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faster_rcnn_inception_resnet_v2_keras_feature_extractor.py

Source:faster_rcnn_inception_resnet_v2_keras_feature_extractor.py

1# Copyright 2019 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Inception Resnet v2 Faster R-CNN implementation in Keras.16See "Inception-v4, Inception-ResNet and the Impact of Residual Connections on17Learning" by Szegedy et al. (https://arxiv.org/abs/1602.07261)18as well as19"Speed/accuracy trade-offs for modern convolutional object detectors" by20Huang et al. (https://arxiv.org/abs/1611.10012)21"""22# Skip pylint for this file because it times out23# pylint: skip-file24import tensorflow as tf25from object_detection.meta_architectures import faster_rcnn_meta_arch26from object_detection.models.keras_models import inception_resnet_v227from object_detection.utils import model_util28from object_detection.utils import variables_helper29class FasterRCNNInceptionResnetV2KerasFeatureExtractor(30    faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor):31  """Faster R-CNN with Inception Resnet v2 feature extractor implementation."""32  def __init__(self,33               is_training,34               first_stage_features_stride,35               batch_norm_trainable=False,36               weight_decay=0.0):37    """Constructor.38    Args:39      is_training: See base class.40      first_stage_features_stride: See base class.41      batch_norm_trainable: See base class.42      weight_decay: See base class.43    Raises:44      ValueError: If `first_stage_features_stride` is not 8 or 16.45    """46    if first_stage_features_stride != 8 and first_stage_features_stride != 16:47      raise ValueError('`first_stage_features_stride` must be 8 or 16.')48    super(FasterRCNNInceptionResnetV2KerasFeatureExtractor, self).__init__(49        is_training, first_stage_features_stride, batch_norm_trainable,50        weight_decay)51  def preprocess(self, resized_inputs):52    """Faster R-CNN with Inception Resnet v2 preprocessing.53    Maps pixel values to the range [-1, 1].54    Args:55      resized_inputs: A [batch, height_in, width_in, channels] float32 tensor56        representing a batch of images with values between 0 and 255.0.57    Returns:58      preprocessed_inputs: A [batch, height_out, width_out, channels] float3259        tensor representing a batch of images.60    """61    return (2.0 / 255.0) * resized_inputs - 1.062  def get_proposal_feature_extractor_model(self, name=None):63    """Returns a model that extracts first stage RPN features.64    Extracts features using the first half of the Inception Resnet v2 network.65    We construct the network in `align_feature_maps=True` mode, which means66    that all VALID paddings in the network are changed to SAME padding so that67    the feature maps are aligned.68    Args:69      name: A scope name to construct all variables within.70    Returns:71      A Keras model that takes preprocessed_inputs:72        A [batch, height, width, channels] float32 tensor73        representing a batch of images.74      And returns rpn_feature_map:75        A tensor with shape [batch, height, width, depth]76    """77    with tf.name_scope(name):78      with tf.name_scope('InceptionResnetV2'):79        model = inception_resnet_v2.inception_resnet_v2(80              self._train_batch_norm,81              output_stride=self._first_stage_features_stride,82              align_feature_maps=True,83              weight_decay=self._weight_decay,84              weights=None,85              include_top=False)86        proposal_features = model.get_layer(87            name='block17_20_ac').output88        return tf.keras.Model(89            inputs=model.inputs,90            outputs=proposal_features)91  def get_box_classifier_feature_extractor_model(self, name=None):92    """Returns a model that extracts second stage box classifier features.93    This function reconstructs the "second half" of the Inception ResNet v294    network after the part defined in `get_proposal_feature_extractor_model`.95    Args:96      name: A scope name to construct all variables within.97    Returns:98      A Keras model that takes proposal_feature_maps:99        A 4-D float tensor with shape100        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]101        representing the feature map cropped to each proposal.102      And returns proposal_classifier_features:103        A 4-D float tensor with shape104        [batch_size * self.max_num_proposals, height, width, depth]105        representing box classifier features for each proposal.106    """107    with tf.name_scope(name):108      with tf.name_scope('InceptionResnetV2'):109        model = inception_resnet_v2.inception_resnet_v2(110            self._train_batch_norm,111            output_stride=16,112            align_feature_maps=False,113            weight_decay=self._weight_decay,114            weights=None,115            include_top=False)116        proposal_feature_maps = model.get_layer(117            name='block17_20_ac').output118        proposal_classifier_features = model.get_layer(119            name='conv_7b_ac').output120        return model_util.extract_submodel(121            model=model,122            inputs=proposal_feature_maps,123            outputs=proposal_classifier_features)124  def restore_from_classification_checkpoint_fn(125      self,126      first_stage_feature_extractor_scope,127      second_stage_feature_extractor_scope):128    """Returns a map of variables to load from a foreign checkpoint.129    This uses a hard-coded conversion to load into Keras from a slim-trained130    inception_resnet_v2 checkpoint.131    Note that this overrides the default implementation in132    faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor which does not work133    for InceptionResnetV2 checkpoints.134    Args:135      first_stage_feature_extractor_scope: A scope name for the first stage136        feature extractor.137      second_stage_feature_extractor_scope: A scope name for the second stage138        feature extractor.139    Returns:140      A dict mapping variable names (to load from a checkpoint) to variables in141      the model graph.142    """143    keras_to_slim_name_mapping = {144        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d/kernel': 'InceptionResnetV2/Conv2d_1a_3x3/weights',145        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/beta': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta',146        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/moving_mean': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean',147        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/moving_variance': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance',148        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_1/kernel': 'InceptionResnetV2/Conv2d_2a_3x3/weights',149        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/beta': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta',150        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/moving_mean': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_mean',151        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/moving_variance': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_variance',152        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_2/kernel': 'InceptionResnetV2/Conv2d_2b_3x3/weights',153        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/beta': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/beta',154        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/moving_mean': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_mean',155        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/moving_variance': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_variance',156        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_3/kernel': 'InceptionResnetV2/Conv2d_3b_1x1/weights',157        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/beta': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/beta',158        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/moving_mean': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_mean',159        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/moving_variance': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_variance',160        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_4/kernel': 'InceptionResnetV2/Conv2d_4a_3x3/weights',161        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/beta': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/beta',162        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/moving_mean': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_mean',163        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/moving_variance': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_variance',164        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_5/kernel': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/weights',165        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/beta': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/beta',166        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',167        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',168        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_6/kernel': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights',169        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/beta': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',170        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',171        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',172        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_7/kernel': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/weights',173        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/beta': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/beta',174        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_mean',175        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_variance',176        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_8/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights',177        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',178        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',179        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',180        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_9/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/weights',181        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',182        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',183        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',184        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_10/kernel': 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'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean',191        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_variance',192        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_12/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/weights',193        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/beta',194        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',195        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',196        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_13/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/weights',197        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',198        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',199        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',200        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_14/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/weights',201        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',202        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',203        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',204        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_15/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/weights',205        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',206        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',207        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',208        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_16/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/weights',209        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',210        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',211        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',212        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_17/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/weights',213        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',214        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',215        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',216        'FirstStageFeatureExtractor/InceptionResnetV2/block35_1_conv/kernel': 'InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/weights',217        'FirstStageFeatureExtractor/InceptionResnetV2/block35_1_conv/bias': 'InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/biases',218        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_18/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/weights',219        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/beta',220        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',221        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',222        'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_19/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/weights',223        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',224        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',225        'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/moving_variance': 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'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',1012        'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_401/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/weights',1013        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',1014        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',1015        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',1016        'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/weights',1017        'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/bias': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/biases',1018        'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_402/kernel': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/weights',1019        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/beta': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta',1020        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_mean': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',1021        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_variance': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',1022        'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_403/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/weights',1023        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',1024        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',1025        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',1026        'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_404/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/weights',1027        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',1028        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',1029        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',1030        'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_405/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/weights',1031        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',1032        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',1033        'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',1034        'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/kernel': 'InceptionResnetV2/Block8/Conv2d_1x1/weights',1035        'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/bias': 'InceptionResnetV2/Block8/Conv2d_1x1/biases',1036        'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b/kernel': 'InceptionResnetV2/Conv2d_7b_1x1/weights',1037        'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/beta': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta',1038        'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_mean': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean',1039        'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_variance': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance',1040    }1041    variables_to_restore = {}1042    for variable in variables_helper.get_global_variables_safely():1043      var_name = keras_to_slim_name_mapping.get(variable.op.name)1044      if var_name:1045        variables_to_restore[var_name] = variable...

