function_name stringlengths 1 63 | docstring stringlengths 50 5.89k | masked_code stringlengths 50 882k | implementation stringlengths 169 12.9k | start_line int32 1 14.6k | end_line int32 16 14.6k | file_content stringlengths 274 882k |
|---|---|---|---|---|---|---|
test_set_one_ca_list | If passed a list containing a single X509Name,
`Context.set_client_ca_list` configures the context to send
that CA name to the client and, on both the server and client sides,
`Connection.get_client_ca_list` returns a list containing that
X509Name after the connection is set up. | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... | def test_set_one_ca_list(self):
"""
If passed a list containing a single X509Name,
`Context.set_client_ca_list` configures the context to send
that CA name to the client and, on both the server and client sides,
`Connection.get_client_ca_list` returns a list containing that
... | 3,514 | 3,528 | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... |
test_set_multiple_ca_list | If passed a list containing multiple X509Name objects,
`Context.set_client_ca_list` configures the context to send
those CA names to the client and, on both the server and client sides,
`Connection.get_client_ca_list` returns a list containing those
X509Names after the connection is set up. | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... | def test_set_multiple_ca_list(self):
"""
If passed a list containing multiple X509Name objects,
`Context.set_client_ca_list` configures the context to send
those CA names to the client and, on both the server and client sides,
`Connection.get_client_ca_list` returns a list co... | 3,530 | 3,548 | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... |
test_set_after_add_client_ca | A call to `Context.set_client_ca_list` after a call to
`Context.add_client_ca` replaces the CA name specified by the
former call with the names specified by the latter call. | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... | def test_set_after_add_client_ca(self):
"""
A call to `Context.set_client_ca_list` after a call to
`Context.add_client_ca` replaces the CA name specified by the
former call with the names specified by the latter call.
"""
cacert = load_certificate(FILETYPE_PEM, root_c... | 3,648 | 3,666 | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... |
test_integers | All of the info constants are integers.
This is a very weak test. It would be nice to have one that actually
verifies that as certain info events happen, the value passed to the
info callback matches up with the constant exposed by OpenSSL.SSL. | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... | def test_integers(self):
"""
All of the info constants are integers.
This is a very weak test. It would be nice to have one that actually
verifies that as certain info events happen, the value passed to the
info callback matches up with the constant exposed by OpenSSL.SSL.
... | 3,673 | 3,694 | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... |
_client_connection | Builds a client connection suitable for using OCSP.
:param callback: The callback to register for OCSP.
:param data: The opaque data object that will be handed to the
OCSP callback.
:param request_ocsp: Whether the client will actually ask for OCSP
stapling. Useful for testing only. | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... | def _client_connection(self, callback, data, request_ocsp=True):
"""
Builds a client connection suitable for using OCSP.
:param callback: The callback to register for OCSP.
:param data: The opaque data object that will be handed to the
OCSP callback.
:param reque... | 3,741 | 3,759 | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... |
_server_connection | Builds a server connection suitable for using OCSP.
:param callback: The callback to register for OCSP.
:param data: The opaque data object that will be handed to the
OCSP callback. | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... | def _server_connection(self, callback, data):
"""
Builds a server connection suitable for using OCSP.
:param callback: The callback to register for OCSP.
:param data: The opaque data object that will be handed to the
OCSP callback.
"""
ctx = Context(SSLv2... | 3,761 | 3,775 | # Copyright (C) Jean-Paul Calderone
# See LICENSE for details.
"""
Unit tests for :mod:`OpenSSL.SSL`.
"""
import datetime
import sys
import uuid
from gc import collect, get_referrers
from errno import (
EAFNOSUPPORT, ECONNREFUSED, EINPROGRESS, EWOULDBLOCK, EPIPE, ESHUTDOWN)
from sys import platform, getfilesyste... |
_GenerateCategories | Generates category string for each of the specified apps.
Args:
apps: list of tuples containing information about the apps.
Returns:
String containing concatenated copies of the category string for each app
in apps, each populated with the appropriate app-specific strings. | # Copyright 2015 Google 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,... | def _GenerateCategories(apps):
"""Generates category string for each of the specified apps.
Args:
apps: list of tuples containing information about the apps.
Returns:
String containing concatenated copies of the category string for each app
in apps, each populated with the appropriate ... | 385 | 409 | # Copyright 2015 Google 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,... |
_GeneratePolicies | Generates policy string for each of the specified apps.
Args:
apps: list of tuples containing information about the apps.
Returns:
String containing concatenated copies of the policy template for each app
in apps, each populated with the appropriate app-specific strings. | # Copyright 2015 Google 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,... | def _GeneratePolicies(apps):
"""Generates policy string for each of the specified apps.
Args:
apps: list of tuples containing information about the apps.
Returns:
String containing concatenated copies of the policy template for each app
in apps, each populated with the appropriate app-... | 411 | 434 | # Copyright 2015 Google 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,... |
GetLoss | Compute loss and also deriv w.r.t to it if asked for.
Compute the loss function. Targets should be in self.data, predictions
should be in self.state.
Args:
get_deriv: If True, compute the derivative w.r.t the loss function and put
it in self.deriv. | from layer import *
class LogisticLayer(Layer):
def __init__(self, *args, **kwargs):
super(LogisticLayer, self).__init__(*args, **kwargs)
@classmethod
def IsLayerType(cls, proto):
return proto.hyperparams.activation == deepnet_pb2.Hyperparams.LOGISTIC
def ApplyActivation(self):
cm.sigmoid(self.st... | def GetLoss(self, get_deriv=False, acc_deriv=False, **kwargs):
"""Compute loss and also deriv w.r.t to it if asked for.
Compute the loss function. Targets should be in self.data, predictions
should be in self.state.
Args:
get_deriv: If True, compute the derivative w.r.t the loss function and pu... | 21 | 62 | from layer import *
class LogisticLayer(Layer):
def __init__(self, *args, **kwargs):
super(LogisticLayer, self).__init__(*args, **kwargs)
@classmethod
def IsLayerType(cls, proto):
return proto.hyperparams.activation == deepnet_pb2.Hyperparams.LOGISTIC
def ApplyActivation(self):
cm.sigmoid(self.st... |
__str__ | [summary] BoxWindow: :math:`[a_1, b_1] imes [a_2, b_2] imes \cdots`
Returns:
[str]: [description of the Box's bounds] | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def __str__(self):
"""[summary] BoxWindow: :math:`[a_1, b_1] \times [a_2, b_2] \times \cdots`
Returns:
[str]: [description of the Box's bounds]
"""
shape = (self.bounds).shape
representation = "BoxWindow: "
# * consider for a, b in self.bounds
# ... | 18 | 49 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
__contains__ | [summary]This method tests if an element (args) is inside the box
Args:
args ([numpy array list]): [the element to test]
Returns:
[bool]: [True if the element is inside the box , False if not] | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def __contains__(self, args):
"""[summary]This method tests if an element (args) is inside the box
Args:
args ([numpy array list]): [the element to test]
Returns:
[bool]: [True if the element is inside the box , False if not]
"""
# * consider for (a,... | 59 | 77 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
center | [summary] determinate the center of the box
Returns:
[numpy array list]: [the center of the box] | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def center(self):
"""[summary] determinate the center of the box
Returns:
[numpy array list]: [the center of the box]
"""
# * Nice try!
# ? how about np.mean(self.bounds)
c = np.zeros(self.__len__())
for i in range(self.__len__()):
c[i... | 115 | 126 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
rand | [summary]
Generate ``n`` points uniformly at random inside the :py:class:`BoxWindow`.
