n.append([v*x*2 for x in range(1,11)]) print(n) for i in range(len(n)): print('N[%d] = %d' % (i, n[i]))

18.857143

38

0.5

n = [] v = int(input()) n.append([v*x*2 for x in range(1,11)]) print(n) for i in range(len(n)): print('N[%d] = %d' % (i, n[i]))

true

790d781330116ec3665c91ad77ec24b53c2d4fc6

1,572

py

Python

problem_6.py

johangenis/problems_vs_algorithms

9925d7319de849fd7814cf87050232c22d8c2a96

[ "MIT" ]

null

problem_6.py

johangenis/problems_vs_algorithms

9925d7319de849fd7814cf87050232c22d8c2a96

[ "MIT" ]

null

problem_6.py

johangenis/problems_vs_algorithms

9925d7319de849fd7814cf87050232c22d8c2a96

[ "MIT" ]

null

def get_min_max(ints): """ Return a tuple(min, max) out of list of unsorted integers. Args: ints(list): list of integers containing one or more integers """ # Handle non-list input if not isinstance(ints, list): return None, None # Define variables for min and max value and initialize to None min_value = None max_value = None for index, value in enumerate(ints): if index == 0: min_value = value max_value = value if value < min_value: min_value = value elif value > max_value: max_value = value return min_value, max_value # Example Test Case of Ten Integers import random # Test case 1: random int array l = [i for i in range(0, 10)] # a list containing 0 - 9 print(f"Test case 1 - random list of int: {l}") random.shuffle(l) # Should print "Pass" as the result should be (0, 9) print ("Pass" if ((0, 9) == get_min_max(l)) else "Fail") # Test case 2: empty array print(f"Test case 2 - empty array") # Should print "Pass" as the result should be (None, None) print ("Pass" if ((None, None) == get_min_max([])) else "Fail") # Test case 3: array with single item print(f"Test case 3 - array with single item") # Should print "Pass" as the result should be (None, None) print ("Pass" if ((1, 1) == get_min_max([1])) else "Fail") # Test case 4: non array input print(f"Test case 4 - non array input") # Should print "Pass" as the result should be (None, None) print ("Pass" if ((None, None) == get_min_max(10)) else "Fail")

26.644068

67

0.636768

def get_min_max(ints): if not isinstance(ints, list): return None, None min_value = None max_value = None for index, value in enumerate(ints): if index == 0: min_value = value max_value = value if value < min_value: min_value = value elif value > max_value: max_value = value return min_value, max_value import random l = [i for i in range(0, 10)] print(f"Test case 1 - random list of int: {l}") random.shuffle(l) print ("Pass" if ((0, 9) == get_min_max(l)) else "Fail") print(f"Test case 2 - empty array") print ("Pass" if ((None, None) == get_min_max([])) else "Fail") print(f"Test case 3 - array with single item") print ("Pass" if ((1, 1) == get_min_max([1])) else "Fail") print(f"Test case 4 - non array input") print ("Pass" if ((None, None) == get_min_max(10)) else "Fail")

true

790d782a620aacd6fa936c2d559a372314eb37d6

2,806

py

Python

clients/kratos/python/test/test_request_method_config.py

UkonnRa/sdk

23ab5408a89cdf6ba7a6d8944f8d1b1cdc68aa4c

[ "Apache-2.0" ]

null

clients/kratos/python/test/test_request_method_config.py

UkonnRa/sdk

23ab5408a89cdf6ba7a6d8944f8d1b1cdc68aa4c

[ "Apache-2.0" ]

null

clients/kratos/python/test/test_request_method_config.py

UkonnRa/sdk

23ab5408a89cdf6ba7a6d8944f8d1b1cdc68aa4c

[ "Apache-2.0" ]

null

# coding: utf-8 """ Ory Kratos Welcome to the ORY Kratos HTTP API documentation! # noqa: E501 The version of the OpenAPI document: latest Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import datetime import ory_kratos_client from ory_kratos_client.models.request_method_config import RequestMethodConfig # noqa: E501 from ory_kratos_client.rest import ApiException class TestRequestMethodConfig(unittest.TestCase): """RequestMethodConfig unit test stubs""" def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): """Test RequestMethodConfig include_option is a boolean, when False only required params are included, when True both required and optional params are included """ # model = ory_kratos_client.models.request_method_config.RequestMethodConfig() # noqa: E501 if include_optional : return RequestMethodConfig( action = '0', errors = [ ory_kratos_client.models.error.Error( message = '0', ) ], fields = [ ory_kratos_client.models.form_field.formField( disabled = True, errors = [ ory_kratos_client.models.error.Error( message = '0', ) ], name = '0', pattern = '0', required = True, type = '0', value = ory_kratos_client.models.value.value(), ) ], method = '0' ) else : return RequestMethodConfig( action = '0', fields = [ ory_kratos_client.models.form_field.formField( disabled = True, errors = [ ory_kratos_client.models.error.Error( message = '0', ) ], name = '0', pattern = '0', required = True, type = '0', value = ory_kratos_client.models.value.value(), ) ], method = '0', ) def testRequestMethodConfig(self): """Test RequestMethodConfig""" inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()

32.627907

100

0.497149

from __future__ import absolute_import import unittest import datetime import ory_kratos_client from ory_kratos_client.models.request_method_config import RequestMethodConfig from ory_kratos_client.rest import ApiException class TestRequestMethodConfig(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): include_optional : return RequestMethodConfig( action = '0', errors = [ ory_kratos_client.models.error.Error( message = '0', ) ], fields = [ ory_kratos_client.models.form_field.formField( disabled = True, errors = [ ory_kratos_client.models.error.Error( message = '0', ) ], name = '0', pattern = '0', required = True, type = '0', value = ory_kratos_client.models.value.value(), ) ], method = '0' ) else : return RequestMethodConfig( action = '0', fields = [ ory_kratos_client.models.form_field.formField( disabled = True, errors = [ ory_kratos_client.models.error.Error( message = '0', ) ], name = '0', pattern = '0', required = True, type = '0', value = ory_kratos_client.models.value.value(), ) ], method = '0', ) def testRequestMethodConfig(self): inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()

true

790d78f20215b94be671773f0601de32147c393d

2,646

py

Python

detr_tensorflow/models/custom_layers.py

Leonardo-Blanger/detr_tensorflow

38fc3c586b6767deed09bd7ec6c2a2fd7002346e

[ "MIT" ]

59

2020-07-04T19:14:31.000Z

2022-03-08T14:30:27.000Z

detr_tensorflow/models/custom_layers.py

Leonardo-Blanger/detr_tensorflow

38fc3c586b6767deed09bd7ec6c2a2fd7002346e

[ "MIT" ]

7

2020-08-17T23:57:43.000Z

2022-03-22T02:52:20.000Z

detr_tensorflow/models/custom_layers.py

Leonardo-Blanger/detr_tensorflow

38fc3c586b6767deed09bd7ec6c2a2fd7002346e

[ "MIT" ]

14

2020-08-17T04:10:16.000Z

2022-02-06T05:48:33.000Z

import tensorflow as tf class FrozenBatchNorm2D(tf.keras.layers.Layer): def __init__(self, eps=1e-5, **kwargs): super().__init__(**kwargs) self.eps = eps def build(self, input_shape): self.weight = self.add_weight(name='weight', shape=[input_shape[-1]], initializer='zeros', trainable=False) self.bias = self.add_weight(name='bias', shape=[input_shape[-1]], initializer='zeros', trainable=False) self.running_mean = self.add_weight(name='running_mean', shape=[input_shape[-1]], initializer='zeros', trainable=False) self.running_var = self.add_weight(name='running_var', shape=[input_shape[-1]], initializer='ones', trainable=False) def call(self, x): scale = self.weight * tf.math.rsqrt(self.running_var + self.eps) shift = self.bias - self.running_mean * scale return x * scale + shift def compute_output_shape(self, input_shape): return input_shape class Linear(tf.keras.layers.Layer): ''' Use this custom layer instead of tf.keras.layers.Dense to allow loading converted PyTorch Dense weights that have shape (output_dim, input_dim) ''' def __init__(self, output_dim, **kwargs): super().__init__(**kwargs) self.output_dim = output_dim def build(self, input_shape): self.kernel = self.add_weight(name='kernel', shape=[self.output_dim, input_shape[-1]], initializer='zeros', trainable=True) self.bias = self.add_weight(name='bias', shape=[self.output_dim], initializer='zeros', trainable=True) def call(self, x): return tf.matmul(x, self.kernel, transpose_b=True) + self.bias def compute_output_shape(self, input_shape): return input_shape.as_list()[:-1] + [self.output_dim] class FixedEmbedding(tf.keras.layers.Layer): def __init__(self, embed_shape, **kwargs): super().__init__(**kwargs) self.embed_shape = embed_shape def build(self, input_shape): self.w = self.add_weight(name='kernel', shape=self.embed_shape, initializer='zeros', trainable=True) def call(self, x=None): return self.w

38.911765

79

0.544974

import tensorflow as tf class FrozenBatchNorm2D(tf.keras.layers.Layer): def __init__(self, eps=1e-5, **kwargs): super().__init__(**kwargs) self.eps = eps def build(self, input_shape): self.weight = self.add_weight(name='weight', shape=[input_shape[-1]], initializer='zeros', trainable=False) self.bias = self.add_weight(name='bias', shape=[input_shape[-1]], initializer='zeros', trainable=False) self.running_mean = self.add_weight(name='running_mean', shape=[input_shape[-1]], initializer='zeros', trainable=False) self.running_var = self.add_weight(name='running_var', shape=[input_shape[-1]], initializer='ones', trainable=False) def call(self, x): scale = self.weight * tf.math.rsqrt(self.running_var + self.eps) shift = self.bias - self.running_mean * scale return x * scale + shift def compute_output_shape(self, input_shape): return input_shape class Linear(tf.keras.layers.Layer): def __init__(self, output_dim, **kwargs): super().__init__(**kwargs) self.output_dim = output_dim def build(self, input_shape): self.kernel = self.add_weight(name='kernel', shape=[self.output_dim, input_shape[-1]], initializer='zeros', trainable=True) self.bias = self.add_weight(name='bias', shape=[self.output_dim], initializer='zeros', trainable=True) def call(self, x): return tf.matmul(x, self.kernel, transpose_b=True) + self.bias def compute_output_shape(self, input_shape): return input_shape.as_list()[:-1] + [self.output_dim] class FixedEmbedding(tf.keras.layers.Layer): def __init__(self, embed_shape, **kwargs): super().__init__(**kwargs) self.embed_shape = embed_shape def build(self, input_shape): self.w = self.add_weight(name='kernel', shape=self.embed_shape, initializer='zeros', trainable=True) def call(self, x=None): return self.w

true

790d794890d607e5896329ec11df3c8e12aae1c0

18,344

py

Python

lldb/test/API/python_api/process/TestProcessAPI.py

acidburn0zzz/llvm-project

7ca7a2547f00e34f5ec91be776a1d0bbca74b7a9

[ "Apache-2.0" ]

61

2019-04-12T18:49:57.000Z

2022-03-19T22:23:16.000Z

lldb/test/API/python_api/process/TestProcessAPI.py

acidburn0zzz/llvm-project

7ca7a2547f00e34f5ec91be776a1d0bbca74b7a9

[ "Apache-2.0" ]

127

2019-04-09T00:55:50.000Z

2022-03-21T15:35:41.000Z

lldb/test/API/python_api/process/TestProcessAPI.py

acidburn0zzz/llvm-project

7ca7a2547f00e34f5ec91be776a1d0bbca74b7a9

[ "Apache-2.0" ]

10

2019-04-02T18:25:40.000Z

2022-02-15T07:11:37.000Z

""" Test SBProcess APIs, including ReadMemory(), WriteMemory(), and others. """ from __future__ import print_function import lldb from lldbsuite.test.decorators import * from lldbsuite.test.lldbtest import * from lldbsuite.test.lldbutil import get_stopped_thread, state_type_to_str class ProcessAPITestCase(TestBase): mydir = TestBase.compute_mydir(__file__) def setUp(self): # Call super's setUp(). TestBase.setUp(self) # Find the line number to break inside main(). self.line = line_number( "main.cpp", "// Set break point at this line and check variable 'my_char'.") @skipIfReproducer # SBProcess::ReadMemory is not instrumented. def test_read_memory(self): """Test Python SBProcess.ReadMemory() API.""" self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) thread = get_stopped_thread(process, lldb.eStopReasonBreakpoint) self.assertTrue( thread.IsValid(), "There should be a thread stopped due to breakpoint") frame = thread.GetFrameAtIndex(0) # Get the SBValue for the global variable 'my_char'. val = frame.FindValue("my_char", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # Due to the typemap magic (see lldb.swig), we pass in 1 to ReadMemory and # expect to get a Python string as the result object! error = lldb.SBError() self.assertFalse(val.TypeIsPointerType()) content = process.ReadMemory( val.AddressOf().GetValueAsUnsigned(), 1, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") if self.TraceOn(): print("memory content:", content) self.expect( content, "Result from SBProcess.ReadMemory() matches our expected output: 'x'", exe=False, startstr=b'x') # Read (char *)my_char_ptr. val = frame.FindValue("my_char_ptr", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) cstring = process.ReadCStringFromMemory( val.GetValueAsUnsigned(), 256, error) if not error.Success(): self.fail("SBProcess.ReadCStringFromMemory() failed") if self.TraceOn(): print("cstring read is:", cstring) self.expect( cstring, "Result from SBProcess.ReadCStringFromMemory() matches our expected output", exe=False, startstr='Does it work?') # Get the SBValue for the global variable 'my_cstring'. val = frame.FindValue("my_cstring", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # Due to the typemap magic (see lldb.swig), we pass in 256 to read at most 256 bytes # from the address, and expect to get a Python string as the result # object! self.assertFalse(val.TypeIsPointerType()) cstring = process.ReadCStringFromMemory( val.AddressOf().GetValueAsUnsigned(), 256, error) if not error.Success(): self.fail("SBProcess.ReadCStringFromMemory() failed") if self.TraceOn(): print("cstring read is:", cstring) self.expect( cstring, "Result from SBProcess.ReadCStringFromMemory() matches our expected output", exe=False, startstr='lldb.SBProcess.ReadCStringFromMemory() works!') # Get the SBValue for the global variable 'my_uint32'. val = frame.FindValue("my_uint32", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # Due to the typemap magic (see lldb.swig), we pass in 4 to read 4 bytes # from the address, and expect to get an int as the result! self.assertFalse(val.TypeIsPointerType()) my_uint32 = process.ReadUnsignedFromMemory( val.AddressOf().GetValueAsUnsigned(), 4, error) if not error.Success(): self.fail("SBProcess.ReadCStringFromMemory() failed") if self.TraceOn(): print("uint32 read is:", my_uint32) if my_uint32 != 12345: self.fail( "Result from SBProcess.ReadUnsignedFromMemory() does not match our expected output") @skipIfReproducer # SBProcess::WriteMemory is not instrumented. def test_write_memory(self): """Test Python SBProcess.WriteMemory() API.""" self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) thread = get_stopped_thread(process, lldb.eStopReasonBreakpoint) self.assertTrue( thread.IsValid(), "There should be a thread stopped due to breakpoint") frame = thread.GetFrameAtIndex(0) # Get the SBValue for the global variable 'my_char'. val = frame.FindValue("my_char", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # If the variable does not have a load address, there's no sense # continuing. if not val.GetLocation().startswith("0x"): return # OK, let's get the hex location of the variable. location = int(val.GetLocation(), 16) # The program logic makes the 'my_char' variable to have memory content as 'x'. # But we want to use the WriteMemory() API to assign 'a' to the # variable. # Now use WriteMemory() API to write 'a' into the global variable. error = lldb.SBError() result = process.WriteMemory(location, 'a', error) if not error.Success() or result != 1: self.fail("SBProcess.WriteMemory() failed") # Read from the memory location. This time it should be 'a'. # Due to the typemap magic (see lldb.swig), we pass in 1 to ReadMemory and # expect to get a Python string as the result object! content = process.ReadMemory(location, 1, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") if self.TraceOn(): print("memory content:", content) self.expect( content, "Result from SBProcess.ReadMemory() matches our expected output: 'a'", exe=False, startstr=b'a') @skipIfReproducer # SBProcess::WriteMemory is not instrumented. def test_access_my_int(self): """Test access 'my_int' using Python SBProcess.GetByteOrder() and other APIs.""" self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) thread = get_stopped_thread(process, lldb.eStopReasonBreakpoint) self.assertTrue( thread.IsValid(), "There should be a thread stopped due to breakpoint") frame = thread.GetFrameAtIndex(0) # Get the SBValue for the global variable 'my_int'. val = frame.FindValue("my_int", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # If the variable does not have a load address, there's no sense # continuing. if not val.GetLocation().startswith("0x"): return # OK, let's get the hex location of the variable. location = int(val.GetLocation(), 16) # Note that the canonical from of the bytearray is little endian. from lldbsuite.test.lldbutil import int_to_bytearray, bytearray_to_int byteSize = val.GetByteSize() bytes = int_to_bytearray(256, byteSize) byteOrder = process.GetByteOrder() if byteOrder == lldb.eByteOrderBig: bytes.reverse() elif byteOrder == lldb.eByteOrderLittle: pass else: # Neither big endian nor little endian? Return for now. # Add more logic here if we want to handle other types. return # The program logic makes the 'my_int' variable to have int type and value of 0. # But we want to use the WriteMemory() API to assign 256 to the # variable. # Now use WriteMemory() API to write 256 into the global variable. error = lldb.SBError() result = process.WriteMemory(location, bytes, error) if not error.Success() or result != byteSize: self.fail("SBProcess.WriteMemory() failed") # Make sure that the val we got originally updates itself to notice the # change: self.expect( val.GetValue(), "SBProcess.ReadMemory() successfully writes (int)256 to the memory location for 'my_int'", exe=False, startstr='256') # And for grins, get the SBValue for the global variable 'my_int' # again, to make sure that also tracks the new value: val = frame.FindValue("my_int", lldb.eValueTypeVariableGlobal) self.expect( val.GetValue(), "SBProcess.ReadMemory() successfully writes (int)256 to the memory location for 'my_int'", exe=False, startstr='256') # Now read the memory content. The bytearray should have (byte)1 as # the second element. content = process.ReadMemory(location, byteSize, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") # The bytearray_to_int utility function expects a little endian # bytearray. if byteOrder == lldb.eByteOrderBig: content = bytearray(content, 'ascii') content.reverse() new_value = bytearray_to_int(content, byteSize) if new_value != 256: self.fail("Memory content read from 'my_int' does not match (int)256") # Dump the memory content.... if self.TraceOn(): for i in content: print("byte:", i) def test_remote_launch(self): """Test SBProcess.RemoteLaunch() API with a process not in eStateConnected, and it should fail.""" self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) if self.TraceOn(): print("process state:", state_type_to_str(process.GetState())) self.assertTrue(process.GetState() != lldb.eStateConnected) error = lldb.SBError() success = process.RemoteLaunch( None, None, None, None, None, None, 0, False, error) self.assertTrue( not success, "RemoteLaunch() should fail for process state != eStateConnected") def test_get_num_supported_hardware_watchpoints(self): """Test SBProcess.GetNumSupportedHardwareWatchpoints() API with a process.""" self.build() exe = self.getBuildArtifact("a.out") self.runCmd("file " + exe, CURRENT_EXECUTABLE_SET) target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) error = lldb.SBError() num = process.GetNumSupportedHardwareWatchpoints(error) if self.TraceOn() and error.Success(): print("Number of supported hardware watchpoints: %d" % num) @no_debug_info_test def test_get_process_info(self): """Test SBProcess::GetProcessInfo() API with a locally launched process.""" self.build() exe = self.getBuildArtifact("a.out") self.runCmd("file " + exe, CURRENT_EXECUTABLE_SET) target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) # Launch the process and stop at the entry point. launch_info = target.GetLaunchInfo() launch_info.SetWorkingDirectory(self.get_process_working_directory()) launch_flags = launch_info.GetLaunchFlags() launch_flags |= lldb.eLaunchFlagStopAtEntry launch_info.SetLaunchFlags(launch_flags) error = lldb.SBError() process = target.Launch(launch_info, error) if not error.Success(): self.fail("Failed to launch process") # Verify basic process info can be retrieved successfully process_info = process.GetProcessInfo() self.assertTrue(process_info.IsValid()) file_spec = process_info.GetExecutableFile() self.assertTrue(file_spec.IsValid()) process_name = process_info.GetName() self.assertIsNotNone(process_name, "Process has a name") self.assertGreater(len(process_name), 0, "Process name isn't blank") self.assertEqual(file_spec.GetFilename(), "a.out") self.assertNotEqual( process_info.GetProcessID(), lldb.LLDB_INVALID_PROCESS_ID, "Process ID is valid") triple = process_info.GetTriple() self.assertIsNotNone(triple, "Process has a triple") # Additional process info varies by platform, so just check that # whatever info was retrieved is consistent and nothing blows up. if process_info.UserIDIsValid(): self.assertNotEqual( process_info.GetUserID(), lldb.UINT32_MAX, "Process user ID is valid") else: self.assertEqual( process_info.GetUserID(), lldb.UINT32_MAX, "Process user ID is invalid") if process_info.GroupIDIsValid(): self.assertNotEqual( process_info.GetGroupID(), lldb.UINT32_MAX, "Process group ID is valid") else: self.assertEqual( process_info.GetGroupID(), lldb.UINT32_MAX, "Process group ID is invalid") if process_info.EffectiveUserIDIsValid(): self.assertNotEqual( process_info.GetEffectiveUserID(), lldb.UINT32_MAX, "Process effective user ID is valid") else: self.assertEqual( process_info.GetEffectiveUserID(), lldb.UINT32_MAX, "Process effective user ID is invalid") if process_info.EffectiveGroupIDIsValid(): self.assertNotEqual( process_info.GetEffectiveGroupID(), lldb.UINT32_MAX, "Process effective group ID is valid") else: self.assertEqual( process_info.GetEffectiveGroupID(), lldb.UINT32_MAX, "Process effective group ID is invalid") process_info.GetParentProcessID() def test_allocate_deallocate_memory(self): """Test Python SBProcess.AllocateMemory() and SBProcess.DeallocateMemory() APIs.""" self.build() (target, process, main_thread, main_breakpoint) = lldbutil.run_to_source_breakpoint( self, "// Set break point at this line", lldb.SBFileSpec("main.cpp")) # Allocate a block of memory in the target process error = lldb.SBError() addr = process.AllocateMemory(16384, lldb.ePermissionsReadable, error) if not error.Success() or addr == lldb.LLDB_INVALID_ADDRESS: self.fail("SBProcess.AllocateMemory() failed") # Now use WriteMemory() API to write 'a' into the allocated # memory. Note that the debugger can do this even though the # block is not set writable. result = process.WriteMemory(addr, 'a', error) if not error.Success() or result != 1: self.fail("SBProcess.WriteMemory() failed") # Read from the memory location. This time it should be 'a'. # Due to the typemap magic (see lldb.swig), we pass in 1 to ReadMemory and # expect to get a Python string as the result object! content = process.ReadMemory(addr, 1, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") if self.TraceOn(): print("memory content:", content) self.expect( content, "Result from SBProcess.ReadMemory() matches our expected output: 'a'", exe=False, startstr=b'a') # Verify that the process itself can read the allocated memory frame = main_thread.GetFrameAtIndex(0) val = frame.EvaluateExpression( "test_read(reinterpret_cast<char *>({:#x}))".format(addr)) self.expect(val.GetValue(), "Result of test_read() matches expected output 'a'", exe=False, startstr="'a'") # Verify that the process cannot write into the block val = frame.EvaluateExpression( "test_write(reinterpret_cast<char *>({:#x}), 'b')".format(addr)) if val.GetError().Success(): self.fail( "test_write() to allocated memory without write permission unexpectedly succeeded") # Deallocate the memory error = process.DeallocateMemory(addr) if not error.Success(): self.fail("SBProcess.DeallocateMemory() failed")

40.22807

106

0.629089

from __future__ import print_function import lldb from lldbsuite.test.decorators import * from lldbsuite.test.lldbtest import * from lldbsuite.test.lldbutil import get_stopped_thread, state_type_to_str class ProcessAPITestCase(TestBase): mydir = TestBase.compute_mydir(__file__) def setUp(self): TestBase.setUp(self) # Find the line number to break inside main(). self.line = line_number( "main.cpp", "// Set break point at this line and check variable 'my_char'.") @skipIfReproducer # SBProcess::ReadMemory is not instrumented. def test_read_memory(self): self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) thread = get_stopped_thread(process, lldb.eStopReasonBreakpoint) self.assertTrue( thread.IsValid(), "There should be a thread stopped due to breakpoint") frame = thread.GetFrameAtIndex(0) # Get the SBValue for the global variable 'my_char'. val = frame.FindValue("my_char", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # Due to the typemap magic (see lldb.swig), we pass in 1 to ReadMemory and # expect to get a Python string as the result object! error = lldb.SBError() self.assertFalse(val.TypeIsPointerType()) content = process.ReadMemory( val.AddressOf().GetValueAsUnsigned(), 1, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") if self.TraceOn(): print("memory content:", content) self.expect( content, "Result from SBProcess.ReadMemory() matches our expected output: 'x'", exe=False, startstr=b'x') # Read (char *)my_char_ptr. val = frame.FindValue("my_char_ptr", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) cstring = process.ReadCStringFromMemory( val.GetValueAsUnsigned(), 256, error) if not error.Success(): self.fail("SBProcess.ReadCStringFromMemory() failed") if self.TraceOn(): print("cstring read is:", cstring) self.expect( cstring, "Result from SBProcess.ReadCStringFromMemory() matches our expected output", exe=False, startstr='Does it work?') # Get the SBValue for the global variable 'my_cstring'. val = frame.FindValue("my_cstring", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # Due to the typemap magic (see lldb.swig), we pass in 256 to read at most 256 bytes # from the address, and expect to get a Python string as the result # object! self.assertFalse(val.TypeIsPointerType()) cstring = process.ReadCStringFromMemory( val.AddressOf().GetValueAsUnsigned(), 256, error) if not error.Success(): self.fail("SBProcess.ReadCStringFromMemory() failed") if self.TraceOn(): print("cstring read is:", cstring) self.expect( cstring, "Result from SBProcess.ReadCStringFromMemory() matches our expected output", exe=False, startstr='lldb.SBProcess.ReadCStringFromMemory() works!') # Get the SBValue for the global variable 'my_uint32'. val = frame.FindValue("my_uint32", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # Due to the typemap magic (see lldb.swig), we pass in 4 to read 4 bytes # from the address, and expect to get an int as the result! self.assertFalse(val.TypeIsPointerType()) my_uint32 = process.ReadUnsignedFromMemory( val.AddressOf().GetValueAsUnsigned(), 4, error) if not error.Success(): self.fail("SBProcess.ReadCStringFromMemory() failed") if self.TraceOn(): print("uint32 read is:", my_uint32) if my_uint32 != 12345: self.fail( "Result from SBProcess.ReadUnsignedFromMemory() does not match our expected output") @skipIfReproducer # SBProcess::WriteMemory is not instrumented. def test_write_memory(self): self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) thread = get_stopped_thread(process, lldb.eStopReasonBreakpoint) self.assertTrue( thread.IsValid(), "There should be a thread stopped due to breakpoint") frame = thread.GetFrameAtIndex(0) # Get the SBValue for the global variable 'my_char'. val = frame.FindValue("my_char", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # If the variable does not have a load address, there's no sense if not val.GetLocation().startswith("0x"): return location = int(val.GetLocation(), 16) # The program logic makes the 'my_char' variable to have memory content as 'x'. # But we want to use the WriteMemory() API to assign 'a' to the # variable. # Now use WriteMemory() API to write 'a' into the global variable. error = lldb.SBError() result = process.WriteMemory(location, 'a', error) if not error.Success() or result != 1: self.fail("SBProcess.WriteMemory() failed") # Read from the memory location. This time it should be 'a'. # Due to the typemap magic (see lldb.swig), we pass in 1 to ReadMemory and # expect to get a Python string as the result object! content = process.ReadMemory(location, 1, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") if self.TraceOn(): print("memory content:", content) self.expect( content, "Result from SBProcess.ReadMemory() matches our expected output: 'a'", exe=False, startstr=b'a') @skipIfReproducer # SBProcess::WriteMemory is not instrumented. def test_access_my_int(self): self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) thread = get_stopped_thread(process, lldb.eStopReasonBreakpoint) self.assertTrue( thread.IsValid(), "There should be a thread stopped due to breakpoint") frame = thread.GetFrameAtIndex(0) # Get the SBValue for the global variable 'my_int'. val = frame.FindValue("my_int", lldb.eValueTypeVariableGlobal) self.DebugSBValue(val) # If the variable does not have a load address, there's no sense if not val.GetLocation().startswith("0x"): return location = int(val.GetLocation(), 16) # Note that the canonical from of the bytearray is little endian. from lldbsuite.test.lldbutil import int_to_bytearray, bytearray_to_int byteSize = val.GetByteSize() bytes = int_to_bytearray(256, byteSize) byteOrder = process.GetByteOrder() if byteOrder == lldb.eByteOrderBig: bytes.reverse() elif byteOrder == lldb.eByteOrderLittle: pass else: # Neither big endian nor little endian? Return for now. # Add more logic here if we want to handle other types. return # The program logic makes the 'my_int' variable to have int type and value of 0. # But we want to use the WriteMemory() API to assign 256 to the # variable. # Now use WriteMemory() API to write 256 into the global variable. error = lldb.SBError() result = process.WriteMemory(location, bytes, error) if not error.Success() or result != byteSize: self.fail("SBProcess.WriteMemory() failed") # Make sure that the val we got originally updates itself to notice the # change: self.expect( val.GetValue(), "SBProcess.ReadMemory() successfully writes (int)256 to the memory location for 'my_int'", exe=False, startstr='256') # And for grins, get the SBValue for the global variable 'my_int' # again, to make sure that also tracks the new value: val = frame.FindValue("my_int", lldb.eValueTypeVariableGlobal) self.expect( val.GetValue(), "SBProcess.ReadMemory() successfully writes (int)256 to the memory location for 'my_int'", exe=False, startstr='256') # Now read the memory content. The bytearray should have (byte)1 as # the second element. content = process.ReadMemory(location, byteSize, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") # The bytearray_to_int utility function expects a little endian # bytearray. if byteOrder == lldb.eByteOrderBig: content = bytearray(content, 'ascii') content.reverse() new_value = bytearray_to_int(content, byteSize) if new_value != 256: self.fail("Memory content read from 'my_int' does not match (int)256") # Dump the memory content.... if self.TraceOn(): for i in content: print("byte:", i) def test_remote_launch(self): self.build() exe = self.getBuildArtifact("a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) if self.TraceOn(): print("process state:", state_type_to_str(process.GetState())) self.assertTrue(process.GetState() != lldb.eStateConnected) error = lldb.SBError() success = process.RemoteLaunch( None, None, None, None, None, None, 0, False, error) self.assertTrue( not success, "RemoteLaunch() should fail for process state != eStateConnected") def test_get_num_supported_hardware_watchpoints(self): self.build() exe = self.getBuildArtifact("a.out") self.runCmd("file " + exe, CURRENT_EXECUTABLE_SET) target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.cpp", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) error = lldb.SBError() num = process.GetNumSupportedHardwareWatchpoints(error) if self.TraceOn() and error.Success(): print("Number of supported hardware watchpoints: %d" % num) @no_debug_info_test def test_get_process_info(self): self.build() exe = self.getBuildArtifact("a.out") self.runCmd("file " + exe, CURRENT_EXECUTABLE_SET) target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) # Launch the process and stop at the entry point. launch_info = target.GetLaunchInfo() launch_info.SetWorkingDirectory(self.get_process_working_directory()) launch_flags = launch_info.GetLaunchFlags() launch_flags |= lldb.eLaunchFlagStopAtEntry launch_info.SetLaunchFlags(launch_flags) error = lldb.SBError() process = target.Launch(launch_info, error) if not error.Success(): self.fail("Failed to launch process") # Verify basic process info can be retrieved successfully process_info = process.GetProcessInfo() self.assertTrue(process_info.IsValid()) file_spec = process_info.GetExecutableFile() self.assertTrue(file_spec.IsValid()) process_name = process_info.GetName() self.assertIsNotNone(process_name, "Process has a name") self.assertGreater(len(process_name), 0, "Process name isn't blank") self.assertEqual(file_spec.GetFilename(), "a.out") self.assertNotEqual( process_info.GetProcessID(), lldb.LLDB_INVALID_PROCESS_ID, "Process ID is valid") triple = process_info.GetTriple() self.assertIsNotNone(triple, "Process has a triple") if process_info.UserIDIsValid(): self.assertNotEqual( process_info.GetUserID(), lldb.UINT32_MAX, "Process user ID is valid") else: self.assertEqual( process_info.GetUserID(), lldb.UINT32_MAX, "Process user ID is invalid") if process_info.GroupIDIsValid(): self.assertNotEqual( process_info.GetGroupID(), lldb.UINT32_MAX, "Process group ID is valid") else: self.assertEqual( process_info.GetGroupID(), lldb.UINT32_MAX, "Process group ID is invalid") if process_info.EffectiveUserIDIsValid(): self.assertNotEqual( process_info.GetEffectiveUserID(), lldb.UINT32_MAX, "Process effective user ID is valid") else: self.assertEqual( process_info.GetEffectiveUserID(), lldb.UINT32_MAX, "Process effective user ID is invalid") if process_info.EffectiveGroupIDIsValid(): self.assertNotEqual( process_info.GetEffectiveGroupID(), lldb.UINT32_MAX, "Process effective group ID is valid") else: self.assertEqual( process_info.GetEffectiveGroupID(), lldb.UINT32_MAX, "Process effective group ID is invalid") process_info.GetParentProcessID() def test_allocate_deallocate_memory(self): self.build() (target, process, main_thread, main_breakpoint) = lldbutil.run_to_source_breakpoint( self, "// Set break point at this line", lldb.SBFileSpec("main.cpp")) error = lldb.SBError() addr = process.AllocateMemory(16384, lldb.ePermissionsReadable, error) if not error.Success() or addr == lldb.LLDB_INVALID_ADDRESS: self.fail("SBProcess.AllocateMemory() failed") result = process.WriteMemory(addr, 'a', error) if not error.Success() or result != 1: self.fail("SBProcess.WriteMemory() failed") content = process.ReadMemory(addr, 1, error) if not error.Success(): self.fail("SBProcess.ReadMemory() failed") if self.TraceOn(): print("memory content:", content) self.expect( content, "Result from SBProcess.ReadMemory() matches our expected output: 'a'", exe=False, startstr=b'a') frame = main_thread.GetFrameAtIndex(0) val = frame.EvaluateExpression( "test_read(reinterpret_cast<char *>({:#x}))".format(addr)) self.expect(val.GetValue(), "Result of test_read() matches expected output 'a'", exe=False, startstr="'a'") val = frame.EvaluateExpression( "test_write(reinterpret_cast<char *>({:#x}), 'b')".format(addr)) if val.GetError().Success(): self.fail( "test_write() to allocated memory without write permission unexpectedly succeeded") error = process.DeallocateMemory(addr) if not error.Success(): self.fail("SBProcess.DeallocateMemory() failed")

true

790d79ee904552647ec607d5d99d7c416fe813e2

2,996

py

Python

src/avm2/generated/generate.py

paolodm/shumway

75c8d387b48a2f2e561eb4bc3458162b7cc71b16

[ "Apache-2.0" ]

1

2015-01-17T05:42:59.000Z

src/avm2/generated/generate.py

Acidburn0zzz/shumway

ef61c3211b91cb62f22441a29b59a0bdbcc2bf93

[ "Apache-2.0" ]

null

src/avm2/generated/generate.py

Acidburn0zzz/shumway

ef61c3211b91cb62f22441a29b59a0bdbcc2bf93

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- Mode: Python; c-basic-offset: 4; indent-tabs-mode: nil; tab-width: 4 -*- # vi: set ts=4 sw=4 expandtab: # ***** BEGIN LICENSE BLOCK ***** # Version: MPL 1.1/GPL 2.0/LGPL 2.1 # # The contents of this file are subject to the Mozilla Public License Version # 1.1 (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # http://www.mozilla.org/MPL/ # # Software distributed under the License is distributed on an "AS IS" basis, # WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License # for the specific language governing rights and limitations under the # License. # # The Original Code is [Open Source Virtual Machine.]. # # The Initial Developer of the Original Code is # Adobe System Incorporated. # Portions created by the Initial Developer are Copyright (C) 2004-2006 # the Initial Developer. All Rights Reserved. # # Contributor(s): # Adobe AS3 Team # # Alternatively, the contents of this file may be used under the terms of # either the GNU General Public License Version 2 or later (the "GPL"), or # the GNU Lesser General Public License Version 2.1 or later (the "LGPL"), # in which case the provisions of the GPL or the LGPL are applicable instead # of those above. If you wish to allow use of your version of this file only # under the terms of either the GPL or the LGPL, and not to allow others to # use your version of this file under the terms of the MPL, indicate your # decision by deleting the provisions above and replace them with the notice # and other provisions required by the GPL or the LGPL. If you do not delete # the provisions above, a recipient may use your version of this file under # the terms of any one of the MPL, the GPL or the LGPL. # # ***** END LICENSE BLOCK ***** import os import subprocess import sys def compile_abc(target, files, deps=None, configs=None): asc_jar = os.environ.get('ASC', os.path.realpath('../../../utils/asc.jar')) javacmd = ['java', '-ea', '-DAS3', '-DAVMPLUS', '-classpath', asc_jar, 'macromedia.asc.embedding.ScriptCompiler', '-builtin'] if deps: javacmd.extend("../%s/%s.abc" % (dep, dep) for dep in deps) javacmd.extend(['-out', target]) javacmd.extend(files) javacmd.extend(configs) p = subprocess.Popen(javacmd, cwd=target) p.wait() def main(): configs = sys.argv[1:] if configs == []: # Build without float suppot by default configs = ['-config', 'CONFIG::VMCFG_FLOAT=false'] compile_abc("builtin", ["builtin.as", "Vector.as", "DescribeType.as", "JSON.as", "Math.as", "Error.as", "Date.as", "RegExp.as", "IDataInput.as", "IDataOutput.as", "ByteArray.as", "Proxy.as", "XML.as", "Dictionary.as"], configs=configs) compile_abc("shell", ["Capabilities.as", "Domain.as", "System.as"], deps=["builtin"], configs=configs) compile_abc("avmplus", ["avmplus.as"], deps=["builtin"], configs=configs) if __name__ == "__main__": main()

42.8

239

0.697263

import os import subprocess import sys def compile_abc(target, files, deps=None, configs=None): asc_jar = os.environ.get('ASC', os.path.realpath('../../../utils/asc.jar')) javacmd = ['java', '-ea', '-DAS3', '-DAVMPLUS', '-classpath', asc_jar, 'macromedia.asc.embedding.ScriptCompiler', '-builtin'] if deps: javacmd.extend("../%s/%s.abc" % (dep, dep) for dep in deps) javacmd.extend(['-out', target]) javacmd.extend(files) javacmd.extend(configs) p = subprocess.Popen(javacmd, cwd=target) p.wait() def main(): configs = sys.argv[1:] if configs == []: configs = ['-config', 'CONFIG::VMCFG_FLOAT=false'] compile_abc("builtin", ["builtin.as", "Vector.as", "DescribeType.as", "JSON.as", "Math.as", "Error.as", "Date.as", "RegExp.as", "IDataInput.as", "IDataOutput.as", "ByteArray.as", "Proxy.as", "XML.as", "Dictionary.as"], configs=configs) compile_abc("shell", ["Capabilities.as", "Domain.as", "System.as"], deps=["builtin"], configs=configs) compile_abc("avmplus", ["avmplus.as"], deps=["builtin"], configs=configs) if __name__ == "__main__": main()

true

790d7b3bbafa0650cab614e8411daae9b0927426

29,770

py

Python

include/users_pb2.py

toyan/TinkoffNewAPI_Python_use_example

983c2743b472b3444f77fd06279e2a8f715fb951

[ "MIT" ]

1

2022-01-20T21:43:31.000Z

include/users_pb2.py

toyan/TinkoffNewAPI_Python_use_example

983c2743b472b3444f77fd06279e2a8f715fb951

[ "MIT" ]

null

include/users_pb2.py

toyan/TinkoffNewAPI_Python_use_example

983c2743b472b3444f77fd06279e2a8f715fb951

[ "MIT" ]

1

2022-01-13T03:38:45.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: users.proto """Generated protocol buffer code.""" from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 import include.common_pb2 as common__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='users.proto', package='tinkoff.public.invest.api.contract.v1', syntax='proto3', serialized_options=b'\n\034ru.tinkoff.piapi.contract.v1P\001Z\021Tinkoff/investAPI\242\002\005TIAPI\252\002\024Tinkoff.InvestAPI.V1\312\002\021Tinkoff\\Invest\\V1', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x0busers.proto\x12%tinkoff.public.invest.api.contract.v1\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x0c\x63ommon.proto\"\x14\n\x12GetAccountsRequest\"W\n\x13GetAccountsResponse\x12@\n\x08\x61\x63\x63ounts\x18\x01 \x03(\x0b\x32..tinkoff.public.invest.api.contract.v1.Account\"\x8d\x02\n\x07\x41\x63\x63ount\x12\n\n\x02id\x18\x01 \x01(\t\x12@\n\x04type\x18\x02 \x01(\x0e\x32\x32.tinkoff.public.invest.api.contract.v1.AccountType\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x44\n\x06status\x18\x04 \x01(\x0e\x32\x34.tinkoff.public.invest.api.contract.v1.AccountStatus\x12/\n\x0bopened_date\x18\x05 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12/\n\x0b\x63losed_date\x18\x06 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\"0\n\x1aGetMarginAttributesRequest\x12\x12\n\naccount_id\x18\x01 \x01(\t\"\xa8\x03\n\x1bGetMarginAttributesResponse\x12K\n\x10liquid_portfolio\x18\x01 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\x12J\n\x0fstarting_margin\x18\x02 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\x12I\n\x0eminimal_margin\x18\x03 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\x12Q\n\x17\x66unds_sufficiency_level\x18\x04 \x01(\x0b\x32\x30.tinkoff.public.invest.api.contract.v1.Quotation\x12R\n\x17\x61mount_of_missing_funds\x18\x05 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\"\x16\n\x14GetUserTariffRequest\"\xab\x01\n\x15GetUserTariffResponse\x12G\n\x0cunary_limits\x18\x01 \x03(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.UnaryLimit\x12I\n\rstream_limits\x18\x02 \x03(\x0b\x32\x32.tinkoff.public.invest.api.contract.v1.StreamLimit\"7\n\nUnaryLimit\x12\x18\n\x10limit_per_minute\x18\x01 \x01(\x05\x12\x0f\n\x07methods\x18\x02 \x03(\t\"-\n\x0bStreamLimit\x12\r\n\x05limit\x18\x01 \x01(\x05\x12\x0f\n\x07streams\x18\x02 \x03(\t\"\x10\n\x0eGetInfoRequest\"\\\n\x0fGetInfoResponse\x12\x13\n\x0bprem_status\x18\x01 \x01(\x08\x12\x13\n\x0bqual_status\x18\x02 \x01(\x08\x12\x1f\n\x17qualified_for_work_with\x18\x03 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, dependencies=[google_dot_protobuf_dot_timestamp__pb2.DESCRIPTOR,common__pb2.DESCRIPTOR,]) _ACCOUNTTYPE = _descriptor.EnumDescriptor( name='AccountType', full_name='tinkoff.public.invest.api.contract.v1.AccountType', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_UNSPECIFIED', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_TINKOFF', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_TINKOFF_IIS', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_INVEST_BOX', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=1376, serialized_end=1504, ) _sym_db.RegisterEnumDescriptor(_ACCOUNTTYPE) AccountType = enum_type_wrapper.EnumTypeWrapper(_ACCOUNTTYPE) _ACCOUNTSTATUS = _descriptor.EnumDescriptor( name='AccountStatus', full_name='tinkoff.public.invest.api.contract.v1.AccountStatus', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_UNSPECIFIED', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_NEW', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_OPEN', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_CLOSED', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=1506, serialized_end=1629, ) _sym_db.RegisterEnumDescriptor(_ACCOUNTSTATUS) AccountStatus = enum_type_wrapper.EnumTypeWrapper(_ACCOUNTSTATUS) ACCOUNT_TYPE_UNSPECIFIED = 0 ACCOUNT_TYPE_TINKOFF = 1 ACCOUNT_TYPE_TINKOFF_IIS = 2 ACCOUNT_TYPE_INVEST_BOX = 3 ACCOUNT_STATUS_UNSPECIFIED = 0 ACCOUNT_STATUS_NEW = 1 ACCOUNT_STATUS_OPEN = 2 ACCOUNT_STATUS_CLOSED = 3 _GETACCOUNTSREQUEST = _descriptor.Descriptor( name='GetAccountsRequest', full_name='tinkoff.public.invest.api.contract.v1.GetAccountsRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=101, serialized_end=121, ) _GETACCOUNTSRESPONSE = _descriptor.Descriptor( name='GetAccountsResponse', full_name='tinkoff.public.invest.api.contract.v1.GetAccountsResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='accounts', full_name='tinkoff.public.invest.api.contract.v1.GetAccountsResponse.accounts', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=123, serialized_end=210, ) _ACCOUNT = _descriptor.Descriptor( name='Account', full_name='tinkoff.public.invest.api.contract.v1.Account', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='tinkoff.public.invest.api.contract.v1.Account.id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='tinkoff.public.invest.api.contract.v1.Account.type', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='tinkoff.public.invest.api.contract.v1.Account.name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='status', full_name='tinkoff.public.invest.api.contract.v1.Account.status', index=3, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='opened_date', full_name='tinkoff.public.invest.api.contract.v1.Account.opened_date', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='closed_date', full_name='tinkoff.public.invest.api.contract.v1.Account.closed_date', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=213, serialized_end=482, ) _GETMARGINATTRIBUTESREQUEST = _descriptor.Descriptor( name='GetMarginAttributesRequest', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='account_id', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest.account_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=484, serialized_end=532, ) _GETMARGINATTRIBUTESRESPONSE = _descriptor.Descriptor( name='GetMarginAttributesResponse', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='liquid_portfolio', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.liquid_portfolio', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='starting_margin', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.starting_margin', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='minimal_margin', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.minimal_margin', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='funds_sufficiency_level', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.funds_sufficiency_level', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='amount_of_missing_funds', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.amount_of_missing_funds', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=535, serialized_end=959, ) _GETUSERTARIFFREQUEST = _descriptor.Descriptor( name='GetUserTariffRequest', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=961, serialized_end=983, ) _GETUSERTARIFFRESPONSE = _descriptor.Descriptor( name='GetUserTariffResponse', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='unary_limits', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffResponse.unary_limits', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='stream_limits', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffResponse.stream_limits', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=986, serialized_end=1157, ) _UNARYLIMIT = _descriptor.Descriptor( name='UnaryLimit', full_name='tinkoff.public.invest.api.contract.v1.UnaryLimit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='limit_per_minute', full_name='tinkoff.public.invest.api.contract.v1.UnaryLimit.limit_per_minute', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='methods', full_name='tinkoff.public.invest.api.contract.v1.UnaryLimit.methods', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1159, serialized_end=1214, ) _STREAMLIMIT = _descriptor.Descriptor( name='StreamLimit', full_name='tinkoff.public.invest.api.contract.v1.StreamLimit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='limit', full_name='tinkoff.public.invest.api.contract.v1.StreamLimit.limit', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='streams', full_name='tinkoff.public.invest.api.contract.v1.StreamLimit.streams', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1216, serialized_end=1261, ) _GETINFOREQUEST = _descriptor.Descriptor( name='GetInfoRequest', full_name='tinkoff.public.invest.api.contract.v1.GetInfoRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1263, serialized_end=1279, ) _GETINFORESPONSE = _descriptor.Descriptor( name='GetInfoResponse', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='prem_status', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse.prem_status', index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qual_status', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse.qual_status', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qualified_for_work_with', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse.qualified_for_work_with', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1281, serialized_end=1373, ) _GETACCOUNTSRESPONSE.fields_by_name['accounts'].message_type = _ACCOUNT _ACCOUNT.fields_by_name['type'].enum_type = _ACCOUNTTYPE _ACCOUNT.fields_by_name['status'].enum_type = _ACCOUNTSTATUS _ACCOUNT.fields_by_name['opened_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _ACCOUNT.fields_by_name['closed_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _GETMARGINATTRIBUTESRESPONSE.fields_by_name['liquid_portfolio'].message_type = common__pb2._MONEYVALUE _GETMARGINATTRIBUTESRESPONSE.fields_by_name['starting_margin'].message_type = common__pb2._MONEYVALUE _GETMARGINATTRIBUTESRESPONSE.fields_by_name['minimal_margin'].message_type = common__pb2._MONEYVALUE _GETMARGINATTRIBUTESRESPONSE.fields_by_name['funds_sufficiency_level'].message_type = common__pb2._QUOTATION _GETMARGINATTRIBUTESRESPONSE.fields_by_name['amount_of_missing_funds'].message_type = common__pb2._MONEYVALUE _GETUSERTARIFFRESPONSE.fields_by_name['unary_limits'].message_type = _UNARYLIMIT _GETUSERTARIFFRESPONSE.fields_by_name['stream_limits'].message_type = _STREAMLIMIT DESCRIPTOR.message_types_by_name['GetAccountsRequest'] = _GETACCOUNTSREQUEST DESCRIPTOR.message_types_by_name['GetAccountsResponse'] = _GETACCOUNTSRESPONSE DESCRIPTOR.message_types_by_name['Account'] = _ACCOUNT DESCRIPTOR.message_types_by_name['GetMarginAttributesRequest'] = _GETMARGINATTRIBUTESREQUEST DESCRIPTOR.message_types_by_name['GetMarginAttributesResponse'] = _GETMARGINATTRIBUTESRESPONSE DESCRIPTOR.message_types_by_name['GetUserTariffRequest'] = _GETUSERTARIFFREQUEST DESCRIPTOR.message_types_by_name['GetUserTariffResponse'] = _GETUSERTARIFFRESPONSE DESCRIPTOR.message_types_by_name['UnaryLimit'] = _UNARYLIMIT DESCRIPTOR.message_types_by_name['StreamLimit'] = _STREAMLIMIT DESCRIPTOR.message_types_by_name['GetInfoRequest'] = _GETINFOREQUEST DESCRIPTOR.message_types_by_name['GetInfoResponse'] = _GETINFORESPONSE DESCRIPTOR.enum_types_by_name['AccountType'] = _ACCOUNTTYPE DESCRIPTOR.enum_types_by_name['AccountStatus'] = _ACCOUNTSTATUS _sym_db.RegisterFileDescriptor(DESCRIPTOR) GetAccountsRequest = _reflection.GeneratedProtocolMessageType('GetAccountsRequest', (_message.Message,), { 'DESCRIPTOR' : _GETACCOUNTSREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetAccountsRequest) }) _sym_db.RegisterMessage(GetAccountsRequest) GetAccountsResponse = _reflection.GeneratedProtocolMessageType('GetAccountsResponse', (_message.Message,), { 'DESCRIPTOR' : _GETACCOUNTSRESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetAccountsResponse) }) _sym_db.RegisterMessage(GetAccountsResponse) Account = _reflection.GeneratedProtocolMessageType('Account', (_message.Message,), { 'DESCRIPTOR' : _ACCOUNT, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.Account) }) _sym_db.RegisterMessage(Account) GetMarginAttributesRequest = _reflection.GeneratedProtocolMessageType('GetMarginAttributesRequest', (_message.Message,), { 'DESCRIPTOR' : _GETMARGINATTRIBUTESREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest) }) _sym_db.RegisterMessage(GetMarginAttributesRequest) GetMarginAttributesResponse = _reflection.GeneratedProtocolMessageType('GetMarginAttributesResponse', (_message.Message,), { 'DESCRIPTOR' : _GETMARGINATTRIBUTESRESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse) }) _sym_db.RegisterMessage(GetMarginAttributesResponse) GetUserTariffRequest = _reflection.GeneratedProtocolMessageType('GetUserTariffRequest', (_message.Message,), { 'DESCRIPTOR' : _GETUSERTARIFFREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetUserTariffRequest) }) _sym_db.RegisterMessage(GetUserTariffRequest) GetUserTariffResponse = _reflection.GeneratedProtocolMessageType('GetUserTariffResponse', (_message.Message,), { 'DESCRIPTOR' : _GETUSERTARIFFRESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetUserTariffResponse) }) _sym_db.RegisterMessage(GetUserTariffResponse) UnaryLimit = _reflection.GeneratedProtocolMessageType('UnaryLimit', (_message.Message,), { 'DESCRIPTOR' : _UNARYLIMIT, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.UnaryLimit) }) _sym_db.RegisterMessage(UnaryLimit) StreamLimit = _reflection.GeneratedProtocolMessageType('StreamLimit', (_message.Message,), { 'DESCRIPTOR' : _STREAMLIMIT, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.StreamLimit) }) _sym_db.RegisterMessage(StreamLimit) GetInfoRequest = _reflection.GeneratedProtocolMessageType('GetInfoRequest', (_message.Message,), { 'DESCRIPTOR' : _GETINFOREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetInfoRequest) }) _sym_db.RegisterMessage(GetInfoRequest) GetInfoResponse = _reflection.GeneratedProtocolMessageType('GetInfoResponse', (_message.Message,), { 'DESCRIPTOR' : _GETINFORESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetInfoResponse) }) _sym_db.RegisterMessage(GetInfoResponse) DESCRIPTOR._options = None _USERSSERVICE = _descriptor.ServiceDescriptor( name='UsersService', full_name='tinkoff.public.invest.api.contract.v1.UsersService', file=DESCRIPTOR, index=0, serialized_options=None, create_key=_descriptor._internal_create_key, serialized_start=1632, serialized_end=2203, methods=[ _descriptor.MethodDescriptor( name='GetAccounts', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetAccounts', index=0, containing_service=None, input_type=_GETACCOUNTSREQUEST, output_type=_GETACCOUNTSRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='GetMarginAttributes', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetMarginAttributes', index=1, containing_service=None, input_type=_GETMARGINATTRIBUTESREQUEST, output_type=_GETMARGINATTRIBUTESRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='GetUserTariff', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetUserTariff', index=2, containing_service=None, input_type=_GETUSERTARIFFREQUEST, output_type=_GETUSERTARIFFRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='GetInfo', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetInfo', index=3, containing_service=None, input_type=_GETINFOREQUEST, output_type=_GETINFORESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), ]) _sym_db.RegisterServiceDescriptor(_USERSSERVICE) DESCRIPTOR.services_by_name['UsersService'] = _USERSSERVICE # @@protoc_insertion_point(module_scope)

42.347084

3,323

0.777158

from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 import include.common_pb2 as common__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='users.proto', package='tinkoff.public.invest.api.contract.v1', syntax='proto3', serialized_options=b'\n\034ru.tinkoff.piapi.contract.v1P\001Z\021Tinkoff/investAPI\242\002\005TIAPI\252\002\024Tinkoff.InvestAPI.V1\312\002\021Tinkoff\\Invest\\V1', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x0busers.proto\x12%tinkoff.public.invest.api.contract.v1\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x0c\x63ommon.proto\"\x14\n\x12GetAccountsRequest\"W\n\x13GetAccountsResponse\x12@\n\x08\x61\x63\x63ounts\x18\x01 \x03(\x0b\x32..tinkoff.public.invest.api.contract.v1.Account\"\x8d\x02\n\x07\x41\x63\x63ount\x12\n\n\x02id\x18\x01 \x01(\t\x12@\n\x04type\x18\x02 \x01(\x0e\x32\x32.tinkoff.public.invest.api.contract.v1.AccountType\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x44\n\x06status\x18\x04 \x01(\x0e\x32\x34.tinkoff.public.invest.api.contract.v1.AccountStatus\x12/\n\x0bopened_date\x18\x05 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12/\n\x0b\x63losed_date\x18\x06 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\"0\n\x1aGetMarginAttributesRequest\x12\x12\n\naccount_id\x18\x01 \x01(\t\"\xa8\x03\n\x1bGetMarginAttributesResponse\x12K\n\x10liquid_portfolio\x18\x01 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\x12J\n\x0fstarting_margin\x18\x02 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\x12I\n\x0eminimal_margin\x18\x03 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\x12Q\n\x17\x66unds_sufficiency_level\x18\x04 \x01(\x0b\x32\x30.tinkoff.public.invest.api.contract.v1.Quotation\x12R\n\x17\x61mount_of_missing_funds\x18\x05 \x01(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.MoneyValue\"\x16\n\x14GetUserTariffRequest\"\xab\x01\n\x15GetUserTariffResponse\x12G\n\x0cunary_limits\x18\x01 \x03(\x0b\x32\x31.tinkoff.public.invest.api.contract.v1.UnaryLimit\x12I\n\rstream_limits\x18\x02 \x03(\x0b\x32\x32.tinkoff.public.invest.api.contract.v1.StreamLimit\"7\n\nUnaryLimit\x12\x18\n\x10limit_per_minute\x18\x01 \x01(\x05\x12\x0f\n\x07methods\x18\x02 \x03(\t\"-\n\x0bStreamLimit\x12\r\n\x05limit\x18\x01 \x01(\x05\x12\x0f\n\x07streams\x18\x02 \x03(\t\"\x10\n\x0eGetInfoRequest\"\\\n\x0fGetInfoResponse\x12\x13\n\x0bprem_status\x18\x01 \x01(\x08\x12\x13\n\x0bqual_status\x18\x02 \x01(\x08\x12\x1f\n\x17qualified_for_work_with\x18\x03 \x03(\t*\x80\x01\n\x0b\x41\x63\x63ountType\x12\x1c\n\x18\x41\x43\x43OUNT_TYPE_UNSPECIFIED\x10\x00\x12\x18\n\x14\x41\x43\x43OUNT_TYPE_TINKOFF\x10\x01\x12\x1c\n\x18\x41\x43\x43OUNT_TYPE_TINKOFF_IIS\x10\x02\x12\x1b\n\x17\x41\x43\x43OUNT_TYPE_INVEST_BOX\x10\x03*{\n\rAccountStatus\x12\x1e\n\x1a\x41\x43\x43OUNT_STATUS_UNSPECIFIED\x10\x00\x12\x16\n\x12\x41\x43\x43OUNT_STATUS_NEW\x10\x01\x12\x17\n\x13\x41\x43\x43OUNT_STATUS_OPEN\x10\x02\x12\x19\n\x15\x41\x43\x43OUNT_STATUS_CLOSED\x10\x03\x32\xbb\x04\n\x0cUsersService\x12\x84\x01\n\x0bGetAccounts\x12\x39.tinkoff.public.invest.api.contract.v1.GetAccountsRequest\x1a:.tinkoff.public.invest.api.contract.v1.GetAccountsResponse\x12\x9c\x01\n\x13GetMarginAttributes\x12\x41.tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest\x1a\x42.tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse\x12\x8a\x01\n\rGetUserTariff\x12;.tinkoff.public.invest.api.contract.v1.GetUserTariffRequest\x1a<.tinkoff.public.invest.api.contract.v1.GetUserTariffResponse\x12x\n\x07GetInfo\x12\x35.tinkoff.public.invest.api.contract.v1.GetInfoRequest\x1a\x36.tinkoff.public.invest.api.contract.v1.GetInfoResponseBf\n\x1cru.tinkoff.piapi.contract.v1P\x01Z\x11Tinkoff/investAPI\xa2\x02\x05TIAPI\xaa\x02\x14Tinkoff.InvestAPI.V1\xca\x02\x11Tinkoff\\Invest\\V1b\x06proto3' , dependencies=[google_dot_protobuf_dot_timestamp__pb2.DESCRIPTOR,common__pb2.DESCRIPTOR,]) _ACCOUNTTYPE = _descriptor.EnumDescriptor( name='AccountType', full_name='tinkoff.public.invest.api.contract.v1.AccountType', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_UNSPECIFIED', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_TINKOFF', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_TINKOFF_IIS', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_TYPE_INVEST_BOX', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=1376, serialized_end=1504, ) _sym_db.RegisterEnumDescriptor(_ACCOUNTTYPE) AccountType = enum_type_wrapper.EnumTypeWrapper(_ACCOUNTTYPE) _ACCOUNTSTATUS = _descriptor.EnumDescriptor( name='AccountStatus', full_name='tinkoff.public.invest.api.contract.v1.AccountStatus', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_UNSPECIFIED', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_NEW', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_OPEN', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ACCOUNT_STATUS_CLOSED', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=1506, serialized_end=1629, ) _sym_db.RegisterEnumDescriptor(_ACCOUNTSTATUS) AccountStatus = enum_type_wrapper.EnumTypeWrapper(_ACCOUNTSTATUS) ACCOUNT_TYPE_UNSPECIFIED = 0 ACCOUNT_TYPE_TINKOFF = 1 ACCOUNT_TYPE_TINKOFF_IIS = 2 ACCOUNT_TYPE_INVEST_BOX = 3 ACCOUNT_STATUS_UNSPECIFIED = 0 ACCOUNT_STATUS_NEW = 1 ACCOUNT_STATUS_OPEN = 2 ACCOUNT_STATUS_CLOSED = 3 _GETACCOUNTSREQUEST = _descriptor.Descriptor( name='GetAccountsRequest', full_name='tinkoff.public.invest.api.contract.v1.GetAccountsRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=101, serialized_end=121, ) _GETACCOUNTSRESPONSE = _descriptor.Descriptor( name='GetAccountsResponse', full_name='tinkoff.public.invest.api.contract.v1.GetAccountsResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='accounts', full_name='tinkoff.public.invest.api.contract.v1.GetAccountsResponse.accounts', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=123, serialized_end=210, ) _ACCOUNT = _descriptor.Descriptor( name='Account', full_name='tinkoff.public.invest.api.contract.v1.Account', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='tinkoff.public.invest.api.contract.v1.Account.id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='tinkoff.public.invest.api.contract.v1.Account.type', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='tinkoff.public.invest.api.contract.v1.Account.name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='status', full_name='tinkoff.public.invest.api.contract.v1.Account.status', index=3, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='opened_date', full_name='tinkoff.public.invest.api.contract.v1.Account.opened_date', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='closed_date', full_name='tinkoff.public.invest.api.contract.v1.Account.closed_date', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=213, serialized_end=482, ) _GETMARGINATTRIBUTESREQUEST = _descriptor.Descriptor( name='GetMarginAttributesRequest', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='account_id', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest.account_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=484, serialized_end=532, ) _GETMARGINATTRIBUTESRESPONSE = _descriptor.Descriptor( name='GetMarginAttributesResponse', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='liquid_portfolio', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.liquid_portfolio', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='starting_margin', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.starting_margin', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='minimal_margin', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.minimal_margin', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='funds_sufficiency_level', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.funds_sufficiency_level', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='amount_of_missing_funds', full_name='tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse.amount_of_missing_funds', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=535, serialized_end=959, ) _GETUSERTARIFFREQUEST = _descriptor.Descriptor( name='GetUserTariffRequest', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=961, serialized_end=983, ) _GETUSERTARIFFRESPONSE = _descriptor.Descriptor( name='GetUserTariffResponse', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='unary_limits', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffResponse.unary_limits', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='stream_limits', full_name='tinkoff.public.invest.api.contract.v1.GetUserTariffResponse.stream_limits', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=986, serialized_end=1157, ) _UNARYLIMIT = _descriptor.Descriptor( name='UnaryLimit', full_name='tinkoff.public.invest.api.contract.v1.UnaryLimit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='limit_per_minute', full_name='tinkoff.public.invest.api.contract.v1.UnaryLimit.limit_per_minute', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='methods', full_name='tinkoff.public.invest.api.contract.v1.UnaryLimit.methods', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1159, serialized_end=1214, ) _STREAMLIMIT = _descriptor.Descriptor( name='StreamLimit', full_name='tinkoff.public.invest.api.contract.v1.StreamLimit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='limit', full_name='tinkoff.public.invest.api.contract.v1.StreamLimit.limit', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='streams', full_name='tinkoff.public.invest.api.contract.v1.StreamLimit.streams', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1216, serialized_end=1261, ) _GETINFOREQUEST = _descriptor.Descriptor( name='GetInfoRequest', full_name='tinkoff.public.invest.api.contract.v1.GetInfoRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1263, serialized_end=1279, ) _GETINFORESPONSE = _descriptor.Descriptor( name='GetInfoResponse', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='prem_status', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse.prem_status', index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qual_status', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse.qual_status', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qualified_for_work_with', full_name='tinkoff.public.invest.api.contract.v1.GetInfoResponse.qualified_for_work_with', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1281, serialized_end=1373, ) _GETACCOUNTSRESPONSE.fields_by_name['accounts'].message_type = _ACCOUNT _ACCOUNT.fields_by_name['type'].enum_type = _ACCOUNTTYPE _ACCOUNT.fields_by_name['status'].enum_type = _ACCOUNTSTATUS _ACCOUNT.fields_by_name['opened_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _ACCOUNT.fields_by_name['closed_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _GETMARGINATTRIBUTESRESPONSE.fields_by_name['liquid_portfolio'].message_type = common__pb2._MONEYVALUE _GETMARGINATTRIBUTESRESPONSE.fields_by_name['starting_margin'].message_type = common__pb2._MONEYVALUE _GETMARGINATTRIBUTESRESPONSE.fields_by_name['minimal_margin'].message_type = common__pb2._MONEYVALUE _GETMARGINATTRIBUTESRESPONSE.fields_by_name['funds_sufficiency_level'].message_type = common__pb2._QUOTATION _GETMARGINATTRIBUTESRESPONSE.fields_by_name['amount_of_missing_funds'].message_type = common__pb2._MONEYVALUE _GETUSERTARIFFRESPONSE.fields_by_name['unary_limits'].message_type = _UNARYLIMIT _GETUSERTARIFFRESPONSE.fields_by_name['stream_limits'].message_type = _STREAMLIMIT DESCRIPTOR.message_types_by_name['GetAccountsRequest'] = _GETACCOUNTSREQUEST DESCRIPTOR.message_types_by_name['GetAccountsResponse'] = _GETACCOUNTSRESPONSE DESCRIPTOR.message_types_by_name['Account'] = _ACCOUNT DESCRIPTOR.message_types_by_name['GetMarginAttributesRequest'] = _GETMARGINATTRIBUTESREQUEST DESCRIPTOR.message_types_by_name['GetMarginAttributesResponse'] = _GETMARGINATTRIBUTESRESPONSE DESCRIPTOR.message_types_by_name['GetUserTariffRequest'] = _GETUSERTARIFFREQUEST DESCRIPTOR.message_types_by_name['GetUserTariffResponse'] = _GETUSERTARIFFRESPONSE DESCRIPTOR.message_types_by_name['UnaryLimit'] = _UNARYLIMIT DESCRIPTOR.message_types_by_name['StreamLimit'] = _STREAMLIMIT DESCRIPTOR.message_types_by_name['GetInfoRequest'] = _GETINFOREQUEST DESCRIPTOR.message_types_by_name['GetInfoResponse'] = _GETINFORESPONSE DESCRIPTOR.enum_types_by_name['AccountType'] = _ACCOUNTTYPE DESCRIPTOR.enum_types_by_name['AccountStatus'] = _ACCOUNTSTATUS _sym_db.RegisterFileDescriptor(DESCRIPTOR) GetAccountsRequest = _reflection.GeneratedProtocolMessageType('GetAccountsRequest', (_message.Message,), { 'DESCRIPTOR' : _GETACCOUNTSREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetAccountsRequest) }) _sym_db.RegisterMessage(GetAccountsRequest) GetAccountsResponse = _reflection.GeneratedProtocolMessageType('GetAccountsResponse', (_message.Message,), { 'DESCRIPTOR' : _GETACCOUNTSRESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetAccountsResponse) }) _sym_db.RegisterMessage(GetAccountsResponse) Account = _reflection.GeneratedProtocolMessageType('Account', (_message.Message,), { 'DESCRIPTOR' : _ACCOUNT, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.Account) }) _sym_db.RegisterMessage(Account) GetMarginAttributesRequest = _reflection.GeneratedProtocolMessageType('GetMarginAttributesRequest', (_message.Message,), { 'DESCRIPTOR' : _GETMARGINATTRIBUTESREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetMarginAttributesRequest) }) _sym_db.RegisterMessage(GetMarginAttributesRequest) GetMarginAttributesResponse = _reflection.GeneratedProtocolMessageType('GetMarginAttributesResponse', (_message.Message,), { 'DESCRIPTOR' : _GETMARGINATTRIBUTESRESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetMarginAttributesResponse) }) _sym_db.RegisterMessage(GetMarginAttributesResponse) GetUserTariffRequest = _reflection.GeneratedProtocolMessageType('GetUserTariffRequest', (_message.Message,), { 'DESCRIPTOR' : _GETUSERTARIFFREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetUserTariffRequest) }) _sym_db.RegisterMessage(GetUserTariffRequest) GetUserTariffResponse = _reflection.GeneratedProtocolMessageType('GetUserTariffResponse', (_message.Message,), { 'DESCRIPTOR' : _GETUSERTARIFFRESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetUserTariffResponse) }) _sym_db.RegisterMessage(GetUserTariffResponse) UnaryLimit = _reflection.GeneratedProtocolMessageType('UnaryLimit', (_message.Message,), { 'DESCRIPTOR' : _UNARYLIMIT, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.UnaryLimit) }) _sym_db.RegisterMessage(UnaryLimit) StreamLimit = _reflection.GeneratedProtocolMessageType('StreamLimit', (_message.Message,), { 'DESCRIPTOR' : _STREAMLIMIT, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.StreamLimit) }) _sym_db.RegisterMessage(StreamLimit) GetInfoRequest = _reflection.GeneratedProtocolMessageType('GetInfoRequest', (_message.Message,), { 'DESCRIPTOR' : _GETINFOREQUEST, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetInfoRequest) }) _sym_db.RegisterMessage(GetInfoRequest) GetInfoResponse = _reflection.GeneratedProtocolMessageType('GetInfoResponse', (_message.Message,), { 'DESCRIPTOR' : _GETINFORESPONSE, '__module__' : 'users_pb2' # @@protoc_insertion_point(class_scope:tinkoff.public.invest.api.contract.v1.GetInfoResponse) }) _sym_db.RegisterMessage(GetInfoResponse) DESCRIPTOR._options = None _USERSSERVICE = _descriptor.ServiceDescriptor( name='UsersService', full_name='tinkoff.public.invest.api.contract.v1.UsersService', file=DESCRIPTOR, index=0, serialized_options=None, create_key=_descriptor._internal_create_key, serialized_start=1632, serialized_end=2203, methods=[ _descriptor.MethodDescriptor( name='GetAccounts', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetAccounts', index=0, containing_service=None, input_type=_GETACCOUNTSREQUEST, output_type=_GETACCOUNTSRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='GetMarginAttributes', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetMarginAttributes', index=1, containing_service=None, input_type=_GETMARGINATTRIBUTESREQUEST, output_type=_GETMARGINATTRIBUTESRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='GetUserTariff', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetUserTariff', index=2, containing_service=None, input_type=_GETUSERTARIFFREQUEST, output_type=_GETUSERTARIFFRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='GetInfo', full_name='tinkoff.public.invest.api.contract.v1.UsersService.GetInfo', index=3, containing_service=None, input_type=_GETINFOREQUEST, output_type=_GETINFORESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), ]) _sym_db.RegisterServiceDescriptor(_USERSSERVICE) DESCRIPTOR.services_by_name['UsersService'] = _USERSSERVICE # @@protoc_insertion_point(module_scope)

true

790d7b95c2ddf064518121d57e15ffbe76b1f1e1

5,575

py

Python

test/expected/python.tornado/actual_base/ttypes.py

dustyholmes-wf/frugal

915ccfc58fcc9baabc4549c522e3acd2975a2e0b

[ "Apache-2.0" ]

null

test/expected/python.tornado/actual_base/ttypes.py

dustyholmes-wf/frugal

915ccfc58fcc9baabc4549c522e3acd2975a2e0b

[ "Apache-2.0" ]

null

test/expected/python.tornado/actual_base/ttypes.py

dustyholmes-wf/frugal

915ccfc58fcc9baabc4549c522e3acd2975a2e0b

[ "Apache-2.0" ]

null

# # Autogenerated by Frugal Compiler (3.4.7) # # DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING # from thrift.Thrift import TType, TMessageType, TException, TApplicationException from frugal.util import make_hashable from thrift.transport import TTransport from thrift.protocol import TBinaryProtocol, TProtocol class base_health_condition(int): PASS = 1 WARN = 2 FAIL = 3 UNKNOWN = 4 _VALUES_TO_NAMES = { 1: "PASS", 2: "WARN", 3: "FAIL", 4: "UNKNOWN", } _NAMES_TO_VALUES = { "PASS": 1, "WARN": 2, "FAIL": 3, "UNKNOWN": 4, } class thing(object): """ Attributes: - an_id - a_string """ def __init__(self, an_id=None, a_string=None): self.an_id = an_id self.a_string = a_string def read(self, iprot): iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.I32: self.an_id = iprot.readI32() else: iprot.skip(ftype) elif fid == 2: if ftype == TType.STRING: self.a_string = iprot.readString() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() self.validate() def write(self, oprot): self.validate() oprot.writeStructBegin('thing') if self.an_id is not None: oprot.writeFieldBegin('an_id', TType.I32, 1) oprot.writeI32(self.an_id) oprot.writeFieldEnd() if self.a_string is not None: oprot.writeFieldBegin('a_string', TType.STRING, 2) oprot.writeString(self.a_string) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(make_hashable(self.an_id)) value = (value * 31) ^ hash(make_hashable(self.a_string)) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class nested_thing(object): """ Attributes: - things """ def __init__(self, things=None): self.things = things def read(self, iprot): iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.LIST: self.things = [] (_, elem78) = iprot.readListBegin() for _ in range(elem78): elem79 = thing() elem79.read(iprot) self.things.append(elem79) iprot.readListEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() self.validate() def write(self, oprot): self.validate() oprot.writeStructBegin('nested_thing') if self.things is not None: oprot.writeFieldBegin('things', TType.LIST, 1) oprot.writeListBegin(TType.STRUCT, len(self.things)) for elem80 in self.things: elem80.write(oprot) oprot.writeListEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(make_hashable(self.things)) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class api_exception(TException): def read(self, iprot): iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() self.validate() def write(self, oprot): self.validate() oprot.writeStructBegin('api_exception') oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __str__(self): return repr(self) def __hash__(self): value = 17 return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other)

