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class | is_sharp_comment_removed bool 1
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f72c7eba9adcee72dd6bb67f5e168999469e292c | 1,058 | py | Python | XXBDailyFresh/apps/user/urls.py | sixTiger/XXBDailyFresh | 5c6976eff8e073f79b50e7829e10332ccd8df43d | [
"MIT"
] | null | null | null | XXBDailyFresh/apps/user/urls.py | sixTiger/XXBDailyFresh | 5c6976eff8e073f79b50e7829e10332ccd8df43d | [
"MIT"
] | null | null | null | XXBDailyFresh/apps/user/urls.py | sixTiger/XXBDailyFresh | 5c6976eff8e073f79b50e7829e10332ccd8df43d | [
"MIT"
] | null | null | null | """XXBDailyFresh URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
# url(r'^register$', RegisterView.as_view(), name='register'), # 注册
# url(r'^active/(?P<token>.*)$', ActiveView.as_view(), name='active'), # 用户激活
# url(r'^login$', LoginView.as_view(), name='login'), # 登录
from django.urls import path
from apps.user.views import RegisterView
urlpatterns = [
path('register/', RegisterView.as_view(), name='register'), # 首页
# path('', views.index, name='index'), # 首页
] | 39.185185 | 81 | 0.680529 |
.urls import path
from apps.user.views import RegisterView
urlpatterns = [
path('register/', RegisterView.as_view(), name='register'),
| true | true |
f72c7fa8536014c7a70bc5bf40a892ab8804afca | 4,820 | py | Python | Tsukihime/nscript_parser.py | Samyuth/LomandoCrawler | 2d6bc7bd79678b78ac7c30e88b72127134e99b91 | [
"MIT"
] | null | null | null | Tsukihime/nscript_parser.py | Samyuth/LomandoCrawler | 2d6bc7bd79678b78ac7c30e88b72127134e99b91 | [
"MIT"
] | 1 | 2022-03-31T09:40:48.000Z | 2022-03-31T09:44:48.000Z | Tsukihime/nscript_parser.py | Samyuth/LomandoCrawler | 2d6bc7bd79678b78ac7c30e88b72127134e99b91 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 16 02:05:23 2022
@author: Sagi
"""
'''
Sample choice node text:
;-BLOCK-------------------------------------------------------------------------
*f20 # Label
gosub *regard_update
!sd
if %sceneskip==1 && %1020==1 skip 4
gosub *s20
mov %1020,1
skip 9
`You have already viewed this scene.
`Would you like to skip?
br
selgosub `1. Skip`, *skip20,
`2. Don't skip`, *s20
skip 3
*skip20
return
;gosub *s20
select `1. There's only a few minutes until homeroom. I have to head there right away.`, *f21,
`2. || I'm curious, so I'll go take a look.`, *f22
'''
import re
from Graph import *
class TextNode():
def __init__(self, label=None, text=None, children=None):
if label is not None:
self.label = label
else:
self.label = None
if text is not None:
self.text = text
else:
self.text = ""
if children is not None:
self.children = children
else:
self.children = []
def get_text(self):
if self.text:
return self.text
else:
return None
def get_label(self):
if self.label:
return self.label
else:
return None
def add_text(self, text):
self.text += text
def change_label(self, label):
self.label = label
def add_children(self, children):
self.children += children
class ChoiceNode(TextNode):
def add_choices(self, choices):
self.choices = choices
def get_choices(self):
if self.choices:
return self.choices
else:
return None
class TsukihimeNode(TextNode):
def get_labels(self, string):
return re.findall("\*.*(?=,)|\*.*(?=\s)|\*.*", string)
def parse_text(self):
if self.text is None:
print("No text to parse")
return -1
line_ctr = 0
lines = self.text.splitlines()
no_lines = len(lines)
while (line_ctr < no_lines):
if lines[line_ctr].find("select") != -1:
children = []
while (line_ctr < no_lines
and re.search("`[0-9].*`", lines[line_ctr])):
children += self.get_labels(lines[line_ctr])
line_ctr += 1
self.add_children(children)
elif lines[line_ctr].find("goto") != -1:
self.add_children(self.get_labels(lines[line_ctr]))
line_ctr += 1
class NscriptParser(Graph):
# method to parse the script
def parse(self):
nscript = open("./nsdec/NSDEC/result.txt", encoding="cp932")
line = nscript.readline()
header = open("./parsed_texts/header.txt", "w", encoding="cp932")
remaining = open("./parsed_texts/remaining.txt", "w", encoding="cp932")
choices = open("./parsed_texts/choices.txt", "w", encoding="cp932")
choice_nodes = []
nodes = []
nodes_present = False
while (line and line.strip() != "*start"):
header.writelines(line)
line = nscript.readline()
while (line and line.strip() != "; $Id: 4.txt 1282 2006-08-04 18:12:29Z chendo $"):
if re.match("\*f.*", line):
nodes_present = True
choice_nodes.append(TsukihimeNode(text=""))
if nodes_present:
choice_nodes[-1].add_text(line)
if re.match("^\*f", line):
choice_nodes[-1].change_label(line.strip())
choices.writelines(line)
line = nscript.readline()
while (line):
if re.match("^\*", line):
nodes.append(TextNode(line))
remaining.writelines(line)
line = nscript.readline()
nscript.close()
header.close()
remaining.close()
choices.close()
choice_nodes = list(filter(lambda x: x.get_label() is not None, choice_nodes))
for node in choice_nodes:
node.parse_text()
for node in choice_nodes:
self.graph.add_node(node.label)
for child in node.children:
if child not in self.graph:
self.graph.add_node(child)
self.graph.add_edge(node.label, child)
return choice_nodes
if __name__ == "__main__":
parser = NscriptParser()
choice_nodes = parser.parse()
leveled_tree = parser.get_leveled_tree()
output = parser.output_tree_sideways()
with open("ouput.txt", "w") as outfile:
outfile.write(output)
#parser.plot()
#parser.plot_pretty()
| 27.542857 | 94 | 0.527386 |
import re
from Graph import *
class TextNode():
def __init__(self, label=None, text=None, children=None):
if label is not None:
self.label = label
else:
self.label = None
if text is not None:
self.text = text
else:
self.text = ""
if children is not None:
self.children = children
else:
self.children = []
def get_text(self):
if self.text:
return self.text
else:
return None
def get_label(self):
if self.label:
return self.label
else:
return None
def add_text(self, text):
self.text += text
def change_label(self, label):
self.label = label
def add_children(self, children):
self.children += children
class ChoiceNode(TextNode):
def add_choices(self, choices):
self.choices = choices
def get_choices(self):
if self.choices:
return self.choices
else:
return None
class TsukihimeNode(TextNode):
def get_labels(self, string):
return re.findall("\*.*(?=,)|\*.*(?=\s)|\*.*", string)
def parse_text(self):
if self.text is None:
print("No text to parse")
return -1
line_ctr = 0
lines = self.text.splitlines()
no_lines = len(lines)
while (line_ctr < no_lines):
if lines[line_ctr].find("select") != -1:
children = []
while (line_ctr < no_lines
and re.search("`[0-9].*`", lines[line_ctr])):
children += self.get_labels(lines[line_ctr])
line_ctr += 1
self.add_children(children)
elif lines[line_ctr].find("goto") != -1:
self.add_children(self.get_labels(lines[line_ctr]))
line_ctr += 1
class NscriptParser(Graph):
def parse(self):
nscript = open("./nsdec/NSDEC/result.txt", encoding="cp932")
line = nscript.readline()
header = open("./parsed_texts/header.txt", "w", encoding="cp932")
remaining = open("./parsed_texts/remaining.txt", "w", encoding="cp932")
choices = open("./parsed_texts/choices.txt", "w", encoding="cp932")
choice_nodes = []
nodes = []
nodes_present = False
while (line and line.strip() != "*start"):
header.writelines(line)
line = nscript.readline()
while (line and line.strip() != "; $Id: 4.txt 1282 2006-08-04 18:12:29Z chendo $"):
if re.match("\*f.*", line):
nodes_present = True
choice_nodes.append(TsukihimeNode(text=""))
if nodes_present:
choice_nodes[-1].add_text(line)
if re.match("^\*f", line):
choice_nodes[-1].change_label(line.strip())
choices.writelines(line)
line = nscript.readline()
while (line):
if re.match("^\*", line):
nodes.append(TextNode(line))
remaining.writelines(line)
line = nscript.readline()
nscript.close()
header.close()
remaining.close()
choices.close()
choice_nodes = list(filter(lambda x: x.get_label() is not None, choice_nodes))
for node in choice_nodes:
node.parse_text()
for node in choice_nodes:
self.graph.add_node(node.label)
for child in node.children:
if child not in self.graph:
self.graph.add_node(child)
self.graph.add_edge(node.label, child)
return choice_nodes
if __name__ == "__main__":
parser = NscriptParser()
choice_nodes = parser.parse()
leveled_tree = parser.get_leveled_tree()
output = parser.output_tree_sideways()
with open("ouput.txt", "w") as outfile:
outfile.write(output)
| true | true |
f72c80155df71399c41c13f3793341aca06db318 | 2,677 | py | Python | plugins/cylance_protect/unit_test/test_update_agent.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 46 | 2019-06-05T20:47:58.000Z | 2022-03-29T10:18:01.000Z | plugins/cylance_protect/unit_test/test_update_agent.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 386 | 2019-06-07T20:20:39.000Z | 2022-03-30T17:35:01.000Z | plugins/cylance_protect/unit_test/test_update_agent.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 43 | 2019-07-09T14:13:58.000Z | 2022-03-28T12:04:46.000Z | import sys
import os
sys.path.append(os.path.abspath("../"))
from unittest import TestCase
from icon_cylance_protect.connection.connection import Connection
from icon_cylance_protect.actions.update_agent import UpdateAgent
import json
import logging
class TestUpdateAgent(TestCase):
def test_integration_update_agent(self):
"""
TODO: Implement assertions at the end of this test case
This is an integration test that will connect to the services your plugin uses. It should be used
as the basis for tests below that can run independent of a "live" connection.
This test assumes a normal plugin structure with a /tests directory. In that /tests directory should
be json samples that contain all the data needed to run this test. To generate samples run:
icon-plugin generate samples
"""
log = logging.getLogger("Test")
test_conn = Connection()
test_action = UpdateAgent()
test_conn.logger = log
test_action.logger = log
try:
with open("../tests/update_agent.json") as file:
test_json = json.loads(file.read()).get("body")
connection_params = test_json.get("connection")
action_params = test_json.get("input")
except Exception as e:
message = """
Could not find or read sample tests from /tests directory
An exception here likely means you didn't fill out your samples correctly in the /tests directory
Please use 'icon-plugin generate samples', and fill out the resulting test files in the /tests directory
"""
self.fail(message)
test_conn.connect(connection_params)
test_action.connection = test_conn
results = test_action.run(action_params)
# TODO: Remove this line
self.fail("Unimplemented test case")
# TODO: The following assert should be updated to look for data from your action
# For example: self.assertEquals({"success": True}, results)
self.assertEquals({}, results)
def test_update_agent(self):
"""
TODO: Implement test cases here
Here you can mock the connection with data returned from the above integration test.
For information on mocking and unit testing please go here:
https://docs.google.com/document/d/1PifePDG1-mBcmNYE8dULwGxJimiRBrax5BIDG_0TFQI/edit?usp=sharing
You can either create a formal Mock for this, or you can create a fake connection class to pass to your
action for testing.
"""
self.fail("Unimplemented Test Case")
| 36.671233 | 116 | 0.668285 | import sys
import os
sys.path.append(os.path.abspath("../"))
from unittest import TestCase
from icon_cylance_protect.connection.connection import Connection
from icon_cylance_protect.actions.update_agent import UpdateAgent
import json
import logging
class TestUpdateAgent(TestCase):
def test_integration_update_agent(self):
log = logging.getLogger("Test")
test_conn = Connection()
test_action = UpdateAgent()
test_conn.logger = log
test_action.logger = log
try:
with open("../tests/update_agent.json") as file:
test_json = json.loads(file.read()).get("body")
connection_params = test_json.get("connection")
action_params = test_json.get("input")
except Exception as e:
message = """
Could not find or read sample tests from /tests directory
An exception here likely means you didn't fill out your samples correctly in the /tests directory
Please use 'icon-plugin generate samples', and fill out the resulting test files in the /tests directory
"""
self.fail(message)
test_conn.connect(connection_params)
test_action.connection = test_conn
results = test_action.run(action_params)
# TODO: Remove this line
self.fail("Unimplemented test case")
# TODO: The following assert should be updated to look for data from your action
# For example: self.assertEquals({"success": True}, results)
self.assertEquals({}, results)
def test_update_agent(self):
self.fail("Unimplemented Test Case")
| true | true |
f72c80b9bc4510e5476205c3adf1bfd5dea678af | 1,931 | py | Python | model-optimizer/extensions/front/onnx/detectionoutput_ext.py | Andruxin52rus/openvino | d824e371fe7dffb90e6d3d58e4e34adecfce4606 | [
"Apache-2.0"
] | 2 | 2020-11-18T14:14:06.000Z | 2020-11-28T04:55:57.000Z | model-optimizer/extensions/front/onnx/detectionoutput_ext.py | Andruxin52rus/openvino | d824e371fe7dffb90e6d3d58e4e34adecfce4606 | [
"Apache-2.0"
] | 30 | 2020-11-13T11:44:07.000Z | 2022-02-21T13:03:16.000Z | model-optimizer/extensions/front/onnx/detectionoutput_ext.py | mmakridi/openvino | 769bb7709597c14debdaa356dd60c5a78bdfa97e | [
"Apache-2.0"
] | 3 | 2021-03-09T08:27:29.000Z | 2021-04-07T04:58:54.000Z | """
Copyright (C) 2018-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from math import log
import numpy as np
from extensions.ops.detectionoutput_onnx import ExperimentalDetectronDetectionOutput
from mo.front.extractor import FrontExtractorOp
from mo.front.onnx.extractors.utils import onnx_attr
class ExperimentalDetectronDetectionOutputFrontExtractor(FrontExtractorOp):
op = 'ExperimentalDetectronDetectionOutput'
enabled = True
@classmethod
def extract(cls, node):
attrs = dict(class_agnostic_box_regression=onnx_attr(node, 'class_agnostic_box_regression', 'i', 0),
max_detections_per_image=onnx_attr(node, 'max_detections_per_image', 'i', 100),
nms_threshold=onnx_attr(node, 'nms_threshold', 'f', 0.5),
num_classes=onnx_attr(node, 'num_classes', 'i', 81),
post_nms_count=onnx_attr(node, 'post_nms_count', 'i', 2000),
score_threshold=onnx_attr(node, 'score_threshold', 'f', 0.05),
max_delta_log_wh=onnx_attr(node, 'max_delta_log_wh', 'f', log(1000. / 16.)),
deltas_weights=np.array(onnx_attr(node, 'deltas_weights', 'floats', [10., 10., 5., 5.]),
dtype=np.float32)
)
ExperimentalDetectronDetectionOutput.update_node_stat(node, attrs)
return cls.enabled
| 43.886364 | 109 | 0.684619 |
from math import log
import numpy as np
from extensions.ops.detectionoutput_onnx import ExperimentalDetectronDetectionOutput
from mo.front.extractor import FrontExtractorOp
from mo.front.onnx.extractors.utils import onnx_attr
class ExperimentalDetectronDetectionOutputFrontExtractor(FrontExtractorOp):
op = 'ExperimentalDetectronDetectionOutput'
enabled = True
@classmethod
def extract(cls, node):
attrs = dict(class_agnostic_box_regression=onnx_attr(node, 'class_agnostic_box_regression', 'i', 0),
max_detections_per_image=onnx_attr(node, 'max_detections_per_image', 'i', 100),
nms_threshold=onnx_attr(node, 'nms_threshold', 'f', 0.5),
num_classes=onnx_attr(node, 'num_classes', 'i', 81),
post_nms_count=onnx_attr(node, 'post_nms_count', 'i', 2000),
score_threshold=onnx_attr(node, 'score_threshold', 'f', 0.05),
max_delta_log_wh=onnx_attr(node, 'max_delta_log_wh', 'f', log(1000. / 16.)),
deltas_weights=np.array(onnx_attr(node, 'deltas_weights', 'floats', [10., 10., 5., 5.]),
dtype=np.float32)
)
ExperimentalDetectronDetectionOutput.update_node_stat(node, attrs)
return cls.enabled
| true | true |
f72c811a9b903e14fbd5d11e5e45b9449c6237b3 | 2,159 | py | Python | test/chemistry/test_driver_gaussian_extra.py | hushaohan/aqua | 8512bc6ce246a8b3cca1e5edb1703b6885aa7c5d | [
"Apache-2.0"
] | 2 | 2020-06-29T16:08:12.000Z | 2020-08-07T22:42:13.000Z | test/chemistry/test_driver_gaussian_extra.py | hushaohan/aqua | 8512bc6ce246a8b3cca1e5edb1703b6885aa7c5d | [
"Apache-2.0"
] | null | null | null | test/chemistry/test_driver_gaussian_extra.py | hushaohan/aqua | 8512bc6ce246a8b3cca1e5edb1703b6885aa7c5d | [
"Apache-2.0"
] | 1 | 2022-01-25T07:09:10.000Z | 2022-01-25T07:09:10.000Z | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# 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.
""" Test Driver Gaussian internals - does not require Gaussian installed """
import unittest
from test.chemistry import QiskitChemistryTestCase
from qiskit.chemistry.drivers import GaussianDriver
# We need to have an instance so we can test function but constructor calls
# an internal method to check G16 installed. We need to replace that with
# the following dummy for things to work and we do it for each test so the
# class ends up as it was
def _check_valid():
pass
class TestDriverGaussianExtra(QiskitChemistryTestCase):
"""Gaussian Driver extra tests for driver specifics, errors etc """
def setUp(self):
super().setUp()
self.good_check = GaussianDriver._check_valid
GaussianDriver._check_valid = _check_valid
# We can now create a driver without the installed (check valid) test failing
def tearDown(self):
GaussianDriver._check_valid = self.good_check
def test_cfg_augment(self):
""" test input configuration augmentation """
cfg = '# rhf/sto-3g scf(conventional)\n\n' \
'h2 molecule\n\n0 1\nH 0.0 0.0 0.0\nH 0.0 0.0 0.735\n\n'
g16 = GaussianDriver(cfg)
aug_cfg = g16._augment_config("mymatfile.mat", cfg)
expected = '# rhf/sto-3g scf(conventional)\n' \
'# Window=Full Int=NoRaff Symm=(NoInt,None)' \
' output=(matrix,i4labels,mo2el) tran=full\n\n' \
'h2 molecule\n\n0 1\nH 0.0 0.0 0.0\nH 0.0 0.0 0.735' \
'\n\nmymatfile.mat\n\n'
self.assertEqual(aug_cfg, expected)
if __name__ == '__main__':
unittest.main()
| 36.59322 | 85 | 0.672534 |
import unittest
from test.chemistry import QiskitChemistryTestCase
from qiskit.chemistry.drivers import GaussianDriver
def _check_valid():
pass
class TestDriverGaussianExtra(QiskitChemistryTestCase):
def setUp(self):
super().setUp()
self.good_check = GaussianDriver._check_valid
GaussianDriver._check_valid = _check_valid
def tearDown(self):
GaussianDriver._check_valid = self.good_check
def test_cfg_augment(self):
cfg = '# rhf/sto-3g scf(conventional)\n\n' \
'h2 molecule\n\n0 1\nH 0.0 0.0 0.0\nH 0.0 0.0 0.735\n\n'
g16 = GaussianDriver(cfg)
aug_cfg = g16._augment_config("mymatfile.mat", cfg)
expected = '# rhf/sto-3g scf(conventional)\n' \
'# Window=Full Int=NoRaff Symm=(NoInt,None)' \
' output=(matrix,i4labels,mo2el) tran=full\n\n' \
'h2 molecule\n\n0 1\nH 0.0 0.0 0.0\nH 0.0 0.0 0.735' \
'\n\nmymatfile.mat\n\n'
self.assertEqual(aug_cfg, expected)
if __name__ == '__main__':
unittest.main()
| true | true |
f72c82f4acdaefe35bcc5d195dbe520974fda99d | 1,269 | py | Python | qiskit_nature/algorithms/excited_states_solvers/__init__.py | divshacker/qiskit-nature | 08f6dcec5e4ac8c08f5b84e764ee78cc3d12facb | [
"Apache-2.0"
] | 132 | 2021-01-28T14:51:11.000Z | 2022-03-25T21:10:47.000Z | qiskit_nature/algorithms/excited_states_solvers/__init__.py | divshacker/qiskit-nature | 08f6dcec5e4ac8c08f5b84e764ee78cc3d12facb | [
"Apache-2.0"
] | 449 | 2021-01-28T19:57:43.000Z | 2022-03-31T17:01:50.000Z | qiskit_nature/algorithms/excited_states_solvers/__init__.py | divshacker/qiskit-nature | 08f6dcec5e4ac8c08f5b84e764ee78cc3d12facb | [
"Apache-2.0"
] | 109 | 2021-01-28T13:17:46.000Z | 2022-03-30T23:53:39.000Z | # This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 2021.
#
# 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.
"""
Excited State Solving Algorithms (:mod:`qiskit_nature.algorithms.excited_states_solvers`)
=========================================================================================
.. currentmodule:: qiskit_nature.algorithms.excited_states_solvers
.. autosummary::
:toctree: ../stubs/
eigensolver_factories
.. autosummary::
:toctree: ../stubs/
:nosignatures:
ExcitedStatesEigensolver
QEOM
"""
from .excited_states_solver import ExcitedStatesSolver
from .qeom import QEOM
from .eigensolver_factories import EigensolverFactory, NumPyEigensolverFactory
from .excited_states_eigensolver import ExcitedStatesEigensolver
__all__ = [
"ExcitedStatesSolver",
"ExcitedStatesEigensolver",
"EigensolverFactory",
"NumPyEigensolverFactory",
"QEOM",
]
| 28.840909 | 89 | 0.711584 |
from .excited_states_solver import ExcitedStatesSolver
from .qeom import QEOM
from .eigensolver_factories import EigensolverFactory, NumPyEigensolverFactory
from .excited_states_eigensolver import ExcitedStatesEigensolver
__all__ = [
"ExcitedStatesSolver",
"ExcitedStatesEigensolver",
"EigensolverFactory",
"NumPyEigensolverFactory",
"QEOM",
]
| true | true |
f72c838e66c47527c0a178012ed8acbdbcfe18e4 | 827 | gyp | Python | ui/aura_extra/aura_extra.gyp | hefen1/chromium | 52f0b6830e000ca7c5e9aa19488af85be792cc88 | [
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | null | null | null | ui/aura_extra/aura_extra.gyp | hefen1/chromium | 52f0b6830e000ca7c5e9aa19488af85be792cc88 | [
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | null | null | null | ui/aura_extra/aura_extra.gyp | hefen1/chromium | 52f0b6830e000ca7c5e9aa19488af85be792cc88 | [
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | 2 | 2020-04-04T13:34:56.000Z | 2020-11-04T07:17:52.000Z | # Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'variables': {
'chromium_code': 1,
},
'targets': [
{
# GN version: //ui/aura_extra
'target_name': 'aura_extra',
'type': '<(component)',
'dependencies': [
'../../base/base.gyp:base',
'../../skia/skia.gyp:skia',
'../aura/aura.gyp:aura',
'../base/ui_base.gyp:ui_base',
'../events/events.gyp:events',
'../gfx/gfx.gyp:gfx',
'../gfx/gfx.gyp:gfx_geometry',
],
'defines': [
'AURA_EXTRA_IMPLEMENTATION',
],
'sources': [
'aura_extra_export.h',
'image_window_delegate.cc',
'image_window_delegate.h',
],
},
],
}
| 24.323529 | 72 | 0.53688 |
{
'variables': {
'chromium_code': 1,
},
'targets': [
{
'target_name': 'aura_extra',
'type': '<(component)',
'dependencies': [
'../../base/base.gyp:base',
'../../skia/skia.gyp:skia',
'../aura/aura.gyp:aura',
'../base/ui_base.gyp:ui_base',
'../events/events.gyp:events',
'../gfx/gfx.gyp:gfx',
'../gfx/gfx.gyp:gfx_geometry',
],
'defines': [
'AURA_EXTRA_IMPLEMENTATION',
],
'sources': [
'aura_extra_export.h',
'image_window_delegate.cc',
'image_window_delegate.h',
],
},
],
}
| true | true |
f72c844451481add20eff334fb82624c5d7efbe7 | 1,662 | py | Python | lab-05-1-logistic_regression.py | KANG91/Deep_Learning | e3e9de769ab835215d0ebeee79ff869afbe64ebf | [
"MIT"
] | null | null | null | lab-05-1-logistic_regression.py | KANG91/Deep_Learning | e3e9de769ab835215d0ebeee79ff869afbe64ebf | [
"MIT"
] | null | null | null | lab-05-1-logistic_regression.py | KANG91/Deep_Learning | e3e9de769ab835215d0ebeee79ff869afbe64ebf | [
"MIT"
] | null | null | null | # Lab 5 Logistic Regression Classifier
import tensorflow as tf
tf.set_random_seed(777) # for reproducibility
x_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]
y_data = [[0], [0], [0], [1], [1], [1]]
# placeholders for a tensor that will be always fed.
X = tf.placeholder(tf.float32, shape=[None, 2])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([2, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
# Cost function
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
tf.log(1 - hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
# Accuracy computation
# True if hypothesis>0.5 else False
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
# Launch graph
with tf.Session() as sess:
# Initialize TensorFlow variables
sess.run(tf.global_variables_initializer())
feed = {X: x_data, Y: y_data}
for step in range(10001):
sess.run(train, feed_dict=feed)
if step % 200 == 0:
print(step, sess.run(cost, feed_dict=feed), sess.run(W))
# Accuracy report
h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict=feed)
print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)
'''
Hypothesis: [[ 0.03074029]
[ 0.15884677]
[ 0.30486736]
[ 0.78138196]
[ 0.93957496]
[ 0.98016882]]
Correct (Y): [[ 0.]
[ 0.]
[ 0.]
[ 1.]
[ 1.]
[ 1.]]
Accuracy: 1.0
'''
| 28.169492 | 76 | 0.628159 |
import tensorflow as tf
tf.set_random_seed(777)
x_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]
y_data = [[0], [0], [0], [1], [1], [1]]
X = tf.placeholder(tf.float32, shape=[None, 2])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([2, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
tf.log(1 - hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
feed = {X: x_data, Y: y_data}
for step in range(10001):
sess.run(train, feed_dict=feed)
if step % 200 == 0:
print(step, sess.run(cost, feed_dict=feed), sess.run(W))
h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict=feed)
print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)
| true | true |
f72c8477a6936f3991993793141885a0bb21af12 | 4,436 | py | Python | tests/unit/models/physics/MeniscusTest.py | edgargmartinez/OpenPNM | c68745993b3e9895f53938164a9cf6305500748e | [
"MIT"
] | 3 | 2019-07-05T22:07:21.000Z | 2019-07-05T22:07:30.000Z | tests/unit/models/physics/MeniscusTest.py | edgargmartinez/OpenPNM | c68745993b3e9895f53938164a9cf6305500748e | [
"MIT"
] | null | null | null | tests/unit/models/physics/MeniscusTest.py | edgargmartinez/OpenPNM | c68745993b3e9895f53938164a9cf6305500748e | [
"MIT"
] | null | null | null | import openpnm as op
import openpnm.models.physics as pm
import scipy as sp
class MeniscusTest:
def setup_class(self):
sp.random.seed(1)
self.net = op.network.Cubic(shape=[5, 1, 5], spacing=5e-5)
self.geo = op.geometry.StickAndBall(network=self.net,
pores=self.net.pores(),
throats=self.net.throats())
self.phase = op.phases.Water(network=self.net)
self.phys = op.physics.Standard(network=self.net,
phase=self.phase,
geometry=self.geo)
def test_toroidal_touch(self):
phys = self.phys
r_tor = 1e-6
self.geo['throat.touch_length'] = 2e-6
phys.add_model(propname='throat.tor_max',
model=pm.meniscus.purcell,
mode='max',
r_toroid=r_tor)
phys.add_model(propname='throat.tor_touch',
model=pm.meniscus.purcell,
mode='touch',
r_toroid=r_tor)
assert sp.any(phys['throat.tor_touch'] < phys['throat.tor_max'])
def test_sinusoidal_touch(self):
phys = self.phys
self.geo['throat.amplitude'] = 5e-6
self.geo['throat.touch_length'] = 1e-6
phys.add_model(propname='throat.sin_pressure_max',
model=pm.meniscus.sinusoidal,
mode='max')
phys.add_model(propname='throat.sin_pressure_touch',
model=pm.meniscus.sinusoidal,
mode='touch')
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
assert sp.any((phys['throat.sin_pressure_touch'] <
phys['throat.sin_pressure_max']))
def test_sinusoidal(self):
phys = self.phys
self.geo['throat.amplitude'] = 5e-6
phys.add_model(propname='throat.sin_pressure',
model=pm.meniscus.sinusoidal,
mode='max')
phys.add_model(propname='throat.sin_meniscus',
model=pm.meniscus.sinusoidal,
mode='men',
target_Pc=5000)
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
def test_toroidal(self):
phys = self.phys
r_tor = 1e-6
phys.add_model(propname='throat.purcell_pressure',
model=pm.capillary_pressure.purcell,
r_toroid=r_tor)
phys.add_model(propname='throat.tor_pressure',
model=pm.meniscus.purcell,
mode='max',
r_toroid=r_tor,
num_points=1000)
phys.add_model(propname='throat.tor_meniscus',
model=pm.meniscus.purcell,
mode='men',
r_toroid=r_tor,
target_Pc=5000)
a = sp.around(phys['throat.purcell_pressure'], 10)
b = sp.around(phys['throat.tor_pressure'], 10)
assert sp.allclose(a, b)
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
def test_general_toroidal(self):
phys = self.phys
r_tor = 1e-6
phys.add_model(propname='throat.purcell_pressure',
model=pm.capillary_pressure.purcell,
r_toroid=r_tor)
phys['throat.scale_a'] = r_tor
phys['throat.scale_b'] = r_tor
phys.add_model(propname='throat.general_pressure',
model=pm.meniscus.general_toroidal,
mode='max',
num_points=1000)
a = sp.around(phys['throat.purcell_pressure'], 10)
b = sp.around(phys['throat.general_pressure'], 10)
assert sp.allclose(a, b)
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
if __name__ == '__main__':
t = MeniscusTest()
self = t
t.setup_class()
for item in t.__dir__():
if item.startswith('test'):
print('running test: '+item)
t.__getattribute__(item)()
| 37.277311 | 72 | 0.511046 | import openpnm as op
import openpnm.models.physics as pm
import scipy as sp
class MeniscusTest:
def setup_class(self):
sp.random.seed(1)
self.net = op.network.Cubic(shape=[5, 1, 5], spacing=5e-5)
self.geo = op.geometry.StickAndBall(network=self.net,
pores=self.net.pores(),
throats=self.net.throats())
self.phase = op.phases.Water(network=self.net)
self.phys = op.physics.Standard(network=self.net,
phase=self.phase,
geometry=self.geo)
def test_toroidal_touch(self):
phys = self.phys
r_tor = 1e-6
self.geo['throat.touch_length'] = 2e-6
phys.add_model(propname='throat.tor_max',
model=pm.meniscus.purcell,
mode='max',
r_toroid=r_tor)
phys.add_model(propname='throat.tor_touch',
model=pm.meniscus.purcell,
mode='touch',
r_toroid=r_tor)
assert sp.any(phys['throat.tor_touch'] < phys['throat.tor_max'])
def test_sinusoidal_touch(self):
phys = self.phys
self.geo['throat.amplitude'] = 5e-6
self.geo['throat.touch_length'] = 1e-6
phys.add_model(propname='throat.sin_pressure_max',
model=pm.meniscus.sinusoidal,
mode='max')
phys.add_model(propname='throat.sin_pressure_touch',
model=pm.meniscus.sinusoidal,
mode='touch')
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
assert sp.any((phys['throat.sin_pressure_touch'] <
phys['throat.sin_pressure_max']))
def test_sinusoidal(self):
phys = self.phys
self.geo['throat.amplitude'] = 5e-6
phys.add_model(propname='throat.sin_pressure',
model=pm.meniscus.sinusoidal,
mode='max')
phys.add_model(propname='throat.sin_meniscus',
model=pm.meniscus.sinusoidal,
mode='men',
target_Pc=5000)
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
def test_toroidal(self):
phys = self.phys
r_tor = 1e-6
phys.add_model(propname='throat.purcell_pressure',
model=pm.capillary_pressure.purcell,
r_toroid=r_tor)
phys.add_model(propname='throat.tor_pressure',
model=pm.meniscus.purcell,
mode='max',
r_toroid=r_tor,
num_points=1000)
phys.add_model(propname='throat.tor_meniscus',
model=pm.meniscus.purcell,
mode='men',
r_toroid=r_tor,
target_Pc=5000)
a = sp.around(phys['throat.purcell_pressure'], 10)
b = sp.around(phys['throat.tor_pressure'], 10)
assert sp.allclose(a, b)
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
def test_general_toroidal(self):
phys = self.phys
r_tor = 1e-6
phys.add_model(propname='throat.purcell_pressure',
model=pm.capillary_pressure.purcell,
r_toroid=r_tor)
phys['throat.scale_a'] = r_tor
phys['throat.scale_b'] = r_tor
phys.add_model(propname='throat.general_pressure',
model=pm.meniscus.general_toroidal,
mode='max',
num_points=1000)
a = sp.around(phys['throat.purcell_pressure'], 10)
b = sp.around(phys['throat.general_pressure'], 10)
assert sp.allclose(a, b)
h = phys.check_data_health()
for check in h.values():
if len(check) > 0:
assert 1 == 2
if __name__ == '__main__':
t = MeniscusTest()
self = t
t.setup_class()
for item in t.__dir__():
if item.startswith('test'):
print('running test: '+item)
t.__getattribute__(item)()
| true | true |
f72c85576b8389695f555dc9f2032aaaf2f1f2df | 19,881 | py | Python | plugins/modules/oci_network_drg.py | LaudateCorpus1/oci-ansible-collection | 2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7 | [
"Apache-2.0"
] | null | null | null | plugins/modules/oci_network_drg.py | LaudateCorpus1/oci-ansible-collection | 2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7 | [
"Apache-2.0"
] | null | null | null | plugins/modules/oci_network_drg.py | LaudateCorpus1/oci-ansible-collection | 2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
# Copyright (c) 2020, 2022 Oracle and/or its affiliates.
# This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license.
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
# Apache License v2.0
# See LICENSE.TXT for details.
# GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {
"metadata_version": "1.1",
"status": ["preview"],
"supported_by": "community",
}
DOCUMENTATION = """
---
module: oci_network_drg
short_description: Manage a Drg resource in Oracle Cloud Infrastructure
description:
- This module allows the user to create, update and delete a Drg resource in Oracle Cloud Infrastructure
- For I(state=present), creates a new dynamic routing gateway (DRG) in the specified compartment. For more information,
see L(Dynamic Routing Gateways (DRGs),https://docs.cloud.oracle.com/iaas/Content/Network/Tasks/managingDRGs.htm).
- For the purposes of access control, you must provide the L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the
compartment where you want
the DRG to reside. Notice that the DRG doesn't have to be in the same compartment as the VCN,
the DRG attachment, or other Networking Service components. If you're not sure which compartment
to use, put the DRG in the same compartment as the VCN. For more information about compartments
and access control, see L(Overview of the IAM Service,https://docs.cloud.oracle.com/iaas/Content/Identity/Concepts/overview.htm).
For information about OCIDs, see L(Resource Identifiers,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm).
- "You may optionally specify a *display name* for the DRG, otherwise a default is provided.
It does not have to be unique, and you can change it. Avoid entering confidential information."
- "This resource has the following action operations in the M(oracle.oci.oci_network_drg_actions) module: change_compartment, get_all_drg_attachments,
upgrade."
version_added: "2.9.0"
author: Oracle (@oracle)
options:
compartment_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the compartment to contain the DRG.
- Required for create using I(state=present).
- Required for update when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is set.
- Required for delete when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is set.
type: str
defined_tags:
description:
- Defined tags for this resource. Each key is predefined and scoped to a
namespace. For more information, see L(Resource Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Operations\\": {\\"CostCenter\\": \\"42\\"}}`"
- This parameter is updatable.
type: dict
display_name:
description:
- A user-friendly name. Does not have to be unique, and it's changeable.
Avoid entering confidential information.
- Required for create, update, delete when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is set.
- This parameter is updatable when C(OCI_USE_NAME_AS_IDENTIFIER) is not set.
type: str
aliases: ["name"]
freeform_tags:
description:
- Free-form tags for this resource. Each tag is a simple key-value pair with no
predefined name, type, or namespace. For more information, see L(Resource
Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Department\\": \\"Finance\\"}`"
- This parameter is updatable.
type: dict
drg_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the DRG.
- Required for update using I(state=present) when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is not set.
- Required for delete using I(state=absent) when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is not set.
type: str
aliases: ["id"]
default_drg_route_tables:
description:
- ""
- This parameter is updatable.
type: dict
suboptions:
vcn:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to DRG
attachments
of type VCN on creation.
- This parameter is updatable.
type: str
ipsec_tunnel:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table assigned to DRG
attachments
of type IPSEC_TUNNEL on creation.
- This parameter is updatable.
type: str
virtual_circuit:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to
DRG attachments
of type VIRTUAL_CIRCUIT on creation.
- This parameter is updatable.
type: str
remote_peering_connection:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to
DRG attachments
of type REMOTE_PEERING_CONNECTION on creation.
- This parameter is updatable.
type: str
state:
description:
- The state of the Drg.
- Use I(state=present) to create or update a Drg.
- Use I(state=absent) to delete a Drg.
type: str
required: false
default: 'present'
choices: ["present", "absent"]
extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_creatable_resource, oracle.oci.oracle_wait_options ]
"""
EXAMPLES = """
- name: Create drg
oci_network_drg:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
# optional
defined_tags: {'Operations': {'CostCenter': 'US'}}
display_name: display_name_example
freeform_tags: {'Department': 'Finance'}
- name: Update drg
oci_network_drg:
# required
drg_id: "ocid1.drg.oc1..xxxxxxEXAMPLExxxxxx"
# optional
defined_tags: {'Operations': {'CostCenter': 'US'}}
display_name: display_name_example
freeform_tags: {'Department': 'Finance'}
default_drg_route_tables:
# optional
vcn: vcn_example
ipsec_tunnel: ipsec_tunnel_example
virtual_circuit: virtual_circuit_example
remote_peering_connection: remote_peering_connection_example
- name: Update drg using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
oci_network_drg:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
display_name: display_name_example
# optional
defined_tags: {'Operations': {'CostCenter': 'US'}}
freeform_tags: {'Department': 'Finance'}
default_drg_route_tables:
# optional
vcn: vcn_example
ipsec_tunnel: ipsec_tunnel_example
virtual_circuit: virtual_circuit_example
remote_peering_connection: remote_peering_connection_example
- name: Delete drg
oci_network_drg:
# required
drg_id: "ocid1.drg.oc1..xxxxxxEXAMPLExxxxxx"
state: absent
- name: Delete drg using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
oci_network_drg:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
display_name: display_name_example
state: absent
"""
RETURN = """
drg:
description:
- Details of the Drg resource acted upon by the current operation
returned: on success
type: complex
contains:
compartment_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the compartment containing the DRG.
returned: on success
type: str
sample: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
defined_tags:
description:
- Defined tags for this resource. Each key is predefined and scoped to a
namespace. For more information, see L(Resource Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Operations\\": {\\"CostCenter\\": \\"42\\"}}`"
returned: on success
type: dict
sample: {'Operations': {'CostCenter': 'US'}}
display_name:
description:
- A user-friendly name. Does not have to be unique, and it's changeable.
Avoid entering confidential information.
returned: on success
type: str
sample: display_name_example
freeform_tags:
description:
- Free-form tags for this resource. Each tag is a simple key-value pair with no
predefined name, type, or namespace. For more information, see L(Resource
Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Department\\": \\"Finance\\"}`"
returned: on success
type: dict
sample: {'Department': 'Finance'}
id:
description:
- The DRG's Oracle ID (L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm)).
returned: on success
type: str
sample: "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx"
lifecycle_state:
description:
- The DRG's current state.
returned: on success
type: str
sample: PROVISIONING
time_created:
description:
- The date and time the DRG was created, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339).
- "Example: `2016-08-25T21:10:29.600Z`"
returned: on success
type: str
sample: "2013-10-20T19:20:30+01:00"
default_drg_route_tables:
description:
- ""
returned: on success
type: complex
contains:
vcn:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to
DRG attachments
of type VCN on creation.
returned: on success
type: str
sample: vcn_example
ipsec_tunnel:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table assigned to DRG
attachments
of type IPSEC_TUNNEL on creation.
returned: on success
type: str
sample: ipsec_tunnel_example
virtual_circuit:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned
to DRG attachments
of type VIRTUAL_CIRCUIT on creation.
returned: on success
type: str
sample: virtual_circuit_example
remote_peering_connection:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned
to DRG attachments
of type REMOTE_PEERING_CONNECTION on creation.
returned: on success
type: str
sample: remote_peering_connection_example
default_export_drg_route_distribution_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of this DRG's default export route distribution for the DRG
attachments.
returned: on success
type: str
sample: "ocid1.defaultexportdrgroutedistribution.oc1..xxxxxxEXAMPLExxxxxx"
sample: {
"compartment_id": "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx",
"defined_tags": {'Operations': {'CostCenter': 'US'}},
"display_name": "display_name_example",
"freeform_tags": {'Department': 'Finance'},
"id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx",
"lifecycle_state": "PROVISIONING",
"time_created": "2013-10-20T19:20:30+01:00",
"default_drg_route_tables": {
"vcn": "vcn_example",
"ipsec_tunnel": "ipsec_tunnel_example",
"virtual_circuit": "virtual_circuit_example",
"remote_peering_connection": "remote_peering_connection_example"
},
"default_export_drg_route_distribution_id": "ocid1.defaultexportdrgroutedistribution.oc1..xxxxxxEXAMPLExxxxxx"
}
"""
from ansible.module_utils.basic import AnsibleModule
from ansible_collections.oracle.oci.plugins.module_utils import (
oci_common_utils,
oci_wait_utils,
)
from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import (
OCIResourceHelperBase,
get_custom_class,
)
try:
from oci.core import VirtualNetworkClient
from oci.core.models import CreateDrgDetails
from oci.core.models import UpdateDrgDetails
HAS_OCI_PY_SDK = True
except ImportError:
HAS_OCI_PY_SDK = False
class DrgHelperGen(OCIResourceHelperBase):
"""Supported operations: create, update, get, list and delete"""
def get_possible_entity_types(self):
return super(DrgHelperGen, self).get_possible_entity_types() + [
"drg",
"drgs",
"coredrg",
"coredrgs",
"drgresource",
"drgsresource",
"core",
]
def get_module_resource_id_param(self):
return "drg_id"
def get_module_resource_id(self):
return self.module.params.get("drg_id")
def get_get_fn(self):
return self.client.get_drg
def get_resource(self):
return oci_common_utils.call_with_backoff(
self.client.get_drg, drg_id=self.module.params.get("drg_id"),
)
def get_required_kwargs_for_list(self):
required_list_method_params = [
"compartment_id",
]
return dict(
(param, self.module.params[param]) for param in required_list_method_params
)
def get_optional_kwargs_for_list(self):
return dict()
def list_resources(self):
required_kwargs = self.get_required_kwargs_for_list()
optional_kwargs = self.get_optional_kwargs_for_list()
kwargs = oci_common_utils.merge_dicts(required_kwargs, optional_kwargs)
return oci_common_utils.list_all_resources(self.client.list_drgs, **kwargs)
def get_create_model_class(self):
return CreateDrgDetails
def create_resource(self):
create_details = self.get_create_model()
return oci_wait_utils.call_and_wait(
call_fn=self.client.create_drg,
call_fn_args=(),
call_fn_kwargs=dict(create_drg_details=create_details,),
waiter_type=oci_wait_utils.LIFECYCLE_STATE_WAITER_KEY,
operation=oci_common_utils.CREATE_OPERATION_KEY,
waiter_client=self.get_waiter_client(),
resource_helper=self,
wait_for_states=self.get_wait_for_states_for_operation(
oci_common_utils.CREATE_OPERATION_KEY,
),
)
def get_update_model_class(self):
return UpdateDrgDetails
def update_resource(self):
update_details = self.get_update_model()
return oci_wait_utils.call_and_wait(
call_fn=self.client.update_drg,
call_fn_args=(),
call_fn_kwargs=dict(
drg_id=self.module.params.get("drg_id"),
update_drg_details=update_details,
),
waiter_type=oci_wait_utils.LIFECYCLE_STATE_WAITER_KEY,
operation=oci_common_utils.UPDATE_OPERATION_KEY,
waiter_client=self.get_waiter_client(),
resource_helper=self,
wait_for_states=self.get_wait_for_states_for_operation(
oci_common_utils.UPDATE_OPERATION_KEY,
),
)
def delete_resource(self):
return oci_wait_utils.call_and_wait(
call_fn=self.client.delete_drg,
call_fn_args=(),
call_fn_kwargs=dict(drg_id=self.module.params.get("drg_id"),),
waiter_type=oci_wait_utils.LIFECYCLE_STATE_WAITER_KEY,
operation=oci_common_utils.DELETE_OPERATION_KEY,
waiter_client=self.get_waiter_client(),
resource_helper=self,
wait_for_states=self.get_wait_for_states_for_operation(
oci_common_utils.DELETE_OPERATION_KEY,
),
)
DrgHelperCustom = get_custom_class("DrgHelperCustom")
class ResourceHelper(DrgHelperCustom, DrgHelperGen):
pass
def main():
module_args = oci_common_utils.get_common_arg_spec(
supports_create=True, supports_wait=True
)
module_args.update(
dict(
compartment_id=dict(type="str"),
defined_tags=dict(type="dict"),
display_name=dict(aliases=["name"], type="str"),
freeform_tags=dict(type="dict"),
drg_id=dict(aliases=["id"], type="str"),
default_drg_route_tables=dict(
type="dict",
options=dict(
vcn=dict(type="str"),
ipsec_tunnel=dict(type="str"),
virtual_circuit=dict(type="str"),
remote_peering_connection=dict(type="str"),
),
),
state=dict(type="str", default="present", choices=["present", "absent"]),
)
)
module = AnsibleModule(argument_spec=module_args, supports_check_mode=True)
if not HAS_OCI_PY_SDK:
module.fail_json(msg="oci python sdk required for this module.")
resource_helper = ResourceHelper(
module=module,
resource_type="drg",
service_client_class=VirtualNetworkClient,
namespace="core",
)
result = dict(changed=False)
if resource_helper.is_delete_using_name():
result = resource_helper.delete_using_name()
elif resource_helper.is_delete():
result = resource_helper.delete()
elif resource_helper.is_update_using_name():
result = resource_helper.update_using_name()
elif resource_helper.is_update():
result = resource_helper.update()
elif resource_helper.is_create():
result = resource_helper.create()
module.exit_json(**result)
if __name__ == "__main__":
main()
| 41.076446 | 160 | 0.632866 |
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {
"metadata_version": "1.1",
"status": ["preview"],
"supported_by": "community",
}
DOCUMENTATION = """
---
module: oci_network_drg
short_description: Manage a Drg resource in Oracle Cloud Infrastructure
description:
- This module allows the user to create, update and delete a Drg resource in Oracle Cloud Infrastructure
- For I(state=present), creates a new dynamic routing gateway (DRG) in the specified compartment. For more information,
see L(Dynamic Routing Gateways (DRGs),https://docs.cloud.oracle.com/iaas/Content/Network/Tasks/managingDRGs.htm).
- For the purposes of access control, you must provide the L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the
compartment where you want
the DRG to reside. Notice that the DRG doesn't have to be in the same compartment as the VCN,
the DRG attachment, or other Networking Service components. If you're not sure which compartment
to use, put the DRG in the same compartment as the VCN. For more information about compartments
and access control, see L(Overview of the IAM Service,https://docs.cloud.oracle.com/iaas/Content/Identity/Concepts/overview.htm).
For information about OCIDs, see L(Resource Identifiers,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm).
- "You may optionally specify a *display name* for the DRG, otherwise a default is provided.
It does not have to be unique, and you can change it. Avoid entering confidential information."
- "This resource has the following action operations in the M(oracle.oci.oci_network_drg_actions) module: change_compartment, get_all_drg_attachments,
upgrade."
version_added: "2.9.0"
author: Oracle (@oracle)
options:
compartment_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the compartment to contain the DRG.
- Required for create using I(state=present).
- Required for update when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is set.
- Required for delete when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is set.
type: str
defined_tags:
description:
- Defined tags for this resource. Each key is predefined and scoped to a
namespace. For more information, see L(Resource Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Operations\\": {\\"CostCenter\\": \\"42\\"}}`"
- This parameter is updatable.
type: dict
display_name:
description:
- A user-friendly name. Does not have to be unique, and it's changeable.
Avoid entering confidential information.
- Required for create, update, delete when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is set.
- This parameter is updatable when C(OCI_USE_NAME_AS_IDENTIFIER) is not set.
type: str
aliases: ["name"]
freeform_tags:
description:
- Free-form tags for this resource. Each tag is a simple key-value pair with no
predefined name, type, or namespace. For more information, see L(Resource
Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Department\\": \\"Finance\\"}`"
- This parameter is updatable.
type: dict
drg_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the DRG.
- Required for update using I(state=present) when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is not set.
- Required for delete using I(state=absent) when environment variable C(OCI_USE_NAME_AS_IDENTIFIER) is not set.
type: str
aliases: ["id"]
default_drg_route_tables:
description:
- ""
- This parameter is updatable.
type: dict
suboptions:
vcn:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to DRG
attachments
of type VCN on creation.
- This parameter is updatable.
type: str
ipsec_tunnel:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table assigned to DRG
attachments
of type IPSEC_TUNNEL on creation.
- This parameter is updatable.
type: str
virtual_circuit:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to
DRG attachments
of type VIRTUAL_CIRCUIT on creation.
- This parameter is updatable.
type: str
remote_peering_connection:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to
DRG attachments
of type REMOTE_PEERING_CONNECTION on creation.
- This parameter is updatable.
type: str
state:
description:
- The state of the Drg.
- Use I(state=present) to create or update a Drg.
- Use I(state=absent) to delete a Drg.
type: str
required: false
default: 'present'
choices: ["present", "absent"]
extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_creatable_resource, oracle.oci.oracle_wait_options ]
"""
EXAMPLES = """
- name: Create drg
oci_network_drg:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
# optional
defined_tags: {'Operations': {'CostCenter': 'US'}}
display_name: display_name_example
freeform_tags: {'Department': 'Finance'}
- name: Update drg
oci_network_drg:
# required
drg_id: "ocid1.drg.oc1..xxxxxxEXAMPLExxxxxx"
# optional
defined_tags: {'Operations': {'CostCenter': 'US'}}
display_name: display_name_example
freeform_tags: {'Department': 'Finance'}
default_drg_route_tables:
# optional
vcn: vcn_example
ipsec_tunnel: ipsec_tunnel_example
virtual_circuit: virtual_circuit_example
remote_peering_connection: remote_peering_connection_example
- name: Update drg using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
oci_network_drg:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
display_name: display_name_example
# optional
defined_tags: {'Operations': {'CostCenter': 'US'}}
freeform_tags: {'Department': 'Finance'}
default_drg_route_tables:
# optional
vcn: vcn_example
ipsec_tunnel: ipsec_tunnel_example
virtual_circuit: virtual_circuit_example
remote_peering_connection: remote_peering_connection_example
- name: Delete drg
oci_network_drg:
# required
drg_id: "ocid1.drg.oc1..xxxxxxEXAMPLExxxxxx"
state: absent
- name: Delete drg using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
oci_network_drg:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
display_name: display_name_example
state: absent
"""
RETURN = """
drg:
description:
- Details of the Drg resource acted upon by the current operation
returned: on success
type: complex
contains:
compartment_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the compartment containing the DRG.
returned: on success
type: str
sample: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
defined_tags:
description:
- Defined tags for this resource. Each key is predefined and scoped to a
namespace. For more information, see L(Resource Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Operations\\": {\\"CostCenter\\": \\"42\\"}}`"
returned: on success
type: dict
sample: {'Operations': {'CostCenter': 'US'}}
display_name:
description:
- A user-friendly name. Does not have to be unique, and it's changeable.
Avoid entering confidential information.
returned: on success
type: str
sample: display_name_example
freeform_tags:
description:
- Free-form tags for this resource. Each tag is a simple key-value pair with no
predefined name, type, or namespace. For more information, see L(Resource
Tags,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
- "Example: `{\\"Department\\": \\"Finance\\"}`"
returned: on success
type: dict
sample: {'Department': 'Finance'}
id:
description:
- The DRG's Oracle ID (L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm)).
returned: on success
type: str
sample: "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx"
lifecycle_state:
description:
- The DRG's current state.
returned: on success
type: str
sample: PROVISIONING
time_created:
description:
- The date and time the DRG was created, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339).
- "Example: `2016-08-25T21:10:29.600Z`"
returned: on success
type: str
sample: "2013-10-20T19:20:30+01:00"
default_drg_route_tables:
description:
- ""
returned: on success
type: complex
contains:
vcn:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned to
DRG attachments
of type VCN on creation.
returned: on success
type: str
sample: vcn_example
ipsec_tunnel:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table assigned to DRG
attachments
of type IPSEC_TUNNEL on creation.
returned: on success
type: str
sample: ipsec_tunnel_example
virtual_circuit:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned
to DRG attachments
of type VIRTUAL_CIRCUIT on creation.
returned: on success
type: str
sample: virtual_circuit_example
remote_peering_connection:
description:
- The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the default DRG route table to be assigned
to DRG attachments
of type REMOTE_PEERING_CONNECTION on creation.
returned: on success
type: str
sample: remote_peering_connection_example
default_export_drg_route_distribution_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of this DRG's default export route distribution for the DRG
attachments.
returned: on success
type: str
sample: "ocid1.defaultexportdrgroutedistribution.oc1..xxxxxxEXAMPLExxxxxx"
sample: {
"compartment_id": "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx",
"defined_tags": {'Operations': {'CostCenter': 'US'}},
"display_name": "display_name_example",
"freeform_tags": {'Department': 'Finance'},
"id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx",
"lifecycle_state": "PROVISIONING",
"time_created": "2013-10-20T19:20:30+01:00",
"default_drg_route_tables": {
"vcn": "vcn_example",
"ipsec_tunnel": "ipsec_tunnel_example",
"virtual_circuit": "virtual_circuit_example",
"remote_peering_connection": "remote_peering_connection_example"
},
"default_export_drg_route_distribution_id": "ocid1.defaultexportdrgroutedistribution.oc1..xxxxxxEXAMPLExxxxxx"
}
"""
from ansible.module_utils.basic import AnsibleModule
from ansible_collections.oracle.oci.plugins.module_utils import (
oci_common_utils,
oci_wait_utils,
)
from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import (
OCIResourceHelperBase,
get_custom_class,
)
try:
from oci.core import VirtualNetworkClient
from oci.core.models import CreateDrgDetails
from oci.core.models import UpdateDrgDetails
HAS_OCI_PY_SDK = True
except ImportError:
HAS_OCI_PY_SDK = False
class DrgHelperGen(OCIResourceHelperBase):
def get_possible_entity_types(self):
return super(DrgHelperGen, self).get_possible_entity_types() + [
"drg",
"drgs",
"coredrg",
"coredrgs",
"drgresource",
"drgsresource",
"core",
]
def get_module_resource_id_param(self):
return "drg_id"
def get_module_resource_id(self):
return self.module.params.get("drg_id")
def get_get_fn(self):
return self.client.get_drg
def get_resource(self):
return oci_common_utils.call_with_backoff(
self.client.get_drg, drg_id=self.module.params.get("drg_id"),
)
def get_required_kwargs_for_list(self):
required_list_method_params = [
"compartment_id",
]
return dict(
(param, self.module.params[param]) for param in required_list_method_params
)
def get_optional_kwargs_for_list(self):
return dict()
def list_resources(self):
required_kwargs = self.get_required_kwargs_for_list()
optional_kwargs = self.get_optional_kwargs_for_list()
kwargs = oci_common_utils.merge_dicts(required_kwargs, optional_kwargs)
return oci_common_utils.list_all_resources(self.client.list_drgs, **kwargs)
def get_create_model_class(self):
return CreateDrgDetails
def create_resource(self):
create_details = self.get_create_model()
return oci_wait_utils.call_and_wait(
call_fn=self.client.create_drg,
call_fn_args=(),
call_fn_kwargs=dict(create_drg_details=create_details,),
waiter_type=oci_wait_utils.LIFECYCLE_STATE_WAITER_KEY,
operation=oci_common_utils.CREATE_OPERATION_KEY,
waiter_client=self.get_waiter_client(),
resource_helper=self,
wait_for_states=self.get_wait_for_states_for_operation(
oci_common_utils.CREATE_OPERATION_KEY,
),
)
def get_update_model_class(self):
return UpdateDrgDetails
def update_resource(self):
update_details = self.get_update_model()
return oci_wait_utils.call_and_wait(
call_fn=self.client.update_drg,
call_fn_args=(),
call_fn_kwargs=dict(
drg_id=self.module.params.get("drg_id"),
update_drg_details=update_details,
),
waiter_type=oci_wait_utils.LIFECYCLE_STATE_WAITER_KEY,
operation=oci_common_utils.UPDATE_OPERATION_KEY,
waiter_client=self.get_waiter_client(),
resource_helper=self,
wait_for_states=self.get_wait_for_states_for_operation(
oci_common_utils.UPDATE_OPERATION_KEY,
),
)
def delete_resource(self):
return oci_wait_utils.call_and_wait(
call_fn=self.client.delete_drg,
call_fn_args=(),
call_fn_kwargs=dict(drg_id=self.module.params.get("drg_id"),),
waiter_type=oci_wait_utils.LIFECYCLE_STATE_WAITER_KEY,
operation=oci_common_utils.DELETE_OPERATION_KEY,
waiter_client=self.get_waiter_client(),
resource_helper=self,
wait_for_states=self.get_wait_for_states_for_operation(
oci_common_utils.DELETE_OPERATION_KEY,
),
)
DrgHelperCustom = get_custom_class("DrgHelperCustom")
class ResourceHelper(DrgHelperCustom, DrgHelperGen):
pass
def main():
module_args = oci_common_utils.get_common_arg_spec(
supports_create=True, supports_wait=True
)
module_args.update(
dict(
compartment_id=dict(type="str"),
defined_tags=dict(type="dict"),
display_name=dict(aliases=["name"], type="str"),
freeform_tags=dict(type="dict"),
drg_id=dict(aliases=["id"], type="str"),
default_drg_route_tables=dict(
type="dict",
options=dict(
vcn=dict(type="str"),
ipsec_tunnel=dict(type="str"),
virtual_circuit=dict(type="str"),
remote_peering_connection=dict(type="str"),
),
),
state=dict(type="str", default="present", choices=["present", "absent"]),
)
)
module = AnsibleModule(argument_spec=module_args, supports_check_mode=True)
if not HAS_OCI_PY_SDK:
module.fail_json(msg="oci python sdk required for this module.")
resource_helper = ResourceHelper(
module=module,
resource_type="drg",
service_client_class=VirtualNetworkClient,
namespace="core",
)
result = dict(changed=False)
if resource_helper.is_delete_using_name():
result = resource_helper.delete_using_name()
elif resource_helper.is_delete():
result = resource_helper.delete()
elif resource_helper.is_update_using_name():
result = resource_helper.update_using_name()
elif resource_helper.is_update():
result = resource_helper.update()
elif resource_helper.is_create():
result = resource_helper.create()
module.exit_json(**result)
if __name__ == "__main__":
main()
| true | true |
f72c8559dfd7a6014d9c69e946707586ad068801 | 2,817 | py | Python | src/dashboard/pages/visualization/visualization.py | ddlatumalea/disease_and_life | aa8c84fdd4a0b41bc0ee275538ac70a362eb26ba | [
"Apache-2.0"
] | null | null | null | src/dashboard/pages/visualization/visualization.py | ddlatumalea/disease_and_life | aa8c84fdd4a0b41bc0ee275538ac70a362eb26ba | [
"Apache-2.0"
] | null | null | null | src/dashboard/pages/visualization/visualization.py | ddlatumalea/disease_and_life | aa8c84fdd4a0b41bc0ee275538ac70a362eb26ba | [
"Apache-2.0"
] | null | null | null | from pathlib import Path
import panel as pn
import pandas as pd
import plotly.express as px
from models.pages import Page
from models.utils.paths import get_prepared_data_path, get_standardized_data_file
from dashboard.widgets import heatmap
PREPARED_DATA_DIR = get_prepared_data_path()
PREPARED_DATA_FILE = get_standardized_data_file()
COLUMNS = ['non-communicable chronic disease [deaths]',
'cancer [deaths]', 'cardiovascular disease [deaths]',
'diabetes mellitus [deaths]', 'chronic respiratory diseases [deaths]',
'diseases of digestive system [deaths]',
'life expectancy [age]']
def get_correlation_heatmap(df, columns):
corr = df[columns].corr()
z = corr.values.round(decimals=2)
x = corr.index
y = corr.index
return heatmap(z, x, y, labels=dict(color='Correlation'))
def get_line_plot(df, x_col, y_col, index, title, width=500):
if width is None:
fig = px.line(df, x=x_col, y=y_col, color=index, title=title)
return pn.pane.Plotly(fig)
else:
fig = px.line(df, x=x_col, y=y_col, color=index, title=title, width=width)
return pn.pane.Plotly(fig)
data = pd.read_csv(Path(PREPARED_DATA_DIR, PREPARED_DATA_FILE))
df = data[data['sex'] == 3]
class VisualizationPage(Page):
def __init__(self):
super().__init__()
self.df = df
self.checkbutton = pn.widgets.CheckButtonGroup(name='Countries', value=['Netherlands'],
options=['Netherlands', 'Japan', 'Canada'])
self.pane = pn.Column(self.checkbutton, self.get_plot(self.checkbutton))
self.button = pn.widgets.Button(name='Visualization')
self.checkbutton.param.watch(self.update, 'value')
def get_plot(self, checkbutton):
gspec = pn.GridSpec(ncols=2, nrows=4, width=1200, height=1800)
selection = df.loc[df['country'].isin(checkbutton.value)]
# life expectancy plot
life_exp_plot = pn.pane.Plotly(
px.line(selection, x='year', y='life expectancy [age]', color='country', title='life expectancy'))
# plots about disease
plots = []
for col in COLUMNS[:-1]:
plots.append(pn.pane.Plotly(
px.line(selection, x='year', y=col, labels={col: 'Deaths per 100.000 people'}, color='country',
title=col.replace('[deaths]', ''))))
gspec[0, :] = life_exp_plot
gspec[1, 0] = plots[0]
gspec[1, 1] = plots[1]
gspec[2, 0] = plots[2]
gspec[2, 1] = plots[3]
gspec[3, 0] = plots[4]
gspec[3, 1] = plots[5]
return gspec
def update(self, event):
self.pane[1] = self.get_plot(self.checkbutton)
def get_contents(self):
return self.pane, self.button
| 32.011364 | 111 | 0.624778 | from pathlib import Path
import panel as pn
import pandas as pd
import plotly.express as px
from models.pages import Page
from models.utils.paths import get_prepared_data_path, get_standardized_data_file
from dashboard.widgets import heatmap
PREPARED_DATA_DIR = get_prepared_data_path()
PREPARED_DATA_FILE = get_standardized_data_file()
COLUMNS = ['non-communicable chronic disease [deaths]',
'cancer [deaths]', 'cardiovascular disease [deaths]',
'diabetes mellitus [deaths]', 'chronic respiratory diseases [deaths]',
'diseases of digestive system [deaths]',
'life expectancy [age]']
def get_correlation_heatmap(df, columns):
corr = df[columns].corr()
z = corr.values.round(decimals=2)
x = corr.index
y = corr.index
return heatmap(z, x, y, labels=dict(color='Correlation'))
def get_line_plot(df, x_col, y_col, index, title, width=500):
if width is None:
fig = px.line(df, x=x_col, y=y_col, color=index, title=title)
return pn.pane.Plotly(fig)
else:
fig = px.line(df, x=x_col, y=y_col, color=index, title=title, width=width)
return pn.pane.Plotly(fig)
data = pd.read_csv(Path(PREPARED_DATA_DIR, PREPARED_DATA_FILE))
df = data[data['sex'] == 3]
class VisualizationPage(Page):
def __init__(self):
super().__init__()
self.df = df
self.checkbutton = pn.widgets.CheckButtonGroup(name='Countries', value=['Netherlands'],
options=['Netherlands', 'Japan', 'Canada'])
self.pane = pn.Column(self.checkbutton, self.get_plot(self.checkbutton))
self.button = pn.widgets.Button(name='Visualization')
self.checkbutton.param.watch(self.update, 'value')
def get_plot(self, checkbutton):
gspec = pn.GridSpec(ncols=2, nrows=4, width=1200, height=1800)
selection = df.loc[df['country'].isin(checkbutton.value)]
life_exp_plot = pn.pane.Plotly(
px.line(selection, x='year', y='life expectancy [age]', color='country', title='life expectancy'))
plots = []
for col in COLUMNS[:-1]:
plots.append(pn.pane.Plotly(
px.line(selection, x='year', y=col, labels={col: 'Deaths per 100.000 people'}, color='country',
title=col.replace('[deaths]', ''))))
gspec[0, :] = life_exp_plot
gspec[1, 0] = plots[0]
gspec[1, 1] = plots[1]
gspec[2, 0] = plots[2]
gspec[2, 1] = plots[3]
gspec[3, 0] = plots[4]
gspec[3, 1] = plots[5]
return gspec
def update(self, event):
self.pane[1] = self.get_plot(self.checkbutton)
def get_contents(self):
return self.pane, self.button
| true | true |
f72c85a0942c0540d2abee2d9180bd484b5864a7 | 6,141 | py | Python | main.py | akashrchandran/pokeowo | 0b2621494ef56f350239817546b843814fe3448e | [
"MIT"
] | null | null | null | main.py | akashrchandran/pokeowo | 0b2621494ef56f350239817546b843814fe3448e | [
"MIT"
] | null | null | null | main.py | akashrchandran/pokeowo | 0b2621494ef56f350239817546b843814fe3448e | [
"MIT"
] | null | null | null | import datetime
import json
import multiprocessing
import os
import random
import re
import time
import discum
version = 'v0.01'
config_path = 'data/config.json'
logo = f'''
###### ### ### ## ####### ### ## ## ###
## ## ## ## ## ## ## ## ## ## ## ## ## ##
## ## ## ## ## ## ## ## ## ## ## ## ##
##### ## ## #### #### ## ## ## # ## ## ##
## ## ## ## ## ## ## ## ####### ## ##
## ## ## ## ## ## ## ## ## ### ### ## ##
#### ### ### ## ####### ### ## ## ###
~ Pokétwo Autocatcher {version}
'''
num_pokemon = 0
shiny = 0
legendary = 0
mythical = 0
poketwo_id = '716390085896962058'
def auto_config():
global user_token, channel_id
if not os.path.exists(config_path):
with open(config_path, "a") as file:
auth_token = input("Enter you Discord auth token: ")
channel_id = input("Enter the preferred Channel ID for spamming and catching: ")
file.write("{\n")
file.write(f' "user_token" : "{auth_token}",\n')
file.write(f' "channel_id" : "{channel_id}"\n')
file.write("}")
os.system('cls' if os.name=='nt' else 'clear')
with open(config_path,'r') as file:
info = json.loads(file.read())
user_token = info['user_token']
channel_id = info['channel_id']
with open('data/pokemon.txt', 'r', encoding='utf8') as file:
pokemon_list = file.read()
with open('data/legendary.txt','r') as file:
legendary_list = file.read()
with open('data/mythical.txt','r') as file:
mythical_list = file.read()
auto_config()
print(logo)
bot = discum.Client(token=user_token, log=False)
def solve(message):
hint = [message[i] for i in range(15, len(message) - 1) if message[i] != '\\']
hint_string = ''.join(hint)
return re.findall(
'^' + hint_string.replace('_', '.') + '$', pokemon_list, re.MULTILINE
)
def spam():
while True:
random_number = random.getrandbits(128)
bot.sendMessage(channel_id, random_number)
intervals = [2.0,2.1,2.2,2.3,2.4,2.5]
time.sleep(random.choice(intervals))
def start_spam():
new_process = multiprocessing.Process(target=spam)
new_process.start()
return new_process
def stop(process):
process.terminate()
def log(string):
now = datetime.datetime.now()
current_time = now.strftime('%H:%M:%S')
print(f'[{current_time}]', string)
@bot.gateway.command
def on_ready(resp):
if resp.event.ready_supplemental:
user = bot.gateway.session.user
log(f'Logged into account: {user["username"]}#{user["discriminator"]}')
@bot.gateway.command
def on_message(resp):
global spam_process
if resp.event.message:
m = resp.parsed.auto()
if m['channel_id'] == channel_id and m['author']['id'] == poketwo_id:
if m['embeds']:
embed_title = m['embeds'][0]['title']
if 'wild pokémon has appeared!' in embed_title:
stop(spam_process)
time.sleep(2)
bot.sendMessage(channel_id, '<@716390085896962058> h')
elif "Congratulations" in embed_title:
embed_content = m['embeds'][0]['description']
if 'now level' in embed_content:
stop(spam_process)
split = embed_content.split(' ')
a = embed_content.count(' ')
level = int(split[a].replace('!', ''))
if level == 100:
#wait will implement in next update
pass
spam_process = start_spam()
else:
content = m['content']
if 'The pokémon is ' in content:
if len(solve(content)) == 0:
log('Pokemon not found.')
else:
for i in solve(content):
stop(spam_process)
time.sleep(2)
bot.sendMessage(channel_id, f'<@716390085896962058> c {i}')
time.sleep(2)
spam_process = start_spam()
elif 'Congratulations' in content:
global shiny
global legendary
global num_pokemon
global mythical
num_pokemon += 1
split = content.split(' ')
pokemon = split[7].replace('!','')
if 'These colors seem unusual...' in content:
shiny += 1
log(f'A shiny Pokémon was caught! Pokémon: {pokemon}')
log(f'Shiny: {shiny} | Legendary: {legendary} | Mythical: {mythical}')
elif re.findall(
f'^{pokemon}$', legendary_list, re.MULTILINE
):
legendary += 1
log(f'A legendary Pokémon was caught! Pokémon: {pokemon}')
log(f'Shiny: {shiny} | Legendary: {legendary} | Mythical: {mythical}')
elif re.findall(f'^{pokemon}$', mythical_list, re.MULTILINE):
mythical += 1
log(f'A mythical Pokémon was caught! Pokémon: {pokemon}')
log(f'Shiny: {shiny} | Legendary: {legendary} | Mythical: {mythical}')
else:
print(f'Total Pokémon Caught: {num_pokemon}')
elif 'human' in content:
stop(spam_process)
log('Captcha Detected; Autocatcher Paused. Press enter to restart.')
input()
bot.sendMessage(channel_id, '<@716390085896962058> h')
if __name__ == '__main__':
print('\nEvent Log:')
spam_process = start_spam()
bot.gateway.run(auto_reconnect=True)
| 37.218182 | 96 | 0.485752 | import datetime
import json
import multiprocessing
import os
import random
import re
import time
import discum
version = 'v0.01'
config_path = 'data/config.json'
logo = f'''
###### ### ### ## ####### ### ## ## ###
## ## ## ## ## ## ## ## ## ## ## ## ## ##
## ## ## ## ## ## ## ## ## ## ## ## ##
##### ## ## #### #### ## ## ## # ## ## ##
## ## ## ## ## ## ## ## ####### ## ##
## ## ## ## ## ## ## ## ## ### ### ## ##
#### ### ### ## ####### ### ## ## ###
~ Pokétwo Autocatcher {version}
'''
num_pokemon = 0
shiny = 0
legendary = 0
mythical = 0
poketwo_id = '716390085896962058'
def auto_config():
global user_token, channel_id
if not os.path.exists(config_path):
with open(config_path, "a") as file:
auth_token = input("Enter you Discord auth token: ")
channel_id = input("Enter the preferred Channel ID for spamming and catching: ")
file.write("{\n")
file.write(f' "user_token" : "{auth_token}",\n')
file.write(f' "channel_id" : "{channel_id}"\n')
file.write("}")
os.system('cls' if os.name=='nt' else 'clear')
with open(config_path,'r') as file:
info = json.loads(file.read())
user_token = info['user_token']
channel_id = info['channel_id']
with open('data/pokemon.txt', 'r', encoding='utf8') as file:
pokemon_list = file.read()
with open('data/legendary.txt','r') as file:
legendary_list = file.read()
with open('data/mythical.txt','r') as file:
mythical_list = file.read()
auto_config()
print(logo)
bot = discum.Client(token=user_token, log=False)
def solve(message):
hint = [message[i] for i in range(15, len(message) - 1) if message[i] != '\\']
hint_string = ''.join(hint)
return re.findall(
'^' + hint_string.replace('_', '.') + '$', pokemon_list, re.MULTILINE
)
def spam():
while True:
random_number = random.getrandbits(128)
bot.sendMessage(channel_id, random_number)
intervals = [2.0,2.1,2.2,2.3,2.4,2.5]
time.sleep(random.choice(intervals))
def start_spam():
new_process = multiprocessing.Process(target=spam)
new_process.start()
return new_process
def stop(process):
process.terminate()
def log(string):
now = datetime.datetime.now()
current_time = now.strftime('%H:%M:%S')
print(f'[{current_time}]', string)
@bot.gateway.command
def on_ready(resp):
if resp.event.ready_supplemental:
user = bot.gateway.session.user
log(f'Logged into account: {user["username"]}#{user["discriminator"]}')
@bot.gateway.command
def on_message(resp):
global spam_process
if resp.event.message:
m = resp.parsed.auto()
if m['channel_id'] == channel_id and m['author']['id'] == poketwo_id:
if m['embeds']:
embed_title = m['embeds'][0]['title']
if 'wild pokémon has appeared!' in embed_title:
stop(spam_process)
time.sleep(2)
bot.sendMessage(channel_id, '<@716390085896962058> h')
elif "Congratulations" in embed_title:
embed_content = m['embeds'][0]['description']
if 'now level' in embed_content:
stop(spam_process)
split = embed_content.split(' ')
a = embed_content.count(' ')
level = int(split[a].replace('!', ''))
if level == 100:
pass
spam_process = start_spam()
else:
content = m['content']
if 'The pokémon is ' in content:
if len(solve(content)) == 0:
log('Pokemon not found.')
else:
for i in solve(content):
stop(spam_process)
time.sleep(2)
bot.sendMessage(channel_id, f'<@716390085896962058> c {i}')
time.sleep(2)
spam_process = start_spam()
elif 'Congratulations' in content:
global shiny
global legendary
global num_pokemon
global mythical
num_pokemon += 1
split = content.split(' ')
pokemon = split[7].replace('!','')
if 'These colors seem unusual...' in content:
shiny += 1
log(f'A shiny Pokémon was caught! Pokémon: {pokemon}')
log(f'Shiny: {shiny} | Legendary: {legendary} | Mythical: {mythical}')
elif re.findall(
f'^{pokemon}$', legendary_list, re.MULTILINE
):
legendary += 1
log(f'A legendary Pokémon was caught! Pokémon: {pokemon}')
log(f'Shiny: {shiny} | Legendary: {legendary} | Mythical: {mythical}')
elif re.findall(f'^{pokemon}$', mythical_list, re.MULTILINE):
mythical += 1
log(f'A mythical Pokémon was caught! Pokémon: {pokemon}')
log(f'Shiny: {shiny} | Legendary: {legendary} | Mythical: {mythical}')
else:
print(f'Total Pokémon Caught: {num_pokemon}')
elif 'human' in content:
stop(spam_process)
log('Captcha Detected; Autocatcher Paused. Press enter to restart.')
input()
bot.sendMessage(channel_id, '<@716390085896962058> h')
if __name__ == '__main__':
print('\nEvent Log:')
spam_process = start_spam()
bot.gateway.run(auto_reconnect=True)
| true | true |
f72c86f6141b9e5ce714030b5766cf7cff25194c | 1,327 | py | Python | isolated_functions.py | wonabru/chainnet | f8ec1e2b580af837cba3322ffe69b95156b1b9a1 | [
"MIT"
] | 5 | 2019-04-20T18:54:55.000Z | 2019-08-23T09:17:20.000Z | isolated_functions.py | wonabru/chainnet | f8ec1e2b580af837cba3322ffe69b95156b1b9a1 | [
"MIT"
] | null | null | null | isolated_functions.py | wonabru/chainnet | f8ec1e2b580af837cba3322ffe69b95156b1b9a1 | [
"MIT"
] | null | null | null | import ast
import re
import pickle
from Crypto.PublicKey import RSA
from base64 import b64decode,b64encode
from tkinter import messagebox
def str2obj(s):
return ast.literal_eval(s.replace('true', 'True').replace('false', 'False'))
def trim_name(name):
return name.replace('@','').replace('#','')
def remove_special_char(in_seq):
"""
Function is responsible for normalize strings to defined format (UPPERCASE with '_' replacing any special character)
:param in_seq: list of strings
:return: list of strings
"""
_sub = re.sub(" {1,5}", "_", in_seq.strip()).lower()
_chars = ['*', '\\', '&', '/', '+']
for x in _chars: _sub = _sub.replace(x, '_')
return _sub
class CFinish:
finish = False
def serialize(message):
return pickle.dumps(message)
def unserialize(ser_message):
return pickle.loads(ser_message)
def encode(n):
b = bytearray()
while n:
b.append(n & 0xFF)
n >>= 8
return b64encode(b).decode('utf-8')
def decode(s):
b = bytearray(b64decode(s.encode('utf-8'))) # in case you're passing in a bytes/str
return sum((1 << (bi * 8)) * bb for (bi, bb) in enumerate(b))
class rsa_temp:
key = RSA.generate(1024)
def showError(ex):
if len(ex.args) > 1:
_title, _err = ex.args
else:
_title, _err = 'Other error', ex.args
messagebox.showerror(title=str(_title), message=str(_err)) | 21.063492 | 117 | 0.679729 | import ast
import re
import pickle
from Crypto.PublicKey import RSA
from base64 import b64decode,b64encode
from tkinter import messagebox
def str2obj(s):
return ast.literal_eval(s.replace('true', 'True').replace('false', 'False'))
def trim_name(name):
return name.replace('@','').replace('#','')
def remove_special_char(in_seq):
_sub = re.sub(" {1,5}", "_", in_seq.strip()).lower()
_chars = ['*', '\\', '&', '/', '+']
for x in _chars: _sub = _sub.replace(x, '_')
return _sub
class CFinish:
finish = False
def serialize(message):
return pickle.dumps(message)
def unserialize(ser_message):
return pickle.loads(ser_message)
def encode(n):
b = bytearray()
while n:
b.append(n & 0xFF)
n >>= 8
return b64encode(b).decode('utf-8')
def decode(s):
b = bytearray(b64decode(s.encode('utf-8')))
return sum((1 << (bi * 8)) * bb for (bi, bb) in enumerate(b))
class rsa_temp:
key = RSA.generate(1024)
def showError(ex):
if len(ex.args) > 1:
_title, _err = ex.args
else:
_title, _err = 'Other error', ex.args
messagebox.showerror(title=str(_title), message=str(_err)) | true | true |
f72c87804c39b074934faddfa6a15a81e1a36cb8 | 4,587 | py | Python | robot/hsin_agent.py | kanokkorn/watering_robot | b39fed532519e2b89a9f1ae1a3d1b72bb550cc1b | [
"MIT"
] | 5 | 2020-04-01T13:55:12.000Z | 2022-03-04T03:32:25.000Z | robot/hsin_agent.py | kanokkorn/watering_robot | b39fed532519e2b89a9f1ae1a3d1b72bb550cc1b | [
"MIT"
] | 7 | 2019-12-21T10:26:40.000Z | 2021-06-25T15:15:05.000Z | robot/hsin_agent.py | kanokkorn/watering_robot | b39fed532519e2b89a9f1ae1a3d1b72bb550cc1b | [
"MIT"
] | 1 | 2020-06-03T07:41:21.000Z | 2020-06-03T07:41:21.000Z | # import modules
from gps3 import gps3
import serial
import math
import time
import csv
import os
# setup gps socket
ser = serial.Serial("/dev/ttyUSB0", 9600)
gps_socket = gps3.GPSDSocket()
data_stream = gps3.DataStream()
gps_socket.connect()
gps_socket.watch()
# read csv files
def track():
# prefix parameter
distance = 10
earth_radius = 6371e3
k = 1
with open("robot/lat_lon.csv", newline="") as f:
read = csv.reader(f)
for gps_row in read:
# print(gps_row) # check if gps read properly
try:
lat_b = float(gps_row[0]) # unpack list to float
lon_b = float(gps_row[1])
except IndexError:
os.system("clear")
raise Exception("Indexing error...Program terminated.")
ser.write(str.encode("S"))
break
# main function
for new_data in gps_socket:
while new_data and distance > 5:
data_stream.unpack(new_data)
# print('Altitude = ', data_stream.TPV['lat'], 'Latitude = ', data_stream.TPV['lon'])
if (data_stream.TPV["lat"] == "n/a") or (
data_stream.TPV["lon"] != "n/a"
):
pass
if (data_stream.TPV["lat"] != "n/a") or (
data_stream.TPV["lon"] != "n/a"
):
try:
in_lat = float(data_stream.TPV["lat"])
except ValueError:
print("lat N/A value")
in_lat = 10.712709
try:
in_lon = float(data_stream.TPV["lon"])
except ValueError:
print("lon N/A value")
in_lon = 99.378788
lat_A = math.radians(in_lat)
lat_B = math.radians(lat_b)
del_lat = math.radians(lat_b - (in_lat))
del_lon = math.radians(lon_b - (in_lon))
a = (math.sin(del_lat / 2) * math.sin(del_lat / 2)) + math.cos(
lat_A
) * math.cos(lat_B) * (
math.sin(del_lon / 2) * math.sin(del_lon / 2)
)
# check if equal zero
try:
c = 2 * math.atan2(math.sqrt(a), math.sqrt((1 - a)))
except ValueError as identifier:
print("No Value")
distance = earth_radius * c
# os.system('clear')
print("Distance: ", distance, " Status : Running")
ser.write(str.encode("M"))
else:
ser.write(str.encode("S"))
os.system("clear")
print("\n==== Checkpoint ", k, " start ====")
time.sleep(0.3)
print("\nDistance: ", distance, " Status : Stop")
time.sleep(0.3)
print("Serial_STOP")
time.sleep(0.3)
for target in range(10):
ser.write(str.encode("O"))
print("watering" + "." * target, end="\r")
ser.write(str.encode("P"))
time.sleep(0.8)
time.sleep(0.3)
print("\nClassification palm Tree :" + str(k))
time.sleep(0.3)
# classify_edit.main()
for target in range(10):
print("writing csv files" + "." * target, end="\r")
time.sleep(0.8)
distance = 10
in_lat = lat_b
in_lon = lon_b
print("\n==== Checkpoint", k, " done ====\n")
k += 1
time.sleep(1)
print("Start Moving to next checkpoint\n")
time.sleep(1)
else:
ser.write(str.encode("S"))
os.system("clear")
print("\n==== End of lines ====")
time.sleep(1)
print("\nFinished\n")
if __name__ == "__main__":
try:
track()
except KeyboardInterrupt:
print("Serial_STOP")
ser.write(str.encode("S"))
raise Exception("Interrupt...Program terminated.")
| 36.404762 | 105 | 0.419228 |
from gps3 import gps3
import serial
import math
import time
import csv
import os
ser = serial.Serial("/dev/ttyUSB0", 9600)
gps_socket = gps3.GPSDSocket()
data_stream = gps3.DataStream()
gps_socket.connect()
gps_socket.watch()
def track():
distance = 10
earth_radius = 6371e3
k = 1
with open("robot/lat_lon.csv", newline="") as f:
read = csv.reader(f)
for gps_row in read:
lat_b = float(gps_row[0])
lon_b = float(gps_row[1])
except IndexError:
os.system("clear")
raise Exception("Indexing error...Program terminated.")
ser.write(str.encode("S"))
break
for new_data in gps_socket:
while new_data and distance > 5:
data_stream.unpack(new_data)
if (data_stream.TPV["lat"] == "n/a") or (
data_stream.TPV["lon"] != "n/a"
):
pass
if (data_stream.TPV["lat"] != "n/a") or (
data_stream.TPV["lon"] != "n/a"
):
try:
in_lat = float(data_stream.TPV["lat"])
except ValueError:
print("lat N/A value")
in_lat = 10.712709
try:
in_lon = float(data_stream.TPV["lon"])
except ValueError:
print("lon N/A value")
in_lon = 99.378788
lat_A = math.radians(in_lat)
lat_B = math.radians(lat_b)
del_lat = math.radians(lat_b - (in_lat))
del_lon = math.radians(lon_b - (in_lon))
a = (math.sin(del_lat / 2) * math.sin(del_lat / 2)) + math.cos(
lat_A
) * math.cos(lat_B) * (
math.sin(del_lon / 2) * math.sin(del_lon / 2)
)
try:
c = 2 * math.atan2(math.sqrt(a), math.sqrt((1 - a)))
except ValueError as identifier:
print("No Value")
distance = earth_radius * c
print("Distance: ", distance, " Status : Running")
ser.write(str.encode("M"))
else:
ser.write(str.encode("S"))
os.system("clear")
print("\n==== Checkpoint ", k, " start ====")
time.sleep(0.3)
print("\nDistance: ", distance, " Status : Stop")
time.sleep(0.3)
print("Serial_STOP")
time.sleep(0.3)
for target in range(10):
ser.write(str.encode("O"))
print("watering" + "." * target, end="\r")
ser.write(str.encode("P"))
time.sleep(0.8)
time.sleep(0.3)
print("\nClassification palm Tree :" + str(k))
time.sleep(0.3)
for target in range(10):
print("writing csv files" + "." * target, end="\r")
time.sleep(0.8)
distance = 10
in_lat = lat_b
in_lon = lon_b
print("\n==== Checkpoint", k, " done ====\n")
k += 1
time.sleep(1)
print("Start Moving to next checkpoint\n")
time.sleep(1)
else:
ser.write(str.encode("S"))
os.system("clear")
print("\n==== End of lines ====")
time.sleep(1)
print("\nFinished\n")
if __name__ == "__main__":
try:
track()
except KeyboardInterrupt:
print("Serial_STOP")
ser.write(str.encode("S"))
raise Exception("Interrupt...Program terminated.")
| true | true |
f72c8854af948f34376eadc837477a9b431ff2c9 | 2,138 | py | Python | app/core/radiofrequency/__init__.py | FHellmann/MLWTF | 582c3505d638907a848d5a6c739ee99981300f17 | [
"Apache-2.0"
] | null | null | null | app/core/radiofrequency/__init__.py | FHellmann/MLWTF | 582c3505d638907a848d5a6c739ee99981300f17 | [
"Apache-2.0"
] | null | null | null | app/core/radiofrequency/__init__.py | FHellmann/MLWTF | 582c3505d638907a848d5a6c739ee99981300f17 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
"""
Author: Fabio Hellmann <info@fabio-hellmann.de>
This is a layer between the raw execution unit and the database.
"""
import logging
from datetime import datetime
from . import rf_rpi
from .models import Protocol, Signal
from ..gpio import RaspberryPi3 as GPIO_PI
from app.database import db
from app.database.models import DataSource, DataSourceType
from app.database.converter import converter
_LOGGER = logging.getLogger(__name__)
class RfDatabase(object):
def __init__(self):
self.db = db
def save_received(self, signal : Signal):
return self.db.add_event(converter.unstructure(signal), DataSource.LOW_RADIO_FREQUENCY, DataSourceType.SENSOR)
def save_send(self, signal : Signal):
return self.db.add_event(converter.unstructure(signal), DataSource.LOW_RADIO_FREQUENCY, DataSourceType.ACTUATOR)
def get_received_signals_since(self, since : datetime):
result_events = self.db.get_events_by(DataSource.LOW_RADIO_FREQUENCY, DataSourceType.SENSOR, since)
result = []
for event in result_events:
result.append(converter.structure(event.data, Signal))
return result
class RfController(object):
def __init__(self):
self._db = RfDatabase()
self._tx_device = rf_rpi.Device(GPIO_PI.GPIO_17.value)
self._tx_device.enable_tx()
self._rx_device = rf_rpi.Device(GPIO_PI.GPIO_27.value)
self._rx_device.enable_rx()
self._rx_device.add_rx_listener(self._receive)
def __del__(self):
self._tx_device.cleanup()
self._rx_device.cleanup()
def get_received_signals_since(self, since : datetime):
return self._db.get_received_signals_since(since)
def send(self, signal : Signal):
_LOGGER.info("Sending radiofrequency signal: " + str(signal))
success = self._tx_device.tx_code(signal)
self._db.save_send(signal)
return success
def _receive(self, signal : Signal):
_LOGGER.info("Receiving radiofrequency signal: " + str(signal))
self._db.save_received(signal)
rf_controller = RfController()
| 30.985507 | 120 | 0.713751 |
import logging
from datetime import datetime
from . import rf_rpi
from .models import Protocol, Signal
from ..gpio import RaspberryPi3 as GPIO_PI
from app.database import db
from app.database.models import DataSource, DataSourceType
from app.database.converter import converter
_LOGGER = logging.getLogger(__name__)
class RfDatabase(object):
def __init__(self):
self.db = db
def save_received(self, signal : Signal):
return self.db.add_event(converter.unstructure(signal), DataSource.LOW_RADIO_FREQUENCY, DataSourceType.SENSOR)
def save_send(self, signal : Signal):
return self.db.add_event(converter.unstructure(signal), DataSource.LOW_RADIO_FREQUENCY, DataSourceType.ACTUATOR)
def get_received_signals_since(self, since : datetime):
result_events = self.db.get_events_by(DataSource.LOW_RADIO_FREQUENCY, DataSourceType.SENSOR, since)
result = []
for event in result_events:
result.append(converter.structure(event.data, Signal))
return result
class RfController(object):
def __init__(self):
self._db = RfDatabase()
self._tx_device = rf_rpi.Device(GPIO_PI.GPIO_17.value)
self._tx_device.enable_tx()
self._rx_device = rf_rpi.Device(GPIO_PI.GPIO_27.value)
self._rx_device.enable_rx()
self._rx_device.add_rx_listener(self._receive)
def __del__(self):
self._tx_device.cleanup()
self._rx_device.cleanup()
def get_received_signals_since(self, since : datetime):
return self._db.get_received_signals_since(since)
def send(self, signal : Signal):
_LOGGER.info("Sending radiofrequency signal: " + str(signal))
success = self._tx_device.tx_code(signal)
self._db.save_send(signal)
return success
def _receive(self, signal : Signal):
_LOGGER.info("Receiving radiofrequency signal: " + str(signal))
self._db.save_received(signal)
rf_controller = RfController()
| true | true |
f72c886994bd9fb0a5722665191651370d918e92 | 2,908 | py | Python | tests/riscv/APIs/State_force.py | Wlgen/force-riscv | 9f09b86c5a21ca00f8e5ade8e5186d65bc3e26f8 | [
"Apache-2.0"
] | 111 | 2020-06-12T22:31:30.000Z | 2022-03-19T03:45:20.000Z | tests/riscv/APIs/State_force.py | Wlgen/force-riscv | 9f09b86c5a21ca00f8e5ade8e5186d65bc3e26f8 | [
"Apache-2.0"
] | 34 | 2020-06-12T20:23:40.000Z | 2022-03-15T20:04:31.000Z | tests/riscv/APIs/State_force.py | Wlgen/force-riscv | 9f09b86c5a21ca00f8e5ade8e5186d65bc3e26f8 | [
"Apache-2.0"
] | 32 | 2020-06-12T19:15:26.000Z | 2022-02-20T11:38:31.000Z | #
# Copyright (C) [2020] Futurewei Technologies, Inc.
#
# FORCE-RISCV is 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
#
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES
# OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
# NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import RandomUtils
from Enums import EStateElementDuplicateMode
from State import State
from base.Sequence import Sequence
from riscv.EnvRISCV import EnvRISCV
from riscv.GenThreadRISCV import GenThreadRISCV
# This test attempts to add StateElements to a State. There is no direct
# mechanism for retrieving the StateElements after they have been added, so
# this test merely ensures the method calls don't crash or fail.
class MainSequence(Sequence):
def generate(self, **kargs):
state = State()
state.setDuplicateMode(EStateElementDuplicateMode.Replace)
mem_start_addr = (RandomUtils.random64(0, 0xFFFFFFFFFFFF) >> 3) << 3
mem_val = RandomUtils.random64()
state.addMemoryStateElement(mem_start_addr, 8, mem_val)
mem_values = []
for _ in range(RandomUtils.random32(1, 64)):
mem_values.append(RandomUtils.random32(0, 0xFF))
mem_start_addr = RandomUtils.random64(0, 0xFFFFFFFFFFFF)
state.addMemoryStateElementsAsBytes(mem_start_addr, mem_values)
gpr_name = "x%d" % RandomUtils.random32(0, 31)
state.addRegisterStateElement(gpr_name, (RandomUtils.random64(),))
fp_reg_name = "D%d" % RandomUtils.random32(0, 31)
state.addRegisterStateElement(fp_reg_name, (RandomUtils.random64(),))
state.addSystemRegisterStateElementByField("sstatus", "FS", 0x3)
state.addVmContextStateElement("mstatus", "MPRV", 0x1)
state.addPcStateElement(RandomUtils.random64(0, 0xFFFFFFFFFFFF))
# Test creating duplicate StateElements
state.addVmContextStateElement("mstatus", "MPRV", 0x0)
state.setDuplicateMode(EStateElementDuplicateMode.Ignore)
state.addRegisterStateElement("sstatus", (RandomUtils.random64(),))
# Test merging two StateElements
mem_start_addr = (RandomUtils.random64(0, 0xFFFFFFFFFFFF) >> 3) << 3
mem_val = RandomUtils.random32()
state.addMemoryStateElement(mem_start_addr, 4, mem_val)
mem_start_addr += 4
mem_values = []
for _ in range(4):
mem_values.append(RandomUtils.random32(0, 0xFF))
state.addMemoryStateElementsAsBytes(mem_start_addr, mem_values)
MainSequenceClass = MainSequence
GenThreadClass = GenThreadRISCV
EnvClass = EnvRISCV
| 38.263158 | 77 | 0.725241 |
import RandomUtils
from Enums import EStateElementDuplicateMode
from State import State
from base.Sequence import Sequence
from riscv.EnvRISCV import EnvRISCV
from riscv.GenThreadRISCV import GenThreadRISCV
class MainSequence(Sequence):
def generate(self, **kargs):
state = State()
state.setDuplicateMode(EStateElementDuplicateMode.Replace)
mem_start_addr = (RandomUtils.random64(0, 0xFFFFFFFFFFFF) >> 3) << 3
mem_val = RandomUtils.random64()
state.addMemoryStateElement(mem_start_addr, 8, mem_val)
mem_values = []
for _ in range(RandomUtils.random32(1, 64)):
mem_values.append(RandomUtils.random32(0, 0xFF))
mem_start_addr = RandomUtils.random64(0, 0xFFFFFFFFFFFF)
state.addMemoryStateElementsAsBytes(mem_start_addr, mem_values)
gpr_name = "x%d" % RandomUtils.random32(0, 31)
state.addRegisterStateElement(gpr_name, (RandomUtils.random64(),))
fp_reg_name = "D%d" % RandomUtils.random32(0, 31)
state.addRegisterStateElement(fp_reg_name, (RandomUtils.random64(),))
state.addSystemRegisterStateElementByField("sstatus", "FS", 0x3)
state.addVmContextStateElement("mstatus", "MPRV", 0x1)
state.addPcStateElement(RandomUtils.random64(0, 0xFFFFFFFFFFFF))
# Test creating duplicate StateElements
state.addVmContextStateElement("mstatus", "MPRV", 0x0)
state.setDuplicateMode(EStateElementDuplicateMode.Ignore)
state.addRegisterStateElement("sstatus", (RandomUtils.random64(),))
# Test merging two StateElements
mem_start_addr = (RandomUtils.random64(0, 0xFFFFFFFFFFFF) >> 3) << 3
mem_val = RandomUtils.random32()
state.addMemoryStateElement(mem_start_addr, 4, mem_val)
mem_start_addr += 4
mem_values = []
for _ in range(4):
mem_values.append(RandomUtils.random32(0, 0xFF))
state.addMemoryStateElementsAsBytes(mem_start_addr, mem_values)
MainSequenceClass = MainSequence
GenThreadClass = GenThreadRISCV
EnvClass = EnvRISCV
| true | true |
f72c88bad07b64edf6012e96a4a6af0ebf4b41c8 | 12,698 | py | Python | mai_version/trees/TILDEQueryScorer.py | joschout/tilde | 1403b50842b83f2edd6b16b1fbe24b9bec2d0048 | [
"Apache-2.0"
] | 16 | 2019-03-06T06:11:33.000Z | 2022-02-07T21:30:25.000Z | mai_version/trees/TILDEQueryScorer.py | joschout/tilde | 1403b50842b83f2edd6b16b1fbe24b9bec2d0048 | [
"Apache-2.0"
] | 4 | 2019-10-08T14:48:23.000Z | 2020-03-26T00:31:57.000Z | mai_version/trees/TILDEQueryScorer.py | krishnangovindraj/tilde | 5243a02d92f375d56ffc49ab8c3d1a87e31e99b9 | [
"Apache-2.0"
] | 4 | 2019-08-14T05:40:47.000Z | 2020-08-05T13:21:16.000Z | import math
from typing import Iterable, Set, List, Optional
import problog
import time
from problog.logic import And, Term
from mai_version.classification.example_partitioning import ExamplePartitioner
from mai_version.representation.TILDE_query import TILDEQuery
from mai_version.representation.example import ExampleWrapper
from mai_version.representation.example import Label
from mai_version.trees.scoring import entropy, information_gain2
class QueryScoreInfo:
"""Wrapper around the information about best scoring query"""
def __init__(self, best_query: TILDEQuery, score_of_best_query: float,
examples_satisfying_best_query: Set[ExampleWrapper],
examples_not_satisfying_best_query: Set[ExampleWrapper]):
self.best_query = best_query # type: TILDEQuery
self.score_of_best_query = score_of_best_query # type: float
self.examples_satisfying_best_query = examples_satisfying_best_query # type: Set[ExampleWrapper]
self.examples_not_satisfying_best_query = examples_not_satisfying_best_query # type: Set[ExampleWrapper]
class TILDEQueryScorer:
@staticmethod
def get_best_refined_query(refined_queries: Iterable[TILDEQuery], examples: Set[ExampleWrapper],
example_partitioner: ExamplePartitioner, possible_targets: List[Label],
probabilistic: Optional[bool] = False) -> QueryScoreInfo:
# Tuple[Optional[TILDEQuery], float, Optional[Set[ExampleWrapper]], Optional[Set[ExampleWrapper]]]:
best_query = None # type: Optional[TILDEQuery]
score_best_query = - math.inf # type: float
examples_satisfying_best_query = None # type: Optional[Set[ExampleWrapper]]
examples_not_satisfying_best_query = None # type: Optional[Set[ExampleWrapper]]
entropy_complete_set = entropy(examples, possible_targets)
nb_of_examples_complete_set = len(examples)
for q in refined_queries: # type: TILDEQuery
print(q)
# compute the score of the queries
conj_of_tilde_query = q.to_conjunction() # type: And
examples_satisfying_q, examples_not_satisfying_q = example_partitioner.get_examples_satisfying_query(
examples, conj_of_tilde_query) # type: Set[ExampleWrapper]
# examples_not_satisfying_q = examples - examples_satisfying_q # type: Set[ExampleWrapper]
#TODO: no longer probabilistic!
score = information_gain2(examples_satisfying_q, examples_not_satisfying_q, possible_targets, nb_of_examples_complete_set, entropy_complete_set)
if score > score_best_query:
best_query = q
score_best_query = score
examples_satisfying_best_query = examples_satisfying_q
examples_not_satisfying_best_query = examples_not_satisfying_q
return QueryScoreInfo(best_query, score_best_query, examples_satisfying_best_query,
examples_not_satisfying_best_query)
class TILDEQueryScorer2:
@staticmethod
def get_best_refined_query(refined_queries: Iterable[TILDEQuery], examples: Set[ExampleWrapper],
example_partitioner: ExamplePartitioner, possible_targets: List[Label],
probabilistic: Optional[bool] = False) -> QueryScoreInfo:
# Tuple[Optional[TILDEQuery], float, Optional[Set[ExampleWrapper]], Optional[Set[ExampleWrapper]]]:
best_query = None # type: Optional[TILDEQuery]
score_best_query = - math.inf # type: float
# examples_satisfying_best_query = None # type: Optional[Set[ExampleWrapper]]
# examples_not_satisfying_best_query = None # type: Optional[Set[ExampleWrapper]]
entropy_complete_set = entropy(examples, possible_targets)
nb_of_examples_complete_set = len(examples)
# ided_queries = list(zip(range(0,len(refined_queries)), refined_queries))
entropy_dict = {label: 0 for label in possible_targets}
query_entropy_dicts = [(entropy_dict.copy(), entropy_dict.copy()) for q in refined_queries]
for clause_db_ex in examples:
db_to_query = clause_db_ex.extend() # type: ClauseDB
if clause_db_ex.classification_term is not None:
db_to_query += clause_db_ex.classification_term
for id, q in zip(range(0,len(refined_queries)), refined_queries):
to_query = Term('q' + str(id))
db_to_query += Term('query')(to_query)
db_to_query += (to_query << q.to_conjunction())
start_time = time.time()
evaluatable = problog.get_evaluatable()
mid_time1 = time.time()
something = evaluatable.create_from(db_to_query, engine=example_partitioner.engine)
mid_time2 = time.time()
results = something.evaluate()
end_time = time.time()
example_partitioner.nb_partitions_calculated += 1
get_evaluatable_duration = mid_time1 - start_time
example_partitioner.sum_get_evaluatable += get_evaluatable_duration
structure_creation_duration = mid_time2 - mid_time1
example_partitioner.sum_structure_creation_duration += structure_creation_duration
if structure_creation_duration > example_partitioner.max_structure_creation_duration:
example_partitioner.max_structure_creation_duration = structure_creation_duration
if structure_creation_duration < example_partitioner.min_structure_creation_duration:
example_partitioner.min_structure_creation_duration = structure_creation_duration
if structure_creation_duration < 0.000001:
example_partitioner.nb_structure_creation_zero += 1
evalutation_duration = end_time - mid_time2
example_partitioner.sum_evaluation_duration += evalutation_duration
if evalutation_duration > example_partitioner.max_evaluation_duration:
example_partitioner.max_evaluation_duration = evalutation_duration
if evalutation_duration < example_partitioner.min_evaluation_duration:
example_partitioner.min_evaluation_duration = evalutation_duration
if evalutation_duration < 0.000001:
example_partitioner.nb_evaluation_zero += 1
# results = problog.get_evaluatable().create_from(db_to_query, engine=example_partitioner.engine).evaluate()
for to_query, prob in results.items():
id = int(to_query.functor[1:])
if prob > 0.5:
query_entropy_dicts[id][0][clause_db_ex.get_label()] = query_entropy_dicts[id][0][clause_db_ex.get_label()] + 1
else:
query_entropy_dicts[id][1][clause_db_ex.get_label()] = query_entropy_dicts[id][1][
clause_db_ex.get_label()] + 1
for query, (left_dic, right_dic) in zip(refined_queries, query_entropy_dicts):
# -- ig --
ig = 0
if nb_of_examples_complete_set != 0:
ig = entropy_complete_set
nb_examples_left = sum(left_dic.values())
if nb_examples_left > 0:
entropy_left = 0
for label in left_dic.keys():
label_value = left_dic[label]
if label_value != 0:
entropy_left -= label_value / nb_examples_left \
* math.log2(label_value / nb_examples_left)
ig -= nb_examples_left / nb_of_examples_complete_set * entropy_left
# ------
nb_examples_right = sum(right_dic.values())
if nb_examples_right > 0:
entropy_right = 0
for label in right_dic.keys():
label_value = right_dic[label]
if label_value != 0:
entropy_right -= label_value / nb_examples_right \
* math.log2(label_value / nb_examples_right)
ig -= nb_examples_right / nb_of_examples_complete_set * entropy_right
if ig > score_best_query:
best_query = query
score_best_query = ig
# --- we now know the best query, so create the partition again:
examples_satisfying_best_query = set() # type: Optional[Set[ExampleWrapper]]
examples_not_satisfying_best_query = set() # type: Optional[Set[ExampleWrapper]]
to_query = Term('to_query')
to_add1 = Term('query')(to_query)
to_add2 = (to_query << best_query.to_conjunction())
for clause_db_ex in examples:
db_to_query = clause_db_ex.extend() # type: ClauseDB
if clause_db_ex.classification_term is not None:
db_to_query += clause_db_ex.classification_term
# db_to_query = example_db.extend()
db_to_query += to_add1
db_to_query += to_add2
start_time = time.time()
evaluatable = problog.get_evaluatable()
mid_time1 = time.time()
something = evaluatable.create_from(db_to_query, engine=example_partitioner.engine)
mid_time2 = time.time()
query_result = something.evaluate()
end_time = time.time()
example_partitioner.nb_partitions_calculated += 1
get_evaluatable_duration = mid_time1 - start_time
example_partitioner.sum_get_evaluatable += get_evaluatable_duration
structure_creation_duration = mid_time2 - mid_time1
example_partitioner.sum_structure_creation_duration += structure_creation_duration
if structure_creation_duration > example_partitioner.max_structure_creation_duration:
example_partitioner.max_structure_creation_duration = structure_creation_duration
if structure_creation_duration < example_partitioner.min_structure_creation_duration:
example_partitioner.min_structure_creation_duration = structure_creation_duration
if structure_creation_duration < 0.000001:
example_partitioner.nb_structure_creation_zero += 1
evalutation_duration = end_time - mid_time2
example_partitioner.sum_evaluation_duration += evalutation_duration
if evalutation_duration > example_partitioner.max_evaluation_duration:
example_partitioner.max_evaluation_duration = evalutation_duration
if evalutation_duration < example_partitioner.min_evaluation_duration:
example_partitioner.min_evaluation_duration = evalutation_duration
if evalutation_duration < 0.000001:
example_partitioner.nb_evaluation_zero += 1
# query_result = problog.get_evaluatable().create_from(db_to_query,
# engine=example_partitioner.engine).evaluate()
if query_result[to_query] > 0.5:
examples_satisfying_best_query.add(clause_db_ex)
else:
examples_not_satisfying_best_query.add(clause_db_ex)
# for qid, q in enumerate(refined_queries): # type: TILDEQuery
# # compute the score of the queries
# conj_of_tilde_query = q.to_conjunction() # type: And
#
# examples_satisfying_q, examples_not_satisfying_q = example_partitioner.get_examples_satisfying_query(
# examples, conj_of_tilde_query) # type: Set[ExampleWrapper]
# # examples_not_satisfying_q = examples - examples_satisfying_q # type: Set[ExampleWrapper]
#
# #TODO: no longer probabilistic!
# score = information_gain2(examples_satisfying_q, examples_not_satisfying_q, possible_targets, nb_of_examples_complete_set, entropy_complete_set)
#
# if score > score_best_query:
# best_query = q
# score_best_query = score
# examples_satisfying_best_query = examples_satisfying_q
# examples_not_satisfying_best_query = examples_not_satisfying_q
return QueryScoreInfo(best_query, score_best_query, examples_satisfying_best_query,
examples_not_satisfying_best_query) | 52.040984 | 158 | 0.654198 | import math
from typing import Iterable, Set, List, Optional
import problog
import time
from problog.logic import And, Term
from mai_version.classification.example_partitioning import ExamplePartitioner
from mai_version.representation.TILDE_query import TILDEQuery
from mai_version.representation.example import ExampleWrapper
from mai_version.representation.example import Label
from mai_version.trees.scoring import entropy, information_gain2
class QueryScoreInfo:
def __init__(self, best_query: TILDEQuery, score_of_best_query: float,
examples_satisfying_best_query: Set[ExampleWrapper],
examples_not_satisfying_best_query: Set[ExampleWrapper]):
self.best_query = best_query
self.score_of_best_query = score_of_best_query
self.examples_satisfying_best_query = examples_satisfying_best_query
self.examples_not_satisfying_best_query = examples_not_satisfying_best_query
class TILDEQueryScorer:
@staticmethod
def get_best_refined_query(refined_queries: Iterable[TILDEQuery], examples: Set[ExampleWrapper],
example_partitioner: ExamplePartitioner, possible_targets: List[Label],
probabilistic: Optional[bool] = False) -> QueryScoreInfo:
best_query = None
score_best_query = - math.inf
examples_satisfying_best_query = None
examples_not_satisfying_best_query = None
entropy_complete_set = entropy(examples, possible_targets)
nb_of_examples_complete_set = len(examples)
for q in refined_queries:
print(q)
conj_of_tilde_query = q.to_conjunction()
examples_satisfying_q, examples_not_satisfying_q = example_partitioner.get_examples_satisfying_query(
examples, conj_of_tilde_query)
score = information_gain2(examples_satisfying_q, examples_not_satisfying_q, possible_targets, nb_of_examples_complete_set, entropy_complete_set)
if score > score_best_query:
best_query = q
score_best_query = score
examples_satisfying_best_query = examples_satisfying_q
examples_not_satisfying_best_query = examples_not_satisfying_q
return QueryScoreInfo(best_query, score_best_query, examples_satisfying_best_query,
examples_not_satisfying_best_query)
class TILDEQueryScorer2:
@staticmethod
def get_best_refined_query(refined_queries: Iterable[TILDEQuery], examples: Set[ExampleWrapper],
example_partitioner: ExamplePartitioner, possible_targets: List[Label],
probabilistic: Optional[bool] = False) -> QueryScoreInfo:
best_query = None
score_best_query = - math.inf
ts)
nb_of_examples_complete_set = len(examples)
entropy_dict = {label: 0 for label in possible_targets}
query_entropy_dicts = [(entropy_dict.copy(), entropy_dict.copy()) for q in refined_queries]
for clause_db_ex in examples:
db_to_query = clause_db_ex.extend()
if clause_db_ex.classification_term is not None:
db_to_query += clause_db_ex.classification_term
for id, q in zip(range(0,len(refined_queries)), refined_queries):
to_query = Term('q' + str(id))
db_to_query += Term('query')(to_query)
db_to_query += (to_query << q.to_conjunction())
start_time = time.time()
evaluatable = problog.get_evaluatable()
mid_time1 = time.time()
something = evaluatable.create_from(db_to_query, engine=example_partitioner.engine)
mid_time2 = time.time()
results = something.evaluate()
end_time = time.time()
example_partitioner.nb_partitions_calculated += 1
get_evaluatable_duration = mid_time1 - start_time
example_partitioner.sum_get_evaluatable += get_evaluatable_duration
structure_creation_duration = mid_time2 - mid_time1
example_partitioner.sum_structure_creation_duration += structure_creation_duration
if structure_creation_duration > example_partitioner.max_structure_creation_duration:
example_partitioner.max_structure_creation_duration = structure_creation_duration
if structure_creation_duration < example_partitioner.min_structure_creation_duration:
example_partitioner.min_structure_creation_duration = structure_creation_duration
if structure_creation_duration < 0.000001:
example_partitioner.nb_structure_creation_zero += 1
evalutation_duration = end_time - mid_time2
example_partitioner.sum_evaluation_duration += evalutation_duration
if evalutation_duration > example_partitioner.max_evaluation_duration:
example_partitioner.max_evaluation_duration = evalutation_duration
if evalutation_duration < example_partitioner.min_evaluation_duration:
example_partitioner.min_evaluation_duration = evalutation_duration
if evalutation_duration < 0.000001:
example_partitioner.nb_evaluation_zero += 1
for to_query, prob in results.items():
id = int(to_query.functor[1:])
if prob > 0.5:
query_entropy_dicts[id][0][clause_db_ex.get_label()] = query_entropy_dicts[id][0][clause_db_ex.get_label()] + 1
else:
query_entropy_dicts[id][1][clause_db_ex.get_label()] = query_entropy_dicts[id][1][
clause_db_ex.get_label()] + 1
for query, (left_dic, right_dic) in zip(refined_queries, query_entropy_dicts):
ig = 0
if nb_of_examples_complete_set != 0:
ig = entropy_complete_set
nb_examples_left = sum(left_dic.values())
if nb_examples_left > 0:
entropy_left = 0
for label in left_dic.keys():
label_value = left_dic[label]
if label_value != 0:
entropy_left -= label_value / nb_examples_left \
* math.log2(label_value / nb_examples_left)
ig -= nb_examples_left / nb_of_examples_complete_set * entropy_left
nb_examples_right = sum(right_dic.values())
if nb_examples_right > 0:
entropy_right = 0
for label in right_dic.keys():
label_value = right_dic[label]
if label_value != 0:
entropy_right -= label_value / nb_examples_right \
* math.log2(label_value / nb_examples_right)
ig -= nb_examples_right / nb_of_examples_complete_set * entropy_right
if ig > score_best_query:
best_query = query
score_best_query = ig
examples_satisfying_best_query = set()
examples_not_satisfying_best_query = set()
to_query = Term('to_query')
to_add1 = Term('query')(to_query)
to_add2 = (to_query << best_query.to_conjunction())
for clause_db_ex in examples:
db_to_query = clause_db_ex.extend()
if clause_db_ex.classification_term is not None:
db_to_query += clause_db_ex.classification_term
db_to_query += to_add1
db_to_query += to_add2
start_time = time.time()
evaluatable = problog.get_evaluatable()
mid_time1 = time.time()
something = evaluatable.create_from(db_to_query, engine=example_partitioner.engine)
mid_time2 = time.time()
query_result = something.evaluate()
end_time = time.time()
example_partitioner.nb_partitions_calculated += 1
get_evaluatable_duration = mid_time1 - start_time
example_partitioner.sum_get_evaluatable += get_evaluatable_duration
structure_creation_duration = mid_time2 - mid_time1
example_partitioner.sum_structure_creation_duration += structure_creation_duration
if structure_creation_duration > example_partitioner.max_structure_creation_duration:
example_partitioner.max_structure_creation_duration = structure_creation_duration
if structure_creation_duration < example_partitioner.min_structure_creation_duration:
example_partitioner.min_structure_creation_duration = structure_creation_duration
if structure_creation_duration < 0.000001:
example_partitioner.nb_structure_creation_zero += 1
evalutation_duration = end_time - mid_time2
example_partitioner.sum_evaluation_duration += evalutation_duration
if evalutation_duration > example_partitioner.max_evaluation_duration:
example_partitioner.max_evaluation_duration = evalutation_duration
if evalutation_duration < example_partitioner.min_evaluation_duration:
example_partitioner.min_evaluation_duration = evalutation_duration
if evalutation_duration < 0.000001:
example_partitioner.nb_evaluation_zero += 1
if query_result[to_query] > 0.5:
examples_satisfying_best_query.add(clause_db_ex)
else:
examples_not_satisfying_best_query.add(clause_db_ex)
examples_not_satisfying_best_query) | true | true |
f72c8a7510c49c3ae446f48e397b061791a320e4 | 13,771 | py | Python | logreg.py | naver/cog | 5b34ca90757116b9cfae11d8838927ba73e1ede8 | [
"BSD-3-Clause"
] | 13 | 2021-10-13T11:13:55.000Z | 2022-03-11T04:41:41.000Z | logreg.py | naver/cog | 5b34ca90757116b9cfae11d8838927ba73e1ede8 | [
"BSD-3-Clause"
] | null | null | null | logreg.py | naver/cog | 5b34ca90757116b9cfae11d8838927ba73e1ede8 | [
"BSD-3-Clause"
] | null | null | null | # ImageNet-CoG Benchmark
# Copyright 2021-present NAVER Corp.
# 3-Clause BSD License
import argparse
import copy
import logging
import math
import os
import shutil
import time
import optuna
import torch as th
import feature_ops
import metrics
import utils
from iterators import TorchIterator
from meters import AverageMeter, ProgressMeter
logger = logging.getLogger()
class LogReg:
"""
Logistic regression classifier with mini-batch SGD.
"""
def __init__(self, args, cfg):
self.args = args
self.cfg = cfg
# load the training set features
trainset = feature_ops.load_feature_set(
args.train_features_path, "train", cfg.CLF.NORM_FTS
)
if args.val:
# randomly split the training set into train + val
logger.info("Splitting the training set into train and val")
trainset, testset = feature_ops.split_trainset(trainset, cfg.CLF.VAL_PERC)
else:
# load the test set
testset = feature_ops.load_feature_set(args.test_features_path, "test", cfg.CLF.NORM_FTS)
if cfg.CLF.N_SHOT > 0:
logger.info(
"Simulating few-shot learning setting, {} images per class.".format(
cfg.CLF.N_SHOT
)
)
trainset = feature_ops.make_fewshot_dataset(trainset, cfg.CLF.N_SHOT)
self.trainset = trainset
self.testset = testset
self.trainset.print_info()
self.testset.print_info()
# determine number of cases
if len(list(self.trainset.y.shape)) == 1:
classes = th.unique(self.trainset.y)
assert th.all(classes == th.unique(self.testset.y))
args.n_classes = classes.size(0)
# move all features to the device
if args.device == "cuda":
feature_ops.move_data_to_cuda([self.trainset, self.testset])
def __call__(self, trial=None):
"""
The function called by Optuna.