Args:
n (int, optional): [description]. Defaults to 1.
rng ([type], optional): [description]. Defaults to None.
Returns:
Randomly n elements that belong to the box | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def rand(self, n=1, rng=None):
"""[summary]
Generate ``n`` points uniformly at random inside the :py:class:`BoxWindow`.
Args:
n (int, optional): [description]. Defaults to 1.
rng ([type], optional): [description]. Defaults to None.
Returns:
Rando... | 128 | 149 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
__init__ | [summary]Initialization of Box's parameters
Args:
args ([numpy array list]): [this argument represents the bounds of the box] | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def __init__(self, center, radius, dimension):
"""[summary]Initialization of Box's parameters
Args:
args ([numpy array list]): [this argument represents the bounds of the box]
"""
self.dim = dimension
self.rad = radius
self.cent = center | 171 | 179 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
__contains__ | [summary]This method tests if an element (args) is inside the ball
Args:
args ([numpy array list]): [the element to test]
Returns:
[bool]: [True if the element is inside the ball , False if not] | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def __contains__(self, args):
"""[summary]This method tests if an element (args) is inside the ball
Args:
args ([numpy array list]): [the element to test]
Returns:
[bool]: [True if the element is inside the ball , False if not]
"""
# * same remarks a... | 181 | 198 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
center | [summary] determinate the center of the ball
Returns:
[numpy array list]: [the center of the ball] | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... | def center(self):
"""[summary] determinate the center of the ball
Returns:
[numpy array list]: [the center of the ball]
"""
# * interesting try
# * exploit numpy vectorization power
# ? how about np.mean(self.bounds)
c = np.zeros(self.__len__())
... | 236 | 248 | import numpy as np
from lab2.utils import get_random_number_generator
# todo clean up the docstrings
class BoxWindow:
"""[summary]BoxWindow class representing a virtual n-dimensional bounded Box"""
def __init__(self, args):
"""[summary]Initialization of Box's parameters
Args:
ar... |
_get_bbox_regression_labels | Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are similarly expanded.
Returns:
bbox_target_data (ndarray): N x 4K blob of regression ta... | # --------------------------------------------------------
# Adapted from Faster R-CNN (https://github.com/rbgirshick/py-faster-rcnn)
# Written by Danfei Xu
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN network."""
import numpy as np
import numpy.rando... | def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are sim... | 279 | 301 | # --------------------------------------------------------
# Adapted from Faster R-CNN (https://github.com/rbgirshick/py-faster-rcnn)
# Written by Danfei Xu
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN network."""
import numpy as np
import numpy.rando... |
_chain_future | Chain two futures so that when one completes, so does the other.
The result (or exception) of source will be copied to destination.
If destination is cancelled, source gets cancelled too.
Compatible with both asyncio.Future and concurrent.futures.Future. | import concurrent.futures
import threading
from asyncio import coroutines
from asyncio.events import AbstractEventLoop
from asyncio.futures import Future
import attr
import uuid
import asyncio
from asyncio import ensure_future
from typing import Any, Union, Coroutine, Callable, Generator, TypeVar, \
... | def _chain_future(
source: Union[concurrent.futures.Future, Future],
destination: Union[concurrent.futures.Future, Future]) -> None:
"""Chain two futures so that when one completes, so does the other.
The result (or exception) of source will be copied to destination.
If destination is cance... | 125 | 171 | import concurrent.futures
import threading
from asyncio import coroutines
from asyncio.events import AbstractEventLoop
from asyncio.futures import Future
import attr
import uuid
import asyncio
from asyncio import ensure_future
from typing import Any, Union, Coroutine, Callable, Generator, TypeVar, \
... |
_get_through_model | Get the "through" model associated with this field.
Need to handle things differently for Django1.1 vs Django1.2
In 1.1 through is a string and through_model has class
In 1.2 through is the class | from django.db import models
from django.db.models.query import QuerySet, Q
from django.db.models.base import ModelBase
from django.db.models.fields.related import RelatedField
from django.conf import settings
from utils import NestedSet
from signals import pre_publish, post_publish
# this takes some inspiration from... | def _get_through_model(self, field_object):
'''
Get the "through" model associated with this field.
Need to handle things differently for Django1.1 vs Django1.2
In 1.1 through is a string and through_model has class
In 1.2 through is the class
'''
through = fi... | 244 | 256 | from django.db import models
from django.db.models.query import QuerySet, Q
from django.db.models.base import ModelBase
from django.db.models.fields.related import RelatedField
from django.conf import settings
from utils import NestedSet
from signals import pre_publish, post_publish
# this takes some inspiration from... |
get_text_width | Function that utilizes ``wcswidth`` or ``wcwidth`` to determine the
number of columns used to display a text string.
We try first with ``wcswidth``, and fallback to iterating each
character and using wcwidth individually, falling back to a value of 0
for non-printable wide characters
On Py2, this depends on ``locale.... | # (c) 2014, Michael DeHaan <michael.dehaan@gmail.com>
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any lat... | def get_text_width(text):
"""Function that utilizes ``wcswidth`` or ``wcwidth`` to determine the
number of columns used to display a text string.
We try first with ``wcswidth``, and fallback to iterating each
character and using wcwidth individually, falling back to a value of 0
for non-printable w... | 79 | 144 | # (c) 2014, Michael DeHaan <michael.dehaan@gmail.com>
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any lat... |
feature_evaluation | Create scatter plot between each feature and the response.
- Plot title specifies feature name
- Plot title specifies Pearson Correlation between feature and response
- Plot saved under given folder with file name including feature name
Parameters
----------
X : DataFrame of shape (n_samples, n_features)
... | from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from IMLearn.metrics import *
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_whi... | def feature_evaluation(X: pd.DataFrame, y: pd.Series,
output_path: str = ".") -> NoReturn:
"""
Create scatter plot between each feature and the response.
- Plot title specifies feature name
- Plot title specifies Pearson Correlation between feature and response
- P... | 41 | 72 | from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from IMLearn.metrics import *
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_whi... |
get | Get an existing FirewallRule 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: Option... | # 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
__al... | @staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'FirewallRule':
"""
Get an existing FirewallRule resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
... | 80 | 96 | # 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
__al... |
evaluate | Predict with custom classifier model.
Parameters:
estimator: Fitted estimator.
X: Input test data.
y: Labels for test data.
Returns:
Predicted labels. | import matplotlib.pyplot as plt, streamlit as st
from typing import Iterable, Union
from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve, auc, RocCurveDisplay
def train(estimator: object, X: Iterable[Union[int, float]], y: Iterable):
"""
Train custom classifier model.
P... | def evaluate(estimator: object, X: Iterable[Union[int, float]], y: Iterable):
"""
Predict with custom classifier model.
Parameters:
estimator: Fitted estimator.
X: Input test data.
y: Labels for test data.
Returns:
Predicted labels.
"""
pred = estimator.predict(... | 49 | 78 | import matplotlib.pyplot as plt, streamlit as st
from typing import Iterable, Union
from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve, auc, RocCurveDisplay
def train(estimator: object, X: Iterable[Union[int, float]], y: Iterable):
"""
Train custom classifier model... |
fit_shifts | Fits (non-iteratively and without sigma-clipping) a displacement
transformation only between input lists of positions ``xy`` and ``uv``.