27.463054

84

0.535785

from thrift.Thrift import TType, TMessageType, TException, TApplicationException from frugal.util import make_hashable from thrift.transport import TTransport from thrift.protocol import TBinaryProtocol, TProtocol class base_health_condition(int): PASS = 1 WARN = 2 FAIL = 3 UNKNOWN = 4 _VALUES_TO_NAMES = { 1: "PASS", 2: "WARN", 3: "FAIL", 4: "UNKNOWN", } _NAMES_TO_VALUES = { "PASS": 1, "WARN": 2, "FAIL": 3, "UNKNOWN": 4, } class thing(object): def __init__(self, an_id=None, a_string=None): self.an_id = an_id self.a_string = a_string def read(self, iprot): iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.I32: self.an_id = iprot.readI32() else: iprot.skip(ftype) elif fid == 2: if ftype == TType.STRING: self.a_string = iprot.readString() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() self.validate() def write(self, oprot): self.validate() oprot.writeStructBegin('thing') if self.an_id is not None: oprot.writeFieldBegin('an_id', TType.I32, 1) oprot.writeI32(self.an_id) oprot.writeFieldEnd() if self.a_string is not None: oprot.writeFieldBegin('a_string', TType.STRING, 2) oprot.writeString(self.a_string) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(make_hashable(self.an_id)) value = (value * 31) ^ hash(make_hashable(self.a_string)) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class nested_thing(object): def __init__(self, things=None): self.things = things def read(self, iprot): iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.LIST: self.things = [] (_, elem78) = iprot.readListBegin() for _ in range(elem78): elem79 = thing() elem79.read(iprot) self.things.append(elem79) iprot.readListEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() self.validate() def write(self, oprot): self.validate() oprot.writeStructBegin('nested_thing') if self.things is not None: oprot.writeFieldBegin('things', TType.LIST, 1) oprot.writeListBegin(TType.STRUCT, len(self.things)) for elem80 in self.things: elem80.write(oprot) oprot.writeListEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(make_hashable(self.things)) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class api_exception(TException): def read(self, iprot): iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() self.validate() def write(self, oprot): self.validate() oprot.writeStructBegin('api_exception') oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __str__(self): return repr(self) def __hash__(self): value = 17 return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other)

true

790d7d696ec7f804417edc31f8fffd6f6e9ddc48

26,411

py

Python

demos/gce_demo.py

atsaki/libcloud

ae85479e835494e196e2f6e79aae9a475603d8ac

[ "Apache-2.0" ]

3

2015-09-11T15:42:16.000Z

2021-05-12T01:10:05.000Z

demos/gce_demo.py

atsaki/libcloud

ae85479e835494e196e2f6e79aae9a475603d8ac

[ "Apache-2.0" ]

null

demos/gce_demo.py

atsaki/libcloud

ae85479e835494e196e2f6e79aae9a475603d8ac

[ "Apache-2.0" ]

3

2016-02-08T23:38:18.000Z

2019-11-05T00:31:34.000Z

#!/usr/bin/env python # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This example performs several tasks on Google Compute Platform. It can be # run directly or can be imported into an interactive python session. This # can also serve as live integration tests. # # To run directly, use python 2.7 or greater: # - $ python gce_demo.py --help # to see the help screen # - $ python gce_demo.py # to run all demos / tests # # To run interactively: # - Make sure you have valid values in secrets.py # (For more information about setting up your credentials, see the # libcloud/common/google.py docstring) # - Run 'python' in this directory, then: # import gce_demo # gce = gce_demo.get_gce_driver() # gce.list_nodes() # etc. # - Or, to run the full demo from the interactive python shell: # import gce_demo # gce_demo.CLEANUP = False # optional # gce_demo.MAX_NODES = 4 # optional # gce_demo.DATACENTER = 'us-central1-a' # optional # gce_demo.main_compute() # 'compute' only demo # gce_demo.main_load_balancer() # 'load_balancer' only demo # gce_demo.main_dns() # 'dns only demo # gce_demo.main() # all demos / tests import os.path import sys import datetime import time try: import argparse except: print('This script uses the python "argparse" module. Please use Python ' '2.7 or greater.') raise try: import secrets except ImportError: print('"demos/secrets.py" not found.\n\n' 'Please copy secrets.py-dist to secrets.py and update the GCE* ' 'values with appropriate authentication information.\n' 'Additional information about setting these values can be found ' 'in the docstring for:\n' 'libcloud/common/google.py\n') sys.exit(1) # Add parent dir of this file's dir to sys.path (OS-agnostically) sys.path.append(os.path.normpath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from libcloud.compute.types import Provider from libcloud.compute.providers import get_driver from libcloud.common.google import ResourceNotFoundError from libcloud.loadbalancer.types import Provider as Provider_lb from libcloud.loadbalancer.providers import get_driver as get_driver_lb from libcloud.dns.types import Provider as Provider_dns from libcloud.dns.providers import get_driver as get_driver_dns from libcloud.dns.base import Record, Zone from libcloud.utils.py3 import PY3 if PY3: import urllib.request as url_req else: import urllib2 as url_req # Maximum number of 1-CPU nodes to allow to run simultaneously MAX_NODES = 5 # String that all resource names created by the demo will start with # WARNING: Any resource that has a matching name will be destroyed. DEMO_BASE_NAME = 'lct' # Datacenter to create resources in DATACENTER = 'us-central1-f' # Clean up resources at the end (can be set to false in order to # inspect resources at the end of the run). Resources will be cleaned # at the beginning regardless. CLEANUP = True args = getattr(secrets, 'GCE_PARAMS', ()) kwargs = getattr(secrets, 'GCE_KEYWORD_PARAMS', {}) # Add datacenter to kwargs for Python 2.5 compatibility kwargs = kwargs.copy() kwargs['datacenter'] = DATACENTER # ==== HELPER FUNCTIONS ==== def get_gce_driver(): driver = get_driver(Provider.GCE)(*args, **kwargs) return driver def get_gcelb_driver(gce_driver=None): # The GCE Load Balancer driver uses the GCE Compute driver for all of its # API calls. You can either provide the driver directly, or provide the # same authentication information so the LB driver can get its own # Compute driver. if gce_driver: driver = get_driver_lb(Provider_lb.GCE)(gce_driver=gce_driver) else: driver = get_driver_lb(Provider_lb.GCE)(*args, **kwargs) return driver def get_dns_driver(gce_driver=None): # The Google DNS driver uses the GCE Compute driver for all of its # API calls. You can either provide the driver directly, or provide the # same authentication information so the LB driver can get its own # Compute driver. if gce_driver: driver = get_driver_dns(Provider_dns.GOOGLE)(gce_driver=gce_driver) else: driver = get_driver_dns(Provider_dns.GOOGLE)(*args, **kwargs) return driver def display(title, resource_list=[]): """ Display a list of resources. :param title: String to be printed at the heading of the list. :type title: ``str`` :param resource_list: List of resources to display :type resource_list: Any ``object`` with a C{name} attribute """ print('=> %s' % title) for item in resource_list: if isinstance(item, Record): if item.name.startswith(DEMO_BASE_NAME): print('=> name=%s, type=%s' % (item.name, item.type)) else: print(' name=%s, type=%s' % (item.name, item.type)) elif isinstance(item, Zone): if item.domain.startswith(DEMO_BASE_NAME): print('=> name=%s, dnsname=%s' % (item.id, item.domain)) else: print(' name=%s, dnsname=%s' % (item.id, item.domain)) elif hasattr(item, 'name'): if item.name.startswith(DEMO_BASE_NAME): print('=> %s' % item.name) else: print(' %s' % item.name) else: if item.startswith(DEMO_BASE_NAME): print('=> %s' % item) else: print(' %s' % item) def cleanup_only(): start_time = datetime.datetime.now() display('Clean-up start time: %s' % str(start_time)) gce = get_gce_driver() # Get project info and print name project = gce.ex_get_project() display('Project: %s' % project.name) # == Get Lists of Everything and Display the lists (up to 10) == # These can either just return values for the current datacenter (zone) # or for everything. all_nodes = gce.list_nodes(ex_zone='all') display('Nodes:', all_nodes) all_addresses = gce.ex_list_addresses(region='all') display('Addresses:', all_addresses) all_volumes = gce.list_volumes(ex_zone='all') display('Volumes:', all_volumes) # This can return everything, but there is a large amount of overlap, # so we'll just get the sizes from the current zone. sizes = gce.list_sizes() display('Sizes:', sizes) # These are global firewalls = gce.ex_list_firewalls() display('Firewalls:', firewalls) networks = gce.ex_list_networks() display('Networks:', networks) images = gce.list_images() display('Images:', images) locations = gce.list_locations() display('Locations:', locations) zones = gce.ex_list_zones() display('Zones:', zones) snapshots = gce.ex_list_snapshots() display('Snapshots:', snapshots) # == Clean up any old demo resources == display('Cleaning up any "%s" resources' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, all_nodes, all_addresses + all_volumes + firewalls + networks + snapshots) volumes = gce.list_volumes() clean_up(gce, DEMO_BASE_NAME, None, volumes) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) def clean_up(gce, base_name, node_list=None, resource_list=None): """ Destroy all resources that have a name beginning with 'base_name'. :param base_name: String with the first part of the name of resources to destroy :type base_name: ``str`` :keyword node_list: List of nodes to consider for deletion :type node_list: ``list`` of :class:`Node` :keyword resource_list: List of resources to consider for deletion :type resource_list: ``list`` of I{Resource Objects} """ if node_list is None: node_list = [] if resource_list is None: resource_list = [] # Use ex_destroy_multiple_nodes to destroy nodes del_nodes = [] for node in node_list: if node.name.startswith(base_name): del_nodes.append(node) result = gce.ex_destroy_multiple_nodes(del_nodes) for i, success in enumerate(result): if success: display(' Deleted %s' % del_nodes[i].name) else: display(' Failed to delete %s' % del_nodes[i].name) # Destroy everything else with just the destroy method for resrc in resource_list: if resrc.name.startswith(base_name): try: resrc.destroy() except ResourceNotFoundError: display(' Not found: %s (%s)' % (resrc.name, resrc.__class__.__name__)) except: class_name = resrc.__class__.__name__ display(' Failed to Delete %s (%s)' % (resrc.name, class_name)) raise # ==== COMPUTE CODE STARTS HERE ==== def main_compute(): start_time = datetime.datetime.now() display('Compute demo/test start time: %s' % str(start_time)) gce = get_gce_driver() # Get project info and print name project = gce.ex_get_project() display('Project: %s' % project.name) # == Get Lists of Everything and Display the lists (up to 10) == # These can either just return values for the current datacenter (zone) # or for everything. all_nodes = gce.list_nodes(ex_zone='all') display('Nodes:', all_nodes) all_addresses = gce.ex_list_addresses(region='all') display('Addresses:', all_addresses) all_volumes = gce.list_volumes(ex_zone='all') display('Volumes:', all_volumes) # This can return everything, but there is a large amount of overlap, # so we'll just get the sizes from the current zone. sizes = gce.list_sizes() display('Sizes:', sizes) # These are global firewalls = gce.ex_list_firewalls() display('Firewalls:', firewalls) networks = gce.ex_list_networks() display('Networks:', networks) images = gce.list_images() display('Images:', images) locations = gce.list_locations() display('Locations:', locations) zones = gce.ex_list_zones() display('Zones:', zones) snapshots = gce.ex_list_snapshots() display('Snapshots:', snapshots) # == Clean up any old demo resources == display('Cleaning up any "%s" resources' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, all_nodes, all_addresses + all_volumes + firewalls + networks + snapshots) # == Create Node with disk auto-created == if MAX_NODES > 1: display('Creating a node with boot/local-ssd using GCE structure:') name = '%s-gstruct' % DEMO_BASE_NAME img_url = "projects/debian-cloud/global/images/" img_url += "backports-debian-7-wheezy-v20141205" disk_type_url = "projects/%s/zones/us-central1-f/" % project.name disk_type_url += "diskTypes/local-ssd" gce_disk_struct = [ { "type": "PERSISTENT", "deviceName": '%s-gstruct' % DEMO_BASE_NAME, "initializeParams": { "diskName": '%s-gstruct' % DEMO_BASE_NAME, "sourceImage": img_url }, "boot": True, "autoDelete": True }, { "type": "SCRATCH", "deviceName": '%s-gstruct-lssd' % DEMO_BASE_NAME, "initializeParams": { "diskType": disk_type_url }, "autoDelete": True } ] node_gstruct = gce.create_node(name, 'n1-standard-1', None, 'us-central1-f', ex_disks_gce_struct=gce_disk_struct) num_disks = len(node_gstruct.extra['disks']) display(' Node %s created with %d disks' % (node_gstruct.name, num_disks)) display('Creating Node with auto-created SSD:') name = '%s-np-node' % DEMO_BASE_NAME node_1 = gce.create_node(name, 'n1-standard-1', 'debian-7', ex_tags=['libcloud'], ex_disk_type='pd-ssd', ex_disk_auto_delete=False) display(' Node %s created' % name) # == Create, and attach a disk == display('Creating a new disk:') disk_name = '%s-attach-disk' % DEMO_BASE_NAME volume = gce.create_volume(10, disk_name) if volume.attach(node_1): display(' Attached %s to %s' % (volume.name, node_1.name)) display(' Disabled auto-delete for %s on %s' % (volume.name, node_1.name)) gce.ex_set_volume_auto_delete(volume, node_1, auto_delete=False) if CLEANUP: # == Detach the disk == if gce.detach_volume(volume, ex_node=node_1): display(' Detached %s from %s' % (volume.name, node_1.name)) # == Create Snapshot == display('Creating a snapshot from existing disk:') # Create a disk to snapshot vol_name = '%s-snap-template' % DEMO_BASE_NAME image = gce.ex_get_image('debian-7') vol = gce.create_volume(None, vol_name, image=image) display('Created disk %s to shapshot:' % DEMO_BASE_NAME) # Snapshot volume snapshot = vol.snapshot('%s-snapshot' % DEMO_BASE_NAME) display(' Snapshot %s created' % snapshot.name) # == Create Node with existing disk == display('Creating Node with existing disk:') name = '%s-persist-node' % DEMO_BASE_NAME # Use objects this time instead of names # Get latest Debian 7 image image = gce.ex_get_image('debian-7') # Get Machine Size size = gce.ex_get_size('n1-standard-1') # Create Disk from Snapshot created above volume_name = '%s-boot-disk' % DEMO_BASE_NAME volume = gce.create_volume(None, volume_name, snapshot=snapshot) display(' Created %s from snapshot' % volume.name) # Create Node with Disk node_2 = gce.create_node(name, size, image, ex_tags=['libcloud'], ex_boot_disk=volume, ex_disk_auto_delete=False) display(' Node %s created with attached disk %s' % (node_2.name, volume.name)) # == Update Tags for Node == display('Updating Tags for %s:' % node_2.name) tags = node_2.extra['tags'] tags.append('newtag') if gce.ex_set_node_tags(node_2, tags): display(' Tags updated for %s' % node_2.name) check_node = gce.ex_get_node(node_2.name) display(' New tags: %s' % check_node.extra['tags']) # == Setting Metadata for Node == display('Setting Metadata for %s:' % node_2.name) if gce.ex_set_node_metadata(node_2, {'foo': 'bar', 'baz': 'foobarbaz'}): display(' Metadata updated for %s' % node_2.name) check_node = gce.ex_get_node(node_2.name) display(' New Metadata: %s' % check_node.extra['metadata']) # == Create Multiple nodes at once == base_name = '%s-multiple-nodes' % DEMO_BASE_NAME number = MAX_NODES - 2 if number > 0: display('Creating Multiple Nodes (%s):' % number) multi_nodes = gce.ex_create_multiple_nodes(base_name, size, image, number, ex_tags=['libcloud'], ex_disk_auto_delete=True) for node in multi_nodes: display(' Node %s created' % node.name) # == Create a Network == display('Creating Network:') name = '%s-network' % DEMO_BASE_NAME cidr = '10.10.0.0/16' network_1 = gce.ex_create_network(name, cidr) display(' Network %s created' % network_1.name) # == Create a Firewall == display('Creating a Firewall:') name = '%s-firewall' % DEMO_BASE_NAME allowed = [{'IPProtocol': 'tcp', 'ports': ['3141']}] firewall_1 = gce.ex_create_firewall(name, allowed, network=network_1, source_tags=['libcloud']) display(' Firewall %s created' % firewall_1.name) # == Create a Static Address == display('Creating an Address:') name = '%s-address' % DEMO_BASE_NAME address_1 = gce.ex_create_address(name) display(' Address %s created with IP %s' % (address_1.name, address_1.address)) # == List Updated Resources in current zone/region == display('Updated Resources in current zone/region') nodes = gce.list_nodes() display('Nodes:', nodes) addresses = gce.ex_list_addresses() display('Addresses:', addresses) firewalls = gce.ex_list_firewalls() display('Firewalls:', firewalls) networks = gce.ex_list_networks() display('Networks:', networks) snapshots = gce.ex_list_snapshots() display('Snapshots:', snapshots) if CLEANUP: display('Cleaning up %s resources created' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, nodes, addresses + firewalls + networks + snapshots) volumes = gce.list_volumes() clean_up(gce, DEMO_BASE_NAME, None, volumes) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) # ==== LOAD BALANCER CODE STARTS HERE ==== def main_load_balancer(): start_time = datetime.datetime.now() display('Load-balancer demo/test start time: %s' % str(start_time)) gce = get_gce_driver() gcelb = get_gcelb_driver(gce) # Get project info and print name project = gce.ex_get_project() display('Project: %s' % project.name) # Existing Balancers balancers = gcelb.list_balancers() display('Load Balancers', balancers) # Protocols protocols = gcelb.list_protocols() display('Protocols', protocols) # Healthchecks healthchecks = gcelb.ex_list_healthchecks() display('Health Checks', healthchecks) # This demo is based on the GCE Load Balancing Quickstart described here: # https://developers.google.com/compute/docs/load-balancing/lb-quickstart # == Clean-up and existing demo resources == all_nodes = gce.list_nodes(ex_zone='all') firewalls = gce.ex_list_firewalls() display('Cleaning up any "%s" resources' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, all_nodes, balancers + healthchecks + firewalls) # == Create 3 nodes to balance between == startup_script = ('apt-get -y update && ' 'apt-get -y install apache2 && ' 'hostname > /var/www/index.html') tag = '%s-www' % DEMO_BASE_NAME base_name = '%s-www' % DEMO_BASE_NAME image = gce.ex_get_image('debian-7') size = gce.ex_get_size('n1-standard-1') number = 3 display('Creating %d nodes' % number) metadata = {'items': [{'key': 'startup-script', 'value': startup_script}]} lb_nodes = gce.ex_create_multiple_nodes(base_name, size, image, number, ex_tags=[tag], ex_metadata=metadata, ex_disk_auto_delete=True, ignore_errors=False) display('Created Nodes', lb_nodes) # == Create a Firewall for instances == display('Creating a Firewall') name = '%s-firewall' % DEMO_BASE_NAME allowed = [{'IPProtocol': 'tcp', 'ports': ['80']}] firewall = gce.ex_create_firewall(name, allowed, source_tags=[tag]) display(' Firewall %s created' % firewall.name) # == Create a Health Check == display('Creating a HealthCheck') name = '%s-healthcheck' % DEMO_BASE_NAME # These are all the default values, but listed here as an example. To # create a healthcheck with the defaults, only name is required. hc = gcelb.ex_create_healthcheck(name, host=None, path='/', port='80', interval=5, timeout=5, unhealthy_threshold=2, healthy_threshold=2) display('Healthcheck %s created' % hc.name) # == Create Load Balancer == display('Creating Load Balancer') name = '%s-lb' % DEMO_BASE_NAME port = 80 protocol = 'tcp' algorithm = None members = lb_nodes[:2] # Only attach the first two initially healthchecks = [hc] balancer = gcelb.create_balancer(name, port, protocol, algorithm, members, ex_healthchecks=healthchecks) display(' Load Balancer %s created' % balancer.name) # == Attach third Node == display('Attaching additional node to Load Balancer') member = balancer.attach_compute_node(lb_nodes[2]) display(' Attached %s to %s' % (member.id, balancer.name)) # == Show Balancer Members == members = balancer.list_members() display('Load Balancer Members') for member in members: display(' ID: %s IP: %s' % (member.id, member.ip)) # == Remove a Member == display('Removing a Member') detached = members[0] detach = balancer.detach_member(detached) if detach: display(' Member %s detached from %s' % (detached.id, balancer.name)) # == Show Updated Balancer Members == members = balancer.list_members() display('Updated Load Balancer Members') for member in members: display(' ID: %s IP: %s' % (member.id, member.ip)) # == Reattach Member == display('Reattaching Member') member = balancer.attach_member(detached) display(' Member %s attached to %s' % (member.id, balancer.name)) # == Test Load Balancer by connecting to it multiple times == PAUSE = 60 display('Sleeping for %d seconds for LB members to serve...' % PAUSE) time.sleep(PAUSE) rounds = 200 url = 'http://%s/' % balancer.ip line_length = 75 display('Connecting to %s %s times' % (url, rounds)) for x in range(rounds): response = url_req.urlopen(url) if PY3: output = str(response.read(), encoding='utf-8').strip() else: output = response.read().strip() if 'www-001' in output: padded_output = output.center(line_length) elif 'www-002' in output: padded_output = output.rjust(line_length) else: padded_output = output.ljust(line_length) sys.stdout.write('\r%s' % padded_output) sys.stdout.flush() time.sleep(.25) print "" if CLEANUP: balancers = gcelb.list_balancers() healthchecks = gcelb.ex_list_healthchecks() nodes = gce.list_nodes(ex_zone='all') firewalls = gce.ex_list_firewalls() display('Cleaning up %s resources created' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, nodes, balancers + healthchecks + firewalls) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) # ==== GOOGLE DNS CODE STARTS HERE ==== def main_dns(): start_time = datetime.datetime.now() display('DNS demo/test start time: %s' % str(start_time)) gce = get_gce_driver() gdns = get_dns_driver() # Get project info and print name project = gce.ex_get_project() display('Project: %s' % project.name) # Get list of managed zones zones = gdns.iterate_zones() display('Zones', zones) # Get list of records zones = gdns.iterate_zones() for z in zones: records = gdns.iterate_records(z) display('Records for managed zone "%s"' % z.id, records) # TODO(erjohnso): Finish this DNS section. Challenging in that you need to # own a domain, so testing will require user customization. Perhaps a new # command-line required flag unless --skip-dns is supplied. Also, real # e2e testing should try to do DNS lookups on new records, but DNS TTL # and propagation delays will introduce limits on what can be tested. end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) if __name__ == '__main__': parser = argparse.ArgumentParser( description='Google Cloud Platform Demo / Live Test Script') parser.add_argument("--compute", help="perform compute demo / live tests", dest="compute", action="store_true") parser.add_argument("--load-balancer", help="perform load-balancer demo / live tests", dest="lb", action="store_true") parser.add_argument("--dns", help="perform DNS demo / live tests", dest="dns", action="store_true") parser.add_argument("--cleanup-only", help="perform clean-up (skips all tests)", dest="cleanup", action="store_true") cl_args = parser.parse_args() if cl_args.cleanup: cleanup_only() else: if cl_args.compute: main_compute() if cl_args.lb: main_load_balancer() if cl_args.dns: main_dns()

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try: import argparse except: print('This script uses the python "argparse" module. Please use Python ' '2.7 or greater.') raise try: import secrets except ImportError: print('"demos/secrets.py" not found.\n\n' 'Please copy secrets.py-dist to secrets.py and update the GCE* ' 'values with appropriate authentication information.\n' 'Additional information about setting these values can be found ' 'in the docstring for:\n' 'libcloud/common/google.py\n') sys.exit(1) # Add parent dir of this file's dir to sys.path (OS-agnostically) sys.path.append(os.path.normpath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from libcloud.compute.types import Provider from libcloud.compute.providers import get_driver from libcloud.common.google import ResourceNotFoundError from libcloud.loadbalancer.types import Provider as Provider_lb from libcloud.loadbalancer.providers import get_driver as get_driver_lb from libcloud.dns.types import Provider as Provider_dns from libcloud.dns.providers import get_driver as get_driver_dns from libcloud.dns.base import Record, Zone from libcloud.utils.py3 import PY3 if PY3: import urllib.request as url_req else: import urllib2 as url_req MAX_NODES = 5 DEMO_BASE_NAME = 'lct' DATACENTER = 'us-central1-f' CLEANUP = True args = getattr(secrets, 'GCE_PARAMS', ()) kwargs = getattr(secrets, 'GCE_KEYWORD_PARAMS', {}) kwargs = kwargs.copy() kwargs['datacenter'] = DATACENTER def get_gce_driver(): driver = get_driver(Provider.GCE)(*args, **kwargs) return driver def get_gcelb_driver(gce_driver=None): if gce_driver: driver = get_driver_lb(Provider_lb.GCE)(gce_driver=gce_driver) else: driver = get_driver_lb(Provider_lb.GCE)(*args, **kwargs) return driver def get_dns_driver(gce_driver=None): if gce_driver: driver = get_driver_dns(Provider_dns.GOOGLE)(gce_driver=gce_driver) else: driver = get_driver_dns(Provider_dns.GOOGLE)(*args, **kwargs) return driver def display(title, resource_list=[]): """ Display a list of resources. :param title: String to be printed at the heading of the list. :type title: ``str`` :param resource_list: List of resources to display :type resource_list: Any ``object`` with a C{name} attribute """ print('=> %s' % title) for item in resource_list: if isinstance(item, Record): if item.name.startswith(DEMO_BASE_NAME): print('=> name=%s, type=%s' % (item.name, item.type)) else: print(' name=%s, type=%s' % (item.name, item.type)) elif isinstance(item, Zone): if item.domain.startswith(DEMO_BASE_NAME): print('=> name=%s, dnsname=%s' % (item.id, item.domain)) else: print(' name=%s, dnsname=%s' % (item.id, item.domain)) elif hasattr(item, 'name'): if item.name.startswith(DEMO_BASE_NAME): print('=> %s' % item.name) else: print(' %s' % item.name) else: if item.startswith(DEMO_BASE_NAME): print('=> %s' % item) else: print(' %s' % item) def cleanup_only(): start_time = datetime.datetime.now() display('Clean-up start time: %s' % str(start_time)) gce = get_gce_driver() project = gce.ex_get_project() display('Project: %s' % project.name) all_nodes = gce.list_nodes(ex_zone='all') display('Nodes:', all_nodes) all_addresses = gce.ex_list_addresses(region='all') display('Addresses:', all_addresses) all_volumes = gce.list_volumes(ex_zone='all') display('Volumes:', all_volumes) sizes = gce.list_sizes() display('Sizes:', sizes) # These are global firewalls = gce.ex_list_firewalls() display('Firewalls:', firewalls) networks = gce.ex_list_networks() display('Networks:', networks) images = gce.list_images() display('Images:', images) locations = gce.list_locations() display('Locations:', locations) zones = gce.ex_list_zones() display('Zones:', zones) snapshots = gce.ex_list_snapshots() display('Snapshots:', snapshots) # == Clean up any old demo resources == display('Cleaning up any "%s" resources' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, all_nodes, all_addresses + all_volumes + firewalls + networks + snapshots) volumes = gce.list_volumes() clean_up(gce, DEMO_BASE_NAME, None, volumes) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) def clean_up(gce, base_name, node_list=None, resource_list=None): """ Destroy all resources that have a name beginning with 'base_name'. :param base_name: String with the first part of the name of resources to destroy :type base_name: ``str`` :keyword node_list: List of nodes to consider for deletion :type node_list: ``list`` of :class:`Node` :keyword resource_list: List of resources to consider for deletion :type resource_list: ``list`` of I{Resource Objects} """ if node_list is None: node_list = [] if resource_list is None: resource_list = [] # Use ex_destroy_multiple_nodes to destroy nodes del_nodes = [] for node in node_list: if node.name.startswith(base_name): del_nodes.append(node) result = gce.ex_destroy_multiple_nodes(del_nodes) for i, success in enumerate(result): if success: display(' Deleted %s' % del_nodes[i].name) else: display(' Failed to delete %s' % del_nodes[i].name) # Destroy everything else with just the destroy method for resrc in resource_list: if resrc.name.startswith(base_name): try: resrc.destroy() except ResourceNotFoundError: display(' Not found: %s (%s)' % (resrc.name, resrc.__class__.__name__)) except: class_name = resrc.__class__.__name__ display(' Failed to Delete %s (%s)' % (resrc.name, class_name)) raise # ==== COMPUTE CODE STARTS HERE ==== def main_compute(): start_time = datetime.datetime.now() display('Compute demo/test start time: %s' % str(start_time)) gce = get_gce_driver() # Get project info and print name project = gce.ex_get_project() display('Project: %s' % project.name) # == Get Lists of Everything and Display the lists (up to 10) == # These can either just return values for the current datacenter (zone) # or for everything. all_nodes = gce.list_nodes(ex_zone='all') display('Nodes:', all_nodes) all_addresses = gce.ex_list_addresses(region='all') display('Addresses:', all_addresses) all_volumes = gce.list_volumes(ex_zone='all') display('Volumes:', all_volumes) # This can return everything, but there is a large amount of overlap, # so we'll just get the sizes from the current zone. sizes = gce.list_sizes() display('Sizes:', sizes) firewalls = gce.ex_list_firewalls() display('Firewalls:', firewalls) networks = gce.ex_list_networks() display('Networks:', networks) images = gce.list_images() display('Images:', images) locations = gce.list_locations() display('Locations:', locations) zones = gce.ex_list_zones() display('Zones:', zones) snapshots = gce.ex_list_snapshots() display('Snapshots:', snapshots) display('Cleaning up any "%s" resources' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, all_nodes, all_addresses + all_volumes + firewalls + networks + snapshots) if MAX_NODES > 1: display('Creating a node with boot/local-ssd using GCE structure:') name = '%s-gstruct' % DEMO_BASE_NAME img_url = "projects/debian-cloud/global/images/" img_url += "backports-debian-7-wheezy-v20141205" disk_type_url = "projects/%s/zones/us-central1-f/" % project.name disk_type_url += "diskTypes/local-ssd" gce_disk_struct = [ { "type": "PERSISTENT", "deviceName": '%s-gstruct' % DEMO_BASE_NAME, "initializeParams": { "diskName": '%s-gstruct' % DEMO_BASE_NAME, "sourceImage": img_url }, "boot": True, "autoDelete": True }, { "type": "SCRATCH", "deviceName": '%s-gstruct-lssd' % DEMO_BASE_NAME, "initializeParams": { "diskType": disk_type_url }, "autoDelete": True } ] node_gstruct = gce.create_node(name, 'n1-standard-1', None, 'us-central1-f', ex_disks_gce_struct=gce_disk_struct) num_disks = len(node_gstruct.extra['disks']) display(' Node %s created with %d disks' % (node_gstruct.name, num_disks)) display('Creating Node with auto-created SSD:') name = '%s-np-node' % DEMO_BASE_NAME node_1 = gce.create_node(name, 'n1-standard-1', 'debian-7', ex_tags=['libcloud'], ex_disk_type='pd-ssd', ex_disk_auto_delete=False) display(' Node %s created' % name) display('Creating a new disk:') disk_name = '%s-attach-disk' % DEMO_BASE_NAME volume = gce.create_volume(10, disk_name) if volume.attach(node_1): display(' Attached %s to %s' % (volume.name, node_1.name)) display(' Disabled auto-delete for %s on %s' % (volume.name, node_1.name)) gce.ex_set_volume_auto_delete(volume, node_1, auto_delete=False) if CLEANUP: if gce.detach_volume(volume, ex_node=node_1): display(' Detached %s from %s' % (volume.name, node_1.name)) display('Creating a snapshot from existing disk:') vol_name = '%s-snap-template' % DEMO_BASE_NAME image = gce.ex_get_image('debian-7') vol = gce.create_volume(None, vol_name, image=image) display('Created disk %s to shapshot:' % DEMO_BASE_NAME) snapshot = vol.snapshot('%s-snapshot' % DEMO_BASE_NAME) display(' Snapshot %s created' % snapshot.name) display('Creating Node with existing disk:') name = '%s-persist-node' % DEMO_BASE_NAME image = gce.ex_get_image('debian-7') size = gce.ex_get_size('n1-standard-1') volume_name = '%s-boot-disk' % DEMO_BASE_NAME volume = gce.create_volume(None, volume_name, snapshot=snapshot) display(' Created %s from snapshot' % volume.name) node_2 = gce.create_node(name, size, image, ex_tags=['libcloud'], ex_boot_disk=volume, ex_disk_auto_delete=False) display(' Node %s created with attached disk %s' % (node_2.name, volume.name)) display('Updating Tags for %s:' % node_2.name) tags = node_2.extra['tags'] tags.append('newtag') if gce.ex_set_node_tags(node_2, tags): display(' Tags updated for %s' % node_2.name) check_node = gce.ex_get_node(node_2.name) display(' New tags: %s' % check_node.extra['tags']) display('Setting Metadata for %s:' % node_2.name) if gce.ex_set_node_metadata(node_2, {'foo': 'bar', 'baz': 'foobarbaz'}): display(' Metadata updated for %s' % node_2.name) check_node = gce.ex_get_node(node_2.name) display(' New Metadata: %s' % check_node.extra['metadata']) base_name = '%s-multiple-nodes' % DEMO_BASE_NAME number = MAX_NODES - 2 if number > 0: display('Creating Multiple Nodes (%s):' % number) multi_nodes = gce.ex_create_multiple_nodes(base_name, size, image, number, ex_tags=['libcloud'], ex_disk_auto_delete=True) for node in multi_nodes: display(' Node %s created' % node.name) display('Creating Network:') name = '%s-network' % DEMO_BASE_NAME cidr = '10.10.0.0/16' network_1 = gce.ex_create_network(name, cidr) display(' Network %s created' % network_1.name) display('Creating a Firewall:') name = '%s-firewall' % DEMO_BASE_NAME allowed = [{'IPProtocol': 'tcp', 'ports': ['3141']}] firewall_1 = gce.ex_create_firewall(name, allowed, network=network_1, source_tags=['libcloud']) display(' Firewall %s created' % firewall_1.name) display('Creating an Address:') name = '%s-address' % DEMO_BASE_NAME address_1 = gce.ex_create_address(name) display(' Address %s created with IP %s' % (address_1.name, address_1.address)) display('Updated Resources in current zone/region') nodes = gce.list_nodes() display('Nodes:', nodes) addresses = gce.ex_list_addresses() display('Addresses:', addresses) firewalls = gce.ex_list_firewalls() display('Firewalls:', firewalls) networks = gce.ex_list_networks() display('Networks:', networks) snapshots = gce.ex_list_snapshots() display('Snapshots:', snapshots) if CLEANUP: display('Cleaning up %s resources created' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, nodes, addresses + firewalls + networks + snapshots) volumes = gce.list_volumes() clean_up(gce, DEMO_BASE_NAME, None, volumes) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) def main_load_balancer(): start_time = datetime.datetime.now() display('Load-balancer demo/test start time: %s' % str(start_time)) gce = get_gce_driver() gcelb = get_gcelb_driver(gce) project = gce.ex_get_project() display('Project: %s' % project.name) balancers = gcelb.list_balancers() display('Load Balancers', balancers) protocols = gcelb.list_protocols() display('Protocols', protocols) healthchecks = gcelb.ex_list_healthchecks() display('Health Checks', healthchecks) all_nodes = gce.list_nodes(ex_zone='all') firewalls = gce.ex_list_firewalls() display('Cleaning up any "%s" resources' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, all_nodes, balancers + healthchecks + firewalls) startup_script = ('apt-get -y update && ' 'apt-get -y install apache2 && ' 'hostname > /var/www/index.html') tag = '%s-www' % DEMO_BASE_NAME base_name = '%s-www' % DEMO_BASE_NAME image = gce.ex_get_image('debian-7') size = gce.ex_get_size('n1-standard-1') number = 3 display('Creating %d nodes' % number) metadata = {'items': [{'key': 'startup-script', 'value': startup_script}]} lb_nodes = gce.ex_create_multiple_nodes(base_name, size, image, number, ex_tags=[tag], ex_metadata=metadata, ex_disk_auto_delete=True, ignore_errors=False) display('Created Nodes', lb_nodes) display('Creating a Firewall') name = '%s-firewall' % DEMO_BASE_NAME allowed = [{'IPProtocol': 'tcp', 'ports': ['80']}] firewall = gce.ex_create_firewall(name, allowed, source_tags=[tag]) display(' Firewall %s created' % firewall.name) display('Creating a HealthCheck') name = '%s-healthcheck' % DEMO_BASE_NAME hc = gcelb.ex_create_healthcheck(name, host=None, path='/', port='80', interval=5, timeout=5, unhealthy_threshold=2, healthy_threshold=2) display('Healthcheck %s created' % hc.name) display('Creating Load Balancer') name = '%s-lb' % DEMO_BASE_NAME port = 80 protocol = 'tcp' algorithm = None members = lb_nodes[:2] healthchecks = [hc] balancer = gcelb.create_balancer(name, port, protocol, algorithm, members, ex_healthchecks=healthchecks) display(' Load Balancer %s created' % balancer.name) display('Attaching additional node to Load Balancer') member = balancer.attach_compute_node(lb_nodes[2]) display(' Attached %s to %s' % (member.id, balancer.name)) members = balancer.list_members() display('Load Balancer Members') for member in members: display(' ID: %s IP: %s' % (member.id, member.ip)) display('Removing a Member') detached = members[0] detach = balancer.detach_member(detached) if detach: display(' Member %s detached from %s' % (detached.id, balancer.name)) members = balancer.list_members() display('Updated Load Balancer Members') for member in members: display(' ID: %s IP: %s' % (member.id, member.ip)) display('Reattaching Member') member = balancer.attach_member(detached) display(' Member %s attached to %s' % (member.id, balancer.name)) PAUSE = 60 display('Sleeping for %d seconds for LB members to serve...' % PAUSE) time.sleep(PAUSE) rounds = 200 url = 'http://%s/' % balancer.ip line_length = 75 display('Connecting to %s %s times' % (url, rounds)) for x in range(rounds): response = url_req.urlopen(url) if PY3: output = str(response.read(), encoding='utf-8').strip() else: output = response.read().strip() if 'www-001' in output: padded_output = output.center(line_length) elif 'www-002' in output: padded_output = output.rjust(line_length) else: padded_output = output.ljust(line_length) sys.stdout.write('\r%s' % padded_output) sys.stdout.flush() time.sleep(.25) print "" if CLEANUP: balancers = gcelb.list_balancers() healthchecks = gcelb.ex_list_healthchecks() nodes = gce.list_nodes(ex_zone='all') firewalls = gce.ex_list_firewalls() display('Cleaning up %s resources created' % DEMO_BASE_NAME) clean_up(gce, DEMO_BASE_NAME, nodes, balancers + healthchecks + firewalls) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) def main_dns(): start_time = datetime.datetime.now() display('DNS demo/test start time: %s' % str(start_time)) gce = get_gce_driver() gdns = get_dns_driver() project = gce.ex_get_project() display('Project: %s' % project.name) zones = gdns.iterate_zones() display('Zones', zones) zones = gdns.iterate_zones() for z in zones: records = gdns.iterate_records(z) display('Records for managed zone "%s"' % z.id, records) end_time = datetime.datetime.now() display('Total runtime: %s' % str(end_time - start_time)) if __name__ == '__main__': parser = argparse.ArgumentParser( description='Google Cloud Platform Demo / Live Test Script') parser.add_argument("--compute", help="perform compute demo / live tests", dest="compute", action="store_true") parser.add_argument("--load-balancer", help="perform load-balancer demo / live tests", dest="lb", action="store_true") parser.add_argument("--dns", help="perform DNS demo / live tests", dest="dns", action="store_true") parser.add_argument("--cleanup-only", help="perform clean-up (skips all tests)", dest="cleanup", action="store_true") cl_args = parser.parse_args() if cl_args.cleanup: cleanup_only() else: if cl_args.compute: main_compute() if cl_args.lb: main_load_balancer() if cl_args.dns: main_dns()

false

true

790d7d7ba0053e639f2f0a33658279ba5db13313

3,474

py

Python

vaccines.py

Karalius/get-vaccine-vilnius

49a918cdef6fedc7538f7e49210b18fb1f03f7f4

[ "MIT" ]

null

vaccines.py

Karalius/get-vaccine-vilnius

49a918cdef6fedc7538f7e49210b18fb1f03f7f4

[ "MIT" ]

null

vaccines.py

Karalius/get-vaccine-vilnius

49a918cdef6fedc7538f7e49210b18fb1f03f7f4

[ "MIT" ]

null

import time from bs4 import BeautifulSoup import requests import json from datetime import datetime, timedelta import psycopg2 import smtplib import os DATABASE = os.environ["DATABASE"] USER = os.environ["USER"] PASSWORD = os.environ["PASSWORD"] HOST = os.environ["HOST"] def send_email(message: str) -> None: """ Sends an email to target email with given message. Args: message (str): message you're sending """ with open("../creds.json", "r") as f: creds = json.loads(f) gmail_user = creds["user"] gmail_pass = creds["pass"] try: server = smtplib.SMTP("smtp.gmail.com", 587) server.starttls() server.login(gmail_user, gmail_pass) server.sendmail(gmail_user, creds["target"], message) except: print("Email didnt work...") def get_data() -> None: """ Infinite loop of every 10min requests to Vilnius vaccination center. Collects count of vaccines and adds to PostgreSQL database. Sends an email if Pfizer vaccine is available. """ while True: sql_connection = psycopg2.connect( database=DATABASE, user=USER, password=PASSWORD, host=HOST ) # Connect to DB cur = sql_connection.cursor() headers = { "Connection": "keep-alive", "Cache-Control": "max-age=0", "sec-ch-ua": "^\\^", "sec-ch-ua-mobile": "?0", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.93 Safari/537.36", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Sec-Fetch-Site": "cross-site", "Sec-Fetch-Mode": "navigate", "Sec-Fetch-User": "?1", "Sec-Fetch-Dest": "document", "Accept-Language": "en-US,en;q=0.9", } page = requests.get( "https://vilnius-vac.myhybridlab.com/selfregister/vaccine", headers=headers ) soup = BeautifulSoup(page.content, "html.parser") vaccines = soup.find("vaccine-rooms", class_=None)[":vaccine-rooms"] json_object = json.loads(vaccines) # Time time_raw = soup.find("small", class_="text-muted").get_text().split() time_str = time_raw[2] + " " + time_raw[3] dt = datetime.fromisoformat(time_str) now = datetime.now().replace(microsecond=0) eet_dt = now + timedelta(hours=3) diff_secs = (eet_dt - dt).seconds total_sleep = 602 - diff_secs moderna = json_object[0]["free_total"] pfizer = json_object[1]["free_total"] astra = json_object[2]["free_total"] janssen = json_object[3]["free_total"] cur.execute( f"INSERT INTO vilnius_vakcinos (time, moderna, pfizer, astra_zeneca, janssen) VALUES ('{time_str}', {moderna}, {pfizer}, {astra}, {janssen});" ) sql_connection.commit() sql_connection.close() if pfizer > 0: send_email( "Pfizer count: {pfizer}, link to register: https://vilnius-vac.myhybridlab.com/selfregister/vaccine" ) time.sleep(total_sleep) if __name__ == "__main__": get_data()

32.46729

161

0.582614

import time from bs4 import BeautifulSoup import requests import json from datetime import datetime, timedelta import psycopg2 import smtplib import os DATABASE = os.environ["DATABASE"] USER = os.environ["USER"] PASSWORD = os.environ["PASSWORD"] HOST = os.environ["HOST"] def send_email(message: str) -> None: with open("../creds.json", "r") as f: creds = json.loads(f) gmail_user = creds["user"] gmail_pass = creds["pass"] try: server = smtplib.SMTP("smtp.gmail.com", 587) server.starttls() server.login(gmail_user, gmail_pass) server.sendmail(gmail_user, creds["target"], message) except: print("Email didnt work...") def get_data() -> None: while True: sql_connection = psycopg2.connect( database=DATABASE, user=USER, password=PASSWORD, host=HOST ) cur = sql_connection.cursor() headers = { "Connection": "keep-alive", "Cache-Control": "max-age=0", "sec-ch-ua": "^\\^", "sec-ch-ua-mobile": "?0", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.93 Safari/537.36", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Sec-Fetch-Site": "cross-site", "Sec-Fetch-Mode": "navigate", "Sec-Fetch-User": "?1", "Sec-Fetch-Dest": "document", "Accept-Language": "en-US,en;q=0.9", } page = requests.get( "https://vilnius-vac.myhybridlab.com/selfregister/vaccine", headers=headers ) soup = BeautifulSoup(page.content, "html.parser") vaccines = soup.find("vaccine-rooms", class_=None)[":vaccine-rooms"] json_object = json.loads(vaccines) time_raw = soup.find("small", class_="text-muted").get_text().split() time_str = time_raw[2] + " " + time_raw[3] dt = datetime.fromisoformat(time_str) now = datetime.now().replace(microsecond=0) eet_dt = now + timedelta(hours=3) diff_secs = (eet_dt - dt).seconds total_sleep = 602 - diff_secs moderna = json_object[0]["free_total"] pfizer = json_object[1]["free_total"] astra = json_object[2]["free_total"] janssen = json_object[3]["free_total"] cur.execute( f"INSERT INTO vilnius_vakcinos (time, moderna, pfizer, astra_zeneca, janssen) VALUES ('{time_str}', {moderna}, {pfizer}, {astra}, {janssen});" ) sql_connection.commit() sql_connection.close() if pfizer > 0: send_email( "Pfizer count: {pfizer}, link to register: https://vilnius-vac.myhybridlab.com/selfregister/vaccine" ) time.sleep(total_sleep) if __name__ == "__main__": get_data()

true

790d7db67280443b19cd4193370f605802115a87

847

py

Python

salt/runners/ssh.py

bogdanr/salt

4f198525873a1b7da3fbb9994dbb40d381494922

[ "Apache-2.0" ]

2

2015-08-04T21:54:38.000Z

2019-04-25T21:47:08.000Z

salt/runners/ssh.py

bogdanr/salt

4f198525873a1b7da3fbb9994dbb40d381494922

[ "Apache-2.0" ]

1

2015-09-02T12:49:48.000Z

2015-09-02T19:22:58.000Z

salt/runners/ssh.py

bogdanr/salt

4f198525873a1b7da3fbb9994dbb40d381494922

[ "Apache-2.0" ]

1

2020-10-19T11:49:50.000Z

# -*- coding: utf-8 -*- ''' A Runner module interface on top of the salt-ssh Python API. This allows for programmatic use from salt-api, the Reactor, Orchestrate, etc. ''' # Import Python Libs from __future__ import absolute_import # Import Salt Libs import salt.client.ssh.client def cmd( tgt, fun, arg=(), timeout=None, expr_form='glob', kwarg=None): ''' Execute a single command via the salt-ssh subsystem and return all routines at once .. versionaddedd:: 2015.2 A wrapper around the :py:meth:`SSHClient.cmd <salt.client.ssh.client.SSHClient.cmd>` method. ''' client = salt.client.ssh.client.SSHClient(mopts=__opts__) return client.cmd( tgt, fun, arg, timeout, expr_form, kwarg)

21.175

78

0.602125

from __future__ import absolute_import import salt.client.ssh.client def cmd( tgt, fun, arg=(), timeout=None, expr_form='glob', kwarg=None): client = salt.client.ssh.client.SSHClient(mopts=__opts__) return client.cmd( tgt, fun, arg, timeout, expr_form, kwarg)

true

790d7dc3297a1f9745e929ec6e91dfe5c2d85a35

13,991

py

Python

superset/dashboards/commands/importers/v0.py

Jacob-ru/superset

148409214ce760368e3bf8122eb0d79297606a0a

[ "Apache-2.0" ]

null

superset/dashboards/commands/importers/v0.py

Jacob-ru/superset

148409214ce760368e3bf8122eb0d79297606a0a

[ "Apache-2.0" ]

null

superset/dashboards/commands/importers/v0.py

Jacob-ru/superset

148409214ce760368e3bf8122eb0d79297606a0a

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import json import logging import time from copy import copy from datetime import datetime from typing import Any, Dict, Optional from flask_babel import lazy_gettext as _ from sqlalchemy.orm import make_transient, Session from superset import ConnectorRegistry, db from superset.commands.base import BaseCommand from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn from superset.datasets.commands.importers.v0 import import_dataset from superset.exceptions import DashboardImportException from superset.models.dashboard import Dashboard from superset.models.slice import Slice from superset.models.core import Database from superset.utils.dashboard_filter_scopes_converter import ( convert_filter_scopes, copy_filter_scopes, ) logger = logging.getLogger(__name__) def import_chart( slc_to_import: Slice, slc_to_override: Optional[Slice], import_time: Optional[int] = None, ) -> int: """Inserts or overrides slc in the database. remote_id and import_time fields in params_dict are set to track the slice origin and ensure correct overrides for multiple imports. Slice.perm is used to find the datasources and connect them. :param Slice slc_to_import: Slice object to import :param Slice slc_to_override: Slice to replace, id matches remote_id :returns: The resulting id for the imported slice :rtype: int """ session = db.session make_transient(slc_to_import) slc_to_import.dashboards = [] slc_to_import.alter_params(remote_id=slc_to_import.id, import_time=import_time) slc_to_import = slc_to_import.copy() slc_to_import.reset_ownership() params = slc_to_import.params_dict datasource = ConnectorRegistry.get_datasource_by_name( session, slc_to_import.datasource_type, params["datasource_name"], params["schema"], params["database_name"], ) slc_to_import.datasource_id = datasource.id # type: ignore if slc_to_override: slc_to_override.override(slc_to_import) session.flush() return slc_to_override.id session.add(slc_to_import) logger.info("Final slice: %s", str(slc_to_import.to_json())) session.flush() return slc_to_import.id def import_dashboard( # pylint: disable=too-many-locals,too-many-statements dashboard_to_import: Dashboard, dataset_id_mapping: Optional[Dict[int, int]] = None, import_time: Optional[int] = None, database_id: Optional[int] = None, ) -> int: """Imports the dashboard from the object to the database. Once dashboard is imported, json_metadata field is extended and stores remote_id and import_time. It helps to decide if the dashboard has to be overridden or just copies over. Slices that belong to this dashboard will be wired to existing tables. This function can be used to import/export dashboards between multiple superset instances. Audit metadata isn't copied over. """ def alter_positions( dashboard: Dashboard, old_to_new_slc_id_dict: Dict[int, int] ) -> None: """Updates slice_ids in the position json. Sample position_json data: { "DASHBOARD_VERSION_KEY": "v2", "DASHBOARD_ROOT_ID": { "type": "DASHBOARD_ROOT_TYPE", "id": "DASHBOARD_ROOT_ID", "children": ["DASHBOARD_GRID_ID"] }, "DASHBOARD_GRID_ID": { "type": "DASHBOARD_GRID_TYPE", "id": "DASHBOARD_GRID_ID", "children": ["DASHBOARD_CHART_TYPE-2"] }, "DASHBOARD_CHART_TYPE-2": { "type": "CHART", "id": "DASHBOARD_CHART_TYPE-2", "children": [], "meta": { "width": 4, "height": 50, "chartId": 118 } }, } """ position_data = json.loads(dashboard.position_json) position_json = position_data.values() for value in position_json: if ( isinstance(value, dict) and value.get("meta") and value.get("meta", {}).get("chartId") ): old_slice_id = value["meta"]["chartId"] if old_slice_id in old_to_new_slc_id_dict: value["meta"]["chartId"] = old_to_new_slc_id_dict[old_slice_id] dashboard.position_json = json.dumps(position_data) def alter_native_filters(dashboard: Dashboard) -> None: json_metadata = json.loads(dashboard.json_metadata) native_filter_configuration = json_metadata.get("native_filter_configuration") if not native_filter_configuration: return for native_filter in native_filter_configuration: for target in native_filter.get("targets", []): old_dataset_id = target.get("datasetId") if dataset_id_mapping and old_dataset_id is not None: target["datasetId"] = dataset_id_mapping.get( old_dataset_id, old_dataset_id, ) dashboard.json_metadata = json.dumps(json_metadata) logger.info("Started import of the dashboard: %s", dashboard_to_import.to_json()) session = db.session logger.info("Dashboard has %d slices", len(dashboard_to_import.slices)) # copy slices object as Slice.import_slice will mutate the slice # and will remove the existing dashboard - slice association slices = copy(dashboard_to_import.slices) # Clearing the slug to avoid conflicts dashboard_to_import.slug = None old_json_metadata = json.loads(dashboard_to_import.json_metadata or "{}") old_to_new_slc_id_dict: Dict[int, int] = {} new_timed_refresh_immune_slices = [] new_expanded_slices = {} new_filter_scopes = {} i_params_dict = dashboard_to_import.params_dict remote_id_slice_map = { slc.params_dict["remote_id"]: slc for slc in session.query(Slice) .filter(Slice.datasource_id.in_(list(dataset_id_mapping.values()))) .all() if "remote_id" in slc.params_dict } for slc in slices: logger.info( "Importing slice %s from the dashboard: %s", slc.to_json(), dashboard_to_import.dashboard_title, ) # Change database name in params due to using new database for imported dashboard if database_id: database_name = session.query(Database).filter(Database.id == database_id).first().name slc.alter_params(database_name=database_name) remote_slc = remote_id_slice_map.get(slc.id) new_slc_id = import_chart(slc, remote_slc, import_time=import_time) old_to_new_slc_id_dict[slc.id] = new_slc_id # update json metadata that deals with slice ids new_slc_id_str = str(new_slc_id) old_slc_id_str = str(slc.id) if ( "timed_refresh_immune_slices" in i_params_dict and old_slc_id_str in i_params_dict["timed_refresh_immune_slices"] ): new_timed_refresh_immune_slices.append(new_slc_id_str) if ( "expanded_slices" in i_params_dict and old_slc_id_str in i_params_dict["expanded_slices"] ): new_expanded_slices[new_slc_id_str] = i_params_dict["expanded_slices"][ old_slc_id_str ] # since PR #9109, filter_immune_slices and filter_immune_slice_fields # are converted to filter_scopes # but dashboard create from import may still have old dashboard filter metadata # here we convert them to new filter_scopes metadata first filter_scopes = {} if ( "filter_immune_slices" in i_params_dict or "filter_immune_slice_fields" in i_params_dict ): filter_scopes = convert_filter_scopes(old_json_metadata, slices) if "filter_scopes" in i_params_dict: filter_scopes = old_json_metadata.get("filter_scopes") # then replace old slice id to new slice id: if filter_scopes: new_filter_scopes = copy_filter_scopes( old_to_new_slc_id_dict=old_to_new_slc_id_dict, old_filter_scopes=filter_scopes, ) # override the dashboard existing_dashboard = None for dash in session.query(Dashboard).all(): if ( "remote_id" in dash.params_dict and dash.params_dict["remote_id"] == dashboard_to_import.id ): existing_dashboard = dash dashboard_to_import = dashboard_to_import.copy() dashboard_to_import.id = None dashboard_to_import.reset_ownership() # position_json can be empty for dashboards # with charts added from chart-edit page and without re-arranging if dashboard_to_import.position_json: alter_positions(dashboard_to_import, old_to_new_slc_id_dict) dashboard_to_import.alter_params(import_time=import_time) dashboard_to_import.remove_params(param_to_remove="filter_immune_slices") dashboard_to_import.remove_params(param_to_remove="filter_immune_slice_fields") if new_filter_scopes: dashboard_to_import.alter_params(filter_scopes=new_filter_scopes) if new_expanded_slices: dashboard_to_import.alter_params(expanded_slices=new_expanded_slices) if new_timed_refresh_immune_slices: dashboard_to_import.alter_params( timed_refresh_immune_slices=new_timed_refresh_immune_slices ) alter_native_filters(dashboard_to_import) new_slices = ( session.query(Slice).filter(Slice.id.in_(old_to_new_slc_id_dict.values())).all() ) if existing_dashboard: existing_dashboard.override(dashboard_to_import) existing_dashboard.slices = new_slices session.flush() return existing_dashboard.id dashboard_to_import.slices = new_slices session.add(dashboard_to_import) session.flush() return dashboard_to_import.id # type: ignore def decode_dashboards( # pylint: disable=too-many-return-statements o: Dict[str, Any] ) -> Any: """ Function to be passed into json.loads obj_hook parameter Recreates the dashboard object from a json representation. """ # pylint: disable=import-outside-toplevel from superset.connectors.druid.models import ( DruidCluster, DruidColumn, DruidDatasource, DruidMetric, ) if "__Dashboard__" in o: return Dashboard(**o["__Dashboard__"]) if "__Slice__" in o: return Slice(**o["__Slice__"]) if "__TableColumn__" in o: return TableColumn(**o["__TableColumn__"]) if "__SqlaTable__" in o: return SqlaTable(**o["__SqlaTable__"]) if "__SqlMetric__" in o: return SqlMetric(**o["__SqlMetric__"]) if "__DruidCluster__" in o: return DruidCluster(**o["__DruidCluster__"]) if "__DruidColumn__" in o: return DruidColumn(**o["__DruidColumn__"]) if "__DruidDatasource__" in o: return DruidDatasource(**o["__DruidDatasource__"]) if "__DruidMetric__" in o: return DruidMetric(**o["__DruidMetric__"]) if "__datetime__" in o: return datetime.strptime(o["__datetime__"], "%Y-%m-%dT%H:%M:%S") return o def import_dashboards( session: Session, content: str, database_id: Optional[int] = None, import_time: Optional[int] = None, ) -> None: """Imports dashboards from a stream to databases""" current_tt = int(time.time()) import_time = current_tt if import_time is None else import_time data = json.loads(content, object_hook=decode_dashboards) if not data: raise DashboardImportException(_("No data in file")) dataset_id_mapping: Dict[int, int] = {} for table in data["datasources"]: new_dataset_id = import_dataset(table, database_id, import_time=import_time) params = json.loads(table.params) dataset_id_mapping[params["remote_id"]] = new_dataset_id session.commit() for dashboard in data["dashboards"]: import_dashboard(dashboard, dataset_id_mapping, import_time=import_time, database_id=database_id) session.commit() class ImportDashboardsCommand(BaseCommand): """ Import dashboard in JSON format. This is the original unversioned format used to export and import dashboards in Superset. """ # pylint: disable=unused-argument def __init__( self, contents: Dict[str, str], database_id: Optional[int] = None, **kwargs: Any ): self.contents = contents self.database_id = database_id def run(self) -> None: self.validate() for file_name, content in self.contents.items(): logger.info("Importing dashboard from file %s", file_name) import_dashboards(db.session, content, self.database_id) def validate(self) -> None: # ensure all files are JSON for content in self.contents.values(): try: json.loads(content) except ValueError: logger.exception("Invalid JSON file") raise

