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f73bd153e1be469d4bf090142eeaf0c8c121dfd4 | 737 | py | Python | paragen/modules/search/abstract_search.py | godweiyang/ParaGen | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | [
"Apache-2.0"
] | 50 | 2022-01-18T07:25:46.000Z | 2022-03-14T13:06:18.000Z | paragen/modules/search/abstract_search.py | JiangtaoFeng/ParaGen | 509334bf16e3674e009bb9dc37ecc38ae3b5c977 | [
"Apache-2.0"
] | 2 | 2022-01-19T09:36:42.000Z | 2022-02-23T07:16:02.000Z | paragen/modules/search/abstract_search.py | JiangtaoFeng/ParaGen | 509334bf16e3674e009bb9dc37ecc38ae3b5c977 | [
"Apache-2.0"
] | 6 | 2022-01-19T09:28:53.000Z | 2022-03-10T10:20:08.000Z | from torch.nn import Module
class AbstractSearch(Module):
"""
AbstractSearch is search algorithm on original neural model to perform special inference.
"""
def __init__(self):
super().__init__()
self._mode = 'infer'
def build(self, *args, **kwargs):
"""
Build search algorithm with task instance
"""
raise NotImplementedError
def forward(self, *args, **kwargs):
"""
Process forward of search algorithm.
"""
raise NotImplementedError
def reset(self, mode):
"""
Reset encoder and switch running mode
Args:
mode: running mode in [train, valid, infer]
"""
self._mode = mode
| 22.333333 | 93 | 0.576662 | from torch.nn import Module
class AbstractSearch(Module):
def __init__(self):
super().__init__()
self._mode = 'infer'
def build(self, *args, **kwargs):
raise NotImplementedError
def forward(self, *args, **kwargs):
raise NotImplementedError
def reset(self, mode):
self._mode = mode
| true | true |
f73bd166cfd3612d52cefc09b1c71b0426f1c099 | 1,991 | py | Python | stellar_sdk/operation/utils.py | bantalon/py-stellar-base | b452f0f92be0387c3e78c8149103978788d7ec0f | [
"Apache-2.0"
] | null | null | null | stellar_sdk/operation/utils.py | bantalon/py-stellar-base | b452f0f92be0387c3e78c8149103978788d7ec0f | [
"Apache-2.0"
] | null | null | null | stellar_sdk/operation/utils.py | bantalon/py-stellar-base | b452f0f92be0387c3e78c8149103978788d7ec0f | [
"Apache-2.0"
] | null | null | null | from decimal import Decimal
from typing import Optional, Union
from ..asset import Asset
from ..exceptions import ValueError, TypeError
from ..keypair import Keypair
from ..muxed_account import MuxedAccount
from ..price import Price
from ..strkey import StrKey
_LOWER_LIMIT = "0"
_UPPER_LIMIT = "922337203685.4775807"
_EXPONENT = 7
def check_source(source: Optional[str]) -> None:
if source is not None:
check_muxed_ed25519_account(source)
def check_ed25519_public_key(public_key: str) -> None:
Keypair.from_public_key(public_key)
def check_muxed_ed25519_account(muxed_account: str) -> None:
StrKey.decode_muxed_account(muxed_account)
def check_asset_code(asset_code: str) -> None:
Asset.check_if_asset_code_is_valid(asset_code)
def check_price(price: Union[str, Decimal, Price]) -> None:
if not (
isinstance(price, str) or isinstance(price, Decimal) or isinstance(price, Price)
):
raise TypeError(
"amount should be type of {}, {} or {}.".format(str, Decimal, Price)
)
if isinstance(price, str) or isinstance(price, Decimal):
check_amount(price)
def check_amount(amount: Union[str, Decimal]) -> None:
if not (isinstance(amount, str) or isinstance(amount, Decimal)):
raise TypeError("amount should be type of {} or {}.".format(str, Decimal))
amount = Decimal(amount)
if abs(amount.as_tuple().exponent) > _EXPONENT:
raise ValueError(
"Value of '{}' must have at most 7 digits after the decimal.".format(amount)
)
if amount < Decimal(_LOWER_LIMIT) or amount > Decimal(_UPPER_LIMIT):
raise ValueError(
"Value of '{}' must represent a positive number "
"and the max valid value is {}.".format(amount, _UPPER_LIMIT)
)
def parse_mux_account_from_account(account: Union[str, MuxedAccount]) -> MuxedAccount:
if isinstance(account, str):
return MuxedAccount.from_account(account)
return account
| 31.109375 | 88 | 0.696133 | from decimal import Decimal
from typing import Optional, Union
from ..asset import Asset
from ..exceptions import ValueError, TypeError
from ..keypair import Keypair
from ..muxed_account import MuxedAccount
from ..price import Price
from ..strkey import StrKey
_LOWER_LIMIT = "0"
_UPPER_LIMIT = "922337203685.4775807"
_EXPONENT = 7
def check_source(source: Optional[str]) -> None:
if source is not None:
check_muxed_ed25519_account(source)
def check_ed25519_public_key(public_key: str) -> None:
Keypair.from_public_key(public_key)
def check_muxed_ed25519_account(muxed_account: str) -> None:
StrKey.decode_muxed_account(muxed_account)
def check_asset_code(asset_code: str) -> None:
Asset.check_if_asset_code_is_valid(asset_code)
def check_price(price: Union[str, Decimal, Price]) -> None:
if not (
isinstance(price, str) or isinstance(price, Decimal) or isinstance(price, Price)
):
raise TypeError(
"amount should be type of {}, {} or {}.".format(str, Decimal, Price)
)
if isinstance(price, str) or isinstance(price, Decimal):
check_amount(price)
def check_amount(amount: Union[str, Decimal]) -> None:
if not (isinstance(amount, str) or isinstance(amount, Decimal)):
raise TypeError("amount should be type of {} or {}.".format(str, Decimal))
amount = Decimal(amount)
if abs(amount.as_tuple().exponent) > _EXPONENT:
raise ValueError(
"Value of '{}' must have at most 7 digits after the decimal.".format(amount)
)
if amount < Decimal(_LOWER_LIMIT) or amount > Decimal(_UPPER_LIMIT):
raise ValueError(
"Value of '{}' must represent a positive number "
"and the max valid value is {}.".format(amount, _UPPER_LIMIT)
)
def parse_mux_account_from_account(account: Union[str, MuxedAccount]) -> MuxedAccount:
if isinstance(account, str):
return MuxedAccount.from_account(account)
return account
| true | true |
f73bd18b9f473cc357560a995441df733d0f54c0 | 6,550 | py | Python | datasets.py | smarsu/mtcnn | 98c3839e250b18c310efa920bc6289a70379f07d | [
"MIT"
] | null | null | null | datasets.py | smarsu/mtcnn | 98c3839e250b18c310efa920bc6289a70379f07d | [
"MIT"
] | null | null | null | datasets.py | smarsu/mtcnn | 98c3839e250b18c310efa920bc6289a70379f07d | [
"MIT"
] | null | null | null | # --------------------------------------------------------
# Face Datasets
# Licensed under The MIT License [see LICENSE for details]
# Copyright 2019 smarsu. All Rights Reserved.
# --------------------------------------------------------
import os.path as osp
import numpy as np
class Dataset(object):
"""The base class of dataset.
self._train_datas = [n, str]
self._train_labels = [n, list of box]
"""
def __init__(self):
pass
@property
def size(self):
"""Return the number of train datas."""
raise NotImplementedError
def train_datas_debug(self, batch_size):
"""Yield batch size train datas per step.
Train datas should be shuffled.
Args:
batch_size: int, > 0
"""
if not isinstance(batch_size, int):
raise ValueError('In Dataset, batch_size should be int, get '
'{}'.format(type(batch_size)))
if batch_size <= 0:
raise ValueError('In Dataset, batch_size should larger equal to '
'1, get {}'.format(batch_size))
indices = list(range(batch_size))
datas = []
# for label, we have box and landmark which is 0.
datas.append([self._train_datas[:batch_size],
self._train_labels[:batch_size]])
return datas
def train_datas(self, batch_size):
"""Yield batch size train datas per step.
Train datas should be shuffled.
Args:
batch_size: int, > 0
"""
if not isinstance(batch_size, int):
raise ValueError('In Dataset, batch_size should be int, get '
'{}'.format(type(batch_size)))
if batch_size <= 0:
raise ValueError('In Dataset, batch_size should larger equal to '
'1, get {}'.format(batch_size))
indices = list(range(self.size))
np.random.shuffle(indices)
epoch_size = self.size // batch_size * batch_size
self._train_datas = self._train_datas[indices][:epoch_size] # [epoch_size, ...]
self._train_labels = self._train_labels[indices][:epoch_size] # [epoch_size, ...]
datas = []
for i in range(self.size // batch_size):
# for label, we have box and landmark which is 0.
datas.append([self._train_datas[i*batch_size:(i+1)*batch_size],
self._train_labels[i*batch_size:(i+1)*batch_size]])
return datas
def merge(self, other):
"""Merge the other datas to self.
Args:
other: Dataset
"""
self._train_datas = np.concatenate(
[self._train_datas, other._train_datas], 0)
self._train_labels = np.concatenate(
[self._train_labels, other._train_labels], 0)
class WiderFace(Dataset):
def __init__(self, train_image_path, label_path, value_image_path=None,
test_image_path=None):
"""
TODO(smarsu): Add way to read `value_image_path` and `test_image_path`.
Add way to read `value_label_path` and `test_label_path`.
Args:
train_image_path: str, the path of train images.
label_path: str
"""
self._data_map = {}
self.train_image_path = train_image_path
self.label_path = label_path
self.train_label_path = self.label_path
self._train_datas, self._train_labels = self._read_train_datas()
@property
def size(self):
"""Return the number of train datas.
Assert the size of self._train_datas and self._train_labels is equal.
"""
return len(self._train_datas)
def data_map(self, key):
""""""
if key not in self._data_map:
raise KeyError('{} not in the data map.'.format(key))
return self._data_map[key]
def _real_image_path(self, path):
"""Get real path of image.
self.train_image_path + '/' + path
Args:
path: str, the image name(id) of labels.
"""
return osp.join(self.train_image_path, path)
def _read_train_datas(self):
"""The special way to read wider face labels.
Args:
label_path: str,
"""
with open(self.train_label_path, 'r') as fb:
lines = fb.readlines()
return self._parse_raw_labels(lines)
def _parse_raw_labels(self, lines):
"""Parse raw str lines to python object.
Args:
lines: list of str, with the structure of
File name
Number of bounding box
x1, y1, w, h, blur, expression, illumination, invalid, occlusion, pose
Returns:
images: numpy array, [n], image paths
labels: numpy array, [n, 4], [x1, y1, x2, y2]
"""
images = []
labels = []
idx = 0
while idx < len(lines):
image_path = lines[idx].strip()
images.append(self._real_image_path(image_path))
idx += 1
num = int(lines[idx])
idx += 1
labels_ = []
for _ in range(num):
x1, y1, w, h, blur, expression, illumination, invalid, \
occlusion, pose = [int(v)
for v in lines[idx].strip().split()]
x2, y2 = x1 + w - 1, y1 + h - 1 # -1 to get the read x2, y2
labels_.append([x1, y1, x2, y2])
idx += 1
labels.append(np.array(labels_))
self._data_map[self._real_image_path(image_path)] = np.array(labels_)
return np.array(images), np.array(labels)
if __name__ == '__main__':
import time
# Test wider face dataset
wider = WiderFace('/datasets/wider/images',
'/datasets/wider/wider_face_split/wider_face_train_bbx_gt.txt')
t1 = time.time()
for data, label in wider.train_datas(32):
print(data, label)
t2 = time.time()
print('Time for read wider dataset:', t2 - t1) # 2.467153787612915s with `print`
print(type(wider._train_datas))
print(type(wider._train_labels))
| 32.26601 | 90 | 0.528244 |
import os.path as osp
import numpy as np
class Dataset(object):
def __init__(self):
pass
@property
def size(self):
raise NotImplementedError
def train_datas_debug(self, batch_size):
if not isinstance(batch_size, int):
raise ValueError('In Dataset, batch_size should be int, get '
'{}'.format(type(batch_size)))
if batch_size <= 0:
raise ValueError('In Dataset, batch_size should larger equal to '
'1, get {}'.format(batch_size))
indices = list(range(batch_size))
datas = []
datas.append([self._train_datas[:batch_size],
self._train_labels[:batch_size]])
return datas
def train_datas(self, batch_size):
if not isinstance(batch_size, int):
raise ValueError('In Dataset, batch_size should be int, get '
'{}'.format(type(batch_size)))
if batch_size <= 0:
raise ValueError('In Dataset, batch_size should larger equal to '
'1, get {}'.format(batch_size))
indices = list(range(self.size))
np.random.shuffle(indices)
epoch_size = self.size // batch_size * batch_size
self._train_datas = self._train_datas[indices][:epoch_size]
self._train_labels = self._train_labels[indices][:epoch_size]
datas = []
for i in range(self.size // batch_size):
datas.append([self._train_datas[i*batch_size:(i+1)*batch_size],
self._train_labels[i*batch_size:(i+1)*batch_size]])
return datas
def merge(self, other):
self._train_datas = np.concatenate(
[self._train_datas, other._train_datas], 0)
self._train_labels = np.concatenate(
[self._train_labels, other._train_labels], 0)
class WiderFace(Dataset):
def __init__(self, train_image_path, label_path, value_image_path=None,
test_image_path=None):
self._data_map = {}
self.train_image_path = train_image_path
self.label_path = label_path
self.train_label_path = self.label_path
self._train_datas, self._train_labels = self._read_train_datas()
@property
def size(self):
return len(self._train_datas)
def data_map(self, key):
if key not in self._data_map:
raise KeyError('{} not in the data map.'.format(key))
return self._data_map[key]
def _real_image_path(self, path):
return osp.join(self.train_image_path, path)
def _read_train_datas(self):
with open(self.train_label_path, 'r') as fb:
lines = fb.readlines()
return self._parse_raw_labels(lines)
def _parse_raw_labels(self, lines):
images = []
labels = []
idx = 0
while idx < len(lines):
image_path = lines[idx].strip()
images.append(self._real_image_path(image_path))
idx += 1
num = int(lines[idx])
idx += 1
labels_ = []
for _ in range(num):
x1, y1, w, h, blur, expression, illumination, invalid, \
occlusion, pose = [int(v)
for v in lines[idx].strip().split()]
x2, y2 = x1 + w - 1, y1 + h - 1
labels_.append([x1, y1, x2, y2])
idx += 1
labels.append(np.array(labels_))
self._data_map[self._real_image_path(image_path)] = np.array(labels_)
return np.array(images), np.array(labels)
if __name__ == '__main__':
import time
wider = WiderFace('/datasets/wider/images',
'/datasets/wider/wider_face_split/wider_face_train_bbx_gt.txt')
t1 = time.time()
for data, label in wider.train_datas(32):
print(data, label)
t2 = time.time()
print('Time for read wider dataset:', t2 - t1)
print(type(wider._train_datas))
print(type(wider._train_labels))
| true | true |
f73bd1bb0270072247ca3475da9fed4222ddf23e | 164 | py | Python | learning/drafts/random_nums.py | medale/pyschool | 2bec74d602e3d323e6645c9ec661086cfa076037 | [
"Apache-2.0"
] | null | null | null | learning/drafts/random_nums.py | medale/pyschool | 2bec74d602e3d323e6645c9ec661086cfa076037 | [
"Apache-2.0"
] | null | null | null | learning/drafts/random_nums.py | medale/pyschool | 2bec74d602e3d323e6645c9ec661086cfa076037 | [
"Apache-2.0"
] | null | null | null | import random
def random_nums():
random_float = random.random()
random_int = random.randint(1, 100)
random_elem = random.choice(['heads', 'tails'])
| 16.4 | 51 | 0.670732 | import random
def random_nums():
random_float = random.random()
random_int = random.randint(1, 100)
random_elem = random.choice(['heads', 'tails'])
| true | true |
f73bd205e0400dad8111ce0cd70ef44926b2fd7f | 4,930 | py | Python | examples/learning_graph_embedding_and_predicting.py | YannCabanes/geomstats | ce3f4bab6cd59c2f071371a46e336086771d0493 | [
"MIT"
] | 743 | 2018-05-23T02:23:29.000Z | 2022-03-29T22:59:22.000Z | examples/learning_graph_embedding_and_predicting.py | YannCabanes/geomstats | ce3f4bab6cd59c2f071371a46e336086771d0493 | [
"MIT"
] | 1,119 | 2018-05-15T05:29:38.000Z | 2022-03-31T18:27:02.000Z | examples/learning_graph_embedding_and_predicting.py | YannCabanes/geomstats | ce3f4bab6cd59c2f071371a46e336086771d0493 | [
"MIT"
] | 159 | 2018-05-23T17:49:24.000Z | 2022-03-30T16:44:47.000Z | """Learning embedding of graph using Poincare Ball Model."""
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import geomstats.backend as gs
import geomstats.visualization as visualization
from geomstats.datasets.prepare_graph_data import HyperbolicEmbedding
from geomstats.datasets.utils import load_karate_graph
from geomstats.learning.kmeans import RiemannianKMeans
from geomstats.learning.kmedoids import RiemannianKMedoids
def main():
"""Learning Poincaré graph embedding.
Learns Poincaré Ball embedding by using Riemannian
gradient descent algorithm. Then K-means is applied
to learn labels of each data sample.
"""
gs.random.seed(1234)
karate_graph = load_karate_graph()
hyperbolic_embedding = HyperbolicEmbedding(max_epochs=3)
embeddings = hyperbolic_embedding.embed(karate_graph)
colors = {1: "b", 2: "r"}
group_1 = mpatches.Patch(color=colors[1], label="Group 1")
group_2 = mpatches.Patch(color=colors[2], label="Group 2")
circle = visualization.PoincareDisk(point_type="ball")
_, ax = plt.subplots(figsize=(8, 8))
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
circle.set_ax(ax)
circle.draw(ax=ax)
for i_embedding, embedding in enumerate(embeddings):
x_coords = embedding[0]
y_coords = embedding[1]
pt_id = i_embedding
plt.scatter(x_coords, y_coords, c=colors[karate_graph.labels[pt_id][0]], s=150)
ax.annotate(pt_id, (x_coords, y_coords))
plt.tick_params(which="both")
plt.title("Poincare Ball Embedding of the Karate Club Network")
plt.legend(handles=[group_1, group_2])
plt.show()
n_clusters = 2
kmeans = RiemannianKMeans(
metric=hyperbolic_embedding.manifold.metric,
n_clusters=n_clusters,
init="random",
)
centroids = kmeans.fit(X=embeddings)
labels = kmeans.predict(X=embeddings)
colors = ["g", "c", "m"]
circle = visualization.PoincareDisk(point_type="ball")
_, ax2 = plt.subplots(figsize=(8, 8))
circle.set_ax(ax2)
circle.draw(ax=ax2)
ax2.axes.xaxis.set_visible(False)
ax2.axes.yaxis.set_visible(False)
group_1_predicted = mpatches.Patch(color=colors[0], label="Predicted Group 1")
group_2_predicted = mpatches.Patch(color=colors[1], label="Predicted Group 2")
group_centroids = mpatches.Patch(color=colors[2], label="Cluster centroids")
for _ in range(n_clusters):
for i_embedding, embedding in enumerate(embeddings):
x_coords = embedding[0]
y_coords = embedding[1]
pt_id = i_embedding
if labels[i_embedding] == 0:
color = colors[0]
else:
color = colors[1]
plt.scatter(x_coords, y_coords, c=color, s=150)
ax2.annotate(pt_id, (x_coords, y_coords))
for _, centroid in enumerate(centroids):
x_coords = centroid[0]
y_coords = centroid[1]
plt.scatter(
x_coords,
y_coords,
c=colors[2],
marker="*",
s=150,
)
plt.title("K-means applied to Karate club embedding")
plt.legend(handles=[group_1_predicted, group_2_predicted, group_centroids])
plt.show()
kmedoid = RiemannianKMedoids(
metric=hyperbolic_embedding.manifold.metric,
n_clusters=n_clusters,
init="random",
n_jobs=2,
)
centroids = kmedoid.fit(data=embeddings, max_iter=100)
labels = kmedoid.predict(data=embeddings)
colors = ["g", "c", "m"]
circle = visualization.PoincareDisk(point_type="ball")
_, ax2 = plt.subplots(figsize=(8, 8))
circle.set_ax(ax2)
circle.draw(ax=ax2)
ax2.axes.xaxis.set_visible(False)
ax2.axes.yaxis.set_visible(False)
group_1_predicted = mpatches.Patch(color=colors[0], label="Predicted Group 1")
group_2_predicted = mpatches.Patch(color=colors[1], label="Predicted Group 2")
group_centroids = mpatches.Patch(color=colors[2], label="Cluster centroids")
for _ in range(n_clusters):
for i_embedding, embedding in enumerate(embeddings):
x_coords = embedding[0]
y_coords = embedding[1]
pt_id = i_embedding
if labels[i_embedding] == 0:
color = colors[0]
else:
color = colors[1]
plt.scatter(x_coords, y_coords, c=color, s=150)
ax2.annotate(pt_id, (x_coords, y_coords))
for _, centroid in enumerate(centroids):
x_coords = centroid[0]
y_coords = centroid[1]
plt.scatter(
x_coords,
y_coords,
c=colors[2],
marker="*",
s=150,
)
plt.title("K-Medoids applied to Karate club embedding")
plt.legend(handles=[group_1_predicted, group_2_predicted, group_centroids])
plt.show()
if __name__ == "__main__":
main()
| 32.866667 | 87 | 0.65071 |
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import geomstats.backend as gs
import geomstats.visualization as visualization
from geomstats.datasets.prepare_graph_data import HyperbolicEmbedding
from geomstats.datasets.utils import load_karate_graph
from geomstats.learning.kmeans import RiemannianKMeans
from geomstats.learning.kmedoids import RiemannianKMedoids
def main():
gs.random.seed(1234)
karate_graph = load_karate_graph()
hyperbolic_embedding = HyperbolicEmbedding(max_epochs=3)
embeddings = hyperbolic_embedding.embed(karate_graph)
colors = {1: "b", 2: "r"}
group_1 = mpatches.Patch(color=colors[1], label="Group 1")
group_2 = mpatches.Patch(color=colors[2], label="Group 2")
circle = visualization.PoincareDisk(point_type="ball")
_, ax = plt.subplots(figsize=(8, 8))
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
circle.set_ax(ax)
circle.draw(ax=ax)
for i_embedding, embedding in enumerate(embeddings):
x_coords = embedding[0]
y_coords = embedding[1]
pt_id = i_embedding
plt.scatter(x_coords, y_coords, c=colors[karate_graph.labels[pt_id][0]], s=150)
ax.annotate(pt_id, (x_coords, y_coords))
plt.tick_params(which="both")
plt.title("Poincare Ball Embedding of the Karate Club Network")
plt.legend(handles=[group_1, group_2])
plt.show()
n_clusters = 2
kmeans = RiemannianKMeans(
metric=hyperbolic_embedding.manifold.metric,
n_clusters=n_clusters,
init="random",
)
centroids = kmeans.fit(X=embeddings)
labels = kmeans.predict(X=embeddings)
colors = ["g", "c", "m"]
circle = visualization.PoincareDisk(point_type="ball")
_, ax2 = plt.subplots(figsize=(8, 8))
circle.set_ax(ax2)
circle.draw(ax=ax2)
ax2.axes.xaxis.set_visible(False)
ax2.axes.yaxis.set_visible(False)
group_1_predicted = mpatches.Patch(color=colors[0], label="Predicted Group 1")
group_2_predicted = mpatches.Patch(color=colors[1], label="Predicted Group 2")
group_centroids = mpatches.Patch(color=colors[2], label="Cluster centroids")
for _ in range(n_clusters):
for i_embedding, embedding in enumerate(embeddings):
x_coords = embedding[0]
y_coords = embedding[1]
pt_id = i_embedding
if labels[i_embedding] == 0:
color = colors[0]
else:
color = colors[1]
plt.scatter(x_coords, y_coords, c=color, s=150)
ax2.annotate(pt_id, (x_coords, y_coords))
for _, centroid in enumerate(centroids):
x_coords = centroid[0]
y_coords = centroid[1]
plt.scatter(
x_coords,
y_coords,
c=colors[2],
marker="*",
s=150,
)
plt.title("K-means applied to Karate club embedding")
plt.legend(handles=[group_1_predicted, group_2_predicted, group_centroids])
plt.show()
kmedoid = RiemannianKMedoids(
metric=hyperbolic_embedding.manifold.metric,
n_clusters=n_clusters,
init="random",
n_jobs=2,
)
centroids = kmedoid.fit(data=embeddings, max_iter=100)
labels = kmedoid.predict(data=embeddings)
colors = ["g", "c", "m"]
circle = visualization.PoincareDisk(point_type="ball")
_, ax2 = plt.subplots(figsize=(8, 8))
circle.set_ax(ax2)
circle.draw(ax=ax2)
ax2.axes.xaxis.set_visible(False)
ax2.axes.yaxis.set_visible(False)
group_1_predicted = mpatches.Patch(color=colors[0], label="Predicted Group 1")
group_2_predicted = mpatches.Patch(color=colors[1], label="Predicted Group 2")
group_centroids = mpatches.Patch(color=colors[2], label="Cluster centroids")
for _ in range(n_clusters):
for i_embedding, embedding in enumerate(embeddings):
x_coords = embedding[0]
y_coords = embedding[1]
pt_id = i_embedding
if labels[i_embedding] == 0:
color = colors[0]
else:
color = colors[1]
plt.scatter(x_coords, y_coords, c=color, s=150)
ax2.annotate(pt_id, (x_coords, y_coords))
for _, centroid in enumerate(centroids):
x_coords = centroid[0]
y_coords = centroid[1]
plt.scatter(
x_coords,
y_coords,
c=colors[2],
marker="*",
s=150,
)
plt.title("K-Medoids applied to Karate club embedding")
plt.legend(handles=[group_1_predicted, group_2_predicted, group_centroids])
plt.show()
if __name__ == "__main__":
main()
| true | true |
f73bd25ec6cf4c0e5139d31719717efc84f6ced8 | 5,798 | py | Python | tests/test_config_parser.py | songchenwen/icloud-drive-docker | 7188dfbcc34e29ddbeeb1324c62ea77bed8f0576 | [
"BSD-3-Clause"
] | null | null | null | tests/test_config_parser.py | songchenwen/icloud-drive-docker | 7188dfbcc34e29ddbeeb1324c62ea77bed8f0576 | [
"BSD-3-Clause"
] | null | null | null | tests/test_config_parser.py | songchenwen/icloud-drive-docker | 7188dfbcc34e29ddbeeb1324c62ea77bed8f0576 | [
"BSD-3-Clause"
] | null | null | null | __author__ = 'Mandar Patil (mandarons@pm.me)'
import os
import unittest
from src import config_parser, constants
class TestConfigParser(unittest.TestCase):
def setUp(self) -> None:
pass
def tearDown(self) -> None:
pass
def test_read_config_valids(self):
# Default config path
self.assertIsNotNone(config_parser.read_config())
# Overridden config path
self.assertIsNotNone(config_parser.read_config(config_path=constants.DEFAULT_CONFIG_FILE_PATH))
def test_read_config_invalids(self):
# Invalid config path
self.assertIsNone(config_parser.read_config(config_path='invalid/path'))
# None config path
self.assertIsNone(config_parser.read_config(config_path=None))
def test_get_sync_interval_valids(self):
# Given sync interval
config = config_parser.read_config()
self.assertEqual(config['settings']['sync_interval'], config_parser.get_sync_interval(config=config))
# Default sync interval
del config['settings']['sync_interval']
self.assertEqual(constants.DEFAULT_SYNC_INTERVAL_SEC, config_parser.get_sync_interval(config=config))
def test_get_sync_interval_invalids(self):
# None config
self.assertEqual(constants.DEFAULT_SYNC_INTERVAL_SEC, config_parser.get_sync_interval(config=None))
def test_prepare_destination_valids(self):
config = config_parser.read_config()
# Given destination
actual = config_parser.prepare_destination(config=config)
self.assertEqual(os.path.abspath(config['settings']['destination']), actual)
self.assertTrue(os.path.exists(actual))
self.assertTrue(os.path.isdir(actual))
os.rmdir(actual)
# Default destination
del config['settings']['destination']
actual = config_parser.prepare_destination(config=config)
self.assertEqual(os.path.abspath(constants.DEFAULT_DRIVE_DESTINATION), actual)
self.assertTrue(os.path.exists(actual))
self.assertTrue(os.path.isdir(actual))
os.rmdir(actual)
def test_prepare_destination_invalids(self):
# None config
actual = config_parser.prepare_destination(config=None)
self.assertEqual(os.path.abspath(constants.DEFAULT_DRIVE_DESTINATION), actual)
self.assertTrue(os.path.exists(actual))
self.assertTrue(os.path.isdir(actual))
os.rmdir(actual)
def test_get_username_valids(self):
config = config_parser.read_config()
# Given username
self.assertEqual(config['credentials']['username'], config_parser.get_username(config=config))
def test_get_username_invalids(self):
config = config_parser.read_config()
# None config
self.assertIsNone(config_parser.get_username(config=None))
# Empty username
config['credentials']['username'] = ''
self.assertIsNone(config_parser.get_username(config=config))
def test_get_remove_obsolete_valids(self):
config = config_parser.read_config()
config['settings']['remove_obsolete'] = True
self.assertTrue(config_parser.get_remove_obsolete(config=config))
del config['settings']['remove_obsolete']
self.assertFalse(config_parser.get_remove_obsolete(config=config))
def test_get_remove_obsolete_invalids(self):
self.assertFalse(config_parser.get_remove_obsolete(config=None))
def test_get_verbose_valids(self):
config = config_parser.read_config()
self.assertEqual(config['settings']['verbose'],config_parser.get_verbose(config=config))
config['settings']['verbose'] = True
self.assertTrue(config_parser.get_verbose(config=config))
def test_get_verbose_invalids(self):
config = config_parser.read_config()
config['settings']['verbose'] = None
self.assertFalse(config_parser.get_verbose(config=config))
del config['settings']['verbose']
self.assertFalse(config_parser.get_verbose(config=config))
del config['settings']
self.assertFalse(config_parser.get_verbose(config=config))
def test_get_smtp_no_tls(self):
config = {'smtp':{'no_tls':True}}
self.assertTrue(config_parser.get_smtp_no_tls(config=config))
config = {'smtp':{'no_tls':False}}
self.assertFalse(config_parser.get_smtp_no_tls(config=config))
del config['smtp']['no_tls']
self.assertFalse(config_parser.get_smtp_no_tls(config=config))
def test_get_smtp_email_valids(self):
# Given email
config = {'smtp':{'email':'user@test.com'}}
self.assertEqual(config['smtp']['email'], config_parser.get_smtp_email(config=config))
def test_smtp_email_invalids(self):
self.assertIsNone(config_parser.get_smtp_email(config=None))
def test_get_smtp_host_valids(self):
# Given host
config = {'smtp':{'host':'smtp.test.com'}}
self.assertEqual(config['smtp']['host'], config_parser.get_smtp_host(config=config))
def test_smtp_host_invalids(self):
self.assertIsNone(config_parser.get_smtp_host(config=None))
def test_get_smtp_port_valids(self):
# Given port
config = {'smtp':{'port':'587'}}
self.assertEqual(config['smtp']['port'], config_parser.get_smtp_port(config=config))
def test_smtp_port_invalids(self):
self.assertIsNone(config_parser.get_smtp_port(config=None))
def test_get_smtp_password_valids(self):
# Given password
config = {'smtp':{'password':'password'}}
self.assertEqual(config['smtp']['password'], config_parser.get_smtp_password(config=config))
def test_smtp_password_invalids(self):
self.assertIsNone(config_parser.get_smtp_password(config=None))
| 40.830986 | 109 | 0.703518 | __author__ = 'Mandar Patil (mandarons@pm.me)'
import os
import unittest
from src import config_parser, constants
class TestConfigParser(unittest.TestCase):
def setUp(self) -> None:
pass
def tearDown(self) -> None:
pass
def test_read_config_valids(self):
self.assertIsNotNone(config_parser.read_config())
self.assertIsNotNone(config_parser.read_config(config_path=constants.DEFAULT_CONFIG_FILE_PATH))
def test_read_config_invalids(self):
self.assertIsNone(config_parser.read_config(config_path='invalid/path'))
self.assertIsNone(config_parser.read_config(config_path=None))
def test_get_sync_interval_valids(self):
config = config_parser.read_config()
self.assertEqual(config['settings']['sync_interval'], config_parser.get_sync_interval(config=config))
del config['settings']['sync_interval']
self.assertEqual(constants.DEFAULT_SYNC_INTERVAL_SEC, config_parser.get_sync_interval(config=config))
def test_get_sync_interval_invalids(self):
self.assertEqual(constants.DEFAULT_SYNC_INTERVAL_SEC, config_parser.get_sync_interval(config=None))
def test_prepare_destination_valids(self):
config = config_parser.read_config()
actual = config_parser.prepare_destination(config=config)
self.assertEqual(os.path.abspath(config['settings']['destination']), actual)
self.assertTrue(os.path.exists(actual))
self.assertTrue(os.path.isdir(actual))
os.rmdir(actual)
del config['settings']['destination']
actual = config_parser.prepare_destination(config=config)
self.assertEqual(os.path.abspath(constants.DEFAULT_DRIVE_DESTINATION), actual)
self.assertTrue(os.path.exists(actual))
self.assertTrue(os.path.isdir(actual))
os.rmdir(actual)
def test_prepare_destination_invalids(self):
actual = config_parser.prepare_destination(config=None)
self.assertEqual(os.path.abspath(constants.DEFAULT_DRIVE_DESTINATION), actual)
self.assertTrue(os.path.exists(actual))
self.assertTrue(os.path.isdir(actual))
os.rmdir(actual)
def test_get_username_valids(self):
config = config_parser.read_config()
self.assertEqual(config['credentials']['username'], config_parser.get_username(config=config))
def test_get_username_invalids(self):
config = config_parser.read_config()
self.assertIsNone(config_parser.get_username(config=None))
config['credentials']['username'] = ''
self.assertIsNone(config_parser.get_username(config=config))
def test_get_remove_obsolete_valids(self):
config = config_parser.read_config()
config['settings']['remove_obsolete'] = True
self.assertTrue(config_parser.get_remove_obsolete(config=config))
del config['settings']['remove_obsolete']
self.assertFalse(config_parser.get_remove_obsolete(config=config))
def test_get_remove_obsolete_invalids(self):
self.assertFalse(config_parser.get_remove_obsolete(config=None))
def test_get_verbose_valids(self):
config = config_parser.read_config()
self.assertEqual(config['settings']['verbose'],config_parser.get_verbose(config=config))
config['settings']['verbose'] = True
self.assertTrue(config_parser.get_verbose(config=config))
def test_get_verbose_invalids(self):
config = config_parser.read_config()
config['settings']['verbose'] = None
self.assertFalse(config_parser.get_verbose(config=config))
del config['settings']['verbose']
self.assertFalse(config_parser.get_verbose(config=config))
del config['settings']
self.assertFalse(config_parser.get_verbose(config=config))
def test_get_smtp_no_tls(self):
config = {'smtp':{'no_tls':True}}
self.assertTrue(config_parser.get_smtp_no_tls(config=config))
config = {'smtp':{'no_tls':False}}
self.assertFalse(config_parser.get_smtp_no_tls(config=config))
del config['smtp']['no_tls']
self.assertFalse(config_parser.get_smtp_no_tls(config=config))
def test_get_smtp_email_valids(self):
config = {'smtp':{'email':'user@test.com'}}
self.assertEqual(config['smtp']['email'], config_parser.get_smtp_email(config=config))
def test_smtp_email_invalids(self):
self.assertIsNone(config_parser.get_smtp_email(config=None))
def test_get_smtp_host_valids(self):
config = {'smtp':{'host':'smtp.test.com'}}
self.assertEqual(config['smtp']['host'], config_parser.get_smtp_host(config=config))
def test_smtp_host_invalids(self):
self.assertIsNone(config_parser.get_smtp_host(config=None))
def test_get_smtp_port_valids(self):
config = {'smtp':{'port':'587'}}
self.assertEqual(config['smtp']['port'], config_parser.get_smtp_port(config=config))
def test_smtp_port_invalids(self):
self.assertIsNone(config_parser.get_smtp_port(config=None))
def test_get_smtp_password_valids(self):
config = {'smtp':{'password':'password'}}
self.assertEqual(config['smtp']['password'], config_parser.get_smtp_password(config=config))
def test_smtp_password_invalids(self):
self.assertIsNone(config_parser.get_smtp_password(config=None))
| true | true |
f73bd334cf9a9c49b25222410d6a13cf4e8cb04d | 4,386 | py | Python | codesearch/retrieval.py | chinchila/codesearch | eba4c08d9098cbbfbf9caf252c608b61a63d1d1a | [
"BSD-3-Clause"
] | 30 | 2020-08-27T13:54:23.000Z | 2022-03-17T03:41:05.000Z | codesearch/retrieval.py | pritam004/Neural-Code-Search | 9b91ce40e8a727932302436c4de2eddb04ebb396 | [
"BSD-3-Clause"
] | 1 | 2021-06-07T11:22:52.000Z | 2021-06-07T11:22:52.000Z | codesearch/retrieval.py | pritam004/Neural-Code-Search | 9b91ce40e8a727932302436c4de2eddb04ebb396 | [
"BSD-3-Clause"
] | 4 | 2021-06-06T02:52:40.000Z | 2022-01-26T07:05:58.000Z | # © 2020 Nokia
#
# Licensed under the BSD 3 Clause license
#
# SPDX-License-Identifier: BSD-3-Clause
# ============================================
import functools
import sys
import numpy as np
from codesearch.utils import Saveable
from codesearch.data_config import DESCRIPTION_FIELD, CODE_FIELD
class RetrievalModel(Saveable):
def __init__(self):
super().__init__()
self._id2snippet = {}
def query(self, query, n=10, projection=[], **kwargs):
n = min(n, len(self._id2snippet))
if projection and "id" not in projection:
projection = ["id"] + projection
sims = self.similarities(query, **kwargs)
return_sims = "score" in projection or not projection
if return_sims:
ranking_ids, sims = self._sims_to_ranking(sims, n, return_sims)
else:
ranking_ids = self._sims_to_ranking(sims, n, return_sims)
sims = None
ranking_snippets = [dict(self._id2snippet[id_]) for id_ in ranking_ids]
if sims:
for s, sim in zip(ranking_snippets, sims):
s["score"] = sim
if projection:
ranking_snippets = [ {f: r[f] for f in projection} for r in ranking_snippets]
return ranking_snippets
def log_query_results(self, queries, relevant_ids=[], projection=[DESCRIPTION_FIELD, CODE_FIELD], log_file=None):
if log_file:
stdout = sys.stdout
with open(log_file, "a") as f:
sys.stdout = f
self._log_query_results(queries, relevant_ids, projection)
sys.stdout = stdout
else:
self._log_query_results(queries, relevant_ids, projection)
def _log_query_results(self, queries, relevant_ids=[], projection=[DESCRIPTION_FIELD, CODE_FIELD]):
line1 = "*" * 40
line2 = "-" * 40
line3 = "-" * 10
for q in queries:
results = self.query(q, n=5, projection=projection)
print(line1)
print(f"QUERY: {q}")
print(line1)
print()
for i, r in enumerate(results):
annotation = ""
if relevant_ids and r["id"] in relevant_ids:
annotation = " - RELEVANT"
print(line2)
print(f"RANK {i+1}{annotation}")
print(line2)
for f in projection:
if "\n" in str(r[f]):
print(f"{f.upper()}:")
print(r[f])
else:
print(f"{f.upper()}: {r[f]}")
print(line2)
print()
print()
print()
def _sims_to_ranking(self, sims, n, return_sims):
idxs = np.argpartition(sims, -n)[-n:]
top_idxs = idxs[np.argsort(sims[idxs])][::-1]
snippet_ids = [self.get_snippetid(top_idx) for top_idx in top_idxs]
if return_sims:
top_sims = [sims[top_idx] for top_idx in top_idxs]
return snippet_ids, top_sims
return snippet_ids
def query_batch(self, queries, n=10, return_sims=False, **kwargs):
sims_batch = self.similarities_batch(tuple(queries), **kwargs)
snippet_ids_batch = []
for sims in sims_batch:
snippet_ids = self._sims_to_ranking(sims, n, return_sims)
snippet_ids_batch.append(snippet_ids)
return snippet_ids_batch
def add_snippets(self, snippets):
for s in snippets:
self._id2snippet[s["id"]] = s
def get_snippetid(self, idx):
pass
def similarities(self, query, **kwargs):
pass
def save(self, path):
super().save(path)
class RetrievalEnsemble(RetrievalModel):
def __init__(self, retrieval_models, weights):
super().__init__()
self.retrieval_models = retrieval_models
self.weights = weights
def similarities(self, query, model_kwargs=None):
N = len(self.snippets)
sims = np.zeros(shape=(N,), dtype=np.float32)
for i, model in enumerate(self.retrieval_models):
weight = self.weights[i] if self.weights else 1.
sims += (weight * model.similarities(query))
return sims | 33.480916 | 117 | 0.553808 |
import functools
import sys
import numpy as np
from codesearch.utils import Saveable
from codesearch.data_config import DESCRIPTION_FIELD, CODE_FIELD
class RetrievalModel(Saveable):
def __init__(self):
super().__init__()
self._id2snippet = {}
def query(self, query, n=10, projection=[], **kwargs):
n = min(n, len(self._id2snippet))
if projection and "id" not in projection:
projection = ["id"] + projection
sims = self.similarities(query, **kwargs)
return_sims = "score" in projection or not projection
if return_sims:
ranking_ids, sims = self._sims_to_ranking(sims, n, return_sims)
else:
ranking_ids = self._sims_to_ranking(sims, n, return_sims)
sims = None
ranking_snippets = [dict(self._id2snippet[id_]) for id_ in ranking_ids]
if sims:
for s, sim in zip(ranking_snippets, sims):
s["score"] = sim
if projection:
ranking_snippets = [ {f: r[f] for f in projection} for r in ranking_snippets]
return ranking_snippets
def log_query_results(self, queries, relevant_ids=[], projection=[DESCRIPTION_FIELD, CODE_FIELD], log_file=None):
if log_file:
stdout = sys.stdout
with open(log_file, "a") as f:
sys.stdout = f
self._log_query_results(queries, relevant_ids, projection)
sys.stdout = stdout
else:
self._log_query_results(queries, relevant_ids, projection)
def _log_query_results(self, queries, relevant_ids=[], projection=[DESCRIPTION_FIELD, CODE_FIELD]):
line1 = "*" * 40
line2 = "-" * 40
line3 = "-" * 10
for q in queries:
results = self.query(q, n=5, projection=projection)
print(line1)
print(f"QUERY: {q}")
print(line1)
print()
for i, r in enumerate(results):
annotation = ""
if relevant_ids and r["id"] in relevant_ids:
annotation = " - RELEVANT"
print(line2)
print(f"RANK {i+1}{annotation}")
print(line2)
for f in projection:
if "\n" in str(r[f]):
print(f"{f.upper()}:")
print(r[f])
else:
print(f"{f.upper()}: {r[f]}")
print(line2)
print()
print()
print()
def _sims_to_ranking(self, sims, n, return_sims):
idxs = np.argpartition(sims, -n)[-n:]
top_idxs = idxs[np.argsort(sims[idxs])][::-1]
snippet_ids = [self.get_snippetid(top_idx) for top_idx in top_idxs]
if return_sims:
top_sims = [sims[top_idx] for top_idx in top_idxs]
return snippet_ids, top_sims
return snippet_ids
def query_batch(self, queries, n=10, return_sims=False, **kwargs):
sims_batch = self.similarities_batch(tuple(queries), **kwargs)
snippet_ids_batch = []
for sims in sims_batch:
snippet_ids = self._sims_to_ranking(sims, n, return_sims)
snippet_ids_batch.append(snippet_ids)
return snippet_ids_batch
def add_snippets(self, snippets):
for s in snippets:
self._id2snippet[s["id"]] = s
def get_snippetid(self, idx):
pass
def similarities(self, query, **kwargs):
pass
def save(self, path):
super().save(path)
class RetrievalEnsemble(RetrievalModel):
def __init__(self, retrieval_models, weights):
super().__init__()
self.retrieval_models = retrieval_models
self.weights = weights
def similarities(self, query, model_kwargs=None):
N = len(self.snippets)
sims = np.zeros(shape=(N,), dtype=np.float32)
for i, model in enumerate(self.retrieval_models):
weight = self.weights[i] if self.weights else 1.
sims += (weight * model.similarities(query))
return sims | true | true |
f73bd3c4156de1c614cdd8360761e6080312f66a | 470 | py | Python | 876. Middle of the Linked List.py | rohitpatwa/leetcode | f4826763e8f154cac9134d53b154b8299acd39a8 | [
"Xnet",
"X11",
"CECILL-B"
] | 1 | 2020-07-15T20:48:27.000Z | 2020-07-15T20:48:27.000Z | 876. Middle of the Linked List.py | rohitpatwa/leetcode | f4826763e8f154cac9134d53b154b8299acd39a8 | [
"Xnet",
"X11",
"CECILL-B"
] | null | null | null | 876. Middle of the Linked List.py | rohitpatwa/leetcode | f4826763e8f154cac9134d53b154b8299acd39a8 | [
"Xnet",
"X11",
"CECILL-B"
] | null | null | null | # Use slow pointer, fast pointer approach
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def middleNode(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
fast = head
while fast and fast.next:
head = head.next
fast = fast.next.next
return head
| 22.380952 | 41 | 0.53617 |
class Solution(object):
def middleNode(self, head):
fast = head
while fast and fast.next:
head = head.next
fast = fast.next.next
return head
| true | true |
f73bd3e16dc98f8ade8ad548b8b995c01e0d8edf | 11,059 | py | Python | tests/v2/test_users.py | dennisdnyce/Questioner | 7d981019786d20ee5352b3379b6c34f3636417c0 | [
"MIT"
] | null | null | null | tests/v2/test_users.py | dennisdnyce/Questioner | 7d981019786d20ee5352b3379b6c34f3636417c0 | [
"MIT"
] | 7 | 2019-01-06T16:12:44.000Z | 2022-03-08T21:09:34.000Z | tests/v2/test_users.py | dennisdnyce/Questioner | 7d981019786d20ee5352b3379b6c34f3636417c0 | [
"MIT"
] | 2 | 2019-01-09T07:04:49.000Z | 2019-01-09T13:08:29.000Z | import os
import json
import unittest
import pytest
from app import create_app
from app.api.v2.models.user_models import UserRegistration
from app.api.v2.views.user_views import myuser
from app.api.v2.utils.validators import validate_users
from app.api.v2.models.database_test import QuestionerTestDatabase
connector = QuestionerTestDatabase()
class TestUserRegistration(unittest.TestCase):
''' This class represents the User Registration test case '''
def setUp(self):
''' define test variables and initialize the app '''
self.app = create_app(config='testing')
self.client = self.app.test_client()
connector.destroy_questioner_test_tables()
connector.create_questioner_test_tables()
self.user ={
'firstname': 'dennis',
'lastname': 'juma',
'othername': 'wafula',
'phoneNumber': '0716714835',
'username': 'dennisd',
'isAdmin': 'True',
'email': 'wafula@gmail.com',
'password': 'thisispass',
'confirm_password': 'thisispass'
}
def test_user_registration_no_firstname(self):
''' tests that a user cannot signup without a firstname '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='', lastname='jumaa', othername='wafula', username="dennisdnycd",
phoneNumber='0713714835', isAdmin='True', email="jumaspayd@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("firstname is required", response_msg["error"])
def test_user_registration_no_lastname(self):
''' tests that a user cannot signup without a lastname '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennis', lastname='', othername='wafula', username="dennisdnye",
phoneNumber='0713714835', isAdmin='True', email="jumaspaye@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("lastname is required", response_msg["error"])
def test_user_registration_no_othername(self):
''' tests that a user cannot signup without othername '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennis', lastname='jumaa', othername='', username="dennisdnyf",
phoneNumber='0713714835', isAdmin='True', email="jumaspayf@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("othername is required", response_msg["error"])
def test_user_registration_no_phone(self):
''' tests that a user cannot signup without a phone '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennis', lastname='jumaa', othername='wafula', username="dennisdnyg",
phoneNumber='', isAdmin='True', email="jumaspayg@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("phoneNumber is required", response_msg["error"])
def test_user_registration_no_username(self):
''' tests that a user cannot signup without a username '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="",
phoneNumber='0713714835', isAdmin='True', email="jumaspayh@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Username is required", response_msg["error"])
def test_user_registration_invalid_username(self):
''' tests that a user cannot signup with an invalid username '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dny",
phoneNumber='0713714835', isAdmin='True', email="jumaspayi@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid username, make sure its 5 to 12 characters long", response_msg["error"])
def test_user_registration_no_password(self):
''' tests that a user cannot signup without a password '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1j",
phoneNumber='0713714835', isAdmin='True', email="jumaspayj@gmail.com", password="", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password is required", response_msg["error"])
def test_user_registration_no_password_confirmation(self):
''' tests that a user cannot signup without password confirmation '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1k",
phoneNumber='0713714835', isAdmin='True', email="jumaspayk@gmail.com", password="thisispass", confirm_password="")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password confirmation is required", response_msg["error"])
def test_user_registration_password_mismatch(self):
''' tests that a user cannot signup without confirming registered password '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1l",
phoneNumber='0713714835', isAdmin='True', email="jumaspayl@gmail.com", password="thisispass2", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password mismatch", response_msg["error"])
def test_user_registration_password_too_short(self):
''' tests that a user cannot signup with password length less than 8 characters '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnycem2",
phoneNumber='0713714835', isAdmin='True', email="jumaspaym2@gmail.com", password="thisisp", confirm_password="thisisp")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password length should be atleast 8 characters long and atmost 12 characters long", response_msg["error"])
def test_user_registration_password_too_long(self):
''' tests that a user cannot signup with password length more than 12 characters '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyceu1",
phoneNumber='0713714835', isAdmin='True', email="jumaspayu3n@gmail.com", password="thisispasswordlong", confirm_password="thisispasswordlong")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password length should be atleast 8 characters long and atmost 12 characters long", response_msg["error"])
def test_user_registration_no_email(self):
''' tests that a user cannot signup without an email address '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1op",
phoneNumber='0713714835', isAdmin='True', email="", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Email is required", response_msg["error"])
def test_user_registration_invalid_email1(self):
''' tests that a user cannot signup with an invalid email address '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyceq1",
phoneNumber='0713714835', isAdmin='True', email="jumaspay", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid email", response_msg["error"])
def test_user_registration_invalid_email2(self):
''' tests that a user cannot signup with an invalid email address '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnycer1",
phoneNumber='0713714835', isAdmin='True', email="jumaspay3@gmail", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid email", response_msg["error"])
def test_user_registration_invalid_email3(self):
''' tests that a user cannot signup with an invalid email address '''
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyces1",
phoneNumber='0713714835', isAdmin='True', email="jumaspay3@gmail.", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid email", response_msg["error"])
def tearDown(self):
''' destroys the test variables after the tests finish executing '''
connector.destroy_questioner_test_tables()
''' make tests conveniently executable '''
if __name__ == '__main__':
unittest.main()
| 67.432927 | 185 | 0.707116 | import os
import json
import unittest
import pytest
from app import create_app
from app.api.v2.models.user_models import UserRegistration
from app.api.v2.views.user_views import myuser
from app.api.v2.utils.validators import validate_users
from app.api.v2.models.database_test import QuestionerTestDatabase
connector = QuestionerTestDatabase()
class TestUserRegistration(unittest.TestCase):
def setUp(self):
self.app = create_app(config='testing')
self.client = self.app.test_client()
connector.destroy_questioner_test_tables()
connector.create_questioner_test_tables()
self.user ={
'firstname': 'dennis',
'lastname': 'juma',
'othername': 'wafula',
'phoneNumber': '0716714835',
'username': 'dennisd',
'isAdmin': 'True',
'email': 'wafula@gmail.com',
'password': 'thisispass',
'confirm_password': 'thisispass'
}
def test_user_registration_no_firstname(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='', lastname='jumaa', othername='wafula', username="dennisdnycd",
phoneNumber='0713714835', isAdmin='True', email="jumaspayd@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("firstname is required", response_msg["error"])
def test_user_registration_no_lastname(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennis', lastname='', othername='wafula', username="dennisdnye",
phoneNumber='0713714835', isAdmin='True', email="jumaspaye@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("lastname is required", response_msg["error"])
def test_user_registration_no_othername(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennis', lastname='jumaa', othername='', username="dennisdnyf",
phoneNumber='0713714835', isAdmin='True', email="jumaspayf@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("othername is required", response_msg["error"])
def test_user_registration_no_phone(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennis', lastname='jumaa', othername='wafula', username="dennisdnyg",
phoneNumber='', isAdmin='True', email="jumaspayg@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("phoneNumber is required", response_msg["error"])
def test_user_registration_no_username(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="",
phoneNumber='0713714835', isAdmin='True', email="jumaspayh@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Username is required", response_msg["error"])
def test_user_registration_invalid_username(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dny",
phoneNumber='0713714835', isAdmin='True', email="jumaspayi@gmail.com", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid username, make sure its 5 to 12 characters long", response_msg["error"])
def test_user_registration_no_password(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1j",
phoneNumber='0713714835', isAdmin='True', email="jumaspayj@gmail.com", password="", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password is required", response_msg["error"])
def test_user_registration_no_password_confirmation(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1k",
phoneNumber='0713714835', isAdmin='True', email="jumaspayk@gmail.com", password="thisispass", confirm_password="")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password confirmation is required", response_msg["error"])
def test_user_registration_password_mismatch(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1l",
phoneNumber='0713714835', isAdmin='True', email="jumaspayl@gmail.com", password="thisispass2", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password mismatch", response_msg["error"])
def test_user_registration_password_too_short(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnycem2",
phoneNumber='0713714835', isAdmin='True', email="jumaspaym2@gmail.com", password="thisisp", confirm_password="thisisp")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password length should be atleast 8 characters long and atmost 12 characters long", response_msg["error"])
def test_user_registration_password_too_long(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyceu1",
phoneNumber='0713714835', isAdmin='True', email="jumaspayu3n@gmail.com", password="thisispasswordlong", confirm_password="thisispasswordlong")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Password length should be atleast 8 characters long and atmost 12 characters long", response_msg["error"])
def test_user_registration_no_email(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyce1op",
phoneNumber='0713714835', isAdmin='True', email="", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 406)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Email is required", response_msg["error"])
def test_user_registration_invalid_email1(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyceq1",
phoneNumber='0713714835', isAdmin='True', email="jumaspay", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid email", response_msg["error"])
def test_user_registration_invalid_email2(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnycer1",
phoneNumber='0713714835', isAdmin='True', email="jumaspay3@gmail", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid email", response_msg["error"])
def test_user_registration_invalid_email3(self):
response = self.client.post("/api/v2/auth/signup", data=json.dumps(dict(firstname='dennisa', lastname='jumaa', othername='wafula', username="dnyces1",
phoneNumber='0713714835', isAdmin='True', email="jumaspay3@gmail.", password="thisispass", confirm_password="thisispass")), content_type="application/json")
self.assertEqual(response.status_code, 422)
response_msg = json.loads(response.data.decode("UTF-8"))
self.assertIn("Invalid email", response_msg["error"])
def tearDown(self):
connector.destroy_questioner_test_tables()
if __name__ == '__main__':
unittest.main()
| true | true |
f73bd557f268ab27de93314a2c8acfc4ab2c6627 | 32,036 | py | Python | sdk/network/azure-mgmt-network/azure/mgmt/network/v2017_08_01/operations/_packet_captures_operations.py | vbarbaresi/azure-sdk-for-python | 397ba46c51d001ff89c66b170f5576cf8f49c05f | [
"MIT"
] | 8 | 2021-01-13T23:44:08.000Z | 2021-03-17T10:13:36.000Z | sdk/network/azure-mgmt-network/azure/mgmt/network/v2017_08_01/operations/_packet_captures_operations.py | vbarbaresi/azure-sdk-for-python | 397ba46c51d001ff89c66b170f5576cf8f49c05f | [
"MIT"
] | null | null | null | sdk/network/azure-mgmt-network/azure/mgmt/network/v2017_08_01/operations/_packet_captures_operations.py | vbarbaresi/azure-sdk-for-python | 397ba46c51d001ff89c66b170f5576cf8f49c05f | [
"MIT"
] | null | null | null | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpRequest, HttpResponse
from azure.core.polling import LROPoller, NoPolling, PollingMethod
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.arm_polling import ARMPolling
from .. import models
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
class PacketCapturesOperations(object):
"""PacketCapturesOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.network.v2017_08_01.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def _create_initial(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
parameters, # type: "models.PacketCapture"
**kwargs # type: Any
):
# type: (...) -> "models.PacketCaptureResult"
cls = kwargs.pop('cls', None) # type: ClsType["models.PacketCaptureResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json, text/json"
# Construct URL
url = self._create_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(parameters, 'PacketCapture')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('PacketCaptureResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'} # type: ignore
def begin_create(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
parameters, # type: "models.PacketCapture"
**kwargs # type: Any
):
# type: (...) -> LROPoller["models.PacketCaptureResult"]
"""Create and start a packet capture on the specified VM.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_watcher_name: The name of the network watcher.
:type network_watcher_name: str
:param packet_capture_name: The name of the packet capture session.
:type packet_capture_name: str
:param parameters: Parameters that define the create packet capture operation.
:type parameters: ~azure.mgmt.network.v2017_08_01.models.PacketCapture
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either PacketCaptureResult or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2017_08_01.models.PacketCaptureResult]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["models.PacketCaptureResult"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._create_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
parameters=parameters,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('PacketCaptureResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'} # type: ignore
def get(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "models.PacketCaptureResult"
"""Gets a packet capture session by name.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_watcher_name: The name of the network watcher.
:type network_watcher_name: str
:param packet_capture_name: The name of the packet capture session.
:type packet_capture_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PacketCaptureResult, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2017_08_01.models.PacketCaptureResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.PacketCaptureResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
accept = "application/json, text/json"
# Construct URL
url = self.get.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('PacketCaptureResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'} # type: ignore
def _delete_initial(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> None
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
# Construct URL
url = self._delete_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'} # type: ignore
def begin_delete(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> LROPoller[None]
"""Deletes the specified packet capture session.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_watcher_name: The name of the network watcher.
:type network_watcher_name: str
:param packet_capture_name: The name of the packet capture session.
:type packet_capture_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._delete_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'} # type: ignore
def _stop_initial(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> None
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
# Construct URL
url = self._stop_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_stop_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/stop'} # type: ignore
def begin_stop(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> LROPoller[None]
"""Stops a specified packet capture session.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_watcher_name: The name of the network watcher.
:type network_watcher_name: str
:param packet_capture_name: The name of the packet capture session.
:type packet_capture_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._stop_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_stop.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/stop'} # type: ignore
def _get_status_initial(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "models.PacketCaptureQueryStatusResult"
cls = kwargs.pop('cls', None) # type: ClsType["models.PacketCaptureQueryStatusResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
accept = "application/json, text/json"
# Construct URL
url = self._get_status_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize('PacketCaptureQueryStatusResult', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('PacketCaptureQueryStatusResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_get_status_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/queryStatus'} # type: ignore
def begin_get_status(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
packet_capture_name, # type: str
**kwargs # type: Any
):
# type: (...) -> LROPoller["models.PacketCaptureQueryStatusResult"]
"""Query the status of a running packet capture session.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_watcher_name: The name of the Network Watcher resource.
:type network_watcher_name: str
:param packet_capture_name: The name given to the packet capture session.
:type packet_capture_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either PacketCaptureQueryStatusResult or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2017_08_01.models.PacketCaptureQueryStatusResult]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["models.PacketCaptureQueryStatusResult"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._get_status_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('PacketCaptureQueryStatusResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_get_status.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/queryStatus'} # type: ignore
def list(
self,
resource_group_name, # type: str
network_watcher_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Iterable["models.PacketCaptureListResult"]
"""Lists all packet capture sessions within the specified resource group.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_watcher_name: The name of the Network Watcher resource.
:type network_watcher_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either PacketCaptureListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2017_08_01.models.PacketCaptureListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.PacketCaptureListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
accept = "application/json, text/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('PacketCaptureListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures'} # type: ignore
| 49.362096 | 240 | 0.667 |
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpRequest, HttpResponse
from azure.core.polling import LROPoller, NoPolling, PollingMethod
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.arm_polling import ARMPolling
from .. import models
if TYPE_CHECKING:
from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
class PacketCapturesOperations(object):
models = models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def _create_initial(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
parameters,
**kwargs
):
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json, text/json"
url = self._create_initial.metadata['url']
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(parameters, 'PacketCapture')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('PacketCaptureResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'}
def begin_create(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
parameters,
**kwargs
):
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None)
if cont_token is None:
raw_result = self._create_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
parameters=parameters,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('PacketCaptureResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'}
def get(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
accept = "application/json, text/json"
url = self.get.metadata['url']
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('PacketCaptureResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'}
def _delete_initial(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
url = self._delete_initial.metadata['url']
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'}
def begin_delete(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None)
if cont_token is None:
raw_result = self._delete_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}'}
def _stop_initial(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
url = self._stop_initial.metadata['url']
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_stop_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/stop'}
def begin_stop(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None)
if cont_token is None:
raw_result = self._stop_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_stop.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/stop'}
def _get_status_initial(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
accept = "application/json, text/json"
url = self._get_status_initial.metadata['url']
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'packetCaptureName': self._serialize.url("packet_capture_name", packet_capture_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize('PacketCaptureQueryStatusResult', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('PacketCaptureQueryStatusResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_get_status_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/queryStatus'}
def begin_get_status(
self,
resource_group_name,
network_watcher_name,
packet_capture_name,
**kwargs
):
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None)
if cont_token is None:
raw_result = self._get_status_initial(
resource_group_name=resource_group_name,
network_watcher_name=network_watcher_name,
packet_capture_name=packet_capture_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('PacketCaptureQueryStatusResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_get_status.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures/{packetCaptureName}/queryStatus'}
def list(
self,
resource_group_name,
network_watcher_name,
**kwargs
):
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2017-08-01"
accept = "application/json, text/json"
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
url = self.list.metadata['url']
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkWatcherName': self._serialize.url("network_watcher_name", network_watcher_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('PacketCaptureListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkWatchers/{networkWatcherName}/packetCaptures'}
| true | true |
f73bd73b96026f6ece6556dc2babadc1d614547e | 2,001 | py | Python | generator.py | Victorwz/fast-weights-pytorch | cef79631c9e41ae48fedbbc11e52be0cf084bfde | [
"Apache-2.0"
] | 2 | 2020-04-22T20:01:40.000Z | 2020-05-07T03:48:02.000Z | generator.py | Victorwz/fast-weights-pytorch | cef79631c9e41ae48fedbbc11e52be0cf084bfde | [
"Apache-2.0"
] | null | null | null | generator.py | Victorwz/fast-weights-pytorch | cef79631c9e41ae48fedbbc11e52be0cf084bfde | [
"Apache-2.0"
] | null | null | null | import numpy as np
import random
import pickle
num_train = 60000
num_val = 10000
num_test = 10000
step_num = 4
elem_num = 26 + 10 + 1
x_train = np.zeros([num_train, step_num * 2 + 3, elem_num], dtype=np.float32)
x_val = np.zeros([num_val, step_num * 2 + 3, elem_num], dtype=np.float32)
x_test = np.zeros([num_test, step_num * 2 + 3, elem_num], dtype=np.float32)
y_train = np.zeros([num_train, elem_num], dtype=np.float32)
y_val = np.zeros([num_val, elem_num], dtype=np.float32)
y_test = np.zeros([num_test, elem_num], dtype=np.float32)
def get_one_hot(c):
a = np.zeros([elem_num])
if ord('a') <= ord(c) <= ord('z'):
a[ord(c) - ord('a')] = 1
elif ord('0') <= ord(c) <= ord('9'):
a[ord(c) - ord('0') + 26] = 1
else:
a[-1] = 1
return a
def generate_one():
a = np.zeros([step_num * 2 + 3, elem_num])
d = {}
st = ''
for i in range(0, step_num):
c = random.randint(0, 25)
while d.has_key(c):
c = random.randint(0, 25)
b = random.randint(0, 9)
d[c] = b
s, t = chr(c + ord('a')), chr(b + ord('0'))
st += s + t
a[i*2] = get_one_hot(s)
a[i*2+1] = get_one_hot(t)
s = random.choice(d.keys())
t = chr(s + ord('a'))
r = chr(d[s] + ord('0'))
a[step_num * 2] = get_one_hot('?')
a[step_num * 2 + 1] = get_one_hot('?')
a[step_num * 2 + 2] = get_one_hot(t)
st += '??' + t + r
e = get_one_hot(r)
return a, e
if __name__ == '__main__':
for i in range(0, num_train):
x_train[i], y_train[i] = generate_one()
for i in range(0, num_test):
x_test[i], y_test[i] = generate_one()
for i in range(0, num_val):
x_val[i], y_val[i] = generate_one()
d = {
'x_train': x_train,
'x_test': x_test,
'x_val': x_val,
'y_train': y_train,
'y_test': y_test,
'y_val': y_val
}
with open('associative-retrieval.pkl', 'wb') as f:
pickle.dump(d, f, protocol=2) | 25.987013 | 77 | 0.541229 | import numpy as np
import random
import pickle
num_train = 60000
num_val = 10000
num_test = 10000
step_num = 4
elem_num = 26 + 10 + 1
x_train = np.zeros([num_train, step_num * 2 + 3, elem_num], dtype=np.float32)
x_val = np.zeros([num_val, step_num * 2 + 3, elem_num], dtype=np.float32)
x_test = np.zeros([num_test, step_num * 2 + 3, elem_num], dtype=np.float32)
y_train = np.zeros([num_train, elem_num], dtype=np.float32)
y_val = np.zeros([num_val, elem_num], dtype=np.float32)
y_test = np.zeros([num_test, elem_num], dtype=np.float32)
def get_one_hot(c):
a = np.zeros([elem_num])
if ord('a') <= ord(c) <= ord('z'):
a[ord(c) - ord('a')] = 1
elif ord('0') <= ord(c) <= ord('9'):
a[ord(c) - ord('0') + 26] = 1
else:
a[-1] = 1
return a
def generate_one():
a = np.zeros([step_num * 2 + 3, elem_num])
d = {}
st = ''
for i in range(0, step_num):
c = random.randint(0, 25)
while d.has_key(c):
c = random.randint(0, 25)
b = random.randint(0, 9)
d[c] = b
s, t = chr(c + ord('a')), chr(b + ord('0'))
st += s + t
a[i*2] = get_one_hot(s)
a[i*2+1] = get_one_hot(t)
s = random.choice(d.keys())
t = chr(s + ord('a'))
r = chr(d[s] + ord('0'))
a[step_num * 2] = get_one_hot('?')
a[step_num * 2 + 1] = get_one_hot('?')
a[step_num * 2 + 2] = get_one_hot(t)
st += '??' + t + r
e = get_one_hot(r)
return a, e
if __name__ == '__main__':
for i in range(0, num_train):
x_train[i], y_train[i] = generate_one()
for i in range(0, num_test):
x_test[i], y_test[i] = generate_one()
for i in range(0, num_val):
x_val[i], y_val[i] = generate_one()
d = {
'x_train': x_train,
'x_test': x_test,
'x_val': x_val,
'y_train': y_train,
'y_test': y_test,
'y_val': y_val
}
with open('associative-retrieval.pkl', 'wb') as f:
pickle.dump(d, f, protocol=2) | true | true |
f73bd882a3ea329ad2d594ef0164253c0426c469 | 2,975 | py | Python | DeepFashion2/model.py | lordtt13/Cybint-AI-projects | 87ad57a2e9c432483c2256408dd15762b7897b56 | [
"Apache-2.0"
] | 2 | 2020-02-19T06:50:59.000Z | 2020-02-19T06:51:04.000Z | DeepFashion2/model.py | lordtt13/Cybint-AI-projects | 87ad57a2e9c432483c2256408dd15762b7897b56 | [
"Apache-2.0"
] | null | null | null | DeepFashion2/model.py | lordtt13/Cybint-AI-projects | 87ad57a2e9c432483c2256408dd15762b7897b56 | [
"Apache-2.0"
] | 1 | 2020-02-14T06:00:50.000Z | 2020-02-14T06:00:50.000Z | from __future__ import print_function
import config
from config import *
from utils import *
# INPUT:
# VGG16 - block5_pool (MaxPooling2D) (None, 7, 7, 512)
# OUTPUT:
# Branch1 - Class Prediction
# Branch2 - IOU Prediction
# NOTE: Both models in create_model_train() and create_model_predict() should be exaclty same
def create_model(is_input_bottleneck, is_load_weights, input_shape, output_classes, optimizer='Adagrad', learn_rate=None, decay=0.0, momentum=0.0, activation='relu', dropout_rate=0.5):
logging.debug('input_shape {}'.format(input_shape))
logging.debug('input_shape {}'.format(type(input_shape)))
# Optimizer
optimizer, learn_rate = get_optimizer(optimizer, learn_rate, decay, momentum)
# Train
if is_input_bottleneck is True:
model_inputs = Input(shape=(input_shape))
common_inputs = model_inputs
# Predict
else: #input_shape = (img_width, img_height, 3)
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
#base_model = applications.inception_v3.InceptionV3(include_top=False, weights='imagenet', input_shape=input_shape)
logging.debug('base_model inputs {}'.format(base_model.input)) # shape=(?, 224, 224, 3)
logging.debug('base_model outputs {}'.format(base_model.output)) # shape=(?, 7, 7, 512)
model_inputs = base_model.input
common_inputs = base_model.output
## Model Classification
x = Flatten()(common_inputs)
x = Dense(256, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
predictions_class = Dense(output_classes, activation='softmax', name='predictions_class')(x)
## Model (Regression) IOU score
x = Flatten()(common_inputs)
x = Dense(256, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
x = Dense(256, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
predictions_iou = Dense(1, activation='sigmoid', name='predictions_iou')(x)
## Create Model
model = Model(inputs=model_inputs, outputs=[predictions_class, predictions_iou])
# logging.debug('model summary {}'.format(model.summary()))
## Load weights
if is_load_weights is True:
model.load_weights(top_model_weights_path_load, by_name=True)
## Compile
model.compile(optimizer=optimizer,
loss={'predictions_class': 'sparse_categorical_crossentropy', 'predictions_iou': 'mean_squared_error'}, metrics=['accuracy'],
loss_weights={'predictions_class': predictions_class_weight, 'predictions_iou': predictions_iou_weight})
logging.info('optimizer:{} learn_rate:{} decay:{} momentum:{} activation:{} dropout_rate:{}'.format(
optimizer, learn_rate, decay, momentum, activation, dropout_rate))
return model | 39.666667 | 184 | 0.65916 | from __future__ import print_function
import config
from config import *
from utils import *
def create_model(is_input_bottleneck, is_load_weights, input_shape, output_classes, optimizer='Adagrad', learn_rate=None, decay=0.0, momentum=0.0, activation='relu', dropout_rate=0.5):
logging.debug('input_shape {}'.format(input_shape))
logging.debug('input_shape {}'.format(type(input_shape)))
optimizer, learn_rate = get_optimizer(optimizer, learn_rate, decay, momentum)
if is_input_bottleneck is True:
model_inputs = Input(shape=(input_shape))
common_inputs = model_inputs
else:
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
logging.debug('base_model inputs {}'.format(base_model.input))
logging.debug('base_model outputs {}'.format(base_model.output))
model_inputs = base_model.input
common_inputs = base_model.output
mon_inputs)
x = Dense(256, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
predictions_class = Dense(output_classes, activation='softmax', name='predictions_class')(x)
ts)
x = Dense(256, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
x = Dense(256, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
predictions_iou = Dense(1, activation='sigmoid', name='predictions_iou')(x)
odel(inputs=model_inputs, outputs=[predictions_class, predictions_iou])
d_weights is True:
model.load_weights(top_model_weights_path_load, by_name=True)
l.compile(optimizer=optimizer,
loss={'predictions_class': 'sparse_categorical_crossentropy', 'predictions_iou': 'mean_squared_error'}, metrics=['accuracy'],
loss_weights={'predictions_class': predictions_class_weight, 'predictions_iou': predictions_iou_weight})
logging.info('optimizer:{} learn_rate:{} decay:{} momentum:{} activation:{} dropout_rate:{}'.format(
optimizer, learn_rate, decay, momentum, activation, dropout_rate))
return model | true | true |
f73bd9fef40d9af195f1e1eb4ae5b47154f56eb1 | 461 | py | Python | sim/Simulator.py | adhocmaster/pyns | 607feb56baf0900535130195163eac331e131a2e | [
"MIT"
] | 1 | 2021-06-15T06:21:14.000Z | 2021-06-15T06:21:14.000Z | sim/Simulator.py | adhocmaster/pyns | 607feb56baf0900535130195163eac331e131a2e | [
"MIT"
] | null | null | null | sim/Simulator.py | adhocmaster/pyns | 607feb56baf0900535130195163eac331e131a2e | [
"MIT"
] | 1 | 2021-06-15T06:21:18.000Z | 2021-06-15T06:21:18.000Z | from abc import ABC
class Simulator(ABC):
def __init__(self):
self.stats = {}
self.stats['dataInFlight'] = []
self.stats['dataInQueue'] = []
self.stats['packetsInFlight'] = []
self.stats['packetsInQueue'] = []
self.stats['queueSize'] = []
self.stats['packetsSent'] = []
self.stats['packetsAcked'] = []
self.stats['totalPacketsSent'] = []
self.stats['totalPacketsAcked'] = [] | 28.8125 | 44 | 0.553145 | from abc import ABC
class Simulator(ABC):
def __init__(self):
self.stats = {}
self.stats['dataInFlight'] = []
self.stats['dataInQueue'] = []
self.stats['packetsInFlight'] = []
self.stats['packetsInQueue'] = []
self.stats['queueSize'] = []
self.stats['packetsSent'] = []
self.stats['packetsAcked'] = []
self.stats['totalPacketsSent'] = []
self.stats['totalPacketsAcked'] = [] | true | true |
f73bda976a8336d5699bf9fa8b6ceb17884743d6 | 5,187 | py | Python | src/kids/cache/__init__.py | vaab/kids.cache | 668f3b966877c4a0855d60e05cc3706cf37e4570 | [
"BSD-2-Clause"
] | 25 | 2015-08-03T08:39:49.000Z | 2021-02-10T13:49:10.000Z | src/kids/cache/__init__.py | vaab/kids.cache | 668f3b966877c4a0855d60e05cc3706cf37e4570 | [
"BSD-2-Clause"
] | 10 | 2015-02-02T02:12:04.000Z | 2018-05-24T09:12:40.000Z | src/kids/cache/__init__.py | vaab/kids.cache | 668f3b966877c4a0855d60e05cc3706cf37e4570 | [
"BSD-2-Clause"
] | 4 | 2016-11-28T17:59:13.000Z | 2019-10-03T10:19:20.000Z | # Package placeholder
import threading
import functools
import collections
CacheInfo = collections.namedtuple(
'CacheInfo', 'type hits misses maxsize currsize')
def make_key(obj, typed=True):
args, kwargs = obj
key = (tuple(args), tuple(sorted(kwargs.items())))
if typed:
key += tuple(type(v) for v in args)
key += tuple(type(v) for _, v in sorted(kwargs.items()))
return key
def is_hashable(obj):
try:
hash(obj)
return True
except Exception: ## pylint: disable-msg=W0703
return False
def make_key_hippie(obj, typed=True):
"""Return hashable structure from non-hashable structure using hippie means
dict and set are sorted and their content subjected to same hippie means.
Note that the key identifies the current content of the structure.
"""
ftype = type if typed else lambda o: None
if is_hashable(obj):
## DO NOT RETURN hash(obj), as hash collision would generate bad
## cache collisions.
return obj, ftype(obj)
## should we try to convert to frozen{set,dict} to get the C
## hashing function speed ? But the convertion has a cost also.
if isinstance(obj, set):
obj = sorted(obj)
if isinstance(obj, (list, tuple)):
return tuple(make_key_hippie(e, typed) for e in obj)
if isinstance(obj, dict):
return tuple(sorted(((make_key_hippie(k, typed),
make_key_hippie(v, typed))
for k, v in obj.items())))
raise ValueError(
"%r can not be hashed. Try providing a custom key function."
% obj)
def hashing(typed=True, strict=False):
"""Returns a typed and/or strict key callable.
A strict key callable will fail on traditionaly non-hashable object,
while a strict=False hashing will use hippie hashing that can hash
mutable object.
A typed key callable will use type of each object in the hash and will
distinguish with same hash but different type (example: 2 and 2.0).
"""
hashable_struct_producer = make_key if strict else make_key_hippie
def _make_key(*args, **kwargs):
## use a list to avoid using hash of tuples...
return hashable_struct_producer([list(args), kwargs], typed=typed)
return _make_key
SUPPORTED_DECORATOR = {
property: lambda f: f.fget,
classmethod: lambda f: f.__func__,
staticmethod: lambda f: f.__func__,
}
def undecorate(func):
"""Returns the decorator and the undecorated function of given object."""
orig_call_wrapper = lambda x: x
for call_wrapper, unwrap in SUPPORTED_DECORATOR.items():
if isinstance(func, call_wrapper):
func = unwrap(func)
orig_call_wrapper = call_wrapper
break
return orig_call_wrapper, func
## inspired by cachetools.decorators.cachedfunc
def cachedfunc(cache_store, key=make_key_hippie):
context = threading.RLock() ## stats lock
def decorator(func):
stats = [0, 0]
wrapper, wrapped = undecorate(func)
@functools.wraps(wrapped)
def _cache_wrapper(*args, **kwargs):
k = key(*args, **kwargs)
with context:
try:
result = cache_store[k]
stats[0] += 1
return result
except KeyError:
stats[1] += 1
result = wrapped(*args, **kwargs)
with context:
try:
cache_store[k] = result
except ValueError:
## Value 'too large', only casted with cachetools stores.
pass
return result
## mimic's python3 ``lru_cache`` facilities.
def cache_info():
with context:
hits, misses = stats
maxsize = getattr(cache_store, "maxsize", None)
currsize = getattr(cache_store, "currsize", len(cache_store))
return CacheInfo(
type(cache_store).__name__, hits, misses, maxsize, currsize)
def cache_clear():
with context:
cache_store.clear()
_cache_wrapper.cache_info = cache_info
_cache_wrapper.cache_clear = cache_clear
return wrapper(_cache_wrapper)
return decorator
def cache(*args, **kwargs):
"""The @cache decorator
Compatility with using ``@cache()`` and ``@cache`` is managed in
the current function.
"""
## only one argument ?
if len(args) == 1 and len(kwargs) == 0 and \
(callable(args[0]) or \
isinstance(args[0], tuple(SUPPORTED_DECORATOR.keys()))):
return _cache_w_args(args[0])
return lambda f: _cache_w_args(f, *args, **kwargs)
## No locking mecanism because this should be implemented in the Cache
## objects if needed.
def _cache_w_args(f, use=None, cache_factory=dict,
key=None, strict=False, typed=False):
if key is None:
key = hashing(strict=strict, typed=typed)
if use is None:
use = cache_factory()
return cachedfunc(cache_store=use, key=key)(f)
hippie_hashing = hashing()
| 30.511765 | 79 | 0.614806 |
import threading
import functools
import collections
CacheInfo = collections.namedtuple(
'CacheInfo', 'type hits misses maxsize currsize')
def make_key(obj, typed=True):
args, kwargs = obj
key = (tuple(args), tuple(sorted(kwargs.items())))
if typed:
key += tuple(type(v) for v in args)
key += tuple(type(v) for _, v in sorted(kwargs.items()))
return key
def is_hashable(obj):
try:
hash(obj)
return True
except Exception: make_key_hippie(obj, typed=True):
ftype = type if typed else lambda o: None
if is_hashable(obj):
j)
if isinstance(obj, dict):
return tuple(sorted(((make_key_hippie(k, typed),
make_key_hippie(v, typed))
for k, v in obj.items())))
raise ValueError(
"%r can not be hashed. Try providing a custom key function."
% obj)
def hashing(typed=True, strict=False):
hashable_struct_producer = make_key if strict else make_key_hippie
def _make_key(*args, **kwargs):
t(args), kwargs], typed=typed)
return _make_key
SUPPORTED_DECORATOR = {
property: lambda f: f.fget,
classmethod: lambda f: f.__func__,
staticmethod: lambda f: f.__func__,
}
def undecorate(func):
orig_call_wrapper = lambda x: x
for call_wrapper, unwrap in SUPPORTED_DECORATOR.items():
if isinstance(func, call_wrapper):
func = unwrap(func)
orig_call_wrapper = call_wrapper
break
return orig_call_wrapper, func
ie):
context = threading.RLock() corator(func):
stats = [0, 0]
wrapper, wrapped = undecorate(func)
@functools.wraps(wrapped)
def _cache_wrapper(*args, **kwargs):
k = key(*args, **kwargs)
with context:
try:
result = cache_store[k]
stats[0] += 1
return result
except KeyError:
stats[1] += 1
result = wrapped(*args, **kwargs)
with context:
try:
cache_store[k] = result
except ValueError:
h context:
hits, misses = stats
maxsize = getattr(cache_store, "maxsize", None)
currsize = getattr(cache_store, "currsize", len(cache_store))
return CacheInfo(
type(cache_store).__name__, hits, misses, maxsize, currsize)
def cache_clear():
with context:
cache_store.clear()
_cache_wrapper.cache_info = cache_info
_cache_wrapper.cache_clear = cache_clear
return wrapper(_cache_wrapper)
return decorator
def cache(*args, **kwargs):
## only one argument ?
if len(args) == 1 and len(kwargs) == 0 and \
(callable(args[0]) or \
isinstance(args[0], tuple(SUPPORTED_DECORATOR.keys()))):
return _cache_w_args(args[0])
return lambda f: _cache_w_args(f, *args, **kwargs)
## No locking mecanism because this should be implemented in the Cache
## objects if needed.
def _cache_w_args(f, use=None, cache_factory=dict,
key=None, strict=False, typed=False):
if key is None:
key = hashing(strict=strict, typed=typed)
if use is None:
use = cache_factory()
return cachedfunc(cache_store=use, key=key)(f)
hippie_hashing = hashing()
| true | true |
f73bdbe602e188715f74357a12989a8527413446 | 2,383 | py | Python | horovod/spark/mpi_run.py | hgao10/horovod_simulation | 3678a7d1d424931f48af4b53ef3293073af71c2e | [
"Apache-2.0"
] | null | null | null | horovod/spark/mpi_run.py | hgao10/horovod_simulation | 3678a7d1d424931f48af4b53ef3293073af71c2e | [
"Apache-2.0"
] | null | null | null | horovod/spark/mpi_run.py | hgao10/horovod_simulation | 3678a7d1d424931f48af4b53ef3293073af71c2e | [
"Apache-2.0"
] | null | null | null | # Copyright 2020 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import sys
from horovod.run.mpi_run import mpi_run as hr_mpi_run
from horovod.run.common.util import codec, secret
def mpi_run(settings, nics, driver, env, stdout=None, stderr=None):
"""
Runs mpirun.
:param settings: Settings for running MPI.
Note: settings.num_proc and settings.hosts must not be None.
:param nics: Interfaces to include by MPI.
:param driver: The Spark driver service that tasks are connected to.
:param env: Environment dictionary to use for running MPI.
:param stdout: Stdout of the mpi process.
Only used when settings.run_func_mode is True.
:param stderr: Stderr of the mpi process.
Only used when settings.run_func_mode is True.
"""
if env is None:
env = os.environ.copy()
# Pass secret key through the environment variables.
env[secret.HOROVOD_SECRET_KEY] = codec.dumps_base64(settings.key)
rsh_agent = (sys.executable,
'-m', 'horovod.spark.driver.mpirun_rsh',
codec.dumps_base64(driver.addresses()),
codec.dumps_base64(settings))
settings.extra_mpi_args = ('{extra_mpi_args} -x NCCL_DEBUG=INFO -mca plm_rsh_agent "{rsh_agent}"'
.format(extra_mpi_args=settings.extra_mpi_args if settings.extra_mpi_args else '',
rsh_agent=' '.join(rsh_agent)))
command = (sys.executable,
'-m', 'horovod.spark.task.mpirun_exec_fn',
codec.dumps_base64(driver.addresses()),
codec.dumps_base64(settings))
hr_mpi_run(settings, nics, env, command, stdout=stdout, stderr=stderr)
| 43.327273 | 113 | 0.652539 |
import os
import sys
from horovod.run.mpi_run import mpi_run as hr_mpi_run
from horovod.run.common.util import codec, secret
def mpi_run(settings, nics, driver, env, stdout=None, stderr=None):
if env is None:
env = os.environ.copy()
env[secret.HOROVOD_SECRET_KEY] = codec.dumps_base64(settings.key)
rsh_agent = (sys.executable,
'-m', 'horovod.spark.driver.mpirun_rsh',
codec.dumps_base64(driver.addresses()),
codec.dumps_base64(settings))
settings.extra_mpi_args = ('{extra_mpi_args} -x NCCL_DEBUG=INFO -mca plm_rsh_agent "{rsh_agent}"'
.format(extra_mpi_args=settings.extra_mpi_args if settings.extra_mpi_args else '',
rsh_agent=' '.join(rsh_agent)))
command = (sys.executable,
'-m', 'horovod.spark.task.mpirun_exec_fn',
codec.dumps_base64(driver.addresses()),
codec.dumps_base64(settings))
hr_mpi_run(settings, nics, env, command, stdout=stdout, stderr=stderr)
| true | true |
f73bdc331de92a67cf6fdaa7ca361d0d53cdf647 | 1,080 | py | Python | widgy/generic/__init__.py | isopets/django-widgy | de6920b2b25699888eee1cc9b4d28f2aa5905ce1 | [
"Apache-2.0"
] | 168 | 2015-01-04T17:22:45.000Z | 2022-01-28T09:53:35.000Z | widgy/generic/__init__.py | isopets/django-widgy | de6920b2b25699888eee1cc9b4d28f2aa5905ce1 | [
"Apache-2.0"
] | 82 | 2015-01-09T18:14:32.000Z | 2020-10-08T18:13:07.000Z | widgy/generic/__init__.py | isopets/django-widgy | de6920b2b25699888eee1cc9b4d28f2aa5905ce1 | [
"Apache-2.0"
] | 61 | 2015-01-09T17:16:51.000Z | 2021-07-03T08:52:27.000Z | from django.contrib.contenttypes.fields import GenericForeignKey, GenericRelation
class ProxyGenericForeignKey(GenericForeignKey):
def __init__(self, *args, **kwargs):
kwargs['for_concrete_model'] = False
super(ProxyGenericForeignKey, self).__init__(*args, **kwargs)
class ProxyGenericRelation(GenericRelation):
def __init__(self, *args, **kwargs):
kwargs['for_concrete_model'] = False
super(ProxyGenericRelation, self).__init__(*args, **kwargs)
class WidgyGenericForeignKey(ProxyGenericForeignKey):
def __get__(self, instance, instance_type=None):
try:
return super(WidgyGenericForeignKey, self).__get__(instance, instance_type)
except AttributeError:
# The model for this content type couldn't be loaded. Use an
# UnknownWidget instead.
from widgy.models import UnknownWidget
ret = UnknownWidget(getattr(instance, self.ct_field), getattr(instance, self.fk_field), instance)
ret.node = instance
ret.warn()
return ret
| 38.571429 | 109 | 0.690741 | from django.contrib.contenttypes.fields import GenericForeignKey, GenericRelation
class ProxyGenericForeignKey(GenericForeignKey):
def __init__(self, *args, **kwargs):
kwargs['for_concrete_model'] = False
super(ProxyGenericForeignKey, self).__init__(*args, **kwargs)
class ProxyGenericRelation(GenericRelation):
def __init__(self, *args, **kwargs):
kwargs['for_concrete_model'] = False
super(ProxyGenericRelation, self).__init__(*args, **kwargs)
class WidgyGenericForeignKey(ProxyGenericForeignKey):
def __get__(self, instance, instance_type=None):
try:
return super(WidgyGenericForeignKey, self).__get__(instance, instance_type)
except AttributeError:
# UnknownWidget instead.
from widgy.models import UnknownWidget
ret = UnknownWidget(getattr(instance, self.ct_field), getattr(instance, self.fk_field), instance)
ret.node = instance
ret.warn()
return ret
| true | true |
f73bdd24d80de4652f229c6057b882f0ba52dc44 | 4,108 | py | Python | tests/test_flights/test_flight.py | PatrickCmd/flight-booking-application | a521932530d622c6eef46ea6a0f968c8267b622e | [
"MIT"
] | null | null | null | tests/test_flights/test_flight.py | PatrickCmd/flight-booking-application | a521932530d622c6eef46ea6a0f968c8267b622e | [
"MIT"
] | 8 | 2020-02-12T01:01:51.000Z | 2022-03-11T23:59:40.000Z | tests/test_flights/test_flight.py | PatrickCmd/flight-booking-application | a521932530d622c6eef46ea6a0f968c8267b622e | [
"MIT"
] | 1 | 2019-08-13T19:04:11.000Z | 2019-08-13T19:04:11.000Z | from tests.base_test_case import FlightBaseTestCase
class TestFlight(FlightBaseTestCase):
def test_create_flight(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
params = {
"name": "Entebbe to Denver",
"origin": "Entebbe",
"destination": "Denver",
"departure": "2019-08-02T08:00:00Z",
"arrival": "2019-08-03T07:00:00Z",
"aircraft": "Vintage",
"status": "ON_TIME",
"number": "KPQYWT72839",
"capacity": 120,
}
response = self.client.post(self.flight_uri, params, format="json")
self.assertEqual(
response.status_code,
201,
"Expected Response Code 201, received {0} instead.".format(
response.status_code
),
)
self.assertIn("Entebbe to Denver", str(response.data))
self.assertIn("capacity", str(response.data))
def test_create_flight_when_not_admin_user(self):
login_uri = "/fbs-api/users/login/"
params_user = {"email": "test@testuser.com", "password": "Testuser12344#"}
self.set_authorization_header(login_uri, params_user)
params = {
"name": "Entebbe to Denver",
"origin": "Entebbe",
"destination": "Denver",
"departure": "2019-08-02T08:00:00Z",
"arrival": "2019-08-03T07:00:00Z",
"aircraft": "Vintage",
"status": "ON_TIME",
"number": "KPQYWT72839",
"capacity": 120,
}
response = self.client.post(self.flight_uri, params, format="json")
self.assertEqual(
response.status_code,
403,
"Expected Response Code 403, received {0} instead.".format(
response.status_code
),
)
self.assertNotIn("Entebbe to Denver", str(response.data))
self.assertIn(
"You don't have permissions to carry out this action.", str(response.data)
)
def test_view_flights(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
response = self.client.get(self.flight_uri, format="json")
self.assertEqual(
response.status_code,
200,
"Expected Response Code 200, received {0} instead.".format(
response.status_code
),
)
def test_get_flight_details(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
response = self.client.get(f"{self.flight_uri}{self.flight.pk}", format="json")
self.assertEqual(
response.status_code,
200,
"Expected Response Code 200, received {0} instead.".format(
response.status_code
),
)
self.assertNotIn("Entebbe to Denver", str(response.data))
self.assertIn("test flight", str(response.data))
def test_get_non_existant_flight_details(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
pk = 10
response = self.client.get(f"{self.flight_uri}{pk}", format="json")
self.assertEqual(
response.status_code,
404,
"Expected Response Code 404, received {0} instead.".format(
response.status_code
),
)
self.assertIn("Not found", str(response.data))
| 36.353982 | 87 | 0.56037 | from tests.base_test_case import FlightBaseTestCase
class TestFlight(FlightBaseTestCase):
def test_create_flight(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
params = {
"name": "Entebbe to Denver",
"origin": "Entebbe",
"destination": "Denver",
"departure": "2019-08-02T08:00:00Z",
"arrival": "2019-08-03T07:00:00Z",
"aircraft": "Vintage",
"status": "ON_TIME",
"number": "KPQYWT72839",
"capacity": 120,
}
response = self.client.post(self.flight_uri, params, format="json")
self.assertEqual(
response.status_code,
201,
"Expected Response Code 201, received {0} instead.".format(
response.status_code
),
)
self.assertIn("Entebbe to Denver", str(response.data))
self.assertIn("capacity", str(response.data))
def test_create_flight_when_not_admin_user(self):
login_uri = "/fbs-api/users/login/"
params_user = {"email": "test@testuser.com", "password": "Testuser12344#"}
self.set_authorization_header(login_uri, params_user)
params = {
"name": "Entebbe to Denver",
"origin": "Entebbe",
"destination": "Denver",
"departure": "2019-08-02T08:00:00Z",
"arrival": "2019-08-03T07:00:00Z",
"aircraft": "Vintage",
"status": "ON_TIME",
"number": "KPQYWT72839",
"capacity": 120,
}
response = self.client.post(self.flight_uri, params, format="json")
self.assertEqual(
response.status_code,
403,
"Expected Response Code 403, received {0} instead.".format(
response.status_code
),
)
self.assertNotIn("Entebbe to Denver", str(response.data))
self.assertIn(
"You don't have permissions to carry out this action.", str(response.data)
)
def test_view_flights(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
response = self.client.get(self.flight_uri, format="json")
self.assertEqual(
response.status_code,
200,
"Expected Response Code 200, received {0} instead.".format(
response.status_code
),
)
def test_get_flight_details(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
response = self.client.get(f"{self.flight_uri}{self.flight.pk}", format="json")
self.assertEqual(
response.status_code,
200,
"Expected Response Code 200, received {0} instead.".format(
response.status_code
),
)
self.assertNotIn("Entebbe to Denver", str(response.data))
self.assertIn("test flight", str(response.data))
def test_get_non_existant_flight_details(self):
login_uri = "/fbs-api/users/login/"
params_user = {
"email": "test@testadminuser.com",
"password": "Testadminuser12344#",
}
self.set_authorization_header(login_uri, params_user)
pk = 10
response = self.client.get(f"{self.flight_uri}{pk}", format="json")
self.assertEqual(
response.status_code,
404,
"Expected Response Code 404, received {0} instead.".format(
response.status_code
),
)
self.assertIn("Not found", str(response.data))
| true | true |
f73bdd63271386f7b70e3a2f607104f30a30ed3e | 2,413 | py | Python | tensorflow_examples/lite/model_maker/__init__.py | ufocruz/examples | e1142ebb269802b5b518219a19cba2f6e1f54258 | [
"Apache-2.0"
] | 3 | 2021-05-04T20:00:41.000Z | 2021-09-14T08:56:05.000Z | tensorflow_examples/lite/model_maker/__init__.py | ufocruz/examples | e1142ebb269802b5b518219a19cba2f6e1f54258 | [
"Apache-2.0"
] | null | null | null | tensorflow_examples/lite/model_maker/__init__.py | ufocruz/examples | e1142ebb269802b5b518219a19cba2f6e1f54258 | [
"Apache-2.0"
] | 1 | 2021-04-20T17:33:19.000Z | 2021-04-20T17:33:19.000Z | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A transfer learning library to train custom TFLite models."""
from tensorflow_examples.lite.model_maker.core import compat
from tensorflow_examples.lite.model_maker.core.data_util import audio_dataloader
from tensorflow_examples.lite.model_maker.core.data_util import image_dataloader
from tensorflow_examples.lite.model_maker.core.data_util import object_detector_dataloader
from tensorflow_examples.lite.model_maker.core.data_util import text_dataloader
from tensorflow_examples.lite.model_maker.core.data_util.audio_dataloader import DataLoader as AudioDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.image_dataloader import ImageClassifierDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.object_detector_dataloader import DataLoader as ObjectDetectorDataloader
from tensorflow_examples.lite.model_maker.core.data_util.recommendation_dataloader import RecommendationDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.text_dataloader import QuestionAnswerDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.text_dataloader import TextClassifierDataLoader
from tensorflow_examples.lite.model_maker.core.export_format import ExportFormat
from tensorflow_examples.lite.model_maker.core.task import audio_classifier
from tensorflow_examples.lite.model_maker.core.task import configs
from tensorflow_examples.lite.model_maker.core.task import image_classifier
from tensorflow_examples.lite.model_maker.core.task import model_spec
from tensorflow_examples.lite.model_maker.core.task import object_detector
from tensorflow_examples.lite.model_maker.core.task import question_answer
from tensorflow_examples.lite.model_maker.core.task import recommendation
from tensorflow_examples.lite.model_maker.core.task import text_classifier
| 61.871795 | 129 | 0.862826 |
from tensorflow_examples.lite.model_maker.core import compat
from tensorflow_examples.lite.model_maker.core.data_util import audio_dataloader
from tensorflow_examples.lite.model_maker.core.data_util import image_dataloader
from tensorflow_examples.lite.model_maker.core.data_util import object_detector_dataloader
from tensorflow_examples.lite.model_maker.core.data_util import text_dataloader
from tensorflow_examples.lite.model_maker.core.data_util.audio_dataloader import DataLoader as AudioDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.image_dataloader import ImageClassifierDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.object_detector_dataloader import DataLoader as ObjectDetectorDataloader
from tensorflow_examples.lite.model_maker.core.data_util.recommendation_dataloader import RecommendationDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.text_dataloader import QuestionAnswerDataLoader
from tensorflow_examples.lite.model_maker.core.data_util.text_dataloader import TextClassifierDataLoader
from tensorflow_examples.lite.model_maker.core.export_format import ExportFormat
from tensorflow_examples.lite.model_maker.core.task import audio_classifier
from tensorflow_examples.lite.model_maker.core.task import configs
from tensorflow_examples.lite.model_maker.core.task import image_classifier
from tensorflow_examples.lite.model_maker.core.task import model_spec
from tensorflow_examples.lite.model_maker.core.task import object_detector
from tensorflow_examples.lite.model_maker.core.task import question_answer
from tensorflow_examples.lite.model_maker.core.task import recommendation
from tensorflow_examples.lite.model_maker.core.task import text_classifier
| true | true |
f73bdd8a837582d5bc07ef5d0cc81f29fba71831 | 3,737 | py | Python | drmaa2/reservation_info.py | gridengine/drmaa2-python | 36e84e8dc0079c9e3d772c1536f07ecb1e435684 | [
"Apache-2.0"
] | 10 | 2019-05-28T23:17:39.000Z | 2022-01-14T08:52:54.000Z | drmaa2/reservation_info.py | iamh2o/drmaa2-python | 36e84e8dc0079c9e3d772c1536f07ecb1e435684 | [
"Apache-2.0"
] | 5 | 2019-11-01T10:50:19.000Z | 2021-12-13T11:56:19.000Z | drmaa2/reservation_info.py | iamh2o/drmaa2-python | 36e84e8dc0079c9e3d772c1536f07ecb1e435684 | [
"Apache-2.0"
] | 2 | 2019-02-26T16:36:07.000Z | 2019-10-29T02:02:06.000Z | #!/usr/bin/env python
# ___INFO__MARK_BEGIN__
#######################################################################################
# Copyright 2008-2021 Univa Corporation (acquired and owned by Altair Engineering Inc.)
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License.
#
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# See the License for the specific language governing permissions and
# limitations under the License.
#######################################################################################
# ___INFO__MARK_END__
from ctypes import POINTER
from ctypes import pointer
from .drmaa2_ctypes import drmaa2_rinfo
from .drmaa2_object import Drmaa2Object
from .drmaa2_exceptions import InvalidArgument
class ReservationInfo(Drmaa2Object):
""" High-level DRMAA2 reservation info class. """
reservation_id = Drmaa2Object.StringDescriptor('reservationId')
""" Reservation id (str). """
reservation_name = Drmaa2Object.StringDescriptor('reservationName')
""" Reservation name (str). """
reserved_start_time = Drmaa2Object.TimeDescriptor('reservedStartTime')
""" Reserved start time (datetime). """
reserved_end_time = Drmaa2Object.TimeDescriptor('reservedEndTime')
""" Reserved end time (datetime). """
users_acl = Drmaa2Object.StringListDescriptor('usersACL')
""" Users ACL ([str]). """
reserved_slots = Drmaa2Object.LongLongDescriptor('reservedSlots')
""" Reserved slots (long). """
reserved_machines = Drmaa2Object.StringListDescriptor('reservedMachines')
""" Reserved machines ([str]). """
implementation_specific = Drmaa2Object.ImplSpecDescriptor('implementationSpecific')
""" Implementation specific dictionary ({str:str}). """
def __init__(self, reservation_info={}):
"""
Constructor.
:param reservation_info: Input structure representing the object; this structure is typically a dictionary, but it can also be a low-level drmaa2_rinfo struct.
:type reservation_info: dict
:raises InvalidArgument: in case of an invalid input argument.
:raises Drmaa2Exception: for all other errors.
>>> ri = ReservationInfo({'reservation_name' : 'sv-01'})
>>> print(ri.reservation_name)
sv-01
>>> ri.reserved_slots = 3
"""
Drmaa2Object.__init__(self)
if isinstance(reservation_info, dict):
self._struct = self.get_drmaa2_library().drmaa2_rinfo_create()
self.init_impl_spec_key_values()
self.from_dict(reservation_info)
elif isinstance(reservation_info, POINTER(drmaa2_rinfo)):
self._struct = reservation_info
else:
raise InvalidArgument('Invalid argument: %s' % str(reservation_info))
def __del__(self):
# self.get_drmaa2_library().drmaa2_rinfo_free(pointer(self._struct))
pass
@classmethod
def get_implementation_specific_keys(cls):
"""
Retrieve list of implementation-specific keys.
:returns: String list of implementation-specific keys.
>>> print(ReservationInfo.get_implementation_specific_keys())
['uge_ri_ar_json']
"""
if cls.implementation_specific_keys is None:
cls.implementation_specific_keys = cls.to_py_string_list(cls.get_drmaa2_library().drmaa2_rinfo_impl_spec())
return cls.implementation_specific_keys
| 41.065934 | 167 | 0.676478 | true | true | |
f73bdde09b10c8d24414c68d205cd8684a52d650 | 1,979 | py | Python | alibi_detect/base.py | Clusks/alibi-detect | b39406a6cf88f315f401562d4fea93a42aa6dcc1 | [
"ECL-2.0",
"Apache-2.0",
"CC0-1.0"
] | 1 | 2021-01-19T09:13:12.000Z | 2021-01-19T09:13:12.000Z | alibi_detect/base.py | Clusks/alibi-detect | b39406a6cf88f315f401562d4fea93a42aa6dcc1 | [
"ECL-2.0",
"Apache-2.0",
"CC0-1.0"
] | null | null | null | alibi_detect/base.py | Clusks/alibi-detect | b39406a6cf88f315f401562d4fea93a42aa6dcc1 | [
"ECL-2.0",
"Apache-2.0",
"CC0-1.0"
] | null | null | null | from abc import ABC, abstractmethod
import copy
import numpy as np
from typing import Dict
DEFAULT_META = {
"name": None,
"detector_type": None, # online or offline
"data_type": None # tabular, image or time-series
} # type: Dict
def outlier_prediction_dict():
data = {
'instance_score': None,
'feature_score': None,
'is_outlier': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
def adversarial_prediction_dict():
data = {
'instance_score': None,
'is_adversarial': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
def adversarial_correction_dict():
data = {
'instance_score': None,
'is_adversarial': None,
'corrected': None,
'no_defense': None,
'defense': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
def concept_drift_dict():
data = {
'is_drift': None,
'distance': None,
'p_val': None,
'threshold': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
class BaseDetector(ABC):
""" Base class for outlier detection algorithms. """
def __init__(self):
self.meta = copy.deepcopy(DEFAULT_META)
self.meta['name'] = self.__class__.__name__
def __repr__(self):
return self.__class__.__name__
@property
def meta(self) -> Dict:
return self._meta
@meta.setter
def meta(self, value: Dict):
if not isinstance(value, dict):
raise TypeError('meta must be a dictionary')
self._meta = value
@abstractmethod
def score(self, X: np.ndarray):
pass
@abstractmethod
def predict(self, X: np.ndarray):
pass
class FitMixin(ABC):
@abstractmethod
def fit(self, X: np.ndarray) -> None:
pass
class ThresholdMixin(ABC):
@abstractmethod
def infer_threshold(self, X: np.ndarray) -> None:
pass
| 21.988889 | 62 | 0.608893 | from abc import ABC, abstractmethod
import copy
import numpy as np
from typing import Dict
DEFAULT_META = {
"name": None,
"detector_type": None,
"data_type": None
}
def outlier_prediction_dict():
data = {
'instance_score': None,
'feature_score': None,
'is_outlier': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
def adversarial_prediction_dict():
data = {
'instance_score': None,
'is_adversarial': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
def adversarial_correction_dict():
data = {
'instance_score': None,
'is_adversarial': None,
'corrected': None,
'no_defense': None,
'defense': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
def concept_drift_dict():
data = {
'is_drift': None,
'distance': None,
'p_val': None,
'threshold': None
}
return copy.deepcopy({"data": data, "meta": DEFAULT_META})
class BaseDetector(ABC):
def __init__(self):
self.meta = copy.deepcopy(DEFAULT_META)
self.meta['name'] = self.__class__.__name__
def __repr__(self):
return self.__class__.__name__
@property
def meta(self) -> Dict:
return self._meta
@meta.setter
def meta(self, value: Dict):
if not isinstance(value, dict):
raise TypeError('meta must be a dictionary')
self._meta = value
@abstractmethod
def score(self, X: np.ndarray):
pass
@abstractmethod
def predict(self, X: np.ndarray):
pass
class FitMixin(ABC):
@abstractmethod
def fit(self, X: np.ndarray) -> None:
pass
class ThresholdMixin(ABC):
@abstractmethod
def infer_threshold(self, X: np.ndarray) -> None:
pass
| true | true |
f73bdf294246507d49633746f6c969ffda3ef049 | 2,219 | py | Python | ldbc_snb_datagen-dev/tools/sparkbench/distributions_extractor.py | netdb407/skh_project | 8893132f898d21149f73cfb3a5f7806ce5518be5 | [
"Apache-2.0"
] | null | null | null | ldbc_snb_datagen-dev/tools/sparkbench/distributions_extractor.py | netdb407/skh_project | 8893132f898d21149f73cfb3a5f7806ce5518be5 | [
"Apache-2.0"
] | 9 | 2020-05-15T22:29:42.000Z | 2021-12-14T21:44:49.000Z | ldbc_snb_datagen-dev/tools/sparkbench/distributions_extractor.py | netdb407/skh_project | 8893132f898d21149f73cfb3a5f7806ce5518be5 | [
"Apache-2.0"
] | null | null | null |
import sys
input_file = open(sys.argv[1])
def parse_line( input_file ):
line = input_file.readline()
if len(line) > 0:
if line[0] == '(':
output = line.replace("\n","").replace(" ","")
while output.find(')') == -1:
line = input_file.readline()
output = output.replace("\n","").replace(" ","") + line.replace("\n","").replace(" ","")
return output
return line
def parse_distribution(input_file, output_file):
line = parse_line(input_file)
accum = 0
entry_labels = []
entry_freq = []
while(len(line) > 0 and line[0] == "(" ):
line = line.replace("(","")
line = line.replace(")","")
line = line.split(",")
freq = int(line[0])
accum += freq
prev = 0
if(len(entry_freq) > 0):
prev = entry_freq[len(entry_freq)-1]
entry_freq.append(prev + freq)
entry_labels.append(line[1])
line = parse_line(input_file)
index = 0
while(index < len(entry_labels)):
output_file.write(str(entry_freq[index] / float(accum))+" "+entry_labels[index]+"\n")
index += 1
return line
words_file = open("words.csv", "w")
hashtags_file = open("hashtags.csv", "w")
sentence_count_file = open("sentence_count.csv", "w")
sentence_lengths_file = open("sentence_lengths.csv", "w")
line = parse_line(input_file)
while(len(line) > 0):
if line.find("Number of unique words",0,len("Number of unique words")) == 0:
line = parse_distribution(input_file, words_file )
elif line.find("Number of unique hashtags",0,len("Number of unique hashtags")) == 0:
line = parse_distribution(input_file, hashtags_file )
elif line.find("Number of sentence counts",0,len("Number of sentence counts")) == 0:
line = parse_distribution(input_file, sentence_count_file )
elif line.find("Words per sentence count",0,len("Words per sentence count")) == 0:
line = parse_distribution(input_file, sentence_lengths_file )
else:
line = parse_line(input_file)
input_file.close()
words_file.close()
hashtags_file.close()
sentence_count_file.close()
sentence_lengths_file.close()
| 34.138462 | 105 | 0.611987 |
import sys
input_file = open(sys.argv[1])
def parse_line( input_file ):
line = input_file.readline()
if len(line) > 0:
if line[0] == '(':
output = line.replace("\n","").replace(" ","")
while output.find(')') == -1:
line = input_file.readline()
output = output.replace("\n","").replace(" ","") + line.replace("\n","").replace(" ","")
return output
return line
def parse_distribution(input_file, output_file):
line = parse_line(input_file)
accum = 0
entry_labels = []
entry_freq = []
while(len(line) > 0 and line[0] == "(" ):
line = line.replace("(","")
line = line.replace(")","")
line = line.split(",")
freq = int(line[0])
accum += freq
prev = 0
if(len(entry_freq) > 0):
prev = entry_freq[len(entry_freq)-1]
entry_freq.append(prev + freq)
entry_labels.append(line[1])
line = parse_line(input_file)
index = 0
while(index < len(entry_labels)):
output_file.write(str(entry_freq[index] / float(accum))+" "+entry_labels[index]+"\n")
index += 1
return line
words_file = open("words.csv", "w")
hashtags_file = open("hashtags.csv", "w")
sentence_count_file = open("sentence_count.csv", "w")
sentence_lengths_file = open("sentence_lengths.csv", "w")
line = parse_line(input_file)
while(len(line) > 0):
if line.find("Number of unique words",0,len("Number of unique words")) == 0:
line = parse_distribution(input_file, words_file )
elif line.find("Number of unique hashtags",0,len("Number of unique hashtags")) == 0:
line = parse_distribution(input_file, hashtags_file )
elif line.find("Number of sentence counts",0,len("Number of sentence counts")) == 0:
line = parse_distribution(input_file, sentence_count_file )
elif line.find("Words per sentence count",0,len("Words per sentence count")) == 0:
line = parse_distribution(input_file, sentence_lengths_file )
else:
line = parse_line(input_file)
input_file.close()
words_file.close()
hashtags_file.close()
sentence_count_file.close()
sentence_lengths_file.close()
| true | true |
f73be0ead8db0c9b2b9580b0fc630b9d96a79294 | 1,312 | py | Python | setup.py | dlbrittain/celery-exporter | c7295bda6ce17c8a4602203ef26b8a9608aab6f1 | [
"MIT"
] | null | null | null | setup.py | dlbrittain/celery-exporter | c7295bda6ce17c8a4602203ef26b8a9608aab6f1 | [
"MIT"
] | null | null | null | setup.py | dlbrittain/celery-exporter | c7295bda6ce17c8a4602203ef26b8a9608aab6f1 | [
"MIT"
] | null | null | null | import io
from setuptools import setup
from setuptools_rust import Binding, RustExtension
long_description = "See https://github.com/OvalMoney/celery-exporter"
with io.open("README.md", encoding="utf-8") as fp:
long_description = fp.read()
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name="celery-exporter",
description="Prometheus metrics exporter for Celery",
long_description=long_description,
long_description_content_type="text/markdown",
version="1.4.0",
author="Fabio Todaro, Nicola Martino",
license="MIT",
author_email="fbregist@gmail.com, mroci@bruttocarattere.org",
url="https://github.com/OvalMoney/celery-exporter",
classifiers=[
"Development Status :: 3 - Alpha",
"Environment :: Console",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3 :: Only",
"Operating System :: OS Independent",
],
rust_extensions=[RustExtension("celery_state", binding=Binding.PyO3, debug=False)],
packages=["celery_exporter"],
install_requires=["click>=7", "celery>=4", "prometheus_client>=0.0.20"],
entry_points={
"console_scripts": ["celery-exporter = celery_exporter.__main__:main"]
},
)
| 34.526316 | 87 | 0.677591 | import io
from setuptools import setup
from setuptools_rust import Binding, RustExtension
long_description = "See https://github.com/OvalMoney/celery-exporter"
with io.open("README.md", encoding="utf-8") as fp:
long_description = fp.read()
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name="celery-exporter",
description="Prometheus metrics exporter for Celery",
long_description=long_description,
long_description_content_type="text/markdown",
version="1.4.0",
author="Fabio Todaro, Nicola Martino",
license="MIT",
author_email="fbregist@gmail.com, mroci@bruttocarattere.org",
url="https://github.com/OvalMoney/celery-exporter",
classifiers=[
"Development Status :: 3 - Alpha",
"Environment :: Console",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3 :: Only",
"Operating System :: OS Independent",
],
rust_extensions=[RustExtension("celery_state", binding=Binding.PyO3, debug=False)],
packages=["celery_exporter"],
install_requires=["click>=7", "celery>=4", "prometheus_client>=0.0.20"],
entry_points={
"console_scripts": ["celery-exporter = celery_exporter.__main__:main"]
},
)
| true | true |
f73be0f3da49275a7dea5a705f01b7713d6fe45d | 2,852 | py | Python | utils/PullData.py | MaxineSun/Paper_Graph | a7e0350c80309b35152e4bce939f522236e8efe0 | [
"MIT"
] | null | null | null | utils/PullData.py | MaxineSun/Paper_Graph | a7e0350c80309b35152e4bce939f522236e8efe0 | [
"MIT"
] | null | null | null | utils/PullData.py | MaxineSun/Paper_Graph | a7e0350c80309b35152e4bce939f522236e8efe0 | [
"MIT"
] | null | null | null | from crossref_commons.retrieval import get_entity
from crossref_commons.types import EntityType, OutputType
class PullData: #transform this function as a class
def __init__(self, doi):
self.doi = doi
def getInitials(self, str): #Put getInitials brfore as a alone function instead of part of pullData
allNames = str.split()
initials = [names[0] for names in allNames]
initialsProcessced = ". ".join(initials) + "."
return initialsProcessced
def pullData(self):
try:
self.art = get_entity(self.doi, EntityType.PUBLICATION, OutputType.JSON)
if 'DOI' in self.art:
doi1 = self.art[ 'DOI' ]
else:
doi1 = None
if 'type' in self.art:
typ = self.art[ 'type' ] #book-chapter
else:
typ = 'None'
if 'volume' in self.art:
vol = self.art[ 'volume' ]
else:
vol = None
if 'issue' in self.art:
iss = self.art[ 'issue' ]
else:
iss = None
if 'page' in self.art:
pgs = self.art[ 'page' ]
else:
pgs = None
if 'author' in self.art:
aut__ = self.art[ 'author' ]
aut = []
for a in aut__:
if 'given' in a:
firstName = a[ 'given' ]
aut.append(self.getInitials(firstName) + ' ' + a[ 'family' ])
elif 'family' in a:
aut.append(a['family'])
else:
aut = None
else:
aut = None
if 'title' in self.art:
tit = self.art[ 'title' ][ 0 ]
else:
tit = None
if 'publisher' in self.art:
pub = self.art[ 'publisher' ]
else:
pub = None
if 'reference-count' in self.art:
cit = self.art[ 'reference-count' ]
else:
cit = 'None'
if 'container-title' in self.art and not len(self.art['container-title']) == 0: #prevents empty lists
jnl = self.art[ 'container-title' ][ 0 ]
else:
jnl = None
if 'reference' in self.art:
ref = self.art[ 'reference' ]
else:
ref = None
if 'created' in self.art:
ymd = self.art[ 'created' ][ 'date-time' ][ 0:10 ]
else:
ymd = None
# if 'reference-count' in self.art:
# rct = self.art[ 'reference-count' ]
# else:
# rct = None
if 'ISSN' in self.art:
issn = self.art[ 'ISSN' ]
else:
issn = None
if 'ISBN' in self.art:
isbn = self.art[ 'ISBN' ]
else:
isbn = None
if 'publisher-location' in self.art:
loc = self.art[ 'publisher-location' ]
else:
loc = None
if 'URL' in self.art:
url = self.art[ 'URL' ]
else:
url = None
if 'editor' in self.art:
edt = self.art[ 'editor' ]
else:
edt = None
if 'article-number' in self.art:
anm = self.art[ 'article-number' ]
else:
anm = None
return doi1, typ, vol, iss, pgs, aut, tit, pub, cit, jnl, ref, ymd, issn, isbn, loc, url, edt, anm
except:
pass
| 22.634921 | 105 | 0.56101 | from crossref_commons.retrieval import get_entity
from crossref_commons.types import EntityType, OutputType
class PullData:
def __init__(self, doi):
self.doi = doi
def getInitials(self, str):
allNames = str.split()
initials = [names[0] for names in allNames]
initialsProcessced = ". ".join(initials) + "."
return initialsProcessced
def pullData(self):
try:
self.art = get_entity(self.doi, EntityType.PUBLICATION, OutputType.JSON)
if 'DOI' in self.art:
doi1 = self.art[ 'DOI' ]
else:
doi1 = None
if 'type' in self.art:
typ = self.art[ 'type' ]
else:
typ = 'None'
if 'volume' in self.art:
vol = self.art[ 'volume' ]
else:
vol = None
if 'issue' in self.art:
iss = self.art[ 'issue' ]
else:
iss = None
if 'page' in self.art:
pgs = self.art[ 'page' ]
else:
pgs = None
if 'author' in self.art:
aut__ = self.art[ 'author' ]
aut = []
for a in aut__:
if 'given' in a:
firstName = a[ 'given' ]
aut.append(self.getInitials(firstName) + ' ' + a[ 'family' ])
elif 'family' in a:
aut.append(a['family'])
else:
aut = None
else:
aut = None
if 'title' in self.art:
tit = self.art[ 'title' ][ 0 ]
else:
tit = None
if 'publisher' in self.art:
pub = self.art[ 'publisher' ]
else:
pub = None
if 'reference-count' in self.art:
cit = self.art[ 'reference-count' ]
else:
cit = 'None'
if 'container-title' in self.art and not len(self.art['container-title']) == 0:
jnl = self.art[ 'container-title' ][ 0 ]
else:
jnl = None
if 'reference' in self.art:
ref = self.art[ 'reference' ]
else:
ref = None
if 'created' in self.art:
ymd = self.art[ 'created' ][ 'date-time' ][ 0:10 ]
else:
ymd = None
if 'ISSN' in self.art:
issn = self.art[ 'ISSN' ]
else:
issn = None
if 'ISBN' in self.art:
isbn = self.art[ 'ISBN' ]
else:
isbn = None
if 'publisher-location' in self.art:
loc = self.art[ 'publisher-location' ]
else:
loc = None
if 'URL' in self.art:
url = self.art[ 'URL' ]
else:
url = None
if 'editor' in self.art:
edt = self.art[ 'editor' ]
else:
edt = None
if 'article-number' in self.art:
anm = self.art[ 'article-number' ]
else:
anm = None
return doi1, typ, vol, iss, pgs, aut, tit, pub, cit, jnl, ref, ymd, issn, isbn, loc, url, edt, anm
except:
pass
| true | true |
f73be1498618c64c0e1b596460a231018146a7a2 | 154,704 | py | Python | tensorflow/python/framework/ops.py | ml-resources/tensorflow | 4ecd72b68cd70c3930551aebbf0c80badc301d28 | [
"Apache-2.0"
] | 1 | 2019-06-19T08:43:26.000Z | 2019-06-19T08:43:26.000Z | tensorflow/python/framework/ops.py | liudgit/tensorflow | 4ecd72b68cd70c3930551aebbf0c80badc301d28 | [
"Apache-2.0"
] | null | null | null | tensorflow/python/framework/ops.py | liudgit/tensorflow | 4ecd72b68cd70c3930551aebbf0c80badc301d28 | [
"Apache-2.0"
] | 1 | 2019-06-19T08:43:23.000Z | 2019-06-19T08:43:23.000Z | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Classes and functions used to construct graphs."""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import contextlib
import copy
import linecache
import re
import sys
import threading
import six
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.framework import versions_pb2
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import op_def_registry
from tensorflow.python.framework import registry
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import versions
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import compat
from tensorflow.python.util import decorator_utils
def _override_helper(clazz_object, operator, func):
"""Overrides (string) operator on Tensors to call func.
Args:
clazz_object: the class to override for; either Tensor or SparseTensor.
operator: the string name of the operator to override.
func: the function that replaces the overridden operator.
Raises:
ValueError: If operator has already been overwritten,
or if operator is not allowed to be overwritten.
"""
existing = getattr(clazz_object, operator, None)
if existing is not None:
# Check to see if this is a default method-wrapper or slot wrapper which
# will be true for the comparison operators.
if not isinstance(existing, type(object.__lt__)):
raise ValueError("operator %s cannot be overwritten again on class %s." %
(operator, clazz_object))
if operator not in Tensor.OVERLOADABLE_OPERATORS:
raise ValueError("Overriding %s is disallowed" % operator)
setattr(clazz_object, operator, func)
def _convert_stack(stack):
"""Converts a stack extracted using _extract_stack() to a traceback stack.
Args:
stack: A list of n 4-tuples, (filename, lineno, name, frame_globals).
Returns:
A list of n 4-tuples (filename, lineno, name, code), where the code tuple
element is calculated from the corresponding elements of the input tuple.
"""
ret = []
for filename, lineno, name, frame_globals in stack:
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, frame_globals)
if line:
line = line.strip()
else:
line = None
ret.append((filename, lineno, name, line))
return ret
# pylint: disable=line-too-long
def _extract_stack():
"""A lightweight re-implementation of traceback.extract_stack.
NOTE(mrry): traceback.extract_stack eagerly retrieves the line of code for
each stack frame using linecache, which results in an abundance of stat()
calls. This implementation does not retrieve the code, and any consumer
should apply _convert_stack to the result to obtain a traceback that can
be formatted etc. using traceback methods.
Returns:
A list of 4-tuples (filename, lineno, name, frame_globals) corresponding to
the call stack of the current thread.
"""
# pylint: enable=line-too-long
try:
raise ZeroDivisionError
except ZeroDivisionError:
f = sys.exc_info()[2].tb_frame.f_back
ret = []
while f is not None:
lineno = f.f_lineno
co = f.f_code
filename = co.co_filename
name = co.co_name
frame_globals = f.f_globals
ret.append((filename, lineno, name, frame_globals))
f = f.f_back
ret.reverse()
return ret
def _as_graph_element(obj):
"""Convert `obj` to a graph element if possible, otherwise return `None`.
Args:
obj: Object to convert.
Returns:
The result of `obj._as_graph_element()` if that method is available;
otherwise `None`.
"""
conv_fn = getattr(obj, "_as_graph_element", None)
if conv_fn and callable(conv_fn):
return conv_fn()
return None
_TENSOR_LIKE_TYPES = tuple()
def is_dense_tensor_like(t):
"""EXPERIMENTAL: Returns true if `t` implements the tensor interface.
See `register_dense_tensor_like_type()` for the current definition of a
"tensor-like type".
Args:
t: An object.
Returns:
True iff `t` is an instance of one of the registered "tensor-like" types.
"""
return isinstance(t, _TENSOR_LIKE_TYPES)
def register_dense_tensor_like_type(tensor_type):
"""EXPERIMENTAL: Registers `tensor_type` as implementing the tensor interface.
A "tensor-like type" can represent a single dense tensor, and implements
the `name` and `dtype` properties.
Args:
tensor_type: A type implementing the tensor interface.
Raises:
TypeError: If `tensor_type` does not implement the tensor interface.
"""
try:
if not isinstance(tensor_type.name, property):
raise TypeError("Type %s does not define a `name` property")
except AttributeError:
raise TypeError("Type %s does not define a `name` property")
try:
if not isinstance(tensor_type.dtype, property):
raise TypeError("Type %s does not define a `dtype` property")
except AttributeError:
raise TypeError("Type %s does not define a `dtype` property")
# We expect this list to be small, so choose quadratic complexity
# for registration, so that we have a tuple that can be used for
# more efficient `isinstance` checks later.
global _TENSOR_LIKE_TYPES
_TENSOR_LIKE_TYPES = tuple(list(_TENSOR_LIKE_TYPES) + [tensor_type])
# NOTE(ebrevdo): Do not subclass this. If you do, I will break you on purpose.
class _TensorLike(object):
"""Internal cls for grouping Tensor, SparseTensor, ..., for is_instance."""
pass
class Tensor(_TensorLike):
"""Represents one of the outputs of an `Operation`.
A `Tensor` is a symbolic handle to one of the outputs of an
`Operation`. It does not hold the values of that operation's output,
but instead provides a means of computing those values in a
TensorFlow [`Session`](../../api_docs/python/client.md#Session).
This class has two primary purposes:
1. A `Tensor` can be passed as an input to another `Operation`.
This builds a dataflow connection between operations, which
enables TensorFlow to execute an entire `Graph` that represents a
large, multi-step computation.
2. After the graph has been launched in a session, the value of the
`Tensor` can be computed by passing it to
[`Session.run()`](../../api_docs/python/client.md#Session.run).
`t.eval()` is a shortcut for calling
`tf.get_default_session().run(t)`.
In the following example, `c`, `d`, and `e` are symbolic `Tensor`
objects, whereas `result` is a numpy array that stores a concrete
value:
```python
# Build a dataflow graph.
c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)
# Construct a `Session` to execute the graph.
sess = tf.Session()
# Execute the graph and store the value that `e` represents in `result`.
result = sess.run(e)
```
@@dtype
@@name
@@value_index
@@graph
@@op
@@consumers
@@eval
@@get_shape
@@shape
@@set_shape
"""
# List of Python operators that we allow to override.
OVERLOADABLE_OPERATORS = {
# Binary.
"__add__",
"__radd__",
"__sub__",
"__rsub__",
"__mul__",
"__rmul__",
"__div__",
"__rdiv__",
"__truediv__",
"__rtruediv__",
"__floordiv__",
"__rfloordiv__",
"__mod__",
"__rmod__",
"__lt__",
"__le__",
"__gt__",
"__ge__",
"__and__",
"__rand__",
"__or__",
"__ror__",
"__xor__",
"__rxor__",
"__getitem__",
"__pow__",
"__rpow__",
# Unary.
"__invert__",
"__neg__",
"__abs__"
}
def __init__(self, op, value_index, dtype):
"""Creates a new `Tensor`.
Args:
op: An `Operation`. `Operation` that computes this tensor.
value_index: An `int`. Index of the operation's endpoint that produces
this tensor.
dtype: A `DType`. Type of elements stored in this tensor.
Raises:
TypeError: If the op is not an `Operation`.
"""
if not isinstance(op, Operation):
raise TypeError("op needs to be an Operation: %s" % op)
self._op = op
self._value_index = value_index
self._dtype = dtypes.as_dtype(dtype)
self._shape = tensor_shape.unknown_shape()
# List of operations that use this Tensor as input. We maintain this list
# to easily navigate a computation graph.
self._consumers = []
# Attributes used for C++ shape inference. Not inspected, only forwarded.
self._handle_shape = tensor_shape_pb2.TensorShapeProto()
self._handle_dtype = types_pb2.DT_INVALID
@property
def op(self):
"""The `Operation` that produces this tensor as an output."""
return self._op
@property
def dtype(self):
"""The `DType` of elements in this tensor."""
return self._dtype
@property
def graph(self):
"""The `Graph` that contains this tensor."""
return self._op.graph
@property
def name(self):
"""The string name of this tensor."""
if not self._op.name:
raise ValueError("Operation was not named: %s" % self._op)
return "%s:%d" % (self._op.name, self._value_index)
@property
def device(self):
"""The name of the device on which this tensor will be produced, or None."""
return self._op.device
@property
def shape(self):
"""Returns the `TensorShape` that represents the shape of this tensor.
The shape is computed using shape inference functions that are
registered in the Op for each `Operation`. See
[`TensorShape`](../../api_docs/python/framework.md#TensorShape)
for more details of what a shape represents.
The inferred shape of a tensor is used to provide shape
information without having to launch the graph in a session. This
can be used for debugging, and providing early error messages. For
example:
```python
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
print(c.shape)
==> TensorShape([Dimension(2), Dimension(3)])
d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
print(d.shape)
==> TensorShape([Dimension(4), Dimension(2)])
# Raises a ValueError, because `c` and `d` do not have compatible
# inner dimensions.
e = tf.matmul(c, d)
f = tf.matmul(c, d, transpose_a=True, transpose_b=True)
print(f.shape)
==> TensorShape([Dimension(3), Dimension(4)])
```
In some cases, the inferred shape may have unknown dimensions. If
the caller has additional information about the values of these
dimensions, `Tensor.set_shape()` can be used to augment the
inferred shape.
Returns:
A `TensorShape` representing the shape of this tensor.
"""
return self._shape
def _shape_as_list(self):
if self._shape.ndims is not None:
return [dim.value for dim in self._shape.dims]
else:
return None
def get_shape(self):
"""Alias of Tensor.shape."""
return self.shape
def set_shape(self, shape):
"""Updates the shape of this tensor.
This method can be called multiple times, and will merge the given
`shape` with the current shape of this tensor. It can be used to
provide additional information about the shape of this tensor that
cannot be inferred from the graph alone. For example, this can be used
to provide additional information about the shapes of images:
```python
_, image_data = tf.TFRecordReader(...).read(...)
image = tf.image.decode_png(image_data, channels=3)
# The height and width dimensions of `image` are data dependent, and
# cannot be computed without executing the op.
print(image.shape)
==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])
# We know that each image in this dataset is 28 x 28 pixels.
image.set_shape([28, 28, 3])
print(image.shape)
==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])
```
Args:
shape: A `TensorShape` representing the shape of this tensor.
Raises:
ValueError: If `shape` is not compatible with the current shape of
this tensor.
"""
self._shape = self._shape.merge_with(shape)
@property
def value_index(self):
"""The index of this tensor in the outputs of its `Operation`."""
return self._value_index
def consumers(self):
"""Returns a list of `Operation`s that consume this tensor.
Returns:
A list of `Operation`s.
"""
return self._consumers
def _add_consumer(self, consumer):
"""Add a consumer to this tensor.
Args:
consumer: an Operation.
Raises:
TypeError: if the consumer is not an Operation.
"""
if not isinstance(consumer, Operation):
raise TypeError("Consumer must be an Operation: %s" % consumer)
self._consumers.append(consumer)
def _as_node_def_input(self):
"""Return a value to use for the NodeDef "input" attribute.
The returned string can be used in a NodeDef "input" attribute
to indicate that the NodeDef uses this Tensor as input.
Raises:
ValueError: if this Tensor's Operation does not have a name.
Returns:
a string.
"""
if not self._op.name:
raise ValueError("Operation was not named: %s" % self._op)
if self._value_index == 0:
return self._op.name
else:
return "%s:%d" % (self._op.name, self._value_index)
def __str__(self):
return "Tensor(\"%s\"%s%s%s)" % (
self.name,
(", shape=%s" % self.get_shape())
if self.get_shape().ndims is not None else "",
(", dtype=%s" % self._dtype.name) if self._dtype else "",
(", device=%s" % self.device) if self.device else "")
def __repr__(self):
return "<tf.Tensor '%s' shape=%s dtype=%s>" % (
self.name, self.get_shape(), self._dtype.name)
def __hash__(self):
# Necessary to support Python's collection membership operators
return id(self)
def __eq__(self, other):
# Necessary to support Python's collection membership operators
return id(self) == id(other)
# NOTE(mrry): This enables the Tensor's overloaded "right" binary
# operators to run when the left operand is an ndarray, because it
# accords the Tensor class higher priority than an ndarray, or a
# numpy matrix.
# TODO(mrry): Convert this to using numpy's __numpy_ufunc__
# mechanism, which allows more control over how Tensors interact
# with ndarrays.
__array_priority__ = 100
@staticmethod
def _override_operator(operator, func):
_override_helper(Tensor, operator, func)
def __iter__(self):
"""Dummy method to prevent iteration. Do not call.
NOTE(mrry): If we register __getitem__ as an overloaded operator,
Python will valiantly attempt to iterate over the Tensor from 0 to
infinity. Declaring this method prevents this unintended
behavior.
Raises:
TypeError: when invoked.
"""
raise TypeError("'Tensor' object is not iterable.")
def __bool__(self):
"""Dummy method to prevent a tensor from being used as a Python `bool`.
This overload raises a `TypeError` when the user inadvertently
treats a `Tensor` as a boolean (e.g. in an `if` statement). For
example:
```python
if tf.constant(True): # Will raise.
# ...
if tf.constant(5) < tf.constant(7): # Will raise.
# ...
```
This disallows ambiguities between testing the Python value vs testing the
dynamic condition of the `Tensor`.
Raises:
`TypeError`.
"""
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
"Use `if t is not None:` instead of `if t:` to test if a "
"tensor is defined, and use TensorFlow ops such as "
"tf.cond to execute subgraphs conditioned on the value of "
"a tensor.")
def __nonzero__(self):
"""Dummy method to prevent a tensor from being used as a Python `bool`.
This is the Python 2.x counterpart to `__bool__()` above.
Raises:
`TypeError`.
"""
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
"Use `if t is not None:` instead of `if t:` to test if a "
"tensor is defined, and use TensorFlow ops such as "
"tf.cond to execute subgraphs conditioned on the value of "
"a tensor.")
def eval(self, feed_dict=None, session=None):
"""Evaluates this tensor in a `Session`.
Calling this method will execute all preceding operations that
produce the inputs needed for the operation that produces this
tensor.
*N.B.* Before invoking `Tensor.eval()`, its graph must have been
launched in a session, and either a default session must be
available, or `session` must be specified explicitly.
Args:
feed_dict: A dictionary that maps `Tensor` objects to feed values.
See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
description of the valid feed values.
session: (Optional.) The `Session` to be used to evaluate this tensor. If
none, the default session will be used.
Returns:
A numpy array corresponding to the value of this tensor.
"""
return _eval_using_default_session(self, feed_dict, self.graph, session)
def _TensorTensorConversionFunction(t, dtype=None, name=None, as_ref=False):
_ = name, as_ref
if dtype and not dtype.is_compatible_with(t.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
% (dtype.name, t.dtype.name, str(t)))
return t
_tensor_conversion_func_registry = {
0: [(Tensor, _TensorTensorConversionFunction)]}
register_dense_tensor_like_type(Tensor)
def convert_to_tensor(value,
dtype=None,
name=None,
preferred_dtype=None):
"""Converts the given `value` to a `Tensor`.
This function converts Python objects of various types to `Tensor`
objects. It accepts `Tensor` objects, numpy arrays, Python lists,
and Python scalars. For example:
```python
import numpy as np
def my_func(arg):
arg = tf.convert_to_tensor(arg, dtype=tf.float32)
return tf.matmul(arg, arg) + arg
# The following calls are equivalent.
value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
```
This function can be useful when composing a new operation in Python
(such as `my_func` in the example above). All standard Python op
constructors apply this function to each of their Tensor-valued
inputs, which allows those ops to accept numpy arrays, Python lists,
and scalars in addition to `Tensor` objects.
Args:
value: An object whose type has a registered `Tensor` conversion function.
dtype: Optional element type for the returned tensor. If missing, the
type is inferred from the type of `value`.
name: Optional name to use if a new `Tensor` is created.
preferred_dtype: Optional element type for the returned tensor,
used when dtype is None. In some cases, a caller may not have a
dtype in mind when converting to a tensor, so preferred_dtype
can be used as a soft preference. If the conversion to
`preferred_dtype` is not possible, this argument has no effect.
Returns:
An `Output` based on `value`.
Raises:
TypeError: If no conversion function is registered for `value`.
RuntimeError: If a registered conversion function returns an invalid value.
"""
return internal_convert_to_tensor(
value=value,
dtype=dtype,
name=name,
preferred_dtype=preferred_dtype,
as_ref=False)
def internal_convert_to_tensor(value,
dtype=None,
name=None,
as_ref=False,
preferred_dtype=None):
"""Converts the given `value` to an `Tensor`.
This function converts Python objects of various types to `Tensor`
objects. It accepts `Tensor` objects, numpy arrays, Python lists,
and Python scalars. For example:
This function can be useful when composing a new operation in Python
All standard Python op constructors apply this function to each of their
Tensor-valued inputs, which allows those ops to accept numpy arrays, Python
lists, and scalars in addition to `Tensor` objects.
Args:
value: An object whose type has a registered `Tensor` conversion function.
dtype: Optional element type for the returned tensor. If missing, the
type is inferred from the type of `value`.
name: Optional name to use if a new `Tensor` is created.
as_ref: True if we want the mutable view of Variables, if applicable.
preferred_dtype: Optional element type for the returned tensor,
used when dtype is None. In some cases, a caller may not have a
dtype in mind when converting to a tensor, so preferred_dtype
can be used as a soft preference. If the conversion to
`preferred_dtype` is not possible, this argument has no effect.
Returns:
A `Tensor` based on `value`.
Raises:
TypeError: If no conversion function is registered for `value`.
RuntimeError: If a registered conversion function returns an invalid value.
"""
error_prefix = "" if name is None else "%s: " % name
if dtype is not None:
dtype = dtypes.as_dtype(dtype)
for _, funcs_at_priority in sorted(_tensor_conversion_func_registry.items()):
for base_type, conversion_func in funcs_at_priority:
if isinstance(value, base_type):
# If dtype is None but preferred_dtype is not None, we try to
# cast to preferred_dtype first.
ret = None
if dtype is None and preferred_dtype is not None:
try:
ret = conversion_func(
value, dtype=preferred_dtype, name=name, as_ref=as_ref)
except (TypeError, ValueError):
# Could not coerce the conversion to use the preferred dtype.
ret = None
if ret is not None and ret is not NotImplemented:
if (ret.dtype.base_dtype !=
dtypes.as_dtype(preferred_dtype).base_dtype):
raise TypeError("convert_to_tensor did not convert to "
"the preferred dtype: %s vs %s " %
(ret.dtype.base_dtype,
dtypes.as_dtype(preferred_dtype).base_dtype))
if ret is None:
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
if ret is NotImplemented:
continue
if not isinstance(ret, Tensor):
raise RuntimeError(
"%sConversion function %r for type %s returned non-Tensor: %r"
% (error_prefix, conversion_func, base_type, ret))
if dtype and not dtype.is_compatible_with(ret.dtype):
raise RuntimeError(
"%sConversion function %r for type %s returned incompatible "
"dtype: requested = %s, actual = %s"
% (error_prefix, conversion_func, base_type,
dtype.name, ret.dtype.name))
return ret
raise TypeError("%sCannot convert %r with type %s to Tensor: "
"no conversion function registered."
% (error_prefix, value, type(value)))
def internal_convert_n_to_tensor(values,
dtype=None,
name=None,
as_ref=False,
preferred_dtype=None):
"""Converts `values` to a list of `Tensor` objects.
Args:
values: A list of objects that can be consumed by `tf.convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` objects.
name: (Optional.) A name prefix to used when a new `Tensor` is
created, in which case element `i` will be given the name `name
+ '_' + i`.
as_ref: True if the caller wants the results as ref tensors.
preferred_dtype: Optional element type for the returned tensors,
used when dtype is None. In some cases, a caller may not have a
dtype in mind when converting to a tensor, so preferred_dtype
can be used as a soft preference. If the conversion to
`preferred_dtype` is not possible, this argument has no effect.
Returns:
A list of `Tensor` and/or `IndexedSlices` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
if not isinstance(values, collections.Sequence):
raise TypeError("values must be a list.")
ret = []
for i, value in enumerate(values):
n = None if name is None else "%s_%d" % (name, i)
ret.append(
internal_convert_to_tensor(
value,
dtype=dtype,
name=n,
as_ref=as_ref,
preferred_dtype=preferred_dtype))
return ret
def convert_n_to_tensor(values,
dtype=None,
name=None,
preferred_dtype=None):
"""Converts `values` to a list of `Tensor` objects.
Args:
values: A list of objects that can be consumed by `tf.convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` objects.
name: (Optional.) A name prefix to used when a new `Tensor` is
created, in which case element `i` will be given the name `name
+ '_' + i`.
preferred_dtype: Optional element type for the returned tensors,
used when dtype is None. In some cases, a caller may not have a
dtype in mind when converting to a tensor, so preferred_dtype
can be used as a soft preference. If the conversion to
`preferred_dtype` is not possible, this argument has no effect.
Returns:
A list of `Tensor` and/or `IndexedSlices` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
return internal_convert_n_to_tensor(values=values,
dtype=dtype,
name=name,
preferred_dtype=preferred_dtype,
as_ref=False)
def convert_to_tensor_or_indexed_slices(value, dtype=None, name=None):
"""Converts the given object to a `Tensor` or an `IndexedSlices`.
If `value` is an `IndexedSlices` or `SparseTensor` it is returned
unmodified. Otherwise, it is converted to a `Tensor` using
`convert_to_tensor()`.
Args:
value: An `IndexedSlices`, `SparseTensor`, or an object that can be consumed
by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` or
`IndexedSlices`.
name: (Optional.) A name to use if a new `Tensor` is created.
Returns:
An `Tensor`, `IndexedSlices`, or `SparseTensor` based on `value`.
Raises:
ValueError: If `dtype` does not match the element type of `value`.
"""
return internal_convert_to_tensor_or_indexed_slices(
value=value, dtype=dtype, name=name, as_ref=False)
def internal_convert_to_tensor_or_indexed_slices(value, dtype=None, name=None,
as_ref=False):
"""Converts the given object to an `Tensor` or an `IndexedSlices`.
If `value` is an `IndexedSlices` or `SparseTensor` it is returned
unmodified. Otherwise, it is converted to a `Tensor` using
`convert_to_tensor()`.
Args:
value: An `IndexedSlices`, `SparseTensor`, or an object that can be consumed
by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` or
`IndexedSlices`.
name: (Optional.) A name to use if a new `Tensor` is created.
as_ref: True if the caller wants the results as ref tensors.
Returns:
An `Tensor`, `IndexedSlices`, or `SparseTensor` based on `value`.
Raises:
ValueError: If `dtype` does not match the element type of `value`.
"""
if isinstance(value, _TensorLike):
if dtype and not dtypes.as_dtype(dtype).is_compatible_with(value.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
% (dtypes.as_dtype(dtype).name, value.dtype.name, str(value)))
return value
else:
return internal_convert_to_tensor(value,
dtype=dtype,
name=name,
as_ref=as_ref)
def internal_convert_n_to_tensor_or_indexed_slices(values, dtype=None,
name=None, as_ref=False):
"""Converts `values` to a list of `Tensor` or `IndexedSlices` objects.
Any `IndexedSlices` or `SparseTensor` objects in `values` are returned
unmodified.
Args:
values: A list of `None`, `IndexedSlices`, `SparseTensor`, or objects that
can be consumed by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor`
`IndexedSlices`.
name: (Optional.) A name prefix to used when a new `Tensor` is
created, in which case element `i` will be given the name `name
+ '_' + i`.
as_ref: True if the caller wants the results as ref tensors.
Returns:
A list of `Tensor`, `IndexedSlices`, and/or `SparseTensor` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
if not isinstance(values, collections.Sequence):
raise TypeError("values must be a list.")
ret = []
for i, value in enumerate(values):
if value is None:
ret.append(value)
else:
n = None if name is None else "%s_%d" % (name, i)
ret.append(
internal_convert_to_tensor_or_indexed_slices(
value, dtype=dtype, name=n, as_ref=as_ref))
return ret
def convert_n_to_tensor_or_indexed_slices(values, dtype=None, name=None):
"""Converts `values` to a list of `Output` or `IndexedSlices` objects.
Any `IndexedSlices` or `SparseTensor` objects in `values` are returned
unmodified.
Args:
values: A list of `None`, `IndexedSlices`, `SparseTensor`, or objects that
can be consumed by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor`
`IndexedSlices`.
name: (Optional.) A name prefix to used when a new `Tensor` is
created, in which case element `i` will be given the name `name
+ '_' + i`.
Returns:
A list of `Tensor`, `IndexedSlices`, and/or `SparseTensor` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
return internal_convert_n_to_tensor_or_indexed_slices(
values=values, dtype=dtype, name=name, as_ref=False)
def register_tensor_conversion_function(base_type, conversion_func,
priority=100):
"""Registers a function for converting objects of `base_type` to `Tensor`.
The conversion function must have the following signature:
```python
def conversion_func(value, dtype=None, name=None, as_ref=False):
# ...
```
It must return a `Tensor` with the given `dtype` if specified. If the
conversion function creates a new `Tensor`, it should use the given
`name` if specified. All exceptions will be propagated to the caller.
