code
stringlengths 22
1.05M
| apis
listlengths 1
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| extract_api
stringlengths 75
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import seaborn as sns
from cleu.embeddings import Embedding,Embeddings
from cleu.utils import utils
import matplotlib.pyplot as plt
import altair as alt
import pandas as pd
import numpy as np
def plot_similarity(list_embedding_src,list_embedding_tgt,distance_function='cosine',csls_k=5):
assert(len(list_embedding_src) > 0 and len(list_embedding_tgt) >0)
assert (len(list_embedding_src) == len(list_embedding_tgt))
words_1 = list(map(lambda embedding: embedding.word , list_embedding_src))
words_2 = list(map(lambda embedding: embedding.word , list_embedding_tgt))
sns.set(rc={'figure.figsize':(13.7,10.27), 'image.cmap' : "Blues" })
similarity_matrix = utils.create_similarity_matrix(list_embedding_src,list_embedding_tgt,distance_function,csls_k)
plot_title = 'Cosine' if distance_function =='cosine' else 'Cross-domain Similarity Local Scaling'
ax = sns.heatmap(similarity_matrix ,annot=True,xticklabels=words_2, yticklabels=words_1)
ax.set_title(plot_title)
ax.xaxis.set_ticks_position('top')
plt.show()
def plot_embeddings_2d(list_embeddings,width,height,dimensionality_reduction='umap'):
"""Plot embeddings that are reduced to 2d
Args:
list_embeddings (list[Embeddings]): List of Embeddings to plot
width (int): Plot width
height (int): Plot height
dimensionality_reduction (str, optional): ('umap','pca,'tsne'). Defaults to 'umap'.
"""
assert(len(list_embeddings) >0)
embeddings_matrix = utils.combine_embeddings_matrix(list_embeddings)
list_word = utils.combine_embeddings_word(list_embeddings)
color = utils.combine_embeddings_language(list_embeddings)
# assert(len(list_word) <=5000)
embeddings_2d = utils.reduce_dimensionality_2d(embeddings_matrix,dimensionality_reduction)
plot_df = pd.DataFrame(
{
"X-Axis": embeddings_2d[:,0],
"Y-Axis": embeddings_2d[:,1],
"name": list_word,
"Language" : color
}
)
plot = alt.Chart(plot_df).mark_circle(size=60).encode(
x='X-Axis',
y='Y-Axis',
tooltip=["name"],
color= 'Language',
).properties(
width=width,
height=height
)
text = (
alt.Chart(plot_df)
.mark_text(dx=-15, dy=3, color="black")
.encode(
x="X-Axis",
y="Y-Axis",
text="name",
)
)
plot =plot.interactive()
plot = plot + text
return plot
def plot_embeddings_neighbours(
list_embedding,
list_tgt_embeddings,
width,
height,
dimensionality_reduction='umap',
k=5,
distance_function='cosine',
csls_k=10
):
"""Accept list of Embedding as query and plot its target nearest neighbours
Args:
list_embedding (list[Embedding]): List of Embedding as Query
list_tgt_embeddings (list[Embeddings]): List of Embeddings as Target
width (int): Plot width
height (int): Plot height
dimensionality_reduction (str, optional): ('umap','pca,'tsne'). Defaults to 'umap'.
k (int, optional): Number of neighbours returned. Defaults to 5.
distance_function (str, optional): Distance function that will be used to measure two different embedding vectors. csls or cosine(Default).
csls_k (int, optional): Number of neighbours that will be used for CSLS mean similarity. Defaults to 10.
"""
# assert((len(list_tgt_embeddings) >0), "Target Embeddings must not be empty")
topic = []
lang = []
neighbours_embeddings = []
for tgt_embeddings in list_tgt_embeddings:
for embedding in list_embedding:
similarities,neighbours = tgt_embeddings.get_nearest_neighbours(embedding,k,distance_function,csls_k)
neighbours_embeddings = neighbours_embeddings + neighbours
topic = topic + ([embedding.word] * len(neighbours) )
lang = lang + ([tgt_embeddings.lang] * len(neighbours))
embeddings_matrix = list(map(lambda embedding: embedding.vector,neighbours_embeddings))
list_word = list(map(lambda embedding: embedding.word,neighbours_embeddings))
color = topic
assert(len(list_word) <=5000)
embeddings_2d = utils.reduce_dimensionality_2d(embeddings_matrix,dimensionality_reduction)
plot_df = pd.DataFrame(
{
"X-Axis": embeddings_2d[:,0],
"Y-Axis": embeddings_2d[:,1],
"name": list_word,
"language" : lang,
"Topic" : color
}
)
plot = alt.Chart(plot_df).mark_circle(size=60).encode(
x='X-Axis',
y='Y-Axis',
tooltip=["name","language"],
color= 'Topic',
).properties(
width=width,
height=height
)
text = (
alt.Chart(plot_df)
.mark_text(dx=-15, dy=3, color="black")
.encode(
x="X-Axis",
y="Y-Axis",
text="name",
)
)
plot =plot.interactive()
plot = plot + text
return plot
|
[
"pandas.DataFrame",
"seaborn.heatmap",
"matplotlib.pyplot.show",
"altair.Chart",
"cleu.utils.utils.combine_embeddings_language",
"cleu.utils.utils.combine_embeddings_word",
"cleu.utils.utils.reduce_dimensionality_2d",
"cleu.utils.utils.combine_embeddings_matrix",
"seaborn.set",
"cleu.utils.utils.create_similarity_matrix"
] |
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|
from django import template
from django.conf import settings
import seed_control_interface
import re
register = template.Library()
@register.simple_tag
def current_version():
return seed_control_interface.__version__
@register.filter
def unslug(value):
return re.sub('[^0-9a-zA-Z]+', ' ', value).strip()
@register.filter
def is_dict(value):
return isinstance(value, dict)
@register.filter
def get_identity(obj):
return obj.get(settings.IDENTITY_FIELD)
def _get_label_for_key(choices, key):
for choice_key, label in choices:
if choice_key == key:
return label
@register.filter
def get_stage(obj):
stage = obj.get(settings.STAGE_FIELD)
return _get_label_for_key(settings.STAGES, stage)
@register.filter
def get_action(obj):
return _get_label_for_key(settings.ACTIONS, obj.get('action'))
@register.filter
def get_user(obj, users):
return users[str(obj["action_by"])]
|
[
"django.template.Library",
"re.sub"
] |
[((115, 133), 'django.template.Library', 'template.Library', ([], {}), '()\n', (131, 133), False, 'from django import template\n'), ((275, 310), 're.sub', 're.sub', (['"""[^0-9a-zA-Z]+"""', '""" """', 'value'], {}), "('[^0-9a-zA-Z]+', ' ', value)\n", (281, 310), False, 'import re\n')]
|
#!/usr/bin/python
"""The primary script to execute the tensorflow models."""
from __future__ import print_function
from munch import munchify
from six.moves import cPickle
from config.arguments import parser
""" Default model """
from model.model import Model
from utils.processor import BatchLoader, DataLoader, eval_loader
from utils.strings import FILES
import utils.adaptive as adaptive
import numpy as np
import tensorflow as tf
import json
import logging
import os
import sys
import time
import yaml
tf.reset_default_graph()
def main():
"""The main method of script."""
args = parser.parse_args()
with open(args.config_file, 'r') as stream:
args.config = munchify(yaml.load(stream))
args.save_dir = os.path.join(args.save_dir, args.job_id)
args.best_dir = os.path.join(args.best_dir, args.job_id)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
if not os.path.exists(args.best_dir):
os.makedirs(args.best_dir)
print(args)
if args.mode == 'train':
train(args)
elif args.mode == 'test' or args.mode == 'valid':
test(args)
elif args.mode == 'generate':
generate(args)
def generate(args):
args.config.timesteps = 1
data_loader = DataLoader(args)
with tf.Session() as sess:
initializer = tf.random_uniform_initializer(-0.05, 0.05)
with tf.variable_scope("model", reuse=None, initializer=initializer):
model = Model(args, args.config.batch_size, mode='train')
steps_done = initialize_weights(sess, model, args, mode='test')
with tf.variable_scope("model", reuse=True, initializer=initializer):
model = Model(args, batch_size=1, mode='eval')
print("Loaded %d completed steps", steps_done)
states = sess.run(model.initial_states)
# First feed in the prior letters
probs = None
for i in args.line.split():
feed = {
model.input_data: np.array([[data_loader.vocab[i]]]),
model.initial_states: states
}
[states, probs] = sess.run([model.final_states, model.probs], feed)
# Now, time to begin the sampling process
def weighted_pick(weights):
t = np.cumsum(weights)
s = np.sum(weights)
return(int(np.searchsorted(t, np.random.rand(1) * s)))
# Weird construct to optimize code
prior_length = len(args.line.split())
output = [' '] * (args.words + prior_length)
for i in range(prior_length):
output[i] = args.line.split()[i]
for i in range(args.words):
if i % 100 == 0:
print("%d out of %d generated" % (i, args.words))
token = weighted_pick(np.squeeze(probs))
if token == len(np.squeeze(probs)):
token = token - 1
output[i + prior_length] = data_loader.rev_vocab[token]
feed = {
model.input_data: np.array([[token]]),
model.initial_states: states
}
[states, probs] = sess.run([model.final_states, model.probs], feed)
output = ' '.join(output)
output = output.replace('</s>', '\n')
output = output + "\n"
# with open(os.path.join(args.save_dir, "generate_{0}.txt".format(args.job_id)), 'w') as f:
# f.write(output)
print(output)
def initialize_weights(sess, model, args, mode):
ckpt = tf.train.get_checkpoint_state(args.save_dir)
ckpt_best = tf.train.get_checkpoint_state(args.best_dir)
if mode == 'test' and ckpt_best:
print("Reading best model parameters from %s", ckpt_best.model_checkpoint_path)
model.saver.restore(sess, ckpt_best.model_checkpoint_path)
steps_done = int(ckpt_best.model_checkpoint_path.split('-')[-1])
# Since local variables are not saved
sess.run([
tf.local_variables_initializer()
])
elif mode == 'train' and ckpt:
print("Reading model parameters from %s", ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
steps_done = int(ckpt.model_checkpoint_path.split('-')[-1])
# Since local variables are not saved
sess.run([
tf.local_variables_initializer()
])
else:
steps_done = 0
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
return steps_done
def evaluate(sess, model, eval_data, args, calculate_prob=False, rev_vocab=None):
"""Calculate perplexity after every epoch."""
states = sess.run(model.initial_states)
total_loss = 0.0
prob_output = ""
eval_x, eval_y, eval_len = eval_data['x'], eval_data['y'], eval_data['len']
for i in range(eval_x.shape[0]):
# Need to pass L1 to get evaluation perplexity
feed = {
model.input_data: eval_x[i:i + 1, :],
model.targets: eval_y[i:i + 1, :],
model.initial_states: states
}
if calculate_prob is True:
[states, loss, probs] = sess.run([model.final_states, model.loss, model.probs], feed)
total_loss += loss.sum()
for j in range(len(probs)):
position = i * args.config.timesteps + j
if position >= eval_len - 1:
continue
token = eval_y[i][j]
prob_output += rev_vocab[token] + " " + str(probs[j, token]) + "\n"
else:
[states, loss] = sess.run([model.final_states, model.loss], feed)
total_loss += loss.sum()
# need to subtract off loss from padding tokens
extra_tokens = (args.config.timesteps - eval_len % args.config.timesteps) % args.config.timesteps + 1
total_loss -= loss[-extra_tokens:].sum()
avg_entropy = total_loss / eval_len
ppl = np.exp(avg_entropy)
if calculate_prob is True:
return ppl, prob_output
else:
return ppl
def test(args):
data_loader = DataLoader(args)
if args.device == "gpu":
cfg_proto = tf.ConfigProto(intra_op_parallelism_threads=2)
cfg_proto.gpu_options.allow_growth = True
else:
cfg_proto = None
with tf.Session(config=cfg_proto) as sess:
initializer = tf.random_uniform_initializer(-0.05, 0.05)
with tf.variable_scope("model", reuse=None, initializer=initializer):
model = Model(args, args.config.batch_size, mode='train')
steps_done = initialize_weights(sess, model, args, mode='test')
with tf.variable_scope("model", reuse=True, initializer=initializer):
model_eval = Model(args, batch_size=1, mode='eval')
print("loaded %d completed steps", steps_done)
test_data = {}
test_data['x'], test_data['y'], test_data['len'] = eval_loader(args, data_loader.vocab, split=args.mode)
ppl, prob_output = evaluate(
sess, model_eval, test_data, args, calculate_prob=True, rev_vocab=data_loader.rev_vocab
)
with open(os.path.join(args.save_dir, "probs_{0}_{1}.txt".format(args.mode,args.job_id)), 'w') as f:
f.write(prob_output)
print("Perplexity is %.4f", ppl)
def train(args):
"""Prepare the data and begins training."""
# Load the text and vocabulary
data_loader = DataLoader(args)
# Prepare batches for training
batch_loader = BatchLoader(args, data_loader)
if args.device == "gpu":
cfg_proto = tf.ConfigProto(intra_op_parallelism_threads=2)
cfg_proto.gpu_options.allow_growth = True
else:
cfg_proto = None
with tf.Session(config=cfg_proto) as sess:
# Build training model, load old weights
initializer = tf.random_uniform_initializer(-0.05, 0.05)
with tf.variable_scope("model", reuse=None, initializer=initializer):
model = Model(args, args.config.batch_size, mode='train')
steps_done = initialize_weights(sess, model, args, mode='train')
print("loaded %d completed steps", steps_done)
# Reusing weights for evaluation model
with tf.variable_scope("model", reuse=True, initializer=initializer):
model_eval = Model(args, batch_size=1, mode='eval')
valid_data = {}
valid_data['x'], valid_data['y'], valid_data['len'] = eval_loader(args, data_loader.vocab, split='valid')
batch_loader.eval_data = valid_data
train_writer = tf.summary.FileWriter(args.save_dir + '/logs/', tf.get_default_graph())
# Making the graph read-only to prevent memory leaks
# https://stackoverflow.com/documentation/tensorflow/3883/how-to-debug-a-memory-leak-in-tensorflow/13426/use-graph-finalize-to-catch-nodes-being-added-to-the-graph#t=201612280201558374055
sess.graph.finalize()
start_epoch = model.epoch.eval()
for epoch in range(start_epoch, args.config.num_epochs):
run_epoch(sess, model, model_eval, args, batch_loader, epoch)
def run_epoch(sess, model, model_eval, args, batch_loader, epoch):
"""Run one epoch of training."""
best_ppl = model.best_ppl.eval()
last_ppl_update = model.last_ppl_update.eval()
margin_ppl = model.margin_ppl.eval()
adaptive_loss = getattr(adaptive, args.loss_mode)
# Reset batch pointer back to zero
batch_loader.reset_batch_pointer()
# Start from an empty RNN state
states = sess.run(model.initial_states)
start_batch = model.global_step.eval() % batch_loader.num_batches
if start_batch != 0:
print("Starting from batch %d / %d", start_batch, batch_loader.num_batches)
batch_loader.pointer += start_batch
for b in range(start_batch, batch_loader.num_batches):
start = time.time()
l1 = adaptive_loss(epoch, b, args=args)
sess.run(model.l1_assign, feed_dict={model.l1_new: l1})
time1 = time.time()
x, y, ngram = batch_loader.next_batch(l1)
time2 = time.time()
print("Time for loading ngram distribution - %.2f", time2 - time1)
# With probability 0.01 feed the initial state
if np.random.randint(1, 101) <= 1:
states = sess.run(model.initial_states)
feed = {model.input_data: x,
model.targets: y,
model.ngram: ngram,
model.initial_states: states}
time3 = time.time()
train_loss, l1, states, _ = sess.run([model.final_cost,
model.cost,
model.final_states,
model.train_op], feed)
end = time.time()
# print the result so far on terminal
batch_num = epoch * batch_loader.num_batches + b
total_num = args.config.num_epochs * batch_loader.num_batches
print("Time for TensorFlow calculations - %.2f", end - time3)
print("Epoch %d, %d / %d. Loss - %.4f, Time - %.2f", epoch, batch_num, total_num, train_loss, end - start)
# Save after `args.eval_freq` batches or at the very end
if batch_num != 0 and (batch_num % args.config.eval_freq == 0 or b == batch_loader.num_batches - 1):
ppl = evaluate(sess, model_eval, batch_loader.eval_data, args)
print("Perplexity after %d steps - %.4f", batch_num, ppl)
# Update rules for best_ppl / training schedule
print("Best ppl is %.4f, ppl < best_ppl is %s", model.best_ppl.eval(), str(ppl < best_ppl))
if ppl < best_ppl:
print("Saving Best Model")
# Storing perplexity and in TensorFlow variable and Python variable
best_ppl = ppl
sess.run(model.best_ppl_assign, feed_dict={model.best_ppl_new: ppl})
if margin_ppl - ppl > args.config.margin_ppl:
# In the case there has been a perplexity change of more than `margin_ppl`
# update the `last_ppl_update` and `margin_ppl` values
# This indicates a "significant" change in ppl
print("Updating margin_ppl, Change of %.4f", margin_ppl - ppl)
last_ppl_update = batch_num
margin_ppl = ppl
sess.run(model.last_ppl_update_assign, feed_dict={model.last_ppl_update_new: batch_num})
sess.run(model.margin_ppl_assign, feed_dict={model.margin_ppl_new: ppl})
# Saving the best model
checkpoint_path = os.path.join(args.best_dir, "lm.ckpt")
model.best_saver.save(sess, checkpoint_path, global_step=model.global_step, write_meta_graph=False)
else:
# Decay learning rate whenever ppl is greater than best_ppl so far
sess.run(model.lr_decay)
print("decaying lr after %d epochs to %.4f" % (model.epoch.eval(), model.lr.eval()))
checkpoint_path = os.path.join(args.save_dir, "lm.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step, write_meta_graph=False)
sess.run(model.epoch_incr)
if __name__ == '__main__':
main()
|
[
"yaml.load",
"numpy.sum",
"tensorflow.reset_default_graph",
"tensorflow.local_variables_initializer",
"tensorflow.ConfigProto",
"numpy.random.randint",
"config.arguments.parser.parse_args",
"numpy.exp",
"tensorflow.get_default_graph",
"os.path.join",
"tensorflow.random_uniform_initializer",
"utils.processor.eval_loader",
"os.path.exists",
"tensorflow.variable_scope",
"model.model.Model",
"numpy.cumsum",
"utils.processor.DataLoader",
"tensorflow.train.get_checkpoint_state",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"utils.processor.BatchLoader",
"numpy.squeeze",
"os.makedirs",
"time.time",
"numpy.array",
"numpy.random.rand"
] |
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|
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
from tqdm import tqdm
tf.compat.v1.enable_v2_behavior()
from tf_agents.agents.reinforce import reinforce_agent
from tf_agents.drivers import dynamic_step_driver
from tf_agents.environments import suite_gym
from tf_agents.environments import tf_py_environment
from tf_agents.eval import metric_utils
from tf_agents.metrics import tf_metrics, py_metrics
from tf_agents.policies import random_tf_policy, epsilon_greedy_policy
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.networks import actor_distribution_network
from tf_agents.trajectories import trajectory
from tf_agents.utils import common
from tf_agents.specs import array_spec
from tf_agents.environments import utils, wrappers
from tf_agents.trajectories import time_step as ts
from tf_agents.drivers import dynamic_episode_driver
from tf_agents.drivers import py_driver
# Standard functions for training
def compute_avg_return(environment, policy, num_episodes=10):
total_return = 0.0
for _ in range(num_episodes):
time_step = environment.reset()
episode_return = 0.0
while not time_step.is_last():
action_step = policy.action(time_step)
time_step = environment.step(action_step.action)
episode_return += time_step.reward
total_return += episode_return
avg_return = total_return / num_episodes
return avg_return.numpy()[0]
def collect_episode(environment, policy, num_episodes, replay_buffers):
episode_counter = 0
environment.reset()
while episode_counter < num_episodes:
time_step = environment.current_time_step()
action_step = policy.action(time_step)
next_time_step = environment.step(action_step.action)
traj = trajectory.from_transition(time_step, action_step, next_time_step)
# Add trajectory to the replay buffer
for replay_buffer in replay_buffers:
replay_buffer.add_batch(traj)
if traj.is_boundary():
episode_counter += 1
|
[
"tf_agents.trajectories.trajectory.from_transition",
"tensorflow.compat.v1.enable_v2_behavior"
] |
[((103, 136), 'tensorflow.compat.v1.enable_v2_behavior', 'tf.compat.v1.enable_v2_behavior', ([], {}), '()\n', (134, 136), True, 'import tensorflow as tf\n'), ((1759, 1825), 'tf_agents.trajectories.trajectory.from_transition', 'trajectory.from_transition', (['time_step', 'action_step', 'next_time_step'], {}), '(time_step, action_step, next_time_step)\n', (1785, 1825), False, 'from tf_agents.trajectories import trajectory\n')]
|
""""CDMI storage
Copyright 2015 Archive Analytics Solutions
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import re
from collections import OrderedDict
from cdmi.capabilities import (
StorageSystemMetadataCapabilities, ContainerCapabilities, DataObjectCapabilities)
class CDMIDataAccessObject(object):
"""CDMI Data Access Object to interact with a data store.
Enhances the Abstract Base Data Access Object, providing CDMI capabilities
and CDMI compliant identifier manipulation.
"""
def __init__(self, config):
#super(CDMIDataAccessObject, self).__init__(config)
self.metdataCapabilities = StorageSystemMetadataCapabilities(
cdmi_acl=False,
cdmi_size=False,
cdmi_ctime=False,
cdmi_atime=False,
cdmi_mtime=False,
cdmi_acount=False,
cdmi_mcount=False
)
self.containerCapabilities = ContainerCapabilities(
cdmi_create_container=False,
cdmi_delete_container=False,
cdmi_create_queue=False,
cdmi_copy_queue=False,
cdmi_move_queue=False,
cdmi_read_metadata=False,
cdmi_modify_metadata=False,
cdmi_list_children=False,
cdmi_list_children_range=False,
cdmi_create_dataobject=False,
cdmi_post_dataobject=False,
cdmi_create_reference=False,
cdmi_copy_dataobject=False,
cdmi_move_dataobject=False
)
self.dataObjectCapabilities = DataObjectCapabilities(
cdmi_read_metadata=False,
cdmi_read_value=False,
cdmi_read_value_range=False,
cdmi_modify_metadata=False,
cdmi_modify_value=False,
cdmi_delete_dataobject=False
)
def _CDMIify_metadata(self, metadata):
"""Impose CDMI compliance on metadata dictionary.
:type metadata: dict
:returns: CDMI compliant metadata dictionary
:rtype: dict
"""
cdmi_md = OrderedDict()
for k, val in metadata.iteritems():
# This may be improved
if not k.startswith(METADATA_PREFIX):
# Replace keys containing internally used "alloy_" prefix with
# CDMI mandated reversed URL style prefixes
cdmi_k = re.sub('^alloy', METADATA_PREFIX, k, 1)
else:
cdmi_k = k
cdmi_md[cdmi_k] = val
return cdmi_md
def _unCDMIify_metadata(self, metadata):
"""Strip CDMI cruft from metadata dictionary for internal storage.
:type metadata: dict
:returns: metadata dictionary for internal storage
:rtype: dict
"""
store_md = {}
for k, val in metadata.iteritems():
if k.startswith('cdmi_'):
# cdmi_ values are prohibited by CDMI spec for user-defined
# metadata
continue
else:
store_md[k] = val
return store_md
|
[
"cdmi.capabilities.DataObjectCapabilities",
"cdmi.capabilities.StorageSystemMetadataCapabilities",
"collections.OrderedDict",
"cdmi.capabilities.ContainerCapabilities",
"re.sub"
] |
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|
import io
import matplotlib.pyplot as plt
import tensorflow as tf
def log_as_histogram(name, values):
"""Logs values as histogram."""
values *= tf.math.rsqrt(tf.reduce_mean(tf.square(values)) + 1e-10)
data = []
scale = 100
for i in range(values.shape[0]):
d = tf.zeros(tf.cast(tf.round(scale * values[i]), dtype=tf.int64)) + i
data.append(d)
data = tf.concat(data, axis=0)
data = tf.cast(data, tf.float32) + tf.random.uniform(tf.shape(data), -0.5, 0.5)
tf.summary.histogram(name, data, buckets=values.shape[0] + 1)
def log_discreate_as_histogram(name, values):
"""Logs values as histogram."""
data = tf.TensorArray(tf.int32, size=0, dynamic_size=True, clear_after_read=True)
for i in tf.range(tf.shape(values)[0]):
d = tf.zeros(shape=tf.cast(tf.round(values[i]), dtype=tf.int64), dtype=tf.int32) + i
data = data.write(i, d)
data = data.concat()
data = tf.cast(data, tf.float32) + tf.random.uniform(tf.shape(data), -0.5, 0.5)
tf.summary.histogram(name, data, buckets=tf.shape(values)[0] + 1)
def plot_to_image(figure):
"""Converts the matplotlib plot specified by 'figure' to a PNG image and
returns it. The supplied figure is closed and inaccessible after this call."""
# Save the plot to a PNG in memory.
buf = io.BytesIO()
plt.savefig(buf, format='png')
# Closing the figure prevents it from being displayed directly inside
# the notebook.
plt.close(figure)
buf.seek(0)
# Convert PNG buffer to TF image
image = tf.image.decode_png(buf.getvalue(), channels=4)
# Add the batch dimension
image = tf.expand_dims(image, 0)
return image
|
[
"io.BytesIO",
"matplotlib.pyplot.close",
"tensorflow.concat",
"tensorflow.cast",
"tensorflow.shape",
"tensorflow.summary.histogram",
"tensorflow.round",
"tensorflow.TensorArray",
"tensorflow.square",
"tensorflow.expand_dims",
"matplotlib.pyplot.savefig"
] |
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|
import os
from keyring import get_password
from werkzeug.utils import import_string
DB = "notifications_service"
PORT = 27017
MONGO_IP = "127.0.0.1"
KAFKA_BROKERS = "127.0.0.1:9092"
BALANCES_TOPIC_NAME = "balances"
class BaseConfig(object):
DEBUG = False
TESTING = False
SERVERNAME = "localhost"
PORT = PORT
DATABASE = DB
USERNAME = ""
PASSWORD = ""
LOGS_PATH = '../CryptoNotificationsService/logs/CryptoNotificationsService.log'
KAFKA_BROKERS = KAFKA_BROKERS
BALANCES_TOPIC_NAME = BALANCES_TOPIC_NAME
class DevelopmentConfig(BaseConfig):
DEBUG = True
TESTING = True
SERVERNAME = "127.0.0.1"
PORT = PORT
DATABASE = DB
USERNAME = "admin"
PASSWORD = "<PASSWORD>"
LOGS_PATH = '../CryptoNotificationsService/logs/CryptoNotificationsService.log'
KAFKA_BROKERS = KAFKA_BROKERS
BALANCES_TOPIC_NAME = BALANCES_TOPIC_NAME
class ProductionConfig(BaseConfig):
DEBUG = False
TESTING = False
SERVERNAME = MONGO_IP
PORT = PORT
DATABASE = DB
USERNAME = ""
PASSWORD = ""
LOGS_PATH = '../CryptoNotificationsService/logs/CryptoNotificationsService.log'
KAFKA_BROKERS = KAFKA_BROKERS
BALANCES_TOPIC_NAME = BALANCES_TOPIC_NAME
config = {
"development": "CryptoNotificationsService.config.DevelopmentConfig",
"production": "CryptoNotificationsService.config.ProductionConfig",
"default": "CryptoNotificationsService.config.DevelopmentConfig",
}
def configure_app():
config_name = os.getenv('FLASK_ENV', 'CryptoNotificationsService.config.DevelopmentConfig')
cfg = import_string(config_name)()
cfg.USERNAME = get_password('CryptoNotificationsService', 'USERNAME')
cfg.PASSWORD = get_password('<PASSWORD>', cfg.USERNAME)
return cfg
|
[
"keyring.get_password",
"os.getenv",
"werkzeug.utils.import_string"
] |
[((1506, 1583), 'os.getenv', 'os.getenv', (['"""FLASK_ENV"""', '"""CryptoNotificationsService.config.DevelopmentConfig"""'], {}), "('FLASK_ENV', 'CryptoNotificationsService.config.DevelopmentConfig')\n", (1515, 1583), False, 'import os\n'), ((1642, 1696), 'keyring.get_password', 'get_password', (['"""CryptoNotificationsService"""', '"""USERNAME"""'], {}), "('CryptoNotificationsService', 'USERNAME')\n", (1654, 1696), False, 'from keyring import get_password\n'), ((1716, 1756), 'keyring.get_password', 'get_password', (['"""<PASSWORD>"""', 'cfg.USERNAME'], {}), "('<PASSWORD>', cfg.USERNAME)\n", (1728, 1756), False, 'from keyring import get_password\n'), ((1594, 1620), 'werkzeug.utils.import_string', 'import_string', (['config_name'], {}), '(config_name)\n', (1607, 1620), False, 'from werkzeug.utils import import_string\n')]
|
# -*- coding: utf-8 -*-
#
# This file is part of Invenio.
#
# Copyright (C) 2021 Graz University of Technology.
#
# Invenio-Records-Marc21 is free software; you can redistribute it and/or modify it
# under the terms of the MIT License; see LICENSE file for more details.
"""Tests for Resources UI marc21 serializer."""
from invenio_records_marc21.resources.serializers.ui import (
Marc21UIJSONSerializer,
Marc21UIXMLSerializer,
)
from invenio_records_marc21.resources.serializers.ui.schema import Marc21UISchema
def test_ui_marcxml_serializer_init():
marc = Marc21UIXMLSerializer()
assert marc._object_key == "ui"
assert marc._schema_cls == Marc21UISchema
def test_ui_marcxml_serializer_dump_one(full_record):
marc = Marc21UIXMLSerializer()
obj = marc.dump_one(full_record)
assert isinstance(obj["metadata"], dict)
assert full_record["metadata"] == obj["metadata"]
assert marc._object_key in obj
obj_ui = obj[marc._object_key]
assert "metadata" in obj_ui
assert isinstance(obj_ui["metadata"], bytes)
def test_ui_marcxml_serializer_dump_many(list_records):
marc = Marc21UIXMLSerializer()
obj_list = marc.dump_many(list_records)
for record, obj in zip(obj_list["hits"]["hits"], list_records["hits"]["hits"]):
assert marc._object_key in obj
assert "metadata" in obj
assert record["metadata"] == obj["metadata"]
obj_ui = obj[marc._object_key]
assert "metadata" in obj_ui
assert isinstance(obj_ui["metadata"], bytes)
def test_ui_json_serializer_init():
marc = Marc21UIJSONSerializer()
assert marc._object_key == "ui"
assert marc._schema_cls == Marc21UISchema
def test_ui_json_serializer_dump_one(full_record):
marc = Marc21UIJSONSerializer()
obj = marc.dump_one(full_record)
assert isinstance(obj["metadata"], dict)
assert full_record["metadata"] == obj["metadata"]
assert marc._object_key in obj
obj_ui = obj[marc._object_key]
assert "metadata" in obj_ui
assert isinstance(obj_ui["metadata"], dict)
def test_ui_json_serializer_dump_many(list_records):
marc = Marc21UIJSONSerializer()
obj_list = marc.dump_many(list_records)
for record, obj in zip(obj_list["hits"]["hits"], list_records["hits"]["hits"]):
assert marc._object_key in obj
assert "metadata" in obj
assert record["metadata"] == obj["metadata"]
obj_ui = obj[marc._object_key]
assert "metadata" in obj_ui
assert isinstance(obj_ui["metadata"], dict)
|
[
"invenio_records_marc21.resources.serializers.ui.Marc21UIJSONSerializer",
"invenio_records_marc21.resources.serializers.ui.Marc21UIXMLSerializer"
] |
[((576, 599), 'invenio_records_marc21.resources.serializers.ui.Marc21UIXMLSerializer', 'Marc21UIXMLSerializer', ([], {}), '()\n', (597, 599), False, 'from invenio_records_marc21.resources.serializers.ui import Marc21UIJSONSerializer, Marc21UIXMLSerializer\n'), ((749, 772), 'invenio_records_marc21.resources.serializers.ui.Marc21UIXMLSerializer', 'Marc21UIXMLSerializer', ([], {}), '()\n', (770, 772), False, 'from invenio_records_marc21.resources.serializers.ui import Marc21UIJSONSerializer, Marc21UIXMLSerializer\n'), ((1130, 1153), 'invenio_records_marc21.resources.serializers.ui.Marc21UIXMLSerializer', 'Marc21UIXMLSerializer', ([], {}), '()\n', (1151, 1153), False, 'from invenio_records_marc21.resources.serializers.ui import Marc21UIJSONSerializer, Marc21UIXMLSerializer\n'), ((1586, 1610), 'invenio_records_marc21.resources.serializers.ui.Marc21UIJSONSerializer', 'Marc21UIJSONSerializer', ([], {}), '()\n', (1608, 1610), False, 'from invenio_records_marc21.resources.serializers.ui import Marc21UIJSONSerializer, Marc21UIXMLSerializer\n'), ((1757, 1781), 'invenio_records_marc21.resources.serializers.ui.Marc21UIJSONSerializer', 'Marc21UIJSONSerializer', ([], {}), '()\n', (1779, 1781), False, 'from invenio_records_marc21.resources.serializers.ui import Marc21UIJSONSerializer, Marc21UIXMLSerializer\n'), ((2136, 2160), 'invenio_records_marc21.resources.serializers.ui.Marc21UIJSONSerializer', 'Marc21UIJSONSerializer', ([], {}), '()\n', (2158, 2160), False, 'from invenio_records_marc21.resources.serializers.ui import Marc21UIJSONSerializer, Marc21UIXMLSerializer\n')]
|
"""
"""
import shlex
import sys
import os
import json
from setuptools import setup, find_packages
params = {}
args = shlex.split(' '.join(sys.argv))
print(args)
if('--kallisto' in args):
index = args.index('--kallisto')
assert(index < len(args)), \
'--kallisto option requires a path'
kallisto_path = args[index + 1]
assert os.path.exists(kallisto_path), \
'kallisto path is invalid.\n%s' % kallisto_path
params['kallisto'] = kallisto_path
sys.argv.remove('--kallisto')
sys.argv.remove(kallisto_path)
else:
params['kallisto'] = 'kallisto'
"""
prepare params.json
"""
with open('./sircel/params.json', 'w') as writer:
writer.write(json.dumps(params, indent = 3))
setup(name='sircel',
version = '0.1.2',
packages = find_packages(),
install_requires = [
'numpy',
'scipy',
'python-Levenshtein',
'matplotlib',
'scikit-learn'],
package_data = {'': ['params.json']},
include_package_data = True,
entry_points = {
'console_scripts' : [
'sircel = sircel.__main__:main']},
description = 'Identify and error correct barcodes for single-cell genomics',
url = 'https://github.com/pachterlab/Sircel',
author = '<NAME>',
author_email = '<EMAIL>',
license = 'MIT',)
|
[
"sys.argv.remove",
"os.path.exists",
"setuptools.find_packages",
"json.dumps"
] |
[((337, 366), 'os.path.exists', 'os.path.exists', (['kallisto_path'], {}), '(kallisto_path)\n', (351, 366), False, 'import os\n'), ((459, 488), 'sys.argv.remove', 'sys.argv.remove', (['"""--kallisto"""'], {}), "('--kallisto')\n", (474, 488), False, 'import sys\n'), ((490, 520), 'sys.argv.remove', 'sys.argv.remove', (['kallisto_path'], {}), '(kallisto_path)\n', (505, 520), False, 'import sys\n'), ((656, 684), 'json.dumps', 'json.dumps', (['params'], {'indent': '(3)'}), '(params, indent=3)\n', (666, 684), False, 'import json\n'), ((752, 767), 'setuptools.find_packages', 'find_packages', ([], {}), '()\n', (765, 767), False, 'from setuptools import setup, find_packages\n')]
|
"""Supporting definitions for the Python regression tests."""
import sys
HOST = 'localhost'
class Error(Exception):
"""Base class for regression test exceptions."""
class TestFailed(Error):
"""Test failed."""
class TestSkipped(Error):
"""Test skipped.
This can be raised to indicate that a test was deliberatly
skipped, but not because a feature wasn't available. For
example, if some resource can't be used, such as the network
appears to be unavailable, this should be raised instead of
TestFailed.
"""
class ResourceDenied(TestSkipped):
"""Test skipped because it requested a disallowed resource.
This is raised when a test calls requires() for a resource that
has not be enabled. It is used to distinguish between expected
and unexpected skips.
"""
verbose = 1 # Flag set to 0 by regrtest.py
use_resources = None # Flag set to [] by regrtest.py
max_memuse = 0 # Disable bigmem tests (they will still be run with
# small sizes, to make sure they work.)
# _original_stdout is meant to hold stdout at the time regrtest began.
# This may be "the real" stdout, or IDLE's emulation of stdout, or whatever.
# The point is to have some flavor of stdout the user can actually see.
_original_stdout = None
def record_original_stdout(stdout):
global _original_stdout
_original_stdout = stdout
def get_original_stdout():
return _original_stdout or sys.stdout
def unload(name):
try:
del sys.modules[name]
except KeyError:
pass
def unlink(filename):
import os
try:
os.unlink(filename)
except OSError:
pass
def forget(modname):
'''"Forget" a module was ever imported by removing it from sys.modules and
deleting any .pyc and .pyo files.'''
unload(modname)
import os
for dirname in sys.path:
unlink(os.path.join(dirname, modname + os.extsep + 'pyc'))
# Deleting the .pyo file cannot be within the 'try' for the .pyc since
# the chance exists that there is no .pyc (and thus the 'try' statement
# is exited) but there is a .pyo file.
unlink(os.path.join(dirname, modname + os.extsep + 'pyo'))
def is_resource_enabled(resource):
"""Test whether a resource is enabled. Known resources are set by
regrtest.py."""
return use_resources is not None and resource in use_resources
def requires(resource, msg=None):
"""Raise ResourceDenied if the specified resource is not available.
If the caller's module is __main__ then automatically return True. The
possibility of False being returned occurs when regrtest.py is executing."""
# see if the caller's module is __main__ - if so, treat as if
# the resource was set
return
if sys._getframe().f_back.f_globals.get("__name__") == "__main__":
return
if not is_resource_enabled(resource):
if msg is None:
msg = "Use of the `%s' resource not enabled" % resource
raise ResourceDenied(msg)
import socket
def find_unused_port(family=socket.AF_INET, socktype=socket.SOCK_STREAM):
"""Returns an unused port that should be suitable for binding. This is
achieved by creating a temporary socket with the same family and type as
the 'sock' parameter (default is AF_INET, SOCK_STREAM), and binding it to
the specified host address (defaults to 0.0.0.0) with the port set to 0,
eliciting an unused ephemeral port from the OS. The temporary socket is
then closed and deleted, and the ephemeral port is returned.
Either this method or bind_port() should be used for any tests where a
server socket needs to be bound to a particular port for the duration of
the test. Which one to use depends on whether the calling code is creating
a python socket, or if an unused port needs to be provided in a constructor
or passed to an external program (i.e. the -accept argument to openssl's
s_server mode). Always prefer bind_port() over find_unused_port() where
possible. Hard coded ports should *NEVER* be used. As soon as a server
socket is bound to a hard coded port, the ability to run multiple instances
of the test simultaneously on the same host is compromised, which makes the
test a ticking time bomb in a buildbot environment. On Unix buildbots, this
may simply manifest as a failed test, which can be recovered from without
intervention in most cases, but on Windows, the entire python process can
completely and utterly wedge, requiring someone to log in to the buildbot
and manually kill the affected process.
(This is easy to reproduce on Windows, unfortunately, and can be traced to
the SO_REUSEADDR socket option having different semantics on Windows versus
Unix/Linux. On Unix, you can't have two AF_INET SOCK_STREAM sockets bind,
listen and then accept connections on identical host/ports. An EADDRINUSE
socket.error will be raised at some point (depending on the platform and
the order bind and listen were called on each socket).
However, on Windows, if SO_REUSEADDR is set on the sockets, no EADDRINUSE
will ever be raised when attempting to bind two identical host/ports. When
accept() is called on each socket, the second caller's process will steal
the port from the first caller, leaving them both in an awkwardly wedged
state where they'll no longer respond to any signals or graceful kills, and
must be forcibly killed via OpenProcess()/TerminateProcess().
The solution on Windows is to use the SO_EXCLUSIVEADDRUSE socket option
instead of SO_REUSEADDR, which effectively affords the same semantics as
SO_REUSEADDR on Unix. Given the propensity of Unix developers in the Open
Source world compared to Windows ones, this is a common mistake. A quick
look over OpenSSL's 0.9.8g source shows that they use SO_REUSEADDR when
openssl.exe is called with the 's_server' option, for example. See
http://bugs.python.org/issue2550 for more info. The following site also
has a very thorough description about the implications of both REUSEADDR
and EXCLUSIVEADDRUSE on Windows:
http://msdn2.microsoft.com/en-us/library/ms740621(VS.85).aspx)
XXX: although this approach is a vast improvement on previous attempts to
elicit unused ports, it rests heavily on the assumption that the ephemeral
port returned to us by the OS won't immediately be dished back out to some
other process when we close and delete our temporary socket but before our
calling code has a chance to bind the returned port. We can deal with this
issue if/when we come across it."""
tempsock = socket.socket(family, socktype)
port = bind_port(tempsock)
tempsock.close()
del tempsock
return port
def bind_port(sock, host='', preferred_port=54321):
"""Try to bind the sock to a port. If we are running multiple
tests and we don't try multiple ports, the test can fails. This
makes the test more robust."""
import socket, errno
# Find some random ports that hopefully no one is listening on.
# Ideally each test would clean up after itself and not continue listening
# on any ports. However, this isn't the case. The last port (0) is
# a stop-gap that asks the O/S to assign a port. Whenever the warning
# message below is printed, the test that is listening on the port should
# be fixed to close the socket at the end of the test.
# Another reason why we can't use a port is another process (possibly
# another instance of the test suite) is using the same port.
for port in [preferred_port, 9907, 10243, 32999, 0]:
try:
sock.bind((host, port))
if port == 0:
port = sock.getsockname()[1]
return port
except socket.error:
if sys.exc_info()[1].args[0] != errno.EADDRINUSE:
raise
print(' WARNING: failed to listen on port %d, trying another' % port, file=sys.__stderr__)
raise TestFailed('unable to find port to listen on')
FUZZ = 1e-6
def fcmp(x, y): # fuzzy comparison function
if type(x) == type(0.0) or type(y) == type(0.0):
try:
x, y = coerce(x, y)
fuzz = (abs(x) + abs(y)) * FUZZ
if abs(x-y) <= fuzz:
return 0
except:
pass
elif type(x) == type(y) and type(x) in (type(()), type([])):
for i in range(min(len(x), len(y))):
outcome = fcmp(x[i], y[i])
if outcome != 0:
return outcome
return cmp(len(x), len(y))
return cmp(x, y)
try:
str
have_unicode = 1
except NameError:
have_unicode = 0
is_jython = sys.platform.startswith('java')
import os
# Filename used for testing
if os.name == 'java':
# Jython disallows @ in module names
TESTFN = '$test'
elif os.name == 'riscos':
TESTFN = 'testfile'
else:
TESTFN = '@test'
# Unicode name only used if TEST_FN_ENCODING exists for the platform.
if have_unicode:
# Assuming sys.getfilesystemencoding()!=sys.getdefaultencoding()
# TESTFN_UNICODE is a filename that can be encoded using the
# file system encoding, but *not* with the default (ascii) encoding
if isinstance('', str):
# python -U
# XXX perhaps unicode() should accept Unicode strings?
TESTFN_UNICODE = "@test-\xe0\xf2"
else:
# 2 latin characters.
TESTFN_UNICODE = str("@test-\xe0\xf2", "latin-1")
TESTFN_ENCODING = sys.getfilesystemencoding()
# TESTFN_UNICODE_UNENCODEABLE is a filename that should *not* be
# able to be encoded by *either* the default or filesystem encoding.
# This test really only makes sense on Windows NT platforms
# which have special Unicode support in posixmodule.
if (not hasattr(sys, "getwindowsversion") or
sys.getwindowsversion()[3] < 2): # 0=win32s or 1=9x/ME
TESTFN_UNICODE_UNENCODEABLE = None
else:
# Japanese characters (I think - from bug 846133)
TESTFN_UNICODE_UNENCODEABLE = eval('u"@test-\u5171\u6709\u3055\u308c\u308b"')
try:
# XXX - Note - should be using TESTFN_ENCODING here - but for
# Windows, "mbcs" currently always operates as if in
# errors=ignore' mode - hence we get '?' characters rather than
# the exception. 'Latin1' operates as we expect - ie, fails.
# See [ 850997 ] mbcs encoding ignores errors
TESTFN_UNICODE_UNENCODEABLE.encode("Latin1")
except UnicodeEncodeError:
pass
else:
sys.stderr.write('WARNING: The filename %r CAN be encoded by the filesystem. '
'Unicode filename tests may not be effective'
% TESTFN_UNICODE_UNENCODEABLE)
# Make sure we can write to TESTFN, try in /tmp if we can't
fp = None
try:
fp = open(TESTFN, 'w+')
except IOError:
TMP_TESTFN = os.path.join('/tmp', TESTFN)
try:
fp = open(TMP_TESTFN, 'w+')
TESTFN = TMP_TESTFN
del TMP_TESTFN
except IOError:
print(('WARNING: tests will fail, unable to write to: %s or %s' %
(TESTFN, TMP_TESTFN)))
if fp is not None:
fp.close()
unlink(TESTFN)
del os, fp
def findfile(file, here=__file__):
"""Try to find a file on sys.path and the working directory. If it is not
found the argument passed to the function is returned (this does not
necessarily signal failure; could still be the legitimate path)."""
import os
if os.path.isabs(file):
return file
path = sys.path
path = [os.path.dirname(here)] + path
for dn in path:
fn = os.path.join(dn, file)
if os.path.exists(fn): return fn
return file
def verify(condition, reason='test failed'):
"""Verify that condition is true. If not, raise TestFailed.
The optional argument reason can be given to provide
a better error text.
"""
if not condition:
raise TestFailed(reason)
def vereq(a, b):
"""Raise TestFailed if a == b is false.
This is better than verify(a == b) because, in case of failure, the
error message incorporates repr(a) and repr(b) so you can see the
inputs.
Note that "not (a == b)" isn't necessarily the same as "a != b"; the
former is tested.
"""
if not (a == b):
raise TestFailed("%r == %r" % (a, b))
def sortdict(dict):
"Like repr(dict), but in sorted order."
items = list(dict.items())
items.sort()
reprpairs = ["%r: %r" % pair for pair in items]
withcommas = ", ".join(reprpairs)
return "{%s}" % withcommas
def check_syntax(statement):
try:
compile(statement, '<string>', 'exec')
except SyntaxError:
pass
else:
print(('Missing SyntaxError: "%s"' % statement))
def open_urlresource(url):
import urllib.request, urllib.parse, urllib.error, urllib.parse
import os.path
filename = urllib.parse.urlparse(url)[2].split('/')[-1] # '/': it's URL!
for path in [os.path.curdir, os.path.pardir]:
fn = os.path.join(path, filename)
if os.path.exists(fn):
return open(fn)
requires('urlfetch')
print('\tfetching %s ...' % url, file=get_original_stdout())
fn, _ = urllib.request.urlretrieve(url, filename)
return open(fn)
#=======================================================================
# Decorator for running a function in a different locale, correctly resetting
# it afterwards.
def run_with_locale(catstr, *locales):
def decorator(func):
def inner(*args, **kwds):
try:
import locale
category = getattr(locale, catstr)
orig_locale = locale.setlocale(category)
except AttributeError:
# if the test author gives us an invalid category string
raise
except:
# cannot retrieve original locale, so do nothing
locale = orig_locale = None
else:
for loc in locales:
try:
locale.setlocale(category, loc)
break
except:
pass
# now run the function, resetting the locale on exceptions
try:
return func(*args, **kwds)
finally:
if locale and orig_locale:
locale.setlocale(category, orig_locale)
inner.__name__ = func.__name__
inner.__doc__ = func.__doc__
return inner
return decorator
#=======================================================================
# Big-memory-test support. Separate from 'resources' because memory use should be configurable.
# Some handy shorthands. Note that these are used for byte-limits as well
# as size-limits, in the various bigmem tests
_1M = 1024*1024
_1G = 1024 * _1M
_2G = 2 * _1G
# Hack to get at the maximum value an internal index can take.
class _Dummy:
def __getslice__(self, i, j):
return j
try:
MAX_Py_ssize_t = _Dummy()[:]
except TypeError:
MAX_Py_ssize_t = sys.maxsize
def set_memlimit(limit):
import re
global max_memuse
sizes = {
'k': 1024,
'm': _1M,
'g': _1G,
't': 1024*_1G,
}
m = re.match(r'(\d+(\.\d+)?) (K|M|G|T)b?$', limit,
re.IGNORECASE | re.VERBOSE)
if m is None:
raise ValueError('Invalid memory limit %r' % (limit,))
memlimit = int(float(m.group(1)) * sizes[m.group(3).lower()])
if memlimit > MAX_Py_ssize_t:
memlimit = MAX_Py_ssize_t
if memlimit < _2G - 1:
raise ValueError('Memory limit %r too low to be useful' % (limit,))
max_memuse = memlimit
def bigmemtest(minsize, memuse, overhead=5*_1M):
"""Decorator for bigmem tests.
'minsize' is the minimum useful size for the test (in arbitrary,
test-interpreted units.) 'memuse' is the number of 'bytes per size' for
the test, or a good estimate of it. 'overhead' specifies fixed overhead,
independant of the testsize, and defaults to 5Mb.
The decorator tries to guess a good value for 'size' and passes it to
the decorated test function. If minsize * memuse is more than the
allowed memory use (as defined by max_memuse), the test is skipped.
Otherwise, minsize is adjusted upward to use up to max_memuse.
"""
def decorator(f):
def wrapper(self):
if not max_memuse:
# If max_memuse is 0 (the default),
# we still want to run the tests with size set to a few kb,
# to make sure they work. We still want to avoid using
# too much memory, though, but we do that noisily.
maxsize = 5147
self.failIf(maxsize * memuse + overhead > 20 * _1M)
else:
maxsize = int((max_memuse - overhead) / memuse)
if maxsize < minsize:
# Really ought to print 'test skipped' or something
if verbose:
sys.stderr.write("Skipping %s because of memory "
"constraint\n" % (f.__name__,))
return
# Try to keep some breathing room in memory use
maxsize = max(maxsize - 50 * _1M, minsize)
return f(self, maxsize)
wrapper.minsize = minsize
wrapper.memuse = memuse
wrapper.overhead = overhead
return wrapper
return decorator
def bigaddrspacetest(f):
"""Decorator for tests that fill the address space."""
def wrapper(self):
if max_memuse < MAX_Py_ssize_t:
if verbose:
sys.stderr.write("Skipping %s because of memory "
"constraint\n" % (f.__name__,))
else:
return f(self)
return wrapper
#=======================================================================
# Preliminary PyUNIT integration.
import unittest
class BasicTestRunner:
def run(self, test):
result = unittest.TestResult()
test(result)
return result
def run_suite(suite, testclass=None):
"""Run tests from a unittest.TestSuite-derived class."""
if verbose:
runner = unittest.TextTestRunner(sys.stdout, verbosity=2)
else:
runner = BasicTestRunner()
result = runner.run(suite)
if not result.wasSuccessful():
if len(result.errors) == 1 and not result.failures:
err = result.errors[0][1]
elif len(result.failures) == 1 and not result.errors:
err = result.failures[0][1]
else:
if testclass is None:
msg = "errors occurred; run in verbose mode for details"
else:
msg = "errors occurred in %s.%s" \
% (testclass.__module__, testclass.__name__)
raise TestFailed(msg)
raise TestFailed(err)
def run_unittest(*classes):
"""Run tests from unittest.TestCase-derived classes."""
suite = unittest.TestSuite()
for cls in classes:
if isinstance(cls, (unittest.TestSuite, unittest.TestCase)):
suite.addTest(cls)
else:
suite.addTest(unittest.makeSuite(cls))
if len(classes)==1:
testclass = classes[0]
else:
testclass = None
run_suite(suite, testclass)
#=======================================================================
# doctest driver.
def run_doctest(module, verbosity=None):
"""Run doctest on the given module. Return (#failures, #tests).
If optional argument verbosity is not specified (or is None), pass
test_support's belief about verbosity on to doctest. Else doctest's
usual behavior is used (it searches sys.argv for -v).
"""
import doctest
if verbosity is None:
verbosity = verbose
else:
verbosity = None
# Direct doctest output (normally just errors) to real stdout; doctest
# output shouldn't be compared by regrtest.
save_stdout = sys.stdout
sys.stdout = get_original_stdout()
try:
f, t = doctest.testmod(module, verbose=verbosity)
if f:
raise TestFailed("%d of %d doctests failed" % (f, t))
finally:
sys.stdout = save_stdout
if verbose:
print(('doctest (%s) ... %d tests with zero failures' % (module.__name__, t)))
return f, t
#=======================================================================
# Threading support to prevent reporting refleaks when running regrtest.py -R
def threading_setup():
import threading
return len(threading._active), len(threading._limbo)
def threading_cleanup(num_active, num_limbo):
import threading
import time
_MAX_COUNT = 10
count = 0
while len(threading._active) != num_active and count < _MAX_COUNT:
print((threading._active))
count += 1
time.sleep(0.1)
count = 0
while len(threading._limbo) != num_limbo and count < _MAX_COUNT:
print((threading._limbo))
count += 1
time.sleep(0.1)
def reap_children():
"""Use this function at the end of test_main() whenever sub-processes
are started. This will help ensure that no extra children (zombies)
stick around to hog resources and create problems when looking
for refleaks.
"""
# Reap all our dead child processes so we don't leave zombies around.
# These hog resources and might be causing some of the buildbots to die.
import os
if hasattr(os, 'waitpid'):
any_process = -1
while True:
try:
# This will raise an exception on Windows. That's ok.
pid, status = os.waitpid(any_process, os.WNOHANG)
if pid == 0:
break
except:
break
|
[
"sys.platform.startswith",
"os.unlink",
"socket.socket",
"sys.exc_info",
"locale.setlocale",
"os.path.join",
"doctest.testmod",
"os.waitpid",
"os.path.dirname",
"os.path.exists",
"unittest.makeSuite",
"sys.getfilesystemencoding",
"unittest.TestSuite",
"re.match",
"sys._getframe",
"time.sleep",
"unittest.TestResult",
"os.path.isabs",
"sys.getwindowsversion",
"unittest.TextTestRunner",
"sys.stderr.write"
] |
[((8785, 8816), 'sys.platform.startswith', 'sys.platform.startswith', (['"""java"""'], {}), "('java')\n", (8808, 8816), False, 'import sys\n'), ((6727, 6758), 'socket.socket', 'socket.socket', (['family', 'socktype'], {}), '(family, socktype)\n', (6740, 6758), False, 'import socket, errno\n'), ((11748, 11767), 'os.path.isabs', 'os.path.isabs', (['file'], {}), '(file)\n', (11761, 11767), False, 'import os\n'), ((15552, 15628), 're.match', 're.match', (['"""(\\\\d+(\\\\.\\\\d+)?) (K|M|G|T)b?$"""', 'limit', '(re.IGNORECASE | re.VERBOSE)'], {}), "('(\\\\d+(\\\\.\\\\d+)?) (K|M|G|T)b?$', limit, re.IGNORECASE | re.VERBOSE)\n", (15560, 15628), False, 'import re\n'), ((19328, 19348), 'unittest.TestSuite', 'unittest.TestSuite', ([], {}), '()\n', (19346, 19348), False, 'import unittest\n'), ((1630, 1649), 'os.unlink', 'os.unlink', (['filename'], {}), '(filename)\n', (1639, 1649), False, 'import os\n'), ((11145, 11173), 'os.path.join', 'os.path.join', (['"""/tmp"""', 'TESTFN'], {}), "('/tmp', TESTFN)\n", (11157, 11173), False, 'import os\n'), ((11884, 11906), 'os.path.join', 'os.path.join', (['dn', 'file'], {}), '(dn, file)\n', (11896, 11906), False, 'import os\n'), ((11918, 11936), 'os.path.exists', 'os.path.exists', (['fn'], {}), '(fn)\n', (11932, 11936), False, 'import os\n'), ((13297, 13325), 'os.path.join', 'os.path.join', (['path', 'filename'], {}), '(path, filename)\n', (13309, 13325), False, 'import os\n'), ((13337, 13355), 'os.path.exists', 'os.path.exists', (['fn'], {}), '(fn)\n', (13351, 13355), False, 'import os\n'), ((18345, 18366), 'unittest.TestResult', 'unittest.TestResult', ([], {}), '()\n', (18364, 18366), False, 'import unittest\n'), ((18544, 18592), 'unittest.TextTestRunner', 'unittest.TextTestRunner', (['sys.stdout'], {'verbosity': '(2)'}), '(sys.stdout, verbosity=2)\n', (18567, 18592), False, 'import unittest\n'), ((20401, 20443), 'doctest.testmod', 'doctest.testmod', (['module'], {'verbose': 'verbosity'}), '(module, verbose=verbosity)\n', (20416, 20443), False, 'import doctest\n'), ((21195, 21210), 'time.sleep', 'time.sleep', (['(0.1)'], {}), '(0.1)\n', (21205, 21210), False, 'import time\n'), ((21356, 21371), 'time.sleep', 'time.sleep', (['(0.1)'], {}), '(0.1)\n', (21366, 21371), False, 'import time\n'), ((1903, 1953), 'os.path.join', 'os.path.join', (['dirname', "(modname + os.extsep + 'pyc')"], {}), "(dirname, modname + os.extsep + 'pyc')\n", (1915, 1953), False, 'import os\n'), ((2176, 2226), 'os.path.join', 'os.path.join', (['dirname', "(modname + os.extsep + 'pyo')"], {}), "(dirname, modname + os.extsep + 'pyo')\n", (2188, 2226), False, 'import os\n'), ((9635, 9662), 'sys.getfilesystemencoding', 'sys.getfilesystemencoding', ([], {}), '()\n', (9660, 9662), False, 'import sys\n'), ((11821, 11842), 'os.path.dirname', 'os.path.dirname', (['here'], {}), '(here)\n', (11836, 11842), False, 'import os\n'), ((13946, 13972), 'locale.setlocale', 'locale.setlocale', (['category'], {}), '(category)\n', (13962, 13972), False, 'import locale\n'), ((17978, 18056), 'sys.stderr.write', 'sys.stderr.write', (["('Skipping %s because of memory constraint\\n' % (f.__name__,))"], {}), "('Skipping %s because of memory constraint\\n' % (f.__name__,))\n", (17994, 18056), False, 'import sys\n'), ((19513, 19536), 'unittest.makeSuite', 'unittest.makeSuite', (['cls'], {}), '(cls)\n', (19531, 19536), False, 'import unittest\n'), ((21994, 22029), 'os.waitpid', 'os.waitpid', (['any_process', 'os.WNOHANG'], {}), '(any_process, os.WNOHANG)\n', (22004, 22029), False, 'import os\n'), ((10819, 10982), 'sys.stderr.write', 'sys.stderr.write', (["('WARNING: The filename %r CAN be encoded by the filesystem. Unicode filename tests may not be effective'\n % TESTFN_UNICODE_UNENCODEABLE)"], {}), "(\n 'WARNING: The filename %r CAN be encoded by the filesystem. Unicode filename tests may not be effective'\n % TESTFN_UNICODE_UNENCODEABLE)\n", (10835, 10982), False, 'import sys\n'), ((14670, 14709), 'locale.setlocale', 'locale.setlocale', (['category', 'orig_locale'], {}), '(category, orig_locale)\n', (14686, 14709), False, 'import locale\n'), ((2798, 2813), 'sys._getframe', 'sys._getframe', ([], {}), '()\n', (2811, 2813), False, 'import sys\n'), ((10011, 10034), 'sys.getwindowsversion', 'sys.getwindowsversion', ([], {}), '()\n', (10032, 10034), False, 'import sys\n'), ((14335, 14366), 'locale.setlocale', 'locale.setlocale', (['category', 'loc'], {}), '(category, loc)\n', (14351, 14366), False, 'import locale\n'), ((17335, 17413), 'sys.stderr.write', 'sys.stderr.write', (["('Skipping %s because of memory constraint\\n' % (f.__name__,))"], {}), "('Skipping %s because of memory constraint\\n' % (f.__name__,))\n", (17351, 17413), False, 'import sys\n'), ((7912, 7926), 'sys.exc_info', 'sys.exc_info', ([], {}), '()\n', (7924, 7926), False, 'import sys\n')]
|
import argparse
import sys
from typing import Optional
from typing import Sequence
import toml
def main(argv: Optional[Sequence[str]] = None) -> int:
parser = argparse.ArgumentParser()
parser.add_argument('filenames', nargs='*', help='Filenames to check.')
args = parser.parse_args(argv)
retval = 0
for filename in args.filenames:
try:
with open(filename) as file_handler:
toml.load(file_handler)
except toml.TomlDecodeError as exc:
print('{}: {}'.format(filename, exc))
retval = 1
return retval
if __name__ == '__main__':
sys.exit(main())
|
[
"toml.load",
"argparse.ArgumentParser"
] |
[((166, 191), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (189, 191), False, 'import argparse\n'), ((433, 456), 'toml.load', 'toml.load', (['file_handler'], {}), '(file_handler)\n', (442, 456), False, 'import toml\n')]
|
import os
class Domain(object):
def __init__(self, api_client):
self.api_client = api_client
def import_domain(self, url):
data = {'domain': os.path.basename(url), 'location': url}
self.api_client.session.post(self.api_client.get_server() + '/users/' +
self.api_client.get_username() + '/domains/importDomain', data)
def get_domain_details(self, domain):
response = self.api_client.session.get(
self.api_client.get_server() + '/users/' +
self.api_client.get_username() + '/domains/getDomainDetails?domain=' + domain
)
if response.text:
return response.json()
else:
return None
def select_default_domain(self, domain):
data = {'domain': domain}
self.api_client.session.post(self.api_client.get_server() + '/users/' +
self.api_client.get_username() + '/domains/selectDomain', data)
def delete_domain(self, domain):
data = {'domain': domain}
self.api_client.session.post(self.api_client.get_server() + '/users/' +
self.api_client.get_username() + '/domains/deleteDomain', data)
|
[
"os.path.basename"
] |
[((169, 190), 'os.path.basename', 'os.path.basename', (['url'], {}), '(url)\n', (185, 190), False, 'import os\n')]
|
from web3.auto import w3
from json import loads
from os.path import dirname, abspath
def init():
path = dirname(dirname(dirname(abspath(__file__))))
all_address_path = path + '/test/address.json'
all_address = loads(open(all_address_path).read())
dapp_hub_abi_path = path + '/build/contracts/DappHub.json'
store_info_abi_path = path + '/build/contracts/DappStoreInfo.json'
method_info_abi_path = path + '/build/contracts/DappMethodInfo.json'
store_admin_abi_path = path + '/build/contracts/DappStoreAdmin.json'
method_admin_abi_path = path + '/build/contracts/DappMethodAdmin.json'
test_abi_path = path + '/build/contracts/DappMethodAdmin.json'
contract_dapp_hub_abi = loads(open(dapp_hub_abi_path).read())['abi']
contract_store_info_abi = loads(
open(store_info_abi_path).read())['abi']
contract_method_info_abi = loads(
open(method_info_abi_path).read())['abi']
contract_store_admin_abi = loads(
open(store_admin_abi_path).read())['abi']
contract_method_admin_abi = loads(
open(method_admin_abi_path).read())['abi']
contract_test_abi = loads(open(test_abi_path).read())['abi']
contract_dapp_hub = w3.eth.contract(
address=all_address["DappHub"], abi=contract_dapp_hub_abi)
contract_store_info = w3.eth.contract(
address=all_address["DappStoreInfo"], abi=contract_store_info_abi)
contract_method_info = w3.eth.contract(
address=all_address["DappMethodInfo"], abi=contract_method_info_abi)
contract_store_admin = w3.eth.contract(
address=all_address["DappStoreAdmin"], abi=contract_store_admin_abi)
contract_method_admin = w3.eth.contract(
address=all_address["DappMethodAdmin"], abi=contract_method_admin_abi)
contract_test = w3.eth.contract(
address=all_address["Test"], abi=contract_test_abi)
return contract_dapp_hub, contract_store_info, contract_method_info, contract_store_admin,contract_method_admin,contract_test
DappHub, DappStoreInfo, DappMethodInfo, DappStoreAdmin,DappMethodAdmin,Test = init()
|
[
"web3.auto.w3.eth.contract",
"os.path.abspath"
] |
[((1201, 1275), 'web3.auto.w3.eth.contract', 'w3.eth.contract', ([], {'address': "all_address['DappHub']", 'abi': 'contract_dapp_hub_abi'}), "(address=all_address['DappHub'], abi=contract_dapp_hub_abi)\n", (1216, 1275), False, 'from web3.auto import w3\n'), ((1311, 1398), 'web3.auto.w3.eth.contract', 'w3.eth.contract', ([], {'address': "all_address['DappStoreInfo']", 'abi': 'contract_store_info_abi'}), "(address=all_address['DappStoreInfo'], abi=\n contract_store_info_abi)\n", (1326, 1398), False, 'from web3.auto import w3\n'), ((1430, 1519), 'web3.auto.w3.eth.contract', 'w3.eth.contract', ([], {'address': "all_address['DappMethodInfo']", 'abi': 'contract_method_info_abi'}), "(address=all_address['DappMethodInfo'], abi=\n contract_method_info_abi)\n", (1445, 1519), False, 'from web3.auto import w3\n'), ((1551, 1640), 'web3.auto.w3.eth.contract', 'w3.eth.contract', ([], {'address': "all_address['DappStoreAdmin']", 'abi': 'contract_store_admin_abi'}), "(address=all_address['DappStoreAdmin'], abi=\n contract_store_admin_abi)\n", (1566, 1640), False, 'from web3.auto import w3\n'), ((1673, 1764), 'web3.auto.w3.eth.contract', 'w3.eth.contract', ([], {'address': "all_address['DappMethodAdmin']", 'abi': 'contract_method_admin_abi'}), "(address=all_address['DappMethodAdmin'], abi=\n contract_method_admin_abi)\n", (1688, 1764), False, 'from web3.auto import w3\n'), ((1789, 1856), 'web3.auto.w3.eth.contract', 'w3.eth.contract', ([], {'address': "all_address['Test']", 'abi': 'contract_test_abi'}), "(address=all_address['Test'], abi=contract_test_abi)\n", (1804, 1856), False, 'from web3.auto import w3\n'), ((134, 151), 'os.path.abspath', 'abspath', (['__file__'], {}), '(__file__)\n', (141, 151), False, 'from os.path import dirname, abspath\n')]
|
import math
import metrics.util
class Sc2MetricAnalyzer(object):
"""A class that derives useful metrics from replay files
This class is an extension to the ``sc2reader`` player class. It is meant
to be used to offer useful metric data to each player in a Starcraft II replay.
The class contains raw data gathered from each replay to derive its metrics.
The raw data is filled from the ``plugins`` associated with this package.
Todo:
- #TotalSupply : Total supply created by the player.
(This is NOT current supply at given time)
- #Workers : Total number of workers created by the player.
- #Army : Total army supply created by the player.
- #Upgrades : Total count of upgrades. (+1/2/3 weapons, psionic storm,
charge, etc.)
- TimeTo66Workers : Time at which the user created 66 workers
(3-base saturation).
- TimeTo75Workers : Time at which the user created 75 workers.
- TimeTo3Bases : Time at which the 3 bases are finished.
- TimeTo4Bases : Time at which the 4 bases are finished.
- TimeToMax : Time at which the total supply created is 199 or above
(stops counting workers above 75)
- SupplyCapped : Time spent supply blocked.
- #SupplyCreateRate : Average rate at which supply buildings are
created until 200 supply
- AvgAPM : Average APM
- #AvgEPM : Average EPM
- #AvgSPM : Average SPM
- #AvgMacroCycleTime : Average time spent issuing macro commands
(vs army commands) (or maybe the avg time between giving a worker a
command and issuing another command not to the worker)
- #AvgHarassDeflection : A score that rates the player's ability
to deflect and minimize damage incurred from harassment attacks.
(works off of minerals/probes/tech/mining time lost?)
- #IdleBaseTime66 : Total time town halls are idle (not making workers) before 66 workers
- #IdleBaseTime75 : Total time town halls are idle (not making workers) before 75 workers
- AvgSQ : Spending Quotient. SQ(i,u)=35(0.00137i-ln(u))+240,
where i=resource collection rate, u=average unspent resources
- AvgSQPreMax : Spending Quotient before maxed out.
- AvgUnspent : Average unspent resources during the game.
- AvgUnspentPreMax : Average unspent resources before maxed out.
- AvgColRate : Average resource collection rate during the game.
- AvgColRatePreMax : Average resource collection rate before maxed out.
- #Units : Dictionary of all the units created, keyed by the units'
names.
- #PPM : average(mean) PAC per minute
- #PAL : PAC action latency. e.g.: how long it takes you to take your
first action after each fixation shift. (mean average)
- #APP : Actions per PAC. The average(mean) number of actions you
take each PAC
- #GAP : How long it takes you, after finishing your actions in
one PAC to establish a new fixaction. (mean average)
"""
def __init__(self):
"""Initialization of the class"""
#: A list of :class:`~metrics.metric_containers.SupplyCount` for each army supply created.
self.army_created = []
#: A list of :class:`~metrics.metric_containers.SupplyCount` for each worker created.
self.workers_created = []
#: A list of :class:`~metrics.metric_containers.SupplyCount` for each unit created.
self.supply_created = []
#: A list of :class:`~metrics.metric_containers.BaseCount` for each base created.
self.bases_created = []
#: A list of :class:`~metrics.metric_containers.FoodCount` for each unit and supply provider
#: created.
self.supply = []
#: A list of :class:`~metrics.metric_containers.ResourceCount` for resources collected and
#: resources unspent every 10 seconds.
self.resources = []
#: The player's average apm
self.avg_apm = 0
#: The player's average spm
self.avg_spm = 0
def metrics(self):
"""A dictionairy of basic metrics."""
return {'TimeToMax' : self.time_to_supply_created_max_workers(198, 75),
'TimeTo3Bases' : self.time_to_bases_created(3),
'TimeTo4Bases' : self.time_to_bases_created(4),
'TimeTo66Workers' : self.time_to_workers_created(66),
'TimeTo75Workers' : self.time_to_workers_created(75),
'AvgAPM' : self.avg_apm,
'AvgSQ' : self.avg_sq(),
'AvgSQPreMax' : self.avg_sq_pre_max(),
'AvgUnspent' : self.aur(),
'AvgUnspentPreMax' : self.aur_pre_max(),
'AvgColRate' : self.avg_rcr(),
'AvgColRatePreMax' : self.avg_rcr_pre_max(),
'SupplyCapped' : self.supply_capped()
}
def first_max(self):
"""Returns the time at which the player first reached max supply."""
return next((sp.second for sp in self.supply
if sp.supply_used >= 200), -1)
def _sq(self, resources):
sum_res_col_rate = 0
sum_unspent_res = 0
for res in resources:
sum_res_col_rate += res.res_col
sum_unspent_res += res.res_unspent
avg_col_rate = sum_res_col_rate / len(resources)
avg_unspent = sum_unspent_res / len(resources)
# SQ(i,u)=35(0.00137i-ln(u))+240, where i=resource collection rate,
# u=average unspent resources
sq = 35 * (0.00137 * avg_col_rate - math.log(avg_unspent)) + 240
return sq
def avg_sq(self):
"""Average Spending Quotient
Calculates the average Spending Quotient (SQ).
Returns:
int: The average Spending Quotient (SQ).
"""
return self._sq(self.resources)
def avg_sq_at_time(self, time_s):
"""Average Spending Quotient up to a specified time
Calculates the average Spending Quotient (SQ) up until the specified
time.
Args:
time_s (int): The time in the replay to stop calculating spending
quotient.
Returns:
int: The average Spending Quotient (SQ) up until the specified time.
"""
resources = list(filter(lambda res: res.second <= time_s,
self.resources))
if len(resources) > 0:
return self._sq(resources)
else:
return 0
def avg_sq_pre_max(self):
"""Average Spending Quotient before max supply
Calculates the average Spending Quotient (SQ) up until the player first
reaches max supply.
Returns:
int: The average Spending Quotient (SQ) up until the player first
maxes.
"""
max_s = self.first_max()
if max_s >= 0:
return self.avg_sq_at_time(max_s)
else:
return self.avg_sq()
def _aur(self, resources):
if len(resources) > 0:
sum_aur = 0
for res in resources:
sum_aur += res.res_unspent
return sum_aur / len(resources)
def aur(self):
"""Average Unspent Resources
Calculates the Average Unspent Resources (AUR) during the game.
Returns:
int: The Average Unspent Resources (AUR)
"""
return self._aur(self.resources)
def aur_at_time(self, time_s):
"""Averague Unspent Resources up to a specified time
Calculates the Average Unspent Resources (AUR) up until the specified
time.
Args:
time_s (int): The time in the replay to stop calculating average
unspent resources.
Returns:
int: The Average Unspent Resources (AUR) up until the specified
time.
"""
resources = list(filter(lambda res: res.second <= time_s,
self.resources))
return self._aur(resources)
def aur_pre_max(self):
"""Average Unspent Resources before max supply
Calculates the Average Unspent Resources (AUR) up until the player first
reaches max supply.
Returns:
int: The Average Unspent Resources (AUR) up until the player first
maxes.
"""
max_s = self.first_max()
if max_s >= 0:
return self.aur_at_time(max_s)
else:
return self.aur()
def _avg_rcr(self, resources):
if len(resources) > 0:
sum_rcr = 0
for res in resources:
sum_rcr += res.res_col
return sum_rcr / len(resources)
def avg_rcr(self):
"""Average Resource Collection Rate
Calculates the average Resource Collection Rate (RCR) during the game.
Returns:
int: The average Resource Collection Rate (RCR)
"""
return self._avg_rcr(self.resources)
def avg_rcr_at_time(self, time_s):
"""Average Resource Collection Rate up to a specified time
Calculates the average Resource Collection Rate (RCR) up until the
specified time.
Args:
time_s (int): The time in the replay to stop calculating average
resource collection rate.
Returns:
int: The average Resource Collection Rate (RCR) up until the
specified time.
"""
resources = list(filter(lambda res: res.second <= time_s,
self.resources))
return self._avg_rcr(resources)
def avg_rcr_pre_max(self):
"""Average Resource Collection Rate before max supply
Calculates the average Resource Collection Rate (RCR) up until the
player first reaches max supply.
Returns:
int: The average Resource Collection Rate (RCR) up until the player
first maxes.