37.509383

99

0.67715

import json import logging import time from copy import copy from datetime import datetime from typing import Any, Dict, Optional from flask_babel import lazy_gettext as _ from sqlalchemy.orm import make_transient, Session from superset import ConnectorRegistry, db from superset.commands.base import BaseCommand from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn from superset.datasets.commands.importers.v0 import import_dataset from superset.exceptions import DashboardImportException from superset.models.dashboard import Dashboard from superset.models.slice import Slice from superset.models.core import Database from superset.utils.dashboard_filter_scopes_converter import ( convert_filter_scopes, copy_filter_scopes, ) logger = logging.getLogger(__name__) def import_chart( slc_to_import: Slice, slc_to_override: Optional[Slice], import_time: Optional[int] = None, ) -> int: session = db.session make_transient(slc_to_import) slc_to_import.dashboards = [] slc_to_import.alter_params(remote_id=slc_to_import.id, import_time=import_time) slc_to_import = slc_to_import.copy() slc_to_import.reset_ownership() params = slc_to_import.params_dict datasource = ConnectorRegistry.get_datasource_by_name( session, slc_to_import.datasource_type, params["datasource_name"], params["schema"], params["database_name"], ) slc_to_import.datasource_id = datasource.id if slc_to_override: slc_to_override.override(slc_to_import) session.flush() return slc_to_override.id session.add(slc_to_import) logger.info("Final slice: %s", str(slc_to_import.to_json())) session.flush() return slc_to_import.id def import_dashboard( dashboard_to_import: Dashboard, dataset_id_mapping: Optional[Dict[int, int]] = None, import_time: Optional[int] = None, database_id: Optional[int] = None, ) -> int: def alter_positions( dashboard: Dashboard, old_to_new_slc_id_dict: Dict[int, int] ) -> None: position_data = json.loads(dashboard.position_json) position_json = position_data.values() for value in position_json: if ( isinstance(value, dict) and value.get("meta") and value.get("meta", {}).get("chartId") ): old_slice_id = value["meta"]["chartId"] if old_slice_id in old_to_new_slc_id_dict: value["meta"]["chartId"] = old_to_new_slc_id_dict[old_slice_id] dashboard.position_json = json.dumps(position_data) def alter_native_filters(dashboard: Dashboard) -> None: json_metadata = json.loads(dashboard.json_metadata) native_filter_configuration = json_metadata.get("native_filter_configuration") if not native_filter_configuration: return for native_filter in native_filter_configuration: for target in native_filter.get("targets", []): old_dataset_id = target.get("datasetId") if dataset_id_mapping and old_dataset_id is not None: target["datasetId"] = dataset_id_mapping.get( old_dataset_id, old_dataset_id, ) dashboard.json_metadata = json.dumps(json_metadata) logger.info("Started import of the dashboard: %s", dashboard_to_import.to_json()) session = db.session logger.info("Dashboard has %d slices", len(dashboard_to_import.slices)) slices = copy(dashboard_to_import.slices) dashboard_to_import.slug = None old_json_metadata = json.loads(dashboard_to_import.json_metadata or "{}") old_to_new_slc_id_dict: Dict[int, int] = {} new_timed_refresh_immune_slices = [] new_expanded_slices = {} new_filter_scopes = {} i_params_dict = dashboard_to_import.params_dict remote_id_slice_map = { slc.params_dict["remote_id"]: slc for slc in session.query(Slice) .filter(Slice.datasource_id.in_(list(dataset_id_mapping.values()))) .all() if "remote_id" in slc.params_dict } for slc in slices: logger.info( "Importing slice %s from the dashboard: %s", slc.to_json(), dashboard_to_import.dashboard_title, ) if database_id: database_name = session.query(Database).filter(Database.id == database_id).first().name slc.alter_params(database_name=database_name) remote_slc = remote_id_slice_map.get(slc.id) new_slc_id = import_chart(slc, remote_slc, import_time=import_time) old_to_new_slc_id_dict[slc.id] = new_slc_id new_slc_id_str = str(new_slc_id) old_slc_id_str = str(slc.id) if ( "timed_refresh_immune_slices" in i_params_dict and old_slc_id_str in i_params_dict["timed_refresh_immune_slices"] ): new_timed_refresh_immune_slices.append(new_slc_id_str) if ( "expanded_slices" in i_params_dict and old_slc_id_str in i_params_dict["expanded_slices"] ): new_expanded_slices[new_slc_id_str] = i_params_dict["expanded_slices"][ old_slc_id_str ] ilter_immune_slices" in i_params_dict or "filter_immune_slice_fields" in i_params_dict ): filter_scopes = convert_filter_scopes(old_json_metadata, slices) if "filter_scopes" in i_params_dict: filter_scopes = old_json_metadata.get("filter_scopes") if filter_scopes: new_filter_scopes = copy_filter_scopes( old_to_new_slc_id_dict=old_to_new_slc_id_dict, old_filter_scopes=filter_scopes, ) existing_dashboard = None for dash in session.query(Dashboard).all(): if ( "remote_id" in dash.params_dict and dash.params_dict["remote_id"] == dashboard_to_import.id ): existing_dashboard = dash dashboard_to_import = dashboard_to_import.copy() dashboard_to_import.id = None dashboard_to_import.reset_ownership() if dashboard_to_import.position_json: alter_positions(dashboard_to_import, old_to_new_slc_id_dict) dashboard_to_import.alter_params(import_time=import_time) dashboard_to_import.remove_params(param_to_remove="filter_immune_slices") dashboard_to_import.remove_params(param_to_remove="filter_immune_slice_fields") if new_filter_scopes: dashboard_to_import.alter_params(filter_scopes=new_filter_scopes) if new_expanded_slices: dashboard_to_import.alter_params(expanded_slices=new_expanded_slices) if new_timed_refresh_immune_slices: dashboard_to_import.alter_params( timed_refresh_immune_slices=new_timed_refresh_immune_slices ) alter_native_filters(dashboard_to_import) new_slices = ( session.query(Slice).filter(Slice.id.in_(old_to_new_slc_id_dict.values())).all() ) if existing_dashboard: existing_dashboard.override(dashboard_to_import) existing_dashboard.slices = new_slices session.flush() return existing_dashboard.id dashboard_to_import.slices = new_slices session.add(dashboard_to_import) session.flush() return dashboard_to_import.id def decode_dashboards( o: Dict[str, Any] ) -> Any: from superset.connectors.druid.models import ( DruidCluster, DruidColumn, DruidDatasource, DruidMetric, ) if "__Dashboard__" in o: return Dashboard(**o["__Dashboard__"]) if "__Slice__" in o: return Slice(**o["__Slice__"]) if "__TableColumn__" in o: return TableColumn(**o["__TableColumn__"]) if "__SqlaTable__" in o: return SqlaTable(**o["__SqlaTable__"]) if "__SqlMetric__" in o: return SqlMetric(**o["__SqlMetric__"]) if "__DruidCluster__" in o: return DruidCluster(**o["__DruidCluster__"]) if "__DruidColumn__" in o: return DruidColumn(**o["__DruidColumn__"]) if "__DruidDatasource__" in o: return DruidDatasource(**o["__DruidDatasource__"]) if "__DruidMetric__" in o: return DruidMetric(**o["__DruidMetric__"]) if "__datetime__" in o: return datetime.strptime(o["__datetime__"], "%Y-%m-%dT%H:%M:%S") return o def import_dashboards( session: Session, content: str, database_id: Optional[int] = None, import_time: Optional[int] = None, ) -> None: current_tt = int(time.time()) import_time = current_tt if import_time is None else import_time data = json.loads(content, object_hook=decode_dashboards) if not data: raise DashboardImportException(_("No data in file")) dataset_id_mapping: Dict[int, int] = {} for table in data["datasources"]: new_dataset_id = import_dataset(table, database_id, import_time=import_time) params = json.loads(table.params) dataset_id_mapping[params["remote_id"]] = new_dataset_id session.commit() for dashboard in data["dashboards"]: import_dashboard(dashboard, dataset_id_mapping, import_time=import_time, database_id=database_id) session.commit() class ImportDashboardsCommand(BaseCommand): def __init__( self, contents: Dict[str, str], database_id: Optional[int] = None, **kwargs: Any ): self.contents = contents self.database_id = database_id def run(self) -> None: self.validate() for file_name, content in self.contents.items(): logger.info("Importing dashboard from file %s", file_name) import_dashboards(db.session, content, self.database_id) def validate(self) -> None: for content in self.contents.values(): try: json.loads(content) except ValueError: logger.exception("Invalid JSON file") raise

true

790d7dd48cc8d6d26ccd217555dd59ffcf548329

19,845

py

Python

nemo/collections/asr/metrics/rnnt_wer.py

Zenodia/NeMo

3c288d8a7caf667c95444c39434e3ebc5f53d911

[ "Apache-2.0" ]

null

nemo/collections/asr/metrics/rnnt_wer.py

Zenodia/NeMo

3c288d8a7caf667c95444c39434e3ebc5f53d911

[ "Apache-2.0" ]

null

nemo/collections/asr/metrics/rnnt_wer.py

Zenodia/NeMo

3c288d8a7caf667c95444c39434e3ebc5f53d911

[ "Apache-2.0" ]

null

# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from typing import List, Optional, Union import editdistance import torch from pytorch_lightning.metrics import Metric from nemo.collections.asr.parts import rnnt_beam_decoding as beam_decode from nemo.collections.asr.parts import rnnt_greedy_decoding as greedy_decode from nemo.collections.asr.parts.rnnt_utils import Hypothesis, NBestHypotheses from nemo.utils import logging __all__ = ['RNNTDecoding', 'RNNTWER'] class AbstractRNNTDecoding(ABC): """ Used for performing RNN-T auto-regressive decoding of the Decoder+Joint network given the encoder state. Args: decoding_cfg: A dict-like object which contains the following key-value pairs. strategy: str value which represents the type of decoding that can occur. Possible values are : - greedy, greedy_batch (for greedy decoding). - beam, tsd, alsd (for beam search decoding). compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded tokens as well as the decoded string. Default is False in order to avoid double decoding unless required. The config may further contain the following sub-dictionaries: "greedy": max_symbols: int, describing the maximum number of target tokens to decode per timestep during greedy decoding. Setting to larger values allows longer sentences to be decoded, at the cost of increased execution time. "beam": beam_size: int, defining the beam size for beam search. Must be >= 1. If beam_size == 1, will perform cached greedy search. This might be slightly different results compared to the greedy search above. score_norm: optional bool, whether to normalize the returned beam score in the hypotheses. Set to True by default. return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the hypotheses after beam search has concluded. This flag is set by default. tsd_max_sym_exp: optional int, determines number of symmetric expansions of the target symbols per timestep of the acoustic model. Larger values will allow longer sentences to be decoded, at increased cost to execution time. alsd_max_target_len: optional int or float, determines the potential maximum target sequence length. If an integer is provided, it can decode sequences of that particular maximum length. If a float is provided, it can decode sequences of int(alsd_max_target_len * seq_len), where seq_len is the length of the acoustic model output (T). NOTE: If a float is provided, it can be greater than 1! By default, a float of 2.0 is used so that a target sequence can be at most twice as long as the acoustic model output length T. decoder: The Decoder/Prediction network module. joint: The Joint network module. blank_id: The id of the RNNT blank token. """ def __init__(self, decoding_cfg, decoder, joint, blank_id: int): super(AbstractRNNTDecoding, self).__init__() self.cfg = decoding_cfg self.blank_id = blank_id self.compute_hypothesis_token_set = self.cfg.get("compute_hypothesis_token_set", False) possible_strategies = ['greedy', 'greedy_batch', 'beam', 'tsd', 'alsd'] if self.cfg.strategy not in possible_strategies: raise ValueError(f"Decoding strategy must be one of {possible_strategies}") if self.cfg.strategy == 'greedy': self.decoding = greedy_decode.GreedyRNNTInfer( decoder_model=decoder, joint_model=joint, blank_index=self.blank_id, max_symbols_per_step=self.cfg.greedy.get('max_symbols', None), ) elif self.cfg.strategy == 'greedy_batch': self.decoding = greedy_decode.GreedyBatchedRNNTInfer( decoder_model=decoder, joint_model=joint, blank_index=self.blank_id, max_symbols_per_step=self.cfg.greedy.get('max_symbols', None), ) elif self.cfg.strategy == 'beam': self.decoding = beam_decode.BeamRNNTInfer( decoder_model=decoder, joint_model=joint, beam_size=self.cfg.beam.beam_size, return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), search_type='default', score_norm=self.cfg.beam.get('score_norm', True), ) elif self.cfg.strategy == 'tsd': self.decoding = beam_decode.BeamRNNTInfer( decoder_model=decoder, joint_model=joint, beam_size=self.cfg.beam.beam_size, return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), search_type='tsd', score_norm=self.cfg.beam.get('score_norm', True), tsd_max_sym_exp_per_step=self.cfg.beam.get('tsd_max_sym_exp', 50), ) elif self.cfg.strategy == 'alsd': self.decoding = beam_decode.BeamRNNTInfer( decoder_model=decoder, joint_model=joint, beam_size=self.cfg.beam.beam_size, return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), search_type='alsd', score_norm=self.cfg.beam.get('score_norm', True), alsd_max_target_len=self.cfg.beam.get('alsd_max_target_len', 2), ) def rnnt_decoder_predictions_tensor( self, encoder_output: torch.Tensor, encoded_lengths: torch.Tensor, return_hypotheses: bool = False ) -> (List[str], Optional[List[List[str]]], Optional[Union[Hypothesis, NBestHypotheses]]): """ Decode an encoder output by autoregressive decoding of the Decoder+Joint networks. Args: encoder_output: torch.Tensor of shape [B, D, T]. encoded_lengths: torch.Tensor containing lengths of the padded encoder outputs. Shape [B]. return_hypotheses: bool. If set to True it will return list of Hypothesis or NBestHypotheses Returns: If `return_best_hypothesis` is set: A tuple (hypotheses, None): hypotheses - list of Hypothesis (best hypothesis per sample). Look at rnnt_utils.Hypothesis for more information. If `return_best_hypothesis` is not set: A tuple(hypotheses, all_hypotheses) hypotheses - list of Hypothesis (best hypothesis per sample). Look at rnnt_utils.Hypothesis for more information. all_hypotheses - list of NBestHypotheses. Each NBestHypotheses further contains a sorted list of all the hypotheses of the model per sample. Look at rnnt_utils.NBestHypotheses for more information. """ # Compute hypotheses with torch.no_grad(): hypotheses_list = self.decoding( encoder_output=encoder_output, encoded_lengths=encoded_lengths ) # type: [List[Hypothesis]] # extract the hypotheses hypotheses_list = hypotheses_list[0] # type: List[Hypothesis] prediction_list = hypotheses_list if isinstance(prediction_list[0], NBestHypotheses): hypotheses = [] all_hypotheses = [] for nbest_hyp in prediction_list: # type: NBestHypotheses n_hyps = nbest_hyp.n_best_hypotheses # Extract all hypotheses for this sample decoded_hyps = self.decode_hypothesis(n_hyps) # type: List[str] hypotheses.append(decoded_hyps[0]) # best hypothesis all_hypotheses.append(decoded_hyps) if return_hypotheses: return hypotheses, all_hypotheses best_hyp_text = [h.text for h in hypotheses] all_hyp_text = [h.text for hh in all_hypotheses for h in hh] return best_hyp_text, all_hyp_text else: hypotheses = self.decode_hypothesis(prediction_list) # type: List[str] if return_hypotheses: return hypotheses, None best_hyp_text = [h.text for h in hypotheses] return best_hyp_text, None def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hypothesis, NBestHypotheses]]: """ Decode a list of hypotheses into a list of strings. Args: hypotheses_list: List of Hypothesis. Returns: A list of strings. """ for ind in range(len(hypotheses_list)): # Extract the integer encoded hypothesis prediction = hypotheses_list[ind].y_sequence if type(prediction) != list: prediction = prediction.tolist() # RNN-T sample level is already preprocessed by implicit CTC decoding # Simply remove any blank tokens prediction = [p for p in prediction if p != self.blank_id] # De-tokenize the integer tokens hypothesis = self.decode_tokens_to_str(prediction) hypotheses_list[ind].text = hypothesis if self.compute_hypothesis_token_set: hypotheses_list[ind].tokens = self.decode_ids_to_tokens(prediction) return hypotheses_list @abstractmethod def decode_tokens_to_str(self, tokens: List[int]) -> str: """ Implemented by subclass in order to decoder a token id list into a string. Args: tokens: List of int representing the token ids. Returns: A decoded string. """ raise NotImplementedError() @abstractmethod def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: """ Implemented by subclass in order to decode a token id list into a token list. A token list is the string representation of each token id. Args: tokens: List of int representing the token ids. Returns: A list of decoded tokens. """ raise NotImplementedError() class RNNTDecoding(AbstractRNNTDecoding): """ Used for performing RNN-T auto-regressive decoding of the Decoder+Joint network given the encoder state. Args: decoding_cfg: A dict-like object which contains the following key-value pairs. strategy: str value which represents the type of decoding that can occur. Possible values are : - greedy, greedy_batch (for greedy decoding). - beam, tsd, alsd (for beam search decoding). compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded tokens as well as the decoded string. Default is False in order to avoid double decoding unless required. The config may further contain the following sub-dictionaries: "greedy": max_symbols: int, describing the maximum number of target tokens to decode per timestep during greedy decoding. Setting to larger values allows longer sentences to be decoded, at the cost of increased execution time. "beam": beam_size: int, defining the beam size for beam search. Must be >= 1. If beam_size == 1, will perform cached greedy search. This might be slightly different results compared to the greedy search above. score_norm: optional bool, whether to normalize the returned beam score in the hypotheses. Set to True by default. return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the hypotheses after beam search has concluded. This flag is set by default. tsd_max_sym_exp: optional int, determines number of symmetric expansions of the target symbols per timestep of the acoustic model. Larger values will allow longer sentences to be decoded, at increased cost to execution time. alsd_max_target_len: optional int or float, determines the potential maximum target sequence length. If an integer is provided, it can decode sequences of that particular maximum length. If a float is provided, it can decode sequences of int(alsd_max_target_len * seq_len), where seq_len is the length of the acoustic model output (T). NOTE: If a float is provided, it can be greater than 1! By default, a float of 2.0 is used so that a target sequence can be at most twice as long as the acoustic model output length T. decoder: The Decoder/Prediction network module. joint: The Joint network module. vocabulary: The vocabulary (excluding the RNNT blank token) which will be used for decoding. """ def __init__( self, decoding_cfg, decoder, joint, vocabulary, ): blank_id = len(vocabulary) self.labels_map = dict([(i, vocabulary[i]) for i in range(len(vocabulary))]) super(RNNTDecoding, self).__init__(decoding_cfg=decoding_cfg, decoder=decoder, joint=joint, blank_id=blank_id) def decode_tokens_to_str(self, tokens: List[int]) -> str: """ Implemented by subclass in order to decoder a token list into a string. Args: tokens: List of int representing the token ids. Returns: A decoded string. """ hypothesis = ''.join([self.labels_map[c] for c in tokens if c != self.blank_id]) return hypothesis def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: """ Implemented by subclass in order to decode a token id list into a token list. A token list is the string representation of each token id. Args: tokens: List of int representing the token ids. Returns: A list of decoded tokens. """ token_list = [self.labels_map[c] for c in tokens if c != self.blank_id] return token_list class RNNTWER(Metric): """ This metric computes numerator and denominator for Overall Word Error Rate (WER) between prediction and reference texts. When doing distributed training/evaluation the result of res=WER(predictions, targets, target_lengths) calls will be all-reduced between all workers using SUM operations. Here contains two numbers res=[wer_numerator, wer_denominator]. WER=wer_numerator/wer_denominator. If used with PytorchLightning LightningModule, include wer_numerator and wer_denominators inside validation_step results. Then aggregate (sum) then at the end of validation epoch to correctly compute validation WER. Example: def validation_step(self, batch, batch_idx): ... wer_num, wer_denom = self.__wer(predictions, transcript, transcript_len) return {'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom} def validation_epoch_end(self, outputs): ... wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() tensorboard_logs = {'validation_loss': val_loss_mean, 'validation_avg_wer': wer_num / wer_denom} return {'val_loss': val_loss_mean, 'log': tensorboard_logs} Args: decoding: RNNTDecoding object that will perform autoregressive decoding of the RNNT model. batch_dim_index: Index of the batch dimension. use_cer: Whether to use Character Error Rate isntead of Word Error Rate. log_prediction: Whether to log a single decoded sample per call. Returns: res: a torch.Tensor object with two elements: [wer_numerator, wer_denominator]. To correctly compute average text word error rate, compute wer=wer_numerator/wer_denominator """ def __init__( self, decoding: RNNTDecoding, batch_dim_index=0, use_cer=False, log_prediction=True, dist_sync_on_step=False ): super(RNNTWER, self).__init__(dist_sync_on_step=dist_sync_on_step, compute_on_step=False) self.decoding = decoding self.batch_dim_index = batch_dim_index self.use_cer = use_cer self.log_prediction = log_prediction self.blank_id = self.decoding.blank_id self.labels_map = self.decoding.labels_map self.add_state("scores", default=torch.tensor(0), dist_reduce_fx='sum', persistent=False) self.add_state("words", default=torch.tensor(0), dist_reduce_fx='sum', persistent=False) def update( self, encoder_output: torch.Tensor, encoded_lengths: torch.Tensor, targets: torch.Tensor, target_lengths: torch.Tensor, ) -> torch.Tensor: words = 0.0 scores = 0.0 references = [] with torch.no_grad(): # prediction_cpu_tensor = tensors[0].long().cpu() targets_cpu_tensor = targets.long().cpu() tgt_lenths_cpu_tensor = target_lengths.long().cpu() # iterate over batch for ind in range(targets_cpu_tensor.shape[self.batch_dim_index]): tgt_len = tgt_lenths_cpu_tensor[ind].item() target = targets_cpu_tensor[ind][:tgt_len].numpy().tolist() reference = self.decoding.decode_tokens_to_str(target) references.append(reference) hypotheses, _ = self.decoding.rnnt_decoder_predictions_tensor(encoder_output, encoded_lengths) if self.log_prediction: logging.info(f"\n") logging.info(f"reference :{references[0]}") logging.info(f"predicted :{hypotheses[0]}") for h, r in zip(hypotheses, references): if self.use_cer: h_list = list(h) r_list = list(r) else: h_list = h.split() r_list = r.split() words += len(r_list) # Compute Levenshtein's distance scores += editdistance.eval(h_list, r_list) self.scores += torch.tensor(scores, device=self.scores.device, dtype=self.scores.dtype) self.words += torch.tensor(words, device=self.words.device, dtype=self.words.dtype) # return torch.tensor([scores, words]).to(predictions.device) def compute(self): wer = self.scores.float() / self.words return wer, self.scores.detach(), self.words.detach()

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from abc import ABC, abstractmethod from typing import List, Optional, Union import editdistance import torch from pytorch_lightning.metrics import Metric from nemo.collections.asr.parts import rnnt_beam_decoding as beam_decode from nemo.collections.asr.parts import rnnt_greedy_decoding as greedy_decode from nemo.collections.asr.parts.rnnt_utils import Hypothesis, NBestHypotheses from nemo.utils import logging __all__ = ['RNNTDecoding', 'RNNTWER'] class AbstractRNNTDecoding(ABC): def __init__(self, decoding_cfg, decoder, joint, blank_id: int): super(AbstractRNNTDecoding, self).__init__() self.cfg = decoding_cfg self.blank_id = blank_id self.compute_hypothesis_token_set = self.cfg.get("compute_hypothesis_token_set", False) possible_strategies = ['greedy', 'greedy_batch', 'beam', 'tsd', 'alsd'] if self.cfg.strategy not in possible_strategies: raise ValueError(f"Decoding strategy must be one of {possible_strategies}") if self.cfg.strategy == 'greedy': self.decoding = greedy_decode.GreedyRNNTInfer( decoder_model=decoder, joint_model=joint, blank_index=self.blank_id, max_symbols_per_step=self.cfg.greedy.get('max_symbols', None), ) elif self.cfg.strategy == 'greedy_batch': self.decoding = greedy_decode.GreedyBatchedRNNTInfer( decoder_model=decoder, joint_model=joint, blank_index=self.blank_id, max_symbols_per_step=self.cfg.greedy.get('max_symbols', None), ) elif self.cfg.strategy == 'beam': self.decoding = beam_decode.BeamRNNTInfer( decoder_model=decoder, joint_model=joint, beam_size=self.cfg.beam.beam_size, return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), search_type='default', score_norm=self.cfg.beam.get('score_norm', True), ) elif self.cfg.strategy == 'tsd': self.decoding = beam_decode.BeamRNNTInfer( decoder_model=decoder, joint_model=joint, beam_size=self.cfg.beam.beam_size, return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), search_type='tsd', score_norm=self.cfg.beam.get('score_norm', True), tsd_max_sym_exp_per_step=self.cfg.beam.get('tsd_max_sym_exp', 50), ) elif self.cfg.strategy == 'alsd': self.decoding = beam_decode.BeamRNNTInfer( decoder_model=decoder, joint_model=joint, beam_size=self.cfg.beam.beam_size, return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), search_type='alsd', score_norm=self.cfg.beam.get('score_norm', True), alsd_max_target_len=self.cfg.beam.get('alsd_max_target_len', 2), ) def rnnt_decoder_predictions_tensor( self, encoder_output: torch.Tensor, encoded_lengths: torch.Tensor, return_hypotheses: bool = False ) -> (List[str], Optional[List[List[str]]], Optional[Union[Hypothesis, NBestHypotheses]]): with torch.no_grad(): hypotheses_list = self.decoding( encoder_output=encoder_output, encoded_lengths=encoded_lengths ) hypotheses_list = hypotheses_list[0] prediction_list = hypotheses_list if isinstance(prediction_list[0], NBestHypotheses): hypotheses = [] all_hypotheses = [] for nbest_hyp in prediction_list: n_hyps = nbest_hyp.n_best_hypotheses decoded_hyps = self.decode_hypothesis(n_hyps) hypotheses.append(decoded_hyps[0]) all_hypotheses.append(decoded_hyps) if return_hypotheses: return hypotheses, all_hypotheses best_hyp_text = [h.text for h in hypotheses] all_hyp_text = [h.text for hh in all_hypotheses for h in hh] return best_hyp_text, all_hyp_text else: hypotheses = self.decode_hypothesis(prediction_list) if return_hypotheses: return hypotheses, None best_hyp_text = [h.text for h in hypotheses] return best_hyp_text, None def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hypothesis, NBestHypotheses]]: for ind in range(len(hypotheses_list)): prediction = hypotheses_list[ind].y_sequence if type(prediction) != list: prediction = prediction.tolist() prediction = [p for p in prediction if p != self.blank_id] hypothesis = self.decode_tokens_to_str(prediction) hypotheses_list[ind].text = hypothesis if self.compute_hypothesis_token_set: hypotheses_list[ind].tokens = self.decode_ids_to_tokens(prediction) return hypotheses_list @abstractmethod def decode_tokens_to_str(self, tokens: List[int]) -> str: raise NotImplementedError() @abstractmethod def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: raise NotImplementedError() class RNNTDecoding(AbstractRNNTDecoding): def __init__( self, decoding_cfg, decoder, joint, vocabulary, ): blank_id = len(vocabulary) self.labels_map = dict([(i, vocabulary[i]) for i in range(len(vocabulary))]) super(RNNTDecoding, self).__init__(decoding_cfg=decoding_cfg, decoder=decoder, joint=joint, blank_id=blank_id) def decode_tokens_to_str(self, tokens: List[int]) -> str: hypothesis = ''.join([self.labels_map[c] for c in tokens if c != self.blank_id]) return hypothesis def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: token_list = [self.labels_map[c] for c in tokens if c != self.blank_id] return token_list class RNNTWER(Metric): def __init__( self, decoding: RNNTDecoding, batch_dim_index=0, use_cer=False, log_prediction=True, dist_sync_on_step=False ): super(RNNTWER, self).__init__(dist_sync_on_step=dist_sync_on_step, compute_on_step=False) self.decoding = decoding self.batch_dim_index = batch_dim_index self.use_cer = use_cer self.log_prediction = log_prediction self.blank_id = self.decoding.blank_id self.labels_map = self.decoding.labels_map self.add_state("scores", default=torch.tensor(0), dist_reduce_fx='sum', persistent=False) self.add_state("words", default=torch.tensor(0), dist_reduce_fx='sum', persistent=False) def update( self, encoder_output: torch.Tensor, encoded_lengths: torch.Tensor, targets: torch.Tensor, target_lengths: torch.Tensor, ) -> torch.Tensor: words = 0.0 scores = 0.0 references = [] with torch.no_grad(): targets_cpu_tensor = targets.long().cpu() tgt_lenths_cpu_tensor = target_lengths.long().cpu() for ind in range(targets_cpu_tensor.shape[self.batch_dim_index]): tgt_len = tgt_lenths_cpu_tensor[ind].item() target = targets_cpu_tensor[ind][:tgt_len].numpy().tolist() reference = self.decoding.decode_tokens_to_str(target) references.append(reference) hypotheses, _ = self.decoding.rnnt_decoder_predictions_tensor(encoder_output, encoded_lengths) if self.log_prediction: logging.info(f"\n") logging.info(f"reference :{references[0]}") logging.info(f"predicted :{hypotheses[0]}") for h, r in zip(hypotheses, references): if self.use_cer: h_list = list(h) r_list = list(r) else: h_list = h.split() r_list = r.split() words += len(r_list) scores += editdistance.eval(h_list, r_list) self.scores += torch.tensor(scores, device=self.scores.device, dtype=self.scores.dtype) self.words += torch.tensor(words, device=self.words.device, dtype=self.words.dtype) # return torch.tensor([scores, words]).to(predictions.device) def compute(self): wer = self.scores.float() / self.words return wer, self.scores.detach(), self.words.detach()

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# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. import string from telethon import events from telethon.utils import add_surrogate from telethon.tl.types import MessageEntityPre from telethon.tl.tlobject import TLObject import datetime PRINTABLE_SET = set(bytes(string.printable, 'ascii')) STR_LEN_MAX = 256 BYTE_LEN_MAX = 64 def parse_pre(text): text = text.strip() return ( text, [MessageEntityPre(offset=0, length=len(add_surrogate(text)), language='potato')] ) def yaml_format(obj, indent=0): """ Pretty formats the given object as a YAML string which is returned. (based on TLObject.pretty_format) """ result = [] if isinstance(obj, TLObject): obj = obj.to_dict() if isinstance(obj, dict): result.append(obj.get('_', 'dict') + ':') if obj: items = obj.items() has_multiple_items = len(items) > 2 if has_multiple_items: result.append('\n') indent += 2 for k, v in items: if k == '_' or v is None: continue formatted = yaml_format(v, indent) if not formatted.strip(): continue result.append(' ' * (indent if has_multiple_items else 1)) result.append(f'{k}: {formatted}') result.append('\n') result.pop() indent -= 2 result.append(' ' * indent) elif isinstance(obj, str): # truncate long strings and display elipsis result.append(repr(obj[:STR_LEN_MAX])) if len(obj) > STR_LEN_MAX: result.append('…') elif isinstance(obj, bytes): # repr() bytes if it's printable, hex like "FF EE BB" otherwise if all(c in PRINTABLE_SET for c in obj): result.append(repr(obj)) else: if len(obj) > BYTE_LEN_MAX: result.append('<…>') else: result.append(' '.join(f'{b:02X}' for b in obj)) elif isinstance(obj, datetime.datetime): # ISO-8601 without timezone offset (telethon dates are always UTC) result.append(obj.strftime('%Y-%m-%d %H:%M:%S')) elif hasattr(obj, '__iter__'): # display iterables one after another at the base indentation level result.append('\n') indent += 2 for x in obj: result.append(' ' * indent) result.append(yaml_format(x, indent)) result.append('\n') result.pop() indent -= 2 result.append(' ' * indent) else: result.append(repr(obj)) return ''.join(result) @borg.on(events.NewMessage(pattern=r"\.new", outgoing=True)) async def _(event): if not event.message.is_reply: return msg = await event.message.get_reply_message() yaml_text = yaml_format(msg) await event.edit( yaml_text, parse_mode=parse_pre )

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import string from telethon import events from telethon.utils import add_surrogate from telethon.tl.types import MessageEntityPre from telethon.tl.tlobject import TLObject import datetime PRINTABLE_SET = set(bytes(string.printable, 'ascii')) STR_LEN_MAX = 256 BYTE_LEN_MAX = 64 def parse_pre(text): text = text.strip() return ( text, [MessageEntityPre(offset=0, length=len(add_surrogate(text)), language='potato')] ) def yaml_format(obj, indent=0): result = [] if isinstance(obj, TLObject): obj = obj.to_dict() if isinstance(obj, dict): result.append(obj.get('_', 'dict') + ':') if obj: items = obj.items() has_multiple_items = len(items) > 2 if has_multiple_items: result.append('\n') indent += 2 for k, v in items: if k == '_' or v is None: continue formatted = yaml_format(v, indent) if not formatted.strip(): continue result.append(' ' * (indent if has_multiple_items else 1)) result.append(f'{k}: {formatted}') result.append('\n') result.pop() indent -= 2 result.append(' ' * indent) elif isinstance(obj, str): result.append(repr(obj[:STR_LEN_MAX])) if len(obj) > STR_LEN_MAX: result.append('…') elif isinstance(obj, bytes): if all(c in PRINTABLE_SET for c in obj): result.append(repr(obj)) else: if len(obj) > BYTE_LEN_MAX: result.append('<…>') else: result.append(' '.join(f'{b:02X}' for b in obj)) elif isinstance(obj, datetime.datetime): # ISO-8601 without timezone offset (telethon dates are always UTC) result.append(obj.strftime('%Y-%m-%d %H:%M:%S')) elif hasattr(obj, '__iter__'): # display iterables one after another at the base indentation level result.append('\n') indent += 2 for x in obj: result.append(' ' * indent) result.append(yaml_format(x, indent)) result.append('\n') result.pop() indent -= 2 result.append(' ' * indent) else: result.append(repr(obj)) return ''.join(result) @borg.on(events.NewMessage(pattern=r"\.new", outgoing=True)) async def _(event): if not event.message.is_reply: return msg = await event.message.get_reply_message() yaml_text = yaml_format(msg) await event.edit( yaml_text, parse_mode=parse_pre )

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47 Setters_Property Decorators/main1.py

codewithsandy/Python-Basic-Exp

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47 Setters_Property Decorators/main1.py

codewithsandy/Python-Basic-Exp

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47 Setters_Property Decorators/main1.py

codewithsandy/Python-Basic-Exp

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null

class Employee: def __init__(self, fname, lname): self.fname = fname self.lname = lname # self.email = f"{fname}.{lname}@sandy.com" def explain(self): return f"This employee is {self.fname} {self.lname}" def email(self): return f"{self.fname}.{self.lname} @parker.com" obj1 = Employee("Peter", "Parkar") print(obj1.email()) obj1.fname = "Spider" print(obj1.email()) #required call email() function to print

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class Employee: def __init__(self, fname, lname): self.fname = fname self.lname = lname def explain(self): return f"This employee is {self.fname} {self.lname}" def email(self): return f"{self.fname}.{self.lname} @parker.com" obj1 = Employee("Peter", "Parkar") print(obj1.email()) obj1.fname = "Spider" print(obj1.email())

true

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py

Python

h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py

ahmedengu/h2o-3

ac2c0a6fbe7f8e18078278bf8a7d3483d41aca11

[ "Apache-2.0" ]

2

2018-09-20T03:28:46.000Z

2018-12-06T21:39:29.000Z

h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py

ahmedengu/h2o-3

ac2c0a6fbe7f8e18078278bf8a7d3483d41aca11

[ "Apache-2.0" ]

2

2021-06-02T02:24:03.000Z

2021-11-15T17:51:49.000Z

h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py

ahmedengu/h2o-3

ac2c0a6fbe7f8e18078278bf8a7d3483d41aca11

[ "Apache-2.0" ]

1

2020-04-17T13:06:26.000Z

from __future__ import print_function import sys import random import os from builtins import range import time import json sys.path.insert(1, "../../../") import h2o from tests import pyunit_utils from h2o.estimators.glm import H2OGeneralizedLinearEstimator from h2o.grid.grid_search import H2OGridSearch class Test_glm_random_grid_search: """ This class is created to test the three stopping conditions for randomized gridsearch using GLM Binomial family. The three stopping conditions are : 1. max_runtime_secs: 2. max_models: 3. metrics. We will be picking 2 stopping metrics to test this stopping condition with. One metric will be optimized if it increases and the other one should be optimized if it decreases. I have written 4 tests: 1. test1_glm_random_grid_search_model_number: this test will not put any stopping conditions on randomized search. The purpose here is to make sure that randomized search will give us all possible hyper-parameter combinations. 2. test2_glm_random_grid_search_max_model: this test the stopping condition of setting the max_model in search criteria; 3. test3_glm_random_grid_search_max_runtime_secs: this test the stopping condition max_runtime_secs in search criteria; 4. test4_glm_random_grid_search_metric: this test the stopping condition of using a metric which can be increasing or decreasing. """ # parameters set by users, change with care curr_time = str(round(time.time())) # parameters denoting filenames of interested that store training/validation/test data sets in csv format training1_filename = "smalldata/gridsearch/gaussian_training1_set.csv" json_filename = "random_gridsearch_GLM_Gaussian_hyper_parameter_" + curr_time + ".json" allowed_diff = 0.5 # error tolerance allowed allowed_time_diff = 1e-1 # fraction of max_runtime_secs allowed for max run time stopping criteria # System parameters, do not change. Dire consequences may follow if you do current_dir = os.path.dirname(os.path.realpath(sys.argv[1])) # directory of this test file train_row_count = 0 # training data row count, randomly generated later train_col_count = 0 # training data column count, randomly generated later max_int_val = 1000 # maximum size of random integer values min_int_val = 0 # minimum size of random integer values max_int_number = 3 # maximum number of integer random grid values to generate max_real_val = 1 # maximum size of random float values min_real_val = 0.0 # minimum size of random float values max_real_number = 3 # maximum number of real grid values to generate lambda_scale = 100 # scale lambda value to be from 0 to 100 instead of 0 to 1 max_runtime_scale = 3 # scale the max runtime to be different from 0 to 1 one_model_time = 0 # time taken to build one barebone model possible_number_models = 0 # possible number of models built based on hyper-parameter specification max_model_number = 0 # maximum number of models specified to test for stopping conditions, generated later max_grid_runtime = 1 # maximum runtime value in seconds, 1 minute max allowed_scaled_overtime = 1 # used to set max_allowed_runtime as allowed_scaled_overtime * total model run time allowed_scaled_time = 1 # how much to scale back max time allowed_scaled_model_number = 1.5 # used to set max_model_number as # possible_number_models * allowed_scaled_model_number max_stopping_rounds = 5 # maximum stopping rounds allowed to be used for early stopping metric max_tolerance = 0.01 # maximum tolerance to be used for early stopping metric family = 'gaussian' # set gaussian as default test_name = "pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py" # name of this test sandbox_dir = "" # sandbox directory where we are going to save our failed test data sets # store information about training/test data sets x_indices = [] # store predictor indices in the data set y_index = 0 # store response index in the data set training1_data = [] # store training data sets total_test_number = 5 # number of tests carried out test_failed = 0 # count total number of tests that have failed test_failed_array = [0]*total_test_number # denote test results for all tests run. 1 error, 0 pass test_num = 0 # index representing which test is being run # give the user opportunity to pre-assign hyper parameters for fixed values hyper_params = {} # parameters to be excluded from hyper parameter list even though they may be gridable exclude_parameter_lists = ['tweedie_link_power', 'tweedie_variance_power'] # do not need these # these are supposed to be gridable but not really exclude_parameter_lists.extend(['fold_column', 'weights_column', 'offset_column']) # these are excluded for extracting parameters to manually build H2O GLM models exclude_parameter_lists.extend(['model_id']) gridable_parameters = [] # store griddable parameter names gridable_types = [] # store the corresponding griddable parameter types gridable_defaults = [] # store the gridabble parameter default values correct_model_number = 0 # count number of models built with correct hyper-parameter specification nfolds = 5 # enable cross validation to test fold_assignment def __init__(self, family): """ Constructor. :param family: distribution family for tests :return: None """ self.setup_data() # setup_data training data self.setup_grid_params() # setup_data grid hyper-parameters def setup_data(self): """ This function performs all initializations necessary: load the data sets and set the training set indices and response column index """ # clean out the sandbox directory first self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True) # preload data sets self.training1_data = h2o.import_file(path=pyunit_utils.locate(self.training1_filename)) # set data set indices for predictors and response self.y_index = self.training1_data.ncol-1 self.x_indices = list(range(self.y_index)) # save the training data files just in case the code crashed. pyunit_utils.remove_csv_files(self.current_dir, ".csv", action='copy', new_dir_path=self.sandbox_dir) def setup_grid_params(self): """ This function setup the randomized gridsearch parameters that will be used later on: 1. It will first try to grab all the parameters that are griddable and parameters used by GLM. 2. It will find the intersection of parameters that are both griddable and used by GLM. 3. There are several extra parameters that are used by GLM that are denoted as griddable but actually is not. These parameters have to be discovered manually and they These are captured in self.exclude_parameter_lists. 4. We generate the gridsearch hyper-parameter. For numerical parameters, we will generate those randomly. For enums, we will include all of them. :return: None """ # build bare bone model to get all parameters model = H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds) model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) self.one_model_time = pyunit_utils.find_grid_runtime([model]) # find model train time print("Time taken to build a base barebone model is {0}".format(self.one_model_time)) # grab all gridable parameters and its type (self.gridable_parameters, self.gridable_types, self.gridable_defaults) = \ pyunit_utils.get_gridables(model._model_json["parameters"]) # give the user opportunity to pre-assign hyper parameters for fixed values self.hyper_params = {} self.hyper_params["fold_assignment"] = ['AUTO', 'Random', 'Modulo'] self.hyper_params["missing_values_handling"] = ['MeanImputation', 'Skip'] # randomly generate griddable parameters (self.hyper_params, self.gridable_parameters, self.gridable_types, self.gridable_defaults) = \ pyunit_utils.gen_grid_search(model.full_parameters.keys(), self.hyper_params, self.exclude_parameter_lists, self.gridable_parameters, self.gridable_types, self.gridable_defaults, random.randint(1, self.max_int_number), self.max_int_val, self.min_int_val, random.randint(1, self.max_real_number), self.max_real_val, self.min_real_val) # change the value of lambda parameters to be from 0 to self.lambda_scale instead of 0 to 1. if "lambda" in list(self.hyper_params): self.hyper_params["lambda"] = [self.lambda_scale * x for x in self.hyper_params["lambda"]] time_scale = self.max_runtime_scale * self.one_model_time # change the value of runtime parameters to be from 0 to self.lambda_scale instead of 0 to 1. if "max_runtime_secs" in list(self.hyper_params): self.hyper_params["max_runtime_secs"] = [time_scale * x for x in self.hyper_params["max_runtime_secs"]] # number of possible models being built: self.possible_number_models = pyunit_utils.count_models(self.hyper_params) # save hyper-parameters in sandbox and current test directories. pyunit_utils.write_hyper_parameters_json(self.current_dir, self.sandbox_dir, self.json_filename, self.hyper_params) def tear_down(self): """ This function performs teardown after the dynamic test is completed. If all tests passed, it will delete all data sets generated since they can be quite large. It will move the training/validation/test data sets into a Rsandbox directory so that we can re-run the failed test. """ if self.test_failed: # some tests have failed. Need to save data sets for later re-runs # create Rsandbox directory to keep data sets and weight information self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True) # Do not want to save all data sets. Only save data sets that are needed for failed tests pyunit_utils.move_files(self.sandbox_dir, self.training1_data_file, self.training1_filename) # write out the jenkins job info into log files. json_file = os.path.join(self.sandbox_dir, self.json_filename) with open(json_file,'wb') as test_file: json.dump(self.hyper_params, test_file) else: # all tests have passed. Delete sandbox if if was not wiped before pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, False) def test1_glm_random_grid_search_model_number(self, metric_name): """ This test is used to make sure the randomized gridsearch will generate all models specified in the hyperparameters if no stopping condition is given in the search criterion. :param metric_name: string to denote what grid search model should be sort by :return: None """ print("*******************************************************************************************") print("test1_glm_random_grid_search_model_number for GLM " + self.family) h2o.cluster_info() # setup_data our stopping condition here, random discrete and find all models search_criteria = {'strategy': 'RandomDiscrete', "stopping_rounds": 0, "seed": round(time.time())} print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) # fire off random grid-search random_grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) random_grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) # compare number of models built from both gridsearch if not (len(random_grid_model) == self.possible_number_models): self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test1_glm_random_grid_search_model_number for GLM: failed, number of models generated" "possible model number {0} and randomized gridsearch model number {1} are not " "equal.".format(self.possible_number_models, len(random_grid_model))) else: self.max_grid_runtime = pyunit_utils.find_grid_runtime(random_grid_model) # time taken to build all models if self.test_failed_array[self.test_num] == 0: print("test1_glm_random_grid_search_model_number for GLM: passed!") self.test_num += 1 sys.stdout.flush() def test2_glm_random_grid_search_max_model(self): """ This test is used to test the stopping condition max_model_number in the randomized gridsearch. The max_models parameter is randomly generated. If it is higher than the actual possible number of models that can be generated with the current hyper-space parameters, randomized grid search should generate all the models. Otherwise, grid search shall return a model that equals to the max_model setting. """ print("*******************************************************************************************") print("test2_glm_random_grid_search_max_model for GLM " + self.family) h2o.cluster_info() # setup_data our stopping condition here self.max_model_number = random.randint(1, int(self.allowed_scaled_model_number * self.possible_number_models)) search_criteria = {'strategy': 'RandomDiscrete', 'max_models': self.max_model_number, "seed": round(time.time())} print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) print("Possible number of models built is {0}".format(self.possible_number_models)) # fire off random grid-search grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) number_model_built = len(grid_model) # count actual number of models built print("Maximum model limit is {0}. Number of models built is {1}".format(search_criteria["max_models"], number_model_built)) if self.possible_number_models >= self.max_model_number: # stopping condition restricts model number if not (number_model_built == self.max_model_number): print("test2_glm_random_grid_search_max_model: failed. Number of model built {0} " "does not match stopping condition number{1}.".format(number_model_built, self.max_model_number)) self.test_failed += 1 self.test_failed_array[self.test_num] = 1 else: print("test2_glm_random_grid_search_max_model for GLM: passed.") else: # stopping condition is too loose if not (number_model_built == self.possible_number_models): self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test2_glm_random_grid_search_max_model: failed. Number of model built {0} does not equal " "to possible model number {1}.".format(number_model_built, self.possible_number_models)) else: print("test2_glm_random_grid_search_max_model for GLM: passed.") self.test_num += 1 sys.stdout.flush() def test3_glm_random_grid_search_max_runtime_secs(self): """ This function will test the stopping criteria max_runtime_secs. For each model built, the field run_time actually denote the time in ms used to build the model. We will add up the run_time from all models and check against the stopping criteria max_runtime_secs. Since each model will check its run time differently, there is some inaccuracies in the actual run time. For example, if we give a model 10 ms to build. The GLM may check and see if it has used up all the time for every 10 epochs that it has run. On the other hand, deeplearning may check the time it has spent after every epoch of training. If we are able to restrict the runtime to not exceed the specified max_runtime_secs by a certain percentage, we will consider the test a success. :return: None """ print("*******************************************************************************************") print("test3_glm_random_grid_search_max_runtime_secs for GLM " + self.family) h2o.cluster_info() if "max_runtime_secs" in list(self.hyper_params): del self.hyper_params['max_runtime_secs'] # number of possible models being built: self.possible_number_models = pyunit_utils.count_models(self.hyper_params) # setup_data our stopping condition here max_run_time_secs = random.uniform(self.one_model_time, self.allowed_scaled_time*self.max_grid_runtime) search_criteria = {'strategy': 'RandomDiscrete', 'max_runtime_secs': max_run_time_secs, "seed": round(time.time())} # search_criteria = {'strategy': 'RandomDiscrete', 'max_runtime_secs': 1/1e8} print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) # fire off random grid-search grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) actual_run_time_secs = pyunit_utils.find_grid_runtime(grid_model) print("Maximum time limit is {0}. Time taken to build all model is " "{1}".format(search_criteria["max_runtime_secs"], actual_run_time_secs)) print("Maximum model number is {0}. Actual number of models built is {1}".format(self.possible_number_models, len(grid_model))) if actual_run_time_secs <= search_criteria["max_runtime_secs"]*(1+self.allowed_diff): print("test3_glm_random_grid_search_max_runtime_secs: passed!") if len(grid_model) > self.possible_number_models: # generate too many models, something is wrong self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test3_glm_random_grid_search_max_runtime_secs: failed. Generated {0} models " " which exceeds maximum possible model number {1}".format(len(grid_model), self.possible_number_models)) elif len(grid_model) == 1: # will always generate 1 model print("test3_glm_random_grid_search_max_runtime_secs: passed!") else: self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test3_glm_random_grid_search_max_runtime_secs: failed. Model takes time {0}" " seconds which exceeds allowed time {1}".format(actual_run_time_secs, max_run_time_secs*(1+self.allowed_diff))) self.test_num += 1 sys.stdout.flush() def test4_glm_random_grid_search_metric(self, metric_name, bigger_is_better): """ This function will test the last stopping condition using metrics. :param metric_name: metric we want to use to test the last stopping condition :param bigger_is_better: higher metric value indicates better model performance :return: None """ print("*******************************************************************************************") print("test4_glm_random_grid_search_metric using " + metric_name + " for family " + self.family) h2o.cluster_info() search_criteria = { "strategy": "RandomDiscrete", "stopping_metric": metric_name, "stopping_tolerance": random.uniform(1e-8, self.max_tolerance), "stopping_rounds": random.randint(1, self.max_stopping_rounds), "seed": round(time.time()) } print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) # add max_runtime_secs back into hyper-parameters to limit model runtime. self.hyper_params["max_runtime_secs"] = [0.3] # arbitrarily set to 0.1 second # fire off random grid-search grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) # bool indicating if randomized grid search has calculated the early stopping condition correctly stopped_correctly = \ pyunit_utils.evaluate_metrics_stopping(grid_model.models, metric_name, bigger_is_better, search_criteria, self.possible_number_models) if stopped_correctly: print("test4_glm_random_grid_search_metric " + metric_name + ": passed. ") else: self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test4_glm_random_grid_search_metric " + metric_name + ": failed. ") self.test_num += 1 def test_random_grid_search_for_glm(): """ Create and instantiate classes, call test methods to test randomize grid search for GLM Gaussian or Binomial families. :return: None """ # randomize grid search for Gaussian test_glm_gaussian_random_grid = Test_glm_random_grid_search("gaussian") test_glm_gaussian_random_grid.test1_glm_random_grid_search_model_number("mse(xval=True)") # this test must be run. test_glm_gaussian_random_grid.test2_glm_random_grid_search_max_model() test_glm_gaussian_random_grid.test3_glm_random_grid_search_max_runtime_secs() test_glm_gaussian_random_grid.test4_glm_random_grid_search_metric("MSE", False) # test_glm_gaussian_random_grid.test4_glm_random_grid_search_metric("r2", True) # R2 was removed as a stopping metric # test_glm_gaussian_random_grid.tear_down() # obsolete # exit with error if any tests have failed if test_glm_gaussian_random_grid.test_failed > 0: sys.exit(1) else: pyunit_utils.remove_files(os.path.join(test_glm_gaussian_random_grid.current_dir, test_glm_gaussian_random_grid.json_filename)) if __name__ == "__main__": pyunit_utils.standalone_test(test_random_grid_search_for_glm) else: test_random_grid_search_for_glm()

53.324444

121

0.67257

from __future__ import print_function import sys import random import os from builtins import range import time import json sys.path.insert(1, "../../../") import h2o from tests import pyunit_utils from h2o.estimators.glm import H2OGeneralizedLinearEstimator from h2o.grid.grid_search import H2OGridSearch class Test_glm_random_grid_search: curr_time = str(round(time.time())) training1_filename = "smalldata/gridsearch/gaussian_training1_set.csv" json_filename = "random_gridsearch_GLM_Gaussian_hyper_parameter_" + curr_time + ".json" allowed_diff = 0.5 allowed_time_diff = 1e-1 current_dir = os.path.dirname(os.path.realpath(sys.argv[1])) train_row_count = 0 train_col_count = 0 max_int_val = 1000 min_int_val = 0 max_int_number = 3 max_real_val = 1 min_real_val = 0.0 max_real_number = 3 lambda_scale = 100 max_runtime_scale = 3 one_model_time = 0 possible_number_models = 0 max_model_number = 0 max_grid_runtime = 1 allowed_scaled_overtime = 1 allowed_scaled_time = 1 allowed_scaled_model_number = 1.5 max_stopping_rounds = 5 max_tolerance = 0.01 family = 'gaussian' test_name = "pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py" sandbox_dir = "" x_indices = [] y_index = 0 training1_data = [] total_test_number = 5 test_failed = 0 test_failed_array = [0]*total_test_number test_num = 0 hyper_params = {} exclude_parameter_lists = ['tweedie_link_power', 'tweedie_variance_power'] exclude_parameter_lists.extend(['fold_column', 'weights_column', 'offset_column']) exclude_parameter_lists.extend(['model_id']) gridable_parameters = [] gridable_types = [] gridable_defaults = [] correct_model_number = 0 nfolds = 5 def __init__(self, family): self.setup_data() self.setup_grid_params() def setup_data(self): self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True) self.training1_data = h2o.import_file(path=pyunit_utils.locate(self.training1_filename)) self.y_index = self.training1_data.ncol-1 self.x_indices = list(range(self.y_index)) pyunit_utils.remove_csv_files(self.current_dir, ".csv", action='copy', new_dir_path=self.sandbox_dir) def setup_grid_params(self): model = H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds) model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) self.one_model_time = pyunit_utils.find_grid_runtime([model]) print("Time taken to build a base barebone model is {0}".format(self.one_model_time)) (self.gridable_parameters, self.gridable_types, self.gridable_defaults) = \ pyunit_utils.get_gridables(model._model_json["parameters"]) self.hyper_params = {} self.hyper_params["fold_assignment"] = ['AUTO', 'Random', 'Modulo'] self.hyper_params["missing_values_handling"] = ['MeanImputation', 'Skip'] (self.hyper_params, self.gridable_parameters, self.gridable_types, self.gridable_defaults) = \ pyunit_utils.gen_grid_search(model.full_parameters.keys(), self.hyper_params, self.exclude_parameter_lists, self.gridable_parameters, self.gridable_types, self.gridable_defaults, random.randint(1, self.max_int_number), self.max_int_val, self.min_int_val, random.randint(1, self.max_real_number), self.max_real_val, self.min_real_val) if "lambda" in list(self.hyper_params): self.hyper_params["lambda"] = [self.lambda_scale * x for x in self.hyper_params["lambda"]] time_scale = self.max_runtime_scale * self.one_model_time if "max_runtime_secs" in list(self.hyper_params): self.hyper_params["max_runtime_secs"] = [time_scale * x for x in self.hyper_params["max_runtime_secs"]] self.possible_number_models = pyunit_utils.count_models(self.hyper_params) pyunit_utils.write_hyper_parameters_json(self.current_dir, self.sandbox_dir, self.json_filename, self.hyper_params) def tear_down(self): if self.test_failed: self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True) pyunit_utils.move_files(self.sandbox_dir, self.training1_data_file, self.training1_filename) json_file = os.path.join(self.sandbox_dir, self.json_filename) with open(json_file,'wb') as test_file: json.dump(self.hyper_params, test_file) else: pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, False) def test1_glm_random_grid_search_model_number(self, metric_name): print("*******************************************************************************************") print("test1_glm_random_grid_search_model_number for GLM " + self.family) h2o.cluster_info() search_criteria = {'strategy': 'RandomDiscrete', "stopping_rounds": 0, "seed": round(time.time())} print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) random_grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) random_grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) if not (len(random_grid_model) == self.possible_number_models): self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test1_glm_random_grid_search_model_number for GLM: failed, number of models generated" "possible model number {0} and randomized gridsearch model number {1} are not " "equal.".format(self.possible_number_models, len(random_grid_model))) else: self.max_grid_runtime = pyunit_utils.find_grid_runtime(random_grid_model) if self.test_failed_array[self.test_num] == 0: print("test1_glm_random_grid_search_model_number for GLM: passed!") self.test_num += 1 sys.stdout.flush() def test2_glm_random_grid_search_max_model(self): print("*******************************************************************************************") print("test2_glm_random_grid_search_max_model for GLM " + self.family) h2o.cluster_info() self.max_model_number = random.randint(1, int(self.allowed_scaled_model_number * self.possible_number_models)) search_criteria = {'strategy': 'RandomDiscrete', 'max_models': self.max_model_number, "seed": round(time.time())} print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) print("Possible number of models built is {0}".format(self.possible_number_models)) grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) number_model_built = len(grid_model) print("Maximum model limit is {0}. Number of models built is {1}".format(search_criteria["max_models"], number_model_built)) if self.possible_number_models >= self.max_model_number: if not (number_model_built == self.max_model_number): print("test2_glm_random_grid_search_max_model: failed. Number of model built {0} " "does not match stopping condition number{1}.".format(number_model_built, self.max_model_number)) self.test_failed += 1 self.test_failed_array[self.test_num] = 1 else: print("test2_glm_random_grid_search_max_model for GLM: passed.") else: if not (number_model_built == self.possible_number_models): self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test2_glm_random_grid_search_max_model: failed. Number of model built {0} does not equal " "to possible model number {1}.".format(number_model_built, self.possible_number_models)) else: print("test2_glm_random_grid_search_max_model for GLM: passed.") self.test_num += 1 sys.stdout.flush() def test3_glm_random_grid_search_max_runtime_secs(self): print("*******************************************************************************************") print("test3_glm_random_grid_search_max_runtime_secs for GLM " + self.family) h2o.cluster_info() if "max_runtime_secs" in list(self.hyper_params): del self.hyper_params['max_runtime_secs'] self.possible_number_models = pyunit_utils.count_models(self.hyper_params) max_run_time_secs = random.uniform(self.one_model_time, self.allowed_scaled_time*self.max_grid_runtime) search_criteria = {'strategy': 'RandomDiscrete', 'max_runtime_secs': max_run_time_secs, "seed": round(time.time())} print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) actual_run_time_secs = pyunit_utils.find_grid_runtime(grid_model) print("Maximum time limit is {0}. Time taken to build all model is " "{1}".format(search_criteria["max_runtime_secs"], actual_run_time_secs)) print("Maximum model number is {0}. Actual number of models built is {1}".format(self.possible_number_models, len(grid_model))) if actual_run_time_secs <= search_criteria["max_runtime_secs"]*(1+self.allowed_diff): print("test3_glm_random_grid_search_max_runtime_secs: passed!") if len(grid_model) > self.possible_number_models: self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test3_glm_random_grid_search_max_runtime_secs: failed. Generated {0} models " " which exceeds maximum possible model number {1}".format(len(grid_model), self.possible_number_models)) elif len(grid_model) == 1: print("test3_glm_random_grid_search_max_runtime_secs: passed!") else: self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test3_glm_random_grid_search_max_runtime_secs: failed. Model takes time {0}" " seconds which exceeds allowed time {1}".format(actual_run_time_secs, max_run_time_secs*(1+self.allowed_diff))) self.test_num += 1 sys.stdout.flush() def test4_glm_random_grid_search_metric(self, metric_name, bigger_is_better): print("*******************************************************************************************") print("test4_glm_random_grid_search_metric using " + metric_name + " for family " + self.family) h2o.cluster_info() search_criteria = { "strategy": "RandomDiscrete", "stopping_metric": metric_name, "stopping_tolerance": random.uniform(1e-8, self.max_tolerance), "stopping_rounds": random.randint(1, self.max_stopping_rounds), "seed": round(time.time()) } print("GLM Gaussian grid search_criteria: {0}".format(search_criteria)) self.hyper_params["max_runtime_secs"] = [0.3] grid_model = \ H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria) grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data) stopped_correctly = \ pyunit_utils.evaluate_metrics_stopping(grid_model.models, metric_name, bigger_is_better, search_criteria, self.possible_number_models) if stopped_correctly: print("test4_glm_random_grid_search_metric " + metric_name + ": passed. ") else: self.test_failed += 1 self.test_failed_array[self.test_num] = 1 print("test4_glm_random_grid_search_metric " + metric_name + ": failed. ") self.test_num += 1 def test_random_grid_search_for_glm(): test_glm_gaussian_random_grid = Test_glm_random_grid_search("gaussian") test_glm_gaussian_random_grid.test1_glm_random_grid_search_model_number("mse(xval=True)") test_glm_gaussian_random_grid.test2_glm_random_grid_search_max_model() test_glm_gaussian_random_grid.test3_glm_random_grid_search_max_runtime_secs() test_glm_gaussian_random_grid.test4_glm_random_grid_search_metric("MSE", False) d.test_failed > 0: sys.exit(1) else: pyunit_utils.remove_files(os.path.join(test_glm_gaussian_random_grid.current_dir, test_glm_gaussian_random_grid.json_filename)) if __name__ == "__main__": pyunit_utils.standalone_test(test_random_grid_search_for_glm) else: test_random_grid_search_for_glm()

true

790d7f2469b623df18560268f0e89fc2f0e10bab

2,558

py

Python

Good_Boids_module/tests/test_the_Good_Boids.py

anest1s/Refactoring_the_Bad_Boids

d569de4372d96917ef6aa7f1ca8acdaa09c26e0f

[ "MIT" ]

null

Good_Boids_module/tests/test_the_Good_Boids.py

anest1s/Refactoring_the_Bad_Boids

d569de4372d96917ef6aa7f1ca8acdaa09c26e0f

[ "MIT" ]

null

Good_Boids_module/tests/test_the_Good_Boids.py

anest1s/Refactoring_the_Bad_Boids

d569de4372d96917ef6aa7f1ca8acdaa09c26e0f

[ "MIT" ]

null

from Good_Boids_module.Update_Boids import Boids import numpy as np from nose.tools import assert_almost_equal, assert_greater from nose.tools import assert_less, assert_equal from numpy.testing import assert_array_equal import os import yaml from Good_Boids_module.tests.record_fixtures import configuration_file fixtures = yaml.load(open('fixture.yaml')) configuration_file_data = yaml.load(open(configuration_file)) def test_good_boids_for_regression(): before_positions = list(fixtures["before_positions"]) before_velocities = list(fixtures["before_velocities"]) new_positions = list(Boids(configuration_file).get_raw_positions(before_positions, before_velocities)) after_positions = list(fixtures["after_positions"]) new_velocities = list(Boids(configuration_file).get_raw_velocities(before_positions, before_velocities)) after_velocities = list(fixtures["after_velocities"]) for i in range(len(new_positions)): assert_almost_equal(new_positions[0][i], after_positions[0][i], delta=0.1) assert_almost_equal(new_positions[1][i], after_positions[1][i], delta=0.1) assert_almost_equal(new_velocities[0][i], after_velocities[0][i], delta=15) assert_almost_equal(new_velocities[1][i], after_velocities[1][i], delta=15) test_good_boids_for_regression() def test_good_boids_initialization(): boids_positions = Boids(configuration_file).positions boids_velocities = Boids(configuration_file).velocities assert_equal(configuration_file_data['birds_number'], len(boids_positions[0])) assert_equal(configuration_file_data['birds_number'], Boids(configuration_file).birds_num) for boid in range(Boids(configuration_file).birds_num): assert_less(boids_positions[0][boid], configuration_file_data['position_upper_limits'][0]) assert_greater(boids_positions[0][boid], configuration_file_data['position_lower_limits'][0]) assert_less(boids_positions[1][boid], configuration_file_data['position_upper_limits'][1]) assert_greater(boids_positions[1][boid], configuration_file_data['position_lower_limits'][1]) assert_less(boids_velocities[0][boid], configuration_file_data['velocity_upper_limits'][0]) assert_greater(boids_velocities[0][boid], configuration_file_data['velocity_lower_limits'][0]) assert_less(boids_velocities[1][boid], configuration_file_data['velocity_upper_limits'][1]) assert_greater(boids_velocities[1][boid], configuration_file_data['velocity_lower_limits'][1]) test_good_boids_initialization()