"""
# empty the cache allocated in the previous call
th.cuda.empty_cache()
args = copy.deepcopy(self.args)
cfg = self.cfg
x_train = self.trainset.x
y_train = self.trainset.y
x_test = self.testset.x
y_test = self.testset.y
# create training and test set iterators
train_iter = TorchIterator((x_train, y_train), cfg.CLF.BATCH_SIZE, shuffle=True)
test_iter = TorchIterator((x_test, y_test), cfg.CLF.BATCH_SIZE, shuffle=False)
# define logistic classifier
model = th.nn.Linear(x_train.size(1), args.n_classes).to(args.device)
crit = th.nn.CrossEntropyLoss().to(args.device)
# sample a learning rate and weight decay
if trial is not None:
lr_intv = cfg.CLF.LR_INTV
wd_intv = cfg.CLF.WD_INTV
args.lr = trial.suggest_loguniform("lr", lr_intv[0], lr_intv[1])
args.wd = trial.suggest_loguniform("wd", wd_intv[0], wd_intv[1])
optim = th.optim.SGD(
model.parameters(), lr=args.lr, momentum=args.mom, weight_decay=args.wd
)
args.exp_dir = os.path.join(
args.output_dir,
"{}-lr-{}_wd-{}".format("val" if args.val else "final", args.lr, args.wd),
)
os.makedirs(args.exp_dir, exist_ok=True)
# write the model definition into exp_dir
utils.write_to_file(str(model), os.path.join(args.exp_dir, "model.txt"))
# logs computed during training / evaluation
args.logs = {
"train/loss": [],
"train/top1": [],
"train/top5": [],
"test/loss": [],
"test/top1": [],
"test/top5": [],
"lr": [],
}
# predictions over the evaluation sets
args.preds = []
for epoch in range(cfg.CLF.N_EPOCHS):
if not args.val:
logger.info(f"**Epoch:{epoch}**")
args.epoch = epoch
train_stat = train(train_iter, model, crit, optim, epoch, args)
validate(test_iter, model, crit, args)
adjust_learning_rate(optim, args, cfg)
# if something went wrong during training
# e.g. SGD diverged
if train_stat == -1:
break
# save the logs
utils.save_pickle(args.logs, f"{args.exp_dir}/logs.pkl")
# save the predictions
utils.save_pickle(args.preds, f"{args.exp_dir}/preds.pkl")
# save the whole args, for ease of access
utils.save_pickle(vars(args), f"{args.exp_dir}/args.pkl")
# save also the final model
th.save(
{
"model": model.state_dict(),
},
f"{args.exp_dir}/model.pth",
)
# return the last test accuracy
return args.logs["test/top1"][-1]
def train(train_loader, model, criterion, optimizer, epoch, args):
"""
Train the classifier for one epoch.
"""
batch_time = AverageMeter("Time", ":6.3f")
losses = AverageMeter("Loss", ":.4e")
top1 = AverageMeter("Acc@1", ":6.2f")
top5 = AverageMeter("Acc@5", ":6.2f")
progress = ProgressMeter(
len(train_loader),
[batch_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch),
)
# switch to train mode
model.train()
end = time.time()
for i, (fts, lbls) in enumerate(train_loader):
fts = fts.to(args.device)
lbls = lbls.to(args.device)
# compute output
output = model(fts)
loss = criterion(output, lbls)
if not th.isfinite(loss):
logger.info("Loss ({}) is not finite, terminating".format(loss.item()))
optimizer.zero_grad()
return -1
# measure accuracy and record loss
acc1, acc5 = metrics.accuracy(output, lbls, topk=(1, 5))
losses.update(loss.item(), fts.size(0))
top1.update(acc1.item(), fts.size(0))
top5.update(acc5.item(), fts.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if (not args.val) and (i % args.print_freq == 0):
progress.display(i)
args.logs["train/loss"].append(losses.avg)
args.logs["train/top1"].append(top1.avg)
args.logs["train/top5"].append(top5.avg)
return 0
def validate(val_loader, model, criterion, args):
losses = AverageMeter("Loss", ":.4e")
top1 = AverageMeter("Acc@1", ":6.2f")
top5 = AverageMeter("Acc@5", ":6.2f")
# switch to evaluate mode
model.eval()
# keep predictions per class
preds = th.ones(len(val_loader.tensors[0]), dtype=th.int32, device=args.device) * -1.
six = 0
with th.no_grad():
for i, (fts, lbls) in enumerate(val_loader):
fts = fts.to(args.device)
lbls = lbls.to(args.device)
bs = fts.size(0)
# compute output
output = model(fts)
loss = criterion(output, lbls)
# store the predicted classes
preds[six:six + bs] = th.argmax(output, dim=1)
six += bs
# measure accuracy and record loss
acc1, acc5 = metrics.accuracy(output, lbls, topk=(1, 5))
losses.update(loss.item(), bs)
top1.update(acc1[0].item(), bs)
top5.update(acc5[0].item(), bs)
# make sure that there is no invalid prediction
assert th.all(preds >= 0).item()
args.preds.append(preds.detach().cpu())
args.logs["test/loss"].append(losses.avg)
args.logs["test/top1"].append(top1.avg)
args.logs["test/top5"].append(top5.avg)
if not args.val:
logger.info(
" * Acc@1:{top1.avg:.3f} - Acc@5:{top5.avg:.3f}".format(
top1=top1, top5=top5
)
)
def adjust_learning_rate(optimizer, args, cfg):
"""Decay the learning rate based on cosine schedule"""
lr = args.lr
lr *= 0.5 * (1.0 + math.cos(math.pi * args.epoch / cfg.CLF.N_EPOCHS))
for param_group in optimizer.param_groups:
param_group["lr"] = lr
args.logs["lr"].append(lr)
def save_checkpoint(state, is_best, filename="checkpoint.pth.tar"):
th.save(state, filename)
if is_best:
shutil.copyfile(filename, "model_best.pth.tar")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=utils.none_or_string_flag,
help='Name of the model in the <model_title>_<architecture_name> form.'
'See the table of models in ./prepare_models/README.md for all the model names we support.'
'This is an optional argument that needs to be set along with --models_root_dir and --dataset.'
'When these three arguments are set, the script will load features from:'
'<models_root_dir>/<model_title>/<architecture_name>/<dataset>/features_*/X_Y.pth.'
'If you would like to load pre-extracted features from somewhere else'
'then ignore this argument and provide the --train_features_dir and --test_features_dir arguments accordingly')
parser.add_argument('--models_root_dir', type=utils.none_or_string_flag,
help='Root directory for all models, see prepare_models/README.md for a detailed explanation.'
'This is an optional argument that needs to be set along with --model and --dataset.'
'Please see the help message for the --model argument as well.')
parser.add_argument("--dataset", type=utils.none_or_string_flag,
help="On which dataset to learn classifiers"
'Possible values are ("in1k", "cog_l1", "cog_l2", "cog_l3", "cog_l4", "cog_l5")'
'This is an optional argument that needs to be set along with --models_root_dir and --model.'
'Please see the help message for the --model argument as well.')
parser.add_argument('--train_features_dir', type=utils.none_or_string_flag,
help='Path to the directory containing pre-extracted training set features.'
'We expect a features file "X_Y.pth" under <train_features_dir>.'
'This is an optional argument that needs to be set if --models_root_dir, --model and --dataset are not set.')
parser.add_argument('--test_features_dir', type=utils.none_or_string_flag,
help='Path to the directory containing pre-extracted test set features.'
'We expect a features file "X_Y.pth" under <test_features_dir>.'
'This is an optional argument that needs to be set if --models_root_dir, --model and --dataset are not set.')
parser.add_argument('--output_dir', type=utils.none_or_string_flag,
help='Where to log program logs.'
'This is an optional argument that needs to be set if --models_root_dir is not set.'
'If not provided, we try to save the logs under'
'<models_root_dir>/<model_title>/<architecture_name>/<dataset>/eval_logreg/seed*')
# learning rate and momentum are tuned in this program, do not manually set.
parser.add_argument("--lr", type=float, default=0.0, help="initial learning rate")
parser.add_argument("--wd", type=float, default=0.0, help="weight decay")
parser.add_argument("--mom", type=float, default=0.9, help="momentum")
# program-related options
parser.add_argument("--print_freq", default=100, type=int, help="print frequency (default: 10)")
parser.add_argument("--device", type=str, default="cuda")
# optionally to overwrite the default config
parser.add_argument("opts", default=None,
help="see configs/default.py for all options",
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.device == "cuda" and not th.cuda.is_available():
print("CUDA is not available, I will run on CPU.")
args.device = "cpu"
# load the config file
# create output directory,
# locate pre-extracted features,
# initialize program logger,
# save args and cfg
# this function sets the following arg variables:
# - train_features_path, type=str
# - test_features_path, type=str
# - output_dir, type=str
args, cfg = utils.init_program(args, _for="logreg")
# tune hyper-parameters with optuna
logger.info("Running Optuna...")
hps_sampler = optuna.samplers.TPESampler(multivariate=True, seed=cfg.EVAL.SEED)
study = optuna.create_study(sampler=hps_sampler, direction="maximize")
args.val = True
logreg = LogReg(args, cfg)
study.optimize(logreg, n_trials=cfg.CLF.N_TRIALS, n_jobs=1, show_progress_bar=False)
utils.save_pickle(study, os.path.join(args.output_dir, "study.pkl"))
logger.info("")
logger.info("*" * 50)
logger.info("Hyper-parameter search ended")
logger.info("best_trial:")
logger.info(str(study.best_trial))
logger.info("best_params:")
logger.info(str(study.best_params))
logger.info("*" * 50)
logger.info("")
# train the final classifier with the tuned hyper-parameters
del logreg
th.cuda.empty_cache()
args.lr = study.best_params["lr"]
args.wd = study.best_params["wd"]
args.val = False
logreg = LogReg(args, cfg)
logreg()
| 37.625683 | 140 | 0.600392 |
import argparse
import copy
import logging
import math
import os
import shutil
import time
import optuna
import torch as th
import feature_ops
import metrics
import utils
from iterators import TorchIterator
from meters import AverageMeter, ProgressMeter
logger = logging.getLogger()
class LogReg:
def __init__(self, args, cfg):
self.args = args
self.cfg = cfg
trainset = feature_ops.load_feature_set(
args.train_features_path, "train", cfg.CLF.NORM_FTS
)
if args.val:
logger.info("Splitting the training set into train and val")
trainset, testset = feature_ops.split_trainset(trainset, cfg.CLF.VAL_PERC)
else:
testset = feature_ops.load_feature_set(args.test_features_path, "test", cfg.CLF.NORM_FTS)
if cfg.CLF.N_SHOT > 0:
logger.info(
"Simulating few-shot learning setting, {} images per class.".format(
cfg.CLF.N_SHOT
)
)
trainset = feature_ops.make_fewshot_dataset(trainset, cfg.CLF.N_SHOT)
self.trainset = trainset
self.testset = testset
self.trainset.print_info()
self.testset.print_info()
if len(list(self.trainset.y.shape)) == 1:
classes = th.unique(self.trainset.y)
assert th.all(classes == th.unique(self.testset.y))
args.n_classes = classes.size(0)
if args.device == "cuda":
feature_ops.move_data_to_cuda([self.trainset, self.testset])
def __call__(self, trial=None):
th.cuda.empty_cache()
args = copy.deepcopy(self.args)
cfg = self.cfg
x_train = self.trainset.x
y_train = self.trainset.y
x_test = self.testset.x
y_test = self.testset.y
train_iter = TorchIterator((x_train, y_train), cfg.CLF.BATCH_SIZE, shuffle=True)
test_iter = TorchIterator((x_test, y_test), cfg.CLF.BATCH_SIZE, shuffle=False)
model = th.nn.Linear(x_train.size(1), args.n_classes).to(args.device)
crit = th.nn.CrossEntropyLoss().to(args.device)
if trial is not None:
lr_intv = cfg.CLF.LR_INTV
wd_intv = cfg.CLF.WD_INTV
args.lr = trial.suggest_loguniform("lr", lr_intv[0], lr_intv[1])
args.wd = trial.suggest_loguniform("wd", wd_intv[0], wd_intv[1])
optim = th.optim.SGD(
model.parameters(), lr=args.lr, momentum=args.mom, weight_decay=args.wd
)
args.exp_dir = os.path.join(
args.output_dir,
"{}-lr-{}_wd-{}".format("val" if args.val else "final", args.lr, args.wd),
)
os.makedirs(args.exp_dir, exist_ok=True)
utils.write_to_file(str(model), os.path.join(args.exp_dir, "model.txt"))
args.logs = {
"train/loss": [],
"train/top1": [],
"train/top5": [],
"test/loss": [],
"test/top1": [],
"test/top5": [],
"lr": [],
}
args.preds = []
for epoch in range(cfg.CLF.N_EPOCHS):
if not args.val:
logger.info(f"**Epoch:{epoch}**")
args.epoch = epoch
train_stat = train(train_iter, model, crit, optim, epoch, args)
validate(test_iter, model, crit, args)
adjust_learning_rate(optim, args, cfg)
if train_stat == -1:
break
utils.save_pickle(args.logs, f"{args.exp_dir}/logs.pkl")
utils.save_pickle(args.preds, f"{args.exp_dir}/preds.pkl")
utils.save_pickle(vars(args), f"{args.exp_dir}/args.pkl")
th.save(
{
"model": model.state_dict(),
},
f"{args.exp_dir}/model.pth",
)
return args.logs["test/top1"][-1]
def train(train_loader, model, criterion, optimizer, epoch, args):
batch_time = AverageMeter("Time", ":6.3f")
losses = AverageMeter("Loss", ":.4e")
top1 = AverageMeter("Acc@1", ":6.2f")
top5 = AverageMeter("Acc@5", ":6.2f")
progress = ProgressMeter(
len(train_loader),
[batch_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch),
)
model.train()
end = time.time()
for i, (fts, lbls) in enumerate(train_loader):
fts = fts.to(args.device)
lbls = lbls.to(args.device)
output = model(fts)
loss = criterion(output, lbls)
if not th.isfinite(loss):
logger.info("Loss ({}) is not finite, terminating".format(loss.item()))
optimizer.zero_grad()
return -1
acc1, acc5 = metrics.accuracy(output, lbls, topk=(1, 5))
losses.update(loss.item(), fts.size(0))
top1.update(acc1.item(), fts.size(0))
top5.update(acc5.item(), fts.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
if (not args.val) and (i % args.print_freq == 0):
progress.display(i)
args.logs["train/loss"].append(losses.avg)
args.logs["train/top1"].append(top1.avg)
args.logs["train/top5"].append(top5.avg)
return 0
def validate(val_loader, model, criterion, args):
losses = AverageMeter("Loss", ":.4e")
top1 = AverageMeter("Acc@1", ":6.2f")
top5 = AverageMeter("Acc@5", ":6.2f")
model.eval()
preds = th.ones(len(val_loader.tensors[0]), dtype=th.int32, device=args.device) * -1.
six = 0
with th.no_grad():
for i, (fts, lbls) in enumerate(val_loader):
fts = fts.to(args.device)
lbls = lbls.to(args.device)
bs = fts.size(0)
output = model(fts)
loss = criterion(output, lbls)
preds[six:six + bs] = th.argmax(output, dim=1)
six += bs
acc1, acc5 = metrics.accuracy(output, lbls, topk=(1, 5))
losses.update(loss.item(), bs)
top1.update(acc1[0].item(), bs)
top5.update(acc5[0].item(), bs)
assert th.all(preds >= 0).item()
args.preds.append(preds.detach().cpu())
args.logs["test/loss"].append(losses.avg)
args.logs["test/top1"].append(top1.avg)
args.logs["test/top5"].append(top5.avg)
if not args.val:
logger.info(
" * Acc@1:{top1.avg:.3f} - Acc@5:{top5.avg:.3f}".format(
top1=top1, top5=top5
)
)
def adjust_learning_rate(optimizer, args, cfg):
lr = args.lr
lr *= 0.5 * (1.0 + math.cos(math.pi * args.epoch / cfg.CLF.N_EPOCHS))
for param_group in optimizer.param_groups:
param_group["lr"] = lr
args.logs["lr"].append(lr)
def save_checkpoint(state, is_best, filename="checkpoint.pth.tar"):
th.save(state, filename)
if is_best:
shutil.copyfile(filename, "model_best.pth.tar")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=utils.none_or_string_flag,
help='Name of the model in the <model_title>_<architecture_name> form.'
'See the table of models in ./prepare_models/README.md for all the model names we support.'
'This is an optional argument that needs to be set along with --models_root_dir and --dataset.'
'When these three arguments are set, the script will load features from:'
'<models_root_dir>/<model_title>/<architecture_name>/<dataset>/features_*/X_Y.pth.'
'If you would like to load pre-extracted features from somewhere else'
'then ignore this argument and provide the --train_features_dir and --test_features_dir arguments accordingly')
parser.add_argument('--models_root_dir', type=utils.none_or_string_flag,
help='Root directory for all models, see prepare_models/README.md for a detailed explanation.'
'This is an optional argument that needs to be set along with --model and --dataset.'
'Please see the help message for the --model argument as well.')
parser.add_argument("--dataset", type=utils.none_or_string_flag,
help="On which dataset to learn classifiers"
'Possible values are ("in1k", "cog_l1", "cog_l2", "cog_l3", "cog_l4", "cog_l5")'
'This is an optional argument that needs to be set along with --models_root_dir and --model.'
'Please see the help message for the --model argument as well.')
parser.add_argument('--train_features_dir', type=utils.none_or_string_flag,
help='Path to the directory containing pre-extracted training set features.'
'We expect a features file "X_Y.pth" under <train_features_dir>.'
'This is an optional argument that needs to be set if --models_root_dir, --model and --dataset are not set.')
parser.add_argument('--test_features_dir', type=utils.none_or_string_flag,
help='Path to the directory containing pre-extracted test set features.'
'We expect a features file "X_Y.pth" under <test_features_dir>.'
'This is an optional argument that needs to be set if --models_root_dir, --model and --dataset are not set.')
parser.add_argument('--output_dir', type=utils.none_or_string_flag,
help='Where to log program logs.'
'This is an optional argument that needs to be set if --models_root_dir is not set.'
'If not provided, we try to save the logs under'
'<models_root_dir>/<model_title>/<architecture_name>/<dataset>/eval_logreg/seed*')
parser.add_argument("--lr", type=float, default=0.0, help="initial learning rate")
parser.add_argument("--wd", type=float, default=0.0, help="weight decay")
parser.add_argument("--mom", type=float, default=0.9, help="momentum")
parser.add_argument("--print_freq", default=100, type=int, help="print frequency (default: 10)")
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("opts", default=None,
help="see configs/default.py for all options",
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.device == "cuda" and not th.cuda.is_available():
print("CUDA is not available, I will run on CPU.")
args.device = "cpu"
args, cfg = utils.init_program(args, _for="logreg")
logger.info("Running Optuna...")
hps_sampler = optuna.samplers.TPESampler(multivariate=True, seed=cfg.EVAL.SEED)
study = optuna.create_study(sampler=hps_sampler, direction="maximize")
args.val = True
logreg = LogReg(args, cfg)
study.optimize(logreg, n_trials=cfg.CLF.N_TRIALS, n_jobs=1, show_progress_bar=False)
utils.save_pickle(study, os.path.join(args.output_dir, "study.pkl"))
logger.info("")
logger.info("*" * 50)
logger.info("Hyper-parameter search ended")
logger.info("best_trial:")
logger.info(str(study.best_trial))
logger.info("best_params:")
logger.info(str(study.best_params))
logger.info("*" * 50)
logger.info("")
del logreg
th.cuda.empty_cache()
args.lr = study.best_params["lr"]
args.wd = study.best_params["wd"]
args.val = False
logreg = LogReg(args, cfg)
logreg()
| true | true |
f72c8ab58a23d585b39a3037e18747d52bcb4b75 | 1,132 | py | Python | Chapter02/Ch02_Code/GUI_tabbed_two_mighty_labels.py | mr4dsd43/Python-GUI-Programming-Cookbook-Second-Edition | 18e4632106169991e9b75680bdd7250c9d77c3be | [
"MIT"
] | 2 | 2021-01-12T03:13:29.000Z | 2021-01-12T03:13:31.000Z | Chapter02/Ch02_Code/GUI_tabbed_two_mighty_labels.py | mr4dsd43/Python-GUI-Programming-Cookbook-Second-Edition | 18e4632106169991e9b75680bdd7250c9d77c3be | [
"MIT"
] | null | null | null | Chapter02/Ch02_Code/GUI_tabbed_two_mighty_labels.py | mr4dsd43/Python-GUI-Programming-Cookbook-Second-Edition | 18e4632106169991e9b75680bdd7250c9d77c3be | [
"MIT"
] | 1 | 2022-02-22T02:06:32.000Z | 2022-02-22T02:06:32.000Z | '''
May 2017
@author: Burkhard A. Meier
'''
#======================
# imports
#======================
import tkinter as tk
from tkinter import ttk
# Create instance
win = tk.Tk()
# Add a title
win.title("Python GUI")
tabControl = ttk.Notebook(win) # Create Tab Control
tab1 = ttk.Frame(tabControl) # Create a tab
tabControl.add(tab1, text='Tab 1') # Add the tab
tab2 = ttk.Frame(tabControl) # Add a second tab
tabControl.add(tab2, text='Tab 2') # Make second tab visible
tabControl.pack(expand=1, fill="both") # Pack to make visible
# LabelFrame using tab1 as the parent
mighty = ttk.LabelFrame(tab1, text=' Mighty Python ')
mighty.grid(column=0, row=0, padx=8, pady=4)
# Label using mighty as the parent
a_label = ttk.Label(mighty, text="Enter a name:")
a_label.grid(column=0, row=0, sticky='W')
# Add another label
ttk.Label(mighty, text="Choose a number:").grid(column=1, row=0)
# Add some space around each label
for child in mighty.winfo_children():
child.grid_configure(padx=8)
#======================
# Start GUI
#======================
win.mainloop()
| 25.155556 | 65 | 0.620141 |
import tkinter as tk
from tkinter import ttk
win = tk.Tk()
win.title("Python GUI")
tabControl = ttk.Notebook(win)
tab1 = ttk.Frame(tabControl)
tabControl.add(tab1, text='Tab 1')
tab2 = ttk.Frame(tabControl)
tabControl.add(tab2, text='Tab 2')
tabControl.pack(expand=1, fill="both")
mighty = ttk.LabelFrame(tab1, text=' Mighty Python ')
mighty.grid(column=0, row=0, padx=8, pady=4)
a_label = ttk.Label(mighty, text="Enter a name:")
a_label.grid(column=0, row=0, sticky='W')
ttk.Label(mighty, text="Choose a number:").grid(column=1, row=0)
for child in mighty.winfo_children():
child.grid_configure(padx=8)
win.mainloop()
| true | true |
f72c8ecfbd321747538079c852e41d9f1f85d700 | 3,446 | py | Python | sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_validate.py | kashifkhan/azure-sdk-for-python | 9c28b76e89b0855e41bd12d5b4a59b51acd47eec | [
"MIT"
] | null | null | null | sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_validate.py | kashifkhan/azure-sdk-for-python | 9c28b76e89b0855e41bd12d5b4a59b51acd47eec | [
"MIT"
] | null | null | null | sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_validate.py | kashifkhan/azure-sdk-for-python | 9c28b76e89b0855e41bd12d5b4a59b51acd47eec | [
"MIT"
] | null | null | null | # ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import functools
from ._version import VERSIONS_SUPPORTED
def check_for_unsupported_actions_types(*args, **kwargs):
client = args[0]
# this assumes the client has an _api_version attribute
selected_api_version = client._api_version # pylint: disable=protected-access
if "actions" not in kwargs:
actions = args[2]
else:
actions = kwargs.get("actions")
if actions is None:
return
actions_version_mapping = {
"2022-03-01-preview":
[
"ExtractSummaryAction",
"RecognizeCustomEntitiesAction",
"SingleCategoryClassifyAction",
"MultiCategoryClassifyAction"
]
}
unsupported = {
arg: version
for version, args in actions_version_mapping.items()
for arg in args
if arg in [action.__class__.__name__ for action in actions]
and selected_api_version != version
and VERSIONS_SUPPORTED.index(selected_api_version) < VERSIONS_SUPPORTED.index(version)
}
if unsupported:
error_strings = [
f"'{param}' is only available for API version {version} and up.\n"
for param, version in unsupported.items()
]
raise ValueError("".join(error_strings))
def validate_multiapi_args(**kwargs):
args_mapping = kwargs.pop("args_mapping", None)
version_method_added = kwargs.pop("version_method_added", None)
custom_wrapper = kwargs.pop("custom_wrapper", None)
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
try:
# this assumes the client has an _api_version attribute
client = args[0]
selected_api_version = client._api_version # pylint: disable=protected-access
except AttributeError:
return func(*args, **kwargs)
# the latest version is selected, we assume all features supported
if selected_api_version == VERSIONS_SUPPORTED[-1]:
return func(*args, **kwargs)
if version_method_added and version_method_added != selected_api_version and \
VERSIONS_SUPPORTED.index(selected_api_version) < VERSIONS_SUPPORTED.index(version_method_added):
raise ValueError(f"'{func.__name__}' is only available for API version {version_method_added} and up.")
if args_mapping:
unsupported = {
arg: version
for version, args in args_mapping.items()
for arg in args
if arg in kwargs.keys()
and selected_api_version != version
and VERSIONS_SUPPORTED.index(selected_api_version) < VERSIONS_SUPPORTED.index(version)
}
if unsupported:
error_strings = [
f"'{param}' is only available for API version {version} and up.\n"
for param, version in unsupported.items()
]
raise ValueError("".join(error_strings))
if custom_wrapper:
custom_wrapper(*args, **kwargs)
return func(*args, **kwargs)
return wrapper
return decorator
| 35.163265 | 119 | 0.589089 |
import functools
from ._version import VERSIONS_SUPPORTED
def check_for_unsupported_actions_types(*args, **kwargs):
client = args[0]
selected_api_version = client._api_version
if "actions" not in kwargs:
actions = args[2]
else:
actions = kwargs.get("actions")
if actions is None:
return
actions_version_mapping = {
"2022-03-01-preview":
[
"ExtractSummaryAction",
"RecognizeCustomEntitiesAction",
"SingleCategoryClassifyAction",
"MultiCategoryClassifyAction"
]
}
unsupported = {
arg: version
for version, args in actions_version_mapping.items()
for arg in args
if arg in [action.__class__.__name__ for action in actions]
and selected_api_version != version
and VERSIONS_SUPPORTED.index(selected_api_version) < VERSIONS_SUPPORTED.index(version)
}
if unsupported:
error_strings = [
f"'{param}' is only available for API version {version} and up.\n"
for param, version in unsupported.items()
]
raise ValueError("".join(error_strings))
def validate_multiapi_args(**kwargs):
args_mapping = kwargs.pop("args_mapping", None)
version_method_added = kwargs.pop("version_method_added", None)
custom_wrapper = kwargs.pop("custom_wrapper", None)
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
try:
client = args[0]
selected_api_version = client._api_version
except AttributeError:
return func(*args, **kwargs)
if selected_api_version == VERSIONS_SUPPORTED[-1]:
return func(*args, **kwargs)
if version_method_added and version_method_added != selected_api_version and \
VERSIONS_SUPPORTED.index(selected_api_version) < VERSIONS_SUPPORTED.index(version_method_added):
raise ValueError(f"'{func.__name__}' is only available for API version {version_method_added} and up.")
if args_mapping:
unsupported = {
arg: version
for version, args in args_mapping.items()
for arg in args
if arg in kwargs.keys()
and selected_api_version != version
and VERSIONS_SUPPORTED.index(selected_api_version) < VERSIONS_SUPPORTED.index(version)
}
if unsupported:
error_strings = [
f"'{param}' is only available for API version {version} and up.\n"
for param, version in unsupported.items()
]
raise ValueError("".join(error_strings))
if custom_wrapper:
custom_wrapper(*args, **kwargs)
return func(*args, **kwargs)
return wrapper
return decorator
| true | true |
f72c8ed99253eaa655d08778cb9bf6fa834191af | 10,956 | py | Python | google/ads/google_ads/v0/proto/resources/shared_criterion_pb2.py | jwygoda/google-ads-python | 863892b533240cb45269d9c2cceec47e2c5a8b68 | [
"Apache-2.0"
] | null | null | null | google/ads/google_ads/v0/proto/resources/shared_criterion_pb2.py | jwygoda/google-ads-python | 863892b533240cb45269d9c2cceec47e2c5a8b68 | [
"Apache-2.0"
] | null | null | null | google/ads/google_ads/v0/proto/resources/shared_criterion_pb2.py | jwygoda/google-ads-python | 863892b533240cb45269d9c2cceec47e2c5a8b68 | [
"Apache-2.0"
] | null | null | null | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/ads/googleads_v0/proto/resources/shared_criterion.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from google.ads.google_ads.v0.proto.common import criteria_pb2 as google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2
from google.ads.google_ads.v0.proto.enums import criterion_type_pb2 as google_dot_ads_dot_googleads__v0_dot_proto_dot_enums_dot_criterion__type__pb2
from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='google/ads/googleads_v0/proto/resources/shared_criterion.proto',
package='google.ads.googleads.v0.resources',
syntax='proto3',
serialized_options=_b('\n%com.google.ads.googleads.v0.resourcesB\024SharedCriterionProtoP\001ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v0/resources;resources\242\002\003GAA\252\002!Google.Ads.GoogleAds.V0.Resources\312\002!Google\\Ads\\GoogleAds\\V0\\Resources\352\002%Google::Ads::GoogleAds::V0::Resources'),
serialized_pb=_b('\n>google/ads/googleads_v0/proto/resources/shared_criterion.proto\x12!google.ads.googleads.v0.resources\x1a\x33google/ads/googleads_v0/proto/common/criteria.proto\x1a\x38google/ads/googleads_v0/proto/enums/criterion_type.proto\x1a\x1egoogle/protobuf/wrappers.proto\"\xdc\x04\n\x0fSharedCriterion\x12\x15\n\rresource_name\x18\x01 \x01(\t\x12\x30\n\nshared_set\x18\x02 \x01(\x0b\x32\x1c.google.protobuf.StringValue\x12\x31\n\x0c\x63riterion_id\x18\x1a \x01(\x0b\x32\x1b.google.protobuf.Int64Value\x12L\n\x04type\x18\x04 \x01(\x0e\x32>.google.ads.googleads.v0.enums.CriterionTypeEnum.CriterionType\x12>\n\x07keyword\x18\x03 \x01(\x0b\x32+.google.ads.googleads.v0.common.KeywordInfoH\x00\x12I\n\ryoutube_video\x18\x05 \x01(\x0b\x32\x30.google.ads.googleads.v0.common.YouTubeVideoInfoH\x00\x12M\n\x0fyoutube_channel\x18\x06 \x01(\x0b\x32\x32.google.ads.googleads.v0.common.YouTubeChannelInfoH\x00\x12\x42\n\tplacement\x18\x07 \x01(\x0b\x32-.google.ads.googleads.v0.common.PlacementInfoH\x00\x12T\n\x13mobile_app_category\x18\x08 \x01(\x0b\x32\x35.google.ads.googleads.v0.common.MobileAppCategoryInfoH\x00\x42\x0b\n\tcriterionB\x81\x02\n%com.google.ads.googleads.v0.resourcesB\x14SharedCriterionProtoP\x01ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v0/resources;resources\xa2\x02\x03GAA\xaa\x02!Google.Ads.GoogleAds.V0.Resources\xca\x02!Google\\Ads\\GoogleAds\\V0\\Resources\xea\x02%Google::Ads::GoogleAds::V0::Resourcesb\x06proto3')
,
dependencies=[google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v0_dot_proto_dot_enums_dot_criterion__type__pb2.DESCRIPTOR,google_dot_protobuf_dot_wrappers__pb2.DESCRIPTOR,])
_SHAREDCRITERION = _descriptor.Descriptor(
name='SharedCriterion',
full_name='google.ads.googleads.v0.resources.SharedCriterion',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='resource_name', full_name='google.ads.googleads.v0.resources.SharedCriterion.resource_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='shared_set', full_name='google.ads.googleads.v0.resources.SharedCriterion.shared_set', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='criterion_id', full_name='google.ads.googleads.v0.resources.SharedCriterion.criterion_id', index=2,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='google.ads.googleads.v0.resources.SharedCriterion.type', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='keyword', full_name='google.ads.googleads.v0.resources.SharedCriterion.keyword', index=4,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='youtube_video', full_name='google.ads.googleads.v0.resources.SharedCriterion.youtube_video', index=5,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='youtube_channel', full_name='google.ads.googleads.v0.resources.SharedCriterion.youtube_channel', index=6,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='placement', full_name='google.ads.googleads.v0.resources.SharedCriterion.placement', index=7,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='mobile_app_category', full_name='google.ads.googleads.v0.resources.SharedCriterion.mobile_app_category', index=8,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='criterion', full_name='google.ads.googleads.v0.resources.SharedCriterion.criterion',
index=0, containing_type=None, fields=[]),
],
serialized_start=245,
serialized_end=849,
)
_SHAREDCRITERION.fields_by_name['shared_set'].message_type = google_dot_protobuf_dot_wrappers__pb2._STRINGVALUE
_SHAREDCRITERION.fields_by_name['criterion_id'].message_type = google_dot_protobuf_dot_wrappers__pb2._INT64VALUE
_SHAREDCRITERION.fields_by_name['type'].enum_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_enums_dot_criterion__type__pb2._CRITERIONTYPEENUM_CRITERIONTYPE
_SHAREDCRITERION.fields_by_name['keyword'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._KEYWORDINFO
_SHAREDCRITERION.fields_by_name['youtube_video'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._YOUTUBEVIDEOINFO
_SHAREDCRITERION.fields_by_name['youtube_channel'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._YOUTUBECHANNELINFO
_SHAREDCRITERION.fields_by_name['placement'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._PLACEMENTINFO
_SHAREDCRITERION.fields_by_name['mobile_app_category'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._MOBILEAPPCATEGORYINFO
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['keyword'])
_SHAREDCRITERION.fields_by_name['keyword'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['youtube_video'])
_SHAREDCRITERION.fields_by_name['youtube_video'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['youtube_channel'])
_SHAREDCRITERION.fields_by_name['youtube_channel'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['placement'])
_SHAREDCRITERION.fields_by_name['placement'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['mobile_app_category'])
_SHAREDCRITERION.fields_by_name['mobile_app_category'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
DESCRIPTOR.message_types_by_name['SharedCriterion'] = _SHAREDCRITERION
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
SharedCriterion = _reflection.GeneratedProtocolMessageType('SharedCriterion', (_message.Message,), dict(
DESCRIPTOR = _SHAREDCRITERION,
__module__ = 'google.ads.googleads_v0.proto.resources.shared_criterion_pb2'
,
__doc__ = """A criterion belonging to a shared set.
Attributes:
resource_name:
The resource name of the shared criterion. Shared set resource
names have the form: ``customers/{customer_id}/sharedCriteria
/{shared_set_id}_{criterion_id}``
shared_set:
The shared set to which the shared criterion belongs.
criterion_id:
The ID of the criterion. This field is ignored for mutates.
type:
The type of the criterion.
criterion:
The criterion. Exactly one must be set.
keyword:
Keyword.
youtube_video:
YouTube Video.
youtube_channel:
YouTube Channel.
placement:
Placement.
mobile_app_category:
Mobile App Category.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v0.resources.SharedCriterion)
))
_sym_db.RegisterMessage(SharedCriterion)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| 59.221622 | 1,457 | 0.793264 |
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
_sym_db = _symbol_database.Default()
from google.ads.google_ads.v0.proto.common import criteria_pb2 as google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2
from google.ads.google_ads.v0.proto.enums import criterion_type_pb2 as google_dot_ads_dot_googleads__v0_dot_proto_dot_enums_dot_criterion__type__pb2
from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='google/ads/googleads_v0/proto/resources/shared_criterion.proto',
package='google.ads.googleads.v0.resources',
syntax='proto3',
serialized_options=_b('\n%com.google.ads.googleads.v0.resourcesB\024SharedCriterionProtoP\001ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v0/resources;resources\242\002\003GAA\252\002!Google.Ads.GoogleAds.V0.Resources\312\002!Google\\Ads\\GoogleAds\\V0\\Resources\352\002%Google::Ads::GoogleAds::V0::Resources'),
serialized_pb=_b('\n>google/ads/googleads_v0/proto/resources/shared_criterion.proto\x12!google.ads.googleads.v0.resources\x1a\x33google/ads/googleads_v0/proto/common/criteria.proto\x1a\x38google/ads/googleads_v0/proto/enums/criterion_type.proto\x1a\x1egoogle/protobuf/wrappers.proto\"\xdc\x04\n\x0fSharedCriterion\x12\x15\n\rresource_name\x18\x01 \x01(\t\x12\x30\n\nshared_set\x18\x02 \x01(\x0b\x32\x1c.google.protobuf.StringValue\x12\x31\n\x0c\x63riterion_id\x18\x1a \x01(\x0b\x32\x1b.google.protobuf.Int64Value\x12L\n\x04type\x18\x04 \x01(\x0e\x32>.google.ads.googleads.v0.enums.CriterionTypeEnum.CriterionType\x12>\n\x07keyword\x18\x03 \x01(\x0b\x32+.google.ads.googleads.v0.common.KeywordInfoH\x00\x12I\n\ryoutube_video\x18\x05 \x01(\x0b\x32\x30.google.ads.googleads.v0.common.YouTubeVideoInfoH\x00\x12M\n\x0fyoutube_channel\x18\x06 \x01(\x0b\x32\x32.google.ads.googleads.v0.common.YouTubeChannelInfoH\x00\x12\x42\n\tplacement\x18\x07 \x01(\x0b\x32-.google.ads.googleads.v0.common.PlacementInfoH\x00\x12T\n\x13mobile_app_category\x18\x08 \x01(\x0b\x32\x35.google.ads.googleads.v0.common.MobileAppCategoryInfoH\x00\x42\x0b\n\tcriterionB\x81\x02\n%com.google.ads.googleads.v0.resourcesB\x14SharedCriterionProtoP\x01ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v0/resources;resources\xa2\x02\x03GAA\xaa\x02!Google.Ads.GoogleAds.V0.Resources\xca\x02!Google\\Ads\\GoogleAds\\V0\\Resources\xea\x02%Google::Ads::GoogleAds::V0::Resourcesb\x06proto3')
,
dependencies=[google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v0_dot_proto_dot_enums_dot_criterion__type__pb2.DESCRIPTOR,google_dot_protobuf_dot_wrappers__pb2.DESCRIPTOR,])
_SHAREDCRITERION = _descriptor.Descriptor(
name='SharedCriterion',
full_name='google.ads.googleads.v0.resources.SharedCriterion',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='resource_name', full_name='google.ads.googleads.v0.resources.SharedCriterion.resource_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='shared_set', full_name='google.ads.googleads.v0.resources.SharedCriterion.shared_set', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='criterion_id', full_name='google.ads.googleads.v0.resources.SharedCriterion.criterion_id', index=2,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='google.ads.googleads.v0.resources.SharedCriterion.type', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='keyword', full_name='google.ads.googleads.v0.resources.SharedCriterion.keyword', index=4,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='youtube_video', full_name='google.ads.googleads.v0.resources.SharedCriterion.youtube_video', index=5,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='youtube_channel', full_name='google.ads.googleads.v0.resources.SharedCriterion.youtube_channel', index=6,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='placement', full_name='google.ads.googleads.v0.resources.SharedCriterion.placement', index=7,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='mobile_app_category', full_name='google.ads.googleads.v0.resources.SharedCriterion.mobile_app_category', index=8,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='criterion', full_name='google.ads.googleads.v0.resources.SharedCriterion.criterion',
index=0, containing_type=None, fields=[]),
],
serialized_start=245,
serialized_end=849,
)
_SHAREDCRITERION.fields_by_name['shared_set'].message_type = google_dot_protobuf_dot_wrappers__pb2._STRINGVALUE
_SHAREDCRITERION.fields_by_name['criterion_id'].message_type = google_dot_protobuf_dot_wrappers__pb2._INT64VALUE
_SHAREDCRITERION.fields_by_name['type'].enum_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_enums_dot_criterion__type__pb2._CRITERIONTYPEENUM_CRITERIONTYPE
_SHAREDCRITERION.fields_by_name['keyword'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._KEYWORDINFO
_SHAREDCRITERION.fields_by_name['youtube_video'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._YOUTUBEVIDEOINFO
_SHAREDCRITERION.fields_by_name['youtube_channel'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._YOUTUBECHANNELINFO
_SHAREDCRITERION.fields_by_name['placement'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._PLACEMENTINFO
_SHAREDCRITERION.fields_by_name['mobile_app_category'].message_type = google_dot_ads_dot_googleads__v0_dot_proto_dot_common_dot_criteria__pb2._MOBILEAPPCATEGORYINFO
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['keyword'])
_SHAREDCRITERION.fields_by_name['keyword'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['youtube_video'])
_SHAREDCRITERION.fields_by_name['youtube_video'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['youtube_channel'])
_SHAREDCRITERION.fields_by_name['youtube_channel'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['placement'])
_SHAREDCRITERION.fields_by_name['placement'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
_SHAREDCRITERION.oneofs_by_name['criterion'].fields.append(
_SHAREDCRITERION.fields_by_name['mobile_app_category'])
_SHAREDCRITERION.fields_by_name['mobile_app_category'].containing_oneof = _SHAREDCRITERION.oneofs_by_name['criterion']
DESCRIPTOR.message_types_by_name['SharedCriterion'] = _SHAREDCRITERION
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
SharedCriterion = _reflection.GeneratedProtocolMessageType('SharedCriterion', (_message.Message,), dict(
DESCRIPTOR = _SHAREDCRITERION,
__module__ = 'google.ads.googleads_v0.proto.resources.shared_criterion_pb2'
,
__doc__ = """A criterion belonging to a shared set.
Attributes:
resource_name:
The resource name of the shared criterion. Shared set resource
names have the form: ``customers/{customer_id}/sharedCriteria
/{shared_set_id}_{criterion_id}``
shared_set:
The shared set to which the shared criterion belongs.
criterion_id:
The ID of the criterion. This field is ignored for mutates.
type:
The type of the criterion.
criterion:
The criterion. Exactly one must be set.
keyword:
Keyword.
youtube_video:
YouTube Video.
youtube_channel:
YouTube Channel.
placement:
Placement.
mobile_app_category:
Mobile App Category.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v0.resources.SharedCriterion)
))
_sym_db.RegisterMessage(SharedCriterion)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| true | true |
f72c907b1f918fdf342d234b59f8c92fc6aa1d93 | 2,070 | py | Python | cows_bulls.py | hmlewis-astro/coding_practice | a781443399766bf13df0d2de93f0ce3acda0c77d | [
"MIT"
] | null | null | null | cows_bulls.py | hmlewis-astro/coding_practice | a781443399766bf13df0d2de93f0ce3acda0c77d | [
"MIT"
] | null | null | null | cows_bulls.py | hmlewis-astro/coding_practice | a781443399766bf13df0d2de93f0ce3acda0c77d | [
"MIT"
] | null | null | null | '''
File name: pythonpractice.py
Author: Hannah Lewis
Date created: 08/03/2020
Date last modified: 08/03/2020
Python Version: 3.7
'''
import random
def main():
'''
Create a program that will play the “cows and bulls” game with the user.
'''
print("You will try to guess a random 4-digit number.")
print("A 'cow' is a correct digit in the correct place.")
print("A 'bull' is a correct digit in the wrong place.")
print("The game ends when you get 4 cows!\n")
print("You can type 'exit' at any time to end the game.\n")
num = str(random.randint(10000, 99999))[1:5] # Get random number, remove first digit so that first digit can be 0
guess = input("Give me your best guess: ") # Get first guess
count = 0
cow = 0
bull = 0
guessing = True
while guessing:
assert len(guess) == 4, "Input must be 4-digits long."
if guess == 'exit': # Player can exit at any time
print("The number was " + str(num) + ".")
print("Better luck next time.")
guessing = False
break
count += 1
for i in range(0,4): # Compare digits
if num[i] == guess[i]:
cow+=1
elif num[i] in guess:
bull+=1
print("You got {} cows, and {} bulls.".format(cow,bull)) # How many cows and bulls
if cow == 4: # If all digits are correct
if count == 1:
print("You got it on the first try!")
guessing = False
if count > 1:
print("You got it! It took you", count, "tries.")
print("The number was " + str(num) + ".")
guessing = False
else: # Guess again
cow = bull = 0
guess = input("Guess again: ")
#TODO: ask if they want to play another game
return
if __name__ == '__main__':
print("Ready to Cows and Bulls?")
main() # Runs exercise
| 27.972973 | 117 | 0.522705 |
import random
def main():
print("You will try to guess a random 4-digit number.")
print("A 'cow' is a correct digit in the correct place.")
print("A 'bull' is a correct digit in the wrong place.")
print("The game ends when you get 4 cows!\n")
print("You can type 'exit' at any time to end the game.\n")
num = str(random.randint(10000, 99999))[1:5]
guess = input("Give me your best guess: ")
count = 0
cow = 0
bull = 0
guessing = True
while guessing:
assert len(guess) == 4, "Input must be 4-digits long."
if guess == 'exit':
print("The number was " + str(num) + ".")
print("Better luck next time.")
guessing = False
break
count += 1
for i in range(0,4):
if num[i] == guess[i]:
cow+=1
elif num[i] in guess:
bull+=1
print("You got {} cows, and {} bulls.".format(cow,bull))
if cow == 4:
if count == 1:
print("You got it on the first try!")
guessing = False
if count > 1:
print("You got it! It took you", count, "tries.")
print("The number was " + str(num) + ".")
guessing = False
else:
cow = bull = 0
guess = input("Guess again: ")
return
if __name__ == '__main__':
print("Ready to Cows and Bulls?")
main()
| true | true |
f72c90b37b41d597ef1c839e1131577727a7329a | 227 | py | Python | game/admin.py | zxalif/simpleapi | 89d9f1c81b7c8e46d9764573fc1070a453751b4a | [
"MIT"
] | null | null | null | game/admin.py | zxalif/simpleapi | 89d9f1c81b7c8e46d9764573fc1070a453751b4a | [
"MIT"
] | 8 | 2020-06-05T23:34:44.000Z | 2022-02-10T09:11:05.000Z | game/admin.py | zxalif/simpleapi | 89d9f1c81b7c8e46d9764573fc1070a453751b4a | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import (
Category,
Game,
Thread,
ThreadImage
)
admin.site.register(Category)
admin.site.register(Game)
admin.site.register(Thread)
admin.site.register(ThreadImage) | 17.461538 | 32 | 0.748899 | from django.contrib import admin
from .models import (
Category,
Game,
Thread,
ThreadImage
)
admin.site.register(Category)
admin.site.register(Game)
admin.site.register(Thread)
admin.site.register(ThreadImage) | true | true |
f72c9168c0692e02e3f54b61d9c5f5e6399fc4d3 | 867 | py | Python | blog/pelican-plugins/headerid/headerid.py | lemonsong/lemonsong.github.io | 14a65b8c2506c95bab64f50143f3850be3edadc1 | [
"MIT"
] | null | null | null | blog/pelican-plugins/headerid/headerid.py | lemonsong/lemonsong.github.io | 14a65b8c2506c95bab64f50143f3850be3edadc1 | [
"MIT"
] | 1 | 2022-01-10T04:39:05.000Z | 2022-01-10T04:39:05.000Z | blog/pelican-plugins/headerid/headerid.py | lemonsong/lemonsong.github.io | 14a65b8c2506c95bab64f50143f3850be3edadc1 | [
"MIT"
] | null | null | null | from pelican import readers
from pelican.readers import PelicanHTMLTranslator
from pelican import signals
from docutils import nodes
def register():
class HeaderIDPatchedPelicanHTMLTranslator(PelicanHTMLTranslator):
def depart_title(self, node):
close_tag = self.context[-1]
parent = node.parent
if isinstance(parent, nodes.section) and parent.hasattr('ids') and parent['ids']:
anchor_name = parent['ids'][0]
# add permalink anchor
if close_tag.startswith('</h'):
self.body.append(
'<a class="headerlink" href="#%s" title="Permalink to this headline">*</a>' % anchor_name
)
PelicanHTMLTranslator.depart_title(self, node)
readers.PelicanHTMLTranslator = HeaderIDPatchedPelicanHTMLTranslator
| 43.35 | 113 | 0.635525 | from pelican import readers
from pelican.readers import PelicanHTMLTranslator
from pelican import signals
from docutils import nodes
def register():
class HeaderIDPatchedPelicanHTMLTranslator(PelicanHTMLTranslator):
def depart_title(self, node):
close_tag = self.context[-1]
parent = node.parent
if isinstance(parent, nodes.section) and parent.hasattr('ids') and parent['ids']:
anchor_name = parent['ids'][0]
if close_tag.startswith('</h'):
self.body.append(
'<a class="headerlink" href="#%s" title="Permalink to this headline">*</a>' % anchor_name
)
PelicanHTMLTranslator.depart_title(self, node)
readers.PelicanHTMLTranslator = HeaderIDPatchedPelicanHTMLTranslator
| true | true |
f72c916ef8e95900c5ab3a87d685611c982bda39 | 2,960 | py | Python | linsae/cogs/Events.py | drakedeveloper/Linsae | 1a866fbb95df3a7270e446dca18e9dca8beb2c3a | [
"Apache-2.0"
] | 1 | 2019-06-27T00:47:21.000Z | 2019-06-27T00:47:21.000Z | linsae/cogs/Events.py | drakedeveloper/Linsae | 1a866fbb95df3a7270e446dca18e9dca8beb2c3a | [
"Apache-2.0"
] | null | null | null | linsae/cogs/Events.py | drakedeveloper/Linsae | 1a866fbb95df3a7270e446dca18e9dca8beb2c3a | [
"Apache-2.0"
] | null | null | null | import discord
import time
import asyncio
from datetime import datetime
import time
from discord.ext import tasks, commands
from tinydb import TinyDB, Query
import re
class Events(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.Cog.listener()
async def on_guild_join(self, guild):
role = await guild.create_role(name="Muted", colour=discord.Colour.dark_grey())
for channel in guild.channels:
await channel.set_permissions(role, send_messages = False)
await asyncio.sleep(delay=5)
for member in guild.members:
if member.guild_permissions.administrator and member.id != self.bot.user.id:
join_message = discord.Embed(title="__**Linsae!**__",
description=f"**Hello, {member.mention}, This is me linsae and in order for me to work you need to do some configuration, sooo let's get started!**",
colour=0x4298f4, timestamp=datetime.utcnow())
join_message.add_field(name="__Knowledge__",
value=f"""**First of all, {member.mention} let me introduce my self:
- My name as you know is Linsae and i'm glad to meet you.
- My developer is Ɗrake#7418 and if you need any help with bots or something feel free to contact him!
- My birthday is 6/25/2019.**""")
join_message.add_field(name="__Configuration__", value=""" Alright so i'm a support bot that helps moderators and make their lifes easier, so what do i do ?
.If a member needs help with something he can just type ***?support*** in a specific channel that i will menion later.
.i have many moderator commands like ban, warn, kick, mute and more....
--> Now in order to do all that the i need to config somethings in the server and don't worry i won't do harm to it!i will just create some channels and roles and ask you things but for that to work you need to type ***?ticketconfig*** in any channel and i will give you instructions!""")
join_message.set_footer(
text="For more help just try to read this embed again or contact the developer!",
icon_url=self.bot.user.avatar_url)
join_message.set_author(name=self.bot.user)
join_message.set_thumbnail(url=guild.icon_url)
await member.send(embed=join_message)
@commands.Cog.listener()
async def on_message(self, message):
if str(message.channel) == "ticket-request":
if message.content != "?support":
await message.delete()
if message.content == "nigga" or message.content == "nigger" or message.content == "nigro":
await message.delete()
await message.channel.send("You can't say that!")
def setup(bot):
bot.add_cog(Events(bot)) | 55.849057 | 294 | 0.631419 | import discord
import time
import asyncio
from datetime import datetime
import time
from discord.ext import tasks, commands
from tinydb import TinyDB, Query
import re
class Events(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.Cog.listener()
async def on_guild_join(self, guild):
role = await guild.create_role(name="Muted", colour=discord.Colour.dark_grey())
for channel in guild.channels:
await channel.set_permissions(role, send_messages = False)
await asyncio.sleep(delay=5)
for member in guild.members:
if member.guild_permissions.administrator and member.id != self.bot.user.id:
join_message = discord.Embed(title="__**Linsae!**__",
description=f"**Hello, {member.mention}, This is me linsae and in order for me to work you need to do some configuration, sooo let's get started!**",
colour=0x4298f4, timestamp=datetime.utcnow())
join_message.add_field(name="__Knowledge__",
value=f"""**First of all, {member.mention} let me introduce my self:
- My name as you know is Linsae and i'm glad to meet you.
- My developer is Ɗrake#7418 and if you need any help with bots or something feel free to contact him!
- My birthday is 6/25/2019.**""")
join_message.add_field(name="__Configuration__", value=""" Alright so i'm a support bot that helps moderators and make their lifes easier, so what do i do ?
.If a member needs help with something he can just type ***?support*** in a specific channel that i will menion later.
.i have many moderator commands like ban, warn, kick, mute and more....
--> Now in order to do all that the i need to config somethings in the server and don't worry i won't do harm to it!i will just create some channels and roles and ask you things but for that to work you need to type ***?ticketconfig*** in any channel and i will give you instructions!""")
join_message.set_footer(
text="For more help just try to read this embed again or contact the developer!",
icon_url=self.bot.user.avatar_url)
join_message.set_author(name=self.bot.user)
join_message.set_thumbnail(url=guild.icon_url)
await member.send(embed=join_message)
@commands.Cog.listener()
async def on_message(self, message):
if str(message.channel) == "ticket-request":
if message.content != "?support":
await message.delete()
if message.content == "nigga" or message.content == "nigger" or message.content == "nigro":
await message.delete()
await message.channel.send("You can't say that!")
def setup(bot):
bot.add_cog(Events(bot)) | true | true |
f72c919a5fbacff307b79548546b94830a8d5ed5 | 26,995 | py | Python | kivymd/uix/list.py | akaminetzkyp/KivyMD | 940791ee1217e09184d8916c0eccc7534f097a48 | [
"MIT"
] | 1 | 2020-07-01T12:39:51.000Z | 2020-07-01T12:39:51.000Z | kivymd/uix/list.py | ayo6706/KivyMD | c67850fd9f505d20a9e86ab89a39918daf34cd43 | [
"MIT"
] | null | null | null | kivymd/uix/list.py | ayo6706/KivyMD | c67850fd9f505d20a9e86ab89a39918daf34cd43 | [
"MIT"
] | null | null | null | """
Components/List
===============
.. seealso::
`Material Design spec, Lists <https://material.io/components/lists>`_
.. rubric:: Lists are continuous, vertical indexes of text or images.
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/lists.png
:align: center
The class :class:`~MDList` in combination with a :class:`~BaseListItem` like
:class:`~OneLineListItem` will create a list that expands as items are added to
it, working nicely with `Kivy's` :class:`~kivy.uix.scrollview.ScrollView`.
Due to the variety in sizes and controls in the `Material Design spec`,
this module suffers from a certain level of complexity to keep the widgets
compliant, flexible and performant.
For this `KivyMD` provides list items that try to cover the most common usecases,
when those are insufficient, there's a base class called :class:`~BaseListItem`
which you can use to create your own list items. This documentation will only
cover the provided ones, for custom implementations please refer to this
module's source code.
`KivyMD` provides the following list items classes for use:
Text only ListItems
-------------------
- OneLineListItem_
- TwoLineListItem_
- ThreeLineListItem_
ListItems with widget containers
--------------------------------
These widgets will take other widgets that inherit from :class:`~ILeftBody`,
:class:`ILeftBodyTouch`, :class:`~IRightBody` or :class:`~IRightBodyTouch` and
put them in their corresponding container.
As the name implies, :class:`~ILeftBody` and :class:`~IRightBody` will signal
that the widget goes into the left or right container, respectively.
:class:`~ILeftBodyTouch` and :class:`~IRightBodyTouch` do the same thing,
except these widgets will also receive touch events that occur within their
surfaces.
`KivyMD` provides base classes such as :class:`~ImageLeftWidget`,
:class:`~ImageRightWidget`, :class:`~IconRightWidget`, :class:`~IconLeftWidget`,
based on the above classes.
.. rubric:: Allows the use of items with custom widgets on the left.
- OneLineAvatarListItem_
- TwoLineAvatarListItem_
- ThreeLineAvatarListItem_
- OneLineIconListItem_
- TwoLineIconListItem_
- ThreeLineIconListItem_
.. rubric:: It allows the use of elements with custom widgets on the left
and the right.
- OneLineAvatarIconListItem_
- TwoLineAvatarIconListItem_
- ThreeLineAvatarIconListItem_
Usage
-----
.. code-block:: python
from kivy.lang import Builder
from kivymd.app import MDApp
from kivymd.uix.list import OneLineListItem
KV = '''
ScrollView:
MDList:
id: container
'''
class Test(MDApp):
def build(self):
return Builder.load_string(KV)
def on_start(self):
for i in range(20):
self.root.ids.container.add_widget(
OneLineListItem(text=f"Single-line item {i}")
)
Test().run()
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/lists.gif
:align: center
.. OneLineListItem:
OneLineListItem
---------------
.. code-block:: kv
OneLineListItem:
text: "Single-line item"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/OneLineListItem.png
:align: center
.. TwoLineListItem:
TwoLineListItem
---------------
.. code-block:: kv
TwoLineListItem:
text: "Two-line item"
secondary_text: "Secondary text here"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/TwoLineListItem.png
:align: center
.. ThreeLineListItem:
ThreeLineListItem
-----------------
.. code-block:: kv
ThreeLineListItem:
text: "Three-line item"
secondary_text: "This is a multi-line label where you can"
tertiary_text: "fit more text than usual"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/ThreeLineListItem.png
:align: center
.. OneLineAvatarListItem:
OneLineAvatarListItem
---------------------
.. code-block:: kv
OneLineAvatarListItem:
text: "Single-line item with avatar"
ImageLeftWidget:
source: "data/logo/kivy-icon-256.png"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/lists-map.png
:align: center
.. TwoLineAvatarListItem:
TwoLineAvatarListItem
---------------------
.. code-block:: kv
TwoLineAvatarListItem:
text: "Two-line item with avatar"
secondary_text: "Secondary text here"
ImageLeftWidget:
source: "data/logo/kivy-icon-256.png"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/TwoLineAvatarListItem.png
:align: center
.. ThreeLineAvatarListItem:
ThreeLineAvatarListItem
-----------------------
.. code-block:: kv
ThreeLineAvatarListItem:
text: "Three-line item with avatar"
secondary_text: "Secondary text here"
tertiary_text: "fit more text than usual"
ImageLeftWidget:
source: "data/logo/kivy-icon-256.png"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/ThreeLineAvatarListItem.png
:align: center
.. OneLineIconListItem:
OneLineIconListItem
-------------------
.. code-block:: kv
OneLineAvatarListItem:
text: "Single-line item with avatar"
IconLeftWidget:
icon: "language-python"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/OneLineIconListItem.png
:align: center
.. TwoLineIconListItem:
TwoLineIconListItem
-------------------
.. code-block:: kv
TwoLineIconListItem:
text: "Two-line item with avatar"
secondary_text: "Secondary text here"
IconLeftWidget:
icon: "language-python"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/TwoLineIconListItem.png
:align: center
.. ThreeLineIconListItem:
ThreeLineIconListItem
---------------------
.. code-block:: kv
ThreeLineIconListItem:
text: "Three-line item with avatar"
secondary_text: "Secondary text here"
tertiary_text: "fit more text than usual"
IconLeftWidget:
icon: "language-python"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/ThreeLineIconListItem.png
:align: center
.. OneLineAvatarIconListItem:
OneLineAvatarIconListItem
-------------------------
.. code-block:: kv
OneLineAvatarIconListItem:
text: "One-line item with avatar"
IconLeftWidget:
icon: "plus"
IconRightWidget:
icon: "minus"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/OneLineAvatarIconListItem.png
:align: center
.. TwoLineAvatarIconListItem:
TwoLineAvatarIconListItem
-------------------------
.. code-block:: kv
TwoLineAvatarIconListItem:
text: "Two-line item with avatar"
secondary_text: "Secondary text here"
IconLeftWidget:
icon: "plus"
IconRightWidget:
icon: "minus"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/TwoLineAvatarIconListItem.png
:align: center
.. ThreeLineAvatarIconListItem:
ThreeLineAvatarIconListItem
---------------------------
.. code-block:: kv
ThreeLineAvatarIconListItem:
text: "Three-line item with avatar"
secondary_text: "Secondary text here"
tertiary_text: "fit more text than usual"
IconLeftWidget:
icon: "plus"
IconRightWidget:
icon: "minus"
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/ThreeLineAvatarIconListItem.png
:align: center
Custom list item
----------------
.. code-block:: python
from kivy.lang import Builder
from kivy.properties import StringProperty
from kivymd.app import MDApp
from kivymd.uix.list import IRightBodyTouch, OneLineAvatarIconListItem
from kivymd.uix.selectioncontrol import MDCheckbox
from kivymd.icon_definitions import md_icons
KV = '''
<ListItemWithCheckbox>:
IconLeftWidget:
icon: root.icon
RightCheckbox:
BoxLayout:
ScrollView:
MDList:
id: scroll
'''
class ListItemWithCheckbox(OneLineAvatarIconListItem):
'''Custom list item.'''
icon = StringProperty("android")
class RightCheckbox(IRightBodyTouch, MDCheckbox):
'''Custom right container.'''
class MainApp(MDApp):
def build(self):
return Builder.load_string(KV)
def on_start(self):
icons = list(md_icons.keys())
for i in range(30):
self.root.ids.scroll.add_widget(
ListItemWithCheckbox(text=f"Item {i}", icon=icons[i])
)
MainApp().run()
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/custom-list-item.png
:align: center
.. code-block:: python
from kivy.lang import Builder
from kivymd.app import MDApp
from kivymd.uix.boxlayout import MDBoxLayout
from kivymd.uix.list import IRightBodyTouch
KV = '''
OneLineAvatarIconListItem:
text: "One-line item with avatar"
on_size:
self.ids._right_container.width = container.width
self.ids._right_container.x = container.width
IconLeftWidget:
icon: "settings"
Container:
id: container
MDIconButton:
icon: "minus"
MDIconButton:
icon: "plus"
'''
class Container(IRightBodyTouch, MDBoxLayout):
adaptive_width = True
class MainApp(MDApp):
def build(self):
return Builder.load_string(KV)
MainApp().run()
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/custom-list-right-container.png
:align: center
"""
from kivy.lang import Builder
from kivy.metrics import dp
from kivy.properties import (
StringProperty,
NumericProperty,
ListProperty,
OptionProperty,
BooleanProperty,
)
from kivy.uix.behaviors import ButtonBehavior
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.image import Image
import kivymd.material_resources as m_res
from kivymd.uix.behaviors import RectangularRippleBehavior
from kivymd.uix.button import MDIconButton
from kivymd.theming import ThemableBehavior
from kivymd.font_definitions import theme_font_styles
from kivymd.uix.gridlayout import MDGridLayout
from kivymd.uix.selectioncontrol import MDCheckbox
Builder.load_string(
"""
#:import m_res kivymd.material_resources
<MDList>
cols: 1
adaptive_height: True
padding: 0, self._list_vertical_padding
<BaseListItem>
size_hint_y: None
canvas:
Color:
rgba:
self.theme_cls.divider_color if root.divider is not None\
else (0, 0, 0, 0)
Line:
points: (root.x ,root.y, root.x+self.width, root.y)\
if root.divider == 'Full' else\
(root.x+root._txt_left_pad, root.y,\
root.x+self.width-root._txt_left_pad-root._txt_right_pad,\
root.y)
Color:
rgba: root.bg_color if root.bg_color else (0, 0, 0, 0)
Rectangle:
pos: self.pos
size: self.size
BoxLayout:
id: _text_container
orientation: 'vertical'
pos: root.pos
padding:
root._txt_left_pad, root._txt_top_pad,\
root._txt_right_pad, root._txt_bot_pad
MDLabel:
id: _lbl_primary
text: root.text
font_style: root.font_style
theme_text_color: root.theme_text_color
text_color: root.text_color
size_hint_y: None
height: self.texture_size[1]
markup: True
shorten_from: 'right'
shorten: True
MDLabel:
id: _lbl_secondary
text: '' if root._num_lines == 1 else root.secondary_text
font_style: root.secondary_font_style
theme_text_color: root.secondary_theme_text_color
text_color: root.secondary_text_color
size_hint_y: None
height: 0 if root._num_lines == 1 else self.texture_size[1]
shorten: True
shorten_from: 'right'
markup: True
MDLabel:
id: _lbl_tertiary
text: '' if root._num_lines == 1 else root.tertiary_text
font_style: root.tertiary_font_style
theme_text_color: root.tertiary_theme_text_color
text_color: root.tertiary_text_color
size_hint_y: None
height: 0 if root._num_lines == 1 else self.texture_size[1]
shorten: True
shorten_from: 'right'
markup: True
<OneLineAvatarListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height/2 - self.height/2
size: dp(40), dp(40)
<ThreeLineAvatarListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height - root._txt_top_pad - self.height - dp(5)
size: dp(40), dp(40)
<OneLineIconListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<ThreeLineIconListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height - root._txt_top_pad - self.height - dp(5)
size: dp(48), dp(48)
<OneLineRightIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<ThreeLineRightIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<OneLineAvatarIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<TwoLineAvatarIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<ThreeLineAvatarIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height - root._txt_top_pad - self.height - dp(5)
size: dp(48), dp(48)
"""
)
class MDList(MDGridLayout):
"""ListItem container. Best used in conjunction with a
:class:`kivy.uix.ScrollView`.
When adding (or removing) a widget, it will resize itself to fit its
children, plus top and bottom paddings as described by the `MD` spec.
"""
_list_vertical_padding = NumericProperty("8dp")
def add_widget(self, widget, index=0, canvas=None):
super().add_widget(widget, index, canvas)
self.height += widget.height
def remove_widget(self, widget):
super().remove_widget(widget)
self.height -= widget.height
class BaseListItem(
ThemableBehavior, RectangularRippleBehavior, ButtonBehavior, FloatLayout
):
"""
Base class to all ListItems. Not supposed to be instantiated on its own.
"""
text = StringProperty()
"""
Text shown in the first line.
:attr:`text` is a :class:`~kivy.properties.StringProperty`
and defaults to `''`.
"""
text_color = ListProperty(None)
"""
Text color in ``rgba`` format used if :attr:`~theme_text_color` is set
to `'Custom'`.
:attr:`text_color` is a :class:`~kivy.properties.ListProperty`
and defaults to `None`.
"""
font_style = OptionProperty("Subtitle1", options=theme_font_styles)
"""
Text font style. See ``kivymd.font_definitions.py``.
:attr:`font_style` is a :class:`~kivy.properties.OptionProperty`
and defaults to `'Subtitle1'`.
"""
theme_text_color = StringProperty("Primary", allownone=True)
"""
Theme text color in ``rgba`` format for primary text.
:attr:`theme_text_color` is a :class:`~kivy.properties.StringProperty`
and defaults to `'Primary'`.
"""
secondary_text = StringProperty()
"""
Text shown in the second line.
:attr:`secondary_text` is a :class:`~kivy.properties.StringProperty`
and defaults to `''`.
"""
tertiary_text = StringProperty()
"""
The text is displayed on the third line.
:attr:`tertiary_text` is a :class:`~kivy.properties.StringProperty`
and defaults to `''`.
"""
secondary_text_color = ListProperty(None)
"""
Text color in ``rgba`` format used for secondary text
if :attr:`~secondary_theme_text_color` is set to `'Custom'`.
:attr:`secondary_text_color` is a :class:`~kivy.properties.ListProperty`
and defaults to `None`.
"""
tertiary_text_color = ListProperty(None)
"""
Text color in ``rgba`` format used for tertiary text
if :attr:`~secondary_theme_text_color` is set to 'Custom'.
:attr:`tertiary_text_color` is a :class:`~kivy.properties.ListProperty`
and defaults to `None`.
"""
secondary_theme_text_color = StringProperty("Secondary", allownone=True)
"""
Theme text color for secondary text.
:attr:`secondary_theme_text_color` is a :class:`~kivy.properties.StringProperty`
and defaults to `'Secondary'`.
"""
tertiary_theme_text_color = StringProperty("Secondary", allownone=True)
"""
Theme text color for tertiary text.
:attr:`tertiary_theme_text_color` is a :class:`~kivy.properties.StringProperty`
and defaults to `'Secondary'`.
"""
secondary_font_style = OptionProperty("Body1", options=theme_font_styles)
"""
Font style for secondary line. See ``kivymd.font_definitions.py``.
:attr:`secondary_font_style` is a :class:`~kivy.properties.OptionProperty`
and defaults to `'Body1'`.
"""
tertiary_font_style = OptionProperty("Body1", options=theme_font_styles)
"""
Font style for tertiary line. See ``kivymd.font_definitions.py``.
:attr:`tertiary_font_style` is a :class:`~kivy.properties.OptionProperty`
and defaults to `'Body1'`.
"""
divider = OptionProperty(
"Full", options=["Full", "Inset", None], allownone=True
)
"""
Divider mode. Available options are: `'Full'`, `'Inset'`
and default to `'Full'`.
:attr:`tertiary_font_style` is a :class:`~kivy.properties.OptionProperty`
and defaults to `'Body1'`.
"""
bg_color = ListProperty()
"""
Background color for menu item.
:attr:`bg_color` is a :class:`~kivy.properties.ListProperty`
and defaults to `[]`.
"""
_txt_left_pad = NumericProperty("16dp")
_txt_top_pad = NumericProperty()
_txt_bot_pad = NumericProperty()
_txt_right_pad = NumericProperty(m_res.HORIZ_MARGINS)
_num_lines = 3
_no_ripple_effect = BooleanProperty(False)
class ILeftBody:
"""
Pseudo-interface for widgets that go in the left container for
ListItems that support it.
Implements nothing and requires no implementation, for annotation only.
"""
pass
class ILeftBodyTouch:
"""
Same as :class:`~ILeftBody`, but allows the widget to receive touch
events instead of triggering the ListItem's ripple effect.
"""
pass
class IRightBody:
"""
Pseudo-interface for widgets that go in the right container for
ListItems that support it.
Implements nothing and requires no implementation, for annotation only.
"""
pass
class IRightBodyTouch:
"""
Same as :class:`~IRightBody`, but allows the widget to receive touch
events instead of triggering the ``ListItem``'s ripple effect
"""
pass
class ContainerSupport:
"""
Overrides ``add_widget`` in a ``ListItem`` to include support
for ``I*Body`` widgets when the appropiate containers are present.
"""
_touchable_widgets = ListProperty()
def add_widget(self, widget, index=0):
if issubclass(widget.__class__, ILeftBody):
self.ids._left_container.add_widget(widget)
elif issubclass(widget.__class__, ILeftBodyTouch):
self.ids._left_container.add_widget(widget)
self._touchable_widgets.append(widget)
elif issubclass(widget.__class__, IRightBody):
self.ids._right_container.add_widget(widget)
elif issubclass(widget.__class__, IRightBodyTouch):
self.ids._right_container.add_widget(widget)
self._touchable_widgets.append(widget)
else:
return super().add_widget(widget)
def remove_widget(self, widget):
super().remove_widget(widget)
if widget in self._touchable_widgets:
self._touchable_widgets.remove(widget)
def on_touch_down(self, touch):
if self.propagate_touch_to_touchable_widgets(touch, "down"):
return
super().on_touch_down(touch)
def on_touch_move(self, touch, *args):
if self.propagate_touch_to_touchable_widgets(touch, "move", *args):
return
super().on_touch_move(touch, *args)
def on_touch_up(self, touch):
if self.propagate_touch_to_touchable_widgets(touch, "up"):
return
super().on_touch_up(touch)
def propagate_touch_to_touchable_widgets(self, touch, touch_event, *args):
triggered = False
for i in self._touchable_widgets:
if i.collide_point(touch.x, touch.y):
triggered = True
if touch_event == "down":
i.on_touch_down(touch)
elif touch_event == "move":
i.on_touch_move(touch, *args)
elif touch_event == "up":
i.on_touch_up(touch)
return triggered
class OneLineListItem(BaseListItem):
"""A one line list item."""
_txt_top_pad = NumericProperty("16dp")
_txt_bot_pad = NumericProperty("15dp") # dp(20) - dp(5)
_height = NumericProperty()
_num_lines = 1
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(48) if not self._height else self._height
class TwoLineListItem(BaseListItem):
"""A two line list item."""
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp") # dp(20) - dp(5)
_height = NumericProperty()
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineListItem(BaseListItem):
"""A three line list item."""
_txt_top_pad = NumericProperty("16dp")
_txt_bot_pad = NumericProperty("15dp") # dp(20) - dp(5)
_height = NumericProperty()
_num_lines = 3
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(88) if not self._height else self._height
class OneLineAvatarListItem(ContainerSupport, BaseListItem):
_txt_left_pad = NumericProperty("72dp")
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("19dp") # dp(24) - dp(5)
_height = NumericProperty()
_num_lines = 1
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(56) if not self._height else self._height
class TwoLineAvatarListItem(OneLineAvatarListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp") # dp(20) - dp(5)
_height = NumericProperty()
_num_lines = 2
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineAvatarListItem(ContainerSupport, ThreeLineListItem):
_txt_left_pad = NumericProperty("72dp")
class OneLineIconListItem(ContainerSupport, OneLineListItem):
_txt_left_pad = NumericProperty("72dp")
class TwoLineIconListItem(OneLineIconListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp") # dp(20) - dp(5)
_height = NumericProperty()
_num_lines = 2
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineIconListItem(ContainerSupport, ThreeLineListItem):
_txt_left_pad = NumericProperty("72dp")
class OneLineRightIconListItem(ContainerSupport, OneLineListItem):
# dp(40) = dp(16) + dp(24):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class TwoLineRightIconListItem(OneLineRightIconListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp") # dp(20) - dp(5)
_height = NumericProperty()
_num_lines = 2
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineRightIconListItem(ContainerSupport, ThreeLineListItem):
# dp(40) = dp(16) + dp(24):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class OneLineAvatarIconListItem(OneLineAvatarListItem):
# dp(40) = dp(16) + dp(24):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class TwoLineAvatarIconListItem(TwoLineAvatarListItem):
# dp(40) = dp(16) + dp(24):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class ThreeLineAvatarIconListItem(ThreeLineAvatarListItem):
# dp(40) = dp(16) + dp(24):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class ImageLeftWidget(ILeftBody, Image):
pass
class ImageRightWidget(IRightBodyTouch, Image):
pass
class IconRightWidget(IRightBodyTouch, MDIconButton):
pass
class IconLeftWidget(ILeftBodyTouch, MDIconButton):
pass
class CheckboxLeftWidget(ILeftBodyTouch, MDCheckbox):
pass
| 27.185297 | 113 | 0.655195 |
from kivy.lang import Builder
from kivy.metrics import dp
from kivy.properties import (
StringProperty,
NumericProperty,
ListProperty,
OptionProperty,
BooleanProperty,
)
from kivy.uix.behaviors import ButtonBehavior
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.image import Image
import kivymd.material_resources as m_res
from kivymd.uix.behaviors import RectangularRippleBehavior
from kivymd.uix.button import MDIconButton
from kivymd.theming import ThemableBehavior
from kivymd.font_definitions import theme_font_styles
from kivymd.uix.gridlayout import MDGridLayout
from kivymd.uix.selectioncontrol import MDCheckbox
Builder.load_string(
"""
#:import m_res kivymd.material_resources
<MDList>
cols: 1
adaptive_height: True
padding: 0, self._list_vertical_padding
<BaseListItem>
size_hint_y: None
canvas:
Color:
rgba:
self.theme_cls.divider_color if root.divider is not None\
else (0, 0, 0, 0)
Line:
points: (root.x ,root.y, root.x+self.width, root.y)\
if root.divider == 'Full' else\
(root.x+root._txt_left_pad, root.y,\
root.x+self.width-root._txt_left_pad-root._txt_right_pad,\
root.y)
Color:
rgba: root.bg_color if root.bg_color else (0, 0, 0, 0)
Rectangle:
pos: self.pos
size: self.size
BoxLayout:
id: _text_container
orientation: 'vertical'
pos: root.pos
padding:
root._txt_left_pad, root._txt_top_pad,\
root._txt_right_pad, root._txt_bot_pad
MDLabel:
id: _lbl_primary
text: root.text
font_style: root.font_style
theme_text_color: root.theme_text_color
text_color: root.text_color
size_hint_y: None
height: self.texture_size[1]
markup: True
shorten_from: 'right'
shorten: True
MDLabel:
id: _lbl_secondary
text: '' if root._num_lines == 1 else root.secondary_text
font_style: root.secondary_font_style
theme_text_color: root.secondary_theme_text_color
text_color: root.secondary_text_color
size_hint_y: None
height: 0 if root._num_lines == 1 else self.texture_size[1]
shorten: True
shorten_from: 'right'
markup: True
MDLabel:
id: _lbl_tertiary
text: '' if root._num_lines == 1 else root.tertiary_text
font_style: root.tertiary_font_style
theme_text_color: root.tertiary_theme_text_color
text_color: root.tertiary_text_color
size_hint_y: None
height: 0 if root._num_lines == 1 else self.texture_size[1]
shorten: True
shorten_from: 'right'
markup: True
<OneLineAvatarListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height/2 - self.height/2
size: dp(40), dp(40)
<ThreeLineAvatarListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height - root._txt_top_pad - self.height - dp(5)
size: dp(40), dp(40)
<OneLineIconListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<ThreeLineIconListItem>
BoxLayout:
id: _left_container
size_hint: None, None
x: root.x + dp(16)
y: root.y + root.height - root._txt_top_pad - self.height - dp(5)
size: dp(48), dp(48)
<OneLineRightIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<ThreeLineRightIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<OneLineAvatarIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<TwoLineAvatarIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height/2 - self.height/2
size: dp(48), dp(48)
<ThreeLineAvatarIconListItem>
BoxLayout:
id: _right_container
size_hint: None, None
x: root.x + root.width - m_res.HORIZ_MARGINS - self.width
y: root.y + root.height - root._txt_top_pad - self.height - dp(5)
size: dp(48), dp(48)
"""
)
class MDList(MDGridLayout):
_list_vertical_padding = NumericProperty("8dp")
def add_widget(self, widget, index=0, canvas=None):
super().add_widget(widget, index, canvas)
self.height += widget.height
def remove_widget(self, widget):
super().remove_widget(widget)
self.height -= widget.height
class BaseListItem(
ThemableBehavior, RectangularRippleBehavior, ButtonBehavior, FloatLayout
):
text = StringProperty()
text_color = ListProperty(None)
font_style = OptionProperty("Subtitle1", options=theme_font_styles)
theme_text_color = StringProperty("Primary", allownone=True)
secondary_text = StringProperty()
tertiary_text = StringProperty()
secondary_text_color = ListProperty(None)
tertiary_text_color = ListProperty(None)
secondary_theme_text_color = StringProperty("Secondary", allownone=True)
tertiary_theme_text_color = StringProperty("Secondary", allownone=True)
secondary_font_style = OptionProperty("Body1", options=theme_font_styles)
tertiary_font_style = OptionProperty("Body1", options=theme_font_styles)
divider = OptionProperty(
"Full", options=["Full", "Inset", None], allownone=True
)
bg_color = ListProperty()
_txt_left_pad = NumericProperty("16dp")
_txt_top_pad = NumericProperty()
_txt_bot_pad = NumericProperty()
_txt_right_pad = NumericProperty(m_res.HORIZ_MARGINS)
_num_lines = 3
_no_ripple_effect = BooleanProperty(False)
class ILeftBody:
pass
class ILeftBodyTouch:
pass
class IRightBody:
pass
class IRightBodyTouch:
pass
class ContainerSupport:
_touchable_widgets = ListProperty()
def add_widget(self, widget, index=0):
if issubclass(widget.__class__, ILeftBody):
self.ids._left_container.add_widget(widget)
elif issubclass(widget.__class__, ILeftBodyTouch):
self.ids._left_container.add_widget(widget)
self._touchable_widgets.append(widget)
elif issubclass(widget.__class__, IRightBody):
self.ids._right_container.add_widget(widget)
elif issubclass(widget.__class__, IRightBodyTouch):
self.ids._right_container.add_widget(widget)
self._touchable_widgets.append(widget)
else:
return super().add_widget(widget)
def remove_widget(self, widget):
super().remove_widget(widget)
if widget in self._touchable_widgets:
self._touchable_widgets.remove(widget)
def on_touch_down(self, touch):
if self.propagate_touch_to_touchable_widgets(touch, "down"):
return
super().on_touch_down(touch)
def on_touch_move(self, touch, *args):
if self.propagate_touch_to_touchable_widgets(touch, "move", *args):
return
super().on_touch_move(touch, *args)
def on_touch_up(self, touch):
if self.propagate_touch_to_touchable_widgets(touch, "up"):
return
super().on_touch_up(touch)
def propagate_touch_to_touchable_widgets(self, touch, touch_event, *args):
triggered = False
for i in self._touchable_widgets:
if i.collide_point(touch.x, touch.y):
triggered = True
if touch_event == "down":
i.on_touch_down(touch)
elif touch_event == "move":
i.on_touch_move(touch, *args)
elif touch_event == "up":
i.on_touch_up(touch)
return triggered
class OneLineListItem(BaseListItem):
_txt_top_pad = NumericProperty("16dp")
_txt_bot_pad = NumericProperty("15dp")
_height = NumericProperty()
_num_lines = 1
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(48) if not self._height else self._height
class TwoLineListItem(BaseListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp")
_height = NumericProperty()
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineListItem(BaseListItem):
_txt_top_pad = NumericProperty("16dp")
_txt_bot_pad = NumericProperty("15dp")
_height = NumericProperty()
_num_lines = 3
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(88) if not self._height else self._height
class OneLineAvatarListItem(ContainerSupport, BaseListItem):
_txt_left_pad = NumericProperty("72dp")
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("19dp")
_height = NumericProperty()
_num_lines = 1
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(56) if not self._height else self._height
class TwoLineAvatarListItem(OneLineAvatarListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp")
_height = NumericProperty()
_num_lines = 2
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineAvatarListItem(ContainerSupport, ThreeLineListItem):
_txt_left_pad = NumericProperty("72dp")
class OneLineIconListItem(ContainerSupport, OneLineListItem):
_txt_left_pad = NumericProperty("72dp")
class TwoLineIconListItem(OneLineIconListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp")
_height = NumericProperty()
_num_lines = 2
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineIconListItem(ContainerSupport, ThreeLineListItem):
_txt_left_pad = NumericProperty("72dp")
class OneLineRightIconListItem(ContainerSupport, OneLineListItem):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class TwoLineRightIconListItem(OneLineRightIconListItem):
_txt_top_pad = NumericProperty("20dp")
_txt_bot_pad = NumericProperty("15dp")
_height = NumericProperty()
_num_lines = 2
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.height = dp(72) if not self._height else self._height
class ThreeLineRightIconListItem(ContainerSupport, ThreeLineListItem):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class OneLineAvatarIconListItem(OneLineAvatarListItem):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class TwoLineAvatarIconListItem(TwoLineAvatarListItem):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class ThreeLineAvatarIconListItem(ThreeLineAvatarListItem):
_txt_right_pad = NumericProperty("40dp")
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._txt_right_pad = dp(40) + m_res.HORIZ_MARGINS
class ImageLeftWidget(ILeftBody, Image):
pass
class ImageRightWidget(IRightBodyTouch, Image):
pass
class IconRightWidget(IRightBodyTouch, MDIconButton):
pass
class IconLeftWidget(ILeftBodyTouch, MDIconButton):
pass
class CheckboxLeftWidget(ILeftBodyTouch, MDCheckbox):
pass
| true | true |
f72c928677b51e691762e5e54a0552edfcb7fb7d | 4,361 | py | Python | lab4/predict_income_romain_claret_and_sylvain_robert-nicoud_lab4.py | RomainClaret/msc.ml.labs | 4e6b8e1c1ab841ab8ebbaee13f6ae43e9a1c44a5 | [
"MIT"
] | null | null | null | lab4/predict_income_romain_claret_and_sylvain_robert-nicoud_lab4.py | RomainClaret/msc.ml.labs | 4e6b8e1c1ab841ab8ebbaee13f6ae43e9a1c44a5 | [
"MIT"
] | null | null | null | lab4/predict_income_romain_claret_and_sylvain_robert-nicoud_lab4.py | RomainClaret/msc.ml.labs | 4e6b8e1c1ab841ab8ebbaee13f6ae43e9a1c44a5 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# 12.04.21
# Assignment lab 04
# Master Class: Machine Learning (5MI2018)
# Faculty of Economic Science
# University of Neuchatel (Switzerland)
# Lab 4, see ML21_Exercise_4.pdf for more information
# https://github.com/RomainClaret/msc.ml.labs
# Authors:
# - Romain Claret @RomainClaret
# - Sylvain Robert-Nicoud @Nic0uds
import warnings
import pickle
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
warnings.filterwarnings("ignore")
# SPLITING ADULT.TEST FILE IN SUBFILES
#spliting the adult.test file into several files to simulate weeks
filename = 'adult.test'
file_handler = open(filename, 'r').readlines()[1:]
prefix_file = "adult_2021_cw_"
week_number = 1
split_into = 10
line_count = 0
file_length = len(file_handler)
for i in range(0,file_length):
if i % ((file_length)//split_into) == 0 and i+((file_length//split_into)//2) < file_length:
open(str(prefix_file)+str(week_number) + ".csv", "w+").writelines(file_handler[i:i+(file_length//split_into)])
week_number += 1
# RUN PIPELINE MODEL FROM OTHER FILE
#input file, and save the predictions into a different file.
#Example:
#Let's say you have the input data weekly in the file adult_2021_cw_12.csv.
#This second script should read the input from this file and use the classifier to make predictions and write those predictions in the file adult_2021_cw_12_pred.csv .
# load pipeline model
pipeline_model = pickle.load( open("grid_search_model.pickle", "rb" ))
weeks_count = 10
filename = 'adult.test'
prefix_file = "adult_2021_cw_"
# get the features names and the values of the categories from adult.names (build a dictionary)
data_dict = {}
with open('adult.names') as f:
for l in f:
if l[0] == '|' or ':' not in l: continue
c = l.split(':')
if c[1].startswith(' continuous'): data_dict[c[0]] = ""
else: data_dict[c[0]] = c[1].replace("\n","").replace(".","").replace(" ","").split(",")
header = list(data_dict.keys())+['income']
# for each week based on a count and a naming convention
for i in range (weeks_count):
filename = str(prefix_file)+str(i+1)+".csv"
df_weekly = pd.read_table(filename, sep=r',\s', na_values='?', skiprows=[0], header=None, names=header).dropna()
drop_list = ["education", "occupation", "relationship"]
df_weekly = df_weekly.drop(columns=drop_list)
dict_replace = {
'marital-status' : {
'Never-married': 'Not-Married',
'Married-civ-spouse': 'Married',
'Divorced': 'Not-Married',
'Married-spouse-absent': 'Married',
'Separated': 'Married',
'Married-AF-spouse': 'Married',
'Widowed': 'Not-Married'
},
'workclass': {
'State-gov': 'Government',
'Self-emp-not-inc': 'Self-Employment',
'Federal-gov': 'Government',
'Local-gov': 'Government',
'Self-emp-inc': 'Self-Employment'
}
}
df_weekly.replace(dict_replace, inplace=True)
df_weekly["income"].replace({"<=50K.": "<=50K", ">50K.": ">50K"}, inplace=True)
for l in ["marital-status", "sex", "income"]:
l_enc = LabelEncoder()
encoder_weekly = l_enc.fit(df_weekly[l])
df_weekly["encoded_"+l] = encoder_weekly.transform(df_weekly[l])
y_hat_dtree_weekly = pipeline_model.predict(df_weekly)
pref_filename = str(prefix_file)+str(i+1)+"_pred.csv"
print(pref_filename, "accuracy_score:",accuracy_score(df_weekly["encoded_income"],y_hat_dtree_weekly),"\n")
# save the prediction into file
pd.DataFrame(y_hat_dtree_weekly).to_csv(str(pref_filename),header=["pred_income"], index=None)
# lab 03 results:
# adult_2021_cw_1.csv accuracy_score: 0.8293736501079914
# adult_2021_cw_2.csv accuracy_score: 0.8503253796095445
# adult_2021_cw_3.csv accuracy_score: 0.8427807486631016
# adult_2021_cw_4.csv accuracy_score: 0.8307860262008734
# adult_2021_cw_5.csv accuracy_score: 0.8507462686567164
# adult_2021_cw_6.csv accuracy_score: 0.854978354978355
# adult_2021_cw_7.csv accuracy_score: 0.8545454545454545
# adult_2021_cw_8.csv accuracy_score: 0.8514531754574811
# adult_2021_cw_9.csv accuracy_score: 0.8296943231441049
# adult_2021_cw_10.csv accuracy_score: 0.8574537540805223 | 36.341667 | 167 | 0.687686 |
import warnings
import pickle
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
warnings.filterwarnings("ignore")
filename = 'adult.test'
file_handler = open(filename, 'r').readlines()[1:]
prefix_file = "adult_2021_cw_"
week_number = 1
split_into = 10
line_count = 0
file_length = len(file_handler)
for i in range(0,file_length):
if i % ((file_length)//split_into) == 0 and i+((file_length//split_into)//2) < file_length:
open(str(prefix_file)+str(week_number) + ".csv", "w+").writelines(file_handler[i:i+(file_length//split_into)])
week_number += 1
#This second script should read the input from this file and use the classifier to make predictions and write those predictions in the file adult_2021_cw_12_pred.csv .
# load pipeline model
pipeline_model = pickle.load( open("grid_search_model.pickle", "rb" ))
weeks_count = 10
filename = 'adult.test'
prefix_file = "adult_2021_cw_"
# get the features names and the values of the categories from adult.names (build a dictionary)
data_dict = {}
with open('adult.names') as f:
for l in f:
if l[0] == '|' or ':' not in l: continue
c = l.split(':')
if c[1].startswith(' continuous'): data_dict[c[0]] = ""
else: data_dict[c[0]] = c[1].replace("\n","").replace(".","").replace(" ","").split(",")
header = list(data_dict.keys())+['income']
# for each week based on a count and a naming convention
for i in range (weeks_count):
filename = str(prefix_file)+str(i+1)+".csv"
df_weekly = pd.read_table(filename, sep=r',\s', na_values='?', skiprows=[0], header=None, names=header).dropna()
drop_list = ["education", "occupation", "relationship"]
df_weekly = df_weekly.drop(columns=drop_list)
dict_replace = {
'marital-status' : {
'Never-married': 'Not-Married',
'Married-civ-spouse': 'Married',
'Divorced': 'Not-Married',
'Married-spouse-absent': 'Married',
'Separated': 'Married',
'Married-AF-spouse': 'Married',
'Widowed': 'Not-Married'
},
'workclass': {
'State-gov': 'Government',
'Self-emp-not-inc': 'Self-Employment',
'Federal-gov': 'Government',
'Local-gov': 'Government',
'Self-emp-inc': 'Self-Employment'
}
}
df_weekly.replace(dict_replace, inplace=True)
df_weekly["income"].replace({"<=50K.": "<=50K", ">50K.": ">50K"}, inplace=True)
for l in ["marital-status", "sex", "income"]:
l_enc = LabelEncoder()
encoder_weekly = l_enc.fit(df_weekly[l])
df_weekly["encoded_"+l] = encoder_weekly.transform(df_weekly[l])
y_hat_dtree_weekly = pipeline_model.predict(df_weekly)
pref_filename = str(prefix_file)+str(i+1)+"_pred.csv"
print(pref_filename, "accuracy_score:",accuracy_score(df_weekly["encoded_income"],y_hat_dtree_weekly),"\n")
# save the prediction into file
pd.DataFrame(y_hat_dtree_weekly).to_csv(str(pref_filename),header=["pred_income"], index=None)
# lab 03 results:
# adult_2021_cw_1.csv accuracy_score: 0.8293736501079914
# adult_2021_cw_2.csv accuracy_score: 0.8503253796095445
# adult_2021_cw_3.csv accuracy_score: 0.8427807486631016
# adult_2021_cw_4.csv accuracy_score: 0.8307860262008734
# adult_2021_cw_5.csv accuracy_score: 0.8507462686567164
# adult_2021_cw_6.csv accuracy_score: 0.854978354978355
# adult_2021_cw_7.csv accuracy_score: 0.8545454545454545
# adult_2021_cw_8.csv accuracy_score: 0.8514531754574811
# adult_2021_cw_9.csv accuracy_score: 0.8296943231441049
# adult_2021_cw_10.csv accuracy_score: 0.8574537540805223 | true | true |
f72c93dc9d0c650ab8f3bacc646cd04dbfed3888 | 92 | py | Python | app/admin/__init__.py | baz1nga/Work-Shift | 77df03120c4bc512703f02a653a6bbc982b14857 | [
"MIT"
] | null | null | null | app/admin/__init__.py | baz1nga/Work-Shift | 77df03120c4bc512703f02a653a6bbc982b14857 | [
"MIT"
] | null | null | null | app/admin/__init__.py | baz1nga/Work-Shift | 77df03120c4bc512703f02a653a6bbc982b14857 | [
"MIT"
] | null | null | null | from flask import Blueprint
bp = Blueprint('admin', __name__)
from app.admin import views
| 15.333333 | 33 | 0.771739 | from flask import Blueprint
bp = Blueprint('admin', __name__)
from app.admin import views
| true | true |
f72c94f9f4bcc67b00da8e6ffb7d26d5bc04f527 | 1,108 | py | Python | puzzle14/14a.py | muellerd/advent_of_code20 | 4d9619de165b584f406ef8a1b136d79355dfe3e1 | [
"MIT"
] | null | null | null | puzzle14/14a.py | muellerd/advent_of_code20 | 4d9619de165b584f406ef8a1b136d79355dfe3e1 | [
"MIT"
] | null | null | null | puzzle14/14a.py | muellerd/advent_of_code20 | 4d9619de165b584f406ef8a1b136d79355dfe3e1 | [
"MIT"
] | null | null | null | rows = []
with open("C:\\Privat\\advent_of_code20\\puzzle14\\input1.txt") as f:
for line in f:
rows.append(line.strip())
#print(rows)
memory = {}
currentMask = ""
for line in rows:
split = line.split(' = ')
if 'mask' in split[0]:
currentMask = split[1].strip()
else:
# value in bit
bit = format(int(split[1]), '036b')
# bit through mask
maskl = len(currentMask)
bitl = len(bit)
result = ''
#print(bit)
#print(currentMask)
for i in range(0, len(bit)):
maskBit = currentMask[i]
bitBit = bit[i]
if maskBit != 'X':
result += maskBit
else:
result += bitBit
#print(result)
toWrite = int(result, 2)
# replace in memory
memoryPosition = split[0][4:-1]
if not memoryPosition in memory:
memory[memoryPosition] = 0
memory[memoryPosition] = toWrite
#print(memory)
sum = 0
for key in memory:
sum += memory[key]
print("Sum of all values in memory: " + str(sum))
| 21.307692 | 69 | 0.525271 | rows = []
with open("C:\\Privat\\advent_of_code20\\puzzle14\\input1.txt") as f:
for line in f:
rows.append(line.strip())
memory = {}
currentMask = ""
for line in rows:
split = line.split(' = ')
if 'mask' in split[0]:
currentMask = split[1].strip()
else:
bit = format(int(split[1]), '036b')
maskl = len(currentMask)
bitl = len(bit)
result = ''
for i in range(0, len(bit)):
maskBit = currentMask[i]
bitBit = bit[i]
if maskBit != 'X':
result += maskBit
else:
result += bitBit
toWrite = int(result, 2)
memoryPosition = split[0][4:-1]
if not memoryPosition in memory:
memory[memoryPosition] = 0
memory[memoryPosition] = toWrite
sum = 0
for key in memory:
sum += memory[key]
print("Sum of all values in memory: " + str(sum))
| true | true |
f72c9587c2b7459c937e13b276ff7e0feb632297 | 3,314 | py | Python | detect_image.py | YunYang1994/CodeFun | 36fcdbfb4ed55fbb8f8dbc6f900842cc7bb9f068 | [
"MIT"
] | 150 | 2019-06-19T03:54:40.000Z | 2019-10-21T07:09:02.000Z | detect_image.py | YunYang1994/cv-notebooks | 36fcdbfb4ed55fbb8f8dbc6f900842cc7bb9f068 | [
"MIT"
] | 7 | 2019-11-26T07:27:42.000Z | 2020-04-02T03:35:29.000Z | detect_image.py | YunYang1994/cv-notebooks | 36fcdbfb4ed55fbb8f8dbc6f900842cc7bb9f068 | [
"MIT"
] | 25 | 2019-11-27T11:07:56.000Z | 2020-03-19T15:44:20.000Z | #! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2020 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : detect_image.py
# Author : YunYang1994
# Created date: 2020-03-19 14:05:53
# Description :
#
#================================================================
import os
import cv2
import time
import numpy as np
import tensorflow as tf
from PIL import Image, ImageFont, ImageDraw
from mtcnn import pnet, rnet, onet
from models import IResnet
from utils import detect_face, align_face, recognize_face
model = IResnet(tflite_model="IResnet.tflite")
font = ImageFont.truetype('weghts/HuaWenXinWei-1.ttf', 30)
image = cv2.imread("/Users/yangyun/多人照片/5.jpg")
image_h, image_w, _ = image.shape
org_image = image.copy()
image = cv2.cvtColor(image ,cv2.COLOR_BGR2RGB)
total_boxes, points = detect_face(image, 20, pnet, rnet, onet, [0.6, 0.7, 0.9], 0.709)
for idx, (bounding_box, keypoints) in enumerate(zip(total_boxes, points.T)):
bounding_boxes = {
'box': [int(bounding_box[0]), int(bounding_box[1]),
int(bounding_box[2]-bounding_box[0]), int(bounding_box[3]-bounding_box[1])],
'confidence': bounding_box[-1],
'keypoints': {
'left_eye': (int(keypoints[0]), int(keypoints[5])),
'right_eye': (int(keypoints[1]), int(keypoints[6])),
'nose': (int(keypoints[2]), int(keypoints[7])),
'mouth_left': (int(keypoints[3]), int(keypoints[8])),
'mouth_right': (int(keypoints[4]), int(keypoints[9])),
}
}
bounding_box = bounding_boxes['box']
keypoints = bounding_boxes['keypoints']
cv2.circle(org_image,(keypoints['left_eye']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['right_eye']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['nose']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['mouth_left']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['mouth_right']),2, (255,0,0), 3)
cv2.rectangle(org_image,
(bounding_box[0], bounding_box[1]),
(bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
(0,255,0), 2)
# align face and extract it out
align_image = align_face(image, keypoints)
marigin = 16
xmin = max(bounding_box[0] - marigin, 0)
ymin = max(bounding_box[1] - marigin, 0)
xmax = min(bounding_box[0] + bounding_box[2] + marigin, image_w)
ymax = min(bounding_box[1] + bounding_box[3] + marigin, image_h)
crop_image = align_image[ymin:ymax, xmin:xmax, :]
if crop_image is not None:
t1 = time.time()
embedding = model(crop_image)
person = recognize_face(embedding)
org_image_pil = Image.fromarray(org_image)
draw = ImageDraw.Draw(org_image_pil)
text_size = draw.textsize(person, font)
draw.text((bounding_box[0], bounding_box[1]-16), person, fill=(0, 0, 255), font=font)
org_image = np.array(org_image_pil)
t2 = time.time()
print("time: %.2fms" %((t2-t1)*1000))
org_image = cv2.cvtColor(org_image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(org_image)
image.show()
# image.save("test.png")
| 36.822222 | 96 | 0.601992 |
import os
import cv2
import time
import numpy as np
import tensorflow as tf
from PIL import Image, ImageFont, ImageDraw
from mtcnn import pnet, rnet, onet
from models import IResnet
from utils import detect_face, align_face, recognize_face
model = IResnet(tflite_model="IResnet.tflite")
font = ImageFont.truetype('weghts/HuaWenXinWei-1.ttf', 30)
image = cv2.imread("/Users/yangyun/多人照片/5.jpg")
image_h, image_w, _ = image.shape
org_image = image.copy()
image = cv2.cvtColor(image ,cv2.COLOR_BGR2RGB)
total_boxes, points = detect_face(image, 20, pnet, rnet, onet, [0.6, 0.7, 0.9], 0.709)
for idx, (bounding_box, keypoints) in enumerate(zip(total_boxes, points.T)):
bounding_boxes = {
'box': [int(bounding_box[0]), int(bounding_box[1]),
int(bounding_box[2]-bounding_box[0]), int(bounding_box[3]-bounding_box[1])],
'confidence': bounding_box[-1],
'keypoints': {
'left_eye': (int(keypoints[0]), int(keypoints[5])),
'right_eye': (int(keypoints[1]), int(keypoints[6])),
'nose': (int(keypoints[2]), int(keypoints[7])),
'mouth_left': (int(keypoints[3]), int(keypoints[8])),
'mouth_right': (int(keypoints[4]), int(keypoints[9])),
}
}
bounding_box = bounding_boxes['box']
keypoints = bounding_boxes['keypoints']
cv2.circle(org_image,(keypoints['left_eye']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['right_eye']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['nose']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['mouth_left']), 2, (255,0,0), 3)
cv2.circle(org_image,(keypoints['mouth_right']),2, (255,0,0), 3)
cv2.rectangle(org_image,
(bounding_box[0], bounding_box[1]),
(bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
(0,255,0), 2)
align_image = align_face(image, keypoints)
marigin = 16
xmin = max(bounding_box[0] - marigin, 0)
ymin = max(bounding_box[1] - marigin, 0)
xmax = min(bounding_box[0] + bounding_box[2] + marigin, image_w)
ymax = min(bounding_box[1] + bounding_box[3] + marigin, image_h)
crop_image = align_image[ymin:ymax, xmin:xmax, :]
if crop_image is not None:
t1 = time.time()
embedding = model(crop_image)
person = recognize_face(embedding)
org_image_pil = Image.fromarray(org_image)
draw = ImageDraw.Draw(org_image_pil)
text_size = draw.textsize(person, font)
draw.text((bounding_box[0], bounding_box[1]-16), person, fill=(0, 0, 255), font=font)
org_image = np.array(org_image_pil)
t2 = time.time()
print("time: %.2fms" %((t2-t1)*1000))
org_image = cv2.cvtColor(org_image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(org_image)
image.show()
| true | true |
f72c966881d67f6b446e37599487a4a5d041df9b | 60,197 | py | Python | heat/engine/resources/openstack/nova/server.py | maestro-hybrid-cloud/heat | 91a4bb3170bd81b1c67a896706851e55709c9b5a | [
"Apache-2.0"
] | null | null | null | heat/engine/resources/openstack/nova/server.py | maestro-hybrid-cloud/heat | 91a4bb3170bd81b1c67a896706851e55709c9b5a | [
"Apache-2.0"
] | null | null | null | heat/engine/resources/openstack/nova/server.py | maestro-hybrid-cloud/heat | 91a4bb3170bd81b1c67a896706851e55709c9b5a | [
"Apache-2.0"
] | null | null | null | #
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import copy
import uuid
from oslo_config import cfg
from oslo_log import log as logging
from oslo_serialization import jsonutils
from oslo_utils import uuidutils
import six
from heat.common import exception
from heat.common.i18n import _
from heat.engine import attributes
from heat.engine.clients import progress
from heat.engine import constraints
from heat.engine import function
from heat.engine import properties
from heat.engine.resources.openstack.neutron import port as neutron_port
from heat.engine.resources.openstack.neutron import subnet
from heat.engine.resources.openstack.nova import server_network_mixin
from heat.engine.resources import scheduler_hints as sh
from heat.engine.resources import stack_user
from heat.engine import support
from heat.rpc import api as rpc_api
cfg.CONF.import_opt('default_software_config_transport', 'heat.common.config')
LOG = logging.getLogger(__name__)
class Server(stack_user.StackUser, sh.SchedulerHintsMixin,
server_network_mixin.ServerNetworkMixin):
PROPERTIES = (
NAME, IMAGE, BLOCK_DEVICE_MAPPING, BLOCK_DEVICE_MAPPING_V2,
FLAVOR, FLAVOR_UPDATE_POLICY, IMAGE_UPDATE_POLICY, KEY_NAME,
ADMIN_USER, AVAILABILITY_ZONE, SECURITY_GROUPS, NETWORKS,
SCHEDULER_HINTS, METADATA, USER_DATA_FORMAT, USER_DATA,
RESERVATION_ID, CONFIG_DRIVE, DISK_CONFIG, PERSONALITY,
ADMIN_PASS, SOFTWARE_CONFIG_TRANSPORT
) = (
'name', 'image', 'block_device_mapping', 'block_device_mapping_v2',
'flavor', 'flavor_update_policy', 'image_update_policy', 'key_name',
'admin_user', 'availability_zone', 'security_groups', 'networks',
'scheduler_hints', 'metadata', 'user_data_format', 'user_data',
'reservation_id', 'config_drive', 'diskConfig', 'personality',
'admin_pass', 'software_config_transport'
)
_BLOCK_DEVICE_MAPPING_KEYS = (
BLOCK_DEVICE_MAPPING_DEVICE_NAME, BLOCK_DEVICE_MAPPING_VOLUME_ID,
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID,
BLOCK_DEVICE_MAPPING_VOLUME_SIZE,
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM,
) = (
'device_name', 'volume_id',
'snapshot_id',
'volume_size',
'delete_on_termination',
)
_BLOCK_DEVICE_MAPPING_V2_KEYS = (
BLOCK_DEVICE_MAPPING_DEVICE_NAME,
BLOCK_DEVICE_MAPPING_VOLUME_ID,
BLOCK_DEVICE_MAPPING_IMAGE_ID,
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID,
BLOCK_DEVICE_MAPPING_SWAP_SIZE,
BLOCK_DEVICE_MAPPING_DEVICE_TYPE,
BLOCK_DEVICE_MAPPING_DISK_BUS,
BLOCK_DEVICE_MAPPING_BOOT_INDEX,
BLOCK_DEVICE_MAPPING_VOLUME_SIZE,
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM,
) = (
'device_name',
'volume_id',
'image_id',
'snapshot_id',
'swap_size',
'device_type',
'disk_bus',
'boot_index',
'volume_size',
'delete_on_termination',
)
_NETWORK_KEYS = (
NETWORK_UUID, NETWORK_ID, NETWORK_FIXED_IP, NETWORK_PORT,
NETWORK_SUBNET, NETWORK_PORT_EXTRA
) = (
'uuid', 'network', 'fixed_ip', 'port',
'subnet', 'port_extra_properties'
)
_SOFTWARE_CONFIG_FORMATS = (
HEAT_CFNTOOLS, RAW, SOFTWARE_CONFIG
) = (
'HEAT_CFNTOOLS', 'RAW', 'SOFTWARE_CONFIG'
)
_SOFTWARE_CONFIG_TRANSPORTS = (
POLL_SERVER_CFN, POLL_SERVER_HEAT, POLL_TEMP_URL, ZAQAR_MESSAGE
) = (
'POLL_SERVER_CFN', 'POLL_SERVER_HEAT', 'POLL_TEMP_URL', 'ZAQAR_MESSAGE'
)
ATTRIBUTES = (
NAME_ATTR, ADDRESSES, NETWORKS_ATTR, FIRST_ADDRESS,
INSTANCE_NAME, ACCESSIPV4, ACCESSIPV6, CONSOLE_URLS,
) = (
'name', 'addresses', 'networks', 'first_address',
'instance_name', 'accessIPv4', 'accessIPv6', 'console_urls',
)
properties_schema = {
NAME: properties.Schema(
properties.Schema.STRING,
_('Server name.'),
update_allowed=True
),
IMAGE: properties.Schema(
properties.Schema.STRING,
_('The ID or name of the image to boot with.'),
constraints=[
constraints.CustomConstraint('glance.image')
],
update_allowed=True
),
BLOCK_DEVICE_MAPPING: properties.Schema(
properties.Schema.LIST,
_('Block device mappings for this server.'),
schema=properties.Schema(
properties.Schema.MAP,
schema={
BLOCK_DEVICE_MAPPING_DEVICE_NAME: properties.Schema(
properties.Schema.STRING,
_('A device name where the volume will be '
'attached in the system at /dev/device_name. '
'This value is typically vda.'),
required=True
),
BLOCK_DEVICE_MAPPING_VOLUME_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the volume to boot from. Only one '
'of volume_id or snapshot_id should be '
'provided.'),
constraints=[
constraints.CustomConstraint('cinder.volume')
]
),
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the snapshot to create a volume '
'from.'),
constraints=[
constraints.CustomConstraint('cinder.snapshot')
]
),
BLOCK_DEVICE_MAPPING_VOLUME_SIZE: properties.Schema(
properties.Schema.INTEGER,
_('The size of the volume, in GB. It is safe to '
'leave this blank and have the Compute service '
'infer the size.')
),
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM: properties.Schema(
properties.Schema.BOOLEAN,
_('Indicate whether the volume should be deleted '
'when the server is terminated.')
),
},
)
),
BLOCK_DEVICE_MAPPING_V2: properties.Schema(
properties.Schema.LIST,
_('Block device mappings v2 for this server.'),
schema=properties.Schema(
properties.Schema.MAP,
schema={
BLOCK_DEVICE_MAPPING_DEVICE_NAME: properties.Schema(
properties.Schema.STRING,
_('A device name where the volume will be '
'attached in the system at /dev/device_name. '
'This value is typically vda.'),
),
BLOCK_DEVICE_MAPPING_VOLUME_ID: properties.Schema(
properties.Schema.STRING,
_('The volume_id can be boot or non-boot device '
'to the server.'),
constraints=[
constraints.CustomConstraint('cinder.volume')
]
),
BLOCK_DEVICE_MAPPING_IMAGE_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the image to create a volume from.'),
constraints=[
constraints.CustomConstraint('glance.image')
],
),
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the snapshot to create a volume '
'from.'),
constraints=[
constraints.CustomConstraint('cinder.snapshot')
]
),
BLOCK_DEVICE_MAPPING_SWAP_SIZE: properties.Schema(
properties.Schema.INTEGER,
_('The size of the swap, in MB.')
),
BLOCK_DEVICE_MAPPING_DEVICE_TYPE: properties.Schema(
properties.Schema.STRING,
_('Device type: at the moment we can make distinction'
' only between disk and cdrom.'),
constraints=[
constraints.AllowedValues(['cdrom', 'disk']),
],
),
BLOCK_DEVICE_MAPPING_DISK_BUS: properties.Schema(
properties.Schema.STRING,
_('Bus of the device: hypervisor driver chooses a '
'suitable default if omitted.'),
constraints=[
constraints.AllowedValues(['ide', 'lame_bus',
'scsi', 'usb',
'virtio']),
],
),
BLOCK_DEVICE_MAPPING_BOOT_INDEX: properties.Schema(
properties.Schema.INTEGER,
_('Integer used for ordering the boot disks.'),
),
BLOCK_DEVICE_MAPPING_VOLUME_SIZE: properties.Schema(
properties.Schema.INTEGER,
_('Size of the block device in GB. If it is omitted, '
'hypervisor driver calculates size.'),
),
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM: properties.Schema(
properties.Schema.BOOLEAN,
_('Indicate whether the volume should be deleted '
'when the server is terminated.')
),
},
),
support_status=support.SupportStatus(version='2015.1')
),
FLAVOR: properties.Schema(
properties.Schema.STRING,
_('The ID or name of the flavor to boot onto.'),
required=True,
update_allowed=True,
constraints=[
constraints.CustomConstraint('nova.flavor')
]
),
FLAVOR_UPDATE_POLICY: properties.Schema(
properties.Schema.STRING,
_('Policy on how to apply a flavor update; either by requesting '
'a server resize or by replacing the entire server.'),
default='RESIZE',
constraints=[
constraints.AllowedValues(['RESIZE', 'REPLACE']),
],
update_allowed=True
),
IMAGE_UPDATE_POLICY: properties.Schema(
properties.Schema.STRING,
_('Policy on how to apply an image-id update; either by '
'requesting a server rebuild or by replacing the entire server'),
default='REBUILD',
constraints=[
constraints.AllowedValues(['REBUILD', 'REPLACE',
'REBUILD_PRESERVE_EPHEMERAL']),
],
update_allowed=True
),
KEY_NAME: properties.Schema(
properties.Schema.STRING,
_('Name of keypair to inject into the server.'),
constraints=[
constraints.CustomConstraint('nova.keypair')
]
),
ADMIN_USER: properties.Schema(
properties.Schema.STRING,
_('Name of the administrative user to use on the server.'),
support_status=support.SupportStatus(
status=support.HIDDEN,
version='5.0.0',
message=_('The default cloud-init user set up for each image '
'(e.g. "ubuntu" for Ubuntu 12.04+, "fedora" for '
'Fedora 19+ and "cloud-user" for CentOS/RHEL 6.5).'),
previous_status=support.SupportStatus(
status=support.DEPRECATED,
version='2014.1',
previous_status=support.SupportStatus(version='2013.2')
)
)
),
AVAILABILITY_ZONE: properties.Schema(
properties.Schema.STRING,
_('Name of the availability zone for server placement.')
),
SECURITY_GROUPS: properties.Schema(
properties.Schema.LIST,
_('List of security group names or IDs. Cannot be used if '
'neutron ports are associated with this server; assign '
'security groups to the ports instead.'),
default=[]
),
NETWORKS: properties.Schema(
properties.Schema.LIST,
_('An ordered list of nics to be added to this server, with '
'information about connected networks, fixed ips, port etc.'),
schema=properties.Schema(
properties.Schema.MAP,
schema={
NETWORK_UUID: properties.Schema(
properties.Schema.STRING,
_('ID of network to create a port on.'),
support_status=support.SupportStatus(
status=support.HIDDEN,
version='5.0.0',
previous_status=support.SupportStatus(
status=support.DEPRECATED,
message=_('Use property %s.') % NETWORK_ID,
version='2014.1'
)
),
constraints=[
constraints.CustomConstraint('neutron.network')
]
),
NETWORK_ID: properties.Schema(
properties.Schema.STRING,
_('Name or ID of network to create a port on.'),
constraints=[
constraints.CustomConstraint('neutron.network')
]
),
NETWORK_FIXED_IP: properties.Schema(
properties.Schema.STRING,
_('Fixed IP address to specify for the port '
'created on the requested network.'),
constraints=[
constraints.CustomConstraint('ip_addr')
]
),
NETWORK_PORT: properties.Schema(
properties.Schema.STRING,
_('ID of an existing port to associate with this '
'server.'),
constraints=[
constraints.CustomConstraint('neutron.port')
]
),
NETWORK_PORT_EXTRA: properties.Schema(
properties.Schema.MAP,
_('Dict, which has expand properties for port. '
'Used only if port property is not specified '
'for creating port.'),
schema=neutron_port.Port.extra_properties_schema,
support_status=support.SupportStatus(version='6.0.0')
),
NETWORK_SUBNET: properties.Schema(
properties.Schema.STRING,
_('Subnet in which to allocate the IP address for '
'port. Used for creating port, based on derived '
'properties. If subnet is specified, network '
'property becomes optional.'),
support_status=support.SupportStatus(version='5.0.0')
)
},
),
update_allowed=True
),
SCHEDULER_HINTS: properties.Schema(
properties.Schema.MAP,
_('Arbitrary key-value pairs specified by the client to help '
'boot a server.')
),
METADATA: properties.Schema(
properties.Schema.MAP,
_('Arbitrary key/value metadata to store for this server. Both '
'keys and values must be 255 characters or less. Non-string '
'values will be serialized to JSON (and the serialized '
'string must be 255 characters or less).'),
update_allowed=True
),
USER_DATA_FORMAT: properties.Schema(
properties.Schema.STRING,
_('How the user_data should be formatted for the server. For '
'HEAT_CFNTOOLS, the user_data is bundled as part of the '
'heat-cfntools cloud-init boot configuration data. For RAW '
'the user_data is passed to Nova unmodified. '
'For SOFTWARE_CONFIG user_data is bundled as part of the '
'software config data, and metadata is derived from any '
'associated SoftwareDeployment resources.'),
default=HEAT_CFNTOOLS,
constraints=[
constraints.AllowedValues(_SOFTWARE_CONFIG_FORMATS),
]
),
SOFTWARE_CONFIG_TRANSPORT: properties.Schema(
properties.Schema.STRING,
_('How the server should receive the metadata required for '
'software configuration. POLL_SERVER_CFN will allow calls to '
'the cfn API action DescribeStackResource authenticated with '
'the provided keypair. POLL_SERVER_HEAT will allow calls to '
'the Heat API resource-show using the provided keystone '
'credentials. POLL_TEMP_URL will create and populate a '
'Swift TempURL with metadata for polling. ZAQAR_MESSAGE will '
'create a dedicated zaqar queue and post the metadata '
'for polling.'),
default=cfg.CONF.default_software_config_transport,
constraints=[
constraints.AllowedValues(_SOFTWARE_CONFIG_TRANSPORTS),
]
),
USER_DATA: properties.Schema(
properties.Schema.STRING,
_('User data script to be executed by cloud-init.'),
default=''
),
RESERVATION_ID: properties.Schema(
properties.Schema.STRING,
_('A UUID for the set of servers being requested.')
),
CONFIG_DRIVE: properties.Schema(
properties.Schema.BOOLEAN,
_('If True, enable config drive on the server.')