When weights are provided, a weighted fit is performed. Parameter
descriptions and return values are identical to those in `iter_linear_fit`,
except returned ``fit`` dictionary does n... | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
A module that provides algorithms for performing linear fit between
sets of 2D points.
:Authors: Mihai Cara, Warren Hack
:License: :doc:`../LICENSE`
"""
import logging
import numbers
import numpy as np
from .linalg import inv
from . import __versio... | def fit_shifts(xy, uv, wxy=None, wuv=None):
""" Fits (non-iteratively and without sigma-clipping) a displacement
transformation only between input lists of positions ``xy`` and ``uv``.
When weights are provided, a weighted fit is performed. Parameter
descriptions and return values are identical to those... | 356 | 412 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
A module that provides algorithms for performing linear fit between
sets of 2D points.
:Authors: Mihai Cara, Warren Hack
:License: :doc:`../LICENSE`
"""
import logging
import numbers
import numpy as np
from .linalg import inv
from . import __versio... |
_encode_files | Build the body for a multipart/form-data request.
Will successfully encode files when passed as a dict or a list of
tuples. Order is retained if data is a list of tuples but arbitrary
if parameters are supplied as a dict.
The tuples may be 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype)
or 4-tup... | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... | @staticmethod
def _encode_files(files, data):
"""Build the body for a multipart/form-data request.
Will successfully encode files when passed as a dict or a list of
tuples. Order is retained if data is a list of tuples but arbitrary
if parameters are supplied as a dict.
... | 114 | 176 | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... |
iter_content | Iterates over the response data. When stream=True is set on the
request, this avoids reading the content at once into memory for
large responses. The chunk size is the number of bytes it should
read into memory. This is not necessarily the length of each item
returned as decoding can take place.
chunk_size must be ... | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... | def iter_content(self, chunk_size=1, decode_unicode=False):
"""Iterates over the response data. When stream=True is set on the
request, this avoids reading the content at once into memory for
large responses. The chunk size is the number of bytes it should
read into memory. This i... | 737 | 790 | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... |
iter_lines | Iterates over the response data, one line at a time. When
stream=True is set on the request, this avoids reading the
content at once into memory for large responses.
.. note:: This method is not reentrant safe. | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... | def iter_lines(self, chunk_size=ITER_CHUNK_SIZE, decode_unicode=False, delimiter=None):
"""Iterates over the response data, one line at a time. When
stream=True is set on the request, this avoids reading the
content at once into memory for large responses.
.. note:: This method is ... | 792 | 821 | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... |
json | Returns the json-encoded content of a response, if any.
:param \*\*kwargs: Optional arguments that ``json.loads`` takes.
:raises ValueError: If the response body does not contain valid json. | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... | def json(self, **kwargs):
r"""Returns the json-encoded content of a response, if any.
:param \*\*kwargs: Optional arguments that ``json.loads`` takes.
:raises ValueError: If the response body does not contain valid json.
"""
if not self.encoding and self.content and len(sel... | 881 | 905 | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... |
close | Releases the connection back to the pool. Once this method has been
called the underlying ``raw`` object must not be accessed again.
*Note: Should not normally need to be called explicitly.* | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... | def close(self):
"""Releases the connection back to the pool. Once this method has been
called the underlying ``raw`` object must not be accessed again.
*Note: Should not normally need to be called explicitly.*
"""
if not self._content_consumed:
self.raw.close()
... | 950 | 961 | # coding: utf-8
# Modified Work: Copyright (c) 2018, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE... |
initialize | Initialize a module.
Args:
module (``torch.nn.Module``): the module will be initialized.
init_cfg (dict | list[dict]): initialization configuration dict to
define initializer. OpenMMLab has implemented 6 initializers
including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
``Kaiming... | #!/usr/bin/env python
# -*- coding=utf8 -*-
"""
# Author: achao
# File Name: weight_init.py
# Description:
"""
import copy
import math
import warnings
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from deep3dmap.core.utils import Registry, build_from_cfg, get_logger, print_log
INITIA... | def initialize(module, init_cfg):
"""Initialize a module.
Args:
module (``torch.nn.Module``): the module will be initialized.
init_cfg (dict | list[dict]): initialization configuration dict to
define initializer. OpenMMLab has implemented 6 initializers
including ``Const... | 556 | 625 | #!/usr/bin/env python
# -*- coding=utf8 -*-
"""
# Author: achao
# File Name: weight_init.py
# Description:
"""
import copy
import math
import warnings
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from deep3dmap.core.utils import Registry, build_from_cfg, get_logger, print_log
INITIA... |
patch_settings | Merge settings with global cms settings, so all required attributes
will exist. Never override, just append non existing settings.
Also check for setting inconsistencies if settings.DEBUG | # -*- coding: utf-8 -*-
from cms.exceptions import CMSDeprecationWarning
from django.conf import settings
from patch import post_patch, post_patch_check, pre_patch
import warnings
# MASKED: patch_settings function (lines 9-37)
patch_settings.ALREADY_PATCHED = False | def patch_settings():
"""Merge settings with global cms settings, so all required attributes
will exist. Never override, just append non existing settings.
Also check for setting inconsistencies if settings.DEBUG
"""
if patch_settings.ALREADY_PATCHED:
return
patch_settings.ALREADY_P... | 9 | 37 | # -*- coding: utf-8 -*-
from cms.exceptions import CMSDeprecationWarning
from django.conf import settings
from patch import post_patch, post_patch_check, pre_patch
import warnings
def patch_settings():
"""Merge settings with global cms settings, so all required attributes
will exist. Never override, just app... |
list_database_account_keys | The access keys for the given database account.
:param str account_name: Cosmos DB database account name.
:param str resource_group_name: Name of an Azure resource group. | # 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, overload
from ... import _utilities
__... | def list_database_account_keys(account_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListDatabaseAccountKeysResult:
"""
The access keys for the given database account.
... | 81 | 104 | # 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, overload
from ... import _utilities
__... |
run_recipe | Given a recipe, calls the appropriate query and returns the result.
The provided recipe name is used to make a call to the modules.
:param str recipe: name of the recipe to be run.
:param list args: remainder arguments that were unparsed.
:param Configuration config: config object.
:returns: string | from __future__ import print_function, absolute_import
import importlib
import logging
import os
from argparse import ArgumentParser
from six import string_types
from adr.formatter import all_formatters
from .errors import MissingDataError
log = logging.getLogger('adr')
here = os.path.abspath(os.path.dirname(__file... | def run_recipe(recipe, args, config):
"""Given a recipe, calls the appropriate query and returns the result.
The provided recipe name is used to make a call to the modules.
:param str recipe: name of the recipe to be run.
:param list args: remainder arguments that were unparsed.
:param Configurati... | 95 | 116 | from __future__ import print_function, absolute_import
import importlib
import logging
import os
from argparse import ArgumentParser
from six import string_types
from adr.formatter import all_formatters
from .errors import MissingDataError
log = logging.getLogger('adr')
here = os.path.abspath(os.path.dirname(__file... |
read_args | Reads command line arguments.
Returns: Parsed arguments. | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
# MASKED: read_args function (lines 10-18)
def plane_err(data,coeffs):
'''Calculates the total squared error of the data wrt a ... | def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--file', type=str, help='path to .csv file', default='orbit.csv')
parser.add_argument('-u', '--units', type=str, help='units of distance (m or km)', defa... | 10 | 18 | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... |
conv_to_2D | Finds coordinates of points in a plane wrt a basis.
Given a list of points in a plane, and a basis of the plane,
this function returns the coordinates of those points
wrt this basis.