The conversion function may return `NotImplemented` for some
inputs. In this case, the conversion process will continue to try
subsequent conversion functions.
If `as_ref` is true, the function must return a `Tensor` reference,
such as a `Variable`.
NOTE: The conversion functions will execute in order of priority,
followed by order of registration. To ensure that a conversion function
`F` runs before another conversion function `G`, ensure that `F` is
registered with a smaller priority than `G`.
Args:
base_type: The base type or tuple of base types for all objects that
`conversion_func` accepts.
conversion_func: A function that converts instances of `base_type` to
`Tensor`.
priority: Optional integer that indicates the priority for applying this
conversion function. Conversion functions with smaller priority values
run earlier than conversion functions with larger priority values.
Defaults to 100.
Raises:
TypeError: If the arguments do not have the appropriate type.
"""
if not (isinstance(base_type, type) or
(isinstance(base_type, tuple)
and all(isinstance(x, type) for x in base_type))):
raise TypeError("base_type must be a type or a tuple of types.")
if not callable(conversion_func):
raise TypeError("conversion_func must be callable.")
try:
funcs_at_priority = _tensor_conversion_func_registry[priority]
except KeyError:
funcs_at_priority = []
_tensor_conversion_func_registry[priority] = funcs_at_priority
funcs_at_priority.append((base_type, conversion_func))
class IndexedSlices(_TensorLike):
"""A sparse representation of a set of tensor slices at given indices.
This class is a simple wrapper for a pair of `Tensor` objects:
* `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.
* `indices`: A 1-D integer `Tensor` with shape `[D0]`.
An `IndexedSlices` is typically used to represent a subset of a larger
tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.
The values in `indices` are the indices in the first dimension of
the slices that have been extracted from the larger tensor.
The dense tensor `dense` represented by an `IndexedSlices` `slices` has
```python
dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]
```
The `IndexedSlices` class is used principally in the definition of
gradients for operations that have sparse gradients
(e.g. [`tf.gather`](../../api_docs/python/array_ops.md#gather)).
Contrast this representation with
[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
which uses multi-dimensional indices and scalar values.
@@__init__
@@values
@@indices
@@dense_shape
@@name
@@dtype
@@device
@@op
"""
def __init__(self, values, indices, dense_shape=None):
"""Creates an `IndexedSlices`."""
_get_graph_from_inputs([values, indices, dense_shape])
self._values = values
self._indices = indices
self._dense_shape = dense_shape
@property
def values(self):
"""A `Tensor` containing the values of the slices."""
return self._values
@property
def indices(self):
"""A 1-D `Tensor` containing the indices of the slices."""
return self._indices
@property
def dense_shape(self):
"""A 1-D `Tensor` containing the shape of the corresponding dense tensor."""
return self._dense_shape
@property
def name(self):
"""The name of this `IndexedSlices`."""
return self.values.name
@property
def device(self):
"""The name of the device on which `values` will be produced, or `None`."""
return self.values.device
@property
def op(self):
"""The `Operation` that produces `values` as an output."""
return self.values.op
@property
def dtype(self):
"""The `DType` of elements in this tensor."""
return self.values.dtype
@property
def graph(self):
"""The `Graph` that contains the values, indices, and shape tensors."""
return self._values.graph
def __str__(self):
return "IndexedSlices(indices=%s, values=%s%s)" % (
self._indices, self._values,
(", dense_shape=%s" % self._dense_shape)
if self._dense_shape is not None else "")
def __neg__(self):
return IndexedSlices(-self.values, self.indices, self.dense_shape)
IndexedSlicesValue = collections.namedtuple(
"IndexedSlicesValue", ["values", "indices", "dense_shape"])
def _device_string(dev_spec):
if isinstance(dev_spec, pydev.DeviceSpec):
return dev_spec.to_string()
else:
return dev_spec
def _NodeDef(op_type, name, device=None, attrs=None):
"""Create a NodeDef proto.
Args:
op_type: Value for the "op" attribute of the NodeDef proto.
name: Value for the "name" attribute of the NodeDef proto.
device: string, device, or function from NodeDef to string.
Value for the "device" attribute of the NodeDef proto.
attrs: Optional dictionary where the key is the attribute name (a string)
and the value is the respective "attr" attribute of the NodeDef proto (an
AttrValue).
Returns:
A node_def_pb2.NodeDef protocol buffer.
"""
node_def = node_def_pb2.NodeDef()
node_def.op = compat.as_bytes(op_type)
node_def.name = compat.as_bytes(name)
if attrs is not None:
for k, v in six.iteritems(attrs):
node_def.attr[k].CopyFrom(v)
if device is not None:
if callable(device):
node_def.device = device(node_def)
else:
node_def.device = _device_string(device)
return node_def
# Copied from core/framework/node_def_util.cc
# TODO(mrry,josh11b): Consolidate this validation in C++ code.
_VALID_OP_NAME_REGEX = re.compile("^[A-Za-z0-9.][A-Za-z0-9_.\\-/]*$")
_VALID_SCOPE_NAME_REGEX = re.compile("^[A-Za-z0-9_.\\-/]*$")
class Operation(object):
"""Represents a graph node that performs computation on tensors.
An `Operation` is a node in a TensorFlow `Graph` that takes zero or
more `Tensor` objects as input, and produces zero or more `Tensor`
objects as output. Objects of type `Operation` are created by
calling a Python op constructor (such as
[`tf.matmul()`](../../api_docs/python/math_ops.md#matmul))
or [`Graph.create_op()`](../../api_docs/python/framework.md#Graph.create_op).
For example `c = tf.matmul(a, b)` creates an `Operation` of type
"MatMul" that takes tensors `a` and `b` as input, and produces `c`
as output.
After the graph has been launched in a session, an `Operation` can
be executed by passing it to
[`Session.run()`](../../api_docs/python/client.md#Session.run).
`op.run()` is a shortcut for calling `tf.get_default_session().run(op)`.
@@name
@@type
@@inputs
@@control_inputs
@@outputs
@@device
@@graph
@@run
@@get_attr
@@traceback
"""
def __init__(self, node_def, g, inputs=None, output_types=None,
control_inputs=None, input_types=None, original_op=None,
op_def=None):
r"""Creates an `Operation`.
NOTE: This constructor validates the name of the `Operation` (passed
as `node_def.name`). Valid `Operation` names match the following
regular expression:
[A-Za-z0-9.][A-Za-z0-9_.\\-/]*
Args:
node_def: `node_def_pb2.NodeDef`. `NodeDef` for the `Operation`.
Used for attributes of `node_def_pb2.NodeDef`, typically `name`,
`op`, and `device`. The `input` attribute is irrelevant here
as it will be computed when generating the model.
g: `Graph`. The parent graph.
inputs: list of `Tensor` objects. The inputs to this `Operation`.
output_types: list of `DType` objects. List of the types of the
`Tensors` computed by this operation. The length of this list indicates
the number of output endpoints of the `Operation`.
control_inputs: list of operations or tensors from which to have a
control dependency.
input_types: List of `DType` objects representing the
types of the tensors accepted by the `Operation`. By default
uses `[x.dtype.base_dtype for x in inputs]`. Operations that expect
reference-typed inputs must specify these explicitly.
original_op: Optional. Used to associate the new `Operation` with an
existing `Operation` (for example, a replica with the op that was
replicated).
op_def: Optional. The `op_def_pb2.OpDef` proto that describes the
op type that this `Operation` represents.
Raises:
TypeError: if control inputs are not Operations or Tensors,
or if `node_def` is not a `NodeDef`,
or if `g` is not a `Graph`,
or if `inputs` are not tensors,
or if `inputs` and `input_types` are incompatible.
ValueError: if the `node_def` name is not valid.
"""
if not isinstance(node_def, node_def_pb2.NodeDef):
raise TypeError("node_def needs to be a NodeDef: %s" % node_def)
if node_def.ByteSize() >= (1 << 31) or node_def.ByteSize() < 0:
raise ValueError(
"Cannot create a tensor proto whose content is larger than 2GB.")
if not _VALID_OP_NAME_REGEX.match(node_def.name):
raise ValueError("'%s' is not a valid node name" % node_def.name)
if not isinstance(g, Graph):
raise TypeError("g needs to be a Graph: %s" % g)
self._node_def = copy.deepcopy(node_def)
self._graph = g
if inputs is None:
inputs = []
elif not isinstance(inputs, list):
raise TypeError("inputs needs to be a list of Tensors: %s" % inputs)
self._inputs = list(inputs) # Defensive copy.
for a in self._inputs:
if not isinstance(a, Tensor):
raise TypeError("input needs to be a Tensor: %s" % a)
# Mark that we consume the inputs.
a._add_consumer(self) # pylint: disable=protected-access
if output_types is None:
output_types = []
self._output_types = output_types
self._outputs = [Tensor(self, i, output_type)
for i, output_type in enumerate(output_types)]
if input_types is None:
input_types = [i.dtype.base_dtype for i in self._inputs]
else:
if not all(x.is_compatible_with(i.dtype)
for i, x in zip(self._inputs, input_types)):
raise TypeError("Inputs are not compatible with input types")
self._input_types = input_types
# Build the list of control inputs.
self._control_inputs = []
if control_inputs:
for c in control_inputs:
c_op = None
if isinstance(c, Operation):
c_op = c
elif isinstance(c, (Tensor, IndexedSlices)):
c_op = c.op
else:
raise TypeError("Control input must be an Operation, "
"a Tensor, or IndexedSlices: %s" % c)
self._control_inputs.append(c_op)
self._original_op = original_op
self._op_def = op_def
self._traceback = _extract_stack()
# Add this op to the current control flow context:
self._control_flow_context = g._get_control_flow_context()
if self._control_flow_context is not None:
self._control_flow_context.AddOp(self)
# NOTE(keveman): Control flow context's AddOp could be creating new ops and
# setting op.inputs[index] = new_op. Thus the new ops' id could be larger
# than this op's id even though this op depend on them. Therefore, delaying
# assigning id to this op until all ops this could be dependent on are
# created.
self._id_value = self._graph._next_id() # pylint: disable=protected-access
self._recompute_node_def()
def colocation_groups(self):
"""Returns the list of colocation groups of the op."""
default_colocation_group = [compat.as_bytes("loc:@%s" %
self._node_def.name)]
if "_class" not in self._node_def.attr:
# This op has no explicit colocation group, so it is itself its
# own root of a colocation group.
return default_colocation_group
attr_groups = [class_name
for class_name in self.get_attr("_class")
if class_name.startswith(b"loc:@")]
# If there are no colocation groups in the explicit _class field,
# return the default colocation group.
return attr_groups if attr_groups else default_colocation_group
def values(self):
"""DEPRECATED: Use outputs."""
return tuple(self.outputs)
def _get_control_flow_context(self):
"""Returns the control flow context of this op.
Returns:
A context object.
"""
return self._control_flow_context
def _set_control_flow_context(self, context):
"""Sets the current control flow context of this op.
Args:
context: a context object.
"""
self._control_flow_context = context
@property
def name(self):
"""The full name of this operation."""
return self._node_def.name
@property
def _id(self):
"""The unique integer id of this operation."""
return self._id_value
@property
def device(self):
"""The name of the device to which this op has been assigned, if any.
Returns:
The string name of the device to which this op has been
assigned, or an empty string if it has not been assigned to a
device.
"""
return self._node_def.device
def _set_device(self, device):
"""Set the device of this operation.
Args:
device: string or device.. The device to set.
"""
self._node_def.device = _device_string(device)
def _add_input(self, tensor, dtype=None):
"""Add a new input to this operation.
Args:
tensor: the Tensor to add as an input.
dtype: tf.DType: type of the input; defaults to
the tensor's dtype.
Raises:
TypeError: if tensor is not a Tensor,
or if input tensor type is not convertible to dtype.
ValueError: if the Tensor is from a different graph.
"""
if not isinstance(tensor, Tensor):
raise TypeError("tensor must be a Tensor: %s" % tensor)
_assert_same_graph(self, tensor)
if dtype is None:
dtype = tensor.dtype
else:
dtype = dtypes.as_dtype(dtype)
if not dtype.is_compatible_with(tensor.dtype):
raise TypeError(
"Cannot convert a tensor of type %s to an input of type %s"
% (tensor.dtype.name, dtype.name))
self._inputs.append(tensor)
self._input_types.append(dtype)
tensor._add_consumer(self) # pylint: disable=protected-access
self._recompute_node_def()
def _update_input(self, index, tensor, dtype=None):
"""Update the input to this operation at the given index.
NOTE: This is for TF internal use only. Please don't use it.
Args:
index: the index of the input to update.
tensor: the Tensor to be used as the input at the given index.
dtype: tf.DType: type of the input; defaults to
the tensor's dtype.
Raises:
TypeError: if tensor is not a Tensor,
or if input tensor type is not convertible to dtype.
ValueError: if the Tensor is from a different graph.
"""
if not isinstance(tensor, Tensor):
raise TypeError("tensor must be a Tensor: %s" % tensor)
_assert_same_graph(self, tensor)
if dtype is None:
dtype = tensor.dtype
else:
dtype = dtypes.as_dtype(dtype)
if not dtype.is_compatible_with(tensor.dtype):
raise TypeError(
"Cannot convert a tensor of type %s to an input of type %s"
% (tensor.dtype.name, dtype.name))
self._inputs[index].consumers().remove(self)
self._inputs[index] = tensor
self._input_types[index] = dtype
tensor._add_consumer(self) # pylint: disable=protected-access
self._recompute_node_def()
def _add_control_inputs(self, ops):
"""Add a list of new control inputs to this operation.
Args:
ops: the list of Operations to add as control input.
Raises:
TypeError: if ops is not a list of Operations.
ValueError: if any op in ops is from a different graph.
"""
if ops:
for op in ops:
if not isinstance(op, Operation):
raise TypeError("op must be an Operation: %s" % op)
_assert_same_graph(self, op)
self._control_inputs.append(op)
self._recompute_node_def()
def _add_control_input(self, op):
"""Add a new control input to this operation.
Args:
op: the Operation to add as control input.
Raises:
TypeError: if op is not an Operation.
ValueError: if op is from a different graph.
"""
self._add_control_inputs([op])
# Methods below are used when building the NodeDef and Graph proto.
def _recompute_node_def(self):
del self._node_def.input[:]
self._node_def.input.extend([t._as_node_def_input() for t in self._inputs])
if self._control_inputs:
self._node_def.input.extend(["^%s" % op.name for op in
self._control_inputs])
def __str__(self):
return str(self._node_def)
def __repr__(self):
return "<tf.Operation '%s' type=%s>" % (self.name, self.type)
@property
def outputs(self):
"""The list of `Tensor` objects representing the outputs of this op."""
return self._outputs
# pylint: disable=protected-access
class _InputList(object):
"""Immutable input list wrapper."""
def __init__(self, op):
self._op = op
def __iter__(self):
return iter(self._op._inputs)
def __len__(self):
return len(self._op._inputs)
def __bool__(self):
return bool(self._op._inputs)
# Python 3 wants __bool__, Python 2.7 wants __nonzero__
__nonzero__ = __bool__
def __getitem__(self, i):
return self._op._inputs[i]
# pylint: enable=protected-access
@property
def inputs(self):
"""The list of `Tensor` objects representing the data inputs of this op."""
return Operation._InputList(self)
@property
def _input_dtypes(self):
return self._input_types
@property
def control_inputs(self):
"""The `Operation` objects on which this op has a control dependency.
Before this op is executed, TensorFlow will ensure that the
operations in `self.control_inputs` have finished executing. This
mechanism can be used to run ops sequentially for performance
reasons, or to ensure that the side effects of an op are observed
in the correct order.
Returns:
A list of `Operation` objects.
"""
return self._control_inputs
@property
def type(self):
"""The type of the op (e.g. `"MatMul"`)."""
return self._node_def.op
@property
def graph(self):
"""The `Graph` that contains this operation."""
return self._graph
@property
def node_def(self):
"""Returns a serialized `NodeDef` representation of this operation.
Returns:
A
[`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/node_def.proto)
protocol buffer.
"""
return self._node_def
@property
def op_def(self):
"""Returns the `OpDef` proto that represents the type of this op.
Returns:
An
[`OpDef`](https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto)
protocol buffer.
"""
return self._op_def
@property
def traceback(self):
"""Returns the call stack from when this operation was constructed."""
return _convert_stack(self._traceback)
def get_attr(self, name):
"""Returns the value of the attr of this op with the given `name`.
Args:
name: The name of the attr to fetch.
Returns:
The value of the attr, as a Python object.
Raises:
ValueError: If this op does not have an attr with the given `name`.
"""
fields = ["s", "i", "f", "b", "type", "shape", "tensor"]
if name not in self._node_def.attr:
raise ValueError("No attr named '" + name + "' in " +
str(self._node_def))
x = self._node_def.attr[name]
# Treat an empty oneof value as an empty list.
if not x.WhichOneof("value"):
return []
if x.HasField("list"):
for f in fields:
if getattr(x.list, f):
return list(getattr(x.list, f))
return []
else:
for f in fields:
if x.HasField(f):
return getattr(x, f)
assert False, "Unsupported field type in " + str(x)
def run(self, feed_dict=None, session=None):
"""Runs this operation in a `Session`.
Calling this method will execute all preceding operations that
produce the inputs needed for this operation.
*N.B.* Before invoking `Operation.run()`, its graph must have been
launched in a session, and either a default session must be
available, or `session` must be specified explicitly.
Args:
feed_dict: A dictionary that maps `Tensor` objects to feed values.
See [`Session.run()`](../../api_docs/python/client.md#Session.run)
for a description of the valid feed values.
session: (Optional.) The `Session` to be used to run to this operation. If
none, the default session will be used.
"""
_run_using_default_session(self, feed_dict, self.graph, session)
_gradient_registry = registry.Registry("gradient")
class RegisterGradient(object):
"""A decorator for registering the gradient function for an op type.
This decorator is only used when defining a new op type. For an op
with `m` inputs and `n` outputs, the gradient function is a function
that takes the original `Operation` and `n` `Tensor` objects
(representing the gradients with respect to each output of the op),
and returns `m` `Tensor` objects (representing the partial gradients
with respect to each input of the op).
For example, assuming that operations of type `"Sub"` take two
inputs `x` and `y`, and return a single output `x - y`, the
following gradient function would be registered:
```python
@tf.RegisterGradient("Sub")
def _sub_grad(unused_op, grad):
return grad, tf.negative(grad)
```
The decorator argument `op_type` is the string type of an
operation. This corresponds to the `OpDef.name` field for the proto
that defines the operation.
@@__init__
"""
def __init__(self, op_type):
"""Creates a new decorator with `op_type` as the Operation type.
Args:
op_type: The string type of an operation. This corresponds to the
`OpDef.name` field for the proto that defines the operation.
"""
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string")
self._op_type = op_type
def __call__(self, f):
"""Registers the function `f` as gradient function for `op_type`."""
_gradient_registry.register(f, self._op_type)
return f
def NotDifferentiable(op_type):
"""Specifies that ops of type `op_type` is not differentiable.
This function should *not* be used for operations that have a
well-defined gradient that is not yet implemented.
This function is only used when defining a new op type. It may be
used for ops such as `tf.size()` that are not differentiable. For
example:
```python
tf.NotDifferentiable("Size")
```
The gradient computed for 'op_type' will then propagate zeros.
For ops that have a well-defined gradient but are not yet implemented,
no declaration should be made, and an error *must* be thrown if
an attempt to request its gradient is made.
Args:
op_type: The string type of an operation. This corresponds to the
`OpDef.name` field for the proto that defines the operation.
Raises:
TypeError: If `op_type` is not a string.
"""
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string")
_gradient_registry.register(None, op_type)
# Alias for the old name, will be eventually removed.
NoGradient = NotDifferentiable
def get_gradient_function(op):
"""Returns the function that computes gradients for "op"."""
if not op.inputs: return None
try:
op_type = op.get_attr("_gradient_op_type")
except ValueError:
op_type = op.type
return _gradient_registry.lookup(op_type)
_shape_registry = registry.Registry("shape functions")
_default_shape_function_registry = registry.Registry("default shape functions")
# These are set to common_shapes.call_cpp_shape_fn by op generated code
# (generated by python_op_gen.cc).
# It is set outside ops.py to avoid a circular dependency.
_call_cpp_shape_fn = None
_call_cpp_shape_fn_and_require_op = None
def _set_call_cpp_shape_fn(call_cpp_shape_fn):
"""Sets default shape fns from passed common_shapes.call_cpp_shape_fn."""
global _call_cpp_shape_fn, _call_cpp_shape_fn_and_require_op
if _call_cpp_shape_fn:
return # already registered
def call_without_requiring(op):
return call_cpp_shape_fn(op, require_shape_fn=False)
_call_cpp_shape_fn = call_without_requiring
def call_with_requiring(op):
return call_cpp_shape_fn(op, require_shape_fn=True)
_call_cpp_shape_fn_and_require_op = call_with_requiring
class RegisterShape(object):
"""No longer used. Was: A decorator for registering a shape function.
Shape functions must now be registered via the SetShapeFn on the
original Op specification in C++.
"""
def __init__(self, op_type):
"""Saves the `op_type` as the `Operation` type."""
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string")
self._op_type = op_type
def __call__(self, f):
"""Registers "f" as the shape function for "op_type"."""
if f is None:
assert _call_cpp_shape_fn
# None is a special "weak" value that provides a default shape function,
# and can be overridden by a non-None registration.
try:
_default_shape_function_registry.register(_call_cpp_shape_fn,
self._op_type)
except KeyError:
# Ignore duplicate registrations of the weak value. This can
# occur if the op library input to wrapper generation
# inadvertently links in one or more of the standard op
# libraries.
pass
else:
_shape_registry.register(f, self._op_type)
return f
def set_shapes_for_outputs(op):
"""Uses the registered shape functions to set the shapes for op's outputs."""
try:
shape_func = _shape_registry.lookup(op.type)
except LookupError:
try:
shape_func = _default_shape_function_registry.lookup(op.type)
except LookupError:
shape_func = _call_cpp_shape_fn_and_require_op
shapes = shape_func(op)
if shapes is None:
raise RuntimeError(
"Shape function for op %s did not return any shapes" % op)
elif isinstance(shapes, dict):
# Returned by call_cpp_shape_fn
shapes_dict = shapes
shapes = shapes_dict["shapes"]
handle_shapes = shapes_dict["handle_shapes"]
handle_dtypes = shapes_dict["handle_dtypes"]
for output, handle_shape, handle_dtype in zip(op.outputs, handle_shapes, handle_dtypes):
# pylint: disable=protected-access
output._handle_shape = handle_shape
output._handle_dtype = handle_dtype
# pylint: enable=protected-access
if len(op.outputs) != len(shapes):
raise RuntimeError(
"Shape function for op %s returned %d shapes but expected %d %s %s" %
(op, len(shapes), len(op.outputs), shape_func.__name__, str(shapes)))
for output, s in zip(op.outputs, shapes):
output.set_shape(s)
class OpStats(object):
"""A holder for statistics about an operator.
This class holds information about the resource requirements for an op,
including the size of its weight parameters on-disk and how many FLOPS it
requires to execute forward inference.
If you define a new operation, you can create a function that will return a
set of information about its usage of the CPU and disk space when serialized.
The function itself takes a Graph object that's been set up so you can call
methods like get_tensor_by_name to help calculate the results, and a NodeDef
argument.
"""
def __init__(self, statistic_type, value=None):
"""Sets up the initial placeholders for the statistics."""
self.statistic_type = statistic_type
self.value = value
@property
def statistic_type(self):
return self._statistic_type
@statistic_type.setter
def statistic_type(self, statistic_type):
self._statistic_type = statistic_type
@property
def value(self):
return self._value
@value.setter
def value(self, value):
self._value = value
def __iadd__(self, other):
if other.statistic_type != self.statistic_type:
raise ValueError("Can't add an OpStat of type %s to one of %s.",
self.statistic_type, other.statistic_type)
if self.value is None:
self.value = other.value
elif other.value is not None:
self._value += other.value
return self
_stats_registry = registry.Registry("statistical functions")
class RegisterStatistics(object):
"""A decorator for registering the statistics function for an op type.
This decorator can be defined for an op type so that it gives a
report on the resources used by an instance of an operator, in the
form of an OpStats object.
Well-known types of statistics include these so far:
- flops: When running a graph, the bulk of the computation happens doing
numerical calculations like matrix multiplications. This type allows a node
to return how many floating-point operations it takes to complete. The
total number of FLOPs for a graph is a good guide to its expected latency.
You can add your own statistics just by picking a new type string, registering
functions for the ops you care about, and then calling get_stats_for_node_def.
If a statistic for an op is registered multiple times, a KeyError will be
raised.
Since the statistics is counted on a per-op basis. It is not suitable for
model parameters (capacity), which is expected to be counted only once, even
if it is shared by multiple ops. (e.g. RNN)
For example, you can define a new metric called doohickey for a Foo operation
by placing this in your code:
```python
@ops.RegisterStatistics("Foo", "doohickey")
def _calc_foo_bojangles(unused_graph, unused_node_def):
return ops.OpStats("doohickey", 20)
```
Then in client code you can retrieve the value by making this call:
```python
doohickey = ops.get_stats_for_node_def(graph, node_def, "doohickey")
```
If the NodeDef is for an op with a registered doohickey function, you'll get
back the calculated amount in doohickey.value, or None if it's not defined.
"""
def __init__(self, op_type, statistic_type):
"""Saves the `op_type` as the `Operation` type."""
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string.")
if "," in op_type:
raise TypeError("op_type must not contain a comma.")
self._op_type = op_type
if not isinstance(statistic_type, six.string_types):
raise TypeError("statistic_type must be a string.")
if "," in statistic_type:
raise TypeError("statistic_type must not contain a comma.")
self._statistic_type = statistic_type
def __call__(self, f):
"""Registers "f" as the statistics function for "op_type"."""
_stats_registry.register(f, self._op_type + "," + self._statistic_type)
return f
def get_stats_for_node_def(graph, node, statistic_type):
"""Looks up the node's statistics function in the registry and calls it.
This function takes a Graph object and a NodeDef from a GraphDef, and if
there's an associated statistics method, calls it and returns a result. If no
function has been registered for the particular node type, it returns an empty
statistics object.
Args:
graph: A Graph object that's been set up with the node's graph.
node: A NodeDef describing the operator.
statistic_type: A string identifying the statistic we're interested in.
Returns:
An OpStats object containing information about resource usage.
"""
try:
stats_func = _stats_registry.lookup(node.op + "," + statistic_type)
result = stats_func(graph, node)
except LookupError:
result = OpStats(statistic_type)
return result
def _name_from_scope_name(name):
"""Returns the name of an op given the name of its scope.
Args:
name: the name of the scope.
Returns:
the name of the op (equal to scope name minus any trailing slash).
"""
return name[:-1] if name[-1] == "/" else name
class Graph(object):
"""A TensorFlow computation, represented as a dataflow graph.
A `Graph` contains a set of
[`Operation`](../../api_docs/python/framework.md#Operation) objects,
which represent units of computation; and
[`Tensor`](../../api_docs/python/framework.md#Tensor) objects, which represent
the units of data that flow between operations.
A default `Graph` is always registered, and accessible by calling
[`tf.get_default_graph()`](../../api_docs/python/framework.md#get_default_graph).
To add an operation to the default graph, simply call one of the functions
that defines a new `Operation`:
```python
c = tf.constant(4.0)
assert c.graph is tf.get_default_graph()
```
Another typical usage involves the
[`Graph.as_default()`](../../api_docs/python/framework.md#Graph.as_default)
context manager, which overrides the current default graph for the
lifetime of the context:
```python
g = tf.Graph()
with g.as_default():
# Define operations and tensors in `g`.
c = tf.constant(30.0)
assert c.graph is g
```
Important note: This class *is not* thread-safe for graph construction. All
operations should be created from a single thread, or external
synchronization must be provided. Unless otherwise specified, all methods
are not thread-safe.
@@__init__
@@as_default
@@as_graph_def
@@finalize
@@finalized
@@control_dependencies
@@device
@@name_scope
A `Graph` instance supports an arbitrary number of "collections"
that are identified by name. For convenience when building a large
graph, collections can store groups of related objects: for
example, the `tf.Variable` uses a collection (named
[`tf.GraphKeys.GLOBAL_VARIABLES`](../../api_docs/python/framework.md#GraphKeys)) for
all variables that are created during the construction of a graph. The caller
may define additional collections by specifying a new name.
@@add_to_collection
@@add_to_collections
@@get_collection
@@get_collection_ref
@@as_graph_element
@@get_operation_by_name
@@get_tensor_by_name
@@get_operations
@@seed
@@unique_name
@@version
@@graph_def_versions
@@create_op
@@gradient_override_map
"""
def __init__(self):
"""Creates a new, empty Graph."""
# Protects the core state that may be accessed by multiple readers.
# Only state that can be returned via public accessors (`as_graph_def()`,
# `get_operations()`, `as_graph_element()`, `get_collection()`, and
# `get_collection_ref()`) is by the lock. Thread-safety is provided on a
# best-effort basis to support buggy programs, and is not guaranteed by the
# public `tf.Graph` API.
# NOTE(mrry): This does not protect the various stacks. A warning will
# be reported if these are used from multiple threads
self._lock = threading.Lock()
self._nodes_by_id = dict() # GUARDED_BY(self._lock)
self._next_id_counter = 0 # GUARDED_BY(self._lock)
self._nodes_by_name = dict() # GUARDED_BY(self._lock)
self._version = 0 # GUARDED_BY(self._lock)
# Current name stack: uniquified names
self._name_stack = ""
# Maps a name used in the graph to the next id to use for that name.
self._names_in_use = {}
# Functions that will be applied to choose a device if none is specified.
self._device_function_stack = []
# Default original_op applied to new ops.
self._default_original_op = None
# Current control flow context. It could be either CondContext or
# WhileContext defined in ops/control_flow_ops.py
self._control_flow_context = None
# A new node will depend of the union of all of the nodes in the stack.
self._control_dependencies_stack = []
# Arbritrary collections of objects.
self._collections = {}
# The graph-level random seed
self._seed = None
# A dictionary of attributes that should be applied to all ops.
self._attr_scope_map = {}
# A map from op type to the kernel label that should be used.
self._op_to_kernel_label_map = {}
# A map from op type to an alternative op type that should be used when
# computing gradients.
self._gradient_override_map = {}
# True if the graph is considered "finalized". In that case no
# new operations can be added.
self._finalized = False
# Functions defined in the graph
self._functions = collections.OrderedDict()
# Default GraphDef versions
self._graph_def_versions = versions_pb2.VersionDef(
producer=versions.GRAPH_DEF_VERSION,
min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER)
self._building_function = False
# Stack of colocate_with ops
self._colocation_stack = []
# Set of tensors that are dangerous to feed!
self._unfeedable_tensors = set()
# Set of operations that are dangerous to fetch!
self._unfetchable_ops = set()
# A map of tensor handle placeholder to tensor dtype.
self._handle_feeders = {}
# A map from tensor handle to its read op.
self._handle_readers = {}
# A map from tensor handle to its move op.
self._handle_movers = {}
# A map from tensor handle to its delete op.
self._handle_deleters = {}
# Resource container.
self._container = ""
self._registered_ops = op_def_registry.get_registered_ops()
def _check_not_finalized(self):
"""Check if the graph is finalized.
Raises:
RuntimeError: If the graph finalized.
"""
if self._finalized:
raise RuntimeError("Graph is finalized and cannot be modified.")
def _add_op(self, op):
"""Adds 'op' to the graph.
Args:
op: the Operator or Tensor to add.
Raises:
TypeError: if op is not an Operation or Tensor.
ValueError: if the op.name or op._id are already used.
"""
self._check_not_finalized()
if not isinstance(op, (Tensor, Operation)):
raise TypeError("op must be a Tensor or Operation: %s" % op)
with self._lock:
# pylint: disable=protected-access
if op._id in self._nodes_by_id:
raise ValueError("cannot add an op with id %d as it already "
"exists in the graph" % op._id)
if op.name in self._nodes_by_name:
raise ValueError("cannot add op with name %s as that name "
"is already used" % op.name)
self._nodes_by_id[op._id] = op
self._nodes_by_name[op.name] = op
self._version = max(self._version, op._id)
# pylint: enable=protected-access
@property
def version(self):
"""Returns a version number that increases as ops are added to the graph.
Note that this is unrelated to the
[GraphDef version](#Graph.graph_def_version).
"""
if self._finalized:
return self._version
with self._lock:
return self._version
@property
def graph_def_versions(self):
"""The GraphDef version information of this graph.
For details on the meaning of each version, see
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto).
Returns:
A `VersionDef`.
"""
return self._graph_def_versions
@property
def seed(self):
"""The graph-level random seed of this graph."""
return self._seed
@seed.setter
def seed(self, seed):
self._seed = seed
@property
def finalized(self):
"""True if this graph has been finalized."""
return self._finalized
def finalize(self):
"""Finalizes this graph, making it read-only.
After calling `g.finalize()`, no new operations can be added to
`g`. This method is used to ensure that no operations are added
to a graph when it is shared between multiple threads, for example
when using a [`QueueRunner`](../../api_docs/python/train.md#QueueRunner).
"""
self._finalized = True
def _unsafe_unfinalize(self):
"""Opposite of `finalize`. Internal interface.
NOTE: Unfinalizing a graph could have negative impact on performance,
especially in a multi-threaded environment. Unfinalizing a graph
when it is in use by a Session may lead to undefined behavior. Ensure
that all sessions using a graph are closed before calling this method.
"""
self._finalized = False
def _get_control_flow_context(self):
"""Returns the current control flow context.
Returns:
A context object.
"""
return self._control_flow_context
def _set_control_flow_context(self, context):
"""Sets the current control flow context.
Args:
context: a context object.
"""
self._control_flow_context = context
def _as_graph_def(self, from_version=None, add_shapes=False):
"""Returns a serialized `GraphDef` representation of this graph.
The serialized `GraphDef` can be imported into another `Graph`
(using [`import_graph_def()`](#import_graph_def)) or used with the
[C++ Session API](../../api_docs/cc/index.md).
This method is thread-safe.
Args:
from_version: Optional. If this is set, returns a `GraphDef`
containing only the nodes that were added to this graph since
its `version` property had the given value.
add_shapes: If true, adds an "_output_shapes" list attr to each
node with the inferred shapes of each of its outputs.
Returns:
A tuple containing a
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
protocol buffer, and the version of the graph to which that
`GraphDef` corresponds.
Raises:
ValueError: If the `graph_def` would be too large.
"""
with self._lock:
graph = graph_pb2.GraphDef()
graph.versions.CopyFrom(self._graph_def_versions)
bytesize = 0
for op_id in sorted(self._nodes_by_id):
op = self._nodes_by_id[op_id]
if from_version is None or op_id > from_version:
graph.node.extend([op.node_def])
if op.outputs and add_shapes:
assert "_output_shapes" not in graph.node[-1].attr
graph.node[-1].attr["_output_shapes"].list.shape.extend([
output.get_shape().as_proto() for output in op.outputs])
bytesize += op.node_def.ByteSize()
if bytesize >= (1 << 31) or bytesize < 0:
raise ValueError("GraphDef cannot be larger than 2GB.")
if self._functions:
for f in self._functions.values():
bytesize += f.definition.ByteSize()
if bytesize >= (1 << 31) or bytesize < 0:
raise ValueError("GraphDef cannot be larger than 2GB.")
graph.library.function.extend([f.definition])
if f.grad_func_name:
grad_def = function_pb2.GradientDef()
grad_def.function_name = f.name
grad_def.gradient_func = f.grad_func_name
graph.library.gradient.extend([grad_def])
return graph, self._version
def as_graph_def(self, from_version=None, add_shapes=False):
"""Returns a serialized `GraphDef` representation of this graph.
The serialized `GraphDef` can be imported into another `Graph`
(using [`import_graph_def()`](#import_graph_def)) or used with the
[C++ Session API](../../api_docs/cc/index.md).
This method is thread-safe.
Args:
from_version: Optional. If this is set, returns a `GraphDef`
containing only the nodes that were added to this graph since
its `version` property had the given value.
add_shapes: If true, adds an "_output_shapes" list attr to each
node with the inferred shapes of each of its outputs.
Returns:
A [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
protocol buffer.
Raises:
ValueError: If the `graph_def` would be too large.
"""
result, _ = self._as_graph_def(from_version, add_shapes)
return result
def _is_function(self, name):
"""Tests whether 'name' is registered in this graph's function library.
Args:
name: string op name.
Returns:
bool indicating whether or not 'name' is registered in function library.
"""
return name in self._functions
def _get_function(self, name):
"""Returns the function definition for 'name'.
Args:
name: string function name.
Returns:
The function def proto.
"""
return self._functions.get(name, None)
def _add_function(self, function):
"""Adds a function to the graph.
After the function has been added, you can call to the function by
passing the function name in place of an op name to
`Graph.create_op()`.
Args:
function: A `_DefinedFunction` object.
Raises:
ValueError: if another function is defined with the same name.
"""
name = function.name
previous = self._functions.get(name, None)
if previous:
raise ValueError("Another function is already defined with that name")
# Sanity checks on gradient definition.
if (function.grad_func_name is not None) and (
function.python_grad_func is not None):
raise ValueError("Gradient defined twice for function %s" % name)
# Need a new-enough consumer to support the functions we add to the graph.
if self._graph_def_versions.min_consumer < 12:
self._graph_def_versions.min_consumer = 12
self._functions[name] = function
@property
def building_function(self):
"""Returns True iff this graph represents a function."""
return self._building_function
# Helper functions to create operations.
def create_op(self, op_type, inputs, dtypes,
input_types=None, name=None, attrs=None, op_def=None,
compute_shapes=True, compute_device=True):
"""Creates an `Operation` in this graph.
This is a low-level interface for creating an `Operation`. Most
programs will not call this method directly, and instead use the
Python op constructors, such as `tf.constant()`, which add ops to
the default graph.
Args:
op_type: The `Operation` type to create. This corresponds to the
`OpDef.name` field for the proto that defines the operation.
inputs: A list of `Tensor` objects that will be inputs to the `Operation`.
dtypes: A list of `DType` objects that will be the types of the tensors
that the operation produces.
input_types: (Optional.) A list of `DType`s that will be the types of
the tensors that the operation consumes. By default, uses the base
`DType` of each input in `inputs`. Operations that expect
reference-typed inputs must specify `input_types` explicitly.
name: (Optional.) A string name for the operation. If not specified, a
name is generated based on `op_type`.
attrs: (Optional.) A dictionary where the key is the attribute name (a
string) and the value is the respective `attr` attribute of the
`NodeDef` proto that will represent the operation (an `AttrValue`
proto).
op_def: (Optional.) The `OpDef` proto that describes the `op_type` that
the operation will have.
compute_shapes: (Optional.) If True, shape inference will be performed
to compute the shapes of the outputs.
compute_device: (Optional.) If True, device functions will be executed
to compute the device property of the Operation.
Raises:
TypeError: if any of the inputs is not a `Tensor`.
ValueError: if colocation conflicts with existing device assignment.
Returns:
An `Operation` object.
"""
self._check_not_finalized()
for idx, a in enumerate(inputs):
if not isinstance(a, Tensor):
raise TypeError("Input #%d is not a tensor: %s" % (idx, a))
if name is None:
name = op_type
# If a names ends with a '/' it is a "name scope" and we use it as-is,
# after removing the trailing '/'.
if name and name[-1] == "/":
name = _name_from_scope_name(name)
else:
name = self.unique_name(name)
node_def = _NodeDef(op_type, name, device=None, attrs=attrs)
# Apply any additional attributes requested. Do not overwrite any existing
# attributes.
for key, value in self._attr_scope_map.items():
if key not in node_def.attr:
if callable(value):
value = value(node_def)
if not isinstance(value, (type(None), attr_value_pb2.AttrValue)):
raise TypeError(
"Callable for scope map key '%s' must return either None or "
"an AttrValue protocol buffer; but it returned: %s" %
(key, value))
node_def.attr[key].CopyFrom(value)
# Apply a kernel label if one has been specified for this op_type.
try:
kernel_label = self._op_to_kernel_label_map[op_type]
node_def.attr["_kernel"].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(kernel_label)))
except KeyError:
pass
# Apply the overriding op_type for gradients if one has been
# specified for this op_type.
try:
mapped_op_type = self._gradient_override_map[op_type]
node_def.attr["_gradient_op_type"].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(mapped_op_type)))
except KeyError:
pass
control_inputs = self._control_dependencies_for_inputs(inputs)
ret = Operation(node_def, self, inputs=inputs, output_types=dtypes,
control_inputs=control_inputs, input_types=input_types,
original_op=self._default_original_op, op_def=op_def)
if compute_shapes:
set_shapes_for_outputs(ret)
self._add_op(ret)
self._record_op_seen_by_control_dependencies(ret)
if compute_device:
self._apply_device_functions(ret)
if self._colocation_stack:
all_colocation_groups = []
for colocation_op in self._colocation_stack:
all_colocation_groups.extend(colocation_op.colocation_groups())
if colocation_op.device:
# Make this device match the device of the colocated op, to
# provide consistency between the device and the colocation
# property.
if ret.device and ret.device != colocation_op.device:
logging.warning("Tried to colocate %s with an op %s that had "
"a different device: %s vs %s. "
"Ignoring colocation property.",
name, colocation_op.name,
ret.device, colocation_op.device)
else:
ret._set_device(colocation_op.device)
all_colocation_groups = sorted(set(all_colocation_groups))
ret.node_def.attr["_class"].CopyFrom(attr_value_pb2.AttrValue(
list=attr_value_pb2.AttrValue.ListValue(s=all_colocation_groups)))
# Sets "container" attribute if
# (1) self._container is not None
# (2) "is_stateful" is set in OpDef
# (3) "container" attribute is in OpDef
# (4) "container" attribute is None
if (self._container and
op_type in self._registered_ops and
self._registered_ops[op_type].is_stateful and
"container" in ret.node_def.attr and
not ret.node_def.attr["container"].s):
ret.node_def.attr["container"].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(self._container)))
return ret
def as_graph_element(self, obj, allow_tensor=True, allow_operation=True):
"""Returns the object referred to by `obj`, as an `Operation` or `Tensor`.
This function validates that `obj` represents an element of this
graph, and gives an informative error message if it is not.
This function is the canonical way to get/validate an object of
one of the allowed types from an external argument reference in the
Session API.
This method may be called concurrently from multiple threads.
Args:
obj: A `Tensor`, an `Operation`, or the name of a tensor or operation.
Can also be any object with an `_as_graph_element()` method that returns
a value of one of these types.
allow_tensor: If true, `obj` may refer to a `Tensor`.
allow_operation: If true, `obj` may refer to an `Operation`.
Returns:
The `Tensor` or `Operation` in the Graph corresponding to `obj`.
Raises:
TypeError: If `obj` is not a type we support attempting to convert
to types.
ValueError: If `obj` is of an appropriate type but invalid. For
example, an invalid string.
KeyError: If `obj` is not an object in the graph.
"""
if self._finalized:
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
with self._lock:
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):
"""See `Graph.as_graph_element()` for details."""
# The vast majority of this function is figuring
# out what an API user might be doing wrong, so
# that we can give helpful error messages.
#
# Ideally, it would be nice to split it up, but we
# need context to generate nice error messages.
if allow_tensor and allow_operation:
types_str = "Tensor or Operation"
elif allow_tensor:
types_str = "Tensor"
elif allow_operation:
types_str = "Operation"
else:
raise ValueError("allow_tensor and allow_operation can't both be False.")
temp_obj = _as_graph_element(obj)
if temp_obj is not None:
obj = temp_obj
# If obj appears to be a name...
if isinstance(obj, compat.bytes_or_text_types):
name = compat.as_str(obj)
if ":" in name and allow_tensor:
# Looks like a Tensor name and can be a Tensor.
try:
op_name, out_n = name.split(":")
out_n = int(out_n)
except:
raise ValueError("The name %s looks a like a Tensor name, but is "
"not a valid one. Tensor names must be of the "
"form \"<op_name>:<output_index>\"." % repr(name))
if op_name in self._nodes_by_name:
op = self._nodes_by_name[op_name]
else:
raise KeyError("The name %s refers to a Tensor which does not "
"exist. The operation, %s, does not exist in the "
"graph." % (repr(name), repr(op_name)))
try:
return op.outputs[out_n]
except:
raise KeyError("The name %s refers to a Tensor which does not "
"exist. The operation, %s, exists but only has "
"%s outputs."
% (repr(name), repr(op_name), len(op.outputs)))
elif ":" in name and not allow_tensor:
# Looks like a Tensor name but can't be a Tensor.
raise ValueError("Name %s appears to refer to a Tensor, not a %s."
% (repr(name), types_str))
elif ":" not in name and allow_operation:
# Looks like an Operation name and can be an Operation.
if name not in self._nodes_by_name:
raise KeyError("The name %s refers to an Operation not in the "
"graph." % repr(name))
return self._nodes_by_name[name]
elif ":" not in name and not allow_operation:
# Looks like an Operation name but can't be an Operation.
if name in self._nodes_by_name:
# Yep, it's an Operation name
err_msg = ("The name %s refers to an Operation, not a %s."
% (repr(name), types_str))
else:
err_msg = ("The name %s looks like an (invalid) Operation name, "
"not a %s." % (repr(name), types_str))
err_msg += (" Tensor names must be of the form "
"\"<op_name>:<output_index>\".")
raise ValueError(err_msg)
elif isinstance(obj, Tensor) and allow_tensor:
# Actually obj is just the object it's referring to.
if obj.graph is not self:
raise ValueError("Tensor %s is not an element of this graph." % obj)
return obj
elif isinstance(obj, Operation) and allow_operation:
# Actually obj is just the object it's referring to.
if obj.graph is not self:
raise ValueError("Operation %s is not an element of this graph." % obj)
return obj
else:
# We give up!
raise TypeError("Can not convert a %s into a %s."
% (type(obj).__name__, types_str))
def get_operations(self):
"""Return the list of operations in the graph.
You can modify the operations in place, but modifications
to the list such as inserts/delete have no effect on the
list of operations known to the graph.
This method may be called concurrently from multiple threads.
Returns:
A list of Operations.
"""
if self._finalized:
return list(self._nodes_by_id.values())
with self._lock:
return list(self._nodes_by_id.values())
def get_operation_by_name(self, name):
"""Returns the `Operation` with the given `name`.
This method may be called concurrently from multiple threads.
Args:
name: The name of the `Operation` to return.
Returns:
The `Operation` with the given `name`.
Raises:
TypeError: If `name` is not a string.
KeyError: If `name` does not correspond to an operation in this graph.
"""
if not isinstance(name, six.string_types):
raise TypeError("Operation names are strings (or similar), not %s."
% type(name).__name__)
return self.as_graph_element(name, allow_tensor=False, allow_operation=True)
def get_tensor_by_name(self, name):
"""Returns the `Tensor` with the given `name`.
This method may be called concurrently from multiple threads.
Args:
name: The name of the `Tensor` to return.
Returns:
The `Tensor` with the given `name`.
Raises:
TypeError: If `name` is not a string.
KeyError: If `name` does not correspond to a tensor in this graph.
"""
# Names should be strings.
if not isinstance(name, six.string_types):
raise TypeError("Tensor names are strings (or similar), not %s."
% type(name).__name__)
return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
def _next_id(self):
"""Id for next Operation instance. Also increments the internal id."""
self._check_not_finalized()
with self._lock:
self._next_id_counter += 1
return self._next_id_counter
@property
def _last_id(self):
return self._next_id_counter
def as_default(self):
"""Returns a context manager that makes this `Graph` the default graph.
This method should be used if you want to create multiple graphs
in the same process. For convenience, a global default graph is
provided, and all ops will be added to this graph if you do not
create a new graph explicitly. Use this method with the `with` keyword
to specify that ops created within the scope of a block should be
added to this graph.
The default graph is a property of the current thread. If you
create a new thread, and wish to use the default graph in that
thread, you must explicitly add a `with g.as_default():` in that
thread's function.
The following code examples are equivalent:
```python
# 1. Using Graph.as_default():
g = tf.Graph()
with g.as_default():
c = tf.constant(5.0)
assert c.graph is g
# 2. Constructing and making default:
with tf.Graph().as_default() as g:
c = tf.constant(5.0)
assert c.graph is g
```
Returns:
A context manager for using this graph as the default graph.
"""
return _default_graph_stack.get_controller(self)
def add_to_collection(self, name, value):
"""Stores `value` in the collection with the given `name`.
Note that collections are not sets, so it is possible to add a value to
a collection several times.
Args:
name: The key for the collection. The `GraphKeys` class
contains many standard names for collections.
value: The value to add to the collection.
"""
self._check_not_finalized()
with self._lock:
if name not in self._collections:
self._collections[name] = [value]
else:
self._collections[name].append(value)
def add_to_collections(self, names, value):
"""Stores `value` in the collections given by `names`.
Note that collections are not sets, so it is possible to add a value to
a collection several times. This function makes sure that duplicates in
`names` are ignored, but it will not check for pre-existing membership of
`value` in any of the collections in `names`.
`names` can be any iterable, but if `names` is a string, it is treated as a
single collection name.
Args:
names: The keys for the collections to add to. The `GraphKeys` class
contains many standard names for collections.
value: The value to add to the collections.
"""
# Make sure names are unique, but treat strings as a single collection name
names = (names,) if isinstance(names, six.string_types) else set(names)
for name in names:
self.add_to_collection(name, value)
def get_collection_ref(self, name):
"""Returns a list of values in the collection with the given `name`.
If the collection exists, this returns the list itself, which can
be modified in place to change the collection. If the collection does
not exist, it is created as an empty list and the list is returned.
This is different from `get_collection()` which always returns a copy of
the collection list if it exists and never creates an empty collection.
Args:
name: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
Returns:
The list of values in the collection with the given `name`, or an empty
list if no value has been added to that collection.
"""
with self._lock:
coll_list = self._collections.get(name, None)
if coll_list is None:
coll_list = []
self._collections[name] = coll_list
return coll_list
def get_collection(self, name, scope=None):
"""Returns a list of values in the collection with the given `name`.
This is different from `get_collection_ref()` which always returns the
actual collection list if it exists in that it returns a new list each time
it is called.
Args:
name: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
scope: (Optional.) If supplied, the resulting list is filtered to include
only items whose `name` attribute matches using `re.match`. Items
without a `name` attribute are never returned if a scope is supplied and
the choice or `re.match` means that a `scope` without special tokens
filters by prefix.
Returns:
The list of values in the collection with the given `name`, or
an empty list if no value has been added to that collection. The
list contains the values in the order under which they were
collected.
"""
with self._lock:
coll_list = self._collections.get(name, None)
if coll_list is None:
return []
if scope is None:
return list(coll_list)
else:
c = []
regex = re.compile(scope)
for item in coll_list:
if hasattr(item, "name") and regex.match(item.name):
c.append(item)
return c
def get_all_collection_keys(self):
"""Returns a list of collections used in this graph."""
with self._lock:
return [x for x in self._collections if isinstance(x, six.string_types)]
def clear_collection(self, name):
"""Clears all values in a collection.
Args:
name: The key for the collection. The `GraphKeys` class contains many
standard names for collections.
"""
self._check_not_finalized()
with self._lock:
if name in self._collections:
del self._collections[name]
@contextlib.contextmanager
def _original_op(self, op):
"""Python 'with' handler to help annotate ops with their originator.
An op may have an 'original_op' property that indicates the op on which
it was based. For example a replica op is based on the op that was
replicated and a gradient op is based on the op that was differentiated.
All ops created in the scope of this 'with' handler will have
the given 'op' as their original op.
Args:
op: The Operation that all ops created in this scope will have as their
original op.
Yields:
Nothing.
"""
old_original_op = self._default_original_op
try:
self._default_original_op = op
yield
finally:
self._default_original_op = old_original_op
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def name_scope(self, name):
r"""Returns a context manager that creates hierarchical names for operations.
A graph maintains a stack of name scopes. A `with name_scope(...):`
statement pushes a new name onto the stack for the lifetime of the context.
The `name` argument will be interpreted as follows:
* A string (not ending with '/') will create a new name scope, in which
`name` is appended to the prefix of all operations created in the
context. If `name` has been used before, it will be made unique by
calling `self.unique_name(name)`.
* A scope previously captured from a `with g.name_scope(...) as
scope:` statement will be treated as an "absolute" name scope, which
makes it possible to re-enter existing scopes.
* A value of `None` or the empty string will reset the current name scope
to the top-level (empty) name scope.
For example:
```python
with tf.Graph().as_default() as g:
c = tf.constant(5.0, name="c")
assert c.op.name == "c"
c_1 = tf.constant(6.0, name="c")
assert c_1.op.name == "c_1"
# Creates a scope called "nested"
with g.name_scope("nested") as scope:
nested_c = tf.constant(10.0, name="c")
assert nested_c.op.name == "nested/c"
# Creates a nested scope called "inner".
with g.name_scope("inner"):
nested_inner_c = tf.constant(20.0, name="c")
assert nested_inner_c.op.name == "nested/inner/c"
# Create a nested scope called "inner_1".
with g.name_scope("inner"):
nested_inner_1_c = tf.constant(30.0, name="c")
assert nested_inner_1_c.op.name == "nested/inner_1/c"
# Treats `scope` as an absolute name scope, and
# switches to the "nested/" scope.
with g.name_scope(scope):
nested_d = tf.constant(40.0, name="d")
assert nested_d.op.name == "nested/d"
with g.name_scope(""):
e = tf.constant(50.0, name="e")
assert e.op.name == "e"
```
The name of the scope itself can be captured by `with
g.name_scope(...) as scope:`, which stores the name of the scope
in the variable `scope`. This value can be used to name an
operation that represents the overall result of executing the ops
in a scope. For example:
```python
inputs = tf.constant(...)
with g.name_scope('my_layer') as scope:
weights = tf.Variable(..., name="weights")
biases = tf.Variable(..., name="biases")
affine = tf.matmul(inputs, weights) + biases
output = tf.nn.relu(affine, name=scope)
```
NOTE: This constructor validates the given `name`. Valid scope
names match one of the following regular expressions:
[A-Za-z0-9.][A-Za-z0-9_.\\-/]* (for scopes at the root)
[A-Za-z0-9_.\\-/]* (for other scopes)
Args:
name: A name for the scope.
Returns:
A context manager that installs `name` as a new name scope.
Raises:
ValueError: If `name` is not a valid scope name, according to the rules
above.
"""
if name:
if self._name_stack:
# Scopes created in a nested scope may have initial characters
# that are illegal as the initial character of an op name
# (viz. '-', '\', '/', and '_').
if not _VALID_SCOPE_NAME_REGEX.match(name):
raise ValueError("'%s' is not a valid scope name" % name)
else:
# Scopes created in the root must match the more restrictive
# op name regex, which constrains the initial character.
if not _VALID_OP_NAME_REGEX.match(name):
raise ValueError("'%s' is not a valid scope name" % name)
try:
old_stack = self._name_stack
if not name: # Both for name=None and name="" we re-set to empty scope.
new_stack = None
elif name and name[-1] == "/":
new_stack = _name_from_scope_name(name)
else:
new_stack = self.unique_name(name)
self._name_stack = new_stack
yield "" if new_stack is None else new_stack + "/"
finally:
self._name_stack = old_stack
# pylint: enable=g-doc-return-or-yield
def unique_name(self, name, mark_as_used=True):
"""Return a unique operation name for `name`.
Note: You rarely need to call `unique_name()` directly. Most of
the time you just need to create `with g.name_scope()` blocks to
generate structured names.
`unique_name` is used to generate structured names, separated by
`"/"`, to help identify operations when debugging a graph.
Operation names are displayed in error messages reported by the
TensorFlow runtime, and in various visualization tools such as
TensorBoard.
If `mark_as_used` is set to `True`, which is the default, a new
unique name is created and marked as in use. If it's set to `False`,
the unique name is returned without actually being marked as used.
This is useful when the caller simply wants to know what the name
to be created will be.
Args:
name: The name for an operation.
mark_as_used: Whether to mark this name as being used.
Returns:
A string to be passed to `create_op()` that will be used
to name the operation being created.
"""
if self._name_stack:
name = self._name_stack + "/" + name
i = self._names_in_use.get(name, 0)
# Increment the number for "name".
if mark_as_used:
self._names_in_use[name] = i + 1
if i > 0:
base_name = name
# Make sure the composed name is not already used.
while name in self._names_in_use:
name = "%s_%d" % (base_name, i)
i += 1
# Mark the composed name as used in case someone wants
# to call unique_name("name_1").
if mark_as_used:
self._names_in_use[name] = 1
return name
@contextlib.contextmanager
def colocate_with(self, op, ignore_existing=False):
"""Returns a context manager that specifies an op to colocate with.
Note: this function is not for public use, only for internal libraries.
For example:
```python
a = tf.Variable([1.0])
with g.colocate_with(a):
b = tf.constant(1.0)
c = tf.add(a, b)
```
`b` and `c` will always be colocated with `a`, no matter where `a`
is eventually placed.
**NOTE** Using a colocation scope resets any existing device constraints.
If `op` is `None` then `ignore_existing` must be `True` and the new
scope resets all colocation and device constraints.
Args:
op: The op to colocate all created ops with, or `None`.
ignore_existing: If true, only applies colocation of this op within
the context, rather than applying all colocation properties
on the stack. If `op` is `None`, this value must be `True`.
Raises:
ValueError: if op is None but ignore_existing is False.
Yields:
A context manager that specifies the op with which to colocate
newly created ops.
"""
if op is None and not ignore_existing:
raise ValueError(
"Trying to reset colocation (op is None) but "
"ignore_existing is not True")
if op is not None and not isinstance(op, Operation):
# We always want to colocate with the reference op.
op = internal_convert_to_tensor_or_indexed_slices(op, as_ref=True).op
# By default, colocate_with resets the device function stack,
# since colocate_with is typically used in specific internal
# library functions where colocation is intended to be "stronger"
# than device functions.
#
# In the future, a caller may specify that device_functions win
# over colocation, in which case we can add support.
device_fn_tmp = self._device_function_stack
self._device_function_stack = []
if ignore_existing:
current_stack = self._colocation_stack
self._colocation_stack = []
if op is not None:
self._colocation_stack.append(op)
try:
yield
finally:
# Restore device function stack
self._device_function_stack = device_fn_tmp
if op is not None:
self._colocation_stack.pop()
# Reset the colocation stack if requested.
if ignore_existing:
self._colocation_stack = current_stack
@contextlib.contextmanager
def device(self, device_name_or_function):
"""Returns a context manager that specifies the default device to use.
The `device_name_or_function` argument may either be a device name
string, a device function, or None:
* If it is a device name string, all operations constructed in
this context will be assigned to the device with that name, unless
overridden by a nested `device()` context.
* If it is a function, it will be treated as a function from
Operation objects to device name strings, and invoked each time
a new Operation is created. The Operation will be assigned to
the device with the returned name.
* If it is None, all `device()` invocations from the enclosing context
will be ignored.
For information about the valid syntax of device name strings, see
the documentation in
[`DeviceNameUtils`](https://www.tensorflow.org/code/tensorflow/core/util/device_name_utils.h).
For example:
```python
with g.device('/gpu:0'):
# All operations constructed in this context will be placed
# on GPU 0.
with g.device(None):
# All operations constructed in this context will have no
# assigned device.
# Defines a function from `Operation` to device string.
def matmul_on_gpu(n):
if n.type == "MatMul":
return "/gpu:0"
else:
return "/cpu:0"
with g.device(matmul_on_gpu):
# All operations of type "MatMul" constructed in this context
# will be placed on GPU 0; all other operations will be placed
# on CPU 0.
```
**N.B.** The device scope may be overridden by op wrappers or
other library code. For example, a variable assignment op
`v.assign()` must be colocated with the `tf.Variable` `v`, and
incompatible device scopes will be ignored.
Args:
device_name_or_function: The device name or function to use in
the context.
Returns:
A context manager that specifies the default device to use for newly
created ops.
"""
if (device_name_or_function is not None
and not callable(device_name_or_function)):
device_function = pydev.merge_device(device_name_or_function)
else:
device_function = device_name_or_function
try:
self._device_function_stack.append(device_function)
yield
finally:
self._device_function_stack.pop()
def _apply_device_functions(self, op):
"""Applies the current device function stack to the given operation."""
# Apply any device functions in reverse order, so that the most recently
# pushed function has the first chance to apply a device to the op.
# We apply here because the result can depend on the Operation's
# signature, which is computed in the Operation constructor.
for device_function in reversed(self._device_function_stack):
if device_function is None:
break
op._set_device(device_function(op))
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def container(self, container_name):
"""Returns a context manager that specifies the resource container to use.
Stateful operations, such as variables and queues, can maintain their
states on devices so that they can be shared by multiple processes.
A resource container is a string name under which these stateful
operations are tracked. These resources can be released or cleared
with `tf.Session.reset()`.
For example:
```python
with g.container('experiment0'):
# All stateful Operations constructed in this context will be placed
# in resource container "experiment0".
v1 = tf.Variable([1.0])
v2 = tf.Variable([2.0])
with g.container("experiment1"):
# All stateful Operations constructed in this context will be
# placed in resource container "experiment1".
v3 = tf.Variable([3.0])
q1 = tf.FIFOQueue(10, tf.float32)
# All stateful Operations constructed in this context will be
# be created in the "experiment0".
v4 = tf.Variable([4.0])
q1 = tf.FIFOQueue(20, tf.float32)
with g.container(""):
# All stateful Operations constructed in this context will be
# be placed in the default resource container.
v5 = tf.Variable([5.0])
q3 = tf.FIFOQueue(30, tf.float32)
# Resets container "experiment0", after which the state of v1, v2, v4, q1
# will become undefined (such as uninitialized).
tf.Session.reset(target, ["experiment0"])
```
Args:
container_name: container name string.
Returns:
A context manager for defining resource containers for stateful ops,
yields the container name.
"""
original_container = self._container
try:
self._container = container_name
yield self._container
finally:
self._container = original_container
# pylint: enable=g-doc-return-or-yield
class _ControlDependenciesController(object):
"""Context manager for `control_dependencies()`."""
def __init__(self, graph, control_inputs):
"""Create a new `_ControlDependenciesController`.
A `_ControlDependenciesController` is the context manager for
`with tf.control_dependencies()` blocks. These normally nest,
as described in the documentation for `control_dependencies()`.
The `control_inputs` argument list control dependencies that must be
added to the current set of control dependencies. Because of
uniquification the set can be empty even if the caller passed a list of
ops. The special value `None` indicates that we want to start a new
empty set of control dependencies instead of extending the current set.
In that case we also clear the current control flow context, which is an
additional mechanism to add control dependencies.
Args:
graph: The graph that this controller is managing.
control_inputs: List of ops to use as control inputs in addition
to the current control dependencies. None to indicate that
the dependencies should be cleared.
"""
self._graph = graph
if control_inputs is None:
self._control_inputs = []
self._new_stack = True
else:
self._control_inputs = control_inputs
self._new_stack = False
self._seen_nodes = set()
self._old_stack = None
self._old_control_flow_context = None
# pylint: disable=protected-access
def __enter__(self):
if self._new_stack:
# Clear the control_dependencies graph.
self._old_stack = self._graph._control_dependencies_stack
self._graph._control_dependencies_stack = []
# Clear the control_flow_context too.
self._old_control_flow_context = self._graph._get_control_flow_context()
self._graph._set_control_flow_context(None)
self._graph._push_control_dependencies_controller(self)
def __exit__(self, unused_type, unused_value, unused_traceback):
self._graph._pop_control_dependencies_controller(self)
if self._new_stack:
self._graph._control_dependencies_stack = self._old_stack
self._graph._set_control_flow_context(self._old_control_flow_context)
# pylint: enable=protected-access
@property
def control_inputs(self):
return self._control_inputs
def add_op(self, op):
self._seen_nodes.add(op)
def op_in_group(self, op):
return op in self._seen_nodes
def _push_control_dependencies_controller(self, controller):
self._control_dependencies_stack.append(controller)
def _pop_control_dependencies_controller(self, controller):
assert self._control_dependencies_stack[-1] is controller
self._control_dependencies_stack.pop()
def _current_control_dependencies(self):
ret = set()
for controller in self._control_dependencies_stack:
for op in controller.control_inputs:
ret.add(op)
return ret
def _control_dependencies_for_inputs(self, input_tensors):
"""For an op that takes `input_tensors` as inputs, compute control inputs.
The returned control dependencies should yield an execution that
is equivalent to adding all control inputs in
self._control_dependencies_stack to a newly created op. However,
this function attempts to prune the returned control dependencies
by observing that nodes created within the same `with
control_dependencies(...):` block may have data dependencies that make
the explicit approach redundant.
Args:
input_tensors: The direct data dependencies for an op to be created.
Returns:
A list of control inputs for the op to be created.
"""
ret = []
input_ops = set([t.op for t in input_tensors])
for controller in self._control_dependencies_stack:
# If any of the input_ops already depends on the inputs from controller,
# we say that the new op is dominated (by that input), and we therefore
# do not need to add control dependencies for this controller's inputs.
dominated = False
for op in input_ops:
if controller.op_in_group(op):
dominated = True
break
if not dominated:
# Don't add a control input if we already have a data dependency on i.
# NOTE(mrry): We do not currently track transitive data dependencies,
# so we may add redundant control inputs.
ret.extend([c for c in controller.control_inputs if c not in input_ops])
return ret
def _record_op_seen_by_control_dependencies(self, op):
"""Record that the given op depends on all registered control dependencies.
Args:
op: An Operation.
"""
for controller in self._control_dependencies_stack:
controller.add_op(op)
def control_dependencies(self, control_inputs):
"""Returns a context manager that specifies control dependencies.
Use with the `with` keyword to specify that all operations constructed
within the context should have control dependencies on
`control_inputs`. For example:
```python
with g.control_dependencies([a, b, c]):
# `d` and `e` will only run after `a`, `b`, and `c` have executed.
d = ...
e = ...
```
Multiple calls to `control_dependencies()` can be nested, and in
that case a new `Operation` will have control dependencies on the union
of `control_inputs` from all active contexts.
```python
with g.control_dependencies([a, b]):
# Ops constructed here run after `a` and `b`.
with g.control_dependencies([c, d]):
# Ops constructed here run after `a`, `b`, `c`, and `d`.
```
You can pass None to clear the control dependencies:
```python
with g.control_dependencies([a, b]):
# Ops constructed here run after `a` and `b`.
with g.control_dependencies(None):
# Ops constructed here run normally, not waiting for either `a` or `b`.
with g.control_dependencies([c, d]):
# Ops constructed here run after `c` and `d`, also not waiting
# for either `a` or `b`.
```
*N.B.* The control dependencies context applies *only* to ops that
are constructed within the context. Merely using an op or tensor
in the context does not add a control dependency. The following
example illustrates this point:
```python
# WRONG
def my_func(pred, tensor):
t = tf.matmul(tensor, tensor)
with tf.control_dependencies([pred]):
# The matmul op is created outside the context, so no control
# dependency will be added.
return t
# RIGHT
def my_func(pred, tensor):
with tf.control_dependencies([pred]):
# The matmul op is created in the context, so a control dependency
# will be added.
return tf.matmul(tensor, tensor)
```
Args:
control_inputs: A list of `Operation` or `Tensor` objects which
must be executed or computed before running the operations
defined in the context. Can also be `None` to clear the control
dependencies.
Returns:
A context manager that specifies control dependencies for all
operations constructed within the context.
Raises:
TypeError: If `control_inputs` is not a list of `Operation` or
`Tensor` objects.
"""
if control_inputs is None:
return self._ControlDependenciesController(self, None)
# First convert the inputs to ops, and deduplicate them.
# NOTE(mrry): Other than deduplication, we do not currently track direct
# or indirect dependencies between control_inputs, which may result in
# redundant control inputs.
control_ops = []
current = self._current_control_dependencies()
for c in control_inputs:
c = self.as_graph_element(c)
if isinstance(c, Tensor):
c = c.op
elif not isinstance(c, Operation):
raise TypeError("Control input must be Operation or Tensor: %s" % c)
if c not in current:
control_ops.append(c)
current.add(c)
return self._ControlDependenciesController(self, control_ops)
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def _attr_scope(self, attr_map):
"""EXPERIMENTAL: A context manager for setting attributes on operators.
This context manager can be used to add additional
attributes to operators within the scope of the context.
For example:
with ops.Graph().as_default() as g:
f_1 = Foo() # No extra attributes
with g._attr_scope({"_a": tf.attr_value_pb2.AttrValue(b=False)}):
f_2 = Foo() # Additional attribute _a=False
with g._attr_scope({"_a": tf.attr_value_pb2.AttrValue(b=True)}):
f_3 = Foo() # Additional attribute _a=False
with g._attr_scope({"_a": None}):
f_4 = Foo() # No additional attributes.
Args:
attr_map: A dictionary mapping attr name strings to
AttrValue protocol buffers or None.
Returns:
A context manager that sets the kernel label to be used for one or more
ops created in that context.
Raises:
TypeError: If attr_map is not a dictionary mapping
strings to AttrValue protobufs.
"""
if not isinstance(attr_map, dict):
raise TypeError("attr_map must be a dictionary mapping "
"strings to AttrValue protocol buffers")
# The saved_attrs dictionary stores any currently-set labels that
# will be overridden by this context manager.
saved_attrs = {}
# Install the given attribute
for name, attr in attr_map.items():
if not (isinstance(name, six.string_types) and
(isinstance(attr, (type(None), attr_value_pb2.AttrValue)) or
callable(attr))):
raise TypeError("attr_map must be a dictionary mapping "
"strings to AttrValue protocol buffers or "
"callables that emit AttrValue protocol buffers")
try:
saved_attrs[name] = self._attr_scope_map[name]
except KeyError:
pass
if attr is None:
del self._attr_scope_map[name]
else:
self._attr_scope_map[name] = attr
try:
yield # The code within the context runs here.
finally:
# Remove the attributes set for this context, and restore any saved
# attributes.
for name, attr in attr_map.items():
try:
self._attr_scope_map[name] = saved_attrs[name]
except KeyError:
del self._attr_scope_map[name]
# pylint: enable=g-doc-return-or-yield
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def _kernel_label_map(self, op_to_kernel_label_map):
"""EXPERIMENTAL: A context manager for setting kernel labels.
This context manager can be used to select particular
implementations of kernels within the scope of the context.
For example:
with ops.Graph().as_default() as g:
f_1 = Foo() # Uses the default registered kernel for the Foo op.
with g.kernel_label_map({"Foo": "v_2"}):
f_2 = Foo() # Uses the registered kernel with label "v_2"
# for the Foo op.
with g.kernel_label_map({"Foo": "v_3"}):
f_3 = Foo() # Uses the registered kernel with label "v_3"
# for the Foo op.
with g.kernel_label_map({"Foo": ""}):
f_4 = Foo() # Uses the default registered kernel
# for the Foo op.
Args:
op_to_kernel_label_map: A dictionary mapping op type strings to
kernel label strings.
Returns:
A context manager that sets the kernel label to be used for one or more
ops created in that context.
Raises:
TypeError: If op_to_kernel_label_map is not a dictionary mapping
strings to strings.
"""
if not isinstance(op_to_kernel_label_map, dict):
raise TypeError("op_to_kernel_label_map must be a dictionary mapping "
"strings to strings")
# The saved_labels dictionary stores any currently-set labels that
# will be overridden by this context manager.
saved_labels = {}
# Install the given label
for op_type, label in op_to_kernel_label_map.items():
if not (isinstance(op_type, six.string_types)
and isinstance(label, six.string_types)):
raise TypeError("op_to_kernel_label_map must be a dictionary mapping "
"strings to strings")
try:
saved_labels[op_type] = self._op_to_kernel_label_map[op_type]
except KeyError:
pass
self._op_to_kernel_label_map[op_type] = label
try:
yield # The code within the context runs here.
finally:
# Remove the labels set for this context, and restore any saved labels.
for op_type, label in op_to_kernel_label_map.items():
try:
self._op_to_kernel_label_map[op_type] = saved_labels[op_type]
except KeyError:
del self._op_to_kernel_label_map[op_type]
# pylint: enable=g-doc-return-or-yield
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def gradient_override_map(self, op_type_map):
"""EXPERIMENTAL: A context manager for overriding gradient functions.
This context manager can be used to override the gradient function
that will be used for ops within the scope of the context.
For example:
```python
@tf.RegisterGradient("CustomSquare")
def _custom_square_grad(op, grad):
# ...
with tf.Graph().as_default() as g:
c = tf.constant(5.0)
s_1 = tf.square(c) # Uses the default gradient for tf.square.
with g.gradient_override_map({"Square": "CustomSquare"}):
s_2 = tf.square(s_2) # Uses _custom_square_grad to compute the
# gradient of s_2.
```
Args:
op_type_map: A dictionary mapping op type strings to alternative op
type strings.
Returns:
A context manager that sets the alternative op type to be used for one
or more ops created in that context.
Raises:
TypeError: If `op_type_map` is not a dictionary mapping strings to
strings.
"""
if not isinstance(op_type_map, dict):
raise TypeError("op_type_map must be a dictionary mapping "
"strings to strings")
# The saved_mappings dictionary stores any currently-set mappings that
# will be overridden by this context manager.
saved_mappings = {}
# Install the given label
for op_type, mapped_op_type in op_type_map.items():
if not (isinstance(op_type, six.string_types)
and isinstance(mapped_op_type, six.string_types)):
raise TypeError("op_type_map must be a dictionary mapping "
"strings to strings")
try:
saved_mappings[op_type] = self._gradient_override_map[op_type]
except KeyError:
pass
self._gradient_override_map[op_type] = mapped_op_type
try:
yield # The code within the context runs here.
finally:
# Remove the labels set for this context, and restore any saved labels.
for op_type, mapped_op_type in op_type_map.items():
try:
self._gradient_override_map[op_type] = saved_mappings[op_type]
except KeyError:
del self._gradient_override_map[op_type]
# pylint: enable=g-doc-return-or-yield
def prevent_feeding(self, tensor):
"""Marks the given `tensor` as unfeedable in this graph."""
self._unfeedable_tensors.add(tensor)
def is_feedable(self, tensor):
"""Returns `True` if and only if `tensor` is feedable."""
return tensor not in self._unfeedable_tensors
def prevent_fetching(self, op):
"""Marks the given `op` as unfetchable in this graph."""
self._unfetchable_ops.add(op)
def is_fetchable(self, tensor_or_op):
"""Returns `True` if and only if `tensor_or_op` is fetchable."""
if isinstance(tensor_or_op, Tensor):
return tensor_or_op.op not in self._unfetchable_ops
else:
return tensor_or_op not in self._unfetchable_ops
def device(device_name_or_function):
"""Wrapper for `Graph.device()` using the default graph.
See
[`Graph.device()`](../../api_docs/python/framework.md#Graph.device)
for more details.
Args:
device_name_or_function: The device name or function to use in
the context.
Returns:
A context manager that specifies the default device to use for newly
created ops.
"""
return get_default_graph().device(device_name_or_function)
def container(container_name):
"""Wrapper for `Graph.container()` using the default graph.
Args:
container_name: The container string to use in the context.
Returns:
A context manager that specifies the default container to use for newly
created stateful ops.
"""
return get_default_graph().container(container_name)
def colocate_with(op, ignore_existing=False):
return get_default_graph().colocate_with(op, ignore_existing)
def control_dependencies(control_inputs):
"""Wrapper for `Graph.control_dependencies()` using the default graph.
See [`Graph.control_dependencies()`](../../api_docs/python/framework.md#Graph.control_dependencies)
for more details.
Args:
control_inputs: A list of `Operation` or `Tensor` objects which
must be executed or computed before running the operations
defined in the context. Can also be `None` to clear the control
dependencies.
Returns:
A context manager that specifies control dependencies for all
operations constructed within the context.
"""
return get_default_graph().control_dependencies(control_inputs)
class _DefaultStack(threading.local):
"""A thread-local stack of objects for providing implicit defaults."""
def __init__(self):
super(_DefaultStack, self).__init__()
self._enforce_nesting = True
self.stack = []
def get_default(self):
return self.stack[-1] if len(self.stack) >= 1 else None
def reset(self):
self.stack = []
@property
def enforce_nesting(self):
return self._enforce_nesting
@enforce_nesting.setter
def enforce_nesting(self, value):
self._enforce_nesting = value
@contextlib.contextmanager
def get_controller(self, default):
"""A context manager for manipulating a default stack."""
try:
self.stack.append(default)
yield default
finally:
if self._enforce_nesting:
if self.stack[-1] is not default:
raise AssertionError(
"Nesting violated for default stack of %s objects"
% type(default))
self.stack.pop()
else:
self.stack.remove(default)
_default_session_stack = _DefaultStack()
def default_session(session):
"""Python "with" handler for defining a default session.
This function provides a means of registering a session for handling
Tensor.eval() and Operation.run() calls. It is primarily intended for use
by session.Session, but can be used with any object that implements
the Session.run() interface.
Use with the "with" keyword to specify that Tensor.eval() and Operation.run()
invocations within the scope of a block should be executed by a particular
session.
The default session applies to the current thread only, so it is always
possible to inspect the call stack and determine the scope of a default
session. If you create a new thread, and wish to use the default session
in that thread, you must explicitly add a "with ops.default_session(sess):"
block in that thread's function.
Example:
The following code examples are equivalent:
# 1. Using the Session object directly:
sess = ...
c = tf.constant(5.0)
sess.run(c)
# 2. Using default_session():
sess = ...
with ops.default_session(sess):
c = tf.constant(5.0)
result = c.eval()
# 3. Overriding default_session():
sess = ...
with ops.default_session(sess):
c = tf.constant(5.0)
with ops.default_session(...):
c.eval(session=sess)
Args:
session: The session to be installed as the default session.
Returns:
A context manager for the default session.
"""
return _default_session_stack.get_controller(session)
def get_default_session():
"""Returns the default session for the current thread.
The returned `Session` will be the innermost session on which a
`Session` or `Session.as_default()` context has been entered.
NOTE: The default session is a property of the current thread. If you
create a new thread, and wish to use the default session in that
thread, you must explicitly add a `with sess.as_default():` in that
thread's function.
Returns:
The default `Session` being used in the current thread.
"""
return _default_session_stack.get_default()
def _eval_using_default_session(tensors, feed_dict, graph, session=None):
"""Uses the default session to evaluate one or more tensors.
Args:
tensors: A single Tensor, or a list of Tensor objects.
feed_dict: A dictionary that maps Tensor objects (or tensor names) to lists,
numpy ndarrays, TensorProtos, or strings.
graph: The graph in which the tensors are defined.
session: (Optional) A different session to use to evaluate "tensors".
Returns:
Either a single numpy ndarray if "tensors" is a single tensor; or a list
of numpy ndarrays that each correspond to the respective element in
"tensors".
Raises:
ValueError: If no default session is available; the default session
does not have "graph" as its graph; or if "session" is specified,
and it does not have "graph" as its graph.
"""
if session is None:
session = get_default_session()
if session is None:
raise ValueError("Cannot evaluate tensor using `eval()`: No default "
"session is registered. Use `with "
"sess.as_default()` or pass an explicit session to "
"`eval(session=sess)`")
if session.graph is not graph:
raise ValueError("Cannot use the default session to evaluate tensor: "
"the tensor's graph is different from the session's "
"graph. Pass an explicit session to "
"`eval(session=sess)`.")
else:
if session.graph is not graph:
raise ValueError("Cannot use the given session to evaluate tensor: "
"the tensor's graph is different from the session's "
"graph.")
return session.run(tensors, feed_dict)
def _run_using_default_session(operation, feed_dict, graph, session=None):
"""Uses the default session to run "operation".
Args:
operation: The Operation to be run.
feed_dict: A dictionary that maps Tensor objects (or tensor names) to lists,
numpy ndarrays, TensorProtos, or strings.
graph: The graph in which "operation" is defined.
session: (Optional) A different session to use to run "operation".
Raises:
ValueError: If no default session is available; the default session
does not have "graph" as its graph; or if "session" is specified,
and it does not have "graph" as its graph.
"""
if session is None:
session = get_default_session()
if session is None:
raise ValueError("Cannot execute operation using `run()`: No default "
"session is registered. Use `with "
"sess.as_default():` or pass an explicit session to "
"`run(session=sess)`")
if session.graph is not graph:
raise ValueError("Cannot use the default session to execute operation: "
"the operation's graph is different from the "
"session's graph. Pass an explicit session to "
"run(session=sess).")
else:
if session.graph is not graph:
raise ValueError("Cannot use the given session to execute operation: "
"the operation's graph is different from the session's "
"graph.")
session.run(operation, feed_dict)
class _DefaultGraphStack(_DefaultStack):
"""A thread-local stack of objects for providing an implicit default graph."""
def __init__(self):
super(_DefaultGraphStack, self).__init__()
self._global_default_graph = None
def get_default(self):
"""Override that returns a global default if the stack is empty."""
ret = super(_DefaultGraphStack, self).get_default()
if ret is None:
ret = self._GetGlobalDefaultGraph()
return ret
def _GetGlobalDefaultGraph(self):
if self._global_default_graph is None:
# TODO(mrry): Perhaps log that the default graph is being used, or set
# provide some other feedback to prevent confusion when a mixture of
# the global default graph and an explicit graph are combined in the
# same process.
self._global_default_graph = Graph()
return self._global_default_graph
def reset(self):
super(_DefaultGraphStack, self).reset()
self._global_default_graph = None
_default_graph_stack = _DefaultGraphStack()
def reset_default_graph():
"""Clears the default graph stack and resets the global default graph.
NOTE: The default graph is a property of the current thread. This
function applies only to the current thread. Calling this function while
a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects
after calling this function will result in undefined behavior.
"""
_default_graph_stack.reset()
def get_default_graph():
"""Returns the default graph for the current thread.
The returned graph will be the innermost graph on which a
`Graph.as_default()` context has been entered, or a global default
graph if none has been explicitly created.
NOTE: The default graph is a property of the current thread. If you
create a new thread, and wish to use the default graph in that
thread, you must explicitly add a `with g.as_default():` in that
thread's function.
Returns:
The default `Graph` being used in the current thread.
"""
return _default_graph_stack.get_default()
def _assert_same_graph(original_item, item):
"""Fail if the 2 items are from different graphs.
Args:
original_item: Original item to check against.
item: Item to check.
Raises:
ValueError: if graphs do not match.
"""
if original_item.graph is not item.graph:
raise ValueError(
"%s must be from the same graph as %s." % (item, original_item))
def _get_graph_from_inputs(op_input_list, graph=None):
"""Returns the appropriate graph to use for the given inputs.
This library method provides a consistent algorithm for choosing the graph
in which an Operation should be constructed:
1. If the default graph is being used to construct a function, we
use the default graph.
2. If the "graph" is specified explicitly, we validate that all of the inputs
in "op_input_list" are compatible with that graph.
3. Otherwise, we attempt to select a graph from the first Operation-
or Tensor-valued input in "op_input_list", and validate that all other
such inputs are in the same graph.
4. If the graph was not specified and it could not be inferred from
"op_input_list", we attempt to use the default graph.
Args:
op_input_list: A list of inputs to an operation, which may include `Tensor`,
`Operation`, and other objects that may be converted to a graph element.
graph: (Optional) The explicit graph to use.
Raises:
TypeError: If op_input_list is not a list or tuple, or if graph is not a
Graph.
ValueError: If a graph is explicitly passed and not all inputs are from it,
or if the inputs are from multiple graphs, or we could not find a graph
and there was no default graph.
Returns:
The appropriate graph to use for the given inputs.
"""
if get_default_graph().building_function:
return get_default_graph()
op_input_list = tuple(op_input_list) # Handle generators correctly
if graph and not isinstance(graph, Graph):
raise TypeError("Input graph needs to be a Graph: %s" % graph)
# 1. We validate that all of the inputs are from the same graph. This is
# either the supplied graph parameter, or the first one selected from one
# the graph-element-valued inputs. In the latter case, we hold onto
# that input in original_graph_element so we can provide a more
# informative error if a mismatch is found.
original_graph_element = None
for op_input in op_input_list:
# Determine if this is a valid graph_element.
graph_element = None
if isinstance(op_input, (Operation, _TensorLike)):
graph_element = op_input
else:
graph_element = _as_graph_element(op_input)
if graph_element is not None:
if not graph:
original_graph_element = graph_element
graph = graph_element.graph
elif original_graph_element is not None:
_assert_same_graph(original_graph_element, graph_element)
elif graph_element.graph is not graph:
raise ValueError(
"%s is not from the passed-in graph." % graph_element)
# 2. If all else fails, we use the default graph, which is always there.
return graph or get_default_graph()
class GraphKeys(object):
"""Standard names to use for graph collections.
The standard library uses various well-known names to collect and
retrieve values associated with a graph. For example, the
`tf.Optimizer` subclasses default to optimizing the variables
collected under `tf.GraphKeys.TRAINABLE_VARIABLES` if none is
specified, but it is also possible to pass an explicit list of
variables.
The following standard keys are defined:
* `GLOBAL_VARIABLES`: the default collection of `Variable` objects, shared
across distributed environment (model variables are subset of these). See
[`tf.global_variables()`](../../api_docs/python/state_ops.md#global_variables)
for more details.
Commonly, all `TRAINABLE_VARIABLES` variables will be in `MODEL_VARIABLES`,
and all `MODEL_VARIABLES` variables will be in `GLOBAL_VARIABLES`.
* `LOCAL_VARIABLES`: the subset of `Variable` objects that are local to each
machine. Usually used for temporarily variables, like counters.
Note: use `tf.contrib.framework.local_variable` to add to this collection.
* `MODEL_VARIABLES`: the subset of `Variable` objects that are used in the
model for inference (feed forward). Note: use
`tf.contrib.framework.model_variable` to add to this collection.
* `TRAINABLE_VARIABLES`: the subset of `Variable` objects that will
be trained by an optimizer. See
[`tf.trainable_variables()`](../../api_docs/python/state_ops.md#trainable_variables)
for more details.
* `SUMMARIES`: the summary `Tensor` objects that have been created in the
graph. See
[`tf.summary.merge_all()`](../../api_docs/python/summary.md#merge_all)
for more details.
* `QUEUE_RUNNERS`: the `QueueRunner` objects that are used to
produce input for a computation. See
[`tf.start_queue_runners()`](../../api_docs/python/train.md#start_queue_runners)
for more details.
* `MOVING_AVERAGE_VARIABLES`: the subset of `Variable` objects that will also
keep moving averages. See
[`tf.moving_average_variables()`](../../api_docs/python/state_ops.md#moving_average_variables)
for more details.
* `REGULARIZATION_LOSSES`: regularization losses collected during graph
construction.
* `WEIGHTS`: weights inside neural network layers
* `BIASES`: biases inside neural network layers
* `ACTIVATIONS`: activations of neural network layers
"""
# Key to collect Variable objects that are global (shared across machines).
# Default collection for all variables, except local ones.
GLOBAL_VARIABLES = "variables"
# Key to collect local variables that are local to the machine and are not
# saved/restored.
LOCAL_VARIABLES = "local_variables"
# Key to collect model variables defined by layers.
MODEL_VARIABLES = "model_variables"
# Key to collect Variable objects that will be trained by the
# optimizers.
TRAINABLE_VARIABLES = "trainable_variables"
# Key to collect summaries.
SUMMARIES = "summaries"
# Key to collect QueueRunners.
QUEUE_RUNNERS = "queue_runners"
# Key to collect table initializers.
TABLE_INITIALIZERS = "table_initializer"
# Key to collect asset filepaths. An asset represents an external resource
# like a vocabulary file.
ASSET_FILEPATHS = "asset_filepaths"
# Key to collect Variable objects that keep moving averages.
MOVING_AVERAGE_VARIABLES = "moving_average_variables"
# Key to collect regularization losses at graph construction.
REGULARIZATION_LOSSES = "regularization_losses"
# Key to collect concatenated sharded variables.
CONCATENATED_VARIABLES = "concatenated_variables"
# Key to collect savers.
SAVERS = "savers"
# Key to collect weights
WEIGHTS = "weights"
# Key to collect biases
BIASES = "biases"
# Key to collect activations
ACTIVATIONS = "activations"
# Key to collect update_ops
UPDATE_OPS = "update_ops"
# Key to collect losses
LOSSES = "losses"
# Key to collect BaseSaverBuilder.SaveableObject instances for checkpointing.
SAVEABLE_OBJECTS = "saveable_objects"
# Key to collect all shared resources used by the graph which need to be
# initialized once per cluster.
RESOURCES = "resources"
# Key to collect all shared resources used in this graph which need to be
# initialized once per session.
LOCAL_RESOURCES = "local_resources"
# Trainable resource-style variables.
TRAINABLE_RESOURCE_VARIABLES = "trainable_resource_variables"
# Key to indicate various ops.
INIT_OP = "init_op"
LOCAL_INIT_OP = "local_init_op"
READY_OP = "ready_op"
READY_FOR_LOCAL_INIT_OP = "ready_for_local_init_op"
SUMMARY_OP = "summary_op"
GLOBAL_STEP = "global_step"
# Used to count the number of evaluations performed during a single evaluation
# run.
EVAL_STEP = "eval_step"
TRAIN_OP = "train_op"
# Key for control flow context.
COND_CONTEXT = "cond_context"
WHILE_CONTEXT = "while_context"
@decorator_utils.classproperty
def VARIABLES(cls): # pylint: disable=no-self-argument
logging.warning("VARIABLES collection name is deprecated, "
"please use GLOBAL_VARIABLES instead; "
"VARIABLES will be removed after 2017-03-02.")
return cls.GLOBAL_VARIABLES
def add_to_collection(name, value):
"""Wrapper for `Graph.add_to_collection()` using the default graph.
See [`Graph.add_to_collection()`](../../api_docs/python/framework.md#Graph.add_to_collection)
for more details.
Args:
name: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
value: The value to add to the collection.
"""
get_default_graph().add_to_collection(name, value)
def add_to_collections(names, value):
"""Wrapper for `Graph.add_to_collections()` using the default graph.
See [`Graph.add_to_collections()`](../../api_docs/python/framework.md#Graph.add_to_collections)
for more details.
Args:
names: The key for the collections. The `GraphKeys` class
contains many standard names for collections.
value: The value to add to the collections.
"""
get_default_graph().add_to_collections(names, value)
def get_collection_ref(key):
"""Wrapper for `Graph.get_collection_ref()` using the default graph.
See [`Graph.get_collection_ref()`](../../api_docs/python/framework.md#Graph.get_collection_ref)
for more details.
Args:
key: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
Returns:
The list of values in the collection with the given `name`, or an empty
list if no value has been added to that collection. Note that this returns
the collection list itself, which can be modified in place to change the
collection.
"""
return get_default_graph().get_collection_ref(key)
def get_collection(key, scope=None):
"""Wrapper for `Graph.get_collection()` using the default graph.
See [`Graph.get_collection()`](../../api_docs/python/framework.md#Graph.get_collection)
for more details.
Args:
key: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
scope: (Optional.) If supplied, the resulting list is filtered to include
only items whose `name` attribute matches using `re.match`. Items
without a `name` attribute are never returned if a scope is supplied and
the choice or `re.match` means that a `scope` without special tokens
filters by prefix.
Returns:
The list of values in the collection with the given `name`, or
an empty list if no value has been added to that collection. The
list contains the values in the order under which they were
collected.
"""
return get_default_graph().get_collection(key, scope)
def get_all_collection_keys():
"""Returns a list of collections used in the default graph."""
return get_default_graph().get_all_collection_keys()
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def name_scope(name, default_name=None, values=None):
"""Returns a context manager for use when defining a Python op.
This context manager validates that the given `values` are from the
same graph, makes that graph the default graph, and pushes a
name scope in that graph (see
[`Graph.name_scope()`](../../api_docs/python/framework.md#Graph.name_scope)
for more details on that).
For example, to define a new Python op called `my_op`:
```python
def my_op(a, b, c, name=None):
with tf.name_scope(name, "MyOp", [a, b, c]) as scope:
a = tf.convert_to_tensor(a, name="a")
b = tf.convert_to_tensor(b, name="b")
c = tf.convert_to_tensor(c, name="c")
# Define some computation that uses `a`, `b`, and `c`.
return foo_op(..., name=scope)
```
Args:
name: The name argument that is passed to the op function.
default_name: The default name to use if the `name` argument is `None`.
values: The list of `Tensor` arguments that are passed to the op function.
Returns:
A context manager for use in defining Python ops. Yields the name scope.
Raises:
ValueError: if neither `name` nor `default_name` is provided
but `values` are.
"""
n = default_name if name is None else name
if n is None and values is not None:
# We only raise an error if values is not None (provided) because currently
# tf.name_scope(None) (values=None then) is sometimes used as an idiom
# to reset to top scope.
raise ValueError(
"At least one of name (%s) and default_name (%s) must be provided." % (
name, default_name))
if values is None:
values = []
g = _get_graph_from_inputs(values)
with g.as_default(), g.name_scope(n) as scope:
yield scope
# pylint: enable=g-doc-return-or-yield
def strip_name_scope(name, export_scope):
"""Removes name scope from a name.
Args:
name: A `string` name.
export_scope: Optional `string`. Name scope to remove.
Returns:
Name with name scope removed, or the original name if export_scope
is None.
"""
if export_scope:
# Strips export_scope/, export_scope///,
# ^export_scope/, loc:@export_scope/.
str_to_replace = r"([\^]|loc:@|^)" + export_scope + r"[\/]+(.*)"
return re.sub(str_to_replace, r"\1\2", compat.as_str(name), count=1)
else:
return name
def prepend_name_scope(name, import_scope):
"""Prepends name scope to a name.
Args:
name: A `string` name.
import_scope: Optional `string`. Name scope to add.
Returns:
Name with name scope added, or the original name if import_scope
is None.
"""
if import_scope:
str_to_replace = r"([\^]|loc:@|^)(.*)"
return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
compat.as_str(name))
else:
return name
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def op_scope(values, name, default_name=None):
"""DEPRECATED. Same as name_scope above, just different argument order."""
logging.warn("tf.op_scope(values, name, default_name) is deprecated,"
" use tf.name_scope(name, default_name, values)")
with name_scope(name, default_name=default_name, values=values) as scope:
yield scope
_proto_function_registry = registry.Registry("proto functions")
def register_proto_function(collection_name, proto_type=None, to_proto=None,
from_proto=None):
"""Registers `to_proto` and `from_proto` functions for collection_name.
`to_proto` function converts a Python object to the corresponding protocol
buffer, and returns the protocol buffer.
`from_proto` function converts protocol buffer into a Python object, and
returns the object..
Args:
collection_name: Name of the collection.
proto_type: Protobuf type, such as `saver_pb2.SaverDef`,
`variable_pb2.VariableDef`, `queue_runner_pb2.QueueRunnerDef`..
to_proto: Function that implements Python object to protobuf conversion.
from_proto: Function that implements protobuf to Python object conversion.
"""
if to_proto and not callable(to_proto):
raise TypeError("to_proto must be callable.")
if from_proto and not callable(from_proto):
raise TypeError("from_proto must be callable.")
_proto_function_registry.register((proto_type, to_proto, from_proto),
collection_name)
def get_collection_proto_type(collection_name):
"""Returns the proto_type for collection_name."""
try:
return _proto_function_registry.lookup(collection_name)[0]
except LookupError:
return None
def get_to_proto_function(collection_name):
"""Returns the to_proto function for collection_name."""
try:
return _proto_function_registry.lookup(collection_name)[1]
except LookupError:
return None
def get_from_proto_function(collection_name):
"""Returns the from_proto function for collection_name."""
try:
return _proto_function_registry.lookup(collection_name)[2]
except LookupError:
return None
def _operation_conversion_error(op, dtype=None, name=None, as_ref=False):
"""Produce a nice error if someone converts an Operation to a Tensor."""
raise TypeError(
("Can't convert Operation '%s' to Tensor "
"(target dtype=%r, name=%r, as_ref=%r)") %
(op.name, dtype, name, as_ref))
register_tensor_conversion_function(Operation, _operation_conversion_error)
| 35.596871 | 101 | 0.679944 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import contextlib
import copy
import linecache
import re
import sys
import threading
import six
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.framework import versions_pb2
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import op_def_registry
from tensorflow.python.framework import registry
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import versions
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import compat
from tensorflow.python.util import decorator_utils
def _override_helper(clazz_object, operator, func):
existing = getattr(clazz_object, operator, None)
if existing is not None:
if not isinstance(existing, type(object.__lt__)):
raise ValueError("operator %s cannot be overwritten again on class %s." %
(operator, clazz_object))
if operator not in Tensor.OVERLOADABLE_OPERATORS:
raise ValueError("Overriding %s is disallowed" % operator)
setattr(clazz_object, operator, func)
def _convert_stack(stack):
ret = []
for filename, lineno, name, frame_globals in stack:
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, frame_globals)
if line:
line = line.strip()
else:
line = None
ret.append((filename, lineno, name, line))
return ret
def _extract_stack():
try:
raise ZeroDivisionError
except ZeroDivisionError:
f = sys.exc_info()[2].tb_frame.f_back
ret = []
while f is not None:
lineno = f.f_lineno
co = f.f_code
filename = co.co_filename
name = co.co_name
frame_globals = f.f_globals
ret.append((filename, lineno, name, frame_globals))
f = f.f_back
ret.reverse()
return ret
def _as_graph_element(obj):
conv_fn = getattr(obj, "_as_graph_element", None)
if conv_fn and callable(conv_fn):
return conv_fn()
return None
_TENSOR_LIKE_TYPES = tuple()
def is_dense_tensor_like(t):
return isinstance(t, _TENSOR_LIKE_TYPES)
def register_dense_tensor_like_type(tensor_type):
try:
if not isinstance(tensor_type.name, property):
raise TypeError("Type %s does not define a `name` property")
except AttributeError:
raise TypeError("Type %s does not define a `name` property")
try:
if not isinstance(tensor_type.dtype, property):
raise TypeError("Type %s does not define a `dtype` property")
except AttributeError:
raise TypeError("Type %s does not define a `dtype` property")
global _TENSOR_LIKE_TYPES
_TENSOR_LIKE_TYPES = tuple(list(_TENSOR_LIKE_TYPES) + [tensor_type])
class _TensorLike(object):
pass
class Tensor(_TensorLike):
OVERLOADABLE_OPERATORS = {
"__add__",
"__radd__",
"__sub__",
"__rsub__",
"__mul__",
"__rmul__",
"__div__",
"__rdiv__",
"__truediv__",
"__rtruediv__",
"__floordiv__",
"__rfloordiv__",
"__mod__",
"__rmod__",
"__lt__",
"__le__",
"__gt__",
"__ge__",
"__and__",
"__rand__",
"__or__",
"__ror__",
"__xor__",
"__rxor__",
"__getitem__",
"__pow__",
"__rpow__",
"__invert__",
"__neg__",
"__abs__"
}
def __init__(self, op, value_index, dtype):
if not isinstance(op, Operation):
raise TypeError("op needs to be an Operation: %s" % op)
self._op = op
self._value_index = value_index
self._dtype = dtypes.as_dtype(dtype)
self._shape = tensor_shape.unknown_shape()
self._consumers = []
self._handle_shape = tensor_shape_pb2.TensorShapeProto()
self._handle_dtype = types_pb2.DT_INVALID
@property
def op(self):
return self._op
@property
def dtype(self):
return self._dtype
@property
def graph(self):
return self._op.graph
@property
def name(self):
if not self._op.name:
raise ValueError("Operation was not named: %s" % self._op)
return "%s:%d" % (self._op.name, self._value_index)
@property
def device(self):
return self._op.device
@property
def shape(self):
return self._shape
def _shape_as_list(self):
if self._shape.ndims is not None:
return [dim.value for dim in self._shape.dims]
else:
return None
def get_shape(self):
return self.shape
def set_shape(self, shape):
self._shape = self._shape.merge_with(shape)
@property
def value_index(self):
return self._value_index
def consumers(self):
return self._consumers
def _add_consumer(self, consumer):
if not isinstance(consumer, Operation):
raise TypeError("Consumer must be an Operation: %s" % consumer)
self._consumers.append(consumer)
def _as_node_def_input(self):
if not self._op.name:
raise ValueError("Operation was not named: %s" % self._op)
if self._value_index == 0:
return self._op.name
else:
return "%s:%d" % (self._op.name, self._value_index)
def __str__(self):
return "Tensor(\"%s\"%s%s%s)" % (
self.name,
(", shape=%s" % self.get_shape())
if self.get_shape().ndims is not None else "",
(", dtype=%s" % self._dtype.name) if self._dtype else "",
(", device=%s" % self.device) if self.device else "")
def __repr__(self):
return "<tf.Tensor '%s' shape=%s dtype=%s>" % (
self.name, self.get_shape(), self._dtype.name)
def __hash__(self):
return id(self)
def __eq__(self, other):
# Necessary to support Python's collection membership operators
return id(self) == id(other)
# operators to run when the left operand is an ndarray, because it
# accords the Tensor class higher priority than an ndarray, or a
# numpy matrix.
# TODO(mrry): Convert this to using numpy's __numpy_ufunc__
__array_priority__ = 100
@staticmethod
def _override_operator(operator, func):
_override_helper(Tensor, operator, func)
def __iter__(self):
raise TypeError("'Tensor' object is not iterable.")
def __bool__(self):
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
"Use `if t is not None:` instead of `if t:` to test if a "
"tensor is defined, and use TensorFlow ops such as "
"tf.cond to execute subgraphs conditioned on the value of "
"a tensor.")
def __nonzero__(self):
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
"Use `if t is not None:` instead of `if t:` to test if a "
"tensor is defined, and use TensorFlow ops such as "
"tf.cond to execute subgraphs conditioned on the value of "
"a tensor.")
def eval(self, feed_dict=None, session=None):
return _eval_using_default_session(self, feed_dict, self.graph, session)
def _TensorTensorConversionFunction(t, dtype=None, name=None, as_ref=False):
_ = name, as_ref
if dtype and not dtype.is_compatible_with(t.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
% (dtype.name, t.dtype.name, str(t)))
return t
_tensor_conversion_func_registry = {
0: [(Tensor, _TensorTensorConversionFunction)]}
register_dense_tensor_like_type(Tensor)
def convert_to_tensor(value,
dtype=None,
name=None,
preferred_dtype=None):
return internal_convert_to_tensor(
value=value,
dtype=dtype,
name=name,
preferred_dtype=preferred_dtype,
as_ref=False)
def internal_convert_to_tensor(value,
dtype=None,
name=None,
as_ref=False,
preferred_dtype=None):
error_prefix = "" if name is None else "%s: " % name
if dtype is not None:
dtype = dtypes.as_dtype(dtype)
for _, funcs_at_priority in sorted(_tensor_conversion_func_registry.items()):
for base_type, conversion_func in funcs_at_priority:
if isinstance(value, base_type):
ret = None
if dtype is None and preferred_dtype is not None:
try:
ret = conversion_func(
value, dtype=preferred_dtype, name=name, as_ref=as_ref)
except (TypeError, ValueError):
ret = None
if ret is not None and ret is not NotImplemented:
if (ret.dtype.base_dtype !=
dtypes.as_dtype(preferred_dtype).base_dtype):
raise TypeError("convert_to_tensor did not convert to "
"the preferred dtype: %s vs %s " %
(ret.dtype.base_dtype,
dtypes.as_dtype(preferred_dtype).base_dtype))
if ret is None:
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
if ret is NotImplemented:
continue
if not isinstance(ret, Tensor):
raise RuntimeError(
"%sConversion function %r for type %s returned non-Tensor: %r"
% (error_prefix, conversion_func, base_type, ret))
if dtype and not dtype.is_compatible_with(ret.dtype):
raise RuntimeError(
"%sConversion function %r for type %s returned incompatible "
"dtype: requested = %s, actual = %s"
% (error_prefix, conversion_func, base_type,
dtype.name, ret.dtype.name))
return ret
raise TypeError("%sCannot convert %r with type %s to Tensor: "
"no conversion function registered."
% (error_prefix, value, type(value)))
def internal_convert_n_to_tensor(values,
dtype=None,
name=None,
as_ref=False,
preferred_dtype=None):
if not isinstance(values, collections.Sequence):
raise TypeError("values must be a list.")
ret = []
for i, value in enumerate(values):
n = None if name is None else "%s_%d" % (name, i)
ret.append(
internal_convert_to_tensor(
value,
dtype=dtype,
name=n,
as_ref=as_ref,
preferred_dtype=preferred_dtype))
return ret
def convert_n_to_tensor(values,
dtype=None,
name=None,
preferred_dtype=None):
return internal_convert_n_to_tensor(values=values,
dtype=dtype,
name=name,
preferred_dtype=preferred_dtype,
as_ref=False)
def convert_to_tensor_or_indexed_slices(value, dtype=None, name=None):
return internal_convert_to_tensor_or_indexed_slices(
value=value, dtype=dtype, name=name, as_ref=False)
def internal_convert_to_tensor_or_indexed_slices(value, dtype=None, name=None,
as_ref=False):
if isinstance(value, _TensorLike):
if dtype and not dtypes.as_dtype(dtype).is_compatible_with(value.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
% (dtypes.as_dtype(dtype).name, value.dtype.name, str(value)))
return value
else:
return internal_convert_to_tensor(value,
dtype=dtype,
name=name,
as_ref=as_ref)
def internal_convert_n_to_tensor_or_indexed_slices(values, dtype=None,
name=None, as_ref=False):
if not isinstance(values, collections.Sequence):
raise TypeError("values must be a list.")
ret = []
for i, value in enumerate(values):
if value is None:
ret.append(value)
else:
n = None if name is None else "%s_%d" % (name, i)
ret.append(
internal_convert_to_tensor_or_indexed_slices(
value, dtype=dtype, name=n, as_ref=as_ref))
return ret
def convert_n_to_tensor_or_indexed_slices(values, dtype=None, name=None):
return internal_convert_n_to_tensor_or_indexed_slices(
values=values, dtype=dtype, name=name, as_ref=False)
def register_tensor_conversion_function(base_type, conversion_func,
priority=100):
if not (isinstance(base_type, type) or
(isinstance(base_type, tuple)
and all(isinstance(x, type) for x in base_type))):
raise TypeError("base_type must be a type or a tuple of types.")
if not callable(conversion_func):
raise TypeError("conversion_func must be callable.")
try:
funcs_at_priority = _tensor_conversion_func_registry[priority]
except KeyError:
funcs_at_priority = []
_tensor_conversion_func_registry[priority] = funcs_at_priority
funcs_at_priority.append((base_type, conversion_func))
class IndexedSlices(_TensorLike):
def __init__(self, values, indices, dense_shape=None):
_get_graph_from_inputs([values, indices, dense_shape])
self._values = values
self._indices = indices
self._dense_shape = dense_shape
@property
def values(self):
return self._values
@property
def indices(self):
return self._indices
@property
def dense_shape(self):
return self._dense_shape
@property
def name(self):
return self.values.name
@property
def device(self):
return self.values.device
@property
def op(self):
return self.values.op
@property
def dtype(self):
return self.values.dtype
@property
def graph(self):
return self._values.graph
def __str__(self):
return "IndexedSlices(indices=%s, values=%s%s)" % (
self._indices, self._values,
(", dense_shape=%s" % self._dense_shape)
if self._dense_shape is not None else "")
def __neg__(self):
return IndexedSlices(-self.values, self.indices, self.dense_shape)
IndexedSlicesValue = collections.namedtuple(
"IndexedSlicesValue", ["values", "indices", "dense_shape"])
def _device_string(dev_spec):
if isinstance(dev_spec, pydev.DeviceSpec):
return dev_spec.to_string()
else:
return dev_spec
def _NodeDef(op_type, name, device=None, attrs=None):
node_def = node_def_pb2.NodeDef()
node_def.op = compat.as_bytes(op_type)
node_def.name = compat.as_bytes(name)
if attrs is not None:
for k, v in six.iteritems(attrs):
node_def.attr[k].CopyFrom(v)
if device is not None:
if callable(device):
node_def.device = device(node_def)
else:
node_def.device = _device_string(device)
return node_def
_VALID_OP_NAME_REGEX = re.compile("^[A-Za-z0-9.][A-Za-z0-9_.\\-/]*$")
_VALID_SCOPE_NAME_REGEX = re.compile("^[A-Za-z0-9_.\\-/]*$")
class Operation(object):
def __init__(self, node_def, g, inputs=None, output_types=None,
control_inputs=None, input_types=None, original_op=None,
op_def=None):
if not isinstance(node_def, node_def_pb2.NodeDef):
raise TypeError("node_def needs to be a NodeDef: %s" % node_def)
if node_def.ByteSize() >= (1 << 31) or node_def.ByteSize() < 0:
raise ValueError(
"Cannot create a tensor proto whose content is larger than 2GB.")
if not _VALID_OP_NAME_REGEX.match(node_def.name):
raise ValueError("'%s' is not a valid node name" % node_def.name)
if not isinstance(g, Graph):
raise TypeError("g needs to be a Graph: %s" % g)
self._node_def = copy.deepcopy(node_def)
self._graph = g
if inputs is None:
inputs = []
elif not isinstance(inputs, list):
raise TypeError("inputs needs to be a list of Tensors: %s" % inputs)
self._inputs = list(inputs)
for a in self._inputs:
if not isinstance(a, Tensor):
raise TypeError("input needs to be a Tensor: %s" % a)
a._add_consumer(self)
if output_types is None:
output_types = []
self._output_types = output_types
self._outputs = [Tensor(self, i, output_type)
for i, output_type in enumerate(output_types)]
if input_types is None:
input_types = [i.dtype.base_dtype for i in self._inputs]
else:
if not all(x.is_compatible_with(i.dtype)
for i, x in zip(self._inputs, input_types)):
raise TypeError("Inputs are not compatible with input types")
self._input_types = input_types
self._control_inputs = []
if control_inputs:
for c in control_inputs:
c_op = None
if isinstance(c, Operation):
c_op = c
elif isinstance(c, (Tensor, IndexedSlices)):
c_op = c.op
else:
raise TypeError("Control input must be an Operation, "
"a Tensor, or IndexedSlices: %s" % c)
self._control_inputs.append(c_op)
self._original_op = original_op
self._op_def = op_def
self._traceback = _extract_stack()
self._control_flow_context = g._get_control_flow_context()
if self._control_flow_context is not None:
self._control_flow_context.AddOp(self)
# setting op.inputs[index] = new_op. Thus the new ops' id could be larger
# assigning id to this op until all ops this could be dependent on are
# created.
self._id_value = self._graph._next_id() # pylint: disable=protected-access
self._recompute_node_def()
def colocation_groups(self):
default_colocation_group = [compat.as_bytes("loc:@%s" %
self._node_def.name)]
if "_class" not in self._node_def.attr:
# This op has no explicit colocation group, so it is itself its
# own root of a colocation group.
return default_colocation_group
attr_groups = [class_name
for class_name in self.get_attr("_class")
if class_name.startswith(b"loc:@")]
# If there are no colocation groups in the explicit _class field,
# return the default colocation group.
return attr_groups if attr_groups else default_colocation_group
def values(self):
return tuple(self.outputs)
def _get_control_flow_context(self):
return self._control_flow_context
def _set_control_flow_context(self, context):
self._control_flow_context = context
@property
def name(self):
return self._node_def.name
@property
def _id(self):
return self._id_value
@property
def device(self):
return self._node_def.device
def _set_device(self, device):
self._node_def.device = _device_string(device)
def _add_input(self, tensor, dtype=None):
if not isinstance(tensor, Tensor):
raise TypeError("tensor must be a Tensor: %s" % tensor)
_assert_same_graph(self, tensor)
if dtype is None:
dtype = tensor.dtype
else:
dtype = dtypes.as_dtype(dtype)
if not dtype.is_compatible_with(tensor.dtype):
raise TypeError(
"Cannot convert a tensor of type %s to an input of type %s"
% (tensor.dtype.name, dtype.name))
self._inputs.append(tensor)
self._input_types.append(dtype)
tensor._add_consumer(self) # pylint: disable=protected-access
self._recompute_node_def()
def _update_input(self, index, tensor, dtype=None):
if not isinstance(tensor, Tensor):
raise TypeError("tensor must be a Tensor: %s" % tensor)
_assert_same_graph(self, tensor)
if dtype is None:
dtype = tensor.dtype
else:
dtype = dtypes.as_dtype(dtype)
if not dtype.is_compatible_with(tensor.dtype):
raise TypeError(
"Cannot convert a tensor of type %s to an input of type %s"
% (tensor.dtype.name, dtype.name))
self._inputs[index].consumers().remove(self)
self._inputs[index] = tensor
self._input_types[index] = dtype
tensor._add_consumer(self) # pylint: disable=protected-access
self._recompute_node_def()
def _add_control_inputs(self, ops):
if ops:
for op in ops:
if not isinstance(op, Operation):
raise TypeError("op must be an Operation: %s" % op)
_assert_same_graph(self, op)
self._control_inputs.append(op)
self._recompute_node_def()
def _add_control_input(self, op):
self._add_control_inputs([op])
# Methods below are used when building the NodeDef and Graph proto.
def _recompute_node_def(self):
del self._node_def.input[:]
self._node_def.input.extend([t._as_node_def_input() for t in self._inputs])
if self._control_inputs:
self._node_def.input.extend(["^%s" % op.name for op in
self._control_inputs])
def __str__(self):
return str(self._node_def)
def __repr__(self):
return "<tf.Operation '%s' type=%s>" % (self.name, self.type)
@property
def outputs(self):
return self._outputs
# pylint: disable=protected-access
class _InputList(object):
def __init__(self, op):
self._op = op
def __iter__(self):
return iter(self._op._inputs)
def __len__(self):
return len(self._op._inputs)
def __bool__(self):
return bool(self._op._inputs)
# Python 3 wants __bool__, Python 2.7 wants __nonzero__
__nonzero__ = __bool__
def __getitem__(self, i):
return self._op._inputs[i]
# pylint: enable=protected-access
@property
def inputs(self):
return Operation._InputList(self)
@property
def _input_dtypes(self):
return self._input_types
@property
def control_inputs(self):
return self._control_inputs
@property
def type(self):
return self._node_def.op
@property
def graph(self):
return self._graph
@property
def node_def(self):
return self._node_def
@property
def op_def(self):
return self._op_def
@property
def traceback(self):
return _convert_stack(self._traceback)
def get_attr(self, name):
fields = ["s", "i", "f", "b", "type", "shape", "tensor"]
if name not in self._node_def.attr:
raise ValueError("No attr named '" + name + "' in " +
str(self._node_def))
x = self._node_def.attr[name]
# Treat an empty oneof value as an empty list.
if not x.WhichOneof("value"):
return []
if x.HasField("list"):
for f in fields:
if getattr(x.list, f):
return list(getattr(x.list, f))
return []
else:
for f in fields:
if x.HasField(f):
return getattr(x, f)
assert False, "Unsupported field type in " + str(x)
def run(self, feed_dict=None, session=None):
_run_using_default_session(self, feed_dict, self.graph, session)
_gradient_registry = registry.Registry("gradient")
class RegisterGradient(object):
def __init__(self, op_type):
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string")
self._op_type = op_type
def __call__(self, f):
_gradient_registry.register(f, self._op_type)
return f
def NotDifferentiable(op_type):
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string")
_gradient_registry.register(None, op_type)
# Alias for the old name, will be eventually removed.
NoGradient = NotDifferentiable
def get_gradient_function(op):
if not op.inputs: return None
try:
op_type = op.get_attr("_gradient_op_type")
except ValueError:
op_type = op.type
return _gradient_registry.lookup(op_type)
_shape_registry = registry.Registry("shape functions")
_default_shape_function_registry = registry.Registry("default shape functions")
# These are set to common_shapes.call_cpp_shape_fn by op generated code
# (generated by python_op_gen.cc).
# It is set outside ops.py to avoid a circular dependency.
_call_cpp_shape_fn = None
_call_cpp_shape_fn_and_require_op = None
def _set_call_cpp_shape_fn(call_cpp_shape_fn):
global _call_cpp_shape_fn, _call_cpp_shape_fn_and_require_op
if _call_cpp_shape_fn:
return # already registered
def call_without_requiring(op):
return call_cpp_shape_fn(op, require_shape_fn=False)
_call_cpp_shape_fn = call_without_requiring
def call_with_requiring(op):
return call_cpp_shape_fn(op, require_shape_fn=True)
_call_cpp_shape_fn_and_require_op = call_with_requiring
class RegisterShape(object):
def __init__(self, op_type):
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string")
self._op_type = op_type
def __call__(self, f):
if f is None:
assert _call_cpp_shape_fn
# None is a special "weak" value that provides a default shape function,
# and can be overridden by a non-None registration.
try:
_default_shape_function_registry.register(_call_cpp_shape_fn,
self._op_type)
except KeyError:
# Ignore duplicate registrations of the weak value. This can
# occur if the op library input to wrapper generation
# inadvertently links in one or more of the standard op
# libraries.
pass
else:
_shape_registry.register(f, self._op_type)
return f
def set_shapes_for_outputs(op):
try:
shape_func = _shape_registry.lookup(op.type)
except LookupError:
try:
shape_func = _default_shape_function_registry.lookup(op.type)
except LookupError:
shape_func = _call_cpp_shape_fn_and_require_op
shapes = shape_func(op)
if shapes is None:
raise RuntimeError(
"Shape function for op %s did not return any shapes" % op)
elif isinstance(shapes, dict):
# Returned by call_cpp_shape_fn
shapes_dict = shapes
shapes = shapes_dict["shapes"]
handle_shapes = shapes_dict["handle_shapes"]
handle_dtypes = shapes_dict["handle_dtypes"]
for output, handle_shape, handle_dtype in zip(op.outputs, handle_shapes, handle_dtypes):
# pylint: disable=protected-access
output._handle_shape = handle_shape
output._handle_dtype = handle_dtype
# pylint: enable=protected-access
if len(op.outputs) != len(shapes):
raise RuntimeError(
"Shape function for op %s returned %d shapes but expected %d %s %s" %
(op, len(shapes), len(op.outputs), shape_func.__name__, str(shapes)))
for output, s in zip(op.outputs, shapes):
output.set_shape(s)
class OpStats(object):
def __init__(self, statistic_type, value=None):
self.statistic_type = statistic_type
self.value = value
@property
def statistic_type(self):
return self._statistic_type
@statistic_type.setter
def statistic_type(self, statistic_type):
self._statistic_type = statistic_type
@property
def value(self):
return self._value
@value.setter
def value(self, value):
self._value = value
def __iadd__(self, other):
if other.statistic_type != self.statistic_type:
raise ValueError("Can't add an OpStat of type %s to one of %s.",
self.statistic_type, other.statistic_type)
if self.value is None:
self.value = other.value
elif other.value is not None:
self._value += other.value
return self
_stats_registry = registry.Registry("statistical functions")
class RegisterStatistics(object):
def __init__(self, op_type, statistic_type):
if not isinstance(op_type, six.string_types):
raise TypeError("op_type must be a string.")
if "," in op_type:
raise TypeError("op_type must not contain a comma.")
self._op_type = op_type
if not isinstance(statistic_type, six.string_types):
raise TypeError("statistic_type must be a string.")
if "," in statistic_type:
raise TypeError("statistic_type must not contain a comma.")
self._statistic_type = statistic_type
def __call__(self, f):
_stats_registry.register(f, self._op_type + "," + self._statistic_type)
return f
def get_stats_for_node_def(graph, node, statistic_type):
try:
stats_func = _stats_registry.lookup(node.op + "," + statistic_type)
result = stats_func(graph, node)
except LookupError:
result = OpStats(statistic_type)
return result
def _name_from_scope_name(name):
return name[:-1] if name[-1] == "/" else name
class Graph(object):
def __init__(self):
self._lock = threading.Lock()
self._nodes_by_id = dict()
self._next_id_counter = 0
self._nodes_by_name = dict()
self._version = 0
self._name_stack = ""
self._names_in_use = {}
self._device_function_stack = []
self._default_original_op = None
self._control_flow_context = None
self._control_dependencies_stack = []
self._collections = {}
self._seed = None
self._attr_scope_map = {}
self._op_to_kernel_label_map = {}
self._gradient_override_map = {}
self._finalized = False
self._functions = collections.OrderedDict()
self._graph_def_versions = versions_pb2.VersionDef(
producer=versions.GRAPH_DEF_VERSION,
min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER)
self._building_function = False
self._colocation_stack = []
self._unfeedable_tensors = set()
self._unfetchable_ops = set()
self._handle_feeders = {}
self._handle_readers = {}
self._handle_movers = {}
self._handle_deleters = {}
self._container = ""
self._registered_ops = op_def_registry.get_registered_ops()
def _check_not_finalized(self):
if self._finalized:
raise RuntimeError("Graph is finalized and cannot be modified.")
def _add_op(self, op):
self._check_not_finalized()
if not isinstance(op, (Tensor, Operation)):
raise TypeError("op must be a Tensor or Operation: %s" % op)
with self._lock:
if op._id in self._nodes_by_id:
raise ValueError("cannot add an op with id %d as it already "
"exists in the graph" % op._id)
if op.name in self._nodes_by_name:
raise ValueError("cannot add op with name %s as that name "
"is already used" % op.name)
self._nodes_by_id[op._id] = op
self._nodes_by_name[op.name] = op
self._version = max(self._version, op._id)
@property
def version(self):
if self._finalized:
return self._version
with self._lock:
return self._version
@property
def graph_def_versions(self):
return self._graph_def_versions
@property
def seed(self):
return self._seed
@seed.setter
def seed(self, seed):
self._seed = seed
@property
def finalized(self):
return self._finalized
def finalize(self):
self._finalized = True
def _unsafe_unfinalize(self):
self._finalized = False
def _get_control_flow_context(self):
return self._control_flow_context
def _set_control_flow_context(self, context):
self._control_flow_context = context
def _as_graph_def(self, from_version=None, add_shapes=False):
with self._lock:
graph = graph_pb2.GraphDef()
graph.versions.CopyFrom(self._graph_def_versions)
bytesize = 0
for op_id in sorted(self._nodes_by_id):
op = self._nodes_by_id[op_id]
if from_version is None or op_id > from_version:
graph.node.extend([op.node_def])
if op.outputs and add_shapes:
assert "_output_shapes" not in graph.node[-1].attr
graph.node[-1].attr["_output_shapes"].list.shape.extend([
output.get_shape().as_proto() for output in op.outputs])
bytesize += op.node_def.ByteSize()
if bytesize >= (1 << 31) or bytesize < 0:
raise ValueError("GraphDef cannot be larger than 2GB.")
if self._functions:
for f in self._functions.values():
bytesize += f.definition.ByteSize()
if bytesize >= (1 << 31) or bytesize < 0:
raise ValueError("GraphDef cannot be larger than 2GB.")
graph.library.function.extend([f.definition])
if f.grad_func_name:
grad_def = function_pb2.GradientDef()
grad_def.function_name = f.name
grad_def.gradient_func = f.grad_func_name
graph.library.gradient.extend([grad_def])
return graph, self._version
def as_graph_def(self, from_version=None, add_shapes=False):
result, _ = self._as_graph_def(from_version, add_shapes)
return result
def _is_function(self, name):
return name in self._functions
def _get_function(self, name):
return self._functions.get(name, None)
def _add_function(self, function):
name = function.name
previous = self._functions.get(name, None)
if previous:
raise ValueError("Another function is already defined with that name")
if (function.grad_func_name is not None) and (
function.python_grad_func is not None):
raise ValueError("Gradient defined twice for function %s" % name)
if self._graph_def_versions.min_consumer < 12:
self._graph_def_versions.min_consumer = 12
self._functions[name] = function
@property
def building_function(self):
return self._building_function
def create_op(self, op_type, inputs, dtypes,
input_types=None, name=None, attrs=None, op_def=None,
compute_shapes=True, compute_device=True):
self._check_not_finalized()
for idx, a in enumerate(inputs):
if not isinstance(a, Tensor):
raise TypeError("Input #%d is not a tensor: %s" % (idx, a))
if name is None:
name = op_type
if name and name[-1] == "/":
name = _name_from_scope_name(name)
else:
name = self.unique_name(name)
node_def = _NodeDef(op_type, name, device=None, attrs=attrs)
for key, value in self._attr_scope_map.items():
if key not in node_def.attr:
if callable(value):
value = value(node_def)
if not isinstance(value, (type(None), attr_value_pb2.AttrValue)):
raise TypeError(
"Callable for scope map key '%s' must return either None or "
"an AttrValue protocol buffer; but it returned: %s" %
(key, value))
node_def.attr[key].CopyFrom(value)
try:
kernel_label = self._op_to_kernel_label_map[op_type]
node_def.attr["_kernel"].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(kernel_label)))
except KeyError:
pass
try:
mapped_op_type = self._gradient_override_map[op_type]
node_def.attr["_gradient_op_type"].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(mapped_op_type)))
except KeyError:
pass
control_inputs = self._control_dependencies_for_inputs(inputs)
ret = Operation(node_def, self, inputs=inputs, output_types=dtypes,
control_inputs=control_inputs, input_types=input_types,
original_op=self._default_original_op, op_def=op_def)
if compute_shapes:
set_shapes_for_outputs(ret)
self._add_op(ret)
self._record_op_seen_by_control_dependencies(ret)
if compute_device:
self._apply_device_functions(ret)
if self._colocation_stack:
all_colocation_groups = []
for colocation_op in self._colocation_stack:
all_colocation_groups.extend(colocation_op.colocation_groups())
if colocation_op.device:
if ret.device and ret.device != colocation_op.device:
logging.warning("Tried to colocate %s with an op %s that had "
"a different device: %s vs %s. "
"Ignoring colocation property.",
name, colocation_op.name,
ret.device, colocation_op.device)
else:
ret._set_device(colocation_op.device)
all_colocation_groups = sorted(set(all_colocation_groups))
ret.node_def.attr["_class"].CopyFrom(attr_value_pb2.AttrValue(
list=attr_value_pb2.AttrValue.ListValue(s=all_colocation_groups)))
if (self._container and
op_type in self._registered_ops and
self._registered_ops[op_type].is_stateful and
"container" in ret.node_def.attr and
not ret.node_def.attr["container"].s):
ret.node_def.attr["container"].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(self._container)))
return ret
def as_graph_element(self, obj, allow_tensor=True, allow_operation=True):
if self._finalized:
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
with self._lock:
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):
if allow_tensor and allow_operation:
types_str = "Tensor or Operation"
elif allow_tensor:
types_str = "Tensor"
elif allow_operation:
types_str = "Operation"
else:
raise ValueError("allow_tensor and allow_operation can't both be False.")
temp_obj = _as_graph_element(obj)
if temp_obj is not None:
obj = temp_obj
# If obj appears to be a name...
if isinstance(obj, compat.bytes_or_text_types):
name = compat.as_str(obj)
if ":" in name and allow_tensor:
# Looks like a Tensor name and can be a Tensor.
try:
op_name, out_n = name.split(":")
out_n = int(out_n)
except:
raise ValueError("The name %s looks a like a Tensor name, but is "
"not a valid one. Tensor names must be of the "
"form \"<op_name>:<output_index>\"." % repr(name))
if op_name in self._nodes_by_name:
op = self._nodes_by_name[op_name]
else:
raise KeyError("The name %s refers to a Tensor which does not "
"exist. The operation, %s, does not exist in the "
"graph." % (repr(name), repr(op_name)))
try:
return op.outputs[out_n]
except:
raise KeyError("The name %s refers to a Tensor which does not "
"exist. The operation, %s, exists but only has "
"%s outputs."
% (repr(name), repr(op_name), len(op.outputs)))
elif ":" in name and not allow_tensor:
# Looks like a Tensor name but can't be a Tensor.
raise ValueError("Name %s appears to refer to a Tensor, not a %s."
% (repr(name), types_str))
elif ":" not in name and allow_operation:
if name not in self._nodes_by_name:
raise KeyError("The name %s refers to an Operation not in the "
"graph." % repr(name))
return self._nodes_by_name[name]
elif ":" not in name and not allow_operation:
if name in self._nodes_by_name:
# Yep, it's an Operation name
err_msg = ("The name %s refers to an Operation, not a %s."
% (repr(name), types_str))
else:
err_msg = ("The name %s looks like an (invalid) Operation name, "
"not a %s." % (repr(name), types_str))
err_msg += (" Tensor names must be of the form "
"\"<op_name>:<output_index>\".")
raise ValueError(err_msg)
elif isinstance(obj, Tensor) and allow_tensor:
if obj.graph is not self:
raise ValueError("Tensor %s is not an element of this graph." % obj)
return obj
elif isinstance(obj, Operation) and allow_operation:
# Actually obj is just the object it's referring to.
if obj.graph is not self:
raise ValueError("Operation %s is not an element of this graph." % obj)
return obj
else:
raise TypeError("Can not convert a %s into a %s."
% (type(obj).__name__, types_str))
def get_operations(self):
if self._finalized:
return list(self._nodes_by_id.values())
with self._lock:
return list(self._nodes_by_id.values())
def get_operation_by_name(self, name):
if not isinstance(name, six.string_types):
raise TypeError("Operation names are strings (or similar), not %s."
% type(name).__name__)
return self.as_graph_element(name, allow_tensor=False, allow_operation=True)
def get_tensor_by_name(self, name):
if not isinstance(name, six.string_types):
raise TypeError("Tensor names are strings (or similar), not %s."
% type(name).__name__)
return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
def _next_id(self):
self._check_not_finalized()
with self._lock:
self._next_id_counter += 1
return self._next_id_counter
@property
def _last_id(self):
return self._next_id_counter
def as_default(self):
return _default_graph_stack.get_controller(self)
def add_to_collection(self, name, value):
self._check_not_finalized()
with self._lock:
if name not in self._collections:
self._collections[name] = [value]
else:
self._collections[name].append(value)
def add_to_collections(self, names, value):
names = (names,) if isinstance(names, six.string_types) else set(names)
for name in names:
self.add_to_collection(name, value)
def get_collection_ref(self, name):
with self._lock:
coll_list = self._collections.get(name, None)
if coll_list is None:
coll_list = []
self._collections[name] = coll_list
return coll_list
def get_collection(self, name, scope=None):
with self._lock:
coll_list = self._collections.get(name, None)
if coll_list is None:
return []
if scope is None:
return list(coll_list)
else:
c = []
regex = re.compile(scope)
for item in coll_list:
if hasattr(item, "name") and regex.match(item.name):
c.append(item)
return c
def get_all_collection_keys(self):
with self._lock:
return [x for x in self._collections if isinstance(x, six.string_types)]
def clear_collection(self, name):
self._check_not_finalized()
with self._lock:
if name in self._collections:
del self._collections[name]
@contextlib.contextmanager
def _original_op(self, op):
old_original_op = self._default_original_op
try:
self._default_original_op = op
yield
finally:
self._default_original_op = old_original_op
@contextlib.contextmanager
def name_scope(self, name):
if name:
if self._name_stack:
if not _VALID_SCOPE_NAME_REGEX.match(name):
raise ValueError("'%s' is not a valid scope name" % name)
else:
if not _VALID_OP_NAME_REGEX.match(name):
raise ValueError("'%s' is not a valid scope name" % name)
try:
old_stack = self._name_stack
if not name:
new_stack = None
elif name and name[-1] == "/":
new_stack = _name_from_scope_name(name)
else:
new_stack = self.unique_name(name)
self._name_stack = new_stack
yield "" if new_stack is None else new_stack + "/"
finally:
self._name_stack = old_stack
def unique_name(self, name, mark_as_used=True):
if self._name_stack:
name = self._name_stack + "/" + name
i = self._names_in_use.get(name, 0)
if mark_as_used:
self._names_in_use[name] = i + 1
if i > 0:
base_name = name
while name in self._names_in_use:
name = "%s_%d" % (base_name, i)
i += 1
if mark_as_used:
self._names_in_use[name] = 1
return name
@contextlib.contextmanager
def colocate_with(self, op, ignore_existing=False):
if op is None and not ignore_existing:
raise ValueError(
"Trying to reset colocation (op is None) but "
"ignore_existing is not True")
if op is not None and not isinstance(op, Operation):
op = internal_convert_to_tensor_or_indexed_slices(op, as_ref=True).op
device_fn_tmp = self._device_function_stack
self._device_function_stack = []
if ignore_existing:
current_stack = self._colocation_stack
self._colocation_stack = []
if op is not None:
self._colocation_stack.append(op)
try:
yield
finally:
self._device_function_stack = device_fn_tmp
if op is not None:
self._colocation_stack.pop()
if ignore_existing:
self._colocation_stack = current_stack
@contextlib.contextmanager
def device(self, device_name_or_function):
if (device_name_or_function is not None
and not callable(device_name_or_function)):
device_function = pydev.merge_device(device_name_or_function)
else:
device_function = device_name_or_function
try:
self._device_function_stack.append(device_function)
yield
finally:
self._device_function_stack.pop()
def _apply_device_functions(self, op):
# signature, which is computed in the Operation constructor.
for device_function in reversed(self._device_function_stack):
if device_function is None:
break
op._set_device(device_function(op))
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def container(self, container_name):
original_container = self._container
try:
self._container = container_name
yield self._container
finally:
self._container = original_container
# pylint: enable=g-doc-return-or-yield
class _ControlDependenciesController(object):
def __init__(self, graph, control_inputs):
self._graph = graph
if control_inputs is None:
self._control_inputs = []
self._new_stack = True
else:
self._control_inputs = control_inputs
self._new_stack = False
self._seen_nodes = set()
self._old_stack = None
self._old_control_flow_context = None
# pylint: disable=protected-access
def __enter__(self):
if self._new_stack:
# Clear the control_dependencies graph.
self._old_stack = self._graph._control_dependencies_stack
self._graph._control_dependencies_stack = []
# Clear the control_flow_context too.
self._old_control_flow_context = self._graph._get_control_flow_context()
self._graph._set_control_flow_context(None)
self._graph._push_control_dependencies_controller(self)
def __exit__(self, unused_type, unused_value, unused_traceback):
self._graph._pop_control_dependencies_controller(self)
if self._new_stack:
self._graph._control_dependencies_stack = self._old_stack
self._graph._set_control_flow_context(self._old_control_flow_context)
# pylint: enable=protected-access
@property
def control_inputs(self):
return self._control_inputs
def add_op(self, op):
self._seen_nodes.add(op)
def op_in_group(self, op):
return op in self._seen_nodes
def _push_control_dependencies_controller(self, controller):
self._control_dependencies_stack.append(controller)
def _pop_control_dependencies_controller(self, controller):
assert self._control_dependencies_stack[-1] is controller
self._control_dependencies_stack.pop()
def _current_control_dependencies(self):
ret = set()
for controller in self._control_dependencies_stack:
for op in controller.control_inputs:
ret.add(op)
return ret
def _control_dependencies_for_inputs(self, input_tensors):
ret = []
input_ops = set([t.op for t in input_tensors])
for controller in self._control_dependencies_stack:
# If any of the input_ops already depends on the inputs from controller,
# we say that the new op is dominated (by that input), and we therefore
# do not need to add control dependencies for this controller's inputs.
dominated = False
for op in input_ops:
if controller.op_in_group(op):
dominated = True
break
if not dominated:
# NOTE(mrry): We do not currently track transitive data dependencies,
# so we may add redundant control inputs.
ret.extend([c for c in controller.control_inputs if c not in input_ops])
return ret
def _record_op_seen_by_control_dependencies(self, op):
for controller in self._control_dependencies_stack:
controller.add_op(op)
def control_dependencies(self, control_inputs):
if control_inputs is None:
return self._ControlDependenciesController(self, None)
# First convert the inputs to ops, and deduplicate them.
# NOTE(mrry): Other than deduplication, we do not currently track direct
# or indirect dependencies between control_inputs, which may result in
# redundant control inputs.
control_ops = []
current = self._current_control_dependencies()
for c in control_inputs:
c = self.as_graph_element(c)
if isinstance(c, Tensor):
c = c.op
elif not isinstance(c, Operation):
raise TypeError("Control input must be Operation or Tensor: %s" % c)
if c not in current:
control_ops.append(c)
current.add(c)
return self._ControlDependenciesController(self, control_ops)
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def _attr_scope(self, attr_map):
if not isinstance(attr_map, dict):
raise TypeError("attr_map must be a dictionary mapping "
"strings to AttrValue protocol buffers")
# The saved_attrs dictionary stores any currently-set labels that
# will be overridden by this context manager.
saved_attrs = {}
# Install the given attribute
for name, attr in attr_map.items():
if not (isinstance(name, six.string_types) and
(isinstance(attr, (type(None), attr_value_pb2.AttrValue)) or
callable(attr))):
raise TypeError("attr_map must be a dictionary mapping "
"strings to AttrValue protocol buffers or "
"callables that emit AttrValue protocol buffers")
try:
saved_attrs[name] = self._attr_scope_map[name]
except KeyError:
pass
if attr is None:
del self._attr_scope_map[name]
else:
self._attr_scope_map[name] = attr
try:
yield # The code within the context runs here.
finally:
# Remove the attributes set for this context, and restore any saved
# attributes.
for name, attr in attr_map.items():
try:
self._attr_scope_map[name] = saved_attrs[name]
except KeyError:
del self._attr_scope_map[name]
# pylint: enable=g-doc-return-or-yield
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def _kernel_label_map(self, op_to_kernel_label_map):
if not isinstance(op_to_kernel_label_map, dict):
raise TypeError("op_to_kernel_label_map must be a dictionary mapping "
"strings to strings")
# The saved_labels dictionary stores any currently-set labels that
# will be overridden by this context manager.
saved_labels = {}
# Install the given label
for op_type, label in op_to_kernel_label_map.items():
if not (isinstance(op_type, six.string_types)
and isinstance(label, six.string_types)):
raise TypeError("op_to_kernel_label_map must be a dictionary mapping "
"strings to strings")
try:
saved_labels[op_type] = self._op_to_kernel_label_map[op_type]
except KeyError:
pass
self._op_to_kernel_label_map[op_type] = label
try:
yield # The code within the context runs here.
finally:
# Remove the labels set for this context, and restore any saved labels.
for op_type, label in op_to_kernel_label_map.items():
try:
self._op_to_kernel_label_map[op_type] = saved_labels[op_type]
except KeyError:
del self._op_to_kernel_label_map[op_type]
# pylint: enable=g-doc-return-or-yield
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def gradient_override_map(self, op_type_map):
if not isinstance(op_type_map, dict):
raise TypeError("op_type_map must be a dictionary mapping "
"strings to strings")
# The saved_mappings dictionary stores any currently-set mappings that
# will be overridden by this context manager.
saved_mappings = {}
# Install the given label
for op_type, mapped_op_type in op_type_map.items():
if not (isinstance(op_type, six.string_types)
and isinstance(mapped_op_type, six.string_types)):
raise TypeError("op_type_map must be a dictionary mapping "
"strings to strings")
try:
saved_mappings[op_type] = self._gradient_override_map[op_type]
except KeyError:
pass
self._gradient_override_map[op_type] = mapped_op_type
try:
yield # The code within the context runs here.
finally:
# Remove the labels set for this context, and restore any saved labels.
for op_type, mapped_op_type in op_type_map.items():
try:
self._gradient_override_map[op_type] = saved_mappings[op_type]
except KeyError:
del self._gradient_override_map[op_type]
# pylint: enable=g-doc-return-or-yield
def prevent_feeding(self, tensor):
self._unfeedable_tensors.add(tensor)
def is_feedable(self, tensor):
return tensor not in self._unfeedable_tensors
def prevent_fetching(self, op):
self._unfetchable_ops.add(op)
def is_fetchable(self, tensor_or_op):
if isinstance(tensor_or_op, Tensor):
return tensor_or_op.op not in self._unfetchable_ops
else:
return tensor_or_op not in self._unfetchable_ops
def device(device_name_or_function):
return get_default_graph().device(device_name_or_function)
def container(container_name):
return get_default_graph().container(container_name)
def colocate_with(op, ignore_existing=False):
return get_default_graph().colocate_with(op, ignore_existing)
def control_dependencies(control_inputs):
return get_default_graph().control_dependencies(control_inputs)
class _DefaultStack(threading.local):
def __init__(self):
super(_DefaultStack, self).__init__()
self._enforce_nesting = True
self.stack = []
def get_default(self):
return self.stack[-1] if len(self.stack) >= 1 else None
def reset(self):
self.stack = []
@property
def enforce_nesting(self):
return self._enforce_nesting
@enforce_nesting.setter
def enforce_nesting(self, value):
self._enforce_nesting = value
@contextlib.contextmanager
def get_controller(self, default):
try:
self.stack.append(default)
yield default
finally:
if self._enforce_nesting:
if self.stack[-1] is not default:
raise AssertionError(
"Nesting violated for default stack of %s objects"
% type(default))
self.stack.pop()
else:
self.stack.remove(default)
_default_session_stack = _DefaultStack()
def default_session(session):
return _default_session_stack.get_controller(session)
def get_default_session():
return _default_session_stack.get_default()
def _eval_using_default_session(tensors, feed_dict, graph, session=None):
if session is None:
session = get_default_session()
if session is None:
raise ValueError("Cannot evaluate tensor using `eval()`: No default "
"session is registered. Use `with "
"sess.as_default()` or pass an explicit session to "
"`eval(session=sess)`")
if session.graph is not graph:
raise ValueError("Cannot use the default session to evaluate tensor: "
"the tensor's graph is different from the session's "
"graph. Pass an explicit session to "
"`eval(session=sess)`.")
else:
if session.graph is not graph:
raise ValueError("Cannot use the given session to evaluate tensor: "
"the tensor's graph is different from the session's "
"graph.")
return session.run(tensors, feed_dict)
def _run_using_default_session(operation, feed_dict, graph, session=None):
if session is None:
session = get_default_session()
if session is None:
raise ValueError("Cannot execute operation using `run()`: No default "
"session is registered. Use `with "
"sess.as_default():` or pass an explicit session to "
"`run(session=sess)`")
if session.graph is not graph:
raise ValueError("Cannot use the default session to execute operation: "
"the operation's graph is different from the "
"session's graph. Pass an explicit session to "
"run(session=sess).")
else:
if session.graph is not graph:
raise ValueError("Cannot use the given session to execute operation: "
"the operation's graph is different from the session's "
"graph.")
session.run(operation, feed_dict)
class _DefaultGraphStack(_DefaultStack):
def __init__(self):
super(_DefaultGraphStack, self).__init__()
self._global_default_graph = None
def get_default(self):
ret = super(_DefaultGraphStack, self).get_default()
if ret is None:
ret = self._GetGlobalDefaultGraph()
return ret
def _GetGlobalDefaultGraph(self):
if self._global_default_graph is None:
# TODO(mrry): Perhaps log that the default graph is being used, or set
# provide some other feedback to prevent confusion when a mixture of
# the global default graph and an explicit graph are combined in the
# same process.
self._global_default_graph = Graph()
return self._global_default_graph
def reset(self):
super(_DefaultGraphStack, self).reset()
self._global_default_graph = None
_default_graph_stack = _DefaultGraphStack()
def reset_default_graph():
_default_graph_stack.reset()
def get_default_graph():
return _default_graph_stack.get_default()
def _assert_same_graph(original_item, item):
if original_item.graph is not item.graph:
raise ValueError(
"%s must be from the same graph as %s." % (item, original_item))
def _get_graph_from_inputs(op_input_list, graph=None):
if get_default_graph().building_function:
return get_default_graph()
op_input_list = tuple(op_input_list) # Handle generators correctly
if graph and not isinstance(graph, Graph):
raise TypeError("Input graph needs to be a Graph: %s" % graph)
# 1. We validate that all of the inputs are from the same graph. This is
# either the supplied graph parameter, or the first one selected from one
# the graph-element-valued inputs. In the latter case, we hold onto
# that input in original_graph_element so we can provide a more
# informative error if a mismatch is found.
original_graph_element = None
for op_input in op_input_list:
# Determine if this is a valid graph_element.
graph_element = None
if isinstance(op_input, (Operation, _TensorLike)):
graph_element = op_input
else:
graph_element = _as_graph_element(op_input)
if graph_element is not None:
if not graph:
original_graph_element = graph_element
graph = graph_element.graph
elif original_graph_element is not None:
_assert_same_graph(original_graph_element, graph_element)
elif graph_element.graph is not graph:
raise ValueError(
"%s is not from the passed-in graph." % graph_element)
# 2. If all else fails, we use the default graph, which is always there.
return graph or get_default_graph()
class GraphKeys(object):
# Key to collect Variable objects that are global (shared across machines).
# Default collection for all variables, except local ones.
GLOBAL_VARIABLES = "variables"
# Key to collect local variables that are local to the machine and are not
# saved/restored.
LOCAL_VARIABLES = "local_variables"
# Key to collect model variables defined by layers.
MODEL_VARIABLES = "model_variables"
# Key to collect Variable objects that will be trained by the
# optimizers.
TRAINABLE_VARIABLES = "trainable_variables"
# Key to collect summaries.
SUMMARIES = "summaries"
# Key to collect QueueRunners.
QUEUE_RUNNERS = "queue_runners"
# Key to collect table initializers.
TABLE_INITIALIZERS = "table_initializer"
# Key to collect asset filepaths. An asset represents an external resource
# like a vocabulary file.
ASSET_FILEPATHS = "asset_filepaths"
# Key to collect Variable objects that keep moving averages.
MOVING_AVERAGE_VARIABLES = "moving_average_variables"
# Key to collect regularization losses at graph construction.
REGULARIZATION_LOSSES = "regularization_losses"
# Key to collect concatenated sharded variables.
CONCATENATED_VARIABLES = "concatenated_variables"
# Key to collect savers.
SAVERS = "savers"
# Key to collect weights
WEIGHTS = "weights"
# Key to collect biases
BIASES = "biases"
# Key to collect activations
ACTIVATIONS = "activations"
# Key to collect update_ops
UPDATE_OPS = "update_ops"
# Key to collect losses
LOSSES = "losses"
# Key to collect BaseSaverBuilder.SaveableObject instances for checkpointing.
SAVEABLE_OBJECTS = "saveable_objects"
# Key to collect all shared resources used by the graph which need to be
# initialized once per cluster.
RESOURCES = "resources"
# Key to collect all shared resources used in this graph which need to be
# initialized once per session.
LOCAL_RESOURCES = "local_resources"
# Trainable resource-style variables.
TRAINABLE_RESOURCE_VARIABLES = "trainable_resource_variables"
# Key to indicate various ops.
INIT_OP = "init_op"
LOCAL_INIT_OP = "local_init_op"
READY_OP = "ready_op"
READY_FOR_LOCAL_INIT_OP = "ready_for_local_init_op"
SUMMARY_OP = "summary_op"
GLOBAL_STEP = "global_step"
# Used to count the number of evaluations performed during a single evaluation
# run.
EVAL_STEP = "eval_step"
TRAIN_OP = "train_op"
# Key for control flow context.
COND_CONTEXT = "cond_context"
WHILE_CONTEXT = "while_context"
@decorator_utils.classproperty
def VARIABLES(cls): # pylint: disable=no-self-argument
logging.warning("VARIABLES collection name is deprecated, "
"please use GLOBAL_VARIABLES instead; "
"VARIABLES will be removed after 2017-03-02.")
return cls.GLOBAL_VARIABLES
def add_to_collection(name, value):
get_default_graph().add_to_collection(name, value)
def add_to_collections(names, value):
get_default_graph().add_to_collections(names, value)
def get_collection_ref(key):
return get_default_graph().get_collection_ref(key)
def get_collection(key, scope=None):
return get_default_graph().get_collection(key, scope)
def get_all_collection_keys():
return get_default_graph().get_all_collection_keys()
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def name_scope(name, default_name=None, values=None):
n = default_name if name is None else name
if n is None and values is not None:
# We only raise an error if values is not None (provided) because currently
# tf.name_scope(None) (values=None then) is sometimes used as an idiom
# to reset to top scope.
raise ValueError(
"At least one of name (%s) and default_name (%s) must be provided." % (
name, default_name))
if values is None:
values = []
g = _get_graph_from_inputs(values)
with g.as_default(), g.name_scope(n) as scope:
yield scope
# pylint: enable=g-doc-return-or-yield
def strip_name_scope(name, export_scope):
if export_scope:
# Strips export_scope/, export_scope///,
# ^export_scope/, loc:@export_scope/.
str_to_replace = r"([\^]|loc:@|^)" + export_scope + r"[\/]+(.*)"
return re.sub(str_to_replace, r"\1\2", compat.as_str(name), count=1)
else:
return name
def prepend_name_scope(name, import_scope):
if import_scope:
str_to_replace = r"([\^]|loc:@|^)(.*)"
return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
compat.as_str(name))
else:
return name
# pylint: disable=g-doc-return-or-yield
@contextlib.contextmanager
def op_scope(values, name, default_name=None):
logging.warn("tf.op_scope(values, name, default_name) is deprecated,"
" use tf.name_scope(name, default_name, values)")
with name_scope(name, default_name=default_name, values=values) as scope:
yield scope
_proto_function_registry = registry.Registry("proto functions")
def register_proto_function(collection_name, proto_type=None, to_proto=None,
from_proto=None):
if to_proto and not callable(to_proto):
raise TypeError("to_proto must be callable.")
if from_proto and not callable(from_proto):
raise TypeError("from_proto must be callable.")