commctrl.py

Source:commctrl.py

1# Generated by h2py from COMMCTRL.H2WM_USER = 10243ICC_LISTVIEW_CLASSES = 1		# listview, header4ICC_TREEVIEW_CLASSES = 2		# treeview, tooltips5ICC_BAR_CLASSES = 4			# toolbar, statusbar, trackbar, tooltips6ICC_TAB_CLASSES = 8			# tab, tooltips7ICC_UPDOWN_CLASS = 16			# updown8ICC_PROGRESS_CLASS = 32		# progress9ICC_HOTKEY_CLASS = 64			# hotkey10ICC_ANIMATE_CLASS = 128		# animate11ICC_WIN95_CLASSES = 25512ICC_DATE_CLASSES = 256			# month picker, date picker, time picker, updown13ICC_USEREX_CLASSES = 512		# comboex14ICC_COOL_CLASSES = 1024			# rebar (coolbar) control15ICC_INTERNET_CLASSES = 204816ICC_PAGESCROLLER_CLASS = 4096		# page scroller17ICC_NATIVEFNTCTL_CLASS = 8192		# native font control18ODT_HEADER = 10019ODT_TAB = 10120ODT_LISTVIEW = 10221PY_0U = 022NM_FIRST = (PY_0U)				# generic to all controls23NM_LAST = (PY_0U- 99)24LVN_FIRST = (PY_0U-100)			# listview25LVN_LAST = (PY_0U-199)26HDN_FIRST = (PY_0U-300)			# header27HDN_LAST = (PY_0U-399)28TVN_FIRST = (PY_0U-400)			# treeview29TVN_LAST = (PY_0U-499)30TTN_FIRST = (PY_0U-520)			# tooltips31TTN_LAST = (PY_0U-549)32TCN_FIRST = (PY_0U-550)			# tab control33TCN_LAST = (PY_0U-580)34CDN_FIRST = (PY_0U-601)			# common dialog (new)35CDN_LAST = (PY_0U-699)36TBN_FIRST = (PY_0U-700)			# toolbar37TBN_LAST = (PY_0U-720)38UDN_FIRST = (PY_0U-721)			# updown39UDN_LAST = (PY_0U-740)40MCN_FIRST = (PY_0U-750)			# monthcal41MCN_LAST = (PY_0U-759)42DTN_FIRST = (PY_0U-760)			# datetimepick43DTN_LAST = (PY_0U-799)44CBEN_FIRST = (PY_0U-800)		# combo box ex45CBEN_LAST = (PY_0U-830)46RBN_FIRST = (PY_0U-831)			# rebar47RBN_LAST = (PY_0U-859)48IPN_FIRST = (PY_0U-860)			# internet address49IPN_LAST = (PY_0U-879)			# internet address50SBN_FIRST = (PY_0U-880)			# status bar51SBN_LAST = (PY_0U-899)52PGN_FIRST = (PY_0U-900)			# Pager Control53PGN_LAST = (PY_0U-950)54LVM_FIRST = 4096				# ListView messages55TV_FIRST = 4352					# TreeView messages56HDM_FIRST = 4608				# Header messages57TCM_FIRST = 4864				# Tab control messages58PGM_FIRST = 5120				# Pager control messages59CCM_FIRST = 8192				# Common control shared messages60CCM_SETBKCOLOR = (CCM_FIRST + 1)		# lParam is bkColor61CCM_SETCOLORSCHEME = (CCM_FIRST + 2)	# lParam is color scheme62CCM_GETCOLORSCHEME = (CCM_FIRST + 3)	# fills in COLORSCHEME pointed to by lParam63CCM_GETDROPTARGET = (CCM_FIRST + 4)64CCM_SETUNICODEFORMAT = (CCM_FIRST + 5)65CCM_GETUNICODEFORMAT = (CCM_FIRST + 6)66INFOTIPSIZE = 102467NM_OUTOFMEMORY = (NM_FIRST-1)68NM_CLICK = (NM_FIRST-2)				# uses NMCLICK struct69NM_DBLCLK = (NM_FIRST-3)70NM_RETURN = (NM_FIRST-4)71NM_RCLICK = (NM_FIRST-5)			# uses NMCLICK struct72NM_RDBLCLK = (NM_FIRST-6)73NM_SETFOCUS = (NM_FIRST-7)74NM_KILLFOCUS = (NM_FIRST-8)75NM_CUSTOMDRAW = (NM_FIRST-12)76NM_HOVER = (NM_FIRST-13)77NM_NCHITTEST = (NM_FIRST-14)			# uses NMMOUSE struct78NM_KEYDOWN = (NM_FIRST-15)			# uses NMKEY struct79NM_RELEASEDCAPTURE = (NM_FIRST-16)80NM_SETCURSOR = (NM_FIRST-17)			# uses NMMOUSE struct81NM_CHAR = (NM_FIRST-18)				# uses NMCHAR struct82MSGF_COMMCTRL_BEGINDRAG = 1689683MSGF_COMMCTRL_SIZEHEADER = 1689784MSGF_COMMCTRL_DRAGSELECT = 1689885MSGF_COMMCTRL_TOOLBARCUST = 1689986CDRF_DODEFAULT = 087CDRF_NEWFONT = 288CDRF_SKIPDEFAULT = 489CDRF_NOTIFYPOSTPAINT = 1690CDRF_NOTIFYITEMDRAW = 3291CDRF_NOTIFYSUBITEMDRAW = 32  # flags are the same, we can distinguish by context92CDRF_NOTIFYPOSTERASE = 6493CDDS_PREPAINT = 194CDDS_POSTPAINT = 295CDDS_PREERASE = 396CDDS_POSTERASE = 497CDDS_ITEM = 6553698CDDS_ITEMPREPAINT = (CDDS_ITEM | CDDS_PREPAINT)99CDDS_ITEMPOSTPAINT = (CDDS_ITEM | CDDS_POSTPAINT)100CDDS_ITEMPREERASE = (CDDS_ITEM | CDDS_PREERASE)101CDDS_ITEMPOSTERASE = (CDDS_ITEM | CDDS_POSTERASE)102CDDS_SUBITEM = 131072103CDIS_SELECTED = 1104CDIS_GRAYED = 2105CDIS_DISABLED = 4106CDIS_CHECKED = 8107CDIS_FOCUS = 16108CDIS_DEFAULT = 32109CDIS_HOT = 64110CDIS_MARKED = 128111CDIS_INDETERMINATE = 256112CLR_NONE = -1 # 0xFFFFFFFFL113CLR_DEFAULT = -16777216 # 0xFF000000L114ILC_MASK = 1115ILC_COLOR = 0116ILC_COLORDDB = 254117ILC_COLOR4 = 4118ILC_COLOR8 = 8119ILC_COLOR16 = 16120ILC_COLOR24 = 24121ILC_COLOR32 = 32122ILC_PALETTE = 2048      # (not implemented)123ILD_NORMAL = 0124ILD_TRANSPARENT = 1125ILD_MASK = 16126ILD_IMAGE = 32127ILD_ROP = 64128ILD_BLEND25 = 2129ILD_BLEND50 = 4130ILD_OVERLAYMASK = 3840131ILD_SELECTED = ILD_BLEND50132ILD_FOCUS = ILD_BLEND25133ILD_BLEND = ILD_BLEND50134CLR_HILIGHT = CLR_DEFAULT135ILCF_MOVE = (0)136ILCF_SWAP = (1)137WC_HEADERA = "SysHeader32"138WC_HEADER = WC_HEADERA139HDS_HORZ = 0140HDS_BUTTONS = 2141HDS_HOTTRACK = 4142HDS_HIDDEN = 8143HDS_DRAGDROP = 64144HDS_FULLDRAG = 128145HDI_WIDTH = 1146HDI_HEIGHT = HDI_WIDTH147HDI_TEXT = 2148HDI_FORMAT = 4149HDI_LPARAM = 8150HDI_BITMAP = 16151HDI_IMAGE = 32152HDI_DI_SETITEM = 64153HDI_ORDER = 128154HDF_LEFT = 0155HDF_RIGHT = 1156HDF_CENTER = 2157HDF_JUSTIFYMASK = 3158HDF_RTLREADING = 4159HDF_OWNERDRAW = 32768160HDF_STRING = 16384161HDF_BITMAP = 8192162HDF_BITMAP_ON_RIGHT = 4096163HDF_IMAGE = 2048164HDM_GETITEMCOUNT = (HDM_FIRST + 0)165HDM_INSERTITEMA = (HDM_FIRST + 1)166HDM_INSERTITEMW = (HDM_FIRST + 10)167HDM_INSERTITEM = HDM_INSERTITEMA168HDM_DELETEITEM = (HDM_FIRST + 2)169HDM_GETITEMA = (HDM_FIRST + 3)170HDM_GETITEMW = (HDM_FIRST + 11)171HDM_GETITEM = HDM_GETITEMA172HDM_SETITEMA = (HDM_FIRST + 4)173HDM_SETITEMW = (HDM_FIRST + 12)174HDM_SETITEM = HDM_SETITEMA175HDM_LAYOUT = (HDM_FIRST + 5)176HHT_NOWHERE = 1177HHT_ONHEADER = 2178HHT_ONDIVIDER = 4179HHT_ONDIVOPEN = 8180HHT_ABOVE = 256181HHT_BELOW = 512182HHT_TORIGHT = 1024183HHT_TOLEFT = 2048184HDM_HITTEST = (HDM_FIRST + 6)185HDM_GETITEMRECT = (HDM_FIRST + 7)186HDM_SETIMAGELIST = (HDM_FIRST + 8)187HDM_GETIMAGELIST = (HDM_FIRST + 9)188HDM_ORDERTOINDEX = (HDM_FIRST + 15)189HDM_CREATEDRAGIMAGE = (HDM_FIRST + 16)  # wparam = which item (by index)190HDM_GETORDERARRAY = (HDM_FIRST + 17)191HDM_SETORDERARRAY = (HDM_FIRST + 18)192HDM_SETHOTDIVIDER = (HDM_FIRST + 19)193HDM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT194HDM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT195HDN_ITEMCHANGINGA = (HDN_FIRST-0)196HDN_ITEMCHANGINGW = (HDN_FIRST-20)197HDN_ITEMCHANGEDA = (HDN_FIRST-1)198HDN_ITEMCHANGEDW = (HDN_FIRST-21)199HDN_ITEMCLICKA = (HDN_FIRST-2)200HDN_ITEMCLICKW = (HDN_FIRST-22)201HDN_ITEMDBLCLICKA = (HDN_FIRST-3)202HDN_ITEMDBLCLICKW = (HDN_FIRST-23)203HDN_DIVIDERDBLCLICKA = (HDN_FIRST-5)204HDN_DIVIDERDBLCLICKW = (HDN_FIRST-25)205HDN_BEGINTRACKA = (HDN_FIRST-6)206HDN_BEGINTRACKW = (HDN_FIRST-26)207HDN_ENDTRACKA = (HDN_FIRST-7)208HDN_ENDTRACKW = (HDN_FIRST-27)209HDN_TRACKA = (HDN_FIRST-8)210HDN_TRACKW = (HDN_FIRST-28)211HDN_GETDISPINFOA = (HDN_FIRST-9)212HDN_GETDISPINFOW = (HDN_FIRST-29)213HDN_BEGINDRAG = (HDN_FIRST-10)214HDN_ENDDRAG = (HDN_FIRST-11)215HDN_ITEMCHANGING = HDN_ITEMCHANGINGA216HDN_ITEMCHANGED = HDN_ITEMCHANGEDA217HDN_ITEMCLICK = HDN_ITEMCLICKA218HDN_ITEMDBLCLICK = HDN_ITEMDBLCLICKA219HDN_DIVIDERDBLCLICK = HDN_DIVIDERDBLCLICKA220HDN_BEGINTRACK = HDN_BEGINTRACKA221HDN_ENDTRACK = HDN_ENDTRACKA222HDN_TRACK = HDN_TRACKA223HDN_GETDISPINFO = HDN_GETDISPINFOA224TOOLBARCLASSNAMEA = "ToolbarWindow32"225TOOLBARCLASSNAME = TOOLBARCLASSNAMEA226CMB_MASKED = 2227TBSTATE_CHECKED = 1228TBSTATE_PRESSED = 2229TBSTATE_ENABLED = 4230TBSTATE_HIDDEN = 8231TBSTATE_INDETERMINATE = 16232TBSTATE_WRAP = 32233TBSTATE_ELLIPSES = 64234TBSTATE_MARKED = 128235TBSTYLE_BUTTON = 0236TBSTYLE_SEP = 1237TBSTYLE_CHECK = 2238TBSTYLE_GROUP = 4239TBSTYLE_CHECKGROUP = (TBSTYLE_GROUP | TBSTYLE_CHECK)240TBSTYLE_DROPDOWN = 8241TBSTYLE_AUTOSIZE = 16 # automatically calculate the cx of the button242TBSTYLE_NOPREFIX = 32 # if this button should not have accel prefix243TBSTYLE_TOOLTIPS = 256244TBSTYLE_WRAPABLE = 512245TBSTYLE_ALTDRAG = 1024246TBSTYLE_FLAT = 2048247TBSTYLE_LIST = 4096248TBSTYLE_CUSTOMERASE = 8192249TBSTYLE_REGISTERDROP = 16384250TBSTYLE_TRANSPARENT = 32768251TBSTYLE_EX_DRAWDDARROWS = 1252BTNS_BUTTON = TBSTYLE_BUTTON253BTNS_SEP = TBSTYLE_SEP         # 0x0001254BTNS_CHECK = TBSTYLE_CHECK       # 0x0002255BTNS_GROUP = TBSTYLE_GROUP       # 0x0004256BTNS_CHECKGROUP = TBSTYLE_CHECKGROUP # (TBSTYLE_GROUP | TBSTYLE_CHECK)257BTNS_DROPDOWN = TBSTYLE_DROPDOWN    # 0x0008258BTNS_AUTOSIZE = TBSTYLE_AUTOSIZE    # 0x0010; automatically calculate the cx of the button259BTNS_NOPREFIX = TBSTYLE_NOPREFIX    # 0x0020; this button should not have accel prefix260BTNS_SHOWTEXT   = 64 # 0x0040              // ignored unless TBSTYLE_EX_MIXEDBUTTONS is set261BTNS_WHOLEDROPDOWN  = 128 # 0x0080          // draw drop-down arrow, but without split arrow section262TBCDRF_NOEDGES = 65536  # Don't draw button edges263TBCDRF_HILITEHOTTRACK = 131072  # Use color of the button bk when hottracked264TBCDRF_NOOFFSET = 262144  # Don't offset button if pressed265TBCDRF_NOMARK = 524288  # Don't draw default highlight of image/text for TBSTATE_MARKED266TBCDRF_NOETCHEDEFFECT = 1048576  # Don't draw etched effect for disabled items267TB_ENABLEBUTTON = (WM_USER + 1)268TB_CHECKBUTTON = (WM_USER + 2)269TB_PRESSBUTTON = (WM_USER + 3)270TB_HIDEBUTTON = (WM_USER + 4)271TB_INDETERMINATE = (WM_USER + 5)272TB_MARKBUTTON = (WM_USER + 6)273TB_ISBUTTONENABLED = (WM_USER + 9)274TB_ISBUTTONCHECKED = (WM_USER + 10)275TB_ISBUTTONPRESSED = (WM_USER + 11)276TB_ISBUTTONHIDDEN = (WM_USER + 12)277TB_ISBUTTONINDETERMINATE = (WM_USER + 13)278TB_ISBUTTONHIGHLIGHTED = (WM_USER + 14)279TB_SETSTATE = (WM_USER + 17)280TB_GETSTATE = (WM_USER + 18)281TB_ADDBITMAP = (WM_USER + 19)282HINST_COMMCTRL = -1283IDB_STD_SMALL_COLOR = 0284IDB_STD_LARGE_COLOR = 1285IDB_VIEW_SMALL_COLOR = 4286IDB_VIEW_LARGE_COLOR = 5287IDB_HIST_SMALL_COLOR = 8288IDB_HIST_LARGE_COLOR = 9289STD_CUT = 0290STD_COPY = 1291STD_PASTE = 2292STD_UNDO = 3293STD_REDOW = 4294STD_DELETE = 5295STD_FILENEW = 6296STD_FILEOPEN = 7297STD_FILESAVE = 8298STD_PRINTPRE = 9299STD_PROPERTIES = 10300STD_HELP = 11301STD_FIND = 12302STD_REPLACE = 13303STD_PRINT = 14304VIEW_LARGEICONS = 0305VIEW_SMALLICONS = 1306VIEW_LIST = 2307VIEW_DETAILS = 3308VIEW_SORTNAME = 4309VIEW_SORTSIZE = 5310VIEW_SORTDATE = 6311VIEW_SORTTYPE = 7312VIEW_PARENTFOLDER = 8313VIEW_NETCONNECT = 9314VIEW_NETDISCONNECT = 10315VIEW_NEWFOLDER = 11316VIEW_VIEWMENU = 12317HIST_BACK = 0318HIST_FORWARD = 1319HIST_FAVORITES = 2320HIST_ADDTOFAVORITES = 3321HIST_VIEWTREE = 4322TB_ADDBUTTONSA = (WM_USER + 20)323TB_INSERTBUTTONA = (WM_USER + 21)324TB_ADDBUTTONS = (WM_USER + 20)325TB_INSERTBUTTON = (WM_USER + 21)326TB_DELETEBUTTON = (WM_USER + 22)327TB_GETBUTTON = (WM_USER + 23)328TB_BUTTONCOUNT = (WM_USER + 24)329TB_COMMANDTOINDEX = (WM_USER + 25)330TB_SAVERESTOREA = (WM_USER + 26)331TB_SAVERESTOREW = (WM_USER + 76)332TB_CUSTOMIZE = (WM_USER + 27)333TB_ADDSTRINGA = (WM_USER + 28)334TB_ADDSTRINGW = (WM_USER + 77)335TB_GETITEMRECT = (WM_USER + 29)336TB_BUTTONSTRUCTSIZE = (WM_USER + 30)337TB_SETBUTTONSIZE = (WM_USER + 31)338TB_SETBITMAPSIZE = (WM_USER + 32)339TB_AUTOSIZE = (WM_USER + 33)340TB_GETTOOLTIPS = (WM_USER + 35)341TB_SETTOOLTIPS = (WM_USER + 36)342TB_SETPARENT = (WM_USER + 37)343TB_SETROWS = (WM_USER + 39)344TB_GETROWS = (WM_USER + 40)345TB_SETCMDID = (WM_USER + 42)346TB_CHANGEBITMAP = (WM_USER + 43)347TB_GETBITMAP = (WM_USER + 44)348TB_GETBUTTONTEXTA = (WM_USER + 45)349TB_GETBUTTONTEXTW = (WM_USER + 75)350TB_REPLACEBITMAP = (WM_USER + 46)351TB_SETINDENT = (WM_USER + 47)352TB_SETIMAGELIST = (WM_USER + 48)353TB_GETIMAGELIST = (WM_USER + 49)354TB_LOADIMAGES = (WM_USER + 50)355TB_GETRECT = (WM_USER + 51) # wParam is the Cmd instead of index356TB_SETHOTIMAGELIST = (WM_USER + 52)357TB_GETHOTIMAGELIST = (WM_USER + 53)358TB_SETDISABLEDIMAGELIST = (WM_USER + 54)359TB_GETDISABLEDIMAGELIST = (WM_USER + 55)360TB_SETSTYLE = (WM_USER + 56)361TB_GETSTYLE = (WM_USER + 57)362TB_GETBUTTONSIZE = (WM_USER + 58)363TB_SETBUTTONWIDTH = (WM_USER + 59)364TB_SETMAXTEXTROWS = (WM_USER + 60)365TB_GETTEXTROWS = (WM_USER + 61)366TB_GETBUTTONTEXT = TB_GETBUTTONTEXTW367TB_SAVERESTORE = TB_SAVERESTOREW368TB_ADDSTRING = TB_ADDSTRINGW369TB_GETBUTTONTEXT = TB_GETBUTTONTEXTA370TB_SAVERESTORE = TB_SAVERESTOREA371TB_ADDSTRING = TB_ADDSTRINGA372TB_GETOBJECT = (WM_USER + 62)  # wParam == IID, lParam void **ppv373TB_GETHOTITEM = (WM_USER + 71)374TB_SETHOTITEM = (WM_USER + 72)  # wParam == iHotItem375TB_SETANCHORHIGHLIGHT = (WM_USER + 73)  # wParam == TRUE/FALSE376TB_GETANCHORHIGHLIGHT = (WM_USER + 74)377TB_MAPACCELERATORA = (WM_USER + 78)  # wParam == ch, lParam int * pidBtn378TBIMHT_AFTER = 1 # TRUE = insert After iButton, otherwise before379TBIMHT_BACKGROUND = 2 # TRUE iff missed buttons completely380TB_GETINSERTMARK = (WM_USER + 79)  # lParam == LPTBINSERTMARK381TB_SETINSERTMARK = (WM_USER + 80)  # lParam == LPTBINSERTMARK382TB_INSERTMARKHITTEST = (WM_USER + 81)  # wParam == LPPOINT lParam == LPTBINSERTMARK383TB_MOVEBUTTON = (WM_USER + 82)384TB_GETMAXSIZE = (WM_USER + 83)  # lParam == LPSIZE385TB_SETEXTENDEDSTYLE = (WM_USER + 84)  # For TBSTYLE_EX_*386TB_GETEXTENDEDSTYLE = (WM_USER + 85)  # For TBSTYLE_EX_*387TB_GETPADDING = (WM_USER + 86)388TB_SETPADDING = (WM_USER + 