Arguments:
points: A numpy array of points.
x: One vector of the basis.
y: Another vector of the basis.
Returns:
Coordinates of the po... | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... | def conv_to_2D(points,x,y):
'''Finds coordinates of points in a plane wrt a basis.
Given a list of points in a plane, and a basis of the plane,
this function returns the coordinates of those points
wrt this basis.
Arguments:
points: A numpy array of points.
x: One vector ... | 60 | 79 | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... |
cart_to_pol | Converts a list of cartesian coordinates into polar ones.
Arguments:
points: The list of points in the format [x,y].
Returns:
A list of polar coordinates in the format [radius,angle]. | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... | def cart_to_pol(points):
'''Converts a list of cartesian coordinates into polar ones.
Arguments:
points: The list of points in the format [x,y].
Returns:
A list of polar coordinates in the format [radius,angle].'''
pol = np.empty(points.shape)
pol[:,0] = np.sqrt(points[:,0]**2... | 81 | 94 | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... |
ellipse_err | Calculates the total squared error of the data wrt an ellipse.
params is a 3 element array used to define an ellipse.
It contains 3 elements a,e, and t0.
a is the semi-major axis
e is the eccentricity
t0 is the angle of the major axis wrt the x-axis.
These 3 elements define an ellipse with one focus at origin.
Equat... | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... | def ellipse_err(polar_coords,params):
'''Calculates the total squared error of the data wrt an ellipse.
params is a 3 element array used to define an ellipse.
It contains 3 elements a,e, and t0.
a is the semi-major axis
e is the eccentricity
t0 is the angle of the major axis wrt... | 96 | 124 | import argparse
from functools import partial
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
def read_args():
'''Reads command line arguments.
Returns: Parsed arguments.'''
parser = argparse.ArgumentParser()
... |
leakyrelu | leakyrelu激活函数
Args:
x (Tensor): input
leak (int): x<0时的斜率
Returns:
Tensor | import tensorflow as tf
# MASKED: leakyrelu function (lines 4-16)
| def leakyrelu(x, leak=0.01):
"""
leakyrelu激活函数
Args:
x (Tensor): input
leak (int): x<0时的斜率
Returns:
Tensor
"""
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * tf.abs(x) | 4 | 16 | import tensorflow as tf
def leakyrelu(x, leak=0.01):
"""
leakyrelu激活函数
Args:
x (Tensor): input
leak (int): x<0时的斜率
Returns:
Tensor
"""
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * tf.abs(x)
|
est_fpos_rate | Estimate false positive rate of a single-token signature.
Estimates using the 'tokensplit' and trace-modeling methods,
and returns the higher (most pessimistic of the two). Note that both
of these estimates are strictly equal to or higher than the actual
fraction of streams that 'token' occurs in within the trace. | # Polygraph (release 0.1)
# Signature generation algorithms for polymorphic worms
#
# Copyright (c) 2004-2005, Intel Corporation
# All Rights Reserved
#
# This software is distributed under the terms of the Eclipse Public
# License, Version 1.0 which can be found in the file named LICENSE.
# ANY ... | def est_fpos_rate(token, trace=None, stats=None):
"""
Estimate false positive rate of a single-token signature.
Estimates using the 'tokensplit' and trace-modeling methods,
and returns the higher (most pessimistic of the two). Note that both
of these estimates are strictly equal to or higher th... | 60 | 95 | # Polygraph (release 0.1)
# Signature generation algorithms for polymorphic worms
#
# Copyright (c) 2004-2005, Intel Corporation
# All Rights Reserved
#
# This software is distributed under the terms of the Eclipse Public
# License, Version 1.0 which can be found in the file named LICENSE.
# ANY ... |
input_fn | Input function which provides a single batch for train or eval.
Returns:
A `tf.data.Dataset` object. | # 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 applica... | def input_fn(self):
"""Input function which provides a single batch for train or eval.
Returns:
A `tf.data.Dataset` object.
"""
if self.data_dir is None:
tf.logging.info('Using fake input.')
return self.input_fn_null()
# Shuffle the filenames to ensure better randomization.
... | 100 | 138 | # 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 applica... |
login | Log user in using GitHub OAuth
arguments:
:request: GET HTTP request
returns:
Redirects to index | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... | def login(request):
""" Log user in using GitHub OAuth
arguments:
:request: GET HTTP request
returns:
Redirects to index
"""
# Create keys if not yet there!
if not request.session.get('github_token'):
request.session['github_token'] = None # To keep API token
... | 59 | 83 | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... |
callback | GitHub redirect here, then retrieves token for API
arguments:
:request: GET HTTP request
returns:
Redirects to index | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... | def callback(request):
"""
GitHub redirect here, then retrieves token for API
arguments:
:request: GET HTTP request
returns:
Redirects to index
"""
# Get code supplied by github
code = request.GET.get('code')
# Payload to fetch
payload = {'client_id': GITHUB_CLIENT... | 86 | 117 | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... |
logout | Logs user out but keep authorization ot OAuth GitHub
arguments:
:request: GET HTTP request
returns:
Redirects to index | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... | def logout(request):
"""
Logs user out but keep authorization ot OAuth GitHub
arguments:
:request: GET HTTP request
returns:
Redirects to index
"""
# Flush the session
request.session['github_token'] = None
request.session['github_info'] = None
return HttpResponse... | 120 | 135 | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... |
recommendations | Get recommended packages for the repo
arguments:
:request: GET/POST HTTP request
:name: repo name
returns:
Rendered recommendation page | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... | def recommendations(request, name):
"""
Get recommended packages for the repo
arguments:
:request: GET/POST HTTP request
:name: repo name
returns:
Rendered recommendation page
"""
# Convert encoded URL back to string e.g. hello%2world -> hello/world
repo_name = url... | 188 | 240 | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... |
recommendations_json | Get recommended packages for the repo in JSON format
arguments:
:request: GET HTTP request
:name: repo name
returns:
JSON object with recommendations | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... | def recommendations_json(request, name):
"""
Get recommended packages for the repo in JSON format
arguments:
:request: GET HTTP request
:name: repo name
returns:
JSON object with recommendations
"""
# Convert encoded URL back to string e.g. hello%2world -> hello/world
... | 243 | 286 | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... |
recommendations_service_api | Returns package recommendations for API POST call without authentication
arguments:
:request: POST request of application/json type
returns:
list of package recommendations | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... | @csrf_exempt
def recommendations_service_api(request):
"""
Returns package recommendations for API POST call without authentication
arguments:
:request: POST request of application/json type
returns:
list of package recommendations
"""
if request.method == 'POST':
# Fe... | 289 | 369 | """
Views for the web service
"""
import os
import json
import urllib.parse
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponseNotAllowed, HttpResponseBadRequest
from django.http import HttpResponse
from django.urls import reverse
import requests
from django.views.decorator... |
link_iterable_by_fields | Generic function to link objects in ``unlinked`` to objects in ``other`` using fields ``fields``.
The database to be linked must have uniqueness for each object for the given ``fields``.
If ``kind``, limit objects in ``unlinked`` of type ``kind``.
If ``relink``, link to objects which already have an ``input``. Other... | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from bw2data import mapping, Database, databases
from ..units import normalize_units as normalize_units_function
from ..errors import StrategyError
from ..utils import activity_hash, DEFAULT_FIELDS
from copy import deep... | def link_iterable_by_fields(unlinked, other=None, fields=None, kind=None,
internal=False, relink=False):
"""Generic function to link objects in ``unlinked`` to objects in ``other`` using fields ``fields``.