51.16

108

0.788898

from Good_Boids_module.Update_Boids import Boids import numpy as np from nose.tools import assert_almost_equal, assert_greater from nose.tools import assert_less, assert_equal from numpy.testing import assert_array_equal import os import yaml from Good_Boids_module.tests.record_fixtures import configuration_file fixtures = yaml.load(open('fixture.yaml')) configuration_file_data = yaml.load(open(configuration_file)) def test_good_boids_for_regression(): before_positions = list(fixtures["before_positions"]) before_velocities = list(fixtures["before_velocities"]) new_positions = list(Boids(configuration_file).get_raw_positions(before_positions, before_velocities)) after_positions = list(fixtures["after_positions"]) new_velocities = list(Boids(configuration_file).get_raw_velocities(before_positions, before_velocities)) after_velocities = list(fixtures["after_velocities"]) for i in range(len(new_positions)): assert_almost_equal(new_positions[0][i], after_positions[0][i], delta=0.1) assert_almost_equal(new_positions[1][i], after_positions[1][i], delta=0.1) assert_almost_equal(new_velocities[0][i], after_velocities[0][i], delta=15) assert_almost_equal(new_velocities[1][i], after_velocities[1][i], delta=15) test_good_boids_for_regression() def test_good_boids_initialization(): boids_positions = Boids(configuration_file).positions boids_velocities = Boids(configuration_file).velocities assert_equal(configuration_file_data['birds_number'], len(boids_positions[0])) assert_equal(configuration_file_data['birds_number'], Boids(configuration_file).birds_num) for boid in range(Boids(configuration_file).birds_num): assert_less(boids_positions[0][boid], configuration_file_data['position_upper_limits'][0]) assert_greater(boids_positions[0][boid], configuration_file_data['position_lower_limits'][0]) assert_less(boids_positions[1][boid], configuration_file_data['position_upper_limits'][1]) assert_greater(boids_positions[1][boid], configuration_file_data['position_lower_limits'][1]) assert_less(boids_velocities[0][boid], configuration_file_data['velocity_upper_limits'][0]) assert_greater(boids_velocities[0][boid], configuration_file_data['velocity_lower_limits'][0]) assert_less(boids_velocities[1][boid], configuration_file_data['velocity_upper_limits'][1]) assert_greater(boids_velocities[1][boid], configuration_file_data['velocity_lower_limits'][1]) test_good_boids_initialization()

true

790d7fd564a4906d9c3188c0bff4c57844454fd8

681

py

Python

seija/reusables/verification.py

MapsetManagementServer/Seija

88acafaa311970df5ae881b7eda48ea780a18d03

[ "MIT" ]

3

2019-07-25T18:27:13.000Z

2021-11-28T18:51:09.000Z

seija/reusables/verification.py

MapsetManagementServer/Seija

88acafaa311970df5ae881b7eda48ea780a18d03

[ "MIT" ]

null

seija/reusables/verification.py

MapsetManagementServer/Seija

88acafaa311970df5ae881b7eda48ea780a18d03

[ "MIT" ]

6

2019-12-17T19:48:10.000Z

2022-03-11T04:29:06.000Z

import discord async def get_role_based_on_reputation(self, guild, ranked_amount): if ranked_amount >= 10: return await get_role_from_db(self, "experienced_mapper", guild) elif ranked_amount >= 1: return await get_role_from_db(self, "ranked_mapper", guild) else: return await get_role_from_db(self, "mapper", guild) async def get_role_from_db(self, setting, guild): async with self.bot.db.execute("SELECT role_id FROM roles WHERE setting = ? AND guild_id = ?", [setting, int(guild.id)]) as cursor: role_id = await cursor.fetchone() return discord.utils.get(guild.roles, id=int(role_id[0]))

37.833333

98

0.675477

import discord async def get_role_based_on_reputation(self, guild, ranked_amount): if ranked_amount >= 10: return await get_role_from_db(self, "experienced_mapper", guild) elif ranked_amount >= 1: return await get_role_from_db(self, "ranked_mapper", guild) else: return await get_role_from_db(self, "mapper", guild) async def get_role_from_db(self, setting, guild): async with self.bot.db.execute("SELECT role_id FROM roles WHERE setting = ? AND guild_id = ?", [setting, int(guild.id)]) as cursor: role_id = await cursor.fetchone() return discord.utils.get(guild.roles, id=int(role_id[0]))

true

790d80d0c914106a10d2850b810eb380fd4604ed

840

py

Python

LeetCode/581.py

KevinTMtz/CompetitiveProgramming

0bf8a297c404073df707b6d7b06965b055ccd872

[ "MIT" ]

1

2020-12-08T02:01:18.000Z

LeetCode/581.py

KevinTMtz/CompetitiveProgramming

0bf8a297c404073df707b6d7b06965b055ccd872

[ "MIT" ]

null

LeetCode/581.py

KevinTMtz/CompetitiveProgramming

0bf8a297c404073df707b6d7b06965b055ccd872

[ "MIT" ]

null

# # LeetCode # # Problem - 581 # URL - https://leetcode.com/problems/shortest-unsorted-continuous-subarray/ # class Solution: def findUnsortedSubarray(self, arr: List[int]) -> int: if (not arr): 0 index1 = -1 index2 = -1 for i in range(1, len(arr)): if (arr[i] < arr[i-1]): index1 = i-1 break for i in range(len(arr)-2, -1, -1): if (arr[i] > arr[i+1]): index2 = i+1 break if (index1 == -1): return 0 else: maxSubArr = max(arr[index1:index2+1]) minSubArr = min(arr[index1:index2+1]) for i in range(0, index1): if (arr[i] > minSubArr): index1 = i break for i in range(len(arr)-1, index2, -1): if (arr[i] < maxSubArr): index2 = i break return index2 - index1 + 1

19.534884

76

0.515476

class Solution: def findUnsortedSubarray(self, arr: List[int]) -> int: if (not arr): 0 index1 = -1 index2 = -1 for i in range(1, len(arr)): if (arr[i] < arr[i-1]): index1 = i-1 break for i in range(len(arr)-2, -1, -1): if (arr[i] > arr[i+1]): index2 = i+1 break if (index1 == -1): return 0 else: maxSubArr = max(arr[index1:index2+1]) minSubArr = min(arr[index1:index2+1]) for i in range(0, index1): if (arr[i] > minSubArr): index1 = i break for i in range(len(arr)-1, index2, -1): if (arr[i] < maxSubArr): index2 = i break return index2 - index1 + 1

true

790d8113d67e2f8121e30d836a9d637643f5563b

814

py

Python

nebula2/gclient/net/__init__.py

xiaoronghuang/nebula-python

fe2a85639dd1500133a63bad50f72b3c0370d1de

[ "Apache-2.0" ]

110

2019-10-24T09:21:07.000Z

2022-03-31T07:06:00.000Z

nebula2/gclient/net/__init__.py

xiaoronghuang/nebula-python

fe2a85639dd1500133a63bad50f72b3c0370d1de

[ "Apache-2.0" ]

83

2019-11-20T07:55:05.000Z

2022-03-23T10:55:14.000Z

nebula2/gclient/net/__init__.py

xiaoronghuang/nebula-python

fe2a85639dd1500133a63bad50f72b3c0370d1de

[ "Apache-2.0" ]

56

2019-10-11T07:01:05.000Z

2022-03-11T09:09:15.000Z

#!/usr/bin/env python # --coding:utf-8-- # Copyright (c) 2020 vesoft inc. All rights reserved. # # This source code is licensed under Apache 2.0 License, # attached with Common Clause Condition 1.0, found in the LICENSES directory. import logging from nebula2.common.ttypes import ErrorCode from nebula2.Exception import ( AuthFailedException, IOErrorException, NotValidConnectionException, InValidHostname, ) from nebula2.data.ResultSet import ResultSet from nebula2.gclient.net.AuthResult import AuthResult from nebula2.gclient.net.Session import Session from nebula2.gclient.net.Connection import Connection from nebula2.gclient.net.ConnectionPool import ConnectionPool logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)-8s [%(filename)s:%(lineno)d]:%(message)s')

29.071429

117

0.782555

import logging from nebula2.common.ttypes import ErrorCode from nebula2.Exception import ( AuthFailedException, IOErrorException, NotValidConnectionException, InValidHostname, ) from nebula2.data.ResultSet import ResultSet from nebula2.gclient.net.AuthResult import AuthResult from nebula2.gclient.net.Session import Session from nebula2.gclient.net.Connection import Connection from nebula2.gclient.net.ConnectionPool import ConnectionPool logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)-8s [%(filename)s:%(lineno)d]:%(message)s')

true

790d81313101dbd0c7e9056bdded1306b21ca4f5

1,586

py

Python

src/scheduler/models/dao/connection/ConnectionDatabase.py

jedicontributors/pythondataintegrator

3e877b367ab9b20185476128ec053db41087879f

[ "MIT" ]

14

2020-12-19T15:06:13.000Z

2022-01-12T19:52:17.000Z

src/scheduler/models/dao/connection/ConnectionDatabase.py

jedicontributors/pythondataintegrator

3e877b367ab9b20185476128ec053db41087879f

[ "MIT" ]

43

2021-01-06T22:05:22.000Z

2022-03-10T10:30:30.000Z

src/scheduler/models/dao/connection/ConnectionDatabase.py

jedicontributors/pythondataintegrator

3e877b367ab9b20185476128ec053db41087879f

[ "MIT" ]

4

2020-12-18T23:10:09.000Z

2021-04-02T13:03:12.000Z

from sqlalchemy import Column, String, Integer, ForeignKey from sqlalchemy.orm import relationship from IocManager import IocManager from models.dao.Entity import Entity class ConnectionDatabase(Entity, IocManager.Base): __tablename__ = "ConnectionDatabase" __table_args__ = {"schema": "Connection"} ConnectionId = Column(Integer, ForeignKey('Connection.Connection.Id')) ConnectorTypeId = Column(Integer, ForeignKey('Connection.ConnectorType.Id')) Sid = Column(String(100), index=False, unique=False, nullable=True) ServiceName = Column(String(100), index=False, unique=False, nullable=True) DatabaseName = Column(String(100), index=False, unique=False, nullable=True) ConnectorType = relationship("ConnectorType", back_populates="Databases") def __init__(self, ConnectionId: int = None, ConnectorTypeId: int = None, Host: str = None, Port: int = None, Sid: str = None, ServiceName: str = None, DatabaseName: str = None, Connection = None, ConnectorType = None, *args, **kwargs): super().__init__(*args, **kwargs) self.ConnectionId: str = ConnectionId self.ConnectorTypeId: str = ConnectorTypeId self.Host: str = Host self.Port: int = Port self.Sid: str = Sid self.ServiceName: str = ServiceName self.DatabaseName: str = DatabaseName self.Connection = Connection self.ConnectorType = ConnectorType

42.864865

80

0.639344

from sqlalchemy import Column, String, Integer, ForeignKey from sqlalchemy.orm import relationship from IocManager import IocManager from models.dao.Entity import Entity class ConnectionDatabase(Entity, IocManager.Base): __tablename__ = "ConnectionDatabase" __table_args__ = {"schema": "Connection"} ConnectionId = Column(Integer, ForeignKey('Connection.Connection.Id')) ConnectorTypeId = Column(Integer, ForeignKey('Connection.ConnectorType.Id')) Sid = Column(String(100), index=False, unique=False, nullable=True) ServiceName = Column(String(100), index=False, unique=False, nullable=True) DatabaseName = Column(String(100), index=False, unique=False, nullable=True) ConnectorType = relationship("ConnectorType", back_populates="Databases") def __init__(self, ConnectionId: int = None, ConnectorTypeId: int = None, Host: str = None, Port: int = None, Sid: str = None, ServiceName: str = None, DatabaseName: str = None, Connection = None, ConnectorType = None, *args, **kwargs): super().__init__(*args, **kwargs) self.ConnectionId: str = ConnectionId self.ConnectorTypeId: str = ConnectorTypeId self.Host: str = Host self.Port: int = Port self.Sid: str = Sid self.ServiceName: str = ServiceName self.DatabaseName: str = DatabaseName self.Connection = Connection self.ConnectorType = ConnectorType

true

790d825980ab768a98f6d16fa5cacee688c8eb00

550

py

Python

backend/home/migrations/0001_load_initial_data.py

crowdbotics-apps/solitary-morning-29716

d7672250b446b91af96f5bf0f75838d96d7c5b3a

[ "FTL", "AML", "RSA-MD" ]

null

backend/home/migrations/0001_load_initial_data.py

crowdbotics-apps/solitary-morning-29716

d7672250b446b91af96f5bf0f75838d96d7c5b3a

[ "FTL", "AML", "RSA-MD" ]

43

2021-08-11T09:52:00.000Z

2022-02-06T17:28:12.000Z

backend/home/migrations/0001_load_initial_data.py

crowdbotics-apps/solitary-morning-29716

d7672250b446b91af96f5bf0f75838d96d7c5b3a

[ "FTL", "AML", "RSA-MD" ]

null

from django.db import migrations def create_site(apps, schema_editor): Site = apps.get_model("sites", "Site") custom_domain = "solitary-morning-29716.botics.co" site_params = { "name": "Solitary Morning", } if custom_domain: site_params["domain"] = custom_domain Site.objects.update_or_create(defaults=site_params, id=1) class Migration(migrations.Migration): dependencies = [ ("sites", "0002_alter_domain_unique"), ] operations = [ migrations.RunPython(create_site), ]

21.153846

61

0.661818

from django.db import migrations def create_site(apps, schema_editor): Site = apps.get_model("sites", "Site") custom_domain = "solitary-morning-29716.botics.co" site_params = { "name": "Solitary Morning", } if custom_domain: site_params["domain"] = custom_domain Site.objects.update_or_create(defaults=site_params, id=1) class Migration(migrations.Migration): dependencies = [ ("sites", "0002_alter_domain_unique"), ] operations = [ migrations.RunPython(create_site), ]

true

790d828e52a2e702df0df87304c75254ba67a0fa

603

py

Python

blog/migrations/0001_initial.py

bwarren2/django-basic-blog

20d4c40f19054d4aa8899240d211781624a7e0c7

[ "MIT" ]

1

2019-08-14T13:26:24.000Z

blog/migrations/0001_initial.py

bwarren2/django-basic-blog

20d4c40f19054d4aa8899240d211781624a7e0c7

[ "MIT" ]

1

2015-07-25T15:23:41.000Z

blog/migrations/0001_initial.py

bwarren2/django-basic-blog

20d4c40f19054d4aa8899240d211781624a7e0c7

[ "MIT" ]

2

2015-07-25T01:42:10.000Z

2019-08-14T13:26:33.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Entry', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('title', models.CharField(max_length=200)), ('content', models.TextField()), ], options={ }, bases=(models.Model,), ), ]

24.12

114

0.538972

from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Entry', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('title', models.CharField(max_length=200)), ('content', models.TextField()), ], options={ }, bases=(models.Model,), ), ]

true

790d82adcd085ab6a86925542f75f0ad1cbc516c

2,617

py

Python

tools/convert.py

tjclement/cz19-badge

6d04756f61053f2ddd97ed60cfb008393d476721

[ "MIT" ]

null

tools/convert.py

tjclement/cz19-badge

6d04756f61053f2ddd97ed60cfb008393d476721

[ "MIT" ]

null

tools/convert.py

tjclement/cz19-badge

6d04756f61053f2ddd97ed60cfb008393d476721

[ "MIT" ]

null

#!/usr/bin/python import sys from PIL import Image import argparse parser = argparse.ArgumentParser(description='Convert animated or static images to CampZone2019 badge code') parser.add_argument('image', help='The path to an image to read from (e.g. .gif, .jpg, .png)') parser.add_argument('--start_x', type=int, default=0, help='The X offset in the image to start reading from') parser.add_argument('--start_y', type=int, default=0, help='The Y offset in the image to start reading from') parser.add_argument('--length_x', type=int, default=32, help='The width to read from the image, starting at start_x') parser.add_argument('--length_y', type=int, default=8, help='The height to read from the image, starting at start_y') parser.add_argument('--start_at_frame', type=int, default=0, help='The frame to start from, if the image is animated') parser.add_argument('--lim_frames', type=int, default=16, help='The number of frames to parse, if the image is animated') parser.add_argument('--skip_frames', type=int, default=1, help='The number of frames to parse, if the image is animated') parser.add_argument('--is_icon', type=bool, default=False, help='Set to "true" to output rgb.image() instead of rgb.gif()') args = parser.parse_args() start_x = args.start_x start_y = args.start_y length_x = args.length_x length_y = args.length_y start_at_frame = args.start_at_frame lim_frames = args.lim_frames skip_frames = args.skip_frames is_icon = args.is_icon frames = [] image = Image.open(sys.argv[1]) n_frames, width, height = image.n_frames if hasattr(image, 'n_frames') else 1, image.width, image.height used_frames = min((n_frames - start_at_frame) / skip_frames, lim_frames) used_width = min(length_x, image.width) used_height = min(length_y, image.height) for frame_no in range(start_at_frame, start_at_frame + used_frames): image.seek(frame_no) frame = list(image.convert('RGBA').getdata()) cut_frame = [] for y in range(start_y, start_y + used_height): for x in range(start_x, start_x + used_width): cut_frame.append(frame[x + width * y]) frames.append(cut_frame) if is_icon: print('icon = ([0x' + ', 0x'.join([', 0x'.join([format(r << 24 | g << 16 | b << 8 | a, '08x') for r, g, b, a in frame]) for frame in frames]) + '], %d)' % used_frames) else: print('rgb.gif([0x' + ', 0x'.join([', 0x'.join([format(r << 24 | g << 16 | b << 8 | a, '08x') for r, g, b, a in frame]) for frame in frames]) + '], %d, %d, %d, %d, %d)' % (0, 0, used_width, used_height, used_frames))

45.912281

123

0.680168

import sys from PIL import Image import argparse parser = argparse.ArgumentParser(description='Convert animated or static images to CampZone2019 badge code') parser.add_argument('image', help='The path to an image to read from (e.g. .gif, .jpg, .png)') parser.add_argument('--start_x', type=int, default=0, help='The X offset in the image to start reading from') parser.add_argument('--start_y', type=int, default=0, help='The Y offset in the image to start reading from') parser.add_argument('--length_x', type=int, default=32, help='The width to read from the image, starting at start_x') parser.add_argument('--length_y', type=int, default=8, help='The height to read from the image, starting at start_y') parser.add_argument('--start_at_frame', type=int, default=0, help='The frame to start from, if the image is animated') parser.add_argument('--lim_frames', type=int, default=16, help='The number of frames to parse, if the image is animated') parser.add_argument('--skip_frames', type=int, default=1, help='The number of frames to parse, if the image is animated') parser.add_argument('--is_icon', type=bool, default=False, help='Set to "true" to output rgb.image() instead of rgb.gif()') args = parser.parse_args() start_x = args.start_x start_y = args.start_y length_x = args.length_x length_y = args.length_y start_at_frame = args.start_at_frame lim_frames = args.lim_frames skip_frames = args.skip_frames is_icon = args.is_icon frames = [] image = Image.open(sys.argv[1]) n_frames, width, height = image.n_frames if hasattr(image, 'n_frames') else 1, image.width, image.height used_frames = min((n_frames - start_at_frame) / skip_frames, lim_frames) used_width = min(length_x, image.width) used_height = min(length_y, image.height) for frame_no in range(start_at_frame, start_at_frame + used_frames): image.seek(frame_no) frame = list(image.convert('RGBA').getdata()) cut_frame = [] for y in range(start_y, start_y + used_height): for x in range(start_x, start_x + used_width): cut_frame.append(frame[x + width * y]) frames.append(cut_frame) if is_icon: print('icon = ([0x' + ', 0x'.join([', 0x'.join([format(r << 24 | g << 16 | b << 8 | a, '08x') for r, g, b, a in frame]) for frame in frames]) + '], %d)' % used_frames) else: print('rgb.gif([0x' + ', 0x'.join([', 0x'.join([format(r << 24 | g << 16 | b << 8 | a, '08x') for r, g, b, a in frame]) for frame in frames]) + '], %d, %d, %d, %d, %d)' % (0, 0, used_width, used_height, used_frames))

true

790d82f674b469724c789de3e008a86a773eabf8

1,670

py

Python

tools/clean_file_locks.py

bopopescu/extra-specs-1

6a14d8d7807727023b4d589af47e8a9605f12db1

[ "Apache-2.0" ]

null

tools/clean_file_locks.py

bopopescu/extra-specs-1

6a14d8d7807727023b4d589af47e8a9605f12db1

[ "Apache-2.0" ]

1

2020-07-24T14:14:13.000Z

tools/clean_file_locks.py

bopopescu/extra-specs-1

6a14d8d7807727023b4d589af47e8a9605f12db1

[ "Apache-2.0" ]

1

2020-07-24T10:40:59.000Z

#!/usr/bin/env python # Copyright 2012 La Honda Research Center, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """clean_file_locks.py - Cleans stale interprocess locks This rountine can be used to find and delete stale lock files from nova's interprocess synchroization. It can be used safely while services are running. """ import logging import optparse from nova import flags from nova import log from nova import utils LOG = log.getLogger('nova.utils') FLAGS = flags.FLAGS def parse_options(): """process command line options.""" parser = optparse.OptionParser('usage: %prog [options]') parser.add_option('--verbose', action='store_true', help='List lock files found and deleted') options, args = parser.parse_args() return options, args def main(): """Main loop.""" options, args = parse_options() verbose = options.verbose if verbose: LOG.logger.setLevel(logging.DEBUG) else: LOG.logger.setLevel(logging.INFO) LOG.info('Cleaning stale locks from %s' % FLAGS.lock_path) utils.cleanup_file_locks() LOG.info('Finished') if __name__ == '__main__': main()

26.09375

74

0.713772

import logging import optparse from nova import flags from nova import log from nova import utils LOG = log.getLogger('nova.utils') FLAGS = flags.FLAGS def parse_options(): parser = optparse.OptionParser('usage: %prog [options]') parser.add_option('--verbose', action='store_true', help='List lock files found and deleted') options, args = parser.parse_args() return options, args def main(): options, args = parse_options() verbose = options.verbose if verbose: LOG.logger.setLevel(logging.DEBUG) else: LOG.logger.setLevel(logging.INFO) LOG.info('Cleaning stale locks from %s' % FLAGS.lock_path) utils.cleanup_file_locks() LOG.info('Finished') if __name__ == '__main__': main()

true

790d832d1bb65414d4f691b248e8be0d69894926

458

py

Python

data/scripts/templates/object/tangible/loot/quest/shared_nym_droid_memory_chip.py

obi-two/GameServer

7d37024e2291a97d49522610cd8f1dbe5666afc2

[ "MIT" ]

20

2015-02-23T15:11:56.000Z

2022-03-18T20:56:48.000Z

data/scripts/templates/object/tangible/loot/quest/shared_nym_droid_memory_chip.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

null

data/scripts/templates/object/tangible/loot/quest/shared_nym_droid_memory_chip.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

20

2015-04-04T16:35:59.000Z

2022-03-24T14:54:37.000Z

#### NOTICE: THIS FILE IS AUTOGENERATED #### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY #### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES from swgpy.object import * def create(kernel): result = Tangible() result.template = "object/tangible/loot/quest/shared_nym_droid_memory_chip.iff" result.attribute_template_id = -1 result.stfName("item_n","nym_memory_chip") #### BEGIN MODIFICATIONS #### #### END MODIFICATIONS #### return result

26.941176

80

0.731441

true

790d83c21c632207c9ab00fc86d6452ee5f648d5

3,011

py

Python

code/logz.py

edwithschoolofai/ARS

22ec3c58637a84e374611d3f8bf05b89a9468f8a

[ "BSD-2-Clause" ]

398

2018-03-20T07:08:01.000Z

2022-03-14T05:51:47.000Z

code/logz.py

edwithschoolofai/ARS

22ec3c58637a84e374611d3f8bf05b89a9468f8a

[ "BSD-2-Clause" ]

13

2018-03-28T19:12:07.000Z

2021-03-19T03:49:49.000Z

code/logz.py

edwithschoolofai/ARS

22ec3c58637a84e374611d3f8bf05b89a9468f8a

[ "BSD-2-Clause" ]

96

2018-03-20T21:17:33.000Z

2021-12-23T02:58:40.000Z

# Code in this file is copied and adapted from # https://github.com/berkeleydeeprlcourse import json """ Some simple logging functionality, inspired by rllab's logging. Assumes that each diagnostic gets logged each iteration Call logz.configure_output_dir() to start logging to a tab-separated-values file (some_folder_name/log.txt) """ import os.path as osp, shutil, time, atexit, os, subprocess color2num = dict( gray=30, red=31, green=32, yellow=33, blue=34, magenta=35, cyan=36, white=37, crimson=38 ) def colorize(string, color, bold=False, highlight=False): attr = [] num = color2num[color] if highlight: num += 10 attr.append(str(num)) if bold: attr.append('1') return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) class G(object): output_dir = None output_file = None first_row = True log_headers = [] log_current_row = {} def configure_output_dir(d=None): """ Set output directory to d, or to /tmp/somerandomnumber if d is None """ G.first_row = True G.log_headers = [] G.log_current_row = {} G.output_dir = d or "/tmp/experiments/%i"%int(time.time()) if not osp.exists(G.output_dir): os.makedirs(G.output_dir) G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w') atexit.register(G.output_file.close) print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True)) def log_tabular(key, val): """ Log a value of some diagnostic Call this once for each diagnostic quantity, each iteration """ if G.first_row: G.log_headers.append(key) else: assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key G.log_current_row[key] = val def save_params(params): with open(osp.join(G.output_dir, "params.json"), 'w') as out: out.write(json.dumps(params, separators=(',\n','\t:\t'), sort_keys=True)) def dump_tabular(): """ Write all of the diagnostics from the current iteration """ vals = [] key_lens = [len(key) for key in G.log_headers] max_key_len = max(15,max(key_lens)) keystr = '%'+'%d'%max_key_len fmt = "| " + keystr + "s | %15s |" n_slashes = 22 + max_key_len print("-"*n_slashes) for key in G.log_headers: val = G.log_current_row.get(key, "") if hasattr(val, "__float__"): valstr = "%8.3g"%val else: valstr = val print(fmt%(key, valstr)) vals.append(val) print("-"*n_slashes) if G.output_file is not None: if G.first_row: G.output_file.write("\t".join(G.log_headers)) G.output_file.write("\n") G.output_file.write("\t".join(map(str,vals))) G.output_file.write("\n") G.output_file.flush() G.log_current_row.clear() G.first_row=False

28.67619

122

0.63733

import json import os.path as osp, shutil, time, atexit, os, subprocess color2num = dict( gray=30, red=31, green=32, yellow=33, blue=34, magenta=35, cyan=36, white=37, crimson=38 ) def colorize(string, color, bold=False, highlight=False): attr = [] num = color2num[color] if highlight: num += 10 attr.append(str(num)) if bold: attr.append('1') return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) class G(object): output_dir = None output_file = None first_row = True log_headers = [] log_current_row = {} def configure_output_dir(d=None): G.first_row = True G.log_headers = [] G.log_current_row = {} G.output_dir = d or "/tmp/experiments/%i"%int(time.time()) if not osp.exists(G.output_dir): os.makedirs(G.output_dir) G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w') atexit.register(G.output_file.close) print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True)) def log_tabular(key, val): if G.first_row: G.log_headers.append(key) else: assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key G.log_current_row[key] = val def save_params(params): with open(osp.join(G.output_dir, "params.json"), 'w') as out: out.write(json.dumps(params, separators=(',\n','\t:\t'), sort_keys=True)) def dump_tabular(): vals = [] key_lens = [len(key) for key in G.log_headers] max_key_len = max(15,max(key_lens)) keystr = '%'+'%d'%max_key_len fmt = "| " + keystr + "s | %15s |" n_slashes = 22 + max_key_len print("-"*n_slashes) for key in G.log_headers: val = G.log_current_row.get(key, "") if hasattr(val, "__float__"): valstr = "%8.3g"%val else: valstr = val print(fmt%(key, valstr)) vals.append(val) print("-"*n_slashes) if G.output_file is not None: if G.first_row: G.output_file.write("\t".join(G.log_headers)) G.output_file.write("\n") G.output_file.write("\t".join(map(str,vals))) G.output_file.write("\n") G.output_file.flush() G.log_current_row.clear() G.first_row=False

true

790d848466bbf50695151b9cdec24f3f85cd12db

24,285

py

Python

src/image-gallery/azext_image_gallery/vendored_sdks/azure_mgmt_compute/operations/_virtual_machine_scale_set_rolling_upgrades_operations.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

1

2022-02-01T18:50:12.000Z

src/image-gallery/azext_image_gallery/vendored_sdks/azure_mgmt_compute/operations/_virtual_machine_scale_set_rolling_upgrades_operations.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

9

2022-03-25T19:35:49.000Z

2022-03-31T06:09:47.000Z

src/image-gallery/azext_image_gallery/vendored_sdks/azure_mgmt_compute/operations/_virtual_machine_scale_set_rolling_upgrades_operations.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

1

2022-03-10T22:13:02.000Z

# pylint: disable=too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Callable, Dict, Optional, TypeVar, Union from msrest import Serializer from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models from .._vendor import _convert_request, _format_url_section T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_cancel_request_initial( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") # type: str # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/cancel") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') return HttpRequest( method="POST", url=_url, params=_query_parameters, **kwargs ) def build_start_os_upgrade_request_initial( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") # type: str # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/osRollingUpgrade") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') return HttpRequest( method="POST", url=_url, params=_query_parameters, **kwargs ) def build_start_extension_upgrade_request_initial( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") # type: str # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/extensionRollingUpgrade") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') return HttpRequest( method="POST", url=_url, params=_query_parameters, **kwargs ) def build_get_latest_request( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") # type: str accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/latest") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) class VirtualMachineScaleSetRollingUpgradesOperations(object): """VirtualMachineScaleSetRollingUpgradesOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.compute.v2021_07_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def _cancel_initial( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") # type: str request = build_cancel_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._cancel_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _cancel_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/cancel"} # type: ignore @distributed_trace def begin_cancel( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> LROPoller[None]: """Cancels the current virtual machine scale set rolling upgrade. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[None] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2021-07-01") # type: str polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._cancel_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_cancel.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/cancel"} # type: ignore def _start_os_upgrade_initial( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") # type: str request = build_start_os_upgrade_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._start_os_upgrade_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _start_os_upgrade_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/osRollingUpgrade"} # type: ignore @distributed_trace def begin_start_os_upgrade( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> LROPoller[None]: """Starts a rolling upgrade to move all virtual machine scale set instances to the latest available Platform Image OS version. Instances which are already running the latest available OS version are not affected. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[None] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2021-07-01") # type: str polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._start_os_upgrade_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_start_os_upgrade.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/osRollingUpgrade"} # type: ignore def _start_extension_upgrade_initial( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") # type: str request = build_start_extension_upgrade_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._start_extension_upgrade_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _start_extension_upgrade_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/extensionRollingUpgrade"} # type: ignore @distributed_trace def begin_start_extension_upgrade( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> LROPoller[None]: """Starts a rolling upgrade to move all extensions for all virtual machine scale set instances to the latest available extension version. Instances which are already running the latest extension versions are not affected. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[None] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2021-07-01") # type: str polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._start_extension_upgrade_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_start_extension_upgrade.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/extensionRollingUpgrade"} # type: ignore @distributed_trace def get_latest( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> "_models.RollingUpgradeStatusInfo": """Gets the status of the latest virtual machine scale set rolling upgrade. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: RollingUpgradeStatusInfo, or the result of cls(response) :rtype: ~azure.mgmt.compute.v2021_07_01.models.RollingUpgradeStatusInfo :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.RollingUpgradeStatusInfo"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") # type: str request = build_get_latest_request( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self.get_latest.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('RollingUpgradeStatusInfo', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_latest.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/latest"} # type: ignore

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from typing import Any, Callable, Dict, Optional, TypeVar, Union from msrest import Serializer from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models from .._vendor import _convert_request, _format_url_section T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_cancel_request_initial( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/cancel") path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) _query_parameters = kwargs.pop("params", {}) _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') return HttpRequest( method="POST", url=_url, params=_query_parameters, **kwargs ) def build_start_os_upgrade_request_initial( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/osRollingUpgrade") path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) _query_parameters = kwargs.pop("params", {}) _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') return HttpRequest( method="POST", url=_url, params=_query_parameters, **kwargs ) def build_start_extension_upgrade_request_initial( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/extensionRollingUpgrade") path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) _query_parameters = kwargs.pop("params", {}) _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') return HttpRequest( method="POST", url=_url, params=_query_parameters, **kwargs ) def build_get_latest_request( resource_group_name: str, vm_scale_set_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2021-07-01") accept = "application/json" _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/latest") path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "vmScaleSetName": _SERIALIZER.url("vm_scale_set_name", vm_scale_set_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) _query_parameters = kwargs.pop("params", {}) _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') _header_parameters = kwargs.pop("headers", {}) _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) class VirtualMachineScaleSetRollingUpgradesOperations(object): models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def _cancel_initial( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") request = build_cancel_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._cancel_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _cancel_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/cancel"} @distributed_trace def begin_cancel( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> LROPoller[None]: api_version = kwargs.pop('api_version', "2021-07-01") polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = self._cancel_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_cancel.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/cancel"} def _start_os_upgrade_initial( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") request = build_start_os_upgrade_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._start_os_upgrade_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _start_os_upgrade_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/osRollingUpgrade"} @distributed_trace def begin_start_os_upgrade( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> LROPoller[None]: api_version = kwargs.pop('api_version', "2021-07-01") polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = self._start_os_upgrade_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_start_os_upgrade.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/osRollingUpgrade"} def _start_extension_upgrade_initial( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") request = build_start_extension_upgrade_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._start_extension_upgrade_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _start_extension_upgrade_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/extensionRollingUpgrade"} @distributed_trace def begin_start_extension_upgrade( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> LROPoller[None]: api_version = kwargs.pop('api_version', "2021-07-01") polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = self._start_extension_upgrade_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_start_extension_upgrade.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/extensionRollingUpgrade"} @distributed_trace def get_latest( self, resource_group_name: str, vm_scale_set_name: str, **kwargs: Any ) -> "_models.RollingUpgradeStatusInfo": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2021-07-01") request = build_get_latest_request( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self.get_latest.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('RollingUpgradeStatusInfo', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_latest.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/rollingUpgrades/latest"}

true

790d87bd4bdeedbce5f096b24423ab2e051374d9

122

py

Python

api/uploader/__init__.py

StepaTa/vkbottle

3b04a5343380cbabe782151e7cb1c1645a9fa9ce

[ "MIT" ]

null

api/uploader/__init__.py

StepaTa/vkbottle

3b04a5343380cbabe782151e7cb1c1645a9fa9ce

[ "MIT" ]

null

api/uploader/__init__.py

StepaTa/vkbottle

3b04a5343380cbabe782151e7cb1c1645a9fa9ce

[ "MIT" ]

null

from .base import Uploader from .photo import PhotoUploader from .doc import DocUploader from .audio import AudioUploader

24.4

32

0.836066

from .base import Uploader from .photo import PhotoUploader from .doc import DocUploader from .audio import AudioUploader

true

790d881362b5d343de0d5f2fd2d6938e5126cc81

12,685

py

Python

python_modules/dagster/dagster/core/code_pointer.py

mpkocher/dagster

c25c07de0e9259b08d6227f82d7aaa24f23bee85

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/code_pointer.py

mpkocher/dagster

c25c07de0e9259b08d6227f82d7aaa24f23bee85

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/code_pointer.py

mpkocher/dagster

c25c07de0e9259b08d6227f82d7aaa24f23bee85

[ "Apache-2.0" ]

null

import importlib import inspect import os import sys import warnings from abc import ABCMeta, abstractmethod from collections import namedtuple import six from dagster import check from dagster.core.errors import DagsterInvariantViolationError from dagster.serdes import whitelist_for_serdes from dagster.seven import import_module_from_path from dagster.utils import alter_sys_path, load_yaml_from_path class CodePointer(six.with_metaclass(ABCMeta)): @abstractmethod def load_target(self): pass @abstractmethod def describe(self): pass @staticmethod def from_module(module_name, definition): check.str_param(module_name, 'module_name') check.str_param(definition, 'definition') return ModuleCodePointer(module_name, definition) @staticmethod def from_python_package(module_name, attribute): check.str_param(module_name, 'module_name') check.str_param(attribute, 'attribute') return PackageCodePointer(module_name, attribute) @staticmethod def from_python_file(python_file, definition, working_directory): check.str_param(python_file, 'python_file') check.str_param(definition, 'definition') check.opt_str_param(working_directory, 'working_directory') if working_directory: return FileInDirectoryCodePointer( python_file=python_file, fn_name=definition, working_directory=working_directory ) return FileCodePointer(python_file=python_file, fn_name=definition) @staticmethod def from_legacy_repository_yaml(file_path): check.str_param(file_path, 'file_path') config = load_yaml_from_path(file_path) repository_config = check.dict_elem(config, 'repository') module_name = check.opt_str_elem(repository_config, 'module') file_name = check.opt_str_elem(repository_config, 'file') fn_name = check.str_elem(repository_config, 'fn') return ( CodePointer.from_module(module_name, fn_name) if module_name # rebase file in config off of the path in the config file else CodePointer.from_python_file(rebase_file(file_name, file_path), fn_name, None) ) def rebase_file(relative_path_in_file, file_path_resides_in): ''' In config files, you often put file paths that are meant to be relative to the location of that config file. This does that calculation. ''' check.str_param(relative_path_in_file, 'relative_path_in_file') check.str_param(file_path_resides_in, 'file_path_resides_in') return os.path.join( os.path.dirname(os.path.abspath(file_path_resides_in)), relative_path_in_file ) def load_python_file(python_file, working_directory): ''' Takes a path to a python file and returns a loaded module ''' check.str_param(python_file, 'python_file') module_name = os.path.splitext(os.path.basename(python_file))[0] cwd = sys.path[0] if working_directory: with alter_sys_path(to_add=[working_directory], to_remove=[cwd]): return import_module_from_path(module_name, python_file) error = None sys_modules = {k: v for k, v in sys.modules.items()} with alter_sys_path(to_add=[], to_remove=[cwd]): try: module = import_module_from_path(module_name, python_file) except ImportError as ie: # importing alters sys.modules in ways that may interfere with the import below, even # if the import has failed. to work around this, we need to manually clear any modules # that have been cached in sys.modules due to the speculative import call # Also, we are mutating sys.modules instead of straight-up assigning to sys_modules, # because some packages will do similar shenanigans to sys.modules (e.g. numpy) to_delete = set(sys.modules) - set(sys_modules) for key in to_delete: del sys.modules[key] error = ie if not error: return module try: module = import_module_from_path(module_name, python_file) # if here, we were able to resolve the module with the working directory on the # path, but should error because we may not always invoke from the same directory # (e.g. from cron) warnings.warn( ( 'Module `{module}` was resolved using the working directory. The ability to ' 'implicitly load modules from the working directory is deprecated and ' 'will be removed in a future release. Please explicitly specify the ' '`working_directory` config option in your workspace.yaml or install `{module}` to ' 'your python environment.' ).format(module=error.name if hasattr(error, 'name') else module_name) ) return module except ImportError: raise error def load_python_module(module_name, warn_only=False, remove_from_path_fn=None): check.str_param(module_name, 'module_name') check.bool_param(warn_only, 'warn_only') check.opt_callable_param(remove_from_path_fn, 'remove_from_path_fn') error = None remove_paths = remove_from_path_fn() if remove_from_path_fn else [] # hook for tests remove_paths.insert(0, sys.path[0]) # remove the working directory with alter_sys_path(to_add=[], to_remove=remove_paths): try: module = importlib.import_module(module_name) except ImportError as ie: error = ie if error: try: module = importlib.import_module(module_name) # if here, we were able to resolve the module with the working directory on the path, # but should error because we may not always invoke from the same directory (e.g. from # cron) if warn_only: warnings.warn( ( 'Module {module} was resolved using the working directory. The ability to ' 'load uninstalled modules from the working directory is deprecated and ' 'will be removed in a future release. Please use the python-file based ' 'load arguments or install {module} to your python environment.' ).format(module=module_name) ) else: six.raise_from( DagsterInvariantViolationError( ( 'Module {module} not found. Packages must be installed rather than ' 'relying on the working directory to resolve module loading.' ).format(module=module_name) ), error, ) except ImportError as ie: raise error return module @whitelist_for_serdes class FileCodePointer(namedtuple('_FileCodePointer', 'python_file fn_name'), CodePointer): def __new__(cls, python_file, fn_name): return super(FileCodePointer, cls).__new__( cls, check.str_param(python_file, 'python_file'), check.str_param(fn_name, 'fn_name'), ) def load_target(self): module = load_python_file(self.python_file, None) if not hasattr(module, self.fn_name): raise DagsterInvariantViolationError( '{name} not found at module scope in file {file}.'.format( name=self.fn_name, file=self.python_file ) ) return getattr(module, self.fn_name) def describe(self): return '{self.python_file}::{self.fn_name}'.format(self=self) def get_cli_args(self): return '-f {python_file} -a {fn_name}'.format( python_file=os.path.abspath(os.path.expanduser(self.python_file)), fn_name=self.fn_name ) @whitelist_for_serdes class FileInDirectoryCodePointer( namedtuple('_FileInDirectoryCodePointer', 'python_file fn_name working_directory'), CodePointer ): ''' Same as FileCodePointer, but with an additional field `working_directory` to help resolve modules that are resolved from the python invocation directory. Required so other processes that need to resolve modules (e.g. cron scheduler) can do so. This could be merged with the `FileCodePointer` with `working_directory` as a None-able field, but not without changing the origin_id for schedules. This would require purging schedule storage to resolve. Should strongly consider merging when we need to do a storage migration. https://github.com/dagster-io/dagster/issues/2673 ''' def __new__(cls, python_file, fn_name, working_directory): return super(FileInDirectoryCodePointer, cls).__new__( cls, check.str_param(python_file, 'python_file'), check.str_param(fn_name, 'fn_name'), check.str_param(working_directory, 'working_directory'), ) def load_target(self): module = load_python_file(self.python_file, self.working_directory) if not hasattr(module, self.fn_name): raise DagsterInvariantViolationError( '{name} not found at module scope in file {file}.'.format( name=self.fn_name, file=self.python_file ) ) return getattr(module, self.fn_name) def describe(self): return '{self.python_file}::{self.fn_name} -- [dir {self.working_directory}]'.format( self=self ) def get_cli_args(self): return '-f {python_file} -a {fn_name} -d {directory}'.format( python_file=os.path.abspath(os.path.expanduser(self.python_file)), fn_name=self.fn_name, directory=self.working_directory, ) @whitelist_for_serdes class ModuleCodePointer(namedtuple('_ModuleCodePointer', 'module fn_name'), CodePointer): def __new__(cls, module, fn_name): return super(ModuleCodePointer, cls).__new__( cls, check.str_param(module, 'module'), check.str_param(fn_name, 'fn_name') ) def load_target(self): module = load_python_module(self.module, warn_only=True) if not hasattr(module, self.fn_name): raise DagsterInvariantViolationError( '{name} not found in module {module}. dir: {dir}'.format( name=self.fn_name, module=self.module, dir=dir(module) ) ) return getattr(module, self.fn_name) def describe(self): return 'from {self.module} import {self.fn_name}'.format(self=self) def get_cli_args(self): return '-m {module} -a {fn_name}'.format(module=self.module, fn_name=self.fn_name) @whitelist_for_serdes class PackageCodePointer(namedtuple('_PackageCodePointer', 'module attribute'), CodePointer): def __new__(cls, module, attribute): return super(PackageCodePointer, cls).__new__( cls, check.str_param(module, 'module'), check.str_param(attribute, 'attribute') ) def load_target(self): module = load_python_module(self.module) if not hasattr(module, self.attribute): raise DagsterInvariantViolationError( '{name} not found in module {module}. dir: {dir}'.format( name=self.attribute, module=self.module, dir=dir(module) ) ) return getattr(module, self.attribute) def describe(self): return 'from {self.module} import {self.attribute}'.format(self=self) def get_cli_args(self): return '-m {module} -a {attribute}'.format(module=self.module, attribute=self.attribute) def get_python_file_from_previous_stack_frame(): '''inspect.stack() lets us introspect the call stack; inspect.stack()[1] is the previous stack frame. In Python < 3.5, this is just a tuple, of which the python file of the previous frame is the 1st element. In Python 3.5+, this is a FrameInfo namedtuple instance; the python file of the previous frame remains the 1st element. ''' # Since this is now a function in this file, we need to go back two hops to find the # callsite file. previous_stack_frame = inspect.stack(0)[2] # See: https://docs.python.org/3/library/inspect.html if sys.version_info.major == 3 and sys.version_info.minor >= 5: check.inst(previous_stack_frame, inspect.FrameInfo) else: check.inst(previous_stack_frame, tuple) python_file = previous_stack_frame[1] return os.path.abspath(python_file)

39.030769

100

0.659361

import importlib import inspect import os import sys import warnings from abc import ABCMeta, abstractmethod from collections import namedtuple import six from dagster import check from dagster.core.errors import DagsterInvariantViolationError from dagster.serdes import whitelist_for_serdes from dagster.seven import import_module_from_path from dagster.utils import alter_sys_path, load_yaml_from_path class CodePointer(six.with_metaclass(ABCMeta)): @abstractmethod def load_target(self): pass @abstractmethod def describe(self): pass @staticmethod def from_module(module_name, definition): check.str_param(module_name, 'module_name') check.str_param(definition, 'definition') return ModuleCodePointer(module_name, definition) @staticmethod def from_python_package(module_name, attribute): check.str_param(module_name, 'module_name') check.str_param(attribute, 'attribute') return PackageCodePointer(module_name, attribute) @staticmethod def from_python_file(python_file, definition, working_directory): check.str_param(python_file, 'python_file') check.str_param(definition, 'definition') check.opt_str_param(working_directory, 'working_directory') if working_directory: return FileInDirectoryCodePointer( python_file=python_file, fn_name=definition, working_directory=working_directory ) return FileCodePointer(python_file=python_file, fn_name=definition) @staticmethod def from_legacy_repository_yaml(file_path): check.str_param(file_path, 'file_path') config = load_yaml_from_path(file_path) repository_config = check.dict_elem(config, 'repository') module_name = check.opt_str_elem(repository_config, 'module') file_name = check.opt_str_elem(repository_config, 'file') fn_name = check.str_elem(repository_config, 'fn') return ( CodePointer.from_module(module_name, fn_name) if module_name else CodePointer.from_python_file(rebase_file(file_name, file_path), fn_name, None) ) def rebase_file(relative_path_in_file, file_path_resides_in): check.str_param(relative_path_in_file, 'relative_path_in_file') check.str_param(file_path_resides_in, 'file_path_resides_in') return os.path.join( os.path.dirname(os.path.abspath(file_path_resides_in)), relative_path_in_file ) def load_python_file(python_file, working_directory): check.str_param(python_file, 'python_file') module_name = os.path.splitext(os.path.basename(python_file))[0] cwd = sys.path[0] if working_directory: with alter_sys_path(to_add=[working_directory], to_remove=[cwd]): return import_module_from_path(module_name, python_file) error = None sys_modules = {k: v for k, v in sys.modules.items()} with alter_sys_path(to_add=[], to_remove=[cwd]): try: module = import_module_from_path(module_name, python_file) except ImportError as ie: to_delete = set(sys.modules) - set(sys_modules) for key in to_delete: del sys.modules[key] error = ie if not error: return module try: module = import_module_from_path(module_name, python_file) warnings.warn( ( 'Module `{module}` was resolved using the working directory. The ability to ' 'implicitly load modules from the working directory is deprecated and ' 'will be removed in a future release. Please explicitly specify the ' '`working_directory` config option in your workspace.yaml or install `{module}` to ' 'your python environment.' ).format(module=error.name if hasattr(error, 'name') else module_name) ) return module except ImportError: raise error def load_python_module(module_name, warn_only=False, remove_from_path_fn=None): check.str_param(module_name, 'module_name') check.bool_param(warn_only, 'warn_only') check.opt_callable_param(remove_from_path_fn, 'remove_from_path_fn') error = None remove_paths = remove_from_path_fn() if remove_from_path_fn else [] remove_paths.insert(0, sys.path[0]) with alter_sys_path(to_add=[], to_remove=remove_paths): try: module = importlib.import_module(module_name) except ImportError as ie: error = ie if error: try: module = importlib.import_module(module_name) if warn_only: warnings.warn( ( 'Module {module} was resolved using the working directory. The ability to ' 'load uninstalled modules from the working directory is deprecated and ' 'will be removed in a future release. Please use the python-file based ' 'load arguments or install {module} to your python environment.' ).format(module=module_name) ) else: six.raise_from( DagsterInvariantViolationError( ( 'Module {module} not found. Packages must be installed rather than ' 'relying on the working directory to resolve module loading.' ).format(module=module_name) ), error, ) except ImportError as ie: raise error return module @whitelist_for_serdes class FileCodePointer(namedtuple('_FileCodePointer', 'python_file fn_name'), CodePointer): def __new__(cls, python_file, fn_name): return super(FileCodePointer, cls).__new__( cls, check.str_param(python_file, 'python_file'), check.str_param(fn_name, 'fn_name'), ) def load_target(self): module = load_python_file(self.python_file, None) if not hasattr(module, self.fn_name): raise DagsterInvariantViolationError( '{name} not found at module scope in file {file}.'.format( name=self.fn_name, file=self.python_file ) ) return getattr(module, self.fn_name) def describe(self): return '{self.python_file}::{self.fn_name}'.format(self=self) def get_cli_args(self): return '-f {python_file} -a {fn_name}'.format( python_file=os.path.abspath(os.path.expanduser(self.python_file)), fn_name=self.fn_name ) @whitelist_for_serdes class FileInDirectoryCodePointer( namedtuple('_FileInDirectoryCodePointer', 'python_file fn_name working_directory'), CodePointer ): def __new__(cls, python_file, fn_name, working_directory): return super(FileInDirectoryCodePointer, cls).__new__( cls, check.str_param(python_file, 'python_file'), check.str_param(fn_name, 'fn_name'), check.str_param(working_directory, 'working_directory'), ) def load_target(self): module = load_python_file(self.python_file, self.working_directory) if not hasattr(module, self.fn_name): raise DagsterInvariantViolationError( '{name} not found at module scope in file {file}.'.format( name=self.fn_name, file=self.python_file ) ) return getattr(module, self.fn_name) def describe(self): return '{self.python_file}::{self.fn_name} -- [dir {self.working_directory}]'.format( self=self ) def get_cli_args(self): return '-f {python_file} -a {fn_name} -d {directory}'.format( python_file=os.path.abspath(os.path.expanduser(self.python_file)), fn_name=self.fn_name, directory=self.working_directory, ) @whitelist_for_serdes class ModuleCodePointer(namedtuple('_ModuleCodePointer', 'module fn_name'), CodePointer): def __new__(cls, module, fn_name): return super(ModuleCodePointer, cls).__new__( cls, check.str_param(module, 'module'), check.str_param(fn_name, 'fn_name') ) def load_target(self): module = load_python_module(self.module, warn_only=True) if not hasattr(module, self.fn_name): raise DagsterInvariantViolationError( '{name} not found in module {module}. dir: {dir}'.format( name=self.fn_name, module=self.module, dir=dir(module) ) ) return getattr(module, self.fn_name) def describe(self): return 'from {self.module} import {self.fn_name}'.format(self=self) def get_cli_args(self): return '-m {module} -a {fn_name}'.format(module=self.module, fn_name=self.fn_name) @whitelist_for_serdes class PackageCodePointer(namedtuple('_PackageCodePointer', 'module attribute'), CodePointer): def __new__(cls, module, attribute): return super(PackageCodePointer, cls).__new__( cls, check.str_param(module, 'module'), check.str_param(attribute, 'attribute') ) def load_target(self): module = load_python_module(self.module) if not hasattr(module, self.attribute): raise DagsterInvariantViolationError( '{name} not found in module {module}. dir: {dir}'.format( name=self.attribute, module=self.module, dir=dir(module) ) ) return getattr(module, self.attribute) def describe(self): return 'from {self.module} import {self.attribute}'.format(self=self) def get_cli_args(self): return '-m {module} -a {attribute}'.format(module=self.module, attribute=self.attribute) def get_python_file_from_previous_stack_frame(): previous_stack_frame = inspect.stack(0)[2] if sys.version_info.major == 3 and sys.version_info.minor >= 5: check.inst(previous_stack_frame, inspect.FrameInfo) else: check.inst(previous_stack_frame, tuple) python_file = previous_stack_frame[1] return os.path.abspath(python_file)

true

790d886400acb4a4a179d95f684170cd908e401b

4,777

py

Python

metrics/__init__.py

nathan-bennett/skellam

8a1fff14ac8c5f6bd415a51befab818f864ab3e5

[ "Apache-2.0" ]

null

metrics/__init__.py

nathan-bennett/skellam

8a1fff14ac8c5f6bd415a51befab818f864ab3e5

[ "Apache-2.0" ]

null

metrics/__init__.py

nathan-bennett/skellam

8a1fff14ac8c5f6bd415a51befab818f864ab3e5

[ "Apache-2.0" ]

null

#!/usr/bin/env python import numpy as np import scipy from shared_utils import ArrayUtils class SkellamMetrics: def __init__(self, x_metrics, y_metrics, y_hat, model, l0, l1, training_values): self._y = y_metrics self._y_hat = y_hat self.model = model self.l0 = ArrayUtils.convert_to_array(l0) self.l1 = ArrayUtils.convert_to_array(l1) self.training_values = training_values self._x0, self._x1 = self.split_or_duplicate_x(x_metrics) self.max_ll = self.model.fun self.coeff_size = self._x0.shape[1] self.lambda_0_coefficients = self.model.x[0 : self.coeff_size].reshape(-1, 1) self.lambda_1_coefficients = self.model.x[self.coeff_size :].reshape(-1, 1) self.train_length = len(training_values[0]) @staticmethod def split_or_duplicate_x(x): return ArrayUtils.split_or_duplicate_x(x, False) def sse(self): return ((self._y - self._y_hat) ** 2).sum() def _y_bar(self): return self._y.mean() def sst(self): return ((self._y - self._y_bar()) ** 2).sum() def r2(self): """Calculate R2 for either the train model or the test model""" sse_sst = self.sse() / self.sst() return 1 - sse_sst def adjusted_r2(self): """Calculate adjusted R2 for either the train model or the test model""" r2 = self.r2() return 1 - (1-r2)*(self.train_length - 1)/(self.train_length - self.coeff_size - 1) def log_likelihood(self): """Returns the maximum of the log likelihood function""" return self.max_ll def aic(self): return 2*self.coeff_size - 2*np.log(self.max_ll) def bic(self): return self.coeff_size*np.log(self.train_length) - 2*np.log(self.max_ll) def _calculate_lambda(self): """Create arrays for our predictions of the two Poisson distributions """ _lambda0 = ArrayUtils.convert_to_array( np.exp(np.squeeze(self._x0 @ self.lambda_0_coefficients)) ) _lambda1 = ArrayUtils.convert_to_array( np.exp(np.squeeze(self._x1 @ self.lambda_1_coefficients)) ) return _lambda0, _lambda1 def _calculate_v(self): """Create diagonal matrix consisting of our predictions of the Poisson distributions """ _lambda0, _lambda1 = self._calculate_lambda() _v0 = np.diagflat(_lambda0) _v1 = np.diagflat(_lambda1) return _v0, _v1 def _calculate_w(self): """Create a diagonal matrix consisting of the difference between our predictions of the 2 Poisson distributions with their observed values """ _lambda0, _lambda1 = self._calculate_lambda() _w0 = np.diagflat((self.l0 - _lambda0.reshape(-1, 1)) ** 2) _w1 = np.diagflat((self.l1 - _lambda1.reshape(-1, 1)) ** 2) return _w0, _w1 def _calculate_robust_covariance(self): """Calculate robust variance covariance matrices for our two sets of coefficients """ _v0, _v1 = self._calculate_v() _w0, _w1 = self._calculate_w() _robust_cov0 = ( np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) * np.dot(np.dot(self._x0.T, _w0), self._x0) * np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) ) _robust_cov1 = ( np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)) * np.dot(np.dot(self._x1.T, _w1), self._x1) * np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)) ) return _robust_cov0, _robust_cov1 def _calculate_robust_standard_errors(self): """Calculate robust standard errors for our two sets of coefficients by taking the square root of the diagonal values in the variance covariance matrices """ _robust_cov0, _robust_cov1 = self._calculate_robust_covariance() _std_error0 = np.sqrt(np.diag(_robust_cov0)) _std_error1 = np.sqrt(np.diag(_robust_cov1)) return _std_error0, _std_error1 def _calculate_z_values(self): """Calculate z statistics for our two sets of coefficients """ _std_error0, _std_error1 = self._calculate_robust_standard_errors() _z_values0 = self.lambda_0_coefficients[:, 0] / _std_error0 _z_values1 = self.lambda_1_coefficients[:, 0] / _std_error1 return _z_values0, _z_values1 def _calculate_p_values(self): """Calculate p values for our two sets of coefficients """ _z_values0, _z_values1 = self._calculate_z_values() _p_values0 = scipy.stats.norm.sf(abs(_z_values0)) * 2 _p_values1 = scipy.stats.norm.sf(abs(_z_values1)) * 2 return _p_values0, _p_values1

38.524194

119

0.639941

import numpy as np import scipy from shared_utils import ArrayUtils class SkellamMetrics: def __init__(self, x_metrics, y_metrics, y_hat, model, l0, l1, training_values): self._y = y_metrics self._y_hat = y_hat self.model = model self.l0 = ArrayUtils.convert_to_array(l0) self.l1 = ArrayUtils.convert_to_array(l1) self.training_values = training_values self._x0, self._x1 = self.split_or_duplicate_x(x_metrics) self.max_ll = self.model.fun self.coeff_size = self._x0.shape[1] self.lambda_0_coefficients = self.model.x[0 : self.coeff_size].reshape(-1, 1) self.lambda_1_coefficients = self.model.x[self.coeff_size :].reshape(-1, 1) self.train_length = len(training_values[0]) @staticmethod def split_or_duplicate_x(x): return ArrayUtils.split_or_duplicate_x(x, False) def sse(self): return ((self._y - self._y_hat) ** 2).sum() def _y_bar(self): return self._y.mean() def sst(self): return ((self._y - self._y_bar()) ** 2).sum() def r2(self): sse_sst = self.sse() / self.sst() return 1 - sse_sst def adjusted_r2(self): r2 = self.r2() return 1 - (1-r2)*(self.train_length - 1)/(self.train_length - self.coeff_size - 1) def log_likelihood(self): return self.max_ll def aic(self): return 2*self.coeff_size - 2*np.log(self.max_ll) def bic(self): return self.coeff_size*np.log(self.train_length) - 2*np.log(self.max_ll) def _calculate_lambda(self): _lambda0 = ArrayUtils.convert_to_array( np.exp(np.squeeze(self._x0 @ self.lambda_0_coefficients)) ) _lambda1 = ArrayUtils.convert_to_array( np.exp(np.squeeze(self._x1 @ self.lambda_1_coefficients)) ) return _lambda0, _lambda1 def _calculate_v(self): _lambda0, _lambda1 = self._calculate_lambda() _v0 = np.diagflat(_lambda0) _v1 = np.diagflat(_lambda1) return _v0, _v1 def _calculate_w(self): _lambda0, _lambda1 = self._calculate_lambda() _w0 = np.diagflat((self.l0 - _lambda0.reshape(-1, 1)) ** 2) _w1 = np.diagflat((self.l1 - _lambda1.reshape(-1, 1)) ** 2) return _w0, _w1 def _calculate_robust_covariance(self): _v0, _v1 = self._calculate_v() _w0, _w1 = self._calculate_w() _robust_cov0 = ( np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) * np.dot(np.dot(self._x0.T, _w0), self._x0) * np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) ) _robust_cov1 = ( np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)) * np.dot(np.dot(self._x1.T, _w1), self._x1) * np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)) ) return _robust_cov0, _robust_cov1 def _calculate_robust_standard_errors(self): _robust_cov0, _robust_cov1 = self._calculate_robust_covariance() _std_error0 = np.sqrt(np.diag(_robust_cov0)) _std_error1 = np.sqrt(np.diag(_robust_cov1)) return _std_error0, _std_error1 def _calculate_z_values(self): _std_error0, _std_error1 = self._calculate_robust_standard_errors() _z_values0 = self.lambda_0_coefficients[:, 0] / _std_error0 _z_values1 = self.lambda_1_coefficients[:, 0] / _std_error1 return _z_values0, _z_values1 def _calculate_p_values(self): _z_values0, _z_values1 = self._calculate_z_values() _p_values0 = scipy.stats.norm.sf(abs(_z_values0)) * 2 _p_values1 = scipy.stats.norm.sf(abs(_z_values1)) * 2 return _p_values0, _p_values1

true

790d890e572f0484dc39deb4959d6eb47614406d

1,683

py

Python

compecon/demos/demddp04.py

daniel-schaefer/CompEcon-python

d3f66e04a7e02be648fc5a68065806ec7cc6ffd6

[ "MIT" ]

23

2016-12-14T13:21:27.000Z

2020-08-23T21:04:34.000Z

compecon/demos/demddp04.py

daniel-schaefer/CompEcon-python

d3f66e04a7e02be648fc5a68065806ec7cc6ffd6

[ "MIT" ]

1

2017-09-10T04:48:54.000Z

2018-03-31T01:36:46.000Z

compecon/demos/demddp04.py

daniel-schaefer/CompEcon-python

d3f66e04a7e02be648fc5a68065806ec7cc6ffd6

[ "MIT" ]

13

2017-02-25T08:10:38.000Z

2020-05-15T09:49:16.000Z

__author__ = 'Randall' from demos.setup import np, plt, demo from compecon import DDPmodel # DEMDDP04 Binomial American put option model # Model Parameters T = 0.5 # years to expiration sigma = 0.2 # annual volatility r = 0.05 # annual interest rate strike = 2.1 # option strike price p0 = 2.0 # current asset price # Discretization Parameters N = 100 # number of time intervals tau = T / N # length of time intervals delta = np.exp(-r * tau) # discount factor u = np.exp(sigma * np.sqrt(tau)) # up jump factor q = 0.5 + np.sqrt(tau) * (r - (sigma**2) / 2) / (2 * sigma) # up jump probability # State Space price = p0 * u ** np.arange(-N, N+1) # asset prices n = price.size # number of states # Action Space (hold=1, exercise=2) X = ['hold', 'exercise'] # vector of actions m = len(X) # number of actions # Reward Function f = np.zeros((m,n)) f[1] = strike - price # State Transition Probability Matrix P = np.zeros((m, n, n)) for i in range(n): P[0, i, min(i + 1, n - 1)] = q P[0, i, max(i - 1, 0)] = 1 - q # Model Structure model = DDPmodel(f, P, delta, horizon=N) model.solve() ## Analysis # Plot Optimal Exercise Boundary i, j = np.where(np.diff(model.policy[:-1], 1)) temp = (i * tau)[::-1] demo.figure('Put Option Optimal Exercise Boundary', 'Time to Maturity', 'Asset Price') plt.plot(temp, price[j]) # Plot Option Premium vs. Asset Price demo.figure('Put Option Value', 'Asset Price', 'Premium', [0, 2 * strike]) plt.plot([0, strike],[strike, 0], 'k--', lw=2) plt.plot(price, model.value[0], lw=3) plt.show()

29.017241

86

0.59893

__author__ = 'Randall' from demos.setup import np, plt, demo from compecon import DDPmodel T = 0.5 sigma = 0.2 r = 0.05 strike = 2.1 p0 = 2.0 N = 100 tau = T / N delta = np.exp(-r * tau) u = np.exp(sigma * np.sqrt(tau)) q = 0.5 + np.sqrt(tau) * (r - (sigma**2) / 2) / (2 * sigma) price = p0 * u ** np.arange(-N, N+1) n = price.size X = ['hold', 'exercise'] m = len(X) f = np.zeros((m,n)) f[1] = strike - price P = np.zeros((m, n, n)) for i in range(n): P[0, i, min(i + 1, n - 1)] = q P[0, i, max(i - 1, 0)] = 1 - q model = DDPmodel(f, P, delta, horizon=N) model.solve() np.where(np.diff(model.policy[:-1], 1)) temp = (i * tau)[::-1] demo.figure('Put Option Optimal Exercise Boundary', 'Time to Maturity', 'Asset Price') plt.plot(temp, price[j]) demo.figure('Put Option Value', 'Asset Price', 'Premium', [0, 2 * strike]) plt.plot([0, strike],[strike, 0], 'k--', lw=2) plt.plot(price, model.value[0], lw=3) plt.show()

true

790d89446cea7063ba034aae3075bfd56fe8dac1

11,029

py

Python

benchmarks/crypto.py

codeclimate-testing/falcon

c2d0b9da4d4cffd39cd489ffa886ee745d06f063

[ "Apache-2.0" ]

115

2015-01-18T13:28:05.000Z

2022-03-01T23:45:44.000Z

benchmarks/crypto.py

codeclimate-testing/falcon

c2d0b9da4d4cffd39cd489ffa886ee745d06f063

[ "Apache-2.0" ]

null

benchmarks/crypto.py

codeclimate-testing/falcon

c2d0b9da4d4cffd39cd489ffa886ee745d06f063

[ "Apache-2.0" ]