_proto_function_registry.register((proto_type, to_proto, from_proto),
collection_name)
def get_collection_proto_type(collection_name):
try:
return _proto_function_registry.lookup(collection_name)[0]
except LookupError:
return None
def get_to_proto_function(collection_name):
try:
return _proto_function_registry.lookup(collection_name)[1]
except LookupError:
return None
def get_from_proto_function(collection_name):
try:
return _proto_function_registry.lookup(collection_name)[2]
except LookupError:
return None
def _operation_conversion_error(op, dtype=None, name=None, as_ref=False):
raise TypeError(
("Can't convert Operation '%s' to Tensor "
"(target dtype=%r, name=%r, as_ref=%r)") %
(op.name, dtype, name, as_ref))
register_tensor_conversion_function(Operation, _operation_conversion_error)
| true | true |
f73be1b188ec3aa0232075720e09d33b10e6ae07 | 5,793 | py | Python | src/qfit/vdw_radii.py | ExcitedStates/qfit-3.0 | 8ed8e8f44015e4eb30fed7a5da65819a586c2bbf | [
"Artistic-2.0",
"MIT"
] | 13 | 2019-05-22T00:07:39.000Z | 2022-03-09T18:12:47.000Z | src/qfit/vdw_radii.py | ExcitedStates/qfit-3.0 | 8ed8e8f44015e4eb30fed7a5da65819a586c2bbf | [
"Artistic-2.0",
"MIT"
] | 84 | 2019-02-14T00:46:57.000Z | 2022-03-03T21:53:15.000Z | src/qfit/vdw_radii.py | ExcitedStates/qfit-3.0 | 8ed8e8f44015e4eb30fed7a5da65819a586c2bbf | [
"Artistic-2.0",
"MIT"
] | 7 | 2019-03-08T01:08:05.000Z | 2021-10-02T14:40:45.000Z | '''
Excited States software: qFit 3.0
Contributors: Saulo H. P. de Oliveira, Gydo van Zundert, and Henry van den Bedem.
Contact: vdbedem@stanford.edu
Copyright (C) 2009-2019 Stanford University
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:
This entire text, including 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, CONTRIBUTORS 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.
'''
# ElementList=[
# " H", "HE",
# "LI", "BE", " B", " C", " N", " O", " F", "NE",
# "NA", "MG", "AL", "SI", " P", " S", "CL", "AR",
# " K", "CA",
# "SC", "TI", " V", "CR", "MN", "FE",
# "CO", "NI", "CU", "ZN",
# "GA", "GE", "AS", "SE", "BR", "KR",
# "RB", "SR",
# " Y", "ZR", "NB", "MO", "TC", "RU",
# "RH", "PD", "AG", "CD",
# "IN", "SN", "SB", "TE", " I", "XE",
# "CS", "BA",
# "LA", "CE", "PR", "ND", "PM", "SM", "EU",
# "GD", "TB", "DY", "HO", "ER", "TM", "YB",
# "LU", "HF", "TA", " W", "RE", "OS",
# "IR", "PT", "AU", "HG",
# "TL", "PB", "BI", "PO", "AT", "RN",
# "FR", "RA",
# "AC", "TH", "PA", " U", "NP", "PU", "AM",
# "CM", "BK", "CF", "ES", "FM", "MD", "NO",
# "LR", "RF", "DB", "SG", "BH", "HS",
# "MT", "UN", "UU", "UB",
# "UQ", "UH", "UO",
# " D", "AN"
# ]
#
#
# VanderWaalsRadiiList = [
# 1.20, 1.40,
# 1.82, 1.78, 1.74, 1.70, 1.55, 1.52, 1.47, 1.54,
# 2.27, 1.73, 1.80, 2.10, 1.80, 1.80, 1.75, 1.88,
# 2.75, 2.65,
# 2.55, 2.45, 2.35, 2.20, 1.73, 1.90,
# 1.75, 1.63, 1.40, 1.39,
# 1.87, 1.86, 1.85, 1.90, 1.85, 2.02,
# 2.75, 2.65,
# 2.55, 2.45, 2.35, 2.20, 2.05, 1.90,
# 1.75, 1.63, 1.72, 1.58,
# 1.93, 2.17, 2.10, 2.06, 1.98, 2.16,
# 2.75, 2.75,
# 2.75, 2.75, 2.75, 2.75, 2.75, 2.75, 2.75,
# 2.75, 2.75, 2.75, 2.75, 2.75, 2.65, 2.55,
# 2.45, 2.35, 2.25, 2.15, 2.05, 1.95,
# 1.85, 1.75, 1.66, 1.55,
# 1.96, 2.02, 2.00, 2.00, 2.00, 2.00,
# 2.75, 2.75,
# 2.50, 2.25, 1.95, 1.86, 1.80, 1.80, 1.80,
# 1.80, 1.80, 1.80, 1.80, 1.80, 1.80, 1.80,
# 1.80, 1.80, 1.80, 1.80, 1.80, 1.80,
# 1.80, 1.80, 1.80, 1.80,
# 1.80, 1.80, 1.80,
# 1.30, 1.50
# ]
# print("vdwRadiiTable={")
# for ele,radius in zip(ElementList,VanderWaalsRadiiList):
# print(f"\'{ele}\':{radius},")
# print("}")
vdwRadiiTable={
'H':1.2,
'HE':1.4,
'LI':1.82,
'BE':1.78,
'B':1.74,
'C':1.7,
'N':1.55,
'O':1.52,
'F':1.47,
'NE':1.54,
'NA':2.27,
'MG':1.73,
'AL':1.8,
'SI':2.1,
'P':1.8,
'S':1.8,
'CL':1.75,
'AR':1.88,
'K':2.75,
'CA':2.65,
'SC':2.55,
'TI':2.45,
'V':2.35,
'CR':2.2,
'MN':1.73,
'FE':1.9,
'CO':1.75,
'NI':1.63,
'CU':1.4,
'ZN':1.39,
'GA':1.87,
'GE':1.86,
'AS':1.85,
'SE':1.9,
'BR':1.85,
'KR':2.02,
'RB':2.75,
'SR':2.65,
'Y':2.55,
'ZR':2.45,
'NB':2.35,
'MO':2.2,
'TC':2.05,
'RU':1.9,
'RH':1.75,
'PD':1.63,
'AG':1.72,
'CD':1.58,
'IN':1.93,
'SN':2.17,
'SB':2.1,
'TE':2.06,
'I':1.98,
'XE':2.16,
'CS':2.75,
'BA':2.75,
'LA':2.75,
'CE':2.75,
'PR':2.75,
'ND':2.75,
'PM':2.75,
'SM':2.75,
'EU':2.75,
'GD':2.75,
'TB':2.75,
'DY':2.75,
'HO':2.75,
'ER':2.75,
'TM':2.65,
'YB':2.55,
'LU':2.45,
'HF':2.35,
'TA':2.25,
'W':2.15,
'RE':2.05,
'OS':1.95,
'IR':1.85,
'PT':1.75,
'AU':1.66,
'HG':1.55,
'TL':1.96,
'PB':2.02,
'BI':2.0,
'PO':2.0,
'AT':2.0,
'RN':2.0,
'FR':2.75,
'RA':2.75,
'AC':2.5,
'TH':2.25,
'PA':1.95,
'U':1.86,
'NP':1.8,
'PU':1.8,
'AM':1.8,
'CM':1.8,
'BK':1.8,
'CF':1.8,
'ES':1.8,
'FM':1.8,
'MD':1.8,
'NO':1.8,
'LR':1.8,
'RF':1.8,
'DB':1.8,
'SG':1.8,
'BH':1.8,
'HS':1.8,
'MT':1.8,
'UN':1.8,
'UU':1.8,
'UB':1.8,
'UQ':1.8,
'UH':1.8,
'UO':1.8,
'D':1.3,
'AN':1.5,
}
# from xml.dom import minidom
#
# # parse an xml file by name
# mydoc = minidom.parse('/data/sauloho/qfit/qfit-3.0/qfit/epsilon.xml')
#
# firsts = mydoc.getElementsByTagName('first')
# seconds = mydoc.getElementsByTagName('second')
# epsilons = mydoc.getElementsByTagName('epsilon')
#
# print("EpsilonTable={")
# for first,second,epsilon in zip(firsts,seconds,epsilons):
# print(f"\'{first.firstChild.data}\':{{\'{second.firstChild.data}\':{epsilon.firstChild.data}}},")
# print("}")
EpsilonTable={
'C':{'C':0.150, 'N':0.155, 'O':0.173, 'S':0.173, 'H':0.055},
'N':{'C':0.155, 'N':0.160, 'O':0.179, 'S':0.179, 'H':0.057},
'O':{'C':0.173, 'N':0.179, 'O':0.200, 'S':0.200, 'H':0.063},
'S':{'C':0.173, 'N':0.179, 'O':0.200, 'S':0.200, 'H':0.063},
'H':{'C':0.055, 'N':0.057, 'O':0.063, 'S':0.063, 'H':0.020},
}
EpsilonIndex = ["H", "C", "N", "O", "S"]
EpsilonArray = [[0.020, 0.055, 0.057, 0.063, 0.063], # H
[0.055, 0.150, 0.155, 0.173, 0.173], # C
[0.057, 0.155, 0.160, 0.179, 0.179], # N
[0.063, 0.173, 0.179, 0.200, 0.200], # O
[0.063, 0.173, 0.179, 0.200, 0.200]] # S
| 24.969828 | 103 | 0.493181 |
vdwRadiiTable={
'H':1.2,
'HE':1.4,
'LI':1.82,
'BE':1.78,
'B':1.74,
'C':1.7,
'N':1.55,
'O':1.52,
'F':1.47,
'NE':1.54,
'NA':2.27,
'MG':1.73,
'AL':1.8,
'SI':2.1,
'P':1.8,
'S':1.8,
'CL':1.75,
'AR':1.88,
'K':2.75,
'CA':2.65,
'SC':2.55,
'TI':2.45,
'V':2.35,
'CR':2.2,
'MN':1.73,
'FE':1.9,
'CO':1.75,
'NI':1.63,
'CU':1.4,
'ZN':1.39,
'GA':1.87,
'GE':1.86,
'AS':1.85,
'SE':1.9,
'BR':1.85,
'KR':2.02,
'RB':2.75,
'SR':2.65,
'Y':2.55,
'ZR':2.45,
'NB':2.35,
'MO':2.2,
'TC':2.05,
'RU':1.9,
'RH':1.75,
'PD':1.63,
'AG':1.72,
'CD':1.58,
'IN':1.93,
'SN':2.17,
'SB':2.1,
'TE':2.06,
'I':1.98,
'XE':2.16,
'CS':2.75,
'BA':2.75,
'LA':2.75,
'CE':2.75,
'PR':2.75,
'ND':2.75,
'PM':2.75,
'SM':2.75,
'EU':2.75,
'GD':2.75,
'TB':2.75,
'DY':2.75,
'HO':2.75,
'ER':2.75,
'TM':2.65,
'YB':2.55,
'LU':2.45,
'HF':2.35,
'TA':2.25,
'W':2.15,
'RE':2.05,
'OS':1.95,
'IR':1.85,
'PT':1.75,
'AU':1.66,
'HG':1.55,
'TL':1.96,
'PB':2.02,
'BI':2.0,
'PO':2.0,
'AT':2.0,
'RN':2.0,
'FR':2.75,
'RA':2.75,
'AC':2.5,
'TH':2.25,
'PA':1.95,
'U':1.86,
'NP':1.8,
'PU':1.8,
'AM':1.8,
'CM':1.8,
'BK':1.8,
'CF':1.8,
'ES':1.8,
'FM':1.8,
'MD':1.8,
'NO':1.8,
'LR':1.8,
'RF':1.8,
'DB':1.8,
'SG':1.8,
'BH':1.8,
'HS':1.8,
'MT':1.8,
'UN':1.8,
'UU':1.8,
'UB':1.8,
'UQ':1.8,
'UH':1.8,
'UO':1.8,
'D':1.3,
'AN':1.5,
}
'C':{'C':0.150, 'N':0.155, 'O':0.173, 'S':0.173, 'H':0.055},
'N':{'C':0.155, 'N':0.160, 'O':0.179, 'S':0.179, 'H':0.057},
'O':{'C':0.173, 'N':0.179, 'O':0.200, 'S':0.200, 'H':0.063},
'S':{'C':0.173, 'N':0.179, 'O':0.200, 'S':0.200, 'H':0.063},
'H':{'C':0.055, 'N':0.057, 'O':0.063, 'S':0.063, 'H':0.020},
}
EpsilonIndex = ["H", "C", "N", "O", "S"]
EpsilonArray = [[0.020, 0.055, 0.057, 0.063, 0.063],
[0.055, 0.150, 0.155, 0.173, 0.173],
[0.057, 0.155, 0.160, 0.179, 0.179],
[0.063, 0.173, 0.179, 0.200, 0.200],
[0.063, 0.173, 0.179, 0.200, 0.200]]
| true | true |
f73be2df781224e713a842b5feab5f7542120fad | 3,683 | py | Python | selfdrive/loggerd/tests/test_uploader.py | 919bot/Tessa | 9b48ff9020e8fb6992fc78271f2720fd19e01093 | [
"MIT"
] | null | null | null | selfdrive/loggerd/tests/test_uploader.py | 919bot/Tessa | 9b48ff9020e8fb6992fc78271f2720fd19e01093 | [
"MIT"
] | null | null | null | selfdrive/loggerd/tests/test_uploader.py | 919bot/Tessa | 9b48ff9020e8fb6992fc78271f2720fd19e01093 | [
"MIT"
] | null | null | null | import os
import time
import threading
import logging
import json
from selfdrive.swaglog import cloudlog
import selfdrive.loggerd.uploader as uploader
from common.timeout import Timeout
from selfdrive.loggerd.tests.loggerd_tests_common import UploaderTestCase
class TestLogHandler(logging.Handler):
def __init__(self):
logging.Handler.__init__(self)
self.reset()
def reset(self):
self.upload_order = list()
def emit(self, record):
try:
j = json.loads(record.message)
if j["event"] == "upload_success":
self.upload_order.append(j["key"])
except BaseException:
pass
log_handler = TestLogHandler()
cloudlog.addHandler(log_handler)
class TestUploader(UploaderTestCase):
def setUp(self):
super(TestUploader, self).setUp()
log_handler.reset()
def tearDown(self):
super(TestUploader, self).tearDown()
def start_thread(self):
self.end_event = threading.Event()
self.up_thread = threading.Thread(target=uploader.uploader_fn, args=[self.end_event])
self.up_thread.daemon = True
self.up_thread.start()
def join_thread(self):
self.end_event.set()
self.up_thread.join()
def gen_files(self, lock=False):
f_paths = list()
for t in ["bootlog.bz2", "qlog.bz2", "rlog.bz2", "dcamera.hevc", "fcamera.hevc"]:
f_paths.append(self.make_file_with_data(self.seg_dir, t, 1, lock=lock))
return f_paths
def gen_order(self, seg1, seg2):
keys = [f"{self.seg_format.format(i)}/qlog.bz2" for i in seg1]
keys += [f"{self.seg_format.format(i)}/rlog.bz2" for i in seg1]
keys += [f"{self.seg_format2.format(i)}/qlog.bz2" for i in seg2]
keys += [f"{self.seg_format2.format(i)}/rlog.bz2" for i in seg2]
for i in seg1:
keys += [f"{self.seg_format.format(i)}/{f}" for f in ['fcamera.hevc','dcamera.hevc']]
for i in seg2:
keys += [f"{self.seg_format2.format(i)}/{f}" for f in ['fcamera.hevc','dcamera.hevc']]
keys += [f"{self.seg_format.format(i)}/bootlog.bz2" for i in seg1]
keys += [f"{self.seg_format2.format(i)}/bootlog.bz2" for i in seg2]
return keys
def test_upload(self):
f_paths = self.gen_files(lock=False)
self.start_thread()
with Timeout(5, "Timeout waiting for file to be uploaded"):
while len(os.listdir(self.root)):
time.sleep(0.01)
self.join_thread()
for f_path in f_paths:
self.assertFalse(os.path.exists(f_path), "All files not uploaded")
exp_order = self.gen_order([self.seg_num], [])
self.assertTrue(log_handler.upload_order == exp_order, "Files uploaded in wrong order")
def test_upload_files_in_create_order(self):
f_paths = list()
seg1_nums = [0,1,2,10,20]
for i in seg1_nums:
self.seg_dir = self.seg_format.format(i)
f_paths += self.gen_files()
seg2_nums = [5,50,51]
for i in seg2_nums:
self.seg_dir = self.seg_format2.format(i)
f_paths += self.gen_files()
self.start_thread()
with Timeout(5, "Timeout waiting for file to be upload"):
while len(os.listdir(self.root)):
time.sleep(0.01)
self.join_thread()
for f_path in f_paths:
self.assertFalse(os.path.exists(f_path), "All files not uploaded")
#exp_order = self.gen_order(seg1_nums, seg2_nums)
#self.assertTrue(log_handler.upload_order == exp_order, "Files uploaded in wrong order")
def test_no_upload_with_lock_file(self):
f_paths = self.gen_files(lock=True)
self.start_thread()
# allow enough time that files should have been uploaded if they would be uploaded
time.sleep(5)
self.join_thread()
for f_path in f_paths:
self.assertTrue(os.path.exists(f_path), "File upload when locked")
| 30.94958 | 92 | 0.685039 | import os
import time
import threading
import logging
import json
from selfdrive.swaglog import cloudlog
import selfdrive.loggerd.uploader as uploader
from common.timeout import Timeout
from selfdrive.loggerd.tests.loggerd_tests_common import UploaderTestCase
class TestLogHandler(logging.Handler):
def __init__(self):
logging.Handler.__init__(self)
self.reset()
def reset(self):
self.upload_order = list()
def emit(self, record):
try:
j = json.loads(record.message)
if j["event"] == "upload_success":
self.upload_order.append(j["key"])
except BaseException:
pass
log_handler = TestLogHandler()
cloudlog.addHandler(log_handler)
class TestUploader(UploaderTestCase):
def setUp(self):
super(TestUploader, self).setUp()
log_handler.reset()
def tearDown(self):
super(TestUploader, self).tearDown()
def start_thread(self):
self.end_event = threading.Event()
self.up_thread = threading.Thread(target=uploader.uploader_fn, args=[self.end_event])
self.up_thread.daemon = True
self.up_thread.start()
def join_thread(self):
self.end_event.set()
self.up_thread.join()
def gen_files(self, lock=False):
f_paths = list()
for t in ["bootlog.bz2", "qlog.bz2", "rlog.bz2", "dcamera.hevc", "fcamera.hevc"]:
f_paths.append(self.make_file_with_data(self.seg_dir, t, 1, lock=lock))
return f_paths
def gen_order(self, seg1, seg2):
keys = [f"{self.seg_format.format(i)}/qlog.bz2" for i in seg1]
keys += [f"{self.seg_format.format(i)}/rlog.bz2" for i in seg1]
keys += [f"{self.seg_format2.format(i)}/qlog.bz2" for i in seg2]
keys += [f"{self.seg_format2.format(i)}/rlog.bz2" for i in seg2]
for i in seg1:
keys += [f"{self.seg_format.format(i)}/{f}" for f in ['fcamera.hevc','dcamera.hevc']]
for i in seg2:
keys += [f"{self.seg_format2.format(i)}/{f}" for f in ['fcamera.hevc','dcamera.hevc']]
keys += [f"{self.seg_format.format(i)}/bootlog.bz2" for i in seg1]
keys += [f"{self.seg_format2.format(i)}/bootlog.bz2" for i in seg2]
return keys
def test_upload(self):
f_paths = self.gen_files(lock=False)
self.start_thread()
with Timeout(5, "Timeout waiting for file to be uploaded"):
while len(os.listdir(self.root)):
time.sleep(0.01)
self.join_thread()
for f_path in f_paths:
self.assertFalse(os.path.exists(f_path), "All files not uploaded")
exp_order = self.gen_order([self.seg_num], [])
self.assertTrue(log_handler.upload_order == exp_order, "Files uploaded in wrong order")
def test_upload_files_in_create_order(self):
f_paths = list()
seg1_nums = [0,1,2,10,20]
for i in seg1_nums:
self.seg_dir = self.seg_format.format(i)
f_paths += self.gen_files()
seg2_nums = [5,50,51]
for i in seg2_nums:
self.seg_dir = self.seg_format2.format(i)
f_paths += self.gen_files()
self.start_thread()
with Timeout(5, "Timeout waiting for file to be upload"):
while len(os.listdir(self.root)):
time.sleep(0.01)
self.join_thread()
for f_path in f_paths:
self.assertFalse(os.path.exists(f_path), "All files not uploaded")
def test_no_upload_with_lock_file(self):
f_paths = self.gen_files(lock=True)
self.start_thread()
time.sleep(5)
self.join_thread()
for f_path in f_paths:
self.assertTrue(os.path.exists(f_path), "File upload when locked")
| true | true |
f73be3a7b3130605aab47153b280e3d8ce8398c6 | 111 | py | Python | back/db/gustafdb.py | rkohser/gustaf2 | b9f4dc0a9b5adca94161f9c59fa9907e1842b091 | [
"MIT"
] | null | null | null | back/db/gustafdb.py | rkohser/gustaf2 | b9f4dc0a9b5adca94161f9c59fa9907e1842b091 | [
"MIT"
] | null | null | null | back/db/gustafdb.py | rkohser/gustaf2 | b9f4dc0a9b5adca94161f9c59fa9907e1842b091 | [
"MIT"
] | null | null | null | from eve import Eve
app = Eve()
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5001, debug=True)
| 18.5 | 50 | 0.63964 | from eve import Eve
app = Eve()
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5001, debug=True)
| true | true |
f73be3ae374fd3d931826c70906f0dcb8897a992 | 3,374 | py | Python | main.py | charlespcamargo/pycococreator | 764ea9055dca173012c36569369315e57fee4682 | [
"Apache-2.0"
] | null | null | null | main.py | charlespcamargo/pycococreator | 764ea9055dca173012c36569369315e57fee4682 | [
"Apache-2.0"
] | 1 | 2020-11-21T00:12:49.000Z | 2020-11-21T00:12:49.000Z | main.py | charlespcamargo/pycococreator | 764ea9055dca173012c36569369315e57fee4682 | [
"Apache-2.0"
] | null | null | null | from pycococreator import PyCocoCreator
from pycococreatortools import PyCocoCreatorTools
from coco_dataset import CocoDataset
from coco_json_utils import CocoJsonCreator
from args import Args
import os
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Generate")
parser.add_argument("-dn", "--database_name", dest="database_name",
default="hedychium_coronarium", help="path to root of datasets")
parser.add_argument("-b", "--base_path", dest="base_path",
default="../images/train/", help="base path to images")
parser.add_argument("-i", "--images_path", dest="images_path",
default="images/", help="path to images")
parser.add_argument("-m", "--masks_path", dest="masks_path",
default="annotations/", help="path to masks")
parser.add_argument("-j", "--instances_json", dest="instances_json", default="coco_instances.json",
help="path to JSON path of coco instances")
parser.add_argument("-mw", "--max_width", dest="max_width", default=920, type=int,
help="max width to show images")
parser.add_argument("-id", "--image_id", dest="image_id", default=10, type=int,
help="image to open/generate HTML")
# Generate COCO JSON - coco_json_utils
parser.add_argument("-md", "--mask_definition", dest="mask_definition", default="mask_definition.json",
help="path to a mask definition JSON file, generated by MaskJsonUtils module")
parser.add_argument("-di", "--dataset_info", dest="dataset_info",
help="path to a dataset info JSON file")
parser.add_argument("-at", "--generate_automatic_info", dest="generate_automatic_info", default=1, type=int,
help="to generate automatic info: 0 or 1")
parser.add_argument("-rw", "--width", dest="width", default=4000, type=int,
help="width to resize images")
parser.add_argument("-rh", "--height", dest="height", default=3000, type=int,
help="height to resize images")
#args = parser.parse_args()
args = Args()
args.show()
coco_dataset = CocoDataset()
pycococreator = PyCocoCreator()
pycococreatortools = PyCocoCreatorTools()
cocojsoncreator = CocoJsonCreator()
pycococreator.main(args, pycococreatortools)
cocojsoncreator.main(args)
try:
# just to show and check
coco_dataset.main(args)
except:
print("In File('coco_instances.json') not found Error, please generate before run")
# In error
# File not found "coco_instances.json", please generate before run.
#if(args.generate_automatic_info == 1):
#cocojsoncreator.main(args)
if(not os.path.exists(args.annotation_path)):
raise Exception(f'File not found {args.annotation_path}')
# all loaded images
images_ids = coco_dataset.images
# take just some of all
# if(True):
# n = 10
# images_ids = list(images_ids)[0:n]
#
#images_ids = list(images_ids)
#filteredList = filter('DJI_0594', images_ids)
coco_dataset.display_categories()
coco_dataset.save_images_to_html(images_ids, max_width=900) | 43.25641 | 112 | 0.635448 | from pycococreator import PyCocoCreator
from pycococreatortools import PyCocoCreatorTools
from coco_dataset import CocoDataset
from coco_json_utils import CocoJsonCreator
from args import Args
import os
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Generate")
parser.add_argument("-dn", "--database_name", dest="database_name",
default="hedychium_coronarium", help="path to root of datasets")
parser.add_argument("-b", "--base_path", dest="base_path",
default="../images/train/", help="base path to images")
parser.add_argument("-i", "--images_path", dest="images_path",
default="images/", help="path to images")
parser.add_argument("-m", "--masks_path", dest="masks_path",
default="annotations/", help="path to masks")
parser.add_argument("-j", "--instances_json", dest="instances_json", default="coco_instances.json",
help="path to JSON path of coco instances")
parser.add_argument("-mw", "--max_width", dest="max_width", default=920, type=int,
help="max width to show images")
parser.add_argument("-id", "--image_id", dest="image_id", default=10, type=int,
help="image to open/generate HTML")
parser.add_argument("-md", "--mask_definition", dest="mask_definition", default="mask_definition.json",
help="path to a mask definition JSON file, generated by MaskJsonUtils module")
parser.add_argument("-di", "--dataset_info", dest="dataset_info",
help="path to a dataset info JSON file")
parser.add_argument("-at", "--generate_automatic_info", dest="generate_automatic_info", default=1, type=int,
help="to generate automatic info: 0 or 1")
parser.add_argument("-rw", "--width", dest="width", default=4000, type=int,
help="width to resize images")
parser.add_argument("-rh", "--height", dest="height", default=3000, type=int,
help="height to resize images")
args = Args()
args.show()
coco_dataset = CocoDataset()
pycococreator = PyCocoCreator()
pycococreatortools = PyCocoCreatorTools()
cocojsoncreator = CocoJsonCreator()
pycococreator.main(args, pycococreatortools)
cocojsoncreator.main(args)
try:
coco_dataset.main(args)
except:
print("In File('coco_instances.json') not found Error, please generate before run")
if(not os.path.exists(args.annotation_path)):
raise Exception(f'File not found {args.annotation_path}')
images_ids = coco_dataset.images
coco_dataset.display_categories()
coco_dataset.save_images_to_html(images_ids, max_width=900) | true | true |
f73be43cfc5eaa1bca66f8efa2cb926b32ef81d2 | 11,979 | py | Python | django_cyverse_auth/protocol/cas.py | simpsonw/django-cyverse-auth | 969ff75f0068a116dce2aaf92c78e1f1675eccbe | [
"BSD-3-Clause"
] | 1 | 2017-04-27T20:06:39.000Z | 2017-04-27T20:06:39.000Z | django_cyverse_auth/protocol/cas.py | simpsonw/django-cyverse-auth | 969ff75f0068a116dce2aaf92c78e1f1675eccbe | [
"BSD-3-Clause"
] | 14 | 2021-04-09T01:09:39.000Z | 2021-10-22T01:14:28.000Z | django_cyverse_auth/protocol/cas.py | benlazarine/django-cyverse-auth | 1664a11c5d9ab223d4e476d78e0513adf8eb27e0 | [
"BSD-3-Clause"
] | 10 | 2017-01-05T16:18:33.000Z | 2020-04-20T20:47:53.000Z | """
CAS authentication protocol
Contact: Steven Gregory <sgregory@iplantcollaborative.org>
"""
from datetime import timedelta
import time
from django.conf import settings
from django.contrib.auth import get_user_model
from django.core.urlresolvers import reverse
from django.http import HttpResponse, HttpResponseRedirect
from django.utils import timezone
from caslib import CASClient, SAMLClient, OAuthClient
import logging
logger = logging.getLogger(__name__)
from django_cyverse_auth.session import create_session_token
from django_cyverse_auth.settings import auth_settings
from django_cyverse_auth.models import UserProxy
###########################
# CAS-SPECIFIC SSO METHODS
###########################
PROXY_TICKET_EXPIRY = timedelta(days=1)
User = get_user_model()
def get_cas_client():
"""
This is how you initialize a CAS Client
"""
return CASClient(auth_settings.CAS_SERVER,
settings.SERVICE_URL,
proxy_url=settings.PROXY_URL,
proxy_callback=settings.PROXY_CALLBACK_URL,
auth_prefix=auth_settings.CAS_AUTH_PREFIX,
self_signed_cert=auth_settings.SELF_SIGNED_CERT)
def cas_logoutRedirect():
return HttpResponseRedirect(auth_settings.CAS_SERVER +
"/cas/logout?service=" + settings.SERVER_URL)
def cas_loginRedirect(request, redirect=None, gateway=False):
if not redirect:
redirect = request.get_full_path()
redirect_to = "%s/CAS_serviceValidater?sendback=%s" \
% (settings.SERVER_URL, redirect)
login_url = "%s%s/login?service=%s" \
% (auth_settings.CAS_SERVER, auth_settings.CAS_AUTH_PREFIX, redirect_to)
if gateway:
login_url += '&gateway=true'
return HttpResponseRedirect(login_url)
def cas_validateUser(username):
"""
Because this is a programmatic request
and CAS requires user input when expired,
We MUST use CAS Proxy Service,
and see if we can reauthenticate the user.
"""
try:
userProxy = UserProxy.objects.filter(username=username).order_by('pk').first()
logger.debug("[CAS] Validation Test - %s" % username)
if userProxy is None:
logger.debug("User %s does not have a proxy" % username)
return (False, None)
proxyTicket = userProxy.proxyTicket
caslib = get_cas_client()
(validUser, cas_response) =\
caslib.reauthenticate(proxyTicket, username=username)
logger.debug("Valid User: %s Proxy response: %s"
% (validUser, cas_response))
return (validUser, cas_response)
except Exception:
logger.exception('Error validating user %s' % username)
return (False, None)
def cas_updateUserProxy(user, pgtIou, max_try=3):
attempts = 0
while attempts < max_try:
try:
# If PGTIOU exists, a UserProxy object was created
# match the user to this ticket.
userProxy = UserProxy.objects.get(proxyIOU=pgtIou)
userProxy.username = user
userProxy.expiresOn = timezone.now() + PROXY_TICKET_EXPIRY
logger.debug("Found a matching proxy IOU for %s"
% userProxy.username)
userProxy.save()
return True
except UserProxy.DoesNotExist:
logger.error("Could not find UserProxy object!"
"ProxyIOU & ID was not saved "
"at proxy url endpoint.")
time.sleep(min(2**attempts, 8))
attempts += 1
return False
def cas_set_redirect_url(sendback, request):
absolute_url = request.build_absolute_uri(
reverse('django_cyverse_auth:cas-service-validate-link'))
return "%s?sendback=%s" % (absolute_url, sendback)
def cas_validateTicket(request):
"""
Method expects 2 GET parameters: 'ticket' & 'sendback'
After a CAS Login:
Redirects the request based on the GET param 'ticket'
Unauthorized Users are redirected to '/' In the event of failure.
Authorized Users are redirected to the GET param 'sendback'
"""
redirect_logout_url = settings.REDIRECT_URL + "/login/"
no_user_url = settings.REDIRECT_URL + "/no_user/"
logger.debug('GET Variables:%s' % request.GET)
ticket = request.GET.get('ticket', None)
sendback = request.GET.get('sendback', None)
if not ticket:
logger.warn("No Ticket received in GET string "
"-- Logout user: %s" % redirect_logout_url)
return HttpResponseRedirect(redirect_logout_url)
logger.debug("ServiceValidate endpoint includes a ticket."
" Ticket must now be validated with CAS")
# ReturnLocation set, apply on successful authentication
caslib = get_cas_client()
caslib.service_url = cas_set_redirect_url(sendback, request)
cas_response = caslib.cas_serviceValidate(ticket)
if not cas_response.success:
logger.debug("CAS Server did NOT validate ticket:%s"
" and included this response:%s (Err:%s)"
% (ticket, cas_response.object, cas_response.error_str))
return HttpResponseRedirect(redirect_logout_url)
if not cas_response.user:
logger.debug("User attribute missing from cas response!"
"This may require a fix to caslib.py")
return HttpResponseRedirect(redirect_logout_url)
if not cas_response.proxy_granting_ticket:
logger.error("""Proxy Granting Ticket missing!
Atmosphere requires CAS proxy as a service to authenticate users.
Possible Causes:
* ServerName variable is wrong in /etc/apache2/apache2.conf
* Proxy URL does not exist
* Proxy URL is not a valid RSA-2/VeriSigned SSL certificate
* /etc/host and hostname do not match machine.""")
return HttpResponseRedirect(redirect_logout_url)
updated = cas_updateUserProxy(
cas_response.user, cas_response.proxy_granting_ticket)
if not updated:
return HttpResponseRedirect(redirect_logout_url)
logger.info("Updated proxy for <%s> -- Auth success!" % cas_response.user)
try:
user = User.objects.get(username=cas_response.user)
except User.DoesNotExist:
return HttpResponseRedirect(no_user_url)
auth_token = create_session_token(None, user, request, issuer="CAS")
if auth_token is None:
logger.warn("Failed to create AuthToken")
HttpResponseRedirect(redirect_logout_url)
return_to = request.GET['sendback']
logger.info("Session token created, User logged in, return to: %s"
% return_to)
return HttpResponseRedirect(return_to)
"""
CAS as a proxy service is a useful feature to renew
a users token/authentication without having to explicitly redirect the browser.
These two functions will be called
if caslib has been configured for proxy usage. (See #settings.py)
"""
def cas_storeProxyIOU_ID(request):
"""
Any request to the proxy url will contain the PROXY-TICKET IOU and ID
IOU and ID are mapped to a DB so they can be used later
"""
if "pgtIou" in request.GET and "pgtId" in request.GET:
iou_token = request.GET["pgtIou"]
proxy_ticket = request.GET["pgtId"]
logger.debug("PROXY HIT 2 - CAS server sends two IDs: "
"1.ProxyIOU (%s) 2. ProxyGrantingTicket (%s)"
% (iou_token, proxy_ticket))
proxy = UserProxy(
proxyIOU=iou_token,
proxyTicket=proxy_ticket
)
proxy.save()
logger.debug("Proxy ID has been saved, match ProxyIOU(%s) "
"from the proxyIOU returned in service validate."
% (proxy.proxyIOU,))
else:
logger.debug("Proxy HIT 1 - CAS server tests that this link is HTTPS")
return HttpResponse("Received proxy request. Thank you.")
def cas_proxyCallback(request):
"""
This is a placeholder for a proxyCallback service
needed for CAS authentication
"""
logger.debug("Incoming request to CASPROXY (Proxy Callback):")
return HttpResponse("I am at a RSA-2 or VeriSigned SSL Cert. website.")
###########################
# CAS-SPECIFIC SAML METHODS
###########################
def get_saml_client():
s_client = SAMLClient(auth_settings.CAS_SERVER,
settings.SERVER_URL,
auth_prefix=auth_settings.CAS_AUTH_PREFIX)
return s_client
def saml_loginRedirect(request, redirect=None, gateway=False):
login_url = "%s%s/login?service=%s/s_serviceValidater%s" %\
(auth_settings.CAS_SERVER, auth_settings.CAS_AUTH_PREFIX,
settings.SERVER_URL,
"?sendback=%s" % redirect if redirect else "")
if gateway:
login_url += '&gateway=true'
return HttpResponseRedirect(login_url)
def saml_validateTicket(request):
"""
Method expects 2 GET parameters: 'ticket' & 'sendback'
After a CAS Login:
Redirects the request based on the GET param 'ticket'
Unauthorized Users are redirected to '/' In the event of failure.
Authorized Users are redirected to the GET param 'sendback'
"""
redirect_logout_url = settings.REDIRECT_URL + "/login/"
no_user_url = settings.REDIRECT_URL + "/no_user/"
logger.debug('GET Variables:%s' % request.GET)
ticket = request.GET.get('ticket', None)
if not ticket:
logger.warn("No Ticket received in GET string "
"-- Logout user: %s" % redirect_logout_url)
return HttpResponseRedirect(redirect_logout_url)
logger.debug("ServiceValidate endpoint includes a ticket."
" Ticket must now be validated with SAML")
# ReturnLocation set, apply on successful authentication
saml_client = get_saml_client()
saml_response = saml_client.saml_serviceValidate(ticket)
if not saml_response.success:
logger.debug("CAS Server did NOT validate ticket:%s"
" and included this response:%s"
% (ticket, saml_response.xml))
return HttpResponseRedirect(redirect_logout_url)
try:
user = User.objects.get(username=saml_response.user)
except User.DoesNotExist:
return HttpResponseRedirect(no_user_url)
auth_token = create_session_token(None, user, request, issuer="CAS+SAML")
if auth_token is None:
logger.warn("Failed to create AuthToken")
HttpResponseRedirect(redirect_logout_url)
return_to = request.GET.get('sendback')
if not return_to:
return HttpResponse(saml_response.response,
content_type="text/xml; charset=utf-8")
return_to += "?token=%s" % auth_token
logger.info("Session token created, return to: %s" % return_to)
return HttpResponseRedirect(return_to)
###########################
# CAS-SPECIFIC OAUTH METHODS
###########################
def get_cas_oauth_client():
o_client = OAuthClient(auth_settings.CAS_SERVER,
auth_settings.OAUTH_CLIENT_CALLBACK,
auth_settings.OAUTH_CLIENT_KEY,
auth_settings.OAUTH_CLIENT_SECRET,
auth_prefix=auth_settings.CAS_AUTH_PREFIX)
return o_client
def cas_profile_contains(attrs, test_value):
# Two basic types of 'values'
# Lists: e.g. attrs['entitlement'] = ['group1','group2','group3']
# Objects: e.g. attrs['email'] = 'test@email.com'
for attr in attrs:
for (key, value) in attr.items():
if isinstance(value, list) and test_value in value:
return True
elif value == test_value:
return True
return False
def cas_profile_for_token(access_token):
oauth_client = get_cas_oauth_client()
profile = oauth_client.get_profile(access_token)
return profile
| 36.745399 | 86 | 0.652642 | from datetime import timedelta
import time
from django.conf import settings
from django.contrib.auth import get_user_model
from django.core.urlresolvers import reverse
from django.http import HttpResponse, HttpResponseRedirect
from django.utils import timezone
from caslib import CASClient, SAMLClient, OAuthClient
import logging
logger = logging.getLogger(__name__)
from django_cyverse_auth.session import create_session_token
from django_cyverse_auth.settings import auth_settings
from django_cyverse_auth.models import UserProxy
request.get_full_path()
redirect_to = "%s/CAS_serviceValidater?sendback=%s" \
% (settings.SERVER_URL, redirect)
login_url = "%s%s/login?service=%s" \
% (auth_settings.CAS_SERVER, auth_settings.CAS_AUTH_PREFIX, redirect_to)
if gateway:
login_url += '&gateway=true'
return HttpResponseRedirect(login_url)
def cas_validateUser(username):
try:
userProxy = UserProxy.objects.filter(username=username).order_by('pk').first()
logger.debug("[CAS] Validation Test - %s" % username)
if userProxy is None:
logger.debug("User %s does not have a proxy" % username)
return (False, None)
proxyTicket = userProxy.proxyTicket
caslib = get_cas_client()
(validUser, cas_response) =\
caslib.reauthenticate(proxyTicket, username=username)
logger.debug("Valid User: %s Proxy response: %s"
% (validUser, cas_response))
return (validUser, cas_response)
except Exception:
logger.exception('Error validating user %s' % username)
return (False, None)
def cas_updateUserProxy(user, pgtIou, max_try=3):
attempts = 0
while attempts < max_try:
try:
userProxy = UserProxy.objects.get(proxyIOU=pgtIou)
userProxy.username = user
userProxy.expiresOn = timezone.now() + PROXY_TICKET_EXPIRY
logger.debug("Found a matching proxy IOU for %s"
% userProxy.username)
userProxy.save()
return True
except UserProxy.DoesNotExist:
logger.error("Could not find UserProxy object!"
"ProxyIOU & ID was not saved "
"at proxy url endpoint.")
time.sleep(min(2**attempts, 8))
attempts += 1
return False
def cas_set_redirect_url(sendback, request):
absolute_url = request.build_absolute_uri(
reverse('django_cyverse_auth:cas-service-validate-link'))
return "%s?sendback=%s" % (absolute_url, sendback)
def cas_validateTicket(request):
redirect_logout_url = settings.REDIRECT_URL + "/login/"
no_user_url = settings.REDIRECT_URL + "/no_user/"
logger.debug('GET Variables:%s' % request.GET)
ticket = request.GET.get('ticket', None)
sendback = request.GET.get('sendback', None)
if not ticket:
logger.warn("No Ticket received in GET string "
"-- Logout user: %s" % redirect_logout_url)
return HttpResponseRedirect(redirect_logout_url)
logger.debug("ServiceValidate endpoint includes a ticket."
" Ticket must now be validated with CAS")
caslib = get_cas_client()
caslib.service_url = cas_set_redirect_url(sendback, request)
cas_response = caslib.cas_serviceValidate(ticket)
if not cas_response.success:
logger.debug("CAS Server did NOT validate ticket:%s"
" and included this response:%s (Err:%s)"
% (ticket, cas_response.object, cas_response.error_str))
return HttpResponseRedirect(redirect_logout_url)
if not cas_response.user:
logger.debug("User attribute missing from cas response!"
"This may require a fix to caslib.py")
return HttpResponseRedirect(redirect_logout_url)
if not cas_response.proxy_granting_ticket:
logger.error("""Proxy Granting Ticket missing!
Atmosphere requires CAS proxy as a service to authenticate users.
Possible Causes:
* ServerName variable is wrong in /etc/apache2/apache2.conf
* Proxy URL does not exist
* Proxy URL is not a valid RSA-2/VeriSigned SSL certificate
* /etc/host and hostname do not match machine.""")
return HttpResponseRedirect(redirect_logout_url)
updated = cas_updateUserProxy(
cas_response.user, cas_response.proxy_granting_ticket)
if not updated:
return HttpResponseRedirect(redirect_logout_url)
logger.info("Updated proxy for <%s> -- Auth success!" % cas_response.user)
try:
user = User.objects.get(username=cas_response.user)
except User.DoesNotExist:
return HttpResponseRedirect(no_user_url)
auth_token = create_session_token(None, user, request, issuer="CAS")
if auth_token is None:
logger.warn("Failed to create AuthToken")
HttpResponseRedirect(redirect_logout_url)
return_to = request.GET['sendback']
logger.info("Session token created, User logged in, return to: %s"
% return_to)
return HttpResponseRedirect(return_to)
def cas_storeProxyIOU_ID(request):
if "pgtIou" in request.GET and "pgtId" in request.GET:
iou_token = request.GET["pgtIou"]
proxy_ticket = request.GET["pgtId"]
logger.debug("PROXY HIT 2 - CAS server sends two IDs: "
"1.ProxyIOU (%s) 2. ProxyGrantingTicket (%s)"
% (iou_token, proxy_ticket))
proxy = UserProxy(
proxyIOU=iou_token,
proxyTicket=proxy_ticket
)
proxy.save()
logger.debug("Proxy ID has been saved, match ProxyIOU(%s) "
"from the proxyIOU returned in service validate."
% (proxy.proxyIOU,))
else:
logger.debug("Proxy HIT 1 - CAS server tests that this link is HTTPS")
return HttpResponse("Received proxy request. Thank you.")
def cas_proxyCallback(request):
logger.debug("Incoming request to CASPROXY (Proxy Callback):")
return HttpResponse("I am at a RSA-2 or VeriSigned SSL Cert. website.")
+ "/login/"
no_user_url = settings.REDIRECT_URL + "/no_user/"
logger.debug('GET Variables:%s' % request.GET)
ticket = request.GET.get('ticket', None)
if not ticket:
logger.warn("No Ticket received in GET string "
"-- Logout user: %s" % redirect_logout_url)
return HttpResponseRedirect(redirect_logout_url)
logger.debug("ServiceValidate endpoint includes a ticket."
" Ticket must now be validated with SAML")
saml_client = get_saml_client()
saml_response = saml_client.saml_serviceValidate(ticket)
if not saml_response.success:
logger.debug("CAS Server did NOT validate ticket:%s"
" and included this response:%s"
% (ticket, saml_response.xml))
return HttpResponseRedirect(redirect_logout_url)
try:
user = User.objects.get(username=saml_response.user)
except User.DoesNotExist:
return HttpResponseRedirect(no_user_url)
auth_token = create_session_token(None, user, request, issuer="CAS+SAML")
if auth_token is None:
logger.warn("Failed to create AuthToken")
HttpResponseRedirect(redirect_logout_url)
return_to = request.GET.get('sendback')
if not return_to:
return HttpResponse(saml_response.response,
content_type="text/xml; charset=utf-8")
return_to += "?token=%s" % auth_token
logger.info("Session token created, return to: %s" % return_to)
return HttpResponseRedirect(return_to)
:
oauth_client = get_cas_oauth_client()
profile = oauth_client.get_profile(access_token)
return profile
| true | true |
f73be4a4ef5b959c7ff8ded4c4dafe4cdd762858 | 7,640 | py | Python | conans/tools.py | johnmcfarlane/conan | 725bd0cee4e53f35521aef7eeb61d4772c460d5e | [
"MIT"
] | null | null | null | conans/tools.py | johnmcfarlane/conan | 725bd0cee4e53f35521aef7eeb61d4772c460d5e | [
"MIT"
] | null | null | null | conans/tools.py | johnmcfarlane/conan | 725bd0cee4e53f35521aef7eeb61d4772c460d5e | [
"MIT"
] | null | null | null | """
Conan tools: classes and function in this module are intended to be used out of the box
with the Conan configuration already currified into them. This configuration refers
mainly to two items:
- requester: on network calls, this will include proxy definition.
- output: the output configuration
Here in this module there should be no logic, all functions and classes must be implemented
elsewhere (mainly in conans.util or conans.client.tools) and ready to be used without
the currification.
"""
import requests
from conans.client.output import ConanOutput
# Tools from conans.client.tools
from conans.client.tools import files as tools_files, net as tools_net, oss as tools_oss, \
system_pm as tools_system_pm, win as tools_win
from conans.client.tools.env import * # pylint: disable=unused-import
from conans.client.tools.pkg_config import * # pylint: disable=unused-import
from conans.client.tools.scm import * # pylint: disable=unused-import
from conans.client.tools.settings import * # pylint: disable=unused-import
from conans.client.tools.apple import *
from conans.client.tools.android import *
# Tools form conans.util
from conans.util.env_reader import get_env
from conans.util.files import _generic_algorithm_sum, load, md5, md5sum, mkdir, relative_dirs, \
rmdir, save as files_save, save_append, sha1sum, sha256sum, to_file_bytes, touch
from conans.util.log import logger
from conans.client.tools.version import Version
from conans.client.build.cppstd_flags import cppstd_flag_new as cppstd_flag # pylint: disable=unused-import
# This global variables are intended to store the configuration of the running Conan application
_global_output = None
_global_requester = None
_global_config = None
def set_global_instances(the_output, the_requester, config):
global _global_output
global _global_requester
global _global_config
# TODO: pass here the configuration file, and make the work here (explicit!)
_global_output = the_output
_global_requester = the_requester
_global_config = config
def get_global_instances():
return _global_output, _global_requester
# Assign a default, will be overwritten in the factory of the ConanAPI
set_global_instances(the_output=ConanOutput(sys.stdout, sys.stderr, True), the_requester=requests,
config=None)
"""
From here onwards only currification is expected, no logic
"""
def save(path, content, append=False):
# TODO: All this three functions: save, save_append and this one should be merged into one.
if append:
save_append(path=path, content=content)
else:
files_save(path=path, content=content, only_if_modified=False)
# From conans.client.tools.net
ftp_download = tools_net.ftp_download
def download(*args, **kwargs):
return tools_net.download(out=_global_output, requester=_global_requester, *args, **kwargs)
def get(*args, **kwargs):
return tools_net.get(output=_global_output, requester=_global_requester, *args, **kwargs)
# from conans.client.tools.files
chdir = tools_files.chdir
human_size = tools_files.human_size
untargz = tools_files.untargz
check_with_algorithm_sum = tools_files.check_with_algorithm_sum
check_sha1 = tools_files.check_sha1
check_md5 = tools_files.check_md5
check_sha256 = tools_files.check_sha256
patch = tools_files.patch
replace_prefix_in_pc_file = tools_files.replace_prefix_in_pc_file
collect_libs = tools_files.collect_libs
which = tools_files.which
unix2dos = tools_files.unix2dos
dos2unix = tools_files.dos2unix
def unzip(*args, **kwargs):
return tools_files.unzip(output=_global_output, *args, **kwargs)
def replace_in_file(*args, **kwargs):
return tools_files.replace_in_file(output=_global_output, *args, **kwargs)
def replace_path_in_file(*args, **kwargs):
return tools_files.replace_path_in_file(output=_global_output, *args, **kwargs)
# from conans.client.tools.oss
args_to_string = tools_oss.args_to_string
detected_architecture = tools_oss.detected_architecture
detected_os = tools_oss.detected_os
OSInfo = tools_oss.OSInfo
cross_building = tools_oss.cross_building
get_cross_building_settings = tools_oss.get_cross_building_settings
get_gnu_triplet = tools_oss.get_gnu_triplet
def cpu_count(*args, **kwargs):
return tools_oss.cpu_count(output=_global_output, *args, **kwargs)
# from conans.client.tools.system_pm
class SystemPackageTool(tools_system_pm.SystemPackageTool):
def __init__(self, *args, **kwargs):
super(SystemPackageTool, self).__init__(output=_global_output, *args, **kwargs)
class NullTool(tools_system_pm.NullTool):
def __init__(self, *args, **kwargs):
super(NullTool, self).__init__(output=_global_output, *args, **kwargs)
class AptTool(tools_system_pm.AptTool):
def __init__(self, *args, **kwargs):
super(AptTool, self).__init__(output=_global_output, *args, **kwargs)
class DnfTool(tools_system_pm.DnfTool):
def __init__(self, *args, **kwargs):
super(DnfTool, self).__init__(output=_global_output, *args, **kwargs)
class YumTool(tools_system_pm.YumTool):
def __init__(self, *args, **kwargs):
super(YumTool, self).__init__(output=_global_output, *args, **kwargs)
class BrewTool(tools_system_pm.BrewTool):
def __init__(self, *args, **kwargs):
super(BrewTool, self).__init__(output=_global_output, *args, **kwargs)
class PkgTool(tools_system_pm.PkgTool):
def __init__(self, *args, **kwargs):
super(PkgTool, self).__init__(output=_global_output, *args, **kwargs)
class ChocolateyTool(tools_system_pm.ChocolateyTool):
def __init__(self, *args, **kwargs):
super(ChocolateyTool, self).__init__(output=_global_output, *args, **kwargs)
class PkgUtilTool(tools_system_pm.PkgUtilTool):
def __init__(self, *args, **kwargs):
super(PkgUtilTool, self).__init__(output=_global_output, *args, **kwargs)
class PacManTool(tools_system_pm.PacManTool):
def __init__(self, *args, **kwargs):
super(PacManTool, self).__init__(output=_global_output, *args, **kwargs)
class ZypperTool(tools_system_pm.ZypperTool):
def __init__(self, *args, **kwargs):
super(ZypperTool, self).__init__(output=_global_output, *args, **kwargs)
# from conans.client.tools.win
vs_installation_path = tools_win.vs_installation_path
vswhere = tools_win.vswhere
vs_comntools = tools_win.vs_comntools
find_windows_10_sdk = tools_win.find_windows_10_sdk
escape_windows_cmd = tools_win.escape_windows_cmd
get_cased_path = tools_win.get_cased_path
MSYS2 = tools_win.MSYS2
MSYS = tools_win.MSYS
CYGWIN = tools_win.CYGWIN
WSL = tools_win.WSL
SFU = tools_win.SFU
unix_path = tools_win.unix_path
run_in_windows_bash = tools_win.run_in_windows_bash
@contextmanager
def vcvars(*args, **kwargs):
with tools_win.vcvars(output=_global_output, *args, **kwargs):
yield
def msvc_build_command(*args, **kwargs):
return tools_win.msvc_build_command(output=_global_output, *args, **kwargs)
def build_sln_command(*args, **kwargs):
return tools_win.build_sln_command(output=_global_output, *args, **kwargs)
def vcvars_command(*args, **kwargs):
return tools_win.vcvars_command(output=_global_output, *args, **kwargs)
def vcvars_dict(*args, **kwargs):
return tools_win.vcvars_dict(output=_global_output, *args, **kwargs)
def latest_vs_version_installed(*args, **kwargs):
return tools_win.latest_vs_version_installed(output=_global_output, *args, **kwargs)
# Ready to use objects.
try:
os_info = OSInfo()
except Exception as exc:
logger.error(exc)
_global_output.error("Error detecting os_info")
| 32.931034 | 108 | 0.767277 |
import requests
from conans.client.output import ConanOutput
from conans.client.tools import files as tools_files, net as tools_net, oss as tools_oss, \
system_pm as tools_system_pm, win as tools_win
from conans.client.tools.env import *
from conans.client.tools.pkg_config import *
from conans.client.tools.scm import *
from conans.client.tools.settings import *
from conans.client.tools.apple import *
from conans.client.tools.android import *
from conans.util.env_reader import get_env
from conans.util.files import _generic_algorithm_sum, load, md5, md5sum, mkdir, relative_dirs, \
rmdir, save as files_save, save_append, sha1sum, sha256sum, to_file_bytes, touch
from conans.util.log import logger
from conans.client.tools.version import Version
from conans.client.build.cppstd_flags import cppstd_flag_new as cppstd_flag
_global_output = None
_global_requester = None
_global_config = None
def set_global_instances(the_output, the_requester, config):
global _global_output
global _global_requester
global _global_config
_global_output = the_output
_global_requester = the_requester
_global_config = config
def get_global_instances():
return _global_output, _global_requester
set_global_instances(the_output=ConanOutput(sys.stdout, sys.stderr, True), the_requester=requests,
config=None)
def save(path, content, append=False):
if append:
save_append(path=path, content=content)
else:
files_save(path=path, content=content, only_if_modified=False)
ftp_download = tools_net.ftp_download
def download(*args, **kwargs):
return tools_net.download(out=_global_output, requester=_global_requester, *args, **kwargs)
def get(*args, **kwargs):
return tools_net.get(output=_global_output, requester=_global_requester, *args, **kwargs)
chdir = tools_files.chdir
human_size = tools_files.human_size
untargz = tools_files.untargz
check_with_algorithm_sum = tools_files.check_with_algorithm_sum
check_sha1 = tools_files.check_sha1
check_md5 = tools_files.check_md5
check_sha256 = tools_files.check_sha256
patch = tools_files.patch
replace_prefix_in_pc_file = tools_files.replace_prefix_in_pc_file
collect_libs = tools_files.collect_libs
which = tools_files.which
unix2dos = tools_files.unix2dos
dos2unix = tools_files.dos2unix
def unzip(*args, **kwargs):
return tools_files.unzip(output=_global_output, *args, **kwargs)
def replace_in_file(*args, **kwargs):
return tools_files.replace_in_file(output=_global_output, *args, **kwargs)
def replace_path_in_file(*args, **kwargs):
return tools_files.replace_path_in_file(output=_global_output, *args, **kwargs)
args_to_string = tools_oss.args_to_string
detected_architecture = tools_oss.detected_architecture
detected_os = tools_oss.detected_os
OSInfo = tools_oss.OSInfo
cross_building = tools_oss.cross_building
get_cross_building_settings = tools_oss.get_cross_building_settings
get_gnu_triplet = tools_oss.get_gnu_triplet
def cpu_count(*args, **kwargs):
return tools_oss.cpu_count(output=_global_output, *args, **kwargs)
class SystemPackageTool(tools_system_pm.SystemPackageTool):
def __init__(self, *args, **kwargs):
super(SystemPackageTool, self).__init__(output=_global_output, *args, **kwargs)
class NullTool(tools_system_pm.NullTool):
def __init__(self, *args, **kwargs):
super(NullTool, self).__init__(output=_global_output, *args, **kwargs)
class AptTool(tools_system_pm.AptTool):
def __init__(self, *args, **kwargs):
super(AptTool, self).__init__(output=_global_output, *args, **kwargs)
class DnfTool(tools_system_pm.DnfTool):
def __init__(self, *args, **kwargs):
super(DnfTool, self).__init__(output=_global_output, *args, **kwargs)
class YumTool(tools_system_pm.YumTool):
def __init__(self, *args, **kwargs):
super(YumTool, self).__init__(output=_global_output, *args, **kwargs)
class BrewTool(tools_system_pm.BrewTool):
def __init__(self, *args, **kwargs):
super(BrewTool, self).__init__(output=_global_output, *args, **kwargs)
class PkgTool(tools_system_pm.PkgTool):
def __init__(self, *args, **kwargs):
super(PkgTool, self).__init__(output=_global_output, *args, **kwargs)
class ChocolateyTool(tools_system_pm.ChocolateyTool):
def __init__(self, *args, **kwargs):
super(ChocolateyTool, self).__init__(output=_global_output, *args, **kwargs)
class PkgUtilTool(tools_system_pm.PkgUtilTool):
def __init__(self, *args, **kwargs):
super(PkgUtilTool, self).__init__(output=_global_output, *args, **kwargs)
class PacManTool(tools_system_pm.PacManTool):
def __init__(self, *args, **kwargs):
super(PacManTool, self).__init__(output=_global_output, *args, **kwargs)
class ZypperTool(tools_system_pm.ZypperTool):
def __init__(self, *args, **kwargs):
super(ZypperTool, self).__init__(output=_global_output, *args, **kwargs)
vs_installation_path = tools_win.vs_installation_path
vswhere = tools_win.vswhere
vs_comntools = tools_win.vs_comntools
find_windows_10_sdk = tools_win.find_windows_10_sdk
escape_windows_cmd = tools_win.escape_windows_cmd
get_cased_path = tools_win.get_cased_path
MSYS2 = tools_win.MSYS2
MSYS = tools_win.MSYS
CYGWIN = tools_win.CYGWIN
WSL = tools_win.WSL
SFU = tools_win.SFU
unix_path = tools_win.unix_path
run_in_windows_bash = tools_win.run_in_windows_bash
@contextmanager
def vcvars(*args, **kwargs):
with tools_win.vcvars(output=_global_output, *args, **kwargs):
yield
def msvc_build_command(*args, **kwargs):
return tools_win.msvc_build_command(output=_global_output, *args, **kwargs)
def build_sln_command(*args, **kwargs):
return tools_win.build_sln_command(output=_global_output, *args, **kwargs)
def vcvars_command(*args, **kwargs):
return tools_win.vcvars_command(output=_global_output, *args, **kwargs)
def vcvars_dict(*args, **kwargs):
return tools_win.vcvars_dict(output=_global_output, *args, **kwargs)
def latest_vs_version_installed(*args, **kwargs):
return tools_win.latest_vs_version_installed(output=_global_output, *args, **kwargs)
try:
os_info = OSInfo()
except Exception as exc:
logger.error(exc)
_global_output.error("Error detecting os_info")
| true | true |
f73be4b323b6f527fad757910c441cb65ddf9c62 | 116,467 | py | Python | tools/machine_learning/dataset/dataset_2000.py | emiliopomares/flow-separation-prediction | ca4585a8263cd5933889fbd763154e2f3851969a | [
"MIT"
] | null | null | null | tools/machine_learning/dataset/dataset_2000.py | emiliopomares/flow-separation-prediction | ca4585a8263cd5933889fbd763154e2f3851969a | [
"MIT"
] | null | null | null | tools/machine_learning/dataset/dataset_2000.py | emiliopomares/flow-separation-prediction | ca4585a8263cd5933889fbd763154e2f3851969a | [
"MIT"
] | null | null | null | dataset = [[575, 1.8478238142818033, 335.84132856507614, 4.0, 40.0, 18.0, 1.0], [576, 15.77686287348893, 42.441254859835766, 4.0, 40.0, 18.0, 1.0], [577, 2.5651959697179394, 460.4148384334217, 4.0, 40.0, 18.0, 0.796539971089987], [578, 17.085286455903624, 1722.0569227642409, 4.0, 40.0, 18.0, 0.20868652310905142], [579, 0.8922404456295938, 544.2232815722436, 4.0, 40.0, 18.0, 0.8391968483418055], [580, 1.855625614157908, 324.0611877697628, 4.0, 40.0, 18.0, 1.0], [581, 13.887583805638116, 370.66355345479735, 4.0, 40.0, 18.0, 0.4156268407842482], [582, 4.574319040915774, 1916.680593529377, 4.0, 40.0, 18.0, 0.4731431536067498], [583, 11.620584764397769, 1765.727157030207, 4.0, 40.0, 18.0, 0.3125490112539554], [584, 2.6235186150459104, 1833.1693569752063, 4.0, 40.0, 18.0, 0.5374621823117757], [585, 13.484073962894696, 660.3699928897363, 4.0, 40.0, 18.0, 0.3539297235585189], [586, 14.039143301499495, 1194.4759299020527, 4.0, 40.0, 18.0, 0.2909060179303773], [587, 13.912217642029711, 1554.008389226998, 4.0, 40.0, 18.0, 0.2743234627435817], [588, 13.99433118037543, 1828.6039911998955, 4.0, 40.0, 18.0, 0.26137292500800485], [589, 13.97985812120516, 1457.7115189147835, 4.0, 40.0, 18.0, 0.277698127963447], [590, 14.235082927476668, 1533.990342569619, 4.0, 40.0, 18.0, 0.2684974619970663], [591, 0.14438911944825494, 1552.4111707538157, 4.0, 40.0, 18.0, 0.6646778218759786], [592, 12.838639587489782, 897.1618056405601, 4.0, 40.0, 18.0, 0.3404965898150903], [593, 11.149353897963573, 598.125634720337, 4.0, 40.0, 18.0, 0.42099275208739695], [594, 2.6770942046494586, 1985.9408533826909, 4.0, 40.0, 18.0, 0.5310687434251105], [595, 2.9997563631283897, 1040.9042861817666, 4.0, 40.0, 18.0, 0.6086804760399638], [596, 2.3022674475226914, 307.9928831694318, 4.0, 40.0, 18.0, 1.0], [597, 16.291429852780407, 1256.6722690719946, 4.0, 40.0, 18.0, 0.24371063020372327], [598, 15.922124691331389, 507.48840748529494, 4.0, 40.0, 18.0, 0.32681641236328557], [599, 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0.2551645921835903], [1885, 18.82343096465152, 1549.2431926707948, 4.0, 40.0, 18.0, 0.18682981601075516], [1886, 23.61101116343376, 56.31308310153682, 4.0, 40.0, 18.0, 0.49506669102635725], [1887, 23.95830884279107, 1045.6197660382663, 4.0, 40.0, 18.0, 0.14087027259529966], [1888, 23.345318437250764, 319.4774483010274, 4.0, 40.0, 18.0, 0.23487003957382097], [1889, 12.174557295359497, 931.7795219644557, 4.0, 40.0, 18.0, 0.3516587971098201], [1890, 3.5628851270324864, 76.89602618966957, 4.0, 40.0, 18.0, 1.0], [1891, 11.649118247663527, 105.2911591759369, 4.0, 40.0, 18.0, 0.725188645412746], [1892, 3.9847097518608683, 1168.6613357106414, 4.0, 40.0, 18.0, 0.5462011252226638], [1893, 12.98039814328282, 338.80503922976135, 4.0, 40.0, 18.0, 0.4440051464878844], [1894, 21.76384222653067, 480.8128790233031, 4.0, 40.0, 18.0, 0.22292047912716645], [1895, 10.824715885623428, 1343.2037601109316, 4.0, 40.0, 18.0, 0.35043707750717284], [1896, 14.377652345347828, 82.74520524662913, 4.0, 40.0, 18.0, 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3.8926884753925113, 95.320417891007, 4.0, 40.0, 18.0, 1.0], [1910, 12.547220265154015, 91.96867547022983, 4.0, 40.0, 18.0, 0.728614752969701], [1911, 4.923249396313468, 310.3760108864668, 4.0, 40.0, 18.0, 0.7306932411166738], [1912, 10.02610472558947, 990.2987729242266, 4.0, 40.0, 18.0, 0.39704974290989803], [1913, 3.9078072998742024, 387.5135572264773, 4.0, 40.0, 18.0, 0.7258905416879472], [1914, 21.744716139095313, 1767.8478011771438, 4.0, 40.0, 18.0, 0.13908581986860621], [1915, 2.258711681922873, 1442.7725169981622, 4.0, 40.0, 18.0, 0.5786283798840285], [1916, 4.43420285521203, 156.40248211760132, 4.0, 40.0, 18.0, 1.0], [1917, 3.007989913402234, 1436.7553318710109, 4.0, 40.0, 18.0, 0.5534230178826371], [1918, 13.744860551147333, 1572.8546028465985, 4.0, 40.0, 18.0, 0.27685456524960983], [1919, 23.27995927255027, 156.23765138005308, 4.0, 40.0, 18.0, 0.32000516996572254], [1920, 8.349389741242916, 640.1012606436612, 4.0, 40.0, 18.0, 0.48700336274058853], [1921, 24.598200648696576, 137.44811527476065, 4.0, 40.0, 18.0, 0.3128780433590026], [1922, 12.93047186229432, 813.7423213302408, 4.0, 40.0, 18.0, 0.3469308457087111], [1923, 6.262485283124522, 1271.038040522172, 4.0, 40.0, 18.0, 0.4669558449987634], [1924, 6.50291860444277, 1966.668743292896, 4.0, 40.0, 18.0, 0.42030857145850026], [1925, 7.307331785538926, 1816.7122146640293, 4.0, 40.0, 18.0, 0.4081257902198177], [1926, 10.492943735372195, 46.371938240960525, 4.0, 40.0, 18.0, 1.0], [1927, 11.596304058530341, 1138.2027878444414, 4.0, 40.0, 18.0, 0.3468385222973252], [1928, 24.347352509983764, 58.69897882967909, 4.0, 40.0, 18.0, 0.46688910309491094], [1929, 3.6471385177059306, 1746.3387323521263, 4.0, 40.0, 18.0, 0.5101054449988423], [1930, 11.984096587369834, 624.0367089266477, 4.0, 40.0, 18.0, 0.396616617947586], [1931, 20.746839831400308, 187.8661004123425, 4.0, 40.0, 18.0, 0.3452682640833985], [1932, 10.23441975893285, 139.37534073080505, 4.0, 40.0, 18.0, 0.7057650418001672], [1933, 14.985214285443957, 600.4120009643879, 4.0, 40.0, 18.0, 0.33084454126775564], [1934, 22.611523606726216, 1237.5895238501282, 4.0, 40.0, 18.0, 0.1473302200483939], [1935, 15.907275581625383, 1903.0670364742768, 4.0, 40.0, 18.0, 0.2238641850440459], [1936, 11.023549498851404, 1110.8333859056133, 4.0, 40.0, 18.0, 0.3623046943340012], [1937, 13.69754230078197, 1073.0787824364565, 4.0, 40.0, 18.0, 0.3057402903004179], [1938, 17.440538792545222, 1033.3911317848074, 4.0, 40.0, 18.0, 0.2359067931255248], [1939, 13.64670934972993, 423.3326930470313, 4.0, 40.0, 18.0, 0.40049820796241], [1940, 13.764247823540893, 1966.937016735742, 4.0, 40.0, 18.0, 0.261919817737229], [1941, 0.9288291950075078, 1792.9297687215046, 4.0, 40.0, 18.0, 0.5984580405373244], [1942, 13.212485977818167, 672.3530570879083, 4.0, 40.0, 18.0, 0.3592985569326885], [1943, 1.0258959085792245, 1092.2137459756923, 4.0, 40.0, 18.0, 0.6617951591553232], [1944, 17.8351020228825, 1726.787789756901, 4.0, 40.0, 18.0, 0.19635421570752118], [1945, 21.21943335691722, 419.59032057933365, 4.0, 40.0, 18.0, 0.24388329033583123], [1946, 24.22709591969878, 1396.7414599108656, 4.0, 40.0, 18.0, 0.12309308896902814], [1947, 20.924883922295642, 989.2047567721401, 4.0, 40.0, 18.0, 0.18260497786986535], [1948, 18.0435504079589, 89.18567663017862, 4.0, 40.0, 18.0, 0.5433952214790109], [1949, 24.27081830466515, 41.89502986189963, 4.0, 40.0, 18.0, 0.5537057327452106], [1950, 21.27899200474617, 835.5406116648644, 4.0, 40.0, 18.0, 0.1885390296117541], [1951, 20.364665078772774, 422.6417340808581, 4.0, 40.0, 18.0, 0.25794499753128536], [1952, 5.103262203791576, 767.5841880106092, 4.0, 40.0, 18.0, 0.5650524825877493], [1953, 1.681210788030052, 820.1569171059258, 4.0, 40.0, 18.0, 0.680437337467596], [1954, 21.288120100189246, 1634.73591190358, 4.0, 40.0, 18.0, 0.14941347100784588], [1955, 11.086447344058723, 68.02700472787981, 4.0, 40.0, 18.0, 1.0], [1956, 9.448475175225138, 129.90593672321307, 4.0, 40.0, 18.0, 0.7561368113011686], [1957, 2.1903150727018206, 194.4411860511767, 4.0, 40.0, 18.0, 1.0], [1958, 17.75500781863005, 66.80001080240127, 4.0, 40.0, 18.0, 0.6271295197613048], [1959, 19.762523777533932, 726.9728304536992, 4.0, 40.0, 18.0, 0.22202010047235224], [1960, 20.82066505363703, 1872.6929550943491, 4.0, 40.0, 18.0, 0.14777804591993463], [1961, 19.218788295963247, 1791.6591725648584, 4.0, 40.0, 18.0, 0.17300785394950557], [1962, 7.867769431003787, 55.0881303536056, 4.0, 40.0, 18.0, 1.0], [1963, 19.899012972436378, 690.9612458112083, 4.0, 40.0, 18.0, 0.22372600571775045], [1964, 13.741683645695776, 1418.9175494785252, 4.0, 40.0, 18.0, 0.28411193805018514], [1965, 9.256316527727579, 614.500089599888, 4.0, 40.0, 18.0, 0.4663692566048468], [1966, 4.772804792592991, 1608.3447522206286, 4.0, 40.0, 18.0, 0.48498603836504206], [1967, 10.60974817077909, 155.3744712390088, 4.0, 40.0, 18.0, 0.6641080621927825], [1968, 11.77922600197693, 829.1528369129031, 4.0, 40.0, 18.0, 0.3719392095098326], [1969, 13.380973365094222, 1077.8286832314043, 4.0, 40.0, 18.0, 0.3118559156014599], [1970, 22.466102035208284, 66.22839536718286, 4.0, 40.0, 18.0, 0.4874530554474939], [1971, 16.16565090160762, 1796.780949291825, 4.0, 40.0, 18.0, 0.22256554305893014], [1972, 5.588177042494847, 1697.075855727831, 4.0, 40.0, 18.0, 0.4562990451243489], [1973, 7.107503324884531, 1949.8536251772614, 4.0, 40.0, 18.0, 0.40726697206450485], [1974, 2.7187146569001452, 904.0802167604023, 4.0, 40.0, 18.0, 0.6260851635866334], [1975, 14.935093211954534, 607.3240779492281, 4.0, 40.0, 18.0, 0.33079591695055427], [1976, 24.270104775462823, 392.3622987549394, 4.0, 40.0, 18.0, 0.20260298752096173], [1977, 17.993690554562384, 80.3881711350263, 4.0, 40.0, 18.0, 0.5699337243185855], [1978, 22.297523888819526, 1349.9246942381608, 4.0, 40.0, 18.0, 0.14648876337987388], [1979, 1.0352863870984663, 50.09247613746339, 4.0, 40.0, 18.0, 1.0], [1980, 6.468476365464065, 113.04680706875692, 4.0, 40.0, 18.0, 1.0], [1981, 1.1031548990482447, 1878.4048572142203, 4.0, 40.0, 18.0, 0.5865813653678678], [1982, 4.010539300634067, 207.12738672209704, 4.0, 40.0, 18.0, 0.8637305682980617], [1983, 23.308990416271254, 76.40168591179443, 4.0, 40.0, 18.0, 0.4368493801397688], [1984, 14.862987689927046, 1805.1760032035231, 4.0, 40.0, 18.0, 0.24608120532147834], [1985, 2.825312477235914, 352.84885975814996, 4.0, 40.0, 18.0, 0.7894656488806221], [1986, 24.022808787226566, 1050.5860653565364, 4.0, 40.0, 18.0, 0.139899557763747], [1987, 7.51679142904062, 90.9751315562337, 4.0, 40.0, 18.0, 1.0], [1988, 9.429987701915419, 1216.7729076176734, 4.0, 40.0, 18.0, 0.39225766456653555], [1989, 1.0944192219756483, 1233.398827848469, 4.0, 40.0, 18.0, 0.641796622013196], [1990, 10.388878917456587, 1418.7904022947578, 4.0, 40.0, 18.0, 0.35602975557568084], [1991, 14.842402896319818, 1977.013567387179, 4.0, 40.0, 18.0, 0.2410396884651546], [1992, 11.482628687738552, 1011.5742119700101, 4.0, 40.0, 18.0, 0.35999433660814334], [1993, 23.346790872516337, 1162.0536614731188, 4.0, 40.0, 18.0, 0.14216513276602566], [1994, 3.5491767050882834, 933.3096372785426, 4.0, 40.0, 18.0, 0.5913856475535725], [1995, 3.7449721566626626, 97.5434341757727, 4.0, 40.0, 18.0, 1.0]]
| true | true |
f73be5427fbe24030a0f1a642b8b36c5bc757baa | 7,447 | py | Python | git/objects/fun.py | yarikoptic/GitPython | 7576b282013249a2b20ccda4acacefd5e625ea39 | [
"BSD-3-Clause"
] | 1 | 2020-10-15T06:16:48.000Z | 2020-10-15T06:16:48.000Z | git/objects/fun.py | yarikoptic/GitPython | 7576b282013249a2b20ccda4acacefd5e625ea39 | [
"BSD-3-Clause"
] | null | null | null | git/objects/fun.py | yarikoptic/GitPython | 7576b282013249a2b20ccda4acacefd5e625ea39 | [
"BSD-3-Clause"
] | null | null | null | """Module with functions which are supposed to be as fast as possible"""
from stat import S_ISDIR
__all__ = ('tree_to_stream', 'tree_entries_from_data', 'traverse_trees_recursive',
'traverse_tree_recursive')
def tree_to_stream(entries, write):
"""Write the give list of entries into a stream using its write method
:param entries: **sorted** list of tuples with (binsha, mode, name)
:param write: write method which takes a data string"""
ord_zero = ord('0')
bit_mask = 7 # 3 bits set
for binsha, mode, name in entries:
mode_str = ''
for i in xrange(6):
mode_str = chr(((mode >> (i*3)) & bit_mask) + ord_zero) + mode_str
# END for each 8 octal value
# git slices away the first octal if its zero
if mode_str[0] == '0':
mode_str = mode_str[1:]
# END save a byte
# here it comes: if the name is actually unicode, the replacement below
# will not work as the binsha is not part of the ascii unicode encoding -
# hence we must convert to an utf8 string for it to work properly.
# According to my tests, this is exactly what git does, that is it just
# takes the input literally, which appears to be utf8 on linux.
if isinstance(name, unicode):
name = name.encode("utf8")
write("%s %s\0%s" % (mode_str, name, binsha))
# END for each item
def tree_entries_from_data(data):
"""Reads the binary representation of a tree and returns tuples of Tree items
:param data: data block with tree data
:return: list(tuple(binsha, mode, tree_relative_path), ...)"""
ord_zero = ord('0')
len_data = len(data)
i = 0
out = list()
while i < len_data:
mode = 0
# read mode
# Some git versions truncate the leading 0, some don't
# The type will be extracted from the mode later
while data[i] != ' ':
# move existing mode integer up one level being 3 bits
# and add the actual ordinal value of the character
mode = (mode << 3) + (ord(data[i]) - ord_zero)
i += 1
# END while reading mode
# byte is space now, skip it
i += 1
# parse name, it is NULL separated
ns = i
while data[i] != '\0':
i += 1
# END while not reached NULL
# default encoding for strings in git is utf8
# Only use the respective unicode object if the byte stream was encoded
name = data[ns:i]
name_enc = name.decode("utf-8")
if len(name) > len(name_enc):
name = name_enc
# END handle encoding
# byte is NULL, get next 20
i += 1
sha = data[i:i+20]
i = i + 20
out.append((sha, mode, name))
# END for each byte in data stream
return out
def _find_by_name(tree_data, name, is_dir, start_at):
"""return data entry matching the given name and tree mode
or None.
Before the item is returned, the respective data item is set
None in the tree_data list to mark it done"""
try:
item = tree_data[start_at]
if item and item[2] == name and S_ISDIR(item[1]) == is_dir:
tree_data[start_at] = None
return item
except IndexError:
pass
# END exception handling
for index, item in enumerate(tree_data):
if item and item[2] == name and S_ISDIR(item[1]) == is_dir:
tree_data[index] = None
return item
# END if item matches
# END for each item
return None
def _to_full_path(item, path_prefix):
"""Rebuild entry with given path prefix"""
if not item:
return item
return (item[0], item[1], path_prefix+item[2])
def traverse_trees_recursive(odb, tree_shas, path_prefix):
"""
:return: list with entries according to the given binary tree-shas.
The result is encoded in a list
of n tuple|None per blob/commit, (n == len(tree_shas)), where
* [0] == 20 byte sha
* [1] == mode as int
* [2] == path relative to working tree root
The entry tuple is None if the respective blob/commit did not
exist in the given tree.
:param tree_shas: iterable of shas pointing to trees. All trees must
be on the same level. A tree-sha may be None in which case None
:param path_prefix: a prefix to be added to the returned paths on this level,
set it '' for the first iteration
:note: The ordering of the returned items will be partially lost"""
trees_data = list()
nt = len(tree_shas)
for tree_sha in tree_shas:
if tree_sha is None:
data = list()
else:
data = tree_entries_from_data(odb.stream(tree_sha).read())
# END handle muted trees
trees_data.append(data)
# END for each sha to get data for
out = list()
out_append = out.append
# find all matching entries and recursively process them together if the match
# is a tree. If the match is a non-tree item, put it into the result.
# Processed items will be set None
for ti, tree_data in enumerate(trees_data):
for ii, item in enumerate(tree_data):
if not item:
continue
# END skip already done items
entries = [ None for n in range(nt) ]
entries[ti] = item
sha, mode, name = item # its faster to unpack
is_dir = S_ISDIR(mode) # type mode bits
# find this item in all other tree data items
# wrap around, but stop one before our current index, hence
# ti+nt, not ti+1+nt
for tio in range(ti+1, ti+nt):
tio = tio % nt
entries[tio] = _find_by_name(trees_data[tio], name, is_dir, ii)
# END for each other item data
# if we are a directory, enter recursion
if is_dir:
out.extend(traverse_trees_recursive(odb, [((ei and ei[0]) or None) for ei in entries], path_prefix+name+'/'))
else:
out_append(tuple(_to_full_path(e, path_prefix) for e in entries))
# END handle recursion
# finally mark it done
tree_data[ii] = None
# END for each item
# we are done with one tree, set all its data empty
del(tree_data[:])
# END for each tree_data chunk
return out
def traverse_tree_recursive(odb, tree_sha, path_prefix):
"""
:return: list of entries of the tree pointed to by the binary tree_sha. An entry
has the following format:
* [0] 20 byte sha
* [1] mode as int
* [2] path relative to the repository
:param path_prefix: prefix to prepend to the front of all returned paths"""
entries = list()
data = tree_entries_from_data(odb.stream(tree_sha).read())
# unpacking/packing is faster than accessing individual items
for sha, mode, name in data:
if S_ISDIR(mode):
entries.extend(traverse_tree_recursive(odb, sha, path_prefix+name+'/'))
else:
entries.append((sha, mode, path_prefix+name))
# END for each item
return entries
| 37.235 | 125 | 0.587485 | from stat import S_ISDIR
__all__ = ('tree_to_stream', 'tree_entries_from_data', 'traverse_trees_recursive',
'traverse_tree_recursive')
def tree_to_stream(entries, write):
ord_zero = ord('0')
bit_mask = 7
for binsha, mode, name in entries:
mode_str = ''
for i in xrange(6):
mode_str = chr(((mode >> (i*3)) & bit_mask) + ord_zero) + mode_str
if mode_str[0] == '0':
mode_str = mode_str[1:]
if isinstance(name, unicode):
name = name.encode("utf8")
write("%s %s\0%s" % (mode_str, name, binsha))
def tree_entries_from_data(data):
ord_zero = ord('0')
len_data = len(data)
i = 0
out = list()
while i < len_data:
mode = 0
# The type will be extracted from the mode later
while data[i] != ' ':
# move existing mode integer up one level being 3 bits
# and add the actual ordinal value of the character
mode = (mode << 3) + (ord(data[i]) - ord_zero)
i += 1
# END while reading mode
# byte is space now, skip it
i += 1
# parse name, it is NULL separated
ns = i
while data[i] != '\0':
i += 1
# END while not reached NULL
# default encoding for strings in git is utf8
# Only use the respective unicode object if the byte stream was encoded
name = data[ns:i]
name_enc = name.decode("utf-8")
if len(name) > len(name_enc):
name = name_enc
# END handle encoding
# byte is NULL, get next 20
i += 1
sha = data[i:i+20]
i = i + 20
out.append((sha, mode, name))
# END for each byte in data stream
return out
def _find_by_name(tree_data, name, is_dir, start_at):
try:
item = tree_data[start_at]
if item and item[2] == name and S_ISDIR(item[1]) == is_dir:
tree_data[start_at] = None
return item
except IndexError:
pass
# END exception handling
for index, item in enumerate(tree_data):
if item and item[2] == name and S_ISDIR(item[1]) == is_dir:
tree_data[index] = None
return item
# END if item matches
# END for each item
return None
def _to_full_path(item, path_prefix):
if not item:
return item
return (item[0], item[1], path_prefix+item[2])
def traverse_trees_recursive(odb, tree_shas, path_prefix):
trees_data = list()
nt = len(tree_shas)
for tree_sha in tree_shas:
if tree_sha is None:
data = list()
else:
data = tree_entries_from_data(odb.stream(tree_sha).read())
# END handle muted trees
trees_data.append(data)
# END for each sha to get data for
out = list()
out_append = out.append
# find all matching entries and recursively process them together if the match
# is a tree. If the match is a non-tree item, put it into the result.
# Processed items will be set None
for ti, tree_data in enumerate(trees_data):
for ii, item in enumerate(tree_data):
if not item:
continue
# END skip already done items
entries = [ None for n in range(nt) ]
entries[ti] = item
sha, mode, name = item # its faster to unpack
is_dir = S_ISDIR(mode) # type mode bits
# find this item in all other tree data items
# wrap around, but stop one before our current index, hence
# ti+nt, not ti+1+nt
for tio in range(ti+1, ti+nt):
tio = tio % nt
entries[tio] = _find_by_name(trees_data[tio], name, is_dir, ii)
# END for each other item data
# if we are a directory, enter recursion
if is_dir:
out.extend(traverse_trees_recursive(odb, [((ei and ei[0]) or None) for ei in entries], path_prefix+name+'/'))
else:
out_append(tuple(_to_full_path(e, path_prefix) for e in entries))
# END handle recursion
# finally mark it done
tree_data[ii] = None
# END for each item
# we are done with one tree, set all its data empty
del(tree_data[:])
# END for each tree_data chunk
return out
def traverse_tree_recursive(odb, tree_sha, path_prefix):
entries = list()
data = tree_entries_from_data(odb.stream(tree_sha).read())
# unpacking/packing is faster than accessing individual items
for sha, mode, name in data:
if S_ISDIR(mode):
entries.extend(traverse_tree_recursive(odb, sha, path_prefix+name+'/'))
else:
entries.append((sha, mode, path_prefix+name))
# END for each item
return entries
| true | true |
f73be5dcad677cd24db42711894839a5ab1fc3b9 | 1,614 | py | Python | installers/aws-eks-cdk/lib/handlers/eksAlbLoadBalancer/index.py | hagerupe/legend | 0131369145adb997832c99f0ef886ff9f7cc67f4 | [
"Apache-2.0"
] | 1 | 2021-02-02T00:12:25.000Z | 2021-02-02T00:12:25.000Z | installers/aws-eks-cdk/lib/handlers/eksAlbLoadBalancer/index.py | epsstan/legend | a0b1ee08bc92fd95514fe2e985fa8b809c3870d7 | [
"Apache-2.0"
] | 1 | 2021-04-07T15:34:22.000Z | 2021-04-07T15:34:22.000Z | installers/aws-eks-cdk/lib/handlers/eksAlbLoadBalancer/index.py | hagerupe/legend | 0131369145adb997832c99f0ef886ff9f7cc67f4 | [
"Apache-2.0"
] | 2 | 2021-01-27T15:41:10.000Z | 2021-02-13T23:40:19.000Z | import logging as log
import cfnresponse
import boto3
import hashlib
import time
log.getLogger().setLevel(log.INFO)
client = boto3.client('elbv2')
def main(event, context):
fqn = event['StackId'] + event['LogicalResourceId']
physical_id = hashlib.md5(fqn.encode('utf-8')).hexdigest()
log.info(physical_id)
try:
log.info('Input event: %s', event)
eksCluster = event['ResourceProperties']['Cluster']
stack = event['ResourceProperties']['ClusterStack']
for x in range(20):
loadBalancers = client.describe_load_balancers()
for lb in loadBalancers['LoadBalancers']:
tagDescriptions = client.describe_tags(ResourceArns = [ lb['LoadBalancerArn'] ])
tags = tagDescriptions['TagDescriptions'][0]['Tags']
stackTag = list(filter(lambda tag: tag['Key'] == 'ingress.k8s.aws/stack', tags))
clusterTag = list(filter(lambda tag: tag['Key'] == 'elbv2.k8s.aws/cluster', tags))
if len(stackTag) > 0 and stackTag[0]['Value'] == stack and len(clusterTag) > 0 and clusterTag[0]['Value'] == eksCluster:
attributes = {
'loadBalancerDnsName': lb['DNSName'],
'loadBalancerCanonicalHostedZoneId': lb['CanonicalHostedZoneId'],
}
cfnresponse.send(event, context, cfnresponse.SUCCESS, attributes, physical_id)
return None
time.sleep(30)
cfnresponse.send(event, context, cfnresponse.FAILED, {}, physical_id)
except Exception as e:
log.exception(e)
# cfnresponse's error message is always "see CloudWatch"
cfnresponse.send(event, context, cfnresponse.FAILED, {}, physical_id) | 38.428571 | 128 | 0.67658 | import logging as log
import cfnresponse
import boto3
import hashlib
import time
log.getLogger().setLevel(log.INFO)
client = boto3.client('elbv2')
def main(event, context):
fqn = event['StackId'] + event['LogicalResourceId']
physical_id = hashlib.md5(fqn.encode('utf-8')).hexdigest()
log.info(physical_id)
try:
log.info('Input event: %s', event)
eksCluster = event['ResourceProperties']['Cluster']
stack = event['ResourceProperties']['ClusterStack']
for x in range(20):
loadBalancers = client.describe_load_balancers()
for lb in loadBalancers['LoadBalancers']:
tagDescriptions = client.describe_tags(ResourceArns = [ lb['LoadBalancerArn'] ])
tags = tagDescriptions['TagDescriptions'][0]['Tags']
stackTag = list(filter(lambda tag: tag['Key'] == 'ingress.k8s.aws/stack', tags))
clusterTag = list(filter(lambda tag: tag['Key'] == 'elbv2.k8s.aws/cluster', tags))
if len(stackTag) > 0 and stackTag[0]['Value'] == stack and len(clusterTag) > 0 and clusterTag[0]['Value'] == eksCluster:
attributes = {
'loadBalancerDnsName': lb['DNSName'],
'loadBalancerCanonicalHostedZoneId': lb['CanonicalHostedZoneId'],
}
cfnresponse.send(event, context, cfnresponse.SUCCESS, attributes, physical_id)
return None
time.sleep(30)
cfnresponse.send(event, context, cfnresponse.FAILED, {}, physical_id)
except Exception as e:
log.exception(e)
cfnresponse.send(event, context, cfnresponse.FAILED, {}, physical_id) | true | true |
f73be7b40925c0d767653e9194b3df819eeb147d | 975 | py | Python | quantize_models.py | LordRaivo/ToxicBot | ebc09064afef0f4af5def1022eada9ef109d27ac | [
"MIT"
] | null | null | null | quantize_models.py | LordRaivo/ToxicBot | ebc09064afef0f4af5def1022eada9ef109d27ac | [
"MIT"
] | null | null | null | quantize_models.py | LordRaivo/ToxicBot | ebc09064afef0f4af5def1022eada9ef109d27ac | [
"MIT"
] | 1 | 2022-02-03T23:49:29.000Z | 2022-02-03T23:49:29.000Z | import torch
import torch.nn as nn
import models
import os
import pickle
import glob
import json
import numpy as np
backend = 'fbgemm'
def split_path(path):
_, path = os.path.splitdrive(path)
folders = []
while 1:
path, folder = os.path.split(path)
if folder != "":
folders.append(folder)
elif path == "\\" or path == "":
break
folders.reverse()
return folders
if __name__ == "__main__":
folder = 'models'
for path in glob.glob(f'{folder}/*/'):
model_name = split_path(path)[-1].split('_')[0]
model_class = getattr(models, model_name)
modelpath = os.path.join(path, 'model.pt')
smallmodelpath = os.path.join(path, 'model_sml.pt')
argspath = os.path.join(path, 'args.txt')
with open(argspath, 'r') as f:
args = json.loads(f.readline())
model = model_class(**args)
model.load_state_dict(torch.load(modelpath, map_location=torch.device('cpu')))
model = model.half()
model.eval()
torch.save(model.state_dict(), smallmodelpath) | 25 | 80 | 0.683077 | import torch
import torch.nn as nn
import models
import os
import pickle
import glob
import json
import numpy as np
backend = 'fbgemm'
def split_path(path):
_, path = os.path.splitdrive(path)
folders = []
while 1:
path, folder = os.path.split(path)
if folder != "":
folders.append(folder)
elif path == "\\" or path == "":
break
folders.reverse()
return folders
if __name__ == "__main__":
folder = 'models'
for path in glob.glob(f'{folder}/*/'):
model_name = split_path(path)[-1].split('_')[0]
model_class = getattr(models, model_name)
modelpath = os.path.join(path, 'model.pt')
smallmodelpath = os.path.join(path, 'model_sml.pt')
argspath = os.path.join(path, 'args.txt')
with open(argspath, 'r') as f:
args = json.loads(f.readline())
model = model_class(**args)
model.load_state_dict(torch.load(modelpath, map_location=torch.device('cpu')))
model = model.half()
model.eval()
torch.save(model.state_dict(), smallmodelpath) | true | true |
f73be7c1487f54c21bfe94df00b0db808b57f4ff | 1,278 | py | Python | nuitka/codegen/OperatorCodes.py | sthagen/Nuitka-Nuitka | 023dc76eeafd9c53ee2a51931474ddd98a3ba083 | [
"Apache-2.0"
] | null | null | null | nuitka/codegen/OperatorCodes.py | sthagen/Nuitka-Nuitka | 023dc76eeafd9c53ee2a51931474ddd98a3ba083 | [
"Apache-2.0"
] | null | null | null | nuitka/codegen/OperatorCodes.py | sthagen/Nuitka-Nuitka | 023dc76eeafd9c53ee2a51931474ddd98a3ba083 | [
"Apache-2.0"
] | null | null | null | # Copyright 2022, Kay Hayen, mailto:kay.hayen@gmail.com
#
# Part of "Nuitka", an optimizing Python compiler that is compatible and
# integrates with CPython, but also works on its own.
#
# 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.
#
""" Operator code tables
These are mostly used to look up the Python C/API from operations or a wrapper used.
"""
unary_operator_codes = {
"UAdd": ("PyNumber_Positive", 1),
"USub": ("PyNumber_Negative", 1),
"Invert": ("PyNumber_Invert", 1),
"Repr": ("PyObject_Repr", 1),
"Not": ("UNARY_NOT", 0),
}
rich_comparison_codes = {
"Lt": "LT",
"LtE": "LE",
"Eq": "EQ",
"NotEq": "NE",
"Gt": "GT",
"GtE": "GE",
}
containing_comparison_codes = ("In", "NotIn")
| 30.428571 | 84 | 0.659624 |
unary_operator_codes = {
"UAdd": ("PyNumber_Positive", 1),
"USub": ("PyNumber_Negative", 1),
"Invert": ("PyNumber_Invert", 1),
"Repr": ("PyObject_Repr", 1),
"Not": ("UNARY_NOT", 0),
}
rich_comparison_codes = {
"Lt": "LT",
"LtE": "LE",
"Eq": "EQ",
"NotEq": "NE",
"Gt": "GT",
"GtE": "GE",
}
containing_comparison_codes = ("In", "NotIn")
| true | true |
f73be9e2c4accbcd5522d0c33b4871bb452b4176 | 158 | py | Python | contrib/wallettools/walletunlock.py | GoldRushGOLDR/Gold-Rush_GOLDR | 97c25d6e1fcdd2e8a243d267b2c3c280e6fa8ad4 | [
"MIT"
] | null | null | null | contrib/wallettools/walletunlock.py | GoldRushGOLDR/Gold-Rush_GOLDR | 97c25d6e1fcdd2e8a243d267b2c3c280e6fa8ad4 | [
"MIT"
] | null | null | null | contrib/wallettools/walletunlock.py | GoldRushGOLDR/Gold-Rush_GOLDR | 97c25d6e1fcdd2e8a243d267b2c3c280e6fa8ad4 | [
"MIT"
] | null | null | null | from jsonrpc import ServiceProxy
access = ServiceProxy("http://127.0.0.1:4354")
pwd = raw_input("Enter wallet passphrase: ")
access.walletpassphrase(pwd, 60)
| 31.6 | 46 | 0.765823 | from jsonrpc import ServiceProxy
access = ServiceProxy("http://127.0.0.1:4354")
pwd = raw_input("Enter wallet passphrase: ")
access.walletpassphrase(pwd, 60)
| true | true |
f73bea86057b0a672eb66c19ecef01f2e50c44e4 | 8,094 | py | Python | msgraph-cli-extensions/v1_0/files_v1_0/azext_files_v1_0/vendored_sdks/files/aio/_files.py | thewahome/msgraph-cli | 33127d9efa23a0e5f5303c93242fbdbb73348671 | [
"MIT"
] | null | null | null | msgraph-cli-extensions/v1_0/files_v1_0/azext_files_v1_0/vendored_sdks/files/aio/_files.py | thewahome/msgraph-cli | 33127d9efa23a0e5f5303c93242fbdbb73348671 | [
"MIT"
] | 22 | 2022-03-29T22:54:37.000Z | 2022-03-29T22:55:27.000Z | msgraph-cli-extensions/v1_0/files_v1_0/azext_files_v1_0/vendored_sdks/files/aio/_files.py | thewahome/msgraph-cli | 33127d9efa23a0e5f5303c93242fbdbb73348671 | [
"MIT"
] | null | null | null | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Optional, TYPE_CHECKING
from azure.mgmt.core import AsyncARMPipelineClient
from msrest import Deserializer, Serializer
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from azure.core.credentials_async import AsyncTokenCredential
from ._configuration import FilesConfiguration
from .operations import DrivesDriveOperations
from .operations import DrivesOperations
from .operations import DrivesListOperations
from .operations import DrivesListContentTypesOperations
from .operations import DrivesListItemsOperations
from .operations import DrivesListItemsVersionsOperations
from .operations import GroupsOperations
from .operations import SharesSharedDriveItemOperations
from .operations import SharesOperations
from .operations import SharesListOperations
from .operations import SharesListContentTypesOperations
from .operations import SharesListItemsOperations
from .operations import SharesListItemsVersionsOperations
from .operations import SharesListItemOperations
from .operations import SharesListItemVersionsOperations
from .operations import SharesPermissionOperations
from .operations import UsersOperations
from .. import models
class Files(object):
"""Files.
:ivar drives_drive: DrivesDriveOperations operations
:vartype drives_drive: files.aio.operations.DrivesDriveOperations
:ivar drives: DrivesOperations operations
:vartype drives: files.aio.operations.DrivesOperations
:ivar drives_list: DrivesListOperations operations
:vartype drives_list: files.aio.operations.DrivesListOperations
:ivar drives_list_content_types: DrivesListContentTypesOperations operations
:vartype drives_list_content_types: files.aio.operations.DrivesListContentTypesOperations
:ivar drives_list_items: DrivesListItemsOperations operations
:vartype drives_list_items: files.aio.operations.DrivesListItemsOperations
:ivar drives_list_items_versions: DrivesListItemsVersionsOperations operations
:vartype drives_list_items_versions: files.aio.operations.DrivesListItemsVersionsOperations
:ivar groups: GroupsOperations operations
:vartype groups: files.aio.operations.GroupsOperations
:ivar shares_shared_drive_item: SharesSharedDriveItemOperations operations
:vartype shares_shared_drive_item: files.aio.operations.SharesSharedDriveItemOperations
:ivar shares: SharesOperations operations
:vartype shares: files.aio.operations.SharesOperations
:ivar shares_list: SharesListOperations operations
:vartype shares_list: files.aio.operations.SharesListOperations
:ivar shares_list_content_types: SharesListContentTypesOperations operations
:vartype shares_list_content_types: files.aio.operations.SharesListContentTypesOperations
:ivar shares_list_items: SharesListItemsOperations operations
:vartype shares_list_items: files.aio.operations.SharesListItemsOperations
:ivar shares_list_items_versions: SharesListItemsVersionsOperations operations
:vartype shares_list_items_versions: files.aio.operations.SharesListItemsVersionsOperations
:ivar shares_list_item: SharesListItemOperations operations
:vartype shares_list_item: files.aio.operations.SharesListItemOperations
:ivar shares_list_item_versions: SharesListItemVersionsOperations operations
:vartype shares_list_item_versions: files.aio.operations.SharesListItemVersionsOperations
:ivar shares_permission: SharesPermissionOperations operations
:vartype shares_permission: files.aio.operations.SharesPermissionOperations
:ivar users: UsersOperations operations
:vartype users: files.aio.operations.UsersOperations
:param credential: Credential needed for the client to connect to Azure.
:type credential: ~azure.core.credentials_async.AsyncTokenCredential
:param top: Show only the first n items.
:type top: int
:param skip: Skip the first n items.
:type skip: int
:param search: Search items by search phrases.
:type search: str
:param filter: Filter items by property values.
:type filter: str
:param count: Include count of items.
:type count: bool
:param str base_url: Service URL
"""
def __init__(
self,
credential: "AsyncTokenCredential",
top: Optional[int] = None,
skip: Optional[int] = None,
search: Optional[str] = None,
filter: Optional[str] = None,
count: Optional[bool] = None,
base_url: Optional[str] = None,
**kwargs: Any
) -> None:
if not base_url:
base_url = 'https://graph.microsoft.com/v1.0'
self._config = FilesConfiguration(credential, top, skip, search, filter, count, **kwargs)
self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
self._serialize = Serializer(client_models)
self._serialize.client_side_validation = False
self._deserialize = Deserializer(client_models)
self.drives_drive = DrivesDriveOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives = DrivesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list = DrivesListOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list_content_types = DrivesListContentTypesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list_items = DrivesListItemsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list_items_versions = DrivesListItemsVersionsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.groups = GroupsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_shared_drive_item = SharesSharedDriveItemOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares = SharesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list = SharesListOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_content_types = SharesListContentTypesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_items = SharesListItemsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_items_versions = SharesListItemsVersionsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_item = SharesListItemOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_item_versions = SharesListItemVersionsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_permission = SharesPermissionOperations(
self._client, self._config, self._serialize, self._deserialize)
self.users = UsersOperations(
self._client, self._config, self._serialize, self._deserialize)
async def close(self) -> None:
await self._client.close()
async def __aenter__(self) -> "Files":
await self._client.__aenter__()
return self
async def __aexit__(self, *exc_details) -> None:
await self._client.__aexit__(*exc_details)
| 51.884615 | 97 | 0.75315 |
from typing import Any, Optional, TYPE_CHECKING
from azure.mgmt.core import AsyncARMPipelineClient
from msrest import Deserializer, Serializer
if TYPE_CHECKING:
from azure.core.credentials_async import AsyncTokenCredential
from ._configuration import FilesConfiguration
from .operations import DrivesDriveOperations
from .operations import DrivesOperations
from .operations import DrivesListOperations
from .operations import DrivesListContentTypesOperations
from .operations import DrivesListItemsOperations
from .operations import DrivesListItemsVersionsOperations
from .operations import GroupsOperations
from .operations import SharesSharedDriveItemOperations
from .operations import SharesOperations
from .operations import SharesListOperations
from .operations import SharesListContentTypesOperations
from .operations import SharesListItemsOperations
from .operations import SharesListItemsVersionsOperations
from .operations import SharesListItemOperations
from .operations import SharesListItemVersionsOperations
from .operations import SharesPermissionOperations
from .operations import UsersOperations
from .. import models
class Files(object):
def __init__(
self,
credential: "AsyncTokenCredential",
top: Optional[int] = None,
skip: Optional[int] = None,
search: Optional[str] = None,
filter: Optional[str] = None,
count: Optional[bool] = None,
base_url: Optional[str] = None,
**kwargs: Any
) -> None:
if not base_url:
base_url = 'https://graph.microsoft.com/v1.0'
self._config = FilesConfiguration(credential, top, skip, search, filter, count, **kwargs)
self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
self._serialize = Serializer(client_models)
self._serialize.client_side_validation = False
self._deserialize = Deserializer(client_models)
self.drives_drive = DrivesDriveOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives = DrivesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list = DrivesListOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list_content_types = DrivesListContentTypesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list_items = DrivesListItemsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.drives_list_items_versions = DrivesListItemsVersionsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.groups = GroupsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_shared_drive_item = SharesSharedDriveItemOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares = SharesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list = SharesListOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_content_types = SharesListContentTypesOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_items = SharesListItemsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_items_versions = SharesListItemsVersionsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_item = SharesListItemOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_list_item_versions = SharesListItemVersionsOperations(
self._client, self._config, self._serialize, self._deserialize)
self.shares_permission = SharesPermissionOperations(
self._client, self._config, self._serialize, self._deserialize)
self.users = UsersOperations(
self._client, self._config, self._serialize, self._deserialize)
async def close(self) -> None:
await self._client.close()
async def __aenter__(self) -> "Files":
await self._client.__aenter__()
return self
async def __aexit__(self, *exc_details) -> None:
await self._client.__aexit__(*exc_details)
| true | true |
f73beb3153c6c928fad7b0cca32a698113dbfc52 | 3,605 | py | Python | PreProcessing/compare_3km_12km_snapshot.py | fuxingwang2018/HCLIMAI | 8673d1b2cb9c5c17e70ba523fa64a8579c48798a | [
"MIT"
] | null | null | null | PreProcessing/compare_3km_12km_snapshot.py | fuxingwang2018/HCLIMAI | 8673d1b2cb9c5c17e70ba523fa64a8579c48798a | [
"MIT"
] | null | null | null | PreProcessing/compare_3km_12km_snapshot.py | fuxingwang2018/HCLIMAI | 8673d1b2cb9c5c17e70ba523fa64a8579c48798a | [
"MIT"
] | null | null | null |
#%matplotlib inline
import matplotlib.pyplot as plt
import netCDF4
plt.switch_backend('agg')
from map_plot import MapPlot
import numpy as np
data_folder = '/nobackup/rossby26/users/sm_fuxwa/AI/standard_data/'
fig_out_path = '/home/sm_fuxwa/Figures/AI/'
# 3km: tas, pr
# 12km: ta500, ta700, ta850, ta950,
# hus500, hus700, hus850, hus950,
# ua500, ua700, ua850, ua950,
# va500, va700, va850, va950,
# phi500, phi700, phi850, phi950,
# tas, pr
var = 'tas'
exp_name = '3km' # 'EOBS20', '3km', '12km'
plot_period = '1h'
if plot_period == '1h':
rec_start = 6590
rec_end = rec_start + 1
ymdhm = 200010011330
scale_factor = 3600
elif plot_period == '1d':
rec_start = 6577
rec_end = 6601
ymdhm = 20001001
scale_factor = 86400
month = 10
period = '200001010000-200912312300' #'200001010000-200912312100' #'200001010030-200912312330'
freq='1hr'
month_dic = { '1':'January',
'2':'February',
'3':'March',
'4':'April',
'5':'May',
'6':'June',
'7':'July',
'8':'August',
'9':'September',
'10':'October',
'11':'November',
'12':'December' }
filein = data_folder + '/' + str(exp_name) + '/' + str(freq) + '/' + str(var) + '/' + str(var) + '_' + str(exp_name) + '_' + str(freq) + '_' + str(period) + '.nc'
#width_def = 12E5
#height_def = 8E5
#lat_0_def = 46.0
#lon_0_def = 11.0
width_def = 4E5
height_def = 3E5
lat_0_def = 46.6
lon_0_def = 11.0
#if exp_name == '12km':
# width_def = 12E5
# height_def = 8E5
# lat_0_def = 46.0
# lon_0_def = 11.0 #14.0
#elif exp_name == '3km':
# width_def = 12E5
# height_def = 8E5
# lat_0_def = 46.0 #45.5
# lon_0_def = 11.0 #16.0
fig_title = " "
lat_name = 'lat' #'latitude'
lon_name = 'lon' #'longitude'
proj_def = 'lcc' # lcc, cyl, ortho
res_def = 'i'
fig_type = '.png'
label_def = ''
extend_def = 'max' #'min', 'max', 'neither', 'both'
cmap_def = 'rainbow'
#cmap_def = 'RdBu_r'
if 'tas' in var:
variable_list = ['tas']
unit = 'K'
extend_def = 'both'
if month >= 5 and month <= 8:
scale_min_def = 275
scale_max_def = 305
else:
scale_min_def = 255
scale_max_def = 285
elif var == 'pr':
variable_list = ['pr']
scale_min_def = 0.0
if plot_period == '1h':
scale_max_def = 12.0
unit = 'mm/hour'
elif plot_period == '1d':
scale_max_def = 150.0
unit = 'mm/day'
nc = netCDF4.Dataset(filein)
lat_sim = nc.variables[lat_name][:]
lon_sim = nc.variables[lon_name][:]
if np.array(lat_sim).ndim == 1 and np.array(lon_sim).ndim == 1:
lon_sim_2d, lat_sim_2d = np.meshgrid(lon_sim, lat_sim)
elif np.array(lat_sim).ndim == 2 and np.array(lon_sim).ndim == 2:
lon_sim_2d = lon_sim
lat_sim_2d = lat_sim
#print nc.variables.keys()
# examine the variables
for var_to_plot in variable_list:
# average over all steps
var_sim_3d = nc.variables[var_to_plot][:,:,:]
#var_sim_2d = var_sim_3d[rec_start - 1 : rec_end - 1, :, :] * scale_factor #np.nanmean(var_sim_3d, axis=0)
var_sim_2d = np.nanmean(var_sim_3d[rec_start - 1 : rec_end - 1, :, :], axis=0) * scale_factor
title_def = var_to_plot + '(' + unit + ')'
fig_out = str(fig_out_path) + exp_name + '_' + var_to_plot + '_' + str(plot_period) + '_' + str(ymdhm) + fig_type
map_plot = MapPlot(fig_out, proj_def, res_def, width_def, height_def, lat_0_def, lon_0_def)
map_plot.Plot_2DField(lat_sim_2d, lon_sim_2d, var_sim_2d[:,:], scale_min_def, scale_max_def, title_def, label_def, cmap_def, extend_def)
#map_plot.Plot_ortho(lat_sim_2d, lon_sim_2d, title_def)
| 28.164063 | 162 | 0.628294 |
import matplotlib.pyplot as plt
import netCDF4
plt.switch_backend('agg')
from map_plot import MapPlot
import numpy as np
data_folder = '/nobackup/rossby26/users/sm_fuxwa/AI/standard_data/'
fig_out_path = '/home/sm_fuxwa/Figures/AI/'
var = 'tas'
exp_name = '3km'
plot_period = '1h'
if plot_period == '1h':
rec_start = 6590
rec_end = rec_start + 1
ymdhm = 200010011330
scale_factor = 3600
elif plot_period == '1d':
rec_start = 6577
rec_end = 6601
ymdhm = 20001001
scale_factor = 86400
month = 10
period = '200001010000-200912312300' ':'January',
'2':'February',
'3':'March',
'4':'April',
'5':'May',
'6':'June',
'7':'July',
'8':'August',
'9':'September',
'10':'October',
'11':'November',
'12':'December' }
filein = data_folder + '/' + str(exp_name) + '/' + str(freq) + '/' + str(var) + '/' + str(var) + '_' + str(exp_name) + '_' + str(freq) + '_' + str(period) + '.nc'
width_def = 4E5
height_def = 3E5
lat_0_def = 46.6
lon_0_def = 11.0
le = " "
lat_name = 'lat'
lon_name = 'lon'
proj_def = 'lcc'
res_def = 'i'
fig_type = '.png'
label_def = ''
extend_def = 'max'
cmap_def = 'rainbow'
if 'tas' in var:
variable_list = ['tas']
unit = 'K'
extend_def = 'both'
if month >= 5 and month <= 8:
scale_min_def = 275
scale_max_def = 305
else:
scale_min_def = 255
scale_max_def = 285
elif var == 'pr':
variable_list = ['pr']
scale_min_def = 0.0
if plot_period == '1h':
scale_max_def = 12.0
unit = 'mm/hour'
elif plot_period == '1d':
scale_max_def = 150.0
unit = 'mm/day'
nc = netCDF4.Dataset(filein)
lat_sim = nc.variables[lat_name][:]
lon_sim = nc.variables[lon_name][:]
if np.array(lat_sim).ndim == 1 and np.array(lon_sim).ndim == 1:
lon_sim_2d, lat_sim_2d = np.meshgrid(lon_sim, lat_sim)
elif np.array(lat_sim).ndim == 2 and np.array(lon_sim).ndim == 2:
lon_sim_2d = lon_sim
lat_sim_2d = lat_sim
for var_to_plot in variable_list:
var_sim_3d = nc.variables[var_to_plot][:,:,:]
n(var_sim_3d[rec_start - 1 : rec_end - 1, :, :], axis=0) * scale_factor
title_def = var_to_plot + '(' + unit + ')'
fig_out = str(fig_out_path) + exp_name + '_' + var_to_plot + '_' + str(plot_period) + '_' + str(ymdhm) + fig_type
map_plot = MapPlot(fig_out, proj_def, res_def, width_def, height_def, lat_0_def, lon_0_def)
map_plot.Plot_2DField(lat_sim_2d, lon_sim_2d, var_sim_2d[:,:], scale_min_def, scale_max_def, title_def, label_def, cmap_def, extend_def)
| true | true |
f73beb4c1b32163d91ff788de411bab4fbb108e3 | 6,897 | py | Python | pybaseball/league_batting_stats.py | mdrews93/pybaseball | 0dab4a2a3e27dd9fa27285d63a1f6f829dfcf4c5 | [
"MIT"
] | null | null | null | pybaseball/league_batting_stats.py | mdrews93/pybaseball | 0dab4a2a3e27dd9fa27285d63a1f6f829dfcf4c5 | [
"MIT"
] | null | null | null | pybaseball/league_batting_stats.py | mdrews93/pybaseball | 0dab4a2a3e27dd9fa27285d63a1f6f829dfcf4c5 | [
"MIT"
] | null | null | null | import requests
import pandas as pd
import datetime
import io
from bs4 import BeautifulSoup
def validate_datestring(date_text):
try:
datetime.datetime.strptime(date_text, '%Y-%m-%d')
except ValueError:
raise ValueError("Incorrect data format, should be YYYY-MM-DD")
def sanitize_input(start_dt, end_dt):
# if no dates are supplied, assume they want yesterday's data
# send a warning in case they wanted to specify
if start_dt is None and end_dt is None:
today = datetime.datetime.today()
start_dt = (today - datetime.timedelta(1)).strftime("%Y-%m-%d")
end_dt = today.strftime("%Y-%m-%d")
print("Warning: no date range supplied. Returning yesterday's data. For a different date range, try batting_stats_range(start_dt, end_dt) or batting_stats(season).")
#if only one date is supplied, assume they only want that day's stats
#query in this case is from date 1 to date 1
if start_dt is None:
start_dt = end_dt
if end_dt is None:
end_dt = start_dt
#if end date occurs before start date, swap them
if end_dt < start_dt:
temp = start_dt
start_dt = end_dt
end_dt = temp
# now that both dates are not None, make sure they are valid date strings
validate_datestring(start_dt)
validate_datestring(end_dt)
return start_dt, end_dt
def get_soup(start_dt, end_dt):
# get most recent standings if date not specified
# if((start_dt is None) or (end_dt is None)):
# print('Error: a date range needs to be specified')
# return None
url = "http://www.baseball-reference.com/leagues/daily.cgi?user_team=&bust_cache=&type=b&lastndays=7&dates=fromandto&fromandto={}.{}&level=mlb&franch=&stat=&stat_value=0".format(start_dt, end_dt)
s = requests.get(url).content
return BeautifulSoup(s, "html.parser")
def get_id_table(soup):
table = soup.find_all('table')[0]
data = []
headings = [th.get_text() for th in table.find("tr").find_all("th")][1:]
data.append(headings)
table_body = table.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
cols = row.find_all('td')
try:
pid = cols[0]["data-append-csv"].split("=")[-1]
cols = [ele.text.strip() for ele in cols]
cols[0] = pid
data.append([ele for ele in cols])
except:
pass
data = pd.DataFrame(data)
data = data.rename(columns=data.iloc[0])
data = data.reindex(data.index.drop(0))
return data
def get_table(soup):
table = soup.find_all('table')[0]
data = []
headings = [th.get_text() for th in table.find("tr").find_all("th")][1:]
data.append(headings)
table_body = table.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
cols = row.find_all('td')
cols = [ele.text.strip() for ele in cols]
data.append([ele for ele in cols])
data = pd.DataFrame(data)
data = data.rename(columns=data.iloc[0])
data = data.reindex(data.index.drop(0))
return data
def batting_stats_range_by_id(start_dt=None, end_dt=None):
"""
Get all batting stats for a set time range. This can be the past week, the
month of August, anything. Just supply the start and end date in YYYY-MM-DD
format.
"""
# make sure date inputs are valid
start_dt, end_dt = sanitize_input(start_dt, end_dt)
if datetime.datetime.strptime(start_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
if datetime.datetime.strptime(end_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
# retrieve html from baseball reference
soup = get_soup(start_dt, end_dt)
table = get_id_table(soup)
table = table.dropna(how='all') # drop if all columns are NA
# scraped data is initially in string format.
# convert the necessary columns to numeric.
for column in ['Age', '#days', 'G', 'PA', 'AB', 'R', 'H', '2B', '3B',
'HR', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SH', 'SF', 'GDP',
'SB', 'CS', 'BA', 'OBP', 'SLG', 'OPS']:
#table[column] = table[column].astype('float')
table[column] = pd.to_numeric(table[column])
#table['column'] = table['column'].convert_objects(convert_numeric=True)
table = table.drop('', 1)
return table
def batting_stats_range(start_dt=None, end_dt=None):
"""
Get all batting stats for a set time range. This can be the past week, the
month of August, anything. Just supply the start and end date in YYYY-MM-DD
format.
"""
# make sure date inputs are valid
start_dt, end_dt = sanitize_input(start_dt, end_dt)
if datetime.datetime.strptime(start_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
if datetime.datetime.strptime(end_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
# retrieve html from baseball reference
soup = get_soup(start_dt, end_dt)
table = get_table(soup)
table = table.dropna(how='all') # drop if all columns are NA
# scraped data is initially in string format.
# convert the necessary columns to numeric.
for column in ['Age', '#days', 'G', 'PA', 'AB', 'R', 'H', '2B', '3B',
'HR', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SH', 'SF', 'GDP',
'SB', 'CS', 'BA', 'OBP', 'SLG', 'OPS']:
#table[column] = table[column].astype('float')
table[column] = pd.to_numeric(table[column])
#table['column'] = table['column'].convert_objects(convert_numeric=True)
table = table.drop('', 1)
return table
def batting_stats_bref(season=None):
"""
Get all batting stats for a set season. If no argument is supplied, gives
stats for current season to date.
"""
if season is None:
season = datetime.datetime.today().strftime("%Y")
season = str(season)
start_dt = season + '-03-01' #opening day is always late march or early april
end_dt = season + '-11-01' #season is definitely over by November
return(batting_stats_range(start_dt, end_dt))
def bwar_bat(return_all=False):
"""
Get data from war_daily_bat table. Returns WAR, its components, and a few other useful stats.
To get all fields from this table, supply argument return_all=True.
"""
url = "http://www.baseball-reference.com/data/war_daily_bat.txt"
s = requests.get(url).content
c=pd.read_csv(io.StringIO(s.decode('utf-8')))
if return_all:
return c
else:
cols_to_keep = ['name_common', 'mlb_ID', 'player_ID', 'year_ID', 'team_ID', 'stint_ID', 'lg_ID',
'pitcher','G', 'PA', 'salary', 'runs_above_avg', 'runs_above_avg_off','runs_above_avg_def',
'WAR_rep','WAA','WAR']
return c[cols_to_keep] | 40.098837 | 199 | 0.634624 | import requests
import pandas as pd
import datetime
import io
from bs4 import BeautifulSoup
def validate_datestring(date_text):
try:
datetime.datetime.strptime(date_text, '%Y-%m-%d')
except ValueError:
raise ValueError("Incorrect data format, should be YYYY-MM-DD")
def sanitize_input(start_dt, end_dt):
# send a warning in case they wanted to specify
if start_dt is None and end_dt is None:
today = datetime.datetime.today()
start_dt = (today - datetime.timedelta(1)).strftime("%Y-%m-%d")
end_dt = today.strftime("%Y-%m-%d")
print("Warning: no date range supplied. Returning yesterday's data. For a different date range, try batting_stats_range(start_dt, end_dt) or batting_stats(season).")