The database to be linked must have uniqueness for each object for the given ... | 25 | 71 | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from bw2data import mapping, Database, databases
from ..units import normalize_units as normalize_units_function
from ..errors import StrategyError
from ..utils import activity_hash, DEFAULT_FIELDS
from copy import deep... |
assign_only_product_as_production | Assign only product as reference product.
Skips datasets that already have a reference product or no production exchanges. Production exchanges must have a ``name`` and an amount.
Will replace the following activity fields, if not already specified:
* 'name' - name of reference product
* 'unit' - unit of reference p... | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from bw2data import mapping, Database, databases
from ..units import normalize_units as normalize_units_function
from ..errors import StrategyError
from ..utils import activity_hash, DEFAULT_FIELDS
from copy import deep... | def assign_only_product_as_production(db):
"""Assign only product as reference product.
Skips datasets that already have a reference product or no production exchanges. Production exchanges must have a ``name`` and an amount.
Will replace the following activity fields, if not already specified:
* 'na... | 74 | 97 | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from bw2data import mapping, Database, databases
from ..units import normalize_units as normalize_units_function
from ..errors import StrategyError
from ..utils import activity_hash, DEFAULT_FIELDS
from copy import deep... |
link_technosphere_by_activity_hash | Link technosphere exchanges using ``activity_hash`` function.
If ``external_db_name``, link against a different database; otherwise link internally.
If ``fields``, link using only certain fields. | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from bw2data import mapping, Database, databases
from ..units import normalize_units as normalize_units_function
from ..errors import StrategyError
from ..utils import activity_hash, DEFAULT_FIELDS
from copy import deep... | def link_technosphere_by_activity_hash(db, external_db_name=None, fields=None):
"""Link technosphere exchanges using ``activity_hash`` function.
If ``external_db_name``, link against a different database; otherwise link internally.
If ``fields``, link using only certain fields."""
TECHNOSPHERE_TYPES =... | 100 | 117 | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from bw2data import mapping, Database, databases
from ..units import normalize_units as normalize_units_function
from ..errors import StrategyError
from ..utils import activity_hash, DEFAULT_FIELDS
from copy import deep... |
AccountListVolumes | Show the list of volumes for an account
Args:
account_name: the name of the account
account_id: the ID of the account
by_id: show volume IDs instead of names
mvip: the management IP of the cluster
username: the admin user of the cluster
pa... | #!/usr/bin/env python
"""
This action will display a list of volumes for an account
"""
from libsf.apputil import PythonApp
from libsf.argutil import SFArgumentParser, GetFirstLine, SFArgFormatter
from libsf.logutil import GetLogger, logargs
from libsf.sfcluster import SFCluster
from libsf.util import ValidateAndDefa... | @logargs
@ValidateAndDefault({
# "arg_name" : (arg_type, arg_default)
"account_name" : (OptionalValueType(StrType), None),
"account_id" : (OptionalValueType(SolidFireIDType), None),
"by_id" : (BoolType, False),
"mvip" : (IPv4AddressType, sfdefaults.mvip),
"username" : (StrType, sfdefaults.userna... | 17 | 87 | #!/usr/bin/env python
"""
This action will display a list of volumes for an account
"""
from libsf.apputil import PythonApp
from libsf.argutil import SFArgumentParser, GetFirstLine, SFArgFormatter
from libsf.logutil import GetLogger, logargs
from libsf.sfcluster import SFCluster
from libsf.util import ValidateAndDefa... |
_add_processname_features | Add process name default features.
Parameters
----------
output_df : pd.DataFrame
The dataframe to add features to
force : bool
If True overwrite existing feature columns
path_separator : str
Path separator for OS | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
r"""
event... | def _add_processname_features(
output_df: pd.DataFrame, force: bool, path_separator: str
):
"""
Add process name default features.
Parameters
----------
output_df : pd.DataFrame
The dataframe to add features to
force : bool
If True overwrite existing feature columns
path... | 316 | 347 | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
r"""
event... |
_add_commandline_features | Add commandline default features.
Parameters
----------
output_df : pd.DataFrame
The dataframe to add features to
force : bool
If True overwrite existing feature columns | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
r"""
event... | def _add_commandline_features(output_df: pd.DataFrame, force: bool):
"""
Add commandline default features.
Parameters
----------
output_df : pd.DataFrame
The dataframe to add features to
force : bool
If True overwrite existing feature columns
"""
if "commandlineLen" not... | 350 | 382 | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
r"""
event... |
user_to_dict | Returns a dictionary based on a user object.
Extra attributes to be retrieved must be set in this module's
configuration.
:param user:
User object: an instance the custom user model.
:returns:
A dictionary with user data. | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... | def user_to_dict(self, user):
"""Returns a dictionary based on a user object.
Extra attributes to be retrieved must be set in this module's
configuration.
:param user:
User object: an instance the custom user model.
:returns:
A dictionary with user d... | 133 | 149 | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... |
default_token_validator | Validates a token.
Tokens are random strings used to authenticate temporarily. They are
used to validate sessions or service requests.
:param user_id:
User id.
:param token:
Token to be checked.
:param token_ts:
Optional token timestamp used to pre-validate the token age.
:returns:
A tuple ``(user_dic... | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... | def default_token_validator(self, user_id, token, token_ts=None):
"""Validates a token.
Tokens are random strings used to authenticate temporarily. They are
used to validate sessions or service requests.
:param user_id:
User id.
:param token:
Token t... | 284 | 321 | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... |
get_user_by_session | Returns a user based on the current session.
:param save_session:
If True, saves the user in the session if authentication succeeds.
:returns:
A user dict or None. | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... | def get_user_by_session(self, save_session=True):
"""Returns a user based on the current session.
:param save_session:
If True, saves the user in the session if authentication succeeds.
:returns:
A user dict or None.
"""
if self._user is None:
... | 351 | 370 | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... |
get_user_by_password | Returns a user based on password credentials.
:param auth_id:
Authentication id.
:param password:
User password.
:param remember:
If True, saves permanent sessions.
:param save_session:
If True, saves the user in the session if authentication succeeds.
:param silent:
If True, raises an exception if... | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... | def get_user_by_password(self, auth_id, password, remember=False,
save_session=True, silent=False):
"""Returns a user based on password credentials.
:param auth_id:
Authentication id.
:param password:
User password.
:param remembe... | 430 | 461 | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... |
set_session | Saves a user in the session.
:param user:
A dictionary with user data.
:param token:
A unique token to be persisted. If None, a new one is created.
:param token_ts:
Token timestamp. If None, a new one is created.
:param cache_ts:
Token cache timestamp. If None, a new one is created.
:remember:
If T... | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... | def set_session(self, user, token=None, token_ts=None, cache_ts=None,
remember=False, **session_args):
"""Saves a user in the session.
:param user:
A dictionary with user data.
:param token:
A unique token to be persisted. If None, a new one is cr... | 470 | 508 | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... |
get_session_data | Returns the session data as a dictionary.
:param pop:
If True, removes the session.
:returns:
A deserialized session, or None. | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... | def get_session_data(self, pop=False):
"""Returns the session data as a dictionary.
:param pop:
If True, removes the session.
:returns:
A deserialized session, or None.
"""
func = self.session.pop if pop else self.session.get
rv = func('_user'... | 518 | 529 | # -*- coding: utf-8 -*-
"""
webapp2_extras.auth
===================
Utilities for authentication and authorization.