),
DISK_CONFIG: properties.Schema(
properties.Schema.STRING,
_('Control how the disk is partitioned when the server is '
'created.'),
constraints=[
constraints.AllowedValues(['AUTO', 'MANUAL']),
]
),
PERSONALITY: properties.Schema(
properties.Schema.MAP,
_('A map of files to create/overwrite on the server upon boot. '
'Keys are file names and values are the file contents.'),
default={}
),
ADMIN_PASS: properties.Schema(
properties.Schema.STRING,
_('The administrator password for the server.'),
update_allowed=True
),
}
attributes_schema = {
NAME_ATTR: attributes.Schema(
_('Name of the server.'),
type=attributes.Schema.STRING
),
ADDRESSES: attributes.Schema(
_('A dict of all network addresses with corresponding port_id. '
'Each network will have two keys in dict, they are network '
'name and network id. '
'The port ID may be obtained through the following expression: '
'"{get_attr: [<server>, addresses, <network name_or_id>, 0, '
'port]}".'),
type=attributes.Schema.MAP
),
NETWORKS_ATTR: attributes.Schema(
_('A dict of assigned network addresses of the form: '
'{"public": [ip1, ip2...], "private": [ip3, ip4], '
'"public_uuid": [ip1, ip2...], "private_uuid": [ip3, ip4]}. '
'Each network will have two keys in dict, they are network '
'name and network id. '),
type=attributes.Schema.MAP
),
FIRST_ADDRESS: attributes.Schema(
_('Convenience attribute to fetch the first assigned network '
'address, or an empty string if nothing has been assigned at '
'this time. Result may not be predictable if the server has '
'addresses from more than one network.'),
support_status=support.SupportStatus(
status=support.HIDDEN,
version='5.0.0',
message=_('Use the networks attribute instead of '
'first_address. For example: "{get_attr: '
'[<server name>, networks, <network name>, 0]}"'),
previous_status=support.SupportStatus(
status=support.DEPRECATED,
version='2014.2',
previous_status=support.SupportStatus(version='2013.2')
)
)
),
INSTANCE_NAME: attributes.Schema(
_('AWS compatible instance name.'),
type=attributes.Schema.STRING
),
ACCESSIPV4: attributes.Schema(
_('The manually assigned alternative public IPv4 address '
'of the server.'),
type=attributes.Schema.STRING
),
ACCESSIPV6: attributes.Schema(
_('The manually assigned alternative public IPv6 address '
'of the server.'),
type=attributes.Schema.STRING
),
CONSOLE_URLS: attributes.Schema(
_("URLs of server's consoles. "
"To get a specific console type, the requested type "
"can be specified as parameter to the get_attr function, "
"e.g. get_attr: [ <server>, console_urls, novnc ]. "
"Currently supported types are "
"novnc, xvpvnc, spice-html5, rdp-html5, serial."),
support_status=support.SupportStatus(version='2015.1'),
type=attributes.Schema.MAP
),
}
# Server host name limit to 53 characters by due to typical default
# linux HOST_NAME_MAX of 64, minus the .novalocal appended to the name
physical_resource_name_limit = 53
default_client_name = 'nova'
entity = 'servers'
def translation_rules(self):
return [properties.TranslationRule(
self.properties,
properties.TranslationRule.REPLACE,
source_path=[self.NETWORKS, self.NETWORK_ID],
value_name=self.NETWORK_UUID)]
def __init__(self, name, json_snippet, stack):
super(Server, self).__init__(name, json_snippet, stack)
if self.user_data_software_config():
self._register_access_key()
def _server_name(self):
name = self.properties[self.NAME]
if name:
return name
return self.physical_resource_name()
def _config_drive(self):
# This method is overridden by the derived CloudServer resource
return self.properties[self.CONFIG_DRIVE]
def _populate_deployments_metadata(self, meta):
meta['deployments'] = meta.get('deployments', [])
meta['os-collect-config'] = meta.get('os-collect-config', {})
if self.transport_poll_server_heat():
meta['os-collect-config'].update({'heat': {
'user_id': self._get_user_id(),
'password': self.password,
'auth_url': self.context.auth_url,
'project_id': self.stack.stack_user_project_id,
'stack_id': self.stack.identifier().stack_path(),
'resource_name': self.name}})
if self.transport_zaqar_message():
queue_id = self.physical_resource_name()
self.data_set('metadata_queue_id', queue_id)
zaqar_plugin = self.client_plugin('zaqar')
zaqar = zaqar_plugin.create_for_tenant(
self.stack.stack_user_project_id)
queue = zaqar.queue(queue_id)
queue.post({'body': meta, 'ttl': zaqar_plugin.DEFAULT_TTL})
meta['os-collect-config'].update({'zaqar': {
'user_id': self._get_user_id(),
'password': self.password,
'auth_url': self.context.auth_url,
'project_id': self.stack.stack_user_project_id,
'queue_id': queue_id}})
elif self.transport_poll_server_cfn():
meta['os-collect-config'].update({'cfn': {
'metadata_url': '%s/v1/' % cfg.CONF.heat_metadata_server_url,
'access_key_id': self.access_key,
'secret_access_key': self.secret_key,
'stack_name': self.stack.name,
'path': '%s.Metadata' % self.name}})
elif self.transport_poll_temp_url():
container = self.physical_resource_name()
object_name = str(uuid.uuid4())
self.client('swift').put_container(container)
url = self.client_plugin('swift').get_temp_url(
container, object_name, method='GET')
put_url = self.client_plugin('swift').get_temp_url(
container, object_name)
self.data_set('metadata_put_url', put_url)
self.data_set('metadata_object_name', object_name)
meta['os-collect-config'].update({'request': {
'metadata_url': url}})
self.client('swift').put_object(
container, object_name, jsonutils.dumps(meta))
self.metadata_set(meta)
def _register_access_key(self):
"""Access is limited to this resource, which created the keypair."""
def access_allowed(resource_name):
return resource_name == self.name
if self.transport_poll_server_cfn():
self.stack.register_access_allowed_handler(
self.access_key, access_allowed)
elif self.transport_poll_server_heat():
self.stack.register_access_allowed_handler(
self._get_user_id(), access_allowed)
def _create_transport_credentials(self):
if self.transport_poll_server_cfn():
self._create_user()
self._create_keypair()
elif (self.transport_poll_server_heat() or
self.transport_zaqar_message()):
self.password = uuid.uuid4().hex
self._create_user()
self._register_access_key()
@property
def access_key(self):
return self.data().get('access_key')
@property
def secret_key(self):
return self.data().get('secret_key')
@property
def password(self):
return self.data().get('password')
@password.setter
def password(self, password):
if password is None:
self.data_delete('password')
else:
self.data_set('password', password, True)
def user_data_raw(self):
return self.properties[self.USER_DATA_FORMAT] == self.RAW
def user_data_software_config(self):
return self.properties[
self.USER_DATA_FORMAT] == self.SOFTWARE_CONFIG
def transport_poll_server_cfn(self):
return self.properties[
self.SOFTWARE_CONFIG_TRANSPORT] == self.POLL_SERVER_CFN
def transport_poll_server_heat(self):
return self.properties[
self.SOFTWARE_CONFIG_TRANSPORT] == self.POLL_SERVER_HEAT
def transport_poll_temp_url(self):
return self.properties[
self.SOFTWARE_CONFIG_TRANSPORT] == self.POLL_TEMP_URL
def transport_zaqar_message(self):
return self.properties.get(
self.SOFTWARE_CONFIG_TRANSPORT) == self.ZAQAR_MESSAGE
def get_software_config(self, ud_content):
try:
sc = self.rpc_client().show_software_config(
self.context, ud_content)
return sc[rpc_api.SOFTWARE_CONFIG_CONFIG]
except Exception as ex:
self.rpc_client().ignore_error_named(ex, 'NotFound')
return ud_content
def handle_create(self):
security_groups = self.properties[self.SECURITY_GROUPS]
user_data_format = self.properties[self.USER_DATA_FORMAT]
ud_content = self.properties[self.USER_DATA]
if self.user_data_software_config() or self.user_data_raw():
if uuidutils.is_uuid_like(ud_content):
# attempt to load the userdata from software config
ud_content = self.get_software_config(ud_content)
metadata = self.metadata_get(True) or {}
if self.user_data_software_config():
self._create_transport_credentials()
self._populate_deployments_metadata(metadata)
userdata = self.client_plugin().build_userdata(
metadata,
ud_content,
instance_user=None,
user_data_format=user_data_format)
flavor = self.properties[self.FLAVOR]
availability_zone = self.properties[self.AVAILABILITY_ZONE]
image = self.properties[self.IMAGE]
if image:
image = self.client_plugin('glance').get_image_id(image)
flavor_id = self.client_plugin().get_flavor_id(flavor)
instance_meta = self.properties[self.METADATA]
if instance_meta is not None:
instance_meta = self.client_plugin().meta_serialize(
instance_meta)
scheduler_hints = self._scheduler_hints(
self.properties[self.SCHEDULER_HINTS])
nics = self._build_nics(self.properties[self.NETWORKS])
block_device_mapping = self._build_block_device_mapping(
self.properties[self.BLOCK_DEVICE_MAPPING])
block_device_mapping_v2 = self._build_block_device_mapping_v2(
self.properties[self.BLOCK_DEVICE_MAPPING_V2])
reservation_id = self.properties[self.RESERVATION_ID]
disk_config = self.properties[self.DISK_CONFIG]
admin_pass = self.properties[self.ADMIN_PASS] or None
personality_files = self.properties[self.PERSONALITY]
key_name = self.properties[self.KEY_NAME]
server = None
try:
server = self.client().servers.create(
name=self._server_name(),
image=image,
flavor=flavor_id,
key_name=key_name,
security_groups=security_groups,
userdata=userdata,
meta=instance_meta,
scheduler_hints=scheduler_hints,
nics=nics,
availability_zone=availability_zone,
block_device_mapping=block_device_mapping,
block_device_mapping_v2=block_device_mapping_v2,
reservation_id=reservation_id,
config_drive=self._config_drive(),
disk_config=disk_config,
files=personality_files,
admin_pass=admin_pass)
finally:
# Avoid a race condition where the thread could be canceled
# before the ID is stored
if server is not None:
self.resource_id_set(server.id)
return server.id
def check_create_complete(self, server_id):
check = self.client_plugin()._check_active(server_id)
if check:
self.store_external_ports()
return check
def handle_check(self):
server = self.client().servers.get(self.resource_id)
status = self.client_plugin().get_status(server)
checks = [{'attr': 'status', 'expected': 'ACTIVE', 'current': status}]
self._verify_check_conditions(checks)
@classmethod
def _build_block_device_mapping(cls, bdm):
if not bdm:
return None
bdm_dict = {}
for mapping in bdm:
mapping_parts = []
snapshot_id = mapping.get(cls.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
if snapshot_id:
mapping_parts.append(snapshot_id)
mapping_parts.append('snap')
else:
volume_id = mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_ID)
mapping_parts.append(volume_id)
mapping_parts.append('')
volume_size = mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_SIZE)
delete = mapping.get(cls.BLOCK_DEVICE_MAPPING_DELETE_ON_TERM)
if volume_size:
mapping_parts.append(str(volume_size))
else:
mapping_parts.append('')
if delete:
mapping_parts.append(str(delete))
device_name = mapping.get(cls.BLOCK_DEVICE_MAPPING_DEVICE_NAME)
bdm_dict[device_name] = ':'.join(mapping_parts)
return bdm_dict
@classmethod
def _build_block_device_mapping_v2(cls, bdm_v2):
if not bdm_v2:
return None
bdm_v2_list = []
for mapping in bdm_v2:
bmd_dict = None
if mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_ID):
bmd_dict = {
'uuid': mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_ID),
'source_type': 'volume',
'destination_type': 'volume',
'boot_index': 0,
'delete_on_termination': False,
}
elif mapping.get(cls.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID):
bmd_dict = {
'uuid': mapping.get(cls.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID),
'source_type': 'snapshot',
'destination_type': 'volume',
'boot_index': 0,
'delete_on_termination': False,
}
elif mapping.get(cls.BLOCK_DEVICE_MAPPING_IMAGE_ID):
bmd_dict = {
'uuid': mapping.get(cls.BLOCK_DEVICE_MAPPING_IMAGE_ID),
'source_type': 'image',
'destination_type': 'volume',
'boot_index': 0,
'delete_on_termination': False,
}
elif mapping.get(cls.BLOCK_DEVICE_MAPPING_SWAP_SIZE):
bmd_dict = {
'source_type': 'blank',
'destination_type': 'local',
'boot_index': -1,
'delete_on_termination': True,
'guest_format': 'swap',
'volume_size': mapping.get(
cls.BLOCK_DEVICE_MAPPING_SWAP_SIZE),
}
# NOTE(prazumovsky): In case of server doesn't take empty value of
# device name, need to escape from such situation.
device_name = mapping.get(cls.BLOCK_DEVICE_MAPPING_DEVICE_NAME)
if device_name:
bmd_dict[cls.BLOCK_DEVICE_MAPPING_DEVICE_NAME] = device_name
update_props = (cls.BLOCK_DEVICE_MAPPING_DEVICE_TYPE,
cls.BLOCK_DEVICE_MAPPING_DISK_BUS,
cls.BLOCK_DEVICE_MAPPING_BOOT_INDEX,
cls.BLOCK_DEVICE_MAPPING_VOLUME_SIZE,
cls.BLOCK_DEVICE_MAPPING_DELETE_ON_TERM)
for update_prop in update_props:
if mapping.get(update_prop) is not None:
bmd_dict[update_prop] = mapping.get(update_prop)
if bmd_dict:
bdm_v2_list.append(bmd_dict)
return bdm_v2_list
def _add_port_for_address(self, server):
"""Method adds port id to list of addresses.
This method is used only for resolving attributes.
"""
nets = copy.deepcopy(server.addresses)
ifaces = server.interface_list()
ip_mac_mapping_on_port_id = dict(((iface.fixed_ips[0]['ip_address'],
iface.mac_addr), iface.port_id)
for iface in ifaces)
for net_name in nets:
for addr in nets[net_name]:
addr['port'] = ip_mac_mapping_on_port_id.get(
(addr['addr'], addr['OS-EXT-IPS-MAC:mac_addr']))
return self._extend_networks(nets)
def _extend_networks(self, networks):
"""Method adds same networks with replaced name on network id.
This method is used only for resolving attributes.
"""
nets = copy.deepcopy(networks)
for key in list(nets.keys()):
try:
net_id = self.client_plugin().get_net_id_by_label(key)
except (exception.NovaNetworkNotFound,
exception.PhysicalResourceNameAmbiguity):
net_id = None
if net_id:
nets[net_id] = nets[key]
return nets
def _resolve_attribute(self, name):
if name == self.FIRST_ADDRESS:
return self.client_plugin().server_to_ipaddress(
self.resource_id) or ''
if name == self.NAME_ATTR:
return self._server_name()
try:
server = self.client().servers.get(self.resource_id)
except Exception as e:
self.client_plugin().ignore_not_found(e)
return ''
if name == self.ADDRESSES:
return self._add_port_for_address(server)
if name == self.NETWORKS_ATTR:
return self._extend_networks(server.networks)
if name == self.INSTANCE_NAME:
return getattr(server, 'OS-EXT-SRV-ATTR:instance_name', None)
if name == self.ACCESSIPV4:
return server.accessIPv4
if name == self.ACCESSIPV6:
return server.accessIPv6
if name == self.CONSOLE_URLS:
return self.client_plugin('nova').get_console_urls(server)
def add_dependencies(self, deps):
super(Server, self).add_dependencies(deps)
# Depend on any Subnet in this template with the same
# network_id as the networks attached to this server.
# It is not known which subnet a server might be assigned
# to so all subnets in a network should be created before
# the servers in that network.
nets = self.properties[self.NETWORKS]
if not nets:
return
for res in six.itervalues(self.stack):
if res.has_interface('OS::Neutron::Subnet'):
subnet_net = (res.properties.get(subnet.Subnet.NETWORK_ID)
or res.properties.get(subnet.Subnet.NETWORK))
for net in nets:
# worry about network_id because that could be the match
# assigned to the subnet as well and could have been
# created by this stack. Regardless, the server should
# still wait on the subnet.
net_id = (net.get(self.NETWORK_ID) or
net.get(self.NETWORK_UUID))
if net_id and net_id == subnet_net:
deps += (self, res)
break
def _update_flavor(self, prop_diff):
flavor = prop_diff[self.FLAVOR]
flavor_id = self.client_plugin().get_flavor_id(flavor)
handler_args = {'args': (flavor_id,)}
checker_args = {'args': (flavor_id, flavor)}
prg_resize = progress.ServerUpdateProgress(self.resource_id,
'resize',
handler_extra=handler_args,
checker_extra=checker_args)
prg_verify = progress.ServerUpdateProgress(self.resource_id,
'verify_resize')
return prg_resize, prg_verify
def _update_image(self, prop_diff):
image_update_policy = (
prop_diff.get(self.IMAGE_UPDATE_POLICY) or
self.properties[self.IMAGE_UPDATE_POLICY])
image = prop_diff[self.IMAGE]
image_id = self.client_plugin('glance').get_image_id(image)
preserve_ephemeral = (
image_update_policy == 'REBUILD_PRESERVE_EPHEMERAL')
password = (prop_diff.get(self.ADMIN_PASS) or
self.properties[self.ADMIN_PASS])
kwargs = {'password': password,
'preserve_ephemeral': preserve_ephemeral}
prg = progress.ServerUpdateProgress(self.resource_id,
'rebuild',
handler_extra={'args': (image_id,),
'kwargs': kwargs})
return prg
def _update_networks(self, server, prop_diff):
updaters = []
new_networks = prop_diff.get(self.NETWORKS)
old_networks = self.properties[self.NETWORKS]
if not server:
server = self.client().servers.get(self.resource_id)
interfaces = server.interface_list()
remove_ports, add_nets = self.calculate_networks(
old_networks, new_networks, interfaces)
for port in remove_ports:
updaters.append(
progress.ServerUpdateProgress(
self.resource_id, 'interface_detach',
complete=True,
handler_extra={'args': (port,)})
)
for args in add_nets:
updaters.append(
progress.ServerUpdateProgress(
self.resource_id, 'interface_attach',
complete=True,
handler_extra={'kwargs': args})
)
return updaters
def _needs_update(self, after, before, after_props, before_props,
prev_resource, check_init_complete=True):
result = super(Server, self)._needs_update(
after, before, after_props, before_props, prev_resource,
check_init_complete=check_init_complete)
prop_diff = self.update_template_diff_properties(after_props,
before_props)
if self.FLAVOR in prop_diff:
flavor_update_policy = (
prop_diff.get(self.FLAVOR_UPDATE_POLICY) or
self.properties[self.FLAVOR_UPDATE_POLICY])
if flavor_update_policy == 'REPLACE':
raise exception.UpdateReplace(self.name)
if self.IMAGE in prop_diff:
image_update_policy = (
prop_diff.get(self.IMAGE_UPDATE_POLICY) or
self.properties[self.IMAGE_UPDATE_POLICY])
if image_update_policy == 'REPLACE':
raise exception.UpdateReplace(self.name)
return result
def handle_update(self, json_snippet, tmpl_diff, prop_diff):
if 'Metadata' in tmpl_diff:
# If SOFTWARE_CONFIG user_data_format is enabled we require
# the "deployments" and "os-collect-config" keys for Deployment
# polling. We can attempt to merge the occ data, but any
# metadata update containing deployments will be discarded.
if self.user_data_software_config():
metadata = self.metadata_get(True) or {}
new_occ_md = tmpl_diff['Metadata'].get('os-collect-config', {})
occ_md = metadata.get('os-collect-config', {})
occ_md.update(new_occ_md)
tmpl_diff['Metadata']['os-collect-config'] = occ_md
deployment_md = metadata.get('deployments', [])
tmpl_diff['Metadata']['deployments'] = deployment_md
self.metadata_set(tmpl_diff['Metadata'])
updaters = []
server = None
if self.METADATA in prop_diff:
server = self.client().servers.get(self.resource_id)
self.client_plugin().meta_update(server,
prop_diff[self.METADATA])
if self.FLAVOR in prop_diff:
updaters.extend(self._update_flavor(prop_diff))
if self.IMAGE in prop_diff:
updaters.append(self._update_image(prop_diff))
elif self.ADMIN_PASS in prop_diff:
if not server:
server = self.client().servers.get(self.resource_id)
server.change_password(prop_diff[self.ADMIN_PASS])
if self.NAME in prop_diff:
if not server:
server = self.client().servers.get(self.resource_id)
self.client_plugin().rename(server, prop_diff[self.NAME])
if self.NETWORKS in prop_diff:
updaters.extend(self._update_networks(server, prop_diff))
# NOTE(pas-ha) optimization is possible (starting first task
# right away), but we'd rather not, as this method already might
# have called several APIs
return updaters
def check_update_complete(self, updaters):
"""Push all updaters to completion in list order."""
for prg in updaters:
if not prg.called:
handler = getattr(self.client_plugin(), prg.handler)
prg.called = handler(*prg.handler_args,
**prg.handler_kwargs)
return False
if not prg.complete:
check_complete = getattr(self.client_plugin(), prg.checker)
prg.complete = check_complete(*prg.checker_args,
**prg.checker_kwargs)
break
status = all(prg.complete for prg in updaters)
if status:
self.store_external_ports()
return status
def metadata_update(self, new_metadata=None):
"""Refresh the metadata if new_metadata is None."""
if new_metadata is None:
# Re-resolve the template metadata and merge it with the
# current resource metadata. This is necessary because the
# attributes referenced in the template metadata may change
# and the resource itself adds keys to the metadata which
# are not specified in the template (e.g the deployments data)
meta = self.metadata_get(refresh=True) or {}
tmpl_meta = self.t.metadata()
meta.update(tmpl_meta)
self.metadata_set(meta)
@staticmethod
def _check_maximum(count, maximum, msg):
"""Check a count against a maximum.
Unless maximum is -1 which indicates that there is no limit.
"""
if maximum != -1 and count > maximum:
raise exception.StackValidationFailed(message=msg)
def _validate_block_device_mapping(self):
# either volume_id or snapshot_id needs to be specified, but not both
# for block device mapping.
bdm = self.properties[self.BLOCK_DEVICE_MAPPING] or []
bootable_vol = False
for mapping in bdm:
device_name = mapping[self.BLOCK_DEVICE_MAPPING_DEVICE_NAME]
if device_name == 'vda':
bootable_vol = True
volume_id = mapping.get(self.BLOCK_DEVICE_MAPPING_VOLUME_ID)
snapshot_id = mapping.get(self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
if volume_id is not None and snapshot_id is not None:
raise exception.ResourcePropertyConflict(
self.BLOCK_DEVICE_MAPPING_VOLUME_ID,
self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
if volume_id is None and snapshot_id is None:
msg = _('Either volume_id or snapshot_id must be specified for'
' device mapping %s') % device_name
raise exception.StackValidationFailed(message=msg)
bdm_v2 = self.properties[self.BLOCK_DEVICE_MAPPING_V2] or []
if bdm and bdm_v2:
raise exception.ResourcePropertyConflict(
self.BLOCK_DEVICE_MAPPING, self.BLOCK_DEVICE_MAPPING_V2)
for mapping in bdm_v2:
volume_id = mapping.get(self.BLOCK_DEVICE_MAPPING_VOLUME_ID)
snapshot_id = mapping.get(self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
image_id = mapping.get(self.BLOCK_DEVICE_MAPPING_IMAGE_ID)
swap_size = mapping.get(self.BLOCK_DEVICE_MAPPING_SWAP_SIZE)
property_tuple = (volume_id, snapshot_id, image_id, swap_size)
if property_tuple.count(None) < 3:
raise exception.ResourcePropertyConflict(
self.BLOCK_DEVICE_MAPPING_VOLUME_ID,
self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID,
self.BLOCK_DEVICE_MAPPING_IMAGE_ID,
self.BLOCK_DEVICE_MAPPING_SWAP_SIZE)
if property_tuple.count(None) == 4:
msg = _('Either volume_id, snapshot_id, image_id or '
'swap_size must be specified.')
raise exception.StackValidationFailed(message=msg)
if any((volume_id, snapshot_id, image_id)):
bootable_vol = True
return bootable_vol
def validate(self):
"""Validate any of the provided params."""
super(Server, self).validate()
if self.user_data_software_config():
if 'deployments' in self.t.metadata():
msg = _('deployments key not allowed in resource metadata '
'with user_data_format of SOFTWARE_CONFIG')
raise exception.StackValidationFailed(message=msg)
bootable_vol = self._validate_block_device_mapping()
# make sure the image exists if specified.
image = self.properties[self.IMAGE]
if not image and not bootable_vol:
msg = _('Neither image nor bootable volume is specified for'
' instance %s') % self.name
raise exception.StackValidationFailed(message=msg)
# network properties 'uuid' and 'network' shouldn't be used
# both at once for all networks
networks = self.properties[self.NETWORKS] or []
# record if any networks include explicit ports
networks_with_port = False
for network in networks:
networks_with_port = (networks_with_port or
network.get(self.NETWORK_PORT))
self._validate_network(network)
# retrieve provider's absolute limits if it will be needed
metadata = self.properties[self.METADATA]
personality = self.properties[self.PERSONALITY]
if metadata is not None or personality:
limits = self.client_plugin().absolute_limits()
# if 'security_groups' present for the server and explict 'port'
# in one or more entries in 'networks', raise validation error
if networks_with_port and self.properties[self.SECURITY_GROUPS]:
raise exception.ResourcePropertyConflict(
self.SECURITY_GROUPS,
"/".join([self.NETWORKS, self.NETWORK_PORT]))
# verify that the number of metadata entries is not greater
# than the maximum number allowed in the provider's absolute
# limits
if metadata is not None:
msg = _('Instance metadata must not contain greater than %s '
'entries. This is the maximum number allowed by your '
'service provider') % limits['maxServerMeta']
self._check_maximum(len(metadata),
limits['maxServerMeta'], msg)
# verify the number of personality files and the size of each
# personality file against the provider's absolute limits
if personality:
msg = _("The personality property may not contain "
"greater than %s entries.") % limits['maxPersonality']
self._check_maximum(len(personality),
limits['maxPersonality'], msg)
for path, contents in personality.items():
msg = (_("The contents of personality file \"%(path)s\" "
"is larger than the maximum allowed personality "
"file size (%(max_size)s bytes).") %
{'path': path,
'max_size': limits['maxPersonalitySize']})
self._check_maximum(len(bytes(contents.encode('utf-8'))),
limits['maxPersonalitySize'], msg)
def _delete_temp_url(self):
object_name = self.data().get('metadata_object_name')
if not object_name:
return
try:
container = self.physical_resource_name()
swift = self.client('swift')
swift.delete_object(container, object_name)
headers = swift.head_container(container)
if int(headers['x-container-object-count']) == 0:
swift.delete_container(container)
except Exception as ex:
self.client_plugin('swift').ignore_not_found(ex)
def _delete_queue(self):
queue_id = self.data().get('metadata_queue_id')
if not queue_id:
return
client_plugin = self.client_plugin('zaqar')
zaqar = client_plugin.create_for_tenant(
self.stack.stack_user_project_id)
try:
zaqar.queue(queue_id).delete()
except Exception as ex:
client_plugin.ignore_not_found(ex)
self.data_delete('metadata_queue_id')
def handle_snapshot_delete(self, state):
if state[0] != self.FAILED:
image_id = self.client().servers.create_image(
self.resource_id, self.physical_resource_name())
return progress.ServerDeleteProgress(
self.resource_id, image_id, False)
return self.handle_delete()
def handle_delete(self):
if self.resource_id is None:
return
if self.user_data_software_config():
self._delete_user()
self._delete_temp_url()
self._delete_queue()
# remove internal and external ports
self._delete_internal_ports()
self.data_delete('external_ports')
try:
self.client().servers.delete(self.resource_id)
except Exception as e:
self.client_plugin().ignore_not_found(e)
return
return progress.ServerDeleteProgress(self.resource_id)
def check_delete_complete(self, prg):
if not prg:
return True
if not prg.image_complete:
image = self.client().images.get(prg.image_id)
if image.status in ('DELETED', 'ERROR'):
raise exception.Error(image.status)
elif image.status == 'ACTIVE':
prg.image_complete = True
if not self.handle_delete():
return True
return False
return self.client_plugin().check_delete_server_complete(
prg.server_id)
def handle_suspend(self):
"""Suspend a server.
Note we do not wait for the SUSPENDED state, this is polled for by
check_suspend_complete in a similar way to the create logic so we can
take advantage of coroutines.
"""
if self.resource_id is None:
raise exception.Error(_('Cannot suspend %s, resource_id not set') %
self.name)
try:
server = self.client().servers.get(self.resource_id)
except Exception as e:
if self.client_plugin().is_not_found(e):
raise exception.NotFound(_('Failed to find server %s') %
self.resource_id)
else:
raise
else:
# if the server has been suspended successful,
# no need to suspend again
if self.client_plugin().get_status(server) != 'SUSPENDED':
LOG.debug('suspending server %s' % self.resource_id)
server.suspend()
return server.id
def check_suspend_complete(self, server_id):
cp = self.client_plugin()
server = cp.fetch_server(server_id)
if not server:
return False
status = cp.get_status(server)
LOG.debug('%(name)s check_suspend_complete status = %(status)s'
% {'name': self.name, 'status': status})
if status in list(cp.deferred_server_statuses + ['ACTIVE']):
return status == 'SUSPENDED'
else:
exc = exception.ResourceUnknownStatus(
result=_('Suspend of server %s failed') % server.name,
resource_status=status)
raise exc
def handle_resume(self):
"""Resume a server.
Note we do not wait for the ACTIVE state, this is polled for by
check_resume_complete in a similar way to the create logic so we can
take advantage of coroutines.
"""
if self.resource_id is None:
raise exception.Error(_('Cannot resume %s, resource_id not set') %
self.name)
try:
server = self.client().servers.get(self.resource_id)
except Exception as e:
if self.client_plugin().is_not_found(e):
raise exception.NotFound(_('Failed to find server %s') %
self.resource_id)
else:
raise
else:
# if the server has been resumed successful,
# no need to resume again
if self.client_plugin().get_status(server) != 'ACTIVE':
LOG.debug('resuming server %s' % self.resource_id)
server.resume()
return server.id
def check_resume_complete(self, server_id):
return self.client_plugin()._check_active(server_id)
def handle_snapshot(self):
image_id = self.client().servers.create_image(
self.resource_id, self.physical_resource_name())
self.data_set('snapshot_image_id', image_id)
return image_id
def check_snapshot_complete(self, image_id):
image = self.client().images.get(image_id)
if image.status == 'ACTIVE':
return True
elif image.status == 'ERROR' or image.status == 'DELETED':
raise exception.Error(image.status)
return False
def handle_delete_snapshot(self, snapshot):
image_id = snapshot['resource_data'].get('snapshot_image_id')
try:
self.client().images.delete(image_id)
except Exception as e:
self.client_plugin().ignore_not_found(e)
def handle_restore(self, defn, restore_data):
image_id = restore_data['resource_data']['snapshot_image_id']
props = function.resolve(self.properties.data)
props[self.IMAGE] = image_id
return defn.freeze(properties=props)
def prepare_for_replace(self):
self.prepare_ports_for_replace()
def restore_prev_rsrc(self, convergence=False):
self.restore_ports_after_rollback(convergence=convergence)
def resource_mapping():
return {
'OS::Nova::Server': Server,
}
| 42.037011 | 79 | 0.570842 |
import copy
import uuid
from oslo_config import cfg
from oslo_log import log as logging
from oslo_serialization import jsonutils
from oslo_utils import uuidutils
import six
from heat.common import exception
from heat.common.i18n import _
from heat.engine import attributes
from heat.engine.clients import progress
from heat.engine import constraints
from heat.engine import function
from heat.engine import properties
from heat.engine.resources.openstack.neutron import port as neutron_port
from heat.engine.resources.openstack.neutron import subnet
from heat.engine.resources.openstack.nova import server_network_mixin
from heat.engine.resources import scheduler_hints as sh
from heat.engine.resources import stack_user
from heat.engine import support
from heat.rpc import api as rpc_api
cfg.CONF.import_opt('default_software_config_transport', 'heat.common.config')
LOG = logging.getLogger(__name__)
class Server(stack_user.StackUser, sh.SchedulerHintsMixin,
server_network_mixin.ServerNetworkMixin):
PROPERTIES = (
NAME, IMAGE, BLOCK_DEVICE_MAPPING, BLOCK_DEVICE_MAPPING_V2,
FLAVOR, FLAVOR_UPDATE_POLICY, IMAGE_UPDATE_POLICY, KEY_NAME,
ADMIN_USER, AVAILABILITY_ZONE, SECURITY_GROUPS, NETWORKS,
SCHEDULER_HINTS, METADATA, USER_DATA_FORMAT, USER_DATA,
RESERVATION_ID, CONFIG_DRIVE, DISK_CONFIG, PERSONALITY,
ADMIN_PASS, SOFTWARE_CONFIG_TRANSPORT
) = (
'name', 'image', 'block_device_mapping', 'block_device_mapping_v2',
'flavor', 'flavor_update_policy', 'image_update_policy', 'key_name',
'admin_user', 'availability_zone', 'security_groups', 'networks',
'scheduler_hints', 'metadata', 'user_data_format', 'user_data',
'reservation_id', 'config_drive', 'diskConfig', 'personality',
'admin_pass', 'software_config_transport'
)
_BLOCK_DEVICE_MAPPING_KEYS = (
BLOCK_DEVICE_MAPPING_DEVICE_NAME, BLOCK_DEVICE_MAPPING_VOLUME_ID,
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID,
BLOCK_DEVICE_MAPPING_VOLUME_SIZE,
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM,
) = (
'device_name', 'volume_id',
'snapshot_id',
'volume_size',
'delete_on_termination',
)
_BLOCK_DEVICE_MAPPING_V2_KEYS = (
BLOCK_DEVICE_MAPPING_DEVICE_NAME,
BLOCK_DEVICE_MAPPING_VOLUME_ID,
BLOCK_DEVICE_MAPPING_IMAGE_ID,
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID,
BLOCK_DEVICE_MAPPING_SWAP_SIZE,
BLOCK_DEVICE_MAPPING_DEVICE_TYPE,
BLOCK_DEVICE_MAPPING_DISK_BUS,
BLOCK_DEVICE_MAPPING_BOOT_INDEX,
BLOCK_DEVICE_MAPPING_VOLUME_SIZE,
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM,
) = (
'device_name',
'volume_id',
'image_id',
'snapshot_id',
'swap_size',
'device_type',
'disk_bus',
'boot_index',
'volume_size',
'delete_on_termination',
)
_NETWORK_KEYS = (
NETWORK_UUID, NETWORK_ID, NETWORK_FIXED_IP, NETWORK_PORT,
NETWORK_SUBNET, NETWORK_PORT_EXTRA
) = (
'uuid', 'network', 'fixed_ip', 'port',
'subnet', 'port_extra_properties'
)
_SOFTWARE_CONFIG_FORMATS = (
HEAT_CFNTOOLS, RAW, SOFTWARE_CONFIG
) = (
'HEAT_CFNTOOLS', 'RAW', 'SOFTWARE_CONFIG'
)
_SOFTWARE_CONFIG_TRANSPORTS = (
POLL_SERVER_CFN, POLL_SERVER_HEAT, POLL_TEMP_URL, ZAQAR_MESSAGE
) = (
'POLL_SERVER_CFN', 'POLL_SERVER_HEAT', 'POLL_TEMP_URL', 'ZAQAR_MESSAGE'
)
ATTRIBUTES = (
NAME_ATTR, ADDRESSES, NETWORKS_ATTR, FIRST_ADDRESS,
INSTANCE_NAME, ACCESSIPV4, ACCESSIPV6, CONSOLE_URLS,
) = (
'name', 'addresses', 'networks', 'first_address',
'instance_name', 'accessIPv4', 'accessIPv6', 'console_urls',
)
properties_schema = {
NAME: properties.Schema(
properties.Schema.STRING,
_('Server name.'),
update_allowed=True
),
IMAGE: properties.Schema(
properties.Schema.STRING,
_('The ID or name of the image to boot with.'),
constraints=[
constraints.CustomConstraint('glance.image')
],
update_allowed=True
),
BLOCK_DEVICE_MAPPING: properties.Schema(
properties.Schema.LIST,
_('Block device mappings for this server.'),
schema=properties.Schema(
properties.Schema.MAP,
schema={
BLOCK_DEVICE_MAPPING_DEVICE_NAME: properties.Schema(
properties.Schema.STRING,
_('A device name where the volume will be '
'attached in the system at /dev/device_name. '
'This value is typically vda.'),
required=True
),
BLOCK_DEVICE_MAPPING_VOLUME_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the volume to boot from. Only one '
'of volume_id or snapshot_id should be '
'provided.'),
constraints=[
constraints.CustomConstraint('cinder.volume')
]
),
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the snapshot to create a volume '
'from.'),
constraints=[
constraints.CustomConstraint('cinder.snapshot')
]
),
BLOCK_DEVICE_MAPPING_VOLUME_SIZE: properties.Schema(
properties.Schema.INTEGER,
_('The size of the volume, in GB. It is safe to '
'leave this blank and have the Compute service '
'infer the size.')
),
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM: properties.Schema(
properties.Schema.BOOLEAN,
_('Indicate whether the volume should be deleted '
'when the server is terminated.')
),
},
)
),
BLOCK_DEVICE_MAPPING_V2: properties.Schema(
properties.Schema.LIST,
_('Block device mappings v2 for this server.'),
schema=properties.Schema(
properties.Schema.MAP,
schema={
BLOCK_DEVICE_MAPPING_DEVICE_NAME: properties.Schema(
properties.Schema.STRING,
_('A device name where the volume will be '
'attached in the system at /dev/device_name. '
'This value is typically vda.'),
),
BLOCK_DEVICE_MAPPING_VOLUME_ID: properties.Schema(
properties.Schema.STRING,
_('The volume_id can be boot or non-boot device '
'to the server.'),
constraints=[
constraints.CustomConstraint('cinder.volume')
]
),
BLOCK_DEVICE_MAPPING_IMAGE_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the image to create a volume from.'),
constraints=[
constraints.CustomConstraint('glance.image')
],
),
BLOCK_DEVICE_MAPPING_SNAPSHOT_ID: properties.Schema(
properties.Schema.STRING,
_('The ID of the snapshot to create a volume '
'from.'),
constraints=[
constraints.CustomConstraint('cinder.snapshot')
]
),
BLOCK_DEVICE_MAPPING_SWAP_SIZE: properties.Schema(
properties.Schema.INTEGER,
_('The size of the swap, in MB.')
),
BLOCK_DEVICE_MAPPING_DEVICE_TYPE: properties.Schema(
properties.Schema.STRING,
_('Device type: at the moment we can make distinction'
' only between disk and cdrom.'),
constraints=[
constraints.AllowedValues(['cdrom', 'disk']),
],
),
BLOCK_DEVICE_MAPPING_DISK_BUS: properties.Schema(
properties.Schema.STRING,
_('Bus of the device: hypervisor driver chooses a '
'suitable default if omitted.'),
constraints=[
constraints.AllowedValues(['ide', 'lame_bus',
'scsi', 'usb',
'virtio']),
],
),
BLOCK_DEVICE_MAPPING_BOOT_INDEX: properties.Schema(
properties.Schema.INTEGER,
_('Integer used for ordering the boot disks.'),
),
BLOCK_DEVICE_MAPPING_VOLUME_SIZE: properties.Schema(
properties.Schema.INTEGER,
_('Size of the block device in GB. If it is omitted, '
'hypervisor driver calculates size.'),
),
BLOCK_DEVICE_MAPPING_DELETE_ON_TERM: properties.Schema(
properties.Schema.BOOLEAN,
_('Indicate whether the volume should be deleted '
'when the server is terminated.')
),
},
),
support_status=support.SupportStatus(version='2015.1')
),
FLAVOR: properties.Schema(
properties.Schema.STRING,
_('The ID or name of the flavor to boot onto.'),
required=True,
update_allowed=True,
constraints=[
constraints.CustomConstraint('nova.flavor')
]
),
FLAVOR_UPDATE_POLICY: properties.Schema(
properties.Schema.STRING,
_('Policy on how to apply a flavor update; either by requesting '
'a server resize or by replacing the entire server.'),
default='RESIZE',
constraints=[
constraints.AllowedValues(['RESIZE', 'REPLACE']),
],
update_allowed=True
),
IMAGE_UPDATE_POLICY: properties.Schema(
properties.Schema.STRING,
_('Policy on how to apply an image-id update; either by '
'requesting a server rebuild or by replacing the entire server'),
default='REBUILD',
constraints=[
constraints.AllowedValues(['REBUILD', 'REPLACE',
'REBUILD_PRESERVE_EPHEMERAL']),
],
update_allowed=True
),
KEY_NAME: properties.Schema(
properties.Schema.STRING,
_('Name of keypair to inject into the server.'),
constraints=[
constraints.CustomConstraint('nova.keypair')
]
),
ADMIN_USER: properties.Schema(
properties.Schema.STRING,
_('Name of the administrative user to use on the server.'),
support_status=support.SupportStatus(
status=support.HIDDEN,
version='5.0.0',
message=_('The default cloud-init user set up for each image '
'(e.g. "ubuntu" for Ubuntu 12.04+, "fedora" for '
'Fedora 19+ and "cloud-user" for CentOS/RHEL 6.5).'),
previous_status=support.SupportStatus(
status=support.DEPRECATED,
version='2014.1',
previous_status=support.SupportStatus(version='2013.2')
)
)
),
AVAILABILITY_ZONE: properties.Schema(
properties.Schema.STRING,
_('Name of the availability zone for server placement.')
),
SECURITY_GROUPS: properties.Schema(
properties.Schema.LIST,
_('List of security group names or IDs. Cannot be used if '
'neutron ports are associated with this server; assign '
'security groups to the ports instead.'),
default=[]
),
NETWORKS: properties.Schema(
properties.Schema.LIST,
_('An ordered list of nics to be added to this server, with '
'information about connected networks, fixed ips, port etc.'),
schema=properties.Schema(
properties.Schema.MAP,
schema={
NETWORK_UUID: properties.Schema(
properties.Schema.STRING,
_('ID of network to create a port on.'),
support_status=support.SupportStatus(
status=support.HIDDEN,
version='5.0.0',
previous_status=support.SupportStatus(
status=support.DEPRECATED,
message=_('Use property %s.') % NETWORK_ID,
version='2014.1'
)
),
constraints=[
constraints.CustomConstraint('neutron.network')
]
),
NETWORK_ID: properties.Schema(
properties.Schema.STRING,
_('Name or ID of network to create a port on.'),
constraints=[
constraints.CustomConstraint('neutron.network')
]
),
NETWORK_FIXED_IP: properties.Schema(
properties.Schema.STRING,
_('Fixed IP address to specify for the port '
'created on the requested network.'),
constraints=[
constraints.CustomConstraint('ip_addr')
]
),
NETWORK_PORT: properties.Schema(
properties.Schema.STRING,
_('ID of an existing port to associate with this '
'server.'),
constraints=[
constraints.CustomConstraint('neutron.port')
]
),
NETWORK_PORT_EXTRA: properties.Schema(
properties.Schema.MAP,
_('Dict, which has expand properties for port. '
'Used only if port property is not specified '
'for creating port.'),
schema=neutron_port.Port.extra_properties_schema,
support_status=support.SupportStatus(version='6.0.0')
),
NETWORK_SUBNET: properties.Schema(
properties.Schema.STRING,
_('Subnet in which to allocate the IP address for '
'port. Used for creating port, based on derived '
'properties. If subnet is specified, network '
'property becomes optional.'),
support_status=support.SupportStatus(version='5.0.0')
)
},
),
update_allowed=True
),
SCHEDULER_HINTS: properties.Schema(
properties.Schema.MAP,
_('Arbitrary key-value pairs specified by the client to help '
'boot a server.')
),
METADATA: properties.Schema(
properties.Schema.MAP,
_('Arbitrary key/value metadata to store for this server. Both '
'keys and values must be 255 characters or less. Non-string '
'values will be serialized to JSON (and the serialized '
'string must be 255 characters or less).'),
update_allowed=True
),
USER_DATA_FORMAT: properties.Schema(
properties.Schema.STRING,
_('How the user_data should be formatted for the server. For '
'HEAT_CFNTOOLS, the user_data is bundled as part of the '
'heat-cfntools cloud-init boot configuration data. For RAW '
'the user_data is passed to Nova unmodified. '
'For SOFTWARE_CONFIG user_data is bundled as part of the '
'software config data, and metadata is derived from any '
'associated SoftwareDeployment resources.'),
default=HEAT_CFNTOOLS,
constraints=[
constraints.AllowedValues(_SOFTWARE_CONFIG_FORMATS),
]
),
SOFTWARE_CONFIG_TRANSPORT: properties.Schema(
properties.Schema.STRING,
_('How the server should receive the metadata required for '
'software configuration. POLL_SERVER_CFN will allow calls to '
'the cfn API action DescribeStackResource authenticated with '
'the provided keypair. POLL_SERVER_HEAT will allow calls to '
'the Heat API resource-show using the provided keystone '
'credentials. POLL_TEMP_URL will create and populate a '
'Swift TempURL with metadata for polling. ZAQAR_MESSAGE will '
'create a dedicated zaqar queue and post the metadata '
'for polling.'),
default=cfg.CONF.default_software_config_transport,
constraints=[
constraints.AllowedValues(_SOFTWARE_CONFIG_TRANSPORTS),
]
),
USER_DATA: properties.Schema(
properties.Schema.STRING,
_('User data script to be executed by cloud-init.'),
default=''
),
RESERVATION_ID: properties.Schema(
properties.Schema.STRING,
_('A UUID for the set of servers being requested.')
),
CONFIG_DRIVE: properties.Schema(
properties.Schema.BOOLEAN,
_('If True, enable config drive on the server.')
),
DISK_CONFIG: properties.Schema(
properties.Schema.STRING,
_('Control how the disk is partitioned when the server is '
'created.'),
constraints=[
constraints.AllowedValues(['AUTO', 'MANUAL']),
]
),
PERSONALITY: properties.Schema(
properties.Schema.MAP,
_('A map of files to create/overwrite on the server upon boot. '
'Keys are file names and values are the file contents.'),
default={}
),
ADMIN_PASS: properties.Schema(
properties.Schema.STRING,
_('The administrator password for the server.'),
update_allowed=True
),
}
attributes_schema = {
NAME_ATTR: attributes.Schema(
_('Name of the server.'),
type=attributes.Schema.STRING
),
ADDRESSES: attributes.Schema(
_('A dict of all network addresses with corresponding port_id. '
'Each network will have two keys in dict, they are network '
'name and network id. '
'The port ID may be obtained through the following expression: '
'"{get_attr: [<server>, addresses, <network name_or_id>, 0, '
'port]}".'),
type=attributes.Schema.MAP
),
NETWORKS_ATTR: attributes.Schema(
_('A dict of assigned network addresses of the form: '
'{"public": [ip1, ip2...], "private": [ip3, ip4], '
'"public_uuid": [ip1, ip2...], "private_uuid": [ip3, ip4]}. '
'Each network will have two keys in dict, they are network '
'name and network id. '),
type=attributes.Schema.MAP
),
FIRST_ADDRESS: attributes.Schema(
_('Convenience attribute to fetch the first assigned network '
'address, or an empty string if nothing has been assigned at '
'this time. Result may not be predictable if the server has '
'addresses from more than one network.'),
support_status=support.SupportStatus(
status=support.HIDDEN,
version='5.0.0',
message=_('Use the networks attribute instead of '
'first_address. For example: "{get_attr: '
'[<server name>, networks, <network name>, 0]}"'),
previous_status=support.SupportStatus(
status=support.DEPRECATED,
version='2014.2',
previous_status=support.SupportStatus(version='2013.2')
)
)
),
INSTANCE_NAME: attributes.Schema(
_('AWS compatible instance name.'),
type=attributes.Schema.STRING
),
ACCESSIPV4: attributes.Schema(
_('The manually assigned alternative public IPv4 address '
'of the server.'),
type=attributes.Schema.STRING
),
ACCESSIPV6: attributes.Schema(
_('The manually assigned alternative public IPv6 address '
'of the server.'),
type=attributes.Schema.STRING
),
CONSOLE_URLS: attributes.Schema(
_("URLs of server's consoles. "
"To get a specific console type, the requested type "
"can be specified as parameter to the get_attr function, "
"e.g. get_attr: [ <server>, console_urls, novnc ]. "
"Currently supported types are "
"novnc, xvpvnc, spice-html5, rdp-html5, serial."),
support_status=support.SupportStatus(version='2015.1'),
type=attributes.Schema.MAP
),
}
# Server host name limit to 53 characters by due to typical default
# linux HOST_NAME_MAX of 64, minus the .novalocal appended to the name
physical_resource_name_limit = 53
default_client_name = 'nova'
entity = 'servers'
def translation_rules(self):
return [properties.TranslationRule(
self.properties,
properties.TranslationRule.REPLACE,
source_path=[self.NETWORKS, self.NETWORK_ID],
value_name=self.NETWORK_UUID)]
def __init__(self, name, json_snippet, stack):
super(Server, self).__init__(name, json_snippet, stack)
if self.user_data_software_config():
self._register_access_key()
def _server_name(self):
name = self.properties[self.NAME]
if name:
return name
return self.physical_resource_name()
def _config_drive(self):
# This method is overridden by the derived CloudServer resource
return self.properties[self.CONFIG_DRIVE]
def _populate_deployments_metadata(self, meta):
meta['deployments'] = meta.get('deployments', [])
meta['os-collect-config'] = meta.get('os-collect-config', {})
if self.transport_poll_server_heat():
meta['os-collect-config'].update({'heat': {
'user_id': self._get_user_id(),
'password': self.password,
'auth_url': self.context.auth_url,
'project_id': self.stack.stack_user_project_id,
'stack_id': self.stack.identifier().stack_path(),
'resource_name': self.name}})
if self.transport_zaqar_message():
queue_id = self.physical_resource_name()
self.data_set('metadata_queue_id', queue_id)
zaqar_plugin = self.client_plugin('zaqar')
zaqar = zaqar_plugin.create_for_tenant(
self.stack.stack_user_project_id)
queue = zaqar.queue(queue_id)
queue.post({'body': meta, 'ttl': zaqar_plugin.DEFAULT_TTL})
meta['os-collect-config'].update({'zaqar': {
'user_id': self._get_user_id(),
'password': self.password,
'auth_url': self.context.auth_url,
'project_id': self.stack.stack_user_project_id,
'queue_id': queue_id}})
elif self.transport_poll_server_cfn():
meta['os-collect-config'].update({'cfn': {
'metadata_url': '%s/v1/' % cfg.CONF.heat_metadata_server_url,
'access_key_id': self.access_key,
'secret_access_key': self.secret_key,
'stack_name': self.stack.name,
'path': '%s.Metadata' % self.name}})
elif self.transport_poll_temp_url():
container = self.physical_resource_name()
object_name = str(uuid.uuid4())
self.client('swift').put_container(container)
url = self.client_plugin('swift').get_temp_url(
container, object_name, method='GET')
put_url = self.client_plugin('swift').get_temp_url(
container, object_name)
self.data_set('metadata_put_url', put_url)
self.data_set('metadata_object_name', object_name)
meta['os-collect-config'].update({'request': {
'metadata_url': url}})
self.client('swift').put_object(
container, object_name, jsonutils.dumps(meta))
self.metadata_set(meta)
def _register_access_key(self):
def access_allowed(resource_name):
return resource_name == self.name
if self.transport_poll_server_cfn():
self.stack.register_access_allowed_handler(
self.access_key, access_allowed)
elif self.transport_poll_server_heat():
self.stack.register_access_allowed_handler(
self._get_user_id(), access_allowed)
def _create_transport_credentials(self):
if self.transport_poll_server_cfn():
self._create_user()
self._create_keypair()
elif (self.transport_poll_server_heat() or
self.transport_zaqar_message()):
self.password = uuid.uuid4().hex
self._create_user()
self._register_access_key()
@property
def access_key(self):
return self.data().get('access_key')
@property
def secret_key(self):
return self.data().get('secret_key')
@property
def password(self):
return self.data().get('password')
@password.setter
def password(self, password):
if password is None:
self.data_delete('password')
else:
self.data_set('password', password, True)
def user_data_raw(self):
return self.properties[self.USER_DATA_FORMAT] == self.RAW
def user_data_software_config(self):
return self.properties[
self.USER_DATA_FORMAT] == self.SOFTWARE_CONFIG
def transport_poll_server_cfn(self):
return self.properties[
self.SOFTWARE_CONFIG_TRANSPORT] == self.POLL_SERVER_CFN
def transport_poll_server_heat(self):
return self.properties[
self.SOFTWARE_CONFIG_TRANSPORT] == self.POLL_SERVER_HEAT
def transport_poll_temp_url(self):
return self.properties[
self.SOFTWARE_CONFIG_TRANSPORT] == self.POLL_TEMP_URL
def transport_zaqar_message(self):
return self.properties.get(
self.SOFTWARE_CONFIG_TRANSPORT) == self.ZAQAR_MESSAGE
def get_software_config(self, ud_content):
try:
sc = self.rpc_client().show_software_config(
self.context, ud_content)
return sc[rpc_api.SOFTWARE_CONFIG_CONFIG]
except Exception as ex:
self.rpc_client().ignore_error_named(ex, 'NotFound')
return ud_content
def handle_create(self):
security_groups = self.properties[self.SECURITY_GROUPS]
user_data_format = self.properties[self.USER_DATA_FORMAT]
ud_content = self.properties[self.USER_DATA]
if self.user_data_software_config() or self.user_data_raw():
if uuidutils.is_uuid_like(ud_content):
# attempt to load the userdata from software config
ud_content = self.get_software_config(ud_content)
metadata = self.metadata_get(True) or {}
if self.user_data_software_config():
self._create_transport_credentials()
self._populate_deployments_metadata(metadata)
userdata = self.client_plugin().build_userdata(
metadata,
ud_content,
instance_user=None,
user_data_format=user_data_format)
flavor = self.properties[self.FLAVOR]
availability_zone = self.properties[self.AVAILABILITY_ZONE]
image = self.properties[self.IMAGE]
if image:
image = self.client_plugin('glance').get_image_id(image)
flavor_id = self.client_plugin().get_flavor_id(flavor)
instance_meta = self.properties[self.METADATA]
if instance_meta is not None:
instance_meta = self.client_plugin().meta_serialize(
instance_meta)
scheduler_hints = self._scheduler_hints(
self.properties[self.SCHEDULER_HINTS])
nics = self._build_nics(self.properties[self.NETWORKS])
block_device_mapping = self._build_block_device_mapping(
self.properties[self.BLOCK_DEVICE_MAPPING])
block_device_mapping_v2 = self._build_block_device_mapping_v2(
self.properties[self.BLOCK_DEVICE_MAPPING_V2])
reservation_id = self.properties[self.RESERVATION_ID]
disk_config = self.properties[self.DISK_CONFIG]
admin_pass = self.properties[self.ADMIN_PASS] or None
personality_files = self.properties[self.PERSONALITY]
key_name = self.properties[self.KEY_NAME]
server = None
try:
server = self.client().servers.create(
name=self._server_name(),
image=image,
flavor=flavor_id,
key_name=key_name,
security_groups=security_groups,
userdata=userdata,
meta=instance_meta,
scheduler_hints=scheduler_hints,
nics=nics,
availability_zone=availability_zone,
block_device_mapping=block_device_mapping,
block_device_mapping_v2=block_device_mapping_v2,
reservation_id=reservation_id,
config_drive=self._config_drive(),
disk_config=disk_config,
files=personality_files,
admin_pass=admin_pass)
finally:
# Avoid a race condition where the thread could be canceled
# before the ID is stored
if server is not None:
self.resource_id_set(server.id)
return server.id
def check_create_complete(self, server_id):
check = self.client_plugin()._check_active(server_id)
if check:
self.store_external_ports()
return check
def handle_check(self):
server = self.client().servers.get(self.resource_id)
status = self.client_plugin().get_status(server)
checks = [{'attr': 'status', 'expected': 'ACTIVE', 'current': status}]
self._verify_check_conditions(checks)
@classmethod
def _build_block_device_mapping(cls, bdm):
if not bdm:
return None
bdm_dict = {}
for mapping in bdm:
mapping_parts = []
snapshot_id = mapping.get(cls.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
if snapshot_id:
mapping_parts.append(snapshot_id)
mapping_parts.append('snap')
else:
volume_id = mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_ID)
mapping_parts.append(volume_id)
mapping_parts.append('')
volume_size = mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_SIZE)
delete = mapping.get(cls.BLOCK_DEVICE_MAPPING_DELETE_ON_TERM)
if volume_size:
mapping_parts.append(str(volume_size))
else:
mapping_parts.append('')
if delete:
mapping_parts.append(str(delete))
device_name = mapping.get(cls.BLOCK_DEVICE_MAPPING_DEVICE_NAME)
bdm_dict[device_name] = ':'.join(mapping_parts)
return bdm_dict
@classmethod
def _build_block_device_mapping_v2(cls, bdm_v2):
if not bdm_v2:
return None
bdm_v2_list = []
for mapping in bdm_v2:
bmd_dict = None
if mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_ID):
bmd_dict = {
'uuid': mapping.get(cls.BLOCK_DEVICE_MAPPING_VOLUME_ID),
'source_type': 'volume',
'destination_type': 'volume',
'boot_index': 0,
'delete_on_termination': False,
}
elif mapping.get(cls.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID):
bmd_dict = {
'uuid': mapping.get(cls.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID),
'source_type': 'snapshot',
'destination_type': 'volume',
'boot_index': 0,
'delete_on_termination': False,
}
elif mapping.get(cls.BLOCK_DEVICE_MAPPING_IMAGE_ID):
bmd_dict = {
'uuid': mapping.get(cls.BLOCK_DEVICE_MAPPING_IMAGE_ID),
'source_type': 'image',
'destination_type': 'volume',
'boot_index': 0,
'delete_on_termination': False,
}
elif mapping.get(cls.BLOCK_DEVICE_MAPPING_SWAP_SIZE):
bmd_dict = {
'source_type': 'blank',
'destination_type': 'local',
'boot_index': -1,
'delete_on_termination': True,
'guest_format': 'swap',
'volume_size': mapping.get(
cls.BLOCK_DEVICE_MAPPING_SWAP_SIZE),
}
# NOTE(prazumovsky): In case of server doesn't take empty value of
device_name = mapping.get(cls.BLOCK_DEVICE_MAPPING_DEVICE_NAME)
if device_name:
bmd_dict[cls.BLOCK_DEVICE_MAPPING_DEVICE_NAME] = device_name
update_props = (cls.BLOCK_DEVICE_MAPPING_DEVICE_TYPE,
cls.BLOCK_DEVICE_MAPPING_DISK_BUS,
cls.BLOCK_DEVICE_MAPPING_BOOT_INDEX,
cls.BLOCK_DEVICE_MAPPING_VOLUME_SIZE,
cls.BLOCK_DEVICE_MAPPING_DELETE_ON_TERM)
for update_prop in update_props:
if mapping.get(update_prop) is not None:
bmd_dict[update_prop] = mapping.get(update_prop)
if bmd_dict:
bdm_v2_list.append(bmd_dict)
return bdm_v2_list
def _add_port_for_address(self, server):
nets = copy.deepcopy(server.addresses)
ifaces = server.interface_list()
ip_mac_mapping_on_port_id = dict(((iface.fixed_ips[0]['ip_address'],
iface.mac_addr), iface.port_id)
for iface in ifaces)
for net_name in nets:
for addr in nets[net_name]:
addr['port'] = ip_mac_mapping_on_port_id.get(
(addr['addr'], addr['OS-EXT-IPS-MAC:mac_addr']))
return self._extend_networks(nets)
def _extend_networks(self, networks):
nets = copy.deepcopy(networks)
for key in list(nets.keys()):
try:
net_id = self.client_plugin().get_net_id_by_label(key)
except (exception.NovaNetworkNotFound,
exception.PhysicalResourceNameAmbiguity):
net_id = None
if net_id:
nets[net_id] = nets[key]
return nets
def _resolve_attribute(self, name):
if name == self.FIRST_ADDRESS:
return self.client_plugin().server_to_ipaddress(
self.resource_id) or ''
if name == self.NAME_ATTR:
return self._server_name()
try:
server = self.client().servers.get(self.resource_id)
except Exception as e:
self.client_plugin().ignore_not_found(e)
return ''
if name == self.ADDRESSES:
return self._add_port_for_address(server)
if name == self.NETWORKS_ATTR:
return self._extend_networks(server.networks)
if name == self.INSTANCE_NAME:
return getattr(server, 'OS-EXT-SRV-ATTR:instance_name', None)
if name == self.ACCESSIPV4:
return server.accessIPv4
if name == self.ACCESSIPV6:
return server.accessIPv6
if name == self.CONSOLE_URLS:
return self.client_plugin('nova').get_console_urls(server)
def add_dependencies(self, deps):
super(Server, self).add_dependencies(deps)
nets = self.properties[self.NETWORKS]
if not nets:
return
for res in six.itervalues(self.stack):
if res.has_interface('OS::Neutron::Subnet'):
subnet_net = (res.properties.get(subnet.Subnet.NETWORK_ID)
or res.properties.get(subnet.Subnet.NETWORK))
for net in nets:
net_id = (net.get(self.NETWORK_ID) or
net.get(self.NETWORK_UUID))
if net_id and net_id == subnet_net:
deps += (self, res)
break
def _update_flavor(self, prop_diff):
flavor = prop_diff[self.FLAVOR]
flavor_id = self.client_plugin().get_flavor_id(flavor)
handler_args = {'args': (flavor_id,)}
checker_args = {'args': (flavor_id, flavor)}
prg_resize = progress.ServerUpdateProgress(self.resource_id,
'resize',
handler_extra=handler_args,
checker_extra=checker_args)
prg_verify = progress.ServerUpdateProgress(self.resource_id,
'verify_resize')
return prg_resize, prg_verify
def _update_image(self, prop_diff):
image_update_policy = (
prop_diff.get(self.IMAGE_UPDATE_POLICY) or
self.properties[self.IMAGE_UPDATE_POLICY])
image = prop_diff[self.IMAGE]
image_id = self.client_plugin('glance').get_image_id(image)
preserve_ephemeral = (
image_update_policy == 'REBUILD_PRESERVE_EPHEMERAL')
password = (prop_diff.get(self.ADMIN_PASS) or
self.properties[self.ADMIN_PASS])
kwargs = {'password': password,
'preserve_ephemeral': preserve_ephemeral}
prg = progress.ServerUpdateProgress(self.resource_id,
'rebuild',
handler_extra={'args': (image_id,),
'kwargs': kwargs})
return prg
def _update_networks(self, server, prop_diff):
updaters = []
new_networks = prop_diff.get(self.NETWORKS)
old_networks = self.properties[self.NETWORKS]
if not server:
server = self.client().servers.get(self.resource_id)
interfaces = server.interface_list()
remove_ports, add_nets = self.calculate_networks(
old_networks, new_networks, interfaces)
for port in remove_ports:
updaters.append(
progress.ServerUpdateProgress(
self.resource_id, 'interface_detach',
complete=True,
handler_extra={'args': (port,)})
)
for args in add_nets:
updaters.append(
progress.ServerUpdateProgress(
self.resource_id, 'interface_attach',
complete=True,
handler_extra={'kwargs': args})
)
return updaters
def _needs_update(self, after, before, after_props, before_props,
prev_resource, check_init_complete=True):
result = super(Server, self)._needs_update(
after, before, after_props, before_props, prev_resource,
check_init_complete=check_init_complete)
prop_diff = self.update_template_diff_properties(after_props,
before_props)
if self.FLAVOR in prop_diff:
flavor_update_policy = (
prop_diff.get(self.FLAVOR_UPDATE_POLICY) or
self.properties[self.FLAVOR_UPDATE_POLICY])
if flavor_update_policy == 'REPLACE':
raise exception.UpdateReplace(self.name)
if self.IMAGE in prop_diff:
image_update_policy = (
prop_diff.get(self.IMAGE_UPDATE_POLICY) or
self.properties[self.IMAGE_UPDATE_POLICY])
if image_update_policy == 'REPLACE':
raise exception.UpdateReplace(self.name)
return result
def handle_update(self, json_snippet, tmpl_diff, prop_diff):
if 'Metadata' in tmpl_diff:
if self.user_data_software_config():
metadata = self.metadata_get(True) or {}
new_occ_md = tmpl_diff['Metadata'].get('os-collect-config', {})
occ_md = metadata.get('os-collect-config', {})
occ_md.update(new_occ_md)
tmpl_diff['Metadata']['os-collect-config'] = occ_md
deployment_md = metadata.get('deployments', [])
tmpl_diff['Metadata']['deployments'] = deployment_md
self.metadata_set(tmpl_diff['Metadata'])
updaters = []
server = None
if self.METADATA in prop_diff:
server = self.client().servers.get(self.resource_id)
self.client_plugin().meta_update(server,
prop_diff[self.METADATA])
if self.FLAVOR in prop_diff:
updaters.extend(self._update_flavor(prop_diff))
if self.IMAGE in prop_diff:
updaters.append(self._update_image(prop_diff))
elif self.ADMIN_PASS in prop_diff:
if not server:
server = self.client().servers.get(self.resource_id)
server.change_password(prop_diff[self.ADMIN_PASS])
if self.NAME in prop_diff:
if not server:
server = self.client().servers.get(self.resource_id)
self.client_plugin().rename(server, prop_diff[self.NAME])
if self.NETWORKS in prop_diff:
updaters.extend(self._update_networks(server, prop_diff))
# have called several APIs
return updaters
def check_update_complete(self, updaters):
for prg in updaters:
if not prg.called:
handler = getattr(self.client_plugin(), prg.handler)
prg.called = handler(*prg.handler_args,
**prg.handler_kwargs)
return False
if not prg.complete:
check_complete = getattr(self.client_plugin(), prg.checker)
prg.complete = check_complete(*prg.checker_args,
**prg.checker_kwargs)
break
status = all(prg.complete for prg in updaters)
if status:
self.store_external_ports()
return status
def metadata_update(self, new_metadata=None):
if new_metadata is None:
# Re-resolve the template metadata and merge it with the
# current resource metadata. This is necessary because the
# attributes referenced in the template metadata may change
# and the resource itself adds keys to the metadata which
# are not specified in the template (e.g the deployments data)
meta = self.metadata_get(refresh=True) or {}
tmpl_meta = self.t.metadata()
meta.update(tmpl_meta)
self.metadata_set(meta)
@staticmethod
def _check_maximum(count, maximum, msg):
if maximum != -1 and count > maximum:
raise exception.StackValidationFailed(message=msg)
def _validate_block_device_mapping(self):
# either volume_id or snapshot_id needs to be specified, but not both
# for block device mapping.
bdm = self.properties[self.BLOCK_DEVICE_MAPPING] or []
bootable_vol = False
for mapping in bdm:
device_name = mapping[self.BLOCK_DEVICE_MAPPING_DEVICE_NAME]
if device_name == 'vda':
bootable_vol = True
volume_id = mapping.get(self.BLOCK_DEVICE_MAPPING_VOLUME_ID)
snapshot_id = mapping.get(self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
if volume_id is not None and snapshot_id is not None:
raise exception.ResourcePropertyConflict(
self.BLOCK_DEVICE_MAPPING_VOLUME_ID,
self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
if volume_id is None and snapshot_id is None:
msg = _('Either volume_id or snapshot_id must be specified for'
' device mapping %s') % device_name
raise exception.StackValidationFailed(message=msg)
bdm_v2 = self.properties[self.BLOCK_DEVICE_MAPPING_V2] or []
if bdm and bdm_v2:
raise exception.ResourcePropertyConflict(
self.BLOCK_DEVICE_MAPPING, self.BLOCK_DEVICE_MAPPING_V2)
for mapping in bdm_v2:
volume_id = mapping.get(self.BLOCK_DEVICE_MAPPING_VOLUME_ID)
snapshot_id = mapping.get(self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID)
image_id = mapping.get(self.BLOCK_DEVICE_MAPPING_IMAGE_ID)
swap_size = mapping.get(self.BLOCK_DEVICE_MAPPING_SWAP_SIZE)
property_tuple = (volume_id, snapshot_id, image_id, swap_size)
if property_tuple.count(None) < 3:
raise exception.ResourcePropertyConflict(
self.BLOCK_DEVICE_MAPPING_VOLUME_ID,
self.BLOCK_DEVICE_MAPPING_SNAPSHOT_ID,
self.BLOCK_DEVICE_MAPPING_IMAGE_ID,
self.BLOCK_DEVICE_MAPPING_SWAP_SIZE)
if property_tuple.count(None) == 4:
msg = _('Either volume_id, snapshot_id, image_id or '
'swap_size must be specified.')
raise exception.StackValidationFailed(message=msg)
if any((volume_id, snapshot_id, image_id)):
bootable_vol = True
return bootable_vol
def validate(self):
super(Server, self).validate()
if self.user_data_software_config():
if 'deployments' in self.t.metadata():
msg = _('deployments key not allowed in resource metadata '
'with user_data_format of SOFTWARE_CONFIG')
raise exception.StackValidationFailed(message=msg)
bootable_vol = self._validate_block_device_mapping()
# make sure the image exists if specified.
image = self.properties[self.IMAGE]
if not image and not bootable_vol:
msg = _('Neither image nor bootable volume is specified for'
' instance %s') % self.name
raise exception.StackValidationFailed(message=msg)
# network properties 'uuid' and 'network' shouldn't be used
networks = self.properties[self.NETWORKS] or []
networks_with_port = False
for network in networks:
networks_with_port = (networks_with_port or
network.get(self.NETWORK_PORT))
self._validate_network(network)
metadata = self.properties[self.METADATA]
personality = self.properties[self.PERSONALITY]
if metadata is not None or personality:
limits = self.client_plugin().absolute_limits()
# if 'security_groups' present for the server and explict 'port'
# in one or more entries in 'networks', raise validation error
if networks_with_port and self.properties[self.SECURITY_GROUPS]:
raise exception.ResourcePropertyConflict(
self.SECURITY_GROUPS,
"/".join([self.NETWORKS, self.NETWORK_PORT]))
# verify that the number of metadata entries is not greater
# than the maximum number allowed in the provider's absolute
if metadata is not None:
msg = _('Instance metadata must not contain greater than %s '
'entries. This is the maximum number allowed by your '
'service provider') % limits['maxServerMeta']
self._check_maximum(len(metadata),
limits['maxServerMeta'], msg)
if personality:
msg = _("The personality property may not contain "
"greater than %s entries.") % limits['maxPersonality']
self._check_maximum(len(personality),
limits['maxPersonality'], msg)
for path, contents in personality.items():
msg = (_("The contents of personality file \"%(path)s\" "
"is larger than the maximum allowed personality "
"file size (%(max_size)s bytes).") %
{'path': path,
'max_size': limits['maxPersonalitySize']})
self._check_maximum(len(bytes(contents.encode('utf-8'))),
limits['maxPersonalitySize'], msg)
def _delete_temp_url(self):
object_name = self.data().get('metadata_object_name')
if not object_name:
return
try:
container = self.physical_resource_name()
swift = self.client('swift')
swift.delete_object(container, object_name)
headers = swift.head_container(container)
if int(headers['x-container-object-count']) == 0:
swift.delete_container(container)
except Exception as ex:
self.client_plugin('swift').ignore_not_found(ex)
def _delete_queue(self):
queue_id = self.data().get('metadata_queue_id')
if not queue_id:
return
client_plugin = self.client_plugin('zaqar')
zaqar = client_plugin.create_for_tenant(
self.stack.stack_user_project_id)
try:
zaqar.queue(queue_id).delete()
except Exception as ex:
client_plugin.ignore_not_found(ex)
self.data_delete('metadata_queue_id')
def handle_snapshot_delete(self, state):
if state[0] != self.FAILED:
image_id = self.client().servers.create_image(
self.resource_id, self.physical_resource_name())
return progress.ServerDeleteProgress(
self.resource_id, image_id, False)
return self.handle_delete()
def handle_delete(self):
if self.resource_id is None:
return
if self.user_data_software_config():
self._delete_user()
self._delete_temp_url()
self._delete_queue()
# remove internal and external ports
self._delete_internal_ports()
self.data_delete('external_ports')
try:
self.client().servers.delete(self.resource_id)
except Exception as e:
self.client_plugin().ignore_not_found(e)
return
return progress.ServerDeleteProgress(self.resource_id)
def check_delete_complete(self, prg):
if not prg:
return True
if not prg.image_complete:
image = self.client().images.get(prg.image_id)
if image.status in ('DELETED', 'ERROR'):
raise exception.Error(image.status)
elif image.status == 'ACTIVE':
prg.image_complete = True
if not self.handle_delete():
return True
return False
return self.client_plugin().check_delete_server_complete(
prg.server_id)
def handle_suspend(self):
if self.resource_id is None:
raise exception.Error(_('Cannot suspend %s, resource_id not set') %
self.name)
try:
server = self.client().servers.get(self.resource_id)
except Exception as e:
if self.client_plugin().is_not_found(e):
raise exception.NotFound(_('Failed to find server %s') %
self.resource_id)
else:
raise
else:
# if the server has been suspended successful,
# no need to suspend again
if self.client_plugin().get_status(server) != 'SUSPENDED':
LOG.debug('suspending server %s' % self.resource_id)
server.suspend()
return server.id
def check_suspend_complete(self, server_id):
cp = self.client_plugin()
server = cp.fetch_server(server_id)
if not server:
return False
status = cp.get_status(server)
LOG.debug('%(name)s check_suspend_complete status = %(status)s'
% {'name': self.name, 'status': status})
if status in list(cp.deferred_server_statuses + ['ACTIVE']):
return status == 'SUSPENDED'
else:
exc = exception.ResourceUnknownStatus(
result=_('Suspend of server %s failed') % server.name,
resource_status=status)
raise exc
def handle_resume(self):
if self.resource_id is None:
raise exception.Error(_('Cannot resume %s, resource_id not set') %
self.name)
try:
server = self.client().servers.get(self.resource_id)
except Exception as e:
if self.client_plugin().is_not_found(e):
raise exception.NotFound(_('Failed to find server %s') %
self.resource_id)
else:
raise
else:
# if the server has been resumed successful,
# no need to resume again
if self.client_plugin().get_status(server) != 'ACTIVE':
LOG.debug('resuming server %s' % self.resource_id)
server.resume()
return server.id
def check_resume_complete(self, server_id):
return self.client_plugin()._check_active(server_id)
def handle_snapshot(self):
image_id = self.client().servers.create_image(
self.resource_id, self.physical_resource_name())
self.data_set('snapshot_image_id', image_id)
return image_id
def check_snapshot_complete(self, image_id):
image = self.client().images.get(image_id)
if image.status == 'ACTIVE':
return True
elif image.status == 'ERROR' or image.status == 'DELETED':
raise exception.Error(image.status)
return False
def handle_delete_snapshot(self, snapshot):
image_id = snapshot['resource_data'].get('snapshot_image_id')
try:
self.client().images.delete(image_id)
except Exception as e:
self.client_plugin().ignore_not_found(e)
def handle_restore(self, defn, restore_data):
image_id = restore_data['resource_data']['snapshot_image_id']
props = function.resolve(self.properties.data)
props[self.IMAGE] = image_id
return defn.freeze(properties=props)
def prepare_for_replace(self):
self.prepare_ports_for_replace()
def restore_prev_rsrc(self, convergence=False):
self.restore_ports_after_rollback(convergence=convergence)
def resource_mapping():
return {
'OS::Nova::Server': Server,
}
| true | true |
f72c9842321b24921292819e0294421b24b2f549 | 3,305 | py | Python | src/draw.py | lRomul/argus-bengali-ai | e64374230f5390a17305769126ff4bfc9a2a8644 | [
"MIT"
] | 2 | 2020-05-08T09:25:38.000Z | 2020-10-04T16:15:29.000Z | src/draw.py | lRomul/argus-bengali-ai | e64374230f5390a17305769126ff4bfc9a2a8644 | [
"MIT"
] | 2 | 2022-01-13T03:19:24.000Z | 2022-03-12T00:48:13.000Z | src/draw.py | lRomul/argus-bengali-ai | e64374230f5390a17305769126ff4bfc9a2a8644 | [
"MIT"
] | null | null | null | import time
import random
import numpy as np
from pathlib import Path
from PIL import Image, ImageDraw, ImageFont, ImageFilter
import torch
from torch.utils.data import Dataset
from src import config
def draw_grapheme(grapheme, font_path, size=(137, 236)):
height, width = size
image = Image.new('RGB', (width, height))
draw = ImageDraw.Draw(image)
font_size = np.random.randint(70, 110)
font = ImageFont.truetype(str(font_path), font_size)
w, h = draw.textsize(grapheme, font=font)
width_ratio = np.random.uniform(1.5, 2.5)
height_ratio = np.random.uniform(2.5, 3.5)
fill = np.random.randint(200, 255)
draw.text(((width - w) / width_ratio, (height - h) / height_ratio),
grapheme, font=font, fill=fill)
image = image.filter(ImageFilter.BLUR)
return np.array(image)[:, :, 0]
def get_draw_data():
graphemes = []
for grapheme_root_idx, grapheme_root in config.class_map['grapheme_root'].items():
for vowel_diacritic_idx, vowel_diacritic in config.class_map['vowel_diacritic'].items():
for consonant_diacritic_idx, consonant_diacritic in config.class_map['consonant_diacritic'].items():
consonant_diacritic, grapheme_root, vowel_diacritic = [c if c != '0' else '' for c in
[consonant_diacritic, grapheme_root,
vowel_diacritic]]
grapheme = consonant_diacritic + grapheme_root + vowel_diacritic
graphemes.append({
'grapheme': grapheme,
'grapheme_root': grapheme_root_idx,
'vowel_diacritic': vowel_diacritic_idx,
'consonant_diacritic': consonant_diacritic_idx
})
return graphemes
class BengaliDrawDataset(Dataset):
def __init__(self,
fonts_dir,
transform=None,
mixer=None):
self.fonts_dir = fonts_dir
self.transform = transform
self.mixer = mixer
self.data = get_draw_data()
self.font_paths = sorted(Path(fonts_dir).glob('*.ttf'))
def __len__(self):
return len(self.data)
def get_sample(self, idx):
sample = self.data[idx]
font_path = np.random.choice(self.font_paths)
image = draw_grapheme(sample['grapheme'], font_path,
size=config.raw_image_shape)
grapheme = torch.tensor(sample['grapheme_root'], dtype=torch.int64)
vowel = torch.tensor(sample['vowel_diacritic'], dtype=torch.int64)
consonant = torch.tensor(sample['consonant_diacritic'], dtype=torch.int64)
target = grapheme, vowel, consonant
return image, target
def _set_random_seed(self, idx):
seed = int(time.time() * 1000.0) + idx
random.seed(seed)
np.random.seed(seed % (2**32 - 1))
@torch.no_grad()
def __getitem__(self, idx):
self._set_random_seed(idx)
image, target = self.get_sample(idx)
if self.mixer is not None:
image, target = self.mixer(self, image, target)
if self.transform is not None:
image = self.transform(image)
return image, target
| 36.318681 | 112 | 0.607867 | import time
import random
import numpy as np
from pathlib import Path
from PIL import Image, ImageDraw, ImageFont, ImageFilter
import torch
from torch.utils.data import Dataset
from src import config
def draw_grapheme(grapheme, font_path, size=(137, 236)):
height, width = size
image = Image.new('RGB', (width, height))
draw = ImageDraw.Draw(image)
font_size = np.random.randint(70, 110)
font = ImageFont.truetype(str(font_path), font_size)
w, h = draw.textsize(grapheme, font=font)
width_ratio = np.random.uniform(1.5, 2.5)
height_ratio = np.random.uniform(2.5, 3.5)
fill = np.random.randint(200, 255)
draw.text(((width - w) / width_ratio, (height - h) / height_ratio),
grapheme, font=font, fill=fill)
image = image.filter(ImageFilter.BLUR)
return np.array(image)[:, :, 0]
def get_draw_data():
graphemes = []
for grapheme_root_idx, grapheme_root in config.class_map['grapheme_root'].items():
for vowel_diacritic_idx, vowel_diacritic in config.class_map['vowel_diacritic'].items():
for consonant_diacritic_idx, consonant_diacritic in config.class_map['consonant_diacritic'].items():
consonant_diacritic, grapheme_root, vowel_diacritic = [c if c != '0' else '' for c in
[consonant_diacritic, grapheme_root,
vowel_diacritic]]
grapheme = consonant_diacritic + grapheme_root + vowel_diacritic
graphemes.append({
'grapheme': grapheme,
'grapheme_root': grapheme_root_idx,
'vowel_diacritic': vowel_diacritic_idx,
'consonant_diacritic': consonant_diacritic_idx
})
return graphemes
class BengaliDrawDataset(Dataset):
def __init__(self,
fonts_dir,
transform=None,
mixer=None):
self.fonts_dir = fonts_dir
self.transform = transform
self.mixer = mixer
self.data = get_draw_data()
self.font_paths = sorted(Path(fonts_dir).glob('*.ttf'))
def __len__(self):
return len(self.data)
def get_sample(self, idx):
sample = self.data[idx]
font_path = np.random.choice(self.font_paths)
image = draw_grapheme(sample['grapheme'], font_path,
size=config.raw_image_shape)
grapheme = torch.tensor(sample['grapheme_root'], dtype=torch.int64)
vowel = torch.tensor(sample['vowel_diacritic'], dtype=torch.int64)
consonant = torch.tensor(sample['consonant_diacritic'], dtype=torch.int64)
target = grapheme, vowel, consonant
return image, target
def _set_random_seed(self, idx):
seed = int(time.time() * 1000.0) + idx
random.seed(seed)
np.random.seed(seed % (2**32 - 1))
@torch.no_grad()
def __getitem__(self, idx):
self._set_random_seed(idx)
image, target = self.get_sample(idx)
if self.mixer is not None:
image, target = self.mixer(self, image, target)
if self.transform is not None:
image = self.transform(image)
return image, target
| true | true |
f72c98f625fd6ff9df578e247df919138a312028 | 1,260 | py | Python | selectionsort.py | maxProgrammer/Entendendo_Algoritmos | 8bc6ef9b7869150ef624333490b68d94b197cb75 | [
"MIT"
] | null | null | null | selectionsort.py | maxProgrammer/Entendendo_Algoritmos | 8bc6ef9b7869150ef624333490b68d94b197cb75 | [
"MIT"
] | null | null | null | selectionsort.py | maxProgrammer/Entendendo_Algoritmos | 8bc6ef9b7869150ef624333490b68d94b197cb75 | [
"MIT"
] | null | null | null | #algoritmo utilizado para ordenação de uma lista.
#a cada execução ele percorre toda lista e coloca o menor na posição (n-1)
def encontraMenor(lista):
#armazena o valor do indice 0 a variavel
menorValor = lista[0]
#considera que index zero tem o menor valor
menorIndex = 0
#percorre lista do indice 1 ao ultimo
for i in range(1,len(lista) - 1):
#compra se lista[i] é menor que menor valor e
#se verdadeiro atualiza menorValor e menorIndex
if lista[i] < menorValor:
menorValor = lista[i]
menorIndex = i
#retorna o index do menor valor encontrado
return menorIndex
#funcao que utiliza a funcaencontra menor
#para gerar outra lista ordenada
def ordenaSelecao(lista):
#lista que receberá itens ordenados
ordLista = []
#percorre todos elementos da lista
for x in range(len(lista)):
# a cada iteracao encontra menor item e o insere
# na nova lista. Funcao pop armazena o item na nova lista
# e apaga na antiga ao mesmo tempo.
menor = encontraMenor(lista)
ordLista.append(lista.pop(menor))
#retorna nova lista ordenada
return ordLista
#teste programa
lista = [3,1,13,5,0,100]
print(ordenaSelecao(lista))
| 28 | 74 | 0.674603 |
def encontraMenor(lista):
menorValor = lista[0]
menorIndex = 0
for i in range(1,len(lista) - 1):
if lista[i] < menorValor:
menorValor = lista[i]
menorIndex = i
return menorIndex
def ordenaSelecao(lista):
ordLista = []
for x in range(len(lista)):
menor = encontraMenor(lista)
ordLista.append(lista.pop(menor))
return ordLista
lista = [3,1,13,5,0,100]
print(ordenaSelecao(lista))
| true | true |
f72c99bc69fba8eb8ab5186eeff081f18b9e24a7 | 3,124 | py | Python | src/dataset_prepare.py | dd-dos/Emotion-detection | 23eb94cbceb70890cf6b0f63e84d80eae7336c85 | [
"MIT"
] | null | null | null | src/dataset_prepare.py | dd-dos/Emotion-detection | 23eb94cbceb70890cf6b0f63e84d80eae7336c85 | [
"MIT"
] | null | null | null | src/dataset_prepare.py | dd-dos/Emotion-detection | 23eb94cbceb70890cf6b0f63e84d80eae7336c85 | [
"MIT"
] | null | null | null | import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
import os
# convert string to integer
def atoi(s):
n = 0
for i in s:
n = n*10 + ord(i) - ord("0")
return n
# making folders
outer_names = ['test','train']
inner_names = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral']
os.makedirs('data', exist_ok=True)
for outer_name in outer_names:
os.makedirs(os.path.join('data',outer_name), exist_ok=True)
for inner_name in inner_names:
os.makedirs(os.path.join('data',outer_name,inner_name), exist_ok=True)
# to keep count of each category
angry = 0
disgusted = 0
fearful = 0
happy = 0
sad = 0
surprised = 0
neutral = 0
angry_test = 0
disgusted_test = 0
fearful_test = 0
happy_test = 0
sad_test = 0
surprised_test = 0
neutral_test = 0
df = pd.read_csv('./fer2013.csv')
mat = np.zeros((48,48),dtype=np.uint8)
print("Saving images...")
# read the csv file line by line
for i in tqdm(range(len(df))):
txt = df['pixels'][i]
words = txt.split()
# the image size is 48x48
for j in range(2304):
xind = j // 48
yind = j % 48
mat[xind][yind] = atoi(words[j])
img = Image.fromarray(mat)
# train
if i < 28709:
if df['emotion'][i] == 0:
img.save('./data/train/angry/im'+str(angry)+'.png')
angry += 1
elif df['emotion'][i] == 1:
img.save('./data/train/disgusted/im'+str(disgusted)+'.png')
disgusted += 1
elif df['emotion'][i] == 2:
img.save('./data/train/fearful/im'+str(fearful)+'.png')
fearful += 1
elif df['emotion'][i] == 3:
img.save('./data/train/happy/im'+str(happy)+'.png')
happy += 1
elif df['emotion'][i] == 4:
img.save('./data/train/sad/im'+str(sad)+'.png')
sad += 1
elif df['emotion'][i] == 5:
img.save('./data/train/surprised/im'+str(surprised)+'.png')
surprised += 1
elif df['emotion'][i] == 6:
img.save('./data/train/neutral/im'+str(neutral)+'.png')
neutral += 1
# test
else:
if df['emotion'][i] == 0:
img.save('./data/test/angry/im'+str(angry_test)+'.png')
angry_test += 1
elif df['emotion'][i] == 1:
img.save('./data/test/disgusted/im'+str(disgusted_test)+'.png')
disgusted_test += 1
elif df['emotion'][i] == 2:
img.save('./data/test/fearful/im'+str(fearful_test)+'.png')
fearful_test += 1
elif df['emotion'][i] == 3:
img.save('./data/test/happy/im'+str(happy_test)+'.png')
happy_test += 1
elif df['emotion'][i] == 4:
img.save('./data/test/sad/im'+str(sad_test)+'.png')
sad_test += 1
elif df['emotion'][i] == 5:
img.save('./data/test/surprised/im'+str(surprised_test)+'.png')
surprised_test += 1
elif df['emotion'][i] == 6:
img.save('./data/test/neutral/im'+str(neutral_test)+'.png')
neutral_test += 1
print("Done!") | 30.330097 | 87 | 0.546735 | import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
import os
def atoi(s):
n = 0
for i in s:
n = n*10 + ord(i) - ord("0")
return n
outer_names = ['test','train']
inner_names = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral']
os.makedirs('data', exist_ok=True)
for outer_name in outer_names:
os.makedirs(os.path.join('data',outer_name), exist_ok=True)
for inner_name in inner_names:
os.makedirs(os.path.join('data',outer_name,inner_name), exist_ok=True)
angry = 0
disgusted = 0
fearful = 0
happy = 0
sad = 0
surprised = 0
neutral = 0
angry_test = 0
disgusted_test = 0
fearful_test = 0
happy_test = 0
sad_test = 0
surprised_test = 0
neutral_test = 0
df = pd.read_csv('./fer2013.csv')
mat = np.zeros((48,48),dtype=np.uint8)
print("Saving images...")
for i in tqdm(range(len(df))):
txt = df['pixels'][i]
words = txt.split()
for j in range(2304):
xind = j // 48
yind = j % 48
mat[xind][yind] = atoi(words[j])
img = Image.fromarray(mat)
if i < 28709:
if df['emotion'][i] == 0:
img.save('./data/train/angry/im'+str(angry)+'.png')
angry += 1
elif df['emotion'][i] == 1:
img.save('./data/train/disgusted/im'+str(disgusted)+'.png')
disgusted += 1
elif df['emotion'][i] == 2:
img.save('./data/train/fearful/im'+str(fearful)+'.png')
fearful += 1
elif df['emotion'][i] == 3:
img.save('./data/train/happy/im'+str(happy)+'.png')
happy += 1
elif df['emotion'][i] == 4:
img.save('./data/train/sad/im'+str(sad)+'.png')
sad += 1
elif df['emotion'][i] == 5:
img.save('./data/train/surprised/im'+str(surprised)+'.png')
surprised += 1
elif df['emotion'][i] == 6:
img.save('./data/train/neutral/im'+str(neutral)+'.png')
neutral += 1
else:
if df['emotion'][i] == 0:
img.save('./data/test/angry/im'+str(angry_test)+'.png')
angry_test += 1
elif df['emotion'][i] == 1:
img.save('./data/test/disgusted/im'+str(disgusted_test)+'.png')
disgusted_test += 1
elif df['emotion'][i] == 2:
img.save('./data/test/fearful/im'+str(fearful_test)+'.png')
fearful_test += 1
elif df['emotion'][i] == 3:
img.save('./data/test/happy/im'+str(happy_test)+'.png')
happy_test += 1
elif df['emotion'][i] == 4:
img.save('./data/test/sad/im'+str(sad_test)+'.png')
sad_test += 1
elif df['emotion'][i] == 5:
img.save('./data/test/surprised/im'+str(surprised_test)+'.png')
surprised_test += 1
elif df['emotion'][i] == 6:
img.save('./data/test/neutral/im'+str(neutral_test)+'.png')
neutral_test += 1
print("Done!") | true | true |
f72c9a79f61fe1255118ac76e5e76311780f9ee8 | 1,612 | py | Python | demos/grouped_mr_heart/demo_predict.py | mathpluscode/DeepReg | 80854094feafec998fa6237199066556c73f31f9 | [
"Apache-2.0"
] | null | null | null | demos/grouped_mr_heart/demo_predict.py | mathpluscode/DeepReg | 80854094feafec998fa6237199066556c73f31f9 | [
"Apache-2.0"
] | null | null | null | demos/grouped_mr_heart/demo_predict.py | mathpluscode/DeepReg | 80854094feafec998fa6237199066556c73f31f9 | [
"Apache-2.0"
] | null | null | null | import argparse
from datetime import datetime
from deepreg.predict import predict
name = "grouped_mr_heart"
# parser is used to simplify testing, by default it is not used
# please run the script with --no-test flag to ensure non-testing mode
# for instance:
# python script.py --no-test
parser = argparse.ArgumentParser()
parser.add_argument(
"--test",
help="Execute the script for test purpose",
dest="test",
action="store_true",
)
parser.add_argument(
"--no-test",
help="Execute the script for non-test purpose",
dest="test",
action="store_false",
)
parser.set_defaults(test=False)
args = parser.parse_args()
print(
"\n\n\n\n\n"
"=========================================================\n"
"The prediction can also be launched using the following command.\n"
"deepreg_predict --gpu '' "
f"--config_path demos/{name}/{name}.yaml "
f"--ckpt_path demos/{name}/dataset/pretrained/ckpt-4000 "
f"--log_root demos/{name} "
"--log_dir logs_predict "
"--save_png --mode test\n"
"=========================================================\n"
"\n\n\n\n\n"
)
log_root = f"demos/{name}"
log_dir = "logs_predict/" + datetime.now().strftime("%Y%m%d-%H%M%S")
ckpt_path = f"{log_root}/dataset/pretrained/ckpt-4000"
config_path = [f"{log_root}/{name}.yaml"]
if args.test:
config_path.append("config/test/demo_unpaired_grouped.yaml")
predict(
gpu="0",
gpu_allow_growth=True,
ckpt_path=ckpt_path,
mode="test",
batch_size=1,
log_root=log_root,
log_dir=log_dir,
sample_label="all",
config_path=config_path,
)
| 26.866667 | 72 | 0.628412 | import argparse
from datetime import datetime
from deepreg.predict import predict
name = "grouped_mr_heart"
parser = argparse.ArgumentParser()
parser.add_argument(
"--test",
help="Execute the script for test purpose",
dest="test",
action="store_true",
)
parser.add_argument(
"--no-test",
help="Execute the script for non-test purpose",
dest="test",
action="store_false",
)
parser.set_defaults(test=False)
args = parser.parse_args()
print(
"\n\n\n\n\n"
"=========================================================\n"
"The prediction can also be launched using the following command.\n"
"deepreg_predict --gpu '' "
f"--config_path demos/{name}/{name}.yaml "
f"--ckpt_path demos/{name}/dataset/pretrained/ckpt-4000 "
f"--log_root demos/{name} "
"--log_dir logs_predict "
"--save_png --mode test\n"
"=========================================================\n"
"\n\n\n\n\n"
)
log_root = f"demos/{name}"
log_dir = "logs_predict/" + datetime.now().strftime("%Y%m%d-%H%M%S")
ckpt_path = f"{log_root}/dataset/pretrained/ckpt-4000"
config_path = [f"{log_root}/{name}.yaml"]
if args.test:
config_path.append("config/test/demo_unpaired_grouped.yaml")
predict(
gpu="0",
gpu_allow_growth=True,
ckpt_path=ckpt_path,
mode="test",
batch_size=1,
log_root=log_root,
log_dir=log_dir,
sample_label="all",
config_path=config_path,
)
| true | true |
f72c9cd27adbff3953b5021bda4fe373f564264d | 2,110 | py | Python | core/helper/config.py | caostorm/smng | f1cff4010a0645ae8e1182cd3c961d97cecf4a6e | [
"MIT"
] | null | null | null | core/helper/config.py | caostorm/smng | f1cff4010a0645ae8e1182cd3c961d97cecf4a6e | [
"MIT"
] | null | null | null | core/helper/config.py | caostorm/smng | f1cff4010a0645ae8e1182cd3c961d97cecf4a6e | [
"MIT"
] | 1 | 2019-06-26T13:05:45.000Z | 2019-06-26T13:05:45.000Z | #coding=utf-8
import json
from core.helper.crypt import pwd_crypt
from core.helper.globalvar import global_const
import sys
class options_config:
class ErrorTypeNotSupport(BaseException):
def __init__(self):
pass
def __str__(self):
return "This type didn't support"
def __init__(self):
self._config_file_path = global_const().get_value('BASEDIR') + "/etc/options.json"
with open(self._config_file_path, "a+") as f:
try:
# 读取文件，从文件初始化_config对象
f.seek(0)
self._config = json.loads(f.read())
except:
self._config = {}
def _sync_file(self):
with open(self._config_file_path, "w+") as f:
f.write(json.dumps(self._config))
pass
def write(self, key, value):
obj = {}
if type(value) == type('1.2'):
# string
obj['type'] = 'string'
elif type(value) == type(1.2):
# float
obj['type'] = 'float'
elif type(value) == type(1):
# int
obj['type'] = 'int'
elif type(value) == type(True):
# bool
obj['type'] = 'bool'
else:
raise self.ErrorTypeNotSupport
encrypto = pwd_crypt()
obj['value'] = encrypto.encrypt(str(value))
self._config[key] = obj
self._sync_file()
def read(self, key):
obj = self._config[key]
encrypto = pwd_crypt()
if obj['type'] == 'string':
return str(encrypto.decrypt(obj['value']))
elif obj['type'] == 'float':
return float(encrypto.decrypt(obj['value']))
elif obj['type'] == 'int':
return int(encrypto.decrypt(obj['value']))
elif obj['type'] == 'bool':
real_value = encrypto.decrypt(obj['value'])
if 'True' == real_value:
return True
elif 'False' == real_value:
return False
elif obj['type'] == 'long':
return long(encrypto.decrypt(obj['value']))
| 30.142857 | 90 | 0.519431 |
import json
from core.helper.crypt import pwd_crypt
from core.helper.globalvar import global_const
import sys
class options_config:
class ErrorTypeNotSupport(BaseException):
def __init__(self):
pass
def __str__(self):
return "This type didn't support"
def __init__(self):
self._config_file_path = global_const().get_value('BASEDIR') + "/etc/options.json"
with open(self._config_file_path, "a+") as f:
try:
# 读取文件，从文件初始化_config对象
f.seek(0)
self._config = json.loads(f.read())
except:
self._config = {}
def _sync_file(self):
with open(self._config_file_path, "w+") as f:
f.write(json.dumps(self._config))
pass
def write(self, key, value):
obj = {}
if type(value) == type('1.2'):
# string
obj['type'] = 'string'
elif type(value) == type(1.2):
# float
obj['type'] = 'float'
elif type(value) == type(1):
# int
obj['type'] = 'int'
elif type(value) == type(True):
# bool
obj['type'] = 'bool'
else:
raise self.ErrorTypeNotSupport
encrypto = pwd_crypt()
obj['value'] = encrypto.encrypt(str(value))
self._config[key] = obj
self._sync_file()
def read(self, key):
obj = self._config[key]
encrypto = pwd_crypt()
if obj['type'] == 'string':
return str(encrypto.decrypt(obj['value']))
elif obj['type'] == 'float':
return float(encrypto.decrypt(obj['value']))
elif obj['type'] == 'int':
return int(encrypto.decrypt(obj['value']))
elif obj['type'] == 'bool':
real_value = encrypto.decrypt(obj['value'])
if 'True' == real_value:
return True
elif 'False' == real_value:
return False
elif obj['type'] == 'long':
return long(encrypto.decrypt(obj['value']))
| true | true |
f72c9d1c53416fbc1312ed7ced97e6c382733715 | 12,177 | py | Python | tensorflow_addons/layers/normalizations.py | tzachar/addons | e352207da32e4670a36a295ea477c476118cb0d9 | [
"Apache-2.0"
] | null | null | null | tensorflow_addons/layers/normalizations.py | tzachar/addons | e352207da32e4670a36a295ea477c476118cb0d9 | [
"Apache-2.0"
] | null | null | null | tensorflow_addons/layers/normalizations.py | tzachar/addons | e352207da32e4670a36a295ea477c476118cb0d9 | [
"Apache-2.0"
] | null | null | null | # 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.
# Orginal implementation from keras_contrib/layer/normalization
# =============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import tensorflow as tf
@tf.keras.utils.register_keras_serializable(package='Addons')
class GroupNormalization(tf.keras.layers.Layer):
"""Group normalization layer.
Group Normalization divides the channels into groups and computes
within each group the mean and variance for normalization.
Empirically, its accuracy is more stable than batch norm in a wide
range of small batch sizes, if learning rate is adjusted linearly
with batch sizes.
Relation to Layer Normalization:
If the number of groups is set to 1, then this operation becomes identical
to Layer Normalization.
Relation to Instance Normalization:
If the number of groups is set to the
input dimension (number of groups is equal
to number of channels), then this operation becomes
identical to Instance Normalization.
Arguments
groups: Integer, the number of groups for Group Normalization.
Can be in the range [1, N] where N is the input dimension.
The input dimension must be divisible by the number of groups.
axis: Integer, the axis that should be normalized.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: If True, multiply by `gamma`.
If False, `gamma` is not used.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: Optional constraint for the beta weight.
gamma_constraint: Optional constraint for the gamma weight.
Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as input.
References
- [Group Normalization](https://arxiv.org/abs/1803.08494)
"""
def __init__(self,
groups=2,
axis=-1,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(GroupNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.groups = groups
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = tf.keras.initializers.get(beta_initializer)
self.gamma_initializer = tf.keras.initializers.get(gamma_initializer)
self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer)
self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer)
self.beta_constraint = tf.keras.constraints.get(beta_constraint)
self.gamma_constraint = tf.keras.constraints.get(gamma_constraint)
self._check_axis()
def build(self, input_shape):
self._check_if_input_shape_is_none(input_shape)
self._set_number_of_groups_for_instance_norm(input_shape)
self._check_size_of_dimensions(input_shape)
self._create_input_spec(input_shape)
self._add_gamma_weight(input_shape)
self._add_beta_weight(input_shape)
self.built = True
super(GroupNormalization, self).build(input_shape)
def call(self, inputs):
input_shape = tf.keras.backend.int_shape(inputs)
tensor_input_shape = tf.shape(inputs)
reshaped_inputs, group_shape = self._reshape_into_groups(
inputs, input_shape, tensor_input_shape)
normalized_inputs = self._apply_normalization(reshaped_inputs,
input_shape)
outputs = tf.reshape(normalized_inputs, tensor_input_shape)
return outputs
def get_config(self):
config = {
'groups':
self.groups,
'axis':
self.axis,
'epsilon':
self.epsilon,
'center':
self.center,
'scale':
self.scale,
'beta_initializer':
tf.keras.initializers.serialize(self.beta_initializer),
'gamma_initializer':
tf.keras.initializers.serialize(self.gamma_initializer),
'beta_regularizer':
tf.keras.regularizers.serialize(self.beta_regularizer),
'gamma_regularizer':
tf.keras.regularizers.serialize(self.gamma_regularizer),
'beta_constraint':
tf.keras.constraints.serialize(self.beta_constraint),
'gamma_constraint':
tf.keras.constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
group_shape[self.axis] = input_shape[self.axis] // self.groups
group_shape.insert(1, self.groups)
group_shape = tf.stack(group_shape)
reshaped_inputs = tf.reshape(inputs, group_shape)
return reshaped_inputs, group_shape
def _apply_normalization(self, reshaped_inputs, input_shape):
group_shape = tf.keras.backend.int_shape(reshaped_inputs)
group_reduction_axes = list(range(len(group_shape)))
# Remember the ordering of the tensor is [batch, group , steps]. Jump
# the first 2 to calculate the variance and the mean
mean, variance = tf.nn.moments(
reshaped_inputs, group_reduction_axes[2:], keepdims=True)
gamma, beta = self._get_reshaped_weights(input_shape)
normalized_inputs = tf.nn.batch_normalization(
reshaped_inputs,
mean=mean,
variance=variance,
scale=gamma,
offset=beta,
variance_epsilon=self.epsilon)
return normalized_inputs
def _get_reshaped_weights(self, input_shape):
broadcast_shape = self._create_broadcast_shape(input_shape)
gamma = None
beta = None
if self.scale:
gamma = tf.reshape(self.gamma, broadcast_shape)
if self.center:
beta = tf.reshape(self.beta, broadcast_shape)
return gamma, beta
def _check_if_input_shape_is_none(self, input_shape):
dim = input_shape[self.axis]
if dim is None:
raise ValueError('Axis ' + str(self.axis) + ' of '
'input tensor should have a defined dimension '
'but the layer received an input with shape ' +
str(input_shape) + '.')
def _set_number_of_groups_for_instance_norm(self, input_shape):
dim = input_shape[self.axis]
if self.groups == -1:
self.groups = dim
def _check_size_of_dimensions(self, input_shape):
dim = input_shape[self.axis]
if dim < self.groups:
raise ValueError(
'Number of groups (' + str(self.groups) + ') cannot be '
'more than the number of channels (' + str(dim) + ').')
if dim % self.groups != 0:
raise ValueError(
'Number of groups (' + str(self.groups) + ') must be a '
'multiple of the number of channels (' + str(dim) + ').')
def _check_axis(self):
if self.axis == 0:
raise ValueError(
"You are trying to normalize your batch axis. Do you want to "
"use tf.layer.batch_normalization instead")
def _create_input_spec(self, input_shape):
dim = input_shape[self.axis]
self.input_spec = tf.keras.layers.InputSpec(
ndim=len(input_shape), axes={self.axis: dim})
def _add_gamma_weight(self, input_shape):
dim = input_shape[self.axis]
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(
shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
def _add_beta_weight(self, input_shape):
dim = input_shape[self.axis]
shape = (dim,)
if self.center:
self.beta = self.add_weight(
shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
def _create_broadcast_shape(self, input_shape):
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
broadcast_shape.insert(1, self.groups)
return broadcast_shape
@tf.keras.utils.register_keras_serializable(package='Addons')
class InstanceNormalization(GroupNormalization):
"""Instance normalization layer.
Instance Normalization is an specific case of ```GroupNormalization```since
it normalizes all features of one channel. The Groupsize is equal to the
channel size. Empirically, its accuracy is more stable than batch norm in a
wide range of small batch sizes, if learning rate is adjusted linearly
with batch sizes.
Arguments
axis: Integer, the axis that should be normalized.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: If True, multiply by `gamma`.
If False, `gamma` is not used.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: Optional constraint for the beta weight.
gamma_constraint: Optional constraint for the gamma weight.
Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as input.