"""
max_s = self.first_max()
if max_s >= 0:
return self.avg_rcr_at_time(max_s)
else:
return self.avg_rcr()
def supply_capped(self):
"""Time spent supply capped
Determines the amount of time the player was supply capped this game.
Supply capped is acknowledged when supply of units = supply created
when supply created < 200.
Returns:
int: The amount of time spent supply capped.
"""
supply_blocked = False
time_of_supply_block = 0
sc = 0
for sp in self.supply:
if (supply_blocked == False and
sp.supply_used >= sp.supply_made and
sp.supply_made < 200):
supply_blocked = True
time_of_supply_block = sp.second
elif (supply_blocked == True and
(sp.supply_used < sp.supply_made or
sp.supply_made >= 200)):
sc += (sp.second - time_of_supply_block)
supply_blocked = False
time_of_supply_block = 0
return sc
def supply_at_time(self, real_time_s):
"""The current supply used at the specified time.
Calculates the current supply used at the time.
Args:
real_time_s (int): The time (in seconds) of the replay to count
supply.
Returns:
int: The current amount of supply used in the given time.
"""
return next((sp.supply_used for sp in reversed(self.supply)
if sp.second <= real_time_s), 0)
def first_time_to_supply(self, supply):
"""The first time that the specified supply was reached.
Finds the time when the specified supply was reached.
Args:
supply (int): The supply desired.
Returns:
int: The time when the supply was first reached.
"""
return next((sp.second for sp in self.supply
if sp.supply_used >= supply), -1)
def workers_created_at_time(self, time_s):
"""Number of workers created up to the specified time
Determines the total number of workers created at the specified time.
This function is accumulative and does not handle workers lost.
Args:
time_s (int): The time (in seconds) of the replay to count workers.
Returns:
int: The number of workers created.
"""
workers = 0
idx = 0
while (len(self.workers_created) > idx
and self.workers_created[idx].second <= time_s):
workers += 1
idx += 1
if (len(self.workers_created) > 0 and self.workers_created[0].second <= time_s):
return workers
else:
return 0
def army_created_at_time(self, game_time_s):
"""The army supply created up to a specified time
Calculate the total army supply created at the specified time.
Args:
game_time_s (int): The time (in seconds) of the replay to count army
supply.
Returns:
int: The amount of army supply created in the given time.
"""
idx = 0
while (len(self.army_created) > idx
and self.army_created[idx].second <= game_time_s):
idx += 1
if (len(self.army_created) > 0 and self.army_created[0].second <= game_time_s):
return self.army_created[idx-1].supply
else:
return 0
def supply_created_at_time(self, real_time_s):
"""The total supply created up to a specified time
Calculate the total supply created at the specified time.
Args:
real_time_s (int): The time (in seconds) of the replay to count
supply.
Returns:
int: The amount of supply created in the given time.
"""
idx = 0
while (len(self.supply_created) > idx
and self.supply_created[idx].second <= real_time_s):
idx += 1
if (len(self.supply_created) > 0 and self.supply_created[0].second <= real_time_s):
return self.supply_created[idx-1].supply
else:
return 0
def time_to_workers_created(self, worker_count):
"""The time at which the number of workers had been created
Finds the time that the specified number of workers have been created.
This does not account for loss of workers.
Args:
worker_count (int): The number of workers to find the time created.
Returns:
int: The time at which the total number of workers were created.
"""
if worker_count > 0 and worker_count <= len(self.workers_created):
return self.workers_created[worker_count-1].second
else:
return None
def time_to_supply_created(self, supply_count):
"""The time at which the specified supply had been created
Finds the time when the specified supply was created. This does not
take into account supply lost.
Args:
supply_count (int): The supply desired.
Returns:
int: The time when the supply was created in the replay.
"""
#supp = list(filter(lambda sp: sp.supply <= supply_count,
# self.supply_created))
#supp = []
#for sp in self.supply_created:
# if sp.supply < supply_count:
# supp.append(sp)
# elif sp.supply >= supply_count and len(supp) > 0:
# supp.append(sp)
# break
#
#if len(supp) > 0:
# return supp[len(supp)-1].second
#else:
# return None
for sc in self.supply_created:
if sc.supply >= supply_count:
return sc.second
return None
def time_to_supply_created_max_workers(self, supply_count,
max_workers_counted):
"""The time at which the specified supply had been created
Finds the time when the specified supply was reached, but only counts a specified
number of workers towards that supply created count. This does not take into
account supply lost.
Args:
supply_count (int): The supply desired.
max_workers_counted (int): The maximum number of workers to count
towards the supply created.
Returns:
int: The time when the supply was created in the replay.
"""
if (supply_count <= 0 or
(len(self.supply_created) > 0 and
self.supply_created[len(self.supply_created)-1].supply < (supply_count - max_workers_counted))):
return None
if len(self.supply_created) == 0:
return None
supp = 0
workers = 0
for sc in self.supply_created:
if not sc.is_worker or workers < max_workers_counted:
supp += sc.unit_supply
if supp >= supply_count:
return sc.second
if sc.is_worker:
workers += 1
return self.supply_created[len(self.supply_created)-1].second
def time_to_bases_created(self, base_count):
"""The time at which the number of bases had been created
Finds the time when the specified number of bases has been reached.
This does not take into account any bases lost.
Args:
base_count (int): The number of bases desired.
Returns:
int: The time when the number of bases had been created.
"""
if base_count <= len(self.bases_created) and base_count > 0:
return self.bases_created[base_count-1].second
else:
return None
## def units_created(self, real_time_s):
## if not 'UnitBornEvent' in self._events or not 'UnitInitEvent' in self._events:
## return 0
## units_created = {"Probe": 0, "Zealot": 0, "Sentry": 0, "Stalker": 0, "Adept": 0, "HighTemplar": 0,
## "DarkTemplar": 0, "Archon": 0, "Observer": 0, "Immortal": 0, "Colossus": 0,
## "Disruptor": 0, "Phoenix": 0, "Oracle": 0, "VoidRay": 0, "Carrier": 0, "Tempest": 0}
##
## unit_born_events = self._events['UnitBornEvent']
## unit_init_events = self._events['UnitInitEvent']
## game_time_s = util.convert_to_gametime_r(self._replay, real_time_s)
##
## player_ube = list(filter(lambda ube: ube.control_pid == self._player_id, unit_born_events))
## player_uie = list(filter(lambda uie: uie.control_pid == self._player_id, unit_init_events))
##
## for ube in player_ube:
## if (ube.second <= game_time_s) and (ube.unit.name in units_created) and (not self._isHallucinated(ube.unit)):
## units_created[ube.unit.name] += 1
##
## for uie in player_uie:
## if (uie.second <= game_time_s) and (uie.unit.name in units_created) and (not self._isHallucinated(uie.unit)):
## units_created[uie.unit.name] += 1
##
## return units_created
## def encode_json(self):
## if isinstance(self, Sc2MetricAnalyzer):
## res_dict = {'__ResourceCount__': True}
## for res in self.resources:
## res_dict.update(res.to_dict())
##
##
##
## return {'__Sc2MetricAnalyzer__': True,
## 'army_created': self.army_created,
## 'workers_created': self.workers_created,
## 'supply_created': self.supply_created,
## 'bases_created': self.bases_created,
## 'current_food_used': self.current_food_used,
## 'current_food_made': self.current_food_made,
## 'resources': self.resources,
## 'avg_apm': self.avg_apm
## }
## else:
## type_name = z.__class__.__name__
## raise TypeError(f"Object of type '{type_name}' is not JSON serializable")
########## Testing ###########
if __name__ == '__main__':
ma = Sc2MetricAnalyzer("..\\test\\test_replays\\Year Zero LE (9).SC2Replay",
1)
|
[
"math.log"
] |
[((5911, 5932), 'math.log', 'math.log', (['avg_unspent'], {}), '(avg_unspent)\n', (5919, 5932), False, 'import math\n')]
|
from django.db import models
# Create your models here.
class Cases(models.Model):
title = models.CharField(max_length=126)
|
[
"django.db.models.CharField"
] |
[((97, 129), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(126)'}), '(max_length=126)\n', (113, 129), False, 'from django.db import models\n')]
|
from authors.base_test_config import TestUsingLoggedInUser
from .test_config import TestConfig
from django.urls import reverse
from rest_framework import status
from authors.response import RESPONSE
class TestArticles(TestUsingLoggedInUser, TestConfig):
"""
test suite for creation for articles
"""
def create_article(self, data):
response = self.client.post(
reverse("articles"),
data,
content_type='application/json',
HTTP_AUTHORIZATION='Token {}'.format(self.access_token)
)
return response
def test_create_article(self):
"""
test create an article
"""
response = self.create_article(self.article_data)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_create_article_with_invalid_tag_list(self):
"""
test invalid tag field
"""
response = self.create_article(self.article_data_invalid_tag_field)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(response.data["tag_list"][0],
RESPONSE["invalid_field"].format("tag_list"))
def test_create_article_with_invalid_tag(self):
"""
test invalid tag
"""
response = self.create_article(self.article_data_invalid_tag)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(response.data["tag_list"][0],
RESPONSE["invalid_field"].format("tag"))
def test_create_article_with_empty_taglist(self):
"""
test user can create an article with an empty tag field
"""
response = self.create_article(self.article_data_2)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_unauthorized_post_article(self):
"""
test unauthorized posting of an article
"""
response = self.client.post(
reverse("articles"),
self.article_data,
content_type='application/json',
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
|
[
"django.urls.reverse"
] |
[((400, 419), 'django.urls.reverse', 'reverse', (['"""articles"""'], {}), "('articles')\n", (407, 419), False, 'from django.urls import reverse\n'), ((2010, 2029), 'django.urls.reverse', 'reverse', (['"""articles"""'], {}), "('articles')\n", (2017, 2029), False, 'from django.urls import reverse\n')]
|
#######################################################
#This script is for evaluating for the task of SLR #
#######################################################
import argparse
import time
import collections
import os
import sys
import torch
import torch.nn
from torch.autograd import Variable
import torch.nn as nn
import numpy as np
import datetime as dt
import _pickle as pickle
from collections import OrderedDict
import cv2
from transformer_slr import make_model as TRANSFORMER
from dataloader_slr import loader #For SLR
from utils import path_data, Batch, greedy_decode
#Progress bar to visualize training progress
import progressbar
import matplotlib.pyplot as plt
#Evaluation metrics
from bleu import compute_bleu
from rouge import rouge
from beam_search import beam_decode
from nltk.translate.bleu_score import sentence_bleu, corpus_bleu
#https://pypi.org/project/py-rouge/
#import rouge
#Lavenshtein distance (WER)
from jiwer import wer
import tensorflow.compat.v1 as tf
tf.enable_eager_execution()
###
# Arg parsing
##############
parser = argparse.ArgumentParser(description='Evaluation')
parser.add_argument('--data', type=str, default=os.path.join('data','phoenix-2014.v3', 'phoenix2014-release','phoenix-2014-multisigner'),
help='location of the test data corpus')
parser.add_argument('--model_path', type=str, default=os.path.join("EXPERIMENTATIONS"),
help='location of the entire trained model')
parser.add_argument('--lookup_table', type=str, default=os.path.join('data','slr_lookup.txt'),
help='location of the words lookup table')
parser.add_argument('--rescale', type=int, default=224,
help='rescale data images. NOTE: use same image size as the training or else you get worse results.')
#Put to 0 to avoid memory segementation fault
parser.add_argument('--num_workers', type=int, default=0,
help='NOTE: put num of workers to 0 to avoid memory saturation.')
parser.add_argument('--image_type', type=str, default='rgb',
help='Evaluate on rgb/grayscale images')
parser.add_argument('--show_sample', action='store_true',
help='Show a sample a preprocessed data.')
parser.add_argument('--batch_size', type=int, default=1,
help='size of one minibatch')
parser.add_argument('--save', action='store_true',
help='save the results of the evaluation')
parser.add_argument('--hand_query', action='store_true',
help='Set hand cropped image as a query for transformer network.')
parser.add_argument('--data_stats', type=str, default='data_stats.pt',
help='Normalize images using the dataset stats (mean/std).')
parser.add_argument('--hand_stats', type=str, default='hand_stats.pt',
help='Normalize images using the dataset stats (mean/std).')
parser.add_argument('--emb_type', type=str, default='2d',
help='Type of image embeddings 2d or 3d.')
parser.add_argument('--emb_network', type=str, default='mb2',
help='Image embeddings network: mb2/mb2-ssd/rcnn')
parser.add_argument('--decoding', type=str, default='greedy',
help='Decoding method (greedy/beam).')
parser.add_argument('--n_beam', type=int, default=4,
help='Beam width when using bean search for decoding.')
parser.add_argument('--rel_window', type=int, default=None)
parser.add_argument('--bleu', action='store_true',
help='Use bleu for evaluation.')
parser.add_argument('--rouge', action='store_true',
help='Use rouge for evaluation.')
parser.add_argument('--txt', type=str, default=None,
help='Run evaluation from txt files.')
parser.add_argument('--heatmap', action='store_true',
help='produce heatmap.')
#----------------------------------------------------------------------------------------
#Same seed for reproducibility)
parser.add_argument('--seed', type=int, default=1111, help='random seed')
#Save folder with the date
start_date = dt.datetime.now().strftime("%Y-%m-%d-%H.%M")
print ("Start Time: "+start_date)
args = parser.parse_args()
#Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)
#experiment_path = PureWindowsPath('EXPERIMENTATIONS\\' + start_date)
save_path = os.path.join('EVALUATION', start_date)
# Creates an experimental directory and dumps all the args to a text file
if(args.save):
if(os.path.exists(save_path)):
print('Evaluation already exists..')
else:
os.makedirs(save_path)
print ("\nPutting log in EVALUATION/%s"%start_date)
#Dump all configurations/hyperparameters in txt
with open (os.path.join(save_path,'eval_config.txt'), 'w') as f:
f.write('Experimentation done at: '+ str(start_date)+' with current configurations:\n')
for arg in vars(args):
f.write(arg+' : '+str(getattr(args, arg))+'\n')
#-------------------------------------------------------------------------------
train_path, valid_path, test_path = path_data(data_path=args.data, task='SLR', hand_query=args.hand_query)
#Load stats
if(args.data_stats):
args.data_stats = torch.load(args.data_stats, map_location=torch.device('cpu'))
if(args.hand_stats):
args.hand_stats = torch.load(args.hand_stats, map_location=torch.device('cpu'))
if (args.image_type == 'rgb'):
channels = 3
elif(args.image_type == 'grayscale'):
channels = 1
else:
print('Image type is not supported!')
quit(0)
#No data augmentation for test data
test_dataloader, test_size = loader(csv_file=test_path[1],
root_dir=test_path[0],
lookup=args.lookup_table,
rescale = args.rescale,
augmentation = None,
batch_size = args.batch_size,
num_workers = args.num_workers,
show_sample = args.show_sample,
istrain=False,
hand_dir=test_path[2],
data_stats=args.data_stats,
hand_stats=args.hand_stats,
channels=channels
)
#No data augmentation for test data
valid_dataloader, valid_size = loader(csv_file=valid_path[1],
root_dir=valid_path[0],
lookup=args.lookup_table,
rescale = args.rescale,
augmentation = None,
batch_size = args.batch_size,
num_workers = args.num_workers,
show_sample = args.show_sample,
istrain=False,
hand_dir=valid_path[2],
data_stats=args.data_stats,
hand_stats=args.hand_stats,
channels=channels
)
print('Test dataset size: '+str(test_size))
print('Valid dataset size: '+str(valid_size))
#Retrieve size of target vocab
with open(args.lookup_table, 'rb') as pickle_file:
vocab = pickle.load(pickle_file)
vocab_size = len(vocab)
#Switch keys and values of vocab to easily look for words
vocab = {y:x for x,y in vocab.items()}
print('vocabulary size:' + str(vocab_size))
#Loop through test and val sets
dataloaders = [valid_dataloader, test_dataloader]
sizes = [valid_size, test_size]
dataset = ['valid', 'test']
#Blank token index
blank_index = 1232
#-------------------------------------------------------------------------------
#Run evaluation from txt files
if(args.txt):
for d in range(len(dataset)):
print(dataset[d])
#Hypotheses file
with open(os.path.join(args.txt,'simpl_translations_'+dataset[d]+'.txt')) as f:
hyp = f.read().splitlines()
#Reference file
with open(os.path.join(args.txt,'simpl_references_'+dataset[d]+'.txt')) as f:
ref = f.read().splitlines()
assert len(hyp) == len(ref)
total_wer_score = 0.0
count = 0
#Measuring WER
for i in range(len(ref)):
total_wer_score += wer(ref[i], hyp[i], standardize=True)
count += 1
print(total_wer_score/count)
quit(0)
#-------------------------------------------------------------------------------
#Run on GPU
if torch.cuda.is_available():
print("Using GPU")
device = torch.device("cuda:0")
else:
#Run on CPU
print("WARNING: You are about to run on cpu, and this will likely run out \
of memory. \n You can try setting batch_size=1 to reduce memory usage")
device = torch.device("cpu")
#-------------------------------------------------------------------------------
#Load the whole model with state dict
model = TRANSFORMER(tgt_vocab=vocab_size, n_stacks=2, n_units=1280,
n_heads=10, d_ff=2048, dropout=0.3, image_size=224,
emb_type='2d', emb_network='mb2')
model.load_state_dict(torch.load(args.model_path)['state_dict'])
#Load entire model w/ weights
#model = torch.load(args.model_path, map_location=device)
model = model.to(device)
print("Model successfully loaded")
model.eval() # Set model to evaluate mode
print ("Evaluating..")
start_time = time.time()
for d in range(len(sizes)):
dataloader = dataloaders[d]
size = sizes[d]
print(dataset[d])
#For progress bar
bar = progressbar.ProgressBar(maxval=size, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
bar.start()
i = 0
count = 0
#Save translation and reference sentences
translation_corpus = []
reference_corpus = []
total_wer_score = 0.0
count = 0
#Loop over minibatches
for step, (x, x_lengths, y, y_lengths, hand_regions, hand_lengths, name) in enumerate(dataloader):
#Update progress bar with every iter
i += len(x)
bar.update(i)
if(args.hand_query):
hand_regions = hand_regions.to(device)
else:
hand_regions = None
y = torch.from_numpy(y).to(device)
x = x.to(device)
batch = Batch(x_lengths, y_lengths, hand_lengths, trg=None, DEVICE=device, emb_type=args.emb_type, fixed_padding=None, rel_window=args.rel_window)
#with torch.no_grad():
output, output_context, output_hand = model.forward(x, batch.src_mask, batch.rel_mask, hand_regions)
#CTC loss expects (Seq, batch, vocab)
if(args.hand_query):
output = output.transpose(0,1)
output_context = output_context.transpose(0,1)
output_hand = output_hand.transpose(0,1)
else:
output = output_context.transpose(0,1)
#Predicted words with highest prob
_, pred = torch.max(output, dim=-1)
#Remove <BLANK>
#pred = pred[pred != blank_index]
if(args.heatmap):
output[17, 0, pred[17].item()].backward(retain_graph=True)
# pull the gradients out of the images feature map
#They should have same shape
gradients = model.get_activations_gradient()
activations = model.get_activations().detach()
# pool the gradients across the channels
pooled_gradients = torch.mean(gradients, dim=[2, 3])
# weight the channels by corresponding gradients
for i in range(57):
for j in range(1280):
activations[i, j, :, :] = activations[i, j, :, :] * pooled_gradients[i, j]
# average the channels of the activations
heatmap = torch.mean(activations, dim=1).squeeze()
maxi = torch.max(heatmap)
# relu on top of the heatmap
heatmap = np.maximum(heatmap.cpu().numpy(), 0)
# normalize the heatmap
heatmap /= maxi.cpu().numpy()
for i in range(heatmap.shape[0]):
#Get image
img = cv2.imread(os.path.join(args.data, 'keyfeatures/fullFrame-210x260px/dev/10January_2011_Monday_tagesschau_default-7', 'images'+'{:04d}'.format(i+1)+'.png'))
img = cv2.resize(img, (args.rescale, args.rescale))
h = heatmap[i]
h = cv2.resize(h, (args.rescale, args.rescale))
h = np.uint8(255 * h)
h = cv2.applyColorMap(h, cv2.COLORMAP_JET)
assert img.shape == h.shape
#h = h*0.4 + img
h = cv2.addWeighted(h, 0.5, img, 0.8, 0)
cv2.imwrite("samples/heatmap"+str(i)+".png", h)
x_lengths = torch.IntTensor(x_lengths)
y_lengths = torch.IntTensor(y_lengths)
decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=output.cpu().detach().numpy(),
sequence_length=x_lengths.cpu().detach().numpy(), merge_repeated=False, beam_width=10, top_paths=1)
pred = decodes[0]
pred = tf.sparse.to_dense(pred).numpy()
#Loop over translations and references
for j in range(len(y)):
ys = y[j, :y_lengths[j]]
p = pred[j]
#Remove <UNK> token
p = p[p != 0]
ys = ys[ys != 0]
hyp = (' '.join([vocab[x.item()] for x in p]))
gt = (' '.join([vocab[x.item()] for x in ys]))
total_wer_score += wer(gt, hyp, standardize=True)
count += 1
#Convert index tokens to words
translation_corpus.append(hyp)
reference_corpus.append(gt)
#Free some memory
#NOTE: this helps alot in avoiding cuda out of memory
del x, y, batch
assert len(translation_corpus) == len(reference_corpus)
print('WER score:'+str(total_wer_score/count))
if(args.save):
#Save results in txt files
with open(os.path.join(save_path, 'translations_'+dataset[d]+'.txt') ,'w') as trans_file:
trans_file.write("\n".join(translation_corpus))
with open(os.path.join(save_path, 'references_'+dataset[d]+'.txt'), 'w') as ref_file:
ref_file.write("\n".join(reference_corpus))
if(args.bleu):
#Default return
#NOTE: bleu score of camgoz results is slightly better than ntlk -> use it instead
#bleu_4 = corpus_bleu(reference_corpus, translation_corpus)
bleu_4, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=4)
#weights = (1.0/1.0, )
bleu_1, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=1)
#weights = (1.0/2.0, 1.0/2.0, )
#bleu_2 = corpus_bleu(reference_corpus, translation_corpus, weights)
bleu_2, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=2)
#weights = (1.0/3.0, 1.0/3.0, 1.0/3.0,)
#bleu_3 = corpus_bleu(reference_corpus, translation_corpus, weights)
bleu_3, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=3)
log_str = 'Bleu Evaluation: ' + '\t' \
+ 'Bleu_1: ' + str(bleu_1) + '\t' \
+ 'Bleu_2: ' + str(bleu_2) + '\t' \
+ 'Bleu_3: ' + str(bleu_3) + '\t' \
+ 'Bleu_4: ' + str(bleu_4)
print(log_str)
if(args.save):
#Save evaluation results in a log file
with open(os.path.join(args.save_path, 'log.txt'), 'a') as f:
f.write(log_str+'\n')
if(args.rouge):
reference_corpus = [" ".join(reference) for reference in reference_corpus]
translation_corpus = [" ".join(hypothesis) for hypothesis in translation_corpus]
score = rouge(translation_corpus, reference_corpus)
print(score["rouge_l/f_score"])
log_str = 'Rouge Evaluation: ' + '\t'
print(log_str)
if(args.save):
#Save evaluation results in a log file
with open(os.path.join(args.save_path, 'log.txt'), 'a') as f:
f.write(log_str+'\n')
|
[
"argparse.ArgumentParser",
"progressbar.Percentage",
"torch.device",
"os.path.join",
"_pickle.load",
"torch.load",
"os.path.exists",
"progressbar.Bar",
"dataloader_slr.loader",
"tensorflow.compat.v1.sparse.to_dense",
"utils.Batch",
"datetime.datetime.now",
"cv2.resize",
"torch.mean",
"transformer_slr.make_model",
"numpy.uint8",
"tensorflow.compat.v1.enable_eager_execution",
"torch.manual_seed",
"cv2.addWeighted",
"bleu.compute_bleu",
"torch.cuda.is_available",
"torch.max",
"torch.IntTensor",
"cv2.applyColorMap",
"utils.path_data",
"torch.from_numpy",
"rouge.rouge",
"os.makedirs",
"time.time",
"jiwer.wer"
] |
[((997, 1024), 'tensorflow.compat.v1.enable_eager_execution', 'tf.enable_eager_execution', ([], {}), '()\n', (1022, 1024), True, 'import tensorflow.compat.v1 as tf\n'), ((1070, 1119), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Evaluation"""'}), "(description='Evaluation')\n", (1093, 1119), False, 'import argparse\n'), ((4318, 4346), 'torch.manual_seed', 'torch.manual_seed', (['args.seed'], {}), '(args.seed)\n', (4335, 4346), False, 'import torch\n'), ((4430, 4468), 'os.path.join', 'os.path.join', (['"""EVALUATION"""', 'start_date'], {}), "('EVALUATION', start_date)\n", (4442, 4468), False, 'import os\n'), ((5166, 5236), 'utils.path_data', 'path_data', ([], {'data_path': 'args.data', 'task': '"""SLR"""', 'hand_query': 'args.hand_query'}), "(data_path=args.data, task='SLR', hand_query=args.hand_query)\n", (5175, 5236), False, 'from utils import path_data, Batch, greedy_decode\n'), ((5692, 6030), 'dataloader_slr.loader', 'loader', ([], {'csv_file': 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'num_workers': 'args.num_workers', 'show_sample': 'args.show_sample', 'istrain': '(False)', 'hand_dir': 'valid_path[2]', 'data_stats': 'args.data_stats', 'hand_stats': 'args.hand_stats', 'channels': 'channels'}), '(csv_file=valid_path[1], root_dir=valid_path[0], lookup=args.\n lookup_table, rescale=args.rescale, augmentation=None, batch_size=args.\n batch_size, num_workers=args.num_workers, show_sample=args.show_sample,\n istrain=False, hand_dir=valid_path[2], data_stats=args.data_stats,\n hand_stats=args.hand_stats, channels=channels)\n', (6306, 6641), False, 'from dataloader_slr import loader\n'), ((8284, 8309), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (8307, 8309), False, 'import torch\n'), ((8710, 8859), 'transformer_slr.make_model', 'TRANSFORMER', ([], {'tgt_vocab': 'vocab_size', 'n_stacks': '(2)', 'n_units': '(1280)', 'n_heads': '(10)', 'd_ff': '(2048)', 'dropout': '(0.3)', 'image_size': '(224)', 'emb_type': '"""2d"""', 'emb_network': '"""mb2"""'}), "(tgt_vocab=vocab_size, n_stacks=2, n_units=1280, n_heads=10,\n d_ff=2048, dropout=0.3, image_size=224, emb_type='2d', emb_network='mb2')\n", (8721, 8859), True, 'from transformer_slr import make_model as TRANSFORMER\n'), ((9236, 9247), 'time.time', 'time.time', ([], {}), '()\n', (9245, 9247), False, 'import time\n'), ((4566, 4591), 'os.path.exists', 'os.path.exists', (['save_path'], {}), '(save_path)\n', (4580, 4591), False, 'import os\n'), ((7028, 7052), '_pickle.load', 'pickle.load', (['pickle_file'], {}), '(pickle_file)\n', (7039, 7052), True, 'import _pickle as pickle\n'), ((8347, 8369), 'torch.device', 'torch.device', (['"""cuda:0"""'], {}), "('cuda:0')\n", (8359, 8369), False, 'import torch\n'), ((8559, 8578), 'torch.device', 'torch.device', (['"""cpu"""'], {}), "('cpu')\n", (8571, 8578), False, 'import torch\n'), ((1170, 1264), 'os.path.join', 'os.path.join', (['"""data"""', '"""phoenix-2014.v3"""', '"""phoenix2014-release"""', '"""phoenix-2014-multisigner"""'], {}), "('data', 'phoenix-2014.v3', 'phoenix2014-release',\n 'phoenix-2014-multisigner')\n", (1182, 1264), False, 'import os\n'), ((1376, 1408), 'os.path.join', 'os.path.join', (['"""EXPERIMENTATIONS"""'], {}), "('EXPERIMENTATIONS')\n", (1388, 1408), False, 'import os\n'), ((1533, 1571), 'os.path.join', 'os.path.join', (['"""data"""', '"""slr_lookup.txt"""'], {}), "('data', 'slr_lookup.txt')\n", (1545, 1571), False, 'import os\n'), ((4159, 4176), 'datetime.datetime.now', 'dt.datetime.now', ([], {}), '()\n', (4174, 4176), True, 'import datetime as dt\n'), ((4657, 4679), 'os.makedirs', 'os.makedirs', (['save_path'], {}), '(save_path)\n', (4668, 4679), False, 'import os\n'), ((8961, 8988), 'torch.load', 'torch.load', (['args.model_path'], {}), '(args.model_path)\n', (8971, 8988), False, 'import torch\n'), ((10112, 10255), 'utils.Batch', 'Batch', (['x_lengths', 'y_lengths', 'hand_lengths'], {'trg': 'None', 'DEVICE': 'device', 'emb_type': 'args.emb_type', 'fixed_padding': 'None', 'rel_window': 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+ '.txt')\n", (7649, 7704), False, 'import os\n'), ((7790, 7855), 'os.path.join', 'os.path.join', (['args.txt', "('simpl_references_' + dataset[d] + '.txt')"], {}), "(args.txt, 'simpl_references_' + dataset[d] + '.txt')\n", (7802, 7855), False, 'import os\n'), ((9432, 9462), 'progressbar.Bar', 'progressbar.Bar', (['"""="""', '"""["""', '"""]"""'], {}), "('=', '[', ']')\n", (9447, 9462), False, 'import progressbar\n'), ((9469, 9493), 'progressbar.Percentage', 'progressbar.Percentage', ([], {}), '()\n', (9491, 9493), False, 'import progressbar\n'), ((10039, 10058), 'torch.from_numpy', 'torch.from_numpy', (['y'], {}), '(y)\n', (10055, 10058), False, 'import torch\n'), ((12118, 12163), 'cv2.resize', 'cv2.resize', (['img', '(args.rescale, args.rescale)'], {}), '(img, (args.rescale, args.rescale))\n', (12128, 12163), False, 'import cv2\n'), ((12215, 12258), 'cv2.resize', 'cv2.resize', (['h', '(args.rescale, args.rescale)'], {}), '(h, (args.rescale, args.rescale))\n', (12225, 12258), False, 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'.txt')\n", (13978, 14026), False, 'import os\n'), ((11585, 11615), 'torch.mean', 'torch.mean', (['activations'], {'dim': '(1)'}), '(activations, dim=1)\n', (11595, 11615), False, 'import torch\n'), ((15302, 15341), 'os.path.join', 'os.path.join', (['args.save_path', '"""log.txt"""'], {}), "(args.save_path, 'log.txt')\n", (15314, 15341), False, 'import os\n'), ((15854, 15893), 'os.path.join', 'os.path.join', (['args.save_path', '"""log.txt"""'], {}), "(args.save_path, 'log.txt')\n", (15866, 15893), False, 'import os\n')]
|
"""
Test program for Data Bootcamp course.
Repository of materials (including this file):
* https://github.com/DaveBackus/Data_Bootcamp/
* https://github.com/DaveBackus/Data_Bootcamp/Code/Python
Written by <NAME>, March 2015
Created with Python 3.4
"""
print('\nWelcome to Data Bootcamp!')
import datetime
print('Today is: ', datetime.date.today(), '\n')
"""
Check Python version
"""
import sys
print('\nWhat version of Python are we running? \n', sys.version, '\n', sep='')
if float(sys.version_info[0]) < 3.0:
raise Exception('Program halted, old version of Python. ' +
'Sorry, you need to install Anaconda again.')
else:
print('Congratulations, Python is up to date!')
sys.exit(0) # this halts execution
#%%
"""
Check path of current working directory (just for the heck of it)
"""
# https://docs.python.org/2/library/os.path.html
import os
print('Current path:\n', os.getcwd(), sep='')
|
[
"os.getcwd",
"datetime.date.today",
"sys.exit"
] |
[((329, 350), 'datetime.date.today', 'datetime.date.today', ([], {}), '()\n', (348, 350), False, 'import datetime\n'), ((710, 721), 'sys.exit', 'sys.exit', (['(0)'], {}), '(0)\n', (718, 721), False, 'import sys\n'), ((914, 925), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (923, 925), False, 'import os\n')]
|
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import numpy as np
import paddle
import pandas as pd
import yaml
from paddle import nn
from paddle.io import DataLoader
from sklearn.metrics import classification_report
from sklearn.metrics import precision_recall_fscore_support
from yacs.config import CfgNode
from paddlespeech.text.models.ernie_linear import ErnieLinear
from paddlespeech.text.models.ernie_linear import PuncDataset
from paddlespeech.text.models.ernie_linear import PuncDatasetFromErnieTokenizer
DefinedClassifier = {
'ErnieLinear': ErnieLinear,
}
DefinedLoss = {
"ce": nn.CrossEntropyLoss,
}
DefinedDataset = {
'Punc': PuncDataset,
'Ernie': PuncDatasetFromErnieTokenizer,
}
def evaluation(y_pred, y_test):
precision, recall, f1, _ = precision_recall_fscore_support(
y_test, y_pred, average=None, labels=[1, 2, 3])
overall = precision_recall_fscore_support(
y_test, y_pred, average='macro', labels=[1, 2, 3])
result = pd.DataFrame(
np.array([precision, recall, f1]),
columns=list(['O', 'COMMA', 'PERIOD', 'QUESTION'])[1:],
index=['Precision', 'Recall', 'F1'])
result['OVERALL'] = overall[:3]
return result
def test(args):
with open(args.config) as f:
config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(config)
test_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["test_path"], **config["data_params"])
test_loader = DataLoader(
test_dataset,
batch_size=config.batch_size,
shuffle=False,
drop_last=False)
model = DefinedClassifier[config["model_type"]](**config["model"])
state_dict = paddle.load(args.checkpoint)
model.set_state_dict(state_dict["main_params"])
model.eval()
punc_list = []
for i in range(len(test_loader.dataset.id2punc)):
punc_list.append(test_loader.dataset.id2punc[i])
test_total_label = []
test_total_predict = []
for i, batch in enumerate(test_loader):
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = model(input)
pred = paddle.argmax(logit, axis=1)
test_total_label.extend(label.numpy().tolist())
test_total_predict.extend(pred.numpy().tolist())
t = classification_report(
test_total_label, test_total_predict, target_names=punc_list)
print(t)
t2 = evaluation(test_total_label, test_total_predict)
print('=========================================================')
print(t2)
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(description="Test a ErnieLinear model.")
parser.add_argument("--config", type=str, help="ErnieLinear config file.")
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
args = parser.parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
test(args)
if __name__ == "__main__":
main()
|
[
"argparse.ArgumentParser",
"paddle.load",
"paddle.reshape",
"paddle.argmax",
"sklearn.metrics.classification_report",
"numpy.array",
"yaml.safe_load",
"paddle.set_device",
"paddle.io.DataLoader",
"sklearn.metrics.precision_recall_fscore_support"
] |
[((1357, 1436), 'sklearn.metrics.precision_recall_fscore_support', 'precision_recall_fscore_support', (['y_test', 'y_pred'], {'average': 'None', 'labels': '[1, 2, 3]'}), '(y_test, y_pred, average=None, labels=[1, 2, 3])\n', (1388, 1436), False, 'from sklearn.metrics import precision_recall_fscore_support\n'), ((1460, 1546), 'sklearn.metrics.precision_recall_fscore_support', 'precision_recall_fscore_support', (['y_test', 'y_pred'], {'average': '"""macro"""', 'labels': '[1, 2, 3]'}), "(y_test, y_pred, average='macro', labels=[1,\n 2, 3])\n", (1491, 1546), False, 'from sklearn.metrics import precision_recall_fscore_support\n'), ((2149, 2239), 'paddle.io.DataLoader', 'DataLoader', (['test_dataset'], {'batch_size': 'config.batch_size', 'shuffle': '(False)', 'drop_last': '(False)'}), '(test_dataset, batch_size=config.batch_size, shuffle=False,\n drop_last=False)\n', (2159, 2239), False, 'from paddle.io import DataLoader\n'), ((2357, 2385), 'paddle.load', 'paddle.load', (['args.checkpoint'], {}), '(args.checkpoint)\n', (2368, 2385), False, 'import paddle\n'), ((2962, 3050), 'sklearn.metrics.classification_report', 'classification_report', (['test_total_label', 'test_total_predict'], {'target_names': 'punc_list'}), '(test_total_label, test_total_predict, target_names=\n punc_list)\n', (2983, 3050), False, 'from sklearn.metrics import classification_report\n'), ((3291, 3355), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Test a ErnieLinear model."""'}), "(description='Test a ErnieLinear model.')\n", (3314, 3355), False, 'import argparse\n'), ((1587, 1620), 'numpy.array', 'np.array', (['[precision, recall, f1]'], {}), '([precision, recall, f1])\n', (1595, 1620), True, 'import numpy as np\n'), ((2731, 2764), 'paddle.reshape', 'paddle.reshape', (['label'], {'shape': '[-1]'}), '(label, shape=[-1])\n', (2745, 2764), False, 'import paddle\n'), ((2812, 2840), 'paddle.argmax', 'paddle.argmax', (['logit'], {'axis': '(1)'}), '(logit, axis=1)\n', (2825, 2840), False, 'import paddle\n'), ((3667, 3691), 'paddle.set_device', 'paddle.set_device', (['"""cpu"""'], {}), "('cpu')\n", (3684, 3691), False, 'import paddle\n'), ((1861, 1878), 'yaml.safe_load', 'yaml.safe_load', (['f'], {}), '(f)\n', (1875, 1878), False, 'import yaml\n'), ((3724, 3748), 'paddle.set_device', 'paddle.set_device', (['"""gpu"""'], {}), "('gpu')\n", (3741, 3748), False, 'import paddle\n')]
|
import os
import glob
from segmentation import *
from utils.image_io import prepare_image
import numpy as np
from cv2.ximgproc import guidedFilter
from matplotlib import pyplot as plt
from _utils import *
image_dir = '../double_dip/images'
prior_hint_dir_fg = '../double_dip/saliency/output_fg'
prior_hint_dir_bg = '../double_dip/saliency/output_bg'
saliency_dir_fg = '../double_dip/saliency/output_fg'
saliency_dir_bg = '../double_dip/saliency/output_bg'
def _clear_output():
os.system('rm -rf output/*')
def _make_dir(image_name):
os.system('mkdir output/{}'.format(image_name))
def _copy_output_to_dir(image_name):
os.system('mv output/*{}* output/{}/'.format(image_name, image_name))
def _plot_learning_curve(image_name, s_obj, title):
fig, ax1 = plt.subplots()
color = 'tab:red'
ax1.set_xlabel('epochs')
ax1.set_ylabel('loss', color=color)
ax1.plot(s_obj.learning_curve, color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('PSNR', color=color)
ax2.plot(s_obj.psnr_learning_curve, color=color)
ax2.tick_params(axis='y', labelcolor=color)
fig.tight_layout()
plt.title(title)
fig.savefig('output/{}_{}.jpg'.format(
image_name.split('.')[0], '_'.join(title.split(' '))))
def _compare_learning_curve(image_name, s_obj_1, s_obj_2, title, legend):
fig = plt.figure()
plt.plot(s_obj_1.psnr_learning_curve)
plt.plot(s_obj_2.psnr_learning_curve)
plt.legend(legend)
plt.title(title)
fig.savefig('output/{}_{}.jpg'.format(
image_name.split('.')[0], '_'.join(title.split(' '))))
_clear_output()
for input_path in glob.glob(image_dir + '/*'):
image_name = os.path.basename(input_path).split('.')[0]
print('----> started training on image << {} >>'.format(image_name))
if image_name == 'eagle':
continue ###!!!
_make_dir(image_name)
im = prepare_image(os.path.join(image_dir, image_name + '.jpg'))
orig_fg = prepare_image(os.path.join(saliency_dir_fg, image_name + '.jpg'))
orig_bg = prepare_image(os.path.join(saliency_dir_bg, image_name + '.jpg'))
prior_hint_name = image_name.split('.')[0] + '_cluster_hint' + '.jpg'
prior_fg = prepare_image(os.path.join(prior_hint_dir_fg, prior_hint_name))
prior_bg = prepare_image(os.path.join(prior_hint_dir_bg, prior_hint_name))
# Configs
stage_1_iter = 500
stage_2_iter = 500
show_every = 200
# Original training
s = Segmentation(
"{}_orig".format(image_name),
im,
bg_hint=orig_bg,
fg_hint=orig_fg,
plot_during_training=True,
show_every=show_every,
first_step_iter_num=stage_1_iter,
second_step_iter_num=stage_2_iter)
s.optimize()
s.finalize()
_plot_learning_curve(image_name, s, 'original learning curve')
# Prior-based hint training
s_prior = Segmentation(
"{}_prior".format(image_name),
im,
bg_hint=prior_bg,
fg_hint=prior_fg,
plot_during_training=True,
show_every=show_every,
first_step_iter_num=stage_1_iter,
second_step_iter_num=stage_2_iter)
s_prior.optimize()
s_prior.finalize()
_plot_learning_curve(image_name, s_prior, 'prior hint learning curve')
_compare_learning_curve(
image_name,
s,
s_prior,
'orig vs prior hint learning curve',
['orig', 'with_prior'])
# Debug mask
def _fix_mask(src_image, learned_mask):
"""
fixing the masks using soft matting
:return:
"""
new_mask = guidedFilter(
src_image.transpose(1, 2, 0).astype(np.float32),
learned_mask[0].astype(np.float32),
radius=7,
eps=1e-4)
def to_bin(x):
v = np.zeros_like(x)
v[x > 0.5] = 1
return v
return to_bin(np.array([new_mask]))
src_image = s_prior.images[0]
learned_mask_np = torch_to_np(s_prior.mask_net_outputs[0])
fixed_mask_np = s_prior.fixed_masks[0]
better_mask_np = _fix_mask(src_image, learned_mask_np)
better_mask = np_to_pil(better_mask_np)
better_mask.save('output/{}_better_mask_prior.jpg'.format(image_name))
src_image = s.images[0]
learned_mask_np = torch_to_np(s.mask_net_outputs[0])
fixed_mask_np = s.fixed_masks[0]
better_mask_np = _fix_mask(src_image, learned_mask_np)
better_mask = np_to_pil(better_mask_np)
better_mask.save('output/{}_better_mask_orig.jpg'.format(image_name))
_copy_output_to_dir(image_name)
|
[
"matplotlib.pyplot.title",
"numpy.zeros_like",
"os.path.join",
"matplotlib.pyplot.plot",
"os.path.basename",
"matplotlib.pyplot.legend",
"os.system",
"matplotlib.pyplot.figure",
"numpy.array",
"glob.glob",
"matplotlib.pyplot.subplots"
] |
[((1711, 1738), 'glob.glob', 'glob.glob', (["(image_dir + '/*')"], {}), "(image_dir + '/*')\n", (1720, 1738), False, 'import glob\n'), ((486, 514), 'os.system', 'os.system', (['"""rm -rf output/*"""'], {}), "('rm -rf output/*')\n", (495, 514), False, 'import os\n'), ((787, 801), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {}), '()\n', (799, 801), True, 'from matplotlib import pyplot as plt\n'), ((1208, 1224), 'matplotlib.pyplot.title', 'plt.title', (['title'], {}), '(title)\n', (1217, 1224), True, 'from matplotlib import pyplot as plt\n'), ((1416, 1428), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1426, 1428), True, 'from matplotlib import pyplot as plt\n'), ((1433, 1470), 'matplotlib.pyplot.plot', 'plt.plot', (['s_obj_1.psnr_learning_curve'], {}), '(s_obj_1.psnr_learning_curve)\n', (1441, 1470), True, 'from matplotlib import pyplot as plt\n'), ((1475, 1512), 'matplotlib.pyplot.plot', 'plt.plot', (['s_obj_2.psnr_learning_curve'], {}), '(s_obj_2.psnr_learning_curve)\n', (1483, 1512), True, 'from matplotlib import pyplot as plt\n'), ((1517, 1535), 'matplotlib.pyplot.legend', 'plt.legend', (['legend'], {}), '(legend)\n', (1527, 1535), True, 'from matplotlib import pyplot as plt\n'), ((1540, 1556), 'matplotlib.pyplot.title', 'plt.title', (['title'], {}), '(title)\n', (1549, 1556), True, 'from matplotlib import pyplot as plt\n'), ((1996, 2040), 'os.path.join', 'os.path.join', (['image_dir', "(image_name + '.jpg')"], {}), "(image_dir, image_name + '.jpg')\n", (2008, 2040), False, 'import os\n'), ((2071, 2121), 'os.path.join', 'os.path.join', (['saliency_dir_fg', "(image_name + '.jpg')"], {}), "(saliency_dir_fg, image_name + '.jpg')\n", (2083, 2121), False, 'import os\n'), ((2151, 2201), 'os.path.join', 'os.path.join', (['saliency_dir_bg', "(image_name + '.jpg')"], {}), "(saliency_dir_bg, image_name + '.jpg')\n", (2163, 2201), False, 'import os\n'), ((2309, 2357), 'os.path.join', 'os.path.join', (['prior_hint_dir_fg', 'prior_hint_name'], {}), '(prior_hint_dir_fg, prior_hint_name)\n', (2321, 2357), False, 'import os\n'), ((2388, 2436), 'os.path.join', 'os.path.join', (['prior_hint_dir_bg', 'prior_hint_name'], {}), '(prior_hint_dir_bg, prior_hint_name)\n', (2400, 2436), False, 'import os\n'), ((3917, 3933), 'numpy.zeros_like', 'np.zeros_like', (['x'], {}), '(x)\n', (3930, 3933), True, 'import numpy as np\n'), ((4005, 4025), 'numpy.array', 'np.array', (['[new_mask]'], {}), '([new_mask])\n', (4013, 4025), True, 'import numpy as np\n'), ((1758, 1786), 'os.path.basename', 'os.path.basename', (['input_path'], {}), '(input_path)\n', (1774, 1786), False, 'import os\n')]
|
#coding:utf-8
import os
import json
import torch
from dense_coattn.utils import (AverageMeter, Initializer, StopwatchMeter,
TimeMeter, extract_statedict, move_to_cuda,
save_checkpoint)
def evalEpoch(epoch, dataloader, model, criterion, opt, writer):
torch.set_grad_enabled(False)
model.eval()
losses = AverageMeter()
accuracies = AverageMeter()
#for batch in dataloader:
for i, batch in enumerate(dataloader):
img_info, ques, ques_mask, ans_idx = move_to_cuda(batch[:-1], devices=opt.gpus)
ques = model.word_embedded(ques).detach()
if False:
img, img_mask = img_info
else:
img = img_info
img_mask = img_info
score = model(img, ques, img_mask, ques_mask)
loss = criterion(score, ans_idx)
accuracy = evaluate(score, ans_idx)
losses.update((loss.item() / opt.batch_size))
accuracies.update(accuracy.item())
if (epoch * len(dataloader) + i + 1) >= opt.num_iter and opt.num_iter > 0:
break
if writer is not None:
writer.add_scalar("epoch/val_loss", losses.avg, global_step=epoch)
writer.add_scalar("epoch/val_accuracy", accuracies.avg, global_step=epoch)
return losses.avg, accuracies.avg
def vqaEval(dataloader, model, criterion, idx2ans, opt):
criterion.reduction = 'none'
torch.set_grad_enabled(False)
model.eval()
result = []
dataset_length = len(dataloader.dataset)
if os.path.exists(os.path.join(opt.result_file, '{}.json'.format(opt.model))):
result = json.load(open(os.path.join(opt.result_file, '{}.json'.format(opt.model))))
else:
for i, batch in enumerate(dataloader):
ques_idx = batch[-1]
img_info, ques, ques_mask, ans_idx = move_to_cuda(batch[:-1], devices=opt.gpus)
ques = model.word_embedded(ques)
if False:
img, img_mask = img_info
else:
img = img_info
img_mask = img_info
score = model(img, ques, img_mask, ques_mask)
loss = criterion(score, ans_idx)
_, inds = torch.sort(score, dim=1, descending=True)
for j in range(min(ques_idx.size(0), dataset_length - i * opt.batch_size)):
result.append({"question_id": ques_idx[j].item(),
"answer": idx2ans[inds[j, 0].item()],
"entropy": loss[j].mean().item()})
json.dump(result, open(os.path.join(opt.result_file, '{}.json'.format(opt.model)), "w"))
vqa = VQA(opt.ann_file, opt.ques_file)
vqa.load_result(result)
vqa_eval = VQAEval(vqa)
vqa_eval.evaluate()
vqa_eval.compute_entropy()
print("\n")
print(">>>> Overall Accuracy is: %.02f\n" % (vqa_eval.accuracy["overall"]))
print(">>>> Per Question Type Accuracy & Entropy is the following:")
for quesType in vqa_eval.accuracy["per_questype"]:
print("%s : %.02f, %.04f" % (quesType,
vqa_eval.accuracy["per_questype"][quesType],
vqa_eval.entropy["per_questype"][quesType]))
print("\n")
print(">>>> Per Answer Type Accuracy & Entropy is the following:")
for ansType in vqa_eval.accuracy["per_anstype"]:
print("%s : %.02f, %.04f" % (ansType,
vqa_eval.accuracy["per_anstype"][ansType],
vqa_eval.entropy["per_anstype"][ansType]))
print("\n")
json.dump({"accuracy": vqa_eval.accuracy, "entropy": vqa_eval.entropy},
open(os.path.join(opt.result_file, '{}_acc.json'.format(opt.model)), "w"))
|
[
"dense_coattn.utils.AverageMeter",
"torch.sort",
"dense_coattn.utils.move_to_cuda",
"torch.set_grad_enabled"
] |
[((322, 351), 'torch.set_grad_enabled', 'torch.set_grad_enabled', (['(False)'], {}), '(False)\n', (344, 351), False, 'import torch\n'), ((382, 396), 'dense_coattn.utils.AverageMeter', 'AverageMeter', ([], {}), '()\n', (394, 396), False, 'from dense_coattn.utils import AverageMeter, Initializer, StopwatchMeter, TimeMeter, extract_statedict, move_to_cuda, save_checkpoint\n'), ((414, 428), 'dense_coattn.utils.AverageMeter', 'AverageMeter', ([], {}), '()\n', (426, 428), False, 'from dense_coattn.utils import AverageMeter, Initializer, StopwatchMeter, TimeMeter, extract_statedict, move_to_cuda, save_checkpoint\n'), ((1432, 1461), 'torch.set_grad_enabled', 'torch.set_grad_enabled', (['(False)'], {}), '(False)\n', (1454, 1461), False, 'import torch\n'), ((548, 590), 'dense_coattn.utils.move_to_cuda', 'move_to_cuda', (['batch[:-1]'], {'devices': 'opt.gpus'}), '(batch[:-1], devices=opt.gpus)\n', (560, 590), False, 'from dense_coattn.utils import AverageMeter, Initializer, StopwatchMeter, TimeMeter, extract_statedict, move_to_cuda, save_checkpoint\n'), ((1856, 1898), 'dense_coattn.utils.move_to_cuda', 'move_to_cuda', (['batch[:-1]'], {'devices': 'opt.gpus'}), '(batch[:-1], devices=opt.gpus)\n', (1868, 1898), False, 'from dense_coattn.utils import AverageMeter, Initializer, StopwatchMeter, TimeMeter, extract_statedict, move_to_cuda, save_checkpoint\n'), ((2218, 2259), 'torch.sort', 'torch.sort', (['score'], {'dim': '(1)', 'descending': '(True)'}), '(score, dim=1, descending=True)\n', (2228, 2259), False, 'import torch\n')]
|
import json
import pickle
import sys
from datetime import datetime
from json import JSONEncoder
import numpy as np
import pandas as pd
import watchdog.events
import watchdog.observers
import time
import tensorflow as tf
import configparser
import os
from kafka import KafkaProducer
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from tensorflow.python.keras.models import load_model
sys.path.append(sys.path[0] + '/..')
from mmt.readerMMT import eventsToFeatures
import warnings
warnings.filterwarnings('ignore')
conf_path = './config.config'
max_message_size = 104857600 #bytes
# ndarray json encoder
class NumpyArrayEncoder(JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
return JSONEncoder.default(self, obj)
# Kafka Producer
producer = KafkaProducer(bootstrap_servers=['localhost:9092'],max_request_size=max_message_size) # value_serializer=serializer
# Watchdog part for monitoring creation of csv files from mmt
class Handler(watchdog.events.PatternMatchingEventHandler):
def __init__(self):
# Watch for new csvs from mmt probe folder (./server/csv/)
watchdog.events.PatternMatchingEventHandler.__init__(self, patterns=['*_1__data.csv'],
## Monitoring csv repot files (_1_) with name with _data
ignore_directories=True, case_sensitive=False)
def on_closed(self, event):
print("Closing action on csv - % s." % event.src_path)
start_time = time.time()
mmt_csv = event.src_path
ips, x_features = eventsToFeatures(mmt_csv)
# if there are more ips then grouped samples from features (i.e. there is an ip but no features for the ip) -> we delete the ip from ip list
ips = pd.merge(ips, x_features, how='inner', on=['ip.session_id', 'meta.direction'])
ips = ips[['ip.session_id', 'meta.direction', 'ip']]
x_features.drop(columns=['ip.session_id', 'meta.direction'], inplace=True)
print("Prediction - test")
# rescaling with scaler used with trained model
x_test = np.asarray(x_features, np.float32)
x_test = scaler.transform(x_test)
# prediction
y_pred = model.predict(x_test)
y_pred = np.transpose(np.round(y_pred)).reshape(y_pred.shape[0], )
preds = np.array([y_pred]).T
# adding predictions to features as last column
res = np.append(x_features, preds, axis=1)
res = np.append(ips, res, axis=1)
# print(res.nbytes)
# results json encoding
j_res = json.dumps(res, cls=NumpyArrayEncoder).encode('utf-8')
print(f'Producing message @ {datetime.now()} | Message') # = {str(j_res)}')
psend = producer.send('predictions', j_res)
# print(psend)
producer.flush()
# pd.DataFrame(res).to_csv(f"{predictions_dir}predictions_{classification_id}.csv", index=False,
# header=prediction_names)
print("--- %s seconds ---" % (time.time() - start_time))
y_pred = None
res = None
features = None
if __name__ == "__main__":
config = configparser.ConfigParser()
config.read(conf_path)
mmt_csv_dir = config['DEFAULT']['mmt_probe_csv_dir']
model_path = config['DEFAULT']['model_path']
scaler_path = config['DEFAULT']['scaler_path']
print(f'{mmt_csv_dir},{model_path},{scaler_path}')
if not mmt_csv_dir or not model_path or not scaler_path:
exit('Config does not contain all needed paths')
print("Loading model...")
model = load_model(model_path)
print("Model loaded.\nLoading scaler...")
scaler = pickle.load(open(scaler_path, 'rb')) # "./saved_scalers/scaler_2022-03-02_10-37-27.pkl"
print("Scaler loaded.")
res=np.ndarray(shape=(2,2), dtype=float, order='F')
j_res = json.dumps(res, cls=NumpyArrayEncoder).encode('utf-8')
print(f'Producing message @ {datetime.now()} | Message') # = {str(j_res)}')
asd = producer.send('messages', j_res)
# asd = producer.send('messages', 'j_res')
print(asd)
producer.flush()
event_handler = Handler()
observer = watchdog.observers.Observer()
print("Starting watchdog.")
observer.schedule(event_handler, path=mmt_csv_dir, recursive=True)
observer.start()
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
observer.stop()
observer.join()
|
[
"sys.path.append",
"tensorflow.python.keras.models.load_model",
"warnings.filterwarnings",
"pandas.merge",
"numpy.asarray",
"kafka.KafkaProducer",
"json.dumps",
"time.time",
"time.sleep",
"numpy.append",
"datetime.datetime.now",
"tensorflow.compat.v1.logging.set_verbosity",
"numpy.array",
"numpy.round",
"mmt.readerMMT.eventsToFeatures",
"configparser.ConfigParser",
"numpy.ndarray",
"json.JSONEncoder.default"
] |
[((284, 346), 'tensorflow.compat.v1.logging.set_verbosity', 'tf.compat.v1.logging.set_verbosity', (['tf.compat.v1.logging.ERROR'], {}), '(tf.compat.v1.logging.ERROR)\n', (318, 346), True, 'import tensorflow as tf\n'), ((402, 438), 'sys.path.append', 'sys.path.append', (["(sys.path[0] + '/..')"], {}), "(sys.path[0] + '/..')\n", (417, 438), False, 'import sys\n'), ((499, 532), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (522, 532), False, 'import warnings\n'), ((837, 928), 'kafka.KafkaProducer', 'KafkaProducer', ([], {'bootstrap_servers': "['localhost:9092']", 'max_request_size': 'max_message_size'}), "(bootstrap_servers=['localhost:9092'], max_request_size=\n max_message_size)\n", (850, 928), False, 'from kafka import KafkaProducer\n'), ((3257, 3284), 'configparser.ConfigParser', 'configparser.ConfigParser', ([], {}), '()\n', (3282, 3284), False, 'import configparser\n'), ((3686, 3708), 'tensorflow.python.keras.models.load_model', 'load_model', (['model_path'], {}), '(model_path)\n', (3696, 3708), False, 'from tensorflow.python.keras.models import load_model\n'), ((3894, 3942), 'numpy.ndarray', 'np.ndarray', ([], {'shape': '(2, 2)', 'dtype': 'float', 'order': '"""F"""'}), "(shape=(2, 2), dtype=float, order='F')\n", (3904, 3942), True, 'import numpy as np\n'), ((776, 806), 'json.JSONEncoder.default', 'JSONEncoder.default', (['self', 'obj'], {}), '(self, obj)\n', (795, 806), False, 'from json import JSONEncoder\n'), ((1607, 1618), 'time.time', 'time.time', ([], {}), '()\n', (1616, 1618), False, 'import time\n'), ((1678, 1703), 'mmt.readerMMT.eventsToFeatures', 'eventsToFeatures', (['mmt_csv'], {}), '(mmt_csv)\n', (1694, 1703), False, 'from mmt.readerMMT import eventsToFeatures\n'), ((1868, 1946), 'pandas.merge', 'pd.merge', (['ips', 'x_features'], {'how': '"""inner"""', 'on': "['ip.session_id', 'meta.direction']"}), "(ips, x_features, how='inner', on=['ip.session_id', 'meta.direction'])\n", (1876, 1946), True, 'import pandas as pd\n'), ((2200, 2234), 'numpy.asarray', 'np.asarray', (['x_features', 'np.float32'], {}), '(x_features, np.float32)\n', (2210, 2234), True, 'import numpy as np\n'), ((2522, 2558), 'numpy.append', 'np.append', (['x_features', 'preds'], {'axis': '(1)'}), '(x_features, preds, axis=1)\n', (2531, 2558), True, 'import numpy as np\n'), ((2573, 2600), 'numpy.append', 'np.append', (['ips', 'res'], {'axis': '(1)'}), '(ips, res, axis=1)\n', (2582, 2600), True, 'import numpy as np\n'), ((2430, 2448), 'numpy.array', 'np.array', (['[y_pred]'], {}), '([y_pred])\n', (2438, 2448), True, 'import numpy as np\n'), ((3954, 3992), 'json.dumps', 'json.dumps', (['res'], {'cls': 'NumpyArrayEncoder'}), '(res, cls=NumpyArrayEncoder)\n', (3964, 3992), False, 'import json\n'), ((4458, 4471), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (4468, 4471), False, 'import time\n'), ((2678, 2716), 'json.dumps', 'json.dumps', (['res'], {'cls': 'NumpyArrayEncoder'}), '(res, cls=NumpyArrayEncoder)\n', (2688, 2716), False, 'import json\n'), ((4043, 4057), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4055, 4057), False, 'from datetime import datetime\n'), ((2368, 2384), 'numpy.round', 'np.round', (['y_pred'], {}), '(y_pred)\n', (2376, 2384), True, 'import numpy as np\n'), ((2771, 2785), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (2783, 2785), False, 'from datetime import datetime\n'), ((3123, 3134), 'time.time', 'time.time', ([], {}), '()\n', (3132, 3134), False, 'import time\n')]
|
import os
import json
import pandas as pd
import gzip
from methods import sanitize
from datetime import datetime
from config import DOCUMENT_INDEX
from methods.tweets import tweet_parser, tweet_to_namedtuple
from db.elastic import Elastic
class Fill:
def __init__(self):
self.keywords = self.set_keywords()
self.es = Elastic()
def set_keywords(self):
df = pd.read_excel('input/twitter_supported_languages.xlsx')
df = df[df['implemented'] == True].set_index('language_code')['floods_filtered']
keywords = {}
for language, words in df.iteritems():
keywords[language] = set([word.strip().lower() for word in words.split(',')])
return keywords
def open(self, fp):
if fp.endswith('.gzip') or fp.endswith('.gz'):
with gzip.open(fp, 'r') as gz:
for tweet in gz:
yield tweet.decode('utf-8')
elif fp.endswith('.jsonl'):
with open(fp, 'r', encoding='utf-8') as f:
for line in f.readlines():
if line.startswith('#'):
continue
yield line.strip()
else:
raise NotImplementedError(f'reader for extension {fp.split(".")[-1]} not implemented')
def generate_tweets(self, fp, start=datetime(1970, 1, 1)):
for tweet in self.open(fp):
try:
tweet = json.loads(tweet)
except json.decoder.JSONDecodeError:
continue
yield tweet
def prepare_doc(self, json_doc):
if 'limit' in json_doc:
return None
doc2es = tweet_parser(json_doc)
if not doc2es:
return None
language = doc2es['source']['lang']
clean_text = sanitize.clean_text(doc2es['text'], lower=False)
clean_text_lower = clean_text.lower()
try:
if not any(keyword in clean_text_lower for keyword in self.keywords[language]):
return None
except KeyError:
return None
doc2es['_index'] = DOCUMENT_INDEX
doc2es['_id'] = doc2es['id']
doc2es['source']['type'] = 'tweet'
return doc2es
def prepare_docs(self, docs):
for doc in docs:
doc2es = self.prepare_doc(doc)
if doc2es:
yield doc2es
def commit_docs(self, docs):
self.es.bulk_operation(docs)
def __call__(self, fp):
tweets = self.generate_tweets(fp)
tweets = self.prepare_docs(tweets)
self.commit_docs(tweets)
if __name__ == '__main__':
filler = Fill()
filler('input/example.jsonl')
|
[
"gzip.open",
"json.loads",
"methods.tweets.tweet_parser",
"datetime.datetime",
"pandas.read_excel",
"methods.sanitize.clean_text",
"db.elastic.Elastic"
] |
[((340, 349), 'db.elastic.Elastic', 'Elastic', ([], {}), '()\n', (347, 349), False, 'from db.elastic import Elastic\n'), ((392, 447), 'pandas.read_excel', 'pd.read_excel', (['"""input/twitter_supported_languages.xlsx"""'], {}), "('input/twitter_supported_languages.xlsx')\n", (405, 447), True, 'import pandas as pd\n'), ((1329, 1349), 'datetime.datetime', 'datetime', (['(1970)', '(1)', '(1)'], {}), '(1970, 1, 1)\n', (1337, 1349), False, 'from datetime import datetime\n'), ((1656, 1678), 'methods.tweets.tweet_parser', 'tweet_parser', (['json_doc'], {}), '(json_doc)\n', (1668, 1678), False, 'from methods.tweets import tweet_parser, tweet_to_namedtuple\n'), ((1791, 1839), 'methods.sanitize.clean_text', 'sanitize.clean_text', (["doc2es['text']"], {'lower': '(False)'}), "(doc2es['text'], lower=False)\n", (1810, 1839), False, 'from methods import sanitize\n'), ((817, 835), 'gzip.open', 'gzip.open', (['fp', '"""r"""'], {}), "(fp, 'r')\n", (826, 835), False, 'import gzip\n'), ((1429, 1446), 'json.loads', 'json.loads', (['tweet'], {}), '(tweet)\n', (1439, 1446), False, 'import json\n')]
|
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests host_event_api module."""
import datetime
import unittest
import mock
from tradefed_cluster import api_test
from tradefed_cluster import datastore_entities
from tradefed_cluster import device_manager
from tradefed_cluster import host_event
from tradefed_cluster import host_event_api
from tradefed_cluster.services import task_scheduler
device_info_emulator = {
'product': 'unknown',
'state': 'Available',
'device_serial': 'emulator-5554',
'sdk_version': 'unknown',
'build_id': 'unknown',
'run_target': 'unknown',
'product_variant': 'unknown'
}
device_info_hammerhead = {
'product': 'hammerhead',
'state': 'Available',
'device_serial': '021f34c1d01e5a59',
'sdk_version': '23',
'build_id': 'MASTER',
'run_target': 'hammerhead',
'product_variant': 'hammerhead'
}
snapshot_event = {
'time': 1234567890,
'data': {},
'cluster': 'example-cluster',
'hostname': 'example0.mtv.corp.example.com',
'lab_name': 'alab',
'event_type': 'DEVICE_SNAPSHOT',
'device_infos': [device_info_emulator, device_info_hammerhead]
}
host_state_event = {
'time': 1234567890,
'cluster': 'example-cluster',
'hostname': 'example0.mtv.corp.example.com',
'event_type': 'HOST_STATE_CHANGED',
'host_state': 'KILLING',
}
class MockDefer(object):
"""Mock methods to replace host_event_api deferred methods.
We cannot use the mock library to do this as deferred will error out with an
error related to module name changes (some black magic that the mock library
uses when replacing method implementations).
"""
deferred_ran = None
events = None
@classmethod
def ProcessHostEventA(cls, events):
assert events is not None
cls.deferred_ran = 'A'
cls.events = events
@classmethod
def ProcessHostEventB(cls, events):
assert events is not None
cls.deferred_ran = 'B'
cls.events = events
class HostEventApiTest(api_test.ApiTest):
def testSubmitHostEvents(self):
request = {'host_events': [snapshot_event]}
# Verify that there aren't any entities persisted before the API call
hosts = datastore_entities.HostInfo.query().fetch()
self.assertEqual(0, len(hosts))
devices = datastore_entities.DeviceInfo.query().fetch()
self.assertEqual(0, len(devices))
self.testapp.post_json('/_ah/api/HostEventApi.SubmitHostEvents', request)
tasks = self.mock_task_scheduler.GetTasks(
queue_names=host_event.HOST_EVENT_QUEUE_NDB)
self.assertEqual(len(tasks), 1)
task_scheduler.RunCallableTask(tasks[0].payload)
# Verify host info in datastore.
hosts = datastore_entities.HostInfo.query().fetch()
timestamp = datetime.datetime.utcfromtimestamp(int(snapshot_event['time']))
self.assertEqual(1, len(hosts))
self.assertEqual(snapshot_event['hostname'], hosts[0].hostname)
self.assertEqual(snapshot_event['lab_name'], hosts[0].lab_name)
self.assertEqual(snapshot_event['cluster'], hosts[0].physical_cluster)
self.assertEqual(timestamp, hosts[0].timestamp)
devices = datastore_entities.DeviceInfo.query().fetch()
self.assertEqual(2, len(devices))
def testSubmitHostEvents_hostStateChangedEvent(self):
request = {'host_events': [host_state_event]}
self.testapp.post_json('/_ah/api/HostEventApi.SubmitHostEvents', request)
tasks = self.mock_task_scheduler.GetTasks(
queue_names=host_event.HOST_EVENT_QUEUE_NDB)
self.assertEqual(len(tasks), 1)
host_event_api.HostEventApi._ProcessHostEventWithNDB = (
MockDefer.ProcessHostEventA)
task_scheduler.RunCallableTask(tasks[0].payload)
event = host_event.HostEvent(**MockDefer.events[0])
self.assertEqual('HOST_STATE_CHANGED', event.type)
self.assertEqual('KILLING', event.host_state)
def testSubmitHostEvents_changeDeferredFunction(self):
"""Tests that tasks execute the latest deferred function implementation."""