87)389TB_SETINSERTMARKCOLOR = (WM_USER + 88)390TB_GETINSERTMARKCOLOR = (WM_USER + 89)391TB_SETCOLORSCHEME = CCM_SETCOLORSCHEME  # lParam is color scheme392TB_GETCOLORSCHEME = CCM_GETCOLORSCHEME	# fills in COLORSCHEME pointed to by lParam393TB_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT394TB_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT395TB_MAPACCELERATORW = (WM_USER + 90)  # wParam == ch, lParam int * pidBtn396TB_MAPACCELERATOR = TB_MAPACCELERATORW397TB_MAPACCELERATOR = TB_MAPACCELERATORA398TBBF_LARGE = 1399TB_GETBITMAPFLAGS = (WM_USER + 41)400TBIF_IMAGE = 1401TBIF_TEXT = 2402TBIF_STATE = 4403TBIF_STYLE = 8404TBIF_LPARAM = 16405TBIF_COMMAND = 32406TBIF_SIZE = 64407TB_GETBUTTONINFOW = (WM_USER + 63)408TB_SETBUTTONINFOW = (WM_USER + 64)409TB_GETBUTTONINFOA = (WM_USER + 65)410TB_SETBUTTONINFOA = (WM_USER + 66)411TB_INSERTBUTTONW = (WM_USER + 67)412TB_ADDBUTTONSW = (WM_USER + 68)413TB_HITTEST = (WM_USER + 69)414TB_SETDRAWTEXTFLAGS = (WM_USER + 70)  # wParam == mask lParam == bit values415TBN_GETBUTTONINFOA = (TBN_FIRST-0)416TBN_GETBUTTONINFOW = (TBN_FIRST-20)417TBN_BEGINDRAG = (TBN_FIRST-1)418TBN_ENDDRAG = (TBN_FIRST-2)419TBN_BEGINADJUST = (TBN_FIRST-3)420TBN_ENDADJUST = (TBN_FIRST-4)421TBN_RESET = (TBN_FIRST-5)422TBN_QUERYINSERT = (TBN_FIRST-6)423TBN_QUERYDELETE = (TBN_FIRST-7)424TBN_TOOLBARCHANGE = (TBN_FIRST-8)425TBN_CUSTHELP = (TBN_FIRST-9)426TBN_DROPDOWN = (TBN_FIRST - 10)427TBN_GETOBJECT = (TBN_FIRST - 12)428HICF_OTHER = 0429HICF_MOUSE = 1          # Triggered by mouse430HICF_ARROWKEYS = 2          # Triggered by arrow keys431HICF_ACCELERATOR = 4          # Triggered by accelerator432HICF_DUPACCEL = 8          # This accelerator is not unique433HICF_ENTERING = 16          # idOld is invalid434HICF_LEAVING = 32          # idNew is invalid435HICF_RESELECT = 64          # hot item reselected436TBN_HOTITEMCHANGE = (TBN_FIRST - 13)437TBN_DRAGOUT = (TBN_FIRST - 14) # this is sent when the user clicks down on a button then drags off the button438TBN_DELETINGBUTTON = (TBN_FIRST - 15) # uses TBNOTIFY439TBN_GETDISPINFOA = (TBN_FIRST - 16) # This is sent when the  toolbar needs  some display information440TBN_GETDISPINFOW = (TBN_FIRST - 17) # This is sent when the  toolbar needs  some display information441TBN_GETINFOTIPA = (TBN_FIRST - 18)442TBN_GETINFOTIPW = (TBN_FIRST - 19)443TBN_GETINFOTIP = TBN_GETINFOTIPA444TBNF_IMAGE = 1445TBNF_TEXT = 2446TBNF_DI_SETITEM = 268435456447TBN_GETDISPINFO = TBN_GETDISPINFOA448TBDDRET_DEFAULT = 0449TBDDRET_NODEFAULT = 1450TBDDRET_TREATPRESSED = 2       # Treat as a standard press button451TBN_GETBUTTONINFO = TBN_GETBUTTONINFOA452REBARCLASSNAMEA = "ReBarWindow32"453REBARCLASSNAME = REBARCLASSNAMEA454RBIM_IMAGELIST = 1455RBS_TOOLTIPS = 256456RBS_VARHEIGHT = 512457RBS_BANDBORDERS = 1024458RBS_FIXEDORDER = 2048459RBS_REGISTERDROP = 4096460RBS_AUTOSIZE = 8192461RBS_VERTICALGRIPPER = 16384  # this always has the vertical gripper (default for horizontal mode)462RBS_DBLCLKTOGGLE = 32768463RBS_TOOLTIPS = 256464RBS_VARHEIGHT = 512465RBS_BANDBORDERS = 1024466RBS_FIXEDORDER = 2048467RBBS_BREAK = 1  # break to new line468RBBS_FIXEDSIZE = 2  # band can't be sized469RBBS_CHILDEDGE = 4  # edge around top & bottom of child window470RBBS_HIDDEN = 8  # don't show471RBBS_NOVERT = 16  # don't show when vertical472RBBS_FIXEDBMP = 32  # bitmap doesn't move during band resize473RBBS_VARIABLEHEIGHT = 64  # allow autosizing of this child vertically474RBBS_GRIPPERALWAYS = 128  # always show the gripper475RBBS_NOGRIPPER = 256  # never show the gripper476RBBIM_STYLE = 1477RBBIM_COLORS = 2478RBBIM_TEXT = 4479RBBIM_IMAGE = 8480RBBIM_CHILD = 16481RBBIM_CHILDSIZE = 32482RBBIM_SIZE = 64483RBBIM_BACKGROUND = 128484RBBIM_ID = 256485RBBIM_IDEALSIZE = 512486RBBIM_LPARAM = 1024487RB_INSERTBANDA = (WM_USER +  1)488RB_DELETEBAND = (WM_USER +  2)489RB_GETBARINFO = (WM_USER +  3)490RB_SETBARINFO = (WM_USER +  4)491RB_GETBANDINFO = (WM_USER +  5)492RB_SETBANDINFOA = (WM_USER +  6)493RB_SETPARENT = (WM_USER +  7)494RB_HITTEST = (WM_USER +  8)495RB_GETRECT = (WM_USER +  9)496RB_INSERTBANDW = (WM_USER +  10)497RB_SETBANDINFOW = (WM_USER +  11)498RB_GETBANDCOUNT = (WM_USER +  12)499RB_GETROWCOUNT = (WM_USER +  13)500RB_GETROWHEIGHT = (WM_USER +  14)501RB_IDTOINDEX = (WM_USER +  16) # wParam == id502RB_GETTOOLTIPS = (WM_USER +  17)503RB_SETTOOLTIPS = (WM_USER +  18)504RB_SETBKCOLOR = (WM_USER +  19) # sets the default BK color505RB_GETBKCOLOR = (WM_USER +  20) # defaults to CLR_NONE506RB_SETTEXTCOLOR = (WM_USER +  21)507RB_GETTEXTCOLOR = (WM_USER +  22) # defaults to 0x00000000508RB_SIZETORECT = (WM_USER +  23) # resize the rebar/break bands and such to this rect (lparam)509RB_SETCOLORSCHEME = CCM_SETCOLORSCHEME  # lParam is color scheme510RB_GETCOLORSCHEME = CCM_GETCOLORSCHEME  # fills in COLORSCHEME pointed to by lParam511RB_INSERTBAND = RB_INSERTBANDA512RB_SETBANDINFO = RB_SETBANDINFOA513RB_BEGINDRAG = (WM_USER + 24)514RB_ENDDRAG = (WM_USER + 25)515RB_DRAGMOVE = (WM_USER + 26)516RB_GETBARHEIGHT = (WM_USER + 27)517RB_GETBANDINFOW = (WM_USER + 28)518RB_GETBANDINFOA = (WM_USER + 29)519RB_GETBANDINFO = RB_GETBANDINFOA520RB_MINIMIZEBAND = (WM_USER + 30)521RB_MAXIMIZEBAND = (WM_USER + 31)522RB_GETDROPTARGET = (CCM_GETDROPTARGET)523RB_GETBANDBORDERS = (WM_USER + 34)  # returns in lparam = lprc the amount of edges added to band wparam524RB_SHOWBAND = (WM_USER + 35)      # show/hide band525RB_SETPALETTE = (WM_USER + 37)526RB_GETPALETTE = (WM_USER + 38)527RB_MOVEBAND = (WM_USER + 39)528RB_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT529RB_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT530RBN_HEIGHTCHANGE = (RBN_FIRST - 0)531RBN_GETOBJECT = (RBN_FIRST - 1)532RBN_LAYOUTCHANGED = (RBN_FIRST - 2)533RBN_AUTOSIZE = (RBN_FIRST - 3)534RBN_BEGINDRAG = (RBN_FIRST - 4)535RBN_ENDDRAG = (RBN_FIRST - 5)536RBN_DELETINGBAND = (RBN_FIRST - 6)     # Uses NMREBAR537RBN_DELETEDBAND = (RBN_FIRST - 7)     # Uses NMREBAR538RBN_CHILDSIZE = (RBN_FIRST - 8)539RBNM_ID = 1540RBNM_STYLE = 2541RBNM_LPARAM = 4542RBHT_NOWHERE = 1543RBHT_CAPTION = 2544RBHT_CLIENT = 3545RBHT_GRABBER = 4546TOOLTIPS_CLASSA = "tooltips_class32"547TOOLTIPS_CLASS = TOOLTIPS_CLASSA548TTS_ALWAYSTIP = 1549TTS_NOPREFIX = 2550TTF_IDISHWND = 1551TTF_CENTERTIP = 2552TTF_RTLREADING = 4553TTF_SUBCLASS = 16554TTF_TRACK = 32555TTF_ABSOLUTE = 128556TTF_TRANSPARENT = 256557TTF_DI_SETITEM = 32768       # valid only on the TTN_NEEDTEXT callback558TTDT_AUTOMATIC = 0559TTDT_RESHOW = 1560TTDT_AUTOPOP = 2561TTDT_INITIAL = 3562TTM_ACTIVATE = (WM_USER + 1)563TTM_SETDELAYTIME = (WM_USER + 3)564TTM_ADDTOOLA = (WM_USER + 4)565TTM_ADDTOOLW = (WM_USER + 50)566TTM_DELTOOLA = (WM_USER + 5)567TTM_DELTOOLW = (WM_USER + 51)568TTM_NEWTOOLRECTA = (WM_USER + 6)569TTM_NEWTOOLRECTW = (WM_USER + 52)570TTM_RELAYEVENT = (WM_USER + 7)571TTM_GETTOOLINFOA = (WM_USER + 8)572TTM_GETTOOLINFOW = (WM_USER + 53)573TTM_SETTOOLINFOA = (WM_USER + 9)574TTM_SETTOOLINFOW = (WM_USER + 54)575TTM_HITTESTA = (WM_USER +10)576TTM_HITTESTW = (WM_USER +55)577TTM_GETTEXTA = (WM_USER +11)578TTM_GETTEXTW = (WM_USER +56)579TTM_UPDATETIPTEXTA = (WM_USER +12)580TTM_UPDATETIPTEXTW = (WM_USER +57)581TTM_GETTOOLCOUNT = (WM_USER +13)582TTM_ENUMTOOLSA = (WM_USER +14)583TTM_ENUMTOOLSW = (WM_USER +58)584TTM_GETCURRENTTOOLA = (WM_USER + 15)585TTM_GETCURRENTTOOLW = (WM_USER + 59)586TTM_WINDOWFROMPOINT = (WM_USER + 16)587TTM_TRACKACTIVATE = (WM_USER + 17)  # wParam = TRUE/FALSE start end  lparam = LPTOOLINFO588TTM_TRACKPOSITION = (WM_USER + 18)  # lParam = dwPos589TTM_SETTIPBKCOLOR = (WM_USER + 19)590TTM_SETTIPTEXTCOLOR = (WM_USER + 20)591TTM_GETDELAYTIME = (WM_USER + 21)592TTM_GETTIPBKCOLOR = (WM_USER + 22)593TTM_GETTIPTEXTCOLOR = (WM_USER + 23)594TTM_SETMAXTIPWIDTH = (WM_USER + 24)595TTM_GETMAXTIPWIDTH = (WM_USER + 25)596TTM_SETMARGIN = (WM_USER + 26)  # lParam = lprc597TTM_GETMARGIN = (WM_USER + 27)  # lParam = lprc598TTM_POP = (WM_USER + 28)599TTM_UPDATE = (WM_USER + 29)600TTM_ADDTOOL = TTM_ADDTOOLA601TTM_DELTOOL = TTM_DELTOOLA602TTM_NEWTOOLRECT = TTM_NEWTOOLRECTA603TTM_GETTOOLINFO = TTM_GETTOOLINFOA604TTM_SETTOOLINFO = TTM_SETTOOLINFOA605TTM_HITTEST = TTM_HITTESTA606TTM_GETTEXT = TTM_GETTEXTA607TTM_UPDATETIPTEXT = TTM_UPDATETIPTEXTA608TTM_ENUMTOOLS = TTM_ENUMTOOLSA609TTM_GETCURRENTTOOL = TTM_GETCURRENTTOOLA610TTN_GETDISPINFOA = (TTN_FIRST - 0)611TTN_GETDISPINFOW = (TTN_FIRST - 10)612TTN_SHOW = (TTN_FIRST - 1)613TTN_POP = (TTN_FIRST - 2)614TTN_GETDISPINFO = TTN_GETDISPINFOA615TTN_NEEDTEXT = TTN_GETDISPINFO616TTN_NEEDTEXTA = TTN_GETDISPINFOA617TTN_NEEDTEXTW = TTN_GETDISPINFOW618SBARS_SIZEGRIP = 256619SBARS_TOOLTIPS = 2048620STATUSCLASSNAMEA = "msctls_statusbar32"621STATUSCLASSNAME = STATUSCLASSNAMEA622SB_SETTEXTA = (WM_USER+1)623SB_SETTEXTW = (WM_USER+11)624SB_GETTEXTA = (WM_USER+2)625SB_GETTEXTW = (WM_USER+13)626SB_GETTEXTLENGTHA = (WM_USER+3)627SB_GETTEXTLENGTHW = (WM_USER+12)628SB_GETTEXT = SB_GETTEXTA629SB_SETTEXT = SB_SETTEXTA630SB_GETTEXTLENGTH = SB_GETTEXTLENGTHA631SB_SETPARTS = (WM_USER+4)632SB_GETPARTS = (WM_USER+6)633SB_GETBORDERS = (WM_USER+7)634SB_SETMINHEIGHT = (WM_USER+8)635SB_SIMPLE = (WM_USER+9)636SB_GETRECT = (WM_USER+10)637SB_ISSIMPLE = (WM_USER+14)638SB_SETICON = (WM_USER+15)639SB_SETTIPTEXTA = (WM_USER+16)640SB_SETTIPTEXTW = (WM_USER+17)641SB_GETTIPTEXTA = (WM_USER+18)642SB_GETTIPTEXTW = (WM_USER+19)643SB_GETICON = (WM_USER+20)644SB_SETTIPTEXT = SB_SETTIPTEXTA645SB_GETTIPTEXT = SB_GETTIPTEXTA646SB_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT647SB_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT648SBT_OWNERDRAW = 4096649SBT_NOBORDERS = 256650SBT_POPOUT = 512651SBT_RTLREADING = 1024652SBT_NOTABPARSING = 2048653SBT_TOOLTIPS = 2048654SB_SETBKCOLOR = CCM_SETBKCOLOR      # lParam = bkColor655SBN_SIMPLEMODECHANGE = (SBN_FIRST - 0)656TRACKBAR_CLASSA = "msctls_trackbar32"657TRACKBAR_CLASS = TRACKBAR_CLASSA658TBS_AUTOTICKS = 1659TBS_VERT = 2660TBS_HORZ = 0661TBS_TOP = 4662TBS_BOTTOM = 0663TBS_LEFT = 4664TBS_RIGHT = 0665TBS_BOTH = 8666TBS_NOTICKS = 16667TBS_ENABLESELRANGE = 32668TBS_FIXEDLENGTH = 64669TBS_NOTHUMB = 128670TBS_TOOLTIPS = 256671TBM_GETPOS = (WM_USER)672TBM_GETRANGEMIN = (WM_USER+1)673TBM_GETRANGEMAX = (WM_USER+2)674TBM_GETTIC = (WM_USER+3)675TBM_SETTIC = (WM_USER+4)676TBM_SETPOS = (WM_USER+5)677TBM_SETRANGE = (WM_USER+6)678TBM_SETRANGEMIN = (WM_USER+7)679TBM_SETRANGEMAX = (WM_USER+8)680TBM_CLEARTICS = (WM_USER+9)681TBM_SETSEL = (WM_USER+10)682TBM_SETSELSTART = (WM_USER+11)683TBM_SETSELEND = (WM_USER+12)684TBM_GETPTICS = (WM_USER+14)685TBM_GETTICPOS = (WM_USER+15)686TBM_GETNUMTICS = (WM_USER+16)687TBM_GETSELSTART = (WM_USER+17)688TBM_GETSELEND = (WM_USER+18)689TBM_CLEARSEL = (WM_USER+19)690TBM_SETTICFREQ = (WM_USER+20)691TBM_SETPAGESIZE = (WM_USER+21)692TBM_GETPAGESIZE = (WM_USER+22)693TBM_SETLINESIZE = (WM_USER+23)694TBM_GETLINESIZE = (WM_USER+24)695TBM_GETTHUMBRECT = (WM_USER+25)696TBM_GETCHANNELRECT = (WM_USER+26)697TBM_SETTHUMBLENGTH = (WM_USER+27)698TBM_GETTHUMBLENGTH = (WM_USER+28)699TBM_SETTOOLTIPS = (WM_USER+29)700TBM_GETTOOLTIPS = (WM_USER+30)701TBM_SETTIPSIDE = (WM_USER+31)702TBTS_TOP = 0703TBTS_LEFT = 1704TBTS_BOTTOM = 2705TBTS_RIGHT = 3706TBM_SETBUDDY = (WM_USER+32) # wparam = BOOL fLeft; (or right)707TBM_GETBUDDY = (WM_USER+33) # wparam = BOOL fLeft; (or right)708TBM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT709TBM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT710TB_LINEUP = 0711TB_LINEDOWN = 1712TB_PAGEUP = 2713TB_PAGEDOWN = 3714TB_THUMBPOSITION = 4715TB_THUMBTRACK = 5716TB_TOP = 6717TB_BOTTOM = 7718TB_ENDTRACK = 8719TBCD_TICS = 1720TBCD_THUMB = 2721TBCD_CHANNEL = 3722DL_BEGINDRAG = (WM_USER+133)723DL_DRAGGING = (WM_USER+134)724DL_DROPPED = (WM_USER+135)725DL_CANCELDRAG = (WM_USER+136)726DL_CURSORSET = 0727DL_STOPCURSOR = 1728DL_COPYCURSOR = 2729DL_MOVECURSOR = 3730DRAGLISTMSGSTRING = "commctrl_DragListMsg"731UPDOWN_CLASSA = "msctls_updown32"732UPDOWN_CLASS = UPDOWN_CLASSA733UD_MAXVAL = 32767734UD_MINVAL = (-UD_MAXVAL)735UDS_WRAP = 1736UDS_SETBUDDYINT = 2737UDS_ALIGNRIGHT = 4738UDS_ALIGNLEFT = 8739UDS_AUTOBUDDY = 16740UDS_ARROWKEYS = 32741UDS_HORZ = 64742UDS_NOTHOUSANDS = 128743UDS_HOTTRACK = 256744UDM_SETRANGE = (WM_USER+101)745UDM_GETRANGE = (WM_USER+102)746UDM_SETPOS = (WM_USER+103)747UDM_GETPOS = (WM_USER+104)748UDM_SETBUDDY = (WM_USER+105)749UDM_GETBUDDY = (WM_USER+106)750UDM_SETACCEL = (WM_USER+107)751UDM_GETACCEL = (WM_USER+108)752UDM_SETBASE = (WM_USER+109)753UDM_GETBASE = (WM_USER+110)754UDM_SETRANGE32 = (WM_USER+111)755UDM_GETRANGE32 = (WM_USER+112) # wParam & lParam are LPINT756UDM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT757UDM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT758UDN_DELTAPOS = (UDN_FIRST - 1)759PROGRESS_CLASSA = "msctls_progress32"760PROGRESS_CLASS = PROGRESS_CLASSA761PBS_SMOOTH = 1762PBS_VERTICAL = 4763PBM_SETRANGE = (WM_USER+1)764PBM_SETPOS = (WM_USER+2)765PBM_DELTAPOS = (WM_USER+3)766PBM_SETSTEP = (WM_USER+4)767PBM_STEPIT = (WM_USER+5)768PBM_SETRANGE32 = (WM_USER+6)  # lParam = high, wParam = low769PBM_GETRANGE = (WM_USER+7)  # wParam = return (TRUE ? low : high). lParam = PPBRANGE or NULL770PBM_GETPOS = (WM_USER+8)771PBM_SETBARCOLOR = (WM_USER+9)		# lParam = bar color772PBM_SETBKCOLOR = CCM_SETBKCOLOR  # lParam = bkColor773HOTKEYF_SHIFT = 1774HOTKEYF_CONTROL = 2775HOTKEYF_ALT = 4776HOTKEYF_EXT = 128777HOTKEYF_EXT = 8778HKCOMB_NONE = 1779HKCOMB_S = 2780HKCOMB_C = 4781HKCOMB_A = 8782HKCOMB_SC = 16783HKCOMB_SA = 32784HKCOMB_CA = 64785HKCOMB_SCA = 128786HKM_SETHOTKEY = (WM_USER+1)787HKM_GETHOTKEY = (WM_USER+2)788HKM_SETRULES = (WM_USER+3)789HOTKEY_CLASSA = "msctls_hotkey32"790HOTKEY_CLASS = HOTKEY_CLASSA791CCS_TOP = 0x00000001792CCS_NOMOVEY = 0x00000002793CCS_BOTTOM = 0x00000003794CCS_NORESIZE = 0x00000004795CCS_NOPARENTALIGN = 0x00000008796CCS_ADJUSTABLE = 0x00000020797CCS_NODIVIDER = 0x00000040798CCS_VERT = 0x00000080799CCS_LEFT = (CCS_VERT | CCS_TOP)800CCS_RIGHT = (CCS_VERT | CCS_BOTTOM)801CCS_NOMOVEX = (CCS_VERT | CCS_NOMOVEY)802WC_LISTVIEWA = "SysListView32"803WC_LISTVIEW = WC_LISTVIEWA804LVS_ICON = 0805LVS_REPORT = 1806LVS_SMALLICON = 2807LVS_LIST = 3808LVS_TYPEMASK = 3809LVS_SINGLESEL = 4810LVS_SHOWSELALWAYS = 8811LVS_SORTASCENDING = 16812LVS_SORTDESCENDING = 32813LVS_SHAREIMAGELISTS = 64814LVS_NOLABELWRAP = 128815LVS_AUTOARRANGE = 256816LVS_EDITLABELS = 512817LVS_OWNERDATA = 4096818LVS_NOSCROLL = 8192819LVS_TYPESTYLEMASK = 64512820LVS_ALIGNTOP = 0821LVS_ALIGNLEFT = 2048822LVS_ALIGNMASK = 3072823LVS_OWNERDRAWFIXED = 1024824LVS_NOCOLUMNHEADER = 16384825LVS_NOSORTHEADER = 32768826LVM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT827LVM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT828LVM_GETBKCOLOR = (LVM_FIRST + 0)829LVM_SETBKCOLOR = (LVM_FIRST + 1)830LVM_GETIMAGELIST = (LVM_FIRST + 2)831LVSIL_NORMAL = 0832LVSIL_SMALL = 1833LVSIL_STATE = 2834LVM_SETIMAGELIST = (LVM_FIRST + 3)835LVM_GETITEMCOUNT = (LVM_FIRST + 4)836LVIF_TEXT = 1837LVIF_IMAGE = 2838LVIF_PARAM = 4839LVIF_STATE = 8840LVIF_INDENT = 16841LVIF_NORECOMPUTE = 2048842LVIS_FOCUSED = 1843LVIS_SELECTED = 2844LVIS_CUT = 4845LVIS_DROPHILITED = 8846LVIS_ACTIVATING = 32847LVIS_OVERLAYMASK = 3840848LVIS_STATEIMAGEMASK = 61440849I_INDENTCALLBACK = (-1)850LPSTR_TEXTCALLBACKA = -1851LPSTR_TEXTCALLBACK = LPSTR_TEXTCALLBACKA852I_IMAGECALLBACK = (-1)853LVM_GETITEMA = (LVM_FIRST + 5)854LVM_GETITEMW = (LVM_FIRST + 75)855LVM_GETITEM = LVM_GETITEMW856LVM_GETITEM = LVM_GETITEMA857LVM_SETITEMA = (LVM_FIRST + 6)858LVM_SETITEMW = (LVM_FIRST + 76)859LVM_SETITEM = LVM_SETITEMW860LVM_SETITEM = LVM_SETITEMA861LVM_INSERTITEMA = (LVM_FIRST + 7)862LVM_INSERTITEMW = (LVM_FIRST + 77)863LVM_INSERTITEM = LVM_INSERTITEMA864LVM_DELETEITEM = (LVM_FIRST + 8)865LVM_DELETEALLITEMS = (LVM_FIRST + 9)866LVM_GETCALLBACKMASK = (LVM_FIRST + 10)867LVM_SETCALLBACKMASK = (LVM_FIRST + 11)868LVNI_ALL = 0869LVNI_FOCUSED = 1870LVNI_SELECTED = 2871LVNI_CUT = 4872LVNI_DROPHILITED = 8873LVNI_ABOVE = 256874LVNI_BELOW = 512875LVNI_TOLEFT = 1024876LVNI_TORIGHT = 2048877LVM_GETNEXTITEM = (LVM_FIRST + 12)878LVFI_PARAM = 1879LVFI_STRING = 2880LVFI_PARTIAL = 8881LVFI_WRAP = 32882LVFI_NEARESTXY = 64883LVM_FINDITEMA = (LVM_FIRST + 13)884LVM_FINDITEMW = (LVM_FIRST + 83)885LVM_FINDITEM = LVM_FINDITEMA886LVIR_BOUNDS = 0887LVIR_ICON = 1888LVIR_LABEL = 2889LVIR_SELECTBOUNDS = 3890LVM_GETITEMRECT = (LVM_FIRST + 14)891LVM_SETITEMPOSITION = (LVM_FIRST + 15)892LVM_GETITEMPOSITION = (LVM_FIRST + 16)893LVM_GETSTRINGWIDTHA = (LVM_FIRST + 17)894LVM_GETSTRINGWIDTHW = (LVM_FIRST + 87)895LVM_GETSTRINGWIDTH = LVM_GETSTRINGWIDTHA896LVHT_NOWHERE = 1897LVHT_ONITEMICON = 2898LVHT_ONITEMLABEL = 4899LVHT_ONITEMSTATEICON = 8900LVHT_ONITEM = (LVHT_ONITEMICON | LVHT_ONITEMLABEL | LVHT_ONITEMSTATEICON)901LVHT_ABOVE = 8902LVHT_BELOW = 16903LVHT_TORIGHT = 32904LVHT_TOLEFT = 64905LVM_HITTEST = (LVM_FIRST + 18)906LVM_ENSUREVISIBLE = (LVM_FIRST + 19)907LVM_SCROLL = (LVM_FIRST + 20)908LVM_REDRAWITEMS = (LVM_FIRST + 21)909LVA_DEFAULT = 0910LVA_ALIGNLEFT = 1911LVA_ALIGNTOP = 2912LVA_SNAPTOGRID = 5913LVM_ARRANGE = (LVM_FIRST + 22)914LVM_EDITLABELA = (LVM_FIRST + 23)915LVM_EDITLABELW = (LVM_FIRST + 118)916LVM_EDITLABEL = LVM_EDITLABELW917LVM_EDITLABEL = LVM_EDITLABELA918LVM_GETEDITCONTROL = (LVM_FIRST + 24)919LVCF_FMT = 1920LVCF_WIDTH = 2921LVCF_TEXT = 4922LVCF_SUBITEM = 8923LVCF_IMAGE = 16924LVCF_ORDER = 32925LVCFMT_LEFT = 0926LVCFMT_RIGHT = 1927LVCFMT_CENTER = 2928LVCFMT_JUSTIFYMASK = 3929LVCFMT_IMAGE = 2048930LVCFMT_BITMAP_ON_RIGHT = 4096931LVCFMT_COL_HAS_IMAGES = 32768932LVM_GETCOLUMNA = (LVM_FIRST + 25)933LVM_GETCOLUMNW = (LVM_FIRST + 95)934LVM_GETCOLUMN = LVM_GETCOLUMNA935LVM_SETCOLUMNA = (LVM_FIRST + 26)936LVM_SETCOLUMNW = (LVM_FIRST + 96)937LVM_SETCOLUMN = LVM_SETCOLUMNA938LVM_INSERTCOLUMNA = (LVM_FIRST + 27)939LVM_INSERTCOLUMNW = (LVM_FIRST + 97)940LVM_INSERTCOLUMN = LVM_INSERTCOLUMNA941LVM_DELETECOLUMN = (LVM_FIRST + 28)942LVM_GETCOLUMNWIDTH = (LVM_FIRST + 29)943LVSCW_AUTOSIZE = -1944LVSCW_AUTOSIZE_USEHEADER = -2945LVM_SETCOLUMNWIDTH = (LVM_FIRST + 30)946LVM_GETHEADER = (LVM_FIRST + 31)947LVM_CREATEDRAGIMAGE = (LVM_FIRST + 33)948LVM_GETVIEWRECT = (LVM_FIRST + 34)949LVM_GETTEXTCOLOR = (LVM_FIRST + 35)950LVM_SETTEXTCOLOR = (LVM_FIRST + 36)951LVM_GETTEXTBKCOLOR = (LVM_FIRST + 37)952LVM_SETTEXTBKCOLOR = (LVM_FIRST + 38)953LVM_GETTOPINDEX = (LVM_FIRST + 39)954LVM_GETCOUNTPERPAGE = (LVM_FIRST + 40)955LVM_GETORIGIN = (LVM_FIRST + 41)956LVM_UPDATE = (LVM_FIRST + 42)957LVM_SETITEMSTATE = (LVM_FIRST + 43)958LVM_GETITEMSTATE = (LVM_FIRST + 44)959LVM_GETITEMTEXTA = (LVM_FIRST + 45)960LVM_GETITEMTEXTW = (LVM_FIRST + 115)961LVM_GETITEMTEXT = LVM_GETITEMTEXTW962LVM_GETITEMTEXT = LVM_GETITEMTEXTA963LVM_SETITEMTEXTA = (LVM_FIRST + 46)964LVM_SETITEMTEXTW = (LVM_FIRST + 116)965LVM_SETITEMTEXT = LVM_SETITEMTEXTW966LVM_SETITEMTEXT = LVM_SETITEMTEXTA967LVSICF_NOINVALIDATEALL = 1968LVSICF_NOSCROLL = 2969LVM_SETITEMCOUNT = (LVM_FIRST + 47)970LVM_SORTITEMS = (LVM_FIRST + 48)971LVM_SETITEMPOSITION32 = (LVM_FIRST + 49)972LVM_GETSELECTEDCOUNT = (LVM_FIRST + 50)973LVM_GETITEMSPACING = (LVM_FIRST + 51)974LVM_GETISEARCHSTRINGA = (LVM_FIRST + 52)975LVM_GETISEARCHSTRINGW = (LVM_FIRST + 117)976LVM_GETISEARCHSTRING = LVM_GETISEARCHSTRINGA977LVM_SETICONSPACING = (LVM_FIRST + 53)978LVM_SETEXTENDEDLISTVIEWSTYLE = (LVM_FIRST + 54)   # optional wParam == mask979LVM_GETEXTENDEDLISTVIEWSTYLE = (LVM_FIRST + 55)980LVS_EX_GRIDLINES = 1981LVS_EX_SUBITEMIMAGES = 2982LVS_EX_CHECKBOXES = 4983LVS_EX_TRACKSELECT = 8984LVS_EX_HEADERDRAGDROP = 16985LVS_EX_FULLROWSELECT = 32 # applies to report mode only986LVS_EX_ONECLICKACTIVATE = 64987LVS_EX_TWOCLICKACTIVATE = 128988LVS_EX_FLATSB = 256989LVS_EX_REGIONAL = 512990LVS_EX_INFOTIP = 1024 # listview does InfoTips for you991LVS_EX_UNDERLINEHOT = 2048992LVS_EX_UNDERLINECOLD = 4096993LVS_EX_MULTIWORKAREAS = 8192994LVM_GETSUBITEMRECT = (LVM_FIRST + 56)995LVM_SUBITEMHITTEST = (LVM_FIRST + 57)996LVM_SETCOLUMNORDERARRAY = (LVM_FIRST + 58)997LVM_GETCOLUMNORDERARRAY = (LVM_FIRST + 59)998LVM_SETHOTITEM = (LVM_FIRST + 60)999LVM_GETHOTITEM = (LVM_FIRST + 61)1000LVM_SETHOTCURSOR = (LVM_FIRST + 62)1001LVM_GETHOTCURSOR = (LVM_FIRST + 63)1002LVM_APPROXIMATEVIEWRECT = (LVM_FIRST + 64)1003LV_MAX_WORKAREAS = 161004LVM_SETWORKAREAS = (LVM_FIRST + 65)1005LVM_GETWORKAREAS = (LVM_FIRST + 70)1006LVM_GETNUMBEROFWORKAREAS = (LVM_FIRST + 73)1007LVM_GETSELECTIONMARK = (LVM_FIRST + 66)1008LVM_SETSELECTIONMARK = (LVM_FIRST + 67)1009LVM_SETHOVERTIME = (LVM_FIRST + 71)1010LVM_GETHOVERTIME = (LVM_FIRST + 72)1011LVM_SETTOOLTIPS = (LVM_FIRST + 74)1012LVM_GETTOOLTIPS = (LVM_FIRST + 78)1013LVBKIF_SOURCE_NONE = 01014LVBKIF_SOURCE_HBITMAP = 11015LVBKIF_SOURCE_URL = 21016LVBKIF_SOURCE_MASK = 31017LVBKIF_STYLE_NORMAL = 01018LVBKIF_STYLE_TILE = 161019LVBKIF_STYLE_MASK = 161020LVM_SETBKIMAGEA = (LVM_FIRST + 68)1021LVM_SETBKIMAGEW = (LVM_FIRST + 138)1022LVM_GETBKIMAGEA = (LVM_FIRST + 69)1023LVM_GETBKIMAGEW = (LVM_FIRST + 139)1024LVKF_ALT = 11025LVKF_CONTROL = 21026LVKF_SHIFT = 41027LVN_ITEMCHANGING = (LVN_FIRST-0)1028LVN_ITEMCHANGED = (LVN_FIRST-1)1029LVN_INSERTITEM = (LVN_FIRST-2)1030LVN_DELETEITEM = (LVN_FIRST-3)1031LVN_DELETEALLITEMS = (LVN_FIRST-4)1032LVN_BEGINLABELEDITA = (LVN_FIRST-5)1033LVN_BEGINLABELEDITW = (LVN_FIRST-75)1034LVN_ENDLABELEDITA = (LVN_FIRST-6)1035LVN_ENDLABELEDITW = (LVN_FIRST-76)1036LVN_COLUMNCLICK = (LVN_FIRST-8)1037LVN_BEGINDRAG = (LVN_FIRST-9)1038LVN_BEGINRDRAG = (LVN_FIRST-11)1039LVN_ODCACHEHINT = (LVN_FIRST-13)1040LVN_ODFINDITEMA = (LVN_FIRST-52)1041LVN_ODFINDITEMW = (LVN_FIRST-79)1042LVN_ITEMACTIVATE = (LVN_FIRST-14)1043LVN_ODSTATECHANGED = (LVN_FIRST-15)1044LVN_ODFINDITEM = LVN_ODFINDITEMA1045LVN_HOTTRACK = (LVN_FIRST-21)1046LVN_GETDISPINFOA = (LVN_FIRST-50)1047LVN_GETDISPINFOW = (LVN_FIRST-77)1048LVN_SETDISPINFOA = (LVN_FIRST-51)1049LVN_SETDISPINFOW = (LVN_FIRST-78)1050LVN_BEGINLABELEDIT = LVN_BEGINLABELEDITA1051LVN_ENDLABELEDIT = LVN_ENDLABELEDITA1052LVN_GETDISPINFO = LVN_GETDISPINFOA1053LVN_SETDISPINFO = LVN_SETDISPINFOA1054LVIF_DI_SETITEM = 40961055LVN_KEYDOWN = (LVN_FIRST-55)1056LVN_MARQUEEBEGIN = (LVN_FIRST-56)1057LVGIT_UNFOLDED = 11058LVN_GETINFOTIPA = (LVN_FIRST-57)1059LVN_GETINFOTIPW = (LVN_FIRST-58)1060LVN_GETINFOTIP = LVN_GETINFOTIPA1061WC_TREEVIEWA = "SysTreeView32"1062WC_TREEVIEW = WC_TREEVIEWA1063TVS_HASBUTTONS = 11064TVS_HASLINES = 21065TVS_LINESATROOT = 41066TVS_EDITLABELS = 81067TVS_DISABLEDRAGDROP = 161068TVS_SHOWSELALWAYS = 321069TVS_RTLREADING = 641070TVS_NOTOOLTIPS = 1281071TVS_CHECKBOXES = 2561072TVS_TRACKSELECT = 5121073TVS_SINGLEEXPAND = 10241074TVS_INFOTIP = 20481075TVS_FULLROWSELECT = 40961076TVS_NOSCROLL = 81921077TVS_NONEVENHEIGHT = 163841078TVIF_TEXT = 11079TVIF_IMAGE = 21080TVIF_PARAM = 41081TVIF_STATE = 81082TVIF_HANDLE = 161083TVIF_SELECTEDIMAGE = 321084TVIF_CHILDREN = 641085TVIF_INTEGRAL = 1281086TVIS_SELECTED = 21087TVIS_CUT = 41088TVIS_DROPHILITED = 81089TVIS_BOLD = 161090TVIS_EXPANDED = 321091TVIS_EXPANDEDONCE = 641092TVIS_EXPANDPARTIAL = 1281093TVIS_OVERLAYMASK = 38401094TVIS_STATEIMAGEMASK = 614401095TVIS_USERMASK = 614401096I_CHILDRENCALLBACK = (-1)1097TVI_ROOT = -655361098TVI_FIRST = -655351099TVI_LAST = -655341100TVI_SORT = -655331101TVM_INSERTITEMA = (TV_FIRST + 0)1102TVM_INSERTITEMW = (TV_FIRST + 50)1103TVM_INSERTITEM = TVM_INSERTITEMW1104TVM_INSERTITEM = TVM_INSERTITEMA1105TVM_DELETEITEM = (TV_FIRST + 1)1106TVM_EXPAND = (TV_FIRST + 2)1107TVE_COLLAPSE = 11108TVE_EXPAND = 21109TVE_TOGGLE = 31110TVE_EXPANDPARTIAL = 163841111TVE_COLLAPSERESET = 327681112TVM_GETITEMRECT = (TV_FIRST + 4)1113TVM_GETCOUNT = (TV_FIRST + 5)1114TVM_GETINDENT = (TV_FIRST + 6)1115TVM_SETINDENT = (TV_FIRST + 7)1116TVM_GETIMAGELIST = (TV_FIRST + 8)1117TVSIL_NORMAL = 01118TVSIL_STATE = 21119TVM_SETIMAGELIST = (TV_FIRST + 9)1120TVM_GETNEXTITEM = (TV_FIRST + 10)1121TVGN_ROOT = 01122TVGN_NEXT = 11123TVGN_PREVIOUS = 21124TVGN_PARENT = 31125TVGN_CHILD = 41126TVGN_FIRSTVISIBLE = 51127TVGN_NEXTVISIBLE = 61128TVGN_PREVIOUSVISIBLE = 71129TVGN_DROPHILITE = 81130TVGN_CARET = 91131TVGN_LASTVISIBLE = 101132TVM_SELECTITEM = (TV_FIRST + 11)1133TVM_GETITEMA = (TV_FIRST + 12)1134TVM_GETITEMW = (TV_FIRST + 62)1135TVM_GETITEM = TVM_GETITEMW1136TVM_GETITEM = TVM_GETITEMA1137TVM_SETITEMA = (TV_FIRST + 13)1138TVM_SETITEMW = (TV_FIRST + 63)1139TVM_SETITEM = TVM_SETITEMW1140TVM_SETITEM = TVM_SETITEMA1141TVM_EDITLABELA = (TV_FIRST + 14)1142TVM_EDITLABELW = (TV_FIRST + 65)1143TVM_EDITLABEL = TVM_EDITLABELW1144TVM_EDITLABEL = TVM_EDITLABELA1145TVM_GETEDITCONTROL = (TV_FIRST + 15)1146TVM_GETVISIBLECOUNT = (TV_FIRST + 16)1147TVM_HITTEST = (TV_FIRST + 17)1148TVHT_NOWHERE = 11149TVHT_ONITEMICON = 21150TVHT_ONITEMLABEL = 41151TVHT_ONITEMINDENT = 81152TVHT_ONITEMBUTTON = 161153TVHT_ONITEMRIGHT = 321154TVHT_ONITEMSTATEICON = 641155TVHT_ABOVE = 2561156TVHT_BELOW = 5121157TVHT_TORIGHT = 10241158TVHT_TOLEFT = 20481159TVHT_ONITEM = (TVHT_ONITEMICON | TVHT_ONITEMLABEL | TVHT_ONITEMSTATEICON)1160TVM_CREATEDRAGIMAGE = (TV_FIRST + 18)1161TVM_SORTCHILDREN = (TV_FIRST + 19)1162TVM_ENSUREVISIBLE = (TV_FIRST + 20)1163TVM_SORTCHILDRENCB = (TV_FIRST + 21)1164TVM_ENDEDITLABELNOW = (TV_FIRST + 22)1165TVM_GETISEARCHSTRINGA = (TV_FIRST + 23)1166TVM_GETISEARCHSTRINGW = (TV_FIRST + 64)1167TVM_GETISEARCHSTRING = TVM_GETISEARCHSTRINGA1168TVM_SETTOOLTIPS = (TV_FIRST + 24)1169TVM_GETTOOLTIPS = (TV_FIRST + 25)1170TVM_SETINSERTMARK = (TV_FIRST + 26)1171TVM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1172TVM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1173TVM_SETITEMHEIGHT = (TV_FIRST + 27)1174TVM_GETITEMHEIGHT = (TV_FIRST + 28)1175TVM_SETBKCOLOR = (TV_FIRST + 29)1176TVM_SETTEXTCOLOR = (TV_FIRST + 30)1177TVM_GETBKCOLOR = (TV_FIRST + 31)1178TVM_GETTEXTCOLOR = (TV_FIRST + 32)1179TVM_SETSCROLLTIME = (TV_FIRST + 33)1180TVM_GETSCROLLTIME = (TV_FIRST + 34)1181TVM_SETINSERTMARKCOLOR = (TV_FIRST + 37)1182TVM_GETINSERTMARKCOLOR = (TV_FIRST + 38)1183TVN_SELCHANGINGA = (TVN_FIRST-1)1184TVN_SELCHANGINGW = (TVN_FIRST-50)1185TVN_SELCHANGEDA = (TVN_FIRST-2)1186TVN_SELCHANGEDW = (TVN_FIRST-51)1187TVC_UNKNOWN = 01188TVC_BYMOUSE = 11189TVC_BYKEYBOARD = 21190TVN_GETDISPINFOA = (TVN_FIRST-3)1191TVN_GETDISPINFOW = (TVN_FIRST-52)1192TVN_SETDISPINFOA = (TVN_FIRST-4)1193TVN_SETDISPINFOW = (TVN_FIRST-53)1194TVIF_DI_SETITEM = 40961195TVN_ITEMEXPANDINGA = (TVN_FIRST-5)1196TVN_ITEMEXPANDINGW = (TVN_FIRST-54)1197TVN_ITEMEXPANDEDA = (TVN_FIRST-6)1198TVN_ITEMEXPANDEDW = (TVN_FIRST-55)1199TVN_BEGINDRAGA = (TVN_FIRST-7)1200TVN_BEGINDRAGW = (TVN_FIRST-56)1201TVN_BEGINRDRAGA = (TVN_FIRST-8)1202TVN_BEGINRDRAGW = (TVN_FIRST-57)1203TVN_DELETEITEMA = (TVN_FIRST-9)1204TVN_DELETEITEMW = (TVN_FIRST-58)1205TVN_BEGINLABELEDITA = (TVN_FIRST-10)1206TVN_BEGINLABELEDITW = (TVN_FIRST-59)1207TVN_ENDLABELEDITA = (TVN_FIRST-11)1208TVN_ENDLABELEDITW = (TVN_FIRST-60)1209TVN_KEYDOWN = (TVN_FIRST-12)1210TVN_GETINFOTIPA = (TVN_FIRST-13)1211TVN_GETINFOTIPW = (TVN_FIRST-14)1212TVN_SINGLEEXPAND = (TVN_FIRST-15)1213TVN_SELCHANGING = TVN_SELCHANGINGA1214TVN_SELCHANGED = TVN_SELCHANGEDA1215TVN_GETDISPINFO = TVN_GETDISPINFOA1216TVN_SETDISPINFO = TVN_SETDISPINFOA1217TVN_ITEMEXPANDING = TVN_ITEMEXPANDINGA1218TVN_ITEMEXPANDED = TVN_ITEMEXPANDEDA1219TVN_BEGINDRAG = TVN_BEGINDRAGA1220TVN_BEGINRDRAG = TVN_BEGINRDRAGA1221TVN_DELETEITEM = TVN_DELETEITEMA1222TVN_BEGINLABELEDIT = TVN_BEGINLABELEDITA1223TVN_ENDLABELEDIT = TVN_ENDLABELEDITA1224TVN_GETINFOTIP = TVN_GETINFOTIPA1225TVCDRF_NOIMAGES = 655361226WC_COMBOBOXEXA = "ComboBoxEx32"1227WC_COMBOBOXEX = WC_COMBOBOXEXA1228CBEIF_TEXT = 11229CBEIF_IMAGE = 21230CBEIF_SELECTEDIMAGE = 41231CBEIF_OVERLAY = 81232CBEIF_INDENT = 161233CBEIF_LPARAM = 321234CBEIF_DI_SETITEM = 2684354561235CBEM_INSERTITEMA = (WM_USER + 1)1236CBEM_SETIMAGELIST = (WM_USER + 2)1237CBEM_GETIMAGELIST = (WM_USER + 3)1238CBEM_GETITEMA = (WM_USER + 4)1239CBEM_SETITEMA = (WM_USER + 5)1240#CBEM_DELETEITEM = CB_DELETESTRING1241CBEM_GETCOMBOCONTROL = (WM_USER + 6)1242CBEM_GETEDITCONTROL = (WM_USER + 7)1243CBEM_SETEXSTYLE = (WM_USER + 8)  # use  SETEXTENDEDSTYLE instead1244CBEM_SETEXTENDEDSTYLE = (WM_USER + 14)   # lparam == new style, wParam (optional) == mask1245CBEM_GETEXSTYLE = (WM_USER + 9) # use GETEXTENDEDSTYLE instead1246CBEM_GETEXTENDEDSTYLE = (WM_USER + 9)1247CBEM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1248CBEM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1249CBEM_SETEXSTYLE = (WM_USER + 8)1250CBEM_GETEXSTYLE = (WM_USER + 9)1251CBEM_HASEDITCHANGED = (WM_USER + 10)1252CBEM_INSERTITEMW = (WM_USER + 11)1253CBEM_SETITEMW = (WM_USER + 12)1254CBEM_GETITEMW = (WM_USER + 13)1255CBEM_INSERTITEM = CBEM_INSERTITEMA1256CBEM_SETITEM = CBEM_SETITEMA1257CBEM_GETITEM = CBEM_GETITEMA1258CBES_EX_NOEDITIMAGE = 11259CBES_EX_NOEDITIMAGEINDENT = 21260CBES_EX_PATHWORDBREAKPROC = 41261CBES_EX_NOSIZELIMIT = 81262CBES_EX_CASESENSITIVE = 161263CBEN_GETDISPINFO = (CBEN_FIRST - 0)1264CBEN_GETDISPINFOA = (CBEN_FIRST - 0)1265CBEN_INSERTITEM = (CBEN_FIRST - 1)1266CBEN_DELETEITEM = (CBEN_FIRST - 2)1267CBEN_BEGINEDIT = (CBEN_FIRST - 4)1268CBEN_ENDEDITA = (CBEN_FIRST - 5)1269CBEN_ENDEDITW = (CBEN_FIRST - 6)1270CBEN_GETDISPINFOW = (CBEN_FIRST - 7)1271CBEN_DRAGBEGINA = (CBEN_FIRST - 8)1272CBEN_DRAGBEGINW = (CBEN_FIRST - 9)1273CBEN_DRAGBEGIN = CBEN_DRAGBEGINA1274CBEN_ENDEDIT = CBEN_ENDEDITA1275CBENF_KILLFOCUS = 11276CBENF_RETURN = 21277CBENF_ESCAPE = 31278CBENF_DROPDOWN = 41279CBEMAXSTRLEN = 2601280WC_TABCONTROLA = "SysTabControl32"1281WC_TABCONTROL = WC_TABCONTROLA1282TCS_SCROLLOPPOSITE = 1   # assumes multiline tab1283TCS_BOTTOM = 21284TCS_RIGHT = 21285TCS_MULTISELECT = 4  # allow multi-select in button mode1286TCS_FLATBUTTONS = 81287TCS_FORCEICONLEFT = 161288TCS_FORCELABELLEFT = 321289TCS_HOTTRACK = 641290TCS_VERTICAL = 1281291TCS_TABS = 01292TCS_BUTTONS = 2561293TCS_SINGLELINE = 01294TCS_MULTILINE = 5121295TCS_RIGHTJUSTIFY = 01296TCS_FIXEDWIDTH = 10241297TCS_RAGGEDRIGHT = 20481298TCS_FOCUSONBUTTONDOWN = 40961299TCS_OWNERDRAWFIXED = 81921300TCS_TOOLTIPS = 163841301TCS_FOCUSNEVER = 327681302TCS_EX_FLATSEPARATORS = 11303TCS_EX_REGISTERDROP = 21304TCM_GETIMAGELIST = (TCM_FIRST + 2)1305TCM_SETIMAGELIST = (TCM_FIRST + 3)1306TCM_GETITEMCOUNT = (TCM_FIRST + 4)1307TCIF_TEXT = 11308TCIF_IMAGE = 21309TCIF_RTLREADING = 41310TCIF_PARAM = 81311TCIF_STATE = 161312TCIS_BUTTONPRESSED = 11313TCIS_HIGHLIGHTED = 21314TCM_GETITEMA = (TCM_FIRST + 5)1315TCM_GETITEMW = (TCM_FIRST + 60)1316TCM_GETITEM = TCM_GETITEMA1317TCM_SETITEMA = (TCM_FIRST + 6)1318TCM_SETITEMW = (TCM_FIRST + 61)1319TCM_SETITEM = TCM_SETITEMA1320TCM_INSERTITEMA = (TCM_FIRST + 7)1321TCM_INSERTITEMW = (TCM_FIRST + 62)1322TCM_INSERTITEM = TCM_INSERTITEMA1323TCM_DELETEITEM = (TCM_FIRST + 8)1324TCM_DELETEALLITEMS = (TCM_FIRST + 9)1325TCM_GETITEMRECT = (TCM_FIRST + 10)1326TCM_GETCURSEL = (TCM_FIRST + 11)1327TCM_SETCURSEL = (TCM_FIRST + 12)1328TCHT_NOWHERE = 11329TCHT_ONITEMICON = 21330TCHT_ONITEMLABEL = 41331TCHT_ONITEM = (TCHT_ONITEMICON | TCHT_ONITEMLABEL)1332TCM_HITTEST = (TCM_FIRST + 13)1333TCM_SETITEMEXTRA = (TCM_FIRST + 14)1334TCM_ADJUSTRECT = (TCM_FIRST + 40)1335TCM_SETITEMSIZE = (TCM_FIRST + 41)1336TCM_REMOVEIMAGE = (TCM_FIRST + 42)1337TCM_SETPADDING = (TCM_FIRST + 43)1338TCM_GETROWCOUNT = (TCM_FIRST + 44)1339TCM_GETTOOLTIPS = (TCM_FIRST + 45)1340TCM_SETTOOLTIPS = (TCM_FIRST + 46)1341TCM_GETCURFOCUS = (TCM_FIRST + 47)1342TCM_SETCURFOCUS = (TCM_FIRST + 48)1343TCM_SETMINTABWIDTH = (TCM_FIRST + 49)1344TCM_DESELECTALL = (TCM_FIRST + 50)1345TCM_HIGHLIGHTITEM = (TCM_FIRST + 51)1346TCM_SETEXTENDEDSTYLE = (TCM_FIRST + 52)  # optional wParam == mask1347TCM_GETEXTENDEDSTYLE = (TCM_FIRST + 53)1348TCM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1349TCM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1350TCN_KEYDOWN = (TCN_FIRST - 0)1351ANIMATE_CLASSA = "SysAnimate32"1352ANIMATE_CLASS = ANIMATE_CLASSA1353ACS_CENTER = 11354ACS_TRANSPARENT = 21355ACS_AUTOPLAY = 41356ACS_TIMER = 8  # don't use threads... use timers1357ACM_OPENA = (WM_USER+100)1358ACM_OPENW = (WM_USER+103)1359ACM_OPEN = ACM_OPENW1360ACM_OPEN = ACM_OPENA1361ACM_PLAY = (WM_USER+101)1362ACM_STOP = (WM_USER+102)1363ACN_START = 11364ACN_STOP = 21365MONTHCAL_CLASSA = "SysMonthCal32"1366MONTHCAL_CLASS = MONTHCAL_CLASSA1367MCM_FIRST = 40961368MCM_GETCURSEL = (MCM_FIRST + 1)1369MCM_SETCURSEL = (MCM_FIRST + 2)1370MCM_GETMAXSELCOUNT = (MCM_FIRST + 3)1371MCM_SETMAXSELCOUNT = (MCM_FIRST + 4)1372MCM_GETSELRANGE = (MCM_FIRST + 5)1373MCM_SETSELRANGE = (MCM_FIRST + 6)1374MCM_GETMONTHRANGE = (MCM_FIRST + 7)1375MCM_SETDAYSTATE = (MCM_FIRST + 8)1376MCM_GETMINREQRECT = (MCM_FIRST + 9)1377MCM_SETCOLOR = (MCM_FIRST + 10)1378MCM_GETCOLOR = (MCM_FIRST + 11)1379MCSC_BACKGROUND = 0   # the background color (between months)1380MCSC_TEXT = 1   # the dates1381MCSC_TITLEBK = 2   # background of the title1382MCSC_TITLETEXT = 31383MCSC_MONTHBK = 4   # background within the month cal1384MCSC_TRAILINGTEXT = 5   # the text color of header & trailing days1385MCM_SETTODAY = (MCM_FIRST + 12)1386MCM_GETTODAY = (MCM_FIRST + 13)1387MCM_HITTEST = (MCM_FIRST + 14)1388MCHT_TITLE = 655361389MCHT_CALENDAR = 1310721390MCHT_TODAYLINK = 1966081391MCHT_NEXT = 16777216   # these indicate that hitting1392MCHT_PREV = 33554432  # here will go to the next/prev month1393MCHT_NOWHERE = 01394MCHT_TITLEBK = (MCHT_TITLE)1395MCHT_TITLEMONTH = (MCHT_TITLE | 1)1396MCHT_TITLEYEAR = (MCHT_TITLE | 2)1397MCHT_TITLEBTNNEXT = (MCHT_TITLE | MCHT_NEXT | 3)1398MCHT_TITLEBTNPREV = (MCHT_TITLE | MCHT_PREV | 3)1399MCHT_CALENDARBK = (MCHT_CALENDAR)1400MCHT_CALENDARDATE = (MCHT_CALENDAR | 1)1401MCHT_CALENDARDATENEXT = (MCHT_CALENDARDATE | MCHT_NEXT)1402MCHT_CALENDARDATEPREV = (MCHT_CALENDARDATE | MCHT_PREV)1403MCHT_CALENDARDAY = (MCHT_CALENDAR | 2)1404MCHT_CALENDARWEEKNUM = (MCHT_CALENDAR | 3)1405MCM_SETFIRSTDAYOFWEEK = (MCM_FIRST + 15)1406MCM_GETFIRSTDAYOFWEEK = (MCM_FIRST + 16)1407MCM_GETRANGE = (MCM_FIRST + 17)1408MCM_SETRANGE = (MCM_FIRST + 18)1409MCM_GETMONTHDELTA = (MCM_FIRST + 19)1410MCM_SETMONTHDELTA = (MCM_FIRST + 20)1411MCM_GETMAXTODAYWIDTH = (MCM_FIRST + 21)1412MCM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1413MCM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1414MCN_SELCHANGE = (MCN_FIRST + 1)1415MCN_GETDAYSTATE = (MCN_FIRST + 3)1416MCN_SELECT = (MCN_FIRST + 4)1417MCS_DAYSTATE = 11418MCS_MULTISELECT = 21419MCS_WEEKNUMBERS = 41420MCS_NOTODAYCIRCLE = 81421MCS_NOTODAY = 161422MCS_NOTODAY = 81423GMR_VISIBLE = 0       # visible portion of display1424GMR_DAYSTATE = 1       # above plus the grayed out parts of1425DATETIMEPICK_CLASSA = "SysDateTimePick32"1426DATETIMEPICK_CLASS = DATETIMEPICK_CLASSA1427DTM_FIRST = 40961428DTM_GETSYSTEMTIME = (DTM_FIRST + 1)1429DTM_SETSYSTEMTIME = (DTM_FIRST + 2)1430DTM_GETRANGE = (DTM_FIRST + 3)1431DTM_SETRANGE = (DTM_FIRST + 4)1432DTM_SETFORMATA = (DTM_FIRST + 5)1433DTM_SETFORMATW = (DTM_FIRST + 50)1434DTM_SETFORMAT = DTM_SETFORMATW1435DTM_SETFORMAT = DTM_SETFORMATA1436DTM_SETMCCOLOR = (DTM_FIRST + 6)1437DTM_GETMCCOLOR = (DTM_FIRST + 7)1438DTM_GETMONTHCAL = (DTM_FIRST + 8)1439DTM_SETMCFONT = (DTM_FIRST + 9)1440DTM_GETMCFONT = (DTM_FIRST + 10)1441DTS_UPDOWN = 1 # use UPDOWN instead of MONTHCAL1442DTS_SHOWNONE = 2 # allow a NONE selection1443DTS_SHORTDATEFORMAT = 0 # use the short date format (app must forward WM_WININICHANGE messages)1444DTS_LONGDATEFORMAT = 4 # use the long date format (app must forward WM_WININICHANGE messages)1445DTS_TIMEFORMAT = 9 # use the time format (app must forward WM_WININICHANGE messages)1446DTS_APPCANPARSE = 16 # allow user entered strings (app MUST respond to DTN_USERSTRING)1447DTS_RIGHTALIGN = 32 # right-align popup instead of left-align it1448DTN_DATETIMECHANGE = (DTN_FIRST + 1) # the systemtime has changed1449DTN_USERSTRINGA = (DTN_FIRST + 2) # the user has entered a string1450DTN_USERSTRINGW = (DTN_FIRST + 15)1451DTN_USERSTRING = DTN_USERSTRINGW1452DTN_WMKEYDOWNA = (DTN_FIRST + 3) # modify keydown on app format field (X)1453DTN_WMKEYDOWNW = (DTN_FIRST + 16)1454DTN_WMKEYDOWN = DTN_WMKEYDOWNA1455DTN_FORMATA = (DTN_FIRST + 4) # query display for app format field (X)1456DTN_FORMATW = (DTN_FIRST + 17)1457DTN_FORMAT = DTN_FORMATA1458DTN_FORMATQUERYA = (DTN_FIRST + 5) # query formatting info for app format field (X)1459DTN_FORMATQUERYW = (DTN_FIRST + 18)1460DTN_FORMATQUERY = DTN_FORMATQUERYA1461DTN_DROPDOWN = (DTN_FIRST + 6) # MonthCal has dropped down1462DTN_CLOSEUP = (DTN_FIRST + 7) # MonthCal is popping up1463GDTR_MIN = 11464GDTR_MAX = 21465GDT_ERROR = -11466GDT_VALID = 01467GDT_NONE = 11468IPM_CLEARADDRESS = (WM_USER+100) # no parameters1469IPM_SETADDRESS = (WM_USER+101) # lparam = TCP/IP address1470IPM_GETADDRESS = (WM_USER+102) # lresult = # of non black fields.  lparam = LPDWORD for TCP/IP address1471IPM_SETRANGE = (WM_USER+103) # wparam = field, lparam = range1472IPM_SETFOCUS = (WM_USER+104) # wparam = field1473IPM_ISBLANK = (WM_USER+105) # no parameters1474WC_IPADDRESSA = "SysIPAddress32"1475WC_IPADDRESS = WC_IPADDRESSA1476IPN_FIELDCHANGED = (IPN_FIRST - 0)1477WC_PAGESCROLLERA = "SysPager"1478WC_PAGESCROLLER = WC_PAGESCROLLERA1479PGS_VERT = 01480PGS_HORZ = 11481PGS_AUTOSCROLL = 21482PGS_DRAGNDROP = 41483PGF_INVISIBLE = 0      # Scroll button is not visible1484PGF_NORMAL = 1      # Scroll button is in normal state1485PGF_GRAYED = 2      # Scroll button is in grayed state1486PGF_DEPRESSED = 4      # Scroll button is in depressed state1487PGF_HOT = 8      # Scroll button is in hot state1488PGB_TOPORLEFT = 01489PGB_BOTTOMORRIGHT = 11490PGM_SETCHILD = (PGM_FIRST + 1)  # lParam == hwnd1491PGM_RECALCSIZE = (PGM_FIRST + 2)1492PGM_FORWARDMOUSE = (PGM_FIRST + 3)1493PGM_SETBKCOLOR = (PGM_FIRST + 4)1494PGM_GETBKCOLOR = (PGM_FIRST + 5)1495PGM_SETBORDER = (PGM_FIRST + 6)1496PGM_GETBORDER = (PGM_FIRST + 7)1497PGM_SETPOS = (PGM_FIRST + 8)1498PGM_GETPOS = (PGM_FIRST + 9)1499PGM_SETBUTTONSIZE = (PGM_FIRST + 10)1500PGM_GETBUTTONSIZE = (PGM_FIRST + 11)1501PGM_GETBUTTONSTATE = (PGM_FIRST + 12)1502PGM_GETDROPTARGET = CCM_GETDROPTARGET1503PGN_SCROLL = (PGN_FIRST-1)1504PGF_SCROLLUP = 11505PGF_SCROLLDOWN = 21506PGF_SCROLLLEFT = 41507PGF_SCROLLRIGHT = 81508PGK_SHIFT = 11509PGK_CONTROL = 21510PGK_MENU = 41511PGN_CALCSIZE = (PGN_FIRST-2)1512PGF_CALCWIDTH = 11513PGF_CALCHEIGHT = 21514WC_NATIVEFONTCTLA = "NativeFontCtl"1515WC_NATIVEFONTCTL = WC_NATIVEFONTCTLA1516NFS_EDIT = 11517NFS_STATIC = 21518NFS_LISTCOMBO = 41519NFS_BUTTON = 81520NFS_ALL = 161521WM_MOUSEHOVER = 6731522WM_MOUSELEAVE = 6751523TME_HOVER = 11524TME_LEAVE = 21525TME_QUERY = 10737418241526TME_CANCEL = -21474836481527HOVER_DEFAULT = -11528WSB_PROP_CYVSCROLL = 0x000000011529WSB_PROP_CXHSCROLL = 0x000000021530WSB_PROP_CYHSCROLL = 0x000000041531WSB_PROP_CXVSCROLL = 0x000000081532WSB_PROP_CXHTHUMB = 0x000000101533WSB_PROP_CYVTHUMB = 0x000000201534WSB_PROP_VBKGCOLOR = 0x000000401535WSB_PROP_HBKGCOLOR = 0x000000801536WSB_PROP_VSTYLE = 0x000001001537WSB_PROP_HSTYLE = 0x000002001538WSB_PROP_WINSTYLE = 0x000004001539WSB_PROP_PALETTE = 0x000008001540WSB_PROP_MASK = 0x00000FFF1541FSB_FLAT_MODE = 21542FSB_ENCARTA_MODE = 11543FSB_REGULAR_MODE = 01544def INDEXTOOVERLAYMASK(i):1545    return i << 81546def INDEXTOSTATEIMAGEMASK(i):...