:copyright: 2011 by tipfy.org.
:license: Apache Sotware License, see LICENSE for details.
"""
import time
import webapp2
from webapp2_extras import security
from webapp2_extras imp... |
stage | Parametrize tests for Ingress/Egress stage testing.
Args:
request: A fixture to interact with Pytest data.
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
Returns:
str: The ACL stage to be tested. | from tests.common import reboot, port_toggle
import os
import time
import random
import logging
import pprint
import pytest
import json
import ptf.testutils as testutils
import ptf.mask as mask
import ptf.packet as packet
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from tests.common i... | @pytest.fixture(scope="module", params=["ingress", "egress"])
def stage(request, duthosts, rand_one_dut_hostname):
"""Parametrize tests for Ingress/Egress stage testing.
Args:
request: A fixture to interact with Pytest data.
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostnam... | 286 | 305 | from tests.common import reboot, port_toggle
import os
import time
import random
import logging
import pprint
import pytest
import json
import ptf.testutils as testutils
import ptf.mask as mask
import ptf.packet as packet
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from tests.common i... |
acl_table | Apply ACL table configuration and remove after tests.
Args:
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
setup: Parameters for the ACL tests.
stage: The ACL stage under test.
ip_version: The IP version under test
Yields:
The ACL ... | from tests.common import reboot, port_toggle
import os
import time
import random
import logging
import pprint
import pytest
import json
import ptf.testutils as testutils
import ptf.mask as mask
import ptf.packet as packet
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from tests.common i... | @pytest.fixture(scope="module")
def acl_table(duthosts, rand_one_dut_hostname, setup, stage, ip_version):
"""Apply ACL table configuration and remove after tests.
Args:
duthosts: All DUTs belong to the testbed.
rand_one_dut_hostname: hostname of a random chosen dut to run test.
setup: P... | 324 | 372 | from tests.common import reboot, port_toggle
import os
import time
import random
import logging
import pprint
import pytest
import json
import ptf.testutils as testutils
import ptf.mask as mask
import ptf.packet as packet
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from tests.common i... |
compute_ade | Compute the average displacement error for a set of K predicted trajectories (for the same actor).
Args:
forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
gt_trajectory: (N, 2) ground truth trajectory.
Returns:
(K,) Average displacement error for each of the predicted... | # <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Utilities to evaluate motion forecasting predictions and compute metrics."""
import numpy as np
from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
# MASKED: compute_ade function (lines 9-22)
def compute_fde(forecasted_trajecto... | def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNumber) -> NDArrayFloat:
"""Compute the average displacement error for a set of K predicted trajectories (for the same actor).
Args:
forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
... | 9 | 22 | # <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Utilities to evaluate motion forecasting predictions and compute metrics."""
import numpy as np
from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNum... |
compute_fde | Compute the final displacement error for a set of K predicted trajectories (for the same actor).
Args:
forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
gt_trajectory: (N, 2) ground truth trajectory, FDE will be evaluated against true position at index `N-1`.
Returns:
... | # <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Utilities to evaluate motion forecasting predictions and compute metrics."""
import numpy as np
from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNum... | def compute_fde(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNumber) -> NDArrayFloat:
"""Compute the final displacement error for a set of K predicted trajectories (for the same actor).
Args:
forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
... | 25 | 38 | # <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Utilities to evaluate motion forecasting predictions and compute metrics."""
import numpy as np
from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNum... |
compute_is_missed_prediction | Compute whether each of K predicted trajectories (for the same actor) missed by more than a distance threshold.
Args:
forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
gt_trajectory: (N, 2) ground truth trajectory, miss will be evaluated against true position at index `N-1... | # <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Utilities to evaluate motion forecasting predictions and compute metrics."""
import numpy as np
from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNum... | def compute_is_missed_prediction(
forecasted_trajectories: NDArrayNumber,
gt_trajectory: NDArrayNumber,
miss_threshold_m: float = 2.0,
) -> NDArrayBool:
"""Compute whether each of K predicted trajectories (for the same actor) missed by more than a distance threshold.
Args:
forecasted_trajec... | 41 | 58 | # <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Utilities to evaluate motion forecasting predictions and compute metrics."""
import numpy as np
from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNum... |
undo_logger_setup | Undoes the automatic logging setup done by OpenAI Gym. You should call
this function if you want to manually configure logging
yourself. Typical usage would involve putting something like the
following at the top of your script:
gym.undo_logger_setup()
logger = logging.getLogger()
logger.addHandler(logging.StreamHandl... | import logging
import sys
import gym
logger = logging.getLogger(__name__)
root_logger = logging.getLogger()
requests_logger = logging.getLogger('requests')
# Set up the default handler
formatter = logging.Formatter('[%(asctime)s] %(message)s')
handler = logging.StreamHandler(sys.stderr)
handler.setFormatter(formatt... | def undo_logger_setup():
"""Undoes the automatic logging setup done by OpenAI Gym. You should call
this function if you want to manually configure logging
yourself. Typical usage would involve putting something like the
following at the top of your script:
gym.undo_logger_setup()
logger = loggi... | 25 | 37 | import logging
import sys
import gym
logger = logging.getLogger(__name__)
root_logger = logging.getLogger()
requests_logger = logging.getLogger('requests')
# Set up the default handler
formatter = logging.Formatter('[%(asctime)s] %(message)s')
handler = logging.StreamHandler(sys.stderr)
handler.setFormatter(formatt... |
_predict_var | predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_like
Array of mean predictions for main model.
prob_inlf : array_like
Array of predicted probabilities ... | __all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
"ZeroInflatedNegativeBinomialP"]
import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model impo... | def _predict_var(self, params, mu, prob_infl):
"""predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_l... | 665 | 684 | __all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
"ZeroInflatedNegativeBinomialP"]
import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model impo... |
_predict_var | predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_like
Array of mean predictions for main model.
prob_inlf : array_like
Array of predicted probabilities ... | __all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
"ZeroInflatedNegativeBinomialP"]
import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model impo... | def _predict_var(self, params, mu, prob_infl):
"""predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_l... | 803 | 824 | __all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
"ZeroInflatedNegativeBinomialP"]
import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model impo... |
_predict_var | predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_like
Array of mean predictions for main model.
prob_inlf : array_like
Array of predicted probabilities ... | __all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
"ZeroInflatedNegativeBinomialP"]
import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model impo... | def _predict_var(self, params, mu, prob_infl):
"""predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_l... | 919 | 940 | __all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
"ZeroInflatedNegativeBinomialP"]
import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model impo... |
reproduce | Reproduce the specified experiments.
Args:
revs: If revs is not specified, all stashed experiments will be
reproduced.
keep_stash: If True, stashed experiments will be preserved if they
fail to reproduce successfully. | import logging
import os
import re
import signal
from collections import defaultdict, namedtuple
from concurrent.futures import CancelledError, ProcessPoolExecutor, wait
from contextlib import contextmanager
from functools import wraps
from multiprocessing import Manager
from typing import Iterable, Mapping, Optional
... | @scm_locked
def reproduce(
self,
revs: Optional[Iterable] = None,
keep_stash: Optional[bool] = True,
**kwargs,
):
"""Reproduce the specified experiments.