MockDefer.deferred_ran = None
request = {'host_events': [snapshot_event]}
# Sending 3 requests and run each with a different version
self.testapp.post_json('/_ah/api/HostEventApi.SubmitHostEvents', request)
self.testapp.post_json('/_ah/api/HostEventApi.SubmitHostEvents', request)
self.testapp.post_json('/_ah/api/HostEventApi.SubmitHostEvents', request)
tasks = self.mock_task_scheduler.GetTasks(
queue_names=host_event.HOST_EVENT_QUEUE_NDB)
self.assertEqual(len(tasks), 3)
task_scheduler.RunCallableTask(tasks[0].payload)
self.assertIsNone(MockDefer.deferred_ran)
host_event_api.HostEventApi._ProcessHostEventWithNDB = (
MockDefer.ProcessHostEventA)
task_scheduler.RunCallableTask(tasks[1].payload)
self.assertEqual('A', MockDefer.deferred_ran)
host_event_api.HostEventApi._ProcessHostEventWithNDB = (
MockDefer.ProcessHostEventB)
task_scheduler.RunCallableTask(tasks[2].payload)
self.assertEqual('B', MockDefer.deferred_ran)
@mock.patch.object(task_scheduler, 'AddCallableTask')
def testSubmitHostEvents_multipleChunks(self, mock_defer):
"""Tests a request with more vents than the chunk size."""
host_event_api.CHUNK_SIZE = 5
request = {'host_events': [snapshot_event] * 50}
self.testapp.post_json('/_ah/api/HostEventApi.SubmitHostEvents', request)
# Should be called 10 times with 5 chunks each
mock_defer.assert_has_calls([
mock.call(mock.ANY, [snapshot_event] * 5,
_queue=host_event.HOST_EVENT_QUEUE_NDB,
_target='testbed-version.default')] * 10)
def testSubmitHostEvents_emptyRequest(self):
request = {}
res = self.testapp.post_json(
'/_ah/api/HostEventApi.SubmitHostEvents', request,
expect_errors=True)
self.assertEqual('400 Bad Request', res.status)
@mock.patch.object(device_manager, 'HandleDeviceSnapshotWithNDB')
@mock.patch.object(device_manager, 'IsHostEventValid', return_value=False)
def testProcessHostEvent_invalidEvent(self, mock_valid, mock_handle):
"""Tests _ProcessHostEvent with an invalid event."""
host_event_api.HostEventApi()._ProcessHostEventWithNDB([snapshot_event])
mock_valid.assert_called_once_with(snapshot_event)
self.assertFalse(mock_handle.called)
def testChunks(self):
"""Test host_event_api.chunks()."""
l = [1, 2, 3, 4, 5]
result = [c for c in host_event_api.chunks(l, 2)]
self.assertEqual(3, len(result))
self.assertEqual([1, 2], result[0])
self.assertEqual([3, 4], result[1])
self.assertEqual([5], result[2])
def testChunks_emptyList(self):
"""Test host_event_api.chunks() given an empty list."""
result = [c for c in host_event_api.chunks([], 2)]
self.assertEqual(0, len(result))
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"mock.patch.object",
"tradefed_cluster.host_event_api.HostEventApi",
"mock.call",
"tradefed_cluster.datastore_entities.DeviceInfo.query",
"tradefed_cluster.host_event_api.chunks",
"tradefed_cluster.services.task_scheduler.RunCallableTask",
"tradefed_cluster.host_event.HostEvent",
"tradefed_cluster.datastore_entities.HostInfo.query"
] |
[((5507, 5559), 'mock.patch.object', 'mock.patch.object', (['task_scheduler', '"""AddCallableTask"""'], {}), "(task_scheduler, 'AddCallableTask')\n", (5524, 5559), False, 'import mock\n'), ((6344, 6408), 'mock.patch.object', 'mock.patch.object', (['device_manager', '"""HandleDeviceSnapshotWithNDB"""'], {}), "(device_manager, 'HandleDeviceSnapshotWithNDB')\n", (6361, 6408), False, 'import mock\n'), ((6412, 6485), 'mock.patch.object', 'mock.patch.object', (['device_manager', '"""IsHostEventValid"""'], {'return_value': '(False)'}), "(device_manager, 'IsHostEventValid', return_value=False)\n", (6429, 6485), False, 'import mock\n'), ((7303, 7318), 'unittest.main', 'unittest.main', ([], {}), '()\n', (7316, 7318), False, 'import unittest\n'), ((3097, 3145), 'tradefed_cluster.services.task_scheduler.RunCallableTask', 'task_scheduler.RunCallableTask', (['tasks[0].payload'], {}), '(tasks[0].payload)\n', (3127, 3145), False, 'from tradefed_cluster.services import task_scheduler\n'), ((4139, 4187), 'tradefed_cluster.services.task_scheduler.RunCallableTask', 'task_scheduler.RunCallableTask', (['tasks[0].payload'], {}), '(tasks[0].payload)\n', (4169, 4187), False, 'from tradefed_cluster.services import task_scheduler\n'), ((4200, 4243), 'tradefed_cluster.host_event.HostEvent', 'host_event.HostEvent', ([], {}), '(**MockDefer.events[0])\n', (4220, 4243), False, 'from tradefed_cluster import host_event\n'), ((5006, 5054), 'tradefed_cluster.services.task_scheduler.RunCallableTask', 'task_scheduler.RunCallableTask', (['tasks[0].payload'], {}), '(tasks[0].payload)\n', (5036, 5054), False, 'from tradefed_cluster.services import task_scheduler\n'), ((5203, 5251), 'tradefed_cluster.services.task_scheduler.RunCallableTask', 'task_scheduler.RunCallableTask', (['tasks[1].payload'], {}), '(tasks[1].payload)\n', (5233, 5251), False, 'from tradefed_cluster.services import task_scheduler\n'), ((5404, 5452), 'tradefed_cluster.services.task_scheduler.RunCallableTask', 'task_scheduler.RunCallableTask', (['tasks[2].payload'], {}), '(tasks[2].payload)\n', (5434, 5452), False, 'from tradefed_cluster.services import task_scheduler\n'), ((2700, 2735), 'tradefed_cluster.datastore_entities.HostInfo.query', 'datastore_entities.HostInfo.query', ([], {}), '()\n', (2733, 2735), False, 'from tradefed_cluster import datastore_entities\n'), ((2794, 2831), 'tradefed_cluster.datastore_entities.DeviceInfo.query', 'datastore_entities.DeviceInfo.query', ([], {}), '()\n', (2829, 2831), False, 'from tradefed_cluster import datastore_entities\n'), ((3195, 3230), 'tradefed_cluster.datastore_entities.HostInfo.query', 'datastore_entities.HostInfo.query', ([], {}), '()\n', (3228, 3230), False, 'from tradefed_cluster import datastore_entities\n'), ((3632, 3669), 'tradefed_cluster.datastore_entities.DeviceInfo.query', 'datastore_entities.DeviceInfo.query', ([], {}), '()\n', (3667, 3669), False, 'from tradefed_cluster import datastore_entities\n'), ((6619, 6648), 'tradefed_cluster.host_event_api.HostEventApi', 'host_event_api.HostEventApi', ([], {}), '()\n', (6646, 6648), False, 'from tradefed_cluster import host_event_api\n'), ((6902, 6929), 'tradefed_cluster.host_event_api.chunks', 'host_event_api.chunks', (['l', '(2)'], {}), '(l, 2)\n', (6923, 6929), False, 'from tradefed_cluster import host_event_api\n'), ((7205, 7233), 'tradefed_cluster.host_event_api.chunks', 'host_event_api.chunks', (['[]', '(2)'], {}), '([], 2)\n', (7226, 7233), False, 'from tradefed_cluster import host_event_api\n'), ((5942, 6063), 'mock.call', 'mock.call', (['mock.ANY', '([snapshot_event] * 5)'], {'_queue': 'host_event.HOST_EVENT_QUEUE_NDB', '_target': '"""testbed-version.default"""'}), "(mock.ANY, [snapshot_event] * 5, _queue=host_event.\n HOST_EVENT_QUEUE_NDB, _target='testbed-version.default')\n", (5951, 6063), False, 'import mock\n')]
|
import unittest
from tilecloud import Bounds
class TestBounds(unittest.TestCase):
def test_empty(self) -> None:
bounds = Bounds()
assert len(bounds) == 0
assert 1 not in bounds
self.assertRaises(StopIteration, next, iter(bounds))
assert bounds == bounds
def test_init_one_argument(self) -> None:
bounds = Bounds(1)
assert list(bounds) == [1]
def test_init_two_arguments(self) -> None:
bounds = Bounds(1, 3)
assert list(bounds) == [1, 2]
def test_add(self) -> None:
bounds = Bounds()
assert len(bounds) == 0
bounds.add(1)
assert list(bounds) == [1]
bounds.add(1)
assert list(bounds) == [1]
bounds.add(2)
assert list(bounds) == [1, 2]
def test_update(self) -> None:
bounds1 = Bounds(1, 3)
bounds2 = Bounds(3, 5)
self.assertTrue(bounds1.update(bounds2) is bounds1)
self.assertEqual(len(bounds1), 4)
self.assertEqual(list(bounds1), [1, 2, 3, 4])
def test_update_empty(self) -> None:
bounds1 = Bounds()
bounds2 = Bounds(3, 5)
assert bounds1.update(bounds2) is bounds1
assert list(bounds1) == [3, 4]
def test_union_empty_empty(self) -> None:
bounds1 = Bounds()
bounds2 = Bounds()
bounds3 = bounds1.union(bounds2)
assert bounds3 is not bounds1
assert bounds3 is not bounds2
assert len(bounds3) == 0
def test_union_empty_normal(self) -> None:
bounds1 = Bounds()
bounds2 = Bounds(3, 5)
bounds3 = bounds1.union(bounds2)
assert bounds3 is not bounds1
assert bounds3 is not bounds2
assert list(bounds3) == [3, 4]
def test_union_normal_empty(self) -> None:
bounds1 = Bounds(1, 3)
bounds2 = Bounds()
bounds3 = bounds1.union(bounds2)
assert bounds3 is not bounds1
assert bounds3 is not bounds2
assert list(bounds3) == [1, 2]
def test_union_normal_normal(self) -> None:
bounds1 = Bounds(1, 3)
bounds2 = Bounds(3, 5)
bounds3 = bounds1.union(bounds2)
assert bounds3 is not bounds1
assert bounds3 is not bounds2
assert list(bounds3) == [1, 2, 3, 4]
|
[
"tilecloud.Bounds"
] |
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|
import sys
from rich import print
import pytorch_lightning as pl
from argparse import ArgumentParser
from data_modules import get_data_module
from networks_modules import Conv2, Conv4, Conv6
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBar
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--lr",type=float,default=50, help="Learning Rate [default 50]")
parser.add_argument("--momentum", type=float, default=0.9, help="Momentum [default 0.9]")
parser.add_argument("--batch-size", type=int, default=256, help="batch size [default 256]")
parser.add_argument("--strategy", type=str, default="ASLP", help="Which method to use [default ASLP]")
parser.add_argument("--weight-rescale",action="store_true", help="Whether to use smart rescale or not [default False]")
parser.add_argument("--signed-constant", action="store_true", help="Whether to use Signed Constant (SC) or not [default False]")
parser.add_argument("--network", type=str, default="Conv4", help="Select network architecture (Conv2, 4 or 6) [default Conv4]")
parser.add_argument("--data-path", type=str, default=".", help="Specify where to download dataset [default '.' (current directory)]")
parser.add_argument("--name", type=str, default="Experiment", help="Name of the experiment for tensorboard logger [default Experiment]")
parser.add_argument("--prune-and-test", action="store_true", help="Whether to prune and evaluate network on the test dataset [default False]")
parser.add_argument("--no-data-augment", action="store_true", help="Whether to enable or disable data augmentation [default False]")
args = parser.parse_args()
config = {
"dataset": "cifar10", # Use CIFAR10 dataset
"val_split":0.1, # Use 10% of images from train split as a validation split
"max_epochs": 1000, # Train during 1000 epochs maximum
"target_sparsity":0.5, # Only applicable if strategy is edge-popup
"init_scheme": "kaiming_uniform_fan_in", # Which weight initialization (Pytorch Standard Init)
"init_value":0, # Initial value for the masks
"sp_lr":1e-3, # Learning rate for the scaling parameters (Smart Rescale)
}
# Update config with values form the CLI arguments
config.update(vars(args))
# Print config dict
print(config)
# Get DataModule with our parameters
dm = get_data_module(config)
if config["network"] not in ["Conv2", "Conv4", "Conv6"]:
print("##### The network {} is not defined".format(config["network"]),file=sys.stderr)
sys.exit()
else:
Model = eval(config["network"])
# Create model
net = Model(config)
print(net)
# Create logger
logger = TensorBoardLogger("tb_logs", name=config["name"])
# Create Early Stopping callback
early_stopper = EarlyStopping(
monitor="val_acc",
patience=100,
mode="max",
)
# Create Model Checkpoint Callback
mdl_chkpt = ModelCheckpoint(
monitor="val_acc",
mode="max"
)
# Create trainer object
trainer = pl.Trainer(accelerator="gpu",
devices=1,
max_epochs=config["max_epochs"],
callbacks=[early_stopper,mdl_chkpt,RichProgressBar()])
# Train model
trainer.fit(net, dm)
# Test best model
results = trainer.test(net, dm, ckpt_path=mdl_chkpt.best_model_path)
print("Best accuracy: {:.2f}%".format(results[0]["test_acc"]*100))
print("Best model path = " + mdl_chkpt.best_model_path)
if config["prune_and_test"]:
net = Model.load_from_checkpoint(mdl_chkpt.best_model_path)
if config["strategy"] == "supermask":
# With this strategy, no pruning but 10 sampled topologies which accuracies are averaged
accuracies = []
for _ in range(10):
test_acc = trainer.test(net,dm)[0]["test_acc"]
accuracies.append(test_acc)
final_test_acc = sum(accuracies)/10
elif config["strategy"] == "edge-popup":
# With this strategy, no pruning since it is enforced during the forward pass
final_test_acc = trainer.test(net,dm)[0]["test_acc"]
elif config["strategy"] == "ASLP":
net.strategy.prune_net(net)
final_test_acc = trainer.test(net,dm)[0]["test_acc"]
print("Pruned model test accuracy: {:.2f}%".format(final_test_acc*100))
|
[
"data_modules.get_data_module",
"argparse.ArgumentParser",
"rich.print",
"pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
"pytorch_lightning.callbacks.progress.rich_progress.RichProgressBar",
"pytorch_lightning.loggers.TensorBoardLogger",
"pytorch_lightning.callbacks.early_stopping.EarlyStopping",
"sys.exit"
] |
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|
from setuptools import setup, find_packages
import codegrapher
setup(
name='codegrapher',
version=codegrapher.__version__,
description='Code that graphs code',
url='http://github.com/LaurEars/codegrapher',
author='<NAME>',
author_email='<EMAIL>',
packages=find_packages(),
include_package_data=True,
install_requires=[
'click',
'graphviz'
],
tests_require=[
'nose',
'coverage'
],
entry_points='''
[console_scripts]
codegrapher=cli:cli
''',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 2 :: Only',
'Topic :: Software Development :: Documentation',
]
)
|
[
"setuptools.find_packages"
] |
[((287, 302), 'setuptools.find_packages', 'find_packages', ([], {}), '()\n', (300, 302), False, 'from setuptools import setup, find_packages\n')]
|
# Generated by Django 2.1.4 on 2020-05-03 15:54
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Movie',
fields=[
('id', models.IntegerField(primary_key=True, serialize=False)),
('name', models.CharField(max_length=100)),
('year', models.IntegerField(null=True)),
('summary', models.TextField(max_length=500, null=True)),
('poster_url', models.URLField(blank=True, null=True)),
('slug', models.SlugField(blank=True, null=True, unique=True)),
],
options={
'ordering': ['-year'],
},
),
]
|
[
"django.db.models.TextField",
"django.db.models.URLField",
"django.db.models.CharField",
"django.db.models.SlugField",
"django.db.models.IntegerField"
] |
[((301, 355), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'primary_key': '(True)', 'serialize': '(False)'}), '(primary_key=True, serialize=False)\n', (320, 355), False, 'from django.db import migrations, models\n'), ((383, 415), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)'}), '(max_length=100)\n', (399, 415), False, 'from django.db import migrations, models\n'), ((443, 473), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'null': '(True)'}), '(null=True)\n', (462, 473), False, 'from django.db import migrations, models\n'), ((504, 547), 'django.db.models.TextField', 'models.TextField', ([], {'max_length': '(500)', 'null': '(True)'}), '(max_length=500, null=True)\n', (520, 547), False, 'from django.db import migrations, models\n'), ((581, 619), 'django.db.models.URLField', 'models.URLField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (596, 619), False, 'from django.db import migrations, models\n'), ((647, 699), 'django.db.models.SlugField', 'models.SlugField', ([], {'blank': '(True)', 'null': '(True)', 'unique': '(True)'}), '(blank=True, null=True, unique=True)\n', (663, 699), False, 'from django.db import migrations, models\n')]
|
import logging
import sys
from reconcile.utils import gql
from reconcile.utils import expiration
from reconcile.utils.aggregated_list import (AggregatedList,
AggregatedDiffRunner,
RunnerException)
from reconcile.quay_base import get_quay_api_store
from reconcile.status import ExitCodes
from reconcile.utils.quay_api import QuayTeamNotFoundException
QUAY_ORG_QUERY = """
{
roles: roles_v1 {
name
users {
name
quay_username
}
bots {
name
quay_username
}
permissions {
service
...on PermissionQuayOrgTeam_v1 {
quayOrg {
name
instance {
name
}
}
team
}
}
expirationDate
}
}
"""
QONTRACT_INTEGRATION = 'quay-membership'
def process_permission(permission):
"""Returns a new permission object with the right keys
State needs these fields: service, org, team.
But the input (coming from QUAY_ORG_QUERY) will have:
service, quayOrg, team
"""
return {
'service': permission['service'],
'team': permission['team'],
'org': (permission['quayOrg']['instance']['name'],
permission['quayOrg']['name'])
}
def fetch_current_state(quay_api_store):
state = AggregatedList()
for org_key, org_data in quay_api_store.items():
quay_api = org_data['api']
teams = org_data['teams']
if not teams:
continue
for team in teams:
try:
members = quay_api.list_team_members(team)
except QuayTeamNotFoundException:
logging.warning("Attempted to list members for team %s in "
"org %s/%s, but it doesn't exist", team,
org_key.instance, org_key.org_name)
else:
# Teams are only added to the state if they exist so that
# there is a proper diff between the desired and current state.
state.add({
'service': 'quay-membership',
'org': org_key,
'team': team
}, members)
return state
def fetch_desired_state():
gqlapi = gql.get_api()
roles = expiration.filter(gqlapi.query(QUAY_ORG_QUERY)['roles'])
state = AggregatedList()
for role in roles:
permissions = [
process_permission(p)
for p in role['permissions']
if p.get('service') == 'quay-membership'
]
if permissions:
members = []
def append_quay_username_members(member):
quay_username = member.get('quay_username')
if quay_username:
members.append(quay_username)
for user in role['users']:
append_quay_username_members(user)
for bot in role['bots']:
append_quay_username_members(bot)
for p in permissions:
state.add(p, members)
return state
class RunnerAction:
def __init__(self, dry_run, quay_api_store):
self.dry_run = dry_run
self.quay_api_store = quay_api_store
def add_to_team(self):
label = "add_to_team"
def action(params, items):
org = params["org"]
team = params["team"]
missing_users = False
quay_api = self.quay_api_store[org]['api']
for member in items:
logging.info([label, member, org, team])
user_exists = quay_api.user_exists(member)
if user_exists:
if not self.dry_run:
quay_api.add_user_to_team(member, team)
else:
missing_users = True
logging.error(f'quay user {member} does not exist.')
# This will be evaluated by AggregatedDiffRunner.run(). The happy
# case is to return True: no missing users
return not missing_users
return action
def create_team(self):
"""
Create an empty team in Quay. This method avoids adding users to the
new team. add_to_team() will handle updating the member list the
next time run() is executed, while keeping this action very simple.
"""
label = "create_team"
def action(params, items):
org = params["org"]
team = params["team"]
# Ensure all quay org/teams are declared as dependencies in a
# `/dependencies/quay-org-1.yml` datafile.
if team not in self.quay_api_store[org]["teams"]:
raise RunnerException(f"Quay team {team} is not defined as a "
f"managedTeam in the {org} org.")
logging.info([label, org, team])
if not self.dry_run:
quay_api = self.quay_api_store[org]['api']
quay_api.create_or_update_team(team)
return True
return action
def del_from_team(self):
label = "del_from_team"
def action(params, items):
org = params["org"]
team = params["team"]
if self.dry_run:
for member in items:
logging.info([label, member, org, team])
else:
quay_api = self.quay_api_store[org]['api']
for member in items:
logging.info([label, member, org, team])
quay_api.remove_user_from_team(member, team)
return True
return action
def run(dry_run):
quay_api_store = get_quay_api_store()
current_state = fetch_current_state(quay_api_store)
desired_state = fetch_desired_state()
# calculate diff
diff = current_state.diff(desired_state)
logging.debug("State diff: %s", diff)
# Run actions
runner_action = RunnerAction(dry_run, quay_api_store)
runner = AggregatedDiffRunner(diff)
runner.register("insert", runner_action.create_team())
runner.register("update-insert", runner_action.add_to_team())
runner.register("update-delete", runner_action.del_from_team())
runner.register("delete", runner_action.del_from_team())
status = runner.run()
if not status:
sys.exit(ExitCodes.ERROR)
|
[
"logging.error",
"logging.debug",
"reconcile.utils.aggregated_list.RunnerException",
"reconcile.utils.gql.get_api",
"logging.warning",
"reconcile.utils.aggregated_list.AggregatedDiffRunner",
"logging.info",
"reconcile.utils.aggregated_list.AggregatedList",
"reconcile.quay_base.get_quay_api_store",
"sys.exit"
] |
[((1355, 1371), 'reconcile.utils.aggregated_list.AggregatedList', 'AggregatedList', ([], {}), '()\n', (1369, 1371), False, 'from reconcile.utils.aggregated_list import AggregatedList, AggregatedDiffRunner, RunnerException\n'), ((2310, 2323), 'reconcile.utils.gql.get_api', 'gql.get_api', ([], {}), '()\n', (2321, 2323), False, 'from reconcile.utils import gql\n'), ((2406, 2422), 'reconcile.utils.aggregated_list.AggregatedList', 'AggregatedList', ([], {}), '()\n', (2420, 2422), False, 'from reconcile.utils.aggregated_list import AggregatedList, AggregatedDiffRunner, RunnerException\n'), ((5755, 5775), 'reconcile.quay_base.get_quay_api_store', 'get_quay_api_store', ([], {}), '()\n', (5773, 5775), False, 'from reconcile.quay_base import get_quay_api_store\n'), ((5946, 5983), 'logging.debug', 'logging.debug', (['"""State diff: %s"""', 'diff'], {}), "('State diff: %s', diff)\n", (5959, 5983), False, 'import logging\n'), ((6074, 6100), 'reconcile.utils.aggregated_list.AggregatedDiffRunner', 'AggregatedDiffRunner', (['diff'], {}), '(diff)\n', (6094, 6100), False, 'from reconcile.utils.aggregated_list import AggregatedList, AggregatedDiffRunner, RunnerException\n'), ((6410, 6435), 'sys.exit', 'sys.exit', (['ExitCodes.ERROR'], {}), '(ExitCodes.ERROR)\n', (6418, 6435), False, 'import sys\n'), ((4907, 4939), 'logging.info', 'logging.info', (['[label, org, team]'], {}), '([label, org, team])\n', (4919, 4939), False, 'import logging\n'), ((3574, 3614), 'logging.info', 'logging.info', (['[label, member, org, team]'], {}), '([label, member, org, team])\n', (3586, 3614), False, 'import logging\n'), ((4765, 4856), 'reconcile.utils.aggregated_list.RunnerException', 'RunnerException', (['f"""Quay team {team} is not defined as a managedTeam in the {org} org."""'], {}), "(\n f'Quay team {team} is not defined as a managedTeam in the {org} org.')\n", (4780, 4856), False, 'from reconcile.utils.aggregated_list import AggregatedList, AggregatedDiffRunner, RunnerException\n'), ((1703, 1845), 'logging.warning', 'logging.warning', (['"""Attempted to list members for team %s in org %s/%s, but it doesn\'t exist"""', 'team', 'org_key.instance', 'org_key.org_name'], {}), '(\n "Attempted to list members for team %s in org %s/%s, but it doesn\'t exist",\n team, org_key.instance, org_key.org_name)\n', (1718, 1845), False, 'import logging\n'), ((3894, 3946), 'logging.error', 'logging.error', (['f"""quay user {member} does not exist."""'], {}), "(f'quay user {member} does not exist.')\n", (3907, 3946), False, 'import logging\n'), ((5385, 5425), 'logging.info', 'logging.info', (['[label, member, org, team]'], {}), '([label, member, org, team])\n', (5397, 5425), False, 'import logging\n'), ((5560, 5600), 'logging.info', 'logging.info', (['[label, member, org, team]'], {}), '([label, member, org, team])\n', (5572, 5600), False, 'import logging\n')]
|
# Compatibility Python 2/3
from __future__ import division, print_function, absolute_import
from builtins import range
# ----------------------------------------------------------------------------------------------------------------------
import sys
import scipyplot.log as log
import matplotlib.pyplot as plt
def save2file(nameFile, fig=plt.gcf(), fileFormat='pdf', verbosity=1, indent=0, dpi=100):
"""
:param fig:
:param nameFile:
:param fileFormat:
:param verbosity:
:param indent:
:param dpi:
:return:
"""
fullNameFile = nameFile + '.' + fileFormat
log.cnd_msg(verbosity, 0, 'Saving to file: ' + fullNameFile, indent_depth=indent)
try:
fig.savefig(fullNameFile, dpi=dpi, bbox_inches='tight', pad_inches=0)
status = 0
except:
log.cnd_warning(verbosity, 1, str(sys.exc_info()[0]))
status = -1
log.cnd_status(verbosity, 0, status)
|
[
"scipyplot.log.cnd_msg",
"matplotlib.pyplot.gcf",
"sys.exc_info",
"scipyplot.log.cnd_status"
] |
[((342, 351), 'matplotlib.pyplot.gcf', 'plt.gcf', ([], {}), '()\n', (349, 351), True, 'import matplotlib.pyplot as plt\n'), ((602, 688), 'scipyplot.log.cnd_msg', 'log.cnd_msg', (['verbosity', '(0)', "('Saving to file: ' + fullNameFile)"], {'indent_depth': 'indent'}), "(verbosity, 0, 'Saving to file: ' + fullNameFile, indent_depth=\n indent)\n", (613, 688), True, 'import scipyplot.log as log\n'), ((888, 924), 'scipyplot.log.cnd_status', 'log.cnd_status', (['verbosity', '(0)', 'status'], {}), '(verbosity, 0, status)\n', (902, 924), True, 'import scipyplot.log as log\n'), ((844, 858), 'sys.exc_info', 'sys.exc_info', ([], {}), '()\n', (856, 858), False, 'import sys\n')]
|
# -*- coding: utf-8 -*-
#---------------------------------------------------------------------------
# Copyright 2020 VMware, Inc. All rights reserved.
# AUTO GENERATED FILE -- DO NOT MODIFY!
#
# vAPI stub file for package com.vmware.nsx.fabric.
#---------------------------------------------------------------------------
"""
"""
__author__ = 'VMware, Inc.'
__docformat__ = 'restructuredtext en'
import sys
from vmware.vapi.bindings import type
from vmware.vapi.bindings.converter import TypeConverter
from vmware.vapi.bindings.enum import Enum
from vmware.vapi.bindings.error import VapiError
from vmware.vapi.bindings.struct import VapiStruct
from vmware.vapi.bindings.stub import (
ApiInterfaceStub, StubFactoryBase, VapiInterface)
from vmware.vapi.bindings.common import raise_core_exception
from vmware.vapi.data.validator import (UnionValidator, HasFieldsOfValidator)
from vmware.vapi.exception import CoreException
from vmware.vapi.lib.constants import TaskType
from vmware.vapi.lib.rest import OperationRestMetadata
class ComputeCollectionFabricTemplates(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.compute_collection_fabric_templates'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ComputeCollectionFabricTemplatesStub)
self._VAPI_OPERATION_IDS = {}
def create(self,
compute_collection_fabric_template,
):
"""
Fabric templates are fabric configurations applied at the compute
collection level. This configurations is used to decide what automated
operations should be a run when a host membership changes.
:type compute_collection_fabric_template: :class:`com.vmware.nsx.model_client.ComputeCollectionFabricTemplate`
:param compute_collection_fabric_template: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeCollectionFabricTemplate`
:return: com.vmware.nsx.model.ComputeCollectionFabricTemplate
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create',
{
'compute_collection_fabric_template': compute_collection_fabric_template,
})
def delete(self,
fabric_template_id,
):
"""
Deletes compute collection fabric template for the given id
:type fabric_template_id: :class:`str`
:param fabric_template_id: (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('delete',
{
'fabric_template_id': fabric_template_id,
})
def get(self,
fabric_template_id,
):
"""
Get compute collection fabric template for the given id
:type fabric_template_id: :class:`str`
:param fabric_template_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeCollectionFabricTemplate`
:return: com.vmware.nsx.model.ComputeCollectionFabricTemplate
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'fabric_template_id': fabric_template_id,
})
def list(self,
compute_collection_id=None,
):
"""
Returns compute collection fabric templates
:type compute_collection_id: :class:`str` or ``None``
:param compute_collection_id: Compute collection id (optional)
:rtype: :class:`com.vmware.nsx.model_client.ComputeCollectionFabricTemplateListResult`
:return: com.vmware.nsx.model.ComputeCollectionFabricTemplateListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'compute_collection_id': compute_collection_id,
})
def update(self,
fabric_template_id,
compute_collection_fabric_template,
):
"""
Updates compute collection fabric template for the given id
:type fabric_template_id: :class:`str`
:param fabric_template_id: (required)
:type compute_collection_fabric_template: :class:`com.vmware.nsx.model_client.ComputeCollectionFabricTemplate`
:param compute_collection_fabric_template: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeCollectionFabricTemplate`
:return: com.vmware.nsx.model.ComputeCollectionFabricTemplate
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('update',
{
'fabric_template_id': fabric_template_id,
'compute_collection_fabric_template': compute_collection_fabric_template,
})
class ComputeCollections(VapiInterface):
"""
"""
CREATE_ACTION_NSX = "remove_nsx"
"""
Possible value for ``action`` of method :func:`ComputeCollections.create`.
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.compute_collections'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ComputeCollectionsStub)
self._VAPI_OPERATION_IDS = {}
def create(self,
cc_ext_id,
action=None,
):
"""
Perform action specific to NSX on the compute-collection. cc-ext-id
should be of type VC_Cluster.
:type cc_ext_id: :class:`str`
:param cc_ext_id: (required)
:type action: :class:`str` or ``None``
:param action: Supported actions on compute-collection (optional)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create',
{
'cc_ext_id': cc_ext_id,
'action': action,
})
def get(self,
cc_ext_id,
):
"""
Returns information about a specific compute collection.
:type cc_ext_id: :class:`str`
:param cc_ext_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeCollection`
:return: com.vmware.nsx.model.ComputeCollection
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'cc_ext_id': cc_ext_id,
})
def list(self,
cm_local_id=None,
cursor=None,
discovered_node_id=None,
display_name=None,
external_id=None,
included_fields=None,
node_id=None,
origin_id=None,
origin_type=None,
owner_id=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all compute collections.
:type cm_local_id: :class:`str` or ``None``
:param cm_local_id: Local Id of the compute collection in the Compute Manager
(optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type discovered_node_id: :class:`str` or ``None``
:param discovered_node_id: Id of the discovered node which belongs to this Compute Collection
(optional)
:type display_name: :class:`str` or ``None``
:param display_name: Name of the ComputeCollection in source compute manager (optional)
:type external_id: :class:`str` or ``None``
:param external_id: External ID of the ComputeCollection in the source Compute manager,
e.g. mo-ref in VC (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type node_id: :class:`str` or ``None``
:param node_id: Id of the fabric node created from a discovered node belonging to
this Compute Collection (optional)
:type origin_id: :class:`str` or ``None``
:param origin_id: Id of the compute manager from where this Compute Collection was
discovered (optional)
:type origin_type: :class:`str` or ``None``
:param origin_type: ComputeCollection type like VC_Cluster. Here the Compute Manager
type prefix would help in differentiating similar named Compute
Collection types from different Compute Managers (optional)
:type owner_id: :class:`str` or ``None``
:param owner_id: Id of the owner of compute collection in the Compute Manager
(optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ComputeCollectionListResult`
:return: com.vmware.nsx.model.ComputeCollectionListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cm_local_id': cm_local_id,
'cursor': cursor,
'discovered_node_id': discovered_node_id,
'display_name': display_name,
'external_id': external_id,
'included_fields': included_fields,
'node_id': node_id,
'origin_id': origin_id,
'origin_type': origin_type,
'owner_id': owner_id,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ComputeManagers(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.compute_managers'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ComputeManagersStub)
self._VAPI_OPERATION_IDS = {}
def create(self,
compute_manager,
):
"""
Registers compute manager with NSX. Inventory service will collect data
from the registered compute manager
:type compute_manager: :class:`com.vmware.nsx.model_client.ComputeManager`
:param compute_manager: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeManager`
:return: com.vmware.nsx.model.ComputeManager
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create',
{
'compute_manager': compute_manager,
})
def delete(self,
compute_manager_id,
):
"""
Unregisters a specified compute manager
:type compute_manager_id: :class:`str`
:param compute_manager_id: (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('delete',
{
'compute_manager_id': compute_manager_id,
})
def get(self,
compute_manager_id,
):
"""
Returns information about a specific compute manager
:type compute_manager_id: :class:`str`
:param compute_manager_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeManager`
:return: com.vmware.nsx.model.ComputeManager
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'compute_manager_id': compute_manager_id,
})
def list(self,
cursor=None,
included_fields=None,
origin_type=None,
page_size=None,
server=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all compute managers.
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type origin_type: :class:`str` or ``None``
:param origin_type: Compute manager type like vCenter (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type server: :class:`str` or ``None``
:param server: IP address or hostname of compute manager (optional)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ComputeManagerListResult`
:return: com.vmware.nsx.model.ComputeManagerListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cursor': cursor,
'included_fields': included_fields,
'origin_type': origin_type,
'page_size': page_size,
'server': server,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
def update(self,
compute_manager_id,
compute_manager,
):
"""
Updates a specified compute manager
:type compute_manager_id: :class:`str`
:param compute_manager_id: (required)
:type compute_manager: :class:`com.vmware.nsx.model_client.ComputeManager`
:param compute_manager: (required)
:rtype: :class:`com.vmware.nsx.model_client.ComputeManager`
:return: com.vmware.nsx.model.ComputeManager
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('update',
{
'compute_manager_id': compute_manager_id,
'compute_manager': compute_manager,
})
class ContainerApplicationInstances(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_application_instances'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerApplicationInstancesStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
container_application_instance_id,
):
"""
Returns information about a specific container application instance.
:type container_application_instance_id: :class:`str`
:param container_application_instance_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerApplicationInstance`
:return: com.vmware.nsx.model.ContainerApplicationInstance
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'container_application_instance_id': container_application_instance_id,
})
def list(self,
container_application_id=None,
container_cluster_id=None,
container_project_id=None,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all container application instance.
:type container_application_id: :class:`str` or ``None``
:param container_application_id: Identifier of the container application (optional)
:type container_cluster_id: :class:`str` or ``None``
:param container_cluster_id: Identifier of the container cluster (optional)
:type container_project_id: :class:`str` or ``None``
:param container_project_id: Identifier of the container project (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerApplicationInstanceListResult`
:return: com.vmware.nsx.model.ContainerApplicationInstanceListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'container_application_id': container_application_id,
'container_cluster_id': container_cluster_id,
'container_project_id': container_project_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ContainerApplications(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_applications'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerApplicationsStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
container_application_id,
):
"""
Returns information about a specific Container Application within a
project.
:type container_application_id: :class:`str`
:param container_application_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerApplication`
:return: com.vmware.nsx.model.ContainerApplication
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'container_application_id': container_application_id,
})
def list(self,
container_cluster_id=None,
container_project_id=None,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all Container Applications.
:type container_cluster_id: :class:`str` or ``None``
:param container_cluster_id: Identifier of the container cluster (optional)
:type container_project_id: :class:`str` or ``None``
:param container_project_id: Identifier of the container project (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerApplicationListResult`
:return: com.vmware.nsx.model.ContainerApplicationListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'container_cluster_id': container_cluster_id,
'container_project_id': container_project_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ContainerClusterNodes(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_cluster_nodes'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerClusterNodesStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
container_cluster_node_id,
):
"""
Returns information about a specific container cluster node.
:type container_cluster_node_id: :class:`str`
:param container_cluster_node_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerClusterNode`
:return: com.vmware.nsx.model.ContainerClusterNode
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'container_cluster_node_id': container_cluster_node_id,
})
def list(self,
container_cluster_id=None,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all container cluster nodes.
:type container_cluster_id: :class:`str` or ``None``
:param container_cluster_id: Identifier of the container cluster (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerClusterNodeListResult`
:return: com.vmware.nsx.model.ContainerClusterNodeListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'container_cluster_id': container_cluster_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ContainerClusters(VapiInterface):
"""
"""
LIST_CLUSTER_TYPE_PAS = "PAS"
"""
Possible value for ``clusterType`` of method :func:`ContainerClusters.list`.
"""
LIST_CLUSTER_TYPE_PKS = "PKS"
"""
Possible value for ``clusterType`` of method :func:`ContainerClusters.list`.
"""
LIST_CLUSTER_TYPE_KUBERNETES = "Kubernetes"
"""
Possible value for ``clusterType`` of method :func:`ContainerClusters.list`.
"""
LIST_CLUSTER_TYPE_OPENSHIFT = "Openshift"
"""
Possible value for ``clusterType`` of method :func:`ContainerClusters.list`.
"""
LIST_CLUSTER_TYPE_WCP = "WCP"
"""
Possible value for ``clusterType`` of method :func:`ContainerClusters.list`.
"""
LIST_CLUSTER_TYPE_OTHER = "Other"
"""
Possible value for ``clusterType`` of method :func:`ContainerClusters.list`.
"""
LIST_INFRA_TYPE_VSPHERE = "vSphere"
"""
Possible value for ``infraType`` of method :func:`ContainerClusters.list`.
"""
LIST_INFRA_TYPE_AWS = "AWS"
"""
Possible value for ``infraType`` of method :func:`ContainerClusters.list`.
"""
LIST_INFRA_TYPE_AZURE = "Azure"
"""
Possible value for ``infraType`` of method :func:`ContainerClusters.list`.
"""
LIST_INFRA_TYPE_VMC = "VMC"
"""
Possible value for ``infraType`` of method :func:`ContainerClusters.list`.
"""
LIST_INFRA_TYPE_KVM = "KVM"
"""
Possible value for ``infraType`` of method :func:`ContainerClusters.list`.
"""
LIST_INFRA_TYPE_BAREMETAL = "Baremetal"
"""
Possible value for ``infraType`` of method :func:`ContainerClusters.list`.
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_clusters'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerClustersStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
container_cluster_id,
):
"""
Returns information about a specific container cluster
:type container_cluster_id: :class:`str`
:param container_cluster_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerCluster`
:return: com.vmware.nsx.model.ContainerCluster
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'container_cluster_id': container_cluster_id,
})
def list(self,
cluster_type=None,
cursor=None,
included_fields=None,
infra_type=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all Container Clusters.
:type cluster_type: :class:`str` or ``None``
:param cluster_type: Type of container cluster (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type infra_type: :class:`str` or ``None``
:param infra_type: Type of infrastructure (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerClusterListResult`
:return: com.vmware.nsx.model.ContainerClusterListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cluster_type': cluster_type,
'cursor': cursor,
'included_fields': included_fields,
'infra_type': infra_type,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ContainerIngressPolicies(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_ingress_policies'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerIngressPoliciesStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
ingress_policy_id,
):
"""
Returns information about a specific ingress policy.
:type ingress_policy_id: :class:`str`
:param ingress_policy_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerIngressPolicy`
:return: com.vmware.nsx.model.ContainerIngressPolicy
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'ingress_policy_id': ingress_policy_id,
})
def list(self,
container_cluster_id=None,
container_project_id=None,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all ingress policies.
:type container_cluster_id: :class:`str` or ``None``
:param container_cluster_id: Identifier of the container cluster (optional)
:type container_project_id: :class:`str` or ``None``
:param container_project_id: Identifier of the container project (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerIngressPolicyListResult`
:return: com.vmware.nsx.model.ContainerIngressPolicyListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'container_cluster_id': container_cluster_id,
'container_project_id': container_project_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ContainerNetworkPolicies(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_network_policies'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerNetworkPoliciesStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
network_policy_id,
):
"""
Returns information about a specific network policy.
:type network_policy_id: :class:`str`
:param network_policy_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerNetworkPolicy`
:return: com.vmware.nsx.model.ContainerNetworkPolicy
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'network_policy_id': network_policy_id,
})
def list(self,
container_cluster_id=None,
container_project_id=None,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all network policies.
:type container_cluster_id: :class:`str` or ``None``
:param container_cluster_id: Identifier of the container cluster (optional)
:type container_project_id: :class:`str` or ``None``
:param container_project_id: Identifier of the container project (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerNetworkPolicyListResult`
:return: com.vmware.nsx.model.ContainerNetworkPolicyListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'container_cluster_id': container_cluster_id,
'container_project_id': container_project_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class ContainerProjects(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.container_projects'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _ContainerProjectsStub)
self._VAPI_OPERATION_IDS = {}
def get(self,
container_project_id,
):
"""
Returns information about a specific project
:type container_project_id: :class:`str`
:param container_project_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.ContainerProject`
:return: com.vmware.nsx.model.ContainerProject
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'container_project_id': container_project_id,
})
def list(self,
container_cluster_id=None,
cursor=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all container projects
:type container_cluster_id: :class:`str` or ``None``
:param container_cluster_id: Identifier of the container cluster (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.ContainerProjectListResult`
:return: com.vmware.nsx.model.ContainerProjectListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'container_cluster_id': container_cluster_id,
'cursor': cursor,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
class DiscoveredNodes(VapiInterface):
"""
"""
LIST_HAS_PARENT_TRUE = "true"
"""
Possible value for ``hasParent`` of method :func:`DiscoveredNodes.list`.
"""
LIST_HAS_PARENT_FALSE = "false"
"""
Possible value for ``hasParent`` of method :func:`DiscoveredNodes.list`.
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.discovered_nodes'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _DiscoveredNodesStub)
self._VAPI_OPERATION_IDS = {}
def createtransportnode(self,
node_ext_id,
transport_node,
):
"""
NSX components are installaed on host and transport node is created
with given configurations.
:type node_ext_id: :class:`str`
:param node_ext_id: (required)
:type transport_node: :class:`com.vmware.nsx.model_client.TransportNode`
:param transport_node: (required)
:rtype: :class:`com.vmware.nsx.model_client.TransportNode`
:return: com.vmware.nsx.model.TransportNode
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('createtransportnode',
{
'node_ext_id': node_ext_id,
'transport_node': transport_node,
})
def get(self,
node_ext_id,
):
"""
Returns information about a specific discovered node.
:type node_ext_id: :class:`str`
:param node_ext_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.DiscoveredNode`
:return: com.vmware.nsx.model.DiscoveredNode
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'node_ext_id': node_ext_id,
})
def hostprep(self,
node_ext_id,
):
"""
:type node_ext_id: :class:`str`
:param node_ext_id: (required)
:rtype: :class:`vmware.vapi.struct.VapiStruct`
:return: com.vmware.nsx.model.Node
The return value will contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('hostprep',
{
'node_ext_id': node_ext_id,
})
def list(self,
cm_local_id=None,
cursor=None,
display_name=None,
external_id=None,
has_parent=None,
included_fields=None,
ip_address=None,
node_id=None,
node_type=None,
origin_id=None,
page_size=None,
parent_compute_collection=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all discovered nodes.
:type cm_local_id: :class:`str` or ``None``
:param cm_local_id: Local Id of the discovered node in the Compute Manager (optional)
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type display_name: :class:`str` or ``None``
:param display_name: Display name of discovered node (optional)
:type external_id: :class:`str` or ``None``
:param external_id: External id of the discovered node, ex. a mo-ref from VC (optional)
:type has_parent: :class:`str` or ``None``
:param has_parent: Discovered node has a parent compute collection or is a standalone
host (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type ip_address: :class:`str` or ``None``
:param ip_address: IP address of the discovered node (optional)
:type node_id: :class:`str` or ``None``
:param node_id: Id of the fabric node created from the discovered node (optional)
:type node_type: :class:`str` or ``None``
:param node_type: Discovered Node type like HostNode (optional)
:type origin_id: :class:`str` or ``None``
:param origin_id: Id of the compute manager from where this node was discovered
(optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type parent_compute_collection: :class:`str` or ``None``
:param parent_compute_collection: External id of the compute collection to which this node belongs
(optional)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.DiscoveredNodeListResult`
:return: com.vmware.nsx.model.DiscoveredNodeListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cm_local_id': cm_local_id,
'cursor': cursor,
'display_name': display_name,
'external_id': external_id,
'has_parent': has_parent,
'included_fields': included_fields,
'ip_address': ip_address,
'node_id': node_id,
'node_type': node_type,
'origin_id': origin_id,
'page_size': page_size,
'parent_compute_collection': parent_compute_collection,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
def reapplyclusterconfig(self,
node_ext_id,
):
"""
:type node_ext_id: :class:`str`
:param node_ext_id: (required)
:rtype: :class:`com.vmware.nsx.model_client.TransportNode`
:return: com.vmware.nsx.model.TransportNode
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('reapplyclusterconfig',
{
'node_ext_id': node_ext_id,
})
class Nodes(VapiInterface):
"""
"""
LIST_HYPERVISOR_OS_TYPE_ESXI = "ESXI"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_RHELKVM = "RHELKVM"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_UBUNTUKVM = "UBUNTUKVM"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_HYPERV = "HYPERV"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_RHELCONTAINER = "RHELCONTAINER"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_CENTOSCONTAINER = "CENTOSCONTAINER"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_RHELSERVER = "RHELSERVER"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_UBUNTUSERVER = "UBUNTUSERVER"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_CENTOSSERVER = "CENTOSSERVER"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_CENTOSKVM = "CENTOSKVM"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_SLESKVM = "SLESKVM"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_HYPERVISOR_OS_TYPE_SLESSERVER = "SLESSERVER"
"""
Possible value for ``hypervisorOsType`` of method :func:`Nodes.list`.
"""
LIST_RESOURCE_TYPE_HOSTNODE = "HostNode"
"""
Possible value for ``resourceType`` of method :func:`Nodes.list`.
"""
LIST_RESOURCE_TYPE_EDGENODE = "EdgeNode"
"""
Possible value for ``resourceType`` of method :func:`Nodes.list`.
"""
LIST_RESOURCE_TYPE_PUBLICCLOUDGATEWAYNODE = "PublicCloudGatewayNode"
"""
Possible value for ``resourceType`` of method :func:`Nodes.list`.
"""
PERFORMACTION_ACTION_ENTER_MAINTENANCE_MODE = "enter_maintenance_mode"
"""
Possible value for ``action`` of method :func:`Nodes.performaction`.
"""
PERFORMACTION_ACTION_EXIT_MAINTENANCE_MODE = "exit_maintenance_mode"
"""
Possible value for ``action`` of method :func:`Nodes.performaction`.
"""
PERFORMACTION_ACTION_GET_MAINTENANCE_MODE_STATE = "get_maintenance_mode_state"
"""
Possible value for ``action`` of method :func:`Nodes.performaction`.
"""
PERFORMACTION_VSAN_MODE_EVACUATE_ALL_DATA = "evacuate_all_data"
"""
Possible value for ``vsanMode`` of method :func:`Nodes.performaction`.
"""
PERFORMACTION_VSAN_MODE_ENSURE_OBJECT_ACCESSIBILITY = "ensure_object_accessibility"
"""
Possible value for ``vsanMode`` of method :func:`Nodes.performaction`.
"""
PERFORMACTION_VSAN_MODE_NO_ACTION = "no_action"
"""
Possible value for ``vsanMode`` of method :func:`Nodes.performaction`.
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.nodes'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _NodesStub)
self._VAPI_OPERATION_IDS = {}
def create(self,
node,
):
"""
Creates a host node (hypervisor) or edge node (router) in the transport
network. When you run this command for a host, NSX Manager attempts to
install the NSX kernel modules, which are packaged as VIB, RPM, or DEB
files. For the installation to succeed, you must provide the host login
credentials and the host thumbprint. To get the ESXi host thumbprint,
SSH to the host and run the **openssl x509 -in /etc/vmware/ssl/rui.crt
-fingerprint -sha256 -noout** command. To generate host key thumbprint
using SHA-256 algorithm please follow the steps below. Log into the
host, making sure that the connection is not vulnerable to a man in the
middle attack. Check whether a public key already exists. Host public
key is generally located at '/etc/ssh/ssh_host_rsa_key.pub'. If the key
is not present then generate a new key by running the following command
and follow the instructions. **ssh-keygen -t rsa** Now generate a
SHA256 hash of the key using the following command. Please make sure to
pass the appropriate file name if the public key is stored with a
different file name other than the default 'id_rsa.pub'. **awk '{print
$2}' id_rsa.pub | base64 -d | sha256sum -b | sed 's/ .\*$//' | xxd -r
-p | base64** This api is deprecated as part of FN+TN unification.
Please use Transport Node API POST /transport-nodes to install NSX
components on a node.
:type node: :class:`vmware.vapi.struct.VapiStruct`
:param node: (required)
The parameter must contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:rtype: :class:`vmware.vapi.struct.VapiStruct`
:return: com.vmware.nsx.model.Node
The return value will contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create',
{
'node': node,
})
def create_0(self,
target_node_id,
target_uri,
):
"""
Invoke POST request on target fabric node. This api is deprecated as
part of FN+TN unification. Please use Transport Node API POST
/transport-nodes/<transport-node-id>/<target-node-id>/<target-uri>
:type target_node_id: :class:`str`
:param target_node_id: Target node UUID (required)
:type target_uri: :class:`str`
:param target_uri: URI of API to invoke on target node (required)
:raise: :class:`com.vmware.vapi.std.errors_client.TimedOut`
Gateway Timeout
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('create_0',
{
'target_node_id': target_node_id,
'target_uri': target_uri,
})
def delete(self,
node_id,
unprepare_host=None,
):
"""
:type node_id: :class:`str`
:param node_id: (required)
:type unprepare_host: :class:`bool` or ``None``
:param unprepare_host: Delete a host and uninstall NSX components (optional, default to
true)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('delete',
{
'node_id': node_id,
'unprepare_host': unprepare_host,
})
def delete_0(self,
target_node_id,
target_uri,
):
"""
Invoke DELETE request on target fabric node. This api is deprecated as
part of FN+TN unification. Please use Transport Node API DELETE
/transport-nodes/<transport-node-id>/<target-node-id>/<target-uri>
:type target_node_id: :class:`str`
:param target_node_id: Target node UUID (required)
:type target_uri: :class:`str`
:param target_uri: URI of API to invoke on target node (required)
:raise: :class:`com.vmware.vapi.std.errors_client.TimedOut`
Gateway Timeout
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('delete_0',
{
'target_node_id': target_node_id,
'target_uri': target_uri,
})
def get(self,
node_id,
):
"""
Returns information about a specific fabric node (host or edge). This
api is deprecated, use Transport Node API GET
/transport-nodes/<transport-node-id> to get fabric node information.
:type node_id: :class:`str`
:param node_id: (required)
:rtype: :class:`vmware.vapi.struct.VapiStruct`
:return: com.vmware.nsx.model.Node
The return value will contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'node_id': node_id,
})
def get_0(self,
target_node_id,
target_uri,
):
"""
Invoke GET request on target fabric node. This api is deprecated as
part of FN+TN unification. Please use Transport Node API GET
/transport-nodes/<transport-node-id>/<target-node-id>/<target-uri>
:type target_node_id: :class:`str`
:param target_node_id: Target node UUID (required)
:type target_uri: :class:`str`
:param target_uri: URI of API to invoke on target node (required)
:raise: :class:`com.vmware.vapi.std.errors_client.TimedOut`
Gateway Timeout
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get_0',
{
'target_node_id': target_node_id,
'target_uri': target_uri,
})
def list(self,
cursor=None,
discovered_node_id=None,
display_name=None,
external_id=None,
hardware_id=None,
hypervisor_os_type=None,
included_fields=None,
ip_address=None,
page_size=None,
resource_type=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all fabric nodes (hosts and edges). This api
is deprecated as part of FN+TN unification. Please use Transport Node
API GET /transport-nodes to list all fabric nodes.
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type discovered_node_id: :class:`str` or ``None``
:param discovered_node_id: Id of the discovered node which was converted to create this node
(optional)
:type display_name: :class:`str` or ``None``
:param display_name: HostNode display name (optional)
:type external_id: :class:`str` or ``None``
:param external_id: HostNode external id (optional)
:type hardware_id: :class:`str` or ``None``
:param hardware_id: Hardware Id of the host (optional)
:type hypervisor_os_type: :class:`str` or ``None``
:param hypervisor_os_type: HostNode's Hypervisor type, for example ESXi, RHEL KVM or UBUNTU
KVM. (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type ip_address: :class:`str` or ``None``
:param ip_address: Management IP address of the node (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type resource_type: :class:`str` or ``None``
:param resource_type: Node type from 'HostNode', 'EdgeNode', 'PublicCloudGatewayNode'
(optional)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.NodeListResult`
:return: com.vmware.nsx.model.NodeListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cursor': cursor,
'discovered_node_id': discovered_node_id,
'display_name': display_name,
'external_id': external_id,
'hardware_id': hardware_id,
'hypervisor_os_type': hypervisor_os_type,
'included_fields': included_fields,
'ip_address': ip_address,
'page_size': page_size,
'resource_type': resource_type,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
def performaction(self,
node_id,
action=None,
evacuate_powered_off_vms=None,
vsan_mode=None,
):
"""
The supported fabric node actions are enter_maintenance_mode,
exit_maintenance_mode for EdgeNode. This API is deprecated, please call
TransportNode maintenance mode API to update maintenance mode, refer to
\"Update transport node maintenance mode\".
:type node_id: :class:`str`
:param node_id: (required)
:type action: :class:`str` or ``None``
:param action: Supported fabric node actions (optional)
:type evacuate_powered_off_vms: :class:`bool` or ``None``
:param evacuate_powered_off_vms: Evacuate powered-off vms (optional, default to false)
:type vsan_mode: :class:`str` or ``None``
:param vsan_mode: Vsan decommission mode (optional, default to
ensure_object_accessibility)
:rtype: :class:`vmware.vapi.struct.VapiStruct`
:return: com.vmware.nsx.model.Node
The return value will contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('performaction',
{
'node_id': node_id,
'action': action,
'evacuate_powered_off_vms': evacuate_powered_off_vms,
'vsan_mode': vsan_mode,
})
def restartinventorysync(self,
node_id,
):
"""
Restart the inventory sync for the node if it is currently internally
paused. After this action the next inventory sync coming from the node
is processed. This api is deprecated as part of FN+TN unification.
Please use Transport Node API POST
/transport-nodes/<transport-node-id>?action=restart_inventory_sync to
restart inventory sync of node.
:type node_id: :class:`str`
:param node_id: (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('restartinventorysync',
{
'node_id': node_id,
})
def update(self,
node_id,
node,
):
"""
:type node_id: :class:`str`
:param node_id: (required)
:type node: :class:`vmware.vapi.struct.VapiStruct`
:param node: (required)
The parameter must contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:rtype: :class:`vmware.vapi.struct.VapiStruct`
:return: com.vmware.nsx.model.Node
The return value will contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('update',
{
'node_id': node_id,
'node': node,
})
def update_0(self,
target_node_id,
target_uri,
):
"""
Invoke PUT request on target fabric node. This api is deprecated as
part of FN+TN unification. Please use Transport Node API PUT
/transport-nodes/<transport-node-id>/<target-node-id>/<target-uri>
:type target_node_id: :class:`str`
:param target_node_id: Target node UUID (required)
:type target_uri: :class:`str`
:param target_uri: URI of API to invoke on target node (required)
:raise: :class:`com.vmware.vapi.std.errors_client.TimedOut`
Gateway Timeout
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('update_0',
{
'target_node_id': target_node_id,
'target_uri': target_uri,
})
def upgradeinfra(self,
node_id,
disable_vm_migration=None,
):
"""
Perform a service deployment upgrade on a host node
:type node_id: :class:`str`
:param node_id: (required)
:type disable_vm_migration: :class:`bool` or ``None``
:param disable_vm_migration: Should VM migration be disabled during upgrade (optional, default
to false)
:rtype: :class:`vmware.vapi.struct.VapiStruct`
:return: com.vmware.nsx.model.Node
The return value will contain all the attributes defined in
:class:`com.vmware.nsx.model_client.Node`.
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('upgradeinfra',
{
'node_id': node_id,
'disable_vm_migration': disable_vm_migration,
})
class Ostypes(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.ostypes'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _OstypesStub)
self._VAPI_OPERATION_IDS = {}
def get(self):
"""
Returns names of all supported host OS.
:rtype: :class:`com.vmware.nsx.model_client.SupportedHostOSListResult`
:return: com.vmware.nsx.model.SupportedHostOSListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get', None)
class Vifs(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.vifs'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _VifsStub)
self._VAPI_OPERATION_IDS = {}
def list(self,
cursor=None,
host_id=None,
included_fields=None,
lport_attachment_id=None,
owner_vm_id=None,
page_size=None,
sort_ascending=None,
sort_by=None,
vm_id=None,
):
"""
Returns information about all VIFs. A virtual network interface
aggregates network interfaces into a logical interface unit that is
indistinuishable from a physical network interface.
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type host_id: :class:`str` or ``None``
:param host_id: Id of the host where this vif is located. (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type lport_attachment_id: :class:`str` or ``None``
:param lport_attachment_id: LPort Attachment Id of the virtual network interface. (optional)
:type owner_vm_id: :class:`str` or ``None``
:param owner_vm_id: External id of the virtual machine. (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:type vm_id: :class:`str` or ``None``
:param vm_id: External id of the virtual machine. (optional)
:rtype: :class:`com.vmware.nsx.model_client.VirtualNetworkInterfaceListResult`
:return: com.vmware.nsx.model.VirtualNetworkInterfaceListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cursor': cursor,
'host_id': host_id,
'included_fields': included_fields,
'lport_attachment_id': lport_attachment_id,
'owner_vm_id': owner_vm_id,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
'vm_id': vm_id,
})
class VirtualMachines(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx.fabric.virtual_machines'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _VirtualMachinesStub)
self._VAPI_OPERATION_IDS = {}
def list(self,
cursor=None,
display_name=None,
external_id=None,
host_id=None,
included_fields=None,
page_size=None,
sort_ascending=None,
sort_by=None,
):
"""
Returns information about all virtual machines.
:type cursor: :class:`str` or ``None``
:param cursor: Opaque cursor to be used for getting next page of records (supplied
by current result page) (optional)
:type display_name: :class:`str` or ``None``
:param display_name: Display Name of the virtual machine (optional)
:type external_id: :class:`str` or ``None``
:param external_id: External id of the virtual machine (optional)
:type host_id: :class:`str` or ``None``
:param host_id: Id of the host where this vif is located (optional)
:type included_fields: :class:`str` or ``None``
:param included_fields: Comma separated list of fields that should be included in query
result (optional)
:type page_size: :class:`long` or ``None``
:param page_size: Maximum number of results to return in this page (server may return
fewer) (optional, default to 1000)
:type sort_ascending: :class:`bool` or ``None``
:param sort_ascending: (optional)
:type sort_by: :class:`str` or ``None``
:param sort_by: Field by which records are sorted (optional)
:rtype: :class:`com.vmware.nsx.model_client.VirtualMachineListResult`
:return: com.vmware.nsx.model.VirtualMachineListResult
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('list',
{
'cursor': cursor,
'display_name': display_name,
'external_id': external_id,
'host_id': host_id,
'included_fields': included_fields,
'page_size': page_size,
'sort_ascending': sort_ascending,
'sort_by': sort_by,
})
def updatetags(self,
virtual_machine_tag_update,
):
"""
Update tags applied to the virtual machine. External id of the virtual
machine will be specified in the request body. Request body should
contain all the tags to be applied. To clear all tags, provide an empty
list. User can apply maximum 25 tags on a virtual machine. The
remaining 5 are reserved for system defined tags.
:type virtual_machine_tag_update: :class:`com.vmware.nsx.model_client.VirtualMachineTagUpdate`
:param virtual_machine_tag_update: (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('updatetags',
{
'virtual_machine_tag_update': virtual_machine_tag_update,
})
class _ComputeCollectionFabricTemplatesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for create operation
create_input_type = type.StructType('operation-input', {
'compute_collection_fabric_template': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionFabricTemplate'),
})
create_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_input_value_validator_list = [
]
create_output_validator_list = [
]
create_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/compute-collection-fabric-templates',
request_body_parameter='compute_collection_fabric_template',
path_variables={
},
query_parameters={
},
content_type='application/json'
)
# properties for delete operation
delete_input_type = type.StructType('operation-input', {
'fabric_template_id': type.StringType(),
})
delete_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
delete_input_value_validator_list = [
]
delete_output_validator_list = [
]
delete_rest_metadata = OperationRestMetadata(
http_method='DELETE',
url_template='/api/v1/fabric/compute-collection-fabric-templates/{fabric-template-id}',
path_variables={
'fabric_template_id': 'fabric-template-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'fabric_template_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/compute-collection-fabric-templates/{fabric-template-id}',
path_variables={
'fabric_template_id': 'fabric-template-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'compute_collection_id': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/compute-collection-fabric-templates',
path_variables={
},
query_parameters={
'compute_collection_id': 'compute_collection_id',
},
content_type='application/json'
)
# properties for update operation
update_input_type = type.StructType('operation-input', {
'fabric_template_id': type.StringType(),
'compute_collection_fabric_template': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionFabricTemplate'),
})
update_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
update_input_value_validator_list = [
]
update_output_validator_list = [
]
update_rest_metadata = OperationRestMetadata(
http_method='PUT',
url_template='/api/v1/fabric/compute-collection-fabric-templates/{fabric-template-id}',
request_body_parameter='compute_collection_fabric_template',
path_variables={
'fabric_template_id': 'fabric-template-id',
},
query_parameters={
},
content_type='application/json'
)
operations = {
'create': {
'input_type': create_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionFabricTemplate'),
'errors': create_error_dict,
'input_value_validator_list': create_input_value_validator_list,
'output_validator_list': create_output_validator_list,
'task_type': TaskType.NONE,
},
'delete': {
'input_type': delete_input_type,
'output_type': type.VoidType(),
'errors': delete_error_dict,
'input_value_validator_list': delete_input_value_validator_list,
'output_validator_list': delete_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionFabricTemplate'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionFabricTemplateListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
'update': {
'input_type': update_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionFabricTemplate'),
'errors': update_error_dict,
'input_value_validator_list': update_input_value_validator_list,
'output_validator_list': update_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'create': create_rest_metadata,
'delete': delete_rest_metadata,
'get': get_rest_metadata,
'list': list_rest_metadata,
'update': update_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.compute_collection_fabric_templates',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ComputeCollectionsStub(ApiInterfaceStub):
def __init__(self, config):
# properties for create operation
create_input_type = type.StructType('operation-input', {
'cc_ext_id': type.StringType(),
'action': type.OptionalType(type.StringType()),
})
create_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_input_value_validator_list = [
]
create_output_validator_list = [
]
create_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/compute-collections/{cc-ext-id}',
path_variables={
'cc_ext_id': 'cc-ext-id',
},
query_parameters={
'action': 'action',
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'cc_ext_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/compute-collections/{cc-ext-id}',
path_variables={
'cc_ext_id': 'cc-ext-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cm_local_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'discovered_node_id': type.OptionalType(type.StringType()),
'display_name': type.OptionalType(type.StringType()),
'external_id': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'node_id': type.OptionalType(type.StringType()),
'origin_id': type.OptionalType(type.StringType()),
'origin_type': type.OptionalType(type.StringType()),
'owner_id': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/compute-collections',
path_variables={
},
query_parameters={
'cm_local_id': 'cm_local_id',
'cursor': 'cursor',
'discovered_node_id': 'discovered_node_id',
'display_name': 'display_name',
'external_id': 'external_id',
'included_fields': 'included_fields',
'node_id': 'node_id',
'origin_id': 'origin_id',
'origin_type': 'origin_type',
'owner_id': 'owner_id',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'create': {
'input_type': create_input_type,
'output_type': type.VoidType(),
'errors': create_error_dict,
'input_value_validator_list': create_input_value_validator_list,
'output_validator_list': create_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollection'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeCollectionListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'create': create_rest_metadata,
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.compute_collections',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ComputeManagersStub(ApiInterfaceStub):
def __init__(self, config):
# properties for create operation
create_input_type = type.StructType('operation-input', {
'compute_manager': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeManager'),
})
create_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_input_value_validator_list = [
HasFieldsOfValidator()
]
create_output_validator_list = [
HasFieldsOfValidator()
]
create_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/compute-managers',
request_body_parameter='compute_manager',
path_variables={
},
query_parameters={
},
content_type='application/json'
)
# properties for delete operation
delete_input_type = type.StructType('operation-input', {
'compute_manager_id': type.StringType(),
})
delete_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
delete_input_value_validator_list = [
]
delete_output_validator_list = [
]
delete_rest_metadata = OperationRestMetadata(
http_method='DELETE',
url_template='/api/v1/fabric/compute-managers/{compute-manager-id}',
path_variables={
'compute_manager_id': 'compute-manager-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'compute_manager_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
HasFieldsOfValidator()
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/compute-managers/{compute-manager-id}',
path_variables={
'compute_manager_id': 'compute-manager-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'origin_type': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'server': type.OptionalType(type.StringType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
HasFieldsOfValidator()
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/compute-managers',
path_variables={
},
query_parameters={
'cursor': 'cursor',
'included_fields': 'included_fields',
'origin_type': 'origin_type',
'page_size': 'page_size',
'server': 'server',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
# properties for update operation
update_input_type = type.StructType('operation-input', {
'compute_manager_id': type.StringType(),
'compute_manager': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeManager'),
})
update_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
update_input_value_validator_list = [
HasFieldsOfValidator()
]
update_output_validator_list = [
HasFieldsOfValidator()
]
update_rest_metadata = OperationRestMetadata(
http_method='PUT',
url_template='/api/v1/fabric/compute-managers/{compute-manager-id}',
request_body_parameter='compute_manager',
path_variables={
'compute_manager_id': 'compute-manager-id',
},
query_parameters={
},
content_type='application/json'
)
operations = {
'create': {
'input_type': create_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeManager'),
'errors': create_error_dict,
'input_value_validator_list': create_input_value_validator_list,
'output_validator_list': create_output_validator_list,
'task_type': TaskType.NONE,
},
'delete': {
'input_type': delete_input_type,
'output_type': type.VoidType(),
'errors': delete_error_dict,
'input_value_validator_list': delete_input_value_validator_list,
'output_validator_list': delete_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeManager'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeManagerListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
'update': {
'input_type': update_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ComputeManager'),
'errors': update_error_dict,
'input_value_validator_list': update_input_value_validator_list,
'output_validator_list': update_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'create': create_rest_metadata,
'delete': delete_rest_metadata,
'get': get_rest_metadata,
'list': list_rest_metadata,
'update': update_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.compute_managers',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerApplicationInstancesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'container_application_instance_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-application-instances/{container-application-instance-id}',
path_variables={
'container_application_instance_id': 'container-application-instance-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'container_application_id': type.OptionalType(type.StringType()),
'container_cluster_id': type.OptionalType(type.StringType()),
'container_project_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-application-instances',
path_variables={
},
query_parameters={
'container_application_id': 'container_application_id',
'container_cluster_id': 'container_cluster_id',
'container_project_id': 'container_project_id',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerApplicationInstance'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerApplicationInstanceListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_application_instances',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerApplicationsStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'container_application_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-applications/{container-application-id}',
path_variables={
'container_application_id': 'container-application-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'container_cluster_id': type.OptionalType(type.StringType()),
'container_project_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-applications',
path_variables={
},
query_parameters={
'container_cluster_id': 'container_cluster_id',
'container_project_id': 'container_project_id',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerApplication'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerApplicationListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_applications',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerClusterNodesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'container_cluster_node_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-cluster-nodes/{container-cluster-node-id}',
path_variables={
'container_cluster_node_id': 'container-cluster-node-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'container_cluster_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-cluster-nodes',
path_variables={
},
query_parameters={
'container_cluster_id': 'container_cluster_id',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerClusterNode'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerClusterNodeListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_cluster_nodes',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerClustersStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'container_cluster_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-clusters/{container-cluster-id}',
path_variables={
'container_cluster_id': 'container-cluster-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cluster_type': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'infra_type': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-clusters',
path_variables={
},
query_parameters={
'cluster_type': 'cluster_type',
'cursor': 'cursor',
'included_fields': 'included_fields',
'infra_type': 'infra_type',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerCluster'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerClusterListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_clusters',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerIngressPoliciesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'ingress_policy_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-ingress-policies/{ingress-policy-id}',
path_variables={
'ingress_policy_id': 'ingress-policy-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'container_cluster_id': type.OptionalType(type.StringType()),
'container_project_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-ingress-policies',
path_variables={
},
query_parameters={
'container_cluster_id': 'container_cluster_id',
'container_project_id': 'container_project_id',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerIngressPolicy'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerIngressPolicyListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_ingress_policies',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerNetworkPoliciesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'network_policy_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-network-policies/{network-policy-id}',
path_variables={
'network_policy_id': 'network-policy-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'container_cluster_id': type.OptionalType(type.StringType()),
'container_project_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-network-policies',
path_variables={
},
query_parameters={
'container_cluster_id': 'container_cluster_id',
'container_project_id': 'container_project_id',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerNetworkPolicy'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerNetworkPolicyListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_network_policies',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _ContainerProjectsStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {
'container_project_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-projects/{container-project-id}',
path_variables={
'container_project_id': 'container-project-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'container_cluster_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/container-projects',
path_variables={
},
query_parameters={
'container_cluster_id': 'container_cluster_id',
'cursor': 'cursor',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerProject'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'ContainerProjectListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.container_projects',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _DiscoveredNodesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for createtransportnode operation
createtransportnode_input_type = type.StructType('operation-input', {
'node_ext_id': type.StringType(),
'transport_node': type.ReferenceType('com.vmware.nsx.model_client', 'TransportNode'),
})
createtransportnode_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
createtransportnode_input_value_validator_list = [
HasFieldsOfValidator()
]
createtransportnode_output_validator_list = [
HasFieldsOfValidator()
]
createtransportnode_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/discovered-nodes/{node-ext-id}?action=create_transport_node',
request_body_parameter='transport_node',
path_variables={
'node_ext_id': 'node-ext-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'node_ext_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/discovered-nodes/{node-ext-id}',
path_variables={
'node_ext_id': 'node-ext-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for hostprep operation
hostprep_input_type = type.StructType('operation-input', {
'node_ext_id': type.StringType(),
})
hostprep_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
hostprep_input_value_validator_list = [
]
hostprep_output_validator_list = [
HasFieldsOfValidator()
]
hostprep_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/discovered-nodes/{node-ext-id}?action=hostprep',
path_variables={
'node_ext_id': 'node-ext-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cm_local_id': type.OptionalType(type.StringType()),
'cursor': type.OptionalType(type.StringType()),
'display_name': type.OptionalType(type.StringType()),
'external_id': type.OptionalType(type.StringType()),
'has_parent': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'ip_address': type.OptionalType(type.StringType()),
'node_id': type.OptionalType(type.StringType()),
'node_type': type.OptionalType(type.StringType()),
'origin_id': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'parent_compute_collection': type.OptionalType(type.StringType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/discovered-nodes',
path_variables={
},
query_parameters={
'cm_local_id': 'cm_local_id',
'cursor': 'cursor',
'display_name': 'display_name',
'external_id': 'external_id',
'has_parent': 'has_parent',
'included_fields': 'included_fields',
'ip_address': 'ip_address',
'node_id': 'node_id',
'node_type': 'node_type',
'origin_id': 'origin_id',
'page_size': 'page_size',
'parent_compute_collection': 'parent_compute_collection',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
# properties for reapplyclusterconfig operation
reapplyclusterconfig_input_type = type.StructType('operation-input', {
'node_ext_id': type.StringType(),
})
reapplyclusterconfig_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
reapplyclusterconfig_input_value_validator_list = [
]
reapplyclusterconfig_output_validator_list = [
HasFieldsOfValidator()
]
reapplyclusterconfig_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/discovered-nodes/{node-ext-id}?action=reapply_cluster_config',
path_variables={
'node_ext_id': 'node-ext-id',
},
query_parameters={
},
content_type='application/json'
)
operations = {
'createtransportnode': {
'input_type': createtransportnode_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'TransportNode'),
'errors': createtransportnode_error_dict,
'input_value_validator_list': createtransportnode_input_value_validator_list,
'output_validator_list': createtransportnode_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DiscoveredNode'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'hostprep': {
'input_type': hostprep_input_type,
'output_type': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
'errors': hostprep_error_dict,
'input_value_validator_list': hostprep_input_value_validator_list,
'output_validator_list': hostprep_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'DiscoveredNodeListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
'reapplyclusterconfig': {
'input_type': reapplyclusterconfig_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'TransportNode'),
'errors': reapplyclusterconfig_error_dict,
'input_value_validator_list': reapplyclusterconfig_input_value_validator_list,
'output_validator_list': reapplyclusterconfig_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'createtransportnode': createtransportnode_rest_metadata,
'get': get_rest_metadata,
'hostprep': hostprep_rest_metadata,
'list': list_rest_metadata,
'reapplyclusterconfig': reapplyclusterconfig_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.discovered_nodes',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _NodesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for create operation
create_input_type = type.StructType('operation-input', {
'node': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
})
create_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_input_value_validator_list = [
HasFieldsOfValidator()
]
create_output_validator_list = [
HasFieldsOfValidator()
]
create_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/nodes',
request_body_parameter='node',
path_variables={
},
query_parameters={
},
content_type='application/json'
)
# properties for create_0 operation
create_0_input_type = type.StructType('operation-input', {
'target_node_id': type.StringType(),
'target_uri': type.StringType(),
})
create_0_error_dict = {
'com.vmware.vapi.std.errors.timed_out':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'TimedOut'),
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
create_0_input_value_validator_list = [
]
create_0_output_validator_list = [
]
create_0_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/nodes/{target-node-id}/{target-uri}',
path_variables={
'target_node_id': 'target-node-id',
'target_uri': 'target-uri',
},
query_parameters={
},
content_type='application/json'
)
# properties for delete operation
delete_input_type = type.StructType('operation-input', {
'node_id': type.StringType(),
'unprepare_host': type.OptionalType(type.BooleanType()),
})
delete_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
delete_input_value_validator_list = [
]
delete_output_validator_list = [
]
delete_rest_metadata = OperationRestMetadata(
http_method='DELETE',
url_template='/api/v1/fabric/nodes/{node-id}',
path_variables={
'node_id': 'node-id',
},
query_parameters={
'unprepare_host': 'unprepare_host',
},
content_type='application/json'
)
# properties for delete_0 operation
delete_0_input_type = type.StructType('operation-input', {
'target_node_id': type.StringType(),
'target_uri': type.StringType(),
})
delete_0_error_dict = {
'com.vmware.vapi.std.errors.timed_out':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'TimedOut'),
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
delete_0_input_value_validator_list = [
]
delete_0_output_validator_list = [
]
delete_0_rest_metadata = OperationRestMetadata(
http_method='DELETE',
url_template='/api/v1/fabric/nodes/{target-node-id}/{target-uri}',
path_variables={
'target_node_id': 'target-node-id',
'target_uri': 'target-uri',
},
query_parameters={
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'node_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
HasFieldsOfValidator()
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/nodes/{node-id}',
path_variables={
'node_id': 'node-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for get_0 operation
get_0_input_type = type.StructType('operation-input', {
'target_node_id': type.StringType(),
'target_uri': type.StringType(),
})
get_0_error_dict = {
'com.vmware.vapi.std.errors.timed_out':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'TimedOut'),
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_0_input_value_validator_list = [
]
get_0_output_validator_list = [
]
get_0_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/nodes/{target-node-id}/{target-uri}',
path_variables={
'target_node_id': 'target-node-id',
'target_uri': 'target-uri',
},
query_parameters={
},
content_type='application/json'
)
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cursor': type.OptionalType(type.StringType()),
'discovered_node_id': type.OptionalType(type.StringType()),
'display_name': type.OptionalType(type.StringType()),
'external_id': type.OptionalType(type.StringType()),
'hardware_id': type.OptionalType(type.StringType()),
'hypervisor_os_type': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'ip_address': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'resource_type': type.OptionalType(type.StringType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
HasFieldsOfValidator()
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/nodes',
path_variables={
},
query_parameters={
'cursor': 'cursor',
'discovered_node_id': 'discovered_node_id',
'display_name': 'display_name',
'external_id': 'external_id',
'hardware_id': 'hardware_id',
'hypervisor_os_type': 'hypervisor_os_type',
'included_fields': 'included_fields',
'ip_address': 'ip_address',
'page_size': 'page_size',
'resource_type': 'resource_type',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
# properties for performaction operation
performaction_input_type = type.StructType('operation-input', {
'node_id': type.StringType(),
'action': type.OptionalType(type.StringType()),
'evacuate_powered_off_vms': type.OptionalType(type.BooleanType()),
'vsan_mode': type.OptionalType(type.StringType()),
})
performaction_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
performaction_input_value_validator_list = [
]
performaction_output_validator_list = [
HasFieldsOfValidator()
]
performaction_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/nodes/{node-id}',
path_variables={
'node_id': 'node-id',
},
query_parameters={
'action': 'action',
'evacuate_powered_off_vms': 'evacuate_powered_off_vms',
'vsan_mode': 'vsan_mode',
},
content_type='application/json'
)
# properties for restartinventorysync operation
restartinventorysync_input_type = type.StructType('operation-input', {
'node_id': type.StringType(),
})
restartinventorysync_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
restartinventorysync_input_value_validator_list = [
]
restartinventorysync_output_validator_list = [
]
restartinventorysync_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/nodes/{node-id}?action=restart_inventory_sync',
path_variables={
'node_id': 'node-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for update operation
update_input_type = type.StructType('operation-input', {
'node_id': type.StringType(),
'node': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
})
update_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
update_input_value_validator_list = [
HasFieldsOfValidator()
]
update_output_validator_list = [
HasFieldsOfValidator()
]
update_rest_metadata = OperationRestMetadata(
http_method='PUT',
url_template='/api/v1/fabric/nodes/{node-id}',
request_body_parameter='node',
path_variables={
'node_id': 'node-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for update_0 operation
update_0_input_type = type.StructType('operation-input', {
'target_node_id': type.StringType(),
'target_uri': type.StringType(),
})
update_0_error_dict = {
'com.vmware.vapi.std.errors.timed_out':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'TimedOut'),
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
update_0_input_value_validator_list = [
]
update_0_output_validator_list = [
]
update_0_rest_metadata = OperationRestMetadata(
http_method='PUT',
url_template='/api/v1/fabric/nodes/{target-node-id}/{target-uri}',
path_variables={
'target_node_id': 'target-node-id',
'target_uri': 'target-uri',
},
query_parameters={
},
content_type='application/json'
)
# properties for upgradeinfra operation
upgradeinfra_input_type = type.StructType('operation-input', {
'node_id': type.StringType(),
'disable_vm_migration': type.OptionalType(type.BooleanType()),
})
upgradeinfra_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
upgradeinfra_input_value_validator_list = [
]
upgradeinfra_output_validator_list = [
HasFieldsOfValidator()
]
upgradeinfra_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/nodes/{node-id}?action=upgrade_infra',
path_variables={
'node_id': 'node-id',
},
query_parameters={
'disable_vm_migration': 'disable_vm_migration',
},
content_type='application/json'
)
operations = {
'create': {
'input_type': create_input_type,
'output_type': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
'errors': create_error_dict,
'input_value_validator_list': create_input_value_validator_list,
'output_validator_list': create_output_validator_list,
'task_type': TaskType.NONE,
},
'create_0': {
'input_type': create_0_input_type,
'output_type': type.VoidType(),
'errors': create_0_error_dict,
'input_value_validator_list': create_0_input_value_validator_list,
'output_validator_list': create_0_output_validator_list,
'task_type': TaskType.NONE,
},
'delete': {
'input_type': delete_input_type,
'output_type': type.VoidType(),
'errors': delete_error_dict,
'input_value_validator_list': delete_input_value_validator_list,
'output_validator_list': delete_output_validator_list,
'task_type': TaskType.NONE,
},
'delete_0': {
'input_type': delete_0_input_type,
'output_type': type.VoidType(),
'errors': delete_0_error_dict,
'input_value_validator_list': delete_0_input_value_validator_list,
'output_validator_list': delete_0_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'get_0': {
'input_type': get_0_input_type,
'output_type': type.VoidType(),
'errors': get_0_error_dict,
'input_value_validator_list': get_0_input_value_validator_list,
'output_validator_list': get_0_output_validator_list,
'task_type': TaskType.NONE,
},
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'NodeListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
'performaction': {
'input_type': performaction_input_type,
'output_type': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
'errors': performaction_error_dict,
'input_value_validator_list': performaction_input_value_validator_list,
'output_validator_list': performaction_output_validator_list,
'task_type': TaskType.NONE,
},
'restartinventorysync': {
'input_type': restartinventorysync_input_type,
'output_type': type.VoidType(),
'errors': restartinventorysync_error_dict,
'input_value_validator_list': restartinventorysync_input_value_validator_list,
'output_validator_list': restartinventorysync_output_validator_list,
'task_type': TaskType.NONE,
},
'update': {
'input_type': update_input_type,
'output_type': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
'errors': update_error_dict,
'input_value_validator_list': update_input_value_validator_list,
'output_validator_list': update_output_validator_list,
'task_type': TaskType.NONE,
},
'update_0': {
'input_type': update_0_input_type,
'output_type': type.VoidType(),
'errors': update_0_error_dict,
'input_value_validator_list': update_0_input_value_validator_list,
'output_validator_list': update_0_output_validator_list,
'task_type': TaskType.NONE,
},
'upgradeinfra': {
'input_type': upgradeinfra_input_type,
'output_type': type.DynamicStructType('vmware.vapi.dynamic_struct', {}, VapiStruct, [type.ReferenceType('com.vmware.nsx.model_client', 'Node')]),
'errors': upgradeinfra_error_dict,
'input_value_validator_list': upgradeinfra_input_value_validator_list,
'output_validator_list': upgradeinfra_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'create': create_rest_metadata,
'create_0': create_0_rest_metadata,
'delete': delete_rest_metadata,
'delete_0': delete_0_rest_metadata,
'get': get_rest_metadata,
'get_0': get_0_rest_metadata,
'list': list_rest_metadata,
'performaction': performaction_rest_metadata,
'restartinventorysync': restartinventorysync_rest_metadata,
'update': update_rest_metadata,
'update_0': update_0_rest_metadata,
'upgradeinfra': upgradeinfra_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.nodes',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _OstypesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for get operation
get_input_type = type.StructType('operation-input', {})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/ostypes',
path_variables={
},
query_parameters={
},
content_type='application/json'
)
operations = {
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'SupportedHostOSListResult'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'get': get_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.ostypes',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _VifsStub(ApiInterfaceStub):
def __init__(self, config):
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cursor': type.OptionalType(type.StringType()),
'host_id': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'lport_attachment_id': type.OptionalType(type.StringType()),
'owner_vm_id': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
'vm_id': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/vifs',
path_variables={
},
query_parameters={
'cursor': 'cursor',
'host_id': 'host_id',
'included_fields': 'included_fields',
'lport_attachment_id': 'lport_attachment_id',
'owner_vm_id': 'owner_vm_id',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
'vm_id': 'vm_id',
},
content_type='application/json'
)
operations = {
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'VirtualNetworkInterfaceListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'list': list_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.vifs',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class _VirtualMachinesStub(ApiInterfaceStub):
def __init__(self, config):
# properties for list operation
list_input_type = type.StructType('operation-input', {
'cursor': type.OptionalType(type.StringType()),
'display_name': type.OptionalType(type.StringType()),
'external_id': type.OptionalType(type.StringType()),
'host_id': type.OptionalType(type.StringType()),
'included_fields': type.OptionalType(type.StringType()),
'page_size': type.OptionalType(type.IntegerType()),
'sort_ascending': type.OptionalType(type.BooleanType()),
'sort_by': type.OptionalType(type.StringType()),
})
list_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
list_input_value_validator_list = [
]
list_output_validator_list = [
]
list_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/api/v1/fabric/virtual-machines',
path_variables={
},
query_parameters={
'cursor': 'cursor',
'display_name': 'display_name',
'external_id': 'external_id',
'host_id': 'host_id',
'included_fields': 'included_fields',
'page_size': 'page_size',
'sort_ascending': 'sort_ascending',
'sort_by': 'sort_by',
},
content_type='application/json'
)
# properties for updatetags operation
updatetags_input_type = type.StructType('operation-input', {
'virtual_machine_tag_update': type.ReferenceType('com.vmware.nsx.model_client', 'VirtualMachineTagUpdate'),
})
updatetags_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
updatetags_input_value_validator_list = [
]
updatetags_output_validator_list = [
]
updatetags_rest_metadata = OperationRestMetadata(
http_method='POST',
url_template='/api/v1/fabric/virtual-machines?action=update_tags',
request_body_parameter='virtual_machine_tag_update',
path_variables={
},
query_parameters={
},
content_type='application/json'
)
operations = {
'list': {
'input_type': list_input_type,
'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'VirtualMachineListResult'),
'errors': list_error_dict,
'input_value_validator_list': list_input_value_validator_list,
'output_validator_list': list_output_validator_list,
'task_type': TaskType.NONE,
},
'updatetags': {
'input_type': updatetags_input_type,
'output_type': type.VoidType(),
'errors': updatetags_error_dict,
'input_value_validator_list': updatetags_input_value_validator_list,
'output_validator_list': updatetags_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'list': list_rest_metadata,
'updatetags': updatetags_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx.fabric.virtual_machines',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class StubFactory(StubFactoryBase):
_attrs = {
'ComputeCollectionFabricTemplates': ComputeCollectionFabricTemplates,
'ComputeCollections': ComputeCollections,
'ComputeManagers': ComputeManagers,
'ContainerApplicationInstances': ContainerApplicationInstances,
'ContainerApplications': ContainerApplications,
'ContainerClusterNodes': ContainerClusterNodes,
'ContainerClusters': ContainerClusters,
'ContainerIngressPolicies': ContainerIngressPolicies,
'ContainerNetworkPolicies': ContainerNetworkPolicies,
'ContainerProjects': ContainerProjects,
'DiscoveredNodes': DiscoveredNodes,
'Nodes': Nodes,
'Ostypes': Ostypes,
'Vifs': Vifs,
'VirtualMachines': VirtualMachines,
'compute_collections': 'com.vmware.nsx.fabric.compute_collections_client.StubFactory',
'compute_managers': 'com.vmware.nsx.fabric.compute_managers_client.StubFactory',
'nodes': 'com.vmware.nsx.fabric.nodes_client.StubFactory',
'virtual_machines': 'com.vmware.nsx.fabric.virtual_machines_client.StubFactory',
}
|
[
"vmware.vapi.bindings.stub.ApiInterfaceStub.__init__",
"vmware.vapi.bindings.type.StringType",
"vmware.vapi.bindings.type.BooleanType",
"vmware.vapi.bindings.type.StructType",
"vmware.vapi.bindings.type.ReferenceType",
"vmware.vapi.bindings.type.VoidType",
"vmware.vapi.bindings.type.IntegerType",
"vmware.vapi.data.validator.HasFieldsOfValidator",
"vmware.vapi.bindings.stub.VapiInterface.__init__",
"vmware.vapi.lib.rest.OperationRestMetadata"
] |
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|
'''
Created on Oct 5, 2016
@author: theo
'''
from django.core.management.base import BaseCommand
from django.contrib.auth.models import User
from optparse import make_option
from django.contrib.gis.geos import Point
from django.utils import timezone
from django.conf import settings
from dateutil import parser
import os,pytz,datetime
import json,logging
import xlrd
import re
from acacia.data.models import ProjectLocatie
from iom import util
from iom.akvo import FlowAPI, as_timestamp
from iom.models import AkvoFlow, CartoDb, Meetpunt, Waarnemer, Alias
from iom.exif import Exif
logger = logging.getLogger(__name__)
def maak_naam(parameter,diep):
if diep and not diep.endswith('iep'):
diep = None
return parameter + '_' + diep if diep else parameter
def get_or_create_waarnemer(akvoname):
try:
# is deze alias al geregistreerd?
alias = Alias.objects.get(alias=akvoname)
waarnemer = alias.waarnemer
logger.debug('Waarnemer {name} gevonden met alias {alias}'.format(name=unicode(waarnemer),alias=alias))
except Alias.DoesNotExist:
# Bestaat er al een waarnemer met deze naam?
words = akvoname.split()
if len(words) > 1:
voornaam = words[0]
initialen = voornaam[0].upper()
achternaam = words[-1].title()
if len(words) > 2:
tussenvoegsel = ' '.join(words[1:-1])
else:
tussenvoegsel = ''
waarnemer, created = Waarnemer.objects.get_or_create(initialen=initialen, voornaam=voornaam, tussenvoegsel=tussenvoegsel, achternaam=achternaam)
else:
waarnemer, created = Waarnemer.objects.get_or_create(achternaam=akvoname)
if created:
logger.info('Waarnemer {} aangemaakt'.format(unicode(waarnemer)))
# alias toevoegen aan waarnemer
alias = waarnemer.alias_set.create(alias=akvoname)
logger.info('alias {alias} toegevoegd aan waarnemer {name}'.format(alias=unicode(alias),name=unicode(waarnemer)))
return waarnemer
def download_photo(url):
# copy photo to local storage and rotate if necessary
try:
filename = os.path.basename(url)
Exif.copyImage(url, os.path.join(settings.PHOTO_DIR,filename))
return os.path.join(settings.PHOTO_URL,filename)
except:
return url
def importRegistrationForm(sheet,projectlocatie,user):
meetpunten=set()
waarnemingen=set()
num_meetpunten = 0
keys = [sheet.cell(0, col).value.split('|')[-1] for col in xrange(sheet.ncols)]
for row in xrange(1,sheet.nrows):
cells = {keys[col]: sheet.cell(row, col).value for col in xrange(sheet.ncols)}
identifier = cells['Identifier']
displayName = cells['Display Name']
submitter = cells['Submitter']
device = cells['Device identifier']
date = parser.parse(cells['Submission Date'],dayfirst=True)
akvowaarnemer = cells['Waarnemer']
meetid = cells['Meetpunt ID']
foto = cells['Maak een foto van het meetgebied']
lat = cells['Latitude']
lon = cells['Longitude']
omschrijving = cells['Meetpunt omschrijving']
num_meetpunten += 1
try:
location = Point(float(lon),float(lat),srid=4326)
location.transform(28992)
except:
logger.error('Probleem met coordinaten {loc}. waarnemer = {waar}, meetpunt = {mp}'.format(loc=(lat,lon), waar = akvowaarnemer or submitter, mp=meetid))
continue
akvoname = akvowaarnemer or submitter
waarnemer = get_or_create_waarnemer(akvoname)
# change reference to photo from smartphone storage to amazon storage and download to this server
if foto:
foto = download_photo(foto)
if meetid:
# Gebuik waarnemer naam + meetid
meetName = '{name} - {id}'. format(name=akvoname, id=meetid)
else:
meetName = displayName
name = meetName or 'Naamloos'
meetpunt = None
for dup in range(10):
try:
meetpunt, created = waarnemer.meetpunt_set.get_or_create(identifier=identifier, defaults={
'name': name,
'projectlocatie': projectlocatie,
'location': location,
'displayname': displayName,
'device': device,
'photo_url': foto,
'description': omschrijving})
break
except Exception as e:
logger.error('Dubbel meetpunt {mname} voor waarnemer {wname}'.format(mname=meetName, wname=unicode(waarnemer)))
name = '{} ({})'.format(meetName, dup+1)
continue
if not meetpunt:
raise Exception('Te veel dubbele meetopunten met naam {name}'.format(name=meetName))
if created:
logger.info('Meetpunt {id} aangemaakt voor waarnemer {name}'.format(id=name,name=unicode(waarnemer)))
meetpunten.add(meetpunt)
#if device != 'IMPORTER':
ec = cells['Meet EC waarde - ECOND']
diep = cells['Diep of ondiep gemeten?']
waarneming_naam = maak_naam('EC',diep)
try:
waarneming, created = meetpunt.waarneming_set.get_or_create(naam=waarneming_naam, waarnemer=waarnemer, datum=date,
defaults = {'waarde': ec, 'device': device, 'opmerking': '', 'foto_url': foto, 'eenheid': 'uS/cm'})
except Exception as e:
logger.exception('Probleem bij toevoegen waarneming {wname} aan meetpunt {mname}'.format(wname=waarneming_naam, mname=unicode(meetpunt)))
continue
if created:
logger.debug('{id}, {date}, EC={ec}'.format(id=waarneming.naam, date=waarneming.datum, ec=waarneming.waarde))
waarnemingen.add(waarneming)
meetpunten.add(meetpunt)
elif waarneming.waarde != ec:
waarneming.waarde = ec
waarneming.save()
waarnemingen.add(waarneming)
meetpunten.add(meetpunt)
#endif
logger.info('Aantal meetpunten: {aantal}, nieuwe meetpunten: {new}'.format(aantal=num_meetpunten, new=len(meetpunten)))
return meetpunten, waarnemingen
def importMonitoringForm(sheet):
meetpunten = set()
waarnemingen = set()
num_waarnemingen = 0
num_replaced = 0
keys = [sheet.cell(0, col).value.split('|')[-1] for col in xrange(sheet.ncols)]
for row in xrange(1,sheet.nrows):
cells = {keys[col]: sheet.cell(row, col).value for col in xrange(sheet.ncols)}
submitter = cells['Submitter']
waarnemer = get_or_create_waarnemer(submitter)
#find related registration form (meetpunt)
identifier = cells['Identifier']
try:
meetpunt = Meetpunt.objects.get(identifier=identifier)
except Meetpunt.DoesNotExist:
logger.error('Meetpunt {identifier} niet gevonden voor {submitter}'.format(identifier=identifier, submitter=submitter))
continue
device = cells['Device identifier']
date = parser.parse(cells['Submission Date'],dayfirst=True) # format: day first
ec=cells['EC waarde - ECOND']
foto=cells['Maak een foto van het meetgebied']
diep=cells['Diep of ondiep']
waarneming_naam = maak_naam('EC',diep)
if foto:
foto = download_photo(foto)
if foto:
# update meetpunt photo along the way..
meetpunt.photo_url = foto
meetpunt.save(update_fields=['photo_url'])
try:
waarneming, created = meetpunt.waarneming_set.get_or_create(naam=waarneming_naam, waarnemer=waarnemer, datum=date,
defaults = {'waarde': ec, 'device': device, 'opmerking': '', 'foto_url': foto, 'eenheid': 'uS/cm'})
except Exception as ex:
logger.exception('Probleem met toevoegen van waarneming {waar} met waarde {waarde} aan meetpunt {meetpunt}'.format(waar=waarneming_naam, waarde=ec, meetpunt=meetpunt))
continue
if created:
logger.info('created {identifier}={mp}, {id}({date})={ec}'.format(identifier=identifier, mp=unicode(meetpunt), id=waarneming.naam, date=waarneming.datum, ec=waarneming.waarde))
num_waarnemingen += 1
waarnemingen.add(waarneming)
meetpunten.add(meetpunt)
elif waarneming.waarde != ec:
waarneming.waarde = ec
waarneming.save()
logger.info('updated {identifier}={mp}, {id}({date})={ec}'.format(identifier=identifier, mp=unicode(meetpunt), id=waarneming.naam, date=waarneming.datum, ec=waarneming.waarde))
num_replaced += 1
waarnemingen.add(waarneming)
meetpunten.add(meetpunt)
logger.info('Aantal nieuwe metingen: {meet}, bijgewerkt: {repl}'.format(meet=num_waarnemingen,repl=num_replaced))
return meetpunten, waarnemingen
class Command(BaseCommand):
args = ''
help = 'Importeer cleaned_data spreadsheet'
option_list = BaseCommand.option_list + (
make_option('--file',
action='store',
dest = 'fname',
default='/media/sf_C_DRIVE/Users/theo/Downloads/NZG_RAW_DATA-10410916.xlsx',
help = 'xlsx file'),
make_option('--user',
action='store',
dest = 'user',
default = 'akvo',
help = 'user name'),
)
def handle(self, *args, **options):
user = User.objects.get(username=options.get('user'))
filename = args[0]
logger.debug('Importing '+filename)
locatie = None
match = re.search('RAW_DATA\-(\d+)\.', filename)
if match:
surveyId = match.group(1)
# find surveyid in akvo config
for candidate in ProjectLocatie.objects.all():
if surveyId == candidate.akvoflow.regform:
locatie = candidate
isRegForm = True
break
if surveyId == candidate.akvoflow.monforms:
locatie = candidate
isRegForm = False
break
if not locatie:
logger.error('Geen geregistreerde survey gevonden voor dit bestand')
return
book = xlrd.open_workbook(filename)
sheet = book.sheet_by_name('Raw Data')
try:
if isRegForm:
logger.debug('Meetpuntgegevens ophalen voor {}'.format(locatie))
mp,wn = importRegistrationForm(sheet,projectlocatie=locatie,user=user)
else:
logger.debug('Waarnemingen ophalen voor {}'.format(locatie))
mp,wn=importMonitoringForm(sheet)
#if mp:
#logger.debug('Grafieken aanpassen')
#util.updateSeries(mp, user)
#logger.debug('Cartodb actualiseren')
#cartodb = locatie.cartodb
#util.exportCartodb(cartodb, mp)
#akvo = locatie.akvoflow
#akvo.last_update = timezone.now()
#akvo.save()
except Exception as e:
logger.exception('Probleem met verwerken nieuwe metingen: %s',e)
finally:
pass
|
[
"dateutil.parser.parse",
"acacia.data.models.ProjectLocatie.objects.all",
"os.path.basename",
"iom.models.Meetpunt.objects.get",
"optparse.make_option",
"xlrd.open_workbook",
"iom.models.Waarnemer.objects.get_or_create",
"re.search",
"iom.models.Alias.objects.get",
"os.path.join",
"logging.getLogger"
] |
[((595, 622), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (612, 622), False, 'import json, logging\n'), ((881, 914), 'iom.models.Alias.objects.get', 'Alias.objects.get', ([], {'alias': 'akvoname'}), '(alias=akvoname)\n', (898, 914), False, 'from iom.models import AkvoFlow, CartoDb, Meetpunt, Waarnemer, Alias\n'), ((2173, 2194), 'os.path.basename', 'os.path.basename', (['url'], {}), '(url)\n', (2189, 2194), False, 'import os, pytz, datetime\n'), ((2281, 2323), 'os.path.join', 'os.path.join', (['settings.PHOTO_URL', 'filename'], {}), '(settings.PHOTO_URL, filename)\n', (2293, 2323), False, 'import os, pytz, datetime\n'), ((2885, 2938), 'dateutil.parser.parse', 'parser.parse', (["cells['Submission Date']"], {'dayfirst': '(True)'}), "(cells['Submission Date'], dayfirst=True)\n", (2897, 2938), False, 'from dateutil import parser\n'), ((7219, 7272), 'dateutil.parser.parse', 'parser.parse', (["cells['Submission Date']"], {'dayfirst': '(True)'}), "(cells['Submission Date'], dayfirst=True)\n", (7231, 7272), False, 'from dateutil import parser\n'), ((9864, 9907), 're.search', 're.search', (['"""RAW_DATA\\\\-(\\\\d+)\\\\."""', 'filename'], {}), "('RAW_DATA\\\\-(\\\\d+)\\\\.', filename)\n", (9873, 9907), False, 'import re\n'), ((10531, 10559), 'xlrd.open_workbook', 'xlrd.open_workbook', (['filename'], {}), '(filename)\n', (10549, 10559), False, 'import xlrd\n'), ((2223, 2265), 'os.path.join', 'os.path.join', (['settings.PHOTO_DIR', 'filename'], {}), '(settings.PHOTO_DIR, filename)\n', (2235, 2265), False, 'import os, pytz, datetime\n'), ((6916, 6959), 'iom.models.Meetpunt.objects.get', 'Meetpunt.objects.get', ([], {'identifier': 'identifier'}), '(identifier=identifier)\n', (6936, 6959), False, 'from iom.models import AkvoFlow, CartoDb, Meetpunt, Waarnemer, Alias\n'), ((9257, 9412), 'optparse.make_option', 'make_option', (['"""--file"""'], {'action': '"""store"""', 'dest': '"""fname"""', 'default': '"""/media/sf_C_DRIVE/Users/theo/Downloads/NZG_RAW_DATA-10410916.xlsx"""', 'help': '"""xlsx file"""'}), "('--file', action='store', dest='fname', default=\n '/media/sf_C_DRIVE/Users/theo/Downloads/NZG_RAW_DATA-10410916.xlsx',\n help='xlsx file')\n", (9268, 9412), False, 'from optparse import make_option\n'), ((9485, 9574), 'optparse.make_option', 'make_option', (['"""--user"""'], {'action': '"""store"""', 'dest': '"""user"""', 'default': '"""akvo"""', 'help': '"""user name"""'}), "('--user', action='store', dest='user', default='akvo', help=\n 'user name')\n", (9496, 9574), False, 'from optparse import make_option\n'), ((10033, 10061), 'acacia.data.models.ProjectLocatie.objects.all', 'ProjectLocatie.objects.all', ([], {}), '()\n', (10059, 10061), False, 'from acacia.data.models import ProjectLocatie\n'), ((1497, 1624), 'iom.models.Waarnemer.objects.get_or_create', 'Waarnemer.objects.get_or_create', ([], {'initialen': 'initialen', 'voornaam': 'voornaam', 'tussenvoegsel': 'tussenvoegsel', 'achternaam': 'achternaam'}), '(initialen=initialen, voornaam=voornaam,\n tussenvoegsel=tussenvoegsel, achternaam=achternaam)\n', (1528, 1624), False, 'from iom.models import AkvoFlow, CartoDb, Meetpunt, Waarnemer, Alias\n'), ((1668, 1720), 'iom.models.Waarnemer.objects.get_or_create', 'Waarnemer.objects.get_or_create', ([], {'achternaam': 'akvoname'}), '(achternaam=akvoname)\n', (1699, 1720), False, 'from iom.models import AkvoFlow, CartoDb, Meetpunt, Waarnemer, Alias\n')]
|
import match_transactions
if __name__ == "__main__":
match_transactions.main()
|
[
"match_transactions.main"
] |
[((58, 83), 'match_transactions.main', 'match_transactions.main', ([], {}), '()\n', (81, 83), False, 'import match_transactions\n')]
|
# Generated by Django 3.2.8 on 2021-11-07 20:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('dsaii', '0010_auto_20211108_0215'),
]
operations = [
migrations.AddField(
model_name='event',
name='date',
field=models.CharField(default='Enter date here', max_length=255),
),
]
|
[
"django.db.models.CharField"
] |
[((329, 388), 'django.db.models.CharField', 'models.CharField', ([], {'default': '"""Enter date here"""', 'max_length': '(255)'}), "(default='Enter date here', max_length=255)\n", (345, 388), False, 'from django.db import migrations, models\n')]
|
import os
import sys
import numpy as np
import pandas as pd
import logging
import gc
import tqdm
import pickle
import time
gc.enable()
cwd = os.getcwd()
train_path = os.path.join(cwd, 'train_artifact')
test_path = os.path.join(cwd, 'test_artifact')
input_path = os.path.join(cwd, 'input_artifact')
embed_path = os.path.join(cwd, 'embed_artifact')
def initiate_logger(log_path):
"""
Initialize a logger with file handler and stream handler
"""
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s %(levelname)-s: %(message)s', datefmt='%H:%M:%S')
fh = logging.FileHandler(log_path)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
sh = logging.StreamHandler(sys.stdout)
sh.setLevel(logging.INFO)
sh.setFormatter(formatter)
logger.addHandler(sh)
logger.info('===================================')
logger.info('Begin executing at {}'.format(time.ctime()))
logger.info('===================================')
return logger
def rough_split(logger=None):
"""
Split training data (900,000) records into training file and validation file using ratio 9:1.
"""
for npy_path in ['train_idx_shuffle.npy', 'train_age.npy', 'train_gender.npy']:
with open(os.path.join(input_path, npy_path), 'rb') as f:
npy = np.load(f)
with open(os.path.join(input_path, '{}_tra.npy'.format(npy_path.split('.')[0])), 'wb') as f:
np.save(f, npy[:810000])
with open(os.path.join(input_path, '{}_val.npy'.format(npy_path.split('.')[0])), 'wb') as f:
np.save(f, npy[810000:])
if logger: logger.info('{} splitted'.format(npy_path))
for pkl_path in ['train_creative_id_seq.pkl', 'train_ad_id_seq.pkl', 'train_advertiser_id_seq.pkl', 'train_product_id_seq.pkl']:
with open(os.path.join(input_path, pkl_path), 'rb') as f:
pkl = pickle.load(f)
with open(os.path.join(input_path, '{}_tra.pkl'.format(pkl_path.split('.')[0])), 'wb') as f:
pickle.dump(pkl[:810000], f)
with open(os.path.join(input_path, '{}_val.pkl'.format(pkl_path.split('.')[0])), 'wb') as f:
pickle.dump(pkl[810000:], f)
if logger: logger.info('{} splitted'.format(pkl_path))
def fine_split(logger=None):
"""
Split train data (900,000 records) into 10 files
Split test data (1,000,000 records) into 10 files
"""
input_split_path = os.path.join(cwd, 'input_split_artifact')
if not os.path.isdir(input_split_path): os.mkdir(input_split_path)
for npy_path in ['train_idx_shuffle.npy', 'train_age.npy', 'train_gender.npy']:
with open(os.path.join(input_path, npy_path), 'rb') as f:
npy = np.load(f)
for i in range(10):
with open(os.path.join(input_split_path, '{}_{}.npy'.format(npy_path.split('.')[0], i+1)), 'wb') as f:
np.save(f, npy[i*90000:(i+1)*90000])
if logger: logger.info('{} splitted'.format(npy_path))
for pkl_path in ['train_creative_id_seq.pkl', 'train_ad_id_seq.pkl', 'train_advertiser_id_seq.pkl', 'train_product_id_seq.pkl',]:
with open(os.path.join(input_path, pkl_path), 'rb') as f:
pkl = pickle.load(f)
for i in range(10):
with open(os.path.join(input_split_path, '{}_{}.pkl'.format(pkl_path.split('.')[0], i+1)), 'wb') as f:
pickle.dump(pkl[i*90000:(i+1)*90000], f)
if logger: logger.info('{} splitted'.format(pkl_path))
for npy_path in ['test_idx_shuffle.npy']:
with open(os.path.join(input_path, npy_path), 'rb') as f:
npy = np.load(f)
for i in range(10):
with open(os.path.join(input_split_path, '{}_{}.npy'.format(npy_path.split('.')[0], i+1)), 'wb') as f:
np.save(f, npy[i*100000:(i+1)*100000])
if logger: logger.info('{} splitted'.format(npy_path))
for pkl_path in ['test_creative_id_seq.pkl', 'test_ad_id_seq.pkl', 'test_advertiser_id_seq.pkl', 'test_product_id_seq.pkl',]:
with open(os.path.join(input_path, pkl_path), 'rb') as f:
pkl = pickle.load(f)
for i in range(10):
with open(os.path.join(input_split_path, '{}_{}.pkl'.format(pkl_path.split('.')[0], i+1)), 'wb') as f:
pickle.dump(pkl[i*100000:(i+1)*100000], f)
if logger: logger.info('{} splitted'.format(pkl_path))
if __name__=='__main__':
logger = initiate_logger('input_split.log')
if len(sys.argv)==1:
rough_split(logger=logger)
else:
fine_split(logger=logger)
|
[
"os.mkdir",
"numpy.load",
"numpy.save",
"pickle.dump",
"os.path.join",
"logging.FileHandler",
"os.getcwd",
"os.path.isdir",
"logging.StreamHandler",
"time.ctime",
"logging.Formatter",
"pickle.load",
"gc.enable",
"logging.getLogger"
] |
[((125, 136), 'gc.enable', 'gc.enable', ([], {}), '()\n', (134, 136), False, 'import gc\n'), ((143, 154), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (152, 154), False, 'import os\n'), ((168, 203), 'os.path.join', 'os.path.join', (['cwd', '"""train_artifact"""'], {}), "(cwd, 'train_artifact')\n", (180, 203), False, 'import os\n'), ((216, 250), 'os.path.join', 'os.path.join', (['cwd', '"""test_artifact"""'], {}), "(cwd, 'test_artifact')\n", (228, 250), False, 'import os\n'), ((264, 299), 'os.path.join', 'os.path.join', (['cwd', '"""input_artifact"""'], {}), "(cwd, 'input_artifact')\n", (276, 299), False, 'import os\n'), ((313, 348), 'os.path.join', 'os.path.join', (['cwd', '"""embed_artifact"""'], {}), "(cwd, 'embed_artifact')\n", (325, 348), False, 'import os\n'), ((459, 486), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (476, 486), False, 'import logging\n'), ((531, 616), 'logging.Formatter', 'logging.Formatter', (['"""%(asctime)s %(levelname)-s: %(message)s"""'], {'datefmt': '"""%H:%M:%S"""'}), "('%(asctime)s %(levelname)-s: %(message)s', datefmt='%H:%M:%S'\n )\n", (548, 616), False, 'import logging\n'), ((618, 647), 'logging.FileHandler', 'logging.FileHandler', (['log_path'], {}), '(log_path)\n', (637, 647), False, 'import logging\n'), ((732, 765), 'logging.StreamHandler', 'logging.StreamHandler', (['sys.stdout'], {}), '(sys.stdout)\n', (753, 765), False, 'import logging\n'), ((2308, 2349), 'os.path.join', 'os.path.join', (['cwd', '"""input_split_artifact"""'], {}), "(cwd, 'input_split_artifact')\n", (2320, 2349), False, 'import os\n'), ((2358, 2389), 'os.path.isdir', 'os.path.isdir', (['input_split_path'], {}), '(input_split_path)\n', (2371, 2389), False, 'import os\n'), ((2391, 2417), 'os.mkdir', 'os.mkdir', (['input_split_path'], {}), '(input_split_path)\n', (2399, 2417), False, 'import os\n'), ((940, 952), 'time.ctime', 'time.ctime', ([], {}), '()\n', (950, 952), False, 'import time\n'), ((1308, 1318), 'numpy.load', 'np.load', (['f'], {}), '(f)\n', (1315, 1318), True, 'import numpy as np\n'), ((1417, 1441), 'numpy.save', 'np.save', (['f', 'npy[:810000]'], {}), '(f, npy[:810000])\n', (1424, 1441), True, 'import numpy as np\n'), ((1540, 1564), 'numpy.save', 'np.save', (['f', 'npy[810000:]'], {}), '(f, npy[810000:])\n', (1547, 1564), True, 'import numpy as np\n'), ((1821, 1835), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (1832, 1835), False, 'import pickle\n'), ((1934, 1962), 'pickle.dump', 'pickle.dump', (['pkl[:810000]', 'f'], {}), '(pkl[:810000], f)\n', (1945, 1962), False, 'import pickle\n'), ((2061, 2089), 'pickle.dump', 'pickle.dump', (['pkl[810000:]', 'f'], {}), '(pkl[810000:], f)\n', (2072, 2089), False, 'import pickle\n'), ((2570, 2580), 'numpy.load', 'np.load', (['f'], {}), '(f)\n', (2577, 2580), True, 'import numpy as np\n'), ((3007, 3021), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (3018, 3021), False, 'import pickle\n'), ((3365, 3375), 'numpy.load', 'np.load', (['f'], {}), '(f)\n', (3372, 3375), True, 'import numpy as np\n'), ((3800, 3814), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (3811, 3814), False, 'import pickle\n'), ((1251, 1285), 'os.path.join', 'os.path.join', (['input_path', 'npy_path'], {}), '(input_path, npy_path)\n', (1263, 1285), False, 'import os\n'), ((1764, 1798), 'os.path.join', 'os.path.join', (['input_path', 'pkl_path'], {}), '(input_path, pkl_path)\n', (1776, 1798), False, 'import os\n'), ((2513, 2547), 'os.path.join', 'os.path.join', (['input_path', 'npy_path'], {}), '(input_path, npy_path)\n', (2525, 2547), False, 'import os\n'), ((2713, 2755), 'numpy.save', 'np.save', (['f', 'npy[i * 90000:(i + 1) * 90000]'], {}), '(f, npy[i * 90000:(i + 1) * 90000])\n', (2720, 2755), True, 'import numpy as np\n'), ((2950, 2984), 'os.path.join', 'os.path.join', (['input_path', 'pkl_path'], {}), '(input_path, pkl_path)\n', (2962, 2984), False, 'import os\n'), ((3154, 3200), 'pickle.dump', 'pickle.dump', (['pkl[i * 90000:(i + 1) * 90000]', 'f'], {}), '(pkl[i * 90000:(i + 1) * 90000], f)\n', (3165, 3200), False, 'import pickle\n'), ((3308, 3342), 'os.path.join', 'os.path.join', (['input_path', 'npy_path'], {}), '(input_path, npy_path)\n', (3320, 3342), False, 'import os\n'), ((3508, 3552), 'numpy.save', 'np.save', (['f', 'npy[i * 100000:(i + 1) * 100000]'], {}), '(f, npy[i * 100000:(i + 1) * 100000])\n', (3515, 3552), True, 'import numpy as np\n'), ((3743, 3777), 'os.path.join', 'os.path.join', (['input_path', 'pkl_path'], {}), '(input_path, pkl_path)\n', (3755, 3777), False, 'import os\n'), ((3947, 3995), 'pickle.dump', 'pickle.dump', (['pkl[i * 100000:(i + 1) * 100000]', 'f'], {}), '(pkl[i * 100000:(i + 1) * 100000], f)\n', (3958, 3995), False, 'import pickle\n')]
|
from typing import Union, List, Dict, Optional, Callable, Set
from skdecide.builders.solver.policy import DeterministicPolicies, UncertainPolicies
from skdecide import Domain, Solver
from skdecide.builders.scheduling.modes import SingleMode
from skdecide.builders.scheduling.scheduling_domains_modelling import State, SchedulingAction, SchedulingActionEnum
from skdecide.builders.scheduling.scheduling_domains import SchedulingDomain, D, MultiModeRCPSP, SingleModeRCPSP
from skdecide import rollout_episode
from skdecide.hub.solver.sgs_policies.sgs_policies import PolicyMethodParams, BasePolicyMethod, PolicyRCPSP
from skdecide.hub.solver.do_solver.do_solver_scheduling import PolicyRCPSP, DOSolver, PolicyMethodParams, BasePolicyMethod, SolvingMethod
from skdecide.builders.discrete_optimization.rcpsp.solver.cpm import CPM
from skdecide.hub.solver.do_solver.sk_to_do_binding import build_do_domain
from skdecide.builders.discrete_optimization.rcpsp.rcpsp_model import RCPSPSolution
from enum import Enum
from deap.gp import PrimitiveSet, PrimitiveTree, genHalfAndHalf
from deap import gp
from deap import algorithms
from deap.base import Toolbox, Fitness
from deap import creator
from deap import tools
import operator
import itertools
import numpy as np
import random
from scipy import stats
from scipy.spatial import distance
def if_then_else(input, output1, output2):
if input: return output1
else: return output2
def protected_div(left, right):
if right != 0.:
return left/right
else:
return 1.
def max_operator(left, right):
return max(left, right)
def min_operator(left, right):
return min(left, right)
def feature_task_duration(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
return domain.sample_task_duration(task_id)
def feature_total_n_res(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
val = 0
mode_consumption = domain.get_task_modes(task_id)[1]
for res in mode_consumption.get_ressource_names():
val += mode_consumption.get_resource_need(res)
return val
def feature_n_successors(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
return len(domain.get_successors_task(task_id))/ len(domain.get_tasks_ids())
def feature_n_predecessors(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
return len(domain.get_predecessors_task(task_id))/ len(domain.get_tasks_ids())
def get_resource_requirements_across_duration(domain: SchedulingDomain, task_id: int, **kwargs):
values = []
mode_consumption = domain.get_task_modes(task_id)[1]
duration = domain.get_latest_sampled_duration(task_id, 1, 0.)
if duration > 0:
for res in mode_consumption.get_ressource_names():
tmp = 0
for t in range(duration):
need = domain.get_task_modes(task_id)[1].get_resource_need_at_time(res, t)
total = domain.sample_quantity_resource(res, t)
tmp += need / total
values.append(tmp/duration)
else:
values = [0.]
# print(task_id,':', values)
return values
def feature_average_resource_requirements(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
values = get_resource_requirements_across_duration(domain=domain, task_id=task_id)
val = np.mean(values)
return val
def feature_minimum_resource_requirements(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
values = get_resource_requirements_across_duration(domain=domain, task_id=task_id)
val = np.min(values)
return val
def feature_non_zero_minimum_resource_requirements(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
values = get_resource_requirements_across_duration(domain=domain, task_id=task_id)
if np.sum(values) > 0.:
val = np.min([x for x in values if x > 0.])
else:
val = np.min(values)
return val
def feature_maximum_resource_requirements(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
values = get_resource_requirements_across_duration(domain=domain, task_id=task_id)
val = np.max(values)
return val
def feature_resource_requirements(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
values = get_resource_requirements_across_duration(domain=domain, task_id=task_id)
val = len([x for x in values if x > 0.]) / len(values)
return val
def feature_all_descendants(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
return len(domain.full_successors[task_id]) / len(domain.get_tasks_ids())
def feature_precedence_done(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, state: State, **kwargs):
return task_id in domain.task_possible_to_launch_precedence(state=state)
def compute_cpm(do_domain):
cpm_solver = CPM(do_domain)
path = cpm_solver.run_classic_cpm()
cpm = cpm_solver.map_node
cpm_esd = cpm[path[-1]]._ESD # to normalize...
return cpm, cpm_esd
def feature_esd(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
""" Will only work if you store cpm results into the object. dirty trick"""
return cpm[task_id]._ESD/cpm_esd
def feature_lsd(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
""" Will only work if you store cpm results into the object. dirty trick"""
return cpm[task_id]._LSD/cpm_esd
def feature_efd(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
""" Will only work if you store cpm results into the object. dirty trick"""
return cpm[task_id]._EFD/cpm_esd
def feature_lfd(domain: SchedulingDomain, cpm, cpm_esd, task_id: int, **kwargs):
""" Will only work if you store cpm results into the object. dirty trick"""
return cpm[task_id]._LFD/cpm_esd
class D(SchedulingDomain, SingleMode):
pass
class FeatureEnum(Enum):
TASK_DURATION = "task_duration"
RESSOURCE_TOTAL = "total_nres"
N_SUCCESSORS = "n_successors"
N_PREDECESSORS = "n_predecessors"
RESSOURCE_REQUIRED = "res_requ"
RESSOURCE_AVG = "avg_res_requ"
RESSOURCE_MIN = "min_res_requ"
RESSOURCE_NZ_MIN = "nz_min_res_requ"
RESSOURCE_MAX = "max_res_requ"
ALL_DESCENDANTS = "all_descendants"
PRECEDENCE_DONE = "precedence_done"
EARLIEST_START_DATE = "ESD"
LATEST_START_DATE = "LSD"
EARLIEST_FINISH_DATE = "EFD"
LATEST_FINISH_DATE = "LFD"
feature_function_map = {FeatureEnum.TASK_DURATION: feature_task_duration,
FeatureEnum.RESSOURCE_TOTAL: feature_total_n_res,
FeatureEnum.N_SUCCESSORS: feature_n_successors,
FeatureEnum.N_PREDECESSORS: feature_n_predecessors, #
FeatureEnum.RESSOURCE_REQUIRED: feature_resource_requirements, #
FeatureEnum.RESSOURCE_AVG: feature_average_resource_requirements, #
FeatureEnum.RESSOURCE_MIN: feature_minimum_resource_requirements, #
FeatureEnum.RESSOURCE_NZ_MIN: feature_non_zero_minimum_resource_requirements, #
FeatureEnum.RESSOURCE_MAX: feature_maximum_resource_requirements, #
FeatureEnum.ALL_DESCENDANTS: feature_all_descendants, #
FeatureEnum.PRECEDENCE_DONE: feature_precedence_done,
FeatureEnum.EARLIEST_START_DATE: feature_esd, #
FeatureEnum.EARLIEST_FINISH_DATE: feature_efd, #
FeatureEnum.LATEST_START_DATE: feature_lsd, #
FeatureEnum.LATEST_FINISH_DATE: feature_lfd} #
feature_static_map = {FeatureEnum.TASK_DURATION: True,
FeatureEnum.RESSOURCE_TOTAL: True,
FeatureEnum.N_SUCCESSORS: True,
FeatureEnum.N_PREDECESSORS: True, #
FeatureEnum.RESSOURCE_REQUIRED: True, #
FeatureEnum.RESSOURCE_AVG: True, #
FeatureEnum.RESSOURCE_MIN: True, #
FeatureEnum.RESSOURCE_NZ_MIN: True, #
FeatureEnum.RESSOURCE_MAX: True, #
FeatureEnum.ALL_DESCENDANTS: True, #
FeatureEnum.PRECEDENCE_DONE: False,
FeatureEnum.EARLIEST_START_DATE: True, #
FeatureEnum.EARLIEST_FINISH_DATE: True, #
FeatureEnum.LATEST_START_DATE: True, #
FeatureEnum.LATEST_FINISH_DATE: True} #
class EvaluationGPHH(Enum):
SGS = 0
PERMUTATION_DISTANCE = 1
# SGS_DEVIATION = 2
class PermutationDistance(Enum):
KTD = 0
HAMMING = 1
KTD_HAMMING = 2
class ParametersGPHH:
set_feature: Set[FeatureEnum] = None
set_primitves: PrimitiveSet = None
tournament_ratio: float = None
pop_size: int = None
n_gen: int = None
min_tree_depth: int = None
max_tree_depth: int = None
crossover_rate: float = None
mutation_rate: float = None
base_policy_method = None
delta_index_freedom: int = None
delta_time_freedom: int = None
deap_verbose: bool = None
evaluation: EvaluationGPHH = None
permutation_distance = PermutationDistance.KTD
def __init__(self,
set_feature,
set_primitves,
tournament_ratio, pop_size, n_gen, min_tree_depth, max_tree_depth, crossover_rate, mutation_rate, base_policy_method, delta_index_freedom, delta_time_freedom, deap_verbose, evaluation, permutation_distance
):
self.set_feature = set_feature
self.set_primitves = set_primitves
self.tournament_ratio = tournament_ratio
self.pop_size = pop_size
self.n_gen = n_gen
self.min_tree_depth = min_tree_depth
self.max_tree_depth = max_tree_depth
self.crossover_rate = crossover_rate
self.mutation_rate = mutation_rate
self.base_policy_method = base_policy_method
self.delta_index_freedom = delta_index_freedom
self.delta_time_freedom = delta_time_freedom
self.deap_verbose = deap_verbose
self.evaluation = evaluation
self.permutation_distance = permutation_distance
@staticmethod
def default():
set_feature = {FeatureEnum.EARLIEST_FINISH_DATE,
FeatureEnum.EARLIEST_START_DATE,
FeatureEnum.LATEST_FINISH_DATE,
FeatureEnum.LATEST_START_DATE,
FeatureEnum.N_PREDECESSORS,
FeatureEnum.N_SUCCESSORS,
FeatureEnum.ALL_DESCENDANTS,
FeatureEnum.RESSOURCE_REQUIRED,
FeatureEnum.RESSOURCE_AVG,
FeatureEnum.RESSOURCE_MAX,
# FeatureEnum.RESSOURCE_MIN
FeatureEnum.RESSOURCE_NZ_MIN
}
pset = PrimitiveSet("main", len(set_feature))
pset.addPrimitive(operator.add, 2)
pset.addPrimitive(operator.sub, 2)
pset.addPrimitive(operator.mul, 2)
pset.addPrimitive(protected_div, 2)
pset.addPrimitive(max_operator, 2)
pset.addPrimitive(min_operator, 2)
pset.addPrimitive(operator.neg, 1)
return ParametersGPHH(
set_feature=set_feature,
set_primitves=pset,
tournament_ratio=0.1,
pop_size=40,
n_gen=100,
min_tree_depth=1,
max_tree_depth=4,
crossover_rate=0.7,
mutation_rate=0.3,
base_policy_method=BasePolicyMethod.FOLLOW_GANTT,
delta_index_freedom=0,
delta_time_freedom=0,
deap_verbose=True,
# evaluation=EvaluationGPHH.PERMUTATION_DISTANCE,
evaluation=EvaluationGPHH.SGS,
permutation_distance=PermutationDistance.KTD)
@staticmethod
def fast_test():
set_feature = {FeatureEnum.EARLIEST_FINISH_DATE,
FeatureEnum.EARLIEST_START_DATE,
FeatureEnum.LATEST_FINISH_DATE,
FeatureEnum.LATEST_START_DATE,
FeatureEnum.N_PREDECESSORS,
FeatureEnum.N_SUCCESSORS,
FeatureEnum.ALL_DESCENDANTS,
FeatureEnum.RESSOURCE_REQUIRED,
FeatureEnum.RESSOURCE_AVG,
FeatureEnum.RESSOURCE_MAX,
# FeatureEnum.RESSOURCE_MIN
FeatureEnum.RESSOURCE_NZ_MIN
}
pset = PrimitiveSet("main", len(set_feature))
pset.addPrimitive(operator.add, 2)
pset.addPrimitive(operator.sub, 2)
pset.addPrimitive(operator.mul, 2)
pset.addPrimitive(protected_div, 2)
pset.addPrimitive(max_operator, 2)
pset.addPrimitive(min_operator, 2)
pset.addPrimitive(operator.neg, 1)
return ParametersGPHH(
set_feature=set_feature,
set_primitves=pset,
tournament_ratio=0.1,
pop_size=10,
n_gen=2,
min_tree_depth=1,
max_tree_depth=4,
crossover_rate=0.7,
mutation_rate=0.3,
base_policy_method=BasePolicyMethod.FOLLOW_GANTT,
delta_index_freedom=0,
delta_time_freedom=0,
deap_verbose=True,
evaluation=EvaluationGPHH.SGS,
# evaluation=EvaluationGPHH.PERMUTATION_DISTANCE,
permutation_distance=PermutationDistance.KTD)
@staticmethod
def default_for_set_features(set_feature: Set[FeatureEnum]):
pset = PrimitiveSet("main", len(set_feature))
pset.addPrimitive(operator.add, 2)
pset.addPrimitive(operator.sub, 2)
pset.addPrimitive(operator.mul, 2)
# pset.addPrimitive(protected_div, 2)
pset.addPrimitive(max_operator, 2)
pset.addPrimitive(min_operator, 2)
pset.addPrimitive(operator.neg, 1)
return ParametersGPHH(
set_feature=set_feature,
set_primitves=pset,
tournament_ratio=0.25,
pop_size=20,
n_gen=20,
min_tree_depth=1,
max_tree_depth=4,
crossover_rate=0.7,
mutation_rate=0.1,
base_policy_method=BasePolicyMethod.SGS_READY,
delta_index_freedom=0,
delta_time_freedom=0,
deap_verbose=True,
evaluation=EvaluationGPHH.PERMUTATION_DISTANCE,
permutation_distance=PermutationDistance.KTD)
class GPHH(Solver, DeterministicPolicies):
T_domain = D
training_domains: List[T_domain]
verbose: bool
weight: int
pset: PrimitiveSet
toolbox: Toolbox
policy: DeterministicPolicies
params_gphh: ParametersGPHH
# policy: GPHHPolicy
evaluation_method: EvaluationGPHH
reference_permutations: Dict
permutation_distance: PermutationDistance
def __init__(self,
training_domains: List[T_domain],
domain_model: SchedulingDomain,
weight: int,
# set_feature: Set[FeatureEnum]=None,
params_gphh: ParametersGPHH=ParametersGPHH.default(),
reference_permutations=None,
# reference_makespans=None,
training_domains_names=None,
verbose: bool=False):
self.training_domains = training_domains
self.domain_model = domain_model
self.params_gphh = params_gphh
# self.set_feature = set_feature
self.set_feature = self.params_gphh.set_feature
print('self.set_feature: ', self.set_feature)
print('Evaluation: ', self.params_gphh.evaluation)
# if set_feature is None:
# self.set_feature = {FeatureEnum.RESSOURCE_TOTAL,
# FeatureEnum.TASK_DURATION,
# FeatureEnum.N_SUCCESSORS,
# FeatureEnum.N_SUCCESSORS,
# FeatureEnum.RESSOURCE_AVG}
self.list_feature = list(self.set_feature)
self.verbose = verbose
self.pset = self.init_primitives(self.params_gphh.set_primitves)
self.weight = weight
self.evaluation_method = self.params_gphh.evaluation
self.initialize_cpm_data_for_training()
if self.evaluation_method == EvaluationGPHH.PERMUTATION_DISTANCE:
self.init_reference_permutations(reference_permutations, training_domains_names)
self.permutation_distance = self.params_gphh.permutation_distance
# if self.evaluation_method == EvaluationGPHH.SGS_DEVIATION:
# self.init_reference_makespans(reference_makespans, training_domains_names)
def init_reference_permutations(self, reference_permutations={}, training_domains_names=[]) -> None:
self.reference_permutations = {}
for i in range(len(self.training_domains)):
td = self.training_domains[i]
td_name = training_domains_names[i]
if td_name not in reference_permutations.keys():
# Run CP
td.set_inplace_environment(False)
solver = DOSolver(policy_method_params=PolicyMethodParams(base_policy_method=BasePolicyMethod.SGS_PRECEDENCE,
delta_index_freedom=0,
delta_time_freedom=0),
method=SolvingMethod.CP)
solver.solve(domain_factory=lambda: td)
raw_permutation = solver.best_solution.rcpsp_permutation
full_permutation = [x+2 for x in raw_permutation]
full_permutation.insert(0, 1)
full_permutation.append(np.max(full_permutation)+1)
print('full_perm: ', full_permutation)
self.reference_permutations[td] = full_permutation
else:
self.reference_permutations[td] = reference_permutations[td_name]
# def init_reference_makespans(self, reference_makespans={}, training_domains_names=[]) -> None:
# self.reference_makespans = {}
# for i in range(len(self.training_domains)):
# td = self.training_domains[i]
# td_name = training_domains_names[i]
# # for td in self.training_domains:
# print('td:',td)
# if td_name not in reference_makespans.keys():
# # Run CP
# td.set_inplace_environment(False)
# solver = DOSolver(policy_method_params=PolicyMethodParams(base_policy_method=BasePolicyMethod.FOLLOW_GANTT,
# delta_index_freedom=0,
# delta_time_freedom=0),
# method=SolvingMethod.CP)
# solver.solve(domain_factory=lambda: td)
#
# state = td.get_initial_state()
# states, actions, values = rollout_episode(domain=td,
# max_steps=1000,
# solver=solver,
# from_memory=state,
# verbose=False,
# outcome_formatter=lambda
# o: f'{o.observation} - cost: {o.value.cost:.2f}')
#
# makespan = sum([v.cost for v in values])
# self.reference_makespans[td] = makespan
# else:
# self.reference_makespans[td] = reference_makespans[td_name]
def _solve_domain(self, domain_factory: Callable[[], D]) -> None:
self.domain = domain_factory()
tournament_ratio = self.params_gphh.tournament_ratio
pop_size = self.params_gphh.pop_size
n_gen = self.params_gphh.n_gen
min_tree_depth = self.params_gphh.min_tree_depth
max_tree_depth = self.params_gphh.max_tree_depth
crossover_rate = self.params_gphh.crossover_rate
mutation_rate = self.params_gphh.mutation_rate
creator.create("FitnessMin", Fitness, weights=(self.weight,))
creator.create("Individual", PrimitiveTree, fitness=creator.FitnessMin)
self.toolbox = Toolbox()
self.toolbox.register("expr", genHalfAndHalf, pset=self.pset, min_=min_tree_depth, max_=max_tree_depth)
self.toolbox.register("individual", tools.initIterate, creator.Individual, self.toolbox.expr)
self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
self.toolbox.register("compile", gp.compile, pset=self.pset)
if self.evaluation_method == EvaluationGPHH.SGS:
self.toolbox.register("evaluate", self.evaluate_heuristic, domains=self.training_domains)
# if self.evaluation_method == EvaluationGPHH.SGS_DEVIATION:
# self.toolbox.register("evaluate", self.evaluate_heuristic_sgs_deviation, domains=self.training_domains)
elif self.evaluation_method == EvaluationGPHH.PERMUTATION_DISTANCE:
self.toolbox.register("evaluate", self.evaluate_heuristic_permutation, domains=self.training_domains)
# self.toolbox.register("evaluate", self.evaluate_heuristic, domains=[self.training_domains[1]])
self.toolbox.register("select", tools.selTournament, tournsize=int(tournament_ratio * pop_size))
self.toolbox.register("mate", gp.cxOnePoint)
self.toolbox.register("expr_mut", gp.genFull, min_=0, max_=max_tree_depth)
self.toolbox.register("mutate", gp.mutUniform, expr=self.toolbox.expr_mut, pset=self.pset)
self.toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
self.toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", np.mean)
mstats.register("std", np.std)
mstats.register("min", np.min)
mstats.register("max", np.max)
pop = self.toolbox.population(n=pop_size)
hof = tools.HallOfFame(1)
self.hof = hof
pop, log = algorithms.eaSimple(pop, self.toolbox, crossover_rate, mutation_rate, n_gen, stats=mstats,
halloffame=hof, verbose=True)
self.best_heuristic = hof[0]
print('best_heuristic: ', self.best_heuristic)
self.func_heuristic = self.toolbox.compile(expr=self.best_heuristic)
self.policy = GPHHPolicy(self.domain, self.domain_model,
self.func_heuristic,
features=self.list_feature,
params_gphh=self.params_gphh,
recompute_cpm=True)
def _get_next_action(self, observation: D.T_agent[D.T_observation]) -> D.T_agent[D.T_concurrency[D.T_event]]:
action = self.policy.sample_action(observation)
# print('action_1: ', action.action)
return action
def _is_policy_defined_for(self, observation: D.T_agent[D.T_observation]) -> bool:
return True
def init_primitives(self, pset) -> PrimitiveSet:
for i in range(len(self.list_feature)):
pset.renameArguments(**{"ARG"+str(i): self.list_feature[i].value})
return pset
def evaluate_heuristic(self, individual, domains) -> float:
vals = []
func_heuristic = self.toolbox.compile(expr=individual)
# print('individual', individual)
for domain in domains:
###
initial_state = domain.get_initial_state()
do_model = build_do_domain(domain)
modes = [initial_state.tasks_mode.get(j, 1) for j in sorted(domain.get_tasks_ids())]
modes = modes[1:-1]
cpm = self.cpm_data[domain]['cpm']
cpm_esd = self.cpm_data[domain]['cpm_esd']
raw_values = []
for task_id in domain.get_available_tasks(initial_state):
input_features = [feature_function_map[lf](domain=domain,
cpm=cpm,
cpm_esd=cpm_esd,
task_id=task_id,
state=initial_state)
for lf in self.list_feature]
output_value = func_heuristic(*input_features)
raw_values.append(output_value)
normalized_values = [x + 1 for x in sorted(range(len(raw_values)), key=lambda k: raw_values[k],
reverse=False)]
normalized_values_for_do = [normalized_values[i] - 2 for i in range(len(normalized_values)) if
normalized_values[i] not in {1, len(normalized_values)}]
solution = RCPSPSolution(problem=do_model,
rcpsp_permutation=normalized_values_for_do,
rcpsp_modes=modes,
)
last_activity = max(list(solution.rcpsp_schedule.keys()))
do_makespan = solution.rcpsp_schedule[last_activity]['end_time']
vals.append(do_makespan)
fitness = [np.mean(vals)]
# fitness = [np.max(vals)]
return fitness
# def evaluate_heuristic_sgs_deviation(self, individual, domains) -> float:
# vals = []
# func_heuristic = self.toolbox.compile(expr=individual)
# # selected_domains = random.sample(domains, 3)
# selected_domains = domains
#
# for domain in selected_domains:
# policy = GPHHPolicy(domain, domain,
# func_heuristic,
# features=self.list_feature,
# params_gphh=self.params_gphh, recompute_cpm=False, cpm_data=self.cpm_data
# )
# state = domain.get_initial_state().copy()
# domain.set_inplace_environment(True) # we can use True because we don't use the value
#
# states, actions, values = rollout_episode(domain=domain,
# max_steps=10000,
# solver=policy,
# from_memory=state,
# verbose=False,
# outcome_formatter=lambda
# o: f'{o.observation} - cost: {o.value.cost:.2f}')
#
# makespan = states[-1].t
# ref_makespan = self.reference_makespans[domain]
# makespan_deviation = (makespan - ref_makespan) / ref_makespan
# # print('mk: ', makespan, ' - mk_dev: ', makespan_deviation, ' - ref: ', ref_makespan)
# vals.append(makespan_deviation)
#
# # fitness = [np.mean(vals)]
# fitness = [np.mean(vals)]
# return fitness
def initialize_cpm_data_for_training(self):
self.cpm_data = {}
for domain in self.training_domains:
do_model = build_do_domain(domain)
cpm, cpm_esd = compute_cpm(do_model)
self.cpm_data[domain] = {'cpm': cpm,
'cpm_esd': cpm_esd}
def evaluate_heuristic_permutation(self, individual, domains) -> float:
vals = []
func_heuristic = self.toolbox.compile(expr=individual)
# print('individual', individual)
for domain in domains:
raw_values = []
initial_state = domain.get_initial_state()
regenerate_cpm = False
if regenerate_cpm:
do_model = build_do_domain(domain)
cpm, cpm_esd = compute_cpm(do_model)
else:
cpm = self.cpm_data[domain]['cpm']
cpm_esd = self.cpm_data[domain]['cpm_esd']
for task_id in domain.get_available_tasks(state=initial_state):
input_features = [feature_function_map[lf](domain=domain,
cpm = cpm,
cpm_esd=cpm_esd,
task_id=task_id,
state=initial_state)
for lf in self.list_feature]
output_value = func_heuristic(*input_features)
raw_values.append(output_value)
most_common_raw_val = max(raw_values, key=raw_values.count)
most_common_count = raw_values.count(most_common_raw_val)
heuristic_permutation = [x + 1 for x in sorted(range(len(raw_values)), key=lambda k: raw_values[k],
reverse=False)]
if self.permutation_distance == PermutationDistance.KTD:
dist, p_value = stats.kendalltau(heuristic_permutation, self.reference_permutations[domain])
dist = -dist
if self.permutation_distance == PermutationDistance.HAMMING:
dist = distance.hamming(heuristic_permutation, self.reference_permutations[domain])
if self.permutation_distance == PermutationDistance.KTD_HAMMING:
ktd, _ = stats.kendalltau(heuristic_permutation, self.reference_permutations[domain])
dist = -ktd + distance.hamming(heuristic_permutation, self.reference_permutations[domain])
penalty = most_common_count / len(raw_values)
# penalty = 0.
penalized_distance = dist + penalty
vals.append(penalized_distance)
fitness = [np.mean(vals)]
# fitness = [np.max(vals)]
return fitness
def test_features(self, domain, task_id, observation):
for f in FeatureEnum:
print('feature: ', f)
calculated_feature = feature_function_map[f](domain=domain,
task_id=task_id,
state=observation)
print('\tcalculated_feature: ',calculated_feature)
def set_domain(self, domain):
self.domain = domain
if self.policy is not None:
self.policy.domain = domain
class GPHHPolicy(DeterministicPolicies):
def __init__(self, domain: SchedulingDomain, domain_model: SchedulingDomain, func_heuristic, features: List[FeatureEnum]=None, params_gphh=None, recompute_cpm=True, cpm_data=None):
self.domain = domain
self.domain_model = domain_model
self.func_heuristic = func_heuristic
self.list_feature = features
self.params_gphh = params_gphh
self.recompute_cpm = recompute_cpm
self.cpm_data = cpm_data
def reset(self):
pass
def _get_next_action(self, observation: D.T_agent[D.T_observation]) -> D.T_agent[D.T_concurrency[D.T_event]]:
run_sgs = True
cheat_mode = False
do_model = build_do_domain(self.domain_model)
modes = [observation.tasks_mode.get(j, 1) for j in sorted(self.domain.get_tasks_ids())]
modes = modes[1:-1]
if run_sgs:
scheduled_tasks_start_times = {}
for j in observation.tasks_details.keys():
if observation.tasks_details[j].start is not None:
scheduled_tasks_start_times[j] = observation.tasks_details[j].start
do_model.mode_details[j][1]['duration'] = observation.tasks_details[j].sampled_duration
# do_model = build_do_domain(self.domain)
# modes = [observation.tasks_mode.get(j, 1) for j in sorted(self.domain.get_tasks_ids())]
# modes = modes[1:-1]
#
# if run_sgs:
# scheduled_tasks_start_times = {}
# for j in observation.tasks_details.keys():
# if observation.tasks_details[j].start is not None:
# scheduled_tasks_start_times[j] = observation.tasks_details[j].start
# else:
# if not cheat_mode:
# do_model.mode_details[j][1]['duration'] = self.domain_model.sample_task_duration(j, 1, 0.)
if self.recompute_cpm:
cpm, cpm_esd = compute_cpm(do_model)
else:
cpm = self.cpm_data[self.domain]['cpm']
cpm_esd = self.cpm_data[self.domain]['cpm_esd']
t = observation.t
raw_values = []
for task_id in self.domain.get_available_tasks(observation):
input_features = [feature_function_map[lf](domain=self.domain,
cpm = cpm,
cpm_esd=cpm_esd,
task_id=task_id,
state=observation)
for lf in self.list_feature]
output_value = self.func_heuristic(*input_features)
raw_values.append(output_value)
normalized_values = [x+1 for x in sorted(range(len(raw_values)), key=lambda k: raw_values[k],
reverse=False)]
normalized_values_for_do = [normalized_values[i] - 2 for i in range(len(normalized_values)) if
normalized_values[i] not in {1, len(normalized_values)}]
# print(t, ': ', normalized_values)
# print('normalized_values_for_do: ', normalized_values_for_do)
modes_dictionnary = {}
for i in range(len(normalized_values)):
modes_dictionnary[i+1] = 1
if run_sgs:
solution = RCPSPSolution(problem=do_model,
rcpsp_permutation=normalized_values_for_do,
rcpsp_modes=modes,
)
solution.generate_schedule_from_permutation_serial_sgs_2(current_t=t,
completed_tasks=
{j: observation.tasks_details[j]
for j in observation.tasks_complete},
scheduled_tasks_start_times=scheduled_tasks_start_times)
schedule = solution.rcpsp_schedule
else:
schedule = None
sgs_policy = PolicyRCPSP(domain=self.domain,
schedule=schedule,
policy_method_params=PolicyMethodParams(
# base_policy_method=BasePolicyMethod.SGS_PRECEDENCE,
# base_policy_method=BasePolicyMethod.SGS_READY,
base_policy_method=self.params_gphh.base_policy_method,
delta_index_freedom=self.params_gphh.delta_index_freedom,
delta_time_freedom=self.params_gphh.delta_time_freedom),
permutation_task=normalized_values,
modes_dictionnary=modes_dictionnary)
action: SchedulingAction = sgs_policy.sample_action(observation)
# print('action_2: ', action.action)
return action
def _is_policy_defined_for(self, observation: D.T_agent[D.T_observation]) -> bool:
return True
class PoolAggregationMethod(Enum):
MEAN = "mean"
MEDIAN = "median"
RANDOM = "random"
class PooledGPHHPolicy(DeterministicPolicies):
def __init__(self, domain: SchedulingDomain, domain_model: SchedulingDomain, func_heuristics, pool_aggregation_method: PoolAggregationMethod = PoolAggregationMethod.MEAN, remove_extremes_values:int = 0, features: List[FeatureEnum]=None, params_gphh=None):
self.domain = domain
self.domain_model = domain_model
self.func_heuristics = func_heuristics
self.list_feature = features
self.params_gphh = params_gphh
self.pool_aggregation_method = pool_aggregation_method
self.remove_extremes_values = remove_extremes_values
def reset(self):
pass
def _get_next_action(self, observation: D.T_agent[D.T_observation]) -> D.T_agent[D.T_concurrency[D.T_event]]:
run_sgs = True
cheat_mode = False
regenerate_cpm = True
do_model = build_do_domain(self.domain_model)
modes = [observation.tasks_mode.get(j, 1) for j in sorted(self.domain.get_tasks_ids())]
modes = modes[1:-1]
if run_sgs:
scheduled_tasks_start_times = {}
for j in observation.tasks_details.keys():
if observation.tasks_details[j].start is not None:
scheduled_tasks_start_times[j] = observation.tasks_details[j].start
do_model.mode_details[j][1]['duration'] = observation.tasks_details[j].sampled_duration
# do_model = build_do_domain(self.domain)
# modes = [observation.tasks_mode.get(j, 1) for j in sorted(self.domain.get_tasks_ids())]
# modes = modes[1:-1]
#
# if run_sgs:
# scheduled_tasks_start_times = {}
# for j in observation.tasks_details.keys():
# # schedule[j] = {}
# if observation.tasks_details[j].start is not None:
# # schedule[j]["start_time"] = observation.tasks_details[j].start
# scheduled_tasks_start_times[j] = observation.tasks_details[j].start
# # if observation.tasks_details[j].end is not None:
# # schedule[j]["end_time"] = observation.tasks_details[j].end
# else:
# if not cheat_mode:
# do_model.mode_details[j][1]['duration'] = self.domain_model.sample_task_duration(j, 1, 0.)
if regenerate_cpm:
cpm, cpm_esd = compute_cpm(do_model)
t = observation.t
raw_values = []
for task_id in self.domain.get_available_tasks(observation):
input_features = [feature_function_map[lf](
domain=self.domain,
cpm = cpm,
cpm_esd=cpm_esd,
task_id=task_id,
state=observation)
for lf in self.list_feature]
output_values = []
for f in self.func_heuristics:
output_value = f(*input_features)
output_values.append(output_value)
# print('output_values: ', output_values)
if self.remove_extremes_values > 0:
the_median = float(np.median(output_values))
tmp = {}
for i in range(len(output_values)):
tmp[i] = abs(output_values[i] - the_median)
tmp = sorted(tmp.items(), key=lambda x: x[1], reverse=True)
to_remove = [tmp[i][0] for i in range(self.remove_extremes_values)]
output_values = list(np.delete(output_values, to_remove))
# print('output_values filtered: ', output_values)
if self.pool_aggregation_method == PoolAggregationMethod.MEAN:
agg_value = np.mean(output_values)
elif self.pool_aggregation_method == PoolAggregationMethod.MEDIAN:
agg_value = np.median(output_values)
elif self.pool_aggregation_method == PoolAggregationMethod.RANDOM:
index = random.randint(len(output_values))
agg_value = output_values[index]
# print('agg_value: ', agg_value)
raw_values.append(agg_value)
normalized_values = [x+1 for x in sorted(range(len(raw_values)), key=lambda k: raw_values[k],
reverse=False)]
normalized_values_for_do = [normalized_values[i] - 2 for i in range(len(normalized_values)) if
normalized_values[i] not in {1, len(normalized_values)}]
# print('normalized_values: ', normalized_values)
# print('normalized_values_for_do: ', normalized_values_for_do)
modes_dictionnary = {}
for i in range(len(normalized_values)):
modes_dictionnary[i+1] = 1
if run_sgs:
solution = RCPSPSolution(problem=do_model,
rcpsp_permutation=normalized_values_for_do,
rcpsp_modes=modes,
)
solution.generate_schedule_from_permutation_serial_sgs_2(current_t=t,
completed_tasks=
{j: observation.tasks_details[j]
for j in observation.tasks_complete},
scheduled_tasks_start_times=scheduled_tasks_start_times)
schedule = solution.rcpsp_schedule
else:
schedule = None
sgs_policy = PolicyRCPSP(domain=self.domain,
schedule=schedule,
policy_method_params=PolicyMethodParams(
# base_policy_method=BasePolicyMethod.SGS_PRECEDENCE,
# base_policy_method=BasePolicyMethod.SGS_READY,
base_policy_method=self.params_gphh.base_policy_method,
delta_index_freedom=self.params_gphh.delta_index_freedom,
delta_time_freedom=self.params_gphh.delta_time_freedom),
permutation_task=normalized_values,
modes_dictionnary=modes_dictionnary)
action: SchedulingAction = sgs_policy.sample_action(observation)
# print('action_2: ', action.action)
return action
def _is_policy_defined_for(self, observation: D.T_agent[D.T_observation]) -> bool:
return True
class FixedPermutationPolicy(DeterministicPolicies):
def __init__(self, domain: SchedulingDomain, domain_model: SchedulingDomain, fixed_perm):
self.domain = domain
self.domain_model = domain_model
self.fixed_perm = fixed_perm
def reset(self):
pass
def _get_next_action(self, observation: D.T_agent[D.T_observation]) -> D.T_agent[D.T_concurrency[D.T_event]]:
run_sgs = True
cheat_mode = False
do_model = build_do_domain(self.domain_model)
modes = [observation.tasks_mode.get(j, 1) for j in sorted(self.domain.get_tasks_ids())]
modes = modes[1:-1]
if run_sgs:
scheduled_tasks_start_times = {}
for j in observation.tasks_details.keys():
if observation.tasks_details[j].start is not None:
scheduled_tasks_start_times[j] = observation.tasks_details[j].start
do_model.mode_details[j][1]['duration'] = observation.tasks_details[j].sampled_duration