8

2015-02-12T04:08:42.000Z

2018-09-11T20:55:29.000Z

""" A pure python (slow) implementation of rijndael with a decent interface To include - from rijndael import rijndael To do a key setup - r = rijndael(key, block_size = 16) key must be a string of length 16, 24, or 32 blocksize must be 16, 24, or 32. Default is 16 To use - ciphertext = r.encrypt(plaintext) plaintext = r.decrypt(ciphertext) If any strings are of the wrong length a ValueError is thrown """ # ported from the Java reference code by Bram Cohen, April 2001 # this code is public domain, unless someone makes # an intellectual property claim against the reference # code, in which case it can be made public domain by # deleting all the comments and renaming all the variables import copy import string shifts = [[[0, 0], [1, 3], [2, 2], [3, 1]], [[0, 0], [1, 5], [2, 4], [3, 3]], [[0, 0], [1, 7], [3, 5], [4, 4]]] # [keysize][block_size] num_rounds = {16: {16: 10, 24: 12, 32: 14}, 24: {16: 12, 24: 12, 32: 14}, 32: {16: 14, 24: 14, 32: 14}} A = [[1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1], [1, 0, 0, 0, 1, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1], [1, 1, 1, 1, 0, 0, 0, 1]] # produce log and alog tables, needed for multiplying in the # field GF(2^m) (generator = 3) alog = [1] for i in range(255): j = (alog[-1] << 1) ^ alog[-1] if j & 0x100 != 0: j ^= 0x11B alog.append(j) log = [0] * 256 for i in range(1, 255): log[alog[i]] = i # multiply two elements of GF(2^m) def mul(a, b): if a == 0 or b == 0: return 0 return alog[(log[a & 0xFF] + log[b & 0xFF]) % 255] # substitution box based on F^{-1}(x) box = [[0] * 8 for i in range(256)] box[1][7] = 1 for i in range(2, 256): j = alog[255 - log[i]] for t in range(8): box[i][t] = (j >> (7 - t)) & 0x01 B = [0, 1, 1, 0, 0, 0, 1, 1] # affine transform: box[i] <- B + A*box[i] cox = [[0] * 8 for i in range(256)] for i in range(256): for t in range(8): cox[i][t] = B[t] for j in range(8): cox[i][t] ^= A[t][j] * box[i][j] # S-boxes and inverse S-boxes S = [0] * 256 Si = [0] * 256 for i in range(256): S[i] = cox[i][0] << 7 for t in range(1, 8): S[i] ^= cox[i][t] << (7-t) Si[S[i] & 0xFF] = i # T-boxes G = [[2, 1, 1, 3], [3, 2, 1, 1], [1, 3, 2, 1], [1, 1, 3, 2]] AA = [[0] * 8 for i in range(4)] for i in range(4): for j in range(4): AA[i][j] = G[i][j] AA[i][i+4] = 1 for i in range(4): pivot = AA[i][i] if pivot == 0: t = i + 1 while AA[t][i] == 0 and t < 4: t += 1 assert t != 4, 'G matrix must be invertible' for j in range(8): AA[i][j], AA[t][j] = AA[t][j], AA[i][j] pivot = AA[i][i] for j in range(8): if AA[i][j] != 0: AA[i][j] = alog[(255 + log[AA[i][j] & 0xFF] - log[pivot & 0xFF]) % 255] for t in range(4): if i != t: for j in range(i+1, 8): AA[t][j] ^= mul(AA[i][j], AA[t][i]) AA[t][i] = 0 iG = [[0] * 4 for i in range(4)] for i in range(4): for j in range(4): iG[i][j] = AA[i][j + 4] def mul4(a, bs): if a == 0: return 0 r = 0 for b in bs: r <<= 8 if b != 0: r = r | mul(a, b) return r T1 = [] T2 = [] T3 = [] T4 = [] T5 = [] T6 = [] T7 = [] T8 = [] U1 = [] U2 = [] U3 = [] U4 = [] for t in range(256): s = S[t] T1.append(mul4(s, G[0])) T2.append(mul4(s, G[1])) T3.append(mul4(s, G[2])) T4.append(mul4(s, G[3])) s = Si[t] T5.append(mul4(s, iG[0])) T6.append(mul4(s, iG[1])) T7.append(mul4(s, iG[2])) T8.append(mul4(s, iG[3])) U1.append(mul4(t, iG[0])) U2.append(mul4(t, iG[1])) U3.append(mul4(t, iG[2])) U4.append(mul4(t, iG[3])) # round constants rcon = [1] r = 1 for t in range(1, 30): r = mul(2, r) rcon.append(r) del A del AA del pivot del B del G del box del log del alog del i del j del r del s del t del mul del mul4 del cox del iG class rijndael(object): def __init__(self, key, block_size = 16): if block_size != 16 and block_size != 24 and block_size != 32: raise ValueError('Invalid block size: ' + str(block_size)) if len(key) != 16 and len(key) != 24 and len(key) != 32: raise ValueError('Invalid key size: ' + str(len(key))) self.block_size = block_size ROUNDS = num_rounds[len(key)][block_size] BC = block_size // 4 # encryption round keys Ke = [[0] * BC for i in range(ROUNDS + 1)] # decryption round keys Kd = [[0] * BC for i in range(ROUNDS + 1)] ROUND_KEY_COUNT = (ROUNDS + 1) * BC KC = len(key) // 4 # copy user material bytes into temporary ints tk = [] for i in range(0, KC): tk.append((ord(key[i * 4]) << 24) | (ord(key[i * 4 + 1]) << 16) | (ord(key[i * 4 + 2]) << 8) | ord(key[i * 4 + 3])) # copy values into round key arrays t = 0 j = 0 while j < KC and t < ROUND_KEY_COUNT: Ke[t // BC][t % BC] = tk[j] Kd[ROUNDS - (t // BC)][t % BC] = tk[j] j += 1 t += 1 tt = 0 rconpointer = 0 while t < ROUND_KEY_COUNT: # extrapolate using phi (the round key evolution function) tt = tk[KC - 1] tk[0] ^= (S[(tt >> 16) & 0xFF] & 0xFF) << 24 ^ \ (S[(tt >> 8) & 0xFF] & 0xFF) << 16 ^ \ (S[ tt & 0xFF] & 0xFF) << 8 ^ \ (S[(tt >> 24) & 0xFF] & 0xFF) ^ \ (rcon[rconpointer] & 0xFF) << 24 rconpointer += 1 if KC != 8: for i in range(1, KC): tk[i] ^= tk[i-1] else: for i in range(1, KC // 2): tk[i] ^= tk[i-1] tt = tk[KC // 2 - 1] tk[KC // 2] ^= (S[ tt & 0xFF] & 0xFF) ^ \ (S[(tt >> 8) & 0xFF] & 0xFF) << 8 ^ \ (S[(tt >> 16) & 0xFF] & 0xFF) << 16 ^ \ (S[(tt >> 24) & 0xFF] & 0xFF) << 24 for i in range(KC // 2 + 1, KC): tk[i] ^= tk[i-1] # copy values into round key arrays j = 0 while j < KC and t < ROUND_KEY_COUNT: Ke[t // BC][t % BC] = tk[j] Kd[ROUNDS - (t // BC)][t % BC] = tk[j] j += 1 t += 1 # inverse MixColumn where needed for r in range(1, ROUNDS): for j in range(BC): tt = Kd[r][j] Kd[r][j] = U1[(tt >> 24) & 0xFF] ^ \ U2[(tt >> 16) & 0xFF] ^ \ U3[(tt >> 8) & 0xFF] ^ \ U4[ tt & 0xFF] self.Ke = Ke self.Kd = Kd def encrypt(self, plaintext): if len(plaintext) != self.block_size: raise ValueError('wrong block length, expected ' + str(self.block_size) + ' got ' + str(len(plaintext))) Ke = self.Ke BC = self.block_size // 4 ROUNDS = len(Ke) - 1 if BC == 4: SC = 0 elif BC == 6: SC = 1 else: SC = 2 s1 = shifts[SC][1][0] s2 = shifts[SC][2][0] s3 = shifts[SC][3][0] a = [0] * BC # temporary work array t = [] # plaintext to ints + key for i in range(BC): t.append((ord(plaintext[i * 4 ]) << 24 | ord(plaintext[i * 4 + 1]) << 16 | ord(plaintext[i * 4 + 2]) << 8 | ord(plaintext[i * 4 + 3]) ) ^ Ke[0][i]) # apply round transforms for r in range(1, ROUNDS): for i in range(BC): a[i] = (T1[(t[ i ] >> 24) & 0xFF] ^ T2[(t[(i + s1) % BC] >> 16) & 0xFF] ^ T3[(t[(i + s2) % BC] >> 8) & 0xFF] ^ T4[ t[(i + s3) % BC] & 0xFF] ) ^ Ke[r][i] t = copy.copy(a) # last round is special result = [] for i in range(BC): tt = Ke[ROUNDS][i] result.append((S[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((S[(t[(i + s1) % BC] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((S[(t[(i + s2) % BC] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((S[ t[(i + s3) % BC] & 0xFF] ^ tt ) & 0xFF) return ''.join(map(chr, result)) def decrypt(self, ciphertext): if len(ciphertext) != self.block_size: raise ValueError('wrong block length, expected ' + str(self.block_size) + ' got ' + str(len(ciphertext))) Kd = self.Kd BC = self.block_size // 4 ROUNDS = len(Kd) - 1 if BC == 4: SC = 0 elif BC == 6: SC = 1 else: SC = 2 s1 = shifts[SC][1][1] s2 = shifts[SC][2][1] s3 = shifts[SC][3][1] a = [0] * BC # temporary work array t = [0] * BC # ciphertext to ints + key for i in range(BC): t[i] = (ord(ciphertext[i * 4 ]) << 24 | ord(ciphertext[i * 4 + 1]) << 16 | ord(ciphertext[i * 4 + 2]) << 8 | ord(ciphertext[i * 4 + 3]) ) ^ Kd[0][i] # apply round transforms for r in range(1, ROUNDS): for i in range(BC): a[i] = (T5[(t[ i ] >> 24) & 0xFF] ^ T6[(t[(i + s1) % BC] >> 16) & 0xFF] ^ T7[(t[(i + s2) % BC] >> 8) & 0xFF] ^ T8[ t[(i + s3) % BC] & 0xFF] ) ^ Kd[r][i] t = copy.copy(a) # last round is special result = [] for i in range(BC): tt = Kd[ROUNDS][i] result.append((Si[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((Si[(t[(i + s1) % BC] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((Si[(t[(i + s2) % BC] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((Si[ t[(i + s3) % BC] & 0xFF] ^ tt ) & 0xFF) return ''.join(map(chr, result)) def encrypt(key, block): return rijndael(key, len(block)).encrypt(block) def decrypt(key, block): return rijndael(key, len(block)).decrypt(block) def t(kl, bl): b = 'b' * bl r = rijndael('a' * kl, bl) assert r.decrypt(r.encrypt(b)) == b def multiple_calls(N): for _ in xrange(N): t(16, 24) t(16, 32) t(24, 16) t(24, 24) t(24, 32) t(32, 16) t(32, 24) t(32, 32) if __name__ == '__main__': n_repeats = 50 multiple_calls(n_repeats)

28.871728

117

0.430683

import copy import string shifts = [[[0, 0], [1, 3], [2, 2], [3, 1]], [[0, 0], [1, 5], [2, 4], [3, 3]], [[0, 0], [1, 7], [3, 5], [4, 4]]] num_rounds = {16: {16: 10, 24: 12, 32: 14}, 24: {16: 12, 24: 12, 32: 14}, 32: {16: 14, 24: 14, 32: 14}} A = [[1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1], [1, 0, 0, 0, 1, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1], [1, 1, 1, 1, 0, 0, 0, 1]] alog = [1] for i in range(255): j = (alog[-1] << 1) ^ alog[-1] if j & 0x100 != 0: j ^= 0x11B alog.append(j) log = [0] * 256 for i in range(1, 255): log[alog[i]] = i def mul(a, b): if a == 0 or b == 0: return 0 return alog[(log[a & 0xFF] + log[b & 0xFF]) % 255] box = [[0] * 8 for i in range(256)] box[1][7] = 1 for i in range(2, 256): j = alog[255 - log[i]] for t in range(8): box[i][t] = (j >> (7 - t)) & 0x01 B = [0, 1, 1, 0, 0, 0, 1, 1] cox = [[0] * 8 for i in range(256)] for i in range(256): for t in range(8): cox[i][t] = B[t] for j in range(8): cox[i][t] ^= A[t][j] * box[i][j] S = [0] * 256 Si = [0] * 256 for i in range(256): S[i] = cox[i][0] << 7 for t in range(1, 8): S[i] ^= cox[i][t] << (7-t) Si[S[i] & 0xFF] = i G = [[2, 1, 1, 3], [3, 2, 1, 1], [1, 3, 2, 1], [1, 1, 3, 2]] AA = [[0] * 8 for i in range(4)] for i in range(4): for j in range(4): AA[i][j] = G[i][j] AA[i][i+4] = 1 for i in range(4): pivot = AA[i][i] if pivot == 0: t = i + 1 while AA[t][i] == 0 and t < 4: t += 1 assert t != 4, 'G matrix must be invertible' for j in range(8): AA[i][j], AA[t][j] = AA[t][j], AA[i][j] pivot = AA[i][i] for j in range(8): if AA[i][j] != 0: AA[i][j] = alog[(255 + log[AA[i][j] & 0xFF] - log[pivot & 0xFF]) % 255] for t in range(4): if i != t: for j in range(i+1, 8): AA[t][j] ^= mul(AA[i][j], AA[t][i]) AA[t][i] = 0 iG = [[0] * 4 for i in range(4)] for i in range(4): for j in range(4): iG[i][j] = AA[i][j + 4] def mul4(a, bs): if a == 0: return 0 r = 0 for b in bs: r <<= 8 if b != 0: r = r | mul(a, b) return r T1 = [] T2 = [] T3 = [] T4 = [] T5 = [] T6 = [] T7 = [] T8 = [] U1 = [] U2 = [] U3 = [] U4 = [] for t in range(256): s = S[t] T1.append(mul4(s, G[0])) T2.append(mul4(s, G[1])) T3.append(mul4(s, G[2])) T4.append(mul4(s, G[3])) s = Si[t] T5.append(mul4(s, iG[0])) T6.append(mul4(s, iG[1])) T7.append(mul4(s, iG[2])) T8.append(mul4(s, iG[3])) U1.append(mul4(t, iG[0])) U2.append(mul4(t, iG[1])) U3.append(mul4(t, iG[2])) U4.append(mul4(t, iG[3])) rcon = [1] r = 1 for t in range(1, 30): r = mul(2, r) rcon.append(r) del A del AA del pivot del B del G del box del log del alog del i del j del r del s del t del mul del mul4 del cox del iG class rijndael(object): def __init__(self, key, block_size = 16): if block_size != 16 and block_size != 24 and block_size != 32: raise ValueError('Invalid block size: ' + str(block_size)) if len(key) != 16 and len(key) != 24 and len(key) != 32: raise ValueError('Invalid key size: ' + str(len(key))) self.block_size = block_size ROUNDS = num_rounds[len(key)][block_size] BC = block_size // 4 Ke = [[0] * BC for i in range(ROUNDS + 1)] Kd = [[0] * BC for i in range(ROUNDS + 1)] ROUND_KEY_COUNT = (ROUNDS + 1) * BC KC = len(key) // 4 tk = [] for i in range(0, KC): tk.append((ord(key[i * 4]) << 24) | (ord(key[i * 4 + 1]) << 16) | (ord(key[i * 4 + 2]) << 8) | ord(key[i * 4 + 3])) t = 0 j = 0 while j < KC and t < ROUND_KEY_COUNT: Ke[t // BC][t % BC] = tk[j] Kd[ROUNDS - (t // BC)][t % BC] = tk[j] j += 1 t += 1 tt = 0 rconpointer = 0 while t < ROUND_KEY_COUNT: tt = tk[KC - 1] tk[0] ^= (S[(tt >> 16) & 0xFF] & 0xFF) << 24 ^ \ (S[(tt >> 8) & 0xFF] & 0xFF) << 16 ^ \ (S[ tt & 0xFF] & 0xFF) << 8 ^ \ (S[(tt >> 24) & 0xFF] & 0xFF) ^ \ (rcon[rconpointer] & 0xFF) << 24 rconpointer += 1 if KC != 8: for i in range(1, KC): tk[i] ^= tk[i-1] else: for i in range(1, KC // 2): tk[i] ^= tk[i-1] tt = tk[KC // 2 - 1] tk[KC // 2] ^= (S[ tt & 0xFF] & 0xFF) ^ \ (S[(tt >> 8) & 0xFF] & 0xFF) << 8 ^ \ (S[(tt >> 16) & 0xFF] & 0xFF) << 16 ^ \ (S[(tt >> 24) & 0xFF] & 0xFF) << 24 for i in range(KC // 2 + 1, KC): tk[i] ^= tk[i-1] j = 0 while j < KC and t < ROUND_KEY_COUNT: Ke[t // BC][t % BC] = tk[j] Kd[ROUNDS - (t // BC)][t % BC] = tk[j] j += 1 t += 1 for r in range(1, ROUNDS): for j in range(BC): tt = Kd[r][j] Kd[r][j] = U1[(tt >> 24) & 0xFF] ^ \ U2[(tt >> 16) & 0xFF] ^ \ U3[(tt >> 8) & 0xFF] ^ \ U4[ tt & 0xFF] self.Ke = Ke self.Kd = Kd def encrypt(self, plaintext): if len(plaintext) != self.block_size: raise ValueError('wrong block length, expected ' + str(self.block_size) + ' got ' + str(len(plaintext))) Ke = self.Ke BC = self.block_size // 4 ROUNDS = len(Ke) - 1 if BC == 4: SC = 0 elif BC == 6: SC = 1 else: SC = 2 s1 = shifts[SC][1][0] s2 = shifts[SC][2][0] s3 = shifts[SC][3][0] a = [0] * BC t = [] for i in range(BC): t.append((ord(plaintext[i * 4 ]) << 24 | ord(plaintext[i * 4 + 1]) << 16 | ord(plaintext[i * 4 + 2]) << 8 | ord(plaintext[i * 4 + 3]) ) ^ Ke[0][i]) for r in range(1, ROUNDS): for i in range(BC): a[i] = (T1[(t[ i ] >> 24) & 0xFF] ^ T2[(t[(i + s1) % BC] >> 16) & 0xFF] ^ T3[(t[(i + s2) % BC] >> 8) & 0xFF] ^ T4[ t[(i + s3) % BC] & 0xFF] ) ^ Ke[r][i] t = copy.copy(a) result = [] for i in range(BC): tt = Ke[ROUNDS][i] result.append((S[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((S[(t[(i + s1) % BC] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((S[(t[(i + s2) % BC] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((S[ t[(i + s3) % BC] & 0xFF] ^ tt ) & 0xFF) return ''.join(map(chr, result)) def decrypt(self, ciphertext): if len(ciphertext) != self.block_size: raise ValueError('wrong block length, expected ' + str(self.block_size) + ' got ' + str(len(ciphertext))) Kd = self.Kd BC = self.block_size // 4 ROUNDS = len(Kd) - 1 if BC == 4: SC = 0 elif BC == 6: SC = 1 else: SC = 2 s1 = shifts[SC][1][1] s2 = shifts[SC][2][1] s3 = shifts[SC][3][1] a = [0] * BC t = [0] * BC for i in range(BC): t[i] = (ord(ciphertext[i * 4 ]) << 24 | ord(ciphertext[i * 4 + 1]) << 16 | ord(ciphertext[i * 4 + 2]) << 8 | ord(ciphertext[i * 4 + 3]) ) ^ Kd[0][i] for r in range(1, ROUNDS): for i in range(BC): a[i] = (T5[(t[ i ] >> 24) & 0xFF] ^ T6[(t[(i + s1) % BC] >> 16) & 0xFF] ^ T7[(t[(i + s2) % BC] >> 8) & 0xFF] ^ T8[ t[(i + s3) % BC] & 0xFF] ) ^ Kd[r][i] t = copy.copy(a) result = [] for i in range(BC): tt = Kd[ROUNDS][i] result.append((Si[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((Si[(t[(i + s1) % BC] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((Si[(t[(i + s2) % BC] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((Si[ t[(i + s3) % BC] & 0xFF] ^ tt ) & 0xFF) return ''.join(map(chr, result)) def encrypt(key, block): return rijndael(key, len(block)).encrypt(block) def decrypt(key, block): return rijndael(key, len(block)).decrypt(block) def t(kl, bl): b = 'b' * bl r = rijndael('a' * kl, bl) assert r.decrypt(r.encrypt(b)) == b def multiple_calls(N): for _ in xrange(N): t(16, 24) t(16, 32) t(24, 16) t(24, 24) t(24, 32) t(32, 16) t(32, 24) t(32, 32) if __name__ == '__main__': n_repeats = 50 multiple_calls(n_repeats)

true

790d894ac649eab4e1f3c6ca5bc2cad193bdd4e5

19,599

py

Python

tests/unit/test_validators/test_linearity_validator.py

abxsantos/analytical-validation-backend

1ea980f17be10562f2b9e9db384076374f445642

[ "MIT" ]

null

tests/unit/test_validators/test_linearity_validator.py

abxsantos/analytical-validation-backend

1ea980f17be10562f2b9e9db384076374f445642

[ "MIT" ]

6

2021-03-20T04:28:03.000Z

2022-01-21T20:32:07.000Z

tests/unit/test_validators/test_linearity_validator.py

abxsantos/analytical-validation-backend

1ea980f17be10562f2b9e9db384076374f445642

[ "MIT" ]

null

from unittest.mock import call, PropertyMock, MagicMock import pytest from analytical_validation.exceptions import DataWasNotFitted from src.analytical_validation.validators.linearity_validator import LinearityValidator @pytest.fixture(scope='function') def fitted_result_obj(mocker): mock = mocker.Mock(create=True) mock.params = (mocker.Mock(), mocker.Mock()) mock.pvalues = (mocker.Mock(), mocker.Mock()) mock.ess = MagicMock() mock.ssr = MagicMock() mock.df_model = MagicMock() mock.df_resid = MagicMock() mock.resid = mocker.Mock() return mock @pytest.fixture(scope='function') def linearity_validator_obj(fitted_result_obj): analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.1, 0.2, 0.3]] linearity_validator = LinearityValidator(analytical_data, concentration_data) linearity_validator.fitted_result = fitted_result_obj return linearity_validator @pytest.fixture(scope='function') def linearity_validator_outlier_obj(): analytical_data = [[1.0, 1.0, 10.0], [2.0, 6.0, 2.0]] concentration_data = [[1.0, 2.0, 3.0], [8.0, 9.0, 10.0]] return LinearityValidator(analytical_data, concentration_data) @pytest.fixture(scope='function') def het_breuschpagan_mock(mocker): het_breuschpagan_mock = mocker.patch('analytical_validation.validators.linearity_validator.' 'statsmodelsapi.het_breuschpagan') het_breuschpagan_mock.return_value = (33, 42) return het_breuschpagan_mock @pytest.fixture(scope='function') def shapiro_mock(mocker, linearity_validator_obj): shapiro_mock = mocker.patch('analytical_validation.validators.linearity_validator.scipy.stats') shapiro_mock.shapiro(linearity_validator_obj.analytical_data).return_value = (0, 1) return shapiro_mock @pytest.fixture(scope='function') def durbin_watson_mock(mocker): durbin_watson_mock = mocker.patch('analytical_validation.validators.linearity_validator.stattools.durbin_watson') durbin_watson_mock.return_value = 1 return durbin_watson_mock @pytest.fixture(scope='function') def add_constant_mock(mocker): add_constant_mock = mocker.patch( 'analytical_validation.validators.linearity_validator.statsmodels.add_constant') return add_constant_mock @pytest.fixture(scope='function') def ordinary_least_squares_regression_mock(mocker): ordinary_least_squares_regression_mock = mocker.patch( 'analytical_validation.validators.linearity_validator.statsmodels.OLS') return ordinary_least_squares_regression_mock class TestLinearityValidator(object): def test_constructor_must_create_object_when_analytical_data_has_float_values(self, linearity_validator_obj): """Given analytical data The LinearityValidator Should create a list of floats """ # Assert assert linearity_validator_obj.analytical_data == [0.100, 0.200, 0.150] assert linearity_validator_obj.concentration_data == [0.1, 0.2, 0.3] def test_ordinary_least_squares_linear_regression_must_pass_float_when_given_correct_data(self, ordinary_least_squares_regression_mock, add_constant_mock, linearity_validator_obj): """Given concentration values = float The ordinary_least_squares_linear_regression Then must set properties""" # Act linearity_validator_obj.ordinary_least_squares_linear_regression() # Assert assert linearity_validator_obj.fitted_result == ordinary_least_squares_regression_mock.return_value.fit.return_value # Garante que a regressao e resultado do resultado do metodo statsmodels.OLS(), aplicado .fit(). assert ordinary_least_squares_regression_mock.called # Garante que o metodo ols esta sendo chamado assert ordinary_least_squares_regression_mock.call_args_list == [ call(linearity_validator_obj.analytical_data, add_constant_mock.return_value) # Garante que os arquivos de entrada definidos no call foram utilizados ] assert add_constant_mock.called assert add_constant_mock.call_args_list == [ call(linearity_validator_obj.concentration_data) ] def test_slope_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.slope == fitted_result_obj.params[1] def test_intercept_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.intercept == fitted_result_obj.params[0] def test_r_squared_adjusted_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.r_squared_adj == fitted_result_obj.rsquared_adj def test_r_squared_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.r_squared == fitted_result_obj.rsquared def test_regression_residues_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): """Given a regression model when regression_residues is called the regression residues must be created""" assert linearity_validator_obj.regression_residues == fitted_result_obj.resid.tolist() def test_sum_of_squares_model_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.sum_of_squares_model == fitted_result_obj.ess def test_sum_of_squares_total_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.sum_of_squares_total == fitted_result_obj.ess + fitted_result_obj.ssr def test_sum_of_squares_resid_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.sum_of_squares_resid == fitted_result_obj.ssr def test_degrees_of_freedom_model_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.degrees_of_freedom_model == fitted_result_obj.df_model def test_degrees_of_freedom_residues_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.degrees_of_freedom_residues == fitted_result_obj.df_resid def test_degrees_of_freedom_total_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.degrees_of_freedom_total == fitted_result_obj.df_model + fitted_result_obj.df_resid def test_mean_squared_error_model_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.mean_squared_error_model == fitted_result_obj.mse_model def test_mean_squared_error_residues_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.mean_squared_error_residues == fitted_result_obj.mse_resid def test_anova_f_value_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.anova_f_value == fitted_result_obj.fvalue def test_anova_f_pvalue_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): # Act & assert assert linearity_validator_obj.anova_f_pvalue == fitted_result_obj.f_pvalue @pytest.mark.parametrize('param_anova_f_pvalue, param_alpha, expected_result', [ (0.051, 0.05, False), (10, 0.1, False), (0.049, 0.05, True), (0.001, 0.10, True) ]) def test_valid_anova_f_pvalue_must_return_true_when_r_squared_is_greater_than_0990(self, param_alpha, linearity_validator_obj, param_anova_f_pvalue, expected_result): """Given data with an aceptable regression model When valid_anova_f_pvalue is called Then anova_f_pvalue < alpha must assert true""" # Arrange linearity_validator_obj.alpha = param_alpha linearity_validator_obj.fitted_result.f_pvalue = param_anova_f_pvalue # Act & Assert assert linearity_validator_obj.valid_anova_f_pvalue is expected_result @pytest.mark.parametrize('param_alpha, param_breusch_pagan_pvalue, expected_result', [ (1, -10, False), (0.05, 0.049, False), (0.10, 0.11, True), (0.05, 10, True) ]) def test_is_homokedastic_must_return_false_when_breusch_pagan_pvalue_is_smaller_than_alpha_otherwise_true(self, param_alpha, param_breusch_pagan_pvalue, expected_result): # Arrange analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.1, 0.2, 0.3]] linearity_validator = LinearityValidator(analytical_data, concentration_data, param_alpha) linearity_validator.breusch_pagan_pvalue = param_breusch_pagan_pvalue # Act & Assert assert linearity_validator.is_homoscedastic is expected_result @pytest.mark.parametrize('param_significant_slope, param_alpha, expected_result', [ (0.051, 0.05, False), (10, 0.1, False), (0.049, 0.05, True), (0.001, 0.10, True) ]) def test_significant_slope_must_return_true_when_slope_pvalue_is_smaller_than_alpha(self, linearity_validator_obj, param_significant_slope, param_alpha, expected_result): """Given homokedastic data When check_hypothesis is called Then slope_is_significant must assert true""" # Arrange linearity_validator_obj.alpha = param_alpha linearity_validator_obj.fitted_result.pvalues = ("mock value", param_significant_slope) # Act & Assert assert linearity_validator_obj.significant_slope is expected_result @pytest.mark.parametrize('param_insignificant_intercept, param_alpha, expected_result', [ (0.051, 0.05, True), (10, 0.1, True), (0.049, 0.05, False), (0.001, 0.10, False) ]) def test_insignificant_intercept_must_return_true_when_intercept_pvalue_is_greater_than_alpha(self, linearity_validator_obj, param_alpha, param_insignificant_intercept, expected_result): """Given homokedastic data When check_hypothesis is called Then intercept_not_significant must assert true""" # Arrange linearity_validator_obj.alpha = param_alpha linearity_validator_obj.fitted_result.pvalues = (param_insignificant_intercept, "mock value") # Act & Assert assert linearity_validator_obj.insignificant_intercept is expected_result @pytest.mark.parametrize('param_r_squared, expected_result', [ (1, True), (0.99, True), (0.98, False) ]) def test_valid_r_squared_must_return_true_when_r_squared_is_greater_than_0990(self, linearity_validator_obj, param_r_squared, expected_result): """Given homokedastic data When check_hypothesis is called Then r_squared > 0.990 must assert true""" # Arrange linearity_validator_obj.fitted_result.rsquared = param_r_squared # Act & Assert assert linearity_validator_obj.valid_r_squared is expected_result @pytest.mark.parametrize( 'param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result', [ (True, True, True, True), (True, False, False, False), (True, True, False, False), (False, True, True, False), (False, True, False, False), (False, False, False, False) ]) def test_valid_regression_model(self, mocker, param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result): # Arrange mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.significant_slope', new_callable=PropertyMock, return_value=param_significant_slope) mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.insignificant_intercept', new_callable=PropertyMock, return_value=param_insignificant_intercept) mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.valid_r_squared', new_callable=PropertyMock, return_value=param_valid_r_squared) analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.2, 0.2, 0.3]] linearity_validator = LinearityValidator(analytical_data, concentration_data) # Act & Assert assert linearity_validator.valid_regression_model is expected_result def test_check_outliers_when_given_list_of_list_data(self, linearity_validator_outlier_obj): linearity_validator_outlier_obj.check_outliers() assert linearity_validator_outlier_obj.outliers == [[10.0], [6.0]] assert linearity_validator_outlier_obj.cleaned_analytical_data == [[1.0, 1.0], [2.0, 2.0]] assert linearity_validator_outlier_obj.cleaned_concentration_data == [[1.0, 2.0], [8.0, 10.0]] @pytest.mark.parametrize('param_shapiro_pvalue, param_alpha, expected_result', [ (10, 0.05, True), (0.01, 0.1, False), (0.0501, 0.05, True), (0.099, 0.1, False) ]) def test_is_normal_distribution(self, param_shapiro_pvalue, param_alpha, expected_result): analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.2, 0.2, 0.3]] validator = LinearityValidator(analytical_data, concentration_data, param_alpha) validator.shapiro_pvalue = param_shapiro_pvalue # Assert assert validator.is_normal_distribution is expected_result def test_run_breusch_pagan_test_must_raise_exception_when_model_is_none(self): """Not given a model parameter The check_homokedasticity Should raise exception""" # Arrange analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.2, 0.2, 0.3]] # Act & Assert with pytest.raises(DataWasNotFitted): LinearityValidator(analytical_data, concentration_data).run_breusch_pagan_test() def test_run_breusch_pagan_test(self, linearity_validator_obj, het_breuschpagan_mock): """Given heterokedastic data When check_homokedasticity is called Then must return false""" # Act linearity_validator_obj.run_breusch_pagan_test() # Assert assert linearity_validator_obj.breusch_pagan_pvalue == 42 assert het_breuschpagan_mock.called assert het_breuschpagan_mock.call_args_list == [ call(linearity_validator_obj.fitted_result.resid, linearity_validator_obj.fitted_result.model.exog) ] @pytest.mark.parametrize('durbin_watson_pvalue', [ 0.1, 1, 2, 2.5, 3, 3.9 ]) def test_check_residual_autocorrelation(self, linearity_validator_obj, durbin_watson_mock, durbin_watson_pvalue): """Given data When residual_autocorrelation is called Then must create durbin_watson_value""" # Arrange durbin_watson_mock.return_value = durbin_watson_pvalue # Act linearity_validator_obj.check_residual_autocorrelation() # Assert assert linearity_validator_obj.durbin_watson_value == durbin_watson_mock.return_value assert durbin_watson_mock.called assert durbin_watson_mock.call_args_list == [ call(linearity_validator_obj.fitted_result.resid) ] def test_check_residual_autocorrelation_must_raise_exception_when_data_not_fitted(self, linearity_validator_obj): """Given data, if no regression was calculated Should raise an exception""" # Arrange linearity_validator_obj.fitted_result = None # Act & assert with pytest.raises(DataWasNotFitted): linearity_validator_obj.check_residual_autocorrelation() @pytest.mark.parametrize('durbin_watson_pvalue', [ -1, 10, 4.1 ]) def test_check_residual_autocorrelation_must_pass_when_durbin_watson_value_is_between_0_and_4(self, linearity_validator_obj, durbin_watson_mock, durbin_watson_pvalue): """Given data, When check_residual is called after fitting the model Should pass creating 0 < durbin_watson_value < 4""" # Arrange durbin_watson_mock.return_value = durbin_watson_pvalue # Act & Assert assert linearity_validator_obj.durbin_watson_value is None

54.140884

222

0.643247

from unittest.mock import call, PropertyMock, MagicMock import pytest from analytical_validation.exceptions import DataWasNotFitted from src.analytical_validation.validators.linearity_validator import LinearityValidator @pytest.fixture(scope='function') def fitted_result_obj(mocker): mock = mocker.Mock(create=True) mock.params = (mocker.Mock(), mocker.Mock()) mock.pvalues = (mocker.Mock(), mocker.Mock()) mock.ess = MagicMock() mock.ssr = MagicMock() mock.df_model = MagicMock() mock.df_resid = MagicMock() mock.resid = mocker.Mock() return mock @pytest.fixture(scope='function') def linearity_validator_obj(fitted_result_obj): analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.1, 0.2, 0.3]] linearity_validator = LinearityValidator(analytical_data, concentration_data) linearity_validator.fitted_result = fitted_result_obj return linearity_validator @pytest.fixture(scope='function') def linearity_validator_outlier_obj(): analytical_data = [[1.0, 1.0, 10.0], [2.0, 6.0, 2.0]] concentration_data = [[1.0, 2.0, 3.0], [8.0, 9.0, 10.0]] return LinearityValidator(analytical_data, concentration_data) @pytest.fixture(scope='function') def het_breuschpagan_mock(mocker): het_breuschpagan_mock = mocker.patch('analytical_validation.validators.linearity_validator.' 'statsmodelsapi.het_breuschpagan') het_breuschpagan_mock.return_value = (33, 42) return het_breuschpagan_mock @pytest.fixture(scope='function') def shapiro_mock(mocker, linearity_validator_obj): shapiro_mock = mocker.patch('analytical_validation.validators.linearity_validator.scipy.stats') shapiro_mock.shapiro(linearity_validator_obj.analytical_data).return_value = (0, 1) return shapiro_mock @pytest.fixture(scope='function') def durbin_watson_mock(mocker): durbin_watson_mock = mocker.patch('analytical_validation.validators.linearity_validator.stattools.durbin_watson') durbin_watson_mock.return_value = 1 return durbin_watson_mock @pytest.fixture(scope='function') def add_constant_mock(mocker): add_constant_mock = mocker.patch( 'analytical_validation.validators.linearity_validator.statsmodels.add_constant') return add_constant_mock @pytest.fixture(scope='function') def ordinary_least_squares_regression_mock(mocker): ordinary_least_squares_regression_mock = mocker.patch( 'analytical_validation.validators.linearity_validator.statsmodels.OLS') return ordinary_least_squares_regression_mock class TestLinearityValidator(object): def test_constructor_must_create_object_when_analytical_data_has_float_values(self, linearity_validator_obj): assert linearity_validator_obj.analytical_data == [0.100, 0.200, 0.150] assert linearity_validator_obj.concentration_data == [0.1, 0.2, 0.3] def test_ordinary_least_squares_linear_regression_must_pass_float_when_given_correct_data(self, ordinary_least_squares_regression_mock, add_constant_mock, linearity_validator_obj): linearity_validator_obj.ordinary_least_squares_linear_regression() assert linearity_validator_obj.fitted_result == ordinary_least_squares_regression_mock.return_value.fit.return_value assert ordinary_least_squares_regression_mock.called assert ordinary_least_squares_regression_mock.call_args_list == [ call(linearity_validator_obj.analytical_data, add_constant_mock.return_value) ] assert add_constant_mock.called assert add_constant_mock.call_args_list == [ call(linearity_validator_obj.concentration_data) ] def test_slope_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.slope == fitted_result_obj.params[1] def test_intercept_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.intercept == fitted_result_obj.params[0] def test_r_squared_adjusted_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.r_squared_adj == fitted_result_obj.rsquared_adj def test_r_squared_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.r_squared == fitted_result_obj.rsquared def test_regression_residues_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.regression_residues == fitted_result_obj.resid.tolist() def test_sum_of_squares_model_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.sum_of_squares_model == fitted_result_obj.ess def test_sum_of_squares_total_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.sum_of_squares_total == fitted_result_obj.ess + fitted_result_obj.ssr def test_sum_of_squares_resid_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.sum_of_squares_resid == fitted_result_obj.ssr def test_degrees_of_freedom_model_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.degrees_of_freedom_model == fitted_result_obj.df_model def test_degrees_of_freedom_residues_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.degrees_of_freedom_residues == fitted_result_obj.df_resid def test_degrees_of_freedom_total_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.degrees_of_freedom_total == fitted_result_obj.df_model + fitted_result_obj.df_resid def test_mean_squared_error_model_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.mean_squared_error_model == fitted_result_obj.mse_model def test_mean_squared_error_residues_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.mean_squared_error_residues == fitted_result_obj.mse_resid def test_anova_f_value_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.anova_f_value == fitted_result_obj.fvalue def test_anova_f_pvalue_property_exists_when_fitted_result_not_none(self, linearity_validator_obj, fitted_result_obj): assert linearity_validator_obj.anova_f_pvalue == fitted_result_obj.f_pvalue @pytest.mark.parametrize('param_anova_f_pvalue, param_alpha, expected_result', [ (0.051, 0.05, False), (10, 0.1, False), (0.049, 0.05, True), (0.001, 0.10, True) ]) def test_valid_anova_f_pvalue_must_return_true_when_r_squared_is_greater_than_0990(self, param_alpha, linearity_validator_obj, param_anova_f_pvalue, expected_result): linearity_validator_obj.alpha = param_alpha linearity_validator_obj.fitted_result.f_pvalue = param_anova_f_pvalue assert linearity_validator_obj.valid_anova_f_pvalue is expected_result @pytest.mark.parametrize('param_alpha, param_breusch_pagan_pvalue, expected_result', [ (1, -10, False), (0.05, 0.049, False), (0.10, 0.11, True), (0.05, 10, True) ]) def test_is_homokedastic_must_return_false_when_breusch_pagan_pvalue_is_smaller_than_alpha_otherwise_true(self, param_alpha, param_breusch_pagan_pvalue, expected_result): analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.1, 0.2, 0.3]] linearity_validator = LinearityValidator(analytical_data, concentration_data, param_alpha) linearity_validator.breusch_pagan_pvalue = param_breusch_pagan_pvalue assert linearity_validator.is_homoscedastic is expected_result @pytest.mark.parametrize('param_significant_slope, param_alpha, expected_result', [ (0.051, 0.05, False), (10, 0.1, False), (0.049, 0.05, True), (0.001, 0.10, True) ]) def test_significant_slope_must_return_true_when_slope_pvalue_is_smaller_than_alpha(self, linearity_validator_obj, param_significant_slope, param_alpha, expected_result): linearity_validator_obj.alpha = param_alpha linearity_validator_obj.fitted_result.pvalues = ("mock value", param_significant_slope) assert linearity_validator_obj.significant_slope is expected_result @pytest.mark.parametrize('param_insignificant_intercept, param_alpha, expected_result', [ (0.051, 0.05, True), (10, 0.1, True), (0.049, 0.05, False), (0.001, 0.10, False) ]) def test_insignificant_intercept_must_return_true_when_intercept_pvalue_is_greater_than_alpha(self, linearity_validator_obj, param_alpha, param_insignificant_intercept, expected_result): linearity_validator_obj.alpha = param_alpha linearity_validator_obj.fitted_result.pvalues = (param_insignificant_intercept, "mock value") assert linearity_validator_obj.insignificant_intercept is expected_result @pytest.mark.parametrize('param_r_squared, expected_result', [ (1, True), (0.99, True), (0.98, False) ]) def test_valid_r_squared_must_return_true_when_r_squared_is_greater_than_0990(self, linearity_validator_obj, param_r_squared, expected_result): linearity_validator_obj.fitted_result.rsquared = param_r_squared assert linearity_validator_obj.valid_r_squared is expected_result @pytest.mark.parametrize( 'param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result', [ (True, True, True, True), (True, False, False, False), (True, True, False, False), (False, True, True, False), (False, True, False, False), (False, False, False, False) ]) def test_valid_regression_model(self, mocker, param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result): mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.significant_slope', new_callable=PropertyMock, return_value=param_significant_slope) mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.insignificant_intercept', new_callable=PropertyMock, return_value=param_insignificant_intercept) mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.valid_r_squared', new_callable=PropertyMock, return_value=param_valid_r_squared) analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.2, 0.2, 0.3]] linearity_validator = LinearityValidator(analytical_data, concentration_data) assert linearity_validator.valid_regression_model is expected_result def test_check_outliers_when_given_list_of_list_data(self, linearity_validator_outlier_obj): linearity_validator_outlier_obj.check_outliers() assert linearity_validator_outlier_obj.outliers == [[10.0], [6.0]] assert linearity_validator_outlier_obj.cleaned_analytical_data == [[1.0, 1.0], [2.0, 2.0]] assert linearity_validator_outlier_obj.cleaned_concentration_data == [[1.0, 2.0], [8.0, 10.0]] @pytest.mark.parametrize('param_shapiro_pvalue, param_alpha, expected_result', [ (10, 0.05, True), (0.01, 0.1, False), (0.0501, 0.05, True), (0.099, 0.1, False) ]) def test_is_normal_distribution(self, param_shapiro_pvalue, param_alpha, expected_result): analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.2, 0.2, 0.3]] validator = LinearityValidator(analytical_data, concentration_data, param_alpha) validator.shapiro_pvalue = param_shapiro_pvalue assert validator.is_normal_distribution is expected_result def test_run_breusch_pagan_test_must_raise_exception_when_model_is_none(self): analytical_data = [[0.100, 0.200, 0.150]] concentration_data = [[0.2, 0.2, 0.3]] with pytest.raises(DataWasNotFitted): LinearityValidator(analytical_data, concentration_data).run_breusch_pagan_test() def test_run_breusch_pagan_test(self, linearity_validator_obj, het_breuschpagan_mock): linearity_validator_obj.run_breusch_pagan_test() assert linearity_validator_obj.breusch_pagan_pvalue == 42 assert het_breuschpagan_mock.called assert het_breuschpagan_mock.call_args_list == [ call(linearity_validator_obj.fitted_result.resid, linearity_validator_obj.fitted_result.model.exog) ] @pytest.mark.parametrize('durbin_watson_pvalue', [ 0.1, 1, 2, 2.5, 3, 3.9 ]) def test_check_residual_autocorrelation(self, linearity_validator_obj, durbin_watson_mock, durbin_watson_pvalue): durbin_watson_mock.return_value = durbin_watson_pvalue linearity_validator_obj.check_residual_autocorrelation() assert linearity_validator_obj.durbin_watson_value == durbin_watson_mock.return_value assert durbin_watson_mock.called assert durbin_watson_mock.call_args_list == [ call(linearity_validator_obj.fitted_result.resid) ] def test_check_residual_autocorrelation_must_raise_exception_when_data_not_fitted(self, linearity_validator_obj): linearity_validator_obj.fitted_result = None with pytest.raises(DataWasNotFitted): linearity_validator_obj.check_residual_autocorrelation() @pytest.mark.parametrize('durbin_watson_pvalue', [ -1, 10, 4.1 ]) def test_check_residual_autocorrelation_must_pass_when_durbin_watson_value_is_between_0_and_4(self, linearity_validator_obj, durbin_watson_mock, durbin_watson_pvalue): durbin_watson_mock.return_value = durbin_watson_pvalue assert linearity_validator_obj.durbin_watson_value is None

true

790d89b051ad928e9d6e848f5756aacde4baebf3

1,049

py

Python

xlsxwriter/test/comparison/test_comment06.py

dthadi3/XlsxWriter

f1801e82240aa9c746ce14948ef95990b83162cf

[ "BSD-2-Clause-FreeBSD" ]

1

2020-07-01T07:24:37.000Z

xlsxwriter/test/comparison/test_comment06.py

dthadi3/XlsxWriter

f1801e82240aa9c746ce14948ef95990b83162cf

[ "BSD-2-Clause-FreeBSD" ]

null

xlsxwriter/test/comparison/test_comment06.py

dthadi3/XlsxWriter

f1801e82240aa9c746ce14948ef95990b83162cf

[ "BSD-2-Clause-FreeBSD" ]

null

############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013-2020, John McNamara, jmcnamara@cpan.org # from ..excel_comparison_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.set_filename('comment06.xlsx') def test_create_file(self): """Test the creation of a simple XlsxWriter file with comments.""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() worksheet.write_comment('A1', 'Some text') worksheet.write_comment('A2', 'Some text') worksheet.write_comment('A3', 'Some text', {'visible': True}) worksheet.write_comment('A4', 'Some text') worksheet.write_comment('A5', 'Some text') worksheet.set_comments_author('John') workbook.close() self.assertExcelEqual()

26.225

79

0.618684

true

790d8a145788a182d3cee1348ee6a22e7eba58ed

3,354

py

Python

google-cloud-sdk/lib/googlecloudsdk/core/diagnostics/diagnostic_base.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

1

2017-11-29T18:52:27.000Z

google-cloud-sdk/lib/googlecloudsdk/core/diagnostics/diagnostic_base.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

null

google-cloud-sdk/lib/googlecloudsdk/core/diagnostics/diagnostic_base.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

3

2017-07-27T18:44:13.000Z

2020-07-25T17:48:53.000Z

# Copyright 2016 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Base classes for diagnostics.""" from googlecloudsdk.core import log from googlecloudsdk.core import properties from googlecloudsdk.core.console import progress_tracker class Diagnostic(object): """Base class for diagnostics. Attributes: intro: A message to introduce the objectives and tasks of the diagnostic. title: The name of the diagnostic. checklist: An iterator of checkbase.Check objects to be run by the diagnostic. """ _MAX_RETRIES = 5 def __init__(self, intro, title, checklist): """Initializes Diagnostic with neccessary attributes. Args: intro: A message to introduce the objectives and tasks of the diagnostic. title: The name of the diagnostic. checklist: An iterable of checkbase.Check objects to be run by the diagnostic. """ self.intro = intro self.title = title self.checklist = checklist def RunChecks(self): """Runs one or more checks, tries fixes, and outputs results. Returns: True if the diagnostic ultimately passed. """ self._Print(self.intro) num_checks_passed = 0 for check in self.checklist: result, fixer = self._RunCheck(check) if properties.VALUES.core.disable_prompts.GetBool(): continue # If the initial check failed, and a fixer is available try to fix issue # and recheck. num_retries = 0 while not result.passed and fixer and num_retries < self._MAX_RETRIES: num_retries += 1 should_check_again = fixer() if should_check_again: result, fixer = self._RunCheck(check, first_run=False) else: fixer = None if not result.passed and fixer and num_retries == self._MAX_RETRIES: log.warn('Unable to fix {0} failure after {1} attempts.'.format( self.title, num_retries)) if result.passed: num_checks_passed += 1 num_checks = len(self.checklist) passed = (num_checks_passed == num_checks) summary = '{check} ({num_passed}/{num_checks} checks) {passed}.\n'.format( check=self.title, num_passed=num_checks_passed, num_checks=num_checks, passed='passed' if passed else 'failed') self._Print(summary, as_error=not passed) return passed def _RunCheck(self, check, first_run=True): with progress_tracker.ProgressTracker('{0} {1}'.format( 'Checking' if first_run else 'Rechecking', check.issue)): result, fixer = check.Check(first_run=first_run) self._PrintResult(result) return result, fixer def _Print(self, message, as_error=False): logger = log.status.Print if not as_error else log.error logger(message) def _PrintResult(self, result): self._Print(result.message, not result.passed)

33.207921

79

0.699463

from googlecloudsdk.core import log from googlecloudsdk.core import properties from googlecloudsdk.core.console import progress_tracker class Diagnostic(object): _MAX_RETRIES = 5 def __init__(self, intro, title, checklist): self.intro = intro self.title = title self.checklist = checklist def RunChecks(self): self._Print(self.intro) num_checks_passed = 0 for check in self.checklist: result, fixer = self._RunCheck(check) if properties.VALUES.core.disable_prompts.GetBool(): continue num_retries = 0 while not result.passed and fixer and num_retries < self._MAX_RETRIES: num_retries += 1 should_check_again = fixer() if should_check_again: result, fixer = self._RunCheck(check, first_run=False) else: fixer = None if not result.passed and fixer and num_retries == self._MAX_RETRIES: log.warn('Unable to fix {0} failure after {1} attempts.'.format( self.title, num_retries)) if result.passed: num_checks_passed += 1 num_checks = len(self.checklist) passed = (num_checks_passed == num_checks) summary = '{check} ({num_passed}/{num_checks} checks) {passed}.\n'.format( check=self.title, num_passed=num_checks_passed, num_checks=num_checks, passed='passed' if passed else 'failed') self._Print(summary, as_error=not passed) return passed def _RunCheck(self, check, first_run=True): with progress_tracker.ProgressTracker('{0} {1}'.format( 'Checking' if first_run else 'Rechecking', check.issue)): result, fixer = check.Check(first_run=first_run) self._PrintResult(result) return result, fixer def _Print(self, message, as_error=False): logger = log.status.Print if not as_error else log.error logger(message) def _PrintResult(self, result): self._Print(result.message, not result.passed)

true

790d8aa3dd20e63992a5022c4c718a8f25bcdb4a

371

py

Python

doc/for_dev/scikit-image/setup_codes/cmorph__dilate.py

fluiddyn/transonic

a460e9f6d1139f79b668cb3306d1e8a7e190b72d

[ "BSD-3-Clause" ]

88

2019-01-08T16:39:08.000Z

2022-02-06T14:19:23.000Z

doc/for_dev/scikit-image/setup_codes/cmorph__dilate.py

fluiddyn/transonic

a460e9f6d1139f79b668cb3306d1e8a7e190b72d

[ "BSD-3-Clause" ]

13

2019-06-20T15:53:10.000Z

2021-02-09T11:03:29.000Z

doc/for_dev/scikit-image/setup_codes/cmorph__dilate.py

fluiddyn/transonic

a460e9f6d1139f79b668cb3306d1e8a7e190b72d

[ "BSD-3-Clause" ]

1

2019-11-05T03:03:14.000Z

import numpy as np from future.cmorph import _dilate rows = 1024 cols = 1024 srows = 64 scols = 64 image = np.random.randint(0, 255, rows * cols, dtype=np.uint8).reshape( (rows, cols) ) selem = np.random.randint(0, 1, srows * scols, dtype=np.uint8).reshape( (srows, scols) ) out = np.zeros((rows, cols), dtype=np.uint8) shift_x = np.int8(2) shift_y = np.int8(2)

21.823529

71

0.679245

import numpy as np from future.cmorph import _dilate rows = 1024 cols = 1024 srows = 64 scols = 64 image = np.random.randint(0, 255, rows * cols, dtype=np.uint8).reshape( (rows, cols) ) selem = np.random.randint(0, 1, srows * scols, dtype=np.uint8).reshape( (srows, scols) ) out = np.zeros((rows, cols), dtype=np.uint8) shift_x = np.int8(2) shift_y = np.int8(2)

true

790d8c973d8f3371872a0ff0b9877108bc43a039

622

py

Python

sdk/keyvault/azure-mgmt-keyvault/azure/mgmt/keyvault/v2016_10_01/aio/operations_async/__init__.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

2

2019-05-17T21:24:53.000Z

2020-02-12T11:13:42.000Z

sdk/keyvault/azure-mgmt-keyvault/azure/mgmt/keyvault/v2016_10_01/aio/operations_async/__init__.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

15

2019-07-12T18:18:04.000Z

2019-07-25T20:55:51.000Z

sdk/keyvault/azure-mgmt-keyvault/azure/mgmt/keyvault/v2016_10_01/aio/operations_async/__init__.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

2

2020-05-21T22:51:22.000Z

2020-05-26T20:53:01.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from ._vaults_operations_async import VaultsOperations from ._operations_async import Operations __all__ = [ 'VaultsOperations', 'Operations', ]

38.875

94

0.588424

from ._vaults_operations_async import VaultsOperations from ._operations_async import Operations __all__ = [ 'VaultsOperations', 'Operations', ]

true

790d8d10c93b27d59e0cddbc0638ac05326fbd57

3,359

py

Python

djng/middleware.py

shriDeveloper/django-angular

b32a910b0e154e5707a10fe3e58de1542fd4183b

[ "MIT" ]

1

2020-01-09T12:18:59.000Z

djng/middleware.py

shriDeveloper/django-angular

b32a910b0e154e5707a10fe3e58de1542fd4183b

[ "MIT" ]

null

djng/middleware.py

shriDeveloper/django-angular

b32a910b0e154e5707a10fe3e58de1542fd4183b

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import six from django import http from django.urls import reverse from django.utils.http import unquote try: from django.utils.deprecation import MiddlewareMixin except ImportError: MiddlewareMixin = object class AngularUrlMiddleware(MiddlewareMixin): """ If the request path is <ANGULAR_REVERSE> it should be resolved to actual view, otherwise return ``None`` and continue as usual. This must be the first middleware in the MIDDLEWARE_CLASSES tuple! """ ANGULAR_REVERSE = '/angular/reverse/' def process_request(self, request): """ Reads url name, args, kwargs from GET parameters, reverses the url and resolves view function Returns the result of resolved view function, called with provided args and kwargs Since the view function is called directly, it isn't ran through middlewares, so the middlewares must be added manually The final result is exactly the same as if the request was for the resolved view. Parametrized urls: djangoUrl.reverse can be used with parametrized urls of $resource In that case the reverse url is something like: /angular/reverse/?djng_url_name=orders&djng_url_kwarg_id=:id $resource can either replace the ':id' part with say 2 and we can proceed as usual, reverse with reverse('orders', kwargs={'id': 2}). If it's not replaced we want to reverse to url we get a request to url '/angular/reverse/?djng_url_name=orders&djng_url_kwarg_id=' which gives a request.GET QueryDict {u'djng_url_name': [u'orders'], u'djng_url_kwarg_id': [u'']} In that case we want to ignore the id param and only reverse to url with name 'orders' and no params. So we ignore args and kwargs that are empty strings. """ if request.path == self.ANGULAR_REVERSE: url_name = request.GET.get('djng_url_name') url_args = request.GET.getlist('djng_url_args', []) url_kwargs = {} # Remove falsy values (empty strings) url_args = filter(lambda x: x, url_args) # Read kwargs for param in request.GET: if param.startswith('djng_url_kwarg_'): # Ignore kwargs that are empty strings if request.GET[param]: url_kwargs[param[15:]] = request.GET[param] # [15:] to remove 'djng_url_kwarg' prefix url = unquote(reverse(url_name, args=url_args, kwargs=url_kwargs)) assert not url.startswith(self.ANGULAR_REVERSE), "Prevent recursive requests" # rebuild the request object with a different environ request.path = request.path_info = url request.environ['PATH_INFO'] = url query = request.GET.copy() for key in request.GET: if key.startswith('djng_url'): query.pop(key, None) if six.PY3: request.environ['QUERY_STRING'] = query.urlencode() else: request.environ['QUERY_STRING'] = query.urlencode().encode('utf-8') # Reconstruct GET QueryList in the same way WSGIRequest.GET function works request.GET = http.QueryDict(request.environ['QUERY_STRING'])

45.391892

116

0.650789

from __future__ import unicode_literals import six from django import http from django.urls import reverse from django.utils.http import unquote try: from django.utils.deprecation import MiddlewareMixin except ImportError: MiddlewareMixin = object class AngularUrlMiddleware(MiddlewareMixin): ANGULAR_REVERSE = '/angular/reverse/' def process_request(self, request): if request.path == self.ANGULAR_REVERSE: url_name = request.GET.get('djng_url_name') url_args = request.GET.getlist('djng_url_args', []) url_kwargs = {} url_args = filter(lambda x: x, url_args) for param in request.GET: if param.startswith('djng_url_kwarg_'): if request.GET[param]: url_kwargs[param[15:]] = request.GET[param] url = unquote(reverse(url_name, args=url_args, kwargs=url_kwargs)) assert not url.startswith(self.ANGULAR_REVERSE), "Prevent recursive requests" request.path = request.path_info = url request.environ['PATH_INFO'] = url query = request.GET.copy() for key in request.GET: if key.startswith('djng_url'): query.pop(key, None) if six.PY3: request.environ['QUERY_STRING'] = query.urlencode() else: request.environ['QUERY_STRING'] = query.urlencode().encode('utf-8') request.GET = http.QueryDict(request.environ['QUERY_STRING'])

true

790d8d6c49aacc1ce2a70311d3da94451ba61e50

1,214

py

Python

Toolz/sqlmap/tamper/charunicodeescape.py

thezakman/CTF-Toolz

b369246ea6766165cce0852e537fb6a0c970869b

[ "Unlicense" ]

71

2019-02-02T11:38:46.000Z

2022-03-31T14:08:27.000Z

tools/sqlmap/tamper/charunicodeescape.py

sravani-m/Web-Application-Security-Framework

d9f71538f5cba6fe1d8eabcb26c557565472f6a6

[ "MIT" ]

null

tools/sqlmap/tamper/charunicodeescape.py

sravani-m/Web-Application-Security-Framework

d9f71538f5cba6fe1d8eabcb26c557565472f6a6

[ "MIT" ]

15

2019-08-07T06:32:04.000Z

2022-03-09T12:48:20.000Z

#!/usr/bin/env python """ Copyright (c) 2006-2019 sqlmap developers (http://sqlmap.org/) See the file 'LICENSE' for copying permission """ import string from lib.core.enums import PRIORITY __priority__ = PRIORITY.NORMAL def tamper(payload, **kwargs): """ Unicode-escapes non-encoded characters in a given payload (not processing already encoded) (e.g. SELECT -> \u0053\u0045\u004C\u0045\u0043\u0054) Notes: * Useful to bypass weak filtering and/or WAFs in JSON contexes >>> tamper('SELECT FIELD FROM TABLE') '\\\\u0053\\\\u0045\\\\u004C\\\\u0045\\\\u0043\\\\u0054\\\\u0020\\\\u0046\\\\u0049\\\\u0045\\\\u004C\\\\u0044\\\\u0020\\\\u0046\\\\u0052\\\\u004F\\\\u004D\\\\u0020\\\\u0054\\\\u0041\\\\u0042\\\\u004C\\\\u0045' """ retVal = payload if payload: retVal = "" i = 0 while i < len(payload): if payload[i] == '%' and (i < len(payload) - 2) and payload[i + 1:i + 2] in string.hexdigits and payload[i + 2:i + 3] in string.hexdigits: retVal += "\\u00%s" % payload[i + 1:i + 3] i += 3 else: retVal += '\\u%.4X' % ord(payload[i]) i += 1 return retVal

30.35

213

0.57084

import string from lib.core.enums import PRIORITY __priority__ = PRIORITY.NORMAL def tamper(payload, **kwargs): retVal = payload if payload: retVal = "" i = 0 while i < len(payload): if payload[i] == '%' and (i < len(payload) - 2) and payload[i + 1:i + 2] in string.hexdigits and payload[i + 2:i + 3] in string.hexdigits: retVal += "\\u00%s" % payload[i + 1:i + 3] i += 3 else: retVal += '\\u%.4X' % ord(payload[i]) i += 1 return retVal

true

790d8d88be5c0cc63cebc70f5ce66d0a202515c4

3,957

py

Python

tests/lib/copy_engines/test_bbcp_copier.py

SVilgelm/CloudFerry

4459c0d21ba7ccffe51176932197b352e426ba63

[ "Apache-2.0" ]

6

2017-04-20T00:49:49.000Z

2020-12-20T16:27:10.000Z

tests/lib/copy_engines/test_bbcp_copier.py

SVilgelm/CloudFerry

4459c0d21ba7ccffe51176932197b352e426ba63

[ "Apache-2.0" ]

3

2017-04-08T15:47:16.000Z

2017-05-18T17:40:59.000Z

tests/lib/copy_engines/test_bbcp_copier.py

SVilgelm/CloudFerry

4459c0d21ba7ccffe51176932197b352e426ba63

[ "Apache-2.0" ]

8

2017-04-07T23:42:36.000Z

2021-08-10T11:05:10.000Z

# Copyright (c) 2016 Mirantis Inc. # # Licensed under the Apache License, Version 2.0 (the License); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an AS IS BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and# # limitations under the License. import mock from cloudferry.lib.copy_engines import base from cloudferry.lib.copy_engines import bbcp_copier from cloudferry.lib.utils import remote_runner from tests.lib.copy_engines import test_base from tests import test class BbcpCopierTestCase(test_base.BaseTestCase): copier_class = bbcp_copier.BbcpCopier def setUp(self): super(BbcpCopierTestCase, self).setUp() self.src_cloud.hosts_with_bbcp = set() self.dst_cloud.hosts_with_bbcp = set() @mock.patch('cloudferry.lib.utils.utils.forward_agent') @mock.patch('os.path.isfile') def test_usage_false(self, mock_isfile, _): mock_isfile.return_value = False self.assertFalse(self.copier.check_usage(self.data)) mock_isfile.return_value = True with mock.patch.object(self.copier, 'copy_bbcp', side_effect=remote_runner.RemoteExecutionError): self.assertFalse(self.copier.check_usage(self.data)) @mock.patch('os.path.isfile') def test_usage_true(self, mock_isfile): mock_isfile.return_value = True with mock.patch.object(self.copier, 'copy_bbcp') as mock_copy_bbcp: self.assertTrue(self.copier.check_usage(self.data)) self.assertEqual(2, mock_copy_bbcp.call_count) mock_copy_bbcp.reset_mock() self.assertTrue(self.copier.check_usage(self.data)) mock_copy_bbcp.assert_not_called() def test_transfer_direct_true(self): with self.mock_runner() as mock_runner: self.copier.transfer(self.data) self.assertCalledOnce(mock_runner.run) mock_runner.reset_mock() self.cfg.set_override('retry', 2, 'migrate') mock_runner.run.side_effect = remote_runner.RemoteExecutionError() with mock.patch.object(self.copier, 'clean_dst') as mock_clean_dst: self.assertRaises(base.FileCopyError, self.copier.transfer, self.data) self.assertEqual(2, mock_runner.run.call_count) self.assertCalledOnce(mock_clean_dst) @mock.patch('cloudferry.lib.utils.local.run') def test_transfer_direct_false(self, mock_run): self.cfg.set_override('direct_transfer', False, 'migrate') self.copier.transfer(self.data) self.assertCalledOnce(mock_run) @mock.patch('cloudferry.lib.utils.local.run') def test_copy_bbcp(self, mock_run): with self.mock_runner() as runner: self.copier.copy_bbcp('fake_host', 'src') self.assertCalledOnce(runner.run) mock_run.assert_not_called() runner.reset_mock() runner.run.side_effect = (remote_runner.RemoteExecutionError, None) self.copier.copy_bbcp('fake_host', 'src') self.assertEqual(2, runner.run.call_count) self.assertCalledOnce(mock_run) class RemoveBBCPTestCase(test.TestCase): @mock.patch('cloudferry.lib.utils.remote_runner.RemoteRunner.' 'run_ignoring_errors') def test_remove_bbcp(self, mock_run_ignoring_errors): cloud = mock.Mock() cloud.hosts_with_bbcp = {'fake_host_1', 'fake_host_2'} cloud.position = 'src' bbcp_copier.remove_bbcp(cloud) self.assertEqual(2, mock_run_ignoring_errors.call_count)

39.969697

79

0.682841

import mock from cloudferry.lib.copy_engines import base from cloudferry.lib.copy_engines import bbcp_copier from cloudferry.lib.utils import remote_runner from tests.lib.copy_engines import test_base from tests import test class BbcpCopierTestCase(test_base.BaseTestCase): copier_class = bbcp_copier.BbcpCopier def setUp(self): super(BbcpCopierTestCase, self).setUp() self.src_cloud.hosts_with_bbcp = set() self.dst_cloud.hosts_with_bbcp = set() @mock.patch('cloudferry.lib.utils.utils.forward_agent') @mock.patch('os.path.isfile') def test_usage_false(self, mock_isfile, _): mock_isfile.return_value = False self.assertFalse(self.copier.check_usage(self.data)) mock_isfile.return_value = True with mock.patch.object(self.copier, 'copy_bbcp', side_effect=remote_runner.RemoteExecutionError): self.assertFalse(self.copier.check_usage(self.data)) @mock.patch('os.path.isfile') def test_usage_true(self, mock_isfile): mock_isfile.return_value = True with mock.patch.object(self.copier, 'copy_bbcp') as mock_copy_bbcp: self.assertTrue(self.copier.check_usage(self.data)) self.assertEqual(2, mock_copy_bbcp.call_count) mock_copy_bbcp.reset_mock() self.assertTrue(self.copier.check_usage(self.data)) mock_copy_bbcp.assert_not_called() def test_transfer_direct_true(self): with self.mock_runner() as mock_runner: self.copier.transfer(self.data) self.assertCalledOnce(mock_runner.run) mock_runner.reset_mock() self.cfg.set_override('retry', 2, 'migrate') mock_runner.run.side_effect = remote_runner.RemoteExecutionError() with mock.patch.object(self.copier, 'clean_dst') as mock_clean_dst: self.assertRaises(base.FileCopyError, self.copier.transfer, self.data) self.assertEqual(2, mock_runner.run.call_count) self.assertCalledOnce(mock_clean_dst) @mock.patch('cloudferry.lib.utils.local.run') def test_transfer_direct_false(self, mock_run): self.cfg.set_override('direct_transfer', False, 'migrate') self.copier.transfer(self.data) self.assertCalledOnce(mock_run) @mock.patch('cloudferry.lib.utils.local.run') def test_copy_bbcp(self, mock_run): with self.mock_runner() as runner: self.copier.copy_bbcp('fake_host', 'src') self.assertCalledOnce(runner.run) mock_run.assert_not_called() runner.reset_mock() runner.run.side_effect = (remote_runner.RemoteExecutionError, None) self.copier.copy_bbcp('fake_host', 'src') self.assertEqual(2, runner.run.call_count) self.assertCalledOnce(mock_run) class RemoveBBCPTestCase(test.TestCase): @mock.patch('cloudferry.lib.utils.remote_runner.RemoteRunner.' 'run_ignoring_errors') def test_remove_bbcp(self, mock_run_ignoring_errors): cloud = mock.Mock() cloud.hosts_with_bbcp = {'fake_host_1', 'fake_host_2'} cloud.position = 'src' bbcp_copier.remove_bbcp(cloud) self.assertEqual(2, mock_run_ignoring_errors.call_count)

true

790d8e57faed3e19d353ef1b42ede6c299f99f05

66

py

Python

src/pomodoro/__main__.py

Dev3XOR/pomodoro

d4dc48b6ebb86ebcccf0897faac7bba36d0319aa

[ "MIT" ]

null

src/pomodoro/__main__.py

Dev3XOR/pomodoro

d4dc48b6ebb86ebcccf0897faac7bba36d0319aa

[ "MIT" ]