#query in this case is from date 1 to date 1
if start_dt is None:
start_dt = end_dt
if end_dt is None:
end_dt = start_dt
#if end date occurs before start date, swap them
if end_dt < start_dt:
temp = start_dt
start_dt = end_dt
end_dt = temp
# now that both dates are not None, make sure they are valid date strings
validate_datestring(start_dt)
validate_datestring(end_dt)
return start_dt, end_dt
def get_soup(start_dt, end_dt):
# get most recent standings if date not specified
# if((start_dt is None) or (end_dt is None)):
# print('Error: a date range needs to be specified')
# return None
url = "http://www.baseball-reference.com/leagues/daily.cgi?user_team=&bust_cache=&type=b&lastndays=7&dates=fromandto&fromandto={}.{}&level=mlb&franch=&stat=&stat_value=0".format(start_dt, end_dt)
s = requests.get(url).content
return BeautifulSoup(s, "html.parser")
def get_id_table(soup):
table = soup.find_all('table')[0]
data = []
headings = [th.get_text() for th in table.find("tr").find_all("th")][1:]
data.append(headings)
table_body = table.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
cols = row.find_all('td')
try:
pid = cols[0]["data-append-csv"].split("=")[-1]
cols = [ele.text.strip() for ele in cols]
cols[0] = pid
data.append([ele for ele in cols])
except:
pass
data = pd.DataFrame(data)
data = data.rename(columns=data.iloc[0])
data = data.reindex(data.index.drop(0))
return data
def get_table(soup):
table = soup.find_all('table')[0]
data = []
headings = [th.get_text() for th in table.find("tr").find_all("th")][1:]
data.append(headings)
table_body = table.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
cols = row.find_all('td')
cols = [ele.text.strip() for ele in cols]
data.append([ele for ele in cols])
data = pd.DataFrame(data)
data = data.rename(columns=data.iloc[0])
data = data.reindex(data.index.drop(0))
return data
def batting_stats_range_by_id(start_dt=None, end_dt=None):
# make sure date inputs are valid
start_dt, end_dt = sanitize_input(start_dt, end_dt)
if datetime.datetime.strptime(start_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
if datetime.datetime.strptime(end_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
# retrieve html from baseball reference
soup = get_soup(start_dt, end_dt)
table = get_id_table(soup)
table = table.dropna(how='all') # drop if all columns are NA
# scraped data is initially in string format.
# convert the necessary columns to numeric.
for column in ['Age', '
'HR', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SH', 'SF', 'GDP',
'SB', 'CS', 'BA', 'OBP', 'SLG', 'OPS']:
#table[column] = table[column].astype('float')
table[column] = pd.to_numeric(table[column])
#table['column'] = table['column'].convert_objects(convert_numeric=True)
table = table.drop('', 1)
return table
def batting_stats_range(start_dt=None, end_dt=None):
# make sure date inputs are valid
start_dt, end_dt = sanitize_input(start_dt, end_dt)
if datetime.datetime.strptime(start_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
if datetime.datetime.strptime(end_dt, "%Y-%m-%d").year < 2008:
raise ValueError("Year must be 2008 or later")
# retrieve html from baseball reference
soup = get_soup(start_dt, end_dt)
table = get_table(soup)
table = table.dropna(how='all') # drop if all columns are NA
# scraped data is initially in string format.
# convert the necessary columns to numeric.
for column in ['Age', '
'HR', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SH', 'SF', 'GDP',
'SB', 'CS', 'BA', 'OBP', 'SLG', 'OPS']:
#table[column] = table[column].astype('float')
table[column] = pd.to_numeric(table[column])
#table['column'] = table['column'].convert_objects(convert_numeric=True)
table = table.drop('', 1)
return table
def batting_stats_bref(season=None):
if season is None:
season = datetime.datetime.today().strftime("%Y")
season = str(season)
start_dt = season + '-03-01' #opening day is always late march or early april
end_dt = season + '-11-01' #season is definitely over by November
return(batting_stats_range(start_dt, end_dt))
def bwar_bat(return_all=False):
url = "http://www.baseball-reference.com/data/war_daily_bat.txt"
s = requests.get(url).content
c=pd.read_csv(io.StringIO(s.decode('utf-8')))
if return_all:
return c
else:
cols_to_keep = ['name_common', 'mlb_ID', 'player_ID', 'year_ID', 'team_ID', 'stint_ID', 'lg_ID',
'pitcher','G', 'PA', 'salary', 'runs_above_avg', 'runs_above_avg_off','runs_above_avg_def',
'WAR_rep','WAA','WAR']
return c[cols_to_keep] | true | true |
f73bebdfdde04ee93dea82677a22edd4015cfe32 | 9,016 | py | Python | hours/tests/test_resource_periods_as_text.py | City-of-Helsinki/hauki | f49628c0533baa68c7deb83224065c3b1158f807 | [
"MIT"
] | 3 | 2020-03-26T05:04:30.000Z | 2022-03-22T15:57:18.000Z | hours/tests/test_resource_periods_as_text.py | City-of-Helsinki/hauki | f49628c0533baa68c7deb83224065c3b1158f807 | [
"MIT"
] | 81 | 2020-06-17T14:31:11.000Z | 2022-02-20T19:01:54.000Z | hours/tests/test_resource_periods_as_text.py | City-of-Helsinki/hauki | f49628c0533baa68c7deb83224065c3b1158f807 | [
"MIT"
] | 9 | 2020-06-18T10:52:09.000Z | 2022-02-11T13:05:59.000Z | import datetime
import pytest
from django.utils import translation
from hours.enums import FrequencyModifier, RuleContext, RuleSubject, State, Weekday
from hours.models import Rule
from hours.tests.conftest import (
DatePeriodFactory,
RuleFactory,
TimeSpanFactory,
TimeSpanGroupFactory,
)
@pytest.mark.django_db
@pytest.mark.parametrize("lang", ["en", "fi"])
def test_resource_opening_hours_as_text_no_date_periods(resource, lang):
with translation.override(lang):
assert resource._get_date_periods_as_text() == ""
@pytest.mark.django_db
def test_resource_opening_hours_as_text(resource):
DatePeriodFactory(
name="Special hours",
resource=resource,
resource_state=State.CLOSED,
start_date=datetime.date(year=2021, month=12, day=27),
end_date=datetime.date(year=2022, month=1, day=2),
override=True,
)
date_period = DatePeriodFactory(
name="Regular opening hours",
resource=resource,
resource_state=State.OPEN,
start_date=datetime.date(year=2021, month=1, day=1),
end_date=datetime.date(year=2022, month=12, day=31),
)
time_span_group = TimeSpanGroupFactory(period=date_period)
TimeSpanFactory(
name="Test time span",
group=time_span_group,
start_time=datetime.time(hour=9, minute=0),
end_time=datetime.time(hour=17, minute=0),
weekdays=[Weekday.MONDAY, Weekday.TUESDAY, Weekday.THURSDAY],
)
TimeSpanFactory(
name="Test time span...",
group=time_span_group,
start_time=datetime.time(hour=9, minute=0),
end_time=datetime.time(hour=19, minute=0),
weekdays=[Weekday.FRIDAY, Weekday.SATURDAY],
)
TimeSpanFactory(
name="Test time span 2",
group=time_span_group,
start_time=datetime.time(hour=10, minute=0),
end_time=datetime.time(hour=14, minute=0),
weekdays=[Weekday.SUNDAY],
)
RuleFactory(
group=time_span_group,
context=RuleContext.PERIOD,
subject=RuleSubject.WEEK,
frequency_modifier=FrequencyModifier.EVEN,
)
time_span_group2 = TimeSpanGroupFactory(period=date_period)
TimeSpanFactory(
name="Test time span 3",
group=time_span_group2,
start_time=datetime.time(hour=8, minute=0),
end_time=datetime.time(hour=16, minute=0),
weekdays=[Weekday.MONDAY, Weekday.TUESDAY],
)
TimeSpanFactory(
name="Test time span 4",
group=time_span_group2,
start_time=datetime.time(hour=9, minute=0),
end_time=datetime.time(hour=13, minute=0),
weekdays=Weekday.weekend(),
)
RuleFactory(
group=time_span_group2,
context=RuleContext.PERIOD,
subject=RuleSubject.MONTH,
frequency_ordinal=2,
)
RuleFactory(
group=time_span_group2,
context=RuleContext.PERIOD,
subject=RuleSubject.WEEK,
frequency_modifier=FrequencyModifier.ODD,
)
with translation.override("en"):
assert resource._get_date_periods_as_text() == (
"\n"
"========================================\n"
"Regular opening hours\n"
"Date period: Jan. 1, 2021 - Dec. 31, 2022\n"
"Opening hours:\n"
"\n"
" Monday-Tuesday, Thursday 9 a.m.-5 p.m. Open\n"
" Friday-Saturday 9 a.m.-7 p.m. Open\n"
" Sunday 10 a.m.-2 p.m. Open\n"
"\n"
" In effect when every one of these match:\n"
" - On even weeks in the period\n"
"\n"
" ---------------------------------------\n"
"\n"
" Monday-Tuesday 8 a.m.-4 p.m. Open\n"
" Saturday-Sunday 9 a.m.-1 p.m. Open\n"
"\n"
" In effect when every one of these match:\n"
" - Every 2nd month in the period\n"
" - On odd weeks in the period\n"
"\n"
"========================================\n"
"Special hours\n"
"Date period: Dec. 27, 2021 - Jan. 2, 2022\n"
"Opening hours:\n"
"\n"
" Closed\n"
"\n"
"========================================\n"
)
with translation.override("fi"):
assert resource._get_date_periods_as_text() == (
"\n"
"========================================\n"
"Regular opening hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Maanantai-Tiistai, Torstai 9.00-17.00 Auki\n"
" Perjantai-Lauantai 9.00-19.00 Auki\n"
" Sunnuntai 10.00-14.00 Auki\n"
"\n"
" Voimassa kun kaikki seuraavat pätevät:\n"
" - Jakson jokainen parillinen viikko\n"
"\n"
" ---------------------------------------\n"
"\n"
" Maanantai-Tiistai 8.00-16.00 Auki\n"
" Lauantai-Sunnuntai 9.00-13.00 Auki\n"
"\n"
" Voimassa kun kaikki seuraavat pätevät:\n"
" - Jakson joka 2. kuukausi\n"
" - Jakson jokainen pariton viikko\n"
"\n"
"========================================\n"
"Special hours\n"
"Aikajakso: 27. joulukuuta 2021 - 2. tammikuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Suljettu\n"
"\n"
"========================================\n"
)
@pytest.mark.django_db
@pytest.mark.parametrize(
"modifier", [None, FrequencyModifier.EVEN, FrequencyModifier.ODD]
)
@pytest.mark.parametrize("start", [None, 1, 2, -1, -2])
@pytest.mark.parametrize("ordinal", [None, 1, 4])
@pytest.mark.parametrize("subject", list(RuleSubject))
@pytest.mark.parametrize("context", list(RuleContext))
def test_rule_as_text_frequency_ordinal(context, subject, start, ordinal, modifier):
if not any([start, ordinal, modifier]) or (
subject == RuleSubject.MONTH and context == RuleContext.MONTH
):
pytest.skip("Won't test this combination as it's an invalid rule")
rule = Rule(
context=context,
subject=subject,
start=start,
frequency_ordinal=ordinal,
frequency_modifier=modifier,
)
with translation.override("en"):
rule_as_text_en = rule.as_text()
with translation.override("fi"):
rule_as_text_fi = rule.as_text()
assert rule_as_text_en
assert rule_as_text_fi
assert rule_as_text_en != rule_as_text_fi
@pytest.mark.django_db
@pytest.mark.parametrize("lang", ["en", "fi"])
def test_resource_date_periods_as_text_is_kept_up_to_date(resource, lang):
assert resource.date_periods_as_text == ""
date_period = DatePeriodFactory(
name="Test hours",
resource=resource,
resource_state=State.OPEN,
start_date=datetime.date(year=2021, month=1, day=1),
end_date=datetime.date(year=2022, month=12, day=31),
)
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Auki\n"
"\n"
"========================================\n"
)
date_period.resource_state = State.CLOSED
date_period.save()
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Suljettu\n"
"\n"
"========================================\n"
)
time_span_group = TimeSpanGroupFactory(period=date_period)
TimeSpanFactory(
group=time_span_group,
start_time=datetime.time(hour=10, minute=0),
end_time=datetime.time(hour=12, minute=0),
weekdays=[Weekday.MONDAY],
resource_state=State.OPEN,
)
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Maanantai 10.00-12.00 Auki\n"
"\n"
"========================================\n"
)
RuleFactory(
group=time_span_group,
context=RuleContext.PERIOD,
subject=RuleSubject.WEEK,
frequency_ordinal=2,
)
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Maanantai 10.00-12.00 Auki\n"
"\n"
" Voimassa kun kaikki seuraavat pätevät:\n"
" - Jakson joka 2. viikko\n"
"\n"
"========================================\n"
)
| 31.746479 | 84 | 0.542702 | import datetime
import pytest
from django.utils import translation
from hours.enums import FrequencyModifier, RuleContext, RuleSubject, State, Weekday
from hours.models import Rule
from hours.tests.conftest import (
DatePeriodFactory,
RuleFactory,
TimeSpanFactory,
TimeSpanGroupFactory,
)
@pytest.mark.django_db
@pytest.mark.parametrize("lang", ["en", "fi"])
def test_resource_opening_hours_as_text_no_date_periods(resource, lang):
with translation.override(lang):
assert resource._get_date_periods_as_text() == ""
@pytest.mark.django_db
def test_resource_opening_hours_as_text(resource):
DatePeriodFactory(
name="Special hours",
resource=resource,
resource_state=State.CLOSED,
start_date=datetime.date(year=2021, month=12, day=27),
end_date=datetime.date(year=2022, month=1, day=2),
override=True,
)
date_period = DatePeriodFactory(
name="Regular opening hours",
resource=resource,
resource_state=State.OPEN,
start_date=datetime.date(year=2021, month=1, day=1),
end_date=datetime.date(year=2022, month=12, day=31),
)
time_span_group = TimeSpanGroupFactory(period=date_period)
TimeSpanFactory(
name="Test time span",
group=time_span_group,
start_time=datetime.time(hour=9, minute=0),
end_time=datetime.time(hour=17, minute=0),
weekdays=[Weekday.MONDAY, Weekday.TUESDAY, Weekday.THURSDAY],
)
TimeSpanFactory(
name="Test time span...",
group=time_span_group,
start_time=datetime.time(hour=9, minute=0),
end_time=datetime.time(hour=19, minute=0),
weekdays=[Weekday.FRIDAY, Weekday.SATURDAY],
)
TimeSpanFactory(
name="Test time span 2",
group=time_span_group,
start_time=datetime.time(hour=10, minute=0),
end_time=datetime.time(hour=14, minute=0),
weekdays=[Weekday.SUNDAY],
)
RuleFactory(
group=time_span_group,
context=RuleContext.PERIOD,
subject=RuleSubject.WEEK,
frequency_modifier=FrequencyModifier.EVEN,
)
time_span_group2 = TimeSpanGroupFactory(period=date_period)
TimeSpanFactory(
name="Test time span 3",
group=time_span_group2,
start_time=datetime.time(hour=8, minute=0),
end_time=datetime.time(hour=16, minute=0),
weekdays=[Weekday.MONDAY, Weekday.TUESDAY],
)
TimeSpanFactory(
name="Test time span 4",
group=time_span_group2,
start_time=datetime.time(hour=9, minute=0),
end_time=datetime.time(hour=13, minute=0),
weekdays=Weekday.weekend(),
)
RuleFactory(
group=time_span_group2,
context=RuleContext.PERIOD,
subject=RuleSubject.MONTH,
frequency_ordinal=2,
)
RuleFactory(
group=time_span_group2,
context=RuleContext.PERIOD,
subject=RuleSubject.WEEK,
frequency_modifier=FrequencyModifier.ODD,
)
with translation.override("en"):
assert resource._get_date_periods_as_text() == (
"\n"
"========================================\n"
"Regular opening hours\n"
"Date period: Jan. 1, 2021 - Dec. 31, 2022\n"
"Opening hours:\n"
"\n"
" Monday-Tuesday, Thursday 9 a.m.-5 p.m. Open\n"
" Friday-Saturday 9 a.m.-7 p.m. Open\n"
" Sunday 10 a.m.-2 p.m. Open\n"
"\n"
" In effect when every one of these match:\n"
" - On even weeks in the period\n"
"\n"
" ---------------------------------------\n"
"\n"
" Monday-Tuesday 8 a.m.-4 p.m. Open\n"
" Saturday-Sunday 9 a.m.-1 p.m. Open\n"
"\n"
" In effect when every one of these match:\n"
" - Every 2nd month in the period\n"
" - On odd weeks in the period\n"
"\n"
"========================================\n"
"Special hours\n"
"Date period: Dec. 27, 2021 - Jan. 2, 2022\n"
"Opening hours:\n"
"\n"
" Closed\n"
"\n"
"========================================\n"
)
with translation.override("fi"):
assert resource._get_date_periods_as_text() == (
"\n"
"========================================\n"
"Regular opening hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Maanantai-Tiistai, Torstai 9.00-17.00 Auki\n"
" Perjantai-Lauantai 9.00-19.00 Auki\n"
" Sunnuntai 10.00-14.00 Auki\n"
"\n"
" Voimassa kun kaikki seuraavat pätevät:\n"
" - Jakson jokainen parillinen viikko\n"
"\n"
" ---------------------------------------\n"
"\n"
" Maanantai-Tiistai 8.00-16.00 Auki\n"
" Lauantai-Sunnuntai 9.00-13.00 Auki\n"
"\n"
" Voimassa kun kaikki seuraavat pätevät:\n"
" - Jakson joka 2. kuukausi\n"
" - Jakson jokainen pariton viikko\n"
"\n"
"========================================\n"
"Special hours\n"
"Aikajakso: 27. joulukuuta 2021 - 2. tammikuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Suljettu\n"
"\n"
"========================================\n"
)
@pytest.mark.django_db
@pytest.mark.parametrize(
"modifier", [None, FrequencyModifier.EVEN, FrequencyModifier.ODD]
)
@pytest.mark.parametrize("start", [None, 1, 2, -1, -2])
@pytest.mark.parametrize("ordinal", [None, 1, 4])
@pytest.mark.parametrize("subject", list(RuleSubject))
@pytest.mark.parametrize("context", list(RuleContext))
def test_rule_as_text_frequency_ordinal(context, subject, start, ordinal, modifier):
if not any([start, ordinal, modifier]) or (
subject == RuleSubject.MONTH and context == RuleContext.MONTH
):
pytest.skip("Won't test this combination as it's an invalid rule")
rule = Rule(
context=context,
subject=subject,
start=start,
frequency_ordinal=ordinal,
frequency_modifier=modifier,
)
with translation.override("en"):
rule_as_text_en = rule.as_text()
with translation.override("fi"):
rule_as_text_fi = rule.as_text()
assert rule_as_text_en
assert rule_as_text_fi
assert rule_as_text_en != rule_as_text_fi
@pytest.mark.django_db
@pytest.mark.parametrize("lang", ["en", "fi"])
def test_resource_date_periods_as_text_is_kept_up_to_date(resource, lang):
assert resource.date_periods_as_text == ""
date_period = DatePeriodFactory(
name="Test hours",
resource=resource,
resource_state=State.OPEN,
start_date=datetime.date(year=2021, month=1, day=1),
end_date=datetime.date(year=2022, month=12, day=31),
)
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Auki\n"
"\n"
"========================================\n"
)
date_period.resource_state = State.CLOSED
date_period.save()
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Suljettu\n"
"\n"
"========================================\n"
)
time_span_group = TimeSpanGroupFactory(period=date_period)
TimeSpanFactory(
group=time_span_group,
start_time=datetime.time(hour=10, minute=0),
end_time=datetime.time(hour=12, minute=0),
weekdays=[Weekday.MONDAY],
resource_state=State.OPEN,
)
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Maanantai 10.00-12.00 Auki\n"
"\n"
"========================================\n"
)
RuleFactory(
group=time_span_group,
context=RuleContext.PERIOD,
subject=RuleSubject.WEEK,
frequency_ordinal=2,
)
assert resource.date_periods_as_text == (
"\n========================================\n"
"Test hours\n"
"Aikajakso: 1. tammikuuta 2021 - 31. joulukuuta 2022\n"
"Aukioloajat:\n"
"\n"
" Maanantai 10.00-12.00 Auki\n"
"\n"
" Voimassa kun kaikki seuraavat pätevät:\n"
" - Jakson joka 2. viikko\n"
"\n"
"========================================\n"
)
| true | true |
f73bebf5f22f828fa0dddcbd7d2a4deae02f54a0 | 179 | py | Python | plugins/clients/apps-client/test/mock_client_app/mock_client_event.py | pcanto-hopeit/hopeit.engine | c17b0438e56940a4d1b2f071cca90ae8b6f70629 | [
"Apache-2.0"
] | 15 | 2020-07-09T17:41:14.000Z | 2021-10-04T20:13:08.000Z | plugins/clients/apps-client/test/mock_client_app/mock_client_event.py | pcanto-hopeit/hopeit.engine | c17b0438e56940a4d1b2f071cca90ae8b6f70629 | [
"Apache-2.0"
] | 48 | 2020-07-10T15:16:17.000Z | 2022-03-03T19:46:46.000Z | plugins/clients/apps-client/test/mock_client_app/mock_client_event.py | pcanto-hopeit/hopeit.engine | c17b0438e56940a4d1b2f071cca90ae8b6f70629 | [
"Apache-2.0"
] | 3 | 2020-07-08T20:12:58.000Z | 2021-01-10T15:57:21.000Z | from hopeit.app.context import EventContext
__steps__ = ['test_app_call']
async def test_app_call(payload: None, context: EventContext) -> str:
raise NotImplementedError()
| 22.375 | 69 | 0.77095 | from hopeit.app.context import EventContext
__steps__ = ['test_app_call']
async def test_app_call(payload: None, context: EventContext) -> str:
raise NotImplementedError()
| true | true |
f73bed0be17ea24ab6ebb469bbeb064f4f2841f5 | 898 | py | Python | Build.py | wasimakh2/FacebookBot | fe3adc1dfbc0babb0b54b8265b0c27eadbe22422 | [
"Apache-2.0"
] | 312 | 2016-02-16T22:52:32.000Z | 2022-03-11T05:34:59.000Z | Build.py | wasimakh2/FacebookBot | fe3adc1dfbc0babb0b54b8265b0c27eadbe22422 | [
"Apache-2.0"
] | 17 | 2016-12-18T17:35:13.000Z | 2021-04-30T08:58:03.000Z | Build.py | wasimakh2/FacebookBot | fe3adc1dfbc0babb0b54b8265b0c27eadbe22422 | [
"Apache-2.0"
] | 130 | 2016-02-03T12:34:18.000Z | 2022-03-22T08:10:59.000Z | import os
import sys
script="""
from cx_Freeze import setup, Executable
import sys
base = None
if sys.platform == "win32":
base = "Win32GUI"
setup(
name = "$FILENOTPY$",
version = "1.0",
description = "$FILENOTPY$",
executables = [Executable("$FILENAME$",appendScriptToExe = False,appendScriptToLibrary = True,icon="icon.ico")]
)"""
def main(filename):
global script
file=open("temp.py","wt")
script=script.replace("$FILENAME$",filename)
name=filename[:-3]
script=script.replace("$FILENOTPY$",name)
file.write(script)
file.close()
os.system("python temp.py build")
os.remove("temp.py")
input("\nDONE")
if __name__=="__main__":
if len(sys.argv)==2:
print("Starting build ->")
print(sys.argv[1])
main(sys.argv[1])
else:
print("No argument\n Pass: Filename")
| 24.944444 | 116 | 0.601336 | import os
import sys
script="""
from cx_Freeze import setup, Executable
import sys
base = None
if sys.platform == "win32":
base = "Win32GUI"
setup(
name = "$FILENOTPY$",
version = "1.0",
description = "$FILENOTPY$",
executables = [Executable("$FILENAME$",appendScriptToExe = False,appendScriptToLibrary = True,icon="icon.ico")]
)"""
def main(filename):
global script
file=open("temp.py","wt")
script=script.replace("$FILENAME$",filename)
name=filename[:-3]
script=script.replace("$FILENOTPY$",name)
file.write(script)
file.close()
os.system("python temp.py build")
os.remove("temp.py")
input("\nDONE")
if __name__=="__main__":
if len(sys.argv)==2:
print("Starting build ->")
print(sys.argv[1])
main(sys.argv[1])
else:
print("No argument\n Pass: Filename")
| true | true |
f73beebf053c139436dce4f12aff85bc275759a9 | 2,041 | py | Python | src/api/bkuser_core/categories/handlers.py | trueware/bk-user | 8c633e0a3821beb839ed120c4514c5733e675862 | [
"MIT"
] | null | null | null | src/api/bkuser_core/categories/handlers.py | trueware/bk-user | 8c633e0a3821beb839ed120c4514c5733e675862 | [
"MIT"
] | null | null | null | src/api/bkuser_core/categories/handlers.py | trueware/bk-user | 8c633e0a3821beb839ed120c4514c5733e675862 | [
"MIT"
] | 1 | 2021-12-31T06:48:41.000Z | 2021-12-31T06:48:41.000Z | # -*- coding: utf-8 -*-
"""
TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
import logging
from bkuser_core.bkiam.constants import IAMAction, ResourceType
from bkuser_core.bkiam.helper import IAMHelper
from bkuser_core.categories.signals import post_category_create
from django.dispatch import receiver
from .plugins.ldap.handlers import create_sync_tasks, delete_sync_tasks, update_sync_tasks # noqa
from .plugins.local.handlers import make_local_default_settings # noqa
logger = logging.getLogger(__name__)
@receiver(post_category_create)
def create_creator_actions(sender, category, **kwargs):
"""请求权限中心，创建新建关联权限记录"""
logger.info("going to create resource_creator_action for Category<%s>", category.id)
helper = IAMHelper()
try:
helper.create_creator_actions(kwargs["creator"], category)
except Exception: # pylint: disable=broad-except
logger.exception("failed to create resource_creator_action (category related)")
# 创建目录之后，默认拥有了目录 & 组织的管理能力
try:
helper.create_auth_by_ancestor(
username=kwargs["creator"],
ancestor=category,
target_type=ResourceType.DEPARTMENT.value,
action_ids=[IAMAction.MANAGE_DEPARTMENT, IAMAction.VIEW_DEPARTMENT],
)
except Exception: # pylint: disable=broad-except
logger.exception("failed to create resource_creator_action (department related)")
| 46.386364 | 115 | 0.763351 |
import logging
from bkuser_core.bkiam.constants import IAMAction, ResourceType
from bkuser_core.bkiam.helper import IAMHelper
from bkuser_core.categories.signals import post_category_create
from django.dispatch import receiver
from .plugins.ldap.handlers import create_sync_tasks, delete_sync_tasks, update_sync_tasks
from .plugins.local.handlers import make_local_default_settings
logger = logging.getLogger(__name__)
@receiver(post_category_create)
def create_creator_actions(sender, category, **kwargs):
logger.info("going to create resource_creator_action for Category<%s>", category.id)
helper = IAMHelper()
try:
helper.create_creator_actions(kwargs["creator"], category)
except Exception:
logger.exception("failed to create resource_creator_action (category related)")
try:
helper.create_auth_by_ancestor(
username=kwargs["creator"],
ancestor=category,
target_type=ResourceType.DEPARTMENT.value,
action_ids=[IAMAction.MANAGE_DEPARTMENT, IAMAction.VIEW_DEPARTMENT],
)
except Exception:
logger.exception("failed to create resource_creator_action (department related)")
| true | true |
f73bef434d57e5ad068ebf3139df0ab3c82e5c73 | 5,308 | py | Python | src/resultGen/apisUsed.py | RRua/AnaDroid | 7417b117a50149a6f210cd334de71b814db8d6c7 | [
"MIT"
] | 7 | 2019-01-17T18:37:59.000Z | 2020-11-16T13:42:29.000Z | src/resultGen/apisUsed.py | RRua/AnaDroid | 7417b117a50149a6f210cd334de71b814db8d6c7 | [
"MIT"
] | null | null | null | src/resultGen/apisUsed.py | RRua/AnaDroid | 7417b117a50149a6f210cd334de71b814db8d6c7 | [
"MIT"
] | null | null | null |
import json,sys,os,re
# Linares Vasquez
red_apis_file=os.getenv("ANADROID_PATH")+"/resources/redAPIS.json"
sec_apis_file=os.getenv("ANADROID_PATH")+"/resources/secAPIS.json"
# only needed fot tests executed before 27/08/2020
# created to undo error that caused malformed methoddefinitions in instrumentation phase
# remove later
# goal: to fix things like this : sample.andremion.musicplayer.view.ProgressView->onMeasure.SavedState|-1183758944
# expected result : sample.andremion.musicplayer.view.ProgressView->onMeasure|-1183758944
def fixMethodDefinition(met_id):
simpl_m_name= met_id.split("->")[1].split("|")[0]
if(len(simpl_m_name.split("."))>1):
met_id=met_id.replace(simpl_m_name,simpl_m_name.split(".")[0] )
return met_id
def areMethodEquals(met1,met2id,met2_val):
met2id = fixMethodDefinition(met2id)
return met1['method_name'] == met2id.split("|")[0] and len(met1['method_args']) == len(met2_val['method_args']) and ( met1['method_length'] == met2_val['method_length'] or met2_val['method_length']==-1 ) # and ( met1['method_locals'] == met2_val['method_locals'] or met2_val['method_locals']==-1 )
def methodWasInvoked(method_obj, traced_methods):
#return method_obj['method_name'] in traced_methods
return len (list(filter( lambda x : areMethodEquals(method_obj, x[0], x[1] ), traced_methods.items() ))) > 0
def methodRedAPIs(method_obj, red_apis_obj):
#print(method_obj['method_apis'])
#print("-----\n")
#print(red_apis_obj)
l=[]
for method_call in method_obj['method_apis']:
if 'name' in method_call and method_call['name'] in red_apis_obj:
l.append(method_call['name'])
#m_apis=method_obj
return l
def methodSecAPIs(method_obj, sec_apis_obj):
#print(method_obj['method_apis'])
#print("-----\n")
#print(red_apis_obj)
l=[]
for method_call in method_obj['method_apis']:
#print(method_call)
if 'name' in method_call:
sec_api_match = list(filter( lambda x : re.search( x , method_call['name'] ), sec_apis_obj.keys() ))
if len (sec_api_match)>0:
l.append(method_call['name'])
# l.append(method_call['name'])
#m_apis=method_obj
return l
def getRedApis(androguard_out_filename, all_traced_filename ):
# load files
red_apis_used={}
with open(androguard_out_filename) as json_file:
app_apis = json.load(json_file)
with open(red_apis_file, encoding='utf-8') as json_file:
red_apis = json.load(json_file)
red_apis_dict={}
for x in red_apis:
red_apis_dict[x['fullMethodDefinition']]=x
with open(all_traced_filename) as json_file:
traced_methods = json.load(json_file)
traced_methods_dict={}
#print(traced_methods.keys())
for testid in traced_methods.keys():
traced_methods_dict[testid]={}
for me , y in traced_methods[testid].items():
#print(me)
traced_methods_dict[testid][me]= y
ct=0
for classe in app_apis.values():
for method in classe['class_methods'].values():
for testid, test_methods in traced_methods_dict.items():
if methodWasInvoked(method, test_methods):
#print("invocado %s" % method['method_name'] )
l= methodRedAPIs(method, red_apis_dict)
if len(l)>0:
if not testid in red_apis_used:
red_apis_used[testid]={}
red_apis_used[testid][method['method_name']]=l
ct=ct+1
return red_apis_used , ct
def getSecApis(androguard_out_filename, all_traced_filename ):
# load files
sec_apis_used={}
with open(androguard_out_filename) as json_file:
app_apis = json.load(json_file)
with open(sec_apis_file, encoding='utf-8') as json_file:
sec_apis = json.load(json_file)
sec_apis_dict={}
for x in sec_apis:
sec_apis_dict[x['APIPrefix']]=x
# load alltracedmethods.json
with open(all_traced_filename) as json_file:
traced_methods = json.load(json_file)
traced_methods_dict={}
#print(traced_methods.keys())
for testid in traced_methods.keys():
traced_methods_dict[testid]={}
for me , y in traced_methods[testid].items():
#print(me)
traced_methods_dict[testid][me]= y
#print(traced_methods_dict)
ct=0
for classe in app_apis.values():
for method in classe['class_methods'].values():
for testid, test_methods in traced_methods_dict.items():
if methodWasInvoked(method, test_methods):
l= methodSecAPIs(method, sec_apis_dict)
if len(l)>0:
if not testid in sec_apis_used:
sec_apis_used[testid]={}
sec_apis_used[testid][method['method_name']]=l
ct=ct+1
return sec_apis_used , ct
if __name__ == '__main__':
if len(sys.argv)>2:
red_apis , ct_red = getRedApis(androguard_out_filename=sys.argv[1],all_traced_filename=sys.argv[2])
sec_apis , ct_sec = getSecApis(androguard_out_filename=sys.argv[1],all_traced_filename=sys.argv[2])
print("methods with red apis: " + str(ct_red) )
print("methods with sec apis: " + str(ct_sec) )
target_dir_path = os.path.dirname(os.path.realpath(sys.argv[2]))
print("dumping red apis to "+ str(target_dir_path) + "/redAPIs.json")
with open( str(target_dir_path) + "/redAPIs.json", 'w') as outfile:
json.dump(red_apis, outfile)
print("dumping sec apis to "+ str(target_dir_path) + "/secAPIs.json")
with open( str(target_dir_path) + "/secAPIs.json", 'w') as outfile:
json.dump(sec_apis, outfile)
else:
print("bad input len. please provide androguard output file and allTracedMethods.json filepaths as args") | 36.356164 | 300 | 0.727581 |
import json,sys,os,re
red_apis_file=os.getenv("ANADROID_PATH")+"/resources/redAPIS.json"
sec_apis_file=os.getenv("ANADROID_PATH")+"/resources/secAPIS.json"
def fixMethodDefinition(met_id):
simpl_m_name= met_id.split("->")[1].split("|")[0]
if(len(simpl_m_name.split("."))>1):
met_id=met_id.replace(simpl_m_name,simpl_m_name.split(".")[0] )
return met_id
def areMethodEquals(met1,met2id,met2_val):
met2id = fixMethodDefinition(met2id)
return met1['method_name'] == met2id.split("|")[0] and len(met1['method_args']) == len(met2_val['method_args']) and ( met1['method_length'] == met2_val['method_length'] or met2_val['method_length']==-1 )
def methodWasInvoked(method_obj, traced_methods):
return len (list(filter( lambda x : areMethodEquals(method_obj, x[0], x[1] ), traced_methods.items() ))) > 0
def methodRedAPIs(method_obj, red_apis_obj):
l=[]
for method_call in method_obj['method_apis']:
if 'name' in method_call and method_call['name'] in red_apis_obj:
l.append(method_call['name'])
return l
def methodSecAPIs(method_obj, sec_apis_obj):
l=[]
for method_call in method_obj['method_apis']:
if 'name' in method_call:
sec_api_match = list(filter( lambda x : re.search( x , method_call['name'] ), sec_apis_obj.keys() ))
if len (sec_api_match)>0:
l.append(method_call['name'])
return l
def getRedApis(androguard_out_filename, all_traced_filename ):
red_apis_used={}
with open(androguard_out_filename) as json_file:
app_apis = json.load(json_file)
with open(red_apis_file, encoding='utf-8') as json_file:
red_apis = json.load(json_file)
red_apis_dict={}
for x in red_apis:
red_apis_dict[x['fullMethodDefinition']]=x
with open(all_traced_filename) as json_file:
traced_methods = json.load(json_file)
traced_methods_dict={}
for testid in traced_methods.keys():
traced_methods_dict[testid]={}
for me , y in traced_methods[testid].items():
traced_methods_dict[testid][me]= y
ct=0
for classe in app_apis.values():
for method in classe['class_methods'].values():
for testid, test_methods in traced_methods_dict.items():
if methodWasInvoked(method, test_methods):
l= methodRedAPIs(method, red_apis_dict)
if len(l)>0:
if not testid in red_apis_used:
red_apis_used[testid]={}
red_apis_used[testid][method['method_name']]=l
ct=ct+1
return red_apis_used , ct
def getSecApis(androguard_out_filename, all_traced_filename ):
sec_apis_used={}
with open(androguard_out_filename) as json_file:
app_apis = json.load(json_file)
with open(sec_apis_file, encoding='utf-8') as json_file:
sec_apis = json.load(json_file)
sec_apis_dict={}
for x in sec_apis:
sec_apis_dict[x['APIPrefix']]=x
with open(all_traced_filename) as json_file:
traced_methods = json.load(json_file)
traced_methods_dict={}
for testid in traced_methods.keys():
traced_methods_dict[testid]={}
for me , y in traced_methods[testid].items():
traced_methods_dict[testid][me]= y
ct=0
for classe in app_apis.values():
for method in classe['class_methods'].values():
for testid, test_methods in traced_methods_dict.items():
if methodWasInvoked(method, test_methods):
l= methodSecAPIs(method, sec_apis_dict)
if len(l)>0:
if not testid in sec_apis_used:
sec_apis_used[testid]={}
sec_apis_used[testid][method['method_name']]=l
ct=ct+1
return sec_apis_used , ct
if __name__ == '__main__':
if len(sys.argv)>2:
red_apis , ct_red = getRedApis(androguard_out_filename=sys.argv[1],all_traced_filename=sys.argv[2])
sec_apis , ct_sec = getSecApis(androguard_out_filename=sys.argv[1],all_traced_filename=sys.argv[2])
print("methods with red apis: " + str(ct_red) )
print("methods with sec apis: " + str(ct_sec) )
target_dir_path = os.path.dirname(os.path.realpath(sys.argv[2]))
print("dumping red apis to "+ str(target_dir_path) + "/redAPIs.json")
with open( str(target_dir_path) + "/redAPIs.json", 'w') as outfile:
json.dump(red_apis, outfile)
print("dumping sec apis to "+ str(target_dir_path) + "/secAPIs.json")
with open( str(target_dir_path) + "/secAPIs.json", 'w') as outfile:
json.dump(sec_apis, outfile)
else:
print("bad input len. please provide androguard output file and allTracedMethods.json filepaths as args") | true | true |
f73befd25e262931487e0a2a67889b9f79f6c43d | 2,187 | py | Python | caffe2/quantization/server/tanh_dnnlowp_op_test.py | Hacky-DH/pytorch | 80dc4be615854570aa39a7e36495897d8a040ecc | [
"Intel"
] | 60,067 | 2017-01-18T17:21:31.000Z | 2022-03-31T21:37:45.000Z | caffe2/quantization/server/tanh_dnnlowp_op_test.py | Hacky-DH/pytorch | 80dc4be615854570aa39a7e36495897d8a040ecc | [
"Intel"
] | 66,955 | 2017-01-18T17:21:38.000Z | 2022-03-31T23:56:11.000Z | caffe2/quantization/server/tanh_dnnlowp_op_test.py | Hacky-DH/pytorch | 80dc4be615854570aa39a7e36495897d8a040ecc | [
"Intel"
] | 19,210 | 2017-01-18T17:45:04.000Z | 2022-03-31T23:51:56.000Z |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPTanhOpTest(hu.HypothesisTestCase):
@given(size=st.integers(1024, 2048), is_empty=st.booleans(), **hu.gcs_cpu_only)
@settings(max_examples=10, deadline=None)
def test_dnnlowp_tanh(self, size, is_empty, gc, dc):
if is_empty:
size = 0
X = (np.random.rand(size) * 10 - 5).astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Tanh", ""), ("Tanh", "DNNLOWP"), ("Int8Tanh", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
engine=engine,
device_option=gc,
followed_by="Tanh",
)
net.Proto().op.extend([quantize])
tanh = core.CreateOperator(
op_type,
["X_q" if engine == "DNNLOWP" else "X"],
["Y_q" if engine == "DNNLOWP" else "Y"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([tanh])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
for o in outputs:
np.testing.assert_allclose(o.Y, outputs[0].Y, atol=0.02, rtol=0)
| 32.641791 | 85 | 0.541838 |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPTanhOpTest(hu.HypothesisTestCase):
@given(size=st.integers(1024, 2048), is_empty=st.booleans(), **hu.gcs_cpu_only)
@settings(max_examples=10, deadline=None)
def test_dnnlowp_tanh(self, size, is_empty, gc, dc):
if is_empty:
size = 0
X = (np.random.rand(size) * 10 - 5).astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Tanh", ""), ("Tanh", "DNNLOWP"), ("Int8Tanh", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
engine=engine,
device_option=gc,
followed_by="Tanh",
)
net.Proto().op.extend([quantize])
tanh = core.CreateOperator(
op_type,
["X_q" if engine == "DNNLOWP" else "X"],
["Y_q" if engine == "DNNLOWP" else "Y"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([tanh])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
for o in outputs:
np.testing.assert_allclose(o.Y, outputs[0].Y, atol=0.02, rtol=0)
| true | true |
f73bf01e0e8932369dd3f787db82c08609a7e6aa | 2,988 | py | Python | sdk/keyvault/azure-keyvault-administration/azure/keyvault/administration/_internal/__init__.py | rsdoherty/azure-sdk-for-python | 6bba5326677468e6660845a703686327178bb7b1 | [
"MIT"
] | 3 | 2020-06-23T02:25:27.000Z | 2021-09-07T18:48:11.000Z | sdk/keyvault/azure-keyvault-administration/azure/keyvault/administration/_internal/__init__.py | rsdoherty/azure-sdk-for-python | 6bba5326677468e6660845a703686327178bb7b1 | [
"MIT"
] | 510 | 2019-07-17T16:11:19.000Z | 2021-08-02T08:38:32.000Z | sdk/keyvault/azure-keyvault-administration/azure/keyvault/administration/_internal/__init__.py | rsdoherty/azure-sdk-for-python | 6bba5326677468e6660845a703686327178bb7b1 | [
"MIT"
] | 5 | 2019-09-04T12:51:37.000Z | 2020-09-16T07:28:40.000Z | # ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
from collections import namedtuple
from six.moves.urllib_parse import urlparse
from .challenge_auth_policy import ChallengeAuthPolicy, ChallengeAuthPolicyBase
from .client_base import KeyVaultClientBase
from .http_challenge import HttpChallenge
from . import http_challenge_cache as HttpChallengeCache
__all__ = [
"ChallengeAuthPolicy",
"ChallengeAuthPolicyBase",
"HttpChallenge",
"HttpChallengeCache",
"KeyVaultClientBase",
]
_VaultId = namedtuple("_VaultId", ["vault_url", "collection", "name", "version"])
def parse_vault_id(url):
try:
parsed_uri = urlparse(url)
except Exception: # pylint: disable=broad-except
raise ValueError("'{}' is not not a valid url".format(url))
if not (parsed_uri.scheme and parsed_uri.hostname):
raise ValueError("'{}' is not not a valid url".format(url))
path = list(filter(None, parsed_uri.path.split("/")))
if len(path) < 2 or len(path) > 3:
raise ValueError("'{}' is not not a valid vault url".format(url))
return _VaultId(
vault_url="{}://{}".format(parsed_uri.scheme, parsed_uri.hostname),
collection=path[0],
name=path[1],
version=path[2] if len(path) == 3 else None,
)
BackupLocation = namedtuple("BackupLocation", ["container_url", "folder_name"])
def parse_folder_url(folder_url):
# type: (str) -> BackupLocation
"""Parse the blob container URL and folder name from a backup's blob storage URL.
For example, https://<account>.blob.core.windows.net/backup/mhsm-account-2020090117323313 parses to
(container_url="https://<account>.blob.core.windows.net/backup", folder_name="mhsm-account-2020090117323313").
"""
try:
parsed = urlparse(folder_url)
# the first segment of the path is the container name
stripped_path = parsed.path.strip("/")
container = stripped_path.split("/")[0]
# the rest of the path is the folder name
folder_name = stripped_path[len(container) + 1 :]
# this intentionally discards any SAS token in the URL--methods require the SAS token as a separate parameter
container_url = "{}://{}/{}".format(parsed.scheme, parsed.netloc, container)
return BackupLocation(container_url, folder_name)
except: # pylint:disable=broad-except
raise ValueError(
'"folder_url" should be the URL of a blob holding a Key Vault backup, for example '
'"https://<account>.blob.core.windows.net/backup/mhsm-account-2020090117323313"'
)
try:
# pylint:disable=unused-import
from .async_challenge_auth_policy import AsyncChallengeAuthPolicy
from .async_client_base import AsyncKeyVaultClientBase
__all__.extend(["AsyncChallengeAuthPolicy", "AsyncKeyVaultClientBase"])
except (SyntaxError, ImportError):
pass
| 34.344828 | 117 | 0.678715 |
from collections import namedtuple
from six.moves.urllib_parse import urlparse
from .challenge_auth_policy import ChallengeAuthPolicy, ChallengeAuthPolicyBase
from .client_base import KeyVaultClientBase
from .http_challenge import HttpChallenge
from . import http_challenge_cache as HttpChallengeCache
__all__ = [
"ChallengeAuthPolicy",
"ChallengeAuthPolicyBase",
"HttpChallenge",
"HttpChallengeCache",
"KeyVaultClientBase",
]
_VaultId = namedtuple("_VaultId", ["vault_url", "collection", "name", "version"])
def parse_vault_id(url):
try:
parsed_uri = urlparse(url)
except Exception:
raise ValueError("'{}' is not not a valid url".format(url))
if not (parsed_uri.scheme and parsed_uri.hostname):
raise ValueError("'{}' is not not a valid url".format(url))
path = list(filter(None, parsed_uri.path.split("/")))
if len(path) < 2 or len(path) > 3:
raise ValueError("'{}' is not not a valid vault url".format(url))
return _VaultId(
vault_url="{}://{}".format(parsed_uri.scheme, parsed_uri.hostname),
collection=path[0],
name=path[1],
version=path[2] if len(path) == 3 else None,
)
BackupLocation = namedtuple("BackupLocation", ["container_url", "folder_name"])
def parse_folder_url(folder_url):
try:
parsed = urlparse(folder_url)
stripped_path = parsed.path.strip("/")
container = stripped_path.split("/")[0]
folder_name = stripped_path[len(container) + 1 :]
container_url = "{}://{}/{}".format(parsed.scheme, parsed.netloc, container)
return BackupLocation(container_url, folder_name)
except:
raise ValueError(
'"folder_url" should be the URL of a blob holding a Key Vault backup, for example '
'"https://<account>.blob.core.windows.net/backup/mhsm-account-2020090117323313"'
)
try:
from .async_challenge_auth_policy import AsyncChallengeAuthPolicy
from .async_client_base import AsyncKeyVaultClientBase
__all__.extend(["AsyncChallengeAuthPolicy", "AsyncKeyVaultClientBase"])
except (SyntaxError, ImportError):
pass
| true | true |
f73bf1a1f7a6df4e14a3529dfff37120bc1158be | 376 | py | Python | TestDemo/changeApp.py | mocne/handerCode | bd6f9c1c5ec9a2e8be79146748f19a430ba30074 | [
"MIT"
] | 1 | 2018-04-21T13:54:56.000Z | 2018-04-21T13:54:56.000Z | TestDemo/changeApp.py | mocne/handerCode | bd6f9c1c5ec9a2e8be79146748f19a430ba30074 | [
"MIT"
] | null | null | null | TestDemo/changeApp.py | mocne/handerCode | bd6f9c1c5ec9a2e8be79146748f19a430ba30074 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import time
from DriverInit import initAppiumDriver
def change2app(driver, package, activity):
driver.quit()
driver = initAppiumDriver.initAppiumWithInfo(package=package, activity=activity)
time.sleep(10)
driver.get_screenshot_as_file('./img/%s.png' % time.strftime('%Y-%m-%d_%H_%M_%S', time.localtime(time.time())))
time.sleep(3) | 34.181818 | 115 | 0.712766 |
import time
from DriverInit import initAppiumDriver
def change2app(driver, package, activity):
driver.quit()
driver = initAppiumDriver.initAppiumWithInfo(package=package, activity=activity)
time.sleep(10)
driver.get_screenshot_as_file('./img/%s.png' % time.strftime('%Y-%m-%d_%H_%M_%S', time.localtime(time.time())))
time.sleep(3) | true | true |
f73bf271bcf6b41568d091472a46e83b99a55918 | 417 | py | Python | torchsparse/nn/modules/crop.py | f-sky/torchsparse | 65466a10c6fa54bff17c6429706b7019a2a59409 | [
"MIT"
] | 1 | 2021-03-16T02:47:56.000Z | 2021-03-16T02:47:56.000Z | torchsparse/nn/modules/crop.py | f-sky/torchsparse | 65466a10c6fa54bff17c6429706b7019a2a59409 | [
"MIT"
] | null | null | null | torchsparse/nn/modules/crop.py | f-sky/torchsparse | 65466a10c6fa54bff17c6429706b7019a2a59409 | [
"MIT"
] | null | null | null | import torch
from torch import nn
from torchsparse.nn.functional import spcrop
__all__ = ['SparseCrop']
class SparseCrop(nn.Module):
def __init__(self, loc_min, loc_max):
super().__init__()
self.loc_min = torch.cuda.IntTensor([list(loc_min)])
self.loc_max = torch.cuda.IntTensor([list(loc_max)])
def forward(self, inputs):
return spcrop(inputs, self.loc_min, self.loc_max)
| 26.0625 | 60 | 0.693046 | import torch
from torch import nn
from torchsparse.nn.functional import spcrop
__all__ = ['SparseCrop']
class SparseCrop(nn.Module):
def __init__(self, loc_min, loc_max):
super().__init__()
self.loc_min = torch.cuda.IntTensor([list(loc_min)])
self.loc_max = torch.cuda.IntTensor([list(loc_max)])
def forward(self, inputs):
return spcrop(inputs, self.loc_min, self.loc_max)
| true | true |
f73bf2e03a18009163612bcd49527a1e50bbe4eb | 6,768 | py | Python | dqn_zoo/networks_test.py | khushjammu/dqn_zoo | 249af96717d605cc1a62fc2b69941881c9661249 | [
"Apache-2.0"
] | 3 | 2021-02-04T23:13:51.000Z | 2021-11-06T10:21:50.000Z | dqn_zoo/networks_test.py | khushjammu/dqn_zoo | 249af96717d605cc1a62fc2b69941881c9661249 | [
"Apache-2.0"
] | null | null | null | dqn_zoo/networks_test.py | khushjammu/dqn_zoo | 249af96717d605cc1a62fc2b69941881c9661249 | [
"Apache-2.0"
] | 1 | 2021-12-20T13:42:35.000Z | 2021-12-20T13:42:35.000Z | # Copyright 2019 DeepMind Technologies Limited. 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.
# ==============================================================================
"""Tests for networks."""
# pylint: disable=g-bad-import-order
import haiku as hk
import jax
from jax.config import config
import jax.numpy as jnp
import numpy as np
import tree
from dqn_zoo import networks
from absl.testing import absltest
def _sample_input(input_shape):
return jnp.zeros((1,) + input_shape, dtype=jnp.float32)
class SimpleLayersTest(absltest.TestCase):
def test_linear(self):
layer = hk.transform(networks.linear(4))
params = layer.init(jax.random.PRNGKey(1), _sample_input((3,)))
self.assertCountEqual(['linear'], params)
lin_params = params['linear']
self.assertCountEqual(['w', 'b'], lin_params)
self.assertEqual((3, 4), lin_params['w'].shape)
self.assertEqual((4,), lin_params['b'].shape)
def test_conv(self):
layer = hk.transform(networks.conv(4, (3, 3), 2))
params = layer.init(jax.random.PRNGKey(1), _sample_input((7, 7, 3)))
self.assertCountEqual(['conv2_d'], params)
conv_params = params['conv2_d']
self.assertCountEqual(['w', 'b'], conv_params)
self.assertEqual((3, 3, 3, 4), conv_params['w'].shape)
self.assertEqual((4,), conv_params['b'].shape)
class LinearWithSharedBiasTest(absltest.TestCase):
def setUp(self):
super().setUp()
rng_key = jax.random.PRNGKey(1)
self.init_rng_key, self.apply_rng_key = jax.random.split(rng_key)
self.input_shape = (4,)
self.output_shape = (3,)
self.weights_shape = (self.input_shape[0], self.output_shape[0])
network_fn = networks.linear_with_shared_bias(self.output_shape[0])
self.network = hk.transform(network_fn)
def test_bias_parameter_shape(self):
params = self.network.init(self.init_rng_key,
_sample_input(self.input_shape))
self.assertLen(tree.flatten(params), 2)
def check_params(path, param):
if path[-1] == 'b':
self.assertNotEqual(self.output_shape, param.shape)
self.assertEqual((1,), param.shape)
elif path[-1] == 'w':
self.assertEqual(self.weights_shape, param.shape)
else:
self.fail('Unexpected parameter %s.' % path)
tree.map_structure_with_path(check_params, params)
def test_output_shares_bias(self):
bias = 1.23
params = self.network.init(self.init_rng_key,
_sample_input(self.input_shape))
def replace_params(path, param):
if path[-1] == 'b':
return jnp.ones_like(param) * bias
else:
return jnp.zeros_like(param)
params = tree.map_structure_with_path(replace_params, params)
output = self.network.apply(params, self.apply_rng_key,
jnp.zeros((1,) + self.input_shape))
self.assertEqual((1,) + self.output_shape, output.shape)
np.testing.assert_allclose([bias] * self.output_shape[0], list(output[0]))
class NoisyLinearTest(absltest.TestCase):
def setUp(self):
super().setUp()
rng_key = jax.random.PRNGKey(1)
self.init_rng_key, self.apply_rng_key = jax.random.split(rng_key)
self.input_shape = (4,)
self.output_shape = (3,)
self.network_fn = networks.noisy_linear(self.output_shape[0], 0.1)
self.network = hk.transform(self.network_fn)
self.params = self.network.init(self.init_rng_key,
_sample_input(self.input_shape))
self.inputs = jnp.zeros((2,) + self.input_shape)
def test_basic(self):
self.network.apply(self.params, self.apply_rng_key, self.inputs)
def test_error_raised_if_rng_is_not_passed_in(self):
with self.assertRaisesRegex(ValueError, 'must be called with an RNG'):
self.network.apply(self.params, self.inputs)
def test_error_raised_if_transformed_without_rng_1(self):
network = hk.without_apply_rng(hk.transform(self.network_fn))
with self.assertRaisesRegex(ValueError, 'PRNGKey'):
network.apply(self.params, self.inputs)
def test_error_raised_if_transformed_without_rng_2(self):
network = hk.without_apply_rng(hk.transform(self.network_fn))
with self.assertRaisesRegex(TypeError, 'positional argument'):
network.apply(self.params, self.apply_rng_key, self.inputs)
def test_same_rng_produces_same_outputs(self):
outputs_1 = self.network.apply(self.params, self.apply_rng_key, self.inputs)
outputs_2 = self.network.apply(self.params, self.apply_rng_key, self.inputs)
np.testing.assert_allclose(outputs_1, outputs_2)
def test_different_rngs_produce_different_outputs(self):
rng_1, rng_2 = jax.random.split(jax.random.PRNGKey(1))
outputs_1 = self.network.apply(self.params, rng_1, self.inputs)
outputs_2 = self.network.apply(self.params, rng_2, self.inputs)
self.assertFalse(np.allclose(outputs_1, outputs_2))
def test_number_of_params_with_bias_correct(self):
net_fn = networks.noisy_linear(self.output_shape[0], 0.1, with_bias=True)
network = hk.transform(net_fn)
params = network.init(self.init_rng_key, _sample_input(self.input_shape))
self.assertCountEqual(['mu', 'sigma'], params)
self.assertCountEqual(['b', 'w'], params['mu'])
self.assertCountEqual(['b', 'w'], params['sigma'])
def test_number_of_params_without_bias_correct(self):
net_fn = networks.noisy_linear(self.output_shape[0], 0.1, with_bias=False)
network = hk.transform(net_fn)
params = network.init(self.init_rng_key, _sample_input(self.input_shape))
self.assertCountEqual(['mu', 'sigma'], params)
self.assertCountEqual(['w'], params['mu'])
self.assertCountEqual(['b', 'w'], params['sigma'])
def test_sigma_params_are_constant(self):
self.assertCountEqual(['mu', 'sigma'], self.params)
sigma_params = self.params['sigma']
sigma_w_values = np.unique(sigma_params['w'])
sigma_b_values = np.unique(sigma_params['b'])
self.assertLen(sigma_w_values, 1)
self.assertLen(sigma_b_values, 1)
value = 0.1 / np.sqrt(self.input_shape[0])
self.assertAlmostEqual(value, sigma_w_values)
self.assertAlmostEqual(value, sigma_b_values)
if __name__ == '__main__':
config.update('jax_numpy_rank_promotion', 'raise')
absltest.main()
| 38.896552 | 80 | 0.700502 |
import haiku as hk
import jax
from jax.config import config
import jax.numpy as jnp
import numpy as np
import tree
from dqn_zoo import networks
from absl.testing import absltest
def _sample_input(input_shape):
return jnp.zeros((1,) + input_shape, dtype=jnp.float32)
class SimpleLayersTest(absltest.TestCase):
def test_linear(self):
layer = hk.transform(networks.linear(4))
params = layer.init(jax.random.PRNGKey(1), _sample_input((3,)))
self.assertCountEqual(['linear'], params)
lin_params = params['linear']
self.assertCountEqual(['w', 'b'], lin_params)
self.assertEqual((3, 4), lin_params['w'].shape)
self.assertEqual((4,), lin_params['b'].shape)
def test_conv(self):
layer = hk.transform(networks.conv(4, (3, 3), 2))
params = layer.init(jax.random.PRNGKey(1), _sample_input((7, 7, 3)))
self.assertCountEqual(['conv2_d'], params)
conv_params = params['conv2_d']
self.assertCountEqual(['w', 'b'], conv_params)
self.assertEqual((3, 3, 3, 4), conv_params['w'].shape)
self.assertEqual((4,), conv_params['b'].shape)
class LinearWithSharedBiasTest(absltest.TestCase):
def setUp(self):
super().setUp()
rng_key = jax.random.PRNGKey(1)
self.init_rng_key, self.apply_rng_key = jax.random.split(rng_key)
self.input_shape = (4,)
self.output_shape = (3,)
self.weights_shape = (self.input_shape[0], self.output_shape[0])
network_fn = networks.linear_with_shared_bias(self.output_shape[0])
self.network = hk.transform(network_fn)
def test_bias_parameter_shape(self):
params = self.network.init(self.init_rng_key,
_sample_input(self.input_shape))
self.assertLen(tree.flatten(params), 2)
def check_params(path, param):
if path[-1] == 'b':
self.assertNotEqual(self.output_shape, param.shape)
self.assertEqual((1,), param.shape)
elif path[-1] == 'w':
self.assertEqual(self.weights_shape, param.shape)
else:
self.fail('Unexpected parameter %s.' % path)
tree.map_structure_with_path(check_params, params)
def test_output_shares_bias(self):
bias = 1.23
params = self.network.init(self.init_rng_key,
_sample_input(self.input_shape))
def replace_params(path, param):
if path[-1] == 'b':
return jnp.ones_like(param) * bias
else:
return jnp.zeros_like(param)
params = tree.map_structure_with_path(replace_params, params)
output = self.network.apply(params, self.apply_rng_key,
jnp.zeros((1,) + self.input_shape))
self.assertEqual((1,) + self.output_shape, output.shape)
np.testing.assert_allclose([bias] * self.output_shape[0], list(output[0]))
class NoisyLinearTest(absltest.TestCase):
def setUp(self):
super().setUp()
rng_key = jax.random.PRNGKey(1)
self.init_rng_key, self.apply_rng_key = jax.random.split(rng_key)
self.input_shape = (4,)
self.output_shape = (3,)
self.network_fn = networks.noisy_linear(self.output_shape[0], 0.1)
self.network = hk.transform(self.network_fn)
self.params = self.network.init(self.init_rng_key,
_sample_input(self.input_shape))
self.inputs = jnp.zeros((2,) + self.input_shape)
def test_basic(self):
self.network.apply(self.params, self.apply_rng_key, self.inputs)
def test_error_raised_if_rng_is_not_passed_in(self):
with self.assertRaisesRegex(ValueError, 'must be called with an RNG'):
self.network.apply(self.params, self.inputs)
def test_error_raised_if_transformed_without_rng_1(self):
network = hk.without_apply_rng(hk.transform(self.network_fn))
with self.assertRaisesRegex(ValueError, 'PRNGKey'):
network.apply(self.params, self.inputs)
def test_error_raised_if_transformed_without_rng_2(self):
network = hk.without_apply_rng(hk.transform(self.network_fn))
with self.assertRaisesRegex(TypeError, 'positional argument'):
network.apply(self.params, self.apply_rng_key, self.inputs)
def test_same_rng_produces_same_outputs(self):
outputs_1 = self.network.apply(self.params, self.apply_rng_key, self.inputs)
outputs_2 = self.network.apply(self.params, self.apply_rng_key, self.inputs)
np.testing.assert_allclose(outputs_1, outputs_2)
def test_different_rngs_produce_different_outputs(self):
rng_1, rng_2 = jax.random.split(jax.random.PRNGKey(1))
outputs_1 = self.network.apply(self.params, rng_1, self.inputs)
outputs_2 = self.network.apply(self.params, rng_2, self.inputs)
self.assertFalse(np.allclose(outputs_1, outputs_2))
def test_number_of_params_with_bias_correct(self):
net_fn = networks.noisy_linear(self.output_shape[0], 0.1, with_bias=True)
network = hk.transform(net_fn)
params = network.init(self.init_rng_key, _sample_input(self.input_shape))
self.assertCountEqual(['mu', 'sigma'], params)
self.assertCountEqual(['b', 'w'], params['mu'])
self.assertCountEqual(['b', 'w'], params['sigma'])
def test_number_of_params_without_bias_correct(self):
net_fn = networks.noisy_linear(self.output_shape[0], 0.1, with_bias=False)
network = hk.transform(net_fn)
params = network.init(self.init_rng_key, _sample_input(self.input_shape))
self.assertCountEqual(['mu', 'sigma'], params)
self.assertCountEqual(['w'], params['mu'])
self.assertCountEqual(['b', 'w'], params['sigma'])
def test_sigma_params_are_constant(self):
self.assertCountEqual(['mu', 'sigma'], self.params)
sigma_params = self.params['sigma']
sigma_w_values = np.unique(sigma_params['w'])
sigma_b_values = np.unique(sigma_params['b'])
self.assertLen(sigma_w_values, 1)
self.assertLen(sigma_b_values, 1)
value = 0.1 / np.sqrt(self.input_shape[0])
self.assertAlmostEqual(value, sigma_w_values)
self.assertAlmostEqual(value, sigma_b_values)
if __name__ == '__main__':
config.update('jax_numpy_rank_promotion', 'raise')
absltest.main()
| true | true |
f73bf43a853a72ce52ed11b2221e3d69c86939c4 | 72 | py | Python | pagebits/tests/__init__.py | nngroup/django-pagebits | 5580219de66ce5da1b3dda0e82fc32713153f0fc | [
"BSD-3-Clause"
] | 4 | 2016-04-10T13:17:26.000Z | 2019-03-12T17:18:59.000Z | pagebits/tests/__init__.py | nngroup/django-pagebits | 5580219de66ce5da1b3dda0e82fc32713153f0fc | [
"BSD-3-Clause"
] | null | null | null | pagebits/tests/__init__.py | nngroup/django-pagebits | 5580219de66ce5da1b3dda0e82fc32713153f0fc | [
"BSD-3-Clause"
] | 6 | 2015-03-27T15:50:46.000Z | 2017-03-09T18:05:47.000Z | from .models import *
from .views import *
from .templatetags import *
| 14.4 | 27 | 0.736111 | from .models import *
from .views import *
from .templatetags import *
| true | true |
f73bf45e888c263f9c6a0a064dddd9dc61be0e1d | 4,994 | py | Python | bench/sqlite3-search-bench.py | daq-tools/PyTables | 0949d41e611e6882a49248c6d82b1da9a994e788 | [
"BSD-3-Clause"
] | null | null | null | bench/sqlite3-search-bench.py | daq-tools/PyTables | 0949d41e611e6882a49248c6d82b1da9a994e788 | [
"BSD-3-Clause"
] | null | null | null | bench/sqlite3-search-bench.py | daq-tools/PyTables | 0949d41e611e6882a49248c6d82b1da9a994e788 | [
"BSD-3-Clause"
] | 2 | 2021-02-09T22:58:15.000Z | 2021-12-23T14:59:22.000Z | import os
import os.path
from time import perf_counter as clock
import numpy
import random
# in order to always generate the same random sequence
random.seed(19)
def fill_arrays(start, stop):
col_i = numpy.arange(start, stop, dtype=numpy.int32)
if userandom:
col_j = numpy.random.uniform(0, nrows, stop - start)
else:
col_j = numpy.array(col_i, dtype=numpy.float64)
return col_i, col_j
# Generator for ensure pytables benchmark compatibility
def int_generator(nrows):
step = 1000 * 100
j = 0
for i in range(nrows):
if i >= step * j:
stop = (j + 1) * step
if stop > nrows: # Seems unnecessary
stop = nrows
col_i, col_j = fill_arrays(i, stop)
j += 1
k = 0
yield (col_i[k], col_j[k])
k += 1
def int_generator_slow(nrows):
for i in range(nrows):
if userandom:
yield (i, float(random.randint(0, nrows)))
else:
yield (i, float(i))
def open_db(filename, remove=0):
if remove and os.path.exists(filename):
os.remove(filename)
con = sqlite.connect(filename)
cur = con.cursor()
return con, cur
def create_db(filename, nrows):
con, cur = open_db(filename, remove=1)
cur.execute("create table ints(i integer, j real)")
t1 = clock()
# This is twice as fast as a plain loop
cur.executemany("insert into ints(i,j) values (?,?)", int_generator(nrows))
con.commit()
ctime = clock() - t1
if verbose:
print(f"insert time: {ctime:.5f}")
print(f"Krows/s: {nrows / 1000 / ctime:.5f}")
close_db(con, cur)
def index_db(filename):
con, cur = open_db(filename)
t1 = clock()
cur.execute("create index ij on ints(j)")
con.commit()
itime = clock() - t1
if verbose:
print(f"index time: {itime:.5f}")
print(f"Krows/s: {nrows / itime:.5f}")
# Close the DB
close_db(con, cur)
def query_db(filename, rng):
con, cur = open_db(filename)
t1 = clock()
ntimes = 10
for i in range(ntimes):
# between clause does not seem to take advantage of indexes
# cur.execute("select j from ints where j between %s and %s" % \
cur.execute("select i from ints where j >= %s and j <= %s" %
# cur.execute("select i from ints where i >= %s and i <=
# %s" %
(rng[0] + i, rng[1] + i))
results = cur.fetchall()
con.commit()
qtime = (clock() - t1) / ntimes
if verbose:
print(f"query time: {qtime:.5f}")
print(f"Mrows/s: {nrows / 1000 / qtime:.5f}")
print(results)
close_db(con, cur)
def close_db(con, cur):
cur.close()
con.close()
if __name__ == "__main__":
import sys
import getopt
try:
import psyco
psyco_imported = 1
except:
psyco_imported = 0
usage = """usage: %s [-v] [-p] [-m] [-i] [-q] [-c] [-R range] [-n nrows] file
-v verbose
-p use "psyco" if available
-m use random values to fill the table
-q do query
-c create the database
-i index the table
-2 use sqlite2 (default is use sqlite3)
-R select a range in a field in the form "start,stop" (def "0,10")
-n sets the number of rows (in krows) in each table
\n""" % sys.argv[0]
try:
opts, pargs = getopt.getopt(sys.argv[1:], 'vpmiqc2R:n:')
except:
sys.stderr.write(usage)
sys.exit(0)
# default options
verbose = 0
usepsyco = 0
userandom = 0
docreate = 0
createindex = 0
doquery = 0
sqlite_version = "3"
rng = [0, 10]
nrows = 1
# Get the options
for option in opts:
if option[0] == '-v':
verbose = 1
elif option[0] == '-p':
usepsyco = 1
elif option[0] == '-m':
userandom = 1
elif option[0] == '-i':
createindex = 1
elif option[0] == '-q':
doquery = 1
elif option[0] == '-c':
docreate = 1
elif option[0] == "-2":
sqlite_version = "2"
elif option[0] == '-R':
rng = [int(i) for i in option[1].split(",")]
elif option[0] == '-n':
nrows = int(option[1])
# Catch the hdf5 file passed as the last argument
filename = pargs[0]
if sqlite_version == "2":
import sqlite
else:
from pysqlite2 import dbapi2 as sqlite
if verbose:
print("pysqlite version:", sqlite.version)
if userandom:
print("using random values")
if docreate:
if verbose:
print("writing %s krows" % nrows)
if psyco_imported and usepsyco:
psyco.bind(create_db)
nrows *= 1000
create_db(filename, nrows)
if createindex:
index_db(filename)
if doquery:
query_db(filename, rng)
| 26.284211 | 81 | 0.543452 | import os
import os.path
from time import perf_counter as clock
import numpy
import random
random.seed(19)
def fill_arrays(start, stop):
col_i = numpy.arange(start, stop, dtype=numpy.int32)
if userandom:
col_j = numpy.random.uniform(0, nrows, stop - start)
else:
col_j = numpy.array(col_i, dtype=numpy.float64)
return col_i, col_j
def int_generator(nrows):
step = 1000 * 100
j = 0
for i in range(nrows):
if i >= step * j:
stop = (j + 1) * step
if stop > nrows:
stop = nrows
col_i, col_j = fill_arrays(i, stop)
j += 1
k = 0
yield (col_i[k], col_j[k])
k += 1
def int_generator_slow(nrows):
for i in range(nrows):
if userandom:
yield (i, float(random.randint(0, nrows)))
else:
yield (i, float(i))
def open_db(filename, remove=0):
if remove and os.path.exists(filename):
os.remove(filename)
con = sqlite.connect(filename)
cur = con.cursor()
return con, cur
def create_db(filename, nrows):
con, cur = open_db(filename, remove=1)
cur.execute("create table ints(i integer, j real)")
t1 = clock()
cur.executemany("insert into ints(i,j) values (?,?)", int_generator(nrows))
con.commit()
ctime = clock() - t1
if verbose:
print(f"insert time: {ctime:.5f}")
print(f"Krows/s: {nrows / 1000 / ctime:.5f}")
close_db(con, cur)
def index_db(filename):
con, cur = open_db(filename)
t1 = clock()
cur.execute("create index ij on ints(j)")
con.commit()
itime = clock() - t1
if verbose:
print(f"index time: {itime:.5f}")
print(f"Krows/s: {nrows / itime:.5f}")
close_db(con, cur)
def query_db(filename, rng):
con, cur = open_db(filename)
t1 = clock()
ntimes = 10
for i in range(ntimes):
cur.execute("select i from ints where j >= %s and j <= %s" %
# %s" %
(rng[0] + i, rng[1] + i))
results = cur.fetchall()
con.commit()
qtime = (clock() - t1) / ntimes
if verbose:
print(f"query time: {qtime:.5f}")
print(f"Mrows/s: {nrows / 1000 / qtime:.5f}")
print(results)
close_db(con, cur)
def close_db(con, cur):
cur.close()
con.close()
if __name__ == "__main__":
import sys
import getopt
try:
import psyco
psyco_imported = 1
except:
psyco_imported = 0
usage = """usage: %s [-v] [-p] [-m] [-i] [-q] [-c] [-R range] [-n nrows] file
-v verbose
-p use "psyco" if available
-m use random values to fill the table
-q do query
-c create the database
-i index the table
-2 use sqlite2 (default is use sqlite3)
-R select a range in a field in the form "start,stop" (def "0,10")
-n sets the number of rows (in krows) in each table
\n""" % sys.argv[0]
try:
opts, pargs = getopt.getopt(sys.argv[1:], 'vpmiqc2R:n:')
except:
sys.stderr.write(usage)
sys.exit(0)
verbose = 0
usepsyco = 0
userandom = 0
docreate = 0
createindex = 0
doquery = 0
sqlite_version = "3"
rng = [0, 10]
nrows = 1
for option in opts:
if option[0] == '-v':
verbose = 1
elif option[0] == '-p':
usepsyco = 1
elif option[0] == '-m':
userandom = 1
elif option[0] == '-i':
createindex = 1
elif option[0] == '-q':
doquery = 1
elif option[0] == '-c':
docreate = 1
elif option[0] == "-2":
sqlite_version = "2"
elif option[0] == '-R':
rng = [int(i) for i in option[1].split(",")]
elif option[0] == '-n':
nrows = int(option[1])
filename = pargs[0]
if sqlite_version == "2":
import sqlite
else:
from pysqlite2 import dbapi2 as sqlite
if verbose:
print("pysqlite version:", sqlite.version)
if userandom:
print("using random values")
if docreate:
if verbose:
print("writing %s krows" % nrows)
if psyco_imported and usepsyco:
psyco.bind(create_db)
nrows *= 1000
create_db(filename, nrows)
if createindex:
index_db(filename)
if doquery:
query_db(filename, rng)
| true | true |
f73bf4c22765ae3b9e4134953e252e8c4eeb07b1 | 158 | py | Python | kattis/heartrate.py | terror/Solutions | 1ad33daec95b565a38ac4730261593bcf249ac86 | [
"CC0-1.0"
] | 2 | 2021-04-05T14:26:37.000Z | 2021-06-10T04:22:01.000Z | kattis/heartrate.py | terror/Solutions | 1ad33daec95b565a38ac4730261593bcf249ac86 | [
"CC0-1.0"
] | null | null | null | kattis/heartrate.py | terror/Solutions | 1ad33daec95b565a38ac4730261593bcf249ac86 | [
"CC0-1.0"
] | null | null | null | for i in range(int(input())):
b, p = list(map(float, input().split()))
t = 60 / p
tt = t * b
print("{:.4f} {:.4f} {:.4f}".format(tt - t, tt, tt + t))
| 26.333333 | 58 | 0.481013 | for i in range(int(input())):
b, p = list(map(float, input().split()))
t = 60 / p
tt = t * b
print("{:.4f} {:.4f} {:.4f}".format(tt - t, tt, tt + t))
| true | true |
f73bf4cc862a2490edd9d343c95505e9bf67c7bb | 9,047 | py | Python | tests/test_filters.py | Outflier/PyAV | f3aa2336a9fddfc2ae46e15a26956da08153af7e | [
"BSD-3-Clause"
] | 965 | 2015-01-08T19:11:16.000Z | 2020-04-30T16:27:07.000Z | tests/test_filters.py | Outflier/PyAV | f3aa2336a9fddfc2ae46e15a26956da08153af7e | [
"BSD-3-Clause"
] | 542 | 2015-01-02T12:55:46.000Z | 2020-04-30T16:13:56.000Z | tests/test_filters.py | Outflier/PyAV | f3aa2336a9fddfc2ae46e15a26956da08153af7e | [
"BSD-3-Clause"
] | 211 | 2015-01-10T12:10:02.000Z | 2020-04-29T14:02:51.000Z | from fractions import Fraction
from unittest import SkipTest
import errno
import numpy as np
from av import AudioFrame, VideoFrame
from av.audio.frame import format_dtypes
from av.filter import Filter, Graph
import av
from .common import Image, TestCase, fate_suite
def generate_audio_frame(
frame_num, input_format="s16", layout="stereo", sample_rate=44100, frame_size=1024
):
"""
Generate audio frame representing part of the sinusoidal wave
:param input_format: default: s16
:param layout: default: stereo
:param sample_rate: default: 44100
:param frame_size: default: 1024
:param frame_num: frame number
:return: audio frame for sinusoidal wave audio signal slice
"""
frame = AudioFrame(format=input_format, layout=layout, samples=frame_size)
frame.sample_rate = sample_rate
frame.pts = frame_num * frame_size
for i in range(len(frame.layout.channels)):
data = np.zeros(frame_size, dtype=format_dtypes[input_format])
for j in range(frame_size):
data[j] = np.sin(2 * np.pi * (frame_num + j) * (i + 1) / float(frame_size))
frame.planes[i].update(data)
return frame
def pull_until_blocked(graph):
frames = []
while True:
try:
frames.append(graph.pull())
except av.utils.AVError as e:
if e.errno != errno.EAGAIN:
raise
return frames
class TestFilters(TestCase):
def test_filter_descriptor(self):
f = Filter("testsrc")
self.assertEqual(f.name, "testsrc")
self.assertEqual(f.description, "Generate test pattern.")
self.assertFalse(f.dynamic_inputs)
self.assertEqual(len(f.inputs), 0)
self.assertFalse(f.dynamic_outputs)
self.assertEqual(len(f.outputs), 1)
self.assertEqual(f.outputs[0].name, "default")
self.assertEqual(f.outputs[0].type, "video")
def test_dynamic_filter_descriptor(self):
f = Filter("split")
self.assertFalse(f.dynamic_inputs)
self.assertEqual(len(f.inputs), 1)
self.assertTrue(f.dynamic_outputs)
self.assertEqual(len(f.outputs), 0)
def test_generator_graph(self):
graph = Graph()
src = graph.add("testsrc")
lutrgb = graph.add(
"lutrgb",
"r=maxval+minval-val:g=maxval+minval-val:b=maxval+minval-val",
name="invert",
)
sink = graph.add("buffersink")
src.link_to(lutrgb)
lutrgb.link_to(sink)
# pads and links
self.assertIs(src.outputs[0].link.output, lutrgb.inputs[0])
self.assertIs(lutrgb.inputs[0].link.input, src.outputs[0])
frame = sink.pull()
self.assertIsInstance(frame, VideoFrame)
if Image:
frame.to_image().save(self.sandboxed("mandelbrot2.png"))
def test_auto_find_sink(self):
graph = Graph()
src = graph.add("testsrc")
src.link_to(graph.add("buffersink"))
graph.configure()
frame = graph.pull()
if Image:
frame.to_image().save(self.sandboxed("mandelbrot3.png"))
def test_delegate_sink(self):
graph = Graph()
src = graph.add("testsrc")
src.link_to(graph.add("buffersink"))
graph.configure()
frame = src.pull()
if Image:
frame.to_image().save(self.sandboxed("mandelbrot4.png"))
def test_haldclut_graph(self):
raise SkipTest()
graph = Graph()
img = Image.open(fate_suite("png1/lena-rgb24.png"))
frame = VideoFrame.from_image(img)
img_source = graph.add_buffer(frame)
hald_img = Image.open("hald_7.png")
hald_frame = VideoFrame.from_image(hald_img)
hald_source = graph.add_buffer(hald_frame)
hald_filter = graph.add("haldclut")
sink = graph.add("buffersink")
img_source.link(0, hald_filter, 0)
hald_source.link(0, hald_filter, 1)
hald_filter.link(0, sink, 0)
graph.config()
self.assertIs(img_source.outputs[0].linked_to, hald_filter.inputs[0])
self.assertIs(hald_source.outputs[0].linked_to, hald_filter.inputs[1])
self.assertIs(hald_filter.outputs[0].linked_to, sink.inputs[0])
hald_source.push(hald_frame)
img_source.push(frame)
frame = sink.pull()
self.assertIsInstance(frame, VideoFrame)
frame.to_image().save(self.sandboxed("filtered.png"))
def test_audio_buffer_sink(self):
graph = Graph()
audio_buffer = graph.add_abuffer(
format="fltp",
sample_rate=48000,
layout="stereo",
time_base=Fraction(1, 48000),
)
audio_buffer.link_to(graph.add("abuffersink"))
graph.configure()
try:
graph.pull()
except OSError as e:
# we haven't pushed any input so expect no frames / EAGAIN
if e.errno != errno.EAGAIN:
raise
@staticmethod
def link_nodes(*nodes):
for c, n in zip(nodes, nodes[1:]):
c.link_to(n)
def test_audio_buffer_resample(self):
graph = Graph()
self.link_nodes(
graph.add_abuffer(
format="fltp",
sample_rate=48000,
layout="stereo",
time_base=Fraction(1, 48000),
),
graph.add(
"aformat", "sample_fmts=s16:sample_rates=44100:channel_layouts=stereo"
),
graph.add("abuffersink"),
)
graph.configure()
graph.push(
generate_audio_frame(
0, input_format="fltp", layout="stereo", sample_rate=48000
)
)
out_frame = graph.pull()
self.assertEqual(out_frame.format.name, "s16")
self.assertEqual(out_frame.layout.name, "stereo")
self.assertEqual(out_frame.sample_rate, 44100)
def test_audio_buffer_volume_filter(self):
graph = Graph()
self.link_nodes(
graph.add_abuffer(
format="fltp",
sample_rate=48000,
layout="stereo",
time_base=Fraction(1, 48000),
),
graph.add("volume", volume="0.5"),
graph.add("abuffersink"),
)
graph.configure()
input_frame = generate_audio_frame(
0, input_format="fltp", layout="stereo", sample_rate=48000
)
graph.push(input_frame)
out_frame = graph.pull()
self.assertEqual(out_frame.format.name, "fltp")
self.assertEqual(out_frame.layout.name, "stereo")
self.assertEqual(out_frame.sample_rate, 48000)
input_data = input_frame.to_ndarray()
output_data = out_frame.to_ndarray()
self.assertTrue(
np.allclose(input_data * 0.5, output_data), "Check that volume is reduced"
)
def test_video_buffer(self):
input_container = av.open(format="lavfi", file="color=c=pink:duration=1:r=30")
input_video_stream = input_container.streams.video[0]
graph = av.filter.Graph()
buffer = graph.add_buffer(template=input_video_stream)
bwdif = graph.add("bwdif", "send_field:tff:all")
buffersink = graph.add("buffersink")
buffer.link_to(bwdif)
bwdif.link_to(buffersink)
graph.configure()
for frame in input_container.decode():
self.assertEqual(frame.time_base, Fraction(1, 30))
graph.push(frame)
filtered_frames = pull_until_blocked(graph)
if frame.pts == 0:
# no output for the first input frame
self.assertEqual(len(filtered_frames), 0)
else:
# we expect two filtered frames per input frame
self.assertEqual(len(filtered_frames), 2)
self.assertEqual(filtered_frames[0].pts, (frame.pts - 1) * 2)
self.assertEqual(filtered_frames[0].time_base, Fraction(1, 60))
self.assertEqual(filtered_frames[1].pts, (frame.pts - 1) * 2 + 1)
self.assertEqual(filtered_frames[1].time_base, Fraction(1, 60))
def test_EOF(self):
input_container = av.open(format="lavfi", file="color=c=pink:duration=1:r=30")
video_stream = input_container.streams.video[0]
graph = av.filter.Graph()
video_in = graph.add_buffer(template=video_stream)
palette_gen_filter = graph.add("palettegen")
video_out = graph.add("buffersink")
video_in.link_to(palette_gen_filter)
palette_gen_filter.link_to(video_out)
graph.configure()
for frame in input_container.decode(video=0):
graph.push(frame)
graph.push(None)
# if we do not push None, we get a BlockingIOError
palette_frame = graph.pull()
self.assertIsInstance(palette_frame, av.VideoFrame)
self.assertEqual(palette_frame.width, 16)
self.assertEqual(palette_frame.height, 16)
| 31.522648 | 87 | 0.608378 | from fractions import Fraction
from unittest import SkipTest
import errno
import numpy as np
from av import AudioFrame, VideoFrame
from av.audio.frame import format_dtypes
from av.filter import Filter, Graph
import av
from .common import Image, TestCase, fate_suite
def generate_audio_frame(
frame_num, input_format="s16", layout="stereo", sample_rate=44100, frame_size=1024
):
frame = AudioFrame(format=input_format, layout=layout, samples=frame_size)
frame.sample_rate = sample_rate
frame.pts = frame_num * frame_size
for i in range(len(frame.layout.channels)):
data = np.zeros(frame_size, dtype=format_dtypes[input_format])
for j in range(frame_size):
data[j] = np.sin(2 * np.pi * (frame_num + j) * (i + 1) / float(frame_size))
frame.planes[i].update(data)
return frame
def pull_until_blocked(graph):
frames = []
while True:
try:
frames.append(graph.pull())
except av.utils.AVError as e:
if e.errno != errno.EAGAIN:
raise
return frames
class TestFilters(TestCase):
def test_filter_descriptor(self):
f = Filter("testsrc")
self.assertEqual(f.name, "testsrc")
self.assertEqual(f.description, "Generate test pattern.")
self.assertFalse(f.dynamic_inputs)
self.assertEqual(len(f.inputs), 0)
self.assertFalse(f.dynamic_outputs)
self.assertEqual(len(f.outputs), 1)
self.assertEqual(f.outputs[0].name, "default")
self.assertEqual(f.outputs[0].type, "video")
def test_dynamic_filter_descriptor(self):
f = Filter("split")
self.assertFalse(f.dynamic_inputs)
self.assertEqual(len(f.inputs), 1)
self.assertTrue(f.dynamic_outputs)
self.assertEqual(len(f.outputs), 0)
def test_generator_graph(self):
graph = Graph()
src = graph.add("testsrc")
lutrgb = graph.add(
"lutrgb",
"r=maxval+minval-val:g=maxval+minval-val:b=maxval+minval-val",
name="invert",
)
sink = graph.add("buffersink")
src.link_to(lutrgb)
lutrgb.link_to(sink)
self.assertIs(src.outputs[0].link.output, lutrgb.inputs[0])
self.assertIs(lutrgb.inputs[0].link.input, src.outputs[0])
frame = sink.pull()
self.assertIsInstance(frame, VideoFrame)
if Image:
frame.to_image().save(self.sandboxed("mandelbrot2.png"))
def test_auto_find_sink(self):
graph = Graph()
src = graph.add("testsrc")
src.link_to(graph.add("buffersink"))
graph.configure()
frame = graph.pull()
if Image:
frame.to_image().save(self.sandboxed("mandelbrot3.png"))
def test_delegate_sink(self):
graph = Graph()
src = graph.add("testsrc")
src.link_to(graph.add("buffersink"))
graph.configure()
frame = src.pull()
if Image:
frame.to_image().save(self.sandboxed("mandelbrot4.png"))
def test_haldclut_graph(self):
raise SkipTest()
graph = Graph()
img = Image.open(fate_suite("png1/lena-rgb24.png"))
frame = VideoFrame.from_image(img)
img_source = graph.add_buffer(frame)
hald_img = Image.open("hald_7.png")
hald_frame = VideoFrame.from_image(hald_img)
hald_source = graph.add_buffer(hald_frame)
hald_filter = graph.add("haldclut")
sink = graph.add("buffersink")
img_source.link(0, hald_filter, 0)
hald_source.link(0, hald_filter, 1)
hald_filter.link(0, sink, 0)
graph.config()
self.assertIs(img_source.outputs[0].linked_to, hald_filter.inputs[0])
self.assertIs(hald_source.outputs[0].linked_to, hald_filter.inputs[1])
self.assertIs(hald_filter.outputs[0].linked_to, sink.inputs[0])
hald_source.push(hald_frame)
img_source.push(frame)
frame = sink.pull()
self.assertIsInstance(frame, VideoFrame)
frame.to_image().save(self.sandboxed("filtered.png"))
def test_audio_buffer_sink(self):
graph = Graph()
audio_buffer = graph.add_abuffer(
format="fltp",
sample_rate=48000,
layout="stereo",
time_base=Fraction(1, 48000),
)
audio_buffer.link_to(graph.add("abuffersink"))
graph.configure()
try:
graph.pull()
except OSError as e:
if e.errno != errno.EAGAIN:
raise
@staticmethod
def link_nodes(*nodes):
for c, n in zip(nodes, nodes[1:]):
c.link_to(n)
def test_audio_buffer_resample(self):
graph = Graph()
self.link_nodes(
graph.add_abuffer(
format="fltp",
sample_rate=48000,
layout="stereo",
time_base=Fraction(1, 48000),
),
graph.add(
"aformat", "sample_fmts=s16:sample_rates=44100:channel_layouts=stereo"
),
graph.add("abuffersink"),
)
graph.configure()
graph.push(
generate_audio_frame(
0, input_format="fltp", layout="stereo", sample_rate=48000
)
)
out_frame = graph.pull()
self.assertEqual(out_frame.format.name, "s16")
self.assertEqual(out_frame.layout.name, "stereo")
self.assertEqual(out_frame.sample_rate, 44100)
def test_audio_buffer_volume_filter(self):
graph = Graph()
self.link_nodes(
graph.add_abuffer(
format="fltp",
sample_rate=48000,
layout="stereo",
time_base=Fraction(1, 48000),
),
graph.add("volume", volume="0.5"),
graph.add("abuffersink"),
)
graph.configure()
input_frame = generate_audio_frame(
0, input_format="fltp", layout="stereo", sample_rate=48000
)
graph.push(input_frame)
out_frame = graph.pull()
self.assertEqual(out_frame.format.name, "fltp")
self.assertEqual(out_frame.layout.name, "stereo")
self.assertEqual(out_frame.sample_rate, 48000)
input_data = input_frame.to_ndarray()
output_data = out_frame.to_ndarray()
self.assertTrue(
np.allclose(input_data * 0.5, output_data), "Check that volume is reduced"
)
def test_video_buffer(self):
input_container = av.open(format="lavfi", file="color=c=pink:duration=1:r=30")
input_video_stream = input_container.streams.video[0]
graph = av.filter.Graph()
buffer = graph.add_buffer(template=input_video_stream)
bwdif = graph.add("bwdif", "send_field:tff:all")
buffersink = graph.add("buffersink")
buffer.link_to(bwdif)
bwdif.link_to(buffersink)
graph.configure()
for frame in input_container.decode():
self.assertEqual(frame.time_base, Fraction(1, 30))
graph.push(frame)
filtered_frames = pull_until_blocked(graph)
if frame.pts == 0:
# no output for the first input frame
self.assertEqual(len(filtered_frames), 0)
else:
# we expect two filtered frames per input frame
self.assertEqual(len(filtered_frames), 2)
self.assertEqual(filtered_frames[0].pts, (frame.pts - 1) * 2)
self.assertEqual(filtered_frames[0].time_base, Fraction(1, 60))
self.assertEqual(filtered_frames[1].pts, (frame.pts - 1) * 2 + 1)
self.assertEqual(filtered_frames[1].time_base, Fraction(1, 60))
def test_EOF(self):
input_container = av.open(format="lavfi", file="color=c=pink:duration=1:r=30")
video_stream = input_container.streams.video[0]
graph = av.filter.Graph()
video_in = graph.add_buffer(template=video_stream)
palette_gen_filter = graph.add("palettegen")
video_out = graph.add("buffersink")
video_in.link_to(palette_gen_filter)
palette_gen_filter.link_to(video_out)
graph.configure()
for frame in input_container.decode(video=0):
graph.push(frame)
graph.push(None)
# if we do not push None, we get a BlockingIOError
palette_frame = graph.pull()
self.assertIsInstance(palette_frame, av.VideoFrame)
self.assertEqual(palette_frame.width, 16)
self.assertEqual(palette_frame.height, 16)
| true | true |
f73bf4f126cc8565a9bbb1503b05b3157f9bdb04 | 1,491 | py | Python | extra_tests/ctypes_tests/test_numbers.py | nanjekyejoannah/pypy | e80079fe13c29eda7b2a6b4cd4557051f975a2d9 | [
"Apache-2.0",
"OpenSSL"
] | 333 | 2015-08-08T18:03:38.000Z | 2022-03-22T18:13:12.000Z | extra_tests/ctypes_tests/test_numbers.py | nanjekyejoannah/pypy | e80079fe13c29eda7b2a6b4cd4557051f975a2d9 | [
"Apache-2.0",
"OpenSSL"
] | 7 | 2020-02-16T16:49:05.000Z | 2021-11-26T09:00:56.000Z | extra_tests/ctypes_tests/test_numbers.py | nanjekyejoannah/pypy | e80079fe13c29eda7b2a6b4cd4557051f975a2d9 | [
"Apache-2.0",
"OpenSSL"
] | 55 | 2015-08-16T02:41:30.000Z | 2022-03-20T20:33:35.000Z | import pytest
from ctypes import *
unsigned_types = [c_ubyte, c_ushort, c_uint, c_ulong]
signed_types = [c_byte, c_short, c_int, c_long, c_longlong]
float_types = [c_double, c_float, c_longdouble]
try:
c_ulonglong
c_longlong
except NameError:
pass
else:
unsigned_types.append(c_ulonglong)
signed_types.append(c_longlong)
################################################################
@pytest.mark.parametrize('t', signed_types + unsigned_types + float_types)
def test_init_again(t):
parm = t()
addr1 = addressof(parm)
parm.__init__(0)
addr2 = addressof(parm)
assert addr1 == addr2
def test_subclass():
class enum(c_int):
def __new__(cls, value):
dont_call_me
class S(Structure):
_fields_ = [('t', enum)]
assert isinstance(S().t, enum)
#@pytest.mark.xfail("'__pypy__' not in sys.builtin_module_names")
@pytest.mark.xfail
def test_no_missing_shape_to_ffi_type():
# whitebox test
"re-enable after adding 'g' to _shape_to_ffi_type.typemap, "
"which I think needs fighting all the way up from "
"rpython.rlib.libffi"
from _ctypes.basics import _shape_to_ffi_type
from _rawffi import Array
for i in range(1, 256):
try:
Array(chr(i))
except ValueError:
pass
else:
assert chr(i) in _shape_to_ffi_type.typemap
@pytest.mark.xfail
def test_pointer_to_long_double():
import ctypes
ctypes.POINTER(ctypes.c_longdouble)
| 26.157895 | 74 | 0.653253 | import pytest
from ctypes import *
unsigned_types = [c_ubyte, c_ushort, c_uint, c_ulong]
signed_types = [c_byte, c_short, c_int, c_long, c_longlong]
float_types = [c_double, c_float, c_longdouble]
try:
c_ulonglong
c_longlong
except NameError:
pass
else:
unsigned_types.append(c_ulonglong)
signed_types.append(c_longlong)
| true | true |
f73bf56a0b43782ac33279b2ff8eddf191cc7b6c | 4,515 | py | Python | pyspedas/mms/particles/mms_part_getspec.py | shihikoo/pyspedas | 5251c52d074c4f2c3d91f2f8a457d36f3ad2ddb4 | [
"MIT"
] | null | null | null | pyspedas/mms/particles/mms_part_getspec.py | shihikoo/pyspedas | 5251c52d074c4f2c3d91f2f8a457d36f3ad2ddb4 | [
"MIT"
] | null | null | null | pyspedas/mms/particles/mms_part_getspec.py | shihikoo/pyspedas | 5251c52d074c4f2c3d91f2f8a457d36f3ad2ddb4 | [
"MIT"
] | null | null | null |
import logging
from time import time
import pyspedas
from pyspedas import time_double
from pyspedas.mms.particles.mms_part_products import mms_part_products
logging.captureWarnings(True)
logging.basicConfig(format='%(asctime)s: %(message)s', datefmt='%d-%b-%y %H:%M:%S', level=logging.INFO)
def mms_part_getspec(instrument='fpi', probe='1', species='e', data_rate='fast',
trange=None, output=['energy', 'theta', 'phi'], units='eflux', energy=None,
phi=None, theta=None, pitch=None, gyro=None, mag_data_rate=None, fac_type='mphigeo',
center_measurement=False, spdf=False, correct_photoelectrons=False,
internal_photoelectron_corrections=False, disable_photoelectron_corrections=False):
"""
"""
start_time = time()
if trange is None:
# test data for development
trange = ['2015-10-16/13:06', '2015-10-16/13:07']
# data_rate = 'brst'
if mag_data_rate is None:
if data_rate == 'brst':
mag_data_rate = 'brst'
scpot_data_rate = 'brst'
else:
mag_data_rate = 'srvy'
scpot_data_rate = 'fast'
instrument = instrument.lower()
# HPCA is required to be at the center of the accumulation interval
# due to assumptions made in mms_get_hpca_dist
if instrument == 'hpca' and center_measurement == False:
center_measurement = True
if instrument == 'fpi':
data_vars = pyspedas.mms.fpi(datatype='d'+species+'s-dist', probe=probe, data_rate=data_rate, trange=trange, time_clip=True, center_measurement=center_measurement, spdf=spdf)
elif instrument == 'hpca':
# for HPCA, 'fast' should be 'srvy'
if data_rate == 'fast':
data_rate = 'srvy'
# 'i' and 'e' are only valid for FPI
if species in ['i', 'e']:
species = 'hplus'
data_vars = pyspedas.mms.hpca(datatype='ion', probe=probe, data_rate=data_rate, trange=trange, time_clip=True, center_measurement=center_measurement, get_support_data=True, spdf=spdf)
else:
logging.error('Error, unknown instrument: ' + instrument + '; valid options: fpi, hpca')
return
if data_vars is None or len(data_vars) == 0:
logging.error('Error, no data loaded.')
return None
if not isinstance(probe, list):
probe = [probe]
if instrument == 'fpi' and species == 'e' and not disable_photoelectron_corrections:
correct_photoelectrons = True
support_trange = [time_double(trange[0])-60.0, time_double(trange[1])+60.0]
# load state data (needed for coordinate transformations and field-aligned coordinates)
pos_vars = pyspedas.mms.mec(probe=probe, trange=support_trange, time_clip=True, spdf=spdf)
if len(pos_vars) == 0:
logging.error('Error, no state data loaded.')
return
mag_vars = pyspedas.mms.fgm(probe=probe, trange=support_trange, data_rate=mag_data_rate, time_clip=True, spdf=spdf)
if len(mag_vars) == 0:
logging.error('Error, no magnetic field data loaded.')
return
scpot_vars = pyspedas.mms.edp(probe=probe, trange=support_trange, level='l2', spdf=spdf, data_rate=scpot_data_rate, datatype='scpot', varformat='*_edp_scpot_*')
out_vars = []
for prb in probe:
prb_str = str(prb)
mag_name = 'mms'+prb_str+'_fgm_b_gse_'+mag_data_rate+'_l2_bvec'
pos_name = 'mms'+prb_str+'_mec_r_gse'
if instrument == 'fpi':
tname = 'mms'+prb_str+'_d'+species+'s_dist_'+data_rate
elif instrument == 'hpca':
tname = 'mms'+prb_str+'_hpca_'+species+'_phase_space_density'
scpot_variable = 'mms'+prb_str+'_edp_scpot_'+scpot_data_rate+'_l2'
new_vars = mms_part_products(tname, species=species, instrument=instrument, probe=prb, data_rate=data_rate,
output=output, units=units, energy=energy, phi=phi, theta=theta, pitch=pitch, gyro=gyro,
mag_name=mag_name, pos_name=pos_name, fac_type=fac_type, sc_pot_name=scpot_variable,
correct_photoelectrons=correct_photoelectrons,
internal_photoelectron_corrections=internal_photoelectron_corrections,
disable_photoelectron_corrections=disable_photoelectron_corrections)
if new_vars is None:
continue
out_vars = out_vars + new_vars
logging.info('Finished; time to run: ' + str(round(time()-start_time, 1)) + ' seconds.')
return out_vars | 39.955752 | 191 | 0.659579 |
import logging
from time import time
import pyspedas
from pyspedas import time_double
from pyspedas.mms.particles.mms_part_products import mms_part_products
logging.captureWarnings(True)
logging.basicConfig(format='%(asctime)s: %(message)s', datefmt='%d-%b-%y %H:%M:%S', level=logging.INFO)
def mms_part_getspec(instrument='fpi', probe='1', species='e', data_rate='fast',
trange=None, output=['energy', 'theta', 'phi'], units='eflux', energy=None,
phi=None, theta=None, pitch=None, gyro=None, mag_data_rate=None, fac_type='mphigeo',
center_measurement=False, spdf=False, correct_photoelectrons=False,
internal_photoelectron_corrections=False, disable_photoelectron_corrections=False):
start_time = time()
if trange is None:
trange = ['2015-10-16/13:06', '2015-10-16/13:07']
if mag_data_rate is None:
if data_rate == 'brst':
mag_data_rate = 'brst'
scpot_data_rate = 'brst'
else:
mag_data_rate = 'srvy'
scpot_data_rate = 'fast'
instrument = instrument.lower()
if instrument == 'hpca' and center_measurement == False:
center_measurement = True
if instrument == 'fpi':
data_vars = pyspedas.mms.fpi(datatype='d'+species+'s-dist', probe=probe, data_rate=data_rate, trange=trange, time_clip=True, center_measurement=center_measurement, spdf=spdf)
elif instrument == 'hpca':
if data_rate == 'fast':
data_rate = 'srvy'
if species in ['i', 'e']:
species = 'hplus'
data_vars = pyspedas.mms.hpca(datatype='ion', probe=probe, data_rate=data_rate, trange=trange, time_clip=True, center_measurement=center_measurement, get_support_data=True, spdf=spdf)
else:
logging.error('Error, unknown instrument: ' + instrument + '; valid options: fpi, hpca')
return
if data_vars is None or len(data_vars) == 0:
logging.error('Error, no data loaded.')
return None
if not isinstance(probe, list):
probe = [probe]
if instrument == 'fpi' and species == 'e' and not disable_photoelectron_corrections:
correct_photoelectrons = True
support_trange = [time_double(trange[0])-60.0, time_double(trange[1])+60.0]
pos_vars = pyspedas.mms.mec(probe=probe, trange=support_trange, time_clip=True, spdf=spdf)
if len(pos_vars) == 0:
logging.error('Error, no state data loaded.')
return
mag_vars = pyspedas.mms.fgm(probe=probe, trange=support_trange, data_rate=mag_data_rate, time_clip=True, spdf=spdf)
if len(mag_vars) == 0:
logging.error('Error, no magnetic field data loaded.')
return
scpot_vars = pyspedas.mms.edp(probe=probe, trange=support_trange, level='l2', spdf=spdf, data_rate=scpot_data_rate, datatype='scpot', varformat='*_edp_scpot_*')
out_vars = []
for prb in probe:
prb_str = str(prb)
mag_name = 'mms'+prb_str+'_fgm_b_gse_'+mag_data_rate+'_l2_bvec'
pos_name = 'mms'+prb_str+'_mec_r_gse'
if instrument == 'fpi':
tname = 'mms'+prb_str+'_d'+species+'s_dist_'+data_rate
elif instrument == 'hpca':
tname = 'mms'+prb_str+'_hpca_'+species+'_phase_space_density'
scpot_variable = 'mms'+prb_str+'_edp_scpot_'+scpot_data_rate+'_l2'
new_vars = mms_part_products(tname, species=species, instrument=instrument, probe=prb, data_rate=data_rate,
output=output, units=units, energy=energy, phi=phi, theta=theta, pitch=pitch, gyro=gyro,
mag_name=mag_name, pos_name=pos_name, fac_type=fac_type, sc_pot_name=scpot_variable,
correct_photoelectrons=correct_photoelectrons,
internal_photoelectron_corrections=internal_photoelectron_corrections,
disable_photoelectron_corrections=disable_photoelectron_corrections)
if new_vars is None:
continue
out_vars = out_vars + new_vars
logging.info('Finished; time to run: ' + str(round(time()-start_time, 1)) + ' seconds.')
return out_vars | true | true |
f73bf5fb08198debdf711452852fef0c042753b9 | 36,670 | py | Python | auth-api/tests/unit/services/test_user.py | thorwolpert/sbc-auth | 5da50cde2e5625d1b0ceea090c3656ee374c9b71 | [
"Apache-2.0"
] | 11 | 2019-09-26T06:58:25.000Z | 2022-01-26T06:19:39.000Z | auth-api/tests/unit/services/test_user.py | thorwolpert/sbc-auth | 5da50cde2e5625d1b0ceea090c3656ee374c9b71 | [
"Apache-2.0"
] | 1,622 | 2019-05-07T21:08:38.000Z | 2022-03-28T17:07:15.000Z | auth-api/tests/unit/services/test_user.py | thorwolpert/sbc-auth | 5da50cde2e5625d1b0ceea090c3656ee374c9b71 | [
"Apache-2.0"
] | 98 | 2019-03-01T21:36:15.000Z | 2021-12-01T22:11:25.000Z | # Copyright © 2019 Province of British Columbia
#
# 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 to verify the User Service.
Test-Suite to ensure that the User Service is working as expected.
"""
import json
from unittest.mock import patch
import pytest
from werkzeug.exceptions import HTTPException
from auth_api.exceptions import BusinessException
from auth_api.exceptions.errors import Error
from auth_api.models import Affiliation as AffiliationModel
from auth_api.models import ContactLink as ContactLinkModel
from auth_api.models import Membership as MembershipModel
from auth_api.models import User as UserModel
from auth_api.services import Org as OrgService
from auth_api.services import User as UserService
from auth_api.services.keycloak import KeycloakService
from auth_api.services.keycloak_user import KeycloakUser
from auth_api.utils.enums import IdpHint, ProductCode, Status
from auth_api.utils.roles import ADMIN, COORDINATOR, USER, Role
from tests.utilities.factory_scenarios import (
KeycloakScenario, TestAnonymousMembership, TestContactInfo, TestEntityInfo, TestJwtClaims, TestOrgInfo,
TestUserInfo)
from tests.utilities.factory_utils import (
factory_contact_model, factory_entity_model, factory_membership_model, factory_org_model, factory_product_model,
factory_user_model, get_tos_latest_version, patch_token_info)
def test_as_dict(session): # pylint: disable=unused-argument
"""Assert that a user is rendered correctly as a dictionary."""
user_model = factory_user_model()
user = UserService(user_model)
dictionary = user.as_dict()
assert dictionary['username'] == TestUserInfo.user1['username']
def test_user_save_by_token(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a user can be created by token."""
patch_token_info(TestJwtClaims.user_test, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestJwtClaims.user_test['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.user_test['sub']
def test_bcros_user_save_by_token(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a user can be created by token."""
patch_token_info(TestJwtClaims.anonymous_bcros_role, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestJwtClaims.anonymous_bcros_role['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.anonymous_bcros_role['sub']
def test_bcros_user_update_by_token(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a user can be created by token."""
user_model = factory_user_model(TestUserInfo.user_bcros)
user = UserService(user_model)
dictionary = user.as_dict()
assert dictionary.get('keycloak_guid', None) is None
patch_token_info(TestJwtClaims.anonymous_bcros_role, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestJwtClaims.anonymous_bcros_role['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.anonymous_bcros_role['sub']
def test_user_save_by_token_no_token(session): # pylint: disable=unused-argument
"""Assert that a user cannot be created from an empty token."""
user = UserService.save_from_jwt_token()
assert user is None
def test_create_user_and_add_membership_owner_skip_auth_mode(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert that an owner can be added as anonymous."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
# only one member should be there since its a STAFF created org
assert len(members) == 1
assert members[0].membership_type_code == ADMIN
def test_reset_password(session, auth_mock, keycloak_mock, monkeypatch): # pylint:disable=unused-argument
"""Assert that the password can be changed."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
patch_token_info(claims, monkeypatch)
membership = [TestAnonymousMembership.generate_random_user(USER)]
users = UserService.create_user_and_add_membership(membership, org.id)
user_name = users['users'][0]['username']
user_info = {'username': user_name, 'password': 'password'}
kc_user = UserService.reset_password_for_anon_user(user_info, user_name)
# cant assert anything else since password wont be gotten back
assert kc_user.user_name == user_name.replace(f'{IdpHint.BCROS.value}/', '').lower()
def test_reset_password_by_member(session, auth_mock, keycloak_mock, monkeypatch): # pylint:disable=unused-argument
"""Assert that the password cant be changed by member."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
admin_claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
membership = [TestAnonymousMembership.generate_random_user(USER)]
patch_token_info(admin_claims, monkeypatch)
users = UserService.create_user_and_add_membership(membership, org.id)
user_name = users['users'][0]['username']
user_info = {'username': user_name, 'password': 'password'}
with pytest.raises(HTTPException) as excinfo:
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
UserService.reset_password_for_anon_user(user_info, user_name)
assert excinfo.exception.code == 403
def test_delete_otp_for_user(session, auth_mock, keycloak_mock, monkeypatch):
"""Assert that the otp cant be reset."""
kc_service = KeycloakService()
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
admin_user = factory_user_model()
factory_membership_model(admin_user.id, org.id)
admin_claims = TestJwtClaims.get_test_real_user(admin_user.keycloak_guid)
membership = [TestAnonymousMembership.generate_random_user(USER)]
keycloak_service = KeycloakService()
request = KeycloakScenario.create_user_request()
request.user_name = membership[0]['username']
keycloak_service.add_user(request)
user = kc_service.get_user_by_username(request.user_name)
user = factory_user_model(TestUserInfo.get_bceid_user_with_kc_guid(user.id))
factory_membership_model(user.id, org.id)
patch_token_info(admin_claims, monkeypatch)
UserService.delete_otp_for_user(user.username)
user1 = kc_service.get_user_by_username(request.user_name)
assert 'CONFIGURE_TOTP' in json.loads(user1.value()).get('requiredActions')
def test_create_user_and_add_same_user_name_error_in_kc(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert that same user name cannot be added twice."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
keycloak_service = KeycloakService()
request = KeycloakScenario.create_user_request()
request.user_name = membership[0]['username']
keycloak_service.add_user(request)
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
def test_create_user_and_add_same_user_name_error_in_db(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert that same user name cannot be added twice."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model(TestUserInfo.user_bcros)
factory_membership_model(user.id, org.id)
new_members = TestAnonymousMembership.generate_random_user(ADMIN)
new_members['username'] = user.username.replace(f'{IdpHint.BCROS.value}/', '')
membership = [new_members]
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
def test_create_user_and_add_transaction_membership(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert transactions works fine."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
with patch('auth_api.models.Membership.flush', side_effect=Exception('mocked error')):
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
user_name = IdpHint.BCROS.value + '/' + membership[0]['username']
assert len(users['users']) == 1
assert users['users'][0]['username'] == membership[0]['username']
assert users['users'][0]['http_status'] == 500
assert users['users'][0]['error'] == 'Adding User Failed'
# make sure no records are created
user = UserModel.find_by_username(user_name)
assert user is None
user = UserModel.find_by_username(membership[0]['username'])
assert user is None
members = MembershipModel.find_members_by_org_id(org.id)
# only one member should be there since its a STAFF created org
assert len(members) == 0
def test_create_user_and_add_transaction_membership_1(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert transactions works fine."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
with patch('auth_api.models.User.flush', side_effect=Exception('mocked error')):
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
user_name = IdpHint.BCROS.value + '/' + membership[0]['username']
assert len(users['users']) == 1
assert users['users'][0]['username'] == membership[0]['username']
assert users['users'][0]['http_status'] == 500
assert users['users'][0]['error'] == 'Adding User Failed'
# make sure no records are created
user = UserModel.find_by_username(user_name)
assert user is None
user = UserModel.find_by_username(membership[0]['username'])
assert user is None
members = MembershipModel.find_members_by_org_id(org.id)
# only one member should be there since its a STAFF created org
assert len(members) == 0
def test_create_user_and_add_membership_admin_skip_auth_mode(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert that an admin can be added as anonymous."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(COORDINATOR)]
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
# only one member should be there since its a STAFF created org
assert len(members) == 1
assert members[0].membership_type_code == COORDINATOR
def test_create_user_and_add_membership_admin_bulk_mode(session, auth_mock,
keycloak_mock, monkeypatch): # pylint:disable=unused-argument
"""Assert that an admin can add a member."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
patch_token_info(claims, monkeypatch)
membership = [TestAnonymousMembership.generate_random_user(USER)]
users = UserService.create_user_and_add_membership(membership, org.id)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
# staff didnt create members..so count is count of owner+other 1 member
assert len(members) == 2
def test_create_user_add_membership_reenable(session, auth_mock, keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that an admin can add a member."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
patch_token_info(claims, monkeypatch)
anon_member = TestAnonymousMembership.generate_random_user(USER)
membership = [anon_member]
users = UserService.create_user_and_add_membership(membership, org.id)
user_name = IdpHint.BCROS.value + '/' + membership[0]['username']
assert len(users['users']) == 1
assert users['users'][0]['username'] == user_name
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
# staff didnt create members..so count is count of owner+other 1 member
assert len(members) == 2
# assert cant be readded
users = UserService.create_user_and_add_membership(membership, org.id)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
# deactivate everything and try again
anon_user = UserModel.find_by_username(user_name)
anon_user.status = Status.INACTIVE.value
anon_user.save()
membership_model = MembershipModel.find_membership_by_userid(anon_user.id)
membership_model.status = Status.INACTIVE.value
update_user_request = KeycloakUser()
update_user_request.user_name = membership[0]['username']
update_user_request.enabled = False
KeycloakService.update_user(update_user_request)
org2 = factory_org_model(org_info=TestOrgInfo.org_anonymous_2, org_type_info={'code': 'BASIC'})
factory_membership_model(user.id, org2.id)
factory_product_model(org2.id, product_code=ProductCode.DIR_SEARCH.value)
users = UserService.create_user_and_add_membership(membership, org2.id)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
# add to same org.Should work
users = UserService.create_user_and_add_membership(membership, org.id)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
def test_create_user_and_add_membership_admin_bulk_mode_unauthorised(session, auth_mock,
keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that bulk operation cannot be performed by unauthorised users."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
membership = [TestAnonymousMembership.generate_random_user(USER)]
with pytest.raises(HTTPException) as excinfo:
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
UserService.create_user_and_add_membership(membership, org.id)
assert excinfo.value.code == 403
def test_create_user_and_add_membership_admin_bulk_mode_multiple(session, auth_mock,
keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that an admin can add a group of members."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
membership = [TestAnonymousMembership.generate_random_user(USER),
TestAnonymousMembership.generate_random_user(COORDINATOR)]
patch_token_info(claims, monkeypatch)
users = UserService.create_user_and_add_membership(membership, org.id)
assert len(users['users']) == 2
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
assert users['users'][1]['username'] == IdpHint.BCROS.value + '/' + membership[1]['username']
assert users['users'][1]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
# staff didnt create members..so count is count of owner+other 2 members
assert len(members) == 3
def test_create_user_and_add_membership_member_error_skip_auth_mode(session, auth_mock,
keycloak_mock): # pylint:disable=unused-argument
"""Assert that an member cannot be added as anonymous in single_mode mode."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(USER)]
with pytest.raises(BusinessException) as exception:
UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert exception.value.code == Error.INVALID_USER_CREDENTIALS.name
def test_create_user_and_add_membership_multiple_error_skip_auth_mode(session, auth_mock, keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that multiple user cannot be created in single_mode mode."""
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(USER),
TestAnonymousMembership.generate_random_user(COORDINATOR)]
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert exception.value.code == Error.INVALID_USER_CREDENTIALS.name
def test_user_save_by_token_fail(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a user cannot not be created."""
with patch.object(UserModel, 'create_from_jwt_token', return_value=None):
patch_token_info(TestJwtClaims.user_test, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is None
def test_add_contact_to_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact can be added to a user."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
contact = UserService.add_contact(TestContactInfo.contact1).as_dict()
assert contact['email'] == TestContactInfo.contact1['email']
assert contact['phone'] == TestContactInfo.contact1['phone']
assert contact['phone_extension'] == TestContactInfo.contact1['phoneExtension']
def test_add_contact_user_no_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact cannot be added to a user that does not exist."""
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.add_contact(TestContactInfo.contact1)
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_add_contact_to_user_already_exists(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact cannot be added to a user that already has a contact."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.add_contact(TestContactInfo.contact1)
with pytest.raises(BusinessException) as exception:
UserService.add_contact(TestContactInfo.contact2)
assert exception.value.code == Error.DATA_ALREADY_EXISTS.name
def test_update_contact_for_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact can be updated for a user."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
contact = UserService.add_contact(TestContactInfo.contact1).as_dict()
assert contact is not None
updated_contact = UserService.update_contact(TestContactInfo.contact2).as_dict()
assert updated_contact is not None
assert updated_contact['email'] == TestContactInfo.contact2['email']
def test_update_terms_of_use_for_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a terms of use can be updated for a user."""
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.save_from_jwt_token()
updated_user = UserService.update_terms_of_use(True, 1)
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
def test_terms_of_service_prev_version(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a terms of use can be updated for a user."""
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.save_from_jwt_token()
# update TOS with old version
updated_user = UserService.update_terms_of_use(True, 1)
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
# accepted version from previous step was old.so comparison should return false
updated_user = UserService.save_from_jwt_token()
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is False
# update TOS with latest version
updated_user = UserService.update_terms_of_use(True, get_tos_latest_version())
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
# accepted version from previous step is latest.so comparison should return true
updated_user = UserService.save_from_jwt_token()
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
def test_update_contact_for_user_no_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact cannot be updated for a user that does not exist."""
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.update_contact(TestContactInfo.contact2)
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_update_contact_for_user_no_contact(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact cannot be updated for a user with no contact."""
factory_user_model(user_info=TestUserInfo.user_test)
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.update_contact(TestContactInfo.contact2)
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_delete_contact_for_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a contact can be deleted for a user."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
contact = UserService.add_contact(TestContactInfo.contact1).as_dict()
assert contact is not None
deleted_contact = UserService.delete_contact().as_dict()
assert deleted_contact is not None
contacts = UserService.get_contacts()
assert contacts.get('contacts') == []
def test_delete_contact_for_user_no_user(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that deleting a contact for a non-existent user raises the right exception."""
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.delete_contact()
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_delete_contact_for_user_no_contact(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that deleting a contact for a user with no contact raises the right exception."""
factory_user_model(user_info=TestUserInfo.user_test)
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.delete_contact()
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_find_users(session): # pylint: disable=unused-argument
"""Assert that a list of users can be retrieved and searched on."""
factory_user_model()
factory_user_model(user_info=TestUserInfo.user2)
users = UserService.find_users(last_name='User')
assert users is not None
assert len(users) == 2
def test_user_find_by_token(session, monkeypatch): # pylint: disable=unused-argument
"""Assert that a user can be found by token."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
found_user = UserService.find_by_jwt_token()
assert found_user is None
# User accepted older version terms and conditions should return False
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.update_terms_of_use(True, 1)
found_user = UserService.find_by_jwt_token()
assert found_user is not None
dictionary = found_user.as_dict()
assert dictionary['username'] == TestJwtClaims.user_test['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.user_test['sub']
assert dictionary['user_terms']['isTermsOfUseAccepted'] is False
# User accepted latest version terms and conditions should return True
UserService.update_terms_of_use(True, get_tos_latest_version())
found_user = UserService.find_by_jwt_token()
dictionary = found_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
def test_user_find_by_username(session): # pylint: disable=unused-argument
"""Assert that a user can be found by username."""
user_model = factory_user_model()
user = UserService(user_model)
user = UserService.find_by_username(None)
assert user is None
user = UserService.find_by_username(TestUserInfo.user1['username'])
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestUserInfo.user1['username']
def test_user_find_by_username_no_model_object(session): # pylint: disable=unused-argument
"""Assert that the business can't be found with no model."""
username = TestUserInfo.user_test['username']
user = UserService.find_by_username(username)
assert user is None
def test_user_find_by_username_missing_username(session): # pylint: disable=unused-argument
"""Assert that the business can't be found by incorrect username."""
user_model = factory_user_model(user_info=TestUserInfo.user_test)
user = UserService(user_model)
user = UserService.find_by_username('foo')
assert user is None
def test_delete_contact_user_link(session, auth_mock, keycloak_mock, monkeypatch): # pylint:disable=unused-argument
"""Assert that a contact can not be deleted if contact link exists."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.public_user_role['sub']
user_model = factory_user_model(user_info=user_with_token)
user = UserService(user_model)
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user.identifier)
org_dictionary = org.as_dict()
org_id = org_dictionary['id']
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.org = org._model # pylint:disable=protected-access
contact_link = contact_link.flush()
contact_link.commit()
deleted_contact = UserService.delete_contact()
assert deleted_contact is None
delete_contact_link = ContactLinkModel.find_by_user_id(user.identifier)
assert not delete_contact_link
exist_contact_link = ContactLinkModel.find_by_org_id(org_id)
assert exist_contact_link
def test_delete_user(session, auth_mock, keycloak_mock, monkeypatch): # pylint:disable=unused-argument
"""Assert that a user can be deleted."""
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
user_model = factory_user_model(user_info=user_with_token)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.commit()
patch_token_info(TestJwtClaims.user_test, monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id)
UserService.delete_user()
updated_user = UserModel.find_by_jwt_token()
assert len(updated_user.contacts) == 0
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'INACTIVE'
def test_delete_user_where_org_has_affiliations(session, auth_mock, keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that a user can be deleted."""
user_model = factory_user_model(user_info=TestUserInfo.user_test)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link = contact_link.flush()
contact_link.commit()
patch_token_info(TestJwtClaims.user_test, monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id).as_dict()
org_id = org['id']
entity = factory_entity_model(entity_info=TestEntityInfo.entity_lear_mock)
affiliation = AffiliationModel(org_id=org_id, entity_id=entity.id)
affiliation.save()
with pytest.raises(BusinessException) as exception:
UserService.delete_user()
assert exception.code == Error.DELETE_FAILED_ONLY_OWNER
updated_user = UserModel.find_by_jwt_token()
contacts = UserService.get_contacts()
assert len(contacts) == 1
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'ACTIVE'
def test_delete_user_where_user_is_member_on_org(session, auth_mock, keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that a user can be deleted."""
# Create a user and org
user_model = factory_user_model(user_info=TestUserInfo.user_test)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.commit()
patch_token_info(TestJwtClaims.get_test_user(user_model.keycloak_guid), monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id)
org_dictionary = org.as_dict()
org_id = org_dictionary['id']
entity = factory_entity_model(entity_info=TestEntityInfo.entity_lear_mock)
affiliation = AffiliationModel(org_id=org_id, entity_id=entity.id)
affiliation.save()
# Create another user and add membership to the above org
user_model2 = factory_user_model(user_info=TestUserInfo.user2)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model2
contact_link.commit()
membership = MembershipModel(org_id=org_id, user_id=user_model2.id, membership_type_code='USER',
membership_type_status=Status.ACTIVE.value)
membership.save()
patch_token_info(TestJwtClaims.get_test_user(user_model2.keycloak_guid), monkeypatch)
UserService.delete_user()
updated_user = UserModel.find_by_jwt_token()
assert len(updated_user.contacts) == 0
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'INACTIVE'
def test_delete_user_where_org_has_another_owner(session, auth_mock, keycloak_mock,
monkeypatch): # pylint:disable=unused-argument
"""Assert that a user can be deleted."""
# Create a user and org
user_model = factory_user_model(user_info=TestUserInfo.user_test)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.commit()
patch_token_info(TestJwtClaims.get_test_user(user_model.keycloak_guid), monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id)
org_dictionary = org.as_dict()
org_id = org_dictionary['id']
entity = factory_entity_model(entity_info=TestEntityInfo.entity_lear_mock)
affiliation = AffiliationModel(org_id=org_id, entity_id=entity.id)
affiliation.save()
# Create another user and add membership to the above org
user_model2 = factory_user_model(user_info=TestUserInfo.user2)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model2
contact_link.commit()
membership = MembershipModel(org_id=org_id, user_id=user_model2.id, membership_type_code='ADMIN',
membership_type_status=Status.ACTIVE.value)
membership.save()
membership.commit()
# with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.get_test_user(user_model2.keycloak_guid), monkeypatch)
UserService.delete_user()
updated_user = UserModel.find_by_jwt_token()
assert len(updated_user.contacts) == 0
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'INACTIVE'
| 45.552795 | 118 | 0.74066 |
import json
from unittest.mock import patch
import pytest
from werkzeug.exceptions import HTTPException
from auth_api.exceptions import BusinessException
from auth_api.exceptions.errors import Error
from auth_api.models import Affiliation as AffiliationModel
from auth_api.models import ContactLink as ContactLinkModel
from auth_api.models import Membership as MembershipModel
from auth_api.models import User as UserModel
from auth_api.services import Org as OrgService
from auth_api.services import User as UserService
from auth_api.services.keycloak import KeycloakService
from auth_api.services.keycloak_user import KeycloakUser
from auth_api.utils.enums import IdpHint, ProductCode, Status
from auth_api.utils.roles import ADMIN, COORDINATOR, USER, Role
from tests.utilities.factory_scenarios import (
KeycloakScenario, TestAnonymousMembership, TestContactInfo, TestEntityInfo, TestJwtClaims, TestOrgInfo,
TestUserInfo)
from tests.utilities.factory_utils import (
factory_contact_model, factory_entity_model, factory_membership_model, factory_org_model, factory_product_model,
factory_user_model, get_tos_latest_version, patch_token_info)
def test_as_dict(session):
user_model = factory_user_model()
user = UserService(user_model)
dictionary = user.as_dict()
assert dictionary['username'] == TestUserInfo.user1['username']
def test_user_save_by_token(session, monkeypatch):
patch_token_info(TestJwtClaims.user_test, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestJwtClaims.user_test['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.user_test['sub']
def test_bcros_user_save_by_token(session, monkeypatch):
patch_token_info(TestJwtClaims.anonymous_bcros_role, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestJwtClaims.anonymous_bcros_role['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.anonymous_bcros_role['sub']
def test_bcros_user_update_by_token(session, monkeypatch):
user_model = factory_user_model(TestUserInfo.user_bcros)
user = UserService(user_model)
dictionary = user.as_dict()
assert dictionary.get('keycloak_guid', None) is None
patch_token_info(TestJwtClaims.anonymous_bcros_role, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestJwtClaims.anonymous_bcros_role['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.anonymous_bcros_role['sub']
def test_user_save_by_token_no_token(session):
user = UserService.save_from_jwt_token()
assert user is None
def test_create_user_and_add_membership_owner_skip_auth_mode(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 1
assert members[0].membership_type_code == ADMIN
def test_reset_password(session, auth_mock, keycloak_mock, monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
patch_token_info(claims, monkeypatch)
membership = [TestAnonymousMembership.generate_random_user(USER)]
users = UserService.create_user_and_add_membership(membership, org.id)
user_name = users['users'][0]['username']
user_info = {'username': user_name, 'password': 'password'}
kc_user = UserService.reset_password_for_anon_user(user_info, user_name)
assert kc_user.user_name == user_name.replace(f'{IdpHint.BCROS.value}/', '').lower()
def test_reset_password_by_member(session, auth_mock, keycloak_mock, monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
admin_claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
membership = [TestAnonymousMembership.generate_random_user(USER)]
patch_token_info(admin_claims, monkeypatch)
users = UserService.create_user_and_add_membership(membership, org.id)
user_name = users['users'][0]['username']
user_info = {'username': user_name, 'password': 'password'}
with pytest.raises(HTTPException) as excinfo:
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
UserService.reset_password_for_anon_user(user_info, user_name)
assert excinfo.exception.code == 403
def test_delete_otp_for_user(session, auth_mock, keycloak_mock, monkeypatch):
kc_service = KeycloakService()
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
admin_user = factory_user_model()
factory_membership_model(admin_user.id, org.id)
admin_claims = TestJwtClaims.get_test_real_user(admin_user.keycloak_guid)
membership = [TestAnonymousMembership.generate_random_user(USER)]
keycloak_service = KeycloakService()
request = KeycloakScenario.create_user_request()
request.user_name = membership[0]['username']
keycloak_service.add_user(request)
user = kc_service.get_user_by_username(request.user_name)
user = factory_user_model(TestUserInfo.get_bceid_user_with_kc_guid(user.id))
factory_membership_model(user.id, org.id)
patch_token_info(admin_claims, monkeypatch)
UserService.delete_otp_for_user(user.username)
user1 = kc_service.get_user_by_username(request.user_name)
assert 'CONFIGURE_TOTP' in json.loads(user1.value()).get('requiredActions')
def test_create_user_and_add_same_user_name_error_in_kc(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
keycloak_service = KeycloakService()
request = KeycloakScenario.create_user_request()
request.user_name = membership[0]['username']
keycloak_service.add_user(request)
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
def test_create_user_and_add_same_user_name_error_in_db(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model(TestUserInfo.user_bcros)
factory_membership_model(user.id, org.id)
new_members = TestAnonymousMembership.generate_random_user(ADMIN)
new_members['username'] = user.username.replace(f'{IdpHint.BCROS.value}/', '')
membership = [new_members]
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
def test_create_user_and_add_transaction_membership(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
with patch('auth_api.models.Membership.flush', side_effect=Exception('mocked error')):
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
user_name = IdpHint.BCROS.value + '/' + membership[0]['username']
assert len(users['users']) == 1
assert users['users'][0]['username'] == membership[0]['username']
assert users['users'][0]['http_status'] == 500
assert users['users'][0]['error'] == 'Adding User Failed'
user = UserModel.find_by_username(user_name)
assert user is None
user = UserModel.find_by_username(membership[0]['username'])
assert user is None
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 0
def test_create_user_and_add_transaction_membership_1(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(ADMIN)]
with patch('auth_api.models.User.flush', side_effect=Exception('mocked error')):
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
user_name = IdpHint.BCROS.value + '/' + membership[0]['username']
assert len(users['users']) == 1
assert users['users'][0]['username'] == membership[0]['username']
assert users['users'][0]['http_status'] == 500
assert users['users'][0]['error'] == 'Adding User Failed'
user = UserModel.find_by_username(user_name)
assert user is None
user = UserModel.find_by_username(membership[0]['username'])
assert user is None
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 0
def test_create_user_and_add_membership_admin_skip_auth_mode(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(COORDINATOR)]
users = UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 1
assert members[0].membership_type_code == COORDINATOR
def test_create_user_and_add_membership_admin_bulk_mode(session, auth_mock,
keycloak_mock, monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
patch_token_info(claims, monkeypatch)
membership = [TestAnonymousMembership.generate_random_user(USER)]
users = UserService.create_user_and_add_membership(membership, org.id)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 2
def test_create_user_add_membership_reenable(session, auth_mock, keycloak_mock,
monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
patch_token_info(claims, monkeypatch)
anon_member = TestAnonymousMembership.generate_random_user(USER)
membership = [anon_member]
users = UserService.create_user_and_add_membership(membership, org.id)
user_name = IdpHint.BCROS.value + '/' + membership[0]['username']
assert len(users['users']) == 1
assert users['users'][0]['username'] == user_name
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 2
users = UserService.create_user_and_add_membership(membership, org.id)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
anon_user = UserModel.find_by_username(user_name)
anon_user.status = Status.INACTIVE.value
anon_user.save()
membership_model = MembershipModel.find_membership_by_userid(anon_user.id)
membership_model.status = Status.INACTIVE.value
update_user_request = KeycloakUser()
update_user_request.user_name = membership[0]['username']
update_user_request.enabled = False
KeycloakService.update_user(update_user_request)
org2 = factory_org_model(org_info=TestOrgInfo.org_anonymous_2, org_type_info={'code': 'BASIC'})
factory_membership_model(user.id, org2.id)
factory_product_model(org2.id, product_code=ProductCode.DIR_SEARCH.value)
users = UserService.create_user_and_add_membership(membership, org2.id)
assert users['users'][0]['http_status'] == 409
assert users['users'][0]['error'] == 'The username is already taken'
users = UserService.create_user_and_add_membership(membership, org.id)
assert len(users['users']) == 1
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
def test_create_user_and_add_membership_admin_bulk_mode_unauthorised(session, auth_mock,
keycloak_mock,
monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
membership = [TestAnonymousMembership.generate_random_user(USER)]
with pytest.raises(HTTPException) as excinfo:
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
UserService.create_user_and_add_membership(membership, org.id)
assert excinfo.value.code == 403
def test_create_user_and_add_membership_admin_bulk_mode_multiple(session, auth_mock,
keycloak_mock,
monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
user = factory_user_model()
factory_membership_model(user.id, org.id)
factory_product_model(org.id, product_code=ProductCode.DIR_SEARCH.value)
claims = TestJwtClaims.get_test_real_user(user.keycloak_guid)
membership = [TestAnonymousMembership.generate_random_user(USER),
TestAnonymousMembership.generate_random_user(COORDINATOR)]
patch_token_info(claims, monkeypatch)
users = UserService.create_user_and_add_membership(membership, org.id)
assert len(users['users']) == 2
assert users['users'][0]['username'] == IdpHint.BCROS.value + '/' + membership[0]['username']
assert users['users'][0]['type'] == Role.ANONYMOUS_USER.name
assert users['users'][1]['username'] == IdpHint.BCROS.value + '/' + membership[1]['username']
assert users['users'][1]['type'] == Role.ANONYMOUS_USER.name
members = MembershipModel.find_members_by_org_id(org.id)
assert len(members) == 3
def test_create_user_and_add_membership_member_error_skip_auth_mode(session, auth_mock,
keycloak_mock):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(USER)]
with pytest.raises(BusinessException) as exception:
UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert exception.value.code == Error.INVALID_USER_CREDENTIALS.name
def test_create_user_and_add_membership_multiple_error_skip_auth_mode(session, auth_mock, keycloak_mock,
monkeypatch):
org = factory_org_model(org_info=TestOrgInfo.org_anonymous)
membership = [TestAnonymousMembership.generate_random_user(USER),
TestAnonymousMembership.generate_random_user(COORDINATOR)]
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
UserService.create_user_and_add_membership(membership, org.id, single_mode=True)
assert exception.value.code == Error.INVALID_USER_CREDENTIALS.name
def test_user_save_by_token_fail(session, monkeypatch):
with patch.object(UserModel, 'create_from_jwt_token', return_value=None):
patch_token_info(TestJwtClaims.user_test, monkeypatch)
user = UserService.save_from_jwt_token()
assert user is None
def test_add_contact_to_user(session, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
contact = UserService.add_contact(TestContactInfo.contact1).as_dict()
assert contact['email'] == TestContactInfo.contact1['email']
assert contact['phone'] == TestContactInfo.contact1['phone']
assert contact['phone_extension'] == TestContactInfo.contact1['phoneExtension']
def test_add_contact_user_no_user(session, monkeypatch):
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.add_contact(TestContactInfo.contact1)
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_add_contact_to_user_already_exists(session, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.add_contact(TestContactInfo.contact1)
with pytest.raises(BusinessException) as exception:
UserService.add_contact(TestContactInfo.contact2)
assert exception.value.code == Error.DATA_ALREADY_EXISTS.name
def test_update_contact_for_user(session, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
contact = UserService.add_contact(TestContactInfo.contact1).as_dict()
assert contact is not None
updated_contact = UserService.update_contact(TestContactInfo.contact2).as_dict()
assert updated_contact is not None
assert updated_contact['email'] == TestContactInfo.contact2['email']
def test_update_terms_of_use_for_user(session, monkeypatch):
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.save_from_jwt_token()
updated_user = UserService.update_terms_of_use(True, 1)
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
def test_terms_of_service_prev_version(session, monkeypatch):
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.save_from_jwt_token()
updated_user = UserService.update_terms_of_use(True, 1)
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
updated_user = UserService.save_from_jwt_token()
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is False
updated_user = UserService.update_terms_of_use(True, get_tos_latest_version())
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
updated_user = UserService.save_from_jwt_token()
dictionary = updated_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
def test_update_contact_for_user_no_user(session, monkeypatch):
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.update_contact(TestContactInfo.contact2)
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_update_contact_for_user_no_contact(session, monkeypatch):
factory_user_model(user_info=TestUserInfo.user_test)
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.update_contact(TestContactInfo.contact2)
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_delete_contact_for_user(session, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
patch_token_info(TestJwtClaims.user_test, monkeypatch)
contact = UserService.add_contact(TestContactInfo.contact1).as_dict()
assert contact is not None
deleted_contact = UserService.delete_contact().as_dict()
assert deleted_contact is not None
contacts = UserService.get_contacts()
assert contacts.get('contacts') == []
def test_delete_contact_for_user_no_user(session, monkeypatch):
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.delete_contact()
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_delete_contact_for_user_no_contact(session, monkeypatch):
factory_user_model(user_info=TestUserInfo.user_test)
with pytest.raises(BusinessException) as exception:
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.delete_contact()
assert exception.value.code == Error.DATA_NOT_FOUND.name
def test_find_users(session):
factory_user_model()
factory_user_model(user_info=TestUserInfo.user2)
users = UserService.find_users(last_name='User')
assert users is not None
assert len(users) == 2
def test_user_find_by_token(session, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
factory_user_model(user_info=user_with_token)
found_user = UserService.find_by_jwt_token()
assert found_user is None
patch_token_info(TestJwtClaims.user_test, monkeypatch)
UserService.update_terms_of_use(True, 1)
found_user = UserService.find_by_jwt_token()
assert found_user is not None
dictionary = found_user.as_dict()
assert dictionary['username'] == TestJwtClaims.user_test['preferred_username']
assert dictionary['keycloak_guid'] == TestJwtClaims.user_test['sub']
assert dictionary['user_terms']['isTermsOfUseAccepted'] is False
UserService.update_terms_of_use(True, get_tos_latest_version())
found_user = UserService.find_by_jwt_token()
dictionary = found_user.as_dict()
assert dictionary['user_terms']['isTermsOfUseAccepted'] is True
def test_user_find_by_username(session):
user_model = factory_user_model()
user = UserService(user_model)
user = UserService.find_by_username(None)
assert user is None
user = UserService.find_by_username(TestUserInfo.user1['username'])
assert user is not None
dictionary = user.as_dict()
assert dictionary['username'] == TestUserInfo.user1['username']
def test_user_find_by_username_no_model_object(session):
username = TestUserInfo.user_test['username']
user = UserService.find_by_username(username)
assert user is None
def test_user_find_by_username_missing_username(session):
user_model = factory_user_model(user_info=TestUserInfo.user_test)
user = UserService(user_model)
user = UserService.find_by_username('foo')
assert user is None
def test_delete_contact_user_link(session, auth_mock, keycloak_mock, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.public_user_role['sub']
user_model = factory_user_model(user_info=user_with_token)
user = UserService(user_model)
patch_token_info(TestJwtClaims.public_user_role, monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user.identifier)
org_dictionary = org.as_dict()
org_id = org_dictionary['id']
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.org = org._model
contact_link = contact_link.flush()
contact_link.commit()
deleted_contact = UserService.delete_contact()
assert deleted_contact is None
delete_contact_link = ContactLinkModel.find_by_user_id(user.identifier)
assert not delete_contact_link
exist_contact_link = ContactLinkModel.find_by_org_id(org_id)
assert exist_contact_link
def test_delete_user(session, auth_mock, keycloak_mock, monkeypatch):
user_with_token = TestUserInfo.user_test
user_with_token['keycloak_guid'] = TestJwtClaims.user_test['sub']
user_model = factory_user_model(user_info=user_with_token)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.commit()
patch_token_info(TestJwtClaims.user_test, monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id)
UserService.delete_user()
updated_user = UserModel.find_by_jwt_token()
assert len(updated_user.contacts) == 0
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'INACTIVE'
def test_delete_user_where_org_has_affiliations(session, auth_mock, keycloak_mock,
monkeypatch):
user_model = factory_user_model(user_info=TestUserInfo.user_test)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link = contact_link.flush()
contact_link.commit()
patch_token_info(TestJwtClaims.user_test, monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id).as_dict()
org_id = org['id']
entity = factory_entity_model(entity_info=TestEntityInfo.entity_lear_mock)
affiliation = AffiliationModel(org_id=org_id, entity_id=entity.id)
affiliation.save()
with pytest.raises(BusinessException) as exception:
UserService.delete_user()
assert exception.code == Error.DELETE_FAILED_ONLY_OWNER
updated_user = UserModel.find_by_jwt_token()
contacts = UserService.get_contacts()
assert len(contacts) == 1
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'ACTIVE'
def test_delete_user_where_user_is_member_on_org(session, auth_mock, keycloak_mock,
monkeypatch):
user_model = factory_user_model(user_info=TestUserInfo.user_test)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.commit()
patch_token_info(TestJwtClaims.get_test_user(user_model.keycloak_guid), monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id)
org_dictionary = org.as_dict()
org_id = org_dictionary['id']
entity = factory_entity_model(entity_info=TestEntityInfo.entity_lear_mock)
affiliation = AffiliationModel(org_id=org_id, entity_id=entity.id)
affiliation.save()
user_model2 = factory_user_model(user_info=TestUserInfo.user2)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model2
contact_link.commit()
membership = MembershipModel(org_id=org_id, user_id=user_model2.id, membership_type_code='USER',
membership_type_status=Status.ACTIVE.value)
membership.save()
patch_token_info(TestJwtClaims.get_test_user(user_model2.keycloak_guid), monkeypatch)
UserService.delete_user()
updated_user = UserModel.find_by_jwt_token()
assert len(updated_user.contacts) == 0
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'INACTIVE'
def test_delete_user_where_org_has_another_owner(session, auth_mock, keycloak_mock,
monkeypatch):
user_model = factory_user_model(user_info=TestUserInfo.user_test)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model
contact_link.commit()
patch_token_info(TestJwtClaims.get_test_user(user_model.keycloak_guid), monkeypatch)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user_model.id)
org_dictionary = org.as_dict()
org_id = org_dictionary['id']
entity = factory_entity_model(entity_info=TestEntityInfo.entity_lear_mock)
affiliation = AffiliationModel(org_id=org_id, entity_id=entity.id)
affiliation.save()
user_model2 = factory_user_model(user_info=TestUserInfo.user2)
contact = factory_contact_model()
contact_link = ContactLinkModel()
contact_link.contact = contact
contact_link.user = user_model2
contact_link.commit()
membership = MembershipModel(org_id=org_id, user_id=user_model2.id, membership_type_code='ADMIN',
membership_type_status=Status.ACTIVE.value)
membership.save()
membership.commit()
patch_token_info(TestJwtClaims.get_test_user(user_model2.keycloak_guid), monkeypatch)
UserService.delete_user()
updated_user = UserModel.find_by_jwt_token()
assert len(updated_user.contacts) == 0
user_orgs = MembershipModel.find_orgs_for_user(updated_user.id)
for org in user_orgs:
assert org.status_code == 'INACTIVE'
| true | true |
f73bf60adcc79b502502f15afba074b8a5426043 | 18,617 | py | Python | yfinance/base.py | rdiere/yfinance | 969eeb66b5bc98635aa0289c15a49246248910e4 | [
"Apache-2.0"
] | null | null | null | yfinance/base.py | rdiere/yfinance | 969eeb66b5bc98635aa0289c15a49246248910e4 | [
"Apache-2.0"
] | null | null | null | yfinance/base.py | rdiere/yfinance | 969eeb66b5bc98635aa0289c15a49246248910e4 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
# https://github.com/ranaroussi/yfinance
#
# Copyright 2017-2019 Ran Aroussi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
import time as _time
import datetime as _datetime
import requests as _requests
import pandas as _pd
import numpy as _np
try:
from urllib.parse import quote as urlencode
except ImportError:
from urllib import quote as urlencode
from . import utils
# import json as _json
# import re as _re
# import sys as _sys
from . import shared
class TickerBase():
def __init__(self, ticker):
self.ticker = ticker.upper()
self._history = None
self._base_url = 'https://query1.finance.yahoo.com'
self._scrape_url = 'https://finance.yahoo.com/quote'
self._fundamentals = False
self._info = None
self._sustainability = None
self._recommendations = None
self._major_holders = None
self._institutional_holders = None
self._isin = None
self._calendar = None
self._expirations = {}
self._earnings = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
self._financials = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
self._balancesheet = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
self._cashflow = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
def history(self, period="1mo", interval="1d",
start=None, end=None, prepost=False, actions=True,
auto_adjust=True, back_adjust=False,
proxy=None, rounding=True, tz=None, **kwargs):
"""
:Parameters:
period : str
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
Either Use period parameter or use start and end
interval : str
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
Intraday data cannot extend last 60 days
start: str
Download start date string (YYYY-MM-DD) or _datetime.
Default is 1900-01-01
end: str
Download end date string (YYYY-MM-DD) or _datetime.
Default is now
prepost : bool
Include Pre and Post market data in results?
Default is False
auto_adjust: bool
Adjust all OHLC automatically? Default is True
back_adjust: bool
Back-adjusted data to mimic true historical prices
proxy: str
Optional. Proxy server URL scheme. Default is None
rounding: bool
Round values to 2 decimal places?
Optional. Default is False = precision suggested by Yahoo!
tz: str
Optional timezone locale for dates.
(default data is returned as non-localized dates)
**kwargs: dict
debug: bool
Optional. If passed as False, will suppress
error message printing to console.
"""
if start or period is None or period.lower() == "max":
if start is None:
start = -2208988800
elif isinstance(start, _datetime.datetime):
start = int(_time.mktime(start.timetuple()))
else:
start = int(_time.mktime(
_time.strptime(str(start), '%Y-%m-%d')))
if end is None:
end = int(_time.time())
elif isinstance(end, _datetime.datetime):
end = int(_time.mktime(end.timetuple()))
else:
end = int(_time.mktime(_time.strptime(str(end), '%Y-%m-%d')))
params = {"period1": start, "period2": end}
else:
period = period.lower()
params = {"range": period}
params["interval"] = interval.lower()
params["includePrePost"] = prepost
params["events"] = "div,splits"
# 1) fix weired bug with Yahoo! - returning 60m for 30m bars
if params["interval"] == "30m":
params["interval"] = "15m"
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
# Getting data from json
url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
data = _requests.get(url=url, params=params, proxies=proxy)
if "Will be right back" in data.text:
raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
"Our engineers are working quickly to resolve "
"the issue. Thank you for your patience.")
data = data.json()
# Work with errors
debug_mode = True
if "debug" in kwargs and isinstance(kwargs["debug"], bool):
debug_mode = kwargs["debug"]
err_msg = "No data found for this date range, symbol may be delisted"
if "chart" in data and data["chart"]["error"]:
err_msg = data["chart"]["error"]["description"]
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
elif "chart" not in data or data["chart"]["result"] is None or \
not data["chart"]["result"]:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
# parse quotes
try:
quotes = utils.parse_quotes(data["chart"]["result"][0], tz)
except Exception:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
# 2) fix weired bug with Yahoo! - returning 60m for 30m bars
if interval.lower() == "30m":
quotes2 = quotes.resample('30T')
quotes = _pd.DataFrame(index=quotes2.last().index, data={
'Open': quotes2['Open'].first(),
'High': quotes2['High'].max(),
'Low': quotes2['Low'].min(),
'Close': quotes2['Close'].last(),
'Adj Close': quotes2['Adj Close'].last(),
'Volume': quotes2['Volume'].sum()
})
try:
quotes['Dividends'] = quotes2['Dividends'].max()
except Exception:
pass
try:
quotes['Stock Splits'] = quotes2['Dividends'].max()
except Exception:
pass
if auto_adjust:
quotes = utils.auto_adjust(quotes)
elif back_adjust:
quotes = utils.back_adjust(quotes)
if rounding:
quotes = _np.round(quotes, data[
"chart"]["result"][0]["meta"]["priceHint"])
quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64)
quotes.dropna(inplace=True)
# actions
dividends, splits = utils.parse_actions(data["chart"]["result"][0], tz)
# combine
df = _pd.concat([quotes, dividends, splits], axis=1, sort=True)
df["Dividends"].fillna(0, inplace=True)
df["Stock Splits"].fillna(0, inplace=True)
# index eod/intraday
df.index = df.index.tz_localize("UTC").tz_convert(
data["chart"]["result"][0]["meta"]["exchangeTimezoneName"])
if params["interval"][-1] in {"m", "h"}:
df.index.name = "Datetime"
else:
df.index = _pd.to_datetime(df.index.date)
if tz is not None:
df.index = df.index.tz_localize(tz)
df.index.name = "Date"
self._history = df.copy()
if not actions:
df.drop(columns=["Dividends", "Stock Splits"], inplace=True)
return df
# ------------------------
def _get_fundamentals(self, kind=None, proxy=None):
def cleanup(data):
df = _pd.DataFrame(data).drop(columns=['maxAge'])
for col in df.columns:
df[col] = _np.where(
df[col].astype(str) == '-', _np.nan, df[col])
df.set_index('endDate', inplace=True)
try:
df.index = _pd.to_datetime(df.index, unit='s')
except ValueError:
df.index = _pd.to_datetime(df.index)
df = df.T
df.columns.name = ''
df.index.name = 'Breakdown'
df.index = utils.camel2title(df.index)
return df
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
if self._fundamentals:
return
# get info and sustainability
url = '%s/%s' % (self._scrape_url, self.ticker)
data = utils.get_json(url, proxy)
# holders
url = "{}/{}/holders".format(self._scrape_url, self.ticker)
holders = _pd.read_html(url)
self._major_holders = holders[0]
self._institutional_holders = holders[1]
if 'Date Reported' in self._institutional_holders:
self._institutional_holders['Date Reported'] = _pd.to_datetime(
self._institutional_holders['Date Reported'])
if '% Out' in self._institutional_holders:
self._institutional_holders['% Out'] = self._institutional_holders[
'% Out'].str.replace('%', '').astype(float)/100
# sustainability
d = {}
if isinstance(data.get('esgScores'), dict):
for item in data['esgScores']:
if not isinstance(data['esgScores'][item], (dict, list)):
d[item] = data['esgScores'][item]
s = _pd.DataFrame(index=[0], data=d)[-1:].T
s.columns = ['Value']
s.index.name = '%.f-%.f' % (
s[s.index == 'ratingYear']['Value'].values[0],
s[s.index == 'ratingMonth']['Value'].values[0])
self._sustainability = s[~s.index.isin(
['maxAge', 'ratingYear', 'ratingMonth'])]
# info (be nice to python 2)
self._info = {}
items = ['summaryProfile', 'summaryDetail', 'quoteType',
'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
for item in items:
if isinstance(data.get(item), dict):
self._info.update(data[item])
self._info['regularMarketPrice'] = self._info['regularMarketOpen']
self._info['logo_url'] = ""
try:
domain = self._info['website'].split(
'://')[1].split('/')[0].replace('www.', '')
self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
except Exception:
pass
# events
try:
cal = _pd.DataFrame(
data['calendarEvents']['earnings'])
cal['earningsDate'] = _pd.to_datetime(
cal['earningsDate'], unit='s')
self._calendar = cal.T
self._calendar.index = utils.camel2title(self._calendar.index)
self._calendar.columns = ['Value']
except Exception:
pass
# analyst recommendations
try:
rec = _pd.DataFrame(
data['upgradeDowngradeHistory']['history'])
rec['earningsDate'] = _pd.to_datetime(
rec['epochGradeDate'], unit='s')
rec.set_index('earningsDate', inplace=True)
rec.index.name = 'Date'
rec.columns = utils.camel2title(rec.columns)
self._recommendations = rec[[
'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
except Exception:
pass
# get fundamentals
data = utils.get_json(url+'/financials', proxy)
# generic patterns
for key in (
(self._cashflow, 'cashflowStatement', 'cashflowStatements'),
(self._balancesheet, 'balanceSheet', 'balanceSheetStatements'),
(self._financials, 'incomeStatement', 'incomeStatementHistory')
):
item = key[1] + 'History'
if isinstance(data.get(item), dict):
key[0]['yearly'] = cleanup(data[item][key[2]])
item = key[1]+'HistoryQuarterly'
if isinstance(data.get(item), dict):
key[0]['quarterly'] = cleanup(data[item][key[2]])
# earnings
if isinstance(data.get('earnings'), dict):
earnings = data['earnings']['financialsChart']
df = _pd.DataFrame(earnings['yearly']).set_index('date')
df.columns = utils.camel2title(df.columns)
df.index.name = 'Year'
self._earnings['yearly'] = df
df = _pd.DataFrame(earnings['quarterly']).set_index('date')
df.columns = utils.camel2title(df.columns)
df.index.name = 'Quarter'
self._earnings['quarterly'] = df
self._fundamentals = True
def get_recommendations(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._recommendations
if as_dict:
return data.to_dict()
return data
def get_calendar(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._calendar
if as_dict:
return data.to_dict()
return data
def get_major_holders(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._major_holders
if as_dict:
return data.to_dict()
return data
def get_institutional_holders(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._institutional_holders
if as_dict:
return data.to_dict()
return data
def get_info(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._info
if as_dict:
return data.to_dict()
return data
def get_sustainability(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._sustainability
if as_dict:
return data.to_dict()
return data
def get_earnings(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._earnings[freq]
if as_dict:
return data.to_dict()
return data
def get_financials(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._financials[freq]
if as_dict:
return data.to_dict()
return data
def get_balancesheet(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._balancesheet[freq]
if as_dict:
return data.to_dict()
return data
def get_balance_sheet(self, proxy=None, as_dict=False, freq="yearly"):
return self.get_balancesheet(proxy, as_dict, freq)
def get_cashflow(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._cashflow[freq]
if as_dict:
return data.to_dict()
return data
def get_dividends(self, proxy=None):
if self._history is None:
self.history(period="max", proxy=proxy)
dividends = self._history["Dividends"]
return dividends[dividends != 0]
def get_splits(self, proxy=None):
if self._history is None:
self.history(period="max", proxy=proxy)
splits = self._history["Stock Splits"]
return splits[splits != 0]
def get_actions(self, proxy=None):
if self._history is None:
self.history(period="max", proxy=proxy)
actions = self._history[["Dividends", "Stock Splits"]]
return actions[actions != 0].dropna(how='all').fillna(0)
def get_isin(self, proxy=None):
# *** experimental ***
if self._isin is not None:
return self._isin
ticker = self.ticker.upper()
if "-" in ticker or "^" in ticker:
self._isin = '-'
return self._isin
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
q = ticker
self.get_info(proxy=proxy)
if "shortName" in self._info:
q = self._info['shortName']
url = 'https://markets.businessinsider.com/ajax/' \
'SearchController_Suggest?max_results=25&query=%s' \
% urlencode(q)
data = _requests.get(url=url, proxies=proxy).text
search_str = '"{}|'.format(ticker)
if search_str not in data:
if q.lower() in data.lower():
search_str = '"|'
if search_str not in data:
self._isin = '-'
return self._isin
else:
self._isin = '-'
return self._isin
self._isin = data.split(search_str)[1].split('"')[0].split('|')[0]
return self._isin
| 36.009671 | 84 | 0.556481 |
from __future__ import print_function
import time as _time
import datetime as _datetime
import requests as _requests
import pandas as _pd
import numpy as _np
try:
from urllib.parse import quote as urlencode
except ImportError:
from urllib import quote as urlencode
from . import utils
from . import shared
class TickerBase():
def __init__(self, ticker):
self.ticker = ticker.upper()
self._history = None
self._base_url = 'https://query1.finance.yahoo.com'
self._scrape_url = 'https://finance.yahoo.com/quote'
self._fundamentals = False
self._info = None
self._sustainability = None
self._recommendations = None
self._major_holders = None
self._institutional_holders = None
self._isin = None
self._calendar = None
self._expirations = {}
self._earnings = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
self._financials = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
self._balancesheet = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
self._cashflow = {
"yearly": utils.empty_df(),
"quarterly": utils.empty_df()}
def history(self, period="1mo", interval="1d",
start=None, end=None, prepost=False, actions=True,
auto_adjust=True, back_adjust=False,
proxy=None, rounding=True, tz=None, **kwargs):
if start or period is None or period.lower() == "max":
if start is None:
start = -2208988800
elif isinstance(start, _datetime.datetime):
start = int(_time.mktime(start.timetuple()))
else:
start = int(_time.mktime(
_time.strptime(str(start), '%Y-%m-%d')))
if end is None:
end = int(_time.time())
elif isinstance(end, _datetime.datetime):
end = int(_time.mktime(end.timetuple()))
else:
end = int(_time.mktime(_time.strptime(str(end), '%Y-%m-%d')))
params = {"period1": start, "period2": end}
else:
period = period.lower()
params = {"range": period}
params["interval"] = interval.lower()
params["includePrePost"] = prepost
params["events"] = "div,splits"
if params["interval"] == "30m":
params["interval"] = "15m"
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
data = _requests.get(url=url, params=params, proxies=proxy)
if "Will be right back" in data.text:
raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
"Our engineers are working quickly to resolve "
"the issue. Thank you for your patience.")
data = data.json()
debug_mode = True
if "debug" in kwargs and isinstance(kwargs["debug"], bool):
debug_mode = kwargs["debug"]
err_msg = "No data found for this date range, symbol may be delisted"
if "chart" in data and data["chart"]["error"]:
err_msg = data["chart"]["error"]["description"]
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
elif "chart" not in data or data["chart"]["result"] is None or \
not data["chart"]["result"]:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
try:
quotes = utils.parse_quotes(data["chart"]["result"][0], tz)
except Exception:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
if interval.lower() == "30m":
quotes2 = quotes.resample('30T')
quotes = _pd.DataFrame(index=quotes2.last().index, data={
'Open': quotes2['Open'].first(),
'High': quotes2['High'].max(),
'Low': quotes2['Low'].min(),
'Close': quotes2['Close'].last(),
'Adj Close': quotes2['Adj Close'].last(),
'Volume': quotes2['Volume'].sum()
})
try:
quotes['Dividends'] = quotes2['Dividends'].max()
except Exception:
pass
try:
quotes['Stock Splits'] = quotes2['Dividends'].max()
except Exception:
pass
if auto_adjust:
quotes = utils.auto_adjust(quotes)
elif back_adjust:
quotes = utils.back_adjust(quotes)
if rounding:
quotes = _np.round(quotes, data[
"chart"]["result"][0]["meta"]["priceHint"])
quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64)
quotes.dropna(inplace=True)
dividends, splits = utils.parse_actions(data["chart"]["result"][0], tz)
df = _pd.concat([quotes, dividends, splits], axis=1, sort=True)
df["Dividends"].fillna(0, inplace=True)
df["Stock Splits"].fillna(0, inplace=True)
df.index = df.index.tz_localize("UTC").tz_convert(
data["chart"]["result"][0]["meta"]["exchangeTimezoneName"])
if params["interval"][-1] in {"m", "h"}:
df.index.name = "Datetime"
else:
df.index = _pd.to_datetime(df.index.date)
if tz is not None:
df.index = df.index.tz_localize(tz)
df.index.name = "Date"
self._history = df.copy()
if not actions:
df.drop(columns=["Dividends", "Stock Splits"], inplace=True)
return df
def _get_fundamentals(self, kind=None, proxy=None):
def cleanup(data):
df = _pd.DataFrame(data).drop(columns=['maxAge'])
for col in df.columns:
df[col] = _np.where(
df[col].astype(str) == '-', _np.nan, df[col])
df.set_index('endDate', inplace=True)
try:
df.index = _pd.to_datetime(df.index, unit='s')
except ValueError:
df.index = _pd.to_datetime(df.index)
df = df.T
df.columns.name = ''
df.index.name = 'Breakdown'
df.index = utils.camel2title(df.index)
return df
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
if self._fundamentals:
return
url = '%s/%s' % (self._scrape_url, self.ticker)
data = utils.get_json(url, proxy)
url = "{}/{}/holders".format(self._scrape_url, self.ticker)
holders = _pd.read_html(url)
self._major_holders = holders[0]
self._institutional_holders = holders[1]
if 'Date Reported' in self._institutional_holders:
self._institutional_holders['Date Reported'] = _pd.to_datetime(
self._institutional_holders['Date Reported'])
if '% Out' in self._institutional_holders:
self._institutional_holders['% Out'] = self._institutional_holders[
'% Out'].str.replace('%', '').astype(float)/100
d = {}
if isinstance(data.get('esgScores'), dict):
for item in data['esgScores']:
if not isinstance(data['esgScores'][item], (dict, list)):
d[item] = data['esgScores'][item]
s = _pd.DataFrame(index=[0], data=d)[-1:].T
s.columns = ['Value']
s.index.name = '%.f-%.f' % (
s[s.index == 'ratingYear']['Value'].values[0],
s[s.index == 'ratingMonth']['Value'].values[0])
self._sustainability = s[~s.index.isin(
['maxAge', 'ratingYear', 'ratingMonth'])]
self._info = {}
items = ['summaryProfile', 'summaryDetail', 'quoteType',
'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
for item in items:
if isinstance(data.get(item), dict):
self._info.update(data[item])
self._info['regularMarketPrice'] = self._info['regularMarketOpen']
self._info['logo_url'] = ""
try:
domain = self._info['website'].split(
'://')[1].split('/')[0].replace('www.', '')
self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
except Exception:
pass
try:
cal = _pd.DataFrame(
data['calendarEvents']['earnings'])
cal['earningsDate'] = _pd.to_datetime(
cal['earningsDate'], unit='s')
self._calendar = cal.T
self._calendar.index = utils.camel2title(self._calendar.index)
self._calendar.columns = ['Value']
except Exception:
pass
try:
rec = _pd.DataFrame(
data['upgradeDowngradeHistory']['history'])
rec['earningsDate'] = _pd.to_datetime(
rec['epochGradeDate'], unit='s')
rec.set_index('earningsDate', inplace=True)
rec.index.name = 'Date'
rec.columns = utils.camel2title(rec.columns)
self._recommendations = rec[[
'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
except Exception:
pass
data = utils.get_json(url+'/financials', proxy)
for key in (
(self._cashflow, 'cashflowStatement', 'cashflowStatements'),
(self._balancesheet, 'balanceSheet', 'balanceSheetStatements'),
(self._financials, 'incomeStatement', 'incomeStatementHistory')
):
item = key[1] + 'History'
if isinstance(data.get(item), dict):
key[0]['yearly'] = cleanup(data[item][key[2]])
item = key[1]+'HistoryQuarterly'
if isinstance(data.get(item), dict):
key[0]['quarterly'] = cleanup(data[item][key[2]])
if isinstance(data.get('earnings'), dict):
earnings = data['earnings']['financialsChart']
df = _pd.DataFrame(earnings['yearly']).set_index('date')
df.columns = utils.camel2title(df.columns)
df.index.name = 'Year'
self._earnings['yearly'] = df
df = _pd.DataFrame(earnings['quarterly']).set_index('date')
df.columns = utils.camel2title(df.columns)
df.index.name = 'Quarter'
self._earnings['quarterly'] = df
self._fundamentals = True
def get_recommendations(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._recommendations
if as_dict:
return data.to_dict()
return data
def get_calendar(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._calendar
if as_dict:
return data.to_dict()
return data
def get_major_holders(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._major_holders
if as_dict:
return data.to_dict()
return data
def get_institutional_holders(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._institutional_holders
if as_dict:
return data.to_dict()
return data
def get_info(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._info
if as_dict:
return data.to_dict()
return data
def get_sustainability(self, proxy=None, as_dict=False, *args, **kwargs):
self._get_fundamentals(proxy)
data = self._sustainability
if as_dict:
return data.to_dict()
return data
def get_earnings(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._earnings[freq]
if as_dict:
return data.to_dict()
return data
def get_financials(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._financials[freq]
if as_dict:
return data.to_dict()
return data
def get_balancesheet(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._balancesheet[freq]
if as_dict:
return data.to_dict()
return data
def get_balance_sheet(self, proxy=None, as_dict=False, freq="yearly"):
return self.get_balancesheet(proxy, as_dict, freq)
def get_cashflow(self, proxy=None, as_dict=False, freq="yearly"):
self._get_fundamentals(proxy)
data = self._cashflow[freq]
if as_dict:
return data.to_dict()
return data
def get_dividends(self, proxy=None):
if self._history is None:
self.history(period="max", proxy=proxy)
dividends = self._history["Dividends"]
return dividends[dividends != 0]
def get_splits(self, proxy=None):
if self._history is None:
self.history(period="max", proxy=proxy)
splits = self._history["Stock Splits"]
return splits[splits != 0]
def get_actions(self, proxy=None):
if self._history is None:
self.history(period="max", proxy=proxy)
actions = self._history[["Dividends", "Stock Splits"]]
return actions[actions != 0].dropna(how='all').fillna(0)
def get_isin(self, proxy=None):
if self._isin is not None:
return self._isin
ticker = self.ticker.upper()
if "-" in ticker or "^" in ticker:
self._isin = '-'
return self._isin
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
q = ticker
self.get_info(proxy=proxy)
if "shortName" in self._info:
q = self._info['shortName']
url = 'https://markets.businessinsider.com/ajax/' \
'SearchController_Suggest?max_results=25&query=%s' \
% urlencode(q)
data = _requests.get(url=url, proxies=proxy).text
search_str = '"{}|'.format(ticker)
if search_str not in data:
if q.lower() in data.lower():
search_str = '"|'
if search_str not in data:
self._isin = '-'
return self._isin
else:
self._isin = '-'
return self._isin
self._isin = data.split(search_str)[1].split('"')[0].split('|')[0]
return self._isin
| true | true |
f73bf6dcdd224c16a88b42698e13671d40775354 | 53,040 | py | Python | test/functional/feature_block.py | JoffreyBourdieux/ApsioCoin | 6573a9ab4e6e302f99d203fe0e1e414adb1fd349 | [
"MIT"
] | null | null | null | test/functional/feature_block.py | JoffreyBourdieux/ApsioCoin | 6573a9ab4e6e302f99d203fe0e1e414adb1fd349 | [
"MIT"
] | null | null | null | test/functional/feature_block.py | JoffreyBourdieux/ApsioCoin | 6573a9ab4e6e302f99d203fe0e1e414adb1fd349 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# Copyright (c) 2015-2017 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test block processing.
This reimplements tests from the bitcoinj/FullBlockTestGenerator used
by the pull-tester.
We use the testing framework in which we expect a particular answer from
each test.