References
- [Instance Normalization: The Missing Ingredient for Fast Stylization]
(https://arxiv.org/abs/1607.08022)
"""
def __init__(self, **kwargs):
if "groups" in kwargs:
logging.warning("The given value for groups will be overwritten.")
kwargs["groups"] = -1
super(InstanceNormalization, self).__init__(**kwargs)
| 38.292453 | 79 | 0.642687 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import tensorflow as tf
@tf.keras.utils.register_keras_serializable(package='Addons')
class GroupNormalization(tf.keras.layers.Layer):
def __init__(self,
groups=2,
axis=-1,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(GroupNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.groups = groups
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = tf.keras.initializers.get(beta_initializer)
self.gamma_initializer = tf.keras.initializers.get(gamma_initializer)
self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer)
self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer)
self.beta_constraint = tf.keras.constraints.get(beta_constraint)
self.gamma_constraint = tf.keras.constraints.get(gamma_constraint)
self._check_axis()
def build(self, input_shape):
self._check_if_input_shape_is_none(input_shape)
self._set_number_of_groups_for_instance_norm(input_shape)
self._check_size_of_dimensions(input_shape)
self._create_input_spec(input_shape)
self._add_gamma_weight(input_shape)
self._add_beta_weight(input_shape)
self.built = True
super(GroupNormalization, self).build(input_shape)
def call(self, inputs):
input_shape = tf.keras.backend.int_shape(inputs)
tensor_input_shape = tf.shape(inputs)
reshaped_inputs, group_shape = self._reshape_into_groups(
inputs, input_shape, tensor_input_shape)
normalized_inputs = self._apply_normalization(reshaped_inputs,
input_shape)
outputs = tf.reshape(normalized_inputs, tensor_input_shape)
return outputs
def get_config(self):
config = {
'groups':
self.groups,
'axis':
self.axis,
'epsilon':
self.epsilon,
'center':
self.center,
'scale':
self.scale,
'beta_initializer':
tf.keras.initializers.serialize(self.beta_initializer),
'gamma_initializer':
tf.keras.initializers.serialize(self.gamma_initializer),
'beta_regularizer':
tf.keras.regularizers.serialize(self.beta_regularizer),
'gamma_regularizer':
tf.keras.regularizers.serialize(self.gamma_regularizer),
'beta_constraint':
tf.keras.constraints.serialize(self.beta_constraint),
'gamma_constraint':
tf.keras.constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
group_shape[self.axis] = input_shape[self.axis] // self.groups
group_shape.insert(1, self.groups)
group_shape = tf.stack(group_shape)
reshaped_inputs = tf.reshape(inputs, group_shape)
return reshaped_inputs, group_shape
def _apply_normalization(self, reshaped_inputs, input_shape):
group_shape = tf.keras.backend.int_shape(reshaped_inputs)
group_reduction_axes = list(range(len(group_shape)))
mean, variance = tf.nn.moments(
reshaped_inputs, group_reduction_axes[2:], keepdims=True)
gamma, beta = self._get_reshaped_weights(input_shape)
normalized_inputs = tf.nn.batch_normalization(
reshaped_inputs,
mean=mean,
variance=variance,
scale=gamma,
offset=beta,
variance_epsilon=self.epsilon)
return normalized_inputs
def _get_reshaped_weights(self, input_shape):
broadcast_shape = self._create_broadcast_shape(input_shape)
gamma = None
beta = None
if self.scale:
gamma = tf.reshape(self.gamma, broadcast_shape)
if self.center:
beta = tf.reshape(self.beta, broadcast_shape)
return gamma, beta
def _check_if_input_shape_is_none(self, input_shape):
dim = input_shape[self.axis]
if dim is None:
raise ValueError('Axis ' + str(self.axis) + ' of '
'input tensor should have a defined dimension '
'but the layer received an input with shape ' +
str(input_shape) + '.')
def _set_number_of_groups_for_instance_norm(self, input_shape):
dim = input_shape[self.axis]
if self.groups == -1:
self.groups = dim
def _check_size_of_dimensions(self, input_shape):
dim = input_shape[self.axis]
if dim < self.groups:
raise ValueError(
'Number of groups (' + str(self.groups) + ') cannot be '
'more than the number of channels (' + str(dim) + ').')
if dim % self.groups != 0:
raise ValueError(
'Number of groups (' + str(self.groups) + ') must be a '
'multiple of the number of channels (' + str(dim) + ').')
def _check_axis(self):
if self.axis == 0:
raise ValueError(
"You are trying to normalize your batch axis. Do you want to "
"use tf.layer.batch_normalization instead")
def _create_input_spec(self, input_shape):
dim = input_shape[self.axis]
self.input_spec = tf.keras.layers.InputSpec(
ndim=len(input_shape), axes={self.axis: dim})
def _add_gamma_weight(self, input_shape):
dim = input_shape[self.axis]
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(
shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
def _add_beta_weight(self, input_shape):
dim = input_shape[self.axis]
shape = (dim,)
if self.center:
self.beta = self.add_weight(
shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
def _create_broadcast_shape(self, input_shape):
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
broadcast_shape.insert(1, self.groups)
return broadcast_shape
@tf.keras.utils.register_keras_serializable(package='Addons')
class InstanceNormalization(GroupNormalization):
def __init__(self, **kwargs):
if "groups" in kwargs:
logging.warning("The given value for groups will be overwritten.")
kwargs["groups"] = -1
super(InstanceNormalization, self).__init__(**kwargs)
| true | true |
f72c9e1c750207443829a4d4625294cef174db04 | 4,966 | py | Python | restaurants/views.py | sunilsm7/django_resto | b7698653093af7e6f26dd0d0c7b8d6046b402ea4 | [
"MIT"
] | 1 | 2017-08-03T01:40:12.000Z | 2017-08-03T01:40:12.000Z | restaurants/views.py | sunilsm7/django_resto | b7698653093af7e6f26dd0d0c7b8d6046b402ea4 | [
"MIT"
] | null | null | null | restaurants/views.py | sunilsm7/django_resto | b7698653093af7e6f26dd0d0c7b8d6046b402ea4 | [
"MIT"
] | null | null | null | from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.contrib import messages
from django.contrib.contenttypes.models import ContentType
from django.core.paginator import Paginator
from django.core.urlresolvers import reverse, reverse_lazy
from django.db.models import Q
from django.http import HttpResponse, HttpResponseRedirect
from django.shortcuts import render, get_object_or_404, redirect
from django.views import View
from django.views.generic import (
CreateView,
DetailView,
ListView,
TemplateView,
UpdateView
)
from django.views.generic.edit import FormView, FormMixin
from django.views.generic.detail import SingleObjectMixin
from comments.forms import CommentForm
from comments.models import Comment
from .forms import (
RestaurantCreateForm,
RestaurantLocationCreateForm,
RestaurantSearchForm
)
from .models import RestaurantLocations
# Create your views here.
class RestaurantListView(ListView):
template_name = 'restaurants/restaurants_list_all.html'
paginate_by = 10
form_class = RestaurantSearchForm
def get_queryset(self):
query = self.request.GET.get('q')
queryset = RestaurantLocations.objects.search(query)
return queryset
class RestaurantDetailView(DetailView, FormView):
#form_class = CommentForm
template_name = 'restaurants/restaurantlocations_detail.html'
queryset = RestaurantLocations.objects.all()
# def get_queryset(self):
# queryset = RestaurantLocations.objects.all()
# return queryset
def get_context_data(self, **kwargs):
comments = Comment.objects.filter(object_id=objects.id)
context = super(RestaurantDetailView, self).get_context_data(**kwargs)
return context
def render(self, request):
objects = get_object_or_404(RestaurantLocations, slug=self.kwargs.get('slug'))
comments = Comment.objects.filter(object_id=objects.id)
return render(request, 'restaurants/restaurantlocations_detail.html', {'comment_form': self.form, 'comments':comments, 'object':objects})
def get(self, request, *args, **kwargs):
self.object = self.get_object()
# initial_data = {
# "content_type": self.object.get_content_type,
# "object_id": self.object.id
# }
self.form = CommentForm(initial={"content_type": self.object.get_content_type,"object_id": self.object.id})
return self.render(request)
#return super(RestaurantDetailView, self).get(request, *args, **kwargs)
def post(self, request, *args, **kwargs):
if not request.user.is_authenticated:
return HttpResponseForbidden()
self.object = self.get_object()
self.form = CommentForm(request.POST or None)
form = self.form
if form.is_valid() and request.user.is_authenticated:
c_type = form.cleaned_data["content_type"]
content_qs = ContentType.objects.filter(app_label ='restaurants')
content_type = content_qs.get(model='restaurantlocations')
obj_id = form.cleaned_data['object_id']
content_data = form.cleaned_data["content"]
parent_obj = None
try:
parent_id = int(request.POST.get("parent_id"))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists() and parent_qs.count() == 1:
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(
user = request.user,
content_type= content_type,
object_id = obj_id,
content = content_data,
parent = parent_obj,
)
return HttpResponseRedirect(new_comment.get_absolute_url())
else:
return self.render(request)
#return super(RetaurantComment, self).post(request, *args, **kwargs)
class MyRestaurantListView(LoginRequiredMixin, ListView):
template_name = 'restaurants/restaurants_list.html'
paginate_by = 10
def get_queryset(self):
return RestaurantLocations.objects.filter(owner=self.request.user)
class RestaurantCreateView(LoginRequiredMixin, CreateView):
form_class = RestaurantLocationCreateForm
template_name = 'form.html'
# success_url = '/restaurants/'
login_url = '/login/'
def form_valid(self, form):
instance = form.save(commit=False)
instance.owner = self.request.user
return super(RestaurantCreateView, self).form_valid(form)
def get_context_data(self, *args, **kwargs):
context = super(RestaurantCreateView, self).get_context_data(*args, **kwargs)
context['title'] = 'Add Restaurant'
return context
class RestaurantUpdateView(LoginRequiredMixin, UpdateView):
form_class = RestaurantLocationCreateForm
template_name = 'restaurants/detail-update.html'
# success_url = '/restaurants/'
login_url = '/login/'
def get_context_data(self, *args, **kwargs):
context = super(RestaurantUpdateView, self).get_context_data(*args, **kwargs)
name = self.get_object().name
context['title'] = '{} {}'.format('Update Restaurant: ', name)
return context
def get_queryset(self):
return RestaurantLocations.objects.filter(owner=self.request.user)
| 31.833333 | 139 | 0.762384 | from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.contrib import messages
from django.contrib.contenttypes.models import ContentType
from django.core.paginator import Paginator
from django.core.urlresolvers import reverse, reverse_lazy
from django.db.models import Q
from django.http import HttpResponse, HttpResponseRedirect
from django.shortcuts import render, get_object_or_404, redirect
from django.views import View
from django.views.generic import (
CreateView,
DetailView,
ListView,
TemplateView,
UpdateView
)
from django.views.generic.edit import FormView, FormMixin
from django.views.generic.detail import SingleObjectMixin
from comments.forms import CommentForm
from comments.models import Comment
from .forms import (
RestaurantCreateForm,
RestaurantLocationCreateForm,
RestaurantSearchForm
)
from .models import RestaurantLocations
class RestaurantListView(ListView):
template_name = 'restaurants/restaurants_list_all.html'
paginate_by = 10
form_class = RestaurantSearchForm
def get_queryset(self):
query = self.request.GET.get('q')
queryset = RestaurantLocations.objects.search(query)
return queryset
class RestaurantDetailView(DetailView, FormView):
template_name = 'restaurants/restaurantlocations_detail.html'
queryset = RestaurantLocations.objects.all()
def get_context_data(self, **kwargs):
comments = Comment.objects.filter(object_id=objects.id)
context = super(RestaurantDetailView, self).get_context_data(**kwargs)
return context
def render(self, request):
objects = get_object_or_404(RestaurantLocations, slug=self.kwargs.get('slug'))
comments = Comment.objects.filter(object_id=objects.id)
return render(request, 'restaurants/restaurantlocations_detail.html', {'comment_form': self.form, 'comments':comments, 'object':objects})
def get(self, request, *args, **kwargs):
self.object = self.get_object()
self.form = CommentForm(initial={"content_type": self.object.get_content_type,"object_id": self.object.id})
return self.render(request)
def post(self, request, *args, **kwargs):
if not request.user.is_authenticated:
return HttpResponseForbidden()
self.object = self.get_object()
self.form = CommentForm(request.POST or None)
form = self.form
if form.is_valid() and request.user.is_authenticated:
c_type = form.cleaned_data["content_type"]
content_qs = ContentType.objects.filter(app_label ='restaurants')
content_type = content_qs.get(model='restaurantlocations')
obj_id = form.cleaned_data['object_id']
content_data = form.cleaned_data["content"]
parent_obj = None
try:
parent_id = int(request.POST.get("parent_id"))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists() and parent_qs.count() == 1:
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(
user = request.user,
content_type= content_type,
object_id = obj_id,
content = content_data,
parent = parent_obj,
)
return HttpResponseRedirect(new_comment.get_absolute_url())
else:
return self.render(request)
class MyRestaurantListView(LoginRequiredMixin, ListView):
template_name = 'restaurants/restaurants_list.html'
paginate_by = 10
def get_queryset(self):
return RestaurantLocations.objects.filter(owner=self.request.user)
class RestaurantCreateView(LoginRequiredMixin, CreateView):
form_class = RestaurantLocationCreateForm
template_name = 'form.html'
login_url = '/login/'
def form_valid(self, form):
instance = form.save(commit=False)
instance.owner = self.request.user
return super(RestaurantCreateView, self).form_valid(form)
def get_context_data(self, *args, **kwargs):
context = super(RestaurantCreateView, self).get_context_data(*args, **kwargs)
context['title'] = 'Add Restaurant'
return context
class RestaurantUpdateView(LoginRequiredMixin, UpdateView):
form_class = RestaurantLocationCreateForm
template_name = 'restaurants/detail-update.html'
login_url = '/login/'
def get_context_data(self, *args, **kwargs):
context = super(RestaurantUpdateView, self).get_context_data(*args, **kwargs)
name = self.get_object().name
context['title'] = '{} {}'.format('Update Restaurant: ', name)
return context
def get_queryset(self):
return RestaurantLocations.objects.filter(owner=self.request.user)
| true | true |
f72c9ff03b849eba70778f598d05555ab5123a75 | 1,072 | py | Python | core/tests/test_managers/test_project.py | erexer/polyaxon | be14dae1ed56d568983388736bcdaf27a7baa4a4 | [
"Apache-2.0"
] | null | null | null | core/tests/test_managers/test_project.py | erexer/polyaxon | be14dae1ed56d568983388736bcdaf27a7baa4a4 | [
"Apache-2.0"
] | null | null | null | core/tests/test_managers/test_project.py | erexer/polyaxon | be14dae1ed56d568983388736bcdaf27a7baa4a4 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
#
# Copyright 2018-2020 Polyaxon, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from polyaxon_sdk import V1Project
from tests.utils import BaseTestCase
from polyaxon.managers.project import ProjectManager
@pytest.mark.managers_mark
class TestProjectManager(BaseTestCase):
def test_default_props(self):
assert ProjectManager.is_all_visibility() is True
assert ProjectManager.IS_POLYAXON_DIR is True
assert ProjectManager.CONFIG_FILE_NAME == ".project"
assert ProjectManager.CONFIG == V1Project
| 33.5 | 74 | 0.767724 |
import pytest
from polyaxon_sdk import V1Project
from tests.utils import BaseTestCase
from polyaxon.managers.project import ProjectManager
@pytest.mark.managers_mark
class TestProjectManager(BaseTestCase):
def test_default_props(self):
assert ProjectManager.is_all_visibility() is True
assert ProjectManager.IS_POLYAXON_DIR is True
assert ProjectManager.CONFIG_FILE_NAME == ".project"
assert ProjectManager.CONFIG == V1Project
| true | true |
f72ca02b98c9b0c00c8385d82a02c58fe350bf58 | 16,524 | py | Python | src/ner_model/typer/data_translator.py | fracivilization/distant_ner_using_thesaurus | cebfb2bd950123ce3ef18e501314778cc41de71e | [
"Apache-2.0"
] | null | null | null | src/ner_model/typer/data_translator.py | fracivilization/distant_ner_using_thesaurus | cebfb2bd950123ce3ef18e501314778cc41de71e | [
"Apache-2.0"
] | null | null | null | src/ner_model/typer/data_translator.py | fracivilization/distant_ner_using_thesaurus | cebfb2bd950123ce3ef18e501314778cc41de71e | [
"Apache-2.0"
] | null | null | null | import dataclasses
from enum import unique
import click
import datasets
from datasets import features
from datasets.arrow_dataset import Dataset
from datasets.dataset_dict import DatasetDict
from src.ner_model.chunker.abstract_model import Chunker
from src.utils.utils import remove_BIE
import dataclasses
from seqeval.metrics.sequence_labeling import get_entities
from collections import defaultdict
from logging import getLogger
from src.utils.params import span_length
from hydra.utils import get_original_cwd
from hashlib import md5
import prettytable
from src.ner_model.chunker import ChunkerConfig
from omegaconf import MISSING
logger = getLogger(__name__)
@dataclasses.dataclass
class MSCConfig:
ner_dataset: str = MISSING
output_dir: str = MISSING
with_o: bool = False
chunker: ChunkerConfig = ChunkerConfig()
o_sampling_ratio: float = 1.0
# hard_o_sampling: bool = False
# o_outside_entity: bool = False
# weight_of_hard_o_for_easy_o: float = 0.5 #
from tqdm import tqdm
from collections import Counter
import random
def remove_misguided_fns(starts, ends, labels):
new_starts, new_ends, new_labels = [], [], []
misguided_tokens = set()
for s, e, l in zip(starts, ends, labels):
if l == "MISGUIDANCE":
for i in range(s, e):
misguided_tokens.add(i)
for s, e, l in zip(starts, ends, labels):
if l != "MISGUIDANCE":
if l.startswith("nc"):
span = set(range(s, e))
if span & misguided_tokens:
continue
new_starts.append(s)
new_ends.append(e)
new_labels.append(l)
return new_starts, new_ends, new_labels
def undersample_thesaurus_negatives(pre_span_classification_dataset):
label_counter = Counter(
[label for snt in pre_span_classification_dataset["labels"] for label in snt]
)
pass
positive_labels = [
label for label in label_counter.keys() if not label.startswith("nc-")
]
max_positive_count = max(label_counter[label] for label in positive_labels)
thesaurus_negative_class_sampling_ratio = {
label: max_positive_count / count
for label, count in label_counter.items()
if label != "nc-O" and label.startswith("nc-")
}
new_pre_span_classification_dataset = defaultdict(list)
pscd = pre_span_classification_dataset
for tokens, starts, ends, labels in zip(
pscd["tokens"], pscd["starts"], pscd["ends"], pscd["labels"]
):
new_starts = []
new_ends = []
new_labels = []
for s, e, l in zip(starts, ends, labels):
if (
l != "nc-O"
and l.startswith("nc-")
and random.random() > thesaurus_negative_class_sampling_ratio[l]
):
continue
new_starts.append(s)
new_ends.append(e)
new_labels.append(l)
new_pre_span_classification_dataset["tokens"].append(tokens)
new_pre_span_classification_dataset["starts"].append(new_starts)
new_pre_span_classification_dataset["ends"].append(new_ends)
new_pre_span_classification_dataset["labels"].append(new_labels)
return new_pre_span_classification_dataset
def ner_datasets_to_span_classification_datasets(
ner_datasets: datasets.DatasetDict,
data_args: MSCConfig,
enumerator: Chunker,
) -> datasets.DatasetDict:
pre_span_classification_datasets = dict()
label_names = sorted(
set(
[
remove_BIE(tag)
for tag in ner_datasets["test"].features["ner_tags"].feature.names
if tag != "O"
]
)
)
if data_args.with_o:
if "nc-O" not in label_names:
label_names = ["nc-O"] + label_names
info = datasets.DatasetInfo(
features=datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"starts": datasets.Sequence(datasets.Value("int32")),
"ends": datasets.Sequence(datasets.Value("int32")),
"labels": datasets.Sequence(datasets.ClassLabel(names=label_names)),
}
)
)
for key in ner_datasets:
pre_span_classification_dataset = defaultdict(list)
ner_tag_labels = ner_datasets[key].features["ner_tags"].feature.names
for snt in tqdm(ner_datasets[key]):
registered_chunks = set()
ner_tags = [ner_tag_labels[tag] for tag in snt["ner_tags"]]
starts = []
ends = []
labels = []
for label, s, e in get_entities(ner_tags):
starts.append(s)
ends.append(e + 1)
labels.append(label)
registered_chunks.add((s, e))
if data_args.with_o and key in {"train", "validation"}:
for s, e in enumerator.predict(snt["tokens"]):
if (
(s, e) not in registered_chunks
and data_args.o_sampling_ratio > random.random()
):
starts.append(s)
ends.append(e)
labels.append("nc-O")
starts, ends, labels = remove_misguided_fns(starts, ends, labels)
if labels:
pre_span_classification_dataset["tokens"].append(snt["tokens"])
pre_span_classification_dataset["starts"].append(starts)
pre_span_classification_dataset["ends"].append(ends)
pre_span_classification_dataset["labels"].append(labels)
# if key == "train":
# pre_span_classification_dataset = undersample_thesaurus_negatives(
# pre_span_classification_dataset
# )
pre_span_classification_datasets[key] = datasets.Dataset.from_dict(
pre_span_classification_dataset, info=info
)
return datasets.DatasetDict(pre_span_classification_datasets)
import numpy as np
def label_balancing_span_classification_datasets(
span_classification_datasets: datasets.DatasetDict, o_and_min_label_count_ratio=1
):
ret_datasets = dict()
if "test" in span_classification_datasets:
info = datasets.DatasetInfo(
features=span_classification_datasets["test"].features
)
else:
info = datasets.DatasetInfo(
features=span_classification_datasets["train"].features
)
for split_key, dataset_split in span_classification_datasets.items():
if split_key != "test":
if "labels" in dataset_split.features:
# for multi span classification datasets
span_classification_dataset = {
"tokens": [],
"starts": [],
"ends": [],
"labels": [],
}
label_count = Counter(
[l for snt in dataset_split["labels"] for l in snt]
)
min_label_count = min(label_count.values())
logger.info("min label count: %d" % min_label_count)
undersampling_ratio = {
label: min_label_count / count
for label, count in label_count.items()
}
for snt in tqdm(dataset_split):
starts = []
ends = []
labels = []
for s, e, l in zip(snt["starts"], snt["ends"], snt["labels"]):
if random.random() < undersampling_ratio[l]:
starts.append(s)
ends.append(e)
labels.append(l)
if labels:
span_classification_dataset["tokens"].append(snt["tokens"])
span_classification_dataset["starts"].append(starts)
span_classification_dataset["ends"].append(ends)
span_classification_dataset["labels"].append(labels)
ret_datasets[split_key] = datasets.Dataset.from_dict(
span_classification_dataset, info=info
)
elif "label" in dataset_split.features:
# for one span classification datasets
span_classification_dataset = {
"tokens": [],
"start": [],
"end": [],
"label": [],
}
label_names = dataset_split.features["label"].names
label_count = Counter(dataset_split["label"])
min_label_count = min(label_count.values())
logger.info("min label count: %d" % min_label_count)
undersampling_ratio = dict()
for label, count in label_count.items():
if label_names[label] == "O":
undersampling_ratio[label] = (
min_label_count / count * o_and_min_label_count_ratio
)
else:
undersampling_ratio[label] = min_label_count / count
for snt in tqdm(dataset_split):
if random.random() < undersampling_ratio[snt["label"]]:
for key, value in snt.items():
span_classification_dataset[key].append(value)
ret_datasets[split_key] = datasets.Dataset.from_dict(
span_classification_dataset, info=info
)
else:
raise NotImplementedError
else:
ret_datasets[split_key] = dataset_split
return datasets.DatasetDict(ret_datasets)
import os
from pathlib import Path
def print_label_statistics(span_classification_datasets: datasets.DatasetDict):
for split_key, dataset_split in span_classification_datasets.items():
if "label" in dataset_split.features:
label_names = dataset_split.features["label"].names
label_count = Counter([label_names[l] for l in dataset_split["label"]])
else:
pass
label_names = dataset_split.features["labels"].feature.names
label_count = Counter(
[label_names[l] for snt in dataset_split["labels"] for l in snt]
)
logger.info("label count of %s split: %s" % (split_key, label_count))
from copy import deepcopy
from typing import Dict, List
import random
def load_o_label_spans(unlabelled_corpus: Dataset, span_num: int) -> List:
# 各文から取得するスパン数を指定
# 各文に対してspan_length長のスパンをかき集めてくる
# 各文に定められた個数になるまでサンプリング
# 全体の断片から決められたスパン数になるまでサンプリング
pass
snt_num = len(unlabelled_corpus)
span_num_per_snt = int(span_num / snt_num) + 100
o_label_spans = []
for snt in unlabelled_corpus["tokens"]:
spans = [
(s, e)
for s in range(len(snt))
for e in range(s + 1, len(snt) + 1)
if e - s <= MSCConfig.span_length
]
for s, e in random.sample(spans, min(span_num_per_snt, len(spans))):
o_label_spans.append(snt[s:e])
return random.sample(o_label_spans, min(span_num, len(o_label_spans)))
import spacy
from itertools import islice
from dataclasses import MISSING, dataclass
@dataclass
class Term2CatBasedDatasetArgs:
label_balance: bool = False
pass
def load_term2cat_based_span_classification_dataset(
term2cat: Dict, unlabelled_corpus: Dataset, args: Term2CatBasedDatasetArgs
):
tokenizer = spacy.load("en_core_sci_sm")
tokenizer.remove_pipe("ner")
dataset = {"tokens": [], "start": [], "end": [], "label": []}
label_names = ["O"] + sorted(set(term2cat.values()))
dict_label_count = Counter(term2cat.values())
if args.label_balance:
over_sampling_ratio = {
l: dict_label_count.most_common()[0][1] / dict_label_count[l]
for l in dict_label_count
}
else:
over_sampling_ratio = {l: 1 for l in dict_label_count}
for term, cat in tqdm(term2cat.items()):
osr = over_sampling_ratio[cat]
tokenized_terms = tokenizer(term)
while True:
if 0 < osr < 1:
if osr > random.random():
break
elif osr <= 0:
break
dataset["tokens"].append([w.text for w in tokenized_terms])
dataset["start"].append(0)
dataset["end"].append(len(tokenized_terms))
dataset["label"].append(label_names.index(cat))
osr -= 1
if args.label_balance:
span_num = dict_label_count.most_common()[0][1]
else:
span_num = sum(dict_label_count.values())
o_labeled_spans = load_o_label_spans(unlabelled_corpus, span_num)
for span in o_labeled_spans:
dataset["tokens"].append(span)
dataset["start"].append(0)
dataset["end"].append(len(span))
dataset["label"].append(label_names.index("O"))
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"label": datasets.ClassLabel(names=label_names),
}
)
# new_dataset_dictに追加
return Dataset.from_dict(dataset, features=features)
def split_span_classification_dataset(datasets: Dataset):
features = datasets.features
split_num = int(len(datasets) * 0.9)
splitted_datasets = dict()
from random import shuffle
indexes = list(range(len(datasets)))
shuffle(indexes)
splitted_datasets["train"] = Dataset.from_dict(
datasets.__getitem__(indexes[:split_num]), features=features
)
splitted_datasets["validation"] = Dataset.from_dict(
datasets.__getitem__(indexes[split_num:]), features=features
)
return DatasetDict(splitted_datasets)
def join_span_classification_datasets(
main_datasets: DatasetDict, sub_datasets: DatasetDict
):
pass
new_dataset_dict = dict()
for key, split in main_datasets.items():
if key in sub_datasets:
sub_split = sub_datasets[key]
new_dataset = {feature: split[feature] for feature in split.features}
main_label_names = split.features["label"].names
sub_label_names = sub_split.features["label"].names
assert len(main_label_names) == len(sub_label_names)
assert len(split.features) == len(sub_split.features)
label_map = {
i: sub_label_names.index(l) for i, l in enumerate(main_label_names)
}
for feature in sub_split.features:
if feature == "label":
new_dataset[feature] += [label_map[l] for l in sub_split[feature]]
else:
new_dataset[feature] += sub_split[feature]
new_dataset_dict[key] = Dataset.from_dict(new_dataset, split.features)
else:
new_dataset_dict[key] = split
return DatasetDict(new_dataset_dict)
def log_label_ratio(msc_datasets: DatasetDict):
table = prettytable.PrettyTable(["Label", "Count", "Ratio (%)"])
pass
train_dataset = msc_datasets["train"]
label_names = train_dataset.features["labels"].feature.names
c = Counter([label for snt in train_dataset["labels"] for label in snt])
label_sum = sum(c.values())
for lid, count in c.most_common():
table.add_row([label_names[lid], count, "%.2f" % (100 * count / label_sum)])
logger.info(table.get_string())
def translate_into_msc_datasets(
ner_datasets: DatasetDict,
msc_args: MSCConfig,
enumerator: Chunker,
):
input_hash = {k: v._fingerprint for k, v in ner_datasets.items()}
input_hash["msc_args"] = str(msc_args)
input_hash["enumerator"] = str(enumerator.config)
output_dir = Path(get_original_cwd()).joinpath(
"data", "buffer", md5(str(input_hash).encode()).hexdigest()
)
logger.info("output_dir of msc_datasets: " + str(output_dir))
if not output_dir.exists():
msc_datasets = ner_datasets_to_span_classification_datasets(
ner_datasets, msc_args, enumerator
)
msc_datasets.save_to_disk(output_dir)
else:
msc_datasets = DatasetDict.load_from_disk(output_dir)
log_label_ratio(msc_datasets)
return msc_datasets
| 37.216216 | 86 | 0.60633 | import dataclasses
from enum import unique
import click
import datasets
from datasets import features
from datasets.arrow_dataset import Dataset
from datasets.dataset_dict import DatasetDict
from src.ner_model.chunker.abstract_model import Chunker
from src.utils.utils import remove_BIE
import dataclasses
from seqeval.metrics.sequence_labeling import get_entities
from collections import defaultdict
from logging import getLogger
from src.utils.params import span_length
from hydra.utils import get_original_cwd
from hashlib import md5
import prettytable
from src.ner_model.chunker import ChunkerConfig
from omegaconf import MISSING
logger = getLogger(__name__)
@dataclasses.dataclass
class MSCConfig:
ner_dataset: str = MISSING
output_dir: str = MISSING
with_o: bool = False
chunker: ChunkerConfig = ChunkerConfig()
o_sampling_ratio: float = 1.0
from tqdm import tqdm
from collections import Counter
import random
def remove_misguided_fns(starts, ends, labels):
new_starts, new_ends, new_labels = [], [], []
misguided_tokens = set()
for s, e, l in zip(starts, ends, labels):
if l == "MISGUIDANCE":
for i in range(s, e):
misguided_tokens.add(i)
for s, e, l in zip(starts, ends, labels):
if l != "MISGUIDANCE":
if l.startswith("nc"):
span = set(range(s, e))
if span & misguided_tokens:
continue
new_starts.append(s)
new_ends.append(e)
new_labels.append(l)
return new_starts, new_ends, new_labels
def undersample_thesaurus_negatives(pre_span_classification_dataset):
label_counter = Counter(
[label for snt in pre_span_classification_dataset["labels"] for label in snt]
)
pass
positive_labels = [
label for label in label_counter.keys() if not label.startswith("nc-")
]
max_positive_count = max(label_counter[label] for label in positive_labels)
thesaurus_negative_class_sampling_ratio = {
label: max_positive_count / count
for label, count in label_counter.items()
if label != "nc-O" and label.startswith("nc-")
}
new_pre_span_classification_dataset = defaultdict(list)
pscd = pre_span_classification_dataset
for tokens, starts, ends, labels in zip(
pscd["tokens"], pscd["starts"], pscd["ends"], pscd["labels"]
):
new_starts = []
new_ends = []
new_labels = []
for s, e, l in zip(starts, ends, labels):
if (
l != "nc-O"
and l.startswith("nc-")
and random.random() > thesaurus_negative_class_sampling_ratio[l]
):
continue
new_starts.append(s)
new_ends.append(e)
new_labels.append(l)
new_pre_span_classification_dataset["tokens"].append(tokens)
new_pre_span_classification_dataset["starts"].append(new_starts)
new_pre_span_classification_dataset["ends"].append(new_ends)
new_pre_span_classification_dataset["labels"].append(new_labels)
return new_pre_span_classification_dataset
def ner_datasets_to_span_classification_datasets(
ner_datasets: datasets.DatasetDict,
data_args: MSCConfig,
enumerator: Chunker,
) -> datasets.DatasetDict:
pre_span_classification_datasets = dict()
label_names = sorted(
set(
[
remove_BIE(tag)
for tag in ner_datasets["test"].features["ner_tags"].feature.names
if tag != "O"
]
)
)
if data_args.with_o:
if "nc-O" not in label_names:
label_names = ["nc-O"] + label_names
info = datasets.DatasetInfo(
features=datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"starts": datasets.Sequence(datasets.Value("int32")),
"ends": datasets.Sequence(datasets.Value("int32")),
"labels": datasets.Sequence(datasets.ClassLabel(names=label_names)),
}
)
)
for key in ner_datasets:
pre_span_classification_dataset = defaultdict(list)
ner_tag_labels = ner_datasets[key].features["ner_tags"].feature.names
for snt in tqdm(ner_datasets[key]):
registered_chunks = set()
ner_tags = [ner_tag_labels[tag] for tag in snt["ner_tags"]]
starts = []
ends = []
labels = []
for label, s, e in get_entities(ner_tags):
starts.append(s)
ends.append(e + 1)
labels.append(label)
registered_chunks.add((s, e))
if data_args.with_o and key in {"train", "validation"}:
for s, e in enumerator.predict(snt["tokens"]):
if (
(s, e) not in registered_chunks
and data_args.o_sampling_ratio > random.random()
):
starts.append(s)
ends.append(e)
labels.append("nc-O")
starts, ends, labels = remove_misguided_fns(starts, ends, labels)
if labels:
pre_span_classification_dataset["tokens"].append(snt["tokens"])
pre_span_classification_dataset["starts"].append(starts)
pre_span_classification_dataset["ends"].append(ends)
pre_span_classification_dataset["labels"].append(labels)
pre_span_classification_datasets[key] = datasets.Dataset.from_dict(
pre_span_classification_dataset, info=info
)
return datasets.DatasetDict(pre_span_classification_datasets)
import numpy as np
def label_balancing_span_classification_datasets(
span_classification_datasets: datasets.DatasetDict, o_and_min_label_count_ratio=1
):
ret_datasets = dict()
if "test" in span_classification_datasets:
info = datasets.DatasetInfo(
features=span_classification_datasets["test"].features
)
else:
info = datasets.DatasetInfo(
features=span_classification_datasets["train"].features
)
for split_key, dataset_split in span_classification_datasets.items():
if split_key != "test":
if "labels" in dataset_split.features:
span_classification_dataset = {
"tokens": [],
"starts": [],
"ends": [],
"labels": [],
}
label_count = Counter(
[l for snt in dataset_split["labels"] for l in snt]
)
min_label_count = min(label_count.values())
logger.info("min label count: %d" % min_label_count)
undersampling_ratio = {
label: min_label_count / count
for label, count in label_count.items()
}
for snt in tqdm(dataset_split):
starts = []
ends = []
labels = []
for s, e, l in zip(snt["starts"], snt["ends"], snt["labels"]):
if random.random() < undersampling_ratio[l]:
starts.append(s)
ends.append(e)
labels.append(l)
if labels:
span_classification_dataset["tokens"].append(snt["tokens"])
span_classification_dataset["starts"].append(starts)
span_classification_dataset["ends"].append(ends)
span_classification_dataset["labels"].append(labels)
ret_datasets[split_key] = datasets.Dataset.from_dict(
span_classification_dataset, info=info
)
elif "label" in dataset_split.features:
span_classification_dataset = {
"tokens": [],
"start": [],
"end": [],
"label": [],
}
label_names = dataset_split.features["label"].names
label_count = Counter(dataset_split["label"])
min_label_count = min(label_count.values())
logger.info("min label count: %d" % min_label_count)
undersampling_ratio = dict()
for label, count in label_count.items():
if label_names[label] == "O":
undersampling_ratio[label] = (
min_label_count / count * o_and_min_label_count_ratio
)
else:
undersampling_ratio[label] = min_label_count / count
for snt in tqdm(dataset_split):
if random.random() < undersampling_ratio[snt["label"]]:
for key, value in snt.items():
span_classification_dataset[key].append(value)
ret_datasets[split_key] = datasets.Dataset.from_dict(
span_classification_dataset, info=info
)
else:
raise NotImplementedError
else:
ret_datasets[split_key] = dataset_split
return datasets.DatasetDict(ret_datasets)
import os
from pathlib import Path
def print_label_statistics(span_classification_datasets: datasets.DatasetDict):
for split_key, dataset_split in span_classification_datasets.items():
if "label" in dataset_split.features:
label_names = dataset_split.features["label"].names
label_count = Counter([label_names[l] for l in dataset_split["label"]])
else:
pass
label_names = dataset_split.features["labels"].feature.names
label_count = Counter(
[label_names[l] for snt in dataset_split["labels"] for l in snt]
)
logger.info("label count of %s split: %s" % (split_key, label_count))
from copy import deepcopy
from typing import Dict, List
import random
def load_o_label_spans(unlabelled_corpus: Dataset, span_num: int) -> List:
pass
snt_num = len(unlabelled_corpus)
span_num_per_snt = int(span_num / snt_num) + 100
o_label_spans = []
for snt in unlabelled_corpus["tokens"]:
spans = [
(s, e)
for s in range(len(snt))
for e in range(s + 1, len(snt) + 1)
if e - s <= MSCConfig.span_length
]
for s, e in random.sample(spans, min(span_num_per_snt, len(spans))):
o_label_spans.append(snt[s:e])
return random.sample(o_label_spans, min(span_num, len(o_label_spans)))
import spacy
from itertools import islice
from dataclasses import MISSING, dataclass
@dataclass
class Term2CatBasedDatasetArgs:
label_balance: bool = False
pass
def load_term2cat_based_span_classification_dataset(
term2cat: Dict, unlabelled_corpus: Dataset, args: Term2CatBasedDatasetArgs
):
tokenizer = spacy.load("en_core_sci_sm")
tokenizer.remove_pipe("ner")
dataset = {"tokens": [], "start": [], "end": [], "label": []}
label_names = ["O"] + sorted(set(term2cat.values()))
dict_label_count = Counter(term2cat.values())
if args.label_balance:
over_sampling_ratio = {
l: dict_label_count.most_common()[0][1] / dict_label_count[l]
for l in dict_label_count
}
else:
over_sampling_ratio = {l: 1 for l in dict_label_count}
for term, cat in tqdm(term2cat.items()):
osr = over_sampling_ratio[cat]
tokenized_terms = tokenizer(term)
while True:
if 0 < osr < 1:
if osr > random.random():
break
elif osr <= 0:
break
dataset["tokens"].append([w.text for w in tokenized_terms])
dataset["start"].append(0)
dataset["end"].append(len(tokenized_terms))
dataset["label"].append(label_names.index(cat))
osr -= 1
if args.label_balance:
span_num = dict_label_count.most_common()[0][1]
else:
span_num = sum(dict_label_count.values())
o_labeled_spans = load_o_label_spans(unlabelled_corpus, span_num)
for span in o_labeled_spans:
dataset["tokens"].append(span)
dataset["start"].append(0)
dataset["end"].append(len(span))
dataset["label"].append(label_names.index("O"))
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"label": datasets.ClassLabel(names=label_names),
}
)
return Dataset.from_dict(dataset, features=features)
def split_span_classification_dataset(datasets: Dataset):
features = datasets.features
split_num = int(len(datasets) * 0.9)
splitted_datasets = dict()
from random import shuffle
indexes = list(range(len(datasets)))
shuffle(indexes)
splitted_datasets["train"] = Dataset.from_dict(
datasets.__getitem__(indexes[:split_num]), features=features
)
splitted_datasets["validation"] = Dataset.from_dict(
datasets.__getitem__(indexes[split_num:]), features=features
)
return DatasetDict(splitted_datasets)
def join_span_classification_datasets(
main_datasets: DatasetDict, sub_datasets: DatasetDict
):
pass
new_dataset_dict = dict()
for key, split in main_datasets.items():
if key in sub_datasets:
sub_split = sub_datasets[key]
new_dataset = {feature: split[feature] for feature in split.features}
main_label_names = split.features["label"].names
sub_label_names = sub_split.features["label"].names
assert len(main_label_names) == len(sub_label_names)
assert len(split.features) == len(sub_split.features)
label_map = {
i: sub_label_names.index(l) for i, l in enumerate(main_label_names)
}
for feature in sub_split.features:
if feature == "label":
new_dataset[feature] += [label_map[l] for l in sub_split[feature]]
else:
new_dataset[feature] += sub_split[feature]
new_dataset_dict[key] = Dataset.from_dict(new_dataset, split.features)
else:
new_dataset_dict[key] = split
return DatasetDict(new_dataset_dict)
def log_label_ratio(msc_datasets: DatasetDict):
table = prettytable.PrettyTable(["Label", "Count", "Ratio (%)"])
pass
train_dataset = msc_datasets["train"]
label_names = train_dataset.features["labels"].feature.names
c = Counter([label for snt in train_dataset["labels"] for label in snt])
label_sum = sum(c.values())
for lid, count in c.most_common():
table.add_row([label_names[lid], count, "%.2f" % (100 * count / label_sum)])
logger.info(table.get_string())
def translate_into_msc_datasets(
ner_datasets: DatasetDict,
msc_args: MSCConfig,
enumerator: Chunker,
):
input_hash = {k: v._fingerprint for k, v in ner_datasets.items()}
input_hash["msc_args"] = str(msc_args)
input_hash["enumerator"] = str(enumerator.config)
output_dir = Path(get_original_cwd()).joinpath(
"data", "buffer", md5(str(input_hash).encode()).hexdigest()
)
logger.info("output_dir of msc_datasets: " + str(output_dir))
if not output_dir.exists():
msc_datasets = ner_datasets_to_span_classification_datasets(
ner_datasets, msc_args, enumerator
)
msc_datasets.save_to_disk(output_dir)
else:
msc_datasets = DatasetDict.load_from_disk(output_dir)
log_label_ratio(msc_datasets)
return msc_datasets
| true | true |
f72ca25004c0c4905aca487d4e9c73657cbe9a5d | 482 | py | Python | app/http/middleware/HelloWorldMiddleware.py | llaski/masonite-tutorial | f89dc88ccf7924b477dfe971fdb981a82e63d5fe | [
"MIT"
] | null | null | null | app/http/middleware/HelloWorldMiddleware.py | llaski/masonite-tutorial | f89dc88ccf7924b477dfe971fdb981a82e63d5fe | [
"MIT"
] | 1 | 2021-06-02T00:33:40.000Z | 2021-06-02T00:33:40.000Z | app/http/middleware/HelloWorldMiddleware.py | llaski/masonite-tutorial | f89dc88ccf7924b477dfe971fdb981a82e63d5fe | [
"MIT"
] | null | null | null | """HelloWorld Middleware."""
from masonite.request import Request
class HelloWorldMiddleware:
"""HelloWorld Middleware."""
def __init__(self, request: Request):
"""Inject Any Dependencies From The Service Container.
Arguments:
Request {masonite.request.Request} -- The Masonite request object
"""
self.request = request
def before(self):
print('Hello World')
def after(self):
print('Goodbye World')
| 21.909091 | 77 | 0.636929 |
from masonite.request import Request
class HelloWorldMiddleware:
def __init__(self, request: Request):
self.request = request
def before(self):
print('Hello World')
def after(self):
print('Goodbye World')
| true | true |
f72ca260e47ced61e897e70195c321f15e9d783d | 3,962 | py | Python | misc/learnpy/k-means/loadiris.py | mutazag/mdsi | efecc8f650ddf6866154389f98d4ce0a9803db18 | [
"MIT"
] | null | null | null | misc/learnpy/k-means/loadiris.py | mutazag/mdsi | efecc8f650ddf6866154389f98d4ce0a9803db18 | [
"MIT"
] | null | null | null | misc/learnpy/k-means/loadiris.py | mutazag/mdsi | efecc8f650ddf6866154389f98d4ce0a9803db18 | [
"MIT"
] | null | null | null | import pandas as pd
from sklearn import datasets
# load iris data set
iris = datasets.load_iris()
print(iris)
species = [iris.target_names[x] for x in iris.target]
iris = pd.DataFrame(iris['data'], columns = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris['Species'] = species
iris.head()
iris.dtypes
# quick count
iris['count'] = 1
iris[['Species', 'count']].groupby('Species').count()
iris.groupby('Species').count()
# plot the data set
# %matplotlib inline
def plot_iris(iris, col1, col2):
print("plot_iris")
import seaborn as sns
import matplotlib.pyplot as plt
sns.lmplot(x = col1, y=col2,
data = iris,
hue = "Species",
fit_reg=False)
plt.xlabel(col1)
plt.ylabel(col2)
plt.title('Iris species show by color')
plt.show()
plot_iris(iris, 'Petal_Width', 'Sepal_Length')
plot_iris(iris, 'Sepal_Width', 'Sepal_Length')
# preparing numeric featurs by scaling
from sklearn.preprocessing import scale
import pandas as pd
num_cols = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width']
iris_scaled = scale(iris[num_cols])
iris_scaled = pd.DataFrame(iris_scaled, columns = num_cols)
print(iris_scaled.describe().round(3))
# coding string col 'species' as numeric using a dictionary
levels = {'setosa':0,
'versicolor':1,
'virginica':2}
# add coded species to the new scaled iris data frame
iris_scaled['Species'] = [levels[x] for x in iris['Species']]
iris_scaled.head()
plot_iris(iris_scaled, 'Sepal_Width', 'Sepal_Length')
## split the data into training and tes using Bernoulli sampling
from sklearn.model_selection import train_test_split
import numpy as np
np.random.seed(3456)
iris_split = train_test_split(np.asmatrix(iris_scaled), test_size = 75)
iris_train_features = iris_split[0][:,:4]
iris_train_labels = np.ravel(iris_split[0][:,4])
iris_test_features = iris_split[1][:,:4]
iris_test_labels = np.ravel(iris_split[1][:,4])
print(iris_train_features.shape)
print(iris_train_labels.shape)
print(iris_test_features.shape)
print(iris_test_labels.shape)
# Train and Eval KNN model
#fit model
from sklearn.neighbors import KNeighborsClassifier
KNN_mod = KNeighborsClassifier(n_neighbors=3) # this is K
KNN_mod.fit(iris_train_features, iris_train_labels)
#test model on test data set
iris_test = pd.DataFrame(iris_test_features, columns = num_cols)
iris_test['predicted'] = KNN_mod.predict(iris_test_features)
iris_test['actuals'] = iris_test_labels
iris_test['correct'] = [1 if x == z else 0 for x, z in zip(iris_test['predicted'], iris_test_labels)]
# calculate some accuracy measure
accuracy = 100 * float(sum(iris_test['correct'])) / float(iris_test.shape[0])
print(accuracy)
iris_test[iris_test.correct != 1]
iris_test.loc[iris_test["correct"] != 1]
# plotting the predicted values and highliting incorrectly classified observations
levels = {0:'setosa', 1:'versicolor', 2:'virginica'}
iris_test['Species'] = [levels[x] for x in iris_test['predicted']]
markers = {1:'^', 0:'o'}
colors = {'setosa':'blue', 'versicolor':'green', 'virginica':'red'}
def plot_shapes(df, col1,col2, markers, colors):
import matplotlib.pyplot as plt
import seaborn as sns
ax = plt.figure(figsize=(6, 6)).gca() # define plot axis
for m in markers: # iterate over marker dictioary keys
for c in colors: # iterate over color dictionary keys
df_temp = df[(df['correct'] == m) & (df['Species'] == c)]
sns.regplot(x = col1, y = col2,
data = df_temp,
fit_reg = False,
scatter_kws={'color': colors[c]},
marker = markers[m],
ax = ax)
plt.xlabel(col1)
plt.ylabel(col2)
plt.title('Iris species by color')
return 'Done'
plot_shapes(iris_test, 'Petal_Width', 'Sepal_Length', markers, colors)
plot_shapes(iris_test, 'Sepal_Width', 'Sepal_Length', markers, colors) | 29.132353 | 108 | 0.694346 | import pandas as pd
from sklearn import datasets
iris = datasets.load_iris()
print(iris)
species = [iris.target_names[x] for x in iris.target]
iris = pd.DataFrame(iris['data'], columns = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris['Species'] = species
iris.head()
iris.dtypes
iris['count'] = 1
iris[['Species', 'count']].groupby('Species').count()
iris.groupby('Species').count()
def plot_iris(iris, col1, col2):
print("plot_iris")
import seaborn as sns
import matplotlib.pyplot as plt
sns.lmplot(x = col1, y=col2,
data = iris,
hue = "Species",
fit_reg=False)
plt.xlabel(col1)
plt.ylabel(col2)
plt.title('Iris species show by color')
plt.show()
plot_iris(iris, 'Petal_Width', 'Sepal_Length')
plot_iris(iris, 'Sepal_Width', 'Sepal_Length')
from sklearn.preprocessing import scale
import pandas as pd
num_cols = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width']
iris_scaled = scale(iris[num_cols])
iris_scaled = pd.DataFrame(iris_scaled, columns = num_cols)
print(iris_scaled.describe().round(3))
levels = {'setosa':0,
'versicolor':1,
'virginica':2}
iris_scaled['Species'] = [levels[x] for x in iris['Species']]
iris_scaled.head()
plot_iris(iris_scaled, 'Sepal_Width', 'Sepal_Length')
mpy as np
np.random.seed(3456)
iris_split = train_test_split(np.asmatrix(iris_scaled), test_size = 75)
iris_train_features = iris_split[0][:,:4]
iris_train_labels = np.ravel(iris_split[0][:,4])
iris_test_features = iris_split[1][:,:4]
iris_test_labels = np.ravel(iris_split[1][:,4])
print(iris_train_features.shape)
print(iris_train_labels.shape)
print(iris_test_features.shape)
print(iris_test_labels.shape)
from sklearn.neighbors import KNeighborsClassifier
KNN_mod = KNeighborsClassifier(n_neighbors=3)
KNN_mod.fit(iris_train_features, iris_train_labels)
iris_test = pd.DataFrame(iris_test_features, columns = num_cols)
iris_test['predicted'] = KNN_mod.predict(iris_test_features)
iris_test['actuals'] = iris_test_labels
iris_test['correct'] = [1 if x == z else 0 for x, z in zip(iris_test['predicted'], iris_test_labels)]
accuracy = 100 * float(sum(iris_test['correct'])) / float(iris_test.shape[0])
print(accuracy)
iris_test[iris_test.correct != 1]
iris_test.loc[iris_test["correct"] != 1]
levels = {0:'setosa', 1:'versicolor', 2:'virginica'}
iris_test['Species'] = [levels[x] for x in iris_test['predicted']]
markers = {1:'^', 0:'o'}
colors = {'setosa':'blue', 'versicolor':'green', 'virginica':'red'}
def plot_shapes(df, col1,col2, markers, colors):
import matplotlib.pyplot as plt
import seaborn as sns
ax = plt.figure(figsize=(6, 6)).gca()
for m in markers:
for c in colors:
df_temp = df[(df['correct'] == m) & (df['Species'] == c)]
sns.regplot(x = col1, y = col2,
data = df_temp,
fit_reg = False,
scatter_kws={'color': colors[c]},
marker = markers[m],
ax = ax)
plt.xlabel(col1)
plt.ylabel(col2)
plt.title('Iris species by color')
return 'Done'
plot_shapes(iris_test, 'Petal_Width', 'Sepal_Length', markers, colors)
plot_shapes(iris_test, 'Sepal_Width', 'Sepal_Length', markers, colors) | true | true |
f72ca2f478e2f86936751094fd9d66c1fab0a9ee | 1,734 | py | Python | run-gat-2-8.py | urialon/bottleneck | 481fbb95edc6ae711da40b6305b40c12ce6a6d29 | [
"MIT"
] | null | null | null | run-gat-2-8.py | urialon/bottleneck | 481fbb95edc6ae711da40b6305b40c12ce6a6d29 | [
"MIT"
] | null | null | null | run-gat-2-8.py | urialon/bottleneck | 481fbb95edc6ae711da40b6305b40c12ce6a6d29 | [
"MIT"
] | null | null | null | import main
from common import Task, STOP, GNN_TYPE
from attrdict import AttrDict
from experiment import Experiment
import torch
override_params = {
2: {'batch_size': 64, 'eval_every': 1000},
3: {'batch_size': 64},
4: {'batch_size': 1024},
5: {'batch_size': 1024},
6: {'batch_size': 1024},
7: {'batch_size': 2048},
8: {'batch_size': 1024, 'accum_grad': 2}, # effective batch size of 2048, with less GPU memory
}
class Results:
def __init__(self, train_acc, test_acc, epoch):
self.train_acc = train_acc
self.test_acc = test_acc
self.epoch = epoch
if __name__ == '__main__':
task = Task.DICTIONARY
gnn_type = GNN_TYPE.GAT
stopping_criterion = STOP.TRAIN
min_depth = 2
max_depth = 8
results_all_depths = {}
for depth in range(min_depth, max_depth + 1):
num_layers = depth + 1
args = main.get_fake_args(task=task, depth=depth, num_layers=num_layers, loader_workers=7,
type=gnn_type, stop=stopping_criterion,
no_activation=True, no_residual=False)
if depth in override_params:
for key, value in AttrDict(override_params[depth]).items():
args[key] = value
train_acc, test_acc, epoch = Experiment(args).run()
torch.cuda.empty_cache()
results_all_depths[depth] = Results(train_acc=train_acc, test_acc=test_acc, epoch=epoch)
print()
print(f'Task: {task}')
print('depth, train_acc, test_acc, epoch, train_acc, test_acc, epoch,')
for depth in range(min_depth, max_depth + 1):
res = results_all_depths[depth]
print(f'{depth}, {res.train_acc}, {res.test_acc}, {res.epoch}')
| 33.346154 | 99 | 0.632641 | import main
from common import Task, STOP, GNN_TYPE
from attrdict import AttrDict
from experiment import Experiment
import torch
override_params = {
2: {'batch_size': 64, 'eval_every': 1000},
3: {'batch_size': 64},
4: {'batch_size': 1024},
5: {'batch_size': 1024},
6: {'batch_size': 1024},
7: {'batch_size': 2048},
8: {'batch_size': 1024, 'accum_grad': 2},
}
class Results:
def __init__(self, train_acc, test_acc, epoch):
self.train_acc = train_acc
self.test_acc = test_acc
self.epoch = epoch
if __name__ == '__main__':
task = Task.DICTIONARY
gnn_type = GNN_TYPE.GAT
stopping_criterion = STOP.TRAIN
min_depth = 2
max_depth = 8
results_all_depths = {}
for depth in range(min_depth, max_depth + 1):
num_layers = depth + 1
args = main.get_fake_args(task=task, depth=depth, num_layers=num_layers, loader_workers=7,
type=gnn_type, stop=stopping_criterion,
no_activation=True, no_residual=False)
if depth in override_params:
for key, value in AttrDict(override_params[depth]).items():
args[key] = value
train_acc, test_acc, epoch = Experiment(args).run()
torch.cuda.empty_cache()
results_all_depths[depth] = Results(train_acc=train_acc, test_acc=test_acc, epoch=epoch)
print()
print(f'Task: {task}')
print('depth, train_acc, test_acc, epoch, train_acc, test_acc, epoch,')
for depth in range(min_depth, max_depth + 1):
res = results_all_depths[depth]
print(f'{depth}, {res.train_acc}, {res.test_acc}, {res.epoch}')
| true | true |
f72ca4cbe79a2f6143b41e5d9b7ad5d70a93a0a8 | 884 | py | Python | enrich/followthemoney_enrich/cache.py | achievement008/followthemoney | bda06d62c81c82e62cd0c53117d8804939b40f62 | [
"MIT"
] | 137 | 2017-10-20T09:36:32.000Z | 2022-03-24T18:49:16.000Z | enrich/followthemoney_enrich/cache.py | achievement008/followthemoney | bda06d62c81c82e62cd0c53117d8804939b40f62 | [
"MIT"
] | 505 | 2017-10-24T13:14:06.000Z | 2022-03-28T20:21:45.000Z | enrich/followthemoney_enrich/cache.py | achievement008/followthemoney | bda06d62c81c82e62cd0c53117d8804939b40f62 | [
"MIT"
] | 32 | 2017-12-19T15:22:07.000Z | 2022-02-18T11:01:28.000Z | import os
import json
from redis import Redis
from normality import stringify
class Cache(object):
def get(self, key):
return None
def has(self, key):
return self.get(key) is not None
def store(self, key, value):
pass
class RedisCache(Cache):
EXPIRE = 84600 * 90
URL = os.environ.get("ENRICH_REDIS_URL")
def __init__(self):
self.redis = Redis.from_url(self.URL)
def _prefix_key(self, key):
return "ftm:enrich:%s" % stringify(key)
def store(self, key, value):
key = self._prefix_key(key)
self.redis.set(key, json.dumps(value), ex=self.EXPIRE)
def get(self, key):
value = self.redis.get(self._prefix_key(key))
if value is not None:
return json.loads(value)
def has(self, key):
key = self._prefix_key(key)
return self.redis.exists(key)
| 22.1 | 62 | 0.61991 | import os
import json
from redis import Redis
from normality import stringify
class Cache(object):
def get(self, key):
return None
def has(self, key):
return self.get(key) is not None
def store(self, key, value):
pass
class RedisCache(Cache):
EXPIRE = 84600 * 90
URL = os.environ.get("ENRICH_REDIS_URL")
def __init__(self):
self.redis = Redis.from_url(self.URL)
def _prefix_key(self, key):
return "ftm:enrich:%s" % stringify(key)
def store(self, key, value):
key = self._prefix_key(key)
self.redis.set(key, json.dumps(value), ex=self.EXPIRE)
def get(self, key):
value = self.redis.get(self._prefix_key(key))
if value is not None:
return json.loads(value)
def has(self, key):
key = self._prefix_key(key)
return self.redis.exists(key)
| true | true |
f72ca4f157e0f5d299e44df76de3bb9ba9ff45ad | 13,454 | py | Python | env/lib/python3.7/encodings/mac_cyrillic.py | JacobMiske/nuclear-database-APIs | bc9fb6afb9aa0d98dde5d744d8f22b2791597e78 | [
"MIT"
] | null | null | null | env/lib/python3.7/encodings/mac_cyrillic.py | JacobMiske/nuclear-database-APIs | bc9fb6afb9aa0d98dde5d744d8f22b2791597e78 | [
"MIT"
] | null | null | null | env/lib/python3.7/encodings/mac_cyrillic.py | JacobMiske/nuclear-database-APIs | bc9fb6afb9aa0d98dde5d744d8f22b2791597e78 | [
"MIT"
] | 1 | 2020-05-01T20:23:35.000Z | 2020-05-01T20:23:35.000Z | """ Python Character Mapping Codec mac_cyrillic generated from 'MAPPINGS/VENDORS/APPLE/CYRILLIC.TXT' with gencodec.py.
"""#"
import codecs
### Codec APIs
class Codec(codecs.Codec):
def encode(self,input,errors='strict'):
return codecs.charmap_encode(input,errors,encoding_table)
def decode(self,input,errors='strict'):
return codecs.charmap_decode(input,errors,decoding_table)
class IncrementalEncoder(codecs.IncrementalEncoder):
def encode(self, input, final=False):
return codecs.charmap_encode(input,self.errors,encoding_table)[0]
class IncrementalDecoder(codecs.IncrementalDecoder):
def decode(self, input, final=False):
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
class StreamWriter(Codec,codecs.StreamWriter):
pass
class StreamReader(Codec,codecs.StreamReader):
pass
### encodings module src
def getregentry():
return codecs.CodecInfo(
name='mac-cyrillic',
encode=Codec().encode,
decode=Codec().decode,
incrementalencoder=IncrementalEncoder,
incrementaldecoder=IncrementalDecoder,
streamreader=StreamReader,
streamwriter=StreamWriter,
)
### Decoding Table
decoding_table = (
'\x00' # 0x00 -> CONTROL CHARACTER
'\x01' # 0x01 -> CONTROL CHARACTER
'\x02' # 0x02 -> CONTROL CHARACTER
'\x03' # 0x03 -> CONTROL CHARACTER
'\x04' # 0x04 -> CONTROL CHARACTER
'\x05' # 0x05 -> CONTROL CHARACTER
'\x06' # 0x06 -> CONTROL CHARACTER
'\x07' # 0x07 -> CONTROL CHARACTER
'\x08' # 0x08 -> CONTROL CHARACTER
'\t' # 0x09 -> CONTROL CHARACTER
'\n' # 0x0A -> CONTROL CHARACTER
'\x0b' # 0x0B -> CONTROL CHARACTER
'\x0c' # 0x0C -> CONTROL CHARACTER
'\r' # 0x0D -> CONTROL CHARACTER
'\x0e' # 0x0E -> CONTROL CHARACTER
'\x0f' # 0x0F -> CONTROL CHARACTER
'\x10' # 0x10 -> CONTROL CHARACTER
'\x11' # 0x11 -> CONTROL CHARACTER
'\x12' # 0x12 -> CONTROL CHARACTER
'\x13' # 0x13 -> CONTROL CHARACTER
'\x14' # 0x14 -> CONTROL CHARACTER
'\x15' # 0x15 -> CONTROL CHARACTER
'\x16' # 0x16 -> CONTROL CHARACTER
'\x17' # 0x17 -> CONTROL CHARACTER
'\x18' # 0x18 -> CONTROL CHARACTER
'\x19' # 0x19 -> CONTROL CHARACTER
'\x1a' # 0x1A -> CONTROL CHARACTER
'\x1b' # 0x1B -> CONTROL CHARACTER
'\x1c' # 0x1C -> CONTROL CHARACTER
'\x1d' # 0x1D -> CONTROL CHARACTER
'\x1e' # 0x1E -> CONTROL CHARACTER
'\x1f' # 0x1F -> CONTROL CHARACTER
' ' # 0x20 -> SPACE
'!' # 0x21 -> EXCLAMATION MARK
'"' # 0x22 -> QUOTATION MARK
'#' # 0x23 -> NUMBER SIGN
'$' # 0x24 -> DOLLAR SIGN
'%' # 0x25 -> PERCENT SIGN
'&' # 0x26 -> AMPERSAND
"'" # 0x27 -> APOSTROPHE
'(' # 0x28 -> LEFT PARENTHESIS
')' # 0x29 -> RIGHT PARENTHESIS
'*' # 0x2A -> ASTERISK
'+' # 0x2B -> PLUS SIGN
',' # 0x2C -> COMMA
'-' # 0x2D -> HYPHEN-MINUS
'.' # 0x2E -> FULL STOP
'/' # 0x2F -> SOLIDUS
'0' # 0x30 -> DIGIT ZERO
'1' # 0x31 -> DIGIT ONE
'2' # 0x32 -> DIGIT TWO
'3' # 0x33 -> DIGIT THREE
'4' # 0x34 -> DIGIT FOUR
'5' # 0x35 -> DIGIT FIVE
'6' # 0x36 -> DIGIT SIX
'7' # 0x37 -> DIGIT SEVEN
'8' # 0x38 -> DIGIT EIGHT
'9' # 0x39 -> DIGIT NINE
':' # 0x3A -> COLON
';' # 0x3B -> SEMICOLON
'<' # 0x3C -> LESS-THAN SIGN
'=' # 0x3D -> EQUALS SIGN
'>' # 0x3E -> GREATER-THAN SIGN
'?' # 0x3F -> QUESTION MARK
'@' # 0x40 -> COMMERCIAL AT
'A' # 0x41 -> LATIN CAPITAL LETTER A
'B' # 0x42 -> LATIN CAPITAL LETTER B
'C' # 0x43 -> LATIN CAPITAL LETTER C
'D' # 0x44 -> LATIN CAPITAL LETTER D
'E' # 0x45 -> LATIN CAPITAL LETTER E
'F' # 0x46 -> LATIN CAPITAL LETTER F
'G' # 0x47 -> LATIN CAPITAL LETTER G
'H' # 0x48 -> LATIN CAPITAL LETTER H
'I' # 0x49 -> LATIN CAPITAL LETTER I
'J' # 0x4A -> LATIN CAPITAL LETTER J
'K' # 0x4B -> LATIN CAPITAL LETTER K
'L' # 0x4C -> LATIN CAPITAL LETTER L
'M' # 0x4D -> LATIN CAPITAL LETTER M
'N' # 0x4E -> LATIN CAPITAL LETTER N
'O' # 0x4F -> LATIN CAPITAL LETTER O
'P' # 0x50 -> LATIN CAPITAL LETTER P
'Q' # 0x51 -> LATIN CAPITAL LETTER Q
'R' # 0x52 -> LATIN CAPITAL LETTER R
'S' # 0x53 -> LATIN CAPITAL LETTER S
'T' # 0x54 -> LATIN CAPITAL LETTER T
'U' # 0x55 -> LATIN CAPITAL LETTER U
'V' # 0x56 -> LATIN CAPITAL LETTER V
'W' # 0x57 -> LATIN CAPITAL LETTER W
'X' # 0x58 -> LATIN CAPITAL LETTER X
'Y' # 0x59 -> LATIN CAPITAL LETTER Y
'Z' # 0x5A -> LATIN CAPITAL LETTER Z
'[' # 0x5B -> LEFT SQUARE BRACKET
'\\' # 0x5C -> REVERSE SOLIDUS
']' # 0x5D -> RIGHT SQUARE BRACKET
'^' # 0x5E -> CIRCUMFLEX ACCENT
'_' # 0x5F -> LOW LINE
'`' # 0x60 -> GRAVE ACCENT
'a' # 0x61 -> LATIN SMALL LETTER A
'b' # 0x62 -> LATIN SMALL LETTER B
'c' # 0x63 -> LATIN SMALL LETTER C
'd' # 0x64 -> LATIN SMALL LETTER D
'e' # 0x65 -> LATIN SMALL LETTER E
'f' # 0x66 -> LATIN SMALL LETTER F
'g' # 0x67 -> LATIN SMALL LETTER G
'h' # 0x68 -> LATIN SMALL LETTER H
'i' # 0x69 -> LATIN SMALL LETTER I
'j' # 0x6A -> LATIN SMALL LETTER J
'k' # 0x6B -> LATIN SMALL LETTER K
'l' # 0x6C -> LATIN SMALL LETTER L
'm' # 0x6D -> LATIN SMALL LETTER M
'n' # 0x6E -> LATIN SMALL LETTER N
'o' # 0x6F -> LATIN SMALL LETTER O
'p' # 0x70 -> LATIN SMALL LETTER P
'q' # 0x71 -> LATIN SMALL LETTER Q
'r' # 0x72 -> LATIN SMALL LETTER R
's' # 0x73 -> LATIN SMALL LETTER S
't' # 0x74 -> LATIN SMALL LETTER T
'u' # 0x75 -> LATIN SMALL LETTER U
'v' # 0x76 -> LATIN SMALL LETTER V
'w' # 0x77 -> LATIN SMALL LETTER W
'x' # 0x78 -> LATIN SMALL LETTER X
'y' # 0x79 -> LATIN SMALL LETTER Y
'z' # 0x7A -> LATIN SMALL LETTER Z
'{' # 0x7B -> LEFT CURLY BRACKET
'|' # 0x7C -> VERTICAL LINE
'}' # 0x7D -> RIGHT CURLY BRACKET
'~' # 0x7E -> TILDE
'\x7f' # 0x7F -> CONTROL CHARACTER
'\u0410' # 0x80 -> CYRILLIC CAPITAL LETTER A
'\u0411' # 0x81 -> CYRILLIC CAPITAL LETTER BE
'\u0412' # 0x82 -> CYRILLIC CAPITAL LETTER VE
'\u0413' # 0x83 -> CYRILLIC CAPITAL LETTER GHE
'\u0414' # 0x84 -> CYRILLIC CAPITAL LETTER DE
'\u0415' # 0x85 -> CYRILLIC CAPITAL LETTER IE
'\u0416' # 0x86 -> CYRILLIC CAPITAL LETTER ZHE
'\u0417' # 0x87 -> CYRILLIC CAPITAL LETTER ZE
'\u0418' # 0x88 -> CYRILLIC CAPITAL LETTER I
'\u0419' # 0x89 -> CYRILLIC CAPITAL LETTER SHORT I
'\u041a' # 0x8A -> CYRILLIC CAPITAL LETTER KA
'\u041b' # 0x8B -> CYRILLIC CAPITAL LETTER EL
'\u041c' # 0x8C -> CYRILLIC CAPITAL LETTER EM
'\u041d' # 0x8D -> CYRILLIC CAPITAL LETTER EN
'\u041e' # 0x8E -> CYRILLIC CAPITAL LETTER O
'\u041f' # 0x8F -> CYRILLIC CAPITAL LETTER PE
'\u0420' # 0x90 -> CYRILLIC CAPITAL LETTER ER
'\u0421' # 0x91 -> CYRILLIC CAPITAL LETTER ES
'\u0422' # 0x92 -> CYRILLIC CAPITAL LETTER TE
'\u0423' # 0x93 -> CYRILLIC CAPITAL LETTER U
'\u0424' # 0x94 -> CYRILLIC CAPITAL LETTER EF
'\u0425' # 0x95 -> CYRILLIC CAPITAL LETTER HA
'\u0426' # 0x96 -> CYRILLIC CAPITAL LETTER TSE
'\u0427' # 0x97 -> CYRILLIC CAPITAL LETTER CHE
'\u0428' # 0x98 -> CYRILLIC CAPITAL LETTER SHA
'\u0429' # 0x99 -> CYRILLIC CAPITAL LETTER SHCHA
'\u042a' # 0x9A -> CYRILLIC CAPITAL LETTER HARD SIGN
'\u042b' # 0x9B -> CYRILLIC CAPITAL LETTER YERU
'\u042c' # 0x9C -> CYRILLIC CAPITAL LETTER SOFT SIGN
'\u042d' # 0x9D -> CYRILLIC CAPITAL LETTER E
'\u042e' # 0x9E -> CYRILLIC CAPITAL LETTER YU
'\u042f' # 0x9F -> CYRILLIC CAPITAL LETTER YA
'\u2020' # 0xA0 -> DAGGER
'\xb0' # 0xA1 -> DEGREE SIGN
'\u0490' # 0xA2 -> CYRILLIC CAPITAL LETTER GHE WITH UPTURN
'\xa3' # 0xA3 -> POUND SIGN
'\xa7' # 0xA4 -> SECTION SIGN
'\u2022' # 0xA5 -> BULLET
'\xb6' # 0xA6 -> PILCROW SIGN
'\u0406' # 0xA7 -> CYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN I
'\xae' # 0xA8 -> REGISTERED SIGN
'\xa9' # 0xA9 -> COPYRIGHT SIGN
'\u2122' # 0xAA -> TRADE MARK SIGN
'\u0402' # 0xAB -> CYRILLIC CAPITAL LETTER DJE
'\u0452' # 0xAC -> CYRILLIC SMALL LETTER DJE
'\u2260' # 0xAD -> NOT EQUAL TO
'\u0403' # 0xAE -> CYRILLIC CAPITAL LETTER GJE
'\u0453' # 0xAF -> CYRILLIC SMALL LETTER GJE
'\u221e' # 0xB0 -> INFINITY
'\xb1' # 0xB1 -> PLUS-MINUS SIGN
'\u2264' # 0xB2 -> LESS-THAN OR EQUAL TO
'\u2265' # 0xB3 -> GREATER-THAN OR EQUAL TO
'\u0456' # 0xB4 -> CYRILLIC SMALL LETTER BYELORUSSIAN-UKRAINIAN I
'\xb5' # 0xB5 -> MICRO SIGN
'\u0491' # 0xB6 -> CYRILLIC SMALL LETTER GHE WITH UPTURN
'\u0408' # 0xB7 -> CYRILLIC CAPITAL LETTER JE
'\u0404' # 0xB8 -> CYRILLIC CAPITAL LETTER UKRAINIAN IE
'\u0454' # 0xB9 -> CYRILLIC SMALL LETTER UKRAINIAN IE
'\u0407' # 0xBA -> CYRILLIC CAPITAL LETTER YI
'\u0457' # 0xBB -> CYRILLIC SMALL LETTER YI
'\u0409' # 0xBC -> CYRILLIC CAPITAL LETTER LJE
'\u0459' # 0xBD -> CYRILLIC SMALL LETTER LJE
'\u040a' # 0xBE -> CYRILLIC CAPITAL LETTER NJE
'\u045a' # 0xBF -> CYRILLIC SMALL LETTER NJE
'\u0458' # 0xC0 -> CYRILLIC SMALL LETTER JE
'\u0405' # 0xC1 -> CYRILLIC CAPITAL LETTER DZE
'\xac' # 0xC2 -> NOT SIGN
'\u221a' # 0xC3 -> SQUARE ROOT
'\u0192' # 0xC4 -> LATIN SMALL LETTER F WITH HOOK
'\u2248' # 0xC5 -> ALMOST EQUAL TO
'\u2206' # 0xC6 -> INCREMENT
'\xab' # 0xC7 -> LEFT-POINTING DOUBLE ANGLE QUOTATION MARK
'\xbb' # 0xC8 -> RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK
'\u2026' # 0xC9 -> HORIZONTAL ELLIPSIS
'\xa0' # 0xCA -> NO-BREAK SPACE
'\u040b' # 0xCB -> CYRILLIC CAPITAL LETTER TSHE
'\u045b' # 0xCC -> CYRILLIC SMALL LETTER TSHE
'\u040c' # 0xCD -> CYRILLIC CAPITAL LETTER KJE
'\u045c' # 0xCE -> CYRILLIC SMALL LETTER KJE
'\u0455' # 0xCF -> CYRILLIC SMALL LETTER DZE
'\u2013' # 0xD0 -> EN DASH
'\u2014' # 0xD1 -> EM DASH
'\u201c' # 0xD2 -> LEFT DOUBLE QUOTATION MARK
'\u201d' # 0xD3 -> RIGHT DOUBLE QUOTATION MARK
'\u2018' # 0xD4 -> LEFT SINGLE QUOTATION MARK
'\u2019' # 0xD5 -> RIGHT SINGLE QUOTATION MARK
'\xf7' # 0xD6 -> DIVISION SIGN
'\u201e' # 0xD7 -> DOUBLE LOW-9 QUOTATION MARK
'\u040e' # 0xD8 -> CYRILLIC CAPITAL LETTER SHORT U
'\u045e' # 0xD9 -> CYRILLIC SMALL LETTER SHORT U
'\u040f' # 0xDA -> CYRILLIC CAPITAL LETTER DZHE
'\u045f' # 0xDB -> CYRILLIC SMALL LETTER DZHE
'\u2116' # 0xDC -> NUMERO SIGN
'\u0401' # 0xDD -> CYRILLIC CAPITAL LETTER IO
'\u0451' # 0xDE -> CYRILLIC SMALL LETTER IO
'\u044f' # 0xDF -> CYRILLIC SMALL LETTER YA
'\u0430' # 0xE0 -> CYRILLIC SMALL LETTER A
'\u0431' # 0xE1 -> CYRILLIC SMALL LETTER BE
'\u0432' # 0xE2 -> CYRILLIC SMALL LETTER VE
'\u0433' # 0xE3 -> CYRILLIC SMALL LETTER GHE
'\u0434' # 0xE4 -> CYRILLIC SMALL LETTER DE
'\u0435' # 0xE5 -> CYRILLIC SMALL LETTER IE
'\u0436' # 0xE6 -> CYRILLIC SMALL LETTER ZHE
'\u0437' # 0xE7 -> CYRILLIC SMALL LETTER ZE
'\u0438' # 0xE8 -> CYRILLIC SMALL LETTER I
'\u0439' # 0xE9 -> CYRILLIC SMALL LETTER SHORT I
'\u043a' # 0xEA -> CYRILLIC SMALL LETTER KA
'\u043b' # 0xEB -> CYRILLIC SMALL LETTER EL
'\u043c' # 0xEC -> CYRILLIC SMALL LETTER EM
'\u043d' # 0xED -> CYRILLIC SMALL LETTER EN
'\u043e' # 0xEE -> CYRILLIC SMALL LETTER O
'\u043f' # 0xEF -> CYRILLIC SMALL LETTER PE
'\u0440' # 0xF0 -> CYRILLIC SMALL LETTER ER
'\u0441' # 0xF1 -> CYRILLIC SMALL LETTER ES
'\u0442' # 0xF2 -> CYRILLIC SMALL LETTER TE
'\u0443' # 0xF3 -> CYRILLIC SMALL LETTER U
'\u0444' # 0xF4 -> CYRILLIC SMALL LETTER EF
'\u0445' # 0xF5 -> CYRILLIC SMALL LETTER HA
'\u0446' # 0xF6 -> CYRILLIC SMALL LETTER TSE
'\u0447' # 0xF7 -> CYRILLIC SMALL LETTER CHE
'\u0448' # 0xF8 -> CYRILLIC SMALL LETTER SHA
'\u0449' # 0xF9 -> CYRILLIC SMALL LETTER SHCHA
'\u044a' # 0xFA -> CYRILLIC SMALL LETTER HARD SIGN
'\u044b' # 0xFB -> CYRILLIC SMALL LETTER YERU
'\u044c' # 0xFC -> CYRILLIC SMALL LETTER SOFT SIGN
'\u044d' # 0xFD -> CYRILLIC SMALL LETTER E
'\u044e' # 0xFE -> CYRILLIC SMALL LETTER YU
'\u20ac' # 0xFF -> EURO SIGN
)
### Encoding table
encoding_table=codecs.charmap_build(decoding_table)
| 43.681818 | 118 | 0.549353 |
import codecs
c):
def encode(self,input,errors='strict'):
return codecs.charmap_encode(input,errors,encoding_table)
def decode(self,input,errors='strict'):
return codecs.charmap_decode(input,errors,decoding_table)
class IncrementalEncoder(codecs.IncrementalEncoder):
def encode(self, input, final=False):
return codecs.charmap_encode(input,self.errors,encoding_table)[0]
class IncrementalDecoder(codecs.IncrementalDecoder):
def decode(self, input, final=False):
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
class StreamWriter(Codec,codecs.StreamWriter):
pass
class StreamReader(Codec,codecs.StreamReader):
pass
nfo(
name='mac-cyrillic',
encode=Codec().encode,
decode=Codec().decode,
incrementalencoder=IncrementalEncoder,
incrementaldecoder=IncrementalDecoder,
streamreader=StreamReader,
streamwriter=StreamWriter,
)
'\x01'
'\x02'
'\x03'
'\x04'
'\x05'
'\x06'
'\x07'
'\x08'
'\t'
'\n'
'\x0b'
'\x0c'
'\r'
'\x0e'
'\x0f'
'\x10'
'\x11'
'\x12'
'\x13'
'\x14'
'\x15'
'\x16'
'\x17'
'\x18'
'\x19'
'\x1a'
'\x1b'
'\x1c'
'\x1d'
'\x1e'
'\x1f'
' '
'!'
'"' # 0x22 -> QUOTATION MARK
'#' # 0x23 -> NUMBER SIGN
'$' # 0x24 -> DOLLAR SIGN
'%' # 0x25 -> PERCENT SIGN
'&' # 0x26 -> AMPERSAND
"'" # 0x27 -> APOSTROPHE
'(' # 0x28 -> LEFT PARENTHESIS
')' # 0x29 -> RIGHT PARENTHESIS
'*' # 0x2A -> ASTERISK
'+' # 0x2B -> PLUS SIGN
',' # 0x2C -> COMMA
'-' # 0x2D -> HYPHEN-MINUS
'.' # 0x2E -> FULL STOP
'/' # 0x2F -> SOLIDUS
'0' # 0x30 -> DIGIT ZERO
'1' # 0x31 -> DIGIT ONE
'2' # 0x32 -> DIGIT TWO
'3' # 0x33 -> DIGIT THREE
'4' # 0x34 -> DIGIT FOUR
'5' # 0x35 -> DIGIT FIVE
'6' # 0x36 -> DIGIT SIX
'7' # 0x37 -> DIGIT SEVEN
'8' # 0x38 -> DIGIT EIGHT
'9' # 0x39 -> DIGIT NINE
':' # 0x3A -> COLON
';' # 0x3B -> SEMICOLON
'<' # 0x3C -> LESS-THAN SIGN
'=' # 0x3D -> EQUALS SIGN
'>' # 0x3E -> GREATER-THAN SIGN
'?' # 0x3F -> QUESTION MARK
'@' # 0x40 -> COMMERCIAL AT
'A' # 0x41 -> LATIN CAPITAL LETTER A
'B' # 0x42 -> LATIN CAPITAL LETTER B
'C' # 0x43 -> LATIN CAPITAL LETTER C
'D' # 0x44 -> LATIN CAPITAL LETTER D
'E' # 0x45 -> LATIN CAPITAL LETTER E
'F' # 0x46 -> LATIN CAPITAL LETTER F
'G' # 0x47 -> LATIN CAPITAL LETTER G
'H' # 0x48 -> LATIN CAPITAL LETTER H
'I' # 0x49 -> LATIN CAPITAL LETTER I
'J' # 0x4A -> LATIN CAPITAL LETTER J
'K' # 0x4B -> LATIN CAPITAL LETTER K
'L' # 0x4C -> LATIN CAPITAL LETTER L
'M' # 0x4D -> LATIN CAPITAL LETTER M
'N' # 0x4E -> LATIN CAPITAL LETTER N
'O' # 0x4F -> LATIN CAPITAL LETTER O
'P' # 0x50 -> LATIN CAPITAL LETTER P
'Q' # 0x51 -> LATIN CAPITAL LETTER Q
'R' # 0x52 -> LATIN CAPITAL LETTER R
'S' # 0x53 -> LATIN CAPITAL LETTER S
'T' # 0x54 -> LATIN CAPITAL LETTER T
'U' # 0x55 -> LATIN CAPITAL LETTER U
'V' # 0x56 -> LATIN CAPITAL LETTER V
'W' # 0x57 -> LATIN CAPITAL LETTER W
'X' # 0x58 -> LATIN CAPITAL LETTER X
'Y' # 0x59 -> LATIN CAPITAL LETTER Y
'Z' # 0x5A -> LATIN CAPITAL LETTER Z
'[' # 0x5B -> LEFT SQUARE BRACKET
'\\' # 0x5C -> REVERSE SOLIDUS
']' # 0x5D -> RIGHT SQUARE BRACKET
'^' # 0x5E -> CIRCUMFLEX ACCENT
'_' # 0x5F -> LOW LINE
'`' # 0x60 -> GRAVE ACCENT
'a' # 0x61 -> LATIN SMALL LETTER A
'b' # 0x62 -> LATIN SMALL LETTER B
'c' # 0x63 -> LATIN SMALL LETTER C
'd' # 0x64 -> LATIN SMALL LETTER D
'e' # 0x65 -> LATIN SMALL LETTER E
'f' # 0x66 -> LATIN SMALL LETTER F
'g' # 0x67 -> LATIN SMALL LETTER G
'h' # 0x68 -> LATIN SMALL LETTER H
'i' # 0x69 -> LATIN SMALL LETTER I
'j' # 0x6A -> LATIN SMALL LETTER J
'k' # 0x6B -> LATIN SMALL LETTER K
'l' # 0x6C -> LATIN SMALL LETTER L
'm' # 0x6D -> LATIN SMALL LETTER M
'n' # 0x6E -> LATIN SMALL LETTER N
'o' # 0x6F -> LATIN SMALL LETTER O
'p' # 0x70 -> LATIN SMALL LETTER P
'q' # 0x71 -> LATIN SMALL LETTER Q
'r' # 0x72 -> LATIN SMALL LETTER R
's' # 0x73 -> LATIN SMALL LETTER S
't' # 0x74 -> LATIN SMALL LETTER T
'u' # 0x75 -> LATIN SMALL LETTER U
'v' # 0x76 -> LATIN SMALL LETTER V
'w' # 0x77 -> LATIN SMALL LETTER W
'x' # 0x78 -> LATIN SMALL LETTER X
'y' # 0x79 -> LATIN SMALL LETTER Y
'z' # 0x7A -> LATIN SMALL LETTER Z
'{' # 0x7B -> LEFT CURLY BRACKET
'|' # 0x7C -> VERTICAL LINE
'}' # 0x7D -> RIGHT CURLY BRACKET
'~' # 0x7E -> TILDE
'\x7f' # 0x7F -> CONTROL CHARACTER
'\u0410' # 0x80 -> CYRILLIC CAPITAL LETTER A
'\u0411' # 0x81 -> CYRILLIC CAPITAL LETTER BE
'\u0412' # 0x82 -> CYRILLIC CAPITAL LETTER VE
'\u0413' # 0x83 -> CYRILLIC CAPITAL LETTER GHE
'\u0414' # 0x84 -> CYRILLIC CAPITAL LETTER DE
'\u0415' # 0x85 -> CYRILLIC CAPITAL LETTER IE
'\u0416' # 0x86 -> CYRILLIC CAPITAL LETTER ZHE
'\u0417' # 0x87 -> CYRILLIC CAPITAL LETTER ZE
'\u0418' # 0x88 -> CYRILLIC CAPITAL LETTER I
'\u0419' # 0x89 -> CYRILLIC CAPITAL LETTER SHORT I
'\u041a' # 0x8A -> CYRILLIC CAPITAL LETTER KA
'\u041b' # 0x8B -> CYRILLIC CAPITAL LETTER EL
'\u041c' # 0x8C -> CYRILLIC CAPITAL LETTER EM
'\u041d' # 0x8D -> CYRILLIC CAPITAL LETTER EN
'\u041e' # 0x8E -> CYRILLIC CAPITAL LETTER O
'\u041f' # 0x8F -> CYRILLIC CAPITAL LETTER PE
'\u0420' # 0x90 -> CYRILLIC CAPITAL LETTER ER
'\u0421' # 0x91 -> CYRILLIC CAPITAL LETTER ES
'\u0422' # 0x92 -> CYRILLIC CAPITAL LETTER TE
'\u0423' # 0x93 -> CYRILLIC CAPITAL LETTER U
'\u0424' # 0x94 -> CYRILLIC CAPITAL LETTER EF
'\u0425' # 0x95 -> CYRILLIC CAPITAL LETTER HA
'\u0426' # 0x96 -> CYRILLIC CAPITAL LETTER TSE
'\u0427' # 0x97 -> CYRILLIC CAPITAL LETTER CHE
'\u0428' # 0x98 -> CYRILLIC CAPITAL LETTER SHA
'\u0429' # 0x99 -> CYRILLIC CAPITAL LETTER SHCHA
'\u042a' # 0x9A -> CYRILLIC CAPITAL LETTER HARD SIGN
'\u042b' # 0x9B -> CYRILLIC CAPITAL LETTER YERU
'\u042c' # 0x9C -> CYRILLIC CAPITAL LETTER SOFT SIGN
'\u042d' # 0x9D -> CYRILLIC CAPITAL LETTER E
'\u042e' # 0x9E -> CYRILLIC CAPITAL LETTER YU
'\u042f' # 0x9F -> CYRILLIC CAPITAL LETTER YA
'\u2020' # 0xA0 -> DAGGER
'\xb0' # 0xA1 -> DEGREE SIGN
'\u0490' # 0xA2 -> CYRILLIC CAPITAL LETTER GHE WITH UPTURN
'\xa3' # 0xA3 -> POUND SIGN
'\xa7' # 0xA4 -> SECTION SIGN
'\u2022' # 0xA5 -> BULLET
'\xb6' # 0xA6 -> PILCROW SIGN
'\u0406' # 0xA7 -> CYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN I
'\xae' # 0xA8 -> REGISTERED SIGN
'\xa9' # 0xA9 -> COPYRIGHT SIGN
'\u2122' # 0xAA -> TRADE MARK SIGN
'\u0402' # 0xAB -> CYRILLIC CAPITAL LETTER DJE
'\u0452' # 0xAC -> CYRILLIC SMALL LETTER DJE
'\u2260' # 0xAD -> NOT EQUAL TO
'\u0403' # 0xAE -> CYRILLIC CAPITAL LETTER GJE
'\u0453' # 0xAF -> CYRILLIC SMALL LETTER GJE
'\u221e' # 0xB0 -> INFINITY
'\xb1' # 0xB1 -> PLUS-MINUS SIGN
'\u2264' # 0xB2 -> LESS-THAN OR EQUAL TO
'\u2265' # 0xB3 -> GREATER-THAN OR EQUAL TO
'\u0456' # 0xB4 -> CYRILLIC SMALL LETTER BYELORUSSIAN-UKRAINIAN I
'\xb5' # 0xB5 -> MICRO SIGN
'\u0491' # 0xB6 -> CYRILLIC SMALL LETTER GHE WITH UPTURN
'\u0408' # 0xB7 -> CYRILLIC CAPITAL LETTER JE
'\u0404' # 0xB8 -> CYRILLIC CAPITAL LETTER UKRAINIAN IE
'\u0454' # 0xB9 -> CYRILLIC SMALL LETTER UKRAINIAN IE
'\u0407' # 0xBA -> CYRILLIC CAPITAL LETTER YI
'\u0457' # 0xBB -> CYRILLIC SMALL LETTER YI
'\u0409' # 0xBC -> CYRILLIC CAPITAL LETTER LJE
'\u0459' # 0xBD -> CYRILLIC SMALL LETTER LJE
'\u040a' # 0xBE -> CYRILLIC CAPITAL LETTER NJE
'\u045a' # 0xBF -> CYRILLIC SMALL LETTER NJE
'\u0458' # 0xC0 -> CYRILLIC SMALL LETTER JE
'\u0405' # 0xC1 -> CYRILLIC CAPITAL LETTER DZE
'\xac' # 0xC2 -> NOT SIGN
'\u221a' # 0xC3 -> SQUARE ROOT
'\u0192' # 0xC4 -> LATIN SMALL LETTER F WITH HOOK
'\u2248' # 0xC5 -> ALMOST EQUAL TO
'\u2206' # 0xC6 -> INCREMENT
'\xab' # 0xC7 -> LEFT-POINTING DOUBLE ANGLE QUOTATION MARK
'\xbb' # 0xC8 -> RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK
'\u2026' # 0xC9 -> HORIZONTAL ELLIPSIS
'\xa0' # 0xCA -> NO-BREAK SPACE
'\u040b' # 0xCB -> CYRILLIC CAPITAL LETTER TSHE
'\u045b' # 0xCC -> CYRILLIC SMALL LETTER TSHE
'\u040c' # 0xCD -> CYRILLIC CAPITAL LETTER KJE
'\u045c' # 0xCE -> CYRILLIC SMALL LETTER KJE
'\u0455' # 0xCF -> CYRILLIC SMALL LETTER DZE
'\u2013' # 0xD0 -> EN DASH
'\u2014' # 0xD1 -> EM DASH
'\u201c' # 0xD2 -> LEFT DOUBLE QUOTATION MARK
'\u201d' # 0xD3 -> RIGHT DOUBLE QUOTATION MARK
'\u2018' # 0xD4 -> LEFT SINGLE QUOTATION MARK
'\u2019' # 0xD5 -> RIGHT SINGLE QUOTATION MARK
'\xf7' # 0xD6 -> DIVISION SIGN
'\u201e' # 0xD7 -> DOUBLE LOW-9 QUOTATION MARK
'\u040e' # 0xD8 -> CYRILLIC CAPITAL LETTER SHORT U
'\u045e' # 0xD9 -> CYRILLIC SMALL LETTER SHORT U
'\u040f' # 0xDA -> CYRILLIC CAPITAL LETTER DZHE
'\u045f' # 0xDB -> CYRILLIC SMALL LETTER DZHE
'\u2116' # 0xDC -> NUMERO SIGN
'\u0401' # 0xDD -> CYRILLIC CAPITAL LETTER IO
'\u0451' # 0xDE -> CYRILLIC SMALL LETTER IO
'\u044f' # 0xDF -> CYRILLIC SMALL LETTER YA
'\u0430' # 0xE0 -> CYRILLIC SMALL LETTER A
'\u0431' # 0xE1 -> CYRILLIC SMALL LETTER BE
'\u0432' # 0xE2 -> CYRILLIC SMALL LETTER VE
'\u0433' # 0xE3 -> CYRILLIC SMALL LETTER GHE
'\u0434' # 0xE4 -> CYRILLIC SMALL LETTER DE
'\u0435' # 0xE5 -> CYRILLIC SMALL LETTER IE
'\u0436' # 0xE6 -> CYRILLIC SMALL LETTER ZHE
'\u0437' # 0xE7 -> CYRILLIC SMALL LETTER ZE
'\u0438' # 0xE8 -> CYRILLIC SMALL LETTER I
'\u0439' # 0xE9 -> CYRILLIC SMALL LETTER SHORT I
'\u043a' # 0xEA -> CYRILLIC SMALL LETTER KA
'\u043b' # 0xEB -> CYRILLIC SMALL LETTER EL
'\u043c' # 0xEC -> CYRILLIC SMALL LETTER EM
'\u043d' # 0xED -> CYRILLIC SMALL LETTER EN
'\u043e' # 0xEE -> CYRILLIC SMALL LETTER O
'\u043f' # 0xEF -> CYRILLIC SMALL LETTER PE
'\u0440' # 0xF0 -> CYRILLIC SMALL LETTER ER
'\u0441' # 0xF1 -> CYRILLIC SMALL LETTER ES
'\u0442' # 0xF2 -> CYRILLIC SMALL LETTER TE
'\u0443' # 0xF3 -> CYRILLIC SMALL LETTER U
'\u0444' # 0xF4 -> CYRILLIC SMALL LETTER EF
'\u0445' # 0xF5 -> CYRILLIC SMALL LETTER HA
'\u0446' # 0xF6 -> CYRILLIC SMALL LETTER TSE
'\u0447' # 0xF7 -> CYRILLIC SMALL LETTER CHE
'\u0448' # 0xF8 -> CYRILLIC SMALL LETTER SHA
'\u0449' # 0xF9 -> CYRILLIC SMALL LETTER SHCHA
'\u044a' # 0xFA -> CYRILLIC SMALL LETTER HARD SIGN
'\u044b' # 0xFB -> CYRILLIC SMALL LETTER YERU
'\u044c' # 0xFC -> CYRILLIC SMALL LETTER SOFT SIGN
'\u044d' # 0xFD -> CYRILLIC SMALL LETTER E
'\u044e' # 0xFE -> CYRILLIC SMALL LETTER YU
'\u20ac' # 0xFF -> EURO SIGN
)
### Encoding table
encoding_table=codecs.charmap_build(decoding_table)
| true | true |
f72ca5261e26e28890b2ead99f9ab8ea92310208 | 9,487 | py | Python | test/fb_cases_util.py | savinshynu/turbo_seti | 7d756f130af5a323403affcdcb9f9bfa62325836 | [
"MIT"
] | 33 | 2017-05-09T03:31:38.000Z | 2022-03-26T01:29:35.000Z | test/fb_cases_util.py | savinshynu/turbo_seti | 7d756f130af5a323403affcdcb9f9bfa62325836 | [
"MIT"
] | 284 | 2018-03-13T13:57:09.000Z | 2022-03-30T21:59:34.000Z | test/fb_cases_util.py | savinshynu/turbo_seti | 7d756f130af5a323403affcdcb9f9bfa62325836 | [
"MIT"
] | 116 | 2017-08-08T17:27:30.000Z | 2022-03-24T21:24:40.000Z | r'''
Utility functions for test_fb_cases.py
'''
from os import mkdir, remove
from os.path import dirname
from shutil import rmtree
import logging
import pandas as pd
import numpy as np
import setigen as stg
from turbo_seti.find_doppler.find_doppler import FindDoppler
from fb_cases_def import HERE, DEBUGGING, RTOL_DIFF, TestResultRecord, SetigenParms
DF_REFERENCE = HERE + '/fb_dat_reference.txt'
SEP = r'\s+'
def initialize(arg_dir):
r'''
Recreate working directory, TESTDIR.
Load result reference tables (2).
'''
rmtree(arg_dir, ignore_errors=True)
mkdir(arg_dir)
df = pd.read_csv(DF_REFERENCE, sep=SEP, engine='python', comment='#')
nrows = len(df)
if nrows < 1:
raise ValueError('initialize: Empty reference table')
if nrows % 2 != 0:
raise ValueError('initialize: Reference table row count ({}) is not divisible by 2'
.format(nrows))
if DEBUGGING:
print('initialize: Test case reference results: \n', df)
ref_tophit_1 = []
ref_tophit_2 = []
jj = 0
while jj < nrows:
record = TestResultRecord()
record.fdir = int(df['fdir'][jj])
record.drsign = int(df['drsign'][jj])
record.tophit_id = int(df['tophit'][jj])
record.drate = float(df['drate'][jj])
record.snr = float(df['snr'][jj])
record.freq = float(df['freq'][jj])
record.index = int(df['index'][jj])
ref_tophit_1.append(record)
if DEBUGGING:
print('initialize: appended for hit_1:\n', record.to_string() )
jj += 1
del record
record = TestResultRecord()
record.fdir = int(df['fdir'][jj])
record.drsign = int(df['drsign'][jj])
record.tophit_id = int(df['tophit'][jj])
record.drate = float(df['drate'][jj])
record.snr = float(df['snr'][jj])
record.freq = float(df['freq'][jj])
record.index = int(df['index'][jj])
ref_tophit_2.append(record)
if DEBUGGING:
print('initialize: appended for hit_2:\n', record.to_string() )
jj += 1
if DEBUGGING:
print('initialize: {} test cases loaded.'.format(len(ref_tophit_1)))
return ref_tophit_1, ref_tophit_2
def generate_fil_file(outpath, flag_fascending, flag_sign_drift_rate):
r'''
Using setigen, generate a filterbank file.
Parameters:
outpath - full path of where to store the resultant filterbank file.
flag_fascending - use an ascending (+1) or descending (-1) sequence of frequencies
flag_sign_drift_rate - use a positive (+1) or negative (-1) drift rate
'''
if DEBUGGING:
print('generate_fil_file: flag_fascending={}, flag_sign_drift_rate={}'
.format(flag_fascending, flag_sign_drift_rate))
# Set up setigne parameters
stg_parms = SetigenParms()
if flag_sign_drift_rate < 0:
stg_parms.drift_rate_1 = -stg_parms.drift_rate_1
stg_parms.drift_rate_2 = -stg_parms.drift_rate_2
stg_parms.drift_rate_3 = -stg_parms.drift_rate_3
stg_parms.drift_rate_4 = -stg_parms.drift_rate_4
stg_parms.drift_rate_5 = -stg_parms.drift_rate_5
# Instantiate a setigen Frame object
frame = stg.Frame(fchans=stg_parms.fchans,
tchans=stg_parms.tchans,
df=stg_parms.df,
dt=stg_parms.dt,
fch1=stg_parms.fch1,
ascending=(flag_fascending > 0))
# Add noise to stg object.
frame.add_noise(x_mean=0, x_std=stg_parms.noise_std, noise_type='gaussian')
# Signal 1 will be detected.
signal_1_intensity = frame.get_intensity(snr=stg_parms.snr_1)
frame.add_constant_signal(f_start=frame.get_frequency(stg_parms.signal_start_1),
drift_rate=stg_parms.drift_rate_1,
level=signal_1_intensity,
width=stg_parms.width_1,
f_profile_type='gaussian')
# Signal 2 will be detected.
signal_2_intensity = frame.get_intensity(snr=stg_parms.snr_2)
frame.add_constant_signal(f_start=frame.get_frequency(stg_parms.signal_start_2),
drift_rate=stg_parms.drift_rate_2,
level=signal_2_intensity,
width=stg_parms.width_2,
f_profile_type='gaussian')
# Signal 3 is a symmetric signal with three Gaussians
# that will fall below the SNR requirements.
signal_3_intensity = frame.get_intensity(snr=stg_parms.snr_3)
frame.add_signal(stg.constant_path(f_start=frame.get_frequency(stg_parms.signal_start_3),
drift_rate=stg_parms.drift_rate_3),
stg.constant_t_profile(level=1),
stg.multiple_gaussian_f_profile(width=stg_parms.width_3),
stg.constant_bp_profile(level=signal_3_intensity))
# Signal 4 is a symmetric signal with three Gaussians
# that will be drifting too quickly.
signal_4_intensity = frame.get_intensity(snr=stg_parms.snr_4)
frame.add_signal(stg.constant_path(f_start=frame.get_frequency(stg_parms.signal_start_4),
drift_rate=stg_parms.drift_rate_4),
stg.constant_t_profile(level=1),
stg.multiple_gaussian_f_profile(width=stg_parms.width_4),
stg.constant_bp_profile(level=signal_4_intensity))
# Signal 5 is similar to signal 4 but drifting in the opposite direction.
signal_5_intensity = frame.get_intensity(snr=stg_parms.snr_5)
frame.add_signal(stg.constant_path(f_start=frame.get_frequency(stg_parms.signal_start_5),
drift_rate=stg_parms.drift_rate_5),
stg.constant_t_profile(level=1),
stg.multiple_gaussian_f_profile(width=stg_parms.width_5),
stg.constant_bp_profile(level=signal_5_intensity))
# Save the frame as a filterbank file.
frame.save_fil(filename=outpath)
print("generate_fil_file: generated {}".format(outpath))
del frame
def make_one_dat_file(arg_path_fil, min_drift=0.0, max_drift=4.0, min_snr=25.0, remove_h5=True):
r'''
Make a single DAT file:
* Instantiate the FindDoppler class object.
* With the object, search the H5, creating the DAT file
and a LOG file (not used).
'''
if max_drift is None:
raise ValueError('make_one_dat_file: max_drift not set')
woutdir = dirname(arg_path_fil)
fdop = FindDoppler(datafile=arg_path_fil,
min_drift=min_drift,
max_drift=max_drift,
snr=min_snr,
log_level_int=logging.WARNING,
out_dir=woutdir)
fdop.search()
path_h5_file = arg_path_fil.replace('.fil', '.h5')
if remove_h5:
remove(path_h5_file)
def get_case_results(arg_path_dat):
r'''From the DAT file, extract the data for all top hits.'''
df = pd.read_csv(arg_path_dat, header=None, sep=SEP, engine='python', comment='#')
nrows = len(df)
if nrows != 2:
raise ValueError('get_case_results: Expected 2 rows in DAT but observed {} rows'
.format(nrows))
obs_tophit_1 = TestResultRecord()
obs_tophit_1.tophit_id = int(df[0][0]) # 1st col, 1st row
obs_tophit_1.drate = float(df[1][0])
obs_tophit_1.snr = float(df[2][0])
obs_tophit_1.freq = float(df[4][0])
obs_tophit_1.index = int(df[5][0])
obs_tophit_2 = TestResultRecord()
obs_tophit_2.tophit_id = int(df[0][1]) # 1st col, 2nd row
obs_tophit_2.drate = float(df[1][1])
obs_tophit_2.snr = float(df[2][1])
obs_tophit_2.freq = float(df[4][1])
obs_tophit_2.index = int(df[5][1])
return obs_tophit_1, obs_tophit_2
def case_comparison(obs_tophit, ref_tophit, max_drift):
r'''Compare DAT file observations to the reference.'''
if obs_tophit is None:
if ref_tophit is None:
return # success, both None
# ref_tophit defined, obs_tophit is None
raise ValueError('case_comparison: FAILED, max_drift={}\nobs_tophit is None\nref_tophit:::{}'
.format(max_drift, ref_tophit.to_string()))
if ref_tophit is None: # obs_tophit defined, ref_tophit is None
raise ValueError('case_comparison: FAILED, max_drift={}\nref_tophit is None\nobs_tophit:::{}'
.format(max_drift, obs_tophit.to_string()))
if obs_tophit.tophit_id == ref_tophit.tophit_id \
and np.isclose(obs_tophit.drate, ref_tophit.drate, rtol=RTOL_DIFF) \
and np.isclose(obs_tophit.snr, ref_tophit.snr, rtol=RTOL_DIFF) \
and np.isclose(obs_tophit.freq, ref_tophit.freq, rtol=RTOL_DIFF) \
and obs_tophit.index == ref_tophit.index:
return # success
# Some field(s) did not compare correctly.
raise ValueError('case_comparison: FAILED, max_drift={}\nobs_tophit:::{}\nref_tophit:::{}'
.format(max_drift, obs_tophit.to_string(), ref_tophit.to_string()))
if __name__ == '__main__':
# __main__ is a developer unit test, not normally to be executed.
from fb_cases_def import TESTDIR, PATH_FIL_FILE, MIN_SNR
rmtree(TESTDIR, ignore_errors=True)
mkdir(TESTDIR)
generate_fil_file(PATH_FIL_FILE, -1, -1)
make_one_dat_file(PATH_FIL_FILE, max_drift=5, min_snr=MIN_SNR)
| 41.792952 | 101 | 0.64288 |
from os import mkdir, remove
from os.path import dirname
from shutil import rmtree
import logging
import pandas as pd
import numpy as np
import setigen as stg
from turbo_seti.find_doppler.find_doppler import FindDoppler
from fb_cases_def import HERE, DEBUGGING, RTOL_DIFF, TestResultRecord, SetigenParms
DF_REFERENCE = HERE + '/fb_dat_reference.txt'
SEP = r'\s+'
def initialize(arg_dir):
rmtree(arg_dir, ignore_errors=True)
mkdir(arg_dir)
df = pd.read_csv(DF_REFERENCE, sep=SEP, engine='python', comment='#')
nrows = len(df)
if nrows < 1:
raise ValueError('initialize: Empty reference table')
if nrows % 2 != 0:
raise ValueError('initialize: Reference table row count ({}) is not divisible by 2'
.format(nrows))
if DEBUGGING:
print('initialize: Test case reference results: \n', df)
ref_tophit_1 = []
ref_tophit_2 = []
jj = 0
while jj < nrows:
record = TestResultRecord()
record.fdir = int(df['fdir'][jj])
record.drsign = int(df['drsign'][jj])
record.tophit_id = int(df['tophit'][jj])
record.drate = float(df['drate'][jj])
record.snr = float(df['snr'][jj])
record.freq = float(df['freq'][jj])
record.index = int(df['index'][jj])
ref_tophit_1.append(record)
if DEBUGGING:
print('initialize: appended for hit_1:\n', record.to_string() )
jj += 1
del record
record = TestResultRecord()
record.fdir = int(df['fdir'][jj])
record.drsign = int(df['drsign'][jj])
record.tophit_id = int(df['tophit'][jj])
record.drate = float(df['drate'][jj])
record.snr = float(df['snr'][jj])
record.freq = float(df['freq'][jj])
record.index = int(df['index'][jj])
ref_tophit_2.append(record)
if DEBUGGING:
print('initialize: appended for hit_2:\n', record.to_string() )
jj += 1
if DEBUGGING:
print('initialize: {} test cases loaded.'.format(len(ref_tophit_1)))
return ref_tophit_1, ref_tophit_2
def generate_fil_file(outpath, flag_fascending, flag_sign_drift_rate):
if DEBUGGING:
print('generate_fil_file: flag_fascending={}, flag_sign_drift_rate={}'
.format(flag_fascending, flag_sign_drift_rate))
stg_parms = SetigenParms()
if flag_sign_drift_rate < 0:
stg_parms.drift_rate_1 = -stg_parms.drift_rate_1
stg_parms.drift_rate_2 = -stg_parms.drift_rate_2
stg_parms.drift_rate_3 = -stg_parms.drift_rate_3
stg_parms.drift_rate_4 = -stg_parms.drift_rate_4
stg_parms.drift_rate_5 = -stg_parms.drift_rate_5
frame = stg.Frame(fchans=stg_parms.fchans,
tchans=stg_parms.tchans,
df=stg_parms.df,
dt=stg_parms.dt,
fch1=stg_parms.fch1,
ascending=(flag_fascending > 0))
frame.add_noise(x_mean=0, x_std=stg_parms.noise_std, noise_type='gaussian')
signal_1_intensity = frame.get_intensity(snr=stg_parms.snr_1)
frame.add_constant_signal(f_start=frame.get_frequency(stg_parms.signal_start_1),
drift_rate=stg_parms.drift_rate_1,
level=signal_1_intensity,
width=stg_parms.width_1,
f_profile_type='gaussian')
signal_2_intensity = frame.get_intensity(snr=stg_parms.snr_2)
frame.add_constant_signal(f_start=frame.get_frequency(stg_parms.signal_start_2),
drift_rate=stg_parms.drift_rate_2,
level=signal_2_intensity,
width=stg_parms.width_2,
f_profile_type='gaussian')
signal_3_intensity = frame.get_intensity(snr=stg_parms.snr_3)
frame.add_signal(stg.constant_path(f_start=frame.get_frequency(stg_parms.signal_start_3),
drift_rate=stg_parms.drift_rate_3),
stg.constant_t_profile(level=1),
stg.multiple_gaussian_f_profile(width=stg_parms.width_3),
stg.constant_bp_profile(level=signal_3_intensity))
signal_4_intensity = frame.get_intensity(snr=stg_parms.snr_4)
frame.add_signal(stg.constant_path(f_start=frame.get_frequency(stg_parms.signal_start_4),
drift_rate=stg_parms.drift_rate_4),
stg.constant_t_profile(level=1),
stg.multiple_gaussian_f_profile(width=stg_parms.width_4),
stg.constant_bp_profile(level=signal_4_intensity))
signal_5_intensity = frame.get_intensity(snr=stg_parms.snr_5)
frame.add_signal(stg.constant_path(f_start=frame.get_frequency(stg_parms.signal_start_5),
drift_rate=stg_parms.drift_rate_5),
stg.constant_t_profile(level=1),
stg.multiple_gaussian_f_profile(width=stg_parms.width_5),
stg.constant_bp_profile(level=signal_5_intensity))
frame.save_fil(filename=outpath)
print("generate_fil_file: generated {}".format(outpath))
del frame
def make_one_dat_file(arg_path_fil, min_drift=0.0, max_drift=4.0, min_snr=25.0, remove_h5=True):
if max_drift is None:
raise ValueError('make_one_dat_file: max_drift not set')
woutdir = dirname(arg_path_fil)
fdop = FindDoppler(datafile=arg_path_fil,
min_drift=min_drift,
max_drift=max_drift,
snr=min_snr,
log_level_int=logging.WARNING,
out_dir=woutdir)
fdop.search()
path_h5_file = arg_path_fil.replace('.fil', '.h5')
if remove_h5:
remove(path_h5_file)
def get_case_results(arg_path_dat):
df = pd.read_csv(arg_path_dat, header=None, sep=SEP, engine='python', comment='#')
nrows = len(df)
if nrows != 2:
raise ValueError('get_case_results: Expected 2 rows in DAT but observed {} rows'
.format(nrows))
obs_tophit_1 = TestResultRecord()
obs_tophit_1.tophit_id = int(df[0][0])
obs_tophit_1.drate = float(df[1][0])
obs_tophit_1.snr = float(df[2][0])
obs_tophit_1.freq = float(df[4][0])
obs_tophit_1.index = int(df[5][0])
obs_tophit_2 = TestResultRecord()
obs_tophit_2.tophit_id = int(df[0][1])
obs_tophit_2.drate = float(df[1][1])
obs_tophit_2.snr = float(df[2][1])
obs_tophit_2.freq = float(df[4][1])
obs_tophit_2.index = int(df[5][1])
return obs_tophit_1, obs_tophit_2
def case_comparison(obs_tophit, ref_tophit, max_drift):
if obs_tophit is None:
if ref_tophit is None:
return
raise ValueError('case_comparison: FAILED, max_drift={}\nobs_tophit is None\nref_tophit:::{}'
.format(max_drift, ref_tophit.to_string()))
if ref_tophit is None:
raise ValueError('case_comparison: FAILED, max_drift={}\nref_tophit is None\nobs_tophit:::{}'
.format(max_drift, obs_tophit.to_string()))
if obs_tophit.tophit_id == ref_tophit.tophit_id \
and np.isclose(obs_tophit.drate, ref_tophit.drate, rtol=RTOL_DIFF) \
and np.isclose(obs_tophit.snr, ref_tophit.snr, rtol=RTOL_DIFF) \
and np.isclose(obs_tophit.freq, ref_tophit.freq, rtol=RTOL_DIFF) \
and obs_tophit.index == ref_tophit.index:
return
raise ValueError('case_comparison: FAILED, max_drift={}\nobs_tophit:::{}\nref_tophit:::{}'
.format(max_drift, obs_tophit.to_string(), ref_tophit.to_string()))
if __name__ == '__main__':
from fb_cases_def import TESTDIR, PATH_FIL_FILE, MIN_SNR
rmtree(TESTDIR, ignore_errors=True)
mkdir(TESTDIR)
generate_fil_file(PATH_FIL_FILE, -1, -1)
make_one_dat_file(PATH_FIL_FILE, max_drift=5, min_snr=MIN_SNR)
| true | true |
f72ca647ec6c0d280fd1a1ba4d668d4a17a782b2 | 5,517 | py | Python | backend/course/migrations/0001_initial.py | crowdbotics-apps/utawala-main-altar-29305 | f450b7e301bc63a8400e7a9b0e39f4b7f931e2fd | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | backend/course/migrations/0001_initial.py | crowdbotics-apps/utawala-main-altar-29305 | f450b7e301bc63a8400e7a9b0e39f4b7f931e2fd | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | backend/course/migrations/0001_initial.py | crowdbotics-apps/utawala-main-altar-29305 | f450b7e301bc63a8400e7a9b0e39f4b7f931e2fd | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | # Generated by Django 2.2.24 on 2021-07-31 08:35
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='Course',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(blank=True, max_length=256, null=True)),
('description', models.TextField(blank=True, null=True)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='course_author', to=settings.AUTH_USER_MODEL)),
('categories', models.ManyToManyField(blank=True, related_name='course_categories', to='course.Category')),
],
),
migrations.CreateModel(
name='Event',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
('date', models.DateTimeField()),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='event_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Group',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='SubscriptionType',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='Subscription',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('subscription_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscription_subscription_type', to='course.SubscriptionType')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscription_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Recording',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('media', models.URLField()),
('published', models.DateTimeField()),
('event', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='recording_event', to='course.Event')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='recording_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='PaymentMethod',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('primary', models.BooleanField()),
('token', models.CharField(max_length=256)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='paymentmethod_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Module',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=256)),
('description', models.TextField()),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='module_course', to='course.Course')),
],
),
migrations.CreateModel(
name='Lesson',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=256)),
('description', models.TextField()),
('media', models.URLField()),
('module', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='lesson_module', to='course.Module')),
],
),
migrations.CreateModel(
name='Enrollment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='enrollment_course', to='course.Course')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='enrollment_user', to=settings.AUTH_USER_MODEL)),
],
),
]
| 49.258929 | 179 | 0.595251 |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='Course',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(blank=True, max_length=256, null=True)),
('description', models.TextField(blank=True, null=True)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='course_author', to=settings.AUTH_USER_MODEL)),
('categories', models.ManyToManyField(blank=True, related_name='course_categories', to='course.Category')),
],
),
migrations.CreateModel(
name='Event',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
('date', models.DateTimeField()),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='event_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Group',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='SubscriptionType',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='Subscription',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('subscription_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscription_subscription_type', to='course.SubscriptionType')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscription_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Recording',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('media', models.URLField()),
('published', models.DateTimeField()),
('event', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='recording_event', to='course.Event')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='recording_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='PaymentMethod',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('primary', models.BooleanField()),
('token', models.CharField(max_length=256)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='paymentmethod_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Module',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=256)),
('description', models.TextField()),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='module_course', to='course.Course')),
],
),
migrations.CreateModel(
name='Lesson',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=256)),
('description', models.TextField()),
('media', models.URLField()),
('module', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='lesson_module', to='course.Module')),
],
),
migrations.CreateModel(
name='Enrollment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='enrollment_course', to='course.Course')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='enrollment_user', to=settings.AUTH_USER_MODEL)),
],
),
]
| true | true |
f72ca651974209e177cd6e0b852ffe740ce4bc1b | 57,027 | py | Python | tensorflow/python/ipu/utils.py | DebeshJha/tensorflow-1 | 2b5a225c49d25273532d11c424d37ce394d7579a | [
"Apache-2.0"
] | 2 | 2021-03-08T23:32:06.000Z | 2022-01-13T03:43:49.000Z | tensorflow/python/ipu/utils.py | DebeshJha/tensorflow-1 | 2b5a225c49d25273532d11c424d37ce394d7579a | [
"Apache-2.0"
] | null | null | null | tensorflow/python/ipu/utils.py | DebeshJha/tensorflow-1 | 2b5a225c49d25273532d11c424d37ce394d7579a | [
"Apache-2.0"
] | null | null | null | # 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.
# =============================================================================
"""
General utilities
~~~~~~~~~~~~~~~~~
"""
import collections
from enum import Enum
import os
import time
import numpy as np
from tensorflow.compiler.plugin.poplar.driver.config_pb2 import IpuOptions
from tensorflow.compiler.plugin.poplar.driver.trace_pb2 import IpuTraceEvent
from tensorflow.compiler.plugin.poplar.driver import config_pb2
from tensorflow.compiler.plugin.poplar.ops import gen_ipu_ops
# pylint: disable=unused-import
# These imports are only here to make it easier for the Tensorflow Wheel users
# to use these functions:
# ```
# from tensorflow.python import ipu
# ...
# ipu.utils.export_variables_from_live_session(...)
# ```
from tensorflow.compiler.plugin.poplar.tools.tensorflow_weights_extractor import (
export_variables_from_live_session, export_variables_from_live_model,
import_data_in_live_session, import_data_in_live_model)
# pylint: enable=unused-import
from tensorflow.compat.v1 import executing_eagerly
from tensorflow.core.framework import attr_value_pb2
from tensorflow.python.client import session as session_lib
from tensorflow.python.distribute import values
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.util import deprecation
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.ipu import ipu_infeed_queue
from tensorflow.python.ipu import dataset_extractor
class SelectionOrder(Enum):
"""Depending on the communication pattern of the model, the order in
which the IPUs are selected and mapped to shards can impact the performance.
For example, given a model which executes on multiple IPUs:
.. code-block:: python
def sharded_graph(pa, pb, pc, pd):
with ipu.scopes.ipu_shard(0):
o1 = pa + pb
with ipu.scopes.ipu_shard(1):
o2 = o1 + pc
with ipu.scopes.ipu_shard(2):
o3 = o2 + pd
return o3
and a typical machine with 8 Graphcore C2 cards:
.. code-block:: none
_______ _______
| | | |
| 14 |=============| 15 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 12 |=============| 13 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 10 |=============| 11 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 8 |=============| 9 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 6 |=============| 7 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 4 |=============| 5 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 2 |=============| 3 |
|_______| |_______|
|| ||
_______ _______
| | | |
| 0 |=============| 1 |
|_______| |_______|
(where each numbered square represents an IPU with the given device ID and the
== and || connections represent IPUs being directly connected via IPU-Links)
we can see that the `ipu_shard(0)` directly communicates with `ipu_shard(1)`
and that `ipu_shard(1)` directly communicates with `ipu_shard(2)`.
If the shards 0, 1, 2 were mapped to IPUs 0, 1, 2 in that order, then the
communication between shards 1 and 2 would not have a direct connection via an
IPU-Link and would have to perform a "hop" via an IPU.
If the shards 0, 1, 2 were mapped to IPUs 0, 1, 3 in that order, then the
communication between shards 1 and 2 would have a direct connection via an
IPU-Link which will reduce the communication cost.
This Enum class is used to control the order in which the IPUs are selected.
Currently, the following IPU selection orderings are supported:
* `AUTO`: automatically try and select the best selection given the network.
* `ZIGZAG`: follow the natural ordering of IPUs. In the above example, the
IPUs would be selected in the following order:
`0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15`.
* `SNAKE`: select IPUs such that each consecutive shard is directly
connected via IPU-Links to the shard before and after. In the above example,
the IPUs would be selected in the following order:
`0, 1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12, 13, 15, 14`.
* `HOOF`: select IPUs such that each consecutive shard is directly
connected via IPU-Links to the shard before and after and the last and first
shard are on the same C2 cards. In the above example, the IPUs would be
selected in the following order:
`0, 2, 4, 6, 8, 10, 12, 14, 15, 13, 11, 9, 7, 5, 3, 1`.
The `SNAKE` and `HOOF` IPU selection orders are particularly beneficial for
pipelined models.
"""
AUTO = config_pb2.IpuSelectionOrder.Value("AUTO")
ZIGZAG = config_pb2.IpuSelectionOrder.Value("ZIGZAG")
SNAKE = config_pb2.IpuSelectionOrder.Value("SNAKE")
HOOF = config_pb2.IpuSelectionOrder.Value("HOOF")
class ExecutionProfileType(Enum):
"""The execution profile type indicates the desired information in the
execution profile.
* `NO_PROFILE` indicates that there should be no execution profiling.
* `DEVICE_PROFILE` indicates that the execution profile should contain only
device wide events.
* `IPU_PROFILE` indicates that the profile should contain IPU level
execution events.
* `TILE_PROFILE` indicates that the profile should contain Tile level
execution events.
"""
NO_PROFILE = config_pb2.IpuExecutionProfileType.Value("NO_PROFILE")
DEVICE_PROFILE = config_pb2.IpuExecutionProfileType.Value("DEVICE_PROFILE")
IPU_PROFILE = config_pb2.IpuExecutionProfileType.Value("IPU_PROFILE")
TILE_PROFILE = config_pb2.IpuExecutionProfileType.Value("TILE_PROFILE")
class DeviceConnectionType(Enum):
"""Enumeration to describe the mechanism used to attach to the Poplar
device.
* `ALWAYS` indicates that the system will attach when configuring the
device.
* `ON_DEMAND` will defer connection to when the IPU is needed.
* `NEVER` will never try to attach to a device. Used when compiling offline.
"""
ALWAYS = config_pb2.IpuDeviceConnectionType.Value("ALWAYS")
ON_DEMAND = config_pb2.IpuDeviceConnectionType.Value("ON_DEMAND")
NEVER = config_pb2.IpuDeviceConnectionType.Value("NEVER")
def configure_ipu_system(config, device="cpu"):
"""Configure an IPU system. Passing an IpuOptions protobuf created by the
``create_ipu_config`` function.
Args:
config: An IpuOptions configuration protobuf
device: The CPU device which is local to the IPU hardware
Returns:
None
"""
if not isinstance(config, config_pb2.IpuOptions):
raise Exception("`config` must be an IpuOptions instance")
g = ops.Graph()
with g.as_default():
with ops.device(device):
cfg_op = gen_ipu_ops.ipu_configure_hardware(config.SerializeToString())
with session_lib.Session(graph=g) as sess:
sess.run(cfg_op)
def get_ipu_config(session=None):
"""Get the configuration of an IPU system.
Args:
session: An optional session on which to execute.
Returns:
A list of IpuOption instances, one for each PoplarExecutor.
"""
configurations = None
# Get the serialized output.
if executing_eagerly():
assert not session, "No session is required for eager execution."
configurations = gen_ipu_ops.ipu_get_configuration().numpy()
else:
s = session if session else session_lib.Session()
configurations = s.run(gen_ipu_ops.ipu_get_configuration())
# Deserialize and determine if a valid config exists,
# i.e. user has succesfully called ipu_configure_hardware.
deserialized = []
valid = False
for conf in configurations:
# Deserialize.
opt = IpuOptions()
opt.ParseFromString(conf)
deserialized.append(opt)
valid |= len(opt.device_config) > 0
if not valid:
raise RuntimeError("No IPU devices configured.")
return deserialized
def get_num_of_ipus_in_device(ipu_device, device="cpu"):
"""Get the number of physical IPUs
Args:
ipu_device: The IPU device for which to get the number of devices for.
device: The CPU device which is local to the IPU hardware.
Returns:
A number of physical IPUs configured for a particular TF device.
"""
g = ops.Graph()
with g.as_default():
with ops.device(device):
cfg_op = gen_ipu_ops.ipu_get_num_devices(ipu_device)
with session_lib.Session(graph=g) as sess:
return sess.run(cfg_op)
def running_on_ipu_model():
""" Check if XLA is configured to run on the ipu model.
Returns:
True if XLA is configured to run on the ipu model.
False if XLA is configured to run on real hardware.
"""
return "--use_ipu_model" in os.environ.get("TF_POPLAR_FLAGS", "")
@deprecation.deprecated_args(None, "Use set_optimization_options() instead.",
"max_cross_replica_sum_buffer_size",
"max_inter_ipu_copies_buffer_size")
def create_ipu_config(profiling=False,
enable_ipu_events=False,
use_poplar_text_report=False,
use_poplar_cbor_report=False,
profile_execution=None,
enable_poplar_serialized_graph=False,
report_every_nth_execution=0,
max_report_size=0x10000000,
report_directory="",
scheduler_selection="",
always_rearrange_copies_on_the_host=False,
merge_infeed_io_copies=False,
disable_graph_convolution_caching=False,
disable_graph_outlining=False,
retain_control_dependencies=False,
max_cross_replica_sum_buffer_size=0,
max_inter_ipu_copies_buffer_size=0,
max_scheduler_lookahead_depth=5,
max_scheduler_search_space_size=64,
prefetch_data_streams=True,
selection_order=None,
enable_experimental_remote_buffer_embedding=False):
"""Create an empty IPU session configuration structure.
Args:
profiling: Enable compilation reports, and IPU trace events.
enable_ipu_events: Enable IPU trace events without poplar reports.
use_poplar_text_report: Enable the Poplar textual report summary.
use_poplar_cbor_report: Enable the Poplar CBOR reports.
profile_execution: Include Poplar execution profiles in the execution
events. Can only be enabled if `profiling` is also enabled. If set, can be
`True`, 'False`, or a member of the `ExecutionProfileType` enumeration.
A `True` value indicates `ExecutionProfileType.DEVICE_PROFILE`.
enable_poplar_serialized_graph: Create the Poplar serialized graph and
include in the IPU compilation trace events.
report_every_nth_execution: Only produce an execution report on every Nth
execution. 0 = One report only.
max_report_size: The maximum size of Poplar profiles to include in the
profile events.
report_directory: When set, reports will be written to files in this
directory, instead of being written into the events. The events will
contain the full paths of the report files.
scheduler_selection: When set, this forces the compiler to use a specific
scheduler when ordering the instructions. See the documentation for a
list of valid schedulers.
always_rearrange_copies_on_the_host: *** Experimental Flag ***
The data which is streamed to/from the device might be stored in different
layouts on the device and on the host. If that is the case the
rearrangment is performed on the device by default. By enabling this
option the rearrangment will be performed on the host at the expense of
latency.
merge_infeed_io_copies: When true, this flag will merge the streamed
host->device input copies into one larger copy. This may reduce the time
to copy data from the host, at the expense of increasing the live tensor
memory on the device.
disable_graph_convolution_caching: By default, the convolution operation
searches for an equivalent cached operation, and uses this instead of
creating a new convolution. Setting this flag forces the creation of a
new convolution. This can improve runtime at the expense of graph size.
disable_graph_outlining: By default, some operations, such as matrix
multiplications, which occur in the graph multiple times but with
different input tensors might be optimised to reduce the total code size
of the graph at the expense of the execution time. Setting this flag will
disable these optimisations. This option is not valid for the convolution
operation (also see disable_graph_convolution_caching)
retain_control_dependencies: Deprecated.
max_cross_replica_sum_buffer_size: The maximum number of bytes that can be
waiting before a cross replica sum op is scheduled.
max_inter_ipu_copies_buffer_size: The maximum number of bytes that can be
waiting before a inter IPU copy between IPUs is scheduled.
max_scheduler_lookahead_depth: The maximum distance to look into the future
when considering valid schedules.
max_scheduler_search_space_size: The maximum number of nodes to consider
when building the tree of future schedules.
prefetch_data_streams: When set to true, the prefetching of data for data
streams on the host will be overlapped with execution on the IPU.
selection_order: the order in which IPUs are selected and mapped to physical
IPU devices when using a multi-IPU devices (see `SelectionOrder`). When
not specified, then automatic selection order is used, otherwise an
instance of `SelectionOrder`.
enable_experimental_remote_buffer_embedding: When set to true,
`HostEmbedding` will make use of poplar remote buffers.
Returns:
An IpuOptions configuration protobuf, suitable for passing to
``configure_ipu_system``
"""
if profiling and enable_ipu_events:
raise Exception(
"`profiling` and `enable_ipu_events` are mutually exclusive")
if retain_control_dependencies:
raise Exception("`retain_control_dependencies` is deprecated")
selection_order = selection_order if selection_order else SelectionOrder.AUTO
profile_execution = profile_execution if profile_execution \
else ExecutionProfileType.NO_PROFILE
if isinstance(profile_execution, (np.bool_, bool)):
if profile_execution:
profile_execution = ExecutionProfileType.DEVICE_PROFILE
else:
profile_execution = ExecutionProfileType.NO_PROFILE
if (profile_execution != ExecutionProfileType.NO_PROFILE and not profiling):
raise Exception("`profiling` is required when `profile_execution` is set")
if not isinstance(profile_execution, ExecutionProfileType):
raise Exception("`profile_execution` must be True, False, or an "
"ExecutionProfileType instance")
opts = config_pb2.IpuOptions()
# Default initialize IpuOptions() attributes here.
opts.creator_id = config_pb2.IpuOptionsCreator.IPU_UTILS
opts.ipu_model_config.compile_ipu_code = True
opts.enable_multi_slice_combiner = False
opts.enable_matmul_combiner = False
opts.enable_gather_simplifier = False
opts.device_connection_type = DeviceConnectionType.ALWAYS.value
opts.speed_size_config.allow_recompute = False
# Configure IpuOptions according to the passed arguments.
opts.profiling.enable_ipu_trace_events = profiling or enable_ipu_events
opts.profiling.enable_compilation_trace = profiling
opts.profiling.enable_io_trace = profiling
opts.profiling.execution_trace_type = profile_execution.value
opts.profiling.enable_poplar_reports_text = use_poplar_text_report
opts.profiling.enable_poplar_reports_cbor = use_poplar_cbor_report
opts.profiling.enable_poplar_graph = enable_poplar_serialized_graph
opts.profiling.report_every_nth_execution = report_every_nth_execution
opts.profiling.max_report_size = max_report_size
opts.profiling.report_directory = report_directory
opts.speed_size_config.always_rearrange_copies_on_the_host = \
always_rearrange_copies_on_the_host
opts.speed_size_config.merge_infeed_io_copies = merge_infeed_io_copies
opts.speed_size_config.disable_graph_convolution_caching = \
disable_graph_convolution_caching
opts.speed_size_config.disable_graph_outlining = \
disable_graph_outlining
opts.speed_size_config.scheduler_selection = scheduler_selection
opts.max_cross_replica_sum_buffer_size = max_cross_replica_sum_buffer_size
opts.max_inter_ipu_copies_buffer_size = max_inter_ipu_copies_buffer_size
opts.max_scheduler_lookahead_depth = max_scheduler_lookahead_depth
opts.max_scheduler_search_space_size = max_scheduler_search_space_size
opts.prefetch_data_streams = prefetch_data_streams
opts.selection_order = selection_order.value
opts.verified_transfers.enabled = False
opts = set_verification_options(opts, VerificationOptions())
opts.enable_experimental_remote_buffer_embedding = \
enable_experimental_remote_buffer_embedding
return opts
def set_serialization_options(opts, output_folder=""):
""" Enable / disable the serialization to disk of the compiled executables.