handle.py

Source:handle.py

...238        yield scrapy.Request("http://www.realax.cn/browse?type=dateissued", callback=self.parse_first)239        yield scrapy.Request("http://ir.sia.cn/browse?type=dateissued", callback=self.parse_first)240        yield scrapy.Request("http://ir.stlib.cn/browse?type=dateissued", callback=self.parse_first)241        # yield scrapy.Request("")242    def parse_first(self, response):243        self.log("Crawled %s %d" % (response.url, response.status), level=scrapy.log.INFO)244        if response.status / 100 != 2:245            return246        # ä»ç½é¡µä¸æåæå¤§ææ¡£æ°éï¼ç¨äºæé ç´¢å¼é¡µç¿»é¡µ247        ret = re.search("var totalItemCount = (\d+);", response.body)248        totalItemCount = 0249        if ret:250            totalItemCount = int(ret.groups()[0])251            self.log("Parse %s totalItemCount %d" % (response.url, totalItemCount), level=scrapy.log.INFO)252        else:253            self.log("Parse %s totalItemCount NULL" % (response.url), level=scrapy.log.INFO)254        offset = 0255        # site = get_url_site(response.url)256        # æé ç¿»é¡µå¹¶æå...

ProjectIVs.py

Source:ProjectIVs.py

1import numpy as np2from sklearn.linear_model import LogisticRegression3import statsmodels.formula.api as smf4import matplotlib5matplotlib.use("TkAgg")6from matplotlib import pyplot as plt7from scipy.special import expit8#matplotlib inline9from sklearn.linear_model import LassoCV10from sklearn.linear_model import Ridge11from sklearn.linear_model import LinearRegression12from sklearn.metrics.pairwise import manhattan_distances13import statsmodels.api as sm14from scipy import stats15from linearmodels.iv import IV2SLS16import os, glob17import pandas as pd18#merge sessions19path = "/Users/antoniomoshe/OneDrive - Technion/TECHNION PHD/Winter 2020/Casuality/Devouir_4/Sessions"20all_files = glob.glob(os.path.join(path, "sessions*.csv"))21sessions_month= pd.concat([pd.read_csv(f) for f in all_files])22#---23len(sessions_month) #502,03324#calculate average service time per hour25sessions_month.columns26sessions_AcceptedInvo = sessions_month[sessions_month[" outcome"] < 5]27(sessions_AcceptedInvo[" queue_sec"] == 0).sum() #all are known abandonments28sessions_AcceptedInvo = sessions_AcceptedInvo[sessions_AcceptedInvo[" queue_sec"] > 0]29len(sessions_AcceptedInvo) #2338330#get StartDay and StartHour for invitation acceptance time ------31#convert strings into date32sessions_AcceptedInvo[' invitation_submit_date'] =\33    pd.to_datetime(sessions_AcceptedInvo[' invitation_submit_date'],\34                                      format=' %d/%m/%Y %H:%M:%S')35sessions_AcceptedInvo[' Invitation_Acep_Day_of_week']=\36    sessions_AcceptedInvo[' invitation_submit_date'].dt.dayofweek37sessions_AcceptedInvo['Invitation_Acep_Hour']=\38    sessions_AcceptedInvo[' invitation_submit_date'].dt.hour39#Treatment Wait variable ---Ö±40#short wait is the treatment i would expect them to buy41sessions_AcceptedInvo['WaitTreatment'] = np.where(sessions_AcceptedInvo[' queue_sec']< 60, 1, 0)42#----43sessions_AcceptedInvo['Y'] =\44    np.where(sessions_AcceptedInvo[' conversion_time']> 0, 1, 0)45#calculate total service duration46timezero=sessions_AcceptedInvo[" invitation_submit_time"].min()47sessions_AcceptedInvo['dummy'] =\48sessions_AcceptedInvo[" end_time"]- \49    sessions_AcceptedInvo[" chat_start_time"]50#known abandonments have no service time51sessions_AcceptedInvo['Total_Service_Duration'] =\52    np.where(sessions_AcceptedInvo['dummy']>= timezero, 0, sessions_AcceptedInvo['dummy'])53sessions_AcceptedInvo.drop('dummy', inplace=True, axis=1)54#write csv55sessions_AcceptedInvo.to_csv("sessions_AcceptedInvo.csv")56#using closure time57timemax=sessions_AcceptedInvo[" invitation_submit_time"].max()58#time in seconds59numberofbins=round((timemax-timezero)/3600)60sessions_AcceptedInvo['dummy'] =\61sessions_AcceptedInvo[" end_time"]- \62    sessions_AcceptedInvo[" chat_start_time"]63#known abandonments have no service time64sessions_AcceptedInvo['Total_Service_Duration'] =\65    np.where(sessions_AcceptedInvo['dummy']>= timezero, 0, sessions_AcceptedInvo['dummy'])66sessions_AcceptedInvo.drop('dummy', inplace=True, axis=1)67sessions_AcceptedInvo['Total_Service_Duration'].mean()68( (sessions_AcceptedInvo[' outcome']==1).sum()+(sessions_AcceptedInvo[' outcome']==2).sum()) \69    /  (sessions_AcceptedInvo[' queue_sec'].sum())70#check with linear regression what influences outcome Y -----71#and also is data for other treatments72dataForRegression = pd.read_csv('DataForRegression.csv', index_col=0)73regresion_check = IV2SLS(dataForRegression.Y,\74                         dataForRegression[['queue_sec','invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\75                                             'browser','score','other_time','other_lines','other_number_words',\76                                            'inner_wait', 'visitor_duration',\77                                            'agent_duration', 'visitor_number_words', 'agent_number_words',\78                                        	'visitor_lines', 'agent_lines',	\79                                            'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg',	'first_sent',\80                                            'last_sent', 'id_rep_code', \81                                            'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \82                                            'NumberofAssigned', 'NumberofAssignedwhenAssigned', \83                                            'Rho_atarrival',\84                                             ]], None, None).fit(cov_type='unadjusted')85print(regresion_check)86regresion_check2 = IV2SLS(dataForRegression.Y,\87                         dataForRegression[['queue_sec','invite_type', 'engagement_skill','target_skill','score','other_time',\88                                            'agent_number_words',\89                                        	'visitor_lines', 'agent_lines',	\90                                             ]], None, None).fit(cov_type='unadjusted')91print(regresion_check2)92#-----93res_first = IV2SLS(dataFirstT.WaitTreatment,dataFirstT.iloc[:, 1:14], None, None).fit(cov_type='unadjusted')94res_first = IV2SLS(dataFirstT.WaitTreatment,dataFirstT.iloc[:, 1:14], None, None).fit(cov_type='unadjusted')95print(res_first)96res_second = IV2SLS(dataFirstT.Y,dataFirstT[['region','city','country','continent','user_os',\97                                             'browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour'\98                                             ]],\99                    dataFirstT.queue_sec*60, dataFirstT[['Rho_atarrival','invite_type',\100                                             'engagement_skill','target_skill'\101                                             ]]).fit(cov_type='unadjusted')102def covariance(x, y):103    # Finding the mean of the series x and y104    mean_x = sum(x)/float(len(x))105    mean_y = sum(y)/float(len(y))106    # Subtracting mean from the individual elements107    sub_x = [i - mean_x for i in x]108    sub_y = [i - mean_y for i in y]109    numerator = sum([sub_x[i]*sub_y[i] for i in range(len(sub_x))])110    denominator = len(x)-1111    cov = numerator/denominator112    return cov113covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)114#-0.12115corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\116        'engagement_skill','target_skill']].corr()117#FIRST TREATMENT118#data that we are using after fix in Matlab119dataFirstTi = pd.read_csv('DataForFirstTreatment.csv', index_col=0)120len(dataFirstTi) #23383121#get only service customers122dataFirstT=dataFirstTi123#len(dataFirstT) #13345124dataFirstT.WaitTreatment\125    = np.where(dataFirstT['queue_sec']< 30, 1, 0)126dataFirstT.WaitTreatment\127    = np.where(dataFirstT['queue_sec']< 60, 1, 0)128#dataFirstT['RhoBinary']\129#    = np.where(dataFirstT['Rho_atarrival']< 1, 0, 1)130dFT=dataFirstT[[\131'invite_type','region','city','engagement_skill','target_skill','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\132]]133modelP = LogisticRegression(max_iter=10000)134FirstX = dataFirstT[[\135                     'Invitation_Acep_Hour', \136                    'Rho_atarrival', 'invite_type', \137                    'engagement_skill','target_skill',\138                    'Invitation_Acep_Day_of_week',\139                    'score','region','city','country','continent',\140                    'user_os','browser'\141                     ]]142#Wait time143FirstY = dataFirstT.WaitTreatment144modelP.fit(FirstX, FirstY)145#reg2 = LinearRegression().fit(FirstX, FirstY)146First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))147treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')148cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')149plt.legend();150plt.xlabel('propensity score');151plt.ylabel('number of units');152plt.savefig('prop_score_wait_60000.pdf',format='pdf')153#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])154FirstIndex=\155np.where((First_Propensityscore[:, 1] < 0.59)& (First_Propensityscore[:, 1] > 0.41))156First_PropensityscoreB=\157First_Propensityscore[FirstIndex[0]]158treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')159cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')160plt.legend();161plt.xlabel('propensity score');162plt.ylabel('number of units');163plt.savefig('prop_score_wait_6000000_Last.pdf',format='pdf')164dataFirstT2=dFT.iloc[FirstIndex[0]]165len(dataFirstT2) #30 sec 10041 #60 sec 6,316166len(dataFirstT) #23383167ATE_IPW=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\168-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))169dataFirstT.groupby("Y")["queue_sec"].mean()170#Out[132]:171#Y172#0    70.728748173#1    48.358257174# ----- T-learner --------175#dataFirstTB = dataFirstT2[[\176#                     'Invitation_Acep_Hour', \177#                    'Rho_atarrival', 'invite_type', \178#                     'engagement_skill', 'target_skill',\179#                     'WaitTreatment', 'Y']]180dataFirstTB=dataFirstT2181dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]182dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)183dataFirstTB0x = dataFirstTB0.iloc[:, :-1]184dataFirstTB0Y = dataFirstTB0['Y']185dataFirstTBx = dataFirstTB.iloc[:, :-1]186dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)187dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]188dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)189dataFirstTB1x = dataFirstTB1.iloc[:, :-1]190dataFirstTB1Y = dataFirstTB1['Y']191model_0 = LassoCV()192model_1 = LassoCV()193model_0.fit(dataFirstTB0x, dataFirstTB0Y)194model_1.fit(dataFirstTB1x, dataFirstTB1Y)195prediction0 = model_0.predict(dataFirstTBx)196prediction1 = model_1.predict(dataFirstTBx)197ATE_TLearner = float((prediction1-prediction0).sum()/len(dataFirstTBx))198# ----- T-learner --------199# ------- S-Learner ---------200dataFirstTBX = dataFirstTB.iloc[:, :-1]201dataFirstTBY = dataFirstTB['Y']202modelS = Ridge()203modelS.fit(dataFirstTBX, dataFirstTBY)204#T=1205dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]206dummT1x = dataFirstTB.iloc[:, :-1]207predictionUn = modelS.predict(dummT1x)208dummT1["WaitTreatment"] = 0209dummT1x = dummT1.iloc[:, :-1]210predictionZe = modelS.predict(dummT1x)211ATE_SLearner = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))212# ------- S-Learner ---------213# ---------matching---------214#T=0215dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]216dummT0 = dummT0.drop(["WaitTreatment"], axis=1)217dummT0x = dummT0.iloc[:, :-1]218dummT0Y = dummT0['Y']219#T=1220dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]221dummT1 = dummT1.drop(["WaitTreatment"], axis=1)222dummT1x = dummT1.iloc[:, :-1]223dummT1Y = dummT1['Y']224dummT0V = dummT0.values225dummT1V = dummT1.values226dummT1XV=dummT1x.values227dummT0XV=dummT0x.values228ITE = []229for row in dummT1V:230    dif=abs(row[:-1]-dummT0V[:,:-1])231    #index232    #dif.argmin()233    #value234    distance=row[-1]-dummT0V[dif.argmin(),-1]235    ITE.append(distance)236#Sum of the Treated/number of Treated237ATE_Matchingb = float(sum(ITE))/float(len(dataFirstTB))238# ---------matching---------239#-----------IV's-------------240corralation=dataFirstTB.corr()241res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['invite_type','engagement_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Hour','Invitation_Acep_Day_of_week']],\242                    dataFirstTB.WaitTreatment, dataFirstTB.Rho_atarrival).fit(cov_type='unadjusted')243print(res_second)244covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)245#-0.12246corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\247        'engagement_skill','target_skill']].corr()248#-----------IV's-------------249# treatment two inner Wait ------250dataForRegression['IW_Treatment'] = np.where(dataForRegression['other_time']< 30, 1, 0)251#120 #130252modelP2 = LogisticRegression(max_iter=10000)253SecondX = dataForRegression[[\254                     'invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\255                                             'browser','score','other_number_words',\256                                            'visitor_duration',\257                                            'agent_duration', 'visitor_number_words', 'agent_number_words',\258                                            'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg',	'first_sent',\259                                            'last_sent', 'id_rep_code', \260                                            'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \261                                            'NumberofAssigned', 'NumberofAssignedwhenAssigned', \262                     ]]263SecondY = dataForRegression.IW_Treatment264modelP2.fit(SecondX, SecondY)265Second_Propensityscore = np.asarray(modelP2.predict_proba(SecondX))266treatment_plt2=plt.hist(Second_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')267cont_plt2=plt.hist(Second_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')268plt.legend();269plt.xlabel('propensity score');270plt.ylabel('number of units');271plt.savefig('prop_score_IW2_b.pdf',format='pdf')272ATE_IPW2=(((dataForRegression['IW_Treatment']*dataForRegression['Y'])/(Second_Propensityscore[:, 1])).sum())*(1/len(dataForRegression))\273-( ( ( (1-dataForRegression['IW_Treatment'])*dataForRegression['Y'] )/(Second_Propensityscore[:, 0]) ).sum())*(1/len(dataForRegression))274dataForRegression.groupby("Y")["other_time"].mean()275#Out[132]:276#Y277#0    139.905420278#1    237.277638279dataFirstTB = dataForRegression[[\280                     'invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\281                                             'browser','score','other_number_words',\282                                            'visitor_duration',\283                                            'agent_duration', 'visitor_number_words', 'agent_number_words',\284                                            'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg',	'first_sent',\285                                            'last_sent', 'id_rep_code', \286                                            'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \287                                            'NumberofAssigned', 'NumberofAssignedwhenAssigned','IW_Treatment','Y',\288                     ]]289dataFirstTB0 = dataFirstTB[dataFirstTB["IW_Treatment"] == 0]290dataFirstTB0 = dataFirstTB0.drop(["IW_Treatment"], axis=1)291dataFirstTB0x = dataFirstTB0.iloc[:, :-1]292dataFirstTB0Y = dataFirstTB0['Y']293dataFirstTBx = dataFirstTB.iloc[:, :-1]294dataFirstTBx = dataFirstTBx.drop(["IW_Treatment"], axis=1)295dataFirstTB1 = dataFirstTB[dataFirstTB["IW_Treatment"] == 1]296dataFirstTB1 = dataFirstTB1.drop(["IW_Treatment"], axis=1)297dataFirstTB1x = dataFirstTB1.iloc[:, :-1]298dataFirstTB1Y = dataFirstTB1['Y']299model_0 = LassoCV()300model_1 = LassoCV()301model_0.fit(dataFirstTB0x, dataFirstTB0Y)302model_1.fit(dataFirstTB1x, dataFirstTB1Y)303prediction0 = model_0.predict(dataFirstTBx)304prediction1 = model_1.predict(dataFirstTBx)305ATE_TLearner2 = float((prediction1-prediction0).sum()/len(dataFirstTBx))306#S-Learner307dataFirstTBX = dataFirstTB.iloc[:, :-1]308dataFirstTBY = dataFirstTB['Y']309modelS = Ridge()310modelS.fit(dataFirstTBX, dataFirstTBY)311#T=1312dummT1 = dataFirstTB[dataFirstTB["IW_Treatment"] == 1]313dummT1x = dataFirstTB.iloc[:, :-1]314predictionUn = modelS.predict(dummT1x)315dummT1["IW_Treatment"] = 0316dummT1x = dummT1.iloc[:, :-1]317predictionZe = modelS.predict(dummT1x)318ATE_SLearner2 = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))319# ---------matching---------320#T=0321dummT0 = dataFirstTB[dataFirstTB["IW_Treatment"] == 0]322dummT0 = dummT0.drop(["IW_Treatment"], axis=1)323dummT0x = dummT0.iloc[:, :-1]324dummT0Y = dummT0['Y']325#T=1326dummT1 = dataFirstTB[dataFirstTB["IW_Treatment"] == 1]327dummT1 = dummT1.drop(["IW_Treatment"], axis=1)328dummT1x = dummT1.iloc[:, :-1]329dummT1Y = dummT1['Y']330dummT0V = dummT0.values331dummT1V = dummT1.values332dummT1XV=dummT1x.values333dummT0XV=dummT0x.values334ITE = []335for row in dummT1V:336    dif=abs(row[:-1]-dummT0V[:,:-1])337    #index338    #dif.argmin()339    #value340    distance=row[-1]-dummT0V[dif.argmin(),-1]341    ITE.append(distance)342#Sum of the Treated/number of343ATE_Matchingb2 = float(sum(ITE))/float(len(dataFirstTB))344#--------345corralation=dataFirstTB.corr()346res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[[\347                     'invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\348                                             'browser','score','other_number_words',\349                                            'visitor_duration',\350                                            'agent_duration', 'visitor_number_words', 'agent_number_words',\351                                            'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg',	'first_sent',\352                                            'last_sent', 'id_rep_code', \353                                            'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \354                                            \355                     ]],\356                    dataFirstTB.IW_Treatment,dataFirstTB.NumberofAssigned).fit(cov_type='unadjusted')357print(res_second)358covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)359#-0.12360corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\361        'engagement_skill','target_skill']].corr()362#treatment 3 Invitation type363DataForThirdTreatment = pd.read_csv('DataForThirdTreatment.csv', index_col=0)364modelP3 = LogisticRegression(max_iter=10000)365ThirdX = DataForThirdTreatment[[\366                     'region','city','country','continent','user_os', 'browser', 'score',\367                    'Arrival_Day_of_week','Arrival_Hour',\368                    'LOS_in_website_before_invorRequest',\369                     ]]370ThirdY = DataForThirdTreatment.InvT371modelP3.fit(ThirdX, ThirdY)372Third_Propensityscore = np.asarray(modelP3.predict_proba(ThirdX))373treatment_plt3=plt.hist(Third_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')374cont_plt3=plt.hist(Third_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')375plt.legend();376plt.xlabel('propensity score');377plt.ylabel('number of units');378plt.savefig('prop_score_Inv.pdf',format='pdf')379ThirdIndex=\380np.where((Third_Propensityscore[:,1] < 0.8)& (Third_Propensityscore[:,1]  > 0.25))381Third_PropensityscoreB=\382Third_Propensityscore[ThirdIndex[0]]383treatment_plt3B=plt.hist(Third_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')384cont_plt3B=plt.hist(Third_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')385plt.legend();386plt.xlabel('propensity score');387plt.ylabel('number of units');388plt.savefig('prop_score_Inv2.pdf',format='pdf')389datathird=DataForThirdTreatment[[\390                     'region','city','country','continent','user_os', 'browser', 'score',\391                    'Arrival_Day_of_week','Arrival_Hour',\392                    'LOS_in_website_before_invorRequest',\393                     'InvT','Y']]394dataFirstT2=datathird.iloc[ThirdIndex[0]]395len(dataFirstT2) #6387396len(dataFirstT) #23383397ATE_IPW=(((dataFirstT2['InvT']*dataFirstT2['Y'])/(Third_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\398-( ( ( (1-dataFirstT2['InvT'])*dataFirstT2['Y'] )/(Third_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))399dataFirstT.groupby("Y")["queue_sec"].mean()400#Out[132]:401#Y402#0    70.728748403#1    48.358257404dataFirstTB0 = datathird[datathird["InvT"] == 0]405dataFirstTB0 = dataFirstTB0.drop(["InvT"], axis=1)406dataFirstTB0x = dataFirstTB0.iloc[:, :-1]407dataFirstTB0Y = dataFirstTB0['Y']408dataFirstTBx = datathird.iloc[:, :-1]409dataFirstTBx = dataFirstTBx.drop(["InvT"], axis=1)410dataFirstTB1 = datathird[datathird["InvT"] == 1]411dataFirstTB1 = dataFirstTB1.drop(["InvT"], axis=1)412dataFirstTB1x = dataFirstTB1.iloc[:, :-1]413dataFirstTB1Y = dataFirstTB1['Y']414model_0 = LinearRegression()415model_1 = LinearRegression()416model_0.fit(dataFirstTB0x, dataFirstTB0Y)417model_1.fit(dataFirstTB1x, dataFirstTB1Y)418prediction0 = model_0.predict(dataFirstTBx)419prediction1 = model_1.predict(dataFirstTBx)420ATE_TLearner3 = float((prediction1-prediction0).sum()/len(dataFirstTBx))421#S-Learner422dataFirstTBX = datathird.iloc[:, :-1]423dataFirstTBY = datathird['Y']424modelS = Ridge()425modelS.fit(dataFirstTBX, dataFirstTBY)426#T=1427dummT1 = datathird[datathird["InvT"] == 1]428dummT1x = datathird.iloc[:, :-1]429predictionUn = modelS.predict(dummT1x)430dummT1["InvT"] = 0431dummT1x = dummT1.iloc[:, :-1]432predictionZe = modelS.predict(dummT1x)...