"""
from test_framework.test_framework import ComparisonTestFramework
from test_framework.util import *
from test_framework.comptool import TestManager, TestInstance, RejectResult
from test_framework.blocktools import *
import time
from test_framework.key import CECKey
from test_framework.script import *
from test_framework.mininode import network_thread_start
import struct
class PreviousSpendableOutput():
def __init__(self, tx = CTransaction(), n = -1):
self.tx = tx
self.n = n # the output we're spending
# Use this class for tests that require behavior other than normal "mininode" behavior.
# For now, it is used to serialize a bloated varint (b64).
class CBrokenBlock(CBlock):
def __init__(self, header=None):
super(CBrokenBlock, self).__init__(header)
def initialize(self, base_block):
self.vtx = copy.deepcopy(base_block.vtx)
self.hashMerkleRoot = self.calc_merkle_root()
def serialize(self, with_witness=False):
r = b""
r += super(CBlock, self).serialize()
r += struct.pack("<BQ", 255, len(self.vtx))
for tx in self.vtx:
if with_witness:
r += tx.serialize_with_witness()
else:
r += tx.serialize_without_witness()
return r
def normal_serialize(self):
r = b""
r += super(CBrokenBlock, self).serialize()
return r
class FullBlockTest(ComparisonTestFramework):
# Can either run this test as 1 node with expected answers, or two and compare them.
# Change the "outcome" variable from each TestInstance object to only do the comparison.
def set_test_params(self):
self.num_nodes = 1
self.setup_clean_chain = True
self.block_heights = {}
self.coinbase_key = CECKey()
self.coinbase_key.set_secretbytes(b"horsebattery")
self.coinbase_pubkey = self.coinbase_key.get_pubkey()
self.tip = None
self.blocks = {}
def add_options(self, parser):
super().add_options(parser)
parser.add_option("--runbarelyexpensive", dest="runbarelyexpensive", default=True)
def run_test(self):
self.test = TestManager(self, self.options.tmpdir)
self.test.add_all_connections(self.nodes)
network_thread_start()
self.test.run()
def add_transactions_to_block(self, block, tx_list):
[ tx.rehash() for tx in tx_list ]
block.vtx.extend(tx_list)
# this is a little handier to use than the version in blocktools.py
def create_tx(self, spend_tx, n, value, script=CScript([OP_TRUE])):
tx = create_transaction(spend_tx, n, b"", value, script)
return tx
# sign a transaction, using the key we know about
# this signs input 0 in tx, which is assumed to be spending output n in spend_tx
def sign_tx(self, tx, spend_tx, n):
scriptPubKey = bytearray(spend_tx.vout[n].scriptPubKey)
if (scriptPubKey[0] == OP_TRUE): # an anyone-can-spend
tx.vin[0].scriptSig = CScript()
return
(sighash, err) = SignatureHash(spend_tx.vout[n].scriptPubKey, tx, 0, SIGHASH_ALL)
tx.vin[0].scriptSig = CScript([self.coinbase_key.sign(sighash) + bytes(bytearray([SIGHASH_ALL]))])
def create_and_sign_transaction(self, spend_tx, n, value, script=CScript([OP_TRUE])):
tx = self.create_tx(spend_tx, n, value, script)
self.sign_tx(tx, spend_tx, n)
tx.rehash()
return tx
def next_block(self, number, spend=None, additional_coinbase_value=0, script=CScript([OP_TRUE]), solve=True):
if self.tip == None:
base_block_hash = self.genesis_hash
block_time = int(time.time())+1
else:
base_block_hash = self.tip.sha256
block_time = self.tip.nTime + 1
# First create the coinbase
height = self.block_heights[base_block_hash] + 1
coinbase = create_coinbase(height, self.coinbase_pubkey)
coinbase.vout[0].nValue += additional_coinbase_value
coinbase.rehash()
if spend == None:
block = create_block(base_block_hash, coinbase, block_time)
block.nVersion = 0x20000000
else:
coinbase.vout[0].nValue += spend.tx.vout[spend.n].nValue - 1 # all but one satoshi to fees
coinbase.rehash()
block = create_block(base_block_hash, coinbase, block_time)
block.nVersion = 0x20000000
tx = create_transaction(spend.tx, spend.n, b"", 1, script) # spend 1 satoshi
self.sign_tx(tx, spend.tx, spend.n)
self.add_transactions_to_block(block, [tx])
block.hashMerkleRoot = block.calc_merkle_root()
if solve:
block.solve()
self.tip = block
self.block_heights[block.sha256] = height
assert number not in self.blocks
self.blocks[number] = block
return block
def get_tests(self):
self.genesis_hash = int(self.nodes[0].getbestblockhash(), 16)
self.block_heights[self.genesis_hash] = 0
spendable_outputs = []
# save the current tip so it can be spent by a later block
def save_spendable_output():
spendable_outputs.append(self.tip)
# get an output that we previously marked as spendable
def get_spendable_output():
return PreviousSpendableOutput(spendable_outputs.pop(0).vtx[0], 0)
# returns a test case that asserts that the current tip was accepted
def accepted():
return TestInstance([[self.tip, True]])
# returns a test case that asserts that the current tip was rejected
def rejected(reject = None):
if reject is None:
return TestInstance([[self.tip, False]])
else:
return TestInstance([[self.tip, reject]])
# move the tip back to a previous block
def tip(number):
self.tip = self.blocks[number]
# adds transactions to the block and updates state
def update_block(block_number, new_transactions):
block = self.blocks[block_number]
self.add_transactions_to_block(block, new_transactions)
old_sha256 = block.sha256
block.hashMerkleRoot = block.calc_merkle_root()
block.solve()
# Update the internal state just like in next_block
self.tip = block
if block.sha256 != old_sha256:
self.block_heights[block.sha256] = self.block_heights[old_sha256]
del self.block_heights[old_sha256]
self.blocks[block_number] = block
return block
# shorthand for functions
block = self.next_block
create_tx = self.create_tx
create_and_sign_tx = self.create_and_sign_transaction
# these must be updated if consensus changes
MAX_BLOCK_SIGOPS = 20000
# Create a new block
block(0)
save_spendable_output()
yield accepted()
# Now we need that block to mature so we can spend the coinbase.
test = TestInstance(sync_every_block=False)
for i in range(99):
block(5000 + i)
test.blocks_and_transactions.append([self.tip, True])
save_spendable_output()
yield test
# collect spendable outputs now to avoid cluttering the code later on
out = []
for i in range(33):
out.append(get_spendable_output())
# Start by building a couple of blocks on top (which output is spent is
# in parentheses):
# genesis -> b1 (0) -> b2 (1)
block(1, spend=out[0])
save_spendable_output()
yield accepted()
block(2, spend=out[1])
yield accepted()
save_spendable_output()
# so fork like this:
#
# genesis -> b1 (0) -> b2 (1)
# \-> b3 (1)
#
# Nothing should happen at this point. We saw b2 first so it takes priority.
tip(1)
b3 = block(3, spend=out[1])
txout_b3 = PreviousSpendableOutput(b3.vtx[1], 0)
yield rejected()
# Now we add another block to make the alternative chain longer.
#
# genesis -> b1 (0) -> b2 (1)
# \-> b3 (1) -> b4 (2)
block(4, spend=out[2])
yield accepted()
# ... and back to the first chain.
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b3 (1) -> b4 (2)
tip(2)
block(5, spend=out[2])
save_spendable_output()
yield rejected()
block(6, spend=out[3])
yield accepted()
# Try to create a fork that double-spends
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b7 (2) -> b8 (4)
# \-> b3 (1) -> b4 (2)
tip(5)
block(7, spend=out[2])
yield rejected()
block(8, spend=out[4])
yield rejected()
# Try to create a block that has too much fee
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b9 (4)
# \-> b3 (1) -> b4 (2)
tip(6)
block(9, spend=out[4], additional_coinbase_value=1)
yield rejected(RejectResult(16, b'bad-cb-amount'))
# Create a fork that ends in a block with too much fee (the one that causes the reorg)
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b10 (3) -> b11 (4)
# \-> b3 (1) -> b4 (2)
tip(5)
block(10, spend=out[3])
yield rejected()
block(11, spend=out[4], additional_coinbase_value=1)
yield rejected(RejectResult(16, b'bad-cb-amount'))
# Try again, but with a valid fork first
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b14 (5)
# (b12 added last)
# \-> b3 (1) -> b4 (2)
tip(5)
b12 = block(12, spend=out[3])
save_spendable_output()
b13 = block(13, spend=out[4])
# Deliver the block header for b12, and the block b13.
# b13 should be accepted but the tip won't advance until b12 is delivered.
yield TestInstance([[CBlockHeader(b12), None], [b13, False]])
save_spendable_output()
# b14 is invalid, but the node won't know that until it tries to connect
# Tip still can't advance because b12 is missing
block(14, spend=out[5], additional_coinbase_value=1)
yield rejected()
yield TestInstance([[b12, True, b13.sha256]]) # New tip should be b13.
# Add a block with MAX_BLOCK_SIGOPS and one with one more sigop
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5) -> b16 (6)
# \-> b3 (1) -> b4 (2)
# Test that a block with a lot of checksigs is okay
lots_of_checksigs = CScript([OP_CHECKSIG] * (MAX_BLOCK_SIGOPS - 1))
tip(13)
block(15, spend=out[5], script=lots_of_checksigs)
yield accepted()
save_spendable_output()
# Test that a block with too many checksigs is rejected
too_many_checksigs = CScript([OP_CHECKSIG] * (MAX_BLOCK_SIGOPS))
block(16, spend=out[6], script=too_many_checksigs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# Attempt to spend a transaction created on a different fork
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5) -> b17 (b3.vtx[1])
# \-> b3 (1) -> b4 (2)
tip(15)
block(17, spend=txout_b3)
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# Attempt to spend a transaction created on a different fork (on a fork this time)
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5)
# \-> b18 (b3.vtx[1]) -> b19 (6)
# \-> b3 (1) -> b4 (2)
tip(13)
block(18, spend=txout_b3)
yield rejected()
block(19, spend=out[6])
yield rejected()
# Attempt to spend a coinbase at depth too low
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5) -> b20 (7)
# \-> b3 (1) -> b4 (2)
tip(15)
block(20, spend=out[7])
yield rejected(RejectResult(16, b'bad-txns-premature-spend-of-coinbase'))
# Attempt to spend a coinbase at depth too low (on a fork this time)
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5)
# \-> b21 (6) -> b22 (5)
# \-> b3 (1) -> b4 (2)
tip(13)
block(21, spend=out[6])
yield rejected()
block(22, spend=out[5])
yield rejected()
# Create a block on either side of MAX_BLOCK_BASE_SIZE and make sure its accepted/rejected
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5) -> b23 (6)
# \-> b24 (6) -> b25 (7)
# \-> b3 (1) -> b4 (2)
tip(15)
b23 = block(23, spend=out[6])
tx = CTransaction()
script_length = MAX_BLOCK_BASE_SIZE - len(b23.serialize()) - 69
script_output = CScript([b'\x00' * script_length])
tx.vout.append(CTxOut(0, script_output))
tx.vin.append(CTxIn(COutPoint(b23.vtx[1].sha256, 0)))
b23 = update_block(23, [tx])
# Make sure the math above worked out to produce a max-sized block
assert_equal(len(b23.serialize()), MAX_BLOCK_BASE_SIZE)
yield accepted()
save_spendable_output()
# Make the next block one byte bigger and check that it fails
tip(15)
b24 = block(24, spend=out[6])
script_length = MAX_BLOCK_BASE_SIZE - len(b24.serialize()) - 69
script_output = CScript([b'\x00' * (script_length+1)])
tx.vout = [CTxOut(0, script_output)]
b24 = update_block(24, [tx])
assert_equal(len(b24.serialize()), MAX_BLOCK_BASE_SIZE+1)
yield rejected(RejectResult(16, b'bad-blk-length'))
block(25, spend=out[7])
yield rejected()
# Create blocks with a coinbase input script size out of range
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b15 (5) -> b23 (6) -> b30 (7)
# \-> ... (6) -> ... (7)
# \-> b3 (1) -> b4 (2)
tip(15)
b26 = block(26, spend=out[6])
b26.vtx[0].vin[0].scriptSig = b'\x00'
b26.vtx[0].rehash()
# update_block causes the merkle root to get updated, even with no new
# transactions, and updates the required state.
b26 = update_block(26, [])
yield rejected(RejectResult(16, b'bad-cb-length'))
# Extend the b26 chain to make sure bitcoind isn't accepting b26
block(27, spend=out[7])
yield rejected(False)
# Now try a too-large-coinbase script
tip(15)
b28 = block(28, spend=out[6])
b28.vtx[0].vin[0].scriptSig = b'\x00' * 101
b28.vtx[0].rehash()
b28 = update_block(28, [])
yield rejected(RejectResult(16, b'bad-cb-length'))
# Extend the b28 chain to make sure bitcoind isn't accepting b28
block(29, spend=out[7])
yield rejected(False)
# b30 has a max-sized coinbase scriptSig.
tip(23)
b30 = block(30)
b30.vtx[0].vin[0].scriptSig = b'\x00' * 100
b30.vtx[0].rehash()
b30 = update_block(30, [])
yield accepted()
save_spendable_output()
# b31 - b35 - check sigops of OP_CHECKMULTISIG / OP_CHECKMULTISIGVERIFY / OP_CHECKSIGVERIFY
#
# genesis -> ... -> b30 (7) -> b31 (8) -> b33 (9) -> b35 (10)
# \-> b36 (11)
# \-> b34 (10)
# \-> b32 (9)
#
# MULTISIG: each op code counts as 20 sigops. To create the edge case, pack another 19 sigops at the end.
lots_of_multisigs = CScript([OP_CHECKMULTISIG] * ((MAX_BLOCK_SIGOPS-1) // 20) + [OP_CHECKSIG] * 19)
b31 = block(31, spend=out[8], script=lots_of_multisigs)
assert_equal(get_legacy_sigopcount_block(b31), MAX_BLOCK_SIGOPS)
yield accepted()
save_spendable_output()
# this goes over the limit because the coinbase has one sigop
too_many_multisigs = CScript([OP_CHECKMULTISIG] * (MAX_BLOCK_SIGOPS // 20))
b32 = block(32, spend=out[9], script=too_many_multisigs)
assert_equal(get_legacy_sigopcount_block(b32), MAX_BLOCK_SIGOPS + 1)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# CHECKMULTISIGVERIFY
tip(31)
lots_of_multisigs = CScript([OP_CHECKMULTISIGVERIFY] * ((MAX_BLOCK_SIGOPS-1) // 20) + [OP_CHECKSIG] * 19)
block(33, spend=out[9], script=lots_of_multisigs)
yield accepted()
save_spendable_output()
too_many_multisigs = CScript([OP_CHECKMULTISIGVERIFY] * (MAX_BLOCK_SIGOPS // 20))
block(34, spend=out[10], script=too_many_multisigs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# CHECKSIGVERIFY
tip(33)
lots_of_checksigs = CScript([OP_CHECKSIGVERIFY] * (MAX_BLOCK_SIGOPS - 1))
b35 = block(35, spend=out[10], script=lots_of_checksigs)
yield accepted()
save_spendable_output()
too_many_checksigs = CScript([OP_CHECKSIGVERIFY] * (MAX_BLOCK_SIGOPS))
block(36, spend=out[11], script=too_many_checksigs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# Check spending of a transaction in a block which failed to connect
#
# b6 (3)
# b12 (3) -> b13 (4) -> b15 (5) -> b23 (6) -> b30 (7) -> b31 (8) -> b33 (9) -> b35 (10)
# \-> b37 (11)
# \-> b38 (11/37)
#
# save 37's spendable output, but then double-spend out11 to invalidate the block
tip(35)
b37 = block(37, spend=out[11])
txout_b37 = PreviousSpendableOutput(b37.vtx[1], 0)
tx = create_and_sign_tx(out[11].tx, out[11].n, 0)
b37 = update_block(37, [tx])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# attempt to spend b37's first non-coinbase tx, at which point b37 was still considered valid
tip(35)
block(38, spend=txout_b37)
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# Check P2SH SigOp counting
#
#
# 13 (4) -> b15 (5) -> b23 (6) -> b30 (7) -> b31 (8) -> b33 (9) -> b35 (10) -> b39 (11) -> b41 (12)
# \-> b40 (12)
#
# b39 - create some P2SH outputs that will require 6 sigops to spend:
#
# redeem_script = COINBASE_PUBKEY, (OP_2DUP+OP_CHECKSIGVERIFY) * 5, OP_CHECKSIG
# p2sh_script = OP_HASH160, ripemd160(sha256(script)), OP_EQUAL
#
tip(35)
b39 = block(39)
b39_outputs = 0
b39_sigops_per_output = 6
# Build the redeem script, hash it, use hash to create the p2sh script
redeem_script = CScript([self.coinbase_pubkey] + [OP_2DUP, OP_CHECKSIGVERIFY]*5 + [OP_CHECKSIG])
redeem_script_hash = hash160(redeem_script)
p2sh_script = CScript([OP_HASH160, redeem_script_hash, OP_EQUAL])
# Create a transaction that spends one satoshi to the p2sh_script, the rest to OP_TRUE
# This must be signed because it is spending a coinbase
spend = out[11]
tx = create_tx(spend.tx, spend.n, 1, p2sh_script)
tx.vout.append(CTxOut(spend.tx.vout[spend.n].nValue - 1, CScript([OP_TRUE])))
self.sign_tx(tx, spend.tx, spend.n)
tx.rehash()
b39 = update_block(39, [tx])
b39_outputs += 1
# Until block is full, add tx's with 1 satoshi to p2sh_script, the rest to OP_TRUE
tx_new = None
tx_last = tx
total_size=len(b39.serialize())
while(total_size < MAX_BLOCK_BASE_SIZE):
tx_new = create_tx(tx_last, 1, 1, p2sh_script)
tx_new.vout.append(CTxOut(tx_last.vout[1].nValue - 1, CScript([OP_TRUE])))
tx_new.rehash()
total_size += len(tx_new.serialize())
if total_size >= MAX_BLOCK_BASE_SIZE:
break
b39.vtx.append(tx_new) # add tx to block
tx_last = tx_new
b39_outputs += 1
b39 = update_block(39, [])
yield accepted()
save_spendable_output()
# Test sigops in P2SH redeem scripts
#
# b40 creates 3333 tx's spending the 6-sigop P2SH outputs from b39 for a total of 19998 sigops.
# The first tx has one sigop and then at the end we add 2 more to put us just over the max.
#
# b41 does the same, less one, so it has the maximum sigops permitted.
#
tip(39)
b40 = block(40, spend=out[12])
sigops = get_legacy_sigopcount_block(b40)
numTxes = (MAX_BLOCK_SIGOPS - sigops) // b39_sigops_per_output
assert_equal(numTxes <= b39_outputs, True)
lastOutpoint = COutPoint(b40.vtx[1].sha256, 0)
new_txs = []
for i in range(1, numTxes+1):
tx = CTransaction()
tx.vout.append(CTxOut(1, CScript([OP_TRUE])))
tx.vin.append(CTxIn(lastOutpoint, b''))
# second input is corresponding P2SH output from b39
tx.vin.append(CTxIn(COutPoint(b39.vtx[i].sha256, 0), b''))
# Note: must pass the redeem_script (not p2sh_script) to the signature hash function
(sighash, err) = SignatureHash(redeem_script, tx, 1, SIGHASH_ALL)
sig = self.coinbase_key.sign(sighash) + bytes(bytearray([SIGHASH_ALL]))
scriptSig = CScript([sig, redeem_script])
tx.vin[1].scriptSig = scriptSig
tx.rehash()
new_txs.append(tx)
lastOutpoint = COutPoint(tx.sha256, 0)
b40_sigops_to_fill = MAX_BLOCK_SIGOPS - (numTxes * b39_sigops_per_output + sigops) + 1
tx = CTransaction()
tx.vin.append(CTxIn(lastOutpoint, b''))
tx.vout.append(CTxOut(1, CScript([OP_CHECKSIG] * b40_sigops_to_fill)))
tx.rehash()
new_txs.append(tx)
update_block(40, new_txs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# same as b40, but one less sigop
tip(39)
block(41, spend=None)
update_block(41, b40.vtx[1:-1])
b41_sigops_to_fill = b40_sigops_to_fill - 1
tx = CTransaction()
tx.vin.append(CTxIn(lastOutpoint, b''))
tx.vout.append(CTxOut(1, CScript([OP_CHECKSIG] * b41_sigops_to_fill)))
tx.rehash()
update_block(41, [tx])
yield accepted()
# Fork off of b39 to create a constant base again
#
# b23 (6) -> b30 (7) -> b31 (8) -> b33 (9) -> b35 (10) -> b39 (11) -> b42 (12) -> b43 (13)
# \-> b41 (12)
#
tip(39)
block(42, spend=out[12])
yield rejected()
save_spendable_output()
block(43, spend=out[13])
yield accepted()
save_spendable_output()
# Test a number of really invalid scenarios
#
# -> b31 (8) -> b33 (9) -> b35 (10) -> b39 (11) -> b42 (12) -> b43 (13) -> b44 (14)
# \-> ??? (15)
# The next few blocks are going to be created "by hand" since they'll do funky things, such as having
# the first transaction be non-coinbase, etc. The purpose of b44 is to make sure this works.
height = self.block_heights[self.tip.sha256] + 1
coinbase = create_coinbase(height, self.coinbase_pubkey)
b44 = CBlock()
b44.nVersion = 0x20000000
b44.nTime = self.tip.nTime + 1
b44.hashPrevBlock = self.tip.sha256
b44.nBits = 0x207fffff
b44.vtx.append(coinbase)
b44.hashMerkleRoot = b44.calc_merkle_root()
b44.solve()
self.tip = b44
self.block_heights[b44.sha256] = height
self.blocks[44] = b44
yield accepted()
# A block with a non-coinbase as the first tx
non_coinbase = create_tx(out[15].tx, out[15].n, 1)
b45 = CBlock()
b45.nVersion = 0x20000000
b45.nTime = self.tip.nTime + 1
b45.hashPrevBlock = self.tip.sha256
b45.nBits = 0x207fffff
b45.vtx.append(non_coinbase)
b45.hashMerkleRoot = b45.calc_merkle_root()
b45.calc_sha256()
b45.solve()
self.block_heights[b45.sha256] = self.block_heights[self.tip.sha256]+1
self.tip = b45
self.blocks[45] = b45
yield rejected(RejectResult(16, b'bad-cb-missing'))
# A block with no txns
tip(44)
b46 = CBlock()
b46.nVersion = 0x20000000
b46.nTime = b44.nTime+1
b46.hashPrevBlock = b44.sha256
b46.nBits = 0x207fffff
b46.vtx = []
b46.hashMerkleRoot = 0
b46.solve()
self.block_heights[b46.sha256] = self.block_heights[b44.sha256]+1
self.tip = b46
assert 46 not in self.blocks
self.blocks[46] = b46
s = ser_uint256(b46.hashMerkleRoot)
yield rejected(RejectResult(16, b'bad-blk-length'))
# Apsiocoin: Temporarily disable test
# A block with invalid work
#tip(44)
#b47 = block(47, solve=False)
#target = uint256_from_compact(b47.nBits)
#while b47.scrypt256 < target: #changed > to <
# b47.nNonce += 1
# b47.rehash()
#yield rejected(RejectResult(16, b'high-hash'))
# A block with timestamp > 2 hrs in the future
tip(44)
b48 = block(48, solve=False)
b48.nTime = int(time.time()) + 60 * 60 * 3
b48.solve()
yield rejected(RejectResult(16, b'time-too-new'))
# A block with an invalid merkle hash
tip(44)
b49 = block(49)
b49.hashMerkleRoot += 1
b49.solve()
yield rejected(RejectResult(16, b'bad-txnmrklroot'))
# A block with an incorrect POW limit
tip(44)
b50 = block(50)
b50.nBits = b50.nBits - 1
b50.solve()
yield rejected(RejectResult(16, b'bad-diffbits'))
# A block with two coinbase txns
tip(44)
b51 = block(51)
cb2 = create_coinbase(51, self.coinbase_pubkey)
b51 = update_block(51, [cb2])
yield rejected(RejectResult(16, b'bad-cb-multiple'))
# A block w/ duplicate txns
# Note: txns have to be in the right position in the merkle tree to trigger this error
tip(44)
b52 = block(52, spend=out[15])
tx = create_tx(b52.vtx[1], 0, 1)
b52 = update_block(52, [tx, tx])
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
# Test block timestamps
# -> b31 (8) -> b33 (9) -> b35 (10) -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15)
# \-> b54 (15)
#
tip(43)
block(53, spend=out[14])
yield rejected() # rejected since b44 is at same height
save_spendable_output()
# invalid timestamp (b35 is 5 blocks back, so its time is MedianTimePast)
b54 = block(54, spend=out[15])
b54.nTime = b35.nTime - 1
b54.solve()
yield rejected(RejectResult(16, b'time-too-old'))
# valid timestamp
tip(53)
b55 = block(55, spend=out[15])
b55.nTime = b35.nTime
update_block(55, [])
yield accepted()
save_spendable_output()
# Test CVE-2012-2459
#
# -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57p2 (16)
# \-> b57 (16)
# \-> b56p2 (16)
# \-> b56 (16)
#
# Merkle tree malleability (CVE-2012-2459): repeating sequences of transactions in a block without
# affecting the merkle root of a block, while still invalidating it.
# See: src/consensus/merkle.h
#
# b57 has three txns: coinbase, tx, tx1. The merkle root computation will duplicate tx.
# Result: OK
#
# b56 copies b57 but duplicates tx1 and does not recalculate the block hash. So it has a valid merkle
# root but duplicate transactions.
# Result: Fails
#
# b57p2 has six transactions in its merkle tree:
# - coinbase, tx, tx1, tx2, tx3, tx4
# Merkle root calculation will duplicate as necessary.
# Result: OK.
#
# b56p2 copies b57p2 but adds both tx3 and tx4. The purpose of the test is to make sure the code catches
# duplicate txns that are not next to one another with the "bad-txns-duplicate" error (which indicates
# that the error was caught early, avoiding a DOS vulnerability.)
# b57 - a good block with 2 txs, don't submit until end
tip(55)
b57 = block(57)
tx = create_and_sign_tx(out[16].tx, out[16].n, 1)
tx1 = create_tx(tx, 0, 1)
b57 = update_block(57, [tx, tx1])
# b56 - copy b57, add a duplicate tx
tip(55)
b56 = copy.deepcopy(b57)
self.blocks[56] = b56
assert_equal(len(b56.vtx),3)
b56 = update_block(56, [tx1])
assert_equal(b56.hash, b57.hash)
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
# b57p2 - a good block with 6 tx'es, don't submit until end
tip(55)
b57p2 = block("57p2")
tx = create_and_sign_tx(out[16].tx, out[16].n, 1)
tx1 = create_tx(tx, 0, 1)
tx2 = create_tx(tx1, 0, 1)
tx3 = create_tx(tx2, 0, 1)
tx4 = create_tx(tx3, 0, 1)
b57p2 = update_block("57p2", [tx, tx1, tx2, tx3, tx4])
# b56p2 - copy b57p2, duplicate two non-consecutive tx's
tip(55)
b56p2 = copy.deepcopy(b57p2)
self.blocks["b56p2"] = b56p2
assert_equal(b56p2.hash, b57p2.hash)
assert_equal(len(b56p2.vtx),6)
b56p2 = update_block("b56p2", [tx3, tx4])
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
tip("57p2")
yield accepted()
tip(57)
yield rejected() #rejected because 57p2 seen first
save_spendable_output()
# Test a few invalid tx types
#
# -> b35 (10) -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> ??? (17)
#
# tx with prevout.n out of range
tip(57)
b58 = block(58, spend=out[17])
tx = CTransaction()
assert(len(out[17].tx.vout) < 42)
tx.vin.append(CTxIn(COutPoint(out[17].tx.sha256, 42), CScript([OP_TRUE]), 0xffffffff))
tx.vout.append(CTxOut(0, b""))
tx.calc_sha256()
b58 = update_block(58, [tx])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# tx with output value > input value out of range
tip(57)
b59 = block(59)
tx = create_and_sign_tx(out[17].tx, out[17].n, 51*COIN)
b59 = update_block(59, [tx])
yield rejected(RejectResult(16, b'bad-txns-in-belowout'))
# reset to good chain
tip(57)
b60 = block(60, spend=out[17])
yield accepted()
save_spendable_output()
# Test BIP30
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> b61 (18)
#
# Blocks are not allowed to contain a transaction whose id matches that of an earlier,
# not-fully-spent transaction in the same chain. To test, make identical coinbases;
# the second one should be rejected.
#
tip(60)
b61 = block(61, spend=out[18])
b61.vtx[0].vin[0].scriptSig = b60.vtx[0].vin[0].scriptSig #equalize the coinbases
b61.vtx[0].rehash()
b61 = update_block(61, [])
assert_equal(b60.vtx[0].serialize(), b61.vtx[0].serialize())
yield rejected(RejectResult(16, b'bad-txns-BIP30'))
# Test tx.isFinal is properly rejected (not an exhaustive tx.isFinal test, that should be in data-driven transaction tests)
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> b62 (18)
#
tip(60)
b62 = block(62)
tx = CTransaction()
tx.nLockTime = 0xffffffff #this locktime is non-final
assert(out[18].n < len(out[18].tx.vout))
tx.vin.append(CTxIn(COutPoint(out[18].tx.sha256, out[18].n))) # don't set nSequence
tx.vout.append(CTxOut(0, CScript([OP_TRUE])))
assert(tx.vin[0].nSequence < 0xffffffff)
tx.calc_sha256()
b62 = update_block(62, [tx])
yield rejected(RejectResult(16, b'bad-txns-nonfinal'))
# Test a non-final coinbase is also rejected
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> b63 (-)
#
tip(60)
b63 = block(63)
b63.vtx[0].nLockTime = 0xffffffff
b63.vtx[0].vin[0].nSequence = 0xDEADBEEF
b63.vtx[0].rehash()
b63 = update_block(63, [])
yield rejected(RejectResult(16, b'bad-txns-nonfinal'))
# This checks that a block with a bloated VARINT between the block_header and the array of tx such that
# the block is > MAX_BLOCK_BASE_SIZE with the bloated varint, but <= MAX_BLOCK_BASE_SIZE without the bloated varint,
# does not cause a subsequent, identical block with canonical encoding to be rejected. The test does not
# care whether the bloated block is accepted or rejected; it only cares that the second block is accepted.
#
# What matters is that the receiving node should not reject the bloated block, and then reject the canonical
# block on the basis that it's the same as an already-rejected block (which would be a consensus failure.)
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18)
# \
# b64a (18)
# b64a is a bloated block (non-canonical varint)
# b64 is a good block (same as b64 but w/ canonical varint)
#
tip(60)
regular_block = block("64a", spend=out[18])
# make it a "broken_block," with non-canonical serialization
b64a = CBrokenBlock(regular_block)
b64a.initialize(regular_block)
self.blocks["64a"] = b64a
self.tip = b64a
tx = CTransaction()
# use canonical serialization to calculate size
script_length = MAX_BLOCK_BASE_SIZE - len(b64a.normal_serialize()) - 69
script_output = CScript([b'\x00' * script_length])
tx.vout.append(CTxOut(0, script_output))
tx.vin.append(CTxIn(COutPoint(b64a.vtx[1].sha256, 0)))
b64a = update_block("64a", [tx])
assert_equal(len(b64a.serialize()), MAX_BLOCK_BASE_SIZE + 8)
yield TestInstance([[self.tip, None]])
# comptool workaround: to make sure b64 is delivered, manually erase b64a from blockstore
self.test.block_store.erase(b64a.sha256)
tip(60)
b64 = CBlock(b64a)
b64.vtx = copy.deepcopy(b64a.vtx)
assert_equal(b64.hash, b64a.hash)
assert_equal(len(b64.serialize()), MAX_BLOCK_BASE_SIZE)
self.blocks[64] = b64
update_block(64, [])
yield accepted()
save_spendable_output()
# Spend an output created in the block itself
#
# -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19)
#
tip(64)
block(65)
tx1 = create_and_sign_tx(out[19].tx, out[19].n, out[19].tx.vout[0].nValue)
tx2 = create_and_sign_tx(tx1, 0, 0)
update_block(65, [tx1, tx2])
yield accepted()
save_spendable_output()
# Attempt to spend an output created later in the same block
#
# -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19)
# \-> b66 (20)
tip(65)
block(66)
tx1 = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue)
tx2 = create_and_sign_tx(tx1, 0, 1)
update_block(66, [tx2, tx1])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# Attempt to double-spend a transaction created in a block
#
# -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19)
# \-> b67 (20)
#
#
tip(65)
block(67)
tx1 = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue)
tx2 = create_and_sign_tx(tx1, 0, 1)
tx3 = create_and_sign_tx(tx1, 0, 2)
update_block(67, [tx1, tx2, tx3])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# More tests of block subsidy
#
# -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20)
# \-> b68 (20)
#
# b68 - coinbase with an extra 10 satoshis,
# creates a tx that has 9 satoshis from out[20] go to fees
# this fails because the coinbase is trying to claim 1 satoshi too much in fees
#
# b69 - coinbase with extra 10 satoshis, and a tx that gives a 10 satoshi fee
# this succeeds
#
tip(65)
block(68, additional_coinbase_value=10)
tx = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue-9)
update_block(68, [tx])
yield rejected(RejectResult(16, b'bad-cb-amount'))
tip(65)
b69 = block(69, additional_coinbase_value=10)
tx = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue-10)
update_block(69, [tx])
yield accepted()
save_spendable_output()
# Test spending the outpoint of a non-existent transaction
#
# -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20)
# \-> b70 (21)
#
tip(69)
block(70, spend=out[21])
bogus_tx = CTransaction()
bogus_tx.sha256 = uint256_from_str(b"23c70ed7c0506e9178fc1a987f40a33946d4ad4c962b5ae3a52546da53af0c5c")
tx = CTransaction()
tx.vin.append(CTxIn(COutPoint(bogus_tx.sha256, 0), b"", 0xffffffff))
tx.vout.append(CTxOut(1, b""))
update_block(70, [tx])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# Test accepting an invalid block which has the same hash as a valid one (via merkle tree tricks)
#
# -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20) -> b72 (21)
# \-> b71 (21)
#
# b72 is a good block.
# b71 is a copy of 72, but re-adds one of its transactions. However, it has the same hash as b71.
#
tip(69)
b72 = block(72)
tx1 = create_and_sign_tx(out[21].tx, out[21].n, 2)
tx2 = create_and_sign_tx(tx1, 0, 1)
b72 = update_block(72, [tx1, tx2]) # now tip is 72
b71 = copy.deepcopy(b72)
b71.vtx.append(tx2) # add duplicate tx2
self.block_heights[b71.sha256] = self.block_heights[b69.sha256] + 1 # b71 builds off b69
self.blocks[71] = b71
assert_equal(len(b71.vtx), 4)
assert_equal(len(b72.vtx), 3)
assert_equal(b72.sha256, b71.sha256)
tip(71)
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
tip(72)
yield accepted()
save_spendable_output()
# Test some invalid scripts and MAX_BLOCK_SIGOPS
#
# -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20) -> b72 (21)
# \-> b** (22)
#
# b73 - tx with excessive sigops that are placed after an excessively large script element.
# The purpose of the test is to make sure those sigops are counted.
#
# script is a bytearray of size 20,526
#
# bytearray[0-19,998] : OP_CHECKSIG
# bytearray[19,999] : OP_PUSHDATA4
# bytearray[20,000-20,003]: 521 (max_script_element_size+1, in little-endian format)
# bytearray[20,004-20,525]: unread data (script_element)
# bytearray[20,526] : OP_CHECKSIG (this puts us over the limit)
#
tip(72)
b73 = block(73)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 1 + 5 + 1
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS - 1] = int("4e",16) # OP_PUSHDATA4
element_size = MAX_SCRIPT_ELEMENT_SIZE + 1
a[MAX_BLOCK_SIGOPS] = element_size % 256
a[MAX_BLOCK_SIGOPS+1] = element_size // 256
a[MAX_BLOCK_SIGOPS+2] = 0
a[MAX_BLOCK_SIGOPS+3] = 0
tx = create_and_sign_tx(out[22].tx, 0, 1, CScript(a))
b73 = update_block(73, [tx])
assert_equal(get_legacy_sigopcount_block(b73), MAX_BLOCK_SIGOPS+1)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# b74/75 - if we push an invalid script element, all prevous sigops are counted,
# but sigops after the element are not counted.
#
# The invalid script element is that the push_data indicates that
# there will be a large amount of data (0xffffff bytes), but we only
# provide a much smaller number. These bytes are CHECKSIGS so they would
# cause b75 to fail for excessive sigops, if those bytes were counted.
#
# b74 fails because we put MAX_BLOCK_SIGOPS+1 before the element
# b75 succeeds because we put MAX_BLOCK_SIGOPS before the element
#
#
tip(72)
b74 = block(74)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 42 # total = 20,561
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS] = 0x4e
a[MAX_BLOCK_SIGOPS+1] = 0xfe
a[MAX_BLOCK_SIGOPS+2] = 0xff
a[MAX_BLOCK_SIGOPS+3] = 0xff
a[MAX_BLOCK_SIGOPS+4] = 0xff
tx = create_and_sign_tx(out[22].tx, 0, 1, CScript(a))
b74 = update_block(74, [tx])
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(72)
b75 = block(75)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 42
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS-1] = 0x4e
a[MAX_BLOCK_SIGOPS] = 0xff
a[MAX_BLOCK_SIGOPS+1] = 0xff
a[MAX_BLOCK_SIGOPS+2] = 0xff
a[MAX_BLOCK_SIGOPS+3] = 0xff
tx = create_and_sign_tx(out[22].tx, 0, 1, CScript(a))
b75 = update_block(75, [tx])
yield accepted()
save_spendable_output()
# Check that if we push an element filled with CHECKSIGs, they are not counted
tip(75)
b76 = block(76)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 1 + 5
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS-1] = 0x4e # PUSHDATA4, but leave the following bytes as just checksigs
tx = create_and_sign_tx(out[23].tx, 0, 1, CScript(a))
b76 = update_block(76, [tx])
yield accepted()
save_spendable_output()
# Test transaction resurrection
#
# -> b77 (24) -> b78 (25) -> b79 (26)
# \-> b80 (25) -> b81 (26) -> b82 (27)
#
# b78 creates a tx, which is spent in b79. After b82, both should be in mempool
#
# The tx'es must be unsigned and pass the node's mempool policy. It is unsigned for the
# rather obscure reason that the Python signature code does not distinguish between
# Low-S and High-S values (whereas the bitcoin code has custom code which does so);
# as a result of which, the odds are 50% that the python code will use the right
# value and the transaction will be accepted into the mempool. Until we modify the
# test framework to support low-S signing, we are out of luck.
#
# To get around this issue, we construct transactions which are not signed and which
# spend to OP_TRUE. If the standard-ness rules change, this test would need to be
# updated. (Perhaps to spend to a P2SH OP_TRUE script)
#
tip(76)
block(77)
tx77 = create_and_sign_tx(out[24].tx, out[24].n, 10*COIN)
update_block(77, [tx77])
yield accepted()
save_spendable_output()
block(78)
tx78 = create_tx(tx77, 0, 9*COIN)
update_block(78, [tx78])
yield accepted()
block(79)
tx79 = create_tx(tx78, 0, 8*COIN)
update_block(79, [tx79])
yield accepted()
# mempool should be empty
assert_equal(len(self.nodes[0].getrawmempool()), 0)
tip(77)
block(80, spend=out[25])
yield rejected()
save_spendable_output()
block(81, spend=out[26])
yield rejected() # other chain is same length
save_spendable_output()
block(82, spend=out[27])
yield accepted() # now this chain is longer, triggers re-org
save_spendable_output()
# now check that tx78 and tx79 have been put back into the peer's mempool
mempool = self.nodes[0].getrawmempool()
assert_equal(len(mempool), 2)
assert(tx78.hash in mempool)
assert(tx79.hash in mempool)
# Test invalid opcodes in dead execution paths.
#
# -> b81 (26) -> b82 (27) -> b83 (28)
#
block(83)
op_codes = [OP_IF, OP_INVALIDOPCODE, OP_ELSE, OP_TRUE, OP_ENDIF]
script = CScript(op_codes)
tx1 = create_and_sign_tx(out[28].tx, out[28].n, out[28].tx.vout[0].nValue, script)
tx2 = create_and_sign_tx(tx1, 0, 0, CScript([OP_TRUE]))
tx2.vin[0].scriptSig = CScript([OP_FALSE])
tx2.rehash()
update_block(83, [tx1, tx2])
yield accepted()
save_spendable_output()
# Reorg on/off blocks that have OP_RETURN in them (and try to spend them)
#
# -> b81 (26) -> b82 (27) -> b83 (28) -> b84 (29) -> b87 (30) -> b88 (31)
# \-> b85 (29) -> b86 (30) \-> b89a (32)
#
#
block(84)
tx1 = create_tx(out[29].tx, out[29].n, 0, CScript([OP_RETURN]))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.calc_sha256()
self.sign_tx(tx1, out[29].tx, out[29].n)
tx1.rehash()
tx2 = create_tx(tx1, 1, 0, CScript([OP_RETURN]))
tx2.vout.append(CTxOut(0, CScript([OP_RETURN])))
tx3 = create_tx(tx1, 2, 0, CScript([OP_RETURN]))
tx3.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx4 = create_tx(tx1, 3, 0, CScript([OP_TRUE]))
tx4.vout.append(CTxOut(0, CScript([OP_RETURN])))
tx5 = create_tx(tx1, 4, 0, CScript([OP_RETURN]))
update_block(84, [tx1,tx2,tx3,tx4,tx5])
yield accepted()
save_spendable_output()
tip(83)
block(85, spend=out[29])
yield rejected()
block(86, spend=out[30])
yield accepted()
tip(84)
block(87, spend=out[30])
yield rejected()
save_spendable_output()
block(88, spend=out[31])
yield accepted()
save_spendable_output()
# trying to spend the OP_RETURN output is rejected
block("89a", spend=out[32])
tx = create_tx(tx1, 0, 0, CScript([OP_TRUE]))
update_block("89a", [tx])
yield rejected()
# Test re-org of a week's worth of blocks (1088 blocks)
# This test takes a minute or two and can be accomplished in memory
#
if self.options.runbarelyexpensive:
tip(88)
LARGE_REORG_SIZE = 1088
test1 = TestInstance(sync_every_block=False)
spend=out[32]
for i in range(89, LARGE_REORG_SIZE + 89):
b = block(i, spend)
tx = CTransaction()
script_length = MAX_BLOCK_BASE_SIZE - len(b.serialize()) - 69
script_output = CScript([b'\x00' * script_length])
tx.vout.append(CTxOut(0, script_output))
tx.vin.append(CTxIn(COutPoint(b.vtx[1].sha256, 0)))
b = update_block(i, [tx])
assert_equal(len(b.serialize()), MAX_BLOCK_BASE_SIZE)
test1.blocks_and_transactions.append([self.tip, True])
save_spendable_output()
spend = get_spendable_output()
yield test1
chain1_tip = i
# now create alt chain of same length
tip(88)
test2 = TestInstance(sync_every_block=False)
for i in range(89, LARGE_REORG_SIZE + 89):
block("alt"+str(i))
test2.blocks_and_transactions.append([self.tip, False])
yield test2
# extend alt chain to trigger re-org
block("alt" + str(chain1_tip + 1))
yield accepted()
# ... and re-org back to the first chain
tip(chain1_tip)
block(chain1_tip + 1)
yield rejected()
block(chain1_tip + 2)
yield accepted()
chain1_tip += 2
if __name__ == '__main__':
FullBlockTest().main()
| 40.8 | 131 | 0.5454 |
from test_framework.test_framework import ComparisonTestFramework
from test_framework.util import *
from test_framework.comptool import TestManager, TestInstance, RejectResult
from test_framework.blocktools import *
import time
from test_framework.key import CECKey
from test_framework.script import *
from test_framework.mininode import network_thread_start
import struct
class PreviousSpendableOutput():
def __init__(self, tx = CTransaction(), n = -1):
self.tx = tx
self.n = n
# Use this class for tests that require behavior other than normal "mininode" behavior.
# For now, it is used to serialize a bloated varint (b64).
class CBrokenBlock(CBlock):
def __init__(self, header=None):
super(CBrokenBlock, self).__init__(header)
def initialize(self, base_block):
self.vtx = copy.deepcopy(base_block.vtx)
self.hashMerkleRoot = self.calc_merkle_root()
def serialize(self, with_witness=False):
r = b""
r += super(CBlock, self).serialize()
r += struct.pack("<BQ", 255, len(self.vtx))
for tx in self.vtx:
if with_witness:
r += tx.serialize_with_witness()
else:
r += tx.serialize_without_witness()
return r
def normal_serialize(self):
r = b""
r += super(CBrokenBlock, self).serialize()
return r
class FullBlockTest(ComparisonTestFramework):
# Can either run this test as 1 node with expected answers, or two and compare them.
# Change the "outcome" variable from each TestInstance object to only do the comparison.
def set_test_params(self):
self.num_nodes = 1
self.setup_clean_chain = True
self.block_heights = {}
self.coinbase_key = CECKey()
self.coinbase_key.set_secretbytes(b"horsebattery")
self.coinbase_pubkey = self.coinbase_key.get_pubkey()
self.tip = None
self.blocks = {}
def add_options(self, parser):
super().add_options(parser)
parser.add_option("--runbarelyexpensive", dest="runbarelyexpensive", default=True)
def run_test(self):
self.test = TestManager(self, self.options.tmpdir)
self.test.add_all_connections(self.nodes)
network_thread_start()
self.test.run()
def add_transactions_to_block(self, block, tx_list):
[ tx.rehash() for tx in tx_list ]
block.vtx.extend(tx_list)
# this is a little handier to use than the version in blocktools.py
def create_tx(self, spend_tx, n, value, script=CScript([OP_TRUE])):
tx = create_transaction(spend_tx, n, b"", value, script)
return tx
# sign a transaction, using the key we know about
# this signs input 0 in tx, which is assumed to be spending output n in spend_tx
def sign_tx(self, tx, spend_tx, n):
scriptPubKey = bytearray(spend_tx.vout[n].scriptPubKey)
if (scriptPubKey[0] == OP_TRUE): # an anyone-can-spend
tx.vin[0].scriptSig = CScript()
return
(sighash, err) = SignatureHash(spend_tx.vout[n].scriptPubKey, tx, 0, SIGHASH_ALL)
tx.vin[0].scriptSig = CScript([self.coinbase_key.sign(sighash) + bytes(bytearray([SIGHASH_ALL]))])
def create_and_sign_transaction(self, spend_tx, n, value, script=CScript([OP_TRUE])):
tx = self.create_tx(spend_tx, n, value, script)
self.sign_tx(tx, spend_tx, n)
tx.rehash()
return tx
def next_block(self, number, spend=None, additional_coinbase_value=0, script=CScript([OP_TRUE]), solve=True):
if self.tip == None:
base_block_hash = self.genesis_hash
block_time = int(time.time())+1
else:
base_block_hash = self.tip.sha256
block_time = self.tip.nTime + 1
# First create the coinbase
height = self.block_heights[base_block_hash] + 1
coinbase = create_coinbase(height, self.coinbase_pubkey)
coinbase.vout[0].nValue += additional_coinbase_value
coinbase.rehash()
if spend == None:
block = create_block(base_block_hash, coinbase, block_time)
block.nVersion = 0x20000000
else:
coinbase.vout[0].nValue += spend.tx.vout[spend.n].nValue - 1 # all but one satoshi to fees
coinbase.rehash()
block = create_block(base_block_hash, coinbase, block_time)
block.nVersion = 0x20000000
tx = create_transaction(spend.tx, spend.n, b"", 1, script) # spend 1 satoshi
self.sign_tx(tx, spend.tx, spend.n)
self.add_transactions_to_block(block, [tx])
block.hashMerkleRoot = block.calc_merkle_root()
if solve:
block.solve()
self.tip = block
self.block_heights[block.sha256] = height
assert number not in self.blocks
self.blocks[number] = block
return block
def get_tests(self):
self.genesis_hash = int(self.nodes[0].getbestblockhash(), 16)
self.block_heights[self.genesis_hash] = 0
spendable_outputs = []
# save the current tip so it can be spent by a later block
def save_spendable_output():
spendable_outputs.append(self.tip)
# get an output that we previously marked as spendable
def get_spendable_output():
return PreviousSpendableOutput(spendable_outputs.pop(0).vtx[0], 0)
# returns a test case that asserts that the current tip was accepted
def accepted():
return TestInstance([[self.tip, True]])
# returns a test case that asserts that the current tip was rejected
def rejected(reject = None):
if reject is None:
return TestInstance([[self.tip, False]])
else:
return TestInstance([[self.tip, reject]])
# move the tip back to a previous block
def tip(number):
self.tip = self.blocks[number]
# adds transactions to the block and updates state
def update_block(block_number, new_transactions):
block = self.blocks[block_number]
self.add_transactions_to_block(block, new_transactions)
old_sha256 = block.sha256
block.hashMerkleRoot = block.calc_merkle_root()
block.solve()
# Update the internal state just like in next_block
self.tip = block
if block.sha256 != old_sha256:
self.block_heights[block.sha256] = self.block_heights[old_sha256]
del self.block_heights[old_sha256]
self.blocks[block_number] = block
return block
# shorthand for functions
block = self.next_block
create_tx = self.create_tx
create_and_sign_tx = self.create_and_sign_transaction
# these must be updated if consensus changes
MAX_BLOCK_SIGOPS = 20000
# Create a new block
block(0)
save_spendable_output()
yield accepted()
# Now we need that block to mature so we can spend the coinbase.
test = TestInstance(sync_every_block=False)
for i in range(99):
block(5000 + i)
test.blocks_and_transactions.append([self.tip, True])
save_spendable_output()
yield test
# collect spendable outputs now to avoid cluttering the code later on
out = []
for i in range(33):
out.append(get_spendable_output())
# Start by building a couple of blocks on top (which output is spent is
# in parentheses):
# genesis -> b1 (0) -> b2 (1)
block(1, spend=out[0])
save_spendable_output()
yield accepted()
block(2, spend=out[1])
yield accepted()
save_spendable_output()
# so fork like this:
#
# genesis -> b1 (0) -> b2 (1)
# \-> b3 (1)
#
# Nothing should happen at this point. We saw b2 first so it takes priority.
tip(1)
b3 = block(3, spend=out[1])
txout_b3 = PreviousSpendableOutput(b3.vtx[1], 0)
yield rejected()
# Now we add another block to make the alternative chain longer.
#
# genesis -> b1 (0) -> b2 (1)
# \-> b3 (1) -> b4 (2)
block(4, spend=out[2])
yield accepted()
# ... and back to the first chain.
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b3 (1) -> b4 (2)
tip(2)
block(5, spend=out[2])
save_spendable_output()
yield rejected()
block(6, spend=out[3])
yield accepted()
# Try to create a fork that double-spends
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b7 (2) -> b8 (4)
# \-> b3 (1) -> b4 (2)
tip(5)
block(7, spend=out[2])
yield rejected()
block(8, spend=out[4])
yield rejected()
# Try to create a block that has too much fee
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b9 (4)
# \-> b3 (1) -> b4 (2)
tip(6)
block(9, spend=out[4], additional_coinbase_value=1)
yield rejected(RejectResult(16, b'bad-cb-amount'))
# Create a fork that ends in a block with too much fee (the one that causes the reorg)
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b10 (3) -> b11 (4)
# \-> b3 (1) -> b4 (2)
tip(5)
block(10, spend=out[3])
yield rejected()
block(11, spend=out[4], additional_coinbase_value=1)
yield rejected(RejectResult(16, b'bad-cb-amount'))
# Try again, but with a valid fork first
# genesis -> b1 (0) -> b2 (1) -> b5 (2) -> b6 (3)
# \-> b12 (3) -> b13 (4) -> b14 (5)
# (b12 added last)
# \-> b3 (1) -> b4 (2)
tip(5)
b12 = block(12, spend=out[3])
save_spendable_output()
b13 = block(13, spend=out[4])
# Deliver the block header for b12, and the block b13.
# b13 should be accepted but the tip won't advance until b12 is delivered.
yield TestInstance([[CBlockHeader(b12), None], [b13, False]])
save_spendable_output()
# Tip still can't advance because b12 is missing
block(14, spend=out[5], additional_coinbase_value=1)
yield rejected()
yield TestInstance([[b12, True, b13.sha256]])
lots_of_checksigs = CScript([OP_CHECKSIG] * (MAX_BLOCK_SIGOPS - 1))
tip(13)
block(15, spend=out[5], script=lots_of_checksigs)
yield accepted()
save_spendable_output()
too_many_checksigs = CScript([OP_CHECKSIG] * (MAX_BLOCK_SIGOPS))
block(16, spend=out[6], script=too_many_checksigs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(15)
block(17, spend=txout_b3)
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
tip(13)
block(18, spend=txout_b3)
yield rejected()
block(19, spend=out[6])
yield rejected()
tip(15)
block(20, spend=out[7])
yield rejected(RejectResult(16, b'bad-txns-premature-spend-of-coinbase'))
tip(13)
block(21, spend=out[6])
yield rejected()
block(22, spend=out[5])
yield rejected()
tip(15)
b23 = block(23, spend=out[6])
tx = CTransaction()
script_length = MAX_BLOCK_BASE_SIZE - len(b23.serialize()) - 69
script_output = CScript([b'\x00' * script_length])
tx.vout.append(CTxOut(0, script_output))
tx.vin.append(CTxIn(COutPoint(b23.vtx[1].sha256, 0)))
b23 = update_block(23, [tx])
assert_equal(len(b23.serialize()), MAX_BLOCK_BASE_SIZE)
yield accepted()
save_spendable_output()
tip(15)
b24 = block(24, spend=out[6])
script_length = MAX_BLOCK_BASE_SIZE - len(b24.serialize()) - 69
script_output = CScript([b'\x00' * (script_length+1)])
tx.vout = [CTxOut(0, script_output)]
b24 = update_block(24, [tx])
assert_equal(len(b24.serialize()), MAX_BLOCK_BASE_SIZE+1)
yield rejected(RejectResult(16, b'bad-blk-length'))
block(25, spend=out[7])
yield rejected()
tip(15)
b26 = block(26, spend=out[6])
b26.vtx[0].vin[0].scriptSig = b'\x00'
b26.vtx[0].rehash()
b26 = update_block(26, [])
yield rejected(RejectResult(16, b'bad-cb-length'))
block(27, spend=out[7])
yield rejected(False)
# Now try a too-large-coinbase script
tip(15)
b28 = block(28, spend=out[6])
b28.vtx[0].vin[0].scriptSig = b'\x00' * 101
b28.vtx[0].rehash()
b28 = update_block(28, [])
yield rejected(RejectResult(16, b'bad-cb-length'))
# Extend the b28 chain to make sure bitcoind isn't accepting b28
block(29, spend=out[7])
yield rejected(False)
tip(23)
b30 = block(30)
b30.vtx[0].vin[0].scriptSig = b'\x00' * 100
b30.vtx[0].rehash()
b30 = update_block(30, [])
yield accepted()
save_spendable_output()
lots_of_multisigs = CScript([OP_CHECKMULTISIG] * ((MAX_BLOCK_SIGOPS-1) // 20) + [OP_CHECKSIG] * 19)
b31 = block(31, spend=out[8], script=lots_of_multisigs)
assert_equal(get_legacy_sigopcount_block(b31), MAX_BLOCK_SIGOPS)
yield accepted()
save_spendable_output()
too_many_multisigs = CScript([OP_CHECKMULTISIG] * (MAX_BLOCK_SIGOPS // 20))
b32 = block(32, spend=out[9], script=too_many_multisigs)
assert_equal(get_legacy_sigopcount_block(b32), MAX_BLOCK_SIGOPS + 1)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(31)
lots_of_multisigs = CScript([OP_CHECKMULTISIGVERIFY] * ((MAX_BLOCK_SIGOPS-1) // 20) + [OP_CHECKSIG] * 19)
block(33, spend=out[9], script=lots_of_multisigs)
yield accepted()
save_spendable_output()
too_many_multisigs = CScript([OP_CHECKMULTISIGVERIFY] * (MAX_BLOCK_SIGOPS // 20))
block(34, spend=out[10], script=too_many_multisigs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(33)
lots_of_checksigs = CScript([OP_CHECKSIGVERIFY] * (MAX_BLOCK_SIGOPS - 1))
b35 = block(35, spend=out[10], script=lots_of_checksigs)
yield accepted()
save_spendable_output()
too_many_checksigs = CScript([OP_CHECKSIGVERIFY] * (MAX_BLOCK_SIGOPS))
block(36, spend=out[11], script=too_many_checksigs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(35)
b37 = block(37, spend=out[11])
txout_b37 = PreviousSpendableOutput(b37.vtx[1], 0)
tx = create_and_sign_tx(out[11].tx, out[11].n, 0)
b37 = update_block(37, [tx])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# attempt to spend b37's first non-coinbase tx, at which point b37 was still considered valid
tip(35)
block(38, spend=txout_b37)
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
tip(35)
b39 = block(39)
b39_outputs = 0
b39_sigops_per_output = 6
redeem_script = CScript([self.coinbase_pubkey] + [OP_2DUP, OP_CHECKSIGVERIFY]*5 + [OP_CHECKSIG])
redeem_script_hash = hash160(redeem_script)
p2sh_script = CScript([OP_HASH160, redeem_script_hash, OP_EQUAL])
spend = out[11]
tx = create_tx(spend.tx, spend.n, 1, p2sh_script)
tx.vout.append(CTxOut(spend.tx.vout[spend.n].nValue - 1, CScript([OP_TRUE])))
self.sign_tx(tx, spend.tx, spend.n)
tx.rehash()
b39 = update_block(39, [tx])
b39_outputs += 1
tx_new = None
tx_last = tx
total_size=len(b39.serialize())
while(total_size < MAX_BLOCK_BASE_SIZE):
tx_new = create_tx(tx_last, 1, 1, p2sh_script)
tx_new.vout.append(CTxOut(tx_last.vout[1].nValue - 1, CScript([OP_TRUE])))
tx_new.rehash()
total_size += len(tx_new.serialize())
if total_size >= MAX_BLOCK_BASE_SIZE:
break
b39.vtx.append(tx_new) # add tx to block
tx_last = tx_new
b39_outputs += 1
b39 = update_block(39, [])
yield accepted()
save_spendable_output()
# Test sigops in P2SH redeem scripts
#
# b40 creates 3333 tx's spending the 6-sigop P2SH outputs from b39 for a total of 19998 sigops.
tip(39)
b40 = block(40, spend=out[12])
sigops = get_legacy_sigopcount_block(b40)
numTxes = (MAX_BLOCK_SIGOPS - sigops) // b39_sigops_per_output
assert_equal(numTxes <= b39_outputs, True)
lastOutpoint = COutPoint(b40.vtx[1].sha256, 0)
new_txs = []
for i in range(1, numTxes+1):
tx = CTransaction()
tx.vout.append(CTxOut(1, CScript([OP_TRUE])))
tx.vin.append(CTxIn(lastOutpoint, b''))
tx.vin.append(CTxIn(COutPoint(b39.vtx[i].sha256, 0), b''))
(sighash, err) = SignatureHash(redeem_script, tx, 1, SIGHASH_ALL)
sig = self.coinbase_key.sign(sighash) + bytes(bytearray([SIGHASH_ALL]))
scriptSig = CScript([sig, redeem_script])
tx.vin[1].scriptSig = scriptSig
tx.rehash()
new_txs.append(tx)
lastOutpoint = COutPoint(tx.sha256, 0)
b40_sigops_to_fill = MAX_BLOCK_SIGOPS - (numTxes * b39_sigops_per_output + sigops) + 1
tx = CTransaction()
tx.vin.append(CTxIn(lastOutpoint, b''))
tx.vout.append(CTxOut(1, CScript([OP_CHECKSIG] * b40_sigops_to_fill)))
tx.rehash()
new_txs.append(tx)
update_block(40, new_txs)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(39)
block(41, spend=None)
update_block(41, b40.vtx[1:-1])
b41_sigops_to_fill = b40_sigops_to_fill - 1
tx = CTransaction()
tx.vin.append(CTxIn(lastOutpoint, b''))
tx.vout.append(CTxOut(1, CScript([OP_CHECKSIG] * b41_sigops_to_fill)))
tx.rehash()
update_block(41, [tx])
yield accepted()
tip(39)
block(42, spend=out[12])
yield rejected()
save_spendable_output()
block(43, spend=out[13])
yield accepted()
save_spendable_output()
# the first transaction be non-coinbase, etc. The purpose of b44 is to make sure this works.
height = self.block_heights[self.tip.sha256] + 1
coinbase = create_coinbase(height, self.coinbase_pubkey)
b44 = CBlock()
b44.nVersion = 0x20000000
b44.nTime = self.tip.nTime + 1
b44.hashPrevBlock = self.tip.sha256
b44.nBits = 0x207fffff
b44.vtx.append(coinbase)
b44.hashMerkleRoot = b44.calc_merkle_root()
b44.solve()
self.tip = b44
self.block_heights[b44.sha256] = height
self.blocks[44] = b44
yield accepted()
# A block with a non-coinbase as the first tx
non_coinbase = create_tx(out[15].tx, out[15].n, 1)
b45 = CBlock()
b45.nVersion = 0x20000000
b45.nTime = self.tip.nTime + 1
b45.hashPrevBlock = self.tip.sha256
b45.nBits = 0x207fffff
b45.vtx.append(non_coinbase)
b45.hashMerkleRoot = b45.calc_merkle_root()
b45.calc_sha256()
b45.solve()
self.block_heights[b45.sha256] = self.block_heights[self.tip.sha256]+1
self.tip = b45
self.blocks[45] = b45
yield rejected(RejectResult(16, b'bad-cb-missing'))
# A block with no txns
tip(44)
b46 = CBlock()
b46.nVersion = 0x20000000
b46.nTime = b44.nTime+1
b46.hashPrevBlock = b44.sha256
b46.nBits = 0x207fffff
b46.vtx = []
b46.hashMerkleRoot = 0
b46.solve()
self.block_heights[b46.sha256] = self.block_heights[b44.sha256]+1
self.tip = b46
assert 46 not in self.blocks
self.blocks[46] = b46
s = ser_uint256(b46.hashMerkleRoot)
yield rejected(RejectResult(16, b'bad-blk-length'))
# Apsiocoin: Temporarily disable test
# A block with invalid work
#tip(44)
#b47 = block(47, solve=False)
#target = uint256_from_compact(b47.nBits)
#while b47.scrypt256 < target: #changed > to <
# b47.nNonce += 1
# b47.rehash()
#yield rejected(RejectResult(16, b'high-hash'))
# A block with timestamp > 2 hrs in the future
tip(44)
b48 = block(48, solve=False)
b48.nTime = int(time.time()) + 60 * 60 * 3
b48.solve()
yield rejected(RejectResult(16, b'time-too-new'))
# A block with an invalid merkle hash
tip(44)
b49 = block(49)
b49.hashMerkleRoot += 1
b49.solve()
yield rejected(RejectResult(16, b'bad-txnmrklroot'))
# A block with an incorrect POW limit
tip(44)
b50 = block(50)
b50.nBits = b50.nBits - 1
b50.solve()
yield rejected(RejectResult(16, b'bad-diffbits'))
# A block with two coinbase txns
tip(44)
b51 = block(51)
cb2 = create_coinbase(51, self.coinbase_pubkey)
b51 = update_block(51, [cb2])
yield rejected(RejectResult(16, b'bad-cb-multiple'))
# A block w/ duplicate txns
# Note: txns have to be in the right position in the merkle tree to trigger this error
tip(44)
b52 = block(52, spend=out[15])
tx = create_tx(b52.vtx[1], 0, 1)
b52 = update_block(52, [tx, tx])
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
# Test block timestamps
# -> b31 (8) -> b33 (9) -> b35 (10) -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15)
# \-> b54 (15)
#
tip(43)
block(53, spend=out[14])
yield rejected() # rejected since b44 is at same height
save_spendable_output()
# invalid timestamp (b35 is 5 blocks back, so its time is MedianTimePast)
b54 = block(54, spend=out[15])
b54.nTime = b35.nTime - 1
b54.solve()
yield rejected(RejectResult(16, b'time-too-old'))
# valid timestamp
tip(53)
b55 = block(55, spend=out[15])
b55.nTime = b35.nTime
update_block(55, [])
yield accepted()
save_spendable_output()
# Test CVE-2012-2459
#
# -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57p2 (16)
# \-> b57 (16)
# \-> b56p2 (16)
# \-> b56 (16)
#
# Merkle tree malleability (CVE-2012-2459): repeating sequences of transactions in a block without
# affecting the merkle root of a block, while still invalidating it.
# See: src/consensus/merkle.h
#
# b57 has three txns: coinbase, tx, tx1. The merkle root computation will duplicate tx.
# Result: OK
#
# b56 copies b57 but duplicates tx1 and does not recalculate the block hash. So it has a valid merkle
# root but duplicate transactions.
# Result: Fails
#
# b57p2 has six transactions in its merkle tree:
# - coinbase, tx, tx1, tx2, tx3, tx4
# Merkle root calculation will duplicate as necessary.
# Result: OK.
#
# b56p2 copies b57p2 but adds both tx3 and tx4. The purpose of the test is to make sure the code catches
# duplicate txns that are not next to one another with the "bad-txns-duplicate" error (which indicates
# that the error was caught early, avoiding a DOS vulnerability.)
# b57 - a good block with 2 txs, don't submit until end
tip(55)
b57 = block(57)
tx = create_and_sign_tx(out[16].tx, out[16].n, 1)
tx1 = create_tx(tx, 0, 1)
b57 = update_block(57, [tx, tx1])
tip(55)
b56 = copy.deepcopy(b57)
self.blocks[56] = b56
assert_equal(len(b56.vtx),3)
b56 = update_block(56, [tx1])
assert_equal(b56.hash, b57.hash)
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
tip(55)
b57p2 = block("57p2")
tx = create_and_sign_tx(out[16].tx, out[16].n, 1)
tx1 = create_tx(tx, 0, 1)
tx2 = create_tx(tx1, 0, 1)
tx3 = create_tx(tx2, 0, 1)
tx4 = create_tx(tx3, 0, 1)
b57p2 = update_block("57p2", [tx, tx1, tx2, tx3, tx4])
tip(55)
b56p2 = copy.deepcopy(b57p2)
self.blocks["b56p2"] = b56p2
assert_equal(b56p2.hash, b57p2.hash)
assert_equal(len(b56p2.vtx),6)
b56p2 = update_block("b56p2", [tx3, tx4])
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
tip("57p2")
yield accepted()
tip(57)
yield rejected() #rejected because 57p2 seen first
save_spendable_output()
# Test a few invalid tx types
#
# -> b35 (10) -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> ??? (17)
#
# tx with prevout.n out of range
tip(57)
b58 = block(58, spend=out[17])
tx = CTransaction()
assert(len(out[17].tx.vout) < 42)
tx.vin.append(CTxIn(COutPoint(out[17].tx.sha256, 42), CScript([OP_TRUE]), 0xffffffff))
tx.vout.append(CTxOut(0, b""))
tx.calc_sha256()
b58 = update_block(58, [tx])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# tx with output value > input value out of range
tip(57)
b59 = block(59)
tx = create_and_sign_tx(out[17].tx, out[17].n, 51*COIN)
b59 = update_block(59, [tx])
yield rejected(RejectResult(16, b'bad-txns-in-belowout'))
# reset to good chain
tip(57)
b60 = block(60, spend=out[17])
yield accepted()
save_spendable_output()
# Test BIP30
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> b61 (18)
#
# Blocks are not allowed to contain a transaction whose id matches that of an earlier,
# not-fully-spent transaction in the same chain. To test, make identical coinbases;
# the second one should be rejected.
#
tip(60)
b61 = block(61, spend=out[18])
b61.vtx[0].vin[0].scriptSig = b60.vtx[0].vin[0].scriptSig #equalize the coinbases
b61.vtx[0].rehash()
b61 = update_block(61, [])
assert_equal(b60.vtx[0].serialize(), b61.vtx[0].serialize())
yield rejected(RejectResult(16, b'bad-txns-BIP30'))
# Test tx.isFinal is properly rejected (not an exhaustive tx.isFinal test, that should be in data-driven transaction tests)
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17)
# \-> b62 (18)
#
tip(60)
b62 = block(62)
tx = CTransaction()
tx.nLockTime = 0xffffffff #this locktime is non-final
assert(out[18].n < len(out[18].tx.vout))
tx.vin.append(CTxIn(COutPoint(out[18].tx.sha256, out[18].n))) # don't set nSequence
tx.vout.append(CTxOut(0, CScript([OP_TRUE])))
assert(tx.vin[0].nSequence < 0xffffffff)
tx.calc_sha256()
b62 = update_block(62, [tx])
yield rejected(RejectResult(16, b'bad-txns-nonfinal'))
tip(60)
b63 = block(63)
b63.vtx[0].nLockTime = 0xffffffff
b63.vtx[0].vin[0].nSequence = 0xDEADBEEF
b63.vtx[0].rehash()
b63 = update_block(63, [])
yield rejected(RejectResult(16, b'bad-txns-nonfinal'))
#
# -> b39 (11) -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18)
# \
# b64a (18)
# b64a is a bloated block (non-canonical varint)
# b64 is a good block (same as b64 but w/ canonical varint)
#
tip(60)
regular_block = block("64a", spend=out[18])
# make it a "broken_block," with non-canonical serialization
b64a = CBrokenBlock(regular_block)
b64a.initialize(regular_block)
self.blocks["64a"] = b64a
self.tip = b64a
tx = CTransaction()
# use canonical serialization to calculate size
script_length = MAX_BLOCK_BASE_SIZE - len(b64a.normal_serialize()) - 69
script_output = CScript([b'\x00' * script_length])
tx.vout.append(CTxOut(0, script_output))
tx.vin.append(CTxIn(COutPoint(b64a.vtx[1].sha256, 0)))
b64a = update_block("64a", [tx])
assert_equal(len(b64a.serialize()), MAX_BLOCK_BASE_SIZE + 8)
yield TestInstance([[self.tip, None]])
# comptool workaround: to make sure b64 is delivered, manually erase b64a from blockstore
self.test.block_store.erase(b64a.sha256)
tip(60)
b64 = CBlock(b64a)
b64.vtx = copy.deepcopy(b64a.vtx)
assert_equal(b64.hash, b64a.hash)
assert_equal(len(b64.serialize()), MAX_BLOCK_BASE_SIZE)
self.blocks[64] = b64
update_block(64, [])
yield accepted()
save_spendable_output()
# Spend an output created in the block itself
#
# -> b42 (12) -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19)
#
tip(64)
block(65)
tx1 = create_and_sign_tx(out[19].tx, out[19].n, out[19].tx.vout[0].nValue)
tx2 = create_and_sign_tx(tx1, 0, 0)
update_block(65, [tx1, tx2])
yield accepted()
save_spendable_output()
# Attempt to spend an output created later in the same block
#
# -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19)
# \-> b66 (20)
tip(65)
block(66)
tx1 = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue)
tx2 = create_and_sign_tx(tx1, 0, 1)
update_block(66, [tx2, tx1])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# Attempt to double-spend a transaction created in a block
#
# -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19)
# \-> b67 (20)
#
#
tip(65)
block(67)
tx1 = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue)
tx2 = create_and_sign_tx(tx1, 0, 1)
tx3 = create_and_sign_tx(tx1, 0, 2)
update_block(67, [tx1, tx2, tx3])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# More tests of block subsidy
#
# -> b43 (13) -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20)
# \-> b68 (20)
#
# b68 - coinbase with an extra 10 satoshis,
# creates a tx that has 9 satoshis from out[20] go to fees
# this fails because the coinbase is trying to claim 1 satoshi too much in fees
#
# b69 - coinbase with extra 10 satoshis, and a tx that gives a 10 satoshi fee
# this succeeds
#
tip(65)
block(68, additional_coinbase_value=10)
tx = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue-9)
update_block(68, [tx])
yield rejected(RejectResult(16, b'bad-cb-amount'))
tip(65)
b69 = block(69, additional_coinbase_value=10)
tx = create_and_sign_tx(out[20].tx, out[20].n, out[20].tx.vout[0].nValue-10)
update_block(69, [tx])
yield accepted()
save_spendable_output()
# Test spending the outpoint of a non-existent transaction
#
# -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20)
# \-> b70 (21)
#
tip(69)
block(70, spend=out[21])
bogus_tx = CTransaction()
bogus_tx.sha256 = uint256_from_str(b"23c70ed7c0506e9178fc1a987f40a33946d4ad4c962b5ae3a52546da53af0c5c")
tx = CTransaction()
tx.vin.append(CTxIn(COutPoint(bogus_tx.sha256, 0), b"", 0xffffffff))
tx.vout.append(CTxOut(1, b""))
update_block(70, [tx])
yield rejected(RejectResult(16, b'bad-txns-inputs-missingorspent'))
# Test accepting an invalid block which has the same hash as a valid one (via merkle tree tricks)
#
# -> b53 (14) -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20) -> b72 (21)
# \-> b71 (21)
#
# b72 is a good block.
# b71 is a copy of 72, but re-adds one of its transactions. However, it has the same hash as b71.
#
tip(69)
b72 = block(72)
tx1 = create_and_sign_tx(out[21].tx, out[21].n, 2)
tx2 = create_and_sign_tx(tx1, 0, 1)
b72 = update_block(72, [tx1, tx2]) # now tip is 72
b71 = copy.deepcopy(b72)
b71.vtx.append(tx2) # add duplicate tx2
self.block_heights[b71.sha256] = self.block_heights[b69.sha256] + 1 # b71 builds off b69
self.blocks[71] = b71
assert_equal(len(b71.vtx), 4)
assert_equal(len(b72.vtx), 3)
assert_equal(b72.sha256, b71.sha256)
tip(71)
yield rejected(RejectResult(16, b'bad-txns-duplicate'))
tip(72)
yield accepted()
save_spendable_output()
# Test some invalid scripts and MAX_BLOCK_SIGOPS
#
# -> b55 (15) -> b57 (16) -> b60 (17) -> b64 (18) -> b65 (19) -> b69 (20) -> b72 (21)
# \-> b** (22)
#
# b73 - tx with excessive sigops that are placed after an excessively large script element.
# The purpose of the test is to make sure those sigops are counted.
#
# script is a bytearray of size 20,526
#
# bytearray[0-19,998] : OP_CHECKSIG
# bytearray[19,999] : OP_PUSHDATA4
# bytearray[20,000-20,003]: 521 (max_script_element_size+1, in little-endian format)
# bytearray[20,004-20,525]: unread data (script_element)
# bytearray[20,526] : OP_CHECKSIG (this puts us over the limit)
#
tip(72)
b73 = block(73)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 1 + 5 + 1
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS - 1] = int("4e",16) # OP_PUSHDATA4
element_size = MAX_SCRIPT_ELEMENT_SIZE + 1
a[MAX_BLOCK_SIGOPS] = element_size % 256
a[MAX_BLOCK_SIGOPS+1] = element_size // 256
a[MAX_BLOCK_SIGOPS+2] = 0
a[MAX_BLOCK_SIGOPS+3] = 0
tx = create_and_sign_tx(out[22].tx, 0, 1, CScript(a))
b73 = update_block(73, [tx])
assert_equal(get_legacy_sigopcount_block(b73), MAX_BLOCK_SIGOPS+1)
yield rejected(RejectResult(16, b'bad-blk-sigops'))
# b74/75 - if we push an invalid script element, all prevous sigops are counted,
# but sigops after the element are not counted.
#
# The invalid script element is that the push_data indicates that
# there will be a large amount of data (0xffffff bytes), but we only
# provide a much smaller number. These bytes are CHECKSIGS so they would
# cause b75 to fail for excessive sigops, if those bytes were counted.
#
# b74 fails because we put MAX_BLOCK_SIGOPS+1 before the element
# b75 succeeds because we put MAX_BLOCK_SIGOPS before the element
#
#
tip(72)
b74 = block(74)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 42 # total = 20,561
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS] = 0x4e
a[MAX_BLOCK_SIGOPS+1] = 0xfe
a[MAX_BLOCK_SIGOPS+2] = 0xff
a[MAX_BLOCK_SIGOPS+3] = 0xff
a[MAX_BLOCK_SIGOPS+4] = 0xff
tx = create_and_sign_tx(out[22].tx, 0, 1, CScript(a))
b74 = update_block(74, [tx])
yield rejected(RejectResult(16, b'bad-blk-sigops'))
tip(72)
b75 = block(75)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 42
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS-1] = 0x4e
a[MAX_BLOCK_SIGOPS] = 0xff
a[MAX_BLOCK_SIGOPS+1] = 0xff
a[MAX_BLOCK_SIGOPS+2] = 0xff
a[MAX_BLOCK_SIGOPS+3] = 0xff
tx = create_and_sign_tx(out[22].tx, 0, 1, CScript(a))
b75 = update_block(75, [tx])
yield accepted()
save_spendable_output()
# Check that if we push an element filled with CHECKSIGs, they are not counted
tip(75)
b76 = block(76)
size = MAX_BLOCK_SIGOPS - 1 + MAX_SCRIPT_ELEMENT_SIZE + 1 + 5
a = bytearray([OP_CHECKSIG] * size)
a[MAX_BLOCK_SIGOPS-1] = 0x4e # PUSHDATA4, but leave the following bytes as just checksigs
tx = create_and_sign_tx(out[23].tx, 0, 1, CScript(a))
b76 = update_block(76, [tx])
yield accepted()
save_spendable_output()
# Test transaction resurrection
#
# -> b77 (24) -> b78 (25) -> b79 (26)
# \-> b80 (25) -> b81 (26) -> b82 (27)
#
# b78 creates a tx, which is spent in b79. After b82, both should be in mempool
#
# The tx'es must be unsigned and pass the node's mempool policy. It is unsigned for the
# rather obscure reason that the Python signature code does not distinguish between
# Low-S and High-S values (whereas the bitcoin code has custom code which does so);
# as a result of which, the odds are 50% that the python code will use the right
# value and the transaction will be accepted into the mempool. Until we modify the
# test framework to support low-S signing, we are out of luck.
#
# To get around this issue, we construct transactions which are not signed and which
# spend to OP_TRUE. If the standard-ness rules change, this test would need to be
# updated. (Perhaps to spend to a P2SH OP_TRUE script)
#
tip(76)
block(77)
tx77 = create_and_sign_tx(out[24].tx, out[24].n, 10*COIN)
update_block(77, [tx77])
yield accepted()
save_spendable_output()
block(78)
tx78 = create_tx(tx77, 0, 9*COIN)
update_block(78, [tx78])
yield accepted()
block(79)
tx79 = create_tx(tx78, 0, 8*COIN)
update_block(79, [tx79])
yield accepted()
# mempool should be empty
assert_equal(len(self.nodes[0].getrawmempool()), 0)
tip(77)
block(80, spend=out[25])
yield rejected()
save_spendable_output()
block(81, spend=out[26])
yield rejected() # other chain is same length
save_spendable_output()
block(82, spend=out[27])
yield accepted() # now this chain is longer, triggers re-org
save_spendable_output()
# now check that tx78 and tx79 have been put back into the peer's mempool
mempool = self.nodes[0].getrawmempool()
assert_equal(len(mempool), 2)
assert(tx78.hash in mempool)
assert(tx79.hash in mempool)
block(83)
op_codes = [OP_IF, OP_INVALIDOPCODE, OP_ELSE, OP_TRUE, OP_ENDIF]
script = CScript(op_codes)
tx1 = create_and_sign_tx(out[28].tx, out[28].n, out[28].tx.vout[0].nValue, script)
tx2 = create_and_sign_tx(tx1, 0, 0, CScript([OP_TRUE]))
tx2.vin[0].scriptSig = CScript([OP_FALSE])
tx2.rehash()
update_block(83, [tx1, tx2])
yield accepted()
save_spendable_output()
block(84)
tx1 = create_tx(out[29].tx, out[29].n, 0, CScript([OP_RETURN]))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx1.calc_sha256()
self.sign_tx(tx1, out[29].tx, out[29].n)
tx1.rehash()
tx2 = create_tx(tx1, 1, 0, CScript([OP_RETURN]))
tx2.vout.append(CTxOut(0, CScript([OP_RETURN])))
tx3 = create_tx(tx1, 2, 0, CScript([OP_RETURN]))
tx3.vout.append(CTxOut(0, CScript([OP_TRUE])))
tx4 = create_tx(tx1, 3, 0, CScript([OP_TRUE]))
tx4.vout.append(CTxOut(0, CScript([OP_RETURN])))
tx5 = create_tx(tx1, 4, 0, CScript([OP_RETURN]))
update_block(84, [tx1,tx2,tx3,tx4,tx5])
yield accepted()
save_spendable_output()
tip(83)
block(85, spend=out[29])
yield rejected()
block(86, spend=out[30])
yield accepted()
tip(84)
block(87, spend=out[30])
yield rejected()
save_spendable_output()
block(88, spend=out[31])
yield accepted()
save_spendable_output()
block("89a", spend=out[32])
tx = create_tx(tx1, 0, 0, CScript([OP_TRUE]))
update_block("89a", [tx])
yield rejected()
# This test takes a minute or two and can be accomplished in memory
#
if self.options.runbarelyexpensive:
tip(88)
LARGE_REORG_SIZE = 1088
test1 = TestInstance(sync_every_block=False)
spend=out[32]
for i in range(89, LARGE_REORG_SIZE + 89):
b = block(i, spend)
tx = CTransaction()
script_length = MAX_BLOCK_BASE_SIZE - len(b.serialize()) - 69
script_output = CScript([b'\x00' * script_length])
tx.vout.append(CTxOut(0, script_output))
tx.vin.append(CTxIn(COutPoint(b.vtx[1].sha256, 0)))
b = update_block(i, [tx])
assert_equal(len(b.serialize()), MAX_BLOCK_BASE_SIZE)
test1.blocks_and_transactions.append([self.tip, True])
save_spendable_output()
spend = get_spendable_output()
yield test1
chain1_tip = i
# now create alt chain of same length
tip(88)
test2 = TestInstance(sync_every_block=False)
for i in range(89, LARGE_REORG_SIZE + 89):
block("alt"+str(i))
test2.blocks_and_transactions.append([self.tip, False])
yield test2
# extend alt chain to trigger re-org
block("alt" + str(chain1_tip + 1))
yield accepted()
# ... and re-org back to the first chain
tip(chain1_tip)
block(chain1_tip + 1)
yield rejected()
block(chain1_tip + 2)
yield accepted()
chain1_tip += 2
if __name__ == '__main__':
FullBlockTest().main()
| true | true |
f73bf710cd9d1456b44da0f6400136e906dc7444 | 11,494 | py | Python | lib/galaxy/files/__init__.py | rhpvorderman/galaxy | 178015f8eff0b0c7a59c0d6756658f6428222837 | [
"CC-BY-3.0"
] | 47 | 2015-10-21T23:30:30.000Z | 2022-03-09T06:51:32.000Z | lib/galaxy/files/__init__.py | rhpvorderman/galaxy | 178015f8eff0b0c7a59c0d6756658f6428222837 | [
"CC-BY-3.0"
] | 20 | 2015-09-30T18:56:40.000Z | 2019-04-12T19:32:59.000Z | lib/galaxy/files/__init__.py | rhpvorderman/galaxy | 178015f8eff0b0c7a59c0d6756658f6428222837 | [
"CC-BY-3.0"
] | 35 | 2015-10-30T13:09:40.000Z | 2021-05-03T23:17:46.000Z | import logging
import os
from collections import (
defaultdict,
namedtuple,
)
from galaxy import exceptions
from galaxy.util import (
plugin_config
)
log = logging.getLogger(__name__)
FileSourcePath = namedtuple('FileSourcePath', ['file_source', 'path'])
class ConfiguredFileSources:
"""Load plugins and resolve Galaxy URIs to FileSource objects."""
def __init__(self, file_sources_config, conf_file=None, conf_dict=None, load_stock_plugins=False):
self._file_sources_config = file_sources_config
self._plugin_classes = self._file_source_plugins_dict()
file_sources = []
if conf_file is not None:
file_sources = self._load_plugins_from_file(conf_file)
elif conf_dict is not None:
plugin_source = plugin_config.plugin_source_from_dict(conf_dict)
file_sources = self._parse_plugin_source(plugin_source)
else:
file_sources = []
custom_sources_configured = len(file_sources) > 0
if load_stock_plugins:
stock_file_source_conf_dict = []
def _ensure_loaded(plugin_type):
for file_source in file_sources:
if file_source.plugin_type == plugin_type:
return
stock_file_source_conf_dict.append({'type': plugin_type})
if file_sources_config.ftp_upload_dir is not None:
_ensure_loaded('gxftp')
if file_sources_config.library_import_dir is not None:
_ensure_loaded('gximport')
if file_sources_config.user_library_import_dir is not None:
_ensure_loaded('gxuserimport')
if stock_file_source_conf_dict:
stock_plugin_source = plugin_config.plugin_source_from_dict(stock_file_source_conf_dict)
# insert at begining instead of append so FTP and library import appear
# at the top of the list (presumably the most common options). Admins can insert
# these explicitly for greater control.
file_sources = self._parse_plugin_source(stock_plugin_source) + file_sources
self._file_sources = file_sources
self.custom_sources_configured = custom_sources_configured
def _load_plugins_from_file(self, conf_file):
plugin_source = plugin_config.plugin_source_from_path(conf_file)
return self._parse_plugin_source(plugin_source)
def _file_source_plugins_dict(self):
import galaxy.files.sources
return plugin_config.plugins_dict(galaxy.files.sources, 'plugin_type')
def _parse_plugin_source(self, plugin_source):
extra_kwds = {
'file_sources_config': self._file_sources_config,
}
return plugin_config.load_plugins(
self._plugin_classes,
plugin_source,
extra_kwds,
dict_to_list_key="id",
)
def get_file_source_path(self, uri):
"""Parse uri into a FileSource object and a path relative to its base."""
if "://" not in uri:
raise exceptions.RequestParameterInvalidException(f"Invalid uri [{uri}]")
scheme, rest = uri.split("://", 1)
if scheme not in self.get_schemes():
raise exceptions.RequestParameterInvalidException(f"Unsupported URI scheme [{scheme}]")
if scheme != "gxfiles":
# prefix unused
id_prefix = None
path = rest
else:
if "/" in rest:
id_prefix, path = rest.split("/", 1)
else:
id_prefix, path = rest, "/"
file_source = self.get_file_source(id_prefix, scheme)
return FileSourcePath(file_source, path)
def validate_uri_root(self, uri, user_context):
# validate a URI against Galaxy's configuration, environment, and the current
# user. Throw appropriate exception if there is a problem with the files source
# referenced by the URI.
if uri.startswith("gxuserimport://"):
user_login = user_context.email
user_base_dir = self._file_sources_config.user_library_import_dir
if user_base_dir is None:
raise exceptions.ConfigDoesNotAllowException('The configuration of this Galaxy instance does not allow upload from user directories.')
full_import_dir = os.path.join(user_base_dir, user_login)
if not os.path.exists(full_import_dir):
raise exceptions.ObjectNotFound('Your user import directory does not exist.')
elif uri.startswith("gximport://"):
base_dir = self._file_sources_config.library_import_dir
if base_dir is None:
raise exceptions.ConfigDoesNotAllowException('The configuration of this Galaxy instance does not allow usage of import directory.')
elif uri.startswith("gxftp://"):
user_ftp_base_dir = self._file_sources_config.ftp_upload_dir
if user_ftp_base_dir is None:
raise exceptions.ConfigDoesNotAllowException('The configuration of this Galaxy instance does not allow upload from FTP directories.')
user_ftp_dir = user_context.ftp_dir
if not user_ftp_dir or not os.path.exists(user_ftp_dir):
raise exceptions.ObjectNotFound('Your FTP directory does not exist, attempting to upload files to it may cause it to be created.')
def get_file_source(self, id_prefix, scheme):
for file_source in self._file_sources:
# gxfiles uses prefix to find plugin, other scheme are assumed to have
# at most one file_source.
if scheme != file_source.get_scheme():
continue
prefix_match = scheme != "gxfiles" or file_source.get_prefix() == id_prefix
if prefix_match:
return file_source
def looks_like_uri(self, path_or_uri):
# is this string a URI this object understands how to realize
if path_or_uri.startswith("gx") and "://" in path_or_uri:
for scheme in self.get_schemes():
if path_or_uri.startswith(f"{scheme}://"):
return True
return False
def get_schemes(self):
schemes = set()
for file_source in self._file_sources:
schemes.add(file_source.get_scheme())
return schemes
def plugins_to_dict(self, for_serialization=False, user_context=None):
rval = []
for file_source in self._file_sources:
el = file_source.to_dict(for_serialization=for_serialization, user_context=user_context)
rval.append(el)
return rval
def to_dict(self, for_serialization=False, user_context=None):
return {
'file_sources': self.plugins_to_dict(for_serialization=for_serialization, user_context=user_context),
'config': self._file_sources_config.to_dict()
}
@staticmethod
def from_app_config(config):
config_file = config.file_sources_config_file
if not os.path.exists(config_file):
config_file = None
file_sources_config = ConfiguredFileSourcesConfig.from_app_config(config)
return ConfiguredFileSources(file_sources_config, config_file, load_stock_plugins=True)
@staticmethod
def from_dict(as_dict):
if as_dict is not None:
sources_as_dict = as_dict["file_sources"]
config_as_dict = as_dict["config"]
file_sources_config = ConfiguredFileSourcesConfig.from_dict(config_as_dict)
else:
sources_as_dict = []
file_sources_config = ConfiguredFileSourcesConfig()
return ConfiguredFileSources(file_sources_config, conf_dict=sources_as_dict)
class ConfiguredFileSourcesConfig:
def __init__(self, symlink_allowlist=None, library_import_dir=None, user_library_import_dir=None, ftp_upload_dir=None, ftp_upload_purge=True):
symlink_allowlist = symlink_allowlist or []
self.symlink_allowlist = symlink_allowlist
self.library_import_dir = library_import_dir
self.user_library_import_dir = user_library_import_dir
self.ftp_upload_dir = ftp_upload_dir
self.ftp_upload_purge = ftp_upload_purge
@staticmethod
def from_app_config(config):
# Formalize what we read in from config to create a more clear interface
# for this component.
kwds = {}
kwds["symlink_allowlist"] = getattr(config, "user_library_import_symlink_allowlist", [])
kwds["library_import_dir"] = getattr(config, "library_import_dir", None)
kwds["user_library_import_dir"] = getattr(config, "user_library_import_dir", None)
kwds["ftp_upload_dir"] = getattr(config, "ftp_upload_dir", None)
kwds["ftp_upload_purge"] = getattr(config, "ftp_upload_purge", True)
return ConfiguredFileSourcesConfig(**kwds)
def to_dict(self):
return {
'symlink_allowlist': self.symlink_allowlist,
'library_import_dir': self.library_import_dir,
'user_library_import_dir': self.user_library_import_dir,
'ftp_upload_dir': self.ftp_upload_dir,
'ftp_upload_purge': self.ftp_upload_purge,
}
@staticmethod
def from_dict(as_dict):
return ConfiguredFileSourcesConfig(
symlink_allowlist=as_dict['symlink_allowlist'],
library_import_dir=as_dict['library_import_dir'],
user_library_import_dir=as_dict['user_library_import_dir'],
ftp_upload_dir=as_dict['ftp_upload_dir'],
ftp_upload_purge=as_dict['ftp_upload_purge'],
)
class ProvidesUserFileSourcesUserContext:
"""Implement a FileSourcesUserContext from a Galaxy ProvidesUserContext (e.g. trans)."""
def __init__(self, trans):
self.trans = trans
@property
def email(self):
user = self.trans.user
return user and user.email
@property
def username(self):
user = self.trans.user
return user and user.username
@property
def ftp_dir(self):
return self.trans.user_ftp_dir
@property
def preferences(self):
user = self.trans.user
return user and user.extra_preferences or defaultdict(lambda: None)
@property
def role_names(self):
"""The set of role names of this user."""
user = self.trans.user
return user and set([ura.role.name for ura in user.roles])
@property
def group_names(self):
"""The set of group names to which this user belongs."""
user = self.trans.user
return user and set([ugr.group.name for ugr in user.groups])
@property
def is_admin(self):
"""Whether this user is an administrator."""
return self.trans.user_is_admin
class DictFileSourcesUserContext:
def __init__(self, **kwd):
self._kwd = kwd
@property
def email(self):
return self._kwd.get("email")
@property
def username(self):
return self._kwd.get("username")
@property
def ftp_dir(self):
return self._kwd.get("user_ftp_dir")
@property
def preferences(self):
return self._kwd.get("preferences")
@property
def role_names(self):
return self._kwd.get("role_names")
@property
def group_names(self):
return self._kwd.get("group_names")
@property
def is_admin(self):
return self._kwd.get("is_admin")
| 38.831081 | 150 | 0.66339 | import logging
import os
from collections import (
defaultdict,
namedtuple,
)
from galaxy import exceptions
from galaxy.util import (
plugin_config
)
log = logging.getLogger(__name__)
FileSourcePath = namedtuple('FileSourcePath', ['file_source', 'path'])
class ConfiguredFileSources:
def __init__(self, file_sources_config, conf_file=None, conf_dict=None, load_stock_plugins=False):
self._file_sources_config = file_sources_config
self._plugin_classes = self._file_source_plugins_dict()
file_sources = []
if conf_file is not None:
file_sources = self._load_plugins_from_file(conf_file)
elif conf_dict is not None:
plugin_source = plugin_config.plugin_source_from_dict(conf_dict)
file_sources = self._parse_plugin_source(plugin_source)
else:
file_sources = []
custom_sources_configured = len(file_sources) > 0
if load_stock_plugins:
stock_file_source_conf_dict = []
def _ensure_loaded(plugin_type):
for file_source in file_sources:
if file_source.plugin_type == plugin_type:
return
stock_file_source_conf_dict.append({'type': plugin_type})
if file_sources_config.ftp_upload_dir is not None:
_ensure_loaded('gxftp')
if file_sources_config.library_import_dir is not None:
_ensure_loaded('gximport')
if file_sources_config.user_library_import_dir is not None:
_ensure_loaded('gxuserimport')
if stock_file_source_conf_dict:
stock_plugin_source = plugin_config.plugin_source_from_dict(stock_file_source_conf_dict)
file_sources = self._parse_plugin_source(stock_plugin_source) + file_sources
self._file_sources = file_sources
self.custom_sources_configured = custom_sources_configured
def _load_plugins_from_file(self, conf_file):
plugin_source = plugin_config.plugin_source_from_path(conf_file)
return self._parse_plugin_source(plugin_source)
def _file_source_plugins_dict(self):
import galaxy.files.sources
return plugin_config.plugins_dict(galaxy.files.sources, 'plugin_type')
def _parse_plugin_source(self, plugin_source):
extra_kwds = {
'file_sources_config': self._file_sources_config,
}
return plugin_config.load_plugins(
self._plugin_classes,
plugin_source,
extra_kwds,
dict_to_list_key="id",
)
def get_file_source_path(self, uri):
if "://" not in uri:
raise exceptions.RequestParameterInvalidException(f"Invalid uri [{uri}]")
scheme, rest = uri.split("://", 1)
if scheme not in self.get_schemes():
raise exceptions.RequestParameterInvalidException(f"Unsupported URI scheme [{scheme}]")
if scheme != "gxfiles":
id_prefix = None
path = rest
else:
if "/" in rest:
id_prefix, path = rest.split("/", 1)
else:
id_prefix, path = rest, "/"
file_source = self.get_file_source(id_prefix, scheme)
return FileSourcePath(file_source, path)
def validate_uri_root(self, uri, user_context):
# user. Throw appropriate exception if there is a problem with the files source
# referenced by the URI.
if uri.startswith("gxuserimport://"):
user_login = user_context.email
user_base_dir = self._file_sources_config.user_library_import_dir
if user_base_dir is None:
raise exceptions.ConfigDoesNotAllowException('The configuration of this Galaxy instance does not allow upload from user directories.')
full_import_dir = os.path.join(user_base_dir, user_login)
if not os.path.exists(full_import_dir):
raise exceptions.ObjectNotFound('Your user import directory does not exist.')
elif uri.startswith("gximport://"):
base_dir = self._file_sources_config.library_import_dir
if base_dir is None:
raise exceptions.ConfigDoesNotAllowException('The configuration of this Galaxy instance does not allow usage of import directory.')
elif uri.startswith("gxftp://"):
user_ftp_base_dir = self._file_sources_config.ftp_upload_dir
if user_ftp_base_dir is None:
raise exceptions.ConfigDoesNotAllowException('The configuration of this Galaxy instance does not allow upload from FTP directories.')
user_ftp_dir = user_context.ftp_dir
if not user_ftp_dir or not os.path.exists(user_ftp_dir):
raise exceptions.ObjectNotFound('Your FTP directory does not exist, attempting to upload files to it may cause it to be created.')
def get_file_source(self, id_prefix, scheme):
for file_source in self._file_sources:
# gxfiles uses prefix to find plugin, other scheme are assumed to have
# at most one file_source.
if scheme != file_source.get_scheme():
continue
prefix_match = scheme != "gxfiles" or file_source.get_prefix() == id_prefix
if prefix_match:
return file_source
def looks_like_uri(self, path_or_uri):
# is this string a URI this object understands how to realize
if path_or_uri.startswith("gx") and "://" in path_or_uri:
for scheme in self.get_schemes():
if path_or_uri.startswith(f"{scheme}://"):
return True
return False
def get_schemes(self):
schemes = set()
for file_source in self._file_sources:
schemes.add(file_source.get_scheme())
return schemes
def plugins_to_dict(self, for_serialization=False, user_context=None):
rval = []
for file_source in self._file_sources:
el = file_source.to_dict(for_serialization=for_serialization, user_context=user_context)
rval.append(el)
return rval
def to_dict(self, for_serialization=False, user_context=None):
return {
'file_sources': self.plugins_to_dict(for_serialization=for_serialization, user_context=user_context),
'config': self._file_sources_config.to_dict()
}
@staticmethod
def from_app_config(config):
config_file = config.file_sources_config_file
if not os.path.exists(config_file):
config_file = None
file_sources_config = ConfiguredFileSourcesConfig.from_app_config(config)
return ConfiguredFileSources(file_sources_config, config_file, load_stock_plugins=True)
@staticmethod
def from_dict(as_dict):
if as_dict is not None:
sources_as_dict = as_dict["file_sources"]
config_as_dict = as_dict["config"]
file_sources_config = ConfiguredFileSourcesConfig.from_dict(config_as_dict)
else:
sources_as_dict = []
file_sources_config = ConfiguredFileSourcesConfig()
return ConfiguredFileSources(file_sources_config, conf_dict=sources_as_dict)
class ConfiguredFileSourcesConfig:
def __init__(self, symlink_allowlist=None, library_import_dir=None, user_library_import_dir=None, ftp_upload_dir=None, ftp_upload_purge=True):
symlink_allowlist = symlink_allowlist or []
self.symlink_allowlist = symlink_allowlist
self.library_import_dir = library_import_dir
self.user_library_import_dir = user_library_import_dir
self.ftp_upload_dir = ftp_upload_dir
self.ftp_upload_purge = ftp_upload_purge
@staticmethod
def from_app_config(config):
# Formalize what we read in from config to create a more clear interface
# for this component.
kwds = {}
kwds["symlink_allowlist"] = getattr(config, "user_library_import_symlink_allowlist", [])
kwds["library_import_dir"] = getattr(config, "library_import_dir", None)
kwds["user_library_import_dir"] = getattr(config, "user_library_import_dir", None)
kwds["ftp_upload_dir"] = getattr(config, "ftp_upload_dir", None)
kwds["ftp_upload_purge"] = getattr(config, "ftp_upload_purge", True)
return ConfiguredFileSourcesConfig(**kwds)
def to_dict(self):
return {
'symlink_allowlist': self.symlink_allowlist,
'library_import_dir': self.library_import_dir,
'user_library_import_dir': self.user_library_import_dir,
'ftp_upload_dir': self.ftp_upload_dir,
'ftp_upload_purge': self.ftp_upload_purge,
}
@staticmethod
def from_dict(as_dict):
return ConfiguredFileSourcesConfig(
symlink_allowlist=as_dict['symlink_allowlist'],
library_import_dir=as_dict['library_import_dir'],
user_library_import_dir=as_dict['user_library_import_dir'],
ftp_upload_dir=as_dict['ftp_upload_dir'],
ftp_upload_purge=as_dict['ftp_upload_purge'],
)
class ProvidesUserFileSourcesUserContext:
def __init__(self, trans):
self.trans = trans
@property
def email(self):
user = self.trans.user
return user and user.email
@property
def username(self):
user = self.trans.user
return user and user.username
@property
def ftp_dir(self):
return self.trans.user_ftp_dir
@property
def preferences(self):
user = self.trans.user
return user and user.extra_preferences or defaultdict(lambda: None)
@property
def role_names(self):
user = self.trans.user
return user and set([ura.role.name for ura in user.roles])
@property
def group_names(self):
user = self.trans.user
return user and set([ugr.group.name for ugr in user.groups])
@property
def is_admin(self):
return self.trans.user_is_admin
class DictFileSourcesUserContext:
def __init__(self, **kwd):
self._kwd = kwd
@property
def email(self):
return self._kwd.get("email")
@property
def username(self):
return self._kwd.get("username")
@property
def ftp_dir(self):
return self._kwd.get("user_ftp_dir")
@property
def preferences(self):
return self._kwd.get("preferences")
@property
def role_names(self):
return self._kwd.get("role_names")
@property
def group_names(self):
return self._kwd.get("group_names")
@property
def is_admin(self):
return self._kwd.get("is_admin")
| true | true |
f73bf8dbbefdfad519cdebbeddc7dbf2a4f595ae | 20,759 | py | Python | src/sage/rings/polynomial/pbori/gbcore.py | hsm207/sage | 020bd59ec28717bfab9af44d2231c53da1ff99f1 | [
"BSL-1.0"
] | 1 | 2021-10-18T01:24:04.000Z | 2021-10-18T01:24:04.000Z | src/sage/rings/polynomial/pbori/gbcore.py | hsm207/sage | 020bd59ec28717bfab9af44d2231c53da1ff99f1 | [
"BSL-1.0"
] | null | null | null | src/sage/rings/polynomial/pbori/gbcore.py | hsm207/sage | 020bd59ec28717bfab9af44d2231c53da1ff99f1 | [
"BSL-1.0"
] | null | null | null | from .nf import GeneratorLimitExceeded, symmGB_F2_C, symmGB_F2_python
from .PyPolyBoRi import (Monomial, Polynomial,
GroebnerStrategy, OrderCode, ll_red_nf_redsb)
from .ll import eliminate, ll_encode
from copy import copy
from itertools import chain
from .statistics import used_vars_set
from .heuristics import dense_system, gauss_on_linear
from .easy_polynomials import easy_linear_polynomials
from .interpolate import lex_groebner_basis_for_polynomial_via_variety
from .fglm import _fglm
from inspect import getfullargspec as getargspec
def get_options_from_function(f):
(argnames, varargs, varopts, defaults) = getargspec(f)[:4]
return dict(
zip(
argnames[-len(defaults):], defaults))
def filter_oldstyle_options(**options):
filtered = dict()
for key in options.keys():
newkey = key
for prefix in ['', 'use_', 'opt_allow_', 'opt_']:
newkey = newkey.replace(prefix, '')
filtered[newkey] = options[key]
return filtered
def filter_newstyle_options(func, **options):
allowed = get_options_from_function(func).keys()
filtered = dict()
for key in options.keys():
for prefix in ['', 'use_', 'opt_', 'opt_allow_']:
if prefix + key in allowed:
filtered[prefix + key] = options[key]
return filtered
def owns_one_constant(I):
"""
Determine whether I contains the constant one polynomial.
"""
return any(p.is_one() for p in I)
def want_interpolation_gb(G):
if not G:
return False
if G[0].ring().get_order_code() != OrderCode.lp:
return False
if len(G) != 1:
return False
p = Polynomial(G[0])
if p.lead_deg() <= 1:
return False
if p.set().n_nodes() > 1000:
return False
return True
def ll_is_good(I):
lex_lead = set()
for p in I:
if not p.is_zero():
m = p.lex_lead()
if m.deg() == 1:
lex_lead.add(next(iter(m.variables())).index())
if len(lex_lead) >= 0.8 * len(I):
uv = used_vars_set(I).deg() # don't use len here, which will yield 1
if len(lex_lead) > 0.9 * uv:
if uv - len(lex_lead) > 16:
return "llfirstonthefly"
else:
return "llfirst"
return False
def ll_heuristic(d):
d = copy(d)
I = d["I"]
if ("llfirstonthefly" not in d) and ("llfirst" not in d):
hint = ll_is_good(I)
if hint:
d[hint] = True
return d
def change_order_heuristic(d):
d_orig = d
d = copy(d)
I = d["I"]
if not I:
return d
switch_table = {OrderCode.lp: OrderCode.dp_asc, OrderCode.dlex: OrderCode.
dp_asc}
if "other_ordering_first" not in d:
# TODO after ll situation might look much different, so heuristic is on
# wrong place
code = next(iter(I)).ring().get_order_code()
if code in switch_table:
max_non_linear = len(I) // 2
non_linear = 0
if code == OrderCode.lp:
for p in I:
if p.lead_deg() > 1:
non_linear = non_linear + 1
if non_linear > max_non_linear:
break
if (non_linear > max_non_linear) or (code != OrderCode.lp):
other_ordering_opts = copy(d_orig)
other_ordering_opts["switch_to"] = switch_table[code]
d["other_ordering_first"] = other_ordering_opts
return d
def interpolation_gb_heuristic(d):
d = copy(d)
I = d["I"]
if not d.get("other_ordering_opts", False) and want_interpolation_gb(I):
d["interpolation_gb"] = True
d["other_ordering_first"] = False
return d
def linear_algebra_heuristic(d):
d = copy(d)
I = d["I"]
def want_la():
if not I:
return False
n_used_vars = None
bound = None
if next(iter(I)).ring().has_degree_order():
new_bound = 200
n_used_vars = used_vars_set(I, bound=new_bound).deg()
if n_used_vars < new_bound:
return True
bound = new_bound
if dense_system(I):
new_bound = 100
if not (bound and new_bound < bound):
n_used_vars = used_vars_set(I, bound=new_bound).deg()
bound = new_bound
if n_used_vars < bound:
return True
return False
if not (("faugere" in d and not d["faugere"]) or
("noro" in d and d["noro"])):
if ("faugere" in d and d["faugere"]) or want_la():
d["faugere"] = True
if "red_tail" not in d:
d["red_tail"] = False
if "selection_size" not in d:
d["selection_size"] = 10000
if "ll" not in d:
d["ll"] = True
return d
def trivial_heuristic(d):
return d
class HeuristicalFunction(object):
def __call__(self, *args, **kwds):
complete_dict = copy(kwds)
heuristic = True
try:
heuristic = complete_dict["heuristic"]
except KeyError:
pass
for (k, v) in zip(self.argnames, args):
complete_dict[k] = v
if heuristic:
complete_dict = self.heuristicFunction(complete_dict)
return self.f(**complete_dict)
def __init__(self, f, heuristic_function):
self.argnames, self.varargs, self.varopts, self.defaults = getargspec(f)[:4]
if hasattr(f, "options"):
self.options = f.options
else:
self.options = dict(zip(self.argnames[-len(self.defaults):], self.
defaults))
self.heuristicFunction = heuristic_function
self.f = f
self.__doc__ = f.__doc__
def with_heuristic(heuristic_function):
def make_wrapper(f):
wrapped = HeuristicalFunction(f, heuristic_function)
wrapped.__name__ = f.__name__
return wrapped
return make_wrapper
def clean_polys(I):
I = list(set((Polynomial(p) for p in I if not Polynomial(p).is_zero())))
return I
def clean_polys_pre(I):
return (clean_polys(I), None)
def gb_with_pre_post_option(option, pre=None,
post=None, if_not_option=tuple(),
default=False):
def make_wrapper(f):
def wrapper(I, **kwds):
prot = kwds.get("prot", False)
for o in if_not_option:
if (o in kwds and kwds[o]) or (o not in kwds and
groebner_basis.options[o]):
option_set = False
if "option_set" not in locals():
if option in kwds:
option_set = kwds[option]
else:
option_set = default
kwds = dict(((o, kwds[o]) for o in kwds if o != option))
state = None
if option_set:
if pre:
pre_args = getargspec(pre)[0]
if prot:
print("preprocessing for option:", option)
local_symbols = copy(locals())
(I, state) = pre(**dict([(k, v) for (k, v) in
local_symbols.items() if k in pre_args]))
I = f(I, **kwds)
if option_set:
if post:
post_args = getargspec(post)[0]
if prot:
print("postprocessing for option:", option)
local_symbols = copy(locals())
I = post(**{k: v for (k, v) in local_symbols.items()
if k in post_args})
return I
wrapper.__name__ = f.__name__
wrapper.__doc__ = f.__doc__
if hasattr(f, "options"):
wrapper.options = copy(f.options)
else:
wrapper.options = get_options_from_function(f)
wrapper.options[option] = default
return wrapper
return make_wrapper
def redsb_post(I, state):
if I == []:
return []
else:
return I.minimalize_and_tail_reduce()
def minsb_post(I, state):
if I == []:
return []
else:
return I.minimalize()
def invert_all(I):
return [p.map_every_x_to_x_plus_one() for p in I]
def invert_all_pre(I):
return (invert_all(I), None)
def invert_all_post(I, state):
return invert_all(I)
def llfirst_pre(I, prot):
(eliminated, llnf, I) = eliminate(I, on_the_fly=False, prot=prot)
return (I, eliminated)
def ll_constants_pre(I):
ll_res = []
while len([p for p in I if p.lex_lead_deg() == 1 and
(p + p.lex_lead()).constant()]) > 0:
I_new = []
ll = []
leads = set()
for p in I:
if p.lex_lead_deg() == 1:
l = p.lead()
if not (l in leads) and p.is_singleton_or_pair():
tail = p + l
if tail.deg() <= 0:
ll.append(p)
leads.add(l)
continue
I_new.append(p)
encoded = ll_encode(ll)
reduced = []
for p in I_new:
p = ll_red_nf_redsb(p, encoded)
if not p.is_zero():
reduced.append(p)
I = reduced
ll_res.extend(ll)
return (I, ll_res)
def variety_size_from_gb(I):
"""
TESTS::
sage: from sage.rings.polynomial.pbori.frontend import *
sage: from sage.rings.polynomial.pbori.gbcore import variety_size_from_gb
sage: r=Ring(100)
sage: x = r.variable
sage: variety_size_from_gb([])
1
sage: variety_size_from_gb([Polynomial(0, r)])
1
sage: variety_size_from_gb([Polynomial(1, r)])
0.0
sage: variety_size_from_gb([x(1)])
1.0
sage: variety_size_from_gb([x(1), x(2)])
1.0
sage: variety_size_from_gb([x(1), x(2)*x(3)])
3.0
sage: variety_size_from_gb([x(1), x(1)*x(4), x(2)*x(3)])
6.0
sage: variety_size_from_gb([x(1)*x(2), x(2)*x(3)])
5.0
sage: mons = [Monomial([r.variable(i) for i in range(100) if i!=j])\
for j in range(100)]
sage: variety_size_from_gb(mons)
1.2676506002282294e+30
"""
I = [Polynomial(p) for p in I]
I = [p for p in I if not p.is_zero()]
if len(I) == 0:
return 1
# # TODO Here's something wrong! See the example with 5 solutions.
# # (reverting for now)
# number_of_used_vars = used_vars_set(I).deg()
# leads = set([p.lead() for p in I])
# minimal_leads = BooleSet(leads).minimal_elements()
# number_of_used_vars_minimal_leads =\
# minimal_leads.vars().deg()
# standard_monomials =\
# minimal_leads.include_divisors().diff(minimal_leads)
# return standard_monomials.size_double()*\
# 2**(number_of_used_vars-number_of_used_vars_minimal_leads)
sm = Monomial(used_vars_set(I)).divisors()
for p in I:
m = p.lead()
sm = sm.diff(sm.multiples_of(m))
return sm.size_double()
def other_ordering_pre(I, option_set, kwds):
"""
TESTS::
sage: from sage.rings.polynomial.pbori.blocks import declare_ring
sage: r = declare_ring(['x0', 'x1', 'x2', 'x3', 'x4'], globals())
sage: id = [x1*x3 + x1 + x2*x3 + x3 + x4, x0*x3 + x0 + x1*x2 + x2 + 1, x1*x3 + x1*x4 + x3*x4 + x4 + 1, x0*x2 + x0*x4 + x1 + x3 + x4]
sage: from sage.rings.polynomial.pbori.gbcore import groebner_basis
sage: groebner_basis(id)
[1]
"""
if not I:
return (I, None)
main_kwds = kwds
options = option_set
old_ring = next(iter(I)).ring()
try:
new_ring = old_ring.clone(ordering=options["switch_to"])
kwds = {k: options[k] for k in options
if k not in ("other_ordering_first", "switch_to", "I")}
kwds["redsb"] = True
I = groebner_basis([new_ring(poly) for poly in I], **kwds)
variety_size = variety_size_from_gb(I)
fglm_bound = options.get("fglm_bound") or groebner_basis.options["fglm_bound"]
if variety_size < fglm_bound:
main_kwds["convert_with_fglm_from_ring"] = new_ring
main_kwds["convert_with_fglm_to_ring"] = old_ring
else:
I = [old_ring(poly) for poly in I]
finally:
pass
return (I, None)
def llfirstonthefly_pre(I, prot):
(eliminated, llnf, I) = eliminate(I, on_the_fly=True)
return (I, eliminated)
def gauss_on_linear_pre(I, prot):
return (gauss_on_linear(I), None)
def easy_linear_polynomials_pre(I):
res = []
for p in I:
res.append(p)
res.extend(easy_linear_polynomials(p))
return (list(set(res)), None)
def llfirst_post(I, state, prot, kwds):
eliminated = state
for p in I:
if p.is_one():
return [p]
else:
if len(eliminated) > 0:
I = list(chain(I, eliminated))
# redsb just for safety, as don't know how option is set
kwds = copy(kwds)
kwds.update(
dict(llfirst=False,
llfirstonthefly=False,
ll_constants=False,
deg_bound=False,
other_ordering_first=False,
eliminate_identical_variables=False, redsb=True))
I = groebner_basis(
I, **kwds
)
return I
def ll_constants_post(I, state):
eliminated = state
for p in I:
if p.is_one():
return [p]
else:
if len(eliminated) > 0:
I = list(chain(I, eliminated))
# redsb just for safety, as don't know how option is set
return I
def result_to_list_post(I, state):
return list(I)
def fix_deg_bound_post(I, state):
if isinstance(I, GroebnerStrategy):
return I.all_generators()
else:
return I
def incremental_pre(I, prot, kwds):
def sort_key(p):
p = Polynomial(p)
return (p.navigation().value(), -p.deg())
I = sorted(I, key=sort_key)
inc_sys = []
kwds = copy(kwds)
kwds['incremental'] = False
for p in I[:-1]:
inc_sys.append(p)
inc_sys = groebner_basis(inc_sys, **kwds)
if prot:
print("incrementally calculating GB, adding generator:", p)
inc_sys.append(I[:-1])
return (inc_sys, None)
def eliminate_identical_variables_pre(I, prot):
changed = True
ll_system = []
treated_linears = set()
while changed:
changed = False
rules = dict()
for p in I:
t = p + p.lead()
if p.lead_deg() == 1:
l = p.lead()
if l in treated_linears:
continue
else:
treated_linears.add(l)
if t.deg() > 0:
rules.setdefault(t, [])
leads = rules[t]
leads.append(l)
def my_sort_key(l):
return l.navigation().value()
for (t, leads) in rules.items():
if len(leads) > 1:
changed = True
leads = sorted(leads, key=my_sort_key, reverse=True)
chosen = leads[0]
for v in leads[1:]:
ll_system.append(chosen + v)
if len(ll_system) > 0:
ll_encoded = ll_encode(ll_system, reduce=True)
I = set([ll_red_nf_redsb(p, ll_encoded) for p in I])
return (I, ll_system)
@gb_with_pre_post_option("clean_arguments", pre=clean_polys_pre, default=True)
@gb_with_pre_post_option("easy_linear_polynomials",
pre=easy_linear_polynomials_pre, default=True)
@gb_with_pre_post_option("result_to_list", post=result_to_list_post,
default=True)
@with_heuristic(interpolation_gb_heuristic)
@gb_with_pre_post_option("invert", pre=invert_all_pre,
post=invert_all_post, default=False)
@gb_with_pre_post_option("gauss_on_linear", pre=gauss_on_linear_pre,
default=True)
@gb_with_pre_post_option("ll_constants", pre=ll_constants_pre,
post=ll_constants_post, default=True)
@gb_with_pre_post_option("eliminate_identical_variables",
pre=eliminate_identical_variables_pre, post=llfirst_post, default=True)
@with_heuristic(ll_heuristic)
@gb_with_pre_post_option("llfirst", if_not_option=["llfirstonthefly"],
pre=llfirst_pre, post=llfirst_post, default=False)
@gb_with_pre_post_option("llfirstonthefly", pre=llfirstonthefly_pre,
post=llfirst_post, default=False)
@gb_with_pre_post_option("incremental", pre=incremental_pre)
@with_heuristic(change_order_heuristic)
@gb_with_pre_post_option("other_ordering_first", if_not_option=[
"interpolation_gb"], pre=other_ordering_pre, default=False)
@with_heuristic(linear_algebra_heuristic)
@gb_with_pre_post_option("fix_deg_bound", if_not_option=["interpolation_gb"],
post=fix_deg_bound_post, default=True)
@gb_with_pre_post_option("minsb", post=minsb_post, if_not_option=["redsb",
"deg_bound", "interpolation_gb", "convert_with_fglm_from_ring"],
default=True)
@gb_with_pre_post_option("redsb", post=redsb_post, if_not_option=["deg_bound",
"interpolation_gb", "convert_with_fglm_from_ring"], default=True)
def groebner_basis(I, heuristic=True, unique_ideal_generator=False,
interpolation_gb=False, clean_and_restart_algorithm=False,
convert_with_fglm_from_ring=None, convert_with_fglm_to_ring=None,
fglm_bound=40000,
modified_linear_algebra=True, preprocessor=None, deg_bound=False,
implementation="Python", full_prot=False, prot=False,
draw_matrices=False, preprocess_only=False, **impl_options):
"""Computes a Groebner basis of a given ideal I, w.r.t options."""
if not I:
return I
if full_prot:
prot = True
if prot:
print("number of passed generators:", len(I))
if convert_with_fglm_from_ring is not None:
from_ring = convert_with_fglm_from_ring
to_ring = convert_with_fglm_to_ring
return _fglm(I, from_ring, to_ring)
if interpolation_gb:
first = next(iter(I))
if len(I) != 1 or first.ring().get_order_code() != OrderCode.lp:
raise ValueError
return lex_groebner_basis_for_polynomial_via_variety(first)
if deg_bound is False:
deg_bound = 100000000
I = [Polynomial(p) for p in I if not p.is_zero()]
if unique_ideal_generator and I:
prod = 1
for p in I:
prod = (p + 1) * prod
I = [prod + 1]
if implementation == "Python":
implementation = symmGB_F2_python
else:
implementation = symmGB_F2_C
# custom preprocessing
if preprocessor:
I = preprocessor(I)
if preprocess_only:
for p in I:
print(p)
import sys
sys.exit(0)
def call_algorithm(I, max_generators=None):
return implementation(I,
deg_bound=deg_bound,
full_prot=full_prot,
prot=prot,
max_generators=max_generators, draw_matrices=draw_matrices,
**filter_newstyle_options(implementation, **impl_options))
if clean_and_restart_algorithm:
for max_generators in [1000, 10000, 50000, 100000, 200000, 300000,
400000, None]:
try:
return call_algorithm(I, max_generators=max_generators)
except GeneratorLimitExceeded as e:
I = list(e.strat.all_generators())
del e.strat
if prot:
print("generator limit exceeded:", max_generators,
"restarting algorithm")
else:
return call_algorithm(I)
def build_groebner_basis_doc_string():
additional_options_from_buchberger = filter_oldstyle_options(**
get_options_from_function(symmGB_F2_python))
for k in list(additional_options_from_buchberger):
if k in groebner_basis.options:
del additional_options_from_buchberger[k]
groebner_basis.__doc__ = (groebner_basis.__doc__ + "\nOptions are:\n" +
"\n".join((k + " : " + repr(groebner_basis.options[k]) for k in
groebner_basis.options)) + """
Turn off heuristic by setting heuristic=False
Additional options come from the actual buchberger implementation.
In case of our standard Python implementation these are the following:
""" + "\n".join((k + " : " + repr(additional_options_from_buchberger[k])
for k in additional_options_from_buchberger)))
build_groebner_basis_doc_string()
def _test():
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
| 30.983582 | 141 | 0.58736 | from .nf import GeneratorLimitExceeded, symmGB_F2_C, symmGB_F2_python
from .PyPolyBoRi import (Monomial, Polynomial,
GroebnerStrategy, OrderCode, ll_red_nf_redsb)
from .ll import eliminate, ll_encode
from copy import copy
from itertools import chain
from .statistics import used_vars_set
from .heuristics import dense_system, gauss_on_linear
from .easy_polynomials import easy_linear_polynomials
from .interpolate import lex_groebner_basis_for_polynomial_via_variety
from .fglm import _fglm
from inspect import getfullargspec as getargspec
def get_options_from_function(f):
(argnames, varargs, varopts, defaults) = getargspec(f)[:4]
return dict(
zip(
argnames[-len(defaults):], defaults))
def filter_oldstyle_options(**options):
filtered = dict()
for key in options.keys():
newkey = key
for prefix in ['', 'use_', 'opt_allow_', 'opt_']:
newkey = newkey.replace(prefix, '')
filtered[newkey] = options[key]
return filtered
def filter_newstyle_options(func, **options):
allowed = get_options_from_function(func).keys()
filtered = dict()
for key in options.keys():
for prefix in ['', 'use_', 'opt_', 'opt_allow_']:
if prefix + key in allowed:
filtered[prefix + key] = options[key]
return filtered
def owns_one_constant(I):
return any(p.is_one() for p in I)
def want_interpolation_gb(G):
if not G:
return False
if G[0].ring().get_order_code() != OrderCode.lp:
return False
if len(G) != 1:
return False
p = Polynomial(G[0])
if p.lead_deg() <= 1:
return False
if p.set().n_nodes() > 1000:
return False
return True
def ll_is_good(I):
lex_lead = set()
for p in I:
if not p.is_zero():
m = p.lex_lead()
if m.deg() == 1:
lex_lead.add(next(iter(m.variables())).index())
if len(lex_lead) >= 0.8 * len(I):
uv = used_vars_set(I).deg()
if len(lex_lead) > 0.9 * uv:
if uv - len(lex_lead) > 16:
return "llfirstonthefly"
else:
return "llfirst"
return False
def ll_heuristic(d):
d = copy(d)
I = d["I"]
if ("llfirstonthefly" not in d) and ("llfirst" not in d):
hint = ll_is_good(I)
if hint:
d[hint] = True
return d
def change_order_heuristic(d):
d_orig = d
d = copy(d)
I = d["I"]
if not I:
return d
switch_table = {OrderCode.lp: OrderCode.dp_asc, OrderCode.dlex: OrderCode.
dp_asc}
if "other_ordering_first" not in d:
# TODO after ll situation might look much different, so heuristic is on
# wrong place
code = next(iter(I)).ring().get_order_code()
if code in switch_table:
max_non_linear = len(I) // 2
non_linear = 0
if code == OrderCode.lp:
for p in I:
if p.lead_deg() > 1:
non_linear = non_linear + 1
if non_linear > max_non_linear:
break
if (non_linear > max_non_linear) or (code != OrderCode.lp):
other_ordering_opts = copy(d_orig)
other_ordering_opts["switch_to"] = switch_table[code]
d["other_ordering_first"] = other_ordering_opts
return d
def interpolation_gb_heuristic(d):
d = copy(d)
I = d["I"]
if not d.get("other_ordering_opts", False) and want_interpolation_gb(I):
d["interpolation_gb"] = True
d["other_ordering_first"] = False
return d
def linear_algebra_heuristic(d):
d = copy(d)
I = d["I"]
def want_la():
if not I:
return False
n_used_vars = None
bound = None
if next(iter(I)).ring().has_degree_order():
new_bound = 200
n_used_vars = used_vars_set(I, bound=new_bound).deg()
if n_used_vars < new_bound:
return True
bound = new_bound
if dense_system(I):
new_bound = 100
if not (bound and new_bound < bound):
n_used_vars = used_vars_set(I, bound=new_bound).deg()
bound = new_bound
if n_used_vars < bound:
return True
return False
if not (("faugere" in d and not d["faugere"]) or
("noro" in d and d["noro"])):
if ("faugere" in d and d["faugere"]) or want_la():
d["faugere"] = True
if "red_tail" not in d:
d["red_tail"] = False
if "selection_size" not in d:
d["selection_size"] = 10000
if "ll" not in d:
d["ll"] = True
return d
def trivial_heuristic(d):
return d
class HeuristicalFunction(object):
def __call__(self, *args, **kwds):
complete_dict = copy(kwds)
heuristic = True
try:
heuristic = complete_dict["heuristic"]
except KeyError:
pass
for (k, v) in zip(self.argnames, args):
complete_dict[k] = v
if heuristic:
complete_dict = self.heuristicFunction(complete_dict)
return self.f(**complete_dict)
def __init__(self, f, heuristic_function):
self.argnames, self.varargs, self.varopts, self.defaults = getargspec(f)[:4]
if hasattr(f, "options"):
self.options = f.options
else:
self.options = dict(zip(self.argnames[-len(self.defaults):], self.
defaults))
self.heuristicFunction = heuristic_function
self.f = f
self.__doc__ = f.__doc__
def with_heuristic(heuristic_function):
def make_wrapper(f):
wrapped = HeuristicalFunction(f, heuristic_function)
wrapped.__name__ = f.__name__
return wrapped
return make_wrapper
def clean_polys(I):
I = list(set((Polynomial(p) for p in I if not Polynomial(p).is_zero())))
return I
def clean_polys_pre(I):
return (clean_polys(I), None)
def gb_with_pre_post_option(option, pre=None,
post=None, if_not_option=tuple(),
default=False):
def make_wrapper(f):
def wrapper(I, **kwds):
prot = kwds.get("prot", False)
for o in if_not_option:
if (o in kwds and kwds[o]) or (o not in kwds and
groebner_basis.options[o]):
option_set = False
if "option_set" not in locals():
if option in kwds:
option_set = kwds[option]
else:
option_set = default
kwds = dict(((o, kwds[o]) for o in kwds if o != option))
state = None
if option_set:
if pre:
pre_args = getargspec(pre)[0]
if prot:
print("preprocessing for option:", option)
local_symbols = copy(locals())
(I, state) = pre(**dict([(k, v) for (k, v) in
local_symbols.items() if k in pre_args]))
I = f(I, **kwds)
if option_set:
if post:
post_args = getargspec(post)[0]
if prot:
print("postprocessing for option:", option)
local_symbols = copy(locals())
I = post(**{k: v for (k, v) in local_symbols.items()
if k in post_args})
return I
wrapper.__name__ = f.__name__
wrapper.__doc__ = f.__doc__
if hasattr(f, "options"):
wrapper.options = copy(f.options)
else:
wrapper.options = get_options_from_function(f)
wrapper.options[option] = default
return wrapper
return make_wrapper
def redsb_post(I, state):
if I == []:
return []
else:
return I.minimalize_and_tail_reduce()
def minsb_post(I, state):
if I == []:
return []
else:
return I.minimalize()
def invert_all(I):
return [p.map_every_x_to_x_plus_one() for p in I]
def invert_all_pre(I):
return (invert_all(I), None)
def invert_all_post(I, state):
return invert_all(I)
def llfirst_pre(I, prot):
(eliminated, llnf, I) = eliminate(I, on_the_fly=False, prot=prot)
return (I, eliminated)
def ll_constants_pre(I):
ll_res = []
while len([p for p in I if p.lex_lead_deg() == 1 and
(p + p.lex_lead()).constant()]) > 0:
I_new = []
ll = []
leads = set()
for p in I:
if p.lex_lead_deg() == 1:
l = p.lead()
if not (l in leads) and p.is_singleton_or_pair():
tail = p + l
if tail.deg() <= 0:
ll.append(p)
leads.add(l)
continue
I_new.append(p)
encoded = ll_encode(ll)
reduced = []
for p in I_new:
p = ll_red_nf_redsb(p, encoded)
if not p.is_zero():
reduced.append(p)
I = reduced
ll_res.extend(ll)
return (I, ll_res)
def variety_size_from_gb(I):
I = [Polynomial(p) for p in I]
I = [p for p in I if not p.is_zero()]
if len(I) == 0:
return 1
# # TODO Here's something wrong! See the example with 5 solutions.
onomial(used_vars_set(I)).divisors()
for p in I:
m = p.lead()
sm = sm.diff(sm.multiples_of(m))
return sm.size_double()
def other_ordering_pre(I, option_set, kwds):
if not I:
return (I, None)
main_kwds = kwds
options = option_set
old_ring = next(iter(I)).ring()
try:
new_ring = old_ring.clone(ordering=options["switch_to"])
kwds = {k: options[k] for k in options
if k not in ("other_ordering_first", "switch_to", "I")}
kwds["redsb"] = True
I = groebner_basis([new_ring(poly) for poly in I], **kwds)
variety_size = variety_size_from_gb(I)
fglm_bound = options.get("fglm_bound") or groebner_basis.options["fglm_bound"]
if variety_size < fglm_bound:
main_kwds["convert_with_fglm_from_ring"] = new_ring
main_kwds["convert_with_fglm_to_ring"] = old_ring
else:
I = [old_ring(poly) for poly in I]
finally:
pass
return (I, None)
def llfirstonthefly_pre(I, prot):
(eliminated, llnf, I) = eliminate(I, on_the_fly=True)
return (I, eliminated)
def gauss_on_linear_pre(I, prot):
return (gauss_on_linear(I), None)
def easy_linear_polynomials_pre(I):
res = []
for p in I:
res.append(p)
res.extend(easy_linear_polynomials(p))
return (list(set(res)), None)
def llfirst_post(I, state, prot, kwds):
eliminated = state
for p in I:
if p.is_one():
return [p]
else:
if len(eliminated) > 0:
I = list(chain(I, eliminated))
kwds = copy(kwds)
kwds.update(
dict(llfirst=False,
llfirstonthefly=False,
ll_constants=False,
deg_bound=False,
other_ordering_first=False,
eliminate_identical_variables=False, redsb=True))
I = groebner_basis(
I, **kwds
)
return I
def ll_constants_post(I, state):
eliminated = state
for p in I:
if p.is_one():
return [p]
else:
if len(eliminated) > 0:
I = list(chain(I, eliminated))
# redsb just for safety, as don't know how option is set
return I
def result_to_list_post(I, state):
return list(I)
def fix_deg_bound_post(I, state):
if isinstance(I, GroebnerStrategy):
return I.all_generators()
else:
return I
def incremental_pre(I, prot, kwds):
def sort_key(p):
p = Polynomial(p)
return (p.navigation().value(), -p.deg())
I = sorted(I, key=sort_key)
inc_sys = []
kwds = copy(kwds)
kwds['incremental'] = False
for p in I[:-1]:
inc_sys.append(p)
inc_sys = groebner_basis(inc_sys, **kwds)
if prot:
print("incrementally calculating GB, adding generator:", p)
inc_sys.append(I[:-1])
return (inc_sys, None)
def eliminate_identical_variables_pre(I, prot):
changed = True
ll_system = []
treated_linears = set()
while changed:
changed = False
rules = dict()
for p in I:
t = p + p.lead()
if p.lead_deg() == 1:
l = p.lead()
if l in treated_linears:
continue
else:
treated_linears.add(l)
if t.deg() > 0:
rules.setdefault(t, [])
leads = rules[t]
leads.append(l)
def my_sort_key(l):
return l.navigation().value()
for (t, leads) in rules.items():
if len(leads) > 1:
changed = True
leads = sorted(leads, key=my_sort_key, reverse=True)
chosen = leads[0]
for v in leads[1:]:
ll_system.append(chosen + v)
if len(ll_system) > 0:
ll_encoded = ll_encode(ll_system, reduce=True)
I = set([ll_red_nf_redsb(p, ll_encoded) for p in I])
return (I, ll_system)
@gb_with_pre_post_option("clean_arguments", pre=clean_polys_pre, default=True)
@gb_with_pre_post_option("easy_linear_polynomials",
pre=easy_linear_polynomials_pre, default=True)
@gb_with_pre_post_option("result_to_list", post=result_to_list_post,
default=True)
@with_heuristic(interpolation_gb_heuristic)
@gb_with_pre_post_option("invert", pre=invert_all_pre,
post=invert_all_post, default=False)
@gb_with_pre_post_option("gauss_on_linear", pre=gauss_on_linear_pre,
default=True)
@gb_with_pre_post_option("ll_constants", pre=ll_constants_pre,
post=ll_constants_post, default=True)
@gb_with_pre_post_option("eliminate_identical_variables",
pre=eliminate_identical_variables_pre, post=llfirst_post, default=True)
@with_heuristic(ll_heuristic)
@gb_with_pre_post_option("llfirst", if_not_option=["llfirstonthefly"],
pre=llfirst_pre, post=llfirst_post, default=False)
@gb_with_pre_post_option("llfirstonthefly", pre=llfirstonthefly_pre,
post=llfirst_post, default=False)
@gb_with_pre_post_option("incremental", pre=incremental_pre)
@with_heuristic(change_order_heuristic)
@gb_with_pre_post_option("other_ordering_first", if_not_option=[
"interpolation_gb"], pre=other_ordering_pre, default=False)
@with_heuristic(linear_algebra_heuristic)
@gb_with_pre_post_option("fix_deg_bound", if_not_option=["interpolation_gb"],
post=fix_deg_bound_post, default=True)
@gb_with_pre_post_option("minsb", post=minsb_post, if_not_option=["redsb",
"deg_bound", "interpolation_gb", "convert_with_fglm_from_ring"],
default=True)
@gb_with_pre_post_option("redsb", post=redsb_post, if_not_option=["deg_bound",
"interpolation_gb", "convert_with_fglm_from_ring"], default=True)
def groebner_basis(I, heuristic=True, unique_ideal_generator=False,
interpolation_gb=False, clean_and_restart_algorithm=False,
convert_with_fglm_from_ring=None, convert_with_fglm_to_ring=None,
fglm_bound=40000,
modified_linear_algebra=True, preprocessor=None, deg_bound=False,
implementation="Python", full_prot=False, prot=False,
draw_matrices=False, preprocess_only=False, **impl_options):
if not I:
return I
if full_prot:
prot = True
if prot:
print("number of passed generators:", len(I))
if convert_with_fglm_from_ring is not None:
from_ring = convert_with_fglm_from_ring
to_ring = convert_with_fglm_to_ring
return _fglm(I, from_ring, to_ring)
if interpolation_gb:
first = next(iter(I))
if len(I) != 1 or first.ring().get_order_code() != OrderCode.lp:
raise ValueError
return lex_groebner_basis_for_polynomial_via_variety(first)
if deg_bound is False:
deg_bound = 100000000
I = [Polynomial(p) for p in I if not p.is_zero()]
if unique_ideal_generator and I:
prod = 1
for p in I:
prod = (p + 1) * prod
I = [prod + 1]
if implementation == "Python":
implementation = symmGB_F2_python
else:
implementation = symmGB_F2_C
if preprocessor:
I = preprocessor(I)
if preprocess_only:
for p in I:
print(p)
import sys
sys.exit(0)
def call_algorithm(I, max_generators=None):
return implementation(I,
deg_bound=deg_bound,
full_prot=full_prot,
prot=prot,
max_generators=max_generators, draw_matrices=draw_matrices,
**filter_newstyle_options(implementation, **impl_options))
if clean_and_restart_algorithm:
for max_generators in [1000, 10000, 50000, 100000, 200000, 300000,
400000, None]:
try:
return call_algorithm(I, max_generators=max_generators)
except GeneratorLimitExceeded as e:
I = list(e.strat.all_generators())
del e.strat
if prot:
print("generator limit exceeded:", max_generators,
"restarting algorithm")
else:
return call_algorithm(I)
def build_groebner_basis_doc_string():
additional_options_from_buchberger = filter_oldstyle_options(**
get_options_from_function(symmGB_F2_python))
for k in list(additional_options_from_buchberger):
if k in groebner_basis.options:
del additional_options_from_buchberger[k]
groebner_basis.__doc__ = (groebner_basis.__doc__ + "\nOptions are:\n" +
"\n".join((k + " : " + repr(groebner_basis.options[k]) for k in
groebner_basis.options)) + """
Turn off heuristic by setting heuristic=False
Additional options come from the actual buchberger implementation.
In case of our standard Python implementation these are the following:
""" + "\n".join((k + " : " + repr(additional_options_from_buchberger[k])
for k in additional_options_from_buchberger)))
build_groebner_basis_doc_string()
def _test():
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
| true | true |
f73bfaa83b6b251ff6deddf45ecfca51a29d7b1e | 289 | py | Python | admin_helper/urls.py | truetug/django-admin-helper | a760725610243dedd36c903d4c7d30141e241890 | [
"MIT"
] | null | null | null | admin_helper/urls.py | truetug/django-admin-helper | a760725610243dedd36c903d4c7d30141e241890 | [
"MIT"
] | null | null | null | admin_helper/urls.py | truetug/django-admin-helper | a760725610243dedd36c903d4c7d30141e241890 | [
"MIT"
] | null | null | null | # encoding: utf-8
from django.conf.urls import patterns, url
from admin_helper.views import PopupView, SuggestView
urlpatterns = patterns(
'admin_helper.views',
url(r'^suggest/$', SuggestView.as_view(), name='suggest'),
url(r'^popup/$', PopupView.as_view(), name='popup')
)
| 24.083333 | 62 | 0.705882 |
from django.conf.urls import patterns, url
from admin_helper.views import PopupView, SuggestView
urlpatterns = patterns(
'admin_helper.views',
url(r'^suggest/$', SuggestView.as_view(), name='suggest'),
url(r'^popup/$', PopupView.as_view(), name='popup')
)
| true | true |
f73bfb19701981ec4b479660a9438d906fc3faea | 1,137 | py | Python | pandasread_csv_addheader.py | SecTraversl/python_pandas_Tools | 4b818c993e1587c150d8f94e0c8c6437516deee4 | [
"MIT"
] | null | null | null | pandasread_csv_addheader.py | SecTraversl/python_pandas_Tools | 4b818c993e1587c150d8f94e0c8c6437516deee4 | [
"MIT"
] | null | null | null | pandasread_csv_addheader.py | SecTraversl/python_pandas_Tools | 4b818c993e1587c150d8f94e0c8c6437516deee4 | [
"MIT"
] | null | null | null | # %%
#######################################
def pandasread_csv_addheader(csv_file: str, addheader: list):
"""Returns a pandas dataframe from the given .csv file. Assumes there is no header in the .csv and requires a header to be given as an argument to the 'addheader'.
Example:
>>> myheader = ['NAME','AGE','JOB','DEPARTMENT','PAY']\n
>>> pandasread_csv_addheader('test.csv', addheader=myheader)\n
NAME AGE JOB DEPARTMENT PAY\n
0 bob 21 janitor sanitization team 2\n
1 alice 22 secretary admin team 3\n
2 chuck 23 plumber construction team 4\n
Reference:
https://stackoverflow.com/questions/36828348/pandas-read-csv-reading-a-csv-file-with-a-missing-header-element
Args:
csv_file (str): Reference an existing .csv file.
addheader (list): Reference the header you want to use for the columns.
Returns:
pandas.core.frame.DataFrame: Returns a pandas dataframe.