# do_model = build_do_domain(self.domain)
# modes = [observation.tasks_mode.get(j, 1) for j in sorted(self.domain.get_tasks_ids())]
# modes = modes[1:-1]
#
# if run_sgs:
# scheduled_tasks_start_times = {}
# for j in observation.tasks_details.keys():
# # schedule[j] = {}
# if observation.tasks_details[j].start is not None:
# # schedule[j]["start_time"] = observation.tasks_details[j].start
# scheduled_tasks_start_times[j] = observation.tasks_details[j].start
# # if observation.tasks_details[j].end is not None:
# # schedule[j]["end_time"] = observation.tasks_details[j].end
# else:
# if not cheat_mode:
# # print('do_model: ', do_model)
# do_model.mode_details[j][1]['duration'] = self.domain_model.sample_task_duration(j, 1, 0.)
normalized_values = self.fixed_perm
normalized_values_for_do = [normalized_values[i] - 2 for i in range(len(normalized_values)) if
normalized_values[i] not in {1, len(normalized_values)}]
# print('normalized_values: ', normalized_values)
# print('normalized_values_for_do: ', normalized_values_for_do)
t = observation.t
modes_dictionnary = {}
for i in range(len(normalized_values)):
modes_dictionnary[i+1] = 1
if run_sgs:
solution = RCPSPSolution(problem=do_model,
rcpsp_permutation=normalized_values_for_do,
rcpsp_modes=modes,
)
solution.generate_schedule_from_permutation_serial_sgs_2(current_t=t,
completed_tasks=
{j: observation.tasks_details[j]
for j in observation.tasks_complete},
scheduled_tasks_start_times=scheduled_tasks_start_times)
schedule = solution.rcpsp_schedule
else:
schedule = None
sgs_policy = PolicyRCPSP(domain=self.domain,
schedule=schedule,
policy_method_params=PolicyMethodParams(
# base_policy_method=BasePolicyMethod.SGS_PRECEDENCE,
# base_policy_method=BasePolicyMethod.SGS_READY,
base_policy_method=BasePolicyMethod.FOLLOW_GANTT,
# delta_index_freedom=self.params_gphh.delta_index_freedom,
# delta_time_freedom=self.params_gphh.delta_time_freedom
),
permutation_task=normalized_values,
modes_dictionnary=modes_dictionnary)
action: SchedulingAction = sgs_policy.sample_action(observation)
# print('action_2: ', action.action)
return action
def _is_policy_defined_for(self, observation: D.T_agent[D.T_observation]) -> bool:
return True
|
[
"numpy.sum",
"numpy.mean",
"scipy.stats.kendalltau",
"deap.tools.HallOfFame",
"deap.base.Toolbox",
"numpy.max",
"skdecide.builders.discrete_optimization.rcpsp.solver.cpm.CPM",
"skdecide.hub.solver.do_solver.sk_to_do_binding.build_do_domain",
"deap.algorithms.eaSimple",
"numpy.median",
"operator.attrgetter",
"numpy.min",
"deap.creator.create",
"skdecide.builders.discrete_optimization.rcpsp.rcpsp_model.RCPSPSolution",
"numpy.delete",
"skdecide.hub.solver.do_solver.do_solver_scheduling.PolicyMethodParams",
"deap.tools.MultiStatistics",
"deap.tools.Statistics",
"scipy.spatial.distance.hamming"
] |
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'deap.creator.create', 'creator.create', (['"""FitnessMin"""', 'Fitness'], {'weights': '(self.weight,)'}), "('FitnessMin', Fitness, weights=(self.weight,))\n", (20460, 20507), False, 'from deap import creator\n'), ((20516, 20587), 'deap.creator.create', 'creator.create', (['"""Individual"""', 'PrimitiveTree'], {'fitness': 'creator.FitnessMin'}), "('Individual', PrimitiveTree, fitness=creator.FitnessMin)\n", (20530, 20587), False, 'from deap import creator\n'), ((20612, 20621), 'deap.base.Toolbox', 'Toolbox', ([], {}), '()\n', (20619, 20621), False, 'from deap.base import Toolbox, Fitness\n'), ((22211, 22259), 'deap.tools.Statistics', 'tools.Statistics', (['(lambda ind: ind.fitness.values)'], {}), '(lambda ind: ind.fitness.values)\n', (22227, 22259), False, 'from deap import tools\n'), ((22281, 22302), 'deap.tools.Statistics', 'tools.Statistics', (['len'], {}), '(len)\n', (22297, 22302), False, 'from deap import tools\n'), ((22320, 22377), 'deap.tools.MultiStatistics', 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rcpsp_permutation=normalized_values_for_do,\n rcpsp_modes=modes)\n', (25475, 25560), False, 'from skdecide.builders.discrete_optimization.rcpsp.rcpsp_model import RCPSPSolution\n'), ((25874, 25887), 'numpy.mean', 'np.mean', (['vals'], {}), '(vals)\n', (25881, 25887), True, 'import numpy as np\n'), ((27854, 27877), 'skdecide.hub.solver.do_solver.sk_to_do_binding.build_do_domain', 'build_do_domain', (['domain'], {}), '(domain)\n', (27869, 27877), False, 'from skdecide.hub.solver.do_solver.sk_to_do_binding import build_do_domain\n'), ((30463, 30476), 'numpy.mean', 'np.mean', (['vals'], {}), '(vals)\n', (30470, 30476), True, 'import numpy as np\n'), ((34488, 34586), 'skdecide.builders.discrete_optimization.rcpsp.rcpsp_model.RCPSPSolution', 'RCPSPSolution', ([], {'problem': 'do_model', 'rcpsp_permutation': 'normalized_values_for_do', 'rcpsp_modes': 'modes'}), '(problem=do_model, rcpsp_permutation=normalized_values_for_do,\n rcpsp_modes=modes)\n', (34501, 34586), False, 'from skdecide.builders.discrete_optimization.rcpsp.rcpsp_model import RCPSPSolution\n'), ((41410, 41508), 'skdecide.builders.discrete_optimization.rcpsp.rcpsp_model.RCPSPSolution', 'RCPSPSolution', ([], {'problem': 'do_model', 'rcpsp_permutation': 'normalized_values_for_do', 'rcpsp_modes': 'modes'}), '(problem=do_model, rcpsp_permutation=normalized_values_for_do,\n rcpsp_modes=modes)\n', (41423, 41508), False, 'from skdecide.builders.discrete_optimization.rcpsp.rcpsp_model import RCPSPSolution\n'), ((45812, 45910), 'skdecide.builders.discrete_optimization.rcpsp.rcpsp_model.RCPSPSolution', 'RCPSPSolution', ([], {'problem': 'do_model', 'rcpsp_permutation': 'normalized_values_for_do', 'rcpsp_modes': 'modes'}), '(problem=do_model, rcpsp_permutation=normalized_values_for_do,\n rcpsp_modes=modes)\n', (45825, 45910), False, 'from skdecide.builders.discrete_optimization.rcpsp.rcpsp_model import RCPSPSolution\n'), ((28444, 28467), 'skdecide.hub.solver.do_solver.sk_to_do_binding.build_do_domain', 'build_do_domain', (['domain'], {}), '(domain)\n', (28459, 28467), False, 'from skdecide.hub.solver.do_solver.sk_to_do_binding import build_do_domain\n'), ((29699, 29775), 'scipy.stats.kendalltau', 'stats.kendalltau', (['heuristic_permutation', 'self.reference_permutations[domain]'], {}), '(heuristic_permutation, self.reference_permutations[domain])\n', (29715, 29775), False, 'from scipy import stats\n'), ((29902, 29978), 'scipy.spatial.distance.hamming', 'distance.hamming', (['heuristic_permutation', 'self.reference_permutations[domain]'], {}), '(heuristic_permutation, self.reference_permutations[domain])\n', (29918, 29978), False, 'from scipy.spatial import distance\n'), ((30082, 30158), 'scipy.stats.kendalltau', 'stats.kendalltau', (['heuristic_permutation', 'self.reference_permutations[domain]'], {}), '(heuristic_permutation, self.reference_permutations[domain])\n', (30098, 30158), False, 'from scipy import stats\n'), ((35451, 35647), 'skdecide.hub.solver.do_solver.do_solver_scheduling.PolicyMethodParams', 'PolicyMethodParams', ([], {'base_policy_method': 'self.params_gphh.base_policy_method', 'delta_index_freedom': 'self.params_gphh.delta_index_freedom', 'delta_time_freedom': 'self.params_gphh.delta_time_freedom'}), '(base_policy_method=self.params_gphh.base_policy_method,\n delta_index_freedom=self.params_gphh.delta_index_freedom,\n delta_time_freedom=self.params_gphh.delta_time_freedom)\n', (35469, 35647), False, 'from skdecide.hub.solver.do_solver.do_solver_scheduling import PolicyRCPSP, DOSolver, PolicyMethodParams, BasePolicyMethod, SolvingMethod\n'), ((40320, 40342), 'numpy.mean', 'np.mean', (['output_values'], {}), '(output_values)\n', (40327, 40342), True, 'import numpy as np\n'), ((42373, 42569), 'skdecide.hub.solver.do_solver.do_solver_scheduling.PolicyMethodParams', 'PolicyMethodParams', ([], {'base_policy_method': 'self.params_gphh.base_policy_method', 'delta_index_freedom': 'self.params_gphh.delta_index_freedom', 'delta_time_freedom': 'self.params_gphh.delta_time_freedom'}), '(base_policy_method=self.params_gphh.base_policy_method,\n delta_index_freedom=self.params_gphh.delta_index_freedom,\n delta_time_freedom=self.params_gphh.delta_time_freedom)\n', (42391, 42569), False, 'from skdecide.hub.solver.do_solver.do_solver_scheduling import PolicyRCPSP, DOSolver, PolicyMethodParams, BasePolicyMethod, SolvingMethod\n'), ((46775, 46843), 'skdecide.hub.solver.do_solver.do_solver_scheduling.PolicyMethodParams', 'PolicyMethodParams', ([], {'base_policy_method': 'BasePolicyMethod.FOLLOW_GANTT'}), '(base_policy_method=BasePolicyMethod.FOLLOW_GANTT)\n', (46793, 46843), False, 'from skdecide.hub.solver.do_solver.do_solver_scheduling import PolicyRCPSP, DOSolver, PolicyMethodParams, BasePolicyMethod, SolvingMethod\n'), ((22039, 22068), 'operator.attrgetter', 'operator.attrgetter', (['"""height"""'], {}), "('height')\n", (22058, 22068), False, 'import operator\n'), 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{'base_policy_method': 'BasePolicyMethod.SGS_PRECEDENCE', 'delta_index_freedom': '(0)', 'delta_time_freedom': '(0)'}), '(base_policy_method=BasePolicyMethod.SGS_PRECEDENCE,\n delta_index_freedom=0, delta_time_freedom=0)\n', (17368, 17469), False, 'from skdecide.hub.solver.do_solver.do_solver_scheduling import PolicyRCPSP, DOSolver, PolicyMethodParams, BasePolicyMethod, SolvingMethod\n'), ((17931, 17955), 'numpy.max', 'np.max', (['full_permutation'], {}), '(full_permutation)\n', (17937, 17955), True, 'import numpy as np\n')]
|
from setuptools import setup, find_packages
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='Classifying Personality of a Person Based On Users Twitter Data',
version='1.0.0',
install_requires=["pandas >= 1.0.2",
"pickleshare >= 0.7.5",
"numpy >= 1.17.2",
"scikit-learn >= 0.21.3",
"nltk >= 3.4.5",
"tweepy >= 3.8.0",
"unicodecsv >= 0.14.1"],
description='A Natural Language Processing (NLP), Machine Learning and Data Mining project, which will automate the screening process before hiring a professional or can be used in psychiatry to check effectivity of patient therapy.',
long_description=long_description,
url='https://github.com/heitorsampaio/Personality-Classification-Based-on-Twitter-Data',
authors=' <NAME> ',
author_email='<EMAIL>',
classifiers=[
'Development Status :: 4 - Beta',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
keywords=' Suggestion Mining using textblob nlp tweepy and nltk',
)
|
[
"os.path.dirname",
"os.path.join",
"setuptools.setup"
] |
[((231, 1279), 'setuptools.setup', 'setup', ([], {'name': '"""Classifying Personality of a Person Based On Users Twitter Data"""', 'version': '"""1.0.0"""', 'install_requires': "['pandas >= 1.0.2', 'pickleshare >= 0.7.5', 'numpy >= 1.17.2',\n 'scikit-learn >= 0.21.3', 'nltk >= 3.4.5', 'tweepy >= 3.8.0',\n 'unicodecsv >= 0.14.1']", 'description': '"""A Natural Language Processing (NLP), Machine Learning and Data Mining project, which will automate the screening process before hiring a professional or can be used in psychiatry to check effectivity of patient therapy."""', 'long_description': 'long_description', 'url': '"""https://github.com/heitorsampaio/Personality-Classification-Based-on-Twitter-Data"""', 'authors': '""" <NAME> """', 'author_email': '"""<EMAIL>"""', 'classifiers': "['Development Status :: 4 - Beta', 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6']", 'keywords': '""" Suggestion Mining using textblob nlp tweepy and nltk"""'}), "(name=\n 'Classifying Personality of a Person Based On Users Twitter Data',\n version='1.0.0', install_requires=['pandas >= 1.0.2',\n 'pickleshare >= 0.7.5', 'numpy >= 1.17.2', 'scikit-learn >= 0.21.3',\n 'nltk >= 3.4.5', 'tweepy >= 3.8.0', 'unicodecsv >= 0.14.1'],\n description=\n 'A Natural Language Processing (NLP), Machine Learning and Data Mining project, which will automate the screening process before hiring a professional or can be used in psychiatry to check effectivity of patient therapy.'\n , long_description=long_description, url=\n 'https://github.com/heitorsampaio/Personality-Classification-Based-on-Twitter-Data'\n , authors=' <NAME> ', author_email='<EMAIL>', classifiers=[\n 'Development Status :: 4 - Beta', 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6'], keywords=\n ' Suggestion Mining using textblob nlp tweepy and nltk')\n", (236, 1279), False, 'from setuptools import setup, find_packages\n'), ((109, 131), 'os.path.dirname', 'path.dirname', (['__file__'], {}), '(__file__)\n', (121, 131), False, 'from os import path\n'), ((144, 172), 'os.path.join', 'path.join', (['here', '"""README.md"""'], {}), "(here, 'README.md')\n", (153, 172), False, 'from os import path\n')]
|
from sys import stderr
import HelperFunctions
from HttpRequest import HttpRequest
from Reading import Reading
from dotenv import load_dotenv
import Adafruit_DHT
DHT_SENSOR = Adafruit_DHT.DHT22
DHT_PIN = 4
load_dotenv()
while True:
humidity, temperature = Adafruit_DHT.read_retry(DHT_SENSOR, DHT_PIN)
temperature = HelperFunctions.convert_temp(temperature)
humidity = HelperFunctions.convert_humidity(humidity)
if humidity is not None and temperature is not None:
reading = Reading(temperature=temperature,
humidity=humidity)
reading.log()
request = HttpRequest(reading)
response = request.post()
HelperFunctions.wait()
else:
print("Measurement failed", file=stderr)
|
[
"HelperFunctions.convert_humidity",
"HelperFunctions.wait",
"Adafruit_DHT.read_retry",
"dotenv.load_dotenv",
"HttpRequest.HttpRequest",
"HelperFunctions.convert_temp",
"Reading.Reading"
] |
[((207, 220), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (218, 220), False, 'from dotenv import load_dotenv\n'), ((262, 306), 'Adafruit_DHT.read_retry', 'Adafruit_DHT.read_retry', (['DHT_SENSOR', 'DHT_PIN'], {}), '(DHT_SENSOR, DHT_PIN)\n', (285, 306), False, 'import Adafruit_DHT\n'), ((325, 366), 'HelperFunctions.convert_temp', 'HelperFunctions.convert_temp', (['temperature'], {}), '(temperature)\n', (353, 366), False, 'import HelperFunctions\n'), ((382, 424), 'HelperFunctions.convert_humidity', 'HelperFunctions.convert_humidity', (['humidity'], {}), '(humidity)\n', (414, 424), False, 'import HelperFunctions\n'), ((501, 552), 'Reading.Reading', 'Reading', ([], {'temperature': 'temperature', 'humidity': 'humidity'}), '(temperature=temperature, humidity=humidity)\n', (508, 552), False, 'from Reading import Reading\n'), ((620, 640), 'HttpRequest.HttpRequest', 'HttpRequest', (['reading'], {}), '(reading)\n', (631, 640), False, 'from HttpRequest import HttpRequest\n'), ((684, 706), 'HelperFunctions.wait', 'HelperFunctions.wait', ([], {}), '()\n', (704, 706), False, 'import HelperFunctions\n')]
|
from __future__ import absolute_import
import six
from datetime import datetime
from sentry.testutils import APITestCase
class GroupEventsOldestTest(APITestCase):
def test_simple(self):
self.login_as(user=self.user)
group = self.create_group()
event_1 = self.create_event(
event_id='a',
group=group,
datetime=datetime(2013, 8, 13, 3, 8, 25),
)
self.create_event(
event_id='b',
group=group,
datetime=datetime(2013, 8, 13, 3, 8, 26),
)
url = '/api/0/issues/{}/events/oldest/'.format(group.id)
response = self.client.get(url, format='json')
assert response.status_code == 200
assert response.data['id'] == six.text_type(event_1.id)
|
[
"six.text_type",
"datetime.datetime"
] |
[((767, 792), 'six.text_type', 'six.text_type', (['event_1.id'], {}), '(event_1.id)\n', (780, 792), False, 'import six\n'), ((379, 410), 'datetime.datetime', 'datetime', (['(2013)', '(8)', '(13)', '(3)', '(8)', '(25)'], {}), '(2013, 8, 13, 3, 8, 25)\n', (387, 410), False, 'from datetime import datetime\n'), ((521, 552), 'datetime.datetime', 'datetime', (['(2013)', '(8)', '(13)', '(3)', '(8)', '(26)'], {}), '(2013, 8, 13, 3, 8, 26)\n', (529, 552), False, 'from datetime import datetime\n')]
|
from objreader import read_obj
class SceneDescription:
def __init__(self):
self.meshes = {}
self.light_pos = (5, 5, 5)
self.light_intensity = 100
def import_mesh(self, filename):
mesh = read_obj(filename)
if mesh is not None:
self.meshes[mesh.name] = mesh
|
[
"objreader.read_obj"
] |
[((230, 248), 'objreader.read_obj', 'read_obj', (['filename'], {}), '(filename)\n', (238, 248), False, 'from objreader import read_obj\n')]
|
# Copyright 2017 loblab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import RPi.GPIO as GPIO
import time
from ask_wave import *
class Transmitter:
def __init__(self, pin_tx):
GPIO.setup(pin_tx, GPIO.OUT)
self.pin_tx = pin_tx
def lock(self):
GPIO.output(self.pin_tx, 1)
def unlock(self):
GPIO.output(self.pin_tx, 0)
def send(self, wave):
b = wave.startbit
ts = wave.timestamp
t1 = time.time()
GPIO.output(self.pin_tx, b)
t1 -= ts[0]
for t in ts[1:-1]:
b = 1 - b
wait = t1 + t - time.time()
if wait > 0:
time.sleep(wait)
GPIO.output(self.pin_tx, b)
wait = t1 + ts[-1] - time.time()
if wait > 0:
time.sleep(wait)
class Receiver:
def __init__(self, pin_rx, pin_en, max_len=2048, sample_period=0.05, min_gap=3, max_gap=10):
# Don't enable GPIO.PUD_DOWN nor GPIO.PUD_UP due to the small receiver current
GPIO.setup(pin_rx, GPIO.IN)
if pin_en > 0:
GPIO.setup(pin_en, GPIO.OUT)
GPIO.output(pin_en, GPIO.HIGH)
self.pin_rx = pin_rx
self.max_len = max_len
self.sample_period = 1e-3 * sample_period
self.min_gap = min_gap
self.max_gap = max_gap
def get_sample(self):
self.sample_time += self.sample_period
now = time.time()
wait = self.sample_time - now
if wait > 0:
time.sleep(wait)
b = GPIO.input(self.pin_rx)
now = time.time()
return (b, now)
def receive(self):
wave = BitWave()
ts = wave.timestamp
b = GPIO.input(self.pin_rx)
now = time.time()
ch = GPIO.wait_for_edge(self.pin_rx, GPIO.BOTH, timeout=self.min_gap)
if ch is not None:
return None
b0 = b
t0 = now
ts.append(t0)
ch = GPIO.wait_for_edge(self.pin_rx, GPIO.BOTH, timeout=self.max_gap-self.min_gap)
b = GPIO.input(self.pin_rx)
now = time.time()
if ch is None:
return None
if b == b0:
return None
wave.startbit = b0
self.bit = b
self.sample_time = now
self.edge_time = now
ts.append(now)
min_gap = 1e-3 * self.min_gap
while True:
(b, now) = self.get_sample()
if b == self.bit:
if now - self.edge_time > min_gap:
if b == 0:
ts.append(now)
return wave if len(ts) > 5 else None
else:
if now - self.edge_time < self.sample_period:
return None
self.edge_time = now
self.bit = b
ts.append(now)
if len(ts) > self.max_len:
return None
return wave
|
[
"RPi.GPIO.setup",
"time.time",
"RPi.GPIO.wait_for_edge",
"time.sleep",
"RPi.GPIO.input",
"RPi.GPIO.output"
] |
[((698, 726), 'RPi.GPIO.setup', 'GPIO.setup', (['pin_tx', 'GPIO.OUT'], {}), '(pin_tx, GPIO.OUT)\n', (708, 726), True, 'import RPi.GPIO as GPIO\n'), ((786, 813), 'RPi.GPIO.output', 'GPIO.output', (['self.pin_tx', '(1)'], {}), '(self.pin_tx, 1)\n', (797, 813), True, 'import RPi.GPIO as GPIO\n'), ((845, 872), 'RPi.GPIO.output', 'GPIO.output', (['self.pin_tx', '(0)'], {}), '(self.pin_tx, 0)\n', (856, 872), True, 'import RPi.GPIO as GPIO\n'), ((967, 978), 'time.time', 'time.time', ([], {}), '()\n', (976, 978), False, 'import time\n'), ((987, 1014), 'RPi.GPIO.output', 'GPIO.output', (['self.pin_tx', 'b'], {}), '(self.pin_tx, b)\n', (998, 1014), True, 'import RPi.GPIO as GPIO\n'), ((1524, 1551), 'RPi.GPIO.setup', 'GPIO.setup', (['pin_rx', 'GPIO.IN'], {}), '(pin_rx, GPIO.IN)\n', (1534, 1551), True, 'import RPi.GPIO as GPIO\n'), ((1919, 1930), 'time.time', 'time.time', ([], {}), '()\n', (1928, 1930), False, 'import time\n'), ((2029, 2052), 'RPi.GPIO.input', 'GPIO.input', (['self.pin_rx'], {}), '(self.pin_rx)\n', (2039, 2052), True, 'import RPi.GPIO as GPIO\n'), ((2067, 2078), 'time.time', 'time.time', ([], {}), '()\n', (2076, 2078), False, 'import time\n'), ((2192, 2215), 'RPi.GPIO.input', 'GPIO.input', (['self.pin_rx'], {}), '(self.pin_rx)\n', (2202, 2215), True, 'import RPi.GPIO as GPIO\n'), ((2230, 2241), 'time.time', 'time.time', ([], {}), '()\n', (2239, 2241), False, 'import time\n'), ((2255, 2319), 'RPi.GPIO.wait_for_edge', 'GPIO.wait_for_edge', (['self.pin_rx', 'GPIO.BOTH'], {'timeout': 'self.min_gap'}), '(self.pin_rx, GPIO.BOTH, timeout=self.min_gap)\n', (2273, 2319), True, 'import RPi.GPIO as GPIO\n'), ((2439, 2518), 'RPi.GPIO.wait_for_edge', 'GPIO.wait_for_edge', (['self.pin_rx', 'GPIO.BOTH'], {'timeout': '(self.max_gap - self.min_gap)'}), '(self.pin_rx, GPIO.BOTH, timeout=self.max_gap - self.min_gap)\n', (2457, 2518), True, 'import RPi.GPIO as GPIO\n'), ((2529, 2552), 'RPi.GPIO.input', 'GPIO.input', (['self.pin_rx'], {}), '(self.pin_rx)\n', (2539, 2552), True, 'import RPi.GPIO as GPIO\n'), ((2567, 2578), 'time.time', 'time.time', ([], {}), '()\n', (2576, 2578), False, 'import time\n'), ((1194, 1221), 'RPi.GPIO.output', 'GPIO.output', (['self.pin_tx', 'b'], {}), '(self.pin_tx, b)\n', (1205, 1221), True, 'import RPi.GPIO as GPIO\n'), ((1251, 1262), 'time.time', 'time.time', ([], {}), '()\n', (1260, 1262), False, 'import time\n'), ((1296, 1312), 'time.sleep', 'time.sleep', (['wait'], {}), '(wait)\n', (1306, 1312), False, 'import time\n'), ((1587, 1615), 'RPi.GPIO.setup', 'GPIO.setup', (['pin_en', 'GPIO.OUT'], {}), '(pin_en, GPIO.OUT)\n', (1597, 1615), True, 'import RPi.GPIO as GPIO\n'), ((1628, 1658), 'RPi.GPIO.output', 'GPIO.output', (['pin_en', 'GPIO.HIGH'], {}), '(pin_en, GPIO.HIGH)\n', (1639, 1658), True, 'import RPi.GPIO as GPIO\n'), ((2000, 2016), 'time.sleep', 'time.sleep', (['wait'], {}), '(wait)\n', (2010, 2016), False, 'import time\n'), ((1112, 1123), 'time.time', 'time.time', ([], {}), '()\n', (1121, 1123), False, 'import time\n'), ((1165, 1181), 'time.sleep', 'time.sleep', (['wait'], {}), '(wait)\n', (1175, 1181), False, 'import time\n')]
|
import os, sys, subprocess
os.environ["OMP_NUM_THREADS"] = "1"
env = "starcraft"
# seeds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
# seeds = [1, 2, 3, 4, 5]
seeds = [777]
# seeds = [20]
# your models, graphs and tensorboard logs would be save in trained_models/{exp_name}
methods = ['baseline']
pretrain_exp_name = ''
# for soft_budget in [.5]:
if True:
for method in methods:
num_epochs = 1000
num_proto = 100
exp_name = "sc_" + method
soft_budget = 0.7
# discrete comm is true if you want to use learnable prototype based communication.
discrete_comm = False
if "proto" in method:
discrete_comm = True
hid_size = 64
save_every = 100
comm_action_one = False
comm_action_zero = False
# weight of the gating penalty. 0 means no penalty.
gating_head_cost_factor = 0.
if "fixed" in method or "baseline" in method:
comm_action_one = True
nprocesses = 0
lr = 0.001
nagents = 3
max_steps = 60
run_str = f"python main.py --env_name {env} --nprocesses {nprocesses} "+\
f"--num_epochs {num_epochs} --epoch_size 10 "+\
f"--gating_head_cost_factor {gating_head_cost_factor} "+\
f"--hid_size {hid_size} --comm_dim {hid_size} --soft_budget {soft_budget} "+\
f" --detach_gap 10 --lrate {lr} --ic3net "+\
f"--recurrent --save SC2_models --load SC2_models "+\
f"--max_steps {max_steps} --nagents {nagents} "+\
f"--exp_name {exp_name} --save_every {save_every} "
if discrete_comm:
run_str += f"--discrete_comm --use_proto --num_proto {num_proto} "
if comm_action_one:
run_str += f"--comm_action_one "
if comm_action_zero:
run_str += f"--comm_action_zero "
if 'soft' in method:
run_str += f"--load_pretrain --pretrain_exp_name {pretrain_exp_name} "
if "minComm" in method:
run_str += "--min_comm_loss --eta_comm_loss 1. "
if "autoencoder" in method:
run_str += "--autoencoder "
# Important: If you want to restore training just use the --restore tag
# run for all seeds
for seed in seeds:
# log_path = os.path.join("trained_models", env, exp_name, "seed" + str(seed), "logs")
log_path = os.path.join("SC2_models", env, exp_name, "seed" + str(seed), "logs")
if os.path.exists(log_path):
run_str += f"--restore "
# with open("runLogs/" + exp_name + "Log.txt","wb") as out:
# subprocess.Popen(run_str + f"--seed {seed}", shell=True, stdout=out)#, stderr=out)
os.system(run_str + f"--seed {seed}")
|
[
"os.path.exists",
"os.system"
] |
[((2524, 2548), 'os.path.exists', 'os.path.exists', (['log_path'], {}), '(log_path)\n', (2538, 2548), False, 'import os, sys, subprocess\n'), ((2777, 2814), 'os.system', 'os.system', (["(run_str + f'--seed {seed}')"], {}), "(run_str + f'--seed {seed}')\n", (2786, 2814), False, 'import os, sys, subprocess\n')]
|
"""tldextract integration tests."""
import pytest
import tldextract
def test_bad_kwargs():
with pytest.raises(ValueError):
tldextract.TLDExtract(
cache_dir=None, suffix_list_urls=(), fallback_to_snapshot=False
)
|
[
"pytest.raises",
"tldextract.TLDExtract"
] |
[((104, 129), 'pytest.raises', 'pytest.raises', (['ValueError'], {}), '(ValueError)\n', (117, 129), False, 'import pytest\n'), ((139, 229), 'tldextract.TLDExtract', 'tldextract.TLDExtract', ([], {'cache_dir': 'None', 'suffix_list_urls': '()', 'fallback_to_snapshot': '(False)'}), '(cache_dir=None, suffix_list_urls=(),\n fallback_to_snapshot=False)\n', (160, 229), False, 'import tldextract\n')]
|
import pytest
import gevent
import logging
import time
from volttron.platform import get_services_core, jsonapi
from volttrontesting.utils.utils import get_rand_ip_and_port
from platform_driver.interfaces.modbus_tk.server import Server
from platform_driver.interfaces.modbus_tk.maps import Map, Catalog
from volttron.platform.agent.known_identities import PLATFORM_DRIVER
logger = logging.getLogger(__name__)
IP, _port = get_rand_ip_and_port().split(":")
PORT = int(_port)
# New modbus_tk driver config
DRIVER_CONFIG = {
"driver_config": {
"name": "test",
"device_address": IP,
"port": PORT,
"slave_id": 1,
"addressing": "offset",
"register_map": "config://modbus_tk_map.csv"
},
"driver_type": "modbus_tk",
"registry_config": "config://modbus_tk.csv",
"interval": 60,
"timezone": "UTC"
}
# New modbus_tk csv config
REGISTRY_CONFIG_STRING = """Volttron Point Name,Register Name
BRAND (),BRAND ()
MODEL (),MODEL ()
COMS STATUS (),COMS STATUS ()
COMS QUALITY (),COMS QUALITY ()
NUMBER OF QUERIES (),NUMBER OF QUERIES ()
NUMBER OF FAILS (),NUMBER OF FAILS ()
DATE LAST ACQUISITION (),DATE LAST ACQUISITION ()
LAST SAMPLING DURATION (s),LAST SAMPLING DURATION (s)
ACCUMULATED REAL ENERGY NET (IMPORT-EXPORT) (kWh),ACCUMULATED REAL ENERGY NET (IMPORT-EXPORT) (kWh)
REAL ENERGY QUADRANTS 1-4 IMPORT (kWh),REAL ENERGY QUADRANTS 1-4 IMPORT (kWh)
REAL ENERGY QUADRANTS 2-3 EXPORT (kWh),REAL ENERGY QUADRANTS 2-3 EXPORT (kWh)
REACTIVE ENERGY - QUADRANT 1 IMPORT (kVARh),REACTIVE ENERGY - QUADRANT 1 IMPORT (kVARh)
REACTIVE ENERGY - QUADRANT 2 IMPORT (kVARh),REACTIVE ENERGY - QUADRANT 2 IMPORT (kVARh)
REACTIVE ENERGY - QUADRANT 3 EXPORT (kVARh),REACTIVE ENERGY - QUADRANT 3 EXPORT (kVARh)
REACTIVE ENERGY - QUADRANT 4 EXPORT (kVARh),REACTIVE ENERGY - QUADRANT 4 EXPORT (kVARh)
APPARENT ENERGY NET (IMPORT/EXPORT) (kVAh),APPARENT ENERGY NET (IMPORT/EXPORT) (kVAh)
APPARENT QUADRANTS 1-4 IMPORT (kVAh),APPARENT QUADRANTS 1-4 IMPORT (kVAh)
APPARENT QUADRANTS 2-3 EXPORT (kVAh),APPARENT QUADRANTS 2-3 EXPORT (kVAh)
TOTAL INSTANTANEOUS REAL POWER (kW),TOTAL INSTANTANEOUS REAL POWER (kW)
TOTAL INSTANTANEOUS REACTIVE POWER (kVAR),TOTAL INSTANTANEOUS REACTIVE POWER (kVAR)
TOTAL INSTANTANEOUS APPARENT POWER (kVA),TOTAL INSTANTANEOUS APPARENT POWER (kVA)
TOTAL POWER FACTOR (-),TOTAL POWER FACTOR (-)
AVERAGE VOLTAGE L-L (V),AVERAGE VOLTAGE L-L (V)
AVERAGE VOLTAGE L-N (V),AVERAGE VOLTAGE L-N (V)
AVERAGE CURRENT (A),AVERAGE CURRENT (A)
FREQUENCY (Hz),FREQUENCY (Hz)
TOTAL REAL POWER PRESENT DEMAND (kW),TOTAL REAL POWER PRESENT DEMAND (kW)
TOTAL REACTIVE POWER PRESENT DEMAND (kVAR),TOTAL REACTIVE POWER PRESENT DEMAND (kVAR)
TOTAL APPARENT POWER PRESENT DEMAND (kVA),TOTAL APPARENT POWER PRESENT DEMAND (kVA)
TOTAL REAL POWER MAX. DEMAND IMPORT (kW),TOTAL REAL POWER MAX. DEMAND IMPORT (kW)
TOTAL REACTIVE POWER MAX. DEMAND IMPORT (kVAR),TOTAL REACTIVE POWER MAX. DEMAND IMPORT (kVAR)
TOTAL APPARENT POWER MAX. DEMAND IMPORT (kVA),TOTAL APPARENT POWER MAX. DEMAND IMPORT (kVA)
TOTAL REAL POWER MAX. DEMAND EXPORT (kW),TOTAL REAL POWER MAX. DEMAND EXPORT (kW)
TOTAL REACTIVE POWER MAX. DEMAND EXPORT (kVAR),TOTAL REACTIVE POWER MAX. DEMAND EXPORT (kVAR)
TOTAL APPARENT POWER MAX. DEMAND EXPORT (kVA),TOTAL APPARENT POWER MAX. DEMAND EXPORT (kVA)
PULSE COUNTER 1 (-),PULSE COUNTER 1 (-)
PULSE COUNTER 2 (-),PULSE COUNTER 2 (-)
ACCUMULATED REAL ENERGY PHASE A IMPORT (kWh),ACCUMULATED REAL ENERGY PHASE A IMPORT (kWh)
ACCUMULATED REAL ENERGY PHASE B IMPORT (kWh),ACCUMULATED REAL ENERGY PHASE B IMPORT (kWh)
ACCUMULATED REAL ENERGY PHASE C IMPORT (kWh),ACCUMULATED REAL ENERGY PHASE C IMPORT (kWh)
ACCUMULATED REAL ENERGY PHASE A EXPORT (kWh),ACCUMULATED REAL ENERGY PHASE A EXPORT (kWh)
ACCUMULATED REAL ENERGY PHASE B EXPORT (kWh),ACCUMULATED REAL ENERGY PHASE B EXPORT (kWh)
ACCUMULATED REAL ENERGY PHASE C EXPORT (kWh),ACCUMULATED REAL ENERGY PHASE C EXPORT (kWh)
ACCUMULATED Q1 REACTIVE ENERGY PHASE A IMPORT (kVARh),ACCUMULATED Q1 REACTIVE ENERGY PHASE A IMPORT (kVARh)
ACCUMULATED Q1 REACTIVE ENERGY PHASE B IMPORT (kVARh),ACCUMULATED Q1 REACTIVE ENERGY PHASE B IMPORT (kVARh)
ACCUMULATED Q1 REACTIVE ENERGY PHASE C IMPORT (kVARh),ACCUMULATED Q1 REACTIVE ENERGY PHASE C IMPORT (kVARh)
ACCUMULATED Q2 REACTIVE ENERGY PHASE A IMPORT (kVARh),ACCUMULATED Q2 REACTIVE ENERGY PHASE A IMPORT (kVARh)
ACCUMULATED Q2 REACTIVE ENERGY PHASE B IMPORT (kVARh),ACCUMULATED Q2 REACTIVE ENERGY PHASE B IMPORT (kVARh)
ACCUMULATED Q2 REACTIVE ENERGY PHASE C IMPORT (kVARh),ACCUMULATED Q2 REACTIVE ENERGY PHASE C IMPORT (kVARh)
ACCUMULATED Q3 REACTIVE ENERGY PHASE A EXPORT (kVARh),ACCUMULATED Q3 REACTIVE ENERGY PHASE A EXPORT (kVARh)
ACCUMULATED Q3 REACTIVE ENERGY PHASE B EXPORT (kVARh),ACCUMULATED Q3 REACTIVE ENERGY PHASE B EXPORT (kVARh)
ACCUMULATED Q3 REACTIVE ENERGY PHASE C EXPORT (kVARh),ACCUMULATED Q3 REACTIVE ENERGY PHASE C EXPORT (kVARh)
ACCUMULATED Q4 REACTIVE ENERGY PHASE A EXPORT (kVARh),ACCUMULATED Q4 REACTIVE ENERGY PHASE A EXPORT (kVARh)
ACCUMULATED Q4 REACTIVE ENERGY PHASE B EXPORT (kVARh),ACCUMULATED Q4 REACTIVE ENERGY PHASE B EXPORT (kVARh)
ACCUMULATED Q4 REACTIVE ENERGY PHASE C EXPORT (kVARh),ACCUMULATED Q4 REACTIVE ENERGY PHASE C EXPORT (kVARh)
ACCUMULATED APPARENT ENERGY PHASE A IMPORT (kVAh),ACCUMULATED APPARENT ENERGY PHASE A IMPORT (kVAh)
ACCUMULATED APPARENT ENERGY PHASE B IMPORT (kVAh),ACCUMULATED APPARENT ENERGY PHASE B IMPORT (kVAh)
ACCUMULATED APPARENT ENERGY PHASE C IMPORT (kVAh),ACCUMULATED APPARENT ENERGY PHASE C IMPORT (kVAh)
ACCUMULATED APPARENT ENERGY PHASE A EXPORT (kVAh),ACCUMULATED APPARENT ENERGY PHASE A EXPORT (kVAh)
ACCUMULATED APPARENT ENERGY PHASE B EXPORT (kVAh),ACCUMULATED APPARENT ENERGY PHASE B EXPORT (kVAh)
ACCUMULATED APPARENT ENERGY PHASE C EXPORT (kVAh),ACCUMULATED APPARENT ENERGY PHASE C EXPORT (kVAh)
REAL POWER PHASE A (kW),REAL POWER PHASE A (kW)
REAL POWER PHASE B (kW),REAL POWER PHASE B (kW)
REAL POWER PHASE C (kW),REAL POWER PHASE C (kW)
REACTIVE POWER PHASE A (kVAR),REACTIVE POWER PHASE A (kVAR)
REACTIVE POWER PHASE B (kVAR),REACTIVE POWER PHASE B (kVAR)
REACTIVE POWER PHASE C (kVAR),REACTIVE POWER PHASE C (kVAR)
APPARENT POWER PHASE A (kVA),APPARENT POWER PHASE A (kVA)
APPARENT POWER PHASE B (kVA),APPARENT POWER PHASE B (kVA)
APPARENT POWER PHASE C (kVA),APPARENT POWER PHASE C (kVA)
POWER FACTOR PHASE A (-),POWER FACTOR PHASE A (-)
POWER FACTOR PHASE B (-),POWER FACTOR PHASE B (-)
POWER FACTOR PHASE C (-),POWER FACTOR PHASE C (-)
VOLTAGE PHASE A-B (V),VOLTAGE PHASE A-B (V)
VOLTAGE PHASE B-C (V),VOLTAGE PHASE B-C (V)
VOLTAGE PHASE A-C (V),VOLTAGE PHASE A-C (V)
VOLTAGE PHASE A-N (V),VOLTAGE PHASE A-N (V)
VOLTAGE PHASE B-N (V),VOLTAGE PHASE B-N (V)
VOLTAGE PHASE C-N (V),VOLTAGE PHASE C-N (V)
CURRENT PHASE A (A),CURRENT PHASE A (A)
CURRENT PHASE B (A),CURRENT PHASE B (A)
CURRENT PHASE C (A),CURRENT PHASE C (A)"""
REGISTER_MAP = """Register Name,Address,Type,Units,Writable,Transform,Table,Mixed Endian
ACCUMULATED REAL ENERGY NET (IMPORT-EXPORT) (kWh),399,float,kWh,TRUE,,analog_output_holding_registers,TRUE
REAL ENERGY QUADRANTS 1-4 IMPORT (kWh),401,float,kWh,TRUE,,analog_output_holding_registers,TRUE
REAL ENERGY QUADRANTS 2-3 EXPORT (kWh),403,float,kWh,TRUE,,analog_output_holding_registers,TRUE
REACTIVE ENERGY - QUADRANT 1 IMPORT (kVARh),405,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
REACTIVE ENERGY - QUADRANT 2 IMPORT (kVARh),407,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
REACTIVE ENERGY - QUADRANT 3 EXPORT (kVARh),409,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
REACTIVE ENERGY - QUADRANT 4 EXPORT (kVARh),411,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
APPARENT ENERGY NET (IMPORT/EXPORT) (kVAh),413,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
APPARENT QUADRANTS 1-4 IMPORT (kVAh),415,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
APPARENT QUADRANTS 2-3 EXPORT (kVAh),417,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
TOTAL INSTANTANEOUS REAL POWER (kW),419,float,kW,TRUE,,analog_output_holding_registers,TRUE
TOTAL INSTANTANEOUS REACTIVE POWER (kVAR),421,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
TOTAL INSTANTANEOUS APPARENT POWER (kVA),423,float,kVA,TRUE,,analog_output_holding_registers,TRUE
TOTAL POWER FACTOR (-),425,float,,TRUE,,analog_output_holding_registers,TRUE
AVERAGE VOLTAGE L-L (V),427,float,V,TRUE,,analog_output_holding_registers,TRUE
AVERAGE VOLTAGE L-N (V),429,float,V,TRUE,,analog_output_holding_registers,TRUE
AVERAGE CURRENT (A),431,float,A,TRUE,,analog_output_holding_registers,TRUE
FREQUENCY (Hz),433,float,Hz,TRUE,,analog_output_holding_registers,TRUE
TOTAL REAL POWER PRESENT DEMAND (kW),435,float,kW,TRUE,,analog_output_holding_registers,TRUE
TOTAL REACTIVE POWER PRESENT DEMAND (kVAR),437,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
TOTAL APPARENT POWER PRESENT DEMAND (kVA),439,float,kVA,TRUE,,analog_output_holding_registers,TRUE
TOTAL REAL POWER MAX. DEMAND IMPORT (kW),441,float,kW,TRUE,,analog_output_holding_registers,TRUE
TOTAL REACTIVE POWER MAX. DEMAND IMPORT (kVAR),443,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
TOTAL APPARENT POWER MAX. DEMAND IMPORT (kVA),445,float,kVA,TRUE,,analog_output_holding_registers,TRUE
TOTAL REAL POWER MAX. DEMAND EXPORT (kW),447,float,kW,TRUE,,analog_output_holding_registers,TRUE
TOTAL REACTIVE POWER MAX. DEMAND EXPORT (kVAR),449,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
TOTAL APPARENT POWER MAX. DEMAND EXPORT (kVA),451,float,kVA,TRUE,,analog_output_holding_registers,TRUE
PULSE COUNTER 1 (-),453,float,,TRUE,,analog_output_holding_registers,TRUE
PULSE COUNTER 2 (-),455,float,,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED REAL ENERGY PHASE A IMPORT (kWh),457,float,kWh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED REAL ENERGY PHASE B IMPORT (kWh),459,float,kWh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED REAL ENERGY PHASE C IMPORT (kWh),461,float,kWh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED REAL ENERGY PHASE A EXPORT (kWh),463,float,kWh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED REAL ENERGY PHASE B EXPORT (kWh),465,float,kWh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED REAL ENERGY PHASE C EXPORT (kWh),467,float,kWh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q1 REACTIVE ENERGY PHASE A IMPORT (kVARh),469,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q1 REACTIVE ENERGY PHASE B IMPORT (kVARh),471,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q1 REACTIVE ENERGY PHASE C IMPORT (kVARh),473,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q2 REACTIVE ENERGY PHASE A IMPORT (kVARh),475,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q2 REACTIVE ENERGY PHASE B IMPORT (kVARh),477,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q2 REACTIVE ENERGY PHASE C IMPORT (kVARh),479,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q3 REACTIVE ENERGY PHASE A EXPORT (kVARh),481,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q3 REACTIVE ENERGY PHASE B EXPORT (kVARh),483,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q3 REACTIVE ENERGY PHASE C EXPORT (kVARh),485,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q4 REACTIVE ENERGY PHASE A EXPORT (kVARh),487,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q4 REACTIVE ENERGY PHASE B EXPORT (kVARh),489,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED Q4 REACTIVE ENERGY PHASE C EXPORT (kVARh),491,float,kVARh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED APPARENT ENERGY PHASE A IMPORT (kVAh),493,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED APPARENT ENERGY PHASE B IMPORT (kVAh),495,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED APPARENT ENERGY PHASE C IMPORT (kVAh),497,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED APPARENT ENERGY PHASE A EXPORT (kVAh),499,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED APPARENT ENERGY PHASE B EXPORT (kVAh),501,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
ACCUMULATED APPARENT ENERGY PHASE C EXPORT (kVAh),503,float,kVAh,TRUE,,analog_output_holding_registers,TRUE
REAL POWER PHASE A (kW),505,float,kW,TRUE,,analog_output_holding_registers,TRUE
REAL POWER PHASE B (kW),507,float,kW,TRUE,,analog_output_holding_registers,TRUE
REAL POWER PHASE C (kW),509,float,kW,TRUE,,analog_output_holding_registers,TRUE
REACTIVE POWER PHASE A (kVAR),511,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
REACTIVE POWER PHASE B (kVAR),513,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
REACTIVE POWER PHASE C (kVAR),515,float,kVAR,TRUE,,analog_output_holding_registers,TRUE
APPARENT POWER PHASE A (kVA),517,float,kVA,TRUE,,analog_output_holding_registers,TRUE
APPARENT POWER PHASE B (kVA),519,float,kVA,TRUE,,analog_output_holding_registers,TRUE
APPARENT POWER PHASE C (kVA),521,float,kVA,TRUE,,analog_output_holding_registers,TRUE
POWER FACTOR PHASE A (-),523,float,,TRUE,,analog_output_holding_registers,TRUE
POWER FACTOR PHASE B (-),525,float,,TRUE,,analog_output_holding_registers,TRUE
POWER FACTOR PHASE C (-),527,float,,TRUE,,analog_output_holding_registers,TRUE
VOLTAGE PHASE A-B (V),529,float,V,TRUE,,analog_output_holding_registers,TRUE
VOLTAGE PHASE B-C (V),531,float,V,TRUE,,analog_output_holding_registers,TRUE
VOLTAGE PHASE A-C (V),533,float,V,TRUE,,analog_output_holding_registers,TRUE
VOLTAGE PHASE A-N (V),535,float,V,TRUE,,analog_output_holding_registers,TRUE
VOLTAGE PHASE B-N (V),537,float,V,TRUE,,analog_output_holding_registers,TRUE
VOLTAGE PHASE C-N (V),539,float,V,TRUE,,analog_output_holding_registers,TRUE
CURRENT PHASE A (A),541,float,A,TRUE,,analog_output_holding_registers,TRUE
CURRENT PHASE B (A),543,float,A,TRUE,,analog_output_holding_registers,TRUE
CURRENT PHASE C (A),545,float,A,TRUE,,analog_output_holding_registers,TRUE"""
# Register values dictionary for testing set_point and get_point
registers_dict = {"ACCUMULATED REAL ENERGY NET (IMPORT-EXPORT) (kWh)": 74.0,
"REAL ENERGY QUADRANTS 1-4 IMPORT (kWh)": 73.0,
"REAL ENERGY QUADRANTS 2-3 EXPORT (kWh)": 72.0,
"REACTIVE ENERGY - QUADRANT 1 IMPORT (kVARh)": 71.0,
"REACTIVE ENERGY - QUADRANT 2 IMPORT (kVARh)": 70.0,
"REACTIVE ENERGY - QUADRANT 3 EXPORT (kVARh)": 69.0,
"REACTIVE ENERGY - QUADRANT 4 EXPORT (kVARh)": 68.0,
"APPARENT ENERGY NET (IMPORT/EXPORT) (kVAh)": 67.0,
"APPARENT QUADRANTS 1-4 IMPORT (kVAh)": 66.0,
"APPARENT QUADRANTS 2-3 EXPORT (kVAh)": 65.0,
"TOTAL INSTANTANEOUS REAL POWER (kW)": 64.0,
"TOTAL INSTANTANEOUS REACTIVE POWER (kVAR)": 63.0,
"TOTAL INSTANTANEOUS APPARENT POWER (kVA)": 62.0,
"TOTAL POWER FACTOR (-)": 61.0,
"AVERAGE VOLTAGE L-L (V)": 60.0,
"AVERAGE VOLTAGE L-N (V)": 59.0,
"AVERAGE CURRENT (A)": 58.0,
"FREQUENCY (Hz)": 57.0,
"TOTAL REAL POWER PRESENT DEMAND (kW)": 56.0,
"TOTAL REACTIVE POWER PRESENT DEMAND (kVAR)": 55.0,
"TOTAL APPARENT POWER PRESENT DEMAND (kVA)": 54.0,
"TOTAL REAL POWER MAX. DEMAND IMPORT (kW)": 53.0,
"TOTAL REACTIVE POWER MAX. DEMAND IMPORT (kVAR)": 52.0,
"TOTAL APPARENT POWER MAX. DEMAND IMPORT (kVA)": 51.0,
"TOTAL REAL POWER MAX. DEMAND EXPORT (kW)": 50.0,
"TOTAL REACTIVE POWER MAX. DEMAND EXPORT (kVAR)": 49.0,
"TOTAL APPARENT POWER MAX. DEMAND EXPORT (kVA)": 48.0,
"PULSE COUNTER 1 (-)": 47.0,
"PULSE COUNTER 2 (-)": 46.0,
"ACCUMULATED REAL ENERGY PHASE A IMPORT (kWh)": 45.0,
"ACCUMULATED REAL ENERGY PHASE B IMPORT (kWh)": 44.0,
"ACCUMULATED REAL ENERGY PHASE C IMPORT (kWh)": 43.0,
"ACCUMULATED REAL ENERGY PHASE A EXPORT (kWh)": 42.0,
"ACCUMULATED REAL ENERGY PHASE B EXPORT (kWh)": 41.0,
"ACCUMULATED REAL ENERGY PHASE C EXPORT (kWh)": 40.0,
"ACCUMULATED Q1 REACTIVE ENERGY PHASE A IMPORT (kVARh)": 39.0,
"ACCUMULATED Q1 REACTIVE ENERGY PHASE B IMPORT (kVARh)": 38.0,
"ACCUMULATED Q1 REACTIVE ENERGY PHASE C IMPORT (kVARh)": 37.0,
"ACCUMULATED Q2 REACTIVE ENERGY PHASE A IMPORT (kVARh)": 36.0,
"ACCUMULATED Q2 REACTIVE ENERGY PHASE B IMPORT (kVARh)": 35.0,
"ACCUMULATED Q2 REACTIVE ENERGY PHASE C IMPORT (kVARh)": 34.0,
"ACCUMULATED Q3 REACTIVE ENERGY PHASE A EXPORT (kVARh)": 33.0,
"ACCUMULATED Q3 REACTIVE ENERGY PHASE B EXPORT (kVARh)": 32.0,
"ACCUMULATED Q3 REACTIVE ENERGY PHASE C EXPORT (kVARh)": 31.0,
"ACCUMULATED Q4 REACTIVE ENERGY PHASE A EXPORT (kVARh)": 30.0,
"ACCUMULATED Q4 REACTIVE ENERGY PHASE B EXPORT (kVARh)": 29.0,
"ACCUMULATED Q4 REACTIVE ENERGY PHASE C EXPORT (kVARh)": 28.0,
"ACCUMULATED APPARENT ENERGY PHASE A IMPORT (kVAh)": 27.0,
"ACCUMULATED APPARENT ENERGY PHASE B IMPORT (kVAh)": 26.0,
"ACCUMULATED APPARENT ENERGY PHASE C IMPORT (kVAh)": 25.0,
"ACCUMULATED APPARENT ENERGY PHASE A EXPORT (kVAh)": 24.0,
"ACCUMULATED APPARENT ENERGY PHASE B EXPORT (kVAh)": 23.0,
"ACCUMULATED APPARENT ENERGY PHASE C EXPORT (kVAh)": 22.0,
"REAL POWER PHASE A (kW)": 21.0,
"REAL POWER PHASE B (kW)": 20.0,
"REAL POWER PHASE C (kW)": 19.0,
"REACTIVE POWER PHASE A (kVAR)": 18.0,
"REACTIVE POWER PHASE B (kVAR)": 17.0,
"REACTIVE POWER PHASE C (kVAR)": 16.0,
"APPARENT POWER PHASE A (kVA)": 15.0,
"APPARENT POWER PHASE B (kVA)": 14.0,
"APPARENT POWER PHASE C (kVA)": 13.0,
"POWER FACTOR PHASE A (-)": 12.0,
"POWER FACTOR PHASE B (-)": 11.0,
"POWER FACTOR PHASE C (-)": 10.0,
"VOLTAGE PHASE A-B (V)": 9.0,
"VOLTAGE PHASE B-C (V)": 8.0,
"VOLTAGE PHASE A-C (V)": 7.0,
"VOLTAGE PHASE A-N (V)": 6.0,
"VOLTAGE PHASE B-N (V)": 5.0,
"VOLTAGE PHASE C-N (V)": 4.0,
"CURRENT PHASE A (A)": 3.0,
"CURRENT PHASE B (A)": 2.0,
"CURRENT PHASE C (A)": 1.0}
@pytest.fixture(scope="module")
def agent(request, volttron_instance):
"""
Build PlatformDriverAgent, add modbus driver & csv configurations
"""
# Build platform driver agent
md_agent = volttron_instance.build_agent(identity="test_md_agent")
capabilities = {'edit_config_store': {'identity': PLATFORM_DRIVER}}
volttron_instance.add_capabilities(md_agent.core.publickey, capabilities)
# Clean out platform driver configurations
# wait for it to return before adding new config
md_agent.vip.rpc.call('config.store',
'manage_delete_store',
PLATFORM_DRIVER).get()
# Add driver configurations
md_agent.vip.rpc.call('config.store',
'manage_store',
PLATFORM_DRIVER,
'devices/modbus_tk',
jsonapi.dumps(DRIVER_CONFIG),
config_type='json')
# Add csv configurations
md_agent.vip.rpc.call('config.store',
'manage_store',
PLATFORM_DRIVER,
'modbus_tk.csv',
REGISTRY_CONFIG_STRING,
config_type='csv')
md_agent.vip.rpc.call('config.store',
'manage_store',
PLATFORM_DRIVER,
'modbus_tk_map.csv',
REGISTER_MAP,
config_type='csv')
platform_uuid = volttron_instance.install_agent(
agent_dir=get_services_core("PlatformDriverAgent"),
config_file={},
start=True)
gevent.sleep(10) # wait for the agent to start and start the devices
def stop():
"""
Stop platform driver agent
"""
volttron_instance.stop_agent(platform_uuid)
md_agent.core.stop()
request.addfinalizer(stop)
return md_agent
@pytest.fixture(scope='class')
def modbus_server(request):
modbus_client = Catalog()['battery_meter'].get_class()
server_process = Server(address=IP, port=PORT)
server_process.define_slave(1, modbus_client, unsigned=False)
server_process.start()
time.sleep(1)
yield server_process
time.sleep(1)
server_process.stop()
@pytest.mark.usefixtures("modbus_server")
class TestModbusTKDriver:
"""
Regression tests for the modbus_tk driver interface.
"""
def get_point(self, agent, device_name, point_name):
"""
Issue a get_point RPC call for the named point and return the result.
@param agent: The test Agent.
@param device_name: The driver name, by default: 'devices/device_name'.
@param point_name: The name of the point to query.
@return: The actual reading value of the point name from the RPC call.
"""
return agent.vip.rpc.call(PLATFORM_DRIVER, 'get_point', device_name,
point_name).get(timeout=10)
def set_point(self, agent, device_name, point_name, point_value):
"""
Issue a set_point RPC call for the named point and value, and return the
result.
@param agent: The test Agent.
@param device_name: The driver name, by default: 'devices/device_name'.
@param point_name: The name of the point to query.
@param point_value: The value to set on the point.
@return:The actual reading value of the point name from the RPC call.
"""
return agent.vip.rpc.call(PLATFORM_DRIVER, 'set_point', device_name,
point_name, point_value).get(timeout=10)
def scrape_all(self, agent, device_name):
"""
Issue a get_point RPC call for the device and return the result.
@param agent: The test Agent.
@param device_name: The driver name, by default: 'devices/device_name'.
@return: The dictionary mapping point names to their actual values from
the RPC call.
"""
return agent.vip.rpc.call(PLATFORM_DRIVER, 'scrape_all', device_name)\
.get(timeout=10)
def test_scrape_all(self, agent):
for key in registers_dict.keys():
self.set_point(agent, 'modbus_tk', key, registers_dict[key])
assert self.get_point(agent, 'modbus_tk', key) == \
registers_dict[key]
assert type(self.scrape_all(agent, 'modbus_tk')) is dict
|
[
"platform_driver.interfaces.modbus_tk.maps.Catalog",
"pytest.fixture",
"volttron.platform.jsonapi.dumps",
"logging.getLogger",
"time.sleep",
"volttron.platform.get_services_core",
"volttrontesting.utils.utils.get_rand_ip_and_port",
"platform_driver.interfaces.modbus_tk.server.Server",
"gevent.sleep",
"pytest.mark.usefixtures"
] |
[((383, 410), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (400, 410), False, 'import logging\n'), ((18842, 18872), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (18856, 18872), False, 'import pytest\n'), ((20804, 20833), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""class"""'}), "(scope='class')\n", (20818, 20833), False, 'import pytest\n'), ((21157, 21197), 'pytest.mark.usefixtures', 'pytest.mark.usefixtures', (['"""modbus_server"""'], {}), "('modbus_server')\n", (21180, 21197), False, 'import pytest\n'), ((20522, 20538), 'gevent.sleep', 'gevent.sleep', (['(10)'], {}), '(10)\n', (20534, 20538), False, 'import gevent\n'), ((20943, 20972), 'platform_driver.interfaces.modbus_tk.server.Server', 'Server', ([], {'address': 'IP', 'port': 'PORT'}), '(address=IP, port=PORT)\n', (20949, 20972), False, 'from platform_driver.interfaces.modbus_tk.server import Server\n'), ((21071, 21084), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (21081, 21084), False, 'import time\n'), ((21114, 21127), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (21124, 21127), False, 'import time\n'), ((424, 446), 'volttrontesting.utils.utils.get_rand_ip_and_port', 'get_rand_ip_and_port', ([], {}), '()\n', (444, 446), False, 'from volttrontesting.utils.utils import get_rand_ip_and_port\n'), ((19728, 19756), 'volttron.platform.jsonapi.dumps', 'jsonapi.dumps', (['DRIVER_CONFIG'], {}), '(DRIVER_CONFIG)\n', (19741, 19756), False, 'from volttron.platform import get_services_core, jsonapi\n'), ((20431, 20471), 'volttron.platform.get_services_core', 'get_services_core', (['"""PlatformDriverAgent"""'], {}), "('PlatformDriverAgent')\n", (20448, 20471), False, 'from volttron.platform import get_services_core, jsonapi\n'), ((20882, 20891), 'platform_driver.interfaces.modbus_tk.maps.Catalog', 'Catalog', ([], {}), '()\n', (20889, 20891), False, 'from platform_driver.interfaces.modbus_tk.maps import Map, Catalog\n')]
|
import datetime
import os
import pathlib
import functools
import mimetypes
from uuid import uuid4
from PIL import Image
import boto3
import botocore
import mimetypes
mimetypes.init()
from .celery import app, logger
from celery import group
from config import Config
S3_ENDPOINT = Config.S3_ENDPOINT
S3_ACCESS_KEY = Config.S3_KEY
S3_SECRET_ACCESS_KEY = Config.S3_SECRET
s3 = boto3.client(
's3',
endpoint_url='https://{}'.format(S3_ENDPOINT),
aws_access_key_id=S3_ACCESS_KEY,
aws_secret_access_key=S3_SECRET_ACCESS_KEY,
)
def to_url(bucket, name):
return "https://{}.{}/{}".format(bucket, S3_ENDPOINT, name)
@app.task()
def upload_workflow(path):
"""
Gets image as uploaded and rotates, uploads, and generates thumbnails
"""
rotate_according_to_exif(path)
upload = upload_to_s3.delay(path, 'folks-images')
thumbnails = generate_thumbnails(path, 'folks-thumbnails')
Path(path).unlink()
def rotate_according_to_exif(file_path):
"""
Rotates image to proper orientation according to EXIF metadata
"""
tag = 0x0112
im = Image.open(file_path)
if not has_attr(im, '_getexif'):
return
exif = im._getexif()
orientation = exif[tag]
if orientation is not None:
exif_transpose_sequences = [[], # 0 (reserved)
[], # 1 top left
# 2 top right
[Image.FLIP_LEFT_RIGHT],
# 3 bottom right
[Image.ROTATE_180],
# 4 bottom left
[Image.FLIP_TOP_BOTTOM],
# 5 left top
[Image.FLIP_LEFT_RIGHT, Image.ROTATE_90],
# 6 right top
[Image.ROTATE_270],
# 7 right bottom
[Image.FLIP_TOP_BOTTOM, Image.ROTATE_90],
# 8 left bottom
[Image.ROTATE_90],
]
logger.info('Image {} was captured with orientation {}',
file_path, orientation)
seq = exif_transpose_sequences[orientation]
im = functools.reduce(type(im).transpose, seq, im)
im.save()
@app.task(bind=True)
def upload_to_s3(self, file_path, bucket, acl="public-read"):
"""
Upload file to S3
"""
fp = pathlib.Path(file_path)
content_type, enc = mimetypes.guess_type(fp.name)
if enc is not None:
logger.error('tried to upload {} (originally {})'.format(enc, fp.name))
return None
name = fp.name
logger.info('uploading {} to {} with ContentType {}'.format(
name, bucket, content_type))
try:
with fp.open('rb') as f:
s3.upload_fileobj(
f,
bucket,
name,
ExtraArgs={"ACL": acl, "ContentType": content_type},
)
except Exception as exc:
logger.error(exc)
raise self.retry(exc=exc)
return to_url(bucket, name)
@app.task
def generate_thumbnails(file_path, bucket):
"""Generate thumbnails (1600px, 640px, 320px)"""
fp = pathlib.Path(file_path)
sizes = [
(1600, 1600),
(640, 640),
(320, 320)
]
im = Image.open(file_path)
thumb_paths = []
for s in sizes:
thumb_name = fp.with_name('{}-{}.jpg'.format(fp.stem, s[0]))
logger.info("thumb name of {} is {}".format(s, thumb_name))
im.thumbnail(s, Image.ANTIALIAS)
im.save(thumb_name, "JPEG")
thumb_paths.append(str(thumb_name))
return group(upload_to_s3.s(p, bucket) for p in thumb_paths).apply_async()
|
[
"mimetypes.guess_type",
"mimetypes.init",
"pathlib.Path",
"PIL.Image.open"
] |
[((166, 182), 'mimetypes.init', 'mimetypes.init', ([], {}), '()\n', (180, 182), False, 'import mimetypes\n'), ((1090, 1111), 'PIL.Image.open', 'Image.open', (['file_path'], {}), '(file_path)\n', (1100, 1111), False, 'from PIL import Image\n'), ((2671, 2694), 'pathlib.Path', 'pathlib.Path', (['file_path'], {}), '(file_path)\n', (2683, 2694), False, 'import pathlib\n'), ((2719, 2748), 'mimetypes.guess_type', 'mimetypes.guess_type', (['fp.name'], {}), '(fp.name)\n', (2739, 2748), False, 'import mimetypes\n'), ((3454, 3477), 'pathlib.Path', 'pathlib.Path', (['file_path'], {}), '(file_path)\n', (3466, 3477), False, 'import pathlib\n'), ((3568, 3589), 'PIL.Image.open', 'Image.open', (['file_path'], {}), '(file_path)\n', (3578, 3589), False, 'from PIL import Image\n')]
|
#!/usr/bin/env python3
#
# Copyright (c) 2016-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
import os
import pathlib
import signal
import sys
import typing
import pexpect
from eden.cli.daemon import wait_for_shutdown
from .lib import edenclient, testcase
from .lib.find_executables import FindExe
from .lib.pexpect import PexpectAssertionMixin
from .lib.service_test_case import ServiceTestCaseBase, service_test
class HealthTest(testcase.EdenTestCase):
def test_is_healthy(self) -> None:
self.assertTrue(self.eden.is_healthy())
self.eden.shutdown()
self.assertFalse(self.eden.is_healthy())
def test_disconnected_daemon_is_not_healthy(self) -> None:
# Create a new edenfs instance that is never started, and make sure
# it is not healthy.
with edenclient.EdenFS() as client:
self.assertFalse(client.is_healthy())
@service_test()
class HealthOfFakeEdenFSTest(ServiceTestCaseBase, PexpectAssertionMixin):
def setUp(self) -> None:
super().setUp()
self.temp_dir = pathlib.Path(self.make_temporary_directory())
def test_healthy_daemon_is_healthy(self) -> None:
with self.spawn_fake_edenfs(self.temp_dir):
status_process = self.spawn_status([])
status_process.expect_exact("eden running normally")
self.assert_process_succeeds(status_process)
def test_killed_daemon_is_not_running(self) -> None:
with self.spawn_fake_edenfs(self.temp_dir) as daemon_pid:
os.kill(daemon_pid, signal.SIGKILL)
wait_for_shutdown(pid=daemon_pid, timeout=5)
status_process = self.spawn_status([])
status_process.expect_exact("edenfs not running")
self.assert_process_fails(status_process, exit_code=1)
def test_hanging_thrift_call_reports_daemon_is_unresponsive(self) -> None:
with self.spawn_fake_edenfs(self.temp_dir, ["--sleepBeforeGetPid=5"]):
status_process = self.spawn_status(["--timeout", "1"])
status_process.expect_exact(
"Eden's Thrift server does not appear to be running, but the "
"process is still alive"
)
self.assert_process_fails(status_process, exit_code=1)
def test_slow_thrift_call_reports_daemon_is_healthy(self) -> None:
with self.spawn_fake_edenfs(self.temp_dir, ["--sleepBeforeGetPid=2"]):
status_process = self.spawn_status(["--timeout", "10"])
status_process.expect_exact("eden running normally")
self.assert_process_succeeds(status_process)
def spawn_status(self, extra_args: typing.List[str]) -> "pexpect.spawn[str]":
return pexpect.spawn(
FindExe.EDEN_CLI,
["--config-dir", str(self.temp_dir)]
+ self.get_required_eden_cli_args()
+ ["status"]
+ extra_args,
encoding="utf-8",
logfile=sys.stderr,
)
|
[
"os.kill",
"eden.cli.daemon.wait_for_shutdown"
] |
[((1746, 1781), 'os.kill', 'os.kill', (['daemon_pid', 'signal.SIGKILL'], {}), '(daemon_pid, signal.SIGKILL)\n', (1753, 1781), False, 'import os\n'), ((1794, 1838), 'eden.cli.daemon.wait_for_shutdown', 'wait_for_shutdown', ([], {'pid': 'daemon_pid', 'timeout': '(5)'}), '(pid=daemon_pid, timeout=5)\n', (1811, 1838), False, 'from eden.cli.daemon import wait_for_shutdown\n')]
|
import random
import torch
from allennlp.data.iterators import BasicIterator
from allennlp.data.token_indexers import SingleIdTokenIndexer, ELMoTokenCharactersIndexer
from allennlp.modules import Embedding, Elmo
from torch import nn
from utils import fever_db
import numpy as np
import os
import config
from data_util.data_readers.fever_sselection_reader import SSelectorReader
import nn_doc_retrieval.disabuigation_training as disamb
from sentence_retrieval.sampler_for_nmodel import get_full_list, post_filter, get_additional_list
from data_util.exvocab import load_vocab_embeddings
from log_util import save_tool
import utils
from flint import torch_util
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from neural_modules import biDafAttn
from sample_for_nli.tf_idf_sample_v1_0 import sample_v1_0, select_sent_for_eval, convert_evidence2scoring_format
from utils import c_scorer, common
class ESIM(nn.Module):
# This is ESIM sequence matching model
# lstm
def __init__(self, rnn_size_in=(1024 + 300, 1024 + 300), rnn_size_out=(300, 300), max_l=100,
mlp_d=300, num_of_class=3, drop_r=0.5, activation_type='relu'):
super(ESIM, self).__init__()
self.dropout_layer = nn.Dropout(drop_r)
self.lstm_1 = nn.LSTM(input_size=rnn_size_in[0], hidden_size=rnn_size_out[0],
num_layers=1, bidirectional=True, batch_first=True)
self.lstm_2 = nn.LSTM(input_size=rnn_size_in[1], hidden_size=rnn_size_out[1],
num_layers=1, bidirectional=True, batch_first=True)
self.projection = nn.Linear(rnn_size_out[0] * 2 * 4, rnn_size_out[0])
self.max_l = max_l
self.bidaf = biDafAttn(300)
self.mlp_1 = nn.Linear(rnn_size_out[1] * 2 * 4, mlp_d)
self.sm = nn.Linear(mlp_d, num_of_class)
if activation_type == 'relu':
activation = nn.ReLU()
elif activation_type == 'tanh':
activation = nn.Tanh()
else:
raise ValueError("Not a valid activation!")
self.classifier = nn.Sequential(*[nn.Dropout(drop_r), self.mlp_1, activation, nn.Dropout(drop_r), self.sm])
def count_params(self):
total_c = 0
for param in self.parameters():
if len(param.size()) == 2:
d1, d2 = param.size()[0], param.size()[1]
total_c += d1 * d2
print("Total count:", total_c)
def display(self):
for name, param in self.named_parameters():
print(name, param.data.size())
def forward(self, layer1_s1, layer2_s1, l1, layer1_s2, layer2_s2, l2): # [B, T]
p_s1 = self.dropout_layer(layer1_s1)
p_s2 = self.dropout_layer(layer1_s2)
s1_layer1_out = torch_util.auto_rnn(self.lstm_1, p_s1, l1)
s2_layer1_out = torch_util.auto_rnn(self.lstm_1, p_s2, l2)
S = self.bidaf.similarity(s1_layer1_out, l1, s2_layer1_out, l2)
s1_att, s2_att = self.bidaf.get_both_tile(S, s1_layer1_out, s2_layer1_out)
s1_coattentioned = torch.cat([s1_layer1_out, s1_att, s1_layer1_out - s1_att,
s1_layer1_out * s1_att], dim=2)
s2_coattentioned = torch.cat([s2_layer1_out, s2_att, s2_layer1_out - s2_att,
s2_layer1_out * s2_att], dim=2)
p_s1_coattentioned = F.relu(self.projection(s1_coattentioned))
p_s2_coattentioned = F.relu(self.projection(s2_coattentioned))
s1_coatt_features = torch.cat([p_s1_coattentioned, layer2_s1], dim=2)
s2_coatt_features = torch.cat([p_s2_coattentioned, layer2_s2], dim=2)
s1_coatt_features = self.dropout_layer(s1_coatt_features)
s2_coatt_features = self.dropout_layer(s2_coatt_features)
s1_layer2_out = torch_util.auto_rnn(self.lstm_2, s1_coatt_features, l1)
s2_layer2_out = torch_util.auto_rnn(self.lstm_2, s2_coatt_features, l2)
s1_lay2_maxout = torch_util.max_along_time(s1_layer2_out, l1)
s2_lay2_maxout = torch_util.max_along_time(s2_layer2_out, l2)
features = torch.cat([s1_lay2_maxout, s2_lay2_maxout,
torch.abs(s1_lay2_maxout - s2_lay2_maxout),
s1_lay2_maxout * s2_lay2_maxout], dim=1)
return self.classifier(features)
class Model(nn.Module):
def __init__(self, weight, vocab_size, embedding_dim,
rnn_size_in=(1024 + 300, 1024 + 300),
rnn_size_out=(300, 300), max_l=150,
mlp_d=300, num_of_class=3, drop_r=0.5, activation_type='relu'):
super(Model, self).__init__()
self.glove_embd_layer = Embedding(vocab_size, embedding_dim,
weight=weight, padding_index=0)
options_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json"
weight_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"
num_of_elmo = 1
self.max_l = max_l
self.elmo_embd_layer = Elmo(options_file, weight_file, num_of_elmo, dropout=0)
self.esim_layer = ESIM(rnn_size_in, rnn_size_out, max_l, mlp_d, num_of_class, drop_r, activation_type)
def display(self, exclude=None):
total_p_size = 0
if exclude is None:
exclude = {'glove'}
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.data.size())
exclude_this = False
for exclude_name in exclude:
if exclude_name in str(name):
exclude_this = True
if exclude_this:
continue
nn = 1
for s in list(param.size()):
nn = nn * s
total_p_size += nn
print('Total Size:', total_p_size)
def raw_input_to_esim_input(self, s_tokens, s_elmo_chars):
s_tokens = torch_util.length_truncate(s_tokens, self.max_l)
s1_glove_embd = self.glove_embd_layer(s_tokens)
s1_elmo_out = self.elmo_embd_layer(s_elmo_chars)
s1_elmo_embd = torch_util.length_truncate(s1_elmo_out, self.max_l, is_elmo=True)
s1_mask, s1_len = torch_util.get_length_and_mask(s_tokens)
assert torch.equal(s1_elmo_embd['mask'], s1_mask)
return s1_glove_embd, s1_elmo_embd['elmo_representations'][0], s1_len
def forward(self, batch):
s1_tokens = batch['premise']['tokens'].to(next(self.parameters()).device)
s1_elmo_chars = batch['premise']['elmo_chars'].to(next(self.parameters()).device)
s2_tokens = batch['hypothesis']['tokens'].to(next(self.parameters()).device)
s2_elmo_chars = batch['hypothesis']['elmo_chars'].to(next(self.parameters()).device)
s1_glove_embd, s1_elmo_embd, s1_len = self.raw_input_to_esim_input(s1_tokens, s1_elmo_chars)
s2_glove_embd, s2_elmo_embd, s2_len = self.raw_input_to_esim_input(s2_tokens, s2_elmo_chars)
s1_layer1_in = torch.cat((s1_glove_embd, s1_elmo_embd), dim=2)
s1_layer2_in = s1_elmo_embd
s2_layer1_in = torch.cat((s2_glove_embd, s2_elmo_embd), dim=2)
s2_layer2_in = s2_elmo_embd
# print(s1_layer1_in.size())
# print(s1_layer2_in.size())
# print(s2_layer1_in.size())
# print(s2_layer2_in.size())
esim_out = self.esim_layer(s1_layer1_in, s1_layer2_in, s1_len,
s2_layer1_in, s2_layer2_in, s2_len)
return esim_out
def eval_model(model, data_iter, criterion):
print("Evaluating ...")
model.eval()
n_correct = loss = 0
totoal_size = 0
y_pred_list = []
y_true_list = []
for batch_idx, batch in enumerate(data_iter):
out = model(batch)
y = batch['label']
n_correct += (torch.max(out, 1)[1].view(y.size()) == y).sum().item()
y_pred_list.extend(torch.max(out, 1)[1].view(y.size()).tolist())
y_true_list.extend(y.tolist())
loss += criterion(out, y).item() * y.size(0)
totoal_size += y.size(0)
print('n_correct:', n_correct)
print('total_size:', totoal_size)
avg_acc = 100. * n_correct / totoal_size
avg_loss = loss / totoal_size
return avg_acc, avg_loss
def full_eval_model(model, data_iter, criterion, dev_data_list):
# select < (-.-) > 0
# non-select < (-.-) > 1
# hidden < (-.-) > -2
with torch.no_grad():
id2label = {
0: "true",
1: "false",
-2: "hidden"
}
print("Evaluating ...")
model.eval()
n_correct = loss = 0
totoal_size = 0
y_pred_logits_list = []
y_pred_prob_list = []
y_id_list = []
for batch_idx, batch in enumerate(tqdm(data_iter)):
out = model(batch)
prob = F.softmax(out, dim=1)
y = batch['selection_label']
y_id_list.extend(list(batch['pid']))
n_correct += (torch.max(out, 1)[1].view(y.size()) == y).sum().item()
y_pred_logits_list.extend(out[:, 0].tolist())
y_pred_prob_list.extend(prob[:, 0].tolist())
loss += criterion(out, y).item() * y.size(0)
totoal_size += y.size(0)
assert len(y_id_list) == len(dev_data_list)
assert len(y_pred_logits_list) == len(dev_data_list)
for i in range(len(dev_data_list)):
assert str(y_id_list[i]) == str(dev_data_list[i]['selection_id'])
# Matching id
dev_data_list[i]['score'] = y_pred_logits_list[i]
dev_data_list[i]['prob'] = y_pred_prob_list[i]
# Reset neural set
print('n_correct:', n_correct)
print('total_size:', totoal_size)
avg_acc = 100. * n_correct / totoal_size
avg_loss = loss / totoal_size
return avg_acc, avg_loss, dev_data_list
def hidden_eval(model, data_iter, dev_data_list):
# select < (-.-) > 0
# non-select < (-.-) > 1
# hidden < (-.-) > -2
with torch.no_grad():
id2label = {
0: "true",
1: "false",
-2: "hidden"
}
print("Evaluating ...")
model.eval()
totoal_size = 0
y_pred_logits_list = []
y_pred_prob_list = []
y_id_list = []
for batch_idx, batch in enumerate(tqdm(data_iter)):
out = model(batch)
prob = F.softmax(out, dim=1)
y = batch['selection_label']
y_id_list.extend(list(batch['pid']))
y_pred_logits_list.extend(out[:, 0].tolist())
y_pred_prob_list.extend(prob[:, 0].tolist())
totoal_size += y.size(0)
assert len(y_id_list) == len(dev_data_list)
assert len(y_pred_logits_list) == len(dev_data_list)
for i in range(len(dev_data_list)):
assert str(y_id_list[i]) == str(dev_data_list[i]['selection_id'])
# Matching id
dev_data_list[i]['score'] = y_pred_logits_list[i]
dev_data_list[i]['prob'] = y_pred_prob_list[i]
# Reset neural set
print('total_size:', totoal_size)
return dev_data_list
def train_fever():
num_epoch = 8
seed = 12
batch_size = 128
experiment_name = "simple_nn"
lazy = True
torch.manual_seed(seed)
keep_neg_sample_prob = 0.5
sample_prob_decay = 0.1
dev_upstream_file = config.RESULT_PATH / "sent_retri/2018_07_05_17:17:50_r/dev.jsonl"
train_upstream_file = config.RESULT_PATH / "sent_retri/2018_07_05_17:17:50_r/train.jsonl"
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
train_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy)
# dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=False)
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy)
complete_upstream_dev_data = get_full_list(config.T_FEVER_DEV_JSONL, dev_upstream_file, pred=True)
print("Dev size:", len(complete_upstream_dev_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_dev_data)
# Load Vocabulary
biterator = BasicIterator(batch_size=batch_size)
dev_biterator = BasicIterator(batch_size=batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="labels")
vocab.add_token_to_namespace("false", namespace="labels")
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='labels')
# Label value
vocab.get_index_to_token_vocabulary('labels')
print(vocab.get_token_to_index_vocabulary('labels'))
print(vocab.get_vocab_size('tokens'))
biterator.index_with(vocab)
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=300, num_of_class=2)
model.display()
model.to(device)
# Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name), 'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# Save source code end.
best_dev = -1
iteration = 0
start_lr = 0.0002
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=start_lr)
criterion = nn.CrossEntropyLoss()
for i_epoch in range(num_epoch):
print("Resampling...")