.. code-block:: python
# Create a device that will save to disk all the compiled executables.
opts = create_ipu_config()
opts = set_serialization_options(opts,
output_folder="/tmp/my_network")
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
output_folder: Where to save the compiled executables.
Set to "" to disable serialization.
Returns:
The IpuOptions configuration protobuf.
"""
opts.serialization_folder = output_folder
return opts
def set_optimization_options(opts,
combine_embedding_lookups=False,
combine_matmuls=False,
max_cross_replica_sum_buffer_size=0,
max_reduce_scatter_buffer_size=0,
max_inter_ipu_copies_buffer_size=0,
max_send_recv_cluster_size=0,
gather_simplifier=False,
triangular_solve_expander_block_size=0):
"""Set the IPU options related to performance / optimizations.
.. code-block:: python
# Create a device with fusion for multiSlices sharing the same input
# enabled.
opts = create_ipu_config()
opts = set_optimization_options(opts,
combine_embedding_lookups=True)
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
combine_embedding_lookups: Fuse embedding lookups on the same tensor. This
might improve performance but increase memory usage.
combine_matmuls: Fuse matmul operations if they share the same weights or
the same input.
max_cross_replica_sum_buffer_size: The maximum number of bytes that can be
waiting before a cross replica sum op is scheduled.
max_reduce_scatter_buffer_size: The maximum number of bytes that can be
waiting before a reduce scatter op is scheduled.
max_inter_ipu_copies_buffer_size: The maximum number of bytes that can be
waiting before a inter IPU copy between IPUs is scheduled.
max_send_recv_cluster_size: The maximum number of bytes that can be waiting
before a cluster of send/recv instructions to/from the host is scheduled.
These are lowered to stream copies that can be merged by Poplar.
gather_simplifier: Will enable more aggressive optimisation
for embedding lookups.
triangular_solve_expander_block_size: Defines size for triangular solver
expander blocks. 0 - implementation defined default.
Returns:
The IpuOptions configuration protobuf.
"""
# Internally embedding lookups are implemented using multiSlice operations.
opts.enable_multi_slice_combiner = combine_embedding_lookups
opts.enable_matmul_combiner = combine_matmuls
opts.max_cross_replica_sum_buffer_size = max_cross_replica_sum_buffer_size
opts.max_reduce_scatter_buffer_size = max_reduce_scatter_buffer_size
opts.max_inter_ipu_copies_buffer_size = max_inter_ipu_copies_buffer_size
opts.max_send_recv_cluster_size = max_send_recv_cluster_size
opts.enable_gather_simplifier = gather_simplifier
opts.triangular_solve_expander_block_size = \
triangular_solve_expander_block_size
return opts
def set_norm_options(opts, use_stable_statistics=False):
"""Set the IPU options related to norms.
Args:
use_stable_statistics: If True, computes the mean first and subtracts
the activations by it before computing the variance. The
implementation with this flag set to True is slower than when set
to False.
Returns:
The IpuOptions configuration protobuf.
"""
opts.use_stable_norm_statistics = use_stable_statistics
return opts
def set_transfer_options(opts, use_verified_transfers=False):
"""Set the IPU options related to Poplar data transfers.
Args:
opts: An IpuOptions session control protobuf.
use_verified_transfers: If True, use Poplar's verified transfers.
Returns:
The IpuOptions configuration protobuf.
"""
opts.verified_transfers.enabled = use_verified_transfers
return opts
class KeyId:
def __init__(self, key=0, start_id=-1):
self.key = key
self.start_id = start_id
class VerificationOptions:
"""Store pairs of key / id to use for each type of data used in the graph.
Does nothing unless verified transfers have been enabled by calling
`set_transfer_options(opts, use_verified_transfers=True)`
and an instance of this class has been set by calling
`set_verification_options`:
.. code-block:: python
o = VerificationOptions()
o.inputs.key = 1
o.infeeds["infeed"].key = 3
set_verification_options(opts, o)
"""
def __init__(self):
self.inputs = KeyId()
self.input_parameters = KeyId()
self.outputs = KeyId()
self.output_parameters = KeyId()
self.infeeds = collections.defaultdict(KeyId)
self.outfeeds = collections.defaultdict(KeyId)
self.checkpoint_in = KeyId(0, 0)
self.checkpoint_out = KeyId(0, 0)
def set_verification_options(opts, verification_options):
"""Set the pairs or key / id to use for each type of data used in the graph
when verified transfers are enabled.
.. code-block:: python
# Create a device which will use verified transfers with different keys.
opts = create_ipu_config()
opts = set_transfer_options(opts, use_verified_transfers=True)
o = VerificationOptions()
o.input_parameters = KeyId(1)
o.infeeds["training_feed"] = KeyId(2)
opts = set_verification_options(opts, o)
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
verification_options: a VerificationOptions object that contains
the keys / ids to use.
"""
if not isinstance(verification_options, VerificationOptions):
raise Exception(
"`verification_options` must be of type VerificationOptions")
def _cp_key_and_id(src, dst):
dst.key = src.key
dst.start_id = src.start_id
for attr in [
"inputs", "input_parameters", "outputs", "output_parameters",
"checkpoint_in", "checkpoint_out"
]:
_cp_key_and_id(getattr(verification_options, attr),
getattr(opts.verified_transfers, attr))
for name, options in verification_options.infeeds.items():
_cp_key_and_id(options, opts.verified_transfers.infeeds[name])
for name, options in verification_options.outfeeds.items():
_cp_key_and_id(options, opts.verified_transfers.outfeeds[name])
return opts
def set_compilation_options(opts, compilation_options=None):
"""Set the IPU compilation options for the session.
.. code-block:: python
# Create a device with debug execution profile flag set to "compute_sets"
opts = create_ipu_config()
opts = set_compilation_options(opts,
compilation_options={"debug.instrument": "true",
"debug.allowOutOfMemory": "true"})
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
compilation_options: A dictionary of poplar compilation option flags to be
sent to the executor.
Returns:
The IpuOptions configuration protobuf, with engine compilation options set.
"""
if compilation_options:
if not isinstance(compilation_options, dict):
raise Exception("`compilation_options` must be a dictionary")
for (option_name, value) in compilation_options.items():
compilation_option = opts.compilation_options.add()
compilation_option.option = option_name
compilation_option.value = value
return opts
def set_convolution_options(opts, convolution_options=None):
"""Set the IPU convolution options for the session.
.. code-block:: python
# Set "availableMemoryProportion" flag to "0.1"
opts = create_ipu_config()
opts = set_convolution_options(opts,
convolution_options={"availableMemoryProportion": "0.1"})
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
convolution_options: A dictionary of poplar option flags for
convolutions. The "availableMemoryProportion" flag indicates the
proportion of tile memory to be made available as
temporary memory for convolutions (float between 0 and 1.0).
Less temporary memory will generally result in a convolution that
takes more cycles to complete. However, because always live memory
(such as control code and vertex state) is not tracked when planning it,
a convolution using less temporary memory may use more memory overall,
due to an increase of always live memory.
Returns:
The IpuOptions configuration protobuf, with convolution options set.
"""
if convolution_options:
if not isinstance(convolution_options, dict):
raise Exception("`convolution_options` must be a dictionary")
for (option_name, value) in convolution_options.items():
opt = opts.convolution_options.add()
opt.option = option_name
opt.value = value
return opts
def set_matmul_options(opts, matmul_options=None, clear_pass_type=False):
"""Set the IPU matrix multiplication options for the session.
.. code-block:: python
# Set "availableMemoryProportion" flag to "0.5"
opts = create_ipu_config()
opts = set_matmul_options(opts,
matmul_options={"availableMemoryProportion": "0.5"})
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
matmul_options: A dictionary containing the poplar option flag
"availableMemoryProportion" for the matrix multiplication operations.
It indicates the proportion of tile memory to be made available as
temporary memory for the matrix multiplications (float between 0 and 1.0).
Less temporary memory will generally result in a multiplication that
takes more cycles to complete. However, because always live memory
(like code and vertex state) is not tracked when planning it,
a multiplication using less temporary memory may use more memory overall,
due to an increase of always live memory.
clear_pass_type: When set to True, the Pass type will not
be set in the options passed to the poplar operation.
Returns:
The IpuOptions configuration protobuf, with matmul options set.
"""
if matmul_options:
if not isinstance(matmul_options, dict):
raise Exception("`matmul_options` must be a dictionary")
for (option_name, value) in matmul_options.items():
opt = opts.matmul_options.add()
opt.option = option_name
opt.value = value
opts.clear_matmul_pass_type = clear_pass_type
return opts
def set_pooling_options(opts, pooling_options=None):
"""Set the IPU pooling compilation options for the session.
.. code-block:: python
# Set "poolUseIntrospectiveMapping" flag to "false"
opts = create_ipu_config()
opts = set_pooling_options(opts,
pooling_options={"poolUseIntrospectiveMapping": "false"})
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
pooling_options: A dictionary of poplar option flags for the pooling
operation.
Returns:
The IpuOptions configuration protobuf, with pooling options set.
"""
if pooling_options:
if not isinstance(pooling_options, dict):
raise Exception("`pooling_options` must be a dictionary")
for (option_name, value) in pooling_options.items():
opt = opts.pooling_options.add()
opt.option = option_name
opt.value = value
return opts
@deprecation.deprecated_args(
None, "report_options is deprecated, use graph_options and"
" execution_options instead", "report_options")
def set_report_options(opts,
report_options=None,
graph_options=None,
execution_options=None):
"""Set the options used to influence Poplar graph and execution reports
generation.
.. code-block:: python
opts = create_ipu_config()
opts = set_report_options(opts,
report_options={"reportOption1": "false"},
graph_options={"graphOptions": "false"},
execution_options={"executionOptions": "false"})
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
report_options: (Deprecated) A dictionary of poplar option flags for
the report generation.
graph_options: A dictionary of poplar option flags for the graph report
generation.
execution_options: A dictionary of poplar option flags for the execution
report generation.
Returns:
The IpuOptions configuration protobuf, with convolution options set.
"""
def use_report_options():
if report_options:
if not isinstance(report_options, dict):
raise Exception("`report_options` must be a dictionary")
return report_options
if not graph_options:
graph_options = use_report_options()
if graph_options:
if not isinstance(graph_options, dict):
raise Exception("`graph_options` must be a dictionary")
for (option_name, value) in graph_options.items():
opt = opts.profiling.graph_options.add()
opt.option = option_name
opt.value = value
if not execution_options:
execution_options = use_report_options()
if execution_options:
if not isinstance(execution_options, dict):
raise Exception("`execution_options` must be a dictionary")
for (option_name, value) in execution_options.items():
opt = opts.profiling.execution_options.add()
opt.option = option_name
opt.value = value
return opts
def set_ipu_model_options(opts, compile_ipu_code=True):
"""Set the IPU Model options.
Args:
compile_ipu_code: Whether or not to actually compile real IPU code for
modelling.
Returns:
The IpuOptions configuration protobuf, with IPU model options set.
"""
opts.ipu_model_config.compile_ipu_code = compile_ipu_code
return opts
@deprecation.deprecated_args(
None,
"Pipelining recomputation will recompute all the non-stateful operations "
"when recomputation is enabled.",
"allow_stateful_recompute",
)
def set_recomputation_options(opts,
allow_recompute=True,
allow_stateful_recompute=None): # pylint: disable=unused-argument
"""Set re-computation options.
Args:
allow_recompute: Whether or not to re-compute instructions during training.
If this is enabled then we will attempt to pattern match
instructions/pipeline stages in the forward pass and recompute them in the
backward pass to avoid having to preserve activations which increase the
maximum memory liveness. Enabling this option can reduce memory usage at
the expense of extra computation. Any stateful operations cannot be
recomputed.
allow_stateful_recompute: Deprecated.
Returns:
The IpuOptions configuration protobuf.
"""
opts.speed_size_config.allow_recompute = allow_recompute
return opts
def set_floating_point_behaviour_options(opts,
inv=True,
div0=True,
oflo=True,
esr=True,
nanoo=True):
"""Set the IPU floating point control behaviour bits
See the Poplar API documentation for poplar::FloatingPointBehaviour.
Args:
inv: If true a floating point invalid operation (defined by IEEE 754)
will cause an exception.
div0: If true a floating point divide by zero operation will cause an
exception.
oflo: If true a floating point overflow will cause an exception.
esr: Enable stochastic rounding.
nanoo: Enable Not-a-Number on overflow mode.
"""
opts.floating_point_behaviour.flags_set = True
opts.floating_point_behaviour.inv = inv
opts.floating_point_behaviour.div0 = div0
opts.floating_point_behaviour.oflo = oflo
opts.floating_point_behaviour.esr = esr
opts.floating_point_behaviour.nanoo = nanoo
return opts
def set_gcl_options(opts, num_io_tiles=0, gcl_options=None):
"""Set the IPU options for the Graphcore Communication Library.
Args:
num_io_tiles: Number of tiles to reserve per IPU for the GCL collective
operations.
gcl_options: A dictionary with options for configuring the GCL collective
operations.
Returns:
The IpuOptions configuration protobuf.
"""
opts.gcl_num_io_tiles = num_io_tiles
if gcl_options:
if not isinstance(gcl_options, dict):
raise TypeError("`gcl_options` must be a dictionary")
for (option_name, value) in gcl_options.items():
opt = opts.gcl_options.add()
opt.option = option_name
opt.value = value
return opts
def auto_select_ipus(opts, num_ipus):
"""Configure the IPUs to be used by the session.
The configuration describes a system consisting of multiple Tensorflow
devices, each with control of one of more IPUs. The devices will be labeled
``/device:IPU:0``, ``/device:IPU:1`` and so on.
Each device can control a specific number of IPUs, given by the ``num_ipus``
parameter. The system will automatically select IPU configurations from the
available IPUs, where they match the desired number of IPUs.
Examples:
.. code-block:: python
# Create a single device, with one IPU
opts = create_ipu_config()
opts = auto_select_ipus(opts, num_ipus=1)
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
.. code-block:: python
# Create two devices, with 2 IPUs per device.
opts = create_ipu_config()
opts = auto_select_ipus(opts, num_ipus=[2,2])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
.. code-block:: python
# Create two devices, with 1 IPU in the first device and 2 IPUs
# in the second device.
opts = create_ipu_config()
opts = auto_select_ipus(opts, num_ipus=[1,2])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
num_ipus: List of IPUs per Tensorflow device
Returns:
The IpuOptions configuration protobuf, configured for auto-selecting a set
of IPU devices.
"""
if opts.device_config:
raise Exception("IPU devices have already been configured.")
if not isinstance(num_ipus, (int, list, tuple)):
raise Exception("`num_ipus` must be an integer, list or tuple.")
if isinstance(num_ipus, int):
dev = opts.device_config.add()
dev.auto_count = num_ipus
else:
for n in num_ipus:
dev = opts.device_config.add()
dev.auto_count = n
return opts
def select_ipus(opts, indices):
"""Configure the IPUs to be used by the session.
The configuration describes a system consisting of multiple Tensorflow
devices, each with control of one of more IPUs. The Tensorflow devices will be
labeled ``/device:IPU:0``, ``/device:IPU:1`` and so on.
Each Tensorflow device uses a specific configuration consisting of one or more
IPUs from the list of devices. These can be found by running the Graphcore
utility ``gc-info -l``. For instance, the following listing shows the device
configurations available on a system with 16 IPUs.
.. code-block:: shell
user@host:~$ gc-info -l
Graphcore device listing:
-+- Id: [0], type: [PCIe], PCI Domain: [0000:1a:00.0]
-+- Id: [1], type: [PCIe], PCI Domain: [0000:1b:00.0]
-+- Id: [2], type: [PCIe], PCI Domain: [0000:23:00.0]
-+- Id: [3], type: [PCIe], PCI Domain: [0000:24:00.0]
-+- Id: [4], type: [PCIe], PCI Domain: [0000:3d:00.0]
-+- Id: [5], type: [PCIe], PCI Domain: [0000:3e:00.0]
-+- Id: [6], type: [PCIe], PCI Domain: [0000:43:00.0]
-+- Id: [7], type: [PCIe], PCI Domain: [0000:44:00.0]
-+- Id: [8], type: [PCIe], PCI Domain: [0000:8b:00.0]
-+- Id: [9], type: [PCIe], PCI Domain: [0000:8c:00.0]
-+- Id: [10], type: [PCIe], PCI Domain: [0000:8e:00.0]
-+- Id: [11], type: [PCIe], PCI Domain: [0000:8f:00.0]
-+- Id: [12], type: [PCIe], PCI Domain: [0000:b8:00.0]
-+- Id: [13], type: [PCIe], PCI Domain: [0000:b9:00.0]
-+- Id: [14], type: [PCIe], PCI Domain: [0000:ba:00.0]
-+- Id: [15], type: [PCIe], PCI Domain: [0000:bb:00.0]
-+- Id: [16], type: [Multi IPU]
|--- PCIe Id: [5], DNC Id: [0], PCI Domain: [0000:3e:00.0]
|--- PCIe Id: [7], DNC Id: [1], PCI Domain: [0000:44:00.0]
-+- Id: [17], type: [Multi IPU]
|--- PCIe Id: [4], DNC Id: [0], PCI Domain: [0000:3d:00.0]
|--- PCIe Id: [6], DNC Id: [1], PCI Domain: [0000:43:00.0]
-+- Id: [18], type: [Multi IPU]
|--- PCIe Id: [3], DNC Id: [0], PCI Domain: [0000:24:00.0]
|--- PCIe Id: [1], DNC Id: [1], PCI Domain: [0000:1b:00.0]
-+- Id: [19], type: [Multi IPU]
|--- PCIe Id: [2], DNC Id: [0], PCI Domain: [0000:23:00.0]
|--- PCIe Id: [0], DNC Id: [1], PCI Domain: [0000:1a:00.0]
-+- Id: [20], type: [Multi IPU]
|--- PCIe Id: [13], DNC Id: [0], PCI Domain: [0000:b9:00.0]
|--- PCIe Id: [15], DNC Id: [1], PCI Domain: [0000:bb:00.0]
-+- Id: [21], type: [Multi IPU]
|--- PCIe Id: [12], DNC Id: [0], PCI Domain: [0000:b8:00.0]
|--- PCIe Id: [14], DNC Id: [1], PCI Domain: [0000:ba:00.0]
-+- Id: [22], type: [Multi IPU]
|--- PCIe Id: [9], DNC Id: [0], PCI Domain: [0000:8c:00.0]
|--- PCIe Id: [11], DNC Id: [1], PCI Domain: [0000:8f:00.0]
-+- Id: [23], type: [Multi IPU]
|--- PCIe Id: [10], DNC Id: [0], PCI Domain: [0000:8e:00.0]
|--- PCIe Id: [8], DNC Id: [1], PCI Domain: [0000:8b:00.0]
-+- Id: [24], type: [Multi IPU]
|--- PCIe Id: [5], DNC Id: [0], PCI Domain: [0000:3e:00.0]
|--- PCIe Id: [7], DNC Id: [1], PCI Domain: [0000:44:00.0]
|--- PCIe Id: [4], DNC Id: [2], PCI Domain: [0000:3d:00.0]
|--- PCIe Id: [6], DNC Id: [3], PCI Domain: [0000:43:00.0]
-+- Id: [25], type: [Multi IPU]
|--- PCIe Id: [3], DNC Id: [0], PCI Domain: [0000:24:00.0]
|--- PCIe Id: [1], DNC Id: [1], PCI Domain: [0000:1b:00.0]
|--- PCIe Id: [2], DNC Id: [2], PCI Domain: [0000:23:00.0]
|--- PCIe Id: [0], DNC Id: [3], PCI Domain: [0000:1a:00.0]
-+- Id: [26], type: [Multi IPU]
|--- PCIe Id: [13], DNC Id: [0], PCI Domain: [0000:b9:00.0]
|--- PCIe Id: [15], DNC Id: [1], PCI Domain: [0000:bb:00.0]
|--- PCIe Id: [12], DNC Id: [2], PCI Domain: [0000:b8:00.0]
|--- PCIe Id: [14], DNC Id: [3], PCI Domain: [0000:ba:00.0]
-+- Id: [27], type: [Multi IPU]
|--- PCIe Id: [9], DNC Id: [0], PCI Domain: [0000:8c:00.0]
|--- PCIe Id: [11], DNC Id: [1], PCI Domain: [0000:8f:00.0]
|--- PCIe Id: [10], DNC Id: [2], PCI Domain: [0000:8e:00.0]
|--- PCIe Id: [8], DNC Id: [3], PCI Domain: [0000:8b:00.0]
-+- Id: [28], type: [Multi IPU]
|--- PCIe Id: [5], DNC Id: [0], PCI Domain: [0000:3e:00.0]
|--- PCIe Id: [7], DNC Id: [1], PCI Domain: [0000:44:00.0]
|--- PCIe Id: [4], DNC Id: [2], PCI Domain: [0000:3d:00.0]
|--- PCIe Id: [6], DNC Id: [3], PCI Domain: [0000:43:00.0]
|--- PCIe Id: [3], DNC Id: [4], PCI Domain: [0000:24:00.0]
|--- PCIe Id: [1], DNC Id: [5], PCI Domain: [0000:1b:00.0]
|--- PCIe Id: [2], DNC Id: [6], PCI Domain: [0000:23:00.0]
|--- PCIe Id: [0], DNC Id: [7], PCI Domain: [0000:1a:00.0]
-+- Id: [29], type: [Multi IPU]
|--- PCIe Id: [13], DNC Id: [0], PCI Domain: [0000:b9:00.0]
|--- PCIe Id: [15], DNC Id: [1], PCI Domain: [0000:bb:00.0]
|--- PCIe Id: [12], DNC Id: [2], PCI Domain: [0000:b8:00.0]
|--- PCIe Id: [14], DNC Id: [3], PCI Domain: [0000:ba:00.0]
|--- PCIe Id: [9], DNC Id: [4], PCI Domain: [0000:8c:00.0]
|--- PCIe Id: [11], DNC Id: [5], PCI Domain: [0000:8f:00.0]
|--- PCIe Id: [10], DNC Id: [6], PCI Domain: [0000:8e:00.0]
|--- PCIe Id: [8], DNC Id: [7], PCI Domain: [0000:8b:00.0]
-+- Id: [30], type: [Multi IPU]
|--- PCIe Id: [5], DNC Id: [0], PCI Domain: [0000:3e:00.0]
|--- PCIe Id: [7], DNC Id: [1], PCI Domain: [0000:44:00.0]
|--- PCIe Id: [4], DNC Id: [2], PCI Domain: [0000:3d:00.0]
|--- PCIe Id: [6], DNC Id: [3], PCI Domain: [0000:43:00.0]
|--- PCIe Id: [3], DNC Id: [4], PCI Domain: [0000:24:00.0]
|--- PCIe Id: [1], DNC Id: [5], PCI Domain: [0000:1b:00.0]
|--- PCIe Id: [2], DNC Id: [6], PCI Domain: [0000:23:00.0]
|--- PCIe Id: [0], DNC Id: [7], PCI Domain: [0000:1a:00.0]
|--- PCIe Id: [13], DNC Id: [8], PCI Domain: [0000:b9:00.0]
|--- PCIe Id: [15], DNC Id: [9], PCI Domain: [0000:bb:00.0]
|--- PCIe Id: [12], DNC Id: [10], PCI Domain: [0000:b8:00.0]
|--- PCIe Id: [14], DNC Id: [11], PCI Domain: [0000:ba:00.0]
|--- PCIe Id: [9], DNC Id: [12], PCI Domain: [0000:8c:00.0]
|--- PCIe Id: [11], DNC Id: [13], PCI Domain: [0000:8f:00.0]
|--- PCIe Id: [10], DNC Id: [14], PCI Domain: [0000:8e:00.0]
|--- PCIe Id: [8], DNC Id: [15], PCI Domain: [0000:8b:00.0]
Examples based on the listing above:
.. code-block:: python
# Create a single device with 1 IPU at PCI address 0000:1a:00.0 by using
# IPU configuration index 0
opts = create_ipu_config()
opts = select_ipus(opts, indices=[0])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
.. code-block:: python
# Create a single device with 1 IPU at PCI address 0000:8b:00.0 by using
# IPU configuration index 8
opts = create_ipu_config()
opts = select_ipus(opts, indices=[8])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
.. code-block:: python
# Create two TensorFlow devices, with one IPU each, being devices at
# indices 0 and 1
opts = create_ipu_config()
opts = select_ipus(opts, indices=[0, 1])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
.. code-block:: python
# Create two TensorFlow devices, with four IPUs each. The device
# configurations at indices 24 (0000:3e:00.0, 0000:44:00.0, 0000:3d:00.0,
# 000:43:00.0) and 25 (0000:24:00.0, 0000:1b:00.0, 0000:23:00.0,
# 00:1a:00.0)
opts = create_ipu_config()
opts = select_ipus(opts, indices=[24, 25])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
.. code-block:: python
# Create four TensorFlow devices each with one IPU, at addresses
# 0000:1a:00.0, 0000:1b:00.0, 0000:23:00.0, 0000:24:00.0.
opts = create_ipu_config()
opts = select_ipus(opts, indices=[0, 1, 2, 3])
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
indices: List of IPU configuration indices.
Returns:
The IpuOptions configuration protobuf, with a number of devices selected by
IPU configuration index.
"""
if opts.device_config:
raise Exception("IPU devices have already been configured.")
if not isinstance(indices, (list, tuple)):
raise Exception("`indices` must be a list or tuple.")
if len(set(indices)) != len(indices):
raise Exception("All device indeicies in `indices` must be unique.")
for i in indices:
dev = opts.device_config.add()
dev.cfg_index = i
return opts
def set_ipu_connection_type(opts, connection_type=None, ipu_version=None):
""" Configure when to attach to the device.
.. code-block:: python
# Compile without attaching to the device.
opts = create_ipu_config()
opts = set_ipu_connection_type(opts,
DeviceConnectionType.ON_DEMAND))
ipu.utils.configure_ipu_system(opts)
with tf.Session() as s:
...
Args:
opts: An IpuOptions session control protobuf.
connection_type: One of `DeviceConnectionType`.
Defaults to `DeviceConnectionType.ALWAYS` if None.
ipu_version: Version of the IPU hardware used. Required if the
`connection_type` provided is `DeviceConnectionType.NEVER`.
Returns:
The IpuOptions configuration protobuf.
"""
connection_type = connection_type if connection_type \
else DeviceConnectionType.ALWAYS
if connection_type == DeviceConnectionType.NEVER and ipu_version is None:
raise Exception("`ipu_version` must be set when `connection_type` is set "
"to `DeviceConnectionType.NEVER`")
opts.device_connection_type = connection_type.value
if ipu_version is not None:
opts.ipu_version = ipu_version
opts.has_ipu_version = True
return opts
def reset_ipu_seed(seed, device="/device:IPU:0", cpu_device="cpu"):
"""Reset the seed used to generate stateful random numbers and perform
stochastic rounding.
Args:
seed: The new random number generator seed.
device: The device to which the seed will be applied.
cpu_device: The CPU device which is on the same hardware to the IPU device.
Returns:
None
"""
g = ops.Graph()
with g.as_default():
with ops.device(cpu_device):
cfg_op = gen_ipu_ops.ipu_reset_seed(device, seed)
with session_lib.Session(graph=g) as sess:
sess.run(cfg_op)
def extract_all_strings_from_event_trace(events):
"""Extract a concatenation of all data strings from an IPU event trace.
Args:
events: An array of IPU events as returned from the ``ipu_compile_summary``
operation.
Returns:
A string containing the concatenation of all of the data fields of the
events.
"""
result = ""
for e in events:
evt = IpuTraceEvent.FromString(e)
result = result + ("-" * 70) + "\n=> @ " + \
time.strftime('%F %T %z', time.localtime(evt.timestamp)) + ": "
if evt.type == IpuTraceEvent.COMPILE_BEGIN:
evt_str = "Compile begin: " + \
evt.compile_begin.module_name.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.COMPILE_END:
evt_str = "Compile end: " + \
evt.compile_end.module_name.decode('utf-8') + "\n" + \
"Duration: " + str(evt.compile_end.duration) + " us\n" + \
evt.compile_end.compilation_report.decode('utf-8')
elif evt.type == IpuTraceEvent.HOST_TO_DEVICE_TRANSFER:
evt_str = "Host->Device\n" + \
evt.data_transfer.data_transfer.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.DEVICE_TO_HOST_TRANSFER:
evt_str = "Device->Host\n" + \
evt.data_transfer.data_transfer.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.LOAD_ENGINE:
evt_str = "Load engine: " + \
evt.load_engine.module_name.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.EXECUTE:
evt_str = "Execute: " + \
evt.execute.module_name.decode('utf-8') + "\n" + \
evt.execute.execution_report.decode('utf-8')
else:
evt_str = "Unknown event"
result = result + evt_str + '\n'
return result
def extract_all_types_from_event_trace(events):
"""Return a list of the types of each event in an event trace tensor
Args:
events: A tensor containing a list of IPU events as protobuf strings
Returns:
A list containing the type of each event
"""
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
result += [evt.type]
return result
def extract_all_events(events):
"""Extract a list containing each event as an event object
Args:
events: A tensor containing a list of IPU events as protobuf strings
Returns:
A list containing IpuTraceEvent objects
"""
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
result += [evt]
return result
def extract_compile_reports(events):
"""Get a list of all compiler reports in the event list.
Args:
events: A list of trace event serialized protobufs.
Returns:
A list of tuples containing the module name and report."""
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
if evt.type == IpuTraceEvent.COMPILE_END:
try:
module = evt.compile_end.module_name.decode('utf-8')
rep = evt.compile_end.compilation_report.decode('utf-8')
if rep:
result += [(module, rep)]
except UnicodeDecodeError:
pass
return result
def extract_poplar_serialized_graphs(events):
"""Get a list of all poplar serialized graphs in the event list.
Args:
events: A list of trace event serialized protobufs.
Returns:
A list of tuples containing the module name and report."""
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
if evt.type == IpuTraceEvent.COMPILE_END:
try:
rep = evt.compile_end.poplar_graph.decode('utf-8')
except UnicodeDecodeError:
rep = evt.compile_end.poplar_graph
module = evt.compile_end.module_name.decode('utf-8')
if rep:
result += [(module, rep)]
return result
def extract_execute_reports(events):
"""Get a list of all compiler reports in the event list.
Args:
events: A list of trace event serialized protobufs.
Returns:
A list of tuples containing the module name and report."""
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
if evt.type == IpuTraceEvent.EXECUTE:
try:
module = evt.execute.module_name.decode('utf-8')
rep = evt.execute.execution_report.decode('utf-8')
if rep:
result += [(module, rep)]
except UnicodeDecodeError:
pass
return result
def move_variable_initialization_to_cpu(graph=None):
"""For all variables in the VARIABLES collection, move any initialization
ops onto the CPU.
Args:
graph: Operations are moved around on this graph. The default graph will be
used if not specified.
Returns:
None
"""
if not graph:
graph = ops.get_default_graph()
with ops.device("/device:CPU:0"):
control_flow_ops.no_op(name="cpu")
variables = []
for v in graph.get_collection('variables'):
# We assume a distribution strategy knows better how to
# initialize its own variables, so skip those.
if not isinstance(v, values.DistributedVariable):
variables.append(v)
def _uses_resource(op):
""" Helper to determine if an op uses a resource """
return any(input_tensor.dtype == 'resource' for input_tensor in op.inputs)
init_ops = []
dep_ops = [v.initializer.inputs[1].op for v in variables]
visited = set()
# Depth-first search up the graph starting from all variables in VARIABLES
# Place all touched ops on the CPU, but do not touch or search ops that use
# resource tensors, otherwise device colocation could be violated.
while dep_ops:
op = dep_ops.pop()
if op not in visited and not _uses_resource(op):
visited.add(op)
init_ops += [op]
dep_ops += [x.op for x in op.inputs]
# pylint: disable=protected-access
for op in init_ops:
op._set_device('/device:CPU:0')
op._set_attr(
'_class',
attr_value_pb2.AttrValue(list=attr_value_pb2.AttrValue.ListValue(
s=[b'loc:@cpu'])))
op._set_attr('_XlaCompile', attr_value_pb2.AttrValue(b=False))
op._set_attr('_XlaScope', attr_value_pb2.AttrValue(s=b''))
# pylint: enable=protected-access
return
def export_dataset_to_file(dataset_or_infeed,
output_filename,
num_elements,
feed_name="",
apply_options=True):
"""Export as binary `num_elements` from the given `infeed` to the specified
`output_filename`.
If the infeed elements are tuples then one file per tuple element will be
created.
For example, if `dataset` looks like
.. code-block:: python
[{ "a": A_0, "b": B_0}, { "a": A_1, "b": B_1}, ...]
then `export_dataset_to_file(dataset, "my_dataset.bin", 100)` will generate:
.. code-block:: python
my_dataset.0.bin # Contains tensors [ A_0, A_1, ..., A_99]
my_dataset.1.bin # Contains tensors [ B_0, B_1, ..., B_99]
Args:
dataset_or_infeed: An unary dataset with the same input and output
structure or an `IPUInfeedQueue`.
output_filename: Where to export the tensors to.
num_elements: Number of elements to export from the dataset.
feed_name: Specify the feed name.
apply_options: Whether to apply optimization options which can improve the
dataset performance.
"""
assert isinstance(dataset_or_infeed,
(dataset_ops.Dataset, ipu_infeed_queue.IPUInfeedQueue))
if isinstance(dataset_or_infeed, ipu_infeed_queue.IPUInfeedQueue):
dataset = dataset_or_infeed._dataset # pylint: disable=protected-access
feed_name = feed_name or dataset_or_infeed._id # pylint: disable=protected-access
else:
dataset = dataset_or_infeed
if apply_options:
dataset = dataset._apply_options() # pylint: disable=protected-access
extractor = dataset_extractor.dataset_extractor(dataset, num_elements,
output_filename, feed_name)
with ops.device("cpu"), session_lib.Session() as sess:
sess.run(extractor)
def export_inputs_to_file(inputs, output_filename, feed_dict):
"""Export as binary the list of `inputs` provided to the specified
`output_filename`.
Args:
inputs: List of graph inputs to export.
output_filename: Where to export the tensors to.
feed_dict: Feed dictionary containing the inputs' values.
"""
with ops.device("cpu"), session_lib.Session() as sess:
sess.run(dataset_extractor.export_variables(inputs, output_filename),
feed_dict)
| 37.296926 | 96 | 0.668806 |
import collections
from enum import Enum
import os
import time
import numpy as np
from tensorflow.compiler.plugin.poplar.driver.config_pb2 import IpuOptions
from tensorflow.compiler.plugin.poplar.driver.trace_pb2 import IpuTraceEvent
from tensorflow.compiler.plugin.poplar.driver import config_pb2
from tensorflow.compiler.plugin.poplar.ops import gen_ipu_ops
from tensorflow.compiler.plugin.poplar.tools.tensorflow_weights_extractor import (
export_variables_from_live_session, export_variables_from_live_model,
import_data_in_live_session, import_data_in_live_model)
from tensorflow.compat.v1 import executing_eagerly
from tensorflow.core.framework import attr_value_pb2
from tensorflow.python.client import session as session_lib
from tensorflow.python.distribute import values
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.util import deprecation
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.ipu import ipu_infeed_queue
from tensorflow.python.ipu import dataset_extractor
class SelectionOrder(Enum):
AUTO = config_pb2.IpuSelectionOrder.Value("AUTO")
ZIGZAG = config_pb2.IpuSelectionOrder.Value("ZIGZAG")
SNAKE = config_pb2.IpuSelectionOrder.Value("SNAKE")
HOOF = config_pb2.IpuSelectionOrder.Value("HOOF")
class ExecutionProfileType(Enum):
NO_PROFILE = config_pb2.IpuExecutionProfileType.Value("NO_PROFILE")
DEVICE_PROFILE = config_pb2.IpuExecutionProfileType.Value("DEVICE_PROFILE")
IPU_PROFILE = config_pb2.IpuExecutionProfileType.Value("IPU_PROFILE")
TILE_PROFILE = config_pb2.IpuExecutionProfileType.Value("TILE_PROFILE")
class DeviceConnectionType(Enum):
ALWAYS = config_pb2.IpuDeviceConnectionType.Value("ALWAYS")
ON_DEMAND = config_pb2.IpuDeviceConnectionType.Value("ON_DEMAND")
NEVER = config_pb2.IpuDeviceConnectionType.Value("NEVER")
def configure_ipu_system(config, device="cpu"):
if not isinstance(config, config_pb2.IpuOptions):
raise Exception("`config` must be an IpuOptions instance")
g = ops.Graph()
with g.as_default():
with ops.device(device):
cfg_op = gen_ipu_ops.ipu_configure_hardware(config.SerializeToString())
with session_lib.Session(graph=g) as sess:
sess.run(cfg_op)
def get_ipu_config(session=None):
configurations = None
if executing_eagerly():
assert not session, "No session is required for eager execution."
configurations = gen_ipu_ops.ipu_get_configuration().numpy()
else:
s = session if session else session_lib.Session()
configurations = s.run(gen_ipu_ops.ipu_get_configuration())
deserialized = []
valid = False
for conf in configurations:
opt = IpuOptions()
opt.ParseFromString(conf)
deserialized.append(opt)
valid |= len(opt.device_config) > 0
if not valid:
raise RuntimeError("No IPU devices configured.")
return deserialized
def get_num_of_ipus_in_device(ipu_device, device="cpu"):
g = ops.Graph()
with g.as_default():
with ops.device(device):
cfg_op = gen_ipu_ops.ipu_get_num_devices(ipu_device)
with session_lib.Session(graph=g) as sess:
return sess.run(cfg_op)
def running_on_ipu_model():
return "--use_ipu_model" in os.environ.get("TF_POPLAR_FLAGS", "")
@deprecation.deprecated_args(None, "Use set_optimization_options() instead.",
"max_cross_replica_sum_buffer_size",
"max_inter_ipu_copies_buffer_size")
def create_ipu_config(profiling=False,
enable_ipu_events=False,
use_poplar_text_report=False,
use_poplar_cbor_report=False,
profile_execution=None,
enable_poplar_serialized_graph=False,
report_every_nth_execution=0,
max_report_size=0x10000000,
report_directory="",
scheduler_selection="",
always_rearrange_copies_on_the_host=False,
merge_infeed_io_copies=False,
disable_graph_convolution_caching=False,
disable_graph_outlining=False,
retain_control_dependencies=False,
max_cross_replica_sum_buffer_size=0,
max_inter_ipu_copies_buffer_size=0,
max_scheduler_lookahead_depth=5,
max_scheduler_search_space_size=64,
prefetch_data_streams=True,
selection_order=None,
enable_experimental_remote_buffer_embedding=False):
if profiling and enable_ipu_events:
raise Exception(
"`profiling` and `enable_ipu_events` are mutually exclusive")
if retain_control_dependencies:
raise Exception("`retain_control_dependencies` is deprecated")
selection_order = selection_order if selection_order else SelectionOrder.AUTO
profile_execution = profile_execution if profile_execution \
else ExecutionProfileType.NO_PROFILE
if isinstance(profile_execution, (np.bool_, bool)):
if profile_execution:
profile_execution = ExecutionProfileType.DEVICE_PROFILE
else:
profile_execution = ExecutionProfileType.NO_PROFILE
if (profile_execution != ExecutionProfileType.NO_PROFILE and not profiling):
raise Exception("`profiling` is required when `profile_execution` is set")
if not isinstance(profile_execution, ExecutionProfileType):
raise Exception("`profile_execution` must be True, False, or an "
"ExecutionProfileType instance")
opts = config_pb2.IpuOptions()
opts.creator_id = config_pb2.IpuOptionsCreator.IPU_UTILS
opts.ipu_model_config.compile_ipu_code = True
opts.enable_multi_slice_combiner = False
opts.enable_matmul_combiner = False
opts.enable_gather_simplifier = False
opts.device_connection_type = DeviceConnectionType.ALWAYS.value
opts.speed_size_config.allow_recompute = False
opts.profiling.enable_ipu_trace_events = profiling or enable_ipu_events
opts.profiling.enable_compilation_trace = profiling
opts.profiling.enable_io_trace = profiling
opts.profiling.execution_trace_type = profile_execution.value
opts.profiling.enable_poplar_reports_text = use_poplar_text_report
opts.profiling.enable_poplar_reports_cbor = use_poplar_cbor_report
opts.profiling.enable_poplar_graph = enable_poplar_serialized_graph
opts.profiling.report_every_nth_execution = report_every_nth_execution
opts.profiling.max_report_size = max_report_size
opts.profiling.report_directory = report_directory
opts.speed_size_config.always_rearrange_copies_on_the_host = \
always_rearrange_copies_on_the_host
opts.speed_size_config.merge_infeed_io_copies = merge_infeed_io_copies
opts.speed_size_config.disable_graph_convolution_caching = \
disable_graph_convolution_caching
opts.speed_size_config.disable_graph_outlining = \
disable_graph_outlining
opts.speed_size_config.scheduler_selection = scheduler_selection
opts.max_cross_replica_sum_buffer_size = max_cross_replica_sum_buffer_size
opts.max_inter_ipu_copies_buffer_size = max_inter_ipu_copies_buffer_size
opts.max_scheduler_lookahead_depth = max_scheduler_lookahead_depth
opts.max_scheduler_search_space_size = max_scheduler_search_space_size
opts.prefetch_data_streams = prefetch_data_streams
opts.selection_order = selection_order.value
opts.verified_transfers.enabled = False
opts = set_verification_options(opts, VerificationOptions())
opts.enable_experimental_remote_buffer_embedding = \
enable_experimental_remote_buffer_embedding
return opts
def set_serialization_options(opts, output_folder=""):
opts.serialization_folder = output_folder
return opts
def set_optimization_options(opts,
combine_embedding_lookups=False,
combine_matmuls=False,
max_cross_replica_sum_buffer_size=0,
max_reduce_scatter_buffer_size=0,
max_inter_ipu_copies_buffer_size=0,
max_send_recv_cluster_size=0,
gather_simplifier=False,
triangular_solve_expander_block_size=0):
opts.enable_multi_slice_combiner = combine_embedding_lookups
opts.enable_matmul_combiner = combine_matmuls
opts.max_cross_replica_sum_buffer_size = max_cross_replica_sum_buffer_size
opts.max_reduce_scatter_buffer_size = max_reduce_scatter_buffer_size
opts.max_inter_ipu_copies_buffer_size = max_inter_ipu_copies_buffer_size
opts.max_send_recv_cluster_size = max_send_recv_cluster_size
opts.enable_gather_simplifier = gather_simplifier
opts.triangular_solve_expander_block_size = \
triangular_solve_expander_block_size
return opts
def set_norm_options(opts, use_stable_statistics=False):
opts.use_stable_norm_statistics = use_stable_statistics
return opts
def set_transfer_options(opts, use_verified_transfers=False):
opts.verified_transfers.enabled = use_verified_transfers
return opts
class KeyId:
def __init__(self, key=0, start_id=-1):
self.key = key
self.start_id = start_id
class VerificationOptions:
def __init__(self):
self.inputs = KeyId()
self.input_parameters = KeyId()
self.outputs = KeyId()
self.output_parameters = KeyId()
self.infeeds = collections.defaultdict(KeyId)
self.outfeeds = collections.defaultdict(KeyId)
self.checkpoint_in = KeyId(0, 0)
self.checkpoint_out = KeyId(0, 0)
def set_verification_options(opts, verification_options):
if not isinstance(verification_options, VerificationOptions):
raise Exception(
"`verification_options` must be of type VerificationOptions")
def _cp_key_and_id(src, dst):
dst.key = src.key
dst.start_id = src.start_id
for attr in [
"inputs", "input_parameters", "outputs", "output_parameters",
"checkpoint_in", "checkpoint_out"
]:
_cp_key_and_id(getattr(verification_options, attr),
getattr(opts.verified_transfers, attr))
for name, options in verification_options.infeeds.items():
_cp_key_and_id(options, opts.verified_transfers.infeeds[name])
for name, options in verification_options.outfeeds.items():
_cp_key_and_id(options, opts.verified_transfers.outfeeds[name])
return opts
def set_compilation_options(opts, compilation_options=None):
if compilation_options:
if not isinstance(compilation_options, dict):
raise Exception("`compilation_options` must be a dictionary")
for (option_name, value) in compilation_options.items():
compilation_option = opts.compilation_options.add()
compilation_option.option = option_name
compilation_option.value = value
return opts
def set_convolution_options(opts, convolution_options=None):
if convolution_options:
if not isinstance(convolution_options, dict):
raise Exception("`convolution_options` must be a dictionary")
for (option_name, value) in convolution_options.items():
opt = opts.convolution_options.add()
opt.option = option_name
opt.value = value
return opts
def set_matmul_options(opts, matmul_options=None, clear_pass_type=False):
if matmul_options:
if not isinstance(matmul_options, dict):
raise Exception("`matmul_options` must be a dictionary")
for (option_name, value) in matmul_options.items():
opt = opts.matmul_options.add()
opt.option = option_name
opt.value = value
opts.clear_matmul_pass_type = clear_pass_type
return opts
def set_pooling_options(opts, pooling_options=None):
if pooling_options:
if not isinstance(pooling_options, dict):
raise Exception("`pooling_options` must be a dictionary")
for (option_name, value) in pooling_options.items():
opt = opts.pooling_options.add()
opt.option = option_name
opt.value = value
return opts
@deprecation.deprecated_args(
None, "report_options is deprecated, use graph_options and"
" execution_options instead", "report_options")
def set_report_options(opts,
report_options=None,
graph_options=None,
execution_options=None):
def use_report_options():
if report_options:
if not isinstance(report_options, dict):
raise Exception("`report_options` must be a dictionary")
return report_options
if not graph_options:
graph_options = use_report_options()
if graph_options:
if not isinstance(graph_options, dict):
raise Exception("`graph_options` must be a dictionary")
for (option_name, value) in graph_options.items():
opt = opts.profiling.graph_options.add()
opt.option = option_name
opt.value = value
if not execution_options:
execution_options = use_report_options()
if execution_options:
if not isinstance(execution_options, dict):
raise Exception("`execution_options` must be a dictionary")
for (option_name, value) in execution_options.items():
opt = opts.profiling.execution_options.add()
opt.option = option_name
opt.value = value
return opts
def set_ipu_model_options(opts, compile_ipu_code=True):
opts.ipu_model_config.compile_ipu_code = compile_ipu_code
return opts
@deprecation.deprecated_args(
None,
"Pipelining recomputation will recompute all the non-stateful operations "
"when recomputation is enabled.",
"allow_stateful_recompute",
)
def set_recomputation_options(opts,
allow_recompute=True,
allow_stateful_recompute=None):
opts.speed_size_config.allow_recompute = allow_recompute
return opts
def set_floating_point_behaviour_options(opts,
inv=True,
div0=True,
oflo=True,
esr=True,
nanoo=True):
opts.floating_point_behaviour.flags_set = True
opts.floating_point_behaviour.inv = inv
opts.floating_point_behaviour.div0 = div0
opts.floating_point_behaviour.oflo = oflo
opts.floating_point_behaviour.esr = esr
opts.floating_point_behaviour.nanoo = nanoo
return opts
def set_gcl_options(opts, num_io_tiles=0, gcl_options=None):
opts.gcl_num_io_tiles = num_io_tiles
if gcl_options:
if not isinstance(gcl_options, dict):
raise TypeError("`gcl_options` must be a dictionary")
for (option_name, value) in gcl_options.items():
opt = opts.gcl_options.add()
opt.option = option_name
opt.value = value
return opts
def auto_select_ipus(opts, num_ipus):
if opts.device_config:
raise Exception("IPU devices have already been configured.")
if not isinstance(num_ipus, (int, list, tuple)):
raise Exception("`num_ipus` must be an integer, list or tuple.")
if isinstance(num_ipus, int):
dev = opts.device_config.add()
dev.auto_count = num_ipus
else:
for n in num_ipus:
dev = opts.device_config.add()
dev.auto_count = n
return opts
def select_ipus(opts, indices):
if opts.device_config:
raise Exception("IPU devices have already been configured.")
if not isinstance(indices, (list, tuple)):
raise Exception("`indices` must be a list or tuple.")
if len(set(indices)) != len(indices):
raise Exception("All device indeicies in `indices` must be unique.")
for i in indices:
dev = opts.device_config.add()
dev.cfg_index = i
return opts
def set_ipu_connection_type(opts, connection_type=None, ipu_version=None):
connection_type = connection_type if connection_type \
else DeviceConnectionType.ALWAYS
if connection_type == DeviceConnectionType.NEVER and ipu_version is None:
raise Exception("`ipu_version` must be set when `connection_type` is set "
"to `DeviceConnectionType.NEVER`")
opts.device_connection_type = connection_type.value
if ipu_version is not None:
opts.ipu_version = ipu_version
opts.has_ipu_version = True
return opts
def reset_ipu_seed(seed, device="/device:IPU:0", cpu_device="cpu"):
g = ops.Graph()
with g.as_default():
with ops.device(cpu_device):
cfg_op = gen_ipu_ops.ipu_reset_seed(device, seed)
with session_lib.Session(graph=g) as sess:
sess.run(cfg_op)
def extract_all_strings_from_event_trace(events):
result = ""
for e in events:
evt = IpuTraceEvent.FromString(e)
result = result + ("-" * 70) + "\n=> @ " + \
time.strftime('%F %T %z', time.localtime(evt.timestamp)) + ": "
if evt.type == IpuTraceEvent.COMPILE_BEGIN:
evt_str = "Compile begin: " + \
evt.compile_begin.module_name.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.COMPILE_END:
evt_str = "Compile end: " + \
evt.compile_end.module_name.decode('utf-8') + "\n" + \
"Duration: " + str(evt.compile_end.duration) + " us\n" + \
evt.compile_end.compilation_report.decode('utf-8')
elif evt.type == IpuTraceEvent.HOST_TO_DEVICE_TRANSFER:
evt_str = "Host->Device\n" + \
evt.data_transfer.data_transfer.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.DEVICE_TO_HOST_TRANSFER:
evt_str = "Device->Host\n" + \
evt.data_transfer.data_transfer.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.LOAD_ENGINE:
evt_str = "Load engine: " + \
evt.load_engine.module_name.decode('utf-8') + "\n"
elif evt.type == IpuTraceEvent.EXECUTE:
evt_str = "Execute: " + \
evt.execute.module_name.decode('utf-8') + "\n" + \
evt.execute.execution_report.decode('utf-8')
else:
evt_str = "Unknown event"
result = result + evt_str + '\n'
return result
def extract_all_types_from_event_trace(events):
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
result += [evt.type]
return result
def extract_all_events(events):
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
result += [evt]
return result
def extract_compile_reports(events):
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
if evt.type == IpuTraceEvent.COMPILE_END:
try:
module = evt.compile_end.module_name.decode('utf-8')
rep = evt.compile_end.compilation_report.decode('utf-8')
if rep:
result += [(module, rep)]
except UnicodeDecodeError:
pass
return result
def extract_poplar_serialized_graphs(events):
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
if evt.type == IpuTraceEvent.COMPILE_END:
try:
rep = evt.compile_end.poplar_graph.decode('utf-8')
except UnicodeDecodeError:
rep = evt.compile_end.poplar_graph
module = evt.compile_end.module_name.decode('utf-8')
if rep:
result += [(module, rep)]
return result
def extract_execute_reports(events):
result = []
for e in events:
evt = IpuTraceEvent.FromString(e)
if evt.type == IpuTraceEvent.EXECUTE:
try:
module = evt.execute.module_name.decode('utf-8')
rep = evt.execute.execution_report.decode('utf-8')
if rep:
result += [(module, rep)]
except UnicodeDecodeError:
pass
return result
def move_variable_initialization_to_cpu(graph=None):
if not graph:
graph = ops.get_default_graph()
with ops.device("/device:CPU:0"):
control_flow_ops.no_op(name="cpu")
variables = []
for v in graph.get_collection('variables'):
if not isinstance(v, values.DistributedVariable):
variables.append(v)
def _uses_resource(op):
return any(input_tensor.dtype == 'resource' for input_tensor in op.inputs)
init_ops = []
dep_ops = [v.initializer.inputs[1].op for v in variables]
visited = set()
while dep_ops:
op = dep_ops.pop()
if op not in visited and not _uses_resource(op):
visited.add(op)
init_ops += [op]
dep_ops += [x.op for x in op.inputs]
for op in init_ops:
op._set_device('/device:CPU:0')
op._set_attr(
'_class',
attr_value_pb2.AttrValue(list=attr_value_pb2.AttrValue.ListValue(
s=[b'loc:@cpu'])))
op._set_attr('_XlaCompile', attr_value_pb2.AttrValue(b=False))
op._set_attr('_XlaScope', attr_value_pb2.AttrValue(s=b''))
return
def export_dataset_to_file(dataset_or_infeed,
output_filename,
num_elements,
feed_name="",
apply_options=True):
assert isinstance(dataset_or_infeed,
(dataset_ops.Dataset, ipu_infeed_queue.IPUInfeedQueue))
if isinstance(dataset_or_infeed, ipu_infeed_queue.IPUInfeedQueue):
dataset = dataset_or_infeed._dataset
feed_name = feed_name or dataset_or_infeed._id
else:
dataset = dataset_or_infeed
if apply_options:
dataset = dataset._apply_options()
extractor = dataset_extractor.dataset_extractor(dataset, num_elements,
output_filename, feed_name)
with ops.device("cpu"), session_lib.Session() as sess:
sess.run(extractor)
def export_inputs_to_file(inputs, output_filename, feed_dict):
with ops.device("cpu"), session_lib.Session() as sess:
sess.run(dataset_extractor.export_variables(inputs, output_filename),
feed_dict)
| true | true |
f72ca776ebc6d065e702f3c8cb4da790bde5d2ce | 3,892 | py | Python | tests/data/residues/GLN.py | uw-ipd/privileged_residues | 78078c22ba537651a1b6bd1404c05246ab73a3e3 | [
"Apache-2.0"
] | null | null | null | tests/data/residues/GLN.py | uw-ipd/privileged_residues | 78078c22ba537651a1b6bd1404c05246ab73a3e3 | [
"Apache-2.0"
] | 20 | 2018-08-13T22:50:46.000Z | 2018-11-03T22:29:03.000Z | tests/data/residues/GLN.py | uw-ipd/privileged_residues | 78078c22ba537651a1b6bd1404c05246ab73a3e3 | [
"Apache-2.0"
] | 1 | 2018-08-25T06:03:43.000Z | 2018-08-25T06:03:43.000Z | from tests.util import pick_ray
from pyrosetta import Pose
from pyrosetta.rosetta.core.import_pose import pose_from_pdbstring
name = "GLN"
contents = """
ATOM 1 N ALA A 1 0.000 0.000 0.000 1.00 0.00 N
ATOM 2 CA ALA A 1 1.458 0.000 0.000 1.00 0.00 C
ATOM 3 C ALA A 1 2.009 1.420 0.000 1.00 0.00 C
ATOM 4 O ALA A 1 1.251 2.390 0.000 1.00 0.00 O
ATOM 5 CB ALA A 1 1.988 -0.773 -1.199 1.00 0.00 C
ATOM 6 1H ALA A 1 -0.334 -0.943 -0.000 1.00 0.00 H
ATOM 7 2H ALA A 1 -0.334 0.471 0.816 1.00 0.00 H
ATOM 8 3H ALA A 1 -0.334 0.471 -0.816 1.00 0.00 H
ATOM 9 HA ALA A 1 1.797 -0.490 0.913 1.00 0.00 H
ATOM 10 1HB ALA A 1 3.078 -0.764 -1.185 1.00 0.00 H
ATOM 11 2HB ALA A 1 1.633 -1.802 -1.154 1.00 0.00 H
ATOM 12 3HB ALA A 1 1.633 -0.307 -2.117 1.00 0.00 H
ATOM 13 N GLN A 2 3.332 1.536 0.000 1.00 0.00 N
ATOM 14 CA GLN A 2 3.988 2.839 0.000 1.00 0.00 C
ATOM 15 C GLN A 2 5.504 2.693 0.000 1.00 0.00 C
ATOM 16 O GLN A 2 6.030 1.580 0.000 1.00 0.00 O
ATOM 17 CB GLN A 2 3.542 3.663 1.211 1.00 0.00 C
ATOM 18 CG GLN A 2 2.545 2.955 2.113 1.00 0.00 C
ATOM 19 CD GLN A 2 2.200 1.564 1.615 1.00 0.00 C
ATOM 20 OE1 GLN A 2 2.707 1.116 0.583 1.00 0.00 O
ATOM 21 NE2 GLN A 2 1.333 0.873 2.346 1.00 0.00 N
ATOM 22 H GLN A 2 3.899 0.700 0.000 1.00 0.00 H
ATOM 23 HA GLN A 2 3.702 3.361 -0.913 1.00 0.00 H
ATOM 24 1HB GLN A 2 4.412 3.926 1.812 1.00 0.00 H
ATOM 25 2HB GLN A 2 3.086 4.592 0.870 1.00 0.00 H
ATOM 26 1HG GLN A 2 2.975 2.864 3.111 1.00 0.00 H
ATOM 27 2HG GLN A 2 1.627 3.541 2.153 1.00 0.00 H
ATOM 28 1HE2 GLN A 2 1.066 -0.050 2.067 1.00 0.00 H
ATOM 29 2HE2 GLN A 2 0.945 1.275 3.176 1.00 0.00 H
ATOM 30 N ALA A 3 6.202 3.823 0.000 1.00 0.00 N
ATOM 31 CA ALA A 3 7.660 3.823 0.000 1.00 0.00 C
ATOM 32 C ALA A 3 8.211 5.243 0.000 1.00 0.00 C
ATOM 33 O ALA A 3 8.260 5.868 1.023 1.00 0.00 O
ATOM 34 OXT ALA A 3 8.596 5.737 -1.023 1.00 0.00 O
ATOM 35 CB ALA A 3 8.190 3.050 -1.199 1.00 0.00 C
ATOM 36 H ALA A 3 5.710 4.705 -0.000 1.00 0.00 H
ATOM 37 HA ALA A 3 7.999 3.333 0.913 1.00 0.00 H
ATOM 38 1HB ALA A 3 9.280 3.059 -1.185 1.00 0.00 H
ATOM 39 2HB ALA A 3 7.835 2.021 -1.154 1.00 0.00 H
ATOM 40 3HB ALA A 3 7.835 3.516 -2.117 1.00 0.00 H
TER
"""
pose = Pose()
pose_from_pdbstring(pose, contents)
n_rays = {
1: pick_ray(pose.residue(1), "1H", "N"),
2: pick_ray(pose.residue(2), "H", "N"),
3: pick_ray(pose.residue(3), "H", "N")
}
c_rays = {
1: pick_ray(pose.residue(1), "O", "C"),
2: pick_ray(pose.residue(2), "O", "C"),
3: pick_ray(pose.residue(3), "O", "C")
}
sc_donor = {
2: [
pick_ray(pose.residue(2), "1HE2", "NE2"),
pick_ray(pose.residue(2), "2HE2", "NE2")
]
}
sc_acceptor = {
2: [
pick_ray(pose.residue(2), "OE1", "CD")
]
}
cat_pi = [ ]
| 48.049383 | 78 | 0.43705 | from tests.util import pick_ray
from pyrosetta import Pose
from pyrosetta.rosetta.core.import_pose import pose_from_pdbstring
name = "GLN"
contents = """
ATOM 1 N ALA A 1 0.000 0.000 0.000 1.00 0.00 N
ATOM 2 CA ALA A 1 1.458 0.000 0.000 1.00 0.00 C
ATOM 3 C ALA A 1 2.009 1.420 0.000 1.00 0.00 C
ATOM 4 O ALA A 1 1.251 2.390 0.000 1.00 0.00 O
ATOM 5 CB ALA A 1 1.988 -0.773 -1.199 1.00 0.00 C
ATOM 6 1H ALA A 1 -0.334 -0.943 -0.000 1.00 0.00 H
ATOM 7 2H ALA A 1 -0.334 0.471 0.816 1.00 0.00 H
ATOM 8 3H ALA A 1 -0.334 0.471 -0.816 1.00 0.00 H
ATOM 9 HA ALA A 1 1.797 -0.490 0.913 1.00 0.00 H
ATOM 10 1HB ALA A 1 3.078 -0.764 -1.185 1.00 0.00 H
ATOM 11 2HB ALA A 1 1.633 -1.802 -1.154 1.00 0.00 H
ATOM 12 3HB ALA A 1 1.633 -0.307 -2.117 1.00 0.00 H
ATOM 13 N GLN A 2 3.332 1.536 0.000 1.00 0.00 N
ATOM 14 CA GLN A 2 3.988 2.839 0.000 1.00 0.00 C
ATOM 15 C GLN A 2 5.504 2.693 0.000 1.00 0.00 C
ATOM 16 O GLN A 2 6.030 1.580 0.000 1.00 0.00 O
ATOM 17 CB GLN A 2 3.542 3.663 1.211 1.00 0.00 C
ATOM 18 CG GLN A 2 2.545 2.955 2.113 1.00 0.00 C
ATOM 19 CD GLN A 2 2.200 1.564 1.615 1.00 0.00 C
ATOM 20 OE1 GLN A 2 2.707 1.116 0.583 1.00 0.00 O
ATOM 21 NE2 GLN A 2 1.333 0.873 2.346 1.00 0.00 N
ATOM 22 H GLN A 2 3.899 0.700 0.000 1.00 0.00 H
ATOM 23 HA GLN A 2 3.702 3.361 -0.913 1.00 0.00 H
ATOM 24 1HB GLN A 2 4.412 3.926 1.812 1.00 0.00 H
ATOM 25 2HB GLN A 2 3.086 4.592 0.870 1.00 0.00 H
ATOM 26 1HG GLN A 2 2.975 2.864 3.111 1.00 0.00 H
ATOM 27 2HG GLN A 2 1.627 3.541 2.153 1.00 0.00 H
ATOM 28 1HE2 GLN A 2 1.066 -0.050 2.067 1.00 0.00 H
ATOM 29 2HE2 GLN A 2 0.945 1.275 3.176 1.00 0.00 H
ATOM 30 N ALA A 3 6.202 3.823 0.000 1.00 0.00 N
ATOM 31 CA ALA A 3 7.660 3.823 0.000 1.00 0.00 C
ATOM 32 C ALA A 3 8.211 5.243 0.000 1.00 0.00 C
ATOM 33 O ALA A 3 8.260 5.868 1.023 1.00 0.00 O
ATOM 34 OXT ALA A 3 8.596 5.737 -1.023 1.00 0.00 O
ATOM 35 CB ALA A 3 8.190 3.050 -1.199 1.00 0.00 C
ATOM 36 H ALA A 3 5.710 4.705 -0.000 1.00 0.00 H
ATOM 37 HA ALA A 3 7.999 3.333 0.913 1.00 0.00 H
ATOM 38 1HB ALA A 3 9.280 3.059 -1.185 1.00 0.00 H
ATOM 39 2HB ALA A 3 7.835 2.021 -1.154 1.00 0.00 H
ATOM 40 3HB ALA A 3 7.835 3.516 -2.117 1.00 0.00 H
TER
"""
pose = Pose()
pose_from_pdbstring(pose, contents)
n_rays = {
1: pick_ray(pose.residue(1), "1H", "N"),
2: pick_ray(pose.residue(2), "H", "N"),
3: pick_ray(pose.residue(3), "H", "N")
}
c_rays = {
1: pick_ray(pose.residue(1), "O", "C"),
2: pick_ray(pose.residue(2), "O", "C"),
3: pick_ray(pose.residue(3), "O", "C")
}
sc_donor = {
2: [
pick_ray(pose.residue(2), "1HE2", "NE2"),
pick_ray(pose.residue(2), "2HE2", "NE2")
]
}
sc_acceptor = {
2: [
pick_ray(pose.residue(2), "OE1", "CD")
]
}
cat_pi = [ ]
| true | true |
f72ca7d3d97e12ab7b405dcff314bdb6c0a78755 | 3,337 | py | Python | examples/pointer_generator/preprocess.py | fairseq-FT/fairseq | 18725499144c1bba7c151b796ba774e59d36eaa9 | [
"MIT"
] | 16,259 | 2018-05-02T02:31:30.000Z | 2022-03-31T21:50:23.000Z | examples/pointer_generator/preprocess.py | fairseq-FT/fairseq | 18725499144c1bba7c151b796ba774e59d36eaa9 | [
"MIT"
] | 3,863 | 2018-05-02T13:42:39.000Z | 2022-03-31T19:03:32.000Z | examples/pointer_generator/preprocess.py | fairseq-FT/fairseq | 18725499144c1bba7c151b796ba774e59d36eaa9 | [
"MIT"
] | 4,796 | 2018-05-02T07:55:51.000Z | 2022-03-31T14:46:45.000Z | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from itertools import zip_longest
def replace_oovs(source_in, target_in, vocabulary, source_out, target_out):
"""Replaces out-of-vocabulary words in source and target text with <unk-N>,
where N in is the position of the word in the source sequence.
"""
def format_unk(pos):
return "<unk-{}>".format(pos)
if target_in is None:
target_in = []
for seq_num, (source_seq, target_seq) in enumerate(
zip_longest(source_in, target_in)
):
source_seq_out = []
target_seq_out = []
word_to_pos = dict()
for position, token in enumerate(source_seq.strip().split()):
if token in vocabulary:
token_out = token
else:
if token in word_to_pos:
oov_pos = word_to_pos[token]
else:
word_to_pos[token] = position
oov_pos = position
token_out = format_unk(oov_pos)
source_seq_out.append(token_out)
source_out.write(" ".join(source_seq_out) + "\n")
if target_seq is not None:
for token in target_seq.strip().split():
if token in word_to_pos:
token_out = format_unk(word_to_pos[token])
else:
token_out = token
target_seq_out.append(token_out)
if target_out is not None:
target_out.write(" ".join(target_seq_out) + "\n")
def main():
parser = argparse.ArgumentParser(
description="Replaces out-of-vocabulary words in both source and target "
"sequences with tokens that indicate the position of the word "
"in the source sequence."
)
parser.add_argument(
"--source", type=str, help="text file with source sequences", required=True
)
parser.add_argument(
"--target", type=str, help="text file with target sequences", default=None
)
parser.add_argument("--vocab", type=str, help="vocabulary file", required=True)
parser.add_argument(
"--source-out",
type=str,
help="where to write source sequences with <unk-N> entries",
required=True,
)
parser.add_argument(
"--target-out",
type=str,
help="where to write target sequences with <unk-N> entries",
default=None,
)
args = parser.parse_args()
with open(args.vocab, encoding="utf-8") as vocab:
vocabulary = vocab.read().splitlines()
target_in = (
open(args.target, "r", encoding="utf-8") if args.target is not None else None
)
target_out = (
open(args.target_out, "w", encoding="utf-8")
if args.target_out is not None
else None
)
with open(args.source, "r", encoding="utf-8") as source_in, open(
args.source_out, "w", encoding="utf-8"
) as source_out:
replace_oovs(source_in, target_in, vocabulary, source_out, target_out)
if target_in is not None:
target_in.close()
if target_out is not None:
target_out.close()
if __name__ == "__main__":
main()
| 32.398058 | 85 | 0.605034 |
import argparse
from itertools import zip_longest
def replace_oovs(source_in, target_in, vocabulary, source_out, target_out):
def format_unk(pos):
return "<unk-{}>".format(pos)
if target_in is None:
target_in = []
for seq_num, (source_seq, target_seq) in enumerate(
zip_longest(source_in, target_in)
):
source_seq_out = []
target_seq_out = []
word_to_pos = dict()
for position, token in enumerate(source_seq.strip().split()):
if token in vocabulary:
token_out = token
else:
if token in word_to_pos:
oov_pos = word_to_pos[token]
else:
word_to_pos[token] = position
oov_pos = position
token_out = format_unk(oov_pos)
source_seq_out.append(token_out)
source_out.write(" ".join(source_seq_out) + "\n")
if target_seq is not None:
for token in target_seq.strip().split():
if token in word_to_pos:
token_out = format_unk(word_to_pos[token])
else:
token_out = token
target_seq_out.append(token_out)
if target_out is not None:
target_out.write(" ".join(target_seq_out) + "\n")
def main():
parser = argparse.ArgumentParser(
description="Replaces out-of-vocabulary words in both source and target "
"sequences with tokens that indicate the position of the word "
"in the source sequence."
)
parser.add_argument(
"--source", type=str, help="text file with source sequences", required=True
)
parser.add_argument(
"--target", type=str, help="text file with target sequences", default=None
)
parser.add_argument("--vocab", type=str, help="vocabulary file", required=True)
parser.add_argument(
"--source-out",
type=str,
help="where to write source sequences with <unk-N> entries",
required=True,
)
parser.add_argument(
"--target-out",
type=str,
help="where to write target sequences with <unk-N> entries",
default=None,
)
args = parser.parse_args()
with open(args.vocab, encoding="utf-8") as vocab:
vocabulary = vocab.read().splitlines()
target_in = (
open(args.target, "r", encoding="utf-8") if args.target is not None else None
)
target_out = (
open(args.target_out, "w", encoding="utf-8")
if args.target_out is not None
else None
)
with open(args.source, "r", encoding="utf-8") as source_in, open(
args.source_out, "w", encoding="utf-8"
) as source_out:
replace_oovs(source_in, target_in, vocabulary, source_out, target_out)
if target_in is not None:
target_in.close()
if target_out is not None:
target_out.close()
if __name__ == "__main__":
main()
| true | true |
f72ca9ee9ae4957b92084a00b5624be329e8478f | 349 | py | Python | Capitulo_02/exercise2_4.py | thiagosouzalink/my_codes-exercices-book-curso_intensivo_de_python | 841aa855a7450ad3d0ba65393ba0b6debcd6a770 | [
"MIT"
] | null | null | null | Capitulo_02/exercise2_4.py | thiagosouzalink/my_codes-exercices-book-curso_intensivo_de_python | 841aa855a7450ad3d0ba65393ba0b6debcd6a770 | [
"MIT"
] | null | null | null | Capitulo_02/exercise2_4.py | thiagosouzalink/my_codes-exercices-book-curso_intensivo_de_python | 841aa855a7450ad3d0ba65393ba0b6debcd6a770 | [
"MIT"
] | null | null | null | """
2.4 – Letras maiúsculas e minúsculas em nomes: Armazene o nome de uma pessoa em uma variável e então apresente o nome dessa pessoa em letras minúsculas, em letras maiúsculas e somente com a primeira letra maiúscula.
"""
nome = "José"
# Minúsculas
print(nome.lower())
# Maiúsculas
print(nome.upper())
# Somente a primeira letra
print(nome[0]) | 24.928571 | 215 | 0.747851 |
nome = "José"
print(nome.lower())
print(nome.upper())
print(nome[0]) | true | true |
f72caa1cc50710b6f6793c4a96821b65b2e32acb | 2,025 | py | Python | src/routes/users.py | tombrereton/flask-api-starter-kit | 2e244bfc4f5659e91fd7cd27388c37bf32baeaec | [
"MIT"
] | null | null | null | src/routes/users.py | tombrereton/flask-api-starter-kit | 2e244bfc4f5659e91fd7cd27388c37bf32baeaec | [
"MIT"
] | null | null | null | src/routes/users.py | tombrereton/flask-api-starter-kit | 2e244bfc4f5659e91fd7cd27388c37bf32baeaec | [
"MIT"
] | null | null | null | from http import HTTPStatus
from typing import List
from apifairy import body, other_responses, response
from flask import Blueprint, jsonify
from flask import request
from src.config import DefaultConfig
from src.dtos.user import UserDto
from src.requests.user import CreateUserRequestSchema, CreateUserRequest, CreateManyUsersRequestSchema, \
CreateManyUsersRequest
from src.responses.user import UserResponseSchema
from src.services import queue_client
from src.services.pascal_to_snake_serializer import JSONSerializer as ToSnakeJson
from src.services.snake_to_pascal_serializer import JSONSerializer as ToPascalJson
users_api = Blueprint('users', __name__)
@users_api.route('users', methods=['POST'])
@other_responses({
200: 'User Created',
400: 'Request Body is Invalid'
})
@body(CreateUserRequestSchema())
def post(user_request: CreateUserRequest):
"""Create a User."""
if request.method == 'POST':
user_snake_case = ToSnakeJson.deserialize(UserDto, ToSnakeJson.serialize(user_request))
add_msg = queue_client.add_create_user_job(user_snake_case)
return jsonify(add_msg), 200
@users_api.route('users/many', methods=['POST'])
@other_responses({
200: 'Users Created',
400: 'Request Body is Invalid'
})
@body(CreateManyUsersRequestSchema())
def post_many(user_request: CreateManyUsersRequest):
"""Create a User."""
if request.method == 'POST':
users_snake_case = ToSnakeJson.deserialize(List[UserDto], ToSnakeJson.serialize(user_request.Users))
users_added = []
for user in users_snake_case:
add_msg = queue_client.add_create_user_job(user)
users_added.append(add_msg)
return jsonify(users_added), 200
@users_api.route('users/<int:id>', methods=['GET'])
@response(UserResponseSchema, HTTPStatus.OK.value, "Get Users")
def get_all_users(id: int):
if request.method == 'GET':
user = UserDto(user_name=DefaultConfig.DEFAULT_USERNAME)
return ToPascalJson.serialize(user), 200
| 33.196721 | 108 | 0.74963 | from http import HTTPStatus
from typing import List
from apifairy import body, other_responses, response
from flask import Blueprint, jsonify
from flask import request
from src.config import DefaultConfig
from src.dtos.user import UserDto
from src.requests.user import CreateUserRequestSchema, CreateUserRequest, CreateManyUsersRequestSchema, \
CreateManyUsersRequest
from src.responses.user import UserResponseSchema
from src.services import queue_client
from src.services.pascal_to_snake_serializer import JSONSerializer as ToSnakeJson
from src.services.snake_to_pascal_serializer import JSONSerializer as ToPascalJson
users_api = Blueprint('users', __name__)
@users_api.route('users', methods=['POST'])
@other_responses({
200: 'User Created',
400: 'Request Body is Invalid'
})
@body(CreateUserRequestSchema())
def post(user_request: CreateUserRequest):
if request.method == 'POST':
user_snake_case = ToSnakeJson.deserialize(UserDto, ToSnakeJson.serialize(user_request))
add_msg = queue_client.add_create_user_job(user_snake_case)
return jsonify(add_msg), 200
@users_api.route('users/many', methods=['POST'])
@other_responses({
200: 'Users Created',
400: 'Request Body is Invalid'
})
@body(CreateManyUsersRequestSchema())
def post_many(user_request: CreateManyUsersRequest):
if request.method == 'POST':
users_snake_case = ToSnakeJson.deserialize(List[UserDto], ToSnakeJson.serialize(user_request.Users))
users_added = []
for user in users_snake_case:
add_msg = queue_client.add_create_user_job(user)
users_added.append(add_msg)
return jsonify(users_added), 200
@users_api.route('users/<int:id>', methods=['GET'])
@response(UserResponseSchema, HTTPStatus.OK.value, "Get Users")
def get_all_users(id: int):
if request.method == 'GET':
user = UserDto(user_name=DefaultConfig.DEFAULT_USERNAME)
return ToPascalJson.serialize(user), 200
| true | true |
f72caa4b74837bd62d61442cc130cfd18f4a2cb9 | 602 | py | Python | src/command_modules/azure-cli-find/azure/cli/command_modules/find/_help.py | v-Ajnava/azure-cli | febec631d79bfca151e84267b5b409594bad598e | [
"MIT"
] | null | null | null | src/command_modules/azure-cli-find/azure/cli/command_modules/find/_help.py | v-Ajnava/azure-cli | febec631d79bfca151e84267b5b409594bad598e | [
"MIT"
] | 3 | 2021-03-26T00:48:20.000Z | 2022-03-29T22:05:39.000Z | src/command_modules/azure-cli-find/azure/cli/command_modules/find/_help.py | v-Ajnava/azure-cli | febec631d79bfca151e84267b5b409594bad598e | [
"MIT"
] | 1 | 2017-12-28T04:51:44.000Z | 2017-12-28T04:51:44.000Z | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from azure.cli.core.help_files import helps
helps['find'] = """
type: command
short-summary: Find Azure CLI commands.
examples:
- name: Search for commands containing 'vm' or 'secret'
text: >
az find -q vm secret
"""
| 37.625 | 94 | 0.465116 |
from azure.cli.core.help_files import helps
helps['find'] = """
type: command
short-summary: Find Azure CLI commands.
examples:
- name: Search for commands containing 'vm' or 'secret'
text: >
az find -q vm secret
"""
| true | true |
f72caa944d2ed0ef2d12c5b7459dddcc53fc9b34 | 12,347 | py | Python | EPro-PnP-Det/epropnp_det/core/bbox_3d/misc.py | Lakonik/EPro-PnP | 931df847190ce10eddd1dc3e3168ce1a2f295ffa | [
"Apache-2.0"
] | 19 | 2022-03-21T10:22:24.000Z | 2022-03-30T15:43:46.000Z | EPro-PnP-Det/epropnp_det/core/bbox_3d/misc.py | Lakonik/EPro-PnP | 931df847190ce10eddd1dc3e3168ce1a2f295ffa | [
"Apache-2.0"
] | null | null | null | EPro-PnP-Det/epropnp_det/core/bbox_3d/misc.py | Lakonik/EPro-PnP | 931df847190ce10eddd1dc3e3168ce1a2f295ffa | [
"Apache-2.0"
] | 3 | 2022-03-26T08:08:24.000Z | 2022-03-30T11:17:11.000Z | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
This file is modified from
https://github.com/tjiiv-cprg/MonoRUn
"""
import math
import numpy as np
import torch
from pytorch3d.structures.meshes import Meshes
from epropnp_det.ops.iou3d.iou3d_utils import nms_gpu
def gen_unit_noc(num_pts, device=None):
indices = torch.arange(0, num_pts, dtype=torch.float32, device=device) + 0.5
phi = torch.arccos(1 - 2 * indices / num_pts)
theta = math.pi * (1 + 5**0.5) * indices
xyz = torch.stack(
(torch.cos(theta) * torch.sin(phi),
torch.sin(theta) * torch.sin(phi),
torch.cos(phi)), dim=-1)
return xyz
def project_to_image_r_mat(
x3d, r_mat, t_vec, cam_intrinsic, img_shapes, z_min=0.5, allowed_border=200,
return_z=False, return_clip_mask=False):
"""
Args:
x3d (torch.Tensor): shape (*, num_points, 3)
r_mat (torch.Tensor): shape (*, 3, 3)
t_vec (torch.Tensor): shape (*, 3) in format [x, y, z]
cam_intrinsic (torch.Tensor): shape (*, 3, 3)
img_shapes (torch.Tensor): shape (*, 2)
Returns:
Tensor: x2d_proj, shape (*, num_points, 2)
"""
proj_r_mats = cam_intrinsic @ r_mat # (*, 3, 3)
proj_t_vecs = cam_intrinsic @ t_vec.unsqueeze(-1) # (*, 3, 1)
# (*, num_points, 3) = ((*, 3, 3) @ (*, 3, num_points) + (*, 3, 1)).T
xyz_proj = (proj_r_mats @ x3d.transpose(-1, -2) + proj_t_vecs).transpose(-1, -2)
z_proj = xyz_proj[..., 2:] # (*, num_points, 1)
if return_clip_mask:
z_clip_mask = z_proj < z_min
z_proj = z_proj.clamp(min=z_min)
x2d_proj = xyz_proj[..., :2] / z_proj # (*, num_points, 2)
# clip to border
x2d_min = -allowed_border - 0.5 # Number
x2d_max = img_shapes[..., None, [1, 0]] + (allowed_border - 0.5) # (*, 1, 2)
if return_clip_mask:
x2d_clip_mask = (x2d_proj < x2d_min) | (x2d_proj > x2d_max)
clip_mask = z_clip_mask.squeeze(-1) | x2d_clip_mask.any(-1) # (*, num_points)
x2d_proj = torch.min(x2d_proj.clamp(min=x2d_min), x2d_max)
if not return_z:
if not return_clip_mask:
return x2d_proj
else:
return x2d_proj, clip_mask
else:
if not return_clip_mask:
return x2d_proj, z_proj
else:
return x2d_proj, z_proj, clip_mask
def project_to_image(
x3d, pose, cam_intrinsic, img_shapes, z_min=0.5, allowed_border=200,
return_z=False, return_clip_mask=False):
"""
Args:
x3d (torch.Tensor): shape (*, num_points, 3)
pose (torch.Tensor): shape (*, 4) in format [x, y, z, yaw]
cam_intrinsic (torch.Tensor): shape (*, 3, 3)
img_shapes (torch.Tensor): shape (*, 2)
Returns:
Tensor: x2d_proj, shape (*, num_points, 2)
"""
r_mat = yaw_to_rot_mat(pose[..., 3])
t_vec = pose[..., :3]
return project_to_image_r_mat(x3d, r_mat, t_vec, cam_intrinsic, img_shapes, z_min,
allowed_border, return_z, return_clip_mask)
def yaw_to_rot_mat(yaw):
"""
Args:
yaw: (*)
Returns:
rot_mats: (*, 3, 3)
"""
if isinstance(yaw, torch.Tensor):
pkg = torch
device_kwarg = dict(device=yaw.device)
else:
pkg = np
device_kwarg = dict()
sin_yaw = pkg.sin(yaw)
cos_yaw = pkg.cos(yaw)
# [[ cos_yaw, 0, sin_yaw],
# [ 0, 1, 0],
# [-sin_yaw, 0, cos_yaw]]
rot_mats = pkg.zeros(yaw.shape + (3, 3), dtype=pkg.float32, **device_kwarg)
rot_mats[..., 0, 0] = cos_yaw
rot_mats[..., 2, 2] = cos_yaw
rot_mats[..., 0, 2] = sin_yaw
rot_mats[..., 2, 0] = -sin_yaw
rot_mats[..., 1, 1] = 1
return rot_mats
def rot_mat_to_yaw(rot_mat):
"""
Args:
rot_mat: (*, 3, 3)
Returns:
yaw: (*)
"""
if isinstance(rot_mat, torch.Tensor):
atan2 = torch.atan2
else:
atan2 = np.arctan2
yaw = atan2(rot_mat[..., 0, 2] - rot_mat[..., 2, 0], rot_mat[..., 0, 0] + rot_mat[..., 2, 2])
return yaw
def box_mesh():
return Meshes(
verts=[torch.tensor([[-1, -1, 1],
[ 1, -1, 1],
[-1, 1, 1],
[ 1, 1, 1],
[-1, -1, -1],
[ 1, -1, -1],
[-1, 1, -1],
[ 1, 1, -1]], dtype=torch.float32)],
faces=[torch.tensor([[0, 1, 2],
[1, 3, 2],
[2, 3, 7],
[2, 7, 6],
[1, 7, 3],
[1, 5, 7],
[6, 7, 4],
[7, 5, 4],
[0, 4, 1],
[1, 4, 5],
[2, 6, 4],
[0, 2, 4]], dtype=torch.int)])
def compute_box_3d(bbox_3d):
"""
Args:
bbox_3d: (*, 7)
Returns:
corners: (*, 8, 3)
edge_corner_idx: (12, 2)
"""
bs = bbox_3d.shape[:-1]
rotation_matrix = yaw_to_rot_mat(bbox_3d[..., 6]) # (*bs, 3, 3)
edge_corner_idx = np.array([[0, 1],
[1, 2],
[2, 3],
[3, 0],
[4, 5],
[5, 6],
[6, 7],
[7, 4],
[0, 4],
[1, 5],
[2, 6],
[3, 7]])
corners = np.array([[ 0.5, 0.5, 0.5],
[ 0.5, 0.5, -0.5],
[-0.5, 0.5, -0.5],
[-0.5, 0.5, 0.5],
[ 0.5, -0.5, 0.5],
[ 0.5, -0.5, -0.5],
[-0.5, -0.5, -0.5],
[-0.5, -0.5, 0.5]], dtype=np.float32)
if isinstance(bbox_3d, torch.Tensor):
edge_corner_idx = torch.from_numpy(edge_corner_idx).to(device=bbox_3d.device)
corners = torch.from_numpy(corners).to(device=bbox_3d.device)
corners = corners * bbox_3d[..., None, :3] # (*bs, 8, 3)
corners = (rotation_matrix[..., None, :, :] @ corners[..., None]).reshape(*bs, 8, 3) \
+ bbox_3d[..., None, 3:6]
return corners, edge_corner_idx
def edge_intersection(corners, edge_corner_idx, clip_axis, clip_val, op, edge_valid_mask=None):
"""
Args:
corners: (bs, 8, 3/2)
edge_corner_idx: (12, 2)
clip_val: (bs, )
edge_valid_mask: (bs, 12)
"""
if op == 'greater':
op = torch.greater
elif op == 'less':
op = torch.less
if edge_valid_mask is None:
edge_valid_mask = corners.new_ones(
(corners.size(0), edge_corner_idx.size(0)), dtype=torch.bool)
corners_inside = op(corners[..., clip_axis], clip_val[:, None]) # (bs, 8)
# compute z intersection
edges_0_inside = corners_inside[:, edge_corner_idx[:, 0]] # (bs, 12)
edges_1_inside = corners_inside[:, edge_corner_idx[:, 1]] # (bs, 12)
edges_clipped = (edges_0_inside ^ edges_1_inside) & edge_valid_mask # (bs, 12)
edges_clipped_idx = edges_clipped.nonzero() # (num_nonzero, 2) in [bs_ind, edge_ind]
if edges_clipped_idx.shape[0] > 0:
edge_corner_idx_to_clip = edge_corner_idx[edges_clipped_idx[:, 1], :] # (num_nonzero, 2)
edges_0 = corners[edges_clipped_idx[:, 0], edge_corner_idx_to_clip[:, 0], :] # (num_nonzero, 3)
edges_1 = corners[edges_clipped_idx[:, 0], edge_corner_idx_to_clip[:, 1], :] # (num_nonzero, 3)
axval0 = edges_0[:, clip_axis] # (num_nonzero, )
axval1 = edges_1[:, clip_axis]
clip_val_ = clip_val[edges_clipped_idx[:, 0]]
weight_0 = axval1 - clip_val_ # (num_nonzero, )
weight_1 = clip_val_ - axval0
intersection = (edges_0 * weight_0[:, None] + edges_1 * weight_1[:, None]
) * (1 / (axval1 - axval0)).clamp(min=-1e6, max=1e6)[:, None] # (num_nonzero, 3)
clip_idx = torch.where(op(axval0, clip_val_),
edge_corner_idx_to_clip[:, 1],
edge_corner_idx_to_clip[:, 0]) # (num_nonzero, )
corners[edges_clipped_idx[:, 0], clip_idx, :] = intersection # replace clipped corners with intersection
corners_inside[edges_clipped_idx[:, 0], clip_idx] = True
edge_valid_mask &= corners_inside[:, edge_corner_idx[:, 0]] & corners_inside[:, edge_corner_idx[:, 1]]
else:
edge_valid_mask &= edges_0_inside & edges_1_inside
return corners, corners_inside, edge_valid_mask
def bboxes_3d_to_2d(bbox_3d, cam_intrinsic, imsize, z_clip=0.1, min_size=4.0, clip=False):
"""
Args:
bbox_3d: (bs, 7)
cam_intrinsic: (bs, 3, 3)
imsize: (bs, 2) in [h, w]
"""
assert bbox_3d.dim() == 2
bs = bbox_3d.size(0)
if bs > 0:
# (bs, 8, 3), (12, 2)
corners, edge_corner_idx = compute_box_3d(bbox_3d)
corners, in_front, edge_valid_mask = edge_intersection(
corners, edge_corner_idx, 2, corners.new_tensor([z_clip]).expand(bs), 'greater')
pts_2d = corners @ cam_intrinsic.transpose(-1, -2)
pts_2d = pts_2d[..., :2] / pts_2d[..., 2:].clamp(min=z_clip) + 0.5 # (bs, 8, 2)
in_canvas = in_front
if clip:
pts_2d, in_canvas_x0, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 0, corners.new_tensor([0]).expand(bs), 'greater', edge_valid_mask)
pts_2d, in_canvas_y0, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 1, corners.new_tensor([0]).expand(bs), 'greater', edge_valid_mask)
pts_2d, in_canvas_x1, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 0, imsize[:, 1], 'less', edge_valid_mask)
pts_2d, in_canvas_y1, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 1, imsize[:, 0], 'less', edge_valid_mask)
in_canvas = in_canvas & in_canvas_x0 & in_canvas_x1 & in_canvas_y0 & in_canvas_y1 # (bs, 8)
not_in_canvas = ~in_canvas
pts_2d[not_in_canvas] = imsize[:, None, [1, 0]].expand(-1, 8, -1)[not_in_canvas]
x0y0 = pts_2d.min(dim=1)[0].clamp(min=0) # (bs, 2)
pts_2d[not_in_canvas] = 0
x1y1 = torch.minimum(pts_2d.max(dim=1)[0], imsize[:, [1, 0]])
bbox = torch.cat((x0y0, x1y1), dim=1) # (bs, 4)
bbox_valid_mask = (x1y1 - x0y0).min(dim=1)[0] >= min_size # (bs, )
else:
bbox = bbox_3d.new_empty((0, 4))
bbox_valid_mask = bbox_3d.new_empty((0, ), dtype=torch.bool)
return bbox, bbox_valid_mask
def xywhr2xyxyr(boxes_xywhr):
"""Convert a rotated boxes in XYWHR format to XYXYR format.
Args:
boxes_xywhr (torch.Tensor): Rotated boxes in XYWHR format.
Returns:
torch.Tensor: Converted boxes in XYXYR format.
"""
boxes = torch.zeros_like(boxes_xywhr)
half_w = boxes_xywhr[:, 2] / 2 # l in bbox_3d
half_h = boxes_xywhr[:, 3] / 2 # w in bbox_3d
# x in cam coord
boxes[:, 0] = boxes_xywhr[:, 0] - half_w
# z in cam coord, mirrored_direction
boxes[:, 1] = boxes_xywhr[:, 1] - half_h
boxes[:, 2] = boxes_xywhr[:, 0] + half_w
boxes[:, 3] = boxes_xywhr[:, 1] + half_h
boxes[:, 4] = boxes_xywhr[:, 4]
return boxes
def batched_bev_nms(bbox_3d, batch_inds, nms_thr=0.25):
"""
Args:
bbox_3d (Tensor): tensor shape (N, 8+),
in format [l, h, w, x, y, z, ry, score, ind, *]
batch_inds (Tensor): tensor shape (N, )
nms_thr (float)
Returns:
Tuple:
bbox_3d_out (Tensor)
keep_inds (Tensor)
"""
n = bbox_3d.size(0)
if n > 1:
boxes_for_nms = xywhr2xyxyr(
bbox_3d[:, [3, 5, 0, 2, 6]])
offset_unit = (boxes_for_nms[:, :4].max() - boxes_for_nms[:, :4].min()) * 2
boxes_for_nms[:, :4] = boxes_for_nms[:, :4] + (offset_unit * batch_inds)[:, None]
keep_inds = nms_gpu(
boxes_for_nms, bbox_3d[:, 7], nms_thr)
else:
keep_inds = bbox_3d.new_zeros(0, dtype=torch.int64)
bbox_3d_out = bbox_3d[keep_inds]
return bbox_3d_out, keep_inds
| 37.990769 | 113 | 0.53268 |
import math
import numpy as np
import torch
from pytorch3d.structures.meshes import Meshes
from epropnp_det.ops.iou3d.iou3d_utils import nms_gpu
def gen_unit_noc(num_pts, device=None):
indices = torch.arange(0, num_pts, dtype=torch.float32, device=device) + 0.5
phi = torch.arccos(1 - 2 * indices / num_pts)
theta = math.pi * (1 + 5**0.5) * indices
xyz = torch.stack(
(torch.cos(theta) * torch.sin(phi),
torch.sin(theta) * torch.sin(phi),
torch.cos(phi)), dim=-1)
return xyz
def project_to_image_r_mat(
x3d, r_mat, t_vec, cam_intrinsic, img_shapes, z_min=0.5, allowed_border=200,
return_z=False, return_clip_mask=False):
proj_r_mats = cam_intrinsic @ r_mat
proj_t_vecs = cam_intrinsic @ t_vec.unsqueeze(-1)
xyz_proj = (proj_r_mats @ x3d.transpose(-1, -2) + proj_t_vecs).transpose(-1, -2)
z_proj = xyz_proj[..., 2:]
if return_clip_mask:
z_clip_mask = z_proj < z_min
z_proj = z_proj.clamp(min=z_min)
x2d_proj = xyz_proj[..., :2] / z_proj
x2d_min = -allowed_border - 0.5
x2d_max = img_shapes[..., None, [1, 0]] + (allowed_border - 0.5)
if return_clip_mask:
x2d_clip_mask = (x2d_proj < x2d_min) | (x2d_proj > x2d_max)
clip_mask = z_clip_mask.squeeze(-1) | x2d_clip_mask.any(-1)
x2d_proj = torch.min(x2d_proj.clamp(min=x2d_min), x2d_max)
if not return_z:
if not return_clip_mask:
return x2d_proj
else:
return x2d_proj, clip_mask
else:
if not return_clip_mask:
return x2d_proj, z_proj
else:
return x2d_proj, z_proj, clip_mask
def project_to_image(
x3d, pose, cam_intrinsic, img_shapes, z_min=0.5, allowed_border=200,
return_z=False, return_clip_mask=False):
r_mat = yaw_to_rot_mat(pose[..., 3])
t_vec = pose[..., :3]
return project_to_image_r_mat(x3d, r_mat, t_vec, cam_intrinsic, img_shapes, z_min,
allowed_border, return_z, return_clip_mask)
def yaw_to_rot_mat(yaw):
if isinstance(yaw, torch.Tensor):
pkg = torch
device_kwarg = dict(device=yaw.device)
else:
pkg = np
device_kwarg = dict()
sin_yaw = pkg.sin(yaw)
cos_yaw = pkg.cos(yaw)
rot_mats = pkg.zeros(yaw.shape + (3, 3), dtype=pkg.float32, **device_kwarg)
rot_mats[..., 0, 0] = cos_yaw
rot_mats[..., 2, 2] = cos_yaw
rot_mats[..., 0, 2] = sin_yaw
rot_mats[..., 2, 0] = -sin_yaw
rot_mats[..., 1, 1] = 1
return rot_mats
def rot_mat_to_yaw(rot_mat):
if isinstance(rot_mat, torch.Tensor):
atan2 = torch.atan2
else:
atan2 = np.arctan2
yaw = atan2(rot_mat[..., 0, 2] - rot_mat[..., 2, 0], rot_mat[..., 0, 0] + rot_mat[..., 2, 2])
return yaw
def box_mesh():
return Meshes(
verts=[torch.tensor([[-1, -1, 1],
[ 1, -1, 1],
[-1, 1, 1],
[ 1, 1, 1],
[-1, -1, -1],
[ 1, -1, -1],
[-1, 1, -1],
[ 1, 1, -1]], dtype=torch.float32)],
faces=[torch.tensor([[0, 1, 2],
[1, 3, 2],
[2, 3, 7],
[2, 7, 6],
[1, 7, 3],
[1, 5, 7],
[6, 7, 4],
[7, 5, 4],
[0, 4, 1],
[1, 4, 5],
[2, 6, 4],
[0, 2, 4]], dtype=torch.int)])
def compute_box_3d(bbox_3d):
bs = bbox_3d.shape[:-1]
rotation_matrix = yaw_to_rot_mat(bbox_3d[..., 6])
edge_corner_idx = np.array([[0, 1],
[1, 2],
[2, 3],
[3, 0],
[4, 5],
[5, 6],
[6, 7],
[7, 4],
[0, 4],
[1, 5],
[2, 6],
[3, 7]])
corners = np.array([[ 0.5, 0.5, 0.5],
[ 0.5, 0.5, -0.5],
[-0.5, 0.5, -0.5],
[-0.5, 0.5, 0.5],
[ 0.5, -0.5, 0.5],
[ 0.5, -0.5, -0.5],
[-0.5, -0.5, -0.5],
[-0.5, -0.5, 0.5]], dtype=np.float32)
if isinstance(bbox_3d, torch.Tensor):
edge_corner_idx = torch.from_numpy(edge_corner_idx).to(device=bbox_3d.device)
corners = torch.from_numpy(corners).to(device=bbox_3d.device)
corners = corners * bbox_3d[..., None, :3]
corners = (rotation_matrix[..., None, :, :] @ corners[..., None]).reshape(*bs, 8, 3) \
+ bbox_3d[..., None, 3:6]
return corners, edge_corner_idx
def edge_intersection(corners, edge_corner_idx, clip_axis, clip_val, op, edge_valid_mask=None):
if op == 'greater':
op = torch.greater
elif op == 'less':
op = torch.less
if edge_valid_mask is None:
edge_valid_mask = corners.new_ones(
(corners.size(0), edge_corner_idx.size(0)), dtype=torch.bool)
corners_inside = op(corners[..., clip_axis], clip_val[:, None])
edges_0_inside = corners_inside[:, edge_corner_idx[:, 0]]
edges_1_inside = corners_inside[:, edge_corner_idx[:, 1]]
edges_clipped = (edges_0_inside ^ edges_1_inside) & edge_valid_mask
edges_clipped_idx = edges_clipped.nonzero()
if edges_clipped_idx.shape[0] > 0:
edge_corner_idx_to_clip = edge_corner_idx[edges_clipped_idx[:, 1], :]
edges_0 = corners[edges_clipped_idx[:, 0], edge_corner_idx_to_clip[:, 0], :]
edges_1 = corners[edges_clipped_idx[:, 0], edge_corner_idx_to_clip[:, 1], :]
axval0 = edges_0[:, clip_axis]
axval1 = edges_1[:, clip_axis]
clip_val_ = clip_val[edges_clipped_idx[:, 0]]
weight_0 = axval1 - clip_val_
weight_1 = clip_val_ - axval0
intersection = (edges_0 * weight_0[:, None] + edges_1 * weight_1[:, None]
) * (1 / (axval1 - axval0)).clamp(min=-1e6, max=1e6)[:, None]
clip_idx = torch.where(op(axval0, clip_val_),
edge_corner_idx_to_clip[:, 1],
edge_corner_idx_to_clip[:, 0])
corners[edges_clipped_idx[:, 0], clip_idx, :] = intersection
corners_inside[edges_clipped_idx[:, 0], clip_idx] = True
edge_valid_mask &= corners_inside[:, edge_corner_idx[:, 0]] & corners_inside[:, edge_corner_idx[:, 1]]
else:
edge_valid_mask &= edges_0_inside & edges_1_inside
return corners, corners_inside, edge_valid_mask
def bboxes_3d_to_2d(bbox_3d, cam_intrinsic, imsize, z_clip=0.1, min_size=4.0, clip=False):
assert bbox_3d.dim() == 2
bs = bbox_3d.size(0)
if bs > 0:
corners, edge_corner_idx = compute_box_3d(bbox_3d)
corners, in_front, edge_valid_mask = edge_intersection(
corners, edge_corner_idx, 2, corners.new_tensor([z_clip]).expand(bs), 'greater')
pts_2d = corners @ cam_intrinsic.transpose(-1, -2)
pts_2d = pts_2d[..., :2] / pts_2d[..., 2:].clamp(min=z_clip) + 0.5
in_canvas = in_front
if clip:
pts_2d, in_canvas_x0, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 0, corners.new_tensor([0]).expand(bs), 'greater', edge_valid_mask)
pts_2d, in_canvas_y0, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 1, corners.new_tensor([0]).expand(bs), 'greater', edge_valid_mask)
pts_2d, in_canvas_x1, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 0, imsize[:, 1], 'less', edge_valid_mask)
pts_2d, in_canvas_y1, edge_valid_mask = edge_intersection(
pts_2d, edge_corner_idx, 1, imsize[:, 0], 'less', edge_valid_mask)
in_canvas = in_canvas & in_canvas_x0 & in_canvas_x1 & in_canvas_y0 & in_canvas_y1
not_in_canvas = ~in_canvas
pts_2d[not_in_canvas] = imsize[:, None, [1, 0]].expand(-1, 8, -1)[not_in_canvas]
x0y0 = pts_2d.min(dim=1)[0].clamp(min=0)
pts_2d[not_in_canvas] = 0
x1y1 = torch.minimum(pts_2d.max(dim=1)[0], imsize[:, [1, 0]])
bbox = torch.cat((x0y0, x1y1), dim=1)
bbox_valid_mask = (x1y1 - x0y0).min(dim=1)[0] >= min_size
else:
bbox = bbox_3d.new_empty((0, 4))
bbox_valid_mask = bbox_3d.new_empty((0, ), dtype=torch.bool)
return bbox, bbox_valid_mask
def xywhr2xyxyr(boxes_xywhr):
boxes = torch.zeros_like(boxes_xywhr)
half_w = boxes_xywhr[:, 2] / 2
half_h = boxes_xywhr[:, 3] / 2
boxes[:, 0] = boxes_xywhr[:, 0] - half_w
boxes[:, 1] = boxes_xywhr[:, 1] - half_h
boxes[:, 2] = boxes_xywhr[:, 0] + half_w
boxes[:, 3] = boxes_xywhr[:, 1] + half_h
boxes[:, 4] = boxes_xywhr[:, 4]
return boxes
def batched_bev_nms(bbox_3d, batch_inds, nms_thr=0.25):
n = bbox_3d.size(0)
if n > 1:
boxes_for_nms = xywhr2xyxyr(
bbox_3d[:, [3, 5, 0, 2, 6]])
offset_unit = (boxes_for_nms[:, :4].max() - boxes_for_nms[:, :4].min()) * 2
boxes_for_nms[:, :4] = boxes_for_nms[:, :4] + (offset_unit * batch_inds)[:, None]
keep_inds = nms_gpu(
boxes_for_nms, bbox_3d[:, 7], nms_thr)
else:
keep_inds = bbox_3d.new_zeros(0, dtype=torch.int64)
bbox_3d_out = bbox_3d[keep_inds]
return bbox_3d_out, keep_inds
| true | true |
f72cab0568521a363e71061115573b79f5eea8ff | 22,874 | py | Python | sdk/python/pulumi_azure_nextgen/compute/v20191201/virtual_machine.py | pulumi/pulumi-azure-nextgen | 452736b0a1cf584c2d4c04666e017af6e9b2c15c | [
"Apache-2.0"
] | 31 | 2020-09-21T09:41:01.000Z | 2021-02-26T13:21:59.000Z | sdk/python/pulumi_azure_nextgen/compute/v20191201/virtual_machine.py | pulumi/pulumi-azure-nextgen | 452736b0a1cf584c2d4c04666e017af6e9b2c15c | [
"Apache-2.0"
] | 231 | 2020-09-21T09:38:45.000Z | 2021-03-01T11:16:03.000Z | sdk/python/pulumi_azure_nextgen/compute/v20191201/virtual_machine.py | pulumi/pulumi-azure-nextgen | 452736b0a1cf584c2d4c04666e017af6e9b2c15c | [
"Apache-2.0"
] | 4 | 2020-09-29T14:14:59.000Z | 2021-02-10T20:38:16.000Z | # 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__ = ['VirtualMachine']
class VirtualMachine(pulumi.CustomResource):
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
additional_capabilities: Optional[pulumi.Input[pulumi.InputType['AdditionalCapabilitiesArgs']]] = None,
availability_set: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
billing_profile: Optional[pulumi.Input[pulumi.InputType['BillingProfileArgs']]] = None,
diagnostics_profile: Optional[pulumi.Input[pulumi.InputType['DiagnosticsProfileArgs']]] = None,
eviction_policy: Optional[pulumi.Input[Union[str, 'VirtualMachineEvictionPolicyTypes']]] = None,
hardware_profile: Optional[pulumi.Input[pulumi.InputType['HardwareProfileArgs']]] = None,
host: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
identity: Optional[pulumi.Input[pulumi.InputType['VirtualMachineIdentityArgs']]] = None,
license_type: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
network_profile: Optional[pulumi.Input[pulumi.InputType['NetworkProfileArgs']]] = None,
os_profile: Optional[pulumi.Input[pulumi.InputType['OSProfileArgs']]] = None,
plan: Optional[pulumi.Input[pulumi.InputType['PlanArgs']]] = None,
priority: Optional[pulumi.Input[Union[str, 'VirtualMachinePriorityTypes']]] = None,
proximity_placement_group: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
storage_profile: Optional[pulumi.Input[pulumi.InputType['StorageProfileArgs']]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
virtual_machine_scale_set: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
vm_name: Optional[pulumi.Input[str]] = None,
zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
Describes a Virtual Machine.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['AdditionalCapabilitiesArgs']] additional_capabilities: Specifies additional capabilities enabled or disabled on the virtual machine.
:param pulumi.Input[pulumi.InputType['SubResourceArgs']] availability_set: Specifies information about the availability set that the virtual machine should be assigned to. Virtual machines specified in the same availability set are allocated to different nodes to maximize availability. For more information about availability sets, see [Manage the availability of virtual machines](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-manage-availability?toc=%2fazure%2fvirtual-machines%2fwindows%2ftoc.json). <br><br> For more information on Azure planned maintenance, see [Planned maintenance for virtual machines in Azure](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-planned-maintenance?toc=%2fazure%2fvirtual-machines%2fwindows%2ftoc.json) <br><br> Currently, a VM can only be added to availability set at creation time. The availability set to which the VM is being added should be under the same resource group as the availability set resource. An existing VM cannot be added to an availability set. <br><br>This property cannot exist along with a non-null properties.virtualMachineScaleSet reference.
:param pulumi.Input[pulumi.InputType['BillingProfileArgs']] billing_profile: Specifies the billing related details of a Azure Spot virtual machine. <br><br>Minimum api-version: 2019-03-01.
:param pulumi.Input[pulumi.InputType['DiagnosticsProfileArgs']] diagnostics_profile: Specifies the boot diagnostic settings state. <br><br>Minimum api-version: 2015-06-15.
:param pulumi.Input[Union[str, 'VirtualMachineEvictionPolicyTypes']] eviction_policy: Specifies the eviction policy for the Azure Spot virtual machine and Azure Spot scale set. <br><br>For Azure Spot virtual machines, both 'Deallocate' and 'Delete' are supported and the minimum api-version is 2019-03-01. <br><br>For Azure Spot scale sets, both 'Deallocate' and 'Delete' are supported and the minimum api-version is 2017-10-30-preview.
:param pulumi.Input[pulumi.InputType['HardwareProfileArgs']] hardware_profile: Specifies the hardware settings for the virtual machine.
:param pulumi.Input[pulumi.InputType['SubResourceArgs']] host: Specifies information about the dedicated host that the virtual machine resides in. <br><br>Minimum api-version: 2018-10-01.
:param pulumi.Input[pulumi.InputType['VirtualMachineIdentityArgs']] identity: The identity of the virtual machine, if configured.
:param pulumi.Input[str] license_type: Specifies that the image or disk that is being used was licensed on-premises. This element is only used for images that contain the Windows Server operating system. <br><br> Possible values are: <br><br> Windows_Client <br><br> Windows_Server <br><br> If this element is included in a request for an update, the value must match the initial value. This value cannot be updated. <br><br> For more information, see [Azure Hybrid Use Benefit for Windows Server](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-hybrid-use-benefit-licensing?toc=%2fazure%2fvirtual-machines%2fwindows%2ftoc.json) <br><br> Minimum api-version: 2015-06-15
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[pulumi.InputType['NetworkProfileArgs']] network_profile: Specifies the network interfaces of the virtual machine.
:param pulumi.Input[pulumi.InputType['OSProfileArgs']] os_profile: Specifies the operating system settings used while creating the virtual machine. Some of the settings cannot be changed once VM is provisioned.
:param pulumi.Input[pulumi.InputType['PlanArgs']] plan: Specifies information about the marketplace image used to create the virtual machine. This element is only used for marketplace images. Before you can use a marketplace image from an API, you must enable the image for programmatic use. In the Azure portal, find the marketplace image that you want to use and then click **Want to deploy programmatically, Get Started ->**. Enter any required information and then click **Save**.
:param pulumi.Input[Union[str, 'VirtualMachinePriorityTypes']] priority: Specifies the priority for the virtual machine. <br><br>Minimum api-version: 2019-03-01
:param pulumi.Input[pulumi.InputType['SubResourceArgs']] proximity_placement_group: Specifies information about the proximity placement group that the virtual machine should be assigned to. <br><br>Minimum api-version: 2018-04-01.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[pulumi.InputType['StorageProfileArgs']] storage_profile: Specifies the storage settings for the virtual machine disks.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
:param pulumi.Input[pulumi.InputType['SubResourceArgs']] virtual_machine_scale_set: Specifies information about the virtual machine scale set that the virtual machine should be assigned to. Virtual machines specified in the same virtual machine scale set are allocated to different nodes to maximize availability. Currently, a VM can only be added to virtual machine scale set at creation time. An existing VM cannot be added to a virtual machine scale set. <br><br>This property cannot exist along with a non-null properties.availabilitySet reference. <br><br>Minimum api‐version: 2019‐03‐01
:param pulumi.Input[str] vm_name: The name of the virtual machine.
:param pulumi.Input[Sequence[pulumi.Input[str]]] zones: The virtual machine zones.
"""
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__['additional_capabilities'] = additional_capabilities
__props__['availability_set'] = availability_set
__props__['billing_profile'] = billing_profile
__props__['diagnostics_profile'] = diagnostics_profile
__props__['eviction_policy'] = eviction_policy
__props__['hardware_profile'] = hardware_profile
__props__['host'] = host
__props__['identity'] = identity
__props__['license_type'] = license_type
__props__['location'] = location
__props__['network_profile'] = network_profile
__props__['os_profile'] = os_profile
__props__['plan'] = plan
__props__['priority'] = priority
__props__['proximity_placement_group'] = proximity_placement_group
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__['storage_profile'] = storage_profile
__props__['tags'] = tags
__props__['virtual_machine_scale_set'] = virtual_machine_scale_set
__props__['vm_name'] = vm_name
__props__['zones'] = zones
__props__['instance_view'] = None
__props__['name'] = None
__props__['provisioning_state'] = None
__props__['resources'] = None
__props__['type'] = None
__props__['vm_id'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:compute:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/latest:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20150615:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20160330:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20160430preview:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20170330:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20171201:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20180401:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20180601:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20181001:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20190301:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20190701:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20200601:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20201201:VirtualMachine")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(VirtualMachine, __self__).__init__(
'azure-nextgen:compute/v20191201:VirtualMachine',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'VirtualMachine':
"""
Get an existing VirtualMachine 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()
return VirtualMachine(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="additionalCapabilities")
def additional_capabilities(self) -> pulumi.Output[Optional['outputs.AdditionalCapabilitiesResponse']]:
"""
Specifies additional capabilities enabled or disabled on the virtual machine.
"""
return pulumi.get(self, "additional_capabilities")
@property
@pulumi.getter(name="availabilitySet")
def availability_set(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
"""
Specifies information about the availability set that the virtual machine should be assigned to. Virtual machines specified in the same availability set are allocated to different nodes to maximize availability. For more information about availability sets, see [Manage the availability of virtual machines](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-manage-availability?toc=%2fazure%2fvirtual-machines%2fwindows%2ftoc.json). <br><br> For more information on Azure planned maintenance, see [Planned maintenance for virtual machines in Azure](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-planned-maintenance?toc=%2fazure%2fvirtual-machines%2fwindows%2ftoc.json) <br><br> Currently, a VM can only be added to availability set at creation time. The availability set to which the VM is being added should be under the same resource group as the availability set resource. An existing VM cannot be added to an availability set. <br><br>This property cannot exist along with a non-null properties.virtualMachineScaleSet reference.
"""
return pulumi.get(self, "availability_set")
@property
@pulumi.getter(name="billingProfile")
def billing_profile(self) -> pulumi.Output[Optional['outputs.BillingProfileResponse']]:
"""
Specifies the billing related details of a Azure Spot virtual machine. <br><br>Minimum api-version: 2019-03-01.
"""
return pulumi.get(self, "billing_profile")
@property
@pulumi.getter(name="diagnosticsProfile")
def diagnostics_profile(self) -> pulumi.Output[Optional['outputs.DiagnosticsProfileResponse']]:
"""
Specifies the boot diagnostic settings state. <br><br>Minimum api-version: 2015-06-15.
"""
return pulumi.get(self, "diagnostics_profile")
@property
@pulumi.getter(name="evictionPolicy")
def eviction_policy(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the eviction policy for the Azure Spot virtual machine and Azure Spot scale set. <br><br>For Azure Spot virtual machines, both 'Deallocate' and 'Delete' are supported and the minimum api-version is 2019-03-01. <br><br>For Azure Spot scale sets, both 'Deallocate' and 'Delete' are supported and the minimum api-version is 2017-10-30-preview.
"""
return pulumi.get(self, "eviction_policy")
@property
@pulumi.getter(name="hardwareProfile")
def hardware_profile(self) -> pulumi.Output[Optional['outputs.HardwareProfileResponse']]:
"""
Specifies the hardware settings for the virtual machine.
"""
return pulumi.get(self, "hardware_profile")
@property
@pulumi.getter
def host(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
"""
Specifies information about the dedicated host that the virtual machine resides in. <br><br>Minimum api-version: 2018-10-01.
"""
return pulumi.get(self, "host")
@property
@pulumi.getter
def identity(self) -> pulumi.Output[Optional['outputs.VirtualMachineIdentityResponse']]:
"""
The identity of the virtual machine, if configured.
"""
return pulumi.get(self, "identity")
@property
@pulumi.getter(name="instanceView")
def instance_view(self) -> pulumi.Output['outputs.VirtualMachineInstanceViewResponse']:
"""
The virtual machine instance view.
"""
return pulumi.get(self, "instance_view")
@property
@pulumi.getter(name="licenseType")
def license_type(self) -> pulumi.Output[Optional[str]]:
"""
Specifies that the image or disk that is being used was licensed on-premises. This element is only used for images that contain the Windows Server operating system. <br><br> Possible values are: <br><br> Windows_Client <br><br> Windows_Server <br><br> If this element is included in a request for an update, the value must match the initial value. This value cannot be updated. <br><br> For more information, see [Azure Hybrid Use Benefit for Windows Server](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-hybrid-use-benefit-licensing?toc=%2fazure%2fvirtual-machines%2fwindows%2ftoc.json) <br><br> Minimum api-version: 2015-06-15
"""
return pulumi.get(self, "license_type")
@property
@pulumi.getter
def location(self) -> pulumi.Output[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="networkProfile")
def network_profile(self) -> pulumi.Output[Optional['outputs.NetworkProfileResponse']]:
"""
Specifies the network interfaces of the virtual machine.
"""
return pulumi.get(self, "network_profile")
@property
@pulumi.getter(name="osProfile")
def os_profile(self) -> pulumi.Output[Optional['outputs.OSProfileResponse']]:
"""
Specifies the operating system settings used while creating the virtual machine. Some of the settings cannot be changed once VM is provisioned.
"""
return pulumi.get(self, "os_profile")
@property
@pulumi.getter
def plan(self) -> pulumi.Output[Optional['outputs.PlanResponse']]:
"""
Specifies information about the marketplace image used to create the virtual machine. This element is only used for marketplace images. Before you can use a marketplace image from an API, you must enable the image for programmatic use. In the Azure portal, find the marketplace image that you want to use and then click **Want to deploy programmatically, Get Started ->**. Enter any required information and then click **Save**.
"""
return pulumi.get(self, "plan")
@property
@pulumi.getter
def priority(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the priority for the virtual machine. <br><br>Minimum api-version: 2019-03-01
"""
return pulumi.get(self, "priority")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
"""
The provisioning state, which only appears in the response.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="proximityPlacementGroup")
def proximity_placement_group(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
"""
Specifies information about the proximity placement group that the virtual machine should be assigned to. <br><br>Minimum api-version: 2018-04-01.
"""
return pulumi.get(self, "proximity_placement_group")
@property
@pulumi.getter
def resources(self) -> pulumi.Output[Sequence['outputs.VirtualMachineExtensionResponse']]:
"""
The virtual machine child extension resources.
"""
return pulumi.get(self, "resources")
@property
@pulumi.getter(name="storageProfile")
def storage_profile(self) -> pulumi.Output[Optional['outputs.StorageProfileResponse']]:
"""
Specifies the storage settings for the virtual machine disks.
"""
return pulumi.get(self, "storage_profile")
@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="virtualMachineScaleSet")
def virtual_machine_scale_set(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
"""
Specifies information about the virtual machine scale set that the virtual machine should be assigned to. Virtual machines specified in the same virtual machine scale set are allocated to different nodes to maximize availability. Currently, a VM can only be added to virtual machine scale set at creation time. An existing VM cannot be added to a virtual machine scale set. <br><br>This property cannot exist along with a non-null properties.availabilitySet reference. <br><br>Minimum api‐version: 2019‐03‐01
"""
return pulumi.get(self, "virtual_machine_scale_set")
@property
@pulumi.getter(name="vmId")
def vm_id(self) -> pulumi.Output[str]:
"""
Specifies the VM unique ID which is a 128-bits identifier that is encoded and stored in all Azure IaaS VMs SMBIOS and can be read using platform BIOS commands.
"""
return pulumi.get(self, "vm_id")
@property
@pulumi.getter
def zones(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
The virtual machine zones.