"""
import pandas
df = pandas.read_csv(csv_file, header=None, names=addheader)
return df
| 40.607143 | 168 | 0.614776 | true | true | |
f73bfb9915a9103181b4cb12b36cafe4c81fcb2b | 1,803 | py | Python | test.py | hreshtaksensei/forexpy | 706f9f9ba7dd6ec83edd268b1b287d96db59b2cf | [
"MIT"
] | 1 | 2020-03-21T03:19:36.000Z | 2020-03-21T03:19:36.000Z | test.py | hreshtaksensei/forexpy | 706f9f9ba7dd6ec83edd268b1b287d96db59b2cf | [
"MIT"
] | null | null | null | test.py | hreshtaksensei/forexpy | 706f9f9ba7dd6ec83edd268b1b287d96db59b2cf | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
"""Usage:
test.py FILE
test.py -h --help
"""
import sys
import os.path
from docopt import docopt
from backtest.oanda_backtest import OandaBacktest
from logic.strategy import Strategy
from settings import CANDLES_MINUTES, MAX_PERCENTAGE_ACCOUNT_AT_RISK,\
STOP_LOSS, TRAILING_PERIOD, TAKE_PROFIT, PLOT_RESULTS
from util.plot import Strategyplot
def plot_results(plot_data):
if not plot_data:
return
splot = Strategyplot(plot_data, 2)
splot.plot("RawPrice", 0, "m-")
splot.plot("Sell", 0, "ro")
splot.plot("Buy", 0, "g^")
splot.plot("Close", 0, "b*")
splot.plot("StopLoss", 0, "_")
#splot.plot("TrailingStop", 0, "g-")
splot.plot("TakeProfit", 0, "g-")
#splot.plot("short", 0, "r--")
#splot.plot("medium", 0, "g--")
#splot.plot("long", 0, "b--")
#splot.plot("NetWorth", 1, "r-")
splot.show()
def main(argv):
arguments = docopt(
__doc__, argv, help=True, version=None, options_first=False)
if os.path.isfile(arguments['FILE']) is not True:
print('File not found')
return
oanda_backtest = OandaBacktest(arguments['FILE'])
strategy = Strategy(
oanda_backtest,
CANDLES_MINUTES,
email=None,
risk=MAX_PERCENTAGE_ACCOUNT_AT_RISK,
stoploss=STOP_LOSS,
trailing_period=TRAILING_PERIOD,
take_profit=TAKE_PROFIT)
print('Starting backtest on', argv[0])
strategy.Start()
while oanda_backtest.is_running():
oanda_backtest.update_subscribers()
if PLOT_RESULTS:
print('plotting results...')
plot_data = oanda_backtest.get_plot_data()
plot_results(plot_data)
if __name__ == "__main__":
try:
main(sys.argv[1:])
except KeyboardInterrupt:
sys.exit(0)
| 25.394366 | 70 | 0.645036 |
import sys
import os.path
from docopt import docopt
from backtest.oanda_backtest import OandaBacktest
from logic.strategy import Strategy
from settings import CANDLES_MINUTES, MAX_PERCENTAGE_ACCOUNT_AT_RISK,\
STOP_LOSS, TRAILING_PERIOD, TAKE_PROFIT, PLOT_RESULTS
from util.plot import Strategyplot
def plot_results(plot_data):
if not plot_data:
return
splot = Strategyplot(plot_data, 2)
splot.plot("RawPrice", 0, "m-")
splot.plot("Sell", 0, "ro")
splot.plot("Buy", 0, "g^")
splot.plot("Close", 0, "b*")
splot.plot("StopLoss", 0, "_")
splot.plot("TakeProfit", 0, "g-")
splot.show()
def main(argv):
arguments = docopt(
__doc__, argv, help=True, version=None, options_first=False)
if os.path.isfile(arguments['FILE']) is not True:
print('File not found')
return
oanda_backtest = OandaBacktest(arguments['FILE'])
strategy = Strategy(
oanda_backtest,
CANDLES_MINUTES,
email=None,
risk=MAX_PERCENTAGE_ACCOUNT_AT_RISK,
stoploss=STOP_LOSS,
trailing_period=TRAILING_PERIOD,
take_profit=TAKE_PROFIT)
print('Starting backtest on', argv[0])
strategy.Start()
while oanda_backtest.is_running():
oanda_backtest.update_subscribers()
if PLOT_RESULTS:
print('plotting results...')
plot_data = oanda_backtest.get_plot_data()
plot_results(plot_data)
if __name__ == "__main__":
try:
main(sys.argv[1:])
except KeyboardInterrupt:
sys.exit(0)
| true | true |
f73bfce54e54d377a8a966317f99d18fbadd3fa0 | 2,839 | py | Python | generative_models/lstm_hc/distribution_learner.py | MorganCThomas/MolScore | b12b7b5539bb3211982fc7a1b5938c0f383a05c0 | [
"MIT"
] | 28 | 2020-12-11T22:10:16.000Z | 2022-02-25T05:00:51.000Z | generative_models/lstm_hc/distribution_learner.py | MorganCThomas/MolScore | b12b7b5539bb3211982fc7a1b5938c0f383a05c0 | [
"MIT"
] | 3 | 2021-08-31T22:50:41.000Z | 2021-11-04T15:41:01.000Z | generative_models/lstm_hc/distribution_learner.py | MorganCThomas/MolScore | b12b7b5539bb3211982fc7a1b5938c0f383a05c0 | [
"MIT"
] | 9 | 2021-03-03T12:10:10.000Z | 2022-02-15T06:53:11.000Z | """
Adapted from guacamol_baselines https://github.com/BenevolentAI/guacamol_baselines
"""
import logging
from typing import List
import torch
#from guacamol.distribution_matching_generator import DistributionMatchingGenerator
from model import SmilesRnn
from trainer import SmilesRnnTrainer
from utils import get_tensor_dataset, load_smiles_from_list, set_random_seed
from smiles_char_dict import SmilesCharDictionary
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
class SmilesRnnDistributionLearner:
def __init__(self, output_dir: str, n_epochs=10, hidden_size=512, n_layers=3,
max_len=100, batch_size=64, rnn_dropout=0.2, lr=1e-3, valid_every=100) -> None:
self.n_epochs = n_epochs
self.output_dir = output_dir
self.hidden_size = hidden_size
self.n_layers = n_layers
self.max_len = max_len
self.batch_size = batch_size
self.rnn_dropout = rnn_dropout
self.lr = lr
self.valid_every = valid_every
self.print_every = 10
self.seed = 42
def train(self, training_set: List[str], validation_set: List[str]):# -> DistributionMatchingGenerator:
# GPU if available
cuda_available = torch.cuda.is_available()
device_str = 'cuda' if cuda_available else 'cpu'
device = torch.device(device_str)
logger.info(f'CUDA enabled:\t{cuda_available}')
set_random_seed(self.seed, device)
# load data
train_seqs, _ = load_smiles_from_list(training_set, self.max_len)
valid_seqs, _ = load_smiles_from_list(validation_set, self.max_len)
train_set = get_tensor_dataset(train_seqs)
test_set = get_tensor_dataset(valid_seqs)
sd = SmilesCharDictionary()
n_characters = sd.get_char_num()
# build network
smiles_model = SmilesRnn(input_size=n_characters,
hidden_size=self.hidden_size,
output_size=n_characters,
n_layers=self.n_layers,
rnn_dropout=self.rnn_dropout)
# wire network for training
optimizer = torch.optim.Adam(smiles_model.parameters(), lr=self.lr)
criterion = torch.nn.CrossEntropyLoss(ignore_index=sd.pad_idx)
trainer = SmilesRnnTrainer(model=smiles_model,
criteria=[criterion],
optimizer=optimizer,
device=device,
log_dir=self.output_dir)
trainer.fit(train_set, test_set,
batch_size=self.batch_size,
print_every=self.print_every,
valid_every=self.valid_every,
n_epochs=self.n_epochs)
| 37.355263 | 107 | 0.638253 |
import logging
from typing import List
import torch
from model import SmilesRnn
from trainer import SmilesRnnTrainer
from utils import get_tensor_dataset, load_smiles_from_list, set_random_seed
from smiles_char_dict import SmilesCharDictionary
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
class SmilesRnnDistributionLearner:
def __init__(self, output_dir: str, n_epochs=10, hidden_size=512, n_layers=3,
max_len=100, batch_size=64, rnn_dropout=0.2, lr=1e-3, valid_every=100) -> None:
self.n_epochs = n_epochs
self.output_dir = output_dir
self.hidden_size = hidden_size
self.n_layers = n_layers
self.max_len = max_len
self.batch_size = batch_size
self.rnn_dropout = rnn_dropout
self.lr = lr
self.valid_every = valid_every
self.print_every = 10
self.seed = 42
def train(self, training_set: List[str], validation_set: List[str]):
cuda_available = torch.cuda.is_available()
device_str = 'cuda' if cuda_available else 'cpu'
device = torch.device(device_str)
logger.info(f'CUDA enabled:\t{cuda_available}')
set_random_seed(self.seed, device)
train_seqs, _ = load_smiles_from_list(training_set, self.max_len)
valid_seqs, _ = load_smiles_from_list(validation_set, self.max_len)
train_set = get_tensor_dataset(train_seqs)
test_set = get_tensor_dataset(valid_seqs)
sd = SmilesCharDictionary()
n_characters = sd.get_char_num()
smiles_model = SmilesRnn(input_size=n_characters,
hidden_size=self.hidden_size,
output_size=n_characters,
n_layers=self.n_layers,
rnn_dropout=self.rnn_dropout)
optimizer = torch.optim.Adam(smiles_model.parameters(), lr=self.lr)
criterion = torch.nn.CrossEntropyLoss(ignore_index=sd.pad_idx)
trainer = SmilesRnnTrainer(model=smiles_model,
criteria=[criterion],
optimizer=optimizer,
device=device,
log_dir=self.output_dir)
trainer.fit(train_set, test_set,
batch_size=self.batch_size,
print_every=self.print_every,
valid_every=self.valid_every,
n_epochs=self.n_epochs)
| true | true |
f73bfdd51aa47da6aa7a3fd44829b3bad58f6c35 | 780 | py | Python | db/migrations/0023_auto_20210208_0838.py | matchd-ch/matchd-backend | 84be4aab1b4708cae50a8988301b15df877c8db0 | [
"Apache-2.0"
] | 1 | 2022-03-03T09:55:57.000Z | 2022-03-03T09:55:57.000Z | db/migrations/0023_auto_20210208_0838.py | matchd-ch/matchd-backend | 84be4aab1b4708cae50a8988301b15df877c8db0 | [
"Apache-2.0"
] | 7 | 2022-02-09T10:44:53.000Z | 2022-03-28T03:29:43.000Z | db/migrations/0023_auto_20210208_0838.py | matchd-ch/matchd-backend | 84be4aab1b4708cae50a8988301b15df877c8db0 | [
"Apache-2.0"
] | null | null | null | # Generated by Django 3.1.5 on 2021-02-08 08:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('db', '0022_student_nickname'),
]
operations = [
migrations.AddField(
model_name='student',
name='field_of_study',
field=models.CharField(default='', max_length=255),
preserve_default=False,
),
migrations.AddField(
model_name='student',
name='graduation',
field=models.DateField(blank=True, null=True),
),
migrations.AddField(
model_name='student',
name='school_name',
field=models.CharField(blank=True, max_length=255, null=True),
),
]
| 26 | 74 | 0.571795 |
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('db', '0022_student_nickname'),
]
operations = [
migrations.AddField(
model_name='student',
name='field_of_study',
field=models.CharField(default='', max_length=255),
preserve_default=False,
),
migrations.AddField(
model_name='student',
name='graduation',
field=models.DateField(blank=True, null=True),
),
migrations.AddField(
model_name='student',
name='school_name',
field=models.CharField(blank=True, max_length=255, null=True),
),
]
| true | true |
f73bfe76a3fb3b909b501dcb198eb297cef9e9fc | 5,979 | py | Python | vulnerabilities/tests/test_npm.py | InLaw/vulnerablecode | e93154ce15f577430dda18cabd1feb1dabc7230a | [
"Apache-2.0"
] | null | null | null | vulnerabilities/tests/test_npm.py | InLaw/vulnerablecode | e93154ce15f577430dda18cabd1feb1dabc7230a | [
"Apache-2.0"
] | null | null | null | vulnerabilities/tests/test_npm.py | InLaw/vulnerablecode | e93154ce15f577430dda18cabd1feb1dabc7230a | [
"Apache-2.0"
] | null | null | null | # Author: Navonil Das (@NavonilDas)
# Copyright (c) 2017 nexB Inc. and others. All rights reserved.
# http://nexb.com and https://github.com/nexB/vulnerablecode/
# The VulnerableCode software is licensed under the Apache License version 2.0.
# Data generated with VulnerableCode require an acknowledgment.
#
# You may not use this software except in compliance with the License.
# You may obtain a copy of the License at: http://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.
#
# When you publish or redistribute any data created with VulnerableCode or any VulnerableCode
# derivative work, you must accompany this data with the following acknowledgment:
#
# Generated with VulnerableCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES
# OR CONDITIONS OF ANY KIND, either express or implied. No content created from
# VulnerableCode should be considered or used as legal advice. Consult an Attorney
# for any legal advice.
# VulnerableCode is a free software code scanning tool from nexB Inc. and others.
# Visit https://github.com/nexB/vulnerablecode/ for support and download.
import json
import os
import shutil
import tempfile
from unittest.mock import patch
import zipfile
from django.test import TestCase
from vulnerabilities import models
from vulnerabilities.import_runner import ImportRunner
from vulnerabilities.package_managers import NpmVersionAPI
from vulnerabilities.importers.npm import categorize_versions
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
TEST_DATA = os.path.join(BASE_DIR, "test_data/")
MOCK_VERSION_API = NpmVersionAPI(
cache={
"jquery": {"3.4", "3.8"},
"kerberos": {"0.5.8", "1.2"},
"@hapi/subtext": {"3.7", "4.1.1", "6.1.3", "7.0.0", "7.0.5"},
}
)
@patch("vulnerabilities.importers.NpmDataSource._update_from_remote")
class NpmImportTest(TestCase):
tempdir = None
@classmethod
def setUpClass(cls) -> None:
cls.tempdir = tempfile.mkdtemp()
zip_path = os.path.join(TEST_DATA, "npm.zip")
with zipfile.ZipFile(zip_path, "r") as zip_ref:
zip_ref.extractall(cls.tempdir)
cls.importer = models.Importer.objects.create(
name="npm_unittests",
license="",
last_run=None,
data_source="NpmDataSource",
data_source_cfg={
"repository_url": "https://example.git",
"working_directory": os.path.join(cls.tempdir, "npm_test"),
"create_working_directory": False,
"remove_working_directory": False,
},
)
@classmethod
def tearDownClass(cls) -> None:
# Make sure no requests for unexpected package names have been made during the tests.
shutil.rmtree(cls.tempdir)
assert len(MOCK_VERSION_API.cache) == 3, MOCK_VERSION_API.cache
def test_import(self, _):
runner = ImportRunner(self.importer, 5)
with patch("vulnerabilities.importers.NpmDataSource.versions", new=MOCK_VERSION_API):
with patch("vulnerabilities.importers.NpmDataSource.set_api"):
runner.run()
assert models.Vulnerability.objects.count() == 3
assert models.VulnerabilityReference.objects.count() == 3
assert models.PackageRelatedVulnerability.objects.filter(is_vulnerable=False).count() == 5
assert models.PackageRelatedVulnerability.objects.filter(is_vulnerable=True).count() == 4
expected_package_count = sum([len(v) for v in MOCK_VERSION_API.cache.values()])
assert models.Package.objects.count() == expected_package_count
self.assert_for_package(
"jquery", {"3.4"}, {"3.8"}, "1518", vulnerability_id="CVE-2020-11022"
) # nopep8
self.assert_for_package("kerberos", {"0.5.8"}, {"1.2"}, "1514")
self.assert_for_package("subtext", {"4.1.1", "7.0.0"}, {"3.7", "6.1.3", "7.0.5"}, "1476")
def assert_for_package(
self,
package_name,
impacted_versions,
resolved_versions,
vuln_id,
vulnerability_id=None,
):
vuln = None
for version in impacted_versions:
pkg = models.Package.objects.get(name=package_name, version=version)
assert pkg.vulnerabilities.count() == 1
vuln = pkg.vulnerabilities.first()
if vulnerability_id:
assert vuln.vulnerability_id == vulnerability_id
ref_url = f"https://registry.npmjs.org/-/npm/v1/advisories/{vuln_id}"
assert models.VulnerabilityReference.objects.get(url=ref_url, vulnerability=vuln)
for version in resolved_versions:
pkg = models.Package.objects.get(name=package_name, version=version)
assert models.PackageRelatedVulnerability.objects.filter(
package=pkg, vulnerability=vuln, is_vulnerable=False
)
def test_categorize_versions_simple_ranges():
all_versions = {"3.4", "3.8"}
impacted_ranges = "<3.5.0"
resolved_ranges = ">=3.5.0"
impacted_versions, resolved_versions = categorize_versions(
all_versions, impacted_ranges, resolved_ranges
)
assert impacted_versions == {"3.4"}
assert resolved_versions == {"3.8"}
def test_categorize_versions_complex_ranges():
all_versions = {"3.7", "4.1.1", "6.1.3", "7.0.0", "7.0.5"}
impacted_ranges = ">=4.1.0 <6.1.3 || >= 7.0.0 <7.0.3"
resolved_ranges = ">=6.1.3 <7.0.0 || >=7.0.3"
impacted_versions, resolved_versions = categorize_versions(
all_versions, impacted_ranges, resolved_ranges
)
assert impacted_versions == {"4.1.1", "7.0.0"}
assert resolved_versions == {"3.7", "6.1.3", "7.0.5"}
| 38.326923 | 98 | 0.676367 |
import json
import os
import shutil
import tempfile
from unittest.mock import patch
import zipfile
from django.test import TestCase
from vulnerabilities import models
from vulnerabilities.import_runner import ImportRunner
from vulnerabilities.package_managers import NpmVersionAPI
from vulnerabilities.importers.npm import categorize_versions
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
TEST_DATA = os.path.join(BASE_DIR, "test_data/")
MOCK_VERSION_API = NpmVersionAPI(
cache={
"jquery": {"3.4", "3.8"},
"kerberos": {"0.5.8", "1.2"},
"@hapi/subtext": {"3.7", "4.1.1", "6.1.3", "7.0.0", "7.0.5"},
}
)
@patch("vulnerabilities.importers.NpmDataSource._update_from_remote")
class NpmImportTest(TestCase):
tempdir = None
@classmethod
def setUpClass(cls) -> None:
cls.tempdir = tempfile.mkdtemp()
zip_path = os.path.join(TEST_DATA, "npm.zip")
with zipfile.ZipFile(zip_path, "r") as zip_ref:
zip_ref.extractall(cls.tempdir)
cls.importer = models.Importer.objects.create(
name="npm_unittests",
license="",
last_run=None,
data_source="NpmDataSource",
data_source_cfg={
"repository_url": "https://example.git",
"working_directory": os.path.join(cls.tempdir, "npm_test"),
"create_working_directory": False,
"remove_working_directory": False,
},
)
@classmethod
def tearDownClass(cls) -> None:
shutil.rmtree(cls.tempdir)
assert len(MOCK_VERSION_API.cache) == 3, MOCK_VERSION_API.cache
def test_import(self, _):
runner = ImportRunner(self.importer, 5)
with patch("vulnerabilities.importers.NpmDataSource.versions", new=MOCK_VERSION_API):
with patch("vulnerabilities.importers.NpmDataSource.set_api"):
runner.run()
assert models.Vulnerability.objects.count() == 3
assert models.VulnerabilityReference.objects.count() == 3
assert models.PackageRelatedVulnerability.objects.filter(is_vulnerable=False).count() == 5
assert models.PackageRelatedVulnerability.objects.filter(is_vulnerable=True).count() == 4
expected_package_count = sum([len(v) for v in MOCK_VERSION_API.cache.values()])
assert models.Package.objects.count() == expected_package_count
self.assert_for_package(
"jquery", {"3.4"}, {"3.8"}, "1518", vulnerability_id="CVE-2020-11022"
)
self.assert_for_package("kerberos", {"0.5.8"}, {"1.2"}, "1514")
self.assert_for_package("subtext", {"4.1.1", "7.0.0"}, {"3.7", "6.1.3", "7.0.5"}, "1476")
def assert_for_package(
self,
package_name,
impacted_versions,
resolved_versions,
vuln_id,
vulnerability_id=None,
):
vuln = None
for version in impacted_versions:
pkg = models.Package.objects.get(name=package_name, version=version)
assert pkg.vulnerabilities.count() == 1
vuln = pkg.vulnerabilities.first()
if vulnerability_id:
assert vuln.vulnerability_id == vulnerability_id
ref_url = f"https://registry.npmjs.org/-/npm/v1/advisories/{vuln_id}"
assert models.VulnerabilityReference.objects.get(url=ref_url, vulnerability=vuln)
for version in resolved_versions:
pkg = models.Package.objects.get(name=package_name, version=version)
assert models.PackageRelatedVulnerability.objects.filter(
package=pkg, vulnerability=vuln, is_vulnerable=False
)
def test_categorize_versions_simple_ranges():
all_versions = {"3.4", "3.8"}
impacted_ranges = "<3.5.0"
resolved_ranges = ">=3.5.0"
impacted_versions, resolved_versions = categorize_versions(
all_versions, impacted_ranges, resolved_ranges
)
assert impacted_versions == {"3.4"}
assert resolved_versions == {"3.8"}
def test_categorize_versions_complex_ranges():
all_versions = {"3.7", "4.1.1", "6.1.3", "7.0.0", "7.0.5"}
impacted_ranges = ">=4.1.0 <6.1.3 || >= 7.0.0 <7.0.3"
resolved_ranges = ">=6.1.3 <7.0.0 || >=7.0.3"
impacted_versions, resolved_versions = categorize_versions(
all_versions, impacted_ranges, resolved_ranges
)
assert impacted_versions == {"4.1.1", "7.0.0"}
assert resolved_versions == {"3.7", "6.1.3", "7.0.5"}
| true | true |
f73bff3f87ec54b155cbd3be7d85764e39c7a12b | 2,724 | py | Python | purge_files.py | darkrilin/gamemaker-slack-bot | 623489dddde37bad906dedb40ceda1335da9c90d | [
"MIT"
] | 1 | 2016-08-09T21:28:31.000Z | 2016-08-09T21:28:31.000Z | purge_files.py | darkrilin/gamemaker-slack-bot | 623489dddde37bad906dedb40ceda1335da9c90d | [
"MIT"
] | null | null | null | purge_files.py | darkrilin/gamemaker-slack-bot | 623489dddde37bad906dedb40ceda1335da9c90d | [
"MIT"
] | null | null | null | from urllib.parse import urlencode
from urllib.request import urlopen
from time import time
from json import load
from codecs import getreader
from os import environ
reader = getreader("utf-8")
token = environ['SLACK_TEST_TOKEN'] # Uses legacy test API token - TODO: This will need to be updated
days = 14 # Purge files older than 14 days
timestamp = int(time()) - days * 24 * 60 * 60
def list_files(slack_token, ts_to):
"""
Fetches a list of all the public files on the slack server
:param slack_token:
:param ts_to: Files created before this timestamp
:return: List of public files
"""
params = {
'token': slack_token,
'ts_to': ts_to,
'count': 500,
}
response = reader(urlopen('https://slack.com/api/files.list?' + urlencode(params)))
file_list = load(response)['files']
return file_list
def delete_files(file_ids, slack_token, verbose=False):
"""
Deletes all files with IDs matching the given list
:param file_ids:
:param slack_token:
:param verbose:
"""
size = 0
count = 0
num_files = len(file_ids)
for file_id in file_ids:
count += 1
params = {
'token': slack_token,
'file': file_id
}
response = reader(urlopen('https://slack.com/api/files.info?' + urlencode(params)))
size += load(response)['file']['size']
response = reader(urlopen('https://slack.com/api/files.delete?' + urlencode(params)))
ok = load(response)['ok']
mb = size / 1048576
if verbose:
print("{0} of {1} - {2} {3} ... {4:.2f} MB saved".format(count, num_files, file_id, ok, mb))
def total_file_size(slack_token, verbose=False):
"""
Finds the total size of all files on the slack server
:param slack_token:
:param verbose:
:return:
"""
params = {
'token': slack_token,
'count': 500,
}
response = reader(urlopen('https://slack.com/api/files.list?' + urlencode(params)))
size = 0
file_ids = [f['id'] for f in load(response)['files']]
for file_id in file_ids:
params = {
'token': token,
'file': file_id
}
response = reader(urlopen('https://slack.com/api/files.info?' + urlencode(params)))
size += load(response)['file']['size']
mb = size / 1048576
if verbose:
print('{0:.2f} MB total'.format(mb))
mb = size / 1048576
return '{0:.2f} MB'.format(mb)
if __name__ == '__main__':
files = [f['id'] for f in list_files(token, timestamp)]
delete_files(files, token, verbose=True)
print("{} files deleted".format(len(files)))
print(total_file_size(token))
| 27.24 | 104 | 0.605727 | from urllib.parse import urlencode
from urllib.request import urlopen
from time import time
from json import load
from codecs import getreader
from os import environ
reader = getreader("utf-8")
token = environ['SLACK_TEST_TOKEN']
days = 14
timestamp = int(time()) - days * 24 * 60 * 60
def list_files(slack_token, ts_to):
params = {
'token': slack_token,
'ts_to': ts_to,
'count': 500,
}
response = reader(urlopen('https://slack.com/api/files.list?' + urlencode(params)))
file_list = load(response)['files']
return file_list
def delete_files(file_ids, slack_token, verbose=False):
size = 0
count = 0
num_files = len(file_ids)
for file_id in file_ids:
count += 1
params = {
'token': slack_token,
'file': file_id
}
response = reader(urlopen('https://slack.com/api/files.info?' + urlencode(params)))
size += load(response)['file']['size']
response = reader(urlopen('https://slack.com/api/files.delete?' + urlencode(params)))
ok = load(response)['ok']
mb = size / 1048576
if verbose:
print("{0} of {1} - {2} {3} ... {4:.2f} MB saved".format(count, num_files, file_id, ok, mb))
def total_file_size(slack_token, verbose=False):
params = {
'token': slack_token,
'count': 500,
}
response = reader(urlopen('https://slack.com/api/files.list?' + urlencode(params)))
size = 0
file_ids = [f['id'] for f in load(response)['files']]
for file_id in file_ids:
params = {
'token': token,
'file': file_id
}
response = reader(urlopen('https://slack.com/api/files.info?' + urlencode(params)))
size += load(response)['file']['size']
mb = size / 1048576
if verbose:
print('{0:.2f} MB total'.format(mb))
mb = size / 1048576
return '{0:.2f} MB'.format(mb)
if __name__ == '__main__':
files = [f['id'] for f in list_files(token, timestamp)]
delete_files(files, token, verbose=True)
print("{} files deleted".format(len(files)))
print(total_file_size(token))
| true | true |
f73c01726f9d59c2094688ab60f9d16fd7fb8794 | 2,035 | py | Python | unix/linux/gnu/redhat.py | fmenabe/python-unix | 738907eddcdeece4be8c82f1d5604c296c94e49f | [
"MIT"
] | 7 | 2015-09-17T13:33:10.000Z | 2021-09-30T15:07:59.000Z | unix/linux/gnu/redhat.py | fmenabe/python-unix | 738907eddcdeece4be8c82f1d5604c296c94e49f | [
"MIT"
] | 3 | 2015-09-17T12:53:17.000Z | 2016-09-27T21:10:48.000Z | unix/linux/gnu/redhat.py | fmenabe/python-unix | 738907eddcdeece4be8c82f1d5604c296c94e49f | [
"MIT"
] | 4 | 2017-01-16T15:29:22.000Z | 2019-08-28T21:11:25.000Z | # -*- coding: utf-8 -*-
import re
import os
import unix
import weakref
from .. import Linux, Chroot, LinuxError
from unix.linux.services import Initd, Upstart, Systemd
DISTRIBS = ('RedHat', 'CentOS')
_CONFDIR = '/etc/sysconfig'
_NETFILE = os.path.join(_CONFDIR, 'network')
def RedHat(host, force=False):
unix.isvalid(host)
root = host.__dict__.get('root', None)
instances = unix.instances(host)
if len(instances) >= 1:
host = Linux(getattr(unix, instances[0]).clone(host))
if root:
host = Chroot(host, root)
if host.distrib[0] not in DISTRIBS and not force:
raise LinuxError('invalid distrib')
class RedHatHost(host.__class__):
def __init__(self):
kwargs = {'root': root} if root else {}
host.__class__.__init__(self, **kwargs)
self.__dict__.update(host.__dict__)
def list_packages(self):
return self.execute('dpkg -l')
@property
def hostname(self):
with self.open(_NETFILE) as fhandler:
for line in fhandler.read().splitlines():
attr, value = line.split('=')
if attr == 'HOSTNAME':
return value
@hostname.setter
def hostname(self, value):
contnet = ''
with self.open(_NETFILE) as fhandler:
content = re.sub('HOSTNAME=[^\n]*',
'HOSTNAME=%s\n' % value,
fhandler.read())
with self.open(_NETFILE, 'w') as fhandler:
fhandler.write(content)
@property
def services(self):
major_version = int(self.distrib[1][0])
if major_version <= 5:
service_handler = Initd
elif major_version == 6:
service_handler = Upstart
elif major_version >= 7:
service_handler = Systemd
return service_handler(weakref.ref(self)())
return RedHatHost()
| 29.926471 | 61 | 0.55086 |
import re
import os
import unix
import weakref
from .. import Linux, Chroot, LinuxError
from unix.linux.services import Initd, Upstart, Systemd
DISTRIBS = ('RedHat', 'CentOS')
_CONFDIR = '/etc/sysconfig'
_NETFILE = os.path.join(_CONFDIR, 'network')
def RedHat(host, force=False):
unix.isvalid(host)
root = host.__dict__.get('root', None)
instances = unix.instances(host)
if len(instances) >= 1:
host = Linux(getattr(unix, instances[0]).clone(host))
if root:
host = Chroot(host, root)
if host.distrib[0] not in DISTRIBS and not force:
raise LinuxError('invalid distrib')
class RedHatHost(host.__class__):
def __init__(self):
kwargs = {'root': root} if root else {}
host.__class__.__init__(self, **kwargs)
self.__dict__.update(host.__dict__)
def list_packages(self):
return self.execute('dpkg -l')
@property
def hostname(self):
with self.open(_NETFILE) as fhandler:
for line in fhandler.read().splitlines():
attr, value = line.split('=')
if attr == 'HOSTNAME':
return value
@hostname.setter
def hostname(self, value):
contnet = ''
with self.open(_NETFILE) as fhandler:
content = re.sub('HOSTNAME=[^\n]*',
'HOSTNAME=%s\n' % value,
fhandler.read())
with self.open(_NETFILE, 'w') as fhandler:
fhandler.write(content)
@property
def services(self):
major_version = int(self.distrib[1][0])
if major_version <= 5:
service_handler = Initd
elif major_version == 6:
service_handler = Upstart
elif major_version >= 7:
service_handler = Systemd
return service_handler(weakref.ref(self)())
return RedHatHost()
| true | true |
f73c01b1bfd8189c29eab7f24401720eb6d2b7c0 | 507 | py | Python | src/_dependencies/lazy.py | dry-python/dependencies | 1a8bba41ab42d0b5249b36471f5300d9faba81e7 | [
"BSD-2-Clause"
] | 175 | 2018-07-21T13:04:44.000Z | 2020-05-27T15:31:06.000Z | src/_dependencies/lazy.py | proofit404/dependencies | 204e0cfadca801d64857f24aa4c74e7939ed9af0 | [
"BSD-2-Clause"
] | 325 | 2016-05-16T11:16:11.000Z | 2022-03-04T00:45:57.000Z | src/_dependencies/lazy.py | dry-python/dependencies | 1a8bba41ab42d0b5249b36471f5300d9faba81e7 | [
"BSD-2-Clause"
] | 18 | 2018-06-17T09:33:16.000Z | 2020-05-20T18:12:30.000Z | from _dependencies.graph import _Graph
class _LazyGraph:
def __init__(self, attrname, namespace):
self.attrname = attrname
self.namespace = namespace
def __get__(self, instance, owner):
graph = _Graph()
for base in reversed(owner.__bases__):
graph.update(base.__dependencies__)
for name, dependency in self.namespace.items():
graph.assign(name, dependency)
type.__setattr__(owner, self.attrname, graph)
return graph
| 29.823529 | 55 | 0.656805 | from _dependencies.graph import _Graph
class _LazyGraph:
def __init__(self, attrname, namespace):
self.attrname = attrname
self.namespace = namespace
def __get__(self, instance, owner):
graph = _Graph()
for base in reversed(owner.__bases__):
graph.update(base.__dependencies__)
for name, dependency in self.namespace.items():
graph.assign(name, dependency)
type.__setattr__(owner, self.attrname, graph)
return graph
| true | true |
f73c025048313646ffa657c41d4c35ef79bc7325 | 6,699 | py | Python | pageplot/plotmodel.py | JBorrow/pageplot | 8abad574fda476d26a59fc8b7d36da2838f2c11e | [
"MIT"
] | null | null | null | pageplot/plotmodel.py | JBorrow/pageplot | 8abad574fda476d26a59fc8b7d36da2838f2c11e | [
"MIT"
] | null | null | null | pageplot/plotmodel.py | JBorrow/pageplot | 8abad574fda476d26a59fc8b7d36da2838f2c11e | [
"MIT"
] | null | null | null | """
The base top-level plot model class.
From this all data and plotting flow.
"""
from pageplot.exceptions import PagePlotParserError
from pathlib import Path
from typing import Any, Optional, Dict, List, Union
from pageplot.extensionmodel import PlotExtension
from pageplot.extensions import built_in_extensions
from pageplot.io.spec import IOSpecification
from pageplot.config import GlobalConfig
from pageplot.mask import get_mask
import matplotlib.pyplot as plt
import numpy as np
import unyt
import attr
@attr.s(auto_attribs=True)
class PlotModel:
"""
Model describing an individual plot. De-serializes the input
json describing an individual figure's extension values.
To use this, you'll need to initialise it with the configuration
(for all the extensions!), and then associate the data with
the appropraite method. The plots can then be created using the
methods in the following order:
``setup_figures`` - creates Figure and Axes objects
``run_extensions`` - runs all of the extensions' ``preprocess`` steps
``perform_blitting`` - runs the extensions' ``blit`` functions
``save`` - writes out the figures to disk
``finalize`` - closes the Figure object
You can also serialize the contents of the whole figure to a dictionary
with the ``serialize`` object.
Parameters
----------
name: str
Plot name. This is the filename of the plot (without file extension).
config: GlobalConfig
Global configuration object.
plot_spec: Dict[str, Any]
Data controlling the behaviour of each extension. The keys should
be the same as the used extensions. Mis-matches will raise a
``PagePlotParserError``.
x, y, z: str, optional
Strings to be passed to the data to load appropriate x, y, and z
data. Here only x is required.
x_units, y_units, z_units: Union[str, None, unyt.unyt_quantity]
Expected output units for the plot, to be parsed.
mask: str, optional
Mask text (see :func:`get_mask`).
"""
name: str
config: GlobalConfig
plot_spec: Dict[str, Any]
x: str
y: Optional[str] = None
z: Optional[str] = None
# Output units for the plot.
x_units: Union[str, None, unyt.unyt_quantity] = None
y_units: Union[str, None, unyt.unyt_quantity] = None
z_units: Union[str, None, unyt.unyt_quantity] = None
mask: Optional[str] = None
data: IOSpecification = attr.ib(init=False)
fig: plt.Figure = attr.ib(init=False)
axes: plt.Axes = attr.ib(init=False)
extensions: Dict[str, PlotExtension] = attr.ib(init=False)
def associate_data(self, data: IOSpecification):
"""
Associates the data file (which conforms to the
``IOSpecification``) with the plot.
data: IOSpecification
Any data file that conforms to the specification.
"""
self.data = data
def setup_figures(self):
"""
Sets up the internal figure and axes.
"""
self.fig, self.axes = plt.subplots()
return
def run_extensions(
self, additional_extensions: Optional[Dict[str, PlotExtension]] = None
):
"""
Run the figure extensions (these provide all data for the figures,
excluding the plotting). Internal extensions are performed
first, then any additional extensions are executed.
additional_extensions: Dict[str, PlotExtension]
Any additional extensions conforming to the specification.
"""
# First, sort out units and masking
units = {
"x_units": self.x_units,
"y_units": self.y_units,
"z_units": self.z_units,
}
for name, value in units.items():
if value is None:
if (associated_data := getattr(self, name[0])) is None:
units[name] = unyt.unyt_quantity(1.0, None)
else:
units[name] = unyt.unyt_quantity(
1.0, associated_data.split(" ", 1)[1]
)
else:
units[name] = unyt.unyt_quantity(1.0, value)
mask = get_mask(data=self.data, mask_text=self.mask)
self.extensions = {}
if additional_extensions is None:
additional_extensions = {}
combined_extensions = {**built_in_extensions, **additional_extensions}
for name in self.plot_spec.keys():
try:
Extension = combined_extensions[name]
except KeyError:
raise PagePlotParserError(
name, "Unable to find matching extension for configuration value."
)
extension = Extension(
name=name,
config=self.config,
metadata=self.data.metadata,
x=self.data.data_from_string(self.x, mask=mask),
y=self.data.data_from_string(self.y, mask=mask),
z=self.data.data_from_string(self.z, mask=mask),
**units,
**self.plot_spec.get(name, {}),
)
extension.preprocess()
self.extensions[name] = extension
return
def perform_blitting(self):
"""
Performs the blitting (creating the figure).
Without this, the extensions are just 'created' and pre-processed
without affecting or creating the figure.
"""
for extension in self.extensions.values():
extension.blit(fig=self.fig, axes=self.axes)
def save(self, filename: Path):
"""
Saves the figure to file.
filename: Path
Filename that you would like to save the figure to. Can have
any matplotlib-compatible file extension.
Notes
-----
It's suggested that you run finalzie() after this function, otherwise
there will be lots of figures open at one time causing potential slowdowns.
"""
self.fig.savefig(filename)
return
def serialize(self) -> Dict[str, Any]:
"""
Serializes the contents of the extensions to a dictionary.
Note that you do not have to have 'created' the figure to run this,
if you just want the data you should be able to just request
the serialized data.
"""
serialized = {name: ext.serialize() for name, ext in self.extensions.items()}
return serialized
def finalize(self):
"""
Closes figures and cleans up.
"""
plt.close(self.fig)
class Config:
arbitrary_types_allowed = True
| 29.641593 | 86 | 0.618749 |
from pageplot.exceptions import PagePlotParserError
from pathlib import Path
from typing import Any, Optional, Dict, List, Union
from pageplot.extensionmodel import PlotExtension
from pageplot.extensions import built_in_extensions
from pageplot.io.spec import IOSpecification
from pageplot.config import GlobalConfig
from pageplot.mask import get_mask
import matplotlib.pyplot as plt
import numpy as np
import unyt
import attr
@attr.s(auto_attribs=True)
class PlotModel:
name: str
config: GlobalConfig
plot_spec: Dict[str, Any]
x: str
y: Optional[str] = None
z: Optional[str] = None
x_units: Union[str, None, unyt.unyt_quantity] = None
y_units: Union[str, None, unyt.unyt_quantity] = None
z_units: Union[str, None, unyt.unyt_quantity] = None
mask: Optional[str] = None
data: IOSpecification = attr.ib(init=False)
fig: plt.Figure = attr.ib(init=False)
axes: plt.Axes = attr.ib(init=False)
extensions: Dict[str, PlotExtension] = attr.ib(init=False)
def associate_data(self, data: IOSpecification):
self.data = data
def setup_figures(self):
self.fig, self.axes = plt.subplots()
return
def run_extensions(
self, additional_extensions: Optional[Dict[str, PlotExtension]] = None
):
units = {
"x_units": self.x_units,
"y_units": self.y_units,
"z_units": self.z_units,
}
for name, value in units.items():
if value is None:
if (associated_data := getattr(self, name[0])) is None:
units[name] = unyt.unyt_quantity(1.0, None)
else:
units[name] = unyt.unyt_quantity(
1.0, associated_data.split(" ", 1)[1]
)
else:
units[name] = unyt.unyt_quantity(1.0, value)
mask = get_mask(data=self.data, mask_text=self.mask)
self.extensions = {}
if additional_extensions is None:
additional_extensions = {}
combined_extensions = {**built_in_extensions, **additional_extensions}
for name in self.plot_spec.keys():
try:
Extension = combined_extensions[name]
except KeyError:
raise PagePlotParserError(
name, "Unable to find matching extension for configuration value."
)
extension = Extension(
name=name,
config=self.config,
metadata=self.data.metadata,
x=self.data.data_from_string(self.x, mask=mask),
y=self.data.data_from_string(self.y, mask=mask),
z=self.data.data_from_string(self.z, mask=mask),
**units,
**self.plot_spec.get(name, {}),
)
extension.preprocess()
self.extensions[name] = extension
return
def perform_blitting(self):
for extension in self.extensions.values():
extension.blit(fig=self.fig, axes=self.axes)
def save(self, filename: Path):
self.fig.savefig(filename)
return
def serialize(self) -> Dict[str, Any]:
serialized = {name: ext.serialize() for name, ext in self.extensions.items()}
return serialized
def finalize(self):
plt.close(self.fig)
class Config:
arbitrary_types_allowed = True
| true | true |
f73c033c208b084d389d14b26d72c13c43b1378a | 1,148 | py | Python | jdxapi/routes/preview.py | jobdataexchange/jdx-api | 7815a6463de56423c3b4196648607c4ebe56828c | [
"Apache-2.0"
] | null | null | null | jdxapi/routes/preview.py | jobdataexchange/jdx-api | 7815a6463de56423c3b4196648607c4ebe56828c | [
"Apache-2.0"
] | 9 | 2019-12-26T17:39:58.000Z | 2022-01-13T01:59:49.000Z | jdxapi/routes/preview.py | jobdataexchange/jdx-api | 7815a6463de56423c3b4196648607c4ebe56828c | [
"Apache-2.0"
] | null | null | null | from flask import request, jsonify
from jdxapi.utils.logger_resource import LoggerResource
from jdxapi.app import api, DB
from jdxapi.models import Pipeline
from jdxapi.utils.functions import RequestHandler, ResponseHandler
from jdxapi.utils.error import ApiError
from sqlalchemy.orm.exc import NoResultFound, MultipleResultsFound
import datetime
import jdxapi.utils.constants as c
from jdxapi.services.competensor import get_preview
@api.resource("/preview")
class Preview(LoggerResource):
def post(self):
req = request.get_json()
pipeline_id = RequestHandler.get_pipeline_id(req, True)
_ = Pipeline.get_pipeline_from_id(pipeline_id)
preview_data = get_preview(pipeline_id)
resp_data = self.create_response_data(pipeline_id, preview_data, req)
response = ResponseHandler.create_response(resp_data, 200)
return response
def create_response_data(self, pipeline_id, preview_data, req):
resp_data = {
c.PIPELINE_ID: str(pipeline_id),
c.TIMESTAMP: str(datetime.datetime.now()),
"preview": preview_data
}
return resp_data
| 32.8 | 77 | 0.736063 | from flask import request, jsonify
from jdxapi.utils.logger_resource import LoggerResource
from jdxapi.app import api, DB
from jdxapi.models import Pipeline
from jdxapi.utils.functions import RequestHandler, ResponseHandler
from jdxapi.utils.error import ApiError
from sqlalchemy.orm.exc import NoResultFound, MultipleResultsFound
import datetime
import jdxapi.utils.constants as c
from jdxapi.services.competensor import get_preview
@api.resource("/preview")
class Preview(LoggerResource):
def post(self):
req = request.get_json()
pipeline_id = RequestHandler.get_pipeline_id(req, True)
_ = Pipeline.get_pipeline_from_id(pipeline_id)
preview_data = get_preview(pipeline_id)
resp_data = self.create_response_data(pipeline_id, preview_data, req)
response = ResponseHandler.create_response(resp_data, 200)
return response
def create_response_data(self, pipeline_id, preview_data, req):
resp_data = {
c.PIPELINE_ID: str(pipeline_id),
c.TIMESTAMP: str(datetime.datetime.now()),
"preview": preview_data
}
return resp_data
| true | true |
f73c03827b72ef05b689c636777a89325d1163bc | 43,779 | py | Python | moto/sns/responses.py | thomassross/moto | 407d5c853dbee9b9e132d97b41414b7dca475765 | [
"Apache-2.0"
] | 1 | 2021-12-12T04:23:06.000Z | 2021-12-12T04:23:06.000Z | moto/sns/responses.py | thomassross/moto | 407d5c853dbee9b9e132d97b41414b7dca475765 | [
"Apache-2.0"
] | 4 | 2017-09-30T07:52:52.000Z | 2021-12-13T06:56:55.000Z | moto/sns/responses.py | thomassross/moto | 407d5c853dbee9b9e132d97b41414b7dca475765 | [
"Apache-2.0"
] | 2 | 2021-11-24T08:05:43.000Z | 2021-11-25T16:18:48.000Z | from __future__ import unicode_literals
import json
import re
from collections import defaultdict
from moto.core.responses import BaseResponse
from moto.core.utils import camelcase_to_underscores
from .models import sns_backends
from .exceptions import InvalidParameterValue, SNSNotFoundError
from .utils import is_e164
class SNSResponse(BaseResponse):
SMS_ATTR_REGEX = re.compile(
r"^attributes\.entry\.(?P<index>\d+)\.(?P<type>key|value)$"
)
OPT_OUT_PHONE_NUMBER_REGEX = re.compile(r"^\+?\d+$")
@property
def backend(self):
return sns_backends[self.region]
def _error(self, code, message, sender="Sender"):
template = self.response_template(ERROR_RESPONSE)
return template.render(code=code, message=message, sender=sender)
def _get_attributes(self):
attributes = self._get_list_prefix("Attributes.entry")
return dict((attribute["key"], attribute["value"]) for attribute in attributes)
def _get_tags(self):
tags = self._get_list_prefix("Tags.member")
return {tag["key"]: tag["value"] for tag in tags}
def _parse_message_attributes(self, prefix="", value_namespace="Value."):
message_attributes = self._get_object_map(
"MessageAttributes.entry", name="Name", value="Value"
)
# SNS converts some key names before forwarding messages
# DataType -> Type, StringValue -> Value, BinaryValue -> Value
transformed_message_attributes = {}
for name, value in message_attributes.items():
# validation
data_type = value["DataType"]
if not data_type:
raise InvalidParameterValue(
"The message attribute '{0}' must contain non-empty "
"message attribute value.".format(name)
)
data_type_parts = data_type.split(".")
if len(data_type_parts) > 2 or data_type_parts[0] not in [
"String",
"Binary",
"Number",
]:
raise InvalidParameterValue(
"The message attribute '{0}' has an invalid message "
"attribute type, the set of supported type prefixes is "
"Binary, Number, and String.".format(name)
)
transform_value = None
if "StringValue" in value:
if data_type == "Number":
try:
transform_value = float(value["StringValue"])
except ValueError:
raise InvalidParameterValue(
"An error occurred (ParameterValueInvalid) "
"when calling the Publish operation: "
"Could not cast message attribute '{0}' value to number.".format(
name
)
)
else:
transform_value = value["StringValue"]
elif "BinaryValue" in value:
transform_value = value["BinaryValue"]
if transform_value == "":
raise InvalidParameterValue(
"The message attribute '{0}' must contain non-empty "
"message attribute value for message attribute "
"type '{1}'.".format(name, data_type[0])
)
# transformation
transformed_message_attributes[name] = {
"Type": data_type,
"Value": transform_value,
}
return transformed_message_attributes
def create_topic(self):
name = self._get_param("Name")
attributes = self._get_attributes()
tags = self._get_tags()
topic = self.backend.create_topic(name, attributes, tags)
if self.request_json:
return json.dumps(
{
"CreateTopicResponse": {
"CreateTopicResult": {"TopicArn": topic.arn},
"ResponseMetadata": {
"RequestId": "a8dec8b3-33a4-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(CREATE_TOPIC_TEMPLATE)
return template.render(topic=topic)
def list_topics(self):
next_token = self._get_param("NextToken")
topics, next_token = self.backend.list_topics(next_token=next_token)
if self.request_json:
return json.dumps(
{
"ListTopicsResponse": {
"ListTopicsResult": {
"Topics": [{"TopicArn": topic.arn} for topic in topics],
"NextToken": next_token,
}
},
"ResponseMetadata": {
"RequestId": "a8dec8b3-33a4-11df-8963-01868b7c937a"
},
}
)
template = self.response_template(LIST_TOPICS_TEMPLATE)
return template.render(topics=topics, next_token=next_token)
def delete_topic(self):
topic_arn = self._get_param("TopicArn")
self.backend.delete_topic(topic_arn)
if self.request_json:
return json.dumps(
{
"DeleteTopicResponse": {
"ResponseMetadata": {
"RequestId": "a8dec8b3-33a4-11df-8963-01868b7c937a"
}
}
}
)
template = self.response_template(DELETE_TOPIC_TEMPLATE)
return template.render()
def get_topic_attributes(self):
topic_arn = self._get_param("TopicArn")
topic = self.backend.get_topic(topic_arn)
if self.request_json:
attributes = {
"Owner": topic.account_id,
"Policy": topic.policy,
"TopicArn": topic.arn,
"DisplayName": topic.display_name,
"SubscriptionsPending": topic.subscriptions_pending,
"SubscriptionsConfirmed": topic.subscriptions_confimed,
"SubscriptionsDeleted": topic.subscriptions_deleted,
"DeliveryPolicy": topic.delivery_policy,
"EffectiveDeliveryPolicy": topic.effective_delivery_policy,
}
if topic.kms_master_key_id:
attributes["KmsMasterKeyId"] = topic.kms_master_key_id
response = {
"GetTopicAttributesResponse": {
"GetTopicAttributesResult": {"Attributes": attributes},
"ResponseMetadata": {
"RequestId": "057f074c-33a7-11df-9540-99d0768312d3"
},
}
}
return json.dumps(response)
template = self.response_template(GET_TOPIC_ATTRIBUTES_TEMPLATE)
return template.render(topic=topic)
def set_topic_attributes(self):
topic_arn = self._get_param("TopicArn")
attribute_name = self._get_param("AttributeName")
attribute_name = camelcase_to_underscores(attribute_name)
attribute_value = self._get_param("AttributeValue")
self.backend.set_topic_attribute(topic_arn, attribute_name, attribute_value)
if self.request_json:
return json.dumps(
{
"SetTopicAttributesResponse": {
"ResponseMetadata": {
"RequestId": "a8763b99-33a7-11df-a9b7-05d48da6f042"
}
}
}
)
template = self.response_template(SET_TOPIC_ATTRIBUTES_TEMPLATE)
return template.render()
def subscribe(self):
topic_arn = self._get_param("TopicArn")
endpoint = self._get_param("Endpoint")
protocol = self._get_param("Protocol")
attributes = self._get_attributes()
subscription = self.backend.subscribe(topic_arn, endpoint, protocol)
if attributes is not None:
for attr_name, attr_value in attributes.items():
self.backend.set_subscription_attributes(
subscription.arn, attr_name, attr_value
)
if self.request_json:
return json.dumps(
{
"SubscribeResponse": {
"SubscribeResult": {"SubscriptionArn": subscription.arn},
"ResponseMetadata": {
"RequestId": "a8763b99-33a7-11df-a9b7-05d48da6f042"
},
}
}
)
template = self.response_template(SUBSCRIBE_TEMPLATE)
return template.render(subscription=subscription)
def unsubscribe(self):
subscription_arn = self._get_param("SubscriptionArn")
self.backend.unsubscribe(subscription_arn)
if self.request_json:
return json.dumps(
{
"UnsubscribeResponse": {
"ResponseMetadata": {
"RequestId": "a8763b99-33a7-11df-a9b7-05d48da6f042"
}
}
}
)
template = self.response_template(UNSUBSCRIBE_TEMPLATE)
return template.render()
def list_subscriptions(self):
next_token = self._get_param("NextToken")
subscriptions, next_token = self.backend.list_subscriptions(
next_token=next_token
)
if self.request_json:
return json.dumps(
{
"ListSubscriptionsResponse": {
"ListSubscriptionsResult": {
"Subscriptions": [
{
"TopicArn": subscription.topic.arn,
"Protocol": subscription.protocol,
"SubscriptionArn": subscription.arn,
"Owner": subscription.topic.account_id,
"Endpoint": subscription.endpoint,
}
for subscription in subscriptions
],
"NextToken": next_token,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(LIST_SUBSCRIPTIONS_TEMPLATE)
return template.render(subscriptions=subscriptions, next_token=next_token)
def list_subscriptions_by_topic(self):
topic_arn = self._get_param("TopicArn")
next_token = self._get_param("NextToken")
subscriptions, next_token = self.backend.list_subscriptions(
topic_arn, next_token=next_token
)
if self.request_json:
return json.dumps(
{
"ListSubscriptionsByTopicResponse": {
"ListSubscriptionsByTopicResult": {
"Subscriptions": [
{
"TopicArn": subscription.topic.arn,
"Protocol": subscription.protocol,
"SubscriptionArn": subscription.arn,
"Owner": subscription.topic.account_id,
"Endpoint": subscription.endpoint,
}
for subscription in subscriptions
],
"NextToken": next_token,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(LIST_SUBSCRIPTIONS_BY_TOPIC_TEMPLATE)
return template.render(subscriptions=subscriptions, next_token=next_token)
def publish(self):
target_arn = self._get_param("TargetArn")
topic_arn = self._get_param("TopicArn")
phone_number = self._get_param("PhoneNumber")
subject = self._get_param("Subject")
message_attributes = self._parse_message_attributes()
arn = None
if phone_number is not None:
# Check phone is correct syntax (e164)
if not is_e164(phone_number):
return (
self._error(
"InvalidParameter", "Phone number does not meet the E164 format"
),
dict(status=400),
)
elif target_arn is not None:
arn = target_arn
else:
arn = topic_arn
message = self._get_param("Message")
try:
message_id = self.backend.publish(
message,
arn=arn,
phone_number=phone_number,
subject=subject,
message_attributes=message_attributes,
)
except ValueError as err:
error_response = self._error("InvalidParameter", str(err))
return error_response, dict(status=400)
if self.request_json:
return json.dumps(
{
"PublishResponse": {
"PublishResult": {"MessageId": message_id},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(PUBLISH_TEMPLATE)
return template.render(message_id=message_id)
def create_platform_application(self):
name = self._get_param("Name")
platform = self._get_param("Platform")
attributes = self._get_attributes()
platform_application = self.backend.create_platform_application(
self.region, name, platform, attributes
)
if self.request_json:
return json.dumps(
{
"CreatePlatformApplicationResponse": {
"CreatePlatformApplicationResult": {
"PlatformApplicationArn": platform_application.arn
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937b"
},
}
}
)
template = self.response_template(CREATE_PLATFORM_APPLICATION_TEMPLATE)
return template.render(platform_application=platform_application)
def get_platform_application_attributes(self):
arn = self._get_param("PlatformApplicationArn")
application = self.backend.get_application(arn)
if self.request_json:
return json.dumps(
{
"GetPlatformApplicationAttributesResponse": {
"GetPlatformApplicationAttributesResult": {
"Attributes": application.attributes
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937f"
},
}
}
)
template = self.response_template(GET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE)
return template.render(application=application)
def set_platform_application_attributes(self):
arn = self._get_param("PlatformApplicationArn")
attributes = self._get_attributes()
self.backend.set_application_attributes(arn, attributes)
if self.request_json:
return json.dumps(
{
"SetPlatformApplicationAttributesResponse": {
"ResponseMetadata": {
"RequestId": "384ac68d-3775-12df-8963-01868b7c937f"
}
}
}
)
template = self.response_template(SET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE)
return template.render()
def list_platform_applications(self):
applications = self.backend.list_platform_applications()
if self.request_json:
return json.dumps(
{
"ListPlatformApplicationsResponse": {
"ListPlatformApplicationsResult": {
"PlatformApplications": [
{
"PlatformApplicationArn": application.arn,
"attributes": application.attributes,
}
for application in applications
],
"NextToken": None,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937c"
},
}
}
)
template = self.response_template(LIST_PLATFORM_APPLICATIONS_TEMPLATE)
return template.render(applications=applications)
def delete_platform_application(self):
platform_arn = self._get_param("PlatformApplicationArn")
self.backend.delete_platform_application(platform_arn)
if self.request_json:
return json.dumps(
{
"DeletePlatformApplicationResponse": {
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937e"
}
}
}
)
template = self.response_template(DELETE_PLATFORM_APPLICATION_TEMPLATE)
return template.render()
def create_platform_endpoint(self):
application_arn = self._get_param("PlatformApplicationArn")
application = self.backend.get_application(application_arn)
custom_user_data = self._get_param("CustomUserData")
token = self._get_param("Token")
attributes = self._get_attributes()
platform_endpoint = self.backend.create_platform_endpoint(
self.region, application, custom_user_data, token, attributes
)
if self.request_json:
return json.dumps(
{
"CreatePlatformEndpointResponse": {
"CreatePlatformEndpointResult": {
"EndpointArn": platform_endpoint.arn
},
"ResponseMetadata": {
"RequestId": "384ac68d-3779-11df-8963-01868b7c937b"
},
}
}
)
template = self.response_template(CREATE_PLATFORM_ENDPOINT_TEMPLATE)
return template.render(platform_endpoint=platform_endpoint)
def list_endpoints_by_platform_application(self):
application_arn = self._get_param("PlatformApplicationArn")
endpoints = self.backend.list_endpoints_by_platform_application(application_arn)
if self.request_json:
return json.dumps(
{
"ListEndpointsByPlatformApplicationResponse": {
"ListEndpointsByPlatformApplicationResult": {
"Endpoints": [
{
"Attributes": endpoint.attributes,
"EndpointArn": endpoint.arn,
}
for endpoint in endpoints
],
"NextToken": None,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(
LIST_ENDPOINTS_BY_PLATFORM_APPLICATION_TEMPLATE
)
return template.render(endpoints=endpoints)
def get_endpoint_attributes(self):
arn = self._get_param("EndpointArn")
try:
endpoint = self.backend.get_endpoint(arn)
if self.request_json:
return json.dumps(
{
"GetEndpointAttributesResponse": {
"GetEndpointAttributesResult": {
"Attributes": endpoint.attributes
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937f"
},
}
}
)
template = self.response_template(GET_ENDPOINT_ATTRIBUTES_TEMPLATE)
return template.render(endpoint=endpoint)
except SNSNotFoundError:
error_response = self._error("NotFound", "Endpoint does not exist")
return error_response, dict(status=404)
def set_endpoint_attributes(self):
arn = self._get_param("EndpointArn")
attributes = self._get_attributes()
self.backend.set_endpoint_attributes(arn, attributes)
if self.request_json:
return json.dumps(
{
"SetEndpointAttributesResponse": {
"ResponseMetadata": {
"RequestId": "384bc68d-3775-12df-8963-01868b7c937f"
}
}
}
)
template = self.response_template(SET_ENDPOINT_ATTRIBUTES_TEMPLATE)
return template.render()
def delete_endpoint(self):
arn = self._get_param("EndpointArn")
self.backend.delete_endpoint(arn)
if self.request_json:
return json.dumps(
{
"DeleteEndpointResponse": {
"ResponseMetadata": {
"RequestId": "384bc68d-3775-12df-8963-01868b7c937f"
}
}
}
)
template = self.response_template(DELETE_ENDPOINT_TEMPLATE)
return template.render()
def get_subscription_attributes(self):
arn = self._get_param("SubscriptionArn")
attributes = self.backend.get_subscription_attributes(arn)
template = self.response_template(GET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE)
return template.render(attributes=attributes)
def set_subscription_attributes(self):
arn = self._get_param("SubscriptionArn")
attr_name = self._get_param("AttributeName")
attr_value = self._get_param("AttributeValue")
self.backend.set_subscription_attributes(arn, attr_name, attr_value)
template = self.response_template(SET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE)
return template.render()
def set_sms_attributes(self):
# attributes.entry.1.key
# attributes.entry.1.value
# to
# 1: {key:X, value:Y}
temp_dict = defaultdict(dict)
for key, value in self.querystring.items():
match = self.SMS_ATTR_REGEX.match(key)
if match is not None:
temp_dict[match.group("index")][match.group("type")] = value[0]
# 1: {key:X, value:Y}
# to
# X: Y
# All of this, just to take into account when people provide invalid stuff.
result = {}
for item in temp_dict.values():
if "key" in item and "value" in item:
result[item["key"]] = item["value"]
self.backend.update_sms_attributes(result)
template = self.response_template(SET_SMS_ATTRIBUTES_TEMPLATE)
return template.render()
def get_sms_attributes(self):
filter_list = set()
for key, value in self.querystring.items():
if key.startswith("attributes.member.1"):
filter_list.add(value[0])
if len(filter_list) > 0:
result = {
k: v for k, v in self.backend.sms_attributes.items() if k in filter_list
}
else:
result = self.backend.sms_attributes
template = self.response_template(GET_SMS_ATTRIBUTES_TEMPLATE)
return template.render(attributes=result)
def check_if_phone_number_is_opted_out(self):
number = self._get_param("phoneNumber")
if self.OPT_OUT_PHONE_NUMBER_REGEX.match(number) is None:
error_response = self._error(
code="InvalidParameter",
message="Invalid parameter: PhoneNumber Reason: input incorrectly formatted",
)
return error_response, dict(status=400)
# There should be a nicer way to set if a nubmer has opted out
template = self.response_template(CHECK_IF_OPTED_OUT_TEMPLATE)
return template.render(opt_out=str(number.endswith("99")).lower())
def list_phone_numbers_opted_out(self):
template = self.response_template(LIST_OPTOUT_TEMPLATE)
return template.render(opt_outs=self.backend.opt_out_numbers)
def opt_in_phone_number(self):
number = self._get_param("phoneNumber")
try:
self.backend.opt_out_numbers.remove(number)
except ValueError:
pass
template = self.response_template(OPT_IN_NUMBER_TEMPLATE)
return template.render()
def add_permission(self):
topic_arn = self._get_param("TopicArn")
label = self._get_param("Label")
aws_account_ids = self._get_multi_param("AWSAccountId.member.")
action_names = self._get_multi_param("ActionName.member.")
self.backend.add_permission(topic_arn, label, aws_account_ids, action_names)
template = self.response_template(ADD_PERMISSION_TEMPLATE)
return template.render()
def remove_permission(self):
topic_arn = self._get_param("TopicArn")
label = self._get_param("Label")
self.backend.remove_permission(topic_arn, label)
template = self.response_template(DEL_PERMISSION_TEMPLATE)
return template.render()
def confirm_subscription(self):
arn = self._get_param("TopicArn")
if arn not in self.backend.topics:
error_response = self._error("NotFound", "Topic does not exist")
return error_response, dict(status=404)
# Once Tokens are stored by the `subscribe` endpoint and distributed
# to the client somehow, then we can check validity of tokens
# presented to this method. The following code works, all thats
# needed is to perform a token check and assign that value to the
# `already_subscribed` variable.
#
# token = self._get_param('Token')
# auth = self._get_param('AuthenticateOnUnsubscribe')
# if already_subscribed:
# error_response = self._error(
# code='AuthorizationError',
# message='Subscription already confirmed'
# )
# return error_response, dict(status=400)
template = self.response_template(CONFIRM_SUBSCRIPTION_TEMPLATE)
return template.render(
sub_arn="{0}:68762e72-e9b1-410a-8b3b-903da69ee1d5".format(arn)
)
def list_tags_for_resource(self):
arn = self._get_param("ResourceArn")
result = self.backend.list_tags_for_resource(arn)
template = self.response_template(LIST_TAGS_FOR_RESOURCE_TEMPLATE)
return template.render(tags=result)
def tag_resource(self):
arn = self._get_param("ResourceArn")
tags = self._get_tags()
self.backend.tag_resource(arn, tags)
return self.response_template(TAG_RESOURCE_TEMPLATE).render()
def untag_resource(self):
arn = self._get_param("ResourceArn")
tag_keys = self._get_multi_param("TagKeys.member")
self.backend.untag_resource(arn, tag_keys)
return self.response_template(UNTAG_RESOURCE_TEMPLATE).render()
CREATE_TOPIC_TEMPLATE = """<CreateTopicResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CreateTopicResult>
<TopicArn>{{ topic.arn }}</TopicArn>
</CreateTopicResult>
<ResponseMetadata>
<RequestId>a8dec8b3-33a4-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</CreateTopicResponse>"""
LIST_TOPICS_TEMPLATE = """<ListTopicsResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListTopicsResult>
<Topics>
{% for topic in topics %}
<member>
<TopicArn>{{ topic.arn }}</TopicArn>
</member>
{% endfor %}
</Topics>
{% if next_token %}
<NextToken>{{ next_token }}</NextToken>
{% endif %}
</ListTopicsResult>
<ResponseMetadata>
<RequestId>3f1478c7-33a9-11df-9540-99d0768312d3</RequestId>
</ResponseMetadata>
</ListTopicsResponse>"""
DELETE_TOPIC_TEMPLATE = """<DeleteTopicResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>f3aa9ac9-3c3d-11df-8235-9dab105e9c32</RequestId>
</ResponseMetadata>
</DeleteTopicResponse>"""
GET_TOPIC_ATTRIBUTES_TEMPLATE = """<GetTopicAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetTopicAttributesResult>
<Attributes>
<entry>
<key>Owner</key>
<value>{{ topic.account_id }}</value>
</entry>
<entry>
<key>Policy</key>
<value>{{ topic.policy }}</value>
</entry>
<entry>
<key>TopicArn</key>
<value>{{ topic.arn }}</value>
</entry>
<entry>
<key>DisplayName</key>
<value>{{ topic.display_name }}</value>
</entry>
<entry>
<key>SubscriptionsPending</key>
<value>{{ topic.subscriptions_pending }}</value>
</entry>
<entry>
<key>SubscriptionsConfirmed</key>
<value>{{ topic.subscriptions_confimed }}</value>
</entry>
<entry>
<key>SubscriptionsDeleted</key>
<value>{{ topic.subscriptions_deleted }}</value>
</entry>
<entry>
<key>DeliveryPolicy</key>
<value>{{ topic.delivery_policy }}</value>
</entry>
<entry>
<key>EffectiveDeliveryPolicy</key>
<value>{{ topic.effective_delivery_policy }}</value>
</entry>
{% if topic.kms_master_key_id %}
<entry>
<key>KmsMasterKeyId</key>
<value>{{ topic.kms_master_key_id }}</value>
</entry>
{% endif %}
</Attributes>
</GetTopicAttributesResult>
<ResponseMetadata>
<RequestId>057f074c-33a7-11df-9540-99d0768312d3</RequestId>
</ResponseMetadata>
</GetTopicAttributesResponse>"""
SET_TOPIC_ATTRIBUTES_TEMPLATE = """<SetTopicAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>a8763b99-33a7-11df-a9b7-05d48da6f042</RequestId>
</ResponseMetadata>
</SetTopicAttributesResponse>"""
CREATE_PLATFORM_APPLICATION_TEMPLATE = """<CreatePlatformApplicationResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CreatePlatformApplicationResult>
<PlatformApplicationArn>{{ platform_application.arn }}</PlatformApplicationArn>
</CreatePlatformApplicationResult>
<ResponseMetadata>
<RequestId>b6f0e78b-e9d4-5a0e-b973-adc04e8a4ff9</RequestId>
</ResponseMetadata>
</CreatePlatformApplicationResponse>"""
CREATE_PLATFORM_ENDPOINT_TEMPLATE = """<CreatePlatformEndpointResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CreatePlatformEndpointResult>
<EndpointArn>{{ platform_endpoint.arn }}</EndpointArn>
</CreatePlatformEndpointResult>
<ResponseMetadata>
<RequestId>6613341d-3e15-53f7-bf3c-7e56994ba278</RequestId>
</ResponseMetadata>
</CreatePlatformEndpointResponse>"""
LIST_PLATFORM_APPLICATIONS_TEMPLATE = """<ListPlatformApplicationsResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListPlatformApplicationsResult>
<PlatformApplications>
{% for application in applications %}
<member>
<PlatformApplicationArn>{{ application.arn }}</PlatformApplicationArn>
<Attributes>
{% for attribute in application.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ application.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</member>
{% endfor %}
</PlatformApplications>
</ListPlatformApplicationsResult>
<ResponseMetadata>
<RequestId>315a335e-85d8-52df-9349-791283cbb529</RequestId>
</ResponseMetadata>
</ListPlatformApplicationsResponse>"""
DELETE_PLATFORM_APPLICATION_TEMPLATE = """<DeletePlatformApplicationResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>097dac18-7a77-5823-a8dd-e65476dcb037</RequestId>
</ResponseMetadata>
</DeletePlatformApplicationResponse>"""
GET_ENDPOINT_ATTRIBUTES_TEMPLATE = """<GetEndpointAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetEndpointAttributesResult>
<Attributes>
{% for attribute in endpoint.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ endpoint.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</GetEndpointAttributesResult>
<ResponseMetadata>
<RequestId>6c725a19-a142-5b77-94f9-1055a9ea04e7</RequestId>
</ResponseMetadata>
</GetEndpointAttributesResponse>"""
LIST_ENDPOINTS_BY_PLATFORM_APPLICATION_TEMPLATE = """<ListEndpointsByPlatformApplicationResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListEndpointsByPlatformApplicationResult>
<Endpoints>
{% for endpoint in endpoints %}
<member>
<EndpointArn>{{ endpoint.arn }}</EndpointArn>
<Attributes>
{% for attribute in endpoint.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ endpoint.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</member>
{% endfor %}
</Endpoints>
</ListEndpointsByPlatformApplicationResult>
<ResponseMetadata>
<RequestId>9a48768c-dac8-5a60-aec0-3cc27ea08d96</RequestId>
</ResponseMetadata>
</ListEndpointsByPlatformApplicationResponse>"""
GET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE = """<GetPlatformApplicationAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetPlatformApplicationAttributesResult>
<Attributes>
{% for attribute in application.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ application.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</GetPlatformApplicationAttributesResult>
<ResponseMetadata>
<RequestId>74848df2-87f6-55ed-890c-c7be80442462</RequestId>
</ResponseMetadata>
</GetPlatformApplicationAttributesResponse>"""
PUBLISH_TEMPLATE = """<PublishResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<PublishResult>
<MessageId>{{ message_id }}</MessageId>
</PublishResult>
<ResponseMetadata>
<RequestId>f187a3c1-376f-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</PublishResponse>"""
SET_ENDPOINT_ATTRIBUTES_TEMPLATE = """<SetEndpointAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>2fe0bfc7-3e85-5ee5-a9e2-f58b35e85f6a</RequestId>
</ResponseMetadata>
</SetEndpointAttributesResponse>"""
DELETE_ENDPOINT_TEMPLATE = """<DeleteEndpointResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>c1d2b191-353c-5a5f-8969-fbdd3900afa8</RequestId>
</ResponseMetadata>
</DeleteEndpointResponse>"""
SET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE = """<SetPlatformApplicationAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>cf577bcc-b3dc-5463-88f1-3180b9412395</RequestId>
</ResponseMetadata>
</SetPlatformApplicationAttributesResponse>"""
SUBSCRIBE_TEMPLATE = """<SubscribeResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<SubscribeResult>
<SubscriptionArn>{{ subscription.arn }}</SubscriptionArn>
</SubscribeResult>
<ResponseMetadata>
<RequestId>c4407779-24a4-56fa-982c-3d927f93a775</RequestId>
</ResponseMetadata>
</SubscribeResponse>"""
UNSUBSCRIBE_TEMPLATE = """<UnsubscribeResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>18e0ac39-3776-11df-84c0-b93cc1666b84</RequestId>
</ResponseMetadata>
</UnsubscribeResponse>"""
LIST_SUBSCRIPTIONS_TEMPLATE = """<ListSubscriptionsResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListSubscriptionsResult>
<Subscriptions>
{% for subscription in subscriptions %}
<member>
<TopicArn>{{ subscription.topic.arn }}</TopicArn>
<Protocol>{{ subscription.protocol }}</Protocol>
<SubscriptionArn>{{ subscription.arn }}</SubscriptionArn>
<Owner>{{ subscription.account_id }}</Owner>
<Endpoint>{{ subscription.endpoint }}</Endpoint>
</member>
{% endfor %}
</Subscriptions>
{% if next_token %}
<NextToken>{{ next_token }}</NextToken>
{% endif %}
</ListSubscriptionsResult>
<ResponseMetadata>
<RequestId>384ac68d-3775-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</ListSubscriptionsResponse>"""
LIST_SUBSCRIPTIONS_BY_TOPIC_TEMPLATE = """<ListSubscriptionsByTopicResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListSubscriptionsByTopicResult>
<Subscriptions>
{% for subscription in subscriptions %}
<member>
<TopicArn>{{ subscription.topic.arn }}</TopicArn>
<Protocol>{{ subscription.protocol }}</Protocol>
<SubscriptionArn>{{ subscription.arn }}</SubscriptionArn>
<Owner>{{ subscription.account_id }}</Owner>
<Endpoint>{{ subscription.endpoint }}</Endpoint>
</member>
{% endfor %}
</Subscriptions>
{% if next_token %}
<NextToken>{{ next_token }}</NextToken>
{% endif %}
</ListSubscriptionsByTopicResult>
<ResponseMetadata>
<RequestId>384ac68d-3775-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</ListSubscriptionsByTopicResponse>"""
# Not responding aws system attribetus like 'Owner' and 'SubscriptionArn'
GET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE = """<GetSubscriptionAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetSubscriptionAttributesResult>
<Attributes>
{% for name, value in attributes.items() %}
<entry>
<key>{{ name }}</key>
<value>{{ value }}</value>
</entry>
{% endfor %}
</Attributes>
</GetSubscriptionAttributesResult>
<ResponseMetadata>
<RequestId>057f074c-33a7-11df-9540-99d0768312d3</RequestId>
</ResponseMetadata>
</GetSubscriptionAttributesResponse>"""
SET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE = """<SetSubscriptionAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>a8763b99-33a7-11df-a9b7-05d48da6f042</RequestId>
</ResponseMetadata>
</SetSubscriptionAttributesResponse>"""
SET_SMS_ATTRIBUTES_TEMPLATE = """<SetSMSAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<SetSMSAttributesResult/>
<ResponseMetadata>
<RequestId>26332069-c04a-5428-b829-72524b56a364</RequestId>
</ResponseMetadata>
</SetSMSAttributesResponse>"""
GET_SMS_ATTRIBUTES_TEMPLATE = """<GetSMSAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetSMSAttributesResult>
<attributes>
{% for name, value in attributes.items() %}
<entry>
<key>{{ name }}</key>
<value>{{ value }}</value>
</entry>
{% endfor %}
</attributes>
</GetSMSAttributesResult>
<ResponseMetadata>
<RequestId>287f9554-8db3-5e66-8abc-c76f0186db7e</RequestId>
</ResponseMetadata>
</GetSMSAttributesResponse>"""
CHECK_IF_OPTED_OUT_TEMPLATE = """<CheckIfPhoneNumberIsOptedOutResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CheckIfPhoneNumberIsOptedOutResult>
<isOptedOut>{{ opt_out }}</isOptedOut>
</CheckIfPhoneNumberIsOptedOutResult>
<ResponseMetadata>
<RequestId>287f9554-8db3-5e66-8abc-c76f0186db7e</RequestId>
</ResponseMetadata>
</CheckIfPhoneNumberIsOptedOutResponse>"""
ERROR_RESPONSE = """<ErrorResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<Error>
<Type>{{ sender }}</Type>
<Code>{{ code }}</Code>
<Message>{{ message }}</Message>
</Error>
<RequestId>9dd01905-5012-5f99-8663-4b3ecd0dfaef</RequestId>
</ErrorResponse>"""
LIST_OPTOUT_TEMPLATE = """<ListPhoneNumbersOptedOutResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListPhoneNumbersOptedOutResult>
<phoneNumbers>
{% for item in opt_outs %}
<member>{{ item }}</member>
{% endfor %}
</phoneNumbers>
</ListPhoneNumbersOptedOutResult>
<ResponseMetadata>
<RequestId>985e196d-a237-51b6-b33a-4b5601276b38</RequestId>
</ResponseMetadata>
</ListPhoneNumbersOptedOutResponse>"""
OPT_IN_NUMBER_TEMPLATE = """<OptInPhoneNumberResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<OptInPhoneNumberResult/>
<ResponseMetadata>
<RequestId>4c61842c-0796-50ef-95ac-d610c0bc8cf8</RequestId>
</ResponseMetadata>
</OptInPhoneNumberResponse>"""
ADD_PERMISSION_TEMPLATE = """<AddPermissionResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>c046e713-c5ff-5888-a7bc-b52f0e4f1299</RequestId>
</ResponseMetadata>
</AddPermissionResponse>"""
DEL_PERMISSION_TEMPLATE = """<RemovePermissionResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>e767cc9f-314b-5e1b-b283-9ea3fd4e38a3</RequestId>
</ResponseMetadata>
</RemovePermissionResponse>"""
CONFIRM_SUBSCRIPTION_TEMPLATE = """<ConfirmSubscriptionResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ConfirmSubscriptionResult>
<SubscriptionArn>{{ sub_arn }}</SubscriptionArn>
</ConfirmSubscriptionResult>
<ResponseMetadata>
<RequestId>16eb4dde-7b3c-5b3e-a22a-1fe2a92d3293</RequestId>
</ResponseMetadata>
</ConfirmSubscriptionResponse>"""
LIST_TAGS_FOR_RESOURCE_TEMPLATE = """<ListTagsForResourceResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListTagsForResourceResult>
<Tags>
{% for name, value in tags.items() %}
<member>
<Key>{{ name }}</Key>
<Value>{{ value }}</Value>
</member>
{% endfor %}
</Tags>
</ListTagsForResourceResult>
<ResponseMetadata>
<RequestId>97fa763f-861b-5223-a946-20251f2a42e2</RequestId>
</ResponseMetadata>
</ListTagsForResourceResponse>"""
TAG_RESOURCE_TEMPLATE = """<TagResourceResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<TagResourceResult/>
<ResponseMetadata>
<RequestId>fd4ab1da-692f-50a7-95ad-e7c665877d98</RequestId>
</ResponseMetadata>
</TagResourceResponse>"""
UNTAG_RESOURCE_TEMPLATE = """<UntagResourceResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<UntagResourceResult/>
<ResponseMetadata>
<RequestId>14eb7b1a-4cbd-5a56-80db-2d06412df769</RequestId>
</ResponseMetadata>
</UntagResourceResponse>"""
| 37.163837 | 146 | 0.595856 | from __future__ import unicode_literals
import json
import re
from collections import defaultdict
from moto.core.responses import BaseResponse
from moto.core.utils import camelcase_to_underscores
from .models import sns_backends
from .exceptions import InvalidParameterValue, SNSNotFoundError
from .utils import is_e164
class SNSResponse(BaseResponse):
SMS_ATTR_REGEX = re.compile(
r"^attributes\.entry\.(?P<index>\d+)\.(?P<type>key|value)$"
)
OPT_OUT_PHONE_NUMBER_REGEX = re.compile(r"^\+?\d+$")
@property
def backend(self):
return sns_backends[self.region]
def _error(self, code, message, sender="Sender"):
template = self.response_template(ERROR_RESPONSE)
return template.render(code=code, message=message, sender=sender)
def _get_attributes(self):
attributes = self._get_list_prefix("Attributes.entry")
return dict((attribute["key"], attribute["value"]) for attribute in attributes)
def _get_tags(self):
tags = self._get_list_prefix("Tags.member")
return {tag["key"]: tag["value"] for tag in tags}
def _parse_message_attributes(self, prefix="", value_namespace="Value."):
message_attributes = self._get_object_map(
"MessageAttributes.entry", name="Name", value="Value"
)
transformed_message_attributes = {}
for name, value in message_attributes.items():
data_type = value["DataType"]
if not data_type:
raise InvalidParameterValue(
"The message attribute '{0}' must contain non-empty "
"message attribute value.".format(name)
)
data_type_parts = data_type.split(".")
if len(data_type_parts) > 2 or data_type_parts[0] not in [
"String",
"Binary",
"Number",
]:
raise InvalidParameterValue(
"The message attribute '{0}' has an invalid message "
"attribute type, the set of supported type prefixes is "
"Binary, Number, and String.".format(name)
)
transform_value = None
if "StringValue" in value:
if data_type == "Number":
try:
transform_value = float(value["StringValue"])
except ValueError:
raise InvalidParameterValue(
"An error occurred (ParameterValueInvalid) "
"when calling the Publish operation: "
"Could not cast message attribute '{0}' value to number.".format(
name
)
)
else:
transform_value = value["StringValue"]
elif "BinaryValue" in value:
transform_value = value["BinaryValue"]
if transform_value == "":
raise InvalidParameterValue(
"The message attribute '{0}' must contain non-empty "
"message attribute value for message attribute "
"type '{1}'.".format(name, data_type[0])
)
transformed_message_attributes[name] = {
"Type": data_type,
"Value": transform_value,
}
return transformed_message_attributes
def create_topic(self):
name = self._get_param("Name")
attributes = self._get_attributes()
tags = self._get_tags()
topic = self.backend.create_topic(name, attributes, tags)
if self.request_json:
return json.dumps(
{
"CreateTopicResponse": {
"CreateTopicResult": {"TopicArn": topic.arn},
"ResponseMetadata": {
"RequestId": "a8dec8b3-33a4-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(CREATE_TOPIC_TEMPLATE)
return template.render(topic=topic)
def list_topics(self):
next_token = self._get_param("NextToken")
topics, next_token = self.backend.list_topics(next_token=next_token)
if self.request_json:
return json.dumps(
{
"ListTopicsResponse": {
"ListTopicsResult": {
"Topics": [{"TopicArn": topic.arn} for topic in topics],
"NextToken": next_token,
}
},
"ResponseMetadata": {
"RequestId": "a8dec8b3-33a4-11df-8963-01868b7c937a"
},
}
)
template = self.response_template(LIST_TOPICS_TEMPLATE)
return template.render(topics=topics, next_token=next_token)
def delete_topic(self):
topic_arn = self._get_param("TopicArn")
self.backend.delete_topic(topic_arn)
if self.request_json:
return json.dumps(
{
"DeleteTopicResponse": {
"ResponseMetadata": {
"RequestId": "a8dec8b3-33a4-11df-8963-01868b7c937a"
}
}
}
)
template = self.response_template(DELETE_TOPIC_TEMPLATE)
return template.render()
def get_topic_attributes(self):
topic_arn = self._get_param("TopicArn")
topic = self.backend.get_topic(topic_arn)
if self.request_json:
attributes = {
"Owner": topic.account_id,
"Policy": topic.policy,
"TopicArn": topic.arn,
"DisplayName": topic.display_name,
"SubscriptionsPending": topic.subscriptions_pending,
"SubscriptionsConfirmed": topic.subscriptions_confimed,
"SubscriptionsDeleted": topic.subscriptions_deleted,
"DeliveryPolicy": topic.delivery_policy,
"EffectiveDeliveryPolicy": topic.effective_delivery_policy,
}
if topic.kms_master_key_id:
attributes["KmsMasterKeyId"] = topic.kms_master_key_id
response = {
"GetTopicAttributesResponse": {
"GetTopicAttributesResult": {"Attributes": attributes},
"ResponseMetadata": {
"RequestId": "057f074c-33a7-11df-9540-99d0768312d3"
},
}
}
return json.dumps(response)
template = self.response_template(GET_TOPIC_ATTRIBUTES_TEMPLATE)
return template.render(topic=topic)
def set_topic_attributes(self):
topic_arn = self._get_param("TopicArn")
attribute_name = self._get_param("AttributeName")
attribute_name = camelcase_to_underscores(attribute_name)
attribute_value = self._get_param("AttributeValue")
self.backend.set_topic_attribute(topic_arn, attribute_name, attribute_value)
if self.request_json:
return json.dumps(
{
"SetTopicAttributesResponse": {
"ResponseMetadata": {
"RequestId": "a8763b99-33a7-11df-a9b7-05d48da6f042"
}
}
}
)
template = self.response_template(SET_TOPIC_ATTRIBUTES_TEMPLATE)
return template.render()
def subscribe(self):
topic_arn = self._get_param("TopicArn")
endpoint = self._get_param("Endpoint")
protocol = self._get_param("Protocol")
attributes = self._get_attributes()
subscription = self.backend.subscribe(topic_arn, endpoint, protocol)
if attributes is not None:
for attr_name, attr_value in attributes.items():
self.backend.set_subscription_attributes(
subscription.arn, attr_name, attr_value
)
if self.request_json:
return json.dumps(
{
"SubscribeResponse": {
"SubscribeResult": {"SubscriptionArn": subscription.arn},
"ResponseMetadata": {
"RequestId": "a8763b99-33a7-11df-a9b7-05d48da6f042"
},
}
}
)
template = self.response_template(SUBSCRIBE_TEMPLATE)
return template.render(subscription=subscription)
def unsubscribe(self):
subscription_arn = self._get_param("SubscriptionArn")
self.backend.unsubscribe(subscription_arn)
if self.request_json:
return json.dumps(
{
"UnsubscribeResponse": {
"ResponseMetadata": {
"RequestId": "a8763b99-33a7-11df-a9b7-05d48da6f042"
}
}
}
)
template = self.response_template(UNSUBSCRIBE_TEMPLATE)
return template.render()
def list_subscriptions(self):
next_token = self._get_param("NextToken")
subscriptions, next_token = self.backend.list_subscriptions(
next_token=next_token
)
if self.request_json:
return json.dumps(
{
"ListSubscriptionsResponse": {
"ListSubscriptionsResult": {
"Subscriptions": [
{
"TopicArn": subscription.topic.arn,
"Protocol": subscription.protocol,
"SubscriptionArn": subscription.arn,
"Owner": subscription.topic.account_id,
"Endpoint": subscription.endpoint,
}
for subscription in subscriptions
],
"NextToken": next_token,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(LIST_SUBSCRIPTIONS_TEMPLATE)
return template.render(subscriptions=subscriptions, next_token=next_token)
def list_subscriptions_by_topic(self):
topic_arn = self._get_param("TopicArn")
next_token = self._get_param("NextToken")
subscriptions, next_token = self.backend.list_subscriptions(
topic_arn, next_token=next_token
)
if self.request_json:
return json.dumps(
{
"ListSubscriptionsByTopicResponse": {
"ListSubscriptionsByTopicResult": {
"Subscriptions": [
{
"TopicArn": subscription.topic.arn,
"Protocol": subscription.protocol,
"SubscriptionArn": subscription.arn,
"Owner": subscription.topic.account_id,
"Endpoint": subscription.endpoint,
}
for subscription in subscriptions
],
"NextToken": next_token,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(LIST_SUBSCRIPTIONS_BY_TOPIC_TEMPLATE)
return template.render(subscriptions=subscriptions, next_token=next_token)
def publish(self):
target_arn = self._get_param("TargetArn")
topic_arn = self._get_param("TopicArn")
phone_number = self._get_param("PhoneNumber")
subject = self._get_param("Subject")
message_attributes = self._parse_message_attributes()
arn = None
if phone_number is not None:
if not is_e164(phone_number):
return (
self._error(
"InvalidParameter", "Phone number does not meet the E164 format"
),
dict(status=400),
)
elif target_arn is not None:
arn = target_arn
else:
arn = topic_arn
message = self._get_param("Message")
try:
message_id = self.backend.publish(
message,
arn=arn,
phone_number=phone_number,
subject=subject,
message_attributes=message_attributes,
)
except ValueError as err:
error_response = self._error("InvalidParameter", str(err))
return error_response, dict(status=400)
if self.request_json:
return json.dumps(
{
"PublishResponse": {
"PublishResult": {"MessageId": message_id},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(PUBLISH_TEMPLATE)
return template.render(message_id=message_id)
def create_platform_application(self):
name = self._get_param("Name")
platform = self._get_param("Platform")
attributes = self._get_attributes()
platform_application = self.backend.create_platform_application(
self.region, name, platform, attributes
)
if self.request_json:
return json.dumps(
{
"CreatePlatformApplicationResponse": {
"CreatePlatformApplicationResult": {
"PlatformApplicationArn": platform_application.arn
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937b"
},
}
}
)
template = self.response_template(CREATE_PLATFORM_APPLICATION_TEMPLATE)
return template.render(platform_application=platform_application)
def get_platform_application_attributes(self):
arn = self._get_param("PlatformApplicationArn")
application = self.backend.get_application(arn)
if self.request_json:
return json.dumps(
{
"GetPlatformApplicationAttributesResponse": {
"GetPlatformApplicationAttributesResult": {
"Attributes": application.attributes
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937f"
},
}
}
)
template = self.response_template(GET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE)
return template.render(application=application)
def set_platform_application_attributes(self):
arn = self._get_param("PlatformApplicationArn")
attributes = self._get_attributes()
self.backend.set_application_attributes(arn, attributes)
if self.request_json:
return json.dumps(
{
"SetPlatformApplicationAttributesResponse": {
"ResponseMetadata": {
"RequestId": "384ac68d-3775-12df-8963-01868b7c937f"
}
}
}
)
template = self.response_template(SET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE)
return template.render()
def list_platform_applications(self):
applications = self.backend.list_platform_applications()
if self.request_json:
return json.dumps(
{
"ListPlatformApplicationsResponse": {
"ListPlatformApplicationsResult": {
"PlatformApplications": [
{
"PlatformApplicationArn": application.arn,
"attributes": application.attributes,
}
for application in applications
],
"NextToken": None,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937c"
},
}
}
)
template = self.response_template(LIST_PLATFORM_APPLICATIONS_TEMPLATE)
return template.render(applications=applications)
def delete_platform_application(self):
platform_arn = self._get_param("PlatformApplicationArn")
self.backend.delete_platform_application(platform_arn)
if self.request_json:
return json.dumps(
{
"DeletePlatformApplicationResponse": {
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937e"
}
}
}
)
template = self.response_template(DELETE_PLATFORM_APPLICATION_TEMPLATE)
return template.render()
def create_platform_endpoint(self):
application_arn = self._get_param("PlatformApplicationArn")
application = self.backend.get_application(application_arn)
custom_user_data = self._get_param("CustomUserData")
token = self._get_param("Token")
attributes = self._get_attributes()
platform_endpoint = self.backend.create_platform_endpoint(
self.region, application, custom_user_data, token, attributes
)
if self.request_json:
return json.dumps(
{
"CreatePlatformEndpointResponse": {
"CreatePlatformEndpointResult": {
"EndpointArn": platform_endpoint.arn
},
"ResponseMetadata": {
"RequestId": "384ac68d-3779-11df-8963-01868b7c937b"
},
}
}
)
template = self.response_template(CREATE_PLATFORM_ENDPOINT_TEMPLATE)
return template.render(platform_endpoint=platform_endpoint)
def list_endpoints_by_platform_application(self):
application_arn = self._get_param("PlatformApplicationArn")
endpoints = self.backend.list_endpoints_by_platform_application(application_arn)
if self.request_json:
return json.dumps(
{
"ListEndpointsByPlatformApplicationResponse": {
"ListEndpointsByPlatformApplicationResult": {
"Endpoints": [
{
"Attributes": endpoint.attributes,
"EndpointArn": endpoint.arn,
}
for endpoint in endpoints
],
"NextToken": None,
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937a"
},
}
}
)
template = self.response_template(
LIST_ENDPOINTS_BY_PLATFORM_APPLICATION_TEMPLATE
)
return template.render(endpoints=endpoints)
def get_endpoint_attributes(self):
arn = self._get_param("EndpointArn")
try:
endpoint = self.backend.get_endpoint(arn)
if self.request_json:
return json.dumps(
{
"GetEndpointAttributesResponse": {
"GetEndpointAttributesResult": {
"Attributes": endpoint.attributes
},
"ResponseMetadata": {
"RequestId": "384ac68d-3775-11df-8963-01868b7c937f"
},
}
}
)
template = self.response_template(GET_ENDPOINT_ATTRIBUTES_TEMPLATE)
return template.render(endpoint=endpoint)
except SNSNotFoundError:
error_response = self._error("NotFound", "Endpoint does not exist")
return error_response, dict(status=404)
def set_endpoint_attributes(self):
arn = self._get_param("EndpointArn")
attributes = self._get_attributes()
self.backend.set_endpoint_attributes(arn, attributes)
if self.request_json:
return json.dumps(
{
"SetEndpointAttributesResponse": {
"ResponseMetadata": {
"RequestId": "384bc68d-3775-12df-8963-01868b7c937f"
}
}
}
)
template = self.response_template(SET_ENDPOINT_ATTRIBUTES_TEMPLATE)
return template.render()
def delete_endpoint(self):
arn = self._get_param("EndpointArn")
self.backend.delete_endpoint(arn)
if self.request_json:
return json.dumps(
{
"DeleteEndpointResponse": {
"ResponseMetadata": {
"RequestId": "384bc68d-3775-12df-8963-01868b7c937f"
}
}
}
)
template = self.response_template(DELETE_ENDPOINT_TEMPLATE)
return template.render()
def get_subscription_attributes(self):
arn = self._get_param("SubscriptionArn")
attributes = self.backend.get_subscription_attributes(arn)
template = self.response_template(GET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE)
return template.render(attributes=attributes)
def set_subscription_attributes(self):
arn = self._get_param("SubscriptionArn")
attr_name = self._get_param("AttributeName")
attr_value = self._get_param("AttributeValue")
self.backend.set_subscription_attributes(arn, attr_name, attr_value)
template = self.response_template(SET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE)
return template.render()
def set_sms_attributes(self):
temp_dict = defaultdict(dict)
for key, value in self.querystring.items():
match = self.SMS_ATTR_REGEX.match(key)
if match is not None:
temp_dict[match.group("index")][match.group("type")] = value[0]
result = {}
for item in temp_dict.values():
if "key" in item and "value" in item:
result[item["key"]] = item["value"]
self.backend.update_sms_attributes(result)
template = self.response_template(SET_SMS_ATTRIBUTES_TEMPLATE)
return template.render()
def get_sms_attributes(self):
filter_list = set()
for key, value in self.querystring.items():
if key.startswith("attributes.member.1"):
filter_list.add(value[0])
if len(filter_list) > 0:
result = {
k: v for k, v in self.backend.sms_attributes.items() if k in filter_list
}
else:
result = self.backend.sms_attributes
template = self.response_template(GET_SMS_ATTRIBUTES_TEMPLATE)
return template.render(attributes=result)
def check_if_phone_number_is_opted_out(self):
number = self._get_param("phoneNumber")
if self.OPT_OUT_PHONE_NUMBER_REGEX.match(number) is None:
error_response = self._error(
code="InvalidParameter",
message="Invalid parameter: PhoneNumber Reason: input incorrectly formatted",
)
return error_response, dict(status=400)
template = self.response_template(CHECK_IF_OPTED_OUT_TEMPLATE)
return template.render(opt_out=str(number.endswith("99")).lower())
def list_phone_numbers_opted_out(self):
template = self.response_template(LIST_OPTOUT_TEMPLATE)
return template.render(opt_outs=self.backend.opt_out_numbers)
def opt_in_phone_number(self):
number = self._get_param("phoneNumber")
try:
self.backend.opt_out_numbers.remove(number)
except ValueError:
pass
template = self.response_template(OPT_IN_NUMBER_TEMPLATE)
return template.render()
def add_permission(self):
topic_arn = self._get_param("TopicArn")
label = self._get_param("Label")
aws_account_ids = self._get_multi_param("AWSAccountId.member.")
action_names = self._get_multi_param("ActionName.member.")
self.backend.add_permission(topic_arn, label, aws_account_ids, action_names)
template = self.response_template(ADD_PERMISSION_TEMPLATE)
return template.render()
def remove_permission(self):
topic_arn = self._get_param("TopicArn")
label = self._get_param("Label")
self.backend.remove_permission(topic_arn, label)
template = self.response_template(DEL_PERMISSION_TEMPLATE)
return template.render()
def confirm_subscription(self):
arn = self._get_param("TopicArn")
if arn not in self.backend.topics:
error_response = self._error("NotFound", "Topic does not exist")
return error_response, dict(status=404)
template = self.response_template(CONFIRM_SUBSCRIPTION_TEMPLATE)
return template.render(
sub_arn="{0}:68762e72-e9b1-410a-8b3b-903da69ee1d5".format(arn)
)
def list_tags_for_resource(self):
arn = self._get_param("ResourceArn")
result = self.backend.list_tags_for_resource(arn)
template = self.response_template(LIST_TAGS_FOR_RESOURCE_TEMPLATE)
return template.render(tags=result)
def tag_resource(self):
arn = self._get_param("ResourceArn")
tags = self._get_tags()
self.backend.tag_resource(arn, tags)
return self.response_template(TAG_RESOURCE_TEMPLATE).render()
def untag_resource(self):
arn = self._get_param("ResourceArn")
tag_keys = self._get_multi_param("TagKeys.member")
self.backend.untag_resource(arn, tag_keys)
return self.response_template(UNTAG_RESOURCE_TEMPLATE).render()
CREATE_TOPIC_TEMPLATE = """<CreateTopicResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CreateTopicResult>
<TopicArn>{{ topic.arn }}</TopicArn>
</CreateTopicResult>
<ResponseMetadata>
<RequestId>a8dec8b3-33a4-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</CreateTopicResponse>"""
LIST_TOPICS_TEMPLATE = """<ListTopicsResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListTopicsResult>
<Topics>
{% for topic in topics %}
<member>
<TopicArn>{{ topic.arn }}</TopicArn>
</member>
{% endfor %}
</Topics>
{% if next_token %}
<NextToken>{{ next_token }}</NextToken>
{% endif %}
</ListTopicsResult>
<ResponseMetadata>
<RequestId>3f1478c7-33a9-11df-9540-99d0768312d3</RequestId>
</ResponseMetadata>
</ListTopicsResponse>"""
DELETE_TOPIC_TEMPLATE = """<DeleteTopicResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>f3aa9ac9-3c3d-11df-8235-9dab105e9c32</RequestId>
</ResponseMetadata>
</DeleteTopicResponse>"""
GET_TOPIC_ATTRIBUTES_TEMPLATE = """<GetTopicAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetTopicAttributesResult>
<Attributes>
<entry>
<key>Owner</key>
<value>{{ topic.account_id }}</value>
</entry>
<entry>
<key>Policy</key>
<value>{{ topic.policy }}</value>
</entry>
<entry>
<key>TopicArn</key>
<value>{{ topic.arn }}</value>
</entry>
<entry>
<key>DisplayName</key>
<value>{{ topic.display_name }}</value>
</entry>
<entry>
<key>SubscriptionsPending</key>
<value>{{ topic.subscriptions_pending }}</value>
</entry>
<entry>
<key>SubscriptionsConfirmed</key>
<value>{{ topic.subscriptions_confimed }}</value>
</entry>
<entry>
<key>SubscriptionsDeleted</key>
<value>{{ topic.subscriptions_deleted }}</value>
</entry>
<entry>
<key>DeliveryPolicy</key>
<value>{{ topic.delivery_policy }}</value>
</entry>
<entry>
<key>EffectiveDeliveryPolicy</key>
<value>{{ topic.effective_delivery_policy }}</value>
</entry>
{% if topic.kms_master_key_id %}
<entry>
<key>KmsMasterKeyId</key>
<value>{{ topic.kms_master_key_id }}</value>
</entry>
{% endif %}
</Attributes>
</GetTopicAttributesResult>
<ResponseMetadata>
<RequestId>057f074c-33a7-11df-9540-99d0768312d3</RequestId>
</ResponseMetadata>
</GetTopicAttributesResponse>"""
SET_TOPIC_ATTRIBUTES_TEMPLATE = """<SetTopicAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>a8763b99-33a7-11df-a9b7-05d48da6f042</RequestId>
</ResponseMetadata>
</SetTopicAttributesResponse>"""
CREATE_PLATFORM_APPLICATION_TEMPLATE = """<CreatePlatformApplicationResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CreatePlatformApplicationResult>
<PlatformApplicationArn>{{ platform_application.arn }}</PlatformApplicationArn>
</CreatePlatformApplicationResult>
<ResponseMetadata>
<RequestId>b6f0e78b-e9d4-5a0e-b973-adc04e8a4ff9</RequestId>
</ResponseMetadata>
</CreatePlatformApplicationResponse>"""
CREATE_PLATFORM_ENDPOINT_TEMPLATE = """<CreatePlatformEndpointResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CreatePlatformEndpointResult>
<EndpointArn>{{ platform_endpoint.arn }}</EndpointArn>
</CreatePlatformEndpointResult>
<ResponseMetadata>
<RequestId>6613341d-3e15-53f7-bf3c-7e56994ba278</RequestId>
</ResponseMetadata>
</CreatePlatformEndpointResponse>"""
LIST_PLATFORM_APPLICATIONS_TEMPLATE = """<ListPlatformApplicationsResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListPlatformApplicationsResult>
<PlatformApplications>
{% for application in applications %}
<member>
<PlatformApplicationArn>{{ application.arn }}</PlatformApplicationArn>
<Attributes>
{% for attribute in application.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ application.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</member>
{% endfor %}
</PlatformApplications>
</ListPlatformApplicationsResult>
<ResponseMetadata>
<RequestId>315a335e-85d8-52df-9349-791283cbb529</RequestId>
</ResponseMetadata>
</ListPlatformApplicationsResponse>"""
DELETE_PLATFORM_APPLICATION_TEMPLATE = """<DeletePlatformApplicationResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>097dac18-7a77-5823-a8dd-e65476dcb037</RequestId>
</ResponseMetadata>
</DeletePlatformApplicationResponse>"""
GET_ENDPOINT_ATTRIBUTES_TEMPLATE = """<GetEndpointAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetEndpointAttributesResult>
<Attributes>
{% for attribute in endpoint.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ endpoint.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</GetEndpointAttributesResult>
<ResponseMetadata>
<RequestId>6c725a19-a142-5b77-94f9-1055a9ea04e7</RequestId>
</ResponseMetadata>
</GetEndpointAttributesResponse>"""
LIST_ENDPOINTS_BY_PLATFORM_APPLICATION_TEMPLATE = """<ListEndpointsByPlatformApplicationResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListEndpointsByPlatformApplicationResult>
<Endpoints>
{% for endpoint in endpoints %}
<member>
<EndpointArn>{{ endpoint.arn }}</EndpointArn>
<Attributes>
{% for attribute in endpoint.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ endpoint.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</member>
{% endfor %}
</Endpoints>
</ListEndpointsByPlatformApplicationResult>
<ResponseMetadata>
<RequestId>9a48768c-dac8-5a60-aec0-3cc27ea08d96</RequestId>
</ResponseMetadata>
</ListEndpointsByPlatformApplicationResponse>"""
GET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE = """<GetPlatformApplicationAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetPlatformApplicationAttributesResult>
<Attributes>
{% for attribute in application.attributes %}
<entry>
<key>{{ attribute }}</key>
<value>{{ application.attributes[attribute] }}</value>
</entry>
{% endfor %}
</Attributes>
</GetPlatformApplicationAttributesResult>
<ResponseMetadata>
<RequestId>74848df2-87f6-55ed-890c-c7be80442462</RequestId>
</ResponseMetadata>
</GetPlatformApplicationAttributesResponse>"""
PUBLISH_TEMPLATE = """<PublishResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<PublishResult>
<MessageId>{{ message_id }}</MessageId>
</PublishResult>
<ResponseMetadata>
<RequestId>f187a3c1-376f-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</PublishResponse>"""
SET_ENDPOINT_ATTRIBUTES_TEMPLATE = """<SetEndpointAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>2fe0bfc7-3e85-5ee5-a9e2-f58b35e85f6a</RequestId>
</ResponseMetadata>
</SetEndpointAttributesResponse>"""
DELETE_ENDPOINT_TEMPLATE = """<DeleteEndpointResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>c1d2b191-353c-5a5f-8969-fbdd3900afa8</RequestId>
</ResponseMetadata>
</DeleteEndpointResponse>"""
SET_PLATFORM_APPLICATION_ATTRIBUTES_TEMPLATE = """<SetPlatformApplicationAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>cf577bcc-b3dc-5463-88f1-3180b9412395</RequestId>
</ResponseMetadata>
</SetPlatformApplicationAttributesResponse>"""
SUBSCRIBE_TEMPLATE = """<SubscribeResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<SubscribeResult>
<SubscriptionArn>{{ subscription.arn }}</SubscriptionArn>
</SubscribeResult>
<ResponseMetadata>
<RequestId>c4407779-24a4-56fa-982c-3d927f93a775</RequestId>
</ResponseMetadata>
</SubscribeResponse>"""
UNSUBSCRIBE_TEMPLATE = """<UnsubscribeResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>18e0ac39-3776-11df-84c0-b93cc1666b84</RequestId>
</ResponseMetadata>
</UnsubscribeResponse>"""
LIST_SUBSCRIPTIONS_TEMPLATE = """<ListSubscriptionsResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListSubscriptionsResult>
<Subscriptions>
{% for subscription in subscriptions %}
<member>
<TopicArn>{{ subscription.topic.arn }}</TopicArn>
<Protocol>{{ subscription.protocol }}</Protocol>
<SubscriptionArn>{{ subscription.arn }}</SubscriptionArn>
<Owner>{{ subscription.account_id }}</Owner>
<Endpoint>{{ subscription.endpoint }}</Endpoint>
</member>
{% endfor %}
</Subscriptions>
{% if next_token %}
<NextToken>{{ next_token }}</NextToken>
{% endif %}
</ListSubscriptionsResult>
<ResponseMetadata>
<RequestId>384ac68d-3775-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</ListSubscriptionsResponse>"""
LIST_SUBSCRIPTIONS_BY_TOPIC_TEMPLATE = """<ListSubscriptionsByTopicResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListSubscriptionsByTopicResult>
<Subscriptions>
{% for subscription in subscriptions %}
<member>
<TopicArn>{{ subscription.topic.arn }}</TopicArn>
<Protocol>{{ subscription.protocol }}</Protocol>
<SubscriptionArn>{{ subscription.arn }}</SubscriptionArn>
<Owner>{{ subscription.account_id }}</Owner>
<Endpoint>{{ subscription.endpoint }}</Endpoint>
</member>
{% endfor %}
</Subscriptions>
{% if next_token %}
<NextToken>{{ next_token }}</NextToken>
{% endif %}
</ListSubscriptionsByTopicResult>
<ResponseMetadata>
<RequestId>384ac68d-3775-11df-8963-01868b7c937a</RequestId>
</ResponseMetadata>
</ListSubscriptionsByTopicResponse>"""
GET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE = """<GetSubscriptionAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetSubscriptionAttributesResult>
<Attributes>
{% for name, value in attributes.items() %}
<entry>
<key>{{ name }}</key>
<value>{{ value }}</value>
</entry>
{% endfor %}
</Attributes>
</GetSubscriptionAttributesResult>
<ResponseMetadata>
<RequestId>057f074c-33a7-11df-9540-99d0768312d3</RequestId>
</ResponseMetadata>
</GetSubscriptionAttributesResponse>"""
SET_SUBSCRIPTION_ATTRIBUTES_TEMPLATE = """<SetSubscriptionAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>a8763b99-33a7-11df-a9b7-05d48da6f042</RequestId>
</ResponseMetadata>
</SetSubscriptionAttributesResponse>"""
SET_SMS_ATTRIBUTES_TEMPLATE = """<SetSMSAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<SetSMSAttributesResult/>
<ResponseMetadata>
<RequestId>26332069-c04a-5428-b829-72524b56a364</RequestId>
</ResponseMetadata>
</SetSMSAttributesResponse>"""
GET_SMS_ATTRIBUTES_TEMPLATE = """<GetSMSAttributesResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<GetSMSAttributesResult>
<attributes>
{% for name, value in attributes.items() %}
<entry>
<key>{{ name }}</key>
<value>{{ value }}</value>
</entry>
{% endfor %}
</attributes>
</GetSMSAttributesResult>
<ResponseMetadata>
<RequestId>287f9554-8db3-5e66-8abc-c76f0186db7e</RequestId>
</ResponseMetadata>
</GetSMSAttributesResponse>"""
CHECK_IF_OPTED_OUT_TEMPLATE = """<CheckIfPhoneNumberIsOptedOutResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<CheckIfPhoneNumberIsOptedOutResult>
<isOptedOut>{{ opt_out }}</isOptedOut>
</CheckIfPhoneNumberIsOptedOutResult>
<ResponseMetadata>
<RequestId>287f9554-8db3-5e66-8abc-c76f0186db7e</RequestId>
</ResponseMetadata>
</CheckIfPhoneNumberIsOptedOutResponse>"""
ERROR_RESPONSE = """<ErrorResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<Error>
<Type>{{ sender }}</Type>
<Code>{{ code }}</Code>
<Message>{{ message }}</Message>
</Error>
<RequestId>9dd01905-5012-5f99-8663-4b3ecd0dfaef</RequestId>
</ErrorResponse>"""
LIST_OPTOUT_TEMPLATE = """<ListPhoneNumbersOptedOutResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListPhoneNumbersOptedOutResult>
<phoneNumbers>
{% for item in opt_outs %}
<member>{{ item }}</member>
{% endfor %}
</phoneNumbers>
</ListPhoneNumbersOptedOutResult>
<ResponseMetadata>
<RequestId>985e196d-a237-51b6-b33a-4b5601276b38</RequestId>
</ResponseMetadata>
</ListPhoneNumbersOptedOutResponse>"""
OPT_IN_NUMBER_TEMPLATE = """<OptInPhoneNumberResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<OptInPhoneNumberResult/>
<ResponseMetadata>
<RequestId>4c61842c-0796-50ef-95ac-d610c0bc8cf8</RequestId>
</ResponseMetadata>
</OptInPhoneNumberResponse>"""
ADD_PERMISSION_TEMPLATE = """<AddPermissionResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>c046e713-c5ff-5888-a7bc-b52f0e4f1299</RequestId>
</ResponseMetadata>
</AddPermissionResponse>"""
DEL_PERMISSION_TEMPLATE = """<RemovePermissionResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ResponseMetadata>
<RequestId>e767cc9f-314b-5e1b-b283-9ea3fd4e38a3</RequestId>
</ResponseMetadata>
</RemovePermissionResponse>"""
CONFIRM_SUBSCRIPTION_TEMPLATE = """<ConfirmSubscriptionResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ConfirmSubscriptionResult>
<SubscriptionArn>{{ sub_arn }}</SubscriptionArn>
</ConfirmSubscriptionResult>
<ResponseMetadata>
<RequestId>16eb4dde-7b3c-5b3e-a22a-1fe2a92d3293</RequestId>
</ResponseMetadata>
</ConfirmSubscriptionResponse>"""
LIST_TAGS_FOR_RESOURCE_TEMPLATE = """<ListTagsForResourceResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<ListTagsForResourceResult>
<Tags>
{% for name, value in tags.items() %}
<member>
<Key>{{ name }}</Key>
<Value>{{ value }}</Value>
</member>
{% endfor %}
</Tags>
</ListTagsForResourceResult>
<ResponseMetadata>
<RequestId>97fa763f-861b-5223-a946-20251f2a42e2</RequestId>
</ResponseMetadata>
</ListTagsForResourceResponse>"""
TAG_RESOURCE_TEMPLATE = """<TagResourceResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<TagResourceResult/>
<ResponseMetadata>
<RequestId>fd4ab1da-692f-50a7-95ad-e7c665877d98</RequestId>
</ResponseMetadata>
</TagResourceResponse>"""
UNTAG_RESOURCE_TEMPLATE = """<UntagResourceResponse xmlns="http://sns.amazonaws.com/doc/2010-03-31/">
<UntagResourceResult/>
<ResponseMetadata>
<RequestId>14eb7b1a-4cbd-5a56-80db-2d06412df769</RequestId>
</ResponseMetadata>
</UntagResourceResponse>"""
| true | true |
f73c03fdca9ef3303bbf4e974345b850baa55803 | 17,383 | py | Python | sdk/python/pulumi_azure_native/network/latest/vpn_server_configuration.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_native/network/latest/vpn_server_configuration.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_native/network/latest/vpn_server_configuration.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['VpnServerConfiguration']
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:network:VpnServerConfiguration'.""", DeprecationWarning)
class VpnServerConfiguration(pulumi.CustomResource):
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:network:VpnServerConfiguration'.""", DeprecationWarning)
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
aad_authentication_parameters: Optional[pulumi.Input[pulumi.InputType['AadAuthenticationParametersArgs']]] = None,
id: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
radius_client_root_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigRadiusClientRootCertificateArgs']]]]] = None,
radius_server_address: Optional[pulumi.Input[str]] = None,
radius_server_root_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigRadiusServerRootCertificateArgs']]]]] = None,
radius_server_secret: Optional[pulumi.Input[str]] = None,
radius_servers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RadiusServerArgs']]]]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
vpn_authentication_types: Optional[pulumi.Input[Sequence[pulumi.Input[Union[str, 'VpnAuthenticationType']]]]] = None,
vpn_client_ipsec_policies: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpsecPolicyArgs']]]]] = None,
vpn_client_revoked_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigVpnClientRevokedCertificateArgs']]]]] = None,
vpn_client_root_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigVpnClientRootCertificateArgs']]]]] = None,
vpn_protocols: Optional[pulumi.Input[Sequence[pulumi.Input[Union[str, 'VpnGatewayTunnelingProtocol']]]]] = None,
vpn_server_configuration_name: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
VpnServerConfiguration Resource.
Latest API Version: 2020-08-01.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['AadAuthenticationParametersArgs']] aad_authentication_parameters: The set of aad vpn authentication parameters.
:param pulumi.Input[str] id: Resource ID.
:param pulumi.Input[str] location: Resource location.
:param pulumi.Input[str] name: The name of the VpnServerConfiguration that is unique within a resource group.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigRadiusClientRootCertificateArgs']]]] radius_client_root_certificates: Radius client root certificate of VpnServerConfiguration.
:param pulumi.Input[str] radius_server_address: The radius server address property of the VpnServerConfiguration resource for point to site client connection.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigRadiusServerRootCertificateArgs']]]] radius_server_root_certificates: Radius Server root certificate of VpnServerConfiguration.
:param pulumi.Input[str] radius_server_secret: The radius secret property of the VpnServerConfiguration resource for point to site client connection.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RadiusServerArgs']]]] radius_servers: Multiple Radius Server configuration for VpnServerConfiguration.
:param pulumi.Input[str] resource_group_name: The resource group name of the VpnServerConfiguration.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags.
:param pulumi.Input[Sequence[pulumi.Input[Union[str, 'VpnAuthenticationType']]]] vpn_authentication_types: VPN authentication types for the VpnServerConfiguration.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpsecPolicyArgs']]]] vpn_client_ipsec_policies: VpnClientIpsecPolicies for VpnServerConfiguration.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigVpnClientRevokedCertificateArgs']]]] vpn_client_revoked_certificates: VPN client revoked certificate of VpnServerConfiguration.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigVpnClientRootCertificateArgs']]]] vpn_client_root_certificates: VPN client root certificate of VpnServerConfiguration.
:param pulumi.Input[Sequence[pulumi.Input[Union[str, 'VpnGatewayTunnelingProtocol']]]] vpn_protocols: VPN protocols for the VpnServerConfiguration.
:param pulumi.Input[str] vpn_server_configuration_name: The name of the VpnServerConfiguration being created or updated.
"""
pulumi.log.warn("""VpnServerConfiguration is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:network:VpnServerConfiguration'.""")
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['aad_authentication_parameters'] = aad_authentication_parameters
__props__['id'] = id
__props__['location'] = location
__props__['name'] = name
__props__['radius_client_root_certificates'] = radius_client_root_certificates
__props__['radius_server_address'] = radius_server_address
__props__['radius_server_root_certificates'] = radius_server_root_certificates
__props__['radius_server_secret'] = radius_server_secret
__props__['radius_servers'] = radius_servers
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['tags'] = tags
__props__['vpn_authentication_types'] = vpn_authentication_types
__props__['vpn_client_ipsec_policies'] = vpn_client_ipsec_policies
__props__['vpn_client_revoked_certificates'] = vpn_client_revoked_certificates
__props__['vpn_client_root_certificates'] = vpn_client_root_certificates
__props__['vpn_protocols'] = vpn_protocols
__props__['vpn_server_configuration_name'] = vpn_server_configuration_name
__props__['etag'] = None
__props__['p2_s_vpn_gateways'] = None
__props__['provisioning_state'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/latest:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20190801:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20190801:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20190901:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20190901:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20191101:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20191101:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20191201:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20191201:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200301:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200301:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200401:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200401:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200501:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200501:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200601:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200601:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200701:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200701:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200801:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200801:VpnServerConfiguration")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(VpnServerConfiguration, __self__).__init__(
'azure-native:network/latest:VpnServerConfiguration',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'VpnServerConfiguration':
"""
Get an existing VpnServerConfiguration resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["aad_authentication_parameters"] = None
__props__["etag"] = None
__props__["location"] = None
__props__["name"] = None
__props__["p2_s_vpn_gateways"] = None
__props__["provisioning_state"] = None
__props__["radius_client_root_certificates"] = None
__props__["radius_server_address"] = None
__props__["radius_server_root_certificates"] = None
__props__["radius_server_secret"] = None
__props__["radius_servers"] = None
__props__["tags"] = None
__props__["type"] = None
__props__["vpn_authentication_types"] = None
__props__["vpn_client_ipsec_policies"] = None
__props__["vpn_client_revoked_certificates"] = None
__props__["vpn_client_root_certificates"] = None
__props__["vpn_protocols"] = None
return VpnServerConfiguration(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="aadAuthenticationParameters")
def aad_authentication_parameters(self) -> pulumi.Output[Optional['outputs.AadAuthenticationParametersResponse']]:
"""
The set of aad vpn authentication parameters.
"""
return pulumi.get(self, "aad_authentication_parameters")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[str]:
"""
A unique read-only string that changes whenever the resource is updated.
"""
return pulumi.get(self, "etag")
@property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
"""
Resource location.
"""
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Resource name.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="p2SVpnGateways")
def p2_s_vpn_gateways(self) -> pulumi.Output[Sequence['outputs.P2SVpnGatewayResponse']]:
"""
List of references to P2SVpnGateways.
"""
return pulumi.get(self, "p2_s_vpn_gateways")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
"""
The provisioning state of the VpnServerConfiguration resource. Possible values are: 'Updating', 'Deleting', and 'Failed'.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="radiusClientRootCertificates")
def radius_client_root_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigRadiusClientRootCertificateResponse']]]:
"""
Radius client root certificate of VpnServerConfiguration.
"""
return pulumi.get(self, "radius_client_root_certificates")
@property
@pulumi.getter(name="radiusServerAddress")
def radius_server_address(self) -> pulumi.Output[Optional[str]]:
"""
The radius server address property of the VpnServerConfiguration resource for point to site client connection.
"""
return pulumi.get(self, "radius_server_address")
@property
@pulumi.getter(name="radiusServerRootCertificates")
def radius_server_root_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigRadiusServerRootCertificateResponse']]]:
"""
Radius Server root certificate of VpnServerConfiguration.
"""
return pulumi.get(self, "radius_server_root_certificates")
@property
@pulumi.getter(name="radiusServerSecret")
def radius_server_secret(self) -> pulumi.Output[Optional[str]]:
"""
The radius secret property of the VpnServerConfiguration resource for point to site client connection.
"""
return pulumi.get(self, "radius_server_secret")
@property
@pulumi.getter(name="radiusServers")
def radius_servers(self) -> pulumi.Output[Optional[Sequence['outputs.RadiusServerResponse']]]:
"""
Multiple Radius Server configuration for VpnServerConfiguration.
"""
return pulumi.get(self, "radius_servers")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource tags.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Resource type.
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="vpnAuthenticationTypes")
def vpn_authentication_types(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
VPN authentication types for the VpnServerConfiguration.
"""
return pulumi.get(self, "vpn_authentication_types")
@property
@pulumi.getter(name="vpnClientIpsecPolicies")
def vpn_client_ipsec_policies(self) -> pulumi.Output[Optional[Sequence['outputs.IpsecPolicyResponse']]]:
"""
VpnClientIpsecPolicies for VpnServerConfiguration.
"""
return pulumi.get(self, "vpn_client_ipsec_policies")
@property
@pulumi.getter(name="vpnClientRevokedCertificates")
def vpn_client_revoked_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigVpnClientRevokedCertificateResponse']]]:
"""
VPN client revoked certificate of VpnServerConfiguration.
"""
return pulumi.get(self, "vpn_client_revoked_certificates")
@property
@pulumi.getter(name="vpnClientRootCertificates")
def vpn_client_root_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigVpnClientRootCertificateResponse']]]:
"""
VPN client root certificate of VpnServerConfiguration.
"""
return pulumi.get(self, "vpn_client_root_certificates")
@property
@pulumi.getter(name="vpnProtocols")
def vpn_protocols(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
VPN protocols for the VpnServerConfiguration.
"""
return pulumi.get(self, "vpn_protocols")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 57.180921 | 1,813 | 0.712075 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['VpnServerConfiguration']
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:network:VpnServerConfiguration'.""", DeprecationWarning)
class VpnServerConfiguration(pulumi.CustomResource):
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:network:VpnServerConfiguration'.""", DeprecationWarning)
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
aad_authentication_parameters: Optional[pulumi.Input[pulumi.InputType['AadAuthenticationParametersArgs']]] = None,
id: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
radius_client_root_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigRadiusClientRootCertificateArgs']]]]] = None,
radius_server_address: Optional[pulumi.Input[str]] = None,
radius_server_root_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigRadiusServerRootCertificateArgs']]]]] = None,
radius_server_secret: Optional[pulumi.Input[str]] = None,
radius_servers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RadiusServerArgs']]]]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
vpn_authentication_types: Optional[pulumi.Input[Sequence[pulumi.Input[Union[str, 'VpnAuthenticationType']]]]] = None,
vpn_client_ipsec_policies: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpsecPolicyArgs']]]]] = None,
vpn_client_revoked_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigVpnClientRevokedCertificateArgs']]]]] = None,
vpn_client_root_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnServerConfigVpnClientRootCertificateArgs']]]]] = None,
vpn_protocols: Optional[pulumi.Input[Sequence[pulumi.Input[Union[str, 'VpnGatewayTunnelingProtocol']]]]] = None,
vpn_server_configuration_name: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
pulumi.log.warn("""VpnServerConfiguration is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:network:VpnServerConfiguration'.""")
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['aad_authentication_parameters'] = aad_authentication_parameters
__props__['id'] = id
__props__['location'] = location
__props__['name'] = name
__props__['radius_client_root_certificates'] = radius_client_root_certificates
__props__['radius_server_address'] = radius_server_address
__props__['radius_server_root_certificates'] = radius_server_root_certificates
__props__['radius_server_secret'] = radius_server_secret
__props__['radius_servers'] = radius_servers
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['tags'] = tags
__props__['vpn_authentication_types'] = vpn_authentication_types
__props__['vpn_client_ipsec_policies'] = vpn_client_ipsec_policies
__props__['vpn_client_revoked_certificates'] = vpn_client_revoked_certificates
__props__['vpn_client_root_certificates'] = vpn_client_root_certificates
__props__['vpn_protocols'] = vpn_protocols
__props__['vpn_server_configuration_name'] = vpn_server_configuration_name
__props__['etag'] = None
__props__['p2_s_vpn_gateways'] = None
__props__['provisioning_state'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/latest:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20190801:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20190801:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20190901:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20190901:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20191101:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20191101:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20191201:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20191201:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200301:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200301:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200401:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200401:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200501:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200501:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200601:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200601:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200701:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200701:VpnServerConfiguration"), pulumi.Alias(type_="azure-native:network/v20200801:VpnServerConfiguration"), pulumi.Alias(type_="azure-nextgen:network/v20200801:VpnServerConfiguration")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(VpnServerConfiguration, __self__).__init__(
'azure-native:network/latest:VpnServerConfiguration',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'VpnServerConfiguration':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["aad_authentication_parameters"] = None
__props__["etag"] = None
__props__["location"] = None
__props__["name"] = None
__props__["p2_s_vpn_gateways"] = None
__props__["provisioning_state"] = None
__props__["radius_client_root_certificates"] = None
__props__["radius_server_address"] = None
__props__["radius_server_root_certificates"] = None
__props__["radius_server_secret"] = None
__props__["radius_servers"] = None
__props__["tags"] = None
__props__["type"] = None
__props__["vpn_authentication_types"] = None
__props__["vpn_client_ipsec_policies"] = None
__props__["vpn_client_revoked_certificates"] = None
__props__["vpn_client_root_certificates"] = None
__props__["vpn_protocols"] = None
return VpnServerConfiguration(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="aadAuthenticationParameters")
def aad_authentication_parameters(self) -> pulumi.Output[Optional['outputs.AadAuthenticationParametersResponse']]:
return pulumi.get(self, "aad_authentication_parameters")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[str]:
return pulumi.get(self, "etag")
@property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter(name="p2SVpnGateways")
def p2_s_vpn_gateways(self) -> pulumi.Output[Sequence['outputs.P2SVpnGatewayResponse']]:
return pulumi.get(self, "p2_s_vpn_gateways")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="radiusClientRootCertificates")
def radius_client_root_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigRadiusClientRootCertificateResponse']]]:
return pulumi.get(self, "radius_client_root_certificates")
@property
@pulumi.getter(name="radiusServerAddress")
def radius_server_address(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "radius_server_address")
@property
@pulumi.getter(name="radiusServerRootCertificates")
def radius_server_root_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigRadiusServerRootCertificateResponse']]]:
return pulumi.get(self, "radius_server_root_certificates")
@property
@pulumi.getter(name="radiusServerSecret")
def radius_server_secret(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "radius_server_secret")
@property
@pulumi.getter(name="radiusServers")
def radius_servers(self) -> pulumi.Output[Optional[Sequence['outputs.RadiusServerResponse']]]:
return pulumi.get(self, "radius_servers")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
return pulumi.get(self, "type")
@property
@pulumi.getter(name="vpnAuthenticationTypes")
def vpn_authentication_types(self) -> pulumi.Output[Optional[Sequence[str]]]:
return pulumi.get(self, "vpn_authentication_types")
@property
@pulumi.getter(name="vpnClientIpsecPolicies")
def vpn_client_ipsec_policies(self) -> pulumi.Output[Optional[Sequence['outputs.IpsecPolicyResponse']]]:
return pulumi.get(self, "vpn_client_ipsec_policies")
@property
@pulumi.getter(name="vpnClientRevokedCertificates")
def vpn_client_revoked_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigVpnClientRevokedCertificateResponse']]]:
return pulumi.get(self, "vpn_client_revoked_certificates")
@property
@pulumi.getter(name="vpnClientRootCertificates")
def vpn_client_root_certificates(self) -> pulumi.Output[Optional[Sequence['outputs.VpnServerConfigVpnClientRootCertificateResponse']]]:
return pulumi.get(self, "vpn_client_root_certificates")
@property
@pulumi.getter(name="vpnProtocols")
def vpn_protocols(self) -> pulumi.Output[Optional[Sequence[str]]]:
return pulumi.get(self, "vpn_protocols")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| true | true |
f73c04c3a1d478e20e01cd911a0674b41ed26288 | 18,969 | py | Python | unrestricted-advex/unrestricted_advex/attacks.py | fdcck/unrestricted-adversarial-examples | 125a21d2073308df05ad2dfaf4f9c58ec749b3e4 | [
"Apache-2.0"
] | 1 | 2018-10-08T10:11:51.000Z | 2018-10-08T10:11:51.000Z | unrestricted-advex/unrestricted_advex/attacks.py | fdcck/unrestricted-adversarial-examples | 125a21d2073308df05ad2dfaf4f9c58ec749b3e4 | [
"Apache-2.0"
] | null | null | null | unrestricted-advex/unrestricted_advex/attacks.py | fdcck/unrestricted-adversarial-examples | 125a21d2073308df05ad2dfaf4f9c58ec749b3e4 | [
"Apache-2.0"
] | null | null | null | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
import random
from itertools import product, repeat
import numpy as np
import tensorflow as tf
from cleverhans.attacks import SPSA
from cleverhans.model import Model
from foolbox.attacks import BoundaryAttack as FoolboxBoundaryAttack
from imagenet_c import corrupt
from six.moves import xrange
from unrestricted_advex.cleverhans_fast_spatial_attack import SpatialTransformationMethod
class Attack(object):
name = None
# TODO: Refactor this out of this object
_stop_after_n_datapoints = None # An attack can optionally run on only a subset of the dataset
def __call__(self, *args, **kwargs):
raise NotImplementedError()
class CleanData(Attack):
"""Also known as the "null attack". Just returns the unaltered clean image"""
name = 'clean'
def __call__(self, model_fn, images_batch_nhwc, y_np):
del y_np, model_fn # unused
return images_batch_nhwc
class SpsaAttack(Attack):
name = 'spsa'
def __init__(self, model, image_shape_hwc, epsilon=(16. / 255),
num_steps=200, batch_size=32, is_debug=False):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf.Session(graph=self.graph)
self.x_input = tf.placeholder(tf.float32, shape=(1,) + image_shape_hwc)
self.y_label = tf.placeholder(tf.int32, shape=(1,))
self.model = model
attack = SPSA(CleverhansPyfuncModelWrapper(self.model), sess=self.sess)
self.x_adv = attack.generate(
self.x_input,
y=self.y_label,
epsilon=epsilon,
num_steps=num_steps,
early_stop_loss_threshold=-1.,
batch_size=batch_size,
is_debug=is_debug)
self.graph.finalize()
def __call__(self, model, x_np, y_np): # (4. / 255)):
if model != self.model:
raise ValueError('Cannot call spsa attack on different models')
del model # unused except to check that we already wired it up right
with self.graph.as_default():
all_x_adv_np = []
for i in xrange(len(x_np)):
x_adv_np = self.sess.run(self.x_adv, feed_dict={
self.x_input: np.expand_dims(x_np[i], axis=0),
self.y_label: np.expand_dims(y_np[i], axis=0),
})
all_x_adv_np.append(x_adv_np)
return np.concatenate(all_x_adv_np)
def corrupt_float32_image(x, corruption_name, severity):
"""Convert to uint8 and back to conform to corruption API"""
x = np.copy(x) # We make a copy to avoid changing things in-place
x = (x * 255).astype(np.uint8)
corrupt_x = corrupt(
x,
corruption_name=corruption_name,
severity=severity)
return corrupt_x.astype(np.float32) / 255.
def _corrupt_float32_image_star(args):
return corrupt_float32_image(*args)
class CommonCorruptionsAttack(Attack):
name = "common_corruptions"
def __init__(self, severity=1):
self.corruption_names = [
'gaussian_noise',
'shot_noise',
'impulse_noise',
'defocus_blur',
'glass_blur',
'motion_blur',
'zoom_blur',
# 'snow', # Snow does not work in python 2.7
# 'frost', # Frost is not working correctly
'fog',
'brightness',
'contrast',
'elastic_transform',
'pixelate',
'jpeg_compression',
'speckle_noise',
'gaussian_blur',
'spatter',
'saturate']
self.severity = severity
self.pool = multiprocessing.Pool(len(self.corruption_names))
def __call__(self, model_fn, images_batch_nhwc, y_np):
assert images_batch_nhwc.shape[1:] == (224, 224, 3), \
"Image shape must equal (N, 224, 224, 3)"
batch_size = len(images_batch_nhwc)
# Keep track of the worst corruption for each image
worst_corruption = np.copy(images_batch_nhwc)
worst_loss = [np.NINF] * batch_size
# Iterate through each image in the batch
for batch_idx, x in enumerate(images_batch_nhwc):
corrupt_args = [(x, corruption_name, self.severity)
for corruption_name in self.corruption_names]
corrupt_x_batch = self.pool.map(_corrupt_float32_image_star, corrupt_args)
logits_batch = model_fn(np.array(corrupt_x_batch))
label = y_np[batch_idx]
# This is left un-vectorized for readability
for (logits, corrupt_x) in zip(logits_batch, corrupt_x_batch):
correct_logit, wrong_logit = logits[label], logits[1 - label]
# We can choose different loss functions to optimize in the
# attack. For now, optimize the magnitude of the wrong logit
# because we use this as our confidence threshold
loss = wrong_logit
# loss = wrong_logit - correct_logit
if loss > worst_loss[batch_idx]:
worst_corruption[batch_idx] = corrupt_x
worst_loss[batch_idx] = loss
return worst_corruption
class BoundaryAttack(Attack):
name = "boundary"
def __init__(self, model, image_shape_hwc, max_l2_distortion=4, label_to_examples=None):
if label_to_examples is None:
label_to_examples = {}
self.max_l2_distortion = max_l2_distortion
class Model:
def bounds(self):
return [0, 1]
def predictions(self, img):
return model(img[np.newaxis, :, :, :])[0]
def batch_predictions(self, img):
return model(img)
self.label_to_examples = label_to_examples
h, w, c = image_shape_hwc
mse_threshold = max_l2_distortion ** 2 / (h * w * c)
try:
# Foolbox 1.5 allows us to use a threshold the attack will abort after
# reaching. Because we only care about a distortion of less than 4, as soon
# as we reach it, we can just abort and move on to the next image.
self.attack = FoolboxBoundaryAttack(model=Model(), threshold=mse_threshold)
except:
# Fall back to the original implementation.
print("WARNING: Using foolbox version < 1.5 will cuase the "
"boundary attack to perform more work than is required. "
"Please upgrade to version 1.5")
self.attack = FoolboxBoundaryAttack(model=Model())
def __call__(self, model, x_np, y_np):
r = []
for i in range(len(x_np)):
other = 1 - y_np[i]
initial_adv = random.choice(self.label_to_examples[other])
try:
adv = self.attack(x_np[i], y_np[i],
log_every_n_steps=100, # Reduce verbosity of the attack
starting_point=initial_adv
)
distortion = np.sum((x_np[i] - adv) ** 2) ** .5
if distortion > self.max_l2_distortion:
# project to the surface of the L2 ball
adv = x_np[i] + (adv - x_np[i]) / distortion * self.max_l2_distortion
except AssertionError as error:
if str(error).startswith("Invalid starting point provided."):
print("WARNING: The model misclassified the starting point (the target) "
"from BoundaryAttack. This means that the attack will fail on this "
"specific point (but is likely to succeed on other points.")
adv = x_np[i] # Just return the non-adversarial point
else:
raise error
r.append(adv)
return np.array(r)
class FastSpatialGridAttack(Attack):
"""Fast attack from "A Rotation and a Translation Suffice: Fooling CNNs with
Simple Transformations", Engstrom et al. 2018
https://arxiv.org/pdf/1712.02779.pdf
"""
name = 'spatial_grid'
def __init__(self, model,
image_shape_hwc,
spatial_limits,
grid_granularity,
black_border_size,
):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf.Session(graph=self.graph)
self.x_input = tf.placeholder(
tf.float32, shape=[None] + list(image_shape_hwc))
self.y_input = tf.placeholder(tf.float32, shape=(None, 2))
self.model = model
attack = SpatialTransformationMethod(
CleverhansPyfuncModelWrapper(self.model), sess=self.sess)
self.x_adv = attack.generate(
self.x_input,
y=self.y_input,
n_samples=None,
dx_min=-float(spatial_limits[0]) / image_shape_hwc[0],
dx_max=float(spatial_limits[0]) / image_shape_hwc[0],
n_dxs=grid_granularity[0],
dy_min=-float(spatial_limits[1]) / image_shape_hwc[1],
dy_max=float(spatial_limits[1]) / image_shape_hwc[1],
n_dys=grid_granularity[1],
angle_min=-spatial_limits[2],
angle_max=spatial_limits[2],
n_angles=grid_granularity[2],
black_border_size=black_border_size,
)
self.graph.finalize()
def __call__(self, model_fn, x_np, y_np):
if model_fn != self.model:
raise ValueError('Cannot call spatial attack on different models')
del model_fn # unused except to check that we already wired it up right
y_np_one_hot = np.zeros([len(y_np), 2], np.float32)
y_np_one_hot[np.arange(len(y_np)), y_np] = 1.0
# Reduce the batch size to 1 to avoid OOM errors
with self.graph.as_default():
all_x_adv_np = []
for i in xrange(len(x_np)):
x_adv_np = self.sess.run(self.x_adv, feed_dict={
self.x_input: np.expand_dims(x_np[i], axis=0),
self.y_input: np.expand_dims(y_np_one_hot[i], axis=0),
})
all_x_adv_np.append(x_adv_np)
return np.concatenate(all_x_adv_np)
class SpatialGridAttack(Attack):
"""Attack from "A Rotation and a Translation Suffice: Fooling CNNs with
Simple Transformations", Engstrom et al. 2018
https://arxiv.org/pdf/1712.02779.pdf
"""
name = 'spatial_grid'
def __init__(self, image_shape_hwc,
spatial_limits,
grid_granularity,
black_border_size,
valid_check=None,
):
"""
:param model_fn: a callable: batch-input -> batch-probability in [0, 1]
:param spatial_limits:
:param grid_granularity:
"""
self.limits = spatial_limits
self.granularity = grid_granularity
self.valid_check = valid_check
# Construct graph for spatial attack
self.graph = tf.Graph()
with self.graph.as_default():
self._x_for_trans = tf.placeholder(tf.float32, shape=[None] + list(image_shape_hwc))
self._t_for_trans = tf.placeholder(tf.float32, shape=[None, 3])
x = apply_black_border(
self._x_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
border_size=black_border_size
)
self._tranformed_x_op = apply_transformation(
x,
transform=self._t_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
)
self.session = tf.Session()
self.grid_store = []
def __call__(self, model_fn, x_np, y_np):
n = len(x_np)
grid = product(*list(np.linspace(-l, l, num=g)
for l, g in zip(self.limits, self.granularity)))
worst_x = np.copy(x_np)
max_xent = np.zeros(n)
all_correct = np.ones(n).astype(bool)
trans_np = np.stack(
repeat([0, 0, 0], n))
with self.graph.as_default():
x_downsize_np = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
for horizontal_trans, vertical_trans, rotation in grid:
trans_np = np.stack(
repeat([horizontal_trans, vertical_trans, rotation], n))
# Apply the spatial attack
with self.graph.as_default():
x_np_trans = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
# See how the model_fn performs on the perturbed input
logits = model_fn(x_np_trans)
preds = np.argmax(logits, axis=1)
cur_xent = _sparse_softmax_cross_entropy_with_logits_from_numpy(
logits, y_np, self.graph, self.session)
cur_xent = np.asarray(cur_xent)
cur_correct = np.equal(y_np, preds)
if self.valid_check:
is_valid = self.valid_check(x_downsize_np, x_np_trans)
cur_correct |= ~is_valid
cur_xent -= is_valid * 1e9
# Select indices to update: we choose the misclassified transformation
# of maximum xent (or just highest xent if everything else if correct).
idx = (cur_xent > max_xent) & (cur_correct == all_correct)
idx = idx | (cur_correct < all_correct)
max_xent = np.maximum(cur_xent, max_xent)
all_correct = cur_correct & all_correct
idx = np.expand_dims(idx, axis=-1) # shape (bsize, 1)
idx = np.expand_dims(idx, axis=-1)
idx = np.expand_dims(idx, axis=-1) # shape (bsize, 1, 1, 1)
worst_x = np.where(idx, x_np_trans, worst_x, ) # shape (bsize, 32, 32, 3)
return worst_x
def _sparse_softmax_cross_entropy_with_logits_from_numpy(logits_np, labels_np, graph, sess):
"""Helper that calls the TF sparse_softmax_cross_entropy_with_logits function"""
with graph.as_default():
labels_tf = tf.placeholder(tf.int32, [None])
logits_tf = tf.placeholder(tf.float32, [None, None])
xent_tf = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels_tf, logits=logits_tf)
return sess.run(xent_tf, feed_dict={
labels_tf: labels_np, logits_tf: logits_np})
def apply_black_border(x, image_height, image_width, border_size):
x = tf.image.resize_images(x, (image_width - border_size,
image_height - border_size))
x = tf.pad(x, [[0, 0],
[border_size, border_size],
[border_size, border_size],
[0, 0]], 'CONSTANT')
return x
def apply_transformation(x, transform, image_height, image_width):
# Map a transformation onto the input
trans_x, trans_y, rot = tf.unstack(transform, axis=1)
rot *= np.pi / 180 # convert degrees to radians
# Pad the image to prevent two-step rotation / translation
# resulting in a cropped image
x = tf.pad(x, [[0, 0], [50, 50], [50, 50], [0, 0]], 'CONSTANT')
# rotate and translate image
ones = tf.ones(shape=tf.shape(trans_x))
zeros = tf.zeros(shape=tf.shape(trans_x))
trans = tf.stack([ones, zeros, -trans_x,
zeros, ones, -trans_y,
zeros, zeros], axis=1)
x = tf.contrib.image.rotate(x, rot, interpolation='BILINEAR')
x = tf.contrib.image.transform(x, trans, interpolation='BILINEAR')
return tf.image.resize_image_with_crop_or_pad(
x, image_height, image_width)
class CleverhansPyfuncModelWrapper(Model):
nb_classes = 2
num_classes = 2
def __init__(self, model_fn):
"""
Wrap a callable function that takes a numpy array of shape (N, C, H, W),
and outputs a numpy vector of length N, with each element in range [0, 1].