OWTiii.py

Source:OWTiii.py

1import numpy as np2from sklearn.linear_model import LogisticRegression3import statsmodels.formula.api as smf4import matplotlib5matplotlib.use("TkAgg")6from matplotlib import pyplot as plt7from scipy.special import expit8#matplotlib inline9from sklearn.linear_model import LassoCV10from sklearn.linear_model import Ridge11from sklearn.linear_model import LinearRegression12import statsmodels.api as sm13from scipy import stats14from linearmodels.iv import IV2SLS15import os, glob16import pandas as pd17#FIRST TREATMENT iii18dataFirstT = pd.read_csv('DataForFirstTreatmentiii.csv', index_col=0)19len(dataFirstT)#12,34320dataFirstT.WaitTreatment\21    = np.where(dataFirstT['queue_sec']< 30, 1, 0)22dataFirstT.WaitTreatment\23    = np.where(dataFirstT['queue_sec']< 60, 1, 0)24dFT=dataFirstT[[\25'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\26]]27modelP = LogisticRegression(max_iter=10000)28FirstX = dataFirstT[[\29                     'Invitation_Acep_Hour', \30                    'Rho_atarrival', 'invite_type', \31                     'engagement_skill', 'target_skill',\32                    'Invitation_Acep_Day_of_week',\33                    'score','region','city','country','continent',\34                    'user_os','browser'\35                     ]]36#Wait time37FirstY = dataFirstT.WaitTreatment38modelP.fit(FirstX, FirstY)39reg2 = LinearRegression().fit(FirstX, FirstY)40First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))41treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')42cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')43plt.legend();44plt.xlabel('propensity score');45plt.ylabel('number of units');46plt.savefig('prop_score_wait30iii.pdf',format='pdf')47#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])48FirstIndex=\49np.where((First_Propensityscore[:, 1] < 0.55)& (First_Propensityscore[:, 1] > 0.46))50First_PropensityscoreB=\51First_Propensityscore[FirstIndex[0]]52treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')53cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')54plt.legend();55plt.xlabel('propensity score');56plt.ylabel('number of units');57plt.savefig('prop_score_wait302iii.pdf',format='pdf')58dataFirstT2=dFT.iloc[FirstIndex[0]]59len(dataFirstT2) #30 sec #1295 #60 sec #4,67560len(dataFirstT) #2338361ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\62-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))63dataFirstT.groupby("Y")["queue_sec"].mean()64#Out[132]:65#Y66#0    70.72874867#1    48.35825768# ----- T-learner --------69#dataFirstTB = dataFirstT2[[\70#                     'Invitation_Acep_Hour', \71#                    'Rho_atarrival', 'invite_type', \72#                     'engagement_skill', 'target_skill',\73#                     'WaitTreatment', 'Y']]74dataFirstTB=dataFirstT275dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]76dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)77dataFirstTB0x = dataFirstTB0.iloc[:, :-1]78dataFirstTB0Y = dataFirstTB0['Y']79dataFirstTBx = dataFirstTB.iloc[:, :-1]80dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)81dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]82dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)83dataFirstTB1x = dataFirstTB1.iloc[:, :-1]84dataFirstTB1Y = dataFirstTB1['Y']85model_0 = LassoCV()86model_1 = LassoCV()87model_0.fit(dataFirstTB0x, dataFirstTB0Y)88model_1.fit(dataFirstTB1x, dataFirstTB1Y)89prediction0 = model_0.predict(dataFirstTBx)90prediction1 = model_1.predict(dataFirstTBx)91ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))92# ----- T-learner --------93# ------- S-Learner ---------94dataFirstTBX = dataFirstTB.iloc[:, :-1]95dataFirstTBY = dataFirstTB['Y']96modelS = Ridge()97modelS.fit(dataFirstTBX, dataFirstTBY)98#T=199dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]100dummT1x = dataFirstTB.iloc[:, :-1]101predictionUn = modelS.predict(dummT1x)102dummT1["WaitTreatment"] = 0103dummT1x = dummT1.iloc[:, :-1]104predictionZe = modelS.predict(dummT1x)105ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))106# ------- S-Learner ---------107# ---------matching---------108#T=0109dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]110dummT0 = dummT0.drop(["WaitTreatment"], axis=1)111dummT0x = dummT0.iloc[:, :-1]112dummT0Y = dummT0['Y']113#T=1114dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]115dummT1 = dummT1.drop(["WaitTreatment"], axis=1)116dummT1x = dummT1.iloc[:, :-1]117dummT1Y = dummT1['Y']118dummT0V = dummT0.values119dummT1V = dummT1.values120dummT1XV=dummT1x.values121dummT0XV=dummT0x.values122ITE = []123for row in dummT1V:124    dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)125    #index126    #dif.argmin()127    #value128    distance=row[-1]-dummT0V[dif.argmin(),-1]129    ITE.append(distance)130#Sum of the Treated/number of Treated131ATE_Matchingbii = float(sum(ITE))/float(len(dataFirstTB))132# ---------matching---------133#-----------IV's-------------134corralation=dataFirstTB.corr()135res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['invite_type','engagement_skill','Rho_atarrival','region','city','country','continent','user_os','browser','score','Invitation_Acep_Hour']],\136                    dataFirstTB.WaitTreatment, dataFirstTB.Invitation_Acep_Day_of_week).fit(cov_type='unadjusted')137print(res_second)138covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)139#-0.12140corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\141        'engagement_skill','target_skill']].corr()142#-----------IV's-------------143#FIRST TREATMENT ii -30 sec144dataFirstT = pd.read_csv('DataForFirstTreatmentii.csv', index_col=0)145dataFirstT.WaitTreatment\146    = np.where(dataFirstT['queue_sec']< 30, 1, 0)147len(dataFirstT)#12,343148dFT=dataFirstT[[\149'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\150]]151modelP = LogisticRegression(max_iter=10000)152FirstX = dataFirstT[[\153                     'Invitation_Acep_Hour', \154                    'Rho_atarrival', 'invite_type', \155                     'engagement_skill', 'target_skill',\156                    'Invitation_Acep_Day_of_week',\157                    'score','region','city','country','continent',\158                    'user_os','browser'\159                     ]]160#Wait time161FirstY = dataFirstT.iloc[:, -2]162modelP.fit(FirstX, FirstY)163reg2 = LinearRegression().fit(FirstX, FirstY)164First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))165treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')166cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')167plt.legend();168plt.xlabel('propensity score');169plt.ylabel('number of units');170plt.savefig('prop_score_waiti2.pdf',format='pdf')171#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])172FirstIndex=\173np.where((First_Propensityscore[:, 1] < 0.60)& (First_Propensityscore[:, 1] > 0.45))174First_PropensityscoreB=\175First_Propensityscore[FirstIndex[0]]176treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')177cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')178plt.legend();179plt.xlabel('propensity score');180plt.ylabel('number of units');181plt.savefig('prop_score_wait2ii.pdf',format='pdf')182dataFirstT2=dFT.iloc[FirstIndex[0]]183len(dataFirstT2) #4,667184len(dataFirstT) #23383185ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\186-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))187dataFirstT.groupby("Y")["queue_sec"].mean()188#Out[132]:189#Y190#0    70.728748191#1    48.358257192# ----- T-learner --------193#dataFirstTB = dataFirstT2[[\194#                     'Invitation_Acep_Hour', \195#                    'Rho_atarrival', 'invite_type', \196#                     'engagement_skill', 'target_skill',\197#                     'WaitTreatment', 'Y']]198dataFirstTB=dataFirstT2199dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]200dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)201dataFirstTB0x = dataFirstTB0.iloc[:, :-1]202dataFirstTB0Y = dataFirstTB0['Y']203dataFirstTBx = dataFirstTB.iloc[:, :-1]204dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)205dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]206dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)207dataFirstTB1x = dataFirstTB1.iloc[:, :-1]208dataFirstTB1Y = dataFirstTB1['Y']209model_0 = LassoCV()210model_1 = LassoCV()211model_0.fit(dataFirstTB0x, dataFirstTB0Y)212model_1.fit(dataFirstTB1x, dataFirstTB1Y)213prediction0 = model_0.predict(dataFirstTBx)214prediction1 = model_1.predict(dataFirstTBx)215ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))216# ----- T-learner --------217# ------- S-Learner ---------218dataFirstTBX = dataFirstTB.iloc[:, :-1]219dataFirstTBY = dataFirstTB['Y']220modelS = Ridge()221modelS.fit(dataFirstTBX, dataFirstTBY)222#T=1223dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]224dummT1x = dataFirstTB.iloc[:, :-1]225predictionUn = modelS.predict(dummT1x)226dummT1["WaitTreatment"] = 0227dummT1x = dummT1.iloc[:, :-1]228predictionZe = modelS.predict(dummT1x)229ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))230# ------- S-Learner ---------231# ---------matching---------232#T=0233dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]234dummT0 = dummT0.drop(["WaitTreatment"], axis=1)235dummT0x = dummT0.iloc[:, :-1]236dummT0Y = dummT0['Y']237#T=1238dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]239dummT1 = dummT1.drop(["WaitTreatment"], axis=1)240dummT1x = dummT1.iloc[:, :-1]241dummT1Y = dummT1['Y']242dummT0V = dummT0.values243dummT1V = dummT1.values244dummT1XV=dummT1x.values245dummT0XV=dummT0x.values246ITE = []247for row in dummT1V:248    dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)249    #index250    #dif.argmin()251    #value252    distance=row[-1]-dummT0V[dif.argmin(),-1]253    ITE.append(distance)254#Sum of the Treated/number of Treated255ATT_Matchingbii = float(sum(ITE))/float(len(dummT1V))256# ---------matching---------257#-----------IV's-------------258corralation=dataFirstTB.corr()259res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour']],\260                    dataFirstTB.WaitTreatment, dataFirstTB.Rho_atarrival).fit(cov_type='unadjusted')261print(res_second)262covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)263#-0.12264corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\265        'engagement_skill','target_skill']].corr()...