1

2021-12-17T22:24:00.000Z

src/pomodoro/__main__.py

Dev3XOR/pomodoro

d4dc48b6ebb86ebcccf0897faac7bba36d0319aa

[ "MIT" ]

null

from pomodoro import main if __name__ == "__main__": main()

11

26

0.666667

from pomodoro import main if __name__ == "__main__": main()

true

790d8e8aea11def00cbaffc91e9868fda5dc192f

568

py

Python

src/fidalgo/azext_fidalgo/generated/_params.py

tbyfield/azure-cli-extensions

e7e5f37fdcea3afb5c4aecb61fa72eac72c2128e

[ "MIT" ]

null

src/fidalgo/azext_fidalgo/generated/_params.py

tbyfield/azure-cli-extensions

e7e5f37fdcea3afb5c4aecb61fa72eac72c2128e

[ "MIT" ]

null

src/fidalgo/azext_fidalgo/generated/_params.py

tbyfield/azure-cli-extensions

e7e5f37fdcea3afb5c4aecb61fa72eac72c2128e

[ "MIT" ]

1

2022-02-14T21:43:29.000Z

# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- # pylint: disable=too-many-lines # pylint: disable=too-many-statements def load_arguments(self, _): pass

37.866667

76

0.558099

def load_arguments(self, _): pass

true

790d8ed36d68e07e09425d5ec08df55d0d2d55e4

338

py

Python

tests/devices/eiger/test_eiger_status.py

dls-controls/tickit

00bb013e69674bcfe4926f365ecb3c65c080abe8

[ "Apache-2.0" ]

4

2021-09-16T13:35:33.000Z

2022-02-01T23:35:53.000Z

tests/devices/eiger/test_eiger_status.py

dls-controls/tickit

00bb013e69674bcfe4926f365ecb3c65c080abe8

[ "Apache-2.0" ]

46

2021-09-16T13:44:58.000Z

2022-02-02T13:42:56.000Z

tests/devices/eiger/test_eiger_status.py

dls-controls/tickit

00bb013e69674bcfe4926f365ecb3c65c080abe8

[ "Apache-2.0" ]

null

import pytest from tickit.devices.eiger.eiger_status import EigerStatus # # # # # EigerStatus Tests # # # # # @pytest.fixture def eiger_status() -> EigerStatus: return EigerStatus() def test_eiger_status_constructor(): EigerStatus() def test_eiger_status_getitem(eiger_status): assert 24.5 == eiger_status["th0_temp"]

17.789474

57

0.730769

import pytest from tickit.devices.eiger.eiger_status import EigerStatus status_getitem(eiger_status): assert 24.5 == eiger_status["th0_temp"]

true

790d8ed41339bba225986addcf060058eecda9c0

5,645

py

Python

cogrob_ros/cogrob_pololu_lidar/scripts/cogrob_pololu_lidar.py

CogRob/TritonBot

d05b521ec7a7f54a04409f5a2897f3e5c75fd3bf

[ "BSD-3-Clause" ]

8

2018-09-21T09:56:02.000Z

2021-07-26T14:35:14.000Z

cogrob_ros/cogrob_pololu_lidar/scripts/cogrob_pololu_lidar.py

CogRob/TritonBot

d05b521ec7a7f54a04409f5a2897f3e5c75fd3bf

[ "BSD-3-Clause" ]

null

cogrob_ros/cogrob_pololu_lidar/scripts/cogrob_pololu_lidar.py

CogRob/TritonBot

d05b521ec7a7f54a04409f5a2897f3e5c75fd3bf

[ "BSD-3-Clause" ]

4

2018-08-26T21:44:52.000Z

2019-08-22T07:38:08.000Z

#!/usr/bin/env python # Copyright (c) 2018, The Regents of the University of California # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the University of California nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OF THE UNIVERSITY OF CALIFORNIA # BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import math import serial import sys import threading import rospy import std_msgs.msg import tf class PololuSerial(object): def __init__(self, tty_device='/dev/ttyACM0'): self._serial = serial.Serial(tty_device, timeout=0.2) self._lock = threading.Lock() def _SendCommand(self, command_sequence, reply_length=0): # TODO(shengye): Check command_sequence is an iterable of integers [0, 255] buf_out = bytearray(command_sequence) with self._lock: self._serial.write(buf_out) if reply_length > 0: buf_in = bytearray(self._serial.read(reply_length)) assert len(buf_in) == reply_length else: buf_in = None return buf_in def Close(self): with self._lock: self._serial.close() self._serial = None def SetTarget(self, target_val, channel=0): command = [0x84, channel, target_val & 0x7F, (target_val >> 7) & 0x7F] self._SendCommand(command) def SetSpeed(self, speed_val, channel=0): command = [0x87, channel, speed_val & 0x7F, (speed_val >> 7) & 0x7F] self._SendCommand(command) def SetAcceleration(self, acc_val, channel=0): command = [0x89, channel, acc_val & 0x7F, (acc_val >> 7) & 0x7F] self._SendCommand(command) def GetPos(self, channel=0): command = [0x90, channel] result = self._SendCommand(command, 2) return_val = (result[1] << 8) | result[0] return return_val def main(argv): rospy.init_node("head_laser_servo_tf") # Parameters tty_device = rospy.get_param("~tty_device", "/dev/ttyACM0") acceleration = rospy.get_param("~acceleration", 20) speed = rospy.get_param("~speed", 10) min_val = rospy.get_param("~min_val", 885 * 4) min_deg = rospy.get_param("~min_deg", -90) max_val = rospy.get_param("~max_val", 1900 * 4) max_deg = rospy.get_param("~max_deg", 0) default_deg = rospy.get_param("~default_deg", -90) fixed_frame = rospy.get_param("~fixed_frame", "head_laser_servo_base") rotating_frame = rospy.get_param("~rotating_frame", "head_laser_servo_mount") time_adj = rospy.get_param("~time_adj", 0.125) tf_pub_rate = rospy.get_param("~tf_pub_rate", 20) dev = PololuSerial(tty_device) dev.SetAcceleration(acceleration) dev.SetSpeed(speed) tf_broadcaster = tf.TransformBroadcaster() disable_tf_publisher = [False] latest_deg = [min_deg] def MoveToDeg(target_deg): target = int((target_deg - min_deg) / (max_deg - min_deg) * (max_val - min_val) + min_val) dev.SetTarget(target) pos = float(dev.GetPos()) disable_tf_publisher[0] = True while pos != target: deg = ((pos - min_val) / (max_val - min_val) * (max_deg - min_deg) + min_deg) tf_broadcaster.sendTransform( (0, 0, 0), tf.transformations.quaternion_from_euler( 0, -deg / 180.0 * math.pi, 0), rospy.Time.now() + rospy.Duration(time_adj), rotating_frame, fixed_frame) latest_deg[0] = deg rospy.loginfo("Degree: %f, Value: %f", deg, pos) pos = float(dev.GetPos()) disable_tf_publisher[0] = False def HeadLaserAngleCallback(data): angle = data.data if angle < min_deg or angle > max_deg: rospy.logerr("%f is not between [%f, %f]", angle, min_deg, max_deg) else: MoveToDeg(angle) MoveToDeg(min_deg) MoveToDeg(max_deg) MoveToDeg(default_deg) rospy.Subscriber("/head_laser/angle", std_msgs.msg.Float64, HeadLaserAngleCallback) rospy.loginfo("Ready to serve.") tf_pub_rate_rospy_rate = rospy.Rate(tf_pub_rate) while not rospy.is_shutdown(): if not disable_tf_publisher[0]: tf_broadcaster.sendTransform( (0, 0, 0), tf.transformations.quaternion_from_euler( 0, -latest_deg[0] / 180.0 * math.pi, 0), rospy.Time.now() + rospy.Duration(time_adj), rotating_frame, fixed_frame) tf_pub_rate_rospy_rate.sleep() dev.Close() if __name__ == "__main__": main(sys.argv)

33.205882

79

0.692117

import math import serial import sys import threading import rospy import std_msgs.msg import tf class PololuSerial(object): def __init__(self, tty_device='/dev/ttyACM0'): self._serial = serial.Serial(tty_device, timeout=0.2) self._lock = threading.Lock() def _SendCommand(self, command_sequence, reply_length=0): buf_out = bytearray(command_sequence) with self._lock: self._serial.write(buf_out) if reply_length > 0: buf_in = bytearray(self._serial.read(reply_length)) assert len(buf_in) == reply_length else: buf_in = None return buf_in def Close(self): with self._lock: self._serial.close() self._serial = None def SetTarget(self, target_val, channel=0): command = [0x84, channel, target_val & 0x7F, (target_val >> 7) & 0x7F] self._SendCommand(command) def SetSpeed(self, speed_val, channel=0): command = [0x87, channel, speed_val & 0x7F, (speed_val >> 7) & 0x7F] self._SendCommand(command) def SetAcceleration(self, acc_val, channel=0): command = [0x89, channel, acc_val & 0x7F, (acc_val >> 7) & 0x7F] self._SendCommand(command) def GetPos(self, channel=0): command = [0x90, channel] result = self._SendCommand(command, 2) return_val = (result[1] << 8) | result[0] return return_val def main(argv): rospy.init_node("head_laser_servo_tf") tty_device = rospy.get_param("~tty_device", "/dev/ttyACM0") acceleration = rospy.get_param("~acceleration", 20) speed = rospy.get_param("~speed", 10) min_val = rospy.get_param("~min_val", 885 * 4) min_deg = rospy.get_param("~min_deg", -90) max_val = rospy.get_param("~max_val", 1900 * 4) max_deg = rospy.get_param("~max_deg", 0) default_deg = rospy.get_param("~default_deg", -90) fixed_frame = rospy.get_param("~fixed_frame", "head_laser_servo_base") rotating_frame = rospy.get_param("~rotating_frame", "head_laser_servo_mount") time_adj = rospy.get_param("~time_adj", 0.125) tf_pub_rate = rospy.get_param("~tf_pub_rate", 20) dev = PololuSerial(tty_device) dev.SetAcceleration(acceleration) dev.SetSpeed(speed) tf_broadcaster = tf.TransformBroadcaster() disable_tf_publisher = [False] latest_deg = [min_deg] def MoveToDeg(target_deg): target = int((target_deg - min_deg) / (max_deg - min_deg) * (max_val - min_val) + min_val) dev.SetTarget(target) pos = float(dev.GetPos()) disable_tf_publisher[0] = True while pos != target: deg = ((pos - min_val) / (max_val - min_val) * (max_deg - min_deg) + min_deg) tf_broadcaster.sendTransform( (0, 0, 0), tf.transformations.quaternion_from_euler( 0, -deg / 180.0 * math.pi, 0), rospy.Time.now() + rospy.Duration(time_adj), rotating_frame, fixed_frame) latest_deg[0] = deg rospy.loginfo("Degree: %f, Value: %f", deg, pos) pos = float(dev.GetPos()) disable_tf_publisher[0] = False def HeadLaserAngleCallback(data): angle = data.data if angle < min_deg or angle > max_deg: rospy.logerr("%f is not between [%f, %f]", angle, min_deg, max_deg) else: MoveToDeg(angle) MoveToDeg(min_deg) MoveToDeg(max_deg) MoveToDeg(default_deg) rospy.Subscriber("/head_laser/angle", std_msgs.msg.Float64, HeadLaserAngleCallback) rospy.loginfo("Ready to serve.") tf_pub_rate_rospy_rate = rospy.Rate(tf_pub_rate) while not rospy.is_shutdown(): if not disable_tf_publisher[0]: tf_broadcaster.sendTransform( (0, 0, 0), tf.transformations.quaternion_from_euler( 0, -latest_deg[0] / 180.0 * math.pi, 0), rospy.Time.now() + rospy.Duration(time_adj), rotating_frame, fixed_frame) tf_pub_rate_rospy_rate.sleep() dev.Close() if __name__ == "__main__": main(sys.argv)

true

790d8edc8ab2aa68079caa33608fed0c6a069a7b

2,216

py

Python

src/py-opentimelineio/opentimelineio/algorithms/timeline_algo.py

desruie/OpenTimelineIO

918797b00e840b7de8a15a3b1ab51e35a004c50f

[ "Apache-2.0" ]

1,021

2017-07-29T05:50:20.000Z

2022-03-28T16:53:28.000Z

src/py-opentimelineio/opentimelineio/algorithms/timeline_algo.py

desruie/OpenTimelineIO

918797b00e840b7de8a15a3b1ab51e35a004c50f

[ "Apache-2.0" ]

987

2017-08-01T17:14:57.000Z

2022-03-31T22:49:03.000Z

src/py-opentimelineio/opentimelineio/algorithms/timeline_algo.py

reinecke/OpenTimelineIO

1325927157564989952edf7c5f7c317fb90e1288

[ "Apache-2.0" ]

233

2017-07-28T23:27:10.000Z

2022-03-31T10:40:35.000Z

# # Copyright Contributors to the OpenTimelineIO project # # Licensed under the Apache License, Version 2.0 (the "Apache License") # with the following modification; you may not use this file except in # compliance with the Apache License and the following modification to it: # Section 6. Trademarks. is deleted and replaced with: # # 6. Trademarks. This License does not grant permission to use the trade # names, trademarks, service marks, or product names of the Licensor # and its affiliates, except as required to comply with Section 4(c) of # the License and to reproduce the content of the NOTICE file. # # You may obtain a copy of the Apache License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the Apache License with the above modification is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the Apache License for the specific # language governing permissions and limitations under the Apache License. # """Algorithms for timeline objects.""" import copy from . import ( track_algo ) def timeline_trimmed_to_range(in_timeline, trim_range): """Returns a new timeline that is a copy of the in_timeline, but with items outside the trim_range removed and items on the ends trimmed to the trim_range. Note that the timeline is never expanded, only shortened. Please note that you could do nearly the same thing non-destructively by just setting the Track's source_range but sometimes you want to really cut away the stuff outside and that's what this function is meant for.""" new_timeline = copy.deepcopy(in_timeline) for track_num, child_track in enumerate(in_timeline.tracks): # @TODO: put the trim_range into the space of the tracks # new_range = new_timeline.tracks.transformed_time_range( # trim_range, # child_track # ) # trim the track and assign it to the new stack. new_timeline.tracks[track_num] = track_algo.track_trimmed_to_range( child_track, trim_range ) return new_timeline

38.877193

79

0.733303

import copy from . import ( track_algo ) def timeline_trimmed_to_range(in_timeline, trim_range): new_timeline = copy.deepcopy(in_timeline) for track_num, child_track in enumerate(in_timeline.tracks): new_timeline.tracks[track_num] = track_algo.track_trimmed_to_range( child_track, trim_range ) return new_timeline

true

790d8fb9ce171e31e7cff74bd1b470c81d27f126

4,809

py

Python

sdk/python/pulumi_azure_nextgen/network/v20200301/get_network_interface_tap_configuration.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/network/v20200301/get_network_interface_tap_configuration.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/network/v20200301/get_network_interface_tap_configuration.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs __all__ = [ 'GetNetworkInterfaceTapConfigurationResult', 'AwaitableGetNetworkInterfaceTapConfigurationResult', 'get_network_interface_tap_configuration', ] @pulumi.output_type class GetNetworkInterfaceTapConfigurationResult: """ Tap configuration in a Network Interface. """ def __init__(__self__, etag=None, name=None, provisioning_state=None, type=None, virtual_network_tap=None): if etag and not isinstance(etag, str): raise TypeError("Expected argument 'etag' to be a str") pulumi.set(__self__, "etag", etag) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if virtual_network_tap and not isinstance(virtual_network_tap, dict): raise TypeError("Expected argument 'virtual_network_tap' to be a dict") pulumi.set(__self__, "virtual_network_tap", virtual_network_tap) @property @pulumi.getter def etag(self) -> str: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter def name(self) -> Optional[str]: """ The name of the resource that is unique within a resource group. This name can be used to access the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: """ The provisioning state of the network interface tap configuration resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def type(self) -> str: """ Sub Resource type. """ return pulumi.get(self, "type") @property @pulumi.getter(name="virtualNetworkTap") def virtual_network_tap(self) -> Optional['outputs.VirtualNetworkTapResponse']: """ The reference to the Virtual Network Tap resource. """ return pulumi.get(self, "virtual_network_tap") class AwaitableGetNetworkInterfaceTapConfigurationResult(GetNetworkInterfaceTapConfigurationResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetNetworkInterfaceTapConfigurationResult( etag=self.etag, name=self.name, provisioning_state=self.provisioning_state, type=self.type, virtual_network_tap=self.virtual_network_tap) def get_network_interface_tap_configuration(network_interface_name: Optional[str] = None, resource_group_name: Optional[str] = None, tap_configuration_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNetworkInterfaceTapConfigurationResult: """ Use this data source to access information about an existing resource. :param str network_interface_name: The name of the network interface. :param str resource_group_name: The name of the resource group. :param str tap_configuration_name: The name of the tap configuration. """ __args__ = dict() __args__['networkInterfaceName'] = network_interface_name __args__['resourceGroupName'] = resource_group_name __args__['tapConfigurationName'] = tap_configuration_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:network/v20200301:getNetworkInterfaceTapConfiguration', __args__, opts=opts, typ=GetNetworkInterfaceTapConfigurationResult).value return AwaitableGetNetworkInterfaceTapConfigurationResult( etag=__ret__.etag, name=__ret__.name, provisioning_state=__ret__.provisioning_state, type=__ret__.type, virtual_network_tap=__ret__.virtual_network_tap)

39.743802

180

0.680183

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs __all__ = [ 'GetNetworkInterfaceTapConfigurationResult', 'AwaitableGetNetworkInterfaceTapConfigurationResult', 'get_network_interface_tap_configuration', ] @pulumi.output_type class GetNetworkInterfaceTapConfigurationResult: def __init__(__self__, etag=None, name=None, provisioning_state=None, type=None, virtual_network_tap=None): if etag and not isinstance(etag, str): raise TypeError("Expected argument 'etag' to be a str") pulumi.set(__self__, "etag", etag) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if virtual_network_tap and not isinstance(virtual_network_tap, dict): raise TypeError("Expected argument 'virtual_network_tap' to be a dict") pulumi.set(__self__, "virtual_network_tap", virtual_network_tap) @property @pulumi.getter def etag(self) -> str: return pulumi.get(self, "etag") @property @pulumi.getter def name(self) -> Optional[str]: return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: return pulumi.get(self, "provisioning_state") @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") @property @pulumi.getter(name="virtualNetworkTap") def virtual_network_tap(self) -> Optional['outputs.VirtualNetworkTapResponse']: return pulumi.get(self, "virtual_network_tap") class AwaitableGetNetworkInterfaceTapConfigurationResult(GetNetworkInterfaceTapConfigurationResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetNetworkInterfaceTapConfigurationResult( etag=self.etag, name=self.name, provisioning_state=self.provisioning_state, type=self.type, virtual_network_tap=self.virtual_network_tap) def get_network_interface_tap_configuration(network_interface_name: Optional[str] = None, resource_group_name: Optional[str] = None, tap_configuration_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNetworkInterfaceTapConfigurationResult: __args__ = dict() __args__['networkInterfaceName'] = network_interface_name __args__['resourceGroupName'] = resource_group_name __args__['tapConfigurationName'] = tap_configuration_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:network/v20200301:getNetworkInterfaceTapConfiguration', __args__, opts=opts, typ=GetNetworkInterfaceTapConfigurationResult).value return AwaitableGetNetworkInterfaceTapConfigurationResult( etag=__ret__.etag, name=__ret__.name, provisioning_state=__ret__.provisioning_state, type=__ret__.type, virtual_network_tap=__ret__.virtual_network_tap)

true

790d907ce96067c105d27b4ff18d5cd849620ec7

142

py

Python

tests/functions.py

apragacz/functoolsplus

9d26666a2d017b25dd0e031ddde1570f5a4bffd3

[ "MIT" ]

2

2019-03-01T15:09:16.000Z

2021-01-26T15:18:58.000Z

tests/functions.py

apragacz/functoolsplus

9d26666a2d017b25dd0e031ddde1570f5a4bffd3

[ "MIT" ]

null

tests/functions.py

apragacz/functoolsplus

9d26666a2d017b25dd0e031ddde1570f5a4bffd3

[ "MIT" ]

null

def identity(x): return x def always_false(x): return False def always_true(x): return True def add(x, y): return x + y

9.466667

20

0.612676

def identity(x): return x def always_false(x): return False def always_true(x): return True def add(x, y): return x + y

true

790d90b0c4b8b17bedcb8e7ece4b102f0eef8c66

2,740

py

Python

ghwiz/mshp.py

nerdralph/h2k

187534da4ec1ffb8711e738acd0271b5d3c7a261

[ "MIT" ]

null

ghwiz/mshp.py

nerdralph/h2k

187534da4ec1ffb8711e738acd0271b5d3c7a261

[ "MIT" ]

null

ghwiz/mshp.py

nerdralph/h2k

187534da4ec1ffb8711e738acd0271b5d3c7a261

[ "MIT" ]

null

#!/usr/bin/python # configure mini-split heat pumps for E files # uses NRCan CSV list converted to TSV # https://oee.nrcan.gc.ca/pml-lmp/index.cfm?language_langue=en&action=app.search-recherche&appliance=ASHP2_GH import math, os, sys import xml.etree.ElementTree as ET if len(sys.argv) < 3: print(sys.argv[0], "E-file.h2k AHRI heads|0(ducted)") sys.exit() e_file = sys.argv[1] ahri = sys.argv[2] heads = sys.argv[3] t = ET.parse(e_file) # tsv field list: # Brand Outside model Inside model Furnace model HSPF (Region IV) Rated heating capacity (Btu/hour) Grant amount AHRI / Verification reference AHRI Classification Series name/product line (if applicable) SEER Rated cooling capacity (Btu/hour) Coefficient of Performance (COP) at -15 °C (5 °F) (at maximum capacity) Capacity Maintenance % (Max -15°C/5°F ÷ Rated 8.3°C/47°F) cchp_search = "grep '" + ahri + "' ccashp.tsv" #d = os.popen(cchp_search).read().split('\t') d = os.popen(cchp_search).read().rstrip('\n').split('\t') # 1 kW = 3412 BTU/hr (mfr, model, head_mdl, size_kw, hspf, seer, cop, fraction) = \ d[0], d[1], d[2], str(float(d[5])/3412), d[4], d[10], d[12], d[13] #(ahri, size_kw, hspf, cop, seer) = cols[9], str(float(cols[5])/3412), cols[4], cols[13], cols[12] e = t.find("./ProgramInformation/Information") info = ET.Element("Info", {"code": "Info. 5"}) # no NEEP until spreadsheet and/or H2K is fixed if (int(heads) > 0): info.text = "MSHP-" + heads else: info.text = "CENTRAL-HP" e.append(info) # GHG instructions are to use Info 6 when more than 1 ccASHP system is installed # but ENS wants all heat pumps in Info 6 info = ET.Element("Info", {"code": "Info. 6"}) info.text = mfr + ";AHRI-" + ahri + ';' + model + ';' + head_mdl e.append(info) #print(info, info.attrib, info.text) # Type 2 CCHP heating system type2 = ET.parse("Type2.xml").getroot() ahp = type2.find("AirHeatPump") ei = ahp.find("EquipmentInformation") ei.attrib["AHRI"] = ahri ei.find("Manufacturer").text = mfr ei.find("Model").text = model ahp.find("Equipment").attrib["numberOfHeads"] = heads specs = ahp.find("Specifications") specs.find("OutputCapacity").attrib["value"] = size_kw # use ASHP HSPF/SEER until NEEP spreadsheet or H2K is fixed for ccHP specs.find("HeatingEfficiency").attrib["value"] = str(float(hspf)/1.15) specs.find("CoolingEfficiency").attrib["value"] = seer cchp = ahp.find("ColdClimateHeatPump") cchp.attrib["heatingEfficiency"] = hspf cchp.attrib["coolingEfficiency"] = seer cchp.attrib["capacity"] = size_kw cchp.attrib["cop"] = cop cchp.attrib["capacityMaintenance"] = fraction hc = t.find("./House/HeatingCooling") hc.remove(hc.find("Type2")) hc.append(type2) #outfile = "MSHP-out.h2k" outfile = e_file t.write(outfile, "UTF-8", True)

36.533333

373

0.693796

import math, os, sys import xml.etree.ElementTree as ET if len(sys.argv) < 3: print(sys.argv[0], "E-file.h2k AHRI heads|0(ducted)") sys.exit() e_file = sys.argv[1] ahri = sys.argv[2] heads = sys.argv[3] t = ET.parse(e_file) cchp_search = "grep '" + ahri + "' ccashp.tsv" d = os.popen(cchp_search).read().rstrip('\n').split('\t') (mfr, model, head_mdl, size_kw, hspf, seer, cop, fraction) = \ d[0], d[1], d[2], str(float(d[5])/3412), d[4], d[10], d[12], d[13] e = t.find("./ProgramInformation/Information") info = ET.Element("Info", {"code": "Info. 5"}) if (int(heads) > 0): info.text = "MSHP-" + heads else: info.text = "CENTRAL-HP" e.append(info) info = ET.Element("Info", {"code": "Info. 6"}) info.text = mfr + ";AHRI-" + ahri + ';' + model + ';' + head_mdl e.append(info) type2 = ET.parse("Type2.xml").getroot() ahp = type2.find("AirHeatPump") ei = ahp.find("EquipmentInformation") ei.attrib["AHRI"] = ahri ei.find("Manufacturer").text = mfr ei.find("Model").text = model ahp.find("Equipment").attrib["numberOfHeads"] = heads specs = ahp.find("Specifications") specs.find("OutputCapacity").attrib["value"] = size_kw specs.find("HeatingEfficiency").attrib["value"] = str(float(hspf)/1.15) specs.find("CoolingEfficiency").attrib["value"] = seer cchp = ahp.find("ColdClimateHeatPump") cchp.attrib["heatingEfficiency"] = hspf cchp.attrib["coolingEfficiency"] = seer cchp.attrib["capacity"] = size_kw cchp.attrib["cop"] = cop cchp.attrib["capacityMaintenance"] = fraction hc = t.find("./House/HeatingCooling") hc.remove(hc.find("Type2")) hc.append(type2) outfile = e_file t.write(outfile, "UTF-8", True)

true

790d926797199d18bc0487056b852ac06c0b6295

3,280

py

Python

tests/conftest.py

elin1231/htmap

b9c43ec1d86e90730210c3317409b75595061d91

[ "Apache-2.0" ]

null

tests/conftest.py

elin1231/htmap

b9c43ec1d86e90730210c3317409b75595061d91

[ "Apache-2.0" ]

null

tests/conftest.py

elin1231/htmap

b9c43ec1d86e90730210c3317409b75595061d91

[ "Apache-2.0" ]

null

# Copyright 2018 HTCondor Team, Computer Sciences Department, # University of Wisconsin-Madison, WI. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time from pathlib import Path from copy import copy import pytest import htmap from htmap.settings import BASE_SETTINGS from htmap._startup import ensure_htmap_dir_exists # start with base settings (ignore user settings for tests) htmap.settings.replace(BASE_SETTINGS) htmap.settings[ "DELIVERY_METHOD" ] = "shared" # shared is the default for all tests that aren't parametric htmap.settings["WAIT_TIME"] = 0.1 htmap.settings["MAP_OPTIONS.request_memory"] = "10MB" htmap.settings["MAP_OPTIONS.keep_claim_idle"] = "1" SETTINGS = copy(htmap.settings) @pytest.fixture(scope="function", autouse=True) def reset_settings(): htmap.settings.replace(SETTINGS) @pytest.fixture(scope="function", autouse=True) def set_transplant_dir(tmpdir_factory, reset_settings): path = Path(tmpdir_factory.mktemp("htmap_transplant_dir")) htmap.settings["TRANSPLANT.DIR"] = path @pytest.fixture(scope="function") def delivery_methods(delivery_method, reset_settings): htmap.settings["DELIVERY_METHOD"] = delivery_method def pytest_addoption(parser): parser.addoption( "--delivery", nargs="+", default=["shared"], # shared is the default for parametric delivery testing ) def pytest_generate_tests(metafunc): if "delivery_methods" in metafunc.fixturenames: metafunc.parametrize( "delivery_method", metafunc.config.getoption("delivery"), ) @pytest.fixture(scope="function", autouse=True) def set_htmap_dir_and_clean(tmpdir_factory): map_dir = Path(tmpdir_factory.mktemp("htmap_dir")) htmap.settings["HTMAP_DIR"] = map_dir ensure_htmap_dir_exists() yield htmap.clean(all=True) @pytest.fixture(scope="session") def doubler(): def doubler(x): return 2 * x return doubler @pytest.fixture(scope="session") def mapped_doubler(doubler): mapper = htmap.mapped(doubler) return mapper @pytest.fixture(scope="session") def power(): def power(x=0, p=2): return x ** p return power @pytest.fixture(scope="session") def mapped_power(power): mapper = htmap.mapped(power) return mapper @pytest.fixture(scope="session") def never_returns(): def never(_): while True: time.sleep(1) return never @pytest.fixture(scope="function") def map_that_never_finishes(never_returns): m = htmap.map(never_returns, [None]) yield m m.remove() @pytest.fixture(scope="session") def mapped_exception(): @htmap.mapped def fail(x): raise Exception(str(x)) return fail def exception_msg(exc_info) -> str: return str(exc_info.value)

23.941606

84

0.721341

import time from pathlib import Path from copy import copy import pytest import htmap from htmap.settings import BASE_SETTINGS from htmap._startup import ensure_htmap_dir_exists htmap.settings.replace(BASE_SETTINGS) htmap.settings[ "DELIVERY_METHOD" ] = "shared" htmap.settings["WAIT_TIME"] = 0.1 htmap.settings["MAP_OPTIONS.request_memory"] = "10MB" htmap.settings["MAP_OPTIONS.keep_claim_idle"] = "1" SETTINGS = copy(htmap.settings) @pytest.fixture(scope="function", autouse=True) def reset_settings(): htmap.settings.replace(SETTINGS) @pytest.fixture(scope="function", autouse=True) def set_transplant_dir(tmpdir_factory, reset_settings): path = Path(tmpdir_factory.mktemp("htmap_transplant_dir")) htmap.settings["TRANSPLANT.DIR"] = path @pytest.fixture(scope="function") def delivery_methods(delivery_method, reset_settings): htmap.settings["DELIVERY_METHOD"] = delivery_method def pytest_addoption(parser): parser.addoption( "--delivery", nargs="+", default=["shared"], # shared is the default for parametric delivery testing ) def pytest_generate_tests(metafunc): if "delivery_methods" in metafunc.fixturenames: metafunc.parametrize( "delivery_method", metafunc.config.getoption("delivery"), ) @pytest.fixture(scope="function", autouse=True) def set_htmap_dir_and_clean(tmpdir_factory): map_dir = Path(tmpdir_factory.mktemp("htmap_dir")) htmap.settings["HTMAP_DIR"] = map_dir ensure_htmap_dir_exists() yield htmap.clean(all=True) @pytest.fixture(scope="session") def doubler(): def doubler(x): return 2 * x return doubler @pytest.fixture(scope="session") def mapped_doubler(doubler): mapper = htmap.mapped(doubler) return mapper @pytest.fixture(scope="session") def power(): def power(x=0, p=2): return x ** p return power @pytest.fixture(scope="session") def mapped_power(power): mapper = htmap.mapped(power) return mapper @pytest.fixture(scope="session") def never_returns(): def never(_): while True: time.sleep(1) return never @pytest.fixture(scope="function") def map_that_never_finishes(never_returns): m = htmap.map(never_returns, [None]) yield m m.remove() @pytest.fixture(scope="session") def mapped_exception(): @htmap.mapped def fail(x): raise Exception(str(x)) return fail def exception_msg(exc_info) -> str: return str(exc_info.value)

true

790d9292ec96dd030beb02589ad7db47a61fdcc8

9,395

py

Python

yyskmultilearn/cluster/graphtool.py

yuan776/scikit-multilearn

5ad32df237e6a9746fd5ec2f9543dcd011e8cdd2

[ "BSD-2-Clause" ]

null

yyskmultilearn/cluster/graphtool.py

yuan776/scikit-multilearn

5ad32df237e6a9746fd5ec2f9543dcd011e8cdd2

[ "BSD-2-Clause" ]

null

yyskmultilearn/cluster/graphtool.py

yuan776/scikit-multilearn

5ad32df237e6a9746fd5ec2f9543dcd011e8cdd2

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import from __future__ import print_function import graph_tool.all as gt import numpy as np from .base import LabelGraphClustererBase from .helpers import _membership_to_list_of_communities, _overlapping_membership_to_list_of_communities class StochasticBlockModel: """A Stochastic Blockmodel fit to Label Graph This contains a stochastic block model instance constructed for a block model variant specified in parameters. It can be fit to an instance of a graph and set of weights. More information on how to select parameters can be found in `the extensive introduction into Stochastic Block Models <https://graph-tool.skewed.de/static/doc/demos/inference/inference.html>`_ in graphtool documentation. Parameters ---------- nested: boolean whether to build a nested Stochastic Block Model or the regular variant, will be automatically put under :code:`self.nested`. use_degree_correlation: boolean whether to correct for degree correlation in modeling, will be automatically put under :code:`self.use_degree_correlation`. allow_overlap: boolean whether to allow overlapping clusters or not, will be automatically put under :code:`self.allow_overlap`. weight_model: string or None decide whether to generate a weighted or unweighted graph, will be automatically put under :code:`self.weight_model`. Attributes ---------- model_: graph_tool.inference.BlockState or its subclass an instance of the fitted model obtained from graph-tool """ def __init__(self, nested, use_degree_correlation, allow_overlap, weight_model): self.nested = nested self.use_degree_correlation = use_degree_correlation self.allow_overlap = allow_overlap self.weight_model = weight_model self.model_ = None def fit_predict(self, graph, weights): """Fits model to a given graph and weights list Sets :code:`self.model_` to the state of graphtool's Stochastic Block Model the after fitting. Attributes ---------- graph: graphtool.Graph the graph to fit the model to weights: graphtool.EdgePropertyMap<double> the property map: edge -> weight (double) to fit the model to, if weighted variant is selected Returns ------- numpy.ndarray partition of labels, each sublist contains label indices related to label positions in :code:`y` """ if self.weight_model: self.model_ = self._model_fit_function()( graph, deg_corr=self.use_degree_correlation, overlap=self.allow_overlap, state_args=dict(recs=[weights], rec_types=[self.weight_model]) ) else: self.model_ = self._model_fit_function()( graph, deg_corr=self.use_degree_correlation, overlap=self.allow_overlap ) return self._detect_communities() def _detect_communities(self): if self.nested: lowest_level = self.model_.get_levels()[0] else: lowest_level = self.model_ number_of_communities = lowest_level.get_B() if self.allow_overlap: # the overlaps block returns # membership vector, and also edges vectors, we need just the membership here at the moment membership_vector = list(lowest_level.get_overlap_blocks()[0]) else: membership_vector = list(lowest_level.get_blocks()) if self.allow_overlap: return _overlapping_membership_to_list_of_communities(membership_vector, number_of_communities) return _membership_to_list_of_communities(membership_vector, number_of_communities) def _model_fit_function(self): if self.nested: return gt.minimize_nested_blockmodel_dl else: return gt.minimize_blockmodel_dl class GraphToolLabelGraphClusterer(LabelGraphClustererBase): """Fits a Stochastic Block Model to the Label Graph and infers the communities This clusterer clusters the label space using by fitting a stochastic block model to the label network and inferring the community structure using graph-tool. The obtained community structure is returned as the label clustering. More information on the inference itself can be found in `the extensive introduction into Stochastic Block Models <https://graph-tool.skewed.de/static/doc/demos/inference/inference.html>`_ in graphtool documentation. Parameters ---------- graph_builder: a GraphBuilderBase inherited transformer the graph builder to provide the adjacency matrix and weight map for the underlying graph model: StochasticBlockModel the desired stochastic block model variant to use Attributes ---------- graph_ : graphtool.Graph object representing a label co-occurence graph weights_ : graphtool.EdgeProperty<double> edge weights defined by graph builder stored in a graphtool compatible format .. note :: This functionality is still undergoing research. .. note :: This clusterer is GPL-licenced and will taint your code with GPL restrictions. References ---------- If you use this class please cite: .. code : latex article{peixoto_graph-tool_2014, title = {The graph-tool python library}, url = {http://figshare.com/articles/graph_tool/1164194}, doi = {10.6084/m9.figshare.1164194}, urldate = {2014-09-10}, journal = {figshare}, author = {Peixoto, Tiago P.}, year = {2014}, keywords = {all, complex networks, graph, network, other}} Examples -------- An example code for using this clusterer with a classifier looks like this: .. code-block:: python from sklearn.ensemble import RandomForestClassifier from yyskmultilearn.problem_transform import LabelPowerset from yyskmultilearn.cluster import IGraphLabelGraphClusterer, LabelCooccurrenceGraphBuilder from yyskmultilearn.ensemble import LabelSpacePartitioningClassifier # construct base forest classifier base_classifier = RandomForestClassifier(n_estimators=1000) # construct a graph builder that will include # label relations weighted by how many times they # co-occurred in the data, without self-edges graph_builder = LabelCooccurrenceGraphBuilder( weighted = True, include_self_edges = False ) # select parameters for the model, we fit a flat, # non-degree correlated, partitioning model # which will use fit the normal distribution as the weights model model = StochasticBlockModel( nested=False, use_degree_correlation=True, allow_overlap=False, weight_model='real-normal' ) # setup problem transformation approach with sparse matrices for random forest problem_transform_classifier = LabelPowerset(classifier=base_classifier, require_dense=[False, False]) # setup the clusterer to use, we selected the fast greedy modularity-maximization approach clusterer = GraphToolLabelGraphClusterer(graph_builder=graph_builder, model=model) # setup the ensemble metaclassifier classifier = LabelSpacePartitioningClassifier(problem_transform_classifier, clusterer) # train classifier.fit(X_train, y_train) # predict predictions = classifier.predict(X_test) For more use cases see `the label relations exploration guide <../labelrelations.ipynb>`_. """ def __init__(self, graph_builder, model): super(GraphToolLabelGraphClusterer, self).__init__(graph_builder) self.model = model self.graph_builder = graph_builder def fit_predict(self, X, y): """Performs clustering on y and returns list of label lists Builds a label graph using the provided graph builder's `transform` method on `y` and then detects communities using the selected `method`. Sets :code:`self.weights_` and :code:`self.graph_`. Parameters ---------- X : None currently unused, left for scikit compatibility y : scipy.sparse label space of shape :code:`(n_samples, n_labels)` Returns ------- arrray of arrays of label indexes (numpy.ndarray) label space division, each sublist represents labels that are in that community """ self._build_graph_instance(y) clusters = self.model.fit_predict(self.graph_, weights=self.weights_) return np.array([community for community in clusters if len(community) > 0]) def _build_graph_instance(self, y): edge_map = self.graph_builder.transform(y) g = gt.Graph(directed=False) g.add_vertex(y.shape[1]) self.weights_ = g.new_edge_property('double') for edge, weight in edge_map.items(): e = g.add_edge(edge[0], edge[1]) self.weights_[e] = weight self.graph_ = g

36.699219

115

0.673018

from __future__ import absolute_import from __future__ import print_function import graph_tool.all as gt import numpy as np from .base import LabelGraphClustererBase from .helpers import _membership_to_list_of_communities, _overlapping_membership_to_list_of_communities class StochasticBlockModel: def __init__(self, nested, use_degree_correlation, allow_overlap, weight_model): self.nested = nested self.use_degree_correlation = use_degree_correlation self.allow_overlap = allow_overlap self.weight_model = weight_model self.model_ = None def fit_predict(self, graph, weights): if self.weight_model: self.model_ = self._model_fit_function()( graph, deg_corr=self.use_degree_correlation, overlap=self.allow_overlap, state_args=dict(recs=[weights], rec_types=[self.weight_model]) ) else: self.model_ = self._model_fit_function()( graph, deg_corr=self.use_degree_correlation, overlap=self.allow_overlap ) return self._detect_communities() def _detect_communities(self): if self.nested: lowest_level = self.model_.get_levels()[0] else: lowest_level = self.model_ number_of_communities = lowest_level.get_B() if self.allow_overlap: membership_vector = list(lowest_level.get_overlap_blocks()[0]) else: membership_vector = list(lowest_level.get_blocks()) if self.allow_overlap: return _overlapping_membership_to_list_of_communities(membership_vector, number_of_communities) return _membership_to_list_of_communities(membership_vector, number_of_communities) def _model_fit_function(self): if self.nested: return gt.minimize_nested_blockmodel_dl else: return gt.minimize_blockmodel_dl class GraphToolLabelGraphClusterer(LabelGraphClustererBase): def __init__(self, graph_builder, model): super(GraphToolLabelGraphClusterer, self).__init__(graph_builder) self.model = model self.graph_builder = graph_builder def fit_predict(self, X, y): self._build_graph_instance(y) clusters = self.model.fit_predict(self.graph_, weights=self.weights_) return np.array([community for community in clusters if len(community) > 0]) def _build_graph_instance(self, y): edge_map = self.graph_builder.transform(y) g = gt.Graph(directed=False) g.add_vertex(y.shape[1]) self.weights_ = g.new_edge_property('double') for edge, weight in edge_map.items(): e = g.add_edge(edge[0], edge[1]) self.weights_[e] = weight self.graph_ = g

true

790d9370983b4903b988e873a086e753013d1ced

5,724

py

Python

src/djangoreactredux/djrenv/lib/python3.5/site-packages/faker/providers/address/sv_SE/__init__.py

m2jobe/c_x

ba914449a9a85d82703895fc884733ca20454034

[ "MIT" ]

9

2018-03-29T18:41:22.000Z

2021-03-11T23:35:30.000Z

faker/providers/address/sv_SE/__init__.py

Saber-xxf/faker1

c966a144b370f7abb568a5154c4ef704e846722e

[ "MIT" ]

5

2020-03-24T16:37:25.000Z

2021-06-10T21:24:54.000Z

faker/providers/address/sv_SE/__init__.py

Saber-xxf/faker1

c966a144b370f7abb568a5154c4ef704e846722e

[ "MIT" ]

1

2018-04-05T22:07:48.000Z

# coding=utf-8 from __future__ import unicode_literals from .. import Provider as AddressProvider class Provider(AddressProvider): building_number_formats = ('###', '##', '#') street_name_formats = ('{{street_prefix}}{{street_suffix}}', ) street_address_formats = ('{{street_name}} {{building_number}}',) street_prefixes = ( 'Björk', 'Järnvägs', 'Ring', 'Skol', 'Skogs', 'Ny', 'Gran', 'Idrotts', 'Stor', 'Kyrk', 'Industri', 'Park', 'Strand', 'Skol', 'Trädgårds', 'Industri', 'Ängs', 'Kyrko', 'Park', 'Villa', 'Ek', 'Kvarn', 'Stations', 'Back', 'Furu', 'Gen', 'Fabriks', 'Åker', 'Bäck', 'Asp' ) street_suffixes = ('gatan', 'gatan', 'vägen', 'vägen', 'stigen', 'gränd', 'torget') address_formats = ("{{street_address}}\n{{postcode}} {{city}}", ) postcode_formats = ('#####', ) city_formats = ('{{city_name}}', ) cities = ( 'Stockholm', 'Göteborg', 'Malmö', 'Uppsala', 'Västerås', 'Örebro', 'Linköping', 'Helsingborg', 'Jönköping', 'Norrköping', 'Lund', 'Umeå', 'Gävle', 'Borås', 'Mölndal', 'Södertälje', 'Eskilstuna', 'Karlstad', 'Halmstad', 'Växjö', 'Sundsvall', 'Luleå', 'Trollhättan', 'Östersund', 'Borlänge', 'Falun', 'Kalmar', 'Skövde', 'Kristianstad', 'Karlskrona', 'Skellefteå', 'Uddevalla', 'Lidingö', 'Motala', 'Landskrona', 'Örnsköldsvik', 'Nyköping', 'Karlskoga', 'Varberg', 'Trelleborg', 'Lidköping', 'Alingsås', 'Piteå', 'Sandviken', 'Ängelholm' ) countries = ( 'Afghanistan', 'Albanien', 'Algeriet', 'Amerikanska Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarktis', 'Antigua och Barbuda', 'Argentina', 'Armenien', 'Aruba', 'Ascension', 'Australien', 'Azerbajdzjan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belgien', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia', 'Bosnien och Hercegovina', 'Botswana', 'Brasilien', 'Brittiska Jungfruöarna', 'Brunei', 'Bulgarien', 'Burkina Faso', 'Burma', 'Burundi', 'Caymanöarna', 'Centralafrikanska republiken', 'Chile', 'Colombia', 'Cooköarna', 'Costa Rica', 'Cypern', 'Danmark', 'Diego Garcia', 'Djibouti', 'Dominica', 'Dominikanska republiken', 'Ecuador', 'Egypten', 'Ekvatorialguinea', 'Elfenbenskusten', 'El Salvador', 'Eritrea', 'Estland', 'Etiopien', 'England', 'Falklandsöarna', 'Fiji', 'Filippinerna', 'Finland', 'Frankrike', 'Franska Guyana', 'Franska Polynesien', 'Färöarna', 'Förenade Arabemiraten', 'Gabon', 'Gambia', 'Georgien', 'Ghana', 'Gibraltar', 'Grekland', 'Grenada', 'Grönland', 'Guadeloupe', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras', 'Hongkong', 'Indien', 'Indonesien', 'Irak', 'Iran', 'Irland', 'Island', 'Israel', 'Italien', 'Jamaica', 'Japan', 'Jemen', 'Jordanien', 'Kambodja', 'Kamerun', 'Kanada', 'Kap Verde', 'Kazakstan', 'Kenya', 'Kina', 'Kirgizistan', 'Kiribati', 'Komorerna', 'Kongo-Brazzaville', 'Kongo-Kinshasa', 'Kosovo', 'Kroatien', 'Kuba', 'Kuwait', 'Laos', 'Lesotho', 'Lettland', 'Libanon', 'Liberia', 'Libyen', 'Liechtenstein', 'Litauen', 'Luxemburg', 'Macao', 'Madagaskar', 'Makedonien', 'Malawi', 'Malaysia', 'Maldiverna', 'Mali', 'Malta', 'Marianerna', 'Marocko', 'Marshallöarna', 'Martinique', 'Mauretanien', 'Mauritius', 'Mayotte', 'Mexiko', 'Midwayöarna', 'Mikronesiens federerade stater', 'Moçambique', 'Moldavien', 'Monaco', 'Mongoliet', 'Montenegro', 'Montserrat', 'Namibia', 'Nauru', 'Nederländerna', 'Nederländska Antillerna', 'Nepal', 'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Nordkorea', 'Nordmarianerna', 'Norfolkön', 'Norge', 'Nya Kaledonien', 'Nya Zeeland', 'Oman', 'Pakistan', 'Palau', 'Palestina', 'Panama', 'Papua Nya Guinea', 'Paraguay', 'Peru', 'Pitcairnöarna', 'Polen', 'Portugal', 'Qatar', 'Réunion', 'Rumänien', 'Rwanda', 'Ryssland', 'Saint Kitts och Nevis', 'Saint Lucia', 'Saint-Pierre och Miquelon', 'Saint Vincent och Grenadinerna', 'Salomonöarna', 'Samoa', 'Sankta Helena', 'San Marino', 'São Tomé och Príncipe', 'Saudiarabien', 'Schweiz', 'Senegal', 'Serbien', 'Seychellerna', 'SierraLeone', 'Singapore', 'Sint Maarten', 'Slovakien', 'Slovenien', 'Somalia', 'Spanien', 'Sri Lanka', 'Storbritannien', 'Sudan', 'Surinam', 'Sverige', 'Swaziland', 'Sydafrika', 'Sydkorea', 'Sydsudan', 'Syrien', 'Tadzjikistan', 'Taiwan', 'Tanzania', 'Tchad', 'Thailand', 'Tjeckien', 'Togo', 'Tokelauöarna', 'Tonga', 'Trinidad och Tobago', 'Tunisien', 'Turkiet', 'Turkmenistan', 'Turks-och Caicosöarna', 'Tuvalu', 'Tyskland', 'Uganda', 'Ukraina', 'Ungern', 'Uruguay', 'USA', 'Uzbekistan', 'Vanuatu', 'Vatikanstaten', 'Venezuela', 'Vietnam', 'Vitryssland', 'Wake', 'Wallis-och Futunaöarna', 'Zambia', 'Zimbabwe', 'Österrike', 'Östtimor' ) states = ( 'Stockholms län', 'Uppsala län', 'Södermanlands län' 'Östergötlands län', 'Jönköpings län', 'Kronobergs län', 'Kalmar län', 'Gotlands län', 'Blekinge län', 'Skåne län', 'Hallands län', 'Västra Götalands län', 'Värmlands län', 'Örebro län', 'Västmanlands län', 'Dalarnas län', 'Gävleborgs län', 'Västernorrlands län', 'Jämtlands län', 'Västerbottens län', 'Norrbottens län' ) def street_prefix(self): return self.random_element(self.street_prefixes) def city_name(self): return self.random_element(self.cities) def state(self): return self.random_element(self.states)

51.567568

80

0.599581

from __future__ import unicode_literals from .. import Provider as AddressProvider class Provider(AddressProvider): building_number_formats = ('###', '##', '#') street_name_formats = ('{{street_prefix}}{{street_suffix}}', ) street_address_formats = ('{{street_name}} {{building_number}}',) street_prefixes = ( 'Björk', 'Järnvägs', 'Ring', 'Skol', 'Skogs', 'Ny', 'Gran', 'Idrotts', 'Stor', 'Kyrk', 'Industri', 'Park', 'Strand', 'Skol', 'Trädgårds', 'Industri', 'Ängs', 'Kyrko', 'Park', 'Villa', 'Ek', 'Kvarn', 'Stations', 'Back', 'Furu', 'Gen', 'Fabriks', 'Åker', 'Bäck', 'Asp' ) street_suffixes = ('gatan', 'gatan', 'vägen', 'vägen', 'stigen', 'gränd', 'torget') address_formats = ("{{street_address}}\n{{postcode}} {{city}}", ) postcode_formats = ('#####', ) city_formats = ('{{city_name}}', ) cities = ( 'Stockholm', 'Göteborg', 'Malmö', 'Uppsala', 'Västerås', 'Örebro', 'Linköping', 'Helsingborg', 'Jönköping', 'Norrköping', 'Lund', 'Umeå', 'Gävle', 'Borås', 'Mölndal', 'Södertälje', 'Eskilstuna', 'Karlstad', 'Halmstad', 'Växjö', 'Sundsvall', 'Luleå', 'Trollhättan', 'Östersund', 'Borlänge', 'Falun', 'Kalmar', 'Skövde', 'Kristianstad', 'Karlskrona', 'Skellefteå', 'Uddevalla', 'Lidingö', 'Motala', 'Landskrona', 'Örnsköldsvik', 'Nyköping', 'Karlskoga', 'Varberg', 'Trelleborg', 'Lidköping', 'Alingsås', 'Piteå', 'Sandviken', 'Ängelholm' ) countries = ( 'Afghanistan', 'Albanien', 'Algeriet', 'Amerikanska Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarktis', 'Antigua och Barbuda', 'Argentina', 'Armenien', 'Aruba', 'Ascension', 'Australien', 'Azerbajdzjan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belgien', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia', 'Bosnien och Hercegovina', 'Botswana', 'Brasilien', 'Brittiska Jungfruöarna', 'Brunei', 'Bulgarien', 'Burkina Faso', 'Burma', 'Burundi', 'Caymanöarna', 'Centralafrikanska republiken', 'Chile', 'Colombia', 'Cooköarna', 'Costa Rica', 'Cypern', 'Danmark', 'Diego Garcia', 'Djibouti', 'Dominica', 'Dominikanska republiken', 'Ecuador', 'Egypten', 'Ekvatorialguinea', 'Elfenbenskusten', 'El Salvador', 'Eritrea', 'Estland', 'Etiopien', 'England', 'Falklandsöarna', 'Fiji', 'Filippinerna', 'Finland', 'Frankrike', 'Franska Guyana', 'Franska Polynesien', 'Färöarna', 'Förenade Arabemiraten', 'Gabon', 'Gambia', 'Georgien', 'Ghana', 'Gibraltar', 'Grekland', 'Grenada', 'Grönland', 'Guadeloupe', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras', 'Hongkong', 'Indien', 'Indonesien', 'Irak', 'Iran', 'Irland', 'Island', 'Israel', 'Italien', 'Jamaica', 'Japan', 'Jemen', 'Jordanien', 'Kambodja', 'Kamerun', 'Kanada', 'Kap Verde', 'Kazakstan', 'Kenya', 'Kina', 'Kirgizistan', 'Kiribati', 'Komorerna', 'Kongo-Brazzaville', 'Kongo-Kinshasa', 'Kosovo', 'Kroatien', 'Kuba', 'Kuwait', 'Laos', 'Lesotho', 'Lettland', 'Libanon', 'Liberia', 'Libyen', 'Liechtenstein', 'Litauen', 'Luxemburg', 'Macao', 'Madagaskar', 'Makedonien', 'Malawi', 'Malaysia', 'Maldiverna', 'Mali', 'Malta', 'Marianerna', 'Marocko', 'Marshallöarna', 'Martinique', 'Mauretanien', 'Mauritius', 'Mayotte', 'Mexiko', 'Midwayöarna', 'Mikronesiens federerade stater', 'Moçambique', 'Moldavien', 'Monaco', 'Mongoliet', 'Montenegro', 'Montserrat', 'Namibia', 'Nauru', 'Nederländerna', 'Nederländska Antillerna', 'Nepal', 'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Nordkorea', 'Nordmarianerna', 'Norfolkön', 'Norge', 'Nya Kaledonien', 'Nya Zeeland', 'Oman', 'Pakistan', 'Palau', 'Palestina', 'Panama', 'Papua Nya Guinea', 'Paraguay', 'Peru', 'Pitcairnöarna', 'Polen', 'Portugal', 'Qatar', 'Réunion', 'Rumänien', 'Rwanda', 'Ryssland', 'Saint Kitts och Nevis', 'Saint Lucia', 'Saint-Pierre och Miquelon', 'Saint Vincent och Grenadinerna', 'Salomonöarna', 'Samoa', 'Sankta Helena', 'San Marino', 'São Tomé och Príncipe', 'Saudiarabien', 'Schweiz', 'Senegal', 'Serbien', 'Seychellerna', 'SierraLeone', 'Singapore', 'Sint Maarten', 'Slovakien', 'Slovenien', 'Somalia', 'Spanien', 'Sri Lanka', 'Storbritannien', 'Sudan', 'Surinam', 'Sverige', 'Swaziland', 'Sydafrika', 'Sydkorea', 'Sydsudan', 'Syrien', 'Tadzjikistan', 'Taiwan', 'Tanzania', 'Tchad', 'Thailand', 'Tjeckien', 'Togo', 'Tokelauöarna', 'Tonga', 'Trinidad och Tobago', 'Tunisien', 'Turkiet', 'Turkmenistan', 'Turks-och Caicosöarna', 'Tuvalu', 'Tyskland', 'Uganda', 'Ukraina', 'Ungern', 'Uruguay', 'USA', 'Uzbekistan', 'Vanuatu', 'Vatikanstaten', 'Venezuela', 'Vietnam', 'Vitryssland', 'Wake', 'Wallis-och Futunaöarna', 'Zambia', 'Zimbabwe', 'Österrike', 'Östtimor' ) states = ( 'Stockholms län', 'Uppsala län', 'Södermanlands län' 'Östergötlands län', 'Jönköpings län', 'Kronobergs län', 'Kalmar län', 'Gotlands län', 'Blekinge län', 'Skåne län', 'Hallands län', 'Västra Götalands län', 'Värmlands län', 'Örebro län', 'Västmanlands län', 'Dalarnas län', 'Gävleborgs län', 'Västernorrlands län', 'Jämtlands län', 'Västerbottens län', 'Norrbottens län' ) def street_prefix(self): return self.random_element(self.street_prefixes) def city_name(self): return self.random_element(self.cities) def state(self): return self.random_element(self.states)

true

790d944fc3cfeccd57e1e4f48ccc80a1c326b3c3

11,935

py

Python

kuka_driver/src/kuka_driver/kuka_rsi_router.py

adamleon/kuka

cac2880ff9bf1fb798029280a9baf51450195fc4

[ "BSD-3-Clause" ]

null

kuka_driver/src/kuka_driver/kuka_rsi_router.py

adamleon/kuka

cac2880ff9bf1fb798029280a9baf51450195fc4

[ "BSD-3-Clause" ]

null

kuka_driver/src/kuka_driver/kuka_rsi_router.py

adamleon/kuka

cac2880ff9bf1fb798029280a9baf51450195fc4

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # Copyright (c) 2014, Norwegian University of Science and Technology # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Author: Lars Tingelstad # Maintainer: Lars Tingelstad <lars.tingelstad@ntnu.no> import socket import threading import time import numpy as np import struct import xml.etree.ElementTree as et class UDPServerRealTime(threading.Thread): def __init__(self,name, host, port, handshake=None): threading.Thread.__init__(self) self.daemon = True self.name = name self._host = host self._port = port self._handshake = handshake self._timeout = None self._timeout_count = 0 self._is_timed_out = False self._max_timeout_count = None self._lock = threading.Lock() self._recv_data = None self._send_data = None self._remote_addr = None self.is_connected = False self._stop_flag = threading.Event() self._disconnect_client_flag = threading.Event() self._socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) self._socket.settimeout(self._timeout) self._socket.bind((self._host, self._port)) def set_max_timeout_count(self, timeout_count): self._max_timeout_count = timeout_count def timeout(self): return self._timeout def set_timeout(self, timeout): self._timeout = timeout self._socket.settimeout(self._timeout) def receive(self): try: #self._lock.acquire() data, addr = self._socket.recvfrom(1024) self._recv_data = data #self._lock.release() ## Set connection if handshake mechanism is not used if self._handshake is None and not self.is_connected: self._remote_addr = addr print("{name}: Got connection from: {addr}".format(name=self.name, addr=self._remote_addr)) self.is_connected = True self._timeout_count = 0 return data except socket.timeout, e: if self._max_timeout_count is not None: self._timeout_count += 1 print("{name}: Late package!".format(name=self.name)) if self._timeout_count > self._max_timeout_count: print("{name}: Maximum timeouts. Disconnecting client: {addr}".format(name=self.name, addr=self._remote_addr)) self._disconnect_client_flag.set() return None def send(self, data): #self._lock.acquire() self._send_data = data self._socket.sendto(self._send_data, self._remote_addr) #self._lock.release() def connect(self): ''' Create connection from external client ''' if self._handshake is not None: if not self.is_connected: self._socket.settimeout(None) data, remote_addr = self._socket.recvfrom(1024) if data == self._handshake: self._remote_addr = remote_addr print("{name}: Got connection from: {addr}".format(name=self.name, addr=self._remote_addr)) self.is_connected = True else: print("{name}: Could not accept connection from: {addr}".format(name=self.name, addr=remote_addr)) self._disconnect_client_flag.set() else: print("{name}: Can not create connection without handshake!".format(name=self.name)) if self._timeout is not None: self._socket.settimeout(self._timeout) def stop(self): print("{name}: Stopping!".format(name=self.name)) self._stop_flag.set() def disconnect(self): #print("{name}: Disconnecting!".format(name=self.name)) self._disconnect_client_flag.set() def run(self): while not self._stop_flag.is_set(): print("{name}: Waiting for connection!".format(name=self.name)) if self._handshake is not None: self.connect() self._disconnect_client_flag.wait() print("{name}: Disconnecting client".format(name=self.name)) self.is_connected = False self._remote_addr = None self._disconnect_client_flag.clear() self.join() class KUKARSIRouter(object): def __init__(self): self._lock = threading.Lock() self._joint_correction = np.zeros(6).astype(np.float32) self._joint_setpoint_position_init = None #self._rsi_server = UDPServerRealTime('rsi server','localhost', 49152) self._rsi_server = UDPServerRealTime('rsi server','192.168.1.67', 49152) self._rsi_server.set_max_timeout_count(3) self._ext_control_server = UDPServerRealTime('ext control server', 'localhost', 10000, "RSI") self._ext_control_server.set_timeout(0.004) self._ext_control_server.set_max_timeout_count(3) def _parse_xml_from_robot(self, data): root = et.fromstring(data) # Cartesian actual position RIst = root.find('RIst').attrib cart_actual_pos = np.array([RIst['X'], RIst['Y'], RIst['Z'], RIst['A'], RIst['B'], RIst['C']], dtype=np.float64) # Cartesian setpoint position RSol = root.find('RSol').attrib cart_setpoint_pos = np.array([RSol['X'], RSol['Y'], RSol['Z'], RSol['A'], RSol['B'], RSol['C']], dtype=np.float64) # Axis actual AIPos = root.find('AIPos').attrib axis_actual_pos = np.array([AIPos['A1'], AIPos['A2'],AIPos['A3'], AIPos['A4'], AIPos['A5'],AIPos['A6']], dtype=np.float64) # Axis setpoint pos ASPos = root.find('ASPos').attrib axis_setpoint_pos = np.array([ASPos['A1'], ASPos['A2'],ASPos['A3'], ASPos['A4'], ASPos['A5'],ASPos['A6']], dtype=np.float64) # Number of late packages Delay = root.find('Delay').attrib n_late_packages = int(Delay['D']) # IPOC number IPOC = int(root.find('IPOC').text) return axis_actual_pos, axis_setpoint_pos, n_late_packages, IPOC def _create_xml_to_robot(self, desired_axis_corr, ipoc_cycle_num): dac = desired_axis_corr sen = et.Element('Sen', {'Type':'ImFree'}) akorr = et.SubElement(sen, 'AK', {'A1':str(dac[0]), 'A2':str(dac[1]), 'A3':str(dac[2]), 'A4':str(dac[3]), 'A5':str(dac[4]), 'A6':str(dac[5])}) ipoc = et.SubElement(sen, 'IPOC').text = str(ipoc_cycle_num) return et.tostring(sen) def _create_joint_pos_packet(self, ipoc, axis_actual_pos): return struct.pack('Q6d', ipoc, *axis_actual_pos) def _parse_joint_pos_packet(self, packet): data = struct.unpack('Q6d', packet) ipoc = data[0] q_desired = np.array(data[1:], dtype=np.float64) return ipoc, q_desired def run(self): self._ext_control_server.start() self._rsi_server.start() #while not self._stop_flag.is_set(): while True: ## Receive rsi packet from robot. This is a blocking call if no rsi ## is connected. The timeout is set to 4ms when the robot connects, ## and is reset to None when the robot disconnects. data = self._rsi_server.receive() if self._rsi_server.is_connected: ## Set timeout of receive for RSI client when robot connects if self._rsi_server.timeout() is None: self._rsi_server.set_timeout(0.004) ## Only parse rsi packet if content is not None if data is not None: ## Parse rsi packet xml document q_actual, q_setpoint, late_packages, ipoc = self._parse_xml_from_robot(data) if self._joint_setpoint_position_init is None: self._joint_setpoint_position_init = q_setpoint if self._ext_control_server.is_connected: ipoc_out = ipoc ## Create joint position packet to send to external control client packet = self._create_joint_pos_packet(ipoc_out, q_actual) ## Send send joint position packet to external control client self._ext_control_server.send(packet) ## Receive desired joint position packet data = self._ext_control_server.receive() if data is not None: ## parse data from client ipoc_in, q_desired = self._parse_joint_pos_packet(data) print(q_desired) ## check if the received ipoc timestamp is equal to ## the received ipoc timestamp from the external ## control client if ipoc_in == ipoc_out: ## The joint correction is equal to the desired joint # position minus the current joint setpoint. with self._lock: #self._joint_correction = q_desired - self._joint_setpoint_position_init self._joint_correction = q_desired - q_setpoint with self._lock: data = self._create_xml_to_robot(self._joint_correction, ipoc) print(data) self._rsi_server.send(data) else: print("RSI Router: No connection with robot. Disconnecting all external connections!") self._joint_setpoint_position_init = None self._joint_correction = np.zeros(6).astype(np.float32) self._ext_control_server.disconnect() self._rsi_server.set_timeout(None) self._ext_control_server.stop() self._rsi_server.stop; if __name__ == '__main__': router = KUKARSIRouter() router.run()