"""
return pulumi.get(self, "zones")
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
| 65.354286 | 1,169 | 0.707135 |
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__ = ['VirtualMachine']
class VirtualMachine(pulumi.CustomResource):
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
additional_capabilities: Optional[pulumi.Input[pulumi.InputType['AdditionalCapabilitiesArgs']]] = None,
availability_set: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
billing_profile: Optional[pulumi.Input[pulumi.InputType['BillingProfileArgs']]] = None,
diagnostics_profile: Optional[pulumi.Input[pulumi.InputType['DiagnosticsProfileArgs']]] = None,
eviction_policy: Optional[pulumi.Input[Union[str, 'VirtualMachineEvictionPolicyTypes']]] = None,
hardware_profile: Optional[pulumi.Input[pulumi.InputType['HardwareProfileArgs']]] = None,
host: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
identity: Optional[pulumi.Input[pulumi.InputType['VirtualMachineIdentityArgs']]] = None,
license_type: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
network_profile: Optional[pulumi.Input[pulumi.InputType['NetworkProfileArgs']]] = None,
os_profile: Optional[pulumi.Input[pulumi.InputType['OSProfileArgs']]] = None,
plan: Optional[pulumi.Input[pulumi.InputType['PlanArgs']]] = None,
priority: Optional[pulumi.Input[Union[str, 'VirtualMachinePriorityTypes']]] = None,
proximity_placement_group: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
storage_profile: Optional[pulumi.Input[pulumi.InputType['StorageProfileArgs']]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
virtual_machine_scale_set: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None,
vm_name: Optional[pulumi.Input[str]] = None,
zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None,
__name__=None,
__opts__=None):
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__['additional_capabilities'] = additional_capabilities
__props__['availability_set'] = availability_set
__props__['billing_profile'] = billing_profile
__props__['diagnostics_profile'] = diagnostics_profile
__props__['eviction_policy'] = eviction_policy
__props__['hardware_profile'] = hardware_profile
__props__['host'] = host
__props__['identity'] = identity
__props__['license_type'] = license_type
__props__['location'] = location
__props__['network_profile'] = network_profile
__props__['os_profile'] = os_profile
__props__['plan'] = plan
__props__['priority'] = priority
__props__['proximity_placement_group'] = proximity_placement_group
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__['storage_profile'] = storage_profile
__props__['tags'] = tags
__props__['virtual_machine_scale_set'] = virtual_machine_scale_set
__props__['vm_name'] = vm_name
__props__['zones'] = zones
__props__['instance_view'] = None
__props__['name'] = None
__props__['provisioning_state'] = None
__props__['resources'] = None
__props__['type'] = None
__props__['vm_id'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:compute:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/latest:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20150615:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20160330:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20160430preview:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20170330:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20171201:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20180401:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20180601:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20181001:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20190301:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20190701:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20200601:VirtualMachine"), pulumi.Alias(type_="azure-nextgen:compute/v20201201:VirtualMachine")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(VirtualMachine, __self__).__init__(
'azure-nextgen:compute/v20191201:VirtualMachine',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'VirtualMachine':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
return VirtualMachine(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="additionalCapabilities")
def additional_capabilities(self) -> pulumi.Output[Optional['outputs.AdditionalCapabilitiesResponse']]:
return pulumi.get(self, "additional_capabilities")
@property
@pulumi.getter(name="availabilitySet")
def availability_set(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
return pulumi.get(self, "availability_set")
@property
@pulumi.getter(name="billingProfile")
def billing_profile(self) -> pulumi.Output[Optional['outputs.BillingProfileResponse']]:
return pulumi.get(self, "billing_profile")
@property
@pulumi.getter(name="diagnosticsProfile")
def diagnostics_profile(self) -> pulumi.Output[Optional['outputs.DiagnosticsProfileResponse']]:
return pulumi.get(self, "diagnostics_profile")
@property
@pulumi.getter(name="evictionPolicy")
def eviction_policy(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "eviction_policy")
@property
@pulumi.getter(name="hardwareProfile")
def hardware_profile(self) -> pulumi.Output[Optional['outputs.HardwareProfileResponse']]:
return pulumi.get(self, "hardware_profile")
@property
@pulumi.getter
def host(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
return pulumi.get(self, "host")
@property
@pulumi.getter
def identity(self) -> pulumi.Output[Optional['outputs.VirtualMachineIdentityResponse']]:
return pulumi.get(self, "identity")
@property
@pulumi.getter(name="instanceView")
def instance_view(self) -> pulumi.Output['outputs.VirtualMachineInstanceViewResponse']:
return pulumi.get(self, "instance_view")
@property
@pulumi.getter(name="licenseType")
def license_type(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "license_type")
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter(name="networkProfile")
def network_profile(self) -> pulumi.Output[Optional['outputs.NetworkProfileResponse']]:
return pulumi.get(self, "network_profile")
@property
@pulumi.getter(name="osProfile")
def os_profile(self) -> pulumi.Output[Optional['outputs.OSProfileResponse']]:
return pulumi.get(self, "os_profile")
@property
@pulumi.getter
def plan(self) -> pulumi.Output[Optional['outputs.PlanResponse']]:
return pulumi.get(self, "plan")
@property
@pulumi.getter
def priority(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "priority")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="proximityPlacementGroup")
def proximity_placement_group(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
return pulumi.get(self, "proximity_placement_group")
@property
@pulumi.getter
def resources(self) -> pulumi.Output[Sequence['outputs.VirtualMachineExtensionResponse']]:
return pulumi.get(self, "resources")
@property
@pulumi.getter(name="storageProfile")
def storage_profile(self) -> pulumi.Output[Optional['outputs.StorageProfileResponse']]:
return pulumi.get(self, "storage_profile")
@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="virtualMachineScaleSet")
def virtual_machine_scale_set(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]:
return pulumi.get(self, "virtual_machine_scale_set")
@property
@pulumi.getter(name="vmId")
def vm_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "vm_id")
@property
@pulumi.getter
def zones(self) -> pulumi.Output[Optional[Sequence[str]]]:
return pulumi.get(self, "zones")
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 |
f72cab35e960fe08f1ad7e2c27dda165a8cea5a9 | 352 | py | Python | qualysapi/__init__.py | trolldbois/qualysapi | 33de3cda1e1073e5c960740e38864d1f551bfd3d | [
"Apache-2.0"
] | 4 | 2019-03-20T14:49:01.000Z | 2020-06-19T19:03:54.000Z | qualysapi/__init__.py | trolldbois/qualysapi | 33de3cda1e1073e5c960740e38864d1f551bfd3d | [
"Apache-2.0"
] | 2 | 2019-02-05T16:20:44.000Z | 2019-02-06T09:50:27.000Z | qualysapi/__init__.py | trolldbois/qualysapi | 33de3cda1e1073e5c960740e38864d1f551bfd3d | [
"Apache-2.0"
] | 1 | 2020-06-01T18:57:41.000Z | 2020-06-01T18:57:41.000Z | # -*- coding: future_fstrings -*-
# This is the version string assigned to the entire egg post
# setup.py install
# Ownership and Copyright Information.
from __future__ import absolute_import
__author__ = "Parag Baxi <parag.baxi@gmail.com>"
__copyright__ = "Copyright 2011-2013, Parag Baxi"
__license__ = "BSD-new"
from qualysapi.util import connect
| 29.333333 | 60 | 0.772727 |
from __future__ import absolute_import
__author__ = "Parag Baxi <parag.baxi@gmail.com>"
__copyright__ = "Copyright 2011-2013, Parag Baxi"
__license__ = "BSD-new"
from qualysapi.util import connect
| true | true |
f72cab98f8c6d40b4cfe232aace0f320986b5a88 | 607 | py | Python | biosimulators_utils/sedml/exceptions.py | biosimulators/Biosimulators_utils | c1363467263120bf1166da2b75e38fc7f56dc94f | [
"MIT"
] | 2 | 2021-06-02T13:26:34.000Z | 2021-12-27T23:12:47.000Z | biosimulators_utils/sedml/exceptions.py | biosimulators/Biosimulators_utils | c1363467263120bf1166da2b75e38fc7f56dc94f | [
"MIT"
] | 102 | 2020-12-06T19:47:43.000Z | 2022-03-31T12:56:17.000Z | biosimulators_utils/sedml/exceptions.py | biosimulators/Biosimulators_utils | c1363467263120bf1166da2b75e38fc7f56dc94f | [
"MIT"
] | 4 | 2021-01-27T19:56:34.000Z | 2022-02-03T21:08:20.000Z | """ Exceptions for SED-ML
:Author: Jonathan Karr <karr@mssm.edu>
:Date: 2021-01-12
:Copyright: 2021, Center for Reproducible Biomedical Modeling
:License: MIT
"""
from ..exceptions import BioSimulatorsException
__all__ = [
'SedmlExecutionError',
'UnsupportedModelLanguageError',
]
class SedmlExecutionError(BioSimulatorsException):
""" Error that a SED document could not be executed """
pass # pragma: no cover
class UnsupportedModelLanguageError(BioSimulatorsException, NotImplementedError):
""" Error that a SED document could not be executed """
pass # pragma: no cover
| 24.28 | 81 | 0.742998 |
from ..exceptions import BioSimulatorsException
__all__ = [
'SedmlExecutionError',
'UnsupportedModelLanguageError',
]
class SedmlExecutionError(BioSimulatorsException):
pass
class UnsupportedModelLanguageError(BioSimulatorsException, NotImplementedError):
pass
| true | true |
f72cabca4a5200b7b635654d553d20ae2f30155f | 3,356 | py | Python | tests/bindings/test_python.py | mfkiwl/hgdb | 6279b2d671b09094b7e69c592fa8f2eca3f6bacd | [
"BSD-2-Clause"
] | 34 | 2021-01-19T21:14:06.000Z | 2022-03-31T18:42:58.000Z | tests/bindings/test_python.py | mfkiwl/hgdb | 6279b2d671b09094b7e69c592fa8f2eca3f6bacd | [
"BSD-2-Clause"
] | 33 | 2021-01-12T18:50:16.000Z | 2022-03-23T04:49:20.000Z | tests/bindings/test_python.py | mfkiwl/hgdb | 6279b2d671b09094b7e69c592fa8f2eca3f6bacd | [
"BSD-2-Clause"
] | 2 | 2021-03-28T06:58:46.000Z | 2022-03-31T02:55:53.000Z | import sqlite3
import tempfile
import hgdb
import os
import pytest
def get_conn_cursor(db_name):
conn = sqlite3.connect(db_name)
c = conn.cursor()
return conn, c
def test_store_instance():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
db.store_instance(42, "test")
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM instance WHERE id=?", (42,))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_breakpoint():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
# no instance matching yet
with pytest.raises(hgdb.db.DebugSymbolTableException) as ex:
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
assert ex.value.args[0]
db.store_instance(42, "test")
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM breakpoint WHERE filename=? AND line_num=?", ("/tmp/test.py", 1))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_context_variable():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
# no variable matching yet
with pytest.raises(hgdb.db.DebugSymbolTableException) as ex:
db.store_context_variable("a", 1, 43)
assert ex.value.args[0]
db.store_instance(42, "test")
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
db.store_variable(43, "value")
db.store_context_variable("a", 1, 43)
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM context_variable WHERE breakpoint_id=?", (1, ))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_generator_variable():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
# no instance matching yet
with pytest.raises(hgdb.db.DebugSymbolTableException) as ex:
db.store_generator_variable("a", 42, 43)
assert ex.value.args[0]
db.store_instance(42, "test")
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
db.store_variable(43, "value")
db.store_generator_variable("a", 42, 43)
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM generator_variable WHERE instance_id=?", (42, ))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_scope():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
db.store_instance(42, "test")
for i in range(4):
db.store_breakpoint(i, 42, "/tmp/test.py", i + 1)
db.store_scope(0, *[0, 1, 2, 3])
conn, c = get_conn_cursor(db_name)
c.execute("SELECT breakpoints FROM scope WHERE scope=?", (0, ))
r = c.fetchone()[0]
assert r == " ".join([str(i) for i in range(4)])
conn.close()
if __name__ == "__main__":
test_store_scope()
| 31.660377 | 105 | 0.61025 | import sqlite3
import tempfile
import hgdb
import os
import pytest
def get_conn_cursor(db_name):
conn = sqlite3.connect(db_name)
c = conn.cursor()
return conn, c
def test_store_instance():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
db.store_instance(42, "test")
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM instance WHERE id=?", (42,))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_breakpoint():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
with pytest.raises(hgdb.db.DebugSymbolTableException) as ex:
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
assert ex.value.args[0]
db.store_instance(42, "test")
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM breakpoint WHERE filename=? AND line_num=?", ("/tmp/test.py", 1))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_context_variable():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
with pytest.raises(hgdb.db.DebugSymbolTableException) as ex:
db.store_context_variable("a", 1, 43)
assert ex.value.args[0]
db.store_instance(42, "test")
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
db.store_variable(43, "value")
db.store_context_variable("a", 1, 43)
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM context_variable WHERE breakpoint_id=?", (1, ))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_generator_variable():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
with pytest.raises(hgdb.db.DebugSymbolTableException) as ex:
db.store_generator_variable("a", 42, 43)
assert ex.value.args[0]
db.store_instance(42, "test")
db.store_breakpoint(1, 42, "/tmp/test.py", 1)
db.store_variable(43, "value")
db.store_generator_variable("a", 42, 43)
conn, c = get_conn_cursor(db_name)
c.execute("SELECT COUNT(*) FROM generator_variable WHERE instance_id=?", (42, ))
r = c.fetchone()[0]
assert r == 1
conn.close()
def test_store_scope():
with tempfile.TemporaryDirectory() as temp:
db_name = os.path.join(temp, "debug.db")
db = hgdb.DebugSymbolTable(db_name)
db.store_instance(42, "test")
for i in range(4):
db.store_breakpoint(i, 42, "/tmp/test.py", i + 1)
db.store_scope(0, *[0, 1, 2, 3])
conn, c = get_conn_cursor(db_name)
c.execute("SELECT breakpoints FROM scope WHERE scope=?", (0, ))
r = c.fetchone()[0]
assert r == " ".join([str(i) for i in range(4)])
conn.close()
if __name__ == "__main__":
test_store_scope()
| true | true |
f72cac3af394de7e0476052b87a340105bd5386f | 2,482 | py | Python | bot.py | StarkGang/TagChecker | 390191a03afc17c9003a046954586532947d10d4 | [
"MIT"
] | 1 | 2021-07-18T01:12:55.000Z | 2021-07-18T01:12:55.000Z | bot.py | StarkGang/TagChecker | 390191a03afc17c9003a046954586532947d10d4 | [
"MIT"
] | null | null | null | bot.py | StarkGang/TagChecker | 390191a03afc17c9003a046954586532947d10d4 | [
"MIT"
] | null | null | null | from pyrogram import filters, Client
import logging
import os
from pyrogram.types import (
ChatPermissions,
InlineKeyboardButton,
InlineKeyboardMarkup
)
logging.basicConfig(level=logging.INFO)
API_ID = int(os.environ.get("API_ID", 6))
API_HASH = os.environ.get("API_HASH", "eb06d4abfb49dc3eeb1aeb98ae0f581e")
TOKEN = os.environ.get("TOKEN", None)
TAG = os.environ.get("TAG", None)
OWNER_ID = int(os.environ.get("OWNER_ID", 1704673514))
tagcheck = Client(
"tagcheck",
bot_token=TOKEN,
api_id=API_ID,
api_hash=API_HASH
)
user_s = {}
async def is_admin(message):
user = await tagcheck.get_chat_member(message.chat.id, message.from_user.id)
if user.status in ("administrator", "creator"):
return True
return False
@tagcheck.on_message(filters.command("start") & filters.user(OWNER_ID))
async def start(_, message):
await message.reply("I am Alive.")
@tagcheck.on_message(filters.group)
async def tag_check(_, message):
if await is_admin(message):
return
user = message.from_user.id
if TAG not in message.from_user.first_name:
try:
await tagcheck.restrict_chat_member(
message.chat.id,
user,
ChatPermissions(),
)
except BaseException as be:
await message.reply(f"**Error:**\n`{be}`")
return
text = f"""
**Heya {message.from_user.mention}**
Please add our tag in your name to
chat again in the group.
**Tag:** `{TAG}`
**Note:** __Click The Below Button For
Unmuting YourSelf!__
"""
await message.reply(
text,
reply_markup=InlineKeyboardMarkup([
[InlineKeyboardButton("Unmute Me", callback_data="unmute")]
]
)
)
user_s.update({"user_id": user})
@tagcheck.on_callback_query(filters.regex("unmute"))
async def unmute(client, cb):
try:
user = user_s["user_id"]
except KeyError:
await cb.answer(
"Oops!\nIts looks like i lost your id from my server\nContact Admins For Unmiting",
show_alert=True
)
return
if cb.from_user.id != user:
await cb.answer("This Button is not for you!", show_alert=True)
return
if TAG in cb.from_user.first_name:
await tagcheck.unban_chat_member(cb.message.chat.id, user)
await cb.answer("Succesfully Unmuted!")
await message.delete()
return
await cb.answer("Please add tag in your name!", show_alert=True)
tagcheck.run()
| 26.404255 | 92 | 0.657937 | from pyrogram import filters, Client
import logging
import os
from pyrogram.types import (
ChatPermissions,
InlineKeyboardButton,
InlineKeyboardMarkup
)
logging.basicConfig(level=logging.INFO)
API_ID = int(os.environ.get("API_ID", 6))
API_HASH = os.environ.get("API_HASH", "eb06d4abfb49dc3eeb1aeb98ae0f581e")
TOKEN = os.environ.get("TOKEN", None)
TAG = os.environ.get("TAG", None)
OWNER_ID = int(os.environ.get("OWNER_ID", 1704673514))
tagcheck = Client(
"tagcheck",
bot_token=TOKEN,
api_id=API_ID,
api_hash=API_HASH
)
user_s = {}
async def is_admin(message):
user = await tagcheck.get_chat_member(message.chat.id, message.from_user.id)
if user.status in ("administrator", "creator"):
return True
return False
@tagcheck.on_message(filters.command("start") & filters.user(OWNER_ID))
async def start(_, message):
await message.reply("I am Alive.")
@tagcheck.on_message(filters.group)
async def tag_check(_, message):
if await is_admin(message):
return
user = message.from_user.id
if TAG not in message.from_user.first_name:
try:
await tagcheck.restrict_chat_member(
message.chat.id,
user,
ChatPermissions(),
)
except BaseException as be:
await message.reply(f"**Error:**\n`{be}`")
return
text = f"""
**Heya {message.from_user.mention}**
Please add our tag in your name to
chat again in the group.
**Tag:** `{TAG}`
**Note:** __Click The Below Button For
Unmuting YourSelf!__
"""
await message.reply(
text,
reply_markup=InlineKeyboardMarkup([
[InlineKeyboardButton("Unmute Me", callback_data="unmute")]
]
)
)
user_s.update({"user_id": user})
@tagcheck.on_callback_query(filters.regex("unmute"))
async def unmute(client, cb):
try:
user = user_s["user_id"]
except KeyError:
await cb.answer(
"Oops!\nIts looks like i lost your id from my server\nContact Admins For Unmiting",
show_alert=True
)
return
if cb.from_user.id != user:
await cb.answer("This Button is not for you!", show_alert=True)
return
if TAG in cb.from_user.first_name:
await tagcheck.unban_chat_member(cb.message.chat.id, user)
await cb.answer("Succesfully Unmuted!")
await message.delete()
return
await cb.answer("Please add tag in your name!", show_alert=True)
tagcheck.run()
| true | true |
f72cad1a00cbc3a4cfeedd1cef65f5d5f630641b | 2,143 | py | Python | oneflow/python/framework/watcher.py | 666DZY666/oneflow | 2062cb211dd1e0619d610659e6d41598d5f73e17 | [
"Apache-2.0"
] | null | null | null | oneflow/python/framework/watcher.py | 666DZY666/oneflow | 2062cb211dd1e0619d610659e6d41598d5f73e17 | [
"Apache-2.0"
] | null | null | null | oneflow/python/framework/watcher.py | 666DZY666/oneflow | 2062cb211dd1e0619d610659e6d41598d5f73e17 | [
"Apache-2.0"
] | 1 | 2021-11-10T07:57:01.000Z | 2021-11-10T07:57:01.000Z | """
Copyright 2020 The OneFlow 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.
"""
from __future__ import absolute_import
import traceback
import oneflow.core.record.record_pb2 as record_util
import oneflow.python.framework.local_blob as local_blob_util
import oneflow.python.framework.ofblob as ofblob
import oneflow.python.framework.remote_blob as remote_blob_util
import oneflow.python.framework.session_context as session_ctx
import oneflow.python.framework.typing_util as oft_util
import oneflow_api
from google.protobuf import text_format
def BindUuidAndHandler(uuid, blob_watched, handler):
assert isinstance(blob_watched, oneflow_api.ConsistentBlob)
session_ctx.GetDefaultSession().uuid2watch_handler[uuid] = (blob_watched, handler)
class _Watcher(oneflow_api.ForeignWatcher):
def __init__(self):
oneflow_api.ForeignWatcher.__init__(self)
def Call(self, handler_uuid, of_blob_ptr):
try:
_WatcherHandler(handler_uuid, of_blob_ptr)
except Exception as e:
print(traceback.format_exc())
raise e
def _WatcherHandler(handler_uuid, of_blob_ptr):
uuid2handler = session_ctx.GetDefaultSession().uuid2watch_handler
assert handler_uuid in uuid2handler
blob_watched, handler = uuid2handler[handler_uuid]
assert callable(handler)
ndarray_lists = ofblob.OfBlob(of_blob_ptr).CopyToNdarrayLists()
local_blob = local_blob_util.MakeLocalBlob(ndarray_lists, blob_watched)
handler(oft_util.TransformWatchedBlob(local_blob, handler))
# static lifetime
_global_watcher = _Watcher()
oneflow_api.RegisterWatcherOnlyOnce(_global_watcher)
| 35.716667 | 86 | 0.792814 | from __future__ import absolute_import
import traceback
import oneflow.core.record.record_pb2 as record_util
import oneflow.python.framework.local_blob as local_blob_util
import oneflow.python.framework.ofblob as ofblob
import oneflow.python.framework.remote_blob as remote_blob_util
import oneflow.python.framework.session_context as session_ctx
import oneflow.python.framework.typing_util as oft_util
import oneflow_api
from google.protobuf import text_format
def BindUuidAndHandler(uuid, blob_watched, handler):
assert isinstance(blob_watched, oneflow_api.ConsistentBlob)
session_ctx.GetDefaultSession().uuid2watch_handler[uuid] = (blob_watched, handler)
class _Watcher(oneflow_api.ForeignWatcher):
def __init__(self):
oneflow_api.ForeignWatcher.__init__(self)
def Call(self, handler_uuid, of_blob_ptr):
try:
_WatcherHandler(handler_uuid, of_blob_ptr)
except Exception as e:
print(traceback.format_exc())
raise e
def _WatcherHandler(handler_uuid, of_blob_ptr):
uuid2handler = session_ctx.GetDefaultSession().uuid2watch_handler
assert handler_uuid in uuid2handler
blob_watched, handler = uuid2handler[handler_uuid]
assert callable(handler)
ndarray_lists = ofblob.OfBlob(of_blob_ptr).CopyToNdarrayLists()
local_blob = local_blob_util.MakeLocalBlob(ndarray_lists, blob_watched)
handler(oft_util.TransformWatchedBlob(local_blob, handler))
_global_watcher = _Watcher()
oneflow_api.RegisterWatcherOnlyOnce(_global_watcher)
| true | true |
f72cad26cae4ebe6adabb39d7a5dfcd09cf17363 | 31,637 | py | Python | sdk/python/pulumi_f5bigip/ltm/snat.py | pulumi/pulumi-f5bigip | 4bce074f8bd7cb42f359ef4814ca5b437230fd1c | [
"ECL-2.0",
"Apache-2.0"
] | 4 | 2018-12-21T23:30:33.000Z | 2021-10-12T16:38:27.000Z | sdk/python/pulumi_f5bigip/ltm/snat.py | pulumi/pulumi-f5bigip | 4bce074f8bd7cb42f359ef4814ca5b437230fd1c | [
"ECL-2.0",
"Apache-2.0"
] | 61 | 2019-01-09T01:50:19.000Z | 2022-03-31T15:27:17.000Z | sdk/python/pulumi_f5bigip/ltm/snat.py | pulumi/pulumi-f5bigip | 4bce074f8bd7cb42f359ef4814ca5b437230fd1c | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2019-10-05T10:36:30.000Z | 2019-10-05T10:36:30.000Z | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** 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, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['SnatArgs', 'Snat']
@pulumi.input_type
class SnatArgs:
def __init__(__self__, *,
name: pulumi.Input[str],
origins: pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]],
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None):
"""
The set of arguments for constructing a Snat resource.
:param pulumi.Input[str] name: Name of the snat
:param pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]] origins: IP or hostname of the snat
:param pulumi.Input[str] autolasthop: -(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
:param pulumi.Input[str] full_path: Fullpath
:param pulumi.Input[str] mirror: Enables or disables mirroring of SNAT connections.
:param pulumi.Input[str] partition: Displays the administrative partition within which this profile resides
:param pulumi.Input[str] snatpool: Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
:param pulumi.Input[str] sourceport: Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
:param pulumi.Input[str] translation: Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
:param pulumi.Input[Sequence[pulumi.Input[str]]] vlans: Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
:param pulumi.Input[bool] vlansdisabled: Disables the SNAT on all VLANs.
"""
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "origins", origins)
if autolasthop is not None:
pulumi.set(__self__, "autolasthop", autolasthop)
if full_path is not None:
pulumi.set(__self__, "full_path", full_path)
if mirror is not None:
pulumi.set(__self__, "mirror", mirror)
if partition is not None:
pulumi.set(__self__, "partition", partition)
if snatpool is not None:
pulumi.set(__self__, "snatpool", snatpool)
if sourceport is not None:
pulumi.set(__self__, "sourceport", sourceport)
if translation is not None:
pulumi.set(__self__, "translation", translation)
if vlans is not None:
pulumi.set(__self__, "vlans", vlans)
if vlansdisabled is not None:
pulumi.set(__self__, "vlansdisabled", vlansdisabled)
@property
@pulumi.getter
def name(self) -> pulumi.Input[str]:
"""
Name of the snat
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: pulumi.Input[str]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def origins(self) -> pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]:
"""
IP or hostname of the snat
"""
return pulumi.get(self, "origins")
@origins.setter
def origins(self, value: pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]):
pulumi.set(self, "origins", value)
@property
@pulumi.getter
def autolasthop(self) -> Optional[pulumi.Input[str]]:
"""
-(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
"""
return pulumi.get(self, "autolasthop")
@autolasthop.setter
def autolasthop(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "autolasthop", value)
@property
@pulumi.getter(name="fullPath")
def full_path(self) -> Optional[pulumi.Input[str]]:
"""
Fullpath
"""
return pulumi.get(self, "full_path")
@full_path.setter
def full_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "full_path", value)
@property
@pulumi.getter
def mirror(self) -> Optional[pulumi.Input[str]]:
"""
Enables or disables mirroring of SNAT connections.
"""
return pulumi.get(self, "mirror")
@mirror.setter
def mirror(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "mirror", value)
@property
@pulumi.getter
def partition(self) -> Optional[pulumi.Input[str]]:
"""
Displays the administrative partition within which this profile resides
"""
return pulumi.get(self, "partition")
@partition.setter
def partition(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "partition", value)
@property
@pulumi.getter
def snatpool(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
"""
return pulumi.get(self, "snatpool")
@snatpool.setter
def snatpool(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "snatpool", value)
@property
@pulumi.getter
def sourceport(self) -> Optional[pulumi.Input[str]]:
"""
Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
"""
return pulumi.get(self, "sourceport")
@sourceport.setter
def sourceport(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sourceport", value)
@property
@pulumi.getter
def translation(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
"""
return pulumi.get(self, "translation")
@translation.setter
def translation(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "translation", value)
@property
@pulumi.getter
def vlans(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
"""
return pulumi.get(self, "vlans")
@vlans.setter
def vlans(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "vlans", value)
@property
@pulumi.getter
def vlansdisabled(self) -> Optional[pulumi.Input[bool]]:
"""
Disables the SNAT on all VLANs.
"""
return pulumi.get(self, "vlansdisabled")
@vlansdisabled.setter
def vlansdisabled(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "vlansdisabled", value)
@pulumi.input_type
class _SnatState:
def __init__(__self__, *,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None):
"""
Input properties used for looking up and filtering Snat resources.
:param pulumi.Input[str] autolasthop: -(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
:param pulumi.Input[str] full_path: Fullpath
:param pulumi.Input[str] mirror: Enables or disables mirroring of SNAT connections.
:param pulumi.Input[str] name: Name of the snat
:param pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]] origins: IP or hostname of the snat
:param pulumi.Input[str] partition: Displays the administrative partition within which this profile resides
:param pulumi.Input[str] snatpool: Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
:param pulumi.Input[str] sourceport: Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
:param pulumi.Input[str] translation: Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
:param pulumi.Input[Sequence[pulumi.Input[str]]] vlans: Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
:param pulumi.Input[bool] vlansdisabled: Disables the SNAT on all VLANs.
"""
if autolasthop is not None:
pulumi.set(__self__, "autolasthop", autolasthop)
if full_path is not None:
pulumi.set(__self__, "full_path", full_path)
if mirror is not None:
pulumi.set(__self__, "mirror", mirror)
if name is not None:
pulumi.set(__self__, "name", name)
if origins is not None:
pulumi.set(__self__, "origins", origins)
if partition is not None:
pulumi.set(__self__, "partition", partition)
if snatpool is not None:
pulumi.set(__self__, "snatpool", snatpool)
if sourceport is not None:
pulumi.set(__self__, "sourceport", sourceport)
if translation is not None:
pulumi.set(__self__, "translation", translation)
if vlans is not None:
pulumi.set(__self__, "vlans", vlans)
if vlansdisabled is not None:
pulumi.set(__self__, "vlansdisabled", vlansdisabled)
@property
@pulumi.getter
def autolasthop(self) -> Optional[pulumi.Input[str]]:
"""
-(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
"""
return pulumi.get(self, "autolasthop")
@autolasthop.setter
def autolasthop(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "autolasthop", value)
@property
@pulumi.getter(name="fullPath")
def full_path(self) -> Optional[pulumi.Input[str]]:
"""
Fullpath
"""
return pulumi.get(self, "full_path")
@full_path.setter
def full_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "full_path", value)
@property
@pulumi.getter
def mirror(self) -> Optional[pulumi.Input[str]]:
"""
Enables or disables mirroring of SNAT connections.
"""
return pulumi.get(self, "mirror")
@mirror.setter
def mirror(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "mirror", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the snat
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def origins(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]]:
"""
IP or hostname of the snat
"""
return pulumi.get(self, "origins")
@origins.setter
def origins(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]]):
pulumi.set(self, "origins", value)
@property
@pulumi.getter
def partition(self) -> Optional[pulumi.Input[str]]:
"""
Displays the administrative partition within which this profile resides
"""
return pulumi.get(self, "partition")
@partition.setter
def partition(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "partition", value)
@property
@pulumi.getter
def snatpool(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
"""
return pulumi.get(self, "snatpool")
@snatpool.setter
def snatpool(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "snatpool", value)
@property
@pulumi.getter
def sourceport(self) -> Optional[pulumi.Input[str]]:
"""
Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
"""
return pulumi.get(self, "sourceport")
@sourceport.setter
def sourceport(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sourceport", value)
@property
@pulumi.getter
def translation(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
"""
return pulumi.get(self, "translation")
@translation.setter
def translation(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "translation", value)
@property
@pulumi.getter
def vlans(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
"""
return pulumi.get(self, "vlans")
@vlans.setter
def vlans(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "vlans", value)
@property
@pulumi.getter
def vlansdisabled(self) -> Optional[pulumi.Input[bool]]:
"""
Disables the SNAT on all VLANs.
"""
return pulumi.get(self, "vlansdisabled")
@vlansdisabled.setter
def vlansdisabled(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "vlansdisabled", value)
class Snat(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None,
__props__=None):
"""
`ltm.Snat` Manages a snat configuration
For resources should be named with their "full path". The full path is the combination of the partition + name of the resource. For example /Common/my-pool.
## Example Usage
```python
import pulumi
import pulumi_f5bigip as f5bigip
test_snat = f5bigip.ltm.Snat("test-snat",
autolasthop="default",
full_path="/Common/test-snat",
mirror="disabled",
name="TEST_SNAT_NAME",
origins=[
f5bigip.ltm.SnatOriginArgs(
name="2.2.2.2",
),
f5bigip.ltm.SnatOriginArgs(
name="3.3.3.3",
),
],
partition="Common",
translation="/Common/136.1.1.1",
vlansdisabled=True)
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] autolasthop: -(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
:param pulumi.Input[str] full_path: Fullpath
:param pulumi.Input[str] mirror: Enables or disables mirroring of SNAT connections.
:param pulumi.Input[str] name: Name of the snat
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]] origins: IP or hostname of the snat
:param pulumi.Input[str] partition: Displays the administrative partition within which this profile resides
:param pulumi.Input[str] snatpool: Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
:param pulumi.Input[str] sourceport: Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
:param pulumi.Input[str] translation: Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
:param pulumi.Input[Sequence[pulumi.Input[str]]] vlans: Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
:param pulumi.Input[bool] vlansdisabled: Disables the SNAT on all VLANs.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: SnatArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
`ltm.Snat` Manages a snat configuration
For resources should be named with their "full path". The full path is the combination of the partition + name of the resource. For example /Common/my-pool.
## Example Usage
```python
import pulumi
import pulumi_f5bigip as f5bigip
test_snat = f5bigip.ltm.Snat("test-snat",
autolasthop="default",
full_path="/Common/test-snat",
mirror="disabled",
name="TEST_SNAT_NAME",
origins=[
f5bigip.ltm.SnatOriginArgs(
name="2.2.2.2",
),
f5bigip.ltm.SnatOriginArgs(
name="3.3.3.3",
),
],
partition="Common",
translation="/Common/136.1.1.1",
vlansdisabled=True)
```
:param str resource_name: The name of the resource.
:param SnatArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(SnatArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None,
__props__=None):
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__ = SnatArgs.__new__(SnatArgs)
__props__.__dict__["autolasthop"] = autolasthop
__props__.__dict__["full_path"] = full_path
__props__.__dict__["mirror"] = mirror
if name is None and not opts.urn:
raise TypeError("Missing required property 'name'")
__props__.__dict__["name"] = name
if origins is None and not opts.urn:
raise TypeError("Missing required property 'origins'")
__props__.__dict__["origins"] = origins
__props__.__dict__["partition"] = partition
__props__.__dict__["snatpool"] = snatpool
__props__.__dict__["sourceport"] = sourceport
__props__.__dict__["translation"] = translation
__props__.__dict__["vlans"] = vlans
__props__.__dict__["vlansdisabled"] = vlansdisabled
super(Snat, __self__).__init__(
'f5bigip:ltm/snat:Snat',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None) -> 'Snat':
"""
Get an existing Snat 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.
:param pulumi.Input[str] autolasthop: -(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
:param pulumi.Input[str] full_path: Fullpath
:param pulumi.Input[str] mirror: Enables or disables mirroring of SNAT connections.
:param pulumi.Input[str] name: Name of the snat
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]] origins: IP or hostname of the snat
:param pulumi.Input[str] partition: Displays the administrative partition within which this profile resides
:param pulumi.Input[str] snatpool: Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
:param pulumi.Input[str] sourceport: Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
:param pulumi.Input[str] translation: Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
:param pulumi.Input[Sequence[pulumi.Input[str]]] vlans: Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
:param pulumi.Input[bool] vlansdisabled: Disables the SNAT on all VLANs.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _SnatState.__new__(_SnatState)
__props__.__dict__["autolasthop"] = autolasthop
__props__.__dict__["full_path"] = full_path
__props__.__dict__["mirror"] = mirror
__props__.__dict__["name"] = name
__props__.__dict__["origins"] = origins
__props__.__dict__["partition"] = partition
__props__.__dict__["snatpool"] = snatpool
__props__.__dict__["sourceport"] = sourceport
__props__.__dict__["translation"] = translation
__props__.__dict__["vlans"] = vlans
__props__.__dict__["vlansdisabled"] = vlansdisabled
return Snat(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def autolasthop(self) -> pulumi.Output[Optional[str]]:
"""
-(Optional) Specifies whether to automatically map last hop for pools or not. The default is to use next level's default.
"""
return pulumi.get(self, "autolasthop")
@property
@pulumi.getter(name="fullPath")
def full_path(self) -> pulumi.Output[Optional[str]]:
"""
Fullpath
"""
return pulumi.get(self, "full_path")
@property
@pulumi.getter
def mirror(self) -> pulumi.Output[Optional[str]]:
"""
Enables or disables mirroring of SNAT connections.
"""
return pulumi.get(self, "mirror")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Name of the snat
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def origins(self) -> pulumi.Output[Sequence['outputs.SnatOrigin']]:
"""
IP or hostname of the snat
"""
return pulumi.get(self, "origins")
@property
@pulumi.getter
def partition(self) -> pulumi.Output[Optional[str]]:
"""
Displays the administrative partition within which this profile resides
"""
return pulumi.get(self, "partition")
@property
@pulumi.getter
def snatpool(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the name of a SNAT pool. You can only use this option when automap and translation are not used.
"""
return pulumi.get(self, "snatpool")
@property
@pulumi.getter
def sourceport(self) -> pulumi.Output[Optional[str]]:
"""
Specifies whether the system preserves the source port of the connection. The default is preserve. Use of the preserve-strict setting should be restricted to UDP only under very special circumstances such as nPath or transparent (that is, no translation of any other L3/L4 field), where there is a 1:1 relationship between virtual IP addresses and node addresses, or when clustered multi-processing (CMP) is disabled. The change setting is useful for obfuscating internal network addresses.
"""
return pulumi.get(self, "sourceport")
@property
@pulumi.getter
def translation(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the name of a translated IP address. Note that translated addresses are outside the traffic management system. You can only use this option when automap and snatpool are not used.
"""
return pulumi.get(self, "translation")
@property
@pulumi.getter
def vlans(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
Specifies the name of the VLAN to which you want to assign the SNAT. The default is vlans-enabled.
"""
return pulumi.get(self, "vlans")
@property
@pulumi.getter
def vlansdisabled(self) -> pulumi.Output[Optional[bool]]:
"""
Disables the SNAT on all VLANs.
"""
return pulumi.get(self, "vlansdisabled")
| 46.939169 | 535 | 0.649746 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['SnatArgs', 'Snat']
@pulumi.input_type
class SnatArgs:
def __init__(__self__, *,
name: pulumi.Input[str],
origins: pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]],
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None):
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "origins", origins)
if autolasthop is not None:
pulumi.set(__self__, "autolasthop", autolasthop)
if full_path is not None:
pulumi.set(__self__, "full_path", full_path)
if mirror is not None:
pulumi.set(__self__, "mirror", mirror)
if partition is not None:
pulumi.set(__self__, "partition", partition)
if snatpool is not None:
pulumi.set(__self__, "snatpool", snatpool)
if sourceport is not None:
pulumi.set(__self__, "sourceport", sourceport)
if translation is not None:
pulumi.set(__self__, "translation", translation)
if vlans is not None:
pulumi.set(__self__, "vlans", vlans)
if vlansdisabled is not None:
pulumi.set(__self__, "vlansdisabled", vlansdisabled)
@property
@pulumi.getter
def name(self) -> pulumi.Input[str]:
return pulumi.get(self, "name")
@name.setter
def name(self, value: pulumi.Input[str]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def origins(self) -> pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]:
return pulumi.get(self, "origins")
@origins.setter
def origins(self, value: pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]):
pulumi.set(self, "origins", value)
@property
@pulumi.getter
def autolasthop(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "autolasthop")
@autolasthop.setter
def autolasthop(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "autolasthop", value)
@property
@pulumi.getter(name="fullPath")
def full_path(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "full_path")
@full_path.setter
def full_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "full_path", value)
@property
@pulumi.getter
def mirror(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "mirror")
@mirror.setter
def mirror(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "mirror", value)
@property
@pulumi.getter
def partition(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "partition")
@partition.setter
def partition(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "partition", value)
@property
@pulumi.getter
def snatpool(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "snatpool")
@snatpool.setter
def snatpool(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "snatpool", value)
@property
@pulumi.getter
def sourceport(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "sourceport")
@sourceport.setter
def sourceport(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sourceport", value)
@property
@pulumi.getter
def translation(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "translation")
@translation.setter
def translation(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "translation", value)
@property
@pulumi.getter
def vlans(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
return pulumi.get(self, "vlans")
@vlans.setter
def vlans(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "vlans", value)
@property
@pulumi.getter
def vlansdisabled(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "vlansdisabled")
@vlansdisabled.setter
def vlansdisabled(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "vlansdisabled", value)
@pulumi.input_type
class _SnatState:
def __init__(__self__, *,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None):
if autolasthop is not None:
pulumi.set(__self__, "autolasthop", autolasthop)
if full_path is not None:
pulumi.set(__self__, "full_path", full_path)
if mirror is not None:
pulumi.set(__self__, "mirror", mirror)
if name is not None:
pulumi.set(__self__, "name", name)
if origins is not None:
pulumi.set(__self__, "origins", origins)
if partition is not None:
pulumi.set(__self__, "partition", partition)
if snatpool is not None:
pulumi.set(__self__, "snatpool", snatpool)
if sourceport is not None:
pulumi.set(__self__, "sourceport", sourceport)
if translation is not None:
pulumi.set(__self__, "translation", translation)
if vlans is not None:
pulumi.set(__self__, "vlans", vlans)
if vlansdisabled is not None:
pulumi.set(__self__, "vlansdisabled", vlansdisabled)
@property
@pulumi.getter
def autolasthop(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "autolasthop")
@autolasthop.setter
def autolasthop(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "autolasthop", value)
@property
@pulumi.getter(name="fullPath")
def full_path(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "full_path")
@full_path.setter
def full_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "full_path", value)
@property
@pulumi.getter
def mirror(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "mirror")
@mirror.setter
def mirror(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "mirror", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def origins(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]]:
return pulumi.get(self, "origins")
@origins.setter
def origins(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SnatOriginArgs']]]]):
pulumi.set(self, "origins", value)
@property
@pulumi.getter
def partition(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "partition")
@partition.setter
def partition(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "partition", value)
@property
@pulumi.getter
def snatpool(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "snatpool")
@snatpool.setter
def snatpool(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "snatpool", value)
@property
@pulumi.getter
def sourceport(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "sourceport")
@sourceport.setter
def sourceport(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sourceport", value)
@property
@pulumi.getter
def translation(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "translation")
@translation.setter
def translation(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "translation", value)
@property
@pulumi.getter
def vlans(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
return pulumi.get(self, "vlans")
@vlans.setter
def vlans(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "vlans", value)
@property
@pulumi.getter
def vlansdisabled(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "vlansdisabled")
@vlansdisabled.setter
def vlansdisabled(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "vlansdisabled", value)
class Snat(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None,
__props__=None):
...
@overload
def __init__(__self__,
resource_name: str,
args: SnatArgs,
opts: Optional[pulumi.ResourceOptions] = None):
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(SnatArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None,
__props__=None):
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__ = SnatArgs.__new__(SnatArgs)
__props__.__dict__["autolasthop"] = autolasthop
__props__.__dict__["full_path"] = full_path
__props__.__dict__["mirror"] = mirror
if name is None and not opts.urn:
raise TypeError("Missing required property 'name'")
__props__.__dict__["name"] = name
if origins is None and not opts.urn:
raise TypeError("Missing required property 'origins'")
__props__.__dict__["origins"] = origins
__props__.__dict__["partition"] = partition
__props__.__dict__["snatpool"] = snatpool
__props__.__dict__["sourceport"] = sourceport
__props__.__dict__["translation"] = translation
__props__.__dict__["vlans"] = vlans
__props__.__dict__["vlansdisabled"] = vlansdisabled
super(Snat, __self__).__init__(
'f5bigip:ltm/snat:Snat',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
autolasthop: Optional[pulumi.Input[str]] = None,
full_path: Optional[pulumi.Input[str]] = None,
mirror: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
origins: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SnatOriginArgs']]]]] = None,
partition: Optional[pulumi.Input[str]] = None,
snatpool: Optional[pulumi.Input[str]] = None,
sourceport: Optional[pulumi.Input[str]] = None,
translation: Optional[pulumi.Input[str]] = None,
vlans: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
vlansdisabled: Optional[pulumi.Input[bool]] = None) -> 'Snat':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _SnatState.__new__(_SnatState)
__props__.__dict__["autolasthop"] = autolasthop
__props__.__dict__["full_path"] = full_path
__props__.__dict__["mirror"] = mirror
__props__.__dict__["name"] = name
__props__.__dict__["origins"] = origins
__props__.__dict__["partition"] = partition
__props__.__dict__["snatpool"] = snatpool
__props__.__dict__["sourceport"] = sourceport
__props__.__dict__["translation"] = translation
__props__.__dict__["vlans"] = vlans
__props__.__dict__["vlansdisabled"] = vlansdisabled
return Snat(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def autolasthop(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "autolasthop")
@property
@pulumi.getter(name="fullPath")
def full_path(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "full_path")
@property
@pulumi.getter
def mirror(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "mirror")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter
def origins(self) -> pulumi.Output[Sequence['outputs.SnatOrigin']]:
return pulumi.get(self, "origins")
@property
@pulumi.getter
def partition(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "partition")
@property
@pulumi.getter
def snatpool(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "snatpool")
@property
@pulumi.getter
def sourceport(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "sourceport")
@property
@pulumi.getter
def translation(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "translation")
@property
@pulumi.getter
def vlans(self) -> pulumi.Output[Optional[Sequence[str]]]:
return pulumi.get(self, "vlans")
@property
@pulumi.getter
def vlansdisabled(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "vlansdisabled")
| true | true |
f72cad63668c1a50f31829882512b8a9df77f041 | 12,170 | py | Python | keystone-moon/keystone/tests/unit/test_backend_endpoint_policy.py | hashnfv/hashnfv-moon | daaba34fa2ed4426bc0fde359e54a5e1b872208c | [
"Apache-2.0"
] | 1 | 2019-05-08T06:09:35.000Z | 2019-05-08T06:09:35.000Z | keystone-moon/keystone/tests/unit/test_backend_endpoint_policy.py | hashnfv/hashnfv-moon | daaba34fa2ed4426bc0fde359e54a5e1b872208c | [
"Apache-2.0"
] | 4 | 2018-08-22T14:51:02.000Z | 2018-10-17T14:04:26.000Z | keystone-moon/keystone/tests/unit/test_backend_endpoint_policy.py | hashnfv/hashnfv-moon | daaba34fa2ed4426bc0fde359e54a5e1b872208c | [
"Apache-2.0"
] | 5 | 2018-08-03T17:19:34.000Z | 2019-01-11T15:54:42.000Z | # Copyright 2014 IBM Corp.
#
# 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 uuid
from six.moves import range
from testtools import matchers
from keystone import exception
from keystone.tests import unit
class PolicyAssociationTests(object):
def _assert_correct_policy(self, endpoint, policy):
ref = (
self.endpoint_policy_api.get_policy_for_endpoint(endpoint['id']))
self.assertEqual(policy['id'], ref['id'])
def _assert_correct_endpoints(self, policy, endpoint_list):
endpoint_id_list = [ep['id'] for ep in endpoint_list]
endpoints = (
self.endpoint_policy_api.list_endpoints_for_policy(policy['id']))
self.assertThat(endpoints, matchers.HasLength(len(endpoint_list)))
for endpoint in endpoints:
self.assertIn(endpoint['id'], endpoint_id_list)
def load_sample_data(self):
"""Create sample data to test policy associations.
The following data is created:
- 3 regions, in a hierarchy, 0 -> 1 -> 2 (where 0 is top)
- 3 services
- 6 endpoints, 2 in each region, with a mixture of services:
0 - region 0, Service 0
1 - region 0, Service 1
2 - region 1, Service 1
3 - region 1, Service 2
4 - region 2, Service 2
5 - region 2, Service 0
"""
def new_endpoint(region_id, service_id):
endpoint = unit.new_endpoint_ref(interface='test',
region_id=region_id,
service_id=service_id,
url='/url')
self.endpoint.append(self.catalog_api.create_endpoint(
endpoint['id'], endpoint))
self.policy = []
self.endpoint = []
self.service = []
self.region = []
parent_region_id = None
for i in range(3):
policy = unit.new_policy_ref()
self.policy.append(self.policy_api.create_policy(policy['id'],
policy))
service = unit.new_service_ref()
self.service.append(self.catalog_api.create_service(service['id'],
service))
region = unit.new_region_ref(parent_region_id=parent_region_id)
# Link the regions together as a hierarchy, [0] at the top
parent_region_id = region['id']
self.region.append(self.catalog_api.create_region(region))
new_endpoint(self.region[0]['id'], self.service[0]['id'])
new_endpoint(self.region[0]['id'], self.service[1]['id'])
new_endpoint(self.region[1]['id'], self.service[1]['id'])
new_endpoint(self.region[1]['id'], self.service[2]['id'])
new_endpoint(self.region[2]['id'], self.service[2]['id'])
new_endpoint(self.region[2]['id'], self.service[0]['id'])
def test_policy_to_endpoint_association_crud(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.check_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.delete_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'])
def test_overwriting_policy_to_endpoint_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], endpoint_id=self.endpoint[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.check_policy_association(
self.policy[1]['id'], endpoint_id=self.endpoint[0]['id'])
def test_invalid_policy_to_endpoint_association(self):
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'])
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'],
region_id=self.region[0]['id'])
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'],
service_id=self.service[0]['id'])
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'],
region_id=self.region[0]['id'])
def test_policy_to_explicit_endpoint_association(self):
# Associate policy 0 with endpoint 0
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self._assert_correct_policy(self.endpoint[0], self.policy[0])
self._assert_correct_endpoints(self.policy[0], [self.endpoint[0]])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.get_policy_for_endpoint,
uuid.uuid4().hex)
def test_policy_to_service_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], service_id=self.service[1]['id'])
# Endpoints 0 and 5 are part of service 0
self._assert_correct_policy(self.endpoint[0], self.policy[0])
self._assert_correct_policy(self.endpoint[5], self.policy[0])
self._assert_correct_endpoints(
self.policy[0], [self.endpoint[0], self.endpoint[5]])
# Endpoints 1 and 2 are part of service 1
self._assert_correct_policy(self.endpoint[1], self.policy[1])
self._assert_correct_policy(self.endpoint[2], self.policy[1])
self._assert_correct_endpoints(
self.policy[1], [self.endpoint[1], self.endpoint[2]])
def test_policy_to_region_and_service_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], service_id=self.service[1]['id'],
region_id=self.region[1]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[2]['id'], service_id=self.service[2]['id'],
region_id=self.region[2]['id'])
# Endpoint 0 is in region 0 with service 0, so should get policy 0
self._assert_correct_policy(self.endpoint[0], self.policy[0])
# Endpoint 5 is in Region 2 with service 0, so should also get
# policy 0 by searching up the tree to Region 0
self._assert_correct_policy(self.endpoint[5], self.policy[0])
# Looking the other way round, policy 2 should only be in use by
# endpoint 4, since that's the only endpoint in region 2 with the
# correct service
self._assert_correct_endpoints(
self.policy[2], [self.endpoint[4]])
# Policy 1 should only be in use by endpoint 2, since that's the only
# endpoint in region 1 (and region 2 below it) with the correct service
self._assert_correct_endpoints(
self.policy[1], [self.endpoint[2]])
# Policy 0 should be in use by endpoint 0, as well as 5 (since 5 is
# of the correct service and in region 2 below it)
self._assert_correct_endpoints(
self.policy[0], [self.endpoint[0], self.endpoint[5]])
def test_delete_association_by_entity(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.delete_association_by_endpoint(
self.endpoint[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'])
# Make sure deleting it again is silent - since this method is used
# in response to notifications by the controller.
self.endpoint_policy_api.delete_association_by_endpoint(
self.endpoint[0]['id'])
# Now try with service - ensure both combined region & service
# associations and explicit service ones are removed
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], service_id=self.service[0]['id'],
region_id=self.region[1]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'])
self.endpoint_policy_api.delete_association_by_service(
self.service[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[1]['id'],
service_id=self.service[0]['id'],
region_id=self.region[1]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'])
# Finally, check delete by region
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.endpoint_policy_api.delete_association_by_region(
self.region[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'])
| 48.68 | 79 | 0.607313 |
import uuid
from six.moves import range
from testtools import matchers
from keystone import exception
from keystone.tests import unit
class PolicyAssociationTests(object):
def _assert_correct_policy(self, endpoint, policy):
ref = (
self.endpoint_policy_api.get_policy_for_endpoint(endpoint['id']))
self.assertEqual(policy['id'], ref['id'])
def _assert_correct_endpoints(self, policy, endpoint_list):
endpoint_id_list = [ep['id'] for ep in endpoint_list]
endpoints = (
self.endpoint_policy_api.list_endpoints_for_policy(policy['id']))
self.assertThat(endpoints, matchers.HasLength(len(endpoint_list)))
for endpoint in endpoints:
self.assertIn(endpoint['id'], endpoint_id_list)
def load_sample_data(self):
def new_endpoint(region_id, service_id):
endpoint = unit.new_endpoint_ref(interface='test',
region_id=region_id,
service_id=service_id,
url='/url')
self.endpoint.append(self.catalog_api.create_endpoint(
endpoint['id'], endpoint))
self.policy = []
self.endpoint = []
self.service = []
self.region = []
parent_region_id = None
for i in range(3):
policy = unit.new_policy_ref()
self.policy.append(self.policy_api.create_policy(policy['id'],
policy))
service = unit.new_service_ref()
self.service.append(self.catalog_api.create_service(service['id'],
service))
region = unit.new_region_ref(parent_region_id=parent_region_id)
parent_region_id = region['id']
self.region.append(self.catalog_api.create_region(region))
new_endpoint(self.region[0]['id'], self.service[0]['id'])
new_endpoint(self.region[0]['id'], self.service[1]['id'])
new_endpoint(self.region[1]['id'], self.service[1]['id'])
new_endpoint(self.region[1]['id'], self.service[2]['id'])
new_endpoint(self.region[2]['id'], self.service[2]['id'])
new_endpoint(self.region[2]['id'], self.service[0]['id'])
def test_policy_to_endpoint_association_crud(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.check_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.delete_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'])
def test_overwriting_policy_to_endpoint_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], endpoint_id=self.endpoint[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.check_policy_association(
self.policy[1]['id'], endpoint_id=self.endpoint[0]['id'])
def test_invalid_policy_to_endpoint_association(self):
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'])
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'],
region_id=self.region[0]['id'])
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'],
service_id=self.service[0]['id'])
self.assertRaises(exception.InvalidPolicyAssociation,
self.endpoint_policy_api.create_policy_association,
self.policy[0]['id'],
region_id=self.region[0]['id'])
def test_policy_to_explicit_endpoint_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self._assert_correct_policy(self.endpoint[0], self.policy[0])
self._assert_correct_endpoints(self.policy[0], [self.endpoint[0]])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.get_policy_for_endpoint,
uuid.uuid4().hex)
def test_policy_to_service_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], service_id=self.service[1]['id'])
self._assert_correct_policy(self.endpoint[0], self.policy[0])
self._assert_correct_policy(self.endpoint[5], self.policy[0])
self._assert_correct_endpoints(
self.policy[0], [self.endpoint[0], self.endpoint[5]])
self._assert_correct_policy(self.endpoint[1], self.policy[1])
self._assert_correct_policy(self.endpoint[2], self.policy[1])
self._assert_correct_endpoints(
self.policy[1], [self.endpoint[1], self.endpoint[2]])
def test_policy_to_region_and_service_association(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], service_id=self.service[1]['id'],
region_id=self.region[1]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[2]['id'], service_id=self.service[2]['id'],
region_id=self.region[2]['id'])
self._assert_correct_policy(self.endpoint[0], self.policy[0])
self._assert_correct_policy(self.endpoint[5], self.policy[0])
# correct service
self._assert_correct_endpoints(
self.policy[2], [self.endpoint[4]])
# Policy 1 should only be in use by endpoint 2, since that's the only
self._assert_correct_endpoints(
self.policy[1], [self.endpoint[2]])
self._assert_correct_endpoints(
self.policy[0], [self.endpoint[0], self.endpoint[5]])
def test_delete_association_by_entity(self):
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.delete_association_by_endpoint(
self.endpoint[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
endpoint_id=self.endpoint[0]['id'])
self.endpoint_policy_api.delete_association_by_endpoint(
self.endpoint[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[1]['id'], service_id=self.service[0]['id'],
region_id=self.region[1]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'])
self.endpoint_policy_api.delete_association_by_service(
self.service[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[1]['id'],
service_id=self.service[0]['id'],
region_id=self.region[1]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'])
self.endpoint_policy_api.create_policy_association(
self.policy[0]['id'], service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.endpoint_policy_api.delete_association_by_region(
self.region[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'],
region_id=self.region[0]['id'])
self.assertRaises(exception.NotFound,
self.endpoint_policy_api.check_policy_association,
self.policy[0]['id'],
service_id=self.service[0]['id'])
| true | true |
f72cae2c89049bcff133dee51ea839c617f5fd7f | 372 | py | Python | supervisely/src/mask_image.py | supervisely-ecosystem/ritm-interactive-segmentation | c86df3c7c95ce20ffd3c9cc5e3f07abe8c162f4c | [
"MIT"
] | 1 | 2022-03-25T14:36:18.000Z | 2022-03-25T14:36:18.000Z | supervisely/src/mask_image.py | supervisely-ecosystem/ritm-interactive-segmentation | c86df3c7c95ce20ffd3c9cc5e3f07abe8c162f4c | [
"MIT"
] | null | null | null | supervisely/src/mask_image.py | supervisely-ecosystem/ritm-interactive-segmentation | c86df3c7c95ce20ffd3c9cc5e3f07abe8c162f4c | [
"MIT"
] | 1 | 2022-03-17T06:39:39.000Z | 2022-03-17T06:39:39.000Z | import sly_globals as g
def get_mask_from_clicks(image_np, clicks_list):
g.CONTROLLER.set_image(image_np)
for click in clicks_list:
g.CONTROLLER.add_click(click.coords[1], click.coords[0], click.is_positive)
try:
res_mask = g.CONTROLLER.result_mask
except Exception(f"Couldn't process image"):
res_mask = None
return res_mask
| 28.615385 | 83 | 0.712366 | import sly_globals as g
def get_mask_from_clicks(image_np, clicks_list):
g.CONTROLLER.set_image(image_np)
for click in clicks_list:
g.CONTROLLER.add_click(click.coords[1], click.coords[0], click.is_positive)
try:
res_mask = g.CONTROLLER.result_mask
except Exception(f"Couldn't process image"):
res_mask = None
return res_mask
| true | true |
f72caed168c08d84dcc3dd7cb27e247c5df1716d | 348 | py | Python | Algorithms/kadane_algorithm/python-kadane-algorithm-O(n).py | omega07/Yet_Another_Algorithms_Repository | 7c967e115e96b3c07010a3bf94ca1cdb898a6e82 | [
"MIT"
] | 33 | 2019-10-14T19:19:43.000Z | 2021-11-30T13:40:20.000Z | Algorithms/kadane_algorithm/python-kadane-algorithm-O(n).py | omega07/Yet_Another_Algorithms_Repository | 7c967e115e96b3c07010a3bf94ca1cdb898a6e82 | [
"MIT"
] | 317 | 2019-10-14T18:35:22.000Z | 2020-03-03T17:45:06.000Z | Algorithms/kadane_algorithm/python-kadane-algorithm-O(n).py | omega07/Yet_Another_Algorithms_Repository | 7c967e115e96b3c07010a3bf94ca1cdb898a6e82 | [
"MIT"
] | 332 | 2019-10-14T18:39:08.000Z | 2021-09-02T16:19:11.000Z | def maxSubArraySum(a,size):
max_so_far =a[0]
curr_max = a[0]
for i in range(1,size):
curr_max = max(a[i], curr_max + a[i])
max_so_far = max(max_so_far,curr_max)
return max_so_far
a = [-2, -3, 4, -1, -2, 1, 5, -3]
print("Maximum contiguous sum is" , maxSubArraySum(a,len(a)))
| 23.2 | 61 | 0.531609 | def maxSubArraySum(a,size):
max_so_far =a[0]
curr_max = a[0]
for i in range(1,size):
curr_max = max(a[i], curr_max + a[i])
max_so_far = max(max_so_far,curr_max)
return max_so_far
a = [-2, -3, 4, -1, -2, 1, 5, -3]
print("Maximum contiguous sum is" , maxSubArraySum(a,len(a)))
| true | true |
f72caeec9c99f7dddcbe170095eba9f6591f69ab | 910 | py | Python | dwavebinarycsp/package_info.py | JoelPasvolsky/dwavebinarycsp | ef260bff6d606d8176b287bb6e27a05d6f72de9f | [
"Apache-2.0"
] | null | null | null | dwavebinarycsp/package_info.py | JoelPasvolsky/dwavebinarycsp | ef260bff6d606d8176b287bb6e27a05d6f72de9f | [
"Apache-2.0"
] | null | null | null | dwavebinarycsp/package_info.py | JoelPasvolsky/dwavebinarycsp | ef260bff6d606d8176b287bb6e27a05d6f72de9f | [
"Apache-2.0"
] | null | null | null | # Copyright 2018 D-Wave Systems 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.
#
# ================================================================================================
__version__ = '0.1.3'
__author__ = 'D-Wave Systems Inc.'
__authoremail__ = 'acondello@dwavesys.com'
__description__ = 'Solves constraints satisfaction problems with binary quadratic model samplers'
| 43.333333 | 98 | 0.661538 |
__version__ = '0.1.3'
__author__ = 'D-Wave Systems Inc.'
__authoremail__ = 'acondello@dwavesys.com'
__description__ = 'Solves constraints satisfaction problems with binary quadratic model samplers'
| true | true |
f72cb12504a8487b6ebb6f694946918cd78f6d7b | 267 | py | Python | output/models/nist_data/atomic/integer/schema_instance/nistschema_sv_iv_atomic_integer_total_digits_2_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | 1 | 2021-08-14T17:59:21.000Z | 2021-08-14T17:59:21.000Z | output/models/nist_data/atomic/integer/schema_instance/nistschema_sv_iv_atomic_integer_total_digits_2_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | 4 | 2020-02-12T21:30:44.000Z | 2020-04-15T20:06:46.000Z | output/models/nist_data/atomic/integer/schema_instance/nistschema_sv_iv_atomic_integer_total_digits_2_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | null | null | null | from output.models.nist_data.atomic.integer.schema_instance.nistschema_sv_iv_atomic_integer_total_digits_2_xsd.nistschema_sv_iv_atomic_integer_total_digits_2 import NistschemaSvIvAtomicIntegerTotalDigits2
__all__ = [
"NistschemaSvIvAtomicIntegerTotalDigits2",
]
| 44.5 | 204 | 0.898876 | from output.models.nist_data.atomic.integer.schema_instance.nistschema_sv_iv_atomic_integer_total_digits_2_xsd.nistschema_sv_iv_atomic_integer_total_digits_2 import NistschemaSvIvAtomicIntegerTotalDigits2
__all__ = [
"NistschemaSvIvAtomicIntegerTotalDigits2",
]
| true | true |
f72cb1f015fcd1360def9463bd8e6047da25b737 | 11,947 | py | Python | examples/ex_icub_trust_cognitive_architecture/endorsement.py | riccardobrue/SOM-example-1 | 8a977e73844f9206ee1704be577f8a7521d2b306 | [
"MIT"
] | null | null | null | examples/ex_icub_trust_cognitive_architecture/endorsement.py | riccardobrue/SOM-example-1 | 8a977e73844f9206ee1704be577f8a7521d2b306 | [
"MIT"
] | null | null | null | examples/ex_icub_trust_cognitive_architecture/endorsement.py | riccardobrue/SOM-example-1 | 8a977e73844f9206ee1704be577f8a7521d2b306 | [
"MIT"
] | 1 | 2021-03-16T16:02:16.000Z | 2021-03-16T16:02:16.000Z | #!/usr/bin/python
# The MIT License (MIT)
#
# Copyright (c) 2017 Massimiliano Patacchiola
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#ATTENTION: to work it requires to lunch the iCub world:
# yarpserver
# ./iCub_SIM
# ./iKinGazeCtrl --from configSim.ini
# yarpdev --device opencv_grabber
# yarp connect /grabber /icubSim/texture/screen
#
# For the cartesian controller of the left arm
# ./simCartesianControl
# ./iKinCartesianSolver --context simCartesianControl --part left_arm
# PocketSphinx valid Commands are:
# The prefix [iCub] or [hey] is optional
# learn <object name>
# this is a <object name>
# forget <object name>
# what is this
# find the <object name>
# stop detection
# look at me
from speech_recognition import SpeechRecognizer
from icub import iCub
import cv2
import random
import time
import os
import sys
def initialise():
# Initialise the speech recognition engine and the iCub controller
my_speech = SpeechRecognizer(
hmm_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/model/en-us/en-us",
language_model_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/model/en-us/en-us.lm.bin",
dictionary_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/data/icub.dic",
grammar_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/data/icub.gram",
rule_name='icub.basicCmd',
fsg_name="icub")
# iCub initialization
my_icub = iCub(icub_root='/icubSim')
# Load acapela configuration from file
my_icub.set_acapela_credential("./acapela_config.csv")
account_login, application_login, application_password, service_url = my_icub.get_acapela_credential()
print("[ACAPELA]Acapela configuration parameters:")
print("Account Login: " + str(account_login))
print("Application Login: " + str(application_login))
print("Account Password: " + str(application_password))
print("Service URL: " + str(service_url))
print("")
# Return the objects
return my_speech, my_icub
def speech_to_action(speech_string):
""" Take the sentence from the speech recognition and plan an action
<action> = (learn new object | watch | inspect | find | search | look | what | start | stop);
<target> = (ball | cup | book | dog | chair | table | at me | is this | movement detection);
@param speech_string:
@return:
"""
if speech_string.find('learn') > -1 or speech_string.find('this is a') > -1:
response_list = ['I like to learn! This is a ',
'Ok, this is a ',
'I learned a new object, ',
'']
object_name = speech_string.rsplit(None, 1)[-1]
response_string = response_list[random.randint(0, len(response_list)-1)] + object_name
state = 'learn'
elif speech_string.find('what is this') > -1:
response_string = ""
state = 'what'
elif speech_string.find('find the') > -1 or speech_string.find('search the') > -1:
object_name = speech_string.rsplit(None, 1)[-1]
object_path = "./objects/" + str(object_name) + ".png"
if not os.path.isfile(object_path):
print("[SPEECH-TO-ACTION][WARNING] " + "this file does not exist: " + str(object_path) + "\n")
response_string = "Sorry I do not know this object!"
state = 'key'
else:
response_list = ["Ok, now I'm looking for a ",
'Ok I will track the ',
'Ready to track the ']
response_string = response_list[random.randint(0, len(response_list)-1)] + object_name
state = 'movedetect on'
elif speech_string.find('stop detection') > -1:
response_list = ["Ok, no more movements",
'Ok I will stop it',
"I'm gonna stop it!"]
response_string = response_list[random.randint(0, len(response_list)-1)]
state = 'movedetect off'
elif speech_string.find('look at me') > -1:
response_list = ["Ok!",
'Sure!']
response_string = response_list[random.randint(0, len(response_list)-1)]
state = 'look'
else:
response_list = ["Sorry I did not understand.",
'Sorry, can you repeat?',
'Repeat again please.']
response_string = response_list[random.randint(0,len(response_list)-1)]
state = 'key'
return response_string, state
def main():
inputfile = ''
outputfile = ''
informant_name = ''
if len(sys.argv) == 1 or len(sys.argv) > 4:
print("python familiarization.py <inputfile> <outputfilename> <informant_name>")
elif len(sys.argv) == 4:
inputfile = sys.argv[1]
outputfile = sys.argv[2]
informant_name = sys.argv[3]
print("Input file: " + str(inputfile))
print("Output file: " + str(outputfile))
print("Informant Name: " + str(informant_name))
STATE = 'show'
speech_string = ""
fovea_offset = 40 # side of the fovea square
my_speech, my_icub = initialise()
is_connected = my_icub.check_connection()
if is_connected:
print("[STATE Init] intenet connection present.")
else:
print("[STATE Init][ERROR] internet connection not present!!!")
my_icub.say_something(text="I'm ready!")
cv2.namedWindow('main')
while True:
if STATE == 'record':
#image = my_icub.return_left_camera_image(mode='BGR')
my_speech.record_audio("/tmp/audio.wav", seconds=3, extension='wav', harddev='3,0')
raw_file_path = my_speech.convert_to_raw(file_name="/tmp/audio.wav", file_name_raw="/tmp/audio.raw", extension='wav')
speech_string = my_speech.return_text_from_audio("/tmp/audio.raw")
print("[STATE " + str(STATE) + "] " + "Speech recognised: " + speech_string)
STATE = 'understand'
elif STATE == 'understand':
response_string, local_state = speech_to_action(speech_string)
print("[STATE " + str(STATE) + "] " + "Speech recognised: " + speech_string)
print("[STATE " + str(STATE) + "] " + "Next state: " + local_state)
my_icub.say_something(text=response_string)
STATE = local_state
elif STATE == 'show':
left_image = my_icub.return_left_camera_image(mode='BGR')
img_cx = int(left_image.shape[1] / 2)
img_cy = int(left_image.shape[0] / 2)
cv2.rectangle(left_image,
(img_cx-fovea_offset, img_cy-fovea_offset),
(img_cx+fovea_offset, img_cy+fovea_offset),
(0, 255, 0), 1)
cv2.imshow('main', left_image)
STATE = 'key'
elif STATE == 'movedetect on':
object_name = response_string.rsplit(None, 1)[-1]
print("[STATE " + str(STATE) + "] " + "start tracking of: " + str(object_name) + "\n")
object_path = "./objects/" + str(object_name) + ".png"
if my_icub.is_movement_detection():
my_icub.stop_movement_detection()
time.sleep(0.5)
my_icub.start_movement_detection(template_path=object_path, delay=1.0)
else:
my_icub.start_movement_detection(template_path=object_path, delay=1.0)
STATE = 'key'
elif STATE == 'movedetect off':
print("[STATE " + str(STATE) + "] " + "stop movement tracking" + "\n")
my_icub.stop_movement_detection()
time.sleep(0.5)
my_icub.reset_head_pose()
STATE = 'key'
elif STATE == 'look':
print("[STATE " + str(STATE) + "] " + "gaze reset" + "\n")
my_icub.reset_head_pose()
STATE = 'key'
elif STATE == 'learn':
object_name = response_string.rsplit(None, 1)[-1]
print("[STATE " + str(STATE) + "] " + "Learning new object: " + object_name + "\n")
left_image = my_icub.return_left_camera_image(mode='BGR')
#left_image = image
img_cx = int(left_image.shape[1] / 2)
img_cy = int(left_image.shape[0] / 2)
left_image = left_image[img_cy-fovea_offset:img_cy+fovea_offset,
img_cx-fovea_offset:img_cx+fovea_offset]
my_icub.learn_object_from_histogram(left_image, object_name)
print("[STATE " + str(STATE) + "] " + "Writing new template in ./objects/" + object_name + ".png" + "\n")
cv2.imwrite('./objects/' + str(object_name) + '.png', left_image)
STATE = 'key'
elif STATE == 'what':
print("[STATE " + str(STATE) + "] " + "Recalling object from memory..." + "\n")
left_image = my_icub.return_left_camera_image(mode='BGR')
#left_image = image
img_cx = int(left_image.shape[1] / 2)
img_cy = int(left_image.shape[0] / 2)
left_image = left_image[img_cy-25:img_cy+25, img_cx-25:img_cx+25]
object_name = my_icub.recall_object_from_histogram(left_image)
if object_name is None:
my_icub.say_something("My memory is empty. Teach me something!")
else:
print("[STATE " + str(STATE) + "] " + "Name returned: " + str(object_name) + "\n")
response_list = ["Let me see. I think this is a ",
"Let me think. It's a ",
"Just a second. It may be a ",
"It should be a "]
response_string = response_list[random.randint(0, len(response_list) - 1)]
my_icub.say_something(response_string + str(object_name))
STATE = 'key'
elif STATE == 'key':
key_pressed = cv2.waitKey(10) # delay in millisecond
if key_pressed==113: #q=QUIT
print("[STATE " + str(STATE) + "] " + "Button (q)uit pressed..." + "\n")
STATE = "close"
elif key_pressed==110: #n=
print("[STATE " + str(STATE) + "] " + "Button (n) pressed..." + "\n")
elif key_pressed==102: #f=
print("[STATE " + str(STATE) + "] " + "Button (f) pressed..." + "\n")
elif key_pressed == 114: # r=RECORD
print("[STATE " + str(STATE) + "] " + "Button (r)ecord pressed..." + "\n")
STATE = "record"
else:
STATE = 'show'
elif STATE == 'close':
my_icub.say_something(text="See you soon, bye bye!")
my_icub.stop_movement_detection()
my_icub.close()
cv2.destroyAllWindows()
break
if __name__ == "__main__":
main()
| 44.913534 | 133 | 0.601657 |
from speech_recognition import SpeechRecognizer
from icub import iCub
import cv2
import random
import time
import os
import sys
def initialise():
my_speech = SpeechRecognizer(
hmm_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/model/en-us/en-us",
language_model_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/model/en-us/en-us.lm.bin",
dictionary_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/data/icub.dic",
grammar_path="/home/massimiliano/pyERA/examples/ex_icub_trust_cognitive_architecture/sphinx/data/icub.gram",
rule_name='icub.basicCmd',
fsg_name="icub")
my_icub = iCub(icub_root='/icubSim')
my_icub.set_acapela_credential("./acapela_config.csv")
account_login, application_login, application_password, service_url = my_icub.get_acapela_credential()
print("[ACAPELA]Acapela configuration parameters:")
print("Account Login: " + str(account_login))
print("Application Login: " + str(application_login))
print("Account Password: " + str(application_password))
print("Service URL: " + str(service_url))
print("")
return my_speech, my_icub
def speech_to_action(speech_string):
if speech_string.find('learn') > -1 or speech_string.find('this is a') > -1:
response_list = ['I like to learn! This is a ',
'Ok, this is a ',
'I learned a new object, ',
'']
object_name = speech_string.rsplit(None, 1)[-1]
response_string = response_list[random.randint(0, len(response_list)-1)] + object_name
state = 'learn'
elif speech_string.find('what is this') > -1:
response_string = ""
state = 'what'
elif speech_string.find('find the') > -1 or speech_string.find('search the') > -1:
object_name = speech_string.rsplit(None, 1)[-1]
object_path = "./objects/" + str(object_name) + ".png"
if not os.path.isfile(object_path):
print("[SPEECH-TO-ACTION][WARNING] " + "this file does not exist: " + str(object_path) + "\n")
response_string = "Sorry I do not know this object!"
state = 'key'
else:
response_list = ["Ok, now I'm looking for a ",
'Ok I will track the ',
'Ready to track the ']
response_string = response_list[random.randint(0, len(response_list)-1)] + object_name
state = 'movedetect on'
elif speech_string.find('stop detection') > -1:
response_list = ["Ok, no more movements",
'Ok I will stop it',
"I'm gonna stop it!"]
response_string = response_list[random.randint(0, len(response_list)-1)]
state = 'movedetect off'
elif speech_string.find('look at me') > -1:
response_list = ["Ok!",
'Sure!']
response_string = response_list[random.randint(0, len(response_list)-1)]
state = 'look'
else:
response_list = ["Sorry I did not understand.",
'Sorry, can you repeat?',
'Repeat again please.']
response_string = response_list[random.randint(0,len(response_list)-1)]
state = 'key'
return response_string, state
def main():
inputfile = ''
outputfile = ''
informant_name = ''
if len(sys.argv) == 1 or len(sys.argv) > 4:
print("python familiarization.py <inputfile> <outputfilename> <informant_name>")
elif len(sys.argv) == 4:
inputfile = sys.argv[1]
outputfile = sys.argv[2]
informant_name = sys.argv[3]
print("Input file: " + str(inputfile))
print("Output file: " + str(outputfile))
print("Informant Name: " + str(informant_name))
STATE = 'show'
speech_string = ""
fovea_offset = 40
my_speech, my_icub = initialise()
is_connected = my_icub.check_connection()
if is_connected:
print("[STATE Init] intenet connection present.")
else:
print("[STATE Init][ERROR] internet connection not present!!!")
my_icub.say_something(text="I'm ready!")
cv2.namedWindow('main')
while True:
if STATE == 'record':
#image = my_icub.return_left_camera_image(mode='BGR')
my_speech.record_audio("/tmp/audio.wav", seconds=3, extension='wav', harddev='3,0')
raw_file_path = my_speech.convert_to_raw(file_name="/tmp/audio.wav", file_name_raw="/tmp/audio.raw", extension='wav')
speech_string = my_speech.return_text_from_audio("/tmp/audio.raw")
print("[STATE " + str(STATE) + "] " + "Speech recognised: " + speech_string)
STATE = 'understand'
elif STATE == 'understand':
response_string, local_state = speech_to_action(speech_string)
print("[STATE " + str(STATE) + "] " + "Speech recognised: " + speech_string)
print("[STATE " + str(STATE) + "] " + "Next state: " + local_state)
my_icub.say_something(text=response_string)
STATE = local_state
elif STATE == 'show':
left_image = my_icub.return_left_camera_image(mode='BGR')
img_cx = int(left_image.shape[1] / 2)
img_cy = int(left_image.shape[0] / 2)
cv2.rectangle(left_image,
(img_cx-fovea_offset, img_cy-fovea_offset),
(img_cx+fovea_offset, img_cy+fovea_offset),
(0, 255, 0), 1)
cv2.imshow('main', left_image)
STATE = 'key'
elif STATE == 'movedetect on':
object_name = response_string.rsplit(None, 1)[-1]
print("[STATE " + str(STATE) + "] " + "start tracking of: " + str(object_name) + "\n")
object_path = "./objects/" + str(object_name) + ".png"
if my_icub.is_movement_detection():
my_icub.stop_movement_detection()
time.sleep(0.5)
my_icub.start_movement_detection(template_path=object_path, delay=1.0)
else:
my_icub.start_movement_detection(template_path=object_path, delay=1.0)
STATE = 'key'
elif STATE == 'movedetect off':
print("[STATE " + str(STATE) + "] " + "stop movement tracking" + "\n")
my_icub.stop_movement_detection()
time.sleep(0.5)
my_icub.reset_head_pose()
STATE = 'key'
elif STATE == 'look':
print("[STATE " + str(STATE) + "] " + "gaze reset" + "\n")
my_icub.reset_head_pose()
STATE = 'key'
elif STATE == 'learn':
object_name = response_string.rsplit(None, 1)[-1]
print("[STATE " + str(STATE) + "] " + "Learning new object: " + object_name + "\n")
left_image = my_icub.return_left_camera_image(mode='BGR')
#left_image = image
img_cx = int(left_image.shape[1] / 2)
img_cy = int(left_image.shape[0] / 2)
left_image = left_image[img_cy-fovea_offset:img_cy+fovea_offset,
img_cx-fovea_offset:img_cx+fovea_offset]
my_icub.learn_object_from_histogram(left_image, object_name)
print("[STATE " + str(STATE) + "] " + "Writing new template in ./objects/" + object_name + ".png" + "\n")
cv2.imwrite('./objects/' + str(object_name) + '.png', left_image)
STATE = 'key'
elif STATE == 'what':
print("[STATE " + str(STATE) + "] " + "Recalling object from memory..." + "\n")
left_image = my_icub.return_left_camera_image(mode='BGR')
#left_image = image
img_cx = int(left_image.shape[1] / 2)
img_cy = int(left_image.shape[0] / 2)
left_image = left_image[img_cy-25:img_cy+25, img_cx-25:img_cx+25]
object_name = my_icub.recall_object_from_histogram(left_image)
if object_name is None:
my_icub.say_something("My memory is empty. Teach me something!")
else:
print("[STATE " + str(STATE) + "] " + "Name returned: " + str(object_name) + "\n")
response_list = ["Let me see. I think this is a ",
"Let me think. It's a ",
"Just a second. It may be a ",
"It should be a "]
response_string = response_list[random.randint(0, len(response_list) - 1)]
my_icub.say_something(response_string + str(object_name))
STATE = 'key'
elif STATE == 'key':
key_pressed = cv2.waitKey(10)
if key_pressed==113:
print("[STATE " + str(STATE) + "] " + "Button (q)uit pressed..." + "\n")
STATE = "close"
elif key_pressed==110:
print("[STATE " + str(STATE) + "] " + "Button (n) pressed..." + "\n")
elif key_pressed==102:
print("[STATE " + str(STATE) + "] " + "Button (f) pressed..." + "\n")
elif key_pressed == 114:
print("[STATE " + str(STATE) + "] " + "Button (r)ecord pressed..." + "\n")
STATE = "record"
else:
STATE = 'show'
elif STATE == 'close':
my_icub.say_something(text="See you soon, bye bye!")
my_icub.stop_movement_detection()
my_icub.close()
cv2.destroyAllWindows()
break
if __name__ == "__main__":
main()
| true | true |
f72cb22b484e4768378d4a3b0201733382c540d7 | 2,332 | py | Python | tests/integration/test_sdv.py | joanvaquer/SDV | 83e4fdf0ff72e6c5b72cfc8c6ec9584dbd34de28 | [
"MIT"
] | null | null | null | tests/integration/test_sdv.py | joanvaquer/SDV | 83e4fdf0ff72e6c5b72cfc8c6ec9584dbd34de28 | [
"MIT"
] | null | null | null | tests/integration/test_sdv.py | joanvaquer/SDV | 83e4fdf0ff72e6c5b72cfc8c6ec9584dbd34de28 | [
"MIT"
] | null | null | null | from sdv import SDV, load_demo
def test_sdv():
metadata, tables = load_demo(metadata=True)
sdv = SDV()
sdv.fit(metadata, tables)
# Sample all
sampled = sdv.sample_all()
assert set(sampled.keys()) == {'users', 'sessions', 'transactions'}
assert len(sampled['users']) == 10
# Sample with children
sampled = sdv.sample('users', reset_primary_keys=True)
assert set(sampled.keys()) == {'users', 'sessions', 'transactions'}
assert len(sampled['users']) == 10
# Sample without children
users = sdv.sample('users', sample_children=False)
assert users.shape == tables['users'].shape
assert set(users.columns) == set(tables['users'].columns)
sessions = sdv.sample('sessions', sample_children=False)
assert sessions.shape == tables['sessions'].shape
assert set(sessions.columns) == set(tables['sessions'].columns)
transactions = sdv.sample('transactions', sample_children=False)
assert transactions.shape == tables['transactions'].shape
assert set(transactions.columns) == set(tables['transactions'].columns)
def test_sdv_multiparent():
metadata, tables = load_demo('got_families', metadata=True)
sdv = SDV()
sdv.fit(metadata, tables)
# Sample all
sampled = sdv.sample_all()
assert set(sampled.keys()) == {'characters', 'families', 'character_families'}
assert len(sampled['characters']) == 7
# Sample with children
sampled = sdv.sample('characters', reset_primary_keys=True)
assert set(sampled.keys()) == {'characters', 'character_families'}
assert len(sampled['characters']) == 7
assert 'family_id' in sampled['character_families']
# Sample without children
characters = sdv.sample('characters', sample_children=False)
assert characters.shape == tables['characters'].shape
assert set(characters.columns) == set(tables['characters'].columns)
families = sdv.sample('families', sample_children=False)
assert families.shape == tables['families'].shape
assert set(families.columns) == set(tables['families'].columns)
character_families = sdv.sample('character_families', sample_children=False)
assert character_families.shape == tables['character_families'].shape
assert set(character_families.columns) == set(tables['character_families'].columns)
| 31.945205 | 87 | 0.694683 | from sdv import SDV, load_demo
def test_sdv():
metadata, tables = load_demo(metadata=True)
sdv = SDV()
sdv.fit(metadata, tables)
sampled = sdv.sample_all()
assert set(sampled.keys()) == {'users', 'sessions', 'transactions'}
assert len(sampled['users']) == 10
sampled = sdv.sample('users', reset_primary_keys=True)
assert set(sampled.keys()) == {'users', 'sessions', 'transactions'}
assert len(sampled['users']) == 10
users = sdv.sample('users', sample_children=False)
assert users.shape == tables['users'].shape
assert set(users.columns) == set(tables['users'].columns)
sessions = sdv.sample('sessions', sample_children=False)
assert sessions.shape == tables['sessions'].shape
assert set(sessions.columns) == set(tables['sessions'].columns)
transactions = sdv.sample('transactions', sample_children=False)
assert transactions.shape == tables['transactions'].shape
assert set(transactions.columns) == set(tables['transactions'].columns)
def test_sdv_multiparent():
metadata, tables = load_demo('got_families', metadata=True)
sdv = SDV()
sdv.fit(metadata, tables)
sampled = sdv.sample_all()
assert set(sampled.keys()) == {'characters', 'families', 'character_families'}
assert len(sampled['characters']) == 7
sampled = sdv.sample('characters', reset_primary_keys=True)
assert set(sampled.keys()) == {'characters', 'character_families'}
assert len(sampled['characters']) == 7
assert 'family_id' in sampled['character_families']
characters = sdv.sample('characters', sample_children=False)
assert characters.shape == tables['characters'].shape
assert set(characters.columns) == set(tables['characters'].columns)
families = sdv.sample('families', sample_children=False)
assert families.shape == tables['families'].shape
assert set(families.columns) == set(tables['families'].columns)
character_families = sdv.sample('character_families', sample_children=False)
assert character_families.shape == tables['character_families'].shape
assert set(character_families.columns) == set(tables['character_families'].columns)
| true | true |
f72cb255bbd9dbaa14f82003586431b14c8cdf93 | 340 | py | Python | WebApp/admin.py | divij-pherwani/PythonProject | 3ba262be580022cffc840f4cf967363eb7d3417b | [
"MIT"
] | null | null | null | WebApp/admin.py | divij-pherwani/PythonProject | 3ba262be580022cffc840f4cf967363eb7d3417b | [
"MIT"
] | null | null | null | WebApp/admin.py | divij-pherwani/PythonProject | 3ba262be580022cffc840f4cf967363eb7d3417b | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import StudentDetail, UniversityDetail, CourseDetail, CourseName, ApplicationDetail
admin.site.register(StudentDetail)
admin.site.register(UniversityDetail)
admin.site.register(CourseDetail)
admin.site.register(CourseName)
admin.site.register(ApplicationDetail)
# Register your models here.
| 28.333333 | 96 | 0.844118 | from django.contrib import admin
from .models import StudentDetail, UniversityDetail, CourseDetail, CourseName, ApplicationDetail
admin.site.register(StudentDetail)
admin.site.register(UniversityDetail)
admin.site.register(CourseDetail)
admin.site.register(CourseName)
admin.site.register(ApplicationDetail)
| true | true |
f72cb2583a8f94f5dbbfd81abcd00d5e5a7903fa | 2,490 | py | Python | serpcord/models/guild.py | PgBiel/serpcord | 482736dc691027417edcd6500cdfbf9053f92b63 | [
"MIT"
] | null | null | null | serpcord/models/guild.py | PgBiel/serpcord | 482736dc691027417edcd6500cdfbf9053f92b63 | [
"MIT"
] | null | null | null | serpcord/models/guild.py | PgBiel/serpcord | 482736dc691027417edcd6500cdfbf9053f92b63 | [
"MIT"
] | null | null | null | import typing
import datetime
from typing import Mapping, Any, Optional, Iterable, List
from .model_abc import JsonAPIModel
from .snowflake import Snowflake
from .user import User
from .enums import PermissionFlags
from .permissions import Role
from serpcord.utils.model import _init_model_from_mapping_json_data
if typing.TYPE_CHECKING:
from serpcord.botclient import BotClient
class GuildMember(JsonAPIModel[Mapping[str, Any]]): # TODO: Optional[Guild] - make sure the guild itself adds itself
def __init__(self, client: "BotClient", user: User, # TODO: docs + slots
*, nick: Optional[str] = None, guild_avatar_hash: Optional[str] = None,
role_ids: Iterable[Snowflake], roles: Iterable[Role], joined_at: datetime.datetime,
premium_since: Optional[datetime.datetime] = None,
is_deaf: bool, is_muted: bool, is_pending: bool = False,
permissions: Optional[PermissionFlags] = None,
communication_disabled_until: Optional[datetime.datetime] = None):
self.client: "BotClient" = client
self.user: User = user # NOTE: Must be injected in MESSAGE_CREATE / MESSAGE_UPDATE events (not provided by API)
self.nick: Optional[str] = str(nick) if nick is not None else None
self.guild_avatar_hash: Optional[str] = str(guild_avatar_hash) if guild_avatar_hash is not None else None
self.role_ids: List[Snowflake] = list(role_ids)
self.joined_at: datetime.datetime = joined_at
self.premium_since: Optional[datetime.datetime] = premium_since
self.is_deaf = bool(is_deaf)
self.is_muted = bool(is_muted)
self.is_pending = bool(is_pending)
self.permissions = PermissionFlags(permissions) if permissions is not None else None
self.communication_disabled_until: Optional[datetime.datetime] = communication_disabled_until
@property
def id(self) -> Snowflake:
return self.user.id
@property
def username(self) -> str:
return self.user.username
@property
def display_name(self) -> str:
return self.nick or self.username
@classmethod
def _from_json_data(cls, client: "BotClient", json_data: Mapping[str, Any]):
return _init_model_from_mapping_json_data(cls, client, json_data, rename=dict(
avatar="guild_avatar_hash", roles="role_ids", deaf="is_deaf", muted="is_muted", pending="is_pending"
), type_check_types=True)
| 46.111111 | 120 | 0.701606 | import typing
import datetime
from typing import Mapping, Any, Optional, Iterable, List
from .model_abc import JsonAPIModel
from .snowflake import Snowflake
from .user import User
from .enums import PermissionFlags
from .permissions import Role
from serpcord.utils.model import _init_model_from_mapping_json_data
if typing.TYPE_CHECKING:
from serpcord.botclient import BotClient
class GuildMember(JsonAPIModel[Mapping[str, Any]]):
def __init__(self, client: "BotClient", user: User,
*, nick: Optional[str] = None, guild_avatar_hash: Optional[str] = None,
role_ids: Iterable[Snowflake], roles: Iterable[Role], joined_at: datetime.datetime,
premium_since: Optional[datetime.datetime] = None,
is_deaf: bool, is_muted: bool, is_pending: bool = False,
permissions: Optional[PermissionFlags] = None,
communication_disabled_until: Optional[datetime.datetime] = None):
self.client: "BotClient" = client
self.user: User = user
self.nick: Optional[str] = str(nick) if nick is not None else None
self.guild_avatar_hash: Optional[str] = str(guild_avatar_hash) if guild_avatar_hash is not None else None
self.role_ids: List[Snowflake] = list(role_ids)
self.joined_at: datetime.datetime = joined_at
self.premium_since: Optional[datetime.datetime] = premium_since
self.is_deaf = bool(is_deaf)
self.is_muted = bool(is_muted)
self.is_pending = bool(is_pending)
self.permissions = PermissionFlags(permissions) if permissions is not None else None
self.communication_disabled_until: Optional[datetime.datetime] = communication_disabled_until
@property
def id(self) -> Snowflake:
return self.user.id
@property
def username(self) -> str:
return self.user.username
@property
def display_name(self) -> str:
return self.nick or self.username
@classmethod
def _from_json_data(cls, client: "BotClient", json_data: Mapping[str, Any]):
return _init_model_from_mapping_json_data(cls, client, json_data, rename=dict(
avatar="guild_avatar_hash", roles="role_ids", deaf="is_deaf", muted="is_muted", pending="is_pending"
), type_check_types=True)
| true | true |
f72cb27896211cd7a2fb7552b1e8abcbeb59a726 | 713 | py | Python | dns/migrations/0016_autozones_path.py | prorevizor/noc | 37e44b8afc64318b10699c06a1138eee9e7d6a4e | [
"BSD-3-Clause"
] | 84 | 2017-10-22T11:01:39.000Z | 2022-02-27T03:43:48.000Z | dns/migrations/0016_autozones_path.py | prorevizor/noc | 37e44b8afc64318b10699c06a1138eee9e7d6a4e | [
"BSD-3-Clause"
] | 22 | 2017-12-11T07:21:56.000Z | 2021-09-23T02:53:50.000Z | dns/migrations/0016_autozones_path.py | prorevizor/noc | 37e44b8afc64318b10699c06a1138eee9e7d6a4e | [
"BSD-3-Clause"
] | 23 | 2017-12-06T06:59:52.000Z | 2022-02-24T00:02:25.000Z | # ----------------------------------------------------------------------
# autozones_path
# ----------------------------------------------------------------------
# Copyright (C) 2007-2019 The NOC Project
# See LICENSE for details
# ----------------------------------------------------------------------
# Third-party modules
from django.db import models
# NOC modules
from noc.core.migration.base import BaseMigration
class Migration(BaseMigration):
def migrate(self):
self.db.add_column(
"dns_dnsserver",
"autozones_path",
models.CharField(
"Autozones path", max_length=256, blank=True, null=True, default="autozones"
),
)
| 29.708333 | 92 | 0.4446 |
from django.db import models
from noc.core.migration.base import BaseMigration
class Migration(BaseMigration):
def migrate(self):
self.db.add_column(
"dns_dnsserver",
"autozones_path",
models.CharField(
"Autozones path", max_length=256, blank=True, null=True, default="autozones"
),
)
| true | true |
f72cb379e5c099506c5177d3a7d4578f63d14794 | 8,765 | py | Python | models/resnet_cifar_quant.py | mengjian0502/GroupLasso_Quant | 1c54c940739babf86e362ffc57752c2aa4c8986d | [
"MIT"
] | null | null | null | models/resnet_cifar_quant.py | mengjian0502/GroupLasso_Quant | 1c54c940739babf86e362ffc57752c2aa4c8986d | [
"MIT"
] | null | null | null | models/resnet_cifar_quant.py | mengjian0502/GroupLasso_Quant | 1c54c940739babf86e362ffc57752c2aa4c8986d | [
"MIT"
] | null | null | null | """
ResNet on CIFAR10
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .quant import ClippedReLU, int_conv2d, int_linear
from .mpdr_score import get_mpdr_score
import math
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
class ResNetBasicblock(nn.Module):
expansion = 1
"""
RexNet basicblock (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua)
"""
def __init__(self, inplanes, planes, stride=1, downsample=None, wbit=4, abit=4, alpha_init=10, mode='mean', k=2, ch_group=16, push=False):
super(ResNetBasicblock, self).__init__()
# self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) # quantization
self.conv_a = int_conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=push) # quantization
self.bn_a = nn.BatchNorm2d(planes)
self.relu1 = ClippedReLU(num_bits=abit, alpha=alpha_init, inplace=True) # Clipped ReLU function 4 - bits
# self.relu1 = nn.ReLU(inplace=True)
self.conv_b = int_conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=push) # quantization
# self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) # quantization
self.bn_b = nn.BatchNorm2d(planes)
self.relu2 = ClippedReLU(num_bits=abit, alpha=alpha_init, inplace=True) # Clipped ReLU function 4 - bits
self.downsample = downsample
def forward(self, x):
residual = x
basicblock = self.conv_a(x)
basicblock = self.bn_a(basicblock)
basicblock = self.relu1(basicblock)
basicblock = self.conv_b(basicblock)
basicblock = self.bn_b(basicblock)
if self.downsample is not None:
residual = self.downsample(x)
return self.relu2(residual + basicblock)
class CifarResNet(nn.Module):
"""
ResNet optimized for the Cifar dataset, as specified in
https://arxiv.org/abs/1512.03385.pdf
"""
def __init__(self, depth, num_classes, wbit=4, abit=4, alpha_init=10, mode='mean', k=2, ch_group=16, push=False):
""" Constructor
Args:
depth: number of layers.
num_classes: number of classes
base_width: base width
"""
super(CifarResNet, self).__init__()
block = ResNetBasicblock
#Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
layer_blocks = (depth - 2) // 6
print ('CifarResNet : Depth : {} , Layers for each block : {}'.format(depth, layer_blocks))
self.num_classes = num_classes
self.ch_group = ch_group
# self.conv_1_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.conv_1_3x3 = int_conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=False) # skip the push process for the first conv layer
self.relu0 = ClippedReLU(num_bits=abit, alpha=alpha_init, inplace=True)
self.bn_1 = nn.BatchNorm2d(16)
self.inplanes = 16
self.stage_1 = self._make_layer(block, 16, layer_blocks, 1, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push)
self.stage_2 = self._make_layer(block, 32, layer_blocks, 2, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push)
self.stage_3 = self._make_layer(block, 64, layer_blocks, 2, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push)
self.avgpool = nn.AvgPool2d(8)
self.classifier = int_linear(64*block.expansion, num_classes, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=False) # skip the push process for the last fc layer
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
#m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, wbit=4, abit=4, alpha_init=10, mode='mean', k=2, ch_group=16, push=False):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
int_conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv_1_3x3(x)
x = self.relu0(self.bn_1(x))
x = self.stage_1(x)
x = self.stage_2(x)
x = self.stage_3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return self.classifier(x)
def get_group_val(self):
val = torch.Tensor()
if torch.cuda.is_available():
val = val.cuda()
count = 0
for m in self.modules():
if isinstance(m, int_conv2d):
kw = m.weight.size(2)
if kw != 1:
if not count in [0]:
w_l = m.weight
num_group = w_l.size(0) * w_l.size(1) // self.ch_group
w_l = w_l.view(w_l.size(0), w_l.size(1) // self.ch_group, self.ch_group, kw, kw)
w_l = w_l.contiguous().view((num_group, self.ch_group*kw*kw))
g = w_l.pow(2).sum(dim=1).pow(1/2)
val = torch.cat((val.view(-1), g.view(-1)))
count += 1
return val
def get_global_thre(self, ratio):
grp_val = self.get_group_val()
# grp_mean = grp_val.mean()
# threshold = ratio * grp_mean
sorted_block_values, indices = torch.sort(grp_val.contiguous().view(-1))
thre_index = int(grp_val.data.numel() * ratio)
threshold = sorted_block_values[thre_index]
return threshold
def get_group_mp(self):
val = torch.Tensor()
if torch.cuda.is_available():
val = val.cuda()
count = 0
for m in self.modules():
if isinstance(m, int_conv2d):
kw = m.weight.size(2)
if kw != 1:
if not count in [0]:
w_l = m.weight
num_group = w_l.size(0) * w_l.size(1) // self.ch_group
w_l = w_l.view(w_l.size(0), w_l.size(1) // self.ch_group, self.ch_group, kw, kw)
w_l = w_l.contiguous().view((num_group, self.ch_group*kw*kw))
g = w_l.abs().mean(dim=1)
val = torch.cat((val.view(-1), g.view(-1)))
count += 1
return val
def get_global_mp_thre(self, ratio):
grp_val = self.get_group_mp()
sorted_block_values, indices = torch.sort(grp_val.contiguous().view(-1))
thre_index = int(grp_val.data.numel() * ratio)
threshold = sorted_block_values[thre_index]
return threshold
def get_group_mpdr(self):
val = torch.Tensor()
if torch.cuda.is_available():
val = val.cuda()
count = 0
for m in self.modules():
if isinstance(m, int_conv2d):
kw = m.weight.size(2)
if kw != 1:
if not count in [0]:
w_l = get_mpdr_score(m.weight)
num_group = w_l.size(0) * w_l.size(1) // self.ch_group
w_l = w_l.view(w_l.size(0), w_l.size(1) // self.ch_group, self.ch_group, kw, kw)
w_l = w_l.contiguous().view((num_group, self.ch_group*kw*kw))
g = w_l.mean(dim=1) # compute the mean of the mpdr score
val = torch.cat((val.view(-1), g.view(-1)))
count += 1
return val
def get_global_mpdr_thre(self, ratio):
grp_val = self.get_group_mpdr()
sorted_block_values, indices = torch.sort(grp_val.contiguous().view(-1))
thre_index = int(grp_val.data.numel() * ratio)
threshold = sorted_block_values[thre_index]
return threshold
class resnet20_quant:
base=CifarResNet
args = list()
kwargs = {'depth': 20}
class resnet32_quant:
base=CifarResNet
args = list()
kwargs = {'depth': 32}
| 37.780172 | 196 | 0.650542 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .quant import ClippedReLU, int_conv2d, int_linear
from .mpdr_score import get_mpdr_score
import math
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
class ResNetBasicblock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, wbit=4, abit=4, alpha_init=10, mode='mean', k=2, ch_group=16, push=False):
super(ResNetBasicblock, self).__init__()
_a = int_conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=push)
self.bn_a = nn.BatchNorm2d(planes)
self.relu1 = ClippedReLU(num_bits=abit, alpha=alpha_init, inplace=True)
self.conv_b = int_conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=push)
= nn.BatchNorm2d(planes)
self.relu2 = ClippedReLU(num_bits=abit, alpha=alpha_init, inplace=True)
self.downsample = downsample
def forward(self, x):
residual = x
basicblock = self.conv_a(x)
basicblock = self.bn_a(basicblock)
basicblock = self.relu1(basicblock)
basicblock = self.conv_b(basicblock)
basicblock = self.bn_b(basicblock)
if self.downsample is not None:
residual = self.downsample(x)
return self.relu2(residual + basicblock)
class CifarResNet(nn.Module):
def __init__(self, depth, num_classes, wbit=4, abit=4, alpha_init=10, mode='mean', k=2, ch_group=16, push=False):
super(CifarResNet, self).__init__()
block = ResNetBasicblock
assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
layer_blocks = (depth - 2) // 6
print ('CifarResNet : Depth : {} , Layers for each block : {}'.format(depth, layer_blocks))
self.num_classes = num_classes
self.ch_group = ch_group
self.conv_1_3x3 = int_conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=False)
self.relu0 = ClippedReLU(num_bits=abit, alpha=alpha_init, inplace=True)
self.bn_1 = nn.BatchNorm2d(16)
self.inplanes = 16
self.stage_1 = self._make_layer(block, 16, layer_blocks, 1, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push)
self.stage_2 = self._make_layer(block, 32, layer_blocks, 2, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push)
self.stage_3 = self._make_layer(block, 64, layer_blocks, 2, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push)
self.avgpool = nn.AvgPool2d(8)
self.classifier = int_linear(64*block.expansion, num_classes, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=False)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, wbit=4, abit=4, alpha_init=10, mode='mean', k=2, ch_group=16, push=False):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
int_conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False, nbit=wbit, mode=mode, k=k, ch_group=ch_group, push=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, wbit=wbit, abit=abit, alpha_init=alpha_init, mode=mode, k=k, ch_group=ch_group, push=push))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv_1_3x3(x)
x = self.relu0(self.bn_1(x))
x = self.stage_1(x)
x = self.stage_2(x)
x = self.stage_3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return self.classifier(x)
def get_group_val(self):
val = torch.Tensor()
if torch.cuda.is_available():
val = val.cuda()
count = 0
for m in self.modules():
if isinstance(m, int_conv2d):
kw = m.weight.size(2)
if kw != 1:
if not count in [0]:
w_l = m.weight
num_group = w_l.size(0) * w_l.size(1) // self.ch_group
w_l = w_l.view(w_l.size(0), w_l.size(1) // self.ch_group, self.ch_group, kw, kw)
w_l = w_l.contiguous().view((num_group, self.ch_group*kw*kw))
g = w_l.pow(2).sum(dim=1).pow(1/2)
val = torch.cat((val.view(-1), g.view(-1)))
count += 1
return val
def get_global_thre(self, ratio):
grp_val = self.get_group_val()
sorted_block_values, indices = torch.sort(grp_val.contiguous().view(-1))
thre_index = int(grp_val.data.numel() * ratio)
threshold = sorted_block_values[thre_index]
return threshold
def get_group_mp(self):
val = torch.Tensor()
if torch.cuda.is_available():
val = val.cuda()
count = 0
for m in self.modules():
if isinstance(m, int_conv2d):
kw = m.weight.size(2)
if kw != 1:
if not count in [0]:
w_l = m.weight
num_group = w_l.size(0) * w_l.size(1) // self.ch_group
w_l = w_l.view(w_l.size(0), w_l.size(1) // self.ch_group, self.ch_group, kw, kw)
w_l = w_l.contiguous().view((num_group, self.ch_group*kw*kw))
g = w_l.abs().mean(dim=1)
val = torch.cat((val.view(-1), g.view(-1)))
count += 1
return val
def get_global_mp_thre(self, ratio):
grp_val = self.get_group_mp()
sorted_block_values, indices = torch.sort(grp_val.contiguous().view(-1))
thre_index = int(grp_val.data.numel() * ratio)
threshold = sorted_block_values[thre_index]
return threshold
def get_group_mpdr(self):
val = torch.Tensor()
if torch.cuda.is_available():
val = val.cuda()
count = 0
for m in self.modules():
if isinstance(m, int_conv2d):
kw = m.weight.size(2)
if kw != 1:
if not count in [0]:
w_l = get_mpdr_score(m.weight)
num_group = w_l.size(0) * w_l.size(1) // self.ch_group
w_l = w_l.view(w_l.size(0), w_l.size(1) // self.ch_group, self.ch_group, kw, kw)
w_l = w_l.contiguous().view((num_group, self.ch_group*kw*kw))
g = w_l.mean(dim=1)
val = torch.cat((val.view(-1), g.view(-1)))
count += 1
return val
def get_global_mpdr_thre(self, ratio):
grp_val = self.get_group_mpdr()
sorted_block_values, indices = torch.sort(grp_val.contiguous().view(-1))
thre_index = int(grp_val.data.numel() * ratio)
threshold = sorted_block_values[thre_index]
return threshold
class resnet20_quant:
base=CifarResNet
args = list()
kwargs = {'depth': 20}
class resnet32_quant:
base=CifarResNet
args = list()
kwargs = {'depth': 32}
| true | true |
f72cb40930dc9e29198e8bc1f4a2818b2e161a8f | 449 | py | Python | util.py | codefordc/us-congress-pizza-flag-tracker | 766c72e01e2c01342d4c6dbe2108fded2022ee74 | [
"CC0-1.0"
] | 5 | 2021-01-31T14:29:43.000Z | 2021-07-15T16:22:30.000Z | util.py | rajindermavi/us-congress-pizza-flag-tracker | 10827f3d6f2ef0cc434a475fc9782fc840cb81ab | [
"CC0-1.0"
] | 85 | 2021-05-12T23:31:29.000Z | 2022-03-30T21:23:58.000Z | util.py | rajindermavi/us-congress-pizza-flag-tracker | 10827f3d6f2ef0cc434a475fc9782fc840cb81ab | [
"CC0-1.0"
] | 8 | 2021-04-11T16:44:15.000Z | 2021-10-30T21:14:17.000Z | import json
from config import db
from models import UserModel
def table_record_to_json(record):
modelClass = type(record)
columns = [record for record in filter(lambda item: not item.startswith('_'),modelClass.__dict__)]
json_value = {column_name: str(getattr(record, column_name))for column_name in columns}
return json_value
def table_to_json(table):
return { "data": [table_record_to_json(record) for record in table] } | 28.0625 | 102 | 0.752784 | import json
from config import db
from models import UserModel
def table_record_to_json(record):
modelClass = type(record)
columns = [record for record in filter(lambda item: not item.startswith('_'),modelClass.__dict__)]
json_value = {column_name: str(getattr(record, column_name))for column_name in columns}
return json_value
def table_to_json(table):
return { "data": [table_record_to_json(record) for record in table] } | true | true |
f72cb478099ad21f4b980eaa5ef8fdbe1740ca81 | 519 | py | Python | models/utils.py | clabrugere/numpy-basics | 81efb4b8ac58fc17dc8f6c676004bbc3a99a92c3 | [
"MIT"
] | 1 | 2020-10-27T18:05:26.000Z | 2020-10-27T18:05:26.000Z | models/utils.py | clabrugere/numpy-basics | 81efb4b8ac58fc17dc8f6c676004bbc3a99a92c3 | [
"MIT"
] | null | null | null | models/utils.py | clabrugere/numpy-basics | 81efb4b8ac58fc17dc8f6c676004bbc3a99a92c3 | [
"MIT"
] | null | null | null | import numpy as np
def confusion_matrix(y_true, y_hat, threshold=.5):
def _to_class(y):
return np.array([1 if i >= threshold else 0 for i in y])
n_classes = len(np.unique(y_true))
cm = np.zeros((n_classes, n_classes))
y_hat = _to_class(y_hat)
for a, p in zip(y_true, y_hat):
cm[a, p] += 1
return cm
def f1_score(cm):
precision = cm[0, 0] / cm[0, :].sum()
recall = cm[0, 0] / cm[:, 0].sum()
return 2 * (precision * recall) / (precision + recall) | 24.714286 | 64 | 0.572254 | import numpy as np
def confusion_matrix(y_true, y_hat, threshold=.5):
def _to_class(y):
return np.array([1 if i >= threshold else 0 for i in y])
n_classes = len(np.unique(y_true))
cm = np.zeros((n_classes, n_classes))
y_hat = _to_class(y_hat)
for a, p in zip(y_true, y_hat):
cm[a, p] += 1
return cm
def f1_score(cm):
precision = cm[0, 0] / cm[0, :].sum()
recall = cm[0, 0] / cm[:, 0].sum()
return 2 * (precision * recall) / (precision + recall) | true | true |
f72cb4e3d578253909cb6f62152c5f20859236b5 | 276 | py | Python | translator/app/modules/speech.py | sharad461/nepali-translator | d35ba1586e4ad14ddae71b24caf49aac66d63a2e | [
"Apache-2.0"
] | 29 | 2019-08-04T03:05:23.000Z | 2021-12-14T14:09:57.000Z | translator/app/modules/speech.py | sharad461/nepali-translator | d35ba1586e4ad14ddae71b24caf49aac66d63a2e | [
"Apache-2.0"
] | 3 | 2020-10-09T01:35:45.000Z | 2021-06-02T12:24:31.000Z | translator/app/modules/speech.py | sharad461/nepali-translator | d35ba1586e4ad14ddae71b24caf49aac66d63a2e | [
"Apache-2.0"
] | 9 | 2019-11-04T10:01:34.000Z | 2021-12-20T02:03:40.000Z | import speech_recognition as sr
def rec():
r = sr.Recognizer()
with sr.Microphone() as source:
audio = r.listen(source)
try:
text = r.recognize_google(audio)
return(text)
except:
return("Sorry, couldn't recognize your voice. Please try again.") | 21.230769 | 68 | 0.666667 | import speech_recognition as sr
def rec():
r = sr.Recognizer()
with sr.Microphone() as source:
audio = r.listen(source)
try:
text = r.recognize_google(audio)
return(text)
except:
return("Sorry, couldn't recognize your voice. Please try again.") | true | true |
f72cb63e07c6ebb1781cffd6e5ba78d6f5d59509 | 1,201 | py | Python | sonata/datamodules/base_datamodule.py | sergevkim/sonata | 2250b60174628ee76fb7d54bf50e4b8b07b505d5 | [
"MIT"
] | 1 | 2021-03-15T19:01:43.000Z | 2021-03-15T19:01:43.000Z | sonata/datamodules/base_datamodule.py | sergevkim/sonata | 2250b60174628ee76fb7d54bf50e4b8b07b505d5 | [
"MIT"
] | null | null | null | sonata/datamodules/base_datamodule.py | sergevkim/sonata | 2250b60174628ee76fb7d54bf50e4b8b07b505d5 | [
"MIT"
] | null | null | null | from abc import ABC, abstractmethod
from pathlib import Path
import torch
from torch import Tensor
from torch.utils.data import Dataset, DataLoader
class BaseDataModule(ABC):
def __init__(
self,
data_path: Path,
batch_size: int,
num_workers: int,
):
super().__init__()
self.data_path = data_path
self.batch_size = batch_size
self.num_workers = num_workers
@staticmethod
def prepare_data(
data_path: Path,
):
pass
@abstractmethod
def setup(
self,
val_ratio: float,
) -> None:
pass
def train_dataloader(self) -> DataLoader:
train_dataloader = DataLoader(
dataset=self.train_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
return train_dataloader
def val_dataloader(self) -> DataLoader:
val_dataloader = DataLoader(
dataset=self.val_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
return val_dataloader
def test_dataloader(self):
pass
| 21.836364 | 48 | 0.587843 | from abc import ABC, abstractmethod
from pathlib import Path
import torch
from torch import Tensor
from torch.utils.data import Dataset, DataLoader
class BaseDataModule(ABC):
def __init__(
self,
data_path: Path,
batch_size: int,
num_workers: int,
):
super().__init__()
self.data_path = data_path
self.batch_size = batch_size
self.num_workers = num_workers
@staticmethod
def prepare_data(
data_path: Path,
):
pass
@abstractmethod
def setup(
self,
val_ratio: float,
) -> None:
pass
def train_dataloader(self) -> DataLoader:
train_dataloader = DataLoader(
dataset=self.train_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
return train_dataloader
def val_dataloader(self) -> DataLoader:
val_dataloader = DataLoader(
dataset=self.val_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
return val_dataloader
def test_dataloader(self):
pass
| true | true |
f72cb68570a41741af7a25b02a5d19503e0f3386 | 3,806 | py | Python | netket/operator/boson.py | gpescia/MyNetKet | 958510966a5870d9d491de0628903cf1fc210921 | [
"Apache-2.0"
] | null | null | null | netket/operator/boson.py | gpescia/MyNetKet | 958510966a5870d9d491de0628903cf1fc210921 | [
"Apache-2.0"
] | 11 | 2021-07-12T15:20:14.000Z | 2022-01-17T09:40:41.000Z | netket/operator/boson.py | gpescia/MyNetKet | 958510966a5870d9d491de0628903cf1fc210921 | [
"Apache-2.0"
] | 1 | 2021-04-25T15:47:32.000Z | 2021-04-25T15:47:32.000Z | # Copyright 2021 The NetKet 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.
from netket.utils.types import DType
from netket.hilbert import AbstractHilbert
from ._local_operator import LocalOperator as _LocalOperator
def destroy(
hilbert: AbstractHilbert, site: int, dtype: DType = float
) -> _LocalOperator:
"""
Builds the boson destruction operator :math:`\\hat{a}` acting on the `site`-th of the
Hilbert space `hilbert`.
If `hilbert` is a non-Bosonic space of local dimension M, it is considered
as a bosonic space of local dimension M.
Args:
hilbert: The hilbert space
site: the site on which this operator acts
Returns:
The resulting Local Operator
"""
import numpy as np
N = hilbert.size_at_index(site)
D = np.array([np.sqrt(m) for m in np.arange(1, N)])
mat = np.diag(D, 1)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
def create(hilbert: AbstractHilbert, site: int, dtype: DType = float) -> _LocalOperator:
"""
Builds the boson creation operator :math:`\\hat{a}^\\dagger` acting on the `site`-th of the
Hilbert space `hilbert`.
If `hilbert` is a non-Bosonic space of local dimension M, it is considered
as a bosonic space of local dimension M.
Args:
hilbert: The hilbert space
site: the site on which this operator acts
Returns:
The resulting Local Operator
"""
import numpy as np
N = hilbert.size_at_index(site)
D = np.array([np.sqrt(m) for m in np.arange(1, N)])
mat = np.diag(D, -1)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
def number(hilbert: AbstractHilbert, site: int, dtype: DType = float) -> _LocalOperator:
"""
Builds the number operator :math:`\\hat{a}^\\dagger\\hat{a}` acting on the `site`-th of the
Hilbert space `hilbert`.
If `hilbert` is a non-Bosonic space of local dimension M, it is considered
as a bosonic space of local dimension M.
Args:
hilbert: The hilbert space
site: the site on which this operator acts
Returns:
The resulting Local Operator
"""
import numpy as np
N = hilbert.size_at_index(site)
D = np.array([m for m in np.arange(0, N)])
mat = np.diag(D, 0)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
def proj(
hilbert: AbstractHilbert, site: int, n: int, dtype: DType = float
) -> _LocalOperator:
"""
Builds the projector operator :math:`|n\\rangle\\langle n |` acting on the `site`-th of the
Hilbert space `hilbert` and collapsing on the state with `n` bosons.
If `hilbert` is a non-Bosonic space of local dimension M, it is considered
as a bosonic space of local dimension M.
Args:
hilbert: The hilbert space
site: the site on which this operator acts
n: the state on which to project
Returns:
the resulting operator
"""
import numpy as np
N = hilbert.size_at_index(site)
if n >= N:
raise ValueError("Cannot project on a state above the cutoff.")
D = np.array([0 for m in np.arange(0, N)])
D[n] = 1
mat = np.diag(D, 0)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
# clean up the module
del AbstractHilbert, DType
| 29.503876 | 96 | 0.672622 |
from netket.utils.types import DType
from netket.hilbert import AbstractHilbert
from ._local_operator import LocalOperator as _LocalOperator
def destroy(
hilbert: AbstractHilbert, site: int, dtype: DType = float
) -> _LocalOperator:
import numpy as np
N = hilbert.size_at_index(site)
D = np.array([np.sqrt(m) for m in np.arange(1, N)])
mat = np.diag(D, 1)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
def create(hilbert: AbstractHilbert, site: int, dtype: DType = float) -> _LocalOperator:
import numpy as np
N = hilbert.size_at_index(site)
D = np.array([np.sqrt(m) for m in np.arange(1, N)])
mat = np.diag(D, -1)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
def number(hilbert: AbstractHilbert, site: int, dtype: DType = float) -> _LocalOperator:
import numpy as np
N = hilbert.size_at_index(site)
D = np.array([m for m in np.arange(0, N)])
mat = np.diag(D, 0)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
def proj(
hilbert: AbstractHilbert, site: int, n: int, dtype: DType = float
) -> _LocalOperator:
import numpy as np
N = hilbert.size_at_index(site)
if n >= N:
raise ValueError("Cannot project on a state above the cutoff.")