Args:
revs: If revs is not specified, all stashed experiments will be
reprodu... | 357 | 419 | import logging
import os
import re
import signal
from collections import defaultdict, namedtuple
from concurrent.futures import CancelledError, ProcessPoolExecutor, wait
from contextlib import contextmanager
from functools import wraps
from multiprocessing import Manager
from typing import Iterable, Mapping, Optional
... |
create_model | Creates a CNN model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_cla... | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | def create_model(self,
model_input,
vocab_size,
l2_penalty=1e-8,
**unused_params):
"""Creates a CNN model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dat... | 33 | 68 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
create_model | Creates a ResNet model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_... | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | def create_model(self,
model_input,
vocab_size,
l2_penalty=1e-8,
**unused_params):
"""Creates a ResNet model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the ... | 74 | 122 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
create_model | Creates a Mixture of (Logistic) Experts model.
The model consists of a per-class softmax distribution over a
configurable number of logistic classifiers. One of the classifiers in the
mixture is not trained, and always predicts 0.
Args:
model_input: 'batch_size' x 'num_features' matrix of input features.
vocab... | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | def create_model(self,
model_input,
vocab_size,
num_mixtures=None,
l2_penalty=1e-8,
**unused_params):
"""Creates a Mixture of (Logistic) Experts model.
The model consists of a per-class softmax distribution over a... | 155 | 208 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
get_corner_loss_lidar | Args:
pred_bbox3d: (N, 7) float Tensor.
gt_bbox3d: (N, 7) float Tensor.
Returns:
corner_loss: (N) float Tensor. | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def get_corner_loss_lidar(pred_bbox3d: torch.Tensor, gt_bbox3d: torch.Tensor):
"""
Args:
pred_bbox3d: (N, 7) float Tensor.
gt_bbox3d: (N, 7) float Tensor.
Returns:
corner_loss: (N) float Tensor.
"""
assert pred_bbox3d.shape[0] == gt_bbox3d.shape[0]
pred_box_corners = bo... | 212 | 235 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
__init__ | Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples. | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
# MASKED: __in... | def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples.
"""
super().__init__()
self.alp... | 15 | 23 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
forward | Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
weighted_loss: (B, #anchors, #classes) float... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
"""
Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classifica... | 45 | 73 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
__init__ | Args:
beta: Scalar float.
L1 to L2 change point.
For beta values < 1e-5, L1 loss is computed.
code_weights: (#codes) float list if not None.
Code-wise weights. | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def __init__(self, beta: float = 1.0 / 9.0, code_weights: list = None):
"""
Args:
beta: Scalar float.
L1 to L2 change point.
For beta values < 1e-5, L1 loss is computed.
code_weights: (#codes) float list if not None.
Code-wise w... | 86 | 99 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
forward | Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targets.
weights: (B, #anchors) float tensor if not None.
Returns:
loss: (B, #anchors) float tensor.
Weighted smooth l1 loss without red... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
"""
Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targe... | 111 | 138 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
__init__ | Args:
code_weights: (#codes) float list if not None.
Code-wise weights. | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def __init__(self, code_weights: list = None):
"""
Args:
code_weights: (#codes) float list if not None.
Code-wise weights.
"""
super(WeightedL1Loss, self).__init__()
if code_weights is not None:
self.code_weights = np.array(code_weights... | 142 | 151 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
forward | Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targets.
weights: (B, #anchors) float tensor if not None.
Returns:
loss: (B, #anchors) float tensor.
Weighted smooth l1 loss without red... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
"""
Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targe... | 153 | 180 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
forward | Args:
input: (B, #anchors, #classes) float tensor.
Predited logits for each class.
target: (B, #anchors, #classes) float tensor.
One-hot classification targets.
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
loss: (B, #anchors) float tensor.
Weighted ... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
"""
Args:
input: (B, #anchors, #classes) float tensor.
Predited logits for each class.
target: (B, #anchors, #classes) float tensor.
One-hot classification tar... | 192 | 209 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
_neg_loss | Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
Arguments:
pred (batch x c x h x w)
gt_regr (batch x c x h x w) | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def _neg_loss(self, pred, gt):
""" Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
Arguments:
pred (batch x c x h x w)
gt_regr (batch x c x h x w)
"""
pos_inds = gt.eq(1).float()
ne... | 244 | 270 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
_reg_loss | L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects) | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def _reg_loss(self, regr, gt_regr, mask):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects)
"""
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(... | 306 | 327 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
__init__ | Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples. | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples.
"""
super(ForegroundFocalLoss, self).__... | 340 | 348 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
forward | Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
weighted_loss: (B, #anchors, #classes) float... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def forward(self, input: torch.Tensor, target: torch.Tensor):
"""
Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
... | 370 | 395 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
_smooth_reg_loss | L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects) | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def _smooth_reg_loss(self, regr, gt_regr, mask, sigma=3):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects)
"""
num = mask.float().sum()
mask = mask.unsqueeze... | 410 | 438 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
__init__ | Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples. | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def __init__(self, gamma: float = 2.0, alpha: float = 0.25, reduction='mean'):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting parameter to balance loss for positive and negative examples.
"""
super(E2ESigmoidF... | 448 | 457 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
forward | Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
weights: (B, #anchors) float tensor.
Anchor-wise weights.
Returns:
weighted_loss: (B, #anchors, #classes) float... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... | def forward(self, input: torch.Tensor, target: torch.Tensor):
"""
Args:
input: (B, #anchors, #classes) float tensor.
Predicted logits for each class
target: (B, #anchors, #classes) float tensor.
One-hot encoded classification targets
... | 478 | 503 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pcdet.ops.iou3d_nms_diff.iou3d_nms_diff_utils import boxes_iou3d_gpu_differentiable
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init... |
parse_model_config_params | Args:
model_params:
num_settings:
random_state:
Returns: | import numpy as np
import yaml
from dask.distributed import Client, LocalCluster, as_completed
import argparse
from os.path import exists, join
from os import makedirs
from mlmicrophysics.data import subset_data_files_by_date, assemble_data_files
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifie... | def parse_model_config_params(model_params, num_settings, random_state):
"""
Args:
model_params:
num_settings:
random_state:
Returns:
"""
param_distributions = dict()
dist_types = dict(randint=randint, expon=expon, uniform=uniform)
for param, param_value in model_p... | 28 | 46 | import numpy as np
import yaml
from dask.distributed import Client, LocalCluster, as_completed
import argparse
from os.path import exists, join
from os import makedirs
from mlmicrophysics.data import subset_data_files_by_date, assemble_data_files
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifie... |
test_with_double_quoted_multiple_words | Test with double-quoted multiple words.
A completed quote will trigger this. Unclosed quotes are ignored. | from __future__ import absolute_import, unicode_literals
from unittest import TestCase as UnitTestCase
import django
from django.contrib.contenttypes.models import ContentType
from django.core import serializers
from django.core.exceptions import ImproperlyConfigured, ValidationError
from django.test import TestCase,... | def test_with_double_quoted_multiple_words(self):
"""
Test with double-quoted multiple words.
A completed quote will trigger this. Unclosed quotes are ignored.
"""
self.assertEqual(parse_tags('"one'), ['one'])
self.assertEqual(parse_tags('"one two'), ['one', 'two'])
... | 506 | 516 | from __future__ import absolute_import, unicode_literals
from unittest import TestCase as UnitTestCase
import django
from django.contrib.contenttypes.models import ContentType
from django.core import serializers
from django.core.exceptions import ImproperlyConfigured, ValidationError
from django.test import TestCase,... |
makedir | return a directory path object with the given name. If the
directory does not yet exist, it will be created. You can use it
to manage files likes e. g. store/retrieve database
dumps across test sessions.
:param name: must be a string not containing a ``/`` separator.