43.242754

130

0.600335

import socket import threading import time import numpy as np import struct import xml.etree.ElementTree as et class UDPServerRealTime(threading.Thread): def __init__(self,name, host, port, handshake=None): threading.Thread.__init__(self) self.daemon = True self.name = name self._host = host self._port = port self._handshake = handshake self._timeout = None self._timeout_count = 0 self._is_timed_out = False self._max_timeout_count = None self._lock = threading.Lock() self._recv_data = None self._send_data = None self._remote_addr = None self.is_connected = False self._stop_flag = threading.Event() self._disconnect_client_flag = threading.Event() self._socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) self._socket.settimeout(self._timeout) self._socket.bind((self._host, self._port)) def set_max_timeout_count(self, timeout_count): self._max_timeout_count = timeout_count def timeout(self): return self._timeout def set_timeout(self, timeout): self._timeout = timeout self._socket.settimeout(self._timeout) def receive(self): try: data, addr = self._socket.recvfrom(1024) self._recv_data = data f.is_connected: self._remote_addr = addr print("{name}: Got connection from: {addr}".format(name=self.name, addr=self._remote_addr)) self.is_connected = True self._timeout_count = 0 return data except socket.timeout, e: if self._max_timeout_count is not None: self._timeout_count += 1 print("{name}: Late package!".format(name=self.name)) if self._timeout_count > self._max_timeout_count: print("{name}: Maximum timeouts. Disconnecting client: {addr}".format(name=self.name, addr=self._remote_addr)) self._disconnect_client_flag.set() return None def send(self, data): self._send_data = data self._socket.sendto(self._send_data, self._remote_addr) def connect(self): ''' Create connection from external client ''' if self._handshake is not None: if not self.is_connected: self._socket.settimeout(None) data, remote_addr = self._socket.recvfrom(1024) if data == self._handshake: self._remote_addr = remote_addr print("{name}: Got connection from: {addr}".format(name=self.name, addr=self._remote_addr)) self.is_connected = True else: print("{name}: Could not accept connection from: {addr}".format(name=self.name, addr=remote_addr)) self._disconnect_client_flag.set() else: print("{name}: Can not create connection without handshake!".format(name=self.name)) if self._timeout is not None: self._socket.settimeout(self._timeout) def stop(self): print("{name}: Stopping!".format(name=self.name)) self._stop_flag.set() def disconnect(self): self._disconnect_client_flag.set() def run(self): while not self._stop_flag.is_set(): print("{name}: Waiting for connection!".format(name=self.name)) if self._handshake is not None: self.connect() self._disconnect_client_flag.wait() print("{name}: Disconnecting client".format(name=self.name)) self.is_connected = False self._remote_addr = None self._disconnect_client_flag.clear() self.join() class KUKARSIRouter(object): def __init__(self): self._lock = threading.Lock() self._joint_correction = np.zeros(6).astype(np.float32) self._joint_setpoint_position_init = None self._rsi_server = UDPServerRealTime('rsi server','192.168.1.67', 49152) self._rsi_server.set_max_timeout_count(3) self._ext_control_server = UDPServerRealTime('ext control server', 'localhost', 10000, "RSI") self._ext_control_server.set_timeout(0.004) self._ext_control_server.set_max_timeout_count(3) def _parse_xml_from_robot(self, data): root = et.fromstring(data) RIst = root.find('RIst').attrib cart_actual_pos = np.array([RIst['X'], RIst['Y'], RIst['Z'], RIst['A'], RIst['B'], RIst['C']], dtype=np.float64) RSol = root.find('RSol').attrib cart_setpoint_pos = np.array([RSol['X'], RSol['Y'], RSol['Z'], RSol['A'], RSol['B'], RSol['C']], dtype=np.float64) AIPos = root.find('AIPos').attrib axis_actual_pos = np.array([AIPos['A1'], AIPos['A2'],AIPos['A3'], AIPos['A4'], AIPos['A5'],AIPos['A6']], dtype=np.float64) ASPos = root.find('ASPos').attrib axis_setpoint_pos = np.array([ASPos['A1'], ASPos['A2'],ASPos['A3'], ASPos['A4'], ASPos['A5'],ASPos['A6']], dtype=np.float64) Delay = root.find('Delay').attrib n_late_packages = int(Delay['D']) IPOC = int(root.find('IPOC').text) return axis_actual_pos, axis_setpoint_pos, n_late_packages, IPOC def _create_xml_to_robot(self, desired_axis_corr, ipoc_cycle_num): dac = desired_axis_corr sen = et.Element('Sen', {'Type':'ImFree'}) akorr = et.SubElement(sen, 'AK', {'A1':str(dac[0]), 'A2':str(dac[1]), 'A3':str(dac[2]), 'A4':str(dac[3]), 'A5':str(dac[4]), 'A6':str(dac[5])}) ipoc = et.SubElement(sen, 'IPOC').text = str(ipoc_cycle_num) return et.tostring(sen) def _create_joint_pos_packet(self, ipoc, axis_actual_pos): return struct.pack('Q6d', ipoc, *axis_actual_pos) def _parse_joint_pos_packet(self, packet): data = struct.unpack('Q6d', packet) ipoc = data[0] q_desired = np.array(data[1:], dtype=np.float64) return ipoc, q_desired def run(self): self._ext_control_server.start() self._rsi_server.start() while True: .004) q_setpoint, late_packages, ipoc = self._parse_xml_from_robot(data) if self._joint_setpoint_position_init is None: self._joint_setpoint_position_init = q_setpoint if self._ext_control_server.is_connected: ipoc_out = ipoc poc_out, q_actual) t) xt_control_server.receive() if data is not None: ipoc_in, q_desired = self._parse_joint_pos_packet(data) print(q_desired) with self._lock: self._joint_correction = q_desired - q_setpoint with self._lock: data = self._create_xml_to_robot(self._joint_correction, ipoc) print(data) self._rsi_server.send(data) else: print("RSI Router: No connection with robot. Disconnecting all external connections!") self._joint_setpoint_position_init = None self._joint_correction = np.zeros(6).astype(np.float32) self._ext_control_server.disconnect() self._rsi_server.set_timeout(None) self._ext_control_server.stop() self._rsi_server.stop; if __name__ == '__main__': router = KUKARSIRouter() router.run()

false

true

790d946125e1389709ed26de240d8d12a5c217e1

1,087

py

Python

ML-in-Action/MachineLearning-dev/src/py3.x/ML/15.BigData_MapReduce/mrMeanMapper.py

cherisyu/ML_in_Action

8c1019de911e7fb1bbab973067213f5f62ab9dcd

[ "Apache-2.0" ]

1

2019-01-23T01:47:31.000Z

ML-in-Action/MachineLearning-dev/src/py3.x/ML/15.BigData_MapReduce/mrMeanMapper.py

cherisyu/ML_in_Action

8c1019de911e7fb1bbab973067213f5f62ab9dcd

[ "Apache-2.0" ]

null

ML-in-Action/MachineLearning-dev/src/py3.x/ML/15.BigData_MapReduce/mrMeanMapper.py

cherisyu/ML_in_Action

8c1019de911e7fb1bbab973067213f5f62ab9dcd

[ "Apache-2.0" ]

null

#!/usr/bin/python # coding:utf-8 ''' Created on 2017-04-06 Update on 2017-11-17 Author: Peter/ApacheCN-xy/片刻 GitHub: https://github.com/apachecn/MachineLearning ''' import sys from numpy import mat, mean, power ''' 这个mapper文件按行读取所有的输入并创建一组对应的浮点数，然后得到数组的长度并创建NumPy矩阵。再对所有的值进行平方，最后将均值和平方后的均值发送出去。这些值将用来计算全局的均值和方差。 Args： file 输入数据 Return： ''' def read_input(file): for line in file: yield line.rstrip() # 返回一个 yield 迭代器，每次获取下一个值，节约内存。 input = read_input(sys.stdin) # 创建一个输入的数据行的列表list input = [float(line) for line in input] # 将得到的数据转化为 float 类型 numInputs = len(input) # 获取数据的个数，即输入文件的数据的行数 input = mat(input) # 将 List 转换为矩阵 sqInput = power(input, 2) # 将矩阵的数据分别求平方，即 2次方 # 输出数据的个数，n个数据的均值，n个数据平方之后的均值 # 第一行是标准输出，也就是reducer的输出 # 第二行识标准错误输出，即对主节点作出的响应报告，表明本节点工作正常。 # 【这不就是面试的装逼重点吗？如何设计监听架构细节】注意：一个好的习惯是想标准错误输出发送报告。如果某任务10分钟内没有报告输出，则将被Hadoop中止。 print("%d\t%f\t%f" % (numInputs, mean(input), mean(sqInput))) # 计算均值 print("map report: still alive", file=sys.stderr)

26.512195

78

0.677093

import sys from numpy import mat, mean, power def read_input(file): for line in file: yield line.rstrip() input = read_input(sys.stdin) input = [float(line) for line in input] numInputs = len(input) input = mat(input) sqInput = power(input, 2) print("%d\t%f\t%f" % (numInputs, mean(input), mean(sqInput))) print("map report: still alive", file=sys.stderr)

true

790d94786a3272ddf89bf0cd9092ab9ab9a52f2a

2,337

py

Python

dependencies/generate maps/pythongis/app/tk2/_othermisc/dropdown_works.py

karimbahgat/AutoMap

eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f

[ "MIT" ]

4

2015-12-05T14:31:55.000Z

2018-02-09T05:54:36.000Z

dependencies/generate maps/pythongis/app/tk2/_othermisc/dropdown_works.py

karimbahgat/AutoMap

eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f

[ "MIT" ]

1

2022-01-13T02:52:09.000Z

dependencies/generate maps/pythongis/app/tk2/_othermisc/dropdown_works.py

karimbahgat/AutoMap

eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f

[ "MIT" ]

1

2018-10-24T01:08:11.000Z

import Tkinter as tk class Combobox(tk.Label): def __init__(self, master, choices=[], default=None, direction="down", arrowimage="default", **kwargs): style = {"relief": "groove", "bg":"white"} style.update(kwargs) tk.Label.__init__(self, master, **style) # options if direction not in ("down","up"): raise Exception("Direction must be either down or up") self.direction = direction self.choices = choices # entry self.entry = tk.Entry(self, bg=style["bg"], borderwidth=0) self.entry.pack(side="left", fill="y") if default != None: self.entry.insert(0, default) # dropdown arrow if arrowimage == "default": arrowimage = tk.PhotoImage(file="dropdown.gif") else: pass # image should be passed as a Photoimage self.arrow = tk.Label(self, bg=style["bg"], image=arrowimage) self.arrow.img = arrowimage self.arrow.pack(side="right") self.arrow.bind("<Button-1>", self.dropdown) def dropdown(self, event=None): self.arrow["relief"] = "sunken" self.entry.focus_force() self.entry.select_range(0, tk.END) menu = tk.Menu(self.entry, tearoff=0, bg="white") def changeentry(choice): self.entry.delete(0, tk.END) self.entry.insert(0, choice) self.rollup() if self.direction == "down": choices = self.choices elif self.direction == "up": choices = list(reversed(self.choices)) for choice in choices: menu.add_command(label=repr(choice).ljust(30), command=lambda x=choice: changeentry(x)) x = self.entry.winfo_rootx() if self.direction == "down": y = self.entry.winfo_rooty() + self.entry.winfo_height() elif self.direction == "up": y = self.entry.winfo_rooty() - menu.yposition(0) #menu.winfo_height() menu.post(x, y) def rollup(self, event=None): self.arrow["relief"] = "flat" if __name__ == "__main__": win = tk.Tk() OPTIONS = range(20) cbox = Combobox(win, choices=OPTIONS, default=12, direction="down") cbox.pack(side="left") cbox2 = Combobox(win, choices=OPTIONS, default=24, direction="up") cbox2.pack(side="left") win.mainloop()

35.953846

107

0.595208

import Tkinter as tk class Combobox(tk.Label): def __init__(self, master, choices=[], default=None, direction="down", arrowimage="default", **kwargs): style = {"relief": "groove", "bg":"white"} style.update(kwargs) tk.Label.__init__(self, master, **style) if direction not in ("down","up"): raise Exception("Direction must be either down or up") self.direction = direction self.choices = choices self.entry = tk.Entry(self, bg=style["bg"], borderwidth=0) self.entry.pack(side="left", fill="y") if default != None: self.entry.insert(0, default) if arrowimage == "default": arrowimage = tk.PhotoImage(file="dropdown.gif") else: pass self.arrow = tk.Label(self, bg=style["bg"], image=arrowimage) self.arrow.img = arrowimage self.arrow.pack(side="right") self.arrow.bind("<Button-1>", self.dropdown) def dropdown(self, event=None): self.arrow["relief"] = "sunken" self.entry.focus_force() self.entry.select_range(0, tk.END) menu = tk.Menu(self.entry, tearoff=0, bg="white") def changeentry(choice): self.entry.delete(0, tk.END) self.entry.insert(0, choice) self.rollup() if self.direction == "down": choices = self.choices elif self.direction == "up": choices = list(reversed(self.choices)) for choice in choices: menu.add_command(label=repr(choice).ljust(30), command=lambda x=choice: changeentry(x)) x = self.entry.winfo_rootx() if self.direction == "down": y = self.entry.winfo_rooty() + self.entry.winfo_height() elif self.direction == "up": y = self.entry.winfo_rooty() - menu.yposition(0) menu.post(x, y) def rollup(self, event=None): self.arrow["relief"] = "flat" if __name__ == "__main__": win = tk.Tk() OPTIONS = range(20) cbox = Combobox(win, choices=OPTIONS, default=12, direction="down") cbox.pack(side="left") cbox2 = Combobox(win, choices=OPTIONS, default=24, direction="up") cbox2.pack(side="left") win.mainloop()

true

790d953aa8005e2273ab5dd9fc378123efc23152

474

py

Python

sentence_transformers/losses/__init__.py

WHU-Peter/sentence-transformers

a9acd9e8eb086221c1351ad6489ed29a076ca8f5

[ "Apache-2.0" ]

null

sentence_transformers/losses/__init__.py

WHU-Peter/sentence-transformers

a9acd9e8eb086221c1351ad6489ed29a076ca8f5

[ "Apache-2.0" ]

null

sentence_transformers/losses/__init__.py

WHU-Peter/sentence-transformers

a9acd9e8eb086221c1351ad6489ed29a076ca8f5

[ "Apache-2.0" ]

null

from .CosineSimilarityLoss import * from .SoftmaxLoss import * from .MultipleNegativesRankingLoss import * from .TripletLoss import * from .MSELoss import * from .ContrastiveLoss import * from .OnlineContrastiveLoss import * from .MegaBatchMarginLoss import * from .DenoisingAutoEncoderLoss import * # Triplet losses from .BatchHardTripletLoss import * from .BatchHardSoftMarginTripletLoss import * from .BatchSemiHardTripletLoss import * from .BatchAllTripletLoss import *

31.6

45

0.827004

from .CosineSimilarityLoss import * from .SoftmaxLoss import * from .MultipleNegativesRankingLoss import * from .TripletLoss import * from .MSELoss import * from .ContrastiveLoss import * from .OnlineContrastiveLoss import * from .MegaBatchMarginLoss import * from .DenoisingAutoEncoderLoss import * from .BatchHardTripletLoss import * from .BatchHardSoftMarginTripletLoss import * from .BatchSemiHardTripletLoss import * from .BatchAllTripletLoss import *

true

790d973791541e76f2855f405878a763b947f1a2

8,733

py

Python

packages/structural_dhcp_mriqc/structural_dhcp_mriqc/qc/functional.py

amakropoulos/structural-pipeline-measures

70e22f9ad94cc57e72e510576cfc3129da83f7fc

[ "Apache-2.0" ]

2

2017-09-11T15:25:14.000Z

2019-09-27T17:08:31.000Z

packages/structural_dhcp_mriqc/structural_dhcp_mriqc/qc/functional.py

amakropoulos/structural-pipeline-measures

70e22f9ad94cc57e72e510576cfc3129da83f7fc

[ "Apache-2.0" ]

6

2019-08-22T06:29:45.000Z

2021-09-19T18:59:46.000Z

packages/structural_dhcp_mriqc/structural_dhcp_mriqc/qc/functional.py

amakropoulos/structural-pipeline-measures

70e22f9ad94cc57e72e510576cfc3129da83f7fc

[ "Apache-2.0" ]

1

2018-02-12T14:38:33.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: # pylint: disable=no-member # # @Author: oesteban # @Date: 2016-02-23 19:25:39 # @Email: code@oscaresteban.es # @Last Modified by: oesteban # @Last Modified time: 2016-02-29 11:43:16 """ Computation of the quality assessment measures on functional MRI """ import os.path as op import numpy as np import nibabel as nb from nitime import algorithms as nta import scipy def gsr(epi_data, mask, direction="y", ref_file=None, out_file=None): """ Computes the :abbr:`GSR (ghost to signal ratio)` [Giannelli2010]_. The procedure is as follows: #. Create a Nyquist ghost mask by circle-shifting the original mask by :math:`N/2`. #. Rotate by :math:`N/2` #. Remove the intersection with the original mask #. Generate a non-ghost background #. Calculate the :abbr:`GSR (ghost to signal ratio)` .. warning :: This should be used with EPI images for which the phase encoding direction is known. :param str epi_file: path to epi file :param str mask_file: path to brain mask :param str direction: the direction of phase encoding (x, y, all) :return: the computed gsr """ direction = direction.lower() if direction[-1] not in ['x', 'y', 'all']: raise Exception("Unknown direction %s, should be one of x, -x, y, -y, all" % direction) if direction == 'all': result = [] for newdir in ['x', 'y']: ofile = None if out_file is not None: fname, ext = op.splitext(ofile) if ext == '.gz': fname, ext2 = op.splitext(fname) ext = ext2 + ext ofile = '%s_%s%s' % (fname, newdir, ext) result += [gsr(epi_data, mask, newdir, ref_file=ref_file, out_file=ofile)] return result # Step 1 n2_mask = np.zeros_like(mask) # Step 2 if direction == "x": n2lim = np.floor(mask.shape[0]/2) n2_mask[:n2lim, :, :] = mask[n2lim:(n2lim*2), :, :] n2_mask[n2lim:(n2lim*2), :, :] = mask[:n2lim, :, :] elif direction == "y": n2lim = np.floor(mask.shape[1]/2) n2_mask[:, :n2lim, :] = mask[:, n2lim:(n2lim*2), :] n2_mask[:, n2lim:(n2lim*2), :] = mask[:, :n2lim, :] elif direction == "z": n2lim = np.floor(mask.shape[2]/2) n2_mask[:, :, :n2lim] = mask[:, :, n2lim:(n2lim*2)] n2_mask[:, :, n2lim:(n2lim*2)] = mask[:, :, :n2lim] # Step 3 n2_mask = n2_mask * (1-mask) # Step 4: non-ghost background region is labeled as 2 n2_mask = n2_mask + 2 * (1 - n2_mask - mask) # Save mask if ref_file is not None and out_file is not None: ref = nb.load(ref_file) out = nb.Nifti1Image(n2_mask, ref.get_affine(), ref.get_header()) out.to_filename(out_file) # Step 5: signal is the entire foreground image ghost = epi_data[n2_mask == 1].mean() - epi_data[n2_mask == 2].mean() signal = epi_data[n2_mask == 0].mean() return float(ghost/signal) def dvars(func, mask, output_all=False, out_file=None): """ Compute the mean :abbr:`DVARS (D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels)` [Power2012]_. Particularly, the *standardized* :abbr:`DVARS (D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels)` [Nichols2013]_ are computed. .. note:: Implementation details Uses the implementation of the `Yule-Walker equations from nitime <http://nipy.org/nitime/api/generated/nitime.algorithms.autoregressive.html\ #nitime.algorithms.autoregressive.AR_est_YW>`_ for the :abbr:`AR (auto-regressive)` filtering of the fMRI signal. :param numpy.ndarray func: functional data, after head-motion-correction. :param numpy.ndarray mask: a 3D mask of the brain :param bool output_all: write out all dvars :param str out_file: a path to which the standardized dvars should be saved. :return: the standardized DVARS """ if len(func.shape) != 4: raise RuntimeError( "Input fMRI dataset should be 4-dimensional" % func) # Remove zero-variance voxels across time axis zv_mask = zero_variance(func, mask) idx = np.where(zv_mask > 0) mfunc = func[idx[0], idx[1], idx[2], :] # Robust standard deviation func_sd = (np.percentile(mfunc, 75) - np.percentile(mfunc, 25)) / 1.349 # Demean mfunc -= mfunc.mean(axis=1)[..., np.newaxis] # AR1 ak_coeffs = np.apply_along_axis(nta.AR_est_YW, 1, mfunc, 1) # Predicted standard deviation of temporal derivative func_sd_pd = np.squeeze(np.sqrt((2 * (1 - ak_coeffs[:, 0])).tolist()) * func_sd) diff_sd_mean = func_sd_pd[func_sd_pd > 0].mean() # Compute temporal difference time series func_diff = np.diff(mfunc, axis=1) # DVARS (no standardization) dvars_nstd = func_diff.std(axis=0) # standardization dvars_stdz = dvars_nstd / diff_sd_mean # voxelwise standardization diff_vx_stdz = func_diff / np.array([func_sd_pd] * func_diff.shape[-1]).T dvars_vx_stdz = diff_vx_stdz.std(1, ddof=1) if output_all: gendvars = np.vstack((dvars_stdz, dvars_nstd, dvars_vx_stdz)) else: gendvars = dvars_stdz.reshape(len(dvars_stdz), 1) if out_file is not None: np.savetxt(out_file, gendvars, fmt='%.12f') return gendvars def fd_jenkinson(in_file, rmax=80., out_file=None): """ Compute the :abbr:`FD (framewise displacement)` [Jenkinson2002]_ on a 4D dataset, after ``3dvolreg`` has been executed (generally a file named ``*.affmat12.1D``). :param str in_file: path to epi file :param float rmax: the default radius (as in FSL) of a sphere represents the brain in which the angular displacements are projected. :param str out_file: a path for the output file with the FD :return: the output file with the FD, and the average FD along the time series :rtype: tuple(str, float) .. note :: :code:`infile` should have one 3dvolreg affine matrix in one row - NOT the motion parameters """ import sys import math if out_file is None: fname, ext = op.splitext(op.basename(in_file)) out_file = op.abspath('%s_fdfile%s' % (fname, ext)) # if in_file (coordinate_transformation) is actually the rel_mean output # of the MCFLIRT command, forward that file if 'rel.rms' in in_file: return in_file pm_ = np.genfromtxt(in_file) original_shape = pm_.shape pm = np.zeros((pm_.shape[0], pm_.shape[1] + 4)) pm[:, :original_shape[1]] = pm_ pm[:, original_shape[1]:] = [0.0, 0.0, 0.0, 1.0] # rigid body transformation matrix T_rb_prev = np.matrix(np.eye(4)) flag = 0 X = [0] # First timepoint for i in range(0, pm.shape[0]): # making use of the fact that the order of aff12 matrix is "row-by-row" T_rb = np.matrix(pm[i].reshape(4, 4)) if flag == 0: flag = 1 else: M = np.dot(T_rb, T_rb_prev.I) - np.eye(4) A = M[0:3, 0:3] b = M[0:3, 3] FD_J = math.sqrt( (rmax * rmax / 5) * np.trace(np.dot(A.T, A)) + np.dot(b.T, b)) X.append(FD_J) T_rb_prev = T_rb np.savetxt(out_file, X) return out_file def gcor(func, mask): """ Compute the :abbr:`GCOR (global correlation)`. :param numpy.ndarray func: input fMRI dataset, after motion correction :param numpy.ndarray mask: 3D brain mask :return: the computed GCOR value """ # Remove zero-variance voxels across time axis tv_mask = zero_variance(func, mask) idx = np.where(tv_mask > 0) zscores = scipy.stats.mstats.zscore(func[idx[0], idx[1], idx[2], :], axis=1) avg_ts = zscores.mean(axis=0) return float(avg_ts.transpose().dot(avg_ts) / len(avg_ts)) def zero_variance(func, mask): """ Mask out voxels with zero variance across t-axis :param numpy.ndarray func: input fMRI dataset, after motion correction :param numpy.ndarray mask: 3D brain mask :return: the 3D mask of voxels with nonzero variance across :math:`t`. :rtype: numpy.ndarray """ idx = np.where(mask > 0) func = func[idx[0], idx[1], idx[2], :] tvariance = func.var(axis=1) tv_mask = np.zeros_like(tvariance) tv_mask[tvariance > 0] = 1 newmask = np.zeros_like(mask) newmask[idx] = tv_mask return newmask

31.078292

89

0.620749

import os.path as op import numpy as np import nibabel as nb from nitime import algorithms as nta import scipy def gsr(epi_data, mask, direction="y", ref_file=None, out_file=None): direction = direction.lower() if direction[-1] not in ['x', 'y', 'all']: raise Exception("Unknown direction %s, should be one of x, -x, y, -y, all" % direction) if direction == 'all': result = [] for newdir in ['x', 'y']: ofile = None if out_file is not None: fname, ext = op.splitext(ofile) if ext == '.gz': fname, ext2 = op.splitext(fname) ext = ext2 + ext ofile = '%s_%s%s' % (fname, newdir, ext) result += [gsr(epi_data, mask, newdir, ref_file=ref_file, out_file=ofile)] return result n2_mask = np.zeros_like(mask) if direction == "x": n2lim = np.floor(mask.shape[0]/2) n2_mask[:n2lim, :, :] = mask[n2lim:(n2lim*2), :, :] n2_mask[n2lim:(n2lim*2), :, :] = mask[:n2lim, :, :] elif direction == "y": n2lim = np.floor(mask.shape[1]/2) n2_mask[:, :n2lim, :] = mask[:, n2lim:(n2lim*2), :] n2_mask[:, n2lim:(n2lim*2), :] = mask[:, :n2lim, :] elif direction == "z": n2lim = np.floor(mask.shape[2]/2) n2_mask[:, :, :n2lim] = mask[:, :, n2lim:(n2lim*2)] n2_mask[:, :, n2lim:(n2lim*2)] = mask[:, :, :n2lim] n2_mask = n2_mask * (1-mask) n2_mask = n2_mask + 2 * (1 - n2_mask - mask) if ref_file is not None and out_file is not None: ref = nb.load(ref_file) out = nb.Nifti1Image(n2_mask, ref.get_affine(), ref.get_header()) out.to_filename(out_file) ghost = epi_data[n2_mask == 1].mean() - epi_data[n2_mask == 2].mean() signal = epi_data[n2_mask == 0].mean() return float(ghost/signal) def dvars(func, mask, output_all=False, out_file=None): if len(func.shape) != 4: raise RuntimeError( "Input fMRI dataset should be 4-dimensional" % func) zv_mask = zero_variance(func, mask) idx = np.where(zv_mask > 0) mfunc = func[idx[0], idx[1], idx[2], :] func_sd = (np.percentile(mfunc, 75) - np.percentile(mfunc, 25)) / 1.349 mfunc -= mfunc.mean(axis=1)[..., np.newaxis] ak_coeffs = np.apply_along_axis(nta.AR_est_YW, 1, mfunc, 1) func_sd_pd = np.squeeze(np.sqrt((2 * (1 - ak_coeffs[:, 0])).tolist()) * func_sd) diff_sd_mean = func_sd_pd[func_sd_pd > 0].mean() func_diff = np.diff(mfunc, axis=1) dvars_nstd = func_diff.std(axis=0) dvars_stdz = dvars_nstd / diff_sd_mean diff_vx_stdz = func_diff / np.array([func_sd_pd] * func_diff.shape[-1]).T dvars_vx_stdz = diff_vx_stdz.std(1, ddof=1) if output_all: gendvars = np.vstack((dvars_stdz, dvars_nstd, dvars_vx_stdz)) else: gendvars = dvars_stdz.reshape(len(dvars_stdz), 1) if out_file is not None: np.savetxt(out_file, gendvars, fmt='%.12f') return gendvars def fd_jenkinson(in_file, rmax=80., out_file=None): import sys import math if out_file is None: fname, ext = op.splitext(op.basename(in_file)) out_file = op.abspath('%s_fdfile%s' % (fname, ext)) if 'rel.rms' in in_file: return in_file pm_ = np.genfromtxt(in_file) original_shape = pm_.shape pm = np.zeros((pm_.shape[0], pm_.shape[1] + 4)) pm[:, :original_shape[1]] = pm_ pm[:, original_shape[1]:] = [0.0, 0.0, 0.0, 1.0] T_rb_prev = np.matrix(np.eye(4)) flag = 0 X = [0] for i in range(0, pm.shape[0]): T_rb = np.matrix(pm[i].reshape(4, 4)) if flag == 0: flag = 1 else: M = np.dot(T_rb, T_rb_prev.I) - np.eye(4) A = M[0:3, 0:3] b = M[0:3, 3] FD_J = math.sqrt( (rmax * rmax / 5) * np.trace(np.dot(A.T, A)) + np.dot(b.T, b)) X.append(FD_J) T_rb_prev = T_rb np.savetxt(out_file, X) return out_file def gcor(func, mask): tv_mask = zero_variance(func, mask) idx = np.where(tv_mask > 0) zscores = scipy.stats.mstats.zscore(func[idx[0], idx[1], idx[2], :], axis=1) avg_ts = zscores.mean(axis=0) return float(avg_ts.transpose().dot(avg_ts) / len(avg_ts)) def zero_variance(func, mask): idx = np.where(mask > 0) func = func[idx[0], idx[1], idx[2], :] tvariance = func.var(axis=1) tv_mask = np.zeros_like(tvariance) tv_mask[tvariance > 0] = 1 newmask = np.zeros_like(mask) newmask[idx] = tv_mask return newmask

true

790d9738efeb876e3e52c3f4c9907f9c3bb7fc43

18,305

py

Python

app/src/main/python/KinoCode.py

susumOyaji/chaquopy-matplotlib-master

dda4a8da1391f968023bdd9d4b9c05e63b499390

[ "MIT" ]

null

app/src/main/python/KinoCode.py

susumOyaji/chaquopy-matplotlib-master

dda4a8da1391f968023bdd9d4b9c05e63b499390

[ "MIT" ]

null

app/src/main/python/KinoCode.py

susumOyaji/chaquopy-matplotlib-master

dda4a8da1391f968023bdd9d4b9c05e63b499390

[ "MIT" ]

null

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay from sklearn.metrics import accuracy_score from sklearn.model_selection import TimeSeriesSplit from keras.layers import Dropout from keras.layers import Dense, LSTM from keras.models import Sequential import numpy as np from sklearn.preprocessing import StandardScaler from matplotlib import pyplot as plt from datetime import timedelta from datetime import datetime import pandas as pd import datetime from dateutil.relativedelta import relativedelta from pandas_datareader import data as pdr from sklearn.metrics import r2_score, mean_squared_error # pandasのインポート # データの読み込み #df = pd.read_csv('finance_dataset.csv') # データフレームの表示 #df code = '6976' # '6976'#6758 #2021年から今日までの1年間のデータを取得しましょう。期日を決めて行きます。 # (2021, 1, 1) # 教師データ(今までのデータ) #start_train = datetime.date(2022, 1, 1) # 教師データ(今までのデータ) start_train=datetime.date.today() + relativedelta(days=-700) #dowstart_train = datetime.date(2022, 1, 5)#start_train + relativedelta(days=+3) # 昨日分(today-1日)まで取得できる（当日分は変動しているため） end_train = datetime.date.today() + relativedelta(days=-1) data = pdr.get_data_yahoo(f'{code}.T', start_train, end_train) # 教師データを読み込む。 Dow_df = pdr.get_data_yahoo('^DJI', start_train, end_train) # 試験データのcsvファイルを読み込む。 Nikkei_df = pdr.get_data_yahoo('^N225', start_train, end_train) # 試験データのcsvファイルを読み込む。 #データの前処理 #欠損データがあるので、欠損値NaNを除外する #df_NikkeiAll_drop = df_NikkeiAll.dropna() #df_NikkeiAll_drop.head() # 先頭の5行を表形式で表示 print(data.head()) ''' png インデックスが0から13966までの連番で、カラムに日付('Date')、最高値('High')、最安値('Low')、始値('Open')、終値('Close')が設定されたデータフレームである事が確認できます。日付('Date)は1965年1月5日から2021年10月21日までとなっています。後に詳しく説明を行いますが、予測モデル作成に対して、目的変数の追加や、週ごとにデータを纏める必要があります。そのために、曜日情報や初めの週を基準として何週目となるか等の情報と、今回の目的変数である木曜日の終値から翌日金曜日の始値が上がるかどうかの’Up’(上がる場合は'1', 同じ又は下がる場合は'0')を追加していきます。次に、infoメソッドを用いて、欠損値の有無やカラムのデータ型の確認を行います。 ''' # 各カラムの詳細確認 data.info() ''' png 各カラム欠損値なしである事がわかります。日付('Date')が’object'型となっています。今回の様な時系列データを用いる際には、'datetime64'型を用いる方が利便性が高い為、pandasの'to_datetime'メソッドを用いてデータ型の変換を行います。 ''' # 日付インデックスをりセット data.reset_index(drop=False,inplace=True) Dow_df.reset_index(drop=False,inplace=True) Nikkei_df.reset_index(drop=False, inplace=True) # Dateのデータ型をを'datetime'型へ変更 data['Date'] = pd.to_datetime(data['Date']) Dow_df['Date'] = pd.to_datetime(Dow_df['Date']) Nikkei_df['Date'] = pd.to_datetime(Nikkei_df['Date']) data.info() ''' png 'Date'のカラムが'object'型から'datetime64'型へ代わっていることが確認できます。次に曜日情報のカラムを追加します。'datetime64'型は'dt.weekday'メソッドを用いて、曜日情報を取得する事ができます。月曜日を0として連番の数字を設定されます。実行結果をdfに'weekday'カラムを追加して入力し、実行結果を確認します。 ''' # 曜日情報を追加(月曜:0, 火曜:1, 水曜:2, 木曜:3, 金曜:4、土曜:5、日曜:6) data['weekday'] = data['Date'].dt.weekday #data['Dowweekday'] = Dow_df['Date'].dt.weekday #data['DowDate'] = Dow_df['Date'] #data['Nikkeiweekday'] = Nikkei_df['Date'].dt.weekday print(data) ''' png 'weekday'のカラムが追加され0から4の数字が入力されている事がわかります。また、株取引の行われない土曜日: 5と日曜日: 6のデータは存在していない事もわかります。次に、1965年1月5日の週を基準に何周目となるのかの情報を追加します。 1965年1月5日が火曜日である事がわかるので、その週の頭の月曜日となる1965年1月4日を基準として、何日目となるのかの情報を追加します。 datetimeのライブラリからdatetimeとtimedeltaをインポートします。基準となる日の1965年1月4日をdatetime関数を使って、変数startに代入します。 dfの'Date'カラムから基準のstartと引き算をすることで、何日目となるのかを計算します。これをtimedelta関数を用いて１週間となる7日周期で割ることで何週目かを計算する事ができます。 timedelta(weeks=1)と設定することで１週間となります。この計算結果を'weeks'というカラムをdfに追加します。実行することで初めの週は0から始まり最後の2021年10月18日の週は2963となっている事が分かります。 ''' # 初めの月曜日となる1965/1/4を基準に日数を追加 start = start_train+relativedelta(days=-2) # datetime(1965, 1, 4) start = pd.to_datetime(start) #data['weeks'] = (data['Date'] - start) // timedelta(weeks=1) #data['Dowweeks'] = (Dow_df['Date'] - start) // timedelta(weeks=1) #data['Nikkiweeks'] = (Nikkei_df['Date'] - start) // timedelta(weeks=1) #print(data) #data.to_csv('data/stocks_price_data/KinoCode_data.csv') # csv書き出し ''' png 日付の情報の'Date', 'weekday', 'weeks'のカラムが分かれて表示されているので、見栄えを整理する目的で、一旦カラムの並び替えを行います。先頭に日付の情報をまとめます。並び替えたい順序でカラムを記述しdfを置き換えます。実行する事で、並び替えられている事がわかります。 ''' # Closeの列のデータのみを取り出し data['NikkiClose'] = Nikkei_df['Close'].values # カラムの並べ替え df = data[['Date', 'weekday','High', 'Low', 'Open', 'Close', 'NikkiClose']] #df_dow = Dow_df[['Date', 'weeks', 'weekday', 'High', 'Low', 'Open', 'Close']] #df_nikkei = Nikkei_df[['Date', 'weeks', 'weekday', 'High', 'Low', 'Open', 'Close']] print(df) df.to_csv('data/stocks_price_data/KinoCode_data.csv') # csv書き出し ''' png 今回のような時系列データを処理する際には、set_indexメソッドを使ってindexを日付に設定します。念のためにsort_valuesメソッドを使って日付順に並び替えを行います。実行する事で、日付の'Date'がindexに設定されている事がわかります。 ''' # データの並び替え df.sort_values(by='Date', ascending=True, inplace=True) # 日付をインデックスにセット df.set_index(keys='Date', inplace=True) print(df) ''' png 次に今回予測したい翌日の終値が本日の終値よりも上がるのかどうかの情報を追加します。shiftメソッドを用いてカラムの情報をずらすdfを作成する事ができるので、それを用いて計算を行います。 shift(-1)とする事で、カラムの情報を１行上にずらしたデータフレームを作成する事ができます。 dfを１行分上にずらしたものをdf_shiftとして作成します。実行する事でカラムの情報が１行分上にシフトしている事がわかります。一番下のカラムは欠損値となります。 ''' #カラム情報を1行上にずらしたデータフレームを作成する df_shift = df.shift(-1) df_shift #png #このdf_shiftを用いて、翌日の終値と本日の終値を引き算し、その結果を'delta_Close'というカラムを追加しdfに入力します。 #翌日の始値と本日の終値の差分を追加する df['delta_Close'] = df_shift['Close'] - df['Close'] df ''' png この'delta_Close'が上がる場合1、それ以外を0として目的変数となる'Up'のカラムを追加します。同時に'delta_Close'カラムの削除も行います。 ''' #目的変数Upを追加する(翌日の終値が上がる場合1、それ以外は0とする)、'delta_Close'カラムの削除 df['Up'] = 0 df['Up'][df['delta_Close'] > 0] = 1 df = df.drop('delta_Close', axis=1) df ''' png ここまでで、下準備となる週番号、曜日、目的変数の追加が終わりました。データの全体像をつかむ時系列データをグラフで表示する事で、株価変動の大まかなイメージを掴みます。 'Open', 'High', 'Low', 'Close'を抜き出しdf_newを作成後に、pyplotを用いてグラフ化行います。 matplotlibのライブラリからpyplotをpltという名前でインポートします。 df_newにplotメソッドを用いて、引数'kind=line'とする事で折れ線グラフが作成されます。pyplotのshowメソッドでグラフを表示します。初めの1965年から1990年頃までは、上昇傾向となっています。その後は下がる傾向となり、2010頃より再度上昇傾向である事がわかります。 ''' # 'Open', 'High', 'Low', 'Close'グラフ化のためにカラム抽出 df_new = df[['Open', 'High', 'Low', 'Close']] # matplotlibのインポート # 時系列折れ線グラフの作成 df_new.plot(kind='line') plt.show() ''' png 特徴量を追加する予測を正しく行えるようにする為の情報量(特徴量)を追加します。現在dfに入っている始値、終値、最高値、最安値の情報だけを用いて予測する事も可能ですが、株価の変動に影響すると言われている一般的な情報を追加していきます。終値の前日比率と、始値と終値の差分カラムに追加します。まず終値の前日比率ですが、本日の終値が前日から何％変動したのかを表す値となります。 (今日の終値 - 前日の終値) ÷ 前日の終値で計算します。 shiftメソッドを用いて、今度は1行したにずらしたデータフレームを作成し、終値の前日比率'Close_ratio'を計算しdfにカラムを追加します。 ''' # 終値の前日比の追加 df_shift = df.shift(1) df['Close_ratio'] = (df['Close'] - df_shift['Close']) / df_shift['Close'] df #png #次に、始値と終値の差分'Body'をdfに追加します。 # 始値と終値の差分を追加 df['Body'] = df['Open'] - df['Close'] df ''' png 特徴量の追加は以上になります。次に、不要なデータの削除を行います。今回、月曜日から木曜日までの情報を用いて、金曜日の始値が上がるか下がるのかを予測するモデルを作成するために、各週で月曜日から金曜日までのデータが揃っている週だけ使用します。祝日や年末年始など株取引が行われていない日はデータがない為、5日分のデータが揃っていない週が存在しています。各週毎に何日分のデータが存在しているのかを調べて、5日分揃っている週のデータを持ってきます。手順としては、週番号'weeks'のリストを作成します。その後リストから取り出した同じ週番号のデータ数をカウントして行き結果をdfに格納し、5日揃っている週だけ残す処理をします。週番号は0から2963まで連番で有ると考えられ、0から順番に処理すれば良いと考えられますが、万が一抜けている週が存在して居ても処理が行えるように、あえて週番号を抜き出したリスト(list_weeks)を作成します。 ''' ''' # 週番号をリストに格納 list_weeks = [] list_weeks = df['weeks'].unique() list_weeks #png #リストに従い、for文を用いて、週毎の日数をカウントしたカラム'week_days'にカウント数を入力します。 # 各週ごとの日数を入力 df['week_days'] = 0 for i in list_weeks: df['week_days'][df['weeks'] == i] = len(df[df['weeks'] == i]) df #png #5日データの存在する週(week_daysが5)の週のデータを抜き出して、dfに入力します。 # 月曜〜金曜まで5日分データのある週だけデータを取り出す df = df[df['week_days'] == 5] df #png #予測に使用しない金曜日のデータ(weekdayが4)を削除します。 #金曜日のデータを削除する(weekday:4となるデータ) df = df[df['weekday'] != 4] df ''' #png #不要カラムの削除と並び替えを行います。 # 不要カラムの削除と並べ替え df = df[['weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body', 'Up']] df ''' png ここまでで、データの準備は完了です。学習データと検証データに分割するさて、ここからは直近の2018年以降のデータを使用します。 2018年から2020年を学習データ、2021年以降を検証データとして分割します。 datetime64型をindexに設定している時系列のデータフレームは、期間を設定してデータを抜き出す事ができます。 2018年1月1日から2020年12月31日までのデータを抜き出し、df_trainに入力します。 ''' # 学習データを2018-01-01〜2020-12-31の期間としdf_trainに入力する df_train = df['2018-01-01': '2020-12-31'] df_train #png #同様に、2021年1月1日以降のデータを抜き出し、df_valに入力します。 # 検証データを2021-01-01以降としてとしてdf_valに入力する df_val = df['2021-01-01':] df_val ''' png 学習データと検証データをそれぞれ、説明変数と目的変数に分けます。説明変数のカラムは'weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body'を目的変数のカラムは'Up'になります。学習データの説明変数をX_train、学習データの目的変数をy_trainとしてカラムを指定して、それぞれを入力します。また、表示することでX_train, y_trainそれぞれに指定した期間内のデータが入力されていることが分かります。 ''' # 学習データを説明変数(X_train)と目的変数(y_train)に分ける X_train = df_train[['weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body']] y_train = df_train['Up'] # 学習データの説明変数と目的変数を確認 print(X_train) print(y_train) #png #png #同様に検証データの説明変数をX_val、目的変数をy_valとしてデータを入力し、確認します。 # 検証データを説明変数(X_val)と目的変数(y_val)に分ける X_val = df_val[['weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body']] y_val = df_val['Up'] # 検証データの説明変数と目的変数を確認 print(X_val) print(y_val) #png #png #学習データと検証データの時系列グラフを作成し2021年前後でデータが分かれていることを目で確認します。2021年以前が学習データで青いグラフ、2021年以降が検証データでオレンジのグラフで示されている事が分かります。 # 学習データと検証データの終値(Close)の折れ線グラフ作成 X_train['Close'].plot(kind='line') X_val['Close'].plot(kind='line') # グラフの凡例を設定 plt.legend(['X_train', 'X_val']) # グラフの表示 plt.show() ''' png データを整える予測モデルに学習をさせるために、データを整えます。説明変数は各週毎の月曜日から木曜日の４日間をセットとして一つにまとめます。また、目的変数は翌日の金曜日の始値が上がるか下がるかを示す木曜日のデータを抜き出します。機械学習を行うためには説明変数と目的変数の数を揃える必要があります。 png 説明変数を抜き出す期間により、株価の金額や変動量が違っています。例えば、2020年4月頃は株価が16000円程度であったのに対し、12月頃には25000円を超えていたり、同じ週でも株価の変動が大きい事もあります。このように抜き出している期間内において、データの大きさや変動幅が大きく異なっている場合、機械学習では予測が正しく行えない事があります。このような場合に標準化という処理を行うことが有ります。この処理を行うことで、平均が0で±3以内の範囲に収める事が出来るために、機械は計算の処理がし易くなり、また予測精度が向上する事もあります。 png この4日毎にデータを抜き出して、標準化を行うための処理を、sklearnのpreprocessingというライブラリのStandardScalerという関数を使って、for文の繰り返し処理を用いて次のような関数を定義します。また今回、機械学習に使用する予測モデルはLSTMというニューラルネットのモデルを使用します。このモデルではnumpy配列という形式のデータを用います。 ''' # 標準化関数(StandardScaler)のインポート # numpyのインポート # 4日ごとにデータを抜き出して、標準化ととnumpy配列に変換する関数(std_to_np)の定義 def std_to_np(df): df_list = [] df = np.array(df) for i in range(0, len(df) - 3, 4): df_s = df[i:i+4] scl = StandardScaler() df_std = scl.fit_transform(df_s) df_list.append(df_std) return np.array(df_list) #標準化を行うStandardScalaerをsklearn.preprocessingから、numpyをnpとしてインポートします。 # 次に4日毎にデータを抜き出し、標準化を行い、numpy配列で出力する関数(std_to_np)を定義します。 #df_list = [] でまず空のリストを定義します。ここには標準化をおこなった後の、4日毎にまとまったデータを格納して行きます。 #df = np.array(df) で入力されたデータフレームをまずnumpy配列に変換します。 #この配列に対して、for文を用いて4日ずつのデータ抜き出して、df_sに入力(df_s=df[i:i+4])した後に、StandardScalerをインスタンス化し(scl=　StandardScaler()) 標準化をおこなった結果をdf_stdに入力(df_std=scl.fit_transform(df_s))し、それをはじめに定義したdf_listにappendメソッドを用いて格納(df_list.append(df_std))して行きます。最後の4日分のデータまでこの繰り返し処理を行います。 #繰り返し処理が終了すると、df_listをnumpy配列で出力(return np.array(df_list))します。 #この関数をX_trainとX_valに適用してデータの型を確認します。 # 学習データと検証データの説明変数に関数(std_to_np)を実行 X_train_np_array = std_to_np(X_train) X_val_np_array = std_to_np(X_val) # 学習データと検証データの形の確認 print(X_train_np_array.shape) print(X_val_np_array.shape) ''' png 出力結果から、480日分あったX_trainが4分の１の120個のデータとなり、132日分あったX_valが4分の１の33個のデータになっている事がわかります。それぞれの数に続く'4'は月曜から木曜の4日分のデータ数を表しており、'7'は説明変数('weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body')のカラム数を表しています。続いて、目的変数の木曜日のデータだけ抜き出します。抜き出す前に一旦、学習データと検証データのデータを確認します。 ''' # 学習データと検証データの目的変数を確認 print(y_train) print(y_val) #png #学習データは480個、検証データは132個有ることがわかります。 #これらのデータに対して、各週の4日目(木曜日)のデータを抜き出して確認します。 # 学習データ、検証データの目的変数の間引き # 週の4日目(木曜日)のデータだけ抜き出す y_train_new = y_train[3::4] y_val_new = y_val[3::4] # 間引き後の学習データと検証データの目的変数を確認 print(y_train_new) print(y_val_new) #学習データと検証データそれぞれ各週の4日目のデータのみになっており、個数は120個と33個となっており、4日毎にまとめた説明変数のデータ数と同じになっています。 #png #png #これで、機械学習を行うためのデータは整いました。 ''' 予測モデルの作成ニューラルネットの1種のLSTMを用いて予測モデルの作成と、検証データを用いた予測精度の検証をします。 LSTMを使用するためにkerasのライブラリを使えるようにする必要があります。まずこのためにtensorflowをインストールします。個人の環境で、インストール済みの方は不要ですが、google colabolatoryを使用の方は毎回行う必要があります。インストールは次のコマンドで数秒で完了します。 ''' #!pip install tensorflow #続いて、kerasから必要な関数をインポートします。 # keras.modelsからSequentialのインポート # keras.layersからDense、LSTMのインポート # Dropoutのインポート #ニューラルネットの構築や、パラメータのチューニング方法の説明は省略させて頂きますが、 # 基本的な入力層、中間層と出力層からなるモデルをこのように構築することができます。 # また、このモデルをlstm_compという関数で定義しましょう。 # LSTM構築とコンパイル関数 def lstm_comp(df): # 入力層/中間層/出力層のネットワークを構築 model = Sequential() model.add(LSTM(256, activation='relu', batch_input_shape=( None, df.shape[1], df.shape[2]))) model.add(Dropout(0.2)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(1, activation='sigmoid')) # ネットワークのコンパイル model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model ''' 次に、作成したモデルが本当に予測に使用できるのかを確認する方法として、交差検証をしましょう。正解の分かっている学習データを複数に分割して、交差検証を行うのが有効です。交差検証の手法には複数存在しますが、今回の様な時系列のデータで過去のデータを用いて未来を予測する場合は、時系列分割の交差検証を用いるのが一般的です。今回は学習データを5分割し、学習データと検証データが図の様なイメージの組み合わせで合計4回の学習、予測と精度検証を繰り返します。これらのスコアの平均値から、モデルが予測に使用できるかの判断を行います。この手法では検証データよりも過去のデータのみを用いて学習を行ないます。 png まず、時系列分割交差検証を行うためのTimeSeriesSplitと、予測結果の精度(accuracy)を算出するためにaccuracy_scoreをインポートします。 # 時系列分割のためTimeSeriesSplitのインポート # accuracy算出のためaccuracy_scoreのインポートつぎに、4回分の交差検証の結果を代入する空のリストを作成します。そして、TimeSeriesSplitのインスタンス化を行い変数(tscv)に代入します。 ''' valid_scores = [] tscv = TimeSeriesSplit(n_splits=4) ''' for文を用いて、交差検証を4回繰り返します。具体的にはこのような検証を実施します。 splitメソッドを用いて学習データを分割し、交差検証用の学習データと検証データを作成先に定義したlstm_comp関数よりLSTMモデルを作成交差検証用の学習データより学習検証データの説明変数を用いて予測予測結果の2値化検証データの目的変数(正解データ)を用いて、予測結果の精度算出と表示予測精度のスコアをリストに格納 ''' for fold, (train_indices, valid_indices) in enumerate(tscv.split(X_train_np_array)): X_train, X_valid = X_train_np_array[train_indices], X_train_np_array[valid_indices] y_train, y_valid = y_train_new[train_indices], y_train_new[valid_indices] # LSTM構築とコンパイル関数にX_trainを渡し、変数modelに代入 model = lstm_comp(X_train) '''# モデル学習''' hist = model.fit(X_train, y_train, epochs=10, batch_size=64) # loss(訓練データに対する判定結果)、val_loss(テストデータに対する判定結果)をプロットする #loss = hist.history['loss'] #val_loss = hist.history['val_loss'] #epochs = len(loss) '''''' # 予測 y_valid_pred = model.predict(X_valid) # 予測結果の2値化 y_valid_pred = np.where(y_valid_pred < 0.5, 0, 1) # 予測精度の算出と表示 score = accuracy_score(y_valid, y_valid_pred) print(f'fold {fold} MAE: {score}') # 予測精度スコアをリストに格納 valid_scores.append(score) #4回の交差検証が終了したら、予測精度のスコアが格納されたリストの表示し、スコアの平均値の算出と表示もしてみましょう。 #4回のそれぞれのスコアと、平均値はこのようになりました。 print(f'valid_scores: {valid_scores}') cv_score = np.mean(valid_scores) print(f'CV score: {cv_score}') ''' png 1回目：0.541 2回目：0.708 3回目：0.541 4回目：0.333 平均：0.531 今回のような上がるか下がるかの2値予測の場合、一般的にはスコアが0.5以上であればある程度使用できるという目安となります。算出したスコアと平均値から、このモデルがある程度使用できるものと判断して次に進みましょう。では、このモデルに対して、2018年から2020年の学習データを用いて学習をします。流れは先ほどの交差検証と似ています。まずは標準化した学習データでLSTMモデルを作成します。 ''' # LSTM構築とコンパイル関数にX_train_np_arrayを渡し、変数modelに代入 model = lstm_comp(X_train_np_array) #作成したモデルで、学習します。 #一瞬で学習が終了しました。 # モデルの学習の実行 result = model.fit(X_train_np_array, y_train_new, epochs=10, batch_size=64) #今度は学習したモデルを用いて、検証データについて予測を行い、先頭の10個を表示させてみましょう。 # 作成したモデルより検証データを用いて予測を行う pred = model.predict(X_val_np_array) pred[:10] ''' このように予測した結果が表示されます。 png この数値を、上がるか下がるかの0と1に変換します。numpyのwhereメソッドを用いて0.5を超えるものを1、それ以外を0と修正します。そして再度先頭の10個を表示します。これで、上がるか下がるかの01どちらかの予測ができました。 ''' # 予測結果を0もしくは1に修正（0.5を境にして、1に近いほど株価が上昇、0に近いほど株価が上昇しない） pred = np.where(pred < 0.5, 0, 1) # 修正した予測結果の先頭10件を確認 pred[:10] ''' png 次に、予測モデルの精度確認を行います。この予測結果を実際の値となる検証データの目的変数と比較し、正解率を計算します。sklearnのaccuracy_scoreという関数を使うことで計算が行えます。この結果を表示すると57％の正解率で有ることがわかります。今回の様な株価が上がるか下がるかの2値の予測では、直感的に予測を行う場合50％の正解率となります。機械学習を用いる事でそれを超える正解率となりました。 ''' # 実際の結果から予測値の正解率を計算する print('accuracy = ', accuracy_score(y_true=y_val_new, y_pred=pred)) ''' # モデルの精度を評価する # 決定係数とRMSEを計算する # 決定係数は1.0に、RMSEは0.0に近いほど、モデルの精度は高い r2_score = r2_score(y_test, predictions) rmse = np.sqrt(mean_squared_error(y_test, predictions)) print(f'r2_score: {r2_score:.4f}') print(f'rmse: {rmse:.4f}') ''' ''' png 最後に、予測結果と正解結果を混同行列を用いて確認します。混同行列とは、このように2行2列の表で、真陽性、真陰性、偽陽性、偽陰性の数を表したものです。今回は、予測が0で結果も0、予測が1で結果も1であれば正解です。0と予測して結果が1、1と予測して結果が0なら不正解ということになります。全体の精度だけではなく、0と1それぞれの正解に対する精度を確認することができます。 jpg 混同行列を生成するために、sklern.mericsからconfusion_matrixとConfusionMatrixDisplayをインポートします。また、視覚的にわかりやすい様に、ヒートマップで表示しましょう。このように、正しく予測が行えているのは、右下の真陽性(TP)と左上の真陰性(TN)です。予測結果が、0か1のどちらかに極端に偏っている傾向ではなさそうですが、正しく予測できていないものも存在していることがわかります。予測精度を改善することで、偽陽性(FP)と偽陰性(FN)の数を減らすことができます。 ''' # 混同行列生成のためconfusion_matrixをインポート # 混同行列を表示 cm = confusion_matrix(y_val_new, pred) cmp = ConfusionMatrixDisplay(cm) cmp.plot(cmap=plt.cm.Reds) # グラフの表示 plt.show() ''' 今回は基本的な特徴量や、機械学習モデルの構築方法で予測を行いました。特徴量を追加することや、学習モデルの改良を行うことで、予測精度を向上させることが可能です。とはいえ、データの期間が変わるだけでも精度も変わります。必ずいつも予測がうまくいくわけではありませんのでご注意ください。 ''' ''' Graphics parameter ''' # Closeの列のデータのみを取り出し TergetData = data['Close'].values # datetimeの列のデータのみを取り出し data = data.reset_index(drop=False) TergetDate = data['Date'].values #リシェイプ TergetData = TergetData.reshape(-1, 1) # float64 TergetDate = TergetDate.reshape(-1, 1) # datetime64[ns] # 読み込んだ日経平均をプロット k = 700 # 表示する数 i = TergetData.shape[0]-k j = TergetData.shape[0] xdata = TergetDate[i:j] ydata = TergetData[i:j] #描画するデータの読み込み fig = plt.figure(figsize=(15, 10), dpi=100) ax = fig.add_subplot(2, 1, 1) # 図全体のタイトル fig.suptitle( "Long Short-Term Memory (Deep Larning) of Artificial Intelligence[AI]", fontsize=20) plt.title("Test Graph", {"fontsize": 20}) ax1 = plt.subplot(2, 2, 1) # 2x2の1番目 ax1.plot(xdata, ydata) # 1番目に描画 ax1.legend(loc='best') ax1.grid() ax1.set_xlabel('Date') # 1番目にxラベルを追加 ax1.set_ylabel(f'{code}') # 1番目にyラベルを追加 ax2 = plt.subplot(2, 2, 2) # 2x2の1番目 ax2.plot(range(epochs), loss, marker='.', label='loss(training data)') # 1番目に描画 ax2.plot(range(epochs), val_loss, marker='.', label='val_loss(evaluation data)') # 1番目に追加描画 ax2.legend(loc='best') ax2.grid() ax2.set_xlabel('epoch') # 1番目にxラベルを追加 ax2.set_ylabel('loss') # 1番目にyラベルを追加 ax3 = plt.subplot(2, 2, 3) # 2x2の3番目 ax3.plot(datelabel, predicted_N, marker='.', label='predicted') # 1番目に描画 ax3.plot(datelabel, y_test_price_N, marker='.', label='y_test_price') # 1番目に追加描画 ax3.legend(loc='best') ax3.grid() ax3.set_xlabel('Date') ax3.set_ylabel(f'{code}') ax4 = plt.subplot(2, 2, 4) # 2x2の4番目 ax4.plot(range(len(predicted_futureN)), predicted_futureN, marker='.', label='future predicted') # 1番目に描画 ax4.plot(range(len(predicted_futureN[:10])), predicted_futureN[:10], marker='.', label='real data', color="0.5") # 1番目に追加描画 ax4.legend(loc='best') ax4.grid() ax4.set_xlabel('Date') # 1番目にxラベルを追加 ax4.set_ylabel(f'{code}') # 1番目にyラベルを追加 # グラフを表示する plt.show()

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254

0.781043

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay from sklearn.metrics import accuracy_score from sklearn.model_selection import TimeSeriesSplit from keras.layers import Dropout from keras.layers import Dense, LSTM from keras.models import Sequential import numpy as np from sklearn.preprocessing import StandardScaler from matplotlib import pyplot as plt from datetime import timedelta from datetime import datetime import pandas as pd import datetime from dateutil.relativedelta import relativedelta from pandas_datareader import data as pdr from sklearn.metrics import r2_score, mean_squared_error code = '6976' + relativedelta(days=-700) relativedelta(days=-1) data = pdr.get_data_yahoo(f'{code}.T', start_train, end_train) Dow_df = pdr.get_data_yahoo('^DJI', start_train, end_train) Nikkei_df = pdr.get_data_yahoo('^N225', start_train, end_train) ad()) data.info() data.reset_index(drop=False,inplace=True) Dow_df.reset_index(drop=False,inplace=True) Nikkei_df.reset_index(drop=False, inplace=True) data['Date'] = pd.to_datetime(data['Date']) Dow_df['Date'] = pd.to_datetime(Dow_df['Date']) Nikkei_df['Date'] = pd.to_datetime(Nikkei_df['Date']) data.info() data['weekday'] = data['Date'].dt.weekday print(data) start = start_train+relativedelta(days=-2) start = pd.to_datetime(start) ['NikkiClose'] = Nikkei_df['Close'].values df = data[['Date', 'weekday','High', 'Low', 'Open', 'Close', 'NikkiClose']] print(df) df.to_csv('data/stocks_price_data/KinoCode_data.csv') df.sort_values(by='Date', ascending=True, inplace=True) df.set_index(keys='Date', inplace=True) print(df) df_shift = df.shift(-1) df_shift df['delta_Close'] = df_shift['Close'] - df['Close'] df df['Up'] = 0 df['Up'][df['delta_Close'] > 0] = 1 df = df.drop('delta_Close', axis=1) df df_new = df[['Open', 'High', 'Low', 'Close']] df_new.plot(kind='line') plt.show() df_shift = df.shift(1) df['Close_ratio'] = (df['Close'] - df_shift['Close']) / df_shift['Close'] df df['Body'] = df['Open'] - df['Close'] df df = df[['weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body', 'Up']] df df_train = df['2018-01-01': '2020-12-31'] df_train df_val = df['2021-01-01':] df_val X_train = df_train[['weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body']] y_train = df_train['Up'] print(X_train) print(y_train) X_val = df_val[['weekday', 'High', 'Low', 'Open', 'Close', 'Close_ratio', 'Body']] y_val = df_val['Up'] print(X_val) print(y_val) X_train['Close'].plot(kind='line') X_val['Close'].plot(kind='line') plt.legend(['X_train', 'X_val']) plt.show() def std_to_np(df): df_list = [] df = np.array(df) for i in range(0, len(df) - 3, 4): df_s = df[i:i+4] scl = StandardScaler() df_std = scl.fit_transform(df_s) df_list.append(df_std) return np.array(df_list) X_train_np_array = std_to_np(X_train) X_val_np_array = std_to_np(X_val) print(X_train_np_array.shape) print(X_val_np_array.shape) print(y_train) print(y_val) y_train_new = y_train[3::4] y_val_new = y_val[3::4] print(y_train_new) print(y_val_new) def lstm_comp(df): model = Sequential() model.add(LSTM(256, activation='relu', batch_input_shape=( None, df.shape[1], df.shape[2]))) model.add(Dropout(0.2)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model valid_scores = [] tscv = TimeSeriesSplit(n_splits=4) for fold, (train_indices, valid_indices) in enumerate(tscv.split(X_train_np_array)): X_train, X_valid = X_train_np_array[train_indices], X_train_np_array[valid_indices] y_train, y_valid = y_train_new[train_indices], y_train_new[valid_indices] model = lstm_comp(X_train) hist = model.fit(X_train, y_train, epochs=10, batch_size=64) y_valid_pred = model.predict(X_valid) y_valid_pred = np.where(y_valid_pred < 0.5, 0, 1) score = accuracy_score(y_valid, y_valid_pred) print(f'fold {fold} MAE: {score}') valid_scores.append(score) print(f'valid_scores: {valid_scores}') cv_score = np.mean(valid_scores) print(f'CV score: {cv_score}') model = lstm_comp(X_train_np_array) result = model.fit(X_train_np_array, y_train_new, epochs=10, batch_size=64) pred = model.predict(X_val_np_array) pred[:10] pred = np.where(pred < 0.5, 0, 1) pred[:10] print('accuracy = ', accuracy_score(y_true=y_val_new, y_pred=pred)) cm = confusion_matrix(y_val_new, pred) cmp = ConfusionMatrixDisplay(cm) cmp.plot(cmap=plt.cm.Reds) plt.show() TergetData = data['Close'].values data = data.reset_index(drop=False) TergetDate = data['Date'].values TergetData = TergetData.reshape(-1, 1) TergetDate = TergetDate.reshape(-1, 1) k = 700 i = TergetData.shape[0]-k j = TergetData.shape[0] xdata = TergetDate[i:j] ydata = TergetData[i:j] fig = plt.figure(figsize=(15, 10), dpi=100) ax = fig.add_subplot(2, 1, 1) fig.suptitle( "Long Short-Term Memory (Deep Larning) of Artificial Intelligence[AI]", fontsize=20) plt.title("Test Graph", {"fontsize": 20}) ax1 = plt.subplot(2, 2, 1) ax1.plot(xdata, ydata) ax1.legend(loc='best') ax1.grid() ax1.set_xlabel('Date') ax1.set_ylabel(f'{code}') ax2 = plt.subplot(2, 2, 2) ax2.plot(range(epochs), loss, marker='.', label='loss(training data)') ax2.plot(range(epochs), val_loss, marker='.', label='val_loss(evaluation data)') ax2.legend(loc='best') ax2.grid() ax2.set_xlabel('epoch') ax2.set_ylabel('loss') ax3 = plt.subplot(2, 2, 3) ax3.plot(datelabel, predicted_N, marker='.', label='predicted') ax3.plot(datelabel, y_test_price_N, marker='.', label='y_test_price') ax3.legend(loc='best') ax3.grid() ax3.set_xlabel('Date') ax3.set_ylabel(f'{code}') ax4 = plt.subplot(2, 2, 4) ax4.plot(range(len(predicted_futureN)), predicted_futureN, marker='.', label='future predicted') ax4.plot(range(len(predicted_futureN[:10])), predicted_futureN[:10], marker='.', label='real data', color="0.5") ax4.legend(loc='best') ax4.grid() ax4.set_xlabel('Date') ax4.set_ylabel(f'{code}') plt.show()

true

790d97e566e99e7453937ad785d99d09ad9d43a7

220

py

Python

src/tikkie2/v2/__init__.py

new10com/tikkie-api

9dfa96f46eb5150fb22a9d65b7c90cd2133da442

[ "MIT" ]

null

src/tikkie2/v2/__init__.py

new10com/tikkie-api

9dfa96f46eb5150fb22a9d65b7c90cd2133da442

[ "MIT" ]

null

src/tikkie2/v2/__init__.py

new10com/tikkie-api

9dfa96f46eb5150fb22a9d65b7c90cd2133da442

[ "MIT" ]

null

from . import ( # noqa ideal_qr, ideal_qr_notification, payment, payment_request, payment_request_notification, refund, sandbox_app, transaction_bundle, transactions_notifications, )

18.333333

33

0.7

from . import ( ideal_qr, ideal_qr_notification, payment, payment_request, payment_request_notification, refund, sandbox_app, transaction_bundle, transactions_notifications, )

true

790d985f21e29559ae25c3c407dbec1a5e270d4b

4,351

py

Python

iqs_client/models/twc_repository_info_response.py

thomas-bc/mms-autocref

1db6697f929a1c782c902923209389e337ec6961

[ "Apache-2.0" ]

null

iqs_client/models/twc_repository_info_response.py

thomas-bc/mms-autocref

1db6697f929a1c782c902923209389e337ec6961

[ "Apache-2.0" ]

null

iqs_client/models/twc_repository_info_response.py

thomas-bc/mms-autocref

1db6697f929a1c782c902923209389e337ec6961

[ "Apache-2.0" ]

null

# coding: utf-8 """ IncQuery Server No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 OpenAPI spec version: 0.12.0 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six class TWCRepositoryInfoResponse(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'repository_structure': 'TWCRepositoryStructure', 'last_updated': 'str' } attribute_map = { 'repository_structure': 'repositoryStructure', 'last_updated': 'lastUpdated' } def __init__(self, repository_structure=None, last_updated=None): # noqa: E501 """TWCRepositoryInfoResponse - a model defined in OpenAPI""" # noqa: E501 self._repository_structure = None self._last_updated = None self.discriminator = None self.repository_structure = repository_structure self.last_updated = last_updated @property def repository_structure(self): """Gets the repository_structure of this TWCRepositoryInfoResponse. # noqa: E501 :return: The repository_structure of this TWCRepositoryInfoResponse. # noqa: E501 :rtype: TWCRepositoryStructure """ return self._repository_structure @repository_structure.setter def repository_structure(self, repository_structure): """Sets the repository_structure of this TWCRepositoryInfoResponse. :param repository_structure: The repository_structure of this TWCRepositoryInfoResponse. # noqa: E501 :type: TWCRepositoryStructure """ if repository_structure is None: raise ValueError("Invalid value for `repository_structure`, must not be `None`") # noqa: E501 self._repository_structure = repository_structure @property def last_updated(self): """Gets the last_updated of this TWCRepositoryInfoResponse. # noqa: E501 :return: The last_updated of this TWCRepositoryInfoResponse. # noqa: E501 :rtype: str """ return self._last_updated @last_updated.setter def last_updated(self, last_updated): """Sets the last_updated of this TWCRepositoryInfoResponse. :param last_updated: The last_updated of this TWCRepositoryInfoResponse. # noqa: E501 :type: str """ if last_updated is None: raise ValueError("Invalid value for `last_updated`, must not be `None`") # noqa: E501 self._last_updated = last_updated def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, TWCRepositoryInfoResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

30.858156

124

0.619168

import pprint import re import six class TWCRepositoryInfoResponse(object): openapi_types = { 'repository_structure': 'TWCRepositoryStructure', 'last_updated': 'str' } attribute_map = { 'repository_structure': 'repositoryStructure', 'last_updated': 'lastUpdated' } def __init__(self, repository_structure=None, last_updated=None): self._repository_structure = None self._last_updated = None self.discriminator = None self.repository_structure = repository_structure self.last_updated = last_updated @property def repository_structure(self): return self._repository_structure @repository_structure.setter def repository_structure(self, repository_structure): if repository_structure is None: raise ValueError("Invalid value for `repository_structure`, must not be `None`") self._repository_structure = repository_structure @property def last_updated(self): return self._last_updated @last_updated.setter def last_updated(self, last_updated): if last_updated is None: raise ValueError("Invalid value for `last_updated`, must not be `None`") self._last_updated = last_updated def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, TWCRepositoryInfoResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

790d98a72fe8f84a93bdacf95cb7a6a30c124775

3,462

py

Python

toDoList/toDoList/settings.py

ruoyunruyan/toDoList

99c06b67f3c153ae66871725b44cde907c972a86

[ "MIT" ]

null

toDoList/toDoList/settings.py

ruoyunruyan/toDoList

99c06b67f3c153ae66871725b44cde907c972a86

[ "MIT" ]

null

toDoList/toDoList/settings.py

ruoyunruyan/toDoList

99c06b67f3c153ae66871725b44cde907c972a86

[ "MIT" ]

null

""" Django settings for toDoList project. Generated by 'django-admin startproject' using Django 2.1.7. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'r=cr&4z(#t-&vbyp_71-sy&edioe73mt48%)1ur^g1&@p$m69e' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'apps.todo', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'toDoList.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], 'builtins': ['django.templatetags.static'] }, }, ] WSGI_APPLICATION = 'toDoList.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'things_to_do', 'USER': 'root', 'PASSWORD': '123456', 'PORT': 3306, 'HOST': '127.0.0.1' } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')]

27.046875

92

0.659445

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'r=cr&4z(#t-&vbyp_71-sy&edioe73mt48%)1ur^g1&@p$m69e' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'apps.todo', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'toDoList.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], 'builtins': ['django.templatetags.static'] }, }, ] WSGI_APPLICATION = 'toDoList.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'things_to_do', 'USER': 'root', 'PASSWORD': '123456', 'PORT': 3306, 'HOST': '127.0.0.1' } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')]

true

790d99a94d22472b3df9e52c3412f8a2f82ec3ce

3,270

py

Python

trading_calendars/exchange_calendar_cmes.py

quantrocket-llc/trading-calendars

b72630cbcb288601c62e61ebe002a9043f9a3112

[ "Apache-2.0" ]

1

2020-07-25T06:18:30.000Z

trading_calendars/exchange_calendar_cmes.py

quantrocket-llc/trading-calendars

b72630cbcb288601c62e61ebe002a9043f9a3112

[ "Apache-2.0" ]

13

2021-04-13T06:49:23.000Z

2022-03-31T00:08:10.000Z

trading_calendars/exchange_calendar_cmes.py

quantrocket-llc/trading-calendars

b72630cbcb288601c62e61ebe002a9043f9a3112

[ "Apache-2.0" ]

3

2020-03-05T23:38:14.000Z

2021-12-12T00:31:36.000Z

# # Copyright 2018 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from datetime import time from pandas.tseries.holiday import ( GoodFriday, USLaborDay, USPresidentsDay, USThanksgivingDay, ) from pytz import timezone from .trading_calendar import HolidayCalendar, TradingCalendar from .us_holidays import ( Christmas, ChristmasEveBefore1993, ChristmasEveInOrAfter1993, USBlackFridayInOrAfter1993, USIndependenceDay, USMartinLutherKingJrAfter1998, USMemorialDay, USNationalDaysofMourning, USNewYearsDay, ) # Useful resources for making changes to this file: # http://www.cmegroup.com/tools-information/holiday-calendar.html class CMESExchangeCalendar(TradingCalendar): """ Exchange calendar for the Chicago Mercantile Exchange (CMES). Open Time: 5:00 PM, America/Chicago Close Time: 5:00 PM, America/Chicago Regularly-Observed Holidays: - New Years Day - Good Friday - Christmas """ name = "CME" country_code = "US" tz = timezone("America/Chicago") open_times = ((None, time(17, 1)),) close_times = ((None, time(16)),) @property def open_offset(self): return -1 @property def regular_holidays(self): # The CME has different holiday rules depending on the type of # instrument. For example, http://www.cmegroup.com/tools-information/holiday-calendar/files/2016-4th-of-july-holiday-schedule.pdf # noqa # shows that Equity, Interest Rate, FX, Energy, Metals & DME Products # close at 1200 CT on July 4, 2016, while Grain, Oilseed & MGEX # Products and Livestock, Dairy & Lumber products are completely # closed. # For now, we will treat the CME as having a single calendar, and just # go with the most conservative hours - and treat July 4 as an early # close at noon. return HolidayCalendar( [ USNewYearsDay, GoodFriday, Christmas, ] ) @property def adhoc_holidays(self): return USNationalDaysofMourning @property def special_closes(self): return [ ( time(12), HolidayCalendar( [ USMartinLutherKingJrAfter1998, USPresidentsDay, USMemorialDay, USLaborDay, USIndependenceDay, USThanksgivingDay, USBlackFridayInOrAfter1993, ChristmasEveBefore1993, ChristmasEveInOrAfter1993, ] ), ) ]

29.196429

144

0.624159

from datetime import time from pandas.tseries.holiday import ( GoodFriday, USLaborDay, USPresidentsDay, USThanksgivingDay, ) from pytz import timezone from .trading_calendar import HolidayCalendar, TradingCalendar from .us_holidays import ( Christmas, ChristmasEveBefore1993, ChristmasEveInOrAfter1993, USBlackFridayInOrAfter1993, USIndependenceDay, USMartinLutherKingJrAfter1998, USMemorialDay, USNationalDaysofMourning, USNewYearsDay, ) class CMESExchangeCalendar(TradingCalendar): name = "CME" country_code = "US" tz = timezone("America/Chicago") open_times = ((None, time(17, 1)),) close_times = ((None, time(16)),) @property def open_offset(self): return -1 @property def regular_holidays(self): return HolidayCalendar( [ USNewYearsDay, GoodFriday, Christmas, ] ) @property def adhoc_holidays(self): return USNationalDaysofMourning @property def special_closes(self): return [ ( time(12), HolidayCalendar( [ USMartinLutherKingJrAfter1998, USPresidentsDay, USMemorialDay, USLaborDay, USIndependenceDay, USThanksgivingDay, USBlackFridayInOrAfter1993, ChristmasEveBefore1993, ChristmasEveInOrAfter1993, ] ), ) ]

true

790d9a42da8e6c811ed3b154e78306f6f7e56b9e

144

py

Python

AlteMatrix/ipanalyzer/__init__.py

Ir0n-c0d3X/AlteMatrix

9479ddeec9839b88d8f7079d00fd62f3ee47157d

[ "MIT" ]

10

2021-09-19T13:55:58.000Z

2022-01-16T02:15:28.000Z

AlteMatrix/ipanalyzer/__init__.py

samuelajala01/AlteMatrix

e22fe443241fb646a218100bdcb19e0e4cc85635

[ "MIT" ]

null

AlteMatrix/ipanalyzer/__init__.py

samuelajala01/AlteMatrix

e22fe443241fb646a218100bdcb19e0e4cc85635

[ "MIT" ]

2

2021-09-19T23:51:51.000Z

2022-01-16T02:15:42.000Z

# Use of this source code is governed by the MIT license. __license__ = "MIT" """This is the IP Analyzer program for the AlteMatrix module."""