# Resampling
complete_upstream_train_data = get_full_list(config.T_FEVER_TRAIN_JSONL, train_upstream_file, pred=False)
filtered_train_data = post_filter(complete_upstream_train_data, keep_prob=keep_neg_sample_prob,
seed=12 + i_epoch)
# Change the seed to avoid duplicate sample...
keep_neg_sample_prob -= sample_prob_decay
print("Sampled_length:", len(filtered_train_data))
sampled_train_instances = train_fever_data_reader.read(filtered_train_data)
train_iter = biterator(sampled_train_instances, shuffle=True, num_epochs=1, cuda_device=device_num)
for i, batch in tqdm(enumerate(train_iter)):
model.train()
out = model(batch)
y = batch['selection_label']
loss = criterion(out, y)
# No decay
optimizer.zero_grad()
loss.backward()
optimizer.step()
iteration += 1
if i_epoch <= 4:
mod = 25000
else:
mod = 10000
if iteration % mod == 0:
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
dev_score, dev_loss, complete_upstream_dev_data = full_eval_model(model, eval_iter, criterion,
complete_upstream_dev_data)
dev_results_list = score_converter_v0(config.T_FEVER_DEV_JSONL, complete_upstream_dev_data)
eval_mode = {'check_sent_id_correct': True, 'standard': True}
strict_score, acc_score, pr, rec, f1 = c_scorer.fever_score(dev_results_list, config.T_FEVER_DEV_JSONL,
mode=eval_mode, verbose=False)
total = len(dev_results_list)
hit = eval_mode['check_sent_id_correct_hits']
tracking_score = hit / total
print(f"Dev(clf_acc/pr/rec/f1/loss):{dev_score}/{pr}/{rec}/{f1}/{dev_loss}")
print(f"Tracking score:", f"{tracking_score}")
need_save = False
if tracking_score > best_dev:
best_dev = tracking_score
need_save = True
if need_save:
save_path = os.path.join(
file_path_prefix,
f'i({iteration})_epoch({i_epoch})_'
f'(tra_score:{tracking_score}|clf_acc:{dev_score}|pr:{pr}|rec:{rec}|f1:{f1}|loss:{dev_loss})'
)
torch.save(model.state_dict(), save_path)
def train_fever_v1():
num_epoch = 10
seed = 12
batch_size = 64
dev_batch_size = 128
experiment_name = "simple_nn_doc_first_sent"
# experiment_name = "simple_nn_doc"
lazy = True
torch.manual_seed(seed)
contain_first_sentence = True
pn_ratio = 1.0
# keep_neg_sample_prob = 0.4
# sample_prob_decay = 0.05
dev_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/dev.jsonl"
train_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/train.jsonl"
dev_data_list = common.load_jsonl(dev_upstream_file)
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
train_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=180)
# dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=False)
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=180)
cursor = fever_db.get_cursor()
complete_upstream_dev_data = disamb.sample_disamb_inference(common.load_jsonl(dev_upstream_file), cursor,
contain_first_sentence=contain_first_sentence)
print("Dev size:", len(complete_upstream_dev_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_dev_data)
# Load Vocabulary
biterator = BasicIterator(batch_size=batch_size)
dev_biterator = BasicIterator(batch_size=dev_batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
biterator.index_with(vocab)
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=160, num_of_class=2)
model.display()
model.to(device)
# Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name), 'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# Save source code end.
best_dev = -1
iteration = 0
start_lr = 0.0002
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=start_lr)
criterion = nn.CrossEntropyLoss()
for i_epoch in range(num_epoch):
print("Resampling...")
# Resampling
complete_upstream_train_data = disamb.sample_disamb_training_v0(common.load_jsonl(train_upstream_file),
cursor, pn_ratio, contain_first_sentence,
only_found=False)
random.shuffle(complete_upstream_train_data)
print("Sample Prob.:", pn_ratio)
print("Sampled_length:", len(complete_upstream_train_data))
sampled_train_instances = train_fever_data_reader.read(complete_upstream_train_data)
train_iter = biterator(sampled_train_instances, shuffle=True, num_epochs=1, cuda_device=device_num)
for i, batch in tqdm(enumerate(train_iter)):
model.train()
out = model(batch)
y = batch['selection_label']
loss = criterion(out, y)
# No decay
optimizer.zero_grad()
loss.backward()
optimizer.step()
iteration += 1
if i_epoch <= 5:
mod = 1000
else:
mod = 500
if iteration % mod == 0:
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_dev_data)
disamb.enforce_disabuigation_into_retrieval_result_v0(complete_upstream_dev_data,
dev_data_list)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
need_save = False
if oracle_score > best_dev:
best_dev = oracle_score
need_save = True
if need_save:
save_path = os.path.join(
file_path_prefix,
f'i({iteration})_epoch({i_epoch})_'
f'(tra_score:{oracle_score}|pr:{pr}|rec:{rec}|f1:{f1})'
)
torch.save(model.state_dict(), save_path)
#
print("Epoch Evaluation...")
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_dev_data)
disamb.enforce_disabuigation_into_retrieval_result_v0(complete_upstream_dev_data,
dev_data_list)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
need_save = False
if oracle_score > best_dev:
best_dev = oracle_score
need_save = True
if need_save:
save_path = os.path.join(
file_path_prefix,
f'i({iteration})_epoch({i_epoch})_e'
f'(tra_score:{oracle_score}|pr:{pr}|rec:{rec}|f1:{f1})'
)
torch.save(model.state_dict(), save_path)
def doc_model_eval():
seed = 12
batch_size = 128
dev_batch_size = 128
lazy = True
torch.manual_seed(seed)
contain_first_sentence = True
# keep_neg_sample_prob = 0.4
# sample_prob_decay = 0.05
# model_path = "/home/easonnie/projects/FunEver/saved_models/08-26-15:13:35_simple_nn_doc/i(7000)_epoch(1)_(tra_score:0.9164416441644164|pr:0.4283778377837277|rec:0.8746624662466247|f1:0.575095052581864)"
model_path = "/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/i(9000)_epoch(1)_(tra_score:0.9212421242124212|pr:0.4299679967996279|rec:0.8818631863186318|f1:0.5780819247968391)"
dev_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/dev.jsonl"
# train_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/train.jsonl"
dev_data_list = common.load_jsonl(dev_upstream_file)
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=180)
cursor = fever_db.get_cursor()
complete_upstream_dev_data = disamb.sample_disamb_inference(common.load_jsonl(dev_upstream_file), cursor,
contain_first_sentence=contain_first_sentence)
print("Dev size:", len(complete_upstream_dev_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_dev_data)
# Load Vocabulary
biterator = BasicIterator(batch_size=batch_size)
dev_biterator = BasicIterator(batch_size=dev_batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
biterator.index_with(vocab)
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=160, num_of_class=2)
model.load_state_dict(torch.load(model_path))
model.display()
model.to(device)
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_dev_data)
common.save_jsonl(complete_upstream_dev_data,
"/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/ablation_neural_doc.jsonl")
disamb.enforce_disabuigation_into_retrieval_result_v1(complete_upstream_dev_data,
dev_data_list)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
def pipeline_function(upstream_file, model_path):
seed = 12
batch_size = 128
dev_batch_size = 128
lazy = True
torch.manual_seed(seed)
contain_first_sentence = True
dev_upstream_file = upstream_file
dev_data_list = common.load_jsonl(dev_upstream_file)
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=180)
cursor = fever_db.get_cursor()
complete_upstream_dev_data = disamb.sample_disamb_inference(common.load_jsonl(dev_upstream_file), cursor,
contain_first_sentence=contain_first_sentence)
print("Dev size:", len(complete_upstream_dev_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_dev_data)
# Load Vocabulary
biterator = BasicIterator(batch_size=batch_size)
dev_biterator = BasicIterator(batch_size=dev_batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
biterator.index_with(vocab)
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=160, num_of_class=2)
model.load_state_dict(torch.load(model_path))
model.display()
model.to(device)
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_dev_data)
# common.save_jsonl(complete_upstream_dev_data,
# "/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/ablation_neural_doc.jsonl")
# remember to add this back
# disamb.enforce_disabuigation_into_retrieval_result_v1(complete_upstream_dev_data,
# dev_data_list)
dev_doc_score_list = complete_upstream_dev_data
return dev_doc_score_list
# oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
# print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
# print("Strict score:", oracle_score)
# print(f"Eval Tracking score:", f"{oracle_score}")
def utest_results_debug():
cursor = fever_db.get_cursor()
contain_first_sentence = True
dev_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/dev.jsonl"
# dev_upstream_file = config.RESULT_PATH / "doc_retri/docretri.pageview/dev.jsonl"
# train_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/train.jsonl"
dev_data_list = common.load_jsonl(dev_upstream_file)
# disamb.item_remove_old_rule(dev_data_list)
disamb.item_resorting(dev_data_list, 5)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=10)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
# Dev(raw_acc/pr/rec/f1):0.9198419841984199/0.4589658965896083/0.8797629762976298/0.6032307383226128 length: 3.25
# Dev(raw_acc/pr/rec/f1):0.9202920292029203/0.5114148914891038/0.8804380438043804/0.6470067565581602 length: 2.89
#
# exit(0)
# complete_upstream_dev_data = disamb.sample_disamb_inference(common.load_jsonl(dev_upstream_file), cursor,
# contain_first_sentence=contain_first_sentence)
complete_upstream_dev_data = common.load_jsonl(
"/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/ablation_neural_doc.jsonl")
disamb.enforce_disabuigation_into_retrieval_result_v2(complete_upstream_dev_data,
dev_data_list, prob_sh=0.00005)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
disamb.item_resorting(dev_data_list, 10)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=10)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
# Eval Tracking score: 0.9202420242024203 prob
# Eval Tracking score: 0.9202420242024203 score
def utest_results():
cursor = fever_db.get_cursor()
contain_first_sentence = True
dev_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/dev.jsonl"
#
# dev_upstream_file = config.RESULT_PATH / "doc_retri/docretri.pageview/dev.jsonl"
train_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/train.jsonl"
dev_data_list = common.load_jsonl(dev_upstream_file)
# disamb.item_resorting(dev_data_list)
# disamb.item_remove_old_rule(dev_data_list)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
# oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=10)
#
# print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
# print("Strict score:", oracle_score)
# print(f"Eval Tracking score:", f"{oracle_score}")
# Dev(raw_acc/pr/rec/f1):0.9198419841984199/0.4589658965896083/0.8797629762976298/0.6032307383226128 length: 3.25
# Dev(raw_acc/pr/rec/f1):0.9202920292029203/0.5114148914891038/0.8804380438043804/0.6470067565581602 length: 2.89
# print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
#
# exit(0)
# complete_upstream_dev_data = disamb.sample_disamb_inference(common.load_jsonl(dev_upstream_file), cursor,
# contain_first_sentence=contain_first_sentence)
complete_upstream_dev_data = common.load_jsonl(
"/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/ablation_neural_doc.jsonl")
disamb.enforce_disabuigation_into_retrieval_result_v2(complete_upstream_dev_data,
dev_data_list, prob_sh=0.0001)
oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
print("Strict score:", oracle_score)
print(f"Eval Tracking score:", f"{oracle_score}")
# oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=10)
#
# print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
# print("Strict score:", oracle_score)
# print(f"Eval Tracking score:", f"{oracle_score}")
# Eval Tracking score: 0.9202420242024203 prob
# Eval Tracking score: 0.9202420242024203 score
def build_relatedness_for_train():
seed = 12
batch_size = 128
dev_batch_size = 128
lazy = True
torch.manual_seed(seed)
contain_first_sentence = True
pn_ratio = 1.0
# keep_neg_sample_prob = 0.4
# sample_prob_decay = 0.05
# model_path = "/home/easonnie/projects/FunEver/saved_models/08-26-15:13:35_simple_nn_doc/i(7000)_epoch(1)_(tra_score:0.9164416441644164|pr:0.4283778377837277|rec:0.8746624662466247|f1:0.575095052581864)"
model_path = "/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/i(9000)_epoch(1)_(tra_score:0.9212421242124212|pr:0.4299679967996279|rec:0.8818631863186318|f1:0.5780819247968391)"
dev_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/dev.jsonl"
# train
# train_upstream_file = config.RESULT_PATH / "doc_retri_bls/docretri.basic.nopageview/train.jsonl"
# dev_data_list = common.load_jsonl(dev_upstream_file)
train_upstream_file = dev_upstream_file
train_data_list = common.load_jsonl(train_upstream_file)
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=180)
cursor = fever_db.get_cursor()
# complete_upstream_dev_data = disamb.sample_disamb_inference(common.load_jsonl(dev_upstream_file), cursor,
# contain_first_sentence=contain_first_sentence)
#
complete_upstream_train_data = disamb.sample_disamb_training_v0(
train_data_list,
cursor, pn_ratio, contain_first_sentence,
only_found=False)
# complete_upstream_train_data = complete_upstream_dev_data
print("Train size:", len(complete_upstream_train_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_train_data)
# Load Vocabulary
biterator = BasicIterator(batch_size=batch_size)
dev_biterator = BasicIterator(batch_size=dev_batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
biterator.index_with(vocab)
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=160, num_of_class=2)
model.load_state_dict(torch.load(model_path))
model.display()
model.to(device)
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_train_data)
common.save_jsonl(complete_upstream_dev_data,
"/home/easonnie/projects/FunEver/saved_models/08-26-15:46:10_simple_nn_doc_first_sent/extra_needed_training_data/dev_doc_list.jsonl")
# disamb.enforce_disabuigation_into_retrieval_result_v1(complete_upstream_dev_data,
# dev_data_list)
# oracle_score, pr, rec, f1 = c_scorer.fever_doc_only(dev_data_list, dev_data_list, max_evidence=5)
#
# print(f"Dev(raw_acc/pr/rec/f1):{oracle_score}/{pr}/{rec}/{f1}")
# print("Strict score:", oracle_score)
# print(f"Eval Tracking score:", f"{oracle_score}")
def debug_fever():
num_epoch = 8
seed = 12
batch_size = 128
experiment_name = "simple_nn"
lazy = True
torch.manual_seed(seed)
keep_neg_sample_prob = 0.6
sample_prob_decay = 0.1
dev_upstream_file = config.RESULT_PATH / "doc_retri/cn_util_Jul17_docretri.singularize/dev.jsonl"
train_upstream_file = config.RESULT_PATH / "doc_retri/cn_util_Jul17_docretri.singularize/train.jsonl"
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
train_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=300)
# dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=False)
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy, max_l=300)
complete_upstream_dev_data = get_full_list(config.T_FEVER_DEV_JSONL, dev_upstream_file, pred=True)
print("Dev size:", len(complete_upstream_dev_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_dev_data)
# Load Vocabulary
biterator = BasicIterator(batch_size=batch_size)
dev_biterator = BasicIterator(batch_size=batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
biterator.index_with(vocab)
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=280, num_of_class=2)
model.display()
model.to(device)
# Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name), 'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# Save source code end.
best_dev = -1
iteration = 0
i_epoch = 0
start_lr = 0.0002
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=start_lr)
criterion = nn.CrossEntropyLoss()
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_dev_data)
dev_results_list = score_converter_v0(config.T_FEVER_DEV_JSONL, complete_upstream_dev_data)
eval_mode = {'check_sent_id_correct': True, 'standard': True}
strict_score, acc_score, pr, rec, f1 = c_scorer.fever_score(dev_results_list, config.T_FEVER_DEV_JSONL,
mode=eval_mode, verbose=False)
total = len(dev_results_list)
hit = eval_mode['check_sent_id_correct_hits']
tracking_score = hit / total
print(f"Dev(raw_acc/pr/rec/f1):{acc_score}/{pr}/{rec}/{f1}/")
print("Strict score:", strict_score)
print(f"Eval Tracking score:", f"{tracking_score}")
need_save = False
if tracking_score > best_dev:
best_dev = tracking_score
need_save = True
if need_save:
save_path = os.path.join(
file_path_prefix,
f'i({iteration})_epoch({i_epoch})_'
f'(tra_score:{tracking_score}|raw_acc:{acc_score}|pr:{pr}|rec:{rec}|f1:{f1})'
)
torch.save(model.state_dict(), save_path)
print("Epoch Evaluation...")
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
complete_upstream_dev_data = hidden_eval(model, eval_iter, complete_upstream_dev_data)
dev_results_list = score_converter_v0(config.T_FEVER_DEV_JSONL, complete_upstream_dev_data)
eval_mode = {'check_sent_id_correct': True, 'standard': True}
strict_score, acc_score, pr, rec, f1 = c_scorer.fever_score(dev_results_list, config.T_FEVER_DEV_JSONL,
mode=eval_mode, verbose=False)
total = len(dev_results_list)
hit = eval_mode['check_sent_id_correct_hits']
tracking_score = hit / total
print(f"Dev(raw_acc/pr/rec/f1):{acc_score}/{pr}/{rec}/{f1}/")
print("Strict score:", strict_score)
print(f"Eval Tracking score:", f"{tracking_score}")
if tracking_score > best_dev:
best_dev = tracking_score
save_path = os.path.join(
file_path_prefix,
f'i({iteration})_epoch({i_epoch})_'
f'(tra_score:{tracking_score}|raw_acc:{acc_score}|pr:{pr}|rec:{rec}|f1:{f1})_epoch'
)
torch.save(model.state_dict(), save_path)
def score_converter_v0(org_data_file, full_sent_list):
"""
:param org_data_file:
:param full_sent_list: append full_sent_score list to evidence of original data file
:return:
"""
d_list = common.load_jsonl(org_data_file)
augmented_dict = dict()
print("Build selected sentences file:", len(full_sent_list))
for sent_item in tqdm(full_sent_list):
selection_id = sent_item['selection_id'] # The id for the current one selection.
org_id = int(selection_id.split('<##>')[0])
if org_id in augmented_dict:
augmented_dict[org_id].append(sent_item)
else:
augmented_dict[org_id] = [sent_item]
for item in d_list:
if int(item['id']) not in augmented_dict:
cur_predicted_sentids = []
else:
cur_predicted_sentids = [] # formating doc_id + c_score.SENTLINT + line_number
sents = augmented_dict[int(item['id'])]
# Modify some mechaism here to selection sentence whether by some score or label
for sent_i in sents:
if sent_i['prob'] >= 0.5:
cur_predicted_sentids.append((sent_i['sid'], sent_i['score']))
# del sent_i['prob']
cur_predicted_sentids = sorted(cur_predicted_sentids, key=lambda x: -x[1])
item['scored_sentids'] = cur_predicted_sentids
item['predicted_sentids'] = [sid for sid, _ in item['scored_sentids']][:5]
item['predicted_evidence'] = convert_evidence2scoring_format(item['predicted_sentids'])
item['predicted_label'] = item['label'] # give ground truth label
# Removing all score and prob
for sent_item in full_sent_list:
if 'score' in sent_item.keys():
del sent_item['score']
del sent_item['prob']
return d_list
def pipeline_first_sent_selection(org_t_file, upstream_in_file, model_save_path):
batch_size = 128
lazy = True
SAVE_PATH = model_save_path
print("Model From:", SAVE_PATH)
dev_upstream_file = upstream_in_file
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy)
complete_upstream_dev_data = get_full_list(org_t_file, dev_upstream_file, pred=True)
print("Dev size:", len(complete_upstream_dev_data))
dev_instances = dev_fever_data_reader.read(complete_upstream_dev_data)
# Load Vocabulary
dev_biterator = BasicIterator(batch_size=batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=300, num_of_class=2)
model.load_state_dict(torch.load(SAVE_PATH))
model.display()
model.to(device)
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
dev_sent_full_list = hidden_eval(model, eval_iter, complete_upstream_dev_data)
return dev_sent_full_list
def get_score_multihop(t_data_file, additional_file, model_path, item_key='prioritized_docids_aside', top_k=6):
batch_size = 64
lazy = True
SAVE_PATH = model_path
print("Model From:", SAVE_PATH)
additional_sentence_list = get_additional_list(t_data_file, additional_file, item_key=item_key, top_k=top_k)
# Prepare Data
token_indexers = {
'tokens': SingleIdTokenIndexer(namespace='tokens'), # This is the raw tokens
'elmo_chars': ELMoTokenCharactersIndexer(namespace='elmo_characters') # This is the elmo_characters
}
dev_fever_data_reader = SSelectorReader(token_indexers=token_indexers, lazy=lazy)
print("Additional Dev size:", len(additional_sentence_list))
dev_instances = dev_fever_data_reader.read(additional_sentence_list)
# Load Vocabulary
dev_biterator = BasicIterator(batch_size=batch_size)
vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT / "vocab_cache" / "nli_basic")
# THis is important
vocab.add_token_to_namespace("true", namespace="selection_labels")
vocab.add_token_to_namespace("false", namespace="selection_labels")
vocab.add_token_to_namespace("hidden", namespace="selection_labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='selection_labels')
# Label value
vocab.get_index_to_token_vocabulary('selection_labels')
print(vocab.get_token_to_index_vocabulary('selection_labels'))
print(vocab.get_vocab_size('tokens'))
dev_biterator.index_with(vocab)
# exit(0)
# Build Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", index=0)
device_num = -1 if device.type == 'cpu' else 0
model = Model(weight=weight_dict['glove.840B.300d'],
vocab_size=vocab.get_vocab_size('tokens'),
embedding_dim=300, max_l=300, num_of_class=2)
model.load_state_dict(torch.load(SAVE_PATH))
model.display()
model.to(device)
eval_iter = dev_biterator(dev_instances, shuffle=False, num_epochs=1, cuda_device=device_num)
additional_sentence_list = hidden_eval(model, eval_iter, additional_sentence_list)
return additional_sentence_list
if __name__ == "__main__":
# train_fever_v1()
# doc_model_eval()
# utest_results()
utest_results_debug()
# build_relatedness_for_train()
|
[
"torch.nn.Dropout",
"data_util.data_readers.fever_sselection_reader.SSelectorReader",
"flint.torch_util.auto_rnn",
"random.shuffle",
"torch.cat",
"allennlp.modules.Elmo",
"sentence_retrieval.sampler_for_nmodel.get_additional_list",
"utils.common.save_jsonl",
"utils.common.load_jsonl",
"torch.no_grad",
"os.path.join",
"nn_doc_retrieval.disabuigation_training.enforce_disabuigation_into_retrieval_result_v0",
"flint.torch_util.get_length_and_mask",
"flint.torch_util.length_truncate",
"torch.load",
"log_util.save_tool.gen_file_prefix",
"allennlp.data.token_indexers.SingleIdTokenIndexer",
"nn_doc_retrieval.disabuigation_training.enforce_disabuigation_into_retrieval_result_v2",
"torch.nn.Linear",
"sentence_retrieval.sampler_for_nmodel.get_full_list",
"torch.nn.LSTM",
"tqdm.tqdm",
"data_util.exvocab.load_vocab_embeddings",
"os.path.basename",
"nn_doc_retrieval.disabuigation_training.sample_disamb_training_v0",
"torch.manual_seed",
"torch.nn.Tanh",
"neural_modules.biDafAttn",
"torch.cuda.is_available",
"torch.max",
"nn_doc_retrieval.disabuigation_training.item_resorting",
"torch.nn.ReLU",
"nn_doc_retrieval.disabuigation_training.enforce_disabuigation_into_retrieval_result_v1",
"allennlp.modules.Embedding",
"torch.nn.CrossEntropyLoss",
"flint.torch_util.max_along_time",
"torch.equal",
"sentence_retrieval.sampler_for_nmodel.post_filter",
"sample_for_nli.tf_idf_sample_v1_0.convert_evidence2scoring_format",
"torch.nn.functional.softmax",
"utils.c_scorer.fever_score",
"allennlp.data.token_indexers.ELMoTokenCharactersIndexer",
"allennlp.data.iterators.BasicIterator",
"utils.c_scorer.fever_doc_only",
"torch.abs",
"utils.fever_db.get_cursor"
] |
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'ELMoTokenCharactersIndexer', ([], {'namespace': '"""elmo_characters"""'}), "(namespace='elmo_characters')\n", (40532, 40561), False, 'from allennlp.data.token_indexers import SingleIdTokenIndexer, ELMoTokenCharactersIndexer\n'), ((43886, 44036), 'os.path.join', 'os.path.join', (['file_path_prefix', 'f"""i({iteration})_epoch({i_epoch})_(tra_score:{tracking_score}|raw_acc:{acc_score}|pr:{pr}|rec:{rec}|f1:{f1})"""'], {}), "(file_path_prefix,\n f'i({iteration})_epoch({i_epoch})_(tra_score:{tracking_score}|raw_acc:{acc_score}|pr:{pr}|rec:{rec}|f1:{f1})'\n )\n", (43898, 44036), False, 'import os\n'), ((46832, 46890), 'sample_for_nli.tf_idf_sample_v1_0.convert_evidence2scoring_format', 'convert_evidence2scoring_format', (["item['predicted_sentids']"], {}), "(item['predicted_sentids'])\n", (46863, 46890), False, 'from sample_for_nli.tf_idf_sample_v1_0 import sample_v1_0, select_sent_for_eval, convert_evidence2scoring_format\n'), ((47458, 47498), 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'torch.cuda.is_available', ([], {}), '()\n', (48773, 48775), False, 'import torch\n'), ((50934, 50959), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (50957, 50959), False, 'import torch\n'), ((2007, 2016), 'torch.nn.Tanh', 'nn.Tanh', ([], {}), '()\n', (2014, 2016), False, 'from torch import nn\n'), ((4191, 4233), 'torch.abs', 'torch.abs', (['(s1_lay2_maxout - s2_lay2_maxout)'], {}), '(s1_lay2_maxout - s2_lay2_maxout)\n', (4200, 4233), False, 'import torch\n'), ((15965, 16065), 'utils.c_scorer.fever_score', 'c_scorer.fever_score', (['dev_results_list', 'config.T_FEVER_DEV_JSONL'], {'mode': 'eval_mode', 'verbose': '(False)'}), '(dev_results_list, config.T_FEVER_DEV_JSONL, mode=\n eval_mode, verbose=False)\n', (15985, 16065), False, 'from utils import c_scorer, common\n'), ((21752, 21853), 'nn_doc_retrieval.disabuigation_training.enforce_disabuigation_into_retrieval_result_v0', 'disamb.enforce_disabuigation_into_retrieval_result_v0', 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|
# -*- coding: utf-8 -*-
# author:liuxiaodong
# contact: <EMAIL>
# datetime:2021/7/16 10:55
# software: PyCharm
# python versions: Python3.7
# file: yolov5s
# license: (C)Copyright 2019-2021 liuxiaodong
import os
import matplotlib.pyplot as plt
from osgeo import ogr
def plot_point(point,symbol='ko',**kwargs):
x,y=point.GetX(),point.GetY()
plt.plot(x,y,symbol,**kwargs)
def plot_line(line,symbol='g-',**kwargs):
x,y=zip(*line.GetPoints())
plt.plot(x,y,symbol,**kwargs)
def plot_polygon(poly,symbol='r-',**kwargs):
for i in range(poly.GetGeometryCount()):
subgeom=poly.GetGeometryRef(i)
x,y=zip(*subgeom.GetPoints())
plt.plot(x,y,symbol,**kwargs)
def plot_layer(filename,symbol,layer_index=0,**kwargs):
ds=ogr.Open(filename)
for row in ds.GetLayer(layer_index):
geom=row.geometry()
geom_type=geom.GetGeometryType()
if geom_type==ogr.wkbPoint:
plot_point(geom,symbol,**kwargs)
elif geom_type==ogr.wkbMultiPoint:
for i in range(geom.GetGeometryCount()):
subgeom=geom.GetGeometryRef(i)
plot_point(subgeom,symbol,**kwargs)
elif geom_type==ogr.wkbLineString:
plot_line(geom,symbol,**kwargs)
elif geom_type==ogr.wkbMultiLineString:
for i in range(geom.GetGeometryCount()):
subgeom=geom.GetGeometryRef(i)
plot_line(subgeom,symbol,**kwargs)
elif geom_type == ogr.wkbPolygon:
plot_polygon(geom,symbol,**kwargs)
elif geom_type==ogr.wkbMultiPolygon:
for i in range(geom.GetGeometryCount()):
subgeom=geom.GetGeometryRef(i)
plot_polygon(subgeom,symbol,**kwargs)
os.chdir(r'D:\WorkSpace\Aircarft_oiltank')
#ไธ้ขไธไธช่ฐๅจไธ่พนๅฐฑๅ
ๆพ็คบ่ฐ๏ผๆๅฐฑๆ็
ง็น๏ผ็บฟ๏ผ้ขๆฅไบ
plot_layer('out_beijing.shp','ko',markersize=5)
# plot_layer('็็.shp','r-')
# plot_layer('ไธญๅฝๅฐๅพ_ๆๅฝฑ.shp','g-',markersize=20)
plt.axis('equal')
plt.gca().get_xaxis().set_ticks([])
plt.gca().get_yaxis().set_ticks([])
plt.show()
|
[
"matplotlib.pyplot.show",
"osgeo.ogr.Open",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.gca",
"os.chdir"
] |
[((1741, 1784), 'os.chdir', 'os.chdir', (['"""D:\\\\WorkSpace\\\\Aircarft_oiltank"""'], {}), "('D:\\\\WorkSpace\\\\Aircarft_oiltank')\n", (1749, 1784), False, 'import os\n'), ((1934, 1951), 'matplotlib.pyplot.axis', 'plt.axis', (['"""equal"""'], {}), "('equal')\n", (1942, 1951), True, 'import matplotlib.pyplot as plt\n'), ((2024, 2034), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (2032, 2034), True, 'import matplotlib.pyplot as plt\n'), ((350, 382), 'matplotlib.pyplot.plot', 'plt.plot', (['x', 'y', 'symbol'], {}), '(x, y, symbol, **kwargs)\n', (358, 382), True, 'import matplotlib.pyplot as plt\n'), ((458, 490), 'matplotlib.pyplot.plot', 'plt.plot', (['x', 'y', 'symbol'], {}), '(x, y, symbol, **kwargs)\n', (466, 490), True, 'import matplotlib.pyplot as plt\n'), ((758, 776), 'osgeo.ogr.Open', 'ogr.Open', (['filename'], {}), '(filename)\n', (766, 776), False, 'from osgeo import ogr\n'), ((664, 696), 'matplotlib.pyplot.plot', 'plt.plot', (['x', 'y', 'symbol'], {}), '(x, y, symbol, **kwargs)\n', (672, 696), True, 'import matplotlib.pyplot as plt\n'), ((1952, 1961), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (1959, 1961), True, 'import matplotlib.pyplot as plt\n'), ((1988, 1997), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (1995, 1997), True, 'import matplotlib.pyplot as plt\n')]
|
from django.contrib import admin
from django_loci.admin import ObjectLocationInline
from openwisp_utils.admin import TimeReadonlyAdminMixin
from .models import Device
class DeviceAdmin(TimeReadonlyAdminMixin, admin.ModelAdmin):
list_display = ('name', 'created', 'modified')
save_on_top = True
inlines = [ObjectLocationInline]
admin.site.register(Device, DeviceAdmin)
|
[
"django.contrib.admin.site.register"
] |
[((345, 385), 'django.contrib.admin.site.register', 'admin.site.register', (['Device', 'DeviceAdmin'], {}), '(Device, DeviceAdmin)\n', (364, 385), False, 'from django.contrib import admin\n')]
|
import argparse
from pytube import Playlist
# Script downloads the videos in the place that the script runs
# Parse arguments for the playlist
parser = argparse.ArgumentParser()
parser.add_argument("url",type=str)
args = parser.parse_args()
URL = args.url
# Defines playlist as python object
playlist = Playlist(URL)
# Show how many URL's downloading
print("Collected this many videos to download")
print(len(playlist.video_urls))
# physically downloading the audio track
print("Starting to download...")
for video in playlist.videos:
videoStream = video.streams.get_by_itag('22')
videoStream.download()
print("Downloading complete!")
|
[
"argparse.ArgumentParser",
"pytube.Playlist"
] |
[((154, 179), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (177, 179), False, 'import argparse\n'), ((307, 320), 'pytube.Playlist', 'Playlist', (['URL'], {}), '(URL)\n', (315, 320), False, 'from pytube import Playlist\n')]
|
import json
import os
import jsonschema
from jsonschema import ValidationError
from traitlets.config import LoggingConfigurable
root = os.path.dirname(__file__)
class SchemaMismatchError(Exception):
def __init__(self, message, actual_version, expected_version):
super(SchemaMismatchError, self).__init__(message)
self.actual_version = actual_version
self.expected_version = expected_version
class SchemaTooOldError(SchemaMismatchError):
pass
class SchemaTooNewError(SchemaMismatchError):
pass
class BaseMetadataValidator(LoggingConfigurable):
def __init__(self):
with open(os.path.join(root, "v{:d}.json".format(self.schema_version)), "r") as fh:
self.schema = json.loads(fh.read())
def _remove_extra_keys(self, cell):
meta = cell.metadata['nbgrader']
allowed = set(self.schema["properties"].keys())
keys = set(meta.keys()) - allowed
if len(keys) > 0:
self.log.warning("extra keys detected in metadata, these will be removed: {}".format(keys))
for key in keys:
del meta[key]
def upgrade_notebook_metadata(self, nb):
for cell in nb.cells:
self.upgrade_cell_metadata(cell)
return nb
def upgrade_cell_metadata(self, cell): # pragma: no cover
raise NotImplementedError("this method must be implemented by subclasses")
def validate_cell(self, cell):
if 'nbgrader' not in cell.metadata:
return
schema = cell.metadata['nbgrader'].get('schema_version', 0)
if schema < self.schema_version:
raise SchemaTooOldError(
"Outdated schema version: {} (expected {})".format(schema, self.schema_version),
schema, self.schema_version)
elif schema > self.schema_version:
raise SchemaTooNewError(
"Schema version is too new: {} (expected {})".format(schema, self.schema_version),
schema, self.schema_version)
jsonschema.validate(cell.metadata['nbgrader'], self.schema)
def validate_nb(self, nb):
for cell in nb.cells:
self.validate_cell(cell)
|
[
"jsonschema.validate",
"os.path.dirname"
] |
[((138, 163), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (153, 163), False, 'import os\n'), ((2030, 2089), 'jsonschema.validate', 'jsonschema.validate', (["cell.metadata['nbgrader']", 'self.schema'], {}), "(cell.metadata['nbgrader'], self.schema)\n", (2049, 2089), False, 'import jsonschema\n')]
|
# Generated by Django 1.11.14 on 2019-03-28 12:39
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('tourism', '0004_auto_20190322_1908'),
]
operations = [
migrations.AlterField(
model_name='touristiccontent',
name='type1',
field=models.ManyToManyField(blank=True, db_table='t_r_contenu_touristique_type1', related_name='contents1', to='tourism.TouristicContentType1', verbose_name='Type 1'),
),
migrations.AlterField(
model_name='touristiccontent',
name='type2',
field=models.ManyToManyField(blank=True, db_table='t_r_contenu_touristique_type2', related_name='contents2', to='tourism.TouristicContentType2', verbose_name='Type 2'),
),
]
|
[
"django.db.models.ManyToManyField"
] |
[((347, 516), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'blank': '(True)', 'db_table': '"""t_r_contenu_touristique_type1"""', 'related_name': '"""contents1"""', 'to': '"""tourism.TouristicContentType1"""', 'verbose_name': '"""Type 1"""'}), "(blank=True, db_table='t_r_contenu_touristique_type1',\n related_name='contents1', to='tourism.TouristicContentType1',\n verbose_name='Type 1')\n", (369, 516), False, 'from django.db import migrations, models\n'), ((639, 808), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'blank': '(True)', 'db_table': '"""t_r_contenu_touristique_type2"""', 'related_name': '"""contents2"""', 'to': '"""tourism.TouristicContentType2"""', 'verbose_name': '"""Type 2"""'}), "(blank=True, db_table='t_r_contenu_touristique_type2',\n related_name='contents2', to='tourism.TouristicContentType2',\n verbose_name='Type 2')\n", (661, 808), False, 'from django.db import migrations, models\n')]
|
import sys
from setuptools import find_packages, setup
from setuptools.command.test import test as TestCommand
PACKAGE_TYPE = 'pg-tools'
PACKAGE_NAME = 'pg-export'
PACKAGE_DESC = 'pg to git converter'
PACKAGE_LONG_DESC = 'Convert postgres database to directory with object files'
PACKAGE_VERSION = '2.1.2'
class PyTest(TestCommand):
user_options = [('pytest-args=', 'a', "Arguments to pass to pytest")]
def initialize_options(self):
super().initialize_options()
# default list of options for testing
# https://docs.pytest.org/en/latest/logging.html
self.pytest_args = (
'--flake8 {0} tests examples '
'--junitxml=.reports/{0}_junit.xml '
'--cov={0} --cov=tests '
'-p no:logging'.format(PACKAGE_NAME.replace('-', '_'))
)
def run_tests(self):
import shlex
# import here, cause outside the eggs aren't loaded
import pytest
errno = pytest.main(shlex.split(self.pytest_args))
sys.exit(errno)
# ะงัะพ ะฝัะถะฝะพ ะดะปั ะทะฐะฟััะบะฐ python setup.py <any_cmd>
# ะัะฟะพะปัะทัะตะผ ัััะฝะพะน ะทะฐะฟััะบ ั ะฟะพะผะพััั ะบะปะฐััะฐ PyTest
setup_requires = []
# ะงัะพ ะฝัะถะฝะพ ะดะปั ัััะฐะฝะพะฒะบะธ
install_requires = [
# ะะพะบะฐะปัะฝัะน pypi
]
# ะงัะพ ะฝัะถะฝะพ ะดะปั ะทะฐะฟััะบะฐ python setup.py test
tests_require = [
'pytest',
'pytest-cov',
'pytest-flake8',
'pytest-asyncio',
'pytest-sugar',
'asynctest',
'importlib-metadata<4.3'
]
# ะกะบัะธะฟัั
console_scripts = [
'pg_export=pg_export.main:main'
]
setup(
name=PACKAGE_NAME,
version=PACKAGE_VERSION,
description=PACKAGE_DESC,
long_description=PACKAGE_LONG_DESC,
url='https://git.dev.uiscom.ru/{}/{}'.format(PACKAGE_TYPE, PACKAGE_NAME),
author="<NAME>",
author_email="<EMAIL>",
license="Nodefined",
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Framework :: Pytest',
'Intended Audience :: Customer Service',
'Intended Audience :: Information Technology',
'License :: Other/Proprietary License',
'License :: UIS License',
'Natural Language :: Russian',
'Natural Language :: English',
'Operating System :: POSIX',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
],
zip_safe=False,
packages=find_packages(exclude=['tests', 'examples', '.reports']),
entry_points={'console_scripts': console_scripts},
python_requires='>=3.5',
setup_requires=setup_requires,
install_requires=install_requires,
tests_require=tests_require,
cmdclass={'test': PyTest},
include_package_data=True
)
|
[
"shlex.split",
"setuptools.find_packages",
"sys.exit"
] |
[((1018, 1033), 'sys.exit', 'sys.exit', (['errno'], {}), '(errno)\n', (1026, 1033), False, 'import sys\n'), ((2416, 2472), 'setuptools.find_packages', 'find_packages', ([], {'exclude': "['tests', 'examples', '.reports']"}), "(exclude=['tests', 'examples', '.reports'])\n", (2429, 2472), False, 'from setuptools import find_packages, setup\n'), ((979, 1008), 'shlex.split', 'shlex.split', (['self.pytest_args'], {}), '(self.pytest_args)\n', (990, 1008), False, 'import shlex\n')]
|
from machin.utils.media import (
show_image, create_video, create_video_subproc,
create_image, create_image_subproc
)
from os.path import join
import os
import pytest
import numpy as np
@pytest.fixture(scope="function")
def images():
images = [
np.random.randint(0, 255, size=[128, 128], dtype=np.uint8)
for _ in range(120)
]
return images
@pytest.fixture(scope="function")
def images_f():
images = [
np.random.rand(128, 128)
for _ in range(120)
]
return images
def test_show_image(images):
show_image(images[0], show_normalized=True)
show_image(images[0], show_normalized=False)
def test_create_video(images, tmpdir):
tmp_dir = str(tmpdir.make_numbered_dir())
create_video(images, tmp_dir, "vid", extension=".gif")
assert os.path.exists(join(tmp_dir, "vid.gif"))
create_video(images, tmp_dir, "vid", extension=".mp4")
assert os.path.exists(join(tmp_dir, "vid.mp4"))
def test_create_video_float(images_f, tmpdir):
tmp_dir = str(tmpdir.make_numbered_dir())
create_video(images_f, tmp_dir, "vid", extension=".gif")
assert os.path.exists(join(tmp_dir, "vid.gif"))
create_video(images_f, tmp_dir, "vid", extension=".mp4")
assert os.path.exists(join(tmp_dir, "vid.mp4"))
def test_create_video_subproc(images, tmpdir):
tmp_dir = str(tmpdir.make_numbered_dir())
create_video_subproc([], tmp_dir, "empty", extension=".gif")()
create_video_subproc(images, tmp_dir, "vid", extension=".gif")()
assert os.path.exists(join(tmp_dir, "vid.gif"))
def test_create_image(images, tmpdir):
tmp_dir = str(tmpdir.make_numbered_dir())
create_image(images[0], tmp_dir, "img", extension=".png")
assert os.path.exists(join(tmp_dir, "img.png"))
def test_create_image_float(images_f, tmpdir):
tmp_dir = str(tmpdir.make_numbered_dir())
create_image(images_f[0], tmp_dir, "img", extension=".png")
assert os.path.exists(join(tmp_dir, "img.png"))
def test_create_image_subproc(images, tmpdir):
tmp_dir = str(tmpdir.make_numbered_dir())
create_image_subproc(images[0], tmp_dir, "img", extension=".png")()
assert os.path.exists(join(tmp_dir, "img.png"))
|
[
"machin.utils.media.create_video_subproc",
"machin.utils.media.create_image",
"machin.utils.media.create_image_subproc",
"pytest.fixture",
"numpy.random.randint",
"numpy.random.rand",
"machin.utils.media.create_video",
"os.path.join",
"machin.utils.media.show_image"
] |
[((197, 229), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""function"""'}), "(scope='function')\n", (211, 229), False, 'import pytest\n'), ((381, 413), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""function"""'}), "(scope='function')\n", (395, 413), False, 'import pytest\n'), ((565, 608), 'machin.utils.media.show_image', 'show_image', (['images[0]'], {'show_normalized': '(True)'}), '(images[0], show_normalized=True)\n', (575, 608), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((613, 657), 'machin.utils.media.show_image', 'show_image', (['images[0]'], {'show_normalized': '(False)'}), '(images[0], show_normalized=False)\n', (623, 657), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((749, 803), 'machin.utils.media.create_video', 'create_video', (['images', 'tmp_dir', '"""vid"""'], {'extension': '""".gif"""'}), 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extension='.mp4')\n", (1191, 1235), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((1662, 1719), 'machin.utils.media.create_image', 'create_image', (['images[0]', 'tmp_dir', '"""img"""'], {'extension': '""".png"""'}), "(images[0], tmp_dir, 'img', extension='.png')\n", (1674, 1719), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((1871, 1930), 'machin.utils.media.create_image', 'create_image', (['images_f[0]', 'tmp_dir', '"""img"""'], {'extension': '""".png"""'}), "(images_f[0], tmp_dir, 'img', extension='.png')\n", (1883, 1930), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((267, 325), 'numpy.random.randint', 'np.random.randint', (['(0)', '(255)'], {'size': '[128, 128]', 'dtype': 'np.uint8'}), '(0, 255, size=[128, 128], dtype=np.uint8)\n', (284, 325), True, 'import numpy as np\n'), ((453, 477), 'numpy.random.rand', 'np.random.rand', (['(128)', '(128)'], {}), '(128, 128)\n', (467, 477), True, 'import numpy as np\n'), ((830, 854), 'os.path.join', 'join', (['tmp_dir', '"""vid.gif"""'], {}), "(tmp_dir, 'vid.gif')\n", (834, 854), False, 'from os.path import join\n'), ((941, 965), 'os.path.join', 'join', (['tmp_dir', '"""vid.mp4"""'], {}), "(tmp_dir, 'vid.mp4')\n", (945, 965), False, 'from os.path import join\n'), ((1149, 1173), 'os.path.join', 'join', (['tmp_dir', '"""vid.gif"""'], {}), "(tmp_dir, 'vid.gif')\n", (1153, 1173), False, 'from os.path import join\n'), ((1262, 1286), 'os.path.join', 'join', (['tmp_dir', '"""vid.mp4"""'], {}), "(tmp_dir, 'vid.mp4')\n", (1266, 1286), False, 'from os.path import join\n'), ((1387, 1447), 'machin.utils.media.create_video_subproc', 'create_video_subproc', (['[]', 'tmp_dir', '"""empty"""'], {'extension': '""".gif"""'}), "([], tmp_dir, 'empty', extension='.gif')\n", (1407, 1447), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((1454, 1516), 'machin.utils.media.create_video_subproc', 'create_video_subproc', (['images', 'tmp_dir', '"""vid"""'], {'extension': '""".gif"""'}), "(images, tmp_dir, 'vid', extension='.gif')\n", (1474, 1516), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((1545, 1569), 'os.path.join', 'join', (['tmp_dir', '"""vid.gif"""'], {}), "(tmp_dir, 'vid.gif')\n", (1549, 1569), False, 'from os.path import join\n'), ((1746, 1770), 'os.path.join', 'join', (['tmp_dir', '"""img.png"""'], {}), "(tmp_dir, 'img.png')\n", (1750, 1770), False, 'from os.path import join\n'), ((1957, 1981), 'os.path.join', 'join', (['tmp_dir', '"""img.png"""'], {}), "(tmp_dir, 'img.png')\n", (1961, 1981), False, 'from os.path import join\n'), ((2082, 2147), 'machin.utils.media.create_image_subproc', 'create_image_subproc', (['images[0]', 'tmp_dir', '"""img"""'], {'extension': '""".png"""'}), "(images[0], tmp_dir, 'img', extension='.png')\n", (2102, 2147), False, 'from machin.utils.media import show_image, create_video, create_video_subproc, create_image, create_image_subproc\n'), ((2176, 2200), 'os.path.join', 'join', (['tmp_dir', '"""img.png"""'], {}), "(tmp_dir, 'img.png')\n", (2180, 2200), False, 'from os.path import join\n')]
|
#
# Copyright (c) 2021 <NAME> <<EMAIL>>
#
# SPDX-License-Identifier: Apache-2.0
#
import time
import pytest
from testlib import *
pd_cap = PDCapabilities([
(Capability.OutputControl, 1, 1),
(Capability.LEDControl, 1, 1),
(Capability.AudiableControl, 1, 1),
(Capability.TextOutput, 1, 1),
])
pd_info_list = [
PDInfo(101, scbk=KeyStore.gen_key(), flags=[ LibFlag.EnforceSecure ], name='chn-0'),
PDInfo(102, name='chn-1')
]
secure_pd_addr = pd_info_list[0].address
secure_pd = PeripheralDevice(pd_info_list[0], pd_cap, log_level=LogLevel.Debug)
insecure_pd_addr = pd_info_list[1].address
insecure_pd = PeripheralDevice(pd_info_list[1], pd_cap, log_level=LogLevel.Debug)
pd_list = [
secure_pd,
insecure_pd,
]
cp = ControlPanel(pd_info_list, log_level=LogLevel.Debug)
@pytest.fixture(scope='module', autouse=True)
def setup_test():
for pd in pd_list:
pd.start()
cp.start()
cp.online_wait_all()
yield
teardown_test()
def teardown_test():
cp.teardown()
for pd in pd_list:
pd.teardown()
def test_command_output():
test_cmd = {
'command': Command.Output,
'output_no': 0,
'control_code': 1,
'timer_count': 10
}
assert cp.is_online(secure_pd_addr)
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
def test_command_buzzer():
test_cmd = {
'command': Command.Buzzer,
'reader': 0,
'control_code': 1,
'on_count': 10,
'off_count': 10,
'rep_count': 10
}
assert cp.is_online(secure_pd_addr)
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
def test_command_text():
test_cmd = {
'command': Command.Text,
'reader': 0,
'control_code': 1,
'temp_time': 20,
'offset_row': 1,
'offset_col': 1,
'data': 'PYOSDP'
}
assert cp.is_online(secure_pd_addr)
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
def test_command_led():
test_cmd = {
'command': Command.LED,
'reader': 1,
'led_number': 0,
'control_code': 1,
'on_count': 10,
'off_count': 10,
'on_color': CommandLEDColor.Red,
'off_color': CommandLEDColor.Black,
'timer_count': 10,
'temporary': True
}
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
def test_command_comset():
test_cmd = {
'command': Command.Comset,
'address': secure_pd_addr,
'baud_rate': 9600
}
assert cp.is_online(secure_pd_addr)
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
def test_command_mfg():
test_cmd = {
'command': Command.Manufacturer,
'vendor_code': 0x00030201,
'mfg_command': 13,
'data': bytes([9,1,9,2,6,3,1,7,7,0])
}
assert cp.is_online(secure_pd_addr)
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
def test_command_keyset():
test_cmd = {
'command': Command.Keyset,
'type': 1,
'data': KeyStore.gen_key()
}
assert cp.is_online(secure_pd_addr)
assert cp.send_command(secure_pd_addr, test_cmd)
assert secure_pd.get_command() == test_cmd
# PD must be online and SC active after a KEYSET command
time.sleep(0.5)
assert cp.is_online(secure_pd_addr)
assert cp.is_sc_active(secure_pd_addr)
# When not in SC, KEYSET command should not be accepted.
assert cp.is_sc_active(insecure_pd_addr) == False
assert cp.send_command(insecure_pd_addr, test_cmd) == False
|
[
"pytest.fixture",
"time.sleep"
] |
[((807, 851), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""', 'autouse': '(True)'}), "(scope='module', autouse=True)\n", (821, 851), False, 'import pytest\n'), ((3496, 3511), 'time.sleep', 'time.sleep', (['(0.5)'], {}), '(0.5)\n', (3506, 3511), False, 'import time\n')]
|
import pylast
import time
from platypush.message.response import Response
from .. import Plugin
class LastfmPlugin(Plugin):
def __init__(self, api_key, api_secret, username, password):
self.api_key = api_key
self.api_secret = api_secret
self.username = username
self.password = password
self.lastfm = pylast.LastFMNetwork(
api_key = self.api_key,
api_secret = self.api_secret,
username = self.username,
password_hash = pylast.md5(self.password))
def scrobble(self, artist, title, album=None, **kwargs):
self.lastfm.scrobble(
artist = artist,
title = title,
album = album,
timestamp = int(time.time()),
)
return Response()
def update_now_playing(self, artist, title, album=None, **kwargs):
self.lastfm.update_now_playing(
artist = artist,
title = title,
album = album,
)
return Response()
# vim:sw=4:ts=4:et:
|
[
"platypush.message.response.Response",
"pylast.md5",
"time.time"
] |
[((786, 796), 'platypush.message.response.Response', 'Response', ([], {}), '()\n', (794, 796), False, 'from platypush.message.response import Response\n'), ((1019, 1029), 'platypush.message.response.Response', 'Response', ([], {}), '()\n', (1027, 1029), False, 'from platypush.message.response import Response\n'), ((515, 540), 'pylast.md5', 'pylast.md5', (['self.password'], {}), '(self.password)\n', (525, 540), False, 'import pylast\n'), ((746, 757), 'time.time', 'time.time', ([], {}), '()\n', (755, 757), False, 'import time\n')]
|
from mm2d.core.robot.description import (
BaseDescription,
LinkDescription,
JointDescription,
RobotDescription,
)
from mm2d.core.spatial import Transform
import mm2d.core.kinematics as kinematics
def three_link_robot():
base = BaseDescription("base", [], dofs=(0, 0, 0))
base.append(
JointDescription("joint1", pose=Transform([0.25, 0])),
LinkDescription("link1", pose=Transform([0.5, 0]), fixtures=[]),
JointDescription("joint2", pose=Transform([0.5, 0])),
LinkDescription("link2", pose=Transform([0.5, 0]), fixtures=[]),
JointDescription("joint3", pose=Transform([0.5, 0])),
LinkDescription("link3", pose=Transform([0.5, 0]), fixtures=[]),
)
return RobotDescription(base)
desc = three_link_robot()
model = kinematics.KinematicModel(desc)
# import IPython
# IPython.embed()
|
[
"mm2d.core.kinematics.KinematicModel",
"mm2d.core.spatial.Transform",
"mm2d.core.robot.description.BaseDescription",
"mm2d.core.robot.description.RobotDescription"
] |
[((792, 823), 'mm2d.core.kinematics.KinematicModel', 'kinematics.KinematicModel', (['desc'], {}), '(desc)\n', (817, 823), True, 'import mm2d.core.kinematics as kinematics\n'), ((249, 292), 'mm2d.core.robot.description.BaseDescription', 'BaseDescription', (['"""base"""', '[]'], {'dofs': '(0, 0, 0)'}), "('base', [], dofs=(0, 0, 0))\n", (264, 292), False, 'from mm2d.core.robot.description import BaseDescription, LinkDescription, JointDescription, RobotDescription\n'), ((733, 755), 'mm2d.core.robot.description.RobotDescription', 'RobotDescription', (['base'], {}), '(base)\n', (749, 755), False, 'from mm2d.core.robot.description import BaseDescription, LinkDescription, JointDescription, RobotDescription\n'), ((350, 370), 'mm2d.core.spatial.Transform', 'Transform', (['[0.25, 0]'], {}), '([0.25, 0])\n', (359, 370), False, 'from mm2d.core.spatial import Transform\n'), ((411, 430), 'mm2d.core.spatial.Transform', 'Transform', (['[0.5, 0]'], {}), '([0.5, 0])\n', (420, 430), False, 'from mm2d.core.spatial import Transform\n'), ((486, 505), 'mm2d.core.spatial.Transform', 'Transform', (['[0.5, 0]'], {}), '([0.5, 0])\n', (495, 505), False, 'from mm2d.core.spatial import Transform\n'), ((546, 565), 'mm2d.core.spatial.Transform', 'Transform', (['[0.5, 0]'], {}), '([0.5, 0])\n', (555, 565), False, 'from mm2d.core.spatial import Transform\n'), ((621, 640), 'mm2d.core.spatial.Transform', 'Transform', (['[0.5, 0]'], {}), '([0.5, 0])\n', (630, 640), False, 'from mm2d.core.spatial import Transform\n'), ((681, 700), 'mm2d.core.spatial.Transform', 'Transform', (['[0.5, 0]'], {}), '([0.5, 0])\n', (690, 700), False, 'from mm2d.core.spatial import Transform\n')]
|
# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2020
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hundreds of individual components, each of which was
# individually copyrighted. Each Cloudera open source product is a
# collective work under U.S. Copyright Law. Your license to use the
# collective work is as provided in your written agreement with
# Cloudera. Used apart from the collective work, this file is
# licensed for your use pursuant to the open source license
# identified above.
#
# This code is provided to you pursuant a written agreement with
# (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
# this code. If you do not have a written agreement with Cloudera nor
# with an authorized and properly licensed third party, you do not
# have any rights to access nor to use this code.
#
# Absent a written agreement with Cloudera, Inc. (โClouderaโ) to the
# contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
# KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
# WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
# IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
# AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
# ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
# OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
# CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
# RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
import datetime
import numpy as np
import pandas as pd
from sts.models.prophet import (
add_season_weekday_indicators,
seasonal_daily_prophet_model
)
from sts.data.loader import load_california_electricity_demand
# Load all available data for training
df = load_california_electricity_demand()
# Take log transform for fully multiplicative model
df['y'] = df.y.apply(np.log)
# Fit best current model
model = seasonal_daily_prophet_model(df)
# Make predictions for one year ahead of most recent training data
future = add_season_weekday_indicators(
model.make_future_dataframe(periods=24*365, freq='H')
)
forecast = model.predict(future)
samples = model.predictive_samples(future)
# Reverse log transform
predictions = np.exp(samples['yhat'])
prediction_df = (
future
.merge(pd.DataFrame(predictions), left_index=True, right_index=True)
.drop(['winter_weekday', 'winter_weekend', 'summer_weekday', 'summer_weekend'],
axis='columns')
.tail(24*365) # keep one year of hourly predictions from most recent training date
)
# Save predictions
prediction_df.to_csv('data/forecast.csv', index=False)
|
[
"pandas.DataFrame",
"sts.data.loader.load_california_electricity_demand",
"numpy.exp",
"sts.models.prophet.seasonal_daily_prophet_model"
] |
[((2318, 2354), 'sts.data.loader.load_california_electricity_demand', 'load_california_electricity_demand', ([], {}), '()\n', (2352, 2354), False, 'from sts.data.loader import load_california_electricity_demand\n'), ((2473, 2505), 'sts.models.prophet.seasonal_daily_prophet_model', 'seasonal_daily_prophet_model', (['df'], {}), '(df)\n', (2501, 2505), False, 'from sts.models.prophet import add_season_weekday_indicators, seasonal_daily_prophet_model\n'), ((2793, 2816), 'numpy.exp', 'np.exp', (["samples['yhat']"], {}), "(samples['yhat'])\n", (2799, 2816), True, 'import numpy as np\n'), ((2858, 2883), 'pandas.DataFrame', 'pd.DataFrame', (['predictions'], {}), '(predictions)\n', (2870, 2883), True, 'import pandas as pd\n')]
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Mรณdulo con el modelo del editor de texto.
Autor: <NAME> <<EMAIL>>.
"""
from __future__ import print_function
from text_editor_view import TextEditorDialogs
import codecs
import sys
import os
class TextEditorModel():
"""
Clase TextEditorModel: Modelo del editor de texto.
Atributos:
opened_file_path: String con la ruta al fichero abierto.
opened_file_data: String con el contenido del fichero abierto.
opened_folder_path: String con la ruta a la carpeta abierta.
opened_folder_files: Lista con los ficheros de la carpeta abierta.
"""
def __init__(self):
self.opened_file_path = u""
self.opened_file_data = u""
self.opened_folder_path = u""
self.opened_folder_files = []
def open_folder(self, folder_path):
"""
Abre la carpeta indicada en el argumento folder_path para cargar todos
sus ficheros en la lista de ficheros self.opened_folder_files.
"""
try:
folder_path = folder_path
folder_path = unicode(os.path.abspath(folder_path))
# Obtenemos la lista con los ficheros de la carpeta.
self.opened_folder_files = self._list_not_hidden_files(folder_path)
# Aรฑadimos el separador de fichero ('/' en Linux y Mac, '\' en
# Windows) al final de la ruta que se almacena en la etiqueta.
if (folder_path[-1] != os.path.sep):
self.opened_folder_path = folder_path + os.path.sep
else:
self.opened_folder_path = folder_path
except:
TextEditorDialogs.show_error_message(
u"No se pudo abrir la carpeta \"" + folder_path + u"\"")
def open_file(self, file_path):
"""
Abre el fichero indicado en el argumento file_path.
"""
try:
with codecs.open(file_path, 'r', encoding='utf-8') as file:
self.opened_file_data = unicode(file.read())
self.opened_file_path = file_path
file.close()
except:
TextEditorDialogs.show_error_message(
u"No se pudo abrir el fichero \"" + file_path + u"\"")
def save_file(self, file_path):
"""
Guarda el archivo abierto en la ruta indicada en el argumento file_path.
"""
try:
with codecs.open(file_path, 'w', encoding='utf-8') as file:
file.write(unicode(self.opened_file_data))
file.close()
self.opened_file_path = file_path
# Actualizamos los ficheros de la carpeta para que el nuevo fichero
# aparezca en el menรบ lateral.
self.reload_folder()
TextEditorDialogs.show_info_message(u"Fichero guardado con รฉxito!")
except:
TextEditorDialogs.show_error_message(
u"No se pudo guardar en el fichero \"" + file_path + u"\"")
def reload_folder(self):
"""
Vuelve a cargar los ficheros de la carpeta abierta (para actualizar
la lista y mostrar nuevos ficheros que puedan haber sido creados).
"""
self.open_folder(self.opened_folder_path)
def _list_not_hidden_files(self, folder_path):
"""
Devuelve una lista con los nombres de los ficheros no ocultos dentro de
la carpeta indicada en el argumento folder_path.
"""
list = []
for f in os.listdir(folder_path):
if (os.path.isfile(os.path.join(folder_path, f))): # Si es fichero...
if (not f.startswith('.')): # ... y no estรก oculto...
list.append(f) # ... lo aรฑadimos a la lista.
return list
if __name__ == "__main__":
"""
En caso de que intentemos ejecutar este mรณdulo.
"""
print(u"Este mรณdulo no puede ser ejecutado", file=sys.stderr)
|
[
"os.path.abspath",
"codecs.open",
"text_editor_view.TextEditorDialogs.show_info_message",
"os.path.join",
"os.listdir",
"text_editor_view.TextEditorDialogs.show_error_message"
] |
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|
from django.test import TestCase
from .models import ResourceType, Book, Blog, Website, Meetup, Developer
from django.urls import reverse
from django.contrib.auth.models import User
import datetime
from .forms import WebsiteForm, MeetupForm, DeveloperForm, BookForm, BlogForm
# Testing Models:
# Test ResourceType
class ResourceTypeTest(TestCase):
def test_resourcestringOutput(self):
resource=ResourceType(resource_type='New Type')
self.assertEqual(str(resource), resource.resource_type)
def test_resourcetypetablename(self):
self.assertCountEqual(str(ResourceType._meta.db_table), 'resource')
# Test Book
class BookTest(TestCase):
def setUp(self):
self.type=ResourceType(resource_type='New Book Resource Type')
self.book_detail=Book(book_title='New Title',resource_type=self.type)
def test_string(self):
self.assertEqual(str(self.book_detail),self.book_detail.book_title)
def test_bookstringOutput(self):
book=Book(book_title='New Book')
self.assertEqual(str(book), book.book_title)
def test_booktablename(self):
self.assertCountEqual(str(Book._meta.db_table), 'book')
# Test Blog
class BLogTest(TestCase):
def setUp(self):
self.type=ResourceType(resource_type='New BLog Resource Type')
self.blog_detail=Blog(blog_title='New Blog Title',resource_type=self.type)
def test_string(self):
self.assertEqual(str(self.blog_detail),self.blog_detail.blog_title)
def test_blogstringOutput(self):
blog=Blog(blog_title='New Blog')
self.assertEqual(str(blog), blog.blog_title)
def test_blogtablename(self):
self.assertCountEqual(str(Blog._meta.db_table), 'blog')
# Test Website
class WebsiteTest(TestCase):
def setUp(self):
self.type=ResourceType(resource_type='New Website Resource Type')
self.website_detail=Website(website_title='New Website Title',resource_type=self.type)
def test_string(self):
self.assertEqual(str(self.website_detail),self.website_detail.website_title)
def test_websitestringOutput(self):
website=Website(website_title='New Website')
self.assertEqual(str(website), website.website_title)
def test_websitetablename(self):
self.assertCountEqual(str(Website._meta.db_table), 'website')
# Test Meetup
class MeetupTest(TestCase):
def setUp(self):
self.type=ResourceType(resource_type='New Meetup Resource Type')
self.meetup_detail=Meetup(meetup_title='New Meetup Title',resource_type=self.type)
def test_string(self):
self.assertEqual(str(self.meetup_detail),self.meetup_detail.meetup_title)
def test_meetupstringOutput(self):
meetup=Meetup(meetup_title='New Meetup')
self.assertEqual(str(meetup), meetup.meetup_title)
def test_meetuptablename(self):
self.assertCountEqual(str(Meetup._meta.db_table), 'meetup')
# Test Developer
class DeveloperTest(TestCase):
def setUp(self):
self.type=ResourceType(resource_type='New Developer Resource Type')
self.developer_detail=Developer(dev_first_name='<NAME>',resource_type=self.type)
def test_string(self):
self.assertEqual(str(self.developer_detail),self.developer_detail.dev_first_name)
def test_developerstringOutput(self):
developer=Developer(dev_first_name='<NAME>')
self.assertEqual(str(developer), developer.dev_first_name)
def test_developertablename(self):
self.assertCountEqual(str(Developer._meta.db_table), 'developer')
# Testing Views, Forms, Template:
# Test IWebsiteFormndex
class IndexTest(TestCase):
def test_view_url_accessible_by_name(self):
response=self.client.get(reverse('index'))
self.assertEqual(response.status_code, 200)
def test_view_uses_correct_template(self):
response=self.client.get(reverse('index'))
self.assertTemplateUsed(response, 'codelibapp/index.html')
# Test getResourceTypes
class TestGetResource(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/types')
self.assertEqual(response.status_code, 404)
def test_view_url_accessible_by_name(self):
response = self.client.get(reverse('types'))
self.assertEqual(response.status_code, 200)
#Testing the template resource.html
def test_view_uses_correct_template(self):
response = self.client.get(reverse('types'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'codelibapp/resource_types.html')
# Test getBooks
class TestGetBooks(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/books')
self.assertEqual(response.status_code, 404)
def test_view_url_accessible_by_name(self):
response = self.client.get(reverse('books'))
self.assertEqual(response.status_code, 200)
#Testing the template resource.html
def test_view_uses_correct_template(self):
response = self.client.get(reverse('books'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'codelibapp/book_list.html')
#Test newBook form
class New_Book_Form_Test(TestCase):
def setUp(self):
self.user =User.objects.create(username='TestUser', password='<PASSWORD>')
self.type=ResourceType.objects.create(resource_type='type')
def test_bookFormValid(self):
data={
'book_title': 'TestTitle',
'resource_type' : self.type,
'user' : self.user,
'book_author' : 'TestAuthor',
'book_publisher' : 'TestPublisher'
}
form = BookForm(data=data)
self.assertTrue(form.is_valid)
def test_bookFormInvalid(self):
data={
'book_title': 'TestTitle',
'resource_type' : self.type,
'user' : self.user,
'book_author' : 'TestAuthor',
'book_publisher' : 'TestPublisher'
}
form = BookForm(data=data)
self.assertFalse(form.is_valid())
# Test NewBook authentication
class New_Book_authentication_test(TestCase):
def setUp(self):
self.test_user=User.objects.create_user(username='TestUser', password='<PASSWORD>')
self.type=ResourceType.objects.create(resource_type='TsetBook')
self.book = Book.objects.create(resource_type=self.type, user=self.test_user, book_title='TestTitle', book_author='TestAuthor', book_publisher='TestPublisher', book_pages=250, book_description='New Test Book Authentication')
def test_redirect_if_not_logged_in(self):
response=self.client.get(reverse('newbook'))
self.assertRedirects(response, '/accounts/login/?next=/codelibapp/newBook/')
def test_Logged_in_uses_correct_template(self):
login=self.client.login(username='TestUser', password='<PASSWORD>')
response=self.client.get(reverse('newbook'))
self.assertEqual(str(response.context['user']), 'TestUser')
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'codelibapp/newbook.html')
# Test getBookDetails
class TestGetBookDetails(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/book_detail')
self.assertEqual(response.status_code, 404)
# Test getBlogs
class TestGetBlogs(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/blogs')
self.assertEqual(response.status_code, 404)
def test_view_url_accessible_by_name(self):
response = self.client.get(reverse('blogs'))
self.assertEqual(response.status_code, 200)
#Testing the template resource.html
def test_view_uses_correct_template(self):
response = self.client.get(reverse('blogs'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'codelibapp/blog_list.html')
# Test getBlogDetails
class TestGetBlogDetails(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/blog_detail')
self.assertEqual(response.status_code, 404)
# Test loginmessage
class TestLoginMessage(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/loginmessage')
self.assertEqual(response.status_code, 301)
def test_view_url_accessible_by_name(self):
response = self.client.get(reverse('loginmessage'))
self.assertEqual(response.status_code, 200)
#Testing the template loginmessage.html
def test_view_uses_correct_template(self):
response = self.client.get(reverse('loginmessage'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'codelibapp/loginmessage.html')
# Test logoutmessage
class TestLogoutMessage(TestCase):
def test_view_url_exists_at_desired_location(self):
response = self.client.get('/codelibapp/logoutmessage')
self.assertEqual(response.status_code, 301)
def test_view_url_accessible_by_name(self):
response = self.client.get(reverse('logoutmessage'))
self.assertEqual(response.status_code, 200)
#Testing the template logoutmessage.html
def test_view_uses_correct_template(self):
response = self.client.get(reverse('logoutmessage'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'codelibapp/logoutmessage.html')
|
[
"django.contrib.auth.models.User.objects.create",
"django.urls.reverse",
"django.contrib.auth.models.User.objects.create_user"
] |
[((5520, 5583), 'django.contrib.auth.models.User.objects.create', 'User.objects.create', ([], {'username': '"""TestUser"""', 'password': '"""<PASSWORD>"""'}), "(username='TestUser', password='<PASSWORD>')\n", (5539, 5583), False, 'from django.contrib.auth.models import User\n'), ((6486, 6554), 'django.contrib.auth.models.User.objects.create_user', 'User.objects.create_user', ([], {'username': '"""TestUser"""', 'password': '"""<PASSWORD>"""'}), "(username='TestUser', password='<PASSWORD>')\n", (6510, 6554), False, 'from django.contrib.auth.models import User\n'), ((3859, 3875), 'django.urls.reverse', 'reverse', (['"""index"""'], {}), "('index')\n", (3866, 3875), False, 'from django.urls import reverse\n'), ((4014, 4030), 'django.urls.reverse', 'reverse', (['"""index"""'], {}), "('index')\n", (4021, 4030), False, 'from django.urls import reverse\n'), ((4415, 4431), 'django.urls.reverse', 'reverse', (['"""types"""'], {}), "('types')\n", (4422, 4431), False, 'from django.urls import reverse\n'), ((4617, 4633), 'django.urls.reverse', 'reverse', (['"""types"""'], {}), "('types')\n", (4624, 4633), False, 'from django.urls import reverse\n'), ((5072, 5088), 'django.urls.reverse', 'reverse', (['"""books"""'], {}), "('books')\n", (5079, 5088), False, 'from django.urls import reverse\n'), ((5274, 5290), 'django.urls.reverse', 'reverse', (['"""books"""'], {}), "('books')\n", (5281, 5290), False, 'from django.urls import reverse\n'), ((6945, 6963), 'django.urls.reverse', 'reverse', (['"""newbook"""'], {}), "('newbook')\n", (6952, 6963), False, 'from django.urls import reverse\n'), ((7217, 7235), 'django.urls.reverse', 'reverse', (['"""newbook"""'], {}), "('newbook')\n", (7224, 7235), False, 'from django.urls import reverse\n'), ((7977, 7993), 'django.urls.reverse', 'reverse', (['"""blogs"""'], {}), "('blogs')\n", (7984, 7993), False, 'from django.urls import reverse\n'), ((8179, 8195), 'django.urls.reverse', 'reverse', (['"""blogs"""'], {}), "('blogs')\n", (8186, 8195), False, 'from django.urls import reverse\n'), ((8893, 8916), 'django.urls.reverse', 'reverse', (['"""loginmessage"""'], {}), "('loginmessage')\n", (8900, 8916), False, 'from django.urls import reverse\n'), ((9106, 9129), 'django.urls.reverse', 'reverse', (['"""loginmessage"""'], {}), "('loginmessage')\n", (9113, 9129), False, 'from django.urls import reverse\n'), ((9587, 9611), 'django.urls.reverse', 'reverse', (['"""logoutmessage"""'], {}), "('logoutmessage')\n", (9594, 9611), False, 'from django.urls import reverse\n'), ((9798, 9822), 'django.urls.reverse', 'reverse', (['"""logoutmessage"""'], {}), "('logoutmessage')\n", (9805, 9822), False, 'from django.urls import reverse\n')]
|
from .utils import needs_es, verify_es_response
from elasticsearch_dsl import Search, Q
from services.bitshares_elasticsearch_client import es
from datetime import datetime, timedelta
from services.cache import cache
@needs_es()
def get_account_history(
account_id=None,
operation_type=None,
from_=0,
size=10,
from_date='2015-10-10',
to_date='now',
sort_by='-operation_id_num',
search_after=None,
type='data', # @ReservedAssignment
agg_field='operation_type'
):
s = Search(using=es, index="bitshares-*")
if type == "data":
s = s.extra(size=size)
if search_after and search_after != '':
s = s.extra(search_after=search_after.split(','))
else:
s = s.extra(**{"from": from_})
q = Q()
if account_id and account_id != '':
q = q & Q("match", account_history__account=account_id)
if (operation_type and operation_type != -1) or operation_type == 0:
q = q & Q("match", operation_type=operation_type)
range_query = Q("range", block_data__block_time={'gte': from_date, 'lte': to_date})
s.query = q & range_query
if type != "data":
s.aggs.bucket('per_field', 'terms', field=agg_field, size=size)
s = s.sort(*sort_by.split(','))
response = s.execute()
verify_es_response(response)
if type == "data":
return [hit.to_dict() for hit in response]
else:
return [field.to_dict() for field in response.aggregations.per_field.buckets]
@needs_es()
@cache.memoize()
def get_single_operation(operation_id):
s = Search(using=es, index="bitshares-*")
s.query = Q("match", account_history__operation_id=operation_id)
response = s.execute()
verify_es_response(response)
return [hit.to_dict() for hit in response][0]
@needs_es()
def is_alive():
find_string = datetime.utcnow().strftime("%Y-%m")
from_date = (datetime.utcnow() - timedelta(days=1)).strftime("%Y-%m-%d")
s = Search(using=es, index="bitshares-" + find_string)
s.query = Q("range", block_data__block_time={'gte': from_date, 'lte': "now"})
s.aggs.metric("max_block_time", "max", field="block_data.block_time")
json_response = {
"server_time": datetime.utcnow(),
"head_block_timestamp": None,
"head_block_time": None
}
response = s.execute()
verify_es_response(response)
if response.aggregations.max_block_time.value is not None:
json_response["head_block_time"] = str(response.aggregations.max_block_time.value_as_string)
json_response["head_block_timestamp"] = response.aggregations.max_block_time.value
json_response["deltatime"] = abs((datetime.utcfromtimestamp(json_response["head_block_timestamp"] / 1000) - json_response["server_time"]).total_seconds())
if json_response["deltatime"] < 30:
json_response["status"] = "ok"
else:
json_response["status"] = "out_of_sync"
json_response["error"] = "last_block_too_old"
else:
json_response["status"] = "out_of_sync"
json_response["deltatime"] = "Infinite"
json_response["query_index"] = find_string
json_response["query_from_date"] = from_date
json_response["error"] = "no_blocks_last_24_hours"
return json_response
@needs_es()
@cache.memoize()
def get_trx(trx, from_=0, size=10):
s = Search(using=es, index="bitshares-*", extra={"size": size, "from": from_})
s.query = Q("match", block_data__trx_id=trx)
response = s.execute()
verify_es_response(response)
return [hit.to_dict() for hit in response]
@needs_es()
def get_trade_history(size=10, from_date='2015-10-10', to_date='now', sort_by='-operation_id_num',
search_after=None, base="1.3.0", quote="1.3.121"):
s = Search(using=es, index="bitshares-*")
s = s.extra(size=size)
if search_after and search_after != '':
s = s.extra(search_after=search_after.split(','))
q = Q()
q = q & Q("match", operation_type=4)
q = q & Q("match", operation_history__op_object__is_maker=True)
q = q & Q("match", operation_history__op_object__fill_price__base__asset_id=base)
q = q & Q("match", operation_history__op_object__fill_price__quote__asset_id=quote)
range_query = Q("range", block_data__block_time={'gte': from_date, 'lte': to_date})
s.query = q & range_query
s = s.sort(*sort_by.split(','))
response = s.execute()
verify_es_response(response)
return [hit.to_dict() for hit in response]
|
[
"datetime.datetime.utcnow",
"datetime.datetime.utcfromtimestamp",
"elasticsearch_dsl.Search",
"datetime.timedelta",
"services.cache.cache.memoize",
"elasticsearch_dsl.Q"
] |
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|
from __future__ import print_function
import argparse
import gzip
import json
import logging
import os
import traceback
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Conv2D, Dense, Flatten
logging.basicConfig(level=logging.DEBUG)
# Define the model object
class SmallConv(Model):
def __init__(self):
super(SmallConv, self).__init__()
self.conv1 = Conv2D(32, 3, activation="relu")
self.flatten = Flatten()
self.d1 = Dense(128, activation="relu")
self.d2 = Dense(10)
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
# Decode and preprocess data
def convert_to_numpy(data_dir, images_file, labels_file):
"""Byte string to numpy arrays"""
with gzip.open(os.path.join(data_dir, images_file), "rb") as f:
images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
with gzip.open(os.path.join(data_dir, labels_file), "rb") as f:
labels = np.frombuffer(f.read(), np.uint8, offset=8)
return (images, labels)
def mnist_to_numpy(data_dir, train):
"""Load raw MNIST data into numpy array
Args:
data_dir (str): directory of MNIST raw data.