OWTii.py

Source:OWTii.py

1import numpy as np2from sklearn.linear_model import LogisticRegression3import statsmodels.formula.api as smf4import matplotlib5matplotlib.use("TkAgg")6from matplotlib import pyplot as plt7from scipy.special import expit8#matplotlib inline9from sklearn.linear_model import LassoCV10from sklearn.linear_model import Ridge11from sklearn.linear_model import LinearRegression12import statsmodels.api as sm13from scipy import stats14from linearmodels.iv import IV2SLS15import os, glob16import pandas as pd17#FIRST TREATMENT ii18dataFirstT = pd.read_csv('DataForFirstTreatmentii.csv', index_col=0)19len(dataFirstT)#12,34320dataFirstT.WaitTreatment\21    = np.where(dataFirstT['queue_sec']< 30, 1, 0)22dataFirstT.WaitTreatment\23    = np.where(dataFirstT['queue_sec']< 60, 1, 0)24dFT=dataFirstT[[\25'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\26]]27modelP = LogisticRegression(max_iter=10000)28FirstX = dataFirstT[[\29                     'Invitation_Acep_Hour', \30                    'Rho_atarrival', 'invite_type', \31                     'engagement_skill', 'target_skill',\32                    'Invitation_Acep_Day_of_week',\33                    'score','region','city','country','continent',\34                    'user_os','browser'\35                     ]]36#Wait time37FirstY = dataFirstT.iloc[:, -2]38modelP.fit(FirstX, FirstY)39reg2 = LinearRegression().fit(FirstX, FirstY)40First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))41treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')42cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')43plt.legend();44plt.xlabel('propensity score');45plt.ylabel('number of units');46plt.savefig('prop_score_wait60ii.pdf',format='pdf')47#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])48FirstIndex=\49np.where((First_Propensityscore[:, 1] < 0.55)& (First_Propensityscore[:, 1] > 0.46))50First_PropensityscoreB=\51First_Propensityscore[FirstIndex[0]]52treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')53cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')54plt.legend();55plt.xlabel('propensity score');56plt.ylabel('number of units');57plt.savefig('prop_score_wait602ii.pdf',format='pdf')58dataFirstT2=dFT.iloc[FirstIndex[0]]59len(dataFirstT2) #30 sec #1295 #60 sec #4,67560len(dataFirstT) #2338361ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\62-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))63dataFirstT.groupby("Y")["queue_sec"].mean()64#Out[132]:65#Y66#0    70.72874867#1    48.35825768# ----- T-learner --------69#dataFirstTB = dataFirstT2[[\70#                     'Invitation_Acep_Hour', \71#                    'Rho_atarrival', 'invite_type', \72#                     'engagement_skill', 'target_skill',\73#                     'WaitTreatment', 'Y']]74dataFirstTB=dataFirstT275dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]76dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)77dataFirstTB0x = dataFirstTB0.iloc[:, :-1]78dataFirstTB0Y = dataFirstTB0['Y']79dataFirstTBx = dataFirstTB.iloc[:, :-1]80dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)81dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]82dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)83dataFirstTB1x = dataFirstTB1.iloc[:, :-1]84dataFirstTB1Y = dataFirstTB1['Y']85model_0 = LassoCV()86model_1 = LassoCV()87model_0.fit(dataFirstTB0x, dataFirstTB0Y)88model_1.fit(dataFirstTB1x, dataFirstTB1Y)89prediction0 = model_0.predict(dataFirstTBx)90prediction1 = model_1.predict(dataFirstTBx)91ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))92# ----- T-learner --------93# ------- S-Learner ---------94dataFirstTBX = dataFirstTB.iloc[:, :-1]95dataFirstTBY = dataFirstTB['Y']96modelS = Ridge()97modelS.fit(dataFirstTBX, dataFirstTBY)98#T=199dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]100dummT1x = dataFirstTB.iloc[:, :-1]101predictionUn = modelS.predict(dummT1x)102dummT1["WaitTreatment"] = 0103dummT1x = dummT1.iloc[:, :-1]104predictionZe = modelS.predict(dummT1x)105ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))106# ------- S-Learner ---------107# ---------matching---------108#T=0109dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]110dummT0 = dummT0.drop(["WaitTreatment"], axis=1)111dummT0x = dummT0.iloc[:, :-1]112dummT0Y = dummT0['Y']113#T=1114dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]115dummT1 = dummT1.drop(["WaitTreatment"], axis=1)116dummT1x = dummT1.iloc[:, :-1]117dummT1Y = dummT1['Y']118dummT0V = dummT0.values119dummT1V = dummT1.values120dummT1XV=dummT1x.values121dummT0XV=dummT0x.values122ITE = []123for row in dummT1V:124    dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)125    #index126    #dif.argmin()127    #value128    distance=row[-1]-dummT0V[dif.argmin(),-1]129    ITE.append(distance)130#Sum of the Treated/number of Treated131ATE_Matchingbii = float(sum(ITE))/float(len(dataFirstTB))132# ---------matching---------133#-----------IV's-------------134corralation=dataFirstTB.corr()135res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['invite_type','engagement_skill','Rho_atarrival','region','city','country','continent','user_os','browser','score','Invitation_Acep_Hour']],\136                    dataFirstTB.WaitTreatment, dataFirstTB.Invitation_Acep_Day_of_week).fit(cov_type='unadjusted')137print(res_second)138covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)139#-0.12140corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\141        'engagement_skill','target_skill']].corr()142#-----------IV's-------------143#FIRST TREATMENT ii -30 sec144dataFirstT = pd.read_csv('DataForFirstTreatmentii.csv', index_col=0)145dataFirstT.WaitTreatment\146    = np.where(dataFirstT['queue_sec']< 30, 1, 0)147len(dataFirstT)#12,343148dFT=dataFirstT[[\149'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\150]]151modelP = LogisticRegression(max_iter=10000)152FirstX = dataFirstT[[\153                     'Invitation_Acep_Hour', \154                    'Rho_atarrival', 'invite_type', \155                     'engagement_skill', 'target_skill',\156                    'Invitation_Acep_Day_of_week',\157                    'score','region','city','country','continent',\158                    'user_os','browser'\159                     ]]160#Wait time161FirstY = dataFirstT.iloc[:, -2]162modelP.fit(FirstX, FirstY)163reg2 = LinearRegression().fit(FirstX, FirstY)164First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))165treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')166cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')167plt.legend();168plt.xlabel('propensity score');169plt.ylabel('number of units');170plt.savefig('prop_score_waiti2.pdf',format='pdf')171#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])172FirstIndex=\173np.where((First_Propensityscore[:, 1] < 0.60)& (First_Propensityscore[:, 1] > 0.45))174First_PropensityscoreB=\175First_Propensityscore[FirstIndex[0]]176treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')177cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')178plt.legend();179plt.xlabel('propensity score');180plt.ylabel('number of units');181plt.savefig('prop_score_wait2ii.pdf',format='pdf')182dataFirstT2=dFT.iloc[FirstIndex[0]]183len(dataFirstT2) #4,667184len(dataFirstT) #23383185ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\186-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))187dataFirstT.groupby("Y")["queue_sec"].mean()188#Out[132]:189#Y190#0    70.728748191#1    48.358257192# ----- T-learner --------193#dataFirstTB = dataFirstT2[[\194#                     'Invitation_Acep_Hour', \195#                    'Rho_atarrival', 'invite_type', \196#                     'engagement_skill', 'target_skill',\197#                     'WaitTreatment', 'Y']]198dataFirstTB=dataFirstT2199dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]200dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)201dataFirstTB0x = dataFirstTB0.iloc[:, :-1]202dataFirstTB0Y = dataFirstTB0['Y']203dataFirstTBx = dataFirstTB.iloc[:, :-1]204dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)205dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]206dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)207dataFirstTB1x = dataFirstTB1.iloc[:, :-1]208dataFirstTB1Y = dataFirstTB1['Y']209model_0 = LassoCV()210model_1 = LassoCV()211model_0.fit(dataFirstTB0x, dataFirstTB0Y)212model_1.fit(dataFirstTB1x, dataFirstTB1Y)213prediction0 = model_0.predict(dataFirstTBx)214prediction1 = model_1.predict(dataFirstTBx)215ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))216# ----- T-learner --------217# ------- S-Learner ---------218dataFirstTBX = dataFirstTB.iloc[:, :-1]219dataFirstTBY = dataFirstTB['Y']220modelS = Ridge()221modelS.fit(dataFirstTBX, dataFirstTBY)222#T=1223dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]224dummT1x = dataFirstTB.iloc[:, :-1]225predictionUn = modelS.predict(dummT1x)226dummT1["WaitTreatment"] = 0227dummT1x = dummT1.iloc[:, :-1]228predictionZe = modelS.predict(dummT1x)229ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))230# ------- S-Learner ---------231# ---------matching---------232#T=0233dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]234dummT0 = dummT0.drop(["WaitTreatment"], axis=1)235dummT0x = dummT0.iloc[:, :-1]236dummT0Y = dummT0['Y']237#T=1238dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]239dummT1 = dummT1.drop(["WaitTreatment"], axis=1)240dummT1x = dummT1.iloc[:, :-1]241dummT1Y = dummT1['Y']242dummT0V = dummT0.values243dummT1V = dummT1.values244dummT1XV=dummT1x.values245dummT0XV=dummT0x.values246ITE = []247for row in dummT1V:248    dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)249    #index250    #dif.argmin()251    #value252    distance=row[-1]-dummT0V[dif.argmin(),-1]253    ITE.append(distance)254#Sum of the Treated/number of Treated255ATT_Matchingbii = float(sum(ITE))/float(len(dummT1V))256# ---------matching---------257#-----------IV's-------------258corralation=dataFirstTB.corr()259res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour']],\260                    dataFirstTB.WaitTreatment, dataFirstTB.Rho_atarrival).fit(cov_type='unadjusted')261print(res_second)262covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)263#-0.12264corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\265        'engagement_skill','target_skill']].corr()...

parse_table.py

Source:parse_table.py

1from ll1 import log2'''LL(1) Parse Table3# Constructs an LL(1) parse table from a given Grammer.4    The table is map of nonterminal maps. Table = { nonterminal : { first, rule} }5    Each nonterminal is mapped to a rule by one of it's firsts.6    Allows rules access as table[terminal][nonterminal]7    If terminal is not in table[nonterminal] then reject terminal8    9    Remember, if two distinct rules are mapped from table[nonterminal][terminal]10         then the grammer is not LL(1) !!11# Grammer : map with each nonterminal key is mapped to a list of production rules12# Rules : an in order list of terminals and nonterminals.13 14# Terminals : symbols that are not mapped to production rules15# Epsilon  :  a special terminal that allows nonterminal to terminal  16  17# Symbols : the tag used to identify token tags(terminals) and rule tags (nonterminals)18 19# First set : the set of possible first terminals that match with a production rule for a nonterminal.20              If a terminal parsed is a e, and e is also within the first set of S.21              For each rule for a nonterminal, each rule's first symbol22              If the first symbol is a terminal then add it to first set23              If the first symbol is a nonterminal and not equal to first set's nonterminal24                then union the nonterminals first into first set25# Follow set : the set of terminals that follow a nonterminal symbol in any given production rule.26                For a nonterminal in rule r.27                If the nonterminal has nothing following it or follows EPSILON in rule, 28                    then union the follow set of the nonterminal that is mapped to rule r,29                    the parent symbol, if it is not the same.30                If the following symbol in rule is a nonterminal, 31                    then union the follow set with the following nonterminal's first set. 32                    If one of the firsts is epsilon, then union the follow set of parent symbol 33                By default START is followed by EOI34# Example Grammer and Table Constructed35    Nonterminals : S, E36    Terminals    : a, +, EPSILON37    Grammer = 38    {39        E : [[T, A]],40        A : [[+, T, A],41             [EPSILON]42            ]43        T : [[a]]44    }45    46    first_set(E) = [a]              # first set of T47    first_set(A) = [+, EPSILON]     # + and  epsilon are terminal so add 48    first_set(T) = [a]              # a is terminal so add49    follow_set(E) = [EOI]           # is start so default follows EOI   50    follow_set(A) = [EOI]           # A follows nothing in each rule, so union the follow of rules symbol that is not A,51                                      so follow set of E  52    follow_set(T) = [+, EOI]        # T is followed by A, so follow first set of A, however, epsilon is in first so union53                                    # follow set of A54    Table =55    {56        E : { a : [T, A], }57        A : { + : [T, A], EOI : [EPSILON] }58        T : { a : [a] }59    }60        | a   |  +  | EOI61    ----------------------62    E   | T A |     |63    A   |     | T A | EPSILON64    T   |  a  |     |65'''66class ll1_parse_table(dict): 67    # Inherits dict to allow access to rule for table[nonterminal][terminal]68    def __init__(self, grammer, start_sym, epsilon_sym):69        self.grammer = grammer70        #predefined parsing symbols each self.grammer must use71        self.START = start_sym72        self.EPSILON = epsilon_sym73        self.EOI = 'EOI'# default end of input symbol74        self.construct()75    76    #returns string representation of table77    def __repr__(self):78        table = 'Parse Table\n'79        for symbol in self.keys():80            table += symbol + ':\n'81            for first in self[symbol].keys():82                rule = ''83                line = '\t' + '{:<10}'.format(first+ ':')84                for r in self[symbol][first]:85                    rule += r + ' ' 86                table +=  line + '[' + rule + ']\n'87        return table88    # Constructs the LL(1) parse table grammers rules. 89    def construct(self):90        for symbol in self.grammer.keys():91            self[symbol] = {} # allocate map for symbol92            # find first and follow set93            firsts = self.first_set(symbol)94            follows = self.follow_set(symbol)            95            for first in firsts:96                # add rule whose first matches first of rule97                for rule in self.grammer[symbol]: # for each rule in symbols rule list98                    if first in self.first_set(rule[0]): # if the first matches the first of the rule99                        if first not in self[symbol]: # if symbol, first rule has not been added 100                            if first == self.EPSILON: # get follow of parent symbol if first is epsilon101                                # for each follow, assign epsilon102                                for follow in follows:103                                    self[symbol][follow] = [first] # make epsilon symbol a rule (list)104                            else:105                                self[symbol][first] = rule106                        else:107                            log.error('TABLE: Duplicate Rules for ' + symbol + ',' + first)108    # returns list of possible symbols that are the first symbol in the rule109    def first_set(self, symbol):110        firsts = []111        if symbol in self.grammer: ## if symbols rules exits112            rule_set = self.grammer[symbol]113            for rule in rule_set: # check each rule. Rule is list of inorder symbols114                # if rule's first symbol is not the same as root. add the first set 115                #   of that symbol to this symbols first set116                if len(rule) > 0 and (rule[0] != symbol): 117                    firsts  = firsts + self.first_set(rule[0]) # concat first sets118        else: # no rules119            # the symbol is a terminal, so return it as is120            firsts.append(symbol)121        return firsts122    #returns list of symbols that can follow the given symbol123    def follow_set(self, symbol):124        follows  = [] # set of follow symbols125        # End of input follows start by default 126        if symbol == self.START: 127            follows.append(self.EOI) 128        ''' search for symbol in every rule of every other symbol and find next possible129            terminal symbol that can follow current symbol130            iterate through rule set from each symbol'''131        for rule_symbol in self.grammer.keys():  132            # iterate through each rule in rule set for each symbol133            for rule in self.grammer[rule_symbol]:134                i = 0   # index of the symbol in the rule135                # check every symbol in rule136                while i <  len(rule):   137                    if rule[i] == symbol:   # if rule symbol is equal to symbol138                        if i+1 < len(rule): # try to get the next following symbol in the rule139                            if rule[i+1] not in self.grammer:    # if next symbol is terminal 140                                    follows.append(rule[i+1])   # append rule symbol to follow set141                            else:   # else if next symbol is nonterminal, union nonterminal's firsts into follow142                                for first in self.first_set(rule[i+1]):  # for each first in the next symbols rule set 143                                    if first == self.EPSILON:    # if the first is epsilon144                                        # union symbol's follow set with the follow set of the parent rule symbol                                    145                                        follows = follows + [x for x in iter(self.follow_set(rule_symbol)) if x not in follows]146                                    elif first not in follows:  # if not epsilon and doesnt already exist in follow set147                                        follows.append(first)   # append symbol to follow set148                        # if at the end of rule(no next symbol), 149                        elif rule_symbol != symbol:#and parent symbol is not the same as current symbol150                            #union symbol's follow set with the follow set of the parent rule symbol151                            follows = follows + [x for x in iter(self.follow_set(rule_symbol)) if x not in follows]152                    i = i + 1 ...

test_replace_child.py

Source:test_replace_child.py

1from typing import Tuple2from hypothesis import given3from tests.utils import (BoundPortedNodesPair,4                         are_bound_ported_nodes_equal,5                         are_bound_ported_nodes_sequences_equal)6from . import strategies7@given(strategies.nested_nodes_with_children_pairs, strategies.nodes_pairs)8def test_basic(first_nodes_with_children_pair: Tuple[BoundPortedNodesPair,9                                                     BoundPortedNodesPair],10               second_nodes_pair: BoundPortedNodesPair) -> None:11    first_nodes, first_children = first_nodes_with_children_pair12    first_bound, first_ported = first_nodes13    first_bound_child, first_ported_child = first_children14    second_bound, second_ported = second_nodes_pair15    first_bound.replace_child(first_bound_child, second_bound)16    first_ported.replace_child(first_ported_child, second_ported)17    assert are_bound_ported_nodes_sequences_equal(first_bound_child.parents,18                                                  first_ported_child.parents)19    assert are_bound_ported_nodes_sequences_equal(second_bound.parents,20                                                  second_ported.parents)...

Using AI Code Generation

1const { Verifier } = require('@pact-foundation/pact')2const path = require('path')3const opts = {4  pactUrls: [path.resolve(process.cwd(), 'pacts', 'Consumer-Provider.json')],5}6new Verifier().verifyProvider(opts).then(output => {7  console.log('Pact Verification Complete!')8  console.log(output)9})10{ providerApplicationVersion: '1.0.0',11   [ { success: true,12        [ { state: 'test state',13             { method: 'GET',14               headers: { Accept: 'application/json' } },15             { status: 200,16               headers: { 'Content-Type': 'application/json' },17                [ { id: 1,

Using AI Code Generation

1var Pact = require('pact-foundation-pact-node');2var pact = Pact();3  .setup()4  .then(function(){5  })6  .then(function(){7    pact.finalize();8  })9  .catch(function(e) {10    console.log(e);11  });12    at ChildProcess.<anonymous> (/Users/andrea/Projects/pact-node-test/node_modules/pact-node/src/pact-node.js:100:19)13    at emitTwo (events.js:87:13)14    at ChildProcess.emit (events.js:172:7)15    at Process.ChildProcess._handle.onexit (internal/child_process.js:200:12)16    at ChildProcess.<anonymous> (/Users/andrea/Projects/pact-node-test/node_modules/pact-node/src/pact-node.js:100:19)17    at emitTwo (events.js:87:13)18    at ChildProcess.emit (events.js:172:7)19    at Process.ChildProcess._handle.onexit (internal/child_process.js:200

Automation Testing Tutorials

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