"""
self.model_fn = model_fn
def fprop(self, x, **kwargs):
logits_op = tf.py_func(self.model_fn, [x], tf.float32)
return {'logits': logits_op}
class RandomSpatialAttack(Attack):
"""Apply a single random rotation and translation
as in "A Rotation and a Translation Suffice: Fooling CNNs with
Simple Transformations", Engstrom et al. 2018
https://arxiv.org/pdf/1712.02779.pdf
"""
name = 'random_spatial'
def __init__(self, image_shape_hwc, spatial_limits, black_border_size, valid_check=None):
self.limits = spatial_limits
self.valid_check = valid_check
# Construct graph for spatial attack
self.graph = tf.Graph()
with self.graph.as_default():
self._x_for_trans = tf.placeholder(tf.float32, shape=[None] + list(image_shape_hwc))
self._t_for_trans = tf.placeholder(tf.float32, shape=[None, 3])
x = apply_black_border(
self._x_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
border_size=black_border_size
)
self._tranformed_x_op = apply_transformation(
x,
transform=self._t_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
)
self.session = tf.Session()
def __call__(self, model_fn, x_np, y_np):
# randomize each example separately
with self.graph.as_default():
result = np.zeros(x_np.shape, dtype=x_np.dtype)
did = np.zeros(x_np.shape[0], dtype=np.bool)
trans_np = np.stack(
repeat([0, 0, 0], x_np.shape[0]))
x_downsize_np = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
while True:
random_transforms = (np.random.uniform(-lim, lim, len(x_np)) for lim in self.limits)
trans_np = np.stack(random_transforms, axis=1)
out = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
if self.valid_check is None:
return out
else:
ok = self.valid_check(x_downsize_np, out)
result[ok] = out[ok]
did[ok] = True
if np.all(did):
return result
class SpsaWithRandomSpatialAttack(Attack):
"""Apply a single random rotation and translation and then apply SPSA
to the resulting image
"""
name = "spsa_with_random_spatial"
def __init__(self, model, image_shape_hwc, spatial_limits, black_border_size,
epsilon=(16. / 255), num_steps=32, is_debug=False,
valid_check=None):
self.random_spatial_attack = RandomSpatialAttack(
image_shape_hwc,
valid_check=valid_check,
spatial_limits=spatial_limits,
black_border_size=black_border_size)
self.spsa_attack = SpsaAttack(
model,
image_shape_hwc,
epsilon=epsilon,
num_steps=num_steps,
batch_size=64, # this is number of samples in the new cleverhans
is_debug=is_debug)
def __call__(self, model, x_np, y_np):
x_after_spatial_np = self.random_spatial_attack(model, x_np, y_np)
x_adv = self.spsa_attack(model, x_after_spatial_np, y_np)
return x_adv
class BoundaryWithRandomSpatialAttack(Attack):
"""Apply a single random rotation and translation and then apply SPSA
to the resulting image
"""
name = "boundary_with_random_spatial"
def __init__(self, model, image_shape_hwc, spatial_limits, black_border_size,
max_l2_distortion=4, label_to_examples=None, valid_check=None):
self.random_spatial_attack = RandomSpatialAttack(
image_shape_hwc,
valid_check=valid_check,
spatial_limits=spatial_limits,
black_border_size=black_border_size)
self.boundary_attack = BoundaryAttack(
model,
max_l2_distortion=max_l2_distortion,
image_shape_hwc=image_shape_hwc,
label_to_examples=label_to_examples)
def __call__(self, model, x_np, y_np):
x_after_spatial_np = self.random_spatial_attack(model, x_np, y_np)
x_adv = self.boundary_attack(model, x_after_spatial_np, y_np)
return x_adv
| 33.455026 | 97 | 0.664611 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
import random
from itertools import product, repeat
import numpy as np
import tensorflow as tf
from cleverhans.attacks import SPSA
from cleverhans.model import Model
from foolbox.attacks import BoundaryAttack as FoolboxBoundaryAttack
from imagenet_c import corrupt
from six.moves import xrange
from unrestricted_advex.cleverhans_fast_spatial_attack import SpatialTransformationMethod
class Attack(object):
name = None
_stop_after_n_datapoints = None
def __call__(self, *args, **kwargs):
raise NotImplementedError()
class CleanData(Attack):
name = 'clean'
def __call__(self, model_fn, images_batch_nhwc, y_np):
del y_np, model_fn
return images_batch_nhwc
class SpsaAttack(Attack):
name = 'spsa'
def __init__(self, model, image_shape_hwc, epsilon=(16. / 255),
num_steps=200, batch_size=32, is_debug=False):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf.Session(graph=self.graph)
self.x_input = tf.placeholder(tf.float32, shape=(1,) + image_shape_hwc)
self.y_label = tf.placeholder(tf.int32, shape=(1,))
self.model = model
attack = SPSA(CleverhansPyfuncModelWrapper(self.model), sess=self.sess)
self.x_adv = attack.generate(
self.x_input,
y=self.y_label,
epsilon=epsilon,
num_steps=num_steps,
early_stop_loss_threshold=-1.,
batch_size=batch_size,
is_debug=is_debug)
self.graph.finalize()
def __call__(self, model, x_np, y_np):
if model != self.model:
raise ValueError('Cannot call spsa attack on different models')
del model
with self.graph.as_default():
all_x_adv_np = []
for i in xrange(len(x_np)):
x_adv_np = self.sess.run(self.x_adv, feed_dict={
self.x_input: np.expand_dims(x_np[i], axis=0),
self.y_label: np.expand_dims(y_np[i], axis=0),
})
all_x_adv_np.append(x_adv_np)
return np.concatenate(all_x_adv_np)
def corrupt_float32_image(x, corruption_name, severity):
x = np.copy(x)
x = (x * 255).astype(np.uint8)
corrupt_x = corrupt(
x,
corruption_name=corruption_name,
severity=severity)
return corrupt_x.astype(np.float32) / 255.
def _corrupt_float32_image_star(args):
return corrupt_float32_image(*args)
class CommonCorruptionsAttack(Attack):
name = "common_corruptions"
def __init__(self, severity=1):
self.corruption_names = [
'gaussian_noise',
'shot_noise',
'impulse_noise',
'defocus_blur',
'glass_blur',
'motion_blur',
'zoom_blur',
elastic_transform',
'pixelate',
'jpeg_compression',
'speckle_noise',
'gaussian_blur',
'spatter',
'saturate']
self.severity = severity
self.pool = multiprocessing.Pool(len(self.corruption_names))
def __call__(self, model_fn, images_batch_nhwc, y_np):
assert images_batch_nhwc.shape[1:] == (224, 224, 3), \
"Image shape must equal (N, 224, 224, 3)"
batch_size = len(images_batch_nhwc)
worst_corruption = np.copy(images_batch_nhwc)
worst_loss = [np.NINF] * batch_size
for batch_idx, x in enumerate(images_batch_nhwc):
corrupt_args = [(x, corruption_name, self.severity)
for corruption_name in self.corruption_names]
corrupt_x_batch = self.pool.map(_corrupt_float32_image_star, corrupt_args)
logits_batch = model_fn(np.array(corrupt_x_batch))
label = y_np[batch_idx]
for (logits, corrupt_x) in zip(logits_batch, corrupt_x_batch):
correct_logit, wrong_logit = logits[label], logits[1 - label]
loss = wrong_logit
if loss > worst_loss[batch_idx]:
worst_corruption[batch_idx] = corrupt_x
worst_loss[batch_idx] = loss
return worst_corruption
class BoundaryAttack(Attack):
name = "boundary"
def __init__(self, model, image_shape_hwc, max_l2_distortion=4, label_to_examples=None):
if label_to_examples is None:
label_to_examples = {}
self.max_l2_distortion = max_l2_distortion
class Model:
def bounds(self):
return [0, 1]
def predictions(self, img):
return model(img[np.newaxis, :, :, :])[0]
def batch_predictions(self, img):
return model(img)
self.label_to_examples = label_to_examples
h, w, c = image_shape_hwc
mse_threshold = max_l2_distortion ** 2 / (h * w * c)
try:
self.attack = FoolboxBoundaryAttack(model=Model(), threshold=mse_threshold)
except:
print("WARNING: Using foolbox version < 1.5 will cuase the "
"boundary attack to perform more work than is required. "
"Please upgrade to version 1.5")
self.attack = FoolboxBoundaryAttack(model=Model())
def __call__(self, model, x_np, y_np):
r = []
for i in range(len(x_np)):
other = 1 - y_np[i]
initial_adv = random.choice(self.label_to_examples[other])
try:
adv = self.attack(x_np[i], y_np[i],
log_every_n_steps=100,
starting_point=initial_adv
)
distortion = np.sum((x_np[i] - adv) ** 2) ** .5
if distortion > self.max_l2_distortion:
adv = x_np[i] + (adv - x_np[i]) / distortion * self.max_l2_distortion
except AssertionError as error:
if str(error).startswith("Invalid starting point provided."):
print("WARNING: The model misclassified the starting point (the target) "
"from BoundaryAttack. This means that the attack will fail on this "
"specific point (but is likely to succeed on other points.")
adv = x_np[i]
else:
raise error
r.append(adv)
return np.array(r)
class FastSpatialGridAttack(Attack):
name = 'spatial_grid'
def __init__(self, model,
image_shape_hwc,
spatial_limits,
grid_granularity,
black_border_size,
):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf.Session(graph=self.graph)
self.x_input = tf.placeholder(
tf.float32, shape=[None] + list(image_shape_hwc))
self.y_input = tf.placeholder(tf.float32, shape=(None, 2))
self.model = model
attack = SpatialTransformationMethod(
CleverhansPyfuncModelWrapper(self.model), sess=self.sess)
self.x_adv = attack.generate(
self.x_input,
y=self.y_input,
n_samples=None,
dx_min=-float(spatial_limits[0]) / image_shape_hwc[0],
dx_max=float(spatial_limits[0]) / image_shape_hwc[0],
n_dxs=grid_granularity[0],
dy_min=-float(spatial_limits[1]) / image_shape_hwc[1],
dy_max=float(spatial_limits[1]) / image_shape_hwc[1],
n_dys=grid_granularity[1],
angle_min=-spatial_limits[2],
angle_max=spatial_limits[2],
n_angles=grid_granularity[2],
black_border_size=black_border_size,
)
self.graph.finalize()
def __call__(self, model_fn, x_np, y_np):
if model_fn != self.model:
raise ValueError('Cannot call spatial attack on different models')
del model_fn
y_np_one_hot = np.zeros([len(y_np), 2], np.float32)
y_np_one_hot[np.arange(len(y_np)), y_np] = 1.0
with self.graph.as_default():
all_x_adv_np = []
for i in xrange(len(x_np)):
x_adv_np = self.sess.run(self.x_adv, feed_dict={
self.x_input: np.expand_dims(x_np[i], axis=0),
self.y_input: np.expand_dims(y_np_one_hot[i], axis=0),
})
all_x_adv_np.append(x_adv_np)
return np.concatenate(all_x_adv_np)
class SpatialGridAttack(Attack):
name = 'spatial_grid'
def __init__(self, image_shape_hwc,
spatial_limits,
grid_granularity,
black_border_size,
valid_check=None,
):
self.limits = spatial_limits
self.granularity = grid_granularity
self.valid_check = valid_check
self.graph = tf.Graph()
with self.graph.as_default():
self._x_for_trans = tf.placeholder(tf.float32, shape=[None] + list(image_shape_hwc))
self._t_for_trans = tf.placeholder(tf.float32, shape=[None, 3])
x = apply_black_border(
self._x_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
border_size=black_border_size
)
self._tranformed_x_op = apply_transformation(
x,
transform=self._t_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
)
self.session = tf.Session()
self.grid_store = []
def __call__(self, model_fn, x_np, y_np):
n = len(x_np)
grid = product(*list(np.linspace(-l, l, num=g)
for l, g in zip(self.limits, self.granularity)))
worst_x = np.copy(x_np)
max_xent = np.zeros(n)
all_correct = np.ones(n).astype(bool)
trans_np = np.stack(
repeat([0, 0, 0], n))
with self.graph.as_default():
x_downsize_np = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
for horizontal_trans, vertical_trans, rotation in grid:
trans_np = np.stack(
repeat([horizontal_trans, vertical_trans, rotation], n))
with self.graph.as_default():
x_np_trans = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
logits = model_fn(x_np_trans)
preds = np.argmax(logits, axis=1)
cur_xent = _sparse_softmax_cross_entropy_with_logits_from_numpy(
logits, y_np, self.graph, self.session)
cur_xent = np.asarray(cur_xent)
cur_correct = np.equal(y_np, preds)
if self.valid_check:
is_valid = self.valid_check(x_downsize_np, x_np_trans)
cur_correct |= ~is_valid
cur_xent -= is_valid * 1e9
idx = (cur_xent > max_xent) & (cur_correct == all_correct)
idx = idx | (cur_correct < all_correct)
max_xent = np.maximum(cur_xent, max_xent)
all_correct = cur_correct & all_correct
idx = np.expand_dims(idx, axis=-1)
idx = np.expand_dims(idx, axis=-1)
idx = np.expand_dims(idx, axis=-1)
worst_x = np.where(idx, x_np_trans, worst_x, )
return worst_x
def _sparse_softmax_cross_entropy_with_logits_from_numpy(logits_np, labels_np, graph, sess):
with graph.as_default():
labels_tf = tf.placeholder(tf.int32, [None])
logits_tf = tf.placeholder(tf.float32, [None, None])
xent_tf = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels_tf, logits=logits_tf)
return sess.run(xent_tf, feed_dict={
labels_tf: labels_np, logits_tf: logits_np})
def apply_black_border(x, image_height, image_width, border_size):
x = tf.image.resize_images(x, (image_width - border_size,
image_height - border_size))
x = tf.pad(x, [[0, 0],
[border_size, border_size],
[border_size, border_size],
[0, 0]], 'CONSTANT')
return x
def apply_transformation(x, transform, image_height, image_width):
trans_x, trans_y, rot = tf.unstack(transform, axis=1)
rot *= np.pi / 180
x = tf.pad(x, [[0, 0], [50, 50], [50, 50], [0, 0]], 'CONSTANT')
ones = tf.ones(shape=tf.shape(trans_x))
zeros = tf.zeros(shape=tf.shape(trans_x))
trans = tf.stack([ones, zeros, -trans_x,
zeros, ones, -trans_y,
zeros, zeros], axis=1)
x = tf.contrib.image.rotate(x, rot, interpolation='BILINEAR')
x = tf.contrib.image.transform(x, trans, interpolation='BILINEAR')
return tf.image.resize_image_with_crop_or_pad(
x, image_height, image_width)
class CleverhansPyfuncModelWrapper(Model):
nb_classes = 2
num_classes = 2
def __init__(self, model_fn):
self.model_fn = model_fn
def fprop(self, x, **kwargs):
logits_op = tf.py_func(self.model_fn, [x], tf.float32)
return {'logits': logits_op}
class RandomSpatialAttack(Attack):
name = 'random_spatial'
def __init__(self, image_shape_hwc, spatial_limits, black_border_size, valid_check=None):
self.limits = spatial_limits
self.valid_check = valid_check
self.graph = tf.Graph()
with self.graph.as_default():
self._x_for_trans = tf.placeholder(tf.float32, shape=[None] + list(image_shape_hwc))
self._t_for_trans = tf.placeholder(tf.float32, shape=[None, 3])
x = apply_black_border(
self._x_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
border_size=black_border_size
)
self._tranformed_x_op = apply_transformation(
x,
transform=self._t_for_trans,
image_height=image_shape_hwc[0],
image_width=image_shape_hwc[1],
)
self.session = tf.Session()
def __call__(self, model_fn, x_np, y_np):
with self.graph.as_default():
result = np.zeros(x_np.shape, dtype=x_np.dtype)
did = np.zeros(x_np.shape[0], dtype=np.bool)
trans_np = np.stack(
repeat([0, 0, 0], x_np.shape[0]))
x_downsize_np = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
while True:
random_transforms = (np.random.uniform(-lim, lim, len(x_np)) for lim in self.limits)
trans_np = np.stack(random_transforms, axis=1)
out = self.session.run(self._tranformed_x_op, feed_dict={
self._x_for_trans: x_np,
self._t_for_trans: trans_np,
})
if self.valid_check is None:
return out
else:
ok = self.valid_check(x_downsize_np, out)
result[ok] = out[ok]
did[ok] = True
if np.all(did):
return result
class SpsaWithRandomSpatialAttack(Attack):
name = "spsa_with_random_spatial"
def __init__(self, model, image_shape_hwc, spatial_limits, black_border_size,
epsilon=(16. / 255), num_steps=32, is_debug=False,
valid_check=None):
self.random_spatial_attack = RandomSpatialAttack(
image_shape_hwc,
valid_check=valid_check,
spatial_limits=spatial_limits,
black_border_size=black_border_size)
self.spsa_attack = SpsaAttack(
model,
image_shape_hwc,
epsilon=epsilon,
num_steps=num_steps,
batch_size=64,
is_debug=is_debug)
def __call__(self, model, x_np, y_np):
x_after_spatial_np = self.random_spatial_attack(model, x_np, y_np)
x_adv = self.spsa_attack(model, x_after_spatial_np, y_np)
return x_adv
class BoundaryWithRandomSpatialAttack(Attack):
name = "boundary_with_random_spatial"
def __init__(self, model, image_shape_hwc, spatial_limits, black_border_size,
max_l2_distortion=4, label_to_examples=None, valid_check=None):
self.random_spatial_attack = RandomSpatialAttack(
image_shape_hwc,
valid_check=valid_check,
spatial_limits=spatial_limits,
black_border_size=black_border_size)
self.boundary_attack = BoundaryAttack(
model,
max_l2_distortion=max_l2_distortion,
image_shape_hwc=image_shape_hwc,
label_to_examples=label_to_examples)
def __call__(self, model, x_np, y_np):
x_after_spatial_np = self.random_spatial_attack(model, x_np, y_np)
x_adv = self.boundary_attack(model, x_after_spatial_np, y_np)
return x_adv
| true | true |
f73c04f5cde18f43d9effdd9b7c06fb277ff3e0a | 400 | py | Python | sdl2/api.py | vaiorabbit/python-sdl2 | 77b4ce73d83d19dedc943ae7f56ee6f73f62ecad | [
"Zlib"
] | 1 | 2018-03-12T01:54:40.000Z | 2018-03-12T01:54:40.000Z | sdl2/api.py | vaiorabbit/python-sdl2 | 77b4ce73d83d19dedc943ae7f56ee6f73f62ecad | [
"Zlib"
] | null | null | null | sdl2/api.py | vaiorabbit/python-sdl2 | 77b4ce73d83d19dedc943ae7f56ee6f73f62ecad | [
"Zlib"
] | null | null | null | SDL2_LOADER = None
SDL2_TTF_LOADER = None
SDL2_IMG_LOADER = None
SDL2_GFX_LOADER = None
SDL2_API_NAMES = []
SDL2_API_ARGS_MAP = {}
SDL2_API_RETVAL_MAP = {}
SDL2_TTF_API_NAMES = []
SDL2_TTF_API_ARGS_MAP = {}
SDL2_TTF_API_RETVAL_MAP = {}
SDL2_IMG_API_NAMES = []
SDL2_IMG_API_ARGS_MAP = {}
SDL2_IMG_API_RETVAL_MAP = {}
SDL2_GFX_API_NAMES = []
SDL2_GFX_API_ARGS_MAP = {}
SDL2_GFX_API_RETVAL_MAP = {}
| 19.047619 | 28 | 0.77 | SDL2_LOADER = None
SDL2_TTF_LOADER = None
SDL2_IMG_LOADER = None
SDL2_GFX_LOADER = None
SDL2_API_NAMES = []
SDL2_API_ARGS_MAP = {}
SDL2_API_RETVAL_MAP = {}
SDL2_TTF_API_NAMES = []
SDL2_TTF_API_ARGS_MAP = {}
SDL2_TTF_API_RETVAL_MAP = {}
SDL2_IMG_API_NAMES = []
SDL2_IMG_API_ARGS_MAP = {}
SDL2_IMG_API_RETVAL_MAP = {}
SDL2_GFX_API_NAMES = []
SDL2_GFX_API_ARGS_MAP = {}
SDL2_GFX_API_RETVAL_MAP = {}
| true | true |
f73c05c915497a74bf280bc0b4d01be5e6526e40 | 780 | py | Python | hikvision.py | simpletask1/video_stream | 984f24feb38c778e225d24771c6246577b94e614 | [
"Apache-2.0"
] | null | null | null | hikvision.py | simpletask1/video_stream | 984f24feb38c778e225d24771c6246577b94e614 | [
"Apache-2.0"
] | null | null | null | hikvision.py | simpletask1/video_stream | 984f24feb38c778e225d24771c6246577b94e614 | [
"Apache-2.0"
] | null | null | null | import threading
import traceback
from camera.sdk_gige_hikvision.GrabImage import MVS_Cam # 工业相机SDK读流
class hikCamera(threading.Thread):
def __init__(self, ip_name):
threading.Thread.__init__(self)
self.ip_name = ip_name
# 初始化摄像头
self.device_camera = MVS_Cam(self.ip_name)
def run(self):
i = 0
while i < 100:
try:
# 获取图像
# 该读流方式不会缓存，读到的一定是最新帧
frame = self.device_camera.Get_Frame()
except:
print(traceback.format_exc())
else:
if type(frame) != type(None):
print(type(frame), self.ip_name, frame.shape[:2])
i += 1
# 关闭摄像头
self.device_camera.Close_Cam() | 24.375 | 69 | 0.544872 | import threading
import traceback
from camera.sdk_gige_hikvision.GrabImage import MVS_Cam
class hikCamera(threading.Thread):
def __init__(self, ip_name):
threading.Thread.__init__(self)
self.ip_name = ip_name
self.device_camera = MVS_Cam(self.ip_name)
def run(self):
i = 0
while i < 100:
try:
frame = self.device_camera.Get_Frame()
except:
print(traceback.format_exc())
else:
if type(frame) != type(None):
print(type(frame), self.ip_name, frame.shape[:2])
i += 1
self.device_camera.Close_Cam() | true | true |
f73c06db0b64a1edcd6aa27780845c92273e5b41 | 3,273 | py | Python | test/travis_test_motors.py | Kageken/pimouse_ros | 97f289755506c53e238d8aa9b331ad449aec0301 | [
"BSD-3-Clause"
] | null | null | null | test/travis_test_motors.py | Kageken/pimouse_ros | 97f289755506c53e238d8aa9b331ad449aec0301 | [
"BSD-3-Clause"
] | null | null | null | test/travis_test_motors.py | Kageken/pimouse_ros | 97f289755506c53e238d8aa9b331ad449aec0301 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
#encoding: utf8
import unittest, rostest
import rosnode, rospy
import time
from pimouse_ros.msg import MotorFreqs
from geometry_msgs.msg import Twist
from std_srvs.srv import Trigger, TriggerResponse #追加
from pimouse_ros.srv import TimedMotion
class MotorTest(unittest.TestCase):
def setUp(self): #このメソッドを追加
rospy.wait_for_service('/motor_on')
rospy.wait_for_service('/motor_off')
rospy.wait_for_service('/timed_motion')
on = rospy.ServiceProxy('/motor_on', Trigger)
ret = on()
def file_check(self,dev,value,message):
with open("/dev/" + dev,"r") as f:
self.assertEqual(f.readline(),str(value)+"\n",message)
def test_node_exist(self):
nodes = rosnode.get_node_names()
self.assertIn('/motors', nodes, "node does not exist")
def test_put_freq(self):
pub = rospy.Publisher('/motor_raw', MotorFreqs)
m = MotorFreqs()
m.left_hz = 123
m.right_hz = 456
for i in range(10):
pub.publish(m)
time.sleep(0.1)
self.file_check("rtmotor_raw_l0",m.left_hz,"wrong left value from motor_raw")
self.file_check("rtmotor_raw_r0",m.right_hz,"wrong left value from motor_raw")
def test_put_cmd_vel(self):
pub = rospy.Publisher('/cmd_vel', Twist)
m = Twist()
m.linear.x = 0.1414
m.angular.z = 1.57
for i in range(10):
pub.publish(m)
time.sleep(0.1)
self.file_check("rtmotor_raw_l0",200,"wrong left value from cmd_vel")
self.file_check("rtmotor_raw_r0",600,"wrong right value from cmd_vel")
time.sleep(1.1)
self.file_check("rtmotor_raw_r0",0,"don't stop after 1[s]")
self.file_check("rtmotor_raw_l0",0,"don't stop after 1[s]")
def test_on_off(self): #このメソッドも追加
off = rospy.ServiceProxy('/motor_off', Trigger)
ret = off()
self.assertEqual(ret.success, True, "motor off does not succeeded")
self.assertEqual(ret.message, "OFF", "motor off wrong message")
with open("/dev/rtmotoren0","r") as f:
data = f.readline()
self.assertEqual(data,"0\n","wrong value in rtmotor0 at motor off")
on = rospy.ServiceProxy('/motor_on', Trigger)
ret = on()
self.assertEqual(ret.success, True, "motor on does not succeeded")
self.assertEqual(ret.message, "ON", "motor on wrong message")
with open("/dev/rtmotoren0","r") as f:
data = f.readline()
self.assertEqual(data,"1\n","wrong value in rtmotor0 at motor on")
def test_put_value_timed(self):
tm = rospy.ServiceProxy('/timed_motion', TimedMotion)
tm(-321,654,1500)
with open("/dev/rtmotor0","r") as f:
data = f.readline()
self.assertEqual(data,"-321 654 1500\n","value does not written to rtmotor0")
if __name__ == '__main__':
rospy.init_node('travis_test_motors')
rostest.rosrun('pimouse_ros','travis_test_motors', MotorTest)
# Copyright 2016 Ryuichi Ueda
# Released under the BSD License.
# To make line numbers be identical with the book, this statement is written here. Don't move it to the header.
| 38.05814 | 111 | 0.629086 |
import unittest, rostest
import rosnode, rospy
import time
from pimouse_ros.msg import MotorFreqs
from geometry_msgs.msg import Twist
from std_srvs.srv import Trigger, TriggerResponse
from pimouse_ros.srv import TimedMotion
class MotorTest(unittest.TestCase):
def setUp(self):
rospy.wait_for_service('/motor_on')
rospy.wait_for_service('/motor_off')
rospy.wait_for_service('/timed_motion')
on = rospy.ServiceProxy('/motor_on', Trigger)
ret = on()
def file_check(self,dev,value,message):
with open("/dev/" + dev,"r") as f:
self.assertEqual(f.readline(),str(value)+"\n",message)
def test_node_exist(self):
nodes = rosnode.get_node_names()
self.assertIn('/motors', nodes, "node does not exist")
def test_put_freq(self):
pub = rospy.Publisher('/motor_raw', MotorFreqs)
m = MotorFreqs()
m.left_hz = 123
m.right_hz = 456
for i in range(10):
pub.publish(m)
time.sleep(0.1)
self.file_check("rtmotor_raw_l0",m.left_hz,"wrong left value from motor_raw")
self.file_check("rtmotor_raw_r0",m.right_hz,"wrong left value from motor_raw")
def test_put_cmd_vel(self):
pub = rospy.Publisher('/cmd_vel', Twist)
m = Twist()
m.linear.x = 0.1414
m.angular.z = 1.57
for i in range(10):
pub.publish(m)
time.sleep(0.1)
self.file_check("rtmotor_raw_l0",200,"wrong left value from cmd_vel")
self.file_check("rtmotor_raw_r0",600,"wrong right value from cmd_vel")
time.sleep(1.1)
self.file_check("rtmotor_raw_r0",0,"don't stop after 1[s]")
self.file_check("rtmotor_raw_l0",0,"don't stop after 1[s]")
def test_on_off(self):
off = rospy.ServiceProxy('/motor_off', Trigger)
ret = off()
self.assertEqual(ret.success, True, "motor off does not succeeded")
self.assertEqual(ret.message, "OFF", "motor off wrong message")
with open("/dev/rtmotoren0","r") as f:
data = f.readline()
self.assertEqual(data,"0\n","wrong value in rtmotor0 at motor off")
on = rospy.ServiceProxy('/motor_on', Trigger)
ret = on()
self.assertEqual(ret.success, True, "motor on does not succeeded")
self.assertEqual(ret.message, "ON", "motor on wrong message")
with open("/dev/rtmotoren0","r") as f:
data = f.readline()
self.assertEqual(data,"1\n","wrong value in rtmotor0 at motor on")
def test_put_value_timed(self):
tm = rospy.ServiceProxy('/timed_motion', TimedMotion)
tm(-321,654,1500)
with open("/dev/rtmotor0","r") as f:
data = f.readline()
self.assertEqual(data,"-321 654 1500\n","value does not written to rtmotor0")
if __name__ == '__main__':
rospy.init_node('travis_test_motors')
rostest.rosrun('pimouse_ros','travis_test_motors', MotorTest)
| true | true |
f73c0705e1774ed3975ef5197b448f467103f19d | 13,332 | py | Python | source/winKernel.py | SWEN-712/screen-reader-brandonp728 | e30c25ad2d10ce632fac0548696a61a872328f59 | [
"bzip2-1.0.6"
] | null | null | null | source/winKernel.py | SWEN-712/screen-reader-brandonp728 | e30c25ad2d10ce632fac0548696a61a872328f59 | [
"bzip2-1.0.6"
] | null | null | null | source/winKernel.py | SWEN-712/screen-reader-brandonp728 | e30c25ad2d10ce632fac0548696a61a872328f59 | [
"bzip2-1.0.6"
] | null | null | null | #winKernel.py
#A part of NonVisual Desktop Access (NVDA)
#Copyright (C) 2006-2019 NV Access Limited, Rui Batista, Aleksey Sadovoy, Peter Vagner, Mozilla Corporation, Babbage B.V., Joseph Lee
#This file is covered by the GNU General Public License.
#See the file COPYING for more details.
"""Functions that wrap Windows API functions from kernel32.dll and advapi32.dll"""
import contextlib
import ctypes
import ctypes.wintypes
from ctypes import WinError
from ctypes import *
from ctypes.wintypes import *
kernel32=ctypes.windll.kernel32
advapi32 = windll.advapi32
#Constants
INFINITE = 0xffffffff
#Process control
PROCESS_ALL_ACCESS=0x1F0FFF
PROCESS_TERMINATE=0x1
PROCESS_VM_OPERATION=0x8
PROCESS_VM_READ=0x10
PROCESS_VM_WRITE=0X20
SYNCHRONIZE=0x100000
PROCESS_QUERY_INFORMATION=0x400
READ_CONTROL=0x20000
MEM_COMMIT=0x1000
MEM_RELEASE=0x8000
PAGE_READWRITE=0x4
MAXIMUM_ALLOWED = 0x2000000
STARTF_USESTDHANDLES = 0x00000100
#Console handles
STD_INPUT_HANDLE=-10
STD_OUTPUT_HANDLE=-11
STD_ERROR_HANDLE=-12
LOCALE_USER_DEFAULT=0x0400
LOCALE_NAME_USER_DEFAULT=None
DATE_LONGDATE=0x00000002
TIME_NOSECONDS=0x00000002
# Wait return types
WAIT_ABANDONED = 0x00000080
WAIT_IO_COMPLETION = 0x000000c0
WAIT_OBJECT_0 = 0x00000000
WAIT_TIMEOUT = 0x00000102
WAIT_FAILED = 0xffffffff
# Image file machine constants
IMAGE_FILE_MACHINE_UNKNOWN = 0
def GetStdHandle(handleID):
h=kernel32.GetStdHandle(handleID)
if h==0:
raise WinError()
return h
GENERIC_READ=0x80000000
GENERIC_WRITE=0x40000000
FILE_SHARE_READ=1
FILE_SHARE_WRITE=2
FILE_SHARE_DELETE=4
OPEN_EXISTING=3
def CreateFile(fileName,desiredAccess,shareMode,securityAttributes,creationDisposition,flags,templateFile):
res=kernel32.CreateFileW(fileName,desiredAccess,shareMode,securityAttributes,creationDisposition,flags,templateFile)
if res==0:
raise ctypes.WinError()
return res
def createEvent(eventAttributes=None, manualReset=False, initialState=False, name=None):
res = kernel32.CreateEventW(eventAttributes, manualReset, initialState, name)
if res==0:
raise ctypes.WinError()
return res
def createWaitableTimer(securityAttributes=None, manualReset=False, name=None):
"""Wrapper to the kernel32 CreateWaitableTimer function.
Consult https://msdn.microsoft.com/en-us/library/windows/desktop/ms682492.aspx for Microsoft's documentation.
In contrast with the original function, this wrapper assumes the following defaults.
@param securityAttributes: Defaults to C{None};
The timer object gets a default security descriptor and the handle cannot be inherited.
The ACLs in the default security descriptor for a timer come from the primary or impersonation token of the creator.
@type securityAttributes: pointer to L{SECURITY_ATTRIBUTES}
@param manualReset: Defaults to C{False} which means the timer is a synchronization timer.
If C{True}, the timer is a manual-reset notification timer.
@type manualReset: bool
@param name: Defaults to C{None}, the timer object is created without a name.
@type name: str
"""
res = kernel32.CreateWaitableTimerW(securityAttributes, manualReset, name)
if res==0:
raise ctypes.WinError()
return res
def setWaitableTimer(handle, dueTime, period=0, completionRoutine=None, arg=None, resume=False):
"""Wrapper to the kernel32 SETWaitableTimer function.
Consult https://msdn.microsoft.com/en-us/library/windows/desktop/ms686289.aspx for Microsoft's documentation.
@param handle: A handle to the timer object.
@type handle: int
@param dueTime: Relative time (in miliseconds).
Note that the original function requires relative time to be supplied as a negative nanoseconds value.
@type dueTime: int
@param period: Defaults to 0, timer is only executed once.
Value should be supplied in miliseconds.
@type period: int
@param completionRoutine: The function to be executed when the timer elapses.
@type completionRoutine: L{PAPCFUNC}
@param arg: Defaults to C{None}; a pointer to a structure that is passed to the completion routine.
@type arg: L{ctypes.c_void_p}
@param resume: Defaults to C{False}; the system is not restored.
If this parameter is TRUE, restores a system in suspended power conservation mode
when the timer state is set to signaled.
@type resume: bool
"""
res = kernel32.SetWaitableTimer(
handle,
# due time is in 100 nanosecond intervals, relative time should be negated.
byref(LARGE_INTEGER(dueTime*-10000)),
period,
completionRoutine,
arg,
resume
)
if res==0:
raise ctypes.WinError()
return True
def openProcess(*args):
return kernel32.OpenProcess(*args)
def closeHandle(*args):
return kernel32.CloseHandle(*args)
#added by Rui Batista to use on Say_battery_status script
#copied from platform sdk documentation (with required changes to work in python)
class SYSTEM_POWER_STATUS(ctypes.Structure):
_fields_ = [("ACLineStatus", ctypes.c_byte), ("BatteryFlag", ctypes.c_byte), ("BatteryLifePercent", ctypes.c_byte), ("Reserved1", ctypes.c_byte), ("BatteryLifeTime", ctypes.wintypes.DWORD), ("BatteryFullLiveTime", ctypes.wintypes.DWORD)]
def GetSystemPowerStatus(sps):
return kernel32.GetSystemPowerStatus(ctypes.byref(sps))
def getThreadLocale():
return kernel32.GetThreadLocale()
class SYSTEMTIME(ctypes.Structure):
_fields_ = (
("wYear", WORD),
("wMonth", WORD),
("wDayOfWeek", WORD),
("wDay", WORD),
("wHour", WORD),
("wMinute", WORD),
("wSecond", WORD),
("wMilliseconds", WORD)
)
def GetDateFormat(Locale,dwFlags,date,lpFormat):
"""@Deprecated: use GetDateFormatEx instead."""
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpDate=byref(date)
else:
lpDate=None
bufferLength=kernel32.GetDateFormatW(Locale, dwFlags, lpDate, lpFormat, None, 0)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetDateFormatW(Locale, dwFlags, lpDate, lpFormat, buf, bufferLength)
return buf.value
def GetDateFormatEx(Locale,dwFlags,date,lpFormat):
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpDate=byref(date)
else:
lpDate=None
bufferLength=kernel32.GetDateFormatEx(Locale, dwFlags, lpDate, lpFormat, None, 0, None)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetDateFormatEx(Locale, dwFlags, lpDate, lpFormat, buf, bufferLength, None)
return buf.value
def GetTimeFormat(Locale,dwFlags,date,lpFormat):
"""@Deprecated: use GetTimeFormatEx instead."""
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpTime=byref(date)
else:
lpTime=None
bufferLength=kernel32.GetTimeFormatW(Locale,dwFlags,lpTime,lpFormat, None, 0)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetTimeFormatW(Locale,dwFlags,lpTime,lpFormat, buf, bufferLength)
return buf.value
def GetTimeFormatEx(Locale,dwFlags,date,lpFormat):
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpTime=byref(date)
else:
lpTime=None
bufferLength=kernel32.GetTimeFormatEx(Locale,dwFlags,lpTime,lpFormat, None, 0)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetTimeFormatEx(Locale,dwFlags,lpTime,lpFormat, buf, bufferLength)
return buf.value
def openProcess(*args):
return kernel32.OpenProcess(*args)
def virtualAllocEx(*args):
res = kernel32.VirtualAllocEx(*args)
if res == 0:
raise WinError()
return res
def virtualFreeEx(*args):
return kernel32.VirtualFreeEx(*args)
def readProcessMemory(*args):
return kernel32.ReadProcessMemory(*args)
def writeProcessMemory(*args):
return kernel32.WriteProcessMemory(*args)
def waitForSingleObject(handle,timeout):
res = kernel32.WaitForSingleObject(handle,timeout)
if res==WAIT_FAILED:
raise ctypes.WinError()
return res
def waitForSingleObjectEx(handle,timeout, alertable):
res = kernel32.WaitForSingleObjectEx(handle,timeout, alertable)
if res==WAIT_FAILED:
raise ctypes.WinError()
return res
SHUTDOWN_NORETRY = 0x00000001
def SetProcessShutdownParameters(level, flags):
res = kernel32.SetProcessShutdownParameters(level, flags)
if res == 0:
raise ctypes.WinError()
def GetExitCodeProcess(process):
exitCode = ctypes.wintypes.DWORD()
if not kernel32.GetExitCodeProcess(process, ctypes.byref(exitCode)):
raise ctypes.WinError()
return exitCode.value
def TerminateProcess(process, exitCode):
if not kernel32.TerminateProcess(process, exitCode):
raise ctypes.WinError()
DRIVE_UNKNOWN = 0
DRIVE_NO_ROOT_DIR = 1
DRIVE_REMOVABLE = 2
DRIVE_FIXED = 3
DRIVE_REMOTE = 4
DRIVE_CDROM = 5
DRIVE_RAMDISK = 6
def GetDriveType(rootPathName):
return kernel32.GetDriveTypeW(rootPathName)
class SECURITY_ATTRIBUTES(Structure):
_fields_ = (
("nLength", DWORD),
("lpSecurityDescriptor", LPVOID),
("bInheritHandle", BOOL)
)
def __init__(self, **kwargs):
super(SECURITY_ATTRIBUTES, self).__init__(nLength=sizeof(self), **kwargs)
def CreatePipe(pipeAttributes, size):
read = ctypes.wintypes.HANDLE()
write = ctypes.wintypes.HANDLE()
if kernel32.CreatePipe(ctypes.byref(read), ctypes.byref(write), byref(pipeAttributes) if pipeAttributes else None, ctypes.wintypes.DWORD(size)) == 0:
raise ctypes.WinError()
return read.value, write.value
class STARTUPINFOW(Structure):
_fields_=(
('cb',DWORD),
('lpReserved',LPWSTR),
('lpDesktop',LPWSTR),
('lpTitle',LPWSTR),
('dwX',DWORD),
('dwY',DWORD),
('dwXSize',DWORD),
('dwYSize',DWORD),
('dwXCountChars',DWORD),
('dwYCountChars',DWORD),
('dwFillAttribute',DWORD),
('dwFlags',DWORD),
('wShowWindow',WORD),
('cbReserved2',WORD),
('lpReserved2',POINTER(c_byte)),
('hSTDInput',HANDLE),
('hSTDOutput',HANDLE),
('hSTDError',HANDLE),
)
def __init__(self, **kwargs):
super(STARTUPINFOW, self).__init__(cb=sizeof(self), **kwargs)
STARTUPINFO = STARTUPINFOW
class PROCESS_INFORMATION(Structure):
_fields_=(
('hProcess',HANDLE),
('hThread',HANDLE),
('dwProcessID',DWORD),
('dwThreadID',DWORD),
)
def CreateProcessAsUser(token, applicationName, commandLine, processAttributes, threadAttributes, inheritHandles, creationFlags, environment, currentDirectory, startupInfo, processInformation):
if advapi32.CreateProcessAsUserW(token, applicationName, commandLine, processAttributes, threadAttributes, inheritHandles, creationFlags, environment, currentDirectory, byref(startupInfo), byref(processInformation)) == 0:
raise WinError()
def GetCurrentProcess():
return kernel32.GetCurrentProcess()
def OpenProcessToken(ProcessHandle, DesiredAccess):
token = HANDLE()
if advapi32.OpenProcessToken(ProcessHandle, DesiredAccess, byref(token)) == 0:
raise WinError()
return token.value
DUPLICATE_SAME_ACCESS = 0x00000002
def DuplicateHandle(sourceProcessHandle, sourceHandle, targetProcessHandle, desiredAccess, inheritHandle, options):
targetHandle = HANDLE()
if kernel32.DuplicateHandle(sourceProcessHandle, sourceHandle, targetProcessHandle, byref(targetHandle), desiredAccess, inheritHandle, options) == 0:
raise WinError()
return targetHandle.value
PAPCFUNC = ctypes.WINFUNCTYPE(None, ctypes.wintypes.ULONG)
THREAD_SET_CONTEXT = 16
GMEM_MOVEABLE=2
class HGLOBAL(HANDLE):
"""
A class for the HGLOBAL Windows handle type.
This class can auto-free the handle when it goes out of scope,
and also contains a classmethod for alloc,
And a context manager compatible method for locking.
"""
def __init__(self,h,autoFree=True):
"""
@param h: the raw Windows HGLOBAL handle
@param autoFree: True by default, the handle will automatically be freed with GlobalFree
when this object goes out of scope.
"""
super(HGLOBAL,self).__init__(h)
self._autoFree=autoFree
def __del__(self):
if self and self._autoFree:
windll.kernel32.GlobalFree(self)
@classmethod
def alloc(cls,flags,size):
"""
Allocates global memory with GlobalAlloc
providing it as an instance of this class.
This method Takes the same arguments as GlobalAlloc.
"""
h=windll.kernel32.GlobalAlloc(flags,size)
return cls(h)
@contextlib.contextmanager
def lock(self):
"""
Used as a context manager,
This method locks the global memory with GlobalLock,
providing the usable memory address to the body of the 'with' statement.
When the body completes, GlobalUnlock is automatically called.
"""
try:
yield windll.kernel32.GlobalLock(self)
finally:
windll.kernel32.GlobalUnlock(self)
def forget(self):
"""
Sets this HGLOBAL value to NULL, forgetting the existing value.
Necessary if you pass this HGLOBAL to an API that takes ownership and therefore will handle freeing itself.
"""
self.value=None
MOVEFILE_COPY_ALLOWED = 0x2
MOVEFILE_CREATE_HARDLINK = 0x10
MOVEFILE_DELAY_UNTIL_REBOOT = 0x4
MOVEFILE_FAIL_IF_NOT_TRACKABLE = 0x20
MOVEFILE_REPLACE_EXISTING = 0x1
MOVEFILE_WRITE_THROUGH = 0x8
def moveFileEx(lpExistingFileName: str, lpNewFileName: str, dwFlags: int):
# If MoveFileExW fails, Windows will raise appropriate errors.
if not kernel32.MoveFileExW(lpExistingFileName, lpNewFileName, dwFlags):
raise ctypes.WinError()
# Thread execution states
ES_CONTINUOUS = 0x80000000
ES_DISPLAY_REQUIRED = 0x2
ES_SYSTEM_REQUIRED = 0x1
kernel32.SetThreadExecutionState.restype = ctypes.wintypes.DWORD
def SetThreadExecutionState(esFlags):
res = kernel32.SetThreadExecutionState(esFlags)
if not res:
raise WinError()
return res
| 32.048077 | 238 | 0.779853 |
import contextlib
import ctypes
import ctypes.wintypes
from ctypes import WinError
from ctypes import *
from ctypes.wintypes import *
kernel32=ctypes.windll.kernel32
advapi32 = windll.advapi32
INFINITE = 0xffffffff
PROCESS_ALL_ACCESS=0x1F0FFF
PROCESS_TERMINATE=0x1
PROCESS_VM_OPERATION=0x8
PROCESS_VM_READ=0x10
PROCESS_VM_WRITE=0X20
SYNCHRONIZE=0x100000
PROCESS_QUERY_INFORMATION=0x400
READ_CONTROL=0x20000
MEM_COMMIT=0x1000
MEM_RELEASE=0x8000
PAGE_READWRITE=0x4
MAXIMUM_ALLOWED = 0x2000000
STARTF_USESTDHANDLES = 0x00000100
STD_INPUT_HANDLE=-10
STD_OUTPUT_HANDLE=-11
STD_ERROR_HANDLE=-12
LOCALE_USER_DEFAULT=0x0400
LOCALE_NAME_USER_DEFAULT=None
DATE_LONGDATE=0x00000002
TIME_NOSECONDS=0x00000002
WAIT_ABANDONED = 0x00000080
WAIT_IO_COMPLETION = 0x000000c0
WAIT_OBJECT_0 = 0x00000000
WAIT_TIMEOUT = 0x00000102
WAIT_FAILED = 0xffffffff
IMAGE_FILE_MACHINE_UNKNOWN = 0
def GetStdHandle(handleID):
h=kernel32.GetStdHandle(handleID)
if h==0:
raise WinError()
return h
GENERIC_READ=0x80000000
GENERIC_WRITE=0x40000000
FILE_SHARE_READ=1
FILE_SHARE_WRITE=2
FILE_SHARE_DELETE=4
OPEN_EXISTING=3
def CreateFile(fileName,desiredAccess,shareMode,securityAttributes,creationDisposition,flags,templateFile):
res=kernel32.CreateFileW(fileName,desiredAccess,shareMode,securityAttributes,creationDisposition,flags,templateFile)
if res==0:
raise ctypes.WinError()
return res
def createEvent(eventAttributes=None, manualReset=False, initialState=False, name=None):
res = kernel32.CreateEventW(eventAttributes, manualReset, initialState, name)
if res==0:
raise ctypes.WinError()
return res
def createWaitableTimer(securityAttributes=None, manualReset=False, name=None):
res = kernel32.CreateWaitableTimerW(securityAttributes, manualReset, name)
if res==0:
raise ctypes.WinError()
return res
def setWaitableTimer(handle, dueTime, period=0, completionRoutine=None, arg=None, resume=False):
res = kernel32.SetWaitableTimer(
handle,
byref(LARGE_INTEGER(dueTime*-10000)),
period,
completionRoutine,
arg,
resume
)
if res==0:
raise ctypes.WinError()
return True
def openProcess(*args):
return kernel32.OpenProcess(*args)
def closeHandle(*args):
return kernel32.CloseHandle(*args)
class SYSTEM_POWER_STATUS(ctypes.Structure):
_fields_ = [("ACLineStatus", ctypes.c_byte), ("BatteryFlag", ctypes.c_byte), ("BatteryLifePercent", ctypes.c_byte), ("Reserved1", ctypes.c_byte), ("BatteryLifeTime", ctypes.wintypes.DWORD), ("BatteryFullLiveTime", ctypes.wintypes.DWORD)]
def GetSystemPowerStatus(sps):
return kernel32.GetSystemPowerStatus(ctypes.byref(sps))
def getThreadLocale():
return kernel32.GetThreadLocale()
class SYSTEMTIME(ctypes.Structure):
_fields_ = (
("wYear", WORD),
("wMonth", WORD),
("wDayOfWeek", WORD),
("wDay", WORD),
("wHour", WORD),
("wMinute", WORD),
("wSecond", WORD),
("wMilliseconds", WORD)
)
def GetDateFormat(Locale,dwFlags,date,lpFormat):
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpDate=byref(date)
else:
lpDate=None
bufferLength=kernel32.GetDateFormatW(Locale, dwFlags, lpDate, lpFormat, None, 0)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetDateFormatW(Locale, dwFlags, lpDate, lpFormat, buf, bufferLength)
return buf.value
def GetDateFormatEx(Locale,dwFlags,date,lpFormat):
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpDate=byref(date)
else:
lpDate=None
bufferLength=kernel32.GetDateFormatEx(Locale, dwFlags, lpDate, lpFormat, None, 0, None)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetDateFormatEx(Locale, dwFlags, lpDate, lpFormat, buf, bufferLength, None)
return buf.value
def GetTimeFormat(Locale,dwFlags,date,lpFormat):
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpTime=byref(date)
else:
lpTime=None
bufferLength=kernel32.GetTimeFormatW(Locale,dwFlags,lpTime,lpFormat, None, 0)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetTimeFormatW(Locale,dwFlags,lpTime,lpFormat, buf, bufferLength)
return buf.value
def GetTimeFormatEx(Locale,dwFlags,date,lpFormat):
if date is not None:
date=SYSTEMTIME(date.year,date.month,0,date.day,date.hour,date.minute,date.second,0)
lpTime=byref(date)
else:
lpTime=None
bufferLength=kernel32.GetTimeFormatEx(Locale,dwFlags,lpTime,lpFormat, None, 0)
buf=ctypes.create_unicode_buffer("", bufferLength)
kernel32.GetTimeFormatEx(Locale,dwFlags,lpTime,lpFormat, buf, bufferLength)
return buf.value
def openProcess(*args):
return kernel32.OpenProcess(*args)
def virtualAllocEx(*args):
res = kernel32.VirtualAllocEx(*args)
if res == 0:
raise WinError()
return res
def virtualFreeEx(*args):
return kernel32.VirtualFreeEx(*args)
def readProcessMemory(*args):
return kernel32.ReadProcessMemory(*args)
def writeProcessMemory(*args):
return kernel32.WriteProcessMemory(*args)
def waitForSingleObject(handle,timeout):
res = kernel32.WaitForSingleObject(handle,timeout)
if res==WAIT_FAILED:
raise ctypes.WinError()
return res
def waitForSingleObjectEx(handle,timeout, alertable):
res = kernel32.WaitForSingleObjectEx(handle,timeout, alertable)
if res==WAIT_FAILED:
raise ctypes.WinError()
return res
SHUTDOWN_NORETRY = 0x00000001
def SetProcessShutdownParameters(level, flags):
res = kernel32.SetProcessShutdownParameters(level, flags)
if res == 0:
raise ctypes.WinError()
def GetExitCodeProcess(process):
exitCode = ctypes.wintypes.DWORD()
if not kernel32.GetExitCodeProcess(process, ctypes.byref(exitCode)):
raise ctypes.WinError()
return exitCode.value
def TerminateProcess(process, exitCode):
if not kernel32.TerminateProcess(process, exitCode):
raise ctypes.WinError()
DRIVE_UNKNOWN = 0
DRIVE_NO_ROOT_DIR = 1
DRIVE_REMOVABLE = 2
DRIVE_FIXED = 3
DRIVE_REMOTE = 4
DRIVE_CDROM = 5
DRIVE_RAMDISK = 6
def GetDriveType(rootPathName):
return kernel32.GetDriveTypeW(rootPathName)
class SECURITY_ATTRIBUTES(Structure):
_fields_ = (
("nLength", DWORD),
("lpSecurityDescriptor", LPVOID),
("bInheritHandle", BOOL)
)
def __init__(self, **kwargs):
super(SECURITY_ATTRIBUTES, self).__init__(nLength=sizeof(self), **kwargs)
def CreatePipe(pipeAttributes, size):
read = ctypes.wintypes.HANDLE()
write = ctypes.wintypes.HANDLE()
if kernel32.CreatePipe(ctypes.byref(read), ctypes.byref(write), byref(pipeAttributes) if pipeAttributes else None, ctypes.wintypes.DWORD(size)) == 0:
raise ctypes.WinError()
return read.value, write.value
class STARTUPINFOW(Structure):
_fields_=(
('cb',DWORD),
('lpReserved',LPWSTR),
('lpDesktop',LPWSTR),
('lpTitle',LPWSTR),
('dwX',DWORD),
('dwY',DWORD),
('dwXSize',DWORD),
('dwYSize',DWORD),
('dwXCountChars',DWORD),
('dwYCountChars',DWORD),
('dwFillAttribute',DWORD),
('dwFlags',DWORD),
('wShowWindow',WORD),
('cbReserved2',WORD),
('lpReserved2',POINTER(c_byte)),
('hSTDInput',HANDLE),
('hSTDOutput',HANDLE),
('hSTDError',HANDLE),
)
def __init__(self, **kwargs):
super(STARTUPINFOW, self).__init__(cb=sizeof(self), **kwargs)
STARTUPINFO = STARTUPINFOW
class PROCESS_INFORMATION(Structure):
_fields_=(
('hProcess',HANDLE),
('hThread',HANDLE),
('dwProcessID',DWORD),
('dwThreadID',DWORD),
)
def CreateProcessAsUser(token, applicationName, commandLine, processAttributes, threadAttributes, inheritHandles, creationFlags, environment, currentDirectory, startupInfo, processInformation):
if advapi32.CreateProcessAsUserW(token, applicationName, commandLine, processAttributes, threadAttributes, inheritHandles, creationFlags, environment, currentDirectory, byref(startupInfo), byref(processInformation)) == 0:
raise WinError()
def GetCurrentProcess():
return kernel32.GetCurrentProcess()
def OpenProcessToken(ProcessHandle, DesiredAccess):
token = HANDLE()
if advapi32.OpenProcessToken(ProcessHandle, DesiredAccess, byref(token)) == 0:
raise WinError()
return token.value
DUPLICATE_SAME_ACCESS = 0x00000002
def DuplicateHandle(sourceProcessHandle, sourceHandle, targetProcessHandle, desiredAccess, inheritHandle, options):
targetHandle = HANDLE()
if kernel32.DuplicateHandle(sourceProcessHandle, sourceHandle, targetProcessHandle, byref(targetHandle), desiredAccess, inheritHandle, options) == 0:
raise WinError()
return targetHandle.value
PAPCFUNC = ctypes.WINFUNCTYPE(None, ctypes.wintypes.ULONG)
THREAD_SET_CONTEXT = 16
GMEM_MOVEABLE=2
class HGLOBAL(HANDLE):
def __init__(self,h,autoFree=True):
super(HGLOBAL,self).__init__(h)
self._autoFree=autoFree
def __del__(self):
if self and self._autoFree:
windll.kernel32.GlobalFree(self)
@classmethod
def alloc(cls,flags,size):
h=windll.kernel32.GlobalAlloc(flags,size)
return cls(h)
@contextlib.contextmanager
def lock(self):
try:
yield windll.kernel32.GlobalLock(self)
finally:
windll.kernel32.GlobalUnlock(self)
def forget(self):
self.value=None
MOVEFILE_COPY_ALLOWED = 0x2
MOVEFILE_CREATE_HARDLINK = 0x10
MOVEFILE_DELAY_UNTIL_REBOOT = 0x4
MOVEFILE_FAIL_IF_NOT_TRACKABLE = 0x20
MOVEFILE_REPLACE_EXISTING = 0x1
MOVEFILE_WRITE_THROUGH = 0x8
def moveFileEx(lpExistingFileName: str, lpNewFileName: str, dwFlags: int):
if not kernel32.MoveFileExW(lpExistingFileName, lpNewFileName, dwFlags):
raise ctypes.WinError()
ES_CONTINUOUS = 0x80000000
ES_DISPLAY_REQUIRED = 0x2
ES_SYSTEM_REQUIRED = 0x1
kernel32.SetThreadExecutionState.restype = ctypes.wintypes.DWORD
def SetThreadExecutionState(esFlags):
res = kernel32.SetThreadExecutionState(esFlags)
if not res:
raise WinError()
return res
| true | true |
f73c075a61d79912547acd84cbcd6608b85abf86 | 7,856 | py | Python | tests/katsu_tests/test_authx_diagnoses.py | CanDIG/rego_development_playground | 75ba1aa9895f6d949e4ab80f51bfc474895eb304 | [
"Apache-2.0"
] | null | null | null | tests/katsu_tests/test_authx_diagnoses.py | CanDIG/rego_development_playground | 75ba1aa9895f6d949e4ab80f51bfc474895eb304 | [
"Apache-2.0"
] | 4 | 2021-06-04T21:34:18.000Z | 2021-11-17T19:23:46.000Z | tests/katsu_tests/test_authx_diagnoses.py | CanDIG/rego_development_playground | 75ba1aa9895f6d949e4ab80f51bfc474895eb304 | [
"Apache-2.0"
] | null | null | null | from test_helpers import helper_get_katsu_response
from test_helpers import helper_get_user_token
import pytest
"""
This test suite will cover the manual testsfor KATSU in README.md, ensuring that
authorization happens correctly
- beacon permissions
- registered/controlled access
- modified but live token
"""
KATSU_URL="http://localhost:8001"
OIDC1_NAME="oidc1"
OIDC2_NAME="oidc2"
@pytest.fixture(scope="session")
def user1_token():
"""
Return the token for user1
"""
return helper_get_user_token("user1", "pass1")
def test_user1_diagnoses_access(user1_token):
""""
Make sure user1 has access to open1, open2, registered3 and controlled4
"""
response = helper_get_katsu_response(user1_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 4
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" in diagnoses_dscps
assert "controlled4" in diagnoses_dscps
'''
Make sure user1 has access to open1, open2, registered3 and controlled4 by id
'''
for id in diagnoses_ids:
response = helper_get_katsu_response(user1_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user1_diagnoses_invalid(user1_token):
"""
Make sure invalid token will not have access to datasets other than open datasets
"""
invalid_token = 'A' + user1_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" not in diagnoses_dscps
assert "controlled4" not in diagnoses_dscps
@pytest.fixture(scope="session")
def user2_token():
"""
Return the token for user2
"""
return helper_get_user_token("user2", "pass2")
def test_user2_diagnoses_access(user2_token):
""""
Make sure user2 has access to open1, open2, registered3 and controlled 4
"""
response = helper_get_katsu_response(user2_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 3
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
""""
Make sure user2 has access to open1, open2, and controlled5
"""
for id in diagnoses_ids:
response = helper_get_katsu_response(user2_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user2_diagnoses_invalid(user2_token):
"""
Make sure invalid token will not have access to datasets other than open datasets
"""
invalid_token = 'A' + user2_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" not in diagnoses_dscps
assert "controlled5" not in diagnoses_dscps
@pytest.fixture(scope="session")
def user3_token():
"""
Return the token for user3
"""
return helper_get_user_token("user3", "pass3", OIDC2_NAME)
def test_user3_diagnoses_access(user3_token):
""""
Make sure user3 has access to open1, open2, registered3, controlled4, and controlled6
"""
response = helper_get_katsu_response(user3_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 5
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" in diagnoses_dscps
assert "controlled4" in diagnoses_dscps
assert "controlled6" in diagnoses_dscps
""""
Make sure user3 has access to open1, open2, registered3, controlled4, and controlled6 by id
"""
for id in diagnoses_ids:
response = helper_get_katsu_response(user3_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user3_diagnoses_invalid(user3_token):
"""
Make sure invalid token will not have access to datasets other than open datasets
"""
invalid_token = 'A' + user3_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" not in diagnoses_dscps
assert "controlled4" not in diagnoses_dscps
assert "controlled6" not in diagnoses_dscps
@pytest.fixture(scope="session")
def user4_token():
"""
Return the token for user4
"""
return helper_get_user_token("user4", "pass4", OIDC2_NAME)
def test_user4_diagnoses_access(user4_token):
""""
Make sure user3 has access to open1, open2, and controlled5
"""
response = helper_get_katsu_response(user4_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 3
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "controlled5" in diagnoses_dscps
""""
Make sure user4 has access to open1, open2, and controlled4 by id
"""
for id in diagnoses_ids:
response = helper_get_katsu_response(user4_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user4_diagnoses_invalid(user4_token):
"""
Make sure invalid token will not have access to datasets other than open datasets
"""
invalid_token = 'A' + user4_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "controlled5" not in diagnoses_dscps | 36.37037 | 95 | 0.710412 | from test_helpers import helper_get_katsu_response
from test_helpers import helper_get_user_token
import pytest
KATSU_URL="http://localhost:8001"
OIDC1_NAME="oidc1"
OIDC2_NAME="oidc2"
@pytest.fixture(scope="session")
def user1_token():
return helper_get_user_token("user1", "pass1")
def test_user1_diagnoses_access(user1_token):
response = helper_get_katsu_response(user1_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 4
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" in diagnoses_dscps
assert "controlled4" in diagnoses_dscps
for id in diagnoses_ids:
response = helper_get_katsu_response(user1_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user1_diagnoses_invalid(user1_token):
invalid_token = 'A' + user1_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" not in diagnoses_dscps
assert "controlled4" not in diagnoses_dscps
@pytest.fixture(scope="session")
def user2_token():
return helper_get_user_token("user2", "pass2")
def test_user2_diagnoses_access(user2_token):
response = helper_get_katsu_response(user2_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 3
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
for id in diagnoses_ids:
response = helper_get_katsu_response(user2_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user2_diagnoses_invalid(user2_token):
invalid_token = 'A' + user2_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" not in diagnoses_dscps
assert "controlled5" not in diagnoses_dscps
@pytest.fixture(scope="session")
def user3_token():
return helper_get_user_token("user3", "pass3", OIDC2_NAME)
def test_user3_diagnoses_access(user3_token):
response = helper_get_katsu_response(user3_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 5
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" in diagnoses_dscps
assert "controlled4" in diagnoses_dscps
assert "controlled6" in diagnoses_dscps
for id in diagnoses_ids:
response = helper_get_katsu_response(user3_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user3_diagnoses_invalid(user3_token):
invalid_token = 'A' + user3_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "registered3" not in diagnoses_dscps
assert "controlled4" not in diagnoses_dscps
assert "controlled6" not in diagnoses_dscps
@pytest.fixture(scope="session")
def user4_token():
return helper_get_user_token("user4", "pass4", OIDC2_NAME)
def test_user4_diagnoses_access(user4_token):
response = helper_get_katsu_response(user4_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 3
diagnoses_ids = list()
diagnoses_dscps = list()
for diagnosis in response_json["results"]:
diagnoses_ids.append(diagnosis["id"])
diagnoses_dscps.append(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "controlled5" in diagnoses_dscps
for id in diagnoses_ids:
response = helper_get_katsu_response(user4_token, f"{KATSU_URL}/api/diagnoses/{id}")
assert response.status_code == 200
assert "id" in response.json().keys()
def test_user4_diagnoses_invalid(user4_token):
invalid_token = 'A' + user4_token[1:]
response = helper_get_katsu_response(invalid_token, f"{KATSU_URL}/api/diagnoses")
assert response.status_code == 200
response_json = response.json()
assert response_json["count"] == 2
diagnoses_dscps = set()
for diagnosis in response_json["results"]:
diagnoses_dscps.add(diagnosis["extra_properties"]["description"])
assert "open1" in diagnoses_dscps
assert "open2" in diagnoses_dscps
assert "controlled5" not in diagnoses_dscps | true | true |
f73c076c09cd5d3e41014423f1a1b4021464aba5 | 7,344 | py | Python | settings/common.py | TroJan/EvalAI | e378c5d6fd6a2f5d1ec020198eeae5d17793952f | [
"BSD-3-Clause"
] | null | null | null | settings/common.py | TroJan/EvalAI | e378c5d6fd6a2f5d1ec020198eeae5d17793952f | [
"BSD-3-Clause"
] | null | null | null | settings/common.py | TroJan/EvalAI | e378c5d6fd6a2f5d1ec020198eeae5d17793952f | [
"BSD-3-Clause"
] | 1 | 2020-01-15T17:27:02.000Z | 2020-01-15T17:27:02.000Z | """
Django settings for evalai project.
Generated by 'django-admin startproject' using Django 1.10.2.
For more information on this file, see
https://docs.djangoproject.com/en/1.10/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.10/ref/settings/
"""
import datetime
import os
import sys
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
APPS_DIR = os.path.join(BASE_DIR, 'apps')
sys.path.append(APPS_DIR)
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.10/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = os.environ.get('SECRET_KEY', 'random_secret_key')
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
DEFAULT_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.sites',
]
OUR_APPS = [
'accounts',
'analytics',
'base',
'challenges',
'hosts',
'jobs',
'participants',
'web',
]
THIRD_PARTY_APPS = [
'allauth',
'allauth.account',
'corsheaders',
'import_export',
'rest_auth',
'rest_auth.registration',
'rest_framework.authtoken',
'rest_framework',
'rest_framework_docs',
'rest_framework_expiring_authtoken',
]
INSTALLED_APPS = DEFAULT_APPS + OUR_APPS + THIRD_PARTY_APPS
MIDDLEWARE = [
'corsheaders.middleware.CorsMiddleware',
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'evalai.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'evalai.wsgi.application'
# Password validation
# https://docs.djangoproject.com/en/1.10/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', # noqa
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', # noqa
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', # noqa
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', # noqa
},
]
# Internationalization
# https://docs.djangoproject.com/en/1.10/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.10/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
MEDIA_URL = "/media/"
SITE_ID = 1
REST_FRAMEWORK = {
'DEFAULT_PAGINATION_CLASS': (
'rest_framework.pagination.LimitOffsetPagination'),
'PAGE_SIZE': 10,
'DEFAULT_PERMISSION_CLASSES': [
'rest_framework.permissions.IsAuthenticatedOrReadOnly'
],
'DEFAULT_AUTHENTICATION_CLASSES': [
'rest_framework_expiring_authtoken.authentication.ExpiringTokenAuthentication',
],
'TEST_REQUEST_DEFAULT_FORMAT': 'json',
'DEFAULT_THROTTLE_CLASSES': (
'rest_framework.throttling.AnonRateThrottle',
'rest_framework.throttling.UserRateThrottle'
),
'DEFAULT_THROTTLE_RATES': {
'anon': '100/minute',
'user': '100/minute'
},
'DEFAULT_RENDERER_CLASSES': (
'rest_framework.renderers.JSONRenderer',
)
}
# ALLAUTH SETTINGS
ACCOUNT_EMAIL_REQUIRED = True
OLD_PASSWORD_FIELD_ENABLED = True
ACCOUNT_CONFIRM_EMAIL_ON_GET = True
ACCOUNT_EMAIL_CONFIRMATION_ANONYMOUS_REDIRECT_URL = '/api/auth/email-confirmed/'
ACCOUNT_EMAIL_CONFIRMATION_AUTHENTICATED_REDIRECT_URL = '/api/auth/email-confirmed/'
AUTHENTICATION_BACKENDS = (
# Needed to login by username in Django admin, regardless of `allauth`
'django.contrib.auth.backends.ModelBackend',
# `allauth` specific authentication methods, such as login by e-mail
'allauth.account.auth_backends.AuthenticationBackend',
)
# CORS Settings
CORS_ORIGIN_ALLOW_ALL = True
# REST Framework Expiring Tokens Configuration
EXPIRING_TOKEN_LIFESPAN = datetime.timedelta(days=7)
# Logging
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'root': {
'level': 'INFO',
'handlers': ['console'],
},
'filters': {
'require_debug_false': {
'()': 'django.utils.log.RequireDebugFalse',
},
'require_debug_true': {
'()': 'django.utils.log.RequireDebugTrue',
}
},
'formatters': {
'simple': {
'format': '[%(asctime)s] %(levelname)s %(message)s',
'datefmt': '%Y-%m-%d %H:%M:%S'
},
'verbose': {
'format': '[%(asctime)s] %(levelname)s %(module)s %(message)s',
'datefmt': '%Y-%m-%d %H:%M:%S'
}
},
'handlers': {
'console': {
'level': 'INFO',
'filters': ['require_debug_true'],
'class': 'logging.StreamHandler',
'formatter': 'simple'
},
'logfile': {
'level': 'DEBUG',
'class': 'logging.handlers.RotatingFileHandler',
'filename': "/tmp/logfile",
'maxBytes': 50000,
'backupCount': 10,
'formatter': 'verbose'
},
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler',
'filters': ['require_debug_false'],
}
},
'loggers': {
'django': {
'handlers': ['console'],
'propagate': False,
},
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'django.security': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'django.db.backends': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
}
}
}
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
}
}
RABBITMQ_PARAMETERS = {
'HOST': 'localhost',
'EVALAI_EXCHANGE': {
'NAME': 'evalai_submissions',
'TYPE': 'topic',
},
'SUBMISSION_QUEUE': 'submission_task_queue',
}
| 26.608696 | 99 | 0.6314 |
import datetime
import os
import sys
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
APPS_DIR = os.path.join(BASE_DIR, 'apps')
sys.path.append(APPS_DIR)
SECRET_KEY = os.environ.get('SECRET_KEY', 'random_secret_key')
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
DEFAULT_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.sites',
]
OUR_APPS = [
'accounts',
'analytics',
'base',
'challenges',
'hosts',
'jobs',
'participants',
'web',
]
THIRD_PARTY_APPS = [
'allauth',
'allauth.account',
'corsheaders',
'import_export',
'rest_auth',
'rest_auth.registration',
'rest_framework.authtoken',
'rest_framework',
'rest_framework_docs',
'rest_framework_expiring_authtoken',
]
INSTALLED_APPS = DEFAULT_APPS + OUR_APPS + THIRD_PARTY_APPS
MIDDLEWARE = [
'corsheaders.middleware.CorsMiddleware',
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'evalai.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'evalai.wsgi.application'
# Password validation
# https://docs.djangoproject.com/en/1.10/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', # noqa
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', # noqa
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', # noqa
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', # noqa
},
]
# Internationalization
# https://docs.djangoproject.com/en/1.10/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.10/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
MEDIA_URL = "/media/"
SITE_ID = 1
REST_FRAMEWORK = {
'DEFAULT_PAGINATION_CLASS': (
'rest_framework.pagination.LimitOffsetPagination'),
'PAGE_SIZE': 10,
'DEFAULT_PERMISSION_CLASSES': [
'rest_framework.permissions.IsAuthenticatedOrReadOnly'
],
'DEFAULT_AUTHENTICATION_CLASSES': [
'rest_framework_expiring_authtoken.authentication.ExpiringTokenAuthentication',
],
'TEST_REQUEST_DEFAULT_FORMAT': 'json',
'DEFAULT_THROTTLE_CLASSES': (
'rest_framework.throttling.AnonRateThrottle',
'rest_framework.throttling.UserRateThrottle'
),
'DEFAULT_THROTTLE_RATES': {
'anon': '100/minute',
'user': '100/minute'
},
'DEFAULT_RENDERER_CLASSES': (
'rest_framework.renderers.JSONRenderer',
)
}
# ALLAUTH SETTINGS
ACCOUNT_EMAIL_REQUIRED = True
OLD_PASSWORD_FIELD_ENABLED = True
ACCOUNT_CONFIRM_EMAIL_ON_GET = True
ACCOUNT_EMAIL_CONFIRMATION_ANONYMOUS_REDIRECT_URL = '/api/auth/email-confirmed/'
ACCOUNT_EMAIL_CONFIRMATION_AUTHENTICATED_REDIRECT_URL = '/api/auth/email-confirmed/'
AUTHENTICATION_BACKENDS = (
# Needed to login by username in Django admin, regardless of `allauth`
'django.contrib.auth.backends.ModelBackend',
# `allauth` specific authentication methods, such as login by e-mail
'allauth.account.auth_backends.AuthenticationBackend',
)
# CORS Settings
CORS_ORIGIN_ALLOW_ALL = True
# REST Framework Expiring Tokens Configuration
EXPIRING_TOKEN_LIFESPAN = datetime.timedelta(days=7)
# Logging
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'root': {
'level': 'INFO',
'handlers': ['console'],
},
'filters': {
'require_debug_false': {
'()': 'django.utils.log.RequireDebugFalse',
},
'require_debug_true': {
'()': 'django.utils.log.RequireDebugTrue',
}
},
'formatters': {
'simple': {
'format': '[%(asctime)s] %(levelname)s %(message)s',
'datefmt': '%Y-%m-%d %H:%M:%S'
},
'verbose': {
'format': '[%(asctime)s] %(levelname)s %(module)s %(message)s',
'datefmt': '%Y-%m-%d %H:%M:%S'
}
},
'handlers': {
'console': {
'level': 'INFO',
'filters': ['require_debug_true'],
'class': 'logging.StreamHandler',
'formatter': 'simple'
},
'logfile': {
'level': 'DEBUG',
'class': 'logging.handlers.RotatingFileHandler',
'filename': "/tmp/logfile",
'maxBytes': 50000,
'backupCount': 10,
'formatter': 'verbose'
},
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler',
'filters': ['require_debug_false'],
}
},
'loggers': {
'django': {
'handlers': ['console'],
'propagate': False,
},
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'django.security': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'django.db.backends': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
}
}
}
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
}
}
RABBITMQ_PARAMETERS = {
'HOST': 'localhost',
'EVALAI_EXCHANGE': {
'NAME': 'evalai_submissions',
'TYPE': 'topic',
},
'SUBMISSION_QUEUE': 'submission_task_queue',
}
| true | true |
f73c07bc944c352b0b9d234240f626e277e39eaf | 3,705 | py | Python | openstack_dashboard/theme_settings.py | ankur-gupta91/block_storage | 938548a3d4507dc56c1c26b442767eb41aa2e610 | [
"Apache-2.0"
] | 9 | 2016-06-03T03:53:24.000Z | 2017-05-20T16:53:23.000Z | openstack_dashboard/theme_settings.py | ankur-gupta91/block_storage | 938548a3d4507dc56c1c26b442767eb41aa2e610 | [
"Apache-2.0"
] | 12 | 2022-03-22T07:28:29.000Z | 2022-03-22T07:29:55.000Z | openstack_dashboard/theme_settings.py | ankur-gupta91/block_storage | 938548a3d4507dc56c1c26b442767eb41aa2e610 | [
"Apache-2.0"
] | 4 | 2016-08-01T10:50:15.000Z | 2017-02-22T12:11:19.000Z | # Copyright 2016 Hewlett Packard Enterprise Software, LLC
# 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 logging
import os
from django.utils.translation import pgettext_lazy
def get_theme_static_dirs(available_themes, collection_dir, root):
static_dirs = []
# Collect and expose the themes that have been configured
for theme in available_themes:
theme_name, theme_label, theme_path = theme
theme_url = os.path.join(collection_dir, theme_name)
theme_path = os.path.join(root, theme_path)
if os.path.exists(os.path.join(theme_path, 'static')):
# Only expose the subdirectory 'static' if it exists from a custom
# theme, allowing other logic to live with a theme that we might
# not want to expose statically
theme_path = os.path.join(theme_path, 'static')
static_dirs.append(
(theme_url, theme_path),
)
return static_dirs
def get_available_themes(available_themes, custom_path, default_path,
default_theme):
new_theme_list = []
# We can only support one path at a time, because of static file
# collection.
custom_ndx = -1
default_ndx = -1
default_theme_ndx = -1
for ndx, each_theme in enumerate(available_themes):
# Maintain Backward Compatibility for CUSTOM_THEME_PATH
if custom_path:
if each_theme[2] == custom_path:
custom_ndx = ndx
# Maintain Backward Compatibility for DEFAULT_THEME_PATH
if default_path:
if each_theme[0] == 'default':
default_ndx = ndx
each_theme = (
'default',
pgettext_lazy('Default style theme', 'Default'),
default_path
)
# Make sure that DEFAULT_THEME is configured for use
if each_theme[0] == default_theme:
default_theme_ndx = ndx
new_theme_list.append(each_theme)
if custom_ndx != -1:
# If CUSTOM_THEME_PATH is set, then we should set that as the default
# theme to make sure that upgrading Horizon doesn't jostle anyone
default_theme = available_themes[custom_ndx][0]
logging.warning("Your AVAILABLE_THEMES already contains your "
"CUSTOM_THEME_PATH, therefore using configuration in "
"AVAILABLE_THEMES for %s." % custom_path)
elif custom_path is not None:
new_theme_list.append(
('custom',
pgettext_lazy('Custom style theme', 'Custom'),
custom_path)
)
default_theme = 'custom'
# If 'default' isn't present at all, add it with the default_path
if default_ndx == -1 and default_path is not None:
new_theme_list.append(
('default',
pgettext_lazy('Default style theme', 'Default'),
default_path)
)
# If default is not configured, we have to set one,
# just grab the first theme
if default_theme_ndx == -1 and custom_ndx == -1:
default_theme = available_themes[0][0]
return new_theme_list, default_theme
| 35.625 | 78 | 0.647233 |
import logging
import os
from django.utils.translation import pgettext_lazy
def get_theme_static_dirs(available_themes, collection_dir, root):
static_dirs = []
for theme in available_themes:
theme_name, theme_label, theme_path = theme
theme_url = os.path.join(collection_dir, theme_name)
theme_path = os.path.join(root, theme_path)
if os.path.exists(os.path.join(theme_path, 'static')):
theme_path = os.path.join(theme_path, 'static')
static_dirs.append(
(theme_url, theme_path),
)
return static_dirs
def get_available_themes(available_themes, custom_path, default_path,
default_theme):
new_theme_list = []
custom_ndx = -1
default_ndx = -1
default_theme_ndx = -1
for ndx, each_theme in enumerate(available_themes):
if custom_path:
if each_theme[2] == custom_path:
custom_ndx = ndx
if default_path:
if each_theme[0] == 'default':
default_ndx = ndx
each_theme = (
'default',
pgettext_lazy('Default style theme', 'Default'),
default_path
)
if each_theme[0] == default_theme:
default_theme_ndx = ndx
new_theme_list.append(each_theme)
if custom_ndx != -1:
default_theme = available_themes[custom_ndx][0]
logging.warning("Your AVAILABLE_THEMES already contains your "
"CUSTOM_THEME_PATH, therefore using configuration in "
"AVAILABLE_THEMES for %s." % custom_path)
elif custom_path is not None:
new_theme_list.append(
('custom',
pgettext_lazy('Custom style theme', 'Custom'),
custom_path)
)
default_theme = 'custom'
# If 'default' isn't present at all, add it with the default_path
if default_ndx == -1 and default_path is not None:
new_theme_list.append(
('default',
pgettext_lazy('Default style theme', 'Default'),
default_path)
)
if default_theme_ndx == -1 and custom_ndx == -1:
default_theme = available_themes[0][0]
return new_theme_list, default_theme
| true | true |
f73c07caec4ae8a0fc91ef1c9848cf2ff5d8a5d1 | 120 | py | Python | test_output/for_loop.py | roshangol/executed-path-visualize | 1759c12b0048fe117205990b151d2f5f57ad9616 | [
"MIT"
] | null | null | null | test_output/for_loop.py | roshangol/executed-path-visualize | 1759c12b0048fe117205990b151d2f5f57ad9616 | [
"MIT"
] | null | null | null | test_output/for_loop.py | roshangol/executed-path-visualize | 1759c12b0048fe117205990b151d2f5f57ad9616 | [
"MIT"
] | null | null | null | lst = [1, 2, 3, 4, 5]
for i in range(len(lst)):
print(lst[i], end=" ")
for j in range(0, 10):
print(j, end=" ") | 20 | 26 | 0.5 | lst = [1, 2, 3, 4, 5]
for i in range(len(lst)):
print(lst[i], end=" ")
for j in range(0, 10):
print(j, end=" ") | true | true |
f73c09d1298a19e2c65689de3b32ae95532ab4b1 | 516 | py | Python | alg_insertion_sort.py | lukes1582/algoritmi | 3313c9ae3cb5f9f0c410ca86ea29e23cb1c3c8fd | [
"Apache-2.0"
] | null | null | null | alg_insertion_sort.py | lukes1582/algoritmi | 3313c9ae3cb5f9f0c410ca86ea29e23cb1c3c8fd | [
"Apache-2.0"
] | null | null | null | alg_insertion_sort.py | lukes1582/algoritmi | 3313c9ae3cb5f9f0c410ca86ea29e23cb1c3c8fd | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
"""
l0m1s
lukes1582@gmail.com
algoritmo insertion sort sviluppato per Python
"""
arr = [5,4,3,1,2,11,9,8,0]
print("Array in origine ")
print(arr)
print ("Lunghezza dell'array "+ str(len(arr)))
print(50*"x")
for i in range(1, len(arr)):
key = arr[i]
j = i-1
while j >=0 and key < arr[j] :
arr[j+1] = arr[j]
j -= 1
arr[j+1] = key
print ("l'array ordinato risulta essere: ")
for x in range(len(arr)):
print ("%d" %arr[x])
| 18.428571 | 47 | 0.546512 |
arr = [5,4,3,1,2,11,9,8,0]
print("Array in origine ")
print(arr)
print ("Lunghezza dell'array "+ str(len(arr)))
print(50*"x")
for i in range(1, len(arr)):
key = arr[i]
j = i-1
while j >=0 and key < arr[j] :
arr[j+1] = arr[j]
j -= 1
arr[j+1] = key
print ("l'array ordinato risulta essere: ")
for x in range(len(arr)):
print ("%d" %arr[x])
| true | true |
f73c0a14a8a12842479a4ecfabd7ba1529fc1b0a | 19,243 | py | Python | py/test/fixture/whale/host/interrupt_handler.py | arccode/factory | a1b0fccd68987d8cd9c89710adc3c04b868347ec | [
"BSD-3-Clause"
] | 3 | 2022-01-06T16:52:52.000Z | 2022-03-07T11:30:47.000Z | py/test/fixture/whale/host/interrupt_handler.py | arccode/factory | a1b0fccd68987d8cd9c89710adc3c04b868347ec | [
"BSD-3-Clause"
] | null | null | null | py/test/fixture/whale/host/interrupt_handler.py | arccode/factory | a1b0fccd68987d8cd9c89710adc3c04b868347ec | [
"BSD-3-Clause"
] | 1 | 2021-10-24T01:47:22.000Z | 2021-10-24T01:47:22.000Z | #!/usr/bin/env python3
#
# Copyright 2014 The Chromium OS Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Handles Whale's button click event."""
import argparse
import functools
import logging
import os
import re
import sys
import time
from cros.factory.test.fixture.whale import keyboard_emulator
from cros.factory.test.fixture.whale import serial_client
from cros.factory.test.fixture.whale import servo_client
from cros.factory.utils import gpio_utils
from cros.factory.utils import process_utils
from cros.factory.utils import ssh_utils
from cros.factory.utils import type_utils
ActionType = type_utils.Enum(['PUSH_NEEDLE', 'FIXTURE_STARTED'])
def TimeClassMethodDebug(func):
"""A decorator to log method running time on debug level."""
@functools.wraps(func)
def Wrapped(*args, **kwargs):
logging.debug('Invoking %s()', func.__name__)
start_time = time.time()
result = func(*args, **kwargs)
logging.debug('%s() finished in %.4f secs', func.__name__,
time.time() - start_time)
return result
return Wrapped
class InterruptHandler:
"""Waits for Whale's I/O expanders' interrupt and dispatches it.
It connects to BeagleBone's servod and polld, where servod is used to get
I/O expanders' input status and reset SR latches; polld is used to wait
GPIO 7, the interrupt pin from Whale's I/O expanders.
"""
# Shortcuts to Whale's button and control dict.
_BUTTON = servo_client.WHALE_BUTTON
_CONTROL = servo_client.WHALE_CONTROL
_FIXTURE_FEEDBACK = servo_client.FIXTURE_FEEDBACK
_PLANKTON_FEEDBACK = servo_client.PLANKTON_FEEDBACK
_WHALE_DEBUG_MODE_EN = servo_client.WHALE_DEBUG_MODE_EN
# List of buttons and feedbacks to scan.
# Difference between button and feedback is: button is latched;
# no latch for feedback.
_BUTTON_LIST = servo_client.WHALE_BUTTONS
_FEEDBACK_LIST = servo_client.WHALE_FEEDBACKS
# Buttons that operator can use (non debug mode).
_OPERATOR_BUTTON_LIST = (_BUTTON.FIXTURE_START, _BUTTON.FIXTURE_STOP)
# DUT sensor check list, add (FEEDBACK, Bool) to check if MLB exists.
# example:
# _DUT_SENSOR_CHECK_LIST = dict([
# (_FIXTURE_FEEDBACK.FB8, True),
# (_FIXTURE_FEEDBACK.FB9, False)])
_DUT_SENSOR_CHECK_LIST = dict()
_INPUT_LIST = _BUTTON_LIST + _FEEDBACK_LIST
_INPUT_INTERRUPT_GPIO = 7
# Used to avoid toggle battery too fast.
_BATTERY_CEASE_TOGGLE_SECS = 1.0
_FixtureState = type_utils.Enum(
['WAIT', 'CLOSED', 'ERR_CLOSING', 'CLOSING', 'OPENING'])
# Fixture state to LED light and LCD message (green, red, message).
_FixtureStateParams = {
_FixtureState.WAIT: ('on', 'on', 'ready'),
_FixtureState.CLOSED: ('off', 'off', 'closed'),
_FixtureState.ERR_CLOSING: ('off', 'on', '!!no board inside!!'),
_FixtureState.CLOSING: ('off', 'on', 'closing'),
_FixtureState.OPENING: ('off', 'on', 'opening')}
def __init__(self, host, polld_port, servod_port, dolphin_port, rpc_debug,
polling_wait_secs):
"""Constructor.
Args:
host: BeagleBone's hostname or IP address.
polld_port: port that polld listens. Set to None if not using polld.
servod_port: port that servod listens.
dolphin_port: port that dolphin server listens. Set to None if not using
dolphin server.
rpc_debug: True to enable XMLRPC debug message.
polling_wait_secs: # seconds for polling button clicking event.
"""
self._poll = gpio_utils.GpioManager(
use_polld=polld_port is not None, host=host, tcp_port=polld_port,
verbose=rpc_debug)
self._dolphin = None
if dolphin_port:
self._dolphin = serial_client.SerialClient(
host=host, tcp_port=dolphin_port, verbose=rpc_debug)
self._servo = servo_client.ServoClient(host=host, port=servod_port,
verbose=rpc_debug)
self._polling_wait_secs = polling_wait_secs
# Store last feedback value. The value is initialzed in the very first
# ScanFeedback call.
self._last_feedback = {}
self._starting_fixture_action = None
# Used to avoid toggle battery too fast.
self._last_battery_toggle_time = time.time()
@TimeClassMethodDebug
def Init(self):
"""Resets button latch and records feedback value."""
self._last_feedback = self._servo.MultipleIsOn(self._FEEDBACK_LIST)
self._servo.MultipleSet([(self._CONTROL.LCM_CMD, 'clear'),
(self._CONTROL.LCM_TEXT, 'Initializing...')])
self.ResetLatch()
self.ResetInterrupt()
self.ResetKeyboard()
# Initial fixture state: cover open.
self._HandleStopFixture(show_state=False)
self._SetState(self._FixtureState.WAIT)
def ResetKeyboard(self):
keyboard = keyboard_emulator.KeyboardEmulator(self._servo)
keyboard.SimulateKeystrokes()
def _SetState(self, state):
green, red, message = self._FixtureStateParams[state]
self._servo.MultipleSet([(self._CONTROL.PASS_LED, green),
(self._CONTROL.FAIL_LED, red),
(self._CONTROL.LCM_CMD, 'clear'),
(self._CONTROL.LCM_TEXT, message)])
self.ShowNucIpOnLED()
def _IsMLBInFixture(self):
"""Checks MLB(s) is(are) inside the fixture.
If the project has only one board, check DUT_SENSOR is enough. For two
boards project, ex. lid and base boards, check DUT_SENSOR and BASE_SENSOR.
Returns:
True if MLB(s) is(are) inside the fixture; otherwise False.
"""
if not self._DUT_SENSOR_CHECK_LIST:
logging.info('No dut sensor...')
return True
dut_sensor_list = list(self._DUT_SENSOR_CHECK_LIST)
dut_sensor_status = self._servo.MultipleIsOn(dut_sensor_list)
return dut_sensor_status == self._DUT_SENSOR_CHECK_LIST
@TimeClassMethodDebug
def _HandleStopFixture(self, show_state=True):
"""Stop Fixture Step"""
logging.info('Stopping fixture...')
if show_state:
self._SetState(self._FixtureState.OPENING)
# Disable battery first for safety.
self._servo.Disable(self._CONTROL.BATTERY)
while True:
feedback_status = self._servo.MultipleIsOn(self._FEEDBACK_LIST)
if (not feedback_status[self._FIXTURE_FEEDBACK.FB1] or
not feedback_status[self._FIXTURE_FEEDBACK.FB3]):
self._servo.Disable(self._CONTROL.FIXTURE_PUSH_NEEDLE)
continue
self._starting_fixture_action = None
logging.info('[Fixture stopped]')
break
self._SetState(self._FixtureState.WAIT)
@TimeClassMethodDebug
def _HandleStartFixtureFeedbackChange(self, feedback_status):
"""Processing Start Fixture feedback information"""
if (self._starting_fixture_action is not None and
self._starting_fixture_action != ActionType.FIXTURE_STARTED):
# we are closing the fixture, check if we detect a hand
if feedback_status[self._FIXTURE_FEEDBACK.FB5]:
# detect hand, abort
self._HandleStopFixture()
return
if self._servo.IsOn(self._BUTTON.FIXTURE_START):
if (self._starting_fixture_action == ActionType.PUSH_NEEDLE and
feedback_status[self._FIXTURE_FEEDBACK.FB2] and
feedback_status[self._FIXTURE_FEEDBACK.FB4]):
logging.info('[HandleStartFixture] fixture closed')
self._starting_fixture_action = ActionType.FIXTURE_STARTED
self._SetState(self._FixtureState.CLOSED)
@TimeClassMethodDebug
def _HandleStartFixture(self):
"""Start Fixture Step"""
logging.info('[Fixture Start ...]')
if self._starting_fixture_action == ActionType.FIXTURE_STARTED:
logging.info('[HandleStartFixture] ACTION = FIXTURE_STARTED')
return
if self._last_feedback[self._FIXTURE_FEEDBACK.FB5]:
logging.info('[HandleStartFixture] Detect Hands, stop..')
return
if self._starting_fixture_action is None:
if not self._IsMLBInFixture():
logging.info(
'[HandleStartFixture] OOPS! Cannot close cover without MLBs')
self._SetState(self._FixtureState.ERR_CLOSING)
return
self._ResetWhaleDeviceBeforeClosing()
self._ResetDolphinDeviceBeforeClosing()
self._starting_fixture_action = ActionType.PUSH_NEEDLE
self._SetState(self._FixtureState.CLOSING)
if self._starting_fixture_action == ActionType.PUSH_NEEDLE:
logging.info('[HandleStartFixture] pushing needle')
self._servo.Enable(self._CONTROL.FIXTURE_PUSH_NEEDLE)
@TimeClassMethodDebug
def _ResetWhaleDeviceBeforeClosing(self):
"""Resets devices on Whale if necessary before closing fixture."""
# Release DUT CC2 pull-high
self._servo.Disable(self._CONTROL.DC)
self._servo.Disable(self._CONTROL.OUTPUT_RESERVE_1)
@TimeClassMethodDebug
def _ResetDolphinDeviceBeforeClosing(self):
"""Resets Dolphin if necessary before closing fixture."""
if self._dolphin is None:
return
# Set dolphin to discharging mode, if dolphin is charging, DUT will fail to
# boot up after battery connection.
# Assuming all serial connections are connected to Dolphin.
serial_amount = self._dolphin.GetSerialAmount()
for serial_index in range(serial_amount):
self._dolphin.Send(serial_index, 'usbc_action dev')
@TimeClassMethodDebug
def _ToggleBattery(self):
"""Toggles battery status.
If battery is on, switches it to off and vise versa.
"""
if (time.time() - self._last_battery_toggle_time <
self._BATTERY_CEASE_TOGGLE_SECS):
logging.debug('Toggle too fast, cease toggle for %f second.',
self._BATTERY_CEASE_TOGGLE_SECS)
return
new_battery_status = ('off' if self._servo.IsOn(self._CONTROL.BATTERY)
else 'on')
logging.info('[Toggle battery to %s]', new_battery_status)
self._servo.Set(self._CONTROL.BATTERY, new_battery_status)
self._last_battery_toggle_time = time.time()
@TimeClassMethodDebug
def ScanButton(self):
"""Scans all buttons and invokes button click handler for clicked buttons.
Returns:
True if a button is clicked.
"""
logging.debug('[Scanning button....]')
status = self._servo.MultipleIsOn(self._BUTTON_LIST)
if status[self._BUTTON.FIXTURE_STOP]:
logging.info('Calling _HandleStopFixture because FIXTURE_STOP is True.')
self._HandleStopFixture()
# Disable stop button, and use 'i2cset' to set it back to input mode.
self._servo.Disable(self._BUTTON.FIXTURE_STOP)
process_utils.Spawn(['i2cset', '-y', '1', '0x77', '0x07', '0xff'])
return True
if (self._starting_fixture_action != ActionType.FIXTURE_STARTED and
self._starting_fixture_action is not None and
not status[self._BUTTON.FIXTURE_START]):
logging.info('Calling _HandleStopFixture because FIXTURE_START is False.')
self._HandleStopFixture()
return False
button_clicked = any(status.values())
if not button_clicked:
return False
operator_mode = not self._servo.IsOn(self._WHALE_DEBUG_MODE_EN)
for button, clicked in status.items():
if not clicked:
continue
if operator_mode and button not in self._OPERATOR_BUTTON_LIST:
logging.debug('Supress button %s click because debug mode is off.',
button)
continue
if button == self._BUTTON.FIXTURE_START:
if self._starting_fixture_action == ActionType.FIXTURE_STARTED:
logging.info('[START] ACTION = FIXTURE_STARTED')
else:
self._HandleStartFixture()
elif button == self._BUTTON.RESERVE_1:
self._ToggleBattery()
logging.info('Button %s clicked', button)
return button_clicked
@TimeClassMethodDebug
def ScanFeedback(self):
"""Scans all feedback and invokes handler for those changed feedback.
Returns:
True if any feedback value is clicked.
"""
logging.debug('[Scanning feedback....]')
feedback_status = self._servo.MultipleIsOn(self._FEEDBACK_LIST)
feedback_changed = False
for name, value in feedback_status.items():
if self._last_feedback[name] == value:
continue
self._HandleStartFixtureFeedbackChange(feedback_status)
logging.info('Feedback %s value changed to %r', name, value)
self._last_feedback[name] = value
feedback_changed = True
return feedback_changed
@TimeClassMethodDebug
def ResetLatch(self):
"""Resets SR latch for buttons."""
self._servo.Click(self._CONTROL.INPUT_RESET)
@TimeClassMethodDebug
def WaitForInterrupt(self):
logging.debug('Polling interrupt (GPIO %d %s) for %r seconds',
self._INPUT_INTERRUPT_GPIO, self._poll.GPIO_EDGE_FALLING,
self._polling_wait_secs)
if self._poll.Poll(self._INPUT_INTERRUPT_GPIO,
self._poll.GPIO_EDGE_FALLING,
self._polling_wait_secs):
logging.debug('Interrupt polled')
else:
logging.debug('Polling interrupt timeout')
@TimeClassMethodDebug
def ResetInterrupt(self):
"""Resets I/O expanders' interrupt.
We have four I/O expanders (TCA9539), three of them have inputs. As
BeagleBone can only accept one interrupt, we cascade two expanders'
(0x75, 0x77) INT to 0x76 input pins. So any input changes from 0x75, 0x76,
0x77 will generate interrupt to BeagleBone.
According to TCA9539 manual:
"resetting the interrupt circuit is achieved when data on the port is
changed to the original setting or data is read from the port that
generated the interrupt. ... Because each 8-bit port is read
independently, the interrupt caused by port 0 is not cleared by a read
of port 1, or vice versa",
to reset interrupt, we need to read each changing bit. However, as servod
reads a byte each time we read an input pin, so we only need to read 0x77
byte-0, byte-1, 0x75 byte-1, and 0x76 byte-0, byte-1, in sequence to reset
INT. The reason to read in sequence is that we need to read 0x76 at last
as 0x77 and 0x75 INT reset could change P02 and P03 pin in 0x76.
"""
# Touch I/O expander 0x77 byte 0 & 1, 0x75 byte 1, 0x76 byte 0 & 1.
# Note that we skip I/O expander 0x75 byte-0 as it contains no input
# pin, won't trigger interrupt.
self._servo.MultipleGet([
self._FIXTURE_FEEDBACK.FB1, self._BUTTON.FIXTURE_START,
self._PLANKTON_FEEDBACK.FB1, self._WHALE_DEBUG_MODE_EN,
self._BUTTON.RESERVE_1])
def Run(self):
"""Waits for Whale's button click interrupt and dispatches it."""
while True:
button_clicked = self.ScanButton()
feedback_changed = self.ScanFeedback()
# The reason why we don't poll interrupt right after reset latch is that
# it might be possible that a button is clicked after latch is cleared
# but before I/O expander is touched. In this case, the button is latched
# but the interrupt is consumed (after touching I/O expander) so that the
# following click of that button won't trigger interrupt again, and
# polling is blocked.
#
# The solution is to read button again without waiting for interrupt.
if button_clicked or feedback_changed:
if button_clicked:
self.ResetLatch()
self.ResetInterrupt()
continue
self.WaitForInterrupt()
def ShowNucIpOnLED(self):
"""Shows NUC dongle IP on LED second line"""
nuc_host = '192.168.234.1'
testing_rsa_path = '/usr/local/factory/misc/sshkeys/testing_rsa'
get_dongle_eth_script = (
'timeout 1s /usr/local/factory/py/test/fixture/get_dongle_eth.sh')
# Make identity file less open to make ssh happy
os.chmod(testing_rsa_path, 0o600)
ssh_command_base = ssh_utils.BuildSSHCommand(
identity_file=testing_rsa_path)
try:
interface = process_utils.SpawnOutput(
ssh_command_base + [nuc_host, get_dongle_eth_script]).strip()
except BaseException:
interface = None
if not interface:
ip_address = 'dongle not found...'
else:
ifconfig_command = 'ifconfig %s' % interface
ifconfig_result = process_utils.SpawnOutput(
ssh_command_base + [nuc_host, ifconfig_command]).strip()
ip_matcher = re.search(r'inet (\d+\.\d+\.\d+\.\d+)', ifconfig_result,
re.MULTILINE)
if not ip_matcher:
ip_address = 'dongle not found...'
else:
ip_address = ip_matcher.group(1)
self._servo.MultipleSet([(self._CONTROL.LCM_ROW, 'r1'),
(self._CONTROL.LCM_TEXT, ip_address)])
def ParseArgs():
"""Parses command line arguments.
Returns:
args from argparse.parse_args().
"""
description = (
'Handle Whale button click event.'
)
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter, description=description)
parser.add_argument('-d', '--debug', action='store_true', default=False,
help='enable debug messages')
parser.add_argument('--rpc_debug', action='store_true', default=False,
help='enable debug messages for XMLRPC call')
parser.add_argument('--nouse_dolphin', action='store_false', default=True,
dest='use_dolphin', help='whether to skip dolphin control'
' (remote server). default: %(default)s')
parser.add_argument('--use_polld', action='store_true', default=False,
help='whether to use polld (for polling GPIO port on '
'remote server) or poll local GPIO port, default: '
'%(default)s')
parser.add_argument('--host', default='127.0.0.1', type=str,
help='hostname of server, default: %(default)s')
parser.add_argument('--dolphin_port', default=9997, type=int,
help='port that dolphin_server listens, default: '
'%(default)d')
parser.add_argument('--polld_port', default=9998, type=int,
help='port that polld listens, default: %(default)d')
parser.add_argument('--servod_port', default=9999, type=int,
help='port that servod listens, default: %(default)d')
parser.add_argument('--polling_wait_secs', default=5, type=int,
help=('# seconds for polling button clicking event, '
'default: %(default)d'))
return parser.parse_args()
def main():
args = ParseArgs()
logging.basicConfig(
level=logging.DEBUG if args.debug else logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s')
polld_port = args.polld_port if args.use_polld else None
dolphin_port = args.dolphin_port if args.use_dolphin else None
handler = InterruptHandler(args.host, polld_port, args.servod_port,
dolphin_port, args.rpc_debug,
args.polling_wait_secs)
handler.Init()
handler.Run()
if __name__ == '__main__':
try:
main()
except KeyboardInterrupt:
sys.exit(0)
except gpio_utils.GpioManagerError as e:
sys.stderr.write(str(e) + '\n')
sys.exit(1)
| 37.657534 | 80 | 0.686276 |
import argparse
import functools
import logging
import os
import re
import sys
import time
from cros.factory.test.fixture.whale import keyboard_emulator
from cros.factory.test.fixture.whale import serial_client
from cros.factory.test.fixture.whale import servo_client
from cros.factory.utils import gpio_utils
from cros.factory.utils import process_utils
from cros.factory.utils import ssh_utils
from cros.factory.utils import type_utils
ActionType = type_utils.Enum(['PUSH_NEEDLE', 'FIXTURE_STARTED'])
def TimeClassMethodDebug(func):
@functools.wraps(func)
def Wrapped(*args, **kwargs):
logging.debug('Invoking %s()', func.__name__)
start_time = time.time()
result = func(*args, **kwargs)
logging.debug('%s() finished in %.4f secs', func.__name__,
time.time() - start_time)
return result
return Wrapped
class InterruptHandler:
_BUTTON = servo_client.WHALE_BUTTON
_CONTROL = servo_client.WHALE_CONTROL
_FIXTURE_FEEDBACK = servo_client.FIXTURE_FEEDBACK
_PLANKTON_FEEDBACK = servo_client.PLANKTON_FEEDBACK
_WHALE_DEBUG_MODE_EN = servo_client.WHALE_DEBUG_MODE_EN
# List of buttons and feedbacks to scan.
# Difference between button and feedback is: button is latched;
# no latch for feedback.
_BUTTON_LIST = servo_client.WHALE_BUTTONS
_FEEDBACK_LIST = servo_client.WHALE_FEEDBACKS
# Buttons that operator can use (non debug mode).
_OPERATOR_BUTTON_LIST = (_BUTTON.FIXTURE_START, _BUTTON.FIXTURE_STOP)
# DUT sensor check list, add (FEEDBACK, Bool) to check if MLB exists.
# example:
# _DUT_SENSOR_CHECK_LIST = dict([
# (_FIXTURE_FEEDBACK.FB8, True),
# (_FIXTURE_FEEDBACK.FB9, False)])
_DUT_SENSOR_CHECK_LIST = dict()
_INPUT_LIST = _BUTTON_LIST + _FEEDBACK_LIST
_INPUT_INTERRUPT_GPIO = 7
# Used to avoid toggle battery too fast.
_BATTERY_CEASE_TOGGLE_SECS = 1.0
_FixtureState = type_utils.Enum(
['WAIT', 'CLOSED', 'ERR_CLOSING', 'CLOSING', 'OPENING'])
# Fixture state to LED light and LCD message (green, red, message).
_FixtureStateParams = {
_FixtureState.WAIT: ('on', 'on', 'ready'),
_FixtureState.CLOSED: ('off', 'off', 'closed'),
_FixtureState.ERR_CLOSING: ('off', 'on', '!!no board inside!!'),
_FixtureState.CLOSING: ('off', 'on', 'closing'),
_FixtureState.OPENING: ('off', 'on', 'opening')}
def __init__(self, host, polld_port, servod_port, dolphin_port, rpc_debug,
polling_wait_secs):
self._poll = gpio_utils.GpioManager(
use_polld=polld_port is not None, host=host, tcp_port=polld_port,
verbose=rpc_debug)
self._dolphin = None
if dolphin_port:
self._dolphin = serial_client.SerialClient(
host=host, tcp_port=dolphin_port, verbose=rpc_debug)
self._servo = servo_client.ServoClient(host=host, port=servod_port,
verbose=rpc_debug)
self._polling_wait_secs = polling_wait_secs
# Store last feedback value. The value is initialzed in the very first
# ScanFeedback call.
self._last_feedback = {}
self._starting_fixture_action = None
# Used to avoid toggle battery too fast.
self._last_battery_toggle_time = time.time()
@TimeClassMethodDebug
def Init(self):
self._last_feedback = self._servo.MultipleIsOn(self._FEEDBACK_LIST)
self._servo.MultipleSet([(self._CONTROL.LCM_CMD, 'clear'),
(self._CONTROL.LCM_TEXT, 'Initializing...')])
self.ResetLatch()
self.ResetInterrupt()
self.ResetKeyboard()
# Initial fixture state: cover open.
self._HandleStopFixture(show_state=False)
self._SetState(self._FixtureState.WAIT)
def ResetKeyboard(self):
keyboard = keyboard_emulator.KeyboardEmulator(self._servo)
keyboard.SimulateKeystrokes()
def _SetState(self, state):
green, red, message = self._FixtureStateParams[state]
self._servo.MultipleSet([(self._CONTROL.PASS_LED, green),
(self._CONTROL.FAIL_LED, red),
(self._CONTROL.LCM_CMD, 'clear'),
(self._CONTROL.LCM_TEXT, message)])
self.ShowNucIpOnLED()
def _IsMLBInFixture(self):
if not self._DUT_SENSOR_CHECK_LIST:
logging.info('No dut sensor...')
return True
dut_sensor_list = list(self._DUT_SENSOR_CHECK_LIST)
dut_sensor_status = self._servo.MultipleIsOn(dut_sensor_list)
return dut_sensor_status == self._DUT_SENSOR_CHECK_LIST
@TimeClassMethodDebug
def _HandleStopFixture(self, show_state=True):
logging.info('Stopping fixture...')
if show_state:
self._SetState(self._FixtureState.OPENING)
# Disable battery first for safety.
self._servo.Disable(self._CONTROL.BATTERY)
while True:
feedback_status = self._servo.MultipleIsOn(self._FEEDBACK_LIST)
if (not feedback_status[self._FIXTURE_FEEDBACK.FB1] or
not feedback_status[self._FIXTURE_FEEDBACK.FB3]):
self._servo.Disable(self._CONTROL.FIXTURE_PUSH_NEEDLE)
continue
self._starting_fixture_action = None
logging.info('[Fixture stopped]')
break
self._SetState(self._FixtureState.WAIT)
@TimeClassMethodDebug
def _HandleStartFixtureFeedbackChange(self, feedback_status):
if (self._starting_fixture_action is not None and
self._starting_fixture_action != ActionType.FIXTURE_STARTED):
# we are closing the fixture, check if we detect a hand
if feedback_status[self._FIXTURE_FEEDBACK.FB5]:
# detect hand, abort
self._HandleStopFixture()
return
if self._servo.IsOn(self._BUTTON.FIXTURE_START):
if (self._starting_fixture_action == ActionType.PUSH_NEEDLE and
feedback_status[self._FIXTURE_FEEDBACK.FB2] and
feedback_status[self._FIXTURE_FEEDBACK.FB4]):
logging.info('[HandleStartFixture] fixture closed')
self._starting_fixture_action = ActionType.FIXTURE_STARTED
self._SetState(self._FixtureState.CLOSED)
@TimeClassMethodDebug
def _HandleStartFixture(self):
logging.info('[Fixture Start ...]')
if self._starting_fixture_action == ActionType.FIXTURE_STARTED:
logging.info('[HandleStartFixture] ACTION = FIXTURE_STARTED')
return
if self._last_feedback[self._FIXTURE_FEEDBACK.FB5]:
logging.info('[HandleStartFixture] Detect Hands, stop..')
return
if self._starting_fixture_action is None:
if not self._IsMLBInFixture():
logging.info(
'[HandleStartFixture] OOPS! Cannot close cover without MLBs')
self._SetState(self._FixtureState.ERR_CLOSING)
return
self._ResetWhaleDeviceBeforeClosing()
self._ResetDolphinDeviceBeforeClosing()
self._starting_fixture_action = ActionType.PUSH_NEEDLE
self._SetState(self._FixtureState.CLOSING)
if self._starting_fixture_action == ActionType.PUSH_NEEDLE:
logging.info('[HandleStartFixture] pushing needle')
self._servo.Enable(self._CONTROL.FIXTURE_PUSH_NEEDLE)
@TimeClassMethodDebug
def _ResetWhaleDeviceBeforeClosing(self):
# Release DUT CC2 pull-high
self._servo.Disable(self._CONTROL.DC)
self._servo.Disable(self._CONTROL.OUTPUT_RESERVE_1)
@TimeClassMethodDebug
def _ResetDolphinDeviceBeforeClosing(self):
if self._dolphin is None:
return
# Set dolphin to discharging mode, if dolphin is charging, DUT will fail to
# boot up after battery connection.
# Assuming all serial connections are connected to Dolphin.
serial_amount = self._dolphin.GetSerialAmount()
for serial_index in range(serial_amount):
self._dolphin.Send(serial_index, 'usbc_action dev')
@TimeClassMethodDebug
def _ToggleBattery(self):
if (time.time() - self._last_battery_toggle_time <
self._BATTERY_CEASE_TOGGLE_SECS):
logging.debug('Toggle too fast, cease toggle for %f second.',
self._BATTERY_CEASE_TOGGLE_SECS)
return
new_battery_status = ('off' if self._servo.IsOn(self._CONTROL.BATTERY)
else 'on')
logging.info('[Toggle battery to %s]', new_battery_status)
self._servo.Set(self._CONTROL.BATTERY, new_battery_status)
self._last_battery_toggle_time = time.time()
@TimeClassMethodDebug
def ScanButton(self):
logging.debug('[Scanning button....]')
status = self._servo.MultipleIsOn(self._BUTTON_LIST)
if status[self._BUTTON.FIXTURE_STOP]:
logging.info('Calling _HandleStopFixture because FIXTURE_STOP is True.')
self._HandleStopFixture()
# Disable stop button, and use 'i2cset' to set it back to input mode.
self._servo.Disable(self._BUTTON.FIXTURE_STOP)
process_utils.Spawn(['i2cset', '-y', '1', '0x77', '0x07', '0xff'])
return True
if (self._starting_fixture_action != ActionType.FIXTURE_STARTED and
self._starting_fixture_action is not None and
not status[self._BUTTON.FIXTURE_START]):
logging.info('Calling _HandleStopFixture because FIXTURE_START is False.')
self._HandleStopFixture()
return False
button_clicked = any(status.values())
if not button_clicked:
return False
operator_mode = not self._servo.IsOn(self._WHALE_DEBUG_MODE_EN)
for button, clicked in status.items():
if not clicked:
continue
if operator_mode and button not in self._OPERATOR_BUTTON_LIST:
logging.debug('Supress button %s click because debug mode is off.',
button)
continue
if button == self._BUTTON.FIXTURE_START:
if self._starting_fixture_action == ActionType.FIXTURE_STARTED:
logging.info('[START] ACTION = FIXTURE_STARTED')
else:
self._HandleStartFixture()
elif button == self._BUTTON.RESERVE_1:
self._ToggleBattery()
logging.info('Button %s clicked', button)
return button_clicked
@TimeClassMethodDebug
def ScanFeedback(self):
logging.debug('[Scanning feedback....]')
feedback_status = self._servo.MultipleIsOn(self._FEEDBACK_LIST)
feedback_changed = False
for name, value in feedback_status.items():
if self._last_feedback[name] == value:
continue
self._HandleStartFixtureFeedbackChange(feedback_status)
logging.info('Feedback %s value changed to %r', name, value)
self._last_feedback[name] = value
feedback_changed = True
return feedback_changed
@TimeClassMethodDebug
def ResetLatch(self):
self._servo.Click(self._CONTROL.INPUT_RESET)
@TimeClassMethodDebug
def WaitForInterrupt(self):
logging.debug('Polling interrupt (GPIO %d %s) for %r seconds',
self._INPUT_INTERRUPT_GPIO, self._poll.GPIO_EDGE_FALLING,
self._polling_wait_secs)
if self._poll.Poll(self._INPUT_INTERRUPT_GPIO,
self._poll.GPIO_EDGE_FALLING,
self._polling_wait_secs):
logging.debug('Interrupt polled')
else:
logging.debug('Polling interrupt timeout')
@TimeClassMethodDebug
def ResetInterrupt(self):
# Touch I/O expander 0x77 byte 0 & 1, 0x75 byte 1, 0x76 byte 0 & 1.
# Note that we skip I/O expander 0x75 byte-0 as it contains no input
# pin, won't trigger interrupt.
self._servo.MultipleGet([
self._FIXTURE_FEEDBACK.FB1, self._BUTTON.FIXTURE_START,
self._PLANKTON_FEEDBACK.FB1, self._WHALE_DEBUG_MODE_EN,
self._BUTTON.RESERVE_1])
def Run(self):
while True:
button_clicked = self.ScanButton()
feedback_changed = self.ScanFeedback()
# it might be possible that a button is clicked after latch is cleared
# but before I/O expander is touched. In this case, the button is latched
# but the interrupt is consumed (after touching I/O expander) so that the
# following click of that button won't trigger interrupt again, and
if button_clicked or feedback_changed:
if button_clicked:
self.ResetLatch()
self.ResetInterrupt()
continue
self.WaitForInterrupt()
def ShowNucIpOnLED(self):
nuc_host = '192.168.234.1'
testing_rsa_path = '/usr/local/factory/misc/sshkeys/testing_rsa'
get_dongle_eth_script = (
'timeout 1s /usr/local/factory/py/test/fixture/get_dongle_eth.sh')
os.chmod(testing_rsa_path, 0o600)
ssh_command_base = ssh_utils.BuildSSHCommand(
identity_file=testing_rsa_path)
try:
interface = process_utils.SpawnOutput(
ssh_command_base + [nuc_host, get_dongle_eth_script]).strip()
except BaseException:
interface = None
if not interface:
ip_address = 'dongle not found...'
else:
ifconfig_command = 'ifconfig %s' % interface
ifconfig_result = process_utils.SpawnOutput(
ssh_command_base + [nuc_host, ifconfig_command]).strip()
ip_matcher = re.search(r'inet (\d+\.\d+\.\d+\.\d+)', ifconfig_result,
re.MULTILINE)
if not ip_matcher:
ip_address = 'dongle not found...'
else:
ip_address = ip_matcher.group(1)
self._servo.MultipleSet([(self._CONTROL.LCM_ROW, 'r1'),
(self._CONTROL.LCM_TEXT, ip_address)])
def ParseArgs():
description = (
'Handle Whale button click event.'
)
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter, description=description)
parser.add_argument('-d', '--debug', action='store_true', default=False,
help='enable debug messages')
parser.add_argument('--rpc_debug', action='store_true', default=False,
help='enable debug messages for XMLRPC call')
parser.add_argument('--nouse_dolphin', action='store_false', default=True,
dest='use_dolphin', help='whether to skip dolphin control'
' (remote server). default: %(default)s')
parser.add_argument('--use_polld', action='store_true', default=False,
help='whether to use polld (for polling GPIO port on '
'remote server) or poll local GPIO port, default: '
'%(default)s')
parser.add_argument('--host', default='127.0.0.1', type=str,
help='hostname of server, default: %(default)s')
parser.add_argument('--dolphin_port', default=9997, type=int,
help='port that dolphin_server listens, default: '
'%(default)d')
parser.add_argument('--polld_port', default=9998, type=int,
help='port that polld listens, default: %(default)d')
parser.add_argument('--servod_port', default=9999, type=int,
help='port that servod listens, default: %(default)d')
parser.add_argument('--polling_wait_secs', default=5, type=int,
help=('# seconds for polling button clicking event, '
'default: %(default)d'))
return parser.parse_args()
def main():
args = ParseArgs()
logging.basicConfig(
level=logging.DEBUG if args.debug else logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s')
polld_port = args.polld_port if args.use_polld else None
dolphin_port = args.dolphin_port if args.use_dolphin else None
handler = InterruptHandler(args.host, polld_port, args.servod_port,
dolphin_port, args.rpc_debug,
args.polling_wait_secs)
handler.Init()
handler.Run()
if __name__ == '__main__':
try:
main()
except KeyboardInterrupt:
sys.exit(0)
except gpio_utils.GpioManagerError as e:
sys.stderr.write(str(e) + '\n')
sys.exit(1)
| true | true |
f73c0a586407cfff97d64e2d1de431ef07e72d15 | 1,071 | py | Python | proof_of_work/multiagent/turn_based/v4/selfishagentv4.py | michaelneuder/parkes_lab_fa19 | 18d9f564e0df9c17ac5d54619ed869d778d4f6a4 | [
"MIT"
] | null | null | null | proof_of_work/multiagent/turn_based/v4/selfishagentv4.py | michaelneuder/parkes_lab_fa19 | 18d9f564e0df9c17ac5d54619ed869d778d4f6a4 | [
"MIT"
] | null | null | null | proof_of_work/multiagent/turn_based/v4/selfishagentv4.py | michaelneuder/parkes_lab_fa19 | 18d9f564e0df9c17ac5d54619ed869d778d4f6a4 | [
"MIT"
] | null | null | null | import numpy as np
class SelfishAgent(object):
def __init__(self, T):
self.T = T
self.policy = np.asarray([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 2, 2, 0, 0, 0, 0, 0, 0],
[2, 1, 2, 2, 2, 0, 0, 0, 0],
[2, 2, 1, 2, 2, 2, 0, 0, 0],
[2, 2, 2, 1, 2, 2, 2, 0, 0],
[2, 2, 2, 2, 1, 2, 2, 2, 0],
[2, 2, 2, 2, 2, 1, 2, 2, 0],
[2, 2, 2, 2, 2, 2, 1, 2, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1]
])
def act(self, state):
a, h = state
if h == self.T:
return 'adopt'
if a == self.T:
return 'override'
if h > a:
return 'adopt'
# if (h == a) and (h == 1):
# return 'match'
if (h == a-1) and (h >= 1):
return 'override'
return 'wait'
def act2(self, state):
action = self.policy[state]
if action == 0:
return 'adopt'
if action == 1:
return 'override'
return 'wait'
| 26.775 | 41 | 0.356676 | import numpy as np
class SelfishAgent(object):
def __init__(self, T):
self.T = T
self.policy = np.asarray([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 2, 2, 0, 0, 0, 0, 0, 0],
[2, 1, 2, 2, 2, 0, 0, 0, 0],
[2, 2, 1, 2, 2, 2, 0, 0, 0],
[2, 2, 2, 1, 2, 2, 2, 0, 0],
[2, 2, 2, 2, 1, 2, 2, 2, 0],
[2, 2, 2, 2, 2, 1, 2, 2, 0],
[2, 2, 2, 2, 2, 2, 1, 2, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1]
])
def act(self, state):
a, h = state
if h == self.T:
return 'adopt'
if a == self.T:
return 'override'
if h > a:
return 'adopt'
if (h == a-1) and (h >= 1):
return 'override'
return 'wait'
def act2(self, state):
action = self.policy[state]
if action == 0:
return 'adopt'
if action == 1:
return 'override'
return 'wait'
| true | true |
f73c0a90b7cbe4e9e76241af7bc20068d063d698 | 2,829 | py | Python | openpnm/geometry/_generic.py | lixuekai2001/OpenPNM | 9026f0fed427d37f4caf1a79e4a7684490d52cf6 | [
"MIT"
] | null | null | null | openpnm/geometry/_generic.py | lixuekai2001/OpenPNM | 9026f0fed427d37f4caf1a79e4a7684490d52cf6 | [
"MIT"
] | null | null | null | openpnm/geometry/_generic.py | lixuekai2001/OpenPNM | 9026f0fed427d37f4caf1a79e4a7684490d52cf6 | [
"MIT"
] | null | null | null | from copy import deepcopy
from openpnm.core import Subdomain, ModelsMixin, ParamMixin
from openpnm.utils import Docorator, SettingsAttr
from openpnm.utils import Workspace, logging
logger = logging.getLogger(__name__)
ws = Workspace()
docstr = Docorator()
@docstr.get_sections(base='GeometrySettings', sections=['Parameters'])
@docstr.dedent
class GeometrySettings:
r"""
Parameters
----------
%(BaseSettings.parameters)s
"""
prefix = 'geo'
@docstr.get_sections(base='GenericGeometry', sections=['Parameters',
'Examples'])
@docstr.dedent
class GenericGeometry(ParamMixin, Subdomain, ModelsMixin):
r"""
This generic class is meant as a starter for custom Geometry objects
It has no pore-scale models assigned to it, so is a blank slate. Note that
all OpenPNM Geometry sub-classes are just GenericGeometry instances with a
assortment of models added.
Parameters
----------
pores : array_like
The list of pores where this geometry applies.
throats : array_like
The list of throats where this Geometry applies.
name : str
A unique name to apply to the object. This name will also be used as a
label to identify where this Geometry applies.
Examples
--------
.. plot::
import openpnm as op
import matplotlib.pyplot as plt
pn = op.network.Cubic(shape=[5, 5, 5])
Ps = pn.pores('all') # Get all pores
Ts = pn.throats('all') # Get all throats
geom = op.geometry.GenericGeometry(network=pn, pores=Ps, throats=Ts)
# Now assign pore-scale models to the empty object
geom.add_model(propname='pore.size',
model=op.models.misc.random,
element='pore',
num_range=[0.01, 0.1])
# Confirm that the object has one added model
print(geom.models)
# The results of the model can be seen using the ``show_hist`` function:
geom.show_hist('pore.size')
plt.show()
"""
def __init__(self, network, pores=[], throats=[], settings=None, **kwargs):
self.settings = SettingsAttr(GeometrySettings, settings)
super().__init__(network=network, settings=self.settings, **kwargs)
network[f'pore.{self.name}'] = False
network[f'throat.{self.name}'] = False
try:
self.set_locations(pores=pores, throats=throats, mode='add')
except Exception as e:
logger.error(f'{e}, instantiation cancelled')
network.project.purge_object(self)
raise e
def _get_phys(self):
"""A shortcut to get a handle to the associated physics."""
return self.project.find_physics(geometry=self)
physics = property(fget=_get_phys)
| 32.517241 | 79 | 0.634853 | from copy import deepcopy
from openpnm.core import Subdomain, ModelsMixin, ParamMixin
from openpnm.utils import Docorator, SettingsAttr
from openpnm.utils import Workspace, logging
logger = logging.getLogger(__name__)
ws = Workspace()
docstr = Docorator()
@docstr.get_sections(base='GeometrySettings', sections=['Parameters'])
@docstr.dedent
class GeometrySettings:
prefix = 'geo'
@docstr.get_sections(base='GenericGeometry', sections=['Parameters',
'Examples'])
@docstr.dedent
class GenericGeometry(ParamMixin, Subdomain, ModelsMixin):
def __init__(self, network, pores=[], throats=[], settings=None, **kwargs):
self.settings = SettingsAttr(GeometrySettings, settings)
super().__init__(network=network, settings=self.settings, **kwargs)
network[f'pore.{self.name}'] = False
network[f'throat.{self.name}'] = False
try:
self.set_locations(pores=pores, throats=throats, mode='add')
except Exception as e:
logger.error(f'{e}, instantiation cancelled')
network.project.purge_object(self)
raise e
def _get_phys(self):
return self.project.find_physics(geometry=self)
physics = property(fget=_get_phys)
| true | true |
f73c0ac78aaa312b971829759014cfbdc0a448ff | 1,652 | py | Python | xos/synchronizer/migration/0012_auto_20200227_1323.py | etrirepo/olt_synchronizer | 493d20a2e404fe7aeca73eaa6d4bb18275faf4ec | [
"Apache-2.0"
] | null | null | null | xos/synchronizer/migration/0012_auto_20200227_1323.py | etrirepo/olt_synchronizer | 493d20a2e404fe7aeca73eaa6d4bb18275faf4ec | [
"Apache-2.0"
] | null | null | null | xos/synchronizer/migration/0012_auto_20200227_1323.py | etrirepo/olt_synchronizer | 493d20a2e404fe7aeca73eaa6d4bb18275faf4ec | [
"Apache-2.0"
] | null | null | null | # Copyright 2017-present Open Networking Foundation
#
# 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.
# -*- coding: utf-8 -*-
# Generated by Django 1.11.28 on 2020-02-27 18:23
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('volt', '0011_auto_20190626_2027'),
]
operations = [
migrations.RemoveField(
model_name='voltservice_decl',
name='voltha_pass',
),
migrations.RemoveField(
model_name='voltservice_decl',
name='voltha_user',
),
migrations.AlterField(
model_name='voltservice_decl',
name='voltha_port',
field=models.IntegerField(default=55555, help_text=b'The Voltha API port. By default 55555'),
),
migrations.AlterField(
model_name='voltservice_decl',
name='voltha_url',
field=models.CharField(default=b'voltha-api.voltha.svc.cluster.local', help_text=b'The Voltha API address. By default voltha-api.default.svc.cluster.local', max_length=256),
),
]
| 34.416667 | 185 | 0.676755 |
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('volt', '0011_auto_20190626_2027'),
]
operations = [
migrations.RemoveField(
model_name='voltservice_decl',
name='voltha_pass',
),
migrations.RemoveField(
model_name='voltservice_decl',
name='voltha_user',
),
migrations.AlterField(
model_name='voltservice_decl',
name='voltha_port',
field=models.IntegerField(default=55555, help_text=b'The Voltha API port. By default 55555'),
),
migrations.AlterField(
model_name='voltservice_decl',
name='voltha_url',
field=models.CharField(default=b'voltha-api.voltha.svc.cluster.local', help_text=b'The Voltha API address. By default voltha-api.default.svc.cluster.local', max_length=256),
),
]
| true | true |
f73c0bae41c8dfc2d32ba13db9a1bf05b21cd322 | 2,793 | py | Python | qiskit/extensions/standard/u2.py | qinvador/qiskit-terra | 4e104de3c113c01688a0ed06b2f2cb1a958fce44 | [
"Apache-2.0"
] | 2 | 2019-06-28T19:58:42.000Z | 2019-07-26T05:04:02.000Z | qiskit/extensions/standard/u2.py | qinvador/qiskit-terra | 4e104de3c113c01688a0ed06b2f2cb1a958fce44 | [
"Apache-2.0"
] | 3 | 2018-11-13T17:33:37.000Z | 2018-12-03T09:35:00.000Z | qiskit/extensions/standard/u2.py | qinvador/qiskit-terra | 4e104de3c113c01688a0ed06b2f2cb1a958fce44 | [
"Apache-2.0"
] | 2 | 2017-12-03T15:48:14.000Z | 2018-03-11T13:08:03.000Z | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017.
#
# 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.
"""
One-pulse single-qubit gate.
"""
import numpy
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
from qiskit.qasm import pi
from qiskit.util import deprecate_arguments
class U2Gate(Gate):
"""One-pulse single-qubit gate."""
def __init__(self, phi, lam, label=None):
"""Create new one-pulse single-qubit gate."""
super().__init__('u2', 1, [phi, lam], label=label)
def _define(self):
from qiskit.extensions.standard.u3 import U3Gate
definition = []
q = QuantumRegister(1, 'q')
rule = [(U3Gate(pi / 2, self.params[0], self.params[1]), [q[0]], [])]
for inst in rule:
definition.append(inst)
self.definition = definition
def inverse(self):
"""Invert this gate.
u2(phi,lamb)^dagger = u2(-lamb-pi,-phi+pi)
"""
return U2Gate(-self.params[1] - pi, -self.params[0] + pi)
def to_matrix(self):
"""Return a Numpy.array for the U2 gate."""
isqrt2 = 1 / numpy.sqrt(2)
phi, lam = self.params
phi, lam = float(phi), float(lam)
return numpy.array([
[
isqrt2,
-numpy.exp(1j * lam) * isqrt2
],
[
numpy.exp(1j * phi) * isqrt2,
numpy.exp(1j * (phi + lam)) * isqrt2
]
], dtype=complex)
@deprecate_arguments({'q': 'qubit'})
def u2(self, phi, lam, qubit, *, q=None): # pylint: disable=invalid-name,unused-argument
"""Apply U2 gate with angle phi and lam to a specified qubit (qubit).
u2(φ,λ) := U(π/2,φ,λ) = Rz(φ + π/2)Rx(π/2)Rz(λ − π/2)
Examples:
Circuit Representation:
.. jupyter-execute::
from qiskit.circuit import QuantumCircuit, Parameter
phi = Parameter('φ')
lam = Parameter('λ')
circuit = QuantumCircuit(1)
circuit.u2(phi,lam,0)
circuit.draw()
Matrix Representation:
.. jupyter-execute::
import numpy
from qiskit.extensions.standard.u2 import U2Gate
U2Gate(numpy.pi/2,numpy.pi/2).to_matrix()
"""
return self.append(U2Gate(phi, lam), [qubit], [])
QuantumCircuit.u2 = u2
| 28.793814 | 89 | 0.595059 |
import numpy
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
from qiskit.qasm import pi
from qiskit.util import deprecate_arguments
class U2Gate(Gate):
def __init__(self, phi, lam, label=None):
super().__init__('u2', 1, [phi, lam], label=label)
def _define(self):
from qiskit.extensions.standard.u3 import U3Gate
definition = []
q = QuantumRegister(1, 'q')
rule = [(U3Gate(pi / 2, self.params[0], self.params[1]), [q[0]], [])]
for inst in rule:
definition.append(inst)
self.definition = definition
def inverse(self):
return U2Gate(-self.params[1] - pi, -self.params[0] + pi)
def to_matrix(self):
isqrt2 = 1 / numpy.sqrt(2)
phi, lam = self.params
phi, lam = float(phi), float(lam)
return numpy.array([
[
isqrt2,
-numpy.exp(1j * lam) * isqrt2
],
[
numpy.exp(1j * phi) * isqrt2,
numpy.exp(1j * (phi + lam)) * isqrt2
]
], dtype=complex)
@deprecate_arguments({'q': 'qubit'})
def u2(self, phi, lam, qubit, *, q=None):
return self.append(U2Gate(phi, lam), [qubit], [])
QuantumCircuit.u2 = u2
| true | true |
f73c0c5783bcd381970f18749f58281f9fcb878f | 5,107 | py | Python | export_weekly_ad_contacts.py | AfricasVoices/Project-WUSC-LEAP-II | 76fa04a92c352483abd2e6b696bd0af26eea517a | [
"MIT"
] | null | null | null | export_weekly_ad_contacts.py | AfricasVoices/Project-WUSC-LEAP-II | 76fa04a92c352483abd2e6b696bd0af26eea517a | [
"MIT"
] | null | null | null | export_weekly_ad_contacts.py | AfricasVoices/Project-WUSC-LEAP-II | 76fa04a92c352483abd2e6b696bd0af26eea517a | [
"MIT"
] | null | null | null | import argparse
import csv
import importlib
from core_data_modules.cleaners import Codes
from core_data_modules.logging import Logger
from core_data_modules.traced_data.io import TracedDataJsonIO
from src.engagement_db_to_analysis.membership_group import (get_membership_groups_data)
log = Logger(__name__)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Exports weekly ad contacts from analysis Traced Data")
parser.add_argument("google_cloud_credentials_file_path", metavar="google-cloud-credentials-file-path",
help="Path to a Google Cloud service account credentials file to use to access the "
"credentials bucket"),
parser.add_argument("configuration_module",
help="Configuration module to import e.g. 'configurations.test_config'. "
"This module must contain a PIPELINE_CONFIGURATION property")
parser.add_argument("traced_data_paths", metavar="traced-data-paths", nargs="+",
help="Paths to the traced data files (either messages or individuals) to extract phone "
"numbers from")
parser.add_argument("membership_group_dir_path", metavar="membership-group-dir-path",
help="Path to directory containing de-identified membership groups CSVs containing membership"
"groups data stored as `avf-participant-uuid` column.")
parser.add_argument("csv_output_file_path", metavar="csv-output-file-path",
help="Path to a CSV file to write the contacts from the locations of interest to. "
"Exported file is in a format suitable for direct upload to Rapid Pro")
args = parser.parse_args()
google_cloud_credentials_file_path = args.google_cloud_credentials_file_path
pipeline_config = importlib.import_module(args.configuration_module).PIPELINE_CONFIGURATION
traced_data_paths = args.traced_data_paths
membership_group_dir_path = args.membership_group_dir_path
csv_output_file_path = args.csv_output_file_path
pipeline = pipeline_config.pipeline_name
uuid_table = pipeline_config.uuid_table.init_uuid_table_client(google_cloud_credentials_file_path)
uuids = set()
opt_out_uuids = set()
for path in traced_data_paths:
log.info(f"Loading previous traced data from file '{path}'...")
with open(path) as f:
data = TracedDataJsonIO.import_jsonl_to_traced_data_iterable(f)
log.info(f"Loaded {len(data)} traced data objects")
for td in data:
if td["consent_withdrawn"] == Codes.TRUE:
opt_out_uuids.add(td["participant_uuid"])
uuids.add(td["participant_uuid"])
# If available, add consented membership group uids to advert uuids
if pipeline_config.analysis.membership_group_configuration is not None:
log.info(f"Adding consented membership group uids to advert uuids ")
membership_group_csv_urls = \
pipeline_config.analysis.membership_group_configuration.membership_group_csv_urls.items()
membership_groups_data = get_membership_groups_data(google_cloud_credentials_file_path,
membership_group_csv_urls, membership_group_dir_path)
consented_membership_groups_uuids = 0
opt_out_membership_groups_uuids = 0
for membership_group in membership_groups_data.values():
for uuid in membership_group:
if uuid in opt_out_uuids:
opt_out_membership_groups_uuids += 1
continue
consented_membership_groups_uuids += 1
uuids.add(uuid)
log.info(f"Found {opt_out_membership_groups_uuids} membership_groups_uuids who have opted out")
log.info(f"Added {consented_membership_groups_uuids} membership_groups_uuids to advert uuids")
log.info(f"Loaded {len(uuids)} uuids (of which {len(opt_out_uuids)} uuids withdrew consent)")
advert_uuids = uuids - opt_out_uuids
log.info(f"Proceeding with {len(advert_uuids)} opt-in uuids")
log.info(f"Converting {len(advert_uuids)} uuids to urns...")
urn_lut = uuid_table.uuid_to_data_batch(advert_uuids)
urns = {urn_lut[uuid] for uuid in advert_uuids}
log.info(f"Converted {len(advert_uuids)} uuids to {len(urns)} urns")
# Export contacts CSV
log.warning(f"Exporting {len(urns)} urns to {csv_output_file_path}...")
with open(csv_output_file_path, "w") as f:
urn_namespaces = {urn.split(":")[0] for urn in urns}
headers = [f"URN:{namespace}" for namespace in urn_namespaces]
writer = csv.DictWriter(f, fieldnames=headers, lineterminator="\n")
writer.writeheader()
for urn in urns:
namespace = urn.split(":")[0]
value = urn.split(":")[1]
writer.writerow({
f"URN:{namespace}": value
})
log.info(f"Wrote {len(urns)} urns to {csv_output_file_path}")
| 48.179245 | 118 | 0.682005 | import argparse
import csv
import importlib
from core_data_modules.cleaners import Codes
from core_data_modules.logging import Logger
from core_data_modules.traced_data.io import TracedDataJsonIO
from src.engagement_db_to_analysis.membership_group import (get_membership_groups_data)
log = Logger(__name__)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Exports weekly ad contacts from analysis Traced Data")
parser.add_argument("google_cloud_credentials_file_path", metavar="google-cloud-credentials-file-path",
help="Path to a Google Cloud service account credentials file to use to access the "
"credentials bucket"),
parser.add_argument("configuration_module",
help="Configuration module to import e.g. 'configurations.test_config'. "
"This module must contain a PIPELINE_CONFIGURATION property")
parser.add_argument("traced_data_paths", metavar="traced-data-paths", nargs="+",
help="Paths to the traced data files (either messages or individuals) to extract phone "
"numbers from")
parser.add_argument("membership_group_dir_path", metavar="membership-group-dir-path",
help="Path to directory containing de-identified membership groups CSVs containing membership"
"groups data stored as `avf-participant-uuid` column.")
parser.add_argument("csv_output_file_path", metavar="csv-output-file-path",
help="Path to a CSV file to write the contacts from the locations of interest to. "
"Exported file is in a format suitable for direct upload to Rapid Pro")
args = parser.parse_args()
google_cloud_credentials_file_path = args.google_cloud_credentials_file_path
pipeline_config = importlib.import_module(args.configuration_module).PIPELINE_CONFIGURATION
traced_data_paths = args.traced_data_paths
membership_group_dir_path = args.membership_group_dir_path
csv_output_file_path = args.csv_output_file_path
pipeline = pipeline_config.pipeline_name
uuid_table = pipeline_config.uuid_table.init_uuid_table_client(google_cloud_credentials_file_path)
uuids = set()
opt_out_uuids = set()
for path in traced_data_paths:
log.info(f"Loading previous traced data from file '{path}'...")
with open(path) as f:
data = TracedDataJsonIO.import_jsonl_to_traced_data_iterable(f)
log.info(f"Loaded {len(data)} traced data objects")
for td in data:
if td["consent_withdrawn"] == Codes.TRUE:
opt_out_uuids.add(td["participant_uuid"])
uuids.add(td["participant_uuid"])
if pipeline_config.analysis.membership_group_configuration is not None:
log.info(f"Adding consented membership group uids to advert uuids ")
membership_group_csv_urls = \
pipeline_config.analysis.membership_group_configuration.membership_group_csv_urls.items()
membership_groups_data = get_membership_groups_data(google_cloud_credentials_file_path,
membership_group_csv_urls, membership_group_dir_path)
consented_membership_groups_uuids = 0
opt_out_membership_groups_uuids = 0
for membership_group in membership_groups_data.values():
for uuid in membership_group:
if uuid in opt_out_uuids:
opt_out_membership_groups_uuids += 1
continue
consented_membership_groups_uuids += 1
uuids.add(uuid)
log.info(f"Found {opt_out_membership_groups_uuids} membership_groups_uuids who have opted out")
log.info(f"Added {consented_membership_groups_uuids} membership_groups_uuids to advert uuids")
log.info(f"Loaded {len(uuids)} uuids (of which {len(opt_out_uuids)} uuids withdrew consent)")
advert_uuids = uuids - opt_out_uuids
log.info(f"Proceeding with {len(advert_uuids)} opt-in uuids")
log.info(f"Converting {len(advert_uuids)} uuids to urns...")
urn_lut = uuid_table.uuid_to_data_batch(advert_uuids)
urns = {urn_lut[uuid] for uuid in advert_uuids}
log.info(f"Converted {len(advert_uuids)} uuids to {len(urns)} urns")
log.warning(f"Exporting {len(urns)} urns to {csv_output_file_path}...")
with open(csv_output_file_path, "w") as f:
urn_namespaces = {urn.split(":")[0] for urn in urns}
headers = [f"URN:{namespace}" for namespace in urn_namespaces]
writer = csv.DictWriter(f, fieldnames=headers, lineterminator="\n")
writer.writeheader()
for urn in urns:
namespace = urn.split(":")[0]
value = urn.split(":")[1]
writer.writerow({
f"URN:{namespace}": value
})
log.info(f"Wrote {len(urns)} urns to {csv_output_file_path}")
| true | true |
f73c0c9cfa0ee96cff187a0a7a14716c2d825e1e | 521 | py | Python | WeatherApp/weatherapp.py | ragreenburg/Amazing-Python-Scripts | d91d0ddd312eb82ac307b58d6d09e6ca19384ea8 | [
"MIT"
] | 930 | 2020-09-05T22:07:28.000Z | 2022-03-30T07:56:18.000Z | WeatherApp/weatherapp.py | ragreenburg/Amazing-Python-Scripts | d91d0ddd312eb82ac307b58d6d09e6ca19384ea8 | [
"MIT"
] | 893 | 2020-09-04T07:57:24.000Z | 2022-02-08T02:12:26.000Z | WeatherApp/weatherapp.py | ragreenburg/Amazing-Python-Scripts | d91d0ddd312eb82ac307b58d6d09e6ca19384ea8 | [
"MIT"
] | 497 | 2020-09-05T08:16:24.000Z | 2022-03-31T00:55:57.000Z | import requests
from bs4 import BeautifulSoup
# Taking City You Want to Check Temperaature
city = input("Enter City : ")
# Storing City You Want to Check Temperaature
search = "weather in" + city
# Searching it on google
url = f"https://www.google.com/search?&q={search}"
# Sending and Receiving Requests
r = requests.get(url)
# Scraping Details
s = BeautifulSoup(r.text, "html.parser")
# Storing Details
update = s.find("div", class_="BNeawe").text
# Printing Details
print("Temperature in " + city + " is: " + update)
| 28.944444 | 50 | 0.725528 | import requests
from bs4 import BeautifulSoup
city = input("Enter City : ")
search = "weather in" + city
url = f"https://www.google.com/search?&q={search}"
r = requests.get(url)
s = BeautifulSoup(r.text, "html.parser")
update = s.find("div", class_="BNeawe").text
print("Temperature in " + city + " is: " + update)
| true | true |
f73c0d558d0f1dbc184863759c8cef0fcbc8b587 | 8,519 | py | Python | sympy/physics/units.py | SpeedyiiiTH/sympy | 37b9b84d4c997ccc9307baf29dd83d12ec31298f | [
"BSD-3-Clause"
] | null | null | null | sympy/physics/units.py | SpeedyiiiTH/sympy | 37b9b84d4c997ccc9307baf29dd83d12ec31298f | [
"BSD-3-Clause"
] | 7 | 2017-05-01T14:15:32.000Z | 2017-09-06T20:44:24.000Z | sympy/physics/units.py | SpeedyiiiTH/sympy | 37b9b84d4c997ccc9307baf29dd83d12ec31298f | [
"BSD-3-Clause"
] | null | null | null | """
Physical units and dimensions.
The base class is Unit, where all here defined units (~200) inherit from.
The find_unit function can help you find units for a given quantity:
>>> import sympy.physics.units as u
>>> u.find_unit('coul')
['coulomb', 'coulombs']
>>> u.find_unit(u.charge)
['C', 'charge', 'coulomb', 'coulombs']
>>> u.coulomb
A*s
Units are always given in terms of base units that have a name and
an abbreviation:
>>> u.A.name
'ampere'
>>> u.ampere.abbrev
'A'
The generic name for a unit (like 'length', 'mass', etc...)
can help you find units:
>>> u.find_unit('magnet')
['magnetic_flux', 'magnetic_constant', 'magnetic_flux_density']
>>> u.find_unit(u.magnetic_flux)
['Wb', 'wb', 'weber', 'webers', 'magnetic_flux']
If, for a given session, you wish to add a unit you may do so:
>>> u.find_unit('gal')
[]
>>> u.gal = 4*u.quart
>>> u.gal/u.inch**3
231
To see a given quantity in terms of some other unit, divide by the desired
unit:
>>> mph = u.miles/u.hours
>>> (u.m/u.s/mph).n(2)
2.2
The units are defined in terms of base units, so when you divide similar
units you will obtain a pure number. This means, for example, that if you
divide a real-world mass (like grams) by the atomic mass unit (amu) you
will obtain Avogadro's number. To obtain the answer in moles you
should divide by the unit ``avogadro``:
>>> u.grams/u.amu
602214179000000000000000
>>> _/u.avogadro
mol
For chemical calculations the unit ``mmu`` (molar mass unit) has been
defined so this conversion is handled automatically. For example, the
number of moles in 1 kg of water might be calculated as:
>>> u.kg/(18*u.mmu).n(3)
55.5*mol
If you need the number of atoms in a mol as a pure number you can use
``avogadro_number`` but if you need it as a dimensional quantity you should use
``avogadro_constant``. (``avogadro`` is a shorthand for the dimensional
quantity.)
>>> u.avogadro_number
602214179000000000000000
>>> u.avogadro_constant
602214179000000000000000/mol
"""
from __future__ import print_function, division
from sympy import Rational, pi
from sympy.core import AtomicExpr
class Unit(AtomicExpr):
"""
Base class for base unit of physical units.
>>> from sympy.physics.units import Unit
>>> Unit("meter", "m")
m
Other units are derived from base units:
>>> import sympy.physics.units as u
>>> cm = u.m/100
>>> 100*u.cm
m
"""
is_positive = True # make sqrt(m**2) --> m
is_commutative = True
__slots__ = ["name", "abbrev"]
def __new__(cls, name, abbrev, **assumptions):
obj = AtomicExpr.__new__(cls, **assumptions)
assert isinstance(name, str), repr(type(name))
assert isinstance(abbrev, str), repr(type(abbrev))
obj.name = name
obj.abbrev = abbrev
return obj
def __getnewargs__(self):
return (self.name, self.abbrev)
def __eq__(self, other):
return isinstance(other, Unit) and self.name == other.name
def __hash__(self):
return super(Unit, self).__hash__()
def _hashable_content(self):
return (self.name, self.abbrev)
@property
def free_symbols(self):
return set()
# Dimensionless
percent = percents = Rational(1, 100)
permille = permille = Rational(1, 1000)
ten = Rational(10)
yotta = ten**24
zetta = ten**21
exa = ten**18
peta = ten**15
tera = ten**12
giga = ten**9
mega = ten**6
kilo = ten**3
deca = ten**1
deci = ten**-1
centi = ten**-2
milli = ten**-3
micro = ten**-6
nano = ten**-9
pico = ten**-12
femto = ten**-15
atto = ten**-18
zepto = ten**-21
yocto = ten**-24
rad = radian = radians = 1
deg = degree = degrees = pi/180
sr = steradian = steradians = 1
# Base units
length = m = meter = meters = Unit('meter', 'm')
mass = kg = kilogram = kilograms = Unit('kilogram', 'kg')
time = s = second = seconds = Unit('second', 's')
current = A = ampere = amperes = Unit('ampere', 'A')
temperature = K = kelvin = kelvins = Unit('kelvin', 'K')
amount = mol = mole = moles = Unit('mole', 'mol')
luminosity = cd = candela = candelas = Unit('candela', 'cd')
# Derived units
volume = meter**3
frequency = Hz = hz = hertz = 1/s
force = N = newton = newtons = m*kg/s**2
energy = J = joule = joules = N*m
power = W = watt = watts = J/s
pressure = Pa = pa = pascal = pascals = N/m**2
charge = C = coulomb = coulombs = s*A
voltage = v = V = volt = volts = W/A
resistance = ohm = ohms = V/A
conductance = S = siemens = mho = mhos = A/V
capacitance = F = farad = farads = C/V
magnetic_flux = Wb = wb = weber = webers = J/A
magnetic_flux_density = T = tesla = teslas = V*s/m**2
inductance = H = henry = henrys = V*s/A
speed = m/s
acceleration = m/s**2
density = kg/m**3
optical_power = dioptre = D = 1/m
illuminance = lux = lx = sr*cd/m**2
# Common length units
km = kilometer = kilometers = kilo*m
dm = decimeter = decimeters = deci*m
cm = centimeter = centimeters = centi*m
mm = millimeter = millimeters = milli*m
um = micrometer = micrometers = micron = microns = micro*m
nm = nanometer = nanometers = nano*m
pm = picometer = picometers = pico*m
ft = foot = feet = Rational('0.3048')*m
inch = inches = Rational('25.4')*mm
yd = yard = yards = 3*ft
mi = mile = miles = 5280*ft
# Common volume and area units
l = liter = liters = m**3 / 1000
dl = deciliter = deciliters = deci*l
cl = centiliter = centiliters = centi*l
ml = milliliter = milliliters = milli*l
# Common time units
ms = millisecond = milliseconds = milli*s
us = microsecond = microseconds = micro*s
ns = nanosecond = nanoseconds = nano*s
ps = picosecond = picoseconds = pico*s
minute = minutes = 60*s
h = hour = hours = 60*minute
day = days = 24*hour
anomalistic_year = anomalistic_years = Rational('365.259636')*day
sidereal_year = sidereal_years = Rational('31558149.540')*s
tropical_year = tropical_years = Rational('365.24219')*day
common_year = common_years = Rational('365')*day
julian_year = julian_years = Rational('365.25')*day
draconic_year = draconic_years = Rational('346.62')*day
gaussian_year = gaussian_years = Rational('365.2568983')*day
full_moon_cycle = full_moon_cycles = Rational('411.78443029')*day
year = years = tropical_year
# Common mass units
g = gram = grams = kilogram / kilo
mg = milligram = milligrams = milli * g
ug = microgram = micrograms = micro * g
#----------------------------------------------------------------------------
# Physical constants
#
c = speed_of_light = 299792458 * m/s
G = gravitational_constant = Rational('6.67428') * ten**-11 * m**3 / kg / s**2
u0 = magnetic_constant = 4*pi * ten**-7 * N/A**2
e0 = electric_constant = 1/(u0 * c**2)
Z0 = vacuum_impedance = u0 * c
planck = Rational('6.62606896') * ten**-34 * J*s
hbar = planck / (2*pi)
avogadro_number = Rational('6.02214179') * 10**23
avogadro = avogadro_constant = avogadro_number / mol
boltzmann = Rational('1.3806505') * ten**-23 * J / K
gee = gees = Rational('9.80665') * m/s**2
atmosphere = atmospheres = atm = 101325 * pascal
kPa = kilo*Pa
bar = bars = 100*kPa
pound = pounds = 0.45359237 * kg * gee # exact
psi = pound / inch ** 2
dHg0 = 13.5951 # approx value at 0 C
mmHg = dHg0 * 9.80665 * Pa
amu = amus = gram / avogadro / mol
mmu = mmus = gram / mol
quart = quarts = Rational(231, 4) * inch**3
eV = 1.602176487e-19 * J
# Other convenient units and magnitudes
ly = lightyear = lightyears = c*julian_year
au = astronomical_unit = astronomical_units = 149597870691*m
def find_unit(quantity):
"""
Return a list of matching units names.
if quantity is a string -- units containing the string `quantity`
if quantity is a unit -- units having matching base units
Examples
========
>>> from sympy.physics import units as u
>>> u.find_unit('charge')
['charge']
>>> u.find_unit(u.charge)
['C', 'charge', 'coulomb', 'coulombs']
>>> u.find_unit('volt')
['volt', 'volts', 'voltage']
>>> u.find_unit(u.inch**3)[:5]
['l', 'cl', 'dl', 'ml', 'liter']
"""
import sympy.physics.units as u
rv = []
if isinstance(quantity, str):
rv = [i for i in dir(u) if quantity in i]
else:
units = quantity.as_coeff_Mul()[1]
for i in dir(u):
try:
if units == eval('u.' + i).as_coeff_Mul()[1]:
rv.append(str(i))
except Exception:
pass
return sorted(rv, key=len)
# Delete this so it doesn't pollute the namespace
del Rational, pi
| 27.044444 | 79 | 0.638925 |
from __future__ import print_function, division
from sympy import Rational, pi
from sympy.core import AtomicExpr
class Unit(AtomicExpr):
is_positive = True
is_commutative = True
__slots__ = ["name", "abbrev"]
def __new__(cls, name, abbrev, **assumptions):
obj = AtomicExpr.__new__(cls, **assumptions)
assert isinstance(name, str), repr(type(name))
assert isinstance(abbrev, str), repr(type(abbrev))
obj.name = name
obj.abbrev = abbrev
return obj
def __getnewargs__(self):
return (self.name, self.abbrev)
def __eq__(self, other):
return isinstance(other, Unit) and self.name == other.name
def __hash__(self):
return super(Unit, self).__hash__()
def _hashable_content(self):
return (self.name, self.abbrev)
@property
def free_symbols(self):
return set()
percent = percents = Rational(1, 100)
permille = permille = Rational(1, 1000)
ten = Rational(10)
yotta = ten**24
zetta = ten**21
exa = ten**18
peta = ten**15
tera = ten**12
giga = ten**9
mega = ten**6
kilo = ten**3
deca = ten**1
deci = ten**-1
centi = ten**-2
milli = ten**-3
micro = ten**-6
nano = ten**-9
pico = ten**-12
femto = ten**-15
atto = ten**-18
zepto = ten**-21
yocto = ten**-24
rad = radian = radians = 1
deg = degree = degrees = pi/180
sr = steradian = steradians = 1
length = m = meter = meters = Unit('meter', 'm')
mass = kg = kilogram = kilograms = Unit('kilogram', 'kg')
time = s = second = seconds = Unit('second', 's')
current = A = ampere = amperes = Unit('ampere', 'A')
temperature = K = kelvin = kelvins = Unit('kelvin', 'K')
amount = mol = mole = moles = Unit('mole', 'mol')
luminosity = cd = candela = candelas = Unit('candela', 'cd')
volume = meter**3
frequency = Hz = hz = hertz = 1/s
force = N = newton = newtons = m*kg/s**2
energy = J = joule = joules = N*m
power = W = watt = watts = J/s
pressure = Pa = pa = pascal = pascals = N/m**2
charge = C = coulomb = coulombs = s*A
voltage = v = V = volt = volts = W/A
resistance = ohm = ohms = V/A
conductance = S = siemens = mho = mhos = A/V
capacitance = F = farad = farads = C/V
magnetic_flux = Wb = wb = weber = webers = J/A
magnetic_flux_density = T = tesla = teslas = V*s/m**2
inductance = H = henry = henrys = V*s/A
speed = m/s
acceleration = m/s**2
density = kg/m**3
optical_power = dioptre = D = 1/m
illuminance = lux = lx = sr*cd/m**2
km = kilometer = kilometers = kilo*m
dm = decimeter = decimeters = deci*m
cm = centimeter = centimeters = centi*m
mm = millimeter = millimeters = milli*m
um = micrometer = micrometers = micron = microns = micro*m
nm = nanometer = nanometers = nano*m
pm = picometer = picometers = pico*m
ft = foot = feet = Rational('0.3048')*m
inch = inches = Rational('25.4')*mm
yd = yard = yards = 3*ft
mi = mile = miles = 5280*ft
l = liter = liters = m**3 / 1000
dl = deciliter = deciliters = deci*l
cl = centiliter = centiliters = centi*l
ml = milliliter = milliliters = milli*l
ms = millisecond = milliseconds = milli*s
us = microsecond = microseconds = micro*s
ns = nanosecond = nanoseconds = nano*s
ps = picosecond = picoseconds = pico*s
minute = minutes = 60*s
h = hour = hours = 60*minute
day = days = 24*hour
anomalistic_year = anomalistic_years = Rational('365.259636')*day
sidereal_year = sidereal_years = Rational('31558149.540')*s
tropical_year = tropical_years = Rational('365.24219')*day
common_year = common_years = Rational('365')*day
julian_year = julian_years = Rational('365.25')*day
draconic_year = draconic_years = Rational('346.62')*day
gaussian_year = gaussian_years = Rational('365.2568983')*day
full_moon_cycle = full_moon_cycles = Rational('411.78443029')*day
year = years = tropical_year
g = gram = grams = kilogram / kilo
mg = milligram = milligrams = milli * g
ug = microgram = micrograms = micro * g
c = speed_of_light = 299792458 * m/s
G = gravitational_constant = Rational('6.67428') * ten**-11 * m**3 / kg / s**2
u0 = magnetic_constant = 4*pi * ten**-7 * N/A**2
e0 = electric_constant = 1/(u0 * c**2)
Z0 = vacuum_impedance = u0 * c
planck = Rational('6.62606896') * ten**-34 * J*s
hbar = planck / (2*pi)
avogadro_number = Rational('6.02214179') * 10**23
avogadro = avogadro_constant = avogadro_number / mol
boltzmann = Rational('1.3806505') * ten**-23 * J / K
gee = gees = Rational('9.80665') * m/s**2
atmosphere = atmospheres = atm = 101325 * pascal
kPa = kilo*Pa
bar = bars = 100*kPa
pound = pounds = 0.45359237 * kg * gee
psi = pound / inch ** 2
dHg0 = 13.5951
mmHg = dHg0 * 9.80665 * Pa
amu = amus = gram / avogadro / mol
mmu = mmus = gram / mol
quart = quarts = Rational(231, 4) * inch**3
eV = 1.602176487e-19 * J
ly = lightyear = lightyears = c*julian_year
au = astronomical_unit = astronomical_units = 149597870691*m
def find_unit(quantity):
import sympy.physics.units as u
rv = []
if isinstance(quantity, str):
rv = [i for i in dir(u) if quantity in i]
else:
units = quantity.as_coeff_Mul()[1]
for i in dir(u):
try:
if units == eval('u.' + i).as_coeff_Mul()[1]:
rv.append(str(i))
except Exception:
pass
return sorted(rv, key=len)
del Rational, pi
| true | true |
f73c0dd0bc664d16bb63c14b2c4d78cf60a252b4 | 1,705 | py | Python | tests/settings.py | OlgaBorisova/django-pgfields | fc21efc116d017f1bf75c88fa3502cc23f8923b3 | [
"BSD-3-Clause"
] | null | null | null | tests/settings.py | OlgaBorisova/django-pgfields | fc21efc116d017f1bf75c88fa3502cc23f8923b3 | [
"BSD-3-Clause"
] | null | null | null | tests/settings.py | OlgaBorisova/django-pgfields | fc21efc116d017f1bf75c88fa3502cc23f8923b3 | [
"BSD-3-Clause"
] | 1 | 2018-10-23T21:31:29.000Z | 2018-10-23T21:31:29.000Z | from __future__ import absolute_import, unicode_literals
from django_pg.utils.south import south_installed
import os
import sys
DEBUG = True
TEMPLATE_DEBUG = DEBUG
# Database settings. This assumes that the default user and empty
# password will work.
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': 'django_pg',
'USER': '',
'PASSWORD': '',
'HOST': 'localhost',
'PORT': 5432,
}
}
# Boilerplate settings.
TIME_ZONE = 'America/Chicago'
USE_TZ = True
LANGUAGE_CODE = 'en-us'
ADMIN_MEDIA_PREFIX = '/static/admin/'
STATICFILES_DIRS = ()
SECRET_KEY = 'vu8[/=:+pjz8o:9c6g7spzq_c14ke9zymjq(:m]5_+-gc3l8)]'
# Installed apps. Be smart about this: search for things under the
# `tests/` directory, but add them as applications as they have their
# own models that we need in order to test this stuff.
INSTALLED_APPS = [ 'django_pg', ]
for module in os.listdir(os.path.dirname(__file__)):
full_dir = os.path.dirname(__file__) + '/' + module
if os.path.isdir(full_dir) and os.path.isfile(full_dir + '/__init__.py'):
INSTALLED_APPS.append('tests.' + module)
# If South is installed, then add it to installed apps.
if south_installed:
INSTALLED_APPS.append('south')
# We don't really care what middleware is installed for our purposes,
# but we really want Django 1.7's check/warning system to be quiet about
# this not being explicitly set.
MIDDLEWARE_CLASSES = (
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
)
# Easiest default test settings for django-pgfields itself.
DJANGOPG_IMPROVED_REPR = True
DJANGOPG_REPR_TEMPLATE = 'single_line'
| 29.396552 | 77 | 0.718475 | from __future__ import absolute_import, unicode_literals
from django_pg.utils.south import south_installed
import os
import sys
DEBUG = True
TEMPLATE_DEBUG = DEBUG
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': 'django_pg',
'USER': '',
'PASSWORD': '',
'HOST': 'localhost',
'PORT': 5432,
}
}
TIME_ZONE = 'America/Chicago'
USE_TZ = True
LANGUAGE_CODE = 'en-us'
ADMIN_MEDIA_PREFIX = '/static/admin/'
STATICFILES_DIRS = ()
SECRET_KEY = 'vu8[/=:+pjz8o:9c6g7spzq_c14ke9zymjq(:m]5_+-gc3l8)]'
INSTALLED_APPS = [ 'django_pg', ]
for module in os.listdir(os.path.dirname(__file__)):
full_dir = os.path.dirname(__file__) + '/' + module
if os.path.isdir(full_dir) and os.path.isfile(full_dir + '/__init__.py'):
INSTALLED_APPS.append('tests.' + module)
if south_installed:
INSTALLED_APPS.append('south')
# but we really want Django 1.7's check/warning system to be quiet about
MIDDLEWARE_CLASSES = (
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
)
DJANGOPG_IMPROVED_REPR = True
DJANGOPG_REPR_TEMPLATE = 'single_line'
| true | true |
f73c0e51df590bc8a015c8e97adf0eb20ac088c3 | 1,383 | py | Python | website/canvas/url_util.py | bopopescu/canvas | 2dfd6009eaecd8dac64ccc6125084e65305fb5d0 | [
"BSD-3-Clause"
] | 61 | 2015-11-10T17:13:46.000Z | 2021-08-06T17:58:30.000Z | website/canvas/url_util.py | bopopescu/canvas | 2dfd6009eaecd8dac64ccc6125084e65305fb5d0 | [
"BSD-3-Clause"
] | 13 | 2015-11-11T07:49:41.000Z | 2021-06-09T03:45:31.000Z | website/canvas/url_util.py | bopopescu/canvas | 2dfd6009eaecd8dac64ccc6125084e65305fb5d0 | [
"BSD-3-Clause"
] | 18 | 2015-11-11T04:50:04.000Z | 2021-08-20T00:57:11.000Z | import urlparse
from django.conf.urls.defaults import url
from canvas.exceptions import ServiceError
re_slug = lambda name: "(?P<%s>[a-zA-Z0-9_.,-]+)" % name
re_group_slug = lambda name: "(?P<%s>[-a-zA-Z0-9_]+)" % name
re_int = lambda name: "(?P<%s>\d+)" % name
re_year = re_int('year')
re_month = re_year + "/" + re_int("month")
re_day = re_month + "/" + re_int('day')
def url_decorator(urls):
def decorator(url_regexp, *args, **kwargs):
def wrapper(fun):
urls.append(url(url_regexp, fun, *args, **kwargs))
return fun
return wrapper
return decorator
def dynamic_urls():
"""
Usage Example:
urls, api = dynamic_urls()
"""
urls = []
return urls, url_decorator(urls)
def maybe(regexp):
return '(%s|)' % regexp
def verify_first_party_url(url):
"""
Also allows iTunes store URLs.