This argument can be accessed via SM_CHANNEL_TRAINING
train (bool): use training data
Returns:
tuple of images and labels as numpy array
"""
if train:
images_file = "train-images-idx3-ubyte.gz"
labels_file = "train-labels-idx1-ubyte.gz"
else:
images_file = "t10k-images-idx3-ubyte.gz"
labels_file = "t10k-labels-idx1-ubyte.gz"
return convert_to_numpy(data_dir, images_file, labels_file)
def normalize(x, axis):
eps = np.finfo(float).eps
mean = np.mean(x, axis=axis, keepdims=True)
# avoid division by zero
std = np.std(x, axis=axis, keepdims=True) + eps
return (x - mean) / std
# Training logic
def train(args):
# create data loader from the train / test channels
x_train, y_train = mnist_to_numpy(data_dir=args.train, train=True)
x_test, y_test = mnist_to_numpy(data_dir=args.test, train=False)
x_train, x_test = x_train.astype(np.float32), x_test.astype(np.float32)
# normalize the inputs to mean 0 and std 1
x_train, x_test = normalize(x_train, (1, 2)), normalize(x_test, (1, 2))
# expand channel axis
# tf uses depth minor convention
x_train, x_test = np.expand_dims(x_train, axis=3), np.expand_dims(x_test, axis=3)
# normalize the data to mean 0 and std 1
train_loader = (
tf.data.Dataset.from_tensor_slices((x_train, y_train))
.shuffle(len(x_train))
.batch(args.batch_size)
)
test_loader = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(args.batch_size)
model = SmallConv()
model.compile()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(
learning_rate=args.learning_rate, beta_1=args.beta_1, beta_2=args.beta_2
)
train_loss = tf.keras.metrics.Mean(name="train_loss")
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name="train_accuracy")
test_loss = tf.keras.metrics.Mean(name="test_loss")
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name="test_accuracy")
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images, training=True)
loss = loss_fn(labels, predictions)
grad = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grad, model.trainable_variables))
train_loss(loss)
train_accuracy(labels, predictions)
return
@tf.function
def test_step(images, labels):
predictions = model(images, training=False)
t_loss = loss_fn(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
return
print("Training starts ...")
for epoch in range(args.epochs):
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()
for batch, (images, labels) in enumerate(train_loader):
train_step(images, labels)
for images, labels in test_loader:
test_step(images, labels)
print(
f"Epoch {epoch + 1}, "
f"Loss: {train_loss.result()}, "
f"Accuracy: {train_accuracy.result() * 100}, "
f"Test Loss: {test_loss.result()}, "
f"Test Accuracy: {test_accuracy.result() * 100}"
)
# Save the model
# A version number is needed for the serving container
# to load the model
version = "00000000"
ckpt_dir = os.path.join(args.model_dir, version)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
model.save(ckpt_dir)
return
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--learning-rate", type=float, default=1e-3)
parser.add_argument("--beta_1", type=float, default=0.9)
parser.add_argument("--beta_2", type=float, default=0.999)
# Environment variables given by the training image
parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
parser.add_argument("--train", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
parser.add_argument("--test", type=str, default=os.environ["SM_CHANNEL_TESTING"])
parser.add_argument("--current-host", type=str, default=os.environ["SM_CURRENT_HOST"])
parser.add_argument("--hosts", type=list, default=json.loads(os.environ["SM_HOSTS"]))
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
train(args)
|
[
"argparse.ArgumentParser",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.metrics.Mean",
"numpy.mean",
"os.path.join",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"json.loads",
"numpy.std",
"os.path.exists",
"numpy.finfo",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.layers.Conv2D",
"os.makedirs",
"logging.basicConfig",
"numpy.expand_dims",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.keras.metrics.SparseCategoricalAccuracy",
"tensorflow.GradientTape"
] |
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|
import pathlib
import numpy
import pytest
import helpers
import meshio
@pytest.mark.parametrize(
"mesh", [helpers.tri_mesh, helpers.quad_mesh, helpers.tri_quad_mesh]
)
def test_obj(mesh):
def writer(*args, **kwargs):
return meshio.obj.write(*args, **kwargs)
for k, c in enumerate(mesh.cells):
mesh.cells[k] = meshio.CellBlock(c.type, c.data.astype(numpy.int32))
helpers.write_read(writer, meshio.obj.read, mesh, 1.0e-12)
@pytest.mark.parametrize(
"filename, ref_sum, ref_num_cells", [("elephav.obj", 3.678372172450000e05, 1148)]
)
def test_reference_file(filename, ref_sum, ref_num_cells):
this_dir = pathlib.Path(__file__).resolve().parent
filename = this_dir / "meshes" / "obj" / filename
mesh = meshio.read(filename)
tol = 1.0e-5
s = numpy.sum(mesh.points)
assert abs(s - ref_sum) < tol * abs(ref_sum)
assert mesh.cells[0].type == "triangle"
assert len(mesh.cells[0].data) == ref_num_cells
|
[
"numpy.sum",
"meshio.read",
"pathlib.Path",
"meshio.obj.write",
"pytest.mark.parametrize",
"helpers.write_read"
] |
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|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Directed graph object for representing coupling between physical qubits.
The nodes of the graph correspond to physical qubits (represented as integers) and the
directed edges indicate which physical qubits are coupled and the permitted direction of
CNOT gates. The object has a distance function that can be used to map quantum circuits
onto a device with this coupling.
"""
import io
import warnings
import numpy as np
import retworkx as rx
from qiskit.transpiler.exceptions import CouplingError
from qiskit.exceptions import MissingOptionalLibraryError
class CouplingMap:
"""
Directed graph specifying fixed coupling.
Nodes correspond to physical qubits (integers) and directed edges correspond
to permitted CNOT gates
"""
__slots__ = ("description", "graph", "_dist_matrix", "_qubit_list", "_size", "_is_symmetric")
def __init__(self, couplinglist=None, description=None):
"""
Create coupling graph. By default, the generated coupling has no nodes.
Args:
couplinglist (list or None): An initial coupling graph, specified as
an adjacency list containing couplings, e.g. [[0,1], [0,2], [1,2]].
It is required that nodes are contiguously indexed starting at 0.
Missed nodes will be added as isolated nodes in the coupling map.
description (str): A string to describe the coupling map.
"""
self.description = description
# the coupling map graph
self.graph = rx.PyDiGraph()
# a dict of dicts from node pairs to distances
self._dist_matrix = None
# a sorted list of physical qubits (integers) in this coupling map
self._qubit_list = None
# number of qubits in the graph
self._size = None
self._is_symmetric = None
if couplinglist is not None:
self.graph.extend_from_edge_list([tuple(x) for x in couplinglist])
def size(self):
"""Return the number of physical qubits in this graph."""
if self._size is None:
self._size = len(self.graph)
return self._size
def get_edges(self):
"""
Gets the list of edges in the coupling graph.
Returns:
Tuple(int,int): Each edge is a pair of physical qubits.
"""
return self.graph.edge_list()
def add_physical_qubit(self, physical_qubit):
"""Add a physical qubit to the coupling graph as a node.
physical_qubit (int): An integer representing a physical qubit.
Raises:
CouplingError: if trying to add duplicate qubit
"""
if not isinstance(physical_qubit, int):
raise CouplingError("Physical qubits should be integers.")
if physical_qubit in self.physical_qubits:
raise CouplingError(
"The physical qubit %s is already in the coupling graph" % physical_qubit
)
self.graph.add_node(physical_qubit)
self._dist_matrix = None # invalidate
self._qubit_list = None # invalidate
self._size = None # invalidate
def add_edge(self, src, dst):
"""
Add directed edge to coupling graph.
src (int): source physical qubit
dst (int): destination physical qubit
"""
if src not in self.physical_qubits:
self.add_physical_qubit(src)
if dst not in self.physical_qubits:
self.add_physical_qubit(dst)
self.graph.add_edge(src, dst, None)
self._dist_matrix = None # invalidate
self._is_symmetric = None # invalidate
def subgraph(self, nodelist):
"""Return a CouplingMap object for a subgraph of self.
nodelist (list): list of integer node labels
"""
warnings.warn(
"The .subgraph() method is deprecated and will be removed in a "
"future release. Instead the .reduce() method should be used "
"instead which does the same thing but preserves nodelist order.",
DeprecationWarning,
stacklevel=2,
)
subcoupling = CouplingMap()
subcoupling.graph = self.graph.subgraph(nodelist)
return subcoupling
@property
def physical_qubits(self):
"""Returns a sorted list of physical_qubits"""
if self._qubit_list is None:
self._qubit_list = self.graph.node_indexes()
return self._qubit_list
def is_connected(self):
"""
Test if the graph is connected.
Return True if connected, False otherwise
"""
try:
return rx.is_weakly_connected(self.graph)
except rx.NullGraph:
return False
def neighbors(self, physical_qubit):
"""Return the nearest neighbors of a physical qubit.
Directionality matters, i.e. a neighbor must be reachable
by going one hop in the direction of an edge.
"""
return self.graph.neighbors(physical_qubit)
@property
def distance_matrix(self):
"""Return the distance matrix for the coupling map."""
self.compute_distance_matrix()
return self._dist_matrix
def compute_distance_matrix(self):
"""Compute the full distance matrix on pairs of nodes.
The distance map self._dist_matrix is computed from the graph using
all_pairs_shortest_path_length. This is normally handled internally
by the :attr:`~qiskit.transpiler.CouplingMap.distance_matrix`
attribute or the :meth:`~qiskit.transpiler.CouplingMap.distance` method
but can be called if you're accessing the distance matrix outside of
those or want to pre-generate it.
"""
if self._dist_matrix is None:
if not self.is_connected():
raise CouplingError("coupling graph not connected")
self._dist_matrix = rx.digraph_distance_matrix(self.graph, as_undirected=True)
def distance(self, physical_qubit1, physical_qubit2):
"""Returns the undirected distance between physical_qubit1 and physical_qubit2.
Args:
physical_qubit1 (int): A physical qubit
physical_qubit2 (int): Another physical qubit
Returns:
int: The undirected distance
Raises:
CouplingError: if the qubits do not exist in the CouplingMap
"""
if physical_qubit1 >= self.size():
raise CouplingError("%s not in coupling graph" % physical_qubit1)
if physical_qubit2 >= self.size():
raise CouplingError("%s not in coupling graph" % physical_qubit2)
self.compute_distance_matrix()
return int(self._dist_matrix[physical_qubit1, physical_qubit2])
def shortest_undirected_path(self, physical_qubit1, physical_qubit2):
"""Returns the shortest undirected path between physical_qubit1 and physical_qubit2.
Args:
physical_qubit1 (int): A physical qubit
physical_qubit2 (int): Another physical qubit
Returns:
List: The shortest undirected path
Raises:
CouplingError: When there is no path between physical_qubit1, physical_qubit2.
"""
paths = rx.digraph_dijkstra_shortest_paths(
self.graph, source=physical_qubit1, target=physical_qubit2, as_undirected=True
)
if not paths:
raise CouplingError(
f"Nodes {str(physical_qubit1)} and {str(physical_qubit2)} are not connected"
)
return paths[physical_qubit2]
@property
def is_symmetric(self):
"""
Test if the graph is symmetric.
Return True if symmetric, False otherwise
"""
if self._is_symmetric is None:
self._is_symmetric = self._check_symmetry()
return self._is_symmetric
def make_symmetric(self):
"""
Convert uni-directional edges into bi-directional.
"""
edges = self.get_edges()
for src, dest in edges:
if (dest, src) not in edges:
self.add_edge(dest, src)
self._dist_matrix = None # invalidate
self._is_symmetric = None # invalidate
def _check_symmetry(self):
"""
Calculates symmetry
Returns:
Bool: True if symmetric, False otherwise
"""
return self.graph.is_symmetric()
def reduce(self, mapping):
"""Returns a reduced coupling map that
corresponds to the subgraph of qubits
selected in the mapping.
Args:
mapping (list): A mapping of reduced qubits to device
qubits.
Returns:
CouplingMap: A reduced coupling_map for the selected qubits.
Raises:
CouplingError: Reduced coupling map must be connected.
"""
from scipy.sparse import coo_matrix, csgraph
reduced_qubits = len(mapping)
inv_map = [None] * (max(mapping) + 1)
for idx, val in enumerate(mapping):
inv_map[val] = idx
reduced_cmap = []
for edge in self.get_edges():
if edge[0] in mapping and edge[1] in mapping:
reduced_cmap.append([inv_map[edge[0]], inv_map[edge[1]]])
# Verify coupling_map is connected
rows = np.array([edge[0] for edge in reduced_cmap], dtype=int)
cols = np.array([edge[1] for edge in reduced_cmap], dtype=int)
data = np.ones_like(rows)
mat = coo_matrix((data, (rows, cols)), shape=(reduced_qubits, reduced_qubits)).tocsr()
if csgraph.connected_components(mat)[0] != 1:
raise CouplingError("coupling_map must be connected.")
return CouplingMap(reduced_cmap)
@classmethod
def from_full(cls, num_qubits, bidirectional=True) -> "CouplingMap":
"""Return a fully connected coupling map on n qubits."""
cmap = cls(description="full")
if bidirectional:
cmap.graph = rx.generators.directed_mesh_graph(num_qubits)
else:
edge_list = []
for i in range(num_qubits):
for j in range(i):
edge_list.append((j, i))
cmap.graph.extend_from_edge_list(edge_list)
return cmap
@classmethod
def from_line(cls, num_qubits, bidirectional=True) -> "CouplingMap":
"""Return a coupling map of n qubits connected in a line."""
cmap = cls(description="line")
cmap.graph = rx.generators.directed_path_graph(num_qubits, bidirectional=bidirectional)
return cmap
@classmethod
def from_ring(cls, num_qubits, bidirectional=True) -> "CouplingMap":
"""Return a coupling map of n qubits connected to each of their neighbors in a ring."""
cmap = cls(description="ring")
cmap.graph = rx.generators.directed_cycle_graph(num_qubits, bidirectional=bidirectional)
return cmap
@classmethod
def from_grid(cls, num_rows, num_columns, bidirectional=True) -> "CouplingMap":
"""Return a coupling map of qubits connected on a grid of num_rows x num_columns."""
cmap = cls(description="grid")
cmap.graph = rx.generators.directed_grid_graph(
num_rows, num_columns, bidirectional=bidirectional
)
return cmap
@classmethod
def from_heavy_hex(cls, distance, bidirectional=True) -> "CouplingMap":
"""Return a heavy hexagon graph coupling map.
A heavy hexagon graph is described in:
https://journals.aps.org/prx/abstract/10.1103/PhysRevX.10.011022
Args:
distance (int): The code distance for the generated heavy hex
graph. The value for distance can be any odd positive integer.
The distance relates to the number of qubits by:
:math:`n = \\frac{5d^2 - 2d - 1}{2}` where :math:`n` is the
number of qubits and :math:`d` is the ``distance`` parameter.
bidirectional (bool): Whether the edges in the output coupling
graph are bidirectional or not. By default this is set to
``True``
Returns:
CouplingMap: A heavy hex coupling graph
"""
cmap = cls(description="heavy-hex")
cmap.graph = rx.generators.directed_heavy_hex_graph(distance, bidirectional=bidirectional)
return cmap
@classmethod
def from_heavy_square(cls, distance, bidirectional=True) -> "CouplingMap":
"""Return a heavy square graph coupling map.
A heavy square graph is described in:
https://journals.aps.org/prx/abstract/10.1103/PhysRevX.10.011022
Args:
distance (int): The code distance for the generated heavy square
graph. The value for distance can be any odd positive integer.
The distance relates to the number of qubits by:
:math:`n = 3d^2 - 2d` where :math:`n` is the
number of qubits and :math:`d` is the ``distance`` parameter.
bidirectional (bool): Whether the edges in the output coupling
graph are bidirectional or not. By default this is set to
``True``
Returns:
CouplingMap: A heavy square coupling graph
"""
cmap = cls(description="heavy-square")
cmap.graph = rx.generators.directed_heavy_square_graph(
distance, bidirectional=bidirectional
)
return cmap
@classmethod
def from_hexagonal_lattice(cls, rows, cols, bidirectional=True) -> "CouplingMap":
"""Return a hexagonal lattice graph coupling map.
Args:
rows (int): The number of rows to generate the graph with.
cols (int): The number of columns to generate the graph with.
bidirectional (bool): Whether the edges in the output coupling
graph are bidirectional or not. By default this is set to
``True``
Returns:
CouplingMap: A hexagonal lattice coupling graph
"""
cmap = cls(description="hexagonal-lattice")
cmap.graph = rx.generators.directed_hexagonal_lattice_graph(
rows, cols, bidirectional=bidirectional
)
return cmap
def largest_connected_component(self):
"""Return a set of qubits in the largest connected component."""
return max(rx.weakly_connected_components(self.graph), key=len)
def __str__(self):
"""Return a string representation of the coupling graph."""
string = ""
if self.get_edges():
string += "["
string += ", ".join([f"[{src}, {dst}]" for (src, dst) in self.get_edges()])
string += "]"
return string
def draw(self):
"""Draws the coupling map.
This function needs `pydot <https://github.com/erocarrera/pydot>`_,
which in turn needs `Graphviz <https://www.graphviz.org/>`_ to be
installed. Additionally, `pillow <https://python-pillow.org/>`_ will
need to be installed.
Returns:
PIL.Image: Drawn coupling map.
Raises:
MissingOptionalLibraryError: when pydot or pillow are not installed.
"""
try:
import pydot
except ImportError as ex:
raise MissingOptionalLibraryError(
libname="pydot",
name="coupling map drawer",
pip_install="pip install pydot",
) from ex
try:
from PIL import Image
except ImportError as ex:
raise MissingOptionalLibraryError(
libname="pillow",
name="coupling map drawer",
pip_install="pip install pillow",
) from ex
dot_str = self.graph.to_dot()
dot = pydot.graph_from_dot_data(dot_str)[0]
png = dot.create_png(prog="neato")
return Image.open(io.BytesIO(png))
|
[
"retworkx.generators.directed_grid_graph",
"scipy.sparse.csgraph.connected_components",
"retworkx.is_weakly_connected",
"qiskit.transpiler.exceptions.CouplingError",
"retworkx.digraph_distance_matrix",
"qiskit.exceptions.MissingOptionalLibraryError",
"retworkx.PyDiGraph",
"scipy.sparse.coo_matrix",
"retworkx.generators.directed_path_graph",
"io.BytesIO",
"retworkx.digraph_dijkstra_shortest_paths",
"numpy.ones_like",
"retworkx.generators.directed_heavy_square_graph",
"retworkx.weakly_connected_components",
"retworkx.generators.directed_heavy_hex_graph",
"retworkx.generators.directed_mesh_graph",
"retworkx.generators.directed_cycle_graph",
"pydot.graph_from_dot_data",
"numpy.array",
"warnings.warn",
"retworkx.generators.directed_hexagonal_lattice_graph"
] |
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'(rows)\n', (9997, 10003), True, 'import numpy as np\n'), ((11013, 11087), 'retworkx.generators.directed_path_graph', 'rx.generators.directed_path_graph', (['num_qubits'], {'bidirectional': 'bidirectional'}), '(num_qubits, bidirectional=bidirectional)\n', (11046, 11087), True, 'import retworkx as rx\n'), ((11355, 11430), 'retworkx.generators.directed_cycle_graph', 'rx.generators.directed_cycle_graph', (['num_qubits'], {'bidirectional': 'bidirectional'}), '(num_qubits, bidirectional=bidirectional)\n', (11389, 11430), True, 'import retworkx as rx\n'), ((11706, 11796), 'retworkx.generators.directed_grid_graph', 'rx.generators.directed_grid_graph', (['num_rows', 'num_columns'], {'bidirectional': 'bidirectional'}), '(num_rows, num_columns, bidirectional=\n bidirectional)\n', (11739, 11796), True, 'import retworkx as rx\n'), ((12811, 12888), 'retworkx.generators.directed_heavy_hex_graph', 'rx.generators.directed_heavy_hex_graph', (['distance'], {'bidirectional': 'bidirectional'}), 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is already in the coupling graph' % physical_qubit)"], {}), "('The physical qubit %s is already in the coupling graph' %\n physical_qubit)\n", (3331, 3410), False, 'from qiskit.transpiler.exceptions import CouplingError\n'), ((5125, 5159), 'retworkx.is_weakly_connected', 'rx.is_weakly_connected', (['self.graph'], {}), '(self.graph)\n', (5147, 5159), True, 'import retworkx as rx\n'), ((6398, 6456), 'retworkx.digraph_distance_matrix', 'rx.digraph_distance_matrix', (['self.graph'], {'as_undirected': '(True)'}), '(self.graph, as_undirected=True)\n', (6424, 6456), True, 'import retworkx as rx\n'), ((6951, 7010), 'qiskit.transpiler.exceptions.CouplingError', 'CouplingError', (["('%s not in coupling graph' % physical_qubit1)"], {}), "('%s not in coupling graph' % physical_qubit1)\n", (6964, 7010), False, 'from qiskit.transpiler.exceptions import CouplingError\n'), ((7072, 7131), 'qiskit.transpiler.exceptions.CouplingError', 'CouplingError', (["('%s not in coupling graph' % physical_qubit2)"], 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False, 'import io\n'), ((6320, 6365), 'qiskit.transpiler.exceptions.CouplingError', 'CouplingError', (['"""coupling graph not connected"""'], {}), "('coupling graph not connected')\n", (6333, 6365), False, 'from qiskit.transpiler.exceptions import CouplingError\n'), ((10019, 10091), 'scipy.sparse.coo_matrix', 'coo_matrix', (['(data, (rows, cols))'], {'shape': '(reduced_qubits, reduced_qubits)'}), '((data, (rows, cols)), shape=(reduced_qubits, reduced_qubits))\n', (10029, 10091), False, 'from scipy.sparse import coo_matrix, csgraph\n'), ((10112, 10145), 'scipy.sparse.csgraph.connected_components', 'csgraph.connected_components', (['mat'], {}), '(mat)\n', (10140, 10145), False, 'from scipy.sparse import coo_matrix, csgraph\n'), ((15859, 15968), 'qiskit.exceptions.MissingOptionalLibraryError', 'MissingOptionalLibraryError', ([], {'libname': '"""pydot"""', 'name': '"""coupling map drawer"""', 'pip_install': '"""pip install pydot"""'}), "(libname='pydot', name='coupling map drawer',\n pip_install='pip install pydot')\n", (15886, 15968), False, 'from qiskit.exceptions import MissingOptionalLibraryError\n'), ((16136, 16247), 'qiskit.exceptions.MissingOptionalLibraryError', 'MissingOptionalLibraryError', ([], {'libname': '"""pillow"""', 'name': '"""coupling map drawer"""', 'pip_install': '"""pip install pillow"""'}), "(libname='pillow', name='coupling map drawer',\n pip_install='pip install pillow')\n", (16163, 16247), False, 'from qiskit.exceptions import MissingOptionalLibraryError\n')]
|
# pylint: disable=invalid-name, unused-variable, unused-argument
"""Schedule for binary dense operator."""
from __future__ import absolute_import as _abs
import tvm
from .. import tag
from .. import generic
@generic.schedule_binary_dense.register(["cpu"])
def schedule_binary_dense(outs):
"""Schedule for binary_dense.
Parameters
----------
outs: Array of Tensor
The computation graph description of binary_dense
in the format of an array of tensors.
Returns
-------
s: Schedule
The computation schedule for binary_dense.
"""
outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
s = tvm.create_schedule([x.op for x in outs])
scheduled_ops = []
def _schedule(A, B, C):
s[C].split(s[C].op.reduce_axis[0], factor=8)
s[C].parallel(s[C].op.axis[0])
if C.op in s.outputs:
Out = C
else:
Out = outs[0].op.output(0)
xo, xi = s[Out].split(Out.op.axis[1], factor=8)
s[Out].vectorize(xi)
def traverse(OP):
"""Internal travserse function"""
# inline all one-to-one-mapping operators except the last stage (output)
if tag.is_broadcast(OP.tag):
if OP not in s.outputs:
s[OP].compute_inline()
for tensor in OP.input_tensors:
if tensor.op.input_tensors and tensor.op not in scheduled_ops:
traverse(tensor.op)
# schedule binary_dense
elif OP.tag == 'binary_dense':
output = OP.output(0)
data = OP.input_tensors[0]
weight = OP.input_tensors[1]
_schedule(data, weight, output)
else:
raise RuntimeError("Unsupported operator: %s" % OP.tag)
scheduled_ops.append(OP)
traverse(outs[0].op)
return s
|
[
"tvm.create_schedule"
] |
[((661, 702), 'tvm.create_schedule', 'tvm.create_schedule', (['[x.op for x in outs]'], {}), '([x.op for x in outs])\n', (680, 702), False, 'import tvm\n')]
|
# -*- coding: utf-8 -*-
# Generated by Django 1.10 on 2017-01-12 11:25
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0010_auto_20170112_1124'),
]
operations = [
migrations.AlterField(
model_name='photoext',
name='height',
field=models.FloatField(blank=True, null=True),
),
migrations.AlterField(
model_name='photoext',
name='width',
field=models.FloatField(blank=True, null=True),
),
]
|
[
"django.db.models.FloatField"
] |
[((398, 438), 'django.db.models.FloatField', 'models.FloatField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (415, 438), False, 'from django.db import migrations, models\n'), ((561, 601), 'django.db.models.FloatField', 'models.FloatField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (578, 601), False, 'from django.db import migrations, models\n')]
|
#!/usr/bin/env python
from __future__ import print_function
import os
import sys
chplenv_dir = os.path.join(os.path.dirname(__file__), '..')
sys.path.insert(0, os.path.abspath(chplenv_dir))
from chplenv import chpl_home_utils
from chplenv import chpl_platform
from chplenv import chpl_make
# Activate a virtualenv that has testing infrastructure requirements installed
#
# By default, we will try to use
# $CHPL_HOME/third-party/chpl-venv/install/$CHPL_HOST_PLATFORM/$python_version_dir/chpl-virtualenv
# as our virtualenv. We then check for a sentinel file that test-venv
# creates when it's been successfully installed and finally activate
# the virtualenv.
#
# A user can also set CHPL_TEST_VENV_DIR to specify the path to a custom
# virtualenv that will be activated. "none" is a special value that means skip
# activating a virtualenv. If CHPL_TEST_VENV_DIR is set, a user is asserting
# that their virtualenv or local install has the requirements available.
#
# Note that this method does not allow us to specify python versions to use and
# instead will use the system default. Long term we probably want to have a
# wrapper script that activates the virtualenv and just calls start_test.
def error(message):
print('[Error: {0}]'.format(message))
exit(1)
def activate_venv():
custom_venv_dir_var = 'CHPL_TEST_VENV_DIR'
custom_venv_dir = os.getenv(custom_venv_dir_var, '').strip()
# user asserts that system already has the required dependencies installed:
if custom_venv_dir == 'none':
print('[Skipping virtualenv activation because {0}={1}. test-venv '
'requirements must be available.]'.format(custom_venv_dir_var,
custom_venv_dir))
else:
venv_dir = None
# using custom venv, does not check that our test requirements are met
if custom_venv_dir:
venv_dir = custom_venv_dir
print('[Using custom virtualenv because {0}={1}. test-venv '
'requirements must be available]'.format(custom_venv_dir_var,
custom_venv_dir))
# check Chapel test-venv for successful installation sentinel
else:
chpl_home = os.path.join(chpl_home_utils.get_chpl_home(), '')
third_party = os.path.join(chpl_home, 'third-party')
host_platform = chpl_platform.get('host')
python_version_dir = 'py{0}.{1}'.format(sys.version_info[0], sys.version_info[1])
venv_dir = os.path.join(third_party, 'chpl-venv', 'install',
host_platform, python_version_dir, 'chpl-virtualenv')
sentinel_file = os.path.join(venv_dir, 'chpl-test-reqs')
if not os.path.isfile(sentinel_file):
error('Chapel test virtualenv is not available, run `{0} '
'test-venv` from {1}'.format(chpl_make.get(), chpl_home))
activation_file = os.path.join(venv_dir, 'bin', 'activate_this.py')
if not os.path.isfile(activation_file):
error('Activation file {0} is missing'.format(activation_file))
# actually activate
execfile(activation_file, dict(__file__=activation_file))
activate_venv()
|
[
"os.path.abspath",
"chplenv.chpl_platform.get",
"os.path.dirname",
"os.path.isfile",
"chplenv.chpl_home_utils.get_chpl_home",
"chplenv.chpl_make.get",
"os.path.join",
"os.getenv"
] |
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|
import numpy as np
import pandas as pd
import pytest
from rayml.data_checks import (
DataCheckActionCode,
DataCheckActionOption,
DataCheckMessageCode,
DataCheckWarning,
IDColumnsDataCheck,
)
id_data_check_name = IDColumnsDataCheck.name
def test_id_cols_data_check_init():
id_cols_check = IDColumnsDataCheck()
assert id_cols_check.id_threshold == 1.0
id_cols_check = IDColumnsDataCheck(id_threshold=0.0)
assert id_cols_check.id_threshold == 0
id_cols_check = IDColumnsDataCheck(id_threshold=0.5)
assert id_cols_check.id_threshold == 0.5
id_cols_check = IDColumnsDataCheck(id_threshold=1.0)
assert id_cols_check.id_threshold == 1.0
with pytest.raises(
ValueError, match="id_threshold must be a float between 0 and 1, inclusive."
):
IDColumnsDataCheck(id_threshold=-0.1)
with pytest.raises(
ValueError, match="id_threshold must be a float between 0 and 1, inclusive."
):
IDColumnsDataCheck(id_threshold=1.1)
def test_id_columns_warning():
X_dict = {
"col_1_id": [0, 1, 2, 3],
"col_2": [2, 3, 4, 5],
"col_3_id": [1, 1, 2, 3],
"Id": [3, 1, 2, 0],
"col_5": [0, 0, 1, 2],
"col_6": [0.1, 0.2, 0.3, 0.4],
}
X = pd.DataFrame.from_dict(X_dict)
id_cols_check = IDColumnsDataCheck(id_threshold=0.95)
assert id_cols_check.validate(X) == [
DataCheckWarning(
message="Columns 'Id', 'col_1_id', 'col_2', 'col_3_id' are 95.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": ["Id", "col_1_id", "col_2", "col_3_id"]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": ["Id", "col_1_id", "col_2", "col_3_id"]},
)
],
).to_dict(),
]
X = pd.DataFrame.from_dict(X_dict)
id_cols_check = IDColumnsDataCheck(id_threshold=1.0)
assert id_cols_check.validate(X) == [
DataCheckWarning(
message="Columns 'Id', 'col_1_id' are 100.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": ["Id", "col_1_id"]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": ["Id", "col_1_id"]},
)
],
).to_dict(),
]
def test_id_columns_strings():
X_dict = {
"col_1_id": ["a", "b", "c", "d"],
"col_2": ["w", "x", "y", "z"],
"col_3_id": [
"123456789012345",
"234567890123456",
"3456789012345678",
"45678901234567",
],
"Id": ["z", "y", "x", "a"],
"col_5": ["0", "0", "1", "2"],
"col_6": [0.1, 0.2, 0.3, 0.4],
}
X = pd.DataFrame.from_dict(X_dict)
X.ww.init(
logical_types={
"col_1_id": "categorical",
"col_2": "categorical",
"Id": "categorical",
"col_5": "categorical",
}
)
id_cols_check = IDColumnsDataCheck(id_threshold=0.95)
assert id_cols_check.validate(X) == [
DataCheckWarning(
message="Columns 'Id', 'col_1_id', 'col_2', 'col_3_id' are 95.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": ["Id", "col_1_id", "col_2", "col_3_id"]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": ["Id", "col_1_id", "col_2", "col_3_id"]},
)
],
).to_dict(),
]
id_cols_check = IDColumnsDataCheck(id_threshold=1.0)
assert id_cols_check.validate(X) == [
DataCheckWarning(
message="Columns 'Id', 'col_1_id' are 100.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": ["Id", "col_1_id"]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": ["Id", "col_1_id"]},
)
],
).to_dict(),
]
def test_id_cols_data_check_input_formats():
id_cols_check = IDColumnsDataCheck(id_threshold=0.8)
# test empty pd.DataFrame
assert id_cols_check.validate(pd.DataFrame()) == []
# test Woodwork
ww_input = pd.DataFrame(np.array([[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]]))
ww_input.ww.init()
assert id_cols_check.validate(ww_input) == [
DataCheckWarning(
message="Columns '0', '1' are 80.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": [0, 1]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": [0, 1]},
)
],
).to_dict(),
]
# test 2D list
assert id_cols_check.validate([[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]]) == [
DataCheckWarning(
message="Columns '0', '1' are 80.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": [0, 1]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": [0, 1]},
)
],
).to_dict(),
]
# test np.array
assert id_cols_check.validate(
np.array([[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]])
) == [
DataCheckWarning(
message="Columns '0', '1' are 80.0% or more likely to be an ID column",
data_check_name=id_data_check_name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": [0, 1]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=id_data_check_name,
metadata={"columns": [0, 1]},
)
],
).to_dict(),
]
|
[
"pandas.DataFrame",
"rayml.data_checks.DataCheckActionOption",
"pandas.DataFrame.from_dict",
"pytest.raises",
"numpy.array",
"rayml.data_checks.IDColumnsDataCheck"
] |
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|
"""Model for SetFee pseudo-transaction type."""
from dataclasses import dataclass, field
from typing import Optional
from xrpl.models.required import REQUIRED
from xrpl.models.transactions.pseudo_transactions.pseudo_transaction import (
PseudoTransaction,
)
from xrpl.models.transactions.types import PseudoTransactionType
from xrpl.models.utils import require_kwargs_on_init
@require_kwargs_on_init
@dataclass(frozen=True)
class SetFee(PseudoTransaction):
"""
A SetFee pseudo-transaction marks a change in `transaction cost
<https://xrpl.org/transaction-cost.html>`_ or `reserve requirements
<https://xrpl.org/reserves.html>`_ as a result of `Fee Voting
<https://xrpl.org/fee-voting.html>`_.
"""
#: The charge, in drops of XRP, for the reference transaction, as hex. (This is the
#: transaction cost before scaling for load.)
base_fee: str = REQUIRED # type: ignore
#: The cost, in fee units, of the reference transaction
reference_fee_units: int = REQUIRED # type: ignore
#: The base reserve, in drops
reserve_base: int = REQUIRED # type: ignore
#: The incremental reserve, in drops
reserve_increment: int = REQUIRED # type: ignore
#: The index of the ledger version where this pseudo-transaction appears. This
#: distinguishes the pseudo-transaction from other occurrences of the same change.
#: This field is omitted for some historical SetFee pseudo-transactions.
ledger_sequence: Optional[int] = None
transaction_type: PseudoTransactionType = field(
default=PseudoTransactionType.SET_FEE,
init=False,
)
|
[
"dataclasses.field",
"dataclasses.dataclass"
] |
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|
# Generated by Django 3.1.4 on 2021-01-23 07:08
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('process', '0012_auto_20210123_1607'),
]
operations = [
migrations.AlterField(
model_name='process',
name='process',
field=models.CharField(blank=True, max_length=16, null=True, verbose_name='ๆๆฅใใจใ๏ผๆฅๅนณๅ'),
),
]
|
[
"django.db.models.CharField"
] |
[((338, 423), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'max_length': '(16)', 'null': '(True)', 'verbose_name': '"""ๆๆฅใใจใ๏ผๆฅๅนณๅ"""'}), "(blank=True, max_length=16, null=True, verbose_name='ๆๆฅใใจใ๏ผๆฅๅนณๅ'\n )\n", (354, 423), False, 'from django.db import migrations, models\n')]
|
#
# This file is part of Magnum.
#
# Copyright ยฉ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
# 2020 <NAME> <<EMAIL>>
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
import unittest
# setUpModule gets called before everything else, skipping if GL tests can't
# be run
from . import GLTestCase, setUpModule
from magnum import *
from magnum import gl, meshtools, primitives
class Compile(GLTestCase):
def test_2d(self):
a = meshtools.compile(primitives.square_wireframe())
self.assertEqual(a.primitive, gl.MeshPrimitive.LINE_LOOP)
self.assertEqual(a.count, 4)
def test_3d(self):
a = meshtools.compile(primitives.cube_solid())
self.assertEqual(a.primitive, gl.MeshPrimitive.TRIANGLES)
self.assertEqual(a.count, 36)
|
[
"magnum.primitives.cube_solid",
"magnum.primitives.square_wireframe"
] |
[((1530, 1559), 'magnum.primitives.square_wireframe', 'primitives.square_wireframe', ([], {}), '()\n', (1557, 1559), False, 'from magnum import gl, meshtools, primitives\n'), ((1718, 1741), 'magnum.primitives.cube_solid', 'primitives.cube_solid', ([], {}), '()\n', (1739, 1741), False, 'from magnum import gl, meshtools, primitives\n')]
|
#!/usr/bin/env python
#-*- coding: utf-8 -*-
#pylint: disable=
# Author: <NAME> <<EMAIL>net AT gmail [DOT] com>
"""
Spark script to parse and aggregate DBS and PhEDEx records on HDFS.
"""
# system modules
import os
import re
import sys
import time
import json
# pyspark modules
from pyspark import SparkContext, StorageLevel
from pyspark.sql import SQLContext
from pyspark.sql.functions import lit, sum, count, col, split
from pyspark.sql import functions as F
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType, IntegerType
# CMSSpark modules
from CMSSpark.spark_utils import phedex_summary_tables, print_rows
from CMSSpark.spark_utils import spark_context, split_dataset
from CMSSpark.utils import info
from CMSSpark.conf import OptionParser
def dateStamp(date):
"Convert YYYYMMDD into sec since epoch"
sec = time.mktime(time.strptime(date, '%Y%m%d'))
return sec
def days_present(min_d, max_d, min_rd, max_rd):
"Find total number of days"
print("### days: %s %s %s %s" % (min_d, max_d, min_rd, max_rd))
if min_d and min_rd and max_d and max_rd:
minv = min(int(min_d), int(min_rd))
maxv = max(int(max_d), int(max_rd))
secs = dateStamp(str(maxv)) - dateStamp(str(minv))
secd = 60*60*24
return int(round(secs/secd))
return -1
def run(fout, yarn=None, verbose=None):
"""
Main function to run pyspark job. It requires a schema file, an HDFS directory
with data and optional script with mapper/reducer functions.
"""
# define spark context, it's main object which allow to communicate with spark
ctx = spark_context('cms', yarn, verbose)
sqlContext = SQLContext(ctx)
# read Phedex tables
tables = {}
hdir = 'hdfs:///cms/users/vk/phedex'
tables.update(phedex_summary_tables(sqlContext, hdir=hdir, verbose=verbose))
phedex_summary_df = tables['phedex_summary_df']
phedex_summary_df.persist(StorageLevel.MEMORY_AND_DISK)
print("### schema", phedex_summary_df.printSchema())
phedex_summary_df.show(5)
# register user defined function
days = udf(days_present, IntegerType())
# aggregate phedex info into dataframe
# pdf = phedex_summary_df\
# .groupBy(['site', 'dataset'])\
# .agg(
# F.min(col('date')).alias('min_date'),
# F.max(col('date')).alias('max_date'),
# F.min(col('replica_date')).alias('min_rdate'),
# F.max(col('replica_date')).alias('max_rdate'),
# F.max(col('size')).alias('max_size'),
# F.min(col('size')).alias('min_size')
# )\
# .withColumn('days', days(col('min_date'), col('max_date'), col('min_rdate'), col('max_rdate')))
pdf = phedex_summary_df\
.groupBy(['site', 'dataset', 'size'])\
.agg(
F.min(col('date')).alias('min_date'),
F.max(col('date')).alias('max_date'),
F.min(col('replica_date')).alias('min_rdate'),
F.max(col('replica_date')).alias('max_rdate')
)\
.withColumn('days', days(col('min_date'), col('max_date'), col('min_rdate'), col('max_rdate')))
pdf.persist(StorageLevel.MEMORY_AND_DISK)
pdf.show(5)
print_rows(pdf, 'pdf', verbose=1)
# write out results back to HDFS, the fout parameter defines area on HDFS
# it is either absolute path or area under /user/USERNAME
if fout:
pdf.write.format("com.databricks.spark.csv")\
.option("header", "true").save(fout)
ctx.stop()
@info
def main():
"Main function"
optmgr = OptionParser('phedex_agg')
opts = optmgr.parser.parse_args()
print("Input arguments: %s" % opts)
run(opts.fout, opts.yarn, opts.verbose)
if __name__ == '__main__':
main()
|
[
"pyspark.sql.SQLContext",
"CMSSpark.spark_utils.spark_context",
"pyspark.sql.functions.col",
"CMSSpark.spark_utils.phedex_summary_tables",
"CMSSpark.conf.OptionParser",
"pyspark.sql.types.IntegerType",
"time.strptime",
"CMSSpark.spark_utils.print_rows"
] |
[((1624, 1659), 'CMSSpark.spark_utils.spark_context', 'spark_context', (['"""cms"""', 'yarn', 'verbose'], {}), "('cms', yarn, verbose)\n", (1637, 1659), False, 'from CMSSpark.spark_utils import spark_context, split_dataset\n'), ((1677, 1692), 'pyspark.sql.SQLContext', 'SQLContext', (['ctx'], {}), '(ctx)\n', (1687, 1692), False, 'from pyspark.sql import SQLContext\n'), ((3315, 3348), 'CMSSpark.spark_utils.print_rows', 'print_rows', (['pdf', '"""pdf"""'], {'verbose': '(1)'}), "(pdf, 'pdf', verbose=1)\n", (3325, 3348), False, 'from CMSSpark.spark_utils import phedex_summary_tables, print_rows\n'), ((3676, 3702), 'CMSSpark.conf.OptionParser', 'OptionParser', (['"""phedex_agg"""'], {}), "('phedex_agg')\n", (3688, 3702), False, 'from CMSSpark.conf import OptionParser\n'), ((863, 892), 'time.strptime', 'time.strptime', (['date', '"""%Y%m%d"""'], {}), "(date, '%Y%m%d')\n", (876, 892), False, 'import time\n'), ((1794, 1855), 'CMSSpark.spark_utils.phedex_summary_tables', 'phedex_summary_tables', (['sqlContext'], {'hdir': 'hdir', 'verbose': 'verbose'}), '(sqlContext, hdir=hdir, verbose=verbose)\n', (1815, 1855), False, 'from CMSSpark.spark_utils import phedex_summary_tables, print_rows\n'), ((2123, 2136), 'pyspark.sql.types.IntegerType', 'IntegerType', ([], {}), '()\n', (2134, 2136), False, 'from pyspark.sql.types import StringType, IntegerType\n'), ((3178, 3193), 'pyspark.sql.functions.col', 'col', (['"""min_date"""'], {}), "('min_date')\n", (3181, 3193), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((3195, 3210), 'pyspark.sql.functions.col', 'col', (['"""max_date"""'], {}), "('max_date')\n", (3198, 3210), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((3212, 3228), 'pyspark.sql.functions.col', 'col', (['"""min_rdate"""'], {}), "('min_rdate')\n", (3215, 3228), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((3230, 3246), 'pyspark.sql.functions.col', 'col', (['"""max_rdate"""'], {}), "('max_rdate')\n", (3233, 3246), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((2899, 2910), 'pyspark.sql.functions.col', 'col', (['"""date"""'], {}), "('date')\n", (2902, 2910), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((2957, 2968), 'pyspark.sql.functions.col', 'col', (['"""date"""'], {}), "('date')\n", (2960, 2968), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((3015, 3034), 'pyspark.sql.functions.col', 'col', (['"""replica_date"""'], {}), "('replica_date')\n", (3018, 3034), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n'), ((3082, 3101), 'pyspark.sql.functions.col', 'col', (['"""replica_date"""'], {}), "('replica_date')\n", (3085, 3101), False, 'from pyspark.sql.functions import lit, sum, count, col, split\n')]
|
from common import cache_request, to_list, diff_and_save
import re
from bs4 import BeautifulSoup
re_extra_spaces = re.compile(r'[^\S\n]+')
re_locale_address = re.compile(r'(?P<locale>\S([^\S\n]|\w)*\s*County)\s*Board of Elections\s*(?P<address>\S.*\d{5}(-\d+)?)\s*', flags=re.MULTILINE+re.DOTALL)
re_phone_line = re.compile(r'Phone:\s*(.*)\n')
re_fax_line = re.compile(r'Fax:\s*(.*)\n')
re_phone = re.compile(r'1?\D*\d{3}\D*\d{3}\D*\d{4}')
def parse_county(soup):
# content is within the only table on the page, in 2 th cells
blocks = soup.find_all('th')
text1 = re_extra_spaces.sub(' ', blocks[0].get_text('\n'))
# locale/county/address
match = re_locale_address.search(text1)
results = match.groupdict()
results['county'] = results['locale']
# phone/fax numbers
results['phones'] = []
for phone_line in re_phone_line.findall(text1):
results['phones'] += re_phone.findall(phone_line)
results['faxes'] = []
for fax_line in re_fax_line.findall(text1):
results['faxes'] += re_phone.findall(fax_line)
# emails
results['emails'] = sorted(set(i.get('href').replace('mailto:','').strip() for i in blocks[0].select('a[href^=mailto]')))
results['emails'] = list(filter(lambda x: not x.startswith('http'), results['emails'])) # Erie county apparently only uses a web form
# county elections url
url = blocks[0].find('a', text=lambda x: x and x.startswith('Visit')).get('href')
if url:
results['url'] = url.strip()
# officials
officers = re_extra_spaces.sub(' ', blocks[1].get_text('\n')).split('\n')[1:]
officers = [officer.split(',')[0] for officer in officers]
results['official'] = officers[0]
results['other_officials'] = officers[1:]
for k in ['locale','emails','faxes','phones','county']:
if results['locale'].startswith('Erie') and k == 'emails': #Erie County apparently only uses a web form
continue
assert(results[k])
return results
if __name__ == '__main__':
data = []
text = cache_request('https://www.elections.ny.gov/CountyBoards.html')
soup = BeautifulSoup(text, 'html.parser')
for county_area in soup.find_all('area'):
county_link = county_area['href']
text = cache_request(county_link)
data.append(parse_county(BeautifulSoup(text, 'html.parser')))
# sort by locale for consistent ordering
data.sort(key=lambda x: x['locale'])
diff_and_save(data, 'public/new_york.json')
|
[
"bs4.BeautifulSoup",
"common.diff_and_save",
"common.cache_request",
"re.compile"
] |
[((117, 141), 're.compile', 're.compile', (['"""[^\\\\S\\\\n]+"""'], {}), "('[^\\\\S\\\\n]+')\n", (127, 141), False, 'import re\n'), ((161, 320), 're.compile', 're.compile', (['"""(?P<locale>\\\\S([^\\\\S\\\\n]|\\\\w)*\\\\s*County)\\\\s*Board of Elections\\\\s*(?P<address>\\\\S.*\\\\d{5}(-\\\\d+)?)\\\\s*"""'], {'flags': '(re.MULTILINE + re.DOTALL)'}), "(\n '(?P<locale>\\\\S([^\\\\S\\\\n]|\\\\w)*\\\\s*County)\\\\s*Board of Elections\\\\s*(?P<address>\\\\S.*\\\\d{5}(-\\\\d+)?)\\\\s*'\n , flags=re.MULTILINE + re.DOTALL)\n", (171, 320), False, 'import re\n'), ((315, 346), 're.compile', 're.compile', (['"""Phone:\\\\s*(.*)\\\\n"""'], {}), "('Phone:\\\\s*(.*)\\\\n')\n", (325, 346), False, 'import re\n'), ((360, 389), 're.compile', 're.compile', (['"""Fax:\\\\s*(.*)\\\\n"""'], {}), "('Fax:\\\\s*(.*)\\\\n')\n", (370, 389), False, 'import re\n'), ((400, 446), 're.compile', 're.compile', (['"""1?\\\\D*\\\\d{3}\\\\D*\\\\d{3}\\\\D*\\\\d{4}"""'], {}), "('1?\\\\D*\\\\d{3}\\\\D*\\\\d{3}\\\\D*\\\\d{4}')\n", (410, 446), False, 'import re\n'), ((1968, 2031), 'common.cache_request', 'cache_request', (['"""https://www.elections.ny.gov/CountyBoards.html"""'], {}), "('https://www.elections.ny.gov/CountyBoards.html')\n", (1981, 2031), False, 'from common import cache_request, to_list, diff_and_save\n'), ((2041, 2075), 'bs4.BeautifulSoup', 'BeautifulSoup', (['text', '"""html.parser"""'], {}), "(text, 'html.parser')\n", (2054, 2075), False, 'from bs4 import BeautifulSoup\n'), ((2348, 2391), 'common.diff_and_save', 'diff_and_save', (['data', '"""public/new_york.json"""'], {}), "(data, 'public/new_york.json')\n", (2361, 2391), False, 'from common import cache_request, to_list, diff_and_save\n'), ((2170, 2196), 'common.cache_request', 'cache_request', (['county_link'], {}), '(county_link)\n', (2183, 2196), False, 'from common import cache_request, to_list, diff_and_save\n'), ((2226, 2260), 'bs4.BeautifulSoup', 'BeautifulSoup', (['text', '"""html.parser"""'], {}), "(text, 'html.parser')\n", (2239, 2260), False, 'from bs4 import BeautifulSoup\n')]
|
import tensorflow as tf
"""tf.polygamma(a,x,name=None)
ๅ่ฝ๏ผ่ฎก็ฎpsi^{(a)}(x),psi^{(a)}(x) = ({d^a}/{dx^a})*psi(x),psiๅณไธบpolygammaใ
่พๅ
ฅ๏ผxไธบๅผ ้๏ผๅฏไปฅไธบ`float32`, `float64`็ฑปๅใa=tf.constant(1,tf.float64) """
a = tf.constant(1, tf.float64)
x = tf.constant([[1, 2, 3, 4]], tf.float64)
z = tf.polygamma(a, x)
sess = tf.Session()
print(sess.run(z))
sess.close()
# z==>[[1.64493407 0.64493407 0.39493407 0.28382296]]
|
[
"tensorflow.Session",
"tensorflow.polygamma",
"tensorflow.constant"
] |
[((202, 228), 'tensorflow.constant', 'tf.constant', (['(1)', 'tf.float64'], {}), '(1, tf.float64)\n', (213, 228), True, 'import tensorflow as tf\n'), ((233, 272), 'tensorflow.constant', 'tf.constant', (['[[1, 2, 3, 4]]', 'tf.float64'], {}), '([[1, 2, 3, 4]], tf.float64)\n', (244, 272), True, 'import tensorflow as tf\n'), ((277, 295), 'tensorflow.polygamma', 'tf.polygamma', (['a', 'x'], {}), '(a, x)\n', (289, 295), True, 'import tensorflow as tf\n'), ((304, 316), 'tensorflow.Session', 'tf.Session', ([], {}), '()\n', (314, 316), True, 'import tensorflow as tf\n')]
|
from collections import OrderedDict
from pyramid.compat import bytes_
from pyramid.httpexceptions import HTTPBadRequest
from pyramid.view import view_config
from pyramid.response import Response
from snovault import TYPES
from collections import OrderedDict
from snovault.util import simple_path_ids
from urllib.parse import (
parse_qs,
urlencode,
)
from .search import iter_search_results
from .search import list_visible_columns_for_schemas
import pprint
import csv
import io
import json
import subprocess
import requests
import shlex
import sys
import logging
import re
log = logging.getLogger(__name__)
#log.setLevel(logging.INFO)
def includeme(config):
config.add_route('batch_download', '/batch_download/{search_params}')
config.add_route('metadata', '/metadata/{search_params}/{tsv}')
config.add_route('peak_download', '/peak_download/{search_params}/{tsv}')
config.add_route('report_download', '/report.tsv')
config.scan(__name__)
# includes concatenated properties
_tsv_mapping = OrderedDict([
('File accession', ['files.title']),
('File format', ['files.file_type']),
('Output type', ['files.output_type']),
('Experiment accession', ['accession']),
('Annotation accession', ['accession']),
('Assay', ['assay_term_name']),
('Annotation', ['annotation_type']),
('Biosample term id', ['biosample_term_id']),
('Biosample term name', ['biosample_term_name']),
('Biosample type', ['biosample_type']),
('Biosample synonyms', ['biosample_synonyms']),
('System slims', ['system_slims']),
('Organ slims', ['organ_slims']),
('Biosample life stage', ['replicates.library.biosample.life_stage']),
('Biosample sex', ['replicates.library.biosample.sex']),
('Biosample Age', ['replicates.library.biosample.age',
'replicates.library.biosample.age_units']),
('Biosample organism', ['replicates.library.biosample.organism.scientific_name']),
('Biosample treatments', ['replicates.library.biosample.treatments.treatment_term_name']),
('Biosample subcellular fraction term name', ['replicates.library.biosample.subcellular_fraction_term_name']),
('Biosample phase', ['replicates.library.biosample.phase']),
('Biosample synchronization stage', ['replicates.library.biosample.fly_synchronization_stage',
'replicates.library.biosample.worm_synchronization_stage',
'replicates.library.biosample.post_synchronization_time',
'replicates.library.biosample.post_synchronization_time_units']),
('Experiment target', ['target.name']),
('Antibody accession', ['replicates.antibody.accession']),
('Library made from', ['replicates.library.nucleic_acid_term_name']),
('Library depleted in', ['replicates.library.depleted_in_term_name']),
('Library extraction method', ['replicates.library.extraction_method']),
('Library lysis method', ['replicates.library.lysis_method']),
('Library crosslinking method', ['replicates.library.crosslinking_method']),
('Library strand specific', ['replicates.library.strand_specificity']),
('Experiment date released', ['date_released']),
('Project', ['award.project']),
('RBNS protein concentration', ['files.replicate.rbns_protein_concentration', 'files.replicate.rbns_protein_concentration_units']),
('Library fragmentation method', ['files.replicate.library.fragmentation_method']),
('Library size range', ['files.replicate.library.size_range']),
('Biological replicate(s)', ['files.biological_replicates']),
('Technical replicate', ['files.replicate.technical_replicate_number']),
('Read length', ['files.read_length']),
('Mapped read length', ['files.mapped_read_length']),
('Run type', ['files.run_type']),
('Paired end', ['files.paired_end']),
('Paired with', ['files.paired_with']),
('Derived from', ['files.derived_from']),
('Size', ['files.file_size']),
('Lab', ['files.lab.title']),
('md5sum', ['files.md5sum']),
('bed_file_state', ['files.bed_file_state']),
('bed_file_value', ['files.bed_file_value']),
('file_format', ['files.file_format']),
('files.date_created', ['files.date_created']),
('dbxrefs', ['files.dbxrefs']),
('File download URL', ['files.href']),
('Assembly', ['files.assembly']),
('Platform', ['files.platform.title']),
('Controlled by', ['files.controlled_by']),
('File Status', ['files.status'])
])
_audit_mapping = OrderedDict([
('Audit WARNING', ['audit.WARNING.path',
'audit.WARNING.category',
'audit.WARNING.detail']),
('Audit INTERNAL_ACTION', ['audit.INTERNAL_ACTION.path',
'audit.INTERNAL_ACTION.category',
'audit.INTERNAL_ACTION.detail']),
('Audit NOT_COMPLIANT', ['audit.NOT_COMPLIANT.path',
'audit.NOT_COMPLIANT.category',
'audit.NOT_COMPLIANT.detail']),
('Audit ERROR', ['audit.ERROR.path',
'audit.ERROR.category',
'audit.ERROR.detail'])
])
def get_file_uuids(result_dict):
file_uuids = []
for item in result_dict['@graph']:
for file in item['files']:
file_uuids.append(file['uuid'])
return list(set(file_uuids))
def get_biosample_accessions(file_json, experiment_json):
for f in experiment_json['files']:
if file_json['uuid'] == f['uuid']:
accession = f.get('replicate', {}).get('library', {}).get('biosample', {}).get('accession')
if accession:
return accession
accessions = []
for replicate in experiment_json.get('replicates', []):
accession = replicate['library']['biosample']['accession']
accessions.append(accession)
return ', '.join(list(set(accessions)))
def get_peak_metadata_links(request):
if request.matchdict.get('search_params'):
search_params = request.matchdict['search_params']
else:
search_params = request.query_string
peak_download_tsv_link = '{host_url}/peak_download/{search_params}/peak_download.tsv'.format(
host_url=request.host_url,
search_params= search_params
)
return [peak_download_tsv_link]
def get_region_metadata_links(request):
if request.matchdict.get('search_params'):
search_params = request.matchdict['search_params']
else:
search_params = request.query_string
region_metadata_tsv_link = '{host_url}/region_metadata/{search_params}/region_metadata.tsv'.format(
host_url=request.host_url,
search_params=search_params
)
return [region_metadata_tsv_link]
def make_cell(header_column, row, exp_data_row):
temp = []
for column in _tsv_mapping[header_column]:
c_value = []
for value in simple_path_ids(row, column):
if str(value) not in c_value:
c_value.append(str(value))
if column == 'replicates.library.biosample.post_synchronization_time' and len(temp):
if len(c_value):
temp[0] = temp[0] + ' + ' + c_value[0]
elif len(temp):
if len(c_value):
temp = [x + ' ' + c_value[0] for x in temp]
else:
temp = c_value
exp_data_row.append(', '.join(list(set(temp))))
def make_audit_cell(header_column, experiment_json, file_json):
categories = []
paths = []
for column in _audit_mapping[header_column]:
for value in simple_path_ids(experiment_json, column):
if 'path' in column:
paths.append(value)
elif 'category' in column:
categories.append(value)
data = []
for i, path in enumerate(paths):
if '/files/' in path and file_json.get('title', '') not in path:
# Skip file audits that does't belong to the file
continue
else:
data.append(categories[i])
return ', '.join(list(set(data)))
@view_config(route_name='peak_download', request_method='GET')
def peak_download(context, request):
param_list = parse_qs(request.matchdict['search_params'])
param_list['field'] = []
header = ['annotation_type', 'source', 'coordinates', 'file.accession', 'annotation.accession']
param_list['limit'] = ['all']
path = '/variant-search/?{}&{}'.format(urlencode(param_list, True),'referrer=download_metadata')
results = request.embed(path, as_user=True)
uuids_in_results = get_file_uuids(results)
rows = []
json_doc = {}
for row in results['peaks']:
if row['_id'] in uuids_in_results:
file_json = request.embed(row['_id'])
annotation_json = request.embed(file_json['dataset'])
for hit in row['inner_hits']['positions']['hits']['hits']:
data_row = []
chrom = '{}'.format(row['_index'])
assembly = '{}'.format(row['_type'])
start = int('{}'.format(hit['_source']['start']))
stop = int('{}'.format(hit['_source']['end']))
state = '{}'.format(hit['_source']['state'])
val = '{}'.format(hit['_source']['val'])
file_accession = file_json['accession']
annotation_accession = annotation_json['accession']
coordinates = '{}:{}-{}'.format(row['_index'], hit['_source']['start'], hit['_source']['end'])
annotation = annotation_json['annotation_type']
biosample_term = annotation_json['biosample_term_name']
data_row.extend([annotation, biosample_term, coordinates, file_accession, annotation_accession])
rows.append(data_row)
fout = io.StringIO()
writer = csv.writer(fout, delimiter='\t')
writer.writerow(header)
writer.writerows(rows)
return Response(
content_type='text/tsv',
body=fout.getvalue(),
content_disposition='attachment;filename="%s"' % 'peak_metadata.tsv'
)
@view_config(route_name='metadata', request_method='GET')
def metadata_tsv(context, request):
param_list = parse_qs(request.matchdict['search_params'])
if 'referrer' in param_list:
search_path = '/{}/'.format(param_list.pop('referrer')[0])
else:
search_path = '/search/'
param_list['field'] = []
header = []
file_attributes = []
for prop in _tsv_mapping:
header.append(prop)
param_list['field'] = param_list['field'] + _tsv_mapping[prop]
if _tsv_mapping[prop][0].startswith('files'):
file_attributes = file_attributes + [_tsv_mapping[prop][0]]
param_list['limit'] = ['all']
path = '{}?{}'.format(search_path, urlencode(param_list, True))
results = request.embed(path, as_user=True)
rows = []
for experiment_json in results['@graph']:
if experiment_json['files']:
exp_data_row = []
for column in header:
if not _tsv_mapping[column][0].startswith('files'):
make_cell(column, experiment_json, exp_data_row)
f_attributes = ['files.title', 'files.file_type',
'files.output_type']
for f in experiment_json['files']:
if 'files.file_type' in param_list:
if f['file_type'] not in param_list['files.file_type']:
continue
f['href'] = request.host_url + f['href']
f_row = []
for attr in f_attributes:
f_row.append(f[attr[6:]])
data_row = f_row + exp_data_row
for prop in file_attributes:
if prop in f_attributes:
continue
path = prop[6:]
temp = []
for value in simple_path_ids(f, path):
temp.append(str(value))
if prop == 'files.replicate.rbns_protein_concentration':
if 'replicate' in f and 'rbns_protein_concentration_units' in f['replicate']:
temp[0] = temp[0] + ' ' + f['replicate']['rbns_protein_concentration_units']
if prop in ['files.paired_with', 'files.derived_from']:
# chopping of path to just accession
if len(temp):
new_values = [t[7:-1] for t in temp]
temp = new_values
data = list(set(temp))
data.sort()
data_row.append(', '.join(data))
audit_info = [make_audit_cell(audit_type, experiment_json, f) for audit_type in _audit_mapping]
data_row.extend(audit_info)
rows.append(data_row)
fout = io.StringIO()
writer = csv.writer(fout, delimiter='\t')
header.extend([prop for prop in _audit_mapping])
writer.writerow(header)
writer.writerows(rows)
return Response(
content_type='text/tsv',
body=fout.getvalue(),
content_disposition='attachment;filename="%s"' % 'metadata.tsv'
)
@view_config(route_name='batch_download', request_method='GET')
def batch_download(context, request):
# adding extra params to get required columns
param_list = parse_qs(request.matchdict['search_params'])
param_list['field'] = ['files.href', 'files.file_type']
param_list['limit'] = ['all']
path = '/search/?%s' % urlencode(param_list, True)
results = request.embed(path, as_user=True)
metadata_link = '{host_url}/metadata/{search_params}/metadata.tsv'.format(
host_url=request.host_url,
search_params=request.matchdict['search_params']
)
files = [metadata_link]
if 'files.file_type' in param_list:
for exp in results['@graph']:
for f in exp['files']:
if f['file_type'] in param_list['files.file_type']:
files.append('{host_url}{href}'.format(
host_url=request.host_url,
href=f['href']
))
else:
for exp in results['@graph']:
for f in exp['files']:
files.append('{host_url}{href}'.format(
host_url=request.host_url,
href=f['href']
))
return Response(
content_type='text/plain',
body='\n'.join(files),
content_disposition='attachment; filename="%s"' % 'files.txt'
)
def lookup_column_value(value, path):
nodes = [value]
names = path.split('.')
for name in names:
nextnodes = []
for node in nodes:
if name not in node:
continue
value = node[name]
if isinstance(value, list):
nextnodes.extend(value)
else:
nextnodes.append(value)
nodes = nextnodes
if not nodes:
return ''
# if we ended with an embedded object, show the @id
if nodes and hasattr(nodes[0], '__contains__') and '@id' in nodes[0]:
nodes = [node['@id'] for node in nodes]
seen = set()
deduped_nodes = [n for n in nodes if not (n in seen or seen.add(n))]
return u','.join(u'{}'.format(n) for n in deduped_nodes)
def format_row(columns):
"""Format a list of text columns as a tab-separated byte string."""
return b'\t'.join([bytes_(c, 'utf-8') for c in columns]) + b'\r\n'
@view_config(route_name='report_download', request_method='GET')
def report_download(context, request):
types = request.params.getall('type')
if len(types) != 1:
msg = 'Report view requires specifying a single type.'
raise HTTPBadRequest(explanation=msg)
# Make sure we get all results
request.GET['limit'] = 'all'
schemas = [request.registry[TYPES][types[0]].schema]
columns = list_visible_columns_for_schemas(request, schemas)
# Work around Excel bug; can't open single column TSV with 'ID' header
if len(columns) == 1 and '@id' in columns:
columns['@id']['title'] = 'id'
header = [column.get('title') or field for field, column in columns.items()]
def generate_rows():
yield format_row(header)
for item in iter_search_results(context, request):
values = [lookup_column_value(item, path) for path in columns]
yield format_row(values)
# Stream response using chunked encoding.
request.response.content_type = 'text/tsv'
request.response.content_disposition = 'attachment;filename="%s"' % 'report.tsv'
request.response.app_iter = generate_rows()
return request.response
|
[
"pyramid.view.view_config",
"io.StringIO",
"csv.writer",
"urllib.parse.urlencode",
"pyramid.compat.bytes_",
"urllib.parse.parse_qs",
"snovault.util.simple_path_ids",
"collections.OrderedDict",
"pyramid.httpexceptions.HTTPBadRequest",
"logging.getLogger"
] |
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|
# Test linked list
from app.linked_list import LinkedList
class TestLinkedList:
def test_initialize_ll(self):
ll = LinkedList(4)
assert ll.head.value == 4
assert ll.head.next is None
assert ll.tail.value == 4
assert ll.tail.next is None
assert ll.head is ll.tail
def test_print_ll(self):
ll = LinkedList(4)
ll.append(5)
assert ll.print_list() == "4->5"
def test_print_ll_with_no_inital_head(self):
ll = LinkedList(None)
ll.head = None
ll.append(5)
assert ll.print_list() == "5"
assert ll.head.value == 5
assert ll.tail.value == 5
def test_pop_ll(self):
ll = LinkedList(4)
ll.append(5)
ll.append(6)
assert ll.length == 3
assert ll.print_list() == "4->5->6"
popped_node = ll.pop()
assert popped_node.value == 6
assert popped_node.next is None
assert ll.length == 2
assert ll.print_list() == "4->5"
ll.pop()
assert ll.length == 1
assert ll.print_list() == "4"
ll.pop()
assert ll.length == 0
assert ll.print_list() == ""
def test_pop_ll_with_zero_length(self):
ll = LinkedList(4)
ll.pop()
assert ll.length == 0
ll.pop()
assert ll.length == 0
def test_prepend_ll(self):
ll = LinkedList(4)
ll.prepend(3)
assert ll.length == 2
assert ll.print_list() == "3->4"
def test_prepend_ll_with_no_element(self):
ll = LinkedList(4)
ll.pop()
ll.prepend(3)
assert ll.length == 1
assert ll.print_list() == "3"
def test_pop_first_ll(self):
ll = LinkedList(4)
ll.append(5)
assert ll.print_list() == "4->5"
# pop first element
ll.pop_first()
assert ll.print_list() == "5"
assert ll.length == 1
# pop first with one element
ll.pop_first()
assert ll.print_list() == ""
assert ll.length == 0
# pop first with no element
ll.pop_first()
assert ll.print_list() == ""
assert ll.length == 0
def test_get_ll_with_index(self):
ll = LinkedList(4)
ll.append(5)
ll.append(6)
ll.append(7)
assert ll.print_list() == "4->5->6->7"
assert ll.get(2).value == 6
assert ll.get(3).value == 7
ll.pop_first()
assert ll.get(2).value == 7
ll.pop()
assert ll.get(2) == None
ll.pop()
# one element
assert ll.get(0).value == 5
ll.pop()
# get on ll with no element
assert ll.get(0) == None
def test_set_value_ll_with_index(self):
ll = LinkedList(4)
ll.append(5)
ll.append(6)
ll.append(7)
assert ll.print_list() == "4->5->6->7"
ll.set_value(0, 3)
assert ll.print_list() == "3->5->6->7"
ll.set_value(2, 8)
assert ll.print_list() == "3->5->8->7"
ll.set_value(10, 10)
assert ll.print_list() == "3->5->8->7"
for _ in range(ll.length):
ll.pop()
ll.set_value(0, 1)
assert ll.print_list() == ""
def test_insert_ll(self):
ll = LinkedList(4)
ll.insert(0, 3)
assert ll.print_list() == "3->4"
ll.insert(2, 6)
assert ll.print_list() == "3->4->6"
ll.insert(2, 5)
assert ll.print_list() == "3->4->5->6"
assert ll.insert(10, 7) == ll.insert(-2, 0) == False
def test_remove_ll(self):
ll = LinkedList(4)
ll.append(5)
ll.append(6)
ll.append(7)
ll.append(8)
assert ll.print_list() == "4->5->6->7->8"
ll.remove(2)
assert ll.print_list() == "4->5->7->8"
ll.remove(0)
assert ll.print_list() == "5->7->8"
ll.remove(1)
assert ll.print_list() == "5->8"
ll.remove(1)
assert ll.print_list() == "5"
ll.remove(0)
assert ll.print_list() == ""
assert ll.remove(3) == False
def test_ll_reverse(self):
ll = LinkedList(4)
ll.append(5)
ll.append(6)
ll.append(7)
assert ll.print_list() == "4->5->6->7"
ll.reverse()
assert ll.print_list() == "7->6->5->4"
|
[
"app.linked_list.LinkedList"
] |
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|
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Tests for non_semantic_speech_benchmark.eval_embedding.sklearn.sklearn_utils."""
import os
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
import tensorflow.compat.v1 as tf
from non_semantic_speech_benchmark.eval_embedding.sklearn import sklearn_utils
class SklearnUtilsTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(
{'l2_normalization': True},
{'l2_normalization': False},
)
def test_tfexample_to_nps(self, l2_normalization):
path = os.path.join(absltest.get_default_test_tmpdir(), 'dummy_tfrecords')
embedding_name = 'fake_emb'
label_name = 'label/fake_lbl'
label_list = ['yes', 'no']
np.random.seed(10)
# Generate fake embeddings and labels.
fake_data = [
(np.random.rand(100), 1),
(np.random.rand(100), 0),
(np.random.rand(100), 1),
]
def _emb_lbl_i_to_tfexample(emb, label_index):
"""Package fake data as a tf.Example."""