Make sure the name contains your plugin or a... | """
merged implementation of the cache provider
the name cache was not chosen to ensure pluggy automatically
ignores the external pytest-cache
"""
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import py
import six
import attr
import pytest
import json
import shu... | def makedir(self, name):
""" return a directory path object with the given name. If the
directory does not yet exist, it will be created. You can use it
to manage files likes e. g. store/retrieve database
dumps across test sessions.
:param name: must be a string not contai... | 58 | 73 | """
merged implementation of the cache provider
the name cache was not chosen to ensure pluggy automatically
ignores the external pytest-cache
"""
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import py
import six
import attr
import pytest
import json
import shu... |
set | save value for the given key.
:param key: must be a ``/`` separated value. Usually the first
name is the name of your plugin or your application.
:param value: must be of any combination of basic
python types, including nested types
like e. g. lists of dictionaries. | """
merged implementation of the cache provider
the name cache was not chosen to ensure pluggy automatically
ignores the external pytest-cache
"""
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import py
import six
import attr
import pytest
import json
import shu... | def set(self, key, value):
""" save value for the given key.
:param key: must be a ``/`` separated value. Usually the first
name is the name of your plugin or your application.
:param value: must be of any combination of basic
python types, including nested types... | 96 | 118 | """
merged implementation of the cache provider
the name cache was not chosen to ensure pluggy automatically
ignores the external pytest-cache
"""
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import py
import six
import attr
import pytest
import json
import shu... |
_compute_delta | Compute delta for given log_moments and eps.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
eps: the target epsilon.
Returns:
delta | # Copyright 2016 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 applica... | def _compute_delta(log_moments, eps):
"""Compute delta for given log_moments and eps.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
eps: the target epsilon.
Returns:
delta
"""
min_delta = 1.0
for moment_order, log_moment in log_mome... | 232 | 252 | # Copyright 2016 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 applica... |
_compute_eps | Compute epsilon for given log_moments and delta.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
delta: the target delta.
Returns:
epsilon | # Copyright 2016 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 applica... | def _compute_eps(log_moments, delta):
"""Compute epsilon for given log_moments and delta.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
delta: the target delta.
Returns:
epsilon
"""
min_eps = float("inf")
for moment_order, log_momen... | 255 | 273 | # Copyright 2016 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 applica... |
compute_log_moment | Compute the log moment of Gaussian mechanism for given parameters.
Args:
q: the sampling ratio.
sigma: the noise sigma.
steps: the number of steps.
lmbd: the moment order.
verify: if False, only compute the symbolic version. If True, computes
both symbolic and numerical solutions and verifies the results... | # Copyright 2016 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 applica... | def compute_log_moment(q, sigma, steps, lmbd, verify=False, verbose=False):
"""Compute the log moment of Gaussian mechanism for given parameters.
Args:
q: the sampling ratio.
sigma: the noise sigma.
steps: the number of steps.
lmbd: the moment order.
verify: if False, only compute the symbolic ... | 276 | 302 | # Copyright 2016 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 applica... |
pacf_ols | Calculate partial autocorrelations
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
Number of lags for which pacf is returned. Lag 0 is not returned.
Returns
-------
pacf : 1d array
partial autocorrelations, maxlag+1 elements
Notes
-----
This solves... | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... | def pacf_ols(x, nlags=40):
'''Calculate partial autocorrelations
Parameters
----------
x : 1d array
observations of time series for which pacf is calculated
nlags : int
Number of lags for which pacf is returned. Lag 0 is not returned.
Returns
-------
pacf : 1d array
... | 525 | 557 | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... |
ccovf | crosscovariance for 1D
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators is n-k, otherwise n
Returns
-------
ccovf : array
autocovariance function
Notes
-----
This uses np.correlate which does full convolution. For very long time
series it is recommended to ... | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... | def ccovf(x, y, unbiased=True, demean=True):
''' crosscovariance for 1D
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators is n-k, otherwise n
Returns
-------
ccovf : array
autocovariance function
Notes
----... | 626 | 658 | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... |
periodogram | Returns the periodogram for the natural frequency of X
Parameters
----------
X : array-like
Array for which the periodogram is desired.
Returns
-------
pgram : array
1./len(X) * np.abs(np.fft.fft(X))**2
References
----------
Brockwell and Davis. | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... | def periodogram(X):
"""
Returns the periodogram for the natural frequency of X
Parameters
----------
X : array-like
Array for which the periodogram is desired.
Returns
-------
pgram : array
1./len(X) * np.abs(np.fft.fft(X))**2
References
----------
Brockwe... | 689 | 714 | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... |
_sigma_est_kpss | Computes equation 10, p. 164 of Kwiatkowski et al. (1992). This is the
consistent estimator for the variance. | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... | def _sigma_est_kpss(resids, nobs, lags):
"""
Computes equation 10, p. 164 of Kwiatkowski et al. (1992). This is the
consistent estimator for the variance.
"""
s_hat = sum(resids**2)
for i in range(1, lags + 1):
resids_prod = np.dot(resids[i:], resids[:nobs - i])
s_hat += 2 * resi... | 1,295 | 1,304 | """
Statistical tools for time series analysis
"""
from statsmodels.compat.python import (iteritems, range, lrange, string_types,
lzip, zip, long)
from statsmodels.compat.scipy import _next_regular
import numpy as np
from numpy.linalg import LinAlgError
from scipy import stats
f... |
__init__ | Args:
dataclass_types:
Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.
kwargs:
(Optional) Passed to `argparse.ArgumentParser()` in the regular way. | import json
import re
import sys
from argparse import ArgumentParser, ArgumentTypeError
from enum import Enum
from pathlib import Path
from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
import dataclasses
DataClass = NewType("DataClass", Any)
DataClassType = NewType("DataClassType", Any)
def st... | def __init__(self, dataclass_types: Union[DataClassType, Iterable[DataClassType]], **kwargs):
"""
Args:
dataclass_types:
Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.
kwargs:
(Optional) Pass... | 48 | 61 | import json
import re
import sys
from argparse import ArgumentParser, ArgumentTypeError
from enum import Enum
from pathlib import Path
from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
import dataclasses
DataClass = NewType("DataClass", Any)
DataClassType = NewType("DataClassType", Any)
def st... |
__init__ | :param application: The application to associate this popup dialog with.
:type application: :py:class:`.KingPhisherClientApplication`
:param str hostname: The hostname associated with the key.
:param key: The host's SSH key.
:type key: :py:class:`paramiko.pkey.PKey` | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# king_phisher/client/dialogs/ssh_host_key.py
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
#... | def __init__(self, application, hostname, key):
"""
:param application: The application to associate this popup dialog with.
:type application: :py:class:`.KingPhisherClientApplication`
:param str hostname: The hostname associated with the key.
:param key: The host's SSH key.
:type key: :py:class:`paramiko... | 72 | 88 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# king_phisher/client/dialogs/ssh_host_key.py
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
#... |
__init__ | :param application: The application which is using this policy.
:type application: :py:class:`.KingPhisherClientApplication` | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# king_phisher/client/dialogs/ssh_host_key.py
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
#... | def __init__(self, application):
"""
:param application: The application which is using this policy.
:type application: :py:class:`.KingPhisherClientApplication`
"""
self.application = application
self.logger = logging.getLogger('KingPhisher.Client.' + self.__class__.__name__)
super(MissingHostKeyPolicy,... | 132 | 139 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# king_phisher/client/dialogs/ssh_host_key.py
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
#... |
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