"""
if not url or not url.startswith('/'):
parsed_url = urlparse.urlparse(url)
try:
protocol = parsed_url[0]
domain = parsed_url[1]
except IndexError:
raise ServiceError("Invalid share url.")
if protocol not in ['http', 'https'] or domain not in ['itunes.apple.com', 'example.com']:
# Only 1st party redirects, to avoid security holes that 3rd party redirects imply
raise ServiceError("Invalid share url.")
| 27.117647 | 99 | 0.606652 | import urlparse
from django.conf.urls.defaults import url
from canvas.exceptions import ServiceError
re_slug = lambda name: "(?P<%s>[a-zA-Z0-9_.,-]+)" % name
re_group_slug = lambda name: "(?P<%s>[-a-zA-Z0-9_]+)" % name
re_int = lambda name: "(?P<%s>\d+)" % name
re_year = re_int('year')
re_month = re_year + "/" + re_int("month")
re_day = re_month + "/" + re_int('day')
def url_decorator(urls):
def decorator(url_regexp, *args, **kwargs):
def wrapper(fun):
urls.append(url(url_regexp, fun, *args, **kwargs))
return fun
return wrapper
return decorator
def dynamic_urls():
urls = []
return urls, url_decorator(urls)
def maybe(regexp):
return '(%s|)' % regexp
def verify_first_party_url(url):
if not url or not url.startswith('/'):
parsed_url = urlparse.urlparse(url)
try:
protocol = parsed_url[0]
domain = parsed_url[1]
except IndexError:
raise ServiceError("Invalid share url.")
if protocol not in ['http', 'https'] or domain not in ['itunes.apple.com', 'example.com']:
raise ServiceError("Invalid share url.")
| true | true |
f73c0f2c39b11fc9eed97e1360702015396621e1 | 9,602 | py | Python | oase-root/libs/backyardlibs/oase_action_common_libs.py | wreathvine/oase | e560b124de6239ec94b662ebff3c310c3f2fa631 | [
"Apache-2.0"
] | null | null | null | oase-root/libs/backyardlibs/oase_action_common_libs.py | wreathvine/oase | e560b124de6239ec94b662ebff3c310c3f2fa631 | [
"Apache-2.0"
] | null | null | null | oase-root/libs/backyardlibs/oase_action_common_libs.py | wreathvine/oase | e560b124de6239ec94b662ebff3c310c3f2fa631 | [
"Apache-2.0"
] | null | null | null | # Copyright 2019 NEC Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
[概要]
"""
import json
import os
import sys
import re
import pytz
import datetime
from socket import gethostname
import traceback
# import検索パス追加
my_path = os.path.dirname(os.path.abspath(__file__))
tmp_path = my_path.split('oase-root')
root_dir_path = tmp_path[0] + 'oase-root'
sys.path.append(root_dir_path)
# oaseログモジュールのimport
from web_app.models.models import ActionLog
from libs.commonlibs.oase_logger import OaseLogger
logger = OaseLogger.get_instance() # ロガー初期化
class ConstantModules:
"""
[クラス概要]
アクションドライバ コンスタント定義クラス
"""
## OASE DB USER
DB_OASE_USER = -2140000001
## module result code
# deprecate 以下3つのステータスはITA_DBを使っていたもので廃止予定
RET_REST_ERROR = 100 # REST Access Error
RET_DATA_ERROR = 101 # DBの登録データが異常
RET_REST_MULTI_UPDATE = 102 # 追越し更新
## テーブルカラム位置
## 共通
COL_FUNCTION_NAME = 0
COL_DISUSE_FLAG = 1
## C_MOVEMENT_CLASS_MNG
CMCM_MOVEMENT_CLASS_NO = 2
CMCM_ORCHESTRATOR_ID = 3
CMCM_PATTERN_ID = 4
CMCM_MOVEMENT_SEQ = 5
CMCM_NEXT_PENDING_FLAG = 6
CMCM_DESCRIPTION = 7
CMCM_SYMPHONY_CLASS_NO = 8
CMCM_NOTE = 9
CMCM_LAST_UPDATE_TIMESTAMP = 10
CMCM_UPDATE_LAST_UPDATE_TIMESTAMP = 11
CMCM_LAST_UPDATE_USER = 12
## C_SYMPHONY_CLASS_MNG
CSCM_SYMPHONY_CLASS_ID = 2
CSCM_SYMPHONY_NAME = 3
## C_CONDUCTOR_EDIT_CLASS_MNG
CCCM_CONDUCTOR_CLASS_ID = 2
CCCM_CONDUCTOR_NAME = 3
## C_NODE_CLASS_MNG
CNCM_NODE_CLASS_NO = 2
CNCM_NODE_NAME = 3
CNCM_NODE_TYPE_ID = 4
CNCM_ORCHESTRATOR_ID = 5
CNCM_PATTERN_ID = 6
CNCM_CONDUCTOR_CLASS_NO = 7
CNCM_CONDUCTOR_CALL_CLASS_NO = 8
CNCM_OPERATION_NO_IDBH = 9
CNCM_SKIP_FLAG = 10
## C_STM_LIST
CSL_SYSTEM_ID = 2
CSL_HARDAWRE_TYPE_ID = 3
CSL_HOSTNAME = 4
CSL_IP_ADDRESS = 5
## C_OPERATION_LIST
COL_OPERATION_NO_UAPK = 2
COL_OPERATION_NO_IDBH = 3
COL_OPERATION_NAME = 4
COL_OPERATION_DATE = 5
COL_NOTE = 6
COL_LAST_UPDATE_TIMESTAMP = 7
COL_UPDATE_LAST_UPDATE_TIMESTAMP = 8
COL_LAST_UPDATE_USER = 9
TBL_COL_MAX = COL_LAST_UPDATE_USER + 1
## C_PARAMETER_SHEET
COL_PARAMETER_NO = 2
COL_HOSTNAME = 3
COL_OPERATION_ID = 4
COL_OPERATION_NAME_PARAM = 5
COL_SCHEDULE_TIMESTAMP_ID_NAME = 9
COL_PARAMETER = 10
TBL_CPS_MAX = COL_PARAMETER + 1
## C_PARAMETER_SHEET_VERTICAL
COL_PARAMETER_NO_VERTICAL = 2
COL_HOSTNAME_VERTICAL = 3
COL_OPERATION_ID_VERTICAL = 4
COL_OPERATION_NAME_PARAM_VERTICAL = 5
COL_SCHEDULE_TIMESTAMP_ID_NAME_VERTICAL = 9
COL_SUBSTITUTION_ORDER_VERTICAL = 10
COL_PARAMETER_VERTICAL = 11
## C_PATTERN_PER_ORCH
CPPO_PATTERN_ID = 2
CPPO_PATTERN_NAME = 3
CPPO_ITA_EXT_STM_ID = 4
## B_ANSIBLE_xxx_PHO_LINK
BAPL_PHO_LINK_ID = 2
BAPL_OPERATION_NO_UAPK = 3
BAPL_PATTERN_ID = 4
BAPL_SYSTEM_ID = 5
BAPL_NOTE = 6
BAPL_LAST_UPDATE_TIMESTAMP = 7
BAPL_UPDATE_LAST_UPDATE_TIMESTAMP = 8
BAPL_LAST_UPDATE_USER = 9
TBL_BAPL_MAX = BAPL_LAST_UPDATE_USER + 1
## E_ANSIBLE_xxx_PATTERN
EAP_PATTERN_ID = 2
EAP_PATTERN_NAME = 3
EAP_LEGACY_VAR_COUNT = 10
EAP_PIONEER_VAR_COUNT = 8
EAP_LEGACYROLE_VAR_COUNT = 10
## F_CREATE_MENU_INFO
FCMI_MENU_ID = 2
FCMI_MENU_NAME = 3
FCMI_TARGET = 4
FCMI_USE = 6
FCMI_MENUGROUP_FOR_VERTICAL_2 = 7
FCMI_MENUGROUP_FOR_HOSTGROUP = 8
FCMI_MENUGROUP_FOR_HOST = 9
FCMI_MENUGROUP_FOR_VERTICAL = 11
## A_MENU_GROUP_LIST
AMGL_MENU_GROUP_ID = 2
AMGL_MENU_GROUP_NAME = 3
## A_MENU_LIST
AML_MENU_ID = 2
AML_MENU_GROUP_ID = 3
AML_MENU_GROUP_NAME = 4
AML_MENU_NAME = 6
## D_ANS_LNS_VAL_ASSIGN
DALVA_ID = 2
DALVA_MENU_NAME = 3
DALVA_ITEM_NAME = 4
DALVA_ITEM_ORDER = 5
DALVA_COLUMN_GROUP = 8
#OASE_T_RHDM_RESPONSE
OTRR_RESPONSE_ID = 2
OTRR_REQUEST_TYPE_ID = 5
OTRR_STATUS = 6
OTRR_UPDATE_STATUS_ID = 7
#OASE_T_RHDM_RESPONSE_ACTION
OTRRA_RESPONSE_ID = 3
OTRRA_EXECUTION_ORDER = 5
#OASE_T_ACTION_HISTORY
OTAH_RESPONSE_ID = 3
OTAH_RULE_NAME = 5
OTAH_EXECUTION_ORDER = 6
OTAH_ACTION_START_TIME = 7
OTAH_ACTION_TYPE_ID = 8
OTAH_STATUS = 10
#OASE_T_ITA_DRIVER
OTID_ITA_DISP_NAME = 3
#OASE_T_MAIL_DRIVER
OTMD_MAIL_DISP_NAME = 3
#OASE_T_MAIL_TEMPLATE
OTMT_MAIL_TEMPLATE_NAME = 3
#トレースIDがない場合の設定値
UnsetTraceID = '----------------------------------------'
class ActionDriverCommonModules:
"""
[クラス概要]
アクションドライバ共通処理クラス
"""
def getStringNowDate(self):
"""
[概要]
現在日付を文字列で取得するメソット
"""
now = datetime.datetime.now(pytz.timezone('UTC'))
return now.strftime("%Y/%m/%d")
@classmethod
def getStringNowDateTime(cls, convert_flg=False):
"""
[概要]
現在日時を文字列で取得するメソット
"""
now = datetime.datetime.now(pytz.timezone('UTC'))
if convert_flg:
return now.strftime("%Y-%m-%d %H:%M:%S.%f")
return now
def KeyValueStringFind(self,pattern,string):
"""
[概要]
Key Value型文字列からValueを抜き出すメソット
"""
string_tmp = string.split('=')
if len(string_tmp) < 2:
return None
key = string_tmp[0]
if key != pattern:
return None
val = '='.join(string_tmp[1:])
return val
@classmethod
def back_trace(self):
"""
[概要]
Key Value型文字列からValueを抜き出すメソット
"""
exc_type, exc_value, exc_traceback = sys.exc_info()
stack_trace = traceback.format_exception(exc_type, exc_value, exc_traceback)
edit_trace = ''
for line in stack_trace:
edit_trace = edit_trace + line
return edit_trace
@staticmethod
def SaveActionLog(resp_id, exe_order, trace_id, msg_id, **kwargs):
"""
[概要]
アクション履歴ログを保存する
"""
now = datetime.datetime.now(pytz.timezone('UTC'))
msg_params = None
if kwargs:
msg_params = '%s' % (kwargs)
try:
ActionLog(
response_id = resp_id,
execution_order = exe_order,
trace_id = trace_id,
message_id = msg_id,
message_params = msg_params,
last_update_timestamp = now
).save(force_insert=True)
except Exception as ex:
logger.system_log('LOSM01007', resp_id, exe_order, trace_id, msg_id, msg_params)
# libs/webcommonlibs/common.py から引用
class TimeConversion:
@classmethod
def get_time_conversion(cls, naive, tz):
"""
[概要]
時刻変換処理を行う
[戻り値]
変換した時刻
"""
return naive.astimezone(pytz.timezone(tz)).strftime('%Y-%m-%d %H:%M:%S')
@classmethod
def get_time_conversion_utc(cls, naive, tz):
"""
[概要]
時刻変換処理を行う
[戻り値]
utc_dt : 変換した時刻(UTC)
"""
tz_ex = pytz.timezone(tz)
naive = naive.replace('/', '-')
user_dt = datetime.datetime.strptime(naive, '%Y-%m-%d %H:%M:%S')
cou_dt = tz_ex.localize(user_dt, is_dst=None)
utc_dt = cou_dt.astimezone(pytz.utc)
return utc_dt
| 30.194969 | 92 | 0.532493 |
import json
import os
import sys
import re
import pytz
import datetime
from socket import gethostname
import traceback
my_path = os.path.dirname(os.path.abspath(__file__))
tmp_path = my_path.split('oase-root')
root_dir_path = tmp_path[0] + 'oase-root'
sys.path.append(root_dir_path)
from web_app.models.models import ActionLog
from libs.commonlibs.oase_logger import OaseLogger
logger = OaseLogger.get_instance()
class ConstantModules:
SER = -2140000001
RROR = 100
RET_DATA_ERROR = 101
RET_REST_MULTI_UPDATE = 102
UNCTION_NAME = 0
COL_DISUSE_FLAG = 1
SS_NO = 2
CMCM_ORCHESTRATOR_ID = 3
CMCM_PATTERN_ID = 4
CMCM_MOVEMENT_SEQ = 5
CMCM_NEXT_PENDING_FLAG = 6
CMCM_DESCRIPTION = 7
CMCM_SYMPHONY_CLASS_NO = 8
CMCM_NOTE = 9
CMCM_LAST_UPDATE_TIMESTAMP = 10
CMCM_UPDATE_LAST_UPDATE_TIMESTAMP = 11
CMCM_LAST_UPDATE_USER = 12
SS_ID = 2
CSCM_SYMPHONY_NAME = 3
= 2
CCCM_CONDUCTOR_NAME = 3
SS_NO = 2
CNCM_NODE_NAME = 3
CNCM_NODE_TYPE_ID = 4
CNCM_ORCHESTRATOR_ID = 5
CNCM_PATTERN_ID = 6
CNCM_CONDUCTOR_CLASS_NO = 7
CNCM_CONDUCTOR_CALL_CLASS_NO = 8
CNCM_OPERATION_NO_IDBH = 9
CNCM_SKIP_FLAG = 10
TEM_ID = 2
CSL_HARDAWRE_TYPE_ID = 3
CSL_HOSTNAME = 4
CSL_IP_ADDRESS = 5
_NO_UAPK = 2
COL_OPERATION_NO_IDBH = 3
COL_OPERATION_NAME = 4
COL_OPERATION_DATE = 5
COL_NOTE = 6
COL_LAST_UPDATE_TIMESTAMP = 7
COL_UPDATE_LAST_UPDATE_TIMESTAMP = 8
COL_LAST_UPDATE_USER = 9
TBL_COL_MAX = COL_LAST_UPDATE_USER + 1
NO = 2
COL_HOSTNAME = 3
COL_OPERATION_ID = 4
COL_OPERATION_NAME_PARAM = 5
COL_SCHEDULE_TIMESTAMP_ID_NAME = 9
COL_PARAMETER = 10
TBL_CPS_MAX = COL_PARAMETER + 1
AL = 2
COL_HOSTNAME_VERTICAL = 3
COL_OPERATION_ID_VERTICAL = 4
COL_OPERATION_NAME_PARAM_VERTICAL = 5
COL_SCHEDULE_TIMESTAMP_ID_NAME_VERTICAL = 9
COL_SUBSTITUTION_ORDER_VERTICAL = 10
COL_PARAMETER_VERTICAL = 11
= 2
CPPO_PATTERN_NAME = 3
CPPO_ITA_EXT_STM_ID = 4
= 2
BAPL_OPERATION_NO_UAPK = 3
BAPL_PATTERN_ID = 4
BAPL_SYSTEM_ID = 5
BAPL_NOTE = 6
BAPL_LAST_UPDATE_TIMESTAMP = 7
BAPL_UPDATE_LAST_UPDATE_TIMESTAMP = 8
BAPL_LAST_UPDATE_USER = 9
TBL_BAPL_MAX = BAPL_LAST_UPDATE_USER + 1
= 2
EAP_PATTERN_NAME = 3
EAP_LEGACY_VAR_COUNT = 10
EAP_PIONEER_VAR_COUNT = 8
EAP_LEGACYROLE_VAR_COUNT = 10
= 2
FCMI_MENU_NAME = 3
FCMI_TARGET = 4
FCMI_USE = 6
FCMI_MENUGROUP_FOR_VERTICAL_2 = 7
FCMI_MENUGROUP_FOR_HOSTGROUP = 8
FCMI_MENUGROUP_FOR_HOST = 9
FCMI_MENUGROUP_FOR_VERTICAL = 11
P_ID = 2
AMGL_MENU_GROUP_NAME = 3
_ID = 2
AML_MENU_GROUP_ID = 3
AML_MENU_GROUP_NAME = 4
AML_MENU_NAME = 6
= 2
DALVA_MENU_NAME = 3
DALVA_ITEM_NAME = 4
DALVA_ITEM_ORDER = 5
DALVA_COLUMN_GROUP = 8
OTRR_RESPONSE_ID = 2
OTRR_REQUEST_TYPE_ID = 5
OTRR_STATUS = 6
OTRR_UPDATE_STATUS_ID = 7
OTRRA_RESPONSE_ID = 3
OTRRA_EXECUTION_ORDER = 5
OTAH_RESPONSE_ID = 3
OTAH_RULE_NAME = 5
OTAH_EXECUTION_ORDER = 6
OTAH_ACTION_START_TIME = 7
OTAH_ACTION_TYPE_ID = 8
OTAH_STATUS = 10
OTID_ITA_DISP_NAME = 3
OTMD_MAIL_DISP_NAME = 3
OTMT_MAIL_TEMPLATE_NAME = 3
UnsetTraceID = '----------------------------------------'
class ActionDriverCommonModules:
def getStringNowDate(self):
now = datetime.datetime.now(pytz.timezone('UTC'))
return now.strftime("%Y/%m/%d")
@classmethod
def getStringNowDateTime(cls, convert_flg=False):
now = datetime.datetime.now(pytz.timezone('UTC'))
if convert_flg:
return now.strftime("%Y-%m-%d %H:%M:%S.%f")
return now
def KeyValueStringFind(self,pattern,string):
string_tmp = string.split('=')
if len(string_tmp) < 2:
return None
key = string_tmp[0]
if key != pattern:
return None
val = '='.join(string_tmp[1:])
return val
@classmethod
def back_trace(self):
exc_type, exc_value, exc_traceback = sys.exc_info()
stack_trace = traceback.format_exception(exc_type, exc_value, exc_traceback)
edit_trace = ''
for line in stack_trace:
edit_trace = edit_trace + line
return edit_trace
@staticmethod
def SaveActionLog(resp_id, exe_order, trace_id, msg_id, **kwargs):
now = datetime.datetime.now(pytz.timezone('UTC'))
msg_params = None
if kwargs:
msg_params = '%s' % (kwargs)
try:
ActionLog(
response_id = resp_id,
execution_order = exe_order,
trace_id = trace_id,
message_id = msg_id,
message_params = msg_params,
last_update_timestamp = now
).save(force_insert=True)
except Exception as ex:
logger.system_log('LOSM01007', resp_id, exe_order, trace_id, msg_id, msg_params)
class TimeConversion:
@classmethod
def get_time_conversion(cls, naive, tz):
return naive.astimezone(pytz.timezone(tz)).strftime('%Y-%m-%d %H:%M:%S')
@classmethod
def get_time_conversion_utc(cls, naive, tz):
tz_ex = pytz.timezone(tz)
naive = naive.replace('/', '-')
user_dt = datetime.datetime.strptime(naive, '%Y-%m-%d %H:%M:%S')
cou_dt = tz_ex.localize(user_dt, is_dst=None)
utc_dt = cou_dt.astimezone(pytz.utc)
return utc_dt
| true | true |
f73c0fe703b389cfc864ec8c3e6ef753f1d11bd1 | 38,347 | py | Python | tests/workflow_builder_test.py | isi-vista/vista-pegasus-wrapper | bbc220bb9aa6adaaad1df785b51526b6d4b0fafc | [
"MIT"
] | 2 | 2021-03-11T13:33:52.000Z | 2021-06-18T17:49:41.000Z | tests/workflow_builder_test.py | isi-vista/vista-pegasus-wrapper | bbc220bb9aa6adaaad1df785b51526b6d4b0fafc | [
"MIT"
] | 108 | 2020-02-25T21:42:30.000Z | 2022-03-25T21:59:30.000Z | tests/workflow_builder_test.py | isi-vista/vista-pegasus-wrapper | bbc220bb9aa6adaaad1df785b51526b6d4b0fafc | [
"MIT"
] | 1 | 2020-08-05T15:33:52.000Z | 2020-08-05T15:33:52.000Z | import subprocess
from pathlib import Path
from random import Random
from immutablecollections import immutableset
from vistautils.parameters import Parameters
from pegasus_wrapper import PegasusProfile
from pegasus_wrapper.artifact import ValueArtifact
from pegasus_wrapper.locator import Locator, _parse_parts
from pegasus_wrapper.pegasus_utils import build_submit_script
from pegasus_wrapper.resource_request import SlurmResourceRequest
from pegasus_wrapper.scripts.add_y import main as add_main
from pegasus_wrapper.scripts.multiply_by_x import main as multiply_by_x_main
from pegasus_wrapper.scripts.sort_nums_in_file import main as sort_nums_main
from pegasus_wrapper.workflow import WorkflowBuilder
import pytest
from yaml import SafeLoader, load
def test_simple_dax(tmp_path):
params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(params)
assert workflow_builder.name == "Test"
assert workflow_builder.created_by == "Testing"
assert (
workflow_builder._workflow_directory # pylint:disable=protected-access
== tmp_path / "working"
)
assert workflow_builder._namespace == "test" # pylint:disable=protected-access
assert workflow_builder._default_site == "saga" # pylint:disable=protected-access
assert workflow_builder.default_resource_request # pylint:disable=protected-access
assert workflow_builder._job_graph is not None # pylint:disable=protected-access
assert workflow_builder._experiment_name == "fred" # pylint:disable=protected-access
def test_locator():
job = Locator(_parse_parts("job"))
example = Locator(_parse_parts("example/path"))
combined = example / job
combined_from_string = example / "job"
assert combined.__repr__() == combined_from_string.__repr__()
with pytest.raises(RuntimeError):
_ = combined / 90
# Confirm we can't create a locator with an equals sign in the name
with pytest.raises(ValueError):
_ = Locator(_parse_parts("x=20"))
def test_dax_with_job_on_saga(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
add_output_file = tmp_path / "add_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
add_args = f"{sorted_output_file} {add_output_file} --y 10"
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
),
locator=Locator("multiply"),
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = workflow_builder.directory_for(sort_job_name)
sort_artifact = workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
category="add",
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
add_job_name = Locator(_parse_parts("jobs/add"))
add_dir = workflow_builder.directory_for(add_job_name)
workflow_builder.run_python_on_args(
add_job_name, add_main, add_args, depends_on=[sort_artifact]
)
assert (add_dir / "___run.sh").exists()
dax_file_one = workflow_builder.write_dax_to_dir(tmp_path)
dax_file_two = workflow_builder.write_dax_to_dir()
assert dax_file_one.exists()
assert dax_file_two.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
submit_script_two = tmp_path / "submit_script_two.sh"
build_submit_script(
submit_script_one,
str(dax_file_one),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
build_submit_script(
submit_script_two,
str(dax_file_two),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
assert submit_script_one.exists()
assert submit_script_two.exists()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
submit_script_process = subprocess.Popen(
["sh", str(submit_script_one)],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
encoding="utf-8",
)
stdout, stderr = submit_script_process.communicate()
print(stdout)
print(stderr)
def test_dax_with_checkpointed_jobs_on_saga(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
# Create checkpointed file so that when trying to create the job again,
# Pegasus just adds the file to the Replica Catalog
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
assert checkpointed_multiply_file.exists()
assert multiply_output_file.exists()
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
),
locator=Locator("multiply"),
)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
)
workflow_builder.write_dax_to_dir()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
# Make sure the Replica Catalog is not empty
assert replica_catalog.stat().st_size > 0
def test_clearing_ckpts(monkeypatch, tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "scavenge",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
)
monkeypatch.setattr("builtins.input", lambda _: "y")
workflow_builder.write_dax_to_dir()
assert not checkpointed_multiply_file.exists()
def test_not_clearing_ckpts(monkeypatch, tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "scavenge",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
)
monkeypatch.setattr("builtins.input", lambda _: "n")
workflow_builder.write_dax_to_dir()
assert checkpointed_multiply_file.exists()
def _job_in_dax_has_category(dax_file, target_job_locator, category):
"""
Return whether the given DAX file has a job
corresponding to `target_job_locator`
which has the category `category`.
"""
target_job_name = str(target_job_locator).replace("/", "_")
with dax_file.open("r") as f:
data = load(f, Loader=SafeLoader)
for item in data["jobs"]:
if item["name"] == target_job_name:
if "dagman" in item["profiles"].keys():
if "CATEGORY" in item["profiles"]["dagman"].keys():
if item["profiles"]["dagman"]["CATEGORY"] == category:
return True
return False
def test_dax_with_categories(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiply_job_category = "arithmetic"
workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
category=multiply_job_category,
)
# Check that the multiply job has the appropriate category set in the DAX file
dax_file = workflow_builder.write_dax_to_dir()
assert dax_file.exists()
assert _job_in_dax_has_category(dax_file, multiply_job_name, multiply_job_category)
assert not _job_in_dax_has_category(
dax_file, multiply_job_name, "an-arbitrary-category"
)
def test_dax_with_saga_categories(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
multiply_partition = "gaia"
multiply_slurm_params = Parameters.from_mapping(
{"partition": multiply_partition, "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_resources = SlurmResourceRequest.from_parameters(multiply_slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
resource_request=multiply_resources,
),
locator=Locator("multiply"),
)
sort_partition = "lestat"
sort_slurm_params = Parameters.from_mapping(
{"partition": sort_partition, "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
sort_resources = SlurmResourceRequest.from_parameters(sort_slurm_params)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=sort_resources,
)
dax_file = workflow_builder.write_dax_to_dir()
assert dax_file.exists()
# Check that the multiply and sort jobs have the appropriate partition-defined categories set in
# the DAX file
assert _job_in_dax_has_category(dax_file, multiply_job_name, multiply_partition)
assert not _job_in_dax_has_category(dax_file, multiply_job_name, sort_partition)
assert _job_in_dax_has_category(dax_file, sort_job_name, sort_partition)
assert not _job_in_dax_has_category(dax_file, sort_job_name, multiply_partition)
def test_category_max_jobs(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
multiply_slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_resources = SlurmResourceRequest.from_parameters(multiply_slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
resource_request=multiply_resources,
),
locator=Locator("multiply"),
)
sort_slurm_params = Parameters.from_mapping(
{
"partition": "ephemeral",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G",
"job_time_in_minutes": 120,
}
)
sort_resources = SlurmResourceRequest.from_parameters(sort_slurm_params)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=sort_resources,
)
workflow_builder.limit_jobs_for_category("gaia", 1)
workflow_builder.write_dax_to_dir()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
# Make sure the config contains the appropriate maxjobs lines and no inappropriate maxjobs lines
with properties_file.open("r") as f:
lines = f.readlines()
for line in lines:
print(line)
assert any("dagman.gaia.maxjobs = 1" in line for line in lines)
assert all("dagman.ephemeral.maxjobs =" not in line for line in lines)
def test_dax_test_exclude_nodes_on_saga(tmp_path):
sample_exclude = "saga01,saga03,saga21,saga05"
sample_include = "saga06"
params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"exclude_list": sample_exclude,
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(0, 25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
job_profile = PegasusProfile(
namespace="pegasus", key="transfer.bypass.input.staging", value="True"
)
resources = SlurmResourceRequest.from_parameters(
slurm_params.unify({"run_on_single_node": sample_include})
)
workflow_builder = WorkflowBuilder.from_parameters(params)
multiply_job_locator = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_locator,
multiply_by_x_main,
multiply_params,
depends_on=[],
job_profiles=[job_profile],
),
locator=Locator("multiply"),
)
sort_job_locator = Locator(_parse_parts("jobs/sort"))
workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
)
dax_file = workflow_builder.write_dax_to_dir(tmp_path)
with dax_file.open("r") as dax:
dax_yaml = load(dax, Loader=SafeLoader)
root = dax_yaml["jobs"]
for item in root:
if item["type"] == "job":
if "pegasus" in item["profiles"]:
if item["name"] == "jobs_multiply":
assert (
f"--exclude={sample_exclude}"
in item["profiles"]["pegasus"]["glite.arguments"]
)
elif item["name"] == "jobs_sort":
assert "--exclude=" in item["profiles"]["pegasus"]["glite.arguments"]
assert (
f"--nodelist={sample_include}"
in item["profiles"]["pegasus"]["glite.arguments"]
)
else:
assert False
def test_dax_with_job_in_container(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
# Add Container
example_docker = workflow_builder.add_container(
"example_container", "docker", tmp_path / "docker.img"
)
with pytest.raises(ValueError):
_ = workflow_builder.add_container(
"fake_container", "invalid", tmp_path / "invalid_docker.img"
)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
container=example_docker,
),
locator=Locator("multiply"),
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = workflow_builder.directory_for(sort_job_name)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
container=example_docker,
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
dax_file_one = workflow_builder.write_dax_to_dir()
assert dax_file_one.exists()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
def test_dax_with_job_on_saga_with_dict_as_params(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
add_output_file = tmp_path / "add_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = {
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4,
}
sort_params = {"input_file": multiply_output_file, "output_file": sorted_output_file}
add_args = f"{sorted_output_file} {add_output_file} --y 10"
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
),
locator=Locator("multiply"),
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = workflow_builder.directory_for(sort_job_name)
sort_artifact = workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
category="add",
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
add_job_name = Locator(_parse_parts("jobs/add"))
add_dir = workflow_builder.directory_for(add_job_name)
workflow_builder.run_python_on_args(
add_job_name, "add_job_main.py", add_args, depends_on=[sort_artifact]
)
assert (add_dir / "___run.sh").exists()
dax_file_one = workflow_builder.write_dax_to_dir(tmp_path)
dax_file_two = workflow_builder.write_dax_to_dir()
assert dax_file_one.exists()
assert dax_file_two.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
submit_script_two = tmp_path / "submit_script_two.sh"
build_submit_script(
submit_script_one,
str(dax_file_one),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
build_submit_script(
submit_script_two,
str(dax_file_two),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
assert submit_script_one.exists()
assert submit_script_two.exists()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
def test_dax_with_python_into_container_jobs(tmp_path):
docker_tar = Path(f"{tmp_path}/docker/tar.tar")
docker_build_dir = tmp_path
docker_image_name = "pegasus_wrapper_container_demo"
docker_image_tag = "0.2"
# Generating parameters for initializing a workflow
# We recommend making workflow directory, site, and partition parameters
# in an research workflow
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"home_dir": str(tmp_path),
"partition": "scavenge",
}
)
saga31_request = SlurmResourceRequest.from_parameters(
Parameters.from_mapping({"run_on_single_node": "saga31", "partition": "gaia"})
)
# Our source input for the sample jobs
input_file = tmp_path / "raw_nums.txt"
add_y_output_file_nas = tmp_path / "nums_y.txt"
sorted_output_file_nas = tmp_path / "sorted.txt"
random = Random()
random.seed(0)
nums = [int(random.random() * 100) for _ in range(0, 25)]
# Base Job Locator
job_locator = Locator(("jobs",))
docker_python_root = Path("/home/app/")
# Write a list of numbers out to be able to run the workflow
with input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
build_container_locator = job_locator / "build_docker"
build_container = workflow_builder.run_bash(
build_container_locator,
command=[
"mkdir -p /scratch/dockermount/pegasus_wrapper_tmp",
f"cd {docker_build_dir}",
f"docker build . -t {docker_image_name}:{docker_image_tag}",
f"docker save -o /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_image_name}:{docker_image_tag}",
f"cp /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_tar.absolute()}",
f"chmod go+r {docker_tar.absolute()}",
],
depends_on=[],
resource_request=saga31_request,
)
build_container_dir = workflow_builder.directory_for(build_container_locator)
assert (build_container_dir / "script.sh").exists()
python36 = workflow_builder.add_container(
f"{docker_image_name}:{docker_image_tag}",
"docker",
str(docker_tar.absolute()),
image_site="saga",
bypass_staging=True,
)
job_profile = PegasusProfile(
namespace="pegasus", key="transfer.bypass.input.staging", value="True"
)
mongo4_4 = workflow_builder.add_container(
"mongo:4.4", "docker", "path/to/tar.tar", image_site="saga", bypass_staging=True
)
with pytest.raises(RuntimeError):
_ = workflow_builder.stop_docker_as_service(
mongo4_4, depends_on=[], resource_request=saga31_request
)
start_mongo = workflow_builder.start_docker_as_service(
mongo4_4,
depends_on=[build_container],
docker_args=f"-v /scratch/mongo/data/db:/data/db",
resource_request=saga31_request,
)
mongo4_4_dir = workflow_builder.directory_for(Locator(("containers", mongo4_4.name)))
assert (mongo4_4_dir / "start.sh").exists()
assert (mongo4_4_dir / "stop.sh").exists()
add_y_locator = job_locator / "add"
add_y_job = workflow_builder.run_python_on_args(
add_y_locator,
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file],
output_file_paths=[add_y_output_file_nas],
)
add_y_dir = workflow_builder.directory_for(add_y_locator)
assert (add_y_dir / "___run.sh").exists()
with pytest.raises(RuntimeError):
_ = workflow_builder.run_python_on_args(
add_y_locator,
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file, input_file],
output_file_paths=[add_y_output_file_nas],
)
sort_job_locator = job_locator / "sort"
sort_job = workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
assert sort_job == workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
sort_job_dir = workflow_builder.directory_for(sort_job_locator)
assert (sort_job_dir / "___run.sh").exists()
assert (sort_job_dir / "____params.params").exists()
with pytest.raises(RuntimeError):
_ = workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=[sorted_output_file_nas, sorted_output_file_nas],
)
celebration_bash_locator = job_locator / "celebrate"
celebration_bash = workflow_builder.run_bash(
celebration_bash_locator,
'echo "Jobs Runs Successfully"',
depends_on=[sort_job],
job_profiles=[job_profile],
)
assert celebration_bash == workflow_builder.run_bash(
celebration_bash_locator,
'echo "Jobs Runs Successfully"',
depends_on=[sort_job],
job_profiles=[job_profile],
)
celebration_bash_dir = workflow_builder.directory_for(celebration_bash_locator)
assert (celebration_bash_dir / "script.sh").exists()
_ = workflow_builder.stop_docker_as_service(
mongo4_4, depends_on=[start_mongo, sort_job], resource_request=saga31_request
)
dax_file_one = workflow_builder.write_dax_to_dir(tmp_path)
assert dax_file_one.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
build_submit_script(
submit_script_one,
str(dax_file_one),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
assert submit_script_one.exists()
| 36.108286 | 128 | 0.673534 | import subprocess
from pathlib import Path
from random import Random
from immutablecollections import immutableset
from vistautils.parameters import Parameters
from pegasus_wrapper import PegasusProfile
from pegasus_wrapper.artifact import ValueArtifact
from pegasus_wrapper.locator import Locator, _parse_parts
from pegasus_wrapper.pegasus_utils import build_submit_script
from pegasus_wrapper.resource_request import SlurmResourceRequest
from pegasus_wrapper.scripts.add_y import main as add_main
from pegasus_wrapper.scripts.multiply_by_x import main as multiply_by_x_main
from pegasus_wrapper.scripts.sort_nums_in_file import main as sort_nums_main
from pegasus_wrapper.workflow import WorkflowBuilder
import pytest
from yaml import SafeLoader, load
def test_simple_dax(tmp_path):
params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(params)
assert workflow_builder.name == "Test"
assert workflow_builder.created_by == "Testing"
assert (
workflow_builder._workflow_directory
== tmp_path / "working"
)
assert workflow_builder._namespace == "test"
assert workflow_builder._default_site == "saga"
assert workflow_builder.default_resource_request
assert workflow_builder._job_graph is not None
assert workflow_builder._experiment_name == "fred"
def test_locator():
job = Locator(_parse_parts("job"))
example = Locator(_parse_parts("example/path"))
combined = example / job
combined_from_string = example / "job"
assert combined.__repr__() == combined_from_string.__repr__()
with pytest.raises(RuntimeError):
_ = combined / 90
with pytest.raises(ValueError):
_ = Locator(_parse_parts("x=20"))
def test_dax_with_job_on_saga(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
add_output_file = tmp_path / "add_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
add_args = f"{sorted_output_file} {add_output_file} --y 10"
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
),
locator=Locator("multiply"),
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = workflow_builder.directory_for(sort_job_name)
sort_artifact = workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
category="add",
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
add_job_name = Locator(_parse_parts("jobs/add"))
add_dir = workflow_builder.directory_for(add_job_name)
workflow_builder.run_python_on_args(
add_job_name, add_main, add_args, depends_on=[sort_artifact]
)
assert (add_dir / "___run.sh").exists()
dax_file_one = workflow_builder.write_dax_to_dir(tmp_path)
dax_file_two = workflow_builder.write_dax_to_dir()
assert dax_file_one.exists()
assert dax_file_two.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
submit_script_two = tmp_path / "submit_script_two.sh"
build_submit_script(
submit_script_one,
str(dax_file_one),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
build_submit_script(
submit_script_two,
str(dax_file_two),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
assert submit_script_one.exists()
assert submit_script_two.exists()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
submit_script_process = subprocess.Popen(
["sh", str(submit_script_one)],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
encoding="utf-8",
)
stdout, stderr = submit_script_process.communicate()
print(stdout)
print(stderr)
def test_dax_with_checkpointed_jobs_on_saga(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
# Create checkpointed file so that when trying to create the job again,
# Pegasus just adds the file to the Replica Catalog
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
assert checkpointed_multiply_file.exists()
assert multiply_output_file.exists()
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
),
locator=Locator("multiply"),
)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
)
workflow_builder.write_dax_to_dir()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
# Make sure the Replica Catalog is not empty
assert replica_catalog.stat().st_size > 0
def test_clearing_ckpts(monkeypatch, tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "scavenge",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
)
monkeypatch.setattr("builtins.input", lambda _: "y")
workflow_builder.write_dax_to_dir()
assert not checkpointed_multiply_file.exists()
def test_not_clearing_ckpts(monkeypatch, tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "scavenge",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
)
monkeypatch.setattr("builtins.input", lambda _: "n")
workflow_builder.write_dax_to_dir()
assert checkpointed_multiply_file.exists()
def _job_in_dax_has_category(dax_file, target_job_locator, category):
target_job_name = str(target_job_locator).replace("/", "_")
with dax_file.open("r") as f:
data = load(f, Loader=SafeLoader)
for item in data["jobs"]:
if item["name"] == target_job_name:
if "dagman" in item["profiles"].keys():
if "CATEGORY" in item["profiles"]["dagman"].keys():
if item["profiles"]["dagman"]["CATEGORY"] == category:
return True
return False
def test_dax_with_categories(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiply_job_category = "arithmetic"
workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
category=multiply_job_category,
)
# Check that the multiply job has the appropriate category set in the DAX file
dax_file = workflow_builder.write_dax_to_dir()
assert dax_file.exists()
assert _job_in_dax_has_category(dax_file, multiply_job_name, multiply_job_category)
assert not _job_in_dax_has_category(
dax_file, multiply_job_name, "an-arbitrary-category"
)
def test_dax_with_saga_categories(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
multiply_partition = "gaia"
multiply_slurm_params = Parameters.from_mapping(
{"partition": multiply_partition, "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_resources = SlurmResourceRequest.from_parameters(multiply_slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
resource_request=multiply_resources,
),
locator=Locator("multiply"),
)
sort_partition = "lestat"
sort_slurm_params = Parameters.from_mapping(
{"partition": sort_partition, "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
sort_resources = SlurmResourceRequest.from_parameters(sort_slurm_params)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=sort_resources,
)
dax_file = workflow_builder.write_dax_to_dir()
assert dax_file.exists()
# Check that the multiply and sort jobs have the appropriate partition-defined categories set in
# the DAX file
assert _job_in_dax_has_category(dax_file, multiply_job_name, multiply_partition)
assert not _job_in_dax_has_category(dax_file, multiply_job_name, sort_partition)
assert _job_in_dax_has_category(dax_file, sort_job_name, sort_partition)
assert not _job_in_dax_has_category(dax_file, sort_job_name, multiply_partition)
def test_category_max_jobs(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"home_dir": str(tmp_path),
}
)
multiply_slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_resources = SlurmResourceRequest.from_parameters(multiply_slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
resource_request=multiply_resources,
),
locator=Locator("multiply"),
)
sort_slurm_params = Parameters.from_mapping(
{
"partition": "ephemeral",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G",
"job_time_in_minutes": 120,
}
)
sort_resources = SlurmResourceRequest.from_parameters(sort_slurm_params)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=sort_resources,
)
workflow_builder.limit_jobs_for_category("gaia", 1)
workflow_builder.write_dax_to_dir()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
# Make sure the config contains the appropriate maxjobs lines and no inappropriate maxjobs lines
with properties_file.open("r") as f:
lines = f.readlines()
for line in lines:
print(line)
assert any("dagman.gaia.maxjobs = 1" in line for line in lines)
assert all("dagman.ephemeral.maxjobs =" not in line for line in lines)
def test_dax_test_exclude_nodes_on_saga(tmp_path):
sample_exclude = "saga01,saga03,saga21,saga05"
sample_include = "saga06"
params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"exclude_list": sample_exclude,
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(0, 25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
job_profile = PegasusProfile(
namespace="pegasus", key="transfer.bypass.input.staging", value="True"
)
resources = SlurmResourceRequest.from_parameters(
slurm_params.unify({"run_on_single_node": sample_include})
)
workflow_builder = WorkflowBuilder.from_parameters(params)
multiply_job_locator = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_locator,
multiply_by_x_main,
multiply_params,
depends_on=[],
job_profiles=[job_profile],
),
locator=Locator("multiply"),
)
sort_job_locator = Locator(_parse_parts("jobs/sort"))
workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
)
dax_file = workflow_builder.write_dax_to_dir(tmp_path)
with dax_file.open("r") as dax:
dax_yaml = load(dax, Loader=SafeLoader)
root = dax_yaml["jobs"]
for item in root:
if item["type"] == "job":
if "pegasus" in item["profiles"]:
if item["name"] == "jobs_multiply":
assert (
f"--exclude={sample_exclude}"
in item["profiles"]["pegasus"]["glite.arguments"]
)
elif item["name"] == "jobs_sort":
assert "--exclude=" in item["profiles"]["pegasus"]["glite.arguments"]
assert (
f"--nodelist={sample_include}"
in item["profiles"]["pegasus"]["glite.arguments"]
)
else:
assert False
def test_dax_with_job_in_container(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping(
{"input_file": multiply_input_file, "output_file": multiply_output_file, "x": 4}
)
sort_params = Parameters.from_mapping(
{"input_file": multiply_output_file, "output_file": sorted_output_file}
)
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
# Add Container
example_docker = workflow_builder.add_container(
"example_container", "docker", tmp_path / "docker.img"
)
with pytest.raises(ValueError):
_ = workflow_builder.add_container(
"fake_container", "invalid", tmp_path / "invalid_docker.img"
)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
container=example_docker,
),
locator=Locator("multiply"),
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = workflow_builder.directory_for(sort_job_name)
workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
container=example_docker,
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
dax_file_one = workflow_builder.write_dax_to_dir()
assert dax_file_one.exists()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
def test_dax_with_job_on_saga_with_dict_as_params(tmp_path):
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"partition": "gaia",
"experiment_name": "fred",
"home_dir": str(tmp_path),
}
)
slurm_params = Parameters.from_mapping(
{"partition": "gaia", "num_cpus": 1, "num_gpus": 0, "memory": "4G"}
)
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
add_output_file = tmp_path / "add_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = {
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4,
}
sort_params = {"input_file": multiply_output_file, "output_file": sorted_output_file}
add_args = f"{sorted_output_file} {add_output_file} --y 10"
resources = SlurmResourceRequest.from_parameters(slurm_params)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=workflow_builder.run_python_on_parameters(
multiply_job_name, multiply_by_x_main, multiply_params, depends_on=[]
),
locator=Locator("multiply"),
)
multiple_dir = workflow_builder.directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = workflow_builder.directory_for(sort_job_name)
sort_artifact = workflow_builder.run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
category="add",
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
add_job_name = Locator(_parse_parts("jobs/add"))
add_dir = workflow_builder.directory_for(add_job_name)
workflow_builder.run_python_on_args(
add_job_name, "add_job_main.py", add_args, depends_on=[sort_artifact]
)
assert (add_dir / "___run.sh").exists()
dax_file_one = workflow_builder.write_dax_to_dir(tmp_path)
dax_file_two = workflow_builder.write_dax_to_dir()
assert dax_file_one.exists()
assert dax_file_two.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
submit_script_two = tmp_path / "submit_script_two.sh"
build_submit_script(
submit_script_one,
str(dax_file_one),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
build_submit_script(
submit_script_two,
str(dax_file_two),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
assert submit_script_one.exists()
assert submit_script_two.exists()
site_catalog = workflow_params.existing_directory("workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") / "replicas.yml"
)
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") / "transformations.yml"
)
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") / "pegasus.properties"
)
assert properties_file.exists()
def test_dax_with_python_into_container_jobs(tmp_path):
docker_tar = Path(f"{tmp_path}/docker/tar.tar")
docker_build_dir = tmp_path
docker_image_name = "pegasus_wrapper_container_demo"
docker_image_tag = "0.2"
# Generating parameters for initializing a workflow
# We recommend making workflow directory, site, and partition parameters
# in an research workflow
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"home_dir": str(tmp_path),
"partition": "scavenge",
}
)
saga31_request = SlurmResourceRequest.from_parameters(
Parameters.from_mapping({"run_on_single_node": "saga31", "partition": "gaia"})
)
# Our source input for the sample jobs
input_file = tmp_path / "raw_nums.txt"
add_y_output_file_nas = tmp_path / "nums_y.txt"
sorted_output_file_nas = tmp_path / "sorted.txt"
random = Random()
random.seed(0)
nums = [int(random.random() * 100) for _ in range(0, 25)]
# Base Job Locator
job_locator = Locator(("jobs",))
docker_python_root = Path("/home/app/")
# Write a list of numbers out to be able to run the workflow
with input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
workflow_builder = WorkflowBuilder.from_parameters(workflow_params)
build_container_locator = job_locator / "build_docker"
build_container = workflow_builder.run_bash(
build_container_locator,
command=[
"mkdir -p /scratch/dockermount/pegasus_wrapper_tmp",
f"cd {docker_build_dir}",
f"docker build . -t {docker_image_name}:{docker_image_tag}",
f"docker save -o /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_image_name}:{docker_image_tag}",
f"cp /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_tar.absolute()}",
f"chmod go+r {docker_tar.absolute()}",
],
depends_on=[],
resource_request=saga31_request,
)
build_container_dir = workflow_builder.directory_for(build_container_locator)
assert (build_container_dir / "script.sh").exists()
python36 = workflow_builder.add_container(
f"{docker_image_name}:{docker_image_tag}",
"docker",
str(docker_tar.absolute()),
image_site="saga",
bypass_staging=True,
)
job_profile = PegasusProfile(
namespace="pegasus", key="transfer.bypass.input.staging", value="True"
)
mongo4_4 = workflow_builder.add_container(
"mongo:4.4", "docker", "path/to/tar.tar", image_site="saga", bypass_staging=True
)
with pytest.raises(RuntimeError):
_ = workflow_builder.stop_docker_as_service(
mongo4_4, depends_on=[], resource_request=saga31_request
)
start_mongo = workflow_builder.start_docker_as_service(
mongo4_4,
depends_on=[build_container],
docker_args=f"-v /scratch/mongo/data/db:/data/db",
resource_request=saga31_request,
)
mongo4_4_dir = workflow_builder.directory_for(Locator(("containers", mongo4_4.name)))
assert (mongo4_4_dir / "start.sh").exists()
assert (mongo4_4_dir / "stop.sh").exists()
add_y_locator = job_locator / "add"
add_y_job = workflow_builder.run_python_on_args(
add_y_locator,
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file],
output_file_paths=[add_y_output_file_nas],
)
add_y_dir = workflow_builder.directory_for(add_y_locator)
assert (add_y_dir / "___run.sh").exists()
with pytest.raises(RuntimeError):
_ = workflow_builder.run_python_on_args(
add_y_locator,
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file, input_file],
output_file_paths=[add_y_output_file_nas],
)
sort_job_locator = job_locator / "sort"
sort_job = workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
assert sort_job == workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
sort_job_dir = workflow_builder.directory_for(sort_job_locator)
assert (sort_job_dir / "___run.sh").exists()
assert (sort_job_dir / "____params.params").exists()
with pytest.raises(RuntimeError):
_ = workflow_builder.run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=[sorted_output_file_nas, sorted_output_file_nas],
)
celebration_bash_locator = job_locator / "celebrate"
celebration_bash = workflow_builder.run_bash(
celebration_bash_locator,
'echo "Jobs Runs Successfully"',
depends_on=[sort_job],
job_profiles=[job_profile],
)
assert celebration_bash == workflow_builder.run_bash(
celebration_bash_locator,
'echo "Jobs Runs Successfully"',
depends_on=[sort_job],
job_profiles=[job_profile],
)
celebration_bash_dir = workflow_builder.directory_for(celebration_bash_locator)
assert (celebration_bash_dir / "script.sh").exists()
_ = workflow_builder.stop_docker_as_service(
mongo4_4, depends_on=[start_mongo, sort_job], resource_request=saga31_request
)
dax_file_one = workflow_builder.write_dax_to_dir(tmp_path)
assert dax_file_one.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
build_submit_script(
submit_script_one,
str(dax_file_one),
workflow_builder._workflow_directory, # pylint:disable=protected-access
)
assert submit_script_one.exists()
| true | true |
f73c10ee3e5c24d52e1500d097c6c6a3310f74e0 | 10,284 | py | Python | plugin/radar/depth_net.py | a1600012888/mmdetection3d | 2e01549c56dabf1965abc975a7301a8d746973ad | [
"Apache-2.0"
] | null | null | null | plugin/radar/depth_net.py | a1600012888/mmdetection3d | 2e01549c56dabf1965abc975a7301a8d746973ad | [
"Apache-2.0"
] | null | null | null | plugin/radar/depth_net.py | a1600012888/mmdetection3d | 2e01549c56dabf1965abc975a7301a8d746973ad | [
"Apache-2.0"
] | null | null | null | # Copyright 2020 Toyota Research Institute. All rights reserved.
import torch
import torch.nn as nn
from .layers01 import \
PackLayerConv3d, UnpackLayerConv3d, Conv2D, ResidualBlock, InvDepth
from .utils import DepthPredictHead2Up, get_depth_metrics
from mmdet.models import DETECTORS
import torch.nn.functional as F
@DETECTORS.register_module()
class PackNetSlim01(nn.Module):
"""
PackNet network with 3d convolutions (version 01, from the CVPR paper).
Slimmer version, with fewer feature channels
https://arxiv.org/abs/1905.02693
Parameters
----------
dropout : float
Dropout value to use
version : str
Has a XY format, where:
X controls upsampling variations (not used at the moment).
Y controls feature stacking (A for concatenation and B for addition)
kwargs : dict
Extra parameters
"""
def __init__(self, dropout=None, version=None, min_depth=0.5, **kwargs):
super().__init__()
self.version = version[1:]
# Input/output channels
in_channels = 3
out_channels = 1
# Hyper-parameters
ni, no = 32, out_channels
n1, n2, n3, n4, n5 = 16, 32, 64, 128, 256
#n1, n2, n3, n4, n5 = 32, 64, 128, 256, 512
num_blocks = [2, 2, 3, 3]
pack_kernel = [5, 3, 3, 3, 3]
unpack_kernel = [3, 3, 3, 3, 3]
iconv_kernel = [3, 3, 3, 3, 3]
num_3d_feat = 4
# Initial convolutional layer
#self.down_sample_conv = Conv2D(in_channels, 16, 5, 2)
self.pre_calc = Conv2D(in_channels, ni, 5, 1)
# Support for different versions
if self.version == 'A': # Channel concatenation
n1o, n1i = n1, n1 + ni + no
n2o, n2i = n2, n2 + n1 + no
n3o, n3i = n3, n3 + n2 + no
n4o, n4i = n4, n4 + n3
n5o, n5i = n5, n5 + n4
elif self.version == 'B': # Channel addition
n1o, n1i = n1, n1 + no
n2o, n2i = n2, n2 + no
n3o, n3i = n3//2, n3//2 + no
n4o, n4i = n4//2, n4//2
n5o, n5i = n5//2, n5//2
else:
raise ValueError('Unknown PackNet version {}'.format(version))
# Encoder
self.pack1 = PackLayerConv3d(n1, pack_kernel[0], d=num_3d_feat)
self.pack2 = PackLayerConv3d(n2, pack_kernel[1], d=num_3d_feat)
self.pack3 = PackLayerConv3d(n3, pack_kernel[2], d=num_3d_feat)
self.pack4 = PackLayerConv3d(n4, pack_kernel[3], d=num_3d_feat)
self.pack5 = PackLayerConv3d(n5, pack_kernel[4], d=num_3d_feat)
self.conv1 = Conv2D(ni, n1, 7, 1)
self.conv2 = ResidualBlock(n1, n2, num_blocks[0], 1, dropout=dropout)
self.conv3 = ResidualBlock(n2, n3, num_blocks[1], 1, dropout=dropout)
self.conv4 = ResidualBlock(n3, n4, num_blocks[2], 1, dropout=dropout)
self.conv5 = ResidualBlock(n4, n5, num_blocks[3], 1, dropout=dropout)
# Decoder
self.unpack5 = UnpackLayerConv3d(n5, n5o, unpack_kernel[0], d=num_3d_feat)
self.unpack4 = UnpackLayerConv3d(n5, n4o, unpack_kernel[1], d=num_3d_feat)
self.unpack3 = UnpackLayerConv3d(n4, n3o, unpack_kernel[2], d=num_3d_feat)
self.unpack2 = UnpackLayerConv3d(n3, n2o, unpack_kernel[3], d=num_3d_feat)
self.unpack1 = UnpackLayerConv3d(n2, n1o, unpack_kernel[4], d=num_3d_feat)
self.iconv5 = Conv2D(n5i, n5, iconv_kernel[0], 1)
self.iconv4 = Conv2D(n4i, n4, iconv_kernel[1], 1)
self.iconv3 = Conv2D(n3i, n3, iconv_kernel[2], 1)
self.iconv2 = Conv2D(n2i, n2, iconv_kernel[3], 1)
self.iconv1 = Conv2D(n1i, n1, iconv_kernel[4], 1)
# Depth Layers
self.unpack_disps = nn.PixelShuffle(2)
self.unpack_disp4 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
self.unpack_disp3 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
self.unpack_disp2 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
self.disp4_layer = InvDepth(n4, out_channels=out_channels, min_depth=min_depth)
self.disp3_layer = InvDepth(n3, out_channels=out_channels, min_depth=min_depth)
self.disp2_layer = InvDepth(n2, out_channels=out_channels, min_depth=min_depth)
self.disp1_layer = InvDepth(n1, out_channels=out_channels, min_depth=min_depth)
self.init_weights()
def init_weights(self):
"""Initializes network weights."""
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
def get_pred(self, data, **kwargs):
"""
Runs the network and returns inverse depth maps
(4 scales if training and 1 if not).
"""
# x = data['img']
x = data
#x = self.down_sample_conv(x)
x = self.pre_calc(x)
# Encoder
x1 = self.conv1(x)
x1p = self.pack1(x1)
x2 = self.conv2(x1p)
x2p = self.pack2(x2)
x3 = self.conv3(x2p)
x3p = self.pack3(x3)
x4 = self.conv4(x3p)
x4p = self.pack4(x4)
x5 = self.conv5(x4p)
x5p = self.pack5(x5)
# Skips
skip1 = x
skip2 = x1p
skip3 = x2p
skip4 = x3p
skip5 = x4p
# Decoder
unpack5 = self.unpack5(x5p)
if self.version == 'A':
concat5 = torch.cat((unpack5, skip5), 1)
else:
concat5 = unpack5 + skip5
iconv5 = self.iconv5(concat5)
unpack4 = self.unpack4(iconv5)
if self.version == 'A':
concat4 = torch.cat((unpack4, skip4), 1)
else:
concat4 = unpack4 + skip4
iconv4 = self.iconv4(concat4)
inv_depth4 = self.disp4_layer(iconv4)
up_inv_depth4 = self.unpack_disp4(inv_depth4)
unpack3 = self.unpack3(iconv4)
if self.version == 'A':
concat3 = torch.cat((unpack3, skip3, up_inv_depth4), 1)
else:
concat3 = torch.cat((unpack3 + skip3, up_inv_depth4), 1)
iconv3 = self.iconv3(concat3)
inv_depth3 = self.disp3_layer(iconv3)
up_inv_depth3 = self.unpack_disp3(inv_depth3)
unpack2 = self.unpack2(iconv3)
if self.version == 'A':
concat2 = torch.cat((unpack2, skip2, up_inv_depth3), 1)
else:
concat2 = torch.cat((unpack2 + skip2, up_inv_depth3), 1)
iconv2 = self.iconv2(concat2)
inv_depth2 = self.disp2_layer(iconv2)
up_inv_depth2 = self.unpack_disp2(inv_depth2)
unpack1 = self.unpack1(iconv2)
if self.version == 'A':
concat1 = torch.cat((unpack1, skip1, up_inv_depth2), 1)
else:
concat1 = torch.cat((unpack1 + skip1, up_inv_depth2), 1)
iconv1 = self.iconv1(concat1)
inv_depth1 = self.disp1_layer(iconv1)
if self.training:
inv_depths = [inv_depth1, inv_depth2, inv_depth3, inv_depth4]
#inv_depths = [inv_depth1]
else:
inv_depths = [inv_depth1]
#inv_depths = [F.interpolate(t_inv_depth, scale_factor=2, mode="bilinear", align_corners=False) for t_inv_depth in inv_depths]
# ret depth pred
return inv_depths
def forward(self, return_loss=True, rescale=False, **kwargs):
if not return_loss:
# in evalhook!
x = kwargs['img']
label = kwargs['depth_map']
data = {'img':x, 'depth_map':label}
depth_pred = self.get_pred(data)[0]
label = data['depth_map'].unsqueeze(dim=1)
mask = (label > 0)
#print(depth_pred.shape, label.shape, mask.shape, 'data shape')
loss = torch.abs((label - depth_pred)) * mask
loss = torch.sum(loss) / torch.sum(mask)
with torch.no_grad():
metrics = get_depth_metrics(depth_pred, label, mask)
# abs_diff, abs_rel, sq_rel, rmse, rmse_log
metrics = [m.item() for m in metrics]
# hack the hook
# outputs[0]=None. see https://github.com/open-mmlab/mmdetection/blob/master/mmdet/apis/test.py#L99
#outputs = {'loss': loss, 'log_vars':log_vars, 'num_samples':depth_pred.size(0), 0:None}
#print('val', loss)
metrics.append(loss.item())
return [metrics]
raise NotImplementedError
def train_step(self, data, optimzier):
depth_pred = self.get_pred(data)[0]
label = data['depth_map'].unsqueeze(dim=1)
mask = (label > 0)
#print(depth_pred.shape, label.shape, mask.shape, 'data shape')
#from IPython import embed
#embed()
loss = torch.abs((label - depth_pred)) * mask
loss = torch.sum(loss) / torch.sum(mask)
log_var = {}
with torch.no_grad():
metrics = get_depth_metrics(depth_pred, label, mask)
# abs_diff, abs_rel, sq_rel, rmse, rmse_log
metrics = [m.item() for m in metrics]
abs_diff, abs_rel, sq_rel, rmse, rmse_log = metrics
sparsity = torch.sum(mask) * 1.0 / torch.numel(mask)
std = torch.tensor([58.395, 57.12, 57.375]).cuda().view(1, -1, 1, 1)
mean = torch.tensor([123.675, 116.28, 103.53]).cuda().view(1, -1, 1, 1)
img = data['img'] * std + mean
img = img / 255.0
depth_at_gt = depth_pred * mask
log_vars = {'loss': loss.item(), 'sparsity': sparsity.item(),
'abs_diff': abs_diff, 'abs_rel': abs_rel,
'sq_rel': sq_rel, 'rmse': rmse,
'rmse_log': rmse_log
}
# 'pred', 'data', 'label', 'depth_at_gt' is used for visualization only!
outputs = {'pred': torch.clamp(1.0 / (depth_pred+1e-4), 0, 1), 'data': img,
'label': torch.clamp(1.0 / (label+1e-4), 0, 1),
'depth_at_gt': torch.clamp(1.0 / (depth_at_gt+1e-4), 0., 1),
'loss':loss, 'log_vars':log_vars, 'num_samples':depth_pred.size(0)}
return outputs
def val_step(self, data, optimizer):
return self.train_step(self, data, optimizer)
| 37.808824 | 134 | 0.583333 |
import torch
import torch.nn as nn
from .layers01 import \
PackLayerConv3d, UnpackLayerConv3d, Conv2D, ResidualBlock, InvDepth
from .utils import DepthPredictHead2Up, get_depth_metrics
from mmdet.models import DETECTORS
import torch.nn.functional as F
@DETECTORS.register_module()
class PackNetSlim01(nn.Module):
def __init__(self, dropout=None, version=None, min_depth=0.5, **kwargs):
super().__init__()
self.version = version[1:]
in_channels = 3
out_channels = 1
ni, no = 32, out_channels
n1, n2, n3, n4, n5 = 16, 32, 64, 128, 256
num_blocks = [2, 2, 3, 3]
pack_kernel = [5, 3, 3, 3, 3]
unpack_kernel = [3, 3, 3, 3, 3]
iconv_kernel = [3, 3, 3, 3, 3]
num_3d_feat = 4
self.pre_calc = Conv2D(in_channels, ni, 5, 1)
if self.version == 'A':
n1o, n1i = n1, n1 + ni + no
n2o, n2i = n2, n2 + n1 + no
n3o, n3i = n3, n3 + n2 + no
n4o, n4i = n4, n4 + n3
n5o, n5i = n5, n5 + n4
elif self.version == 'B':
n1o, n1i = n1, n1 + no
n2o, n2i = n2, n2 + no
n3o, n3i = n3//2, n3//2 + no
n4o, n4i = n4//2, n4//2
n5o, n5i = n5//2, n5//2
else:
raise ValueError('Unknown PackNet version {}'.format(version))
self.pack1 = PackLayerConv3d(n1, pack_kernel[0], d=num_3d_feat)
self.pack2 = PackLayerConv3d(n2, pack_kernel[1], d=num_3d_feat)
self.pack3 = PackLayerConv3d(n3, pack_kernel[2], d=num_3d_feat)
self.pack4 = PackLayerConv3d(n4, pack_kernel[3], d=num_3d_feat)
self.pack5 = PackLayerConv3d(n5, pack_kernel[4], d=num_3d_feat)
self.conv1 = Conv2D(ni, n1, 7, 1)
self.conv2 = ResidualBlock(n1, n2, num_blocks[0], 1, dropout=dropout)
self.conv3 = ResidualBlock(n2, n3, num_blocks[1], 1, dropout=dropout)
self.conv4 = ResidualBlock(n3, n4, num_blocks[2], 1, dropout=dropout)
self.conv5 = ResidualBlock(n4, n5, num_blocks[3], 1, dropout=dropout)
self.unpack5 = UnpackLayerConv3d(n5, n5o, unpack_kernel[0], d=num_3d_feat)
self.unpack4 = UnpackLayerConv3d(n5, n4o, unpack_kernel[1], d=num_3d_feat)
self.unpack3 = UnpackLayerConv3d(n4, n3o, unpack_kernel[2], d=num_3d_feat)
self.unpack2 = UnpackLayerConv3d(n3, n2o, unpack_kernel[3], d=num_3d_feat)
self.unpack1 = UnpackLayerConv3d(n2, n1o, unpack_kernel[4], d=num_3d_feat)
self.iconv5 = Conv2D(n5i, n5, iconv_kernel[0], 1)
self.iconv4 = Conv2D(n4i, n4, iconv_kernel[1], 1)
self.iconv3 = Conv2D(n3i, n3, iconv_kernel[2], 1)
self.iconv2 = Conv2D(n2i, n2, iconv_kernel[3], 1)
self.iconv1 = Conv2D(n1i, n1, iconv_kernel[4], 1)
self.unpack_disps = nn.PixelShuffle(2)
self.unpack_disp4 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
self.unpack_disp3 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
self.unpack_disp2 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
self.disp4_layer = InvDepth(n4, out_channels=out_channels, min_depth=min_depth)
self.disp3_layer = InvDepth(n3, out_channels=out_channels, min_depth=min_depth)
self.disp2_layer = InvDepth(n2, out_channels=out_channels, min_depth=min_depth)
self.disp1_layer = InvDepth(n1, out_channels=out_channels, min_depth=min_depth)
self.init_weights()
def init_weights(self):
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
def get_pred(self, data, **kwargs):
x = data
x = self.pre_calc(x)
x1 = self.conv1(x)
x1p = self.pack1(x1)
x2 = self.conv2(x1p)
x2p = self.pack2(x2)
x3 = self.conv3(x2p)
x3p = self.pack3(x3)
x4 = self.conv4(x3p)
x4p = self.pack4(x4)
x5 = self.conv5(x4p)
x5p = self.pack5(x5)
skip1 = x
skip2 = x1p
skip3 = x2p
skip4 = x3p
skip5 = x4p
unpack5 = self.unpack5(x5p)
if self.version == 'A':
concat5 = torch.cat((unpack5, skip5), 1)
else:
concat5 = unpack5 + skip5
iconv5 = self.iconv5(concat5)
unpack4 = self.unpack4(iconv5)
if self.version == 'A':
concat4 = torch.cat((unpack4, skip4), 1)
else:
concat4 = unpack4 + skip4
iconv4 = self.iconv4(concat4)
inv_depth4 = self.disp4_layer(iconv4)
up_inv_depth4 = self.unpack_disp4(inv_depth4)
unpack3 = self.unpack3(iconv4)
if self.version == 'A':
concat3 = torch.cat((unpack3, skip3, up_inv_depth4), 1)
else:
concat3 = torch.cat((unpack3 + skip3, up_inv_depth4), 1)
iconv3 = self.iconv3(concat3)
inv_depth3 = self.disp3_layer(iconv3)
up_inv_depth3 = self.unpack_disp3(inv_depth3)
unpack2 = self.unpack2(iconv3)
if self.version == 'A':
concat2 = torch.cat((unpack2, skip2, up_inv_depth3), 1)
else:
concat2 = torch.cat((unpack2 + skip2, up_inv_depth3), 1)
iconv2 = self.iconv2(concat2)
inv_depth2 = self.disp2_layer(iconv2)
up_inv_depth2 = self.unpack_disp2(inv_depth2)
unpack1 = self.unpack1(iconv2)
if self.version == 'A':
concat1 = torch.cat((unpack1, skip1, up_inv_depth2), 1)
else:
concat1 = torch.cat((unpack1 + skip1, up_inv_depth2), 1)
iconv1 = self.iconv1(concat1)
inv_depth1 = self.disp1_layer(iconv1)
if self.training:
inv_depths = [inv_depth1, inv_depth2, inv_depth3, inv_depth4]
else:
inv_depths = [inv_depth1]
return inv_depths
def forward(self, return_loss=True, rescale=False, **kwargs):
if not return_loss:
x = kwargs['img']
label = kwargs['depth_map']
data = {'img':x, 'depth_map':label}
depth_pred = self.get_pred(data)[0]
label = data['depth_map'].unsqueeze(dim=1)
mask = (label > 0)
loss = torch.abs((label - depth_pred)) * mask
loss = torch.sum(loss) / torch.sum(mask)
with torch.no_grad():
metrics = get_depth_metrics(depth_pred, label, mask)
metrics = [m.item() for m in metrics]
metrics.append(loss.item())
return [metrics]
raise NotImplementedError
def train_step(self, data, optimzier):
depth_pred = self.get_pred(data)[0]
label = data['depth_map'].unsqueeze(dim=1)
mask = (label > 0)
loss = torch.abs((label - depth_pred)) * mask
loss = torch.sum(loss) / torch.sum(mask)
log_var = {}
with torch.no_grad():
metrics = get_depth_metrics(depth_pred, label, mask)
metrics = [m.item() for m in metrics]
abs_diff, abs_rel, sq_rel, rmse, rmse_log = metrics
sparsity = torch.sum(mask) * 1.0 / torch.numel(mask)
std = torch.tensor([58.395, 57.12, 57.375]).cuda().view(1, -1, 1, 1)
mean = torch.tensor([123.675, 116.28, 103.53]).cuda().view(1, -1, 1, 1)
img = data['img'] * std + mean
img = img / 255.0
depth_at_gt = depth_pred * mask
log_vars = {'loss': loss.item(), 'sparsity': sparsity.item(),
'abs_diff': abs_diff, 'abs_rel': abs_rel,
'sq_rel': sq_rel, 'rmse': rmse,
'rmse_log': rmse_log
}
outputs = {'pred': torch.clamp(1.0 / (depth_pred+1e-4), 0, 1), 'data': img,
'label': torch.clamp(1.0 / (label+1e-4), 0, 1),
'depth_at_gt': torch.clamp(1.0 / (depth_at_gt+1e-4), 0., 1),
'loss':loss, 'log_vars':log_vars, 'num_samples':depth_pred.size(0)}
return outputs
def val_step(self, data, optimizer):
return self.train_step(self, data, optimizer)
| true | true |
f73c139ec5017afdf9ac4cced3afcaf2a5531129 | 5,997 | py | Python | pybind/slxos/v17s_1_02/brocade_tunnels_ext_rpc/get_tunnel_info/output/tunnel/nodes/__init__.py | extremenetworks/pybind | 44c467e71b2b425be63867aba6e6fa28b2cfe7fb | [
"Apache-2.0"
] | null | null | null | pybind/slxos/v17s_1_02/brocade_tunnels_ext_rpc/get_tunnel_info/output/tunnel/nodes/__init__.py | extremenetworks/pybind | 44c467e71b2b425be63867aba6e6fa28b2cfe7fb | [
"Apache-2.0"
] | null | null | null | pybind/slxos/v17s_1_02/brocade_tunnels_ext_rpc/get_tunnel_info/output/tunnel/nodes/__init__.py | extremenetworks/pybind | 44c467e71b2b425be63867aba6e6fa28b2cfe7fb | [
"Apache-2.0"
] | 1 | 2021-11-05T22:15:42.000Z | 2021-11-05T22:15:42.000Z |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
class nodes(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-tunnels-ext - based on the path /brocade_tunnels_ext_rpc/get-tunnel-info/output/tunnel/nodes. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Indicates the nodes from which this tunnel
data are retrieved.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__node_id',)
_yang_name = 'nodes'
_rest_name = 'nodes'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__node_id = YANGDynClass(base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'brocade_tunnels_ext_rpc', u'get-tunnel-info', u'output', u'tunnel', u'nodes']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'get-tunnel-info', u'output', u'tunnel', u'nodes']
def _get_node_id(self):
"""
Getter method for node_id, mapped from YANG variable /brocade_tunnels_ext_rpc/get_tunnel_info/output/tunnel/nodes/node_id (uint32)
YANG Description: Node id
"""
return self.__node_id
def _set_node_id(self, v, load=False):
"""
Setter method for node_id, mapped from YANG variable /brocade_tunnels_ext_rpc/get_tunnel_info/output/tunnel/nodes/node_id (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_node_id is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_node_id() directly.
YANG Description: Node id
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """node_id must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)""",
})
self.__node_id = t
if hasattr(self, '_set'):
self._set()
def _unset_node_id(self):
self.__node_id = YANGDynClass(base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)
node_id = __builtin__.property(_get_node_id, _set_node_id)
_pyangbind_elements = {'node_id': node_id, }
| 46.130769 | 458 | 0.717859 |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
class nodes(PybindBase):
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__node_id',)
_yang_name = 'nodes'
_rest_name = 'nodes'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__node_id = YANGDynClass(base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'brocade_tunnels_ext_rpc', u'get-tunnel-info', u'output', u'tunnel', u'nodes']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'get-tunnel-info', u'output', u'tunnel', u'nodes']
def _get_node_id(self):
return self.__node_id
def _set_node_id(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """node_id must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)""",
})
self.__node_id = t
if hasattr(self, '_set'):
self._set()
def _unset_node_id(self):
self.__node_id = YANGDynClass(base=TypedListType(allowed_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)), is_leaf=False, yang_name="node-id", rest_name="node-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, namespace='urn:brocade.com:mgmt:brocade-tunnels-ext', defining_module='brocade-tunnels-ext', yang_type='uint32', is_config=True)
node_id = __builtin__.property(_get_node_id, _set_node_id)
_pyangbind_elements = {'node_id': node_id, }
| true | true |
f73c13bb34372d043ea74f3ff8fb5fc7580b5518 | 35,618 | py | Python | utils/RigidPatching.py | thomaskuestner/CNNArt | c2fc639dd2ce035f6ca90113290682a0ccd26fb8 | [
"Apache-2.0"
] | 22 | 2018-04-27T21:28:46.000Z | 2021-12-24T06:44:55.000Z | utils/RigidPatching.py | thomaskuestner/CNNArt | c2fc639dd2ce035f6ca90113290682a0ccd26fb8 | [
"Apache-2.0"
] | 81 | 2017-11-09T17:23:15.000Z | 2020-01-28T22:54:13.000Z | utils/RigidPatching.py | thomaskuestner/CNNArt | c2fc639dd2ce035f6ca90113290682a0ccd26fb8 | [
"Apache-2.0"
] | 18 | 2017-11-13T16:12:17.000Z | 2020-08-27T10:17:34.000Z | '''
Copyright: 2016-2019 Thomas Kuestner (thomas.kuestner@med.uni-tuebingen.de) under Apache2 license
@author: Thomas Kuestner
'''
import numpy as np
import tensorflow as tf
import math
from utils.Label import Label
#########################################################################################################################################
#Function: fRigidPatching #
#The function fRigidPatching is responsible for splitting the dicom numpy array in patches depending on the patchSize and the #
#patchOverlap. Besides the function creates an 1D array with the corresponding labels. #
# #
#Input: dicom_numpy_array ---> 3D dicom array (height, width, number of slices) #
# patchSize ---> size of patches, example: [40, 40], patchSize[0] = height, patchSize[1] = weight, height and weight can differ #
# patchOverlap ---> the ratio for overlapping, example: 0.25 #
# mask_numpy_array ---> 3D mask array contains information about the areas of artefacts. movement-artefact = 1, shim-artefact = 2 #
# noise-artefact = 3 #
# ratio_labeling ---> set the ratio of the number of 'Pixel-Artefacts' to the whole number of pixels of one patch #
#Output: dPatches ---> 3D-Numpy-Array, which contain all Patches. #
# dLabels ---> 1D-Numpy-Array with all corresponding labels #
#########################################################################################################################################
def fRigidPatching(dicom_numpy_array, patchSize, patchOverlap, mask_numpy_array, ratio_labeling):
move_artefact = False
shim_artefact = False
noise_artefact = False
#dLabels = []
dOverlap = np.multiply(patchSize, patchOverlap)
dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
size_zero_pad = np.array(([math.ceil((dicom_numpy_array.shape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[
0], math.ceil((dicom_numpy_array.shape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1]]))
zero_pad = np.array(([int(size_zero_pad[0]) - dicom_numpy_array.shape[0], int(size_zero_pad[1]) - dicom_numpy_array.shape[1]]))
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)), int(math.ceil(zero_pad[1] / 2))]))
Img_zero_pad = np.lib.pad(dicom_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (0, 0)),
mode='constant')
Mask_zero_pad = np.lib.pad(mask_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (0, 0)),
mode='constant')
nbPatches = int(((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1)*((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1)*dicom_numpy_array.shape[2])
dPatches = np.zeros((patchSize[0], patchSize[1], nbPatches), dtype=float) #dtype=np.float32
dLabels = np.zeros((nbPatches), dtype = float) #dtype = float
idxPatch = 0
for iZ in range(0, dicom_numpy_array.shape[2], 1):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
dPatch = Img_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ]
dPatches[:,:,idxPatch] = dPatch
dPatch_mask = Mask_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ]
patch_number_value = patchSize[0] * patchSize[1]
if np.count_nonzero((dPatch_mask==1).astype(np.int)) > int(ratio_labeling*patch_number_value):
move_artefact = True
if np.count_nonzero((dPatch_mask==2).astype(np.int)) > int(ratio_labeling*patch_number_value):
shim_artefact = True
if np.count_nonzero((dPatch_mask==3).astype(np.int)) > int(ratio_labeling*patch_number_value):
noise_artefact = True
label = 0
if move_artefact == True and shim_artefact != True and noise_artefact != True:
label = 1
elif move_artefact != True and shim_artefact == True and noise_artefact != True:
label = 2
elif move_artefact != True and shim_artefact != True and noise_artefact == True:
label = 3
elif move_artefact == True and shim_artefact == True and noise_artefact != True:
label = 4
elif move_artefact == True and shim_artefact != True and noise_artefact == True:
label = 5
elif move_artefact != True and shim_artefact == True and noise_artefact == True:
label = 6
elif move_artefact == True and shim_artefact == True and noise_artefact == True:
label = 7
print(label)
dLabels[idxPatch] = label
idxPatch += 1
move_artefact = False
shim_artefact = False
noise_artefact = False
print("Rigid done!")
print(dLabels)
return dPatches, dLabels, nbPatches
#########################################################################################################################################
#Function: fRigidPatching3D #
#The function fRigidPatching3D is responsible for splitting the dicom numpy array in patches depending on the patchSize and the #
#patchOverlap. Besides the function creates an 1D array with the corresponding labels. #
# #
#Input: dicom_numpy_array ---> 3D dicom array (height, width, number of slices) #
# patchSize ---> size of patches, example: [40, 40], patchSize[0] = height, patchSize[1] = weight, height and weight can differ #
# patchOverlap ---> the ratio for overlapping, example: 0.25 #
# mask_numpy_array ---> 3D mask array contains information about the areas of artefacts. movement-artefact = 1, shim-artefact = 2 #
# noise-artefact = 3 #
# ratio_labeling ---> set the ratio of the number of 'Pixel-Artefacts' to the whole number of pixels of one patch #
#Output: dPatches ---> 3D-Numpy-Array, which contain all Patches. #
# dLabels ---> 1D-Numpy-Array with all corresponding labels #
#########################################################################################################################################
def fRigidPatching3D(dicom_numpy_array, patchSize, patchOverlap, mask_numpy_array, ratio_labeling):
move_artefact = False
shim_artefact = False
noise_artefact = False
#dLabels = []
print(patchSize)
dOverlap = np.round(np.multiply(patchSize, patchOverlap))
dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
print(dOverlap, dNotOverlap)
size_zero_pad = np.array(([math.ceil((dicom_numpy_array.shape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[
0], math.ceil((dicom_numpy_array.shape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1], math.ceil((dicom_numpy_array.shape[2] - dOverlap[2]) / (dNotOverlap[2])) * dNotOverlap[2] + dOverlap[2]]))
print(size_zero_pad.shape)
zero_pad = np.array(([int(size_zero_pad[0]) - dicom_numpy_array.shape[0], int(size_zero_pad[1]) - dicom_numpy_array.shape[1], int(size_zero_pad[2]) - dicom_numpy_array.shape[2]]))
print(zero_pad.shape)
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)), int(math.ceil(zero_pad[1] / 2)), int(math.ceil(zero_pad[2] / 2))]))
print(zero_pad_part.shape)
Img_zero_pad = np.lib.pad(dicom_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
print(Img_zero_pad.shape)
Mask_zero_pad = np.lib.pad(mask_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
print(Mask_zero_pad.shape)
print(size_zero_pad[2])
print(np.round((1-patchOverlap)*patchSize[2]))
print(((size_zero_pad[2]-patchSize[2])/(np.round((1-patchOverlap)*patchSize[2]))+1))
nbPatches = ((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1)*((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1)*((size_zero_pad[2]-patchSize[2])/(np.round((1-patchOverlap)*patchSize[2]))+1)
print(nbPatches)
dPatches = np.zeros((patchSize[0], patchSize[1], patchSize[2], int(nbPatches)), dtype=float)
dLabels = np.zeros((int(nbPatches)), dtype = int) #float
idxPatch = 0
for iZ in range(0, int(size_zero_pad[2] - dOverlap[2]), int(dNotOverlap[2])):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
dPatch = Img_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ:iZ + patchSize[2]]
print(dPatch.shape)
print(dPatches[:,:,:,idxPatch].shape)
dPatches[:,:,:,idxPatch] = dPatch
dPatch_mask = Mask_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ:iZ + patchSize[2]]
patch_number_value = patchSize[0] * patchSize[1]*patchSize[2]
if np.count_nonzero((dPatch_mask==1).astype(np.int)) > int(ratio_labeling*patch_number_value):
move_artefact = True
if np.count_nonzero((dPatch_mask==2).astype(np.int)) > int(ratio_labeling*patch_number_value):
shim_artefact = True
if np.count_nonzero((dPatch_mask==3).astype(np.int)) > int(ratio_labeling*patch_number_value):
noise_artefact = True
label = 0
if move_artefact == True and shim_artefact != True and noise_artefact != True:
label = 1
elif move_artefact != True and shim_artefact == True and noise_artefact != True:
label = 2
elif move_artefact != True and shim_artefact != True and noise_artefact == True:
label = 3
elif move_artefact == True and shim_artefact == True and noise_artefact != True:
label = 4
elif move_artefact == True and shim_artefact != True and noise_artefact == True:
label = 5
elif move_artefact != True and shim_artefact == True and noise_artefact == True:
label = 6
elif move_artefact == True and shim_artefact == True and noise_artefact == True:
label = 7
print(label)
dLabels[idxPatch] = label
idxPatch += 1
move_artefact = False
shim_artefact = False
noise_artefact = False
print("Rigid done!")
print(dLabels.dtype)
return dPatches, dLabels, nbPatches
def fRigidPatching3DN(dicom_numpy_array, patchSize, patchOverlap, mask_numpy_array, ratio_labeling):
move_artefact = False
shim_artefact = False
noise_artefact = False
#dLabels = []
dOverlap = np.multiply(patchSize, patchOverlap)
dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
print(dOverlap,dNotOverlap)
size_zero_pad = np.array(([math.ceil((dicom_numpy_array.shape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[
0], math.ceil((dicom_numpy_array.shape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1], math.ceil((dicom_numpy_array.shape[2] - dOverlap[2]) / (dNotOverlap[2])) * dNotOverlap[2] + dOverlap[2]]))
zero_pad = np.array(([int(size_zero_pad[0]) - dicom_numpy_array.shape[0], int(size_zero_pad[1]) - dicom_numpy_array.shape[1], int(size_zero_pad[2]) - dicom_numpy_array.shape[2]]))
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)), int(math.ceil(zero_pad[1] / 2)), int(math.ceil(zero_pad[2] / 2))]))
Img_zero_pad = np.lib.pad(dicom_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
print(Img_zero_pad.shape)
Mask_zero_pad = np.lib.pad(mask_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
nbPatches = ((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1)*((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1)*((size_zero_pad[2]-patchSize[2])/((1-patchOverlap)*patchSize[2])+1)
print(((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1))
print(((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1))
print(((size_zero_pad[2]-patchSize[2])/((1-patchOverlap)*patchSize[2])+1))
print(int(patchSize[0]), int(patchSize[1]), int(patchSize[2]), int(nbPatches))
dPatches = np.zeros((int(patchSize[0]), int(patchSize[1]), int(patchSize[2]), int(nbPatches)), dtype=float)
dLabels = np.zeros((int(nbPatches)), dtype = float)
idxPatch = 0
for iZ in range(0, dicom_numpy_array.shape[2], int(dNotOverlap[2])):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
print(iX, iY, iZ)
dPatch = Img_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ:iZ + patchSize[2]]
print(idxPatch)
print(dPatch.shape)
dPatches[:,:,:,idxPatch] = dPatch
dPatch_mask = Mask_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ:iZ + patchSize[2]]
patch_number_value = patchSize[0] * patchSize[1]*patchSize[2]
if np.count_nonzero((dPatch_mask==1).astype(np.int)) > int(ratio_labeling*patch_number_value):
move_artefact = True
if np.count_nonzero((dPatch_mask==2).astype(np.int)) > int(ratio_labeling*patch_number_value):
shim_artefact = True
if np.count_nonzero((dPatch_mask==3).astype(np.int)) > int(ratio_labeling*patch_number_value):
noise_artefact = True
label = 0
if move_artefact == True and shim_artefact != True and noise_artefact != True:
label = 1
elif move_artefact != True and shim_artefact == True and noise_artefact != True:
label = 2
elif move_artefact != True and shim_artefact != True and noise_artefact == True:
label = 3
elif move_artefact == True and shim_artefact == True and noise_artefact != True:
label = 4
elif move_artefact == True and shim_artefact != True and noise_artefact == True:
label = 5
elif move_artefact != True and shim_artefact == True and noise_artefact == True:
label = 6
elif move_artefact == True and shim_artefact == True and noise_artefact == True:
label = 7
dLabels[idxPatch] = label
idxPatch += 1
move_artefact = False
shim_artefact = False
noise_artefact = False
print("Rigid patching done!")
#print("Rigid done!")
#print(dLabels)
return dPatches, dLabels, nbPatches
def fRigidPatching_maskLabeling(dicom_numpy_array, patchSize, patchOverlap, mask_numpy_array, ratio_labeling, dataset):
dPatches = None
move_artefact = False
shim_artefact = False
noise_artefact = False
#body region
bodyRegion, bodyRegionLabel = dataset.getBodyRegion()
# MRT weighting label (T1, T2)
weighting, weightingLabel = dataset.getMRTWeighting()
#dOverlap = np.multiply(patchSize, patchOverlap)
dOverlap = np.round(np.multiply(patchSize, patchOverlap))
#dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
dNotOverlap = [patchSize[0]-dOverlap[0], patchSize[1]-dOverlap[1]]
size_zero_pad = np.array(
([math.ceil((dicom_numpy_array.shape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[0],
math.ceil((dicom_numpy_array.shape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1]]))
zero_pad = np.array(
([int(size_zero_pad[0]) - dicom_numpy_array.shape[0], int(size_zero_pad[1]) - dicom_numpy_array.shape[1]]))
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)), int(math.ceil(zero_pad[1] / 2))]))
Img_zero_pad = np.lib.pad(dicom_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]),
(zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (0, 0)), mode='constant')
Mask_zero_pad = np.lib.pad(mask_numpy_array,
((zero_pad_part[0], zero_pad[0] - zero_pad_part[0]),
(zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (0, 0)),
mode='constant')
nbPatches = int(((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1)*((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1)*dicom_numpy_array.shape[2])
nbPatches_in_Y = int((size_zero_pad[0]-dOverlap[0])/dNotOverlap[0])
nbPatches_in_X = int((size_zero_pad[1]-dOverlap[1])/dNotOverlap[1])
nbPatches_in_Z = dicom_numpy_array.shape[2]
nbPatches = nbPatches_in_X*nbPatches_in_Y*nbPatches_in_Z
dPatches = np.zeros((patchSize[0], patchSize[1], nbPatches), dtype=float) # dtype=np.float32
#dLabels = np.zeros((nbPatches), dtype=float) # dtype = float
dLabels = np.zeros((nbPatches), dtype=np.dtype('i4'))
idxPatch = 0
for iZ in range(0, dicom_numpy_array.shape[2], 1):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
dPatch = Img_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ]
dPatches[:, :, idxPatch] = dPatch
#if idxPatch == 7678:
# print()
dPatch_mask = Mask_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ]
patch_number_value = patchSize[0] * patchSize[1]
if np.count_nonzero((dPatch_mask == 1).astype(np.int)) > int(ratio_labeling * patch_number_value):
move_artefact = True
if np.count_nonzero((dPatch_mask == 2).astype(np.int)) > int(ratio_labeling * patch_number_value):
shim_artefact = True
if np.count_nonzero((dPatch_mask == 3).astype(np.int)) > int(ratio_labeling * patch_number_value):
noise_artefact = True
label = Label.REFERENCE
if move_artefact == True and shim_artefact != True and noise_artefact != True:
label = Label.MOTION
elif move_artefact != True and shim_artefact == True and noise_artefact != True:
label = Label.SHIM
elif move_artefact != True and shim_artefact != True and noise_artefact == True:
label = Label.NOISE
elif move_artefact == True and shim_artefact == True and noise_artefact != True:
label = Label.MOTION_AND_SHIM
elif move_artefact == True and shim_artefact != True and noise_artefact == True:
label = Label.MOTION_AND_NOISE
elif move_artefact != True and shim_artefact == True and noise_artefact == True:
label = Label.SHIM_AND_NOISE
elif move_artefact == True and shim_artefact == True and noise_artefact == True:
label = Label.MOTION_AND_SHIM_AND_NOISE
# calculate final label
label = label + bodyRegionLabel + weightingLabel
#print(label)
dLabels[idxPatch] = label
idxPatch += 1
move_artefact = False
shim_artefact = False
noise_artefact = False
print("Rigid patching done for %s!" % dataset.getPathdata())
#print(dLabels)
#return dPatches, dLabels, nbPatches
return dPatches, dLabels
def fRigidPatching_patchLabeling(dicom_numpy_array, patchSize, patchOverlap, ratio_labeling):
dPatches = None
move_artefact = False
shim_artefact = False
noise_artefact = False
dLabels = []
dOverlap = np.multiply(patchSize, patchOverlap)
dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
size_zero_pad = np.array(
([math.ceil((dicom_numpy_array.shape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[
0],
math.ceil((dicom_numpy_array.shape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1]]))
zero_pad = np.array(
([int(size_zero_pad[0]) - dicom_numpy_array.shape[0], int(size_zero_pad[1]) - dicom_numpy_array.shape[1]]))
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)), int(math.ceil(zero_pad[1] / 2))]))
Img_zero_pad = np.lib.pad(dicom_numpy_array, (
(zero_pad_part[0], zero_pad[0] - zero_pad_part[0]), (zero_pad_part[1], zero_pad[1] - zero_pad_part[1]), (0, 0)),
mode='constant')
for iZ in range(0, dicom_numpy_array.shape[2], 1):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
dPatch = Img_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ]
dPatch = dPatch[:, :, np.newaxis]
if dPatches is None:
dPatches = dPatch
else:
dPatches = np.concatenate((dPatches, dPatch), axis=2)
dLabels = np.ones((dPatches.shape[2]), dtype=np.dtype('i4'))
return dPatches, dLabels
#########################################################################################################################################
#Function: fRigidPatching3D #
#The function fRigidPatching3D is responsible for splitting the dicom numpy array in patches depending on the patchSize and the #
#patchOverlap. Besides the function creates an 1D array with the corresponding labels. #
# #
#Input: dicom_numpy_array ---> 3D dicom array (height, width, number of slices) #
# patchSize ---> size of patches, example: [40, 40], patchSize[0] = height, patchSize[1] = weight, height and weight can differ #
# patchOverlap ---> the ratio for overlapping, example: 0.25 #
# mask_numpy_array ---> 3D mask array contains information about the areas of artefacts. movement-artefact = 1, shim-artefact = 2 #
# noise-artefact = 3 #
# ratio_labeling ---> set the ratio of the number of 'Pixel-Artefacts' to the whole number of pixels of one patch #
#Output: dPatches ---> 3D-Numpy-Array, which contain all Patches. #
# dLabels ---> 1D-Numpy-Array with all corresponding labels #
#########################################################################################################################################
def fRigidPatching3D_maskLabeling(dicom_numpy_array, patchSize, patchOverlap, mask_numpy_array, ratio_labeling, dataset=None, dopatching=True):
#ToDo odd patch size not supported!
move_artefact = False
shim_artefact = False
noise_artefact = False
if isinstance(dataset, int): # already pre-processed label bodyRegionLabel + weightingLabel
bodyRegionweightingLabel = dataset
else:
# body region
bodyRegion, bodyRegionLabel = dataset.getBodyRegion()
# MRT weighting label (T1, T2)
weighting, weightingLabel = dataset.getMRTWeighting()
dOverlap = np.round(np.multiply(patchSize, patchOverlap))
dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
size_zero_pad = np.array(([math.ceil((dicom_numpy_array.shape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[0],
math.ceil((dicom_numpy_array.shape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1],
math.ceil((dicom_numpy_array.shape[2] - dOverlap[2]) / (dNotOverlap[2])) * dNotOverlap[2] + dOverlap[2]]))
zero_pad = np.array(([int(size_zero_pad[0]) - dicom_numpy_array.shape[0],
int(size_zero_pad[1]) - dicom_numpy_array.shape[1],
int(size_zero_pad[2]) - dicom_numpy_array.shape[2]]))
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)),
int(math.ceil(zero_pad[1] / 2)),
int(math.ceil(zero_pad[2] / 2))]))
Img_zero_pad = np.lib.pad(dicom_numpy_array,
((zero_pad_part[0], zero_pad[0] - zero_pad_part[0]),
(zero_pad_part[1], zero_pad[1] - zero_pad_part[1]),
(zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
Mask_zero_pad = np.lib.pad(mask_numpy_array,
((zero_pad_part[0], zero_pad[0] - zero_pad_part[0]),
(zero_pad_part[1], zero_pad[1] - zero_pad_part[1]),
(zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
nbPatches = ((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1)*((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1)*((size_zero_pad[2]-patchSize[2])/(np.round((1-patchOverlap)*patchSize[2]))+1)
nbPatches_in_Y = int((size_zero_pad[0] - dOverlap[0]) / dNotOverlap[0])
nbPatches_in_X = int((size_zero_pad[1] - dOverlap[1]) / dNotOverlap[1])
nbPatches_in_Z = int((size_zero_pad[2] - dOverlap[2]) / dNotOverlap[2])
nbPatches = nbPatches_in_X * nbPatches_in_Y * nbPatches_in_Z
dPatches = np.zeros((patchSize[0], patchSize[1], patchSize[2], int(nbPatches)), dtype=float)
dLabels = np.zeros((int(nbPatches)), dtype = int) #float
idxPatch = 0
for iZ in range(0, int(size_zero_pad[2] - dOverlap[2]), int(dNotOverlap[2])):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
dPatch = Img_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ:iZ + patchSize[2]]
dPatches[:,:,:,idxPatch] = dPatch
dPatch_mask = Mask_zero_pad[iY:iY + patchSize[0], iX:iX + patchSize[1], iZ:iZ + patchSize[2]]
patch_number_value = patchSize[0] * patchSize[1]*patchSize[2]
if np.count_nonzero((dPatch_mask==1).astype(np.int)) > int(ratio_labeling*patch_number_value):
move_artefact = True
if np.count_nonzero((dPatch_mask==2).astype(np.int)) > int(ratio_labeling*patch_number_value):
shim_artefact = True
if np.count_nonzero((dPatch_mask==3).astype(np.int)) > int(ratio_labeling*patch_number_value):
noise_artefact = True
label = Label.REFERENCE
if move_artefact == True and shim_artefact != True and noise_artefact != True:
label = Label.MOTION
elif move_artefact != True and shim_artefact == True and noise_artefact != True:
label = Label.SHIM
elif move_artefact != True and shim_artefact != True and noise_artefact == True:
label = Label.NOISE
elif move_artefact == True and shim_artefact == True and noise_artefact != True:
label = Label.MOTION_AND_SHIM
elif move_artefact == True and shim_artefact != True and noise_artefact == True:
label = Label.MOTION_AND_NOISE
elif move_artefact != True and shim_artefact == True and noise_artefact == True:
label = Label.SHIM_AND_NOISE
elif move_artefact == True and shim_artefact == True and noise_artefact == True:
label = Label.MOTION_AND_SHIM_AND_NOISE
if isinstance(dataset, int):
label = bodyRegionweightingLabel + label
else:
label = weightingLabel + bodyRegionLabel + label
dLabels[idxPatch] = label
idxPatch += 1
move_artefact = False
shim_artefact = False
noise_artefact = False
if isinstance(dataset, int):
return dPatches
else:
print("Rigid patching done for %s " % dataset.getPathdata())
#print(dLabels)
return dPatches, dLabels#, nbPatches
def fRigidPatching3D_maskLabeling_tf(dicom_tensor, patchSize, patchOverlap, mask_numpy_array, ratio_labeling, dataset=None, dopatching=True):
#ToDo odd patch size not supported!
dOverlap = tf.math.round(tf.math.multiply(patchSize, patchOverlap))
dNotOverlap = tf.math.round(tf.math.multiply(patchSize, (1 - patchOverlap)))
imgShape = dicom_tensor.shape.as_list()
size_zero_pad = np.array(([math.ceil((imgShape[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[0],
math.ceil((imgShape[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1],
math.ceil((imgShape[2] - dOverlap[2]) / (dNotOverlap[2])) * dNotOverlap[2] + dOverlap[2]]))
zero_pad = np.array(([int(size_zero_pad[0]) - imgShape[0],
int(size_zero_pad[1]) - imgShape[1],
int(size_zero_pad[2]) - imgShape[2]]))
zero_pad_part = np.array(([int(math.ceil(zero_pad[0] / 2)),
int(math.ceil(zero_pad[1] / 2)),
int(math.ceil(zero_pad[2] / 2))]))
Img_zero_pad = tf.pad(dicom_tensor,
tf.Variable((zero_pad_part[0], zero_pad[0] - zero_pad_part[0]),
(zero_pad_part[1], zero_pad[1] - zero_pad_part[1]),
(zero_pad_part[2], zero_pad[2] - zero_pad_part[2])),
mode='constant')
#nbPatches = ((size_zero_pad[0]-patchSize[0])/((1-patchOverlap)*patchSize[0])+1)*((size_zero_pad[1]-patchSize[1])/((1-patchOverlap)*patchSize[1])+1)*((size_zero_pad[2]-patchSize[2])/(tf.math.round((1-patchOverlap)*patchSize[2]))+1)
#nbPatches_in_Y = int((size_zero_pad[0] - dOverlap[0]) / dNotOverlap[0])
#nbPatches_in_X = int((size_zero_pad[1] - dOverlap[1]) / dNotOverlap[1])
#nbPatches_in_Z = int((size_zero_pad[2] - dOverlap[2]) / dNotOverlap[2])
#nbPatches = nbPatches_in_X * nbPatches_in_Y * nbPatches_in_Z
#dPatches = tf.zeros((patchSize[0], patchSize[1], patchSize[2], int(nbPatches)), dtype=float)
patch = [None for _ in range(fcalculatepatches(imgShape, patchSize, patchOverlap))]
idxPatch = 0
for iZ in range(0, int(size_zero_pad[2] - dOverlap[2]), int(dNotOverlap[2])):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
patch[idxPatch] = tf.slice(Img_zero_pad, begin=[iY, iX, iZ], size=[patchSize[0], patchSize[1], patchSize[2]])
idxPatch += 1
dPatches = tf.stack(patch, axis=3)
return dPatches
def fcalculatepatches(imageSize, patchSize, patchOverlap):
dOverlap = np.round(np.multiply(patchSize, patchOverlap))
dNotOverlap = np.round(np.multiply(patchSize, (1 - patchOverlap)))
size_zero_pad = np.array(
([math.ceil((imageSize[0] - dOverlap[0]) / (dNotOverlap[0])) * dNotOverlap[0] + dOverlap[0],
math.ceil((imageSize[1] - dOverlap[1]) / (dNotOverlap[1])) * dNotOverlap[1] + dOverlap[1],
math.ceil((imageSize[2] - dOverlap[2]) / (dNotOverlap[2])) * dNotOverlap[2] + dOverlap[2]]))
idxPatch = 0
for iZ in range(0, int(size_zero_pad[2] - dOverlap[2]), int(dNotOverlap[2])):
for iY in range(0, int(size_zero_pad[0] - dOverlap[0]), int(dNotOverlap[0])):
for iX in range(0, int(size_zero_pad[1] - dOverlap[1]), int(dNotOverlap[1])):
idxPatch += 1
return idxPatch | 61.199313 | 236 | 0.556264 |
import numpy as np
import tensorflow as tf
import math
from utils.Label import Label
| true | true |
f73c14d25861ed70f0c0e1a0b50b9d6c72f556b2 | 24,829 | py | Python | cirq-core/cirq/protocols/apply_unitary_protocol.py | Zshan0/Cirq | 93bbaa853305faa65117bcbdc2063f741cb2977c | [
"Apache-2.0"
] | 1 | 2022-02-02T07:13:54.000Z | 2022-02-02T07:13:54.000Z | cirq-core/cirq/protocols/apply_unitary_protocol.py | bradyb/Cirq | 610b0d4ea3a7862169610797266734c844ddcc1f | [
"Apache-2.0"
] | null | null | null | cirq-core/cirq/protocols/apply_unitary_protocol.py | bradyb/Cirq | 610b0d4ea3a7862169610797266734c844ddcc1f | [
"Apache-2.0"
] | null | null | null | # Copyright 2018 The Cirq Developers
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A protocol for implementing high performance unitary left-multiplies."""
from typing import (
Any,
cast,
Iterable,
Optional,
Sequence,
Tuple,
TYPE_CHECKING,
TypeVar,
Union,
)
import numpy as np
from typing_extensions import Protocol
from cirq import linalg, qis
from cirq._doc import doc_private
from cirq.protocols import qid_shape_protocol
from cirq.protocols.decompose_protocol import (
_try_decompose_into_operations_and_qubits,
)
from cirq.type_workarounds import NotImplementedType
if TYPE_CHECKING:
import cirq
# This is a special indicator value used by the apply_unitary method
# to determine whether or not the caller provided a 'default' argument. It must
# be of type np.ndarray to ensure the method has the correct type signature in
# that case. It is checked for using `is`, so it won't have a false positive if
# the user provides a different np.array([]) value.
RaiseTypeErrorIfNotProvided: np.ndarray = np.array([])
TDefault = TypeVar('TDefault')
class ApplyUnitaryArgs:
"""Arguments for performing an efficient left-multiplication by a unitary.
The receiving object is expected to mutate `target_tensor` so that it
contains the state after multiplication, and then return `target_tensor`.
Alternatively, if workspace is required, the receiving object can overwrite
`available_buffer` with the results and return `available_buffer`. Or, if
the receiving object is attempting to be simple instead of fast, it can
create an entirely new array and return that.
Attributes:
target_tensor: The input tensor that needs to be left-multiplied by
the unitary effect of the receiving object. The tensor will
have the shape (2, 2, 2, ..., 2). It usually corresponds to
a multi-qubit superposition, but it could also be a multi-qubit
unitary transformation or some other concept.
available_buffer: Pre-allocated workspace with the same shape and
dtype as the target tensor.
axes: Which axes the unitary effect is being applied to (e.g. the
qubits that the gate is operating on).
"""
def __init__(
self, target_tensor: np.ndarray, available_buffer: np.ndarray, axes: Iterable[int]
):
"""Inits ApplyUnitaryArgs.
Args:
target_tensor: The input tensor that needs to be left-multiplied by
the unitary effect of the receiving object. The tensor will
have the shape (2, 2, 2, ..., 2). It usually corresponds to
a multi-qubit superposition, but it could also be a multi-qubit
unitary transformation or some other concept.
available_buffer: Pre-allocated workspace with the same shape and
dtype as the target tensor.
axes: Which axes the unitary effect is being applied to (e.g. the
qubits that the gate is operating on).
"""
self.target_tensor = target_tensor
self.available_buffer = available_buffer
self.axes = tuple(axes)
@staticmethod
def default(
num_qubits: Optional[int] = None, *, qid_shape: Optional[Tuple[int, ...]] = None
) -> 'ApplyUnitaryArgs':
"""A default instance starting in state |0⟩.
Specify exactly one argument.
Args:
num_qubits: The number of qubits to make space for in the state.
qid_shape: The shape of the state, specifying the dimension of each
qid.
Raises:
TypeError: If exactly neither `num_qubits` or `qid_shape` is provided or
both are provided.
"""
if (num_qubits is None) == (qid_shape is None):
raise TypeError('Specify exactly one of num_qubits or qid_shape.')
if num_qubits is not None:
qid_shape = (2,) * num_qubits
qid_shape = cast(Tuple[int, ...], qid_shape) # Satisfy mypy
num_qubits = len(qid_shape)
state = qis.one_hot(index=(0,) * num_qubits, shape=qid_shape, dtype=np.complex128)
return ApplyUnitaryArgs(state, np.empty_like(state), range(num_qubits))
def with_axes_transposed_to_start(self) -> 'ApplyUnitaryArgs':
"""Returns a transposed view of the same arguments.
Returns:
A view over the same target tensor and available workspace, but
with the numpy arrays transposed such that the axes field is
guaranteed to equal `range(len(result.axes))`. This allows one to
say e.g. `result.target_tensor[0, 1, 0, ...]` instead of
`result.target_tensor[result.subspace_index(0b010)]`.
"""
axis_set = set(self.axes)
other_axes = [axis for axis in range(len(self.target_tensor.shape)) if axis not in axis_set]
perm = (*self.axes, *other_axes)
target_tensor = self.target_tensor.transpose(*perm)
available_buffer = self.available_buffer.transpose(*perm)
return ApplyUnitaryArgs(target_tensor, available_buffer, range(len(self.axes)))
def _for_operation_with_qid_shape(
self, indices: Iterable[int], qid_shape: Tuple[int, ...]
) -> 'ApplyUnitaryArgs':
"""Creates a sliced and transposed view of `self` appropriate for an
operation with shape `qid_shape` on qubits with the given indices.
Example:
sub_args = args._for_operation_with_qid_shape(indices, (2, 2, 2))
# Slice where the first qubit is |1>.
sub_args.target_tensor[..., 1, :, :]
Args:
indices: Integer indices into `self.axes` specifying which qubits
the operation applies to.
qid_shape: The qid shape of the operation, the expected number of
quantum levels in each qubit the operation applies to.
Returns: A new `ApplyUnitaryArgs` where `sub_args.target_tensor` and
`sub_args.available_buffer` are sliced and transposed views of
`self.target_tensor` and `self.available_buffer` respectively.
"""
slices = [slice(0, size) for size in qid_shape]
sub_axes = [self.axes[i] for i in indices]
axis_set = set(sub_axes)
other_axes = [axis for axis in range(len(self.target_tensor.shape)) if axis not in axis_set]
ordered_axes = (*other_axes, *sub_axes)
# Transpose sub_axes to the end of the shape and slice them
target_tensor = self.target_tensor.transpose(*ordered_axes)[(..., *slices)]
available_buffer = self.available_buffer.transpose(*ordered_axes)[(..., *slices)]
new_axes = range(len(other_axes), len(ordered_axes))
return ApplyUnitaryArgs(target_tensor, available_buffer, new_axes)
def subspace_index(
self, little_endian_bits_int: int = 0, *, big_endian_bits_int: int = 0
) -> Tuple[Union[slice, int, 'ellipsis'], ...]:
"""An index for the subspace where the target axes equal a value.
Args:
little_endian_bits_int: The desired value of the qubits at the
targeted `axes`, packed into an integer. The least significant
bit of the integer is the desired bit for the first axis, and
so forth in increasing order. Can't be specified at the same
time as `big_endian_bits_int`.
big_endian_bits_int: The desired value of the qubits at the
targeted `axes`, packed into an integer. The most significant
bit of the integer is the desired bit for the first axis, and
so forth in decreasing order. Can't be specified at the same
time as `little_endian_bits_int`.
Returns:
A value that can be used to index into `target_tensor` and
`available_buffer`, and manipulate only the part of Hilbert space
corresponding to a given bit assignment.
Example:
If `target_tensor` is a 4 qubit tensor and `axes` is `[1, 3]` and
then this method will return the following when given
`little_endian_bits=0b01`:
`(slice(None), 0, slice(None), 1, Ellipsis)`
Therefore the following two lines would be equivalent:
args.target_tensor[args.subspace_index(0b01)] += 1
args.target_tensor[:, 0, :, 1] += 1
"""
return linalg.slice_for_qubits_equal_to(
self.axes,
little_endian_qureg_value=little_endian_bits_int,
big_endian_qureg_value=big_endian_bits_int,
qid_shape=self.target_tensor.shape,
)
class SupportsConsistentApplyUnitary(Protocol):
"""An object that can be efficiently left-multiplied into tensors."""
@doc_private
def _apply_unitary_(
self, args: ApplyUnitaryArgs
) -> Union[np.ndarray, None, NotImplementedType]:
"""Left-multiplies a unitary effect onto a tensor with good performance.
This method is given both the target tensor and workspace of the same
shape and dtype. The method then either performs inline modifications of
the target tensor and returns it, or writes its output into the
workspace tensor and returns that. This signature makes it possible to
write specialized simulation methods that run without performing large
allocations, significantly increasing simulation performance.
The target may represent a wave function, a unitary matrix, or some
other tensor. Implementations will work in all of these cases as long as
they correctly focus on only operating on the given axes.
Args:
args: A `cirq.ApplyUnitaryArgs` object with the `args.target_tensor`
to operate on, an `args.available_workspace` buffer to use as
temporary workspace, and the `args.axes` of the tensor to target
with the unitary operation. Note that this method is permitted
(and in fact expected) to mutate `args.target_tensor` and
`args.available_workspace`.
Returns:
If the receiving object is not able to apply its unitary effect,
None or NotImplemented should be returned.
If the receiving object is able to work inline, it should directly
mutate `args.target_tensor` and then return `args.target_tensor`.
The caller will understand this to mean that the result is in
`args.target_tensor`.
If the receiving object is unable to work inline, it can write its
output over `args.available_buffer` and then return
`args.available_buffer`. The caller will understand this to mean
that the result is in `args.available_buffer` (and so what was
`args.available_buffer` will become `args.target_tensor` in the next
call, and vice versa).
The receiving object is also permitted to allocate a new
numpy.ndarray and return that as its result.
"""
def apply_unitary(
unitary_value: Any,
args: ApplyUnitaryArgs,
default: TDefault = RaiseTypeErrorIfNotProvided,
*,
allow_decompose: bool = True,
) -> Union[np.ndarray, TDefault]:
"""High performance left-multiplication of a unitary effect onto a tensor.
Applies the unitary effect of `unitary_value` to the tensor specified in
`args` by using the following strategies:
A. Try to use `unitary_value._apply_unitary_(args)`.
Case a) Method not present or returns `NotImplemented`.
Continue to next strategy.
Case b) Method returns `None`.
Conclude `unitary_value` has no unitary effect.
Case c) Method returns a numpy array.
Forward the successful result to the caller.
B. Try to use `unitary_value._unitary_()`.
Case a) Method not present or returns `NotImplemented`.
Continue to next strategy.
Case b) Method returns `None`.
Conclude `unitary_value` has no unitary effect.
Case c) Method returns a numpy array.
Multiply the matrix onto the target tensor and return to the caller.
C. Try to use `unitary_value._decompose_()` (if `allow_decompose`).
Case a) Method not present or returns `NotImplemented` or `None`.
Continue to next strategy.
Case b) Method returns an OP_TREE.
Delegate to `cirq.apply_unitaries`.
D. Conclude that `unitary_value` has no unitary effect.
The order that the strategies are tried depends on the number of qubits
being operated on. For small numbers of qubits (4 or less) the order is
ABCD. For larger numbers of qubits the order is ACBD (because it is expected
that decomposing will outperform generating the raw matrix).
Args:
unitary_value: The value with a unitary effect to apply to the target.
args: A mutable `cirq.ApplyUnitaryArgs` object describing the target
tensor, available workspace, and axes to operate on. The attributes
of this object will be mutated as part of computing the result.
default: What should be returned if `unitary_value` doesn't have a
unitary effect. If not specified, a TypeError is raised instead of
returning a default value.
allow_decompose: Defaults to True. If set to False, and applying the
unitary effect requires decomposing the object, the method will
pretend the object has no unitary effect.
Returns:
If the receiving object does not have a unitary effect, then the
specified default value is returned (or a TypeError is raised). If
this occurs, then `target_tensor` should not have been mutated.
Otherwise the result is the `np.ndarray` instance storing the result.
This may be `args.target_tensor`, `args.available_workspace`, or some
other numpy array. It is the caller's responsibility to correctly handle
all three of these cases. In all cases `args.target_tensor` and
`args.available_buffer` may have been mutated.
Raises:
TypeError: `unitary_value` doesn't have a unitary effect and `default`
wasn't specified.
"""
# Decide on order to attempt application strategies.
if len(args.axes) <= 4:
strats = [
_strat_apply_unitary_from_apply_unitary,
_strat_apply_unitary_from_unitary,
_strat_apply_unitary_from_decompose,
]
else:
strats = [
_strat_apply_unitary_from_apply_unitary,
_strat_apply_unitary_from_decompose,
_strat_apply_unitary_from_unitary,
]
if not allow_decompose:
strats.remove(_strat_apply_unitary_from_decompose)
# Try each strategy, stopping if one works.
for strat in strats:
result = strat(unitary_value, args)
if result is None:
break
if result is not NotImplemented:
return result
# Don't know how to apply. Fallback to specified default behavior.
if default is not RaiseTypeErrorIfNotProvided:
return default
raise TypeError(
"cirq.apply_unitary failed. "
"Value doesn't have a (non-parameterized) unitary effect.\n"
"\n"
"type: {}\n"
"value: {!r}\n"
"\n"
"The value failed to satisfy any of the following criteria:\n"
"- An `_apply_unitary_(self, args) method that returned a value "
"besides None or NotImplemented.\n"
"- A `_unitary_(self)` method that returned a value "
"besides None or NotImplemented.\n"
"- A `_decompose_(self)` method that returned a "
"list of unitary operations.\n"
"".format(type(unitary_value), unitary_value)
)
def _strat_apply_unitary_from_apply_unitary(
unitary_value: Any, args: ApplyUnitaryArgs
) -> Optional[np.ndarray]:
# Check for magic method.
func = getattr(unitary_value, '_apply_unitary_', None)
if func is None:
return NotImplemented
op_qid_shape = qid_shape_protocol.qid_shape(unitary_value, (2,) * len(args.axes))
sub_args = args._for_operation_with_qid_shape(range(len(op_qid_shape)), op_qid_shape)
sub_result = func(sub_args)
if sub_result is NotImplemented or sub_result is None:
return sub_result
return _incorporate_result_into_target(args, sub_args, sub_result)
def _strat_apply_unitary_from_unitary(
unitary_value: Any, args: ApplyUnitaryArgs
) -> Optional[np.ndarray]:
# Check for magic method.
method = getattr(unitary_value, '_unitary_', None)
if method is None:
return NotImplemented
# Attempt to get the unitary matrix.
matrix = method()
if matrix is NotImplemented or matrix is None:
return matrix
val_qid_shape = qid_shape_protocol.qid_shape(unitary_value, default=(2,) * len(args.axes))
sub_args = args._for_operation_with_qid_shape(range(len(val_qid_shape)), val_qid_shape)
matrix = matrix.astype(sub_args.target_tensor.dtype)
if len(val_qid_shape) == 1 and val_qid_shape[0] <= 2:
# Special case for single-qubit, 2x2 or 1x1 operations.
# np.einsum is faster for larger cases.
subspaces = [(..., level) for level in range(val_qid_shape[0])]
sub_result = linalg.apply_matrix_to_slices(
sub_args.target_tensor, matrix, subspaces, out=sub_args.available_buffer
)
else:
# General case via np.einsum.
sub_result = linalg.targeted_left_multiply(
matrix.reshape(val_qid_shape * 2),
sub_args.target_tensor,
sub_args.axes,
out=sub_args.available_buffer,
)
return _incorporate_result_into_target(args, sub_args, sub_result)
def _strat_apply_unitary_from_decompose(val: Any, args: ApplyUnitaryArgs) -> Optional[np.ndarray]:
operations, qubits, _ = _try_decompose_into_operations_and_qubits(val)
if operations is None:
return NotImplemented
return apply_unitaries(operations, qubits, args, None)
def apply_unitaries(
unitary_values: Iterable[Any],
qubits: Sequence['cirq.Qid'],
args: Optional[ApplyUnitaryArgs] = None,
default: Any = RaiseTypeErrorIfNotProvided,
) -> Optional[np.ndarray]:
"""Apply a series of unitaries onto a state tensor.
Uses `cirq.apply_unitary` on each of the unitary values, to apply them to
the state tensor from the `args` argument.
CAUTION: if one of the given unitary values does not have a unitary effect,
forcing the method to terminate, the method will not rollback changes
from previous unitary values.
Args:
unitary_values: The values with unitary effects to apply to the target.
qubits: The qubits that will be targeted by the unitary values. These
qubits match up, index by index, with the `indices` property of the
`args` argument.
args: A mutable `cirq.ApplyUnitaryArgs` object describing the target
tensor, available workspace, and axes to operate on. The attributes
of this object will be mutated as part of computing the result. If
not specified, this defaults to the zero state of the given qubits
with an axis ordering matching the given qubit ordering.
default: What should be returned if any of the unitary values actually
don't have a unitary effect. If not specified, a TypeError is
raised instead of returning a default value.
Returns:
If any of the unitary values do not have a unitary effect, the
specified default value is returned (or a TypeError is raised).
CAUTION: If this occurs, the contents of `args.target_tensor`
and `args.available_buffer` may have been mutated.
If all of the unitary values had a unitary effect that was
successfully applied, this method returns the `np.ndarray`
storing the final result. This `np.ndarray` may be
`args.target_tensor`, `args.available_buffer`, or some
other instance. The caller is responsible for dealing with
this potential aliasing of the inputs and the result.
Raises:
TypeError: An item from `unitary_values` doesn't have a unitary effect
and `default` wasn't specified.
ValueError: If the number of qubits does not match the number of
axes provided in the `args`.
"""
if args is None:
qid_shape = qid_shape_protocol.qid_shape(qubits)
args = ApplyUnitaryArgs.default(qid_shape=qid_shape)
if len(qubits) != len(args.axes):
raise ValueError('len(qubits) != len(args.axes)')
qubit_map = {q.with_dimension(1): args.axes[i] for i, q in enumerate(qubits)}
state = args.target_tensor
buffer = args.available_buffer
for op in unitary_values:
indices = [qubit_map[q.with_dimension(1)] for q in op.qubits]
result = apply_unitary(
unitary_value=op, args=ApplyUnitaryArgs(state, buffer, indices), default=None
)
# Handle failure.
if result is None:
if default is RaiseTypeErrorIfNotProvided:
raise TypeError(
"cirq.apply_unitaries failed. "
"There was a non-unitary value in the `unitary_values` "
"list.\n"
"\n"
"non-unitary value type: {}\n"
"non-unitary value: {!r}".format(type(op), op)
)
return default
# Handle aliasing of results.
if result is buffer:
buffer = state
state = result
return state
def _incorporate_result_into_target(
args: 'ApplyUnitaryArgs', sub_args: 'ApplyUnitaryArgs', sub_result: np.ndarray
):
"""Takes the result of calling `_apply_unitary_` on `sub_args` and
copies it back into `args.target_tensor` or `args.available_buffer` as
necessary to return the result of applying the unitary to the full args.
Also swaps the buffers so the result is always in `args.target_tensor`.
Args:
args: The original args.
sub_args: A version of `args` with transposed and sliced views of
it's tensors.
sub_result: The result of calling an object's `_apply_unitary_`
method on `sub_args`. A transposed subspace of the desired
result.
Returns:
The full result tensor after applying the unitary. Always
`args.target_tensor`.
Raises:
ValueError: If `sub_args` tensors are not views of `args` tensors.
"""
if not (
np.may_share_memory(args.target_tensor, sub_args.target_tensor)
and np.may_share_memory(args.available_buffer, sub_args.available_buffer)
):
raise ValueError(
'sub_args.target_tensor and subargs.available_buffer must be views of '
'args.target_tensor and args.available_buffer respectively.'
)
is_subspace = sub_args.target_tensor.size < args.target_tensor.size
if sub_result is sub_args.target_tensor:
return args.target_tensor
if sub_result is sub_args.available_buffer:
if is_subspace:
# The subspace that was modified is likely much smaller than
# the whole tensor so copy sub_result back into target_tensor.
sub_args.target_tensor[...] = sub_result
return args.target_tensor
return args.available_buffer
# The subspace that was modified is likely much smaller than
# the whole tensor so copy sub_result back into target_tensor.
# It's an uncommon case where sub_result is a new array.
if np.may_share_memory(sub_args.target_tensor, sub_result):
# Someone did something clever. E.g. implementing SWAP with a
# reshape.
# Copy to available_buffer instead.
if is_subspace:
args.available_buffer[...] = args.target_tensor
sub_args.available_buffer[...] = sub_result
return args.available_buffer
sub_args.target_tensor[...] = sub_result
return args.target_tensor
| 43.407343 | 100 | 0.668452 |
from typing import (
Any,
cast,
Iterable,
Optional,
Sequence,
Tuple,
TYPE_CHECKING,
TypeVar,
Union,
)
import numpy as np
from typing_extensions import Protocol
from cirq import linalg, qis
from cirq._doc import doc_private
from cirq.protocols import qid_shape_protocol
from cirq.protocols.decompose_protocol import (
_try_decompose_into_operations_and_qubits,
)
from cirq.type_workarounds import NotImplementedType
if TYPE_CHECKING:
import cirq
# the user provides a different np.array([]) value.
RaiseTypeErrorIfNotProvided: np.ndarray = np.array([])
TDefault = TypeVar('TDefault')
class ApplyUnitaryArgs:
def __init__(
self, target_tensor: np.ndarray, available_buffer: np.ndarray, axes: Iterable[int]
):
self.target_tensor = target_tensor
self.available_buffer = available_buffer
self.axes = tuple(axes)
@staticmethod
def default(
num_qubits: Optional[int] = None, *, qid_shape: Optional[Tuple[int, ...]] = None
) -> 'ApplyUnitaryArgs':
if (num_qubits is None) == (qid_shape is None):
raise TypeError('Specify exactly one of num_qubits or qid_shape.')
if num_qubits is not None:
qid_shape = (2,) * num_qubits
qid_shape = cast(Tuple[int, ...], qid_shape) # Satisfy mypy
num_qubits = len(qid_shape)
state = qis.one_hot(index=(0,) * num_qubits, shape=qid_shape, dtype=np.complex128)
return ApplyUnitaryArgs(state, np.empty_like(state), range(num_qubits))
def with_axes_transposed_to_start(self) -> 'ApplyUnitaryArgs':
axis_set = set(self.axes)
other_axes = [axis for axis in range(len(self.target_tensor.shape)) if axis not in axis_set]
perm = (*self.axes, *other_axes)
target_tensor = self.target_tensor.transpose(*perm)
available_buffer = self.available_buffer.transpose(*perm)
return ApplyUnitaryArgs(target_tensor, available_buffer, range(len(self.axes)))
def _for_operation_with_qid_shape(
self, indices: Iterable[int], qid_shape: Tuple[int, ...]
) -> 'ApplyUnitaryArgs':
slices = [slice(0, size) for size in qid_shape]
sub_axes = [self.axes[i] for i in indices]
axis_set = set(sub_axes)
other_axes = [axis for axis in range(len(self.target_tensor.shape)) if axis not in axis_set]
ordered_axes = (*other_axes, *sub_axes)
# Transpose sub_axes to the end of the shape and slice them
target_tensor = self.target_tensor.transpose(*ordered_axes)[(..., *slices)]
available_buffer = self.available_buffer.transpose(*ordered_axes)[(..., *slices)]
new_axes = range(len(other_axes), len(ordered_axes))
return ApplyUnitaryArgs(target_tensor, available_buffer, new_axes)
def subspace_index(
self, little_endian_bits_int: int = 0, *, big_endian_bits_int: int = 0
) -> Tuple[Union[slice, int, 'ellipsis'], ...]:
return linalg.slice_for_qubits_equal_to(
self.axes,
little_endian_qureg_value=little_endian_bits_int,
big_endian_qureg_value=big_endian_bits_int,
qid_shape=self.target_tensor.shape,
)
class SupportsConsistentApplyUnitary(Protocol):
@doc_private
def _apply_unitary_(
self, args: ApplyUnitaryArgs
) -> Union[np.ndarray, None, NotImplementedType]:
def apply_unitary(
unitary_value: Any,
args: ApplyUnitaryArgs,
default: TDefault = RaiseTypeErrorIfNotProvided,
*,
allow_decompose: bool = True,
) -> Union[np.ndarray, TDefault]:
# Decide on order to attempt application strategies.
if len(args.axes) <= 4:
strats = [
_strat_apply_unitary_from_apply_unitary,
_strat_apply_unitary_from_unitary,
_strat_apply_unitary_from_decompose,
]
else:
strats = [
_strat_apply_unitary_from_apply_unitary,
_strat_apply_unitary_from_decompose,
_strat_apply_unitary_from_unitary,
]
if not allow_decompose:
strats.remove(_strat_apply_unitary_from_decompose)
# Try each strategy, stopping if one works.
for strat in strats:
result = strat(unitary_value, args)
if result is None:
break
if result is not NotImplemented:
return result
# Don't know how to apply. Fallback to specified default behavior.
if default is not RaiseTypeErrorIfNotProvided:
return default
raise TypeError(
"cirq.apply_unitary failed. "
"Value doesn't have a (non-parameterized) unitary effect.\n"
"\n"
"type: {}\n"
"value: {!r}\n"
"\n"
"The value failed to satisfy any of the following criteria:\n"
"- An `_apply_unitary_(self, args) method that returned a value "
"besides None or NotImplemented.\n"
"- A `_unitary_(self)` method that returned a value "
"besides None or NotImplemented.\n"
"- A `_decompose_(self)` method that returned a "
"list of unitary operations.\n"
"".format(type(unitary_value), unitary_value)
)
def _strat_apply_unitary_from_apply_unitary(
unitary_value: Any, args: ApplyUnitaryArgs
) -> Optional[np.ndarray]:
# Check for magic method.
func = getattr(unitary_value, '_apply_unitary_', None)
if func is None:
return NotImplemented
op_qid_shape = qid_shape_protocol.qid_shape(unitary_value, (2,) * len(args.axes))
sub_args = args._for_operation_with_qid_shape(range(len(op_qid_shape)), op_qid_shape)
sub_result = func(sub_args)
if sub_result is NotImplemented or sub_result is None:
return sub_result
return _incorporate_result_into_target(args, sub_args, sub_result)
def _strat_apply_unitary_from_unitary(
unitary_value: Any, args: ApplyUnitaryArgs
) -> Optional[np.ndarray]:
# Check for magic method.
method = getattr(unitary_value, '_unitary_', None)
if method is None:
return NotImplemented
# Attempt to get the unitary matrix.
matrix = method()
if matrix is NotImplemented or matrix is None:
return matrix
val_qid_shape = qid_shape_protocol.qid_shape(unitary_value, default=(2,) * len(args.axes))
sub_args = args._for_operation_with_qid_shape(range(len(val_qid_shape)), val_qid_shape)
matrix = matrix.astype(sub_args.target_tensor.dtype)
if len(val_qid_shape) == 1 and val_qid_shape[0] <= 2:
# Special case for single-qubit, 2x2 or 1x1 operations.
# np.einsum is faster for larger cases.
subspaces = [(..., level) for level in range(val_qid_shape[0])]
sub_result = linalg.apply_matrix_to_slices(
sub_args.target_tensor, matrix, subspaces, out=sub_args.available_buffer
)
else:
# General case via np.einsum.
sub_result = linalg.targeted_left_multiply(
matrix.reshape(val_qid_shape * 2),
sub_args.target_tensor,
sub_args.axes,
out=sub_args.available_buffer,
)
return _incorporate_result_into_target(args, sub_args, sub_result)
def _strat_apply_unitary_from_decompose(val: Any, args: ApplyUnitaryArgs) -> Optional[np.ndarray]:
operations, qubits, _ = _try_decompose_into_operations_and_qubits(val)
if operations is None:
return NotImplemented
return apply_unitaries(operations, qubits, args, None)
def apply_unitaries(
unitary_values: Iterable[Any],
qubits: Sequence['cirq.Qid'],
args: Optional[ApplyUnitaryArgs] = None,
default: Any = RaiseTypeErrorIfNotProvided,
) -> Optional[np.ndarray]:
if args is None:
qid_shape = qid_shape_protocol.qid_shape(qubits)
args = ApplyUnitaryArgs.default(qid_shape=qid_shape)
if len(qubits) != len(args.axes):
raise ValueError('len(qubits) != len(args.axes)')
qubit_map = {q.with_dimension(1): args.axes[i] for i, q in enumerate(qubits)}
state = args.target_tensor
buffer = args.available_buffer
for op in unitary_values:
indices = [qubit_map[q.with_dimension(1)] for q in op.qubits]
result = apply_unitary(
unitary_value=op, args=ApplyUnitaryArgs(state, buffer, indices), default=None
)
# Handle failure.
if result is None:
if default is RaiseTypeErrorIfNotProvided:
raise TypeError(
"cirq.apply_unitaries failed. "
"There was a non-unitary value in the `unitary_values` "
"list.\n"
"\n"
"non-unitary value type: {}\n"
"non-unitary value: {!r}".format(type(op), op)
)
return default
# Handle aliasing of results.
if result is buffer:
buffer = state
state = result
return state
def _incorporate_result_into_target(
args: 'ApplyUnitaryArgs', sub_args: 'ApplyUnitaryArgs', sub_result: np.ndarray
):
if not (
np.may_share_memory(args.target_tensor, sub_args.target_tensor)
and np.may_share_memory(args.available_buffer, sub_args.available_buffer)
):
raise ValueError(
'sub_args.target_tensor and subargs.available_buffer must be views of '
'args.target_tensor and args.available_buffer respectively.'
)
is_subspace = sub_args.target_tensor.size < args.target_tensor.size
if sub_result is sub_args.target_tensor:
return args.target_tensor
if sub_result is sub_args.available_buffer:
if is_subspace:
# The subspace that was modified is likely much smaller than
# the whole tensor so copy sub_result back into target_tensor.
sub_args.target_tensor[...] = sub_result
return args.target_tensor
return args.available_buffer
# The subspace that was modified is likely much smaller than
# the whole tensor so copy sub_result back into target_tensor.
# It's an uncommon case where sub_result is a new array.
if np.may_share_memory(sub_args.target_tensor, sub_result):
if is_subspace:
args.available_buffer[...] = args.target_tensor
sub_args.available_buffer[...] = sub_result
return args.available_buffer
sub_args.target_tensor[...] = sub_result
return args.target_tensor
| true | true |
f73c15662ec170505e5b105b68fa5669388e46eb | 2,384 | py | Python | test/unit/utils/test_expiring_dict.py | dolphinridercrypto/bxcommon | 8f70557c1dbff785a5dd3fcdf91176066e085c3a | [
"MIT"
] | 12 | 2019-11-06T17:39:10.000Z | 2022-03-01T11:26:19.000Z | test/unit/utils/test_expiring_dict.py | dolphinridercrypto/bxcommon | 8f70557c1dbff785a5dd3fcdf91176066e085c3a | [
"MIT"
] | 8 | 2019-11-06T21:31:11.000Z | 2021-06-02T00:46:50.000Z | test/unit/utils/test_expiring_dict.py | dolphinridercrypto/bxcommon | 8f70557c1dbff785a5dd3fcdf91176066e085c3a | [
"MIT"
] | 5 | 2019-11-14T18:08:11.000Z | 2022-02-08T09:36:22.000Z | import time
import unittest
from mock import MagicMock
from bxcommon.utils.expiring_dict import ExpiringDict
from bxcommon.utils.alarm_queue import AlarmQueue
class ExpiringDictTests(unittest.TestCase):
EXPIRATION_TIME_S = 1
def setUp(self):
self.ALARM_QUEUE = AlarmQueue()
self.e_dict = ExpiringDict(self.ALARM_QUEUE, self.EXPIRATION_TIME_S, "testdict")
def test_cleanup(self):
kv1 = (1, 2)
kv2 = (3, 4)
kv3 = (5, 6)
kv4 = (7, 8)
kv5 = ("str1", 1)
kv6 = ("str2", 2)
# adding first 2 items to the dict
self.e_dict.add(kv1[0], kv1[1])
self.e_dict.add(kv2[0], kv2[1])
time.time = MagicMock(return_value=time.time() + self.EXPIRATION_TIME_S+1)
self.assertEqual(len(self.e_dict.contents), 2)
self.assertTrue(kv1[0] in self.e_dict.contents)
self.assertTrue(kv2[0] in self.e_dict.contents)
self.assertEqual(self.e_dict.contents[kv1[0]], kv1[1])
self.assertEqual(self.e_dict.contents[kv2[0]], kv2[1])
# adding last 2 items to the dict
self.e_dict.add(kv3[0], kv3[1])
self.e_dict.add(kv4[0], kv4[1])
self.e_dict.add(kv5[0], kv5[1])
self.e_dict.add(kv6[0], kv6[1])
self.ALARM_QUEUE.fire_alarms()
# first 2 items are expired, last two have not
self.assertFalse(kv1[0] in self.e_dict.contents)
self.assertFalse(kv2[0] in self.e_dict.contents)
self.assertTrue(kv3[0] in self.e_dict.contents)
self.assertTrue(kv4[0] in self.e_dict.contents)
self.assertTrue(kv5[0] in self.e_dict.contents)
self.assertTrue(kv6[0] in self.e_dict.contents)
def test_remove_item(self):
kv1 = (1, 2)
self.e_dict.add(kv1[0], kv1[1])
self.assertTrue(kv1[0] in self.e_dict.contents)
self.e_dict.remove_item(kv1[0])
self.assertFalse(kv1[0] in self.e_dict.contents)
def test_cleanup__not_existing_item(self):
kv1 = (1, 2)
self.e_dict.add(kv1[0], kv1[1])
self.assertTrue(kv1[0] in self.e_dict.contents)
self.e_dict.remove_item(kv1[0])
self.assertFalse(kv1[0] in self.e_dict.contents)
time.time = MagicMock(return_value=time.time() + self.EXPIRATION_TIME_S + 1)
self.ALARM_QUEUE.fire_alarms()
self.assertFalse(kv1[0] in self.e_dict.contents)
| 31.786667 | 88 | 0.635906 | import time
import unittest
from mock import MagicMock
from bxcommon.utils.expiring_dict import ExpiringDict
from bxcommon.utils.alarm_queue import AlarmQueue
class ExpiringDictTests(unittest.TestCase):
EXPIRATION_TIME_S = 1
def setUp(self):
self.ALARM_QUEUE = AlarmQueue()
self.e_dict = ExpiringDict(self.ALARM_QUEUE, self.EXPIRATION_TIME_S, "testdict")
def test_cleanup(self):
kv1 = (1, 2)
kv2 = (3, 4)
kv3 = (5, 6)
kv4 = (7, 8)
kv5 = ("str1", 1)
kv6 = ("str2", 2)
self.e_dict.add(kv1[0], kv1[1])
self.e_dict.add(kv2[0], kv2[1])
time.time = MagicMock(return_value=time.time() + self.EXPIRATION_TIME_S+1)
self.assertEqual(len(self.e_dict.contents), 2)
self.assertTrue(kv1[0] in self.e_dict.contents)
self.assertTrue(kv2[0] in self.e_dict.contents)
self.assertEqual(self.e_dict.contents[kv1[0]], kv1[1])
self.assertEqual(self.e_dict.contents[kv2[0]], kv2[1])
self.e_dict.add(kv3[0], kv3[1])
self.e_dict.add(kv4[0], kv4[1])
self.e_dict.add(kv5[0], kv5[1])
self.e_dict.add(kv6[0], kv6[1])
self.ALARM_QUEUE.fire_alarms()
self.assertFalse(kv1[0] in self.e_dict.contents)
self.assertFalse(kv2[0] in self.e_dict.contents)
self.assertTrue(kv3[0] in self.e_dict.contents)
self.assertTrue(kv4[0] in self.e_dict.contents)
self.assertTrue(kv5[0] in self.e_dict.contents)
self.assertTrue(kv6[0] in self.e_dict.contents)
def test_remove_item(self):
kv1 = (1, 2)
self.e_dict.add(kv1[0], kv1[1])
self.assertTrue(kv1[0] in self.e_dict.contents)
self.e_dict.remove_item(kv1[0])
self.assertFalse(kv1[0] in self.e_dict.contents)
def test_cleanup__not_existing_item(self):
kv1 = (1, 2)
self.e_dict.add(kv1[0], kv1[1])
self.assertTrue(kv1[0] in self.e_dict.contents)
self.e_dict.remove_item(kv1[0])
self.assertFalse(kv1[0] in self.e_dict.contents)
time.time = MagicMock(return_value=time.time() + self.EXPIRATION_TIME_S + 1)
self.ALARM_QUEUE.fire_alarms()
self.assertFalse(kv1[0] in self.e_dict.contents)
| true | true |
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