ex = tf.train.Example()
ex.features.feature[
f'embedding/{embedding_name}'].float_list.value.extend(emb)
ex.features.feature[label_name].bytes_list.value.append(
label_list[label_index].encode('utf-8'))
return ex
# Write TFRecord of tf.Examples to disk.
with tf.python_io.TFRecordWriter(path) as writer:
for emb, label_index in fake_data:
ex = _emb_lbl_i_to_tfexample(emb, label_index)
writer.write(ex.SerializeToString())
# Convert them back.
npx, npy, _ = sklearn_utils.tfexamples_to_nps(path, embedding_name,
label_name, label_list,
l2_normalization)
# Check that output is correct.
expected_embs = np.array([d[0] for d in fake_data], np.float32)
if l2_normalization:
expected_embs /= np.linalg.norm(expected_embs, axis=1, ord=2,
keepdims=True)
self.assertAllEqual(npx, expected_embs)
self.assertAllEqual(npy, (1, 0, 1))
def test_speaker_normalization(self):
original_embeddings = np.array(
[
[0.5, 2.1],
[0.5, 0.6],
[-.5, 2.1],
],
np.float32)
speaker_ids = np.array(
[
'id1',
'id2',
'id1',
],
np.str)
expected_normalized_embeddings = np.array(
[
[1.0, 0.0],
[0.0, 0.0],
[-1., 0.0],
],
np.float32)
normalized_embeddings = sklearn_utils._speaker_normalization(
original_embeddings, speaker_ids)
self.assertAllEqual(normalized_embeddings, expected_normalized_embeddings)
if __name__ == '__main__':
tf.test.main()
|
[
"non_semantic_speech_benchmark.eval_embedding.sklearn.sklearn_utils.tfexamples_to_nps",
"numpy.random.seed",
"tensorflow.compat.v1.train.Example",
"absl.testing.absltest.get_default_test_tmpdir",
"tensorflow.compat.v1.python_io.TFRecordWriter",
"absl.testing.parameterized.parameters",
"tensorflow.compat.v1.test.main",
"numpy.array",
"numpy.linalg.norm",
"numpy.random.rand",
"non_semantic_speech_benchmark.eval_embedding.sklearn.sklearn_utils._speaker_normalization"
] |
[((999, 1085), 'absl.testing.parameterized.parameters', 'parameterized.parameters', (["{'l2_normalization': True}", "{'l2_normalization': False}"], {}), "({'l2_normalization': True}, {'l2_normalization': \n False})\n", (1023, 1085), False, 'from absl.testing import parameterized\n'), ((3382, 3396), 'tensorflow.compat.v1.test.main', 'tf.test.main', ([], {}), '()\n', (3394, 3396), True, 'import tensorflow.compat.v1 as tf\n'), ((1332, 1350), 'numpy.random.seed', 'np.random.seed', (['(10)'], {}), '(10)\n', (1346, 1350), True, 'import numpy as np\n'), ((2161, 2260), 'non_semantic_speech_benchmark.eval_embedding.sklearn.sklearn_utils.tfexamples_to_nps', 'sklearn_utils.tfexamples_to_nps', (['path', 'embedding_name', 'label_name', 'label_list', 'l2_normalization'], {}), '(path, embedding_name, label_name,\n label_list, l2_normalization)\n', (2192, 2260), False, 'from non_semantic_speech_benchmark.eval_embedding.sklearn import sklearn_utils\n'), ((2414, 2461), 'numpy.array', 'np.array', (['[d[0] for d in fake_data]', 'np.float32'], {}), '([d[0] for d in fake_data], np.float32)\n', (2422, 2461), True, 'import numpy as np\n'), ((2759, 2818), 'numpy.array', 'np.array', (['[[0.5, 2.1], [0.5, 0.6], [-0.5, 2.1]]', 'np.float32'], {}), '([[0.5, 2.1], [0.5, 0.6], [-0.5, 2.1]], np.float32)\n', (2767, 2818), True, 'import numpy as np\n'), ((2900, 2939), 'numpy.array', 'np.array', (["['id1', 'id2', 'id1']", 'np.str'], {}), "(['id1', 'id2', 'id1'], np.str)\n", (2908, 2939), True, 'import numpy as np\n'), ((3041, 3100), 'numpy.array', 'np.array', (['[[1.0, 0.0], [0.0, 0.0], [-1.0, 0.0]]', 'np.float32'], {}), '([[1.0, 0.0], [0.0, 0.0], [-1.0, 0.0]], np.float32)\n', (3049, 3100), True, 'import numpy as np\n'), ((3192, 3262), 'non_semantic_speech_benchmark.eval_embedding.sklearn.sklearn_utils._speaker_normalization', 'sklearn_utils._speaker_normalization', (['original_embeddings', 'speaker_ids'], {}), '(original_embeddings, speaker_ids)\n', (3228, 3262), False, 'from non_semantic_speech_benchmark.eval_embedding.sklearn import sklearn_utils\n'), ((1175, 1209), 'absl.testing.absltest.get_default_test_tmpdir', 'absltest.get_default_test_tmpdir', ([], {}), '()\n', (1207, 1209), False, 'from absl.testing import absltest\n'), ((1630, 1648), 'tensorflow.compat.v1.train.Example', 'tf.train.Example', ([], {}), '()\n', (1646, 1648), True, 'import tensorflow.compat.v1 as tf\n'), ((1931, 1964), 'tensorflow.compat.v1.python_io.TFRecordWriter', 'tf.python_io.TFRecordWriter', (['path'], {}), '(path)\n', (1958, 1964), True, 'import tensorflow.compat.v1 as tf\n'), ((2510, 2569), 'numpy.linalg.norm', 'np.linalg.norm', (['expected_embs'], {'axis': '(1)', 'ord': '(2)', 'keepdims': '(True)'}), '(expected_embs, axis=1, ord=2, keepdims=True)\n', (2524, 2569), True, 'import numpy as np\n'), ((1422, 1441), 'numpy.random.rand', 'np.random.rand', (['(100)'], {}), '(100)\n', (1436, 1441), True, 'import numpy as np\n'), ((1456, 1475), 'numpy.random.rand', 'np.random.rand', (['(100)'], {}), '(100)\n', (1470, 1475), True, 'import numpy as np\n'), ((1490, 1509), 'numpy.random.rand', 'np.random.rand', (['(100)'], {}), '(100)\n', (1504, 1509), True, 'import numpy as np\n')]
|
from __future__ import print_function
import random
import Pyro4
class GameServer(object):
def __init__(self, engine):
self.engine=engine
def register(self, name, observer):
robot=self.engine.signup_robot(name, observer)
self._pyroDaemon.register(robot) # make the robot a pyro object
return robot
class RemoteBot(object):
def __init__(self, robot, engine):
self.robot=robot
self.engine=engine
def get_data(self):
return self.robot.serializable()
def change_direction(self, direction):
self.robot.dx,self.robot.dy = direction
def emote(self, text):
self.robot.emote(text)
def terminate(self):
self.engine.remove_robot(self.robot)
class LocalGameObserver(object):
def __init__(self, name):
self.name=name
self.robot=None
self._pyroOneway=set() # remote observers have this
def world_update(self, iteration, world, robotdata):
# change directions randomly
if random.random()>0.8:
if random.random()>=0.5:
dx,dy=random.randint(-1,1),0
else:
dx,dy=0,random.randint(-1,1)
self.robot.change_direction((dx,dy))
def start(self):
self.robot.emote("Here we go!")
def victory(self):
print("[%s] I WON!!!" % self.name)
def death(self, killer):
if killer:
print("[%s] I DIED (%s did it)" % (self.name, killer.name))
else:
print("[%s] I DIED" % self.name)
class GameObserver(object):
def world_update(self, iteration, world, robotdata):
pass
def start(self):
print("Battle starts!")
def victory(self):
print("I WON!!!")
def death(self, killer):
print("I DIED")
if killer:
print("%s KILLED ME :(" % killer.name)
|
[
"random.random",
"random.randint"
] |
[((1052, 1067), 'random.random', 'random.random', ([], {}), '()\n', (1065, 1067), False, 'import random\n'), ((1089, 1104), 'random.random', 'random.random', ([], {}), '()\n', (1102, 1104), False, 'import random\n'), ((1134, 1155), 'random.randint', 'random.randint', (['(-1)', '(1)'], {}), '(-1, 1)\n', (1148, 1155), False, 'import random\n'), ((1201, 1222), 'random.randint', 'random.randint', (['(-1)', '(1)'], {}), '(-1, 1)\n', (1215, 1222), False, 'import random\n')]
|
from gauss import Gauss
from square_roots import SquareRoots
from running import Running
from lt_rotation import LtRotation
from ui import UI
from termcolor import colored
import time
eps = 0.001
'''task 1'''
t1 = colored('TASK1', 'red')
print(t1)
a = [
[-54, -11, 40, 57, 80, -99, 29, 45, 22, 50],
[19, 40, 89, -36, -63, 22, 57, -62, -14, 39],
[-81, 40, 69, -87, -89, -63, 76, 31, 43, -27],
[18, 42, -69, -88, -37, 31, 91, 91, -37, 14],
[9, -71, 78, 98, -83, 98, 45, 27, -10, 20],
[38, -57, 65, -97, 49, 39, -3, 18, 43, 98],
[-62, 0, 72, -22, -43, 58, 87, 26, 64, 72],
[60, 61, 68, -14, -28, 60, -8, 36, 40, -34],
[73, 17, 62, 15, -46, -22, 71, -50, 72, 38]
]
UI.print_slar(a, 9)
start_time = time.time()
g1 = Gauss(a, 9, eps)
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
lu = LtRotation(a, 9, eps)
print("--- %s seconds ---" % (time.time() - start_time))
'''task 2'''
t2 = colored('TASK2', 'red')
print(t2)
b = [
[414, 250, -51, -38, -129, -77, 208, -8, 239, 259, 240],
[250, 655, 293, 108, -42, 255, -91, -26, 47, 200, 353],
[-51, 293, 643, 184, -228, 94, -22, -117, -141, -235, 284],
[-38, 108, 184, 265, -32, -96, -210, 126, 12, -167, 346],
[-129, -42, -228, -32, 282, 127, -169, 40, 7, 44, -219],
[-77, 255, 94, -96, 127, 466, -162, -98, -248, 169, 219],
[208, -91, -22, -210, -169, -162, 678, -10, 260, -116, 451],
[-8, -26, -117, 126, 40, -98, -10, 655, 22, -166, 205],
[239, 47, -141, 12, 7, -248, 260, 22, 480, -28, 528],
[259, 200, -235, -167, 44, 169, -116, -166, -28, 583, 361]
]
UI.print_slar(b, 10)
start_time = time.time()
sq = SquareRoots(b, 10, eps)
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
g2 = Gauss(b, 10, eps)
print("--- %s seconds ---" % (time.time() - start_time))
t3 = colored('TASK3', 'red')
print(t3)
c = [
[346, -16, 0, 0, 0, 0, 0, 0, 0, 0, 3],
[-65, 248, -80, 0, 0, 0, 0, 0, 0, 0, -137],
[0, 176, 660, 286, 0, 0, 0, 0, 0, 0, 996],
[0, 0, -249, -785, -315, 0, 0, 0, 0, 0, 624],
[0, 0, 0, -6, -20, 8, 0, 0, 0, 0, -29],
[0, 0, 0, 0, -365, -802, 46, 0, 0, 0, 789],
[0, 0, 0, 0, 0, -102, 545, -175, 0, 0, -725],
[0, 0, 0, 0, 0, 0, -93, -322, 3, 0, -220],
[0, 0, 0, 0, 0, 0, 0, 105, 813, -324, 855],
[0, 0, 0, 0, 0, 0, 0, 0, -194, -218, 835]
]
UI.print_slar(c, 10)
start_time = time.time()
run = Running(c, 10, eps)
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
g3 = Gauss(c, 10, eps)
print("--- %s seconds ---" % (time.time() - start_time))
|
[
"square_roots.SquareRoots",
"running.Running",
"gauss.Gauss",
"lt_rotation.LtRotation",
"time.time",
"termcolor.colored",
"ui.UI.print_slar"
] |
[((216, 239), 'termcolor.colored', 'colored', (['"""TASK1"""', '"""red"""'], {}), "('TASK1', 'red')\n", (223, 239), False, 'from termcolor import colored\n'), ((699, 718), 'ui.UI.print_slar', 'UI.print_slar', (['a', '(9)'], {}), '(a, 9)\n', (712, 718), False, 'from ui import UI\n'), ((732, 743), 'time.time', 'time.time', ([], {}), '()\n', (741, 743), False, 'import time\n'), ((749, 765), 'gauss.Gauss', 'Gauss', (['a', '(9)', 'eps'], {}), '(a, 9, eps)\n', (754, 765), False, 'from gauss import Gauss\n'), ((837, 848), 'time.time', 'time.time', ([], {}), '()\n', (846, 848), False, 'import time\n'), ((854, 875), 'lt_rotation.LtRotation', 'LtRotation', (['a', '(9)', 'eps'], {}), '(a, 9, eps)\n', (864, 875), False, 'from lt_rotation import LtRotation\n'), ((952, 975), 'termcolor.colored', 'colored', (['"""TASK2"""', '"""red"""'], {}), "('TASK2', 'red')\n", (959, 975), False, 'from termcolor import colored\n'), ((1610, 1630), 'ui.UI.print_slar', 'UI.print_slar', (['b', '(10)'], {}), '(b, 10)\n', (1623, 1630), False, 'from ui import UI\n'), ((1644, 1655), 'time.time', 'time.time', ([], {}), '()\n', (1653, 1655), False, 'import time\n'), ((1661, 1684), 'square_roots.SquareRoots', 'SquareRoots', (['b', '(10)', 'eps'], {}), '(b, 10, eps)\n', (1672, 1684), False, 'from square_roots import SquareRoots\n'), ((1756, 1767), 'time.time', 'time.time', ([], {}), '()\n', (1765, 1767), False, 'import time\n'), ((1773, 1790), 'gauss.Gauss', 'Gauss', (['b', '(10)', 'eps'], {}), '(b, 10, eps)\n', (1778, 1790), False, 'from gauss import Gauss\n'), ((1854, 1877), 'termcolor.colored', 'colored', (['"""TASK3"""', '"""red"""'], {}), "('TASK3', 'red')\n", (1861, 1877), False, 'from termcolor import colored\n'), ((2367, 2387), 'ui.UI.print_slar', 'UI.print_slar', (['c', '(10)'], {}), '(c, 10)\n', (2380, 2387), False, 'from ui import UI\n'), ((2401, 2412), 'time.time', 'time.time', ([], {}), '()\n', (2410, 2412), False, 'import time\n'), ((2419, 2438), 'running.Running', 'Running', (['c', '(10)', 'eps'], {}), '(c, 10, eps)\n', (2426, 2438), False, 'from running import Running\n'), ((2510, 2521), 'time.time', 'time.time', ([], {}), '()\n', (2519, 2521), False, 'import time\n'), ((2527, 2544), 'gauss.Gauss', 'Gauss', (['c', '(10)', 'eps'], {}), '(c, 10, eps)\n', (2532, 2544), False, 'from gauss import Gauss\n'), ((796, 807), 'time.time', 'time.time', ([], {}), '()\n', (805, 807), False, 'import time\n'), ((906, 917), 'time.time', 'time.time', ([], {}), '()\n', (915, 917), False, 'import time\n'), ((1715, 1726), 'time.time', 'time.time', ([], {}), '()\n', (1724, 1726), False, 'import time\n'), ((1821, 1832), 'time.time', 'time.time', ([], {}), '()\n', (1830, 1832), False, 'import time\n'), ((2469, 2480), 'time.time', 'time.time', ([], {}), '()\n', (2478, 2480), False, 'import time\n'), ((2575, 2586), 'time.time', 'time.time', ([], {}), '()\n', (2584, 2586), False, 'import time\n')]
|
# Generated by Django 3.1.3 on 2020-11-29 09:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("autotasks", "0008_auto_20201030_1515"),
]
operations = [
migrations.AddField(
model_name="automatedtask",
name="run_time_bit_weekdays",
field=models.IntegerField(blank=True, null=True),
),
]
|
[
"django.db.models.IntegerField"
] |
[((358, 400), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (377, 400), False, 'from django.db import migrations, models\n')]
|
# -*- encoding: utf-8 -*-
from django.shortcuts import render
from modules.forms import ModuleFilterForm
from modules.models import Module
# Create your views here.
def home(request):
module_filter_form = ModuleFilterForm(request.POST or None)
from_module = Module.objects.get(name='Entrada') #De momento sรณlo Entrada
modules = Module.objects.filter(shortcut=True).order_by('name')
return render(request, 'index.html', {
'module_filter_form' : module_filter_form,
'from_module' : from_module,
'modules' : modules
})
|
[
"modules.forms.ModuleFilterForm",
"modules.models.Module.objects.filter",
"django.shortcuts.render",
"modules.models.Module.objects.get"
] |
[((213, 251), 'modules.forms.ModuleFilterForm', 'ModuleFilterForm', (['(request.POST or None)'], {}), '(request.POST or None)\n', (229, 251), False, 'from modules.forms import ModuleFilterForm\n'), ((268, 302), 'modules.models.Module.objects.get', 'Module.objects.get', ([], {'name': '"""Entrada"""'}), "(name='Entrada')\n", (286, 302), False, 'from modules.models import Module\n'), ((406, 531), 'django.shortcuts.render', 'render', (['request', '"""index.html"""', "{'module_filter_form': module_filter_form, 'from_module': from_module,\n 'modules': modules}"], {}), "(request, 'index.html', {'module_filter_form': module_filter_form,\n 'from_module': from_module, 'modules': modules})\n", (412, 531), False, 'from django.shortcuts import render\n'), ((343, 379), 'modules.models.Module.objects.filter', 'Module.objects.filter', ([], {'shortcut': '(True)'}), '(shortcut=True)\n', (364, 379), False, 'from modules.models import Module\n')]
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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"
qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
[
"PyQt5.QtCore.qUnregisterResourceData",
"PyQt5.QtCore.qVersion",
"PyQt5.QtCore.qRegisterResourceData"
] |
[((534471, 534572), 'PyQt5.QtCore.qRegisterResourceData', 'QtCore.qRegisterResourceData', (['rcc_version', 'qt_resource_struct', 'qt_resource_name', 'qt_resource_data'], {}), '(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n', (534499, 534572), False, 'from PyQt5 import QtCore\n'), ((534599, 534702), 'PyQt5.QtCore.qUnregisterResourceData', 'QtCore.qUnregisterResourceData', (['rcc_version', 'qt_resource_struct', 'qt_resource_name', 'qt_resource_data'], {}), '(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n', (534629, 534702), False, 'from PyQt5 import QtCore\n'), ((534247, 534264), 'PyQt5.QtCore.qVersion', 'QtCore.qVersion', ([], {}), '()\n', (534262, 534264), False, 'from PyQt5 import QtCore\n')]
|
#!/usr/bin/env python
'''
------------------------------------------------------------------------------------------
Copyright 2020 <NAME>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-------------------------------------------------------------------------------------------
Notes: Oh this is just the signup processing stuff. Grabs the form values from the header
and so on so forth...
'''
import cgi
import theroom
# Create instance of FieldStorage
form = cgi.FieldStorage()
# Now populate it with the stuff taken from the form
gun = str(form.getvalue('uname'))
gpw = str(form.getvalue('pword'))
gfn = str(form.getvalue('fname'))
gln = str(form.getvalue('lname'))
gpn = str(form.getvalue('phone'))
gem = str(form.getvalue('email'))
# Trys to sign up
pros = theroom.makeUser(gun,gpw,acl=0,fname=gfn,lname=gln,phone=gpn,email=gem)
if pros == True:
rpage = '''
changeRoom('newUser',"&yes=yes");
'''
else:
rpage = '''
var erp1 = document.getElementById('errorplace1');
erp1.innerHTML = "The username %s is already taken. Please use a different one";
''' % gun
print("Content-Type: text/xml\r\n")
print(rpage)
|
[
"cgi.FieldStorage",
"theroom.makeUser"
] |
[((999, 1017), 'cgi.FieldStorage', 'cgi.FieldStorage', ([], {}), '()\n', (1015, 1017), False, 'import cgi\n'), ((1310, 1387), 'theroom.makeUser', 'theroom.makeUser', (['gun', 'gpw'], {'acl': '(0)', 'fname': 'gfn', 'lname': 'gln', 'phone': 'gpn', 'email': 'gem'}), '(gun, gpw, acl=0, fname=gfn, lname=gln, phone=gpn, email=gem)\n', (1326, 1387), False, 'import theroom\n')]
|
import pandas as pd
import time
import sys
def df_parser(file) -> tuple:
"""
Returns df and name depending on file type
"""
extention = file.split('.')[1]
name = file.split('.')[0]
if extention == 'csv':
return pd.read_csv(file), name
elif extention == 'xlsx':
return pd.read_excel(file), name
elif extention == 'sql':
return pd.read_sql(file), name
else:
return None
def data_lst(df) -> dict:
"""
Stores input data into a dictionary
"""
data = {}
value = ''
for row in df.index:
value = ','.join(str(x) for x in df.iloc[row].values)
if data.get(value):
data[value]['rows'].append(row)
else:
data[value] = {'rows': [row]}
return data
def val_list_count(lst1, lst2) -> list:
"""
Computes total match and unmatch lengths and returns counts and data list.
"""
match_count = 0
unmatch_count = 0
match_lst = []
unmatch_lst = []
for key in lst1:
if lst2.get(key):
match_count += len(lst1[key]['rows'])
match_lst.append(key.split(','))
else:
unmatch_count += len(lst1[key]['rows'])
unmatch_lst.append(key.split(','))
return match_lst, unmatch_lst, match_count, unmatch_count
def validator(workbook1, workbook2) -> None:
"""
Auto validator that will return percentage of corrlelation either positive or negative
:param workbook1 string: path to workbook 1
:param workbook2 string: path to workbook 2
:param corr boolean: choose whether you would like to search for positive or negative correlation
:return: percentages
"""
start_time = time.time()
df1, name1 = df_parser(workbook1)
df2, name2 = df_parser(workbook2)
val_list_1 = data_lst(df1)
val_list_2 = data_lst(df2)
mlst1, ulst1, mcount1, ucount1 = val_list_count(val_list_1, val_list_2)
mlst2, ulst2, mcount2, ucount2 = val_list_count(val_list_2, val_list_1)
pd.DataFrame(mlst1).to_csv(f'reports/Matching_{name1}.csv')
pd.DataFrame(mlst2).to_csv(f'reports/Matching_{name2}.csv')
pd.DataFrame(ulst1).to_csv(f'reports/Unmatching_{name1}.csv')
pd.DataFrame(ulst2).to_csv(f'reports/Unmatching_{name2}.csv')
output_info = pd.DataFrame((
(
df1.shape[0], df1.shape[1], mcount1, ucount1,
round(mcount1 / df1.shape[0] * 100, 2), round(ucount1 / df1.shape[0] * 100, 2), ''
),
(
df2.shape[0], df2.shape[1], mcount2, ucount2,
round(mcount2 / df2.shape[0] * 100, 2), round(ucount2 / df2.shape[0] * 100, 2), ''
),
(
'', '', '', '', '', '', round(time.time() - start_time, 3)
),
),
columns=['RowCount', 'ColumnCount', 'Matching', 'Unmatching', 'Matching%', 'Unmatching%', 'RunTime'],
index=[name1, name2, 'Total'])
output_info.to_excel('reports/00_FullReport.xlsx')
print('============================================Report================================================')
print(output_info)
print('==================================================================================================')
return None
def field_check(field1, field2) -> None:
"""
For testing
"""
test1 = field1
test2 = field2
file = open('field_check.txt', 'w+')
file.writelines(test1)
file.writelines('\n')
file.writelines(test2)
file.close()
return None
if __name__ == '__main__':
workbook1 = sys.argv[1]
workbook2 = sys.argv[2]
validator(workbook1, workbook2)
|
[
"pandas.DataFrame",
"pandas.read_csv",
"time.time",
"pandas.read_excel",
"pandas.read_sql"
] |
[((1719, 1730), 'time.time', 'time.time', ([], {}), '()\n', (1728, 1730), False, 'import time\n'), ((245, 262), 'pandas.read_csv', 'pd.read_csv', (['file'], {}), '(file)\n', (256, 262), True, 'import pandas as pd\n'), ((2029, 2048), 'pandas.DataFrame', 'pd.DataFrame', (['mlst1'], {}), '(mlst1)\n', (2041, 2048), True, 'import pandas as pd\n'), ((2093, 2112), 'pandas.DataFrame', 'pd.DataFrame', (['mlst2'], {}), '(mlst2)\n', (2105, 2112), True, 'import pandas as pd\n'), ((2157, 2176), 'pandas.DataFrame', 'pd.DataFrame', (['ulst1'], {}), '(ulst1)\n', (2169, 2176), True, 'import pandas as pd\n'), ((2223, 2242), 'pandas.DataFrame', 'pd.DataFrame', (['ulst2'], {}), '(ulst2)\n', (2235, 2242), True, 'import pandas as pd\n'), ((314, 333), 'pandas.read_excel', 'pd.read_excel', (['file'], {}), '(file)\n', (327, 333), True, 'import pandas as pd\n'), ((384, 401), 'pandas.read_sql', 'pd.read_sql', (['file'], {}), '(file)\n', (395, 401), True, 'import pandas as pd\n'), ((2709, 2720), 'time.time', 'time.time', ([], {}), '()\n', (2718, 2720), False, 'import time\n')]
|
import discord
from discord import Colour, Embed
from discord.ext import commands
import re
import random
def replacement_func(match, repl_pattern):
match_str = match.group(0)
repl = ''.join([r_char if m_char.islower() else r_char.upper()
for r_char, m_char in zip(repl_pattern, match_str)])
repl += repl_pattern[len(match_str):]
return repl
class TextCog(commands.Cog, name="Text Manipulation Commands"):
def __init__(self, bot):
self.bot = bot
@commands.command(name='ebin', help="Returns a spurdified version of the sentence passed as parameter")
async def ebin(self, ctx, *args):
bicd = {
"epic":"ebin",
"penis":"benis","wh":"w","th":"d","af":"ab","ap":"ab","ca":"ga","ck":"gg","co":"go",
"ev":"eb","ex":"egs","et":"ed","iv":"ib","it":"id","ke":"ge","op":"ob","ot":"od",
"po":"bo","pe":"be","pi":"bi","up":"ub","va":"ba","cr":"gr","kn":"gn","lt":"ld",
"mm":"m", "nt":"dn","pr":"br","tr":"dr","alk":"olk","ing":"ign","ic":"ig","ng":"nk",
"kek":"geg","some":"sum","meme":"maymay"
}
ebinFaces = [ ':D', ':DD', ':DDD', 'XD', 'XXD', 'XDD', 'XXDD' ]
new_args = " ".join(args)
for k, v in bicd.items():
new_args = re.sub(k, lambda k: replacement_func(k,v), new_args, flags=re.I)
ebinText = new_args+" "+ random.choice(ebinFaces)
await ctx.send(ebinText)
def setup(bot):
bot.add_cog(TextCog(bot))
|
[
"discord.ext.commands.command",
"random.choice"
] |
[((498, 605), 'discord.ext.commands.command', 'commands.command', ([], {'name': '"""ebin"""', 'help': '"""Returns a spurdified version of the sentence passed as parameter"""'}), "(name='ebin', help=\n 'Returns a spurdified version of the sentence passed as parameter')\n", (514, 605), False, 'from discord.ext import commands\n'), ((1392, 1416), 'random.choice', 'random.choice', (['ebinFaces'], {}), '(ebinFaces)\n', (1405, 1416), False, 'import random\n')]
|
import os
import unittest
from PIL import Image
from utils import getdrawer, abspath
from helper import assert_image_similar
class TestRankingScreen(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.tests = []
cls.update = False
cls.tests.append(getdrawer("", "syunn", (123123123, -1)))
cls.tests.append(getdrawer("2", "syunn", (123123123, -1)))
def testframes(self):
for i in range(len(self.tests)):
drawer = self.tests[i][1]
expectf = self.tests[i][0] + "rankingbruh.png"
for x in range(int(0.5 * drawer.settings.fps)):
drawer.draw_rankingpanel()
if self.update:
drawer.pbuffer.save(expectf)
else:
expect = Image.open(expectf).convert("RGBA")
assert_image_similar(drawer.pbuffer, expect, 5)
expectf = self.tests[i][0] + "rankingbruh1.png"
for x in range(int(0.6 * drawer.settings.fps)):
drawer.draw_rankingpanel()
if self.update:
drawer.pbuffer.save(expectf)
else:
expect = Image.open(expectf).convert("RGBA")
assert_image_similar(drawer.pbuffer, expect, 1)
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"utils.getdrawer",
"helper.assert_image_similar",
"PIL.Image.open"
] |
[((1083, 1098), 'unittest.main', 'unittest.main', ([], {}), '()\n', (1096, 1098), False, 'import unittest\n'), ((266, 305), 'utils.getdrawer', 'getdrawer', (['""""""', '"""syunn"""', '(123123123, -1)'], {}), "('', 'syunn', (123123123, -1))\n", (275, 305), False, 'from utils import getdrawer, abspath\n'), ((326, 366), 'utils.getdrawer', 'getdrawer', (['"""2"""', '"""syunn"""', '(123123123, -1)'], {}), "('2', 'syunn', (123123123, -1))\n", (335, 366), False, 'from utils import getdrawer, abspath\n'), ((706, 753), 'helper.assert_image_similar', 'assert_image_similar', (['drawer.pbuffer', 'expect', '(5)'], {}), '(drawer.pbuffer, expect, 5)\n', (726, 753), False, 'from helper import assert_image_similar\n'), ((1005, 1052), 'helper.assert_image_similar', 'assert_image_similar', (['drawer.pbuffer', 'expect', '(1)'], {}), '(drawer.pbuffer, expect, 1)\n', (1025, 1052), False, 'from helper import assert_image_similar\n'), ((666, 685), 'PIL.Image.open', 'Image.open', (['expectf'], {}), '(expectf)\n', (676, 685), False, 'from PIL import Image\n'), ((965, 984), 'PIL.Image.open', 'Image.open', (['expectf'], {}), '(expectf)\n', (975, 984), False, 'from PIL import Image\n')]
|
#!/usr/bin/env python
import os
import plynx
from setuptools import setup, find_packages
def parse_requirements(filename):
""" load requirements from a pip requirements file """
lineiter = (line.strip() for line in open(filename))
return [line for line in lineiter if line and not line.startswith("#")]
DIR = os.path.dirname(os.path.abspath(__file__))
install_requires = parse_requirements(os.path.join(DIR, 'requirements.txt'))
# Extra dependencies for storage
dev_reqs = parse_requirements(os.path.join(DIR, 'requirements-dev.txt'))
gs = [
"google-cloud-storage>=1.13.0",
]
s3 = [
"boto3>=1.9.62",
]
ssh = [
"paramiko>=2.4.2",
]
all_reqs = dev_reqs + gs + s3 + ssh
setup(
name='plynx',
version=plynx.__version__,
description='ML platform',
long_description='Interactive, Scalable, Shareable and Reproducible ML experiments',
url='https://plynx.com',
author='<NAME>',
author_email='<EMAIL>',
classifiers=[
'Development Status :: 4 - Beta',
'Environment :: Console',
'Environment :: Web Environment',
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Information Technology',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: Apache Software License',
'Operating System :: POSIX',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.7',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: Scientific/Engineering :: Information Analysis',
'Topic :: Scientific/Engineering :: Visualization',
],
keywords='data science, machine learning, pipeline, workflow, experiments',
packages=find_packages(exclude=['scripts', 'docker']),
install_requires=install_requires,
extras_require={
'all': all_reqs,
'dev': dev_reqs,
'gs': gs,
's3': s3,
'ssh': ssh,
},
package_data={},
entry_points={
'console_scripts': [
'plynx=plynx.bin:main',
],
},
project_urls={
'Demo': 'https://plynx.com',
'Source': 'https://github.com/plynx-team/plynx',
},
# plynx.graph.base_nodes.collection uses reference to __file__
zip_safe=False,
)
|
[
"os.path.abspath",
"os.path.join",
"setuptools.find_packages"
] |
[((341, 366), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (356, 366), False, 'import os\n'), ((406, 443), 'os.path.join', 'os.path.join', (['DIR', '"""requirements.txt"""'], {}), "(DIR, 'requirements.txt')\n", (418, 443), False, 'import os\n'), ((509, 550), 'os.path.join', 'os.path.join', (['DIR', '"""requirements-dev.txt"""'], {}), "(DIR, 'requirements-dev.txt')\n", (521, 550), False, 'import os\n'), ((1870, 1914), 'setuptools.find_packages', 'find_packages', ([], {'exclude': "['scripts', 'docker']"}), "(exclude=['scripts', 'docker'])\n", (1883, 1914), False, 'from setuptools import setup, find_packages\n')]
|
import os
from flask import Flask, request, abort, jsonify, current_app
from bot import db
from bot import utils
from bot.directive import Directive
from bot.context import Context, PrivateContext, GroupContext
from apscheduler.schedulers.background import BackgroundScheduler
from .utils import send
def create_app(config=None):
app = Flask(__name__)
if config:
# load the config if passed in
app.config.from_mapping(config)
else:
app.config.from_mapping(
DATABASE=os.path.join(app.instance_path, 'database.db')
)
if not os.path.exists(app.instance_path):
os.mkdir(app.instance_path)
app.config.from_pyfile(os.path.join(os.path.dirname(app.root_path), 'settings.cfg'))
# print(os.getcwd())
db.init_database(app)
app.route('/', methods=['POST'])(handler)
app.route('/webhook', methods=['POST'])(webhook_handler)
app.teardown_appcontext(db.close_db)
init_background_tasks(app.config)
return app
def init_background_tasks(config):
apsched = BackgroundScheduler()
apsched.add_job(init_ky_reminder, args=[config], trigger='cron', hour=7, minute=0)
apsched.start()
def init_ky_reminder(config):
from datetime import date
try:
ky_date_str = os.environ["KY_DATE"]
ky_date = date(int(ky_date_str[:4]), int(ky_date_str[4:6]), int(ky_date_str[6:]))
days_to_ky = (ky_date - date.today()).days
for group, name in config["ALLOWED_GROUP"].items():
if '่็ ' in name:
send(GroupContext.build(group_id=group), message=f"่ท็ฆป{ky_date_str[:4]}ๅนดๅบฆ่็ ่ฟๆ{days_to_ky}ๅคฉ")
except KeyError:
for group, name in config["ALLOWED_GROUP"].items():
if '่็ ' in name:
send(GroupContext.build(group_id=group), message="็ฎก็ๅ่ฟๆช่ฎพๅฎ่็ ๆถ้ด๏ผไฝฟ็จ /setky ่ฎพๅฎ่็ ๆถ้ด")
def handler():
payload = request.json
post_type = payload.get("post_type")
if post_type != "message":
abort(400)
if payload['message_type'] != 'group' and payload['message_type'] != 'private':
abort(400)
if payload['message_type'] == 'group' and \
payload['group_id'] not in current_app.config['ALLOWED_GROUP']:
abort(400)
if payload['message_type'] == 'group':
context = GroupContext(payload)
elif payload['message_type'] == 'private':
context = PrivateContext(payload)
pre_process(context)
# map command to Directive class and execute it.
try:
if hasattr(Directive, context.directive):
obj = Directive(context)
response = getattr(obj, context.directive)()
else:
response = ''
except AttributeError: # if a message is not a directive
response = ''
return jsonify(response) if isinstance(response, dict) else ''
# TODO ๅผๆญฅๆง่ก
def pre_process(context: Context):
utils.log(context)
utils.accumulate_exp(context)
utils.randomly_save_message_to_treehole(context)
if context.message_type == 'group':
utils.find_cai(context)
if context.group_id == current_app.config['FORWARDED_QQ_GROUP_ID']:
utils.send_to_tg(context)
def webhook_handler():
context = GroupContext.build('', group_id=current_app.config['WEBHOOK_NOTIFICATION_GROUP'])
# DOC: https://developer.github.com/webhooks/event-payloads/
payload = request.json
if request.headers.get("X-GitHub-Event") == 'check_run':
if payload['action'] != "completed":
return ""
check_run = payload['check_run']
message = f"CI job {check_run['name']} has completed: {check_run['conclusion']}."
utils.send(context, message)
elif request.headers.get("X-GitHub-Event") == 'push':
commits = payload['commits']
message = f"{payload['sender']['login']} has pushed {len(commits)} commit(s)" \
f" to my repository:"
for commit in commits:
message += f"\n{commit['id'][:6]} {commit['message']}"
utils.send(context, message)
elif request.headers.get("X-GitHub-Event") == 'pull_request':
message = f"{payload['sender']['login']} has {payload['action']} a pull request " \
f"{payload['pull_request']['title']}. " \
f"For details see: {payload['pull_request']['url']}"
utils.send(context, message)
return ""
|
[
"apscheduler.schedulers.background.BackgroundScheduler",
"os.mkdir",
"flask.jsonify",
"os.path.join",
"bot.utils.log",
"flask.request.headers.get",
"os.path.dirname",
"flask.abort",
"os.path.exists",
"bot.utils.randomly_save_message_to_treehole",
"bot.context.GroupContext.build",
"bot.utils.send",
"bot.directive.Directive",
"datetime.date.today",
"bot.utils.find_cai",
"bot.utils.send_to_tg",
"bot.db.init_database",
"bot.utils.accumulate_exp",
"bot.context.PrivateContext",
"flask.Flask",
"bot.context.GroupContext"
] |
[((342, 357), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (347, 357), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((787, 808), 'bot.db.init_database', 'db.init_database', (['app'], {}), '(app)\n', (803, 808), False, 'from bot import db\n'), ((1065, 1086), 'apscheduler.schedulers.background.BackgroundScheduler', 'BackgroundScheduler', ([], {}), '()\n', (1084, 1086), False, 'from apscheduler.schedulers.background import BackgroundScheduler\n'), ((2889, 2907), 'bot.utils.log', 'utils.log', (['context'], {}), '(context)\n', (2898, 2907), False, 'from bot import utils\n'), ((2912, 2941), 'bot.utils.accumulate_exp', 'utils.accumulate_exp', (['context'], {}), '(context)\n', (2932, 2941), False, 'from bot import utils\n'), ((2946, 2994), 'bot.utils.randomly_save_message_to_treehole', 'utils.randomly_save_message_to_treehole', (['context'], {}), '(context)\n', (2985, 2994), False, 'from bot import utils\n'), ((3221, 3307), 'bot.context.GroupContext.build', 'GroupContext.build', (['""""""'], {'group_id': "current_app.config['WEBHOOK_NOTIFICATION_GROUP']"}), "('', group_id=current_app.config[\n 'WEBHOOK_NOTIFICATION_GROUP'])\n", (3239, 3307), False, 'from bot.context import Context, PrivateContext, GroupContext\n'), ((1979, 1989), 'flask.abort', 'abort', (['(400)'], {}), '(400)\n', (1984, 1989), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((2083, 2093), 'flask.abort', 'abort', (['(400)'], {}), '(400)\n', (2088, 2093), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((2227, 2237), 'flask.abort', 'abort', (['(400)'], {}), '(400)\n', (2232, 2237), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((2300, 2321), 'bot.context.GroupContext', 'GroupContext', (['payload'], {}), '(payload)\n', (2312, 2321), False, 'from bot.context import Context, PrivateContext, GroupContext\n'), ((2780, 2797), 'flask.jsonify', 'jsonify', (['response'], {}), '(response)\n', (2787, 2797), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((3044, 3067), 'bot.utils.find_cai', 'utils.find_cai', (['context'], {}), '(context)\n', (3058, 3067), False, 'from bot import utils\n'), ((3403, 3440), 'flask.request.headers.get', 'request.headers.get', (['"""X-GitHub-Event"""'], {}), "('X-GitHub-Event')\n", (3422, 3440), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((3663, 3691), 'bot.utils.send', 'utils.send', (['context', 'message'], {}), '(context, message)\n', (3673, 3691), False, 'from bot import utils\n'), ((589, 622), 'os.path.exists', 'os.path.exists', (['app.instance_path'], {}), '(app.instance_path)\n', (603, 622), False, 'import os\n'), ((636, 663), 'os.mkdir', 'os.mkdir', (['app.instance_path'], {}), '(app.instance_path)\n', (644, 663), False, 'import os\n'), ((2387, 2410), 'bot.context.PrivateContext', 'PrivateContext', (['payload'], {}), '(payload)\n', (2401, 2410), False, 'from bot.context import Context, PrivateContext, GroupContext\n'), ((2568, 2586), 'bot.directive.Directive', 'Directive', (['context'], {}), '(context)\n', (2577, 2586), False, 'from bot.directive import Directive\n'), ((3156, 3181), 'bot.utils.send_to_tg', 'utils.send_to_tg', (['context'], {}), '(context)\n', (3172, 3181), False, 'from bot import utils\n'), ((3701, 3738), 'flask.request.headers.get', 'request.headers.get', (['"""X-GitHub-Event"""'], {}), "('X-GitHub-Event')\n", (3720, 3738), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((4021, 4049), 'bot.utils.send', 'utils.send', (['context', 'message'], {}), '(context, message)\n', (4031, 4049), False, 'from bot import utils\n'), ((517, 563), 'os.path.join', 'os.path.join', (['app.instance_path', '"""database.db"""'], {}), "(app.instance_path, 'database.db')\n", (529, 563), False, 'import os\n'), ((708, 738), 'os.path.dirname', 'os.path.dirname', (['app.root_path'], {}), '(app.root_path)\n', (723, 738), False, 'import os\n'), ((1431, 1443), 'datetime.date.today', 'date.today', ([], {}), '()\n', (1441, 1443), False, 'from datetime import date\n'), ((4059, 4096), 'flask.request.headers.get', 'request.headers.get', (['"""X-GitHub-Event"""'], {}), "('X-GitHub-Event')\n", (4078, 4096), False, 'from flask import Flask, request, abort, jsonify, current_app\n'), ((4347, 4375), 'bot.utils.send', 'utils.send', (['context', 'message'], {}), '(context, message)\n', (4357, 4375), False, 'from bot import utils\n'), ((1560, 1594), 'bot.context.GroupContext.build', 'GroupContext.build', ([], {'group_id': 'group'}), '(group_id=group)\n', (1578, 1594), False, 'from bot.context import Context, PrivateContext, GroupContext\n'), ((1778, 1812), 'bot.context.GroupContext.build', 'GroupContext.build', ([], {'group_id': 'group'}), '(group_id=group)\n', (1796, 1812), False, 'from bot.context import Context, PrivateContext, GroupContext\n')]
|
from setuptools import setup
setup(name='pyecoplug',
version='0.0.6',
description='Interface for the EcoPlug outlet',
url='http://github.com/philburr/pyecoplug',
author='<EMAIL>',
license='MIT',
packages=['pyecoplug'],
install_requires=[],
zip_safe=True)
|
[
"setuptools.setup"
] |
[((30, 269), 'setuptools.setup', 'setup', ([], {'name': '"""pyecoplug"""', 'version': '"""0.0.6"""', 'description': '"""Interface for the EcoPlug outlet"""', 'url': '"""http://github.com/philburr/pyecoplug"""', 'author': '"""<EMAIL>"""', 'license': '"""MIT"""', 'packages': "['pyecoplug']", 'install_requires': '[]', 'zip_safe': '(True)'}), "(name='pyecoplug', version='0.0.6', description=\n 'Interface for the EcoPlug outlet', url=\n 'http://github.com/philburr/pyecoplug', author='<EMAIL>', license='MIT',\n packages=['pyecoplug'], install_requires=[], zip_safe=True)\n", (35, 269), False, 'from setuptools import setup\n')]
|
import uuid
import urllib
from PyQt5 import uic, QtWidgets
from ..utils import ui
from ..utils.logging import error
from ..threads.load_api_data_thread import LoadAPIDataThread
from ..models.api import API
FORM_CLASS, _ = uic.loadUiType(ui.path('add_edit_api_dialog.ui'))
class AddEditAPIDialog(QtWidgets.QDialog, FORM_CLASS):
def __init__(self, data={}, hooks={}, parent=None, iface=None):
super(AddEditAPIDialog, self).__init__(parent)
self.data = data
self.hooks = hooks
self.iface = iface
self.setupUi(self)
self.populate_details()
self.populate_auth_method_combo()
self.cancelButton.clicked.connect(self.on_cancel_clicked)
self.removeButton.clicked.connect(self.on_remove_clicked)
self.saveAddButton.clicked.connect(self.on_save_add_clicked)
def on_cancel_clicked(self):
self.reject()
def on_remove_clicked(self):
self.hooks['remove_api'](self.api)
self.accept()
def set_all_enabled(self, enabled):
self.urlEditBox.setEnabled(enabled)
self.authenticationCombo.setEnabled(enabled)
self.removeButton.setEnabled(enabled)
self.cancelButton.setEnabled(enabled)
self.saveAddButton.setEnabled(enabled)
def on_save_add_clicked(self):
self.set_all_enabled(False)
self.saveAddButton.setText('Testing Connection...')
api_id = str(uuid.uuid4())
if self.api is not None:
api_id = self.api.id
api = API({'id': api_id, 'href': self.urlEditBox.text()})
self.loading_thread = LoadAPIDataThread(
api,
on_error=self.on_api_error,
on_finished=self.on_api_success)
self.loading_thread.start()
def on_api_error(self, e):
self.set_all_enabled(True)
if self.api is None:
self.saveAddButton.setText('Add')
else:
self.saveAddButton.setText('Save')
if type(e) == urllib.error.URLError:
error(self.iface, f'Connection Failed; {e.reason}')
else:
error(self.iface, f'Connection Failed; {type(e).__name__}')
def on_api_success(self, api):
if self.api is None:
self.hooks['add_api'](api)
else:
self.hooks['edit_api'](api)
self.accept()
@property
def api(self):
return self.data.get('api', None)
def populate_details(self):
if self.api is None:
self.saveAddButton.setText('Add')
self.removeButton.hide()
return
self.urlEditBox.setText(self.api.href)
def populate_auth_method_combo(self):
self.authenticationCombo.addItem('No Auth')
|
[
"uuid.uuid4"
] |
[((1427, 1439), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (1437, 1439), False, 'import uuid\n')]
|
# Generated by Django 4.0.3 on 2022-03-15 15:56
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('animais', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='animal',
name='domestico',
field=models.CharField(default='n/a', max_length=5),
preserve_default=False,
),
migrations.AddField(
model_name='animal',
name='nome_animal',
field=models.CharField(default='n/a', max_length=20),
preserve_default=False,
),
migrations.AddField(
model_name='animal',
name='predador',
field=models.CharField(default='n/a', max_length=5),
preserve_default=False,
),
migrations.AddField(
model_name='animal',
name='venenozo',
field=models.CharField(default='n/a', max_length=5),
preserve_default=False,
),
]
|
[
"django.db.models.CharField"
] |
[((326, 371), 'django.db.models.CharField', 'models.CharField', ([], {'default': '"""n/a"""', 'max_length': '(5)'}), "(default='n/a', max_length=5)\n", (342, 371), False, 'from django.db import migrations, models\n'), ((532, 578), 'django.db.models.CharField', 'models.CharField', ([], {'default': '"""n/a"""', 'max_length': '(20)'}), "(default='n/a', max_length=20)\n", (548, 578), False, 'from django.db import migrations, models\n'), ((736, 781), 'django.db.models.CharField', 'models.CharField', ([], {'default': '"""n/a"""', 'max_length': '(5)'}), "(default='n/a', max_length=5)\n", (752, 781), False, 'from django.db import migrations, models\n'), ((939, 984), 'django.db.models.CharField', 'models.CharField', ([], {'default': '"""n/a"""', 'max_length': '(5)'}), "(default='n/a', max_length=5)\n", (955, 984), False, 'from django.db import migrations, models\n')]
|
import json
from asgiref.sync import async_to_sync
from tornado.testing import AsyncHTTPTestCase
class TestUserRegistrationApi(AsyncHTTPTestCase):
def get_app(self):
from consoleme.config import config
config.CONFIG.config["auth"]["get_user_by_password"] = True
config.CONFIG.config["auth"]["allow_user_registration"] = True
from consoleme.routes import make_app
return make_app(jwt_validator=lambda x: {})
def tearDown(self) -> None:
from consoleme.config import config
config.CONFIG.config["auth"]["get_user_by_password"] = False
config.CONFIG.config["auth"]["allow_user_registration"] = False
def test_register_user(self):
body = json.dumps(
{
"username": "testuser5",
"password": "<PASSWORD>",
}
)
response = self.fetch("/api/v2/user_registration", method="POST", body=body)
self.assertEqual(response.code, 403)
self.assertEqual(
json.loads(response.body),
{
"status": "error",
"reason": "invalid_request",
"redirect_url": None,
"status_code": 403,
"message": None,
"errors": [
"The email address is not valid. It must have exactly one @-sign."
],
"data": None,
},
)
body = json.dumps(
{
"username": "<EMAIL>",
"password": "<PASSWORD>",
}
)
response = self.fetch("/api/v2/user_registration", method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "success",
"reason": None,
"redirect_url": None,
"status_code": 200,
"message": "Successfully created user <EMAIL>.",
"errors": None,
"data": None,
},
)
class TestLoginApi(AsyncHTTPTestCase):
def get_app(self):
from consoleme.config import config
config.CONFIG.config["auth"]["get_user_by_password"] = True
config.CONFIG.config["auth"]["set_auth_cookie"] = True
from consoleme.routes import make_app
return make_app(jwt_validator=lambda x: {})
def tearDown(self) -> None:
from consoleme.config import config
config.CONFIG.config["auth"]["get_user_by_password"] = False
config.CONFIG.config["auth"]["set_auth_cookie"] = False
def test_login_post_no_user(self):
body = json.dumps(
{"username": "fakeuser", "password": "<PASSWORD>", "after_redirect_uri": "/"}
)
response = self.fetch("/api/v2/login", method="POST", body=body)
self.assertEqual(response.code, 403)
self.assertEqual(
json.loads(response.body),
{
"status": "error",
"reason": "authentication_failure",
"redirect_url": None,
"status_code": 403,
"message": None,
"errors": [
"User doesn't exist, or password is incorrect. ",
"Your next authentication failure will result in a 1 second wait. This wait time will expire after 60 seconds of no authentication failures.",
],
"data": None,
},
)
def test_login_post_invalid_password(self):
from consoleme.lib.dynamo import UserDynamoHandler
ddb = UserDynamoHandler()
ddb.create_user("testuser", "correctpassword", ["group1", "<EMAIL>"])
body = json.dumps(
{"username": "testuser", "password": "<PASSWORD>", "after_redirect_uri": "/"}
)
response = self.fetch("/api/v2/login", method="POST", body=body)
self.assertEqual(response.code, 403)
self.assertEqual(
json.loads(response.body),
{
"status": "error",
"reason": "authentication_failure",
"redirect_url": None,
"status_code": 403,
"message": None,
"errors": [
"User doesn't exist, or password is incorrect. ",
"Your next authentication failure will result in a 1 second wait. This wait time will expire after 60 seconds of no authentication failures.",
],
"data": None,
},
)
def test_login_post_success(self):
from consoleme.lib.dynamo import UserDynamoHandler
ddb = UserDynamoHandler()
ddb.create_user(
"testuser2", "<PASSWORD>", ["group1", "<EMAIL>"]
)
body = json.dumps(
{
"username": "testuser2",
"password": "<PASSWORD>",
"after_redirect_uri": "/",
}
)
response = self.fetch("/api/v2/login", method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "redirect",
"reason": "authenticated_redirect",
"redirect_url": "/",
"status_code": 200,
"message": "User has successfully authenticated. Redirecting to their intended destination.",
"errors": None,
"data": None,
},
)
class TestUserApi(AsyncHTTPTestCase):
def get_app(self):
from consoleme.routes import make_app
return make_app(jwt_validator=lambda x: {})
def test_create_user(self):
from consoleme.config import config
headers = {
config.get("auth.user_header_name"): "<EMAIL>",
config.get("auth.groups_header_name"): "groupa,groupb,groupc",
}
body = json.dumps(
{
"user_management_action": "create",
"username": "testuser3",
"password": "<PASSWORD>",
"groups": ["group1", "group2", "group3"],
}
)
response = self.fetch("/api/v2/user", headers=headers, method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "success",
"reason": None,
"redirect_url": None,
"status_code": 200,
"message": "Successfully created user testuser3.",
"errors": None,
"data": None,
},
)
# Verify new user works
from consoleme.lib.dynamo import UserDynamoHandler
from consoleme.models import LoginAttemptModel
ddb = UserDynamoHandler()
login_attempt_success = LoginAttemptModel(
username="testuser3", password="<PASSWORD>"
)
should_pass = async_to_sync(ddb.authenticate_user)(login_attempt_success)
self.assertEqual(
should_pass.dict(),
{
"authenticated": True,
"errors": None,
"username": "testuser3",
"groups": ["group1", "group2", "group3"],
},
)
login_attempt_fail = LoginAttemptModel(
username="testuser3", password="<PASSWORD>"
)
should_fail = async_to_sync(ddb.authenticate_user)(login_attempt_fail)
self.assertEqual(
should_fail.dict(),
{
"authenticated": False,
"errors": [
"User doesn't exist, or password is incorrect. ",
"Your next authentication failure will result in a 1 second wait. "
"This wait time will expire after 60 seconds of no authentication failures.",
],
"username": None,
"groups": None,
},
)
# Update password
body = json.dumps(
{
"user_management_action": "update",
"username": "testuser3",
"password": "<PASSWORD>",
}
)
response = self.fetch("/api/v2/user", headers=headers, method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "success",
"reason": None,
"redirect_url": None,
"status_code": 200,
"message": "Successfully updated user testuser3.",
"errors": None,
"data": None,
},
)
# Update groups
body = json.dumps(
{
"user_management_action": "update",
"username": "testuser3",
"groups": ["group1", "group2", "group3", "newgroup"],
}
)
response = self.fetch("/api/v2/user", headers=headers, method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "success",
"reason": None,
"redirect_url": None,
"status_code": 200,
"message": "Successfully updated user testuser3.",
"errors": None,
"data": None,
},
)
# Update groups and password AT THE SAME TIME!!1
body = json.dumps(
{
"user_management_action": "update",
"username": "testuser3",
"password": "<PASSWORD>",
"groups": ["group1", "group2", "group3", "newgroup", "newgroup2"],
}
)
response = self.fetch("/api/v2/user", headers=headers, method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "success",
"reason": None,
"redirect_url": None,
"status_code": 200,
"message": "Successfully updated user testuser3.",
"errors": None,
"data": None,
},
)
# Delete the user
body = json.dumps(
{
"user_management_action": "delete",
"username": "testuser3",
}
)
response = self.fetch("/api/v2/user", headers=headers, method="POST", body=body)
self.assertEqual(response.code, 200)
self.assertEqual(
json.loads(response.body),
{
"status": "success",
"reason": None,
"redirect_url": None,
"status_code": 200,
"message": "Successfully deleted user testuser3.",
"errors": None,
"data": None,
},
)
|
[
"consoleme.lib.dynamo.UserDynamoHandler",
"json.loads",
"json.dumps",
"consoleme.routes.make_app",
"consoleme.config.config.get",
"consoleme.models.LoginAttemptModel",
"asgiref.sync.async_to_sync"
] |
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'group3', 'newgroup',\n 'newgroup2']}"], {}), "({'user_management_action': 'update', 'username': 'testuser3',\n 'password': '<PASSWORD>', 'groups': ['group1', 'group2', 'group3',\n 'newgroup', 'newgroup2']})\n", (9695, 9859), False, 'import json\n'), ((10506, 10579), 'json.dumps', 'json.dumps', (["{'user_management_action': 'delete', 'username': 'testuser3'}"], {}), "({'user_management_action': 'delete', 'username': 'testuser3'})\n", (10516, 10579), False, 'import json\n'), ((1026, 1051), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (1036, 1051), False, 'import json\n'), ((1758, 1783), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (1768, 1783), False, 'import json\n'), ((2967, 2992), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (2977, 2992), False, 'import json\n'), ((4041, 4066), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (4051, 4066), False, 'import json\n'), ((5188, 5213), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (5198, 5213), False, 'import json\n'), ((5861, 5896), 'consoleme.config.config.get', 'config.get', (['"""auth.user_header_name"""'], {}), "('auth.user_header_name')\n", (5871, 5896), False, 'from consoleme.config import config\n'), ((5921, 5958), 'consoleme.config.config.get', 'config.get', (['"""auth.groups_header_name"""'], {}), "('auth.groups_header_name')\n", (5931, 5958), False, 'from consoleme.config import config\n'), ((6425, 6450), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (6435, 6450), False, 'import json\n'), ((7085, 7121), 'asgiref.sync.async_to_sync', 'async_to_sync', (['ddb.authenticate_user'], {}), '(ddb.authenticate_user)\n', (7098, 7121), False, 'from asgiref.sync import async_to_sync\n'), ((7549, 7585), 'asgiref.sync.async_to_sync', 'async_to_sync', (['ddb.authenticate_user'], {}), '(ddb.authenticate_user)\n', (7562, 7585), False, 'from asgiref.sync import async_to_sync\n'), ((8512, 8537), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (8522, 8537), False, 'import json\n'), ((9275, 9300), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (9285, 9300), False, 'import json\n'), ((10125, 10150), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (10135, 10150), False, 'import json\n'), ((10822, 10847), 'json.loads', 'json.loads', (['response.body'], {}), '(response.body)\n', (10832, 10847), False, 'import json\n')]
|
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from __future__ import division
import sys
import humanfriendly
BAR_LEN = 70
class ProgressViewBase(object):
""" a view base for progress reporting """
def __init__(self, out):
self.out = out
def write(self, args):
""" writes the progress """
raise NotImplementedError
def flush(self):
""" flushes the message out the pipeline"""
raise NotImplementedError
def clear(self):
""" resets the view to neutral """
pass # pylint: disable=unnecessary-pass
class ProgressReporter(object):
""" generic progress reporter """
def __init__(self, message='', value=None, total_value=None):
self.message = message
self.value = value
self.total_val = total_value
self.closed = False
def add(self, **kwargs):
"""
adds a progress report
:param kwargs: dictionary containing 'message', 'total_val', 'value'
"""
message = kwargs.get('message', self.message)
total_val = kwargs.get('total_val', self.total_val)
value = kwargs.get('value', self.value)
if value and total_val:
assert value >= 0 and value <= total_val and total_val >= 0
self.closed = value == total_val
self.total_val = total_val
self.value = value
self.message = message
def report(self):
""" report the progress """
percent = self.value / self.total_val if self.value is not None and self.total_val else None
return {'message': self.message, 'percent': percent}
class ProgressHook(object):
""" sends the progress to the view """
def __init__(self):
self.reporter = ProgressReporter()
self.active_progress = None
def init_progress(self, progress_view):
""" activate a view """
self.active_progress = progress_view
def add(self, **kwargs):
""" adds a progress report """
self.reporter.add(**kwargs)
self.update()
def update(self):
""" updates the view with the progress """
self.active_progress.write(self.reporter.report())
self.active_progress.flush()
def stop(self):
""" if there is an abupt stop before ending """
self.reporter.closed = True
self.add(message='Interrupted')
self.active_progress.clear()
def begin(self, **kwargs):
""" start reporting progress """
kwargs['message'] = kwargs.get('message', 'Starting')
self.add(**kwargs)
self.reporter.closed = False
def end(self, **kwargs):
""" ending reporting of progress """
kwargs['message'] = kwargs.get('message', 'Finished')
self.reporter.closed = True
self.add(**kwargs)
self.active_progress.clear()
def is_running(self):
""" whether progress is continuing """
return not self.reporter.closed
class IndeterminateStandardOut(ProgressViewBase):
""" custom output for progress reporting """
def __init__(self, out=None):
super(IndeterminateStandardOut, self).__init__(
out if out else sys.stderr)
self.spinner = None
def write(self, args):
"""
writes the progress
:param args: dictionary containing key 'message'
"""
if self.spinner is None:
self.spinner = humanfriendly.Spinner(
label='In Progress', stream=self.out, hide_cursor=False)
msg = args.get('message', 'In Progress')
try:
self.spinner.step(label=msg)
except OSError:
pass
def clear(self):
try:
self.spinner.clear()
except AttributeError:
pass
def flush(self):
self.out.flush()
def _format_value(msg, percent):
bar_len = BAR_LEN - len(msg) - 1
completed = int(bar_len * percent)
message = '\r{}['.format(msg)
message += ('#' * completed).ljust(bar_len)
message += '] {:.4%}'.format(percent)
return message
class DeterminateStandardOut(ProgressViewBase):
""" custom output for progress reporting """
def __init__(self, out=None):
super(DeterminateStandardOut, self).__init__(out if out else sys.stderr)
def write(self, args):
"""
writes the progress
:param args: args is a dictionary containing 'percent', 'message'
"""
percent = args.get('percent', 0)
message = args.get('message', '')
if percent:
progress = _format_value(message, percent)
self.out.write(progress)
def clear(self):
self.out.write('\n')
def flush(self):
self.out.flush()
def get_progress_view(determinant=False, outstream=sys.stderr):
""" gets your view """
if determinant:
return DeterminateStandardOut(out=outstream)
return IndeterminateStandardOut(out=outstream)
|
[
"humanfriendly.Spinner"
] |
[((3716, 3794), 'humanfriendly.Spinner', 'humanfriendly.Spinner', ([], {'label': '"""In Progress"""', 'stream': 'self.out', 'hide_cursor': '(False)'}), "(label='In Progress', stream=self.out, hide_cursor=False)\n", (3737, 3794), False, 'import humanfriendly\n')]
|
import firebase_admin
from firebase_admin import credentials
default_app = firebase_admin.initialize_app()
cred = credentials.Certificate("serviceAccountKey.json")
firebase_admin.initialize_app(cred)
|
[
"firebase_admin.initialize_app",
"firebase_admin.credentials.Certificate"
] |
[((78, 109), 'firebase_admin.initialize_app', 'firebase_admin.initialize_app', ([], {}), '()\n', (107, 109), False, 'import firebase_admin\n'), ((118, 167), 'firebase_admin.credentials.Certificate', 'credentials.Certificate', (['"""serviceAccountKey.json"""'], {}), "('serviceAccountKey.json')\n", (141, 167), False, 'from firebase_admin import credentials\n'), ((168, 203), 'firebase_admin.initialize_app', 'firebase_admin.initialize_app', (['cred'], {}), '(cred)\n', (197, 203), False, 'import firebase_admin\n')]
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Minimal example of using pygimli to simulate the steady heat equation.
"""
import pygimli as pg
import pygimli.meshtools as mt
# Create geometry definition for the modelling domain
world = mt.createWorld(start=[-20, 0], end=[20, -16], layers=[-2, -8],
worldMarker=False)
# Create a heterogeneous block
block = mt.createRectangle(start=[-6, -3.5], end=[6, -6.0], marker=4,
boundaryMarker=10, area=0.1)
# Merge geometrical entities
geom = world + block
pg.show(geom, boundaryMarker=True, savefig='geometry.pdf')
# Create a mesh from the geometry definition
mesh = mt.createMesh(geom, quality=33, area=0.2, smooth=[1, 10])
pg.show(mesh, savefig='mesh.pdf')
# $\diverg(a\grad T)=0$ with $T(bottom)=1$, $T(top)=0$
T = pg.solver.solve(mesh,
a=[[1, 1.0], [2, 2.0], [3, 3.0], [4, 0.1]],
bc={'Dirichlet':{8:1.0, 4:0.0}},
verbose=True)
ax, _ = pg.show(mesh, data=T, label='Temperature $T$', cMap="hot_r",
nLevs=11, showBoundary=True, savefig='T_field.pdf')
|
[
"pygimli.show",
"pygimli.meshtools.createRectangle",
"pygimli.meshtools.createMesh",
"pygimli.solver.solve",
"pygimli.meshtools.createWorld"
] |
[((240, 326), 'pygimli.meshtools.createWorld', 'mt.createWorld', ([], {'start': '[-20, 0]', 'end': '[20, -16]', 'layers': '[-2, -8]', 'worldMarker': '(False)'}), '(start=[-20, 0], end=[20, -16], layers=[-2, -8], worldMarker=\n False)\n', (254, 326), True, 'import pygimli.meshtools as mt\n'), ((384, 478), 'pygimli.meshtools.createRectangle', 'mt.createRectangle', ([], {'start': '[-6, -3.5]', 'end': '[6, -6.0]', 'marker': '(4)', 'boundaryMarker': '(10)', 'area': '(0.1)'}), '(start=[-6, -3.5], end=[6, -6.0], marker=4,\n boundaryMarker=10, area=0.1)\n', (402, 478), True, 'import pygimli.meshtools as mt\n'), ((552, 610), 'pygimli.show', 'pg.show', (['geom'], {'boundaryMarker': '(True)', 'savefig': '"""geometry.pdf"""'}), "(geom, boundaryMarker=True, savefig='geometry.pdf')\n", (559, 610), True, 'import pygimli as pg\n'), ((664, 721), 'pygimli.meshtools.createMesh', 'mt.createMesh', (['geom'], {'quality': '(33)', 'area': '(0.2)', 'smooth': '[1, 10]'}), '(geom, quality=33, area=0.2, smooth=[1, 10])\n', (677, 721), True, 'import pygimli.meshtools as mt\n'), ((722, 755), 'pygimli.show', 'pg.show', (['mesh'], {'savefig': '"""mesh.pdf"""'}), "(mesh, savefig='mesh.pdf')\n", (729, 755), True, 'import pygimli as pg\n'), ((816, 940), 'pygimli.solver.solve', 'pg.solver.solve', (['mesh'], {'a': '[[1, 1.0], [2, 2.0], [3, 3.0], [4, 0.1]]', 'bc': "{'Dirichlet': {(8): 1.0, (4): 0.0}}", 'verbose': '(True)'}), "(mesh, a=[[1, 1.0], [2, 2.0], [3, 3.0], [4, 0.1]], bc={\n 'Dirichlet': {(8): 1.0, (4): 0.0}}, verbose=True)\n", (831, 940), True, 'import pygimli as pg\n'), ((998, 1114), 'pygimli.show', 'pg.show', (['mesh'], {'data': 'T', 'label': '"""Temperature $T$"""', 'cMap': '"""hot_r"""', 'nLevs': '(11)', 'showBoundary': '(True)', 'savefig': '"""T_field.pdf"""'}), "(mesh, data=T, label='Temperature $T$', cMap='hot_r', nLevs=11,\n showBoundary=True, savefig='T_field.pdf')\n", (1005, 1114), True, 'import pygimli as pg\n')]
|
import requests_mock
import koji
import pytest
from koji_builder_kube import cli, errors
def test_session_setup_error_type():
with pytest.raises(errors.KojiError):
cli.session_setup({})
def test_session_setup_error_auth(fixtures_dir):
data = {
'serverca': f'{fixtures_dir}/ca.pem',
'cert': f'{fixtures_dir}/cert.pem',
'server': 'https://koji-hub:8443/koji-hub'
}
with pytest.raises(errors.KojiError):
cli.session_setup(data)
def test_session_setup_success(fixtures_dir, mocker):
data = {
'server': 'https://koji-hub:8443/kojihub',
'serverca': f'{fixtures_dir}/ca.pem',
'cert': f'{fixtures_dir}/cert.pem',
}
mocker.patch('os.access')
mocker.patch('os.path.exists')
mocker.patch.object(koji.ClientSession, 'ssl_login', autospec=True)
cli.session_setup(data)
def test_run_success(fixtures_dir, mocker):
mocker.patch('os.access')
mocker.patch('os.path.exists')
mocker.patch.object(koji.ClientSession, 'ssl_login', autospec=True)
mocker.patch('koji_builder_kube.cli.mode', cli.Modes.TEST)
cli.run(['-c', f'{fixtures_dir}/kojid-ssl.conf'])
|
[
"pytest.raises",
"koji_builder_kube.cli.session_setup",
"koji_builder_kube.cli.run"
] |
[((791, 814), 'koji_builder_kube.cli.session_setup', 'cli.session_setup', (['data'], {}), '(data)\n', (808, 814), False, 'from koji_builder_kube import cli, errors\n'), ((1055, 1104), 'koji_builder_kube.cli.run', 'cli.run', (["['-c', f'{fixtures_dir}/kojid-ssl.conf']"], {}), "(['-c', f'{fixtures_dir}/kojid-ssl.conf'])\n", (1062, 1104), False, 'from koji_builder_kube import cli, errors\n'), ((136, 167), 'pytest.raises', 'pytest.raises', (['errors.KojiError'], {}), '(errors.KojiError)\n', (149, 167), False, 'import pytest\n'), ((173, 194), 'koji_builder_kube.cli.session_setup', 'cli.session_setup', (['{}'], {}), '({})\n', (190, 194), False, 'from koji_builder_kube import cli, errors\n'), ((397, 428), 'pytest.raises', 'pytest.raises', (['errors.KojiError'], {}), '(errors.KojiError)\n', (410, 428), False, 'import pytest\n'), ((434, 457), 'koji_builder_kube.cli.session_setup', 'cli.session_setup', (['data'], {}), '(data)\n', (451, 457), False, 'from koji_builder_kube import cli, errors\n')]
|
import inspect
import itertools
class ParserRule(object):
"""
Describes a parser rule - that is, a production rule from the thinglang grammar
"""
COUNTER = itertools.count(0)
def __init__(self, constructor, pattern, predicate, index=None):
self.constructor, self.pattern, self.predicate, self.size, self.index = \
constructor, pattern, predicate, len(pattern), (index if index is not None else next(ParserRule.COUNTER))
def __repr__(self):
return f'ParserRule<{self.index}>({self.pattern})'
def matches(self, tokens):
for start_idx, start_token in enumerate(tokens):
token_subset = tokens[start_idx:start_idx+self.size]
if self.does_match(token_subset) and self.predicate(tokens, start_idx):
return self.constructor, start_idx, start_idx + self.size
return False
def does_match(self, tokens):
return len(tokens) == self.size and all(self.is_instance(token, cls) for token, cls in zip(tokens, self.pattern))
@staticmethod
def is_instance(inst, cls):
if isinstance(cls, str):
return type(inst).__name__ == cls
elif isinstance(cls, tuple): # To deal with mixed type/string class names
return any(ParserRule.is_instance(inst, x) for x in cls)
else:
return isinstance(inst, cls)
@staticmethod
def mark(func, predicate=lambda *args: True):
args = inspect.signature(func)
func.parser_rule = ParserRule(func, [x.annotation for x in args.parameters.values()], predicate)
return func
@staticmethod
def predicate(predicate):
def wrapped(func):
ParserRule.mark(func, predicate)
return func
return wrapped
|
[
"inspect.signature",
"itertools.count"
] |
[((175, 193), 'itertools.count', 'itertools.count', (['(0)'], {}), '(0)\n', (190, 193), False, 'import itertools\n'), ((1460, 1483), 'inspect.signature', 'inspect.signature', (['func'], {}), '(func)\n', (1477, 1483), False, 'import inspect\n')]
|
import os
from test import support
import unittest
# Skip tests if we don't have concurrent.futures.
support.import_module('concurrent.futures')
def load_tests(loader, _, pattern):
pkg_dir = os.path.dirname(__file__)
suite = AsyncioTestSuite()
return support.load_package_tests(pkg_dir, loader, suite, pattern)
class AsyncioTestSuite(unittest.TestSuite):
"""A custom test suite that also runs setup/teardown for the whole package.
Normally unittest only runs setUpModule() and tearDownModule() within each
test module part of the test suite. Copying those functions to each file
would be tedious, let's run this once and for all.
"""
def run(self, result, debug=False):
ignore = support.ignore_deprecations_from
tokens = {
ignore("asyncio.base_events", like=r".*loop argument.*"),
ignore("asyncio.unix_events", like=r".*loop argument.*"),
ignore("asyncio.futures", like=r".*loop argument.*"),
ignore("asyncio.runners", like=r".*loop argument.*"),
ignore("asyncio.subprocess", like=r".*loop argument.*"),
ignore("asyncio.tasks", like=r".*loop argument.*"),
ignore("test.test_asyncio.test_events", like=r".*loop argument.*"),
ignore("test.test_asyncio.test_queues", like=r".*loop argument.*"),
ignore("test.test_asyncio.test_tasks", like=r".*loop argument.*"),
}
try:
super().run(result, debug=debug)
finally:
support.clear_ignored_deprecations(*tokens)
|
[
"test.support.clear_ignored_deprecations",
"os.path.dirname",
"test.support.import_module",
"test.support.load_package_tests"
] |
[((102, 145), 'test.support.import_module', 'support.import_module', (['"""concurrent.futures"""'], {}), "('concurrent.futures')\n", (123, 145), False, 'from test import support\n'), ((198, 223), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (213, 223), False, 'import os\n'), ((266, 325), 'test.support.load_package_tests', 'support.load_package_tests', (['pkg_dir', 'loader', 'suite', 'pattern'], {}), '(pkg_dir, loader, suite, pattern)\n', (292, 325), False, 'from test import support\n'), ((1522, 1565), 'test.support.clear_ignored_deprecations', 'support.clear_ignored_deprecations', (['*tokens'], {}), '(*tokens)\n', (1556, 1565), False, 'from test import support\n')]
|
from itertools import groupby
# ์์ถ ๋จ์๊ฐ n์ผ ๋, ์์ถ๋ ๋ฌธ์์ด์ ๊ธธ์ด(compressed string length)๋ฅผ ๋ฐํํ๋ ํจ์
def get_comp_str_len(s, n):
splited_str_list = [] # ๋ฌธ์์ด s๋ฅผ n๊ฐ ๋จ์๋ก ์๋ฅธ ๋ฌธ์์ด๋ค์ ์ ์ฅํ ๋ฐฐ์ด
compressed_str_list = [] # ์์ถ๋ ๋ฌธ์์ด์ ์ ์ฅํ ๋ฐฐ์ด
for i in range(0, len(s), n):
splited_str_list.append(s[i:i + n])
"""
groupby()๋ฅผ ์ด์ฉํด ์ฐ์๋ ๋ฌธ์์ด์ ๊ฐ์๋ฅผ ๊ตฌํ ์ ์์
"""
for x, group in groupby(splited_str_list):
group_len = len(list(group))
if group_len == 1:
compressed_str_list.append(x)
else:
compressed_str_list.append(f"{group_len}{x}")
return sum(len(c) for c in compressed_str_list)
def solution(s):
# ์์ธ ์ฒ๋ฆฌ (s์ ๊ธธ์ด๊ฐ 1์ผ ๋)
if len(s) == 1:
return 1
# ์์ถ ๋จ์๋ 1๋ถํฐ (len(s) // 2)๊น์ง
result_list = []
for i in range(1, (len(s)//2) + 1):
result_list.append(get_comp_str_len(s, i))
return min(result_list)
|
[
"itertools.groupby"
] |
[((389, 414), 'itertools.groupby', 'groupby', (['splited_str_list'], {}), '(splited_str_list)\n', (396, 414), False, 'from itertools import groupby\n')]
|
"""Test utils for rule generator scanners."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import yaml
from deploy.rule_generator import project_config
_OVERALL_DICT = {
'domain': 'domain.com',
'organization_id': '246801357924',
'folder_id': '357801357924',
'billing_account': '<KEY>',
'allowed_apis': ['compute.googleapis.com', 'storage.googleapis.com'],
}
_FORSETI_DICT = {
'project': {
'project_id': 'forseti-project',
'owners_group': '<EMAIL>',
'auditors_group': '<EMAIL>',
'data_readwrite_groups': ['<EMAIL>'],
'data_readonly_groups': ['<EMAIL>'],
'generated_fields': {
'project_number':
9999,
'log_sink_service_account': (
'<EMAIL>'),
},
},
'generated_fields': {
'service_account': '<EMAIL>',
'server_bucket': 'gs://forseti-project-server/',
}
}
_PROJECT_YAML = """
projects:
- project_id: %(project_id)s
owners_group: %(project_id)s-<EMAIL>
auditors_group: %(<EMAIL>)<EMAIL>
data_readwrite_groups:
- %(project_id)<EMAIL>
data_readonly_groups:
- %(project_id)<EMAIL>
- %(<EMAIL>)<EMAIL>
generated_fields:
project_number: %(project_num)s
log_sink_service_account: <EMAIL>
gce_instance_info:
- name: 'instance'
id: '123'
data_buckets:
- name_suffix: '-bucket'
location: US-CENTRAL1
storage_class: REGIONAL
gce_instances:
- name: 'instance'
zone: 'us-central1-f'
machine_type: 'n1-standard-1'
existing_boot_image: 'projects/debian-cloud/global/images/family/debian-9'
start_vm: true
audit_logs:
logs_gcs_bucket:
location: US
storage_class: MULTI_REGIONAL
ttl_days: 365
logs_bigquery_dataset:
location: US
"""
def create_test_global_config():
"""Creates a global config dictionary for tests."""
return _OVERALL_DICT.copy()
def create_test_project(project_id, project_num, extra_fields=None,
audit_logs_project=None):
"""Create a test project with optional extra project_config fields."""
config_dict = yaml.load(_PROJECT_YAML % {
'project_id': project_id, 'project_num': project_num})
project = config_dict['projects'][0]
if extra_fields:
project.update(extra_fields)
return project_config.ProjectConfig(
project=project,
audit_logs_project=audit_logs_project,
forseti=_FORSETI_DICT)
def create_test_projects(num_projects):
"""Create a list of test projects."""
return [
create_test_project(project_id='project-%s' % i, project_num=1000000 + i)
for i in range(num_projects)
]
|
[
"deploy.rule_generator.project_config.ProjectConfig",
"yaml.load"
] |
[((2178, 2263), 'yaml.load', 'yaml.load', (["(_PROJECT_YAML % {'project_id': project_id, 'project_num': project_num})"], {}), "(_PROJECT_YAML % {'project_id': project_id, 'project_num':\n project_num})\n", (2187, 2263), False, 'import yaml\n'), ((2367, 2479), 'deploy.rule_generator.project_config.ProjectConfig', 'project_config.ProjectConfig', ([], {'project': 'project', 'audit_logs_project': 'audit_logs_project', 'forseti': '_FORSETI_DICT'}), '(project=project, audit_logs_project=\n audit_logs_project, forseti=_FORSETI_DICT)\n', (2395, 2479), False, 'from deploy.rule_generator import project_config\n')]
|