28.8

64

0.736111

__license__ = "MIT"

true

790d9b66017834e0dfa129ac19fd3acb39af0d07

7,097

py

Python

sb/stable_baselines_ex/common/wrappers_ex.py

artberryx/SAR

e6c79ea271f1033d5ea3c11556aff173adf6d941

[ "MIT" ]

4

2021-11-12T05:24:21.000Z

2021-12-13T01:18:08.000Z

sb/stable_baselines_ex/common/wrappers_ex.py

artberryx/SAR

e6c79ea271f1033d5ea3c11556aff173adf6d941

[ "MIT" ]

null

sb/stable_baselines_ex/common/wrappers_ex.py

artberryx/SAR

e6c79ea271f1033d5ea3c11556aff173adf6d941

[ "MIT" ]

null

import gym import numpy as np from gym import spaces from stable_baselines.common.running_mean_std import RunningMeanStd class ScaleRewardEnv(gym.RewardWrapper): def __init__(self, env: gym.Env, scale): gym.RewardWrapper.__init__(self, env) self.scale = scale def reward(self, reward: float) -> float: return reward * self.scale class RepeatGoalEnv(gym.Wrapper): def __init__( self, env: gym.Env, gamma, max_d, max_t, lambda_dt, anoise_type=None, anoise_prob=0., anoise_std=0., ): gym.Wrapper.__init__(self, env) self.epsilon_std = 1e-3 self.gamma = gamma self.max_d = max_d self.max_t = max_t self.lambda_dt = lambda_dt self.anoise_type = anoise_type self.anoise_prob = anoise_prob self.anoise_std = anoise_std self.body_key = None part_keys = set(self.env.sim.model._body_name2id.keys()) target_keys = ['torso', 'cart', 'body1'] for target_key in target_keys: if target_key in part_keys: self.body_key = target_key break if self.anoise_type in ['ext_fpc']: low = np.concatenate([self.observation_space.low, [-np.inf] * 3]) high = np.concatenate([self.observation_space.high, [np.inf] * 3]) self.observation_space = spaces.Box( low=low, high=high, shape=(self.observation_space.shape[0] + 3,), dtype=self.observation_space.dtype, ) self.obs_dim = self.observation_space.shape[0] + 3 self.cur_force = np.zeros(3) else: self.obs_dim = self.observation_space.shape[0] action_dim = self.env.action_space.shape[0] self.ori_action_dim = action_dim low = self.env.action_space.low high = self.env.action_space.high if self.max_d is not None or self.max_t is not None: action_dim += 1 low = np.r_[low, -1.] high = np.r_[high, 1.] self.action_space = spaces.Box( low=low, high=high, shape=(action_dim,), dtype=env.action_space.dtype ) self.cur_obs = None self.obs_rms = RunningMeanStd(shape=self.observation_space.shape) self.reset_update_obs_estimate = False self.num_steps = 0 self.eval_mode = False def _update_obs_estimate(self, obs): if not self.eval_mode: self.obs_rms.update(obs[:, :self.obs_dim]) def step(self, aug_action): cur_idx = self.ori_action_dim action = aug_action[:self.ori_action_dim] if self.anoise_type == 'action': if np.random.rand() < self.anoise_prob: action = action + np.random.randn(*action.shape) * self.anoise_std action = np.clip(action, self.action_space.low[:len(action)], self.action_space.high[:len(action)]) elif self.anoise_type is not None and 'ext' in self.anoise_type: if np.random.rand() < self.anoise_prob: if self.env.spec.id == 'Reacher-v2': force = np.zeros(3) torque = np.random.randn(3) * self.anoise_std cur_info = torque else: force = np.random.randn(3) * self.anoise_std torque = np.zeros(3) cur_info = force if self.anoise_type == 'ext_fpc': self.cur_force = np.clip(cur_info, -1, 1) self.env.sim.data.xfrc_applied[self.env.sim.model._body_name2id[self.body_key], :] = np.r_[ force, torque] else: self.env.sim.data.xfrc_applied[self.env.sim.model._body_name2id[self.body_key], :] = [0] * 6 if self.max_d is not None or self.max_t is not None: u = aug_action[cur_idx] cur_idx += 1 norm_u = (u + 1) / 2 u = norm_u else: u = None lambda_dt = self.lambda_dt total_reward = 0.0 done = None cur_gamma = 1.0 first_obs = self.cur_obs for i in range(100000000): obs, reward, done, info = self.env.step(action) if self.anoise_type in ['ext_fpc']: obs = np.concatenate([obs, self.cur_force]) if not done: self._update_obs_estimate(obs[np.newaxis, ...]) self.reset_update_obs_estimate = True total_reward += reward * cur_gamma cur_gamma *= self.gamma if done: break if self.max_d is None and self.max_t is None: break if self.max_t is not None: t_delta = (i + 1) * self.env.dt if self.max_d is not None: norm_obs = (obs - self.obs_rms.mean) / (np.sqrt(self.obs_rms.var) + self.epsilon_std) norm_first_obs = (first_obs - self.obs_rms.mean) / (np.sqrt(self.obs_rms.var) + self.epsilon_std) d_delta = np.linalg.norm(norm_obs - norm_first_obs, ord=1) / len(obs) if self.max_d is not None and self.max_t is not None: if lambda_dt is None: if d_delta >= u * self.max_d: break if t_delta >= self.max_t: break else: ori_t_delta = t_delta t_delta = t_delta / self.max_t d_delta = d_delta / self.max_d delta = lambda_dt * d_delta + (1 - lambda_dt) * t_delta if delta >= u: break if ori_t_delta >= self.max_t: break elif self.max_t is not None: if t_delta >= u * self.max_t: break elif self.max_d is not None: if d_delta >= u * self.max_d: break self.cur_obs = obs info['w'] = i + 1 info['t_diff'] = (i + 1) * self.env.dt if u is not None: if self.max_d is not None and self.max_t is not None: pass elif self.max_t is not None: info['t'] = u * self.max_t elif self.max_d is not None: info['d'] = u * self.max_d info['u'] = u if lambda_dt is not None: info['lambda_dt'] = lambda_dt self.num_steps += 1 return self.cur_obs, total_reward, done, info def reset(self, **kwargs): obs = self.env.reset(**kwargs) if self.anoise_type in ['ext_fpc']: self.cur_force = np.zeros(3) obs = np.concatenate([obs, self.cur_force]) if self.reset_update_obs_estimate: self._update_obs_estimate(obs[np.newaxis, ...]) self.reset_update_obs_estimate = False self.cur_obs = obs return self.cur_obs

35.133663

115

0.533888

import gym import numpy as np from gym import spaces from stable_baselines.common.running_mean_std import RunningMeanStd class ScaleRewardEnv(gym.RewardWrapper): def __init__(self, env: gym.Env, scale): gym.RewardWrapper.__init__(self, env) self.scale = scale def reward(self, reward: float) -> float: return reward * self.scale class RepeatGoalEnv(gym.Wrapper): def __init__( self, env: gym.Env, gamma, max_d, max_t, lambda_dt, anoise_type=None, anoise_prob=0., anoise_std=0., ): gym.Wrapper.__init__(self, env) self.epsilon_std = 1e-3 self.gamma = gamma self.max_d = max_d self.max_t = max_t self.lambda_dt = lambda_dt self.anoise_type = anoise_type self.anoise_prob = anoise_prob self.anoise_std = anoise_std self.body_key = None part_keys = set(self.env.sim.model._body_name2id.keys()) target_keys = ['torso', 'cart', 'body1'] for target_key in target_keys: if target_key in part_keys: self.body_key = target_key break if self.anoise_type in ['ext_fpc']: low = np.concatenate([self.observation_space.low, [-np.inf] * 3]) high = np.concatenate([self.observation_space.high, [np.inf] * 3]) self.observation_space = spaces.Box( low=low, high=high, shape=(self.observation_space.shape[0] + 3,), dtype=self.observation_space.dtype, ) self.obs_dim = self.observation_space.shape[0] + 3 self.cur_force = np.zeros(3) else: self.obs_dim = self.observation_space.shape[0] action_dim = self.env.action_space.shape[0] self.ori_action_dim = action_dim low = self.env.action_space.low high = self.env.action_space.high if self.max_d is not None or self.max_t is not None: action_dim += 1 low = np.r_[low, -1.] high = np.r_[high, 1.] self.action_space = spaces.Box( low=low, high=high, shape=(action_dim,), dtype=env.action_space.dtype ) self.cur_obs = None self.obs_rms = RunningMeanStd(shape=self.observation_space.shape) self.reset_update_obs_estimate = False self.num_steps = 0 self.eval_mode = False def _update_obs_estimate(self, obs): if not self.eval_mode: self.obs_rms.update(obs[:, :self.obs_dim]) def step(self, aug_action): cur_idx = self.ori_action_dim action = aug_action[:self.ori_action_dim] if self.anoise_type == 'action': if np.random.rand() < self.anoise_prob: action = action + np.random.randn(*action.shape) * self.anoise_std action = np.clip(action, self.action_space.low[:len(action)], self.action_space.high[:len(action)]) elif self.anoise_type is not None and 'ext' in self.anoise_type: if np.random.rand() < self.anoise_prob: if self.env.spec.id == 'Reacher-v2': force = np.zeros(3) torque = np.random.randn(3) * self.anoise_std cur_info = torque else: force = np.random.randn(3) * self.anoise_std torque = np.zeros(3) cur_info = force if self.anoise_type == 'ext_fpc': self.cur_force = np.clip(cur_info, -1, 1) self.env.sim.data.xfrc_applied[self.env.sim.model._body_name2id[self.body_key], :] = np.r_[ force, torque] else: self.env.sim.data.xfrc_applied[self.env.sim.model._body_name2id[self.body_key], :] = [0] * 6 if self.max_d is not None or self.max_t is not None: u = aug_action[cur_idx] cur_idx += 1 norm_u = (u + 1) / 2 u = norm_u else: u = None lambda_dt = self.lambda_dt total_reward = 0.0 done = None cur_gamma = 1.0 first_obs = self.cur_obs for i in range(100000000): obs, reward, done, info = self.env.step(action) if self.anoise_type in ['ext_fpc']: obs = np.concatenate([obs, self.cur_force]) if not done: self._update_obs_estimate(obs[np.newaxis, ...]) self.reset_update_obs_estimate = True total_reward += reward * cur_gamma cur_gamma *= self.gamma if done: break if self.max_d is None and self.max_t is None: break if self.max_t is not None: t_delta = (i + 1) * self.env.dt if self.max_d is not None: norm_obs = (obs - self.obs_rms.mean) / (np.sqrt(self.obs_rms.var) + self.epsilon_std) norm_first_obs = (first_obs - self.obs_rms.mean) / (np.sqrt(self.obs_rms.var) + self.epsilon_std) d_delta = np.linalg.norm(norm_obs - norm_first_obs, ord=1) / len(obs) if self.max_d is not None and self.max_t is not None: if lambda_dt is None: if d_delta >= u * self.max_d: break if t_delta >= self.max_t: break else: ori_t_delta = t_delta t_delta = t_delta / self.max_t d_delta = d_delta / self.max_d delta = lambda_dt * d_delta + (1 - lambda_dt) * t_delta if delta >= u: break if ori_t_delta >= self.max_t: break elif self.max_t is not None: if t_delta >= u * self.max_t: break elif self.max_d is not None: if d_delta >= u * self.max_d: break self.cur_obs = obs info['w'] = i + 1 info['t_diff'] = (i + 1) * self.env.dt if u is not None: if self.max_d is not None and self.max_t is not None: pass elif self.max_t is not None: info['t'] = u * self.max_t elif self.max_d is not None: info['d'] = u * self.max_d info['u'] = u if lambda_dt is not None: info['lambda_dt'] = lambda_dt self.num_steps += 1 return self.cur_obs, total_reward, done, info def reset(self, **kwargs): obs = self.env.reset(**kwargs) if self.anoise_type in ['ext_fpc']: self.cur_force = np.zeros(3) obs = np.concatenate([obs, self.cur_force]) if self.reset_update_obs_estimate: self._update_obs_estimate(obs[np.newaxis, ...]) self.reset_update_obs_estimate = False self.cur_obs = obs return self.cur_obs

true

790d9c116845947669e908b57d75437bbfcf16c8

4,041

py

Python

conversions/length_conversion.py

NavpreetDevpuri/Python

7ef5ae66d777e8ed702993c6aa9270e0669cb0c6

[ "MIT" ]

145,614

2016-07-21T05:40:05.000Z

2022-03-31T22:17:22.000Z

conversions/length_conversion.py

NavpreetDevpuri/Python

7ef5ae66d777e8ed702993c6aa9270e0669cb0c6

[ "MIT" ]

3,987

2016-07-28T17:31:25.000Z

2022-03-30T23:07:46.000Z

conversions/length_conversion.py

NavpreetDevpuri/Python

7ef5ae66d777e8ed702993c6aa9270e0669cb0c6

[ "MIT" ]

40,014

2016-07-26T15:14:41.000Z

2022-03-31T22:23:03.000Z

""" Conversion of length units. Available Units:- Metre,Kilometre,Feet,Inch,Centimeter,Yard,Foot,Mile,Millimeter USAGE : -> Import this file into their respective project. -> Use the function length_conversion() for conversion of length units. -> Parameters : -> value : The number of from units you want to convert -> from_type : From which type you want to convert -> to_type : To which type you want to convert REFERENCES : -> Wikipedia reference: https://en.wikipedia.org/wiki/Meter -> Wikipedia reference: https://en.wikipedia.org/wiki/Kilometer -> Wikipedia reference: https://en.wikipedia.org/wiki/Feet -> Wikipedia reference: https://en.wikipedia.org/wiki/Inch -> Wikipedia reference: https://en.wikipedia.org/wiki/Centimeter -> Wikipedia reference: https://en.wikipedia.org/wiki/Yard -> Wikipedia reference: https://en.wikipedia.org/wiki/Foot -> Wikipedia reference: https://en.wikipedia.org/wiki/Mile -> Wikipedia reference: https://en.wikipedia.org/wiki/Millimeter """ from collections import namedtuple from_to = namedtuple("from_to", "from_ to") TYPE_CONVERSION = { "millimeter": "mm", "centimeter": "cm", "meter": "m", "kilometer": "km", "inch": "in", "inche": "in", # Trailing 's' has been stripped off "feet": "ft", "foot": "ft", "yard": "yd", "mile": "mi", } METRIC_CONVERSION = { "mm": from_to(0.001, 1000), "cm": from_to(0.01, 100), "m": from_to(1, 1), "km": from_to(1000, 0.001), "in": from_to(0.0254, 39.3701), "ft": from_to(0.3048, 3.28084), "yd": from_to(0.9144, 1.09361), "mi": from_to(1609.34, 0.000621371), } def length_conversion(value: float, from_type: str, to_type: str) -> float: """ Conversion between length units. >>> length_conversion(4, "METER", "FEET") 13.12336 >>> length_conversion(4, "M", "FT") 13.12336 >>> length_conversion(1, "meter", "kilometer") 0.001 >>> length_conversion(1, "kilometer", "inch") 39370.1 >>> length_conversion(3, "kilometer", "mile") 1.8641130000000001 >>> length_conversion(2, "feet", "meter") 0.6096 >>> length_conversion(4, "feet", "yard") 1.333329312 >>> length_conversion(1, "inch", "meter") 0.0254 >>> length_conversion(2, "inch", "mile") 3.15656468e-05 >>> length_conversion(2, "centimeter", "millimeter") 20.0 >>> length_conversion(2, "centimeter", "yard") 0.0218722 >>> length_conversion(4, "yard", "meter") 3.6576 >>> length_conversion(4, "yard", "kilometer") 0.0036576 >>> length_conversion(3, "foot", "meter") 0.9144000000000001 >>> length_conversion(3, "foot", "inch") 36.00001944 >>> length_conversion(4, "mile", "kilometer") 6.43736 >>> length_conversion(2, "miles", "InChEs") 126719.753468 >>> length_conversion(3, "millimeter", "centimeter") 0.3 >>> length_conversion(3, "mm", "in") 0.1181103 >>> length_conversion(4, "wrongUnit", "inch") Traceback (most recent call last): ... ValueError: Invalid 'from_type' value: 'wrongUnit'. Conversion abbreviations are: mm, cm, m, km, in, ft, yd, mi """ new_from = from_type.lower().rstrip("s") new_from = TYPE_CONVERSION.get(new_from, new_from) new_to = to_type.lower().rstrip("s") new_to = TYPE_CONVERSION.get(new_to, new_to) if new_from not in METRIC_CONVERSION: raise ValueError( f"Invalid 'from_type' value: {from_type!r}.\n" f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}" ) if new_to not in METRIC_CONVERSION: raise ValueError( f"Invalid 'to_type' value: {to_type!r}.\n" f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}" ) return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to if __name__ == "__main__": import doctest doctest.testmod()

32.853659

84

0.621133

from collections import namedtuple from_to = namedtuple("from_to", "from_ to") TYPE_CONVERSION = { "millimeter": "mm", "centimeter": "cm", "meter": "m", "kilometer": "km", "inch": "in", "inche": "in", "feet": "ft", "foot": "ft", "yard": "yd", "mile": "mi", } METRIC_CONVERSION = { "mm": from_to(0.001, 1000), "cm": from_to(0.01, 100), "m": from_to(1, 1), "km": from_to(1000, 0.001), "in": from_to(0.0254, 39.3701), "ft": from_to(0.3048, 3.28084), "yd": from_to(0.9144, 1.09361), "mi": from_to(1609.34, 0.000621371), } def length_conversion(value: float, from_type: str, to_type: str) -> float: new_from = from_type.lower().rstrip("s") new_from = TYPE_CONVERSION.get(new_from, new_from) new_to = to_type.lower().rstrip("s") new_to = TYPE_CONVERSION.get(new_to, new_to) if new_from not in METRIC_CONVERSION: raise ValueError( f"Invalid 'from_type' value: {from_type!r}.\n" f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}" ) if new_to not in METRIC_CONVERSION: raise ValueError( f"Invalid 'to_type' value: {to_type!r}.\n" f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}" ) return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to if __name__ == "__main__": import doctest doctest.testmod()

true

790d9c7645afc33219e39cfaad728a2eff4993a3

3,474

py

Python

kinetick/models/position.py

aWFtbGVnaW9u/kinetick

2562a666ff57e72d1314e053db415d2873b8f71f

[ "Apache-2.0" ]

1

2022-01-23T23:00:34.000Z

kinetick/models/position.py

aWFtbGVnaW9u/kinetick

2562a666ff57e72d1314e053db415d2873b8f71f

[ "Apache-2.0" ]

null

kinetick/models/position.py

aWFtbGVnaW9u/kinetick

2562a666ff57e72d1314e053db415d2873b8f71f

[ "Apache-2.0" ]

null

from mongoengine import StringField, DateTimeField, IntField, FloatField, BooleanField, DynamicDocument from datetime import datetime # note: position / order / trade are used interchangeably through the app. class Position(DynamicDocument): """ Position Data Model. holds information relating to either trade/order/position. """ _tickerId = StringField(max_length=50, required=True, db_field="tickerId") _symbol = StringField(max_length=50, required=False, db_field="symbol") datetime = DateTimeField(required=True, default=datetime.utcnow) algo = StringField(max_length=100) _direction = StringField(max_length=20, choices=('LONG', 'SHORT'), db_field="direction") _quantity = IntField(default=0, db_field="quantity") entry_time = DateTimeField() exit_time = DateTimeField() exit_reason = StringField() order_type = StringField() # LIMIT/MARKET _broker_order_id = StringField(db_field="broker_order_id") _variety = StringField(db_field="variety") market_price = FloatField() target = FloatField(default=0.0) stop = FloatField(default=0.0) entry_price = FloatField(default=0.0) exit_price = FloatField(default=0.0) realized_pnl = FloatField(default=0.0) _active = BooleanField(default=False, db_field="active") opt_ticker = StringField(max_length=50, required=False) opt_strike = FloatField(required=False) opt_type = StringField(require=False), opt_expiry = StringField(required=False), sec_type = StringField(default='STK') # TODO add enum underlying = StringField(required=False) meta = { 'indexes': [ { 'fields': ['_active'], 'sparse': True }, { 'fields': ['algo'], 'sparse': True } ] } def open_position(self): if self._direction is None: raise Exception("no direction provided") if self._quantity is None: raise Exception("no quantity provided") if self.entry_time is None: self.entry_time = datetime.now() self._active = True def close_position(self): if self._direction is None: raise Exception("no direction provided") if self._quantity is None: raise Exception("no quantity provided") if self.exit_time is None: self.exit_time = datetime.now() self._active = False def pnl(self): pnl = abs(self.exit_price - self.entry_price) sl_hit = False if self.exit_price <= self.entry_price and self._direction == "LONG": sl_hit = True elif self.exit_price >= self.entry_price and self._direction == "SHORT": sl_hit = True pnl = -pnl if sl_hit else pnl pnl = pnl * self._quantity return pnl @property def active(self): return self._active @property def ticker_id(self): return self._tickerId @property def symbol(self): return self._symbol @property def direction(self): return self._direction @property def quantity(self): return self._quantity @property def broker_order_id(self): return self._broker_order_id @property def variety(self): return self._variety @staticmethod def find(algo, **query) -> list: return Position.objects(algo=algo, **query)

31.297297

103

0.637018

from mongoengine import StringField, DateTimeField, IntField, FloatField, BooleanField, DynamicDocument from datetime import datetime class Position(DynamicDocument): _tickerId = StringField(max_length=50, required=True, db_field="tickerId") _symbol = StringField(max_length=50, required=False, db_field="symbol") datetime = DateTimeField(required=True, default=datetime.utcnow) algo = StringField(max_length=100) _direction = StringField(max_length=20, choices=('LONG', 'SHORT'), db_field="direction") _quantity = IntField(default=0, db_field="quantity") entry_time = DateTimeField() exit_time = DateTimeField() exit_reason = StringField() order_type = StringField() _broker_order_id = StringField(db_field="broker_order_id") _variety = StringField(db_field="variety") market_price = FloatField() target = FloatField(default=0.0) stop = FloatField(default=0.0) entry_price = FloatField(default=0.0) exit_price = FloatField(default=0.0) realized_pnl = FloatField(default=0.0) _active = BooleanField(default=False, db_field="active") opt_ticker = StringField(max_length=50, required=False) opt_strike = FloatField(required=False) opt_type = StringField(require=False), opt_expiry = StringField(required=False), sec_type = StringField(default='STK') underlying = StringField(required=False) meta = { 'indexes': [ { 'fields': ['_active'], 'sparse': True }, { 'fields': ['algo'], 'sparse': True } ] } def open_position(self): if self._direction is None: raise Exception("no direction provided") if self._quantity is None: raise Exception("no quantity provided") if self.entry_time is None: self.entry_time = datetime.now() self._active = True def close_position(self): if self._direction is None: raise Exception("no direction provided") if self._quantity is None: raise Exception("no quantity provided") if self.exit_time is None: self.exit_time = datetime.now() self._active = False def pnl(self): pnl = abs(self.exit_price - self.entry_price) sl_hit = False if self.exit_price <= self.entry_price and self._direction == "LONG": sl_hit = True elif self.exit_price >= self.entry_price and self._direction == "SHORT": sl_hit = True pnl = -pnl if sl_hit else pnl pnl = pnl * self._quantity return pnl @property def active(self): return self._active @property def ticker_id(self): return self._tickerId @property def symbol(self): return self._symbol @property def direction(self): return self._direction @property def quantity(self): return self._quantity @property def broker_order_id(self): return self._broker_order_id @property def variety(self): return self._variety @staticmethod def find(algo, **query) -> list: return Position.objects(algo=algo, **query)

true

790d9e6dfa929eb30227ee925a46f34c1ce1594a

1,642

py

Python

RK45 - Copy.py

Mahdi-Asadi/python_thesis

6cb1dbe24fcf9133e971e64c91e1dde234250da9

[ "MIT" ]

null

RK45 - Copy.py

Mahdi-Asadi/python_thesis

6cb1dbe24fcf9133e971e64c91e1dde234250da9

[ "MIT" ]

null

RK45 - Copy.py

Mahdi-Asadi/python_thesis

6cb1dbe24fcf9133e971e64c91e1dde234250da9

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import RK45 f_out = "E:\\1\\P_rk4.txt" # address file for output f2 = open(f_out,"w+") def du_dx(x,y): wa=1 # atomic frequency wp=0.6 # field frequency g=0.6 # coupling strength n = 1 # number of photons A = n*wp+(wa/2) B = (1+n)*wp-(wa/2) X = n+1 C = np.sqrt(X) dydx_1= A*y[1]+g*C*y[3] dydx_2= -A*y[0]-g*C*y[2] dydx_3= B*y[3]+g*C*y[1] dydx_4= -B*y[2]-g*C*y[0] return [dydx_1,dydx_2,dydx_3,dydx_4] y_0 = (1/np.sqrt(2),0,1/np.sqrt(2),0) # initial value # print("y_0 = ",y_0) m = 1000 ti = 0 tf = 30 h = tf/m tspan = np.arange(ti,tf,h) print(h) for i in tspan: print(i) v = RK45(du_dx,t0 =i,y0 = y_0,t_bound=i) # 4 answer of dydx_1,...,dydx_4 print(v.y[0:]) # print(type(v)) # print("v.t[0] = ",v.t[0]) # print(len(v.t)) # print("------------------") # print(v.y) # print(len(v.t)) # print("------------------") # y_1 = v.y[:,0] # print("y_1 = ",y_1) # print("------------------") # y_2 = v.y[0,:] # print("y_2 = ",y_2) # print("------------------") # y_3 = v.y[0,0] # print("y_3 = ",y_3) # print("------------------") # # -------------------------- # # print in file # count = 0 # while count<1000: # y_i = v.y[:,count] # f2.write(str(v.t[count])) # f2.write(" ") # for i in y_i: # i = round(i,4) # i = str(i) # f2.write(i) # f2.write(len(i)*" ") # f2.write("\n") # count = count+1 # # y_prime = u_s[:,1] # # print(y_prime) # plt.plot(v.t, v.y[0,:],'-', label='r(t)') # plt.xlabel("x") # plt.ylabel("y") # plt.show()

23.457143

76

0.476248

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import RK45 f_out = "E:\\1\\P_rk4.txt" f2 = open(f_out,"w+") def du_dx(x,y): wa=1 wp=0.6 g=0.6 n = 1 A = n*wp+(wa/2) B = (1+n)*wp-(wa/2) X = n+1 C = np.sqrt(X) dydx_1= A*y[1]+g*C*y[3] dydx_2= -A*y[0]-g*C*y[2] dydx_3= B*y[3]+g*C*y[1] dydx_4= -B*y[2]-g*C*y[0] return [dydx_1,dydx_2,dydx_3,dydx_4] y_0 = (1/np.sqrt(2),0,1/np.sqrt(2),0) m = 1000 ti = 0 tf = 30 h = tf/m tspan = np.arange(ti,tf,h) print(h) for i in tspan: print(i) v = RK45(du_dx,t0 =i,y0 = y_0,t_bound=i) print(v.y[0:])

true

790d9e8d01cd1e437033f450863d4865f44cd735

22,915

py

Python

metaflow/datatools/s3.py

oliverholworthy/metaflow

378e718a0091d1189e92f3027e3a52c659be59bc

[ "Apache-2.0" ]

2

2020-06-07T13:52:03.000Z

2020-08-17T17:05:06.000Z

metaflow/datatools/s3.py

oliverholworthy/metaflow

378e718a0091d1189e92f3027e3a52c659be59bc

[ "Apache-2.0" ]

null

metaflow/datatools/s3.py

oliverholworthy/metaflow

378e718a0091d1189e92f3027e3a52c659be59bc

[ "Apache-2.0" ]

1

2020-03-12T11:12:38.000Z

import os import sys import time import shutil import random import subprocess from itertools import starmap from tempfile import mkdtemp, NamedTemporaryFile from .. import current, FlowSpec from ..metaflow_config import DATATOOLS_S3ROOT from ..util import is_stringish,\ to_bytes,\ to_unicode,\ to_fileobj,\ url_quote,\ url_unquote from ..exception import MetaflowException from ..debug import debug from . import s3op try: # python2 from urlparse import urlparse except: # python3 from urllib.parse import urlparse from metaflow.datastore.util.s3util import get_s3_client from botocore.exceptions import ClientError NUM_S3OP_RETRIES = 8 class MetaflowS3InvalidObject(MetaflowException): headline = 'Not a string-like object' class MetaflowS3URLException(MetaflowException): headline = 'Invalid address' class MetaflowS3Exception(MetaflowException): headline = 'S3 access failed' class MetaflowS3NotFound(MetaflowException): headline = 'S3 object not found' class MetaflowS3AccessDenied(MetaflowException): headline = 'S3 access denied' class S3Object(object): """ This object represents a path or an object in S3, with an optional local copy. Get or list calls return one or more of S3Objects. """ def __init__(self, prefix, url, path, size=None): # all fields of S3Object should return a unicode object def ensure_unicode(x): return None if x is None else to_unicode(x) prefix, url, path = map(ensure_unicode, (prefix, url, path)) self._size = size self._url = url self._path = path self._key = None if path: self._size = os.stat(self._path).st_size if prefix is None or prefix == url: self._key = url self._prefix = None else: self._key = url[len(prefix.rstrip('/')) + 1:].rstrip('/') self._prefix = prefix @property def exists(self): """ Does this key correspond to an object in S3? """ return self._size is not None @property def downloaded(self): """ Has this object been downloaded? """ return bool(self._path) @property def url(self): """ S3 location of the object """ return self._url @property def prefix(self): """ Prefix requested that matches the object. """ return self._prefix @property def key(self): """ Key corresponds to the key given to the get call that produced this object. This may be a full S3 URL or a suffix based on what was requested. """ return self._key @property def path(self): """ Path to the local file corresponding to the object downloaded. This file gets deleted automatically when a S3 scope exits. Returns None if this S3Object has not been downloaded. """ return self._path @property def blob(self): """ Contents of the object as a byte string. Returns None if this S3Object has not been downloaded. """ if self._path: with open(self._path, 'rb') as f: return f.read() @property def text(self): """ Contents of the object as a Unicode string. Returns None if this S3Object has not been downloaded. """ if self._path: return self.blob.decode('utf-8', errors='replace') @property def size(self): """ Size of the object in bytes. Returns None if the key does not correspond to an object in S3. """ return self._size def __str__(self): if self._path: return '<S3Object %s (%d bytes, local)>' % (self._url, self._size) elif self._size: return '<S3Object %s (%d bytes, in S3)>' % (self._url, self._size) else: return '<S3Object %s (object does not exist)>' % self._url def __repr__(self): return str(self) class S3(object): def __init__(self, tmproot='.', bucket=None, prefix=None, run=None, s3root=None): """ Initialize a new context for S3 operations. This object is based used as a context manager for a with statement. There are two ways to initialize this object depending whether you want to bind paths to a Metaflow run or not. 1. With a run object: run: (required) Either a FlowSpec object (typically 'self') or a Run object corresponding to an existing Metaflow run. These are used to add a version suffix in the S3 path. bucket: (optional) S3 bucket. prefix: (optional) S3 prefix. 2. Without a run object: s3root: (optional) An S3 root URL for all operations. If this is not specified, all operations require a full S3 URL. These options are supported in both the modes: tmproot: (optional) Root path for temporary files (default: '.') """ if run: # 1. use a (current) run ID with optional customizations parsed = urlparse(DATATOOLS_S3ROOT) if not bucket: bucket = parsed.netloc if not prefix: prefix = parsed.path if isinstance(run, FlowSpec): if current.is_running_flow: prefix = os.path.join(prefix, current.flow_name, current.run_id) else: raise MetaflowS3URLException(\ "Initializing S3 with a FlowSpec outside of a running " "flow is not supported.") else: prefix = os.path.join(prefix, run.parent.id, run.id) self._s3root = u's3://%s' % os.path.join(bucket, prefix.strip('/')) elif s3root: # 2. use an explicit S3 prefix parsed = urlparse(to_unicode(s3root)) if parsed.scheme != 's3': raise MetaflowS3URLException(\ "s3root needs to be an S3 URL prefxied with s3://.") self._s3root = s3root.rstrip('/') else: # 3. use the client only with full URLs self._s3root = None self._tmpdir = mkdtemp(dir=tmproot, prefix='metaflow.s3.') def __enter__(self): return self def __exit__(self, *args): self.close() def close(self): """ Delete all temporary files downloaded in this context. """ try: if not debug.s3client: shutil.rmtree(self._tmpdir) except: pass def _url(self, key): # NOTE: All URLs are handled as Unicode objects (unicde in py2, # string in py3) internally. We expect that all URLs passed to this # class as either Unicode or UTF-8 encoded byte strings. All URLs # returned are Unicode. if self._s3root is None: parsed = urlparse(to_unicode(key)) if parsed.scheme == 's3' and parsed.path: return key else: if current.is_running_flow: raise MetaflowS3URLException(\ "Specify S3(run=self) when you use S3 inside a running " "flow. Otherwise you have to use S3 with full " "s3:// urls.") else: raise MetaflowS3URLException(\ "Initialize S3 with an 's3root' or 'run' if you don't " "want to specify full s3:// urls.") elif key: if key.startswith('s3://'): raise MetaflowS3URLException(\ "Don't use absolute S3 URLs when the S3 client is " "initialized with a prefix. URL: %s" % key) return os.path.join(self._s3root, key) else: return self._s3root def list_paths(self, keys=None): """ List the next level of paths in S3. If multiple keys are specified, listings are done in parallel. The returned S3Objects have .exists == False if the url refers to a prefix, not an existing S3 object. Args: keys: (required) a list of suffixes for paths to list. Returns: a list of S3Objects (not downloaded) Example: Consider the following paths in S3: A/B/C D/E In this case, list_paths(['A', 'D']), returns ['A/B', 'D/E']. The first S3Object has .exists == False, since it does not refer to an object in S3. It is just a prefix. """ def _list(keys): if keys is None: keys = [None] urls = (self._url(key).rstrip('/') + '/' for key in keys) res = self._read_many_files('list', urls) for s3prefix, s3url, size in res: if size: yield s3prefix, s3url, None, int(size) else: yield s3prefix, s3url, None, None return list(starmap(S3Object, _list(keys))) def list_recursive(self, keys=None): """ List objects in S3 recursively. If multiple keys are specified, listings are done in parallel. The returned S3Objects have always .exists == True, since they refer to existing objects in S3. Args: keys: (required) a list of suffixes for paths to list. Returns: a list of S3Objects (not downloaded) Example: Consider the following paths in S3: A/B/C D/E In this case, list_recursive(['A', 'D']), returns ['A/B/C', 'D/E']. """ def _list(keys): if keys is None: keys = [None] res = self._read_many_files('list', map(self._url, keys), recursive=True) for s3prefix, s3url, size in res: yield s3prefix, s3url, None, int(size) return list(starmap(S3Object, _list(keys))) def get(self, key=None, return_missing=False): """ Get a single object from S3. Args: key: (optional) a suffix identifying the object. return_missing: (optional, default False) if set to True, do not raise an exception for a missing key but return it as an S3Object with .exists == False. Returns: an S3Object corresponding to the object requested. """ url = self._url(key) src = urlparse(url) def _download(s3, tmp): s3.download_file(src.netloc, src.path.lstrip('/'), tmp) return url try: path = self._one_boto_op(_download, url) except MetaflowS3NotFound: if return_missing: path = None else: raise return S3Object(self._s3root, url, path) def get_many(self, keys, return_missing=False): """ Get many objects from S3 in parallel. Args: keys: (required) a list of suffixes identifying the objects. return_missing: (optional, default False) if set to True, do not raise an exception for a missing key but return it as an S3Object with .exists == False. Returns: a list of S3Objects corresponding to the objects requested. """ def _get(): res = self._read_many_files('get', map(self._url, keys), allow_missing=return_missing, verify=True, verbose=False, listing=True) for s3prefix, s3url, fname in res: if fname: yield self._s3root, s3url, os.path.join(self._tmpdir, fname) else: # missing entries per return_missing=True yield self._s3root, s3prefix, None, None return list(starmap(S3Object, _get())) def get_recursive(self, keys): """ Get many objects from S3 recursively in parallel. Args: keys: (required) a list of suffixes for paths to download recursively. Returns: a list of S3Objects corresponding to the objects requested. """ def _get(): res = self._read_many_files('get', map(self._url, keys), recursive=True, verify=True, verbose=False, listing=True) for s3prefix, s3url, fname in res: yield s3prefix, s3url, os.path.join(self._tmpdir, fname) return list(starmap(S3Object, _get())) def get_all(self): """ Get all objects from S3 recursively (in parallel). This request only works if S3 is initialized with a run or a s3root prefix. Returns: a list of S3Objects corresponding to the objects requested. """ if self._s3root is None: raise MetaflowS3URLException(\ "Can't get_all() when S3 is initialized without a prefix") else: return self.get_recursive([None]) def put(self, key, obj, overwrite=True): """ Put an object to S3. Args: key: (required) suffix for the object. obj: (required) a bytes, string, or a unicode object to be stored in S3. overwrite: (optional) overwrites the key with obj, if it exists Returns: an S3 URL corresponding to the object stored. """ if not is_stringish(obj): raise MetaflowS3InvalidObject(\ "Object corresponding to the key '%s' is not a string " "or a bytes object." % key) url = self._url(key) src = urlparse(url) def _upload(s3, tmp): # we need to recreate the StringIO object for retries since # apparently upload_fileobj will/may close() it blob = to_fileobj(obj) s3.upload_fileobj(blob, src.netloc, src.path.lstrip('/')) if overwrite: self._one_boto_op(_upload, url) return url else: def _head(s3, tmp): s3.head_object(Bucket=src.netloc, Key=src.path.lstrip('/')) try: self._one_boto_op(_head, url) except MetaflowS3NotFound as err: self._one_boto_op(_upload, url) return url def put_many(self, key_objs, overwrite=True): """ Put objects to S3 in parallel. Args: key_objs: (required) an iterator of (key, value) tuples. Value must be a string, bytes, or a unicode object. overwrite: (optional) overwrites the key with obj, if it exists Returns: a list of (key, S3 URL) tuples corresponding to the files sent. """ def _store(): for key, obj in key_objs: if is_stringish(obj): with NamedTemporaryFile(dir=self._tmpdir, delete=False, mode='wb', prefix='metaflow.s3.put_many.') as tmp: tmp.write(to_bytes(obj)) tmp.close() yield tmp.name, self._url(key), key else: raise MetaflowS3InvalidObject( "Object corresponding to the key '%s' is not a string " "or a bytes object." % key) return self._put_many_files(_store(), overwrite) def put_files(self, key_paths, overwrite=True): """ Put files to S3 in parallel. Args: key_paths: (required) an iterator of (key, path) tuples. overwrite: (optional) overwrites the key with obj, if it exists Returns: a list of (key, S3 URL) tuples corresponding to the files sent. """ def _check(): for key, path in key_paths: if not os.path.exists(path): raise MetaflowS3NotFound("Local file not found: %s" % path) yield path, self._url(key), key return self._put_many_files(_check(), overwrite) def _one_boto_op(self, op, url): error = '' for i in range(NUM_S3OP_RETRIES): tmp = NamedTemporaryFile(dir=self._tmpdir, prefix='metaflow.s3.one_file.', delete=False) try: s3, _ = get_s3_client() op(s3, tmp.name) return tmp.name except ClientError as err: error_code = s3op.normalize_client_error(err) if error_code == 404: raise MetaflowS3NotFound(url) elif error_code == 403: raise MetaflowS3AccessDenied(url) elif error_code == 'NoSuchBucket': raise MetaflowS3URLException("Specified S3 bucket doesn't exist.") error = str(err) except Exception as ex: # TODO specific error message for out of disk space error = str(ex) os.unlink(tmp.name) # add some jitter to make sure retries are not synchronized time.sleep(2**i + random.randint(0, 10)) raise MetaflowS3Exception("S3 operation failed.\n"\ "Key requested: %s\n"\ "Error: %s" % (url, error)) # NOTE: re: _read_many_files and _put_many_files # All file IO is through binary files - we write bytes, we read # bytes. All inputs and outputs from these functions are Unicode. # Conversion between bytes and unicode is done through url_quote # and url_unquote. def _read_many_files(self, op, prefixes, **options): with NamedTemporaryFile(dir=self._tmpdir, mode='wb', delete=not debug.s3client, prefix='metaflow.s3.inputs.') as inputfile: inputfile.write(b'\n'.join(map(url_quote, prefixes))) inputfile.flush() stdout, stderr = self._s3op_with_retries(op, inputs=inputfile.name, **options) if stderr: raise MetaflowS3Exception("Getting S3 files failed.\n"\ "First prefix requested: %s\n"\ "Error: %s" % (prefixes[0], stderr)) else: for line in stdout.splitlines(): yield tuple(map(url_unquote, line.strip(b'\n').split(b' '))) def _put_many_files(self, url_files, overwrite): url_files = list(url_files) with NamedTemporaryFile(dir=self._tmpdir, mode='wb', delete=not debug.s3client, prefix='metaflow.s3.put_inputs.') as inputfile: lines = (b' '.join(map(url_quote, (os.path.realpath(local), url))) for local, url, _ in url_files) inputfile.write(b'\n'.join(lines)) inputfile.flush() stdout, stderr = self._s3op_with_retries('put', filelist=inputfile.name, verbose=False, overwrite=overwrite, listing=True) if stderr: raise MetaflowS3Exception("Uploading S3 files failed.\n"\ "First key: %s\n"\ "Error: %s" % (url_files[0][2], stderr)) else: urls = set() for line in stdout.splitlines(): url, _, _ = map(url_unquote, line.strip(b'\n').split(b' ')) urls.add(url) return [(key, url) for _, url, key in url_files if url in urls] def _s3op_with_retries(self, mode, **options): cmdline = [sys.executable, os.path.abspath(s3op.__file__), mode] for key, value in options.items(): key = key.replace('_', '-') if isinstance(value, bool): if value: cmdline.append('--%s' % key) else: cmdline.append('--no-%s' % key) else: cmdline.extend(('--%s' % key, value)) for i in range(NUM_S3OP_RETRIES): with NamedTemporaryFile(dir=self._tmpdir, mode='wb+', delete=not debug.s3client, prefix='metaflow.s3op.stderr') as stderr: try: debug.s3client_exec(cmdline) stdout = subprocess.check_output(cmdline, cwd=self._tmpdir, stderr=stderr.file) return stdout, None except subprocess.CalledProcessError as ex: stderr.seek(0) err_out = stderr.read().decode('utf-8', errors='replace') stderr.seek(0) if ex.returncode == s3op.ERROR_URL_NOT_FOUND: raise MetaflowS3NotFound(err_out) elif ex.returncode == s3op.ERROR_URL_ACCESS_DENIED: raise MetaflowS3AccessDenied(err_out) time.sleep(2**i + random.randint(0, 10)) return None, err_out

35.472136

86

0.514728

import os import sys import time import shutil import random import subprocess from itertools import starmap from tempfile import mkdtemp, NamedTemporaryFile from .. import current, FlowSpec from ..metaflow_config import DATATOOLS_S3ROOT from ..util import is_stringish,\ to_bytes,\ to_unicode,\ to_fileobj,\ url_quote,\ url_unquote from ..exception import MetaflowException from ..debug import debug from . import s3op try: from urlparse import urlparse except: from urllib.parse import urlparse from metaflow.datastore.util.s3util import get_s3_client from botocore.exceptions import ClientError NUM_S3OP_RETRIES = 8 class MetaflowS3InvalidObject(MetaflowException): headline = 'Not a string-like object' class MetaflowS3URLException(MetaflowException): headline = 'Invalid address' class MetaflowS3Exception(MetaflowException): headline = 'S3 access failed' class MetaflowS3NotFound(MetaflowException): headline = 'S3 object not found' class MetaflowS3AccessDenied(MetaflowException): headline = 'S3 access denied' class S3Object(object): def __init__(self, prefix, url, path, size=None): def ensure_unicode(x): return None if x is None else to_unicode(x) prefix, url, path = map(ensure_unicode, (prefix, url, path)) self._size = size self._url = url self._path = path self._key = None if path: self._size = os.stat(self._path).st_size if prefix is None or prefix == url: self._key = url self._prefix = None else: self._key = url[len(prefix.rstrip('/')) + 1:].rstrip('/') self._prefix = prefix @property def exists(self): return self._size is not None @property def downloaded(self): return bool(self._path) @property def url(self): return self._url @property def prefix(self): return self._prefix @property def key(self): return self._key @property def path(self): return self._path @property def blob(self): if self._path: with open(self._path, 'rb') as f: return f.read() @property def text(self): if self._path: return self.blob.decode('utf-8', errors='replace') @property def size(self): return self._size def __str__(self): if self._path: return '<S3Object %s (%d bytes, local)>' % (self._url, self._size) elif self._size: return '<S3Object %s (%d bytes, in S3)>' % (self._url, self._size) else: return '<S3Object %s (object does not exist)>' % self._url def __repr__(self): return str(self) class S3(object): def __init__(self, tmproot='.', bucket=None, prefix=None, run=None, s3root=None): if run: parsed = urlparse(DATATOOLS_S3ROOT) if not bucket: bucket = parsed.netloc if not prefix: prefix = parsed.path if isinstance(run, FlowSpec): if current.is_running_flow: prefix = os.path.join(prefix, current.flow_name, current.run_id) else: raise MetaflowS3URLException(\ "Initializing S3 with a FlowSpec outside of a running " "flow is not supported.") else: prefix = os.path.join(prefix, run.parent.id, run.id) self._s3root = u's3://%s' % os.path.join(bucket, prefix.strip('/')) elif s3root: parsed = urlparse(to_unicode(s3root)) if parsed.scheme != 's3': raise MetaflowS3URLException(\ "s3root needs to be an S3 URL prefxied with s3://.") self._s3root = s3root.rstrip('/') else: self._s3root = None self._tmpdir = mkdtemp(dir=tmproot, prefix='metaflow.s3.') def __enter__(self): return self def __exit__(self, *args): self.close() def close(self): try: if not debug.s3client: shutil.rmtree(self._tmpdir) except: pass def _url(self, key): if self._s3root is None: parsed = urlparse(to_unicode(key)) if parsed.scheme == 's3' and parsed.path: return key else: if current.is_running_flow: raise MetaflowS3URLException(\ "Specify S3(run=self) when you use S3 inside a running " "flow. Otherwise you have to use S3 with full " "s3:// urls.") else: raise MetaflowS3URLException(\ "Initialize S3 with an 's3root' or 'run' if you don't " "want to specify full s3:// urls.") elif key: if key.startswith('s3://'): raise MetaflowS3URLException(\ "Don't use absolute S3 URLs when the S3 client is " "initialized with a prefix. URL: %s" % key) return os.path.join(self._s3root, key) else: return self._s3root def list_paths(self, keys=None): def _list(keys): if keys is None: keys = [None] urls = (self._url(key).rstrip('/') + '/' for key in keys) res = self._read_many_files('list', urls) for s3prefix, s3url, size in res: if size: yield s3prefix, s3url, None, int(size) else: yield s3prefix, s3url, None, None return list(starmap(S3Object, _list(keys))) def list_recursive(self, keys=None): def _list(keys): if keys is None: keys = [None] res = self._read_many_files('list', map(self._url, keys), recursive=True) for s3prefix, s3url, size in res: yield s3prefix, s3url, None, int(size) return list(starmap(S3Object, _list(keys))) def get(self, key=None, return_missing=False): url = self._url(key) src = urlparse(url) def _download(s3, tmp): s3.download_file(src.netloc, src.path.lstrip('/'), tmp) return url try: path = self._one_boto_op(_download, url) except MetaflowS3NotFound: if return_missing: path = None else: raise return S3Object(self._s3root, url, path) def get_many(self, keys, return_missing=False): def _get(): res = self._read_many_files('get', map(self._url, keys), allow_missing=return_missing, verify=True, verbose=False, listing=True) for s3prefix, s3url, fname in res: if fname: yield self._s3root, s3url, os.path.join(self._tmpdir, fname) else: yield self._s3root, s3prefix, None, None return list(starmap(S3Object, _get())) def get_recursive(self, keys): def _get(): res = self._read_many_files('get', map(self._url, keys), recursive=True, verify=True, verbose=False, listing=True) for s3prefix, s3url, fname in res: yield s3prefix, s3url, os.path.join(self._tmpdir, fname) return list(starmap(S3Object, _get())) def get_all(self): if self._s3root is None: raise MetaflowS3URLException(\ "Can't get_all() when S3 is initialized without a prefix") else: return self.get_recursive([None]) def put(self, key, obj, overwrite=True): if not is_stringish(obj): raise MetaflowS3InvalidObject(\ "Object corresponding to the key '%s' is not a string " "or a bytes object." % key) url = self._url(key) src = urlparse(url) def _upload(s3, tmp): # we need to recreate the StringIO object for retries since # apparently upload_fileobj will/may close() it blob = to_fileobj(obj) s3.upload_fileobj(blob, src.netloc, src.path.lstrip('/')) if overwrite: self._one_boto_op(_upload, url) return url else: def _head(s3, tmp): s3.head_object(Bucket=src.netloc, Key=src.path.lstrip('/')) try: self._one_boto_op(_head, url) except MetaflowS3NotFound as err: self._one_boto_op(_upload, url) return url def put_many(self, key_objs, overwrite=True): def _store(): for key, obj in key_objs: if is_stringish(obj): with NamedTemporaryFile(dir=self._tmpdir, delete=False, mode='wb', prefix='metaflow.s3.put_many.') as tmp: tmp.write(to_bytes(obj)) tmp.close() yield tmp.name, self._url(key), key else: raise MetaflowS3InvalidObject( "Object corresponding to the key '%s' is not a string " "or a bytes object." % key) return self._put_many_files(_store(), overwrite) def put_files(self, key_paths, overwrite=True): def _check(): for key, path in key_paths: if not os.path.exists(path): raise MetaflowS3NotFound("Local file not found: %s" % path) yield path, self._url(key), key return self._put_many_files(_check(), overwrite) def _one_boto_op(self, op, url): error = '' for i in range(NUM_S3OP_RETRIES): tmp = NamedTemporaryFile(dir=self._tmpdir, prefix='metaflow.s3.one_file.', delete=False) try: s3, _ = get_s3_client() op(s3, tmp.name) return tmp.name except ClientError as err: error_code = s3op.normalize_client_error(err) if error_code == 404: raise MetaflowS3NotFound(url) elif error_code == 403: raise MetaflowS3AccessDenied(url) elif error_code == 'NoSuchBucket': raise MetaflowS3URLException("Specified S3 bucket doesn't exist.") error = str(err) except Exception as ex: error = str(ex) os.unlink(tmp.name) time.sleep(2**i + random.randint(0, 10)) raise MetaflowS3Exception("S3 operation failed.\n"\ "Key requested: %s\n"\ "Error: %s" % (url, error)) def _read_many_files(self, op, prefixes, **options): with NamedTemporaryFile(dir=self._tmpdir, mode='wb', delete=not debug.s3client, prefix='metaflow.s3.inputs.') as inputfile: inputfile.write(b'\n'.join(map(url_quote, prefixes))) inputfile.flush() stdout, stderr = self._s3op_with_retries(op, inputs=inputfile.name, **options) if stderr: raise MetaflowS3Exception("Getting S3 files failed.\n"\ "First prefix requested: %s\n"\ "Error: %s" % (prefixes[0], stderr)) else: for line in stdout.splitlines(): yield tuple(map(url_unquote, line.strip(b'\n').split(b' '))) def _put_many_files(self, url_files, overwrite): url_files = list(url_files) with NamedTemporaryFile(dir=self._tmpdir, mode='wb', delete=not debug.s3client, prefix='metaflow.s3.put_inputs.') as inputfile: lines = (b' '.join(map(url_quote, (os.path.realpath(local), url))) for local, url, _ in url_files) inputfile.write(b'\n'.join(lines)) inputfile.flush() stdout, stderr = self._s3op_with_retries('put', filelist=inputfile.name, verbose=False, overwrite=overwrite, listing=True) if stderr: raise MetaflowS3Exception("Uploading S3 files failed.\n"\ "First key: %s\n"\ "Error: %s" % (url_files[0][2], stderr)) else: urls = set() for line in stdout.splitlines(): url, _, _ = map(url_unquote, line.strip(b'\n').split(b' ')) urls.add(url) return [(key, url) for _, url, key in url_files if url in urls] def _s3op_with_retries(self, mode, **options): cmdline = [sys.executable, os.path.abspath(s3op.__file__), mode] for key, value in options.items(): key = key.replace('_', '-') if isinstance(value, bool): if value: cmdline.append('--%s' % key) else: cmdline.append('--no-%s' % key) else: cmdline.extend(('--%s' % key, value)) for i in range(NUM_S3OP_RETRIES): with NamedTemporaryFile(dir=self._tmpdir, mode='wb+', delete=not debug.s3client, prefix='metaflow.s3op.stderr') as stderr: try: debug.s3client_exec(cmdline) stdout = subprocess.check_output(cmdline, cwd=self._tmpdir, stderr=stderr.file) return stdout, None except subprocess.CalledProcessError as ex: stderr.seek(0) err_out = stderr.read().decode('utf-8', errors='replace') stderr.seek(0) if ex.returncode == s3op.ERROR_URL_NOT_FOUND: raise MetaflowS3NotFound(err_out) elif ex.returncode == s3op.ERROR_URL_ACCESS_DENIED: raise MetaflowS3AccessDenied(err_out) time.sleep(2**i + random.randint(0, 10)) return None, err_out

true

790da00e416e6ea53931fd81ed764a45913d098e

1,674

py

Python

week4/week4_additionalexercice_5.py

harshonyou/SOFT1

1bd2b0cc26d39c549bec576389bebd0fd011387d

[ "Apache-2.0" ]

null

week4/week4_additionalexercice_5.py

harshonyou/SOFT1

1bd2b0cc26d39c549bec576389bebd0fd011387d

[ "Apache-2.0" ]

null

week4/week4_additionalexercice_5.py

harshonyou/SOFT1

1bd2b0cc26d39c549bec576389bebd0fd011387d

[ "Apache-2.0" ]

null

''' Exercise 5: Vectors A vector of dimension 𝑛𝑛 can be represented by a list in Python. For example, a vector of dimension 3 could represent a point in space, and a vector of dimension 4 could represent a point in space and time (the fourth dimension being the time). In mathematical notation, a vector of dimension 3 is represented as follow: � 𝑎𝑎 𝑏𝑏 𝑐𝑐 � The vector could be stored in a Python list [a, b, c]. There are two simple operations that can be done on vector, and the result of the two operation is also a vector. The two operations are: Scalar product: 𝜆𝜆 ∙ � 𝑎𝑎 𝑏𝑏 𝑐𝑐 � = � 𝜆𝜆 ∙ 𝑎𝑎 𝜆𝜆 ∙ 𝑏𝑏 𝜆𝜆 ∙ 𝑐𝑐 � Addition: � 𝑎𝑎 𝑏𝑏 𝑐𝑐 � + � 𝑑𝑑 𝑒𝑒 𝑓𝑓 � = � 𝑎𝑎 + 𝑑𝑑 𝑏𝑏 + 𝑒𝑒 𝑐𝑐 + 𝑓𝑓 � Implement two functions: 1. scalar_product(scalar, vector) where scalar is a float and vector is a list of float. The function returns the scalar product of the two parameters. 2. vector_addition(vector1, vector2) where vector1 and vector2 are lists of float. The function returns the vector addition of the two parameters. If vector1 and vector2 don’t have the same dimension, you should print an error message and return None. ''' def scalar_product(scalar, vector): for x in range(len(vector)): vector[x]*=scalar return vector def vector_addition(vector1, vector2): if(len(vector2)!=len(vector1)): return 'Error' for x in range(len(vector1)): vector1[x]=int(vector1[x])+int(vector2[x]) return vector1 print(scalar_product(int(input('Enter Scalar Value: ')),input('Enter a Matrix seperated by coma: ').split(',') )) print(vector_addition( input('Enter first Matrix to add: ').split(',') , input('Enter second Matrix to add: ').split(',') ))

27.442623

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0.717443

def scalar_product(scalar, vector): for x in range(len(vector)): vector[x]*=scalar return vector def vector_addition(vector1, vector2): if(len(vector2)!=len(vector1)): return 'Error' for x in range(len(vector1)): vector1[x]=int(vector1[x])+int(vector2[x]) return vector1 print(scalar_product(int(input('Enter Scalar Value: ')),input('Enter a Matrix seperated by coma: ').split(',') )) print(vector_addition( input('Enter first Matrix to add: ').split(',') , input('Enter second Matrix to add: ').split(',') ))

true