D = np.array([0 for m in np.arange(0, N)])
D[n] = 1
mat = np.diag(D, 0)
return _LocalOperator(hilbert, mat, [site], dtype=dtype)
del AbstractHilbert, DType
| true | true |
f72cb81ea991aa6ce3d971ea1b6e47347518c4cb | 31 | py | Python | day2/oddno1.py | nikhilsamninan/python-files | 15198459081097058a939b40b5e8ef754e578fe0 | [
"Apache-2.0"
] | null | null | null | day2/oddno1.py | nikhilsamninan/python-files | 15198459081097058a939b40b5e8ef754e578fe0 | [
"Apache-2.0"
] | null | null | null | day2/oddno1.py | nikhilsamninan/python-files | 15198459081097058a939b40b5e8ef754e578fe0 | [
"Apache-2.0"
] | null | null | null | print(tuple(range(201,400,2)))
| 15.5 | 30 | 0.709677 | print(tuple(range(201,400,2)))
| true | true |
f72cb9eb47ac3d1bf036724169c33be5cd5d5d60 | 338 | py | Python | dogstatsd/__init__.py | ian28223/datadog-unix-agent | 09c75778b512361c83ff10e7cdb37b887bcaa8fe | [
"Apache-2.0"
] | 13 | 2018-08-11T01:40:51.000Z | 2022-01-02T09:07:43.000Z | dogstatsd/__init__.py | ian28223/datadog-unix-agent | 09c75778b512361c83ff10e7cdb37b887bcaa8fe | [
"Apache-2.0"
] | 21 | 2018-05-28T13:16:23.000Z | 2021-08-19T15:43:40.000Z | dogstatsd/__init__.py | ian28223/datadog-unix-agent | 09c75778b512361c83ff10e7cdb37b887bcaa8fe | [
"Apache-2.0"
] | 15 | 2018-05-10T15:09:41.000Z | 2022-03-21T06:46:21.000Z | # Unless explicitly stated otherwise all files in this repository are licensed
# under the Apache License Version 2.0.
# This product includes software developed at Datadog (https://www.datadoghq.com/).
# Copyright 2018 Datadog, Inc.
from .server import Server
from .reporter import Reporter
__all__ = [
"Server",
"Reporter",
]
| 26 | 83 | 0.748521 |
from .server import Server
from .reporter import Reporter
__all__ = [
"Server",
"Reporter",
]
| true | true |
f72cba691951b7828f5ece31e4d5727f90f7fb13 | 428 | py | Python | gallery/migrations/0006_image_image.py | dennis027/Gallery | 282c1807087beb2e2a5ea1d51b5b6891145c20a0 | [
"MIT"
] | null | null | null | gallery/migrations/0006_image_image.py | dennis027/Gallery | 282c1807087beb2e2a5ea1d51b5b6891145c20a0 | [
"MIT"
] | null | null | null | gallery/migrations/0006_image_image.py | dennis027/Gallery | 282c1807087beb2e2a5ea1d51b5b6891145c20a0 | [
"MIT"
] | null | null | null | # Generated by Django 2.2 on 2021-07-03 12:00
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('gallery', '0005_remove_image_image'),
]
operations = [
migrations.AddField(
model_name='image',
name='image',
field=models.CharField(default=1, max_length=255),
preserve_default=False,
),
]
| 21.4 | 62 | 0.598131 |
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('gallery', '0005_remove_image_image'),
]
operations = [
migrations.AddField(
model_name='image',
name='image',
field=models.CharField(default=1, max_length=255),
preserve_default=False,
),
]
| true | true |
f72cbb0f0e9ed11c2ce819bd187907cbc6229269 | 1,190 | py | Python | examples/pure_jax.py | kingoflolz/DALL-E | d3f3e9a57a31b1e1cc74a449a9e6e5a0442f0ac7 | [
"MIT"
] | 7 | 2021-04-10T15:03:37.000Z | 2021-07-05T02:49:51.000Z | examples/pure_jax.py | kingoflolz/DALL-E | d3f3e9a57a31b1e1cc74a449a9e6e5a0442f0ac7 | [
"MIT"
] | null | null | null | examples/pure_jax.py | kingoflolz/DALL-E | d3f3e9a57a31b1e1cc74a449a9e6e5a0442f0ac7 | [
"MIT"
] | 1 | 2021-10-01T07:47:41.000Z | 2021-10-01T07:47:41.000Z | import io
import jax
import requests
import PIL
from PIL import ImageOps
import numpy as np
import jax.numpy as jnp
from dall_e_jax import get_encoder, get_decoder, map_pixels, unmap_pixels
target_image_size = 256
def download_image(url):
resp = requests.get(url)
resp.raise_for_status()
return PIL.Image.open(io.BytesIO(resp.content))
def preprocess(img):
img = ImageOps.fit(img, [target_image_size,] * 2, method=0, bleed=0.0, centering=(0.5, 0.5))
img = np.expand_dims(np.transpose(np.array(img).astype(np.float32)/255, (2, 0, 1)), 0)
return map_pixels(img)
jax_enc_fn, jax_enc_params = get_encoder("encoder.pkl")
jax_dec_fn, jax_dec_params = get_decoder("decoder.pkl")
x = preprocess(download_image('https://assets.bwbx.io/images/users/iqjWHBFdfxIU/iKIWgaiJUtss/v2/1000x-1.jpg'))
z_logits = jax_enc_fn(jax_enc_params, x)
z = jnp.argmax(z_logits, axis=1)
z = jnp.transpose(jax.nn.one_hot(z, num_classes=8192), (0, 3, 1, 2))
x_stats = jax_dec_fn(jax_dec_params, z)
x_rec = unmap_pixels(jax.nn.sigmoid(x_stats[:, :3]))
x_rec = np.transpose((np.array(x_rec[0]) * 255).astype(np.uint8), (1, 2, 0))
PIL.Image.fromarray(x_rec).save('reconstructed.png')
| 26.444444 | 110 | 0.730252 | import io
import jax
import requests
import PIL
from PIL import ImageOps
import numpy as np
import jax.numpy as jnp
from dall_e_jax import get_encoder, get_decoder, map_pixels, unmap_pixels
target_image_size = 256
def download_image(url):
resp = requests.get(url)
resp.raise_for_status()
return PIL.Image.open(io.BytesIO(resp.content))
def preprocess(img):
img = ImageOps.fit(img, [target_image_size,] * 2, method=0, bleed=0.0, centering=(0.5, 0.5))
img = np.expand_dims(np.transpose(np.array(img).astype(np.float32)/255, (2, 0, 1)), 0)
return map_pixels(img)
jax_enc_fn, jax_enc_params = get_encoder("encoder.pkl")
jax_dec_fn, jax_dec_params = get_decoder("decoder.pkl")
x = preprocess(download_image('https://assets.bwbx.io/images/users/iqjWHBFdfxIU/iKIWgaiJUtss/v2/1000x-1.jpg'))
z_logits = jax_enc_fn(jax_enc_params, x)
z = jnp.argmax(z_logits, axis=1)
z = jnp.transpose(jax.nn.one_hot(z, num_classes=8192), (0, 3, 1, 2))
x_stats = jax_dec_fn(jax_dec_params, z)
x_rec = unmap_pixels(jax.nn.sigmoid(x_stats[:, :3]))
x_rec = np.transpose((np.array(x_rec[0]) * 255).astype(np.uint8), (1, 2, 0))
PIL.Image.fromarray(x_rec).save('reconstructed.png')
| true | true |
f72cbbad2bdf77b532dac0c510c9856f9ed9388e | 12,421 | py | Python | src/run_joint_confidence_cdcOriginalGan.py | williamsashbee/Confident_classifier | cba3ef862b310afc3af6c4a62b524f032f45549e | [
"MIT"
] | null | null | null | src/run_joint_confidence_cdcOriginalGan.py | williamsashbee/Confident_classifier | cba3ef862b310afc3af6c4a62b524f032f45549e | [
"MIT"
] | null | null | null | src/run_joint_confidence_cdcOriginalGan.py | williamsashbee/Confident_classifier | cba3ef862b310afc3af6c4a62b524f032f45549e | [
"MIT"
] | null | null | null | ##############################################
# This code is based on samples from pytorch #
##############################################
# Writer: Kimin Lee
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import data_loader
import numpy as np
import torchvision.utils as vutils
import models
from torchvision import datasets, transforms
from torch.autograd import Variable
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = "5"
# Training settings
parser = argparse.ArgumentParser(description='Training code - joint confidence')
parser.add_argument('--batch-size', type=int, default=128, help='input batch size for training')
parser.add_argument('--epochs', type=int, default=100, help='number of epochs to train')
parser.add_argument('--lr', type=float, default=0.0002, help='learning rate')
parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--log-interval', type=int, default=100,
help='how many batches to wait before logging training status')
parser.add_argument('--dataset', default='mnist', help='cifar10 | svhn')
parser.add_argument('--dataroot', required=True, help='path to dataset')
parser.add_argument('--imageSize', type=int, default=32, help='the height / width of the input image to network')
parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')
parser.add_argument('--wd', type=float, default=0.0, help='weight decay')
parser.add_argument('--droprate', type=float, default=0.1, help='learning rate decay')
parser.add_argument('--decreasing_lr', default='60', help='decreasing strategy')
parser.add_argument('--num_classes', type=int, default=10, help='the # of classes')
parser.add_argument('--beta', type=float, default=1, help='penalty parameter for KL term')
args = parser.parse_args()
if args.dataset == 'cifar10':
args.beta = 0.1
args.batch_size = 64
print(args)
args.cuda = not args.no_cuda and torch.cuda.is_available()
print("Random Seed: ", args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
print('load data: ', args.dataset)
if args.dataset=='mnist':
transform = transforms.Compose([
transforms.Scale(32),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, download=True, transform=transform),
batch_size=128, shuffle=True)
else:
train_loader, test_loader = data_loader.getTargetDataSet(args.dataset, args.batch_size, args.imageSize, args.dataroot)
transform = transforms.Compose([
transforms.Scale(32),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_loader_mnist = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=128, shuffle=True)
print('Load model')
model = models.vgg13()
print(model)
print('load GAN')
nz = 100
G = models.cdcOriginalGenerator(1, nz, 64, 3) # ngpu, nz, ngf, nc
D = models.cdcOriginalDiscriminator(1, 3, 64) # ngpu, nc, ndf
G.weight_init(mean=0.0, std=0.02)
D.weight_init(mean=0.0, std=0.02)
# Initial setup for GAN
real_label = 1
fake_label = 0
criterion = nn.BCELoss()
nz = 100
print('Setup optimizer')
lr = 0.0002
batch_size = 128
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
G_optimizer = optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))
D_optimizer = optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.999))
decreasing_lr = list(map(int, args.decreasing_lr.split(',')))
onehot = torch.zeros(10, 10).cuda()
onehot = onehot.scatter_(1, torch.cuda.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).view(10, 1), 1).view(10, 10, 1, 1)
img_size = 32
num_labels = 10
fraction = 1
fill = torch.zeros([num_labels, num_labels, img_size / fraction, img_size / fraction]).cuda()
for i in range(num_labels):
fill[i, i, :, :] = 1
fill = fill.cuda()
# os.environ["CUDA_LAUNCH_BLOCKING"]="1"
# Binary Cross Entropy loss
BCE_loss = nn.BCELoss()
# fixed_noise = torch.FloatTensor(64, nz, 1, 1).normal_(0, 1)
fixed_noise = torch.randn((64, 100)).view(-1, 100, 1, 1)
fixed_label = None
if args.cuda:
model.cuda()
D.cuda()
G.cuda()
criterion.cuda()
fixed_noise = fixed_noise.cuda()
first = True
def train(epoch):
model.train()
# D_train_loss = 0
# G_train_loss = 3
trg = 0
trd = 0
i = 0
for batch_idx, (data, y_labels) in enumerate(train_loader):
uniform_dist = torch.Tensor(data.size(0), args.num_classes).fill_((1. / args.num_classes)).cuda()
x_ = data.cuda()
assert x_[0, :, :, :].shape == (3, 32, 32)
global first
if first:
global fixed_noise
global fixed_label
first = False
fixed_label = onehot[y_labels.squeeze()[:64]]
print("saving fixed_label!")
vutils.save_image(data[:64],
'{}/{}jointConfidencerealReference{}.png'.format(args.outf, args.dataset, epoch),
normalize=True)
# train discriminator D
D.zero_grad()
y_ = y_labels
mini_batch = x_.size()[0]
y_real_ = torch.ones(mini_batch)
y_fake_ = torch.zeros(mini_batch)
y_real_, y_fake_ = Variable(y_real_.cuda()), Variable(y_fake_.cuda())
y_fill_ = fill[y_.squeeze().tolist()]
# y_fill_ = fill[y_]
assert y_fill_[0, y_.squeeze().tolist()[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * mini_batch
x_, y_fill_ = Variable(x_.cuda()), Variable(y_fill_.cuda())
D_result = D(x_, y_fill_).squeeze()
D_real_loss = BCE_loss(D_result, y_real_)
z_ = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)
y_ = (torch.rand(mini_batch, 1) * num_labels).type(torch.LongTensor).squeeze()
y_label_ = onehot[y_]
y_fill_ = fill[y_]
assert y_label_[0, y_[0]] == 1
assert y_label_.shape == (mini_batch, 10, 1, 1)
assert y_fill_[0, y_[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * mini_batch
z_, y_label_, y_fill_ = Variable(z_.cuda()), Variable(y_label_.cuda()), Variable(y_fill_.cuda())
G_result = G(z_, y_label_)
D_result = D(G_result, y_fill_).squeeze()
D_fake_loss = BCE_loss(D_result, y_fake_)
D_fake_score = D_result.data.mean()
D_train_loss = D_real_loss + D_fake_loss
trg += 1
if D_train_loss > .1:
trd += 1
D_train_loss.backward()
D_optimizer.step()
# D_losses.append(D_train_loss.item())
# train generator G
G.zero_grad()
z_ = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)
y_ = (torch.rand(mini_batch, 1) * num_labels).type(torch.LongTensor).squeeze()
y_label_ = onehot[y_]
y_fill_ = fill[y_]
z_, y_label_, y_fill_ = Variable(z_.cuda()), Variable(y_label_.cuda()), Variable(y_fill_.cuda())
assert y_label_[0, y_[0]] == 1
assert y_label_.shape == (mini_batch, 10, 1, 1)
assert y_fill_[0, y_[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * mini_batch
G_result = G(z_, y_label_)
D_result = D(G_result, y_fill_).squeeze()
G_train_loss = BCE_loss(D_result, y_real_)
# minimize the true distribution
KL_fake_output = F.log_softmax(model(G_result))
errG_KL = F.kl_div(KL_fake_output, uniform_dist) * args.num_classes
generator_loss = G_train_loss + args.beta * errG_KL # 12.0, .65, 0e-8
generator_loss.backward()
G_optimizer.step()
# G_losses.append(G_train_loss.item())
###########################
# (3) Update classifier #
###########################
# cross entropy loss
optimizer.zero_grad()
x_ = Variable(x_)
output = F.log_softmax(model(x_))
loss = F.nll_loss(output.cuda(), y_labels.type(torch.cuda.LongTensor).squeeze())
# KL divergence
####
z_ = torch.randn((data.shape[0], 100)).view(-1, 100, 1, 1).cuda()
y_ = (torch.rand(data.shape[0], 1) * num_labels).type(torch.LongTensor).squeeze().cuda()
y_label_ = onehot[y_]
y_fill_ = fill[y_]
assert y_label_[0, y_[0]] == 1
assert y_label_.shape == (data.shape[0], 10, 1, 1)
assert y_fill_[0, y_[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * data.shape[0]
G_result = G(z_, y_label_)
# !!!#D_result = D(G_result, y_fill_).squeeze()
####
KL_fake_output = F.log_softmax(model(G_result))
KL_loss_fake = F.kl_div(KL_fake_output, uniform_dist) * args.num_classes
total_loss = loss + args.beta * KL_loss_fake
# total_loss = loss
total_loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print(
"Epoch {} , Descriminator loss {:.6f} Generator loss {:.6f} traingenerator {:.6f} traindiscriminator {:.6f}".format(
epoch, D_train_loss, G_train_loss, trg, trd))
print('Classification Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, KL fake Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data.item(), KL_loss_fake.data.item()))
# print('Classification Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, KL fake Loss: {:.6f}'.format(
# epoch, batch_idx * len(data), len(train_loader.dataset),
# 100. * batch_idx / len(train_loader), loss.data.item(), KL_loss_fake.data.item()))
fake = G(fixed_noise.cuda(), fixed_label)
vutils.save_image(fake.data, '%s/MNISTcDCgan_samples_epoch_%03d.png' % (args.outf, epoch), normalize=True)
def test(epoch):
model.eval()
test_loss = 0
correct = 0
total = 0
for data, target in test_loader:
total += data.size(0)
if args.cuda:
data, target = data.cuda(), target.cuda()
# data, target = Variable(data, volatile=True), Variable(target)
output = F.log_softmax(model(data))
target = target.type(
torch.LongTensor) # https://discuss.pytorch.org/t/runtimeerror-multi-target-not-supported-newbie/10216/4
if args.cuda:
output = output.cuda()
target = target.cuda()
target = torch.squeeze(target)
test_loss += F.nll_loss(output, target).data.item()
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
test_loss = test_loss
test_loss /= len(test_loader) # loss function already averages over batch size
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, total,
100. * correct / total))
for epoch in range(1, args.epochs + 1):
train(epoch)
test(epoch)
if epoch in decreasing_lr:
G_optimizer.param_groups[0]['lr'] *= args.droprate
D_optimizer.param_groups[0]['lr'] *= args.droprate
optimizer.param_groups[0]['lr'] *= args.droprate
if epoch % 20 == 0:
# do checkpointing
torch.save(G.state_dict(), '%s/netG_epoch_%d.pth' % (args.outf, epoch))
torch.save(D.state_dict(), '%s/netD_epoch_%d.pth' % (args.outf, epoch))
torch.save(model.state_dict(), '%s/model_epoch_%d.pth' % (args.outf, epoch))
| 37.3003 | 132 | 0.622494 | h.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
print('load data: ', args.dataset)
if args.dataset=='mnist':
transform = transforms.Compose([
transforms.Scale(32),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, download=True, transform=transform),
batch_size=128, shuffle=True)
else:
train_loader, test_loader = data_loader.getTargetDataSet(args.dataset, args.batch_size, args.imageSize, args.dataroot)
transform = transforms.Compose([
transforms.Scale(32),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_loader_mnist = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=128, shuffle=True)
print('Load model')
model = models.vgg13()
print(model)
print('load GAN')
nz = 100
G = models.cdcOriginalGenerator(1, nz, 64, 3)
D = models.cdcOriginalDiscriminator(1, 3, 64)
G.weight_init(mean=0.0, std=0.02)
D.weight_init(mean=0.0, std=0.02)
real_label = 1
fake_label = 0
criterion = nn.BCELoss()
nz = 100
print('Setup optimizer')
lr = 0.0002
batch_size = 128
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
G_optimizer = optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))
D_optimizer = optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.999))
decreasing_lr = list(map(int, args.decreasing_lr.split(',')))
onehot = torch.zeros(10, 10).cuda()
onehot = onehot.scatter_(1, torch.cuda.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).view(10, 1), 1).view(10, 10, 1, 1)
img_size = 32
num_labels = 10
fraction = 1
fill = torch.zeros([num_labels, num_labels, img_size / fraction, img_size / fraction]).cuda()
for i in range(num_labels):
fill[i, i, :, :] = 1
fill = fill.cuda()
BCE_loss = nn.BCELoss()
fixed_noise = torch.randn((64, 100)).view(-1, 100, 1, 1)
fixed_label = None
if args.cuda:
model.cuda()
D.cuda()
G.cuda()
criterion.cuda()
fixed_noise = fixed_noise.cuda()
first = True
def train(epoch):
model.train()
trg = 0
trd = 0
i = 0
for batch_idx, (data, y_labels) in enumerate(train_loader):
uniform_dist = torch.Tensor(data.size(0), args.num_classes).fill_((1. / args.num_classes)).cuda()
x_ = data.cuda()
assert x_[0, :, :, :].shape == (3, 32, 32)
global first
if first:
global fixed_noise
global fixed_label
first = False
fixed_label = onehot[y_labels.squeeze()[:64]]
print("saving fixed_label!")
vutils.save_image(data[:64],
'{}/{}jointConfidencerealReference{}.png'.format(args.outf, args.dataset, epoch),
normalize=True)
D.zero_grad()
y_ = y_labels
mini_batch = x_.size()[0]
y_real_ = torch.ones(mini_batch)
y_fake_ = torch.zeros(mini_batch)
y_real_, y_fake_ = Variable(y_real_.cuda()), Variable(y_fake_.cuda())
y_fill_ = fill[y_.squeeze().tolist()]
assert y_fill_[0, y_.squeeze().tolist()[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * mini_batch
x_, y_fill_ = Variable(x_.cuda()), Variable(y_fill_.cuda())
D_result = D(x_, y_fill_).squeeze()
D_real_loss = BCE_loss(D_result, y_real_)
z_ = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)
y_ = (torch.rand(mini_batch, 1) * num_labels).type(torch.LongTensor).squeeze()
y_label_ = onehot[y_]
y_fill_ = fill[y_]
assert y_label_[0, y_[0]] == 1
assert y_label_.shape == (mini_batch, 10, 1, 1)
assert y_fill_[0, y_[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * mini_batch
z_, y_label_, y_fill_ = Variable(z_.cuda()), Variable(y_label_.cuda()), Variable(y_fill_.cuda())
G_result = G(z_, y_label_)
D_result = D(G_result, y_fill_).squeeze()
D_fake_loss = BCE_loss(D_result, y_fake_)
D_fake_score = D_result.data.mean()
D_train_loss = D_real_loss + D_fake_loss
trg += 1
if D_train_loss > .1:
trd += 1
D_train_loss.backward()
D_optimizer.step()
G.zero_grad()
z_ = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)
y_ = (torch.rand(mini_batch, 1) * num_labels).type(torch.LongTensor).squeeze()
y_label_ = onehot[y_]
y_fill_ = fill[y_]
z_, y_label_, y_fill_ = Variable(z_.cuda()), Variable(y_label_.cuda()), Variable(y_fill_.cuda())
assert y_label_[0, y_[0]] == 1
assert y_label_.shape == (mini_batch, 10, 1, 1)
assert y_fill_[0, y_[0], :, :].sum() == (img_size / fraction) ** 2
assert y_fill_.sum() == (img_size / fraction) ** 2 * mini_batch
G_result = G(z_, y_label_)
D_result = D(G_result, y_fill_).squeeze()
G_train_loss = BCE_loss(D_result, y_real_)
KL_fake_output = F.log_softmax(model(G_result))
errG_KL = F.kl_div(KL_fake_output, uniform_dist) * args.num_classes
generator_loss = G_train_loss + args.beta * errG_KL
generator_loss.backward()
G_optimizer.step()
pe[0]
G_result = G(z_, y_label_)
x(model(G_result))
KL_loss_fake = F.kl_div(KL_fake_output, uniform_dist) * args.num_classes
total_loss = loss + args.beta * KL_loss_fake
total_loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print(
"Epoch {} , Descriminator loss {:.6f} Generator loss {:.6f} traingenerator {:.6f} traindiscriminator {:.6f}".format(
epoch, D_train_loss, G_train_loss, trg, trd))
print('Classification Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, KL fake Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data.item(), KL_loss_fake.data.item()))
fake = G(fixed_noise.cuda(), fixed_label)
vutils.save_image(fake.data, '%s/MNISTcDCgan_samples_epoch_%03d.png' % (args.outf, epoch), normalize=True)
def test(epoch):
model.eval()
test_loss = 0
correct = 0
total = 0
for data, target in test_loader:
total += data.size(0)
if args.cuda:
data, target = data.cuda(), target.cuda()
output = F.log_softmax(model(data))
target = target.type(
torch.LongTensor)
if args.cuda:
output = output.cuda()
target = target.cuda()
target = torch.squeeze(target)
test_loss += F.nll_loss(output, target).data.item()
pred = output.data.max(1)[1]
correct += pred.eq(target.data).cpu().sum()
test_loss = test_loss
test_loss /= len(test_loader)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, total,
100. * correct / total))
for epoch in range(1, args.epochs + 1):
train(epoch)
test(epoch)
if epoch in decreasing_lr:
G_optimizer.param_groups[0]['lr'] *= args.droprate
D_optimizer.param_groups[0]['lr'] *= args.droprate
optimizer.param_groups[0]['lr'] *= args.droprate
if epoch % 20 == 0:
torch.save(G.state_dict(), '%s/netG_epoch_%d.pth' % (args.outf, epoch))
torch.save(D.state_dict(), '%s/netD_epoch_%d.pth' % (args.outf, epoch))
torch.save(model.state_dict(), '%s/model_epoch_%d.pth' % (args.outf, epoch))
| true | true |
f72cbd007d1006b7c1318b34026adba9042de0cd | 5,497 | py | Python | tb_rest_client/models/models_ce/page_data_ota_package_info.py | jernkuan/thingsboard-python-rest-client | 3fb25272507494e6d494b27ca2380d3c543562e5 | [
"Apache-2.0"
] | null | null | null | tb_rest_client/models/models_ce/page_data_ota_package_info.py | jernkuan/thingsboard-python-rest-client | 3fb25272507494e6d494b27ca2380d3c543562e5 | [
"Apache-2.0"
] | null | null | null | tb_rest_client/models/models_ce/page_data_ota_package_info.py | jernkuan/thingsboard-python-rest-client | 3fb25272507494e6d494b27ca2380d3c543562e5 | [
"Apache-2.0"
] | 1 | 2021-11-26T11:24:56.000Z | 2021-11-26T11:24:56.000Z | # coding: utf-8
"""
ThingsBoard REST API
For instructions how to authorize requests please visit <a href='http://thingsboard.io/docs/reference/rest-api/'>REST API documentation page</a>. # noqa: E501
OpenAPI spec version: 2.0
Contact: info@thingsboard.io
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class PageDataOtaPackageInfo(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'data': 'list[OtaPackageInfo]',
'has_next': 'bool',
'total_elements': 'int',
'total_pages': 'int'
}
attribute_map = {
'data': 'data',
'has_next': 'hasNext',
'total_elements': 'totalElements',
'total_pages': 'totalPages'
}
def __init__(self, data=None, has_next=None, total_elements=None, total_pages=None): # noqa: E501
"""PageDataOtaPackageInfo - a model defined in Swagger""" # noqa: E501
self._data = None
self._has_next = None
self._total_elements = None
self._total_pages = None
self.discriminator = None
if data is not None:
self.data = data
if has_next is not None:
self.has_next = has_next
if total_elements is not None:
self.total_elements = total_elements
if total_pages is not None:
self.total_pages = total_pages
@property
def data(self):
"""Gets the data of this PageDataOtaPackageInfo. # noqa: E501
:return: The data of this PageDataOtaPackageInfo. # noqa: E501
:rtype: list[OtaPackageInfo]
"""
return self._data
@data.setter
def data(self, data):
"""Sets the data of this PageDataOtaPackageInfo.
:param data: The data of this PageDataOtaPackageInfo. # noqa: E501
:type: list[OtaPackageInfo]
"""
self._data = data
@property
def has_next(self):
"""Gets the has_next of this PageDataOtaPackageInfo. # noqa: E501
:return: The has_next of this PageDataOtaPackageInfo. # noqa: E501
:rtype: bool
"""
return self._has_next
@has_next.setter
def has_next(self, has_next):
"""Sets the has_next of this PageDataOtaPackageInfo.
:param has_next: The has_next of this PageDataOtaPackageInfo. # noqa: E501
:type: bool
"""
self._has_next = has_next
@property
def total_elements(self):
"""Gets the total_elements of this PageDataOtaPackageInfo. # noqa: E501
:return: The total_elements of this PageDataOtaPackageInfo. # noqa: E501
:rtype: int
"""
return self._total_elements
@total_elements.setter
def total_elements(self, total_elements):
"""Sets the total_elements of this PageDataOtaPackageInfo.
:param total_elements: The total_elements of this PageDataOtaPackageInfo. # noqa: E501
:type: int
"""
self._total_elements = total_elements
@property
def total_pages(self):
"""Gets the total_pages of this PageDataOtaPackageInfo. # noqa: E501
:return: The total_pages of this PageDataOtaPackageInfo. # noqa: E501
:rtype: int
"""
return self._total_pages
@total_pages.setter
def total_pages(self, total_pages):
"""Sets the total_pages of this PageDataOtaPackageInfo.
:param total_pages: The total_pages of this PageDataOtaPackageInfo. # noqa: E501
:type: int
"""
self._total_pages = total_pages
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(PageDataOtaPackageInfo, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, PageDataOtaPackageInfo):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| 29.084656 | 163 | 0.593778 |
import pprint
import re
import six
class PageDataOtaPackageInfo(object):
swagger_types = {
'data': 'list[OtaPackageInfo]',
'has_next': 'bool',
'total_elements': 'int',
'total_pages': 'int'
}
attribute_map = {
'data': 'data',
'has_next': 'hasNext',
'total_elements': 'totalElements',
'total_pages': 'totalPages'
}
def __init__(self, data=None, has_next=None, total_elements=None, total_pages=None):
self._data = None
self._has_next = None
self._total_elements = None
self._total_pages = None
self.discriminator = None
if data is not None:
self.data = data
if has_next is not None:
self.has_next = has_next
if total_elements is not None:
self.total_elements = total_elements
if total_pages is not None:
self.total_pages = total_pages
@property
def data(self):
return self._data
@data.setter
def data(self, data):
self._data = data
@property
def has_next(self):
return self._has_next
@has_next.setter
def has_next(self, has_next):
self._has_next = has_next
@property
def total_elements(self):
return self._total_elements
@total_elements.setter
def total_elements(self, total_elements):
self._total_elements = total_elements
@property
def total_pages(self):
return self._total_pages
@total_pages.setter
def total_pages(self, total_pages):
self._total_pages = total_pages
def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(PageDataOtaPackageInfo, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
return pprint.pformat(self.to_dict())
def __repr__(self):
return self.to_str()
def __eq__(self, other):
if not isinstance(other, PageDataOtaPackageInfo):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other
| true | true |
f72cbd8032bfba00a07e989b6b537df95ff4361b | 8,128 | py | Python | Chapter2/LFM.py | 7125messi/rencommend_system_learning | 4a8bcef241c4c0357cfbe4d1a9828b847974b69c | [
"Apache-2.0"
] | 3 | 2019-10-10T15:49:42.000Z | 2020-05-31T07:39:10.000Z | Chapter2/LFM.py | 7125messi/rencommend_system_learning | 4a8bcef241c4c0357cfbe4d1a9828b847974b69c | [
"Apache-2.0"
] | null | null | null | Chapter2/LFM.py | 7125messi/rencommend_system_learning | 4a8bcef241c4c0357cfbe4d1a9828b847974b69c | [
"Apache-2.0"
] | 2 | 2019-09-18T07:59:48.000Z | 2020-01-16T15:00:48.000Z | # 导入包
import random
import math
import numpy as np
import time
from tqdm import tqdm
from tqdm import trange
# 1 通用函数定义
## 定义装饰器，监控运行时间
def timmer(func):
def wrapper(*args, **kwargs):
start_time = time.time()
res = func(*args, **kwargs)
stop_time = time.time()
print('Func {},run time:{}'.format(func.__name__,stop_time - start_time))
return res
return wrapper
## 数据处理相关
### load data
### split data
class Dataset():
def __init__(self,fp):
self.data = self.loadData(fp)
@timmer
def loadData(self,fp):
data = []
for l in open(fp):
data.append(tuple(map(int, l.strip().split('::')[:2])))
return data
@timmer
def splitData(self, M, k, seed=1):
'''
:params: data, 加载的所有(user, item)数据条目
:params: M, 划分的数目，最后需要取M折的平均
:params: k, 本次是第几次划分，k~[0, M)
:params: seed, random的种子数，对于不同的k应设置成一样的
:return: train, test
'''
train , test = [], []
random.seed(seed)
for user, item in self.data:
# 这里与书中的不一致，本人认为取M-1较为合理，因randint是左右都覆盖的
if random.randint(0, M-1) == k:
test.append((user, item))
else:
train.append((user, item))
# 处理成字典的形式，user->set(items)
def convert_dict(data):
data_dict = {}
for user, item in data:
if user not in data_dict:
data_dict[user] = set()
data_dict[user].add(item)
data_dict = {k: list(data_dict[k]) for k in data_dict}
return data_dict
return convert_dict(train), convert_dict(test)
## 评价指标
### Precision
### Recall
### Coverage
### Popularity(Novelty)
class Metric():
def __init__(self, train, test, GetRecommendation):
'''
:params: train, 训练数据
:params: test, 测试数据
:params: GetRecommendation, 为某个用户获取推荐物品的接口函数
'''
self.train = train
self.test = test
self.GetRecommendation = GetRecommendation
self.recs = self.getRec()
# 为test中的每个用户进行推荐
def getRec(self):
recs = {}
for user in self.test:
rank = self.GetRecommendation(user)
recs[user] = rank
return recs
# 定义精确率指标计算方式
def precision(self):
all, hit = 0, 0
for user in self.test:
test_items = set(self.test[user])
rank = self.recs[user]
for item, score in rank:
if item in test_items:
hit += 1
all += len(rank)
return round(hit / all * 100, 2)
# 定义召回率指标计算方式
def recall(self):
all, hit = 0, 0
for user in self.test:
test_items = set(self.test[user])
rank = self.recs[user]
for item, score in rank:
if item in test_items:
hit += 1
all += len(test_items)
return round(hit / all * 100, 2)
# 定义覆盖率指标计算方式
def coverage(self):
all_item, recom_item = set(), set()
for user in self.test:
for item in self.train[user]:
all_item.add(item)
rank = self.recs[user]
for item, score in rank:
recom_item.add(item)
return round(len(recom_item) / len(all_item) * 100, 2)
# 定义新颖度指标计算方式
def popularity(self):
# 计算物品的流行度
item_pop = {}
for user in self.train:
for item in self.train[user]:
if item not in item_pop:
item_pop[item] = 0
item_pop[item] += 1
num, pop = 0, 0
for user in self.test:
rank = self.recs[user]
for item, score in rank:
# 取对数，防止因长尾问题带来的被流行物品所主导
pop += math.log(1 + item_pop[item])
num += 1
return round(pop / num, 6)
def eval(self):
metric = {'Precision': self.precision(),
'Recall': self.recall(),
'Coverage': self.coverage(),
'Popularity': self.popularity()}
print('Metric:', metric)
return metric
# 2 LFM算法实现
def LFM(train,ratio,K,lr,step,lmbda,N):
'''
:params: train, 训练数据
:params: ratio, 负采样的正负比例
:params: K, 隐语义个数
:params: lr, 初始学习率
:params: step, 迭代次数
:params: lmbda, 正则化系数
:params: N, 推荐TopN物品的个数
:return: GetRecommendation, 获取推荐结果的接口
'''
all_items = {}
for user in train:
for item in train[user]:
if item not in all_items:
all_items[item] = 0
all_items[item] += 1
all_items = list(all_items.items())
items = [x[0] for x in all_items]
pops = [x[1] for x in all_items]
# 负采样函数（按照流行度就行采样）
def nSample(data,ratio):
new_data = {}
# 正样本
for user in data:
if user not in new_data:
new_data[user] = {}
for item in data[user]:
new_data[user][item] = 1
# 负样本
for user in new_data:
seen = set(new_data[user])
pos_num = len(seen)
item = np.random.choice(items, int(pos_num * ratio * 3), pops)
item = [x for x in item if x not in seen][:int(pos_num * ratio)]
new_data[user].update({x: 0 for x in item})
return new_data
# 训练
P, Q = {}, {}
for user in train:
P[user] = np.random.random(K)
for item in items:
Q[item] = np.random.random(K)
for s in trange(step):
data = nSample(train, ratio)
for user in data:
for item in data[user]:
eui = data[user][item] - (P[user] * Q[item]).sum()
P[user] += lr * (Q[item] * eui - lmbda * P[user])
Q[item] += lr * (P[user] * eui - lmbda * Q[item])
lr *= 0.9 # 调整学习率
# 获取接口函数
def GetRecommendation(user):
seen_items = set(train[user])
recs = {}
for item in items:
if item not in seen_items:
recs[item] = (P[user] * Q[item]).sum()
recs = list(sorted(recs.items(), key=lambda x: x[1], reverse=True))[:N]
return recs
return GetRecommendation
# 3 LFM实验
## M=8, N=10, ratio=[1, 2, 3, 5, 10, 20]
class Experiment():
def __init__(self, M, N, ratio=1,
K=100, lr=0.02, step=100, lmbda=0.01, fp='../dataset/ml-1m/ratings.dat'):
'''
:params: M, 进行多少次实验
:params: N, TopN推荐物品的个数
:params: ratio, 正负样本比例
:params: K, 隐语义个数
:params: lr, 学习率
:params: step, 训练步数
:params: lmbda, 正则化系数
:params: fp, 数据文件路径
'''
self.M = M
self.K = K
self.N = N
self.ratio = ratio
self.lr = lr
self.step = step
self.lmbda = lmbda
self.fp = fp
self.alg = LFM
# 定义单次实验
@timmer
def worker(self, train, test):
'''
:params: train, 训练数据集
:params: test, 测试数据集
:return: 各指标的值
'''
getRecommendation = self.alg(train, self.ratio, self.K,
self.lr, self.step, self.lmbda, self.N)
metric = Metric(train, test, getRecommendation)
return metric.eval()
# 多次实验取平均
@timmer
def run(self):
metrics = {'Precision': 0, 'Recall': 0,
'Coverage': 0, 'Popularity': 0}
dataset = Dataset(self.fp)
for ii in range(self.M):
train, test = dataset.splitData(self.M, ii)
print('Experiment {}:'.format(ii))
metric = self.worker(train, test)
metrics = {k: metrics[k]+metric[k] for k in metrics}
metrics = {k: metrics[k] / self.M for k in metrics}
print('Average Result (M={}, N={}, ratio={}): {}'.format(\
self.M, self.N, self.ratio, metrics))
# LFM实验(运行时间较长，这里没贴实验结果)
M, N = 8, 10
for r in [1, 2, 3, 5, 10, 20]:
exp = Experiment(M, N, ratio=r)
exp.run() | 29.028571 | 93 | 0.506275 |
import random
import math
import numpy as np
import time
from tqdm import tqdm
from tqdm import trange
nc):
def wrapper(*args, **kwargs):
start_time = time.time()
res = func(*args, **kwargs)
stop_time = time.time()
print('Func {},run time:{}'.format(func.__name__,stop_time - start_time))
return res
return wrapper
elf.data = self.loadData(fp)
@timmer
def loadData(self,fp):
data = []
for l in open(fp):
data.append(tuple(map(int, l.strip().split('::')[:2])))
return data
@timmer
def splitData(self, M, k, seed=1):
train , test = [], []
random.seed(seed)
for user, item in self.data:
if random.randint(0, M-1) == k:
test.append((user, item))
else:
train.append((user, item))
def convert_dict(data):
data_dict = {}
for user, item in data:
if user not in data_dict:
data_dict[user] = set()
data_dict[user].add(item)
data_dict = {k: list(data_dict[k]) for k in data_dict}
return data_dict
return convert_dict(train), convert_dict(test)
self.test = test
self.GetRecommendation = GetRecommendation
self.recs = self.getRec()
def getRec(self):
recs = {}
for user in self.test:
rank = self.GetRecommendation(user)
recs[user] = rank
return recs
def precision(self):
all, hit = 0, 0
for user in self.test:
test_items = set(self.test[user])
rank = self.recs[user]
for item, score in rank:
if item in test_items:
hit += 1
all += len(rank)
return round(hit / all * 100, 2)
def recall(self):
all, hit = 0, 0
for user in self.test:
test_items = set(self.test[user])
rank = self.recs[user]
for item, score in rank:
if item in test_items:
hit += 1
all += len(test_items)
return round(hit / all * 100, 2)
def coverage(self):
all_item, recom_item = set(), set()
for user in self.test:
for item in self.train[user]:
all_item.add(item)
rank = self.recs[user]
for item, score in rank:
recom_item.add(item)
return round(len(recom_item) / len(all_item) * 100, 2)
def popularity(self):
item_pop = {}
for user in self.train:
for item in self.train[user]:
if item not in item_pop:
item_pop[item] = 0
item_pop[item] += 1
num, pop = 0, 0
for user in self.test:
rank = self.recs[user]
for item, score in rank:
pop += math.log(1 + item_pop[item])
num += 1
return round(pop / num, 6)
def eval(self):
metric = {'Precision': self.precision(),
'Recall': self.recall(),
'Coverage': self.coverage(),
'Popularity': self.popularity()}
print('Metric:', metric)
return metric
def LFM(train,ratio,K,lr,step,lmbda,N):
all_items = {}
for user in train:
for item in train[user]:
if item not in all_items:
all_items[item] = 0
all_items[item] += 1
all_items = list(all_items.items())
items = [x[0] for x in all_items]
pops = [x[1] for x in all_items]
def nSample(data,ratio):
new_data = {}
for user in data:
if user not in new_data:
new_data[user] = {}
for item in data[user]:
new_data[user][item] = 1
for user in new_data:
seen = set(new_data[user])
pos_num = len(seen)
item = np.random.choice(items, int(pos_num * ratio * 3), pops)
item = [x for x in item if x not in seen][:int(pos_num * ratio)]
new_data[user].update({x: 0 for x in item})
return new_data
P, Q = {}, {}
for user in train:
P[user] = np.random.random(K)
for item in items:
Q[item] = np.random.random(K)
for s in trange(step):
data = nSample(train, ratio)
for user in data:
for item in data[user]:
eui = data[user][item] - (P[user] * Q[item]).sum()
P[user] += lr * (Q[item] * eui - lmbda * P[user])
Q[item] += lr * (P[user] * eui - lmbda * Q[item])
lr *= 0.9
def GetRecommendation(user):
seen_items = set(train[user])
recs = {}
for item in items:
if item not in seen_items:
recs[item] = (P[user] * Q[item]).sum()
recs = list(sorted(recs.items(), key=lambda x: x[1], reverse=True))[:N]
return recs
return GetRecommendation
self, M, N, ratio=1,
K=100, lr=0.02, step=100, lmbda=0.01, fp='../dataset/ml-1m/ratings.dat'):
self.M = M
self.K = K
self.N = N
self.ratio = ratio
self.lr = lr
self.step = step
self.lmbda = lmbda
self.fp = fp
self.alg = LFM
@timmer
def worker(self, train, test):
getRecommendation = self.alg(train, self.ratio, self.K,
self.lr, self.step, self.lmbda, self.N)
metric = Metric(train, test, getRecommendation)
return metric.eval()
@timmer
def run(self):
metrics = {'Precision': 0, 'Recall': 0,
'Coverage': 0, 'Popularity': 0}
dataset = Dataset(self.fp)
for ii in range(self.M):
train, test = dataset.splitData(self.M, ii)
print('Experiment {}:'.format(ii))
metric = self.worker(train, test)
metrics = {k: metrics[k]+metric[k] for k in metrics}
metrics = {k: metrics[k] / self.M for k in metrics}
print('Average Result (M={}, N={}, ratio={}): {}'.format(\
self.M, self.N, self.ratio, metrics))
M, N = 8, 10
for r in [1, 2, 3, 5, 10, 20]:
exp = Experiment(M, N, ratio=r)
exp.run() | true | true |
f72cbd82ce65ea7deeb9b12673a6fa17f65eaeaa | 2,015 | py | Python | intake_questgdal/base.py | Aquaveo/intake_questgdal | c11cd111a53b7270391c6923d0e252c4abbbc56b | [
"BSD-3-Clause"
] | null | null | null | intake_questgdal/base.py | Aquaveo/intake_questgdal | c11cd111a53b7270391c6923d0e252c4abbbc56b | [
"BSD-3-Clause"
] | 1 | 2019-06-06T15:28:15.000Z | 2019-06-06T15:28:15.000Z | intake_questgdal/base.py | Aquaveo/intake_questgdal | c11cd111a53b7270391c6923d0e252c4abbbc56b | [
"BSD-3-Clause"
] | null | null | null | from intake.source.base import DataSource, Schema
import rasterio
import xarray as xr
import warnings
# from . import __version__
class quest_gdal_base(DataSource):
"""Reads an HDF5 table
Parameters
----------
path: str
File to load.
tablename: str
Name of table to load.
metadata:
Arbitrary information to associate with this source.
"""
#version = __version__
version = '0.0.1'
container = 'dataframe'
partition_access = False
path = ''
# def _get_schema(self):
# self._schema = Schema(
# datashape=None,
# dtype=None,
# shape=None,
# npartitions=1,
# extra_metadata={}
# )
# return self._schema
def _get_schema(self):
if self.path is not '':
xarr = xr.open_rasterio(self.path)
ds2 = xr.Dataset({'raster': xarr})
metadata = {
'dims': dict(ds2.dims),
'data_vars': {k: list(ds2[k].coords)
for k in ds2.data_vars.keys()},
'coords': tuple(ds2.coords.keys()),
'array': 'raster'
}
atts = ['transform', 'crs', 'res', 'is_tiled', 'nodatavals']
for att in atts:
if att in xarr.attrs:
metadata[att] = xarr.attrs[att]
return Schema(
datashape=None,
dtype = str(xarr.dtype),
shape=xarr.shape,
npartitions=1,
extra_metadata=metadata
)
else:
self._schema = Schema(
datashape=None,
dtype=None,
shape=None,
npartitions=1,
extra_metadata={}
)
return self._schema
def _get_partition(self, _):
return None
def _close(self):
pass
def raster_data(self, path):
return rasterio.open(path)
| 26.168831 | 72 | 0.4933 | from intake.source.base import DataSource, Schema
import rasterio
import xarray as xr
import warnings
class quest_gdal_base(DataSource):
version = '0.0.1'
container = 'dataframe'
partition_access = False
path = ''
def _get_schema(self):
if self.path is not '':
xarr = xr.open_rasterio(self.path)
ds2 = xr.Dataset({'raster': xarr})
metadata = {
'dims': dict(ds2.dims),
'data_vars': {k: list(ds2[k].coords)
for k in ds2.data_vars.keys()},
'coords': tuple(ds2.coords.keys()),
'array': 'raster'
}
atts = ['transform', 'crs', 'res', 'is_tiled', 'nodatavals']
for att in atts:
if att in xarr.attrs:
metadata[att] = xarr.attrs[att]
return Schema(
datashape=None,
dtype = str(xarr.dtype),
shape=xarr.shape,
npartitions=1,
extra_metadata=metadata
)
else:
self._schema = Schema(
datashape=None,
dtype=None,
shape=None,
npartitions=1,
extra_metadata={}
)
return self._schema
def _get_partition(self, _):
return None
def _close(self):
pass
def raster_data(self, path):
return rasterio.open(path)
| true | true |
f72cbdde941379a53be16076b51cf17c429ca67d | 6,353 | py | Python | mooringlicensing/management/commands/approval_renewal_notices.py | jawaidm/mooringlicensing | b22e74209da8655c8ad3af99e00f36d17c8ef73f | [
"Apache-2.0"
] | null | null | null | mooringlicensing/management/commands/approval_renewal_notices.py | jawaidm/mooringlicensing | b22e74209da8655c8ad3af99e00f36d17c8ef73f | [
"Apache-2.0"
] | 2 | 2021-03-05T06:48:11.000Z | 2021-03-26T08:14:17.000Z | mooringlicensing/management/commands/approval_renewal_notices.py | jawaidm/mooringlicensing | b22e74209da8655c8ad3af99e00f36d17c8ef73f | [
"Apache-2.0"
] | 2 | 2021-09-19T15:45:19.000Z | 2021-10-05T05:07:41.000Z | from django.core.management.base import BaseCommand
from django.utils import timezone
from django.conf import settings
from django.core.exceptions import ImproperlyConfigured
from django.db.models import Q
from mooringlicensing.components.approvals.models import (
Approval,
WaitingListAllocation,
AnnualAdmissionPermit,
AuthorisedUserPermit,
MooringLicence,
DcvPermit,
)
from ledger.accounts.models import EmailUser
from datetime import timedelta
from mooringlicensing.components.proposals.email import send_approval_renewal_email_notification
import logging
from mooringlicensing.components.main.models import NumberOfDaysType, NumberOfDaysSetting
from mooringlicensing.settings import (
CODE_DAYS_FOR_RENEWAL_WLA,
CODE_DAYS_FOR_RENEWAL_AAP,
CODE_DAYS_FOR_RENEWAL_AUP,
CODE_DAYS_FOR_RENEWAL_ML,
CODE_DAYS_FOR_RENEWAL_DCVP,
)
logger = logging.getLogger(__name__)
class Command(BaseCommand):
help = 'Send Approval renewal notice when approval is due to expire in 30 days'
def perform_per_type(self, number_of_days_code, approval_class, updates, errors):
today = timezone.localtime(timezone.now()).date()
# Retrieve the number of days before expiry date of the approvals to email
days_type = NumberOfDaysType.objects.get(code=number_of_days_code)
days_setting = NumberOfDaysSetting.get_setting_by_date(days_type, today)
if not days_setting:
# No number of days found
raise ImproperlyConfigured("NumberOfDays: {} is not defined for the date: {}".format(days_type.name, today))
expiry_notification_date = today + timedelta(days=days_setting.number_of_days)
logger.info('Running command {}'.format(__name__))
# Construct queries
queries = Q()
if number_of_days_code == CODE_DAYS_FOR_RENEWAL_DCVP:
queries &= Q(end_date__lte=expiry_notification_date)
queries &= Q(renewal_sent=False)
queries &= Q(status__in=[DcvPermit.DCV_PERMIT_STATUS_CURRENT,])
else:
queries &= Q(expiry_date__lte=expiry_notification_date)
queries &= Q(renewal_sent=False)
queries &= Q(replaced_by__isnull=True)
queries &= Q(status__in=(Approval.APPROVAL_STATUS_CURRENT, Approval.APPROVAL_STATUS_SUSPENDED))
approvals = approval_class.objects.filter(queries)
for a in approvals:
try:
if approval_class == DcvPermit:
# send_approval_renewal_email_notification_dcvp(a)
pass
else:
a.generate_renewal_doc()
send_approval_renewal_email_notification(a)
a.renewal_sent = True
a.save()
logger.info('Renewal notice sent for Approval {}'.format(a.id))
updates.append(a.lodgement_number)
except Exception as e:
err_msg = 'Error sending renewal notice for Approval {}'.format(a.lodgement_number)
logger.error('{}\n{}'.format(err_msg, str(e)))
errors.append(err_msg)
def handle(self, *args, **options):
try:
user = EmailUser.objects.get(email=settings.CRON_EMAIL)
except:
user = EmailUser.objects.create(email=settings.CRON_EMAIL, password='')
updates, errors = [], []
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_WLA, WaitingListAllocation, updates, errors)
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_AAP, AnnualAdmissionPermit, updates, errors)
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_AUP, AuthorisedUserPermit, updates, errors)
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_ML, MooringLicence, updates, errors)
# today = timezone.localtime(timezone.now()).date()
#
# # Retrieve the number of days before expiry date of the approvals to email
# days_type = NumberOfDaysType.objects.get(code=CODE_DAYS_FOR_RENEWAL)
# days_setting = NumberOfDaysSetting.get_setting_by_date(days_type, today)
# if not days_setting:
# # No number of days found
# raise ImproperlyConfigured("NumberOfDays: {} is not defined for the date: {}".format(days_type.name, today))
#
# expiry_notification_date = today + timedelta(days=days_setting.number_of_days)
#
# # Construct queries
# queries = Q()
# queries &= Q(expiry_date__lte=expiry_notification_date)
# queries &= Q(renewal_sent=False)
# queries &= Q(replaced_by__isnull=True)
# queries &= Q(status__in=(Approval.APPROVAL_STATUS_CURRENT, Approval.APPROVAL_STATUS_SUSPENDED))
#
# # For debug
# # params = options.get('params')
# # debug = True if params.get('debug', 'f').lower() in ['true', 't', 'yes', 'y'] else False
# # approval_lodgement_number = params.get('approval_renewal_notices_lodgement_number', 'no-lodgement-number')
# # if debug:
# # queries = queries | Q(lodgement_number__iexact=approval_lodgement_number)
#
# logger.info('Running command {}'.format(__name__))
# # for a in Approval.objects.filter(**renewal_conditions):
# for a in Approval.objects.filter(queries):
# # if a.status == Approval.APPROVAL_STATUS_CURRENT or a.status == Approval.APPROVAL_STATUS_SUSPENDED:
# try:
# a.generate_renewal_doc()
# send_approval_renewal_email_notification(a)
# a.renewal_sent = True
# a.save()
# logger.info('Renewal notice sent for Approval {}'.format(a.id))
# updates.append(a.lodgement_number)
# except Exception as e:
# err_msg = 'Error sending renewal notice for Approval {}'.format(a.lodgement_number)
# logger.error('{}\n{}'.format(err_msg, str(e)))
# errors.append(err_msg)
cmd_name = __name__.split('.')[-1].replace('_', ' ').upper()
err_str = '<strong style="color: red;">Errors: {}</strong>'.format(len(errors)) if len(errors)>0 else '<strong style="color: green;">Errors: 0</strong>'
msg = '<p>{} completed. {}. IDs updated: {}.</p>'.format(cmd_name, err_str, updates)
logger.info(msg)
print(msg) # will redirect to cron_tasks.log file, by the parent script
| 45.705036 | 160 | 0.668346 | from django.core.management.base import BaseCommand
from django.utils import timezone
from django.conf import settings
from django.core.exceptions import ImproperlyConfigured
from django.db.models import Q
from mooringlicensing.components.approvals.models import (
Approval,
WaitingListAllocation,
AnnualAdmissionPermit,
AuthorisedUserPermit,
MooringLicence,
DcvPermit,
)
from ledger.accounts.models import EmailUser
from datetime import timedelta
from mooringlicensing.components.proposals.email import send_approval_renewal_email_notification
import logging
from mooringlicensing.components.main.models import NumberOfDaysType, NumberOfDaysSetting
from mooringlicensing.settings import (
CODE_DAYS_FOR_RENEWAL_WLA,
CODE_DAYS_FOR_RENEWAL_AAP,
CODE_DAYS_FOR_RENEWAL_AUP,
CODE_DAYS_FOR_RENEWAL_ML,
CODE_DAYS_FOR_RENEWAL_DCVP,
)
logger = logging.getLogger(__name__)
class Command(BaseCommand):
help = 'Send Approval renewal notice when approval is due to expire in 30 days'
def perform_per_type(self, number_of_days_code, approval_class, updates, errors):
today = timezone.localtime(timezone.now()).date()
days_type = NumberOfDaysType.objects.get(code=number_of_days_code)
days_setting = NumberOfDaysSetting.get_setting_by_date(days_type, today)
if not days_setting:
raise ImproperlyConfigured("NumberOfDays: {} is not defined for the date: {}".format(days_type.name, today))
expiry_notification_date = today + timedelta(days=days_setting.number_of_days)
logger.info('Running command {}'.format(__name__))
queries = Q()
if number_of_days_code == CODE_DAYS_FOR_RENEWAL_DCVP:
queries &= Q(end_date__lte=expiry_notification_date)
queries &= Q(renewal_sent=False)
queries &= Q(status__in=[DcvPermit.DCV_PERMIT_STATUS_CURRENT,])
else:
queries &= Q(expiry_date__lte=expiry_notification_date)
queries &= Q(renewal_sent=False)
queries &= Q(replaced_by__isnull=True)
queries &= Q(status__in=(Approval.APPROVAL_STATUS_CURRENT, Approval.APPROVAL_STATUS_SUSPENDED))
approvals = approval_class.objects.filter(queries)
for a in approvals:
try:
if approval_class == DcvPermit:
pass
else:
a.generate_renewal_doc()
send_approval_renewal_email_notification(a)
a.renewal_sent = True
a.save()
logger.info('Renewal notice sent for Approval {}'.format(a.id))
updates.append(a.lodgement_number)
except Exception as e:
err_msg = 'Error sending renewal notice for Approval {}'.format(a.lodgement_number)
logger.error('{}\n{}'.format(err_msg, str(e)))
errors.append(err_msg)
def handle(self, *args, **options):
try:
user = EmailUser.objects.get(email=settings.CRON_EMAIL)
except:
user = EmailUser.objects.create(email=settings.CRON_EMAIL, password='')
updates, errors = [], []
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_WLA, WaitingListAllocation, updates, errors)
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_AAP, AnnualAdmissionPermit, updates, errors)
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_AUP, AuthorisedUserPermit, updates, errors)
self.perform_per_type(CODE_DAYS_FOR_RENEWAL_ML, MooringLicence, updates, errors)
| true | true |
f72cbe35893af2f1b2c363e8fe4e587be57b909c | 6,321 | py | Python | InterventionsMIP/main.py | haoxiangyang89/COVID_Staged_Alert | 4c2cc5ef1d38c140875380a5f10a0fe1eaf8a47a | [
"MIT"
] | 1 | 2021-06-24T19:27:01.000Z | 2021-06-24T19:27:01.000Z | InterventionsMIP/main.py | haoxiangyang89/COVID_Staged_Alert | 4c2cc5ef1d38c140875380a5f10a0fe1eaf8a47a | [
"MIT"
] | null | null | null | InterventionsMIP/main.py | haoxiangyang89/COVID_Staged_Alert | 4c2cc5ef1d38c140875380a5f10a0fe1eaf8a47a | [
"MIT"
] | 3 | 2021-12-15T13:32:25.000Z | 2022-02-24T13:57:07.000Z | from InterventionsMIP import project_path, instances_path
import multiprocessing as mp
from threshold_policy import threshold_policy_search
from interventions import Intervension
from epi_params import EpiSetup, ParamDistribution
from utils import parse_arguments
from reporting.plotting import plot_stoch_simulations
from instances import load_instance
if __name__ == '__main__':
# Parse arguments
args = parse_arguments()
# Parse city and get corresponding instance
instance = load_instance(args.city, setup_file_name=args.f)
# TODO Read command line args for n_proc for better integration with crunch
n_proc = args.n_proc
# TODO: pull out n_replicas_train and n_replicas_test to a config file
n_replicas_train = args.train_reps
n_replicas_test = args.test_reps
# Create the pool (Note: pool needs to be created only once to run on a cluster)
mp_pool = mp.Pool(n_proc) if n_proc > 1 else None
for sc in [0]:
for co in [0.95]:
for base_line_train in [0.4]:
for base_line_test in [0.4]:
for const in ['test']: #[10 * i for i in range(0, 21)] + [215, 1000]:
policy_class = 'step'
instance_name = f'local_{instance.city}_SC{sc}_CO{co}_BLTrain{base_line_train}_BLTest_{base_line_test}_{policy_class}_{const}'
print('\n============================================')
print(instance_name)
#TODO: This list should be longe to include all possible transmission reduction values
# that might come in the instance file
interventions_train = [
Intervension(0, 0, 0, instance.epi, instance.N),
Intervension(1, 0, 0, instance.epi, instance.N),
Intervension(0, 0, base_line_train, instance.epi, instance.N),
Intervension(1, 0, base_line_train, instance.epi, instance.N),
Intervension(1, 0, 0.9, instance.epi, instance.N),
Intervension(0, co, base_line_train, instance.epi, instance.N),
Intervension(1, co, base_line_train, instance.epi, instance.N),
Intervension(1, co, 0.9, instance.epi, instance.N),
Intervension(1, 0, 0.95, instance.epi, instance.N),
Intervension(0, 0, 0.95, instance.epi, instance.N)
]
interventions_test = [
Intervension(0, 0, 0, instance.epi, instance.N),
Intervension(1, 0, 0, instance.epi, instance.N),
Intervension(0, 0, base_line_test, instance.epi, instance.N),
Intervension(1, 0, base_line_test, instance.epi, instance.N),
Intervension(1, 0, 0.9, instance.epi, instance.N),
Intervension(0, co, base_line_test, instance.epi, instance.N),
Intervension(1, co, base_line_test, instance.epi, instance.N),
Intervension(1, co, 0.9, instance.epi, instance.N),
Intervension(1, 0, 0.95, instance.epi, instance.N),
Intervension(0, 0, 0.95, instance.epi, instance.N)
]
sd_levels_train = {'H': 0.9, 'L': base_line_train}
sd_levels_test = {'H': 0.9, 'L': base_line_test}
best_policy_replicas, policy_params = threshold_policy_search(instance,
interventions_train,
interventions_test,
sd_levels_train,
sd_levels_test,
cocooning=co,
school_closure=sc,
mp_pool=mp_pool,
n_replicas_train=n_replicas_train,
n_replicas_test=n_replicas_test,
instance_name=instance_name,
policy={
'class': policy_class,
'vals': [120, 216, 9]
},
policy_class=policy_class)
n_replicas = len(best_policy_replicas)
plot_stoch_simulations(
instance_name,
best_policy_replicas,
['sim'] * n_replicas,
plot_left_axis=['IH'],
plot_right_axis=[],
T=instance.T, #437,
hosp_beds=instance.hosp_beds,
population=instance.N.sum(),
interventions=interventions_test,
calendar=instance.cal,
policy_params=policy_params,
plot_triggers=True,
plot_legend=True,
show=True,
align_axes=True,
n_replicas=5,
BL=base_line_test)
| 64.5 | 150 | 0.424933 | from InterventionsMIP import project_path, instances_path
import multiprocessing as mp
from threshold_policy import threshold_policy_search
from interventions import Intervension
from epi_params import EpiSetup, ParamDistribution
from utils import parse_arguments
from reporting.plotting import plot_stoch_simulations
from instances import load_instance
if __name__ == '__main__':
args = parse_arguments()
instance = load_instance(args.city, setup_file_name=args.f)
n_proc = args.n_proc
n_replicas_train = args.train_reps
n_replicas_test = args.test_reps
mp_pool = mp.Pool(n_proc) if n_proc > 1 else None
for sc in [0]:
for co in [0.95]:
for base_line_train in [0.4]:
for base_line_test in [0.4]:
for const in ['test']:
policy_class = 'step'
instance_name = f'local_{instance.city}_SC{sc}_CO{co}_BLTrain{base_line_train}_BLTest_{base_line_test}_{policy_class}_{const}'
print('\n============================================')
print(instance_name)
interventions_train = [
Intervension(0, 0, 0, instance.epi, instance.N),
Intervension(1, 0, 0, instance.epi, instance.N),
Intervension(0, 0, base_line_train, instance.epi, instance.N),
Intervension(1, 0, base_line_train, instance.epi, instance.N),
Intervension(1, 0, 0.9, instance.epi, instance.N),
Intervension(0, co, base_line_train, instance.epi, instance.N),
Intervension(1, co, base_line_train, instance.epi, instance.N),
Intervension(1, co, 0.9, instance.epi, instance.N),
Intervension(1, 0, 0.95, instance.epi, instance.N),
Intervension(0, 0, 0.95, instance.epi, instance.N)
]
interventions_test = [
Intervension(0, 0, 0, instance.epi, instance.N),
Intervension(1, 0, 0, instance.epi, instance.N),
Intervension(0, 0, base_line_test, instance.epi, instance.N),
Intervension(1, 0, base_line_test, instance.epi, instance.N),
Intervension(1, 0, 0.9, instance.epi, instance.N),
Intervension(0, co, base_line_test, instance.epi, instance.N),
Intervension(1, co, base_line_test, instance.epi, instance.N),
Intervension(1, co, 0.9, instance.epi, instance.N),
Intervension(1, 0, 0.95, instance.epi, instance.N),
Intervension(0, 0, 0.95, instance.epi, instance.N)
]
sd_levels_train = {'H': 0.9, 'L': base_line_train}
sd_levels_test = {'H': 0.9, 'L': base_line_test}
best_policy_replicas, policy_params = threshold_policy_search(instance,
interventions_train,
interventions_test,
sd_levels_train,
sd_levels_test,
cocooning=co,
school_closure=sc,
mp_pool=mp_pool,
n_replicas_train=n_replicas_train,
n_replicas_test=n_replicas_test,
instance_name=instance_name,
policy={
'class': policy_class,
'vals': [120, 216, 9]
},
policy_class=policy_class)
n_replicas = len(best_policy_replicas)
plot_stoch_simulations(
instance_name,
best_policy_replicas,
['sim'] * n_replicas,
plot_left_axis=['IH'],
plot_right_axis=[],
T=instance.T,
hosp_beds=instance.hosp_beds,
population=instance.N.sum(),
interventions=interventions_test,
calendar=instance.cal,
policy_params=policy_params,
plot_triggers=True,
plot_legend=True,
show=True,
align_axes=True,
n_replicas=5,
BL=base_line_test)
| true | true |
f72cbe73762b18771ed1651cd35031464722fae9 | 19,152 | py | Python | official/nlp/transformer/transformer_main.py | 873040/Abhishek | 2ddd716e66bc5cc6e6f0787508dd07da0e02e75a | [
"Apache-2.0"
] | 4 | 2020-03-13T14:01:32.000Z | 2021-05-31T17:17:32.000Z | official/nlp/transformer/transformer_main.py | 873040/Abhishek | 2ddd716e66bc5cc6e6f0787508dd07da0e02e75a | [
"Apache-2.0"
] | 10 | 2019-12-28T21:31:19.000Z | 2020-04-12T20:01:58.000Z | official/nlp/transformer/transformer_main.py | 873040/Abhishek | 2ddd716e66bc5cc6e6f0787508dd07da0e02e75a | [
"Apache-2.0"
] | 8 | 2020-04-12T04:30:33.000Z | 2021-09-17T20:54:44.000Z | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Train and evaluate the Transformer model.
See README for description of setting the training schedule and evaluating the
BLEU score.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from official.modeling import performance
from official.nlp.transformer import compute_bleu
from official.nlp.transformer import data_pipeline
from official.nlp.transformer import metrics
from official.nlp.transformer import misc
from official.nlp.transformer import optimizer
from official.nlp.transformer import transformer
from official.nlp.transformer import translate
from official.nlp.transformer.utils import tokenizer
from official.utils.flags import core as flags_core
from official.utils.logs import logger
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
INF = int(1e9)
BLEU_DIR = "bleu"
_SINGLE_SAMPLE = 1
def translate_and_compute_bleu(model,
params,
subtokenizer,
bleu_source,
bleu_ref,
distribution_strategy=None):
"""Translate file and report the cased and uncased bleu scores.
Args:
model: A Keras model, used to generate the translations.
params: A dictionary, containing the translation related parameters.
subtokenizer: A subtokenizer object, used for encoding and decoding source
and translated lines.
bleu_source: A file containing source sentences for translation.
bleu_ref: A file containing the reference for the translated sentences.
distribution_strategy: A platform distribution strategy, used for TPU based
translation.
Returns:
uncased_score: A float, the case insensitive BLEU score.
cased_score: A float, the case sensitive BLEU score.
"""
# Create temporary file to store translation.
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
translate.translate_file(
model,
params,
subtokenizer,
bleu_source,
output_file=tmp_filename,
print_all_translations=False,
distribution_strategy=distribution_strategy)
# Compute uncased and cased bleu scores.
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, True)
os.remove(tmp_filename)
return uncased_score, cased_score
def evaluate_and_log_bleu(model,
params,
bleu_source,
bleu_ref,
vocab_file,
distribution_strategy=None):
"""Calculate and record the BLEU score.
Args:
model: A Keras model, used to generate the translations.
params: A dictionary, containing the translation related parameters.
bleu_source: A file containing source sentences for translation.
bleu_ref: A file containing the reference for the translated sentences.
vocab_file: A file containing the vocabulary for translation.
distribution_strategy: A platform distribution strategy, used for TPU based
translation.
Returns:
uncased_score: A float, the case insensitive BLEU score.
cased_score: A float, the case sensitive BLEU score.
"""
subtokenizer = tokenizer.Subtokenizer(vocab_file)
uncased_score, cased_score = translate_and_compute_bleu(
model, params, subtokenizer, bleu_source, bleu_ref, distribution_strategy)
logging.info("Bleu score (uncased): %s", uncased_score)
logging.info("Bleu score (cased): %s", cased_score)
return uncased_score, cased_score
class TransformerTask(object):
"""Main entry of Transformer model."""
def __init__(self, flags_obj):
"""Init function of TransformerMain.
Args:
flags_obj: Object containing parsed flag values, i.e., FLAGS.
Raises:
ValueError: if not using static batch for input data on TPU.
"""
self.flags_obj = flags_obj
self.predict_model = None
# Add flag-defined parameters to params object
num_gpus = flags_core.get_num_gpus(flags_obj)
self.params = params = misc.get_model_params(flags_obj.param_set, num_gpus)
params["num_gpus"] = num_gpus
params["use_ctl"] = flags_obj.use_ctl
params["data_dir"] = flags_obj.data_dir
params["model_dir"] = flags_obj.model_dir
params["static_batch"] = flags_obj.static_batch
params["max_length"] = flags_obj.max_length
params["decode_batch_size"] = flags_obj.decode_batch_size
params["decode_max_length"] = flags_obj.decode_max_length
params["padded_decode"] = flags_obj.padded_decode
params["num_parallel_calls"] = (
flags_obj.num_parallel_calls or tf.data.experimental.AUTOTUNE)
params["use_synthetic_data"] = flags_obj.use_synthetic_data
params["batch_size"] = flags_obj.batch_size or params["default_batch_size"]
params["repeat_dataset"] = None
params["dtype"] = flags_core.get_tf_dtype(flags_obj)
params["enable_tensorboard"] = flags_obj.enable_tensorboard
params["enable_metrics_in_training"] = flags_obj.enable_metrics_in_training
params["steps_between_evals"] = flags_obj.steps_between_evals
params["enable_checkpointing"] = flags_obj.enable_checkpointing
self.distribution_strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=num_gpus,
all_reduce_alg=flags_obj.all_reduce_alg,
num_packs=flags_obj.num_packs,
tpu_address=flags_obj.tpu or "")
if self.use_tpu:
params["num_replicas"] = self.distribution_strategy.num_replicas_in_sync
if not params["static_batch"]:
raise ValueError("TPU requires static batch for input data.")
else:
logging.info("Running transformer with num_gpus = %d", num_gpus)
if self.distribution_strategy:
logging.info("For training, using distribution strategy: %s",
self.distribution_strategy)
else:
logging.info("Not using any distribution strategy.")
performance.set_mixed_precision_policy(
params["dtype"],
flags_core.get_loss_scale(flags_obj, default_for_fp16="dynamic"))
@property
def use_tpu(self):
if self.distribution_strategy:
return isinstance(self.distribution_strategy,
tf.distribute.experimental.TPUStrategy)
return False
def train(self):
"""Trains the model."""
params = self.params
flags_obj = self.flags_obj
# Sets config options.
keras_utils.set_session_config(enable_xla=flags_obj.enable_xla)
_ensure_dir(flags_obj.model_dir)
with distribution_utils.get_strategy_scope(self.distribution_strategy):
model = transformer.create_model(params, is_train=True)
opt = self._create_optimizer()
current_step = 0
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)
latest_checkpoint = tf.train.latest_checkpoint(flags_obj.model_dir)
if latest_checkpoint:
checkpoint.restore(latest_checkpoint)
logging.info("Loaded checkpoint %s", latest_checkpoint)
current_step = opt.iterations.numpy()
if params["use_ctl"]:
train_loss_metric = tf.keras.metrics.Mean(
"training_loss", dtype=tf.float32)
if params["enable_tensorboard"]:
summary_writer = tf.compat.v2.summary.create_file_writer(
flags_obj.model_dir)
else:
summary_writer = tf.compat.v2.summary.create_noop_writer()
train_metrics = [train_loss_metric]
if params["enable_metrics_in_training"]:
train_metrics = train_metrics + model.metrics
else:
model.compile(opt)
model.summary()
if self.use_tpu:
# Different from experimental_distribute_dataset,
# experimental_distribute_datasets_from_function requires
# per-replica/local batch size.
params["batch_size"] /= self.distribution_strategy.num_replicas_in_sync
train_ds = (
self.distribution_strategy
.experimental_distribute_datasets_from_function(
lambda ctx: data_pipeline.train_input_fn(params, ctx)))
else:
train_ds = data_pipeline.train_input_fn(params)
map_data_fn = data_pipeline.map_data_for_transformer_fn
train_ds = train_ds.map(
map_data_fn, num_parallel_calls=params["num_parallel_calls"])
if params["use_ctl"]:
train_ds_iterator = iter(train_ds)
callbacks = self._create_callbacks(flags_obj.model_dir, 0, params)
# Only TimeHistory callback is supported for CTL
if params["use_ctl"]:
callbacks = [cb for cb in callbacks
if isinstance(cb, keras_utils.TimeHistory)]
# TODO(b/139418525): Refactor the custom training loop logic.
@tf.function
def train_steps(iterator, steps):
"""Training steps function for TPU runs.
Args:
iterator: The input iterator of the training dataset.
steps: An integer, the number of training steps.
Returns:
A float, the loss value.
"""
def _step_fn(inputs):
"""Per-replica step function."""
inputs, targets = inputs
with tf.GradientTape() as tape:
logits = model([inputs, targets], training=True)
loss = metrics.transformer_loss(logits, targets,
params["label_smoothing"],
params["vocab_size"])
# Scales the loss, which results in using the average loss across all
# of the replicas for backprop.
scaled_loss = loss / self.distribution_strategy.num_replicas_in_sync
# De-dupes variables due to keras tracking issues.
tvars = list({id(v): v for v in model.trainable_variables}.values())
grads = tape.gradient(scaled_loss, tvars)
opt.apply_gradients(zip(grads, tvars))
# For reporting, the metric takes the mean of losses.
train_loss_metric.update_state(loss)
for _ in tf.range(steps):
train_loss_metric.reset_states()
self.distribution_strategy.run(
_step_fn, args=(next(iterator),))
cased_score, uncased_score = None, None
cased_score_history, uncased_score_history = [], []
while current_step < flags_obj.train_steps:
remaining_steps = flags_obj.train_steps - current_step
train_steps_per_eval = (
remaining_steps if remaining_steps < flags_obj.steps_between_evals
else flags_obj.steps_between_evals)
current_iteration = current_step // flags_obj.steps_between_evals
logging.info(
"Start train iteration at global step:{}".format(current_step))
history = None
if params["use_ctl"]:
if not self.use_tpu:
raise NotImplementedError(
"Custom training loop on GPUs is not implemented.")
# Runs training steps.
with summary_writer.as_default():
for cb in callbacks:
cb.on_epoch_begin(current_iteration)
cb.on_batch_begin(0)
train_steps(
train_ds_iterator,
tf.convert_to_tensor(train_steps_per_eval, dtype=tf.int32))
current_step += train_steps_per_eval
train_loss = train_loss_metric.result().numpy().astype(float)
logging.info("Train Step: %d/%d / loss = %s", current_step,
flags_obj.train_steps, train_loss)
for cb in callbacks:
cb.on_batch_end(train_steps_per_eval - 1)
cb.on_epoch_end(current_iteration)
if params["enable_tensorboard"]:
for metric_obj in train_metrics:
tf.compat.v2.summary.scalar(metric_obj.name, metric_obj.result(),
current_step)
summary_writer.flush()
for cb in callbacks:
cb.on_train_end()
if flags_obj.enable_checkpointing:
# avoid check-pointing when running for benchmarking.
checkpoint_name = checkpoint.save(
os.path.join(flags_obj.model_dir,
"ctl_step_{}.ckpt".format(current_step)))
logging.info("Saved checkpoint to %s", checkpoint_name)
else:
if self.use_tpu:
raise NotImplementedError(
"Keras model.fit on TPUs is not implemented.")
history = model.fit(
train_ds,
initial_epoch=current_iteration,
epochs=current_iteration + 1,
steps_per_epoch=train_steps_per_eval,
callbacks=callbacks,
# If TimeHistory is enabled, progress bar would be messy. Increase
# the verbose level to get rid of it.
verbose=(2 if flags_obj.enable_time_history else 1))
current_step += train_steps_per_eval
logging.info("Train history: {}".format(history.history))
logging.info("End train iteration at global step:{}".format(current_step))
if (flags_obj.bleu_source and flags_obj.bleu_ref):
uncased_score, cased_score = self.eval()
cased_score_history.append([current_iteration + 1, cased_score])
uncased_score_history.append([current_iteration + 1, uncased_score])
stats = ({
"loss": train_loss
} if history is None else misc.build_stats(history, callbacks))
if uncased_score and cased_score:
stats["bleu_uncased"] = uncased_score
stats["bleu_cased"] = cased_score
stats["bleu_uncased_history"] = uncased_score_history
stats["bleu_cased_history"] = cased_score_history
return stats
def eval(self):
"""Evaluates the model."""
distribution_strategy = self.distribution_strategy if self.use_tpu else None
# We only want to create the model under DS scope for TPU case.
# When 'distribution_strategy' is None, a no-op DummyContextManager will
# be used.
with distribution_utils.get_strategy_scope(distribution_strategy):
if not self.predict_model:
self.predict_model = transformer.create_model(self.params, False)
self._load_weights_if_possible(
self.predict_model,
tf.train.latest_checkpoint(self.flags_obj.model_dir))
self.predict_model.summary()
return evaluate_and_log_bleu(
self.predict_model, self.params, self.flags_obj.bleu_source,
self.flags_obj.bleu_ref, self.flags_obj.vocab_file,
distribution_strategy)
def predict(self):
"""Predicts result from the model."""
params = self.params
flags_obj = self.flags_obj
with tf.name_scope("model"):
model = transformer.create_model(params, is_train=False)
self._load_weights_if_possible(
model, tf.train.latest_checkpoint(self.flags_obj.model_dir))
model.summary()
subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file)
ds = data_pipeline.eval_input_fn(params)
ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE)
ret = model.predict(ds)
val_outputs, _ = ret
length = len(val_outputs)
for i in range(length):
translate.translate_from_input(val_outputs[i], subtokenizer)
def _create_callbacks(self, cur_log_dir, init_steps, params):
"""Creates a list of callbacks."""
sfunc = optimizer.LearningRateFn(params["learning_rate"],
params["hidden_size"],
params["learning_rate_warmup_steps"])
scheduler_callback = optimizer.LearningRateScheduler(sfunc, init_steps)
callbacks = misc.get_callbacks(params["steps_between_evals"])
callbacks.append(scheduler_callback)
if params["enable_checkpointing"]:
ckpt_full_path = os.path.join(cur_log_dir, "cp-{epoch:04d}.ckpt")
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(
ckpt_full_path, save_weights_only=True))
return callbacks
def _load_weights_if_possible(self, model, init_weight_path=None):
"""Loads model weights when it is provided."""
if init_weight_path:
logging.info("Load weights: {}".format(init_weight_path))
# TODO(b/139414977): Having the same variable restoring method for both
# TPU and GPU.
if self.use_tpu:
checkpoint = tf.train.Checkpoint(
model=model, optimizer=self._create_optimizer())
checkpoint.restore(init_weight_path)
else:
model.load_weights(init_weight_path)
else:
logging.info("Weights not loaded from path:{}".format(init_weight_path))
def _create_optimizer(self):
"""Creates optimizer."""
params = self.params
lr_schedule = optimizer.LearningRateSchedule(
params["learning_rate"], params["hidden_size"],
params["learning_rate_warmup_steps"])
opt = tf.keras.optimizers.Adam(
lr_schedule if self.use_tpu else params["learning_rate"],
params["optimizer_adam_beta1"],
params["optimizer_adam_beta2"],
epsilon=params["optimizer_adam_epsilon"])
opt = performance.configure_optimizer(
opt,
use_float16=params["dtype"] == tf.float16,
use_graph_rewrite=self.flags_obj.fp16_implementation == "graph_rewrite",
loss_scale=flags_core.get_loss_scale(
self.flags_obj, default_for_fp16="dynamic"))
return opt
def _ensure_dir(log_dir):
"""Makes log dir if not existed."""
if not tf.io.gfile.exists(log_dir):
tf.io.gfile.makedirs(log_dir)
def main(_):
flags_obj = flags.FLAGS
with logger.benchmark_context(flags_obj):
task = TransformerTask(flags_obj)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
keras_utils.set_gpu_thread_mode_and_count(
per_gpu_thread_count=flags_obj.per_gpu_thread_count,
gpu_thread_mode=flags_obj.tf_gpu_thread_mode,
num_gpus=flags_obj.num_gpus,
datasets_num_private_threads=flags_obj.datasets_num_private_threads)
if flags_obj.mode == "train":
task.train()
elif flags_obj.mode == "predict":
task.predict()
elif flags_obj.mode == "eval":
task.eval()
else:
raise ValueError("Invalid mode {}".format(flags_obj.mode))
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
misc.define_transformer_flags()
app.run(main)
| 38.457831 | 80 | 0.688753 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from official.modeling import performance
from official.nlp.transformer import compute_bleu
from official.nlp.transformer import data_pipeline
from official.nlp.transformer import metrics
from official.nlp.transformer import misc
from official.nlp.transformer import optimizer
from official.nlp.transformer import transformer
from official.nlp.transformer import translate
from official.nlp.transformer.utils import tokenizer
from official.utils.flags import core as flags_core
from official.utils.logs import logger
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
INF = int(1e9)
BLEU_DIR = "bleu"
_SINGLE_SAMPLE = 1
def translate_and_compute_bleu(model,
params,
subtokenizer,
bleu_source,
bleu_ref,
distribution_strategy=None):
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
translate.translate_file(
model,
params,
subtokenizer,
bleu_source,
output_file=tmp_filename,
print_all_translations=False,
distribution_strategy=distribution_strategy)
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, True)
os.remove(tmp_filename)
return uncased_score, cased_score
def evaluate_and_log_bleu(model,
params,
bleu_source,
bleu_ref,
vocab_file,
distribution_strategy=None):
subtokenizer = tokenizer.Subtokenizer(vocab_file)
uncased_score, cased_score = translate_and_compute_bleu(
model, params, subtokenizer, bleu_source, bleu_ref, distribution_strategy)
logging.info("Bleu score (uncased): %s", uncased_score)
logging.info("Bleu score (cased): %s", cased_score)
return uncased_score, cased_score
class TransformerTask(object):
def __init__(self, flags_obj):
self.flags_obj = flags_obj
self.predict_model = None
num_gpus = flags_core.get_num_gpus(flags_obj)
self.params = params = misc.get_model_params(flags_obj.param_set, num_gpus)
params["num_gpus"] = num_gpus
params["use_ctl"] = flags_obj.use_ctl
params["data_dir"] = flags_obj.data_dir
params["model_dir"] = flags_obj.model_dir
params["static_batch"] = flags_obj.static_batch
params["max_length"] = flags_obj.max_length
params["decode_batch_size"] = flags_obj.decode_batch_size
params["decode_max_length"] = flags_obj.decode_max_length
params["padded_decode"] = flags_obj.padded_decode
params["num_parallel_calls"] = (
flags_obj.num_parallel_calls or tf.data.experimental.AUTOTUNE)
params["use_synthetic_data"] = flags_obj.use_synthetic_data
params["batch_size"] = flags_obj.batch_size or params["default_batch_size"]
params["repeat_dataset"] = None
params["dtype"] = flags_core.get_tf_dtype(flags_obj)
params["enable_tensorboard"] = flags_obj.enable_tensorboard
params["enable_metrics_in_training"] = flags_obj.enable_metrics_in_training
params["steps_between_evals"] = flags_obj.steps_between_evals
params["enable_checkpointing"] = flags_obj.enable_checkpointing
self.distribution_strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=num_gpus,
all_reduce_alg=flags_obj.all_reduce_alg,
num_packs=flags_obj.num_packs,
tpu_address=flags_obj.tpu or "")
if self.use_tpu:
params["num_replicas"] = self.distribution_strategy.num_replicas_in_sync
if not params["static_batch"]:
raise ValueError("TPU requires static batch for input data.")
else:
logging.info("Running transformer with num_gpus = %d", num_gpus)
if self.distribution_strategy:
logging.info("For training, using distribution strategy: %s",
self.distribution_strategy)
else:
logging.info("Not using any distribution strategy.")
performance.set_mixed_precision_policy(
params["dtype"],
flags_core.get_loss_scale(flags_obj, default_for_fp16="dynamic"))
@property
def use_tpu(self):
if self.distribution_strategy:
return isinstance(self.distribution_strategy,
tf.distribute.experimental.TPUStrategy)
return False
def train(self):
params = self.params
flags_obj = self.flags_obj
keras_utils.set_session_config(enable_xla=flags_obj.enable_xla)
_ensure_dir(flags_obj.model_dir)
with distribution_utils.get_strategy_scope(self.distribution_strategy):
model = transformer.create_model(params, is_train=True)
opt = self._create_optimizer()
current_step = 0
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)
latest_checkpoint = tf.train.latest_checkpoint(flags_obj.model_dir)
if latest_checkpoint:
checkpoint.restore(latest_checkpoint)
logging.info("Loaded checkpoint %s", latest_checkpoint)
current_step = opt.iterations.numpy()
if params["use_ctl"]:
train_loss_metric = tf.keras.metrics.Mean(
"training_loss", dtype=tf.float32)
if params["enable_tensorboard"]:
summary_writer = tf.compat.v2.summary.create_file_writer(
flags_obj.model_dir)
else:
summary_writer = tf.compat.v2.summary.create_noop_writer()
train_metrics = [train_loss_metric]
if params["enable_metrics_in_training"]:
train_metrics = train_metrics + model.metrics
else:
model.compile(opt)
model.summary()
if self.use_tpu:
params["batch_size"] /= self.distribution_strategy.num_replicas_in_sync
train_ds = (
self.distribution_strategy
.experimental_distribute_datasets_from_function(
lambda ctx: data_pipeline.train_input_fn(params, ctx)))
else:
train_ds = data_pipeline.train_input_fn(params)
map_data_fn = data_pipeline.map_data_for_transformer_fn
train_ds = train_ds.map(
map_data_fn, num_parallel_calls=params["num_parallel_calls"])
if params["use_ctl"]:
train_ds_iterator = iter(train_ds)
callbacks = self._create_callbacks(flags_obj.model_dir, 0, params)
if params["use_ctl"]:
callbacks = [cb for cb in callbacks
if isinstance(cb, keras_utils.TimeHistory)]
@tf.function
def train_steps(iterator, steps):
def _step_fn(inputs):
inputs, targets = inputs
with tf.GradientTape() as tape:
logits = model([inputs, targets], training=True)
loss = metrics.transformer_loss(logits, targets,
params["label_smoothing"],
params["vocab_size"])
scaled_loss = loss / self.distribution_strategy.num_replicas_in_sync
tvars = list({id(v): v for v in model.trainable_variables}.values())
grads = tape.gradient(scaled_loss, tvars)
opt.apply_gradients(zip(grads, tvars))
train_loss_metric.update_state(loss)
for _ in tf.range(steps):
train_loss_metric.reset_states()
self.distribution_strategy.run(
_step_fn, args=(next(iterator),))
cased_score, uncased_score = None, None
cased_score_history, uncased_score_history = [], []
while current_step < flags_obj.train_steps:
remaining_steps = flags_obj.train_steps - current_step
train_steps_per_eval = (
remaining_steps if remaining_steps < flags_obj.steps_between_evals
else flags_obj.steps_between_evals)
current_iteration = current_step // flags_obj.steps_between_evals
logging.info(
"Start train iteration at global step:{}".format(current_step))
history = None
if params["use_ctl"]:
if not self.use_tpu:
raise NotImplementedError(
"Custom training loop on GPUs is not implemented.")
with summary_writer.as_default():
for cb in callbacks:
cb.on_epoch_begin(current_iteration)
cb.on_batch_begin(0)
train_steps(
train_ds_iterator,
tf.convert_to_tensor(train_steps_per_eval, dtype=tf.int32))
current_step += train_steps_per_eval
train_loss = train_loss_metric.result().numpy().astype(float)
logging.info("Train Step: %d/%d / loss = %s", current_step,
flags_obj.train_steps, train_loss)
for cb in callbacks:
cb.on_batch_end(train_steps_per_eval - 1)
cb.on_epoch_end(current_iteration)
if params["enable_tensorboard"]:
for metric_obj in train_metrics:
tf.compat.v2.summary.scalar(metric_obj.name, metric_obj.result(),
current_step)
summary_writer.flush()
for cb in callbacks:
cb.on_train_end()
if flags_obj.enable_checkpointing:
checkpoint_name = checkpoint.save(
os.path.join(flags_obj.model_dir,
"ctl_step_{}.ckpt".format(current_step)))
logging.info("Saved checkpoint to %s", checkpoint_name)
else:
if self.use_tpu:
raise NotImplementedError(
"Keras model.fit on TPUs is not implemented.")
history = model.fit(
train_ds,
initial_epoch=current_iteration,
epochs=current_iteration + 1,
steps_per_epoch=train_steps_per_eval,
callbacks=callbacks,
verbose=(2 if flags_obj.enable_time_history else 1))
current_step += train_steps_per_eval
logging.info("Train history: {}".format(history.history))
logging.info("End train iteration at global step:{}".format(current_step))
if (flags_obj.bleu_source and flags_obj.bleu_ref):
uncased_score, cased_score = self.eval()
cased_score_history.append([current_iteration + 1, cased_score])
uncased_score_history.append([current_iteration + 1, uncased_score])
stats = ({
"loss": train_loss
} if history is None else misc.build_stats(history, callbacks))
if uncased_score and cased_score:
stats["bleu_uncased"] = uncased_score
stats["bleu_cased"] = cased_score
stats["bleu_uncased_history"] = uncased_score_history
stats["bleu_cased_history"] = cased_score_history
return stats
def eval(self):
distribution_strategy = self.distribution_strategy if self.use_tpu else None
with distribution_utils.get_strategy_scope(distribution_strategy):
if not self.predict_model:
self.predict_model = transformer.create_model(self.params, False)
self._load_weights_if_possible(
self.predict_model,
tf.train.latest_checkpoint(self.flags_obj.model_dir))
self.predict_model.summary()
return evaluate_and_log_bleu(
self.predict_model, self.params, self.flags_obj.bleu_source,
self.flags_obj.bleu_ref, self.flags_obj.vocab_file,
distribution_strategy)
def predict(self):
params = self.params
flags_obj = self.flags_obj
with tf.name_scope("model"):
model = transformer.create_model(params, is_train=False)
self._load_weights_if_possible(
model, tf.train.latest_checkpoint(self.flags_obj.model_dir))
model.summary()
subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file)
ds = data_pipeline.eval_input_fn(params)
ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE)
ret = model.predict(ds)
val_outputs, _ = ret
length = len(val_outputs)
for i in range(length):
translate.translate_from_input(val_outputs[i], subtokenizer)
def _create_callbacks(self, cur_log_dir, init_steps, params):
sfunc = optimizer.LearningRateFn(params["learning_rate"],
params["hidden_size"],
params["learning_rate_warmup_steps"])
scheduler_callback = optimizer.LearningRateScheduler(sfunc, init_steps)
callbacks = misc.get_callbacks(params["steps_between_evals"])
callbacks.append(scheduler_callback)
if params["enable_checkpointing"]:
ckpt_full_path = os.path.join(cur_log_dir, "cp-{epoch:04d}.ckpt")
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(
ckpt_full_path, save_weights_only=True))
return callbacks
def _load_weights_if_possible(self, model, init_weight_path=None):
if init_weight_path:
logging.info("Load weights: {}".format(init_weight_path))
if self.use_tpu:
checkpoint = tf.train.Checkpoint(
model=model, optimizer=self._create_optimizer())
checkpoint.restore(init_weight_path)
else:
model.load_weights(init_weight_path)
else:
logging.info("Weights not loaded from path:{}".format(init_weight_path))
def _create_optimizer(self):
params = self.params
lr_schedule = optimizer.LearningRateSchedule(
params["learning_rate"], params["hidden_size"],
params["learning_rate_warmup_steps"])
opt = tf.keras.optimizers.Adam(
lr_schedule if self.use_tpu else params["learning_rate"],
params["optimizer_adam_beta1"],
params["optimizer_adam_beta2"],
epsilon=params["optimizer_adam_epsilon"])
opt = performance.configure_optimizer(
opt,
use_float16=params["dtype"] == tf.float16,
use_graph_rewrite=self.flags_obj.fp16_implementation == "graph_rewrite",
loss_scale=flags_core.get_loss_scale(
self.flags_obj, default_for_fp16="dynamic"))
return opt
def _ensure_dir(log_dir):
if not tf.io.gfile.exists(log_dir):
tf.io.gfile.makedirs(log_dir)
def main(_):
flags_obj = flags.FLAGS
with logger.benchmark_context(flags_obj):
task = TransformerTask(flags_obj)
if flags_obj.tf_gpu_thread_mode:
keras_utils.set_gpu_thread_mode_and_count(
per_gpu_thread_count=flags_obj.per_gpu_thread_count,
gpu_thread_mode=flags_obj.tf_gpu_thread_mode,
num_gpus=flags_obj.num_gpus,
datasets_num_private_threads=flags_obj.datasets_num_private_threads)
if flags_obj.mode == "train":
task.train()
elif flags_obj.mode == "predict":
task.predict()
elif flags_obj.mode == "eval":
task.eval()
else:
raise ValueError("Invalid mode {}".format(flags_obj.mode))
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
misc.define_transformer_flags()
app.run(main)
| true | true |
f72cbea6b5b5fb4a9f0c9efd4d8092605bb087d6 | 18,884 | py | Python | src/sentry/models/dsymfile.py | percipient/sentry | 84c6f75ab40e12677c81d9210c3fe8ad66d7a0c3 | [
"BSD-3-Clause"
] | null | null | null | src/sentry/models/dsymfile.py | percipient/sentry | 84c6f75ab40e12677c81d9210c3fe8ad66d7a0c3 | [
"BSD-3-Clause"
] | 8 | 2019-12-28T23:49:55.000Z | 2022-03-02T04:34:18.000Z | src/sentry/models/dsymfile.py | percipient/sentry | 84c6f75ab40e12677c81d9210c3fe8ad66d7a0c3 | [
"BSD-3-Clause"
] | null | null | null | """
sentry.models.dsymfile
~~~~~~~~~~~~~~~~~~~~~~
:copyright: (c) 2010-2016 by the Sentry Team, see AUTHORS for more details.
:license: BSD, see LICENSE for more details.
"""
from __future__ import absolute_import
import os
import shutil
import hashlib
import six
import tempfile
from requests.exceptions import RequestException
from jsonfield import JSONField
from itertools import chain
from django.db import models, router, transaction, connection, IntegrityError
from django.utils import timezone
from django.utils.translation import ugettext_lazy as _
from symsynd.macho.arch import get_macho_uuids
from sentry.db.models import FlexibleForeignKey, Model, BoundedBigIntegerField, \
sane_repr, BaseManager, BoundedPositiveIntegerField
from sentry.models.file import File
from sentry.utils.zip import safe_extract_zip
from sentry.utils.db import is_sqlite
from sentry.utils.native import parse_addr
from sentry.constants import KNOWN_DSYM_TYPES
from sentry.reprocessing import resolve_processing_issue
class VersionDSymFile(Model):
__core__ = False
objects = BaseManager()
dsym_file = FlexibleForeignKey('sentry.ProjectDSymFile', null=True)
dsym_app = FlexibleForeignKey('sentry.DSymApp')
version = models.CharField(max_length=32)
build = models.CharField(max_length=32, null=True)
date_added = models.DateTimeField(default=timezone.now)
class Meta:
app_label = 'sentry'
db_table = 'sentry_versiondsymfile'
unique_together = (('dsym_file', 'version', 'build'),)
# TODO(dcramer): pull in enum library
class DSymPlatform(object):
GENERIC = 0
APPLE = 1
ANDROID = 2
DSYM_PLATFORMS = {
'generic': DSymPlatform.GENERIC,
'apple': DSymPlatform.APPLE,
'android': DSymPlatform.ANDROID,
}
def _auto_enrich_data(data, app_id, platform):
# If we don't have an icon URL we can try to fetch one from iTunes
if 'icon_url' not in data and platform == DSymPlatform.APPLE:
from sentry.http import safe_urlopen
try:
rv = safe_urlopen('http://itunes.apple.com/lookup', params={
'bundleId': app_id,
})
except RequestException:
pass
else:
if rv.ok:
rv = rv.json()
if rv.get('results'):
data['icon_url'] = rv['results'][0]['artworkUrl512']
class DSymAppManager(BaseManager):
def create_or_update_app(self, sync_id, app_id, project, data=None,
platform=DSymPlatform.GENERIC):
if data is None:
data = {}
_auto_enrich_data(data, app_id, platform)
existing_app = DSymApp.objects.filter(
app_id=app_id, project=project).first()
if existing_app is not None:
now = timezone.now()
existing_app.update(
sync_id=sync_id,
data=data,
last_synced=now,
)
return existing_app
return BaseManager.create(self,
sync_id=sync_id,
app_id=app_id,
data=data,
project=project,
platform=platform
)
class DSymApp(Model):
__core__ = False
objects = DSymAppManager()
project = FlexibleForeignKey('sentry.Project')
app_id = models.CharField(max_length=64)
sync_id = models.CharField(max_length=64, null=True)
data = JSONField()
platform = BoundedPositiveIntegerField(default=0, choices=(
(DSymPlatform.GENERIC, _('Generic')),
(DSymPlatform.APPLE, _('Apple')),
(DSymPlatform.ANDROID, _('Android')),
))
last_synced = models.DateTimeField(default=timezone.now)
date_added = models.DateTimeField(default=timezone.now)
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymapp'
unique_together = (('project', 'platform', 'app_id'),)
class DSymSDKManager(BaseManager):
def enumerate_sdks(self, sdk=None, version=None):
"""Return a grouped list of SDKs."""
filter = ''
args = []
if version is not None:
for col, val in zip(['major', 'minor', 'patchlevel'],
version.split('.')):
if not val.isdigit():
return []
filter += ' and k.version_%s = %d' % (
col,
int(val)
)
if sdk is not None:
filter += ' and k.sdk_name = %s'
args.append(sdk)
cur = connection.cursor()
cur.execute('''
select distinct k.*, count(*) as bundle_count, o.cpu_name
from sentry_dsymsdk k,
sentry_dsymbundle b,
sentry_dsymobject o
where b.sdk_id = k.id and
b.object_id = o.id %s
group by k.id, k.sdk_name, o.cpu_name
''' % filter, args)
rv = []
for row in cur.fetchall():
row = dict(zip([x[0] for x in cur.description], row))
ver = '%s.%s.%s' % (
row['version_major'],
row['version_minor'],
row['version_patchlevel']
)
rv.append({
'sdk_name': row['sdk_name'],
'version': ver,
'build': row['version_build'],
'bundle_count': row['bundle_count'],
'cpu_name': row['cpu_name'],
})
return sorted(rv, key=lambda x: (x['sdk_name'],
x['version'],
x['build'],
x['cpu_name']))
class DSymSDK(Model):
__core__ = False
dsym_type = models.CharField(max_length=20, db_index=True)
sdk_name = models.CharField(max_length=20)
version_major = models.IntegerField()
version_minor = models.IntegerField()
version_patchlevel = models.IntegerField()
version_build = models.CharField(max_length=40)
objects = DSymSDKManager()
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymsdk'
index_together = [
('version_major', 'version_minor', 'version_patchlevel',
'version_build'),
]
class DSymObject(Model):
__core__ = False
cpu_name = models.CharField(max_length=40)
object_path = models.TextField(db_index=True)
uuid = models.CharField(max_length=36, db_index=True)
vmaddr = BoundedBigIntegerField(null=True)
vmsize = BoundedBigIntegerField(null=True)
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymobject'
class DSymBundle(Model):
__core__ = False
sdk = FlexibleForeignKey('sentry.DSymSDK')
object = FlexibleForeignKey('sentry.DSymObject')
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymbundle'
class DSymSymbolManager(BaseManager):
def bulk_insert(self, items):
db = router.db_for_write(DSymSymbol)
items = list(items)
if not items:
return
# On SQLite we don't do this. Two reasons: one, it does not
# seem significantly faster and you're an idiot if you import
# huge amounts of system symbols into sqlite anyways. secondly
# because of the low parameter limit
if not is_sqlite():
try:
with transaction.atomic(using=db):
cur = connection.cursor()
cur.execute('''
insert into sentry_dsymsymbol
(object_id, address, symbol)
values %s
''' % ', '.join(['(%s, %s, %s)'] * len(items)),
list(chain(*items)))
cur.close()
return
except IntegrityError:
pass
cur = connection.cursor()
for item in items:
cur.execute('''
insert into sentry_dsymsymbol
(object_id, address, symbol)
select
%(object_id)s, %(address)s, %(symbol)s
where not exists (
select 1 from sentry_dsymsymbol
where object_id = %(object_id)s
and address = %(address)s);
''', {
'object_id': item[0],
'address': item[1],
'symbol': item[2],
})
cur.close()
def lookup_symbol(self, instruction_addr, image_addr, uuid,
cpu_name=None, object_path=None, sdk_info=None,
image_vmaddr=None):
"""Finds a system symbol."""
# If we use the "none" dsym type we never return a symbol here.
if sdk_info is not None and sdk_info['dsym_type'] == 'none':
return
instruction_addr = parse_addr(instruction_addr)
image_addr = parse_addr(image_addr)
addr_abs = None
if image_vmaddr is not None:
image_vmaddr = parse_addr(image_vmaddr)
addr_abs = image_vmaddr + instruction_addr - image_addr
addr_rel = instruction_addr - image_addr
uuid = six.text_type(uuid).lower()
cur = connection.cursor()
try:
# First try: exact match on uuid (addr_rel)
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o
where o.uuid = %s and
s.object_id = o.id and
s.address <= o.vmaddr + %s and
s.address >= o.vmaddr
order by address desc
limit 1;
''', [uuid, addr_rel])
rv = cur.fetchone()
if rv:
return rv[0]
# Second try: exact match on uuid (addr_abs)
if addr_abs is not None:
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o
where o.uuid = %s and
s.object_id = o.id and
s.address <= %s and
s.address >= %s
order by address desc
limit 1;
''', [uuid, addr_abs, image_vmaddr])
rv = cur.fetchone()
if rv:
return rv[0]
# Third try: exact match on path and arch (addr_rel)
if sdk_info is None or \
cpu_name is None or \
object_path is None:
return
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o,
sentry_dsymsdk k,
sentry_dsymbundle b
where b.sdk_id = k.id and
b.object_id = o.id and
s.object_id = o.id and
k.sdk_name = %s and
k.dsym_type = %s and
k.version_major = %s and
k.version_minor = %s and
k.version_patchlevel = %s and
o.cpu_name = %s and
o.object_path = %s and
s.address <= o.vmaddr + %s and
s.address >= o.vmaddr
order by address desc
limit 1;
''', [sdk_info['sdk_name'], sdk_info['dsym_type'],
sdk_info['version_major'], sdk_info['version_minor'],
sdk_info['version_patchlevel'], cpu_name, object_path,
addr_rel])
rv = cur.fetchone()
if rv:
return rv[0]
# Fourth try: exact match on path and arch (addr_abs)
if addr_abs is not None:
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o,
sentry_dsymsdk k,
sentry_dsymbundle b
where b.sdk_id = k.id and
b.object_id = o.id and
s.object_id = o.id and
k.sdk_name = %s and
k.dsym_type = %s and
k.version_major = %s and
k.version_minor = %s and
k.version_patchlevel = %s and
o.cpu_name = %s and
o.object_path = %s and
s.address <= %s and
s.address >= %s
order by address desc
limit 1;
''', [sdk_info['sdk_name'], sdk_info['dsym_type'],
sdk_info['version_major'], sdk_info['version_minor'],
sdk_info['version_patchlevel'], cpu_name, object_path,
addr_abs, image_vmaddr])
rv = cur.fetchone()
if rv:
return rv[0]
finally:
cur.close()
class DSymSymbol(Model):
__core__ = False
object = FlexibleForeignKey('sentry.DSymObject')
address = BoundedBigIntegerField(db_index=True)
symbol = models.TextField()
objects = DSymSymbolManager()
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymsymbol'
unique_together = [
('object', 'address'),
]
class CommonDSymFile(Model):
"""
A single dsym file that is associated with a project.
"""
__core__ = False
file = FlexibleForeignKey('sentry.File')
object_name = models.TextField()
cpu_name = models.CharField(max_length=40)
__repr__ = sane_repr('object_name', 'cpu_name', 'uuid')
class Meta:
abstract = True
app_label = 'sentry'
@property
def dsym_type(self):
ct = self.file.headers.get('Content-Type').lower()
return KNOWN_DSYM_TYPES.get(ct, 'unknown')
class ProjectDSymFileManager(BaseManager):
def find_missing(self, checksums, project):
if not checksums:
return[]
checksums = [x.lower() for x in checksums]
missing = set(checksums)
found = ProjectDSymFile.objects.filter(
file__checksum__in=checksums,
project=project
).values('file__checksum')
for values in found:
missing.discard(values.values()[0])
return sorted(missing)
def find_by_checksums(self, checksums, project):
if not checksums:
return []
checksums = [x.lower() for x in checksums]
return ProjectDSymFile.objects.filter(
file__checksum__in=checksums,
project=project
)
class ProjectDSymFile(CommonDSymFile):
project = FlexibleForeignKey('sentry.Project', null=True)
uuid = models.CharField(max_length=36)
is_global = False
objects = ProjectDSymFileManager()
class Meta(CommonDSymFile.Meta):
unique_together = (('project', 'uuid'),)
db_table = 'sentry_projectdsymfile'
class GlobalDSymFile(CommonDSymFile):
uuid = models.CharField(max_length=36, unique=True)
is_global = True
class Meta(CommonDSymFile.Meta):
db_table = 'sentry_globaldsymfile'
def _create_macho_dsym_from_uuid(project, cpu_name, uuid, fileobj,
object_name):
"""This creates a mach dsym file from the given uuid and open file
object to a dsym file. This will not verify the uuid. Use
`create_files_from_macho_zip` for doing everything.
"""
extra = {}
if project is None:
cls = GlobalDSymFile
file_type = 'global.dsym'
else:
cls = ProjectDSymFile
extra['project'] = project
file_type = 'project.dsym'
h = hashlib.sha1()
while 1:
chunk = fileobj.read(16384)
if not chunk:
break
h.update(chunk)
checksum = h.hexdigest()
fileobj.seek(0, 0)
try:
rv = cls.objects.get(uuid=uuid, **extra)
if rv.file.checksum == checksum:
return rv
except cls.DoesNotExist:
pass
else:
# The checksum mismatches. In this case we delete the old object
# and perform a re-upload.
rv.delete()
file = File.objects.create(
name=uuid,
type=file_type,
headers={
'Content-Type': 'application/x-mach-binary'
},
)
file.putfile(fileobj)
try:
with transaction.atomic():
rv = cls.objects.create(
file=file,
uuid=uuid,
cpu_name=cpu_name,
object_name=object_name,
**extra
)
except IntegrityError:
file.delete()
rv = cls.objects.get(uuid=uuid, **extra)
resolve_processing_issue(
project=project,
scope='native',
object='dsym:%s' % uuid,
)
return rv
def create_files_from_macho_zip(fileobj, project=None):
"""Creates all missing dsym files from the given zip file. This
returns a list of all files created.
"""
scratchpad = tempfile.mkdtemp()
try:
safe_extract_zip(fileobj, scratchpad)
to_create = []
for dirpath, dirnames, filenames in os.walk(scratchpad):
for fn in filenames:
fn = os.path.join(dirpath, fn)
try:
uuids = get_macho_uuids(fn)
except (IOError, ValueError):
# Whatever was contained there, was probably not a
# macho file.
continue
for cpu, uuid in uuids:
to_create.append((cpu, uuid, fn))
rv = []
for cpu, uuid, filename in to_create:
with open(filename, 'rb') as f:
rv.append((_create_macho_dsym_from_uuid(
project, cpu, uuid, f, os.path.basename(filename))))
return rv
finally:
shutil.rmtree(scratchpad)
def find_dsym_file(project, image_uuid):
"""Finds a dsym file for the given uuid. Looks both within the project
as well the global store.
"""
image_uuid = image_uuid.lower()
try:
return ProjectDSymFile.objects.filter(
uuid=image_uuid,
project=project
).select_related('file').get()
except ProjectDSymFile.DoesNotExist:
pass
try:
return GlobalDSymFile.objects.filter(
uuid=image_uuid
).select_related('file').get()
except GlobalDSymFile.DoesNotExist:
return None
| 32.061121 | 81 | 0.54157 |
from __future__ import absolute_import
import os
import shutil
import hashlib
import six
import tempfile
from requests.exceptions import RequestException
from jsonfield import JSONField
from itertools import chain
from django.db import models, router, transaction, connection, IntegrityError
from django.utils import timezone
from django.utils.translation import ugettext_lazy as _
from symsynd.macho.arch import get_macho_uuids
from sentry.db.models import FlexibleForeignKey, Model, BoundedBigIntegerField, \
sane_repr, BaseManager, BoundedPositiveIntegerField
from sentry.models.file import File
from sentry.utils.zip import safe_extract_zip
from sentry.utils.db import is_sqlite
from sentry.utils.native import parse_addr
from sentry.constants import KNOWN_DSYM_TYPES
from sentry.reprocessing import resolve_processing_issue
class VersionDSymFile(Model):
__core__ = False
objects = BaseManager()
dsym_file = FlexibleForeignKey('sentry.ProjectDSymFile', null=True)
dsym_app = FlexibleForeignKey('sentry.DSymApp')
version = models.CharField(max_length=32)
build = models.CharField(max_length=32, null=True)
date_added = models.DateTimeField(default=timezone.now)
class Meta:
app_label = 'sentry'
db_table = 'sentry_versiondsymfile'
unique_together = (('dsym_file', 'version', 'build'),)
class DSymPlatform(object):
GENERIC = 0
APPLE = 1
ANDROID = 2
DSYM_PLATFORMS = {
'generic': DSymPlatform.GENERIC,
'apple': DSymPlatform.APPLE,
'android': DSymPlatform.ANDROID,
}
def _auto_enrich_data(data, app_id, platform):
if 'icon_url' not in data and platform == DSymPlatform.APPLE:
from sentry.http import safe_urlopen
try:
rv = safe_urlopen('http://itunes.apple.com/lookup', params={
'bundleId': app_id,
})
except RequestException:
pass
else:
if rv.ok:
rv = rv.json()
if rv.get('results'):
data['icon_url'] = rv['results'][0]['artworkUrl512']
class DSymAppManager(BaseManager):
def create_or_update_app(self, sync_id, app_id, project, data=None,
platform=DSymPlatform.GENERIC):
if data is None:
data = {}
_auto_enrich_data(data, app_id, platform)
existing_app = DSymApp.objects.filter(
app_id=app_id, project=project).first()
if existing_app is not None:
now = timezone.now()
existing_app.update(
sync_id=sync_id,
data=data,
last_synced=now,
)
return existing_app
return BaseManager.create(self,
sync_id=sync_id,
app_id=app_id,
data=data,
project=project,
platform=platform
)
class DSymApp(Model):
__core__ = False
objects = DSymAppManager()
project = FlexibleForeignKey('sentry.Project')
app_id = models.CharField(max_length=64)
sync_id = models.CharField(max_length=64, null=True)
data = JSONField()
platform = BoundedPositiveIntegerField(default=0, choices=(
(DSymPlatform.GENERIC, _('Generic')),
(DSymPlatform.APPLE, _('Apple')),
(DSymPlatform.ANDROID, _('Android')),
))
last_synced = models.DateTimeField(default=timezone.now)
date_added = models.DateTimeField(default=timezone.now)
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymapp'
unique_together = (('project', 'platform', 'app_id'),)
class DSymSDKManager(BaseManager):
def enumerate_sdks(self, sdk=None, version=None):
filter = ''
args = []
if version is not None:
for col, val in zip(['major', 'minor', 'patchlevel'],
version.split('.')):
if not val.isdigit():
return []
filter += ' and k.version_%s = %d' % (
col,
int(val)
)
if sdk is not None:
filter += ' and k.sdk_name = %s'
args.append(sdk)
cur = connection.cursor()
cur.execute('''
select distinct k.*, count(*) as bundle_count, o.cpu_name
from sentry_dsymsdk k,
sentry_dsymbundle b,
sentry_dsymobject o
where b.sdk_id = k.id and
b.object_id = o.id %s
group by k.id, k.sdk_name, o.cpu_name
''' % filter, args)
rv = []
for row in cur.fetchall():
row = dict(zip([x[0] for x in cur.description], row))
ver = '%s.%s.%s' % (
row['version_major'],
row['version_minor'],
row['version_patchlevel']
)
rv.append({
'sdk_name': row['sdk_name'],
'version': ver,
'build': row['version_build'],
'bundle_count': row['bundle_count'],
'cpu_name': row['cpu_name'],
})
return sorted(rv, key=lambda x: (x['sdk_name'],
x['version'],
x['build'],
x['cpu_name']))
class DSymSDK(Model):
__core__ = False
dsym_type = models.CharField(max_length=20, db_index=True)
sdk_name = models.CharField(max_length=20)
version_major = models.IntegerField()
version_minor = models.IntegerField()
version_patchlevel = models.IntegerField()
version_build = models.CharField(max_length=40)
objects = DSymSDKManager()
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymsdk'
index_together = [
('version_major', 'version_minor', 'version_patchlevel',
'version_build'),
]
class DSymObject(Model):
__core__ = False
cpu_name = models.CharField(max_length=40)
object_path = models.TextField(db_index=True)
uuid = models.CharField(max_length=36, db_index=True)
vmaddr = BoundedBigIntegerField(null=True)
vmsize = BoundedBigIntegerField(null=True)
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymobject'
class DSymBundle(Model):
__core__ = False
sdk = FlexibleForeignKey('sentry.DSymSDK')
object = FlexibleForeignKey('sentry.DSymObject')
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymbundle'
class DSymSymbolManager(BaseManager):
def bulk_insert(self, items):
db = router.db_for_write(DSymSymbol)
items = list(items)
if not items:
return
# On SQLite we don't do this. Two reasons: one, it does not
# huge amounts of system symbols into sqlite anyways. secondly
# because of the low parameter limit
if not is_sqlite():
try:
with transaction.atomic(using=db):
cur = connection.cursor()
cur.execute('''
insert into sentry_dsymsymbol
(object_id, address, symbol)
values %s
''' % ', '.join(['(%s, %s, %s)'] * len(items)),
list(chain(*items)))
cur.close()
return
except IntegrityError:
pass
cur = connection.cursor()
for item in items:
cur.execute('''
insert into sentry_dsymsymbol
(object_id, address, symbol)
select
%(object_id)s, %(address)s, %(symbol)s
where not exists (
select 1 from sentry_dsymsymbol
where object_id = %(object_id)s
and address = %(address)s);
''', {
'object_id': item[0],
'address': item[1],
'symbol': item[2],
})
cur.close()
def lookup_symbol(self, instruction_addr, image_addr, uuid,
cpu_name=None, object_path=None, sdk_info=None,
image_vmaddr=None):
# If we use the "none" dsym type we never return a symbol here.
if sdk_info is not None and sdk_info['dsym_type'] == 'none':
return
instruction_addr = parse_addr(instruction_addr)
image_addr = parse_addr(image_addr)
addr_abs = None
if image_vmaddr is not None:
image_vmaddr = parse_addr(image_vmaddr)
addr_abs = image_vmaddr + instruction_addr - image_addr
addr_rel = instruction_addr - image_addr
uuid = six.text_type(uuid).lower()
cur = connection.cursor()
try:
# First try: exact match on uuid (addr_rel)
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o
where o.uuid = %s and
s.object_id = o.id and
s.address <= o.vmaddr + %s and
s.address >= o.vmaddr
order by address desc
limit 1;
''', [uuid, addr_rel])
rv = cur.fetchone()
if rv:
return rv[0]
# Second try: exact match on uuid (addr_abs)
if addr_abs is not None:
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o
where o.uuid = %s and
s.object_id = o.id and
s.address <= %s and
s.address >= %s
order by address desc
limit 1;
''', [uuid, addr_abs, image_vmaddr])
rv = cur.fetchone()
if rv:
return rv[0]
# Third try: exact match on path and arch (addr_rel)
if sdk_info is None or \
cpu_name is None or \
object_path is None:
return
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o,
sentry_dsymsdk k,
sentry_dsymbundle b
where b.sdk_id = k.id and
b.object_id = o.id and
s.object_id = o.id and
k.sdk_name = %s and
k.dsym_type = %s and
k.version_major = %s and
k.version_minor = %s and
k.version_patchlevel = %s and
o.cpu_name = %s and
o.object_path = %s and
s.address <= o.vmaddr + %s and
s.address >= o.vmaddr
order by address desc
limit 1;
''', [sdk_info['sdk_name'], sdk_info['dsym_type'],
sdk_info['version_major'], sdk_info['version_minor'],
sdk_info['version_patchlevel'], cpu_name, object_path,
addr_rel])
rv = cur.fetchone()
if rv:
return rv[0]
# Fourth try: exact match on path and arch (addr_abs)
if addr_abs is not None:
cur.execute('''
select s.symbol
from sentry_dsymsymbol s,
sentry_dsymobject o,
sentry_dsymsdk k,
sentry_dsymbundle b
where b.sdk_id = k.id and
b.object_id = o.id and
s.object_id = o.id and
k.sdk_name = %s and
k.dsym_type = %s and
k.version_major = %s and
k.version_minor = %s and
k.version_patchlevel = %s and
o.cpu_name = %s and
o.object_path = %s and
s.address <= %s and
s.address >= %s
order by address desc
limit 1;
''', [sdk_info['sdk_name'], sdk_info['dsym_type'],
sdk_info['version_major'], sdk_info['version_minor'],
sdk_info['version_patchlevel'], cpu_name, object_path,
addr_abs, image_vmaddr])
rv = cur.fetchone()
if rv:
return rv[0]
finally:
cur.close()
class DSymSymbol(Model):
__core__ = False
object = FlexibleForeignKey('sentry.DSymObject')
address = BoundedBigIntegerField(db_index=True)
symbol = models.TextField()
objects = DSymSymbolManager()
class Meta:
app_label = 'sentry'
db_table = 'sentry_dsymsymbol'
unique_together = [
('object', 'address'),
]
class CommonDSymFile(Model):
__core__ = False
file = FlexibleForeignKey('sentry.File')
object_name = models.TextField()
cpu_name = models.CharField(max_length=40)
__repr__ = sane_repr('object_name', 'cpu_name', 'uuid')
class Meta:
abstract = True
app_label = 'sentry'
@property
def dsym_type(self):
ct = self.file.headers.get('Content-Type').lower()
return KNOWN_DSYM_TYPES.get(ct, 'unknown')
class ProjectDSymFileManager(BaseManager):
def find_missing(self, checksums, project):
if not checksums:
return[]
checksums = [x.lower() for x in checksums]
missing = set(checksums)
found = ProjectDSymFile.objects.filter(
file__checksum__in=checksums,
project=project
).values('file__checksum')
for values in found:
missing.discard(values.values()[0])
return sorted(missing)
def find_by_checksums(self, checksums, project):
if not checksums:
return []
checksums = [x.lower() for x in checksums]
return ProjectDSymFile.objects.filter(
file__checksum__in=checksums,
project=project
)
class ProjectDSymFile(CommonDSymFile):
project = FlexibleForeignKey('sentry.Project', null=True)
uuid = models.CharField(max_length=36)
is_global = False
objects = ProjectDSymFileManager()
class Meta(CommonDSymFile.Meta):
unique_together = (('project', 'uuid'),)
db_table = 'sentry_projectdsymfile'
class GlobalDSymFile(CommonDSymFile):
uuid = models.CharField(max_length=36, unique=True)
is_global = True
class Meta(CommonDSymFile.Meta):
db_table = 'sentry_globaldsymfile'
def _create_macho_dsym_from_uuid(project, cpu_name, uuid, fileobj,
object_name):
extra = {}
if project is None:
cls = GlobalDSymFile
file_type = 'global.dsym'
else:
cls = ProjectDSymFile
extra['project'] = project
file_type = 'project.dsym'
h = hashlib.sha1()
while 1:
chunk = fileobj.read(16384)
if not chunk:
break
h.update(chunk)
checksum = h.hexdigest()
fileobj.seek(0, 0)
try:
rv = cls.objects.get(uuid=uuid, **extra)
if rv.file.checksum == checksum:
return rv
except cls.DoesNotExist:
pass
else:
# The checksum mismatches. In this case we delete the old object
# and perform a re-upload.
rv.delete()
file = File.objects.create(
name=uuid,
type=file_type,
headers={
'Content-Type': 'application/x-mach-binary'
},
)
file.putfile(fileobj)
try:
with transaction.atomic():
rv = cls.objects.create(
file=file,
uuid=uuid,
cpu_name=cpu_name,
object_name=object_name,
**extra
)
except IntegrityError:
file.delete()
rv = cls.objects.get(uuid=uuid, **extra)
resolve_processing_issue(
project=project,
scope='native',
object='dsym:%s' % uuid,
)
return rv
def create_files_from_macho_zip(fileobj, project=None):
scratchpad = tempfile.mkdtemp()
try:
safe_extract_zip(fileobj, scratchpad)
to_create = []
for dirpath, dirnames, filenames in os.walk(scratchpad):
for fn in filenames:
fn = os.path.join(dirpath, fn)
try:
uuids = get_macho_uuids(fn)
except (IOError, ValueError):
# Whatever was contained there, was probably not a
# macho file.
continue
for cpu, uuid in uuids:
to_create.append((cpu, uuid, fn))
rv = []
for cpu, uuid, filename in to_create:
with open(filename, 'rb') as f:
rv.append((_create_macho_dsym_from_uuid(
project, cpu, uuid, f, os.path.basename(filename))))
return rv
finally:
shutil.rmtree(scratchpad)
def find_dsym_file(project, image_uuid):
image_uuid = image_uuid.lower()
try:
return ProjectDSymFile.objects.filter(
uuid=image_uuid,
project=project
).select_related('file').get()
except ProjectDSymFile.DoesNotExist:
pass
try:
return GlobalDSymFile.objects.filter(
uuid=image_uuid
).select_related('file').get()
except GlobalDSymFile.DoesNotExist:
return None
| true | true |
f72cbf17e64a21584865047b98978bd2193a31f9 | 53,060 | py | Python | graphics/basic_plot_functions.py | JCSDA/mpas-jedi | e0780d1fd295912ee4cfb758854c52b6764d4ab9 | [
"Apache-2.0"
] | 2 | 2021-09-25T01:20:10.000Z | 2021-12-17T18:44:53.000Z | graphics/basic_plot_functions.py | JCSDA/mpas-jedi | e0780d1fd295912ee4cfb758854c52b6764d4ab9 | [
"Apache-2.0"
] | null | null | null | graphics/basic_plot_functions.py | JCSDA/mpas-jedi | e0780d1fd295912ee4cfb758854c52b6764d4ab9 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
from copy import deepcopy
import cartopy.crs as ccrs
import datetime as dt
import logging
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import matplotlib
matplotlib.use('AGG')
import matplotlib.axes as maxes
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import plot_utils as pu
import var_utils as vu
import os
_logger = logging.getLogger(__name__)
cmGray = plt.cm.get_cmap("gist_gray")
cmRainbow = plt.cm.get_cmap("gist_rainbow")
cmSpectral = plt.cm.get_cmap("nipy_spectral")
cmHeat = plt.cm.get_cmap("gist_heat")
cmOcean = plt.cm.get_cmap("ocean")
cmNCAR = plt.cm.get_cmap("gist_ncar")
WhiteBlack1 = cmGray(np.linspace(1.0,0.0,17)) # white to black (-90 to -74 C)
BlackRed = cmHeat(np.linspace(0.0,0.5,10)) #black to red (-74 to -65 C)
ROYG = cmSpectral(np.linspace(0.9,0.43,27)) # red, orange, yellow, green, blue (-65 to -39 C)
#GreenBlue = cmNCAR(np.linspace(0.05,0.1,8)) # green to blue (-39 to -32 C)
#BlueCyan = cmRainbow(np.linspace(0.8,0.6,13)) # blue to cyan (-32 to -20 C)
GreenBlueCyan = cmNCAR(np.linspace(0.05,0.2,20)) # green to blue (-39 to -20 C)
#WhiteBlack2 = cmGray(np.linspace(0.9,0.0,51)) # white to black (-20 to 30 C)
MVW = cmNCAR(np.linspace(0.8,0.98,21)) # magenta to violet to white (-20 to 0 C)
WhiteBlack2 = cmGray(np.linspace(0.9,0.0,31)) # white to black (0 to 30 C)
#btcolors = np.concatenate((WhiteBlack1, BlackRed, ROYG, GreenBlue, BlueCyan, WhiteBlack2))
#btcolors = np.concatenate((WhiteBlack1, BlackRed, ROYG, GreenBlueCyan, WhiteBlack2))
btcolors = np.concatenate((WhiteBlack1, BlackRed, ROYG, GreenBlueCyan, MVW, WhiteBlack2))
btCMap = colors.ListedColormap(btcolors)
#This script includes basic plotting functions.
distriZooms = {}
#Full Earth
distriZooms['default'] = {
'cLon': None,
'minLon': -180,
'maxLon': 180,
'minLat': -90,
'maxLat': 90,
}
distriZooms['abi'] = {
'cLon': -75.2,
'minLon': None,
'maxLon': None,
'minLat': None,
'maxLat': None,
}
distriZooms['ahi'] = {
'cLon': 140.7,
'minLon': None,
'maxLon': None,
'minLat': None,
'maxLat': None,
}
def plotDistri(lats,lons,values, \
ObsType,VarName,var_unit,out_name,nstation,levbin, \
dmin=None,dmax=None,dotsize=6,color="rainbow"):
#================================================================
#INPUTS:
# lats - latitude
# lons - longitude
# values - values will be plotted
# ObsType - observation type
# VarName - variable name
# var_unit - variable units
# out_name - will be included in output file name. It can be experiment name.
# nstation - station numbers for sondes.
# levbin - plot all levels together (levbin=all); or plot every level.
# dmin, dmax - min/max values of colorbars, optional
# dotsize - dot size, optional
# color - color scheme, optional
#================================================================
# For some plots that need to change longitude from [-180,180] to [0,360]
# tmp = np.logical_not(lons > 0)
# lons[tmp] = lons[tmp] + 360
#set map=======================================================================
cLon = distriZooms['default']['cLon']
minLon = distriZooms['default']['minLon']
maxLon = distriZooms['default']['maxLon']
minLat = distriZooms['default']['minLat']
maxLat = distriZooms['default']['maxLat']
for key, val in distriZooms.items():
if key in ObsType:
cLon = val['cLon']
minLon = val['minLon']
maxLon = val['maxLon']
minLat = val['minLat']
maxLat = val['maxLat']
if cLon is not None:
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection=ccrs.Orthographic(cLon))
else:
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(projection=ccrs.PlateCarree())
ax.set_global()
#draw points onto map =========================================================
if color == "BT":
if ("abi" in ObsType or "ahi" in ObsType):
cm = btCMap
if dmin is None: dmin = 183
if dmax is None: dmax = 303
else:
cm = plt.cm.get_cmap("gist_ncar")
if dmin is None: dmin = 190
if dmax is None: dmax = 270
else:
cm = plt.cm.get_cmap(color)
finite = np.isfinite(values)
if ((("abi" in ObsType or "ahi" in ObsType)
and finite.sum() > 4e4)
or "model" in ObsType):
# option 1: smoothed contours (note: color bar is not quite right)
# sc=m.contourf(lons[finite], lats[finite], values[finite],
# cm.N, cmap = cm, vmin = dmin, vmax = dmax,
# latlon = True, tri = True, extend='both')
# option 2: pixel contours
# first sort by longitude to avoid bug for cyclic projections in basemap
lonsPlot = lons[finite]
lonsPlot[lonsPlot > 180.0] -= 360.0 # fixes latitude swap bug for cyclic projections
latsPlot = lats[finite]
valuesPlot = values[finite]
lonSort = np.argsort(lonsPlot)
p = plt.pcolor(lonsPlot[lonSort], latsPlot[lonSort], valuesPlot[lonSort],
transform = ccrs.PlateCarree(),
cmap = cm, vmin = dmin, vmax = dmax,
latlon = True, tri = True)
else:
p=ax.scatter(lons[finite], lats[finite], c=values[finite],
transform = ccrs.PlateCarree(),
cmap= cm, s = dotsize)
ax.gridlines(draw_labels=True, xlocs=np.arange(-180,180,60),linestyle='--')
ax.coastlines()
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom",size="5%", pad=0.3,axes_class=plt.Axes)
#fig.add_axes(cax)
plt.colorbar(p,cax=cax,orientation='horizontal') #,cax=cax,ax=ax,orientation='horizontal')
#set title ===================================================================
if nstation == 0 or ObsType == 'satwind':
plt.text(0.5, 1.15, '%s %s %s nlocs:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)])), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
else:
if ObsType[:6] == 'gnssro':
plt.text(0.5, 1.15, '%s %s %s nlocs:%s nprofile:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)]),nstation), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
elif ObsType == 'aircraft':
plt.text(0.5, 1.15, '%s %s %s nlocs:%s nflight:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)]),nstation), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
else:
plt.text(0.5, 1.15, '%s %s %s nlocs:%s nstation:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)]),nstation), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
plt.savefig('distri_%s_%s_%s.png'%(VarName,out_name,levbin),dpi=200,bbox_inches='tight')
plt.close()
def scatterMapFields(
lonVals, latVals, fields,
filename,
minLon = -180., maxLon = 180.,
minLat = -90., maxLat = 90.,
cLon = None,
projection = 'default',
dmin = None, dmax = None,
markers = {},
sizes = {},
cmap = 'gist_ncar',
cbarType = None,
c = {},
logVLim = 1.e-12,
):
# setup map
cLons = np.asarray([])
lonVals_180 = {}
for name in lonVals.keys():
cLon = None
# 0 < longitude <= 360
lonVals_360 = deepcopy(lonVals[name])
while np.max(lonVals_360) >= 360.0:
lonVals_360[lonVals_360 >= 360.0] -= 360.0
while np.min(lonVals_360) < 0.0:
lonVals_360[lonVals_360 < 0.0] += 360.0
# -180 < longitude <= 180
lonVals_180[name] = deepcopy(lonVals_360)
lonVals_180[name][lonVals_180[name] > 180.0] -= 360.0
for lon in [lonVals_360, lonVals_180[name]]:
if np.max(lon) - np.min(lon) <= 180.0:
cLon = 0.5*(np.max(lon) + np.min(lon))
cLons = np.append(cLons, cLon)
anycLonNone = np.any([c is None for c in cLons])
if anycLonNone:
# plot entire Earth
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection=ccrs.Mollweide(0.0))
else:
# plot single projected side of Earth
cLon = cLons[0]
if cLon > 180.0: cLon-=360.0
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection=ccrs.Orthographic(cLon))
assert (cbarType is None or cbarType in ['Log', 'SymLog']), \
'scatterMapFields: invalid cbarType: '+cbarType
for name, field in fields.items():
f = c=c.get(name, field)
finite = np.isfinite(f)
lons = lonVals_180[name][finite]
lats = latVals[name][finite]
f = f[finite]
## transform to pcolormesh and cartopy conventions
# longitude monotonically increasing
lonSort = np.argsort(lons)
lons = lons[lonSort]
lats = lats[lonSort]
f = f[lonSort]
if dmin is None:
vmin = f.min()
else:
vmin = dmin
if dmax is None:
vmax = f.max()
else:
vmax = dmax
if cbarType is None:
norm = None
elif cbarType == 'Log':
if vmin <= logVLim: vmin = logVLim
f[f < vmin] = vmin
norm=colors.LogNorm(vmin=vmin, vmax=vmax)
elif cbarType == 'SymLog':
norm=colors.SymLogNorm(vmin=vmin, vmax=vmax,
linthresh=1.e-4*vmax, linscale=1.0, base=10)
sc = ax.scatter(lons, lats, c=f,
s = sizes.get(name, 1),
cmap = cmap,
norm = norm,
marker = markers.get(name, '.'), linewidth = 0,
transform=ccrs.PlateCarree(),
)
# show full projection extent
ax.set_global()
# add coastlines
ax.coastlines()
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom",size="5%", pad=0.3,axes_class=plt.Axes)
cb = plt.colorbar(sc, cax=cax, orientation='horizontal')
plt.savefig(filename, dpi=200, bbox_inches='tight')
plt.close()
def plotTimeserial2D(Stats,xlabeltime,ylevels,VarName):
#================================================================
#INPUTS:
# Stats - statistics
# xlabeltime - time labels for x-axis
# ylevels - vertical levels for y-axis
# VarName - variable name
#================================================================
zgrid = np.loadtxt("/glade/work/jban/pandac/fix_input/graphics/zgrid_v55.txt")
fig, ax1 = plt.subplots()
xarray = range(len(xlabeltime))
valuemin = np.amin(Stats)
valuemax = np.amax(Stats)
# yonggangyu introduce epsilon and xi for plotting absolutely zero field,
# solving vmin, vcenter, vmax ascending order issue
epsilon = 1.e-8
if (valuemin > 0 or valuemax < 0):
color = 'rainbow'
plt.contourf(xarray,ylevels,Stats,40,vmin=valuemin, vmax=valuemax,cmap=color)
xi=-1
else:
cmap = 'coolwarm'
if ( -valuemin < epsilon and valuemax < epsilon ):
xi=1
valuemin = -epsilon
valuemax = epsilon
elif ( -valuemin < epsilon and valuemax > epsilon ):
xi=2
valuemin = -epsilon
elif ( -valuemin > epsilon and valuemax < epsilon ):
xi=3
valuemax = epsilon
else:
xi=4
#print('xi= '+str(xi)+' valuemin= ',str(valuemin)+' valuemax= ',str(valuemax))
norm = matplotlib.colors.DivergingNorm(vmin=valuemin, vcenter=0, vmax=valuemax)
plt.contourf(xarray,ylevels,Stats,40,vmin=valuemin, vmax=valuemax,norm=norm,cmap=cmap)
xarray = range(len(xlabeltime))
major_ticks = np.arange(0, 56, 5)
ax1.set_yticks(major_ticks)
ax1.set_ylim([0,54])
ax1.set_ylabel('Vertical level',fontsize=15)
ax2 = ax1.twinx()
ax2.set_yticks(major_ticks-1)
ax2.set_yticklabels((zgrid[::5]).astype(int))
ax2.set_ylabel('Height (m)',fontsize=13)
FCDay = ''.join(VarName.split("_")[1:][:-3])
if (FCDay == 'day0.0'):
ax1.set_xlabel('Analysis Time',fontsize=15)
ax1.set_xticks(xarray[::4])
ax1.set_xticklabels(xlabeltime[::4],rotation=90)
elif (FCDay == 'day0.25'):
ax1.set_xlabel( '6h Forecast',fontsize=15)
ax1.set_xticks(xarray[::4])
ax1.set_xticklabels(xlabeltime[::4],rotation=90)
else:
ax1.set_xlabel( 'Lead Time',fontsize=15)
plt.colorbar(extend='both',orientation="horizontal",pad=0.2)
ax1.grid(True)
region = ''.join(VarName.split("_")[2:][:-2])
var = ''.join(VarName.split("_")[3:][:-1])
stats = ''.join(VarName.split("_")[4:])
plt.title(stats+' variable:'+vu.varDictModel[var][1]+'('+ vu.varDictModel[var][0]+') '+region, fontsize = 12)
plt.savefig(VarName+'_TS_2d.png',dpi=200,bbox_inches='tight')
plt.close()
maxLegendEntries = 12
###############################################################################
lenWarnSer = 0
nanWarnSer = 0
def plotSeries(fig, \
linesVals, xVals, \
linesLabel, \
title="", dataLabel="y", \
sciticks=False, logscale= False, signdef=False, \
indepLabel="x", invert_ind_axis=False, \
ny=1, nx=1, nplots=1, iplot=0, \
linesValsMinCI=None, linesValsMaxCI=None, \
dmin=np.NaN, dmax=np.NaN, \
lineAttribOffset=0, \
legend_inside=True,
interiorLabels=True):
# ARGUMENTS
# fig - matplotlib figure object
# linesVals - dependent variable (list of arrays)
# xVals - independent variable on x-axis (array)
# linesLabel - legend label for linesVals (list)
# title - subplot title, optional
# dataLabel - label for linesVals, optional
# sciticks - whether linesVals needs scientific formatting for ticks, optional
# logscale - y-axis is scaled logarithmically, optional, overrides sciticks
# signdef - whether linesVals is positive/negative definite, optional
# indepLabel - label for xVals, optional
# invert_ind_axis - whether to invert x-axis orientation, optional
# ny, nx - number of subplots in x/y direction, optional
# nplots - total number of subplots, optional
# iplot - this subplot index (starting at 0), optional
# linesValsMinCI - minimum error bound for linesVals (list of arrays), optional
# linesValsMaxCI - maximum error bound for linesVals (list of arrays), optional
# Note: linesValsMinCI and linesValsMaxCI must be specified together
# lineAttribOffset - offset for selecting line attributes, optional
# dmin, dmax - min/max values of linesVals, optional
# legend_inside - whether legend should be placed inside the subplot, optional
ax = fig.add_subplot(ny, nx, iplot+1)
#title
ax.set_title(title,fontsize=5)
#add lines
plotVals = np.asarray([])
nLines = 0
for iline, lineVals in enumerate(linesVals):
if np.all(np.isnan(lineVals)):
global nanWarnSer
if nanWarnSer==0:
_logger.warning("skipping all-NaN data")
_logger.warning(title+"; "+indepLabel+"; "+linesLabel[iline])
nanWarnSer=nanWarnSer+1
continue
if len(lineVals)!=len(xVals):
global lenWarnSer
if lenWarnSer==0:
_logger.warning("skipping data where len(x)!=len(y)")
_logger.warning(title+"; "+indepLabel+"; "+linesLabel[iline])
lenWarnSer=lenWarnSer+1
continue
# Plot line for each lineVals that has non-missing data
pColor = pu.plotColor(len(linesVals),iline+lineAttribOffset)
ax.plot(xVals, lineVals, \
color=pColor, \
label=linesLabel[iline], \
ls=pu.plotLineStyle(len(linesVals),iline+lineAttribOffset), \
linewidth=0.5)
nLines += 1
plotVals = np.append(plotVals, lineVals)
# Add shaded error regions if specified
if linesValsMinCI is not None and \
linesValsMaxCI is not None:
# test statistical significance versus zero
if signdef:
significant = np.empty(len(lineVals))
significant[:] = np.NaN
else:
significant = np.multiply(linesValsMinCI[iline], linesValsMaxCI[iline])
significant = np.array([x if np.isfinite(x) else -1.0 for x in significant])
lineArr = np.array(lineVals)
xArr = np.array(xVals)
negsiginds = np.array([i for i,x in enumerate(significant)
if (x > 0.0 and lineArr[i] < 0.0)],dtype=int)
if len(negsiginds) > 0:
ax.plot(xArr[negsiginds], lineArr[negsiginds], \
color=pColor, \
ls='', \
marker='v', \
markersize=1.5)
possiginds = np.array([i for i,x in enumerate(significant)
if (x > 0.0 and lineArr[i] > 0.0)],dtype=int)
if len(possiginds) > 0:
ax.plot(xArr[possiginds], lineArr[possiginds], \
color=pColor, \
ls='', \
marker='^', \
markersize=1.5)
ax.plot(xVals, linesValsMinCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.plot(xVals, linesValsMaxCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
color=pColor, \
edgecolor=pColor, \
linewidth=0.0, alpha = 0.1)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
where=significant > 0.0, \
color=pColor, \
edgecolor=pColor, \
linewidth=0.2, alpha = 0.3)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
# add horizontal zero line for unbounded quantities
if not signdef:
ax.plot([xVals[0], xVals[-1]], [0., 0.], ls="--", c=".3", \
linewidth=0.7,markersize=0)
# standardize x-limits
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,plotVals,signdef)
#axes settings
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
isLogScale = logscale
if logscale:
nonzero = np.logical_and(np.greater(np.abs(plotVals), 0.), np.isfinite(plotVals))
if nonzero.sum() > 0:
vmin = np.nanmin(np.abs(plotVals[nonzero]))
vmax = np.nanmax(np.abs(plotVals[nonzero]))
if signdef:
# log tick labels look bad for single decade
if vmax / vmin > 10.0:
ax.set_yscale('log')
else:
isLogScale = False
else:
ax.set_yscale('symlog')
else:
isLogScale = False
if isLogScale and np.isfinite(maxdval) and maxdval > 0.:
ax.set_ylim(None, maxdval)
if np.abs(vmin) > 0.:
ax.set_ylim(vmin, None)
if not isLogScale:
if sciticks:
ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_ylim(mindval,maxdval)
if maxdval-mindval < 1.0 or \
maxdval-mindval > 100.0:
ax.tick_params(axis='y',rotation=-35)
ax.yaxis.get_offset_text().set_fontsize(3)
#handle interior subplot ticks/labels
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_xlabel(indepLabel,fontsize=4)
if interiorLabels or iy == ny-1:
ax.set_ylabel(dataLabel,fontsize=4)
#legend
if nLines <= maxLegendEntries:
if legend_inside:
#INSIDE AXES
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=1)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
#OUTSIDE AXES
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
if invert_ind_axis:
ax.invert_xaxis()
ax.grid()
return
###############################################################################
lenWarnProf = 0
nanWarnProf = 0
def plotProfile(fig, \
linesVals, yVals, \
linesLabel, \
title="", dataLabel="x", \
sciticks=False, logscale=False, signdef=False, \
indepLabel="y", invert_ind_axis=False, \
ny=1, nx=1, nplots=1, iplot=0, \
linesValsMinCI=None, linesValsMaxCI=None, \
dmin=np.NaN, dmax=np.NaN, \
lineAttribOffset=0, \
legend_inside=True,
interiorLabels=True):
# ARGUMENTS
# fig - matplotlib figure object
# linesVals - dependent variable (list of arrays)
# yVals - independent variable on y-axis (array)
# linesLabel - legend label for linesVals (list)
# title - subplot title, optional
# dataLabel - label for linesVals, optional
# sciticks - whether linesVals needs scientific formatting for ticks, optional
# logscale - x-axis is scaled logarithmically, optional, overrides sciticks
# signdef - whether linesVals is positive/negative definite, optional
# indepLabel - label for yVals, optional
# invert_ind_axis - whether to invert y-axis orientation, optional
# ny, nx - number of subplots in x/y direction, optional
# nplots - total number of subplots, optional
# iplot - this subplot index (starting at 0), optional
# linesValsMinCI - minimum error bound for linesVals (list of arrays), optional
# linesValsMaxCI - maximum error bound for linesVals (list of arrays), optional
# Note: linesValsMinCI and linesValsMaxCI must be specified together
# lineAttribOffset - offset for selecting line attributes, optional
# dmin, dmax - min/max values of linesVals, optional
# legend_inside - whether legend should be placed inside the subplot, optional
ax = fig.add_subplot(ny, nx, iplot+1)
#title
ax.set_title(title,fontsize=5)
#add lines
plotVals = np.asarray([])
nLines = 0
for iline, lineVals in enumerate(linesVals):
if np.all(np.isnan(lineVals)):
global nanWarnProf
if nanWarnProf==0:
_logger.warning("skipping all-NaN data")
_logger.warning(title+"; "+dataLabel+"; "+linesLabel[iline])
nanWarnProf=nanWarnProf+1
continue
if len(lineVals)!=len(yVals):
global lenWarnProf
if lenWarnProf==0:
_logger.warning("skipping data where len(x)!=len(y)")
_logger.warning(title+"; "+dataLabel+"; "+linesLabel[iline])
lenWarnProf=lenWarnProf+1
continue
# Plot line for each lineVals that has non-missing data
pColor = pu.plotColor(len(linesVals),iline+lineAttribOffset)
ax.plot(lineVals, yVals, \
color=pColor, \
label=linesLabel[iline], \
ls=pu.plotLineStyle(len(linesVals),iline+lineAttribOffset), \
linewidth=0.5)
nLines += 1
plotVals = np.append(plotVals,lineVals)
# Add shaded error regions if specified
if linesValsMinCI is not None and \
linesValsMaxCI is not None:
# test statistical significance versus zero
if signdef:
significant = np.empty(len(lineVals))
significant[:] = np.NaN
else:
significant = np.multiply(linesValsMinCI[iline], linesValsMaxCI[iline])
significant = np.array([x if np.isfinite(x) else -1.0 for x in significant])
lineArr = np.array(lineVals)
yArr = np.array(yVals)
negsiginds = np.array([i for i,x in enumerate(significant)
if (x > 0.0 and lineArr[i] < 0.0)],dtype=int)
if len(negsiginds) > 0:
ax.plot(lineArr[negsiginds], yArr[negsiginds], \
color=pColor, \
ls='', \
marker='<', \
markersize=1.5)
possiginds = np.array([i for i,x in enumerate(significant)
if (x > 0.0 and lineArr[i] > 0.0)],dtype=int)
if len(possiginds) > 0:
ax.plot(lineArr[possiginds], yArr[possiginds], \
color=pColor, \
ls='', \
marker='>', \
markersize=1.5)
ax.plot(linesValsMinCI[iline], yVals, \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.plot(linesValsMaxCI[iline], yVals, \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.fill_betweenx(yVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
color=pColor, \
edgecolor=pColor, \
linewidth=0.0, alpha = 0.1)
ax.fill_betweenx(yVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
where=significant > 0.0, \
color=pColor, \
edgecolor=pColor, \
linewidth=0.2, alpha = 0.3)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
# add vertical zero line for unbounded quantities
if not signdef:
ax.plot([0., 0.], [yVals[0], yVals[-1]], ls="--", c=".3", \
linewidth=0.7,markersize=0)
# standardize x-limits
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,plotVals,signdef)
#axes settings
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
isLogScale = logscale
if logscale:
nonzero = np.logical_and(np.greater(np.abs(plotVals), 0.), np.isfinite(plotVals))
if nonzero.sum() > 0:
vmin = np.nanmin(np.abs(plotVals[nonzero]))
vmax = np.nanmax(np.abs(plotVals[nonzero]))
if signdef:
# log tick labels look bad for single decade
if vmax / vmin > 10.0:
ax.set_xscale('log')
else:
isLogScale = False
else:
ax.set_xscale('symlog')
else:
isLogScale = False
if isLogScale and np.isfinite(maxdval) and maxdval > 0.:
ax.set_xlim(None, maxdval)
if np.abs(mindval) > 0.:
ax.set_xlim(mindval, None)
if not isLogScale:
if sciticks:
ax.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_xlim(mindval,maxdval)
if maxdval-mindval < 1.0 or \
maxdval-mindval > 100.0:
ax.tick_params(axis='x',rotation=-35)
ax.xaxis.get_offset_text().set_fontsize(3)
#handle interior subplot ticks/labels
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_xlabel(dataLabel,fontsize=4)
if interiorLabels or iy == ny-1:
ax.set_ylabel(indepLabel,fontsize=4)
#legend
if nLines <= maxLegendEntries:
if legend_inside:
#INSIDE AXES
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=1)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
#OUTSIDE AXES
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
if invert_ind_axis:
ax.invert_yaxis()
ax.grid()
return
###############################################################################
lenWarnTS=0
nanWarnTS=0
def plotTimeSeries(fig, \
xsDates, linesVals, \
linesLabel, \
title="", dataLabel="", \
sciticks=False, logscale = False, signdef=False, \
ny=1, nx=1, nplots=1, iplot=0, \
linesValsMinCI=None, linesValsMaxCI=None, \
dmin=np.NaN, dmax=np.NaN, \
lineAttribOffset=0, \
legend_inside=True,
interiorLabels=True):
# ARGUMENTS
# fig - matplotlib figure object
# xsDates - date x-values (list/array or list of lists/arrays
# of float seconds, dt.timedelta, dt.datetime)
# linesVals - dependent variable (list of arrays)
# linesLabel - legend label for linesVals (list)
# title - subplot title, optional
# dataLabel - label for linesVals, optional
# sciticks - whether linesVals needs scientific formatting for ticks, optional
# logscale - y-axis is scaled logarithmically, optional, overrides sciticks
# signdef - whether linesVals is positive/negative definite, optional
# ny, nx - number of subplots in x/y direction, optional
# nplots - total number of subplots, optional
# iplot - this subplot index (starting at 0), optional
# linesValsMinCI - minimum error bound for linesVals (list of arrays), optional
# linesValsMaxCI - maximum error bound for linesVals (list of arrays), optional
# Note: linesValsMinCI and linesValsMaxCI must be specified together
# lineAttribOffset - offset for selecting line attributes, optional
# dmin, dmax - min/max values of linesVals, optional
# legend_inside - whether legend should be placed inside the subplot, optional
ax = fig.add_subplot(ny, nx, iplot+1)
#title
ax.set_title(title,fontsize=5)
#add lines
plotVals = np.asarray([])
nLines = 0
jline = 0
for iline, lineVals in enumerate(linesVals):
if np.all(np.isnan(lineVals)):
global nanWarnTS
if nanWarnTS==0:
_logger.warning("skipping all-NaN data")
_logger.warning(title+"; "+dataLabel+"; "+linesLabel[iline])
nanWarnTS=nanWarnTS+1
continue
#float xVals
if isinstance(xsDates[0],(list,np.ndarray)):
xVals = pu.TDeltas2Seconds(xsDates[min([iline,len(xsDates)-1])])
else:
xVals = pu.TDeltas2Seconds(xsDates)
if len(lineVals)!=len(xVals):
global lenWarnTS
if lenWarnTS==0:
_logger.warning("skipping data where len(x)!=len(y)")
_logger.warning(title+"; "+dataLabel+"; "+linesLabel[iline])
lenWarnTS=lenWarnTS+1
continue
if jline == 0:
minX = xVals[0]
maxX = xVals[-1]
else:
minX = min([xVals[0], minX])
maxX = max([xVals[-1], maxX])
jline += 1
# Plot line for each lineVals that has non-missing data
pColor = pu.plotColor(len(linesVals),iline+lineAttribOffset)
ax.plot(xVals, lineVals, \
label=linesLabel[iline], \
color=pColor, \
ls=pu.plotLineStyle(len(linesVals),iline+lineAttribOffset), \
linewidth=0.5)
nLines += 1
plotVals = np.append(plotVals, lineVals)
# Add shaded CI regions if specified
if linesValsMinCI is not None and \
linesValsMaxCI is not None:
# test statistical significance versus zero
if signdef:
significant = np.empty(len(lineVals))
significant[:] = np.NaN
else:
significant = np.multiply(linesValsMinCI[iline], linesValsMaxCI[iline])
significant = np.array([x if np.isfinite(x) else -1.0 for x in significant])
lineArr = np.array(lineVals)
xArr = np.array(xVals)
negsiginds = np.array([i for i,x in enumerate(significant)
if (x > 0.0 and lineArr[i] < 0.0)],dtype=int)
if len(negsiginds) > 0:
ax.plot(xArr[negsiginds], lineArr[negsiginds], \
color=pColor, \
ls='', \
marker='v', \
markersize=1.5)
possiginds = np.array([i for i,x in enumerate(significant)
if (x > 0.0 and lineArr[i] > 0.0)],dtype=int)
if len(possiginds) > 0:
ax.plot(xArr[possiginds], lineArr[possiginds], \
color=pColor, \
ls='', \
marker='^', \
markersize=1.5)
ax.plot(xVals, linesValsMinCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.plot(xVals, linesValsMaxCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
color=pColor, \
edgecolor=pColor, \
linewidth=0.0, alpha = 0.1)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
where=significant > 0.0, \
color=pColor, \
edgecolor=pColor, \
linewidth=0.2, alpha = 0.3)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
# standardize y-limits
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,plotVals,signdef)
# add horizontal zero line for unbounded quantities
if not signdef:
ax.plot([minX, maxX], [0., 0.], ls="--", c=".3", \
linewidth=0.7,markersize=0)
#axes settings
if isinstance(xsDates[0],(list,np.ndarray)):
pu.format_x_for_dates(ax, xsDates[0])
else:
pu.format_x_for_dates(ax, xsDates)
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
isLogScale = logscale
if logscale:
nonzero = np.logical_and(np.greater(np.abs(plotVals), 0.), np.isfinite(plotVals))
if nonzero.sum() > 0:
vmin = np.nanmin(np.abs(plotVals[nonzero]))
vmax = np.nanmax(np.abs(plotVals[nonzero]))
if signdef:
# log tick labels look bad for single decade
if vmax / vmin > 10.0:
ax.set_yscale('log')
else:
isLogScale = False
else:
ax.set_yscale('symlog')
else:
isLogScale = False
if isLogScale and np.isfinite(maxdval) and maxdval > 0.:
ax.set_ylim(None, maxdval)
if np.abs(vmin) > 0.:
ax.set_ylim(vmin, None)
if not isLogScale:
if sciticks:
ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_ylim(mindval,maxdval)
if maxdval-mindval < 1.0 or \
maxdval-mindval > 100.0:
ax.tick_params(axis='y',rotation=-35)
ax.yaxis.get_offset_text().set_fontsize(3)
ax.grid()
#handle interior subplot ticks/labels
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_ylabel(dataLabel,fontsize=4)
#legend
if nLines <= maxLegendEntries:
if legend_inside:
#INSIDE AXES
nlcol = np.int(np.ceil(np.sqrt(nLines)))
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=nlcol)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
#OUTSIDE AXES
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
return
###############################################################################
def plotTimeSeries2D(fig, \
xDates, yVals, contourVals, \
title="", clabel="", \
sciticks=False, logscale=False, signdef=False, \
dataLabel="y", invert_ind_axis=False, \
ny=1, nx=1, nplots=1, iplot=0, \
dmin=np.NaN, dmax=np.NaN,
interiorLabels=True):
# ARGUMENTS
# fig - matplotlib figure object
# xDates - date x-values (array of float seconds, dt.timedelta, dt.datetime)
# yVals - second independent variable
# contourVals - dependent variable (2d array)
# title - subplot title, optional
# clabel - label for dependent variable, optional
# sciticks - whether contourVals needs scientific formatting for ticks, optional
# logscale - whether contours are spaced logarithmically, optional, overrides sciticks
# signdef - whether contourVals is positive/negative definite, optional
# dataLabel - label for yVals, optional
# invert_ind_axis - whether to invert y-axis orientation, optional
# ny, nx - number of subplots in x/y direction, optional
# nplots - total number of subplots, optional
# iplot - this subplot index (starting at 0), optional
# dmin, dmax - min/max values of contourVals, optional
ax = fig.add_subplot(ny, nx, iplot+1)
if (np.isnan(contourVals)).all():
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
xVals = pu.TDeltas2Seconds(xDates)
# standardize c-limits
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,contourVals,signdef)
if signdef:
cmapName = 'BuPu'
nlevs = 18
# scientific contours
cint = contourVals.astype(int)
isInt = np.all((contourVals - cint) == 0)
if isInt:
minscid = np.nanmax(np.array([1., dmin]))
else:
minscid = maxdval*1.e-5
lognorm = colors.LogNorm(vmin=minscid, vmax=maxdval)
else:
cmapName = 'seismic'
nlevs = 28
# scientific contours
lognorm = colors.SymLogNorm(vmin=mindval, vmax=maxdval,
linthresh=1.e-3*maxdval, linscale=1.3, base=10)
# plot contour
# option 1: smoothed contours
#cp = ax.contourf(xVals, yVals, contourVals, nlevs, cmap=cmapName, extend='both', \
# vmin=mindval, vmax=maxdval)
# option 2: pixel contours
cmap = plt.get_cmap(cmapName)
cmap.set_bad(color = 'k', alpha = 1.0)
if logscale:
norm = lognorm
else:
levels = mticker.MaxNLocator(nbins=nlevs).tick_values(mindval,maxdval)
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
xVals_pcolor, yVals_pcolor = transformXY_for_pcolor(xVals,yVals)
cp = ax.pcolormesh(xVals_pcolor, yVals_pcolor, contourVals, cmap=cmap, norm=norm)
#title
ax.set_title(title,fontsize=5)
#axes settings
pu.format_x_for_dates(ax, xDates)
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
#handle interior subplot ticks/labels
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_ylabel(dataLabel,fontsize=4)
if interiorLabels or ix == nx-1:
#colorbar
m = plt.cm.ScalarMappable(cmap=cmap)
m.set_array(contourVals)
m.set_norm(norm)
if (np.isfinite(mindval) and
np.isfinite(maxdval) and
not logscale):
m.set_clim(mindval,maxdval)
cb = plt.colorbar(m, ax=ax)
#scientific formatting
if sciticks and not logscale:
cb.ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
cb.ax.yaxis.get_offset_text().set_fontsize(3)
cb.ax.tick_params(labelsize=3)
cb.set_label(clabel,fontsize=5)
if invert_ind_axis:
ax.invert_yaxis()
# optionally add a grid
#ax.grid()
return
###############################################################################
def transformXY_for_pcolor(xs,ys):
# adjust centered x and y values to edges to work with pcolormesh
# note: works best for regularly spaced data
xs_diff = xs[1] - xs[0]
# extend xs by 2
# fill in first endpoint
xs_extend = [xs[0]-xs_diff]
# fill in internal values
for x in xs: xs_extend.append(x)
# fill in last endpoint
xs_extend.append(xs_extend[-1]+(xs[-1]-xs[-2]))
# calculate the midpoints
xs_pcolormesh_midpoints = []
for ii, x in enumerate(xs_extend[:-1]):
xs_pcolormesh_midpoints.append(x+0.5*(xs_extend[ii+1] - xs_extend[ii]))
ys_diff = ys[1] - ys[0]
# extend ys by 2
# fill in first endpoint
ys_extend = [ys[0]-ys_diff]
# fill in internal values
for y in ys: ys_extend.append(y)
# fill in last endpoint
ys_extend.append(ys_extend[-1]+(ys[-1]-ys[-2]))
# calculate the midpoints
ys_pcolormesh_midpoints = []
for ii, y in enumerate(ys_extend[:-1]):
ys_pcolormesh_midpoints.append(y+0.5*(ys_extend[ii+1] - ys_extend[ii]))
return xs_pcolormesh_midpoints, ys_pcolormesh_midpoints
###############################################################################
lenWarnPDF = 0
nanWarnPDF = 0
def plotPDF(fig,
countsVals, xVals,
countsLabel,
title="",
indepLabel="x",
ny=1, nx=1, nplots=1, iplot=0,
lineAttribOffset=1,
legend_inside=True,
interiorLabels=True):
# ARGUMENTS
# fig - matplotlib figure object
# countsVals - list of arrays, each containing counts across xVals
# xVals - independent variable on x-axis (array)
# countsLabel - legend label for countsVals (list)
# title - subplot title, optional
# indepLabel - label for xVals, optional
# ny, nx - number of subplots in x/y direction, optional
# nplots - total number of subplots, optional
# iplot - this subplot index (starting at 0), optional
# lineAttribOffset - offset for selecting line attributes, optional
# legend_inside - whether legend should be placed inside the subplot, optional
ax = fig.add_subplot(ny, nx, iplot+1)
#title
ax.set_title(title,fontsize=5)
#add counts
plotVals = []
nPDFs = 0
for ihist, countVals in enumerate(countsVals):
if np.all(np.isnan(countVals)):
global nanWarnPDF
if nanWarnPDF==0:
_logger.warning("skipping all-NaN data")
_logger.warning(title+"; "+indepLabel+"; "+countsLabel[ihist])
nanWarnPDF=nanWarnPDF+1
continue
if len(countVals)!=len(xVals):
global lenWarnPDF
if lenWarnPDF==0:
_logger.warning("skipping data where len(x)!=len(y)")
_logger.warning(title+"; "+indepLabel+"; "+countsLabel[ihist])
lenWarnPDF=lenWarnPDF+1
continue
# Plot line for each countVals that has non-missing data
# assume constant dx between bins
dx = xVals[1] - xVals[0]
ax.plot(xVals, np.divide(countVals,np.sum(countVals)*dx),
color=pu.plotColor(len(countsVals),ihist+lineAttribOffset),
label=countsLabel[ihist],
ls=pu.plotLineStyle(len(countsVals),ihist+lineAttribOffset),
linewidth=0.5)
nPDFs = nPDFs + 1
plotVals.append(countVals)
if nPDFs == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
# add a standard normal pdf
from scipy.stats import norm
ax.plot(xVals, norm.pdf(xVals),
color='k',
ls='-',
linewidth=0.35,
label='N(0,1)'
)
#axes settings
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
plt.yscale('log')
ax.set_ylim(bottom=1.e-6)
#handle interior subplot ticks/labels
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_xlabel(indepLabel,fontsize=4)
ax.set_ylabel('PDF',fontsize=4)
#legend
if legend_inside:
#INSIDE AXES
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=1)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
#OUTSIDE AXES
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
ax.grid()
return
###############################################################################
lenWarnRamp = 0
nanWarnRamp = 0
def plotfitRampComposite(fig,
xVals,
countVals,
meanVals,
rmsVals,
stdVals,
title="", dataLabel="y", \
indepLabel="x",
ny=1, nx=1, nplots=1, iplot=0,
lineAttribOffset=1,
legend_inside=True,
interiorLabels=True):
# ARGUMENTS
# fig - matplotlib figure object
# countVals - Count of quantity (array)
# meanVals - Mean of quantity (array)
# rmsVals - RMS of quantity (array)
# stdVals - STD of quantity (array)
# xVals - independent variable on x-axis (array)
# title - subplot title, optional
# dataLabel - label for y-axis, optional
# indepLabel - label for xVals, optional
# ny, nx - number of subplots in x/y direction, optional
# nplots - total number of subplots, optional
# iplot - this subplot index (starting at 0), optional
# lineAttribOffset - offset for selecting line attributes, optional
# legend_inside - whether legend should be placed inside the subplot, optional
ax = fig.add_subplot(ny, nx, iplot+1)
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
#title
ax.set_title(title,fontsize=5)
#add lines
plotVals = []
nLines = 0
linesLabel = ['RMS','STD','Mean']
for iline, lineVals in enumerate([rmsVals,stdVals,meanVals]):
if np.all(np.isnan(lineVals)):
global nanWarnRamp
if nanWarnRamp==0:
_logger.warning("skipping all-NaN data")
_logger.warning(title+"; "+indepLabel+"; "+linesLabel[iline])
nanWarnRamp=nanWarnRamp+1
continue
if len(lineVals)!=len(xVals):
global lenWarnRamp
if lenWarnRamp==0:
_logger.warning("skipping data where len(x)!=len(y)")
_logger.warning(title+"; "+indepLabel+"; "+linesLabel[iline])
lenWarnRamp=lenWarnRamp+1
continue
# Plot line for each lineVals that has non-missing data
pColor = pu.plotColor(4,iline+lineAttribOffset)
ax.plot(xVals, lineVals,
color=pColor,
label=linesLabel[iline],
ls=pu.plotLineStyle(4,iline+lineAttribOffset),
linewidth=0.6)
nLines += 1
plotVals.append(lineVals)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
# Add fit for stdVals here using info from countVals
ind0 = np.argmax(countVals)
indexMaxX4Std = 0
for ii, std in enumerate(stdVals):
if np.isfinite(std): indexMaxX4Std = ii
indexMaxX = indexMaxX4Std
maxCount = 0
for ii, count in enumerate(countVals):
if count > maxCount: maxCount = count
if count < 0.002*maxCount:
indexMaxX = ii
break
if indexMaxX > indexMaxX4Std:
ind1 = np.argmax(stdVals[0:indexMaxX4Std])
else:
ind1 = np.argmax(stdVals[0:indexMaxX])
weights = [0.2]*(ind1-ind0+1)
weights[0] = 1.0
p = np.polyfit(xVals[ind0:ind1+1],stdVals[ind0:ind1+1],1,
w=weights)
X0 = xVals[ind0]
ERR0 = X0 * p[0] + p[1]
# X1 = xVals[ind1]
# ERR1 = X1 * p[0] + p[1]
ERR1 = stdVals[ind1]
X1 = (ERR1 - p[1]) / p[0]
ERRfitDict = {
'bu':{
'X': [round(X0,2), round(X1,2)],
'ERR': [round(ERR0,2), round(ERR1,2)],
},
'YAML':{
'X0': [round(X0,2)],
'X1': [round(X1,2)],
'ERR0': [round(ERR0,2)],
'ERR1': [round(ERR1,2)],
},
}
fitX = np.asarray([0.0] + ERRfitDict['bu']['X'] + [xVals[indexMaxX4Std]])
fitERR = np.asarray([ERR0] + ERRfitDict['bu']['ERR'] + [ERR1])
plotVals.append(fitERR)
pColor = pu.plotColor(4,1+lineAttribOffset)
ax.plot(fitX, fitERR,
color=pColor,
label='Fit-STD',
ls='-.',
linewidth=1.2,
marker='+',
ms=1.5
)
#axes settings
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
# standardize x-limits
mindval, maxdval = pu.get_clean_ax_limits(plotVals=plotVals)
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_ylim(mindval,maxdval)
#handle interior subplot ticks/labels
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_xlabel(indepLabel,fontsize=4)
if interiorLabels or iy == ny-1:
ax.set_ylabel(dataLabel,fontsize=4)
#legend
if legend_inside:
#INSIDE AXES
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=1)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
#OUTSIDE AXES
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
ax.grid()
# Add count on RHS y-axis
ax2 = ax.twinx()
color = 'black'
if interiorLabels or ix == nx:
ax2.set_ylabel('Count',fontsize=4,color=color)
ax2.plot(xVals[:indexMaxX4Std], countVals[:indexMaxX4Std],
color=color,
label='Count',
ls=':',
linewidth=0.5)
ax2.tick_params(axis='y', labelcolor=color)
ax2.yaxis.set_tick_params(labelsize=3)
plt.yscale('log')
ax2.set_ylim(bottom=100.)
return ERRfitDict
| 35.827144 | 115 | 0.561006 |
from copy import deepcopy
import cartopy.crs as ccrs
import datetime as dt
import logging
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import matplotlib
matplotlib.use('AGG')
import matplotlib.axes as maxes
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import plot_utils as pu
import var_utils as vu
import os
_logger = logging.getLogger(__name__)
cmGray = plt.cm.get_cmap("gist_gray")
cmRainbow = plt.cm.get_cmap("gist_rainbow")
cmSpectral = plt.cm.get_cmap("nipy_spectral")
cmHeat = plt.cm.get_cmap("gist_heat")
cmOcean = plt.cm.get_cmap("ocean")
cmNCAR = plt.cm.get_cmap("gist_ncar")
WhiteBlack1 = cmGray(np.linspace(1.0,0.0,17))
BlackRed = cmHeat(np.linspace(0.0,0.5,10))
ROYG = cmSpectral(np.linspace(0.9,0.43,27))
))
WhiteBlack2 = cmGray(np.linspace(0.9,0.0,31))
btcolors = np.concatenate((WhiteBlack1, BlackRed, ROYG, GreenBlueCyan, MVW, WhiteBlack2))
btCMap = colors.ListedColormap(btcolors)
distriZooms = {}
distriZooms['default'] = {
'cLon': None,
'minLon': -180,
'maxLon': 180,
'minLat': -90,
'maxLat': 90,
}
distriZooms['abi'] = {
'cLon': -75.2,
'minLon': None,
'maxLon': None,
'minLat': None,
'maxLat': None,
}
distriZooms['ahi'] = {
'cLon': 140.7,
'minLon': None,
'maxLon': None,
'minLat': None,
'maxLat': None,
}
def plotDistri(lats,lons,values, \
ObsType,VarName,var_unit,out_name,nstation,levbin, \
dmin=None,dmax=None,dotsize=6,color="rainbow"):
cLon = distriZooms['default']['cLon']
minLon = distriZooms['default']['minLon']
maxLon = distriZooms['default']['maxLon']
minLat = distriZooms['default']['minLat']
maxLat = distriZooms['default']['maxLat']
for key, val in distriZooms.items():
if key in ObsType:
cLon = val['cLon']
minLon = val['minLon']
maxLon = val['maxLon']
minLat = val['minLat']
maxLat = val['maxLat']
if cLon is not None:
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection=ccrs.Orthographic(cLon))
else:
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(projection=ccrs.PlateCarree())
ax.set_global()
if color == "BT":
if ("abi" in ObsType or "ahi" in ObsType):
cm = btCMap
if dmin is None: dmin = 183
if dmax is None: dmax = 303
else:
cm = plt.cm.get_cmap("gist_ncar")
if dmin is None: dmin = 190
if dmax is None: dmax = 270
else:
cm = plt.cm.get_cmap(color)
finite = np.isfinite(values)
if ((("abi" in ObsType or "ahi" in ObsType)
and finite.sum() > 4e4)
or "model" in ObsType):
lonsPlot = lons[finite]
lonsPlot[lonsPlot > 180.0] -= 360.0
latsPlot = lats[finite]
valuesPlot = values[finite]
lonSort = np.argsort(lonsPlot)
p = plt.pcolor(lonsPlot[lonSort], latsPlot[lonSort], valuesPlot[lonSort],
transform = ccrs.PlateCarree(),
cmap = cm, vmin = dmin, vmax = dmax,
latlon = True, tri = True)
else:
p=ax.scatter(lons[finite], lats[finite], c=values[finite],
transform = ccrs.PlateCarree(),
cmap= cm, s = dotsize)
ax.gridlines(draw_labels=True, xlocs=np.arange(-180,180,60),linestyle='--')
ax.coastlines()
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom",size="5%", pad=0.3,axes_class=plt.Axes)
plt.colorbar(p,cax=cax,orientation='horizontal')
if nstation == 0 or ObsType == 'satwind':
plt.text(0.5, 1.15, '%s %s %s nlocs:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)])), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
else:
if ObsType[:6] == 'gnssro':
plt.text(0.5, 1.15, '%s %s %s nlocs:%s nprofile:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)]),nstation), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
elif ObsType == 'aircraft':
plt.text(0.5, 1.15, '%s %s %s nlocs:%s nflight:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)]),nstation), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
else:
plt.text(0.5, 1.15, '%s %s %s nlocs:%s nstation:%s' \
%(ObsType,VarName,var_unit,len(values[~np.isnan(values)]),nstation), \
horizontalalignment='center', \
fontsize=12, transform = ax.transAxes)
plt.savefig('distri_%s_%s_%s.png'%(VarName,out_name,levbin),dpi=200,bbox_inches='tight')
plt.close()
def scatterMapFields(
lonVals, latVals, fields,
filename,
minLon = -180., maxLon = 180.,
minLat = -90., maxLat = 90.,
cLon = None,
projection = 'default',
dmin = None, dmax = None,
markers = {},
sizes = {},
cmap = 'gist_ncar',
cbarType = None,
c = {},
logVLim = 1.e-12,
):
cLons = np.asarray([])
lonVals_180 = {}
for name in lonVals.keys():
cLon = None
lonVals_360 = deepcopy(lonVals[name])
while np.max(lonVals_360) >= 360.0:
lonVals_360[lonVals_360 >= 360.0] -= 360.0
while np.min(lonVals_360) < 0.0:
lonVals_360[lonVals_360 < 0.0] += 360.0
lonVals_180[name] = deepcopy(lonVals_360)
lonVals_180[name][lonVals_180[name] > 180.0] -= 360.0
for lon in [lonVals_360, lonVals_180[name]]:
if np.max(lon) - np.min(lon) <= 180.0:
cLon = 0.5*(np.max(lon) + np.min(lon))
cLons = np.append(cLons, cLon)
anycLonNone = np.any([c is None for c in cLons])
if anycLonNone:
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection=ccrs.Mollweide(0.0))
else:
cLon = cLons[0]
if cLon > 180.0: cLon-=360.0
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection=ccrs.Orthographic(cLon))
assert (cbarType is None or cbarType in ['Log', 'SymLog']), \
'scatterMapFields: invalid cbarType: '+cbarType
for name, field in fields.items():
f = c=c.get(name, field)
finite = np.isfinite(f)
lons = lonVals_180[name][finite]
lats = latVals[name][finite]
f = f[finite]
ons[lonSort]
lats = lats[lonSort]
f = f[lonSort]
if dmin is None:
vmin = f.min()
else:
vmin = dmin
if dmax is None:
vmax = f.max()
else:
vmax = dmax
if cbarType is None:
norm = None
elif cbarType == 'Log':
if vmin <= logVLim: vmin = logVLim
f[f < vmin] = vmin
norm=colors.LogNorm(vmin=vmin, vmax=vmax)
elif cbarType == 'SymLog':
norm=colors.SymLogNorm(vmin=vmin, vmax=vmax,
linthresh=1.e-4*vmax, linscale=1.0, base=10)
sc = ax.scatter(lons, lats, c=f,
s = sizes.get(name, 1),
cmap = cmap,
norm = norm,
marker = markers.get(name, '.'), linewidth = 0,
transform=ccrs.PlateCarree(),
)
ax.set_global()
ax.coastlines()
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom",size="5%", pad=0.3,axes_class=plt.Axes)
cb = plt.colorbar(sc, cax=cax, orientation='horizontal')
plt.savefig(filename, dpi=200, bbox_inches='tight')
plt.close()
def plotTimeserial2D(Stats,xlabeltime,ylevels,VarName):
zgrid = np.loadtxt("/glade/work/jban/pandac/fix_input/graphics/zgrid_v55.txt")
fig, ax1 = plt.subplots()
xarray = range(len(xlabeltime))
valuemin = np.amin(Stats)
valuemax = np.amax(Stats)
epsilon = 1.e-8
if (valuemin > 0 or valuemax < 0):
color = 'rainbow'
plt.contourf(xarray,ylevels,Stats,40,vmin=valuemin, vmax=valuemax,cmap=color)
xi=-1
else:
cmap = 'coolwarm'
if ( -valuemin < epsilon and valuemax < epsilon ):
xi=1
valuemin = -epsilon
valuemax = epsilon
elif ( -valuemin < epsilon and valuemax > epsilon ):
xi=2
valuemin = -epsilon
elif ( -valuemin > epsilon and valuemax < epsilon ):
xi=3
valuemax = epsilon
else:
xi=4
norm = matplotlib.colors.DivergingNorm(vmin=valuemin, vcenter=0, vmax=valuemax)
plt.contourf(xarray,ylevels,Stats,40,vmin=valuemin, vmax=valuemax,norm=norm,cmap=cmap)
xarray = range(len(xlabeltime))
major_ticks = np.arange(0, 56, 5)
ax1.set_yticks(major_ticks)
ax1.set_ylim([0,54])
ax1.set_ylabel('Vertical level',fontsize=15)
ax2 = ax1.twinx()
ax2.set_yticks(major_ticks-1)
ax2.set_yticklabels((zgrid[::5]).astype(int))
ax2.set_ylabel('Height (m)',fontsize=13)
FCDay = ''.join(VarName.split("_")[1:][:-3])
if (FCDay == 'day0.0'):
ax1.set_xlabel('Analysis Time',fontsize=15)
ax1.set_xticks(xarray[::4])
ax1.set_xticklabels(xlabeltime[::4],rotation=90)
elif (FCDay == 'day0.25'):
ax1.set_xlabel( '6h Forecast',fontsize=15)
ax1.set_xticks(xarray[::4])
ax1.set_xticklabels(xlabeltime[::4],rotation=90)
else:
ax1.set_xlabel( 'Lead Time',fontsize=15)
plt.colorbar(extend='both',orientation="horizontal",pad=0.2)
ax1.grid(True)
region = ''.join(VarName.split("_")[2:][:-2])
var = ''.join(VarName.split("_")[3:][:-1])
stats = ''.join(VarName.split("_")[4:])
plt.title(stats+' variable:'+vu.varDictModel[var][1]+'('+ vu.varDictModel[var][0]+') '+region, fontsize = 12)
plt.savefig(VarName+'_TS_2d.png',dpi=200,bbox_inches='tight')
plt.close()
maxLegendEntries = 12
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.plot(xVals, linesValsMaxCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
color=pColor, \
edgecolor=pColor, \
linewidth=0.0, alpha = 0.1)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
where=significant > 0.0, \
color=pColor, \
edgecolor=pColor, \
linewidth=0.2, alpha = 0.3)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
if not signdef:
ax.plot([xVals[0], xVals[-1]], [0., 0.], ls="--", c=".3", \
linewidth=0.7,markersize=0)
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,plotVals,signdef)
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
isLogScale = logscale
if logscale:
nonzero = np.logical_and(np.greater(np.abs(plotVals), 0.), np.isfinite(plotVals))
if nonzero.sum() > 0:
vmin = np.nanmin(np.abs(plotVals[nonzero]))
vmax = np.nanmax(np.abs(plotVals[nonzero]))
if signdef:
if vmax / vmin > 10.0:
ax.set_yscale('log')
else:
isLogScale = False
else:
ax.set_yscale('symlog')
else:
isLogScale = False
if isLogScale and np.isfinite(maxdval) and maxdval > 0.:
ax.set_ylim(None, maxdval)
if np.abs(vmin) > 0.:
ax.set_ylim(vmin, None)
if not isLogScale:
if sciticks:
ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_ylim(mindval,maxdval)
if maxdval-mindval < 1.0 or \
maxdval-mindval > 100.0:
ax.tick_params(axis='y',rotation=-35)
ax.yaxis.get_offset_text().set_fontsize(3)
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_xlabel(indepLabel,fontsize=4)
if interiorLabels or iy == ny-1:
ax.set_ylabel(dataLabel,fontsize=4)
if nLines <= maxLegendEntries:
if legend_inside:
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=1)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
if invert_ind_axis:
ax.invert_xaxis()
ax.grid()
return
als, \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.plot(linesValsMaxCI[iline], yVals, \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.fill_betweenx(yVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
color=pColor, \
edgecolor=pColor, \
linewidth=0.0, alpha = 0.1)
ax.fill_betweenx(yVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
where=significant > 0.0, \
color=pColor, \
edgecolor=pColor, \
linewidth=0.2, alpha = 0.3)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
if not signdef:
ax.plot([0., 0.], [yVals[0], yVals[-1]], ls="--", c=".3", \
linewidth=0.7,markersize=0)
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,plotVals,signdef)
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
isLogScale = logscale
if logscale:
nonzero = np.logical_and(np.greater(np.abs(plotVals), 0.), np.isfinite(plotVals))
if nonzero.sum() > 0:
vmin = np.nanmin(np.abs(plotVals[nonzero]))
vmax = np.nanmax(np.abs(plotVals[nonzero]))
if signdef:
if vmax / vmin > 10.0:
ax.set_xscale('log')
else:
isLogScale = False
else:
ax.set_xscale('symlog')
else:
isLogScale = False
if isLogScale and np.isfinite(maxdval) and maxdval > 0.:
ax.set_xlim(None, maxdval)
if np.abs(mindval) > 0.:
ax.set_xlim(mindval, None)
if not isLogScale:
if sciticks:
ax.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_xlim(mindval,maxdval)
if maxdval-mindval < 1.0 or \
maxdval-mindval > 100.0:
ax.tick_params(axis='x',rotation=-35)
ax.xaxis.get_offset_text().set_fontsize(3)
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_xlabel(dataLabel,fontsize=4)
if interiorLabels or iy == ny-1:
ax.set_ylabel(indepLabel,fontsize=4)
if nLines <= maxLegendEntries:
if legend_inside:
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=1)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
if invert_ind_axis:
ax.invert_yaxis()
ax.grid()
return
if (x > 0.0 and lineArr[i] > 0.0)],dtype=int)
if len(possiginds) > 0:
ax.plot(xArr[possiginds], lineArr[possiginds], \
color=pColor, \
ls='', \
marker='^', \
markersize=1.5)
ax.plot(xVals, linesValsMinCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.plot(xVals, linesValsMaxCI[iline], \
color=pColor, \
alpha=0.4, \
ls='-', \
linewidth=0.5)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
color=pColor, \
edgecolor=pColor, \
linewidth=0.0, alpha = 0.1)
ax.fill_between(xVals, linesValsMinCI[iline], linesValsMaxCI[iline], \
where=significant > 0.0, \
color=pColor, \
edgecolor=pColor, \
linewidth=0.2, alpha = 0.3)
if nLines == 0:
ax.tick_params(axis='x',labelbottom=False)
ax.tick_params(axis='y',labelleft=False)
return
mindval, maxdval = pu.get_clean_ax_limits(dmin,dmax,plotVals,signdef)
if not signdef:
ax.plot([minX, maxX], [0., 0.], ls="--", c=".3", \
linewidth=0.7,markersize=0)
if isinstance(xsDates[0],(list,np.ndarray)):
pu.format_x_for_dates(ax, xsDates[0])
else:
pu.format_x_for_dates(ax, xsDates)
ax.xaxis.set_tick_params(labelsize=3)
ax.yaxis.set_tick_params(labelsize=3)
isLogScale = logscale
if logscale:
nonzero = np.logical_and(np.greater(np.abs(plotVals), 0.), np.isfinite(plotVals))
if nonzero.sum() > 0:
vmin = np.nanmin(np.abs(plotVals[nonzero]))
vmax = np.nanmax(np.abs(plotVals[nonzero]))
if signdef:
if vmax / vmin > 10.0:
ax.set_yscale('log')
else:
isLogScale = False
else:
ax.set_yscale('symlog')
else:
isLogScale = False
if isLogScale and np.isfinite(maxdval) and maxdval > 0.:
ax.set_ylim(None, maxdval)
if np.abs(vmin) > 0.:
ax.set_ylim(vmin, None)
if not isLogScale:
if sciticks:
ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
if (np.isfinite(mindval) and
np.isfinite(maxdval)):
ax.set_ylim(mindval,maxdval)
if maxdval-mindval < 1.0 or \
maxdval-mindval > 100.0:
ax.tick_params(axis='y',rotation=-35)
ax.yaxis.get_offset_text().set_fontsize(3)
ax.grid()
ix = int(iplot)%int(nx)
iy = int(iplot)/int(nx)
if not interiorLabels \
and (iy < ny-2 or ( iy == ny-2 and (int(nplots)%int(nx)==0 or ix <= (int(nplots)%int(nx) - 1)) )):
ax.tick_params(axis='x',labelbottom=False)
if interiorLabels or ix == 0:
ax.set_ylabel(dataLabel,fontsize=4)
if nLines <= maxLegendEntries:
if legend_inside:
nlcol = np.int(np.ceil(np.sqrt(nLines)))
lh = ax.legend(loc='best',fontsize=3,frameon=True,\
framealpha=0.4,ncol=nlcol)
lh.get_frame().set_linewidth(0.0)
elif ix==nx-1 or iplot==nplots-1:
ax.legend(loc='upper left',fontsize=3,frameon=False, \
bbox_to_anchor=(1.02, 1), borderaxespad=0)
return
| true | true |
f72cbfd0caae91239053996913ba8621fe6047da | 636 | py | Python | RestaurantReview/manage.py | sehyun-seankim/Django_project_restaurant_review | 5d2eb90486f8064aec16538a71c667d830d3db37 | [
"MIT"
] | null | null | null | RestaurantReview/manage.py | sehyun-seankim/Django_project_restaurant_review | 5d2eb90486f8064aec16538a71c667d830d3db37 | [
"MIT"
] | null | null | null | RestaurantReview/manage.py | sehyun-seankim/Django_project_restaurant_review | 5d2eb90486f8064aec16538a71c667d830d3db37 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'RestaurantReview.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| 28.909091 | 80 | 0.687107 |
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'RestaurantReview.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| true | true |
f72cbfdf7f7c4f11bbe1601b54f70466e1e3e688 | 5,321 | py | Python | slackclient/_response.py | lovexi/CodingMonkey-Bot-Python-version | 52561d2b15a78119769099d304a98f80da53a010 | [
"MIT"
] | null | null | null | slackclient/_response.py | lovexi/CodingMonkey-Bot-Python-version | 52561d2b15a78119769099d304a98f80da53a010 | [
"MIT"
] | null | null | null | slackclient/_response.py | lovexi/CodingMonkey-Bot-Python-version | 52561d2b15a78119769099d304a98f80da53a010 | [
"MIT"
] | null | null | null | import json
import random
import math
import os
from crawling._twitter import twitter_crawling
class Response(object):
def __init__(self, token):
self.name = ""
self.token = token
self.greetingList = ['Hello {}, welcome to the Equifax Hackathon channel! Have fun :). You can type help for more details!'
, 'Nice to see you here, {} ! What can I do for you (please type help!)'
, 'I am willing to do anything for you {} ! Type help so I can help you!']
self.help_msg = {"text": 'Don\'t Worry {} ! I will show you how to communicate with me :).',
"attachments":[{"pretext": "Command line:",
"color": "#36a64f", "text": "hi: Say hello to me, so that I know you are here!"},
{"color": "#36a64f", "text": "print message: I will grab all detailed ID message for you, such as channel id or user id :)"},
{"color": "#e2ffb6", "text": "help: I can show you all commands I can understand :)"},
{"color": "#415677", "text": "show name or nameID: I can know that your target ID"},
{"color": "#b27485", "text": "select dataLocation: I can know where I can grab data for you"}
]}
self.select_msg = {"text": "Where do you want to grab personal information for {} ?",
"attachments": [{"pretext": "You can choose:", "color": "#36a64f", "text": "Facebook + limits"},
{"color": "#36a64f", "text": "Twitter + limits"},
{"color": "#415677", "text": "Craigslist"}
]}
def response(self, data, channel, sc, user):
type = data["type"]
user_info = sc.api_read("users.info", token = self.token, user = user)
username = user_info["user"]["name"]
if type == "hello":
sc.rtm_send_message(channel, self.greetingList[int(math.floor(random.random()*3))].format(username))
if "user" in data.keys() and data["user"] == user:
if (type == "message"):
text = data["text"].lower()
if (text.startswith("hi")):
sc.rtm_send_message(channel, "I am CodingMonkey Bot. Nice to meet you here {0}!".format(username))
if (text.startswith("print")):
sc.rtm_send_message(channel, data[text[5:].strip()])
if (text.startswith("help")):
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = self.help_msg["text"].format(username), attachments = self.help_msg["attachments"])
if (text.startswith("show")):
command_msg = str(text).split(' ')
self.name = command_msg[1]
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = self.select_msg["text"].format(username),
attachments = self.select_msg["attachments"])
if (text.startswith("select")):
command_msg = str(text).split(' ')
if (command_msg[1].lower() == "twitter"):
twi = twitter_crawling()
limits = 5
if len(command_msg) == 3:
limits = int(command_msg[2])
twitter_info = json.dumps(twi.spiderInfo(self.name, limits))
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = "Here are the results in Twitter:", attachments = twitter_info)
elif (command_msg[1].lower() == "facebook"):
root = os.getcwd()
relative_path = "slackclient/data/facebookY.json"
abs_path = os.path.join(root, relative_path)
with open(abs_path) as facebook_file:
facebook_info = json.load(facebook_file)
facebook_info = json.dumps(facebook_info)
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = "Here are the results in Facebook:", attachments = facebook_info)
elif (command_msg[1].lower() == "craigslist"):
root = os.getcwd()
relative_path = "slackclient/data/craigslist.json"
abs_path = os.path.join(root, relative_path)
with open(abs_path) as craigslist_file:
craigslist_info = json.load(craigslist_file)
craigslist_info = json.dumps(craigslist_info)
craigslist_info = craigslist_info.replace("'", "%100")
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = "Here are the results in Craigslist:", attachments = craigslist_info)
| 58.472527 | 153 | 0.516256 | import json
import random
import math
import os
from crawling._twitter import twitter_crawling
class Response(object):
def __init__(self, token):
self.name = ""
self.token = token
self.greetingList = ['Hello {}, welcome to the Equifax Hackathon channel! Have fun :). You can type help for more details!'
, 'Nice to see you here, {} ! What can I do for you (please type help!)'
, 'I am willing to do anything for you {} ! Type help so I can help you!']
self.help_msg = {"text": 'Don\'t Worry {} ! I will show you how to communicate with me :).',
"attachments":[{"pretext": "Command line:",
"color": "#36a64f", "text": "hi: Say hello to me, so that I know you are here!"},
{"color": "#36a64f", "text": "print message: I will grab all detailed ID message for you, such as channel id or user id :)"},
{"color": "#e2ffb6", "text": "help: I can show you all commands I can understand :)"},
{"color": "#415677", "text": "show name or nameID: I can know that your target ID"},
{"color": "#b27485", "text": "select dataLocation: I can know where I can grab data for you"}
]}
self.select_msg = {"text": "Where do you want to grab personal information for {} ?",
"attachments": [{"pretext": "You can choose:", "color": "#36a64f", "text": "Facebook + limits"},
{"color": "#36a64f", "text": "Twitter + limits"},
{"color": "#415677", "text": "Craigslist"}
]}
def response(self, data, channel, sc, user):
type = data["type"]
user_info = sc.api_read("users.info", token = self.token, user = user)
username = user_info["user"]["name"]
if type == "hello":
sc.rtm_send_message(channel, self.greetingList[int(math.floor(random.random()*3))].format(username))
if "user" in data.keys() and data["user"] == user:
if (type == "message"):
text = data["text"].lower()
if (text.startswith("hi")):
sc.rtm_send_message(channel, "I am CodingMonkey Bot. Nice to meet you here {0}!".format(username))
if (text.startswith("print")):
sc.rtm_send_message(channel, data[text[5:].strip()])
if (text.startswith("help")):
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = self.help_msg["text"].format(username), attachments = self.help_msg["attachments"])
if (text.startswith("show")):
command_msg = str(text).split(' ')
self.name = command_msg[1]
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = self.select_msg["text"].format(username),
attachments = self.select_msg["attachments"])
if (text.startswith("select")):
command_msg = str(text).split(' ')
if (command_msg[1].lower() == "twitter"):
twi = twitter_crawling()
limits = 5
if len(command_msg) == 3:
limits = int(command_msg[2])
twitter_info = json.dumps(twi.spiderInfo(self.name, limits))
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = "Here are the results in Twitter:", attachments = twitter_info)
elif (command_msg[1].lower() == "facebook"):
root = os.getcwd()
relative_path = "slackclient/data/facebookY.json"
abs_path = os.path.join(root, relative_path)
with open(abs_path) as facebook_file:
facebook_info = json.load(facebook_file)
facebook_info = json.dumps(facebook_info)
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = "Here are the results in Facebook:", attachments = facebook_info)
elif (command_msg[1].lower() == "craigslist"):
root = os.getcwd()
relative_path = "slackclient/data/craigslist.json"
abs_path = os.path.join(root, relative_path)
with open(abs_path) as craigslist_file:
craigslist_info = json.load(craigslist_file)
craigslist_info = json.dumps(craigslist_info)
craigslist_info = craigslist_info.replace("'", "%100")
sc.api_call("chat.postMessage", token = self.token, channel = channel,
username = "codingmonkey", text = "Here are the results in Craigslist:", attachments = craigslist_info)
| true | true |
f72cc038fe01f625fd75044fc25d9c661707b934 | 241 | py | Python | fabfile/__init__.py | lem-usp/Bio507 | 67b8f8f677e6c1f39ad257d456f0cc0cac289022 | [
"MIT"
] | null | null | null | fabfile/__init__.py | lem-usp/Bio507 | 67b8f8f677e6c1f39ad257d456f0cc0cac289022 | [
"MIT"
] | null | null | null | fabfile/__init__.py | lem-usp/Bio507 | 67b8f8f677e6c1f39ad257d456f0cc0cac289022 | [
"MIT"
] | null | null | null | from fabric.state import output
from .development import *
#
# Fabric configuration
#
output['debug'] = False # see full command list
def help():
''' Fabfile documentation '''
local('python -c "import fabfile; help(fabfile)"')
| 16.066667 | 54 | 0.680498 | from fabric.state import output
from .development import *
output['debug'] = False
def help():
local('python -c "import fabfile; help(fabfile)"')
| true | true |
f72cc0e34bd07cf91c3cd084ab5e50132bdbe531 | 5,036 | py | Python | sahara/plugins/vanilla/hadoop2/validation.py | hortonworksqe/sahara | b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270 | [
"Apache-2.0"
] | 1 | 2016-04-13T17:07:05.000Z | 2016-04-13T17:07:05.000Z | sahara/plugins/vanilla/hadoop2/validation.py | hortonworksqe/sahara | b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270 | [
"Apache-2.0"
] | null | null | null | sahara/plugins/vanilla/hadoop2/validation.py | hortonworksqe/sahara | b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2014 Mirantis Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sahara.plugins.general import exceptions as ex
from sahara.plugins.general import utils as u
from sahara.plugins.vanilla.hadoop2 import config_helper as cu
from sahara.plugins.vanilla import utils as vu
from sahara.utils import general as gu
def validate_cluster_creating(pctx, cluster):
nn_count = _get_inst_count(cluster, 'namenode')
if nn_count != 1:
raise ex.InvalidComponentCountException('namenode', 1, nn_count)
snn_count = _get_inst_count(cluster, 'secondarynamenode')
if snn_count not in [0, 1]:
raise ex.InvalidComponentCountException('secondarynamenode', '0 or 1',
snn_count)
rm_count = _get_inst_count(cluster, 'resourcemanager')
if rm_count not in [0, 1]:
raise ex.InvalidComponentCountException('resourcemanager', '0 or 1',
rm_count)
hs_count = _get_inst_count(cluster, 'historyserver')
if hs_count not in [0, 1]:
raise ex.InvalidComponentCountException('historyserver', '0 or 1',
hs_count)
nm_count = _get_inst_count(cluster, 'nodemanager')
if rm_count == 0:
if nm_count > 0:
raise ex.RequiredServiceMissingException('resourcemanager',
required_by='nodemanager')
oo_count = _get_inst_count(cluster, 'oozie')
dn_count = _get_inst_count(cluster, 'datanode')
if oo_count not in [0, 1]:
raise ex.InvalidComponentCountException('oozie', '0 or 1', oo_count)
if oo_count == 1:
if dn_count < 1:
raise ex.RequiredServiceMissingException('datanode',
required_by='oozie')
if nm_count < 1:
raise ex.RequiredServiceMissingException('nodemanager',
required_by='oozie')
if hs_count != 1:
raise ex.RequiredServiceMissingException('historyserver',
required_by='oozie')
rep_factor = cu.get_config_value(pctx, 'HDFS', 'dfs.replication', cluster)
if dn_count < rep_factor:
raise ex.InvalidComponentCountException(
'datanode', rep_factor, dn_count, 'Number of datanodes must be not'
' less than dfs.replication.')
def validate_additional_ng_scaling(cluster, additional):
rm = vu.get_resourcemanager(cluster)
scalable_processes = _get_scalable_processes()
for ng_id in additional:
ng = gu.get_by_id(cluster.node_groups, ng_id)
if not set(ng.node_processes).issubset(scalable_processes):
msg = "Vanilla plugin cannot scale nodegroup with processes: %s"
raise ex.NodeGroupCannotBeScaled(ng.name,
msg % ' '.join(ng.node_processes))
if not rm and 'nodemanager' in ng.node_processes:
msg = ("Vanilla plugin cannot scale node group with processes "
"which have no master-processes run in cluster")
raise ex.NodeGroupCannotBeScaled(ng.name, msg)
def validate_existing_ng_scaling(pctx, cluster, existing):
scalable_processes = _get_scalable_processes()
dn_to_delete = 0
for ng in cluster.node_groups:
if ng.id in existing:
if ng.count > existing[ng.id] and "datanode" in ng.node_processes:
dn_to_delete += ng.count - existing[ng.id]
if not set(ng.node_processes).issubset(scalable_processes):
msg = ("Vanilla plugin cannot scale nodegroup "
"with processes: %s")
raise ex.NodeGroupCannotBeScaled(
ng.name, msg % ' '.join(ng.node_processes))
dn_amount = len(vu.get_datanodes(cluster))
rep_factor = cu.get_config_value(pctx, 'HDFS', 'dfs.replication', cluster)
if dn_to_delete > 0 and dn_amount - dn_to_delete < rep_factor:
msg = ("Vanilla plugin cannot shrink cluster because it would be not "
"enough nodes for replicas (replication factor is %s)")
raise ex.ClusterCannotBeScaled(
cluster.name, msg % rep_factor)
def _get_scalable_processes():
return ['datanode', 'nodemanager']
def _get_inst_count(cluster, process):
return sum([ng.count for ng in u.get_node_groups(cluster, process)])
| 41.619835 | 79 | 0.633241 |
from sahara.plugins.general import exceptions as ex
from sahara.plugins.general import utils as u
from sahara.plugins.vanilla.hadoop2 import config_helper as cu
from sahara.plugins.vanilla import utils as vu
from sahara.utils import general as gu
def validate_cluster_creating(pctx, cluster):
nn_count = _get_inst_count(cluster, 'namenode')
if nn_count != 1:
raise ex.InvalidComponentCountException('namenode', 1, nn_count)
snn_count = _get_inst_count(cluster, 'secondarynamenode')
if snn_count not in [0, 1]:
raise ex.InvalidComponentCountException('secondarynamenode', '0 or 1',
snn_count)
rm_count = _get_inst_count(cluster, 'resourcemanager')
if rm_count not in [0, 1]:
raise ex.InvalidComponentCountException('resourcemanager', '0 or 1',
rm_count)
hs_count = _get_inst_count(cluster, 'historyserver')
if hs_count not in [0, 1]:
raise ex.InvalidComponentCountException('historyserver', '0 or 1',
hs_count)
nm_count = _get_inst_count(cluster, 'nodemanager')
if rm_count == 0:
if nm_count > 0:
raise ex.RequiredServiceMissingException('resourcemanager',
required_by='nodemanager')
oo_count = _get_inst_count(cluster, 'oozie')
dn_count = _get_inst_count(cluster, 'datanode')
if oo_count not in [0, 1]:
raise ex.InvalidComponentCountException('oozie', '0 or 1', oo_count)
if oo_count == 1:
if dn_count < 1:
raise ex.RequiredServiceMissingException('datanode',
required_by='oozie')
if nm_count < 1:
raise ex.RequiredServiceMissingException('nodemanager',
required_by='oozie')
if hs_count != 1:
raise ex.RequiredServiceMissingException('historyserver',
required_by='oozie')
rep_factor = cu.get_config_value(pctx, 'HDFS', 'dfs.replication', cluster)
if dn_count < rep_factor:
raise ex.InvalidComponentCountException(
'datanode', rep_factor, dn_count, 'Number of datanodes must be not'
' less than dfs.replication.')
def validate_additional_ng_scaling(cluster, additional):
rm = vu.get_resourcemanager(cluster)
scalable_processes = _get_scalable_processes()
for ng_id in additional:
ng = gu.get_by_id(cluster.node_groups, ng_id)
if not set(ng.node_processes).issubset(scalable_processes):
msg = "Vanilla plugin cannot scale nodegroup with processes: %s"
raise ex.NodeGroupCannotBeScaled(ng.name,
msg % ' '.join(ng.node_processes))
if not rm and 'nodemanager' in ng.node_processes:
msg = ("Vanilla plugin cannot scale node group with processes "
"which have no master-processes run in cluster")
raise ex.NodeGroupCannotBeScaled(ng.name, msg)
def validate_existing_ng_scaling(pctx, cluster, existing):
scalable_processes = _get_scalable_processes()
dn_to_delete = 0
for ng in cluster.node_groups:
if ng.id in existing:
if ng.count > existing[ng.id] and "datanode" in ng.node_processes:
dn_to_delete += ng.count - existing[ng.id]
if not set(ng.node_processes).issubset(scalable_processes):
msg = ("Vanilla plugin cannot scale nodegroup "
"with processes: %s")
raise ex.NodeGroupCannotBeScaled(
ng.name, msg % ' '.join(ng.node_processes))
dn_amount = len(vu.get_datanodes(cluster))
rep_factor = cu.get_config_value(pctx, 'HDFS', 'dfs.replication', cluster)
if dn_to_delete > 0 and dn_amount - dn_to_delete < rep_factor:
msg = ("Vanilla plugin cannot shrink cluster because it would be not "
"enough nodes for replicas (replication factor is %s)")
raise ex.ClusterCannotBeScaled(
cluster.name, msg % rep_factor)
def _get_scalable_processes():
return ['datanode', 'nodemanager']
def _get_inst_count(cluster, process):
return sum([ng.count for ng in u.get_node_groups(cluster, process)])
| true | true |
f72cc0fbea74a83a8775b2c4a4948f97cf3aff29 | 5,959 | py | Python | DeepReinforcementLearning/funcs.py | Christoper-Harvey/1st-Capstone | 93630a4d5f4a2d939c8b5f74f11b5b33052e3f72 | [
"MIT"
] | 1 | 2019-06-13T13:11:52.000Z | 2019-06-13T13:11:52.000Z | DeepReinforcementLearning/funcs.py | Christoper-Harvey/1st-Capstone | 93630a4d5f4a2d939c8b5f74f11b5b33052e3f72 | [
"MIT"
] | null | null | null | DeepReinforcementLearning/funcs.py | Christoper-Harvey/1st-Capstone | 93630a4d5f4a2d939c8b5f74f11b5b33052e3f72 | [
"MIT"
] | 2 | 2019-04-30T19:14:11.000Z | 2019-06-13T13:11:57.000Z | import numpy as np
import random
import loggers as lg
from game import Game, GameState
from model import Residual_CNN
from agent import Agent, User
import config
def playMatchesBetweenVersions(env, run_version, player1version, player2version, EPISODES, logger, turns_until_tau0, goes_first = 0):
if player1version == -1:
player1 = User('player1', env.state_size, env.action_size)
else:
player1_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, env.input_shape, env.action_size, config.HIDDEN_CNN_LAYERS)
if player1version > 0:
player1_network = player1_NN.read(env.name, run_version, player1version)
player1_NN.model.set_weights(player1_network.get_weights())
player1 = Agent('player1', env.state_size, env.action_size, config.p1_MCTS_SIMS, config.CPUCT, player1_NN)
if player2version == -1:
player2 = User('player2', env.state_size, env.action_size)
else:
player2_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, env.input_shape, env.action_size, config.HIDDEN_CNN_LAYERS)
if player2version > 0:
player2_network = player2_NN.read(env.name, run_version, player2version)
player2_NN.model.set_weights(player2_network.get_weights())
player2 = Agent('player2', env.state_size, env.action_size, config.p2_MCTS_SIMS, config.CPUCT, player2_NN)
scores, memory, points, sp_scores = playMatches(player1, player2, EPISODES, logger, turns_until_tau0, None, goes_first)
return (scores, memory, points, sp_scores)
def playMatches(player1, player2, EPISODES, logger, turns_until_tau0, memory = None, goes_first = 0):
env = Game()
scores = {player1.name:0, "drawn": 0, player2.name:0}
sp_scores = {'sp':0, "drawn": 0, 'nsp':0}
points = {player1.name:[], player2.name:[]}
for e in range(EPISODES):
logger.info('====================')
logger.info('EPISODE %d OF %d', e+1, EPISODES)
logger.info('====================')
print (str(e+1) + ' ', end='')
state = env.reset()
done = 0
turn = 0
player1.mcts = None
player2.mcts = None
if goes_first == 0:
player1Starts = random.randint(0,1) * 2 - 1
else:
player1Starts = goes_first
if player1Starts == 1:
players = {1:{"agent": player1, "name":player1.name}
, -1: {"agent": player2, "name":player2.name}
}
logger.info(player1.name + ' plays as X')
else:
players = {1:{"agent": player2, "name":player2.name}
, -1: {"agent": player1, "name":player1.name}
}
logger.info(player2.name + ' plays as X')
logger.info('--------------')
env.gameState.render(logger)
while done == 0:
turn = turn + 1
#### Run the MCTS algo and return an action
if turn < turns_until_tau0:
action, pi, MCTS_value, NN_value = players[state.playerTurn]['agent'].act(state, 1)
else:
action, pi, MCTS_value, NN_value = players[state.playerTurn]['agent'].act(state, 0)
if memory != None:
####Commit the move to memory
memory.commit_stmemory(env.identities, state, pi)
logger.info('action: %d', action)
for r in range(env.grid_shape[0]):
logger.info(['----' if x == 0 else '{0:.2f}'.format(np.round(x,2)) for x in pi[env.grid_shape[1]*r : (env.grid_shape[1]*r + env.grid_shape[1])]])
logger.info('MCTS perceived value for %s: %f', state.pieces[str(state.playerTurn)] ,np.round(MCTS_value,2))
logger.info('NN perceived value for %s: %f', state.pieces[str(state.playerTurn)] ,np.round(NN_value,2))
logger.info('====================')
### Do the action
state, value, done, _ = env.step(action) #the value of the newState from the POV of the new playerTurn i.e. -1 if the previous player played a winning move
env.gameState.render(logger)
if done == 1:
if memory != None:
#### If the game is finished, assign the values correctly to the game moves
for move in memory.stmemory:
if move['playerTurn'] == state.playerTurn:
move['value'] = value
else:
move['value'] = -value
memory.commit_ltmemory()
if value == 1:
logger.info('%s WINS!', players[state.playerTurn]['name'])
scores[players[state.playerTurn]['name']] = scores[players[state.playerTurn]['name']] + 1
if state.playerTurn == 1:
sp_scores['sp'] = sp_scores['sp'] + 1
else:
sp_scores['nsp'] = sp_scores['nsp'] + 1
elif value == -1:
logger.info('%s WINS!', players[-state.playerTurn]['name'])
scores[players[-state.playerTurn]['name']] = scores[players[-state.playerTurn]['name']] + 1
if state.playerTurn == 1:
sp_scores['nsp'] = sp_scores['nsp'] + 1
else:
sp_scores['sp'] = sp_scores['sp'] + 1
else:
logger.info('DRAW...')
scores['drawn'] = scores['drawn'] + 1
sp_scores['drawn'] = sp_scores['drawn'] + 1
pts = state.score
points[players[state.playerTurn]['name']].append(pts[0])
points[players[-state.playerTurn]['name']].append(pts[1])
return (scores, memory, points, sp_scores)
| 41.096552 | 167 | 0.545058 | import numpy as np
import random
import loggers as lg
from game import Game, GameState
from model import Residual_CNN
from agent import Agent, User
import config
def playMatchesBetweenVersions(env, run_version, player1version, player2version, EPISODES, logger, turns_until_tau0, goes_first = 0):
if player1version == -1:
player1 = User('player1', env.state_size, env.action_size)
else:
player1_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, env.input_shape, env.action_size, config.HIDDEN_CNN_LAYERS)
if player1version > 0:
player1_network = player1_NN.read(env.name, run_version, player1version)
player1_NN.model.set_weights(player1_network.get_weights())
player1 = Agent('player1', env.state_size, env.action_size, config.p1_MCTS_SIMS, config.CPUCT, player1_NN)
if player2version == -1:
player2 = User('player2', env.state_size, env.action_size)
else:
player2_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, env.input_shape, env.action_size, config.HIDDEN_CNN_LAYERS)
if player2version > 0:
player2_network = player2_NN.read(env.name, run_version, player2version)
player2_NN.model.set_weights(player2_network.get_weights())
player2 = Agent('player2', env.state_size, env.action_size, config.p2_MCTS_SIMS, config.CPUCT, player2_NN)
scores, memory, points, sp_scores = playMatches(player1, player2, EPISODES, logger, turns_until_tau0, None, goes_first)
return (scores, memory, points, sp_scores)
def playMatches(player1, player2, EPISODES, logger, turns_until_tau0, memory = None, goes_first = 0):
env = Game()
scores = {player1.name:0, "drawn": 0, player2.name:0}
sp_scores = {'sp':0, "drawn": 0, 'nsp':0}
points = {player1.name:[], player2.name:[]}
for e in range(EPISODES):
logger.info('====================')
logger.info('EPISODE %d OF %d', e+1, EPISODES)
logger.info('====================')
print (str(e+1) + ' ', end='')
state = env.reset()
done = 0
turn = 0
player1.mcts = None
player2.mcts = None
if goes_first == 0:
player1Starts = random.randint(0,1) * 2 - 1
else:
player1Starts = goes_first
if player1Starts == 1:
players = {1:{"agent": player1, "name":player1.name}
, -1: {"agent": player2, "name":player2.name}
}
logger.info(player1.name + ' plays as X')
else:
players = {1:{"agent": player2, "name":player2.name}
, -1: {"agent": player1, "name":player1.name}
}
logger.info(player2.name + ' plays as X')
logger.info('--------------')
env.gameState.render(logger)
while done == 0:
turn = turn + 1
t'].act(state, 1)
else:
action, pi, MCTS_value, NN_value = players[state.playerTurn]['agent'].act(state, 0)
if memory != None:
logger.info('action: %d', action)
for r in range(env.grid_shape[0]):
logger.info(['----' if x == 0 else '{0:.2f}'.format(np.round(x,2)) for x in pi[env.grid_shape[1]*r : (env.grid_shape[1]*r + env.grid_shape[1])]])
logger.info('MCTS perceived value for %s: %f', state.pieces[str(state.playerTurn)] ,np.round(MCTS_value,2))
logger.info('NN perceived value for %s: %f', state.pieces[str(state.playerTurn)] ,np.round(NN_value,2))
logger.info('====================')
, _ = env.step(action)
env.gameState.render(logger)
if done == 1:
if memory != None:
move['value'] = -value
memory.commit_ltmemory()
if value == 1:
logger.info('%s WINS!', players[state.playerTurn]['name'])
scores[players[state.playerTurn]['name']] = scores[players[state.playerTurn]['name']] + 1
if state.playerTurn == 1:
sp_scores['sp'] = sp_scores['sp'] + 1
else:
sp_scores['nsp'] = sp_scores['nsp'] + 1
elif value == -1:
logger.info('%s WINS!', players[-state.playerTurn]['name'])
scores[players[-state.playerTurn]['name']] = scores[players[-state.playerTurn]['name']] + 1
if state.playerTurn == 1:
sp_scores['nsp'] = sp_scores['nsp'] + 1
else:
sp_scores['sp'] = sp_scores['sp'] + 1
else:
logger.info('DRAW...')
scores['drawn'] = scores['drawn'] + 1
sp_scores['drawn'] = sp_scores['drawn'] + 1
pts = state.score
points[players[state.playerTurn]['name']].append(pts[0])
points[players[-state.playerTurn]['name']].append(pts[1])
return (scores, memory, points, sp_scores)
| true | true |
f72cc221951afaa2a1888c4d748a5068c84f56dc | 3,916 | py | Python | topasgraphsim/src/functions/dp.py | sebasj13/topas-create-graphs | 5ccdbcbbe39461917cc015aa59805e518421431c | [
"MIT"
] | 1 | 2021-12-20T10:56:40.000Z | 2021-12-20T10:56:40.000Z | topasgraphsim/src/functions/dp.py | sebasj13/topas-create-graphs | 5ccdbcbbe39461917cc015aa59805e518421431c | [
"MIT"
] | null | null | null | topasgraphsim/src/functions/dp.py | sebasj13/topas-create-graphs | 5ccdbcbbe39461917cc015aa59805e518421431c | [
"MIT"
] | 1 | 2021-12-26T06:29:22.000Z | 2021-12-26T06:29:22.000Z | import numpy as np
import scipy.integrate as integrate
import scipy.interpolate as interpolate
def calculate_parameters(axis, dose, cax=False):
"""
A function to calculate the relevant
descriptive parameters of dose profiles.
"""
interpolated_axis = np.linspace(axis[0], axis[-1], len(axis) * 100)
akima_dose_interpolator = interpolate.Akima1DInterpolator(axis, dose)
interpolated_dose = np.flip(akima_dose_interpolator.__call__(interpolated_axis))
D0 = (
interpolated_dose[int(len(interpolated_dose) / 2)]
+ interpolated_dose[int(len(interpolated_dose) / 2) - 1]
) / 2
XL20 = interpolated_axis[: int(len(interpolated_axis) / 2)][
(
np.abs(
interpolated_dose[: int(len(interpolated_axis) / 2)] - 0.2 * max(dose)
)
).argmin()
]
XL50 = interpolated_axis[: int(len(interpolated_axis) / 2)][
(
np.abs(
interpolated_dose[: int(len(interpolated_axis) / 2)] - 0.5 * max(dose)
)
).argmin()
]
XL80 = interpolated_axis[: int(len(interpolated_axis) / 2)][
(
np.abs(
interpolated_dose[: int(len(interpolated_axis) / 2)] - 0.8 * max(dose)
)
).argmin()
]
XR20 = interpolated_axis[int(len(interpolated_axis) / 2) :][
(
np.abs(
interpolated_dose[
int(len(interpolated_axis) / 2) : len(interpolated_axis)
]
- 0.2 * max(dose)
)
).argmin()
]
XR50 = interpolated_axis[int(len(interpolated_axis) / 2) :][
(
np.abs(
interpolated_dose[
int(len(interpolated_axis) / 2) : len(interpolated_axis)
]
- 0.5 * max(dose)
)
).argmin()
]
XR80 = interpolated_axis[int(len(interpolated_axis) / 2) :][
(
np.abs(
interpolated_dose[
int(len(interpolated_axis) / 2) : len(interpolated_axis)
]
- 0.8 * max(dose)
)
).argmin()
]
HWB = round(abs(XR50 - XL50), 3)
CAXdev = round(XL50 + 0.5 * HWB, 3)
Dose80 = [value for value in dose if value >= 0.8 * max(dose)]
if cax == True:
return CAXdev
flat_krieger = round(
max([value for value in dose if value >= 0.95 * max(dose)])
- min([value for value in dose if value >= 0.95 * max(dose)]) / D0,
5,
)
flat_stddev = round(np.std(Dose80), 3)
if len(Dose80) % 2 != 0:
Dose80 = (
Dose80[0 : int(len(Dose80) / 2)]
+ Dose80[int(len(Dose80) / 2) + 1 : len(Dose80)]
)
S = round(
max(
[Dose80[i - 1] / Dose80[len(Dose80) - i] for i in range(1, len(Dose80) + 1)]
),
3,
)
Lpenumbra = round(abs(XL80 - XL20 + CAXdev), 3)
Rpenumbra = round(abs(XR80 - XR20 + CAXdev), 3)
XL20index = np.where(interpolated_axis == XL20)[0][0]
XL80index = np.where(interpolated_axis == XL80)[0][0]
XR20index = np.where(interpolated_axis == XR20)[0][0]
XR80index = np.where(interpolated_axis == XR80)[0][0]
Lintegral = round(
abs(
integrate.simps(
interpolated_dose[XL20index:XL80index],
interpolated_axis[XL20index:XL80index],
)
),
3,
)
Rintegral = round(
abs(
integrate.simps(
interpolated_dose[XR80index:XR20index],
interpolated_axis[XR80index:XR20index],
)
),
3,
)
if CAXdev > 150:
raise Exception
return [
HWB,
CAXdev,
flat_krieger,
flat_stddev,
S,
Lpenumbra,
Rpenumbra,
Lintegral,
Rintegral,
]
| 27.77305 | 88 | 0.516854 | import numpy as np
import scipy.integrate as integrate
import scipy.interpolate as interpolate
def calculate_parameters(axis, dose, cax=False):
interpolated_axis = np.linspace(axis[0], axis[-1], len(axis) * 100)
akima_dose_interpolator = interpolate.Akima1DInterpolator(axis, dose)
interpolated_dose = np.flip(akima_dose_interpolator.__call__(interpolated_axis))
D0 = (
interpolated_dose[int(len(interpolated_dose) / 2)]
+ interpolated_dose[int(len(interpolated_dose) / 2) - 1]
) / 2
XL20 = interpolated_axis[: int(len(interpolated_axis) / 2)][
(
np.abs(
interpolated_dose[: int(len(interpolated_axis) / 2)] - 0.2 * max(dose)
)
).argmin()
]
XL50 = interpolated_axis[: int(len(interpolated_axis) / 2)][
(
np.abs(
interpolated_dose[: int(len(interpolated_axis) / 2)] - 0.5 * max(dose)
)
).argmin()
]
XL80 = interpolated_axis[: int(len(interpolated_axis) / 2)][
(
np.abs(
interpolated_dose[: int(len(interpolated_axis) / 2)] - 0.8 * max(dose)
)
).argmin()
]
XR20 = interpolated_axis[int(len(interpolated_axis) / 2) :][
(
np.abs(
interpolated_dose[
int(len(interpolated_axis) / 2) : len(interpolated_axis)
]
- 0.2 * max(dose)
)
).argmin()
]
XR50 = interpolated_axis[int(len(interpolated_axis) / 2) :][
(
np.abs(
interpolated_dose[
int(len(interpolated_axis) / 2) : len(interpolated_axis)
]
- 0.5 * max(dose)
)
).argmin()
]
XR80 = interpolated_axis[int(len(interpolated_axis) / 2) :][
(
np.abs(
interpolated_dose[
int(len(interpolated_axis) / 2) : len(interpolated_axis)
]
- 0.8 * max(dose)
)
).argmin()
]
HWB = round(abs(XR50 - XL50), 3)
CAXdev = round(XL50 + 0.5 * HWB, 3)
Dose80 = [value for value in dose if value >= 0.8 * max(dose)]
if cax == True:
return CAXdev
flat_krieger = round(
max([value for value in dose if value >= 0.95 * max(dose)])
- min([value for value in dose if value >= 0.95 * max(dose)]) / D0,
5,
)
flat_stddev = round(np.std(Dose80), 3)
if len(Dose80) % 2 != 0:
Dose80 = (
Dose80[0 : int(len(Dose80) / 2)]
+ Dose80[int(len(Dose80) / 2) + 1 : len(Dose80)]
)
S = round(
max(
[Dose80[i - 1] / Dose80[len(Dose80) - i] for i in range(1, len(Dose80) + 1)]
),
3,
)
Lpenumbra = round(abs(XL80 - XL20 + CAXdev), 3)
Rpenumbra = round(abs(XR80 - XR20 + CAXdev), 3)
XL20index = np.where(interpolated_axis == XL20)[0][0]
XL80index = np.where(interpolated_axis == XL80)[0][0]
XR20index = np.where(interpolated_axis == XR20)[0][0]
XR80index = np.where(interpolated_axis == XR80)[0][0]
Lintegral = round(
abs(
integrate.simps(
interpolated_dose[XL20index:XL80index],
interpolated_axis[XL20index:XL80index],
)
),
3,
)
Rintegral = round(
abs(
integrate.simps(
interpolated_dose[XR80index:XR20index],
interpolated_axis[XR80index:XR20index],
)
),
3,
)
if CAXdev > 150:
raise Exception
return [
HWB,
CAXdev,
flat_krieger,
flat_stddev,
S,
Lpenumbra,
Rpenumbra,
Lintegral,
Rintegral,
]
| true | true |
f72cc242a75bff056fc4182f50f291db178b0519 | 5,780 | py | Python | sonarqube/community/user_groups.py | 0x646e78/python-sonarqube-api | c641ab4dd180b4184f2663bd28277aa796b36417 | [
"MIT"
] | null | null | null | sonarqube/community/user_groups.py | 0x646e78/python-sonarqube-api | c641ab4dd180b4184f2663bd28277aa796b36417 | [
"MIT"
] | null | null | null | sonarqube/community/user_groups.py | 0x646e78/python-sonarqube-api | c641ab4dd180b4184f2663bd28277aa796b36417 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# @Author: Jialiang Shi
from sonarqube.utils.rest_client import RestClient
from sonarqube.utils.config import (
API_USER_GROUPS_SEARCH_ENDPOINT,
API_USER_GROUPS_CREATE_ENDPOINT,
API_USER_GROUPS_DELETE_ENDPOINT,
API_USER_GROUPS_UPDATE_ENDPOINT,
API_USER_GROUPS_USERS_ENDPOINT,
API_USER_GROUPS_ADD_USER_ENDPOINT,
API_USER_GROUPS_REMOVE_USER_ENDPOINT
)
class SonarQubeUserGroups(RestClient):
"""
SonarQube user_groups Operations
"""
def __init__(self, **kwargs):
"""
:param kwargs:
"""
super(SonarQubeUserGroups, self).__init__(**kwargs)
def __getitem__(self, name):
result = list(self.search_user_groups(q=name))
for group in result:
if group['name'] == name:
return group
def search_user_groups(self, fields=None, q=None):
"""
Search for user groups.
:param fields: Comma-separated list of the fields to be returned in response.
All the fields are returned by default. Possible values are for:
* name
* description
* membersCount
:param q: Limit search to names that contain the supplied string.
:return:
"""
params = {}
if fields:
params.update({"f": fields})
page_num = 1
page_size = 1
total = 2
if q:
params['q'] = q
while page_num * page_size < total:
resp = self.get(API_USER_GROUPS_SEARCH_ENDPOINT, params=params)
response = resp.json()
page_num = response['paging']['pageIndex']
page_size = response['paging']['pageSize']
total = response['paging']['total']
params['p'] = page_num + 1
for group in response['groups']:
yield group
def create_group(self, group_name, description=None):
"""
Create a group.
:param group_name: Name for the new group. A group name cannot be larger than 255 characters and must be unique.
The value 'anyone' (whatever the case) is reserved and cannot be used.
:param description: Description for the new group. A group description cannot be larger than 200 characters.
:return: request response
"""
params = {
'name': group_name
}
if description:
params.update({'description': description})
return self.post(API_USER_GROUPS_CREATE_ENDPOINT, params=params)
def delete_group(self, group_name):
"""
Delete a group. The default groups cannot be deleted.
:param group_name:
:return:
"""
params = {
'name': group_name
}
self.post(API_USER_GROUPS_DELETE_ENDPOINT, params=params)
def update_group(self, group_id, group_name=None, description=None):
"""
Update a group.
:param group_id: Identifier of the group.
:param group_name: New optional name for the group. A group name cannot be larger than 255 characters and must
be unique. Value 'anyone' (whatever the case) is reserved and cannot be used. If value is empty or not
defined, then name is not changed.
:param description: New optional description for the group. A group description cannot be larger than
200 characters. If value is not defined, then description is not changed.
:return:
"""
params = {'id': group_id}
if group_name:
params.update({'name': group_name})
if description:
params.update({'description': description})
self.post(API_USER_GROUPS_UPDATE_ENDPOINT, params=params)
def add_user_to_group(self, group_name, user_login):
"""
Add a user to a group.
:param group_name: Group name
:param user_login: User login
:return:
"""
params = {
'login': user_login,
'name': group_name
}
self.post(API_USER_GROUPS_ADD_USER_ENDPOINT, params=params)
def remove_user_from_group(self, group_name, user_login):
"""
Remove a user from a group.
:param group_name: Group name
:param user_login: User login
:return:
"""
params = {
'login': user_login,
'name': group_name
}
self.post(API_USER_GROUPS_REMOVE_USER_ENDPOINT, params=params)
def search_users_belong_to_group(self, group_name, q=None, selected="selected"):
"""
Search for users with membership information with respect to a group.
:param group_name: Group name
:param q: Limit search to names or logins that contain the supplied string.
:param selected: Depending on the value, show only selected items (selected=selected), deselected items
(selected=deselected), or all items with their selection status (selected=all).Possible values are for:
* all
* deselected
* selected
default value is selected.
:return:
"""
params = {
'name': group_name,
'selected': selected
}
page_num = 1
page_size = 1
total = 2
if q:
params.update({'q': q})
while page_num * page_size < total:
resp = self.get(API_USER_GROUPS_USERS_ENDPOINT, params=params)
response = resp.json()
page_num = response['p']
page_size = response['ps']
total = response['total']
params['p'] = page_num + 1
for user in response['users']:
yield user
| 30.582011 | 120 | 0.59654 |
from sonarqube.utils.rest_client import RestClient
from sonarqube.utils.config import (
API_USER_GROUPS_SEARCH_ENDPOINT,
API_USER_GROUPS_CREATE_ENDPOINT,
API_USER_GROUPS_DELETE_ENDPOINT,
API_USER_GROUPS_UPDATE_ENDPOINT,
API_USER_GROUPS_USERS_ENDPOINT,
API_USER_GROUPS_ADD_USER_ENDPOINT,
API_USER_GROUPS_REMOVE_USER_ENDPOINT
)
class SonarQubeUserGroups(RestClient):
def __init__(self, **kwargs):
super(SonarQubeUserGroups, self).__init__(**kwargs)
def __getitem__(self, name):
result = list(self.search_user_groups(q=name))
for group in result:
if group['name'] == name:
return group
def search_user_groups(self, fields=None, q=None):
params = {}
if fields:
params.update({"f": fields})
page_num = 1
page_size = 1
total = 2
if q:
params['q'] = q
while page_num * page_size < total:
resp = self.get(API_USER_GROUPS_SEARCH_ENDPOINT, params=params)
response = resp.json()
page_num = response['paging']['pageIndex']
page_size = response['paging']['pageSize']
total = response['paging']['total']
params['p'] = page_num + 1
for group in response['groups']:
yield group
def create_group(self, group_name, description=None):
params = {
'name': group_name
}
if description:
params.update({'description': description})
return self.post(API_USER_GROUPS_CREATE_ENDPOINT, params=params)
def delete_group(self, group_name):
params = {
'name': group_name
}
self.post(API_USER_GROUPS_DELETE_ENDPOINT, params=params)
def update_group(self, group_id, group_name=None, description=None):
params = {'id': group_id}
if group_name:
params.update({'name': group_name})
if description:
params.update({'description': description})
self.post(API_USER_GROUPS_UPDATE_ENDPOINT, params=params)
def add_user_to_group(self, group_name, user_login):
params = {
'login': user_login,
'name': group_name
}
self.post(API_USER_GROUPS_ADD_USER_ENDPOINT, params=params)
def remove_user_from_group(self, group_name, user_login):
params = {
'login': user_login,
'name': group_name
}
self.post(API_USER_GROUPS_REMOVE_USER_ENDPOINT, params=params)
def search_users_belong_to_group(self, group_name, q=None, selected="selected"):
params = {
'name': group_name,
'selected': selected
}
page_num = 1
page_size = 1
total = 2
if q:
params.update({'q': q})
while page_num * page_size < total:
resp = self.get(API_USER_GROUPS_USERS_ENDPOINT, params=params)
response = resp.json()
page_num = response['p']
page_size = response['ps']
total = response['total']
params['p'] = page_num + 1
for user in response['users']:
yield user
| true | true |
f72cc2a756c43756ba71fb67aa4ae3e1efa74f2f | 5,550 | py | Python | userbot/modules/locks.py | RiSecID/Auto | d06ef712666a35ddbf0c123dbb86705096cbbb56 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 1 | 2020-04-10T13:11:46.000Z | 2020-04-10T13:11:46.000Z | userbot/modules/locks.py | RiSecID/Auto | d06ef712666a35ddbf0c123dbb86705096cbbb56 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | userbot/modules/locks.py | RiSecID/Auto | d06ef712666a35ddbf0c123dbb86705096cbbb56 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 1 | 2020-12-02T14:59:04.000Z | 2020-12-02T14:59:04.000Z | # Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.c (the "License");
# you may not use this file except in compliance with the License.
from telethon.tl.functions.messages import EditChatDefaultBannedRightsRequest
from telethon.tl.types import ChatBannedRights
from userbot import CMD_HELP
from userbot.events import register
@register(outgoing=True, pattern=r"^.lock ?(.*)")
async def locks(event):
input_str = event.pattern_match.group(1).lower()
peer_id = event.chat_id
msg = None
media = None
sticker = None
gif = None
gamee = None
ainline = None
gpoll = None
adduser = None
cpin = None
changeinfo = None
if input_str == "msg":
msg = True
what = "messages"
elif input_str == "media":
media = True
what = "media"
elif input_str == "sticker":
sticker = True
what = "stickers"
elif input_str == "gif":
gif = True
what = "GIFs"
elif input_str == "game":
gamee = True
what = "games"
elif input_str == "inline":
ainline = True
what = "inline bots"
elif input_str == "poll":
gpoll = True
what = "polls"
elif input_str == "invite":
adduser = True
what = "invites"
elif input_str == "pin":
cpin = True
what = "pins"
elif input_str == "info":
changeinfo = True
what = "chat info"
elif input_str == "all":
msg = True
media = True
sticker = True
gif = True
gamee = True
ainline = True
gpoll = True
adduser = True
cpin = True
changeinfo = True
what = "everything"
else:
if not input_str:
return await event.edit("`I can't lock nothing !!`")
else:
return await event.edit(f"`Invalid lock type:` {input_str}")
lock_rights = ChatBannedRights(
until_date=None,
send_messages=msg,
send_media=media,
send_stickers=sticker,
send_gifs=gif,
send_games=gamee,
send_inline=ainline,
send_polls=gpoll,
invite_users=adduser,
pin_messages=cpin,
change_info=changeinfo,
)
try:
await event.client(
EditChatDefaultBannedRightsRequest(peer=peer_id,
banned_rights=lock_rights))
await event.edit(f"`Locked {what} for this chat !!`")
except BaseException as e:
return await event.edit(
f"`Do I have proper rights for that ??`\n**Error:** {str(e)}")
@register(outgoing=True, pattern=r"^.unlock ?(.*)")
async def rem_locks(event):
input_str = event.pattern_match.group(1).lower()
peer_id = event.chat_id
msg = None
media = None
sticker = None
gif = None
gamee = None
ainline = None
gpoll = None
adduser = None
cpin = None
changeinfo = None
if input_str == "msg":
msg = False
what = "messages"
elif input_str == "media":
media = False
what = "media"
elif input_str == "sticker":
sticker = False
what = "stickers"
elif input_str == "gif":
gif = False
what = "GIFs"
elif input_str == "game":
gamee = False
what = "games"
elif input_str == "inline":
ainline = False
what = "inline bots"
elif input_str == "poll":
gpoll = False
what = "polls"
elif input_str == "invite":
adduser = False
what = "invites"
elif input_str == "pin":
cpin = False
what = "pins"
elif input_str == "info":
changeinfo = False
what = "chat info"
elif input_str == "all":
msg = False
media = False
sticker = False
gif = False
gamee = False
ainline = False
gpoll = False
adduser = False
cpin = False
changeinfo = False
what = "everything"
else:
if not input_str:
return await event.edit("`I can't unlock nothing !!`")
else:
return await event.edit(f"`Invalid unlock type:` {input_str}")
unlock_rights = ChatBannedRights(
until_date=None,
send_messages=msg,
send_media=media,
send_stickers=sticker,
send_gifs=gif,
send_games=gamee,
send_inline=ainline,
send_polls=gpoll,
invite_users=adduser,
pin_messages=cpin,
change_info=changeinfo,
)
try:
await event.client(
EditChatDefaultBannedRightsRequest(peer=peer_id,
banned_rights=unlock_rights))
await event.edit(f"`Unlocked {what} for this chat !!`")
except BaseException as e:
return await event.edit(
f"`Do I have proper rights for that ??`\n**Error:** {str(e)}")
CMD_HELP.update({
"locks":
">`.lock <all (or) type(s)>` or >`.unlock <all (or) type(s)>`"
"\nUsage: Allows you to lock/unlock some common message types in the chat."
"\n[NOTE: Requires proper admin rights in the chat !!]"
"\n\nAvailable message types to lock/unlock are: "
"\n`all, msg, media, sticker, gif, game, inline, poll, invite, pin, info`"
})
| 29.057592 | 80 | 0.544505 |
from telethon.tl.functions.messages import EditChatDefaultBannedRightsRequest
from telethon.tl.types import ChatBannedRights
from userbot import CMD_HELP
from userbot.events import register
@register(outgoing=True, pattern=r"^.lock ?(.*)")
async def locks(event):
input_str = event.pattern_match.group(1).lower()
peer_id = event.chat_id
msg = None
media = None
sticker = None
gif = None
gamee = None
ainline = None
gpoll = None
adduser = None
cpin = None
changeinfo = None
if input_str == "msg":
msg = True
what = "messages"
elif input_str == "media":
media = True
what = "media"
elif input_str == "sticker":
sticker = True
what = "stickers"
elif input_str == "gif":
gif = True
what = "GIFs"
elif input_str == "game":
gamee = True
what = "games"
elif input_str == "inline":
ainline = True
what = "inline bots"
elif input_str == "poll":
gpoll = True
what = "polls"
elif input_str == "invite":
adduser = True
what = "invites"
elif input_str == "pin":
cpin = True
what = "pins"
elif input_str == "info":
changeinfo = True
what = "chat info"
elif input_str == "all":
msg = True
media = True
sticker = True
gif = True
gamee = True
ainline = True
gpoll = True
adduser = True
cpin = True
changeinfo = True
what = "everything"
else:
if not input_str:
return await event.edit("`I can't lock nothing !!`")
else:
return await event.edit(f"`Invalid lock type:` {input_str}")
lock_rights = ChatBannedRights(
until_date=None,
send_messages=msg,
send_media=media,
send_stickers=sticker,
send_gifs=gif,
send_games=gamee,
send_inline=ainline,
send_polls=gpoll,
invite_users=adduser,
pin_messages=cpin,
change_info=changeinfo,
)
try:
await event.client(
EditChatDefaultBannedRightsRequest(peer=peer_id,
banned_rights=lock_rights))
await event.edit(f"`Locked {what} for this chat !!`")
except BaseException as e:
return await event.edit(
f"`Do I have proper rights for that ??`\n**Error:** {str(e)}")
@register(outgoing=True, pattern=r"^.unlock ?(.*)")
async def rem_locks(event):
input_str = event.pattern_match.group(1).lower()
peer_id = event.chat_id
msg = None
media = None
sticker = None
gif = None
gamee = None
ainline = None
gpoll = None
adduser = None
cpin = None
changeinfo = None
if input_str == "msg":
msg = False
what = "messages"
elif input_str == "media":
media = False
what = "media"
elif input_str == "sticker":
sticker = False
what = "stickers"
elif input_str == "gif":
gif = False
what = "GIFs"
elif input_str == "game":
gamee = False
what = "games"
elif input_str == "inline":
ainline = False
what = "inline bots"
elif input_str == "poll":
gpoll = False
what = "polls"
elif input_str == "invite":
adduser = False
what = "invites"
elif input_str == "pin":
cpin = False
what = "pins"
elif input_str == "info":
changeinfo = False
what = "chat info"
elif input_str == "all":
msg = False
media = False
sticker = False
gif = False
gamee = False
ainline = False
gpoll = False
adduser = False
cpin = False
changeinfo = False
what = "everything"
else:
if not input_str:
return await event.edit("`I can't unlock nothing !!`")
else:
return await event.edit(f"`Invalid unlock type:` {input_str}")
unlock_rights = ChatBannedRights(
until_date=None,
send_messages=msg,
send_media=media,
send_stickers=sticker,
send_gifs=gif,
send_games=gamee,
send_inline=ainline,
send_polls=gpoll,
invite_users=adduser,
pin_messages=cpin,
change_info=changeinfo,
)
try:
await event.client(
EditChatDefaultBannedRightsRequest(peer=peer_id,
banned_rights=unlock_rights))
await event.edit(f"`Unlocked {what} for this chat !!`")
except BaseException as e:
return await event.edit(
f"`Do I have proper rights for that ??`\n**Error:** {str(e)}")
CMD_HELP.update({
"locks":
">`.lock <all (or) type(s)>` or >`.unlock <all (or) type(s)>`"
"\nUsage: Allows you to lock/unlock some common message types in the chat."
"\n[NOTE: Requires proper admin rights in the chat !!]"
"\n\nAvailable message types to lock/unlock are: "
"\n`all, msg, media, sticker, gif, game, inline, poll, invite, pin, info`"
})
| true | true |
f72cc5c07d47e87c78a7d4236d54674e5f436c66 | 230 | py | Python | pycones/sponsorship/managers.py | python-spain/PyConES2015 | af78ad7f1d7df747a2f5428be87a5b061457dd24 | [
"MIT"
] | null | null | null | pycones/sponsorship/managers.py | python-spain/PyConES2015 | af78ad7f1d7df747a2f5428be87a5b061457dd24 | [
"MIT"
] | null | null | null | pycones/sponsorship/managers.py | python-spain/PyConES2015 | af78ad7f1d7df747a2f5428be87a5b061457dd24 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
class SponsorManager(models.Manager):
def active(self):
return self.get_query_set().filter(active=True).order_by("level")
| 23 | 73 | 0.726087 |
from __future__ import unicode_literals
from django.db import models
class SponsorManager(models.Manager):
def active(self):
return self.get_query_set().filter(active=True).order_by("level")
| true | true |
f72cc5f2ca3bea87b59576ba3da7939aab82e2af | 116 | py | Python | URI/1 - INICIANTE/Python/1759 - HoHoHo.py | william-james-pj/LogicaProgramacao | 629f746e34da2e829dc7ea2e489ac36bb1b1fb13 | [
"MIT"
] | 1 | 2020-04-14T16:48:16.000Z | 2020-04-14T16:48:16.000Z | URI/1 - INICIANTE/Python/1759 - HoHoHo.py | william-james-pj/LogicaProgramacao | 629f746e34da2e829dc7ea2e489ac36bb1b1fb13 | [
"MIT"
] | null | null | null | URI/1 - INICIANTE/Python/1759 - HoHoHo.py | william-james-pj/LogicaProgramacao | 629f746e34da2e829dc7ea2e489ac36bb1b1fb13 | [
"MIT"
] | null | null | null | n = int(input())
for y in range(0, n):
if(y == n-1):
print('Ho!')
else:
print('Ho', end=' ') | 19.333333 | 28 | 0.413793 | n = int(input())
for y in range(0, n):
if(y == n-1):
print('Ho!')
else:
print('Ho', end=' ') | true | true |
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